', start_introduction + 1)\r\n\r\n #If the page onl has introduction\r\n if '
' not in page:\r\n stop_introduction = page.find('', start_introduction + 1)\r\n else:\r\n pass\r\n\r\n raw_introduction = page[start_introduction : stop_introduction]\r\n return raw_introduction\r\n\r\n\r\n#Remove all the HTML tags from the introduction to get the pure text\r\n#Eliminate all the text inside '<' & '>'\r\ndef extract_pure_introduction(page):\r\n pure_introduction = (re.sub(r'<.+?>', '', page)) #From '<' to the next '>'\r\n return pure_introduction\r\n\r\n\r\n#Main Crawl function that calls all the above function and crawls the entire site sequentially\r\ndef get_introduction(url):\r\n raw_html = download_page(url)\r\n if raw_html == 'shabi':\r\n return \"None\"\r\n raw_introduction = extract_introduction(raw_html)\r\n pure_introduction = extract_pure_introduction(raw_introduction)\r\n return pure_introduction\r\n\r\n\r\ndf = pd.read_csv('label_foodtype.csv', header=None, names=['id', 'name'])\r\nrecipe_name = [re.sub(r'\\s+', '_', x.strip()) for x in df['name']]\r\nurl_prefix = \"https://en.wikipedia.org/wiki/\"\r\ntext = {}\r\nfor each in recipe_name:\r\n url = url_prefix + each\r\n intro = get_introduction(url)\r\n text[each] = intro\r\n time.sleep(2)\r\n\r\nwith open('wiki.json', 'w') as f:\r\n json.dump(text, f)\r\n\r\n","sub_path":"wiki-crawler.py","file_name":"wiki-crawler.py","file_ext":"py","file_size_in_byte":3456,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"488729515","text":"#a\n#b#せっかくなのでビットでフラグ管理してみる\na,b,c= map(int,input().split())\nflag =0\nif a+b==c:\n flag = flag | (1<<0)\nif a-b==c:\n flag = flag | (1<<1)\n\nif flag ==0b00:\n print(\"!\")\nif flag ==0b01:\n print(\"+\")\nif flag ==0b10:\n print(\"-\")\nif flag ==0b11:\n print(\"?\")\n\n#c\n# warshall_floyd法\ndef warshall_floyd(d):\n for k in range(n):\n for i in range(n):\n for j in range(n):\n d[i][j]= min(d[i][j],d[i][k]+d[k][j])\n return d\n\n# user n,pair m\nn,m = map(int,input().split())\nd = [[float(\"inf\") for i in range(n)] for i in range(n)]\n\nfor i in range(n):\n d[i][i] = 0\n\nfor i in range(m):\n a,b = map(int,input().split())\n a-=1\n b-=1\n d[a][b] = 1\n d[b][a] = 1\n\n# output 友達の友達＝各ユーザの最��距離が2の数を数える\nfor i in warshall_floyd(d):\n print(i.count(2))","sub_path":"ABC016.py","file_name":"ABC016.py","file_ext":"py","file_size_in_byte":872,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"260214054","text":"# Copyright 2020 Tier IV, Inc. All rights reserved.\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 launch\nfrom launch_ros.actions import ComposableNodeContainer, LoadComposableNodes\nfrom launch_ros.descriptions import ComposableNode\nfrom launch.conditions import IfCondition, UnlessCondition\nfrom launch.actions import DeclareLaunchArgument\nfrom launch.substitutions import AnonName, LaunchConfiguration\n\n\ndef generate_launch_description():\n\n ns = 'euclidean_cluster'\n pkg = 'euclidean_cluster'\n\n # declare launch arguments\n input_pointcloud_param = DeclareLaunchArgument(\n 'input_pointcloud',\n default_value='/sensing/lidar/no_ground/pointcloud')\n\n input_map_param = DeclareLaunchArgument(\n 'input_map',\n default_value='/map/pointcloud_map')\n\n output_clusters_param = DeclareLaunchArgument(\n 'output_clusters',\n default_value='clusters')\n\n use_pointcloud_map_param = DeclareLaunchArgument(\n 'use_pointcloud_map',\n default_value='false')\n\n # set voxel grid filter as a component\n voxel_grid_filter_component = ComposableNode(\n package='voxel_grid_filter',\n plugin='pcl::VoxelGrid',\n name=AnonName('voxel_grid_filter'),\n remappings=[('input', LaunchConfiguration('input_pointcloud')),\n ('output', 'voxel_grid_filtered/pointcloud')],\n parameters=[\n {\n 'filter_field_name': 'z',\n 'filter_limit_min': 0.1,\n 'filter_limit_max': 2.5,\n 'filter_limit_negative': False,\n 'leaf_size': 0.1,\n 'input_frame': 'base_link',\n 'output_frame': 'base_link',\n }\n ]\n )\n\n # set compare map filter as a component\n compare_map_filter_component = ComposableNode(\n package='pointcloud_preprocessor',\n plugin='pointcloud_preprocessor::VoxelBasedCompareMapFilterComponent',\n name=AnonName('compare_map_filter'),\n remappings=[('input', 'voxel_grid_filtered/pointcloud'),\n ('map', LaunchConfiguration('input_map')),\n ('output', 'compare_map_filtered/pointcloud')]\n )\n\n use_map_euclidean_cluster_component = ComposableNode(\n package=pkg,\n plugin='euclidean_cluster::EuclideanClusterNodelet',\n name=AnonName('euclidean_cluster'),\n remappings=[('input', 'compare_map_filtered/pointcloud'),\n ('output', LaunchConfiguration('output_clusters'))],\n parameters=[\n {\n 'target_frame': 'base_link',\n 'use_height': False,\n 'tolerance': 0.7,\n 'min_cluster_size': 10,\n 'max_cluster_size': 1000\n }\n ]\n )\n\n disuse_map_euclidean_cluster_component = ComposableNode(\n package=pkg,\n plugin='euclidean_cluster::EuclideanClusterNodelet',\n name=AnonName('euclidean_cluster'),\n remappings=[('input', 'voxel_grid_filtered/pointcloud'),\n ('output', LaunchConfiguration('output_clusters'))],\n parameters=[\n {\n 'target_frame': 'base_link',\n 'use_height': False,\n 'tolerance': 0.7,\n 'min_cluster_size': 10,\n 'max_cluster_size': 1000\n }\n ]\n )\n\n container = ComposableNodeContainer(\n name='euclidean_cluster_container',\n namespace=ns,\n package='rclcpp_components',\n executable='component_container',\n composable_node_descriptions=[voxel_grid_filter_component],\n output='screen',\n )\n\n use_map_loader = LoadComposableNodes(\n composable_node_descriptions=[compare_map_filter_component,\n use_map_euclidean_cluster_component],\n target_container=container,\n condition=IfCondition(LaunchConfiguration('use_pointcloud_map')),\n )\n\n disuse_map_loader = LoadComposableNodes(\n composable_node_descriptions=[disuse_map_euclidean_cluster_component],\n target_container=container,\n condition=UnlessCondition(LaunchConfiguration('use_pointcloud_map')),\n )\n\n return launch.LaunchDescription([\n input_pointcloud_param,\n input_map_param,\n output_clusters_param,\n use_pointcloud_map_param,\n container,\n use_map_loader,\n disuse_map_loader\n ])\n","sub_path":"perception/object_recognition/detection/euclidean_cluster/launch/euclidean_cluster.launch.py","file_name":"euclidean_cluster.launch.py","file_ext":"py","file_size_in_byte":4938,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"558418752","text":"\n\nclass Dog:\n \"\"\"类\"\"\"\n def eat(self, food):\n \"\"\"行为\"\"\"\n print('小狗吃了', food)\n self.food = food\n\n def food_info(self):\n \"\"\"能否得到小狗上次吃的食物是什么\"\"\"\n print(self.food)\n\n\ndog1 = Dog()\ndog1.eat('骨头')\n\ndog2 = Dog()\ndog2.eat('包子')\n\ndog1.food_info()\ndog2.food_info()","sub_path":"pbase/day17/code/instance_method2.py","file_name":"instance_method2.py","file_ext":"py","file_size_in_byte":346,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"429605347","text":"import re\nfrom re import sub\n\nclass RLEString(object):\n\n\tdef __init__(self, string):\n\t\t#check if the string is valid\n\t\tif not re.match('^[a-zA-Z]+$',string):\n\t\t\traise ValueError(\"Text has to consist of alphabetic characters (a-zA-Z))\")\n\t\telse:\n\t\t\tself.__mystring = string\n\t\t\tself.__iscompressed = False\n\n\n\tdef compress(self):\n\t\t#compress internal string\n\t\t#substitute function of regular expression in python\n\t\t#the (.)\\1* is the syntax for finding backreferences -> finding all equal charactes in a row\n\t\t#the sub() function substitutes for example: EEEEE -> 5E\n\t\tif self.__iscompressed:\n\t\t\traise RuntimeError(\"Mystring is already compressed!\")\n\t\telse:\n\t\t\tself.__mystring = sub(r'(.)\\1*', lambda m: str(len(m.group(0))) + m.group(1), self.__mystring)\n\t\t\tself.__iscompressed = True\n\t\t\treturn self.__mystring\n\t\n\n\tdef decompress(self):\n\t\t#substitute function of regular expression in python\n\t\t#the caputuring groups (\\d+ = decimal digit group(1) and (\\D = any non digit character group(2)) has\n\t\t#to be placed in the resulting \"new\" mystring. group(0) would result in any kind of: (5E), group(1): 5, group(2): E\n\t\t#decompressed is the result: EEEEE\n\t\t#the sub() function, substitutes 5E with E*(int)5 -> EEEEE\n\t\tif not self.__iscompressed:\n\t\t\traise RuntimeError(\"Mystring is already decompressed!\")\n\t\telse:\n\t\t\tself.__mystring = sub(r'(\\d+)(\\D)', lambda m: m.group(2) * int(m.group(1)), self.__mystring)\n\t\t\tself.__iscompressed = False\n\t\t\treturn self.__mystring\n\n\n\tdef iscompressed(self):\n\t\tif self.__iscompressed:\n\t\t\treturn self.__iscompressed\n\n\tdef __str__(self):\n\t\treturn self.__mystring\n","sub_path":"ex9/RLEString.py","file_name":"RLEString.py","file_ext":"py","file_size_in_byte":1588,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"500432157","text":"\n\n#calss header\nclass _CHAMPION():\n\tdef __init__(self,): \n\t\tself.name = \"CHAMPION\"\n\t\tself.definitions = [u'someone or something, especially a person or animal, that has beaten all other competitors in a competition: ', u'a person who enthusiastically supports, defends, or fights for a person, belief, right, or principle: ']\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'nouns'\n\n\n\tdef run(self, obj1 = [], obj2 = []):\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/nouns/_champion.py","file_name":"_champion.py","file_ext":"py","file_size_in_byte":500,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"38460391","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 ('index', '0015_kontakt'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='kontakt',\n name='informacja',\n field=models.TextField(max_length=500, default=''),\n ),\n ]\n","sub_path":"index/migrations/0016_kontakt_informacja.py","file_name":"0016_kontakt_informacja.py","file_ext":"py","file_size_in_byte":405,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"318334481","text":"# Problem: Implement a function to reverse a string (a list of characters), in-place.\n# Constraints\n# - Since we need to do this in place, it seems we cannot use the slice operator or reversed function?\n# - Correct\n# - Since Python strings are immutable, can we use a list instead?\n# - Yes\n\n\nclass ReverseString(object):\n\n def reverse(self, chars):\n if chars:\n size = len(chars)\n i = 0\n while i < size // 2:\n chars[i], chars[-1 - i] = chars[-1 - i], chars[i]\n i += 1\n return chars\n","sub_path":"arrays_strings/ReverseString/reverse_string.py","file_name":"reverse_string.py","file_ext":"py","file_size_in_byte":577,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"499024930","text":"# =============================================================================\n# Minet Youtube Captions CLI Action\n# =============================================================================\n#\n# Action reading an input CSV file line by line and retrieving caption about\n# the given Youtube videos.\n#\nimport casanova\nfrom bs4 import BeautifulSoup\nfrom tqdm import tqdm\nfrom minet.cli.utils import print_err\nfrom minet.utils import create_pool, request\n\nREPORT_HEADERS = [\n 'caption_text'\n]\n\nCAPTIONS_URL_TEMPLATE = 'https://www.youtube.com/api/timedtext?lang=%(lang)s&v=%(id)s'\n\n\ndef captions_action(namespace, output_file):\n\n enricher = casanova.enricher(\n namespace.file,\n output_file,\n keep=namespace.select,\n add=REPORT_HEADERS\n )\n\n loading_bar = tqdm(\n desc='Retrieving',\n dynamic_ncols=True,\n unit=' videos',\n )\n\n http = create_pool()\n\n for line, video_id in enricher.cells(namespace.column, with_rows=True):\n url_caption = CAPTIONS_URL_TEMPLATE % {'lang': namespace.lang, 'id': video_id}\n\n err, result_caption = request(http, url_caption)\n\n if err is not None:\n raise err\n elif result_caption.status >= 400:\n print_err('request error %s' % result_caption.status)\n enricher.writerow(line)\n else:\n soup = BeautifulSoup(result_caption.data, 'lxml')\n full_text = []\n\n caption_text = \" \".join(item.get_text() for item in soup.find_all('text'))\n\n enricher.writerow(line, [caption_text])\n\n loading_bar.update()\n","sub_path":"minet/cli/youtube/captions.py","file_name":"captions.py","file_ext":"py","file_size_in_byte":1604,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"563136217","text":"import pytest\nimport todo_app.view_model as view_model\nimport datetime\nimport todo_app.Task as task\n \n@pytest.fixture\ndef test_tasks():\n today = datetime.datetime.utcnow()\n yesterday = today - datetime.timedelta(days=1) \n task_list = [\n task.Task(1, 'To Do', 'Task1', today),\n task.Task(2, 'To Do', 'Task2', today),\n task.Task(3, 'Doing', 'Task3', today),\n task.Task(4, 'Doing', 'Task4', today),\n task.Task(5, 'Doing', 'Task5', today),\n task.Task(6, 'Done', 'Task6', today),\n task.Task(7, 'Done', 'Task7', today),\n task.Task(8, 'Done', 'Task8', today),\n task.Task(9, 'Done', 'Task8', today),\n task.Task(10, 'Done', 'Task10', yesterday),\n task.Task(11, 'Done', 'Task11', yesterday),\n task.Task(12, 'Done', 'Task12', yesterday)\n ]\n\n test_list = view_model.ViewModel(task_list)\n\n return test_list\n\n@pytest.fixture\ndef test_tasks_2():\n today = datetime.datetime.utcnow()\n yesterday = today - datetime.timedelta(days= 1)\n task_list = [ \n task.Task(1, 'To Do', 'Task1', today),\n task.Task(2, 'To Do', 'Task2', today),\n task.Task(3, 'Doing', 'Task3', today),\n task.Task(4, 'Doing', 'Task4', today),\n task.Task(5, 'Done', 'Task5', today),\n task.Task(6, 'Done', 'Task6', yesterday),\n task.Task(7, 'Done', 'Task7', yesterday),\n task.Task(8, 'Done', 'Task8', yesterday)\n ]\n \n test_list = view_model.ViewModel(task_list)\n\n return test_list\n\n@pytest.fixture\ndef test_tasks_3():\n today = datetime.datetime.utcnow()\n yesterday = today - datetime.timedelta(days= 1)\n task_list = [ \n task.Task(1, 'Doing', 'Task1', today),\n task.Task(2, 'Doing', 'Task2', today),\n task.Task(3, 'Done', 'Task3', today),\n task.Task(4, 'Done', 'Task4', today),\n task.Task(5, 'Done', 'Task5', today),\n task.Task(6, 'Done', 'Task6', yesterday),\n task.Task(7, 'Done', 'Task7', yesterday)\n ]\n\n test_list = view_model.ViewModel(task_list)\n\n return test_list\n\ndef test_to_do_items_count(test_tasks):\n todo = test_tasks.tasks_todo\n assert len(todo) == 2\n\ndef test_doing_items_count(test_tasks):\n doing = test_tasks.tasks_doing\n assert len(doing) == 3\n\ndef test_done_items_count(test_tasks):\n done = test_tasks.tasks_done\n assert len(done) == 7\n\ndef test_recent_done_tasks_count(test_tasks):\n today_tasks = test_tasks.tasks_recently_done\n assert len(today_tasks) == 4\n\ndef test_older_done_tasks_count(test_tasks):\n older_tasks = test_tasks.older_done_tasks\n assert len(older_tasks) == 3\n\ndef test_show_all_tasks_more_than_5(test_tasks):\n show_all = test_tasks.show_all_done_tasks\n assert len(show_all) == 4\n\ndef test_show_all_tasks_less_than_5(test_tasks_2):\n show_all = test_tasks_2.show_all_done_tasks\n assert len(show_all) == 4\n\ndef test_show_all_tasks_equal_to_5(test_tasks_3):\n show_all = test_tasks_3.show_all_done_tasks\n assert len(show_all) == 5","sub_path":"todo_app/test_tasks.py","file_name":"test_tasks.py","file_ext":"py","file_size_in_byte":3010,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"109624232","text":"###\nimport sys\nimport time\nimport os\nimport binascii\nimport i2c\nimport sup\nimport struct\n\n##################################\n# VARIABLE DEFINITIONS\n## I2C address for the mcu \nMCU_ADDRESS = 0x53\n## i2c baudrate \nBAUDRATE = 100000\n## i2c bus that hosts the mcu \nBUSNUM = 2\n## unused \nMAX_RUNTIME = 120\n## delay between reading and writing \nREADWRITE_DELAY = 0.005\n## delay between writing and reading \nREAD_DELAY = 0.5\n\n\ndef initI2C():\n # initialize the I2C connection\n global MCU_I2C\n MCU_I2C = i2c.I2C(MCU_ADDRESS)\n\n\ndef get_sup_telem():\n print('SUPMCU TELEMETRY INITIATED')\n telem = []\n sendCMD('SUP:SELF START') # start selftest\n sendCMD('SUP:TEL? 5,DATA') # heartbeat\n time.sleep(READ_DELAY)\n temp = readI2C(13)\n time.sleep(READWRITE_DELAY)\n value = []\n if temp != -1:\n temp = temp[5:]\n value = struct.unpack('Q', temp)[0]\n else:\n temp = 'error'\n print('TELEM:HEARTBEAT: ' + str(value))\n telem.append(temp)\n sendCMD('SUP:TEL? 6,DATA') # context switches\n time.sleep(READ_DELAY)\n temp = readI2C(13)\n time.sleep(READWRITE_DELAY)\n value = []\n if temp != -1:\n temp = temp[5:]\n value = struct.unpack('Q', temp)[0]\n else:\n temp = 'error'\n print('TELEM:CONTEXT: ' + str(value))\n telem.append(temp)\n sendCMD('SUP:TEL? 7,DATA') # idle hooks\n time.sleep(READ_DELAY)\n temp = readI2C(13)\n time.sleep(READWRITE_DELAY)\n value = []\n if temp != -1:\n temp = temp[5:]\n value = struct.unpack('Q', temp)[0]\n else:\n temp = 'error'\n print('TELEM:IDLE: ' + str(value))\n telem.append(temp)\n sendCMD('SUP:TEL? 4,DATA') # selftest\n\n\ndef get_batt_telem():\n print('BATTERY TELEMETRY INITIATED')\n fields = [\n\t#[Field name, Field Number, Field Length, Field Type]\n ['at rate time to full',5,2,'H'],\n ['at rate time to empty',6,2,'H'],\n\n ['temperature',8,2,'H'],\n ['voltage',9,2,'H'],\n ['current',10,2,'h'],\n ['average current',11,2,'h'],\n \n ['relative state of charge',13,1,'B'],\n ['absolute state of charge',14,1,'B'],\n ['remaining capacity',15,2,'H'],\n ['full charge capacity',16,2,'H'],\n ['run time to empty',17,2,'H'],\n ['average time to empty',18,2,'H'],\n ['average time to full',19,2,'H'],\n ['charging current',20,2,'H'],\n ['charging voltage',21,2,'H'],\n ['battery status',22,2,'H'],\n ['cycle count',23,2,'H'],\n ['design capacity',24,2,'h'],\n ['design voltage',25,2,'h'],\n \n ['Extra Temp Sensor 1',48,2,'H'],\n ['Extra Temp Sensor 2',49,2,'H'],\n ['Extra Temp Sensor 3',50,2,'H'],\n ['Extra Temp Sensor 4',51,2,'H'],\n ['BM2 Status',52,1,'c'],\n # ['Permanent Failure Time',53,15,'c'],\n # ['Permanent Failure Registers',54,4,'c'],\n \n ['cell voltage 4',60,2,'H'],\n ['cell voltage 3',61,2,'H'],\n ['cell voltage 2',62,2,'H'],\n ['cell votlage 1',63,2,'H'],\n \n ['safety alert',80,2,'H'],\n ['safety status',81,2,'H'],\n ['pf alert',82,2,'H'],\n ['pf status',83,2,'H'],\n ['operation status',84,2,'H'],\n ['charging status',85,2,'H'],\n \n ['pack voltage',90,2,'H'],\n \n ['average voltage',93,2,'H'],\n ['ts1 temperature',94,2,'h'],\n ['ts2 temperature',95,2,'h'],\n \n ['safety alert 2',104,2,'H'],\n ['safety status 2',105,2,'H'],\n ['pf alert 2',106,2,'H'],\n ['pf status 2',107,2,'H'],\n\n ['temp range',114,2,'H']\n ]\n for each in fields:\n field_name = each[0]\n field_num = each[1]\n field_length = each[2]\n field_type = each[3]\n cmd = 'BM2:TEL? '+str(field_num)+',DATA'\n print(cmd)\n sendCMD(cmd) \n time.sleep(READ_DELAY)\n temp = readI2C(5 + field_length)\n time.sleep(READWRITE_DELAY)\n if temp != -1:\n print('hex: ' + binascii.hexlify(temp))\n print('binary: ' + bin(int(binascii.hexlify(temp[5:]), 16)))\n temp = temp[5:]\n temp = struct.unpack(field_type, temp)[0]\n else:\n temp = 'error'\n print(field_name+': ' + str(temp))\n\ndef get_pim_telem():\n print('PIM TELEMETRY INITIATED')\n fields = [\n\t#[Field name, Field Number, Field Length, Field Type]\n ['Channel Currents',0,8,'HHHH'],\n ['Channel Shunt Resister Values',1,8,'HHHH'],\n ['Channel Current Limits',2,8,'HHHH'],\n ['Channel Current Offsets for linear fit',3,8,'HHHH'],\n ['Channel Current Scaling Factors for linear fit',4,8,'HHHH'],\n ['PIM Status Register',5,1,'B'],\n ['PIM Overcurrent event log',6,8,'HHHH']\n ]\n for each in fields:\n field_name = each[0]\n field_num = each[1]\n field_length = each[2]\n field_type = each[3]\n cmd = 'PIM:TEL? '+str(field_num)+',DATA'\n print(cmd)\n sendCMD(cmd) \n time.sleep(READ_DELAY)\n temp = readI2C(5 + field_length)\n time.sleep(READWRITE_DELAY)\n if temp != -1:\n print('hex: ' + binascii.hexlify(temp))\n print('binary: ' + bin(int(binascii.hexlify(temp[5:]), 16)))\n temp = temp[5:]\n temp = struct.unpack(field_type, temp)\n else:\n temp = 'error'\n print(field_name+': ' + str(temp))\n \n \n \n \ndef sendCMD(cmd):\n initI2C()\n MCU_I2C.write(cmd)\n \n\ndef readI2C(length = 1):\n initI2C()\n telem = MCU_I2C.read(length)\n return telem\n\n\n\nget_sup_telem()\n\nget_pim_telem()\n\n# get_batt_telem()\n","sub_path":"SupMCU_Testing/test_sup.py","file_name":"test_sup.py","file_ext":"py","file_size_in_byte":5638,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"653104170","text":"from django.conf.urls import patterns, url\nfrom . import views\n\nurlpatterns = patterns('',\n url(\n regex=r'^$',\n view=views.ExperienceListView.as_view(),\n name='list',\n ),\n url(\n regex=r'^(?P
[-\\w\\d]+)-(?P\\d+)/$',\n view=views.ExperienceDetailView.as_view(),\n name=\"detail\",\n ),\n)\n","sub_path":"experiences/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":342,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"33163765","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\n\"\"\"\n\ntime - nlogn\nspace- n to create a HEAP - NOT SURE ( Am i right ?)\n\"\"\"\n\n\nimport heapq\n\n\nclass Solution:\n def mergeKLists(self, lists: List[ListNode]) -> ListNode:\n h = [] #list initialized to [] to create a heap\n head = dummy = ListNode(0) # dummy node\n for l in lists: # traverse all lists and create heap of n elements with logn height\n while l:\n heapq.heappush(h, l.val)\n l = l.next\n\n for i in range(len(h)): # pop elements 1 by 1 and create a new merged list\n dummy.next = ListNode(heapq.heappop(h))\n dummy = dummy.next\n return head.next\n\n\n\n\n\n","sub_path":"Problem-2.py","file_name":"Problem-2.py","file_ext":"py","file_size_in_byte":815,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"383392753","text":"\"\"\"\nHere is the model for the Users App.\n\n\"\"\"\n#\ntry:\n import json\nexcept:\n import simplejson as json\n\n# Rammi imports\nfrom rammi.interface import DictableInstance, MongoJSONEncoder\n\nclass User(DictableInstance):\n _fields = (\"username\", \n \"first_name\", \n \"middle_name\", \n \"last_name\",\n \"email\",\n \"date_created\",\n \"open_id\",\n \"fav_courses\",\n )\n \n def to_json(self):\n return json.dumps(self.to_dict(), cls=MongoJSONEncoder)\n","sub_path":"resources/usersmodel.py","file_name":"usersmodel.py","file_ext":"py","file_size_in_byte":555,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"478157623","text":"\nimport cv2\nimport numpy as np\n \npath = 'Small.png'\nimg = cv2.imread(path)\nimgContour = img.copy()\n \nimgGray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\nimgBlur = cv2.GaussianBlur(imgGray,(7,7),1)\nimgCanny = cv2.Canny(imgBlur,50,50)\n\nhsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n#Blue color\nlower_blue = np.array([100,110,110])\nupper_blue = np.array([130,255,255])\nblue_mask = cv2.inRange(hsv_img, lower_blue, upper_blue)\nblue = cv2.bitwise_and(img, img, mask = blue_mask)\n\n# Red Color\nlower_red = np.array([0,100,20])\nupper_red = np.array([8,255,255])\nlower_red2 = np.array([175,100,20])\nupper_red2 = np.array([179,255,255])\n\nred_mask = cv2.inRange(hsv_img, lower_red, upper_red)\nred_mask2 = cv2.inRange(hsv_img, lower_red2, upper_red2)\nmask_red = cv2.add(red_mask, red_mask2)\nred = cv2.bitwise_and(img, img, mask = mask_red)\nmed = cv2.medianBlur(mask_red, 5)\ncv2.imwrite(\"ref_red.png\", med)\n\n#White color\nlower_white = np.array([250,250,250])\nupper_white = np.array([255,255,255])\nwhite_mask = cv2.inRange(hsv_img, lower_white, upper_white)\nwhite = cv2.bitwise_and(img, img, mask = white_mask)\n\ndef getContours(img):\n\n contours,hierarchy = cv2.findContours(img,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)\n tri=0\n sqr=0\n cir=0\n area_tri=0\n area_sqr=0\n area_cir=0\n\n for cnt in contours:\n area = cv2.contourArea(cnt)\n #print(area)\n\n if area>10:\n cv2.drawContours(imgContour, cnt, -1, (0, 255, 0), 1)\n peri = cv2.arcLength(cnt,True)\n approx = cv2.approxPolyDP(cnt,0.02*peri,True)\n #print(len(approx))\n x, y, w, h = cv2.boundingRect(approx)\n \n #if objCor >=3 and objCor <=3.5:\n if len(approx)==3: \n tri+=1\n area_tri+=area+area\n objectType=\"Tri\"\n\n elif len(approx)==4:\n sqr+=1\n area_sqr=area+area\n objectType=\"Squ\"\n\n elif len(approx)>5: \n cir+=1\n area_cir=area+area\n objectType=\"Cir\"\n\n else:objectType=\"None\"\n \n cv2.rectangle(imgContour,(x,y),(x+w,y+h),(0,255,0),2)\n #Put text\n cv2.putText(imgContour,objectType,\n (x+(w//2)-10,y+(h//2)-10),cv2.FONT_HERSHEY_COMPLEX,0.4,\n (0,0,0),2)\n\n totaltri=(tri)\n totalsqr=(sqr)\n totalcir=(cir)\n totalfig=tri+sqr+cir\n total_area_tri=area_tri\n total_area_sqr=area_sqr\n total_area_cir=area_cir\n total_area = total_area_tri+total_area_sqr+total_area_cir\n print(\"--------------------------------- Number of figures ---------------------------------\")\n print(\"Total Number of Triangles: \", totaltri)\n print(\"Total Number of Squares: \", totalsqr)\n print(\"Total Number of Circles: \", totalcir)\n print(\"Total Number of Figures\", totalfig)\n print(\"--------------------------------- Number of areas ---------------------------------\")\n print(\"Total Area of Triangles: \", total_area_tri)\n print(\"Total Area of Squares: \", total_area_sqr)\n print(\"Total Area of Circles: \", total_area_cir)\n print(\"Total Area of Figures: \", total_area)\n\n\ncv2.imshow('Color Segmentation: Red', red)\ncv2.imshow('Color Segmentation: Blue', blue)\n\ngetContours(imgCanny)\nimgBlank = np.zeros_like(img)\ncv2.imshow(\"Final\", imgContour)\n\n#print(tri)\n#print(sqr)\n#print(cir)\ncv2.waitKey(0)\n","sub_path":"final_project/Code1.py","file_name":"Code1.py","file_ext":"py","file_size_in_byte":3382,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"315757741","text":"import csv\nimport json\nimport sys\nfrom datetime import datetime\nfrom os import path\n\nimport pandas as pd\nfrom PyQt5 import QtCore\nfrom PyQt5.QtCore import QSize, Qt\nfrom PyQt5.QtGui import QColor, QDoubleValidator, QIcon, QPalette\nfrom PyQt5.QtWidgets import (QApplication, QCheckBox, QComboBox, QDialog,\n QGroupBox, QHeaderView, QLabel, QLineEdit,\n QMessageBox, QPushButton, QTableWidget,\n QTableWidgetItem, QTabWidget, QVBoxLayout,\n QWidget)\n\n\ndef dark_mode(app):\n \"\"\"\n Set the colour palette of an app to a custom dark mode theme.\n\n Parameters\n ----------\n app : PyQt5.QtWidgets.QApplication\n The Qt App object\n\n Returns\n -------\n PyQt5.QtWidgets.QApplication\n The Qt app with the dark mode theme applied.\n \"\"\"\n palette = QPalette()\n palette.setColor(QPalette.Window, QColor(30, 30, 30))\n palette.setColor(QPalette.WindowText, QColor(225, 225, 225))\n palette.setColor(QPalette.Light, Qt.white)\n palette.setColor(QPalette.Midlight, QColor(225, 225, 225))\n palette.setColor(QPalette.Dark, QColor(65, 65, 65))\n palette.setColor(QPalette.Mid, QColor(160, 160, 160))\n palette.setColor(QPalette.BrightText, QColor(255, 51, 51))\n palette.setColor(QPalette.Button, QColor(40, 40, 40))\n palette.setColor(QPalette.Base, QColor(65, 65, 65))\n palette.setColor(QPalette.AlternateBase, QColor(50, 50, 50))\n palette.setColor(QPalette.ToolTipBase, Qt.white)\n palette.setColor(QPalette.ToolTipText, Qt.white)\n palette.setColor(QPalette.Text, QColor(225, 225, 225))\n palette.setColor(QPalette.ButtonText, QColor(225, 225, 225))\n palette.setColor(QPalette.Link, QColor(42, 130, 218))\n palette.setColor(QPalette.Highlight, QColor(42, 130, 218))\n palette.setColor(QPalette.HighlightedText, Qt.black)\n app.setPalette(palette)\n return app\n\n\nclass WeightLog():\n \"\"\"\n Main class for storing both the Qt app and database, with functions\n that act on both objects.\n \"\"\"\n\n def __init__(self, settings):\n self.settings = settings\n # self.df = pd.read_csv(self.settings['database_path'], encoding='utf-8')\n self.tabdialog = Tab(settings)\n self.tabdialog.show()\n\n\nclass Tab(QDialog):\n \"\"\"\n Child class of a Qt dialog window. Where a dialog window is a top-level\n window mostly used for short-term tasks and brief communications with\n the user.\n\n Parameters\n ----------\n QDialog : PyQt5.QtWidgets.QDialog\n The main Qt dialog window which stores the individual tabs of the\n programme.\n \"\"\"\n\n def __init__(self, settings):\n super().__init__()\n self.setWindowTitle(\"Body Weight Log\")\n self.setWindowIcon(QIcon(\"icon.png\"))\n vbox = QVBoxLayout()\n tabWidget = QTabWidget()\n\n font = tabWidget.font()\n font.setPointSize(settings['font_point_size'])\n tabWidget.setFont(font)\n self.setMinimumSize(\n QSize(\n settings['WindowWidth'],\n settings['WindowHeight']))\n\n tabWidget.addTab(TabAdd(settings), \"Add data\")\n tabWidget.addTab(TabCSV(settings), \"View table\")\n tabWidget.addTab(TabPlotting(settings), \"Plot data\")\n vbox.addWidget(tabWidget)\n self.setLayout(vbox)\n\n\nclass TabAdd(QWidget):\n \"\"\"\n Child class of Qt QWidget object. This class represents the first\n tab of the user interface which contains the database additional\n functionalilty.\n\n Parameters\n ----------\n QWidget : PyQt5.QtWidgets.QWidget\n The widget is the atom of the user interface.\n \"\"\"\n\n def __init__(self, settings):\n \"\"\"\n Setup the \"Add data\" applicaiton tab.\n\n Parameters\n ----------\n settings : dict\n Settings dict imported from the app settings.json file.\n \"\"\"\n super().__init__()\n self.settings = settings\n self.font_size = settings['font_point_size']\n weightlabel = QLabel(\"Weight (kg):\")\n self.weight_edit = QLineEdit()\n self.only_floats = QDoubleValidator()\n self.weight_edit.setValidator(self.only_floats)\n self.weight_edit.setPlaceholderText(self.last_measurement())\n\n date = QLabel(\"Date:\")\n self.date_edit = QLineEdit()\n self.date_edit.setText(datetime.today().strftime(\"%Y-%m-%d\"))\n\n addr = QLabel(\"Comment:\")\n self.comment_edit = QLineEdit()\n self.comment_edit.setPlaceholderText(\"Optional\")\n\n addval = QPushButton(\"Add value\")\n addval.clicked.connect(self.clickMethod)\n\n vbox = QVBoxLayout()\n vbox.setContentsMargins(30, 30, 30, 30)\n vbox.addWidget(weightlabel)\n vbox.addWidget(self.weight_edit)\n vbox.addWidget(date)\n vbox.addWidget(self.date_edit)\n vbox.addWidget(addr)\n vbox.addWidget(self.comment_edit)\n vbox.addStretch(30)\n vbox.addWidget(addval)\n self.setLayout(vbox)\n\n def last_measurement(self):\n \"\"\"\n Get the last weight value entered into the database.\n\n Returns\n -------\n str\n Last measured weight in kg.\n \"\"\"\n with open(self.settings['database_path'], newline='') as f:\n reader = csv.reader(f)\n data = list(reader)\n for entry in reversed(data):\n if entry[1] != '':\n return str(entry[1])\n return ''\n\n def date_already_has_data(self):\n new_val = self.date_edit.text()\n with open(self.settings['database_path'], newline='') as f:\n reader = csv.reader(f)\n data = list(reader)\n\n for entry in reversed(data):\n if entry[1] == new_val:\n True\n return False\n\n def date_format_correct(self):\n \"\"\"\n Check that date entered conforms to \"%Y-%m-%d\" format.\n\n Returns\n -------\n bool\n True if date is of a valid format, else False.\n \"\"\"\n valid_format = True\n try:\n new_val = self.date_edit.text()\n datetime_object = datetime.strptime(new_val, \"%Y-%m-%d\")\n except BaseException:\n valid_format = False\n return valid_format\n\n def date_temporal_paradox_free(self):\n \"\"\"\n Checks that the date entered is not after todays date. You can't know\n tommorrows weight if you haven't measured it yet.\n\n Returns\n -------\n bool\n True if date is not a temporal paradox, else False.\n \"\"\"\n valid_date = True\n new_val = self.date_edit.text()\n datetime_object = datetime.strptime(new_val, \"%Y-%m-%d\")\n\n if datetime_object > datetime.now():\n valid_date = False\n return valid_date\n\n def clickMethod(self):\n \"\"\"\n Defines the response to the \"Add value\" button.\n \"\"\"\n if not self.date_format_correct():\n msg = QMessageBox()\n msg.setWindowTitle(\"Warning\")\n msg.setWindowIcon(QIcon(\"icon.png\"))\n msg.setIcon(QMessageBox.Warning)\n\n font = msg.font()\n font.setPointSize(self.font_size)\n msg.setFont(font)\n\n msg.setText(\"Date entered in incorrect format.\")\n msg.setStandardButtons(QMessageBox.Ok)\n msg.setDefaultButton(QMessageBox.Ok)\n\n x = msg.exec_() # show our messagebox\n\n elif not self.date_temporal_paradox_free():\n msg = QMessageBox()\n msg.setWindowTitle(\"Warning\")\n msg.setWindowIcon(QIcon(\"icon.png\"))\n msg.setIcon(QMessageBox.Warning)\n\n font = msg.font()\n font.setPointSize(self.font_size)\n msg.setFont(font)\n\n msg.setText(\n \"Date entered is invalid since it is after today's date.\")\n msg.setStandardButtons(QMessageBox.Ok)\n msg.setDefaultButton(QMessageBox.Ok)\n\n x = msg.exec_() # show our messagebox\n\n elif not self.date_already_has_data():\n msg = QMessageBox()\n msg.setWindowTitle(\"Warning\")\n msg.setWindowIcon(QIcon(\"icon.png\"))\n msg.setIcon(QMessageBox.Warning)\n\n font = msg.font()\n font.setPointSize(self.font_size)\n msg.setFont(font)\n\n msg.setText(\"Date entered already has a weight value of \"\n f\"{self.weight_edit.text()} in the database. Do you \"\n \"wish to override this value?\")\n msg.setStandardButtons(QMessageBox.Ok | QMessageBox.Cancel)\n msg.setDefaultButton(QMessageBox.Ok)\n msg.buttonClicked.connect(self.okay_button2)\n x = msg.exec_() # show our messagebox\n\n else:\n msg = QMessageBox()\n msg.setWindowTitle(\"New entry\")\n msg.setWindowIcon(QIcon(\"icon.png\"))\n font = msg.font()\n font.setPointSize(self.font_size)\n msg.setFont(font)\n msg.setText(\"New entry added to weight log\")\n msg.setStandardButtons(QMessageBox.Ok | QMessageBox.Cancel)\n msg.setDefaultButton(QMessageBox.Ok)\n msg.buttonClicked.connect(self.okay_button1)\n x = msg.exec_() # show our messagebox\n\n def okay_button1(self, i):\n \"\"\"\n Execute if okay button is pressed from AddTab when no warnings are\n presented.\n\n Parameters\n ----------\n i : PyQt5.QtWidgets.QPushButton\n Button class object\n \"\"\"\n if i.text() == \"OK\":\n print(\"Entry added\")\n\n def okay_button2(self, i):\n \"\"\"\n Execute if okay button is pressed from AddTab when entry for given \"\n \"date already exists in the database.\n\n Parameters\n ----------\n i : PyQt5.QtWidgets.QPushButton\n Button class object\n \"\"\"\n if i.text() == \"OK\":\n print(\"Entry overwritten\")\n\n\nclass PandasWidget(QTableWidget):\n \"\"\"\n Redifinition of the Qt QTableWidget class to implement an editable table\n view of the database.\n\n Parameters\n ----------\n QTableWidget : PyQt5.QtWidgets.QTableWidget\n Table widgets provide standard table display facilities for\n applications.\n \"\"\"\n\n def __init__(self, df):\n super().__init__()\n self.df = df\n self.setStyleSheet('font-size: 28px;')\n self.insert_data(df)\n\n self.verticalHeader().setSectionResizeMode(QHeaderView.Stretch)\n self.horizontalHeader().setSectionResizeMode(QHeaderView.Stretch)\n\n def insert_data(self, data):\n \"\"\"\n Populate the table with the data from the weight database.\n\n Parameters\n ----------\n data : Pandas DataFrame\n The data to be displayed in the table.\n \"\"\"\n # set table dimension\n self.df = data\n nRows, nColumns = data.shape\n self.setColumnCount(nColumns)\n self.setRowCount(nRows)\n self.setHorizontalHeaderLabels(data.columns)\n\n # data insertion\n for i in range(self.rowCount()):\n for j in range(self.columnCount()):\n self.setItem(i, j, QTableWidgetItem(str(data.iloc[i, j])))\n\n # Enable cell updates\n self.cellChanged[int, int].connect(self.updateDF)\n\n self.scrollToBottom()\n\n def updateDF(self, row, column):\n \"\"\"\n Updates a specificed cell in the database.\n\n Parameters\n ----------\n row : int\n The database row index.\n column : int\n The database column index.\n \"\"\"\n text = self.item(row, column).text()\n self.df.iloc[row, column] = text\n\n\nclass TabCSV(QWidget):\n \"\"\"\n Child class of Qt QWidget object. This class represents the second tab in\n the application which presents a table of the weight database which can be\n inspected and edited by the user.\n\n Parameters\n ----------\n QWidget : PyQt5.QtWidgets.QWidget\n The widget is the atom of the user interface.\n \"\"\"\n\n def __init__(self, settings):\n super().__init__()\n self.settings = settings\n self.load_csv()\n\n mainLayout = QVBoxLayout()\n self.tableView = PandasWidget(pd.DataFrame())\n # self.tableView = PandasWidget(self.database)\n\n self.pushButtonLoad = QPushButton(\"Load csv\")\n self.pushButtonLoad.clicked.connect(self.reload_csv)\n\n self.pushButtonWrite = QPushButton(\"Write to csv\")\n self.pushButtonWrite.clicked.connect(self.write_csv)\n\n mainLayout.addWidget(self.tableView)\n mainLayout.addWidget(self.pushButtonLoad)\n mainLayout.addWidget(self.pushButtonWrite)\n self.setLayout(mainLayout)\n\n def load_csv(self):\n \"\"\"\n Load in the weight databse from the location specified in the\n settings json file.\n \"\"\"\n self.database = pd.read_csv(\n self.settings['database_path'],\n encoding='utf-8')\n\n def write_csv(self):\n \"\"\"\n Write to the weight databse in the location specified in the settings\n json file.\n \"\"\"\n self.tableView.df.to_csv('Data export.csv', index=False)\n print('CSV file exported')\n\n @QtCore.pyqtSlot()\n def reload_csv(self):\n \"\"\"\n Full function to load csv data and populate the table with the new\n data.\n \"\"\"\n self.load_csv()\n self.tableView.insert_data(self.database)\n self.update()\n\n\nclass TabPlotting(QWidget):\n \"\"\"\n Child class of Qt QWidget object. This class represents the final tab of\n the application which is used for plotting values in the weight database.\n\n Parameters\n ----------\n QWidget : PyQt5.QtWidgets.QWidget\n The widget is the atom of the user interface.\n \"\"\"\n\n def __init__(self, settings):\n \"\"\"\n Initialse the layout and user iterface of the plotting tab.\n\n Parameters\n ----------\n settings : dict\n Settings from the json settings file.\n \"\"\"\n super().__init__()\n self.settings = settings\n groupBox = QGroupBox(\"Select data range\")\n list = [\"1M\", \"3M\", \"6M\", \"YTD\", \"1Y\", \"MAX\"]\n combo = QComboBox()\n combo.addItems(list)\n vbox = QVBoxLayout()\n vbox.addWidget(combo)\n groupBox.setLayout(vbox)\n groupBox2 = QGroupBox(\"Plot options\")\n av7day = QCheckBox(\"7 day average\")\n av7day.setChecked(1)\n lineplot = QCheckBox(\"Lineplot\")\n plotlbs = QCheckBox(\"Plot in lbs\")\n\n vboxp = QVBoxLayout()\n vboxp.addWidget(av7day)\n vboxp.addWidget(lineplot)\n vboxp.addWidget(plotlbs)\n\n plotvals = QPushButton(\"Plot data\")\n\n groupBox2.setLayout(vboxp)\n mainLayout = QVBoxLayout()\n mainLayout.setContentsMargins(30, 30, 30, 30)\n mainLayout.addWidget(groupBox)\n mainLayout.addWidget(groupBox2)\n mainLayout.addStretch(30)\n mainLayout.addWidget(plotvals)\n self.setLayout(mainLayout)\n\n\ndef make_database(name):\n \"\"\"\n Create database if no current database exists.\n\n Parameters\n ----------\n name : str\n Name of database (must have .csv extension)\n e.g. my_database.csv\n\n \"\"\"\n if path.exists(name):\n return True\n else:\n headers = [['Date', 'Weight(kg)', 'Notes']]\n with open(name, 'w', newline='') as f:\n writer = csv.writer(f)\n writer.writerows(headers)\n return False\n\n\n# The main attraction\nif __name__ == \"__main__\":\n\n # Import settings\n with open('settings.json', 'r') as f:\n settings = json.load(f)\n\n # Create database if one doesn't exist\n db_exists = make_database(settings['database_path'])\n\n # Start the app\n app = QApplication(sys.argv)\n app.setStyle(\"Fusion\") # Force the style to be the same on all OSs\n app = dark_mode(app)\n weightlog = WeightLog(settings)\n app.exec()\n","sub_path":"scale_app.py","file_name":"scale_app.py","file_ext":"py","file_size_in_byte":16070,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"607264488","text":"\n#####VecDyn query\n\n\n\n\n@auth.requires_login()\ndef vec_dyn_query():\n \"\"\"\n Controller to serve a searchable grid view of the vector dynamics\n datasets with a download function. We want to be able to download\n individual records, selected sets of records and all records\n covered by a query.\n\n The individual row / selected sets is handled by a download controller\n that grabs ids from the request variables and send back the data as\n a csv.\n\n The complete set of rows in a grid query is accessible to the export\n buttons within the grid code, so the download all option uses that\n mechanism but expands the rows to the datasets for each row before\n returning. Currently, this won't let you Download All for more than\n 50 MainID records.\n \"\"\"\n\n # control which fields available\n [setattr(f, 'readable', False) for f in db.taxon\n if f.name not in ('db.taxon.tax_species,db.taxon.tax_genus,'\n 'db.taxon.tax_family,db.taxon.tax_order')]\n # [setattr(f, 'readable', False) for f in db.StudyLocation\n # if f.name not in ('db.StudyLocation.Country,db.StudyLocation.CountyStateProvince')]\n # MainID is not made unreadable, so that it can be accessed by the export controller\n [setattr(f, 'readable', False) for f in db.study_data\n if f.name not in ('db.study_data.id, db.study_data.location_description')]\n\n # Add selectability checkboxes\n select = [('Download selected',\n lambda ids : redirect(URL('queries', 'vec_dyn_download', vars=dict(ids=ids))),\n 'btn btn-default')]\n\n # Adding an exporter that grabs all the data from a query,\n # the name _with_hidden_cols is needed to expose the MainID in the\n # rows passed to the exporter class. Note that nothing unreadable\n # can be exposed.\n export = dict(data_with_hidden_cols=(ExporterAll, 'Export All'),\n csv_with_hidden_cols=False,\n csv=False, xml=False, html=False, json=False,\n tsv_with_hidden_cols=False, tsv=False)\n\n # turn the MainID into a download link\n db.study_data.id.represent = lambda value, row: A(value, _href=URL(\"queries\",\"vec_dyn_download\",\n vars={'ids': row.study_data.id}))\n\n # get the grid\n grid = SQLFORM.grid((db.taxon.taxonID == db.study_data.taxon_id)&\n (db.gaul_admin_layers.ADM_CODE == db.study_data.location_ID),\n exportclasses=export,\n ignore_common_filters=True,\n field_id=db.study_data.id,\n fields= [db.study_data.id,\n db.taxon.tax_species, db.taxon.tax_genus,\n db.taxon.tax_family, db.taxon.tax_order,\n db.taxon.tax_class, db.taxon.tax_phylum,\n db.gaul_admin_layers.ADM0_NAME, db.gaul_admin_layers.ADM1_NAME,\n db.study_data.location_description],\n headers={'taxon.tax_species' : 'Taxon Name',\n 'taxon.tax_genus' : 'Genus',\n 'taxon.tax_family' : 'Family',\n 'taxon.tax_order' : 'Order',\n 'taxon.tax_class' : 'Class',\n 'taxon.tax_phylum' : 'Phylum',\n 'gaul_admin_layers.ADM0_NAME' : 'Country',\n 'gaul_admin_layers.ADM1_NAME' : 'Region',\n 'gaul_admin_layers.ADM2_NAME': 'County',\n 'study_data.id' : 'Dataset ID' },\n maxtextlength=100,\n selectable=select,\n deletable=False, editable=False, details=False, create=False)\n\n # The final bit of untidiness is the location of the buttons.\n # - The export 'menu' (a single button here) is at the bottom of the page.\n # This button doesn't submit a form, just calls the page again with _export_type\n # set, so we can simply move it.\n # - The Download selected button is more tricky: selectable turns the grid\n # table into a form, which the download selected button needs to be inside.\n # I've simply hidden it and added a fake button in the right location that\n # uses JS to press the real submit. Having a fake submit and using JS to submit\n # the form directly wasn't getting the request right, so this is easier!\n\n # The script we're setting up for is this:\n\n # \n\n # Create some buttons to add (one is just a link, masquerading\n # as a button, the other presses the hidden submit on the real form).\n # This code shouldn't run if no records are found by a search, since then\n # the export menu and the selectable form don't exist.\n exp_menu = grid.element('.w2p_export_menu')\n if exp_menu is not None:\n\n exp_menu = grid.element('.w2p_export_menu')\n exp_all = A(\"Download all\", _class=\"btn btn-default\",\n _href=exp_menu[1].attributes['_href'],\n _style='padding:6px 12px;line-height:20px')\n fake_exp_sel = INPUT(_value='Download selected', _type='submit',\n _class=\"btn btn-default\", _id='fake_exp_sel',\n _style='padding:6px 12px;line-height:20px')\n\n # add the buttons after the end of the web2py console form\n console = grid.element('.web2py_console')\n console[1].insert(1, CAT(exp_all, fake_exp_sel))\n\n # add an ID to the selection form, to allow JS to link the\n # new button to form submission\n sel_form = grid.element('.web2py_table form')\n sel_form['_id'] = 'select_form'\n\n\n # Delete the original export menu\n export_menu_idx = [x.attributes['_class'] for x in grid].index('w2p_export_menu')\n del grid[export_menu_idx]\n\n # hide the real export selected button and add an ID\n exp_sel = grid.element('.web2py_table .btn')\n exp_sel['_style'] = 'display:none;'\n exp_sel['_id'] = 'exp_sel'\n\n return dict(grid=grid)\n\ndef _get_data_csv(ids):\n \"\"\"\n Internal function that gets the required fields for downloading datasets\n for a given set of MainID ids and returns the data formatted as csv.\n\n This compilation is needed by both the dataset download controller\n and the Exporter class, so define here once and call from each.\n \"\"\"\n\n rows = db((db.study_data.id.belongs(ids) &\n (db.taxon.taxonID == db.study_data.taxon_id) &\n (db.gaul_admin_layers.ADM_CODE == db.study_data.location_ID) &\n (db.collection_info.id == db.study_data.collection_info_id) &\n (db.sample_data.study_data_id == db.study_data.id)),ignore_common_filters=True).select(\n db.study_data.title, db.gaul_admin_layers.ADM0_NAME, db.gaul_admin_layers.ADM1_NAME,db.gaul_admin_layers.ADM2_NAME,\n db.gaul_admin_layers.centroid_latitude, db.gaul_admin_layers.centroid_longitude,\n db.study_data.location_description,db.study_data.location_environment,\n db.study_data.study_lat_DD, db.study_data.study_long_DD, db.study_data.geo_datum,\n db.study_data.spatial_accuracy,db.study_data.location_extent,db.study_data.species_id_method,\n db.study_data.study_design,db.study_data.sampling_strategy,db.study_data.sampling_method,\n db.study_data.sampling_protocol,db.study_data.measurement_unit,db.study_data.study_collection_area,\n db.study_data.value_transform,db.taxon.tax_species, db.taxon.tax_genus, db.taxon.tax_family,\n db.taxon.tax_order, db.taxon.tax_class, db.taxon.tax_phylum,\n db.study_data.location_description,db.sample_data.sample_start_date, db.sample_data.sample_start_time,\n db.sample_data.sample_end_date, db.sample_data.sample_end_time, db.sample_data.value,\n db.sample_data.sample_info, db.sample_data.sample_name,db.sample_data.sample_sex,\n db.sample_data.sample_lat_DD, db.sample_data.sample_long_DD, db.collection_info.title,\n db.collection_info.collection_authority,db.collection_info.DOI,db.collection_info.publication_date,\n db.collection_info.description,db.collection_info.contact_name,db.collection_info.contact_email)\n return rows.as_csv()\n\n\ndef vec_dyn_download():\n\n \"\"\"\n Function to return the data of records matching the ids\n \"\"\"\n\n # Get the ids. If there are multiple ids, then we get a list,\n # and we need an iterable for belongs, so all we have to trap\n # is a single ID which comes in as a string\n ids = request.vars['ids']\n if isinstance(ids, str):\n ids = [ids]\n\n data = _get_data_csv(ids)\n\n # and now poke the text object out to the browser\n response.headers['Content-Type'] = 'text/csv'\n attachment = 'attachment;filename=vec_dyn_download_{}.txt'.format(datetime.date.today().isoformat())\n response.headers['Content-Disposition'] = attachment\n\n raise HTTP(200, data,\n **{'Content-Type':'text/csv',\n 'Content-Disposition':attachment + ';'})\n\n\nclass ExporterAll(object):\n\n \"\"\"\n Used to export all the data associated with rows in the grid\n \"\"\"\n\n file_ext = \"csv\"\n content_type = \"text/csv\"\n\n def __init__(self, rows):\n self.rows = rows\n\n def export(self):\n\n if self.rows:\n # expand rows to get full data and return that\n request.vars._export_filename = \"yourname\"\n ids = [rw.study_data.id for rw in self.rows]\n\n # currently simple check that we aren't trying to export too much\n if len(ids) > 50:\n return 'Download all is currently restricted to searches including fewer than 50 records.'\n else:\n data = _get_data_csv(ids)\n return data\n else:\n return\n","sub_path":"controllers/queries.py","file_name":"queries.py","file_ext":"py","file_size_in_byte":10357,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"146881479","text":"\"\"\"Command line interface for semantic segmentation.\"\"\"\nfrom __future__ import annotations\n\nimport click\n\nfrom tiatoolbox.cli.common import (\n cli_batch_size,\n cli_file_type,\n cli_img_input,\n cli_masks,\n cli_mode,\n cli_num_loader_workers,\n cli_on_gpu,\n cli_output_path,\n cli_pretrained_model,\n cli_pretrained_weights,\n cli_verbose,\n cli_yaml_config_path,\n prepare_ioconfig_seg,\n prepare_model_cli,\n tiatoolbox_cli,\n)\n\n\n@tiatoolbox_cli.command()\n@cli_img_input()\n@cli_output_path(\n usage_help=\"Output directory where model predictions will be saved.\",\n default=\"semantic_segmentation\",\n)\n@cli_file_type(\n default=\"*.png, *.jpg, *.jpeg, *.tif, *.tiff, *.svs, *.ndpi, *.jp2, *.mrxs\",\n)\n@cli_mode(\n usage_help=\"Type of input file to process.\",\n default=\"wsi\",\n input_type=click.Choice([\"patch\", \"wsi\", \"tile\"], case_sensitive=False),\n)\n@cli_pretrained_model(default=\"fcn-tissue_mask\")\n@cli_pretrained_weights(default=None)\n@cli_on_gpu()\n@cli_batch_size()\n@cli_masks(default=None)\n@cli_yaml_config_path()\n@cli_num_loader_workers()\n@cli_verbose()\ndef semantic_segment(\n pretrained_model: str,\n pretrained_weights: str,\n img_input: str,\n file_types: str,\n masks: str | None,\n mode: str,\n output_path: str,\n batch_size: int,\n yaml_config_path: str,\n num_loader_workers: int,\n *,\n on_gpu: bool,\n verbose: bool,\n) -> None:\n \"\"\"Process an image/directory of input images with a patch classification CNN.\"\"\"\n from tiatoolbox.models import IOSegmentorConfig, SemanticSegmentor\n from tiatoolbox.utils import save_as_json\n\n files_all, masks_all, output_path = prepare_model_cli(\n img_input=img_input,\n output_path=output_path,\n masks=masks,\n file_types=file_types,\n )\n\n ioconfig = prepare_ioconfig_seg(\n IOSegmentorConfig,\n pretrained_weights,\n yaml_config_path,\n )\n\n predictor = SemanticSegmentor(\n pretrained_model=pretrained_model,\n pretrained_weights=pretrained_weights,\n batch_size=batch_size,\n num_loader_workers=num_loader_workers,\n verbose=verbose,\n )\n\n output = predictor.predict(\n imgs=files_all,\n masks=masks_all,\n mode=mode,\n on_gpu=on_gpu,\n save_dir=output_path,\n ioconfig=ioconfig,\n )\n\n save_as_json(output, str(output_path.joinpath(\"results.json\")))\n","sub_path":"tiatoolbox/cli/semantic_segment.py","file_name":"semantic_segment.py","file_ext":"py","file_size_in_byte":2414,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"60410759","text":"#!/usr/bin/python\n\n\"\"\"\n\nTODO:\n Put more care into what sort of data is inputted in the calculation of the fwhm. Maybe it's not a peak at all...\n Put all the comments above the functions as docstrings within the functions\n Think of how to deal with L: get from the size of the coefficients_vector or pass as parameter?\n Plotting routines should have control over x, y axis?\n Test convert_to_gnuplot and steady_pdf and stablish consistent functionality: write to file or plot? or both?\n When using np.max to get peak, maybe it's better to interpolate...\n Make plot_eigenfunction and plot_steady_pdf the same function\n\n Approach qualified as \"MINE\" does not work so far...\n HARDCODED size of stats[] output (i.e. number of statistics)\n\nREMARKS:\n Note commented out lines: import lmfit and the whole function fwhm_from_lorentzian() because computer in the office has\n an old version of scipy (0.10.6, and need minimum 0.11...) \n\n\"\"\"\n\nimport numpy as np\nfrom scipy.interpolate import UnivariateSpline\nimport lmfit \nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\n\nimport scipy as sp\n\nimport math\nimport sys\nimport time \n\n# The following function is not very elegant: number of parameters hardcoded and initialization weird. Alternatives?\n# Use named tuple?\n# TODO: When I add a new statistic, I have to change here:\n# stats initialization (first line) and assignment of values\n# when called from run_over_L, we need to change there:\n# nump_output_stats, which has to be the same as the size of stats in get_stats\n# the format of the stats_summary_file, where we have to add a string (moreover, note that there 'L' also appears)\n# Maybe I could add L as well, since we can know it from the size of eigenvalues?\n\ndef get_stats(freq, power_spectrum, eigenvectors, eigenvalues):\n stats = np.zeros(7)\n L = int((np.sqrt(eigenvectors.shape[0]) - 1)/2)\n eigenvector1, eigenvalue1 = get_slowest_mode(eigenvectors, eigenvalues)\n peak_position, peak_value = get_peak_from_lorentzian_fit(freq, power_spectrum)\n stats[0] = L\n stats[1] = get_fwhm_from_lorentzian_fit(freq, power_spectrum) \n stats[2] = peak_position\n stats[3] = peak_value \n stats[4] = get_quality_factor(freq, power_spectrum)\n stats[5] = np.abs(np.real(eigenvalue1))\n stats[6] = np.abs(np.imag(eigenvalue1))\n\n # In case we want to use the data not obtained from the fit\n #stats[0] = get_fwhm(freq, power_spectrum) \n #stats[2] = get_peak_value(power_spectrum)\n return stats\n\n#def get_eigenvalues\n\ndef get_peak_value(y):\n \"\"\"Return the value of y at the peak for data containing a single peak\n TODO:\n This assumes there is a single maximum in y, which is usually the case for the power spectra seen so far. Need to be\n made more robust by finding local extrema. A possible solution can be found in\n http://stackoverflow.com/questions/4624970/finding-local-maxima-minima-with-numpy-in-a-1d-numpy-array\n\n Used only to get some sort of normalization to compare to rtol\n \"\"\"\n return np.max(y)\n\ndef get_peak_from_lorentzian_fit(x, y):\n \"\"\"Return the value of y at the peak for data containing a single peak using a double Lorentzian fit\"\"\"\n fit_result = fit_double_lorentzian(x, y)\n peak_position = fit_result.params.valuesdict()['positive_center']\n # Looks a little hacky but x only admits numpy arrays (we cannot simply do x=peak_position)\n peak_value = fit_result.eval(x=np.array([peak_position]))[0]\n return peak_position, peak_value \n\ndef get_quality_factor(x, y):\n \"\"\"Return the quality factor for data containing a single peak\"\"\"\n #return x[np.argmax(y)]/get_fwhm(x,y)\n return x[np.argmax(y)]/get_fwhm_from_lorentzian_fit(x,y)\n\ndef get_fwhm(x, y):\n \"\"\" Return FWHM for data containing a single peak\n Based on http://stackoverflow.com/questions/10582795/finding-the-full-width-half-maximum-of-a-peak\n It uses interpolation to find the right x-values for the half-maximum (even if they don't exist in the original array.\n\n Another possible implementation would be something like (google \"radpy fwhm\")\n truncated_data = x[where(y > np.max(y)/2)]\n fwhm = truncated_data[-1] - truncated_data[0]\n ... but will suffer if not too many data, interpolation is better\n\n It breaks for large values of the noise where a peak is barely visible. Included a small hack that works for\n intermediate values of the noise (0.5 < D < 1.0), but for larger noise the peak is at f = 0, and we are not talking\n any more of the fwhm of a peak in the power spectrum\n It will give error if the frequency window is not big enough to include the whole peak\n In general, fitting a Lorentzian is more robust!!\n \"\"\"\n\n # create a spline of x and y-np.max(y)/2 \n spline = UnivariateSpline(x, y - np.max(y)/2, s=0)\n x_peak = x[np.argmax(spline(x))]\n if len(spline.roots()) == 2:\n r1, r2 = spline.roots() # find the roots\n fwhm = r2 - r1\n elif len(spline.roots()) == 1 and x_peak < spline.roots()[0]:\n # Assume symmetry along peak, which is not always true...\n hwhm = spline.roots()[0] - x_peak\n fwhm = 2 * hwhm\n else:\n sys.exit(\"window not big enough\")\n\n #plt.plot(x,y)\n #plt.axvspan(r1, r2, facecolor='g', alpha=0.5)\n\n return fwhm\n\n\ndef get_fwhm_from_lorentzian_fit(x, y):\n \"\"\"\n Return FWHM for data containing a single peak using a double-Lorentzian fit\n\n It is thought for a one-sided power spectrum (which is real&even), so that values in x > 0\n \"\"\"\n fit_result = fit_double_lorentzian(x, y)\n\n return fit_result.params.valuesdict()['positive_fwhm']\n\ndef plot_double_lorentzian_fit(x, y):\n fit_result = fit_double_lorentzian(x, y)\n x, y = extend_to_even(x, y)\n positive_x = x > 0 # Determine positive side for plot\n plt.plot(x, y, 'bo', label = 'data')\n #plt.plot(x, fit_result.best_fit, 'r-', label = 'fit', linewidth = 2)\n plt.plot(x[positive_x], fit_result.best_fit[positive_x], 'r-', label = 'fit', linewidth = 2)\n plt.xscale('log')\n plt.yscale('log')\n\ndef extend_to_even(x, y):\n # Mirror (w.r.t. vertical axis) the one-sided spectrum that is passed to the function\n x = np.concatenate((-np.flipud(x),x))\n y = np.concatenate((np.flipud(y),y))\n return x, y\n\ndef fit_double_lorentzian(x, y):\n \"\"\"\n It is thought for a one-sided power spectrum (which is real&even), so that values in x > 0\n\n Args:\n x: e.g. frequencies\n y: e.g. power spectrum\n\n Comments:\n The fit to a single Lorentzian is only good for the vecinity of the peak.\n On the contrary, the double-Lorentzian fit suffers if we have only one-side of the spectrum.\n Fixing amplitude of the Lorentzian on negative side gives good visual agreement, but parameters on (-) side bad.\n Solution: mirror the spectrum with respect to y-axis since we know it is a real and even function\n I could also do here a first round fitting to a single Lorentzian and a second run fitting to 2 Lorentzians\n using as initial values for the parameters the result from the first fit.\n \"\"\"\n ###########\n ## Fit to a single Lorentzian\n #gmod = lmfit.models.LorentzianModel()\n #pars = gmod.guess(y, x=x, center=x[np.argmax(y)])\n #pars = gmod.guess(y, x=x)\n #result = gmod.fit(y, x=x, amplitude=0.5, center=0.07, sigma=0.03)\n #result = gmod.fit(y, pars, x=x)\n #return result.params.valuesdict()['fwhm']\n ########### Comment out until here to do the double-Lorentzian\n\n ##########\n # Fit to two Lorentzians\n # Need to check if frequency 0 is included in the array or not\n if x[0] < 0:\n sys.exit(\"Cannot mirror the data with respect to the y axis; first x-value is negative\")\n elif x[0] == 0:\n print(\"freq[0] = 0.0, removing such a value...\")\n x = x[1:]\n y = y[1:]\n\n x, y = extend_to_even(x, y)\n\n # mod 1 refers to the positive part of the power spectrum, mirror of mod2 (negative part)\n mod1 = lmfit.models.LorentzianModel(prefix=\"positive_\")\n mod2 = lmfit.models.LorentzianModel(prefix=\"negative_\")\n combined_model = mod1 + mod2 \n # Guess parameters for the positive part given the position of the center\n # Note the \"-\" sign, since np.argmax(y) only returns first occurence, which refers to the peak in the negative part\n pars = mod1.guess(y, x=x, positive_center=-x[np.argmax(y)])\n # After guessing, need to reset the value of the center to that of the max. Otherwise everything centered at 0 and\n # routine gives a fit to a function centered at 0 (need to somehow make the system \"unstable\" in order to converge)\n pars['positive_center'].value = -x[np.argmax(y)]\n # Example of how to set constraints on parameters\n #mod2.set_param_hint('negative_amplitude', vary=False)\n # Add parameters for the negative side based on the guesses from the positive \n pars += mod2.make_params(negative_center=-pars.valuesdict()['positive_center'],\n negative_amplitude=pars.valuesdict()['positive_amplitude'],\n negative_sigma=pars.valuesdict()['positive_sigma'] )\n\n result = combined_model.fit(y, pars, x=x)\n ########## Comment out until here to do the single-Lorentzian\n\n # For debugging\n #print(result.fit_report())\n #import matplotlib.pyplot as plt \n #plt.plot(x, y, 'bo')\n #plt.plot(x, result.init_fit, 'k--')\n #plt.plot(x, result.best_fit, 'r-')\n #plt.yscale('log')\n\n return result\n\ndef get_function_arguments():\n \"\"\"Returns tuple containing dictionary of calling function's\n named arguments and a list of calling function's unnamed\n positional arguments (the ones without default value? and \n the ones where the function call has as argument *list)\n\n TODO:\n needs some tweaking to get a proper parameter list\n since it resorts to locals(), it is not a good idea to use it for scripts that are run, since there is a lot of\n metadata and useless objects created by the interpreter there, I think\n\n From: http://kbyanc.blogspot.de/2007/07/python-aggregating-function-arguments.html\n \"\"\"\n from inspect import getargvalues, stack\n posname, kwname, args = getargvalues(stack()[1][0])[-3:]\n posargs = args.pop(posname, [])\n args.update(args.pop(kwname, []))\n return args, posargs\n\n# Print parameters to file at the top of the file, obtained from get_function_arguments()\n# need modification to get the other output (unnamed parameters) \ndef print_params_file(filename, params):\n with open(filename, 'w') as f_handle:\n for key, value in params.iteritems():\n f_handle.write(' '.join(['#',key,str(value),'\\n']))\n\n# Print parameters to stdout\n# TODO: this is just printing a dictionary... Is there a better way to do so?\ndef print_params(params):\n for key, value in params.iteritems():\n print(' '.join(['#',key,str(value),'\\n']))\n\ndef print_stats_format_file(filename):\n \"\"\"TODO: associate with get_stats by using a dictionary or named tuple there and automatically generate format here\"\"\"\n\n################################################################################\n\n# Helper functions to always control the size of vectors and matrices\n# Should I have one for big_c_odd and big_c_even?\n# TODO: I need one that does something like write_to_even_vector(n, L), same for odd\n# TODO: or maybe something like get_odd_index(m, l)\ndef get_size_even_matrices(L):\n return (2*L+1, 2*L+1)\n\ndef get_size_odd_matrices(L):\n return (2*L, 2*L)\n\ndef get_size_even_vector(L):\n return 2*L+1\n\ndef get_size_odd_vector(L):\n return 2*L\n\n\ndef get_number_c_vectors(L):\n return 2*L+1\n\ndef get_L_even_matrix(even_coeffs):\n # Remember: size of each even vector is 2*L+1, and vectors are stacked as column vectors in the big matrix of coeffs\n return int((even_coeffs.shape[0] - 1)/2)\n\ndef get_L_odd_matrix(odd_coeffs):\n # Remember: size of each odd vector is 2*L1, and vectors are stacked as column vectors in the big matrix of coeffs\n return int(odd_coeffs.shape[0]/2)\n\n# Get zero-th column (i.e. c_0) in big_c\ndef get_id_zero_column_big_c(L):\n return L\n\ndef get_id_center_element_even_c(L):\n return L\n# The following is not exactly the center of the vector since it has an even number of elements:\n# I just chose (for a vector c_n) the element that has as first index n, i.e. c_{n,n+1}\ndef get_id_center_element_odd_c(L):\n return L-1\n\n# TODO: GENERAL PROBLEM!! I SHOULD ONLY HAVE 2L ODD VECTORS AND NOT 2L+1.\n# In the current setting, more wave numbers appear on one side that on the other... (positive vs negative I mean)\n# This leads to problems when it comes to finding the conditions for the boundaries of the coefficients\n# (see weird conditions for the odd coefficients)\n\n# Extract a specific coefficient c_{m,k}, with those indices referring to the expansion\n# Rewrite using a function find_index (i.e. given (m, l), find index in odd or even coefficients matrices\n# TODO: Also write the other way round (given index in odd/even, find (m,l) from expansion\ndef get_coefficient(m, l, even_coefficients, odd_coefficients):\n if (even_coefficients.shape[1] != odd_coefficients.shape[1]):\n sys.exit(\"even and odd coefficient matrices have different number of columns\")\n else:\n L = (even_coefficients.shape[1] - 1)/2\n # The following is common to both even and odd coefficients\n id_column_zero = get_id_zero_column_big_c(L)\n \n if np.logical_and(np.logical_and( (m+l) % 2 == 0, np.abs(m+l) <= 2*L), np.abs(m-l) <= 2*L ):\n n = (m+l)/2\n id_center_vector = get_id_center_element_even_c(L)\n return even_coefficients[id_center_vector + m - n, id_column_zero + n]\n\n elif np.logical_and(np.logical_and( (m+l) % 2 != 0, (-2*L+1) < (m+l) <= (2*L+1)), np.abs(m-l) <= (2*L-1) ):\n n = (m+l-1)/2\n id_center_vector = get_id_center_element_odd_c(L)\n \n return odd_coefficients[id_center_vector + m - n, id_column_zero + n]\n\n else:\n return 0.0\n\n# The following function has testing purposes: given a inhomogeneity obtained from full matrix problem, obtain the\n# corresponding mcf inhomogeneity for odd coefficients. The parameter \"full_inhomogeneity\" should have obtained with an\n# L larger than 2*L+1 here in order for all the coefficients to be (in principle) different from zero\ndef set_inhomogeneity_odd_from_full(full_inhomogeneity, L):\n b = np.zeros((2*L,2*L+1), dtype=np.complex128)\n for k in range(-L,L+1):\n for i in range(-L+1, L):\n b[i+L-1, k+L] = full_matrix.get_coefficient(k + i,k+1-i, full_inhomogeneity)\n\n return b \n\n# In its simplest way, we only need a function to set inhomogeneity for matrices of odd coefficients, and we just need\n# to pass even coefficients of the stationary function since the odd ones are all 0\ndef set_inhomogeneity_odd_n(n, stationary_even_coeffs, stationary_odd_coeffs, p=1):\n L_even = get_L_even_matrix(stationary_even_coeffs)\n L_odd = get_L_odd_matrix(stationary_odd_coeffs)\n if L_even != L_odd:\n sys.exit(\"Dimensions of even and odd matrices of coefficients are not coherent\")\n else:\n L = L_even\n\n # To avoid cumbersome notation\n even_coeffs = stationary_even_coeffs\n odd_coeffs = stationary_odd_coeffs\n\n id_0 = get_id_center_element_odd_c(L)\n\n v = np.zeros(get_size_odd_vector(L), dtype=np.complex128)\n\n for i in range(-L+1, L+1):\n v[id_0+i] = 4. * 0.5 * (1j) * ((2*math.pi)**2 * \\\n get_coefficient(n+i, n-i+1, even_coeffs, odd_coeffs) * \\\n (get_coefficient(p, 0, even_coeffs, odd_coeffs) - get_coefficient(-p, 0, even_coeffs, odd_coeffs)) -\\\n (get_coefficient(n+i-p, n-i+1, even_coeffs, odd_coeffs) - get_coefficient(n+i+p, n-i+1, even_coeffs, odd_coeffs)))\n\n return v \n\ndef set_inhomogeneity_odd(stationary_even_coeffs, stationary_odd_coeffs, p=1):\n L_even = get_L_even_matrix(stationary_even_coeffs)\n L_odd = get_L_odd_matrix(stationary_odd_coeffs)\n if L_even != L_odd:\n sys.exit(\"Dimensions of even and odd matrices of coefficients are not coherent\")\n else:\n L = L_even\n\n number_c_vectors = get_number_c_vectors(L)\n size_odd = get_size_odd_vector(L)\n inhomogeneities = np.zeros((size_odd, number_c_vectors), dtype=np.complex128)\n\n # To avoid cumbersome notation\n even_coeffs = stationary_even_coeffs\n odd_coeffs = stationary_odd_coeffs\n\n zero_big_c = get_id_zero_column_big_c(L) \n for n in range(-L, L+1):\n id_n = zero_big_c + n\n inhomogeneities[:, id_n] = set_inhomogeneity_odd_n(n, even_coeffs, odd_coeffs, p)\n\n return inhomogeneities\n\ndef set_inhomogeneity_even_n(n, stationary_even_coeffs, stationary_odd_coeffs, p=1):\n L_even = get_L_even_matrix(stationary_even_coeffs)\n L_odd = get_L_odd_matrix(stationary_odd_coeffs)\n if L_even != L_odd:\n sys.exit(\"Dimensions of even and odd matrices of coefficients are not coherent\")\n else:\n L = L_even\n\n # To avoid cumbersome notation\n even_coeffs = stationary_even_coeffs\n odd_coeffs = stationary_odd_coeffs\n\n id_0 = get_id_center_element_even_c(L)\n\n v = np.zeros(get_size_even_vector(L), dtype=np.complex128)\n\n for i in range(-L, L+1):\n v[id_0+i] = 4. * 0.5 * (1j) * ((2*math.pi)**2 * \\\n get_coefficient(n+i, n-i, even_coeffs, odd_coeffs) * \\\n (get_coefficient(p, 0, even_coeffs, odd_coeffs) - get_coefficient(-p, 0, even_coeffs, odd_coeffs)) -\\\n (get_coefficient(n+i-p, n-i, even_coeffs, odd_coeffs) - get_coefficient(n+i+p, n-i, even_coeffs, odd_coeffs)))\n\n return v \n\ndef set_inhomogeneity_even(stationary_even_coeffs, stationary_odd_coeffs, p=1):\n L_even = get_L_even_matrix(stationary_even_coeffs)\n L_odd = get_L_odd_matrix(stationary_odd_coeffs)\n if L_even != L_odd:\n sys.exit(\"Dimensions of even and odd matrices of coefficients are not coherent\")\n else:\n L = L_even\n\n number_c_vectors = get_number_c_vectors(L)\n size_even = get_size_even_vector(L)\n inhomogeneities = np.zeros((size_even, number_c_vectors), dtype=np.complex128)\n\n # To avoid cumbersome notation\n even_coeffs = stationary_even_coeffs\n odd_coeffs = stationary_odd_coeffs\n\n zero_big_c = get_id_zero_column_big_c(L) \n for n in range(-L, L+1):\n id_n = zero_big_c + n\n inhomogeneities[:, id_n] = set_inhomogeneity_even_n(n, even_coeffs, odd_coeffs, p)\n\n return inhomogeneities\n\n# I should have a single function for even and odd matrices\ndef set_odd_matrices(L=15, D=0.1, alpha=0.1):\n\n number_c_vectors = get_number_c_vectors(L)\n #size_c = get_size_even_vector(L)\n #size_big_Q = (number_c_vectors, size_c, size_c)\n size_odd_matrix = get_size_odd_matrices(L)\n size_big_Q = (number_c_vectors, ) + size_odd_matrix \n\n Q_minus = np.zeros(size_big_Q)\n Q_0 = np.zeros(size_big_Q)\n Q_plus = np.zeros(size_big_Q)\n \n zero_big_c = get_id_zero_column_big_c(L) \n\n #for n in range(number_c_vectors):\n for n in range(-L, L+1):\n id_n = zero_big_c + n\n (Q_minus[id_n,:,:], Q_0[id_n,:,:], Q_plus[id_n,:,:]) = set_odd_matrices_n(n, L, D, alpha)\n\n return (Q_minus, Q_0, Q_plus)\n\ndef set_odd_matrices_n(n, L=15, D=0.1, alpha=0.1):\n size_An = get_size_odd_matrices(L) \n\n # Change these lines for sparse matrices\n Am1 = np.zeros(size_An)\n A0 = np.zeros(size_An)\n Ap1 = np.zeros(size_An)\n\n # It would be cleaner to use in the following something like Am1[id_n, id_n + 1] for the centervalue, for example\n # where id_n = L \n\n # REMEMBER THE SHIFTS IN THE INDICES (my notes use negative indices to indicate row,column, here only positive)\n # Set outer rows and columns for Am1 and Ap1\n Am1[0,0] = -2*L+1\n Am1[0,1] = 2*alpha*(n+L)\n Am1[-1,-1] = 2*L-1 \n Am1[-1,-2] = 2*alpha*(n+L)\n Ap1[0,0] = 2*L-1\n Ap1[0,1] = -2*alpha*(n-L+1) \n Ap1[-1,-1] = -2*L+1\n Ap1[-1,-2] = -2*alpha*(n-L+1) \n #Set rest of the matrices Am1 and Ap1\n # Note the modifications of the call to range. HARDCODED, VERY UGLY\n for k in range(1,(size_An[0]-1)):\n Am1[k,k+1] = 2*alpha*(n+L-k)\n Am1[k,k] = -2*(L-k)+1 \n Am1[k,k-1] = 2*alpha*(n+(k-L)+1)\n Ap1[k,k+1] = -2*alpha*(n+(k-L)+1) \n Ap1[k,k] = 2*(L-k)-1\n Ap1[k,k-1] = -2*alpha*(n-(k-L)) \n\n # Set outer rows and columns for A0\n A0[0,1] = 2*n+1\n A0[0,0] = 4*D*((n-L+1)*(n-L+1)+(n+L)*(n+L))\n A0[-1,-2] = -(2*n+1)\n A0[-1,-1] = 4*D*((n-L+1)*(n-L+1)+(n+L)*(n+L))\n\n # Set rest of matrices Am1 and Ap1\n # Note the modifications of the call to range. HARDCODED, VERY UGLY\n for k in range(1,(size_An[0]-1)):\n A0[k,k+1] = +(2*n+1)\n A0[k,k] = 4*D*((n+(k-L)+1)**2+(n-(k-L))**2)\n A0[k,k-1] = -(2*n+1)\n\n return (Am1, A0, Ap1)\n\ndef set_even_matrices(L=15, D=0.1, alpha=0.1):\n\n number_c_vectors = get_number_c_vectors(L)\n #size_c = get_size_even_vector(L)\n #size_big_Q = (number_c_vectors, size_c, size_c)\n size_even_matrix = get_size_even_matrices(L)\n size_big_Q = (number_c_vectors, ) + size_even_matrix \n\n Q_minus = np.zeros(size_big_Q)\n Q_0 = np.zeros(size_big_Q)\n Q_plus = np.zeros(size_big_Q)\n \n zero_big_c = get_id_zero_column_big_c(L) \n\n #for n in range(number_c_vectors):\n for n in range(-L, L+1):\n id_n = zero_big_c + n\n (Q_minus[id_n,:,:], Q_0[id_n,:,:], Q_plus[id_n,:,:]) = set_even_matrices_n(n, L, D, alpha)\n\n return (Q_minus, Q_0, Q_plus)\n\n\ndef set_even_matrices_n(n, L=15, D=0.1, alpha=0.1):\n size_An = get_size_even_matrices(L) \n\n # Change these lines for sparse matrices\n Am1 = np.zeros(size_An)\n A0 = np.zeros(size_An)\n Ap1 = np.zeros(size_An)\n\n # REMEMBER THE SHIFTS IN THE INDICES (my notes use negative indices to indicate row,column, here only positive)\n # Set outer rows and columns for Am1 and Ap1\n Am1[0,0] = -2*(0-L)\n Am1[0,1] = -2*alpha*(n+L)\n Am1[-1,-1] = -2*(L) \n Am1[-1,-2] = -2*alpha*(n+L)\n Ap1[0,0] = -2*L \n Ap1[0,1] = 2*alpha*(n-L) \n Ap1[-1,-1] = 2*L \n Ap1[-1,-2] = 2*alpha*(n-L) \n #Set rest of the matrices Am1 and Ap1\n # Note the modifications of the call to range. HARDCODED, VERY UGLY\n for k in range(1,(size_An[0]-1)):\n Am1[k,k+1] = -2*alpha*(n-(k-L))\n Am1[k,k] = -2*(k-L) \n Am1[k,k-1] = -2*alpha*(n+(k-L))\n Ap1[k,k+1] = 2*alpha*(n+(k-L)) \n Ap1[k,k] = 2*(k-L) \n Ap1[k,k-1] = 2*alpha*(n-(k-L)) \n\n # Set outer rows and columns for A0\n A0[0,1] = -2*n \n A0[0,0] = -4*D*((n-L)*(n-L)+(n+L)*(n+L))\n A0[-1,-2] = 2*n\n A0[-1,-1] = -4*D*((n-L)*(n-L)+(n+L)*(n+L))\n\n # Set rest of matrices Am1 and Ap1\n # Note the modifications of the call to range. HARDCODED, VERY UGLY\n for k in range(1,(size_An[0]-1)):\n A0[k,k+1] = -2*n \n A0[k,k] = -4*D*((n+(k-L))*(n+(k-L))+(n-(k-L))*(n-(k-L)))\n A0[k,k-1] = +2*n\n\n\n # Set the corresponding Q_minus, Q_plus, Q_0\n # Play with adding a new dimension for block matrix Q_minus, etc.? That way assigment would be automatic\n #Q_minus[n,:,:] = Am1[:,:] \n #Q_0[n,:,:] = A0[:,:]\n #Q_plus[n,:,:] = Ap1[:,:]\n Am1 = -Am1\n A0 = -A0\n Ap1 = -Ap1\n \n return (Am1, A0, Ap1)\n\n# The nasty trick of getting 2L+1 vectos of coefficients for the odd ones allows us to use same function for both?\n# Note that in these 3D arrays \"Q_0[i+1, :, :]\" is equivalent to \"Q_0[i+1]\"\n# Wrap into a function the long statement to calculate S_plus[i], etc.\n# The indices here could be much more informative: iterate from -N to N \n# If detuning = 0, system is singular (row full of zeros in Q_0[0]). I did I dirty trick by finding n=0 and stopping\n# loop, but I guess the right thing to do would be to catch the exception of singular matrix and leave the loop then\n# That dirty trick also uses a dirty way to obtain L from the data\n# TODO: change Q_0 by Q0_detuned!!!!\ndef get_Splus(Q_minus, Q_0, Q_plus, detuning):\n S_plus = np.zeros_like(Q_minus, dtype=np.complex128)\n\n S_plus[-1] = 0.0\n\n # N represents cut-off for big c_n, i.e. c_{-L}... c_{0} .... c_{+L}\n id_N = S_plus.shape[0] - 1\n # TODO: Very dirty way to deal with the indices!!\n L = (S_plus.shape[0] - 1) / 2\n #N = number_big_c_vectors - 1\n\n for i in range(id_N-1,-1,-1): # goes N-1, N-2 ... 0\n #print(modify_diagonal(Q_0[i+1], detuning)+np.dot(Q_plus[i+1],S_plus[i+1]))\n S_plus[i] = -np.dot(np.linalg.inv((modify_diagonal(Q_0[i+1], detuning)+np.dot(Q_plus[i+1],S_plus[i+1]))), Q_minus[i+1])\n # Handle special case of detuning = 0.0 for even coeffs by getting out of the loop (so that error doesn't happen)\n if np.logical_and(detuning == 0.0, i == L):\n break\n \"\"\"\n print(S_plus[i,:,:])\n print(\"\\n\")\n \"\"\"\n return S_plus\n\n# id_N is inconsistent with respect to the one defined for get_Splus()\ndef get_Sminus(Q_minus, Q_0, Q_plus, detuning):\n S_minus = np.zeros_like(Q_minus, dtype=np.complex128)\n\n S_minus[0] = 0.0\n\n #N = number_big_c_vectors - 1\n # get_number_c_vectors() = number_S_matrices\n # TODO: Very dirty way to deal with the indices!!\n L = (S_minus.shape[0] - 1) / 2\n id_N = S_minus.shape[0]\n\n for i in range(1, id_N): # goes N-1, N-2 ... 0\n S_minus[i] = -np.dot(np.linalg.inv((modify_diagonal(Q_0[i-1], detuning) +np.dot(Q_minus[i-1],S_minus[i-1]))), Q_plus[i-1])\n # Handle special case of detuning = 0.0 for even coeffs, which leads to singular matrix.\n if np.logical_and(detuning == 0.0, i == L):\n break\n \"\"\"\n print(S_minus[i,:,:])\n print(\"\\n\")\n \"\"\"\n return S_minus\n\n\n# In the case we are interested, detuning = -i\\omega, or detuning = eigenvalue (with negative real part)\n# Fundamental here is not to modify M within the function!!\n# Distinguish between complex and real detuning to control memory?\ndef modify_diagonal(M, detuning):\n # Think of a better way to deal with the following three lines... global variables?\n #A = np.copy(M)\n # The following copies array and casts it to complex values\n # For in-place type conversion, see\n # http://stackoverflow.com/questions/4389517/in-place-type-conversion-of-a-numpy-array\n A = M.astype(np.complex128)\n rows_A = np.shape(A)[1]\n #print(sp.sparse.issparse(A))\n \n # It works because of some broadcasting magic, possible source of BUGS...\n A = M + 4 * np.eye(rows_A) * detuning\n\n return A \n\n# The following could just be a lambda function? As it is now it doesn't make sense, exactly the same as modify_diagonal\n#def Q0_detuned(Q0_eo, detuning):\n# return modify_diagonal(Q0_eo, detuning)\n\ndef get_indices_expansion(row, column, L, flag_even): \n # Not completely clean, but so far so good \n\n if flag_even == True:\n k = column - get_id_zero_column_big_c(L)\n m = k + (row - get_id_center_element_even_c(L))\n l = k - (row - get_id_center_element_even_c(L))\n else:\n k = column - get_id_zero_column_big_c(L)\n m = k + (row - get_id_center_element_odd_c(L))\n l = k - (row - get_id_center_element_odd_c(L)) + 1\n\n return (m, l)\n\ndef rebuild_eigenfunction_mcf(x, y, even_coeffs, odd_coeffs):\n aux_sum = 0.0+1j*0.0\n \n L_even = get_L_even_matrix(even_coeffs)\n L_odd = get_L_odd_matrix(odd_coeffs)\n if L_even != L_odd:\n sys.exit(\"Dimensions of even and odd matrices of coefficients are not coherent\")\n else:\n L = L_even\n\n for (i, j), value in np.ndenumerate(even_coeffs):\n m, l = get_indices_expansion(i, j, L, flag_even = True)\n aux_sum += even_coeffs[i, j] * np.exp(1j * (m*x+l*y))\n\n for (i, j), value in np.ndenumerate(odd_coeffs):\n m, l = get_indices_expansion(i, j, L, flag_even = False)\n aux_sum += odd_coeffs[i, j] * np.exp(1j * (m*x+l*y))\n \n return aux_sum\n\n# Plot steady pdf\ndef plot_steady_pdf_mcf(even_coeffs, odd_coeffs):\n plot_factor = 1.0\n boundary = np.pi/2\n step = 0.1\n x, y = np.mgrid[-plot_factor*boundary:plot_factor*boundary:step,-plot_factor*boundary:plot_factor*boundary:step]\n #x, y = np.mgrid[0:np.pi:0.01,0:np.pi:0.01]\n fig = plt.figure()\n ax = fig.add_subplot(111,projection='3d')\n #ax = fig.add_subplot(211,projection='3d')\n #ax1 = fig.add_subplot(212,projection='3d')\n #ax.plot_surface(x, y, np.log(steady_prob_density(x,y,c)))\n # Factor 4 in the following line to obtained the normalized pdf in -pi/2,pi/2: nothing to do with factor 4 from\n # eigenvalue problem\n steady_pdf = 4*np.real(rebuild_eigenfunction_mcf(x,y, even_coeffs, odd_coeffs))\n #ax.plot_surface(x,y, steady_pdf)\n ax.scatter(x, y, steady_pdf) \n #ax1.plot_surface(simulation[:,0],simulation[:,1],simulation[:,2])\n #v = np.linspace(-np.pi,np.pi,100)\n #plt.plot(v,steady_prob_density(v,0,c))\n plt.show()\n\n return x, y, steady_pdf \n\ndef power_spectrum_from_coefficients_mcf(Hml_even, Hml_odd, p, q):\n # TODO: need to adapt this function for the cosine (not only p and q are involved, but also the sign in the line):\n #sum_coefficients = get_coefficient(-q,0,Hml_even, Hml_odd) - get_coefficient(q,0,Hml_even, Hml_odd)\n sum_coefficients = -2.0*get_coefficient(q,0,Hml_even, Hml_odd)\n #sum_coefficients = 2.0*get_coefficient(-q,0,Hml_even, Hml_odd)\n\n #print(get_coefficient(-q,0,Hml_even, Hml_odd) + get_coefficient(q,0,Hml_even, Hml_odd))\n #print(get_coefficient(q, 0, Hml_even, Hml_odd))\n #print(get_coefficient(-q, 0, Hml_even, Hml_odd))\n #print(sum_coefficients)\n\n return (2*math.pi)**2*np.real(-1j*sum_coefficients)\n\n# NEED TO MODIFY all the following function to do the whole range -L to +L, when lambda != 0, no symmetry arguments...\ndef up_iteration_coefficients(S_plus, c_0, L):\n # Iterate with matrices {S_n} to obtain the sequence of vectors {c_n=[c_{2n}, c_{2n+1}]}\n c = np.zeros((c_0.shape[0], L+1), dtype=np.complex128)\n #c = np.zeros((2*L+1, L+1), dtype=np.complex128)\n c[:, [0]] = c_0\n for i in range(1,L+1):\n c[:,i] = np.dot(S_plus[i-1,:,:], c[:,i-1])\n return c\n\ndef down_iteration_coefficients(S_minus, c_0, L):\n # Iterate with matrices {S_n} to obtain the sequence of vectors {c_n=[c_{2n}, c_{2n+1}]}\n c = np.zeros((c_0.shape[0], L+1), dtype=np.complex128)\n #c = np.zeros((2*L+1, L+1), dtype=np.complex128)\n c[:, [L]] = c_0\n for i in range(L-1,-1, -1):\n c[:,i] = np.dot(S_minus[i+1,:,:], c[:,i+1])\n return c\n\n\n\n# This is the modification I was talking about. I could have done it on the same function but in this way the tests \n# from the notebook are still OK\n# Note this doesn't work if lambda=0\n# TODO: inconsistency: some functions ask for L whether others \"know\" how to get it from within the function\n# VERY NASTY: I COPIED SOME THINGS FROM get_Splus and get_Sminus, in particular how to do the loop. However, in that\n# case id_N is correctly obtained from the first dimension of S_minus. Here, however, the vectors are stacked in columns\n# in a_plus/minus and c, and therefore id_L has to be obtained from the columns!!\ndef full_up_iteration_coefficients(S_plus, a_plus, L):\n c = np.zeros_like(a_plus)\n c[:, 0] = a_plus[:, 0] \n id_L = a_plus.shape[1]\n\n for i in range(1,id_L):\n c[:,i] = np.dot(S_plus[i-1,:,:], c[:,i-1]) + a_plus[:, i]\n return c\n\ndef full_down_iteration_coefficients(S_minus, a_minus, L):\n c = np.zeros_like(a_minus)\n id_L = a_minus.shape[1] - 1\n c[:, id_L] = a_minus[:, id_L] \n\n for i in range(id_L-1,-1, -1):\n c[:,i] = np.dot(S_minus[i+1,:,:], c[:,i+1]) + a_minus[:, i]\n return c\n\n# TODO: as it stands the arguments aren't passed consistently to the following functions::\n# Q0 is passed unmodified (i.e. without detuning), whereas Sp is passed already with detuning\n# Solutions: either pass Q0 already modified or calculate the Sp(detuned) from within the function (pass detunin)\ndef get_a_plus(Qm, Q0, Qp, Sp, inhomogeneity, L):\n a = np.zeros_like(inhomogeneity)\n a[:, -1] = np.dot(np.linalg.inv(Q0[-1]), inhomogeneity[:, -1])\n id_L = a.shape[1]-1\n\n for i in range(id_L-1,-1,-1): # goes N-1, N-2 ... 0\n a[:, i] = np.dot(np.linalg.inv(Q0[i]+np.dot(Qp[i],Sp[i])), inhomogeneity[:, i] - np.dot(Qm[i],a[:, i+1] ))\n\n return a \n\ndef get_a_minus(Qm, Q0, Qp, Sm, inhomogeneity, L):\n a = np.zeros_like(inhomogeneity)\n a[:, 0] = np.dot(np.linalg.inv(Q0[0]), inhomogeneity[:,0])\n id_L = a.shape[1]\n\n for i in range(1,id_L): \n a[:, i] = np.dot(np.linalg.inv(Q0[i]+np.dot(Qm[i],Sm[i])), inhomogeneity[:, i] - np.dot(Qp[i],a[:, i-1] ))\n\n return a \n\n################################################################################\n# LAST TRIAL, starting from the center: THIS IS THE APPROACH IT WORKS\ndef get_c0(Qm, Q0, Qp, Sm, Sp, a_minus, a_plus, inhomogeneity, L):\n id_0 = get_id_zero_column_big_c(L) \n #M = np.dot(Qm[id_0], Sm[id_0]) + Q0[id_0] + np.dot(Qp[id_0], Sp[id_0])\n #c0 = np.linalg.solve(M, inhomogeneity[:, id_0] - (np.dot(Qm[id_0], a_minus[:, id_0-1]) + np.dot(Qp[id_0], a_plus[:,id_0+1]))) \n\n # Using the symmetries we know...\n UD_m = -np.fliplr(np.eye(Sm[id_0].shape[0]))\n M = np.dot(Qm[id_0], UD_m) + Q0[id_0] + np.dot(Qp[id_0], Sp[id_0])\n c0 = np.linalg.solve(M, inhomogeneity[:, id_0] - (np.dot(Qp[id_0], a_plus[:,id_0+1]))) \n\n #return c0\n #return c0, np.linalg.cond(M)\n return c0, np.linalg.det(M)\n \ndef half_up_iteration_coefficients(S_plus, a_plus, c0, L):\n id_0 = get_id_zero_column_big_c(L) \n c = np.zeros_like(a_plus)\n id_L = a_plus.shape[1]\n\n c[:, id_0] = c0\n\n for i in range(id_0+1,id_L):\n c[:,i] = np.dot(S_plus[i-1,:,:], c[:,i-1]) + a_plus[:, i]\n return c\n\ndef half_down_iteration_coefficients(S_minus, a_minus, c0, L):\n id_0 = get_id_zero_column_big_c(L) \n c = np.zeros_like(a_minus)\n id_L = a_minus.shape[1] - 1\n\n c[:, id_0] = c0\n\n for i in range(id_0-1,-1, -1):\n c[:,i] = np.dot(S_minus[i+1,:,:], c[:,i+1]) + a_minus[:, i]\n return c\n################################################################################\n## NAIVE TEST SUITE\n\ndef test_set_up_steady_pdf(D=0.1, alpha=0.1, L=15):\n detuning = 0.0\n id_0 = get_id_zero_column_big_c(L)\n (Qm_e, Q0_e, Qp_e) = set_even_matrices(L=L, alpha=alpha, D=D)\n (Qm_o, Q0_o, Qp_o) = set_odd_matrices(L=L, alpha=alpha, D=D)\n S_plus_e = get_Splus(Qm_e, Q0_e, Qp_e, detuning)\n S_minus_e = get_Sminus(Qm_e, Q0_e, Qp_e, detuning)\n S_plus_o = get_Splus(Qm_o, Q0_o, Qp_o, detuning)\n S_minus_o = get_Sminus(Qm_o, Q0_o, Qp_o, detuning)\n\n c0_mixed = determine_start_vector(Qm_e[id_0], Q0_e[id_0], Qp_e[id_0], S_plus_e[id_0], S_minus_e[id_0], L,flag_up=0)\n c0_up = determine_start_vector(Qm_e[id_0], Q0_e[id_0], Qp_e[id_0], S_plus_e[id_0], S_minus_e[id_0], L,flag_up=1)\n c0_down = determine_start_vector(Qm_e[id_0], Q0_e[id_0], Qp_e[id_0], S_plus_e[id_0], S_minus_e[id_0], L,flag_up=2)\n\n c_up = up_iteration_coefficients(S_plus_e[L:], c0_mixed, L)\n c_down = down_iteration_coefficients(S_minus_e[:L+1], c0_mixed, L)\n\n # Run tests\n \n assert (compare_Spm_steady(S_plus_e, S_minus_e, 2, L)),\"S_plus and S_minus not related\"\n assert (compare_methods_c0(c0_mixed, c0_up, c0_down)),\"Different c0's for different methods\"\n assert (compare_pos_neg_coefficients_steady(c_up, c_down)),\"Different results up/down iteration\"\n\n return \"Tests passed\"\n \ndef test_mcf_steady_pdf():\n # Tests to compare with known results: textfiles with coefficients obtained in the other module steady_pdf_mcf\n # If coefficients are obtained from main(), they are real arrays and can be accessed as the first example here\n # If coefficients obtaind from savetxt_simple...., then they are complex arrays and can be accessed as second example\n # It is fundamental that the coefficients saved are known to be correct: for that reason a comparison with\n # simulations is provided in module steady_pdf_mcf\n\n #c_even, c_odd = get_stationary_coefficients(Qm_e, Q0_e, Qp_e, Qm_o, Q0_o, Qp_o, L)\n L_test = 15\n c_even, c_odd = get_steady_pdf(D=0.1, alpha=0.1, L=L_test)\n data = np.loadtxt(\"test/D_0.1_alpha_0.1/mcf_coefficients_D_0.1_L_15.dat\")\n assert (np.allclose(c_even[:, L_test:], data)), \"Wrong steady pdf coefficients\"\n\n L_test = 30\n c_even, c_odd = get_steady_pdf(D=0.01, alpha=0.1, L=L_test)\n data = np.loadtxt(\"test/D_0.01_alpha_0.1/mcf_coefficients_D_0.01_L_30.dat\").view(complex)\n assert (np.allclose(c_even[:, L_test:], data)), \"Wrong steady pdf coefficients\"\n\n return \"Tests passed\"\n\n# Given a one-sided (positive) matrix of coefficients, find the negative ones for the stationary case, where they must\n# satisfy c_{k,l} = c_{-k,-l}\ndef transform_positive_coefficients(c_positive):\n return np.flipud(np.fliplr(c_positive)) \n\n# The following is equivalent to multiplying by UD = np.fliplr(np.eye(size_odd/even)) as follows: UD*c_positive*UD^-1,\n# i.e. a \"similarity transformation\". This similarity transformation also relates negative and positive\n# coefficients for the stationary case, so that: c_{-k} = UD c{+k}\ndef transform_matrix(matrix):\n invert_mat = np.fliplr(np.eye(matrix.shape[0]))\n return np.dot(invert_mat, np.dot(matrix, np.linalg.inv(invert_mat)))\n\n# Compare S_plus_even(n) to UD * S_minus_even(-n) UD^-1 (stationary case, even coefficients)\ndef compare_Spm_steady(Sp_e, Sm_e, n, L):\n id_0 = get_id_zero_column_big_c(L) \n transformed_matrix = transform_matrix(Sm_e[id_0-n])\n return np.allclose(Sp_e[id_0+n], transformed_matrix)\n\n# c_up and c_down are obtained from up and down iteration\ndef compare_pos_neg_coefficients_steady(c_up, c_down):\n c_up_transformed = transform_positive_coefficients(c_up)\n return np.allclose(c_down, c_up_transformed)\n \ndef compare_methods_c0(c0_mixed, c0_up, c0_down):\n return np.logical_and(np.allclose(c0_up, c0_down), np.allclose(c0_mixed, c0_up))\n\n################################################################################\n# CHANGE NAME OF THE FOLLOWING FUNCTION\ndef get_steady_pdf(D=0.1, alpha=0.1, L=15):\n \n (Qm_e, Q0_e, Qp_e) = set_even_matrices(L=L, D=D, alpha=alpha)\n (Qm_o, Q0_o, Qp_o) = set_odd_matrices(L=L, D=D, alpha=alpha)\n\n c_even, c_odd = get_stationary_coefficients(Qm_e, Q0_e, Qp_e, Qm_o, Q0_o, Qp_o, L)\n\n #plot_steady_pdf_mcf(c_even, c_odd)\n return c_even, c_odd\n\n\ndef get_stationary_coefficients(Qm_e, Q0_e, Qp_e, Qm_o, Q0_o, Qp_o, L):\n id_0 = get_id_zero_column_big_c(L)\n detuning = 0.0 # This indicates we are dealing with steady pdf\n\n S_plus_e = get_Splus(Qm_e, Q0_e, Qp_e, detuning)\n S_minus_e = get_Sminus(Qm_e, Q0_e, Qp_e, detuning)\n\n c0_e = determine_start_vector(Qm_e[id_0], Q0_e[id_0], Qp_e[id_0], S_plus_e[id_0], S_minus_e[id_0], L,flag_up=0)\n c_up_e = up_iteration_coefficients(S_plus_e[L:], c0_e, L)\n c_down_e = down_iteration_coefficients(S_minus_e[:L+1], c0_e, L)\n\n full_c_e = get_full_from_half_coefficients(c_up_e, c_down_e)\n\n############################################################\n # The following part is only included in case we'd be dealing with non-zero odd coefficients (but still symmetric!)\n # c_odd has to be zero in the end!!\n # Odd coefficients\n S_plus_o = get_Splus(Qm_o, Q0_o, Qp_o, detuning)\n S_minus_o = get_Sminus(Qm_o, Q0_o, Qp_o, detuning)\n\n aux_M_o = get_aux_M(Qm_o[id_0], Q0_o[id_0], Qp_o[id_0], S_plus_o[id_0], S_minus_o[id_0], L,flag_up=0)\n c0_o = np.dot(np.linalg.inv(aux_M_o), np.zeros((get_size_odd_vector(L), 1))) # up_iteration... needs c0_o (2*L(+1) x 1)\n c_up_o = up_iteration_coefficients(S_plus_o[L:], c0_o, L)\n c_down_o = down_iteration_coefficients(S_minus_o[:L+1], c0_o, L)\n full_c_o = get_full_from_half_coefficients(c_up_o, c_down_o)\n############################################################\n\n return full_c_e, full_c_o \n\ndef get_full_from_half_coefficients(c_up, c_down):\n half_c = np.delete(c_down,-1,1)\n full_c = np.hstack((half_c, c_up))\n return full_c\n\n################################################################################\n\n# Maybe in the following functions it would be better to pass the whole series of matrices Qm, Qp, etc. and select then\n# the corresponding 0 matrix (probably better to pass n as an argument)\n# flag_up = 0 (determine from S_m(0) and S_p(0) together), 1 (from S_p(0) and trick) and 2 (from S_m(0) and trick)\ndef get_aux_M(Qm_0,Q0_0, Qp_0, S_plus_0, S_minus_0, L, flag_up=0):\n if flag_up == 0:\n # The auxiliary matrix is determined from both S_plus[0] and S_minus[0] matrices\n return Q0_0+np.dot(Qp_0, S_plus_0)+np.dot(Qm_0, S_minus_0) \n elif flag_up == 1:\n # The auxiliary matrix is determined solely from both S_plus[0] matrices + additional trick with U_D\n return Q0_0+np.dot((Qp_0+np.fliplr(Qm_0)),S_plus_0)\n elif flag_up == 2:\n # The auxiliary matrix is determined solely from both S_minus[0] matrices + additional trick with U_D\n return Q0_0+np.dot((Qm_0+np.fliplr(Qp_0)),S_minus_0)\n else:\n sys.exit(\"the passed flag does not correspond to any valid form of aux_M\")\n\n# This is only valid when det(aux_M) == 0 (as in the steady state probability density), i.e. underdetermined system\n# flag_up = 0 (determine from S_m(0) and S_p(0) together), 1 (from S_p(0) and trick) and 2 (from S_m(0) and trick)\ndef determine_start_vector(Qm_0,Q0_0, Qp_0, S_plus_0, S_minus_0, L, flag_up=0):\n # Obtain initial vector c_0 = [c_{0,-L} ... c_{0,+L} c_{1,-L} ... c_{1,+L} ] from c_{0,0}\n # Note row with index L is the row corresponding to c_{0,0} and is full of zeros for aux_M\n\n aux_M = get_aux_M(Qm_0,Q0_0, Qp_0, S_plus_0, S_minus_0, L, flag_up)\n\n normalization_coefficient = 1./((2*np.pi)*(2*np.pi))\n\n #print(aux_M)\n \"\"\"\n print(aux_M)\n print(aux_M[L,:]) # print row full of zeros\n print(aux_M[:,[L]])\n print(\"\\n\")\n print(aux_M[:,[L]])\n \"\"\"\n # The following should be a call like get_id_center_element_even_c(L):\n id_0 = L\n\n aux_M = np.delete(aux_M, (L), axis=0) #Delete row L from aux_M\n aux_v = aux_M[:,[L]] # With this syntax, I keep it as a column vector\n aux_M = np.delete(aux_M, (L), axis=1) #Delete column L from aux_M\n\n #print(aux_v)\n #print(\"\\n\")\n #c0 = - np.dot(np.linalg.inv(aux_M),aux_v)*normalization_coefficient COMPLETELY WRONG!!!!!!!!!\n\n c0 = - np.dot(np.linalg.inv(aux_M),aux_v*normalization_coefficient)\n \"\"\"\n print(c0)\n print(\"\\n\")\n \"\"\"\n #c[:,[0]] = np.insert(c0, L, normalization_coefficient, axis=0) #Insert in row (axis=0) L of our column vector c0 the number \"normalization coefficient\"\n #print(c[:,[0]])\n c0 = np.insert(c0, L, normalization_coefficient, axis=0) #Insert in row (axis=0) L of our column vector c0 the number \"normalization coefficient\"\n return c0\n################################################################################\n\n# I just have the following as an example for the sparse problem later on\ndef solve_sparse_eigenproblem(D=0.1, alpha = 0.1, L = 15):\n \n M = set_coefficient_matrix(D, alpha, L)\n M_sp = sp.sparse.csr_matrix(M) \n\n # Note division by 4 (provides the correct eigenvalues)\n eigenvalues, eigenvectors = sp.sparse.linalg.eigs(-M_sp/4, k = 3, which = 'LR')\n\n return eigenvalues, eigenvectors\n\n# Translate x,y vectors as generated by np.mgrid (i.e. for pyplot's 3d routine) to gnuplot's format\n# TODO: how to deal with where to store the file... Maybe it's better to return just the np.array?\n# TODO: need to write a function that does the inverse job!!\ndef convert_to_gnuplot_3d(x, y, steady_pdf):\n ux = np.ravel(x)\n uy = np.ravel(y)\n usteady_pdf = np.ravel(steady_pdf)\n \"\"\"\n # Save data to a file so that it can be plotted by gnuplot\n #ux = ux[:, np.newaxis]\n #uy = uy[:, np.newaxis]\n steady_pdf = 4*np.real(rebuild_eigenfunction(ux,uy,stationary_coeffs))\n #output_array = (ux, uy,(np.log(steady_prob_density(ux,uy,c))/(np.log(10))) )\n \"\"\"\n np.savetxt('steady_pdf_gnuplot.dat', np.c_[ux,uy,steady_pdf])\n return ux, uy, usteady_pdf\n\n# The following function is a quick way to get a scatter plot of xyz data in a file\n# If we need a surface plot, have to reformat the data as explained in:\n# http://stackoverflow.com/questions/15118939/fill-2d-numpy-array-from-three-1d-numpy-arrays?answertab=active#tab-top\n# See also the last lines of power_spectrum_sin.py\ndef plot_xyz(filename):\n data = np.loadtxt(filename)\n x = data[:,0]\n y = data[:,1]\n z = data[:,2]\n\n fig = plt.figure()\n ax = fig.add_subplot(111,projection='3d')\n #ax.plot_surface(x, y, np.log(steady_prob_density(x,y,c)))\n ax.scatter(x, y, z) \n plt.show()\n\n# Function that finds the closest element within an array to a certain value (I think now only deals with real part)\n# Need to improve it: at the moment I think it doesn't work with complex values? Be careful! abs indeed works with complex values\ndef find_nearest(array,value):\n idx = (np.abs(array-value)).argmin() \n return idx\n\nif __name__ == '__main__':\n import timeit \n","sub_path":"numerics/matrix-methods/matrix-continued-fraction/utils_mcf.py","file_name":"utils_mcf.py","file_ext":"py","file_size_in_byte":43437,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"542360624","text":"import os\nimport numpy as np\nimport torch\nimport random\nfrom torch.utils.data import Dataset\n\ndef create_dataset(data_path: str, data_index_path: str, config):\n local_conditioning = config[\"model\"][\"local_conditioning\"]\n if local_conditioning == \"mel\":\n return MelDataset(data_path, data_index_path, config)\n raise ValueError(local_conditioning)\n\n\nclass MelDataset(Dataset):\n def __init__(self, data_path: str, data_index_path: str, config):\n self.config = config\n self.data_path = data_path\n self.mel_path = os.path.join(data_path, \"mel\")\n self.wav_path = os.path.join(data_path, \"wav\")\n self.hop_length = config[\"data\"][\"hop_length\"]\n self.data_index_path = data_index_path\n with open(self.data_index_path, 'rt') as f:\n self.data_index = f.readlines()\n\n def __getitem__(self, index):\n mel_win = self.config[\"train\"][\"sequence_length\"]\n fileid = self.data_index[index]\n x = np.load(os.path.join(self.wav_path, '{}.npy'.format(fileid)))\n m = np.load(os.path.join(self.mel_path, '{}.npy'.format(fileid)))\n # file too short, pick another one\n while (m.shape[-1] < mel_win or\n x.shape[-1] != m.shape[-1] * self.hop_length):\n print(\"WARNING: %s has mel len of %d, needing at least %d, wave length is %d with hop size %d\"\n % (fileid, m.shape[-1], mel_win, x[-1], self.hop_length))\n index = random.randint(0, len(self.data_index) - 1)\n file = self.data_index[index]\n x = np.load(os.path.join(self.wav_path, '{}.npy'.format(fileid)))\n m = np.load(os.path.join(self.mel_path, '{}.npy'.format(fileid)))\n\n # randomly pick a segment\n max_mel_start = m.shape[-1] - mel_win - 1\n mel_start = random.randint(0, max_mel_start)\n mel = m[:, mel_start:mel_start + mel_win]\n audio_start = mel_start * self.hop_length\n audio = x[audio_start:audio_start + mel_win * self.hop_length]\n return (torch.from_numpy(mel).float(), torch.from_numpy(audio).float())\n\n def __len__(self):\n return len(self.data_index)\n","sub_path":"narv/dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":2153,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"142175061","text":"# Python 2 Compatibility\r\nfrom __future__ import absolute_import\r\nfrom __future__ import division\r\nfrom __future__ import print_function\r\n\r\n# Imports \r\nimport math\r\nimport time\r\nimport tensorflow as tf\r\n\r\n\r\ndef evaluation_function_scale(sess, eval_op, initializable_iterator, handle, data_handle, data_size, steps_per_epoch, mode, reg_param):\r\n\r\n\t# Start timer\r\n\tstart_time = time.time()\r\n\t\t\t\r\n\t# Initialize iterator\r\n\tsess.run(initializable_iterator.initializer)\r\n\r\n\t# Initialize loss counter\r\n\teval_loss = 0\t\t\r\n\r\n\t# Iterate over training dataset\r\n\tfor _ in range(steps_per_epoch):\r\n\t\teval_loss += sess.run(eval_op, feed_dict={handle: data_handle})\r\n\r\n\t# Calculate average training data evaluation loss\r\n\teval_loss_avg = eval_loss/(data_size*reg_param)\r\n\teval_loss_avg = math.sqrt(2*eval_loss_avg)\r\n\r\n\t# Print training data evaluation results\r\n\tduration = time.time() - start_time\r\n\tif mode=='TRAINING':\r\n\t\tprint('TRAIN DATA EVALUATION: Num examples: %d Avg scale loss: %.10f Time for evaluation: %.2f sec' % (data_size, eval_loss_avg, duration))\r\n\telif mode=='VALIDATION':\r\n\t\tprint('VALIDATION DATA EVALUATION: Num examples: %d Avg scale loss: %.10f Time for evaluation: %.2f sec' % (data_size, eval_loss_avg, duration))\r\n\t\t\t\r\n\t\t\t\r\ndef inference_fun_scale(sess, eval_op, eval_train_iterator, validation_iterator, handle, eval_train_handle, validation_handle, train_size, validation_size, eval_train_steps_per_epoch, validation_steps_per_epoch):\r\n\r\n\t# EVALUATION RUN ON TRAINING SET\r\n\tevaluation_function_scale(sess, eval_op, eval_train_iterator, handle, eval_train_handle, train_size, eval_train_steps_per_epoch, 'TRAINING', 1)\r\n\r\n\t# EVALUATION RUN ON VALIDATION SET\r\n\tevaluation_function_scale(sess, eval_op, validation_iterator, handle, validation_handle, validation_size, validation_steps_per_epoch, 'VALIDATION', 1)","sub_path":"Networks/ScaleNet/Standalone/evaluation.py","file_name":"evaluation.py","file_ext":"py","file_size_in_byte":1823,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"253310379","text":"# Copyright 2015 Hewlett-Packard Development Company, L.P. All Rights Reserved.\n#\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# 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, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\nimport itertools\nimport uuid\nimport eventlet\nfrom flanker import mime as flanker_mime\nfrom flanker.mime.message.headers import encoding\nfrom oslo_config import cfg\nfrom oslo_log import log\nfrom oslo_utils import timeutils\nfrom pidgey.common import context\nfrom pidgey.common import exceptions\nfrom pidgey.common import rpc\nfrom pidgey.common import service\nfrom pidgey.processor import rpcapi\nfrom pidgey.smtp import server\nfrom pidgey.smtp.policies import headers\nfrom pidgey.smtp.policies import split\nfrom pidgey import objects\nfrom pidgey import utils\nfrom pidgey import persistence\nCONF = cfg.CONF\nLOG = log.getLogger(__name__)\n\n\nclass SmtpValidators(object):\n \"\"\"Base class for implementing SMTP command validators.\n\n Sub-classes may implement some or all of the following functions. Leaving\n the `reply` argument untouched will return the default, successful reply\n from the command.\n\n - ``handle_banner(reply, address)``: Validate connecting address before\n sending the SMTP banner.\n - ``handle_ehlo(reply, ehlo_as)``: Validate the EHLO string.\n - ``handle_helo(reply, helo_as)``: Validate the HELO string.\n - ``handle_mail(reply, sender, params)``: Validate the sender address.\n - ``handle_rcpt(reply, recipient, params)``: Validate one recipient\n address.\n - ``handle_data(reply)``: Any remaining validation before receiving data.\n - ``handle_have_data(reply, data)``: Validate the received message data.\n - ``handle_rset(reply)``: Called before replying to an RSET command.\n - ``handle_tls()``: Called after a successful TLS handshake. This may be at\n the beginning of the session or after a `STARTTLS` command.\n\n :param session: When sub-classes are instantiated, instances are passed\n this object, stored and described in :attr:`session` below,\n that have useful information about the current session.\n\n \"\"\"\n\n def __init__(self, session):\n self.session = session\n\n\nclass Validators(SmtpValidators):\n \"\"\"Custom validators doing stuff like validation of domains etc.\"\"\"\n\n def __init__(self, session):\n super(Validators, self).__init__(session)\n\n def handle_rcpt(self, reply, address, params):\n domain = address.split('@')[1]\n try:\n ctxt = context.make_context(is_admin=True)\n objects.Domain.get(ctxt, domain)\n except exceptions.NotFound:\n reply.code = '550'\n reply.enhanced_status_code = '5.7.1'\n reply.message = 'Recipient <%s> Not allowed' % address\n except Exception as e:\n LOG.exception(e)\n\n\nclass SmtpSession(object):\n\n def __init__(self, address, validator_class, handoff):\n self.extended_smtp = False\n self.security = None\n self.address = address\n self.reverse_address = None\n self.handoff = handoff\n self.validators = validator_class(self) if validator_class else None\n self.message = None\n self.ehlo_as = None\n self.auth_result = None\n\n def _call_validator(self, command, *args):\n method = 'handle_' + command\n if hasattr(self.validators, method):\n getattr(self.validators, method)(*args)\n\n @property\n def protocol(self):\n proto = 'SMTP'\n if self.extended_smtp:\n proto = 'ESMTP'\n if self.security == 'TLS':\n proto += 'S'\n if self.auth_result is not None:\n proto += 'A'\n return proto\n\n def banner_(self, reply):\n self._call_validator('banner', reply, self.address)\n\n def ehlo(self, reply, ehlo_as):\n self._call_validator('ehlo', reply, ehlo_as)\n self.extended_smtp = True\n if reply.code == '250':\n self.ehlo_as = ehlo_as\n self.message = None\n\n def helo(self, reply, helo_as):\n self._call_validator('helo', reply, helo_as)\n if reply.code == '250':\n self.ehlo_as = helo_as\n self.message = None\n\n def tlshandshake(self):\n self._call_validator('tls')\n self.security = 'TLS'\n\n def auth(self, reply, result):\n self.auth_result = result\n\n def rset(self, reply):\n self.message = None\n\n def mail(self, reply, address, params):\n try:\n self._call_validator('mail', reply, address, params)\n except TypeError:\n self._call_validator('mail', reply, address)\n\n if reply.code == '250':\n self.message = objects.Message(\n id=str(uuid.uuid4()), sender=address, recipients=[])\n self.message.metadata = {}\n self.message.headers = objects.Headers()\n self.message.headers.obj_set_defaults()\n self.message.content = {}\n self.message.content_map = {}\n self.message.attachments = [\n objects.Attachment(\n size=5, name='foo', url='fooo', content_type='text')]\n\n def rcpt(self, reply, address, params):\n try:\n self._call_validator('rcpt', reply, address, params)\n except TypeError:\n self._call_validator('rcpt', reply, address)\n\n if reply.code == '250':\n self.message.recipients.append(address)\n\n def data(self, reply):\n self._call_validator('data', reply)\n\n def have_data(self, reply, data, err):\n if isinstance(err, exceptions.MessageTooBig):\n reply.code = '552'\n reply.message = '5.3.4 Message exceeded size limit'\n return\n if err:\n raise err\n self._call_validator('have_data', reply, data)\n if reply.code != '250':\n return\n self.message.metadata['client.ip'] = self.address[0]\n self.message.metadata['client.host'] = self.reverse_address\n self.message.metadata['client.name'] = self.ehlo_as\n self.message.metadata['client.protocol'] = self.protocol\n self.message.metadata['client.auth'] = self.auth_result\n\n msg = flanker_mime.from_string(data)\n for hk, hv in msg.headers.iteritems(raw=True):\n if not isinstance(hv, basestring):\n hv = encoding.to_mime(hk, hv)\n self.message.headers.append(hk, hv)\n\n self.message.subject = msg.subject\n if not msg.content_type.is_multipart():\n self.message.content[str(msg.content_type)] = msg.body\n results = self.handoff(self.message, data)\n if isinstance(results[0][1], exceptions.QueueError):\n reply.code = '550'\n reply.message = '5.6.0 Error queuing message'\n elif isinstance(results[0][1], exceptions.RelayError):\n relay_reply = results[0][1].reply\n reply.copy(relay_reply)\n else:\n reply.message = '2.6.0 Message accepted for delivery'\n self.message = None\n\n\nclass MTAService(service.Service):\n\n def __init__(self, *args, **kwargs):\n super(MTAService, self).__init__(*args, **kwargs)\n rpc.init(cfg.CONF)\n listen = (self._service_config.host, self._service_config.port)\n self.socket = eventlet.listen(listen)\n LOG.info('Listening on: %s:%s' % listen)\n self.store = persistence.get_persister(CONF.persistence_driver)\n self.queue_policies = []\n self.validator_class = Validators\n self.auth_class = None\n self.auth_obj = None\n\n @property\n def process_api(self):\n return rpcapi.ProcessorAPI.get_instance()\n\n @property\n def service_name(self):\n return 'mta'\n\n def start(self):\n self.queue_policies.extend([\n headers.AddDateHeader(),\n headers.AddReceivedHeader(),\n headers.AddMessageIdHeader(),\n split.RecipientDomainSplit()])\n super(MTAService, self).start()\n self.tg.add_thread(self.handle)\n\n def _run_policies(self, message):\n results = [message]\n\n def recurse(current, i):\n try:\n policy = self.queue_policies[i]\n except IndexError:\n return\n\n ret = policy.apply(current)\n if ret:\n results.remove(current)\n results.extend(ret)\n for env in ret:\n recurse(env, i + 1)\n\n else:\n recurse(current, i + 1)\n\n recurse(message, 0)\n return results\n\n def handle(self):\n LOG.debug('Handling thread is started..')\n while True:\n client, addr = self.socket.accept()\n self.tg.add_thread(self.dispatch, client, addr)\n\n def dispatch(self, client, addr):\n \"\"\"\n Dispatch a SMTP\n \"\"\"\n s = None\n try:\n session = SmtpSession(addr, self.validator_class, self.handoff)\n s = server.Server(\n client,\n session,\n auth_class=self.auth_class,\n auth_obj=self.auth_obj)\n s.handle()\n except exceptions.ConnectionLost:\n pass\n except Exception as e:\n LOG.exception(e)\n finally:\n if s:\n s.io.close()\n\n def handoff(self, message, data):\n now = timeutils.utcnow()\n message.time = now\n message.metadata[\"receiver\"] = self._host\n try:\n return self.enqueue(message, data)\n except Exception as e:\n LOG.exception(e)\n raise\n\n def enqueue(self, message, data):\n ctxt = context.make_context(is_admin=True)\n\n messages = self._run_policies(message)\n\n ids = utils.pool_imap(\n self.store.write, (messages, itertools.repeat(data), ))\n\n for msg in messages:\n self.process_api.process(ctxt, msg)\n\n results = zip(messages, ids)\n return results\n","sub_path":"pidgey/mta/service.py","file_name":"service.py","file_ext":"py","file_size_in_byte":10468,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"362548180","text":"# Version 1.0 - 12/10/2018\n\nimport csv\n\nclass CSVHandler:\n \"\"\"Pass in an array of objects of the same type and handle to csv creation\"\"\"\n\n keys = []\n objects = []\n\n def __init__(self, objects):\n \"\"\"Uses the first object passed in to define keys. Checks that all objects contain keys\n\n Arguments:\n objects -- Any object of the same type ( object array expected )\n \"\"\"\n\n for key in objects[0].keys():\n self.keys.append(key)\n\n self.add_objects(objects)\n\n def add_objects(self, objects):\n \"\"\"Stores objects in objects if object has correct keys\n \n Arguments:\n objects -- Valid objects to add (object array expected)\"\"\"\n\n for obj in objects:\n if not self.is_valid_object(obj):\n raise Exception(\"Object {0} invalid. Requires keys {1}\".format(obj, self.keys))\n\n for obj in objects:\n self.objects.append(obj)\n\n\n def is_valid_object(self, obj):\n \"\"\"Uses stored keys to check if object is valid\n \n Arguments:\n obj -- The object you want to check\n \"\"\"\n\n for key in self.keys:\n if key not in obj:\n return False\n\n return True\n\n\n def write_csv(self, file_name):\n \"\"\"Write the objects stoored to CSV\n \n Arguments\n file_name -- name of file to write to\n \"\"\"\n\n with open(file_name, 'w') as csv_file:\n writer = csv.writer(csv_file, delimiter=\",\", lineterminator=\"\\n\")\n \n writer.writerow( self.keys )\n\n for ob in self.objects:\n row = []\n\n for key in self.keys:\n row.append( ob[key] )\n \n writer.writerow( row )","sub_path":"tobias_py/basic/csv/csv_handler.py","file_name":"csv_handler.py","file_ext":"py","file_size_in_byte":1565,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"583363156","text":"import csv\nfrom datetime import datetime\n\n\nclass Bill:\n def __init__(self, client_name,\n date, number, sum):\n self.client_name = client_name\n self.date = datetime.strptime(date, \"%d.%m.%Y\")\n self.number = number\n self.sum = float(sum)\n\n def __str__(self):\n return f'{self.client_name} {self.date} {self.number} {self.sum}'\n\n\ndef csv_reader(file):\n bills = []\n with open(file) as f:\n reader = csv.reader(f)\n for row in reader:\n bills.append(row[0])\n\n for i in range(len(bills)):\n bills[i] = bills[i].replace('\\t', ' ')\n\n csv_objects = []\n for i in range(len(bills)-1):\n CSV = Bill(bills[i+1].partition(' ')[0],\n bills[i + 1].partition(' ')[2].partition(' ')[0],\n bills[i + 1].partition(' ')[2].partition(' ')[2].partition(' ')[0],\n bills[i + 1].partition(' ')[2].partition(' ')[2].partition(' ')[2])\n csv_objects.append(CSV)\n\n return csv_objects\n\n\ndef sum_of_payment(payments):\n return sum([float(x.sum) for x in payments])\n\n\ndef sort_by_name(amounts):\n for i in range(len(amounts) - 1):\n for j in range(len(amounts) - i - 1):\n if amounts[j].client_name < amounts[j + 1].client_name:\n amounts[j], amounts[j + 1] = amounts[j + 1], amounts[j]\n\n return amounts\n\n\ndef sort_by_date_after_sort_by_name(amounts):\n for i in range(len(amounts) - 1):\n for j in range(len(amounts) - i - 1):\n if amounts[j].date > \\\n amounts[j + 1].date and \\\n amounts[j].client_name == \\\n amounts[j + 1].client_name:\n amounts[j], amounts[j + 1] = \\\n amounts[j + 1], amounts[j]\n return amounts\n","sub_path":"bills/bills_reworked/csv_script.py","file_name":"csv_script.py","file_ext":"py","file_size_in_byte":1796,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"387098645","text":"import re \nlanuage='PythonC#JavaC#PHPC#'\ndef convert(value):\n matched=value.group()\n return '!!'+matched+'!!'\n#4~8\n# r=re.findall('c#.{3}',lanuage,re.I|re.S)\nr=re.sub('C#',convert,lanuage,1)\n# r=lanuage.replace('C#','GO')\nprint(r)\n\n","sub_path":"python学习/实训/ten/c8.py","file_name":"c8.py","file_ext":"py","file_size_in_byte":238,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"281809639","text":"# Remove every negative value from `numbers', without changing the list itself.\n\nnumbers = [1, -7, 2, -9, 3, -1, 4, -2, 5, -9, 6, -1, 7, -4, 8, -8, 9]\n\n# Simple Solution\n\nresult = []\nfor value in numbers:\n if value < 0:\n result.append(value)\n\n# Advanced Solution: Filter() Function\n\nis_positive = lambda x: x >= 0\nresults = filter(is_positive, numbers)\n","sub_path":"solutions/remove-negatives.py","file_name":"remove-negatives.py","file_ext":"py","file_size_in_byte":363,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"331056246","text":"\"\"\"Logsforhumans pretends logs all models changes to human beings.\"\"\"\n# coding: utf-8\nimport threading\n\nfrom django.conf import settings\nfrom django.contrib.auth import get_user_model\nfrom django.db import models\nfrom django.db.models.fields.related import ManyToManyField\nfrom django.db.models.signals import post_delete, m2m_changed\nfrom django.dispatch import receiver\nfrom django.db.models.fields.related_descriptors import ManyToManyDescriptor\nfrom six import text_type as unicode\n\nDEFAULT_DELETE_MESSAGE = u'The {model_name} \"{instance_str}\" (id={instance_id}) was deleted'\nDEFAULT_CREATION_MESSAGE = u'The {model_name} \"{instance_str}\" was created'\nDEFAULT_FIELD_CHANGE_MESSAGE = u'The field {field_verbose_name} was changed from \"{old_value}\" to \"{new_value}\"'\nDEFAULT_M2M_FIELDS_MESSAGE = u'The {item_model_name} \"{item}\" was {action} in the field {m2m_table}'\nDEFAULT_GENERIC_CHANGE = u'The field {field_verbose_name} was changed'\n\n\ndef get_delete_format_message():\n return getattr(\n settings,\n 'LOGSFORHUMANS_DELETE_MESSAGE',\n DEFAULT_DELETE_MESSAGE)\n\n\ndef get_creation_format_message():\n return getattr(\n settings,\n 'LOGSFORHUMANS_CREATION_MESSAGE',\n DEFAULT_CREATION_MESSAGE)\n\n\ndef get_field_change_format_message():\n return getattr(\n settings,\n 'LOGSFORHUMANS_FIELD_CHANGE_MESSAGE',\n DEFAULT_FIELD_CHANGE_MESSAGE)\n\n\ndef get_m2m_fields_change_format_message():\n return getattr(\n settings,\n 'LOGSFORHUMANS_M2M_FIELDS_CHANGE_MESSAGE',\n DEFAULT_M2M_FIELDS_MESSAGE)\n\n\ndef get_generic_change_format_message():\n return getattr(\n settings,\n 'LOGSFORHUMANS_GENERIC_CHANGE_MESSAGE',\n DEFAULT_GENERIC_CHANGE)\n\n\nclass HumanLog(models.Model):\n description = models.TextField(blank=False)\n\n app_label = models.CharField(max_length=255)\n model_name = models.CharField(max_length=255)\n object_id = models.CharField(max_length=512)\n\n creation_date = models.DateTimeField(\n auto_now_add=True, verbose_name=u'Created in')\n updated_by = models.ForeignKey(\n settings.AUTH_USER_MODEL,\n null=True,\n blank=True,\n editable=False,\n verbose_name=u'Updated by',\n on_delete=models.CASCADE)\n\n class Meta:\n ordering = ('-creation_date', )\n\n def __str__(self):\n return self.description\n\n\nLOGSFORHUMAN_THREAD = threading.local()\n\n\ndef get_models_changelogs(self):\n to_ignore = getattr(self, 'LOGSFORHUMANS_IGNORE_DETAILS', [])\n ignore_changes = getattr(self, 'LOGSFORHUMANS_IGNORE_CHANGES', [])\n model_name = self._meta.verbose_name\n instance_str = unicode(self)\n instance_id = self.pk\n\n tem_pk = bool(instance_id)\n if not tem_pk:\n return get_creation_format_message().format(**locals())\n\n old_instance = self.__class__.objects.filter(pk=self.pk).first()\n if not old_instance:\n return u''\n\n description = u''\n for field in self._meta.get_fields():\n field_name = field.name\n if field_name in ignore_changes:\n continue\n\n if not isinstance(field, ManyToManyField):\n if not hasattr(self, field_name):\n continue\n\n new_value = getattr(self, field_name)\n if hasattr(self, 'get_{}_display'.format(field_name)):\n new_value = getattr(\n self, 'get_{}_display'.format(field_name))()\n\n old_value = getattr(old_instance, field_name)\n if hasattr(old_instance, 'get_{}_display'.format(field_name)):\n old_value = getattr(\n old_instance, 'get_{}_display'.format(field_name))()\n\n if new_value != old_value:\n field_verbose_name = field.verbose_name\n if field_name in to_ignore:\n description += get_generic_change_format_message().format(\n **locals()) + '\\n'\n else:\n description += get_field_change_format_message().format(\n **locals()) + '\\n'\n return description\n\n\ndef can_have_changelog(self):\n if not hasattr(self, 'skip_changelog') or not self.skip_changelog:\n return True\n return False\n\n\ndef get_logs(self):\n return HumanLog.objects.filter(\n model_name=self._meta.model_name,\n app_label=self._meta.app_label,\n object_id=self.pk)\n\n\ndef add_log(self, log):\n user = self.get_current_user()\n if isinstance(user, get_user_model()):\n human_log = HumanLog.objects.create(\n description=log,\n app_label=self._meta.app_label,\n model_name=self._meta.model_name,\n object_id=self.pk,\n updated_by=user\n )\n else:\n human_log = HumanLog.objects.create(\n description=log,\n app_label=self._meta.app_label,\n model_name=self._meta.model_name,\n object_id=self.pk)\n if hasattr(self, 'logsforhumans_onchange'):\n self.logsforhumans_onchange(human_log)\n\n\ndef add_delete_log(self, kwargs):\n if not self.can_have_changelog():\n return\n\n model_name = self._meta.verbose_name\n instance_str = unicode(self)\n instance_id = self.pk\n\n message = get_delete_format_message().format(**locals())\n self.add_log(message)\n\n\ndef get_save_method(original_save):\n def save(self, *args, **kwargs):\n description = None\n if self.can_have_changelog():\n description = self.get_models_changelogs()\n original_save(self, *args, **kwargs)\n if description:\n self.add_log(description)\n\n return save\n\n\ndef get_current_user(*args):\n \"\"\"Return the current user configured in middleware.\"\"\"\n if hasattr(LOGSFORHUMAN_THREAD, 'request'):\n return getattr(LOGSFORHUMAN_THREAD.request, 'user', None)\n\n\ndef generate_m2m_change_logs(**kwargs):\n if not hasattr(kwargs['instance'], 'add_log'):\n return\n\n sender = kwargs['sender']\n create_logs = False\n action = u''\n if kwargs.get('action') == 'post_remove':\n create_logs = True\n action = 'removido'\n elif kwargs.get('action') == 'post_add':\n create_logs = True\n action = 'adicionado'\n if not create_logs:\n return\n log = u''\n for pk in kwargs.get('pk_set', list()):\n item = kwargs.get('model').objects.filter(pk=pk).first()\n item_model_name = kwargs.get('model')._meta.verbose_name\n m2m_table = kwargs.get('sender')._meta.db_table\n\n # getting the m2m field name/verbose_name\n class_obj = kwargs['instance'].__class__\n m2m_field_verbose_name = ''\n for field in dir(class_obj):\n if isinstance(getattr(class_obj, field), ManyToManyDescriptor):\n if getattr(class_obj, field).through == sender:\n m2m_field_verbose_name = getattr(class_obj, field).field.verbose_name\n\n log += get_m2m_fields_change_format_message().format(**locals())\n log += '\\n'\n if log:\n kwargs['instance'].add_log(log)\n\n\ndef generate_humanlogs(class_obj):\n\n class_obj.can_have_changelog = can_have_changelog\n class_obj.get_logs = get_logs\n class_obj.get_models_changelogs = get_models_changelogs\n class_obj.add_log = add_log\n class_obj.add_delete_log = add_delete_log\n class_obj.save = get_save_method(class_obj.save)\n class_obj.get_current_user = get_current_user\n class_obj.generate_m2m_change_logs = generate_m2m_change_logs\n\n @receiver(post_delete, sender=class_obj, weak=False)\n def class_instance_post_delete(**kwargs):\n instance = kwargs['instance']\n instance.add_delete_log(kwargs)\n class_obj.class_instance_post_delete = class_instance_post_delete\n for field in dir(class_obj):\n if isinstance(getattr(class_obj, field), ManyToManyDescriptor):\n through = getattr(class_obj, field).through\n\n # the \"dispatch_uid\" argument is to avoid bind function twice\n # in m2m fields the two classes involved may have the same field\n # in the list \"dir(obj)\"\n @receiver(m2m_changed, sender=through, weak=False, dispatch_uid=unicode(through))\n def class_instance_m2m_changed(**kwargs):\n class_obj.generate_m2m_change_logs(**kwargs)\n\n return class_obj\n","sub_path":"models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":8318,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"259618753","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue May 14 11:53:45 2019\n\n@author: hugomeyer\n\"\"\"\n\n\nimport numpy as np\nfrom scipy.signal import butter, lfilter\nimport matplotlib.pyplot as plt\nimport os\n\n\n\n\nclass Similarity_cut(object):\n def __init__(self, index, time, label, score=-1):\n self.index=index\n self.score=score\n self.label=label\n self.time = time\n \n def info(self):\n return {'index': self.index, 'time': self.time, 'score': self.score, 'label': self.label}\n\n\n\n \nclass Similarity(object):\n def __init__(self, start, end, data, fps):\n if start > len(data) or end > len(data):\n raise ValueError(\"The defined subscene is out of the scene boundaries.\")\n\n self.start=start\n self.end=end\n self.values = data\n self.indices=np.arange(start, end+1, 1)\n self.tops = []\n self.pits = []\n self.fps = fps\n self.features = []\n self.feature_labels = {\n 'valley_pit': 1, \n 'plateau_start': 2, \n 'plateau_end': 3, \n 'hill_start': 4, \n 'hill_end': 5, \n 'hill_top': 6\n }\n \n \n\n \n def processing(self):\n #if sum(self.values) != 0:\n self.remove_discontinuities(mu=0.12, eps=0.25, nb_pts=5)\n self.butter_lowpass_filter(2.5)\n self.find_tops_pits(nb_pts=6)\n # return 1\n #else:\n # return 0\n \n \n \n def remove_discontinuities(self, mu=0.12, eps=0.25, nb_pts=5):\n\n signal = np.asarray(self.values).copy()\n \n \n scores = []\n for i in range(len(signal)-nb_pts+1):\n packet = signal[i:i+nb_pts]\n for j in range(nb_pts-1, 2, -1):\n if abs(packet[0]-packet[j]) < mu and min(packet[0], packet[j])-min(packet[:j+1]) > eps:\n score = np.argmin(packet)+i+1\n if score not in scores:\n #scores.append(score)\n signal[i:i+j+1] = np.linspace(packet[0], packet[j], j+1).tolist()\n\n self.values = signal.tolist()\n \n \n \n\n\n def butter_lowpass_filter(self, freq):\n\n data = np.asarray(self.values).copy()\n if len(data)>10:\n order = 6\n ratio_pts_used_for_side=0.1\n fs = 30\n shift={1: 27, 2: 12, 2.5: 8, 3: 7, 4: 5, 5: 4, 6: 3}\n \n #Avoid side effects\n ext_size = max(round(ratio_pts_used_for_side*data.size), 1)#, shift[freq]+1)\n sig_shift = min(shift[freq], ext_size-1)\n x1 = np.flipud(data[1:ext_size+1])\n x2 = np.flipud(data[-1-ext_size:-1])\n data = np.insert(data, 0, x1)\n data = np.append(data, x2)\n \n #filtering\n b, a = self.butter_lowpass(freq, fs, order=order)\n y = lfilter(b, a, data)\n inf, sup = ext_size+sig_shift, -ext_size+sig_shift\n self.values = y[inf : sup]\n\n \n \n \n def butter_lowpass(self, cutoff, fs, order=5):\n \"\"\"\n PARAMETERS: \n - cutoff: cutoff frequency determining the boudary of the low pass band o the filter\n - fs: sampling frequency\n - order: order of the filter\n \n DESCRIPTION:\n Compute the filtering coefficients given the chosen parameters\n \n RETURN:\n - b, a: filter coefficients \n \"\"\"\n nyq = 0.5 * fs\n normal_cutoff = cutoff / nyq\n b, a = butter(order, normal_cutoff, btype='low', analog=False)\n return b, a\n \n \n \n def extract_features(self):\n if self.tops.any() or self.pits.any():\n \n pits_tops, labels = self.detect_pits_tops()\n \n \n #features = [Similarity_cut(int(el[0]), int(el[0])/self.fps, labels=[], score=self.compute_score_top(el))\n # for el, label in zip(pits_tops, labels) if label=='top']\n \n \n features = self.find_labels(pits_tops, labels)\n \n self.features = np.asarray(features)[np.argsort([cut.index for cut in features])].tolist()\n #self.pit_cuts = self.score_pits(pits_tops, labels)\n \n \n featured_pits_labels = ['hill_start', 'hill_end', 'valley_pit']\n indices_of_featured_pits = [feat.index for feat in self.features if any(elem in feat.label for elem in featured_pits_labels)]\n indices_to_rm = [i for i in range(self.pits.shape[0]) if self.pits[i, 0] in indices_of_featured_pits]\n\n self.pits = np.delete(self.pits, indices_to_rm, axis=0)\n\n \n \n \n \n \n \n def find_labels(self, pits_tops, labels):\n features = []\n double_tops = [i for i, (el1, el2) in enumerate(zip(labels[:-1], labels[1:])) if [el1, el2] == ['top', 'top']]\n double_pits = [i for i, (el1, el2) in enumerate(zip(labels[:-1], labels[1:])) if [el1, el2] == ['pit', 'pit']]\n\n for i in double_tops:\n front_top = pits_tops[i]\n back_top = pits_tops[i+1]\n if i-1 >= 0:\n front_pit = pits_tops[i-1]\n else:\n local_min_left, ind_left = self.find_next_min_local(front_top, 'left', nb_pts=6)\n front_pit = [ind_left, local_min_left]\n if i+2 < len(pits_tops):\n back_pit = pits_tops[i+2]\n else:\n local_min_right, ind_right = self.find_next_min_local(back_top, 'right', nb_pts=6)\n back_pit = [ind_right, local_min_right]\n \n if self.plateau_shaped(front_top, back_top, front_pit, back_pit, spread_factor=1.5):#The higher the spread_factor, the spreader we allow the hill to be\n ## Back top\n front_score = self.compute_score_top(front_top)\n back_score = self.compute_score_top(back_top)\n features.append(Similarity_cut(int(front_top[0]), int(front_top[0])/self.fps, \n label='plateau_start', score=front_score))\n features.append(Similarity_cut(int(back_top[0]), int(back_top[0])/self.fps, \n label='plateau_end', score=back_score))\n '''\n if i-1 >= 0:\n features.append(Similarity_cut(int(pits_tops[i-1][0]), int(pits_tops[i-1][0])/self.fps, \n label='hill_start', score=front_score))\n if i+2 < len(pits_tops):\n features.append(Similarity_cut(int(pits_tops[i+2][0]), int(pits_tops[i+2][0])/self.fps, \n label='hill_end', score=back_score))\n '''\n for i in double_pits:\n if i-1 >= 0:\n features.append(Similarity_cut(int(pits_tops[i-1][0]), int(pits_tops[i-1][0])/self.fps, \n label='valley_start', score=self.compute_score_top(pits_tops[i-1])))\n if i+2 < len(pits_tops):\n features.append(Similarity_cut(int(pits_tops[i+2][0]), int(pits_tops[i+2][0])/self.fps, \n label='valley_end', score=self.compute_score_top(pits_tops[i+2])))\n \n already_visited = [i for i, el in enumerate(pits_tops) if el[0] in [feat.index for feat in features]]\n single_tops = [i for i, label in enumerate(labels) if label == 'top' and i not in already_visited]\n \n for i in single_tops:\n single_top = pits_tops[i]\n if i > 0:\n # Replace top by its previous pit if the slope is low \n right_min_local, _ = self.find_next_min_local(single_top, 'right')\n ratio = (single_top[1]-right_min_local)/(single_top[1]-pits_tops[i-1][1])\n time_ratio = (single_top[1]-pits_tops[i-1][1])*100/(single_top[0]-pits_tops[i-1][0])\n if ratio < 0.25 and ratio > 0 and labels[i-1] != 'top' and time_ratio<2:\n\n features.append(Similarity_cut(int(pits_tops[i-1][0]), int(pits_tops[i-1][0])/self.fps, \n label='valley_pit', score=self.compute_score_top(single_top)))\n \n # if not pit_replaced_top:\n features.append(Similarity_cut(int(single_top[0]), int(single_top[0])/self.fps, \n label='hill_top', score=self.compute_score_top(single_top)))\n return features\n \n \n def find_tops_pits(self, nb_pts):\n tops = []\n pits = []\n\n data =self.values\n nb_pts = min(nb_pts, int(len(data)/2))\n going_down = data[0] > data[nb_pts]\n old_moving_avg = data[0]\n for i in range(len(data)-nb_pts+1):\n new_moving_avg = sum(data[i:i+nb_pts])/len(data[i:i+nb_pts])\n if going_down and new_moving_avg>old_moving_avg:\n pits.append(i+np.argmin(data[i:i+nb_pts])+1)\n going_down = False\n if not going_down and new_moving_avg 0.1]\n else:\n pits = [pit for pit in pits \n if max(data[pit] - self.find_next_max_local([pit, data[pit]], 'right')[0], \n data[pit] - self.find_next_max_local([pit, data[pit]], 'left')[0]) \n > 0.1]\n \n \n pits=np.asarray([(pit_ind, data[pit_ind-1]) for pit_ind in pits])\n tops=np.asarray([(top_ind, data[top_ind-1]) for top_ind in tops])\n\n self.tops, self.pits = tops, pits\n \n \n\n \n def keep_meaningful_tops_pits(self, tops, pits):\n\n data =self.values\n seq = np.sort(tops+pits)\n remove=[]\n start=0\n treshold=0.1\n if pits[0]= 0 and (seq[back] in remove or (seq[back] in tops and data[seq[back]]tresh2:\n remove.append(pits[i+1])\n \n pits = [pit for pit in pits if pit not in remove]\n return [el for el in pits_tops if el not in remove], pits\n \n \n \n \n def respawn_first_top_of_deleted_serie(self, tops_pits, tops, remove, nb_min_el_in_serie):\n if len(remove) < 2:\n return remove\n \n indices = np.asarray([i for i, el in enumerate(tops_pits) if el in remove])\n indices_shifted = np.append(indices[1:], indices[-1]+1)\n diff = indices_shifted-indices-1\n counter = 0\n respawned_indices=[]\n first_index=0\n for i, el in enumerate(diff):\n if el:\n if counter >= nb_min_el_in_serie-1 and indices[i]+1 < len(tops_pits):\n if tops_pits[indices[i]+1] in tops:\n tops_pits[indices[i]]\n respawned_indices.append(first_index)\n counter=0\n else:\n if counter == 0:\n first_index=i\n counter+=1\n if counter >= nb_min_el_in_serie-1 and indices[i]+1 < len(tops_pits):\n if tops_pits[indices[i]+1] in tops:\n respawned_indices.append(first_index)\n respawned_indices = [ind if remove[ind] in tops else ind+1 for ind in respawned_indices]\n return np.delete(remove, respawned_indices).tolist()\n \n \n \n \n \n def rm_more_than_2_consec_tops(self, tops_pits, tops):\n rm_consec = [tops_pits[i] for i in range(1, len(tops_pits)-1) \n if tops_pits[i-1] in tops and tops_pits[i] in tops and tops_pits[i+1] in tops]\n\n return[top for top in tops if top not in rm_consec]\n \n\n \n \n \n \n def detect_pits_tops(self):\n\n if not self.tops.any():\n pits_tops=self.pits\n elif not self.pits.any():\n pits_tops=self.tops\n else:\n pits_tops = np.concatenate((self.tops, self.pits))\n \n indices = np.argsort(pits_tops[:, 0])\n pits_tops = pits_tops[indices]\n labels = np.asarray(['top']*self.tops.shape[0] + ['pit']*self.pits.shape[0])[indices]\n \n \n return pits_tops, labels\n \n \n \n \n def score_pits(self, pits_tops, labels):\n pits = [pits_tops[i] for i in range(len(pits_tops)) if labels[i]=='pit']\n return [Similarity_cut(int(pit[0]), pit[0]/self.fps, [], score=self.compute_score_pit(pit)) for pit in pits]\n \n \n\n \n def compute_score_top(self, top):\n left_min_local, _ = self.find_next_min_local(top, 'left', nb_pts=6)\n right_min_local, _ = self.find_next_min_local(top, 'right', nb_pts=6)\n\n return ((abs(left_min_local-right_min_local)**2*(top[1]-min(left_min_local, right_min_local)))**(1./3))*2\n \n \n \n def compute_score_pit(self, pit):\n left_max_local, _ = self.find_next_max_local(pit, 'left')\n right_max_local, _ = self.find_next_max_local(pit, 'right')\n\n return ((abs(left_max_local-right_max_local)**2*(max(left_max_local, right_max_local)-pit[1]))**(1./3))*2\n \n \n \n def find_next_min_local(self, top, side, nb_pts=10):\n nb_pts = min(nb_pts, int(len(self.values)/2))\n i = int(top[0])-self.start\n old_moving_avg = self.values[i]\n if side == 'right':\n while i+nb_pts < len(self.values)+1:\n new_moving_avg = sum(self.values[i:i+nb_pts])/len(self.values[i:i+nb_pts])\n if new_moving_avg>old_moving_avg:\n min_index=i+np.argmin(self.values[i:i+nb_pts])+1+self.start\n min_value=self.values[min_index-self.start]\n return min_value, min_index#[min_index, min_value]\n\n i+=1\n old_moving_avg = new_moving_avg\n \n return self.values[-1], len(self.values)\n else:\n while i-nb_pts+1 >= 0:\n new_moving_avg = sum(self.values[i-nb_pts+1:i+1])/len(self.values[i-nb_pts+1:i+1])\n if new_moving_avg>old_moving_avg:\n min_index=i-np.argmin(self.values[i-nb_pts+1:i+1][::-1])+self.start\n min_value=self.values[min_index-self.start]\n return min_value, min_index#[min_index, min_value]\n\n i-=1\n old_moving_avg = new_moving_avg\n \n return self.values[0], self.start#[self.start, ]\n \n\n\n def find_next_max_local(self, pit, side, nb_pts=10):\n nb_pts = min(nb_pts, int(len(self.values)/2))\n i = int(pit[0])-self.start\n old_moving_avg = self.values[i]\n if side == 'right':\n while i+nb_pts < len(self.values)+1:\n new_moving_avg = sum(self.values[i:i+nb_pts])/len(self.values[i:i+nb_pts])\n if new_moving_avg= 0:\n new_moving_avg = sum(self.values[i-nb_pts+1:i+1])/len(self.values[i-nb_pts+1:i+1])\n if new_moving_avgspread_factor:\n return False\n elif abs(back_top[1]-front_top[1])<0.1:\n return True\n else:\n return False\n \n \n \n \n \n def lineplot_analog(self, clip_ID, yticks=0.1, duration=None, fps=None, title=None,\n tops_pits=True, save=False, path='../'):\n #plt.figure(num=None, , dpi=80, facecolor='w', edgecolor='k')\n if title is None:\n title = ''\n if duration is None:\n x_label='Frames'\n else:\n x_label = 'Time'\n y_label='Similarity'\n\n \n factor = 1\n if duration is not None:\n factor = 1/self.fps\n elif fps is not None:\n factor = fps/self.fps\n \n x = np.asarray(self.indices)*factor#+self.offset\n y = np.asarray(self.values)\n \n if save:\n dpi = 80\n else:\n dpi = 80\n \n fig, ax = plt.subplots(figsize=(10, 4), dpi=dpi)\n \n if x.shape[0] > 400:\n step = round(50*factor)\n elif x.shape[0] > 200:\n step = round(20*factor)\n elif x.shape[0] > 100:\n step = round(10*factor)\n elif x.shape[0] > 30:\n step = max(round(5*factor, 1), 0.1)\n else:\n step = max(round(1*factor, 1), 0.1)\n \n X_ticks_maj = np.arange(max(int(x[0])-factor, 0), int(x[-1])+2, step)\n y_ticks = np.arange(int(y.min()), round(y.max(), 1), yticks)\n X_ticks_min = np.arange(x[0]-factor, x[-1]+1, step*0.2)\n X_ticks_min[0] = int(x[0])\n \n if duration is not None:\n X_ticks_maj[0] = 0\n else:\n X_ticks_maj[0] = 1\n \n ax.spines['top'].set_color('white')\n ax.spines['right'].set_color('white')\n \n ax.set_xticks(X_ticks_maj)\n ax.set_xticks(X_ticks_min, minor=True)\n ax.set_yticks(y_ticks)\n ax.grid(which='both')\n ax.grid(which='minor', alpha=0.4)\n ax.grid(which='major', alpha=1)\n ax.set_xlabel(x_label)\n ax.set_ylabel(y_label)\n \n\n \n plt.title(title)\n \n plt.plot(x, y)\n \n if tops_pits:\n if self.features:\n for feat in self.features:\n if any(elem in feat.label for elem in ['hill_start', 'hill_end']):\n color='k'\n elif any(elem in feat.label for elem in ['plateau_start', 'plateau_end']):\n color='b'\n elif any(elem in feat.label for elem in ['valley_start', 'valley_end']):\n color='m'\n else:\n color='g'\n \n plt.scatter(feat.time, self.values[feat.index-1], color=color, s=180, alpha=0.2)\n plt.scatter(feat.time, self.values[feat.index-1], color=color, s=40, alpha=1)\n score = '{:.2f}' .format(feat.score)\n ax.annotate(score, (feat.time, self.values[feat.index-1]))\n \n if self.pits.any():\n\n pits_ind = self.pits[:, 0].astype(int)-1\n\n indices = self.indices[pits_ind]*factor\n plt.scatter(indices, self.pits[:, 1], color='r', s=180, alpha=0.2)\n plt.scatter(indices, self.pits[:, 1], color='r', s=40, alpha=1)\n \n \n string = ''\n\n \n if save == True:\n plt.savefig(os.path.join(path, 'Clip_{}_similarity2' .format(clip_ID)+string+'.png'))\n else:\n plt.show()\n \n \n ","sub_path":"src/similarity.py","file_name":"similarity.py","file_ext":"py","file_size_in_byte":24086,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"238946299","text":"#!python3 \n\n\"\"\"\n##### Task 1\nAsk the user to enter an integer.\nPrint the multiplication tables up to 12 for that number\nusing a for loop instead of a while loop.\n(2 points)\n\ninputs:\nint number\n\noutputs:\nmultiples of that number\n\nexample:\nEnter number:4\n4 8 12 16 20 24 28 32 36 40 44 48\n\"\"\"\n\na = int(input(\"Enter an integer: \"))\n\nfor i in range(1,13):\n print(a * i, end=\" \")\n","sub_path":"task1.py","file_name":"task1.py","file_ext":"py","file_size_in_byte":376,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"334054680","text":"from sense_hat import SenseHat\nimport random\nfrom math import floor, ceil\nimport time\nimport sys\nimport os\n\nclass MatrixRect:\n\n def __init__(self, w, h, ledc_on, filled):\n self.matrix = []\n self.ledc_black = (0, 0, 0)\n self.ledc_on = ledc_on\n self.w = w\n self.h = h\n self.filled = filled\n self.createBlackMatrix();\n self.createPattern()\n\n def createBlackMatrix(self):\n for p in range(0,64):\n self.matrix.append(self.ledc_black)\n\n def createPattern(self):\n for r in range(0,self.w):\n for c in range(0, self.h):\n if self.filled == False:\n ledc = self.ledc_black\n if r == 0 or (r == self.w - 1):\n ledc = self.ledc_on\n elif c == 0 or c == self.h - 1:\n ledc = self.ledc_on\n\n self.matrix[r*8+c] = ledc\n else:\n self.matrix[r*8+c] = ledc_on\n\nsense = SenseHat()\ni = 0\ndir = 1\nledc_on = (255, 0, 0)\n\nwhile True:\n try:\n m = MatrixRect(i, i, ledc_on, False)\n sense.set_pixels(m.matrix)\n\n temp_i = i+1*dir\n if temp_i <= 8 and temp_i >= 0:\n i = temp_i\n else:\n ledc_on = (round(random.random()*255), round(random.random()*255), round(random.random()*255))\n dir = dir*-1\n\n time.sleep(0.1)\n except KeyboardInterrupt:\n print('Interrupted')\n sense.clear()\n try:\n sys.exit(0)\n except SystemExit:\n os._exit(0)","sub_path":"sensehat/rect_animated.py","file_name":"rect_animated.py","file_ext":"py","file_size_in_byte":1527,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"612277339","text":"import requests\nfrom order.models import Order\n\n\nclass BaseConnector(object):\n def __init__(self, instance, server_url):\n self.server_url = server_url\n self.instance = Order.objects.get(id=instance)\n\n def sync_status(self):\n data = self.get_serializer(self.instance)\n data_status = {\n 'status': data['status'],\n }\n url = f'{self.server_url}/order/item/{self.instance.number}/'\n self.send(url, data_status, method='patch')\n\n def create_order(self):\n url = f'{self.server_url}/order/create/'\n data = self.get_serializer(self.instance)\n for i in ['id', 'synced']:\n del data[i]\n self.send(url, data, method='post')\n\n def send(self, url, data, method):\n request = {\n 'post': requests.post,\n 'patch': requests.patch,\n }\n resp = request[method](url, data=data)\n print(resp.status_code, resp.text)\n if resp.status_code == 200 or resp.status_code == 201:\n self.instance.synced = True\n else:\n self.instance.synced = False\n self.instance.save()\n\n @staticmethod\n def get_serializer(instance):\n from order.serializers import OrderSerializer\n serializer = OrderSerializer(instance)\n return serializer.data\n","sub_path":"apps/exchange/connector.py","file_name":"connector.py","file_ext":"py","file_size_in_byte":1323,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"411615650","text":"# Copyright (c) 2013 Nexedi SA and Contributors. All Rights Reserved.\nimport transaction\nfrom Products.SlapOS.tests.testSlapOSMixin import \\\n testSlapOSMixin\nfrom zExceptions import Unauthorized\nfrom DateTime import DateTime\nfrom functools import wraps\nfrom Products.ERP5Type.tests.utils import createZODBPythonScript\nimport difflib\n\nclass TestSlapOSSoftwareInstance_requestValidationPayment(testSlapOSMixin):\n\n def beforeTearDown(self):\n transaction.abort()\n\n def createCloudContract(self):\n new_id = self.generateNewId()\n contract = self.portal.cloud_contract_module.newContent(\n portal_type='Cloud Contract',\n title=\"Contract %s\" % new_id,\n reference=\"TESTCONTRACT-%s\" % new_id,\n )\n self.portal.portal_workflow._jumpToStateFor(contract, 'invalidated')\n return contract\n\n def createPaymentTransaction(self):\n new_id = self.generateNewId()\n return self.portal.accounting_module.newContent(\n portal_type='Payment Transaction',\n title=\"Payment %s\" % new_id,\n reference=\"TESTPAY-%s\" % new_id,\n )\n\n def createInvoiceTransaction(self):\n new_id = self.generateNewId()\n return self.portal.accounting_module.newContent(\n portal_type='Sale Invoice Transaction',\n title=\"Invoice %s\" % new_id,\n reference=\"TESTINV-%s\" % new_id,\n created_by_builder=1,\n )\n\n def createNeededDocuments(self):\n new_id = self.generateNewId()\n person = self.portal.person_module.newContent(\n portal_type='Person',\n title=\"Person %s\" % new_id,\n reference=\"TESTPERS-%s\" % new_id,\n )\n subscription = self.portal.hosting_subscription_module.newContent(\n portal_type='Hosting Subscription',\n title=\"Subscription %s\" % new_id,\n reference=\"TESTSUB-%s\" % new_id,\n destination_section_value=person,\n )\n instance = self.portal.software_instance_module.newContent(\n portal_type='Software Instance',\n title=\"Instance %s\" % new_id,\n reference=\"TESTINST-%s\" % new_id,\n specialise_value=subscription,\n )\n return person, instance, subscription\n\n def test_requestValidationPayment_REQUEST_disallowed(self):\n person, instance, subscription = self.createNeededDocuments()\n self.assertRaises(\n Unauthorized,\n instance.SoftwareInstance_requestValidationPayment,\n REQUEST={})\n\n def test_prevent_concurrency(self):\n person, instance, subscription = self.createNeededDocuments()\n tag = \"%s_requestValidationPayment_inProgress\" % person.getUid()\n person.reindexObject(activate_kw={'tag': tag})\n transaction.commit()\n\n result = instance.SoftwareInstance_requestValidationPayment()\n self.assertEquals(result, None)\n\n def test_addCloudContract(self):\n person, instance, subscription = self.createNeededDocuments()\n contract = instance.SoftwareInstance_requestValidationPayment()\n\n # Default property\n self.assertEquals(contract.getPortalType(), 'Cloud Contract')\n self.assertEquals(contract.getValidationState(), 'invalidated')\n self.assertEquals(contract.getDestinationSection(), person.getRelativeUrl())\n self.assertEquals(contract.getTitle(),\n 'Contract for \"%s\"' % person.getTitle())\n\n def test_addCloudContract_do_not_duplicate_contract_if_not_reindexed(self):\n person, instance, subscription = self.createNeededDocuments()\n contract = instance.SoftwareInstance_requestValidationPayment()\n transaction.commit()\n contract2 = instance.SoftwareInstance_requestValidationPayment()\n self.assertNotEquals(contract, None)\n self.assertEquals(contract2, None)\n\n def test_addCloudContract_existing_invalidated_contract(self):\n person, instance, subscription = self.createNeededDocuments()\n contract = instance.SoftwareInstance_requestValidationPayment()\n transaction.commit()\n self.tic()\n contract2 = instance.SoftwareInstance_requestValidationPayment()\n self.assertNotEquals(contract, None)\n self.assertEquals(contract2.getRelativeUrl(), contract.getRelativeUrl())\n\n def test_addCloudContract_existing_validated_contract(self):\n person, instance, subscription = self.createNeededDocuments()\n contract = instance.SoftwareInstance_requestValidationPayment()\n contract.validate()\n transaction.commit()\n self.tic()\n contract2 = instance.SoftwareInstance_requestValidationPayment()\n self.assertNotEquals(contract, None)\n self.assertEquals(contract2.getRelativeUrl(), contract.getRelativeUrl())\n\n def test_do_nothing_if_validated_contract(self):\n person, instance, subscription = self.createNeededDocuments()\n contract = self.createCloudContract()\n contract.edit(destination_section_value=person)\n contract.validate()\n self.tic()\n\n contract2 = instance.SoftwareInstance_requestValidationPayment()\n self.assertEquals(contract2.getRelativeUrl(), contract.getRelativeUrl())\n self.assertEquals(contract2.getCausality(\"\"), \"\")\n self.assertEquals(contract2.getValidationState(), \"validated\")\n\n def test_validate_contract_if_payment_found(self):\n person, instance, subscription = self.createNeededDocuments()\n contract = self.createCloudContract()\n contract.edit(destination_section_value=person)\n payment = self.createPaymentTransaction()\n payment.edit(\n default_destination_section_value=person,\n )\n self.portal.portal_workflow._jumpToStateFor(payment, 'stopped')\n self.assertEquals(contract.getValidationState(), \"invalidated\")\n self.tic()\n\n contract2 = instance.SoftwareInstance_requestValidationPayment()\n self.assertEquals(contract2.getRelativeUrl(), contract.getRelativeUrl())\n self.assertEquals(contract2.getCausality(\"\"), \"\")\n self.assertEquals(contract2.getValidationState(), \"validated\")\n\n def test_create_invoice_if_needed_and_no_payment_found(self):\n person, instance, subscription = self.createNeededDocuments()\n contract = self.createCloudContract()\n contract.edit(destination_section_value=person)\n self.assertEquals(contract.getValidationState(), \"invalidated\")\n self.tic()\n\n before_date = DateTime()\n contract2 = instance.SoftwareInstance_requestValidationPayment()\n after_date = DateTime()\n self.assertEquals(contract2.getRelativeUrl(), contract.getRelativeUrl())\n self.assertNotEquals(contract2.getCausality(\"\"), \"\")\n self.assertEquals(contract2.getValidationState(), \"invalidated\")\n\n invoice = contract2.getCausalityValue()\n self.assertEquals(invoice.getPortalType(), 'Sale Invoice Transaction')\n self.assertEquals(len(invoice.contentValues()), 1)\n self.assertEquals(invoice.getSimulationState(), 'confirmed')\n self.assertEquals(invoice.getCausalityState(), 'building')\n self.assertEquals(invoice.getTitle(), 'Account validation')\n self.assertEquals(invoice.getSource(), person.getRelativeUrl())\n self.assertEquals(invoice.getDestination(), person.getRelativeUrl())\n self.assertEquals(invoice.getDestinationSection(), person.getRelativeUrl())\n self.assertEquals(invoice.getDestinationDecision(), person.getRelativeUrl())\n self.assertTrue(invoice.getStartDate() >= before_date)\n self.assertTrue(invoice.getStartDate() <= after_date)\n self.assertEquals(invoice.getStartDate(), invoice.getStopDate())\n\n def test_do_nothing_if_invoice_is_ongoing(self):\n person, instance, subscription = self.createNeededDocuments()\n contract = self.createCloudContract()\n invoice = self.createInvoiceTransaction()\n self.portal.portal_workflow._jumpToStateFor(invoice, 'confirmed')\n contract.edit(\n destination_section_value=person,\n causality_value=invoice,\n )\n self.assertEquals(contract.getValidationState(), \"invalidated\")\n self.tic()\n\n contract2 = instance.SoftwareInstance_requestValidationPayment()\n self.assertEquals(contract2.getRelativeUrl(), contract.getRelativeUrl())\n self.assertEquals(contract2.getCausality(\"\"), invoice.getRelativeUrl())\n self.assertEquals(contract2.getValidationState(), \"invalidated\")\n\n def test_forget_current_cancelled_invoice(self):\n person, instance, subscription = self.createNeededDocuments()\n contract = self.createCloudContract()\n invoice = self.createInvoiceTransaction()\n self.portal.portal_workflow._jumpToStateFor(invoice, 'cancelled')\n contract.edit(\n destination_section_value=person,\n causality_value=invoice,\n )\n self.assertEquals(contract.getValidationState(), \"invalidated\")\n self.tic()\n\n contract2 = instance.SoftwareInstance_requestValidationPayment()\n self.assertEquals(contract2.getRelativeUrl(), contract.getRelativeUrl())\n self.assertEquals(contract2.getCausality(\"\"), \"\")\n self.assertEquals(contract2.getValidationState(), \"invalidated\")\n\n def test_forget_current_grouped_invoice(self):\n person, instance, subscription = self.createNeededDocuments()\n contract = self.createCloudContract()\n invoice = self.createInvoiceTransaction()\n line = invoice.newContent(\n portal_type=\"Sale Invoice Transaction Line\",\n source=\"account_module/receivable\", \n grouping_reference=\"foo\",\n )\n line.getSourceValue().getAccountType()\n self.portal.portal_workflow._jumpToStateFor(invoice, 'stopped')\n contract.edit(\n destination_section_value=person,\n causality_value=invoice,\n )\n self.assertEquals(contract.getValidationState(), \"invalidated\")\n self.tic()\n\n contract2 = instance.SoftwareInstance_requestValidationPayment()\n self.assertEquals(contract2.getRelativeUrl(), contract.getRelativeUrl())\n self.assertEquals(contract2.getCausality(\"\"), \"\")\n self.assertEquals(contract2.getValidationState(), \"invalidated\")\n\n def test_do_nothing_if_invoice_is_not_grouped(self):\n person, instance, subscription = self.createNeededDocuments()\n contract = self.createCloudContract()\n invoice = self.createInvoiceTransaction()\n invoice.newContent(\n portal_type=\"Sale Invoice Transaction Line\",\n source=\"account_module/receivable\", \n )\n self.portal.portal_workflow._jumpToStateFor(invoice, 'stopped')\n contract.edit(\n destination_section_value=person,\n causality_value=invoice,\n )\n self.assertEquals(contract.getValidationState(), \"invalidated\")\n self.tic()\n\n contract2 = instance.SoftwareInstance_requestValidationPayment()\n self.assertEquals(contract2.getRelativeUrl(), contract.getRelativeUrl())\n self.assertEquals(contract2.getCausality(\"\"), invoice.getRelativeUrl())\n self.assertEquals(contract2.getValidationState(), \"invalidated\")\n\nclass TestSlapOSPerson_isAllowedToAllocate(testSlapOSMixin):\n\n def beforeTearDown(self):\n transaction.abort()\n\n def createPerson(self):\n new_id = self.generateNewId()\n return self.portal.person_module.newContent(\n portal_type='Person',\n title=\"Person %s\" % new_id,\n reference=\"TESTPERS-%s\" % new_id,\n )\n\n def createCloudContract(self):\n new_id = self.generateNewId()\n return self.portal.cloud_contract_module.newContent(\n portal_type='Cloud Contract',\n title=\"Contract %s\" % new_id,\n reference=\"TESTCONTRACT-%s\" % new_id,\n )\n\n def test_not_allowed_by_default(self):\n person = self.createPerson()\n result = person.Person_isAllowedToAllocate()\n self.assertEquals(result, False)\n\n def test_allowed_if_has_a_validated_contract(self):\n person = self.createPerson()\n contract = self.createCloudContract()\n contract.edit(\n destination_section_value=person\n )\n self.portal.portal_workflow._jumpToStateFor(contract, 'validated')\n self.tic()\n result = person.Person_isAllowedToAllocate()\n self.assertEquals(result, True)\n\n def test_not_allowed_if_has_an_invalidated_contract(self):\n person = self.createPerson()\n contract = self.createCloudContract()\n contract.edit(\n destination_section_value=person\n )\n self.portal.portal_workflow._jumpToStateFor(contract, 'invalidated')\n self.tic()\n result = person.Person_isAllowedToAllocate()\n self.assertEquals(result, False)\n\n def test_not_allowed_if_no_related_contract(self):\n person = self.createPerson()\n contract = self.createCloudContract()\n self.portal.portal_workflow._jumpToStateFor(contract, 'validated')\n self.tic()\n result = person.Person_isAllowedToAllocate()\n self.assertEquals(result, False)\n","sub_path":"master/bt5/slapos_accounting/TestTemplateItem/portal_components/test.erp5.testSlapOSContractSkins.py","file_name":"test.erp5.testSlapOSContractSkins.py","file_ext":"py","file_size_in_byte":12267,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"285830902","text":"\r\nimport urllib.request\r\nimport json\r\nime = input(\"Input the name of a youtuber with over 5k subscribers: \")\r\n\r\nkey = '' # input your own key cause i am not going to leak mine\r\n\r\n\r\ntry: \r\n data = urllib.request.urlopen(\"https://www.googleapis.com/youtube/v3/channels?part=statistics&key=\"+key+\"&forUsername=\"+ime).read()\r\n sub = json.loads(data)[\"items\"][0][\"statistics\"][\"subscriberCount\"]\r\n if int(sub) > 5000:\r\n print(ime+\" has \"+\"{:,d}\".format(int(sub)) + \" subscribers\")\r\n else:\r\n print(\"The specified youtuber does not exceed 5,000 subscribers!\")\r\nexcept:\r\n print(\"Channel was not found (try to input original channel name)\")\r\n\r\n ","sub_path":"sourceCode.py","file_name":"sourceCode.py","file_ext":"py","file_size_in_byte":668,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"405453468","text":"import os.path\n\nfrom data.base_dataset import BaseDataset, get_params\nfrom data.image_folder import make_dataset\nimport torchvision.transforms as transforms\nimport glob\n\nfrom PIL import Image\n\n\nclass TextureDataset(BaseDataset):\n \"\"\"A dataset class for paired image dataset.\n\n It assumes that the directory '/path/to/data/train' contains image pairs in the form of {A,B}.\n During test time, you need to prepare a directory '/path/to/data/test'.\n \"\"\"\n\n def __init__(self, opt):\n \"\"\"Initialize this dataset class.\n\n Parameters:\n opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions\n \"\"\"\n BaseDataset.__init__(self, opt)\n\n assert opt.data_class_a is not None, \"--data-class-a is required\"\n assert opt.data_class_b is not None, \"--data-class-b is required\"\n\n self.data_dir_a = opt.data_class_a\n self.data_dir_b = opt.data_class_b\n\n self.data_paths_a = glob.glob(os.path.join(opt.data_class_a, '*'))\n self.data_paths_b = glob.glob(os.path.join(opt.data_class_b, '*'))\n\n # assert(self.opt.load_size >= self.opt.crop_size) # crop_size should be smaller than the size of loaded image\n self.input_nc = self.opt.output_nc if self.opt.direction == 'BtoA' else self.opt.input_nc\n self.output_nc = self.opt.input_nc if self.opt.direction == 'BtoA' else self.opt.output_nc\n\n def __getitem__(self, index):\n \"\"\"Return a data point and its metadata information.\n\n Parameters:\n index - - a random integer for data indexing\n\n Returns a dictionary that contains A, B, A_paths and B_paths\n A (tensor) - - an image in the input domain\n B (tensor) - - its corresponding image in the target domain\n A_paths (str) - - image paths\n B_paths (str) - - image paths\n \"\"\"\n # read a image given a random integer index\n a_path = self.data_paths_a[index]\n b_path = self.data_paths_b[index]\n\n a_image = Image.open(a_path).convert('RGB')\n b_image = Image.open(b_path).convert('RGB')\n\n # apply the same transform to both A and B\n transform_params = get_params(self.opt, a_image.size)\n\n a_transform = get_transform(self.opt, transform_params, grayscale=(self.input_nc == 1))\n b_transform = get_transform(self.opt, transform_params, grayscale=(self.output_nc == 1))\n\n a = a_transform(a_image)\n b = b_transform(b_image)\n\n return {'A': a, 'B': b, 'A_paths': a_path, 'B_paths': b_path}\n\n def __len__(self):\n \"\"\"Return the total number of images in the dataset.\"\"\"\n return min(len(self.data_paths_a), len(self.data_paths_b))\n\n\ndef get_transform(opt, params=None, grayscale=False, method=Image.BICUBIC, convert=True):\n transform_list = []\n if grayscale:\n transform_list.append(transforms.Grayscale(1))\n\n if not opt.no_flip:\n if params is None:\n transform_list.append(transforms.RandomHorizontalFlip())\n elif params['flip']:\n transform_list.append(transforms.Lambda(lambda img: __flip(img, params['flip'])))\n\n transform_list.append(transforms.Lambda(lambda img: __crop(img)))\n\n if 'resize' in opt.preprocess:\n osize = [opt.load_size, opt.load_size]\n transform_list.append(transforms.Resize(osize, method))\n\n if convert:\n transform_list += [transforms.ToTensor()]\n if grayscale:\n transform_list += [transforms.Normalize((0.5,), (0.5,))]\n else:\n transform_list += [transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]\n\n return transforms.Compose(transform_list)\n\n\ndef __flip(img, flip):\n if flip:\n return img.transpose(Image.FLIP_LEFT_RIGHT)\n return img\n\n\ndef __crop(img, texture_size=512):\n s = texture_size * 400 / 512\n return img.crop((texture_size/2 - s/2, texture_size - s, texture_size/2 + s/2, texture_size))\n","sub_path":"data/texture_dataset.py","file_name":"texture_dataset.py","file_ext":"py","file_size_in_byte":3958,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"335078740","text":"\r\nimport os, sys, os.path\r\nimport glob\r\nimport time\r\nimport shutil\r\n\r\ndef debug(str):\r\n\tglobal debugon\r\n\tif debugon==1: print (\"DEBUG: %s\" % str)\r\n\r\ndebugon=1\t\t\t\t\t\t \t\t\t\t\t\t\t\t\r\nheadersize=1\r\nsrcfolder = sys.argv[0][:-15]\r\ntgtfolder = sys.argv[0][:-15] + \"\\Mergedfiles\"\t\r\ndestfolder = sys.argv[0][:-15] + \"\\Processedfiles\"\r\n\r\ndef process():\r\n\tsuffix=\"merged%s\" % time.strftime(\"-%m-%d-%y~RJ\")\t\r\n\toutfilename=\"%s\\\\%s\" % (tgtfolder, suffix)\r\n\tcount = 0\r\n\tfileslist = next(os.walk(srcfolder))[2];\r\n\tprint (fileslist)\r\n\tif(len(fileslist)) > 0:\r\n\t\tmergedfile = open(outfilename, \"w\")\r\n\t\tfor files in fileslist:\r\n\t\t\tfilename = files;\r\n\t\t\t\r\n\t\t\tfiles = \"\\\\\".join([srcfolder,files])\t\t\r\n\t\t\tif (count == 0 ):\r\n\t\t\t\twith open(files) as f1:\r\n\t\t\t\t\tfor lines in f1.readlines():\r\n\t\t\t\t\t\tmergedfile.write(lines)\r\n\t\t\telse:\r\n\t\t\t\twith open(files) as f2:\r\n\t\t\t\t\tfor line in f2.readlines()[headersize:]:\r\n\t\t\t\t\t\tmergedfile.write(line)\r\n\t\t\tcount = count+1\r\n\t\t\tshutil.move( files, \"%s\\\\%s\" %(destfolder, filename))\r\nprocess()\r\n\r\ndebug (\"SUCCESS\")\t\t\r\n","sub_path":"Scriptss/scripts/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":1019,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"603890462","text":"from flask import Flask, render_template, request, redirect, url_for, flash, session, escape, jsonify\nimport os, smtplib, pyrebase\nfrom firebase import firebase \n\napp = Flask(__name__)\n\n# settings\napp.secret_key = 'mysecretkey'\nAPP_ROOT = os.path.dirname(os.path.abspath(__file__))\ntarget = os.path.join(APP_ROOT, 'static/')\ntotal = 0\n\n# firebase\ndb = firebase.FirebaseApplication(\"https://apapachatestore.firebaseio.com\")\n\n# storage\nstorageConfig = {\n \"apiKey\": \"AIzaSyBAnc0Oz9Y5WEyjqyH385ue6L_UpkvLtew\",\n \"authDomain\": \"apapachatestore.firebaseapp.com\",\n \"databaseURL\": \"https://apapachatestore.firebaseio.com\",\n \"projectId\": \"apapachatestore\",\n \"storageBucket\": \"apapachatestore.appspot.com\",\n \"messagingSenderId\": \"529842451934\",\n \"appId\": \"1:529842451934:web:9a29de330667b9727ad94f\"\n}\nfirebase = pyrebase.initialize_app(storageConfig)\nstorage = firebase.storage()\npathCloud = \"Productos/\"\n\n# descarga de archivos\ndata = db.get(\"Productos\", \"\")\nif(data):\n for key in data:\n pathCloud = \"Productos/\"+data[key][\"Imagen\"]\n pathLocal = \"static/\"+data[key][\"Imagen\"]\n storage.child(pathCloud).download(pathLocal)\n\n#email smtp\nemaillist = ['rayma9829@gmail.com',]\nserver = smtplib.SMTP('smtp.gmail.com', 587)\nserver.starttls()\n\n#storeManager\n@app.route(\"/storeManager\", methods =['POST','GET']) \ndef storeManager():\n if \"username\" in session:\n data = db.get(\"Productos\", \"\")\n name = escape(session[\"username\"])\n if(data):\n return render_template(\"storeManager.html\", it = data, opc = True, name = name )\n else:\n return render_template(\"storeManager.html\", opc = True, name = name )\n else:\n if request.method == 'POST':\n user = request.form['usuario']\n password = request.form['contrasena']\n usuarios = db.get(\"Usuarios\", \"\")\n auten = False\n for key in usuarios:\n if usuarios[key][\"Usuario\"]==user and usuarios[key][\"Contraseña\"]==password:\n session[\"username\"] = user\n auten = True\n break\n \n if auten == True: \n data = db.get(\"Productos\", \"\")\n name = escape(session[\"username\"])\n if(data):\n for key in data:\n print(data[key][\"Imagen\"])\n pathCloud = \"Productos/\"+data[key][\"Imagen\"]\n pathLocal = \"static/\"+data[key][\"Imagen\"]\n storage.child(pathCloud).download(pathLocal)\n flash('Bienvenido')\n return render_template(\"storeManager.html\", it = data, opc = True, name = name)\n else:\n return render_template(\"storeManager.html\", opc = True, name = name)\n else:\n flash('El usuario o la contraseña son incorrectos')\n return render_template(\"autenticar.html\")\n else:\n return render_template(\"autenticar.html\")\n\n@app.route('/storeManager/logout')\ndef storeLogout():\n session.pop(\"username\")\n flash('Has cerrado tu sesión')\n return redirect(url_for('storeManager'))\n\n@app.route(\"/add\", methods = ['POST'])\ndef add():\n if not os.path.isdir(target):\n os.mkdir(target)\n if request.method == 'POST':\n producto = request.form['producto']\n imagen = request.files['imagen']\n descripcion = request.form['descripcion']\n precio = request.form['precio']\n inventario = request.form['inventario']\n etiquetas = request.form['etiquetas']\n filename = imagen.filename\n destination = \"/\".join([target, filename])\n if producto == \"\" or precio == \"\" or descripcion==\"\" or (imagen==None) or inventario == None or etiquetas == \"\":\n flash('Llena todos los campos correctamente')\n return redirect(url_for('storeManager'))\n else:\n filename = imagen.filename\n destination = \"/\".join([target, filename])\n precioFormat = int(precio)\n imagen.save(destination)\n pathCloud = \"Productos/\"+filename\n pathLocal = destination\n storage.child(pathCloud).put(pathLocal)\n data = {\n \"Producto\": producto,\n \"Imagen\": filename,\n \"Descripcion\": descripcion,\n \"Precio\": precio,\n \"Inventario\": inventario,\n \"Etiquetas\": etiquetas\n }\n db.post(\"Productos\", data)\n server.login('apapachatestore@gmail.com','apapachatecontrasena')\n message = 'Se ha agregado un producto satisfactoriamente\\nCorreo enviado desde apapachatestore.herokuapp.com'\n subject = 'Producto agregado'\n message = 'Subject: {}\\n\\n{}'.format(subject, message)\n for email in emaillist:\n server.sendmail('apapachatestore@gmail.com', email, message)\n flash('Producto agregado satisfactoriamente')\n return redirect(url_for('storeManager'))\n\n@app.route(\"/delete/\")\ndef delete(id):\n img = db.get(\"Productos\", id)[\"Imagen\"]\n print(img)\n db.delete(\"Productos\", id)\n server.login('apapachatestore@gmail.com','apapachatecontrasena')\n message = 'El producto ha sido eliminado satisfactoriamente\\nCorreo enviado desde apapachatestore.herokuapp.com'\n subject = 'Producto eliminado'\n message = 'Subject: {}\\n\\n{}'.format(subject, message)\n for email in emaillist:\n server.sendmail('apapachatestore@gmail.com', email, message)\n flash('Producto eliminado satisfactoriamente')\n return redirect(url_for('storeManager'))\n\n@app.route(\"/edit/\")\ndef edit(id):\n data = db.get(\"Productos\", id)\n print(data)\n return render_template(\"edit.html\", producto = data, id = id)\n\n@app.route(\"/update/\", methods = ['POST'])\ndef update(id):\n if request.method == 'POST':\n producto = request.form['producto']\n imagen = request.files['imagen']\n descripcion = request.form['descripcion']\n precio = request.form['precio']\n inventario = request.form['inventario']\n etiquetas = request.form['etiquetas']\n filename = imagen.filename\n destination = \"/\".join([target, filename])\n pa = \"Productos/\"+id\n pathCloud = \"Productos\"\n pathLocal = destination\n if imagen:\n img = db.get(\"Productos\", id)[\"Imagen\"]\n os.remove('static/'+img)\n db.put(pa, \"Imagen\", filename)\n imagen.save(destination)\n storage.child(pathCloud).put(pathLocal)\n if producto:\n db.put(pa, \"Producto\", producto)\n if descripcion:\n db.put(pa, \"Descripcion\", descripcion)\n if precio:\n precioFormat = int(precio)\n db.put(pa, \"Precio\", precioFormat)\n if inventario:\n db.put(pa, \"Inventario\", inventario)\n if etiquetas:\n db.put(pa, \"Etiquetas\", etiquetas)\n server.login('apapachatestore@gmail.com','apapachatecontrasena')\n message = 'El producto ha sido actualizado satisfactoriamente\\nCorreo enviado desde apapachatestore.herokuapp.com'\n subject = 'Producto actualizado'\n message = 'Subject: {}\\n\\n{}'.format(subject, message)\n for email in emaillist:\n server.sendmail('apapachatestore@gmail.com', email, message)\n flash('Producto actualizado satisfactoriamente')\n return redirect(url_for('storeManager'))\n\n@app.route(\"/signup\", methods=['GET','POST'])\ndef signup():\n if request.method == 'POST': \n usuario = request.form['usuario']\n contrasena = request.form['contrasena']\n confirmcontrasena = request.form['confirmcontrasena']\n correo = request.form['email']\n users = db.get(\"Usuarios\", \"\")\n registrar = False\n if contrasena == confirmcontrasena:\n for user in users:\n if users[user][\"Usuario\"] == usuario:\n flash('El usuario ya existe, intente con otro')\n return render_template('signup.html')\n else:\n data = {\n \"Usuario\": usuario,\n \"Contraseña\": contrasena,\n \"email\": correo\n }\n db.post(\"Usuarios\", data)\n server.login('apapachatestore@gmail.com','apapachatecontrasena')\n message = 'Se ha registrado un nuevo usuario\\nCorreo enviado desde apapachatestore.herokuapp.com'\n subject = 'Alta de usuario'\n message = 'Subject: {}\\n\\n{}'.format(subject, message)\n for email in emaillist:\n server.sendmail('apapachatestore@gmail.com', email, message)\n flash('Usuario registrado exitosamente')\n return redirect(url_for('storeManager'))\n else:\n flash('Las contraseñas deben coincidir')\n return render_template('signup.html')\n else:\n return render_template('signup.html')\n\n@app.route(\"/adduser\", methods=['POST'])\ndef adduser():\n flash('Usuario registrado satisfactoriamente')\n return redirect(url_for('storeManager'))\n\n\n#apapachateStore\n@app.route(\"/\")\ndef index():\n productos = db.get(\"Productos\", \"\")\n return render_template('index.html', productos = productos)\n\n@app.route(\"/productos\")\ndef product():\n data = db.get(\"Productos\", \"\")\n return render_template(\"productos.html\", productos = data)\n\n@app.route(\"/vaciarCarrito\")\ndef vaciarCarrito():\n session.pop(\"total\")\n flash(\"Has vaciado el carrito\")\n return render_template('carrito.html')\n\n@app.route(\"/addproduct/\")\ndef agregar(precio):\n if 'total' in session:\n session['total'] = int(escape(session['total']))+precio\n else:\n session['total'] = precio\n flash('El total es {}'.format(escape(session['total'])))\n return redirect(url_for('product'))\n\n\n@app.route(\"/successful\")\ndef successful():\n return render_template(\"successful.html\")\n\n@app.route(\"/carrito\")\ndef carrito():\n if 'total' in session:\n return render_template('carrito.html', total = escape(session['total']))\n else:\n return render_template('carrito.html')\n\n@app.route(\"/producto/\")\ndef producto(id):\n producto = db.get(\"Productos\", id)\n return render_template(\"producto.html\", productos = producto, id = id)\n\n\nif __name__ == '__main__': \n app.run(debug=True, port=5500)","sub_path":"index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":10609,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"335428544","text":"from gzip import GzipFile\nimport io\nimport yaml\nimport threading\nfrom contextlib import closing\nimport json\nfrom typing import Optional\nimport pandas as pd\nfrom .bundles.managed_content.content import ManagedContentClient\nfrom .bundles.managed_content.cortex_helpers import load_token, load_api_endpoint\nfrom aistac.handlers.abstract_handlers import AbstractSourceHandler, ConnectorContract, AbstractPersistHandler\n\ntry:\n import cPickel as pickle\nexcept ImportError:\n import pickle\n\n\n__author__ = 'Omar Eid'\n\n\nclass ManagedContentSourceHandler(AbstractSourceHandler):\n \"\"\" A Managed Content Source handler\"\"\"\n\n def __init__(self, connector_contract: ConnectorContract):\n \"\"\" Initialise the handler passing the source_contract dictionary \"\"\"\n super().__init__(connector_contract)\n self.token = self._load_token()\n self.api_endpoint = self._load_api_endpoint()\n self.cortex_mc_client = ManagedContentClient(self.api_endpoint, \"2\", self.token)\n self._file_state = 0\n self._changed_flag = True\n\n def mc_key(self, connector_contract: Optional[ConnectorContract]=None):\n _cc = connector_contract if connector_contract is not None else self.connector_contract\n key = _cc.path\n return key[1:] if key.startswith(\"/\") else key\n\n def _load_token(self):\n return load_token(token=self.connector_contract.kwargs.get(\"token\"))\n\n def _load_api_endpoint(self):\n return load_api_endpoint(endpoint=self.connector_contract.kwargs.get(\"api_endpoint\"))\n\n def supported_types(self) -> list:\n \"\"\" The source types supported with this module\"\"\"\n return ['pickle', \"csv\"] # , \"json\" , \"tsv\"\n\n def _download_key_from_mc(self, key):\n return self.cortex_mc_client.download(key, retries=2)\n\n def _load_dict_from_json_in_mc(self, mc_key: str, load_as_df=None, **json_options) -> pd.DataFrame:\n if not self.exists():\n return pd.DataFrame()\n content = self._download_key_from_mc(mc_key).read()\n data = json.load(io.StringIO(content.decode('utf-8')), **json_options)\n if load_as_df:\n data = pd.DataFrame(data)\n return data\n\n def _load_dict_from_yaml_in_mc(self, mc_key: str) -> pd.DataFrame:\n if not self.exists():\n return pd.DataFrame()\n content = self._download_key_from_mc(mc_key).read()\n return yaml.safe_load(io.StringIO(content.decode('utf-8')))\n\n def _load_df_from_csv_in_mc(self, mc_key: str, **pandas_options) -> pd.DataFrame:\n if not self.exists():\n return pd.DataFrame()\n content = self._download_key_from_mc(mc_key).read()\n return pd.read_csv(io.StringIO(content.decode('utf-8')), **pandas_options)\n\n def _load_df_from_pickle_in_mc(self, mc_key: str, **kwargs) -> pd.DataFrame:\n \"\"\" loads a pickle file \"\"\"\n if not self.exists():\n return pd.DataFrame()\n fix_imports = kwargs.pop('fix_imports', True)\n encoding = kwargs.pop('encoding', 'ASCII')\n errors = kwargs.pop('errors', 'strict')\n with threading.Lock():\n with closing(io.BytesIO(self._download_key_from_mc(mc_key).read())) as f:\n return pickle.load(f, fix_imports=fix_imports, encoding=encoding, errors=errors)\n\n def _load_gz_from_mc(self, mc_key: str) -> [GzipFile, None]:\n if not self.exists():\n return None\n return GzipFile(None, 'rb', fileobj=self._download_key_from_mc(mc_key))\n\n def load_canonical(self) -> [pd.DataFrame, dict, GzipFile]:\n \"\"\" returns the canonical dataset based on the connector contract. This method utilises the pandas\n 'pd.read_' methods and directly passes the kwargs to these methods.\n Extra Parameters in the ConnectorContract kwargs:\n - file_type: (optional) the type of the source file. if not set, inferred from the file extension\n \"\"\"\n if not isinstance(self.connector_contract, ConnectorContract):\n raise ValueError(\"The Managed Content Connector Contract has not been set\")\n _cc = self.connector_contract\n load_params = _cc.kwargs\n load_params.update(_cc.query) # Update kwargs with those in the uri query\n load_params.pop('token', None)\n load_params.pop('api_endpoint', None)\n _, _, _ext = _cc.address.rpartition('.')\n file_type = load_params.get('file_type', _ext if len(_ext) > 0 else 'csv')\n with threading.Lock():\n if file_type.lower() in ['csv']:\n rtn_data = self._load_df_from_csv_in_mc(mc_key=self.mc_key(), **load_params)\n elif file_type.lower() in ['pkl ', 'pickle']:\n rtn_data = self._load_df_from_pickle_in_mc(mc_key=self.mc_key(), **load_params)\n # elif file_type.lower() in ['tsv']:\n # rtn_data = self._load_df_from_csv_in_mc(self.mc_key(, delimiter='\\t', **load_params)\n elif file_type.lower() in ['json']:\n rtn_data = self._load_dict_from_json_in_mc(self.mc_key(), **load_params)\n elif file_type.lower() in ['yaml']:\n rtn_data = self._load_dict_from_yaml_in_mc(self.mc_key())\n elif file_type.lower() in [\"gz\"]:\n rtn_data = self._load_gz_from_mc(self.mc_key())\n else:\n raise LookupError('The source format {} is not currently supported'.format(file_type))\n return rtn_data\n\n def exists(self) -> bool:\n \"\"\" returns True if the file in mc exists \"\"\"\n _cc = self.connector_contract\n mc_key = self.mc_key()\n return self.cortex_mc_client.exists(mc_key)\n\n def get_modified(self) -> [int, float, str]:\n \"\"\" returns the amount of documents in the collection\n ... if the counts change ... then the collection was probably modified ...\n ... this assumes that records are never edited/updated ... nor deleted ...\n \"\"\"\n # Cortex Does Not Currently send back any meta data regarding the file version when checking if it exists ...\n # https://bitbucket.org/cognitivescale/cortex-connections-service/src/d1d2fdae3fc9db398d7873cac58c2dc7db6fb5ff/lib/controllers/content.js#lines-287\n mc_key = self.mc_key()\n uri = f\"content/details/{mc_key}\"\n resp = self.cortex_mc_client.serviceconnector.request('GET', uri)\n response = resp.json()\n return (response or {}).get(\"LastModified\", 0)\n\n\nclass ManagedContentPersistHandler(ManagedContentSourceHandler, AbstractPersistHandler):\n # A Managed Content persist handler\n\n def _persist_df_as_pickle(self, canonical: pd.DataFrame, mc_key: str, **kwargs) -> None:\n \"\"\"dumps a pickle file\"\"\"\n protocol = kwargs.pop('protocol', pickle.HIGHEST_PROTOCOL)\n fix_imports = kwargs.pop('fix_imports', True)\n with threading.Lock():\n # https://stackoverflow.com/questions/13223855/what-is-the-http-content-type-to-use-for-a-blob-of-bytes\n pickle_byte_stream = pickle.dumps(canonical, protocol=protocol, fix_imports=fix_imports)\n self.cortex_mc_client.upload_streaming(mc_key, pickle_byte_stream, \"application/python-pickle\")\n\n def _persist_df_as_csv(self, canonical: pd.DataFrame, mc_key: str, **kwargs):\n return self.cortex_mc_client.upload_streaming(mc_key, canonical.to_csv(**kwargs), \"text/csv\", retries=2)\n\n def _persist_dict_as_json(self, canonical: dict, mc_key: str):\n return self.cortex_mc_client.upload_streaming(mc_key, json.dumps(canonical), \"application/json\", retries=2)\n\n def _persist_dict_as_yaml(self, canonical: dict, mc_key: str):\n return self.cortex_mc_client.upload_streaming(mc_key, yaml.dump(canonical), \"application/yaml\", retries=2)\n\n def persist_canonical(self, canonical: pd.DataFrame, **kwargs) -> bool:\n \"\"\" persists the canonical dataset\n Extra Parameters in the ConnectorContract kwargs:\n - file_type: (optional) the type of the source file. if not set, inferred from the file extension\n \"\"\"\n if not isinstance(self.connector_contract, ConnectorContract):\n return False\n _uri = self.connector_contract.uri\n return self.backup_canonical(uri=_uri, canonical=canonical)\n\n def backup_canonical(self, canonical: [dict, pd.DataFrame], uri: str, ignore_kwargs: bool = False) -> bool:\n \"\"\" creates a backup of the canonical to an alternative URI \"\"\"\n if not isinstance(self.connector_contract, ConnectorContract):\n return False\n _cc = self.connector_contract\n _uri_cc = ConnectorContract(uri, module_name=_cc.module_name, handler=_cc.handler)\n load_params = _cc.kwargs\n load_params.update(_cc.query) # Update kwargs with those in the uri query\n load_params.pop('token', None)\n load_params.pop('api_endpoint', None)\n mc_key = self.mc_key(_uri_cc)\n _, _, _ext = _uri_cc.address.rpartition('.')\n file_type = load_params.get('file_type', _ext if len(_ext) > 0 else 'csv')\n with threading.Lock():\n if file_type.lower() in ['csv']:\n self._persist_df_as_csv(canonical, mc_key=mc_key, **load_params)\n elif file_type.lower() in ['pkl', 'pickle']:\n self._persist_df_as_pickle(canonical, mc_key=mc_key, **load_params)\n # elif file_type.lower() in ['tsv']:\n # rtn_data = self._load_df_from_csv_in_mc(mc_key, delimiter='\\t', **load_params)\n elif file_type.lower() in ['json']:\n self._persist_dict_as_json(canonical=canonical, mc_key=mc_key)\n elif file_type.lower() in ['yaml']:\n self._persist_dict_as_yaml(canonical=canonical, mc_key=mc_key)\n else:\n raise LookupError('The source format {} is not currently supported'.format(file_type))\n\n return True\n\n def remove_canonical(self) -> bool:\n if not isinstance(self.connector_contract, ConnectorContract):\n return False\n _cc = self.connector_contract\n raise NotImplementedError(\"remove_canonical for ManagedContentPersistHandler not yet implemented.\")\n","sub_path":"ds_connectors/handlers/managed_content_handlers.py","file_name":"managed_content_handlers.py","file_ext":"py","file_size_in_byte":10162,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"96193145","text":"#!/usr/bin/env python3\nfrom PIL import Image\n\ndef fill_image(image):\n width,height = image.size\n new_length = 0\n if width > height: \n new_length = width \n else:\n new_length = height\n imageNew = Image.new(image.mode,(new_length,new_length),color='white')\n# if width > height:\n #imageNew.paste(image,(0,int((new_length-height)/2)))\n# else:\n #imageNew.paste(image,(int((new_length-width)/2,0))\n imageNew.paste(image,(0,int((new_length-height)/2)))\n return imageNew\n\ndef cut_image(image):\n width, height = image.size\n item_width = int(width/3)\n box_list = []\n image_list = []\n for i in range(0,3):\n for j in range(0,3):\n box = (j*item_width, i*item_width,(j+1)*item_width, (i+1)*item_width)\n box_list.append(box) \n for box in box_list:\n image_list.append(image.crop(box))\n return image_list\n\ndef save_images(image_list):\n index=1\n for image in image_list:\n image.save('./result/python'+str(index)+'.png','PNG')\n index += 1\n\nif __name__=='__main__':\n file_path='python.JPG'\n image=Image.open(file_path)\n new_image=fill_image(image)\n image_list=cut_image(new_image)\n save_images(image_list)\n","sub_path":"Python_Def_9Grids.py","file_name":"Python_Def_9Grids.py","file_ext":"py","file_size_in_byte":1141,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"487387154","text":"from django.urls import path\nfrom .views import login, internView, logout\n\napp_name = 'user_auth'\nurlpatterns = [\n # wenn eine Anfrage an / reinkommt, dann übergebe das der Funkt. Index aus der views.py\n path('', internView, name='internView'),\n path('login/', login, name='login'),\n path('logout/', logout, name='logout'),\n]","sub_path":"apps/user_auth/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":338,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"85911373","text":"from PyQt5.QtCore import (Qt, pyqtSlot)\nfrom PyQt5.QtWidgets import (QWidget, QPushButton, QVBoxLayout, QHBoxLayout,\n QTreeWidget, QLineEdit, QLabel)\nfrom matplotlib import pyplot as plt\nfrom matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas\n\n\nclass TabSingle(QWidget):\n def __init__(self):\n QWidget.__init__(self, flags=Qt.Widget)\n\n self.lbl = QLabel('일시 입력')\n self.ledt_datetime = QLineEdit()\n self.btn_drawPlot = QPushButton(\"차트그리기\")\n self.btn_drawPlot.clicked.connect(self.drawSp)\n\n self.tree_entitymap = QTreeWidget()\n\n self.fig = plt.Figure()\n self.canvas = FigureCanvas(self.fig)\n\n # Left Layout\n self.leftLayout = QVBoxLayout()\n self.leftLayout.addWidget(self.canvas)\n\n # Right Layout\n self.rightLayout = QVBoxLayout()\n self.rightLayout.addWidget(self.lbl)\n self.rightLayout.addWidget(self.ledt_datetime)\n self.rightLayout.addWidget(self.btn_drawPlot)\n self.rightLayout.addStretch(1)\n\n # Main Layout\n self.mainLayout = QHBoxLayout()\n self.mainLayout.addLayout(self.leftLayout)\n self.mainLayout.addLayout(self.rightLayout)\n self.mainLayout.setStretchFactor(self.leftLayout, 1)\n self.mainLayout.setStretchFactor(self.rightLayout, 0)\n\n self.setLayout(self.mainLayout)\n\n # get PySparkManager\n # self.pysparkmgr = PySparkManager()\n\n def getSpectra(self, datehm):\n import happybase\n connection = happybase.Connection('210.102.142.14')\n print(connection.tables())\n table = connection.table('natural_light')\n return table.row(datehm, ['sp_ird'])\n\n @pyqtSlot(name='drawSpectra')\n def drawSp(self):\n # date = '2017-04-13'\n # time = '1240'\n strdt = self.ledt_datetime.text()\n\n plt.close()\n self.fig.clear()\n\n dict_sp = self.getSpectra(strdt)\n wl = list(dict_sp.keys())\n ird = list(dict_sp.values())\n\n for i in range(len(wl)):\n wl[i] = float(wl[i].decode('utf-8').split(':')[1])\n ird[i] = float(ird[i].decode('utf-8'))\n\n print(wl)\n print(ird)\n print('max ird = %f' % max(ird))\n\n ax = self.fig.add_subplot(111)\n\n ax.scatter(wl, ird, color='blue', s=3, label='%s' % strdt)\n ax.set_ylim(0, max(ird))\n ax.set_xlabel('wavelength [nm]')\n ax.set_ylabel('spectral irradiance [W/m2nm]')\n\n ax.legend(loc='upper right', fontsize=10)\n # plt.show()\n\n self.canvas.draw()\n","sub_path":"tabs/single/tab_single.py","file_name":"tab_single.py","file_ext":"py","file_size_in_byte":2616,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"90373188","text":"import requests\r\nimport json\r\nimport csv\r\nfrom pymongo import MongoClient \r\nfrom constant_women_owned_RAPI_API import gendr\r\nimport gender_guesser.detector as gender\r\ndetector_gender = gender.Detector()\r\nclient = MongoClient()\r\ndb = client['ticker']\r\n \r\n\r\nheaders = {\r\n 'x-rapidapi-host': \"apidojo-yahoo-finance-v1.p.rapidapi.com\",\r\n 'x-rapidapi-key': \"6b1091072bmsh9684ba0675c7b7ep1f8852jsn6c5d63df5caf\"\r\n }\r\nurl = \"https://apidojo-yahoo-finance-v1.p.rapidapi.com/stock/v2/get-profile\"\r\n\r\n \r\n\r\n\r\n\r\n# querystring = {\"symbol\":\"ASML\"}\r\n# response = requests.request(\"GET\", url, headers=headers, params=querystring)\r\n# response = json.loads(response.text)\r\n\r\n \r\n\r\n# response[\"defaultKeyStatistics\"][\"beta\"][\"fmt\"]\r\n\r\n\r\ndef papoulate_data():\r\n with open('/home/fintech/Ahsan/NEW_stocks_BETA_industries_values_with_dereived_industries.csv', 'r') as csv_file:\r\n csv_reader = csv.DictReader(csv_file)\r\n with open('/home/fintech/Ahsan/new_1_stocks_industries_values_with_dereived_industries.csv', 'w') as new_file:\r\n fieldnames = ['Ticker','Industry','Derived Industry','Environment score','Social score','Governance score','Percentile','Controversy','Adult entertainment - negative','Firearms','Animal welfare','Nuclear power','Alcohol','Tobacco','Gambling','Palm oil','Controversial weapons','Fur leather','Gmo','Catholic','Coal','Pesticides','Military Contract','Vegan','Climate change','Alignment with UN SDGs','Labor rights','Diversity','Civil liberties','Water','Wind power - positive','Clean energy','Carbon emissions','Child labor','Stem cell research','Interest bearing instruments','Minority owned','Millennial owned','Gender equity','LGBTQ','Immigration','Family values','Maternity leave (rights)','Abortion','Contraceptives','Beta', \"total_men\", \"total_women\", \"unpredicted_names\", \"all_names\", \"women_percentage\"]\r\n\r\n\r\n csv_writer = csv.DictWriter(new_file, fieldnames=fieldnames)\r\n\r\n\r\n csv_writer.writeheader()\r\n # for rows in csv_reader:\r\n # print(\"------------------------------------------\")\r\n # print(rows)\r\n for rows in csv_reader:\r\n try:\r\n # print(\"------------------------------------------\")\r\n # time.sleep(5)\r\n ticker = rows[\"Ticker\"]\r\n querystring = {\"symbol\":str(ticker)}\r\n response = requests.request(\"GET\", url, headers=headers, params=querystring)\r\n response = json.loads(response.text)\r\n man = 0\r\n women = 0\r\n unpredicted_names = []\r\n all_names = []\r\n for record in response[\"assetProfile\"][\"companyOfficers\"]:\r\n name = record['name']\r\n # print(name)\r\n all_names.append(record['name'])\r\n if \"Mr.\" in name:\r\n man += 1\r\n elif \"Ms.\" in name:\r\n women += 1\r\n else:\r\n\r\n if \"Dr.\" in name:\r\n # print(\"in doctor\")\r\n # print(name)\r\n name = record['name'].split(\"Dr. \")[1]\r\n # print(name)\r\n name = name.split(\" \")[0]\r\n # print(str(name.upper()))\r\n # print(name)\r\n try:\r\n gen = str(gendr[str(name.upper())])\r\n # print(gen)\r\n except:\r\n gen = \"unknown\"\r\n if gen == 'male':\r\n man += 1\r\n elif gen == 'female':\r\n women += 1\r\n else:\r\n if detector_gender.get_gender(str(name)) == 'male':\r\n man += 1\r\n elif detector_gender.get_gender(str(name)) == 'female':\r\n women += 1\r\n else:\r\n unpredicted_names.append(record['name'])\r\n\r\n elif \"Dr.\" not in name and \"Mr.\" not in name and \"Ms.\" not in name:\r\n name = record['name'].split(\" \")[0]\r\n # print(name)\r\n try:\r\n gen = str(gendr[str(name.upper())])\r\n # print(gen)\r\n except:\r\n gen = \"unknown\"\r\n if gen == 'male':\r\n man += 1\r\n elif gen == 'female':\r\n women += 1\r\n else:\r\n if detector_gender.get_gender(str(name)) == 'male':\r\n man += 1\r\n elif detector_gender.get_gender(str(name)) == 'female':\r\n women += 1\r\n else:\r\n unpredicted_names.append(record['name'])\r\n women_owned_percentage = 0.0\r\n if not unpredicted_names:\r\n women_owned_percentage = (women) / (man + women)\r\n women_owned_percentage = round(float(women_owned_percentage * 100) ,2)\r\n\r\n if str(rows['Controversy']) == \"FALSE\":\r\n rows['Controversy'] = 0\r\n\r\n if str(rows['Beta']) == \"NaN\":\r\n rows['Beta'] = 0\r\n\r\n rows[\"total_men\"] = man\r\n rows[\"total_women\"] = women\r\n rows[\"unpredicted_names\"] = unpredicted_names\r\n rows[\"all_names\"] = all_names\r\n rows[\"women_percentage\"] = women_owned_percentage\r\n csv_writer.writerow(rows)\r\n\r\n x={}\r\n # print(ticker,man,women,unpredicted_names,all_names)\r\n x[\"ticker\"] = ticker\r\n x[\"total_men\"] = man\r\n x[\"total_women\"] = women\r\n x[\"unpredicted_names\"] = unpredicted_names\r\n x[\"all_names\"] = all_names\r\n x[\"women_percentage\"] = women_owned_percentage\r\n db.ticker_executives_officers.insert_one(x)\r\n except Exception as e:\r\n if str(rows['Beta']) == \"NaN\":\r\n rows['Beta'] = 0\r\n\r\n rows[\"total_men\"] = 0\r\n rows[\"total_women\"] = 0\r\n rows[\"unpredicted_names\"] = []\r\n rows[\"all_names\"] = []\r\n rows[\"women_percentage\"] = 0.0\r\n csv_writer.writerow(rows)\r\n # rows[\"Beta\"] = str(e)\r\n # print(\"Exception in test_beta1 :\",e)\r\n # break\r\n # csv_writer.writerow(rows)\r\n pass\r\n\r\n\r\n\r\n\r\nif __name__ == '__main__':\r\n papoulate_data()\r\n\r\n\r\n","sub_path":"women_owned_rapid_API.py","file_name":"women_owned_rapid_API.py","file_ext":"py","file_size_in_byte":7540,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"390210569","text":"import bpy\nimport bmesh\nfrom .capify import (capify, is_cap)\n\nclass ObjectCapifier(bpy.types.Operator):\n bl_idname = \"object.capify\"\n bl_label = \"Capify\"\n bl_context = \"objectmode\"\n bl_description = \"Add proper topology to cylinder object caps\"\n bl_options = {\"REGISTER\", \"UNDO\"}\n\n @classmethod\n def poll(cls, context):\n ob = context.active_object\n return (ob and ob.type == 'MESH' and context.mode == 'OBJECT')\n\n def execute(self, context):\n bpy.ops.object.mode_set(mode=\"EDIT\")\n bm = bmesh.from_edit_mesh(context.active_object.data)\n sel = [f for f in bm.faces if is_cap(f)]\n for f in sel:\n capify(f, bm)\n bpy.ops.object.mode_set(mode=\"OBJECT\")\n return {'FINISHED'}","sub_path":"ui/object.py","file_name":"object.py","file_ext":"py","file_size_in_byte":758,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"108928026","text":"\"\"\"\nSettings overrides for development environment.\n\"\"\"\n\nfrom settings import *\n\n# Build paths inside the project like this: os.path.join(BASE_DIR, ...)\nimport os\nBASE_DIR = os.path.dirname(os.path.dirname(__file__))\n\nDEBUG = True\n\nTEMPLATE_DEBUG = True\n\nALLOWED_HOSTS = [\n '.ucdavis.edu',\n '54.243.53.70'\n]\n\n\nMIDDLEWARE_CLASSES += (\n# 'utils.middleware.multiport.MultiPortMiddleware', # this is just an attempt to be able to connect to multiple django servers by different people ~/svn/private/python/utils/\n)\n\nCORS_ORIGIN_WHITELIST = (\n 'meamdev.ucdavis.edu'\n )\n\n# Database\n# https://docs.djangoproject.com/en/1.7/ref/settings/#databases\n\nDATABASES = {\n 'default': {\n 'ENGINE': 'django.db.backends.mysql',\n 'NAME': 'meamcore',\n 'USER': 'ec2-user',\n 'PASSWORD': 'tr1ad',\n 'HOST': '127.0.0.1',\n 'PORT': '',\n }\n}\n\n","sub_path":"meamstream/settings-dev.py","file_name":"settings-dev.py","file_ext":"py","file_size_in_byte":876,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"308333957","text":"from src2.syslog_ng.options_and_drivers.option_data_provider import OptionDataProvider\n\nclass SlngConfigGlobalOptionGenerator(object):\n def __init__(self, option_properties):\n self.option_properties = option_properties\n self.global_option_data_provider = OptionDataProvider(option_properties)\n self.global_option_collector = []\n\n def generate_global_options_for_actual_option(self):\n if self.global_option_data_provider.is_actual_option_in_target_statement_option(target_statement=\"global\"):\n self.add_actual_option_to_global_option_collector()\n\n if self.global_option_data_provider.get_additional_further_options(further_option_type=\"global\"):\n self.add_further_global_options_to_global_option_collector()\n\n return self.global_option_collector\n\n def add_actual_option_to_global_option_collector(self):\n if self.global_option_data_provider.is_option_value_default():\n ### Note: in this case we don't want to generate global option\n pass\n elif self.global_option_data_provider.is_option_value_empty():\n self.global_option_collector.append({\"option_name\": self.option_properties['option_name'], \"option_value\": \"\"})\n else:\n self.global_option_collector.append(self.global_option_data_provider.format_option_properties_for_config())\n\n def add_further_global_options_to_global_option_collector(self):\n further_global_options = self.global_option_data_provider.get_additional_further_options(further_option_type=\"global\")\n for further_global_option in further_global_options:\n self.global_option_collector.append(further_global_option)\n","sub_path":"src2/syslog_ng/config_generator/config_global_options_generator.py","file_name":"config_global_options_generator.py","file_ext":"py","file_size_in_byte":1697,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"10631484","text":"import re\nfrom urllib.parse import urlparse\n\nimport numpy as np\n\nfrom util_core.util.url_analysis import is_social_url\nfrom util_core.util.urls_util import normalise_href_url, url_host, url_type\nfrom util_core.util.html_util import (\n get_start_tag, get_tag_names, get_inner, get_tag_name,\n get_text, get_normalised_attrs\n)\n# from selenium_data_extraction.create_data.create_element_descriptions_util import get_spatial_visibility\n# from selenium_data_extraction.create_data.create_element_descriptions_util import (\n# find_common_features, calculate_computed_style_weights, add_hashes, add_computed_style_weights,\n# get_spatial_visibility, get_tag_desc, get_ancestor_tag_info, get_ancestor_tag_info_bs4\n# )\n\nfrom webextractor.clustering.comparisons.computed_styles import EXPECTED_COMPUTED_STYLE_KEYS\nfrom webextractor.element_descriptions.util import get_spatial_visibility\nfrom webextractor.element_descriptions.feature_set import create_feature_set\nfrom util_core.util.url_analysis import is_social_url, is_google_calendar_link\nfrom util_core.util.html_analysis import create_outer_summary\nfrom util_core.util.html_util import (\n get_xpath_attrs_condition, get_img_url, remove_html_content, get_href\n # normalise_outer\n)\n\nEVENTBRITE_EVENT_REGEX = r'eventbrite\\.[a-z\\.]{2,6}/e/'\nGOOGLE_MAPS_REGEX = r'google\\.[a-z\\.]{2,6}/maps/'\n\nEXPECTED_CSS_DISPLAY_VALS = (\n 'inline', 'block', 'inline-flex', 'flex', 'inline-block', '-webkit-box',\n 'table', 'inline-table', 'table-cell', 'list-item'\n)\n\n\ndef is_eventbrite_event(u):\n return bool(re.search(EVENTBRITE_EVENT_REGEX, u))\n\n\ndef is_google_map(u):\n if 'maps.google.co' in u:\n return True\n if re.search(GOOGLE_MAPS_REGEX, u) or 'maps.google' in u: # todo: this is too simple\n return True\n return False\n\n\ndef is_meetup_event(u):\n pass # todo\n\n\nALL_VISIBLE = {'cssComputed__visibility': 'visible', 'jquery__is_hidden': False, 'driver__is_displayed': True, 'spatial_visibility': 'IN_PAGE'} # 'cssComputed__display': 'block'}\n\n\n# r'[day_num_optword] [month_W]'\nDAY_NUM_OPTWORD_MONTH = '(?P01|02|03|04|05|06|07|08|09|10|11|12|13|14|15|16|17|18|19|20|21|22|23|24|25|26|27|28|29|30|31|1|2|3|4|5|6|7|8|9)(st|nd|rd|th)? (of )?(?Pjanuary|february|march|april|may|june|july|august|september|october|november|december|jan|feb|mar|apr|jun|jul|aug|sep|sept|oct|nov|dec)'\n# r'[month_W] [day_num_word]'\nMONTH_DAY_NUM_WORD = '(?Pjanuary|february|march|april|may|june|july|august|september|october|november|december|jan|feb|mar|apr|jun|jul|aug|sep|sept|oct|nov|dec) (?P01|02|03|04|05|06|07|08|09|10|11|12|13|14|15|16|17|18|19|20|21|22|23|24|25|26|27|28|29|30|31|1|2|3|4|5|6|7|8|9)(st|nd|rd|th)'\n# r'[month_W] [year_4]\nMONTH_YEAR = '(?Pjanuary|february|march|april|may|june|july|august|september|october|november|december|jan|feb|mar|apr|jun|jul|aug|sep|sept|oct|nov|dec) (?P2010|2011|2012|2013|2014|2015|2016|2017|2018|2019|2020|2021|2022|2023|2024)'\n# r'[time]'\nTIME = '((?P00|01|02|03|04|05|06|07|08|09|10|11|12|13|14|15|16|17|18|19|20|21|22|23|0|1|2|3|4|5|6|7|8|9)(:|\\\\.)(?P(0|1|2|3|4|5)(0|5))\\\\s?(?Pa\\\\.m\\\\.|p\\\\.m\\\\.|am|pm)?|(?P00|01|02|03|04|05|06|07|08|09|10|11|12|1|2|3|4|5|6|7|8|9)\\\\s?(?Pa\\\\.m\\\\.|p\\\\.m\\\\.|am|pm))'\n# r'[day_name]'\nDAY_NAME = '(?Pmonday|tuesday|wednesday|thursday|friday|saturday|sunday|mon|tue|wed|thu|thur|thurs|fri|sat|sun)'\n\nTEXT_DATE_REGEXPS = [\n (DAY_NUM_OPTWORD_MONTH, 'DAY_NUM_OPTWORD_MONTH'),\n (MONTH_DAY_NUM_WORD, 'MONTH_DAY_NUM_WORD'),\n (MONTH_YEAR, 'MONTH_YEAR'),\n (TIME, 'TIME'),\n (DAY_NAME, 'DAY_NAME')\n]\n\n\ndef get_url_data(outer_html, context):\n from webextractor.element_descriptions.descriptions import (\n is_eventbrite_event, is_google_calendar_link, is_google_map\n )\n page_url = context['page_url']\n page_url_host = context['page_url_host']\n page_host_base = context['page_host_base']\n\n url = get_href(outer_html)\n url = normalise_href_url(url, page_url, page_host_base)\n url = (url or '').strip()\n\n di = {\n 'url': url,\n 'url_type': url_type(url) or '',\n 'url_host': '',\n 'url_lower': url.lower(),\n }\n\n if '..' in url: # sometimes happens for some reason\n import pdb; pdb.set_trace()\n print()\n\n if url:\n\n if url and is_social_url(url):\n di['url_host'] = 'SOCIAL_URL_HOST'\n elif di['url_type'] == 'WEBPAGE':\n di['url_host'] = url_host(url)\n\n #di['url_no_nums'] = re.sub(r'\\d+', 'N', di['url']) if di['url'] else None\n #di['url_no_args'] = url.split('?')[0] if '?' in url else url\n #url_params, url_param_names = _get_url_params(url, page_url_host, di['url_host'])\n #di['url_params'], di['url_param_names'] = url_params, url_param_names\n di['url__is_google_map'] = is_google_map(url)\n di['url__is_eventbrite_event'] = is_eventbrite_event(url)\n di['url__google_calendar'] = is_google_calendar_link(url)\n di['url__is_social'] = is_social_url(url)\n url_no_params = url.split('?')[0].rstrip('/')\n di['url__contains_event'] = 'event' in url and not url_no_params.endswith('events')\n\n else:\n di.update({\n 'url__is_google_map': False,\n 'url__is_eventbrite_event': False,\n 'url__google_calendar': False,\n 'url__is_social': False,\n 'url__contains_event': False\n })\n\n return di\n\n\ndef _get_url_params(url, page_url_host, url_host_val):\n url_params, url_param_names = {}, []\n\n if url_host_val == page_url_host and '?' in url:\n query = urlparse(url).query.strip().rstrip('&')\n if query:\n if '=' not in query:\n url_params = {query: ''}\n url_param_names = query\n else:\n try:\n url_params = dict([\n (s.split('=', 1) if '=' in s else (s, 'NO_EQUALS'))\n for s in query.split('&') if s\n ])\n except:\n import pdb; pdb.set_trace()\n url_param_names = list(sorted(url_params.keys()))\n return url_params, url_param_names\n\n\nclass ElemDescription(object):\n\n def __init__(self, elem_node, index, context):\n self.elem_node = elem_node\n self.context = context\n self.index = index\n self.page_url_host = context['page_url_host']\n #self.ancestor_path = None\n\n @classmethod\n def create(cls, elem_node, index, context):\n e = cls(elem_node, index, context)\n if elem_node.tag_name == 'a':\n e.__class__ = LinkElemDescription\n return e\n\n def _get_computed_styles(self):\n styles = self.elem_node.all_computed_styles\n styles_array = np.array([\n # clip at 100 characters\n styles.get(k, '')[:100] for k in EXPECTED_COMPUTED_STYLE_KEYS\n ])\n return {\n 'all_computed_styles': styles,\n 'all_computed_styles_str': [\n k + '___' + v for (k, v) in styles.items()\n ],\n 'all_computed_styles__array': styles_array\n }\n\n def _get_html_content(self):\n\n outerL = self.elem_node.outer_htmlL\n txt = self.elem_node.text\n\n def has_date():\n if not txt:\n return None\n for reg, key in TEXT_DATE_REGEXPS:\n if re.search(reg, txt, flags=re.I):\n return key\n return None\n\n di = {\n 'outer_html': self.elem_node.outer_html,\n 'outer_htmlL': self.elem_node.outer_htmlL,\n #'outer_html_normalised': normalise_outer(self.elem_node.outer_htmlL, l=False),\n 'inner_html': get_inner(self.elem_node.outer_html),\n #'outer_desc': get_tag_desc(self.elem_node.outer_html, prepare=True),\n 'outer_html_no_content': remove_html_content(outerL, l=False),\n 'text': txt, # todo: can we do something to minimise calls to this in preload script?\n 'num_tags': round(self.elem_node.outer_html.count('<') / 2), # todo: move this to another method?\n }\n if di['text'] == \"What's On\" and 'visually-hidden' in di['outer_html']:\n import pdb; pdb.set_trace()\n print()\n\n di['text_has_date'] = has_date()\n\n return di\n\n def _get_self_tag_info(self):\n outer = self.elem_node.outer_html\n parent_elem = self.elem_node.ancestor_path.get(1, 'elem')\n parent_outer = parent_elem.outer_html if parent_elem else None\n parent_outerL = parent_outer.lower() if parent_outer else None\n\n return {\n # todo: we should batch-load outer_html for nodes in parent path\n 'parent_tag_name': get_tag_name(parent_outerL) if parent_outerL else None,\n 'parent_outer_html': parent_outer,\n 'parent_outer_htmlL': parent_outerL if parent_elem else None,\n 'parent_outer_html_no_content': remove_html_content(parent_outerL, l=False) if parent_elem else None,\n #'parent_outer_html_normalised': normalise_outer(parent_outerL, l=False) if parent_elem else None,\n 'parent_start_tag': get_start_tag(parent_outer, normalise=True) if parent_elem else None,\n 'start_tag': get_start_tag(outer, normalise=True),\n 'tag_name': self.elem_node.tag_name,\n 'tags_key': self.elem_node.get_tags_key()\n }\n\n def _get_visibility_info(self):\n # is_displayed__full = True\n # is_displayed__fast = True\n # if self.elem_node.get_is_visible_fast() is False:\n # is_displayed__full = False\n # is_displayed__fast = False\n # elif self.elem_node.driver_elem.is_displayed() is False: # bypassing wrapper method here for certainty when gather ground truth\n # is_displayed__full = False\n\n # if is_displayed__fast is False or is_displayed__full is False:\n # import pdb; pdb.set_trace()\n\n if self.elem_node.driver_elem.is_displayed() is False and self.elem_node.is_displayed():\n print('warning: is_displayed() discrepancy detection disabled')\n #import pdb; pdb.set_trace()\n\n di = {\n 'spatial_visibility': get_spatial_visibility(self.elem_node.rect),\n\n 'driver__is_displayed': self.elem_node.is_displayed(),\n 'cssComputed__display': self.elem_node.value_of_css_property('display'),\n 'cssComputed__visibility': self.elem_node.value_of_css_property('visibility'), # usually 'visible'\n 'jquery__is_hidden': self.elem_node.is_hidden_jquery,\n\n #'is_displayed__fast': is_displayed__fast,\n #'is_displayed__full': is_displayed__full,\n\n # also: self.elem_node.is_displayed and elem_node.is_visible but I think\n # these may be redundant or not worth the cost\n }\n #print(self.elem_node.value_of_css_property('display'))\n #if di['jquery__is_hidden'] != (not di['driver__is_displayed']):\n # import pdb; pdb.set_trace() # if this continues to never catch, we can stop using is_displayed()\n\n di['visibility__ALL_VISIBLE'] = True\n for key, visible_val in ALL_VISIBLE.items():\n if di[key] != visible_val:\n di['visibility__ALL_VISIBLE'] = False\n break\n if di['visibility__ALL_VISIBLE'] and di['cssComputed__display'] not in EXPECTED_CSS_DISPLAY_VALS:\n print('cssComputed__display___'+di['cssComputed__display'])\n import pdb; pdb.set_trace()\n di['visibility__ALL_VISIBLE'] = False\n\n return di\n\n def _get_img_info(self, ed):\n\n img_type = None\n if '')\ndef server_static(filename):\n return static_file(filename, root='./public_html')\n\n@route('/api/students')\ndef students():\n pass\n\n@route('/api/clean_students')\ndef clean_students():\n Student_Clean.clean()\n return json.dumps({\n 'message': \"OK\"\n })\n\n\n@route('/api/student/deliver_taskset', method='POST')\ndef deliver_taskset():\n name = request.json[\"name\"]\n taskset = request.json[\"taskset\"]\n\n data = req.post(\"http://localhost:27000/student/\" + name + \"/deliver_taskset\", json={\n 'taskset': taskset\n })\n\n print(data)\n return data.json()\n\n\n\n\n@route('/api/student//taskset')\ndef taskset(student):\n\n data = req.get(\"http://localhost:27000/student/{0}/taskset\".format(student))\n return data.json()\n\n\n\n@route('/api/students')\ndef create_students():\n return json.dumps(StudentEngine.get_students())\n\n@route('/api/create_students')\ndef create_students():\n Student_Create.generate()\n\n\n return json.dumps({\n 'message': \"OK\",\n 'students': StudentEngine.get_students()\n })\n\n\n\n\n\n\n\n\n\n\nif __name__ == \"__main__\":\n run(host='0.0.0.0', port=8080)\n\n","sub_path":"AI/Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":1381,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"396402974","text":"from api.views_helper import *\nfrom api.email_helper import *\n\nfrom django.contrib.auth.models import User, AnonymousUser\nfrom rest_framework import viewsets, filters\nfrom api.models import *\nfrom api.healthfiles.models import *\nfrom api.serializers import *\nfrom api.healthfiles.serializers import *\n\nimport hashlib, os\n\nfrom rest_framework.authtoken.models import Token\n\nfrom rest_framework.views import APIView\nfrom rest_framework.authtoken.serializers import AuthTokenSerializer\nfrom rest_framework import permissions, renderers, parsers, status\nfrom rest_framework.decorators import api_view, authentication_classes, permission_classes\n\n\nfrom rest_framework.response import Response\n\nfrom django.http import HttpResponse\nfrom itertools import chain\n\nfrom datetime import datetime, timedelta\nfrom django.shortcuts import get_object_or_404\n\n#from django.core import exceptions\nfrom django.contrib.auth.hashers import *\n\nimport boto\nfrom boto.s3.key import Key\nfrom django.conf import settings\n\n\n\n\n\n@api_view(['GET',])\n@permission_classes((permissions.AllowAny,))\n#@authentication_classes()\ndef heartbeat(request):\n return HttpResponse(status=204)\n\n@api_view(['POST',])\ndef share_healthfile(request, healthfile_id):\n serializer = FileShareSerializer(data=request.DATA, context={'request': request})\n if serializer.is_valid():\n try:\n m = Healthfile.objects.get(pk=healthfile_id,is_deleted=False)\n\n has_permission = False\n if request.user.is_authenticated():\n pass\n else:\n return Response(status=status.HTTP_401_UNAUTHORIZED)\n\n user_id = int(request.user.id)\n\n if request.user == m.user:\n has_permission = True\n else:\n qqueryset = UserGroupSet.objects.filter(user_id__in=[user_id,m.user_id],group_id__in=[user_id,m.user_id],status='ACTIVE',is_deleted=False)\n for p in qqueryset:\n if(p.user_id != p.group_id):\n has_permission = True\n if has_permission:\n mm = {}\n mm['url'] = 'media/'+str(m.file)\n mm['name'] = m.name\n share_healthfile_email(request.user,serializer.object.get('email'),mm)\n return Response(status=status.HTTP_204_NO_CONTENT)\n else:\n return Response(status=status.HTTP_401_UNAUTHORIZED)\n except Healthfile.DoesNotExist:\n return Response(content, status=status.HTTP_404_NOT_FOUND)\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n@api_view(['GET',])\ndef handles3downloads(request, healthfile_id):\n LOCAL_PATH = settings.S3_LOCAL_DOWNLOAD_LOCATION\n\n try:\n m = Healthfile.objects.get(id=healthfile_id,is_deleted=False)\n\n has_permission = False\n if request.user.is_authenticated():\n pass\n else:\n return Response(status=status.HTTP_401_UNAUTHORIZED)\n\n user_id = int(request.user.id)\n\n if request.user == m.user:\n has_permission = True\n else:\n qqueryset = UserGroupSet.objects.filter(user_id__in=[user_id,m.user_id],group_id__in=[user_id,m.user_id],status='ACTIVE',is_deleted=False)\n for p in qqueryset:\n if(p.user_id != p.group_id):\n has_permission = True\n \n if has_permission:\n try:\n conn = boto.connect_s3(settings.AWS_ACCESS_KEY_ID, settings.AWS_SECRET_ACCESS_KEY)\n \n key = conn.get_bucket(settings.AWS_STORAGE_BUCKET_NAME).get_key('media/'+str(m.file))\n # delete file first and after wards\n if os.path.exists(LOCAL_PATH+str(m.id)+'-'+m.name):\n os.remove(LOCAL_PATH+str(m.id)+'-'+m.name)\n key.get_contents_to_filename(LOCAL_PATH+str(m.id)+'-'+m.name)\n \n response = HttpResponse(file(LOCAL_PATH+str(m.id)+'-'+m.name), content_type = m.mime_type)\n response['Content-Length'] = os.path.getsize(LOCAL_PATH+str(m.id)+'-'+m.name)\n return response\n except:\n return Response(status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n else:\n return Response(status=status.HTTP_401_UNAUTHORIZED)\n except Healthfile.DoesNotExist:\n return Response(status=status.HTTP_404_NOT_FOUND)\n\n\n\n@api_view(['GET',])\ndef api_root(request, format=None):\n return Response({})\n\nclass ObtainAuthToken(APIView):\n throttle_classes = ()\n permission_classes = ()\n parser_classes = (parsers.FormParser, parsers.MultiPartParser, parsers.JSONParser,)\n renderer_classes = (renderers.JSONRenderer,)\n serializer_class = AuthTokenSerializer\n model = Token\n\n def post(self, request):\n access_token = request.POST.get(\"access_token\",None)\n email = request.POST.get(\"email\",None)\n mobile = request.POST.get(\"mobile\",None)\n if access_token is not None:\n serializer = SocialAuthTokenSerializer(data=request.DATA)\n elif email is not None:\n serializer = EmailAuthTokenSerializer(data=request.DATA)\n elif mobile is not None:\n serializer = MobileAuthTokenSerializer(data=request.DATA)\n else:\n serializer = self.serializer_class(data=request.DATA)\n if serializer.is_valid():\n token, created = Token.objects.get_or_create(user=serializer.object['user'])\n return Response({'token': token.key})\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n\n\n\n\n\"\"\"\nfrom data_importer import XLSImporter, BaseImporter\nimport pprint\nfrom api.diet.models import *\nimport fractions\n\n\nclass FoodItemXlsImporterModel(BaseImporter):\n class Meta:\n model = FoodItem\n ignore_first_line = True\n raise_errors = True\n delimiter = ','\n exclude = ['display_image','quantity','calories_unit', 'total_fat_unit', 'saturated_fat_unit', 'polyunsaturated_fat_unit', 'monounsaturated_fat_unit', 'trans_fat_unit', 'cholesterol_unit', 'sodium_unit', 'potassium_unit', 'total_carbohydrates_unit', 'dietary_fiber_unit', 'sugars_unit', 'protein_unit', 'vitamin_a_unit', 'vitamin_c_unit', 'calcium_unit', 'iron_unit',\n 'created_at','updated_at','updated_by',\n ]\n\ndef upload_fdb_files(filename):\n #LOCAL_DIR = os.path.dirname(__file__)\n LOCAL_DIR = '/home/kunalr/codebase/portal/vapi/api/csvs/c'\n #xls_file = os.path.join(LOCAL_DIR, 'csvs/'+filename)\n #xls_file = 'csvs/'+filename\n xls_file = os.path.join(LOCAL_DIR, filename)\n print xls_file\n print filename\n #return\n my_csv_list = FoodItemXlsImporterModel(source=xls_file)\n pprint.pprint(my_csv_list.cleaned_data[1])\n user = User.objects.get(id=1)\n import string\n i = 0\n for k,m in my_csv_list.cleaned_data:\n \n i = i + 1\n #print i\n try:\n m['quantity_unit'] = filter(lambda x: x in string.printable, m['quantity_unit'])\n m['name'] = filter(lambda x: x in string.printable, m['name'])\n\n qstr = m['quantity_unit']\n m['quantity'],abc,m['quantity_unit'] = qstr.partition(\" \")\n #print m['quantity']\n if m['quantity'] == '1/2':\n m['quantity'] = 0.5\n elif m['quantity'] == '1/3':\n m['quantity'] = 0.33;\n elif m['quantity'] == '3/4':\n m['quantity'] = 0.75;\n elif m['quantity'] == '1/4':\n m['quantity'] = 0.25;\n elif m['quantity'] == '2/3':\n m['quantity'] = 0.66;\n elif m['quantity'] == '1/8':\n m['quantity'] = 0.125;\n else:\n try:\n m['quantity'] = float(m['quantity'])\n except:\n m['quantity'] = float(fractions.Fraction(m['quantity']))\n m['updated_by'] = user\n m['status'] = 'ACTIVE'\n except:\n pprint.pprint(my_csv_list.cleaned_data[i-1])\n continue\n fi = FoodItem(**m)\n fi.save(force_insert=True,)\n\n \n\n@api_view(['GET',])\ndef upload_food_items(request, format=None):\n path= os.path.dirname(os.path.abspath(__file__))\n csvs = os.path.join(path, 'csvs')\n os.chdir('/home/kunalr/codebase/portal/vapi/api/csvs/c/')\n for files in os.listdir(\".\"):\n upload_fdb_files(files)\n #upload_fdb_files('fdb3.xls')\n #upload_fdb_files('fdb4.xls')\n\n\"\"\"\n","sub_path":"vapi/api/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":8517,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"253103258","text":"from aocd import get_data\n\ndef part_one(input):\n areas=input.splitlines()\n print(\"Part One: \", areas)\n\ndef part_two(input):\n res=''\n print(\"Part Two: \", res)\n\n\ndata = get_data(day=6)\ndata = \"\"\"1, 1\n1, 6\n8, 3\n3, 4\n5, 5\n8, 9\"\"\"\npart_one(data)\npart_two(data)\n","sub_path":"6.py","file_name":"6.py","file_ext":"py","file_size_in_byte":268,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"433386154","text":"import matplotlib.pyplot as plt\nfrom model_fpn import I2D\nfrom threading import Thread\nfrom torch.autograd import Variable\nfrom torchvision.utils import save_image\nimport argparse, time\nimport cv2\nimport numpy as np\nimport os, sys\nimport timeit\nimport torch, time\nimport imageio\nimport PIL\nfrom torchvision import transforms\nimport open3d as o3d\n\ndef parse_args():\n \"\"\"\n Parse input arguments\n \"\"\"\n parser = argparse.ArgumentParser(description='Normal image estimation from ToF depth image')\n parser.add_argument('--cuda', dest='cuda',\n help='whether use CUDA',\n default=True,\n action='store_true')\n parser.add_argument('--num_workers', dest='num_workers',\n help='num_workers',\n default=1, type=int) \n parser.add_argument('--input_image_path', dest='input_image_path',\n help='path to a single input image for evaluation',\n # default='/media/rambo/ssd2/Szilard/nyu_v2_filter/comparison/depth3_/', type=str)\n default='/media/rambo/ssd2/Szilard/pico_tofnest/4bag_unfiltered/depth3/', type=str)\n parser.add_argument('--eval_folder', dest='eval_folder',\n help='evaluate only one image or the whole folder',\n default=True, type=bool)\n parser.add_argument('--model_path', dest='model_path',\n help='path to the model to use',\n default='saved_models/dfilt_1_9_v33.pth', type=str)\n\n args = parser.parse_args()\n return args\n\ndef point_cloud(depth1):\n \"\"\"Transform a depth image into a point cloud with one point for each\n pixel in the image, using the camera transform for a camera\n centred at cx, cy with field of view fx, fy.\n\n depth is a 2-D ndarray with shape (rows, cols) containing\n depths from 1 to 254 inclusive. The result is a 3-D array with\n shape (rows, cols, 3). Pixels with invalid depth in the input have\n NaN for the z-coordinate in the result.\n\n \"\"\"\n # depth is of shape (1,480,640)\n # K = [460.58518931365654, 0.0, 334.0805877590529, 0.0, 460.2679961517268, 169.80766383231037, 0.0, 0.0, 1.0] # pico zense\n K = [460.585, 0.0, 334.081, 0.0, 460.268, 169.808, 0.0, 0.0, 1.0] # pico zense\n # K = [582.62448167737955, 0.0, 313.04475870804731, 0.0, 582.69103270988637, 238.44389626620386, 0.0, 0.0, 1.0] # nyu_v2_dataset\n # K = [582.624, 0.0, 313.045, 0.0, 582.691, 238.444, 0.0, 0.0, 1.0] # nyu_v2_dataset\n fx = K[0]\n fy = K[4]\n cx = K[2]\n cy = K[5]\n\n depth = depth1.clone()\n # open3d_img = o3d.t.geometry.Image(depth[0])#/1000.0)\n # intrinsics = o3d.camera.PinholeCameraIntrinsic(640,360,fx,fy,cx,cy)\n # pcd = o3d.geometry.create_point_cloud_from_depth_image(open3d_img,intrinsic=intrinsics)\n \n rows, cols = depth[0].shape\n c, _ = torch.meshgrid(torch.arange(cols), torch.arange(cols))\n c = torch.meshgrid(torch.arange(cols))\n new_c = c[0].reshape([1,cols]).to('cuda')\n r = torch.meshgrid(torch.arange(rows))\n new_r = r[0].unsqueeze(-1).to('cuda')\n valid = (depth[0] > 0) & (depth[0] < 65535)\n nan_number = torch.tensor(np.nan).to('cuda')\n zero_number = torch.tensor(0.).to('cuda')\n z = torch.where(valid, depth[0]/1000.0, nan_number) # allways divide with 1000.0\n x = torch.where(valid, z * (new_c - cx) / fx, nan_number)\n y = torch.where(valid, z * (new_r - cy) / fy, nan_number)\n \n\n dimension = rows * cols\n z_ok = z.reshape(dimension)\n x_ok = x.reshape(dimension)\n y_ok = y.reshape(dimension)\n\n return torch.stack((x_ok,y_ok,z_ok),dim=1) \n\nif __name__ == '__main__':\n\n args = parse_args()\n\n if torch.cuda.is_available() and not args.cuda:\n print(\"WARNING: You might want to run with --cuda\")\n \n # network initialization\n print('Initializing model...')\n i2d = I2D(fixed_feature_weights=False)\n if args.cuda:\n i2d = i2d.cuda()\n \n print('Done!')\n \n \n load_name = os.path.join(args.model_path)\n print(\"loading checkpoint %s\" % (load_name))\n state = i2d.state_dict()\n checkpoint = torch.load(load_name)\n checkpoint = {k: v for k, v in checkpoint['model'].items() if k in state}\n state.update(checkpoint)\n i2d.load_state_dict(state)\n if 'pooling_mode' in checkpoint.keys():\n POOLING_MODE = checkpoint['pooling_mode']\n print(\"loaded checkpoint %s\" % (load_name))\n del checkpoint\n torch.cuda.empty_cache()\n\n i2d.eval()\n\n img = Variable(torch.FloatTensor(1))\n\n print('evaluating...')\n if args.eval_folder:\n dlist=os.listdir(args.input_image_path)\n dlist.sort()\n time_sum = 0\n counter = 0\n max_depth=7000.\n min_depth=300.\n nan_number = torch.tensor(np.nan).to('cuda')\n eps_number = torch.tensor(1e-7).to('cuda')\n zero_number = torch.tensor(0.).to('cuda')\n for filename in dlist:\n if filename.endswith(\".png\"):\n path=args.input_image_path+filename\n print(\"Predicting for:\"+filename)\n depth = cv2.imread(path,cv2.IMREAD_UNCHANGED).astype(np.float32)\n if len(depth.shape) < 3:\n print(\"Got 1 channel depth images, creating 3 channel depth images\")\n combine_depth = np.empty((depth.shape[0],depth.shape[1], 3))\n combine_depth[:,:,0] = depth\n combine_depth[:,:,1] = depth\n combine_depth[:,:,2] = depth\n depth = combine_depth\n depth2 = np.moveaxis(depth,-1,0)\n img = torch.from_numpy(depth2).float().unsqueeze(0).cuda()\n \n start = timeit.default_timer()\n # img2=img.clone()\n # img[img>max_depth] = max_depth\n # img[img 0) & (imgmask < max_depth+1)\n # img2=img2-min_depth\n # m_depth=torch.max(img)\n img=img/max_depth \n z_fake = i2d(img)\n # z_fake = torch.where(valid, z_fake*max_depth, zero_number)\n stop = timeit.default_timer()\n time_sum=time_sum+stop-start\n counter=counter+1\n save_path=path[:-4]\n\n # plt.imshow(z_fake[0].cpu().detach().numpy().transpose((1,2,0))*max_depth, vmin=0, vmax=max_depth)\n # plt.colorbar()\n # plt.savefig(save_path +'_pred.png',bbox_inches='tight')\n # plt.close()\n o3d_pcd = o3d.geometry.PointCloud()\n z_fake_pcd = point_cloud(z_fake[0]).cpu().detach().numpy()\n o3d_pcd.points = o3d.utility.Vector3dVector(z_fake_pcd*max_depth)\n o3d.io.write_point_cloud(save_path +'_pred.pcd', o3d_pcd)\n # npimage=(z_fake[0]/max_depth).squeeze(0).cpu().detach().numpy().astype(np.uint16)\n # cv2.imwrite(save_path +'_pred.png', npimage)\n\n else:\n continue\n print('Predicting '+str(counter)+' images took ', time_sum/counter) \n else:\n depth = cv2.imread(args.input_image_path,cv2.IMREAD_UNCHANGED).astype(np.float32)\n if len(depth.shape) < 3:\n print(\"Got 1 channel depth images, creating 3 channel depth images\")\n combine_depth = np.empty((depth.shape[0],depth.shape[1], 3))\n combine_depth[:,:,0] = depth\n combine_depth[:,:,1] = depth\n combine_depth[:,:,2] = depth\n depth = combine_depth\n depth2 = np.moveaxis(depth,-1,0)\n img = torch.from_numpy(depth2).float().unsqueeze(0)\n start = timeit.default_timer()\n z_fake = i2d(img.cuda())\n stop = timeit.default_timer()\n zfv=z_fake*2-1\n z_fake_norm=zfv.pow(2).sum(dim=1).pow(0.5).unsqueeze(1)\n zfv=zfv/z_fake_norm\n z_fake=(zfv+1)/2\n save_path=args.input_image_path[:-4]\n save_image(z_fake[0], save_path +\"_pred\"+'.png')\n print('Predicting the image took ', stop-start)\n \n","sub_path":"eval.py","file_name":"eval.py","file_ext":"py","file_size_in_byte":8246,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"98814793","text":"from datasets import load_dataset\n\n\ndata_files = {}\ntrain_file = '../naver_news/naver_news_train.txt'\nvalidation_file = '../naver_news/naver_news_eval.txt'\ndata_files[\"train\"] = train_file\ndata_files[\"validation\"] = validation_file\n\n\ndatasets = load_dataset('text', data_files=data_files)\ndatasets.save_to_disk('/data/aicc1/datasets')\n","sub_path":"data/save_datasets.py","file_name":"save_datasets.py","file_ext":"py","file_size_in_byte":335,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"50897538","text":"# Autor: Oscar Macias Rodríguez\r\n# Descripción: Lee el número de paquetes comprados y despliega la cantidad descontada(si la hay) y el total a pagar.\r\n\r\n\r\n# Función principal. Imprime el total a pagar y recibe la cantidad de paquetes.\r\ndef main():\r\n cantidadPaquetes = int(input(\"Cantidad de paquetes: \"))\r\n print(\"Total: $\", totalPagar(cantidadPaquetes))\r\n\r\n\r\n# Calcula el precio con descuento.\r\ndef totalPagar(cantidadPaquetes):\r\n precioFijo = 1500\r\n\r\n if 0 >= cantidadPaquetes:\r\n print(\"Error\")\r\n main()\r\n else:\r\n if 1 <= cantidadPaquetes < 9:\r\n return(precioFijo)\r\n if 10 <= cantidadPaquetes < 19:\r\n return(precioFijo*1.20)\r\n if 20 <= cantidadPaquetes < 49:\r\n return(precioFijo*1.30)\r\n if 50 <= cantidadPaquetes < 99:\r\n return(precioFijo*1.40)\r\n if 100 <= cantidadPaquetes:\r\n return(precioFijo*1.50)\r\n return\r\n\r\n\r\nmain()","sub_path":"Software.py","file_name":"Software.py","file_ext":"py","file_size_in_byte":962,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"251766569","text":"import numpy as np\r\nimport nltk\r\nimport os\r\nfrom nltk.corpus import stopwords\r\ndataroot = os.path.abspath('../../Data')\r\n\r\ndef importData(data_type=0, is_rm=True):\r\n if data_type == 0:\r\n ## training data\r\n files = (\r\n './processed/SQuAD1.0/train/sentences.npz', './processed/SQuAD1.0/train/questions.npy',\r\n './processed/SQuAD1.0/train/answers.npy', './processed/SQuAD1.0/train/answers_start.npy')\r\n elif data_type == 1:\r\n ## validation date\r\n files = (\r\n './processed/SQuAD1.0/val/sentences.npz', './processed/SQuAD1.0/val/questions.npy',\r\n './processed/SQuAD1.0/val/answers.npy', './processed/SQuAD1.0/val/answers_start.npy')\r\n else:\r\n ## test data\r\n if not is_rm:\r\n files = (\r\n './processed/SQuAD1.0/test/sentences.npz', './processed/SQuAD1.0/test/questions.npy',\r\n './processed/SQuAD1.0/test/answers.npy', './processed/SQuAD1.0/test/answers_start.npy')\r\n else:\r\n files = (\r\n './processed/SQuAD1.0/test/rm_sentences.npz', './processed/SQuAD1.0/test/rm_questions.npy',\r\n './processed/SQuAD1.0/test/rm_answers.npy', './processed/SQuAD1.0/test/rm_answers_start.npy')\r\n sentences = np.load(os.path.join(dataroot, files[0]))['sent']\r\n questions = np.load(os.path.join(dataroot, files[1]))\r\n answers = np.load(os.path.join(dataroot, files[2]))\r\n answers_start = np.load(os.path.join(dataroot, files[3]))\r\n return sentences, questions, answers, answers_start\r\n\r\n\r\ndef import_entity_data(data_type=0, is_connected=False, is_span_bounds=False):\r\n if is_connected:\r\n if data_type == 0:\r\n entity_file = './processed/SQuAD1.0/train/connected_entity.npy'\r\n entity_location_file = './processed/SQuAD1.0/train/connected_entity_location.npy'\r\n entity_span_bounds_file = './processed/SQuAD1.0/train/connected_entity_span_bounds.npy'\r\n entity_type_file = './processed/SQuAD1.0/train/connected_entity_type.npy'\r\n elif data_type == 1:\r\n entity_file = './processed/SQuAD1.0/val/connected_entity.npy'\r\n entity_location_file = './processed/SQuAD1.0/val/connected_entity_location.npy'\r\n entity_span_bounds_file = './processed/SQuAD1.0/val/connected_entity_span_bounds.npy'\r\n entity_type_file = './processed/SQuAD1.0/val/connected_entity_type.npy'\r\n else:\r\n entity_file = './processed/SQuAD1.0/test/connected_entity.npy'\r\n entity_location_file = './processed/SQuAD1.0/test/connected_entity_location.npy'\r\n entity_span_bounds_file = './processed/SQuAD1.0/test/connected_entity_span_bounds.npy'\r\n entity_type_file = './processed/SQuAD1.0/test/connected_entity_type.npy'\r\n else:\r\n if data_type == 0:\r\n entity_file = './processed/SQuAD1.0/train/entity.npy'\r\n entity_location_file = './processed/SQuAD1.0/train/entity_location.npy'\r\n entity_span_bounds_file = './processed/SQuAD1.0/train/entity_span_bounds.npy'\r\n entity_type_file = './processed/SQuAD1.0/train/entity_type.npy'\r\n elif data_type == 1:\r\n entity_file = './processed/SQuAD1.0/val/entity.npy'\r\n entity_location_file = './processed/SQuAD1.0/val/entity_location.npy'\r\n entity_span_bounds_file = './processed/SQuAD1.0/val/entity_span_bounds.npy'\r\n entity_type_file = './processed/SQuAD1.0/val/entity_type.npy'\r\n else:\r\n entity_file = './processed/SQuAD1.0/test/entity.npy'\r\n entity_location_file = './processed/SQuAD1.0/test/entity_location.npy'\r\n entity_span_bounds_file = './processed/SQuAD1.0/test/entity_span_bounds.npy'\r\n entity_type_file = './processed/SQuAD1.0/test/entity_type.npy'\r\n\r\n entities = np.load(os.path.join(dataroot, entity_file))\r\n entities_location = np.load(os.path.join(dataroot, entity_location_file))\r\n entities_type = np.load(os.path.join(dataroot, entity_type_file))\r\n if not is_span_bounds:\r\n return entities, entities_location, entities_type\r\n else:\r\n entities_span_bounds = np.load(os.path.join(dataroot, entity_span_bounds_file))\r\n return entities, entities_location, entities_span_bounds, entities_type\r\n\r\n\r\ndef save_entity_data(entities, entities_location, entities_type, data_type=0):\r\n if data_type == 0:\r\n save_entity_file = './processed/SQuAD1.0/train/entity'\r\n save_location_file = './processed/SQuAD1.0/train/entity_location'\r\n save_type_file = './processed/SQuAD1.0/train/entity_type'\r\n elif data_type == 1:\r\n save_entity_file = './processed/SQuAD1.0/val/entity'\r\n save_location_file = './processed/SQuAD1.0/val/entity_location'\r\n save_type_file = './processed/SQuAD1.0/val/entity_type'\r\n else:\r\n save_entity_file = './processed/SQuAD1.0/test/entity'\r\n save_location_file = './processed/SQuAD1.0/test/entity_location'\r\n save_type_file = './processed/SQuAD1.0/test/entity_type'\r\n\r\n np.save(os.path.join(dataroot, save_entity_file), entities)\r\n np.save(os.path.join(dataroot, save_location_file), entities_location)\r\n np.save(os.path.join(dataroot, save_type_file), entities_type)\r\n\r\n\r\ndef combine_connected_entities(data_type=0):\r\n sentences, questions, answers_text, _ = importData(data_type=data_type)\r\n entities, entities_location, entities_type = import_entity_data(data_type=data_type)\r\n special_types = ['PERSON', 'ORGANIZATION', 'LOCATION', 'MONEY', 'TRANSPORTATION', 'NUMBER', 'HOLIDAY', 'DATE', 'TIME', 'NORP', 'QUANTITY', 'PERCENT', 'EVENT', 'WORK']\r\n for i, each_sent_entities in enumerate(entities):\r\n j = 1\r\n while j < len(each_sent_entities):\r\n if (entities_type[i][j-1] == special_types[1] and entities_type[i][j] == 'DATE') or (\r\n entities_type[i][j-1] == 'DATE' and entities_type[i][j] == special_types[1]):\r\n j = j + 1\r\n elif (entities_type[i][j-1] == 'DATE' and entities_type[i][j] == 'PERCENT') or (\r\n entities_type[i][j - 1] == 'PERCENT' and entities_type[i][j] == 'DATE'):\r\n j = j + 1\r\n elif (entities_type[i][j - 1] == 'PERCENT' and entities_type[i][j] == special_types[1]) or (\r\n entities_type[i][j - 1] == special_types[1] and entities_type[i][j] == 'PERCENT'):\r\n j = j + 1\r\n elif entities_type[i][j-1] == special_types[1] and entities_type[i][j] == 'QUANTITY':\r\n j = j + 1\r\n elif (entities_type[i][j-1] == 'QUANTITY' and entities_type[i][j] == special_types[-2]) or (\r\n entities_type[i][j - 1] == special_types[-2] and entities_type[i][j] == 'QUANTITY'):\r\n j = j + 1\r\n elif (entities_type[i][j-1] == special_types[-2] and entities_type[i][j] == 'DATE') or (\r\n entities_type[i][j - 1] == 'DATE' and entities_type[i][j] == special_types[-2]):\r\n j = j + 1\r\n elif entities_type[i][j-1] == 'DATE' and entities_type[i][j] == 'NORP':\r\n j = j + 1\r\n elif entities_type[i][j - 1] == 'DATE' and entities_type[i][j] == 'HOLIDAY':\r\n j = j + 1\r\n elif entities_type[i][j-1] == 'HOLIDAY' and entities_type[i][j] == 'DATE':\r\n j = j + 1\r\n elif entities_type[i][j-1] == 'NORP' and entities_type[i][j] == 'PERCENT':\r\n j = j + 1\r\n elif each_sent_entities[j] in each_sent_entities[j-1]:\r\n each_sent_entities.pop(j)\r\n entities_location[i].pop(j)\r\n entities_type[i].pop(j)\r\n elif (entities_location[i][j-1][1] + 1) >= entities_location[i][j][0]:\r\n x = each_sent_entities.pop(j-1)\r\n y = each_sent_entities.pop(j-1)\r\n combined_start_index = entities_location[i].pop(j-1)[0]\r\n combined_end_index = entities_location[i].pop(j-1)[1]\r\n first_type = entities_type[i].pop(j - 1)\r\n second_type = entities_type[i].pop(j - 1)\r\n\r\n if combined_start_index < combined_end_index:\r\n each_sent_entities.insert(j-1, sentences[i][combined_start_index: combined_end_index])\r\n entities_location[i].insert(j-1, (combined_start_index, combined_end_index))\r\n\r\n if first_type == second_type:\r\n entities_type[i].insert(j-1, first_type)\r\n elif first_type == 'CONCEPT' and second_type in special_types:\r\n entities_type[i].insert(j-1, second_type)\r\n elif first_type in special_types and second_type == 'CONCEPT':\r\n entities_type[i].insert(j-1, first_type)\r\n else:\r\n if first_type == special_types[0] or second_type == special_types[0]:\r\n entities_type[i].insert(j-1, special_types[0])\r\n elif first_type == special_types[2] or second_type == special_types[2]:\r\n entities_type[i].insert(j-1, special_types[2])\r\n elif first_type == special_types[3] or second_type == special_types[3]:\r\n entities_type[i].insert(j-1, special_types[3])\r\n elif first_type == special_types[4] or second_type == special_types[4]:\r\n entities_type[i].insert(j-1, special_types[4])\r\n elif (first_type == special_types[1] and second_type == special_types[-1]) or (\r\n first_type == special_types[-1] and second_type == special_types[1]):\r\n entities_type[i].insert(j-1, special_types[1])\r\n # elif (first_type == special_types[1] and second_type == special_types[-2]) or (\r\n elif first_type == special_types[-2] and second_type == special_types[1]:\r\n entities_type[i].insert(j-1, special_types[-2])\r\n elif first_type == special_types[-2] and second_type == special_types[8]:\r\n entities_type[i].insert(j-1, special_types[-2])\r\n elif (first_type == special_types[1] and second_type == special_types[8]) or (\r\n first_type == special_types[8] and second_type == special_types[1]):\r\n entities_type[i].insert(j-1, special_types[8])\r\n elif (first_type == special_types[5] and second_type in (special_types[1:2]+special_types[6:])) or (\r\n first_type in (special_types[1:2]+special_types[6:]) and second_type == special_types[5]):\r\n entities_type[i].insert(j-1, special_types[5])\r\n elif (first_type == 'DATE' and second_type == 'TIME') or (first_type == 'TIME' and second_type == 'DATE'):\r\n entities_type[i].insert(j-1, 'TIME')\r\n elif first_type == special_types[-1]:\r\n entities_type[i].insert(j-1, second_type)\r\n elif second_type == special_types[-1]:\r\n entities_type[i].insert(j-1, first_type)\r\n elif first_type in (special_types[1:2]+special_types[9:10]) and second_type == special_types[-2]:\r\n entities_type[i].insert(j-1, second_type)\r\n elif first_type == special_types[-2] and second_type == special_types[9]:\r\n entities_type[i].insert(j-1, first_type)\r\n elif first_type == 'QUANTITY' and second_type == 'DATE':\r\n entities_type[i].insert(j-1, second_type)\r\n elif (first_type == 'NORP' and second_type in (special_types[1:2] + special_types[7:8])) or (\r\n first_type in (special_types[1:2] + special_types[7:8]) and second_type == 'NORP'):\r\n entities_type[i].insert(j-1, 'NORP')\r\n elif first_type == special_types[8] and second_type == special_types[6]:\r\n entities_type[i].insert(j-1, special_types[8])\r\n elif first_type == special_types[-3] and second_type == special_types[9]:\r\n entities_type[i].insert(j-1, special_types[9])\r\n elif first_type == special_types[9] and second_type == special_types[6]:\r\n entities_type[i].insert(j-1, special_types[6])\r\n elif first_type == special_types[6] and second_type == special_types[8]:\r\n entities_type[i].insert(j-1, special_types[6])\r\n elif first_type == special_types[8] and second_type == special_types[-2]:\r\n entities_type[i].insert(j-1, special_types[-2])\r\n elif first_type == special_types[6] and second_type == special_types[-2]:\r\n entities_type[i].insert(j-1, special_types[-2])\r\n elif first_type == special_types[-3] and second_type == special_types[8]:\r\n entities_type[i].insert(j-1, special_types[-3])\r\n elif first_type == special_types[7] and second_type == special_types[10]:\r\n entities_type[i].insert(j-1, special_types[7])\r\n else:\r\n print(sentences[i])\r\n print(questions[i])\r\n print(answers_text[i])\r\n print(x, '\\t', y, '\\t')\r\n print(each_sent_entities[j-1], first_type, second_type)\r\n entities_type[i].insert(j - 1, special_types[1])\r\n print('#'*80)\r\n else:\r\n j = j - 1\r\n else:\r\n j = j + 1\r\n\r\n if len(each_sent_entities) > 0 and each_sent_entities[-1] == 'rrb':\r\n each_sent_entities.pop()\r\n entities_location[i].pop()\r\n entities_type[i].pop()\r\n\r\n # print(sentences[i])\r\n # print(entities[i])\r\n # print(entities_location[i])\r\n # print('#'*100)\r\n\r\n if i % 1000 == 0:\r\n print(i)\r\n\r\n if data_type == 0:\r\n connected_entity_file = './processed/SQuAD1.0/train/connected_entity.npy'\r\n connected_entity_location_file = './processed/SQuAD1.0/train/connected_entity_location.npy'\r\n connected_entity_type_file = './processed/SQuAD1.0/train/connected_entity_type.npy'\r\n elif data_type == 1:\r\n connected_entity_file = './processed/SQuAD1.0/val/connected_entity.npy'\r\n connected_entity_location_file = './processed/SQuAD1.0/val/connected_entity_location.npy'\r\n connected_entity_type_file = './processed/SQuAD1.0/val/connected_entity_type.npy'\r\n else:\r\n connected_entity_file = './processed/SQuAD1.0/test/connected_entity.npy'\r\n connected_entity_location_file = './processed/SQuAD1.0/test/connected_entity_location.npy'\r\n connected_entity_type_file = './processed/SQuAD1.0/test/connected_entity_type.npy'\r\n\r\n np.save(os.path.join(dataroot, connected_entity_file), entities)\r\n np.save(os.path.join(dataroot, connected_entity_location_file), entities_location)\r\n np.save(os.path.join(dataroot, connected_entity_type_file), entities_type)\r\n\r\n\r\ndef remove_unoverlap_data(sentences, questions, answers, answers_start):\r\n rm_sentences, rm_questions, rm_answers, rm_answers_start = list(), list(), list(), list()\r\n\r\n for i,sent in enumerate(sentences):\r\n sent_words = nltk.word_tokenize(sent)\r\n rm_sent_words = [w for w in sent_words if(w not in stopwords.words('english'))]\r\n rm_sent_words_set = set(rm_sent_words)\r\n\r\n cur_questions, cur_answers, cur_answers_start = list(), list(), list(),\r\n for j,ques in enumerate(questions[i]):\r\n ques_words = nltk.word_tokenize(ques)\r\n rm_ques_words = [w for w in ques_words if(w not in stopwords.words('english'))]\r\n if len(rm_sent_words_set & set(rm_ques_words)) > 0:\r\n cur_questions.append(ques)\r\n cur_answers.append(answers[i][j])\r\n cur_answers_start.append(answers_start[i][j])\r\n if len(cur_questions) > 0:\r\n rm_sentences.append(sent)\r\n rm_questions.append(cur_questions)\r\n rm_answers.append(cur_answers)\r\n rm_answers_start.append(cur_answers_start)\r\n if i % 100 == 0:\r\n print(i)\r\n print('length', len(rm_sentences))\r\n np.savez_compressed('../processed/SQuAD1.0/test/rm_sentences', sent=rm_sentences)\r\n np.save('../processed/SQuAD1.0/test/rm_questions', rm_questions)\r\n np.save('../processed/SQuAD1.0/test/rm_answers', rm_answers)\r\n np.save('../processed/SQuAD1.0/test/rm_answers_start', rm_answers_start)\r\n\r\n\r\ndef tag_answers(sentences, answers, answers_start):\r\n sentences_taggings = list()\r\n for i, each_sent in enumerate(sentences):\r\n sent_length = len(nltk.word_tokenize(each_sent))\r\n for j in range(len(answers[i])):\r\n BIO_tagging = ['O', ] * sent_length\r\n start = len(nltk.word_tokenize(each_sent[:answers_start[i][j]]))\r\n answer_length = len(nltk.word_tokenize(answers[i][j]))\r\n BIO_tagging[start] = 'B'\r\n if answer_length > 1:\r\n BIO_tagging[start+1 : start+answer_length] = ['I', ] * (answer_length - 1)\r\n\r\n sentences_taggings.append(BIO_tagging)\r\n sentences_taggings = np.array(sentences_taggings)\r\n return sentences_taggings\r\n\r\n\r\ndef get_tagging_data(answers_BIO, index=None):\r\n tag_dict = {'B': 1, 'I': 2, 'O': 0}\r\n all_answers_tag = []\r\n for answer_bio in answers_BIO:\r\n vector = list()\r\n for each_tag in answer_bio:\r\n idx = tag_dict.get(each_tag)\r\n vector.append(idx)\r\n all_answers_tag.append(vector)\r\n all_answers_tag = np.array(all_answers_tag)\r\n if index is None:\r\n shuffle_all_answer_tag = all_answers_tag\r\n else:\r\n shuffle_all_answer_tag = all_answers_tag[index]\r\n return shuffle_all_answer_tag, tag_dict\r\n\r\n\r\ndef save_answer_tagging(data_type=0):\r\n if data_type == 0:\r\n answer_taggings_file = './processed/SQuAD1.0/train/answer_labels.npy'\r\n elif data_type ==1:\r\n answer_taggings_file = './processed/SQuAD1.0/val/answer_labels.npy'\r\n else:\r\n answer_taggings_file = './processed/SQuAD1.0/test/answer_labels.npy'\r\n\r\n sentences, _, answers, answers_start = importData(data_type=data_type)\r\n answer_taggings = tag_answers(sentences, answers, answers_start)\r\n all_answer_taggings = get_tagging_data(answer_taggings)[0]\r\n np.save(os.path.join(dataroot, answer_taggings_file), all_answer_taggings)\r\n\r\n\r\nif __name__ == '__main__':\r\n test_sentences, test_questions, test_answers, test_answers_start = importData(data_type=2, is_rm=False)\r\n remove_unoverlap_data(test_sentences, test_questions, test_answers, test_answers_start)\r\n\r\n save_answer_tagging(data_type=0)\r\n save_answer_tagging(data_type=1)\r\n save_answer_tagging(data_type=2)\r\n\r\n\r\n pass\r\n\r\n\r\n","sub_path":"Data/Preprocess/Preprocessing.py","file_name":"Preprocessing.py","file_ext":"py","file_size_in_byte":19447,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"384535942","text":"#Nombre: Alejandro Cadena\r\n#Correo: Dobleaduo@gmail.com\r\n# En un supermercado se hace una promoción mediante la cual el cliente obtiene un descuento dependiendo de un número que se escoge al azar. Si el número escogido \r\n# es menor que 74, se aplicará un descuento del 15% en relación al total de la compra, si es mayor e igual a 74 el descuento aplicado será del 20%. \r\n# Obtener cuanto dinero se le descuenta.\r\n\r\nnumero=int(input(\"Ingrese el numero obtenido \"))\r\ncompra=float(input(\"Ingrese el valor de la compra \"))\r\nif numero>=74:\r\n descuento=compra*0.2\r\n total=compra-descuento\r\n print(\"Felicidades ud obtuvo un descuento del 20% en su compra \",total)\r\nelse:\r\n Descuento=compra*0.15\r\n Total=compra-Descuento\r\n print(\"Felicidades ud obtuvo un descuento del 15% en su compra \",Total)","sub_path":"algoritmo26.py","file_name":"algoritmo26.py","file_ext":"py","file_size_in_byte":810,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"265929131","text":"import unittest\nimport DataSet as ds\nimport numpy as np\n\n\nclass Testing(unittest.TestCase):\n\n def test_subset(self):\n print(\"TEST - DataSet - Creating Subset\")\n data_set_1 = np.array([['No', 'Sunny', 'Hot', 'High', 'Weak'],\n ['No', 'Sunny', 'Hot', 'High', 'Strong'],\n ['Yes', 'Overcast', 'Hot', 'High', 'Weak'],\n ['Yes', 'Rain', 'Mild', 'High', 'Weak'],\n ['Yes', 'Rain', 'Cool', 'Normal', 'Weak'],\n ['No', 'Rain', 'Cool', 'Normal', 'Strong'],\n ['Yes', 'Overcast', 'Cool', 'Normal', 'Strong'],\n ['No', 'Sunny', 'Mild', 'High', 'Weak'],\n ['Yes', 'Sunny', 'Cool', 'Normal', 'Weak'],\n ['Yes', 'Rain', 'Mild', 'Normal', 'Weak'],\n ['Yes', 'Sunny', 'Mild', 'Normal', 'Strong'],\n ['Yes', 'Overcast', 'Mild', 'High', 'Strong'],\n ['Yes', 'Overcast', 'Hot', 'Normal', 'Weak'],\n ['No', 'Rain', 'Mild', 'High', 'Strong']\n ])\n print('\\n DATASET')\n print(data_set_1)\n\n print('\\n LABELS')\n data_labels_1 = ['PlayTennis', 'Outlook', 'Temperature', 'Humidity', 'Wind']\n print(data_labels_1)\n\n dataset = ds.DataSet(data_set_1, data_labels_1)\n\n print('\\n TEST CASE 1')\n print('\\n Class: Outlook')\n print(' Value: Sunny')\n print('\\n\\n RESULT SUBSET')\n result = dataset.create_subset('Outlook', 'Sunny')\n print(result.data)\n print('\\n RESULT LABELS')\n print(result.labels)\n\n expected_labels = ['PlayTennis', 'Temperature', 'Humidity', 'Wind']\n expected_data = [['No', 'Hot', 'High', 'Weak'],\n ['No', 'Hot', 'High', 'Strong'],\n ['No', 'Mild', 'High', 'Weak'],\n ['Yes', 'Cool', 'Normal', 'Weak'],\n ['Yes', 'Mild', 'Normal', 'Strong']\n ]\n\n np.testing.assert_array_equal(expected_labels, result.labels)\n np.testing.assert_array_equal(expected_data, result.data)\n\n print('\\n TEST CASE 2')\n print('\\n Class: Humidity')\n print(' Value: High')\n print('\\n\\n RESULT SUBSET')\n result = dataset.create_subset('Humidity', 'High')\n print(result.data)\n print('\\n RESULT LABELS')\n print(result.labels)\n\n expected_labels = ['PlayTennis', 'Outlook', 'Temperature', 'Wind']\n expected_data = [['No', 'Sunny', 'Hot', 'Weak'],\n ['No', 'Sunny', 'Hot', 'Strong'],\n ['Yes', 'Overcast', 'Hot', 'Weak'],\n ['Yes', 'Rain', 'Mild', 'Weak'],\n ['No', 'Sunny', 'Mild', 'Weak'],\n ['Yes', 'Overcast', 'Mild', 'Strong'],\n ['No', 'Rain', 'Mild', 'Strong']\n ]\n\n np.testing.assert_array_equal(expected_labels, result.labels)\n np.testing.assert_array_equal(expected_data, result.data)\n\n print('\\n TEST CASE 3')\n print('\\n Class: Wind')\n print(' Value: NonExistant Value')\n print('\\n\\n RESULT SUBSET')\n result = dataset.create_subset('Wind', 'NonExistant')\n print(result.data)\n print('\\n RESULT LABELS')\n print(result.labels)\n\n expected_labels = ['PlayTennis', 'Outlook', 'Temperature', 'Humidity']\n expected_data = np.array([])\n\n np.testing.assert_array_equal(expected_labels, result.labels)\n self.assertEquals(expected_data.size, 0)\n\n print('\\n TEST CASE 4')\n print('\\n Class: NonExistant Class')\n print(' Value: N/A')\n print('\\n\\n RESULT SUBSET')\n with self.assertRaises(ValueError):\n dataset.create_subset('N/A', 'N/A')\n\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"DataSetTest.py","file_name":"DataSetTest.py","file_ext":"py","file_size_in_byte":4075,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"136621474","text":"import numpy as np\nimport cv2\nimport imutils\nimport pytesseract\n\nimg = cv2.imread('14.jpg') # Read input image\n\n# Convert to hsv\nhsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\ns = hsv[:, :, 1]\n\nret, thresh = cv2.threshold(s, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n\n# Find contours\ncnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)\ncnts = imutils.grab_contours(cnts)\n\nc = max(cnts, key=cv2.contourArea)\nx, y, w, h = cv2.boundingRect(c)\nthresh_card = thresh[y:y+h, x:x+w].copy()\n\n# OCR\nresult = pytesseract.image_to_string(thresh_card)\nprint(f\"OCR Results:\\n {result}\")\n\n# Show image\ncv2.imshow('s', s)\ncv2.imshow('thresh', thresh)\ncv2.imshow('thresh_card', thresh_card)\ncv2.waitKey(0)\ncv2.destroyAllWindows()","sub_path":"ocr-2.py","file_name":"ocr-2.py","file_ext":"py","file_size_in_byte":734,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"270622212","text":"import fcntl\nimport json\nimport logging\nimport psycopg2\n\nfrom six.moves.BaseHTTPServer import BaseHTTPRequestHandler, HTTPServer\nfrom six.moves.socketserver import ThreadingMixIn\nfrom threading import Thread\n\nlogger = logging.getLogger(__name__)\n\n\nclass RestApiHandler(BaseHTTPRequestHandler):\n\n def do_GET(self):\n response = self.get_postgresql_status()\n\n path = '/master' if self.path == '/' else self.path\n status_code = 200 if response['running'] and 'role' in response and response['role'] in path else 503\n\n self.send_response(status_code)\n self.send_header('Content-Type', 'application/json')\n self.end_headers()\n self.wfile.write(json.dumps(response).encode('utf-8'))\n\n def get_postgresql_status(self):\n try:\n row = self.server.query(\"\"\"SELECT to_char(pg_postmaster_start_time(), 'YYYY-MM-DD HH24:MI:SS.MS TZ'),\n pg_is_in_recovery(),\n CASE WHEN pg_is_in_recovery()\n THEN null\n ELSE pg_current_xlog_location() END,\n pg_last_xlog_receive_location(),\n pg_last_xlog_replay_location(),\n pg_is_in_recovery() AND pg_is_xlog_replay_paused()\"\"\")[0]\n return {\n 'running': True,\n 'postmaster_start_time': row[0],\n 'role': 'slave' if row[1] else 'master',\n 'xlog': ({\n 'received_location': row[3],\n 'replayed_location': row[4],\n 'paused': row[5]} if row[1] else {\n 'location': row[2]\n })\n }\n except (psycopg2.OperationalError, psycopg2.InterfaceError):\n logger.exception('get_postgresql_status')\n return {'running': self.server.patroni.postgresql.is_running()}\n\n\nclass RestApiServer(ThreadingMixIn, HTTPServer, Thread):\n\n def __init__(self, patroni, config):\n self.connection_string = 'http://{}/patroni'.format(config.get('connect_address', None) or config['listen'])\n host, port = config['listen'].split(':')\n HTTPServer.__init__(self, (host, int(port)), RestApiHandler)\n Thread.__init__(self, target=self.serve_forever)\n self._set_fd_cloexec(self.socket)\n self.patroni = patroni\n self.daemon = True\n\n def query(self, sql, *params):\n cursor = self.patroni.postgresql.connection().cursor()\n cursor.execute(sql, params)\n ret = [r for r in cursor]\n cursor.close()\n return ret\n\n @staticmethod\n def _set_fd_cloexec(fd):\n flags = fcntl.fcntl(fd, fcntl.F_GETFD)\n fcntl.fcntl(fd, fcntl.F_SETFD, flags | fcntl.FD_CLOEXEC)\n","sub_path":"helpers/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":2921,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"443969612","text":"from django.conf import settings\nfrom django.contrib.sites.shortcuts import get_current_site\nfrom django.template import loader\nfrom django.utils.safestring import mark_safe\nfrom django.utils.translation import ugettext_lazy as _\n\nfrom .models import MailTemplate\n\n\ndef find_template(template_name):\n template, created = MailTemplate.objects.get_or_create(template_type=template_name, defaults={\n 'subject': get_subject(template_name),\n 'body': get_body(template_name)\n })\n\n return template\n\n\ndef get_subject(template_name):\n config = settings.MAIL_EDITOR_CONF\n\n template_config = config.get(template_name)\n if template_config:\n subject = template_config.get('subject_default')\n if subject:\n return subject\n\n return _('Please fix this template')\n\n\ndef get_body(template_name):\n config = settings.MAIL_EDITOR_CONF\n\n template_config = config.get(template_name)\n default = _('Your content here...')\n if template_config:\n body = template_config.get('body_default')\n if body:\n default = body\n\n template = loader.get_template('mail/_outer_table.html')\n current_site = get_current_site(None)\n return template.render({'domain': current_site.domain, 'default': mark_safe(default)}, None)\n","sub_path":"mail_editor/helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":1285,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"302213011","text":"# File Overlap\n\n\ndef read_to_list(file):\n a = []\n\n with open(file, \"r\") as f:\n data = f.readlines()\n for line in data:\n num = int(line.strip())\n a.append(num)\n\n return a\n\n\nif __name__ == '__main__':\n a1 = read_to_list(\"data/happynumbers.txt\")\n a2 = read_to_list(\"data/primenumbers.txt\")\n\n print(set(a1) & set(a2))\n","sub_path":"ex23.py","file_name":"ex23.py","file_ext":"py","file_size_in_byte":368,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"38419400","text":"\"\"\"\nTests scikit-imputer converter.\n\"\"\"\nimport unittest\nimport numpy as np\nfrom sklearn.preprocessing import Imputer\nfrom onnxmltools import convert_sklearn\nfrom onnxmltools.convert.common.data_types import FloatTensorType, Int64TensorType\n\n\nclass TestSklearnImputerConverter(unittest.TestCase):\n\n def test_model_imputer(self):\n model = Imputer(missing_values='NaN', strategy='mean', axis=0)\n model.fit([[1, 2], [np.nan, 3], [7, 6]])\n model_onnx = convert_sklearn(model, 'scikit-learn imputer', [('input', Int64TensorType([1, 2]))])\n self.assertTrue(model_onnx is not None)\n\n def test_imputer_int_inputs(self):\n model = Imputer(missing_values='NaN', strategy='mean', axis=0)\n model.fit([[1, 2], [np.nan, 3], [7, 6]])\n\n model_onnx = convert_sklearn(model, 'scikit-learn imputer', [('input', Int64TensorType([1, 2]))])\n self.assertEqual(len(model_onnx.graph.node), 2)\n\n # Last node should be Imputer\n outputs = model_onnx.graph.output\n self.assertEqual(len(outputs), 1)\n\n self.assertEqual(outputs[0].type.tensor_type.shape.dim[-1].dim_value, 2)\n\n def test_imputer_float_inputs(self):\n model = Imputer(missing_values='NaN', strategy='mean', axis=0)\n model.fit([[1, 2], [np.nan, 3], [7, 6]])\n\n model_onnx = convert_sklearn(model, 'scikit-learn imputer', [('input', FloatTensorType([1, 2]))])\n self.assertTrue(model_onnx.graph.node is not None)\n\n # should contain only node\n self.assertEqual(len(model_onnx.graph.node), 1)\n\n # last node should contain the Imputer\n outputs = model_onnx.graph.output\n self.assertEqual(len(outputs), 1)\n self.assertEqual(outputs[0].type.tensor_type.shape.dim[-1].dim_value, 2)\n","sub_path":"tests/sklearn/test_ImputerConverter.py","file_name":"test_ImputerConverter.py","file_ext":"py","file_size_in_byte":1766,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"477269142","text":"#---------------------------------------------#\n# Base Class #\n# -------------------------- #\n# start: 2020-06-26 #\n# -------------------------- #\n# #\n# Base(진영) class #\n#---------------------------------------------#\nimport sys\nsys.path.insert(0,'C:/Users/user/IndProj/Project-Game/Common')\nimport cmmlib\nimport pygame\nimport random\n#run 모듈의 screen을 global로 쓰고 싶지만 run에서 base import 하고 base에서 run import 하면 충돌일어나는 듯\n#screen은 초기화에서 매개변수로 받아오기\n\nscreen = cmmlib.screen\n\n#org_pos을 전역변수로 사용하기 위해 임시로 베이스 이미지 하나 꺼내서 사이즈 설정해줌\nbase_img = pygame.image.load('C:/Users/user/IndProj/Project-Game/Img/base1.PNG')\nbase_size = pygame.transform.scale(base_img,(int(screen.get_width()/15),int(screen.get_height()/12)))\n\n#기본 위치 -----> 11시 / 1시 / 5시 / 7시 (각 모퉁이)\norg_x_pos = [0,screen.get_width() - base_size.get_width()]\norg_y_pos = [0,screen.get_height() - base_size.get_height()]\n\n#기본 위치에서 랜덤으로 x좌표 y좌표 하나씩 얻어온 것을 pos으로 지정 (진영 위치 랜덤 생성)\norg_pos = [\n [org_x_pos[0],org_y_pos[0]],\n [org_x_pos[0],org_y_pos[1]],\n [org_x_pos[1],org_y_pos[0]],\n [org_x_pos[1],org_y_pos[1]]\n]\n\n#base는 HP와 고유의 랜덤 위치\n#base에 들어가야될 정보\n#너비 높이 / 팀 / 위치\nclass Base:\n def __init__(self,team):\n global screen\n #기본 베이스\n self.base_info = ['team','hp','pos']\n\n #진영에 따른 베이스 이미지 로드\n if team == 'Blue':\n blue_base = pygame.image.load('C:/Users/user/IndProj/Project-Game/Img/base1.PNG')\n #blue진영 이미지 resize (임시로 screen의 가로 15분의 1 / 세로 10분의 1크기)\n base_size = pygame.transform.scale(blue_base,(int(screen.get_width()/15),int(screen.get_height()/12)))\n elif team == 'Red':\n red_base = pygame.image.load('C:/Users/user/IndProj/Project-Game/Img/base2.PNG')\n #red진영 이미지 resize (임시로 screen의 가로 15분의 1 / 세로 10분의 1크기)\n base_size = pygame.transform.scale(red_base,(int(screen.get_width()/15),int(screen.get_height()/12)))\n\n\n global org_pos\n cur_pos = random.sample(org_pos,1)\n print('org_pos : ',org_pos)\n print('cur_pos : ',cur_pos)\n\n self.base_info[0] = team\n self.base_info[1] = 500\n self.base_info[2] = cur_pos\n\n #self.base_info[3]=size(type : surface) / self.base_info[2]=랜덤 위치(type : surface)에 삽입\n screen.blit(base_size, self.base_info[2][0])\n","sub_path":"GameRun/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":2852,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"369001336","text":"def criar_faixa(musica,artista,album):\n faixa= {'musica':musica,'artista':artista,'album':album}\n return faixa\n\n\n\ndef salvar_faixa(faixa):\n arquivo= open('aula16/faixas.txt','a')\n arquivo.write(f'{faixa[\"musica\"]};{faixa[\"artista\"]};{faixa[\"album\"]}\\n')\n arquivo.close()\n\ndef ler_faixa():\n arquivo= open('aula16/faixas.txt','r')\n lista_faixa= []\n for linha in arquivo:\n linha= linha.strip()\n dados_faixa= linha.split(';')\n faixa= criar_faixa(dados_faixa[0],dados_faixa[1],dados_faixa[2])\n lista_faixa.append(faixa)\n arquivo.close()\n return lista_faixa\n \n\n\n","sub_path":"aula16/faixa.py","file_name":"faixa.py","file_ext":"py","file_size_in_byte":629,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"153179957","text":"def check(A,H,W):\n for h in range(H):\n for w in range(W-1):\n if A[h][w] >= A[h][w+1]:\n return False\n for w in range(W):\n for h in range(H-1):\n if A[h][w] >= A[h+1][w]:\n return False\n return True\n\ndef solve():\n H,W = map(int,input().split())\n A = [list(map(int,input().split())) for i in range(H)]\n B = [list(map(int,input().split())) for i in range(H)]\n if check(A,H,W) and check(B,H,W):\n return 'Possible'\n for h in range(H):\n for w in range(W):\n if A[h][w] > B[h][w]:\n A[h][w], B[h][w] = B[h][w], A[h][w]\n if check(A,H,W) and check(B,H,W):\n return 'Possible'\n return 'Impossible'\n\nprint(solve())\n","sub_path":"codeforces/cr557_2/b.py","file_name":"b.py","file_ext":"py","file_size_in_byte":741,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"216852857","text":"# Copyright (c) SenseTime. All Rights Reserved.\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport math\nimport cv2\n# import cv2.cv2\nimport numpy as np\nfrom pysot.core.config import cfg\nfrom pysot.models.loss import select_cross_entropy_loss, select_iou_loss\nfrom pysot.models.backbone import get_backbone\nfrom pysot.models.head import get_ban_head\nfrom pysot.models.neck import get_neck\nfrom pysot.utils.misc import NestedTensor\nfrom pysot.models.head.trans_module import transmodule\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as patches\n\n\nclass ModelBuilder(nn.Module):\n def __init__(self):\n super(ModelBuilder, self).__init__()\n\n # build backbone\n self.backbone = get_backbone(cfg.BACKBONE.TYPE,\n **cfg.BACKBONE.KWARGS)\n\n # build adjust layer\n if cfg.ADJUST.ADJUST:\n self.neck = get_neck(cfg.ADJUST.TYPE,\n **cfg.ADJUST.KWARGS)\n\n # build ban head\n if cfg.BAN.BAN:\n self.head = get_ban_head(cfg.BAN.TYPE,\n **cfg.BAN.KWARGS)\n\n self.trans_module = transmodule(256)\n self.nan_to_val = 1e-10, 1e-10\n\n\n def template(self, z):\n zf = self.backbone(z)\n if cfg.ADJUST.ADJUST:\n zf = self.neck(zf)\n self.zf = zf\n\n def track(self, x):\n xf = self.backbone(x)\n if cfg.ADJUST.ADJUST:\n xf = self.neck(xf)\n cls, loc = self.head(self.zf, xf)\n return {\n 'cls': cls,\n 'loc': loc\n }\n\n def log_softmax(self, cls):\n if cfg.BAN.BAN:\n cls = cls.permute(0, 2, 3, 1).contiguous()\n cls = F.log_softmax(cls, dim=3)\n return cls\n\n def forward(self, data, idx):\n \"\"\" only used in training\"\"\"\n template = data['template'].cuda()\n update = data['update'].cuda()\n search = data['search'].cuda()\n label_cls = data['label_cls'].cuda()\n label_loc = data['label_loc'].cuda()\n\n\n\n feat_t_list = self.backbone(template)\n feat_s_list = self.backbone(search)\n feat_u_list = self.backbone(update)\n\n if cfg.ADJUST.ADJUST:\n zf = self.neck(feat_t_list) # 3, 32,256,7,7\n xf = self.neck(feat_s_list) # 3, 32,256,31,31\n uf = self.neck(feat_u_list) # 3, 32,256,7,7\n\n update_f = []\n for i in range(len(zf)):\n if i == 0:\n out = self.trans_module(u=uf[i], z=zf[i])\n else:\n out = self.trans_module(u=uf[i], z=zf[i], pre_uf=update_f[i - 1])\n update_f.append(out)\n\n # get feature\n cls, loc = self.head(update_f, xf)\n cls_u, loc_u = self.head(uf, xf)\n\n cls_u = self.log_softmax(cls_u)\n cls_loss_u = select_cross_entropy_loss(cls_u, label_cls)\n loc_loss_u = select_iou_loss(loc_u, label_loc, label_cls)\n\n # get loss\n # cls loss with cross entropy loss # loc loss with iou loss\n cls_ = self.log_softmax(cls)\n cls_loss = select_cross_entropy_loss(cls_, label_cls)\n loc_loss = select_iou_loss(loc, label_loc, label_cls)\n\n outputs = {}\n outputs['total_loss'] = cfg.TRAIN.CLS_WEIGHT * cls_loss + cfg.TRAIN.LOC_WEIGHT * loc_loss \\\n + cfg.TRAIN.CLS_WEIGHT * cls_loss_u + cfg.TRAIN.LOC_WEIGHT * loc_loss_u\n outputs['cls_loss'] = cls_loss + cls_loss_u\n outputs['loc_loss'] = loc_loss + loc_loss_u\n\n return outputs\n","sub_path":"pysot/models/model_builder.py","file_name":"model_builder.py","file_ext":"py","file_size_in_byte":3718,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"340624331","text":"import numpy as np\nimport numpy.linalg as alg\n\n\ndef find_mod_inverse_matrix(input_matrix, mod):\n\tn = len(input_matrix)\n\tmatrix = np.matrix(input_matrix)\n\tadj = np.zeros(shape=(n, n))\n\tfor i in range(0, n):\n\t\tfor j in range(0, n):\n\t\t\tadj[i][j] = ((-1) ** (i + j) * int(round(alg.det(_get_co_factor(matrix, j, i))))) % mod\n\treturn (_mod_inv(int(round(alg.det(matrix))), mod) * adj) % mod\n\n\ndef _mod_inv(a, p):\n\tfor i in range(1, p):\n\t\tif (i * a) % p == 1:\n\t\t\treturn i\n\traise ValueError(str(a) + \" has no inverse mod \" + str(p))\n\n\ndef _get_co_factor(matrix, i, j):\n\tmatrix = np.array(matrix)\n\tminor = np.zeros(shape=(len(matrix) - 1, len(matrix) - 1))\n\tp = 0\n\tfor s in range(0, len(minor)):\n\t\tif p == i:\n\t\t\tp = p + 1\n\t\tq = 0\n\t\tfor t in range(0, len(minor)):\n\t\t\tif q == j:\n\t\t\t\tq = q + 1\n\t\t\tminor[s][t] = matrix[p][q]\n\t\t\tq = q + 1\n\t\tp = p + 1\n\treturn minor\n","sub_path":"DataProtection/util/matrix_inv.py","file_name":"matrix_inv.py","file_ext":"py","file_size_in_byte":852,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"324177627","text":"\n\nfrom xai.brain.wordbase.nouns._birdcage import _BIRDCAGE\n\n#calss header\nclass _BIRDCAGES(_BIRDCAGE, ):\n\tdef __init__(self,): \n\t\t_BIRDCAGE.__init__(self)\n\t\tself.name = \"BIRDCAGES\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"birdcage\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_birdcages.py","file_name":"_birdcages.py","file_ext":"py","file_size_in_byte":252,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"69878527","text":"import os\nimport sys\nimport argparse\nimport matplotlib.pyplot as plt\n\ndef graph_field(file_name):\n \n infile = open(file_name+\".txt\", 'r')\n matrix = []\n temp = []\n temp_numb = []\n for word in infile:\n temp_numb.clear()\n temp = word.rstrip('\\n').split(':')\n for i in temp:\n temp_numb.append(float(i))\n matrix.append(temp_numb[:])\n \n plt.imshow(matrix, cmap='gray', interpolation='nearest')\n plt.show()\n \ndef main():\n \n # Create the parser to deal with the arguments\n parser = argparse.ArgumentParser(\"Plot room states\")\n \n # Set the positional arguments\n parser.add_argument('--RESLT_PATH', dest='RESLT_PATH', type=str, required=True, help='The path with the results')\n \n # parse args\n args = parser.parse_args()\n \n # RESLT folder\n RESLT = args.RESLT_PATH\n\n print(f\"Path to folder: {RESLT}\")\n \n graph_field(RESLT + \"staticField\")\n graph_field(RESLT + \"initialPosition\")\n graph_field(RESLT + \"finalPosition\")\n\nif __name__ == '__main__':\n main()\n","sub_path":"tools/field_grapher.py","file_name":"field_grapher.py","file_ext":"py","file_size_in_byte":1065,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"448437038","text":"# Owner(s): [\"oncall: distributed\"]\n\nimport sys\n\nimport torch\nimport torch.distributed as dist\nfrom torch.distributed._sharded_tensor import (\n shard_parameter,\n)\nfrom torch.distributed._sharded_optim import (\n ShardedOptimizer,\n named_params_with_sharded_tensor,\n)\nfrom torch.testing._internal.common_distributed import (\n requires_nccl,\n skip_if_lt_x_gpu,\n)\nfrom torch.testing._internal.common_utils import (\n TEST_WITH_DEV_DBG_ASAN,\n run_tests,\n)\nfrom torch.testing._internal.distributed._sharded_tensor import (\n TEST_GPU_NUM,\n ShardedTensorTestBase,\n with_comms,\n)\nfrom torch.testing._internal.distributed._sharded_tensor._test_ops_common import (\n generate_chunk_sharding_specs_for_test,\n generate_local_weight_sharding_params_for_test,\n)\n\nif TEST_WITH_DEV_DBG_ASAN:\n print(\n \"Skip dev-asan as torch + multiprocessing spawn have known issues\",\n file=sys.stderr,\n )\n sys.exit(0)\n\n\nclass TestShardedTensorOpsLinear(ShardedTensorTestBase):\n def _run_sharded_linear(self, spec, input_size, linear_size, sharded_dim):\n # Use same seed.\n torch.manual_seed(0)\n local_linear = torch.nn.Linear(*linear_size).cuda(self.rank)\n\n sharded_linear = torch.nn.Linear(*linear_size)\n\n # Copy the weights and bias from local linear\n sharded_linear.weight = torch.nn.Parameter(local_linear.weight.detach().clone())\n sharded_linear.bias = torch.nn.Parameter(local_linear.bias.detach().clone())\n\n # Shard the parameter.\n shard_parameter(sharded_linear, \"weight\", spec)\n\n # Run sharded computation\n torch.manual_seed(self.rank) # inputs different on each rank\n inp = torch.rand(*input_size).cuda(self.rank)\n sharded_output = sharded_linear(inp)\n\n # Run local computation\n local_output = local_linear(inp)\n\n # Verify\n self.assertEqual(local_output, sharded_output)\n\n # Validate for torch.nn.functional.linear version.\n local_output = torch.nn.functional.linear(\n inp, local_linear.weight, local_linear.bias\n )\n sharded_output = torch.nn.functional.linear(\n inp, sharded_linear.weight, sharded_linear.bias\n )\n self.assertEqual(local_output, sharded_output)\n\n # Compute loss and run backward pass.\n local_output.sum().backward()\n sharded_output.sum().backward()\n local_grad = local_linear.weight.grad\n\n # Verify that both weight and bias in the sharded linear has non-None grad.\n sharded_weight = sharded_linear.weight.local_shards()[0].tensor\n self.assertNotEqual(sharded_linear.bias.grad, None)\n self.assertNotEqual(sharded_weight.grad, None)\n\n # Shard the local linear's weight grad so that we can compare.\n dist.all_reduce(local_grad)\n (start_pos, chunk_size) = generate_local_weight_sharding_params_for_test(\n local_linear.weight, sharded_dim, TEST_GPU_NUM, spec, self.rank\n )\n local_grad_narrowed = local_grad.narrow(sharded_dim, start_pos, chunk_size)\n\n # Test backward gradient calculation.\n self.assertEqual(sharded_linear.bias.grad, local_linear.bias.grad)\n self.assertEqual(sharded_weight.grad, local_grad_narrowed)\n\n # Test optimizer.\n previous = local_linear.weight.clone().detach()\n optim = torch.optim.SGD(local_linear.parameters(), lr=0.1)\n optim.step()\n self.assertNotEqual(previous, local_linear.weight)\n previous_sharded_weight = sharded_weight.clone()\n previous_sharded_bias = sharded_linear.bias.clone()\n sharded_optim = ShardedOptimizer(dict(named_params_with_sharded_tensor(sharded_linear)), torch.optim.SGD, lr=0.1)\n sharded_optim.step()\n sharded_weight = sharded_linear.weight.local_shards()[0].tensor\n local_weight_narrowed = local_linear.weight.narrow(\n sharded_dim, start_pos, chunk_size\n )\n self.assertEqual(sharded_weight.size(), local_weight_narrowed.size())\n self.assertNotEqual(previous_sharded_weight, sharded_weight)\n self.assertEqual(sharded_weight, local_weight_narrowed)\n self.assertNotEqual(previous_sharded_bias, sharded_linear.bias)\n self.assertEqual(sharded_linear.bias, local_linear.bias)\n\n @with_comms(init_rpc=False)\n @skip_if_lt_x_gpu(TEST_GPU_NUM)\n @requires_nccl()\n def test_sharded_linear_colwise(self):\n for spec in generate_chunk_sharding_specs_for_test(0):\n self._run_sharded_linear(spec, [2, 17], [17, 12], 0)\n self._run_sharded_linear(spec, [8, 21], [21, 11], 0)\n self._run_sharded_linear(spec, [7, 23], [23, 13], 0)\n self._run_sharded_linear(spec, [4, 15], [15, 14], 0)\n\n @with_comms(init_rpc=False)\n @skip_if_lt_x_gpu(TEST_GPU_NUM)\n @requires_nccl()\n def test_sharded_linear_rowwise(self):\n for spec in generate_chunk_sharding_specs_for_test(1):\n # Test even split.\n self._run_sharded_linear(spec, [8, 16], [16, 11], 1)\n\n # Test uneven split.\n self._run_sharded_linear(spec, [5, 19], [19, 11], 1)\n self._run_sharded_linear(spec, [10, 21], [21, 11], 1)\n\n\nif __name__ == \"__main__\":\n run_tests()\n","sub_path":"test/distributed/_sharded_tensor/ops/test_linear.py","file_name":"test_linear.py","file_ext":"py","file_size_in_byte":5268,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"288474652","text":"# -*- coding: utf-8 -*-\n'''this code is to read latest yeastGEM, estabolish the gene-protein-reaction relation, then the protein information can be merged into this dataframe\nbased on the geneID mapping\n12th, Nov, 2018\nHongzhong Lu\n'''\n\n# Import packages\nimport pandas as pd\nimport os ##for directory\nfrom cobra.io import read_sbml_model\n\nos.chdir('/Users/luho/PycharmProjects/model/cobrapy/code')\n\n\n# import self function\ndef getRXNgeneMapping(rxn0, gpr0):\n '''this function is used to split the GPR;\n input, for example rxn0=['r1','g2']\n gpr0=['a or c','a and b']\n output, each rxn related with each gene'''\n s1 = rxn0\n s2 = gpr0\n s2 = s2.str.replace('and','@')\n s2 = s2.str.replace('or','@')\n s2 = s2.str.replace('\\\\( ','')\n s2 = s2.str.replace('\\\\(\\\\( ','')\n s2 = s2.str.replace('\\\\(', '')\n s2 = s2.str.replace('\\\\(\\\\(', '')\n s2 = s2.str.replace(' \\\\)','')\n s2 = s2.str.replace(' \\\\)\\\\) ','')\n s2 = s2.str.replace('\\\\)', '')\n s2 = s2.str.replace('\\\\)\\\\) ', '')\n s3 = splitAndCombine(s2,s1,sep0=\"@\")\n s3['V2'] = s3['V2'].str.strip()\n s3.columns = ['rxnID', 'gene']\n return s3\n\ndef correctSomeWrongFormat(model0):\n \"\"\"\n This function is used to correct some wrong format when read yeastGEM model from cobratoolbox\n \"\"\"\n # Correct metabolite ids:\n for met in model0.metabolites:\n met.id = met.id.replace('__91__', '_')\n met.id = met.id.replace('__93__', '')\n #for reaction in model0.reactions:\n # reaction.gene_reaction_rule = reaction.gene_reaction_rule.replace('__45__','-')\n for gene in model0.genes:\n gene.id = gene.id.replace('__45__', '-')\n\n return model0\n\n\n\ndef saveExcel(infile, outfile):\n '''\n function to save the dataframe into xlsx format\n :param infile:\n :param outfile:\n :return:\n '''\n writer = pd.ExcelWriter(outfile)\n infile.to_excel(writer,'Sheet1')\n writer.save()\n\n\n\n# input the subsystem information\ngem_dataframe = pd.read_excel('/Users/luho/PycharmProjects/model/model_correction/result/yeastGEM_with subsystem.xlsx')\n\n# input yeast8 for every update from yeastGEM repo\nGEM_nov = read_sbml_model('../data/yeastGEM_nov.xml')\nGEM_nov= correctSomeWrongFormat(GEM_nov)\n#produce the dataframe for the metabolites and the rxn\n\ngem_rxn_nov = produceRxnList(GEM_nov)\n\n\n#establish rxn-gene mapping\nproYeast_DataFrame = getRXNgeneMapping(gem_rxn_nov['rxnID'], gem_rxn_nov['GPR'])\nproYeast_DataFrame0 = pd.merge(proYeast_DataFrame, gem_rxn_nov, on='rxnID')\nsaveExcel(proYeast_DataFrame0, '../result/proYeast_DataFrame_from_yeastGEM_november.xlsx')\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"PDB parameter collection/Connection_between_yeastGEM_and_proYeastDB.py","file_name":"Connection_between_yeastGEM_and_proYeastDB.py","file_ext":"py","file_size_in_byte":2584,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"449352629","text":"\nclass Solution:\n res = 0\n leftmost_pos = {}\n def widthOfBinaryTree(self, root: TreeNode) -> int:\n global res,leftmost_pos\n res = 0\n leftmost_pos = {}\n Solution.maxWidth(self,root,0,0)\n return res\n \n\n def maxWidth(self,treeNode, depth,position):\n global res,leftmost_pos\n if treeNode is None:\n return\n\n if depth not in leftmost_pos:\n leftmost_pos[depth] = position\n\n res = max(res,position-leftmost_pos[depth]+1)\n Solution.maxWidth(self,treeNode.left,depth+1,position*2)\n Solution.maxWidth(self,treeNode.right,depth+1,position*2+1)","sub_path":"Challenges/2020/July Challenge/Week 2/2. Maximum Width of Binary Tree - (IMP).py","file_name":"2. Maximum Width of Binary Tree - (IMP).py","file_ext":"py","file_size_in_byte":650,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"602204909","text":"\"\"\"\n Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.\n\n Permission is hereby granted, free of charge, to any person obtaining a copy of this\n software and associated documentation files (the \"Software\"), to deal in the Software\n without restriction, including without limitation the rights to use, copy, modify,\n merge, publish, distribute, sublicense, and/or sell copies of the Software, and to\n permit persons to whom the Software is furnished to do so.\n\n THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,\n INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A\n PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT\n HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION\n OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\n SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\"\"\"\nfrom cfnlint import CloudFormationLintRule\nfrom cfnlint import RuleMatch\n\n\nclass Policy(CloudFormationLintRule):\n \"\"\"Check if IAM Policy JSON is correct\"\"\"\n id = 'E2507'\n shortdesc = 'Check if IAM Policies are properly configured'\n description = 'See if there elements inside an IAM policy ' + \\\n 'are correct'\n tags = ['base', 'properties', 'iam']\n\n def _check_policy_document(self, branch, policy):\n \"\"\"Check policy document\"\"\"\n matches = list()\n\n valid_keys = [\n 'Version',\n 'Id',\n 'Statement',\n ]\n\n if not isinstance(policy, dict):\n message = \"IAM Policy Documents needs to be JSON\"\n matches.append(\n RuleMatch(branch[:], message))\n return matches\n\n for parent_key, parent_value in policy.items():\n if parent_key not in valid_keys:\n message = \"IAM Policy key %s doesn't exist.\" % (parent_key)\n matches.append(\n RuleMatch(branch[:] + [parent_key], message))\n if parent_key == 'Statement':\n if isinstance(parent_value, (list)):\n for index, statement in enumerate(parent_value):\n matches.extend(\n self._check_policy_statement(\n branch[:] + [parent_key, index],\n statement\n )\n )\n else:\n message = \"IAM Policy statement should be of list.\"\n matches.append(\n RuleMatch(branch[:] + [parent_key], message))\n return matches\n\n def _check_policy_statement(self, branch, statement):\n \"\"\"Check statements\"\"\"\n matches = list()\n statement_valid_keys = [\n 'Effect',\n 'Principal',\n 'NotPrincipal',\n 'Action',\n 'NotAction',\n 'Resource',\n 'NotResource',\n 'Condition',\n 'Sid',\n ]\n\n for key, _ in statement.items():\n if key not in statement_valid_keys:\n message = \"IAM Policy statement key %s isn't valid\" % (key)\n matches.append(\n RuleMatch(branch[:] + [key], message))\n if 'Effect' not in statement:\n message = \"IAM Policy statement missing Effect\"\n matches.append(\n RuleMatch(branch[:], message))\n else:\n effect = statement.get('Effect')\n if effect not in ['Allow', 'Deny']:\n message = \"IAM Policy Effect should be Allow or Deny\"\n matches.append(\n RuleMatch(branch[:] + ['Effect'], message))\n if 'Action' not in statement and 'NotAction' not in statement:\n message = \"IAM Policy statement missing Action or NotAction\"\n matches.append(\n RuleMatch(branch[:], message))\n if 'Principal' in statement:\n message = \"IAM Policy statement shouldn't have Principal\"\n matches.append(\n RuleMatch(branch[:] + ['Principal'], message))\n if 'NotPrincipal' in statement:\n message = \"IAM Policy statement shouldn't have NotPrincipal\"\n matches.append(\n RuleMatch(branch[:] + ['NotPrincipal'], message))\n if 'Resource' not in statement and 'NotResource' not in statement:\n message = \"IAM Policy statement missing Resource or NotResource\"\n matches.append(\n RuleMatch(branch[:], message))\n\n return(matches)\n\n def _check_policy(self, branch, policy):\n \"\"\"Checks a policy\"\"\"\n matches = list()\n policy_document = policy.get('PolicyDocument', {})\n matches.extend(\n self._check_policy_document(\n branch + ['PolicyDocument'], policy_document))\n\n return matches\n\n def match(self, cfn):\n \"\"\"Check IAM Policies Properties\"\"\"\n\n matches = list()\n\n iam_types = [\n 'AWS::IAM::Group',\n 'AWS::IAM::ManagedPolicy',\n 'AWS::IAM::Policy',\n 'AWS::IAM::Role',\n 'AWS::IAM::User',\n ]\n\n resources = cfn.get_resources(iam_types)\n for resource_name, resource_values in resources.items():\n tree = ['Resources', resource_name, 'Properties']\n properties = resource_values.get('Properties', {})\n if properties:\n policy_document = properties.get('PolicyDocument', None)\n if policy_document:\n matches.extend(\n self._check_policy_document(\n tree[:] + ['PolicyDocument'], policy_document))\n policy_documents = properties.get('Policies', [])\n for index, policy_document in enumerate(policy_documents):\n matches.extend(\n self._check_policy(\n tree[:] + ['Policies', index],\n policy_document))\n\n return matches\n","sub_path":"src/cfnlint/rules/resources/iam/Policy.py","file_name":"Policy.py","file_ext":"py","file_size_in_byte":6139,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"206897931","text":"#\n# Author: Kris Swann\n#\n\nclass Zillion:\n \"\"\"\n A class which represents an unbounded positive number by using a list.\n\n Attributes:\n list - A list where each element represents a single digit in an element.\n Initialized to []. Leading 0's are not trimmed.\n Ex. 12345 would be implemented as [1, 2, 3, 4, 5].\n Ex. 01242 would be implemented as [0, 1, 2, 4, 2].\n \"\"\"\n list = []\n\n\n def __init__(self, digits):\n \"\"\"\n Create a Zillion object with the arg digits. Commas and Spaces will be ignored.\n Will remove leading 0's.\n\n Args:\n digits - The string of the number which the Zillion object is created with.\n Can contain spaces, digits or commas. Must be a non-empty string\n and contain at least one digit. If any of these conditions are\n not met, will throw a RuntimeError.\n \"\"\"\n\n # Create sublists of digits & spaces and commas.\n self.list = [int(char) for char in digits if char in map(str, list(range(0, 10)))]\n allowed_extras = [char for char in digits if char == ' ' or char == ',']\n\n # If there were no digits or if there were extra chars that were not digits\n # or spaces or commas, throw an error.\n if self.list == [] or len(digits) > len(self.list) + len(allowed_extras):\n raise RuntimeError\n\n # Remove leading 0's\n # while self.list[0] == 0 and len(self.list) > 1:\n # self.list = self.list[1:]\n\n\n def increment(self):\n \"\"\"\n Increments the number zillion represents by 1. (Works with leading 0's.)\n \"\"\"\n for index in range(len(self.list) - 1, -1, -1):\n if self.list[index] == 9:\n self.list[index] = 0\n else: # The base case.\n self.list[index] += 1\n return None # Exit before hitting next case.\n\n # If no base case was reached, add an additional digit place to the front of\n # the list.\n self.list = [1] + self.list\n\n\n def isZero(self):\n \"\"\"\n Returns if the number that Zillion represents is 0. (Works with Leading 0's.)\n \"\"\"\n return self.list.count(0) == len(self.list)\n\n\n def toString(self):\n response = ''\n for digit in self.list:\n response += str(digit)\n return response\n\n\n\n\n\n#\n# TESTS\n#\ncases = ['', ',, ', '-19224', '9sPfrJj0t9', '343 53 j', '{89489}', 'p', ',,,', ' ',\n '0', '000', '12345', '132 32 23 44', '132,43902,00', '129320,', '341348 ',\n ',13342 ', '9', '999', '99999999999999999999999']\n\nfor case in cases:\n try:\n z = Zillion(case)\n print('toString: ' + z.toString())\n print('\\tisZero: ' + str(z.isZero()))\n z.increment()\n print('\\tincremented by 1: ' + z.toString())\n z.increment()\n print('\\tincremented by 2: ' + z.toString())\n except:\n print('Error')\n\n\n\n\n\n#\n# RESULTS\n#\n# Error\n# Error\n# Error\n# Error\n# Error\n# Error\n# Error\n# Error\n# Error\n# toString: 0\n# \tisZero: True\n# \tincremented by 1: 1\n# \tincremented by 2: 2\n# toString: 000\n# \tisZero: True\n# \tincremented by 1: 001\n# \tincremented by 2: 002\n# toString: 12345\n# \tisZero: False\n# \tincremented by 1: 12346\n# \tincremented by 2: 12347\n# toString: 132322344\n# \tisZero: False\n# \tincremented by 1: 132322345\n# \tincremented by 2: 132322346\n# toString: 1324390200\n# \tisZero: False\n# \tincremented by 1: 1324390201\n# \tincremented by 2: 1324390202\n# toString: 129320\n# \tisZero: False\n# \tincremented by 1: 129321\n# \tincremented by 2: 129322\n# toString: 341348\n# \tisZero: False\n# \tincremented by 1: 341349\n# \tincremented by 2: 341350\n# toString: 13342\n# \tisZero: False\n# \tincremented by 1: 13343\n# \tincremented by 2: 13344\n# toString: 9\n# \tisZero: False\n# \tincremented by 1: 10\n# \tincremented by 2: 11\n# toString: 999\n# \tisZero: False\n# \tincremented by 1: 1000\n# \tincremented by 2: 1001\n# toString: 99999999999999999999999\n# \tisZero: False\n# \tincremented by 1: 100000000000000000000000\n# \tincremented by 2: 100000000000000000000001\n","sub_path":"Lab02/lab_02.py","file_name":"lab_02.py","file_ext":"py","file_size_in_byte":4122,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"353266867","text":"# coding=gbk\r\nfrom tkinter import *\r\nfrom PIL import Image, ImageTk,ImageEnhance,ImageFilter,ImageChops\r\nfrom tkinter import filedialog,font,messagebox\r\nimport numpy as np\r\nimport random\r\nimport math\r\nimport cv2,os\r\n\r\nmaster=Tk()\r\nmaster.title('图像数据增强系统v1.0')\r\nw1=Frame(height=50,width=50,bg='dark grey')\r\nw2=Frame(height=50,width=50,bg='dark grey')\r\nw5=Frame(height=450,width=520,bg='dark grey')\r\nw6=Frame(height=450,width=520,bg='dark grey')\r\n\r\n#利用padx和pady，可以将框架边界区分开\r\nw5.grid(row=0,column=0,columnspan=2, rowspan=3,padx=5, pady=5,sticky=E+S+N+W)\r\nw6.grid(row=0,column=2,columnspan=2, rowspan=3,padx=5, pady=5,sticky=E+S+N+W)\r\nw1.grid(row=3,column=0,columnspan=2, rowspan=3,padx=16, pady=45,sticky=E+S+N+W)\r\nw2.grid(row=3,column=2,columnspan=2, rowspan=3,padx=16, pady=45,sticky=E+S+N+W)\r\nt1=Text(w1,bg='gray')\r\nt2=Text(w2,bg='gray')\r\nt5=Text(w5,bg='gray')\r\nt6=Text(w6,bg='gray')\r\n\r\nt1.grid(padx=2, pady=3)\r\nt2.grid(padx=2, pady=3)\r\nt5.grid(padx=2, pady=3)\r\nt6.grid(padx=2, pady=3)\r\n\r\n# //////////////创建菜单项//////////////\r\nmenubar=Menu(t1)\r\nfmenu1=Menu(t1,tearoff=0)\r\nfmenu1.add_command(label=\"新建\",accelerator='Ctrl+N')\r\nfmenu1.add_separator()# 在两个菜单选项中间添加一条横线\r\nfmenu1.add_command(label=\"打开\",accelerator='Ctrl+O')\r\nfmenu1.add_separator()\r\nfmenu1.add_command(label=\"保存\",accelerator='Ctrl+S')\r\nfmenu1.add_separator()\r\nfmenu1.add_command(label=\"另存为\",accelerator='Alt+F2')\r\nfmenu1.add_separator()\r\nfmenu1.add_command(label=\"退出\",accelerator='Exit')\r\n\r\nfmenu2=Menu(t1,tearoff=0)\r\nfmenu2.add_command(label=\"复制\",accelerator='Ctrl+C')\r\nfmenu2.add_separator()# 在两个菜单选项中间添加一条横线\r\nfmenu2.add_command(label=\"粘贴\",accelerator='Ctrl+复制')\r\nfmenu2.add_separator()\r\nfmenu2.add_command(label=\"剪切\",accelerator='Ctrl+S')\r\nfmenu2.add_separator()\r\nfmenu2.add_command(label=\"全选\",accelerator='Ctrl+A')\r\n\r\nfmenu3=Menu(t1,tearoff=0)\r\nfor item in ['工具栏','状态栏','列表','详细信息']:\r\n fmenu3.add_command(label=item)\r\n fmenu3.add_separator()\r\n\r\nfmenu4=Menu(t1,tearoff=0)\r\nfor item in ['放大','缩小']:\r\n fmenu4.add_command(label=item)\r\n fmenu4.add_separator()\r\n\r\nfmenu5=Menu(t1,tearoff=0)\r\nfor item in ['关于']:\r\n fmenu5.add_command(label=item)\r\n fmenu5.add_separator()\r\n\r\nmenubar.add_cascade(label=\"文件\",menu=fmenu1)\r\nmenubar.add_cascade(label=\"编辑\",menu=fmenu2)\r\nmenubar.add_cascade(label=\"查看\",menu=fmenu3)\r\nmenubar.add_cascade(label=\"工具\",menu=fmenu4)\r\nmenubar.add_cascade(label=\"帮助\",menu=fmenu5)\r\nmenubar.configure(font='Times, 8')\r\nmaster['menu']=menubar\r\n\r\nlabel_txt1 = Label(t5, height='2', text=\"原始图像\",font = \"Helvetica 15 bold\").grid(row=1, column=6, columnspan=2, rowspan=3, padx=5, pady=3, sticky=E + S + N + W)\r\nlabel_txt1 = Label(t6, height='2', text=\"数据增强后的图像\",font = \"Helvetica 15 bold\").grid(row=1, column=6, columnspan=2, rowspan=3, padx=5, pady=3, sticky=E + S + N + W)\r\n\r\n# button_upload_photo1 = Button(t1, text='选择需要增强的图像',font = \"Helvetica 15 bold\", height='2', width='38')\r\n# button_upload_photo1.grid(row=0, column=1, sticky=S, padx=10, pady=3)\r\n\r\n\r\npy = Button(t1, text='平移',font = \"Helvetica 15 bold\", height='1', width='11')\r\npy.grid(row=0, column=0, sticky=S, padx=10, pady=3)\r\n\r\nxzh = Button(t1, text='镜像',font = \"Helvetica 15 bold\", height='1', width='11')\r\nxzh.grid(row=0, column=1, sticky=S, padx=10, pady=3)\r\n\r\nmh = Button(t1, text='模糊',font = \"Helvetica 15 bold\", height='1', width='11')\r\nmh.grid(row=0, column=2, sticky=S, padx=10, pady=3)\r\n\r\nshb = Button(t2, text='颜色抖动',font = \"Helvetica 15 bold\", height='1', width='11')\r\nshb.grid(row=1, column=0, sticky=S, padx=10, pady=3)\r\n\r\nhd = Button(t2, text='灰度拉伸',font = \"Helvetica 15 bold\", height='1', width='11')\r\nhd.grid(row=1, column=1, sticky=S, padx=10, pady=3)\r\n\r\ncc = Button(t2, text='随机擦除',font = \"Helvetica 15 bold\", height='1', width='11')\r\ncc.grid(row=1, column=2, sticky=S, padx=10, pady=3)\r\n\r\ndef RandomErasing(img):\r\n probability = 1\r\n sl = 0.02\r\n sh = 0.4\r\n r1 = 0.3\r\n mean = [0.4914, 0.4822, 0.4465]\r\n\r\n if random.uniform(0, 1) > probability:\r\n return img\r\n\r\n for attempt in range(100):\r\n area = img.size[0] * img.size[1]\r\n target_area = random.uniform(sl, sh) * area\r\n aspect_ratio = random.uniform(r1, 1 / r1)\r\n\r\n h = int(round(0.8*math.sqrt(target_area * aspect_ratio)))\r\n w = int(round(0.8*math.sqrt(target_area / aspect_ratio)))\r\n\r\n if w < img.size[1] and h < img.size[0]:\r\n x1 = random.randint(0, img.size[0] - h)\r\n y1 = random.randint(0, img.size[1] - w)\r\n img = np.array(img)\r\n if img.shape[2] == 3:\r\n img[x1:x1 + h, y1:y1 + w,0] = mean[0]\r\n img[x1:x1 + h, y1:y1 + w,1] = mean[1]\r\n img[x1:x1 + h, y1:y1 + w,2] = mean[2]\r\n else:\r\n img[ x1:x1 + h, y1:y1 + w,0] = mean[0]\r\n return Image.fromarray(np.uint8(img))\r\n return img\r\n\r\ndef one_image_upload():\r\n # global fname\r\n name_ = filedialog.askopenfilename()\r\n if name_ != '':\r\n fname = name_\r\n # print(fname)\r\n im = Image.open(fname)\r\n showImg_yuan(im)\r\n return im\r\n\r\ndef fpy():\r\n name_ = filedialog.askopenfilename()\r\n if name_ != '':\r\n fname = name_\r\n # print(fname)\r\n im = Image.open(fname)\r\n showImg_yuan(im)\r\n out = ImageChops.offset(im,150,150)\r\n showImg_detect(out)\r\n\r\ndef fxzh():\r\n name_ = filedialog.askopenfilename()\r\n if name_ != '':\r\n fname = name_\r\n # print(fname)\r\n im = Image.open(fname)\r\n showImg_yuan(im)\r\n # out = im.rotate(25) # 逆时针旋转45度\r\n out = im.transpose(Image.FLIP_LEFT_RIGHT)\r\n # region = region.transpose(Image.ROTATE_180）\r\n showImg_detect(out)\r\n\r\ndef fmh():\r\n name_ = filedialog.askopenfilename()\r\n if name_ != '':\r\n fname = name_\r\n # print(fname)\r\n im = Image.open(fname)\r\n showImg_yuan(im)\r\n out = im.filter(ImageFilter.BLUR)\r\n showImg_detect(out)\r\n\r\ndef fshb():\r\n name_ = filedialog.askopenfilename()\r\n if name_ != '':\r\n fname = name_\r\n # print(fname)\r\n im = Image.open(fname)\r\n showImg_yuan(im)\r\n random_factor = np.random.randint(0, 31) / 10. # 随机因子\r\n color_image = ImageEnhance.Color(im).enhance(random_factor) # 调整图像的饱和度\r\n random_factor = np.random.randint(10, 21) / 10. # 随机因子\r\n brightness_image = ImageEnhance.Brightness(color_image).enhance(random_factor) # 调整图像的亮度\r\n random_factor = np.random.randint(10, 21) / 10. # 随机因1子\r\n contrast_image = ImageEnhance.Contrast(brightness_image).enhance(random_factor) # 调整图像对比度\r\n random_factor = np.random.randint(0, 31) / 10. # 随机因子\r\n out = ImageEnhance.Sharpness(contrast_image).enhance(random_factor)\r\n showImg_detect(out)\r\n\r\ndef fhd():\r\n name_ = filedialog.askopenfilename()\r\n if name_ != '':\r\n fname = name_\r\n # print(fname)\r\n im = Image.open(fname)\r\n showImg_yuan(im)\r\n out = im.point(lambda i: i * 1.5)\r\n showImg_detect(out)\r\n\r\ndef fcc():\r\n name_ = filedialog.askopenfilename()\r\n if name_ != '':\r\n fname = name_\r\n # print(fname)\r\n im = Image.open(fname)\r\n showImg_yuan(im)\r\n out = RandomErasing(im)\r\n\r\n showImg_detect(out)\r\n\r\ndef showImg_yuan(im):\r\n im = im.resize((500, 350), Image.ANTIALIAS)\r\n photo = ImageTk.PhotoImage(im)\r\n label = Label(t5, image=photo)\r\n label.image = photo\r\n label.grid(row=4, column=6, columnspan=2, rowspan=3, padx=5, pady=5, sticky=E + S + N + W)\r\n\r\ndef showImg_detect(im):\r\n im = im.resize((500, 350), Image.ANTIALIAS)\r\n photo = ImageTk.PhotoImage(im)\r\n label = Label(t6, image=photo)\r\n label.image = photo\r\n label.grid(row=4, column=6, columnspan=2, rowspan=3, padx=5, pady=5, sticky=E + S + N + W)\r\n\r\nif __name__ == '__main__':\r\n\r\n global fname,fname1,fname2\r\n fname2 = 'm.jpg'\r\n im = Image.open(fname2)\r\n showImg_yuan(im)\r\n fname1 = 'h.jpg'\r\n im1 = Image.open(fname1)\r\n showImg_detect(im1)\r\n py['command'] = fpy\r\n xzh['command'] = fxzh\r\n mh['command'] = fmh\r\n shb['command'] = fshb\r\n hd['command'] = fhd\r\n cc['command'] = fcc\r\n\r\n master.mainloop()\r\n","sub_path":"gui_datagenerator.py","file_name":"gui_datagenerator.py","file_ext":"py","file_size_in_byte":8368,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"143512898","text":"# Definition for singly-linked list.\n# class ListNode(object):\n# def __init__(self, x):\n# self.val = x\n# self.next = None\n\nclass Solution(object):\n def isPalindrome(self, head):\n \"\"\"\n :type head: ListNode\n :rtype: bool\n \"\"\"\n p1 = p2 = ListNode(-1) # ues sentinel, so that p1 ends up at the tail and generalizes head = None case\n p1.next = head\n while p2 and p2.next and p2.next.next:\n p1 = p1.next\n p2 = p2.next.next\n # p2 will arrive at either the last node or the last but one node\n head2 = p1.next.next if p2.next else p1.next\n p1.next = None\n head2 = self.reverseList(head2)\n p1, p2 = head, head2\n while p1 and p2:\n if p1.val != p2.val:\n return False\n p1 = p1.next\n p2 = p2.next\n return True\n\n def reverseList(self, head):\n if not head: return None\n p0 = senti = ListNode(-1)\n p1 = p0.next = head\n while p1:\n p2 = p1.next\n p1.next = p0\n p0 = p1\n p1 = p2\n senti.next = None # end last node of reversed list\n return p0","sub_path":"palindrome_linked_list/prac.py","file_name":"prac.py","file_ext":"py","file_size_in_byte":1202,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"73323325","text":"#!/usr/bin/env py_kp\n# -*- coding: utf-8 -*-\n# @Time : 2019/10/12 14:22\n# @Author : song\n# @File : ckpt_pd.py\n\n\nimport tensorflow as tf\nimport pickle\nfrom model.text_cnn import TextCnn\nfrom classification.data_helper import *\n\nconfig = {\n 'n_class': 2,\n 'embed_size': 128,\n 'kernel_size': [3, 4, 5],\n 'n_filters': 50,\n 'top_k': 1,\n 'lr': 1e-3\n}\nif __name__ == '__main__':\n\n # =============================to pd===================================#\n\n ckpt_dir = \"./output/ckpt\"\n word_id = pickle.load(open(\"./word_id.pkl\", 'rb'))\n\n config['vocab_size'] = len(word_id)\n with tf.Session() as sess:\n model = TextCnn(**config)\n cpkt = tf.train.get_checkpoint_state(ckpt_dir)\n if cpkt and cpkt.model_checkpoint_path:\n print(cpkt.model_checkpoint_path)\n model.saver.restore(sess, cpkt.model_checkpoint_path)\n\n constant_graph = tf.graph_util.convert_variables_to_constants(sess, sess.graph_def,\n ['acc/pred', \"fc/logits/BiasAdd\"])\n\n # 写入序列化的 PB 文件\n with tf.gfile.FastGFile(\"./output/pd/\" + 'txt_clf.pb', mode='wb') as f:\n f.write(constant_graph.SerializeToString())\n #\n # =============================predict===================================#\n\n sess = tf.Session()\n with tf.gfile.FastGFile(\"./output/pd/\" + 'txt_clf.pb', 'rb') as f:\n graph_def = tf.GraphDef()\n graph_def.ParseFromString(f.read())\n sess.graph.as_default()\n tf.import_graph_def(graph_def, name='') # 导入计算图\n\n # 需要有一个初始化的过程\n sess.run(tf.global_variables_initializer())\n\n # 输入\n input_x = sess.graph.get_tensor_by_name('input_x:0')\n\n pred = sess.graph.get_tensor_by_name('acc/pred:0')\n BiasAdd = sess.graph.get_tensor_by_name('fc/logits/BiasAdd:0')\n\n content = \"超级好的卖家！之前不小心拍错了，客服非常耐心的帮我解答问题，快递也非常给力，必须赞！！！\"\n\n cut = ' '.join(list(content))\n\n sent = [get_x(cut, word_id, max_len=50)]\n\n pred, BiasAdd = sess.run([pred, BiasAdd], feed_dict={input_x: sent})\n print(pred)\n print(BiasAdd)\n","sub_path":"nlp_prj/app/classification/ckpt_pd.py","file_name":"ckpt_pd.py","file_ext":"py","file_size_in_byte":2238,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"604460785","text":"import csv\nimport numpy as np\nimport statsmodels.api as sm\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import average_precision_score\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import plot_precision_recall_curve\n\n# Logistic regression code for real-world credit card data\n# iterations - controls the number of models fit\n# APs - stores the AP scores\n#\n# Additionally, generates historgrams of AP scores\n\niterations = 30\nAPs = np.zeros((iterations, 2))\n\nresult = pd.read_csv(\"creditcard.csv\")\nY = result.values[:, -1].astype(int)\nTime = result.values[:, 0].astype(int)\nX = result.values[:, 1:-1]\n\nfor i in range(iterations):\n print(i)\n X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2)\n lr = LogisticRegression(max_iter = 2000)\n lr.fit(X_train, y_train)\n # Uncomment to plot PR curve\n# disp = plot_precision_recall_curve(clf, X_test, y_test)\n# plt.show()\n APs[i, 0] = average_precision_score(y_train, lr.predict(X_train))\n APs[i, 1] = average_precision_score(y_test, lr.predict(X_test))\n\nprint(APs)\nprint(np.mean(APs, axis=0))\nprint(np.std(APs, axis=0))\n\nfig, ((ax0), (ax1)) = plt.subplots(nrows=1, ncols=2)\nax0.title.set_text('AP of Logistic Regression on training data')\nax1.title.set_text('AP of Logistic Regression on testing data')\nax0.hist(APs[:,0], alpha=.8, edgecolor='blue')\nax1.hist(APs[:,1], alpha=.8, edgecolor='red')\nplt.show()\n","sub_path":"Project/Logistic.py","file_name":"Logistic.py","file_ext":"py","file_size_in_byte":1515,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"401632384","text":"\"\"\"\n-------------------------------------------------------------------------------\nMODULE\n acs_funding_portfolio_report\n\nDESCRIPTION\n Purpose : Interdesk funding\n Department and Desk : ACS Post Trade Services\n Requester : Jennitha Jugnath and Martin Wortmann\n Developer : Jakub Tomaga\n\nHISTORY\n===============================================================================\nDate CR number Developer Description\n-------------------------------------------------------------------------------\n2019-04-17 CHG1001639975 Jakub Tomaga Initial implementation.\n\"\"\"\n\nimport os\nimport logging\nimport acm\nfrom at_logging import getLogger\nfrom at_ael_variables import AelVariableHandler\nfrom at_report import CSVReportCreator\n\n\n# Logging\nLOGGER = getLogger(__name__)\nLOGGER.setLevel(logging.DEBUG)\nVERSION = \"1.0\"\n\n\nclass ACSFundingPortfolioReport(CSVReportCreator):\n \"\"\"Report displaying portfolio level.\"\"\"\n def __init__(self, full_file_path, compound):\n self.compound = compound\n # Split full path into individual parameters of the parent class.\n file_name_only, file_suffix = os.path.splitext(full_file_path)\n # Remove a dot from the extension\n file_suffix = file_suffix[1:]\n file_path = os.path.dirname(full_file_path)\n super(ACSFundingPortfolioReport, self).__init__(\n file_name_only,\n file_suffix,\n file_path)\n\n def _collect_data(self):\n \"\"\"Collect data relevant for the report.\"\"\"\n for portfolio in self.compound.AllPhysicalPortfolios():\n row = [\n portfolio.Oid(),\n portfolio.Name(),\n portfolio.AdditionalInfo().Prt_BDA_AccountNum(),\n portfolio.AdditionalInfo().CostCenter(),\n portfolio.AdditionalInfo().FundingIndicator()\n ]\n self.content.append(row)\n\n def _header(self):\n \"\"\"Return columns of the header.\"\"\"\n header = [\n \"Portfolio Number\",\n \"Portfolio Name\",\n \"prt_BDA AccountNum\",\n \"CostCenter\",\n \"FundingIndicator\"\n ]\n return header\n\n\nael_variables = AelVariableHandler()\nael_variables.add(\"output_file\",\n label=\"Output file\")\nael_variables.add(\"compound\",\n label=\"Compound portfolio\",\n cls=acm.FCompoundPortfolio,\n default=acm.FCompoundPortfolio[\"ABSA CAPITAL SECURITIES\"])\n\n\ndef ael_main(config):\n \"\"\"Entry point of the script.\"\"\"\n compound = config[\"compound\"]\n output_file = config[\"output_file\"]\n \n # Generate the report\n report = ACSFundingPortfolioReport(output_file, compound)\n try:\n report.create_report()\n LOGGER.info(\"Secondary output wrote to %s\", output_file)\n except Exception as ex:\n LOGGER.exception(\"Failed to generate the report %s\", ex)\n raise ex\n","sub_path":"Python modules/acs_funding_portfolio_report.py","file_name":"acs_funding_portfolio_report.py","file_ext":"py","file_size_in_byte":2957,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"550108966","text":"# Set of possible knight moves\npossible_moves = [(-2, -1), (-2, 1),\n (-1, -2), (-1, 2),\n (1, -2), (1, 2),\n (2, -1), (2, 1)]\n\nN = int(input())\n\nb = []\n\nempty = \".\"\nblocked = \"#\"\nknight = \"K\"\n\nfor x in range(N):\n b.append(input())\n\noriginal_board = []\n\nfor row in range(N):\n index = []\n\n for col in range(N):\n # Gets the position of the knight\n if b[row][col] == knight:\n starting_position = (row, col)\n\n # Appends the string at the spot to the index's array\n index.append(b[row][col])\n\n # Appends the index row to the original board\n original_board.append(index)\n\nprint(starting_position)\n","sub_path":"February 6/Knight_Jump_1.01.py","file_name":"Knight_Jump_1.01.py","file_ext":"py","file_size_in_byte":693,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"594509818","text":"import numpy as np\nimport eagerpy as ep\nfrom functools import partial\n\nfrom ..utils import flatten\nfrom ..utils import atleast_kd\n\n\nclass L2CarliniWagnerAttack:\n \"Carlini Wagner L2 Attack\"\n\n def __init__(self, model):\n self.model = model\n\n def __call__(\n self,\n inputs,\n labels,\n *,\n target_classes=None,\n binary_search_steps=9,\n max_iterations=10000,\n confidence=0,\n learning_rate=1e-2,\n initial_const=1e-3,\n abort_early=True,\n ):\n x = ep.astensor(inputs)\n N = len(x)\n\n targeted = target_classes is not None\n if targeted:\n labels = None\n target_classes = ep.astensor(target_classes)\n assert target_classes.shape == (N,)\n is_adv = partial(\n targeted_is_adv, target_classes=target_classes, confidence=confidence\n )\n else:\n labels = ep.astensor(labels)\n assert labels.shape == (N,)\n is_adv = partial(untargeted_is_adv, labels=labels, confidence=confidence)\n\n bounds = self.model.bounds()\n to_attack_space = partial(_to_attack_space, bounds=bounds)\n to_model_space = partial(_to_model_space, bounds=bounds)\n\n x_attack = to_attack_space(x)\n reconstsructed_x = to_model_space(x_attack)\n\n rows = np.arange(N)\n\n def loss_fun(delta: ep.Tensor, consts: ep.Tensor) -> ep.Tensor:\n assert delta.shape == x_attack.shape\n assert consts.shape == (N,)\n\n x = to_model_space(x_attack + delta)\n logits = ep.astensor(self.model.forward(x.tensor))\n\n if targeted:\n c_minimize = best_other_classes(logits, target_classes)\n c_maximize = target_classes\n else:\n c_minimize = labels\n c_maximize = best_other_classes(logits, labels)\n\n is_adv_loss = logits[rows, c_minimize] - logits[rows, c_maximize]\n assert is_adv_loss.shape == (N,)\n is_adv_loss = is_adv_loss + confidence\n is_adv_loss = ep.maximum(0, is_adv_loss)\n is_adv_loss = is_adv_loss * consts\n\n squared_norms = flatten(x - reconstsructed_x).square().sum(axis=-1)\n loss = is_adv_loss.sum() + squared_norms.sum()\n return loss, (x, logits)\n\n loss_aux_and_grad = ep.value_and_grad_fn(x, loss_fun, has_aux=True)\n\n consts = initial_const * np.ones((N,))\n lower_bounds = np.zeros((N,))\n upper_bounds = np.inf * np.ones((N,))\n\n best_advs = ep.zeros_like(x)\n best_advs_norms = ep.ones(x, (N,)) * np.inf\n\n # the binary search searches for the smallest consts that produce adversarials\n for binary_search_step in range(binary_search_steps):\n if (\n binary_search_step == binary_search_steps - 1\n and binary_search_steps >= 10\n ):\n # in the last iteration, repeat the search once\n consts = np.minimum(upper_bounds, 1e10)\n\n # create a new optimizer find the delta that minimizes the loss\n delta = ep.zeros_like(x_attack)\n optimizer = AdamOptimizer(delta)\n\n found_advs = np.full(\n (N,), fill_value=False\n ) # found adv with the current consts\n loss_at_previous_check = np.inf\n\n consts_ = ep.from_numpy(x, consts.astype(np.float32))\n\n for iteration in range(max_iterations):\n loss, (perturbed, logits), gradient = loss_aux_and_grad(delta, consts_)\n delta += optimizer(gradient, learning_rate)\n\n if abort_early and iteration % (np.ceil(max_iterations / 10)) == 0:\n # after each tenth of the iterations, check progress\n if not (loss <= 0.9999 * loss_at_previous_check):\n break # stop Adam if there has been no progress\n loss_at_previous_check = loss\n\n found_advs_iter = is_adv(logits)\n found_advs = np.logical_or(found_advs, found_advs_iter.numpy())\n\n norms = flatten(perturbed - x).square().sum(axis=-1).sqrt()\n closer = norms < best_advs_norms\n new_best = closer.float32() * found_advs_iter.float32()\n\n best_advs = (\n atleast_kd(new_best, best_advs.ndim) * perturbed\n + (1 - atleast_kd(new_best, best_advs.ndim)) * best_advs\n )\n best_advs_norms = new_best * norms + (1 - new_best) * best_advs_norms\n\n upper_bounds = np.where(found_advs, consts, upper_bounds)\n lower_bounds = np.where(found_advs, lower_bounds, consts)\n\n consts_exponential_search = consts * 10\n consts_binary_search = (lower_bounds + upper_bounds) / 2\n consts = np.where(\n np.isinf(upper_bounds), consts_exponential_search, consts_binary_search\n )\n\n return best_advs.tensor\n\n\nclass AdamOptimizer:\n def __init__(self, x):\n self.m = ep.zeros_like(x)\n self.v = ep.zeros_like(x)\n self.t = 0\n\n def __call__(self, gradient, learning_rate, beta1=0.9, beta2=0.999, epsilon=1e-8):\n self.t += 1\n\n self.m = beta1 * self.m + (1 - beta1) * gradient\n self.v = beta2 * self.v + (1 - beta2) * gradient ** 2\n\n bias_correction_1 = 1 - beta1 ** self.t\n bias_correction_2 = 1 - beta2 ** self.t\n\n m_hat = self.m / bias_correction_1\n v_hat = self.v / bias_correction_2\n\n return -learning_rate * m_hat / (ep.sqrt(v_hat) + epsilon)\n\n\ndef untargeted_is_adv(logits: ep.Tensor, labels: ep.Tensor, confidence) -> ep.Tensor:\n logits = logits + ep.onehot_like(logits, labels, value=confidence)\n classes = logits.argmax(axis=-1)\n return classes != labels\n\n\ndef targeted_is_adv(\n logits: ep.Tensor, target_classes: ep.Tensor, confidence\n) -> ep.Tensor:\n logits = logits - ep.onehot_like(logits, target_classes, value=confidence)\n classes = logits.argmax(axis=-1)\n return classes == target_classes\n\n\ndef best_other_classes(logits, exclude):\n other_logits = logits - ep.onehot_like(logits, exclude, value=np.inf)\n return other_logits.argmax(axis=-1)\n\n\ndef _to_attack_space(x: ep.Tensor, *, bounds: tuple) -> ep.Tensor:\n min_, max_ = bounds\n a = (min_ + max_) / 2\n b = (max_ - min_) / 2\n x = (x - a) / b # map from [min_, max_] to [-1, +1]\n x = x * 0.999999 # from [-1, +1] to approx. (-1, +1)\n x = x.arctanh() # from (-1, +1) to (-inf, +inf)\n return x\n\n\ndef _to_model_space(x: ep.Tensor, *, bounds) -> ep.Tensor:\n min_, max_ = bounds\n x = x.tanh() # from (-inf, +inf) to (-1, +1)\n a = (min_ + max_) / 2\n b = (max_ - min_) / 2\n x = x * b + a # map from (-1, +1) to (min_, max_)\n return x\n","sub_path":"foolbox/ext/native/attacks/carlini_wagner.py","file_name":"carlini_wagner.py","file_ext":"py","file_size_in_byte":6900,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"231574299","text":"import simpy\r\nimport numpy as np\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nimport random\r\n\r\nclass Part:\r\n def __init__(self, id):\r\n self.id = id\r\n self.step = 0\r\n self.process_list = ['process1', 'process2', 'process3', 'sink']\r\n\r\nclass Source:\r\n def __init__(self, env, monitor, model, name, IAT):\r\n self.env = env\r\n self.monitor = monitor\r\n self.model = model\r\n self.name = name\r\n self.IAT = IAT\r\n self.part_id = 0\r\n self.env.process(self.processing())\r\n\r\n def processing(self):\r\n while True:\r\n self.part_id += 1\r\n part = Part(self.part_id)\r\n self.monitor.record(time=self.env.now, part=part.id, process=self.name, event='part created')\r\n yield self.env.process(self.to_next_process(part))\r\n\r\n IAT = self.IAT\r\n yield self.env.timeout(IAT)\r\n\r\n def to_next_process(self,part):\r\n yield self.model[part.process_list[part.step]].store.put(part)\r\n\r\n\r\n\r\nclass Process:\r\n def __init__(self, env, monitor, model, name, setup_time, service_time, capacity):\r\n self.env = env\r\n self.monitor = monitor\r\n self.model = model\r\n self.name = name\r\n self.setup_time = setup_time\r\n self.service_time = service_time\r\n self.store = simpy.Store(env)\r\n self.machines = simpy.Store(env, capacity=capacity)\r\n for i in range(capacity):\r\n self.machines.put('resource'+str(i))\r\n self.env.process(self.processing())\r\n\r\n def processing(self):\r\n while True:\r\n machine = yield self.machines.get()\r\n part = yield self.store.get()\r\n self.env.process(self.servicing(part, machine))\r\n\r\n def servicing(self, part, machine):\r\n setup_time = self.setup_time\r\n\r\n self.monitor.record(time=self.env.now, part=part.id, process=self.name, event='process1 start', resource=machine)\r\n yield self.env.timeout(setup_time)\r\n self.monitor.record(time=self.env.now, part=part.id, process=self.name, event='process1 finish', resource=machine)\r\n\r\n service_time = self.service_time\r\n self.monitor.record(time=self.env.now, part=part.id, process=self.name, event='process2 start', resource=machine)\r\n yield self.env.timeout(service_time)\r\n self.monitor.record(time=self.env.now, part=part.id, process=self.name, event='process2 finish', resource=machine)\r\n\r\n self.env.process(self.to_next_process(part, machine))\r\n\r\n def to_next_process(self, part, machine):\r\n part.step += 1\r\n yield self.model[part.process_list[part.step]].store.put(part)\r\n self.machines.put(machine)\r\n\r\n\r\nclass Sink:\r\n def __init__(self, env, monitor, model, name):\r\n self.env = env\r\n self.monitor = monitor\r\n self.model = model\r\n self.name = name\r\n self.store = simpy.Store(env)\r\n self.env.process(self.processing())\r\n\r\n def processing(self):\r\n while True:\r\n part = yield self.store.get()\r\n self.monitor.record(time=self.env.now, part=part.id, process=self.name, event='part finish')\r\nclass Monitor:\r\n def __init__(self, filepath):\r\n self.filepath = filepath\r\n self.time = list()\r\n self.part = list()\r\n self.process = list()\r\n self.event = list()\r\n self.resource = list()\r\n\r\n def record(self, time, process=None, part=None, event=None, resource=None):\r\n self.time.append(time)\r\n self.part.append(part)\r\n self.process.append(process)\r\n self.event.append(event)\r\n self.resource.append(resource)\r\n\r\n def save_event_tracer(self):\r\n event_tracer = pd.DataFrame(columns=['Time', 'Part', 'Process', 'Event', 'Resource'])\r\n event_tracer['Time'] = self.time\r\n event_tracer['Part'] = self.part\r\n event_tracer['Process'] = self.process\r\n event_tracer['Event'] = self.event\r\n event_tracer['Resource'] = self.resource\r\n\r\n event_tracer.to_csv(self.filepath)\r\n return event_tracer\r\n\r\n\r\nif __name__ == '__main__':\r\n IAT = random.expovariate(1.0 / 3)\r\n process1_time = random.expovariate(1.0 / 30)\r\n process2_time = random.expovariate(1.0 / 40)\r\n process3_time = random.expovariate(1.0 / 50)\r\n capacity = 1\r\n\r\n env = simpy.Environment()\r\n monitor = Monitor('eventlog.csv')\r\n model = {}\r\n model['source'] = Source(env, monitor, model, 'source', IAT)\r\n model['process1'] = Process(env, monitor, model, 'process1', process1_time, capacity)\r\n model['process2'] = Process(env, monitor, model, 'process2', process2_time, capacity)\r\n model['process3'] = Process(env, monitor, model, 'process3', process3_time, capacity)\r\n\r\n model['sink'] = Sink(env, monitor, model, 'sink')\r\n\r\n env.run(until=100)\r\n\r\n monitor.save_event_tracer()","sub_path":"exam/2022-28385_prob4.py","file_name":"2022-28385_prob4.py","file_ext":"py","file_size_in_byte":4876,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"507428941","text":"import numpy as np\nfrom ase import Atoms\nfrom ase.neighborlist import neighbor_list\nfrom itertools import permutations\nfrom helper_functions import veclen\nfrom scipy.special import sph_harm\n\ndef BehParCutOff(r, r_c):\n return (r<=r_c)*0.5*(1+np.cos(np.pi*r/r_c))\n\ndef BehParRadial(atoms, eta, r_c):\n num_atoms = A.get_number_of_atoms()\n num_params = len(eta)\n F = np.zeros((num_atoms, num_params))\n \n r_ij = atoms.get_all_distances()\n for ii in range(num_atoms):\n R = r_ij[ii, r_ij[ii, :] > 0].reshape(num_atoms-1, 1)\n F[ii, :] = np.sum(np.exp(-eta*R**2/r_c**2)*BehParCutOff(R, r_c), axis=0)\n return F\n\ndef BehParAngular(atoms, eta, xi, r_c, lambd=[1, -1]):\n num_atoms = A.get_number_of_atoms()\n num_params = 2*(len(eta)*len(xi))\n F = np.zeros((num_atoms, num_params))\n \n fc = BehParCutOff\n\n r_ij = atoms.get_all_distances()\n for ii in range(num_atoms):\n for jj in range(num_atoms):\n for kk in range(num_atoms):\n if kk != ii and jj != kk and ii != jj:\n theta = atoms.get_angle(jj, ii, kk)*np.pi/180\n c = 0\n fac = fc(r_ij[ii, jj], r_c)*fc(r_ij[ii, kk], r_c)*fc(r_ij[jj, kk], r_c)\n for e in eta:\n for x in xi:\n for lamb in lambd:\n F[ii, c] += 2**(1-x)*(1-lamb*np.cos(theta))**x*np.exp(-e*(r_ij[ii, jj]**2+r_ij[ii, kk]**2+r_ij[jj, kk]**2)/r_c**2)*fac\n c += 1\n return F \n\ndef BehPar(atoms, eta, xi, rc):\n# print(eta)\n# print(xi)\n# print(rc)\n I, J, dists, D = neighbor_list('ijdD', atoms, rc)\n dists = dists[:, np.newaxis]\n\n num_radial = len(eta)\n num_angular = len(xi)*2\n num_atoms = len(atoms)\n F = np.zeros((num_atoms, num_radial+num_angular))\n \n # Adjust angular parameters:\n lamb = np.zeros((num_angular))\n Xi = np.zeros((num_angular))\n c = 0\n for x in xi:\n for ii in [-1, 1]:\n Xi[c] = x\n lamb[c] = ii\n c += 1\n\n # Radial functions given by:\n for i, j, d in zip(I, J, dists):\n F[i, 0:num_radial] += np.exp(-eta*d**2/rc**2)*BehParCutOff(d, rc)\n \n # Angular functions:\n eta_ang = 0.005\n for i in range(num_atoms):\n neigh_mask = I==i\n for Rij, rij, j in zip(D[neigh_mask], dists[neigh_mask].ravel(), J[neigh_mask]):\n for Rik, rik, k in zip(D[neigh_mask], dists[neigh_mask].ravel(), J[neigh_mask]):\n if j < k:\n Rjk = Rik-Rij\n rjk = np.sqrt(Rjk@Rjk)\n theta = (Rij@Rik)/(np.sqrt(Rij@Rij)*np.sqrt(Rik@Rik))\n F[i, num_radial::] += (1+lamb*theta)**Xi*np.exp(-eta_ang*(rij**2+rik**2+rjk**2)/rc**2)*BehParCutOff(rij, rc)*BehParCutOff(rik, rc)*BehParCutOff(rjk, rc)\n F[:, num_radial::] *= 2**(1-Xi)\n \n # Atomic number as last entry:\n Fz = atoms.get_atomic_numbers().reshape(num_atoms, 1)\n \n \n #F[: -1] = [atom.get_atomic_number() for atom in atoms]\n\n F = np.concatenate((Fz, F), axis=1)\n\n return F\n\ndef SivaDescriptor(atoms, eta, xi, rc, L=2):\n \"\"\"\n Calculates the descriptors of an atoms object according to \n 'S. Jindal, S. Chiriki, Spherical Harmonics based descriptor..'\n -- atoms: Atoms objects\n -- eta: Parameters for radial functions\n -- xi: Parameters for angular functions\n -- rc: Cutoff\n -- L: Maximum l used in expansion of spherical harmonics.\n\n Returns local feature vectors of each atom in the structure:\n 1st entry: Chemical species.\n Middle: Radial functions\n Last: Angular functions\n \"\"\"\n\n\n # Obtain data from atoms object\n I, J, dists, Dvecs = neighbor_list('ijdD', atoms, rc)\n num_atoms = atoms.get_number_of_atoms()\n num_eta = len(eta)\n num_xi = len(xi)\n num_l = L+1; Ls = [l for l in range(num_l)]\n num_m = 2*L+1\n\n # Setup storage arrays:\n Fr = np.zeros((num_atoms, num_eta)) # Radial\n Fa = np.zeros((atoms.get_number_of_atoms(), num_xi, num_l)) # Angular\n\n # Calculate radial functions:\n for i, d in zip(I, dists):\n Fr[i, :] += np.exp(-eta*d**2)\n \n # Calculate angular functions:\n c = np.zeros((num_atoms, num_xi, num_l, num_m), dtype=np.complex)\n for i, d, r in zip(I, dists, Dvecs):\n\n pol = np.arccos(r[2]/d) # Polar angle\n azi = np.arctan(r[1]/r[0]) # Azimuthal angle\n fc = BehParCutOff(d, rc)\n\n for xidx, x in enumerate(xi):\n ans = np.exp(-x*d**2)*fc\n for lidx, l in enumerate(range(L+1)):\n for midx, m in enumerate(range(-l, l+1)): \n c[i, xidx, lidx, midx] = ans*sph_harm(m, l, azi, pol)\n\n for i in range(num_atoms):\n for lidx in range(num_l):\n for xidx in range(num_xi):\n Fa[i, xidx, lidx] += 4*np.pi/(2*Ls[lidx]+1)*(np.vdot(c[i, xidx, lidx, :],c[i, xidx, lidx, :])).real\n \n Fa = Fa.reshape(num_atoms, num_xi*num_l)\n Z = atoms.get_atomic_numbers()[:, np.newaxis]\n return np.append(Z, np.append(Fr, Fa, axis=1), axis=1)\n\n\n\n\n\n\n\n","sub_path":"main_files/descriptors.py","file_name":"descriptors.py","file_ext":"py","file_size_in_byte":5131,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"496551949","text":"from array import array\nfrom enum import Enum\n\nwith open('data.txt') as f:\n data = list(map(int, f.read().split(',')))\n\n\nclass Opcode(Enum):\n ADD = 1\n MUL = 2\n IN = 3\n OUT = 4\n JNZ = 5\n JZ = 6\n LT = 7\n EQ = 8\n REL = 9\n HALT = 99\n\n\nclass ParamMode(Enum):\n MEMORY = 0\n IMMEDIATE = 1\n RELATIVE = 2\n\n\nclass Halt(Exception):\n pass\n\n\nclass Memory:\n def __init__(self, initial):\n self.data = array('q', initial)\n\n def __getitem__(self, index):\n return self.data[index] if index < len(self.data) else 0\n\n def __setitem__(self, index, value):\n if index >= len(self.data):\n self.data.extend([0] * (index - len(self.data)))\n self.data.append(value)\n else:\n self.data[index] = value\n\n\nclass Interpreter:\n def __init__(self, inp):\n self.pc = 0\n self.rel = 0\n self.memory = Memory(data)\n self.input = inp\n self.output = []\n\n def _param(self, index):\n mode = ParamMode(self.memory[self.pc] // 10 ** (index + 1) % 10)\n if mode == ParamMode.MEMORY:\n return self.memory[self.pc + index]\n if mode == ParamMode.IMMEDIATE:\n return self.pc + index\n if mode == ParamMode.RELATIVE:\n return self.rel + self.memory[self.pc + index]\n\n def param(self, index):\n return self.memory[self._param(index)]\n\n def store(self, index, value):\n self.memory[self._param(index)] = value\n\n def step(self):\n op = Opcode(self.memory[self.pc] % 100)\n if op == Opcode.ADD:\n self.store(3, self.param(1) + self.param(2))\n self.pc += 4\n elif op == Opcode.MUL:\n self.store(3, self.param(1) * self.param(2))\n self.pc += 4\n elif op == Opcode.IN:\n self.store(1, self.input.pop(0))\n self.pc += 2\n elif op == Opcode.OUT:\n self.output.append(self.param(1))\n self.pc += 2\n elif op == Opcode.JNZ:\n self.pc = self.param(2) if self.param(1) else self.pc + 3\n elif op == Opcode.JZ:\n self.pc = self.pc + 3 if self.param(1) else self.param(2)\n elif op == Opcode.LT:\n self.store(3, self.param(1) < self.param(2))\n self.pc += 4\n elif op == Opcode.EQ:\n self.store(3, self.param(1) == self.param(2))\n self.pc += 4\n elif op == Opcode.REL:\n self.rel += self.param(1)\n self.pc += 2\n elif op == Opcode.HALT:\n raise Halt()\n\n def run_until_output(self):\n length = len(self.output)\n while len(self.output) == length:\n self.step()\n return self.output[-1]\n\n def run_until_halt(self):\n try:\n while 1:\n self.step()\n except Halt:\n return\n\n\ninterpreter = Interpreter([1])\ninterpreter.run_until_halt()\nprint(interpreter.output)\n\ninterpreter = Interpreter([2])\ninterpreter.run_until_halt()\nprint(interpreter.output)\n","sub_path":"python/09/day09.py","file_name":"day09.py","file_ext":"py","file_size_in_byte":3027,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"109565617","text":"# python modules\r\nimport pymysql\r\nimport pyodbc\r\nfrom datetime import datetime\r\n\r\n\r\ndef etl(query, source_cnx, target_cnx):\r\n # extract data from source db\r\n\r\n source_cursor = source_cnx.cursor()\r\n start_time = datetime.now()\r\n source_cursor.execute(query.extract_query)\r\n data = source_cursor.fetchall()\r\n # ------QQ\r\n # I think FETCHALL is non-deterministic and you might fail based on \r\n # number of records you are fetching & resource availabilty on your target server\r\n # It is better to go with deterministic model FETCHMANY(size)\r\n # So that you know your tested limits/resource utilization and can adjust as needed \r\n # --------\r\n source_cursor.close()\r\n # -------QQ:\r\n # It is good to catch and log duration for getting the data from source\r\n # -------\r\n\r\n # load data into warehouse db\r\n if data:\r\n # --------------QQ:\r\n # Is this a good model to use between SET or PROCEDURAL operation\r\n # https://www.codeproject.com/Articles/34142/Understanding-Set-based-and-Procedural-approaches\r\n # --------------\r\n target_cursor = target_cnx.cursor()\r\n # target_cursor.execute(\"USE {}\".format(datawarehouse_name))\r\n target_cursor.executemany(query.load_query, data)\r\n # -----QQ\r\n # I think you have right choice above. \r\n # By calling the executemany() method of the MySQLCursor object, \r\n # the MySQL Connector/Python translates the INSERT statement into \r\n # the one that contains multiple lists of values\r\n # -------\r\n target_cnx.commit()\r\n # -------QQ:\r\n # Measure duration elapsed in milliseconds for Target write operation\r\n # So that you know where you are spending mode of your time\r\n # ------- \r\n end_time = datetime.now()\r\n print('data loaded to warehouse db')\r\n target_cursor.execute('''SELECT highest_id FROM log_table WHERE job_status='success' ORDER BY batch_id DESC LIMIT 1''')\r\n lowest_id = target_cursor.fetchone()\r\n lowest_id = lowest_id[0]\r\n target_cursor.execute('''SELECT bookinginfoid FROM roxy_datawarehouse ORDER BY id DESC LIMIT 1''')\r\n highest_id = target_cursor.fetchone()\r\n log_table_insert_query = ('''INSERT INTO log_table (`job_name`, `job_start_date`, `job_end_date`, \r\n `lowest_id`, `highest_id`, `job_status`) VALUES (%s, %s, %s, %s, %s, %s)''')\r\n log_table_insert_params = ('roxy', start_time,\r\n end_time, (lowest_id+1), highest_id, 'success')\r\n target_cursor.execute(log_table_insert_query, log_table_insert_params)\r\n target_cnx.commit()\r\n # --------QQ\r\n # I'm not a MySQL expert. Is that every insert/update has to use CURSOR\r\n # In TSQL world, CURSOR is bad choice for the reasons discussed above\r\n # --------\r\n target_cursor.close()\r\n else:\r\n print('data empty')\r\n target_cursor = target_cnx.cursor()\r\n data_empty_insert = ('''INSERT INTO log_table (`job_name`, `job_start_date`, `job_status`, `failure_reason`) VALUES (%s, %s, %s, %s)''')\r\n data_empty_params = ('roxy', start_time, 'failed', 'data empty')\r\n target_cursor.execute(data_empty_insert, data_empty_params)\r\n target_cnx.commit()\r\n\r\n\r\ndef etl_process(queries, target_cnx, source_db_config, db_platform):\r\n # establish source db connection\r\n # -------QQ:\r\n # Check for input params are not null\r\n # Throw specic err so you know which input is not valid\r\n # ------- \r\n if db_platform == 'mysql':\r\n source_cnx = pymysql.connect(**source_db_config)\r\n elif db_platform == 'sqlserver':\r\n source_cnx = pyodbc.connect(**source_db_config)\r\n elif db_platform == 'firebird':\r\n source_cnx = fdb.connect(**source_db_config)\r\n elif db_platform == 'sqlite':\r\n source_cnx = sqlite3.connect(**source_db_config)\r\n else:\r\n return 'Error! unrecognised db platform'\r\n # -------QQ:\r\n # You don't want to throw exception? Why string return value?\r\n # I haven't done Python and more of a learning question\r\n # -------\r\n \r\n # loop through sql queries\r\n for query in queries:\r\n etl(query, source_cnx, target_cnx)\r\n\r\n # close the source db connection\r\n # -------QQ:\r\n # After closing the cursor, can't immed. close source connection?\r\n # Do you have to keep it open until Target write Op. is complete?\r\n # -------\r\n source_cnx.close()\r\n","sub_path":"etl.py","file_name":"etl.py","file_ext":"py","file_size_in_byte":4502,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"103606728","text":"# Author: Mingyu Ding\n# Time: 2/1/2020 7:02 PM\n# Copyright 2019. All Rights Reserved.\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# ==============================================================================\n\nfrom importlib import import_module\nfrom getopt import getopt\nimport scipy.io as sio\nimport matplotlib.pyplot as plt\nfrom matplotlib.path import Path\nimport numpy as np\nimport pprint\nimport sys\nimport os\nimport cv2\nimport math\nimport shutil\nimport re\nfrom easydict import EasyDict as edict\nimport json\nimport pickle\nimport mmcv\n\n# stop python from writing so much bytecode\nsys.dont_write_bytecode = True\nsys.path.append(os.getcwd())\nnp.set_printoptions(suppress=True)\n\nmean_3d = [ 1.5319942, 1.6342136, 3.8970344, 1.0385063, -2.2421432, 26.235588, -0.04752721, -0.02627299]\nstd_3d = [ 0.13790289, 0.09824956, 0.42854765, 42.354717, 21.57514, 12.873396, 1.6905682, 1.7750752]\nAw = 123.62374/2 # 69.16813\nAh = 113.182/2 # 51.076797]\n\n# trans_mean = [-0.00378265, -0.00182043, -0.00622482, 0.016801083, -0.03962014, 26.235588, -0.04752721, -0.02627299]\n# trans_std = [0.08565227, 0.06028508, 0.11255758, 0.6852198, 0.38124686, 12.873396, 1.6905682, 1.7750752]\ntrans_mean = [-0.00378265, -0.00182043, -0.00622482, 0, 0, 26.235588, -0.04752721, -0.02627299]\ntrans_std = [0.08565227, 0.06028508, 0.11255758, 1, 1, 12.873396, 1.6905682, 1.7750752]\n\ndef read_kitti_cal(calfile):\n \"\"\"\n Reads the kitti calibration projection matrix (p2) file from disc.\n Args:\n calfile (str): path to single calibration file\n \"\"\"\n\n text_file = open(calfile, 'r')\n\n p2pat = re.compile(('(P2:)\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s+(fpat)' +\n '\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s*\\n').replace('fpat', '[-+]?[\\d]+\\.?[\\d]*[Ee](?:[-+]?[\\d]+)?'))\n\n for line in text_file:\n\n parsed = p2pat.fullmatch(line)\n\n # bbGt annotation in text format of:\n # cls x y w h occ x y w h ign ang\n if parsed is not None:\n p2 = np.zeros([4, 4], dtype=float)\n p2[0, 0] = parsed.group(2)\n p2[0, 1] = parsed.group(3)\n p2[0, 2] = parsed.group(4)\n p2[0, 3] = parsed.group(5)\n p2[1, 0] = parsed.group(6)\n p2[1, 1] = parsed.group(7)\n p2[1, 2] = parsed.group(8)\n p2[1, 3] = parsed.group(9)\n p2[2, 0] = parsed.group(10)\n p2[2, 1] = parsed.group(11)\n p2[2, 2] = parsed.group(12)\n p2[2, 3] = parsed.group(13)\n\n p2[3, 3] = 1\n\n text_file.close()\n\n return p2\n\n\ndef convertRot2Alpha(ry3d, z3d, x3d):\n\n alpha = ry3d - math.atan2(-z3d, x3d) - 0.5 * math.pi\n # alpha = ry3d - math.atan2(x3d, z3d) # equivalent\n\n while alpha > math.pi: alpha -= math.pi * 2\n while alpha < (-math.pi): alpha += math.pi * 2\n\n return alpha\n\n\ndef project_3d(p2, x3d, y3d, z3d, w3d, h3d, l3d, ry3d, return_3d=False):\n \"\"\"\n Projects a 3D box into 2D vertices\n Args:\n p2 (nparray): projection matrix of size 4x3\n x3d: x-coordinate of center of object\n y3d: y-coordinate of center of object\n z3d: z-cordinate of center of object\n w3d: width of object\n h3d: height of object\n l3d: length of object\n ry3d: rotation w.r.t y-axis\n \"\"\"\n\n # compute rotational matrix around yaw axis\n R = np.array([[+math.cos(ry3d), 0, +math.sin(ry3d)],\n [0, 1, 0],\n [-math.sin(ry3d), 0, +math.cos(ry3d)]])\n\n # 3D bounding box corners\n x_corners = np.array([0, l3d, l3d, l3d, l3d, 0, 0, 0])\n y_corners = np.array([0, 0, h3d, h3d, 0, 0, h3d, h3d])\n z_corners = np.array([0, 0, 0, w3d, w3d, w3d, w3d, 0])\n\n x_corners += -l3d / 2\n y_corners += -h3d / 2\n z_corners += -w3d / 2\n\n # bounding box in object co-ordinate\n corners_3d = np.array([x_corners, y_corners, z_corners])\n\n # rotate\n corners_3d = R.dot(corners_3d)\n\n # translate object coordinate to camera coordinate\n corners_3d += np.array([x3d, y3d, z3d]).reshape((3, 1))\n\n corners_3D_1 = np.vstack((corners_3d, np.ones((corners_3d.shape[-1]))))\n corners_2D = p2.dot(corners_3D_1)\n corners_2D = corners_2D / corners_2D[2] # normalize dim 3 -> 2, image coordinate\n\n bb3d_lines_verts_idx = [0, 1, 2, 3, 4, 5, 6, 7]\n\n verts3d = (corners_2D[:, bb3d_lines_verts_idx][:2]).astype(float).T\n\n if return_3d:\n return verts3d, corners_3d # 2d corners in image coordinate and 3d corners in camera coordinate\n else:\n return verts3d\n\n\ndef read_kitti_label(file, calib, dataset):\n \"\"\"\n Reads the kitti label file from disc.\n Args:\n file (str): path to single label file for an image\n p2 (ndarray): projection matrix for the given image\n \"\"\"\n\n gts = []\n calib = calib.astype(np.float32)\n\n text_file = open(file, 'r')\n\n '''\n Values Name Description\n ----------------------------------------------------------------------------\n 1 type Describes the type of object: 'Car', 'Van', 'Truck',\n 'Pedestrian', 'Person_sitting', 'Cyclist', 'Tram',\n 'Misc' or 'DontCare'\n 1 truncated Float from 0 (non-truncated) to 1 (truncated), where\n truncated refers to the object leaving image boundaries\n 1 occluded Integer (0,1,2,3) indicating occlusion state:\n 0 = fully visible, 1 = partly occluded\n 2 = largely occluded, 3 = unknown\n 1 alpha Observation angle of object, ranging [-pi..pi]\n 4 bbox 2D bounding box of object in the image (0-based index):\n contains left, top, right, bottom pixel coordinates\n 3 dimensions 3D object dimensions: height, width, length (in meters)\n 3 location 3D object location x,y,z in camera coordinates (in meters)\n 1 rotation_y Rotation ry around Y-axis in camera coordinates [-pi..pi]\n 1 score Only for results: Float, indicating confidence in\n detection, needed for p/r curves, higher is better.\n '''\n\n pattern = re.compile(('([a-zA-Z\\-\\?\\_]+)\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s+'\n + '(fpat)\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s*((fpat)?)\\n')\n .replace('fpat', '[-+]?\\d*\\.\\d+|[-+]?\\d+'))\n\n bboxes = []\n bboxes_ignore = []\n bboxes_3d = []\n bboxes_3d_ignore = []\n labels = []\n labels_ignore = []\n\n TYPE2LABEL = dict(\n Background=0,\n Car=1,\n Cyclist=2,\n Pedestrian=3,\n # Van=4,\n # Person_sitting=5\n # Truck = 6\n # Tram = 7\n # Misc = 8\n )\n\n for line in text_file:\n\n parsed = pattern.fullmatch(line)\n\n # bbGt annotation in text format of:\n # cls x y w h occ x y w h ign ang\n if parsed is not None:\n\n obj = edict()\n\n ign = False\n\n label_type = parsed.group(1) # type\n trunc = float(parsed.group(2))\n occ = float(parsed.group(3))\n alpha = float(parsed.group(4))\n\n x = float(parsed.group(5)) # left\n y = float(parsed.group(6)) # top\n x2 = float(parsed.group(7)) # right\n y2 = float(parsed.group(8)) # bottom\n\n width = x2 - x + 1\n height = y2 - y + 1\n\n h3d = float(parsed.group(9))\n w3d = float(parsed.group(10))\n l3d = float(parsed.group(11))\n\n cx3d = float(parsed.group(12)) # center of car in 3d\n cy3d = float(parsed.group(13)) # bottom of car in 3d\n cz3d = float(parsed.group(14)) # center of car in 3d\n rotY = float(parsed.group(15))\n\n cy3d -= (h3d / 2)\n\n ign = 0\n verts3d, corners_3d = project_3d(calib, cx3d, cy3d, cz3d, w3d, h3d, l3d, rotY, return_3d=True)\n\n if np.any(corners_3d[2, :] <= 0):\n ign = True\n else: # 3d for 2d\n x = min(verts3d[:, 0])\n y = min(verts3d[:, 1])\n x2 = max(verts3d[:, 0])\n y2 = max(verts3d[:, 1])\n\n width = x2 - x + 1\n height = y2 - y + 1\n\n # rotY_1 = 0\n # if rotY < 0:\n # rotY = rotY + math.pi\n # rotY_1 = 1\n\n x_p, y_p, z_p, _ = calib.dot(np.array([cx3d, cy3d, cz3d, 1]))\n x_p /= z_p\n y_p /= z_p\n\n alpha = convertRot2Alpha(rotY, cz3d, cx3d)\n obj.bbox_3d = [h3d, w3d, l3d, x_p, y_p, z_p, rotY, alpha]\n\n # for i in range(8):\n # obj.bbox_3d[i] = (obj.bbox_3d[i] - mean_3d[i]) / std_3d[i]\n\n for i in range(3):\n obj.bbox_3d[i] = np.log(obj.bbox_3d[i]/mean_3d[i])\n\n for i in range(8):\n obj.bbox_3d[i] = (obj.bbox_3d[i] - trans_mean[i]) / trans_std[i]\n\n obj.bbox_2d = [x, y, x2, y2]\n obj.label = label_type\n\n # gts.append(obj)\n\n if label_type in TYPE2LABEL and occ <= 1 and not ign and height >= 23 and height <= 280:\n # print(obj.bbox_3d)\n bboxes.append(obj.bbox_2d)\n bboxes_3d.append(obj.bbox_3d)\n labels.append(TYPE2LABEL[label_type])\n # if obj.bbox_3d[3] > x2 or obj.bbox_3d[3] < x:\n # print(obj.bbox_2d, obj.bbox_3d)\n if label_type in TYPE2LABEL and occ <= 1 and ign: # or occ >= 2: # label_type == 'DontCare' or\n bboxes_ignore.append(obj.bbox_2d)\n bboxes_3d_ignore.append(obj.bbox_3d)\n labels_ignore.append(0)\n\n if dataset == 'train':\n if bboxes_ignore:\n ann = dict(\n bboxes=np.array(bboxes, dtype=np.float32),\n bboxes_3d=np.array(bboxes_3d, dtype=np.float32),\n calib=calib,\n labels=np.array(labels, dtype=np.int64),\n bboxes_ignore=np.zeros((0, 4), dtype=np.float32),\n bboxes_3d_ignore=np.zeros((0, 8), dtype=np.float32)\n # bboxes_ignore=np.array(bboxes_ignore, dtype=np.float32),\n # bboxes_3d_ignore=np.array(bboxes_3d_ignore, dtype=np.float32),\n )\n else:\n ann = dict(\n bboxes=np.array(bboxes, dtype=np.float32),\n bboxes_3d=np.array(bboxes_3d, dtype=np.float32),\n calib=calib,\n labels=np.array(labels, dtype=np.int64),\n bboxes_ignore=np.zeros((0, 4), dtype=np.float32),\n bboxes_3d_ignore=np.zeros((0, 8), dtype=np.float32),\n )\n else:\n ann = dict(\n bboxes=np.array(bboxes, dtype=np.float32),\n labels=np.array(labels, dtype=np.int64),\n bboxes_3d=np.array(bboxes_3d, dtype=np.float32),\n calib=calib,\n )\n\n return ann\n\n\n\nkitti_raw = dict()\nkitti_raw['base'] = os.path.join(os.getcwd(), 'data', 'kitti')\nkitti_raw['calib'] = os.path.join(kitti_raw['base'], 'training', 'calib')\nkitti_raw['img'] = os.path.join(kitti_raw['base'], 'training', 'image_2')\nkitti_raw['label'] = os.path.join(kitti_raw['base'], 'training', 'label_2')\nkitti_raw['pre'] = os.path.join(kitti_raw['base'], 'training', 'prev_2')\n\ntrain_file = 'kitti_tools/split1/train.txt'\nval_file = 'kitti_tools/split1/val.txt'\ntrain_list = []\nval_list = []\n\n# stats = np.zeros((0, 8), np.float32)\n\nfor item in ['train', 'val']:\n if item == 'train':\n text_file = open(train_file, 'r')\n else:\n text_file = open(val_file, 'r')\n\n for index, line in enumerate(text_file):\n if index % 20 == 0:\n print(index)\n # if index >= 200:\n # break\n parsed = re.search('(\\d+)', line)\n if parsed is not None:\n id = str(parsed[0])\n # print(id)\n\n file_info = dict()\n file_info['filename'] = os.path.join(kitti_raw['img'], id + '.png')\n file_info['calibname'] = os.path.join(kitti_raw['calib'], id + '.txt')\n file_info['labelname'] = os.path.join(kitti_raw['label'], id + '.txt')\n\n file_shape = cv2.imread(file_info['filename']).shape[:2][::-1]\n file_info['width'] = file_shape[0]\n file_info['height'] = file_shape[1]\n\n file_info['calib'] = read_kitti_cal(file_info['calibname'])\n file_info['ann'] = read_kitti_label(file_info['labelname'], file_info['calib'], item)\n # stats = np.concatenate((stats, file_info['ann']['bboxes_3d']), axis=0)\n # print(stats.mean(0), stats.std(0))\n if item == 'train':\n train_list.append(file_info)\n else:\n val_list.append(file_info)\n# output = open(os.path.join(os.getcwd(), 'train.pkl'), 'wb')\n# pickle.dump(train_list, output)\n# print(stats.mean(0),stats.std(0))\nmmcv.dump(train_list, os.path.join(os.getcwd(), os.path.join(os.getcwd(), 'kitti_tools', 'split1', 'train_3d.pkl')))\nmmcv.dump(val_list, os.path.join(os.getcwd(), os.path.join(os.getcwd(), 'kitti_tools', 'split1', 'val_3d.pkl')))","sub_path":"kitti_tools/split1/convert_datasets/kitti_3d.py","file_name":"kitti_3d.py","file_ext":"py","file_size_in_byte":13787,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"644428300","text":"\n# coding: utf-8\n\n# In[1]:\n\n\nimport warnings\nwarnings.filterwarnings(\"ignore\", message=\"numpy.dtype size changed\") # another bogus warning, see https://github.com/numpy/numpy/pull/432\nimport numpy as np\nimport pandas as pd\nimport sys\nfrom collections import defaultdict\nfrom pprint import pformat\nfrom collections import deque\nimport math\nfrom functools import reduce\n\nimport pandas as pd\nfrom copy import copy\nimport numpy as np\n\nfrom pyqstrat.pq_utils import *\nfrom pyqstrat.marketdata import *\nfrom pyqstrat.orders import *\nfrom pyqstrat.plot import *\nfrom pyqstrat.evaluator import *\n\n\n# In[3]:\n\n\ndef _calc_pnl(open_trades, new_trades, ending_close, multiplier):\n '''\n >>> from collections import deque\n >>> trades = deque([Trade('IBM', np.datetime64('2018-01-01 10:15:00'), 3, 51.),\n ... Trade('IBM', np.datetime64('2018-01-01 10:20:00'), 10, 50.),\n ... Trade('IBM', np.datetime64('2018-01-02 11:20:00'), -5, 45.)])\n >>> print(_calc_pnl(open_trades = deque(), new_trades = trades, ending_close = 54, multiplier = 100))\n (deque([IBM 2018-01-01 10:20 qty: 8 prc: 50.0 order: None]), 3200.0, -2800.0)\n >>> trades = deque([Trade('IBM', np.datetime64('2018-01-01 10:15:00'), -8, 10.),\n ... Trade('IBM', np.datetime64('2018-01-01 10:20:00'), 9, 11.),\n ... Trade('IBM', np.datetime64('2018-01-02 11:20:00'), -4, 6.)])\n >>> print(_calc_pnl(open_trades = deque(), new_trades = trades, ending_close = 5.8, multiplier = 100))\n (deque([IBM 2018-01-02 11:20 qty: -3 prc: 6.0 order: None]), 60.00000000000006, -1300.0)\n '''\n \n realized = 0.\n unrealized = 0.\n \n trades = copy(new_trades)\n \n while (len(trades)):\n trade = trades[0]\n if not len(open_trades) or (np.sign(open_trades[-1].qty) == np.sign(trade.qty)):\n open_trades.append(copy(trade))\n trades.popleft()\n continue\n \n if abs(trade.qty) > abs(open_trades[0].qty):\n open_trade = open_trades.popleft()\n realized += open_trade.qty * multiplier * (trade.price - open_trade.price)\n trade.qty += open_trade.qty\n else:\n open_trade = open_trades[0]\n realized += trade.qty * multiplier * (open_trades[-1].price - trade.price)\n trades.popleft()\n open_trade.qty += trade.qty\n \n unrealized = sum([open_trade.qty * (ending_close - open_trade.price) for open_trade in open_trades]) * multiplier\n\n return open_trades, unrealized, realized\n\nclass Trade:\n def __init__(self, symbol, date, qty, price, fee = 0., commission = 0., order = None):\n '''Args:\n symbol: a string\n date: Trade execution datetime\n qty: Number of contracts or shares filled\n price: Trade price\n fee: Fees paid to brokers or others. Default 0\n commision: Commission paid to brokers or others. Default 0\n order: A reference to the order that created this trade. Default None\n '''\n assert(isinstance(symbol, str) and len(symbol) > 0)\n assert(np.isfinite(qty))\n assert(np.isfinite(price))\n assert(np.isfinite(fee))\n assert(np.isfinite(commission))\n \n self.symbol = symbol\n self.date = date\n self.qty = qty\n self.price = price\n self.fee = fee\n self.commission = commission\n self.order = order\n \n def __repr__(self):\n return '{} {:%Y-%m-%d %H:%M} qty: {} prc: {}{}{} order: {}'.format(self.symbol, pd.Timestamp(self.date).to_pydatetime(), \n self.qty, self.price, \n ' ' + str(self.fee) if self.fee != 0 else '', \n ' ' + str(self.commission) if self.commission != 0 else '', \n self.order)\n\n\nclass Portfolio:\n '''A portfolio contains one or more strategies that run concurrently so you can test running strategies that are uncorrelated together.'''\n def __init__(self, name = 'main'):\n '''Args:\n name: String used for displaying this portfolio\n '''\n self.name = name\n self.strategies = {}\n \n def add_strategy(self, name, strategy):\n '''\n Args:\n name: Name of the strategy\n strategy: Strategy object\n '''\n self.strategies[name] = strategy\n strategy.portfolio = self\n strategy.name = name\n \n def run_indicators(self, strategy_names = None):\n '''Compute indicators for the strategies specified\n \n Args:\n strategy_names: A list of strategy names. By default this is set to None and we use all strategies.\n '''\n if strategy_names is None: strategy_names = list(self.strategies.keys())\n if len(strategy_names) == 0: raise Exception('a portofolio must have at least one strategy')\n for name in strategy_names: self.strategies[name].run_indicators()\n \n def run_signals(self, strategy_names = None):\n '''Compute signals for the strategies specified. Must be called after run_indicators\n \n Args:\n strategy_names: A list of strategy names. By default this is set to None and we use all strategies.\n '''\n if strategy_names is None: strategy_names = list(self.strategies.keys())\n if len(strategy_names) == 0: raise Exception('a portofolio must have at least one strategy')\n for name in strategy_names: self.strategies[name].run_signals()\n \n def run_rules(self, strategy_names = None, start_date = None, end_date = None, run_first = False, run_last = True):\n '''Run rules for the strategies specified. Must be called after run_indicators and run_signals. \n See run function for argument descriptions\n '''\n start_date, end_date = str2date(start_date), str2date(end_date)\n if strategy_names is None: strategy_names = list(self.strategies.keys())\n if len(strategy_names) == 0: raise Exception('a portofolio must have at least one strategy')\n\n strategies = [self.strategies[key] for key in strategy_names]\n \n min_date = min([strategy.dates[0] for strategy in strategies])\n if start_date: min_date = max(min_date, start_date)\n max_date = max([strategy.dates[-1] for strategy in strategies])\n if end_date: max_date = min(max_date, end_date)\n \n iter_list = []\n \n for strategy in strategies:\n dates, iterations = strategy._get_iteration_indices(start_date = start_date, end_date = end_date, run_first = run_first, run_last = run_last)\n iter_list.append((strategy, dates, iterations))\n \n dates_list = [tup[1] for tup in iter_list]\n \n #for v in self.rebalance_rules.values():\n # _, freq = v\n # rebalance_dates = pd.date_range(min_date, max_date, freq = freq).values\n # dates_list.append(rebalance_dates)\n \n all_dates = np.array(reduce(np.union1d, dates_list))\n\n iterations = [[] for x in range(len(all_dates))]\n\n for tup in iter_list: # per strategy\n strategy = tup[0]\n dates = tup[1]\n iter_tup = tup[2] # vector with list of (rule, symbol, iter_params dict)\n for i, date in enumerate(dates):\n idx = np.searchsorted(all_dates, date)\n iterations[idx].append((Strategy._iterate, (strategy, i, iter_tup[i])))\n \n #for name, tup in self.rebalance_rules.items():\n # rule, freq = tup\n # rebalance_dates = pd.date_range(all_dates[0], all_dates[-1], freq = freq).values\n # rebalance_indices = np.where(np.in1d(all_dates, rebalance_dates))[0]\n # iterations[idx].append(lambda : Portfolio.rebalance(rule), idx)\n \n self.iterations = iterations # For debugging\n \n for iter_idx, tup_list in enumerate(iterations):\n for tup in tup_list:\n func = tup[0]\n args = tup[1]\n func(*args)\n \n def run(self, strategy_names = None, start_date = None, end_date = None, run_first = False, run_last = True):\n '''\n Run indicators, signals and rules.\n \n Args:\n strategy_names: A list of strategy names. By default this is set to None and we use all strategies.\n start_date: Run rules starting from this date. \n Sometimes we have a few strategies in a portfolio that need different lead times before they are ready to trade\n so you can set this so they are all ready by this date. Default None\n end_date: Don't run rules after this date. Default None\n run_first: Force running rules on the first bar even if signals do not require this. Default False\n run_last: Force running rules on penultimate bar even if signals do not require this. \n '''\n start_date, end_date = str2date(start_date), str2date(end_date)\n self.run_indicators()\n self.run_signals()\n return self.run_rules(strategy_names, start_date, end_date, run_first, run_last)\n \n def df_returns(self, sampling_frequency = 'D', strategy_names = None):\n '''\n Return dataframe containing equity and returns with a date index. Equity and returns are combined from all strategies passed in.\n \n Args:\n sampling_frequency: Date frequency for rows. Default 'D' for daily so we will have one row per day\n strategy_names: A list of strategy names. By default this is set to None and we use all strategies.\n '''\n if strategy_names is None: strategy_names = list(self.strategies.keys())\n if len(strategy_names) == 0: raise Exception('portfolio must have at least one strategy')\n equity_list = []\n for name in strategy_names:\n equity = self.strategies[name].df_returns(sampling_frequency = sampling_frequency)[['equity']]\n equity.columns = [name]\n equity_list.append(equity)\n df = pd.concat(equity_list, axis = 1)\n df['equity'] = df.sum(axis = 1)\n df['ret'] = df.equity.pct_change()\n return df\n \n def evaluate_returns(self, sampling_frequency = 'D', strategy_names = None, plot = True, float_precision = 4):\n '''Returns a dictionary of common return metrics.\n \n Args:\n sampling_frequency: Date frequency. Default 'D' for daily so we downsample to daily returns before computing metrics\n strategy_names: A list of strategy names. By default this is set to None and we use all strategies.\n plot: If set to True, display plots of equity, drawdowns and returns. Default False\n float_precision: Number of significant figures to show in returns. Default 4\n '''\n returns = self.df_returns(sampling_freq, strategy_names)\n ev = compute_return_metrics(returns.index.values, returns.ret.values, returns.equity.values[0])\n display_return_metrics(ev.metrics(), float_precision = float_precision)\n if plot: plot_return_metrics(ev.metrics())\n return ev.metrics()\n \n def plot(self, sampling_frequency = 'D', strategy_names = None):\n '''Display plots of equity, drawdowns and returns\n \n Args:\n sampling_frequency: Date frequency. Default 'D' for daily so we downsample to daily returns before computing metrics\n strategy_names: A list of strategy names. By default this is set to None and we use all strategies.\n '''\n returns = self.df_returns(sampling_frequency, strategy_names)\n ev = compute_return_metrics(returns.index.values, returns.ret.values, returns.equity.values[0])\n plot_return_metrics(ev.metrics())\n \n def __repr__(self):\n return f'{self.name} {self.strategies.keys()}'\n \n \nclass ContractPNL:\n '''Computes pnl for a single contract over time given trades and market data'''\n def __init__(self, contract):\n self.symbol = contract.symbol\n self.multiplier = contract.multiplier\n self.marketdata = contract.marketdata\n self.dates = self.marketdata.dates\n self.unrealized = np.empty(len(self.dates), dtype = np.float) * np.nan; self.unrealized[0] = 0\n self.realized = np.empty(len(self.dates), dtype = np.float) * np.nan; self.realized[0] = 0\n \n #TODO: Add commission and fee from trades\n self.commission = np.empty(len(self.dates), dtype = np.float) * 0; self.commission[0] = 0\n self.fee = np.empty(len(self.dates), dtype = np.float) * 0; self.fee[0] = 0\n \n self.net_pnl = np.empty(len(self.dates), dtype = np.float) * np.nan; self.net_pnl[0] = 0\n self.position = np.empty(len(self.dates), dtype = np.float) * np.nan; self.position[0] = 0\n self.close = self.marketdata.c\n self._trades = []\n self.open_trades = deque()\n \n def add_trades(self, trades):\n '''Args:\n trades: A list of Trade objects\n '''\n self._trades += trades\n \n def calc(self, prev_i, i):\n '''Compute pnl and store it internally\n \n Args:\n prev_i: Start index to compute pnl from\n i: End index to compute pnl to\n '''\n calc_trades = deque([trade for trade in self._trades if trade.date > self.dates[prev_i] and trade.date <= self.dates[i]])\n \n if not np.isfinite(self.close[i]):\n unrealized = self.unrealized[prev_i]\n realized = 0. \n else:\n open_trades, unrealized, realized = _calc_pnl(self.open_trades, calc_trades, self.close[i], self.multiplier)\n self.open_trades = open_trades\n \n self.unrealized[i] = unrealized\n self.realized[i] = self.realized[prev_i] + realized\n trade_qty = sum([trade.qty for trade in calc_trades])\n self.position[i] = self.position[prev_i] + trade_qty\n self.net_pnl[i] = self.realized[i] + self.unrealized[i] - self.commission[i] - self.fee[i]\n \n def trades(self, start_date = None, end_date = None):\n '''Get a list of trades\n \n Args:\n start_date: A string or numpy datetime64. Trades with trade dates >= start_date will be returned. Default None\n end_date: A string or numpy datetime64. Trades with trade dates <= end_date will be returned. Default None\n '''\n start_date, end_date = str2date(start_date), str2date(end_date)\n trades = [trade for trade in self._trades if (start_date is None or trade.date >= start_date) and (end_date is None or trade.date <= end_date)]\n return trades\n \n def df(self):\n '''Returns a pandas dataframe with pnl data, indexed by date'''\n df = pd.DataFrame({'date' : self.dates, 'unrealized' : self.unrealized, 'realized' : self.realized, \n 'fee' : self.fee, 'net_pnl' : self.net_pnl, 'position' : self.position})\n df.dropna(subset = ['unrealized', 'realized'], inplace = True)\n df['symbol'] = self.symbol\n return df[['symbol', 'date', 'unrealized', 'realized', 'fee', 'net_pnl', 'position']].set_index('date')\n \nclass Contract:\n '''A Contract can be a real or virtual instrument. For example, for futures you may wish to create a single continous contract instead of\n a contract for each future series\n '''\n def __init__(self, symbol, marketdata, multiplier = 1.):\n '''\n Args:\n symbol: A unique string reprenting this contract. e.g IBM or WTI_FUTURE\n multiplier: If you have to multiply price to get price per contract, set that multiplier there.\n marketdata: A MarketData object containing prices for this contract.\n '''\n assert(isinstance(symbol, str) and len(symbol) > 0)\n assert(multiplier > 0)\n self.symbol = symbol\n self.multiplier = multiplier\n self.marketdata = marketdata\n\nclass Account:\n '''An Account calculates pnl for a set of contracts'''\n def __init__(self, contracts, starting_equity = 1.0e6, calc_frequency = 'D'):\n '''\n Args:\n contracts: A list of Contract objects\n starting_equity: Starting equity in account currency. Default 1.e6\n calc_frequency: Account will calculate pnl at this frequency. Default 'D' for daily\n '''\n if calc_frequency != 'D': raise Exception('unknown calc frequency: {}'.format(calc_frequency))\n self.calc_freq = calc_frequency\n self.contract_pnls = defaultdict()\n self.current_calc_index = 0\n self.marketdata = {}\n self.all_dates = None\n self.starting_equity = starting_equity\n if len(contracts) == 0:\n raise Exception('must add at least one contract')\n for contract in contracts: \n self.add_contract(contract)\n \n def _set_dates(self, dates):\n if self.all_dates is not None and not np.array_equal(dates, all_dates):\n raise Exception('all symbols in a strategy must have the same dates')\n self.all_dates = dates\n calc_dates = dates.astype('M8[D]')\n self.calc_dates = np.unique(calc_dates)\n self.calc_indices = np.searchsorted(dates, self.calc_dates, side='left') - 1\n if self.calc_indices[0] == -1: self.calc_indices[0] = 0\n self._equity = np.empty(len(dates), np.float) * np.nan; \n self._equity[0] = self.starting_equity\n \n def symbols(self):\n return list(self.contract_pnls.keys())\n \n def add_contract(self, contract):\n if self.all_dates is None: self._set_dates(contract.marketdata.dates)\n self.contract_pnls[contract.symbol] = ContractPNL(contract)\n self.marketdata[contract.symbol] = contract.marketdata\n \n def _add_trades(self, symbol, trades):\n self.contract_pnls[symbol].add_trades(trades)\n \n def calc(self, i):\n '''\n Computes P&L and stores it internally for all contracts.\n \n Args:\n i: Index to compute P&L at. Account remembers the last index it computed P&L up to and will compute P&L between these two indices\n '''\n calc_indices = self.calc_indices[:]\n if self.current_calc_index == i: return\n intermediate_calc_indices = np.ravel(np.where(np.logical_and(calc_indices > self.current_calc_index, calc_indices <= i)))\n\n if not len(intermediate_calc_indices) or calc_indices[intermediate_calc_indices[-1]] != i: \n calc_indices = np.append(calc_indices, i)\n intermediate_calc_indices = np.append(intermediate_calc_indices, len(calc_indices) - 1)\n \n for symbol, symbol_pnl in self.contract_pnls.items():\n prev_calc_index = self.current_calc_index\n for idx in intermediate_calc_indices:\n calc_index = calc_indices[idx]\n symbol_pnl.calc(prev_calc_index, calc_index)\n self._equity[calc_index] = self._equity[prev_calc_index] + symbol_pnl.net_pnl[calc_index] - symbol_pnl.net_pnl[prev_calc_index]\n #print(f'prev_calc_index: {prev_calc_index} calc_index: {calc_index} prev_equity: {self._equity[prev_calc_index]} net_pnl: {symbol_pnl.net_pnl[calc_index]} prev_net_pnl: {symbol_pnl.net_pnl[prev_calc_index]}')\n prev_calc_index = calc_index\n \n self.current_calc_index = i\n \n def position(self, symbol, date):\n '''Returns position for a symbol at a given date in number of contracts or shares. Will cause calculation if Account has not previously calculated\n up to this date'''\n i = self.find_index_before(date)\n self.calc(i)\n return self.contract_pnls[symbol].position[i]\n \n def equity(self, date):\n '''Returns equity in this account in Account currency. Will cause calculation if Account has not previously calculated up to this date'''\n i = self.find_index_before(date)\n self.calc(i)\n return self._equity[i]\n \n def trades(self, symbol = None, start_date = None, end_date = None):\n '''Returns a list of trades with the given symbol and with trade date between (and including) start date and end date if they are specified.\n If symbol is None trades for all symbols are returned'''\n start_date, end_date = str2date(start_date), str2date(end_date)\n if symbol is None:\n trades = []\n for symbol, sym_pnl in self.contract_pnls.items():\n trades += sym_pnl.trades(start_date, end_date)\n return trades\n else:\n return self.contract_pnls[symbol].trades(start_date, end_date)\n \n def find_index_before(self, date):\n '''Returns the market data index before or at date'''\n return np.searchsorted(self.all_dates, date)\n \n def transfer_cash(self, date, amount):\n '''Move cash from one portfolio to another'''\n i = self.find_index_before(date)\n curr_equity = self.equity(date)\n if (amount > curr_equity): amount = curr_equity # Cannot make equity negative\n self._equity[i] -= amount\n return amount\n\n def df_pnl(self, symbol = None):\n '''Returns a dataframe with P&L columns. If symbol is set to None (default), sums up P&L across symbols'''\n if symbol:\n ret = self.contract_pnls[symbol].df()\n else:\n dfs = []\n for symbol, symbol_pnl in self.contract_pnls.items():\n df = symbol_pnl.df()\n dfs.append(df)\n ret = pd.concat(dfs)\n ret = ret.reset_index().groupby('date').sum()\n df_equity = pd.DataFrame({'equity' : self._equity}, index = self.all_dates).dropna()\n ret = pd.merge(ret, df_equity, left_index = True, right_index = True, how = 'outer')\n ret.index.name = 'date'\n return ret\n \n def df_trades(self, symbol = None, start_date = None, end_date = None):\n '''Returns a dataframe with data from trades with the given symbol and with trade date between (and including) start date and end date\n if they are specified. If symbol is None, trades for all symbols are returned'''\n start_date, end_date = str2date(start_date), str2date(end_date)\n if symbol:\n trades = self.contract_pnls[symbol].trades(start_date, end_date)\n else:\n trades = [v.trades(start_date, end_date) for v in self.contract_pnls.values()]\n trades = [trade for sublist in trades for trade in sublist] # flatten list\n df = pd.DataFrame.from_records([(trade.symbol, trade.date, trade.qty, trade.price, trade.fee, trade.commission, trade.order.date, trade.order.qty, trade.order.params()) for trade in trades],\n columns = ['symbol', 'date', 'qty', 'price', 'fee', 'commission', 'order_date', 'order_qty', 'order_params'])\n return df\n \nclass Strategy:\n def __init__(self, contracts, starting_equity = 1.0e6, calc_frequency = 'D'):\n '''\n Args:\n contracts: A list of contract objects\n starting_equity: Starting equity in Strategy currency. Default 1.e6\n calc_frequency: How often P&L is calculated. Default is 'D' for daily\n '''\n self.name = None\n self.account = Account(contracts, starting_equity, calc_frequency)\n self.symbols = [contract.symbol for contract in contracts]\n self.indicators = {}\n self.indicator_values = defaultdict(dict)\n self.signals = {}\n self.signal_values = defaultdict(dict)\n self.rules = {}\n self.rule_signals = {}\n self.market_sims = {}\n self._trades = defaultdict(list)\n self._orders = []\n self.dates = self.account.all_dates\n \n def add_indicator(self, name, indicator_function):\n '''\n Args:\n name: Name of the indicator\n indicator_function: A function taking a MarketData object and returning a numpy array\n containing indicator values. The return array must have the same length as the MarketData object\n '''\n self.indicators[name] = indicator_function\n \n def add_signal(self, name, signal_function):\n '''\n Args:\n name: Name of the signal\n signal_function: A function taking a MarketData object and a dictionary of indicator value arrays as input and returning a numpy array\n containing signal values. The return array must have the same length as the MarketData object\n '''\n self.signals[name] = signal_function\n \n def add_rule(self, name, rule_function, signal_name, sig_true_values):\n '''Add a trading rule\n \n Args:\n name: Name of the trading rule\n rule_function: A trading rule function that returns a list of Orders\n signal_name: The strategy will call the trading rule function when the signal with this name matches sig_true_values\n sig_true_values: A numpy array of values. If the signal value at a bar is equal to one of these, the Strategy will call the trading rule function\n '''\n self.rule_signals[name] = (signal_name, sig_true_values)\n self.rules[name] = rule_function\n \n def add_market_sim(self, market_sim_function, symbols = None):\n '''Add a market simulator. A market simulator takes a list of Orders as input and returns a list of Trade objects.\n \n Args:\n market_sim_function: A function that takes a list of Orders and MarketData as input and returns a list of Trade objects\n symbols: A list of the symbols that this market_sim_function applies to. If None (default) it will apply to all symbols\n '''\n if symbols is None: symbols = self.symbols\n for symbol in symbols: self.market_sims[symbol] = market_sim_function\n \n def run_indicators(self, indicator_names = None, symbols = None):\n '''Calculate values of the indicators specified and store them.\n \n Args:\n indicator_names: List of indicator names. If None (default) run all indicators\n symbols: List of symbols to run these indicators for. If None (default) use all symbols\n '''\n if indicator_names is None: indicator_names = self.indicators.keys()\n if symbols is None: symbols = self.symbols\n \n for indicator_name in indicator_names:\n indicator_function = self.indicators[indicator_name]\n for symbol in symbols:\n marketdata = self.account.marketdata[symbol]\n self.indicator_values[symbol][indicator_name] = series_to_array(indicator_function(marketdata))\n \n def run_signals(self, signal_names = None, symbols = None):\n '''Calculate values of the signals specified and store them.\n \n Args:\n signal_names: List of signal names. If None (default) run all signals\n symbols: List of symbols to run these signals for. If None (default) use all symbols\n '''\n if signal_names is None: signal_names = self.signals.keys()\n if symbols is None: symbols = self.symbols\n \n for signal_name in signal_names:\n signal_function = self.signals[signal_name]\n for symbol in symbols:\n marketdata = self.account.marketdata[symbol]\n self.signal_values[symbol][signal_name] = series_to_array(signal_function(marketdata, self.indicator_values[symbol]))\n \n def run_rules(self, rule_names = None, symbols = None, start_date = None, end_date = None, run_first = False, run_last = True):\n '''Run trading rules.\n \n Args:\n rule_names: List of rule names. If None (default) run all rules\n symbols: List of symbols to run these signals for. If None (default) use all symbols\n start_date: Run rules starting from this date. Default None \n end_date: Don't run rules after this date. Default None\n run_first: Force running rules on the first bar even if signals do not require this. Default False\n run_last: Force running rules on penultimate bar even if signals do not require this. \n '''\n start_date, end_date = str2date(start_date), str2date(end_date)\n dates, iterations = self._get_iteration_indices(rule_names, symbols, start_date, end_date, run_first, run_last)\n # Now we know which rules, symbols need to be applied for each iteration, go through each iteration and apply them\n # in the same order they were added to the strategy\n for i, tup_list in enumerate(iterations):\n self._iterate(i, tup_list)\n \n def _get_iteration_indices(self, rule_names = None, symbols = None, start_date = None, end_date = None, \n run_first = False, run_last = True):\n start_date, end_date = str2date(start_date), str2date(end_date)\n if rule_names is None: rule_names = self.rules.keys()\n if symbols is None: symbols = self.symbols\n \n dates = self.dates\n num_dates = len(dates)\n \n iterations = [[] for x in range(num_dates)]\n self.orders_iter = [[] for x in range(num_dates)]\n \n for rule_name in rule_names:\n rule_function = self.rules[rule_name]\n for symbol in symbols:\n marketdata = self.account.marketdata[symbol]\n market_sim = self.market_sims[symbol]\n signal_name = self.rule_signals[rule_name][0]\n sig_true_values = self.rule_signals[rule_name][1]\n sig_values = self.signal_values[symbol][signal_name]\n dates = marketdata.dates\n null_value = False if sig_values.dtype == np.dtype('bool') else np.nan\n if start_date: sig_values[0:np.searchsorted(dates, start_date)] = null_value\n if end_date: sig_values[np.searchsorted(dates, end_date):] = null_value\n indices = np.nonzero(np.isin(sig_values, sig_true_values))[0]\n if indices[-1] == len(sig_values) -1: indices = indices[:-1] # Don't run rules on last index since we cannot fill any orders\n if run_first and indices[0] != 0: indices = np.insert(indices, 0, 0)\n if run_last and indices[-1] != len(sig_values) - 2: indices = np.append(indices, len(sig_values) - 2)\n indicator_values = self.indicator_values[symbol]\n iteration_params = {'market_sim' : market_sim, 'indicator_values' : indicator_values, 'signal_values' : sig_values, 'marketdata' : marketdata}\n for idx in indices: iterations[idx].append((rule_function, symbol, iteration_params))\n \n self.iterations = iterations # For debugging\n \n return self.dates, iterations\n \n def _iterate(self, i, tup_list):\n for tup in self.orders_iter[i]:\n try:\n open_orders, symbol, params = tup\n open_orders = self._sim_market(i, open_orders, symbol, params)\n if len(open_orders): self.orders_iter[i + 1].append((open_orders, symbol, params))\n except Exception as e:\n raise type(e)(f'Exception: {str(e)} at rule: {type(tup[0])} symbol: {tup[1]} index: {i}').with_traceback(sys.exc_info()[2])\n \n for tup in tup_list:\n try:\n rule_function, symbol, params = tup\n open_orders = self._get_orders(i, rule_function, symbol, params)\n self._orders += open_orders\n if len(open_orders): self.orders_iter[i + 1].append((open_orders, symbol, params))\n except Exception as e:\n raise type(e)(f'Exception: {str(e)} at rule: {type(tup[0])} symbol: {tup[1]} index: {i}').with_traceback(sys.exc_info()[2])\n \n def _get_orders(self, idx, rule_function, symbol, params):\n indicator_values, signal_values, marketdata = (params['indicator_values'], params['signal_values'], params['marketdata'])\n open_orders = rule_function(self, symbol, idx, self.dates[idx], marketdata, indicator_values, signal_values, self.account)\n return open_orders\n \n def _sim_market(self, idx, open_orders, symbol, params):\n '''\n Keep iterating while we have open orders since they may get filled\n TODO: For limit orders and trigger orders we can be smarter here and reduce indices like quantstrat does\n '''\n market_sim_function = params['market_sim']\n trades = market_sim_function(self, open_orders, idx, self.dates[idx], self.account.marketdata[symbol])\n if len(trades) == 0: return []\n self._trades[symbol] += trades\n self.account._add_trades(symbol, trades)\n self.account.calc(idx)\n open_orders = [order for order in open_orders if order.status == 'open']\n return open_orders\n \n def df_data(self, symbols = None, add_pnl = True, start_date = None, end_date = None):\n '''\n Add indicators and signals to end of market data and return as a pandas dataframe.\n \n Args:\n symbols: list of symbols to include. All if set to None (default)\n add_pnl: If True (default), include P&L columns in dataframe\n start_date: string or numpy datetime64. Default None\n end_date: string or numpy datetime64: Default None\n '''\n start_date, end_date = str2date(start_date), str2date(end_date)\n if symbols is None: symbols = self.symbols\n if not isinstance(symbols, list): symbols = [symbols]\n \n mds = []\n \n for symbol in symbols:\n md = self.account.marketdata[symbol].df(start_date, end_date)\n \n md.insert(0, 'symbol', symbol)\n if add_pnl: \n df_pnl = self.account.df_pnl(symbol)\n del df_pnl['symbol']\n\n indicator_values = self.indicator_values[symbol]\n\n for k in sorted(indicator_values.keys()):\n name = k\n if name in md.columns: name = name + '.ind' # if we have a market data column with the same name as the indicator\n md.insert(len(md.columns), name, indicator_values[k])\n\n signal_values = self.signal_values[symbol]\n\n for k in sorted(signal_values.keys()):\n name = k\n if name in md.columns: name = name + '.sig'\n md.insert(len(md.columns), name, signal_values[k])\n \n if add_pnl: md = pd.merge(md, df_pnl, left_index = True, right_index = True, how = 'left')\n # Add counter column for debugging\n md.insert(len(md.columns), 'i', np.arange(len(md)))\n \n mds.append(md)\n \n return pd.concat(mds)\n \n def marketdata(self, symbol):\n '''Return MarketData object for this symbol'''\n return self.account.marketdata[symbol]\n \n def trades(self, symbol = None, start_date = None, end_date = None):\n '''Returns a list of trades with the given symbol and with trade date between (and including) start date and end date if they are specified.\n If symbol is None trades for all symbols are returned'''\n start_date, end_date = str2date(start_date), str2date(end_date)\n return self.account.trades(symbol, start_date, end_date)\n \n def df_trades(self, symbol = None, start_date = None, end_date = None):\n '''Returns a dataframe with data from trades with the given symbol and with trade date between (and including) start date and end date\n if they are specified. If symbol is None, trades for all symbols are returned'''\n start_date, end_date = str2date(start_date), str2date(end_date)\n return self.account.df_trades(symbol, start_date, end_date)\n \n def orders(self, symbol = None, start_date = None, end_date = None):\n '''Returns a list of orders with the given symbol and with order date between (and including) start date and end date if they are specified.\n If symbol is None orders for all symbols are returned'''\n start_date, end_date = str2date(start_date), str2date(end_date)\n return [order for order in self._orders if (symbol is None or order.symbol == symbol) and (\n start_date is None or order.date >= start_date) and (end_date is None or order.date <= end_date)]\n \n def df_orders(self, symbol = None, start_date = None, end_date = None):\n '''Returns a dataframe with data from orders with the given symbol and with order date between (and including) start date and end date\n if they are specified. If symbol is None, orders for all symbols are returned'''\n start_date, end_date = str2date(start_date), str2date(end_date)\n orders = self.orders(symbol, start_date, end_date)\n df_orders = pd.DataFrame.from_records([(order.symbol, type(order).__name__, order.date, order.qty, order.params()) \n for order in orders], columns = ['symbol', 'type', 'date', 'qty', 'params'])\n return df_orders\n \n def df_pnl(self, symbol = None):\n '''Returns a dataframe with P&L columns. If symbol is set to None (default), sums up P&L across symbols'''\n return self.account.df_pnl(symbol)\n \n def df_returns(self, symbol = None, sampling_frequency = 'D'):\n '''Return a dataframe of returns and equity indexed by date.\n \n Args:\n symbol: The symbol to get returns for. If set to None (default), this returns the sum of PNL for all symbols\n sampling_frequency: Downsampling frequency. Default is None. See pandas frequency strings for possible values\n '''\n pnl = self.df_pnl(symbol)[['equity']]\n pnl.equity = pnl.equity.ffill()\n pnl = pnl.resample(sampling_frequency).last()\n pnl['ret'] = pnl.equity.pct_change()\n return pnl\n \n def plot(self, symbols = None, md_columns = 'c', pnl_columns = 'equity', title = None, figsize = (20, 15), date_range = None, \n date_format = None, sampling_frequency = None, trade_marker_properties = None, hspace = 0.15):\n \n '''Plot indicators, signals, trades, position, pnl\n \n Args:\n symbols: List of symbols or None (default) for all symbols\n md_columns: List of columns of market data to plot. Default is 'c' for close price. You can set this to 'ohlcv' if you want to plot\n a candlestick of OHLCV data\n pnl_columns: List of P&L columns to plot. Default is 'equity'\n title: Title of plot (None)\n figsize: Figure size. Default is (20, 15)\n date_range: Tuple of strings or datetime64, e.g. (\"2018-01-01\", \"2018-04-18 15:00\") to restrict the graph. Default None\n date_format: Date format for tick labels on x axis. If set to None (default), will be selected based on date range. See matplotlib date format strings\n sampling_frequency: Downsampling frequency. Default is None. The graph may get too busy if you have too many bars of data, in which case you may want to \n downsample before plotting. See pandas frequency strings for possible values\n trade_marker_properties: A dictionary of order reason code -> marker shape, marker size, marker color for plotting trades with different reason codes.\n Default is None in which case the dictionary from the ReasonCode class is used\n hspace: Height (vertical) space between subplots. Default is 0.15\n '''\n date_range = strtup2date(date_range)\n if symbols is None: symbols = self.symbols\n if not isinstance(symbols, list): symbols = [symbols]\n if not isinstance(md_columns, list): md_columns = [md_columns]\n if not isinstance(pnl_columns, list): pnl_columns = [pnl_columns]\n for symbol in symbols:\n md = self.marketdata(symbol)\n md_dates = md.dates\n if md_columns == ['ohlcv']:\n md_list = [OHLC('price', dates = md_dates, o = md.o, h = md.h, l = md.l, c = md.c, v = md.v)]\n else:\n md_list = [TimeSeries(md_column, dates = md_dates, values = getattr(md, md_column)) for md_column in md_columns]\n indicator_list = [TimeSeries(indicator_name, dates = md_dates, values = self.indicator_values[symbol][indicator_name], line_type = '--'\n ) for indicator_name in self.indicators.keys() if indicator_name in self.indicator_values[symbol]]\n signal_list = [TimeSeries(signal_name, dates = md_dates, values = self.signal_values[symbol][signal_name]\n ) for signal_name in self.signals.keys() if signal_name in self.signal_values[symbol]]\n df_pnl_ = self.df_pnl(symbol)\n pnl_list = [TimeSeries(pnl_column, dates = df_pnl_.index.values, values = df_pnl_[pnl_column].values) for pnl_column in pnl_columns]\n if trade_marker_properties:\n trade_sets = trade_sets_by_reason_code(self._trades[symbol], trade_marker_properties)\n else:\n trade_sets = trade_sets_by_reason_code(self._trades[symbol])\n main_subplot = Subplot(indicator_list + md_list + trade_sets, height_ratio = 0.5, ylabel = 'Indicators')\n signal_subplot = Subplot(signal_list, ylabel = 'Signals', height_ratio = 0.167)\n pnl_subplot = Subplot(pnl_list, ylabel = 'Equity', height_ratio = 0.167, log_y = True, y_tick_format = '${x:,.0f}')\n position = df_pnl_.position.values\n pos_subplot = Subplot([TimeSeries('position', dates = df_pnl_.index.values, values = position, plot_type = 'filled_line')], \n ylabel = 'Position', height_ratio = 0.167)\n plot = Plot([main_subplot, signal_subplot, pos_subplot, pnl_subplot], figsize = figsize,\n date_range = date_range, date_format = date_format, sampling_frequency = sampling_frequency, title = title, hspace = hspace)\n plot.draw()\n \n def evaluate_returns(self, symbol = None, plot = True, float_precision = 4):\n '''Returns a dictionary of common return metrics.\n \n Args:\n sampling_frequency: Date frequency. Default 'D' for daily so we downsample to daily returns before computing metrics\n strategy_names: A list of strategy names. By default this is set to None and we use all strategies.\n plot: If set to True, display plots of equity, drawdowns and returns. Default False\n float_precision: Number of significant figures to show in returns. Default 4\n '''\n returns = self.df_returns(symbol)\n ev = compute_return_metrics(returns.index.values, returns.ret.values, self.account.starting_equity)\n display_return_metrics(ev.metrics(), float_precision = float_precision)\n if plot: plot_return_metrics(ev.metrics())\n return ev.metrics()\n \n def plot_returns(self, symbol = None):\n '''Display plots of equity, drawdowns and returns for the given symbol or for all symbols if symbol is None (default)'''\n if symbol is None:\n symbols = self.symbols()\n else:\n symbols = [symbol]\n \n df_list = []\n \n for symbol in symbols:\n df_list.append(self.df_returns(symbol))\n \n df = pd.concat(df_list, axis = 1)\n \n ev = compute_return_metrics(returns.index.values, returns.ret.values, self.account.starting_equity)\n plot_return_metrics(ev.metrics())\n \n def __repr__(self):\n return f'{pformat(self.indicators)} {pformat(self.rules)} {pformat(self.account)}'\n\ndef test_strategy(): \n from datetime import datetime, timedelta\n \n set_defaults()\n\n def sim_order(order, i, date, md):\n symbol = order.symbol\n trade_price = np.nan\n skid_fraction = 0.5\n \n if not md.valid_row(i): return None\n \n if isinstance(order, MarketOrder):\n if order.qty > 0:\n trade_price = 0.5 * (md.c[i] + md.h[i])\n else:\n trade_price = 0.5 * (md.c[i] + md.l[i])\n elif order.qty > 0 and md.h[i] > order.trigger_price:\n trade_price = skid_fraction * max(md.o[i], order.trigger_price, md.l[i]) + (1 - skid_fraction) * md.h[i]\n elif order.qty < 0 and md.l[i] < order.trigger_price:\n trade_price = skid_fraction * min(md.o[i], order.trigger_price, md.h[i]) + (1 - skid_fraction) * md.l[i]\n else:\n return None\n sim_trade = Trade(symbol, date, order.qty, trade_price, order = order)\n return sim_trade\n \n def market_simulator(strategy, orders, i, date, marketdata):\n trades = []\n for order in orders:\n sim_trade = sim_order(order, i, date, marketdata)\n if sim_trade is None: continue\n if math.isclose(sim_trade.qty, order.qty): order.status = 'filled'\n\n trades.append(sim_trade)\n return trades\n \n def trade_rule(strategy, symbol, i, date, marketdata, indicator_values, signal_values, account):\n heat = 0.05\n reason_code = None\n \n if not marketdata.valid_row(i): return []\n \n curr_pos = account.position(symbol, date)\n \n if i == len(marketdata.dates) - 2: # Last date so get out of position\n if not math.isclose(curr_pos, 0): \n return [MarketOrder(symbol, date, -curr_pos, reason_code = ReasonCode.BACKTEST_END)]\n else:\n return []\n \n trend = signal_values[i]\n fast_resistance, fast_support, slow_resistance, slow_support = (indicator_values['fast_resistance'][i], \n indicator_values['fast_support'][i], indicator_values['slow_resistance'][i], indicator_values['slow_support'][i])\n \n if trend == 1:\n entry_limit = fast_resistance\n stop_limit = fast_support\n elif trend == -1:\n entry_limit = fast_support\n stop_limit = fast_resistance\n else:\n return []\n\n if math.isclose(curr_pos, 0): # We got a trade in the previous bar so put in a stop limit order\n if math.isclose(entry_limit, stop_limit): return []\n curr_equity = account.equity(date)\n order_qty = curr_equity * heat / (entry_limit - stop_limit)\n trigger_price = entry_limit\n reason_code = ReasonCode.ENTER_LONG if order_qty > 0 else ReasonCode.ENTER_SHORT\n else:\n order_qty = -curr_pos\n trigger_price = stop_limit\n reason_code = ReasonCode.EXIT_LONG if order_qty < 0 else ReasonCode.EXIT_SHORT\n \n order_qty = round(order_qty)\n \n if np.isnan(order_qty):\n raise Exception(f'Got nan order qty date: {date} i: {i} curr_pos: {curr_pos} curr_equity: {curr_equity} entry_limit: {entry_limit} stop_limit: {stop_limit}')\n \n if math.isclose(order_qty, 0): return []\n \n order = StopLimitOrder(symbol, date, order_qty, trigger_price, reason_code = reason_code)\n \n return [order]\n \n def get_support(lows, n): return pd.Series(lows).rolling(window = n, min_periods = 1).min().values\n\n def get_resistance(highs, n): return pd.Series(highs).rolling(window = n, min_periods = 1).max().values\n \n def get_trend(md, ind):\n trend = pd.Series(np.where(pd.Series(md.h) > shift_np(ind['slow_resistance'], 1), 1, \n np.where(pd.Series(md.l) < shift_np(ind['slow_support'], 1), -1, \n np.nan)))\n trend.fillna(method = 'ffill', inplace = True)\n return trend.values\n \n def build_strategy(contract, fast_interval, slow_interval):\n strategy = Strategy([contract])\n strategy.add_indicator('slow_resistance', lambda md : get_resistance(md.h, slow_interval))\n strategy.add_indicator('slow_support', lambda md : get_support(md.l, slow_interval))\n strategy.add_indicator('fast_resistance', lambda md : get_resistance(md.h, fast_interval))\n strategy.add_indicator('fast_support', lambda md : get_support(md.l, fast_interval))\n strategy.add_signal('trend', get_trend)\n strategy.add_market_sim(market_simulator)\n strategy.add_rule('trade_rule', trade_rule, 'trend', np.array([-1, 1]))\n return strategy\n \n np.random.seed(0)\n dates = np.arange(datetime(2018, 1, 1, 9, 0, 0), datetime(2018, 3, 1, 16, 0, 0), timedelta(minutes = 5))\n dates = np.array([dt for dt in dates.astype(object) if dt.hour >= 9 and dt.hour <= 16]).astype('M8[m]')\n rets = np.random.normal(size = len(dates)) / 1000\n c_0 = 100\n c = np.round(c_0 * np.cumprod(1 + rets), 2)\n l = np.round(c * (1. - np.abs(np.random.random(size = len(dates)) / 1000.)), 2)\n h = np.round(c * (1. + np.abs(np.random.random(size = len(dates)) / 1000.)), 2)\n o = np.round(l + (h - l) * np.random.random(size = len(dates)), 2)\n v = np.round(np.random.normal(size = len(dates)) * 100)\n \n portfolio = Portfolio()\n \n slow_interval = 0\n \n for days in [0.5, 1, 2]:\n # 1 day from 9 - 4 pm has 7 hours which translate to 7 x 12 = 84 5 minute periods\n fast_interval = round(days * 84) \n slow_interval = round(5 * fast_interval)\n contract = Contract('IBM', MarketData(dates, c, o, h, l, v))\n portfolio.add_strategy(f'strat_{days}', build_strategy(contract, fast_interval, slow_interval))\n \n # Start at max slow days so all strategies start at the same time\n print('running')\n portfolio.run(start_date = dates[slow_interval])\n print('done')\n \n strat1 = portfolio.strategies['strat_0.5']\n portfolio.plot();\n strat1.plot();\n \nif __name__ == \"__main__\":\n test_strategy()\n\n","sub_path":"pyqstrat/strategy.py","file_name":"strategy.py","file_ext":"py","file_size_in_byte":50531,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"510322732","text":"import itertools\nimport os\nimport shutil\nimport tarfile\nfrom urllib import request\nimport zipfile\n\n\nREG = 'Regression'\nBINARY_CLF = 'Binary'\nMULTI_CLF = 'Multiclass'\nMO_BINARY_CLF = 'Multi-output binary'\n\ndef get_data_home(data_home: str = None):\n \"\"\"Return the path of the creme data directory.\n\n By default this will expand the relative path '~/creme_data'.\n\n \"\"\"\n\n if data_home is None:\n data_home = os.environ.get('CREME_DATA', os.path.join('~', 'creme_data'))\n data_home = os.path.expanduser(data_home)\n if not os.path.exists(data_home):\n os.makedirs(data_home)\n return data_home\n\n\ndef download_dataset(url: str, data_home: str, uncompress=True, verbose=True) -> str:\n \"\"\"Downloads/decompresses a dataset locally if does not exist.\n\n Parameters:\n url: From where to download the dataset.\n data_home: The directory where you wish to store the data.\n uncompress: Whether to uncompress the file or not.\n verbose: Whether to indicate download progress or not.\n\n Returns:\n data_dir_path: The dataset's storage location?\n\n \"\"\"\n\n def _print(msg):\n if verbose:\n print(msg)\n\n name = os.path.basename(url)\n extension = '.'.join(name.split('.')[1:])\n data_home = get_data_home(data_home=data_home)\n path = os.path.join(data_home, f'{name}')\n archive_path = path\n if extension:\n path = path[:-(len(extension) + 1)] # e.g. path/to/file.tar.gz becomes path/to/file\n\n # Download if necessary\n if not (os.path.exists(path) or os.path.exists(archive_path)):\n\n _print(f'Downloading {url}')\n with request.urlopen(url) as r, open(archive_path, 'wb') as f:\n shutil.copyfileobj(r, f)\n\n # If no uncompression is required then we're done\n if not uncompress:\n return archive_path\n\n # Uncompress if necessary\n if not os.path.exists(path):\n\n _print(f'Uncompressing into {path}')\n\n if extension.endswith('zip'):\n with zipfile.ZipFile(archive_path, 'r') as zf:\n zf.extractall(path)\n\n elif extension.endswith(('gz', 'tar')):\n mode = 'r:' if extension.endswith('tar') else 'r:gz'\n tar = tarfile.open(archive_path, mode)\n tar.extractall(path)\n tar.close()\n\n else:\n raise RuntimeError(f'Unhandled extension type: {extension}')\n\n # Delete the archive file now that the dataset is available\n os.remove(archive_path)\n\n return path\n\n\nclass Dataset:\n\n def __init__(self, n_features, category):\n self.n_features = n_features\n self.category = category\n\n def __iter__(self):\n raise NotImplementedError\n\n def take(self, k: int):\n \"\"\"Yields the k first (`x`, `y`) pairs.\"\"\"\n return itertools.islice(self, k)\n\n\nclass FileDataset(Dataset):\n\n def __init__(self, n_samples, n_features, category, **dl_params):\n super().__init__(n_features=n_features, category=category)\n self.n_samples = n_samples\n self.dl_params = dl_params\n\n def _stream_X_y(self, dir):\n raise NotImplementedError\n\n @property\n def _remote(self):\n \"\"\"Whether or not the dataset needs downloading or not.\"\"\"\n return 'url' in self.dl_params\n\n @property\n def _ready(self):\n \"\"\"Whether or not the dataset is ready to be read.\"\"\"\n if self._remote:\n return os.path.isdir(os.path.join(\n get_data_home(self.dl_params['data_home']),\n self.dl_params['name']\n ))\n return True\n\n def __iter__(self):\n if self._remote:\n data_dir_path = download_dataset(**self.dl_params)\n else:\n data_dir_path = os.path.dirname(__file__)\n yield from self._stream_X_y(data_dir_path)\n","sub_path":"creme/datasets/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":3817,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"509578989","text":"\n# python 3 basic logging tutorial: https://docs.python.org/3/howto/logging.html\n\nimport logging\nimport os\n\nos.system(\"echo '' > test_logging.log\")\n\nlogging.basicConfig(filename='test_logging.log',level=logging.DEBUG)\n\nlogging.debug('this is a debug level message')\nlogger = logging.getLogger()\nlogger.debug('this is debug msg from logger')\nlogger.setLevel(logging.INFO)\nlogger.debug('this is debug msg from logger') # won't print since DEBUG < INFO level\nlogger.info('this is info msg from logger')\n\n\n# load logging config from file, which is more common for application coding\n\nfrom logging.config import fileConfig\n\nfileConfig('test_logging_config.ini')\nlogger = logging.getLogger()\nlogger.debug('hard to see through %s is', 'the future')\n","sub_path":"test_logging.py","file_name":"test_logging.py","file_ext":"py","file_size_in_byte":743,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"305570978","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Oct 5 13:48:56 2021\n\n@author: janeyalex\n\"\"\"\nimport pandas as pd\nimport collections\n\n\nf = open(\"ulysses.txt\",'r')\nl1=[]\nl2=[]\nfor line in f:\n lineword=line.split(':')\n #if not lineword[0].isdigit():\n l1.append(lineword[0])\n l2.append(int(lineword[1])) \nf.close()\n\ndf = pd.DataFrame({'word': l1,'count':l2})\n\n# 5a\ntotalNumWords = df['count'].sum()\nuniqueWords =len(l2)\nfrac = uniqueWords/totalNumWords\n\nfreq= collections.Counter(l2)\n\n# 5b\nn1=freq[1]/sum(freq.values())\nn2=freq[2]/sum(freq.values())\nn3=freq[3]/sum(freq.values())","sub_path":"HW6q5.py","file_name":"HW6q5.py","file_ext":"py","file_size_in_byte":614,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"55925178","text":"\"\"\"\n * Copyright 2020, Departamento de sistemas y Computación,\n * Universidad de Los Andes\n *\n *\n * Desarrolado para el curso ISIS1225 - Estructuras de Datos y Algoritmos\n *\n *\n * This program is free software: you can redistribute it and/or modify\n * it under the terms of the GNU General Public License as published by\n * the Free Software Foundation, either version 3 of the License, or\n * (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 withthis program. If not, see .\n *\n * Contribuciones:\n *\n * Dario Correal - Version inicial\n \"\"\"\n\n\nfrom DISClib.DataStructures.singlelinkedlist import addLast\nimport config as cf\nfrom DISClib.ADT import list as lt\nfrom DISClib.ADT.graph import gr\nfrom DISClib.ADT import map as mp\nfrom DISClib.DataStructures import mapentry as me\nfrom DISClib.Algorithms.Graphs import scc\nfrom DISClib.ADT import orderedmap as om\nfrom DISClib.Algorithms.Graphs import dijsktra as djk\nassert cf\n\n\"\"\"\nSe define la estructura de un catálogo de videos. El catálogo tendrá dos listas, una para los videos, otra para las categorias de\nlos mismos.\n\"\"\"\n\n# Construccion de modelos\ndef newAnalyzer():\n \"\"\" Inicializa el analizador\n\n landing: Tabla de hash para guardar los vertices del grafo\n connections: Grafo para representar las rutas entre landing points\n components: Almacena la informacion de los componentes conectados\n paths: Estructura que almancena los caminos de costo minimo desde un\n vertice determinado a todos los otros vértices del grafo\n \"\"\"\n analyzer = {\n 'landing': None,\n 'cableconections': None,\n 'countries': None,\n 'ids': None,\n 'countriesinfo': None,\n 'capitales': None,\n 'components': None,\n 'paths': None\n }\n\n analyzer['landing'] = mp.newMap(numelements=14000,\n maptype='PROBING')\n\n analyzer['countries'] = mp.newMap(numelements=14000,\n maptype='PROBING')\n\n analyzer['capitales'] = mp.newMap(numelements=14000,\n maptype='PROBING')\n \n analyzer['countriesinfo'] = mp.newMap(numelements=14000,\n maptype='PROBING')\n\n analyzer['ids'] = mp.newMap(numelements=14000,\n maptype='PROBING')\n\n analyzer['cableconections'] = gr.newGraph(datastructure='ADJ_LIST',\n directed=True,\n size=14000,\n comparefunction=compareVertex)\n return analyzer\n\n# Funciones para agregar informacion al catalogo\ndef addLandingPoint(analyzer, service):\n \"\"\"\n Adiciona los landing points al grafo como vertices y arcos entre los\n landing points adyacentes.\n\n Los vertices tienen por nombre el origen y destino\n seguido de el id del cable. Por ejemplo:\n\n 3316-2africa\n\n Si es un cable con una ruta diferente seria: 3316-africa-coast-to-europe-ace\n \"\"\"\n\n origin = \"<\" + service['\\ufefforigin'] + \"-\" + service['cable_id'] + \">\"\n destination = \"<\" + service['destination'] + \"-\" + service['cable_id'] + \">\"\n length = service['cable_length']\n if service['\\ufefforigin'] == service['destination']:\n length = '0.100 km'\n addGraph(analyzer, origin, destination, length)\n addCapital(analyzer, origin, destination, length)\n return analyzer\n\ndef addGraph(analyzer, origen, destino, length):\n if not gr.containsVertex(analyzer['cableconections'],origen):\n gr.insertVertex(analyzer['cableconections'],origen)\n if not gr.containsVertex(analyzer['cableconections'],destino):\n gr.insertVertex(analyzer['cableconections'],destino)\n edge = gr.getEdge(analyzer['cableconections'], origen, destino)\n if edge is None:\n gr.addEdge(analyzer['cableconections'],origen, destino, length)\n return analyzer\n\ndef addCapital(analyzer, origen, destino, length):\n if mp.get(analyzer['landing'], origen) is not None and mp.get(analyzer['landing'], destino) is not None:\n pais_origen = mp.get(analyzer['landing'], origen)['value']['pais']\n print(pais_origen)\n pais_destino= mp.get(analyzer['landing'], destino)['value']['pais']\n info_origen = {'pais': pais_origen,\n 'origen':lt.newList(),\n 'peso': length}\n info_destino = {'pais': pais_destino,\n 'origen':lt.newList(),\n 'peso': length}\n capital_origen = mp.get(analyzer['countriesinfo'], pais_origen)['value']['CapitalName'].lower().strip()\n capital_destino = mp.get(analyzer['countriesinfo'], pais_destino)['value']['CapitalName'].lower().strip()\n entry = mp.get(analyzer['capitales'], capital_origen)\n if entry is None:\n info_origen = {'pais': pais_origen,\n 'origen':lt.newList(),\n 'peso': length}\n mp.put(analyzer['capitales'], capital_origen, info_origen)\n lt.addLast(entry['value']['origen'], origen)\n if entry['value']['peso'] < length:\n entry['value']['peso'] = length\n \n entrada = mp.get(analyzer['capitales'], capital_destino)\n if entrada is None:\n info_destino = {'pais': pais_destino,\n 'origen':lt.newList(),\n 'peso': length}\n mp.put(analyzer['capitales'], capital_destino, info_destino)\n lt.addLast(entrada['value']['origen'], destino)\n if entrada['value']['peso'] < length:\n entrada['value']['peso'] = length\n\ndef conectarCapitales(analyzer):\n llaves = mp.keySet(analyzer['capitales'])\n for key in llaves:\n if mp.get(analyzer['capitales'], key) is not None:\n entry = mp.get(analyzer['capitales'], key)['value']\n for point in entry['origen']:\n if not gr.containsVertex(analyzer['cableconections'],key):\n gr.insertVertex(analyzer['cableconections'],key)\n edge = gr.getEdge(analyzer['cableconections'], key, point)\n if edge is None:\n gr.addEdge(analyzer['cableconections'],key, point, entry['peso'])\n edge1 = gr.getEdge(analyzer['cableconections'], point, key)\n if edge1 is None:\n gr.addEdge(analyzer['cableconections'],point, key, entry['peso'])\n\n #en otro mapa guardar los ids como llaves y como valor el string service[\"origin\"] y service[\"service[cable id\"]\n\n\ndef loadlandings(analyzer, landing):\n entry = mp.get(analyzer['landing'], landing['landing_point_id'])\n pais= landing['name'].split(\",\")\n if len(pais) == 1:\n info = pais[0].strip()\n else:\n info = pais[1].strip()\n idlanding = landing[\"landing_point_id\"]\n if entry is None:\n addinformacion = {'pais':info,\n 'informacion': landing}\n mp.put(analyzer['landing'], landing['landing_point_id'], addinformacion)\n addCountry(analyzer, info , idlanding)\n \ndef addCountry(analyzer, country, idlanding):\n paises = analyzer['countries']\n entry = mp.get(paises, country)\n if entry is None:\n lista = lt.newList()\n lt.addLast(lista, idlanding)\n mp.put(paises, country, lista)\n else:\n lt.addLast(entry['value'], idlanding)\n #para asegurarme que solo este agregando\n #lo cambie a un order map \n\ndef loadCountries(analyzer, country):\n entry = mp.get(analyzer['countriesinfo'], country['CountryName'].lower().strip())\n if entry is None:\n mp.put(analyzer['countriesinfo'], country['CountryName'].lower().strip(), country)\n return analyzer\n\n\n# Funciones para creacion de datos\n\n# Funciones de consulta\n\ndef connected(analyzer):\n \"\"\"\n Calcula los componentes conectados del grafo\n Se utiliza el algoritmo de Kosaraju\n \"\"\"\n analyzer['vertex'] = scc.KosarajuSCC(analyzer['cableconections'])\n return scc.connectedComponents(analyzer['vertex'])\n\ndef landingPoints(analyzer):\n \"\"\"\n Retorna el total de landing points (vertices) del grafo\n \"\"\"\n \n return gr.numVertices(analyzer['cableconections'])\n\n\n\n\ndef totalConnections(analyzer):\n \"\"\"\n Retorna el total arcos del grafo\n \"\"\"\n return gr.numEdges(analyzer['cableconections'])\n\ndef totalCountries(analyzer):\n return mp.size(analyzer['countries'])\n\ndef informacionLanding(analyzer):\n primero = mp.get(analyzer['landing'], '3316')\n informacion = primero['value']['informacion']\n return informacion\n\ndef informacionCountries(analyzer):\n primero = mp.get(analyzer['countriesinfo'], 'chuuk')\n informacion = primero['value']\n return informacion\n\ndef connectedComponents(analyzer):\n \"\"\"\n Calcula los componentes conectados del grafo\n Se utiliza el algoritmo de Kosaraju\n \"\"\"\n analyzer['components'] = scc.KosarajuSCC(analyzer['cableconections'])\n return scc.connectedComponents(analyzer['components'])\n\ndef sameCluster(analyzer, origen, destino):\n adyacentes = gr.adjacents(analyzer['cableconections'],origen)\n relacion = False\n if lt.isPresent(adyacentes, destino):\n relacion = True\n return relacion\n\ndef minimumCostPaths(analyzer, initialStation):\n \"\"\"\n Calcula los caminos de costo mínimo desde la estacion initialStation\n a todos los demas vertices del grafo\n \"\"\"\n analyzer['paths'] = djk.Dijkstra(analyzer['cableconections'], initialStation)\n return analyzer\n\ndef minimumPath(analyzer, origin, destination):\n \"\"\"\n Retorna el camino de costo minimo entre la estacion de inicio\n y la estacion destino\n Se debe ejecutar primero la funcion minimumCostPaths\n \"\"\"\n\n c1 = me.getValue(mp.get(analyzer['countriesinfo'], origin.lower()))['CapitalName']\n c2 = me.getValue(mp.get(analyzer['countriesinfo'], destination.lower()))['CapitalName']\n\n minimumCostPaths(analyzer, c1.lower().strip())\n path = djk.pathTo(analyzer['paths'], c2.lower().strip())\n return path\n\n\n\"\"\"\ndef sameCluster(analyzer, origen, destino):\n vertice1 = gr.containsVertex(analyzer['cableconections'], origen)\n vertice2 = gr.containsVertex(analyzer['cableconections'], destino)\n if vertice1 is False and vertice2 is False:\n return \"0\"\n elif vertice1 is False:\n return \"1\"\n elif vertice2 is False:\n return \"2\"\n else:\n return scc.stronglyConnected(analyzer['cableconections'], origen, destino)\n\"\"\"\n\n# Funciones utilizadas para comparar elementos dentro de una lista\ndef compareVertex(trip1,trip2):\n if (trip1 == trip2['key']):\n return 0\n elif (trip1 > trip2['key']):\n return 1\n else:\n return -1\n #compara las llaves\ndef compareCountries(c1,c2):\n if (c1 == c2):\n return 0\n elif (c1 > c2):\n return 1\n else:\n return -1\n\n# Funciones de ordenamiento\n","sub_path":"App/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":11343,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"193604128","text":"\"\"\" CSS Style for the Div components\r\n@author: Olivier Nogues\r\n\r\n\"\"\"\r\n\r\nimport CssBase\r\n\r\nclass CssDivNoBorder(CssBase.CssCls):\r\n \"\"\" \"\"\"\r\n __style = [\r\n {'attr': 'margin', 'value': '0'},\r\n {'attr': 'padding', 'value': '0'},\r\n {'attr': 'border', 'value': '0'},\r\n {'attr': 'outline', 'value': 'none'},\r\n ]\r\n\r\n\r\nclass CsssDivBoxMargin(CssBase.CssCls):\r\n \"\"\" CSS Style for Div element with a 5 pixel margin \"\"\"\r\n __style = [\r\n {'attr': 'margin', 'value': '5px'},\r\n ]\r\n\r\n\r\nclass CsssDivWhitePage(CssBase.CssCls):\r\n \"\"\" CSS Style for Div element with a 5 pixel margin \"\"\"\r\n __style = [\r\n {'attr': 'border', 'value': '1px solid black'},\r\n {'attr': 'height', 'value': '80%'},\r\n {'attr': 'min-height', 'value': '600px'},\r\n {'attr': 'width', 'value': '400px'},\r\n {'attr': 'box-shadow', 'value': '10px 10px 8px 10px #888888'},\r\n ]\r\n\r\n\r\nclass CssDivBoxCenter(CssBase.CssCls):\r\n \"\"\" CSS Style for a standard Div item \"\"\"\r\n __style = [{'attr': 'width', 'value': '100%'},\r\n {'attr': 'text-align', 'value': 'center'}]\r\n\r\n\r\nclass CssDivBoxWithDotBorder(CssBase.CssCls):\r\n \"\"\" CSS Style for a Div item with a border with dots\"\"\"\r\n __style = [{'attr': 'margin', 'value': '5px'},\r\n {'attr': 'border', 'value': '1px dashed black;'}]\r\n\r\n\r\nclass CssDivBubble(CssBase.CssCls):\r\n \"\"\" \"\"\"\r\n __style = [\r\n {'attr': 'width', 'value': '100px'},\r\n {'attr': 'margin-left', 'value': 'auto'},\r\n {'attr': 'margin-right', 'value': 'auto'},\r\n {'attr': 'height', 'value': '100px'},\r\n {'attr': 'border-radius', 'value': '50%'},\r\n {'attr': 'padding-top', 'value': '30px'},\r\n {'attr': 'text-align', 'value': 'center'}\r\n ]\r\n\r\n\r\nclass CssDivBox(CssBase.CssCls):\r\n \"\"\" CSS Style for a standard Div item \"\"\"\r\n __style = [{'attr': 'width', 'value': '100%'},\r\n {'attr': 'overflow-x', 'value': 'auto'}]\r\n\r\n\r\nclass CssDivLeft(CssBase.CssCls):\r\n \"\"\" CSS Style for a box located on the left \"\"\"\r\n __style = [{'attr': 'float', 'value': 'left'},\r\n {'attr': 'width', 'value': '20%'}]\r\n\r\n\r\nclass CssDivRight(CssBase.CssCls):\r\n \"\"\" CSS Style for a box located on the right \"\"\"\r\n __style = [{'attr': 'float', 'value': 'right'},\r\n {'attr': 'width', 'value': '80%'}]\r\n\r\n\r\nclass CssDivBorder(CssBase.CssCls):\r\n \"\"\" CSS Style for a div element with a black border \"\"\"\r\n __style = [{'attr': 'border', 'value': '1px solid black'}]\r\n\r\n\r\nclass CssDivShadow(CssBase.CssCls):\r\n \"\"\" CSS Style for a div element with a black border \"\"\"\r\n __style = [{'attr': 'box-shadow', 'value': '10px 10px 8px 10px #888888'}]\r\n\r\n\r\nclass CssDivWhitePage(CssBase.CssCls):\r\n \"\"\" CSS Style for a div white page \"\"\"\r\n reqCss = [CssDivShadow, CssDivBorder]\r\n __style = [{'attr': 'height', 'value': '80%'},\r\n {'attr': 'min-height', 'value': '600px'},\r\n {'attr': 'width', 'value': '400px'},\r\n {'attr': 'background-color', 'value': 'white'}\r\n ]\r\n\r\n\r\nclass CssDivBanner(CssBase.CssCls):\r\n \"\"\" CSS Style for the Index Banner \"\"\"\r\n __style = [{'attr': 'height', 'value': '400px'},\r\n {'attr': 'width', 'value': '100%'},\r\n {'attr': 'margin', 'value': '0'},\r\n {'attr': 'overflow-y', 'value': 'auto'},\r\n {'attr': 'margin-top', 'value': '50px'},\r\n {'attr': 'background', 'value': 'linear-gradient(#292B2C, #29b229)'},\r\n {'attr': 'padding', 'value': '10px'},\r\n {'attr': 'color', 'value': 'white'}]\r\n\r\n\r\nclass CssDivSubBanner(CssBase.CssCls):\r\n \"\"\" CSS Style for the Index Banner \"\"\"\r\n __style = [{'attr': 'height', 'value': '400px'},\r\n {'attr': 'width', 'value': '100%'},\r\n {'attr': 'margin', 'value': '0'},\r\n {'attr': 'overflow-y', 'value': 'auto'},\r\n {'attr': 'margin-top', 'value': '50px'},\r\n {'attr': 'background', 'value': 'white'}, ##a4dba4\r\n {'attr': 'padding', 'value': '0'},\r\n {'attr': 'color', 'value': 'black'}]\r\n\r\n\r\nclass CssDivLabelPoint(CssBase.CssCls):\r\n \"\"\" \"\"\"\r\n __style = [\r\n {'attr': 'background', 'value': '#292B2C'},\r\n {'attr': 'padding', 'value': '10px'},\r\n {'attr': 'margin-left', 'value': '5px'},\r\n {'attr': 'border-radius', 'value': '50%'},\r\n {'attr': 'cursor', 'value': 'pointer'},\r\n {'attr': 'display', 'value': 'inline-block'},\r\n ]\r\n chidrenTag = 'label'\r\n\r\n\r\nclass CssDivBox(CssBase.CssCls):\r\n \"\"\" \"\"\"\r\n __style = [\r\n {'attr': 'background', 'value': 'white'},\r\n {'attr': 'margin', 'value': '5px 0 5px 5px'},\r\n {'attr': 'border', 'value': '1px solid green'},\r\n ]\r\n\r\n\r\nclass CssDivCommBubble(CssBase.CssCls):\r\n \"\"\" \"\"\"\r\n\r\n __style = [\r\n {'attr': 'width', 'value': '100%'},\r\n {'attr': 'vertical-align', 'value': 'top'},\r\n\r\n {'attr': 'top', 'value': '0'},\r\n {'attr': 'height', 'value': '100px'},\r\n {'attr': 'margin-bottom', 'value': '20px'},\r\n {'attr': 'margin-left', 'value': '20px'},\r\n {'attr': 'min-height', 'value': '20px'},\r\n {'attr': 'color', 'value': 'white'},\r\n {'attr': 'display', 'value': 'inline-block'},\r\n ]\r\n\r\n before = [\r\n {'attr': 'content', 'value': \"''\"},\r\n {'attr': 'width', 'value': '0'},\r\n {'attr': 'height', 'value': '0'},\r\n {'attr': 'display', 'value': 'inline-block'},\r\n {'attr': 'border', 'value': '15px solid transparent'},\r\n {'attr': 'border-right-color', 'value': '#398438'},\r\n #{'attr': 'border-left-color', 'value': 'red'},\r\n {'attr': 'margin-left', 'value': '-30px'},\r\n #{'attr': 'margin-top', 'value': '5px'}\r\n ]\r\n","sub_path":"ares/Lib/css/CssDiv.py","file_name":"CssDiv.py","file_ext":"py","file_size_in_byte":5513,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"190526","text":"'''DropBox's logs configuration.\n'''\n\n__author__ = 'Miguel Ojeda'\n__copyright__ = 'Copyright 2012, CERN CMS'\n__credits__ = ['Giacomo Govi', 'Salvatore Di Guida', 'Miguel Ojeda', 'Andreas Pfeiffer']\n__license__ = 'Unknown'\n__maintainer__ = 'Miguel Ojeda'\n__email__ = 'mojedasa@cern.ch'\n\n\nimport service\n\n\n# For integration and production, we use the (reader) production dropBox database\nif service.settings['productionLevel'] in set(['int', 'pro']):\n connectionDictionary = service.secrets['dropBoxConnections']['pro']\n\n# For development, we use the prep dropBox database\nelif service.settings['productionLevel'] in set(['dev']):\n connectionDictionary = service.secrets['dropBoxConnections']['dev']\n\n# In private instances, we take connections from netrc\nelif service.settings['productionLevel'] in set(['private']):\n connectionDictionary = service.getConnectionDictionaryFromNetrc('dropBoxDatabase')\n\nelse:\n raise Exception('Unknown production level.')\n\n\ndef getBackendOldThreshold(backend):\n\n backendsOldThreshold = {\n # Online and offline backends run every 30 seconds, so 60 seconds should note a problem\n 'online': 60,\n 'offline': 60,\n\n # Tier0 backend runs at 10-min boundaries, so 20 minutes should note a problem\n 'tier0': 20 * 60,\n\n # Private instances run every 10 seconds, so 20 seconds should note a problem\n 'private': 20,\n }\n\n if backend in backendsOldThreshold:\n return backendsOldThreshold[backend]\n\n return backendsOldThreshold['private']\n\n","sub_path":"logs/dropBox/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":1536,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"184319450","text":"import tqdm\nimport sys\nimport torch\nfrom losses import gaussian_log_likelihood\nfrom utils import iterate_minibatches\n\n\ndef custom_pbar(iterable, total):\n for i, item in enumerate(iterable):\n sys.stdout.write('%d/%d \\r' % (i, total))\n if i < total:\n yield item\n else:\n sys.stdout.write('\\n')\n break\n\n\ndef train_regressor(model, iters=2000, batchsize=100,\n resample=False, optimizer=None,\n log_likelihood=gaussian_log_likelihood):\n X = (model.X - model.mx)*model.iSx\n Y = (model.Y - model.my)*model.iSy\n N = X.shape[0]\n M = batchsize\n\n if optimizer is None:\n params = filter(lambda p: p.requires_grad, model.parameters())\n optimizer = torch.optim.Adam(params, 1e-3)\n\n pbar = tqdm.tqdm(enumerate(iterate_minibatches(X, Y, M)), total=iters)\n\n for i, batch in pbar:\n x, y = batch\n model.zero_grad()\n outs = model(x, normalize=False, resample=resample)\n Enlml = -log_likelihood(y, *outs).mean()\n loss = Enlml + model.regularization_loss()/N\n loss.backward()\n optimizer.step()\n pbar.set_description('log-likelihood of data: %f' % (-Enlml))\n if i == iters:\n pbar.close()\n break\n","sub_path":"prob_mbrl/core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":1285,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"510820376","text":"from sqlalchemy import *\nfrom migrate import *\n\n\nfrom migrate.changeset import schema\npre_meta = MetaData()\npost_meta = MetaData()\nconstraints = Table('constraints', post_meta,\n Column('id', Integer, primary_key=True, nullable=False),\n Column('constraint1_flt', Float(precision=20)),\n Column('constraint2_bool', Boolean),\n Column('constraint3_str', String(length=80)),\n Column('constraint4_sel', String(length=80)),\n Column('constraint5_selmult', String(length=80)),\n Column('constraint6_txtarea', String(length=200)),\n Column('constraint7_decimal', Float),\n Column('createddate', DateTime),\n)\n\n\ndef upgrade(migrate_engine):\n # Upgrade operations go here. Don't create your own engine; bind\n # migrate_engine to your metadata\n pre_meta.bind = migrate_engine\n post_meta.bind = migrate_engine\n post_meta.tables['constraints'].create()\n\n\ndef downgrade(migrate_engine):\n # Operations to reverse the above upgrade go here.\n pre_meta.bind = migrate_engine\n post_meta.bind = migrate_engine\n post_meta.tables['constraints'].drop()\n","sub_path":"FlaskTest/db_repository/versions/002_migration.py","file_name":"002_migration.py","file_ext":"py","file_size_in_byte":1077,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"632575709","text":"from pydantic import BaseSettings, EmailStr\n\n\nclass Settings(BaseSettings):\n ENV: str = \"dev\"\n SENTRY_DSN: str = None\n API_KEY: str = \"you-will-want-something-safe-here\"\n DEFAULT_EMAIL_ADDRESS: EmailStr = \"default@email.com\"\n SENDGRID_API_KEY: str = \"get_your_api_key_from_sendgrid_dashboard\"\n MAILJET_API_KEY: str = \"get_your_api_key_from_mailjet_dashboard\"\n MAILJET_API_SECRET: str = \"get_your_api_secret_from_mailjet_dashboard\"\n\n class Config:\n env_file = \".env\"\n case_sensitive = True\n\n\nsettings = Settings()\n","sub_path":"app/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":551,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"139191599","text":"import sys\nimport command\nfrom util_usr import receive_msg, get_pos_param\nfrom param_id_list import param_dic as dic\ndef get_param(mavfile):\n while not command.end_entered():\n option = raw_input('Choose an option (1. parameter by ID, 2. parameter by name, 3. all GPS parameters): ')\n if option == 'end':\n sys.exit()\n else:\n try:\n option = int(option)\n except:\n print('Input has to be an integer from the option list')\n pass\n if option == 1:\n param_id = int(raw_input('Type in ID: '))\n name, value, data = receive_msg(mavfile, 'PARAM_VALUE', True, param_id = param_id)\n print('----------\\n' + value[0] + ': ' + str(value[1]) + '\\n' + '----------')\n elif option == 2 or option == '':\n param_name = raw_input('Type in parameter name: ')\n inv_dic = {v: k for k, v in dic.items()}\n if '_' not in param_name:\n param_name_splitted = param_name.split()\n param_name = ''\n for word in param_name_splitted:\n param_name += word.upper()+'_'\n param_name = param_name[:-1] #removing the last _ from the string \n print('Changed input to: ' + param_name)\n else:\n pass\n param_id = int(inv_dic[param_name])\n name, value, data = receive_msg(mavfile, 'PARAM_VALUE', True, param_id = param_id)\n print('----------\\n' + value[0] + ': ' + str(value[1]) + '\\n' + '----------')\n elif option == 3:\n get_pos_param(mavfile, 'print')\n\n else: \n print('Option not defined')\n\n\n\ncmd_get_param = command.cmd('get_param', get_param)\ncmd_get_param.execute_cmd()\n","sub_path":"pyscripts/modules/cmd_get_param.py","file_name":"cmd_get_param.py","file_ext":"py","file_size_in_byte":1887,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"562300601","text":"from proteus import *\nfrom proteus.default_n import *\nfrom wigley import *\nfrom twp_navier_stokes_p import *\n\ntimeIntegration = BackwardEuler_cfl\nstepController = FixedStep\n \nfemSpaces = {0:C0_AffineLinearOnSimplexWithNodalBasis,\n 1:C0_AffineLinearOnSimplexWithNodalBasis,\n 2:C0_AffineLinearOnSimplexWithNodalBasis,\n 3:C0_AffineLinearOnSimplexWithNodalBasis}\n\nelementQuadrature = SimplexGaussQuadrature(nd,quad_order)\nelementBoundaryQuadrature = SimplexGaussQuadrature(nd-1,quad_order)\n\nsubgridError = NavierStokesASGS_velocity_pressure_opt(coefficients,nd,lag=False,delayLagSteps=1,hFactor=1.0)\n\nnumericalFluxType = NavierStokes_Advection_DiagonalUpwind_Diffusion_SIPG_exterior #need weak for parallel and global conservation\n\nmassLumping = False\n\nshockCapturing = NavierStokes_SC_opt(coefficients,nd,ns_shockCapturingFactor,lag=False)\n\n\nmultilevelNonlinearSolver = NewtonNS\nlevelNonlinearSolver = NewtonNS\n\nmaxNonlinearIts = 10\nmaxLineSearches = 0\n\nnonlinearSmoother = None\n\nfullNewtonFlag = True\n\ntolFac = 1e-3\n\nnl_atol_res = 0.0\n\nmatrix = SparseMatrix\n\nmultilevelLinearSolver = PETSc\nlevelLinearSolver = PETSc\nlinear_solver_options_prefix = 'rans2p_'\nlinearSmoother=None\n\nnonlinearSolverConvergenceTest = 'rits'\nlevelNonlinearSolverConvergenceTest = 'rits'\n\nlinTolFac = 0.001\n\n","sub_path":"cases/wigleyRBLES/twp_navier_stokes_n.py","file_name":"twp_navier_stokes_n.py","file_ext":"py","file_size_in_byte":1338,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"74254414","text":"# from torch.utils.data import Dataset\nfrom base.base_data_loader import BaseDataLoader\nimport numpy as np\nimport torch\nimport os\nfrom random import randint\nfrom utils.util import prepare_dataset, pol2cart\nfrom scipy.signal import stft, resample\nfrom matplotlib import pyplot as plt\n\nNOISE_SAMPLES = 100\n\n\nclass SeismicDatasetLoader(BaseDataLoader):\n def __init__(self, root_dir, signal_dir, noise_dir, transform=None):\n self.root_dir = root_dir\n self.signal_dir = signal_dir\n self.noise_dir = noise_dir\n self.transform = transform\n\n assert os.path.exists(os.path.join(self.root_dir, self.signal_dir)), 'Path to signal images cannot be found'\n assert os.path.exists(os.path.join(self.root_dir, self.noise_dir)), 'Path to noise images cannot be found'\n\n self.signal = sorted([os.path.join(self.root_dir, signal_dir, file) for file in\n os.listdir(os.path.join(self.root_dir, self.signal_dir))\n if file.endswith('.npz')]) # and np.isin(int(file[0:4]), self.idx_list)])\n self.noise = sorted([os.path.join(self.root_dir, noise_dir, file) for file in\n os.listdir(os.path.join(self.root_dir, self.noise_dir))\n if file.endswith('.npz')]) # and np.isin(int(file[0:4]), self.idx_list)])\n\n def __len__(self):\n return len(self.signal)\n\n def __getitem__(self, item):\n if torch.is_tensor(item):\n item = item.tolist()\n\n signal = np.load(self.signal[item], allow_pickle=True)['arr_0']\n noise = np.load(self.noise[randint(0, item)], allow_pickle=True)['arr_0']\n\n signal = resample(signal, 25500)\n processed = prepare_dataset(noise, signal)\n processed = resample(processed, len(signal))\n noise = resample(noise, len(signal))\n\n sample = {'signal': signal, 'noise': noise, 'processed': processed}\n\n f, t, Zxx_processed = stft(processed)\n _, _, Zxx_signal = stft(signal)\n _, _, Zxx_noise = stft(noise)\n\n Zxx_processed = pol2cart(np.abs(Zxx_processed), np.angle(Zxx_processed))\n Zxx_signal = pol2cart(np.abs(Zxx_signal), np.angle(Zxx_signal))\n Zxx_noise = pol2cart(np.abs(Zxx_noise), np.angle(Zxx_noise))\n\n Zxx_signal[0] += 1e-10\n Zxx_signal[1] += 1e-10\n\n Zxx_processed = resample(Zxx_processed, 31, axis=1)\n Zxx_signal = resample(Zxx_signal, 31, axis=1)\n Zxx_noise = resample(Zxx_noise, 31, axis=1)\n f = resample(f, 31)\n\n # Ms\n signal_mask = 1 / (1 + np.abs(np.sqrt(Zxx_noise[0] ** 2 + Zxx_noise[1] ** 2)) / np.abs(\n np.sqrt(Zxx_signal[0] ** 2 + Zxx_signal[1] ** 2)))\n\n # Mn\n noise_mask = (np.abs(np.sqrt(Zxx_noise[0] ** 2 + Zxx_noise[1] ** 2)) / np.abs(\n np.sqrt(Zxx_signal[0] ** 2 + Zxx_signal[1] ** 2))) / (\n 1 + np.abs(np.sqrt(Zxx_noise[0] ** 2 + Zxx_noise[1] ** 2)) / np.abs(\n np.sqrt(Zxx_signal[0] ** 2 + Zxx_signal[1] ** 2)))\n\n stft_dict = {'f': f, 't': t, 'Zxx_signal': Zxx_signal, 'Zxx_processed': Zxx_processed}\n\n if self.transform:\n sample = self.transform(sample)\n\n return sample, stft_dict, noise_mask, signal_mask\n","sub_path":"SeismicSignalDenoising/data_loader/data_loaders.py","file_name":"data_loaders.py","file_ext":"py","file_size_in_byte":3288,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"55967252","text":"# dataframe\nimport numpy\nimport pandas\nmyarray = numpy.array([[1, 2, 3], [4, 5, 6]])\nrownames = ['a', 'b']\ncolnames = ['one', 'two', 'three']\nmydataframe = pandas.DataFrame(myarray, index=rownames, columns=colnames)\n#print(mydataframe)\n\n# Load CSV using Pandas from URL\nfrom pandas import read_csv\nurl = \"https://goo.gl/vhm1eU\"\nnames = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']\ndata = read_csv(url, names=names)\nprint(data.shape)\n\ndescription = data.describe()\nprint(description)\n","sub_path":"OLD/MlTest1/test1a.py","file_name":"test1a.py","file_ext":"py","file_size_in_byte":511,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"315545792","text":"\n\n\n\n\"\"\"\n\nTrees\n\nBST\n\"\"\"\n\n\nclass Node:\n def __init__(self, value):\n self.left = None\n self.data = value\n self.right = None\n self.parent = None\n self.height = 0\n\n#self balancing binary search tree\nclass BinarySearchTree:\n\n def __init__(self, value):\n self.root = Node(value)\n\n def insert(self, value, root:Node):\n #this has to be a recursive function\n\n #we might be adding the first value for this subtree - so root will be null\n #so we just have to add a new node here - this is also used for adding a new left child or right child\n if root == None:\n return Node(value)\n #value can be greater than given root\n #recursive call on right child - that will return whatever the balanced subtree right child is\n if value > root.data:\n root.right = self.insert(value, root.right)\n root.right.parent = root\n else:\n root.left = self.insert(value, root.left)\n root.left.parent = root\n #value can be smaller than given root\n #recursive call on left child - that will return whatever the balanced subtree left child is\n\n #so at this point we should have the root, the left.child and the right.child and our parent is expecting the 'root' to be returned\n\n #we just added a child to the current 'root' - so recalculate it's height\n\n #call height\n root.height = self.height(root)\n\n #check balance for this node\n balance = self.height(root.left) - self.height(root.right)\n #height of left child\n #height of right child\n #get difference\n\n #4 cases\n #imbalance can be in\n #right subtree of right child\n #left subtree of right child\n #right subtree of left child\n #left subtree of left child\n\n #left child and if value is > leftchild.value - then right subtree of left child - leftRight rotate\n #if we have a new root - return that to teh recursive parent waiting for the 'root'\n if balance > 1 and value < root.left.data:\n return self.rightRotate(root)\n\n if balance > 1 and value > root.left.data:\n root.left = self.leftRotate(root.left)\n return self.rightRotate(root)\n\n if balance < -1 and value > root.right.data:\n return self.leftRotate(root)\n\n if balance < -1 and value < root.right.data:\n root.right = self.rightRotate(root.right)\n return self.leftRotate(root)\n #similarly we have other cases\n\n #this function has to return the root because the root of the tree/subtree can change\n return root\n\n\n def leftRotate(self, node:Node):\n print(\"leftrotate\")\n temp = node.right\n node.right = temp.left\n if temp.left != None:\n temp.left.parent = node\n temp.left = node\n temp.parent = node.parent\n node.parent = temp\n node.height =self.height(node)\n temp.height = self.height(temp)\n return temp\n\n def rightRotate(self, node:Node):\n print(\"rightRotate\")\n temp = node.left\n node.left = temp.right\n temp.parent = node.parent\n temp.right = node\n node.parent = temp\n node.height = self.height(node)\n temp.height = self.height(temp)\n return temp\n\n def height(self, node: Node):\n if node == None:\n return -1\n else:\n height = 0\n if node.left == None and node.right == None:\n node.height = 0\n return 0\n else:\n if node.left != None:\n height = node.left.height\n\n if node.right != None:\n if node.right.height > height:\n height = node.right.height\n return height + 1\n\n def contain(self,value,node:Node):\n\n print(\"Current node is \", node.data)\n if node.data == value:\n print(\"Value found!\")\n return\n\n if value < node.data:\n if node.left != None:\n self.contain(value, node.left)\n else:\n print(\"Value not found !\")\n return\n else:\n if node.right != None:\n self.contain(value, node.right)\n else:\n print(\"Value not found !\")\n return\n\n return\n\n\n def inOrderTraverse(self, node: Node):\n #first process left node\n if node.left != None:\n self.inOrderTraverse(node.left)\n\n #then process root node\n print(node.data, \"(\", node.height, \")\", end=\" - \")\n\n #then process right node\n if node.right != None:\n self.inOrderTraverse(node.right)\n\n return\n\n def postOrderTraverse(self, root: Node):\n #left - right - root\n\n if root == None:\n return\n\n self.postOrderTraverse(root.left)\n\n self.postOrderTraverse(root.right)\n\n print(root.data, \"(\", root.height, \")\", end=\" - \")\n\n return\n\n\n def preOrderTraverse(self, root: Node):\n #root - left - right\n\n if root == None:\n return\n\n print(root.data, \"(\", root.height, \")\", end=\" - \")\n\n self.preOrderTraverse(root.left)\n\n self.preOrderTraverse(root.right)\n\n return\n\ninput_array = [53, 47, 89, 71, 32, 18, 22, 51]\nnewtree = BinarySearchTree(15)\nfor x in input_array:\n print(\"\")\n print(\"Adding\", x)\n newtree.root = newtree.insert(x, newtree.root)\n\nnewtree.contain(71, newtree.root)\nnewtree.contain(32, newtree.root)\nnewtree.contain(99, newtree.root)\nprint(\"inOrderTraverse\")\nnewtree.inOrderTraverse(newtree.root)\nprint(\"\\npostOrderTraverse\")\nnewtree.postOrderTraverse(newtree.root)\nprint(\"\\npreOrderTraverse\")\nnewtree.preOrderTraverse(newtree.root)\n\n","sub_path":"practice_8.py","file_name":"practice_8.py","file_ext":"py","file_size_in_byte":5901,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"578617885","text":"\"\"\"\nDjango settings for liwi project.\n\nFor more information on this file, see\nhttps://docs.djangoproject.com/en/1.6/topics/settings/\n\nFor the full list of settings and their values, see\nhttps://docs.djangoproject.com/en/1.6/ref/settings/\n\"\"\"\n\n# Build paths inside the project like this: os.path.join(BASE_DIR, ...)\nimport os\nimport sys\nBASE_DIR = os.path.dirname(os.path.dirname(__file__))\n\n\n# Quick-start development settings - unsuitable for production\n# See https://docs.djangoproject.com/en/1.6/howto/deployment/checklist/\n\n# SECURITY WARNING: keep the secret key used in production secret!\nSECRET_KEY = '69)jz9t9^kvci2_8^j732h%a*8yz4h-(kehld1@e2jcw_d9wkn'\n\nAWS_ACCESS_KEY_ID = 'AKIAJEYC7OVXICWHIFMA'\nAWS_SECRET_ACCESS_KEY = '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'\nAWS_SES_REGION_NAME = 'us-west-2'\nAWS_SES_REGION_ENDPOINT = 'email.us-west-2.amazonaws.com'\n\nTESTING = 'test' in sys.argv\n\nDEBUG = True\nTEMPLATE_DEBUG = True\n\n\n# Application definition\n\nINSTALLED_APPS = (\n 'django.contrib.admin',\n 'django.contrib.auth',\n 'django.contrib.contenttypes',\n 'django.contrib.sessions',\n 'django.contrib.messages',\n 'django.contrib.staticfiles',\n 'south',\n 'registration',\n 'authentication',\n 'home',\n 'user_profile',\n 'art',\n 'artists',\n 'cart',\n)\n\nMIDDLEWARE_CLASSES = (\n 'django.contrib.sessions.middleware.SessionMiddleware',\n 'django.middleware.common.CommonMiddleware',\n 'django.middleware.csrf.CsrfViewMiddleware',\n 'django.contrib.auth.middleware.AuthenticationMiddleware',\n 'django.contrib.messages.middleware.MessageMiddleware',\n 'django.middleware.clickjacking.XFrameOptionsMiddleware',\n 'django.contrib.sessions.middleware.SessionMiddleware',\n)\n\nROOT_URLCONF = 'liwi.urls'\n\nWSGI_APPLICATION = 'liwi.wsgi.application'\n\n\n# Database\n# https://docs.djangoproject.com/en/1.6/ref/settings/#databases\n\nDATABASES = {\n 'default': {\n 'ENGINE': 'django.db.backends.postgresql_psycopg2',\n 'NAME': 'liwi',\n 'USER': 'kshafer',\n 'PASSWORD': 'Rubygem14',\n 'HOST': 'liwi-development.c2mw5t19dwbx.us-west-2.rds.amazonaws.com',\n 'PORT': '5432',\n },\n 'liwi_test': {\n 'ENGINE': 'django.db.backends.postgresql_psycopg2',\n 'NAME': 'liwi_test',\n 'USER': 'kshafer',\n 'PASSWORD': 'Rubygem14',\n 'HOST': 'liwi-development.c2mw5t19dwbx.us-west-2.rds.amazonaws.com',\n 'PORT': '5432',\n }\n}\n\n# Internationalization\n# https://docs.djangoproject.com/en/1.6/topics/i18n/\n\nLANGUAGE_CODE = 'en-us'\n\nTIME_ZONE = 'UTC'\n\nUSE_I18N = True\n\nUSE_L10N = True\n\nUSE_TZ = True\n\n\n# Static files (CSS, JavaScript, Images)\n# https://docs.djangoproject.com/en/1.6/howto/static-files/\n\nSTATIC_URL = '/static/'\n\nSTATICFILES_DIRS = (\n os.path.join(BASE_DIR, \"static\"),\n)\n\nTEMPLATE_DIRS = [os.path.join(BASE_DIR, 'templates')]\n\nAUTH_USER_MODEL = 'registration.User'\n\nLOGIN_URL = '/login'\n\nSITE_ROOT = '/home/ubuntu/liwi-site'\n\nif TESTING:\n MEDIA_ROOT = '/home/ubuntu/liwi-site/test_photos/'\n MEDIA_URL = '/test_photos/'\nelse:\n MEDIA_ROOT = '/home/ubuntu/liwi-site/photos/'\n MEDIA_URL = '/photos/'\n\n#if testing, send messages with a file based backend\n#NOTE: the run_tests script will create and tear down the mail directory\nif TESTING:\n EMAIL_BACKEND = 'django.core.mail.backends.filebased.EmailBackend'\n EMAIL_FILE_PATH = 'mail/' \nelse:\n EMAIL_BACKEND = 'django_ses.SESBackend'\n\nLOGGING = {\n 'version': 1,\n 'disable_existing_loggers': True,\n 'formatters': {\n 'standard': {\n 'format' : \"[%(asctime)s] %(levelname)s [%(name)s:%(lineno)s] %(message)s\",\n 'datefmt' : \"%d/%b/%Y %H:%M:%S\"\n },\n },\n 'handlers': {\n 'null': {\n 'level':'DEBUG',\n 'class':'django.utils.log.NullHandler',\n },\n 'logfile': {\n 'level':'DEBUG',\n 'class':'logging.handlers.RotatingFileHandler',\n 'filename': SITE_ROOT + \"/logfile.txt\",\n 'maxBytes': 50000,\n 'backupCount': 2,\n 'formatter': 'standard',\n },\n 'console':{\n 'level':'INFO',\n 'class':'logging.StreamHandler',\n 'formatter': 'standard'\n },\n },\n 'loggers': {\n 'django': {\n 'handlers':['console'],\n 'propagate': True,\n 'level':'WARN',\n },\n 'django.db.backends': {\n 'handlers': ['console'],\n 'level': 'DEBUG',\n 'propagate': False,\n },\n 'liwi': {\n 'handlers': ['console', 'logfile'],\n 'level': 'DEBUG',\n },\n }\n}\n","sub_path":"settings/development.py","file_name":"development.py","file_ext":"py","file_size_in_byte":4624,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"452725530","text":"import pexpect, os\n\nfrom cli_toolkit.vivado.cmdResult import VivadoCmdResult\nfrom cli_toolkit.vivado.config import VivadoConfig\nfrom cli_toolkit.vivado.tcl import VivadoTCL\n\n\ndef mkPackageIp(verdor, user, name, version):\n return ':'.join([verdor, user, name, version])\n\n\n \nclass VivadoCntrl():\n def __init__(self, execFile=VivadoConfig.getExec(), deleteLogsOnExit=True, timeout=6 * 60 * 60, logComunication=False):\n self.execFile = execFile\n self.proc = None\n self.jurnalFile = \"vivado.jou\"\n self.logFile = 'vivado.log'\n self.verbose = True\n self.timeout = timeout\n self.guiOpened = False\n self.logComunication = logComunication\n self.encoding = 'ASCII'\n \n def __enter__(self):\n cmd = [\"-mode\", 'tcl' , \"-notrace\"]\n if self.verbose:\n cmd.append('-verbose')\n self.proc = pexpect.spawn(self.execFile, cmd)\n self.firstCmd = True\n return self\n \n def __exit__(self, exc_type, exc_val, exc_tb):\n p = self.proc\n if p.isalive():\n p.sendline(VivadoTCL.exit())\n p.expect(\"exit\", timeout=self.timeout) # block while cmd ends\n if p.isalive():\n p.terminate()\n \n def openGui(self):\n \"\"\"\n @attention: this method disconnects controller and opens gui\n \"\"\" \n list(self.process([VivadoTCL.start_gui()])) # list to execute because process() is generator\n if self.proc.isalive():\n self.proc.wait()\n \n \n def _process(self, cmds):\n p = self.proc\n for cmd in cmds:\n if self.firstCmd:\n p.expect(\"Vivado%\", timeout=self.timeout) # block while command line init\n self.firstCmd = False\n if self.guiOpened:\n raise Exception(\"Controller have no acces to Vivado because gui is opened\")\n \n p.sendline(cmd)\n # @attention: there is timing issue in reading from tty next command returns corrupted line \n p.readline() # read cmd from tty\n # p.expect(cmd, timeout=self.timeout) \n if cmd == VivadoTCL.start_gui():\n self.guiOpened = True\n try:\n p.expect(\"Vivado%\", timeout=self.timeout) # block while cmd ends\n except pexpect.EOF:\n pass\n t = p.before.decode(self.encoding)\n if self.logComunication:\n print(cmd)\n print(t)\n res = VivadoCmdResult.fromStdoutStr(cmd, t)\n res.raiseOnErrors()\n yield res\n def process(self, cmds, iterator=False):\n \"\"\"\n @attention: if iterator == True you must iterate trough it to execute commands, \n this is how python generator works\n @param iterator: return iterator over cmd results \n \"\"\"\n results = self._process(cmds)\n if iterator:\n return results\n else:\n return list(results)\n def rmLogs(self):\n if os.path.exists(self.logFile):\n os.remove(self.logFile)\n if os.path.exists(self.jurnalFile):\n os.remove(self.jurnalFile) \n \nif __name__ == \"__main__\":\n import os\n with VivadoCntrl() as v: \n _op, _pwd, _dir = v.process(['open_project /home/nic30/Documents/vivado/Sprobe10_board_test/Sprobe10_board_test.xpr', 'pwd', 'dir'])\n print(_op.resultText)\n ls = os.listdir(_pwd.resultText)\n vivadoLs = _dir.resultText.split()\n ls.sort()\n vivadoLs.sort()\n print(ls)\n print(vivadoLs)\n v.openGui()\n print('finished')\n","sub_path":"cli_toolkit/vivado/controller.py","file_name":"controller.py","file_ext":"py","file_size_in_byte":3681,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"621445995","text":"import logging\nimport numpy as np\nfrom PyQt5 import QtWidgets\nfrom matplotlib.figure import Figure\nfrom matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas\nfrom .Logger import log_timing\n__all__ = ('VPICCanvas',)\n\nclass VPICCanvas(FigureCanvas):\n def __init__(self, parent=None, width=5, height=4, dpi=100):\n\n # Logging\n self.logger = logging.getLogger('vpic.canvas')\n\n # Figure\n self.fig = Figure(figsize=(width, height), dpi=dpi)\n self.axes = self.fig.subplots(2, 3,\n gridspec_kw={'width_ratios': [1, 16, 4],\n 'height_ratios': [4, 1],\n 'wspace': 0.05,\n 'hspace': 0.05,\n 'left': 0.12,\n 'right': 0.9,\n 'bottom': 0.1,\n 'top': 0.93})\n\n # Delete un-needed axes.\n self.axes[1, 0].remove()\n self.axes[1, 2].remove()\n\n # Share axes.\n self.axes[0, 1].get_shared_x_axes().join(self.axes[0, 1],\n self.axes[1, 1])\n self.axes[0, 1].get_shared_y_axes().join(self.axes[0, 1],\n self.axes[0, 2])\n\n FigureCanvas.__init__(self, self.fig)\n self.setParent(parent)\n FigureCanvas.setSizePolicy(self,\n QtWidgets.QSizePolicy.Expanding,\n QtWidgets.QSizePolicy.Expanding)\n FigureCanvas.updateGeometry(self)\n\n self.dataset = np.empty([0, 0, 0, 0])\n self.title = 'null'\n self.grid = [np.zeros(1)]*4\n self.slicer = [slice(0, 1)]*4\n self.xaxis = 0\n self.yaxis = 1\n self.vline = None\n self.hline = None\n self.image = None\n self.data = np.empty([0, 0, 0, 0])\n self.data_stale = True\n self.cbar = None\n\n @log_timing\n def read_data(self):\n \"\"\"Read and cache the 2D slice. By caching, we speed up lineouts.\"\"\"\n if self.dataset.size > 0:\n slicer_xy = self.slicer.copy()\n slicer_xy[self.xaxis] = slice(None)\n slicer_xy[self.yaxis] = slice(None)\n self.data = self.dataset[tuple(slicer_xy)]\n else:\n self.data = np.empty([0, 0, 0, 0])\n self.data_stale = True\n\n def set_slice(self, axis, index):\n \"\"\"Set the slice indicies for the line-outs.\"\"\"\n # Data coordinate.\n if isinstance(index, float):\n index = np.searchsorted(self.grid[axis], index)\n index = max(0, index-1)\n\n # Exact index.\n self.slicer[axis] = slice(index, index+1)\n\n def clear(self):\n \"\"\"Clear existing plots.\"\"\"\n self.axes[0, 0].cla()\n self.axes[0, 1].cla()\n self.axes[1, 1].cla()\n self.axes[0, 2].cla()\n\n def draw_2d(self, extent):\n \"\"\"Main 2D plotting. We only need to redraw this if the underlying\n data has changed.\"\"\"\n\n # Grab the data.\n squeeze_axis = [0, 1, 2, 3]\n squeeze_axis.remove(self.xaxis)\n squeeze_axis.remove(self.yaxis)\n if self.data.size > 0:\n data_xy = np.squeeze(self.data, axis=tuple(squeeze_axis))\n if self.xaxis < self.yaxis:\n data_xy = data_xy.T\n else:\n data_xy = np.zeros((1, 1))\n\n if self.image is not None:\n # Just update the underlying data. We do this because if users\n # update preferences (colormap, interpolation, etc.) through the\n # GUI it will be perserved across updates.\n self.image.set_data(data_xy)\n self.image.set_extent(extent)\n self.image.autoscale()\n\n else:\n # Draw the initial plot.\n self.image = self.axes[0, 1].imshow(data_xy,\n origin='lower',\n aspect='auto',\n extent=extent)\n\n # Colobar construction\n self.cbar = self.fig.colorbar(self.image, cax=self.axes[0, 0])\n\n if self.cbar is not None:\n self.cbar.ax.tick_params(axis='y',\n right=False, labelright=False,\n left=True, labelleft=True)\n\n # Set tickparams.\n self.axes[0, 1].tick_params(axis='both',\n right=False, labelright=False,\n left=False, labelleft=False,\n bottom=False, labelbottom=False,\n top=False, labeltop=False)\n\n @log_timing\n def draw_plots(self, rescale=False):\n \"\"\"Draw plots for the dataset and slices.\"\"\"\n if self.dataset.size > 0:\n\n xgrid = self.grid[self.xaxis]\n ygrid = self.grid[self.yaxis]\n xslice = xgrid[self.slicer[self.xaxis]][0]\n yslice = ygrid[self.slicer[self.yaxis]][0]\n\n slicer_x = [slice(None)]*4\n slicer_x[self.yaxis] = self.slicer[self.yaxis]\n\n slicer_y = [slice(None)]*4\n slicer_y[self.xaxis] = self.slicer[self.xaxis]\n\n # Assumes these should be 1D, but is better than squeeze due\n # to implementation inconsistency in squeezing memmaps.\n data_x = self.data[tuple(slicer_x)].flatten()\n data_y = self.data[tuple(slicer_y)].flatten()\n\n else:\n data_x = np.zeros(2)\n data_y = np.zeros(2)\n xgrid = np.atleast_1d([0, 1])\n ygrid = np.atleast_1d([0, 1])\n xslice = 0\n yslice = 0\n\n\n # Save the current limits.\n if rescale:\n xlim = xgrid[[0, -1]]\n ylim = ygrid[[0, -1]]\n else:\n xlim = self.axes[0, 1].get_xlim()\n ylim = self.axes[0, 1].get_ylim()\n\n # If stale, redraw the image.\n if self.data_stale:\n self.draw_2d([xgrid[0], xgrid[-1], ygrid[0], ygrid[-1]])\n self.data_stale = False\n\n # Construct or move the cursors.\n if self.vline is None:\n self.vline = self.axes[0, 1].axvline(xslice, c='0.2')\n else:\n self.vline.set_data([xslice, xslice], [0, 1])\n\n if self.hline is None:\n self.hline = self.axes[0, 1].axhline(yslice, c='0.2')\n else:\n self.hline.set_data([0, 1], [yslice, yslice])\n\n # Plot the lineouts.\n self.axes[0, 2].cla()\n self.axes[1, 1].cla()\n self.axes[0, 2].plot(data_y, ygrid, c='0.2', scaley=False)\n self.axes[1, 1].plot(xgrid, data_x, c='0.2', scalex=False)\n\n # Set limits\n self.axes[0, 1].set_xlim(*xlim)\n self.axes[0, 1].set_ylim(*ylim)\n\n # Set labels\n labels = ['t', 'z', 'y', 'x']\n self.axes[0, 2].set_ylabel(rf'${labels[self.yaxis]}$', fontsize='x-large')\n self.axes[0, 2].yaxis.set_label_position('right')\n self.axes[0, 2].yaxis.label.set_rotation(0)\n self.axes[0, 2].yaxis.labelpad = 10\n self.axes[1, 1].set_xlabel(rf'${labels[self.xaxis]}$', fontsize='x-large')\n\n # Set title\n indices = list(range(4))\n indices.remove(self.xaxis)\n indices.remove(self.yaxis)\n\n slice_labels = [rf'${labels[i]} = {self.grid[i][self.slicer[i]][0]:.4g}$'\n for i in indices]\n title = self.title + ': ' + ', '.join(slice_labels)\n self.axes[0, 1].set_title(title, size='large')\n\n # Set tickparams\n self.axes[0, 2].tick_params(axis='both',\n right=True, labelright=True,\n left=False, labelleft=False,\n bottom=False, labelbottom=False,\n top=True, labeltop=True)\n\n self.axes[1, 1].tick_params(axis='both',\n right=False, labelright=False,\n left=True, labelleft=True,\n bottom=True, labelbottom=True,\n top=False, labeltop=False)\n\n self.draw()\n","sub_path":"pyvpic/viewer/Canvas.py","file_name":"Canvas.py","file_ext":"py","file_size_in_byte":8443,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"566379553","text":"import math\n\ndef fibonacci():\n x, y = 0, 1\n while True:\n x, y = y, x + y\n yield x\n return\n\ndef run():\n i = 1\n fib = fibonacci()\n while True:\n v = next(fib)\n if math.floor(math.log10(v)+1) >= 1000:\n break\n i += 1\n return i\n","sub_path":"src/python/p025.py","file_name":"p025.py","file_ext":"py","file_size_in_byte":289,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"194008732","text":"'''\r\ntf.matmul(a, b, name) #矩阵或者tensor乘法\r\na: `Tensor` of type `float16`, `float32`, `float64`, `int32`, `complex64`,\r\nb: `Tensor` with same type and rank as `a`.（也就是说a ，b的维度和类型必须一致）\r\n1.输入a ，b必须是矩阵或者维度大于2的tensor\r\n2.这个函数是专门矩阵或者tensor乘法，而不是矩阵元素对应元素相乘。这点要注意\r\n'''\r\nimport tensorflow as tf\r\nb1 = tf.Variable(tf.random_normal([2]), name='bias1')\r\nCL = tf.constant([1., 2., 3., 4., 5., 6.], shape=[2, 3])\r\nprint(tf.Session().run(CL))\r\n# W1 = tf.constant([7., 8., 9., 10., 11., 12.], shape=[3, 2])#偏置与W1最后维度一样\r\n# c = tf.matmul(CL, W1) #[array([[ 39., 54., 69.],\r\n# # [ 49., 68., 87.],\r\n# #[ 59., 82., 105.]], dtype=float32)]\r\n# print(c.shape)\r\n# FCNL1 = tf.nn.relu(tf.matmul(CL, W1) + b1)\r\n# FCNL1 = tf.nn.dropout(FCNL1, keep_prob=0.5)\r\n# print(FCNL1.shape)\r\n# with tf.Session() as sess:\r\n# print(sess.run([CL]))\r\n'''[array([[1, 2, 3],\r\n [4, 5, 6]]), \r\n array([[ 7, 8],\r\n [ 9, 10],\r\n [11, 12]]), \r\n array([[ 58, 64],\r\n [139, 154]])]'''","sub_path":"tf.matmul.py","file_name":"tf.matmul.py","file_ext":"py","file_size_in_byte":1226,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"524054966","text":"from flask.views import View\nfrom laserpony.models.project import Project\n\n\nclass BaseView(View):\n context = {}\n\n def prepare(self, *args, **kwargs):\n #Any processing that needs to happen before each request is handled\n #gets taken care of here\n projects = Project.objects\n self.context['navigation'] = {\n 'projects' : projects\n }\n self.context['extends_with']= \"nav.html\"\n\n def handle_request(self):\n \"\"\"Subclasses have to override this method to implement the\n actual view function code. This method is called with all\n the arguments from the URL rule.\n \"\"\"\n raise NotImplementedError()\n\n def dispatch_request(self, *args, **kwargs):\n self.context = dict()\n self.prepare(self, *args, **kwargs)\n return self.handle_request(*args, **kwargs)\n\n\n","sub_path":"laserpony/views/scaffold.py","file_name":"scaffold.py","file_ext":"py","file_size_in_byte":865,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"27351523","text":"# -*- coding:utf-8 -*-\n#!/usr/bin/python\n# Python: 3.4.3\n# Platform: Windows\n# Author: Mr.Yuan\tfusu1435@163.com\n# Program: KNN-Algorithm demo ,dating test & handwriting classify\n# History: 2015.8.31 v1.0.0\n\nfrom numpy import *\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport operator\nimport os\n\ndef createDataset():\n\tgroup = array([[1.0,1.1],[1.0,1.0],[0.0,0],[0,0.1]])\n\tlabels = ['A','A','B','B']\n\treturn group,labels\n\ndef KNNclassify(inV,dataSet,labels,kCount):\n\tdatasetSize = dataSet.shape[0]\n\tdiffMat = tile(inV,(datasetSize,1)) - dataSet\n\tsqDiffMat = diffMat**2\n\tsqDist = sqDiffMat.sum(axis=1)\n\tdist = sqDist ** 0.5\n\tsortedDistIndicies = dist.argsort()\n\tclassCount = {}\n\tfor i in range(kCount):\n\t\tvoteLabel = labels[sortedDistIndicies[i]]\n\t\tclassCount[voteLabel] = classCount.get(voteLabel,0) + 1\n\n\tsortedClassCout = sorted(classCount.items(),\\\n\t\t\t\tkey = operator.itemgetter(1),reverse = True)\n\treturn sortedClassCout[0][0]\n\ndef file2matrix(filename):\n\tfReader = open(filename)\n\tarrayOfLines = fReader.readlines()\n\tnumOfLines = len(arrayOfLines)\n\tretMat = zeros((numOfLines,3))\n\tclassLabelVec = []\n\tindex = 0\n\tfor line in arrayOfLines:\n\t\tline = line.strip()\n\t\tlistFromLine = line.split('\\t')\n\t\tretMat[index,:] = listFromLine[0:3]\n\t\tclassLabelVec.append(int(listFromLine[-1]))\n\t\tindex += 1\n\treturn retMat,classLabelVec\n\ndef autoNorm(dataSet):\n\tminVal = dataSet.min(0)\n\tmaxVal = dataSet.max(0)\n\tranges = maxVal - minVal\n\tnormDataset = zeros(shape(dataSet))\n\tm = dataSet.shape[0]\n\tnormDataset = dataSet - tile(minVal,(m,1))\n\tnormDataset = normDataset/tile(ranges,(m,1))\n\t\n\treturn normDataset,ranges,minVal\n\ndef dateClassTest():\n\thRatio = 0.5\n\tdateMat,dateLabel = file2matrix('datingTest.txt')\n\t#plotData(dateMat)\n\tnormMat,ranges,minVal = autoNorm(dateMat)\n\tm = normMat.shape[0]\n\tnumTestVecs = int(m*hRatio)\n\terrCount = 0.0\n\tfor i in range(numTestVecs):\n\t\tclassifierRes = KNNclassify(normMat[i,:],normMat[numTestVecs:m,:],\\\n\tdateLabel[numTestVecs:m],3)\n\t\tprint (\"the classifier came back with: %d,the real answer is : %d\"\\\n\t\t% (classifierRes,dateLabel[i]))\n\tif (classifierRes != dateLabel[i]):\n\t\terrCount += 1.0\n\tprint (\"the total error rate is : %f\" % (errCount/float(numTestVecs)))\n\ndef plotData(dataSet):\n\tfig = plt.figure()\n\tax = fig.add_subplot(111)\n\tax.scatter(dataSet[:,1],dataSet[:2])\n\tplt.show()\n\n# conver img to vector\ndef img2vector(filename):\n\tretVec = zeros((1,1024))\n\tfReader = open(filename)\n\tfor i in range(32):\n\t\tlineStr = fReader.readline()\n\t\tfor j in range(32):\n\t\t\tretVec[0,32*i+j] = int(lineStr[j])\n\t\n\treturn retVec\n\t\t\t\ndef file2mat(fileDir):\n\tdataLabels = []\n\tfileList = os.listdir(fileDir)\n\tnumFile = len(fileList)\n\tprint('numFile = %d' % numFile)\n\tdataMat = zeros((numFile,1024))\n\tfor i in range(numFile):\n\t\tfileNameStr = fileList[i]\n\t\tfileStr = fileNameStr.split('.')[0]\n\t\tclassNumStr = int(fileStr.split('_')[0])\n\t\tdataLabels.append(classNumStr)\n\t\tdataMat[i,:] = img2vector('%s/%s' % (fileDir,fileNameStr))\n\t \n\treturn dataMat,dataLabels\n\ndef loadDataset():\n\t# Get training data\n\tprint('Get training data:')\n\ttrainingMat,trainingLabels = file2mat('trainingDigits')\n\t# Get test data\n\tprint('Get test data:')\n\ttestMat,testLabels = file2mat('testDigits')\n\t\n\treturn trainingMat,trainingLabels,testMat,testLabels\ndef loadDataset1():\n\t# Get training data\n\ttrainingLabels = []\n\ttrainingFileList = listdir('trainingDigits')\n\tnumTraining= len(trainingFileList)\n\ttrainingMat = zeros(numTraining,1024)\n\tfor i in range(numTraining):\n\t\tfileNameStr = trainingFileList[i]\n\t\tfileStr = fileNameStr.split('.')[0]\n\t\tclassNumStr = int(fileStr.split('_')[0])\n\t\ttrainingLabels.append(classNumStr)\n\t\ttrainingMat[i,:] = img2vector('trainingDigits/%s' % fileNameStr)\n\t\n\t# Get test data\n\ttestLabels = []\n\ttestFileList = listdir('testDigits')\n\tnumTest = len(testFileList)\n\ttestMat = zeros(numTest,1024)\n\tfor i in range(mTest):\n\t\tfileNameStr = testFileList[i]\n\t\tfileStr = fileNameStr.split('.')[0]\n\t\tclassNumStr = int(fileStr.split('_')[0])\n\t\ttestLabels.append(classNumStr)\n\t\ttestMat[i,:] = img2vector('testDigits/%s' % fileNameStr)\n\n\treturn trainingMat,trainingLabels,testMat,testLabels\n# test hand writing class\ndef handwritingClassTest():\n\t# step 1 : load data\n\tprint ('step 1 : load data')\n\ttrainingMat,trainingLabels,testMat,testLabels = loadDataset()\n\t# step 2 : train data\n\tprint ('step 2 : train data')\n\tpass\n\t# step 3 : test data\n\terrCount = 0.0\n\tnumTest = testMat.shape[0]\n\tprint ('step 3 : test data')\n\tfor i in range(numTest):\n\t\tclassifierRes = KNNclassify(testMat,trainingMat,trainingLabels,3)\n\t\tprint (\"the classifier cama back with : %d,the real answer is : %d\" %(classifierRes,testLabels[i]))\n\t\tif (classifierRes != testLabels[i]):\n\t\t\terrCount += 1.0\n\tprint (\"\\nthe total number of errors is : %d\" % errCount)\n\tprint (\"\\nthe total error rate is : %f\" % (errCount/float(numTest)))\n\nif __name__ == '__main__':\n#\tdataSet,labels = createDataset()\n#\tprint(KNNclassfy([0.4,0.6],dataSet,labels,3))\n#\tplotData(dataSet)\n\tdateClassTest()\n#\thandwritingClassTest()","sub_path":"KNN/KNN.py","file_name":"KNN.py","file_ext":"py","file_size_in_byte":5015,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"239306733","text":"# Nicholas Allan, 2015-11-10\n# \n\n#import libraries\nimport pandas as pd \nimport sys\nimport matplotlib.pyplot as plt\n\ndef main():\n \n #output variables\n\toutput_file = sys.argv[2]\n\t#input variables \n\t#action = sys.argv[1]\n\tinput_file = sys.argv[1]\n\n\t\t#load the data\n\tdata = pd.read_csv(input_file, delimiter = ',')\n \n\t\t#print data\n\tplot(data, output_file)\n\n\ndef plot(plot_data, savename):\n #plot data\n\tplt.plot(plot_data)\n #save the plot\n\tplt.savefig(savename)\n \nmain()\n","sub_path":"make_it_happen.py","file_name":"make_it_happen.py","file_ext":"py","file_size_in_byte":494,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"225779705","text":"\"\"\"\n Tests and examples for getting ark info\n\"\"\"\n\n__author__ = 'Matt Gidney'\n__version__ = '1.0'\n__date__ = '2013/01/10'\n\n\nclass ARKManager(object):\n \"\"\"\n This class is used to defer as long as possible the load of ARK modules\n \"\"\"\n def __init__(self, job, tool):\n self.job = job\n self.tool = tool\n \n self._ark = None\n self._arkdao = None\n self._arkutils = None\n self._versionservice = None\n \n @property \n def version_service(self):\n if self._versionservice is None:\n from AL.utils.arkdao.hessianservices.version import VersionService\n self._versionservice = VersionService()\n return self._versionservice\n \n @property \n def arkdao(self):\n if self._arkdao is None:\n import arklib\n from AL.utils.arkdao import arklibdao\n self._ark = arklib.Ark(self.job, self.tool)\n self._arkdao = arklibdao.ArkLibDAO(self.job,self._ark )\n return self._arkdao\n\n @property \n def arkutils(self):\n if self._arkutils is None:\n from AL.utils import arkutils\n self._arkutils = arkutils\n return self._arkutils\n \n \nclass Asset(object): \n def __init__(self,job,title):\n self.job=job\n self.title=title\n self.ark_manager = ARKManager(self.job, self.title)\n \n \nA=Asset(\"kragle\",\"mattg\")\n\n\n \n \n \n \n \n \n \n \n ","sub_path":"python/Binocular/_test_scripts_local/arkdao_tester.py","file_name":"arkdao_tester.py","file_ext":"py","file_size_in_byte":1487,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"493444837","text":"#!/usr/bin/python2\nimport serial\nfrom serial import SerialException\n\n\nclass SwitchSpeaker:\n\n def __init__(self, logger):\n self.logger = logger\n try:\n self.ser = serial.Serial(\n port='/dev/ttyUSB0',\n baudrate=9600\n )\n\n if not self.ser.isOpen():\n self.logger.info(\"Serialport /dev/ttyUSB0 cannot be opened.\")\n except SerialException:\n self.logger.info(\"Serialport /dev/ttyUSB0 already opened.\")\n\n def internal(self):\n close_1 = \"\\xff\\x03\\x01\"\n close_2 = \"\\xff\\x04\\x01\"\n self.ser.write(close_1)\n self.ser.write(close_2)\n self.ser.close()\n self.logger.info(\"switched to internal speaker\")\n\n def external(self):\n open_1 = \"\\xff\\x03\\x00\"\n open_2 = \"\\xff\\x04\\x00\"\n self.ser.write(open_1)\n self.ser.write(open_2)\n self.ser.close()\n self.logger.info(\"switched to external speaker\")\n","sub_path":"bin/Speaker/SwitchSpeaker.py","file_name":"SwitchSpeaker.py","file_ext":"py","file_size_in_byte":984,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"599331053","text":"from server.portal_work import get_weekly_birthdays, get_todays_birthdays\nfrom models import Subscriber\n\n\ndef format_date(str_date):\n try:\n date = str_date.split('-')\n return '{}.{}'.format(date[2], date[1])\n except:\n return 'unknown'\n\n\ndef generate_message(birthday_people, add_date):\n if len(birthday_people) < 1:\n return \"Дни рождения не обнаружены :)\"\n result = \"\"\n try:\n for man in birthday_people:\n result += man['first_name'] + \" \"\n result += man['last_name'] + \" \"\n if add_date:\n result += \"({}) \\n\".format(format_date(man['birth_date']))\n return result\n except:\n return 'Произошла ошибка. Зайди на Портал и посмотри.'\n\n\ndef start_handler(bot, update):\n bot.sendMessage(update.message.chat_id, text='Привет! Используй /help чтобы посмотреть список команд.')\n\n\ndef weekly_birthdays_handler(bot, update):\n birthday_people = get_weekly_birthdays()\n bot.sendMessage(update.message.chat_id, text=generate_message(birthday_people, True))\n\n\ndef today_birthdays_handler(bot, update):\n birthday_people = get_todays_birthdays()\n bot.sendMessage(update.message.chat_id, text=generate_message(birthday_people, False))\n\n\ndef unknown_command_handler(bot, update):\n bot.sendMessage(update.message.chat_id, text=\"Что? Попробуй /help .\")\n\n\ndef add_subscriber_handler(bot, update):\n sub, created = Subscriber.get_or_create(channel=update.message.chat_id)\n if created:\n bot.sendMessage(update.message.chat_id, text=\"Спасибо! Теперь вы подписаны на оповещения.\")\n else:\n bot.sendMessage(update.message.chat_id, text=\"Вы уже подписаны на оповещения.\")\n\n\ndef remove_subscriber_handler(bot, update):\n try:\n sub = Subscriber.get(channel=update.message.chat_id)\n sub.delete_instance()\n bot.sendMessage(update.message.chat_id, text=\"Вы отписались от оповещений.\")\n except:\n bot.sendMessage(update.message.chat_id, text=\"Вы не подписаны на оповещения.\")\n\n","sub_path":"server/handlers.py","file_name":"handlers.py","file_ext":"py","file_size_in_byte":2250,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"555639972","text":"# -*- coding cp949 -*-\n\nimport math\n\ndef Add(a) :\n \"더하기\"\n num = 0\n for i in a :\n num += i\n return num\n\ndef Sub(a) :\n \"빼기\"\n num = a[0]\n for i in range(1, len(a)) :\n num -= a[i]\n return num\n\ndef Mul(a) :\n \"곱하기\"\n num = 1\n for i in a :\n num *= i\n return num\n\ndef Div(a) :\n \"나누기\"\n num = a[0]\n for i in range(1, len(a)) :\n num /= a[i]\n return num\n\ndef TriangleArea(width, height) :\n \"삼각형 넓이\"\n return width * height / 2\n\ndef RectangleArea(width, height) :\n \"사각형 넓이\"\n return width * height\n\ndef CircleArea(radius) :\n \"원의 넓이\"\n return pow(radius, 2) * math.pi\n\n\n\nprint(\"Run Arith Module\")\n\n# 모듈내에서 직접 실행할때\nif __name__ == '__main__' :\n print(\"모듈 직접 실행하였습니다.\")\n print(\"Test Add {0}\".format(Add((10, 20))))\n# 다른 파일에서 import후에 사용할 때\nelse :\n print(\"Import arith Moudle\")","sub_path":"Python/Lecture/2017_06_07_Lecture/01_Inheritance/Arith.py","file_name":"Arith.py","file_ext":"py","file_size_in_byte":976,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"388817663","text":"\n# Emulating 4 inputs to 3 neurons\n# so every neuron will have its own weight set and also their own different biases\n\n\ninputs = [1, 2, 3, 2.5]\nweight1 = [0.2, 0.8, -0.5, 1.0]\nweight2 = [0.5, -0.91, 0.26, -0.5]\nweight3 = [-0.26, 0.27, 0.17, 0.87]\n\nbias1 = 2\nbias2 = 3\nbias3 = 0.5\n\noutput = [inputs[0]*weight1[0] + inputs[1]*weight1[1] + inputs[2]*weight1[2] + inputs[3]*weight1[3] + bias1,inputs[0]*weight2[0] + inputs[1]*weight2[1] + inputs[2]*weight2[2] + inputs[3]*weight2[3] + bias2, inputs[0]*weight3[0] + inputs[1]*weight3[1] + inputs[2]*weight3[2] + inputs[3]*weight3[3] + bias3]\nprint(output)","sub_path":"n2(4insto3layers).py","file_name":"n2(4insto3layers).py","file_ext":"py","file_size_in_byte":600,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"341811165","text":"def ficha(n='', g=0):\n\n print(f'O jogador {n} fez {g} gol(s) no campeonato.')\n\n\n\n\nnome = str(input('Nome do jogador: '))\ngols = str(input('Numero do Gols: '))\n\nif gols.isnumeric():\n gols = int(gols)\nelse:\n gols = 0\n\nif nome.strip() == '':\n ficha(g=gols)\nelse:\n ficha(n=nome, g=gols)\n\n\n","sub_path":"exercicios/ex103-Ficha jogador.py","file_name":"ex103-Ficha jogador.py","file_ext":"py","file_size_in_byte":314,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"457615581","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Feb 23 14:22:52 2019\n\n@author: gabic\n\"\"\"\n\nimport sys\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.lines as mlines\nfrom matplotlib.pyplot import figure\n\n#coefiientes do U \ndef a (i,h,k):\n return -2*((1/(h**2))+(1/((i*h*k)**2)))\n\ndef b (i,h,k):\n return (1/((k*i*h)**2))\n\ndef c (i,h):\n return (1/h**2)-(1/(2*(i*h)**2))\n\ndef d(i,h): \n return (1.0/h**2) + (1.0 / (2.0 * i * h**2)) \n##\n\ndef imprimeMatriz(matriz):\n ordem = len(matriz[0])\n just_space = 5\n for i in range(ordem):\n for j in range(ordem):\n sys.stdout.write(\"{0:.1f}\".format(matriz[i][j]).ljust(just_space))\n #sys.stdout.write(\"| \" + repr(vetor_solucao[i]).ljust(just_space))\n sys.stdout.flush()\n print(\"\")\n print(\"--------------------------\")\n \ndef fatoraLU(A):\n U = np.copy(A) \n n = np.shape(U)[0] \n L = np.eye(n) \n for j in np.arange(n-1): \n for i in np.arange(j+1,n): \n L[i,j] = U[i,j]/U[j,j] \n for k in np.arange(j+1,n): \n U[i,k] = U[i,k] - L[i,j]*U[j,k] \n U[i,j] = 0 \n# print(\"L:\",L)\n# print(\"U\",U)\n return L, U\ndef triangularSuperior(U,b,n):\n x = np.zeros((len(b),1),dtype = np.float64)\n x[n-1] = b[n-1]/U[n-1][n-1]\n for i in range (n-1, -1,-1):\n s = b[i]\n for j in range(i+1, n):\n s = s-U[i][j]*x[j]\n x[i] = s/U[i][i]\n return x\ndef Gauss(a,b,n):\n for k in range(0,n):\n for i in range(k+1,n):\n m = a[i][k]/a[k][k] \n for j in range(k,n):\n a[i][j]=a[i][j]-m*a[k][j]\n b[i] = b[i] - m*b[k]\n x = triangularSuperior(a,b,n)\n return x\n ############################################################################################## \nN=3\n# g=0\nr=1.0\ni=0\ntheta=np.pi*2\nh=(r/N)\nk=theta*h\n\n\n#criando matriz original\nmatriz_original=np.zeros((N**2,N**2), np.float64)\n\n\n#vetores da matriz principal N X N \n\nvetor_dP=np.ones((N**2), np.float64)*a(i+1, h,k)#diagonal principal\nvetor_d1=np.ones(((N**2)-1), np.float64)* b(i+1, h,k)#diagonal parte de cima\nvetor_d2=np.ones(((N**2)-1), np.float64)* b(i+1, h,k)#diagonal parte de baixo\n\n#vetor da matriz_baixo N X N\nvetor_dP2= np.ones((N**2)-3, np.float64)*c(i+1,h)\n\n#vetor da matriz_cima N X N, irei multiplicar de acordo com o tamanho original\nvetor_dP3=np.ones((N**2)-3, np.float64)*d(i+1,h)\n\n#juntando todas as diagonais\nmatriz_original=np.diagflat(vetor_dP, 0)+np.diagflat(vetor_d1,1)+np.diagflat(vetor_d2, -1)+ np.diagflat(vetor_dP3, 3)+np.diagflat(vetor_dP2, -3) \n#agora, irei multiplicar os valores com os seus respectivos valores de i\nvetor_multiplicador=np.array([[1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0],[1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0],[1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0],[2.0,2.0,0.0,4.0,4.0,4.0,2.0,2.0,2.0],[2.0,2.0,2.0,4.0,4.0,4.0,2.0,2.0,2.0],[2.0,2.0,2.0,4.0,4.0,4.0,2.0,2.0,2.0],[0.0,0.0,0.0,3.0,3.0,0.0,9.0,9.0,9.0],[0.0,0.0,0.0,3.0,3.0,3.0,9.0,9.0,9.0],[0.0,0.0,0.0,3.0,3.0,3.0,9.0,9.0,9.0]]) \n#multiplicando pela matriz original\nmatriz_original=matriz_original*vetor_multiplicador\nprint(\"Matriz original já com os valores de i:\")\nimprimeMatriz(matriz_original) \n\n####################################################################################################\n####################### RESOLUÇÃO COM LU #######################################\n####################################################################################################\nF=np.ones((N**2), np.float64)*20 #função f\nL,U = fatoraLU(matriz_original)\nY = Gauss(L,F,len(F))\nx = triangularSuperior(U,Y,len(Y))\nprint(\"Resultado da discretização utilizando coordenadas polares:\",x)","sub_path":"coordenadas_polares.py","file_name":"coordenadas_polares.py","file_ext":"py","file_size_in_byte":3838,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"203656340","text":"import time\n\nimport asyncio\nfrom venom.rpc.comms.grpc import create_server\nfrom venom.rpc.method import rpc\nfrom hello import HelloRequest, HelloResponse\nfrom venom.rpc import Service, Venom\n\n\nclass HelloService(Service):\n @rpc\n async def say_hello(self, request: HelloRequest) -> HelloResponse:\n await asyncio.sleep(1)\n return HelloResponse(message=f\"Hello, {request.name}!\")\n\n\napp = Venom()\napp.add(HelloService)\n\nserver = create_server(app)\n\nif __name__ == '__main__':\n server.add_insecure_port('[::]:50053')\n server.start()\n try:\n while True:\n time.sleep(24 * 60 * 60)\n except KeyboardInterrupt:\n server.stop(0)\n","sub_path":"examples/grpc/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":673,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"648667349","text":"#print('Hola, esta es mi primera calculadora: ')\n#x = int(input('Ingresa el primer numero que quieres sumar: '))\n#xx = str(x)\n#print('Ya tengo el primer numero, Ahora necesito otro para sumar:')\n#y = int(input('¿Cual es el otro numero que quieres sumar: '))\n#yy = str(y)\n#suma = x+y\n#print('¡Listo, la suma de los numeros : ' + xx + ' Y el numero: ' + yy + ' es: ', suma)\n\n#print('Hola, esta es mi primera calculadora: ')\n#x = int(input('Enter the frist number: '))\n#xx = str(x)\n#y = int(input('Enter the secod number: '))\n#yy = str(y)\n#suma = x+y\n#print(xx, '+', yy, '=', suma)\n\n\nprint('Hola, esta es mi primera calculadora: ')\nx = int(input('Enter the frist number: '))\ny = int(input('Enter the secod number: '))\nsuma = x+y\nprint(str(x) + '+' + str(y) + '=' + str(suma))\n\n\n","sub_path":"Estudio_Python/calculadora.py","file_name":"calculadora.py","file_ext":"py","file_size_in_byte":786,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"37354234","text":"\"\"\" Collection of syllogistic helper functions.\n\n\"\"\"\n\n#: List of syllogistic task identifiers.\nSYLLOGISMS = []\nfor _prem1 in ['A', 'I', 'E', 'O']:\n for _prem2 in ['A', 'I', 'E', 'O']:\n for _fig in ['1', '2', '3', '4']:\n SYLLOGISMS.append(_prem1 + _prem2 + _fig)\n\n#: List of syllogistic responses.\nRESPONSES = []\nfor _quant in ['A', 'I', 'E', 'O']:\n for _direction in ['ac', 'ca']:\n RESPONSES.append(_quant + _direction)\nRESPONSES.append('NVC')\n\ndef encode_task(task_tuple):\n \"\"\" Encodes a syllogistic task by transforming a task tuple string into\n its corresponding identifier.\n\n Parameters\n ----------\n task_tuple : list(list(str))\n Task tuple in list representation (e.g.,\n [['Some', 'models', 'managers'], ['All', 'models', 'clerks']])\n\n Returns\n -------\n str\n Syllogistic task identifier (e.g., 'AI1'). Figures are defined in\n accordance to Khemlani et al. (2012).\n\n \"\"\"\n\n prem_1, prem_2 = task_tuple\n\n quant1 = prem_1[0].replace('All', 'A').replace(\n 'Some not', 'O').replace('Some', 'I').replace('No', 'E')\n quant2 = prem_2[0].replace('All', 'A').replace(\n 'Some not', 'O').replace('Some', 'I').replace('No', 'E')\n figure = 1\n\n if prem_1[1] == prem_2[1]:\n figure = 4\n elif prem_1[2] == prem_2[1]:\n figure = 1\n elif prem_1[2] == prem_2[2]:\n figure = 3\n elif prem_1[1] == prem_2[2]:\n figure = 2\n else:\n raise ValueError('Could not determine figure of:', task_tuple)\n\n return quant1 + quant2 + str(figure)\n\ndef encode_response(response, task):\n \"\"\" Encodes a syllogistic response by transforming the tuple containing the\n response string and the task tuple into its string representation.\n\n Parameters\n ----------\n response : list(list(str))\n Response encodings.\n\n task : list(list(str))\n Task tuple representation.\n\n Returns\n -------\n str\n Encoded response (e.g., 'Iac').\n\n \"\"\"\n\n if not isinstance(response[0], list):\n response = [response]\n\n if response[0] == 'NVC':\n return 'NVC'\n\n if response[0][0] == 'NVC':\n return 'NVC'\n\n object_sets = [set(x[1:]) for x in task]\n midterm = object_sets[0].intersection(object_sets[1])\n obj_a = object_sets[0] - midterm\n\n quant = response[0][0].replace('All', 'A').replace(\n 'Some not', 'O').replace('Some', 'I').replace('No', 'E')\n\n return quant + ('ac' if response[0][1] == list(obj_a)[0] else 'ca')\n\ndef decode_response(enc_response, task):\n \"\"\" Decodes an encoded syllogistic response by transforming it to the\n corresponding tuple representation and inserting the appropriate terms.\n\n Parameters\n ----------\n enc_response : str\n Encoded syllogistic response (e.g., 'Aac').\n\n task : list(str)\n Syllogistic task in the tuple list representation (e.g.,\n [['Some', 'models', 'managers'], ['All', 'models', 'clerks']]).\n\n Returns\n -------\n list\n List representation of the response to decode.\n\n \"\"\"\n\n if enc_response == 'NVC':\n return [['NVC']]\n if enc_response == ['NVC']:\n return [enc_response]\n if enc_response == [['NVC']]:\n return enc_response\n\n obj_a = set(task[0][1:]) - set(task[1][1:])\n obj_c = set(task[1][1:]) - set(task[0][1:])\n\n quant = enc_response[0].replace('A', 'All').replace(\n 'I', 'Some').replace('O', 'Some not').replace('E', 'No')\n if enc_response[1:] == 'ac':\n return [[quant, list(obj_a)[0], list(obj_c)[0]]]\n\n return [[quant, list(obj_c)[0], list(obj_a)[0]]]\n","sub_path":"ccobra/syllogistic/parsing.py","file_name":"parsing.py","file_ext":"py","file_size_in_byte":3611,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"425943278","text":"# define global constants for other scripts\nfrom datetime import datetime\n\nGLOBAL_CAP_CONST = 1\n\n# Customize the backtesting start time\nSTART_TRADING_TIME = {\n 'USDTWD': datetime(2008, 1, 1), \n 'USDSGD': datetime(2000, 1, 1), \n 'USDRUB': datetime(2008, 1, 1), \n 'USDCOP': datetime(2000, 1, 1), \n 'USDTRY': datetime(2004, 1, 1)\n}\n","sub_path":"trading/global_constant.py","file_name":"global_constant.py","file_ext":"py","file_size_in_byte":344,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"525794538","text":"from rest_framework import views, status\nfrom rest_framework.response import Response\nimport requests\nimport ujson as json\nimport os\nfrom trello import TrelloClient, Member\nfrom card.models import Card\nfrom task.models import Task\nfrom user.models import User\n\n\nclass FetchView(views.APIView):\n\n def post(self, request, *args, **kwargs):\n\n d3_board = self.get_d3_board()\n lists = d3_board.open_lists()\n cards = lists[0].list_cards()\n\n for c in cards:\n card = self.extract_card_data(c)\n self.save_user(card.get('user'))\n self.save_card(\n card.get('id'),\n card.get('description'),\n card.get('user').get('id'))\n self.save_tasks(card)\n\n return Response({\n 'Response': 'Sucesso'},\n status=status.HTTP_200_OK)\n\n def save_user(self, userToSave):\n\n userID = userToSave.get('id')\n userName = userToSave.get('name')\n\n user = User.objects.filter(idUser=userID).first()\n\n if user:\n return\n\n newUser = User()\n newUser.idUser = userID\n newUser.nome = userName\n newUser.save()\n\n return\n\n def save_card(self, cardID, cardDescr, cardUserID):\n\n card = Card.objects.filter(idCard=cardID).first()\n user = User.objects.filter(idUser=cardUserID).first()\n\n if card:\n return\n\n newCard = Card()\n newCard.idCard = cardID\n newCard.description = cardDescr\n newCard.user = user\n newCard.save()\n\n return\n\n def save_tasks(self, card):\n tasks = card.get('tasks')\n card = Card.objects.filter(idCard=card.get('id')).first()\n\n for t in tasks:\n task = Task()\n task.idTask = t.get('id')\n task.description = t.get('description')[:250]\n task.card = card\n task.save()\n\n def is_d3_board(self, board):\n \"\"\"\n Check if the board is the 'D3 Board' by checking it's id\n \"\"\"\n\n if board is None:\n return False\n elif board.id == os.environ.get('D3_BOARD', None):\n return True\n else:\n return False\n\n def get_d3_board(self):\n client = TrelloClient(\n api_key=os.environ.get('TRELLO_API_KEY', None),\n token=os.environ.get('TRELLO_TOKEN', None),\n )\n\n all_boards = client.list_boards()\n d3_board = None\n for board in all_boards:\n if (self.is_d3_board(board)):\n d3_board = board\n break\n return d3_board\n\n def extract_card_data(self, card):\n \"\"\"\n extract info from trello's card\n\n return an object:\n {\n id,\n description,\n tasks,\n user,\n }\n \"\"\"\n if card is None:\n return None\n\n member = self.get_card_first_member(card)\n\n tasks = self.get_card_tasks(card)\n\n return {\n 'id': card.id,\n 'description': card.desc,\n 'tasks': tasks,\n 'user': member,\n }\n\n def get_card_tasks(self, card):\n \"\"\"\n return an array of objects:\n [{\n id,\n description,\n checked,\n }]\n \"\"\"\n check_lists = card.fetch_checklists()\n tasks = []\n for check_list in check_lists:\n # check_list.name = 'Segunda' | 'Terça' | ...\n tasks.extend(self.format_check_list_tasks(check_list))\n\n return tasks\n\n def format_check_list_tasks(self, check_list):\n \"\"\"\n return an array of objects:\n [{\n id,\n description,\n checked,\n }]\n \"\"\"\n if check_list is None:\n return None\n\n tasks = []\n for task in check_list.items:\n tasks.append({\n 'id': task['id'],\n 'description': task['name'],\n 'checked': task['checked'],\n })\n\n return tasks\n\n def get_card_first_member(self, card):\n \"\"\"\n return an object:\n {\n id,\n name,\n }\n \"\"\"\n memberId = card.idMembers[0]\n trelloMember = Member(card.client, memberId)\n member = trelloMember.fetch()\n\n return {\n 'id': member.id,\n 'name': member.full_name,\n }\n","sub_path":"user/fetch/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4540,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"322979893","text":"#!/usr/bin/python\n# -*- coding:utf-8 -*-\n\"\"\"\n分词模块\n\"\"\"\n\nfrom __future__ import division\nfrom __future__ import print_function\nimport tensorflow as tf\nimport numpy as np\nimport os\nimport json\nimport re\n\nimport data_preprocess\n\nindex_data = 0\n\nfile_name = \"../data/mixCorpus_test.txt\"\nword_id, label_id = data_preprocess.data2id(file_name)\n\n\ndef get_batch(batch_size, time_step, word_id, label_id, label_size):\n \"\"\"\n 产生批量训练��据\n :param batch_size: \n :param time_step: \n :param word_id: \n :param label_id: \n :param label_size: \n :return: train_word_id 维度 [batch_size][time_step]\n train_label_id [batch_size][time_step][label_size]\n \"\"\"\n\n global index_data\n train_word_id = np.zeros(shape=[batch_size, time_step])\n train_label_id = np.zeros(shape=[batch_size, time_step, label_size])\n index = index_data\n for i in range(batch_size):\n index = index % len(word_id)\n while len(word_id[index]) > time_step:\n index = (index + 1) % len(word_id)\n for j in range(len(word_id[index])):\n train_word_id[i, j] = word_id[index][j]\n train_label_id[i, j, label_id[index][j]] = 1\n for j in range(len(word_id[index]), time_step):\n train_label_id[i, j, 0] = 1\n # 下一个句子\n index = index + 1\n index_data = index\n print('*'*30)\n print(index_data)\n print('*' * 30)\n # index_data = index_data + 1\n return train_word_id, train_label_id\n\n\ndef data_tranform(time_step, word_id):\n \"\"\"\n 产生批量训练数据\n :param time_step: \n :param word_id: \n :return: train_word_id 维度 [batch_size][time_step] \n \"\"\"\n train_word_id = np.zeros(shape=[len(word_id), time_step])\n for i in range(len(word_id)):\n for j in range(len(word_id[i])):\n if j < time_step:\n train_word_id[i, j] = word_id[i][j]\n return train_word_id\n\n\nclass ConfigArgs(object):\n \"\"\"分词参数配置\"\"\"\n label_size = 5 # 类别数\n init_scale = 0.01 # 相关参数的初始值为随机均匀分布，范围是[-init_scale,+init_scale]\n learning_rate = 1e-3 # 学习速率,在文本循环次数超过max_epoch以后会逐渐降低\n max_grad_norm = 10 # 用于控制梯度膨胀，如果梯度向量的L2模超过max_grad_norm，则等比例缩小\n num_layers = 1 # lstm层数\n num_steps = 20 # 单个数据中，序列的长度。\n hidden_size = 512 # 隐藏层中单元数目\n keep_prob = 0.5 # 用于dropout.每批数据输入时神经网络中的每个单元会以1-keep_prob的概率不工作，可以防止过拟合\n batch_size = 8 # 每批数据的规模，每批有10个。\n max_loop = 2000 # 运行次数\n charNum = 10000 # 词典大小\n char_dim = 512 # 每个字符嵌入的维度\n# hide_dim = 200 # 隐层维度\n\n\nclass WordSegment(object):\n \"\"\"\n 分词\n \"\"\"\n\n def __init__(self, config, is_training, embedding_char=None):\n \"\"\"\n 初始化函数\n :param config: 模型的配置参数 \n :param emdeding_word: 训练好的词向量\n \"\"\"\n self.config = config\n self.is_training = is_training\n self.embedding_char = embedding_char\n\n # 初始化\n self.sess = None\n self.graph = None\n self.init = None\n self.losses = None\n self._train_op = None\n self.saver = None\n # 构建图，初始化\n self.build_graph()\n self.init_op()\n\n def init_op(self):\n \"\"\"\n 分词初始化\n :return: 无\n \"\"\"\n self.sess = tf.Session(graph=self.graph)\n self.sess.run(self.init)\n\n def build_graph(self):\n \"\"\"\n 模型构建\n \"\"\"\n self.graph = tf.Graph()\n\n with self.graph.as_default():\n # 维度 [batch][char_id]\n self.char_id = tf.placeholder(tf.int32, shape=[None, self.config.num_steps])\n # 维度 [batch][char_id][label_size]\n self.labels = tf.placeholder(tf.float32, shape=[None, self.config.num_steps, self.config.label_size])\n\n def lstm_cell():\n return tf.contrib.rnn.GRUCell(self.config.hidden_size)\n\n attn_cell = lstm_cell\n if self.is_training and self.config.keep_prob < 1: # dropout 防止过拟合\n def attn_cell():\n return tf.contrib.rnn.DropoutWrapper(\n lstm_cell(), output_keep_prob=self.config.keep_prob)\n\n cell_1_forward = tf.contrib.rnn.MultiRNNCell( # 多层lstm\n [attn_cell() for _ in range(self.config.num_layers)], state_is_tuple=True)\n cell_1_backward = tf.contrib.rnn.MultiRNNCell( # 多层lstm\n [attn_cell() for _ in range(self.config.num_layers)], state_is_tuple=True)\n\n cell_2_forward = tf.contrib.rnn.MultiRNNCell( # 多层lstm\n [attn_cell() for _ in range(self.config.num_layers)], state_is_tuple=True)\n cell_2_backward = tf.contrib.rnn.MultiRNNCell( # 多层lstm\n [attn_cell() for _ in range(self.config.num_layers)], state_is_tuple=True)\n\n # 嵌入向量\n with tf.device(\"/cpu:0\"):\n # 获取词嵌入\n if self.embedding_char is not None:\n embedding_char = tf.Variable(self.embedding_char,\n trainable=True, name=\"embedding_char\", dtype=tf.float32)\n else:\n embedding_char = tf.Variable(\n tf.truncated_normal([self.config.charNum, self.config.char_dim], stddev=0.1),\n trainable=True, name=\"embedding_char\", dtype=tf.float32)\n inputs_char = tf.nn.embedding_lookup(embedding_char, self.char_id)\n\n # BiLSTM\n outputs_1, _, _ = tf.contrib.rnn.static_bidirectional_rnn(\n cell_1_forward, cell_1_backward, tf.unstack(inputs_char, self.config.num_steps, 1),\n dtype=tf.float32, scope='bidirectional_rnn_0')\n # dropout\n if self.is_training and self.config.keep_prob < 1:\n outputs_1 = tf.nn.dropout(outputs_1, self.config.keep_prob) # 维度 [step][None][feature*2]\n\n # BiLSTM\n outputs_2, _, _ = tf.contrib.rnn.static_bidirectional_rnn(\n cell_2_forward, cell_2_backward, tf.unstack(outputs_1, self.config.num_steps, 0),\n dtype=tf.float32, scope='bidirectional_rnn_1')\n\n # dropout\n if self.is_training and self.config.keep_prob < 1:\n outputs_2 = tf.nn.dropout(outputs_2, self.config.keep_prob) # 维度 [step][None][feature*2]\n else:\n outputs_2 = tf.stack(outputs_2)\n\n weight = tf.Variable(tf.random_uniform([self.config.hidden_size*2, self.config.label_size], -0.1, 0.1))\n bias = tf.Variable(tf.zeros([self.config.label_size]))\n\n # 预测结果保存\n y_pre_sum = []\n for t_s in range(self.config.num_steps):\n y_pre = tf.nn.softmax(\n tf.matmul(outputs_2[t_s, :, :], weight) + bias\n )\n y_pre_sum.append(y_pre) # 维度 [step][batch_size][labels]\n cross_entropy = -tf.reduce_sum(self.labels[:, t_s, :] * tf.log(y_pre + 1e-10))\n tf.add_to_collection('losses', cross_entropy)\n\n # 结果 [step][batch_size]\n self.correct_prediction = tf.argmax(tf.stack(y_pre_sum), 2)\n\n # 模型评估\n for each_ in range(len(y_pre_sum)):\n correct_prediction = tf.equal(tf.argmax(y_pre_sum[each_], 1),\n tf.argmax(self.labels[:, each_, :], 1))\n tf.add_to_collection('evaluate', tf.reduce_mean(tf.cast(correct_prediction, \"float\")))\n self.evaluate = tf.add_n(tf.get_collection('evaluate'), name=\"evaluate_loss\")/self.config.num_steps\n\n # 优化损失函数\n self.losses = tf.add_n(tf.get_collection('losses'), name=\"total_loss\")\n\n self.train_step = tf.train.GradientDescentOptimizer(1e-3).minimize(self.losses)\n '''\n # 自适应学习率调准\n self.AdaDelta_train_op = tf.train.AdadeltaOptimizer(1e-3).minimize(self.losses)\n self.Adam_train_op = tf.train.AdamOptimizer(1e-3).minimize(self.losses)\n '''\n # 梯度限制， 防止梯度爆炸\n tvars = tf.trainable_variables()\n grads, _ = tf.clip_by_global_norm(tf.gradients(self.losses, tvars),\n self.config.max_grad_norm)\n optimizer = tf.train.AdamOptimizer(self.config.learning_rate)\n self._train_op = optimizer.apply_gradients(\n zip(grads, tvars),\n global_step=tf.contrib.framework.get_or_create_global_step())\n\n # init 和 saver\n self.init = tf.global_variables_initializer()\n self.saver = tf.train.Saver()\n\n def train_model(self):\n \"\"\"\n 模型的训练\n :return: 无 \n \"\"\"\n # 训练模型\n for i in range(self.config.max_loop):\n train_word_id, train_label_id = get_batch(self.config.batch_size, self.config.num_steps,\n word_id, label_id, self.config.label_size)\n feed_dict = {\n self.char_id: train_word_id,\n self.labels: train_label_id\n }\n loss, _, evaluate = self.sess.run([self.losses, self._train_op, self.evaluate], feed_dict=feed_dict)\n if i % 10 == 0:\n print(\"迭代次数：\", i,\" 损失：\", loss, ' 准确率：', evaluate)\n\n def predict(self, train_word_id):\n \"\"\"\n 预测\n :param train_word_id: 预测字符的id \n :return: # 维度 [step][batch_size]\n \"\"\"\n feed_dict = {\n self.char_id: train_word_id,\n }\n # 结果 [step][batch_size]\n correct_prediction = self.sess.run(self.correct_prediction, feed_dict=feed_dict)\n return correct_prediction\n\n def predict_char(self, word, words_dict):\n \"\"\"\n 标注句子，分词\n :param word: 需分词的句子\n :param words_dict: 字符字典\n :return: 返回每个字符的 label id\n 维度 [step][batch_size]\n \"\"\"\n char_id = data_preprocess.char2id(word, words_dict)\n char_id = data_tranform(self.config.num_steps, char_id)\n label_id = self.predict(char_id)\n return label_id\n\n def id2label(self, word, label_id, labels_rdict):\n \"\"\"\n 将分词的标注转为 对应的标签\n :param word: 需标注的文本\n :param label_id: 标注的文本对应的标签 # 维度 [step][batch_size]\n :param labels_dict: 标签对应的字典\n :return: \n \"\"\"\n labels = []\n for i in range(len(word)):\n label = []\n for j in range(len(word[i])):\n if j < self.config.num_steps and label_id[j][i] in labels_rdict.keys():\n label.append(labels_rdict[label_id[j][i]])\n else:\n label.append('o')\n labels.append(label)\n return labels\n\n def save_model(self, save_path):\n \"\"\"\n 模型的保存\n :param save_path: 保存的路径 \n :return: 返回模型路径\n \"\"\"\n if os.path.isfile(save_path):\n raise RuntimeError('the save path should be a dir')\n if not os.path.exists(save_path):\n os.mkdir(save_path)\n # 记录tf模型\n tf_path = os.path.join(save_path, 'tf_vars')\n if os.path.exists(tf_path):\n os.remove(tf_path)\n self.saver.save(self.sess, tf_path)\n print(\"模型保存到: %s\" % save_path)\n return save_path\n\n def restore(self, path):\n \"\"\"\n 模型的加载\n :param path: 模型加载路径\n :return: 无\n \"\"\"\n with self.graph.as_default():\n self.saver.restore(self.sess, path + '/tf_vars')\n\n\ndef load_data(path, name, reverse):\n \"\"\"\n 加载数据\n :param path: 路径 \n :param name: 文件名\n :param reverse: 是否是逆向字典\n :return: 返回对应字典的类型\n \"\"\"\n if reverse is False:\n dictionary = json.load(open(os.path.join(path, name), 'rb'))\n else:\n reverse_dictionary = json.load(open(os.path.join(path, name), 'rb'))\n dictionary = {int(key): val for key, val in reverse_dictionary.items()}\n return dictionary\n\n\ndef load_model_data():\n \"\"\"\n 加载模型需要的数据\n :return: 模型需要的数据 字典\n \"\"\"\n path = './dictionary'\n model_dict = load_data(path, 'model_dict.json', False)\n model_rdict = load_data(path, 'model_rdict.json', True)\n label_dict = load_data(path, 'label_dict.json', False)\n label_rdict = load_data(path, 'label_rdict.json', True)\n return model_dict, model_rdict, label_dict, label_rdict\n\n\ndef load_model(model_path=\"./model_data\"):\n \"\"\"\n 加载模型\n :param model_path: \n :return: 返回模型 \n \"\"\"\n ws = WordSegment(ConfigArgs, False, None)\n ws.restore(model_path)\n return ws\n\n\ndef save_model(model_path=\"./model_data\"):\n ws = WordSegment(ConfigArgs, True, None)\n ws.train_model()\n # 保存模型\n ws.save_model(save_path=model_path)\n\n\ndef predict(ws, sentence):\n char_list = word2list(sentence)\n model_dict, model_rdict, label_dict, label_rdict = load_model_data()\n label_id = ws.predict_char(char_list, model_dict)\n labels = ws.id2label(char_list, label_id, label_rdict)\n return list2json(char_list, labels)\n\n\ndef word2list(sentence):\n \"\"\"\n 把句子转化为相应的字符数组\n :param sentence: 句子\n :return: 字符数组\n \"\"\"\n regular_expression = u'[,.!?，。！？]'\n sentence = unicode(sentence, \"utf-8\")\n sen = re.split(regular_expression, sentence)\n char_list = []\n for i in range(len(sen)):\n words = [w for w in sen[i]]\n char_list.append(words)\n return char_list\n\n\ndef list2json(sentence, char_list):\n result = []\n for i in range(len(char_list)):\n word = []\n w = ''\n k = 0\n for j in range(len(char_list[i])):\n if char_list[i][j] in ['s', 'o']:\n if w.strip() != '':\n temp = dict()\n temp['id'] = k\n temp['cont'] = w\n k = k + 1\n word.append(temp)\n temp = dict()\n temp['id'] = k\n temp['cont'] = sentence[i][j]\n word.append(temp)\n k = k + 1\n w = ''\n if char_list[i][j] in ['b', 'm', 'e']:\n w = w + sentence[i][j]\n if char_list[i][j] == 'e':\n temp = dict()\n temp['id'] = k\n temp['cont'] = w\n word.append(temp)\n k = k + 1\n w = ''\n if j == len(char_list[i])-1 and w.strip() != '':\n temp = dict()\n temp['id'] = k\n temp['cont'] = w\n word.append(temp)\n word.append(w)\n result.append(word)\n return json.dumps(result)\n\n\nif __name__ == '__main__':\n save_model()\n ws = load_model()\n # sentence = ['我是中国人', '结婚的和没结婚的']\n sentence = '我是中国人。结婚的和没结婚的'\n labels = predict(ws, sentence)\n print(labels)\n\n","sub_path":"word_seg/model/wordseg.py","file_name":"wordseg.py","file_ext":"py","file_size_in_byte":15749,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"483943088","text":"#Imports to get django setup\nimport os\nimport sys\nimport django\n\nsys.path.append(\"/code\")\nos.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"core.settings\")\ndjango.setup()\n\n#Rest of the imports\nimport logging\nimport boto3\nimport datetime\nimport requests\nimport pytz\n\nfrom django.conf import settings\nfrom archive.models import Collection, Picture\nfrom django.db import connection,transaction\n\nFORBIDDEN_NAMES = [\"documents\", \"cache\"]\n\nlogger = logging.getLogger('date')\n\nlogger.info(\"STARTING IMAGE UPLOADER\")\n\ndef s3_config():\n return boto3.client('s3',\n endpoint_url=settings.AWS_S3_ENDPOINT_URL,\n aws_access_key_id=settings.AWS_ACCESS_KEY_ID,\n aws_secret_access_key=settings.AWS_SECRET_ACCESS_KEY)\n\nclient = s3_config()\n\nquery_list = []\ncollection_list = []\nyear_list = []\n\n\nresponse = client.list_objects(Bucket=settings.AWS_STORAGE_BUCKET_NAME, Prefix=settings.PRIVATE_MEDIA_LOCATION + \"/\", Delimiter=\"/\")\n\n# Gets a list of years\nfor obj in response.get('CommonPrefixes'):\n year = obj.get('Prefix').replace(settings.PRIVATE_MEDIA_LOCATION,\"\").replace(\"/\",\"\")\n if year not in FORBIDDEN_NAMES:\n year_list.append(year)\n\nfor year in year_list:\n response = client.list_objects(Bucket=settings.AWS_STORAGE_BUCKET_NAME, Prefix=settings.PRIVATE_MEDIA_LOCATION + f\"/{year}/\", Delimiter=\"/\")\n album_year = int(year)\n # For every year, list containing albums\n for obj in response.get('CommonPrefixes'):\n album_name = obj.get('Prefix').replace(settings.PRIVATE_MEDIA_LOCATION + f\"/{year}\",\"\").replace(\"/\",\"\")\n exists_check = Collection.objects.filter(title=album_name, pub_date__year=album_year).count()\n # check if album name not exists\n if exists_check == 0:\n logger.info(\"Album does not exist\")\n # Create a collection from album name\n collection = Collection(title=album_name, type=\"Pictures\", pub_date=datetime.datetime(album_year,1,1,10,10,tzinfo=pytz.timezone('Europe/Helsinki')))\n # If an album with same name in a specific year does not exist, save the collection\n collection.save()\n response = client.list_objects(Bucket=settings.AWS_STORAGE_BUCKET_NAME, Prefix=settings.PRIVATE_MEDIA_LOCATION + f\"/{year}/{album_name}\")\n contents = response.get('Contents')\n # For every album, list its contents\n for data in contents:\n path = data[\"Key\"].replace(settings.PRIVATE_MEDIA_LOCATION + \"/\", \"\")\n logger.info(path)\n picture_data = (path, False, collection.id)\n query_list.append(picture_data)\n\n\n# Creates a connection to the database and inserts the query list to the correct table\ncursor = connection.cursor()\n\nquery = ''' INSERT INTO archive_picture \n (image, favorite, collection_id) \n VALUES (%s,%s,%s) '''\n\ncursor.executemany(query, query_list)\ntransaction.commit()\n\nlogger.info(\"IMAGE UPLOADER COMPLETE\")\n","sub_path":"archive/s3_populate_db.py","file_name":"s3_populate_db.py","file_ext":"py","file_size_in_byte":2967,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"428346681","text":"from tkinter import *\nimport math\nfrom tkinter.scrolledtext import *\n#15/05/21\n#Caitlin Naylor \n#Job Cost/Charge Calulator\n\nclass Job:\n def __init__(self, job_num, name, distance, minutes, wof_and_tune, charge):\n self.job_num = job_num\n self.name = name\n self.distance = distance\n self.minutes = minutes\n self.wof_and_tune = wof_and_tune\n self.charge = charge\n\nclass JobCostGUI:\n def __init__(self, parent):\n self.jobs = []\n self.BG_COLOUR = \"#cceeff\"\n self.BTN_COLOUR = \"white\"\n self.FONT = \"Sans Serif\"\n #Add Jobs Frame\n self.add_job_frame = Frame(parent)\n self.add_job_frame.configure(pady = 10, padx = 10, bg = self.BG_COLOUR)\n self.add_job_frame.grid(row = 0, column = 0)\n\n #Company Logo\n #Suzy has supplied and given permission for this logo to be used in this program\n\n self.logo = PhotoImage(file = \"logo.gif\")\n self.logo_label = Label(self.add_job_frame, image = self.logo, bg = self.BG_COLOUR)\n self.logo_label.grid(row = 0, column = 0, columnspan = 4)\n \n #Add a Job Label\n self.add_job_label = Label(self.add_job_frame, text = \"Add a Job\",\n font = (self.FONT, 17), bg = self.BG_COLOUR)\n self.add_job_label.grid(row = 1, column = 0, sticky = NW, pady = 5)\n\n #Job Number Label\n self.job_num_label = Label(self.add_job_frame, text = \"Job Number\",\n font = (self.FONT, 11), bg = self.BG_COLOUR)\n self.job_num_label.grid(row = 2, column = 0, sticky = NW)\n\n #Job Number Input\n self.job_num_var = StringVar() #StringVar so that the text box is blank upon opening\n self.job_num_var.set(\"\")\n self.job_num_box = Entry(self.add_job_frame, font = (self.FONT, 10),\n textvariable = self.job_num_var, width = 30)\n self.job_num_box.grid(row = 2, column = 1, columnspan = 3, sticky = NW)\n\n #Name Label\n self.name_label = Label(self.add_job_frame, text = \"Customer Name\",\n font = (self.FONT, 11), bg = self.BG_COLOUR)\n self.name_label.grid(row = 3, column = 0, sticky = NW)\n\n #First Name Input\n self.first_name_var = StringVar()\n self.first_name_var.set(\"\")\n self.first_name_box = Entry(self.add_job_frame, font = (self.FONT, 10),\n width = 30, textvariable = self.first_name_var)\n self.first_name_box.grid(row = 4, column = 0, sticky = NW)\n\n #Last Name Input\n self.last_name_var = StringVar()\n self.last_name_var.set(\"\")\n self.last_name_box = Entry(self.add_job_frame, font = (self.FONT, 10),\n width = 30, textvariable = self.last_name_var)\n self.last_name_box.grid(row = 4, column = 1, columnspan = 3, sticky = NW)\n\n #First Name Label\n self.first_name_label = Label(self.add_job_frame, text = \"First Name\",\n font = (self.FONT, 8), bg = self.BG_COLOUR)\n self.first_name_label.grid(row = 5, column = 0, sticky = NW)\n\n #Last Name Label\n self.last_name_label = Label(self.add_job_frame, text = \"Last Name\",\n font = (self.FONT, 8), bg = self.BG_COLOUR)\n self.last_name_label.grid(row = 5, column = 1, sticky = NW)\n\n #Distance Travelled Label\n self.distance_label = Label(self.add_job_frame,\n text = \"Distance Travelled to Client (km)\",\n font = (self.FONT, 11), bg = self.BG_COLOUR)\n self.distance_label.grid(row = 6, column = 0, sticky = NW)\n\n #Distance Input\n self.distance_var = StringVar() #String not Int as IntVar automatically rounds down\n self.distance_var.set(\"\")\n self.distance_box = Entry(self.add_job_frame, font = (self.FONT, 10),\n width = 30, textvariable = self.distance_var)\n self.distance_box.grid(row = 6, column = 1, columnspan = 3, sticky = NW)\n\n #Minutes Spent on Virus Protection Label\n self.minutes_label = Label(self.add_job_frame,\n text = \"Minutes Spent on Virus Protection\",\n font = (self.FONT, 11), bg = self.BG_COLOUR)\n self.minutes_label.grid(row = 7, column = 0, sticky = NW)\n\n #Minutes Input\n self.minutes_var = StringVar() #StringVar so that the text box is blank upon opening\n self.minutes_var.set(\"\") \n self.minutes_box = Entry(self.add_job_frame, font = (self.FONT, 10),\n width = 30, textvariable = self.minutes_var)\n self.minutes_box.grid(row = 7, column = 1, columnspan = 3, sticky = NW)\n\n #Wof and tune Label\n wof_and_tune_label = Label(self.add_job_frame, text = \"Was a WOF and Tune Required?\",\n font = (self.FONT, 11), bg = self.BG_COLOUR)\n wof_and_tune_label.grid(row = 8, column = 0, sticky = NW)\n\n #Wof and tune radiobuttons\n self.wof_tune_var = StringVar()\n self.wof_tune_var.set(0)\n self.WOF_TUNE_RBS_NAMES = [\"Yes\", \"No\"]\n self.wof_tune_rbs = []\n \n for i in range(len(self.WOF_TUNE_RBS_NAMES)):\n self.wof_tune_rbs.append(Radiobutton(self.add_job_frame,\n text = self.WOF_TUNE_RBS_NAMES[i],\n value = self.WOF_TUNE_RBS_NAMES[i],\n variable = self.wof_tune_var,\n font = (self.FONT, 11), bg = self.BG_COLOUR))\n self.wof_tune_rbs[i].grid(row = 8, column = i +1, sticky = NW)\n\n #Enter Button to add a job\n #lambda code from\n #https://www.reddit.com/r/learnpython/comments/cdfmn5/tkinter_help_command_running_before_clicked/\n self.enter_job_btn = Button(self.add_job_frame, text = \"Enter\",\n font = (self.FONT, 11),width = 8, bg = self.BTN_COLOUR, \n command = lambda:self.store_input(Job)) \n self.enter_job_btn.grid(row = 9, column = 2, pady = 5, sticky = NE)\n\n #Reminder jobs a final label\n self.reminder_label = Label(self.add_job_frame,\n text = \"\"\"Note: All job entries are final so please\ndouble check before pressing enter\"\"\", \n font = (self.FONT, 7), bg = self.BG_COLOUR)\n self.reminder_label.grid(row = 10, column = 2, columnspan = 2)\n \n\n #Show Jobs Buttons\n self.show_jobs_btn = Button(self.add_job_frame, text = \"Show Jobs\",\n font = (self.FONT, 11), width = 8, bg = self.BTN_COLOUR, \n command = lambda:self.get_to_job_cards())\n self.show_jobs_btn.grid(row = 9, column = 3, padx = 5, pady = 5, sticky = NE)\n\n #Creation of other frames\n self.job_cards_frame = Frame(parent)\n\n self.error_message_frame = Frame(parent)\n\n self.summary_frame = Frame(parent)\n\n #Error message label creation\n self.error_label = Label(self.error_message_frame, font = (self.FONT, 9))\n\n def store_input(self, Job):\n self.error_label.configure(text = \"\") #Removing any previous error messages\n self.error_message_frame.grid_remove()\n\n #All input places need to be filled in \n if self.minutes_var.get() == \"\" or self.wof_tune_var.get() == \"0\" or \\\n self.first_name_var.get() == \"\" or self.last_name_var.get() == \"\" or \\\n self.job_num_var.get() == \"\" or self.distance_var.get() == \"\":\n self.add_job_frame.update_idletasks()\n self.error_label.configure(text = \"Please fill in all the entry areas before pressing enter.\")\n self.error_label.grid(row = 0, column = 1 )\n self.error_message_frame.grid(row = 0, column = 0, sticky = N)\n else:\n #If letters or decimals are entered in a field that does not take that type \n while True:\n try:\n self.error_label.configure(text = \"\") #Removing any previous error messages\n self.error_message_frame.grid_remove()\n self.add_job_frame.update_idletasks() \n self.minutes = float(self.minutes_var.get())\n self.wof_and_tune = self.wof_tune_var.get()\n #WOF and tune cannot be No if minutes is zero as then no task has been done \n if self.minutes == 0 and self.wof_and_tune == \"No\":\n self.add_job_frame.update_idletasks()\n self.error_label.configure(text = \"\"\"There is no task chosen.\nPlease increase minutes spent on virus protection or change WOF and Tune to 'Yes'\"\"\")\n self.error_label.grid(row = 0, column = 1 )\n self.error_message_frame.grid(row = 0, column = 0, sticky = N)\n break\n\n else:\n self.distance = float(self. distance_var.get())\n self.job_num = int(self.job_num_var.get())\n \n #If numbers below boundary are entered\n #Placed after storage as for calculation variables must be int/float\n if self.minutes < 0 or self.distance < 0 or self.job_num < 1:\n self.add_job_frame.update_idletasks()\n self.error_label.configure(text = \"\"\"That is not a valid entry. Please note that\njob number cannot be below 1, and minutes and distance cannot be below 0.\"\"\")\n self.error_label.grid(row = 0, column = 1 )\n self.error_message_frame.grid(row = 0, column = 0, sticky = N)\n break\n else:\n self.name = self.first_name_var.get().strip().capitalize() + \" \" + \\\n self.last_name_var.get().strip().capitalize()\n\n\n #job number must be unique\n if len(self.jobs)>0:\n self.add_job_frame.update_idletasks()\n for i in range(len(self.jobs)):\n if self.job_num == self.jobs[i].job_num: #comparing with other job numbers\n self.add_job_frame.update_idletasks() \n self.error_label.configure(text = \"\"\"Please change the job number.\nThe entered job number belongs to another job.\"\"\")\n self.error_label.grid(row = 0, column = 1 )\n self.error_message_frame.grid(row = 0, column = 0, sticky = N)\n self.job_num = 0 #this allows this entry to be easily identified as a repeat\n\n #Round Distance\n if self.distance % 1 >=0.5:\n self.distance = math.ceil(self.distance)\n else:\n self.distance = math.floor(self.distance)\n \n\n self.calc_charge()\n\n #Indivual Job Objects\n self.job = Job(self.job_num, self.name, self.distance, self.minutes,\n self.wof_and_tune, self.charge)\n \n #Collection of the Objects \n self.jobs.append(self.job)\n \n #If job number has been repeated, remove the entry that was saved\n if self.jobs[-1].job_num == 0:\n self.jobs.pop(-1)\n \n\n #Reset Input Areas\n if self.job_num !=0:\n self.job_num_var.set(\"\")\n self.first_name_var.set(\"\")\n self.last_name_var.set(\"\")\n self.distance_var.set(\"\")\n self.minutes_var.set(\"\")\n self.wof_tune_var.set(0)\n\n break\n \n \n except:\n self.add_job_frame.update_idletasks() \n self.error_label.configure(text = \"\"\"That is not a valid entry. Please note that job number, minutes,\nand distance must be numbers and that job number must be a whole number.\"\"\")\n self.error_label.grid(row = 0, column = 1 )\n self.error_message_frame.grid(row = 0, column = 0, sticky = N)\n break\n \n\n def calc_charge(self):\n self.charge = 0\n FLAT_RATE = 10\n if self.distance <= 5:\n self.charge += FLAT_RATE\n else:\n self.charge += FLAT_RATE + ((self.distance - 5)*0.5)\n\n if self.wof_and_tune == self.WOF_TUNE_RBS_NAMES[0]: #yes\n self.charge += 100\n\n if self.minutes >0:\n self.charge += 0.80 *self.minutes\n\n #making charge 2dp as common for money\n #code inspired by:\n #https://www.kite.com/python/answers/how-to-print-a-float-with-two-decimal-places-in-python#:~:\n #text=Use%20str.,number%20with%20two%20decimal%20places.\n self.charge = \"{:.2f}\".format(self.charge) \n\n\n def get_to_job_cards(self):\n self.error_label.configure(text = \"\")\n self.error_message_frame.grid_remove() #removing any previous error messages\n if len(self.jobs)>0:\n self.add_job_frame.grid_remove()\n self.summary_frame.grid_remove()\n self.job_cards_frame.grid(row = 0, column = 0)\n self.job_cards_frame.configure(pady = 10, padx = 13, bg = self.BG_COLOUR)\n self.index = 0 #which job the cards are on\n self.job_cards_frame.update_idletasks()\n #Company Logo\n #Suzy has supplied and given permission for this logo to be used in this program\n\n self.logo_label = Label(self.job_cards_frame, image = self.logo, bg = self.BG_COLOUR)\n self.logo_label.grid(row = 0, column = 0, columnspan = 4)\n\n #Jobs Heading Label\n self.jobs_label = Label(self.job_cards_frame, text = \"Jobs\",\n font = (self.FONT, 17), pady = 5, bg = self.BG_COLOUR)\n self.jobs_label.grid(row = 1, column = 0, sticky = NW, pady = 5)\n\n #Text Box of Job Info\n self.job_info = Text(self.job_cards_frame, width = 48, height = 3,\n font = (self.FONT, 13), pady = 8, padx = 8)\n self.job_info.grid(row = 2, column = 0, columnspan = 4)\n\n\n self.job_info.insert(END,\"Job Number: \"+ str(self.jobs[self.index].job_num) + \"\\n\" +\n \"Customer Name: \" + self.jobs[self.index].name + \"\\n\" +\n \"Total Charge: $\" + str(self.jobs[self.index].charge))\n\n self.job_info.configure(state = 'disabled') #Disabling so the box is not typable in\n\n #Next and Prev Buttons\n self.next_card_btn = Button(self.job_cards_frame, text = \"Next Job\",\n font = (self.FONT, 11), width = 8, bg = self.BTN_COLOUR,\n command = self.next_job)\n self.next_card_btn.grid(row = 3, column = 2, sticky = NW, pady = 5)\n\n self.prev_card_btn = Button(self.job_cards_frame, text = \"Prev Job\",\n font = (self.FONT, 11), width = 8, bg = self.BTN_COLOUR,\n command = self.prev_job)\n self.prev_card_btn.grid(row = 3, column = 1, sticky = NE, pady = 5, padx = 5)\n\n #Getting to Add a Job Frame Button\n self.add_job_btn = Button(self.job_cards_frame, text = \"Add a Job\",\n font = (self.FONT, 11), width = 8, bg = self.BTN_COLOUR, \n command = self.get_to_add_jobs)\n self.add_job_btn.grid(row = 4, column = 3, sticky = NE, pady = 5)\n\n #Getting to Summary Frame Button\n #Icon from flaticon, free licensed for use,\n self.icon = PhotoImage(file = \"info-button.png\")\n self.summary_btn = Button(self.job_cards_frame, image = self.icon,\n font = (self.FONT, 11), width = 30, height = 30,\n bg = self.BTN_COLOUR, command = self.get_to_summary)\n self.summary_btn.grid(row = 1, column = 3, sticky = NE, pady = 5)\n \n else:\n self.add_job_frame.update_idletasks()\n self.error_label.configure(text = \"There are no jobs stored\")\n self.error_label.grid(row = 0, column = 1 )\n self.error_message_frame.grid(row = 0, column = 0, sticky = N)\n\n \n def next_job(self):\n if len(self.jobs) > 1:\n if self.index != (len(self.jobs)-1): #if at end of list, go back to start\n self.index+=1\n else:\n self.index = 0\n\n self.job_info.configure(state = \"normal\") #undisabling box so content can change\n #updating text box to info for next job\n self.job_info.delete(1.0, END)\n self.job_info.insert(END,\"Job Number: \"+ str(self.jobs[self.index].job_num) + \"\\n\" +\n \"Customer Name: \" + self.jobs[self.index].name + \"\\n\" +\n \"Total Charge: $\" + str(self.jobs[self.index].charge))\n self.job_info.configure(state = \"disabled\")\n else:\n self.error_label.configure(text = \"There is only one job stored\")\n self.error_label.grid(row = 0, column = 1 )\n self.error_message_frame.grid(row = 0, column = 0, sticky = N) \n\n def prev_job(self):\n if len(self.jobs) > 0:\n if self.index !=0:\n self.index-=1\n else:\n self.index = (len(self.jobs)-1)\n\n self.job_info.configure(state = \"normal\")\n self.job_info.delete(1.0, END)\n self.job_info.insert(END,\"Job Number: \"+ str(self.jobs[self.index].job_num) + \"\\n\" +\n \"Customer Name: \" + self.jobs[self.index].name + \"\\n\" +\n \"Total Charge: $\" + str(self.jobs[self.index].charge))\n self.job_info.configure(state = \"disabled\")\n else:\n self.error_label.configure(text = \"There is only one job stored\")\n self.error_label.grid(row = 0, column = 1 )\n self.error_message_frame.grid(row = 0, column = 0, sticky = N)\n\n def get_to_add_jobs(self):\n self.error_label.configure(text = \"\")\n self.error_message_frame.grid_remove() #removing any previous error messages\n self.job_cards_frame.grid_remove()\n self.summary_frame.grid_remove()\n self.add_job_frame.grid(row = 0, column = 0)\n self.add_job_frame.update_idletasks()\n\n def get_to_summary(self):\n self.job_cards_frame.grid_remove()\n self.summary_frame.grid(row = 0, column = 0)\n self.summary_frame.update_idletasks()\n self.summary_frame.configure(pady = 10, padx = 13, bg = self.BG_COLOUR)\n\n #Company Logo\n #Suzy has supplied and given permission for this logo to be used in this programme\n\n self.logo_label = Label(self.summary_frame, image = self.logo, bg = self.BG_COLOUR)\n self.logo_label.grid(row = 0, column = 0, columnspan = 2)\n\n #Summary Heading Label\n self.summary_label = Label(self.summary_frame, text = \"Summary of Jobs\",\n font = (self.FONT, 17), pady = 5, bg = self.BG_COLOUR)\n self.summary_label.grid(row = 1, column = 0, sticky = NW, pady = 5)\n\n #Scrolled Text box of Job Information\n self.job_summary_box = ScrolledText(self.summary_frame, font = (self.FONT, 13),\n pady = 8, padx = 8, height = 8, width = 46,\n wrap = 'word')\n self.job_summary_box.grid(row = 2, column = 0, columnspan = 2)\n\n for i in range(len(self.jobs)):\n self.job_summary_box.insert(END,\"Job Number: \"+ str(self.jobs[i].job_num) + \"\\n\" +\n \"Customer Name: \" + self.jobs[i].name + \"\\n\" +\n \"Distance Travelled to Client (km): \"\n + str(self.jobs[i].distance) +\n \"\\n\" + \"Minutes spent on Virus Protection: \"\n + str(self.jobs[i].minutes) +\n \"\\n\" + \"Was WOF and Tune Required? \"\n + self.jobs[i].wof_and_tune + \"\\n\"\n + \"Total Charge: $\" + str(self.jobs[i].charge)\n + \"\\n\" + \"_____________________________\" + \"\\n\")\n\n self.job_summary_box.configure(state = 'disabled') #Disabling so the box is not typable in\n\n #Getting to Add job frame button\n self.add_a_job_btn = Button(self.summary_frame, text = \"Add a Job\",\n font = (self.FONT, 11), width = 8, bg = self.BTN_COLOUR, \n command = self.get_to_add_jobs)\n self.add_a_job_btn.grid(row = 3, column = 1, sticky = NE, pady = 10)\n\n #Back to Job Cards frame button\n self.back_to_job_cards_btn = Button(self.summary_frame, text = \"Back\",\n font = (self.FONT, 11), width = 8, bg = self.BTN_COLOUR, \n command = self.get_to_job_cards)\n self.back_to_job_cards_btn.grid(row = 1, column = 1, pady = 5, sticky = NE)\n \n \n\n#Main Routine\nif __name__==\"__main__\":\n root= Tk()\n ratings = JobCostGUI(root)\n root.title(\"Job Cost/Charge Calculator\")\n root.mainloop()\n\n","sub_path":"job_cost_charge_calculator.py","file_name":"job_cost_charge_calculator.py","file_ext":"py","file_size_in_byte":22784,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"539069234","text":"##################################\n# FOR GETTING ANOMALIES\n\ndef version_1(dx): # constant stddev threshold across time\n anomalies = dx\\\n .where(dx.iloc[:, [0]] > dx.iloc[:, [0]].std())\\\n .dropna()\n return anomalies\n\ndef version_2(dx, lookback=10):\n anomalies = dx \\\n .assign(std_dev = dx.rolling(lookback).std())# \\\n anomalies = anomalies \\\n .assign(Interest = anomalies.Interest / anomalies.std_dev)# \\\n anomalies = anomalies \\\n .where(anomalies.Interest > 1.0) \\\n .dropna()\n return anomalies\n\ndef get_anomalies(data, fun, **kwargs):\n # dx = np.asarray(data.Blockchain[1:]) - np.asarray(data.Blockchain[:-1])\n dx = data.diff()\n if fun == version_1:\n anomalies = fun(dx)\n elif fun == version_2:\n anomalies = fun(dx, **kwargs)\n num_anomalies = len(anomalies)\n if num_anomalies >= 11: # more than ten anomalies\n anomalies = anomalies.sort_values(by='Interest', ascending=False)\\\n .head(10)\n return num_anomalies, anomalies.index","sub_path":"client/anomalies.py","file_name":"anomalies.py","file_ext":"py","file_size_in_byte":1064,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"133038259","text":"BENCHMARKS = [\n { 'bench': 'BunnymarkV2', 'lang': 'gd', },\n { 'bench': 'BunnymarkV2', 'lang': 'js', },\n { 'bench': 'BunnymarkV1Sprites', 'lang': 'gd', },\n { 'bench': 'BunnymarkV1Sprites', 'lang': 'js', },\n { 'bench': 'BunnymarkV1DrawTexture', 'lang': 'gd', },\n { 'bench': 'BunnymarkV1DrawTexture', 'lang': 'js', },\n { 'bench': 'fib', 'lang': 'gd', },\n { 'bench': 'fib', 'lang': 'js', },\n]\nimport os\nfor b in BENCHMARKS:\n commands = ' --bench=' + b['bench'] + ' --lang=' + b['lang']\n os.system('~/Documents/Workspace/Develop/Godot/godot/bin/godot.x11.opt.64 --path . ' + commands)","sub_path":"run_benchmarks.py","file_name":"run_benchmarks.py","file_ext":"py","file_size_in_byte":609,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"317590146","text":"from validator import Required, Equals, validate\nfrom ficheros.codigo import Generador\ngenerador = Generador()\nclass HeaderController:\n def validar_header(self,header):\n rules = {\n \"Authorization\": [Required],\n \"Content-Type\": [Required,Equals('application/json')]\n }\n respuesta=validate(rules, header)\n if respuesta[0]:\n return True,''\n else:\n codigo = generador.validarGuardarInformacionError(\"000\",\"validar header- no se enviaron todo los parametros- header_controller\",\"post\",'') \n return False ,codigo\n ","sub_path":"mype/controller/header_controller.py","file_name":"header_controller.py","file_ext":"py","file_size_in_byte":608,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"268681133","text":"from StatFunctions.StatObj import *\nimport numpy as np\nfrom StatFunctions.PDF import *\nimport math\nimport matplotlib.pyplot as plt \n\ndef EStep(data,cluster,Stat):\n ''' Calculates probability of each data for each cluster\n '''\n rValue = np.zeros((len(data),cluster)) \n for x,mean,var,pi in zip(range(cluster),Stat.mean,Stat.variance,Stat.pi):\n rValue[:,x] = pi* pdf(data,mean,var)\n for i in range(len(rValue)):\n rValue[i] = rValue[i]/(np.sum(Stat.pi)*np.sum(rValue,axis=1)[i])\n return rValue\n\ndef MStep(data,cluster,Stat,rValue):\n ''' \n Calculate fraction point for each cluster\n Update all paramaters accordingly - Using M Step Formulas\n '''\n fractPnt = []\n for x in range(len(rValue[0])):\n fractPnt.append(np.sum(rValue[:,x]))\n for k in range(len(fractPnt)):\n Stat.pi[k] = (fractPnt[k]/np.sum(fractPnt))\n Stat.mean = np.sum(data.reshape(len(data),1)*rValue,axis=0)/fractPnt\n var_c = []\n for x in range(len(rValue[0])):\n var_c.append((1/fractPnt[x])*np.dot(((np.array(rValue[:,x]).reshape(len(data),1))*(data.reshape(len(data),1)-Stat.mean[x])).T,(data.reshape(len(data),1)-Stat.mean[x])))\n #for x in range(len(var_c)):\n # Stat.variance[x] = (len(data)-1 /len(data))* var_c[x][0][0]\n return Stat\n\n\ndef LogLikelyHood(data,Stat: Stat):\n ''' \n Find Likelihood for the given data and stat paramaters\n '''\n logLik = 0\n for x in range(len(Stat.mean)):\n varS = Stat.variance[x] * Stat.variance[x]\n term1 = ((len(data)) * math.log(2 * math.pi))/(-1*2)\n term2 = ((len(data)) * math.log(varS))/(-1*2)\n term3 = np.sum((np.power(np.subtract(data,Stat.mean[x]),2))/(-1*2*varS),axis=0)\n logLik += term1+term2+term3\n return logLik\n\ndef FindOptimalClusters(dataArr):\n '''\n Finds Optimal Clusters for the given Data Set\n 1) Run for each cluster starting from 2 to 9\n 2) Execute EStep and Mstep for 10 times\n 3) Record Log likelihood for each iterations\n 4) If the log likelihood values are monotonically increasing, it is the optimal cluster\n 5) Else continue with next cluster\n '''\n optCluster = 0\n for cluster in range(2,10):\n logs=[]\n print('Checking feasibility of Cluster:' + str(cluster))\n data= np.empty(len(dataArr))\n np.copyto(data,dataArr)\n stat = Stat()\n stat.SetInitialValues(cluster,data)\n for x in range(10):\n rValue = EStep(data,cluster,stat)\n stat = MStep(data,cluster,stat,rValue)\n logLikely = LogLikelyHood(data,stat)\n logs.append(logLikely)\n if all(x Null values replaced by zero, we can replace it with any other value depending on circumstances\n 0 1 2\n0 0.0 1.0 2\n1 2.0 3.0 4\n2 3.0 0.0 5\n\"\"\"\n\"\"\"\nOther method:\n Forward fill --> to propagate the previous value forward : df.fillna(method='ffill')\n Back fill --> to propagate the next values backward : df.fillna(method='bfill')\n \n\"\"\"\n","sub_path":"Handling missing values in Pandas.py","file_name":"Handling missing values in Pandas.py","file_ext":"py","file_size_in_byte":3135,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"418783067","text":"import cv2\nfrom recognize_server.face_recognizer import FaceRecognizer\nfrom management_server.user_server import UserServer, User\nimport os\nimport numpy as np\n\nuser_server = UserServer()\n\nrecognizer = FaceRecognizer(recognize_flag=False)\n\n\ndef cv2_imread(filePath):\n cv_img = cv2.imdecode(np.fromfile(filePath, dtype=np.uint8), -1)\n\n return cv_img\n\n\ndef sign_up():\n src_dir = 'xdemo_src_img'\n dirs = os.listdir(src_dir)\n\n for name in dirs:\n user = User()\n\n user.name = name\n user.title = \"學生\"\n user.college = \"工學院\"\n user.department = \"資工系（日）\"\n\n img_dir = src_dir + '/' + name\n for img_name in os.listdir(img_dir):\n img = cv2_imread(img_dir + '/' + img_name)\n frame, face_embs = recognizer.embedding(img)\n user.face_imgs.append(frame)\n user.face_embs.append(face_embs[0])\n\n ok = user_server.new_user(user)\n\n if ok:\n print(\"'{}' sign up success !!\".format(user.name))\n\n else:\n print(\"'{}' is exist !!\".format(user.name))\n\n\nif __name__ == '__main__':\n sign_up()\n","sub_path":"xdemo/demo_signup.py","file_name":"demo_signup.py","file_ext":"py","file_size_in_byte":1135,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"72584366","text":"\"\"\"\nTo use the python script in Command Prompt:\nWrite 'python your_file_name.py'\n\"\"\"\n\n# Four different arrays\nnumbers = [5, 10, 50, 75, 250]\nmoreNumbers = [540, 41, 78, 91, 2]\nonlyTwoNumbers = [0, 1]\nonlyOneNumber = [5]\n\n# Function that finds the minimum and maximum numbers from an array\ndef FindMinMax(arrayOfNumbers):\n # Uses the built-in function min() to find the lowest number\n minimumNumber = min(arrayOfNumbers)\n # Uses the built-in function max() to find the largest number\n maximumNumber = max(arrayOfNumbers)\n # Prints a message with a normal string\n print(\"From the list specified:\")\n # Prints the minimum and maximum number using a formatted (f) string\n print(f\"Minimum is {minimumNumber}\")\n print(f\"Maximum is {maximumNumber}\\n\")\n\n# Calls the functions with arguments\nFindMinMax(numbers)\nFindMinMax(moreNumbers)\nFindMinMax(onlyTwoNumbers)\nFindMinMax(onlyOneNumber)","sub_path":"challenge101/challenge101.py","file_name":"challenge101.py","file_ext":"py","file_size_in_byte":905,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"5750657","text":"import sys\nimport itertools\nimport re\nfrom Bio import SeqIO\n\n# For a fixed positive integer k, order all possible k-mers taken from an underlying alphabet lexicographically.\n#\n# Then the k-mer composition of a string s can be represented by an array A for which A[m] denotes the number of\n# times that the mth k-mer (with respect to the lexicographic order) appears in s.\n#\n# Given: A DNA string s in FASTA format (having length at most 100 kbp).\n#\n# Return: The 4-mer composition of s.\n\nfile_path = sys.argv[1]\nsequence = \"\"\n# read input file into sequence\nwith open(file_path,'r') as handle:\n for record in SeqIO.parse(handle,\"fasta\"):\n sequence = str(record.seq)\n\n# helper function to generate all permutations of a certain alphabet\n# Lexicographical order is automatically achieved through this\ndef generate_permutations(letters):\n return [''.join(i) for i in itertools.product(letters,repeat = 4)]\n\nall_permutations = generate_permutations(\"ACGT\")\nall_counts = []\n# search for all occurences of every permutation in the sequence and count the number of occurences\nfor permutation in all_permutations:\n pattern = re.compile(r'(?=(' + permutation + '))')\n permutation_count = 0\n for occurence in re.findall(pattern,sequence):\n permutation_count += 1\n all_counts.append(permutation_count)\nprint(*all_counts,sep=' ')\n\n\n\n\n\n\n","sub_path":"Bioinformatics_Stronghold/k-Mer_Composition.py","file_name":"k-Mer_Composition.py","file_ext":"py","file_size_in_byte":1356,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"332234746","text":"import os\r\nimport os.path\r\nimport re\r\nimport glob\r\nimport errno\r\nimport io\r\n\r\n#DESCRICAO\r\n# Clean the stamps from CORPO in the text\r\n\r\n\r\npath=[]\r\ncorpo_ident=r'([\\s\\S]*?)<\\/CORPO>'\r\nre_space=r'(\\A\\s*?([\\s\\S]*?)<\\/CORPO>',(''+str(corpo_depois[0][0])+''),text)\r\n\t\t\t\t\t\tf=open(path_clean_total,\"r+\",encoding='utf-8')\r\n\t\t\t\t\t\tf.write(text_cleaned)\r\n\t\t\t\t\t\tf.read()\r\n\t\t\t\t\telse:\r\n\t\t\t\t\t\tcopyfile(name,path_clean_total)\r\n\t\t\t\t\tfile.close()\r\n\t\t\t\t\t\r\n\t\t\t\telse:\r\n\t\t\t\t\tcopyfile(name,path_clean_total)\r\n\t\t\t\t\tprint(name + \" dont have CORPO.\")\r\n\t\t\t\t\tnotcorpo+=1'''\r\n\r\n\t\texcept IOError as exc: \r\n\t\t\tif exc.errno != errno.EISDIR:\r\n\t\t\t\traise\r\n\r\nif __name__== \"__main__\":\r\n\tmain()","sub_path":"Python/Auxiliar/clean_cabecalho.py","file_name":"clean_cabecalho.py","file_ext":"py","file_size_in_byte":2263,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"578470581","text":"# coding: utf-8\n\nfrom hashlib import md5\n\nfrom django.test import TestCase\n\nfrom core import utils as u\n\n\nclass UploadToTestCase(TestCase):\n\n def setUp(self):\n self.filename = 'image.jpg'\n self.hash = md5(self.filename.encode('utf-8')).hexdigest()\n self.upload_path = '/'.join(\n [self.hash[:2], self.hash[2:4], self.hash + '.jpg'])\n\n def test_upload_to_directory(self):\n uploader = u.upload_to('dir')\n expected_dir = 'dir/' + self.upload_path\n assert uploader(instance=None, filename=self.filename) == expected_dir\n\n def test_upload_to_empty_directory(self):\n uploader = u.upload_to()\n assert uploader(instance=None, filename=self.filename) == self.upload_path\n","sub_path":"src/core/tests/test_utils.py","file_name":"test_utils.py","file_ext":"py","file_size_in_byte":737,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"583616000","text":"import pandas as pd\nimport numpy as np\nimport scipy as sp\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom preprocessing import transform_and_extract_train, transform_and_extract_test\nfrom plot_result import plot_all_results\nfrom imblearn.under_sampling import RandomUnderSampler\nfrom sklearn.metrics import accuracy_score,classification_report,roc_auc_score,roc_curve,auc,precision_recall_curve,confusion_matrix\n\nfrom keras.wrappers.scikit_learn import KerasClassifier\nfrom keras.models import Sequential,Model\nfrom keras.layers import Dense, Dropout, LeakyReLU\nfrom keras import backend as K\nfrom keras import regularizers\nfrom keras.optimizers import sgd\n\nurl1 = './data/training_set.csv'\ndf = pd.read_csv(url1)\nX_train, y_train = transform_and_extract_train(df)\n\nurl2 = './data/test_set_sample.csv'\ndf_test = pd.read_csv(url2)\nX_test, y_test = transform_and_extract_test(df_test)\n\nru = RandomUnderSampler('majority')\nX_resampled, y_resampled = ru.fit_sample(X_train, y_train)\n\nK.clear_session()\nK.set_learning_phase(1)\n\ndef prediction_model():\n optimizer = sgd(lr=0.001,momentum=0.001)\n model = Sequential()\n model.add(Dense(X_train.shape[1], input_dim=X_train.shape[1], kernel_initializer='normal', activation='relu'))\n model.add(Dropout(0.1))\n model.add(Dense(int(X_train.shape[1]/2),kernel_initializer='normal', activation='relu'))\n model.add(LeakyReLU(alpha=0.1))\n model.add(Dropout(0.1))\n model.add(Dense(int(X_train.shape[1]/4),kernel_initializer='normal', activation='tanh'))\n model.add(LeakyReLU(alpha=0.1))\n model.add(Dropout(0.1))\n model.add(Dense(1, kernel_initializer='normal', activation='sigmoid'))\n model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n return model\n\nestimator = KerasClassifier(build_fn=prediction_model, epochs=1000, batch_size=50, verbose=1)\nestimator.fit(X_resampled, y_resampled , validation_split=0.1)\n\nresults = estimator.predict(X_test)\n\noutput = pd.DataFrame(results)\noutput.to_csv('./data/output/test_set_sample_predictions.csv',index=None, header= False, columns= None)\n\nif len(y_test)>1: #metrics visualization will only be outputed if 'went_on_backorder' column is present in testing set\n print('===========================')\n print('Accuracy Score: ',accuracy_score(results,y_test))\n print('\\n')\n\n print('===========================')\n print('ROC AUC Score: ',roc_auc_score(results,y_test))\n print('\\n')\n\n print('===========================')\n print('Confusion Matrix')\n print(pd.DataFrame(confusion_matrix(results, y_test),columns=[-1,1],index=[-1,1]))\n print('\\n')\n\n print('===========================')\n print('Classification Report')\n print(classification_report(results,y_test))\n print('\\n')\n\n probs = estimator.predict_proba(X_test)\n plot_all_results(results, probs, y_test)\n\nprint('Finished. Please check data folder for output')\n","sub_path":"final/predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":2915,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"597563624","text":"'''\nCreated on Mar 9, 2018\n\n@author: lorenzo\n'''\nfrom it.ds4biz.annotator.SimpleAnnotator import SimpleAnnotator\nfrom it.ds4biz.builder.TrainBuilders import FilterWindowsTrainBuilder\nfrom it.ds4biz.feature.FeatureExtractor import WindowsTextFeatureExtractor\nfrom it.ds4biz.util.WordTokenizer import word_tokenizer\n\nclass ManualAnnotatorBuilder:\n '''\n crea un dataset con le annotazioni manuali\n '''\n\n\n def __init__(self, train_builder ,num_pred_word = -8,num_post_word = 8):\n '''\n Constructor\n '''\n self.num_pred_word = num_pred_word\n self.num_post_word = num_post_word\n self.annotator = SimpleAnnotator()\n self.train_builder = train_builder\n self.traindata = []\n \n def add_manual_annotation(self, text, target_tokens):\n \n annotext = self.annotator.annotate(text, target_tokens)\n print(list(annotext.all_annotation()))\n new_dataset_part = self.train_builder.build(annotext)\n self.traindata.extend(new_dataset_part)\n \n def all_dataset(self):\n for elment in self.traindata:\n yield elment\n \nif __name__ == '__main__':\n text = \"Buongiorno sono il Dottor. Zingler grande amico del Dott. Giorgio\"\n text = word_tokenizer(text)\n target_tokens = [\"Zingler\", \"Giorgio\"]\n print(text,target_tokens)\n gen=FilterWindowsTrainBuilder(WindowsTextFeatureExtractor(),-100,100)\n\n annbuild = ManualAnnotatorBuilder(gen)\n annbuild.add_manual_annotation(text, target_tokens)\n for a in annbuild.all_dataset():\n print(a)\n \n \n \n ","sub_path":"ds4biz/ds4biz/it/ds4biz/builder/ManualAnnotator.py","file_name":"ManualAnnotator.py","file_ext":"py","file_size_in_byte":1604,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"253669302","text":"import player\nimport stations\nimport gpio\nfrom time import sleep\n\n# load stations\nstations = stations.load(\"stations.txt\")\n\n# checking gpio status and execute whatever stuff\nradio = player.Player()\ngpios = gpio.GPIO()\nurls = []\nbutton_old = 666\nplay_old = 666\nwhile True:\n button, play = gpios.status()\n if (button != button_old) or (play != play_old):\n\n if (button != button_old):\n if button == 0:\n urls = stations[0]\n elif button == 1:\n urls = stations[1]\n elif button == 2:\n urls = stations[2]\n elif button == 3:\n urls = stations[3]\n\n if (play != play_old):\n if play:\n if radio.getIsOn():\n radio.transition(0)\n radio.stop()\n radio.start(urls)\n sleep(1)\n radio.transition(1)\n else:\n radio.stop()\n if (play != play_old):\n if play:\n radio.start(urls)\n sleep(1)\n radio.transition(1)\n else:\n radio.stop()\n\n button_old = button\n play_old = play\n\n sleep(0.05)","sub_path":"sternradio.py","file_name":"sternradio.py","file_ext":"py","file_size_in_byte":1260,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"551013314","text":"import pandas as pd\nimport numpy as np\nimport os\nfrom torch.utils.data import Dataset, DataLoader\nimport math\nimport json\nimport itertools\nimport torch\nfrom tqdm import tqdm\n\ndef correct_batch(batch):\n while len(batch)<4000:\n batch = np.append(batch,0)\n return batch\n\nclass parkinsonsData(Dataset):\n \n def __init__(self, df, col):\n file_df = pd.read_csv(\"../../../data3/mPower/52461358.csv\")\n # if col==14:\n # files = file_df[\"deviceMotion_walking_rest.json.items\"].tolist()\n # elif col==8:\n # files = file_df[\"deviceMotion_walking_outbound.json.items\"].tolist()\n # else:\n # files = file_df[\"deviceMotion_walking_return.json.items\"].tolist()\n\n self.users = pd.read_csv(\"../../../data3/mPower/users.csv\")\n self.col = col\n self.dataset = []\n\n for z in tqdm(file_df.iterrows()):\n healthcode = z[1][3]\n try:\n label = self.users.loc[self.users['healthCode'] == healthcode][\"professional-diagnosis\"]\n \n if label.values[0]:\n label=1\n else:\n label=0\n if(os.path.exists(\"../../../data3/mPower/data/\"+str(int(z[1][col]))+\".json\")):\n f = open(\"../../../data3/mPower/data/\"+str(int(z[1][col]))+\".json\")\n data = json.load(f)\n if data != None:\n \n x=[]\n x.append([])\n x.append([])\n x.append([])\n \n for i in range(0,len(data),2):\n rot = data[i].get(\"rotationRate\")\n x[0].append(rot[\"x\"])\n x[1].append(rot[\"y\"])\n x[2].append(rot[\"z\"])\n \n stdev = np.std(np.asarray(x))\n mean = np.mean(np.asarray(x))\n x = ((np.asarray(x)-mean)/stdev).tolist()\n \n x[0] = correct_batch(x[0])\n x[1] = correct_batch(x[1])\n x[2] = correct_batch(x[2])\n \n # stdev = np.std(np.asarray(x))\n # mean = np.mean(np.asarray(x))\n # x = ((np.asarray(x)-mean)/stdev)\n \n self.dataset.append([np.asarray(x),label])\n except:\n continue\n \n\n def __len__(self):\n return len(self.dataset)\n\n def __getitem__(self, idx):\n print(self.dataset[idx][0])\n return self.dataset[idx][0], self.dataset[idx][1] \n\n","sub_path":"parkinsons_dataset.py","file_name":"parkinsons_dataset.py","file_ext":"py","file_size_in_byte":2654,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"154356381","text":"import json\nimport hashlib\n\n# function to parse DID from public key\ndef didFromPK(pubKey):\n did = \"did:vtn:trustid:\" + str(hashlib.sha256(pubKey).hexdigest())\n return did\n\ndef didFromWallet(did_wallet_path):\n with open(did_wallet_path) as did_file:\n data = json.load(did_file)\n temp_did = data['dids']\n did = temp_did[0]['did']\n return did","sub_path":"app/modbus-sync-server/hfbssisdk/src/hfbssi/didFromPK.py","file_name":"didFromPK.py","file_ext":"py","file_size_in_byte":372,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"477912852","text":"import nltk\nimport random\n\ndef randomize(x):\n\tnew_seq = []\n\trand_list = []\n\twords = nltk.word_tokenize(x)\n\tfor i in words:\n\t\tif len(i) >= 5:\n\t\t\trand_list.append(i)\n\t\t\t#secure_random = random.SystemRandom()\n\t\t\t#selected_word = secure_random.choice(rand_list)\n\t\t\ti = i[:-1]\n\t\t\tprint(i)\n\t\t\tnew_seq.append(i)\n\t\telse:\n\t\t\tnew_seq.append(i)\n\tadversarial = ' '.join(new_seq)\n\treturn(adversarial)\n\nadversarial = randomize(\"This movie is terrible but it has some good effects.\")\nprint(adversarial)\n","sub_path":"code_0.3/randomize_sentence.py","file_name":"randomize_sentence.py","file_ext":"py","file_size_in_byte":488,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"621652078","text":"from lettuce import *\nfrom should_dsl import *\n\nfrom player import Player\nfrom buraco import *\n\n@step(r'I have the players (.*)')\ndef given_i_have_the_players(step, names):\n names = names.split(', ')\n world.players = []\n for name in names:\n world.players.append(Player(name))\n\n@step(r'I initialize the game')\ndef when_i_initialize_the_game(step):\n try:\n world.buraco = Buraco(world.players)\n except InvalidNumberOfPlayers as e:\n world.msg = e.msg\n\n@step(r'each player has 11 cards')\ndef then_each_player_has_11_cards(step):\n for player in world.players:\n len(player.hand) |should| equal_to(11)\n\n@step(r'the game cannot be started')\ndef the_game_cannot_be_started(step):\n world.msg |should_be.equal_to| \"The number of players must be 2, 3 or 4\"\n\n@step(r'the game has 2 pots with 11 cards each')\ndef the_game_has_2_pots_with_11_cards_each(step):\n for pots in world.buraco.pots:\n len(pots) |should| equal_to(11)\n\n@step(r'the game has the stock with (\\d+)')\ndef the_game_has_the_stock_with(step, ramaining_cards):\n ramaining_cards = int(ramaining_cards)\n len(world.buraco.stock) |should| equal_to(ramaining_cards)\n\n","sub_path":"features/steps_definition/steps.py","file_name":"steps.py","file_ext":"py","file_size_in_byte":1176,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"228619713","text":"#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n# vim: tabstop=4 shiftwidth=4 expandtab\n\nimport os\nimport sys\nimport optparse\nfrom repomanager.rpm.repo import RPMRepManager\n\ndef main():\n parser = RPMRepManager.MakeNeededOptions()\n (options, args) = parser.parse_args()\n if not options.base:\n parser.error('No base given')\n if not options.version:\n parser.error('No version given')\n if not options.arch:\n parser.error('No arch given')\n\n rep = RPMRepManager(options)\n rpmlist = []\n for arch in [options.arch, 'noarch']:\n rpmlist += rep.list_rpms([options.base + '/' + options.version + '/' + arch])\n signed, unsigned = rep.sort_signed(rpmlist)\n for u in unsigned:\n sys.stdout.write(u.get(\"fname\") + '\\n')\n sys.stdout.flush()\n\nif __name__ == '__main__':\n main()\n","sub_path":"findunsigned.py","file_name":"findunsigned.py","file_ext":"py","file_size_in_byte":829,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"555180618","text":"#!/usr/bin/python\n\nimport sys, getopt, csv\nfrom datetime import datetime, timedelta\n\n\ndef main(argv):\n inputfile = None\n outputfile = None\n try:\n opts, args = getopt.getopt(argv, \"hf:o:\", [\"ifile=\", \"ofile=\"])\n except getopt.GetoptError:\n print('test.py -f -o ')\n sys.exit(2)\n for opt, arg in opts:\n if opt == '-h':\n print('test.py -f -o ')\n sys.exit()\n elif opt in (\"-f\", \"--ifile\"):\n inputfile = arg\n elif opt in (\"-o\", \"--ofile\"):\n outputfile = arg\n\n print('Input file is ', inputfile)\n print('Output file is ', outputfile)\n\n transform(inputfile, outputfile)\n\ndef transform(inputfile, outputfile):\n print('transforming')\n header = ['ts_name', 'time', 'unit', 'value', 'class']\n read_header = ['stn', 'time', 'tso005s0']\n with open(inputfile, 'r') as file_input, open(outputfile, 'w') as file_output:\n reader = csv.reader(file_input, delimiter=';')\n next(reader, None) # skip the headers\n writer = csv.writer(file_output, delimiter=';')\n writer.writerow(header)\n\n for row in reader:\n row = [row[0], row[1], 'Celsius', row[2], 0]\n writer.writerow(row)\n\n print('transforming done')\n\n\nif __name__ == \"__main__\":\n main(sys.argv[1:])\n","sub_path":"misc/tools/idaweb.py","file_name":"idaweb.py","file_ext":"py","file_size_in_byte":1369,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"48339331","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nimport os\nimport json\nimport codecs\nimport shutil\nimport re\n\nclass JSONSchema2RST:\n def __init__(self):\n self.data = {'type': 'root', 'children': {}}\n self.source = None\n self.target = None\n self.toctrees = []\n\n def travel(self, source='./schemas', target='./source'):\n self.source = source\n self.index_target = target\n self.target = os.path.join(target, 'apis')\n self.blob_template = os.path.join(self.source, '_template/blob.rst')\n self.toc_template = os.path.join(self.source, '_template/toc.rst')\n self.index_template = os.path.join(self.source, '_template/index.rst')\n\n for root, subdirs, files in os.walk(source):\n for f in files:\n if os.path.basename(f) == 'config.json':\n config_path = os.path.join(root, os.path.basename(f))\n config_data = self.parse_config(os.path.abspath(config_path))\n config_type = config_data.get('type', 'toc')\n\n uri = self.get_uri(root)\n config_data.update({\n 'uri': uri\n })\n\n if config_type == 'toc':\n config_data.update({\n 'children': {}\n })\n elif config_type == 'blob':\n request_blob_path = os.path.join(root, 'request.json')\n response_blob_path = os.path.join(root, 'response.json')\n config_data.update({\n 'request': self.parse_config(request_blob_path),\n 'response': self.parse_config(response_blob_path)\n })\n\n self.update_uri_config(root, config_data)\n\n self.export_doc(self.data)\n\n toctrees = ''\n index_target_path = os.path.join(self.index_target,'index.rst')\n index_rst = self.parse_raw(self.index_template)\n\n for toc in self.toctrees:\n toctrees += ' '\n toctrees += os.path.join('.', 'apis', toc, 'doc')\n toctrees += '\\n'\n\n index_rst = re.sub('\\{\\{toctrees\\}\\}', toctrees, index_rst)\n\n index_file = codecs.open(index_target_path, encoding='utf-8', mode='w')\n index_file.write(index_rst)\n index_file.close()\n\n\n def get_uri(self, source):\n return os.path.relpath(os.path.abspath(source), os.path.abspath(self.source))\n\n def update_uri_config(self, source, data):\n keys_str = self.get_uri(source)\n keys_list = keys_str.split('/')\n d = self.data\n\n for key in keys_list:\n if 'children' in d and key not in d['children']:\n d['children'].update({key: data})\n d = d['children'][key]\n\n def export_doc(self, data):\n # print json.dumps(self.data, sort_keys=False, indent=4, separators=(',', ': '))\n for k, v in data['children'].iteritems():\n doc_type = v.get('type', 'toc')\n uri = v.get('uri')\n\n target_path = os.path.join(self.target, uri)\n\n if not os.path.isdir(target_path) or not os.path.exists(target_path):\n shutil.rmtree(target_path, ignore_errors=True)\n os.makedirs(target_path)\n\n target_path = os.path.join(self.target, uri, 'doc.rst')\n rst_raw = self.parse_blob(v) if doc_type == 'blob' else self.parse_toc(v)\n doc = codecs.open(target_path, encoding='utf-8', mode='w')\n doc.write(rst_raw)\n doc.close()\n\n if doc_type == 'toc':\n if 'children' in v:\n self.export_doc(v)\n elif doc_type == 'blob':\n if uri not in self.toctrees:\n self.toctrees.append(uri)\n\n example_source_path = os.path.join(self.source, uri, 'example.json')\n example_target_path = os.path.join(self.target, uri, 'example.json')\n if os.path.exists(example_source_path) and os.path.isfile(example_source_path):\n shutil.copy2(example_source_path, example_target_path)\n else:\n eg = codecs.open(example_target_path, encoding='utf-8', mode='w')\n eg.write('empty')\n eg.close()\n\n @staticmethod\n def parse_config(source):\n if os.path.exists(source):\n x = codecs.open(source, encoding='utf-8')\n data = json.loads(x.read())\n x.close()\n return data\n else:\n return {}\n\n @staticmethod\n def parse_raw(source):\n if os.path.exists(source):\n x = codecs.open(source, encoding='utf-8')\n data = x.read()\n x.close()\n return data\n else:\n return 'null'\n\n def parse_toc(self, toc):\n rst = self.parse_raw(self.toc_template)\n\n rst = re.sub('\\{\\{title\\}\\}', toc.get('title'), rst)\n\n return rst\n\n def parse_blob(self, blob):\n rst = self.parse_raw(self.blob_template)\n\n rst = re.sub('\\{\\{title\\}\\}', blob.get('title'), rst)\n rst = re.sub('\\{\\{uri\\}\\}', blob.get('uri'), rst)\n rst = re.sub('\\{\\{authors\\}\\}', blob.get('authors'), rst)\n rst = re.sub('\\{\\{version\\}\\}', blob.get('version'), rst)\n rst = re.sub('\\{\\{method\\}\\}', blob.get('method'), rst)\n\n request_raw = ''\n if 'request' in blob:\n if not not blob.get('request'):\n request_raw = self.parse_schema(blob.get('request'))\n\n rst = re.sub('\\{\\{request\\}\\}', request_raw, rst)\n\n\n response_raw = ''\n if 'response' in blob:\n if not not blob.get('response'):\n response_raw = self.parse_schema(blob.get('response').get('properties').get('data'))\n\n rst = re.sub('\\{\\{response\\}\\}', response_raw, rst)\n\n return rst\n\n @staticmethod\n def parse_schema(schema):\n\n rst = u'''\n \n \n \n | 键名 | \n 类型 | \n 描述 | \n
\n
\n \n ''' \n items = []\n if 'properties' in schema:\n items = schema['properties']\n elif 'items' in schema:\n items = schema['items'][\"properties\"]\n\n if len(items) > 0:\n i = 0\n for k,v in items.iteritems():\n schema_type = v.get('type', 'string')\n rst += '' if i/2 == 0 else '
'\n\n rst += '| ' + k + ' | ' + schema_type + ' | '\n if schema_type == 'object' or schema_type == 'array':\n rst += '' + v.get('description', '-') + ''\n rst += JSONSchema2RST.parse_schema(v)\n rst += ' | '\n else:\n rst += '' + v.get('description', '-') + ' | '\n\n rst += '
'\n\n i += 1\n else:\n rst += ''\n rst += u'| 未配置 | '\n rst += '
'\n\n rst += '''\n \n
\n '''\n return rst\n\n\nif __name__ == \"__main__\":\n jsonschema2rst = JSONSchema2RST()\n jsonschema2rst.travel()\n","sub_path":"jsonschema2rst.py","file_name":"jsonschema2rst.py","file_ext":"py","file_size_in_byte":7626,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"71942457","text":"#!/usr/bin/python\r\n\r\nimport sys\r\nimport socket\r\nimport threading\r\nimport Node\r\nimport Edge\r\nimport re\r\nimport heapq\r\n\r\ndef linkCheck():\r\n global linkBool\r\n linkBool = True\r\n\r\ndef graphCheck():\r\n global graphBool\r\n graphBool = True\r\n\r\ndef sendPacket():\r\n copyAliveMsgs = dict(numAliveMsgs)\r\n for neighbour in copyAliveMsgs.keys():\r\n\r\n #if a message has not been sent by the neighbour of this node\r\n if copyAliveMsgs[neighbour] == 0:\r\n\r\n #increment the number of missed messages by that neighbour\r\n numMissedMsgs[neighbour] += 1\r\n\r\n else:\r\n #a message has been sent by the neighbour of this node\r\n\r\n #reset numAliveMsgs to 0 to detect whether future messages have been sent or not\r\n numAliveMsgs[neighbour] = 0\r\n\r\n #reset the number of missed messages to 0\r\n numMissedMsgs[neighbour] = 0\r\n\r\n\r\n if numMissedMsgs[neighbour] == 3:\r\n #if a neighbour has failed to send a message 3 times in a row\r\n\r\n #get the regex of the link and delete the neighbour from the message\r\n regex = neighbour + \"=[0-9\\.]+\"\r\n global message\r\n message = re.sub(regex, '', message)\r\n\r\n #delete the neighbour from numAliveMsgs and numMissedMsgs, not needed anymore\r\n del numAliveMsgs[neighbour]\r\n del numMissedMsgs[neighbour]\r\n\r\n #delete the neighbour from the graph if it already hasn't been deleted\r\n if neighbour in graph.keys():\r\n del graph[neighbour]\r\n\r\n #for each node in the graph\r\n for node in graph.values():\r\n copyEdges = list(node.neighbours)\r\n for edge in copyEdges:\r\n\r\n #if the end node is equal to the dead neighbour then remove the edge from the list\r\n if edge.endNode == neighbour:\r\n node.neighbours.remove(edge)\r\n\r\n #generate a node failure message to send to remaining neighbours and flood network\r\n nodeToDelete = \"%s:DEAD\" % neighbour\r\n for port in portList:\r\n messageSocket.sendto(nodeToDelete, (\"127.0.0.1\", port))\r\n\r\n #send the original message/linkStatePacket\r\n for port in portList:\r\n messageSocket.sendto(message, (\"127.0.0.1\", port))\r\n\r\ndef shortestPath():\r\n\r\n #distance and previous dicts returned from dijkstra's\r\n D, P = dijkstra(graph, startNode)\r\n\r\n #for each node in the graph except this programs node\r\n for node in graph.values():\r\n if node.name == startNode.name:\r\n continue\r\n\r\n #the node that we are printing the shortest path to\r\n string = node.name\r\n\r\n #the previous neighbour to the above node\r\n previous = P[node.name]\r\n\r\n #keep working backwards adding each previous neighbour till we hit the start node\r\n while previous != startNode.name:\r\n string = previous + string\r\n previous = P[previous]\r\n string = startNode.name + string\r\n\r\n print(\"least-cost path to node %s: %s and the cost is %.1f\" % (node.name, string, D[node.name]))\r\n\r\ndef dijkstra(graph, startNode):\r\n\r\n #this is dijkstras algorithm using a priority queue\r\n\r\n dist = {}\r\n prev = {}\r\n queue = []\r\n\r\n #distance to reach the start node is 0 obviously\r\n dist[startNode.name] = 0\r\n\r\n #for each node in the graph\r\n for node in graph.values():\r\n\r\n #if the node isn't the start node\r\n if node.name != startNode.name:\r\n\r\n #initialise distance to that node = infinity and previous neighbour of that node = None\r\n dist[node.name] = float('inf')\r\n prev[node.name] = None\r\n\r\n #push the node onto the queue\r\n heapq.heappush(queue, (dist[node.name], node.name))\r\n\r\n while len(queue) != 0:\r\n\r\n #pop the vertex from the queue\r\n vertex = heapq.heappop(queue)\r\n\r\n #for every neighbour in the vertex's neighbours\r\n for neighbour in graph[vertex[1]].neighbours:\r\n\r\n #length = the distance to get to vertex + the distance to get to the neighbour of the vertex\r\n length = dist[vertex[1]] + neighbour.travelCost\r\n\r\n #if the length less dist[neighbour.endNode], found a path with lower cost\r\n if length < dist[neighbour.endNode]:\r\n\r\n #old node to be removed from queue\r\n old = (dist[neighbour.endNode], neighbour.endNode)\r\n queue.remove(old)\r\n\r\n #the distance to reach that neighbour is updated and the previous node is the vertex\r\n dist[neighbour.endNode] = length\r\n prev[neighbour.endNode] = vertex[1]\r\n\r\n #push the updated node onto the queue\r\n heapq.heappush(queue, (length, neighbour.endNode))\r\n\r\n return dist, prev\r\n\r\n#################### initial stage ####################\r\n\r\ngraph = {}\r\nportList = []\r\npacketList = []\r\nnumAliveMsgs = {}\r\nnumMissedMsgs = {}\r\nlinkBool = False\r\ngraphBool = False\r\n\r\nnodeID = str(sys.argv[1])\r\nnodePort = int(sys.argv[2])\r\nfile = str(sys.argv[3])\r\nf = open(file, 'r')\r\n\r\nUPDATE_INTERVAL = 1\r\nROUTE_UPDATE_INTERVAL = 30\r\n\r\nstartNode = Node.Node(nodeID)\r\n\r\n#link state packet - initially\r\nmessage = \"%s: \" % nodeID\r\n\r\nfirstLineCheck = 0\r\nfor line in f:\r\n if firstLineCheck == 0:\r\n firstLineCheck = 1\r\n continue\r\n\r\n strings = line.split()\r\n\r\n #create a new node from the split line\r\n newNode = Node.Node(strings[0])\r\n portList.append(int(strings[2]))\r\n\r\n #initialise statuses to check for dead neighbours later on\r\n numAliveMsgs[strings[0]] = 0\r\n numMissedMsgs[strings[0]] = 0\r\n\r\n #create edges for both nodes\r\n startNodeEdge = Edge.Edge(nodeID, strings[0], strings[1])\r\n newNodeEdge = Edge.Edge(strings[0], nodeID, strings[1])\r\n\r\n #add edges to neighbours list on both nodes\r\n startNode.neighbours.append(startNodeEdge)\r\n newNode.neighbours.append(newNodeEdge)\r\n\r\n #add the newNode to the graph\r\n graph[newNode.name] = newNode\r\n\r\n #add the edge to the link state message\r\n message += \"%s=%s \" % (strings[0], strings[1])\r\n\r\n#add the start node to the graph\r\ngraph[startNode.name] = startNode\r\n\r\n#add the message to the packetList\r\npacketList.append(message)\r\n\r\n#################### moving onto the continuous loop stage ####################\r\n\r\nmessageSocket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\r\nmessageSocket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\r\nmessageSocket.setblocking(0)\r\nmessageSocket.bind(('', nodePort))\r\n\r\n#timer which updates a boolean every second\r\nlinkTimer = threading.Timer(UPDATE_INTERVAL, linkCheck)\r\nlinkTimer.start()\r\n\r\n#time which updates a boolean every thirty seconds\r\ngraphTimer = threading.Timer(ROUTE_UPDATE_INTERVAL, graphCheck)\r\ngraphTimer.start()\r\n\r\ndupCheck = False\r\n\r\nwhile True:\r\n\r\n #if a second has passed\r\n if linkBool == True:\r\n\r\n #execute the sendPacket function, reset the boolean, reset the timer\r\n sendPacket()\r\n linkBool = False\r\n linkTimer.cancel()\r\n linkTimer = threading.Timer(UPDATE_INTERVAL, linkCheck)\r\n linkTimer.start()\r\n\r\n #if thirty seconds have passed\r\n if graphBool == True:\r\n\r\n #execute the shortestPath function, reset the boolean, reset the timer\r\n shortestPath()\r\n graphBool = False\r\n graphTimer.cancel()\r\n graphTimer = threading.Timer(ROUTE_UPDATE_INTERVAL, graphCheck)\r\n graphTimer.start()\r\n\r\n try:\r\n\r\n #receive link state packets from other nodes\r\n linkStatePacket, address = messageSocket.recvfrom(2048)\r\n\r\n #the first character of the packet is the node name\r\n nodeName = linkStatePacket[0]\r\n\r\n #if the link state packet is from one of this nodes neighbours\r\n if nodeName in numAliveMsgs.keys():\r\n numAliveMsgs[nodeName] += 1\r\n\r\n #if the link state packet has already been received then do not forward it\r\n for packet in packetList:\r\n if linkStatePacket == packet:\r\n dupCheck = True\r\n break\r\n\r\n if dupCheck == True:\r\n dupCheck = False\r\n continue\r\n\r\n #append the packet to the list\r\n packetList.append(linkStatePacket)\r\n\r\n #if we have received a failed node message\r\n if linkStatePacket == nodeName + \":DEAD\":\r\n\r\n #delete the node from the graph\r\n del graph[nodeName]\r\n\r\n for node in graph.values():\r\n copyEdges = list(node.neighbours)\r\n for edge in copyEdges:\r\n #delete all edges in the graph which contain the dead node\r\n if edge.endNode == nodeName:\r\n node.neighbours.remove(edge)\r\n\r\n #forward the failed node message to other neighbours to flood the network\r\n for port in portList:\r\n if port == address[1]:\r\n continue\r\n messageSocket.sendto(linkStatePacket, (\"127.0.0.1\", port))\r\n continue\r\n\r\n #else it's just a regular link state packet, but still forward it to other neighbours to flood network\r\n for port in portList:\r\n if port == address[1]:\r\n continue\r\n messageSocket.sendto(linkStatePacket, (\"127.0.0.1\", port))\r\n\r\n #if the graph already has the node in it\r\n if graph.has_key(nodeName):\r\n\r\n #regex to extract all the links to the nodeName\r\n links = re.findall(\"[A-Za-z]=[0-9\\.]+\", linkStatePacket)\r\n\r\n for link in links:\r\n edgeCheck = False\r\n\r\n #create a new edge\r\n newEdge = Edge.Edge(nodeName, link[0], link[2:])\r\n\r\n #if the edge is already in the nodes neighbours\r\n for edge in graph[nodeName].neighbours:\r\n if (edge.startNode == newEdge.startNode and edge.endNode == newEdge.endNode):\r\n edgeCheck = True\r\n\r\n #if the edge isn't in the nodes neighbours\r\n if (edgeCheck == False):\r\n\r\n #append the edge to the node's neighbour list\r\n graph[nodeName].neighbours.append(newEdge)\r\n\r\n #create a neighbour node and an edge going in the opposite direction\r\n neighbourNode = Node.Node(link[0])\r\n neighbourEdge = Edge.Edge(link[0], nodeName, link[2:])\r\n\r\n #if the neighbour node is already in the graph then just append the edge\r\n if neighbourNode.name in graph.keys():\r\n graph[neighbourNode.name].neighbours.append(neighbourEdge)\r\n\r\n else:\r\n #else append the edge to the node and then add the node to the graph\r\n neighbourNode.neighbours.append(neighbourEdge)\r\n graph[link[0]] = neighbourNode\r\n else:\r\n #the graph does not contain the node, so create the new node\r\n newNode = Node.Node(nodeName)\r\n\r\n #extract all links from the link state packet\r\n links = re.findall(\"[A-Za-z]=[0-9\\.]+\", linkStatePacket)\r\n\r\n #add all edges to the new node\r\n for link in links:\r\n newEdge = Edge.Edge(nodeName, link[0], link[2:])\r\n newNode.neighbours.append(newEdge)\r\n\r\n #add the node into the graph\r\n graph[newNode.name] = newNode\r\n\r\n #loop through all the links once more\r\n for link in links:\r\n\r\n #if the neighbour is already in the graph then\r\n if (link[0] in graph.keys()):\r\n\r\n #create an edge going in the opposite direction, add it to the node already in the graph\r\n neighbourEdge = Edge.Edge(link[0], nodeName, link[2:])\r\n graph[link[0]].neighbours.append(neighbourEdge)\r\n\r\n else:\r\n #else we have to create the neighbour node as well\r\n neighbourNode = Node.Node(link[0])\r\n\r\n #create the edge going in the opposite direction\r\n neighbourEdge = Edge.Edge(link[0], nodeName, link[2:])\r\n\r\n #add the edge to the node and then add the node to the graph\r\n neighbourNode.neighbours.append(neighbourEdge)\r\n graph[link[0]] = neighbourNode\r\n\r\n except:\r\n pass\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"assignment2/Lsr.py","file_name":"Lsr.py","file_ext":"py","file_size_in_byte":12567,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"118223759","text":"import multiprocessing\n\ndef consumer(input_q):\n while True:\n item = input_q.get()\n #\n print(item)\n #\n input_q.task_done()\n\ndef producer(sequence, output_q):\n for item in sequence:\n #\n output_q.put(item)\n\n\n# setting\nif __name__ == '__main__':\n q = multiprocessing.JoinableQueue()\n # start consumer process\n cons_p = multiprocessing.Process(target=consumer,args=(q,))\n cons_p.daemon=True\n cons_p.start()\n\n # create sequences\n sequence = [1,2,3,4]\n producer(sequence,q)\n \n q.join()\n\n","sub_path":"multiprocess_practice.py","file_name":"multiprocess_practice.py","file_ext":"py","file_size_in_byte":565,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"311978607","text":"import logging\nfrom pyAitu import executor, Bot, Dispatcher\nfrom pyAitu.models import Message, Options, FormClosed, FormSubmitted, CustomContainer, Indent, \\\n FlexOptions, Image, FileMetadata, Text, Divider, FormAction\nfrom pyAitu.models.constants.alignment import RIGHT\nfrom pyAitu.models.constants.text_size import H3, H4\nfrom pyAitu.models.constants.text_style import BOLD\nfrom pyAitu.utils.strings import UPLOADED_FILES\n\n\nAPI_TOKEN = 'YOUR_API_TOKEN'\n\nbot = Bot(token=API_TOKEN)\ndispatcher = Dispatcher(bot)\n\nlogging.basicConfig(level=logging.INFO)\n\n\n@dispatcher.message_handler()\nasync def handle(message: Message):\n \"\"\"\n Layers of containers:\n Main Container <- Parent Container <- Child Container\n \"\"\"\n\n # Child Component\n child_contact_number_text = Text(\n content_id=\"text_id\",\n title=\"+7 (727) 332-77-22\",\n options=Options(\n text_size=H3,\n text_style=BOLD,\n indent_outer=Indent(left=12, top=4, right=12, bottom=12),\n text_color=\"#A9ADB1\"\n )\n )\n\n # Child Component\n child_contact_title_text = Text(\n content_id=\"text_id\",\n title=\"Контактный телефон:\",\n options=Options(\n text_size=H4,\n indent_outer=Indent(left=12, top=2, right=12),\n text_color=\"#A9ADB1\"\n )\n )\n\n # Child Component\n child_divider = Divider(\n content_id=\"divider_id\",\n options=Options(\n indent_outer=Indent(left=12, top=14, right=12)\n )\n )\n\n # Child Component\n child_subtitle_text = Text(\n content_id=\"text_id\",\n title=\"eubank.kz\",\n options=Options(\n text_size=H4,\n alignment=RIGHT,\n indent_outer=Indent(left=12, top=2, right=12),\n text_color=\"#0075EB\"\n ),\n form_action=FormAction(action=\"open_url\",\n data_template=\"https://eubank.kz\")\n )\n\n # Child Component\n child_title_text = Text(\n content_id=\"text_cat_id\",\n title=\"Евразийский банк\",\n options=Options(\n text_size=H3,\n text_style=BOLD,\n indent_outer=Indent(left=12, top=12, right=12),\n )\n )\n\n # Child Component\n file = await bot.upload_file(\"images/cat.jpg\")\n child_image = Image(\n content_id=\"image_id\",\n options=Options(width=37, height=6,\n flex_options=FlexOptions(align_self=\"center\")),\n file_metadata=FileMetadata(\n file_id=file.get(UPLOADED_FILES)[0][\"fileId\"],\n file_type=\"image\",\n file_name=file.get(UPLOADED_FILES)[0][\"fileName\"]\n )\n )\n\n # Child Container\n child_custom_container = CustomContainer(\n content_id=\"child_id_1\",\n options=Options(width=62, height=16,\n flex_options=FlexOptions(flex_direction=\"column\", align_items=\"center\"),\n background_color=\"#2B296D\"),\n content=[child_image]\n )\n\n # Parent Container\n parent_custom_container = CustomContainer(\n content_id=\"parent_id\",\n options=Options(width=62, flex_options=FlexOptions(flex_direction=\"column\", align_items=\"start\")),\n content=[child_custom_container, child_title_text, child_subtitle_text, child_divider, child_contact_title_text,\n child_contact_number_text]\n )\n\n # Main Container\n main_custom_container = CustomContainer(\n content_id=\"main_id\",\n options=Options(indent_outer=Indent(left=16, right=16, top=8, bottom=8), background=\"card\"),\n content=[parent_custom_container]\n )\n\n content = [main_custom_container, main_custom_container, main_custom_container]\n\n await bot.send_container_message(message.chat.id, content=content)\n\n\n@dispatcher.form_submitted_handler()\nasync def handle_submission(submitted_form: FormSubmitted):\n await bot.send_message(\n submitted_form.chat.id,\n f\"Oh, it seems I have received text from you, look:\\n\\\"{submitted_form.metadata}\\\"\"\n )\n\n\n@dispatcher.form_closed_handler()\nasync def handle_form_closing(closed_form: FormClosed):\n await bot.send_message(closed_form.chat.id, \"Form is closed\")\n\n\nif __name__ == '__main__':\n executor.start_polling(dispatcher)\n","sub_path":"examples/send_container_message_bot.py","file_name":"send_container_message_bot.py","file_ext":"py","file_size_in_byte":4296,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"267059428","text":"\"\"\"\nThis module represents a device.\n\nComputer Systems Architecture Course\nAssignment 1\nMarch 2018\n\"\"\"\n\nfrom threading import Event, Thread, Lock, Condition\n\n\nclass ReusableBarrierCond(object):\n \"\"\"\n Class that represents a reusable barrier\n and is imported from the third lab.\n \"\"\"\n def __init__(self, num_threads):\n \"\"\"\n Constructor.\n\n @type num_threads: Integer\n @param num_threads: The number of threads for barrier\n\n @type count_threads: Integer\n @param count_threads: A variable that counts how\n many threads have arrieved at barrier\n\n @type cond: Condition\n @param cond: Does the synchronization\n \"\"\"\n self.num_threads = num_threads\n self.count_threads = self.num_threads\n self.cond = Condition()\n\n def wait(self):\n \"\"\"\n Wait method.\n \"\"\"\n self.cond.acquire()\n self.count_threads -= 1\n if self.count_threads == 0:\n self.cond.notify_all()\n self.count_threads = self.num_threads\n else:\n self.cond.wait()\n self.cond.release()\n\nclass Device(object):\n \"\"\"\n Class that represents a device.\n \"\"\"\n\n def __init__(self, device_id, sensor_data, supervisor):\n \"\"\"\n Constructor.\n\n @type device_id: Integer\n @param device_id: the unique id of this node; between 0 and N-1\n\n @type sensor_data: List of (Integer, Float)\n @param sensor_data: a list containing (location, data) as measured by this device\n\n @type supervisor: Supervisor\n @param supervisor: the testing infrastructure's control and validation component\n\n @type scripts: List of (script, Integer)\n @param scripts: a list containing (script, location)\n\n @type timepoint_done: Event\n @param timepoint_done: an event that let me know when it\n comes to another timepoint\n\n @type thread: DeviceThread\n @param thread: a thread which manage the work of MyThreads\n\n @type location_lock: Dictionary {Integer : Lock}\n @param location_lock: a dictionary which contains a lock foreach location\n\n @type barrier: ReusableBarrierCond\n @param barrier: a barrier which help me to synchronize the devices\n \"\"\"\n\n self.device_id = device_id\n self.sensor_data = sensor_data\n self.supervisor = supervisor\n self.scripts = []\n self.timepoint_done = Event()\n self.thread = None\n self.location_lock = {}\n self.barrier = None\n\n def __str__(self):\n \"\"\"\n Pretty prints this device.\n\n @rtype: String\n @return: a string containing the id of this device\n \"\"\"\n\n return \"Device %d\" % self.device_id\n\n def setup_devices(self, devices):\n \"\"\"\n Setup the devices before simulation begins.\n\n @type devices: List of Device\n @param devices: list containing all devices\n \"\"\"\n # Create a list with all device_id.\n id_list = []\n for device in devices:\n id_list.append(device.device_id)\n # Find the smallest device_id.\n id_leader = min(id_list)\n\n # Only device with the smallest id will execute the following sequence.\n if self.device_id == id_leader:\n # Create a reusable barrier for synchronize devices.\n barrier = ReusableBarrierCond(len(devices))\n # Each device will share the same barrier.\n # Each device will have a DeviceThread which\n # will start more MyThread.\n for device in devices:\n device.barrier = barrier\n device.thread = DeviceThread(device)\n device.thread.start()\n # Create the dictionary {location:lock}.\n location_list = []\n location_dict = {}\n for device in devices:\n for location in device.sensor_data:\n if location not in location_list:\n location_list.append(location)\n for location in location_list:\n lock = Lock()\n location_dict[location] = lock\n # Each device will share the same dictionary.\n for device in devices:\n device.location_lock = location_dict\n\n def assign_script(self, script, location):\n \"\"\"\n Provide a script for the device to execute.\n\n @type script: Script\n @param script: the script to execute from now on at each timepoint; None if the\n current timepoint has ended\n\n @type location: Integer\n @param location: the location for which the script is interested in\n \"\"\"\n if script is not None:\n self.scripts.append((script, location))\n else:\n self.timepoint_done.set()\n\n def get_data(self, location):\n \"\"\"\n Returns the pollution value this device has for the given location.\n\n @type location: Integer\n @param location: a location for which obtain the data\n\n @rtype: Float\n @return: the pollution value\n \"\"\"\n\n return self.sensor_data[location] if location in self.sensor_data else None\n\n def set_data(self, location, data):\n \"\"\"\n Sets the pollution value stored by this device for the given location.\n\n @type location: Integer\n @param location: a location for which to set the data\n\n @type data: Float\n @param data: the pollution value\n \"\"\"\n\n if location in self.sensor_data:\n self.sensor_data[location] = data\n\n def shutdown(self):\n \"\"\"\n Instructs the device to shutdown (terminate all threads). This method\n is invoked by the tester. This method must block until all the threads\n started by this device terminate.\n \"\"\"\n self.thread.join()\n\n\nclass DeviceThread(Thread):\n \"\"\"\n Class that implements the device's Master thread.\n \"\"\"\n\n def __init__(self, device):\n \"\"\"\n Constructor.\n\n @type device: Device\n @param device: the device which owns this Marster Thread\n \"\"\"\n Thread.__init__(self, name=\"Device Thread %d\" % device.device_id)\n self.device = device\n\n def run(self):\n\n while True:\n # Get the current neighbourhood.\n neighbours = self.device.supervisor.get_neighbours()\n\n if neighbours is None:\n break\n # Wait until device receive a None script.\n self.device.timepoint_done.wait()\n self.device.timepoint_done.clear()\n\n # Create a list of 8 Worker Threads(MyThread).\n threads = []\n for i in range(8):\n scripts_list = {}\n aux = i\n # Create a dictionary of {location : scripts} for each\n # Worker Thread.\n while aux < len(self.device.scripts):\n (script, location) = self.device.scripts[aux]\n scripts_list[location] = script\n aux += 8\n thread = MyThread(self.device, scripts_list, neighbours)\n threads.append(thread)\n # Stats and wait the threads.\n for i in range(8):\n threads[i].start()\n for i in range(8):\n threads[i].join()\n # Wait until all device will arrieve here.\n self.device.barrier.wait()\n\n\nclass MyThread(Thread):\n \"\"\"\n Class that implements the device's Worker thread.\n \"\"\"\n\n def __init__(self, device, scripts_list, neighbours):\n \"\"\"\n Constructor.\n\n @type device: Device\n @param device: the device which owns this Worker thread\n\n @type scripts_list: dictionary {Integer: script}\n @param scripts_list: a disctionary {location:script} which represents\n all scripts that worker thread must to resolve\n\n @type neighbours: List of Device\n @param neighbours: a list of all device in the neighbourhood\n \"\"\"\n Thread.__init__(self, name=\"Device Thread %d\" % device.device_id)\n self.device = device\n self.scripts_list = scripts_list\n self.neighbours = neighbours\n\n def run(self):\n # For each location from dictionary\n for location in self.scripts_list:\n script_data = []\n # Collect data from current neighbours\n # Make acquire for that location.\n self.device.location_lock[location].acquire()\n for dev in self.neighbours:\n data = dev.get_data(location)\n if data is not None:\n script_data.append(data)\n # Add our data, if any.\n data = self.device.get_data(location)\n if data is not None:\n script_data.append(data)\n\n if script_data != []:\n # Run script on data.\n result = self.scripts_list[location].run(script_data)\n\n # Update data of neighbours.\n for dev in self.neighbours:\n dev.set_data(location, result)\n # Update our data.\n self.device.set_data(location, result)\n # Make release for that location.\n self.device.location_lock[location].release()\n","sub_path":"Third Year/Computer Architecture/Homework 1/device.py","file_name":"device.py","file_ext":"py","file_size_in_byte":9362,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"247837526","text":"from toontown.coghq.SpecImports import *\nimport random\nGlobalEntities = {1000: {'type': 'levelMgr',\n 'name': 'LevelMgr',\n 'comment': '',\n 'parentEntId': 0,\n 'cogLevel': 0,\n 'farPlaneDistance': 1500,\n 'modelFilename': 'phase_11/models/lawbotHQ/LB_Zone22a',\n 'wantDoors': 1},\n 1001: {'type': 'editMgr',\n 'name': 'EditMgr',\n 'parentEntId': 0,\n 'insertEntity': None,\n 'removeEntity': None,\n 'requestNewEntity': None,\n 'requestSave': None},\n 0: {'type': 'zone',\n 'name': 'UberZone',\n 'comment': '',\n 'parentEntId': 0,\n 'scale': 1,\n 'description': '',\n 'visibility': []},\n 100030: {'type': 'battleBlocker',\n 'name': '',\n 'comment': '',\n 'parentEntId': 0,\n 'pos': Point3(-0.124318, -27.1644, 0),\n 'hpr': Vec3(0, 0, 0),\n 'scale': Vec3(1, 1, 1),\n 'cellId': 0,\n 'radius': 25.0},\n 100000: {'type': 'elevatorMarker',\n 'name': '',\n 'comment': '',\n 'parentEntId': 0,\n 'pos': Point3(0.199988, -31.3479, 0),\n 'hpr': Vec3(180, 0, 0),\n 'scale': Point3(1, 1, 1),\n 'modelPath': 0},\n 100001: {'type': 'model',\n 'name': '',\n 'comment': '',\n 'parentEntId': 0,\n 'pos': Point3(0, -30.3883, 13.5561),\n 'hpr': Vec3(180, 0, 0),\n 'scale': Vec3(2.6262, 2.6262, 2.6262),\n 'collisionsOnly': 0,\n 'flattenType': 'light',\n 'loadType': 'loadModelCopy',\n 'modelPath': 'phase_11/models/lawbotHQ/LB_bookshelfB'},\n 10000: {'type': 'nodepath',\n 'name': 'cogs',\n 'comment': '',\n 'parentEntId': 0,\n 'pos': Point3(0, 0, 0),\n 'hpr': Vec3(180, 0, 0),\n 'scale': 1}}\nScenario0 = {}\nlevelSpec = {'globalEntities': GlobalEntities,\n 'scenarios': [Scenario0],\n 'titleString': 'MemTag: LawbotOfficeOilRoom_Battle00 %s' % random.random()}\n","sub_path":"toontown/coghq/LawbotOfficeOilRoom_Battle00.py","file_name":"LawbotOfficeOilRoom_Battle00.py","file_ext":"py","file_size_in_byte":2000,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"631579921","text":"from django.test import TestCase\nfrom random import randint\nfrom faker import Factory\nfrom .models import Bookmarklet, Category, Vote, User, Comment\nimport methods\nimport templatetags.decoder\n\n\nclass TestVotes(TestCase):\n def setUp(self):\n self.user = create_user('t', 'ah')\n self.user.save()\n c = Category.objects.create(\n name='c'\n )\n self.b = Bookmarklet.objects.create(\n name='test',\n source='http://dfg',\n content='adfgd',\n user=self.user,\n description='desc',\n )\n self.b.categories.add(c)\n self.b.save()\n\n def test_vote(self):\n u1 = create_user('a', 'b')\n u2 = create_user('f', 'b')\n u3 = create_user('g', 'b')\n u4 = create_user('c', 'b')\n Vote.objects.create(user=u1, bookmarklet=self.b, rating=4)\n Vote.objects.create(user=u2, bookmarklet=self.b, rating=2)\n Vote.objects.create(user=u3, bookmarklet=self.b, rating=1)\n Vote.objects.create(user=u4, bookmarklet=self.b, rating=4)\n self.assertEqual(methods.get_review_avg(1), 2.75)\n\n def test_lot_votes(self):\n self.create_lot_votes(2000)\n\n def create_lot_votes(self, qty):\n # need to profile avg method..\n faker = Factory.create()\n\n for i in range(qty):\n username = faker.user_name() + str(randint(0, qty))\n user = create_user(name=username, passwd='p')\n Vote.objects.create(user=user, bookmarklet=self.b, rating=randint(0, 5))\n self.b.avg_rating = methods.get_review_avg(1)\n self.b.save()\n self.assertGreater(self.b.avg_rating, 0)\n\n\nclass TestCategories(TestCase):\n def test_two_categories(self):\n self.user = create_user('t', 'ah')\n self.user.save()\n c = Category.objects.create(name='c')\n c1 = Category.objects.create(name='c1')\n self.b = Bookmarklet.objects.create(\n name='test',\n source='http://dfg',\n content='adfgd',\n user=self.user,\n description='desc',\n )\n self.b.categories.add(c)\n self.b.categories.add(c1)\n self.b.save()\n self.assertEqual(c, self.b.categories.get(pk=1))\n self.assertEqual(c1, self.b.categories.get(pk=2))\n\n\nclass TestComments(TestCase):\n def setUp(self):\n self.user = create_user('t', 'ah')\n self.user.save()\n c = Category(\n name='c'\n )\n c.save()\n self.b = Bookmarklet.objects.create(\n name='test',\n source='http://dfg',\n content='adfgd',\n user=self.user,\n description='desc',\n )\n self.b.categories.add(c)\n self.b.save()\n\n def test_comment(self):\n comm = Comment.objects.create(\n text=\"my comment!\",\n user=self.user,\n bookmarklet=self.b\n )\n self.assertEqual(comm.text,self.b.comment_set.get(pk=1).text)\n\n\n def test_top_bookmarklets(self):\n for i in range(0, 12):\n self.create_bookmarklet()\n allb = Bookmarklet.objects.all()\n self.assertEqual(13, len(allb))\n qs = Bookmarklet.objects.order_by('avg_rating')[:10]\n self.assertEqual(10, len(qs))\n\n def create_bookmarklet(self):\n faker = Factory.create()\n return Bookmarklet.objects.create(\n name=faker.name(),\n source='http://www.test.com',\n content='not used',\n user=self.user,\n description='desc',\n avg_rating=randint(1, 5),\n )\n\n\ndef create_user(name, passwd):\n return User.objects.create(username=name, password=passwd)\n\n\nclass TestDecoder(TestCase):\n def test_with_js(self):\n value = 'data:text/html,%20%23e{position%3Aabsolute%3Btop%3' \\\n 'A0%3Bright%3A0%3Bbottom%3A0%3Bleft%3A0%3B}<%2Fstyle><%2Fdiv>' \\\n ' -threshold):\n table += \"\\\\textcolor{red}{\\\\textbf{\" + str(round(c, 2)) + \"}}\"\n else:\n table += str(round(c, 2))\n if (j == len(features_name) - 1):\n table += \" \\\\\\\\\\n \\hline \\n\"\n else:\n table += \" & \"\n table += \"\\end{tabular}\\n \\end{center}\"\n # print(table)\n # thre = 0.4\n # d = np.array(data)\n # data_t = np.transpose(d)\n # el = np.arange(0, len(data_t))\n # combo_index = list(itertools.product(el, repeat=2))\n # # print(combo_index)\n # for e in combo_index:\n # ind1 = e.__getitem__(0)\n # ind2 = e.__getitem__(1)\n # c, t = pearsonr(data_t[ind1], data_t[ind2])\n #\n # titolo = '\\n{} - {}\\nP: --> {}'.format(features_name[ind1], features_name[ind2], round(c, 2))\n # # print(titolo)\n return table\n\n\nfeatures_to_remove_single = [\"DateTime\", \"Symbol\"]\nfeatures_to_remove_multi = [\"DateTime\", 'Symbol_1', 'Symbol_2', 'Symbol_3', 'Symbol_4', 'Symbol_5', 'Symbol_6',\n 'Symbol_7',\n 'Symbol_8']\n\nfolder_refers = {\"single\": [\"../crypto_preprocessing/step0_data/\", features_to_remove_single],\n \"single_indicators\": [\"../crypto_preprocessing/step1_indicators/\", features_to_remove_single],\n \"multi\": [\"../crypto_preprocessing/step5_horizontal/\", features_to_remove_multi]}\n\npathfolder = \"../Pearson_table_latex/\"\nos.makedirs(pathfolder, exist_ok=False)\nfor k, v in folder_refers.items():\n files = os.listdir(v[0])\n for each_csv in files:\n dataframe_all = pd.read_csv(v[0] + each_csv, delimiter=',', header=0)\n data_all = dataframe_all.values # per controllare la dimensione - se non dovessi trovarmi\n print(\"All data -> \", np.shape(data_all))\n dataframe = dataframe_all.drop(columns=v[1])\n data = dataframe.values\n columns_name = dataframe.columns.values\n print(\"After reduction -> \", np.shape(data))\n pearson_correlation_table_latex = pearson_correlation(data=data, features_name=columns_name, threshold=0.4)\n with open(pathfolder + each_csv.replace(\".csv\", \"\"), \"w\") as fs:\n fs.write(pearson_correlation_table_latex)\n\n#\n# dataframe = pd.read_csv(\"../crypto_preprocessing/step5_horizontal/horizontal.csv\", delimiter=',', header=0)\n# data = dataframe.values\n#\n# a = dataframe.drop(\n# columns=[\"DateTime\", 'Symbol_1', 'Symbol_2', 'Symbol_3', 'Symbol_4', 'Symbol_5', 'Symbol_6', 'Symbol_7',\n# 'Symbol_8'])\n# d = a.values\n# col_name = a.columns.values\n# print(np.shape(data), \"\\n\", col_name)\n# t = pearson_correlation(data=d, features_name=col_name)\n# with open(\"../multi.txt\", \"w\") as fs:\n# fs.write(t)\n","sub_path":"crypto_utility/pearson_to_latex.py","file_name":"pearson_to_latex.py","file_ext":"py","file_size_in_byte":3558,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"525220312","text":"import csv\nimport os\nimport random\n\nimport settings\nfrom ip_pool import api_settings\n\n\ndef get_random_ip_in_pool():\n with open(api_settings.FILE_NAME, 'a+') as f:\n f.seek(0, 0)\n csv_obj = csv.reader(f)\n file_list = [item for item in csv_obj]\n addr = random.choice(file_list)[0]\n return addr\n\n\ndef get_ip_pool_list():\n if not settings.ACT_PROXY:\n return ['127.0.0.1' for _ in range(100)]\n with open(api_settings.FILE_NAME, 'a+') as f:\n f.seek(0, 0)\n csv_obj = csv.reader(f)\n file_list = [item[0] for item in csv_obj]\n return file_list\n\n\ndef get_last_log():\n with open(get_log_abs_path(), 'rb') as f: # 打开文件\n off = -50 # 设置偏移量\n while True:\n try:\n f.seek(off, 2) # seek(off, 2)表示文件指针：从文件末尾(2)开始向前50个字符(-50)\\\n except IOError:\n return b'0'\n lines = f.readlines() # 读取文件指针范围内所有行\n if len(lines) >= 2: # 判断是否最后至少有两行，这样保证了最后一行是完整的\n last_line = lines[-1] # 取最后一行\n break\n # 如果off为50时得到的readlines只有一行内容，那么不能保证最后一行是完整的\n # 所以off翻倍重新运行，直到readlines不止一行\n off *= 2\n return last_line\n\n\ndef get_last_row_number():\n b_str = get_last_log().decode()\n num = b_str.split('http')[0]\n return num\n\n\ndef get_log_abs_path():\n if 'ip_pool' in os.path.abspath(''):\n return os.path.abspath('../log.md')\n else:\n return os.path.abspath('log.md')\n\n\ndef update_line_to_eof(count):\n \"\"\"将指定行数文件移至末尾\"\"\"\n with open(api_settings.FILE_NAME, 'r+') as f: # 打开文件\n cr = csv.reader(f)\n rows = [cr.__next__() for _ in range(count)]\n if not rows:\n return\n f.seek(0, 2)\n cw = csv.writer(f)\n cw.writerows(rows)\n delete_rows(1, count)\n\n\ndef delete_rows(del_line, count):\n \"\"\"在大量数据文件下删除某几行\n del_line = 1 count = 10 删除(包括)第1行到第10行的数据\n \"\"\"\n with open(api_settings.FILE_NAME, 'r') as old_file:\n with open(api_settings.FILE_NAME, 'r+') as new_file:\n\n current_line = 1\n # 定位到需要删除的行\n while current_line < del_line: # (del_line - 1)\n old_file.readline()\n current_line += 1\n\n # 当前光标在被删除行的行首，记录该位置\n seek_point = old_file.tell()\n\n # 设置光标位置\n new_file.seek(seek_point, 0)\n\n # 读需要删除的行，光标移到下一行行首\n for _ in range(count):\n old_file.readline()\n\n # 被删除行的下一行读给 next_line\n next_line = old_file.readline()\n\n # 连续覆盖剩余行，后面所有行上移一行\n while next_line:\n new_file.write(next_line)\n next_line = old_file.readline()\n\n # 写完最后一行后截断文件，因为删除操作，文件整体少了一行，原文件最后一行需要去掉\n new_file.truncate()\n\n\nif __name__ == '__main__':\n # update_line_to_eof(0)\n a = get_last_row_number()\n b = 1\n","sub_path":"ip_pool/file_handler.py","file_name":"file_handler.py","file_ext":"py","file_size_in_byte":3423,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"207546434","text":"from girl import girl\n\nclass normalGirl( girl ):\n\t\t\n\t\tdef __init__( self, name, attract, maint_bud, intelli_lev,types):\n\t\t\t\t\"Constructor for intializing\"\n\t\t\t\tgirl.__init__( name, attract, maint_bud, intelli_lev,types )\n\t\t\t\t\n\t\t\n\t\tdef calHappiness( self, giftList ):\n\t\t\t\t\"Calculate happiness for Normal Girl\"\n\t\t\t\ta = 0\n\t\t\t\tfor i in giftList:\n\t\t\t\t\t\ta = a+ i.value + i.price\n\t\t\t\tself.happiness = a\n","sub_path":"Ques8/src/normalGirl.py","file_name":"normalGirl.py","file_ext":"py","file_size_in_byte":397,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"620287585","text":"'''\n=> Prefix Product\nO(n^2)\n[10, 5, 2, 6] 110\n 10 50 100 600\n\n'''\nclass Solution:\n \"\"\"\n @param nums: an array\n @param k: an integer\n @return: the number of subarrays where the product of all the elements in the subarray is less than k\n \"\"\"\n def numSubarrayProductLessThanK(self, nums, k):\n # Write your code here\n if not nums or k <= 0:\n return 0\n \n res = 0\n product = 1\n left = 0\n right = 0\n \n while right < len(nums):\n product *= nums[right]\n while product >= k:\n product /= nums[left]\n left += 1\n res += right - left + 1\n right += 1\n \n return res\n\n# Time: O(n)\n# Space: O(1)","sub_path":"1075_subarray-product-less-than-k/subarray-product-less-than-k.py","file_name":"subarray-product-less-than-k.py","file_ext":"py","file_size_in_byte":763,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"357896775","text":"# method_1\nclass Solution:\n def distributeCandies(self, candies: int, num_people: int) -> List[int]:\n cand_list = [0 for _ in range(num_people)]\n # 初始化\n cur_cand = 1\n cur_child = 0\n while candies > 0:\n if cur_child == num_people:\n cur_child = 0\n # 不够发了\n if candies < cur_cand:\n cand_list[cur_child] += candies\n candies = 0\n # 按规则发放\n else:\n cand_list[cur_child] += cur_cand\n candies -= cur_cand\n cur_cand += 1\n cur_child += 1\n return cand_list\n\n# method_1 官解，简洁版，\nclass Solution:\n def distributeCandies(self, candies: int, num_people: int) -> List[int]:\n ans = [0]*num_people\n i = 0\n while candies > 0:\n ans[i % num_people] += min(i+1,candies)\n candies -= min(i+1,candies)\n i += 1\n return ans\n\n# method_2 数学法\nclass Solution:\n def distributeCandies(self, candies: int, num_people: int) -> List[int]:\n ans = [0]*num_people\n # 获得完整礼物的人数 p\n p = int((2*candies + 0.25)**0.5 - 0.5)\n # 完整部分分发\n rows = p // num_people\n cols = p % num_people\n for i in range(num_people):\n ans[i] = (i+1)*rows + int(num_people*rows*(rows-1)/2)\n # 不完整部分分发\n if i < cols:\n ans[i] += (i+1) + num_people*rows\n # 剩余分发\n ans[cols] += candies - int(p*(p + 1)/2)\n \n return ans","sub_path":"Qustion Code/1103. 分糖果 II.py","file_name":"1103. 分糖果 II.py","file_ext":"py","file_size_in_byte":1627,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"344105922","text":"'''\nEnunciado: desenvolva um programa onde 4 jogadores joguem um dado e tenham resultados aleatórios. Guarde esses\nresultados em um dicionário. No final, coloque esse dicionário em ordem, sabendo que o vencedor tirou o maior numero\nno dado.\n'''\n\n# ================================================ #\nfrom random import randint\nfrom time import sleep\n\n# ================================================ #\n# definindo as variaveis\n\njogo = {}\nlista = []\nmaior = 0\npos = 0\n\n# ================================================ #\n# gerando um dicionario com numeros aleatorios de 1 a 6\n\njogo['Jogador1'] = randint(1, 6)\njogo['Jogador2'] = randint(1, 6)\njogo['Jogador3'] = randint(1, 6)\njogo['Jogador4'] = randint(1, 6)\n\nprint('-'*30)\n\n# ================================================ #\nprint('Resultado dos sorteios:')\nprint('-'*30)\nfor k, v in jogo.items():\n print(f' O {k} sorteou o nº {v}')\n sleep(1)\n#print(f'{\" Fim do Sorteio \":=^30}\\n')\n\n# ================================================ #\n# definindo a lógica do sorteio dos ganhadores\n\ncnt = 0\nfor e in jogo.items():\n if cnt == 0 or e[1] <= maior:\n maior = e[1]\n lista.append(e)\n else:\n while pos < len(lista):\n if e[1] >= lista[pos][1]:\n lista.insert(pos, e)\n break\n pos += 1\n cnt += 1\n\nprint(f'\\n{\" RANKING DOS GANHADORES \":=^30}')\nprint('-'*30)\n\nfor c, l in enumerate(lista):\n print(f' {c+1}º Lugar: {l[0]} com {l[1]}')\n sleep(1)\n\nprint(f'\\n{\" Fim do Sorteio \":=^30}')\n","sub_path":"exercicios-cursoemvideo-python3-mundo3/Aula19/exerc091.py","file_name":"exerc091.py","file_ext":"py","file_size_in_byte":1527,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"76226472","text":"# Copyright (c) 2021 Graphcore Ltd. All rights reserved.\n# Copyright 2020 The HuggingFace Team All rights reserved.\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#\n# This file has been modified by Graphcore Ltd.\n\nfrom datetime import datetime\nfrom pathlib import Path\n\nfrom datasets import load_metric\nimport tensorflow as tf\nfrom tensorflow.python import ipu\nfrom transformers import BertConfig, TFBertForQuestionAnswering\n\nfrom data_utils.batch_config import BatchConfig, Task\nfrom data_utils.squad_v1.load_squad_data import format_raw_data_for_metric, get_prediction_dataset, get_squad_data\nfrom data_utils.squad_v1.postprocess_squad_predictions import postprocess_qa_predictions\nfrom keras_extensions.callbacks.callback_factory import CallbackFactory\nfrom keras_extensions.learning_rate.scheduler_builder import get_lr_scheduler\nfrom keras_extensions.optimization import get_optimizer\nfrom model.losses import QuestionAnsweringLossFunction\nfrom model.convert_bert_model import convert_tf_bert_model, post_process_bert_input_layer\nfrom model.pipeline_stage_names import PIPELINE_ALLOCATE_PREVIOUS, PIPELINE_NAMES\nfrom utilities.argparser import parse_arguments\nfrom utilities.assign_pipeline_stages import PipelineStagesAssigner\nfrom utilities.checkpoint_utility import load_checkpoint_into_model\nfrom utilities.ipu_utils import create_ipu_strategy, get_poplar_options_per_pipeline_stage, set_random_seeds\nfrom utilities.loss_enqueuer import wrap_loss_in_enqueuer\n\n\ndef fine_tune_squad(**config):\n # Get checkpoint from Hugging Face's models repo or from our own checkpoint.\n bert_model_name = config.get(\"bert_model_name\", None)\n pretrained_ckpt_path = config.get(\"pretrained_ckpt_path\", None) # This can be passed from both cli and config file.\n assert bert_model_name is not None or pretrained_ckpt_path is not None, \\\n \"SQuAD requires a pretrained model, either `bert_model_name` (via config file) or `pretrained_ckpt_path` \" \\\n \"(via config file or `--pretrained-ckpt-path` command line argument) but none provided.\"\n assert (bert_model_name is not None and pretrained_ckpt_path is None) \\\n or (bert_model_name is None and pretrained_ckpt_path is not None), \\\n f\"Only one checkpoint is accepted, but two provided: `bert_model_name`={bert_model_name}, \" \\\n f\"and `pretrained_ckpt_path`={pretrained_ckpt_path}.\"\n if pretrained_ckpt_path is not None:\n bert_config_params = config[\"bert_config\"]\n\n # Get required options\n micro_batch_size = config[\"micro_batch_size\"]\n num_epochs = config[\"num_epochs\"]\n optimizer_opts = config[\"optimizer_opts\"]\n learning_rate = config['learning_rate']\n replicas = config[\"replicas\"]\n grad_acc_steps_per_replica = config[\"grad_acc_steps_per_replica\"]\n wandb_opts = config[\"wandb_opts\"]\n use_outlining = config[\"use_outlining\"]\n replace_layers = config[\"replace_layers\"]\n enable_recomputation = config[\"enable_recomputation\"]\n embedding_serialization_factor = config[\"embedding_serialization_factor\"]\n optimizer_state_offchip = config[\"optimizer_state_offchip\"]\n matmul_available_memory_proportion_per_pipeline_stage = config[\n \"matmul_available_memory_proportion_per_pipeline_stage\"]\n matmul_partials_type = config[\"matmul_partials_type\"]\n pipeline_stages = config[\"pipeline_stages\"]\n device_mapping = config[\"device_mapping\"]\n global_batches_per_log = config[\"global_batches_per_log\"]\n seed = config[\"seed\"]\n cache_dir = config[\"cache_dir\"]\n output_dir = config[\"output_dir\"]\n\n # Get optional options\n save_ckpt_path = config.get(\"save_ckpt_path\", Path(__file__).parent.joinpath(\"checkpoints\").absolute())\n ckpt_every_n_steps_per_execution = config.get(\"ckpt_every_n_steps_per_execution\", 2000)\n\n universal_run_name = config.get(\"name\", f\"{Path(config['config']).stem}-{wandb_opts['init']['name']}\")\n universal_run_name += f\"-{datetime.now().strftime('%Y%m%d_%H%M%S')}\"\n print(f\"Universal name for run: {universal_run_name}\")\n set_random_seeds(seed)\n num_pipeline_stages = len(device_mapping)\n num_ipus_per_replicas = max(device_mapping) + 1\n num_ipus = replicas * num_ipus_per_replicas\n\n # Load training and validation data\n # =================================\n train_dataset, eval_dataset, num_train_samples, num_eval_samples, raw_datasets = get_squad_data(\n micro_batch_size,\n cache_dir\n )\n train_batch_config = BatchConfig(micro_batch_size=micro_batch_size,\n total_num_train_samples=num_epochs * num_train_samples.numpy(),\n num_replicas=replicas,\n gradient_accumulation_count=grad_acc_steps_per_replica,\n dataset_size=num_train_samples.numpy(),\n global_batches_per_log=global_batches_per_log,\n task=Task.OTHER)\n\n # Create model\n # ============\n policy = tf.keras.mixed_precision.Policy(\"float16\")\n tf.keras.mixed_precision.set_global_policy(policy)\n strategy = create_ipu_strategy(num_ipus, enable_recomputation=enable_recomputation)\n with strategy.scope():\n # Instantiate the pretrained model given in the config.\n if bert_model_name is not None:\n model = TFBertForQuestionAnswering.from_pretrained(bert_model_name)\n else:\n bert_config = BertConfig(**bert_config_params, hidden_act=ipu.nn_ops.gelu)\n model = TFBertForQuestionAnswering(config=bert_config)\n\n # Convert subclass model to functional, expand main layers to enable pipelining, and replace some layers to\n # optimise performance.\n model = convert_tf_bert_model(\n model,\n train_dataset,\n post_process_bert_input_layer,\n replace_layers=replace_layers,\n use_outlining=use_outlining,\n embedding_serialization_factor=embedding_serialization_factor,\n rename_outputs={'tf.compat.v1.squeeze': 'start_positions', 'tf.compat.v1.squeeze_1': 'end_positions'}\n )\n # Load from pretrained checkpoint if requested.\n if pretrained_ckpt_path is not None:\n print(f\"Attempting to load pretrained checkpoint from path {pretrained_ckpt_path}. \"\n f\"This will overwrite the current weights\")\n load_checkpoint_into_model(model, pretrained_ckpt_path)\n\n # Configure pipeline stages\n # =========================\n if num_pipeline_stages > 1:\n pipeline_assigner = PipelineStagesAssigner(PIPELINE_ALLOCATE_PREVIOUS, PIPELINE_NAMES)\n assignments = model.get_pipeline_stage_assignment()\n assignments = pipeline_assigner.assign_pipeline_stages(assignments, pipeline_stages)\n model.set_pipeline_stage_assignment(assignments)\n model.print_pipeline_stage_assignment_summary()\n poplar_options_per_pipeline_stage = get_poplar_options_per_pipeline_stage(\n num_ipus_per_replicas,\n device_mapping,\n matmul_available_memory_proportion_per_pipeline_stage,\n matmul_partials_type\n )\n model.set_pipelining_options(\n gradient_accumulation_steps_per_replica=grad_acc_steps_per_replica,\n pipeline_schedule=ipu.ops.pipelining_ops.PipelineSchedule.Grouped,\n device_mapping=device_mapping,\n offload_weight_update_variables=optimizer_state_offchip,\n forward_propagation_stages_poplar_options=poplar_options_per_pipeline_stage,\n backward_propagation_stages_poplar_options=poplar_options_per_pipeline_stage,\n recomputation_mode=ipu.pipelining_ops.RecomputationMode.RecomputeAndBackpropagateInterleaved,\n )\n\n # Compile the model for training\n # ==============================\n # Wrap loss in an out-feed queue.\n loss_outfeed_queue = ipu.ipu_outfeed_queue.IPUOutfeedQueue()\n qa_loss = wrap_loss_in_enqueuer(QuestionAnsweringLossFunction,\n loss_outfeed_queue,\n [\"end_positions_loss\", \"start_positions_loss\"])()\n # Define optimiser with polynomial decay learning rate.\n learning_rate['lr_schedule_params']['total_steps'] = train_batch_config.num_train_steps\n lr_outfeed_queue = ipu.ipu_outfeed_queue.IPUOutfeedQueue(outfeed_mode=ipu.ipu_outfeed_queue.IPUOutfeedMode.LAST)\n lr_scheduler = get_lr_scheduler(scheduler_name=learning_rate[\"lr_schedule\"],\n schedule_params=learning_rate[\"lr_schedule_params\"],\n queue=lr_outfeed_queue)\n # Prepare optimizer.\n outline_optimizer_apply_gradients = use_outlining\n optimizer = get_optimizer(\n optimizer_opts[\"name\"],\n grad_acc_steps_per_replica,\n replicas,\n lr_scheduler,\n outline_optimizer_apply_gradients,\n weight_decay_rate=optimizer_opts[\"params\"][\"weight_decay_rate\"],\n )\n # Compile the model.\n model.compile(\n optimizer=optimizer,\n loss={\"end_positions\": qa_loss, \"start_positions\": qa_loss},\n metrics='accuracy',\n steps_per_execution=train_batch_config.steps_per_execution\n )\n\n # Train the model\n # ===============\n # Set up callbacks\n callbacks = CallbackFactory.get_callbacks(\n universal_run_name=universal_run_name,\n batch_config=train_batch_config,\n model=model,\n checkpoint_path=save_ckpt_path,\n ckpt_every_n_steps_per_execution=ckpt_every_n_steps_per_execution,\n outfeed_queues=[lr_outfeed_queue, loss_outfeed_queue],\n config=config,\n )\n # Print configs to be logged in wandb's terminal.\n print(config)\n print(f\"Training batch config:\\n{train_batch_config}\")\n # Train the model\n history = model.fit(\n train_dataset,\n steps_per_epoch=train_batch_config.num_micro_batches_per_epoch,\n epochs=num_epochs,\n callbacks=callbacks\n )\n\n # Evaluate the model on the validation set\n # ========================================\n # Prepare the dataset to be evaluated in the IPU.\n eval_batch_config = BatchConfig(micro_batch_size=micro_batch_size,\n total_num_train_samples=num_eval_samples.numpy(),\n num_replicas=replicas,\n gradient_accumulation_count=grad_acc_steps_per_replica,\n dataset_size=num_eval_samples.numpy(),\n task=Task.OTHER)\n max_eval_samples = eval_batch_config.micro_batch_size * eval_batch_config.num_micro_batches_per_epoch\n eval_pred_dataset = get_prediction_dataset(eval_dataset, max_eval_samples)\n with strategy.scope():\n # Re-compile the model for prediction if needed.\n if train_batch_config.steps_per_execution != eval_batch_config.steps_per_execution:\n model.compile(steps_per_execution=eval_batch_config.steps_per_execution)\n # Get predictions for the validation data.\n print(f\"Running inference:\\nGenerating predictions on the validation data...\")\n predictions = model.predict(\n eval_pred_dataset,\n batch_size=eval_batch_config.micro_batch_size\n )\n # The predictions for the end position goes first in the model outputs tuple (note the output of model.summary()).\n end_predictions, start_predictions = predictions\n # Match the predictions to answers in the original context.\n # This will also write out the predictions to a json file in the directory given by `output_dir`.\n final_predictions = postprocess_qa_predictions(\n list(raw_datasets[\"validation\"].as_numpy_iterator()),\n list(eval_dataset.unbatch().as_numpy_iterator()),\n (start_predictions, end_predictions),\n output_dir=output_dir\n )\n # Format the predictions and the actual labels as expected by the metric.\n formatted_predictions = [{\"id\": k, \"prediction_text\": v} for k, v in final_predictions.items()]\n formatted_labels = format_raw_data_for_metric(raw_datasets[\"validation\"])\n metric = load_metric(\"squad\")\n metrics = metric.compute(predictions=formatted_predictions, references=formatted_labels)\n print(\"Evaluation metrics:\")\n for key, value in metrics.items():\n print(f\"{key}: {value:.3f}\")\n\n return history\n\n\nif __name__ == \"__main__\":\n if __name__ == \"__main__\":\n fine_tune_squad(**parse_arguments(\"TF2 BERT SQuAD Fine Tuning\"))\n","sub_path":"applications/tensorflow2/bert/run_squad.py","file_name":"run_squad.py","file_ext":"py","file_size_in_byte":13359,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"213349348","text":"#!/usr/bin/env python3\n\nimport curses\n\nstdscr = curses.initscr() # initialization - curses do their magic with terminal\ncurses.noecho() # turn off automatic echoing of keys\ncurses.cbreak() # react to keys automatically, do now wait for Enter to be pressed\nstdscr.keypad(True)\n\n# Terminating curses application\n# First reverse curses-friendly terminal settings\ncurses.nocbreak()\nstdscr.keypad(False)\ncurses.echo()\ncurses.endwin() # restore terminal to its original operating mode\n","sub_path":"curses/curses_trial.py","file_name":"curses_trial.py","file_ext":"py","file_size_in_byte":483,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"637019151","text":"#!/usr/bin/env python\n\n## Simple talker demo that published std_msgs/Strings messages\n## to the 'chatter' topic\n\nimport rospy\nfrom std_msgs.msg import Int64\nfrom std_msgs.msg import String\n\n\n\ndef talker():\n pub = rospy.Publisher('chatter_num', Int64, queue_size=10)\n pub2 = rospy.Publisher('chatter_text', String, queue_size=10)\n\n rospy.init_node('talker', anonymous=True)\n rate = rospy.Rate(20) # 10hz\n value = 0\n while not rospy.is_shutdown():\n value = value + 1 % rospy.get_time()\n text = 'Hello World'\n rospy.loginfo(value)\n pub.publish(value)\n\n rospy.loginfo(text)\n pub2.publish(text)\n\n rate.sleep()\n\nif __name__ == '__main__':\n try:\n\n talker()\n except rospy.ROSInterruptException:\n pass\n","sub_path":"ros/mic_ros/catkin_ws/src/mic/scripts/testes/mic.py","file_name":"mic.py","file_ext":"py","file_size_in_byte":780,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"509058297","text":"from __future__ import division\nimport os, glob, cv2, random\ndef main(indir, preddir):\n\tprint(\"##Evaluate function\")\n\tindir = os.path.expanduser(indir)\n\tpreddir = os.path.expanduser(preddir)\n\n\tfor root, _, file_names in os.walk(indir):\n\t\tprint('#Root:',root)\n\t\tprint('#file_names: ', file_names)\n\t\troot_part_path = os.path.relpath(root, indir)\n\t\tprint('#root_part_path: ', root_part_path)\n\t\tprint(file_names)\n\t\tdes_file_names = list(filter(lambda x: x.endswith('.txt'), file_names))\n\t\tdes_file_paths = [os.path.join(root, e) for e in des_file_names]\n\t\tprint('ndes_file_names', len(des_file_names))\n\t\tpred_file_path = os.path.join(preddir, root_part_path, 'results.csv')\n\t\tpred_strgs_of_img = dict()\n\t\twith open(pred_file_path) as f:\n\t\t\tlines = f.readlines()\n\t\t\tprint(lines)\n\t\t\tfor line in lines:\n\t\t\t\tprint('line=', line)\n\t\t\t\tcolums = [e.rstrip() for e in line.split(',')]\n\t\t\t\tprint('colums: ', colums)\n\t\t\t\t# if len(colums) > 1:\n\t\t\t\tpred_strgs_of_img[colums[0]] = colums[1:]\n\t\t\t\t# else:\n\t\t\t\t# \tpred_strgs_of_img[colums[0]] = ['']\n\t\t\t\tprint('pred_strgs_of_img[colums[0]]: ', pred_strgs_of_img[colums[0]])\n\t\tprint('pred_strgs_of_img: ', pred_strgs_of_img)\n\t\tcorrect_count = 0\n\t\tn_lp = 0\n\t\tfalse_reports = []\n\t\tfor des_file_name, des_file_path in zip(des_file_names, des_file_paths):\n\t\t\tdes_bfile_name = os.path.splitext(des_file_name)[0]\n\t\t\tprint('des_bfile_name: ', des_bfile_name)\n\t\t\twith open(des_file_path) as f:\n\t\t\t\tlines = f.readlines()\n\t\t\t\tprint(lines)\n\t\t\t\t# actual_strgs = []\n\t\t\t\tfor line in lines:\n\t\t\t\t\tline = line.replace(\"-\", \"\")\n\t\t\t\t\tprint('line=', line)\n\t\t\t\t\tcolums = line.split(',')\n\t\t\t\t\traw_strg = colums[9]\n\t\t\t\t\tactual_strg = raw_strg.split('.')[0]\n\t\t\t\t\t# actual_strgs.append(actual_strg)\n\t\t\t\t\tif actual_strg in pred_strgs_of_img[des_bfile_name]:\n\t\t\t\t\t\tcorrect_count += 1\n\t\t\t\t\t\tprint('True ' + des_bfile_name + ': ' + actual_strg + ' in ' + str(pred_strgs_of_img[des_bfile_name]))\n\t\t\t\t\telse:\n\t\t\t\t\t\tfalse_reports.append('False ' + des_bfile_name + ': ' + actual_strg + ' not in ' + str(pred_strgs_of_img[des_bfile_name]))\n\t\t\t\t\tn_lp += 1\n\t\tacc = correct_count/n_lp\n\t\tprint('correct_count/n_lp = ' + str(correct_count) + '/' + str(n_lp))\n\t\tprint('acc = ', acc)\n\t\tprint('false_report: ')\n\t\tfor false_report in false_reports:\n\t\t\tprint(false_report)\n\t\t\t\t\n\n\nif __name__=='__main__':\n\timport argparse\n\tap = argparse.ArgumentParser()\n\tap.add_argument(\"--indir\", help=\"indir\")\n\tap.add_argument(\"--preddir\", help=\"preddir\")\n\targs= vars(ap.parse_args())\n\tmain(args[\"indir\"], args[\"preddir\"])","sub_path":"code/Librarys/evaluate/evaluate.py","file_name":"evaluate.py","file_ext":"py","file_size_in_byte":2492,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"240624364","text":"import requests\nfrom bs4 import BeautifulSoup\nimport pickle\n\nDESIRED_HUBS = []\n\nret = requests.get('https://habr.com/ru/all/')\nsoup = BeautifulSoup(ret.text, 'html.parser')\nposts = soup.find_all('article', class_='post')\nPOSTS = []\n\n\ndef view_posts():\n for post in posts:\n hubs = post.find_all('a', class_='hub-link')\n for hub in hubs:\n hub_lower = hub.text.lower()\n if any([hub_lower in desired for desired in DESIRED_HUBS]):\n title_element = post.find('a', class_='post__title_link')\n post_preview = title_element.text + \" \" + title_element.attrs.get('href')\n POSTS.append(post_preview)\n return POSTS\n\n\ndef clear_posts():\n POSTS.clear()\n\n\ndef view_hubs():\n try:\n with open('data.pickle', 'rb') as f:\n return pickle.load(f)\n except FileNotFoundError:\n return \"Error!\"\n\n\ndef add_hubs(message):\n with open('data.pickle', 'rb') as f:\n DESIRED_HUBS = pickle.load(f)\n DESIRED_HUBS.append(message.lower())\n with open('data.pickle', 'wb') as f:\n pickle.dump(DESIRED_HUBS, f)\n\n\ndef delete_hub(message):\n with open('data.pickle', 'rb') as f:\n DESIRED_HUBS = pickle.load(f)\n DESIRED_HUBS.remove(message.lower())\n with open('data.pickle', 'wb') as f:\n pickle.dump(DESIRED_HUBS, f)\n\n","sub_path":"digest.py","file_name":"digest.py","file_ext":"py","file_size_in_byte":1359,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"298856810","text":"import sys\n\ninput = lambda: sys.stdin.readline().rstrip()\n\nn = int(input())\nscores_dict = {}\nscores = []\nfor _ in range(n):\n name, score = input().split()\n scores.append(int(score))\n scores_dict[score] = name # score를 key로 넣어 score로 학생 이름 검색이 가능하게 함. \nscores.sort(reverse=True) # 세 번째로 높은학생이니 내림차순 정렬\n# 1. dict 키로 검색\nprint(scores_dict[str(scores[2])])\n\n\n# sorted_dict = sorted(scores_dict.items(), key=operator.itemgetter(1))\n# 인자값에 있는 key=operator.itemgetter(0)는 정렬하고자 하는 키 값을 0번째 인덱스 기준으로 하겠다는 것입니다.\n# 0번째 인자는 Key입니다.\n# 인자값에 있는 key=operator.itemgetter(1)는 정렬하고자 하는 키 값을 1번째 인덱스 기준으로 하겠다는 것입니다.\n# 1번째 인자는 Value입니다.\n\n\n# 2. for문을 활용하여 딕셔너리의 값으로 키를 찾음\n# for key, value in scores_dict.items():\n# if value == scores[2]:\n# print(key)\n","sub_path":"코드업/코드업_1420.py","file_name":"코드업_1420.py","file_ext":"py","file_size_in_byte":1028,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"541091733","text":"from django import forms\nfrom .models import Resume,Upload_resume\n\n\nclass ResumeForm(forms.ModelForm):\n class Meta:\n model = Resume\n fields = [\"school_inter\",\n \"board_inter\",\n \"year_inter\",\n \"inter\",\n \"add\", \n \"phone\",\n \"school_high\",\n \"year_high\",\n \"high\",\n \"board_high\",\n \n \"email\",\n \"skill\",\n \"area_of_skill\",\n \"hob\",\n \"gender\",\n \"dob\",\n \"exp\",\n \"ug\",\n \"ug_coll\",\n \"year_ug\",\n\n\n ]\nclass UploadForm(forms.ModelForm):\n class Meta:\n model = Upload_resume\n fields =[\"resume\"]\n\n \n ","sub_path":"resume/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":675,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"359226531","text":"from bson.json_util import dumps\nfrom pymongo import MongoClient\n\nmg_client = MongoClient('localhost', 27017)\ndb = mg_client.test\n\n#clear collections\ndb.store.remove({})\ndb.transaction.remove({})\ndb.products.remove({})\ndb.productStore.remove({})\n\n\n## insert stores\nstores = [\n {\n \"name\": \"store 1\"\n },\n {\n \"name\": \"store 2\"\n },\n {\n \"name\": \"store 3\"\n }\n]\n\nstore_insert_result = db.store.insert_many(stores)\nstore_ids = store_insert_result.inserted_ids\n\n# insert products\nproducts = [\n {\n \"type\": \"shoe\",\n \"name\": \"authentic\",\n \"company\": \"vans\",\n \"color\": \"blue\",\n \"price\": 50\n },\n {\n \"type\": \"shoe\",\n \"name\": \"authentic\",\n \"company\": \"vans\",\n \"color\": \"yellow\",\n \"price\": 25\n },\n {\n \"type\": \"shoe\",\n \"name\": \"air-jordan\",\n \"company\": \"nike\",\n \"color\": \"green\",\n \"price\": 100\n },\n {\n \"type\": \"sock\",\n \"name\": \"low\",\n \"company\": \"under armour\",\n \"color\": \"white\",\n \"price\": 40\n },\n {\n \"type\": \"shoe\",\n \"name\": \"old skool\",\n \"company\": \"vans\",\n \"color\": \"black\",\n \"price\": 20\n },\n {\n \"type\": \"pants\",\n \"name\": \"chino\",\n \"company\": \"jcrew\",\n \"color\": \"khaki\",\n \"price\": 50\n },\n {\n \"type\": \"shoe\",\n \"name\": \"authentic\",\n \"company\": \"vans\",\n \"color\": \"white\",\n \"price\": 5\n },\n {\n \"type\": \"shoe\",\n \"name\": \"janowski\",\n \"company\": \"nike\",\n \"color\": \"orange\",\n \"price\": 500\n }\n]\n\nproduct_insert_result = db.products.insert_many(products)\nproduct_ids = product_insert_result.inserted_ids\n\n\n# foreach store insert all product ids into productStore collection\nfor storeId in store_ids:\n for productId in product_ids:\n item = {\n \"storeId\": storeId,\n \"productId\": productId,\n \"stock\": 100,\n \"sales\": 0\n }\n db.productStore.insert_one(item)\n\n# ASSUMPTION - all stores have the same products and the same levels of inventory\n\nmg_client.close()\n\n\n\n\n\n\n\n","sub_path":"mongo/insertMany.py","file_name":"insertMany.py","file_ext":"py","file_size_in_byte":2170,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"196942710","text":"import json\nimport os.path\n\nfrom haikunator import Haikunator\n\nfrom .. import CLOUD_PIPE_TEMPLATES_FOLDER\n\nname_generator = Haikunator()\n\n\n\nclass ImageInfo:\n \"\"\"\n \"\"\"\n\n def __init__(self, info):\n \"\"\"\n \"\"\"\n self.memory = info['memory']\n self.image_name = info['image_name']\n self.algorithm_name = info['algorithm_name']\n self.vcpus = info['CPUs'] # Does it have to be an even number?\n self.env_variable = {}\n\n for var in info['user_specified_environment_variables']:\n self.env_variable[var['name']] = self.EnvironmentVariable(var)\n\n class EnvironmentVariable:\n \"\"\"\n \"\"\"\n def __init__(self, var):\n \"\"\"\n \"\"\"\n self.name = var['name']\n self.required = var['required']\n self.initialized = False\n self.value = None\n\n def init_var(self, value):\n \"\"\"\n \"\"\"\n self.initialized = True\n self.value = value\n\n def init_env_variables(self, info):#, credentials):\n \"\"\"\n \"\"\"\n\n for name, value in info['variables'].items():\n if name not in self.env_variable:\n self.env_variable[name] = self.add_required_env_variable(name)\n self.env_variable[name].init_var(value)\n\n # for name, value in credentials.items():\n # if name not in self.env_variable:\n # self.env_variable[name] = self.add_required_variable(name)\n # self.env_variable[name].init_var(value)\n\n def add_required_env_variable(self, var_name):\n \"\"\"\n \"\"\"\n helper = {}\n helper['name'] = var_name\n helper['required'] = True\n return self.EnvironmentVariable(helper)","sub_path":"yunpipe/pipeline/image_info.py","file_name":"image_info.py","file_ext":"py","file_size_in_byte":1765,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"374102484","text":"import os\nimport cpuinfo\nimport psutil\nimport subprocess\nimport time\nfrom psutil._common import bytes2human\n\nclass System_Info():\n ''' This class retrieves some types of system information. '''\n def __init__(self):\n ''' Constructor. '''\n self.cpu_info = cpuinfo.get_cpu_info()\n self.cpu_count_phisycal = psutil.cpu_count(logical=False)\n self.cpu_count_logical = psutil.cpu_count(logical=True)\n self.cpu_percent = psutil.cpu_percent(interval=None, percpu=True)\n self.disk_usage = psutil.disk_usage('/')\n self.network = psutil.net_if_addrs()\n\n def memory(self):\n ''' This function returns a dict of memory informations. '''\n def format_info(info):\n dict_info = dict()\n for name in info._fields:\n value = getattr(info, name)\n if name != 'percent':\n value = bytes2human(value)\n dict_info[name.capitalize()] = value\n return dict_info\n\n memory = format_info(psutil.virtual_memory())\n swap = format_info(psutil.swap_memory())\n return memory, swap\n\n def _disk_usage(self):\n ''' This function returns a dict of disk partitions and your usages. '''\n usage_dict = dict()\n for part in psutil.disk_partitions(all=False):\n if os.name == 'nt':\n if 'cdrom' in part.opts or part.fstype == '':\n continue\n usage = psutil.disk_usage(part.mountpoint)\n usage_dict[part.device] = [bytes2human(usage.total),\n bytes2human(usage.used),\n bytes2human(usage.free),\n int(usage.percent),\n part.fstype,\n part.mountpoint]\n return usage_dict\n\n def _cpu_info(self):\n ''' This function returns a dictionary with informations about the cpu. '''\n cpu_dict = dict()\n cpu_dict['Name: '] = self.cpu_info['brand_raw']\n cpu_dict['Architecture: '] = self.cpu_info['arch']\n cpu_dict['Bits: '] = self.cpu_info['bits']\n if self.cpu_info['hz_actual'][0] % 10**9 > 0:\n cpu_dict['Frequency: '] = str(\n round((self.cpu_info['hz_actual'][0]/10**9), 2)) + ' Ghz'\n elif self.cpu_info['hz_actual'][0] % 10**6 > 0:\n cpu_dict['Frequency: '] = str(\n round((self.cpu_info['hz_actual'][0]/10**9), 2)) + ' Mhz'\n elif self.cpu_info['hz_actual'][0] % 10**3 > 0:\n cpu_dict['Frequency: '] = str(\n round((self.cpu_info['hz_actual'][0]/10**9), 2)) + ' Khz'\n cpu_dict['Physical Cores: '] = self.cpu_count_phisycal\n cpu_dict['Logical Cores: '] = self.cpu_count_logical\n cpu_dict['cpus'] = self.cpu_percent\n return cpu_dict\n\n def _network(self):\n ''' This function get the addresses associated to each network\n interface card (NIC) installed on the system and put in a dict\n {NIC: IP}. '''\n network = {}\n for k, v in self.network.items():\n if k == 'Loopback Pseudo-Interface 1':\n network[k] = [v[0].address]\n else:\n network[k] = [v[1].address]\n return network\n\n def _info_files(self):\n lista = os.listdir()\n dic = dict()\n for i in lista:\n dic[i] = []\n if os.path.isfile(i):\n dic[i].append(bytes2human(os.stat(i).st_size))\n dic[i].append(time.ctime(os.stat(i).st_atime))\n dic[i].append(time.ctime(os.stat(i).st_mtime))\n dic[i].append(os.path.splitext(i)[1])\n dic[i].append('File')\n else:\n dic[i].append(bytes2human(os.stat(i).st_size))\n dic[i].append(time.ctime(os.stat(i).st_atime))\n dic[i].append(time.ctime(os.stat(i).st_mtime))\n dic[i].append(os.path.splitext(i)[1])\n dic[i].append('Directory')\n return dic\n\n def _directory_info(self):\n lista = os.listdir()\n dic = dict()\n for i in lista:\n dic[i] = []\n if os.path.isfile(i):\n dic[i].append(bytes2human(os.stat(i).st_size))\n dic[i].append(time.ctime(os.stat(i).st_atime))\n dic[i].append(time.ctime(os.stat(i).st_mtime))\n dic[i].append(os.path.splitext(i)[1])\n dic[i].append('File')\n else:\n dic[i].append(bytes2human(os.stat(i).st_size))\n dic[i].append(time.ctime(os.stat(i).st_atime))\n dic[i].append(time.ctime(os.stat(i).st_mtime))\n dic[i].append(os.path.splitext(i)[1])\n dic[i].append('Directory')\n return dic\n\n def _pid_info(self):\n name = 'python.exe'\n lp = psutil.pids()\n pid_list = []\n for i in lp:\n p = psutil.Process(i)\n if p.name() == name:\n pid_list.append(i)\n return pid_list","sub_path":"tp8/system.py","file_name":"system.py","file_ext":"py","file_size_in_byte":5111,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"37762398","text":"\"\"\" 動画 : 支持率 -- 不支持率 最新値(２次元プロット)\n\n\"\"\"\nimport sys\nimport os\nimport argparse\nimport time\nfrom datetime import datetime\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as anm\n\nfrom fileio import dt_fm_sn, sn_fm_dt\nfrom polldb import t_max\nfrom db_defs import db_defs\n\ncfg = {\n 'gout_date': datetime.now().strftime('%Y%m%d'), # 出力file 名に付加する日付\n 'avg': False,\n 'args': None, # parse_args() の戻り値を保持する\n}\n\ndef _d(s):\n return datetime.strptime(s, '%Y-%m-%d')\n \ndef options():\n \"\"\" オプション定義\n Returns\n -------\n opt : ArgumentParser() インスタンス\n \n \"\"\"\n opt = argparse.ArgumentParser(description='動画 支持率-不支持率 ２次元プロット')\n \n # DB\n opt.add_argument('-d', dest='db_folder', default='../data',\n help='data folder. (../data)')\n opt.add_argument('-b', dest='db_begin', default='2016-04-01',\n help='DB 読み込み開始日付 (2016-04-01)')\n opt.add_argument('-e', dest='db_end', default='2022-12-31',\n help='DB 読み込み終了日付 (2022-12-31)')\n opt.add_argument('-m', dest='ma_days', type=int, default=180,\n help='DB の長期移動平均の窓サイズ [days]. (180)')\n \n # GOUT\n # opt.add_argument('-g', '--gout', action='store_true',\n # help='グラフのファイル出力')\n opt.add_argument('-f', dest='gout_folder', default='../output',\n help='動画の出力先フォルダ (../out)')\n opt.add_argument('-n', dest='gout_ndx', type=int, default=1,\n help='動画の(先頭)図番号 Mov# (1)')\n \n # APP -- NAP グラフ\n opt.add_argument('-r', dest='xy_range', default=\"20:70\",\n help='X軸, Y軸のレンジ (20:70)')\n # opt.add_argument('-t', dest='trimday', default=None,\n # help='直近 n 日の結果を抽出 (-t14 for last 2 weeks)') \n \n return opt\n \n \nclass UPDATE:\n \n def __init__(self, fig):\n \n # DB 読み込み\n # DB の読み込み日付の指定.\n args = cfg['args']\n d0 = _d(args.db_begin)\n df = _d(args.db_end)\n self.pp2, self.pp3, self.ppj = db_defs(d0, df, args.db_folder)\n \n # 評価日時 (10 日毎)\n self.t0 = sn_fm_dt(d0)\n self.tf = max(t_max(self.pp2), t_max(self.pp3), t_max(self.ppj)) + 10\n self.tt = [a for a in range(int(self.t0), int(self.tf), 10)] + [int(self.tf)]*10 # 終端に tf を 10 回繰り返し。\n \n # figure & axis 生成\n #\n self.fig = fig\n self.ax = fig.add_subplot(1, 1, 1)\n self.fig.subplots_adjust(left=0)\n \n \"\"\"\n if cfg['avg']:\n h = 0.67\n self.fig.text(0.19, h + 0.15, \"グループ H: 読売/日経/共同 の平均\", fontsize=10) #, bbox=bbox)\n self.fig.text(0.19, h + 0.12, \"グループ L: 毎日/朝日/時事/ANN/NHK の平均\", fontsize=10) #, bbox=bbox)\n \"\"\"\n \n def set_labels(self):\n self.ax.set_aspect('equal')\n \n xymin, xymax = [int(a) for a in cfg['args'].xy_range.split(':')] # X軸,Y軸の範囲(X,Y 共通)\n self.ax.set_xlim((xymin, xymax))\n self.ax.set_ylim((xymin, xymax))\n \n y1, m1 = [int(a) for a in dt_fm_sn(self.t0).strftime('%Y %m').split()]\n y2, m2 = [int(a) for a in dt_fm_sn(self.tf).strftime('%Y %m').split()]\n self.ax.set_title('内閣支持率(%d年%d月～%d年%d月)' % (y1, m1, y2, m2))\n \n self.ax.set_xlabel('支持する[%]', fontsize=14)\n self.ax.set_ylabel('支持しない[%]', fontsize=14)\n \n self.ax.legend(loc='upper left', bbox_to_anchor=(1.0, 1.0))\n self.ax.grid(True)\n \n def __call__(self, i):\n # 進行状況を画面出力\n sys.stdout.write('%s %s\\r' % (i, dt_fm_sn(self.tt[i]).strftime('%Y-%m-%d')))\n \n # クリア\n self.ax.cla()\n \n # 描画\n for (pp, yc, g) in [(self.ppj, 'brown', 'jnn'), (self.pp2, 'indigo', 'グループ H'), (self.pp3, 'green', 'グループ L')]:\n if False and cfg['avg']:\n if g != 'jnn':\n x_ = np.mean([p.interp['APP_RATE'](self.tt[i]) for p in pp])\n y_ = np.mean([p.interp['NAP_RATE'](self.tt[i]) for p in pp])\n self.ax.plot(x_, y_, '*', color=yc, label=g)\n else:\n dt = dt_fm_sn(self.tt[i])\n for p in pp:\n if True or p.label in ['読売', '毎日', '朝日']:\n x = p.interp['APP_RATE'](self.tt[i])\n y = p.interp['NAP_RATE'](self.tt[i])\n s = '%s %s' % (p.label, dt_fm_sn(p.db['T'][-1]).strftime('%m/%d'))\n if p.label == '毎日':\n if dt < datetime(2020, 5, 20):\n flg_dsp = True\n else:\n flg_dsp = False\n elif p.label == 'SSRC':\n if dt > datetime(2020, 4, 1):\n flg_dsp = True\n else:\n flg_dsp = False\n else:\n flg_dsp = True\n if flg_dsp:\n self.ax.plot(x, y, p.marker, ms = p.size, color=yc, label=s, alpha=0.5)\n else:\n pass\n x_ = np.mean([p.interp['APP_RATE'](self.tt[i]) for p in self.pp3])\n y_ = np.mean([p.interp['NAP_RATE'](self.tt[i]) for p in self.pp3])\n self.ax.text(x_-8, y_-10, nen_tsuki(dt_fm_sn(self.tt[i])), alpha=0.5, fontsize=16)\n self.ax.plot([0,100], [0,100], '--', color='gray', alpha=0.3)\n \n self.set_labels()\n \ndef nen_tsuki(dt):\n ys = dt.strftime('%Y')\n ms = dt.strftime('%m')\n return '%s年%s月' % (ys, ms)\n \ndef main():\n # オプション設定 (cfg['args'] に記録)\n #\n opt = options()\n cfg['args'] = opt.parse_args()\n \n \"\"\"\n if len(sys.argv) == 2:\n if sys.argv[1] == 'a':\n cfg['avg'] = True\n \n \"\"\"\n \n args = cfg['args']\n fig = plt.figure()\n update = UPDATE(fig)\n ani = anm.FuncAnimation(fig, update, interval=100, frames=len(update.tt))\n fn = os.path.join(args.gout_folder, 'Mov%d_%s.mp4' % (args.gout_ndx, cfg['gout_date']))\n ani.save(fn, writer='ffmpeg')\n \nmain()\n","sub_path":"script/m_app_nap.py","file_name":"m_app_nap.py","file_ext":"py","file_size_in_byte":6630,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"434730082","text":"\n\nfrom xai.brain.wordbase.verbs._discomfit import _DISCOMFIT\n\n#calss header\nclass _DISCOMFITED(_DISCOMFIT, ):\n\tdef __init__(self,): \n\t\t_DISCOMFIT.__init__(self)\n\t\tself.name = \"DISCOMFITED\"\n\t\tself.specie = 'verbs'\n\t\tself.basic = \"discomfit\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/verbs/_discomfited.py","file_name":"_discomfited.py","file_ext":"py","file_size_in_byte":261,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"69092548","text":"from itertools import islice\nimport yaml\n\n\nclass Seed:\n def __init__(self, session):\n self.session = session\n\n def import_yml(self, Model, yaml_path,\n each=1000, clean=False, autocommit=True):\n data = self._load_yaml(yaml_path)\n records = [Model(**record) for record in data]\n\n if clean:\n self._before_delete(Model)\n\n try:\n for chunked_records in self._chunk(records, size=each):\n self.session.bulk_save_objects(\n chunked_records, return_defaults=True)\n except Exception as e:\n self.session.rollback()\n raise e\n else:\n if autocommit:\n self.session.commit()\n\n def _before_delete(self, Model):\n Model.query.delete()\n\n def _load_yaml(self, yaml_path):\n with open(yaml_path) as stream:\n return yaml.load(stream)\n\n def _chunk(self, it, size):\n it = iter(it)\n return iter(lambda: tuple(islice(it, size)), ())\n","sub_path":"backend/models/seed.py","file_name":"seed.py","file_ext":"py","file_size_in_byte":1028,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"100054271","text":"import os\nimport abc\nimport random\nimport imageio\nimport jieba\nimport matplotlib.pyplot as plt\nfrom PIL import Image\nfrom wordcloud import WordCloud\nimport pandas as pd\nfrom sklearn.decomposition import PCA\nfrom mpl_toolkits.mplot3d import Axes3D\n\n\nclass Plotter(abc.ABC):\n @abc.abstractmethod\n def plot(self, data, *args, **kwargs):\n pass\n\n\nclass PointPlotter(Plotter):\n def __init__(self, size):\n self.size = size\n\n def create_test_point(self):\n point_list = []\n for i in range(self.size):\n point_list.append(Point(i, random.randint(1, 100)))\n print('Successfully create ', self.size, 'test data!')\n return point_list\n\n def plot(self, data, *args, **kwargs):\n plt.figure()\n x = []\n y = []\n for point in data:\n x.append(point.x)\n y.append(point.y)\n\n plt.plot(x, y)\n # plt.show()\n plt.savefig('plot.jpg')\n\n\nclass ArrayPlotter(Plotter):\n def __init__(self, size, dim):\n self.size = size\n self.dim = dim\n\n def create_test_array(self):\n point_list = []\n for i in range(self.size):\n if self.dim == 2:\n point_list.append(Point(i, random.randint(1, 100)))\n else:\n point_list.append([random.randint(1, 100) for i in range(self.dim)])\n return point_list\n\n def plot(self, data, *args, **kwargs):\n if self.dim > 3: # PCA降维\n pca = PCA(n_components=3)\n pca.fit(data)\n data = pca.fit_transform(data)\n print('WARNING:Dim 【', self.dim, '】 > 3. PCA Transformed To 3!')\n\n x = []\n y = []\n z = []\n for point in data:\n x.append(point[0])\n y.append(point[1])\n z.append(point[2])\n\n fig = plt.figure()\n ax = Axes3D(fig)\n ax.scatter(x, y, z)\n # plt.show()\n plt.savefig('array_plot_'+str(self.dim)+'.jpg')\n\n\nclass TextPlotter(Plotter):\n def __init__(self, file_path):\n self.file_path = file_path\n\n def create_test_text(self):\n data = pd.read_csv(self.file_path, sep='\\t', header=None, names=['评论', '经度', '纬度', '时间'])\n data_ls = list(data['评论'])\n return data_ls\n\n def plot(self, data, *args, **kwargs):\n # 停用词表\n stop_list = []\n with open('stopwords_list.txt', 'r') as f:\n while True:\n line = f.readline()\n if not line:\n break\n line = line.strip() # 去除\\n\n stop_list.append(line)\n\n all_word_list = []\n for text in data:\n word_list = list(jieba.cut(text))\n if 'http' in word_list:\n word_list = word_list[:word_list.index('http')]\n for word in word_list:\n if word not in stop_list:\n all_word_list.append(word)\n\n plt.figure()\n word_cloud = WordCloud(font_path='/System/Library/Fonts/PingFang.ttc',\n background_color='white',\n width=1000,\n height=1000,\n max_font_size=100,\n min_font_size=10,\n mode='RGBA')\n\n word_str = ' '.join(all_word_list)\n word_cloud.generate(word_str)\n word_cloud.to_file('word_cloud.png')\n\n\nclass ImagePlotter(Plotter):\n def __init__(self, file_path):\n self.file_path = file_path\n\n def create_test_image(self):\n img_list = []\n for file_name in os.listdir(self.file_path):\n if file_name.split('.')[-1] == 'jpeg':\n img_list.append(Image.open(self.file_path + '/' + file_name))\n\n return img_list\n\n def plot(self, data, *args, **kwargs):\n column = args[0]\n line = args[1]\n plt.figure(\"Processed Image\")\n for i in range(column * line):\n plt.subplot(column, line, i + 1)\n plt.axis('off')\n plt.imshow(data[i])\n plt.savefig('image_show.jpg')\n\n\nclass GifPlotter(Plotter):\n def __init__(self, file_path):\n self.file_path = file_path\n\n def create_test_image(self):\n img_list = []\n for file_name in os.listdir(self.file_path):\n if file_name.split('.')[-1] == 'jpeg':\n img_list.append(imageio.imread(self.file_path + '/' + file_name))\n \n return img_list\n\n def plot(self, data, *args, **kwargs):\n imageio.mimsave('test_gif.gif', data, 'GIF', duration=0.35)\n\n\nclass Point:\n def __init__(self, x, y):\n self.x = x\n self.y = y\n\n\ndef main():\n pp = PointPlotter(size=100)\n test_data = pp.create_test_point()\n pp.plot(test_data)\n\n ap = ArrayPlotter(size=100, dim=10)\n test_data = ap.create_test_array()\n ap.plot(test_data)\n\n tp = TextPlotter(file_path='weibo.txt')\n test_data = tp.create_test_text()\n tp.plot(test_data)\n\n ip = ImagePlotter(file_path='pictures')\n test_data = ip.create_test_image()\n ip.plot(test_data, 3, 3)\n\n gp = GifPlotter(file_path='pictures')\n test_data = gp.create_test_image()\n gp.plot(test_data, 3, 3)\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"Homework_9.py","file_name":"Homework_9.py","file_ext":"py","file_size_in_byte":5297,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"504497892","text":"# -*- coding: UTF-8 -*-\nimport argparse\nimport random\n\nimport pandas as pd\nimport string\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.linear_model import LogisticRegression, LinearRegression, Ridge\nfrom sklearn.neural_network import MLPClassifier\nimport numpy as np\n\nimport ReviewProposer\nfrom ReviewProposer import call\nGOOD_FEEDBACK = [\"Wow! Looks like this is a pretty good review\",\n \"That is a great review you got here :)\",\n \"Hehe nice, just need to put it online then\",\n \"Another great review\",\n \"Seems like you have some sucess\"]\nBAD_FEEDBACK = [\"Wow! That is a bad one\",\n \"Seems like someone is not a fan\",\n \"You better avoid that\",\n \"Should avoid such comments on your products!\"]\n\ndef test_review(review):\n review_vect = vectorizer.transform(review)\n print(review_vect.toarray())\n if clr.predict(review_vect) >= 1:\n print(\"Jeff Bezos: \", random.choice(GOOD_FEEDBACK))\n print(\"That is 4 or 5 stars!\")\n else:\n print(\"Jeff Bezos: \", random.choice(BAD_FEEDBACK))\n print(\"That is like 1 or 2 stars...\")\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument('--dataset',default='output.csv', help='reviews_Clothing_Shoes_and_Jewelry_5.json, ')\n parser.add_argument('--dataset3', default='output.csv', help='Software.json, ')\n args = parser.parse_args()\n\n\n print('start processing data')\n\n option = 2\n\n if option == 1: #Claque\n #Accuracy is 100%\n param1 = \"average_review_rating\"\n param2 = \"customer_reviews\"\n perso_dataset = \"amazon_co-ecommerce_sample.csv\"\n input_data = pd.read_csv(perso_dataset)\n elif option ==2:\n param1 = \"overall\"\n param2 = \"reviewText\"\n input_data = pd.read_csv(args.dataset)\n elif option == 3:\n param1 = \"overall\"\n param2 = \"reviewText\"\n input_data = pd.read_csv(args.dataset3)\n\n\n\n\n #print(input_data.describe())\n #print(input_data.head(10))\n\n input_data[param1] = input_data[param1].astype(object) # fix datatype error\n input_data[param2] = input_data[param2].astype(object) # fix datatype error\n\n input_data[param2][:100].to_csv('data_as_text.txt', header=None, index=None, sep=' ', mode='a')\n\n \"\"\"print(\"Describe the dataset\")\n print(input_data.describe())\n print(input_data.head(10))\"\"\"\n\n working_data = {param2: input_data[param2][:100000], param1: input_data[param1][:100000]}\n working_data = pd.DataFrame(data=working_data)\n working_data = working_data.dropna() # ignore if any row contained NaN\n working_data = working_data[working_data[param1] != '3']\n\n #Here we relabel our data such that we work only on good and bad reviews and discard medium ones\n if option==3 or option==2:\n working_data['label'] = working_data[param1].apply(lambda rating: +1 if str(rating).split(\" \", 1)[0] > '4' else 0)\n elif option==1:\n working_data['label'] = working_data[param1].apply(lambda rating: +1 if str(rating).split(\" \", 1)[0] > '4' else 0)\n\n # Splitting data into training set and testing set\n X = pd.DataFrame(working_data, columns=[param2])\n y = pd.DataFrame(working_data, columns=['label'])\n\n print(\"split the dataset...\")\n X_training, test_X, Y_training, test_y = train_test_split(X, y)\n\n\n print(\"vectorize the corpus...\")\n train_vector = vectorizer.fit_transform(X_training[param2])\n test_vector = vectorizer.transform(test_X[param2])\n print('processing ... ok')\n print('start training model')\n\n\n #In case you want to try something else\n #clr = LogisticRegression()\n #clr = LinearRegression()\n #clr = Ridge(alpha=.5)\n #clr = MLPClassifier(solver='lbfgs', alpha=1e-5,hidden_layer_sizes = (5, 2), random_state = 1)\n\n\n\n clr.fit(train_vector, Y_training.values.ravel())\n scores = clr.score(test_vector, test_y)\n print('training ...ok')\n print('accuracy: {}%'.format(scores * 100))\n\n #In case you want a quick check by yourself\n \"\"\" review1_1 = \"That is so bad, the worst ever, not recommend\"\n review1 = [review1_1]\n test_review(review1)\n \n review2 = [\"This is the greatest, I love it so much. Highly recommend it to everyone. It is the best ever\"]\n test_review(review2)\"\"\"\n\n\ndef display_header():\n print()\n print()\n print()\n print(\"___________________________________________________________________________________________\")\n print(\"*******************************************************************************************\")\n print(\"___________________________________________________________________________________________\")\n print()\n print(\" Welcome to the NLP Project!\")\n print()\n print(\"You will soon be greeted by Jeff Bezos who will guide you through the wonders of Amazon...\")\n print(\" Do not hesitate to ask him his opinion about the description of your product\")\n print(\" or certain reviews... \")\n print()\n print(\"___________________________________________________________________________________________\")\n print(\"*******************************************************************************************\")\n print(\"___________________________________________________________________________________________\")\n print()\n\nif __name__ == '__main__':\n NGram = 2\n vectorizer = CountVectorizer(token_pattern=r'\\b\\w+\\b', analyzer='word', ngram_range=(NGram, NGram))\n clr = LogisticRegression()\n main()\n display_header()\n chatbox = ReviewProposer#.call()\n flag = True\n\n while(flag):\n user_response = [chatbox.call()]\n print(\"call done\")\n test_review(user_response)\n print(\"test review done\")\n","sub_path":"text_classification.py","file_name":"text_classification.py","file_ext":"py","file_size_in_byte":5872,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"550687330","text":"# THIS IS NOT USED\n \n \nimport os\nimport sys\n#from arcgis.features import FeatureLayer\n#from arcgis.gis import GIS\nimport requests\nimport geopandas as gpd\nimport fiona\n\n# for arcgis\n#from utils import connect\n\nfrom datetime import datetime\ndatestamp = datetime.now().strftime(\"%Y%m%d_%H%M\")\n\n# Output_Feature_Class = \"K:\\\\Social_Services\\\\NearMe\\\\NearMe.gdb\\\\address_pt_ReverseGeocode\"\n# arcpy.geocoding.ReverseGeocode(in_features=address_pt_2_, in_address_locator=\"\", out_feature_class=Output_Feature_Class, address_type=\"ADDRESS\", search_distance=\"100 Meters\", feature_type=\"\", location_type=\"\")\n\ndef drop_untaxables(df):\n \"\"\" Drop any taxlots that have a non-numeric Taxlot attribute. \"\"\"\n # Change all nonnumerics into NaN\n before = len(df)\n df['Taxlot'] = df['Taxlot'].str.extract('(\\d+)')\n df = df.dropna(subset=['Taxlot'])\n after = len(df)\n print(\"before %d after %d\" % (before, after))\n #print(df)\n return df\n\ndef features_to_points(df):\n \"\"\" Return a centroid point for each polygon. \"\"\"\n\n # This is a horrible implementation, I hang my head in shame.\n # \"Append\" creates a new DF on each pass. You fix please.\n points = gpd.GeoDataFrame()\n for index, row in df.iterrows():\n row.geometry = row.geometry.centroid\n points = points.append(row)\n return points\n\ndef save_df(sdf,target):\n sdf.spatial.to_featureclass(location=target)\n return \n\nif __name__ == '__main__':\n\n\n fgdb = \"K:/Social_Services/NearMe/NearMe.gdb\"\n# fgdb = \"K:/Social_Services/NearMe/addrpts.gdb\"\n taxlots = \"taxlots_centroids\"\n\n #serverUrl = Config.SERVER_URL\n #taxlots_feature_service = serverUrl + '/rest/services/Taxlots/FeatureServer/1'\n\n try:\n taxlots_df = None\n\n #whereclause = 'where=1=1' # get every taxlot.... up to 5000 that is\n #whereclause = 'objectIds=1900267' # get just one, for testing\n\n # We request the data in web mercator because otherwise it complains\n # about how hard it is to accurately calculate the centroid, and we want the centroid!\n# query_url = taxlots_feature_service + '/query?f=geojson' + '&geometryType=esriGeometryPolygon&outSR=EPSG:3857&' + whereclause\n# data = requests.get(query_url)\n# b = bytes(data.content)\n# with fiona.BytesCollection(b) as fp:\n# crs = fp.crs \n# taxlots_df = pd.GeoDataFrame.from_features(fp, crs=\"EPSG:4326\")\n\n# with fiona.open(fgdb, layer=taxlots, mode='r') as fp:\n# print(fp)\n layers = fiona.listlayers(fgdb)\n print(fgdb, layers)\n taxlots_df = gpd.read_file(fgdb, layer=taxlots, rows=5)\n print(taxlots_df)\n\n except Exception as e:\n print(\"Could not query taxlots. \\\"%s\\\"\" % e)\n\n taxable_df = drop_untaxables(taxlots_df) # This drops ONE point sheesh\n\n# address_points_df = features_to_points(taxable_df)\n# print(address_points_df)\n\n # Write the cleaned up data out to address points\n\n # BE CAREFUL what you write here, it gets WEIRD if you accidentally write a SHAPEFILE (the default)\n # into an GDB directory -- then suddenly ogrinfo will see only the shapefile.\n # https://gis.stackexchange.com/questions/231265/geopandas-write-layer-back-into-geodatabase\n\n # OpenFileGDB can't WRITE only read so you have to install an extra library,\n # https://gis.stackexchange.com/questions/193288/how-to-add-support-for-filegdb-esri-file-gdb-api-driver-in-fiona\n\n # How to install FileGDB, follow instructions here\n # https://glenbambrick.com/2017/03/10/setting-up-gdal-with-filegdb/\n # download https://download.lfd.uci.edu/pythonlibs/w4tscw6k/GDAL-3.2.2-cp37-cp37m-win_amd64.whl\n # pip install the wheel file\n # I had to install the fiona wheel too\n #\n # confirm it loaded\n # python\n # from osgeo import gdal\n # gdal.__version__ should show 3.22\n # cd /k/Social_Services\n # dig the filegdb dll out of the zip and put it into your osgeo folder\n #\n #\n\n taxable_df.to_file(fgdb, layer='address_pts_' + datestamp, driver=\"FileGDB\", crs=\"EPSG:4326\")\n \n print(\"Wrote address points.\")\n","sub_path":"Address_points/build_address_points_geopandas.py","file_name":"build_address_points_geopandas.py","file_ext":"py","file_size_in_byte":4130,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"198704197","text":"from sklearn import tree\n\n#features = [[140, \"smooth\"], [130, \"smooth\"], [150, \"bumpy\"], [170, \"bumpy\"]]\nfeatures = [[140, 0], [130, 0], [150, 1], [170, 1]]\n#lables = [\"apple\", \"apple\", \"orange\", \"orange\"]\nlables = [0, 0, 1, 1]\n\nclassfier = tree.DecisionTreeClassifier()\n\nclassfier.fit(features, lables)\n\nprint(classfier.predict([141, 0])) # Should be 0\nprint(classfier.predict([171, 1])) # Should be 1\n\n\n\n","sub_path":"decision_tree_demo/decision_tree_demo.py","file_name":"decision_tree_demo.py","file_ext":"py","file_size_in_byte":406,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"214716100","text":"# uncompyle6 version 3.6.7\n# Python bytecode 3.6 (3379)\n# Decompiled from: Python 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)]\n# Embedded file name: build/bdist.macosx-10.7-x86_64/egg/airflow/contrib/operators/dataproc_operator.py\n# Compiled at: 2019-09-11 03:47:34\n# Size of source mod 2**32: 50223 bytes\n__doc__ = '\\nThis module contains Google Dataproc operators.\\n'\nimport ntpath, os, re, time, uuid\nfrom datetime import timedelta\nfrom airflow.contrib.hooks.gcp_dataproc_hook import DataProcHook\nfrom airflow.contrib.hooks.gcs_hook import GoogleCloudStorageHook\nfrom airflow.exceptions import AirflowException\nfrom airflow.models import BaseOperator\nfrom airflow.utils.decorators import apply_defaults\nfrom airflow.version import version\nfrom airflow.utils import timezone\n\nclass DataprocOperationBaseOperator(BaseOperator):\n \"\"\"DataprocOperationBaseOperator\"\"\"\n\n @apply_defaults\n def __init__(self, project_id, region='global', gcp_conn_id='google_cloud_default', delegate_to=None, *args, **kwargs):\n (super(DataprocOperationBaseOperator, self).__init__)(*args, **kwargs)\n self.gcp_conn_id = gcp_conn_id\n self.delegate_to = delegate_to\n self.project_id = project_id\n self.region = region\n self.hook = DataProcHook(gcp_conn_id=(self.gcp_conn_id),\n delegate_to=(self.delegate_to),\n api_version='v1beta2')\n\n def execute(self, context):\n self.hook.wait(self.start())\n\n def start(self, context):\n raise AirflowException('Please submit an operation')\n\n\nclass DataprocClusterCreateOperator(DataprocOperationBaseOperator):\n \"\"\"DataprocClusterCreateOperator\"\"\"\n template_fields = [\n 'cluster_name', 'project_id', 'zone', 'region']\n\n @apply_defaults\n def __init__(self, project_id, cluster_name, num_workers, zone=None, network_uri=None, subnetwork_uri=None, internal_ip_only=None, tags=None, storage_bucket=None, init_actions_uris=None, init_action_timeout='10m', metadata=None, custom_image=None, image_version=None, autoscaling_policy=None, properties=None, num_masters=1, master_machine_type='n1-standard-4', master_disk_type='pd-standard', master_disk_size=500, worker_machine_type='n1-standard-4', worker_disk_type='pd-standard', worker_disk_size=500, num_preemptible_workers=0, labels=None, region='global', service_account=None, service_account_scopes=None, idle_delete_ttl=None, auto_delete_time=None, auto_delete_ttl=None, customer_managed_key=None, *args, **kwargs):\n (super(DataprocClusterCreateOperator, self).__init__)(args, project_id=project_id, region=region, **kwargs)\n self.cluster_name = cluster_name\n self.num_masters = num_masters\n self.num_workers = num_workers\n self.num_preemptible_workers = num_preemptible_workers\n self.storage_bucket = storage_bucket\n self.init_actions_uris = init_actions_uris\n self.init_action_timeout = init_action_timeout\n self.metadata = metadata\n self.custom_image = custom_image\n self.image_version = image_version\n self.properties = properties or dict()\n self.master_machine_type = master_machine_type\n self.master_disk_type = master_disk_type\n self.master_disk_size = master_disk_size\n self.autoscaling_policy = autoscaling_policy\n self.worker_machine_type = worker_machine_type\n self.worker_disk_type = worker_disk_type\n self.worker_disk_size = worker_disk_size\n self.labels = labels\n self.zone = zone\n self.network_uri = network_uri\n self.subnetwork_uri = subnetwork_uri\n self.internal_ip_only = internal_ip_only\n self.tags = tags\n self.service_account = service_account\n self.service_account_scopes = service_account_scopes\n self.idle_delete_ttl = idle_delete_ttl\n self.auto_delete_time = auto_delete_time\n self.auto_delete_ttl = auto_delete_ttl\n self.customer_managed_key = customer_managed_key\n self.single_node = num_workers == 0\n assert not (self.custom_image and self.image_version), \"custom_image and image_version can't be both set\"\n if not not self.single_node:\n if not (self.single_node and self.num_preemptible_workers == 0):\n raise AssertionError('num_workers == 0 means single node mode - no preemptibles allowed')\n\n def _get_init_action_timeout(self):\n match = re.match('^(\\\\d+)(s|m)$', self.init_action_timeout)\n if match:\n if match.group(2) == 's':\n return self.init_action_timeout\n if match.group(2) == 'm':\n val = float(match.group(1))\n return '{}s'.format(timedelta(minutes=val).seconds)\n raise AirflowException('DataprocClusterCreateOperator init_action_timeout should be expressed in minutes or seconds. i.e. 10m, 30s')\n\n def _build_gce_cluster_config(self, cluster_data):\n if self.zone:\n zone_uri = 'https://www.googleapis.com/compute/v1/projects/{}/zones/{}'.format(self.project_id, self.zone)\n cluster_data['config']['gceClusterConfig']['zoneUri'] = zone_uri\n else:\n if self.metadata:\n cluster_data['config']['gceClusterConfig']['metadata'] = self.metadata\n else:\n if self.network_uri:\n cluster_data['config']['gceClusterConfig']['networkUri'] = self.network_uri\n else:\n if self.subnetwork_uri:\n cluster_data['config']['gceClusterConfig']['subnetworkUri'] = self.subnetwork_uri\n if self.internal_ip_only:\n if not self.subnetwork_uri:\n raise AirflowException('Set internal_ip_only to true only when you pass a subnetwork_uri.')\n cluster_data['config']['gceClusterConfig']['internalIpOnly'] = True\n if self.tags:\n cluster_data['config']['gceClusterConfig']['tags'] = self.tags\n if self.service_account:\n cluster_data['config']['gceClusterConfig']['serviceAccount'] = self.service_account\n if self.service_account_scopes:\n cluster_data['config']['gceClusterConfig']['serviceAccountScopes'] = self.service_account_scopes\n return cluster_data\n\n def _build_lifecycle_config(self, cluster_data):\n if self.idle_delete_ttl:\n cluster_data['config']['lifecycleConfig']['idleDeleteTtl'] = '{}s'.format(self.idle_delete_ttl)\n if self.auto_delete_time:\n utc_auto_delete_time = timezone.convert_to_utc(self.auto_delete_time)\n cluster_data['config']['lifecycleConfig']['autoDeleteTime'] = utc_auto_delete_time.format('%Y-%m-%dT%H:%M:%S.%fZ', formatter='classic')\n else:\n if self.auto_delete_ttl:\n cluster_data['config']['lifecycleConfig']['autoDeleteTtl'] = '{}s'.format(self.auto_delete_ttl)\n return cluster_data\n\n def _build_cluster_data(self):\n if self.zone:\n master_type_uri = 'https://www.googleapis.com/compute/v1/projects/{}/zones/{}/machineTypes/{}'.format(self.project_id, self.zone, self.master_machine_type)\n worker_type_uri = 'https://www.googleapis.com/compute/v1/projects/{}/zones/{}/machineTypes/{}'.format(self.project_id, self.zone, self.worker_machine_type)\n else:\n master_type_uri = self.master_machine_type\n worker_type_uri = self.worker_machine_type\n cluster_data = {'projectId':self.project_id, \n 'clusterName':self.cluster_name, \n 'config':{'gceClusterConfig':{}, 'masterConfig':{'numInstances':self.num_masters, \n 'machineTypeUri':master_type_uri, \n 'diskConfig':{'bootDiskType':self.master_disk_type, \n 'bootDiskSizeGb':self.master_disk_size}}, \n 'workerConfig':{'numInstances':self.num_workers, \n 'machineTypeUri':worker_type_uri, \n 'diskConfig':{'bootDiskType':self.worker_disk_type, \n 'bootDiskSizeGb':self.worker_disk_size}}, \n 'secondaryWorkerConfig':{}, 'softwareConfig':{}, 'lifecycleConfig':{}, 'encryptionConfig':{}, 'autoscalingConfig':{}}}\n if self.num_preemptible_workers > 0:\n cluster_data['config']['secondaryWorkerConfig'] = {'numInstances':self.num_preemptible_workers, 'machineTypeUri':worker_type_uri, \n 'diskConfig':{'bootDiskType':self.worker_disk_type, \n 'bootDiskSizeGb':self.worker_disk_size}, \n 'isPreemptible':True}\n cluster_data['labels'] = self.labels or {}\n cluster_data['labels'].update({'airflow-version': 'v' + version.replace('.', '-').replace('+', '-')})\n if self.storage_bucket:\n cluster_data['config']['configBucket'] = self.storage_bucket\n if self.image_version:\n cluster_data['config']['softwareConfig']['imageVersion'] = self.image_version\n else:\n if self.custom_image:\n custom_image_url = 'https://www.googleapis.com/compute/beta/projects/{}/global/images/{}'.format(self.project_id, self.custom_image)\n cluster_data['config']['masterConfig']['imageUri'] = custom_image_url\n if not self.single_node:\n cluster_data['config']['workerConfig']['imageUri'] = custom_image_url\n cluster_data = self._build_gce_cluster_config(cluster_data)\n if self.single_node:\n self.properties['dataproc:dataproc.allow.zero.workers'] = 'true'\n if self.properties:\n cluster_data['config']['softwareConfig']['properties'] = self.properties\n cluster_data = self._build_lifecycle_config(cluster_data)\n if self.init_actions_uris:\n init_actions_dict = [{'executableFile':uri, 'executionTimeout':self._get_init_action_timeout()} for uri in self.init_actions_uris]\n cluster_data['config']['initializationActions'] = init_actions_dict\n if self.customer_managed_key:\n cluster_data['config']['encryptionConfig'] = {'gcePdKmsKeyName': self.customer_managed_key}\n if self.autoscaling_policy:\n cluster_data['config']['autoscalingConfig'] = {'policyUri': self.autoscaling_policy}\n return cluster_data\n\n def start(self):\n \"\"\"\n Create a new cluster on Google Cloud Dataproc.\n \"\"\"\n self.log.info('Creating cluster: %s', self.cluster_name)\n cluster_data = self._build_cluster_data()\n return self.hook.get_conn().projects().regions().clusters().create(projectId=(self.project_id),\n region=(self.region),\n body=cluster_data,\n requestId=(str(uuid.uuid4()))).execute()\n\n\nclass DataprocClusterScaleOperator(DataprocOperationBaseOperator):\n \"\"\"DataprocClusterScaleOperator\"\"\"\n template_fields = [\n 'cluster_name', 'project_id', 'region']\n\n @apply_defaults\n def __init__(self, cluster_name, project_id, region='global', num_workers=2, num_preemptible_workers=0, graceful_decommission_timeout=None, *args, **kwargs):\n (super(DataprocClusterScaleOperator, self).__init__)(args, project_id=project_id, region=region, **kwargs)\n self.cluster_name = cluster_name\n self.num_workers = num_workers\n self.num_preemptible_workers = num_preemptible_workers\n self.optional_arguments = {}\n if graceful_decommission_timeout:\n self.optional_arguments['gracefulDecommissionTimeout'] = self._get_graceful_decommission_timeout(graceful_decommission_timeout)\n\n def _build_scale_cluster_data(self):\n scale_data = {'config': {'workerConfig':{'numInstances': self.num_workers}, \n 'secondaryWorkerConfig':{'numInstances': self.num_preemptible_workers}}}\n return scale_data\n\n @staticmethod\n def _get_graceful_decommission_timeout(timeout):\n match = re.match('^(\\\\d+)(s|m|h|d)$', timeout)\n if match:\n if match.group(2) == 's':\n return timeout\n else:\n if match.group(2) == 'm':\n val = float(match.group(1))\n return '{}s'.format(timedelta(minutes=val).seconds)\n if match.group(2) == 'h':\n val = float(match.group(1))\n return '{}s'.format(timedelta(hours=val).seconds)\n if match.group(2) == 'd':\n val = float(match.group(1))\n return '{}s'.format(timedelta(days=val).seconds)\n raise AirflowException('DataprocClusterScaleOperator should be expressed in day, hours, minutes or seconds. i.e. 1d, 4h, 10m, 30s')\n\n def start(self):\n \"\"\"\n Scale, up or down, a cluster on Google Cloud Dataproc.\n \"\"\"\n self.log.info('Scaling cluster: %s', self.cluster_name)\n update_mask = 'config.worker_config.num_instances,config.secondary_worker_config.num_instances'\n scaling_cluster_data = self._build_scale_cluster_data()\n return (self.hook.get_conn().projects().regions().clusters().patch)(projectId=self.project_id, \n region=self.region, \n clusterName=self.cluster_name, \n updateMask=update_mask, \n body=scaling_cluster_data, \n requestId=str(uuid.uuid4()), **self.optional_arguments).execute()\n\n\nclass DataprocClusterDeleteOperator(DataprocOperationBaseOperator):\n \"\"\"DataprocClusterDeleteOperator\"\"\"\n template_fields = [\n 'cluster_name', 'project_id', 'region']\n\n @apply_defaults\n def __init__(self, cluster_name, project_id, region='global', *args, **kwargs):\n (super(DataprocClusterDeleteOperator, self).__init__)(args, project_id=project_id, region=region, **kwargs)\n self.cluster_name = cluster_name\n\n def start(self):\n \"\"\"\n Delete a cluster on Google Cloud Dataproc.\n \"\"\"\n self.log.info('Deleting cluster: %s in %s', self.cluster_name, self.region)\n return self.hook.get_conn().projects().regions().clusters().delete(projectId=(self.project_id),\n region=(self.region),\n clusterName=(self.cluster_name),\n requestId=(str(uuid.uuid4()))).execute()\n\n\nclass DataProcJobBaseOperator(BaseOperator):\n \"\"\"DataProcJobBaseOperator\"\"\"\n job_type = ''\n\n @apply_defaults\n def __init__(self, job_name='{{task.task_id}}_{{ds_nodash}}', cluster_name='cluster-1', dataproc_properties=None, dataproc_jars=None, gcp_conn_id='google_cloud_default', delegate_to=None, labels=None, region='global', job_error_states=None, *args, **kwargs):\n (super(DataProcJobBaseOperator, self).__init__)(*args, **kwargs)\n self.gcp_conn_id = gcp_conn_id\n self.delegate_to = delegate_to\n self.labels = labels\n self.job_name = job_name\n self.cluster_name = cluster_name\n self.dataproc_properties = dataproc_properties\n self.dataproc_jars = dataproc_jars\n self.region = region\n self.job_error_states = job_error_states if job_error_states is not None else {'ERROR'}\n self.hook = DataProcHook(gcp_conn_id=gcp_conn_id, delegate_to=delegate_to)\n self.job_template = None\n self.job = None\n self.dataproc_job_id = None\n\n def create_job_template(self):\n \"\"\"\n Initialize `self.job_template` with default values\n \"\"\"\n self.job_template = self.hook.create_job_template(self.task_id, self.cluster_name, self.job_type, self.dataproc_properties)\n self.job_template.set_job_name(self.job_name)\n self.job_template.add_jar_file_uris(self.dataproc_jars)\n self.job_template.add_labels(self.labels)\n\n def execute(self, context):\n \"\"\"\n Build `self.job` based on the job template, and submit it.\n :raises AirflowException if no template has been initialized (see create_job_template)\n \"\"\"\n if self.job_template:\n self.job = self.job_template.build()\n self.dataproc_job_id = self.job['job']['reference']['jobId']\n self.hook.submit(self.hook.project_id, self.job, self.region, self.job_error_states)\n else:\n raise AirflowException('Create a job template before')\n\n def on_kill(self):\n \"\"\"\n Callback called when the operator is killed.\n Cancel any running job.\n \"\"\"\n if self.dataproc_job_id:\n self.hook.cancel(self.hook.project_id, self.dataproc_job_id, self.region)\n\n\nclass DataProcPigOperator(DataProcJobBaseOperator):\n \"\"\"DataProcPigOperator\"\"\"\n template_fields = [\n 'query', 'variables', 'job_name', 'cluster_name', 'region',\n 'dataproc_jars', 'dataproc_properties']\n template_ext = ('.pg', '.pig')\n ui_color = '#0273d4'\n job_type = 'pigJob'\n\n @apply_defaults\n def __init__(self, query=None, query_uri=None, variables=None, dataproc_pig_properties=None, dataproc_pig_jars=None, *args, **kwargs):\n (super(DataProcPigOperator, self).__init__)(args, dataproc_properties=dataproc_pig_properties, \n dataproc_jars=dataproc_pig_jars, **kwargs)\n self.query = query\n self.query_uri = query_uri\n self.variables = variables\n\n def execute(self, context):\n self.create_job_template()\n if self.query is None:\n self.job_template.add_query_uri(self.query_uri)\n else:\n self.job_template.add_query(self.query)\n self.job_template.add_variables(self.variables)\n super(DataProcPigOperator, self).execute(context)\n\n\nclass DataProcHiveOperator(DataProcJobBaseOperator):\n \"\"\"DataProcHiveOperator\"\"\"\n template_fields = [\n 'query', 'variables', 'job_name', 'cluster_name', 'region',\n 'dataproc_jars', 'dataproc_properties']\n template_ext = ('.q', '.hql')\n ui_color = '#0273d4'\n job_type = 'hiveJob'\n\n @apply_defaults\n def __init__(self, query=None, query_uri=None, variables=None, dataproc_hive_properties=None, dataproc_hive_jars=None, *args, **kwargs):\n (super(DataProcHiveOperator, self).__init__)(args, dataproc_properties=dataproc_hive_properties, \n dataproc_jars=dataproc_hive_jars, **kwargs)\n self.query = query\n self.query_uri = query_uri\n self.variables = variables\n if self.query is not None:\n if self.query_uri is not None:\n raise AirflowException('Only one of `query` and `query_uri` can be passed.')\n\n def execute(self, context):\n self.create_job_template()\n if self.query is None:\n self.job_template.add_query_uri(self.query_uri)\n else:\n self.job_template.add_query(self.query)\n self.job_template.add_variables(self.variables)\n super(DataProcHiveOperator, self).execute(context)\n\n\nclass DataProcSparkSqlOperator(DataProcJobBaseOperator):\n \"\"\"DataProcSparkSqlOperator\"\"\"\n template_fields = [\n 'query', 'variables', 'job_name', 'cluster_name', 'region',\n 'dataproc_jars', 'dataproc_properties']\n template_ext = ('.q', )\n ui_color = '#0273d4'\n job_type = 'sparkSqlJob'\n\n @apply_defaults\n def __init__(self, query=None, query_uri=None, variables=None, dataproc_spark_properties=None, dataproc_spark_jars=None, *args, **kwargs):\n (super(DataProcSparkSqlOperator, self).__init__)(args, dataproc_properties=dataproc_spark_properties, \n dataproc_jars=dataproc_spark_jars, **kwargs)\n self.query = query\n self.query_uri = query_uri\n self.variables = variables\n if self.query is not None:\n if self.query_uri is not None:\n raise AirflowException('Only one of `query` and `query_uri` can be passed.')\n\n def execute(self, context):\n self.create_job_template()\n if self.query is None:\n self.job_template.add_query_uri(self.query_uri)\n else:\n self.job_template.add_query(self.query)\n self.job_template.add_variables(self.variables)\n super(DataProcSparkSqlOperator, self).execute(context)\n\n\nclass DataProcSparkOperator(DataProcJobBaseOperator):\n \"\"\"DataProcSparkOperator\"\"\"\n template_fields = [\n 'arguments', 'job_name', 'cluster_name', 'region',\n 'dataproc_jars', 'dataproc_properties']\n ui_color = '#0273d4'\n job_type = 'sparkJob'\n\n @apply_defaults\n def __init__(self, main_jar=None, main_class=None, arguments=None, archives=None, files=None, dataproc_spark_properties=None, dataproc_spark_jars=None, *args, **kwargs):\n (super(DataProcSparkOperator, self).__init__)(args, dataproc_properties=dataproc_spark_properties, \n dataproc_jars=dataproc_spark_jars, **kwargs)\n self.main_jar = main_jar\n self.main_class = main_class\n self.arguments = arguments\n self.archives = archives\n self.files = files\n\n def execute(self, context):\n self.create_job_template()\n self.job_template.set_main(self.main_jar, self.main_class)\n self.job_template.add_args(self.arguments)\n self.job_template.add_archive_uris(self.archives)\n self.job_template.add_file_uris(self.files)\n super(DataProcSparkOperator, self).execute(context)\n\n\nclass DataProcHadoopOperator(DataProcJobBaseOperator):\n \"\"\"DataProcHadoopOperator\"\"\"\n template_fields = [\n 'arguments', 'job_name', 'cluster_name',\n 'region', 'dataproc_jars', 'dataproc_properties']\n ui_color = '#0273d4'\n job_type = 'hadoopJob'\n\n @apply_defaults\n def __init__(self, main_jar=None, main_class=None, arguments=None, archives=None, files=None, dataproc_hadoop_properties=None, dataproc_hadoop_jars=None, *args, **kwargs):\n (super(DataProcHadoopOperator, self).__init__)(args, dataproc_properties=dataproc_hadoop_properties, \n dataproc_jars=dataproc_hadoop_jars, **kwargs)\n self.main_jar = main_jar\n self.main_class = main_class\n self.arguments = arguments\n self.archives = archives\n self.files = files\n\n def execute(self, context):\n self.create_job_template()\n self.job_template.set_main(self.main_jar, self.main_class)\n self.job_template.add_args(self.arguments)\n self.job_template.add_archive_uris(self.archives)\n self.job_template.add_file_uris(self.files)\n super(DataProcHadoopOperator, self).execute(context)\n\n\nclass DataProcPySparkOperator(DataProcJobBaseOperator):\n \"\"\"DataProcPySparkOperator\"\"\"\n template_fields = [\n 'arguments', 'job_name', 'cluster_name',\n 'region', 'dataproc_jars', 'dataproc_properties']\n ui_color = '#0273d4'\n job_type = 'pysparkJob'\n\n @staticmethod\n def _generate_temp_filename(filename):\n date = time.strftime('%Y%m%d%H%M%S')\n return '{}_{}_{}'.format(date, str(uuid.uuid4())[:8], ntpath.basename(filename))\n\n def _upload_file_temp(self, bucket, local_file):\n \"\"\"\n Upload a local file to a Google Cloud Storage bucket.\n \"\"\"\n temp_filename = self._generate_temp_filename(local_file)\n if not bucket:\n raise AirflowException(\"If you want Airflow to upload the local file to a temporary bucket, set the 'temp_bucket' key in the connection string\")\n self.log.info('Uploading %s to %s', local_file, temp_filename)\n GoogleCloudStorageHook(google_cloud_storage_conn_id=(self.gcp_conn_id)).upload(bucket_name=bucket,\n object_name=temp_filename,\n mime_type='application/x-python',\n filename=local_file)\n return 'gs://{}/{}'.format(bucket, temp_filename)\n\n @apply_defaults\n def __init__(self, main, arguments=None, archives=None, pyfiles=None, files=None, dataproc_pyspark_properties=None, dataproc_pyspark_jars=None, *args, **kwargs):\n (super(DataProcPySparkOperator, self).__init__)(args, dataproc_properties=dataproc_pyspark_properties, \n dataproc_jars=dataproc_pyspark_jars, **kwargs)\n self.main = main\n self.arguments = arguments\n self.archives = archives\n self.files = files\n self.pyfiles = pyfiles\n\n def execute(self, context):\n self.create_job_template()\n if os.path.isfile(self.main):\n cluster_info = self.hook.get_cluster(project_id=(self.hook.project_id),\n region=(self.region),\n cluster_name=(self.cluster_name))\n bucket = cluster_info['config']['configBucket']\n self.main = self._upload_file_temp(bucket, self.main)\n self.job_template.set_python_main(self.main)\n self.job_template.add_args(self.arguments)\n self.job_template.add_archive_uris(self.archives)\n self.job_template.add_file_uris(self.files)\n self.job_template.add_python_file_uris(self.pyfiles)\n super(DataProcPySparkOperator, self).execute(context)\n\n\nclass DataprocWorkflowTemplateInstantiateOperator(DataprocOperationBaseOperator):\n \"\"\"DataprocWorkflowTemplateInstantiateOperator\"\"\"\n template_fields = [\n 'template_id']\n\n @apply_defaults\n def __init__(self, template_id, *args, **kwargs):\n (super(DataprocWorkflowTemplateInstantiateOperator, self).__init__)(*args, **kwargs)\n self.template_id = template_id\n\n def start(self):\n \"\"\"\n Instantiate a WorkflowTemplate on Google Cloud Dataproc.\n \"\"\"\n self.log.info('Instantiating Template: %s', self.template_id)\n return self.hook.get_conn().projects().regions().workflowTemplates().instantiate(name=('projects/%s/regions/%s/workflowTemplates/%s' % (\n self.project_id, self.region, self.template_id)),\n body={'requestId': str(uuid.uuid4())}).execute()\n\n\nclass DataprocWorkflowTemplateInstantiateInlineOperator(DataprocOperationBaseOperator):\n \"\"\"DataprocWorkflowTemplateInstantiateInlineOperator\"\"\"\n template_fields = [\n 'template']\n\n @apply_defaults\n def __init__(self, template, *args, **kwargs):\n (super(DataprocWorkflowTemplateInstantiateInlineOperator, self).__init__)(*args, **kwargs)\n self.template = template\n\n def start(self):\n \"\"\"\n Instantiate a WorkflowTemplate Inline on Google Cloud Dataproc.\n \"\"\"\n self.log.info('Instantiating Inline Template')\n return self.hook.get_conn().projects().regions().workflowTemplates().instantiateInline(parent=('projects/%s/regions/%s' % (self.project_id, self.region)),\n requestId=(str(uuid.uuid4())),\n body=(self.template)).execute()","sub_path":"pycfiles/apache_beam-2.20.0-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64/dataproc_operator.cpython-36.py","file_name":"dataproc_operator.cpython-36.py","file_ext":"py","file_size_in_byte":26377,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"344707202","text":"from scapy.all import *\r\n\r\ndef get_pcap_info():\r\n #pcapファイル名の指定\r\n get_file_name = input('ファイル名は？')\r\n\r\n #ファイルを読み込む\r\n pcaps = rdpcap(get_file_name)\r\n\r\n record_counts = len(pcaps)\r\n\r\n for i in range(0,record_counts):\r\n\r\n #i個目のパケット表示\r\n packet = pcaps[i]\r\n \r\n #結果表示\r\n print('info',packet.show())\r\n\r\nget_pcap_info()","sub_path":"pcapcode/pcapcode.py","file_name":"pcapcode.py","file_ext":"py","file_size_in_byte":436,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"6940367","text":"#working code\nimport numpy as np\nimport math as m\nimport matplotlib.pyplot as plt\nimport time\n\n# functions definition:\n\n\ndef Integrated_ode():\n global Aav, Amv, Apv, Atv, Gpw\n global Epua, Epuc, Epuv, Epwc, Epwv\n global yav, ymv, ypv, ytv, ypua, ypuc, ypuv, ypwa, ypwc, ypwv\n global Rav, Rmv, Rpua, Rpuc, Rpuv, Rpv, Rpwc, Rpwv, Rtv, bav, bmv, bpv, btv\n global Spua, Spuc, Spuv, Spwc, Spwv\n global Zpua, Zpuc, Zpuv, Zpwc, Zpwv\n global Caor, Cart, Ccap, Cven, Cvca\n global yaor, yart, ycap, yven, yvca\n global Raor, Rart, Rcap, Rven, Rvca\n global Saor, Sart, Scap, Sven, Svca\n global dvq, P_0d\n global dv, v, q\n global ela, era\n global plv, prv, Sla, Slv, Sra, Srv, ppc, pit, qco, Tduration, ddt, tcr\n\n dvq[0, 0] = q[0, 14] - q[0, 0] # Venous volume dvq(1)= q(15) - q(1);\n\n P_0d[0, 0] = v[0, 0] / Cven + Sven * dv[0, 0] # Venous Pressure\n\n dvq[0, 1] = (v[0, 0] / Cven + Sven * dv[0, 0] - Rven * q[0, 0] - v[0, 1] / Cvca - Svca * dv[0, 1]) / yven # Venous flow\n\n dvq[0, 2] = q[0, 0] - q[0, 1] # VC volume\n\n P_0d[0, 1] = v[0, 1] / Cvca + Svca * dv[0, 1] # VC Pressure\n\n dvq[0, 3] = (v[0, 1] / Cvca - era * v[0, 2] - Rvca * q[0, 1] + Svca * dv[0, 1] - Sra * dv[0, 2] - ppc - pit) / yvca # VC Flow\n\n qco = 0.0\n\n dvq[0, 4] = q[0, 1] + qco - q[0, 2] # RA volume\n\n P_0d[0, 3] = era * v[0, 2] + Sra * dv[0, 2] + ppc + pit # RA pressure\n\n dvq[0, 5] = (era * v[0, 2] - prv - Rtv * q[0, 2] - btv * q[0, 2] * np.abs(q[0, 2]) + Sra * dv[0, 2] - Srv * dv[0, 3]) / ytv # TV flow\n\n dvq[0, 6] = q[0, 2] - q[0, 3] # RV volume\n\n P_0d[0, 5] = prv + Srv * dv[0, 3] + ppc + pit # RV pressure\n\n dvq[0, 7] = (prv - Epua * Zpua - Rpv * q[0, 3] - bpv * q[0, 3] * (np.abs(q[0, 3])) + Srv * dv[0, 3] - Spua * dv[0, 4] + ppc) / ypv # PV flow\n\n dvq[0, 8] = q[0, 3] - q[0, 4] - q[0, 7] # Pulmonary Artery volume\n\n P_0d[0, 7] = Epua * Zpua + Spua * dv[0, 4] + pit # Pulmonary Artery Pressure\n\n dvq[0, 9] = (Epua * Zpua - Epuc * Zpuc - Rpua * q[0, 4] + Spua * dv[0, 4] - Spuc * dv[0, 5]) / ypua # Pulmonary Artery Flow\n\n dvq[0, 10] = q[0, 4] - q[0, 5] # Pulmonary Capillary volume\n\n P_0d[0, 9] = Epuc * Zpuc + Spuc * dv[0, 5] + pit # Pulmonary Capillary Pressure\n\n dvq[0, 11] = (Epuc * Zpuc - Epuv * Zpuv - Rpuc * q[0, 5] + Spuc * dv[0, 5] - Spuv * dv[0, 6]) / ypuc # Pulmonary Capillary Flow\n\n dvq[0, 12] = q[0, 5] - q[0, 6] # Pulmonary Vein volume\n\n P_0d[0, 11] = Epuv * Zpuv + Spuv * dv[0, 6] + pit # Pulmonary Vein Pressure\n\n dvq[0, 13] = (Epuv * Zpuv - ela * v[0, 9] - Rpuv * q[0, 6] + Spuv * dv[0, 6] - Sla * dv[0, 9] - ppc) / ypuv # Pulmonary Vein flow\n\n if Gpw > 0:\n dvq[0, 14] = (Epua * Zpua - Epwc * Zpwc - q[0, 7] / gpw + Spua * dv[0, 4] - Spwc * dv[0, 7]) / ypwa # Pulmonary Wedge ##Artery flow\n else:\n dvq[0, 14] = 0\n\n dvq[0, 15] = q[0, 7] - q[0, 8] # Pulmonary Wedge capillary volume\n\n P_0d[0, 14] = Epwc * Zpwc + Spwc * dv[0, 7] + pit # Pulmonary Wedge capillary Pressure\n\n dvq[0, 16] = (Epwc * Zpwc - Epwv * Zpwv - Rpwc * q[0, 8] + Spwc * dv[0, 7] - Spwv * dv[0, 8]) / ypwc # Pulmonary Wedge capillary Flow\n\n dvq[0, 17] = q[0, 8] - q[0, 9] # Pulmonary Wedge vein Volume\n\n P_0d[0, 16] = Epwv * Zpwv + Spwv * dv[0, 8] + pit # Pulmonary Wedge vein Pressure\n\n dvq[0, 18] = (Epwv * Zpwv - ela * v[0, 9] - Rpwv * q[0, 9] + Spwv * dv[0, 8] - Sla * dv[0, 9] - ppc) / ypwv # Pulmonary Wedge vein Flow\n\n dvq[0, 19] = q[0, 6] + q[0, 9] - q[0, 10] # LA volume\n\n P_0d[0, 18] = ela * v[0, 9] + Sla * dv[0, 9] + ppc + pit # LA Pressure\n\n dvq[0, 20] = (ela * v[0, 9] - plv - Rmv * q[0, 10] - bmv * q[0, 10] * np.abs(q[0, 10]) + Sla * dv[0, 9] - Slv * dv[0, 10]) / ymv # Mitral flow\n\n dvq[0, 21] = q[0, 10] - q[0, 11] # LV volume\n\n P_0d[0, 20] = plv + Slv * dv[0, 10] + ppc + pit # LV Pressure\n\n dvq[0, 22] = (plv - v[0, 11] / Caor - Rav * q[0, 11] - (bav * q[0, 11]) * np.abs(q[0, 11]) + Slv * dv[0, 10] - Saor * dv[0, 11] + ppc + pit) / yav # Aortic Flow\n\n # Adjust the state of cardiac valve\n if Aav == 0.0 and q[0, 11] <= 0.000000001:\n dvq[0, 22] = 0.0\n\n if np.abs(tcr - Tduration) <= ddt * 0.5 or tcr < 0.1 or (Amv == 0.0 and q[0, 10] < 0.000000001):\n dvq[0, 20] = 0.0\n\n if Apv == 0.0 and q[0, 3] <= 0.00000001:\n dvq[0, 7] = 0.0\n\n if np.abs(tcr - Tduration) <= ddt * 0.5 or tcr < 0.1 or (Atv == 0.0 and q[0, 2] <= 0.000000001):\n dvq[0, 5] = 0.0\n\n dvq[0, 23] = q[0, 11] - q[0, 12] # Aorta volume\n\n P_0d[0, 22] = v[0, 11] / Caor + Saor * dv[0, 11] # Aorta Presuure\n\n dvq[0, 24] = (v[0, 11] / Caor + Saor * dv[0, 11] - q[0, 12] * Raor - v[0, 12] / Cart - Sart * dv[0, 12]) / yaor # Aorta Flow\n\n dvq[0, 25] = q[0, 12] - q[0, 13] # Artery Volume\n\n P_0d[0, 24] = v[0, 12] / Cart + Sart * dv[0, 12] # Artery Pressure\n\n dvq[0, 26] = (v[0, 12] / Cart + Sart * dv[0, 12] - q[0, 13] * Rart - v[0, 13] / Ccap - Scap * dv[0, 13]) / yart # Artery Flow\n\n dvq[0, 27] = q[0, 13] - q[0, 14] # Capillarya Volume\n\n P_0d[0, 26] = v[0, 13] / Ccap + Scap * dv[0, 13] # Capillary Pressure\n\n dvq[0, 28] = (v[0, 13] / Ccap + Scap * dv[0, 13] - q[0, 14] * Rcap - v[0, 0] / Cven - Sven * dv[0, 0]) / ycap # Capillary Pressure\n\n\ndef Ecal(EEE, ZZZ, vol):\n EcalR = EEE * np.exp(vol / ZZZ)\n return EcalR\n\n\ndef Lvecal():\n global Elva, Elvb\n global elv, FL\n global tee, tcr\n tcal = tcr\n\n if tcal <= tee:\n elv = FL * Elva * 0.5 * (1.0 - np.cos(3.1415926 * tcal / tee)) + Elvb / FL\n else:\n if tcal <= 1.5 * tee:\n elv = FL * Elva * 0.5 * (1.0 + np.cos(3.1415926 * (tcal - tee) / (0.5 * tee))) + Elvb / FL\n else:\n elv = Elvb / FL\n\n\ndef Laecal():\n global tcr, ela, Elaa, tac, tar, Tduration, Elab\n tcal = tcr\n teec = tar - tac\n teer = teec\n tap = tar + teer - Tduration\n\n if (tcal >= 0.0 and tcal <= tap):\n ela = Elaa * 0.5 * (1.0 + np.cos(3.1415926 * (tcal + Tduration - tar) / teer)) + Elab\n\n if (tcal > tap and tcal <= tac):\n ela = Elab\n\n if (tcal > tac and tcal <= tar):\n ela = Elaa * 0.5 * (1.0 - np.cos(3.1415926 * (tcal - tac) / teec)) + Elab\n\n # c if (tcal > tar. and.tcal <= (tar+teer)) then\n if (tcal > tar and tcal <= Tduration):\n ela = Elaa * 0.5 * (1.0 + np.cos(3.1415926 * (tcal - tar) / teer)) + Elab\n\n\ndef Rvecal():\n global tcr, FR1, Erva, tee, Ervb\n global erv\n tcal = tcr\n\n if tcal <= tee:\n erv = FR1 * Erva * 0.5 * (1.0 - np.cos(3.1415926 * tcal / tee)) + Ervb / FR1\n else:\n if tcal <= 1.5 * tee:\n erv = FR1 * Erva * 0.5 * (1.0 + np.cos(2.0 * 3.1415926 * (tcal - tee) / tee)) + Ervb / FR1\n else:\n erv = Ervb / FR1\n\n\ndef Raecal():\n global Eraa, Erab\n global tar, tac, tcr\n global era, Tduration\n teec = tar - tac\n teer = teec\n tcal = tcr\n tap = tar + teer - Tduration\n\n if 0 <= tcal <= tap:\n era = Eraa * 0.5 * (1.0 + np.cos(3.1415926 * (tcal + Tduration - tar) / teer)) + Erab\n\n if tap < tcal <= tac:\n era = Erab\n\n if tcal > tac and tcal <= tar:\n era = Eraa * 0.5 * (1.0 - np.cos(3.1415926 * (tcal - tac) / teec)) + Erab\n\n if tcal > tar and tcal <= Tduration:\n era = Eraa * 0.5 * (1.0 + np.cos(3.1415926 * (tcal - tar) / teer)) + Erab\n\n\ndef AAav():\n global Caor, dv, v\n global plv, Slv, ppc\n\n intee = plv + Slv * dv[0, 10] + ppc - v[0, 11] / Caor\n\n if intee > 0.0:\n AAav = 4.0\n else:\n AAav = 0.0\n\n return AAav\n\n\ndef AAmv():\n global v, ela, plv\n\n intee = ela * v[0, 9] - plv\n\n if intee > 0.0:\n AAmv = 4.0\n else:\n AAmv = 0.0\n return AAmv\n\n\ndef AApv():\n global Epua, Zpua, prv\n\n intee = prv - Epua * Zpua;\n\n if intee > 0.0:\n AApv = 4.0\n else:\n AApv = 0.0\n return AApv\n\n\ndef AAtv():\n global era, v, prv\n\n intee = era * v[0, 2] - prv\n\n if intee > 0.0:\n AAtv = 4.0\n else:\n AAtv = 0.0\n return AAtv\n\n\ndef rungekutta(subresultcr):\n global Aav, Amv, Apv, Atv, dvq\n global dv, v, dq, q, ddt\n\n # call Integrated_ode % == == == == == = make a note........................\n Integrated_ode()\n\n # Declaration of variables\n dfl = np.zeros(shape=(2, 30))\n dq = np.zeros(shape=(2, 15))\n subrukuk = np.zeros(shape=(4, 29))\n inter = np.zeros(shape=(2, 29))\n newpara = np.zeros(shape=(2, 29))\n\n # -------------------------------------------------------------------------\n dfl[0, :29] = dvq[0, :29]\n dq[0, :7] = dfl[0, 1:14:2]\n dq[0, 7:15] = dfl[0, 14:29:2]\n dv[0, :7] = dfl[0, 0:13:2]\n dv[0, 7:14] = dfl[0, 15:28:2]\n\n\n for nrk in np.arange(4):\n subrukuk[nrk, :29] = np.multiply(ddt, dfl[0, :29])\n\n if nrk == 0:\n Aav = AAav()\n Amv = AAmv()\n Apv = AApv()\n Atv = AAtv()\n\n if nrk < 3:\n inter[0, :29] = np.multiply(0.5, subrukuk[nrk, 0:29])\n else:\n inter[0, :29] = subrukuk[nrk, 0:29]\n\n inter[0, 0:29] = np.multiply(0.5, subrukuk[nrk, 0:29])\n newpara[0, 0:29] = np.add(subresultcr[0, 0:29], inter[0, 0:29])\n\n q[0, :7] = newpara[0, 1:14:2]\n q[0, 7:15] = newpara[0, 14:29:2]\n\n v[0, :7] = newpara[0, 0:13:2]\n v[0, 7:14] = newpara[0, 15:28:2]\n\n return subrukuk\n\n# End of function definition ----------------------------------\n\ndef main():\n global valve, Aav, Amv, Apv, Atv, Gpw\n global E_cardiopul, Elaa, Elab, Elva, Elvb, Eraa, Erab, Erva, Ervb, Epua, Epuc, Epuv, Epwa, Epwc, Epwv\n global yL_cardiopul, yav, ymv, ypv, ytv, ypua, ypuc, ypuv, ypwa, ypwc, ypwv\n global R_cardiopul, Ra, Raa, Rav, Rca, Rda, Rmv, Rpua, Rpuc, Rpuv, Rpv, Rpwa, Rpwc, Rpwv, Rtv, Rv, Rvc, bav, bmv, bpv, btv\n global S_cardiopul, Spua, Spuc, Spuv, Spwa, Spwc, Spwv\n global Z_cardiopul, Zpua, Zpuc, Zpuv, Zpwa, Zpwc, Zpwv\n global C_peripheral, Caor, Cart, Ccap, Cven, Cvca\n global yL_peripheral, yaor, yart, ycap, yven, yvca\n global R_peripheral, Raor, Rart, Rcap, Rven, Rvca\n global S_peripheral, Saor, Sart, Scap, Sven, Svca\n global sdvsdqdvdq, dvq, P_0d\n global dvdq_cardiopul, dv, v, dq, q\n global cardiac_parameter, elv, ela, erv, era, cklr, ckrl, plv, prv, Sla, Slv, Sra, Srv, ppp, ppc, pit, qco, FL, FR1, STR\n global R_cardiopulc, Rav0, Rmv0, Rpv0, Rtv0, bav0, bmv0, bpv0, btv0, Rav1, Rmv1, Rpv1, Rtv1, bav1, bmv1, bpv1, btv1\n global Rav2, Rmv2, Rpv2, Rtv2, bav2, bmv2, bpv2, btv2, yav0, ymv0, ypv0, ytv0, yav1, ymv1, ypv1, ytv1, yav2, ymv2, ypv2, ytv2\n global n_val, m_cvst, m_cvrg, n_vrg\n global timestep, Tduration, ddt, tee, tcr, tac, tar, t, odic\n\n v = np.zeros(shape=(2, 14))\n q = np.zeros(shape=(2, 21))\n dvq = np.zeros(shape=(2, 29))\n result = np.zeros(shape=(2, 101))\n P_0d = np.zeros(shape=(2, 101))\n diffv = np.zeros(shape=(2, 14))\n resultcr = np.zeros(shape=(2, 101))\n dv = np.zeros(shape=(2, 21))\n MyResult = np.zeros(shape=(100000, 28))\n MyResult1 = np.zeros(shape=(100000, 29))\n\n # initial values of all state equations (should be equal to number of equations in dvdqsdvdq.m file)\n odic = np.array(\n [1068.2371, 52.4983, 181.7233, -41.7618, 65.0625, 0, 122.6637, 0, 67.0272, -0.3118, 135.1100, -2.1737, 198.7568,\n -64.1791, 0, 2.7983, 0.1357, 2.7932, 1.1042, 68.8587, 0, 121.2539, 0, 67.3641, 41.8262, 22.0472, 56.6627,\n 1.8539, 57.6473])\n\n Pit = -2.5\n pit = Pit\n jj = 0\n\n # --------------------------------------------------------------------------------\n Elva = 2.87 # !Peak-systolic elastance of left ventricle\n Elvb = 0.06 # !Basic diastolic elastance of left ventricle\n Elaa = 0.07 # !Peak-systolic elastance of left atrium\n Elab = 0.075 # !Basic diastolic elastance of left atrium\n Erva = 0.52 # !Peak-systolic elastance of right ventricle\n Ervb = 0.043 # !Basic diastolic elastance of right ventricle\n Eraa = 0.055 # !Peak-systolic elastance of right atrium\n Erab = 0.06 # !Basic diastolic elastance of right atrium\n\n Vmax = 900 # Reference volume of Frank-Starling law\n Es = 45.9 # !Effective septal elastance\n Vpc0 = 380.0 # !Reference total pericardial and cardiac volume old value 380\n Vpe = 30.0 # !Pericardial volume of heart\n Vcon = 40.0 # !Volume constant\n Sva0 = 0.0005 # !Coefficient of cardiac viscoelasticity\n\n # ! Cardiac valve parameters\n # !(aortic valve(AV),mitral valve(MV), tricuspid valve(TV),pulmonary valve(PV))\n bav = 0.000025 # !Bernoulli's resistance of AV\n bmv = 0.000016 # !Bernoulli's resistance of MV\n btv = 0.000016 # !Bernoulli's resistance of TV\n bpv = 0.000025 # !Bernoulli's resistance of PV\n Rav = 0.005 # !Viscous resistance of AV\n Rmv = 0.005 # !Viscous resistance of MV\n Rtv = 0.005 # !Viscous resistance of TV\n Rpv = 0.005 # !Viscous resistance of PV\n yav = 0.0005 # !Inertance of AV\n ymv = 0.0002 # !Inertance of MV\n ytv = 0.0002 # !Inertance of TV\n ypv = 0.0005 # !Inertance of PV\n\n # ! Pulmonary circulation\n Epua0 = 0.0200\n Epuc0 = 0.0200\n Epuv0 = 0.0200\n Epwc0 = 0.7000\n Epwv0 = 0.7000\n Rpua = 0.04\n Rpuc = 0.04\n Rpuv = 0.005\n Rpwa = 0.0005\n Rpwc = 0.4\n Rpwv = 0.4\n ypua = 0.0005\n ypuc = 0.0005\n ypuv = 0.0005\n ypwa = 0.0005\n ypwc = 0.0005\n ypwv = 0.0005\n Zpua = 20.0\n Zpuc = 60.0\n Zpuv = 200.0\n Zpwa = 1.0\n Zpwc = 1.0\n Zpwv = 1.0\n Spua = 0.01\n Spuc = 0.01\n Spuv = 0.01\n Spwa = 0.01\n Spwc = 0.01\n Spwv = 0.01\n\n # ! Peripheral circulation\n Caor = 0.9\n Cart = 0.3\n Ccap = 0.06\n Cven = 100.0\n Cvca = 30.0\n yaor = 0.015\n yart = 0.05\n ycap = 0.0005\n yven = 0.0005\n yvca = 0.0005\n Raor = 0.08\n Rart = 0.8\n Rcap = 0.35\n Rven = 0.07\n Rvca = 0.001\n Saor = 0.01\n Sart = 0.01\n Scap = 0.01\n Sven = 0.01\n Svca = 0.01\n qco = 0.0\n\n # ------------------------------------------------------------------------------------------------\n Tduration = 1/65 # input('Please specify cardiac duration(s)')\n dt = 0.0001 # input'Please specify time step(s)')\n tee = 0.3*np.sqrt(Tduration) #!Moment when ventricular contractility reaches the peak\n tac = Tduration - 0.5 * tee - 0.02 * (Tduration/0.855) # !Moment when atrium begins to contract\n tar = Tduration - 0.02 * (Tduration / 0.855) # !Moment when atrium begins to relax\n ddt = dt\n ncycle = 0 #GET INPUT FORM USER THROUGH TEXTBOX\n ntotal = ((ncycle) * Tduration / dt)\n ntotal = int(ntotal)\n\n # ------------------------------------------------------------------------\n for nstep in np.arange(ntotal):\n if nstep == 0:\n tcr = 0.0\n ppc = 0.0\n cn =0\n\n i = 0\n for i in range(29):\n result[0, i] = odic[i]\n\n i = 0\n for i in np.arange(14):\n if i <= 6:\n v[0, i] = result[0, 2 * i]\n else:\n v[0, i] = result[0, 2 * i + 1]\n\n i =0\n for i in np.arange(15):\n if i <= 6:\n q[0, i] = result[0, 2 * i + 1]\n else:\n q[0, i] = result[0, 2 * i]\n\n STR = 1.0\n FL = 1.0\n FR1 = 1.0\n Gpw = 0.0\n\n Aav = 0.0\n Amv = 0.0\n Apv = 0.0\n Atv = 0.0\n\n Pit = -2.5\n\n # fid60 = fopen('0Dresult.txt','a+')\n # fid63 = fopen('0Dpress.txt','a+')\n\n # ----------------------Start computation---------------------------\n ncount = 0\n ncountadd = ncount + 1\n\n cn += 1\n tcr = cn * dt % Tduration\n\n print('tcr of ', cn,':', tcr)\n t = cn * dt\n\n # c.... Compute the pulmonary elastances\n Epua = Ecal(Epua0, Zpua, v[0, 4])\n Epuc = Ecal(Epuc0, Zpuc, v[0, 5])\n Epuv = Ecal(Epuv0, Zpuv, v[0, 6])\n Epwc = Ecal(Epwc0, Zpwc, v[0, 7])\n Epwv = Ecal(Epwv0, Zpwv, v[0, 8])\n\n # c.....Update nolinear cardiac parameters\n if tcr == 0.0:\n FL = 1.0 - (result[0, 21] / Vmax) # Left ventricle scaling factor\n FR1 = 1.0 - (result[0, 6] / Vmax) # Right ventricle scaling factor\n\n Lvecal() # LV elastance function calling\n Laecal() # LA elastance function calling\n Rvecal() # RV elastance function calling\n Raecal() # RA elastance function calling\n\n # Spetum cross talk pressure calculations\n cklr = erv / (Es + erv)\n ckrl = elv / (Es + elv)\n plv = ckrl * Es * v[0, 10] + ckrl * cklr * Es * v[0, 3] / (1.0 - cklr)\n prv = cklr * Es * v[0, 3] + ckrl * cklr * Es * v[0, 10] / (1.0 - ckrl)\n\n # All cardiac chambers viscoelastance calculation\n Sla = Sva0 * v[0, 9] * ela\n Slv = Sva0 * plv\n Sra = Sva0 * v[0, 2] * era\n Srv = Sva0 * prv\n\n # Pericardium pressure calculations\n ppp = (v[0, 2] + v[0, 3] + v[0, 9] + v[0, 10] + Vpe - Vpc0) / Vcon\n ppc = np.exp(ppp)\n\n # c....Update dv and state equation function calling\n Integrated_ode() # = == == == == == == == == == == == == == = >> make a note.............................\n\n diffv[0, :7] = dvq[0, 0:13:2]\n diffv[0, 7:14] = dvq[0, 15:28:2]\n dv[0, :14] = diffv[0, :14]\n\n # c.....Implement fourth - order Runge - Kutta method\n resultcr[0, :101] = result[0, :101]\n rukuk = rungekutta(resultcr)\n\n # c.....Update variables with Runge-Kutta method\n for j in np.arange(29):\n result[ncountadd, j] = result[ncount, j] + (rukuk[0, j] + 2.0 * (rukuk[1, j] + rukuk[2, j]) + rukuk[3, j]) / 6.0\n\n delta = 0.00000001\n\n # update all four cardiac valve flow as zero when they were closed\n if (Aav == 0.0 and resultcr[0, 22] <= delta):\n resultcr[0, 22] = 0.0\n result[ncountadd, 22] = 0.0\n q[0, 11] = 0.0\n\n if (np.abs(tcr - Tduration) <= ddt * 0.5 or tcr < 0.1 or (Amv == 0.0 and resultcr[0, 20]) <= delta):\n resultcr[0, 20] = 0.0\n resultcr[ncountadd, 20] = 0.0\n q[0, 10] = 0.0\n\n if (Apv == 0.0) and (resultcr[0, 7] <= delta):\n resultcr[0, 7] = 0.0\n resultcr[ncountadd, 7] = 0.0\n q[0, 3] = 0.0\n\n if np.abs(tcr - Tduration) <= ddt * 0.5 or tcr < 0.1 or Atv == 0.0 and resultcr[0, 5] <= delta:\n resultcr[0, 5] = 0.0\n result[ncountadd, 5] = 0.0\n q[0, 2] = 0.0\n\n # c.....Update q() and v() to be used at next time\n v[0, :7] = result[ncountadd, 0:13:2]\n v[0, 7:14] = result[ncountadd, 15:28:2]\n\n q[0, : 7] = result[ncountadd, 1:14:2]\n q[0, 7: 15] = result[ncountadd, 14:29:2]\n q[0, 15: 21] = result[ncountadd, 29:35:1]\n result[0, :29] = result[1, :29]\n\n # load calculated hemodynamic variables in an array\n jj += 1\n print('Value of JJ:', jj - 1)\n MyResult[jj - 1, :27] = P_0d[0, :27]\n MyResult1[jj - 1, :29] = result[0, :29]\n\n # Update the initial conditions for next cardiac cycle\n if nstep+1 == ntotal:\n odic_new = result[0, :29]\n\n stp_sz = Tduration * ncycle\n t = np.arange(0, stp_sz, dt)\n T = t\n Pa_1 = MyResult1[:ntotal, 23] #23\n F_1 = MyResult1[:ntotal, 22] #22\n\n plt.figure()\n plt.plot(T, F_1, label='Flow')\n lg = plt.legend()\n\n plt.figure()\n plt.plot(T, Pa_1, label='Pressure')\n lg = plt.legend()\n plt.show()\n\nif __name__ == \"__main__\":\n main()","sub_path":"Heartmodel/LATEST_HM.py","file_name":"LATEST_HM.py","file_ext":"py","file_size_in_byte":19582,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"359230972","text":"import sys\nsys.path.append('/home/yang/python-urx')\nsys.path.append('/home/yang/Git/CobotBenchmark/Driver')\nsys.path.append('/home/yang/Git/CobotBenchmark/Functions')\nsys.path.append('/home/yang/Git/CobotBenchmark/ToolKit')\nfrom fc_predictor import Predictor\nfrom realsense_controller import RealsenseController\nimport cv2\nfrom PIL import Image, ImageDraw\nimport time\nimport numpy as np\nimport ur_controller_urx as urc\nimport Calibration_2D as Cali\nfrom datetime import datetime\nfrom success_label import *\nimport random\nfrom random import choice\nimport os\nimport heapq\n\nWorkSpace = [-0.21,-0.37,0.21,-0.68]\nrob = urc.ur5()\ncamera = RealsenseController()\nhand_eye=Cali.calibration()\ncrop_box = [340,160,980,700]\nstr = '/media/yang/Linux/Data/OriginalData/2-Fingers_Guided'\nG = Predictor('/home/yang/Git/CobotBenchmark/Functions/checkpoint/fc_cnn(new)/Toys') # /ur5\ncolor_image,depth_image,infrared_L,infrared_R = camera.getImage()\nrob.homing()\nrob.close_gripper()\nwith open(str+'/TrainingRecord', 'aw') as f:\n f.write('i,x,y,rz,success_label'+'\\n')\n\nfor i in range(999):\n\n color_image1,depth_image,infrared_L,infrared_R = camera.getImage()\n if not os.path.exists(str+\"/\"+\"TrialCount_%01d\"%i):\n os.makedirs(str+\"/\"+\"TrialCount_%01d\"%i)\n strnew=str+\"/\"+\"TrialCount_%01d\"%i\n cv2.imwrite(strnew +\"/\"+\"image_01.jpg\", color_image1)\n img = Image.open(strnew+'/image_01.jpg').crop(crop_box)\n img.save(strnew +\"/\"+\"Croppedimage.jpg\")\n\n rob.open_gripper()\n\n image = np.array(img).reshape(1,(crop_box[3]-crop_box[1]),(crop_box[2]-crop_box[0]),3)\n y_value = G.eval(image,(crop_box[3]-crop_box[1]),(crop_box[2]-crop_box[0]))\n angle_patch,probability_patch = G.parse_eval(y_value)\n probability_patch_sub = []\n for j in range(len(probability_patch)):\n if probability_patch[j] > 0.5:\n probability_patch_sub.append(j)\n if len(probability_patch_sub) == 0:\n max_num_list = map(probability_patch.index, heapq.nlargest(5, probability_patch))\n b = random.sample(max_num_list,1)\n idx = b[0]\n else:\n idx = choice(probability_patch_sub)\n y = (int(idx/y_value.shape[2]))*((crop_box[3]-crop_box[1]-238)/(y_value.shape[1]-1))+111+crop_box[1]\n x = (idx%y_value.shape[2])*((crop_box[2]-crop_box[0]-238)/(y_value.shape[2]-1))+111+crop_box[0]\n rz = (-1.57 + (random.sample(range(18),1)[0]+0.5)*(1.57/9))\n print(x,y,rz)\n x,y = hand_eye.cam2rob(x,y)\n rob.move([x,y,0.25],rz)\n rob.grasping()\n rob.homing()\n time.sleep(5)\n\n x2=random.uniform(WorkSpace[0]+0.1,WorkSpace[2]-0.1)\n y2=random.uniform(WorkSpace[1]-0.1,WorkSpace[3]+0.1)\n rz2=random.uniform(-1.57,1.57)\n color_image2,depth_image,infrared_L,infrared_R = camera.getImage()\n cv2.imwrite(strnew +\"/\"+\"image_02.jpg\", color_image2)\n grasp_label = success_label(color_image1,color_image2)\n\n if grasp_label == 1:\n\n rob.move([x2, y2, 0.20],rz2)\n rob.move([x2, y2, 0.15],rz2)\n\n rob.open_gripper()\n rob.homing()\n rob.close_gripper()\n\n with open(str+'/TrainingRecord', 'aw') as f:\n f.write(\"%d,\"%i+\"%f,\"%x+\"%f,\"%y+\"%f,\"%rz+\"%d\"%grasp_label+'\\n')\n","sub_path":"Functions/TrainingDataCollection/CollectingData_Guided.py","file_name":"CollectingData_Guided.py","file_ext":"py","file_size_in_byte":3149,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"553462260","text":"#good thing the list didn't have any words of the same length and same number of characters\r\n#there must be a name for those kinds of words but i don't remembergit\r\n\r\nwordList = []\r\n\r\nwith open(\"3wordlist.txt\", encoding=\"utf-8\") as file:\r\n\tfor line in file:\r\n\t\twordList.append(line.rstrip(\"\\n\"))\r\n\r\nscrambledList = [\"mkeart\",\r\n\t\t\t\t\"sleewa\",\r\n\t\t\t\t\"edcudls\",\r\n\t\t\t\t\"iragoge\",\r\n\t\t\t\t\"usrlsle\",\r\n\t\t\t\t\"nalraoci\",\r\n\t\t\t\t\"nsdeuto\",\r\n\t\t\t\t\"amrhat\",\r\n\t\t\t\t\"inknsy\",\r\n\t\t\t\t\"iferkna\"]\r\n\r\nalphabet = (\"a\",\r\n\t\t\t\"b\",\r\n\t\t\t\"c\",\r\n\t\t\t\"d\",\r\n\t\t\t\"e\",\r\n\t\t\t\"f\",\r\n\t\t\t\"g\",\r\n\t\t\t\"h\",\r\n\t\t\t\"i\",\r\n\t\t\t\"j\",\r\n\t\t\t\"k\",\r\n\t\t\t\"l\",\r\n\t\t\t\"m\",\r\n\t\t\t\"n\",\r\n\t\t\t\"o\",\r\n\t\t\t\"p\",\r\n\t\t\t\"q\",\r\n\t\t\t\"r\",\r\n\t\t\t\"s\",\r\n\t\t\t\"t\",\r\n\t\t\t\"u\",\r\n\t\t\t\"v\",\r\n\t\t\t\"w\",\r\n\t\t\t\"x\",\r\n\t\t\t\"y\",\r\n\t\t\t\"z\")\r\n\r\npossibleWords = []\r\n\r\ndef checkWord(scrambledWord, word):\r\n\tif(len(word) == len(scrambledWord)):\r\n\t\tfor letter in alphabet:\r\n\t\t\tif(scrambledWord.count(letter) != word.count(letter)):\r\n\t\t\t\treturn\r\n\t\tprint(word)\r\n\r\nfor scrambledWord in scrambledList:\r\n\tfor word in wordList:\r\n\t\tcheckWord(scrambledWord, word)\r\n","sub_path":"Difficult/3.py","file_name":"3.py","file_ext":"py","file_size_in_byte":1024,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"216498248","text":"#!/usr/bin/env python\n# pylint: disable=R,C\n\n\"\"\"Unit tests for the `mapper` module.\"\"\"\n\nimport os\nimport pytest\nfrom unittest.mock import patch, Mock\n\nfrom yorm import exceptions\nfrom yorm.mapper import Helper, Mapper\nfrom yorm.converters import Integer\n\n\n@patch('yorm.settings.fake', True)\nclass TestHelperFake:\n\n \"\"\"Unit tests for fake mappings using the `Helper` class.\"\"\"\n\n def test_create(self):\n \"\"\"Verify fake files can be created.\"\"\"\n mapper = Helper(\"fake/path/to/file\")\n mapper.create(Mock())\n\n assert False is os.path.exists(mapper.path)\n\n def test_delete(self):\n \"\"\"Verify fake files can be deleted.\"\"\"\n mapper = Helper(\"fake/path/to/file\")\n mapper.create(None)\n mapper.delete()\n\n assert False is os.path.exists(mapper.path)\n\n def test_create_after_delete(self):\n mapper = Helper(\"fake/path/to/file\")\n mapper.create(None)\n mapper.delete()\n\n assert False is mapper.exists\n assert True is mapper.deleted\n\n mapper.create(None)\n\n assert True is mapper.exists\n assert False is mapper.deleted\n\n def test_modified(self):\n \"\"\"Verify fake files can be modified.\"\"\"\n mapper = Helper(\"fake/path/to/file\")\n\n assert True is mapper.modified\n\n mapper.create(None)\n\n assert True is mapper.modified\n\n mapper.modified = False\n\n assert False is mapper.modified\n\n mapper.modified = True\n\n assert True is mapper.modified\n\n\nclass TestHelperReal:\n\n \"\"\"Unit tests for real mappings using the `Helper` class.\"\"\"\n\n def test_create(self, tmpdir):\n \"\"\"Verify files can be created.\"\"\"\n tmpdir.chdir()\n mapper = Helper(\"real/path/to/file\")\n mapper.create(None)\n\n assert True is os.path.isfile(mapper.path)\n assert True is mapper.exists\n assert False is mapper.deleted\n\n def test_create_twice(self, tmpdir):\n \"\"\"Verify the second creation is ignored.\"\"\"\n tmpdir.chdir()\n mapper = Helper(\"real/path/to/file\")\n mapper.create(None)\n mapper.create(None)\n\n assert True is os.path.isfile(mapper.path)\n assert True is mapper.exists\n assert False is mapper.deleted\n\n def test_delete(self, tmpdir):\n \"\"\"Verify files can be deleted.\"\"\"\n tmpdir.chdir()\n mapper = Helper(\"real/path/to/file\")\n mapper.create(None)\n mapper.delete()\n\n assert False is os.path.exists(mapper.path)\n assert False is mapper.exists\n assert True is mapper.deleted\n with pytest.raises(exceptions.FileDeletedError):\n mapper.fetch(None, None)\n\n def test_delete_twice(self, tmpdir):\n \"\"\"Verify the second deletion is ignored.\"\"\"\n tmpdir.chdir()\n mapper = Helper(\"real/path/to/file\")\n mapper.delete()\n\n assert False is os.path.exists(mapper.path)\n assert False is mapper.exists\n assert True is mapper.deleted\n\n def test_modified(self, tmpdir):\n \"\"\"Verify files track modifications.\"\"\"\n tmpdir.chdir()\n mapper = Helper(\"real/path/to/file\")\n\n assert True is mapper.modified\n\n mapper.create(None)\n\n assert True is mapper.modified\n\n mapper.modified = False\n\n assert False is mapper.modified\n\n mapper.modified = True\n\n assert True is mapper.modified\n\n def test_modified_deleted(self):\n \"\"\"Verify a deleted file is always modified.\"\"\"\n mapper = Helper(\"fake/path/to/file\")\n\n assert True is mapper.modified\n\n\nclass TestMapper:\n\n \"\"\"Unit tests for the `Mapper` class.\"\"\"\n\n class MyObject:\n foo = 1\n\n def test_store_ignores_auto_off(self, tmpdir):\n tmpdir.chdir()\n obj = self.MyObject()\n attrs = {'number': Integer}\n mapper = Mapper(obj, \"real/path/to/file\", attrs, auto=False)\n\n assert \"\" == mapper.text\n assert False is mapper.auto\n\n mapper.create()\n\n assert \"\" == mapper.text\n assert False is mapper.auto\n\n mapper.store()\n\n assert \"number: 0\\n\" == mapper.text\n assert False is mapper.auto\n\n def test_missing_attributes_added(self):\n obj = self.MyObject()\n path = \"mock/path\"\n attrs = {'bar': Integer, 'qux': Integer}\n mapper = Mapper(obj, path, attrs)\n mapper.create()\n mapper.fetch()\n\n assert 1 == obj.foo\n assert 0 == obj.bar\n assert 0 == obj.qux\n\n\nif __name__ == '__main__':\n pytest.main()\n","sub_path":"yorm/test/test_mapper.py","file_name":"test_mapper.py","file_ext":"py","file_size_in_byte":4531,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"373687145","text":"import os\nimport gc\nimport time\nfrom selenium import webdriver\nfrom selenium.webdriver.firefox.firefox_binary import FirefoxBinary\nfrom PIL import Image, ImageFont, ImageDraw \nimport imageio\nimport folium\nfrom folium.plugins import MarkerCluster, FastMarkerCluster\nfrom folium.plugins import HeatMap\n\nfrom magic_numbers import drug_resistance_pos, drug_resistance_col, drug_resistance_mut\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom copy import deepcopy\n\nimport pyautogui\n\n\ndef html_to_png(map_path, method ,screen_path):\n ## converting html map to png image by screenshot\n maps = []\n for r, d, f in os.walk(map_path):\n for file in f:\n if method+'.html' in file:\n #maps.append(file)\n maps.append(os.path.join(r, file))\n delay=5\n path= os.getcwd()\n \n for mn in maps:\n items = mn.split('-') #parse year and time_step from map_name\n year = items[1]\n time_step = items[2]\n tmpurl= \"file://\" + path + '/' + mn\n img_name = screen_path + 'screen-'+year +'-'+time_step+'-'+ method +'.png'\n\n firefoxpath = \"/usr/bin/firefox\"\n binary = FirefoxBinary(firefoxpath)\n Fprofile = webdriver.FirefoxProfile()\n Fprofile.set_preference(\"xpinstall.signatures.required\", False)\n browser = webdriver.Firefox(firefox_binary= binary, firefox_profile= Fprofile)\n browser.get(tmpurl)\n\n \n #fullscreen to show more regions \n # pyautogui.press('f11')\n \n #Give the map tiles some time to load\n time.sleep(delay)\n\n print(img_name)\n browser.save_screenshot(img_name)\n browser.quit()\n\n #add year to picture\n img = Image.open(img_name)\n draw = ImageDraw.Draw(img)\n font = ImageFont.truetype(\"font/Vonique.ttf\",25)\n draw.text((200, 0),year+'-'+time_step,(255,255,255),font=font)\n img.save(img_name)\n\n\ndef make_movie(screen_path, method ,movie_name= 'simul/movie.gif'):\n files = []\n imgs = []\n for r, d, f in os.walk(screen_path):\n for file in f:\n if method+'.png' in file:\n files.append(os.path.join(r, file))\n #chronological order\n files = sorted(files)\n for f in files:\n imgs.append( imageio.imread(f) )\n\t # gc.collect()\n imageio.mimsave(movie_name, imgs)\n print(movie_name)\n\n\n################################################\n\ndef init_render(dic, regions_trans):\n regions = {}\n #filter the regions\n for k,v in dic.items():\n reg = v[\"region\"]\n if( reg in regions_trans ):\n if( reg not in regions ):\n regions[reg] = regions_trans[reg].copy()\n\n #all location for fastMarkerCluster\n points = []\n for k in regions:\n if(regions[k][\"localisation\"] is None):\n continue\n points.append(regions[k][\"localisation\"])\n\n fol_map = folium.Map( location=[52.520008, 13.404954], #Berlin location\n zoom_start= 3.5,\n tiles='CartoDB dark_matter'\n )\n # FastMarkerCluster(points).add_to(fol_map)\n # folium.LayerControl().add_to(fol_map)\n\n return regions, fol_map\n\ndef render_sim(dic, regions_trans, map_name):\n '''Init Visuals'''\n #parameters\n # A+B <= 1\n A = 0.5\n B = 0.2\n\n regions, fol_map = init_render(dic, regions_trans)\n\n #render regions\n for k,v in regions_trans.items():\n location = v[\"localisation\"]\n if(location is None): #not located, ignore\n continue \n \n circle_size = 5 # big enough so we to click on it\n if(v[\"pop_estimate\"]):\n tot = 0\n air = 0\n transport_types = v[\"type\"]\n for t in transport_types:\n if(t == \"airport\"):\n air += 1\n tot += 1 \n other = tot - air\n pop = v[\"pop_estimate\"] / 1000000\n circle_size += (air*A+other*B) * pop * 0.25\n\n #location = list(location)\n folium.CircleMarker( location = location, \n popup= k,\n radius= circle_size,\n color='#3186cc',\n fill_color='#3186cc',\n fill=False\n ).add_to(fol_map)\n fol_map.save(map_name)\n print(\"Population map generated\")\n\n\n\ndef render_dynamic(dic, regions_trans, map_name):\n '''Init Visuals'''\n #parameters\n # A+B <= 1\n A = 0.5\n B = 0.2\n \n regions, fol_map = init_render(dic, regions_trans)\n\n #render regions\n for k in regions:\n location = regions[k][\"localisation\"]\n if(location is None):\n continue \n \n I_size = 5\n R_size = 5\n if(regions[k][\"pop_estimate\"]):\n tot = 0\n air = 0\n transport_types = regions[k][\"type\"]\n for t in transport_types:\n if(t == \"airport\"):\n air += 1\n tot += 1 \n I = regions[k][\"pop_infected\"] / 1000000\n R = regions[k][\"pop_recovered\"] / 1000000\n I_size += tot * (I*A + I*B) \n R_size += tot * (R*A + R*B) \n #infected population\n folium.CircleMarker( location = location, \n popup= k,\n radius= I_size,\n color='#dd2e44',\n fill_color='#dd2e44',\n fill=False\n ).add_to(fol_map)\n #recovered population\n folium.CircleMarker( location = location, \n popup= k,\n radius= R_size,\n color='#008d23',\n fill_color='#008d23',\n fill=False\n ).add_to(fol_map)\n fol_map.save(map_name)\n print(\"Dynamic map generated\")\n\n\n\ndef render_resistance(dic, regions_trans, map_name):\n '''Init Visuals'''\n #parameters\n max_size = 30\n regions = {}\n #filter the regions\n for k,v in dic.items():\n sequence_mut = v['binary']\n if(sequence_mut is None): #no info\n continue\n mut_found = np.zeros(len(drug_resistance_pos)) \n for i in range(len(drug_resistance_pos)):\n pos = i\n if(sequence_mut[pos]): #found a drug_resistance mut\n mut_found[i] += 1\n\n reg = v[\"region\"]\n if( reg in regions_trans ):\n if( reg not in regions ):\n regions[reg] = regions_trans[reg].copy()\n regions[reg][\"positive\"] = np.zeros(len(drug_resistance_pos)) \n regions[reg]['nb_seq'] = 0\n regions[reg][\"positive\"] += mut_found\n regions[reg][\"nb_seq\"] += 1 \n\n # fol_map = folium.Map( location=[37.983810, 23.727539], #Athen location\n # zoom_start= 3.2,\n # tiles='CartoDB dark_matter'\n # )\n devnull, fol_map = init_render(dic, regions_trans)\n\n #render regions\n for k in regions:\n location = regions[k][\"localisation\"]\n if(location is None): #not located, ignore\n continue \n\n for i in range(len(drug_resistance_pos)):\n circle_color = drug_resistance_col[i]\n circle_size = max_size * regions[k][\"positive\"][i] / regions[k][\"nb_seq\"]\n if(circle_size == 0): #no mut, no plot\n continue\n\n folium.CircleMarker( location = location, \n popup= k,\n radius= circle_size,\n color= circle_color,\n fill_color= circle_color,\n fill=False\n ).add_to(fol_map)\n fol_map.save(map_name)\n print(\"Drug resistance map generated\")\n\n\n#one render to rule them all\ndef render_all(dic, regions_trans, map_name):\n # plot all data on one map\n\n '''Init Visuals'''\n #parameters\n A = 0.5 #airport coef\n B = 0.2 #other transportation coef\n # max_size = 30\n regions = deepcopy(regions_trans)\n\n #set the mutation values\n for k,v in dic.items():\n sequence_mut = v['binary']\n if(sequence_mut is None): #no info\n continue\n mut_found = np.zeros(len(drug_resistance_pos)) \n for i in range(len(drug_resistance_pos)):\n pos = drug_resistance_pos[i]\n if(sequence_mut[pos]): #found a drug_resistance mut\n mut_found[i] += 1\n\n reg = v[\"region\"]\n if( reg in regions ):\n if( 'nb_seq' not in regions[reg] ): #the other key is implicite, they're always called at the same time\n regions[reg][\"positive\"] = np.zeros(len(drug_resistance_pos)) \n regions[reg]['nb_seq'] = 0\n regions[reg][\"positive\"] += mut_found\n regions[reg][\"nb_seq\"] += 1 \n\n\n fol_map = folium.Map( location=[52.520008, 13.404954], #Berlin location\n zoom_start=4,\n tiles='CartoDB dark_matter'\n )\n\n #render regions\n for k,v in regions.items():\n location = v[\"localisation\"]\n if(location is None): #not located, ignore\n continue \n\n #Calculating circle sizes\n #or initiate at higher to see small interaction\n circle_size = 0\n I_size = 0\n R_size = 0\n if(v[\"pop_estimate\"]):\n tot = 0\n air = 0\n transport_types = v[\"type\"]\n tot = len(transport_types)\n air = transport_types.count(\"airport\")\n other = tot - air #number of transportation method other than air\n\n pop = v[\"pop_estimate\"] / 1000000\n I = v[\"pop_infected\"] / 1000000\n R = v[\"pop_recovered\"] / 1000000\n\n circle_size += (A*air + B*other) * pop #will be the maximum size \n I_size += (A*air + B*other) * I\n R_size += (A*air + B*other) * R\n\n else: #there's nothing to plot for this region then\n continue\n\n \n #Population\n folium.CircleMarker( location = location, \n popup= k,\n radius= circle_size,\n color='#3186cc',\n fill_color='#3186cc',\n fill=False\n ).add_to(fol_map)\n\n #infected population\n folium.CircleMarker( location = location, \n popup= k,\n radius= I_size,\n color='#008d23',\n fill_color='#008d23',\n fill=False\n ).add_to(fol_map)\n #recovered population\n folium.CircleMarker( location = location, \n popup= k,\n radius= R_size,\n color='#dd2e44',\n fill_color='#dd2e44',\n fill=False\n ).add_to(fol_map)\n\n\n #Drug resistance\n if( 'nb_seq' in v):\n for i in range(len(drug_resistance_pos)):\n res_color = drug_resistance_col[i]\n res_size = circle_size * v[\"positive\"][i] / v[\"nb_seq\"]\n if(res_size == 0): #no mut, no plot\n continue\n\n #Drug resistance mutation (normalized on population)\n folium.CircleMarker( location = location, \n popup= k,\n radius= res_size,\n color= res_color,\n fill_color= res_color,\n fill=False\n ).add_to(fol_map)\n\n\n fol_map.save(map_name)\n print(\"Full map generated\")\n\n\n\ndef render_infection(dic, regions_trans, map_name):\n '''Init Visuals'''\n #parameters\n A = 0.5\n B = 0.2\n\n regions, fol_map = init_render(dic, regions_trans)\n\n #render regions\n for k,v in regions_trans.items():\n location = v[\"localisation\"]\n if(location is None):\n continue \n \n circle_size = 5 # big enough so we to click on it\n if(v[\"pop_estimate\"]):\n tot = 0\n air = 0\n transport_types = v[\"type\"]\n for t in transport_types:\n if(t == \"airport\"):\n air += 1\n tot += 1 \n other = tot - air\n pop = v[\"pop_estimate\"] / 1000000 \n circle_size += (air*A+other*B) * pop * 0.25\n\n prc_infected = v[\"pop_infected\"] / v[\"pop_estimate\"]\n\n\n point = [ [location[0], location[1], prc_infected * 7500] ]\n # point = [ [location[0], location[1], v[\"pop_infected\"]] ]\n HeatMap( data= point,\n radius= circle_size \n ).add_to(fol_map)\n\n fol_map.save(map_name)\n\n print(\"Infection map generated\")\n\n\n\n\n\ndef mut_bilan( mutations, years, bilanname_):\n x = np.arange(len(years))\n fig = plt.figure()\n for pos in range(len(drug_resistance_pos)):\n muts = []\n for m in mutations:\n if(m is None):\n muts.append(0)\n else:\n muts.append(m[pos])\n plt.plot(muts, label= str(drug_resistance_pos[pos]) )\n plt.legend()\n plt.xticks(x, labels = years,fontsize= 8, rotation='vertical' )\n fig.savefig(bilanname_)\n\n print(bilanname_)\n\n\ndef drift_bilan( values, years, bilanname_):\n x = np.arange(len(years))\n fig = plt.figure()\n \n val = []\n for v in values:\n val.append(v)\n \n plt.plot(val)\n plt.xticks(x, labels = years,fontsize= 8, rotation='vertical' )\n fig.savefig(bilanname_)\n\n print(bilanname_)","sub_path":"rendering.py","file_name":"rendering.py","file_ext":"py","file_size_in_byte":14194,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"521055435","text":"# coding=utf-8\n\n\"\"\"\n337. House Robber III My Submissions QuestionEditorial Solution\nTotal Accepted: 7198 Total Submissions: 19482 Difficulty: Medium\nThe thief has found himself a new place for his thievery again. There is only one entrance to this area,\ncalled the \"root.\" Besides the root, each house has one and only one parent house.\nAfter a tour, the smart thief realized that \"all houses in this place forms a binary tree\".\nIt will automatically contact the police if two directly-linked houses were broken into on the same night.\n\nDetermine the maximum amount of money the thief can rob tonight without alerting the police.\n\nExample 1:\n 3\n / \\\n 2 3\n \\ \\\n 3 1\nMaximum amount of money the thief can rob = 3 + 3 + 1 = 7.\nExample 2:\n 3\n / \\\n 4 5\n / \\ \\\n 1 3 1\nMaximum amount of money the thief can rob = 4 + 5 = 9.\n\"\"\"\n\n\n# Definition for a binary tree node.\nclass TreeNode(object):\n def __init__(self, x):\n self.val = x\n self.left = None\n self.right = None\n\n\n# this solution is AC by using java\n# but time limited in python!!!!!\nclass Solution(object):\n def rob(self, root):\n \"\"\"\n :type root: TreeNode\n :rtype: int\n \"\"\"\n if root is None:\n return 0\n val = 0\n if root.left:\n val += self.rob(root.left.left) + self.rob(root.left.right)\n if root.right:\n val += self.rob(root.right.left) + self.rob(root.right.right)\n return max(root.val + val, self.rob(root.left) + self.rob(root.right))\n\n\nif __name__ == '__main__':\n root = TreeNode(0)\n root.left = TreeNode(1)\n root.left.left = TreeNode(3)\n root.left.right = TreeNode(4)\n root.right = TreeNode(1)\n root.right.left = TreeNode(4)\n root.right.right = TreeNode(3)\n\n print (Solution().rob(root))\n","sub_path":"zishell/solution/medium/solution337_rob.py","file_name":"solution337_rob.py","file_ext":"py","file_size_in_byte":1828,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"503843399","text":"# -*- coding: utf-8 -*-\n\n# ==============================================================\n# 文件读取\n# ==============================================================\ndef data_process(string):\n \"\"\" 删除数据两侧括号，并利用','对字符串进行切片处理\n string: 传入的待处理str\n 返回值: str string\n \"\"\"\n # 删除数据两侧括号\n string = string.replace('(', '')\n string = string.replace(')', '')\n # 利用','对字符串进行切片处理，返回类型为list\n string = string.split(\",\")\n return string\n\ndef read_cars(car_path):\n \"\"\" 读取车辆信息\n car_path: car.txt路径\n 返回值: dict cars{}\n \"\"\"\n cars = {}\n # 以只读模式打开文件\n with open(car_path, 'r') as f:\n # 跳过文件中的第一行注释\n f.readline()\n datas = f.readlines()\n for data in datas:\n # 数据处理\n data = data_process(data)\n # 按照ID索引添加信息到datas\n # 数据内容依次为：id, from, to, speed, planTime\n cars[int(data[0])] = {'from': int(data[1]), 'to': int(data[2]), \\\n 'speed': int(data[3]), 'time': int(data[4]), \\\n 'priority': int(data[5]), 'preset': int(data[6]), \\\n 'state': -1, 's1': -1, 'direction': 'unknown', 'next_cross': -1, 'should_judge': 1\n }\n return cars\n\ndef read_roads(road_path):\n \"\"\" 读取道路信息\n road_path: roads.txt路径\n 返回值: dict roads{}\n \"\"\"\n roads = {}\n with open(road_path, 'r') as f:\n f.readline()\n datas = f.readlines()\n for data in datas:\n data = data_process(data)\n # 数据内容依次为：id, length, speed, channel, from, to, isDuplex\n roads[int(data[0])] = {'length': int(data[1]), 'speed': int(data[2]), \\\n 'channel': int(data[3]), 'from': int(data[4]), \\\n 'to': int(data[5]), 'isDuplex': bool(int(data[6]))\n } \n return roads\n\ndef read_cross(cross_path):\n \"\"\" 读取路口信息\n cross_path: cross.txt路径\n 返回值: dict cross{}\n \"\"\"\n cross = {}\n with open(cross_path, 'r') as f:\n f.readline()\n datas = f.readlines()\n for data in datas:\n data = data_process(data)\n # 数据内容依次为：id, road0, road1, road2, road3,\n cross[int(data[0])] = {0: int(data[1]), 1: int(data[2]), 2: int(data[3]), 3: int(data[4])}\n return cross\n\ndef read_preset_answer(preset_answer_path):\n \"\"\" 读取预置车辆信息\n presetAnswer_path: presetAnswer.txt路径\n 返回值: dict presetAnswer{}\n \"\"\"\n preset_answer = {}\n with open(preset_answer_path, 'r') as f:\n f.readline()\n datas = f.readlines()\n for data in datas:\n path = []\n data = data_process(data)\n for tmp in data:\n path.append(int(tmp))\n path.pop(0)\n path.pop(0)\n # 数据内容依次为：id, road0, road1, road2, road3,\n preset_answer[int(data[0])] = {'time': int(data[1]), 'path': path}\n return preset_answer\n\ndef answer2str(answer_time, graph):\n \"\"\" 将answer转化为最终的字符串形式\n\n \"\"\"\n answer_list = []\n for ans in answer_time:\n path_str = \"\"\n for i in range(0, len(ans['path']) - 1):\n temp = str(graph[ans['path'][i]][ans['path'][i+1]][1]) + ','\n path_str += temp\n # 去末尾逗号\n path = path_str[:-1]\n answer = '(' + str(ans['car_id']) + ',' + str(ans['time']) + ',' + path + ')'\n answer_list.append(answer)\n return answer_list\n\n\ndef output_answer(answer_path, answer_str):\n \"\"\" 将结果写入到answer.txt\n answer_path: answer.txt路径\n answer_list: 结果集 answer_list[], 内部元素为str\n \"\"\"\n with open(answer_path, 'w+') as f:\n f.write(\"#(carId,StartTime,RoadId...)\" + \"\\n\")\n for line in answer_str:\n f.write(line + \"\\n\")\n\n\n# ==============================================================\n# 用以主函数调用\n# ==============================================================\ndef read_input(car_path, road_path, cross_path, preset_answer_path):\n \"\"\" 读取car.txt, road.txt, cross.txt\n car_path: cat.txt路径\n road_path: road.txt路径\n cross_path: cross.txt路径\n 返回值: dict cars{}, roads{}, cross{}\n \"\"\"\n cars = read_cars(car_path)\n roads = read_roads(road_path)\n cross = read_cross(cross_path)\n preset_answer = read_preset_answer(preset_answer_path)\n return cars, roads, cross, preset_answer","sub_path":"CodeCraft-2019-复赛/src/file_io.py","file_name":"file_io.py","file_ext":"py","file_size_in_byte":4886,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"29453278","text":"from flask import (render_template, url_for, flash,\n redirect, request, abort, Blueprint)\nfrom flask_login import current_user, login_required\nfrom blog import db\nfrom blog.models import Abbonati\nfrom blog.posts.forms import NuovoAbbonato\nimport datetime\n\nposts = Blueprint('posts', __name__)\n\n\n@posts.route('/post/new', methods=['GET', 'POST'])\n@login_required\ndef new_post():\n form = NuovoAbbonato()\n if form.validate_on_submit():\n post = Abbonati(nome=form.nome.data, cell=form.cell.data, scadenza=form.scadenza.data, quota=form.quota.data, versato=form.versato.data, note=form.note.data)\n db.session.add(post)\n db.session.commit()\n flash('Socio pubblicato con successo!', 'success')\n return redirect(url_for('main.index'))\n\n return render_template('new_post.html', title='Nuovo abbonato', form=form, legend=\"Nuovo Abbonato\")\n\n\n@posts.route('/post//update', methods=['GET', 'POST'])\n@login_required\ndef update_post(id):\n post = Abbonati.query.get_or_404(id)\n form = NuovoAbbonato()\n if form.validate_on_submit():\n post.nome = form.nome.data\n post.cell = form.cell.data\n post.scadenza = form.scadenza.data\n post.quota = form.quota.data\n post.versato += int(form.versato.data)\n post.note = form.note.data\n db.session.commit()\n flash(\"Socio modificato\", 'info')\n return redirect(url_for('main.index'))\n elif request.method == \"GET\":\n form.nome.data = post.nome\n form.cell.data = post.cell\n form.scadenza.data = post.scadenza\n form.quota.data = post.quota\n form.note.data = post.note\n return render_template('new_post.html', title='Modifica Socio', form=form, legend=\"Modifica Socio\")\n\n\n@posts.route('/post//delete', methods=['POST'])\n@login_required\ndef delete_post(id):\n post = Abbonati.query.get_or_404(id)\n db.session.delete(post)\n db.session.commit()\n flash('Socio eliminato', 'info')\n return redirect(url_for('main.index'))","sub_path":"blog/posts/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":2034,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"256125995","text":"import io\nimport sys\n\ndef fasta_parser(stream):\n \n if isinstance(stream.read(0), bytes):\n db = []\n for line in stream:\n if line[0] == 62: #\n header, desc = line.split(maxsplit=1)\n header = header[1:]\n desc = desc.strip()\n db.append({\"id\":header, \"desc\":desc, \"raw\":io.BytesIO(line), \"sequence\":io.BytesIO()})\n else:\n line = line.strip()\n db[-1]['sequence'].write(line)\n db[-1]['raw'].write(line)\n return(db)\n else:\n print(\"Der Stream muss binaer sein.\", file=sys.stderr)\n exit(-1)\n\n\ndef get_raw(db, index):\n\n return db[index]['raw']\n\ndef get_id(db, index):\n\n return db[index]['id']\n\ndef get_description(db, index):\n\n return db[index]['desc']\n\ndef get_sequence(db, index):\n\n return db[index]['sequence']\n\ndef get_fasta(db, index, line_length=80):\n\n lines = []\n for i in range(0, (len(db[index]['sequence']) // line_length) * line_length, step=line_length):\n lines.append(db[index]['sequence'][i:i + line_length])\n if len(sequence) % line_length > 0:\n lines.append(sequence[i:])\n\n return \"\\n\".join(lines)\n\ndef get_feature(db, index, feature):\n\n return(db[index][feature])\n\ndef add_feature(db, index, feature, value):\n\n db[index][feature] = value\n return db[index]\n\ndef add_sequence_object(db, id, description, sequence, **features):\n\n db.append({\"id\":id, \"desc\":description, \"sequence\":sequence, **features})\n return(db)\n\ndef get_gc_content(db, index):\n\n seq = db[index][\"sequence\"]\n count = 0\n for ind, char in enumerate(seq):\n if (char == \"A\") or (char == \"T\"):\n continue\n else:\n count +=1\n content = count / (ind+1)\n content *= 100\n return content\n\ndef get_output(db, index, type):\n\n if type == \"markdown\":\n output = \"# H1\" + db[index][\"id\"] + \"*\" + db[index][\"desc\"] + \"*\" + \"\\n\" + \"```\" + db[index][\"sequence\"] + \"```\"\n elif type == \"html\":\n output = \"\" + db[index][\"id\"] + \"
\" + db[index][\"desc\"] + \"\" + \"
\" + \"\" + db[index][\"sequence\"] + \"\"\n else:\n output = db[index][\"id\"] + db[index][\"desc\"] + db[index][\"sequence\"]\n return output\n","sub_path":"AlmaBeganovic/ws2016.py","file_name":"ws2016.py","file_ext":"py","file_size_in_byte":2296,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"556945458","text":"import requests\nfrom decouple import config\n\n\napi_url = 'https://api.fortnox.se/3/'\n\nclass FortNoxRequestHandler():\n \"\"\"Handles requests and responses to FortNox API\"\"\"\n def __init__(self):\n self.headers = {\n 'Access-Token': config(\"ACCESS_TOKEN\"),\n 'Client-Secret': config(\"FORTNOX_CLIENT_SECRET\")\n }\n\n def get_all_articles(self):\n '''Return all articles'''\n r = requests.get(f'{api_url}articles', headers=self.headers)\n decoded_response = r.json()\n return decoded_response.get('Articles')\n\n def update_article(self, article):\n '''Update and article'''\n r = requests.put(f'{api_url}article/{article.ArticleNumber}',\n data={\"Article\":article}, headers=self.headers)\n return r.json()\n\n def create_article(self, article):\n '''Create a new article'''\n r = requests.post(f'{api_url}article/', data={\"Article\":article},\n headers=self.headers)\n return r.json()\n\n def get_customers(self, pages=None):\n '''Get a list of customers'''\n if pages is None:\n r = requests.get(f'{api_url}customers/', headers=self.headers)\n decoded_response = r.json()\n return decoded_response.get('Customers')\n\n customers = []\n current_page = 1\n\n while current_page <= pages:\n r = requests.get(f'{api_url}customers/',\n params={'page':current_page}, headers=self.headers)\n decoded_response = r.json()\n customers = customers + decoded_response.get('Customers')\n current_page += 1\n\n return customers\n \n\n\n \n","sub_path":"src/api_calls.py","file_name":"api_calls.py","file_ext":"py","file_size_in_byte":1694,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"450246352","text":"import os \nimport ujson as json\n\nimport networkx as nx\nimport traceback\nimport time\nimport sys\nfrom pathlib import Path\n\nimport logger_utils\nfrom trace_utils import *\nfrom dag_utils import *\nfrom collect import Collector\nfrom replay import Replayer\nfrom progress_utils import progressBar\nimport arg_utils\nimport debug_utils\nimport optimizer\n# from cost_model_xla import XlaDataset, FusionCostModel\n\nargs = arg_utils.SingleArg().args\nlogger = logger_utils.SingleLogger(args.path.split(',')[0], \n args.option, args.logging_level, \n is_clean=args.clean, \n show_progress=args.progress)\nlogger.info(args)\nQueueType(\"NCCL\")\ndebug_utils.DebugRecorder(is_enable=args.debug_traces)\n\nsys.setrecursionlimit(1000000)\n\npath_list = args.path.split(',')\n\"\"\" Read traces and prepare statitic info\"\"\"\nif args.option not in ['critical', 'combine', 'compare', \"replay\", \"topo_sort\", \"collect\", \"3dcompare\", \"optimize\"]:\n pm = PathManager(path_list[0])\n traces = read_traces(pm.search(FileName.TRACE))\n name2sta, cat2sta = return_stat(traces)\n\nif args.option == \"statistic\":\n \"\"\" Output the statistic results \"\"\"\n # \\TODO: device id\n def output(_name2sta):\n logger.info(\"Profile Statistics.\")\n logger.info(\"===================\")\n logger.info(\"%-60s\\t Total Count\\t Time (ms)\\t Min Time (ms)\\t Max Time (ms)\\t Avg Time (ms)\\t Variance (ms^2)\" % \"Name\")\n logger.info(\"%-60s\\t -----------\\t ---------\\t -------------\\t -------------\\t -------------\\t ---------------\" % \"----\")\n line_cnt = 0\n for name, statistic in _name2sta:\n if (args.head and line_cnt >= args.head):\n break \n logger.info(\"%-60s\\t %11d\\t %9.4f\\t %12.4f\\t %13.4f\\t %13.4f\\t %13.4f\" % \n (name,\n statistic[\"cnt\"],\n statistic[\"time\"],\n statistic[\"min_t\"],\n statistic[\"max_t\"],\n statistic[\"avg\"],\n statistic[\"var\"]\n ))\n line_cnt += 1\n\n # output(sorted(name2sta.items(), lambda x, y: cmp(x[1][\"avg\"], y[1][\"avg\"])))\n if args.sort:\n sort_sta = sorted(name2sta.items(), key=lambda x: x[1][\"avg\"], reverse=True)\n else:\n sort_sta = name2sta.items()\n output(sort_sta)\n if args.xlsx:\n export2xlsx([name2sta], os.path.dirname(path_dict[\"trace_path\"]))\n\n # Group by category\n logger.info(\"\")\n logger.info(\"Group by category\")\n logger.info(\"===================\")\n line_cnt = 0\n for cat, statistic in cat2sta.items():\n if (args.head and line_cnt >= args.head):\n break\n logger.info(\"Category: %-10s\\t The most time-consuming OP: %-30s -> %13.4f (ms)\" % (cat, statistic[\"max_name\"], statistic[\"max_t\"] / 1000.0))\n line_cnt += 1\n\nif args.option == \"graph\":\n mygraph = nx.read_gml(pm.search(FileName.DAG))\n visualize_gml(mygraph)\n\nif args.option == \"critical\":\n ''' \n Args:\n -- args.path: the dir of a worker, which contains multiple folders \n storing traces of GPUs of this worker\n '''\n assert pm.dir_level == DirLevel.WORKER\n #! used to store all dags generated from GPUs\n graphs = []\n for _dir in pm.dirs:\n dagmanager = DAGManager(os.path.join(pm.path, _dir), platform=args.platform)\n dagmanager.gen_dag_with_prefix_weight()\n dag_longest_path(dagmanager.dag, dagmanager.pm, weight=\"weight\", default_weight=0)\n graphs.append(dagmanager.dag)\n\n graph = nx.compose_all(graphs)\n dag_longest_path(graph, pm, weight=\"weight\", default_weight=0)\n\nif args.option == \"timeline\":\n raise NotImplementedError()\n\nif args.option == \"replay\":\n ''' Re-generate the timeline according to the dependency \n graph with time for each node.\n Args:\n --path: the root path for \n --step_num: number of steps we want to generate.\n ''' \n clct = Collector(path_list[0], comm_backend=args_.comm_backend, platform=args.platform)\n iter_times = clct.init(args.force)\n\n ### Replay traces\n logger.info(\"# Start to Replay\")\n replayer = Replayer(dag=clct.trail_dag, \n _step_num=args.step_num, \n leaf_dirs=clct.all_prefix_list(), \n dump_path=clct.pm.path,\n comm_backend=clct.comm_backend,\n byteps_graph=clct.byteps_graph)\n \n def replay_with_delay(idx_, rst, node_name=None):\n logger.info(node_name)\n delay_dict = {node_name: {\"delay\": -5, \"ratio\": 1}} if node_name is not None else None\n step_end_time = replayer.replayAndDelay(delay_dict, _ouput=True)\n for trace in replayer.rst_traces:\n trace[\"tid\"] = \"%d-->%s\"%(idx_, trace[\"tid\"] if \"tid\" in trace else \"tid\")\n rst.append(trace) \n return idx_ + 1\n\n if args.sub_option is None:\n ''' Directly replay '''\n replayer.replay()\n cal_edge_cost(replayer.exct_dag)\n critical_path = dag_longest_path(replayer.exct_dag, clct.pm, weight=\"cost\", default_weight=0, _debug_level=1)\n elif args.sub_option == \"smlt_delay_cmp\":\n ''' Replay with computation delays'''\n delay_dict = {\"DELAY_ALL_CMP\": {\"delay\": 0, \"ratio\": args.delay_ratio}}\n step_end_time = replayer.replayAndDelay(delay_dict, _output=True)\n elif args.sub_option == \"smlt_delay_comm\":\n ''' Replay with communication delays'''\n delay_dict = {\"DELAY_ALL_COMM\": {\"delay\": 0, \"ratio\": args.delay_ratio}}\n step_end_time = replayer.replayAndDelay(delay_dict, _output=True)\n elif args.sub_option == \"map_delay\":\n ''' Replay and add delays to each node respectively.'''\n iter_time = max([e[2] for e in iter_times])\n node_lists = list(wk_dag.nodes())\n total_len = len(node_lists)\n pgsbar = progressBar(start=0, end=total_len)\n idx = 0\n while idx < total_len:\n nodename = node_lists[idx]\n delay_dict = {nodename: {\"delay\": 10, \"ratio\": 1.0}}\n step_end_time = replayer.replayAndDelay(delay_dict, _ouput=False)\n logger.info(\"Delay %s ==> %s ==> %s critical path.\" % (nodename, str(step_end_time), \"in\" if nodename in critical_path else \"not in\"))\n if args.progress:\n pgsbar.showBar(idx)\n idx += 10\n\n elif args.sub_option == \"bottleneck\":\n ''' Replay and add delays to some of the node on the critical path respectively.'''\n ### Get the execution graph first\n replayer.replay()\n cal_edge_cost(replayer.exct_dag)\n\n critical_path = dag_longest_path(replayer.exct_dag, clct.pm, weight=\"cost\", default_weight=0, _debug_level=2)\n critical_path = sorted(critical_path, key=lambda x: x[1], reverse=True)\n total_len = len(critical_path)\n pgsbar = progressBar(start=0, end=total_len)\n iter_time = max([e[2] for e in iter_times])\n idx = 0\n max_diff = 0\n bottleneckt_ = None\n\n while idx < total_len:\n nodename, node_len = critical_path[idx]\n if node_len == 0:\n idx += 1\n continue\n ### TODO (huhanpeng): change the value 10\n delay_dict = {nodename: {\"delay\": -5, \"ratio\": 1}}\n step_end_time_ms = [t / 1000 for t in replayer.replayAndDelay(delay_dict, _ouput=False).values()]\n cur_iter_time_ = max(step_end_time_ms)\n diff_ = cur_iter_time_ - iter_time if cur_iter_time_ > iter_time else iter_time - cur_iter_time_\n logger.info(\"Delay %s\" % (nodename))\n logger.info(\" ==> %s.\" % (str(step_end_time_ms)))\n if diff_ > max_diff:\n max_diff = diff_\n bottleneckt_ = nodename\n if args.progress:\n pgsbar.showBar(idx)\n ### TODO (huhanpeng): how to pick these nodes\n idx += 10\n logger.info(\"bottleneckt: %s\" % bottleneckt_)\n elif args.sub_option == \"compare\":\n rst = []\n idx = 0\n idx = replay_with_delay(idx, rst)\n idx = replay_with_delay(idx, rst, \"host0.rank1->FW.bertencoder0_transformer0_multiheadattentioncell0_batch_dot1\")\n # idx = replay_with_delay(idx, rst, \"host1.rank0->BW.bertencoder0_slice0\")\n rst = sorted(rst, key=lambda x: (x[\"pid\"], x[\"tid\"]))\n with open(os.path.join(clct.pm.path, \"replay_compare.json\"), 'w') as f:\n json.dump(rst, f)\n\nif args.option == \"topo_sort\":\n pm = PathManager(path_list[0])\n assert pm.dir_level == DirLevel.GPU\n local_rank = int(pm.path.split(\"/\")[-1])\n dagmanager = DAGManager(pm.path, local_rank, platform=args.platform)\n dagmanager.gen_fw_bw_dag()\n\n'''below options use special --path'''\nif args.option == \"compare\":\n if len(path_list) < 2:\n raise ValueError(\"To compare two files, two paths must be given\")\n if os.path.isfile(path_list[0]):\n traces = [read_traces(path_list[0]), read_traces(path_list[1])]\n else:\n clct = [Collector(path_list[0], comm_backend=args_.comm_backend, platform=args.platform), \n Collector(path_list[1], comm_backend=args_.comm_backend, platform=args.platform)]\n traces = [c.iter_combine() for c in clct]\n name2sta = [return_stat(_traces)[0] for _traces in traces]\n name2compare = {}\n for name, statistic in name2sta[0].items():\n if name not in name2sta[1]:\n continue\n name2compare[name] = {\n \"avg_absolute\": name2sta[1][name][\"avg\"] - statistic[\"avg\"],\n \"avg_relative\": (name2sta[1][name][\"avg\"] - statistic[\"avg\"]) / statistic[\"avg\"]\n }\n\n if args.sort:\n sort_sta = sorted(name2compare.items(), key=lambda x: x[1][\"avg_relative\"], reverse=True)\n else:\n sort_sta = name2compare.items()\n\n name2sta.append(name2compare)\n if args.xlsx:\n def gen_sheet_name(l):\n if len(l) >= 31:\n l = l[-31:]\n return \"_\".join(l.split(\"/\")[1:])\n\n sheet_name = [gen_sheet_name(l) for l in path_list]\n sheet_name.append(\"comparison\")\n export2xlsx(name2sta, \n os.path.abspath(path_list[0]) if os.path.isdir(path_list[0]) else os.path.dirname(path_list[0]), \n filename=\"compare\",\n sheet_name=sheet_name)\n\n logger.info(\"Compare following two files:\")\n logger.info(\"File 1: \" + path_list[0])\n logger.info(\"File 2: \" + path_list[1])\n logger.info(\"===================\")\n logger.info(\"%-100s\\t Absolute Avg Time Increase (ms)\\t Relative Avg Time Increase\" % \"Name\")\n line_cnt = 0\n for name, compare in sort_sta:\n if (args.head and line_cnt >= args.head):\n break \n logger.info(\"%-100s\\t %24.4f\\t %24.4f\" %\n (name, compare[\"avg_absolute\"], compare[\"avg_relative\"]))\n line_cnt += 1\n\nif args.option == \"collect\":\n clct = Collector(path_list[0], comm_backend=args_.comm_backend, platform=args.platform)\n clct.init(args.force)\n if args.sub_option == \"combine\":\n clct.iter_combine()\n elif args.sub_option == \"xlsx\":\n clct.traceM.export2xlsx(path_list[0])\n elif args.sub_option == \"visual_dag\":\n clct.traceM.export2xlsx(path_list[0])\n elif args.sub_option == \"iter_time\":\n clct.iter_time()\n elif args.sub_option == \"straggler\":\n clct.detect_straggler1()\n elif args.sub_option == \"bottleneck\":\n clct.detect_bottleneck1()\n elif args.sub_option == \"query\":\n while True:\n name = input(\"\\nQuerying: \\n\\t 1). The tensor name \\n\\t 2). \\\\sta_by_cnt \\n\\t 3). q or Q to quit \\nInput your command: \")\n if name.lower() == \"q\":\n break\n elif \"\\\\sta_by_cnt\" in name or name == \"2\":\n clct.detect_straggler1()\n else:\n avg = clct.traceM.lookup_stat(None, None, name)\n print(\"Average time: %f ms\" % (avg))\n\nif args.option == \"optimize\":\n\n from cost_model_amp.amp_pred import AMPPredictor, AMPTrainer\n clct = Collector(path_list[0], comm_backend=args_.comm_backend, platform=args.platform)\n clct.init(args.force)\n amp_pred = AMPTrainer(\n os.path.join(path_list[0], \"host0/0/metadata.json\"),\n path_list[0])\n amp_pred.collect_raw_data(path_list[0])\n amp_pred.gen_train_data(clct.trail_dag)\n amp_pred.train(test=True)\n '''\n if len(path_list) < 2:\n raise RuntimeError(\"optimize requires positional path arguments: profile data path & cost model path.\")\n clct = Collector(path_list[0], comm_backend=args_.comm_backend)\n models_dir = path_list[1]\n clct.init(args.force)\n cost_models = {}\n logger.info(\"Searching for model dumps in {}\".format(models_dir))\n for model_dump in os.listdir(models_dir):\n model_path = os.path.join(models_dir, model_dump)\n p = Path(model_path)\n if p.is_file():\n node_name = p.stem\n cm = FusionCostModel(os.path.join(args.cost_model_tmp_dir, node_name))\n cm.load(model_path)\n cost_models[node_name] = cm\n logger.info(\"Added cost model for {}\".format(node_name))\n else:\n logger.warn(\"{} not a file.\".format(model_path))\n if args.optimizer == \"MCTS\":\n opt = optimizer.MCTSOptimizer(clct, cost_models=cost_models, ucb_type=args.ucb_type, no_mutation=args.no_mutation)\n elif args.optimizer == \"MCMC\":\n opt = optimizer.MCMCOptimizer(clct, cost_models=cost_models)\n opt.search()\n '''\n### Output debug traces\ndebug_utils.DebugRecorder().dump_traces(path_list[0])\n\n\n\n","sub_path":"analyze.py","file_name":"analyze.py","file_ext":"py","file_size_in_byte":13561,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"455570930","text":"\"\"\"\nEscreva um programa que leia dois números, imprima o resultado da\nmultiplicação do primeiro pelo segundo. Utilize apenas os operadores\nde soma e subtração para calcular o resultado. Lembre-se de que podemos\nentender a multiplicação de dois números como somas sucessivas de um deles.\nassim, 4 x 5 = 5 + 5 + 5 + 5 = 4 + 4 + 4 + 4 + 4\n\n\"\"\"\n\n# Solução\nproduto = 0\nn1 = int(input(\"Digite o primeiro número\"))\nn2 = int(input(\"Digite o segundo número\"))\n\nx = 1\nwhile x <= n2:\n produto += n1 # n1 + n1 + n1 +n1 ...\n x += 1\nprint(f' {n1} x {n2} = {produto}')\n\n","sub_path":"Livros_Cursos/Nilo_3ed/cap05/Exercicio_05-08.py","file_name":"Exercicio_05-08.py","file_ext":"py","file_size_in_byte":576,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"597978154","text":"#!/usr/bin python\n#!coding=utf8\n\nimport web\n\ndb = web.database(dbn='mysql', user='root', pw='admin', db='webpy_schema');\n\nrender = web.template.render('templates/')\n\nurls = (\n\t\t'/', 'index'\n\t\t)\n\nclass index:\n\tdef GET(self):\n\t\ttodos = db.select('todo')\n\t\treturn render.index(todos)\n\nif __name__ == \"__main__\":\n\tapp = web.application(urls, globals())\n\tapp.run()\n","sub_path":"code/code.py","file_name":"code.py","file_ext":"py","file_size_in_byte":360,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"256126551","text":"#FRANCISCO JAVIER GONZALEZ MOLINA A01748636\r\n#BOLETOS PARA UN CONCIERTO\r\n\r\n#esta funcion calcula el total de pago que se realizara\r\n##puse el valor de los boletos como variable para que se entendiera mas\r\n#al final regresa el total que se pagara por los boletos seleccionados.\r\ndef calcularPago (nOboletosA, nOboletosB, nOboletosC):\r\n boletoA= 3250\r\n boletoB= 1730\r\n boletoC= 850\r\n totalpago= ((nOboletosA*boletoA)+(nOboletosB*boletoB)+(nOboletosC*boletoC))\r\n return totalpago\r\n\r\n#Esta funcion realiza las operaciones para saber el valor total de los boletos\r\n#deseados\r\ndef main ():\r\n nOboletosA= int(input(\"Numero de boletos en zona A: \"))\r\n nOboletosB= int(input(\"Numero de boletos en zona B: \"))\r\n nOboletosC= int(input(\"Numero de boletos en zona C: \"))\r\n total= calcularPago(nOboletosA,nOboletosB, nOboletosC)\r\n print (\"\"\"\r\n Costo total es: $%.2f\"\"\"% (total))\r\n\r\n#llama la funcion \"main ()\"\r\nmain()","sub_path":"Boletos.py","file_name":"Boletos.py","file_ext":"py","file_size_in_byte":936,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"438308053","text":"# coding: utf8\n\"\"\"\nParsing functionality for the SFM variant understood for Dictionaria submissions.\n\"\"\"\nfrom __future__ import unicode_literals, print_function\nfrom collections import defaultdict\nimport re\nfrom copy import copy\n\nfrom clld.db.models import common\nfrom clld.db.fts import tsvector\nfrom clld.db.meta import DBSession\n\nfrom clldutils import sfm\nfrom clldutils.misc import cached_property\n\nfrom dictionaria.lib.ingest import MeaningDescription, split, BaseDictionary\nfrom dictionaria import models\n\n\nclass Concepticon(object):\n def __init__(self):\n self.count = 0\n\n def __call__(self, entry):\n items = []\n for marker, content in entry:\n if marker == 'de':\n md = MeaningDescription(content)\n items.append((marker, md.meanings))\n if md.has_comparison_meaning:\n self.count += 1\n items.append(('comparison_meanings', md.comparison_meanings))\n else:\n items.append((marker, content))\n return entry.__class__(items)\n\n\nclass Meaning(object):\n \"\"\"\n A word can have several meanings, described by meaning description and gloss.\n\n Semantic domain and examples are also meaning specific.\n \"\"\"\n def __init__(self):\n self.de = None\n self.ge = None\n self.gxx = None\n self.gxy = None\n self.re = None\n self.sd = []\n self.xref = []\n\n\nclass Word(object):\n \"\"\"\n A word is the atomic language unit in dictionaria.\n\n A dictionary entry may contain multiple words via sub-entry markers.\n \"\"\"\n def __init__(self, form):\n self.form = form\n self._hm = 0 # homonym marker\n self.ph = None # phonetic representation of the word\n self._ps = None # part-of-speech\n self.data = defaultdict(list) # to store additional marker, value pairs\n self.rel = []\n self.meanings = []\n\n @property\n def ps(self):\n return self._ps\n\n @ps.setter\n def ps(self, value):\n if self._ps and self._ps != value:\n raise ValueError('Multiple assignments of different pos for %s: %s, %s' % (\n self.form, self._ps, value))\n self._ps = value\n\n @property\n def hm(self):\n return '{0}'.format(self._hm) if self._hm else ''\n\n @hm.setter\n def hm(self, value):\n m = re.search('(?P[0-9]+)', value)\n self._hm = int(m.group('number')) if m else 0\n\n def copy(self):\n w = Word(self.form)\n w.ph = self.ph\n if not self.hm:\n self.hm = '1'\n w.hm = '{0}'.format(int(self.hm) + 1)\n w.data = copy(self.data)\n return w\n\n @property\n def id(self):\n return self.form + (self.hm or '')\n\n\nRELATION_MAP = {\n 'cf': 'see also',\n 'mn': 'main entry',\n 'an': 'antonym',\n 'sy': 'synonym',\n}\n\n\nclass Entry(sfm.Entry):\n \"\"\"\n A dictionary entry.\n \"\"\"\n def checked_word(self, word, meaning, pos):\n if meaning:\n if meaning.de or meaning.ge:\n word.meanings.append(meaning)\n #else:\n #print('meaning without description for %s' % word.form)\n if word.ps is None:\n word.ps = pos\n return word\n\n @property\n def files(self):\n res = []\n for marker, mtype in [('sf', 'audio'), ('pc', 'image')]:\n for n in self.getall(marker):\n for nn in split(n):\n res.append((nn, mtype))\n return res\n\n def get_words(self):\n \"\"\"\n :return: generator for the words contained within the entry.\n \"\"\"\n word = None\n # if an entry has only one \\ps marker but multiple words, the value of \\ps is used\n # as part-of-speech for all words.\n pos = None\n\n meaning = None\n\n # flag signaling whether we are dealing with the first meaning of a word or\n # subsequent ones.\n first_meaning = True\n sn_is_se = False\n\n # now we loop over the (marker, value) pairs of the entry:\n for k, v in self:\n # individual words are identified by \\lx or \\se (sub-entry) markers.\n if k in ['lx', 'se']:\n if word:\n yield self.checked_word(word, meaning, pos)\n word = Word(v)\n meaning = Meaning()\n elif k == 'sn': # a new sense number: initialize a new Meaning.\n word.hm = word.hm or v\n if first_meaning:\n # determine whether we are dealing with the case where \\ps comes after\n # \\sn, thus, \\sn has to be treated like \\se:\n sn_is_se = not bool(pos)\n first_meaning = False\n else:\n self.checked_word(word, meaning, pos)\n if sn_is_se and word.form:\n yield word\n word = word.copy()\n meaning = Meaning()\n # meaning-specific markers:\n elif k in ['de', 'ge', 're', 'gxx', 'gxy']:\n # FIXME: we must support multiple meanings expressed by\n # semicolon-separated \\ge values, e.g. \"jump ; jump at\"\n setattr(meaning, k, v)\n elif k == 'sd':\n meaning.sd.append(v)\n elif k == 'xref':\n meaning.xref.append(v)\n # word-specific markers:\n elif k in ['hm', 'ph']:\n if getattr(word, k) is None:\n # only record first occurrence of the marker!\n setattr(word, k, v)\n elif k == 'ps':\n pos = v\n try:\n word.ps = v\n except ValueError:\n self.checked_word(word, meaning, pos)\n if word.form:\n yield word\n word = word.copy()\n word.ps = v\n meaning = Meaning()\n elif k in RELATION_MAP:\n word.rel.extend([(RELATION_MAP[k], vv.strip()) for vv in split(v, ',')])\n else:\n word.data[k].append(v)\n if word and word.form:\n yield self.checked_word(word, meaning, pos)\n\n\nclass Dictionary(BaseDictionary):\n @cached_property()\n def sfm(self):\n return sfm.SFM.from_file(\n self.dir.joinpath('db.sfm'),\n entry_sep='\\\\lx ',\n entry_prefix='\\\\lx ',\n entry_impl=Entry)\n\n def concepticon(self, db):\n visitor = Concepticon()\n self.sfm.visit(visitor)\n print('Found comparison meanings for %s of %s entries' % (\n visitor.count, len(self.sfm)))\n self.sfm.write(db)\n\n def load(\n self,\n submission,\n data,\n vocab,\n lang,\n comparison_meanings,\n labels):\n rel = []\n skipped = []\n words_by_lemma = defaultdict(list)\n\n def meaning_descriptions(s):\n return split((s or '').replace('.', ' ').lower())\n\n wids = set()\n for i, entry in enumerate(self.sfm):\n words = list(entry.get_words())\n headword = None\n\n for j, word in enumerate(words):\n if word.form.startswith('\\\\_'):\n continue\n if not word.meanings:\n skipped.append(word)\n continue\n\n examples = []\n for meaning in word.meanings:\n for xref in meaning.xref:\n if xref in data['Example']:\n examples.append(data['Example'][xref].serialized)\n\n wid = '%s-%s-%s' % (submission.id, i + 1, j + 1)\n # Recognize and keep \\lxid as local entry ID\n if entry.get('lxid'):\n if len(words) == 1:\n _wid = '%s-%s' % (submission.id, entry.get('lxid'))\n if _wid not in wids:\n wids.add(_wid)\n wid = _wid\n #else:\n # print(submission.id, entry.get('lxid'))\n fullentry = '{0}\\n{1}'.format(entry, '\\n'.join(examples))\n w = data.add(\n models.Word,\n word.id,\n id=wid,\n name=word.form,\n entry_comment=word.data.get('links', [None]).pop(),\n number=int(word.hm) if word.hm and word.hm != '-' else 0,\n phonetic=word.ph,\n pos=word.ps,\n fts=tsvector(fullentry),\n serialized=fullentry,\n dictionary=vocab,\n language=lang)\n\n seen = set()\n for bibref in entry.getall('bibref'):\n key, context = bibref, None\n if '[' in key:\n key, _, context = key.partition('[')\n context = context.replace(']', '').strip()\n\n if (key, context) in seen:\n continue\n seen.add((key, context))\n if key in data['DictionarySource']:\n DBSession.add(models.WordReference(\n source=data['DictionarySource'][key],\n word=w,\n description=labels.get(context, context)))\n\n if not headword:\n headword = word.id\n else:\n rel.append((w, 'main entry', headword))\n\n for tw in word.rel:\n rel.append((w, tw[0], tw[1]))\n\n words_by_lemma[word.form].append(w)\n if word.hm:\n words_by_lemma['{0} {1}'.format(word.form, word.hm)].append(w)\n if wid not in words_by_lemma:\n words_by_lemma[wid].append(w)\n DBSession.flush()\n\n for md5, type_ in set(entry.files):\n submission.add_file(type_, md5, common.Unit_files, w)\n\n for k, meaning in enumerate(word.meanings):\n if not (meaning.ge or meaning.de):\n # FIXME: better logging!\n #print('meaning without description for word %s' % w.name)\n continue\n\n if meaning.ge:\n meaning.ge = meaning.ge.replace('.', ' ')\n\n m = models.Meaning(\n id='%s-%s' % (w.id, k + 1),\n name=meaning.de or meaning.ge,\n description=meaning.de,\n gloss=meaning.ge,\n reverse=meaning.re,\n alt_translation1=meaning.gxx,\n alt_translation_language1=submission.props.get('metalanguages', {}).get('gxx'),\n alt_translation2=meaning.gxy,\n alt_translation_language2=submission.props.get('metalanguages', {}).get('gxy'),\n #ord=k + 1,\n word=w,\n semantic_domain=', '.join(meaning.sd))\n\n for xref in meaning.xref:\n s = data['Example'].get(xref)\n if s is None:\n print('missing example referenced: %s' % xref)\n else:\n models.MeaningSentence(meaning=m, sentence=s)\n\n #\n # Lookup comparison meanings.\n #\n for m in re.finditer('\\[(?P[0-9]+)\\]', entry.get('zcom2', '')):\n cid = m.group('id')\n vsid = '%s-%s' % (submission.id, cid)\n if vsid in data['ValueSet']:\n vs = data['ValueSet'][vsid]\n else:\n vs = data.add(\n common.ValueSet,\n vsid,\n id=vsid,\n language=lang,\n contribution=vocab,\n parameter_pk=comparison_meanings[cid])\n\n vid = '%s-%s' % (vsid, w.id)\n if vid not in data['Counterpart']:\n data.add(\n models.Counterpart,\n vid,\n id=vid,\n name=w.name,\n valueset=vs,\n word=w)\n\n for index, (key, label) in enumerate(labels.items()):\n if key in word.data:\n for value in word.data[key]:\n DBSession.add(common.Unit_data(\n object_pk=w.pk, key=labels[key], value=value, ord=index))\n\n for key, value in word.data.items():\n if key.endswith('_links'):\n nkey = key.replace('_links', '')\n for i, value in enumerate(word.data[key]):\n DBSession.add(common.Unit_data(\n object_pk=w.pk,\n key=(labels[nkey] + '_links') if nkey in labels else key,\n value=value,\n ord=i + 1))\n\n # FIXME: vgroup words by description and add synonym relationships!\n for word in list(data['Word'].keys())[:]:\n if '-' in word:\n alt = word.replace('-', '')\n if alt not in data['Word']:\n data['Word'][alt] = data['Word'][word]\n\n for i, (w, d, target) in enumerate(rel):\n for t in words_by_lemma.get(target, []):\n DBSession.add(models.SeeAlso(\n source_pk=w.pk, target_pk=t.pk, description=d, ord=i))\n\n if skipped:\n print('{0} entries with no meaning skipped'.format(len(skipped)))\n","sub_path":"dictionaria/lib/sfm.py","file_name":"sfm.py","file_ext":"py","file_size_in_byte":14194,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"354013706","text":"# -*- coding: utf-8 -*-\n###############################################################################\n#\n# Copyright (c) 2019 HERE Europe B.V.\n#\n# SPDX-License-Identifier: MIT\n# License-Filename: LICENSE\n#\n###############################################################################\n\nfrom qgis.PyQt.uic import loadUiType\nfrom . import config\nimport os\nimport time\n\ndef disconnect_silent(signal):\n ok = True\n try: signal.disconnect()\n except TypeError: ok = False\n return ok\ndef get_ui_class(ui_file):\n \"\"\"return class object of a uifile\"\"\"\n ui_file_full = os.path.join(\n config.PLUGIN_DIR, \"gui\", \"ui\", ui_file\n )\n return loadUiType(ui_file_full)[0]\n\ndef make_unique_full_path(ext=\"json\"):\n return os.path.join(config.TMP_DIR, \"%s.%s\"%(time.time(),ext))\n # return os.path.join(config.TMP_DIR, \"%s.%s\"%(\"test\",ext))\ndef clear_cache():\n files = [os.path.join(config.TMP_DIR, f) for f in os.listdir(config.TMP_DIR)]\n files.append(config.LOG_FILE)\n for f in files:\n try: os.remove(f)\n except OSError: pass # files in used","sub_path":"XYZHubConnector/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1085,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"81402594","text":"import os\nfrom datetime import timedelta\nfrom flask import Flask, jsonify, send_from_directory\nfrom flask_cors import CORS\nfrom api.models import db\nfrom flask_migrate import Migrate\nfrom flask_jwt_extended import JWTManager\nfrom api.routes import api\nfrom api.utils import APIException, generate_sitemap\nfrom api.admin import setup_admin\n\nENV = os.getenv(\"FLASK_ENV\")\nstatic_file_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../public/')\napp = Flask(__name__)\n\n# database configuration:\napp.config['SQLALCHEMY_DATABASE_URI'] = os.environ.get('DATABASE_URL')\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\nMIGRATE = Migrate(app, db)\ndb.init_app(app)\n\n# Allow CORS requests to this API\nCORS(app)\n\napp.config['JWT_SUPER_SECRET_KEY'] = os.environ.get('SECRET_KEY')\n# Token espire time in minutes\napp.config['JWT_ACCESS_TOKEN_EXPIRES'] = timedelta(minutes=5)\napp.config[\"JWT_REFRESH_TOKEN_EXPIRES\"] = timedelta(days=2)\n\njwt = JWTManager(app) \n\n# add the admin\nsetup_admin(app)\n\n# Add all endpoints form the API with a \"api\" prefix\napp.register_blueprint(api, url_prefix=\"/api\")\n\n\n# Handle/serialize errors like a JSON object\n@app.errorhandler(APIException)\ndef handle_invalid_usage(error):\n return jsonify(error.to_dict()), error.status_code\n\n\n# generate sitemap with all your endpoints\n@app.route('/')\ndef sitemap():\n if ENV == \"development\":\n return generate_sitemap(app)\n return send_from_directory(static_file_dir, 'index.html')\n\n\n# any other endpoint will try to serve it like a static file\n@app.route('/', methods=['GET'])\ndef serve_any_other_file(path):\n if not os.path.isfile(os.path.join(static_file_dir, path)):\n path = 'index.html'\n response = send_from_directory(static_file_dir, path)\n # avoid cache memory\n response.cache_control.max_age = 0\n return response\n\n\nif __name__ == \"__main__\":\n app.run(host=\"0.0.0.0\", port=5000, debug=True)\n","sub_path":"src/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1924,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"562795325","text":"# -*- coding: utf-8 -*-\nimport numpy as np\nfrom scipy import signal\nimport matplotlib.pyplot as plt\nplt.style.use('seaborn-whitegrid')\n\nt = np.linspace(0.0, 2 * np.pi, 1000)\n\nc = np.sin(20 * t) # carrier\n\nm_analog = np.sin(2 * t)\nm_digital = 10*signal.square(2 * t)\nfm = np.sin(20 * t + ( 5 * m_analog) )\nfsk = np.cos(2 * np.pi * (2 * np.pi + m_digital) * t )\n\nplots = []\nplot_carrier = plt.subplot(5, 1, 1)\nplt.plot(t, c, color='steelblue')\nplt.ylabel('Portadora')\nplots.append(plot_carrier)\n\nplot_m_analog = plt.subplot(5, 1, 2)\nplt.plot(t, m_analog,color='red')\nplt.ylabel('Analogica')\nplots.append(plot_m_analog)\n\nplot_fm = plt.subplot(5, 1, 3)\nplt.plot(t, fm, color='green')\nplt.ylabel('FM')\nplots.append(plot_fm)\n\nplot_m_digital = plt.subplot(5, 1, 4)\nplt.plot(t, m_digital, color='red')\nplt.ylabel('Digital')\nplots.append(plot_m_digital)\n\nplot_fsk = plt.subplot(5, 1, 5)\nplt.plot(t, fsk, color='green')\nplt.ylabel('FSK')\nplots.append(plot_fsk)\n\n\nfor plot in plots:\n plot.axes.get_xaxis().set_ticks([])\n plot.axes.get_yaxis().set_ticks([])\n\nplt.show()","sub_path":"fsk_plot.py","file_name":"fsk_plot.py","file_ext":"py","file_size_in_byte":1064,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"525286158","text":"#!/usr/bin/env python\n\nimport rospy\nfrom geometry_msgs.msg import Twist\nfrom turtlesim.msg import Pose\nimport math\nimport time\nfrom std_srvs.srv import Empty\n\nx=0\ny=0\nyaw=0\n\ndef poseCallback(pose_message):\n global x\n global y, yaw\n x= pose_message.x\n y= pose_message.y\n yaw = pose_message.theta\n\n #print \"pose callback\"\n #print ('x = {}'.format(pose_message.x)) #new in python 3\n #print ('y = %f' %pose_message.y) #used in python 2\n #print ('yaw = {}'.format(pose_message.theta)) #new in python 3\n\n\ndef move(speed, distance, is_forward):\n #declare a Twist message to send velocity commands\n velocity_message = Twist()\n #get current location\n global x, y\n x0=x\n y0=y\n\n if is_forward:\n velocity_message.linear.x =abs(speed)\n else:\n velocity_message.linear.x =-abs(speed)\n\n distance_moved = 0.0\n loop_rate = rospy.Rate(10) # we publish the velocity at 10 Hz (10 times a second)\n cmd_vel_topic='/turtle1/cmd_vel'\n velocity_publisher = rospy.Publisher(cmd_vel_topic, Twist, queue_size=10)\n\n while True :\n rospy.loginfo(\"Turtlesim moves forwards\")\n velocity_publisher.publish(velocity_message)\n\n loop_rate.sleep()\n\n #rospy.Duration(1.0)\n\n distance_moved = abs(0.5 * math.sqrt(((x-x0) ** 2) + ((y-y0) ** 2)))\n print (distance_moved)\n if not (distance_moved(x0+distance+0.25)) and (y 1)\n new_np_img = np.ones(side_len * side_len * c).reshape(side_len, \n side_len, c) * 100\n\n if h > w:\n for i in range(c):\n # Multiply by 255 because plt read in as vals 0-1, not 0-255\n old_patch = np_img[:, :, i] * 255\n pad = (side_len - w)//2\n new_np_img[:, pad:(pad + w), i] = old_patch\n plt.imsave(new_img_file, new_np_img)\n elif w > h:\n for i in range(c):\n old_patch = np_img[:, :, i] * 255\n pad = (side_len - h)//2\n new_np_img[pad:(pad + h), :, i] = old_patch\n plt.imsave(new_img_file, new_np_img)\n else:\n # Image already square - lucky!\n plt.imsave(new_img_file, np_img)\n\n\ndef main():\n \"\"\"Loops through all image in given input directory. Reads\n each file in a try/except block in the instance the file\n is not an image (it is then skipped).\n\n Calls a function to add padding to square up the image\n and saves it to a new file.\n \"\"\"\n\n # Just grab all files - we'll use try/except to filter\n images = glob.glob(os.path.join(args.input_dir, '*.*'))\n if not os.path.exists(args.output_dir):\n os.makedirs(args.output_dir)\n for img_file in images:\n print(img_file)\n try:\n np_img = plt.imread(img_file)\n print(np_img.shape)\n img_name = img_file.split(os.sep)[-1]\n new_img_file = os.path.join(args.output_dir, img_name)\n pad_image(np_img, new_img_file)\n except Exception as e:\n print('Warning: {}. Skpping file.'.format(e))\n continue\n\nif __name__ == '__main__':\n main()\n \n\n\n\n","sub_path":"scripts/padding.py","file_name":"padding.py","file_ext":"py","file_size_in_byte":2918,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"125187182","text":"from pathlib import Path\n\nimport numpy as np\nimport numpy.lib.recfunctions\n\nimport cv2\n\nNANOSECONDS_IN_SECOND = 10**9\n\nclass MARSMeasurementsReader:\n \"\"\"\n Reads measurements coming from the 'MARS logger' app.\n Currently, doesn't include images ('movie.mp4').\n \"\"\"\n def __init__(self, path):\n \"\"\"\n path:\n pathlib.Path or str\n A directory with 'frame_timestamps.txt', 'gyro_accel.csv', 'movie_metadata.csv', 'movie.mp4'.\n \"\"\"\n self.path = Path(path)\n\n ############################## Read IMU data ##############################\n\n dtype = np.dtype([\n ('time', np.int64),\n ('Wx', np.float64),\n ('Wy', np.float64),\n ('Wz', np.float64),\n ('Ax', np.float64),\n ('Ay', np.float64),\n ('Az', np.float64),\n ])\n IMU_data = np.loadtxt(self.path / \"gyro_accel.csv\", dtype=dtype, delimiter=',', skiprows=1)\n\n # Time in nanoseconds\n self.timestamps_IMU = IMU_data['time']\n # Acceleration along x,y,z (in body frame) in m/s^2\n self.accelerometer = np.lib.recfunctions.structured_to_unstructured(IMU_data[['Ax', 'Ay', 'Az']])\n # Angular velocity around x,y,z (in body frame) in rad/s\n self.gyroscope = np.lib.recfunctions.structured_to_unstructured(IMU_data[['Wx', 'Wy', 'Wz']])\n\n ############################## Read image metadata ##############################\n\n dtype = np.dtype([\n ('time', np.int64),\n ('fx', np.float64),\n ('fy', np.float64),\n ])\n camera_data = np.loadtxt(self.path / \"movie_metadata.csv\", dtype=dtype, delimiter=',', skiprows=1, usecols=(0,1,2))\n\n # Time in nanoseconds (scale is consistent with `self.timestamps_IMU`, but timestamps aren't)\n self.timestamps_camera = camera_data['time']\n self.camera_focal_length = np.lib.recfunctions.structured_to_unstructured(camera_data[['fx', 'fy']])\n\n ############################# Check video for frame size ##############################\n\n ok, sample_frame = cv2.VideoCapture(str(self.path / \"movie.mp4\")).read()\n assert ok\n self.DOWNSCALING = 2.0 # not used yet\n self.IMAGE_SIZE = (sample_frame.shape[1] / self.DOWNSCALING, sample_frame.shape[0] / self.DOWNSCALING)\n\n def get_dt_IMU(self, i):\n \"\"\"\n i:\n int\n Index of the IMU measurement in `self.timestamps_IMU`.\n\n return:\n float\n Time in seconds between i-th and (i+1)-th IMU measurements\n \"\"\"\n if i > len(self.timestamps_IMU) - 2 or i < 0:\n return 1e-16\n else:\n return (self.timestamps_IMU[i+1] - self.timestamps_IMU[i]) / NANOSECONDS_IN_SECOND\n\n def camera_intrinsics(self, i):\n \"\"\"\n i:\n int\n Index of the camera frame in `self.timestamps_camera`.\n\n return:\n gtsam.Cal3DS2\n \"\"\"\n return gtsam.Cal3DS2(\n *self.camera_focal_length[i], # focal length\n 0, # skew\n IMAGE_SIZE[0] / 2, IMAGE_SIZE[1] / 2, # principal point\n 0.25669783, -1.40364704, # distortion for Huawei Honor 10: k1, k2\n -0.00332119, 0.00333635 # distortion for Huawei Honor 10: p1, p2\n )\n\n def get_video_reader(self):\n \"\"\"\n return:\n generator, yields np.ndarray\n \"\"\"\n def generator(video_reader, scaling_factor):\n while video_reader.grab():\n ok, image = video_reader.retrieve()\n assert ok\n image = cv2.resize(\n image, (0, 0), fx=scaling_factor, fy=scaling_factor,\n interpolation=cv2.INTER_AREA)\n\n yield np.rot90(image).copy()\n\n video_reader = cv2.VideoCapture(str(self.path / \"movie.mp4\"))\n return generator(video_reader, 1 / self.DOWNSCALING)\n","sub_path":"measurements_reader.py","file_name":"measurements_reader.py","file_ext":"py","file_size_in_byte":4042,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"531412705","text":"from copy import deepcopy\r\n\r\nimport requests\r\n\r\nfrom init_service import root_dir, nice_json\r\nfrom flask import Flask, request, abort, jsonify\r\nimport json\r\nfrom flask_sqlalchemy import SQLAlchemy\r\nfrom werkzeug.exceptions import NotFound\r\n\r\n\r\napp = Flask(__name__)\r\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///cbookings.db'\r\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\r\n\r\ndb = SQLAlchemy(app)\r\n\r\nclass Bookings(db.Model):\r\n id = db.Column(db.Integer,primary_key=True,nullable=False,autoincrement=True)\r\n user_id = db.Column(db.String(120),nullable=False)\r\n showtime_id = db.Column(db.Integer,nullable=False)\r\n movie_id = db.Column(db.String(120),nullable=False)\r\n\r\ndef fillsqlitetable():\r\n bookings = Bookings.query.all()\r\n for booking in bookings:\r\n db.session.delete(booking)\r\n db.session.commit()\r\n jsondb = json.load(open('../database/bookings.json','r'))\r\n for user_id in jsondb.keys():\r\n for showtime_id in jsondb[user_id].keys():\r\n for movie_id in jsondb[user_id][showtime_id]:\r\n booking = Bookings(user_id=user_id,showtime_id=showtime_id,movie_id=movie_id)\r\n db.session.add(booking)\r\n db.session.commit()\r\n\r\n# with open(\"{}/database/bookings.json\".format(root_dir()), \"r\") as f:\r\n# bookings = json.load(f)\r\n\r\ndef get_bookings():\r\n response = dict()\r\n users = requests.get(\"http://localhost:5000/users\")\r\n showtimes = requests.get(\"http://localhost:5002/showtimes\")\r\n showtimes = showtimes.json()\r\n users = users.json()\r\n for user in users.iterkeys():\r\n _object = dict()\r\n for showtime in showtimes.iterkeys():\r\n bookings = Bookings.query.filter_by(user_id=user, showtime_id=showtime).all()\r\n movie_ids = [booking.movie_id for booking in bookings]\r\n if not movie_ids == []:\r\n _object[showtime] = movie_ids\r\n if not _object == {}:\r\n response[user] = _object\r\n return response\r\n\r\ndef delete_booking_by_id(user_id,showtime_id,movie_id):\r\n booking = Bookings.query.filter_by(user_id=user_id,showtime_id=showtime_id,movie_id=movie_id).first()\r\n db.session.delete(booking)\r\n db.session.commit()\r\n return get_bookings()\r\n\r\ndef add_booking(user_id,showtime_id,movie_id):\r\n booking = Bookings(user_id=user_id,showtime_id=showtime_id,movie_id=movie_id)\r\n db.session.add(booking)\r\n db.session.commit()\r\n\r\n@app.errorhandler(404)\r\ndef not_found(error=None):\r\n message = {\r\n 'status': 404,\r\n 'message': 'Not Found: ' + request.url,\r\n }\r\n resp = jsonify(message)\r\n resp.status_code = 404\r\n\r\n return resp\r\n\r\n@app.route(\"/\", methods=['GET'])\r\ndef hello():\r\n return nice_json({\r\n \"uri\": \"/\",\r\n \"subresource_uris\": {\r\n \"bookings\": \"/bookings\",\r\n \"booking\": \"/bookings/\"\r\n }\r\n })\r\n\r\n\r\n@app.route(\"/bookings\", methods=['GET'])\r\ndef booking_list():\r\n return nice_json(get_bookings())\r\n\r\n\r\n@app.route(\"/bookings/\", methods=['GET'])\r\ndef booking_record(username):\r\n if username not in get_bookings().iterkeys():\r\n return not_found()\r\n\r\n return nice_json(get_bookings()[username])\r\n\r\n@app.route(\"/bookings\",methods=['DELETE'])\r\ndef delete_user_booking():\r\n _user_id,_showtime_id,_movie_id = request.form['userid'],request.form['showtimeid'],request.form['movieid']\r\n if _user_id == None and _showtime_id == None and _movie_id == None:\r\n raise NotFound\r\n else:\r\n delete_booking_by_id(_user_id,_showtime_id,_movie_id)\r\n return nice_json(get_bookings())\r\n\r\n@app.route(\"/bookings\",methods=['POST'])\r\ndef add_user_booking():\r\n movie_id = request.form['movieid']\r\n user_id = request.form['userid']\r\n showtime_id = request.form['showtimeid']\r\n\r\n add_booking(user_id,showtime_id,movie_id)\r\n return nice_json(get_bookings())\r\n\r\n\r\nif __name__ == \"__main__\":\r\n # Only run below commands to initialize database from scratch\r\n # db.create_all()\r\n # fillsqlitetable()\r\n app.run(port=5003, debug=True)\r\n\r\n","sub_path":"services/bookings.py","file_name":"bookings.py","file_ext":"py","file_size_in_byte":4079,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"293783445","text":"from django.urls import path\nfrom . import views\nfrom django.contrib.auth import views as authviews\n\n\nurlpatterns = [path('new/', views.CreateUser.as_view(), name='signup'),\n path('settings/', views.Create_blog, name='blog_settings'),\n path('profile/', views.ShowsUserPage, name=\"user_page\"),\n path('profile/edit/', views.ChangeUserData, name=\"user_cutomize\"),\n path('profile/show/', views.GetUserData, name='show_user'),\n path('blog/delete//',\n views.Delete_blog, name='blog_delete'),\n path('login/', views.Account_login.as_view(), name='login'),\n path('logout/',\n authviews.LogoutView.as_view(next_page='/'), name='logout'),\n path('reject/', views.Reject_user, name='reject'),\n ]\n","sub_path":"accounts/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":854,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"189947348","text":"import json\nimport threading\nfrom django.conf import settings\nfrom kazoo.client import KazooClient\nfrom kazoo.security import make_digest_acl\n\n\nclass Zooconf:\n\tconnection = None\n\twebService = None\n\tapplicationService = None\n\tstorageServicesList = None\n\tauthenticationServiceList = None\n\tstatus = None\n\n\tdef initialize(self):\n\t\tself.__zooConnect()\n\t\tself.__publishService()\n\t\tself.__initAuthenticationServiceWatches()\n\t\tself.__initStorageServiceWatches()\n\t\tself.heartbeat()\n\n\tdef __zooConnect(self):\n\t\tprint(\"Connecting to ZooKeeper\")\n\t\tself.connection = KazooClient(hosts=settings.ZOOKEEPER_HOST)\n\t\tself.connection.start()\n\t\tdigest_auth = \"%s:%s\" % (settings.ZOOKEEPER_USER, settings.ZOOKEEPER_PASSWORD)\n\t\tself.connection.add_auth(\"digest\", digest_auth)\n\n\tdef __publishService(self):\n\t\tacl = make_digest_acl(settings.ZOOKEEPER_USER, settings.ZOOKEEPER_PASSWORD, all=True)\n\t\tdataJsonDict = {\n\t\t\t'SERVER_HOSTNAME': settings.SERVER_HOSTNAME,\n\t\t\t'SERVER_PORT': settings.SERVER_PORT,\n\t\t\t'SHARED_KEY_BASE_64': settings.SHARED_KEY_BASE_64,\n\t\t\t'CHILDREN': []\n\t\t}\n\t\tif settings.ZOOKEEPER_PATH_TO_NODE == 'Auth/':\n\t\t\tdataJsonDict['AUTH_SYSTEM'] = settings.AUTH_SYSTEM\n\t\tif self.connection.exists(path=settings.ZOOKEEPER_ROOT + settings.ZOOKEEPER_PATH_TO_NODE + settings.ZOOKEEPER_NODE_ID):\n\t\t\tself.connection.set(\n\t\t\t\tpath=settings.ZOOKEEPER_ROOT + settings.ZOOKEEPER_PATH_TO_NODE + settings.ZOOKEEPER_NODE_ID,\n\t\t\t\tvalue=json.JSONEncoder().encode(dataJsonDict).encode()\n\t\t\t)\n\t\telse:\n\t\t\tself.connection.create_async(\n\t\t\t\tpath=settings.ZOOKEEPER_ROOT + settings.ZOOKEEPER_PATH_TO_NODE + settings.ZOOKEEPER_NODE_ID,\n\t\t\t\tvalue=json.JSONEncoder().encode(dataJsonDict).encode(),\n\t\t\t\tephemeral=settings.ZOOKEEPER_NODE_EPHIMERAL\n\t\t\t)\n\t\tif settings.ZOOKEEPER_PATH_TO_NODE != '':\n\t\t\tdata, stat = self.connection.get(settings.ZOOKEEPER_ROOT + settings.ZOOKEEPER_PATH_TO_NODE)\n\t\t\tdataJsonDict = json.loads(data.decode(\"utf-8\"))\n\t\t\tif settings.ZOOKEEPER_NODE_ID not in dataJsonDict['CHILDREN']:\n\t\t\t\tdataJsonDict['CHILDREN'].append(settings.ZOOKEEPER_NODE_ID)\n\t\t\tself.connection.set(\n\t\t\t\tpath=settings.ZOOKEEPER_ROOT + settings.ZOOKEEPER_PATH_TO_NODE,\n\t\t\t\tvalue=json.JSONEncoder().encode(dataJsonDict).encode()\n\t\t\t)\n\n\tdef __initStorageServiceWatches(self):\n\t\tself.storageServicesList = []\n\n\t\t@self.connection.ChildrenWatch(settings.ZOOKEEPER_ROOT + \"StorageServices\")\n\t\tdef watch_children(children):\n\t\t\tprint(self.readTree())\n\t\t\tif settings.ZOOKEEPER_PATH_TO_NODE == \"StorageServices/\":\n\t\t\t\tnode = {\n\t\t\t\t\t'SERVER_HOSTNAME': settings.SERVER_HOSTNAME,\n\t\t\t\t\t'SERVER_PORT': settings.SERVER_PORT,\n\t\t\t\t\t'CHILDREN': []\n\t\t\t\t}\n\t\t\t\tif node not in self.storageServicesList:\n\t\t\t\t\tself.__publishService()\n\n\tdef __initAuthenticationServiceWatches(self):\n\t\tself.authenticationServiceList = []\n\n\t\t@self.connection.ChildrenWatch(settings.ZOOKEEPER_ROOT + \"Auth\")\n\t\tdef watch_children(children):\n\t\t\tprint(self.readTree())\n\t\t\tif settings.ZOOKEEPER_PATH_TO_NODE == \"Auth/\":\n\t\t\t\tnode = {\n\t\t\t\t\t'SERVER_HOSTNAME': settings.SERVER_HOSTNAME,\n\t\t\t\t\t'SERVER_PORT': settings.SERVER_PORT,\n\t\t\t\t\t'SHARED_KEY_BASE_64': settings.SHARED_KEY_BASE_64,\n\t\t\t\t\t'CHILDREN': [],\n\t\t\t\t\t'AUTH_SYSTEM': settings.AUTH_SYSTEM\n\t\t\t\t}\n\t\t\t\tif node not in self.authenticationServiceList:\n\t\t\t\t\tself.__publishService()\n\n\tdef getAvailableFs(self): return self.storageServicesList\n\n\tdef getZooConnection(self): return self.connection\n\n\tdef readTree(self):\n\t\tresult = \"{\"\n\t\trootChildren = self.connection.get_children(settings.ZOOKEEPER_ROOT)\n\t\tfor child in rootChildren:\n\t\t\tdata, stat = self.connection.get(settings.ZOOKEEPER_ROOT + child)\n\t\t\tresult += '\"' + child + '\": ' + data.decode(\"utf-8\") + \",\"\n\t\t\ttry:\n\t\t\t\tgrandchildren = self.connection.get_children(settings.ZOOKEEPER_ROOT + child)\n\t\t\t\tfor grandchild in grandchildren:\n\t\t\t\t\tdata, stat = self.connection.get(settings.ZOOKEEPER_ROOT + child + '/' + grandchild)\n\t\t\t\t\tresult += '\"' + grandchild + '\": ' + data.decode(\"utf-8\") + \",\"\n\t\t\t\tdata, stat = self.connection.get(settings.ZOOKEEPER_ROOT + child + '/')\n\t\t\t\tdataJsonDict = json.loads(data.decode(\"utf-8\"))\n\t\t\t\tdataJsonDict['CHILDREN'] = grandchildren\n\t\t\t\tself.connection.set(\n\t\t\t\t\tpath=settings.ZOOKEEPER_ROOT + child,\n\t\t\t\t\tvalue=json.JSONEncoder().encode(dataJsonDict).encode()\n\t\t\t\t)\n\t\t\texcept Exception:\n\t\t\t\tpass\n\t\tresult = result[:-1] + '}'\n\t\tself.status = json.loads(result)\n\t\tself.initZkTree()\n\t\treturn self.status\n\n\tdef getStatus(self):\n\t\ttry:\n\t\t\treturn self.readTree()\n\t\texcept Exception:\n\t\t\tself.__zooConnect()\n\t\t\treturn self.getStatus()\n\n\tdef getNodeData(self, node):\n\t\ttry:\n\t\t\treturn self.status[node]\n\t\texcept Exception:\n\t\t\treturn {}\n\n\tdef initZkTree(self):\n\t\tserviceData = self.getNodeData('WebService')\n\t\tself.webService = serviceData['SERVER_HOSTNAME'] + ':' + serviceData['SERVER_PORT'] + '/'\n\n\t\tserviceData = self.getNodeData('ApplicationService')\n\t\tself.applicationService = serviceData['SERVER_HOSTNAME'] + ':' + serviceData['SERVER_PORT'] + '/'\n\n\t\tserviceData = self.getNodeData('Auth')\n\t\tself.authenticationServiceList = []\n\t\tfor authService in serviceData['CHILDREN']:\n\t\t\tauthServiceData = self.getNodeData(authService)\n\t\t\tif authServiceData != {}:\n\t\t\t\tbaseUrl = authServiceData['SERVER_HOSTNAME'] + ((':' + authServiceData['SERVER_PORT']) if authServiceData['SERVER_PORT'] != '' else '') + '/'\n\t\t\t\tloginUrl = baseUrl + 'login?system=' + authServiceData['AUTH_SYSTEM'] + '&callback='\n\t\t\t\tregisterUrl = baseUrl + 'register?system=' + authServiceData['AUTH_SYSTEM'] + '&callback='\n\t\t\t\tauthServiceDict = {\n\t\t\t\t\t'name': authService,\n\t\t\t\t\t'url': baseUrl,\n\t\t\t\t\t'loginUrl': loginUrl,\n\t\t\t\t\t'registerUrl': registerUrl,\n\t\t\t\t\t'sharedKey': authServiceData['SHARED_KEY_BASE_64'],\n\t\t\t\t\t'system': authServiceData['AUTH_SYSTEM']\n\t\t\t\t}\n\t\t\t\tself.authenticationServiceList.append(authServiceDict)\n\n\t\tserviceData = self.getNodeData('StorageServices')\n\t\tself.storageServicesList = []\n\t\tfor storageService in serviceData['CHILDREN']:\n\t\t\tstorageServiceData = self.getNodeData(storageService)\n\t\t\tif storageServiceData != {}:\n\t\t\t\tstorageServiceDict = {\n\t\t\t\t\t'name': storageService,\n\t\t\t\t\t'url': storageServiceData['SERVER_HOSTNAME'] + ':' + storageServiceData['SERVER_PORT'] + '/',\n\t\t\t\t\t'sharedKey': storageServiceData['SHARED_KEY_BASE_64'],\n\t\t\t\t}\n\t\t\t\tself.storageServicesList.append(storageServiceDict)\n\n\tdef heartbeat(self):\n\t\tthreading.Timer(300.0, self.heartbeat).start()\n\t\tprint(\"Heartbeat\")\n\t\tprint(str(self.getStatus()))\n\n\nzk = Zooconf()\n","sub_path":"WebService/WebService/zoo.py","file_name":"zoo.py","file_ext":"py","file_size_in_byte":6368,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"573607186","text":"from django.test import TestCase\nfrom django.urls import resolve\nfrom django.core.urlresolvers import reverse\n\nimport datetime\n\nfrom . models import Movie, Preformer, Category, MovieWatchList\nfrom . views import home, movies_detail, movie_watch_list\nfrom . forms import NewMovieWatchListForm\n\nclass HomePageTests(TestCase):\n def setUp(self):\n Movie.objects.create(movie_title=\"Test movie\", description=\"testing 123\")\n\n def test_home_view_staus_code(self):\n \"\"\"\n :ac: Returns a status 302 (Found).\n \"\"\"\n url = reverse(\"home\")\n resp = self.client.get(url)\n self.assertEqual(resp.status_code, 302)\n\nclass MoviesRedirectTest(TestCase):\n def setUp(self):\n self.movie = Movie.objects.create(movie_title=\"Test movie\", description=\"testing 123\")\n # self.list = MovieWatchList.objects.create(movie_list_name=\"test_list\", created_by=\"test Person\")\n self.url = reverse(\"movie_list\")\n self.response = self.client.get(self.url)\n\n def test_movies_view_status_code(self):\n \"\"\"\n :ac: Returns a status 200\n \"\"\"\n self.assertEqual(self.response.status_code, 200)\n\n\n def test_create_movie_watch_list_contains_link_to_movie_watch_list(self):\n \"\"\"\n :ac: Test if link between movie list has working link to detail page.\n \"\"\"\n movie_url = reverse(\n 'movies_detail',\n kwargs={'pk': self.movie.pk}\n )\n self.assertContains(self.response, 'href=\"{0}\"'.format(movie_url))\n\n\nclass MovieDetailsViewTests(TestCase):\n def setUp(self):\n self.movie = Movie.objects.create(\n movie_title=\"Thor: Rangnarok\",\n description=\"Marvel studios, rated M\",\n )\n\n self.actor = Preformer.objects.create(\n name=\"Chris Hemsworth\",\n description=\"He plays Thor\",\n # movies=self.movie\n )\n self.actor.movies.add(self.movie) # the parent model must be saved first before adding a m2m\n\n\n def test_movie_detail_status_code(self):\n \"\"\"\n :ac: Returns a status 200\n \"\"\"\n url = reverse(\"movies_detail\", kwargs={\"pk\": self.movie.pk})\n resp = self.client.get(url)\n self.assertEqual(resp.status_code, 200)\n\n def test_movie_not_found_status_code(self):\n \"\"\"\n :ac: Returns a 404, if movie doesn't exist\n \"\"\"\n url = reverse(\"movies_detail\", kwargs={\"pk\": 99})\n resp = self.client.get(url)\n self.assertEqual(resp.status_code, 404)\n\n def test_movie_details_resolves_detail_view(self):\n test_movie_detail = resolve(\"/movies/1/\")\n self.assertEqual(test_movie_detail.func, movies_detail)\n\n def test_movie_detail_contains_information_on_movie(self):\n \"\"\"\n :ac: Can successfully pull details for a given movie\n \"\"\"\n url = reverse(\"movies_detail\", kwargs={\"pk\": self.movie.pk})\n resp = self.client.get(url)\n self.assertEqual(resp.status_code, 200)\n\n def test_create_movie_watch_list_contains_link_to_movie_watch_list(self):\n movie_detail_url = reverse(\n 'movies_detail',\n kwargs={'pk': self.movie.pk}\n )\n resp = self.client.get(movie_detail_url)\n home_url = reverse(\"movie_list\")\n self.assertContains(resp, 'href=\"{0}\"'.format(home_url))\n\n\nclass MovieWatchListTests(TestCase):\n def setUp(self):\n self.movie_list = MovieWatchList.objects.create(\n movie_list_name=\"Felix and Lilyana's favourite movies\",\n created_by=\"A person\"\n )\n\n self.movie = Movie.objects.create(\n movie_title=\"Thor: Rangnarok\",\n description=\"Lilyana and Felix's favourite\",\n recently_added=datetime.date(2018, 1, 1)\n )\n self.movie.movie_lists.add(self.movie_list)\n\n def test_movie_list_exists(self):\n qs = MovieWatchList.objects.all()\n self.assertEqual(len(qs), 1)\n\n def test_csrf(self):\n url = reverse('create_movie_watch_list')\n response = self.client.get(url)\n self.assertContains(response, 'csrfmiddlewaretoken')\n\n def test_new_movie_list_valid_post_data(self):\n url = reverse('create_movie_watch_list')\n data = {\n 'movie_list_name': 'Test title',\n 'created_by': 'A Person',\n }\n resp = self.client.post(url, data)\n self.assertTrue(MovieWatchList.objects.exists())\n self.assertTrue(Movie.objects.exists())\n\n\n def test_contains_form(self):\n \"\"\"\n :ac: Checks there is a form.\n \"\"\"\n url = reverse(\"create_movie_watch_list\")\n resp = self.client.post(url)\n form = resp.context.get(\"form\")\n self.assertIsInstance(form, NewMovieWatchListForm)\n\n def test_new_movie_list_invalid_post_data(self):\n \"\"\"\n Invalid post data should not redirect\n The expected behavior is to show the form again with validation errors\n \"\"\"\n url = reverse('create_movie_watch_list')\n resp = self.client.post(url)\n form = resp.context.get(\"form\")\n self.assertTrue(form.errors)\n self.assertEquals(resp.status_code, 200)\n","sub_path":"moviewatchlist/movies/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":5189,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"126139176","text":"import argparse\nimport logging\nimport numpy as np\nimport torch\nimport torch.nn as nn\n\nfrom tqdm import tqdm\nfrom itertools import islice\nfrom pathlib import Path\n\nfrom torch import optim\nfrom torch.utils.data import DataLoader\nfrom torch.utils.tensorboard import SummaryWriter\n\nfrom vq_vae_text.utils import setup_logging_from_args, save_checkpoint\nfrom vq_vae_text.datasets import LMDBDataset, ByteLevelTextDataset\nfrom vq_vae_text.models.pixelsnail import PixelSNAIL\n\ntry:\n import apex\n from apex import amp\nexcept ImportError:\n amp = None\n\namp = None\n\n\ndef load_data(path, args):\n dataset = LMDBDataset(path)\n data_loader = DataLoader(dataset, batch_size=args.batch_size, shuffle=True)\n return data_loader\n\n\ndef log_summary(summary_writer, prefix, tag_value_pairs, step):\n for tag, val in tag_value_pairs:\n summary_writer.add_scalar(prefix + tag, val, global_step=step)\n\n\ndef compute_accuracy(x, x_hat):\n correct = (x == x_hat).float()\n accuracy = correct.sum() / x.numel()\n return accuracy\n\n\ndef train(model, optimizer, scheduler, criterion, data_loader, target_shape, summary_writer, device, epoch):\n model.train()\n\n if args.steps_per_epoch is not None:\n data_loader = islice(data_loader, 0, args.steps_per_epoch)\n pbar = tqdm(data_loader, total=args.steps_per_epoch)\n else:\n pbar = tqdm(data_loader)\n\n for idx, batch in enumerate(pbar):\n model.zero_grad()\n\n batch = batch.view(batch.size(0), *target_shape).to(device)\n\n out, _ = model(batch)\n\n loss = criterion(out, batch)\n\n if amp is not None:\n with amp.scale_loss(loss, optimizer) as scaled_loss:\n scaled_loss.backward()\n else:\n loss.backward()\n\n optimizer.step()\n\n if scheduler is not None:\n scheduler.step()\n\n accuracy = compute_accuracy(batch, out.argmax(1))\n\n lr = optimizer.param_groups[0]['lr']\n\n pbar.set_description(f'epoch: {epoch + 1}; loss: {loss.item():.5f}; '\n f'acc: {accuracy:.5f}; lr: {lr:.5f}')\n\n #step = optimizer.state[optimizer.param_groups[0]['params'][0]]['step']\n #log_summary(summary_writer, 'train_', zip(['loss', 'acc', 'lr'], [loss, accuracy, lr]), step=step)\n\n\n@torch.no_grad()\ndef evaluate(model, criterion, data_loader, target_shape, summary_writer, device, epoch):\n model.eval()\n\n loss_accum = []\n acc_accum = []\n\n for batch in data_loader:\n batch = batch.view(batch.size(0), *target_shape).to(device)\n\n out, _ = model(batch)\n loss = criterion(out, batch)\n\n accuracy = compute_accuracy(batch, out.argmax(1))\n\n loss_accum.append(loss.item())\n acc_accum.append(accuracy.item())\n\n loss = np.mean(loss_accum)\n acc = np.mean(acc_accum)\n\n log_summary(summary_writer, 'val_', zip(['loss', 'acc'], [loss, acc]), step=epoch)\n\n\n@torch.no_grad()\ndef sample_latents(latent_predictor, device, size, num, temperature):\n row = torch.zeros(num, *size, dtype=torch.int64).to(device)\n cache = {}\n\n for i in tqdm(range(size[0])):\n for j in range(size[1]):\n out, cache = latent_predictor(row[:, : i + 1, :], cache=cache)\n prob = torch.softmax(out[:, :, i, j] / temperature, 1)\n sample = torch.multinomial(prob, 1).squeeze(-1)\n row[:, i, j] = sample\n\n return row\n\n\n@torch.no_grad()\ndef sample(latent_predictor, ae_model, device, latent_size, num, temperature):\n latents = sample_latents(latent_predictor, device, latent_size, num, temperature)\n latents = latents.view(latents.size(0), -1)\n\n pred = ae_model.decode_codes(latents).cpu()\n pred = pred.argmax(-1)\n texts = ByteLevelTextDataset.seq_to_text(pred)\n\n return texts\n\n\ndef save_samples(samples, epoch, result_dir):\n samples_dir = Path(result_dir) / 'samples'\n samples_dir.mkdir(exist_ok=True)\n\n file = samples_dir / f'samples_{epoch}.txt'\n file.write_text('\\n\\n'.join(samples))\n\n\ndef main(args):\n if args.device:\n device = args.device\n else:\n device = 'cuda' if torch.cuda.is_available() else 'cpu'\n\n if args.data_parallel and amp is not None:\n logging.info('Using APEX Distributed Data Parallel training')\n torch.cuda.set_device(args.local_rank)\n torch.distributed.init_process_group(backend='nccl',\n init_method='env://')\n\n save_path = setup_logging_from_args(args)\n\n logging.info(f'Using device {device}')\n\n logging.info('Loading data')\n\n train_gen = load_data(args.train_data, args)\n if args.val_data:\n val_gen = load_data(args.val_data, args)\n else:\n val_gen = None\n\n if args.debug:\n logging.info('Debug mode active')\n train_gen = [next(iter(train_gen))]\n if val_gen:\n val_gen = [next(iter(val_gen))]\n\n logging.info('Preparing model')\n\n shape = list(eval(args.latents_shape))\n model_args = dict(\n shape=shape,\n n_class=512,\n embed_dim=64,\n channel=128,\n kernel_size=5,\n n_block=2,\n n_res_block=2,\n res_channel=128\n )\n if args.model_args:\n model_args.update(dict(eval(args.model_args)))\n\n model = PixelSNAIL(**model_args).to(device)\n\n optimizer = optim.SGD(model.parameters(), lr=args.lr)\n if args.sched == 'cycle':\n scheduler = optim.lr_scheduler.CyclicLR(\n optimizer,\n mode='triangular2',\n base_lr=args.lr / 100.0,\n max_lr=args.lr,\n gamma=args.gamma,\n step_size_up=10000,\n cycle_momentum=False\n )\n elif args.sched == '1cycle':\n scheduler = optim.lr_scheduler.OneCycleLR(optimizer, max_lr=5.0, total_steps=50000, div_factor=100, final_div_factor=100000)\n else:\n scheduler = optim.lr_scheduler.ExponentialLR(\n optimizer,\n gamma=args.gamma\n )\n\n if amp is not None:\n logging.info('Using AMP for training')\n model, optimizer = amp.initialize(model, optimizer, opt_level=args.amp)\n\n if args.data_parallel:\n if amp is not None:\n logging.info('Using APEX Distributed Data Parallel training')\n model = apex.parallel.DistributedDataParallel(model)\n else:\n logging.info('Using PyTorch Data Parallel training')\n model = nn.DataParallel(model)\n\n ae_model = None\n if args.ae_model_checkpoint:\n logging.info(f'Loading autoencoder model checkpoint from {args.ae_model_checkpoint}')\n ae_model = torch.load(args.ae_model_checkpoint).to(device)\n ae_model.eval()\n\n criterion = nn.CrossEntropyLoss()\n\n summary_writer = SummaryWriter(save_path)\n\n try:\n samples = sample(model, ae_model, device, latent_size=shape, num=args.samples_per_epoch, temperature=1.0)\n save_samples(samples, 0, save_path)\n\n for epoch in range(args.epochs):\n logging.info(f'Epoch {epoch + 1}')\n\n train(model, optimizer, scheduler, criterion, train_gen, shape, summary_writer, device, epoch)\n\n if val_gen:\n evaluate(model, criterion, val_gen, shape, summary_writer, device, epoch)\n\n #if scheduler:\n # scheduler.step()\n\n if ae_model:\n logging.info('Sampling')\n\n samples = sample(model, ae_model, device, latent_size=shape, num=args.samples_per_epoch, temperature=1.0)\n save_samples(samples, epoch + 1, save_path)\n\n summary_writer.add_text('samples', '\\n\\n'.join(samples), global_step=epoch)\n\n if not args.debug:\n save_checkpoint(model, epoch, save_path)\n except KeyboardInterrupt:\n logging.info('Gracefully interrupting training')\n\n summary_writer.close()\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument('--model-name', type=str, default='pixelsnail', dest='model')\n parser.add_argument('--device', type=str)\n parser.add_argument('--model-args', type=str)\n parser.add_argument('--latents-shape', type=str, required=True)\n parser.add_argument('--ae-model-checkpoint', type=str)\n parser.add_argument('--samples-per-epoch', type=int, default=16)\n parser.add_argument('--train-data', type=str, required=True)\n parser.add_argument('--val-data', type=str)\n parser.add_argument('--epochs', type=int, default=100)\n parser.add_argument('--steps-per-epoch', type=int, default=None)\n parser.add_argument('--lr', type=float, default=3e-4)\n parser.add_argument('--batch-size', type=int, default=32)\n parser.add_argument('--data-parallel', action='store_true')\n parser.add_argument('--gamma', type=float, default=0.95,\n help='Learning rate decay gamma.')\n parser.add_argument('--sched', choices=['cycle', '1cycle'], default=None)\n parser.add_argument('--amp', type=str, default='O0')\n parser.add_argument('--local_rank', type=int, default=0)\n parser.add_argument('--debug', action='store_true')\n args = parser.parse_args()\n\n main(args)\n","sub_path":"legacy/vq_vae_text/train_pixelsnail.py","file_name":"train_pixelsnail.py","file_ext":"py","file_size_in_byte":9116,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"428030265","text":"import tensorflow as tf\nimport numpy as np\nimport glob\nimport os\nfrom SAD_parameters import *\n\nclass Data:\n def __init__(self, inp_shape=INPUT_SHAPE, feat_dim=FEAT_DIM, batch_size=BATCH_SIZE, val_size=NUM_VAL_SAMPLES, \n speech_dp_train=DATA_PATH_SP_TRAIN, non_speech_dp_train=DATA_PATH_NS_TRAIN,\n speech_dp_val=DATA_PATH_SP_VAL, non_speech_dp_val=DATA_PATH_NS_VAL,\n speech_dp_test=DATA_PATH_SP_TEST, non_speech_dp_test=DATA_PATH_NS_TEST):\n self.inp_shape = inp_shape\n self.feat_dim = feat_dim\n self.batch_size = batch_size\n self.val_size = val_size\n self.data_paths_sp_train = speech_dp_train\n self.data_paths_ns_train = non_speech_dp_train\n self.data_paths_sp_val = speech_dp_val\n self.data_paths_ns_val = non_speech_dp_val\n self.data_paths_sp_test = speech_dp_test\n self.data_paths_ns_test = non_speech_dp_test\n self.data = {}\n self.build_datasets()\n \n def feature_parser(self, utt, label):\n context_features = {'feature_id': tf.FixedLenFeature([], tf.string)}\n sequence_features = {'log_mels': tf.FixedLenSequenceFeature([], tf.string)}\n [utt_id, features_raw] = tf.parse_single_sequence_example(utt,\n context_features=context_features, \n sequence_features=sequence_features)\n features = tf.reshape(tf.decode_raw(features_raw['log_mels'],tf.float32), self.feat_dim)\n return utt_id['feature_id'], features, np.eye(2)[label]\n \n def create_TFRDataset(self, path_to_tfrecords, mode, label):\n files = glob.glob(os.path.join(path_to_tfrecords, '*tfrecord'))\n dataset = tf.data.TFRecordDataset(files)\n dataset = dataset.map(lambda x: self.feature_parser(x, 2*[label]))\n if mode == 'train':\n dataset = dataset.shuffle(self.batch_size)\n dataset = dataset.repeat()\n dataset = dataset.batch(batch_size=int(self.batch_size/4))\n dataset = dataset.prefetch(buffer_size=self.batch_size)\n elif mode == 'val':\n dataset = dataset.batch(batch_size=self.val_size)\n elif mode == 'test':\n dataset = dataset.batch(self.batch_size/2)\n dataset_iterator = dataset.make_one_shot_iterator()\n return dataset, dataset_iterator\n \n def normalize_batch(self, data):\n mean, var = tf.nn.moments(data, axes=[0,1,2])\n norm_data = (data - mean) / tf.sqrt(var + 1e-8)\n return norm_data\n \n def concatenate_and_normalize_batch(self, sp_itr, ns_itr):\n _, X_sp, y_sp = sp_itr.get_next()\n _, X_ns, y_ns = ns_itr.get_next()\n X_batch = tf.concat((X_sp, X_ns), axis=0)\n X_batch = self.normalize_batch(X_batch)\n X_batch = tf.reshape(X_batch, tf.concat(([-1], self.inp_shape), axis=0))\n y_batch = tf.concat((y_sp, y_ns), axis=0)\n y_batch = tf.reshape(tf.cast(y_batch, tf.int32), [-1, 2])\n return X_batch, y_batch\n \n def generate_test_batch(self, sp_ds, ns_ds):\n dataset = sp_ds.concatenate(ns_ds)\n iterator = dataset.make_one_shot_iterator()\n _, X_test, y_test = iterator.get_next()\n X_test = self.normalize_batch(X_test)\n X_test = tf.reshape(X_test, tf.concat(([-1], self.inp_shape), axis=0))\n y_test = tf.reshape(tf.cast(y_test, tf.int32), [-1, 2])\n return X_test, y_test\n \n def build_datasets(self): \n _, self.data['speech_train'] = self.create_TFRDataset(self.data_paths_sp_train, 'train', 1)\n _, self.data['non_speech_train'] = self.create_TFRDataset(self.data_paths_ns_train, 'train', 0)\n _, self.data['speech_val'] = self.create_TFRDataset(self.data_paths_sp_val, 'val', 1)\n _, self.data['non_speech_val'] = self.create_TFRDataset(self.data_paths_ns_val, 'val', 0)\n self.data['speech_test'], _ = self.create_TFRDataset(self.data_paths_sp_test, 'test', 1)\n self.data['non_speech_test'], _ = self.create_TFRDataset(self.data_paths_ns_test, 'test', 0)\n [self.X_batch, self.y_batch] = self.concatenate_and_normalize_batch(self.data['speech_train'], self.data['non_speech_train'])\n [self.X_val, self.y_val] = self.concatenate_and_normalize_batch(self.data['speech_val'], self.data['non_speech_val']) \n [self.X_test, self.y_test] = self.generate_test_batch(self.data['speech_test'], self.data['non_speech_test'])\n","sub_path":"data_loader.py","file_name":"data_loader.py","file_ext":"py","file_size_in_byte":4440,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"277196597","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.stats import linregress\n\n\nfilename = \"final_output.dat\"\n\n# Tokens to extract energies from Dice output\nvar_string = \" 0 -\"\npt_string = \"PTEnergy\"\n\n# Read file\nwith open(filename, \"r\") as f:\n lines = f.readlines()\n\n# Extract energies\nvar_energies = []\ntot_energies = []\ntarget_error = None\n\nfor line in lines:\n if var_string in line:\n var_energy = float(line.split()[1])\n var_energies.append(var_energy)\n\n if pt_string in line and len(line.split()) > 2:\n tot_energy = float(line.split()[1])\n tot_energies.append(tot_energy)\n\n if \"targetError\" in line and len(line.split()) == 2:\n target_error = float(line.split()[-1])\n\n# Convert to np.ndarray\nvar_energies = np.array(var_energies)\ntot_energies = np.array(tot_energies)\npt_correction = tot_energies - var_energies\n\n# Print and check that energies increase monotonically\nheader = \"Data Extracted from {}\".format(filename)\nprint(header + \"\\n\" + \"#\" * len(header) + \"\\n\")\nprint(\"{:11s} {:11s} {:11s}\".format(\"Var.\", \"Total\", \"dPT\"))\nfor i in range(var_energies.size):\n if i > 0:\n if var_energies[i - 1] >= var_energies[i]:\n raise AssertionError(\"Var. energies not increasing!\")\n if tot_energies[i - 1] >= tot_energies[i]:\n raise AssertionError(\"Tot. energies not increasing!\")\n\n print(\n \"{:.6f} {:.6f} {:.6f}\".format(\n var_energies[i], tot_energies[i], pt_correction[i]\n )\n )\n\n# Linear Regression\nslope, intercept, r_value, p_value, std_err = linregress(pt_correction, tot_energies)\nprint(\"R value:\", r_value)\nprint(\"Extrapolated Energy:\", intercept)\n\n# Estimate error\nn_samples = 100\nextrap = np.zeros((pt_correction.size * n_samples))\n\nfor i, en in enumerate(tot_energies):\n # Generate noise\n noise = np.random.normal(0, target_error, n_samples)\n\n # Add noise to the total energy\n tot_plus_noise = tot_energies.copy()\n pt_plus_noise = pt_correction.copy()\n for j, d_noise in enumerate(noise):\n tot_plus_noise[i] = en + d_noise\n pt_plus_noise[i] = pt_correction[i] + d_noise\n extrap[i * n_samples + j] = linregress(pt_plus_noise, tot_plus_noise)[1]\n\n# Print Data\nheader = \"Extrap. Energy Error Analysis (includes noise)\"\nprint(\"\\n\" + header + \"\\n\" + \"#\" * len(header) + \"\\n\")\nprint(\"Energy spread [{:.6f} - {:.6f}] (Ha)\".format(extrap.min(), extrap.max()))\nprint(\n \"Mean Extrap. Energy = {:.6f} +\\- {:.2e} (Ha)\".format(\n np.mean(extrap), np.std(extrap)\n )\n)\n\n\n# Plot Extrapolation\nplt.figure()\nx = np.linspace(pt_correction.min() * 1.1, 0)\ny = x * slope + intercept\nplt.plot(x, y, c=\"r\")\nplt.scatter(pt_correction, tot_energies)\n\nplt.xlabel(\"PT Correction (Ha)\")\nplt.ylabel(\"Total Energy (Ha)\")\nplt.tight_layout()\nplt.savefig(\"extrap.png\", dpi=600)\n\n","sub_path":"pentacene/singlet_geom/1Ag/extrapolate.py","file_name":"extrapolate.py","file_ext":"py","file_size_in_byte":2846,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"308790641","text":"import numpy as np\nimport time\n\n\nfrom _01_params_funct import average_values_params, sigmoid\nfrom _02abc_evolution_funct import evolve_nat, initial_pop\nfrom _03_policy_funct import Policy, save_policy, sample_conf\nfrom _05_optimization import optimize_policy, optimize_policy_stoch, optimize_stoch_decr_uncert, optimize_policy_stoch_evo\nfrom _06b_actions_cont_phys_dist import action_conf, action_conf_deriv, action_conf_deriv_stoch, action_conf_deriv_stoch_previous_policy, action_conf_deriv_stoch_evo, action_conf_avg_stoch_evo, action_conf_stoch_evo\n\n\n\n###############################################################################\n###############################################################################\n#\n# GENERATE CONTINUOUS PHYSICAL DISTANCING POLICY \n#\n###############################################################################\n###############################################################################\n\n\n## offset of the outbreak\noffset = 30.0\n## initial time\nt_0 = 60.0\nt_f = t_0 + 2*365.0\n\n## initial population\nparams = average_values_params()\nx_0 = initial_pop(offset, t_0, params)\n## natural evolution of the disease\nx_nat = evolve_nat(x_0, t_0, t_f, params)\n\n\npol = Policy(t_0, t_f, ethical = True)\nnum_iter = 100\nprint(\"Run with \", num_iter,\" iterations\")\n\n## run the optimisation and time it \ntic = time.time()\noptimize_policy(action_conf, action_conf_deriv, t_0, t_f, pol.confi,\n x_0, params = params, lr = 0.01, num_iter=num_iter)\ntoc = time.time()\nprint(\"Task finished in {} minutes.\". format(int((toc-tic)/60)))\n\nsave_policy(\"Pol_cpd_det_\"+str(num_iter)+\"iter\", pol)\n\n\n###############################################################################\n# STOCHASTIC PARAMETERS\n###############################################################################\n\n#Reset policy\npol = Policy(t_0, t_f, ethical = True)\nnum_iter = 100\n\nnoise_level = [0.05, 0.25 ]\nlabels = ['05', '25' ]\n\nfor k in range(len(noise_level)):\n ## Reset the policy to the initial conditions \n pol = Policy(t_0, t_f, ethical = True)\n num_iter = 100\n\n print(\"Case of stochastic parameters: noise_level =\", noise_level[k], \" Number of iterations = \", num_iter)\n tic = time.time()\n optimize_policy_stoch(action_conf, action_conf_deriv_stoch, t_0, t_f, pol.confi,\n x_0,params = params, lr = 0.01, num_iter = num_iter,\n num_fluct = int(256*noise_level[k]*4), noise_level=noise_level[k] )\n toc = time.time()\n print(\"Task finished in {} minutes.\". format(int((toc-tic)/60)))\n #print(\"Task finished in {} seconds\". format(toc-tic))\n\n save_policy(\"Pol_cpd_stoch_noise\"+labels[k]+\"_\"+str(num_iter)+\"iter\", pol)\n\n\n\n###############################################################################\n# STOCHASTIC PARAMETERS WITH DECREASING NOISE \n###############################################################################\n\n#Reset policy\npol = Policy(t_0, t_f, ethical = True)\nnum_iter = 100\n\nprint(\"Case of stochastic parameters with decreasing noise: noise_level = 0.25\", \" Number of iterations = \", num_iter)\ntic = time.time()\npol.confi = optimize_stoch_decr_uncert(action_conf, action_conf_deriv_stoch_previous_policy, t_0, t_f, pol.confi, x_0,\n lr = 0.01, num_iter=num_iter, num_fluct=50, initial_noise_level=0.25)\n\ntoc = time.time()\nprint(\"Task finished in {} minutes.\". format(int((toc-tic)/60)))\n\n\nsave_policy(\"Pol_cpd_stoch_decr_noise_25_\"+str(num_iter)+\"iter\", pol)\n\n\n\n###############################################################################\n# STOCHASTIC EVOLUTION\n###############################################################################\n\n#Reset policy\npol = Policy(t_0, t_f, ethical = True)\nnum_iter = 100\n\nnoise_level = [0.05,0.25 ]\nlabels =['05', '25' ]\n\nfor k in range(len(noise_level)):\n ## Reset the policy to the initial conditions \n #pol = Policy(t_0, t_f, ethical = True)\n\n\n\n print(\"Stochastic evolution: case of noise_level =\", noise_level[k], \" Number of iterations = \", num_iter)\n tic = time.time()\n optimize_policy_stoch_evo(action_conf_stoch_evo, action_conf_avg_stoch_evo, t_0, t_f, pol.confi,\n x_0,params = params, lr = 0.01, num_iter = num_iter,\n num_fluct = int(256*noise_level[k]*4) , noise_level=noise_level[k] )\n\n toc = time.time()\n print(\"Task finished in {} minutes.\". format(int((toc-tic)/60)))\n #print(\"Task finished in {} seconds\". format(toc-tic))\n\n save_policy(\"Pol_cpd_gauss_stoch_evo\"+labels[k]+\"_\"+str(num_iter)+\"iter_low_fluct\", pol)\n\n\n","sub_path":"Numpy_vers/_07b_gen_pol_cont_phys_dist.py","file_name":"_07b_gen_pol_cont_phys_dist.py","file_ext":"py","file_size_in_byte":4689,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"205612465","text":"# Copyright (C) 2019 Google Inc.\n# Licensed under http://www.apache.org/licenses/LICENSE-2.0 \n\"\"\"Create, description, representation and equal of entities.\"\"\"\n# pylint: disable=too-many-arguments\n# pylint: disable=too-few-public-methods\n\nimport copy\nfrom datetime import datetime\n\nfrom dateutil import parser, tz\n\nfrom lib.utils import help_utils\nfrom lib.utils.string_utils import StringMethods\n\n\nclass Representation(object):\n \"\"\"Class to operate with entities' representation.\"\"\"\n # pylint: disable=too-many-public-methods\n diff_info = None # {\"equal\": {\"atr7\": val7, ...}, \"diff\": {\"atr3\": val3}}\n tree_view_attrs_to_exclude = (\n \"created_at\", \"updated_at\", \"custom_attributes\", \"assertions\")\n people_attrs_names = [\n \"creators\", \"assignees\", \"verifiers\", \"admins\", \"primary_contacts\",\n \"secondary_contacts\", \"audit_captains\", \"auditors\",\n \"control_operators\", \"control_owners\",\n \"principal_assignees\", \"secondary_assignees\", \"managers\", \"editors\",\n \"readers\", \"reviewers\"] # multiply\n\n @property\n def attrs_names(self):\n \"\"\"Entity instance's attributes names according to class model.\"\"\"\n return self.get_attrs_names(self.__class__)\n\n @property\n def attrs_names_to_repr(self):\n \"\"\"Entity instance's attributes names w/o REST and exclude\n 'custom_attribute_definitions', 'custom_attribute_values'.\n \"\"\"\n # todo: add logic to getting 'url', 'id' via UI services\n return [attr_name for attr_name in self.attrs_names if attr_name not in [\n \"custom_attribute_definitions\", \"custom_attribute_values\", \"href\",\n \"url\", \"id\"]]\n\n @classmethod\n def all_attrs_names(cls):\n \"\"\"All possible entities' attributes names include REST.\"\"\"\n return list(set(cls.get_attrs_names() + [\n \"access_control_list\", \"recipients\", \"default_people\",\n \"modal_title\", \"assignee_type\", \"user_roles\"]))\n\n @classmethod\n def get_attrs_names(cls, entity=None):\n \"\"\"Get list unique entities attributes' names. If 'entity' then get\n attributes of one entered entity, else get attributes of all entities.\n \"\"\"\n all_entities_cls = (help_utils.convert_to_list(entity) if entity\n else list(Entity.all_entities_classes()))\n all_entities_attrs_names = StringMethods.convert_list_elements_to_list(\n [entity_cls().__dict__.keys() for entity_cls in all_entities_cls])\n return list(set(all_entities_attrs_names))\n\n def __repr__(self):\n \"\"\"Dictionary representation for entity.\"\"\"\n return str(dict(\n zip(self.attrs_names_to_repr,\n [getattr(self, attr_name_to_repr) for\n attr_name_to_repr in self.attrs_names_to_repr])))\n\n @staticmethod\n def remap_collection():\n \"\"\"Get transformation dictionary {'OLD KEY': 'NEW KEY'}, where\n 'OLD KEY' - UI elements and CSV fields correspond to\n 'NEW KEY' - objects attributes.\n \"\"\"\n from lib.constants import element, files\n els = element.TransformationElements\n csv = files.TransformationCSVFields\n # common for UI and CSV\n result_remap_items = {\n els.TITLE: \"title\", els.ADMIN: \"admins\",\n els.CODE: \"slug\", els.REVIEW_STATE: \"os_state\",\n els.OBJECT_REVIEW: \"os_state\",\n els.OBJECT_REVIEW_FULL: \"object_review_txt\",\n els.STATE: \"status\"\n }\n ui_remap_items = {\n els.PROGRAM_MANAGERS: \"managers\", els.VERIFIED: \"verified\",\n els.STATUS: \"status\", els.LAST_UPDATED: \"updated_at\",\n els.AUDIT_CAPTAINS: \"audit_captains\",\n els.AUDITORS: \"auditors\",\n \"MAPPED_OBJECTS\": \"mapped_objects\", els.ASSIGNEES: \"assignees\",\n els.CREATORS: \"creators\", \"VERIFIERS\": \"verifiers\",\n \"COMMENTS\": \"comments\", \"CREATED_AT\": \"created_at\",\n els.MODIFIED_BY: \"modified_by\", \"LAST_UPDATED_BY\": \"modified_by\",\n \"UPDATED_AT\": \"updated_at\", \"ASSESSMENT_TYPE\": \"assessment_type\",\n \"IS_VERIFIED\": \"verified\",\n \"CUSTOM_ATTRIBUTES\": \"custom_attributes\",\n \"DESCRIPTION\": \"description\",\n \"EVIDENCE_URLS\": \"evidence_urls\",\n \"ASSERTIONS\": \"assertions\",\n \"PRIMARY_CONTACTS\": \"primary_contacts\",\n \"CONTROL_OPERATORS\": \"control_operators\",\n \"CONTROL_OWNERS\": \"control_owners\",\n \"URL\": \"url\",\n \"ID\": \"id\", \"RISK_TYPE\": \"risk_type\",\n \"REVIEWERS\": \"reviewers\"\n }\n csv_remap_items = {\n csv.REVISION_DATE: \"updated_at\"\n }\n result_remap_items.update(ui_remap_items)\n result_remap_items.update(csv_remap_items)\n return StringMethods.dict_keys_to_upper_case(result_remap_items)\n\n @staticmethod\n def repr_obj_to_dict(objs):\n \"\"\"Convert objects' representation to dictionary 'obj.attr_name' =\n 'attr_value' to dictionary or list of dictionaries with items\n {'attr_name': 'attr_value'}.\n \"\"\"\n if objs or isinstance(objs, bool):\n if isinstance(objs, list):\n if (all(not isinstance(_, dict) and\n not isinstance(_, (str, unicode, int)) and\n _ for _ in objs)):\n objs = [_.__dict__ for _ in objs]\n else:\n if (not isinstance(objs, dict) and\n not isinstance(objs, (str, unicode, int))):\n objs = objs.__dict__\n return objs\n\n @staticmethod\n def repr_dict_to_obj(dic):\n \"\"\"Convert dictionary to Entity representation (dictionary's keys and\n values to attributes names and attributes values).\n \"\"\"\n # pylint: disable=expression-not-assigned\n # pylint: disable=consider-iterating-dictionary\n entity = Entity()\n [setattr(entity, k, v) for k, v in dic.iteritems()]\n return entity\n\n def repr_ui(self):\n \"\"\"Convert entity's attributes values from REST like to UI like\n representation.\n \"\"\"\n return self.convert_repr_rest_to_ui(objs=self)\n\n @classmethod # noqa: ignore=C901\n def convert_repr_rest_to_ui(cls, objs):\n \"\"\"Convert object's or objects' attributes values from REST like\n (dict or list of dict) representation to UI like with unicode.\n Examples:\n None to None, u'Ex' to u'Ex', [u'Ex1', u'Ex2', ...] to u'Ex1, Ex2',\n {'name': u'Ex', ...} to u'Ex',\n [{'name': u'Ex1', ...}, {'name': u'Ex2', ...}] to u'Ex1, Ex2'\n \"\"\"\n # pylint: disable=too-many-locals\n # pylint: disable=undefined-loop-variable\n # pylint: disable=invalid-name\n def convert_repr_rest_to_ui(obj):\n \"\"\"Convert object's attributes from REST to UI like representation.\"\"\"\n def convert_attr_val_repr_dict_to_unicode(attr_name, attr_value):\n \"\"\"Convert attribute value from dictionary to unicode representation\n (get value by key from dictionary 'attr_value' where key determine\n according to 'attr_name').\n \"\"\"\n if isinstance(attr_value, dict):\n converted_attr_value = attr_value\n if attr_name in Representation.people_attrs_names + [\n \"created_by\", \"modified_by\"\n ]:\n converted_attr_value = unicode(attr_value.get(\"email\"))\n if attr_name in [\"custom_attribute_definitions\", \"program\", \"audit\",\n \"mapped_objects\"]:\n converted_attr_value = (\n unicode(attr_value.get(\"title\")) if\n attr_name != \"custom_attribute_definitions\" else\n {attr_value.get(\"id\"): attr_value.get(\"title\").upper()}\n )\n if attr_name in [\"custom_attribute_values\"]:\n converted_attr_value = {attr_value.get(\"custom_attribute_id\"):\n attr_value.get(\"attribute_value\")}\n if obj_attr_name == \"comments\":\n converted_attr_value = {\n k: (parser.parse(v).replace(tzinfo=tz.tzutc()) if\n k == \"created_at\" and isinstance(v, unicode) else v)\n for k, v in attr_value.iteritems()\n if k in [\"modified_by\", \"created_at\", \"description\"]}\n if attr_name == \"assertions\":\n for name, assertion_id in ControlEntity.ASSERTIONS.iteritems():\n if assertion_id == attr_value[\"id\"]:\n converted_attr_value = name\n return converted_attr_value\n origin_obj = copy.deepcopy(obj)\n for obj_attr_name in obj.__dict__.keys():\n # 'Ex', u'Ex', 1, None to 'Ex', u'Ex', 1, None\n obj_attr_value = getattr(obj, obj_attr_name)\n # REST like u'08-20-2017T04:30:45' to date=2017-08-20,\n # timetz=04:30:45+00:00\n if (obj_attr_name in [\"updated_at\", \"created_at\"] and\n isinstance(obj_attr_value, unicode)):\n obj_attr_value = (parser.parse(obj_attr_value).\n replace(tzinfo=tz.tzutc()))\n if isinstance(obj_attr_value, dict) and obj_attr_value:\n # \"modified_by\" {\"type\": \"Person\", \"id\": x} to u'user@example.com'\n # todo: deprecated?\n if obj_attr_name == \"modified_by\":\n from lib.service import rest_service\n obj_attr_value = getattr(rest_service.ObjectsInfoService().get_obj(\n obj=Representation.repr_dict_to_obj(obj_attr_value)), \"email\")\n # {'name': u'Ex1', 'type': u'Ex2', ...} to u'Ex1'\n else:\n obj_attr_value = convert_attr_val_repr_dict_to_unicode(\n obj_attr_name, obj_attr_value)\n # [el1, el2, ...] or [{item1}, {item2}, ...] to [u'Ex1, u'Ex2', ...]\n if (isinstance(obj_attr_value, list) and\n all(isinstance(item, dict) for item in obj_attr_value)):\n obj_attr_value = [\n convert_attr_val_repr_dict_to_unicode(obj_attr_name, item) for\n item in obj_attr_value]\n setattr(obj, obj_attr_name, obj_attr_value)\n # merge \"custom_attribute_definitions\" and \"custom_attribute_values\"\n obj_cas_attrs_names = [\n \"custom_attributes\", \"custom_attribute_definitions\",\n \"custom_attribute_values\"]\n if set(obj_cas_attrs_names).issubset(obj.__dict__.keys()):\n cas_def = obj.custom_attribute_definitions\n cas_val = obj.custom_attribute_values\n # form CAs values of CAs definitions exist but CAs values not, or CAs\n # definitions have different then CAs values lengths\n if (cas_def and\n (not cas_val or (isinstance(cas_def and cas_val, list)) and\n len(cas_def) != len(cas_val))):\n from lib.entities.entities_factory import (\n CustomAttributeDefinitionsFactory)\n cas_val_dicts_keys = ([_.keys()[0] for _ in cas_val] if\n isinstance(cas_val, list) else [None])\n _cas_val = [\n {k: v} for k, v in\n CustomAttributeDefinitionsFactory.generate_ca_title_id(\n [Representation.repr_dict_to_obj(cad)\n for cad in origin_obj.custom_attribute_definitions]\n ).iteritems() if k not in cas_val_dicts_keys]\n cas_val = _cas_val if not cas_val else cas_val + _cas_val\n cas_def_dict = (\n dict([_def.iteritems().next() for _def in cas_def]) if\n (isinstance(cas_def, list) and\n all(isinstance(_def, dict)\n for _def in cas_def)) else None)\n cas_val_dict = (\n dict([_val.iteritems().next() for _val in cas_val]) if\n (isinstance(cas_def, list) and\n all(isinstance(_def, dict)\n for _def in cas_def)) else None)\n cas = StringMethods.merge_dicts_by_same_key(cas_def_dict, cas_val_dict)\n if obj.custom_attributes:\n cas.update(obj.custom_attributes)\n if cas in [{None: None}, {}]:\n cas = None\n setattr(obj, \"custom_attributes\", cas)\n return obj\n return help_utils.execute_method_according_to_plurality(\n objs=objs, types=Entity.all_entities_classes(),\n method_name=convert_repr_rest_to_ui)\n\n def repr_snapshot(self, parent_obj):\n \"\"\"Convert entity's attributes values to Snapshot representation.\"\"\"\n return (self.convert_repr_to_snapshot(\n objs=self, parent_obj=parent_obj))\n\n def repr_min_dict(self):\n \"\"\"Get and return entity's minimal dictionary representation w/\n 'type', 'id' keys, e.g. {'type': 'Control', 'id': 1}\n \"\"\"\n return {\"type\": getattr(self, \"type\"),\n \"id\": getattr(self, \"id\")}\n\n @classmethod # noqa: ignore=C901\n def convert_repr_to_snapshot(cls, objs, parent_obj):\n \"\"\"Convert object's or objects' attributes values to Snapshot\n representation.\n Retrieved values will be used for: 'id'.\n Set values will be used for: 'title, 'type', 'slug', 'href'.\n \"\"\"\n def convert_repr_to_snapshot(origin_obj, parent_obj):\n \"\"\"Convert object's attributes to Snapshot representation.\"\"\"\n from lib.service import rest_service\n origin_obj = copy.deepcopy(origin_obj)\n snapshoted_obj = (\n rest_service.ObjectsInfoService().get_snapshoted_obj(\n origin_obj=origin_obj, paren_obj=parent_obj))\n origin_obj.__dict__.update(\n {k: v for k, v in snapshoted_obj.__dict__.iteritems()})\n return origin_obj\n return help_utils.execute_method_according_to_plurality(\n objs=objs, types=Entity.all_entities_classes(),\n method_name=convert_repr_to_snapshot, parent_obj=parent_obj)\n\n def update_attrs(self, is_replace_attrs=True, is_allow_none=True,\n is_replace_dicts_values=False, **attrs):\n \"\"\"Update entity's attributes values according to entered data\n (dictionaries of attributes and values).\n If 'is_replace_values_of_dicts' then update values of dicts in list which\n is value of particular object's attribute name.\n \"\"\"\n return (self.update_objs_attrs_values(\n objs=self, is_replace_attrs_values=is_replace_attrs,\n is_allow_none_values=is_allow_none,\n is_replace_values_of_dicts=is_replace_dicts_values, **attrs))\n\n def delete_attrs(self, *attrs_names):\n \"\"\"Delete entity's attributes according to '*attrs_names'.\"\"\"\n # pylint: disable=expression-not-assigned\n [delattr(self, attr_name)\n for attr_name in attrs_names if hasattr(self, attr_name)]\n\n def set_attrs(self, *attrs_names, **attrs):\n \"\"\"Set entity's attributes according to '**attrs' items if key and value\n are corresponding, otherwise set values to None where '*attrs_names'\n is keys.\n \"\"\"\n # pylint: disable=expression-not-assigned\n [setattr(self, attr_name, attrs.get(attr_name))\n for attr_name in attrs_names]\n\n @classmethod\n def update_objs_attrs_values(\n cls, objs, is_replace_attrs_values=True,\n is_allow_none_values=True, is_replace_values_of_dicts=False, **attrs\n ):\n \"\"\"Update object or list of objects ('objs') attributes values by\n manually entered data if attribute name exist in 'attrs_names' witch equal\n to 'all_objs_attrs_names' according to dictionary of attributes and values\n '**attrs'. If 'is_replace_attrs_values' then replace attributes values,\n if not 'is_replace_attrs_values' then update (merge) attributes values\n witch should be lists. If 'is_allow_none_values' then allow to set None\n object's attributes values, and vice versa.\n If 'is_replace_values_of_dicts' then update values of dicts in list which\n is value of particular object's attribute name:\n (**attrs is attr={'key1': 'new_value2', 'key2': 'new_value2'}).\n \"\"\"\n # pylint: disable=expression-not-assigned\n # pylint: disable=invalid-name\n def update_obj_attrs_values(obj, is_replace_attrs_values,\n is_allow_none_values, **attrs):\n \"\"\"Update object's attributes values.\"\"\"\n for obj_attr_name in attrs:\n obj_attr_value = None\n if obj_attr_name in Representation.all_attrs_names():\n _obj_attr_value = attrs.get(obj_attr_name)\n if not is_replace_values_of_dicts:\n # convert repr from objects to dicts exclude datetime objects\n obj_attr_value = (\n cls.repr_obj_to_dict(_obj_attr_value) if\n not isinstance(_obj_attr_value, datetime) else _obj_attr_value)\n if not is_replace_attrs_values:\n origin_obj_attr_value = getattr(obj, obj_attr_name)\n obj_attr_value = (\n dict(origin_obj_attr_value.items() + obj_attr_value.items())\n if obj_attr_name == \"custom_attributes\" else\n help_utils.convert_to_list(origin_obj_attr_value) +\n help_utils.convert_to_list(obj_attr_value))\n if is_replace_values_of_dicts and isinstance(_obj_attr_value, dict):\n obj_attr_value = StringMethods.exchange_dicts_items(\n transform_dict=_obj_attr_value,\n dicts=help_utils.convert_to_list(\n getattr(obj, obj_attr_name)),\n is_keys_not_values=False)\n obj_attr_value = (\n obj_attr_value if isinstance(getattr(obj, obj_attr_name), list)\n else obj_attr_value[0])\n if (is_allow_none_values is True or\n (is_allow_none_values is False and\n obj_attr_value is not None)):\n setattr(obj, obj_attr_name, obj_attr_value)\n return obj\n return help_utils.execute_method_according_to_plurality(\n objs=objs, types=Entity.all_entities_classes(),\n method_name=update_obj_attrs_values,\n is_replace_attrs_values=is_replace_attrs_values,\n is_allow_none_values=is_allow_none_values, **attrs)\n\n @classmethod\n def filter_objs_attrs(cls, objs, attrs_to_include):\n \"\"\"Make objects's copy and filter objects's attributes (delete attributes\n from objects witch not in list'attrs_to_include').\n 'objs' can be list of objects or object.\n \"\"\"\n # pylint: disable=expression-not-assigned\n def filter_obj_attrs(obj, attrs_to_include):\n \"\"\"Filter one object's attributes.\"\"\"\n obj = copy.deepcopy(obj)\n [delattr(obj, obj_attr) for obj_attr in obj.__dict__.keys()\n if obj_attr not in attrs_to_include]\n return obj\n return ([filter_obj_attrs(obj, attrs_to_include) for obj in objs] if\n isinstance(objs, list) else\n filter_obj_attrs(objs, attrs_to_include))\n\n def __eq__(self, other):\n \"\"\"Extended equal procedure fore self and other entities.\"\"\"\n comparison = Representation.compare_entities(self, other)\n self.diff_info = comparison[\"self_diff\"]\n other.diff_info = comparison[\"other_diff\"]\n return comparison[\"is_equal\"]\n\n @staticmethod\n def attrs_values_types_error(self_attr, other_attr, expected_types):\n raise ValueError(\"'{}' have to be isinstance of classes: {}\\n\".\n format((self_attr, other_attr), expected_types))\n\n @classmethod\n def is_attrs_equal(cls, attr_name, self_attr_value, other_attr_value):\n \"\"\"Compare entities' attributes according to attributes' names and values,\n if is equal then return 'True' and vise versa.\n \"\"\"\n is_equal = False\n if attr_name == \"custom_attributes\":\n is_equal = cls.compare_cas(self_attr_value, other_attr_value)\n elif attr_name in [\"updated_at\", \"created_at\"]:\n is_equal = cls.compare_datetime(self_attr_value, other_attr_value)\n elif attr_name == \"comments\":\n is_equal = cls.compare_comments(self_attr_value, other_attr_value)\n else:\n is_equal = self_attr_value == other_attr_value\n return is_equal\n\n @classmethod\n def is_list_of_attrs_equal(cls, self_list_attrs, other_list_attrs):\n \"\"\"Compare list of entities' attributes according to attributes' names and\n values, if is equal then return 'True' and vise versa.\n \"\"\"\n return (all(all((self_k == other_k and cls.is_attrs_equal(\n attr_name=self_k, self_attr_value=self_attr[self_k],\n other_attr_value=other_attr[self_k])) for self_k, other_k\n in zip(self_attr.keys(), other_attr.keys()))\n for self_attr, other_attr in zip(self_list_attrs, other_list_attrs)))\n\n @staticmethod\n def compare_cas(self_cas, other_cas):\n \"\"\"Compare entities' 'custom_attributes' attributes.\"\"\"\n if (isinstance(self_cas, (dict, type(None))) and\n isinstance(other_cas, (dict, type(None)))):\n is_equal = False\n if (isinstance(self_cas, dict) and isinstance(other_cas, dict)):\n is_equal = StringMethods.is_subset_of_dicts(self_cas, other_cas)\n else:\n is_equal = self_cas == other_cas\n return is_equal\n else:\n Representation.attrs_values_types_error(\n self_attr=self_cas, other_attr=other_cas,\n expected_types=(dict.__name__, type(None).__name__))\n\n @staticmethod\n def compare_datetime(self_datetime, other_datetime):\n \"\"\"Compare entities' datetime ('created_at', 'updated_at') attributes.\"\"\"\n # pylint: disable=superfluous-parens\n if (isinstance(self_datetime, (datetime, type(None))) and\n isinstance(other_datetime, (datetime, type(None)))):\n return self_datetime == other_datetime\n else:\n Representation.attrs_values_types_error(\n self_attr=self_datetime, other_attr=other_datetime,\n expected_types=(datetime.__name__, type(None).__name__))\n\n @staticmethod\n def compare_comments(self_comments, other_comments):\n \"\"\"Compare entities' 'comments' attributes due to specific dictionaries'\n format values in list comments.\n \"\"\"\n # pylint: disable=no-else-return\n if help_utils.is_multiple_objs(\n StringMethods.convert_list_elements_to_list(\n [self_comments, other_comments]), (dict, type(None))):\n if self_comments and other_comments:\n is_comments_equal_list = []\n for self_comment, other_comment in zip(self_comments, other_comments):\n is_comments_equal = False\n if self_comment and other_comment:\n is_comments_equal = (\n all((Representation.compare_datetime(\n self_comment.get(\"created_at\"),\n other_comment.get(\"created_at\")\n ) if (isinstance(_self, datetime) and\n isinstance(_other, datetime))else\n _self == _other) for _self, _other in zip(\n self_comment.iteritems(), other_comment.iteritems())))\n # convert datetime to unicode in order to get visible repr\n if self_comment.get(\"created_at\"):\n self_comment[\"created_at\"] = unicode(\n self_comment.get(\"created_at\"))\n if other_comment.get(\"created_at\"):\n other_comment[\"created_at\"] = unicode(\n other_comment.get(\"created_at\"))\n else:\n is_comments_equal = self_comment == other_comment\n is_comments_equal_list.append(is_comments_equal)\n return all(is_equal for is_equal in is_comments_equal_list)\n else:\n return self_comments == other_comments\n else:\n Representation.attrs_values_types_error(\n self_attr=self_comments, other_attr=other_comments,\n expected_types=(list.__name__, type(None).__name__))\n\n def compare_entities(self, other):\n \"\"\"Extended compare of entities: 'self_entity' and 'other_entity' according\n to specific 'attrs_names_to_repr' and return:\n - 'is_equal' - True if entities equal else False;\n - 'self_diff' - 'equal' and 'diff' parts of 'self_entity' after compare;\n - 'other_diff' - 'equal' and 'diff' parts of 'other_entity' after compare.\n \"\"\"\n # pylint: disable=not-an-iterable\n is_equal = False\n self_equal, self_diff, other_equal, other_diff = {}, {}, {}, {}\n if (isinstance(self, other.__class__) and\n self.attrs_names_to_repr == other.attrs_names_to_repr):\n for attr_name in self.attrs_names_to_repr:\n self_attr_value = None\n other_attr_value = None\n if (attr_name in self.__dict__.keys() and\n attr_name in other.__dict__.keys()):\n self_attr_value = getattr(self, attr_name)\n other_attr_value = getattr(other, attr_name)\n is_equal = self.is_attrs_equal(\n attr_name=attr_name, self_attr_value=self_attr_value,\n other_attr_value=other_attr_value)\n # convert datetime to unicode in order to get visible representation\n if isinstance(self_attr_value, datetime):\n self_attr_value = unicode(self_attr_value)\n if isinstance(other_attr_value, datetime):\n other_attr_value = unicode(other_attr_value)\n if is_equal:\n self_equal[attr_name] = self_attr_value\n other_equal[attr_name] = other_attr_value\n else:\n self_diff[attr_name] = self_attr_value, type(self_attr_value)\n other_diff[attr_name] = other_attr_value, type(other_attr_value)\n is_equal = self_diff == other_diff == {}\n return {\"is_equal\": is_equal,\n \"self_diff\": {\"equal\": self_equal, \"diff\": self_diff},\n \"other_diff\": {\"equal\": other_equal, \"diff\": other_diff}\n }\n\n @classmethod\n def extract_excluding_attrs(cls, expected_objs, actual_objs, *exclude_attrs):\n \"\"\"Extract dictionary which contains collections to compare according to\n exclude attributes.\n Where:\n 'exp_objs_wo_ex_attrs', 'act_objs_wo_ex_attrs' - list objects w/o excluding\n attributes;\n 'exp_ex_attrs', 'act_ex_attrs' - list dictionaries w/ excluding attributes\n (items which contain attributes' names and values);\n ''*exclude_attrs' - tuple of excluding attributes names.\n \"\"\"\n # pylint: disable=invalid-name\n expected_excluded_attrs, actual_excluded_attrs = (\n cls.extract_simple_collections(\n exclude_attrs, actual_objs, *expected_objs))\n expected_objs_wo_excluded_attrs, actual_objs_wo_excluded_attrs = (\n cls.extract_objs(exclude_attrs, actual_objs, *expected_objs))\n return {\"exp_objs_wo_ex_attrs\": expected_objs_wo_excluded_attrs,\n \"act_objs_wo_ex_attrs\": actual_objs_wo_excluded_attrs,\n \"exp_ex_attrs\": expected_excluded_attrs,\n \"act_ex_attrs\": actual_excluded_attrs}\n\n @staticmethod\n def extract_objs_wo_excluded_attrs(objs, *exclude_attrs):\n \"\"\"Return list objects w/ attributes values set to 'None' according to\n '*exclude_attrs' tuple attributes' names.\n \"\"\"\n return [expected_obj.update_attrs(\n **dict([(attr, None) for attr in exclude_attrs]))\n for expected_obj in objs]\n\n @staticmethod\n def extract_excluded_attrs_collection(objs, *exclude_attrs):\n \"\"\"Return list dictionaries (attributes' names and values) according to\n '*exclude_attrs' tuple attributes' names.\n \"\"\"\n # pylint: disable=invalid-name\n return [dict([(attr, getattr(expected_obj, attr))\n for attr in exclude_attrs]) for expected_obj in objs]\n\n @staticmethod\n def extract_simple_collections(expected_objs, actual_objs, *exclude_attrs):\n \"\"\"Extract expected and actual simple collections excluded attributes.\"\"\"\n return [Representation.extract_excluded_attrs_collection(\n copy.deepcopy(objs), *exclude_attrs)\n for objs in [expected_objs, actual_objs]]\n\n @staticmethod\n def extract_objs(expected_objs, actual_objs, *exclude_attrs):\n \"\"\"Extract expected and actual objects w/ set to 'None' excluded\n attributes.\n \"\"\"\n return [Representation.extract_objs_wo_excluded_attrs(\n copy.deepcopy(objs), *exclude_attrs)\n for objs in [expected_objs, actual_objs]]\n\n @staticmethod\n def filter_objs_by_attrs(objs, **attrs):\n \"\"\"Filter objects by attributes' items and return matched according to\n plurality.\n 'objs' - object or list objects;\n '**attrs' - items of attributes' names and values.\n \"\"\"\n list_objs = help_utils.convert_to_list(objs)\n matched_objs = [\n obj for obj in list_objs\n if isinstance(obj, Entity.all_entities_classes()) and\n StringMethods.is_subset_of_dicts(dict(**attrs), obj.__dict__)]\n return (help_utils.get_single_obj(matched_objs)\n if not help_utils.is_multiple_objs(matched_objs) else matched_objs)\n\n\nclass Entity(Representation):\n \"\"\"Class that represent model for base entity.\"\"\"\n __hash__ = None\n\n def __init__(self, **attrs):\n self.set_attrs(\n \"type\", \"slug\", \"id\", \"title\", \"href\", \"url\", \"admins\",\n \"primary_contacts\", \"secondary_contacts\", \"status\",\n \"comments\", \"custom_attribute_definitions\", \"custom_attribute_values\",\n \"custom_attributes\", \"created_at\", \"updated_at\", \"modified_by\",\n \"object_review_txt\", \"description\", **attrs)\n\n @staticmethod\n def all_entities_classes():\n \"\"\"Explicitly return tuple of all entities' classes.\"\"\"\n return (\n PersonEntity, CustomAttributeDefinitionEntity, ProgramEntity,\n ControlEntity, AuditEntity, AssessmentEntity, AssessmentTemplateEntity,\n IssueEntity, CommentEntity, ObjectiveEntity, AccessControlRoleEntity,\n RiskEntity, OrgGroupEntity, ProposalEntity)\n\n def __lt__(self, other):\n return self.slug < other.slug\n\n\nclass CommentEntity(Representation):\n \"\"\"Class that represent model for Comment entity.\"\"\"\n __hash__ = None\n\n def __init__(self, **attrs):\n self.set_attrs(\n \"type\", \"id\", \"href\", \"description\", \"created_at\", \"modified_by\",\n **attrs)\n\n def __lt__(self, other):\n return self.description < other.description\n\n\nclass PersonEntity(Entity):\n \"\"\"Class that represent model for Person entity.\"\"\"\n\n def __init__(self, **attrs):\n super(PersonEntity, self).__init__()\n self.delete_attrs(\n \"slug\", \"title\", \"admins\", \"primary_contacts\", \"secondary_contacts\",\n \"status\", \"comments\")\n self.set_attrs(\n \"name\", \"id\", \"type\", \"email\", \"company\", \"system_wide_role\", **attrs)\n\n def __lt__(self, other):\n return self.email < other.email\n\n\nclass UserRoleEntity(Representation):\n \"\"\"Class that represents model for user role entity\"\"\"\n\n def __init__(self):\n super(UserRoleEntity, self).__init__()\n self.set_attrs(\n \"type\", \"id\", \"created_at\", \"updated_at\", \"modified_by\",\n \"person\", \"role\")\n\n\nclass AccessControlRoleEntity(Representation):\n \"\"\"Class that represents model for ACL role entity.\"\"\"\n\n def __init__(self):\n super(AccessControlRoleEntity, self).__init__()\n self.set_attrs(\n \"context\", \"created_at\", \"default_to_current_user\", \"delete\", \"id\",\n \"mandatory\", \"modified_by\", \"my_work\", \"name\", \"non_editable\",\n \"object_type\", \"parent_type\", \"read\", \"selfLink\", \"tooltip\", \"type\",\n \"update\", \"updated_at\")\n\n\nclass CustomAttributeDefinitionEntity(Representation):\n \"\"\"Class that represent model for Custom Attribute entity.\"\"\"\n __hash__ = None\n\n def __init__(self, **attrs):\n super(CustomAttributeDefinitionEntity, self).__init__()\n self.set_attrs(\n \"title\", \"id\", \"href\", \"type\", \"definition_type\", \"attribute_type\",\n \"helptext\", \"placeholder\", \"mandatory\", \"multi_choice_options\",\n \"created_at\", \"updated_at\", \"modified_by\", \"multi_choice_mandatory\",\n **attrs)\n\n def __lt__(self, other):\n return self.title < other.title\n\n\nclass CustomAttributeValueEntity(Representation):\n \"\"\"Class that represents model for Custom Attribute Value entity.\"\"\"\n\n def __init__(self, **attrs):\n super(CustomAttributeValueEntity, self).__init__()\n self.set_attrs(\n \"attribute_object\", \"attribute_object_id\", \"attribute_value\",\n \"custom_attribute_id\", **attrs)\n\n\nclass ProgramEntity(Entity):\n \"\"\"Class that represent model for Program entity.\"\"\"\n\n def __init__(self, **attrs):\n super(ProgramEntity, self).__init__()\n self.delete_attrs(\"admins\")\n self.set_attrs(\n \"managers\", \"editors\", \"readers\", \"primary_contacts\",\n \"secondary_contacts\", \"reviewers\", **attrs)\n\n\nclass ControlEntity(Entity):\n \"\"\"Class that represent model for Control entity.\"\"\"\n ASSERTIONS = {\"Confidentiality\": 37, \"Integrity\": 38,\n \"Availability\": 39, \"Security\": 40, \"Privacy\": 41}\n __hash__ = None\n\n def __init__(self, **attrs):\n super(ControlEntity, self).__init__()\n self.delete_attrs(\n \"primary_contacts\", \"secondary_contacts\")\n self.set_attrs(\n \"control_operators\", \"control_owners\", \"principal_assignees\",\n \"secondary_assignees\", \"program\",\n \"assertions\", **attrs)\n\n\nclass ObjectiveEntity(Entity):\n \"\"\"Class that represent model for Objective entity.\"\"\"\n\n\nclass RiskEntity(Entity):\n \"\"\"Class that represent model for Risk entity.\"\"\"\n\n def __init__(self, **attrs):\n super(RiskEntity, self).__init__()\n self.set_attrs(\n \"risk_type\", \"threat_source\", \"threat_event\", \"vulnerability\", **attrs)\n\n\nclass OrgGroupEntity(Entity):\n \"\"\"Class that represent model for Org Group entity.\"\"\"\n\n\nclass AuditEntity(Entity):\n \"\"\"Class that represent model for Audit enity.\"\"\"\n\n def __init__(self, **attrs):\n super(AuditEntity, self).__init__()\n self.delete_attrs(\n \"admins\", \"primary_contacts\", \"secondary_contacts\", \"comments\")\n self.set_attrs(\n \"audit_captains\", \"auditors\", \"program\", **attrs)\n\n\nclass AssessmentTemplateEntity(Entity):\n \"\"\"Class that represent model for Assessment Template entity.\"\"\"\n\n def __init__(self, **attrs):\n super(AssessmentTemplateEntity, self).__init__()\n self.delete_attrs(\n \"admins\", \"primary_contacts\", \"secondary_contacts\", \"comments\")\n self.set_attrs(\n \"assignees\", \"verifiers\", \"template_object_type\", \"audit\", **attrs)\n\n\nclass AssessmentEntity(Entity):\n \"\"\"Class that represent model for Assessment entity.\"\"\"\n\n def __init__(self, **attrs):\n super(AssessmentEntity, self).__init__()\n self.delete_attrs(\"admins\")\n self.set_attrs(\n \"creators\", \"assignees\", \"verifiers\", \"assessment_type\", \"verified\",\n \"mapped_objects\", \"audit\", \"template\", \"object\", \"evidence_urls\",\n \"secondary_contacts\", **attrs)\n\n def cads_from_template(self):\n return [definition\n for definition\n in self.custom_attribute_definitions\n if definition[\"definition_id\"] is not None]\n\n\nclass IssueEntity(Entity):\n \"\"\"Class that represent model for Issue entity.\"\"\"\n\n\nclass ProposalEntity(Representation):\n \"\"\"Proposal entity from UI.\n\n Changes attribute should be an array of dictionaries with keys obj_attr_type,\n cur_value, proposed_value.\n \"\"\"\n def __init__(self, **attrs):\n super(ProposalEntity, self).__init__()\n self.set_attrs(\n \"status\", \"author\", \"datetime\", \"changes\", \"comment\", **attrs)\n\n\nclass ProposalEmailUI(Representation):\n \"\"\"Proposal notification email.\"\"\"\n def __init__(self, **attrs):\n super(ProposalEmailUI, self).__init__()\n self.set_attrs(\n \"recipient_email\", \"author\", \"obj_type\", \"changes\", \"comment\", **attrs)\n\n\nclass DailyEmailUI(Representation):\n \"\"\"Daily notification email.\"\"\"\n def __init__(self, **attrs):\n super(DailyEmailUI, self).__init__()\n self.set_attrs(\n \"email_recipient\", \"assigned_tasks\", \"due_soon_tasks\", \"new_wf_cycles\",\n **attrs)\n","sub_path":"test/selenium/src/lib/entities/entity.py","file_name":"entity.py","file_ext":"py","file_size_in_byte":34802,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"174855497","text":"import os\nimport requests\nfrom pprint import pprint\nfrom db import SQliteDB\nfrom pandas import DataFrame\nimport pandas as pd\nfrom azure.cognitiveservices.language.textanalytics import TextAnalyticsClient\nfrom msrest.authentication import CognitiveServicesCredentials\nimport csv\nimport json\n\nclass TextAnalytics:\n def __init__(self, _csv, _api_key, _endpoint):\n self.csv_path = _csv\n self.api_key = _api_key\n self.endpoint = _endpoint\n self.db = SQliteDB()\n\n #\n # Azure text analytic endpoint urls\n #\n self.sentiment_endpoint = self.endpoint + \"text/analytics/v2.1/sentiment\"\n self.keyphrase_endpoint = self.endpoint + \"text/analytics/v2.1/keyPhrases\"\n\n\n self.client = TextAnalyticsClient(endpoint=self.endpoint, credentials=CognitiveServicesCredentials(self.api_key))\n\n def parse_csv(self):\n dataframe = pd.read_csv(self.csv_path)\n return dataframe\n\n def send_to_text_analytics(self, data_frame):\n for index, row in data_frame.iterrows():\n self.db.insert_into_db_2(row[\"game\"], row[\"genre\"], row[\"reviewer_id\"], row[\"overall_score\"])\n\n documents = [\n {\n \"id\": row[\"game\"],\n \"language\" : \"en\",\n \"text\": row[\"review\"]\n }\n ]\n\n json_formatted = json.dumps(documents)\n\n response_sentiment = self.client.sentiment(documents=documents)\n response_key_phrases = self.client.key_phrases(documents=documents)\n\n #\n # Extract sentiment\n #\n extracted_sentiment = int(response_sentiment.documents[0].score * 100)\n # extracted_sentiment = int(extracted_sentiment_response[\"documents\"][\"score\"] * 100)\n\n for document in response_key_phrases.documents:\n for phrase in document.key_phrases:\n self.db.insert_into_db(document.id, extracted_sentiment, phrase)\n\n def print_suggestion(self, key_phrases):\n try:\n print(self.db.generate_suggestions_features(key_phrases).return_game_name())\n except:\n print(\"Didn't find what you were looking for, but you could probably do what you want in Minecraft.\")\n","sub_path":"src/textanalytics.py","file_name":"textanalytics.py","file_ext":"py","file_size_in_byte":2269,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"18524987","text":"def onlyFlag():\n flag = hero.findFlag()\n if flag:\n if hero.isReady(\"jump\"):\n hero.jumpTo(flag.pos)\n else:\n hero.moveXY(flag.pos.x, flag.pos.y)\n hero.pickUpFlag(flag)\n\n\ndef summonIt():\n if hero.canCast(\"summon-burl\"):\n hero.cast(\"summon-burl\")\n if hero.canCast(\"summon-undead\"):\n hero.cast(\"summon-undead\")\n if hero.canCast(\"raise-dead\") and len(hero.findCorpses()):\n hero.cast(\"raise-dead\")\n\n\ndef summonArmy():\n gold = hero.gold\n if gold > hero.costOf(\"soldier\"):\n hero.summon(\"soldier\")\n\n army = hero.findFriends()\n for soldier in army:\n if soldier.type == \"soldier\":\n enemy = soldier.findNearestEnemy()\n if enemy:\n hero.command(soldier, \"attack\", enemy)\n\n\ndef attackIt(enemy):\n distance = hero.distanceTo(enemy)\n if distance < 25 and hero.canCast(\"fear\"):\n hero.cast(\"fear\", enemy)\n elif distance < 30 and hero.canCast(\"chain-lightning\"):\n hero.cast(\"chain-lightning\", enemy)\n elif distance < 30 and hero.canCast(\"poison-cloud\"):\n hero.cast(\"poison-cloud\", enemy)\n elif distance < 15 and hero.health < hero.maxHealth / 1.5:\n hero.cast(\"drain-life\", enemy)\n else:\n hero.attack(enemy)\n\n\ndef drainFriend():\n friend = hero.findNearest(hero.findFriends())\n if friend and friend.type != \"burl\" and hero.distanceTo(friend) <= 15:\n hero.cast(\"drain-life\", friend)\n\n\ndef getBestCoin():\n coins = hero.findItems()\n best = None\n value = 0\n for coin in coins:\n now = coin.value / hero.distanceTo(coin)\n if now > value:\n value = now\n best = coin\n return best\n\n\ndef battleTactics():\n onlyFlag()\n summonIt()\n \n coin = getBestCoin()\n if coin:\n hero.move(coin.pos)\n\n summonArmy()\n enemy = hero.findNearestEnemy()\n if enemy:\n attackIt(enemy)\n\n if hero.health < hero.maxHealth / 2:\n drainFriend()\n \n\nwhile True:\n battleTactics()\n","sub_path":"8_Cloudrip_Mountain/391-Restless_Dead/restless_dead.py","file_name":"restless_dead.py","file_ext":"py","file_size_in_byte":2032,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"350334816","text":"start = int(input(\"Сколько вы пробежали за 1 день?: \"))\nfinish = int(input(\"Сколько хотите пробежать?: \"))\nday = 0\nresult = 0\n\nwhile True:\n if result < finish:\n result = result + (start * 1.1)\n day += 1\n else:\n print(f'На {day} день, вы сможете пробежать не менее: {int(result)} киллометров')\n break\n\n\n\n\n","sub_path":"HomeWorks/les1/6.py","file_name":"6.py","file_ext":"py","file_size_in_byte":426,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"252580636","text":"\"\"\"The unittest module for the admin blueprint.\n\"\"\"\n\nimport unittest\nfrom urllib.parse import urlparse\nfrom flask_login import current_user\n\nfrom app import create_app, db\nfrom app.models.feedback import Feedback\nfrom app.models.administrator import Administrator\nfrom app.models.review_status import ReviewStatus\n\n\nclass AdminBlueprintTestCase(unittest.TestCase):\n \"\"\"The test class for the admin blueprint\"\"\"\n\n def setUp(self) -> None:\n self.app = create_app('testing')\n self.app_context = self.app.app_context()\n self.app_context.push()\n db.create_all()\n Administrator.insert_administrator()\n ReviewStatus.insert_review_status()\n\n self.client = self.app.test_client(use_cookies=True)\n\n # create some feedbacks for testing\n self.test_info = [{\n 'email': f'test{i}@email.com',\n 'title': f'title{i}',\n 'content': f'some content{i}',\n } for i in range(5)]\n\n for info in self.test_info:\n db.session.add(Feedback(**info))\n db.session.commit()\n\n def tearDown(self) -> None:\n db.session.remove()\n db.drop_all()\n self.app_context.pop()\n\n def test_review_all_feedback_page(self) -> None:\n \"\"\"Test for the route /admin/\"\"\"\n\n # login administrator and save the session\n with self.client as client:\n client.post('/login/', data={\n 'name': 'admin',\n 'password': 'admin',\n })\n\n response = self.client.get('/admin')\n self.assertTrue(response.status_code - 300 in range(0, 100),\n 'miss of redirection')\n\n for url in ['/admin', '/admin/']:\n response = self.client.get(url,\n follow_redirects=True)\n self.assertEqual(response.status_code, 200,\n 'wrong status code')\n text = response.get_data(as_text=True)\n for info in self.test_info:\n self.assertTrue(info['title'] in text,\n f'feedback not found: {info[\"title\"]}')\n\n def test_review_a_feedback(self) -> None:\n \"\"\"Test for the route /admin/\"\"\"\n\n # login administrator and save the session\n with self.client as client:\n client.post('/login/', data={\n 'name': 'admin',\n 'password': 'admin',\n })\n\n feedbacks = db.session.query(Feedback).all()\n\n # check the content of each feedback\n for feedback in feedbacks:\n response = self.client.get(f'/admin/{feedback.id}')\n text = response.get_data(as_text=True)\n self.assertTrue(str(feedback.id) in text,\n ('cannot find the correct '\n 'feedback id in the response'))\n self.assertTrue(str(feedback.email) in text,\n ('cannot find the correct '\n 'feedback email in the response'))\n self.assertTrue(feedback.title in text,\n ('cannot find the correct '\n 'feedback title in the response'))\n self.assertTrue(feedback.content in text,\n ('cannot find the correct '\n 'feedback content in the response'))\n status = ReviewStatus.query \\\n .filter_by(id=feedback.review_status_id).first()\n self.assertTrue(status.name in text,\n ('cannot find the correct '\n 'review status name in the response'))\n if feedback.response:\n self.assertTrue(feedback.response in text,\n ('cannot find the correct '\n 'admin response in the response'))\n\n # test for the wrong id\n existed_id = [feedback.id for feedback in feedbacks]\n id_not_exists = 0\n while id_not_exists in existed_id:\n id_not_exists += len(existed_id)\n\n response = self.client.get(f'/admin/{id_not_exists}')\n self.assertTrue(response.status_code - 300 in range(0, 100),\n 'wrong http status code')\n\n url = urlparse(response.location)\n self.assertTrue(url.path in ('/admin/', '/admin'),\n 'wrong routing')\n\n def test_reply_to_a_feedback(self) -> None:\n \"\"\"Test for the POST method to the route /admin/\"\"\"\n\n # login administrator and save the session\n with self.client as client:\n client.post('/login/', data={\n 'name': 'admin',\n 'password': 'admin',\n })\n\n review_status = ReviewStatus.query.all()\n data_list = [{\n 'review_status': status.id,\n 'response': f'response{status.id}',\n } for status in review_status]\n feedbacks = Feedback.query.all()[:len(data_list)]\n\n # post the response and review statuses\n with self.client as client:\n for data, feedback in zip(data_list, feedbacks):\n\n client.post(f'/admin/{feedback.id}',\n data=data)\n\n # check if the database is modified as expectation\n feedback_in_db = Feedback.query \\\n .filter_by(id=feedback.id).first()\n self.assertEqual(data['response'],\n feedback_in_db.response,\n 'response not correct')\n self.assertEqual(data['review_status'],\n feedback_in_db.review_status_id,\n 'review status not correct')\n self.assertEqual(current_user.id,\n feedback_in_db.reviewer_id,\n 'reviewer id not correct')\n\n # check if the view is renewed\n response = client.get(f'/admin/{feedback.id}')\n text = response.get_data(as_text=True)\n status = ReviewStatus.query \\\n .filter_by(id=data['review_status']).first()\n self.assertTrue(status.name in text,\n ('cannot find the correct '\n 'review status name in the response'))\n self.assertTrue(data['response'] in text,\n ('cannot find the correct '\n 'admin response in the response'))\n","sub_path":"tests/test_review.py","file_name":"test_review.py","file_ext":"py","file_size_in_byte":6609,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"408012703","text":"# -*- coding: utf-8 -*-\nfrom automatas import *\n\n\nTokenConfig = [\n ('IF', A_If),\n ('EOF', A_EOF),\n ('ELSE', A_Else),\n ('FALSE', A_False),\n ('FOR', A_FOR),\n ('FUN', A_FUN),\n ('RETURN', A_Return),\n ('TRUE', A_True),\n ('VAR', A_VAR),\n ('WHILE', A_While),\n ('PAREN_OPEN', A_ParenOpen),\n ('PAREN_CLOSE', A_ParenClose),\n ('APOSTROFO', A_Apostrofo),\n ('ASTERISCO', A_Asterisco),\n ('COMA', A_Coma),\n ('LLAVE_ABIERTA', A_LlaveAbierta),\n ('LLAVE_CERRADA', A_LlaveCerrada),\n ('EXCLAMACION', A_Exclamacion),\n ('GUION_MEDIO', A_Guionmedio),\n ('MAS', A_Mas),\n ('PUNTO', A_Punto),\n ('PUNTOCOMA', A_PuntoComa),\n ('SLASH', A_Slash),\n ('IGUAL', A_Igual),\n ('DOBLE_IGUAL', A_DobleIgual),\n ('EXCLAMACION_IGUAL', A_ExclamacionIgual),\n ('MENOR', A_Menor),\n ('MAYOR', A_Mayor),\n ('MAYOR_IGUAL', A_Mayorigual),\n ('MENOR_IGUAL', A_Menorigual),\n ('AND', A_AND),\n ('OR', A_OR),\n (\"NUM\", A_Num),\n (\"STRING\", A_String),\n (\"ID\", A_ID),\n]\n\n\n\ndef lex(cadena):\n\n tokens = []\n index = 0\n start = 0\n\n while index < len(cadena):\n\n #Solución al problema del espacio\n c = cadena[index]\n if c.isspace():\n index+=1\n continue\n \n start = index\n posible_candidato = []\n candidatos = []\n lexeme = \"\"\n cadenaPrefijo = \"\"\n todosTrampa = False\n \n while not todosTrampa:\n todosTrampa = True\n lexeme = cadenaPrefijo\n cadenaPrefijo = cadena[start: index + 1]\n candidatos = posible_candidato \n posible_candidato = []\n\n for tipoDeToken, automata in TokenConfig:\n resultado = automata(cadenaPrefijo)\n if resultado == RESULTADO_ACEPTADO:\n posible_candidato.append(tipoDeToken)\n todosTrampa = False\n elif resultado == RESULTADO_NO_ACEPTADO:\n todosTrampa = False \n elif index > len(cadena): #Solución al bucle del ultimo caracter\n break\n\n index += 1\n\n index -= 1\n if len(candidatos) == 0:\n raise Exception(\"TOKEN NO ACEPTADO \" + lexeme)\n else:\n tipoDeToken = candidatos[0]\n tokens.append((tipoDeToken, lexeme))\n\n return tokens\n\n\n","sub_path":"lex.py","file_name":"lex.py","file_ext":"py","file_size_in_byte":2430,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"513930965","text":"# -*- coding:utf-8 -*-\r\n\r\n\r\n# @version: 1.0\r\n# @author: daichi\r\n# @date: '14-12-18'\r\nfrom crawler.dir_page_crawler.db.mysql import MySqlHelper\r\nfrom base.basicDao import BasicDao\r\n\r\n\r\nclass TaoBaoDao(BasicDao):\r\n def __init__(self, autoConfigure, target_table):\r\n BasicDao.__init__(self, autoConfigure, target_table)\r\n\r\n def load_cinema_info(self, dbHelper=None):\r\n flag = False\r\n if not dbHelper:\r\n dbHelper = MySqlHelper()\r\n dbHelper.openByConfObject(self._autoConfigure.get_db_configure())\r\n flag = True\r\n sql = 'select cinemaId,cityId,cityName from tz_cinema'\r\n rows = dbHelper.read(sql)\r\n cinema_info_list = []\r\n for row in rows:\r\n cinema_info_list.append([row[0], row[1], row[2]])\r\n if flag:\r\n dbHelper.close()\r\n return cinema_info_list\r\n\r\n def load_film_ids(self, dbHelper=None):\r\n if not dbHelper:\r\n dbHelper = MySqlHelper()\r\n dbHelper.openByConfObject(self._autoConfigure.get_db_configure())\r\n sql = 'select distinct FilmID from tb_film_list'\r\n rows = dbHelper.read(sql)\r\n film_id_list = []\r\n for row in rows:\r\n film_id_list.append(row[0])\r\n return film_id_list\r\n\r\nif __name__ == \"__main__\":\r\n pass","sub_path":"cinema-movie-monitor/dao/taobao_dao.py","file_name":"taobao_dao.py","file_ext":"py","file_size_in_byte":1312,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"88726111","text":"#!/usr/bin/python\n\nimport argparse\nimport http.client as httplib\nimport json\nimport os\nimport time\n\nimport httplib2\nimport requests\nfrom google.auth.transport.requests import Request\nfrom google.oauth2 import service_account\nfrom google_auth_oauthlib.flow import InstalledAppFlow\nfrom googleapiclient.discovery import build\n\nfrom flickr import fetch_photos\n\n# Explicitly tell the underlying HTTP transport library not to retry, since\n# we are handling retry logic ourselves.\nhttplib2.RETRIES = 1\n\n# Maximum number of times to retry before giving up.\nMAX_RETRIES = 10\n\n# Always retry when these exceptions are raised.\nRETRIABLE_EXCEPTIONS = (\n httplib2.HttpLib2Error,\n IOError,\n httplib.NotConnected,\n httplib.IncompleteRead,\n httplib.ImproperConnectionState,\n httplib.CannotSendRequest,\n httplib.CannotSendHeader,\n httplib.ResponseNotReady,\n httplib.BadStatusLine,\n)\n\n# Always retry when an apiclient.errors.HttpError with one of these status\n# codes is raised.\nRETRIABLE_STATUS_CODES = [500, 502, 503, 504]\n\n# The CLIENT_SECRETS_FILE variable specifies the name of a file that contains\n# the OAuth 2.0 information for this application, including its client_id and\n# client_secret. You can acquire an OAuth 2.0 client ID and client secret from\n# the {{ Google Cloud Console }} at\n# {{ https://cloud.google.com/console }}.\n# Please ensure that you have enabled the YouTube Data API for your project.\n# For more information about using OAuth2 to access the YouTube Data API, see:\n# https://developers.google.com/youtube/v3/guides/authentication\n# For more information about the client_secrets.json file format, see:\n# https://developers.google.com/api-client-library/python/guide/aaa_client_secrets\nCLIENT_SECRETS_FILE = os.environ['CLIENT_SECRETS_FILE']\nSERVICE_ACCOUNT_FILE = os.environ['SERVICE_ACCOUNT_FILE']\n\n# This OAuth 2.0 access scope allows an application to upload files to the\n# authenticated user's YouTube channel, but doesn't allow other types of access.\nSCOPES = [\"https://www.googleapis.com/auth/photoslibrary\"]\nAPI_SERVICE_NAME = \"photoslibrary\"\nAPI_VERSION = \"v1\"\n\nVALID_PRIVACY_STATUSES = (\"public\", \"private\", \"unlisted\")\n\nMEDIA_FORMATS = {\"jpg\": \"jpeg\", \"jpeg\": \"jpeg\", \"png\": \"png\", \"gif\": \"gif\"}\n\n# Authorize the request and store authorization credentials.\ndef get_authenticated_service():\n flow = InstalledAppFlow.from_client_secrets_file(CLIENT_SECRETS_FILE, SCOPES)\n # credentials = flow.run_console()\n credentials = flow.run_local_server()\n return build(API_SERVICE_NAME, API_VERSION, credentials=credentials)\n\n\ndef get_service_with_service_account():\n credentials = service_account.Credentials.from_service_account_file(\n SERVICE_ACCOUNT_FILE, scopes=SCOPES\n )\n credentials.refresh(Request())\n return build(API_SERVICE_NAME, API_VERSION, credentials=credentials)\n\n\ndef upload(photolib, photo_file, media_format):\n token = photolib._http.credentials.token\n url = \"https://photoslibrary.googleapis.com/v1/uploads\"\n headers = {\n \"Authorization\": f\"Bearer {token}\",\n \"Content-Type\": \"application/octet-stream\",\n \"X-Goog-Upload-Content-Type\": f\"image/{media_format}\",\n \"X-Goog-Upload-Protocol\": \"raw\",\n }\n upload_token = requests.post(\n url, headers=headers, data=open(photo_file, \"rb\").read()\n ).text\n\n payload = json.dumps(\n {\n \"newMediaItems\": [\n {\n \"description\": photo_file,\n \"simpleMediaItem\": {\n \"fileName\": photo_file,\n \"uploadToken\": upload_token,\n },\n }\n ]\n }\n )\n\n url = \"https://photoslibrary.googleapis.com/v1/mediaItems:batchCreate\"\n headers = {\"Authorization\": f\"Bearer {token}\", \"Content-Type\": \"application/json\"}\n r = requests.post(url, headers=headers, data=payload)\n\n # r = photolib.mediaItems().batchCreate(payload).execute()\n if r.ok:\n print(f\"Upload response: {r}\")\n else:\n if r.status_code == 429:\n raise ResourceWarning(r.text)\n elif r.status_code == 401:\n raise PermissionError(r.text)\n else:\n raise Exception(f\"Failed to upload: {r.text}\")\n\n\nif __name__ == \"__main__\":\n photolib = get_authenticated_service()\n # photolib = get_service_with_service_account()\n delay = 5\n for photo_file in fetch_photos():\n if not os.path.isfile(photo_file):\n print(f\"Skip invalid format: {photo_file}\")\n continue\n\n media_format = MEDIA_FORMATS.get(photo_file.split(\".\")[-1])\n if media_format:\n retry_count = 0\n while retry_count < 10:\n try:\n upload(photolib, photo_file, media_format)\n # time.sleep(delay)\n break\n except ResourceWarning as e:\n print(f\"Failed to upload photo (delay - {delay}): {e}\")\n time.sleep(61)\n delay += 5\n retry_count += 1\n except PermissionError as e:\n print(f\"Permission issue: {e}\")\n photolib._http.credentials.refresh(Request())\n\n retry_count += 1\n except Exception as e:\n print(f\"Failed to upload photo (delay - {delay}): {e}\")\n time.sleep(61)\n retry_count += 1\n\n try:\n os.remove(photo_file)\n except Exception as e:\n pass\n else:\n raise Exception(f\"Unsupported media format: {photo_file}\")\n","sub_path":"upload_photo.py","file_name":"upload_photo.py","file_ext":"py","file_size_in_byte":5671,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"204489733","text":"# Copyright 2020 Google LLC\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 collections\nimport json\n\nfrom flask import Blueprint\n\nfrom cache import cache\nfrom services.datacommons import fetch_data\n\nWANTED_PLACE_TYPES = [\"Country\", \"State\", \"County\", \"City\"]\n\n# Define blueprint\nbp = Blueprint(\n \"place\",\n __name__,\n url_prefix='/api/place'\n)\n\n\n@bp.route('/child/')\n@cache.memoize(timeout=3600 * 24) # Cache for one day.\ndef child(dcid):\n \"\"\"\n Get the child places for a place.\n \"\"\"\n return json.dumps(child_fetch(dcid))\n\n\n@cache.memoize(timeout=3600 * 24) # Cache for one day.\ndef child_fetch(dcid):\n response = fetch_data(\n '/node/property-values',\n {\n 'dcids': [dcid],\n 'property': 'containedInPlace',\n 'direction': 'in'\n },\n compress=False,\n post=True\n )\n places = response[dcid].get('in', [])\n result = collections.defaultdict(list)\n for place in places:\n for place_type in place['types']:\n if place_type in WANTED_PLACE_TYPES:\n result[place_type].append({\n 'name': place['name'],\n 'dcid': place['dcid'],\n })\n break\n for place in result:\n result[place].sort(key=lambda x: x['dcid'])\n return result\n","sub_path":"server/routes/api/place.py","file_name":"place.py","file_ext":"py","file_size_in_byte":1809,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"434003292","text":"import random\nimport threading\n\nimport requests\nfrom bs4 import BeautifulSoup\nimport os\nimport json \nimport time\nimport random\n\nimport traceback\n\n\nbase_url = 'https://m.meishij.net/'\n\n\ndef get_page(url):\n headers = {'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.106 Safari/537.36'}\n r = requests.get(url,headers = headers)\n r.encoding = 'utf-8'\n return r.text\n\n\ndef get_cate(url): #得到所有的分类\n cate_url=[]\n cate_name=[]\n html=get_page(url)\n soup=BeautifulSoup(html,'html.parser')\n all_div=soup.find('div',{'class':{'category_box mt20'}})\n all_big_cate =all_div.find_all('div',{'class':{'category_sub clear'}})\n for big_div in all_big_cate:\n big_cate = big_div.find('h3').text\n small_cate_a = big_div.find_all('a',{'target':{'_blank'}})\n for sub_a in small_cate_a:\n small_cate=sub_a.text.strip()\n cate= big_cate+'_'+small_cate\n sub_href=sub_a['href']\n cate_url.append(sub_href)\n cate_name.append(cate)\n return cate_url,cate_name\n\ndef get_recipe_url(url): #得到每个类别里面至少10页的上面所有菜谱的网址\n recipe_url=[]\n for i in range(1,10):\n try:\n url_t=url+'page/'+str(i) + '/'\n html=get_page(url_t)\n soup=BeautifulSoup(html,'html.parser')\n info=soup.find('div',{'class':{'ui_newlist_1 get_num'}})\n all_h2 = info.find_all('h2')\n # all_p=info.find_all('p',{'class':{'name'}})\n for h2 in all_h2:\n a = h2.find('a',{'target':{'_blank'}})\n href=a['href']\n recipe_url.append(href)\n except:\n continue\n return recipe_url\n\ndef get_info(url,cate): #得到每道菜谱的详细信息\n html=get_page(url)\n soup=BeautifulSoup(html,'html.parser')\n title_h=soup.find('h1',{'class':{'recipe_De_title'}}) #菜名\n title = title_h.find('a').text\n name_a=soup.find('a',{'class':{'uright'}})\n name = name_a.find('span').text #作者名称\n yl=[] #用料\n all_tr_div=soup.find_all('div',{'class':{'recipeCategory_sub_R clear'}})\n # print(all_tr)\n all_li = []\n for div in all_tr_div:\n all_li.extend(div.find_all('li'))\n for li in all_li: \n yl_name=li.find_all('span')[0].text.replace(\"\\n\", \"\") \n yl_unit=li.find_all('span')[1].text.replace(\"\\n\", \"\")\n if not yl_unit:\n yl_unit='适量'\n yl.append(yl_name+':'+yl_unit)\n # print(yl)\n steps=[]\n pic_url = []\n all_li_div=soup.find('div',{'class':{'recipeStep'}})\n # print(all_li_div)\n all_li = all_li_div.find_all('li')\n # print(all_li)\n for li_div in all_li:\n steps.append(li_div.find('div',{'class':{'recipeStep_word'}}).text)\n image_div = li_div.find('div',{'class':{'recipeStep_img'}})\n image = image_div.find('img')\n try:\n pic_url.append(image['src'])\n except:\n continue\n name=yes_or_no(name)\n yl=yes_or_no(yl)\n steps=yes_or_no(steps)\n # save_recipe(title,ratingValue,cooked,name,yl,steps,tip,cate,pic_url)\n dish_dict = { \n \"菜名\": title,\n \"这道菜的原作者\":name,\n \"用料\":yl,\n \"步骤\":steps,\n \"图片\":pic_url,\n }\n print('{}--{}爬取完成！'.format(cate,title))\n return dish_dict\n\ndef yes_or_no(item): #判断是不是空的\n if not item:\n item='无'\n return item\n\n\nif __name__ == '__main__':\n # print('正在爬取http://www.xicidaili.com/nn的前1页的ip代理')\n # get_proxy(2) # 1页\n # print('第1页ip代理爬取以及检验完成，并存入useful_ip_proxy.txt文件中')\n start_url='https://home.meishichina.com/recipe-type.html'\n cate_url, cate_name=get_cate(start_url)\n for i in range(len(cate_url)):\n print('开始爬取{}分类'.format(cate_name[i]))\n recipe_url=get_recipe_url(cate_url[i])\n # dish_list = []\n for j in range(len(recipe_url)):\n try:\n dishinfo = get_info(recipe_url[j],cate_name[i])\n # print(dishinfo)\n # dish_list.append(dishinfo)\n with open('meishitianxia.txt','a+',encoding='utf-8')as f:\n json.dump(dishinfo,f,ensure_ascii=False)\n f.write('\\n')\n f.close()\n time.sleep(random.random()*10)\n except Exception as e:\n traceback.print_exc()\n # print('get info fail')\n time.sleep(random.random()*5)\n continue\n # big_cate=cate_name[i].split('_')[0]\n # small_cate=cate_name[i].split('_')[1]\n # path='./'+big_cate+'/'\n # if not os.path.exists(path):\n # os.makedirs(path)\n # path_name=path+small_cate+'.json'\n ","sub_path":"meishitianxia.py","file_name":"meishitianxia.py","file_ext":"py","file_size_in_byte":4892,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"77203061","text":"\"\"\"\nThis module handles acceptance testing for the TriageAPI.\n\"\"\"\n\nfrom api.triage_api import create_app\nimport json\n\n\nclass TestTriageAPI:\n \"\"\"\n The `TestTriageAPI` class contains acceptance tests for general API functions.\n \"\"\"\n\n def setup_class(self):\n \"\"\"\n Test setup that occurs once before all tests are run.\n \"\"\"\n self.test_client = create_app().test_client()\n\n def test_base_route(self):\n \"\"\"\n Test Type: Acceptance\n Test Purpose: Tests Requirement INT-4\n \"\"\"\n\n response = self.test_client.get('/')\n response_data = json.loads(response.data)\n\n assert response.status_code == 200\n assert response_data['api'] == 'Triage API'\n\n def test_base_route_version(self):\n \"\"\"\n Test Type: Acceptance\n Test Purpose: Tests Requirement INT-5\n \"\"\"\n\n response = self.test_client.get('/v1')\n response_data = json.loads(response.data)\n\n assert response.status_code == 200\n assert response_data['version'] == 1\n","sub_path":"api/tests/test_triage_api.py","file_name":"test_triage_api.py","file_ext":"py","file_size_in_byte":1058,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"92679576","text":"\n\"\"\"\nr = read file\nw = write file\na = append at the end file\nr + = read and write\n\"\"\"\n\nemp_file = open('README.md', 'r')\n# print(emp_file.readable()) # returns file is readable or not => TRUE or FALSE\n# print(emp_file.readline()) # returns a single of a file\n# print(emp_file.read()) # returns all lines\n# print(emp_file.readlines()) # put into an array\nfor emp in emp_file.readlines():\n print(emp)\nemp_file.close()\n","sub_path":"YouTubeBasic/20.read_file.py","file_name":"20.read_file.py","file_ext":"py","file_size_in_byte":420,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"317585991","text":"import numpy as np\n\nfrom save.SaveFile import SaveFile\nfrom save.ABCSave import ABCSave\nfrom parameters.SaveParameters import SaveParameters\nfrom save.SaveRestart import SaveRestart\nfrom save.SaveTools import SaveTools\n\nclass SaveOCGenetic(ABCSave):\n def __init__(self):\n super().__init__()\n self.par = SaveParameters()\n self.save_files = []\n self.restart_file = None\n\n self.save_tools = SaveTools()\n\n\n def init_save_file_list(self, oc_iterator, restart_step):\n log = SaveFile(\".log\",\n \"#fields: n_iteration, convergence, J, projection on target state, alpha*integral(field^2)\\n\",\n 1,\n 'log_file',\n oc_iterator)\n\n final_pop = SaveFile(\"_final_pop.dat\",\n \"#Final states populations \\n #fields: n_iteration, final states population \\n\",\n 1,\n 'final_pop',\n oc_iterator)\n\n pop_t = SaveFile(\"_pop_t.dat\",\n \"#pop(t) \\n #fields: n_iteration, nstep, states population(t) \\n\",\n restart_step,\n 'pop_t',\n oc_iterator)\n\n field_t = SaveFile(\"_field_t.dat\",\n \"#field(t) \\n#fields: n_iteration, nstep, time, field(t) x, y, z \\n\",\n restart_step,\n 'field_t',\n oc_iterator)\n\n field_ampl = SaveFile(\"_field_ampl.dat\",\n \"#field_amplitudes \\n #field shape = a0 + sum(ai*sin(wi t)) \\n \"\n +\"#omegas = \"+np.array_repr(oc_iterator.genetic_par.omegas_matrix[:,0]).replace('\\n', ''),\n restart_step,\n 'field_ampl',\n oc_iterator)\n\n\n self.save_files = [log, final_pop, pop_t, field_t, field_ampl]\n self.restart_file = SaveRestart(\"_field_bkp.dat\", restart_step, oc_iterator)\n\n\n\n def init_save(self, save_input, log_input, oc_iterator):\n self.par.folder = save_input.folder\n self.par.filename = save_input.name\n self.par.restart_calculation = save_input.restart_calculation\n self.par.append = save_input.append\n self.init_save_file_list(oc_iterator, save_input.restart_step)\n\n\n for i in np.arange(len(self.save_files)):\n print(\"save: \" + str(i))\n self.save_tools.creation_save_files(self.par.folder + self.par.filename, self.save_files[i], self.par.append)\n self.print_log_header(log_input)\n\n\n\n def save(self, iteration):\n for i in np.arange(len(self.save_files)):\n self.save_tools.save_every_n_iterations(iteration, self.par.folder + self.par.filename, self.save_files[i])\n self.save_tools.save_restart(iteration, self.par.folder + self.par.filename, self.restart_file)\n","sub_path":"save/SaveOCGenetic.py","file_name":"SaveOCGenetic.py","file_ext":"py","file_size_in_byte":2977,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"368722663","text":"from openerp import api, fields, models\n\n\ndef open_clipboard(clipboard):\n if not clipboard or '\\n' not in clipboard:\n return\n lines = filter(lambda x: len(x), clipboard.split('\\n'))\n header = dict([(count, x.lower()) for count, x in enumerate(lines[0].split('\\t'))])\n for row in xrange(1, len(lines)):\n yield dict([(header[count], x.strip()) for count, x in enumerate(lines[row].split('\\t'))])\n\n\ndef parse_quantity(line):\n assert 'quantity' in line, 'Quantity should be specified in column \\'Quantity\\''\n qty = int(line['quantity'] or 1)\n assert qty > 0, 'Quantity must be positive, got %s' % qty\n return qty\n\n\ndef parse_boxed(line):\n return bool(int(line.get('boxed', False) or False))\n\n\ndef parse_date(line):\n date = line.get('date')\n assert date, 'Date should be specified in column \\'Date\\''\n return date\n\n\nclass stock_inventory_import(models.TransientModel):\n _name = 'stock.inventory.import'\n\n def _default_location(self):\n warehouse = self.env.user.warehouses and self.env.user.warehouses[0] or self.env.ref('stock.warehouse0')\n return warehouse.wh_input_stock_loc_id\n\n name = fields.Char('Inventory Reference', required=1)\n location = fields.Many2one('stock.location', 'Inventoried Location', required=1, default=_default_location, domain=[('usage', '=', 'internal')])\n data = fields.Text()\n\n @api.multi\n def import_inventory(self):\n prod_obj, lot_obj = self.env['product.product'], self.env['stock.production.lot']\n loc_obj, partner_obj = self.env['stock.location'], self.env['res.partner']\n inv_obj, inv_line_obj = self.env['stock.inventory'], self.env['stock.inventory.line'].with_context(no_theoretical_qty=True)\n move_obj, status_obj = self.env['stock.move'], self.env['stock.quant.status']\n self.ensure_one()\n\n def fetch_location_names():\n locs = loc_obj.search([])\n return {loc_name[1]: loc_obj.browse(loc_name[0]) for loc_name in locs.name_get()}\n\n def fetch_product(line):\n code = line.get('model')\n assert code, 'Product code should be specified in column \\'Model\\''\n prod = prod_obj.search([('default_code', '=', code)], limit=1) \\\n or prod_obj.create({'default_code': code, 'name': code})\n line['model'] = prod\n\n def fetch_serial(line):\n product, serial = line['model'], False\n lot_name = line.get('serial') if 'serial' in line else line.get('serial number')\n if lot_name:\n serial = lot_obj.search([('name', '=', lot_name), ('product_id', '=', product.id)], limit=1) \\\n or lot_obj.create({'name': lot_name, 'product_id': product.id})\n line['serial'] = serial\n\n def fetch_location(line, location_names):\n loc_name = line.get('location')\n assert loc_name, 'Location should be specified in column \\'Location\\' (e.g. \\'WH/Input\\')'\n location = location_names.get(loc_name)\n assert location, 'Location %s not found' % loc_name\n line['location'] = location\n\n def fetch_owner(line):\n owner_name, owner = line.get('owner'), False\n if owner_name:\n owner = partner_obj.search([('name', '=', owner_name)])\n assert owner, 'Owner %s not found' % owner_name\n line['owner'] = owner\n\n def fetch_status(line):\n status_name, status = line.get('status'), False\n if status_name:\n status = status_obj.search([('name', '=', status_name)])\n assert status, 'Status %s not found' % status_name\n line['status'] = status\n\n def parse_clipboard(clipboard):\n data = []\n location_names = fetch_location_names()\n for line in open_clipboard(clipboard):\n fetch_product(line)\n fetch_serial(line)\n fetch_location(line, location_names)\n fetch_owner(line)\n line['quantity'] = parse_quantity(line)\n line['boxed'] = parse_boxed(line)\n line['condition'] = line.get('condition', 'new').lower() or 'new'\n line['pages'] = line.get('pages') or False\n line['date'] = parse_date(line)\n fetch_status(line)\n line['notes'] = line.get('notes') or False\n data.append(line)\n return data\n\n inv = inv_obj.create({'name': self.name, 'location_id': self.location.id, 'filter': 'partial'})\n inv.prepare_inventory()\n\n data = parse_clipboard(self.data)\n for line in data:\n product, serial, location, owner, qty = line['model'], line['serial'], line['location'], line['owner'], line['quantity']\n inv_line_obj.create({\n 'inventory_id': inv.id,\n 'product_id': product.id,\n 'product_qty': qty,\n 'location_id': location.id,\n 'prod_lot_id': serial and serial.id,\n 'partner_id': owner and owner.id,\n 'product_uom_id': product.uom_id.id,\n })\n\n ordered_moves = []\n inv.with_context(ordered_moves=ordered_moves).action_done()\n\n for idx, line in enumerate(data):\n move = move_obj.browse(ordered_moves[idx])\n quant = move.quant_ids\n status = line['status']\n quant.write({\n 'is_boxed': line['boxed'],\n 'condition': line['condition'],\n 'page_count': line['pages'],\n 'in_date': line['date'],\n 'status': status and status.id,\n 'note': line['notes'],\n })\n move.write({\n 'date': line.get('date'),\n })\n return {\n 'view_type': 'form',\n 'view_mode': 'form',\n 'res_model': 'stock.inventory',\n 'res_id': inv.id,\n 'view_id': False,\n 'type': 'ir.actions.act_window'\n }\n","sub_path":"wizard/inventory_import.py","file_name":"inventory_import.py","file_ext":"py","file_size_in_byte":6086,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"456104203","text":"#!/usr/bin/env python\nimport csv, sys, os\nfrom lxml import etree\n\nDEVICECSV = 'sonic_lab_devices.csv'\nLINKCSV = 'sonic_lab_links.csv'\nLAB_CONNECTION_GRAPH_ROOT_NAME = 'LabConnectionGraph'\nLAB_CONNECTION_GRAPH_DPGL2_NAME = 'DevicesL2Info'\n\nclass LabGraph(object):\n\n \"\"\" \n This is used to create \"graph\" file of lab for all connections and vlan info from csv file\n We(both engineer and lab technician) maintian and modify the csv file to keep track of the lab\n infrastucture for Sonic development and testing environment. \n \"\"\"\n\n def __init__(self, dev_csvfile='', link_csvfile=''):\n #TODO:make generated xml file name as parameters in the future to make it more flexible\n self.devices = []\n self.links = []\n self.devcsv = dev_csvfile\n self.linkcsv = link_csvfile\n self.png_xmlfile = 'str_sonic_png.xml'\n self.dpg_xmlfile = 'str_sonic_dpg.xml'\n self.one_xmlfile = 'lab_connection_graph.xml'\n self.pngroot = etree.Element('PhysicalNetworkGraphDeclaration')\n self.dpgroot = etree.Element('DataPlaneGraph')\n\n\n def read_devices(self):\n csv_dev = open(self.devcsv)\n csv_devices = csv.DictReader(csv_dev)\n devices_root = etree.SubElement(self.pngroot, 'Devices')\n for row in csv_devices:\n attrs = {}\n self.devices.append(row)\n for key in row:\n if key.lower() != 'managementip':\n attrs[key]=row[key].decode('utf-8')\n prod = etree.SubElement(devices_root, 'Device', attrs)\n csv_dev.close()\n \n def read_links(self):\n csv_file = open(self.linkcsv)\n csv_links = csv.DictReader(csv_file)\n links_root = etree.SubElement(self.pngroot, 'DeviceInterfaceLinks')\n for link in csv_links:\n attrs = {}\n for key in link:\n if key.lower() != 'vlanid' and key.lower() != 'vlanmode':\n attrs[key]=link[key].decode('utf-8')\n prod = etree.SubElement(links_root, 'DeviceInterfaceLink', attrs)\n self.links.append(link)\n csv_file.close()\n \n def generate_dpg(self):\n for dev in self.devices:\n hostname = dev.get('Hostname', '')\n managementip = dev.get('ManagementIp', '')\n if hostname and 'fanout' in dev['Type'].lower():\n ###### Build Management interface IP here, if we create each device indivial minigraph file, we may comment this out \n l3inforoot = etree.SubElement(self.dpgroot, 'DevicesL3Info', {'Hostname': hostname})\n etree.SubElement(l3inforoot, 'ManagementIPInterface', {'Name': 'ManagementIp', 'Prefix': managementip})\n ####### Build L2 information Here\n l2inforoot = etree.SubElement(self.dpgroot, LAB_CONNECTION_GRAPH_DPGL2_NAME, {'Hostname': hostname})\n vlanattr = {}\n for link in self.links:\n if link['StartDevice'] == hostname:\n vlanattr['portname'] = link['StartPort']\n if link['EndDevice'] == hostname:\n vlanattr['portname'] = link['EndPort']\n if link['StartDevice'] == hostname or link['EndDevice'] == hostname:\n vlanattr['vlanids'] = link['VlanID']\n vlanattr['mode'] = link['VlanMode']\n etree.SubElement(l2inforoot, 'InterfaceVlan', vlanattr)\n\n def create_xml(self):\n '''\n\n if two seperate file of png and dpg needed, uncomment these part\n\n pngxml = open(self.png_xmlfile, 'w')\n png = etree.tostring(self.pngroot, pretty_print=True)\n pngxml.write(png)\n\n pngxml = open(self.dpg_xmlfile, 'w')\n dpg = etree.tostring(self.dpgroot, pretty_print=True)\n pngxml.write(dpg)\n '''\n\n onexml = open(self.one_xmlfile, 'w')\n root=etree.Element(LAB_CONNECTION_GRAPH_ROOT_NAME)\n root.append(self.pngroot)\n root.append(self.dpgroot)\n result = etree.tostring(root, pretty_print=True)\n onexml.write(result)\n\ndef main():\n\n mygraph = LabGraph(DEVICECSV, LINKCSV)\n\n mygraph.read_devices()\n mygraph.read_links()\n mygraph.generate_dpg()\n mygraph.create_xml()\n\n\nif __name__ == '__main__':\n main()\n\n","sub_path":"sonic-mgmt/ansible/files/creategraph.py","file_name":"creategraph.py","file_ext":"py","file_size_in_byte":4327,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"7467075","text":"#!/usr/bin/env python\n\nfrom __future__ import print_function\nimport math\nimport os\nimport numpy as np\nimport rospy\n\nfrom lxml import etree\n\ntree = etree.parse('/home/sietse/catkin_ws/src/Rowing/boot3_description/config/param.xacro-template')\nroot = tree.getroot()\n\netree.tostring(root)\n\nspandict = root[0].attrib\n\nprint (spandict.get('name'))\nprint (spandict.get('value'))\n\ntochange = ['span', 'hip2', 'elbow_left']\ntovalue = [ '1.0', '4', '56']\n\nfor jnt in tochange:\n value = tovalue[tochange.index(jnt)]\n for child in root:\n if child.attrib['name'] == jnt:\n child.attrib['value'] = value\n\n\nfor child in root:\n print(child.attrib)\n\n#tree.write('new_param.xacro')\n\n \n\n\n","sub_path":"gazebo/boot3_description/closechains/xmltest.py","file_name":"xmltest.py","file_ext":"py","file_size_in_byte":700,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"69751192","text":"import pytest\nimport tempfile\nimport subprocess\nimport random\nimport time\nimport pymongo\n\nfrom blitzdb.backends.mongo import Backend\nfrom blitzdb import Document\nfrom blitzdb.tests.helpers.movie_data import Actor,Director,Movie,generate_test_data\n\n@pytest.fixture(scope = \"function\")\ndef large_test_data(request,backend):\n return generate_test_data(request,backend,100)\n\n@pytest.fixture(scope = \"function\")\ndef large_test_data(request,backend):\n return generate_test_data(request,backend,100)\n\n@pytest.fixture(scope = \"function\")\ndef small_test_data(request,backend):\n return generate_test_data(request,backend,20)\n\n@pytest.fixture(scope = \"module\")\ndef backend(request):\n con = pymongo.MongoClient()\n con.drop_database(\"blitzdb_test_3243213121435312431\")\n db = pymongo.MongoClient()['blitzdb_test_3243213121435312431']\n backend = Backend(db)\n\n for idx in ['name','director']:\n backend.create_index(Movie,idx)\n\n backend.create_index(Actor,'name')\n backend.create_index(Actor,'movies')\n\n return backend\n\ndef test_basic_storage(backend,small_test_data):\n\n (movies,actors,directors) = small_test_data\n\n assert len(backend.filter(Movie,{})) == len(movies)\n assert len(backend.filter(Actor,{})) == len(actors)\n\ndef test_list_query(backend,small_test_data):\n\n (movies,actors,directors) = small_test_data\n\n movie = None\n i = 0\n while not movie or len(movie.cast) < 4:\n movie = movies[i]\n actor = movie.cast[0]['actor']\n i+=1\n\n other_movie = movies[i%len(movies)]\n while other_movie in actor.movies:\n other_movie = movies[i%len(movies)]\n i+=1\n\n assert actor in backend.filter(Actor,{'movies' : movie})\n assert actor not in backend.filter(Actor,{'movies' : other_movie})\n\ndef test_indexed_delete(backend,small_test_data):\n\n (movies,actors,directors) = small_test_data\n\n for movie in movies:\n backend.filter(Actor,{'movies' : movie}).delete()\n\n for actor in backend.filter(Actor,{}):\n assert actor.movies == []\n\ndef test_indexed_delete(backend):\n\n movie = Movie({'name' : 'The Godfather'})\n actor = Actor({'name' : 'Marlon Brando'})\n actor.performances = [movie]\n movie.cast = {'Don Corleone' : actor}\n\n movie.save(backend)\n\ndef test_non_indexed_delete(backend,small_test_data):\n\n (movies,actors,directors) = small_test_data\n\n for movie in movies:\n backend.filter(Director,{'movies' : movie}).delete()\n\n for director in backend.filter(Director,{}):\n assert director.movies == []\n\ndef test_default_backend(backend,small_test_data):\n\n movies = backend.filter(Movie,{})\n old_len = len(movies)\n movie = movies[0]\n movie.delete()\n\n with pytest.raises(Movie.DoesNotExist):\n backend.get(Movie,{'pk' : movie.pk})\n\n assert old_len == len(backend.filter(Movie,{}))+1\n\ndef test_index_reloading(backend,small_test_data):\n\n (movies,actors,directors) = small_test_data\n\n backend.filter(Actor,{'movies' : movies[0]}).delete()\n assert list(backend.filter(Actor,{'movies' : movies[0]})) == []\n\ndef test_positional_query(backend,small_test_data):\n\n \"\"\"\n We test a search query which explicitly references a given list item in an object\n \"\"\"\n\n (movies,actors,directors) = small_test_data\n\n movie = None\n i = 0\n while not movie or len(movie.cast) < 3:\n if len(movies[i].cast):\n movie = movies[i]\n actor = movie.cast[0]['actor']\n index = actor.movies.index(movie)\n i+=1\n\n assert actor in backend.filter(Actor,{'movies.%d' % index : movie})\n\n","sub_path":"blitzdb/tests/test_mongo_backend.py","file_name":"test_mongo_backend.py","file_ext":"py","file_size_in_byte":3584,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"523325002","text":"# Question Generator\nfrom __future__ import division, print_function\n\nimport util.io as io\nimport dataset as dset\nfrom vocab import Vocab\nimport misc\nimport opt_parser as op\nfrom attribute_detector import AttributeDetector\n\nimport torch\nfrom torch.autograd import Variable\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nimport torchvision\nimport torchvision.transforms as transforms\n\nfrom PIL import Image\nimport numpy as np\nimport time\nimport os\nimport sys\n\n\nclass QuestionGenerator(nn.Module):\n '''\n Basic question generator with CNN-LSTM architecture.\n '''\n\n def __init__(self, opts = None, fn = None, gpu_id = 0):\n '''\n opts defined at opts.py\n '''\n assert (opts or fn)\n\n super(QuestionGenerator, self).__init__()\n \n # set model options\n if fn:\n # load opts from file\n self.opts = torch.load(fn)['opts']\n self.opts.gpu_id = gpu_id\n opts = self.opts\n else:\n self.opts = opts\n\n # load cnn\n self.cnn = _get_cnn(opts.cnn_type, opts.cnn_feat_layer) # cnn parameter fixed\n\n if 'pair_method' not in opts:\n opts.pair_method = None # just for compatible with old version\n\n image_feat_size = _get_feat_size(opts.cnn_type, opts.cnn_feat_layer, opts.image_repr, opts.pair_method)\n\n self.cnn_input_size = 224\n\n # load image transform for PIL image\n self.image_transform = transforms.Compose([\\\n misc.Resize(size = self.cnn_input_size),\\\n transforms.ToTensor(),\\\n transforms.Normalize(mean = [0.485, 0.456, 0.406], std = [0.229, 0.224, 0.225])\\\n ])\n\n # load vocabulary\n self.vocab = Vocab(fn = opts.vocab_fn)\n\n # set image embedding\n self.image_embed = nn.Linear(image_feat_size, opts.image_embed_size)\n self.image_embed_drop = nn.Dropout(opts.dropout)\n\n # set word embedding\n self.word_embed = nn.Embedding(len(self.vocab), opts.word_embed_size)\n self.word_embed_drop = nn.Dropout(opts.dropout)\n\n # set attribute embedding\n\n if opts.att_fmt == 'label':\n # train an attribute embedding layer\n self.att_embed = nn.Embedding(opts.att_num+1, opts.att_embed_size)\n self.att_embed_drop = nn.Dropout(opts.dropout)\n\n if 'pair' in opts.image_fmt:\n att_embed_output_size = 2 * opts.att_embed_size\n else:\n att_embed_output_size = opts.att_embed_size\n\n elif opts.att_fmt == 'word2vec' or opts.att_fmt == 'glove':\n # directly use input attribute embedding vector\n if 'pair' in opts.image_fmt:\n att_embed_output_size = 2 * att_embed_size\n else:\n att_embed_output_size = opts.att_embed_size\n\n elif 'score' in opts.att_fmt:\n if opts.att_embed_size != opts.att_num:\n print('automatically change \"att_embed_size\" from %d to %d, in order to match \"att_num\"' % (opts.att_embed_size, opts.att_num))\n opts.att_embed_size = opts.att_num\n\n if opts.att_fmt == 'score_cc':\n att_embed_output_size = 2 * opts.att_embed_size\n elif opts.att_fmt == 'score' and 'pair' in opts.image_fmt:\n att_embed_output_size = 2 * opts.att_embed_size\n else:\n att_embed_output_size = opts.att_embed_size\n\n else:\n # no attribute input\n opts.att_embed_size = 0\n att_embed_output_size = 0\n\n # set rnn\n rnn_input_size = opts.image_embed_size + opts.word_embed_size + att_embed_output_size\n if opts.rnn_type == 'lstm':\n self.rnn = nn.LSTM(input_size = rnn_input_size, hidden_size = opts.rnn_hidden_size, \\\n num_layers = opts.rnn_num_layer, bias = False, dropout = opts.dropout, batch_first = True)\n elif opts.rnn_type == 'gru':\n self.rnn = nn.GRU(input_size = rnn_input_size, hidden_size = opts.rnn_hidden_size, \\\n num_layers = opts.rnn_num_layer, bias = False, dropout = opts.dropout, batch_first = True)\n\n # set word classifer\n self.word_cls = nn.Linear(opts.rnn_hidden_size, len(self.vocab))\n\n # attribute detector status\n if 'att_fn' in opts and opts.att_fn is not None:\n self.mount_attribute_detector(opts.att_fn)\n else:\n self.att_detector = None\n\n if fn:\n self.load_model(fn)\n else:\n self._init_weight()\n\n # move model to gpu\n self.cuda(opts.gpu_id)\n\n\n def learnable_parameters(self):\n '''\n return a generator of learnable parameters\n '''\n\n # currently joint learning is not supported\n memo = set(self.cnn.parameters())\n if isinstance(self.att_detector, nn.Module):\n memo = memo.union(set(self.att_detector.parameters()))\n # return self.parameters(memo = memo)\n for p in self.parameters():\n if p not in memo:\n yield p\n\n\n def _init_weight(self):\n '''\n initialize the weights of layers except CNN and RNN\n '''\n init_range = 0.08 # follow the caffe setting\n\n self.image_embed.weight.data.uniform_(-init_range, init_range)\n self.image_embed.bias.data.fill_(0)\n self.word_embed.weight.data.uniform_(-init_range, init_range)\n self.word_cls.weight.data.uniform_(-init_range, init_range)\n self.word_cls.bias.data.fill_(0)\n\n if self.opts.att_fmt == 'label':\n self.att_embed.weight.data.uniform_(-init_range, init_range)\n\n for w_name, w in self.rnn._parameters.iteritems():\n if w_name.startswith('weight'):\n w.data.uniform_(-init_range, init_range)\n elif w_name.startswith('bias'):\n w.data.fill_(0)\n\n def _init_hidden(self, batch_size):\n '''\n return initial value of RNN hidden nodes\n '''\n w = next(self.parameters()).data # only for getting data type\n rnn_num_layer = self.opts.rnn_num_layer\n rnn_hidden_size = self.opts.rnn_hidden_size\n\n if self.opts.rnn_type == 'lstm':\n return (Variable(w.new(rnn_num_layer, batch_size, rnn_hidden_size).zero_()),\n Variable(w.new(rnn_num_layer, batch_size, rnn_hidden_size).zero_()))\n else:\n return Variable(w.new(rnn_num_layer, batch_size, rnn_hidden_size).zero_())\n\n def _image_embedding(self, input_data, fmt = None):\n '''\n perform image embedding\n output:\n feat\n '''\n if fmt is None:\n fmt = self.opts.image_fmt\n\n image_repr = self.opts.image_repr\n bsz = self._get_batch_size(input_data)\n\n if fmt == 'feat':\n # input_data: (region_feat, image_feat, relative_location) in Tensor\n if image_repr == 'region':\n data = input_data[0]\n elif image_repr == 'full':\n data = torch.cat(input_data, 1)\n else:\n raise Exception('invalid image representation %s for input format %s' % (image_repr, fmt))\n\n embed_input = data\n feat_output = embed_input\n\n elif fmt == 'rgb':\n # input_data: list of PIL image\n if not isinstance(input_data, list): # single input\n input_data = [input_data]\n images = input_data\n data = torch.Tensor(bsz, 3, self.cnn_input_size, self.cnn_input_size)\n\n if image_repr == 'region':\n for i, img in enumerate(images):\n data[i] = self.image_transform(img)\n\n data = data.cuda(self.opts.gpu_id)\n embed_input = self.cnn(Variable(data)).view(bsz, -1)\n feat_output = embed_input\n else:\n raise Exception('invalid image representation %s for input format %s' % (image_repr, fmt))\n\n elif fmt == 'rgb+bbox':\n # \n # input_data: list of (PIL, bbox)\n if not isinstance(input_data, list): # single input\n input_data = [input_data]\n images, bboxs = zip(*input_data)\n regions = [img.crop(bbox) for img, bbox in input_data]\n\n if image_repr == 'region':\n # only use region feature\n data = torch.Tensor(bsz, 3, self.cnn_input_size, self.cnn_input_size)\n\n for i, rg in enumerate(regions):\n data[i] = self.image_transform(rg)\n\n data = data.cuda(self.opts.gpu_id)\n embed_input = self.cnn(Variable(data)).view(bsz, -1)\n feat_output = embed_input\n\n elif image_repr == 'full':\n # use region feature, image feature and location\n data = torch.Tensor(bsz*2, 3, self.cnn_input_size, self.cnn_input_size)\n for i, (rg, img) in enumerate(zip(regions, images)):\n data[2*i] = self.image_transform(rg)\n data[2*i+1] = self.image_transform(img)\n data = data.cuda(self.opts.gpu_id)\n embed_input = self.cnn(Variable(data)).view(bsz, -1)\n\n # relative location\n image_size = [img.size for img in images]\n r_loc = [[x1/w, y1/h, x2/w, y2/h, (x2-x1)*(y2-y1)/w/h] \\\n for (x1, y1, x2, y2), (w, h) in zip(bboxs, image_size)]\n r_loc = Variable(torch.Tensor(r_loc).cuda(self.opts.gpu_id))\n\n embed_input = torch.cat((embed_input, r_loc), 1)\n feat_output = embed_input\n else:\n raise Exception('invalid image representation %s for input format %s' % (image_repr, fmt))\n\n elif fmt == 'feat_pair':\n # input_data: (feat_1, feat_2)\n # feat_k is (region_feat_k, image_feat_k, relative_location_feat_k) in Tensor\n\n if image_repr == 'region':\n feat_1, feat_2 = input_data[0][0], input_data[1][0]\n feat_output = (feat_1, feat_2)\n else:\n raise Exception('invalid image representation %s for input format %s' % (image_repr, fmt))\n\n # fuse features from image pairs\n pair_method = self.opts.pair_method\n \n if pair_method == 'c':\n embed_input = torch.cat((feat_1, feat_2), 1)\n elif pair_method == 'd':\n embed_input = feat_1 - feat_2\n elif pair_method == 'ad':\n embed_input = torch.abs(feat_1 - feat_2)\n elif pair_method == 'c+ad':\n embed_input = torch.cat((feat_1, feat_2, torch.abs(feat_1 - feat_2)), 1)\n elif pair_method == 'f+d':\n embed_input = torch.cat((feat_1, feat_1 - feat_2), 1)\n elif pair_method == 'f+ad':\n embed_input = torch.cat((feat_1, torch.abs(feat_1 - feat_2)), 1)\n else:\n raise Exception('invalid pairing method %s' % pair_method)\n\n else:\n raise Exception('invalid image input format %s'%(fmt))\n\n image_embed = self.image_embed(embed_input)\n image_embed = F.relu(self.image_embed_drop(image_embed))\n\n # alse output feat for attribute model\n return image_embed, feat_output\n\n def _lang_embedding(self, input_data, fmt = None):\n '''\n perform language embedding\n '''\n\n if fmt is None:\n fmt = self.opts.lang_fmt\n\n seq_len = self.opts.seq_len\n\n if fmt == 'seqs':\n # input_data: (seq_input, seq_target, seq_mask), in LongTensor\n seq_input = input_data[0]\n assert(seq_input.size(1) == seq_len)\n\n elif fmt == 'str':\n # seq_input, seq_target, seq_mask = self\n if not isinstance(input_data, list):\n input_data = [input_data]\n seq_input, _, _ = self.vocab.encode_batch(input_data, seq_len)\n seq_input = Variable(torch.LongTensor(seq_input).cuda(self.opts.gpu_id))\n\n else:\n raise Exception('invalid language input format %s'%(fmt))\n\n # get word embedding of input sequence\n seq_embed = self.word_embed(seq_input)\n seq_embed = F.tanh(self.word_embed_drop(seq_embed))\n return seq_embed\n\n def _att_embedding(self, att_input, img_feat = None, fmt = None):\n '''\n perform attribute embedding\n '''\n if fmt is None:\n fmt = self.opts.att_fmt\n\n\n if 'pair' in self.opts.image_fmt:\n # att_input = (att_1, att_2)\n\n if fmt == 'word2vec' or fmt == 'glove':\n assert isinstance(att_input, (tuple, list)) and len(att_input) == 2\n att_input = torch.cat(att_input, 1)\n att_embed = att_input\n\n elif fmt == 'label':\n assert isinstance(att_input, (tuple, list)) and len(att_input) == 2\n att_input = torch.cat(att_input, 1)\n bsz = att_input.size(0)\n att_embed = self.att_embed(att_input.long()).view(bsz, -1)\n att_embed = F.relu(self.att_embed_drop(att_embed))\n\n elif fmt.startswith('score'):\n # soft attribute score\n assert isinstance(img_feat, (tuple, list)) and len(img_feat) == 2\n score_1, score_2 = self.att_detector(img_feat[0]), self.att_detector(img_feat[1])\n\n if fmt == 'score':\n att_embed = torch.cat((score_1, score_2), 1)\n elif fmt == 'score_c':\n att_embed = score_1 * (1 - score_2) + score_2 * (1 - score_1)\n elif fmt == 'score_cc':\n att_embed = torch.cat((\n score_1 * (1 - score_2) + score_2 * (1 - score_1),\n score_1 * score_2), 1)\n\n else:\n if fmt == 'word2vec' or fmt == 'glove':\n att_embed = att_input\n elif fmt == 'label':\n att_embed = self.att_embed(att_input.long().view(-1))\n att_embed = F.relu(self.att_embed_drop(att_embed))\n elif fmt == 'score':\n assert isinstance(img_feat, Variable)\n att_embed = self.att_detector(img_feat)\n\n return att_embed\n\n\n def _get_batch_size(self, image_input):\n '''\n get batch_size from image input\n '''\n\n if self.opts.image_fmt == 'feat':\n\n assert len(image_input) == 3\n return image_input[0].size(0) # (region_feat, image_feat, location_feat)\n\n elif self.opts.image_fmt in {'rgb', 'rgb+bbox'}:\n\n if isinstance(image_input, list):\n return len(image_input) # image list\n else:\n return 1 # single image\n elif self.opts.image_fmt == 'feat_pair':\n return image_input[0][0].size(0)\n\n def forward(self, image_input, lang_input, att_input = None):\n '''\n forward process in training\n '''\n opts = self.opts\n\n # get batch size from input\n bsz = self._get_batch_size(image_input)\n\n # init state. For lstm, state = (h,c); for gru, state = h\n state = self._init_hidden(bsz)\n\n # compute image feature\n image_embed, image_feat = self._image_embedding(image_input)\n\n # compute language sequences\n seq_embed = self._lang_embedding(lang_input)\n\n # compute attribute embeddinf if needed\n # directly add attribute embedding into image embedding for convenience\n if opts.att_fmt != 'none':\n\n att_embed = self._att_embedding(att_input, image_feat)\n if 'norm_before_fusion' in opts and opts.norm_before_fusion == True:\n image_embed = image_embed / image_embed.norm(2, 1).expand_as(image_embed)\n att_embed = att_embed / att_embed.norm(2, 1).expand_as(att_embed)\n \n image_embed = torch.cat((image_embed, att_embed), 1)\n\n # rnn forward\n rnn_input = torch.cat((seq_embed, torch.stack([image_embed]*opts.seq_len, dim = 1)), 2)\n rnn_output, _ = self.rnn(rnn_input, state) # rnn_output.size: bsz x seq_len x H\n\n rnn_output = rnn_output.contiguous()\n\n # word classify\n\n # word_cls_output = self.word_cls(rnn_output.view(bsz * opts.seq_len))\n output = F.log_softmax(self.word_cls(rnn_output.view(opts.seq_len * bsz, -1))).view(bsz, opts.seq_len,-1)\n\n return output\n\n def _predict_next_word(self, input_embed, previous_word, state, wp_method = 3):\n '''\n helper function for sample() and sample_beam(). predict probablity distribution of next word\n input:\n input_embed : (Variable of size n*embed_size) image (and attribute) embedding\n previous_word : previous word\n state : (Variable of size rnn_num_layer*n*rnn_hidden_size) hidden state\n wp_method : method to compute word probability for collaborative generation\n 0 - mean\n 1 - prod\n 2 - min\n 3 - prod / sum\n output:\n log_prob : (Variable of size n*vocab_size) probability distribution of next word\n new_state : (Variable of size rnn_num_layer*n*rnn_hidden_size) new state\n '''\n\n if not isinstance(previous_word, list):\n previous_word = [previous_word]\n\n # M = 1 (single image) or 2 (image pair)\n M = input_embed.size(0)\n assert M == 1 or M == 2\n\n # compute word embedding\n word_idx = Variable(torch.LongTensor(previous_word).cuda(self.opts.gpu_id))\n word_embed = self.word_embed(word_idx) # size = (1, word_embed_size)\n\n # compute rnn input\n word_embed = torch.stack([word_embed]*M, 0)\n input_embed = input_embed.view(M, 1, -1)\n rnn_input = torch.cat((word_embed, input_embed), 2)\n\n rnn_output, new_state = self.rnn(rnn_input, state)\n rnn_output = rnn_output.contiguous().view(M, -1)\n\n prob = F.softmax(self.word_cls(rnn_output)).view(M, -1)\n\n if M == 2:\n if wp_method == 0:\n # method 0: mean\n prob = prob.mean(dim = 0)\n elif wp_method == 1:\n # method 1: prod\n prob = prob.prod(dim = 0)\n elif wp_method == 2:\n # method 2: min\n prob = prob.min(dim = 0)[0]\n elif wp_method == 3:\n # method 3: prod/sum\n prob = prob.prod(dim = 0) / prob.sum(dim = 0)\n else:\n raise Exception('invalid collaborative generation method')\n\n prob /= prob.sum().data[0]\n\n log_prob = torch.log(prob)\n return log_prob, new_state\n\n\n def sample_beam(self, input_embed, att_scores = None, beam_size = 5, max_length = 20, wp_method = 1):\n '''\n sample questions from imput using beam search\n input:\n input_embed : Variable of size (N * M * D), where:\n N: attribute (or attribute pair) number\n M: 1 for single image, 2 for image pair\n D: embedding size (concatenation of image embedding and attribute embedding)\n att_scores : list of M float numbers. The attribute (or attribute pair) score outputed by attribute detector\n if att_scores == None, each attribute will be given socre 1\n beam_size\n max_length\n wp_method: : see _predict_next_word()\n \n output:\n beam : list of K str, where K = beam_size. Generated questions\n beam_prob : prob of each generated beam\n att_idx : attribute indexs (index in input_embed and att_scores) of each generated beam\n '''\n \n N, M = input_embed.size(0), input_embed.size(1)\n assert M == 1 or M == 2 # M: must be 1 (single image) or 2 (image pair)\n if att_scores is None:\n att_scores = [1] * N\n else:\n assert len(att_scores) == N\n\n token_bos = self.vocab['']\n token_eos = self.vocab['']\n\n # init beams\n beam = []\n beam_att_idx = []\n beam_log_prob = []\n beam_state = []\n beam_complete = 0\n\n for idx in xrange(N):\n beam.append([token_bos])\n beam_att_idx.append(idx)\n beam_log_prob.append(np.log(att_scores[idx]))\n beam_state.append(self._init_hidden(M))\n\n while beam_complete < len(beam):\n expansions = []\n for beam_idx, (b, p, state, att_idx) in enumerate(zip(beam, beam_log_prob, beam_state, beam_att_idx)):\n previous_word = b[-1]\n if len(b) >= max_length or previous_word == token_eos:\n exp = {'prefix_beam_idx': beam_idx,\n 'extension': [],\n 'log_prob': p,\n 'state': state\n }\n expansions.append(exp)\n\n else:\n exp_log_prob, new_state = self._predict_next_word(input_embed[att_idx], previous_word, state, wp_method)\n exp_log_prob = exp_log_prob.data.cpu().view(-1).numpy()\n exp_word_idx = exp_log_prob.argsort()[-beam_size:]\n for idx in exp_word_idx:\n exp_p = exp_log_prob[idx]\n exp = {'prefix_beam_idx': beam_idx,\\\n 'extension': [idx],\\\n 'log_prob': p + exp_p,\\\n 'state': new_state}\n expansions.append(exp)\n expansions.sort(key = lambda exp: -1*exp['log_prob'])\n expansions = expansions[0:beam_size]\n\n beam = [beam[exp['prefix_beam_idx']] + exp['extension'] for exp in expansions]\n beam_log_prob = [exp['log_prob'] for exp in expansions]\n beam_state = [exp['state'] for exp in expansions]\n beam_att_idx = [beam_att_idx[exp['prefix_beam_idx']] for exp in expansions]\n \n beam_complete = 0\n for b in beam:\n if b[-1] == token_eos or len(b) >= max_length:\n beam_complete += 1\n\n beam_prob = [np.exp(log_p) for log_p in beam_log_prob]\n return beam, beam_prob, beam_att_idx\n \n def generate(self, image_input, att_input = None, beam_size = 5, max_length = 20, wp_method = 1, num_top_att = 1):\n '''\n generate question from single image or image pair\n\n input:\n image_input : image or image feature\n att_input : if att_input != None, use given attribute(or attribute pair)\n or, use attribute_detector model to output attirbute(or attribute pair)\n beam_size : width of beam search\n max_length : max length of generated sequences\n num_top_att : number of attributes to use\n output:\n ques: question(str)\n prob: probability of question\n '''\n\n # get image embedding and batch_size\n image_embed, image_feat = self._image_embedding(image_input)\n collaboratieve = (image_embed.size(0) == 2)\n image_pair = (self.opts.image_fmt == 'feat_pair')\n\n info = {}\n\n if self.opts.att_fmt == 'none':\n # no attribute condition\n input_embed = image_embed.unsqueeze(dim = 0)\n att_scores = None\n else:\n if att_input is not None:\n # use input attribute (format should be correctly set outside)\n # input should be one attribute or one attribute pair\n att_embed = self._att_embedding(att_input, image_feat)\n input_embed = torch.cat((image_embed, att_embed), 1).unsqueeze(dim = 0)\n att_scores = None\n elif self.opts.att_fmt.startswith('score'):\n # get soft attribute condition from attribute detector\n assert self.att_detector is not None, 'question generator: no attribute detector'\n att_embed = self._att_embedding(None, image_feat)\n input_embed = torch.cat((image_embed, att_embed), 1).unsqueeze(dim = 0)\n att_scores = None\n\n else:\n # get hard attribute condition from attribute detector\n assert self.att_detector is not None, 'question generator: no attribute detector'\n\n # get attribute candidates\n # att_cand : for single attribute: list of (att_idx, att_score)\n # for attribute pair : list of ([att_idx_1, att_idx_2], att_score)\n # where att_idx is an int, att_score is a float\n # att_vec : the attribute score vector (output of att_detector.forward)\n # for single attribute: Variable of size 1*num_att\n # for attribute pair: Variable of size 2*num_att\n\n\n if image_pair:\n if collaboratieve:\n raise Exception('image_pair + collaborate + hard_att is not supported')\n else:\n att_cand, _ = self.att_detector.detect_pair(torch.cat(image_feat, 0), num = num_top_att)\n\n else:\n if collaboratieve:\n att_cand, _ = self.att_detector.detect_pair(image_feat, num = num_top_att)\n else:\n att_cand, _ = self.att_detector.detect(image_feat, num = num_top_att)\n\n # convert attribute candidates into proper format\n input_embed = []\n att_scores = []\n info['att_cand'] = [] # attribute candidates\n for idx, (att, score) in enumerate(att_cand):\n att_input = self.att_detector.att_format_trans(att, 'label', self.opts.att_fmt)\n att_input = Variable(torch.Tensor(att_input).cuda(self.opts.gpu_id))\n if image_pair:\n att_input = (att_input[0].view(1,-1), att_input[1].view(1,-1))\n\n att_embed = self._att_embedding(att_input, image_feat)\n\n input_embed.append(torch.cat((image_embed, att_embed), 1))\n att_scores.append(score)\n\n att_name = self.att_detector.att_format_trans(att, 'label', 'name')\n info['att_cand'].append((att_name, score))\n\n input_embed = torch.stack(input_embed, dim = 0)\n\n # generate question using beam search\n beams, probs, att_idxs = self.sample_beam(input_embed, att_scores, beam_size, max_length, wp_method)\n\n # output top-1 generated question, and all attribute candidates\n ques = self.vocab.decode(beams[0]) # this is a str\n info['prob'] = probs[0] # a float number\n info['att_idx'] = att_idxs[0] # the index of selected attribute\n\n return ques, info\n\n\n def mount_attribute_detector(self, att_fn):\n '''\n mount an attribute detector, which will be used in attribute-conditioned questino generation\n '''\n\n if not att_fn.startswith('models'):\n att_fn = 'models/' + att_fn\n\n self.att_detector = AttributeDetector(fn = att_fn, gpu_id = self.opts.gpu_id)\n self.att_detector.eval()\n self.opts.att_fn = att_fn\n\n print('question generator: mount attribute detector from \"%s\"' % att_fn)\n\n\n def save_model(self, fn):\n '''\n save options and weights into file\n '''\n model_info = {\n 'opts': self.opts,\n 'params': list(self.learnable_parameters())\n }\n\n torch.save(model_info, fn)\n\n def load_model(self, fn):\n '''\n load weights from file\n '''\n print('load model parameters from %s'%fn)\n params = torch.load(fn)['params']\n\n for idx, p in enumerate(self.learnable_parameters()):\n p.data.copy_(params[idx].data)\n\n\n\ndef _get_cnn(cnn_type, feat_layer):\n '''\n customize cnn architecture for feature extraction\n '''\n if cnn_type == 'vgg19':\n cnn_full = torchvision.models.vgg19(pretrained = True)\n cnn_full.eval()\n # fix the cnn parameters\n for param in cnn_full.parameters():\n param.requires_grad = False\n\n if feat_layer == 'fc':\n # feat_size: 1000\n cnn = cnn_full\n\n elif feat_layer == 'conv_map':\n # feat_size: 512 x 7 x 7\n cnn = cnn_full.features\n\n elif feat_layer == 'conv':\n # feat_size: 512\n cnn = nn.Sequential(cnn_full.features, nn.MaxPool2d(7))\n else:\n raise Exception('invalid feature layer \"%s\" for vgg' % feat_layer)\n\n elif cnn_type == 'resnet101':\n cnn_full = torchvision.models.resnet101(pretrained = True)\n cnn_full.eval()\n for param in cnn_full.parameters():\n param.requires_grad = False\n\n if feat_layer == 'fc':\n # feat_size: 1000\n cnn = cnn_full\n\n elif feat_layer == 'conv_map':\n # feat_size: 2048 x 7 x 7\n cnn = nn.Sequential(cnn_full.conv1, cnn_full.bn1, cnn_full.relu, cnn_full.maxpool,\\\n cnn_full.layer1, cnn_full.layer2, cnn_full.layer3, cnn_full.layer4)\n\n elif feat_layer == 'conv':\n cnn = nn.Sequential(cnn_full.conv1, cnn_full.bn1, cnn_full.relu, cnn_full.maxpool,\\\n cnn_full.layer1, cnn_full.layer2, cnn_full.layer3, cnn_full.layer4, cnn_full.avgpool)\n else:\n raise Exception('invalid feature layer \"%s\" for resnet' % feat_layer)\n elif cnn_type == 'resnet152':\n cnn_full = torchvision.models.resnet152(pretrained = True)\n cnn_full.eval()\n for param in cnn_full.parameters():\n param.requires_grad = False\n\n if feat_layer == 'fc':\n cnn = cnn_full\n elif feat_layer == 'conv_map':\n cnn = nn.Sequential(cnn_full.conv1, cnn_full.bn1, cnn_full.relu, cnn_full.maxpool,\\\n cnn_full.layer1, cnn_full.layer2, cnn_full.layer3, cnn_full.layer4)\n elif feat_layer == 'conv':\n cnn = nn.Sequential(cnn_full.conv1, cnn_full.bn1, cnn_full.relu, cnn_full.maxpool,\\\n cnn_full.layer1, cnn_full.layer2, cnn_full.layer3, cnn_full.layer4, cnn_full.avgpool)\n else:\n raise Exception('invalid feature layer \"%s\" for resnet' % feat_layer)\n\n else:\n raise Exception('invalid cnn type \"%s\"' % cnn_type)\n\n return cnn\n\ndef _get_feat_size(cnn_type, cnn_feat_layer, image_repr, pair_method = None):\n \n feat_size_dict = {\n 'vgg19': {'fc': 1000, 'conv': 512},\n 'resnet101': {'fc': 1000, 'conv': 2048},\n 'resnet152': {'fc': 1000, 'conv': 2048},\n }\n\n base_feat_size = feat_size_dict[cnn_type][cnn_feat_layer]\n\n if image_repr == 'full':\n feat_size = base_feat_size * 2 + 5\n else:\n feat_size = base_feat_size\n\n if pair_method in {'c+ad'}:\n feat_size *= 3\n elif pair_method in {'c', 'f+d', 'f+ad'}:\n feat_size *= 2\n\n return feat_size\n\n\n# Train Question Generator\n\ndef train_model(model, opts):\n\n print('Training Question Generator ...')\n model_opts = model.opts\n gpu_id = model_opts.gpu_id\n\n # training on \"single image\" or \"image pair\"\n train_on_pair = (model_opts.image_fmt == 'feat_pair')\n\n # set model id\n prefix = 'pqg_' if train_on_pair else 'qg_'\n\n if not opts.id.startswith(prefix):\n opts.id = prefix + opts.id\n\n # create data loader\n if opts.dataset == 'vqa1.0' or opts.dataset == 'vqa1.9':\n train_feat_fn = 'data/dataset_cache/%s/feat_%s_%s_%s_train.h5' % (opts.dataset, opts.dataset,model_opts.cnn_type, model_opts.cnn_feat_layer)\n train_seq_fn = 'data/dataset_cache/%s/seq_%s_train.h5' % (opts.dataset, opts.dataset)\n train_ans_fn = 'data/dataset_cache/%s/answer_%s_train.h5' % (opts.dataset, opts.dataset)\n test_feat_fn = 'data/dataset_cache/%s/feat_%s_%s_%s_val.h5' % (opts.dataset, opts.dataset, model_opts.cnn_type, model_opts.cnn_feat_layer)\n test_seq_fn = 'data/dataset_cache/%s/seq_%s_val.h5' % (opts.dataset, opts.dataset)\n test_ans_fn = 'data/dataset_cache/%s/answer_%s_val.h5' % (opts.dataset, opts.dataset)\n\n if train_on_pair:\n train_dset = dset.Dataset_VQA_Pair('train', opts.dataset[-3::], train_feat_fn, train_seq_fn, train_ans_fn, opts.yes_no_question, ans_fmt = model_opts.att_fmt)\n test_dset = dset.Dataset_VQA_Pair('val', opts.dataset[-3::], test_feat_fn, test_seq_fn, test_ans_fn, opts.yes_no_question, ans_fmt = model_opts.att_fmt)\n else:\n train_dset = dset.Dataset_VQA('train', opts.dataset[-3::], train_feat_fn, train_seq_fn, train_ans_fn, opts.yes_no_question, ans_fmt = model_opts.att_fmt)\n test_dset = dset.Dataset_VQA('val', opts.dataset[-3::], test_feat_fn, test_seq_fn, test_ans_fn, opts.yes_no_question, ans_fmt = model_opts.att_fmt)\n\n else:\n raise Exception('invalid dataset for training: \"%s\"'%opts.dataset)\n\n if opts.debug:\n train_dset.len = opts.batch_size * 1\n test_dset = train_dset\n\n train_loader = torch.utils.data.DataLoader(train_dset, batch_size = opts.batch_size, shuffle = True, num_workers = 2)\n test_loader = torch.utils.data.DataLoader(test_dset, batch_size = opts.batch_size, num_workers = 2)\n\n def _parse_data(data):\n if train_on_pair:\n feat_1, feat_2, seqs, att_1, att_2, _, _ = data\n \n if torch.rand(1)[0] < opts.pair_swap:\n feat_1, feat_2 = feat_2, feat_1\n att_1, att_2 = att_2, att_1\n\n feat_1 = [Variable(f.cuda(gpu_id)) for f in feat_1]\n feat_2 = [Variable(f.cuda(gpu_id)) for f in feat_2]\n seqs = [Variable(s.cuda(gpu_id)) for s in seqs]\n att_1 = Variable(att_1.cuda(gpu_id))\n att_2 = Variable(att_2.cuda(gpu_id))\n\n feat = (feat_1, feat_2)\n att = (att_1, att_2)\n\n else:\n feat, seqs, att = data\n feat = [Variable(f.cuda(gpu_id)) for f in feat]\n seqs = [Variable(s.cuda(gpu_id)) for s in seqs]\n att = Variable(att.cuda(gpu_id))\n\n return feat, seqs, att\n\n # create optimizer\n if opts.optim == 'sgd':\n optimizer = torch.optim.SGD(model.learnable_parameters(), lr = opts.lr, weight_decay = opts.weight_decay, momentum = opts.momentum)\n elif opts.optim == 'adam':\n optimizer = torch.optim.Adam(model.learnable_parameters(), lr = opts.lr, betas = (opts.optim_alpha, opts.optim_beta), eps = opts.optim_epsilon, weight_decay = opts.weight_decay)\n else:\n raise Exception('invalid optimizer \"%s\"'% opts.optim)\n\n # set crit\n crit = misc.Language_NLLLoss(weight = opts.loss_weight)\n crit_acc = misc.Language_Acc()\n\n # define snapshot information and funtion\n info = {'opts': vars(model.opts),\\\n 'train_opts': vars(opts),\\\n 'loss_history': [],\\\n 'test_loss_history': [],\\\n 'lr_history': []\n }\n log_buffer = []\n\n # remove previous log file\n output_dir = os.path.join('models', opts.id)\n io.mkdir_if_missing(output_dir)\n log_fn = os.path.join('models', opts.id, 'log.txt')\n info_fn = os.path.join(output_dir, 'info.json')\n\n if os.path.isfile(log_fn):\n os.remove(log_fn)\n\n def _snapshot(epoch):\n \n print('saving... checkpoint to %s\\t' % output_dir)\n # save model weights\n weight_fn = os.path.join(output_dir, '%s.pth'%epoch)\n model.save_model(weight_fn)\n\n # save info\n io.save_json(info, info_fn)\n\n # save log\n io.save_str_list(log_buffer, log_fn, mode = 'a')\n log_buffer[:] = []\n\n # create loss buffer\n loss_buffer = misc.Loss_Buffer(opts.average_loss)\n\n # main training loop\n epoch = 0\n while epoch != opts.max_epochs:\n\n # set model mode\n model.train()\n if model.att_detector is not None:\n model.att_detector.eval()\n\n # update lr\n lr = opts.lr * (opts.lr_decay_rate ** (epoch//opts.lr_decay))\n for group in optimizer.param_groups:\n group['lr'] = lr\n\n # train one eopch\n for batch_idx, data in enumerate(train_loader):\n\n optimizer.zero_grad()\n\n # data preproc\n feat, seqs, att = _parse_data(data)\n\n # forward and backward\n output = model(image_input = feat, lang_input = seqs, att_input = att)\n\n # compute loss\n loss = crit(log_prob = output, target = seqs[1], mask = seqs[2])\n loss.backward()\n\n\n # clip gradient\n total_grad_norm = torch.nn.utils.clip_grad_norm(model.learnable_parameters(), opts.clip_grad)\n # if total_grad_norm > opts.clip_grad:\n # print('Clip gradient: %f ==> %f' % (total_grad_norm, opts.clip_grad))\n # print(total_grad_norm)\n\n # optimize\n optimizer.step()\n\n # display loss\n loss_smt = loss_buffer(loss.data[0]) # smooth loss\n\n if batch_idx % opts.display_interval == 0:\n\n log = '[%s] [%s] Train Epoch %d [%d/%d (%.2f%%)] LR: %.3e Loss: %.6f' % (time.ctime(), opts.id,\\\n epoch, batch_idx * train_loader.batch_size, len(train_loader.dataset),\\\n 100.* batch_idx / len(train_loader), lr, loss_smt)\n print(log)\n log_buffer.append(log)\n\n batch_idx_glb = batch_idx + epoch * len(train_loader) # global batch_idx\n info['loss_history'].append({'iteration': batch_idx_glb,'epoch': epoch, 'loss': loss_smt})\n info['lr_history'].append({'iteration': batch_idx_glb, 'epoch': epoch, 'lr': lr})\n\n # update epoch index\n epoch += 1\n\n # test\n if opts.test_interval > 0 and epoch % opts.test_interval == 0:\n\n # set model mode\n model.eval()\n\n # set test iteration\n test_iter = opts.test_iter if opts.test_iter > 0 else len(test_loader)\n\n # statics\n c_num = 0 # number of correct predicted words\n t_num = 0 # number of words\n test_loss = 0 # test loss\n\n # test\n for batch_idx, data in enumerate(test_loader):\n\n # forward\n feat, seqs, att = _parse_data(data)\n\n output = model(image_input = feat, lang_input = seqs, att_input = att)\n\n loss = crit(log_prob = output, target = seqs[1], mask = seqs[2])\n test_loss += loss.data[0]\n\n _, c_num_batch, t_num_batch = crit_acc(log_prob = output, target = seqs[1], mask = seqs[2])\n c_num += c_num_batch\n t_num += t_num_batch\n\n print('\\rTesting %d/%d (%.2f%%)' % (batch_idx, test_iter, 100.*batch_idx/test_iter), end = '')\n if (batch_idx + 1) == test_iter:\n break\n\n # compute average loss and MAP\n test_loss /= test_iter\n test_acc = c_num / t_num\n log = '[%s] [%s] Test Epoch: %d Loss: %.6f Accuracy: %.2f%%' % (time.ctime(), opts.id, epoch, test_loss, test_acc * 100)\n\n print('\\n' + log)\n log_buffer.append(log)\n info['test_loss_history'].append({\\\n 'iteration': epoch*len(train_loader), \\\n 'epoch': epoch, \\\n 'loss': test_loss, \\\n 'acc': test_acc,\\\n })\n\n # snapshot\n if opts.snapshot_interval > 0 and epoch % opts.snapshot_interval == 0:\n _snapshot(epoch)\n\n # final snapshot\n _snapshot(epoch = 'final')\n\n\nif __name__ == '__main__':\n\n command = op.parse_command().command\n\n if command == 'train':\n model_opts = op.parse_qg_opt()\n train_opts = op.parse_train_opt()\n\n model = QuestionGenerator(opts = model_opts)\n train_model(model, train_opts)\n else:\n raise Exception('invalid command \"%s\" for question_generator'%command)","sub_path":"modules/question_generator.py","file_name":"question_generator.py","file_ext":"py","file_size_in_byte":40585,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"636608684","text":"#!/usr/bin/env python3\n\nimport re,sys\ntry:\n\timport igraph as ig\n\thas_igraph = True\nexcept ImportError:\n\timport networkx as nx\n\thas_igraph = False\nexcept ImportError:\n\tsys.stderr.write(\"ERROR: Module requires either igraph (preferred) or networkx (slower)\")\n\tsys.exit(1)\n\n# NetworkX produces GML files that include a numeric 'label' attribute.\n# This attribute prevents Cytoscape from opening the files, so we'll patch it.\ndef patchGML(filename):\n\twith open(filename, \"r+\") as f:\n\t\tcontent = f.read()\n\t\tnewcontent = re.sub(\"label (\\S+)\\s+\", r'', content)\n\t\tf.seek(0, 0)\n\t\tf.truncate()\n\t\tf.write(newcontent)\n\ndef saveNetwork(mapDir, layer, baseUsers, retweeted, mentioned):\n\tnet = None\n\toldMapFilename = None\n\tnewMapFilename = mapDir + \"/layer\" + str(layer+1) + \".gml\"\n\tnewMapFilenameCytoscape = mapDir + \"/layer\" + str(layer+1) + \"_cytoscape.gml\"\n\tif( layer > 0 ):\n\t\toldMapFilename = mapDir + \"/layer\" + str(layer) + \".gml\"\n\n\t# For layer 0 we need to explicitly create seed nodes\n\tusers = dict()\n\tif( layer == 0 ):\n\t\tif( has_igraph ):\n\t\t\tnet = ig.Graph(directed=True)\n\t\t\tnet.add_vertices(len(baseUsers))\n\t\t\tfor i in range(0, len(baseUsers)):\n\t\t\t\tusername = baseUsers[i]\n\t\t\t\tnet.vs[i][\"name\"] = username\n\t\t\t\tnet.vs[i][\"layer\"] = 0\n\t\t\t\tnet.vs[i][\"retweeted\"] = \"false\"\n\t\t\t\tnet.vs[i][\"mentioned\"] = \"false\"\n\t\telse:\n\t\t\tnet = nx.DiGraph()\n\t\t\tfor username in baseUsers:\n\t\t\t\tnet.add_node(username, name=username, layer=0, retweeted=\"false\", mentioned=\"false\")\n\telse:\n\t\tif( has_igraph ):\n\t\t\tnet = ig.Graph.Read_GML(oldMapFilename)\n\t\telse:\n\t\t\tnet = nx.read_gml(oldMapFilename)\n\n\t# Now let's add the new users\n\t# This is messy in networkx, because we have to add all of the users *and*\n\t# their attributes up front -- there's no clean way to update them later\n\tmentionedUsernames = set()\n\tretweetedUsernames = set()\n\tif( has_igraph ):\n\t\tnodeNames = set(net.vs.select()[\"name\"])\n\telse:\n\t\tnodeNames = set(net.nodes())\n\t# Get a set of all the usernames we'll be working with\n\tfor srcUser in retweeted.keys():\n\t\trts = retweeted[srcUser]\n\t\tfor dstUser in rts:\n\t\t\tif( dstUser not in nodeNames ):\n\t\t\t\tretweetedUsernames.add(dstUser)\n\tfor srcUser in mentioned.keys():\n\t\trts = mentioned[srcUser]\n\t\tfor dstUser in rts:\n\t\t\tif( dstUser not in nodeNames ):\n\t\t\t\tmentionedUsernames.add(dstUser)\n\t# Now add those usernames with appropriate retweeted/mentioned attributes\n\tfor username in mentionedUsernames:\n\t\tif( username in nodeNames ):\n\t\t\tnext\n\t\tif( username in retweetedUsernames ):\n\t\t\tif( has_igraph ):\n\t\t\t\tnet.add_vertices(1)\n\t\t\t\ti = len(net.vs)-1\n\t\t\t\tnet.vs[i][\"name\"] = username\n\t\t\t\tnet.vs[i][\"layer\"] = layer+1\n\t\t\t\tnet.vs[i][\"retweeted\"] = \"true\"\n\t\t\t\tnet.vs[i][\"mentioned\"] = \"true\"\n\t\t\telse:\n\t\t\t\tnet.add_node(username, name=username, layer=layer+1, retweeted=\"true\", mentioned=\"true\")\n\t\telse:\n\t\t\tif( has_igraph ):\n\t\t\t\tnet.add_vertices(1)\n\t\t\t\ti = len(net.vs)-1\n\t\t\t\tnet.vs[i][\"name\"] = username\n\t\t\t\tnet.vs[i][\"layer\"] = layer+1\n\t\t\t\tnet.vs[i][\"retweeted\"] = \"false\"\n\t\t\t\tnet.vs[i][\"mentioned\"] = \"true\"\n\t\t\telse:\n\t\t\t\tnet.add_node(username, name=username, layer=layer+1, retweeted=\"false\", mentioned=\"true\")\n\t\tnodeNames.add(username)\n\tfor username in retweetedUsernames:\n\t\tif( username in nodeNames or username in mentionedUsernames ):\n\t\t\tnext\n\t\tif( has_igraph ):\n\t\t\tnet.add_vertices(1)\n\t\t\ti = len(net.vs)-1\n\t\t\tnet.vs[i][\"name\"] = username\n\t\t\tnet.vs[i][\"layer\"] = layer+1\n\t\t\tnet.vs[i][\"retweeted\"] = \"true\"\n\t\t\tnet.vs[i][\"mentioned\"] = \"false\"\n\t\telse:\n\t\t\tnet.add_node(username, name=username, layer=layer+1, retweeted=\"true\", mentioned=\"false\")\n\t\tnodeNames.add(username)\n\n\t# Next, let's add the edges\n\tfor srcUser in retweeted.keys():\n\t\trts = retweeted[srcUser]\n\t\tfor dstUser in rts:\n\t\t\tif( has_igraph ):\n\t\t\t\tsrcID = net.vs.find(name=srcUser).index\n\t\t\t\tdstID = net.vs.find(name=dstUser).index\n\t\t\t\tnet.add_edge(srcID,dstID)\n\t\t\telse:\n\t\t\t\tnet.add_edge(srcUser, dstUser)\n\tfor srcUser in mentioned.keys():\n\t\tmts = mentioned[srcUser]\n\t\tfor dstUser in mts:\n\t\t\tif( has_igraph ):\n\t\t\t\tsrcID = net.vs.find(name=srcUser).index\n\t\t\t\tdstID = net.vs.find(name=dstUser).index\n\t\t\t\tnet.add_edge(srcID,dstID)\n\t\t\telse:\n\t\t\t\tnet.add_edge(srcUser, dstUser)\n\n\t# Finally save it to disk\n\tif( has_igraph ):\n\t\tnet.write_gml(newMapFilename)\n\telse:\n\t\tnx.write_gml(net, newMapFilename)\n\t\tnx.write_gml(net, newMapFilenameCytoscape)\n\t\tpatchGML(newMapFilenameCytoscape)\n","sub_path":"analyze.py","file_name":"analyze.py","file_ext":"py","file_size_in_byte":4311,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"272690403","text":"#!/usr/bin/env python\r\nimport rospy\r\nfrom transitions import Machine\r\nfrom std_msgs.msg import Int32MultiArray, Bool, String\r\nimport datetime\r\n\r\nclass FSM(object): \r\n def __init__ (self, fan, lights, t):\r\n self.st = ['low', 'ok', 'high'] \r\n self.lights = lights # publisher address\r\n self.fan = fan # fan pub address\r\n self.temp_explain = rospy.Publisher(\"temp_down_explain\", String, latch=True, queue_size=1)\r\n self.on = False\r\n self.fsm = Machine(model=self, states=self.st, initial='ok')\r\n self.fsm.add_transition('low', 'ok', 'low', conditions='temp_low', after='reset')\r\n self.fsm.add_transition('low', 'high', 'low', conditions='temp_low', after='reset')\r\n self.fsm.add_transition('low', 'low', 'low', conditions='temp_low', after='reset')\r\n self.fsm.add_transition('normal', 'low', 'ok', conditions='temp_good', after='reset')\r\n self.fsm.add_transition('normal', 'high', 'ok', conditions='temp_good', after='reset') \r\n self.fsm.add_transition('normal', 'ok', 'ok', conditions='temp_good', after='reset') \r\n self.fsm.add_transition('high', 'ok', 'high', conditions='temp_high', after='regulate_high')\r\n self.fsm.add_transition('high', 'low', 'high', conditions='temp_high', after='regulate_high')\r\n self.fsm.add_transition('high', 'high', 'high', conditions='temp_high', after='regulate_high') \r\n self.temp = t\r\n self.low_bound = 24\r\n self.high_bound = 30\r\n\r\n def send_explain(self):\r\n explain_string = \"Temperature Down FSM: At \"\r\n explain_string += str(datetime.timedelta(seconds=rospy.get_rostime().to_sec() % 86400))\r\n explain_string += \", the current state of the Temperature Down FSM is \"\r\n explain_string += self.state\r\n explain_string += \" because the measured temperature is \"\r\n explain_string += str(self.temp)\r\n explain_string += \" which is \"\r\n if self.state == 'low':\r\n explain_string += \"below the lower optimal bound of \" + str(self.low_bound) + \"C, but the FSM doesn't send any actuator commands since we are scheduled to only decrease the temperature.\"\r\n elif self.state == 'ok':\r\n explain_string += \"within the optimal bounds of \" + str(self.low_bound) + \" C and \" + str(self.high_bound) + \"C, so the FSM doesn't send any actuator commands since the temperature is OK.\"\r\n else:\r\n explain_string += \"above the optimal bound of \" + str(self.high_bound) + \"C, so the FSM turns the fans on to decrease the temperature.\"\r\n self.temp_explain.publish(explain_string)\r\n\r\n def check_state(self, data):\r\n if (not self.on):\r\n return\r\n self.temp = data\r\n self.low()\r\n self.normal()\r\n self.high()\r\n self.send_explain()\r\n \r\n def temp_low(self):\r\n return self.temp < self.low_bound\r\n \r\n def temp_good(self):\r\n return self.temp >= self.low_bound and self.temp <= self.high_bound\r\n \r\n def temp_high(self):\r\n return self.temp > self.high_bound\r\n\r\n def toggle(self, flag=False):\r\n self.on = flag\r\n\r\n def fan_on(self):\r\n self.fan.publish(True)\r\n\r\n def fan_off(self):\r\n self.fan.publish(False)\r\n\r\n def lights_on(self):\r\n self.lights.publish(220) # publish on\r\n\r\n def lights_low(self):\r\n self.lights.publish(50)\r\n\r\n def reset(self):\r\n self.fan_off()\r\n\r\n def regulate_high(self):\r\n self.fan_on()\r\n\r\n # def regulate_low(self):\r\n # self.lights_on()\r\n\r\n\r\n","sub_path":"final/t_down.py","file_name":"t_down.py","file_ext":"py","file_size_in_byte":3583,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"183341837","text":"import numpy as np\nimport pickle\nfrom sklearn.feature_selection import SelectKBest\nfrom sklearn.feature_selection import chi2\nfrom sklearn.model_selection import train_test_split\n\n# carregar dataset\nskin_cancer_dataset = np.loadtxt('dataset/skin_cancer_dataset.txt')\n\n# Selecionando as melhores features\nX = SelectKBest(score_func=chi2, k=45000).fit_transform(skin_cancer_dataset[:, 1:], skin_cancer_dataset[:, 0])\n\n# separar dados para treino e validacao\nX_train, X_test, y_train, y_test = train_test_split(X, skin_cancer_dataset[:, 0], random_state=0)\n\n# criar modelo\nmodel = pickle.load(open('./trained_model_svm_selctKBest.sav', 'rb'))\n\n# variaveis falso e verdadeiro, positivo e negativo\ntp = 0\nfn = 0\ntn = 0\nfp = 0\n\n# predizer\npredictedclasses = model.predict(X_test)\n\nindex = 0\nfor flag in y_test:\n if flag == 0:\n if predictedclasses[index] == 0:\n tn += 1\n else:\n fp += 1\n\n else:\n if predictedclasses[index] == 1:\n tp += 1\n else:\n fn += 1\n\n index += 1\n\n# imprimir resultados\nprint('tp', tp)\nprint('tn', tn)\nprint('fp', fp)\nprint('fn', fn)\nprint('----')\nprint('sensitivity: ', \"%.2f\" % (tp/(tp+fn)))\nprint('specificity: ', \"%.2f\" % (tn/(tn+fp)))\n","sub_path":"sens_espec_svm_script.py","file_name":"sens_espec_svm_script.py","file_ext":"py","file_size_in_byte":1233,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"319115672","text":"#!/usr/bin/env python\n# encoding: utf-8\nimport ckanapi\nimport ckan.logic as logic\nimport ckan.plugins.toolkit as toolkit\nimport ckanext.scheming.helpers as scheming_helpers\nimport ckanext.stcndm.helpers as stcndm_helpers\nimport arrow\n\nimport views\n\n_get_or_bust = logic.get_or_bust\n_get_action = toolkit.get_action\n_ValidationError = toolkit.ValidationError\n_NotFound = toolkit.ObjectNotFound\n_NotAuthorized = toolkit.NotAuthorized\n\n\n@logic.side_effect_free\ndef get_next_product_id(context, data_dict):\n \"\"\"\n Returns the next available ProductId (without registering it).\n\n :param parentProductId: Cube Id\n :type parentProductId: str\n :param productType:\n :type productType: str\n\n :return: next available ProductId\n :rtype: str\n\n :raises: ValidationError, ObjectNotFound\n \"\"\"\n\n lc = ckanapi.LocalCKAN(context=context)\n product_id = _get_or_bust(data_dict, 'parentProductId')\n\n # TODO: we need to standardize these. This is a result of swapping\n # everything to java-style params\n data_dict['productId'] = product_id\n\n if not len(str(product_id)) == 8:\n raise _ValidationError('invalid ParentProductId length')\n\n product_type = _get_or_bust(data_dict, 'productType')\n\n # testing for existence of cubeid\n lc.action.GetCube(cubeId=product_id)\n\n subject_code = str(product_id)[:2]\n # TODO Do we want to rely on the subject_code in the cube dict?\n # probably not...\n # TODO this is actually going to be a problem because cubes do\n # not have good data in subj\n sequence_id = str(product_id)[4:8]\n\n product_family = '{subject_code}{product_type}{sequence_id}'.format(\n subject_code=subject_code,\n product_type=product_type,\n sequence_id=sequence_id\n )\n\n query = {\n 'q': 'product_id_new:{product_family}*'.format(\n product_family=product_family\n ),\n 'sort': 'product_id_new desc'\n }\n\n response = _get_action('package_search')(context, query)\n\n product_id_new = \"{subject_code}{product_type}{sequence_id}01\".format(\n subject_code=subject_code,\n product_type=product_type,\n sequence_id=sequence_id\n )\n\n if response['count'] < 1:\n return product_id_new\n\n view_id = response['results'][0]['product_id_new'][-2:]\n\n if view_id == '99':\n # TODO: implement reusing unused IDs\n raise _ValidationError(\n 'All Product IDs have been used. '\n 'Reusing IDs is in development.'\n )\n else:\n try:\n product_id_new = (\n '{subject_code}{product_type}{sequence_id}{view_id}'\n ).format(\n subject_code=subject_code,\n product_type=product_type,\n sequence_id=sequence_id,\n view_id=str(int(view_id)+1).zfill(2)\n )\n except ValueError:\n raise _ValidationError(\n 'Invalid product_id {0}'.format(\n product_id_new\n )\n )\n\n return product_id_new\n\n\n@logic.side_effect_free\ndef get_product(context, data_dict):\n \"\"\"\n Returns a product given its `productId`, if it exists.\n\n :param productId: product id (i.e. 2112002604)\n :type productId: str\n :return: product or 404 if not found.\n :rtype: dict\n :raises: ObjectNotFound, ValidationError\n \"\"\"\n product_id = _get_or_bust(data_dict, 'productId')\n\n lc = ckanapi.LocalCKAN(context=context)\n result = lc.action.package_search(\n q='product_id_new:{product_id}'.format(\n product_id=product_id\n ),\n rows=1\n )\n\n if not result['count']:\n raise _NotFound('product {product_id} not found'.format(\n product_id=product_id\n ))\n\n # If we're getting more than one result for a given product_id\n # something has gone terribly wrong with the database.\n assert(not result['count'] > 1)\n\n product = result['results'][0]\n\n # As part of JIRA-5048 we need to resolve _code fields and return\n # the labels.\n codes_to_lookup = (\n ('frequency_codes', 'frequency'),\n ('geolevel_codes', 'geolevel')\n )\n\n for code_field, code_id in codes_to_lookup:\n cfv = product.get(code_field)\n\n if not cfv:\n continue\n\n codes = cfv if isinstance(cfv, list) else [cfv]\n product[code_field + '_resolved'] = results = {}\n\n for code in codes:\n value = stcndm_helpers.lookup_label(code_id, code, 'codeset')\n results[code] = value\n\n return product\n\n\n@logic.side_effect_free\ndef get_dataset_schema(context, data_dict):\n \"\"\"\n Returns full schema definition for the dataset `name`.\n\n :param name: The name of the schema to return.\n :param expanded: Expand schema presets. Defaults to `True`.\n :returns: A complete dataset schema or 404 if not found.\n :rtype: dict\n \"\"\"\n schema_name = _get_or_bust(data_dict, 'name')\n\n expanded = data_dict.get('expanded', True)\n if isinstance(expanded, basestring):\n expanded = True if expanded == 'true' else False\n\n result = scheming_helpers.scheming_get_dataset_schema(\n schema_name,\n expanded=expanded\n )\n\n if result is None:\n raise _NotFound('no schema by the name {name}'.format(\n name=schema_name\n ))\n\n return result\n\n\n@logic.side_effect_free\ndef get_group_schema(context, data_dict):\n \"\"\"\n Returns full schema definition for the group `name`.\n\n :param name: The name of the schema to return.\n :param expanded: Expand schema presets. Defaults to `True`.\n :returns: A complete dataset schema or 404 if not found.\n :rtype: dict\n \"\"\"\n schema_name = _get_or_bust(data_dict, 'name')\n\n expanded = data_dict.get('expanded', True)\n if isinstance(expanded, basestring):\n expanded = True if expanded == 'true' else False\n\n result = scheming_helpers.scheming_get_group_schema(\n schema_name,\n expanded=expanded\n )\n\n if result is None:\n raise _NotFound('no schema by the name {name}'.format(\n name=schema_name\n ))\n\n return result\n\n\n# noinspection PyUnusedLocal\n@logic.side_effect_free\ndef get_product_type(context, data_dict):\n # noinspection PyUnresolvedReferences\n \"\"\"Return the French and English titles for the given product_type_code.\n\n Example query:\n\n .. code:: python\n\n r = requests.get('/api/3/action/GetProductType?productType=13')\n print(r.json())\n\n Example response:\n\n .. code:: json\n\n {\n \"success\": true,\n \"result\": {\n \"fr\": \"Graphique\",\n \"en\": \"Chart\",\n \"product_type_code\": \"13\"\n }\n }\n\n\n :param productType: Product Type Code (i.e. '10') or '*' to receive a\n list of all product_types\n :type productType: str\n :return: English, French and code values for given product_type\n :rtype: dict\n :raises: ValidationError\n \"\"\"\n massage = lambda in_: {\n 'product_type_code': in_['value'],\n 'en': in_['label'].get('en'),\n 'fr': in_['label'].get('fr')\n }\n\n product_type = _get_or_bust(data_dict, 'productType')\n\n presets = scheming_helpers.scheming_get_preset('ndm_product')\n product_types = presets['choices']\n\n if product_type == '*':\n return [massage(pt) for pt in product_types]\n else:\n for pt in product_types:\n if unicode(pt['value']) == unicode(product_type):\n return massage(pt)\n else:\n raise logic.ValidationError(\n 'productType: \\'{0}\\' not valid'.format(product_type)\n )\n\n\n@logic.side_effect_free\ndef get_last_publish_status(context, data_dict):\n \"\"\"\n Return the French and English values for the given lastpublishstatuscode.\n\n :param lastPublishStatusCode: Publishing Status Code (i.e. '10')\n :type lastPublishStatusCode: str\n :return: English, French and code values for given lastpublishstatuscode\n :rtype: dict\n :raises: ValidationError\n \"\"\"\n massage = lambda in_: {\n 'last_publish_status_code': in_['value'],\n 'en': in_['label'].get('en'),\n 'fr': in_['label'].get('fr')\n }\n\n publish_status = _get_or_bust(\n data_dict,\n 'lastPublishStatusCode'\n ).zfill(2)\n\n presets = scheming_helpers.scheming_get_preset('ndm_publish_status')\n publish_statuses = presets['choices']\n\n for ps in publish_statuses:\n if unicode(ps['value']) == unicode(publish_status):\n return massage(ps)\n else:\n raise logic.ValidationError(\n 'lastPublishStatusCode: \\'{0}\\' invalid'.format(publish_status)\n )\n\n\n@logic.side_effect_free\ndef get_format_description(context, data_dict):\n \"\"\"\n Return the French and English values for the given formatCode.\n\n :param formatCode: Format Code (i.e. '10')\n :type formatCode: str\n\n :return: English, French and code values for given formatCode\n :rtype: dict\n\n :raises: ValidationError\n \"\"\"\n massage = lambda in_: {\n 'format_code': in_['value'],\n 'en': in_['label'].get('en'),\n 'fr': in_['label'].get('fr')\n }\n\n format_code = _get_or_bust(\n data_dict,\n 'formatCode'\n ).zfill(2)\n\n preset = scheming_helpers.scheming_get_preset(u'ndm_format')\n format_codes = preset['choices']\n\n for fc in format_codes:\n if fc['value'] == format_code:\n return massage(fc)\n else:\n raise logic.ValidationError(\n 'formatCode \\'{0}\\' invalid'.format(format_code)\n )\n\n\n@logic.side_effect_free\ndef get_upcoming_releases(context, data_dict):\n # noinspection PyUnresolvedReferences\n \"\"\"\n Return all records with a lastpublishstatuscode of Verified (8) and a\n release date between the two parameters.\n\n :param startDate: Beginning of date range\n :param endDate: End of date range\n\n :return: productId, issueno, correctionid, reference_period,\n release_date for each matching record.\n :rtype: list of dicts\n \"\"\"\n # TODO: date validation? anything else?\n\n start_date = _get_or_bust(data_dict, 'startDate')\n end_date = _get_or_bust(data_dict, 'endDate')\n\n q = {\n 'q': (\n 'release_date:[{startDate}:00Z TO {endDate}:00Z] '\n 'AND last_publish_status_code:8'\n ).format(\n startDate=start_date,\n endDate=end_date\n ),\n 'rows': 500\n }\n\n result = _get_action('package_search')(context, q)\n\n count = result['count']\n\n if count == 0:\n raise _NotFound\n else:\n desired_extras = ['product_id_new',\n 'issue_no',\n 'correction_id_code',\n 'reference_period',\n 'release_date',\n 'url',\n 'product_type_code',\n 'last_publish_status_code']\n\n output = []\n\n for result in result['results']:\n result_dict = {}\n for extra in result['extras']:\n if extra['key'] in desired_extras:\n result_dict[extra['key']] = extra['value'] or ''\n output.append(result_dict)\n\n return {'count': count, 'results': output}\n\n\n@logic.side_effect_free\ndef get_issues_by_pub_status(context, data_dict):\n # noinspection PyUnresolvedReferences\n \"\"\"\n Fields (listed below) for all product issues of type \"productType\" with a\n last_publish_status_code equal to that passed in with a release date\n between the two input parameters\n\n :param lastPublishStatusCode: Possible values are outlined on\n https://confluence.statcan.ca/display/NDMA/Publishing+workflow\n :param startReleaseDate: Beginning of date range\n :param endReleaseDate: End of date range\n :param productTypeCode: Possible values are outlined on\n https://confluence.statcan.ca/pages/viewpage.action?pageId=20416770.\n If no product type is passed in, assume all product types are\n requested.\n\n :rtype: list of dicts\n \"\"\"\n # TODO: date validation? anything else?\n\n get_last_publish_status_code = _get_or_bust(\n data_dict,\n 'lastPublishStatusCode'\n )\n start_release_date = _get_or_bust(data_dict, 'startReleaseDate')\n end_release_date = _get_or_bust(data_dict, 'endReleaseDate')\n if 'productType' in data_dict and data_dict['productType']:\n product_type_code = data_dict['productTypeCode']\n else:\n product_type_code = '[\"\" TO *]'\n\n q = (\n 'extras_release_date:[{startReleaseDate} TO {endReleaseDate}] AND '\n 'extras_last_publish_status_code:{lastPublishStatusCode} AND '\n 'extras_product_type_code:{productTypeCode}'\n ).format(\n startReleaseDate=arrow.get(\n start_release_date\n ).format('YYYY-MM-DDTHH:mm:ss')+'Z',\n endReleaseDate=arrow.get(\n end_release_date\n ).format('YYYY-MM-DDTHH:mm:ss')+'Z',\n lastPublishStatusCode=get_last_publish_status_code,\n productTypeCode=product_type_code\n )\n\n lc = ckanapi.LocalCKAN()\n\n i = 0\n n = 1\n while i < n:\n query_results = lc.action.package_search(\n q=q,\n rows=1000,\n start=i*1000)\n i += 1\n n = query_results['count'] / 1000.0\n count = query_results['count']\n if count == 0:\n raise _NotFound\n\n desired_extras = [\n 'product_type_code',\n 'product_id_new',\n 'issue_no',\n 'correction_id_code',\n 'last_publish_status_code',\n 'release_date',\n 'reference_period',\n 'url'\n ]\n\n output = []\n\n for result in query_results['results']:\n result_dict = {}\n for extra in desired_extras:\n if extra in result:\n result_dict[extra] = result[extra] or ''\n output.append(result_dict)\n\n return {'count': count, 'results': output}\n\n\n@logic.side_effect_free\ndef get_derived_product_list(context, data_dict):\n \"\"\"\n Return a dict with all ProductIDs and French/English titles that are\n associated with a given SubjectCode and ProductType.\n\n Note that this relies on the subject_code field rather than the\n subject code in the cube_id.\n\n :param parentProductId: cube id\n :type parentProductId: str\n :param productType: two-digit product type code\n :type productType: str\n\n :return: registered cubes for the SubjectCode and their French/English\n titles.\n :rtype: list of dicts\n \"\"\"\n\n product_id = _get_or_bust(data_dict, 'parentProductId')\n product_type = _get_or_bust(data_dict, 'productType')\n\n subject_code, sequence_id = product_id[:2], product_id[-4:]\n\n lc = ckanapi.LocalCKAN(context=context)\n\n response = lc.action.package_search(\n q=(\n 'product_id_new:{subject_code}??{sequence_id}* '\n 'AND product_type_code:{product_type}'\n ).format(\n subject_code=subject_code,\n sequence_id=sequence_id,\n product_type=product_type\n ),\n rows=1000\n )\n\n return [{\n 'title': r['title'],\n 'product_id': r['product_id_new']\n } for r in response['results']]\n\n\ndef tv_register_product(context, data_dict):\n \"\"\"\n Register a new product based on a given `parentProductId` (the 8-digit ID\n of a cube) and the desired `productTypeCode`. The new product's fields will\n be populated based on the cube record.\n\n .. note::\n\n If the cube is missing English and French titles, the cube will be\n updated.\n\n .. note::\n\n As the schemas for tables, indicators, charts, and maps do not\n yet exist, this method is not thoroughly tested.\n\n :param parentProductId: 8-digit cube id\n :type parentProductId: str\n :param productType: 2-digit product type code\n :type productType: str\n :param productTitle: EN/FR title dictionary\n :type productTitle: dict\n :return: newly-registered product id\n :rtype: dict\n \"\"\"\n # These are the only product types that can be registered using\n # this method as these are the only \"data products\".\n # TODO: Can we pull this from somewhere? Presets.yaml does not\n # necessarily have the exact schema name in ndm_product_type.\n VALID_DATA_TYPES = {\n u'11': 'view',\n u'12': 'indicator',\n u'13': 'chart',\n u'14': 'map'\n }\n CUBE_PRODUCT_TYPE = u'10'\n VIEW_PRODUCT_TYPE = u'11'\n\n cube_id = _get_or_bust(data_dict, 'parentProductId')\n title = _get_or_bust(data_dict, 'productTitle')\n product_type = _get_or_bust(data_dict, 'productType').zfill(2)\n\n if product_type == CUBE_PRODUCT_TYPE:\n raise _ValidationError(\n 'Please use RegisterCube to register a cube'\n )\n elif product_type not in VALID_DATA_TYPES:\n raise _ValidationError(\n 'Invalid data productType, only data products may be registered '\n 'with this service'\n )\n\n lc = ckanapi.LocalCKAN(context=context)\n cube_dict = lc.action.GetCube(cubeId=cube_id)\n\n product_type_schema = lc.action.GetDatasetSchema(\n name=VALID_DATA_TYPES[product_type]\n )\n\n # Copy fields that overlap between the cubes and the destination\n # type.\n copied_fields = {}\n for field in product_type_schema['dataset_fields']:\n field_name = field['field_name']\n if field_name in cube_dict:\n copied_fields[field_name] = cube_dict[field_name]\n\n # FIXME: This is not atomic. If this API method is called quickly\n # in parallel, this product ID could no longer be free.\n product_id = lc.action.GetNextProductId(\n parentProductId=cube_id,\n productType=product_type\n )\n # The following assumes there can be no more than 99 views\n if product_type == VIEW_PRODUCT_TYPE and product_id.endswith('01'):\n params = {\n \"cubeId\": cube_id,\n \"defaultView\": product_id,\n }\n result = views.update_default_view(context, params)\n\n # Overwrite/add some fields that we don't want to inherit\n # from the cube.\n copied_fields.update({\n 'type': VALID_DATA_TYPES[product_type],\n 'name': product_id,\n 'owner_org': 'statcan',\n 'product_id_new': product_id,\n 'parent_product': cube_id,\n 'title': title,\n 'product_type_code': product_type\n })\n\n lc.action.package_create(**copied_fields)\n try:\n stcndm_helpers.ensure_release_exists(str(product_id))\n except ValueError:\n # We don't create releases for this type of product\n pass\n\n return {'product_id_new': product_id}\n\n\ndef delete_product(context, data_dict):\n \"\"\"\n Set the status of a record to 'Deleted' and remove all metadata associated\n with that record. This will make the productid available for reuse.\n\n This web service is presently being mocked (i.e. it will return a success\n if a valid productid is passed in, but the exact implementation is still\n being discussed.)\n\n :param productId: product id of record to be deleted\n :type productId: str\n :param issueno:\n\n :return: success or failure\n :rtype: dict\n \"\"\"\n product_id = _get_or_bust(data_dict, 'productId')\n\n lc = ckanapi.LocalCKAN(context=context)\n\n result = lc.action.package_search(\n q='product_id_new:{pid}'.format(pid=product_id),\n rows=1,\n fl=['id']\n )\n\n if result['count']:\n lc.action.package_delete(id=result['results'][0]['id'])\n\n return {\n 'message': 'Product successfully deleted',\n 'product_id_new': product_id\n }\n\n\n# noinspection PyUnusedLocal\ndef purge_dataset(context, data_dict):\n # noinspection PyUnresolvedReferences\n \"\"\"\n Purges a dataset from the database.\n\n :param productId: Product ID\n :type productId: str\n\n :return: success or failure\n :rtype: dict\n \"\"\"\n # TODO: Implement user permission validation before deploying this.\n product_id = _get_or_bust(data_dict, 'productId')\n\n import ckan.model as model\n\n dataset = model.Package.get(unicode(product_id))\n\n # rev = model.repo.new_revision()\n\n model.Package.purge(dataset)\n\n # dataset.purge()\n model.repo.commit_and_remove()\n return {'success': True, 'message': '%s purged' % product_id}\n\n\n# TODO: This is out of scope for FY2014.\ndef update_last_publish_status(context, data_dict):\n \"\"\"\n Update the publishing status code\n\n :param productIds: publishing status code\n :type productIds: str\n :param issueNo: publishing status code\n :type issueNo: str\n :param lastPublishStatusCode: publishing status code\n :type lastPublishStatusCode: str\n\n :return: updated package\n :rtype: dict\n \"\"\"\n return [\n _update_single_publish_status(\n context,\n {\n 'productId': product_id,\n 'issueNo': _get_or_bust(data_dict, 'issueNo'),\n 'lastPublishStatusCode': _get_or_bust(\n data_dict,\n 'lastPublishStatusCode'\n )\n }\n ) for product_id in _get_or_bust(data_dict, 'productIds')\n ]\n\n\ndef _update_single_publish_status(context, data_dict):\n \"\"\"\n Update the publishing status code\n\n :param productId: publishing status code\n :type productId: str\n :param issueNo: publishing status code\n :type issueNo: str\n :param lastPublishStatusCode: publishing status code\n :type lastPublishStatusCode: str\n\n :return: updated package\n :rtype: dict\n \"\"\"\n product_id = _get_or_bust(data_dict, 'productId')\n issue_no = _get_or_bust(data_dict, 'issueNo')\n last_publish_status_code = _get_or_bust(data_dict, 'lastPublishStatusCode')\n\n q = {\n 'q': (\n 'extras_product_id_new:{product_id} AND '\n 'extras_issue_no:{issue_no}'\n ).format(\n product_id=product_id,\n issue_no=issue_no\n )\n }\n\n result = _get_action('package_search')(context, q)\n\n if result['count'] == 0:\n raise _ValidationError('Record not found.')\n elif result['count'] > 1:\n raise _ValidationError(\n 'More than one record identified with these values. '\n 'Please contact CKAN IT'\n )\n\n pkg_dict = result['results'][0]\n\n for extra in pkg_dict['extras']:\n if extra['key'] == 'last_publish_status_code':\n extra['value'] = str(last_publish_status_code)\n\n result = _get_action('package_update')(context, pkg_dict)\n\n # TODO: what do we actually want to return here? This is the full\n # package with the k: v formatted extras.\n return result\n\n\ndef update_product_geo(context, data_dict):\n \"\"\"\n Update the specificgeocode_bi_txtm value and sets the geo level\n (geolevel_*) accordingly.\n\n :param productId: ID of the product to update.\n :type productId: str\n\n :param dguids: Geo-code values status code\n :type dguids: list of strings\n\n :return: updated package\n :rtype: dict\n \"\"\"\n product_id = _get_or_bust(data_dict, 'productId')\n dguids = _get_or_bust(data_dict, 'dguids')\n\n lc = ckanapi.LocalCKAN(context=context)\n\n if isinstance(dguids, basestring):\n dguids = [x.strip() for x in dguids.split(';')]\n\n response = lc.action.package_search(\n q='product_id_new:{product_id}'.format(\n product_id=product_id\n )\n )\n\n if response['count'] == 0:\n raise _ValidationError('Record not found.')\n elif response['count'] > 1:\n raise _ValidationError(\n 'More than one record identified with these values. '\n 'Please contact CKAN IT'\n )\n\n pkg_dict = response['results'][0]\n pkg_dict.update({\n # Why only the first 5? What's the business logic here?\n # Validator *requires* that this be a list.\n 'geolevel_codes': list(set(sc[:5] for sc in dguids)),\n 'geodescriptor_codes': dguids\n })\n\n # TODO: Check the results?\n lc.action.package_update(**pkg_dict)\n\n return lc.action.package_show(id=pkg_dict['id'])\n\n\ndef ensure_release_exists(context, data_dict):\n \"\"\"\n Ensure a release exists for the given `productId`.\n\n :param productId: The parent product ID.\n :type productId: str\n \"\"\"\n product_id = _get_or_bust(data_dict, 'productId')\n stcndm_helpers.ensure_release_exists(product_id)\n\n\n@logic.side_effect_free\ndef get_releases_for_product(context, data_dict):\n \"\"\"\n Returns all of the releases for the given `productId`.\n\n :param productId: ID of the parent product.\n :type productId: str\n \"\"\"\n product_id = _get_or_bust(data_dict, 'productId')\n\n lc = ckanapi.LocalCKAN(context=context)\n\n results = lc.action.package_search(\n q='parent_product_id:{pid}'.format(pid=product_id)\n )\n\n return {\n 'count': results['count'],\n 'results': [r for r in results['results']]\n }\n","sub_path":"ckanext/stcndm/logic/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":25072,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"309642457","text":"import pandas as pd\r\nimport geopandas as gp\r\nfrom google.cloud import bigquery\r\nfrom google.oauth2 import service_account\r\n\r\n#Setting up the connection to the project's BigQuery SQL database\r\ncreds = service_account.Credentials.from_service_account_file(\"./Data/data-key-viewer.json\")\r\nproject_id=\"dash-app-318517\"\r\nclient = bigquery.Client(credentials=creds, project=project_id)\r\n\r\ndef furnish_arrests():\r\n df_arrests = client.query(\"select * from mpd_dash_database.arrests\").to_dataframe()\r\n #formating the time data\r\n df_arrests['Arrest_Hour'] = pd.to_datetime(df_arrests['Arrest_Hour'],format='%H')\r\n df_arrests['Arrest_Hour'] = df_arrests['Arrest_Hour'].dt.strftime('%I %p')\r\n \r\n return df_arrests\r\n\r\ndef furnish_stops():\r\n data_full = client.query(\"select * from mpd_dash_database.stops\").to_dataframe()\r\n #dropping na values from important columns. they each have less than 0.01% na\r\n data_full.dropna(subset=['stop_district','stop_time','stop_duration_minutes','race_ethnicity'], inplace=True)\r\n #removing ages = to unknown - count < 0.02%\r\n data_full = data_full[data_full['age']!='Unknown']\r\n #children ages are set to \"Juvenile\" string type. Converting that to 16. Assuming mean.\r\n data_full['age'].replace('Juvenile',16, inplace=True)\r\n #mapping age data to integer\r\n data_full['age']=data_full['age'].map(int)\r\n #creating a stop datetime column with date from both date and time columns for ease of analysis\r\n data_full['stop_datetime'] = pd.to_datetime(data_full['stop_date']+'T'+data_full['stop_time'], format= r'%Y-%m-%dT%H:%M')\r\n data_full['race_ethnicity'].replace(['Unknown','Multiple'],'Other',inplace=True )\r\n #adding a weekday column\r\n week = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\r\n\r\n def date_to_weekday(date):\r\n return week[date.weekday()]\r\n\r\n data_full['weekdays'] = data_full['stop_datetime'].map(date_to_weekday)\r\n\r\n return data_full\r\n\r\ndef furnish_geo(data_full, districts_geo):\r\n #grouping by district and adding metrics to the geofile\r\n districts_geo['avg_age'] = data_full.groupby('stop_district').mean()['age'].values\r\n districts_geo['avg_stop_duration'] = data_full.groupby('stop_district').mean()['stop_duration_minutes'].values\r\n districts_geo['count_child'] = data_full[data_full['age']==16].groupby('stop_district').count()['stop_duration_minutes'].values\r\n districts_geo['count_adult'] = data_full[data_full['age']>16].groupby('stop_district').count()['stop_duration_minutes'].values\r\n districts_geo['person_searches'] = data_full[data_full['person_search_or_protective_pat_down']==1].groupby('stop_district').count()['stop_duration_minutes'].values\r\n districts_geo['property_searches'] = data_full[data_full['property_search_or_protective_pat_down']==1].groupby('stop_district').count()['stop_duration_minutes'].values\r\n districts_geo['person_warrant'] = data_full[data_full['person_search_warrant']==1].groupby('stop_district').count()['stop_duration_minutes'].values\r\n districts_geo['property_warrant'] = data_full[data_full['property_search_warrant']==1].groupby('stop_district').count()['stop_duration_minutes'].values\r\n\r\n districts_geo.index=range(1,8)\r\n\r\n return districts_geo\r\n\r\ndef furnish_daily(data_full):\r\n #creating an array with the counts of daily stops\r\n \r\n df = data_full[['stop_date','stop_duration_minutes']].copy()\r\n df['stop_date'] = pd.to_datetime(df['stop_date'], format='%Y-%m-%d')\r\n df.set_index('stop_date', inplace=True)\r\n daily_count = df['stop_duration_minutes'].resample('D').count()\r\n\r\n\r\n #getting the rolling weekly average\r\n daily_count['rolling avg'] = daily_count.rolling(7).mean()\r\n\r\n return daily_count\r\n\r\ndef furnish_hourly(data_full):\r\n #creating an array with the counts of stops by hour of day\r\n df = data_full[['stop_time','stop_date']].copy()\r\n df['stop_time'] = pd.to_datetime(df['stop_time'], format='%H:%M')\r\n df.set_index('stop_time', inplace=True)\r\n hourly_count = df['stop_date'].resample('H').count()\r\n\r\n return hourly_count","sub_path":"furnish_data.py","file_name":"furnish_data.py","file_ext":"py","file_size_in_byte":4108,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"360426871","text":"# -*- coding: utf-8 -*-\nfrom __future__ import print_function\nfrom scrapy.spiders import CrawlSpider\n\n\nclass BaseCrawlSpider(CrawlSpider):\n\t# 58 和链家有多个城市的租房信息，对这两个网站，会针对支持的每个城市发送请求来获得租房信息\n # 所有的租房网站的爬虫都会继承这个爬虫，但是我们会在具体的爬虫中通过限制域名来限制每个爬虫具体爬取的网站\n # 这样的话每个爬虫就不会爬取所有网站的链接了\n def start_requests(self):\n cities = self.settings.get('cities', [])\n city_url_mappings = self.settings.get('available_cities_map', {})\n\n # 获得每个城市的租房信息\n for city in cities:\n city_url = city_url_mappings[city]\n if city_url is None:\n print('Cannot crawl house renting data from city: ', city)\n else:\n # 向指定城市发送租房请求链接\n yield self.make_requests_from_url(city_url)\n","sub_path":"00 爬虫实战教程/house-renting/house_renting/base_spider.py","file_name":"base_spider.py","file_ext":"py","file_size_in_byte":1019,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"70395721","text":"#------------------------------------------#\n# Exercise on Creating Standard Graphics\n#------------------------------------------#\nimport numpy as np\nfrom numpy.random import randn\nimport pandas as pd\nfrom pandas import Series, DataFrame\n\nimport matplotlib.pyplot as plt\nfrom matplotlib import rcParams\n\n#========================================#\n# Generating Line Charts\n#========================================#\nx=range(1,10)\ny=[1,2,3,4,0,4,3,2,1]\nplt.plot(x,y)\n\n# Please note you need to execute show() method to display the graph in IDLE environment\n# unlike in some other ENV like Jupyter Notes etc.\nplt.show()\n\n#-------------------------------------------------#\n# Lets generate Line Plot for mpg of Cars object #\n#-------------------------------------------------#\naddress='C:\\VINOD\\Technical\\SourceCode\\Python\\DATA\\mtcars.csv'\ncars=pd.read_csv(address)\nprint (cars.head())\n\nmpg=cars['mpg']\n# Note that mpg assigned as above becomes an array of 32 elements.\n# We can simply Line Plot the mpg where the indices 0 thru 31 is taken as x-cordinate and the corresponding\n# element value as y-cordinate\nprint (mpg)\nmpg.plot()\nplt.show()\n\n\n#---------------------------------------------------------#\n# Lets draw Line chart for 3 variables wt,cyl,mpg #\n#---------------------------------------------------------#\ndf=cars[ ['cyl','wt','mpg'] ]\ndf.plot()\nplt.show()\n\n#============================================#\n# Generating BAR Graphs\n#============================================#\n\nx=range(1,10)\ny=[1,2,3,4,0,4,3,2,1]\nplt.bar(x,y)\nplt.show()\n\n# Lets draw Bar Graph from our Pandas mpg variable\nmpg.plot(kind=\"bar\")\nplt.show()\n\n# Lets do horizontal Bars\nmpg.plot(kind=\"barh\")\nplt.show()\n\n#============================================#\n# Generating Pie Charts\n#============================================#\nx=[1,2,5.6,5,3]\nplt.pie(x)\nplt.show()\n\n#===========================================#\n# Saving the Plots\n#-------------------------------------------#\nx=[1,2,5.6,5,3]\nplt.pie(x)\nplt.savefig('C:\\VINOD\\Technical\\SourceCode\\Python\\DATA\\pie.png')\nplt.show()\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"data_visualization_ex1.py","file_name":"data_visualization_ex1.py","file_ext":"py","file_size_in_byte":2106,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"274979978","text":"# coding: utf-8\n\nimport os\nimport math\nimport random\n\nimport numpy as np\nimport tensorflow as tf\nimport cv2\n\nslim = tf.contrib.slim\n\n#get_ipython().magic('matplotlib inline')\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\n\nimport sys\nsys.path.append('../')\n\nfrom nets import ssd_vgg_300, ssd_common, np_methods\nfrom preprocessing import ssd_vgg_preprocessing\nfrom notebooks import visualization\n\n\n# TensorFlow session: grow memory when needed. TF, DO NOT USE ALL MY GPU MEMORY!!!\ngpu_options = tf.GPUOptions(allow_growth=True)\nconfig = tf.ConfigProto(log_device_placement=False, gpu_options=gpu_options)\nisess = tf.InteractiveSession(config=config)\n\n\n# ## SSD 300 Model\n# \n# The SSD 300 network takes 300x300 image inputs. In order to feed any image, the latter is resize to this input shape (i.e.`Resize.WARP_RESIZE`). Note that even though it may change the ratio width / height, the SSD model performs well on resized images (and it is the default behaviour in the original Caffe implementation).\n# \n# SSD anchors correspond to the default bounding boxes encoded in the network. The SSD net output provides offset on the coordinates and dimensions of these anchors.\n\n# Input placeholder.\nnet_shape = (300, 300)\ndata_format = 'NHWC'\nimg_input = tf.placeholder(tf.uint8, shape=(None, None, 3))\n# Evaluation pre-processing: resize to SSD net shape.\nimage_pre, labels_pre, bboxes_pre, bbox_img = ssd_vgg_preprocessing.preprocess_for_eval(\n img_input, None, None, net_shape, data_format, resize=ssd_vgg_preprocessing.Resize.WARP_RESIZE)\nimage_4d = tf.expand_dims(image_pre, 0)\n\n\n# Define the SSD model.\nreuse = True if 'ssd_net' in locals() else None\nssd_net = ssd_vgg_300.SSDNet()\nwith slim.arg_scope(ssd_net.arg_scope(data_format=data_format)):\n predictions, localisations, _, _ = ssd_net.net(image_4d, is_training=False, reuse=reuse)\n\n# Restore SSD model.\nckpt_filename = '../checkpoints/ssd_300_vgg.ckpt'\n# ckpt_filename = '../checkpoints/VGG_VOC0712_SSD_300x300_ft_iter_120000.ckpt'\nisess.run(tf.global_variables_initializer())\nsaver = tf.train.Saver()\nsaver.restore(isess, ckpt_filename)\n\n# SSD default anchor boxes.\nssd_anchors = ssd_net.anchors(net_shape)\n\n\n# ## Post-processing pipeline\n# \n# The SSD outputs need to be post-processed to provide proper detections. Namely, we follow these common steps:\n# \n# * Select boxes above a classification threshold;\n# * Clip boxes to the image shape;\n# * Apply the Non-Maximum-Selection algorithm: fuse together boxes whose Jaccard score > threshold;\n# * If necessary, resize bounding boxes to original image shape.\n\n# Main image processing routine.\ndef process_image(img, select_threshold=0.5, nms_threshold=.45, net_shape=(300, 300)):\n # Run SSD network.\n rimg, rpredictions, rlocalisations, rbbox_img = isess.run([image_4d, predictions, localisations, bbox_img],\n feed_dict={img_input: img})\n \n # Get classes and bboxes from the net outputs.\n rclasses, rscores, rbboxes = np_methods.ssd_bboxes_select(\n rpredictions, rlocalisations, ssd_anchors,\n select_threshold=select_threshold, img_shape=net_shape, num_classes=21, decode=True)\n \n rbboxes = np_methods.bboxes_clip(rbbox_img, rbboxes)\n rclasses, rscores, rbboxes = np_methods.bboxes_sort(rclasses, rscores, rbboxes, top_k=400)\n rclasses, rscores, rbboxes = np_methods.bboxes_nms(rclasses, rscores, rbboxes, nms_threshold=nms_threshold)\n # Resize bboxes to original image shape. Note: useless for Resize.WARP!\n rbboxes = np_methods.bboxes_resize(rbbox_img, rbboxes)\n return rclasses, rscores, rbboxes\n\n\n# Added ----------------------------------------------------------------------\n\n\nVOC_LABELS = {\n 'none': (0, 'Background'),\n 'aeroplane': (1, 'Vehicle'),\n 'bicycle': (2, 'Vehicle'),\n 'bird': (3, 'Animal'),\n 'boat': (4, 'Vehicle'),\n 'bottle': (5, 'Indoor'),\n 'bus': (6, 'Vehicle'),\n 'car': (7, 'Vehicle'),\n 'cat': (8, 'Animal'),\n 'chair': (9, 'Indoor'),\n 'cow': (10, 'Animal'),\n 'diningtable': (11, 'Indoor'),\n 'dog': (12, 'Animal'),\n 'horse': (13, 'Animal'),\n 'motorbike': (14, 'Vehicle'),\n 'person': (15, 'Person'),\n 'pottedplant': (16, 'Indoor'),\n 'sheep': (17, 'Animal'),\n 'sofa': (18, 'Indoor'),\n 'train': (19, 'Vehicle'),\n 'tvmonitor': (20, 'Indoor'),\n}\n\nclass2label = {}\n\nfor key in VOC_LABELS.keys():\n class2label[VOC_LABELS[key][0]] = key\n\nUSE_WEBCAM = False\nCROP = True\n\nif USE_WEBCAM:\n\n cap = cv2.VideoCapture(0)\n print(cap.get(3), cap.get(4))\n #cap.set(3, 1920)\n #cap.set(4, 1080)\n cap.set(3, 640)\n cap.set(4, 480)\n print(cap.get(3), cap.get(4))\n\n colors = dict()\n cnt = 0\n while(True):\n # Capture frame-by-frame\n ret, frame = cap.read()\n\n # Our operations on the frame come here\n #frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n\n img = frame\n \n rclasses, rscores, rbboxes = process_image(img)\n \n '''\n for i in range(rclasses.shape[0]):\n cls_id = int(rclasses[i])\n if cls_id >= 0:\n if cls_id not in colors:\n colors[cls_id] = (int(random.randint(0, 255)), random.randint(0, 255), random.randint(0, 255))\n '''\n \n visualization.bboxes_draw_on_img(img, rclasses, rscores, rbboxes, visualization.colors_tableau, class2label, thickness=2)\n\n\n # Put text on the frame\n caption = u\"Hello\"\n location = (100, 100)\n font = cv2.FONT_HERSHEY_DUPLEX\n scale = 1\n color = (0, 0, 255)\n #cv2.putText(img, caption, location, font, scale, color, 1)\n\n #cv2.addText(img, caption, location, font)\n\n # Display the resulting frame\n if cnt == 0:\n cv2.imshow('frame', img)\n cnt = 1\n\n key = cv2.waitKey(10) & 0xFF\n if key == ord('r'):\n cv2.imshow('frame', img)\n\n if key == ord('q'):\n break\n\n # When everything done, release the capture\n cap.release()\n cv2.destroyAllWindows()\n\n\nelse:\n #img = cv2.imread('messi5.jpg',0)\n\n path = '/media/airc/HDD_2TB/data/keti_people/2/'\n image_names = sorted([f for f in os.listdir(path) if os.path.isfile(os.path.join(path, f))])\n print(image_names)\n #image_name = path + image_names[-2]\n # image_name = '../demo/flowers-248822_640.jpg'\n \n i = 0\n while i < len(image_names):\n image_name = image_names[i]\n head, ext = os.path.splitext(image_name)\n img = cv2.imread(os.path.join(path, image_name), 1)\n #img = cv2.imread('police-car-406893_1920.jpg', 1)\n rclasses, rscores, rbboxes = process_image(img)\n\n print(image_name)\n print(rclasses)\n # visualization.bboxes_draw_on_img(img, rclasses, rscores, rbboxes, visualization.colors_plasma)\n #visualization.plt_bboxes(img, rclasses, rscores, rbboxes)\n\n if CROP:\n imgs = visualization.crop_img(img, rclasses, rbboxes, crop_class=15)\n else:\n visualization.bboxes_draw_on_img(img, rclasses, rscores, rbboxes, visualization.colors_tableau, class2label, thickness=2)\n\n #print(rbboxes)\n # # Put text on the frame\n # caption = u\"Hello\"\n # location = (100, 100)\n # scale = 1\n # color = (0, 0, 255)\n # font = QFont()\n # font.setFamily('Times')\n # font.setPointSize(10)\n\n # #cv2.putText(img, caption, location, font, scale, color, 1)\n\n # cv2.addText(img, caption, location, font)\n\n ## For Displaying Korean\n # from PIL import Image, ImageDraw, ImageFont\n\n # font_size = 36\n # font_color = (0, 0, 0)\n # caption = u' '\n # unicode_font = ImageFont.truetype(\"NanumGothic.ttf\", font_size)\n\n # pil_image = Image.open(image_name).convert('RGB')\n # draw = ImageDraw.Draw(pil_image)\n # draw.text ( (10,10), caption, font=unicode_font, fill=font_color )\n # open_cv_image = np.array(pil_image)\n # open_cv_image = open_cv_image[:, :, ::-1].copy()\n\n # visualization.bboxes_draw_on_img(open_cv_image, rclasses, rscores, rbboxes, visualization.colors_tableau, class2label, thickness=2)\n ##\n\n # cv2.imshow('image', open_cv_image)\n\n if CROP:\n j = 0\n while j < len(imgs):\n #cv2.imshow('image', imgs[j])\n crop_image_name = os.path.join(os.path.join(path, 'crop'), head + '_crop_' + str(j) + ext)\n cv2.imwrite(crop_image_name, imgs[j])\n j += 1\n # if cv2.waitKey(0) == ord('s'):\n # crop_image_name = os.path.join(os.path.join(path, 'crop'), head + '_crop' + ext)\n # cv2.imwrite(crop_image_name, imgs[j])\n # j += 1\n # elif cv2.waitKey(0) == ord('n'):\n # j += 1\n # elif cv2.waitKey(0) & 0xFF == ord('q'):\n # break\n i += 1\n else:\n if img is not None:\n cv2.imshow('image', img)\n if img is None or cv2.waitKey(0) == ord('n'):\n i += 1\n elif cv2.waitKey(0) & 0xFF == ord('q'):\n break\n\n cv2.destroyAllWindows()\n# ----------------------------------------------------------------------------\n\n'''\n# Test on some demo image and visualize output.\npath = '../demo/'\nimage_names = sorted(os.listdir(path))\n\nimg = mpimg.imread(path + image_names[-5])\nrclasses, rscores, rbboxes = process_image(img)\n\n# visualization.bboxes_draw_on_img(img, rclasses, rscores, rbboxes, visualization.colors_plasma)\nvisualization.plt_bboxes(img, rclasses, rscores, rbboxes)\n'''\n\n","sub_path":"people_classification/ssd_notebook_modified_for_cropping.py","file_name":"ssd_notebook_modified_for_cropping.py","file_ext":"py","file_size_in_byte":9741,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"150183243","text":"#!/usr/bin/env python\n# Copyright (c) 2010 __Ricoh Company Ltd.__. All rights reserved.\n\n\"\"\"Infobar operations.\"\"\"\n\nfrom PIL import Image, ImageDraw\n\nInfobar_Height = 66\nInfobar_Size = 825,Infobar_Height\nInfobar_Path = '/data/infobar.pgm'\nButton_Size = 63 #width and height and with border at sides..\n\nfrom ew.util import ew_logging, system_config as sysconfig\nfrom sdk.display_window import DisplayWindow, pending_gui\nfrom sdk.widgets.image_label import ImageLabel\nfrom sdk.display import Display, get_font\nfrom sdk.display_constants import PEN_RESOLUTION, COLOR_WHITE\nfrom sdk.intersect import WidgetMap\nfrom sdk.bounding_box import BoundingBox\nfrom sdk.widgets.image_button import ImageButton\nimport threading, os\n\nInfobar_Scaled_Height = Infobar_Height * PEN_RESOLUTION\nBlank_Infobar = Image.new(\"L\", (825,66), COLOR_WHITE)\n\nlogger = ew_logging.getLogger('ew.launcher.infobar')\n\nclass Infobar(DisplayWindow):\n _static_path = \"/data/static-infobar.pgm\"\n def __init__(self, doc_runner):\n self._runner = doc_runner\n DisplayWindow.__init__(self, 825, Infobar_Height,widget_id='Infobar')\n self._widget_map = WidgetMap(grid = (10,1),screen_size=Infobar_Size)\n self._battery_level = 2 #just made up for now\n self._active_button = None\n self._active_ip = None\n self._document_title = None\n self.create_gui()\n #self.on_inbox(self._inbox_button)\n self._disabled = False\n self._update_ds_lock = threading.RLock()\n self._page_id = None\n\n def launcher(self):\n return self._runner\n\n def identity(self):\n return self._page_id if self._page_id else DisplayWindow.identity(self)\n\n def adjusted_full_box(self):\n return BoundingBox((0,0), Infobar_Size)\n \n def current_page(self):\n pass\n\n def update_using_region_of_page(self, page_id):\n self._page_id = page_id\n with self._update_ds_lock:\n if not self._disabled:\n h, w = Infobar_Size\n self.launcher().update_region_of_page(0, 0, 0, 0, h, w, page_id, self._static_path)\n\n def owns_stroke(self, index, flags, display_id, millis, points):\n contains_point = (points[0][1] < Infobar_Scaled_Height) if points else False\n return flags & 0x10 or contains_point\n\n def set_title(self, doc_title):\n \"\"\"draw the text using white-ish pen more or less centered in the available space.\n Append the current ip-addr in square brackets if there is room. Consider appending\n the pagenumber instead at a later date\"\"\"\n if self._disabled: return\n if self._document_title != doc_title:\n self._document_title = doc_title\n self._draw_text()\n\n\n def set_active_button(self, button):\n with self._update_ds_lock:\n if button != self._active_button:\n if self._active_button:\n self._active_button.state = 'default'\n self._active_button.update()\n self._active_button = button\n if self._active_button:\n self._active_button.state = 'selected'\n self._active_button.update()\n\n def enable(self):\n \"\"\"go back to normal state if in disabled state\"\"\"\n\n if self._disabled:\n with self._update_ds_lock:\n self._disabled = False\n self._settings_button.set_disabled(False)\n #self._templates_button.set_disabled(False)\n self._inbox_button.set_disabled(False)\n self.process_changes()\n\n def disable(self):\n \"\"\"go to disabled state\"\"\"\n with self._update_ds_lock:\n self._settings_button.set_disabled(True)\n #self._templates_button.set_disabled(True)\n self._inbox_button.set_disabled(True)\n self._disabled = True\n try:\n self.process_changes()\n except:\n logger.exception('This is usually ok if we are blanking the infobar')\n\n def blank_image(self):\n Blank_Infobar.save(self._static_path)\n \n def restore_image(self):\n with self._update_ds_lock:\n self.working_image().save(self._static_path)\n\n def _draw_pointer(self, x_location):\n \"\"\"draws the current item bottom pointer\"\"\"\n pass #TBD\n\n def create_gui(self):\n class NumberedImageLabel(ImageLabel):\n def __init__(self, name, root_name, index_range):\n self._range = index_range\n self._index = self._range[0]\n self._root = root_name\n ImageLabel.__init__(self, name, 63, 63,\n self._image_number(index_range[0]),widget_id=name)\n\n def _image_number(self, num):\n image_path = None\n if num in range(*self._range):\n self._index = num\n image_path = '%s_%d.pgm' % (self._root, self._index)\n else:\n logger.error('%r is not in range %r', num, range(*self._range))\n return image_path\n\n def set_image(self, path):\n ImageLabel.set_image(self, path)\n self._index = None\n\n def set_image_number(self, num):\n\n if self._index != num:\n path = self._image_number(num)\n logger.debug('image path for %d is %s', num, path)\n if path:\n self.set_image(path)\n\n with Display.instance().pending:\n self._default_image_file = os.path.join(sysconfig.images_dir, 'static-infobar.pgm')\n self._working_image = Image.open(self._default_image_file).copy()\n button_top = Infobar_Height - Button_Size\n self._is_locked = False\n self._set_up_button_areas(button_top)\n self._wifi = NumberedImageLabel('wifi', 'wifi', (0,4))\n self._wifi.set_image_number(3)\n self._radio = NumberedImageLabel('radio', 'radio', (0,6))\n self._roaming_radio = NumberedImageLabel('radio_r', 'radio_r', (1,6))\n self._airplane_mode = ImageLabel(\"airplane_mode_icon\", 63, 63, \"airplane.pgm\")\n self._no_connection = ImageLabel(\"no_connection\", 63, 63, \"no_connection.pgm\")\n self._network_widgets = {'3GR' : self._roaming_radio, '3g' : self._radio, 'wifi' : self._wifi, 'airplane': self._airplane_mode, 'None': self._no_connection}\n #all our widgets on the infobar are 63,63 size and include sife border.\n #so just get our starting k and start adding\n x = self._settings_button.x + Button_Size\n self.add(self._wifi, x, button_top)\n self.net_location = x, button_top\n self._network_type = 'wifi'\n x += Button_Size\n self._battery = NumberedImageLabel('battery', 'battery', (0,5))\n self._battery.set_image_number(3)\n self.add(self._battery, x, button_top)\n self._text_min_x = self._inbox_button.x + 2 * Button_Size\n text_max_x = self._settings_button.x\n self._text_max_sz = text_max_x - self._text_min_x\n self._blank_text = self._working_image.crop((self._text_min_x, 0, text_max_x, Infobar_Height))\n# self._blank_text = Image.new('L', (self._text_max_sz, Infobar_Height), color=23)\n self._text_font = get_font(22)\n\n def send_changes_to_DS(self):\n if not self._disabled:\n if self._changed_region:\n x,y,w,h = self._changed_region.as_xywh()\n else:\n x,y = 0,0\n w,h = Infobar_Size\n logger.debug('sending changes for %s to DS updating %s',\n self.identity(), (x,y,w,h))\n self._runner.update_region_of_page(x, y, x, y, w, h, \n self.identity(), self.image_path())\n self.reset_changed_region()\n \n def _draw_text(self):\n def too_big(text):\n return self._text_font.getsize(text)[0] > self._text_max_sz\n\n def adjust_text(text):\n n_off = 3\n if too_big(text):\n text = text[:-n_off] + '..'\n while too_big(text):\n n_off += 1\n text = text[:-n_off] + '..'\n return text, self._text_font.getsize(text)\n\n text = '%s' % (self._document_title)\n text, size = adjust_text(text)\n self._document_title = text\n #calculate text start to center it\n width = size[0]\n\n x = self._text_min_x + (self._text_max_sz - width) / 2\n y = (Infobar_Height - size[1]) / 2\n #clear the text area\n self._working_image.paste(self._blank_text, (self._text_min_x, 0))\n #draw the text\n draw = ImageDraw.ImageDraw(self._working_image)\n draw.text((x,y), text, font=self._text_font, fill=255)\n with self._update_ds_lock:\n self.set_dirty()\n self.process_changes()\n\n def image_path(self):\n return self._static_path\n\n def is_infobar(self):\n return True\n\n def _set_up_button_areas(self, button_top):\n def make_button(name, main_image_name, handler):\n default = '%s.pgm' % main_image_name\n selected = '%s_selected.pgm' % main_image_name\n button = ImageButton(name, Button_Size, Button_Size, \"DEFAULT\", default,\n selected_path = selected,\n widget_id = name)\n button.add_callback(handler, 'on_button_press')\n return button\n\n x = 10\n self._inbox_button = make_button('inbox','inbox', self.on_inbox)\n self.add(self._inbox_button, x, button_top)\n x += Button_Size\n self._lock_button = ImageButton('lock', 63, 63, \"DEFAULT\", 'inbox_disabled.pgm', \n selected_path = 'large_lock.pgm', widget_id = 'lock')\n self.add(self._lock_button, x, button_top)\n self._lock_button.add_callback(self.unlock_document, 'on_button_press')\n self._settings_button = make_button('settings', 'settings', self.on_settings)\n self.add(self._settings_button, 634, button_top)\n\n def unlock_document(self, widget):\n if self._is_locked:\n self._settings_button.set_disabled(False)\n self._inbox_button.set_disabled(False)\n self._lock_button.state = 'default'\n self._runner.unlock_document()\n self._is_locked = False\n\n def lock_document(self):\n self._settings_button.set_disabled(True)\n self._inbox_button.set_disabled(True)\n self._lock_button.state = 'selected'\n self._is_locked = True\n\n def _call_in_thread(self, function):\n logger.debug('Starting infobar thread executing %s',\n function.__name__)\n thread = threading.Thread(target=function, name='infobar_thread')\n thread.setDaemon(True)\n thread.start()\n\n def mark_inbox_active(self):\n self.set_active_button(self._inbox_button)\n\n def on_inbox(self, button):\n self.set_active_button(button)\n self._call_in_thread(self._runner.open_inbox)\n\n def on_templates(self, button):\n self.set_active_button(button)\n self._call_in_thread(self._runner.open_templates)\n\n\n def on_settings(self, button):\n self.set_active_button(button)\n self._call_in_thread(self._runner.open_settings)\n\n def set_battery_level(self, level, charging=False):\n \"\"\"should redraw battery level indicator by call\n back to the doc_runner if it has changed enough\"\"\"\n #assumes level is 0-4 inclusive and that if charging\n #we display the battery_none (lightning bold overlaid icon).\n #change to do the converting math if we get a percentage full instead\n if charging:\n self._battery.set_image('battery_none.pgm')\n else:\n self._battery.set_image_number(level)\n self.process_changes()\n\n def _network_widget(self):\n return self._network_widgets[self._network_type]\n\n def set_network_level(self, level, active_ip = None):\n \"\"\"level is 0..3 inclusive for wifi and 0..5 inclusive for 3G. active_ip is the ip address of\n the current wireless.\"\"\"\n self._network_widget().set_image_number(level)\n if active_ip != self._active_ip:\n self._active_ip = active_ip\n\n def set_network_info(self, network_type, network_level, ssid, active_ip = None):\n with pending_gui():\n if network_type == None or network_type == 'None':\n self._network_widget().set_image('no_connection.pgm')\n else:\n self.set_network_type(network_type)\n if network_type == 'wifi' and not ssid:\n self._network_widget().set_image('wifi_none.pgm')\n elif network_type != \"airplane\":\n self.set_network_level(network_level, active_ip)\n self.process_changes()\n\n def set_network_type(self, network_type):\n 'set network type to either \"wifi\" or \"3G\" or \"3GR\" or \"airplane\"'\n if not network_type in ('wifi', '3g', '3GR', 'airplane', 'None'):\n logger.debug('unknown network type %r ignored', network_type)\n return\n\n if network_type != self._network_type:\n old_widget = self._network_widgets.get(self._network_type)\n new_widget = self._network_widgets.get(network_type)\n# logger.debug('network types are %s', self._network_widgets)\n if old_widget in self.elements:\n self.elements.remove(old_widget)\n old_widget.parent = None\n self.add(new_widget, *self.net_location)\n self._network_type = network_type\n\n","sub_path":"ew/launcher/infobar.py","file_name":"infobar.py","file_ext":"py","file_size_in_byte":13883,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"547943445","text":"#stack LIFO\n\"\"\"\nstack=[]\n\nstack.append('a')\nstack.append('b')\nstack.append('c')\n\nprint(stack)\n\nprint(stack.pop())\nprint(stack.pop())\nprint(stack)\n\"\"\"\n\"\"\"\nstack=[]\ndef push(value):\n stack.append(value)\n print(stack)\n\ndef pop():\n if not stack:\n print(\"Stack is empty\")\n else:\n print(stack.pop())\n print(stack)\n\nwhile True:\n n=int(input(\"Enter the operation-1.Push 2.Pop 3.Quit\"))\n if n==1:\n value=int(input())\n push(value)\n elif n==2:\n pop()\n elif n==3:\n break\n else:\n print(\"Enter correct option\")\n\"\"\"\n\"\"\"\n# using oops\nclass Stack:\n def __init__(self):\n self.__stack_list=[]\n def push(self,value):\n self.__stack_list.append(value)\n return self.__stack_list\n def pop(self):\n if not self.__stack_list:\n return \"Stack is empty\"\n else:\n print(self.__stack_list.pop())\n return self.__stack_list\nclass Adding_Stack(Stack):\n def __init__(self):\n Stack.__init__(self)\n self.__sum=0\n def push(self,val):\n self.val=val\n self.__sum=self.__sum+self.val\n Stack.push(self,val)\n def pop(self):\n Stack.pop(self)\n self.__sum=self.__sum-self.val\n \n def getsum(self):\n return self.__sum\n\nobj=Stack()\nobj2=Adding_Stack()\n\nfor i in range(5):\n obj2.push(i)\nprint(obj2.getsum())\nfor j in range(5):\n obj2.pop()\n print(obj2.getsum())\n\"\"\"\n\"\"\"\n#using collections\nimport collections\nstack=collections.deque()\nstack.append(10)\nstack.append(20)\nstack.append(30)\nprint(stack)\nstack.pop()\nprint(stack)\n\"\"\"\n#using deque\nimport queue\nstack=queue.LifoQueue()\nstack.put(1)\nstack.put(3)\nprint(stack)\nstack.get()\n\n","sub_path":"Stack.py","file_name":"Stack.py","file_ext":"py","file_size_in_byte":1719,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"598896860","text":"\n\nfrom xai.brain.wordbase.nouns._blacksmith import _BLACKSMITH\n\n#calss header\nclass _BLACKSMITHS(_BLACKSMITH, ):\n\tdef __init__(self,): \n\t\t_BLACKSMITH.__init__(self)\n\t\tself.name = \"BLACKSMITHS\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"blacksmith\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_blacksmiths.py","file_name":"_blacksmiths.py","file_ext":"py","file_size_in_byte":266,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"307243345","text":"import tensorflow as tf\nimport numpy as np\n\n\ndef temporal_padding(x, padding=(1, 1)):\n \"\"\"Pads the middle dimension of a 3D tensor.\n # Arguments\n x: Tensor or variable.\n padding: Tuple of 2 integers, how many zeros to\n add at the start and end of dim 1.\n # Returns\n A padded 3D tensor.\n \"\"\"\n assert len(padding) == 2\n pattern = [[0, 0], [padding[0], padding[1]], [0, 0]]\n return tf.pad(x, pattern)\n\n\ndef attentionBlock(x, initializer):\n \"\"\"self attention block\n # Arguments\n x: Tensor of shape [N, L, Cin]\n \"\"\"\n\n k_size = x.get_shape()[-1].value\n v_size = x.get_shape()[-1].value\n\n key = tf.layers.dense(x, units=k_size, activation=None, use_bias=False,\n kernel_initializer=initializer) # [N, L, k_size]\n # query = tf.layers.dense(x, units=k_size, activation=None, use_bias=False\n # kernel_initializer=tf.random_normal_initializer(0, 0.01)) # [N, L, k_size]\n value = tf.layers.dense(x, units=v_size, activation=None, use_bias=False,\n kernel_initializer=initializer)\n\n logits = tf.matmul(key, key, transpose_b=True)\n logits = logits / np.sqrt(k_size)\n weights = tf.nn.softmax(logits, name=\"attention_weights\") # N, L, ksize\n output = tf.matmul(weights, value)\n\n return output\n\n\ndef weightNormConvolution1d(x, num_filters, dilation_rate, filter_size=3, stride=[1], initializer=None,\n pad='VALID', init_scale=1., init=False, gated=False, counters={}):\n with tf.variable_scope('weightnorm_conv1d'):\n # currently this part is never used\n if init:\n print(\"initializing weight norm\")\n # data based initialization of parameters\n V = tf.get_variable('V', [filter_size, int(x.get_shape()[-1]), num_filters],\n tf.float32, initializer,\n trainable=True)\n V_norm = tf.nn.l2_normalize(V.initialized_value(), [0, 1])\n\n # pad x\n left_pad = dilation_rate * (filter_size - 1)\n x = temporal_padding(x, (left_pad, 0))\n x_init = tf.nn.convolution(x, V_norm, pad, stride, [dilation_rate])\n # x_init = tf.nn.conv2d(x, V_norm, [1]+stride+[1], pad)\n m_init, v_init = tf.nn.moments(x_init, [0, 1])\n scale_init = init_scale / tf.sqrt(v_init + 1e-8)\n g = tf.get_variable('g', dtype=tf.float32, initializer=scale_init,\n trainable=True)\n b = tf.get_variable('b', dtype=tf.float32, initializer=-m_init * scale_init,\n trainable=True)\n x_init = tf.reshape(scale_init, [1, 1, num_filters]) * (x_init - tf.reshape(m_init, [1, 1, num_filters]))\n # apply non_linearity\n x_init = tf.nn.relu(x_init)\n return x_init\n\n else:\n # Gating mechanism (Dauphin 2016 LM with Gated Conv. Nets)\n if gated:\n num_filters = num_filters * 2\n\n # size of V is L, Cin, Cout\n V = tf.get_variable('V', [filter_size, int(x.get_shape()[-1]), num_filters],\n tf.float32, initializer=None,\n trainable=True)\n g = tf.get_variable('g', shape=[num_filters], dtype=tf.float32,\n initializer=tf.constant_initializer(1.), trainable=True)\n b = tf.get_variable('b', shape=[num_filters], dtype=tf.float32,\n initializer=None, trainable=True)\n\n # size of input x is N, L, Cin\n\n # use weight normalization (Salimans & Kingma, 2016)\n W = tf.reshape(g, [1, 1, num_filters]) * tf.nn.l2_normalize(V, [0, 1])\n\n # pad x for causal convolution\n left_pad = dilation_rate * (filter_size - 1)\n x = temporal_padding(x, (left_pad, 0))\n\n # calculate convolutional layer output\n x = tf.nn.bias_add(tf.nn.convolution(x, W, pad, stride, [dilation_rate]), b)\n\n # GLU\n if gated:\n split0, split1 = tf.split(x, num_or_size_splits=2, axis=2)\n split1 = tf.sigmoid(split1)\n x = tf.multiply(split0, split1)\n # ReLU\n else:\n # apply nonlinearity\n x = tf.nn.relu(x)\n\n print(x.get_shape())\n\n return x\n\n\ndef TemporalBlock(input_layer, out_channels, filter_size, stride, dilation_rate, initializer, counters,\n dropout_keep_prob, init=False, atten=False, highway=False, gated=False):\n in_channels = input_layer.get_shape()[-1]\n with tf.variable_scope('temporal_block'):\n\n # num_filters is the hidden units in TCN\n # which is the number of out channels\n conv1 = weightNormConvolution1d(input_layer, out_channels, dilation_rate, filter_size, [stride], initializer,\n counters=counters, init=init, gated=gated)\n # set noise shape for spatial dropout\n # refer to https://colab.research.google.com/drive/1la33lW7FQV1RicpfzyLq9H0SH1VSD4LE#scrollTo=TcFQu3F0y-fy\n # shape should be [N, 1, C]\n noise_shape = (tf.shape(conv1)[0], tf.constant(1), tf.shape(conv1)[2])\n dropout1 = tf.nn.dropout(conv1, dropout_keep_prob, noise_shape)\n if atten:\n dropout1 = attentionBlock(dropout1, initializer)\n\n conv2 = weightNormConvolution1d(dropout1, out_channels, dilation_rate, filter_size, [stride],\n counters=counters, init=init, gated=gated)\n dropout2 = tf.nn.dropout(conv2, dropout_keep_prob, noise_shape)\n if atten:\n dropout2 = attentionBlock(dropout2, initializer)\n\n # highway connetions or residual connection\n residual = None\n if highway:\n W_h = tf.get_variable('W_h', [1, int(input_layer.get_shape()[-1]), out_channels],\n tf.float32, initializer, trainable=True)\n b_h = tf.get_variable('b_h', shape=[out_channels], dtype=tf.float32,\n initializer=None, trainable=True)\n H = tf.nn.bias_add(tf.nn.convolution(input_layer, W_h, 'SAME'), b_h)\n\n W_t = tf.get_variable('W_t', [1, int(input_layer.get_shape()[-1]), out_channels],\n tf.float32, initializer, trainable=True)\n b_t = tf.get_variable('b_t', shape=[out_channels], dtype=tf.float32,\n initializer=None, trainable=True)\n T = tf.nn.bias_add(tf.nn.convolution(input_layer, W_t, 'SAME'), b_t)\n T = tf.nn.sigmoid(T)\n residual = H * T + input_layer * (1.0 - T)\n elif in_channels != out_channels:\n W_h = tf.get_variable('W_h', [1, int(input_layer.get_shape()[-1]), out_channels],\n tf.float32, initializer, trainable=True)\n b_h = tf.get_variable('b_h', shape=[out_channels], dtype=tf.float32,\n initializer=None, trainable=True)\n residual = tf.nn.bias_add(tf.nn.convolution(input_layer, W_h, 'SAME'), b_h)\n else:\n print(\"no residual convolution\")\n\n res = input_layer if residual is None else residual\n\n return tf.nn.relu(dropout2 + res)\n\n\ndef TemporalConvNet(input_layer, num_channels, kernel_size=2, initializer=tf.random_normal_initializer(0, 0.01),\n dropout_keep_prob=tf.constant(0.0, dtype=tf.float32), init=False, atten=False, highway=False, gated=False):\n num_levels = len(num_channels)\n counters = {}\n for i in range(num_levels):\n print(i)\n dilation_size = 2 ** i\n out_channels = num_channels[i]\n input_layer = TemporalBlock(input_layer, out_channels, kernel_size, stride=1, dilation_rate=dilation_size,\n initializer=initializer, counters=counters, dropout_keep_prob=dropout_keep_prob,\n init=init, atten=atten, highway=highway, gated=gated)\n\n return input_layer\n","sub_path":"models/cnn_module.py","file_name":"cnn_module.py","file_ext":"py","file_size_in_byte":8151,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"306849108","text":"import os\nimport csv\nimport sys\nimport argparse\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--dustmasker', help=\"VCF file for use\")\nparser.add_argument('--chrom', help=\"VCF file for use\")\nargs = parser.parse_args()\nlinestoworkwith = []\n\nfor beta in list(csv.reader(open(args.dustmasker),delimiter='\\t')):\n\tif beta[0] == args.chrom:\n\t\tlinestoworkwith.append(beta[1:])\n\nnewfle = open('/home/vdp5/data/reference_genomes/PVP01_gapfiles/{}_gap.txt'.format(args.chrom), 'w')\n\nnewfle.write('start\\tend\\n')\n\nfor beta in linestoworkwith:\n\tnewfle.write('{}\\t{}\\n'.format(beta[0], beta[1]))\n\nnewfle.close()","sub_path":"fastEPPR/create_gapfile.py","file_name":"create_gapfile.py","file_ext":"py","file_size_in_byte":608,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"397084918","text":"import sys\n\nfrom tastypie.resources import ModelResource\n\n\nclass UnpaginatedResource(ModelResource):\n \"\"\"\n Base ModelResource used to strip out pagination from lists.\n \"\"\"\n class Meta:\n include_resource_uri = False\n limit = sys.maxint\n max_limit = None\n\n def alter_list_data_to_serialize(self, request, data):\n \"\"\"\n Remove the 'meta' dictionary from the response as we're not paginating, so it's unnecessary.\n \"\"\"\n if isinstance(data, dict):\n del data['meta']\n listName = self.getResourceListName()\n if listName:\n data[listName] = data['objects']\n del data['objects']\n\n return data\n\n def getResourceListName(self):\n \"\"\"\n Override to rename the array (returned from get_list) from 'objects' to the return of this method.\n \"\"\"\n return None\n","sub_path":"tablefootball/modelresources/unpaginated.py","file_name":"unpaginated.py","file_ext":"py","file_size_in_byte":903,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"454096977","text":"from threading import Lock\n\nclass BankAccount:\n def __init__(self):\n self.status = False\n self.balance = 0\n self.thread = Lock()\n\n def get_balance(self):\n with self.thread:\n if self.status == False:\n raise ValueError(\"Account is closed\")\n else:\n return self.balance\n\n def open(self):\n with self.thread:\n if self.status == True:\n raise ValueError (\"Account open\")\n else:\n self.status = True\n self.balance = 0\n\n def deposit(self, amount):\n with self.thread:\n if self.status == False:\n raise ValueError(\"Account is closed\")\n else:\n if amount >= 0:\n self.balance += amount\n else:\n raise ValueError(\"Can't deposit given amount\")\n\n def withdraw(self, amount):\n with self.thread:\n if self.status == False:\n raise ValueError(\"Account is closed\")\n else:\n if amount >= 0 and (self.balance - amount) >= 0:\n self.balance -= amount\n else:\n raise ValueError(\"Can't withdraw given amount\")\n\n def close(self):\n with self.thread:\n if self.status == False:\n raise ValueError(\"Account is closed\")\n else:\n self.status = False\n","sub_path":"bank-account/bank_account.py","file_name":"bank_account.py","file_ext":"py","file_size_in_byte":1465,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"114149933","text":"import logging\nfrom logging.handlers import RotatingFileHandler\n\nfrom flask import Flask\n\napp = Flask(__name__)\n\n@app.route('/')\ndef foo():\n app.logger.warning('A warning occurred (%d apples)', 42)\n app.logger.error('An error occurred')\n app.logger.info('Info')\n return \"foo\"\n\nif __name__ == '__main__':\n handler = RotatingFileHandler('foo.log', maxBytes=10000, backupCount=1)\n handler.setLevel(logging.DEBUG)\n formatter = logging.Formatter(\n \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\")\n handler.setFormatter(formatter)\n app.logger.addHandler(handler)\n #app.logger.addHandler(log)\n app.run()\n","sub_path":"web/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":644,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"651556939","text":"import unittest\nfrom Board import board\n\n\nclass testBoardConfig(unittest.TestCase):\n\n def test_board_6_col_6_row(self):\n num_row = 6\n num_col = 6\n\n test_board = board(num_col=6, num_row=6)\n test_num_row = len(test_board.board)\n test_num_col = len(test_board.board[0])\n\n self.assertEqual(test_num_col, num_col)\n self.assertEqual(test_num_row, num_row)\n\n","sub_path":"tests/test_board_config.py","file_name":"test_board_config.py","file_ext":"py","file_size_in_byte":404,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"634041211","text":"# Definition for singly-linked list.\n# class ListNode(object):\n# def __init__(self, val=0, next=None):\n# self.val = val\n# self.next = next\n\nclass Solution(object):\n def addTwoNumbers(self, l1, l2):\n \"\"\"\n :type l1: ListNode\n :type l2: ListNode\n :rtype: ListNode\n \"\"\"\n \n dummy_head = ListNode(-1)\n temp_res = dummy_head\n first_value = ''\n second_value = ''\n temp_value = ''\n carry = 0\n left = -1\n right = -1\n \n while l1:\n first_value += str(l1.val)\n l1 = l1.next\n left += 1\n \n while l2:\n second_value += str(l2.val)\n l2 = l2.next\n right += 1\n \n while left >= 0 or right >= 0 or carry: \n if left >= 0:\n carry += ord(first_value[left]) - ord('0')\n left -= 1\n \n if right >= 0:\n carry += ord(second_value[right]) - ord('0')\n right -= 1\n \n carry, remainder = divmod(carry, 10)\n temp_value = str(remainder) + temp_value\n \n for char in temp_value:\n temp_res.next = ListNode(char)\n temp_res = temp_res.next\n \n return dummy_head.next ","sub_path":"practice/solution/0445_add_two_numbers_ii.py","file_name":"0445_add_two_numbers_ii.py","file_ext":"py","file_size_in_byte":1354,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"237816906","text":"import random\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom k_nearest_neighbor import KNearestNeighbor\nfrom load_pulse_data import load_data\n\nX_data, y_data = load_data(\"data.txt\")\nnum_data = X_data.shape[0]\n\n# cross-validation\nX_train = X_data\ny_train = y_data\nnum_folds = 5\nk_choices = [1, 3, 5, 8, 10, 12, 15, 20, 50, 100]\n\nX_train_folds = np.split(X_train, 5)\ny_train_folds = np.split(y_train, 5)\nclassifier = KNearestNeighbor()\nk_to_accuracies = {}\n\nfor k in k_choices:\n k_to_accuracies[k] = np.zeros(5)\n for i in range(5):\n X_tr = X_train_folds[0:i] + X_train_folds[i+1:5]\n X_tr = np.reshape(X_tr, (X_train.shape[0] * 4 / 5, -1))\n y_tr = y_train_folds[0:i] + y_train_folds[i+1:5]\n y_tr = np.reshape(y_tr, (X_train.shape[0] * 4 / 5))\n X_tt = np.reshape(X_train_folds[i], (X_train.shape[0] / 5, -1))\n classifier.train(X_tr, y_tr)\n y_test_pred = classifier.predict(X_tt, k)\n accuracy = float(np.sum(y_test_pred == y_train_folds[i])) / y_test_pred.shape[0]\n k_to_accuracies[k][i] = accuracy\n\nfor k in k_choices:\n accuracies = k_to_accuracies[k]\n plt.scatter([k] * len(accuracies), accuracies)\n\naccuracies_mean = np.array([np.mean(v) for k, v in sorted(k_to_accuracies.items())])\naccuracies_std = np.array([np.std(v) for k, v in sorted(k_to_accuracies.items())])\nplt.errorbar(k_choices, accuracies_mean, yerr=accuracies_std)\nplt.title('Cross-validation on k')\nplt.xlabel('k')\nplt.ylabel('Cross-validation accuracy')\nplt.show()\n\n# predict\n# X_train = X_data[0:num_data*4/5:]\n# y_train = y_data[0:num_data*4/5:]\n# X_test = X_data[num_data*4/5:]\n# y_test = y_data[num_data*4/5:]\n# num_training = X_train.shape[0]\n# num_test = X_test.shape[0]\n#\n# classifier = KNearestNeighbor()\n# classifier.train(X_train, y_train)\n# y_pred = classifier.predict(X_test, k=12)\n#\n# num_correct = np.sum(y_pred == y_test)\n# accuracy = float(num_correct) / num_test\n# print 'Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy)\n# print(y_pred)\n# print(y_test * 1.0)","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":2043,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"367120058","text":"from __future__ import print_function\nimport re\nimport requests\nimport datetime\n\n\nclass Jira(object):\n def __init__(self, config):\n self.config = {}\n for item in config:\n self.config[item[0]] = item[1]\n\n self.auth_data = (self.config['username'], self.config['password'])\n\n def make_request(self, uri):\n \"\"\"\n Generic method for making requests to JIRA API.\n \"\"\"\n response = requests.get(self.config['api_url'] + uri, auth=self.auth_data)\n response.raise_for_status()\n\n return response.json()\n\n def get_tasks(self):\n \"\"\"\n Generator returning all tasks assigned to current user that match filter specified in configuration\n \"\"\"\n tasks = self.make_request(\n 'search?jql=' + self.config['search_query'] + '&fields=*all,-comment'\n )\n if not tasks:\n return\n\n for issue in tasks['issues']:\n if self.config['economic_field'] not in issue['fields']:\n print('ERROR - task %s is missing economic project ID' % (issue['key']))\n yield None\n else:\n task = {\n 'date': datetime.datetime.now().isoformat()[:10],\n 'project_id': self.get_project_id(issue['fields'][self.config['economic_field']]),\n 'task_description': '%s %s' % (issue['key'], issue['fields']['summary']),\n 'time_spent': str(self.get_hours(issue['key'])).replace('.', ',')\n }\n\n yield task\n\n def get_hours(self, issue):\n hours = 0.0\n if not int(self.config['use_worklog']):\n return hours\n\n now = str(datetime.datetime.now())[:10]\n for worklog in self.get_worklog(issue):\n if worklog['author']['name'] == self.config['username'] and worklog['started'].startswith(now):\n hours += worklog['timeSpentSeconds']\n\n return hours / 3600\n\n def get_worklog(self, issue_id):\n return self.make_request('issue/%s/worklog' % issue_id)['worklogs']\n\n def get_project_id(self, economic_field):\n \"\"\"\n Economic field might be either select box or input field.\n Value might be either numeric or contain number and project's name.\n\n :param economic_field:\n :return: :rtype:\n \"\"\"\n if type(economic_field) is dict:\n project_id = str(economic_field['value'])\n else:\n project_id = str(economic_field)\n\n return int(re.search(r'^\\d+', project_id).group())\n","sub_path":"economicpy/jira.py","file_name":"jira.py","file_ext":"py","file_size_in_byte":2575,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"422254396","text":"# python3\r\n# -*- coding:utf-8 -*-\r\n\r\nimport argparse\r\nimport os,sys\r\nfrom collections import Counter\r\nimport glob\r\nimport random\r\nimport string\r\nimport re\r\nimport resource\r\nimport logbook\r\nfrom logbook.compat import redirect_logging\r\nimport configargparse\r\nimport numpy as np\r\nimport arrow\r\nfrom Bio import SeqIO\r\nimport resource\r\nfrom gensim.models import word2vec\r\n\r\nsys.path.extend(['.', '..'])\r\n\r\nclass Tee(object):\r\n def __init__(self, fptr):\r\n self.file = fptr\r\n def __enter__(self):\r\n self.stdout = sys.stdout\r\n sys.stdout = self\r\n def __exit__(self, exception_type, exception_value, traceback):\r\n sys.stdout = self.stdout\r\n def write(self, data):\r\n self.file.write(data)\r\n self.stdout.write(data)\r\n\r\nclass Fasta_Sequence_cut:\r\n def __init__(self,fasta_files,Min_SeqLength,Max_SeqLength,kmer_low,kmer_high):\r\n self.logger = logbook.Logger(self.__class__.__name__)\r\n self.fasta_files = fasta_files\r\n self.epochs = args.epochs\r\n self.Min_SeqLength = Min_SeqLength\r\n self.Max_SeqLength = Max_SeqLength\r\n self.kmer_low = kmer_low\r\n self.kmer_high = kmer_high\r\n self.kmer_len_counter = Counter()\r\n self.nb_kmers = 0\r\n self.random_num = np.random.RandomState(123)\r\n self.fasta_seq = 0\r\n self.cut_seq = 0\r\n self.cut_seq_lengthlist=[]\r\n self.iter_count = 0\r\n\r\n def SeqGenerator_fastahandle(self):\r\n for curr_epoch in range(self.epochs):\r\n for fasta_file in self.fasta_files:\r\n with open(fasta_file) as fasta:\r\n self.logger.info('Opened file: {}'.format(fasta_file))\r\n self.logger.info('Memory usage: {} MB'.format(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1E6))\r\n self.logger.info('Current epoch: {} / {}'.format(curr_epoch + 1, self.epochs))\r\n yield fasta\r\n\r\n def SeqGenerator(self):\r\n for fasta_handle in self.SeqGenerator_fastahandle():\r\n for seq_record in SeqIO.parse(fasta_handle,'fasta'):\r\n self.fasta_seq +=1\r\n whole_seq = seq_record.seq\r\n self.logger.info('Whole fasta seqlen:{}'.format(len(whole_seq)))\r\n cut_seq_stat = 0\r\n while cut_seq_stat < len(whole_seq):\r\n self.cut_seq +=1\r\n seqlen = random.randint(self.Min_SeqLength,self.Max_SeqLength)\r\n #seqlen = len(seq_record.seq) #miRna长度较短，使用其本身长度作为切割长度\r\n segment = seq_record.seq[cut_seq_stat: cut_seq_stat+seqlen]\r\n cut_seq_stat += seqlen\r\n self.logger.debug('Cut seq len:{}'.format(len(segment)))\r\n self.cut_seq_lengthlist.append(len(segment))\r\n yield segment\r\n\r\n def SlidingKmerFragmenter(self,seq):\r\n return [seq[i: i + self.random_num.randint(self.kmer_low, self.kmer_high + 1)] for i in range(len(seq) - self.kmer_high + 1)]\r\n\r\n def Kmer_Histogram(self,seq):\r\n for kmer in seq:\r\n self.kmer_len_counter[len(kmer)] += 1\r\n self.nb_kmers += 1\r\n\r\n def Kmer_stat(self, fptr):\r\n for kmer_len in sorted(self.kmer_len_counter.keys()):\r\n self.logger.info('Percent of {:2d}-mers: {:3.1f}% ({})'.format(\r\n kmer_len,\r\n 100.0 * self.kmer_len_counter[kmer_len] / self.nb_kmers,\r\n self.kmer_len_counter[kmer_len]))\r\n total_bps = sum([l * c for l, c in self.kmer_len_counter.items()])\r\n self.logger.info('Number of base-pairs: {}'.format(total_bps))\r\n\r\n def sequence_stat(self,fptr):\r\n self.logger.info('Number of fasta sequence: {}'.format(self.fasta_seq))\r\n self.logger.info('After cut Number of sequence: {}'.format(self.cut_seq))\r\n np.savez(args.out_dir+'/cut_seq_length.npz',Cut_Lenght=self.cut_seq_lengthlist)\r\n\r\n def __iter__(self):\r\n self.iter_count += 1\r\n for seq in self.SeqGenerator():\r\n seq = seq.upper()\r\n if True and self.random_num.rand() < 0.5:\r\n seq = seq.reverse_complement()\r\n\r\n acgt_seq_split = list(filter(bool,re.split(r'[^ACGTacgt]+', str(seq))))\r\n for acgt_seq in acgt_seq_split:\r\n kmer_seq = self.SlidingKmerFragmenter(acgt_seq)\r\n if len(kmer_seq) > 0:\r\n if self.iter_count ==1:\r\n self.Kmer_Histogram(kmer_seq)\r\n yield kmer_seq\r\n\r\n\r\nclass Learner:\r\n def __init__(self,outputfile,context_halfsize, gensim_iters, vec_dim):\r\n self.logger = logbook.Logger(self.__class__.__name__)\r\n self.model = None\r\n self.outputfile = outputfile\r\n # 三个word2vec 参数\r\n self.context_halfsize =context_halfsize\r\n self.gensim_iters = gensim_iters\r\n self.vec_dim =vec_dim\r\n self.use_skipgram = 1\r\n # 日志打印信息\r\n self.logger.info('Context window half size: {}'.format(self.context_halfsize))\r\n self.logger.info('Use skipgram: {}'.format(self.use_skipgram))\r\n self.logger.info('gensim_iters: {}'.format(self.gensim_iters))\r\n self.logger.info('vec_dim: {}'.format(self.vec_dim))\r\n\r\n def train(self,kmer_seq_generator):\r\n self.model = word2vec.Word2Vec(\r\n sentences=kmer_seq_generator,\r\n size=self.vec_dim,\r\n window=self.context_halfsize,\r\n min_count=2,\r\n workers=4,\r\n sg=self.use_skipgram,\r\n iter=self.gensim_iters)\r\n\r\n def write_vec(self):\r\n out_filename = '{}.w2v'.format(self.outputfile)\r\n self.model.wv.save_word2vec_format(out_filename, binary=False)\r\n\r\n\r\nif __name__ == '__main__':\r\n argp = configargparse.get_argument_parser()\r\n argp.add('-c', is_config_file=True, help='config file path')\r\n argp.add_argument('--inputs', help='FASTA files', nargs='+', required=True)\r\n argp.add_argument('--k-low', help='k-mer start range (inclusive)', type=int, default=5)\r\n argp.add_argument('--k-high', help='k-mer end range (inclusive)', type=int, default=5)\r\n\r\n argp.add_argument('--Min_seq', help='cut sequence min length', type=int, default=200)\r\n argp.add_argument('--Max_seq', help='cut sequence max length', type=int, default=300)\r\n argp.add_argument('--vec_dim', help='vector dimension', type=int, default=12)\r\n argp.add_argument('--context', help='half size of context window (the total size is 2*c+1)', type=int, default=4)\r\n argp.add_argument('--epochs', help='number of epochs', type=int, default=1)\r\n argp.add_argument('--gensim_iters', help=\"gensim's internal iterations\", type=int, default=1)\r\n argp.add_argument('--out_dir', help=\"output directory\", default='../dataset/dna2vec/results')\r\n args = argp.parse_args()\r\n\r\n '''\r\n step1: \r\n 1\\输入文件，fasta文件\r\n 参数：args.inputs\r\n '''\r\n inputs = [glob.glob(s)[0] for s in args.inputs]\r\n print(inputs)\r\n fileSizeSum = sum([os.stat(f).st_size for f in inputs])\r\n\r\n '''\r\n 2\\定义输出日志文件：\r\n 参数：args.k_low,args.k_high,args.vec_dim,args.context,args.kmer_fragmenter\r\n '''\r\n out_dir = args.out_dir\r\n out_file_suffix_word = ''.join(random.SystemRandom().choice(string.ascii_lowercase +\r\n string.ascii_uppercase + string.digits) for _ in range(3))\r\n out_file_suffix = 'k{}to{}-{}d-{}c-{}Mbp-{}-{}'.format(args.k_low,args.k_high,args.vec_dim,args.context,\r\n fileSizeSum * args.epochs, 'sliding',out_file_suffix_word)\r\n out_file = '{}/{}-{}-{}'.format(out_dir,'testRNA2vec',arrow.utcnow().format('YYYYMMDD-HHmm'),out_file_suffix,)\r\n out_txt_filename = '{}.txt'.format(out_file)\r\n print(out_txt_filename) \r\n\r\n '''\r\n 3\\写到日志文件中\r\n work2vec 开始训练\r\n '''\r\n with open(out_txt_filename, 'w') as summary_fptr:\r\n with Tee(summary_fptr):\r\n logbook.StreamHandler(sys.stdout, level='DEBUG').push_application()\r\n redirect_logging()\r\n SeqGenerator = Fasta_Sequence_cut(fasta_files=inputs,\r\n Min_SeqLength=args.Min_seq,\r\n Max_SeqLength=args.Max_seq,\r\n kmer_low=args.k_low,\r\n kmer_high=args.k_high)\r\n learner = Learner(outputfile=out_file,\r\n context_halfsize=args.context,\r\n gensim_iters=args.gensim_iters,\r\n vec_dim=args.vec_dim)\r\n\r\n learner.train(kmer_seq_generator=SeqGenerator)\r\n learner.write_vec()\r\n SeqGenerator.Kmer_stat(sys.stdout)\r\n SeqGenerator.sequence_stat(sys.stdout)\r\n","sub_path":"train_rna2vec.py","file_name":"train_rna2vec.py","file_ext":"py","file_size_in_byte":8947,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"428401457","text":"from django import forms\n\n#форма для ввода анализируемого текста\nclass UserForm(forms.Form):\n text = forms.CharField(widget=forms.Textarea(), label=\"Результат анализа:\")\n ner = forms.BooleanField(label=\"Разметка распознанных сущностей\")\n morph = forms.BooleanField(label=\"Морфологический анализ\")\n syntax = forms.BooleanField(label=\"Синтаксический анализ\")\n seman = forms.IntegerField(label=\"Номер предложения для семантического анализа\")\n#форма для вывода результата\nclass ResultForm(forms.Form):\n result = forms.CharField()","sub_path":"polls/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":726,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"185233534","text":"# pdflatex.py - render LaTeX file to PDF and optionally open in viewer\n\nimport sys\nimport os\nimport subprocess\n\nfrom . import tools\n\n__all__ = ['render', 'viewer']\n\nVIEW = False\n\n\ndef no_compile(filename, view=VIEW):\n raise NotImplementedError('unsupported platform')\n\n\ndef texlive_compile(filename, view=VIEW):\n pdflatex = ['pdflatex', '-pdf', '-interaction=batchmode', '-halt-on-error']\n compile_dir = os.path.dirname(filename)\n if compile_dir:\n pdflatex.append('-output-directory=%s' % compile_dir)\n pdflatex.append(filename)\n for i in range(3):\n subprocess.Popen(pdflatex).wait()\n if view:\n viewer(tools.swapext(filename, 'pdf'))\n\n\ndef miktex_compile(filename, view=VIEW):\n compile_dir, filename = os.path.split(filename) # texify has issues with remote directory\n texify = ['texify', '--pdf', '--batch', '--verbose', '--quiet']\n if view:\n texify.append('--run-viewer')\n texify.append(filename)\n with tools.chdir(compile_dir):\n subprocess.Popen(texify).wait()\n\n\n@apply\ndef render(platform=sys.platform):\n compile_funcs = {\n 'darwin': texlive_compile,\n 'linux2': texlive_compile,\n 'win32': miktex_compile,\n }\n return compile_funcs.get(platform, no_compile)\n\n\ndef no_view(filepath):\n raise NotImplementedError('unsupported platform')\n\n\ndef darwin_view(filepath):\n subprocess.Popen(['open', filepath])\n\n\ndef linux2_view(filepath):\n subprocess.Popen(['xdg-open', filepath])\n\n\ndef win32_view(filepath):\n os.startfile(os.path.normpath(filepath))\n\n\n@apply\ndef viewer(platform=sys.platform):\n view_funcs = {\n 'darwin': darwin_view,\n 'linux2': linux2_view,\n 'win32': win32_view,\n }\n return view_funcs.get(platform, no_view)\n","sub_path":"dmengine/reporting/pdflatex.py","file_name":"pdflatex.py","file_ext":"py","file_size_in_byte":1763,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"445924466","text":"#Author: Noah Wagnon\n#Description: Implements the API that supports posting\n# and getting play info\n\n\n\n\nfrom flask import Flask\nfrom flask_restful import Resource, Api, reqparse\nimport pandas as pd\nimport ast\n\napp = Flask(__name__)\napi = Api(app)\n\nclass Plays(Resource):\n def get(self):\n data = pd.read_csv('plays.csv') # read local CSV\n data = data.to_dict() # convert dataframe to dict\n return {'data': data}, 200 # return data and 200 OK\n\n def post(self):\n parser = reqparse.RequestParser() # initialize\n parser.add_argument('play_number', required=True) # add args\n parser.add_argument('quarter', required=True) # add args\n parser.add_argument('start_time', required=True) # add args\n parser.add_argument('end_time', required=True) # add args\n parser.add_argument('home', required=True)\n parser.add_argument('away', required=True)\n parser.add_argument('participating_players',required=False, action='append')\n args = parser.parse_args() # parse arguments to dictionary\n\n # read our CSV\n data = pd.read_csv('plays.csv')\n\n if args['play_number'] in list(data['play_number']):\n return {\n 'message': f\"'{args['play_number']}' already exists.\"\n }, 409\n else:\n # create new dataframe containing new values\n new_data = pd.DataFrame({\n 'play_number': [args['play_number']],\n 'quarter': [args['quarter']],\n 'start_time': [args['start_time']],\n 'end_time': [args['end_time']],\n 'home': [args['home']],\n 'away': [args['away']],\n 'participating_players': [args['participating_players']]\n })\n # add the newly provided values\n data = data.append(new_data, ignore_index=True)\n data.to_csv('plays.csv', index=False) # save back to CSV\n return {'data': data.to_dict()}, 200 # return data with 200 OK\n\n\n def put(self): \n parser = reqparse.RequestParser() # initialize\n parser.add_argument('play_number', required=True) # add args\n parser.add_argument('player', required=True)\n args = parser.parse_args() # parse arguments to dictionary\n\n # read our CSV\n data = pd.read_csv('plays.csv')\n print(list(data['play_number']))\n\n if int(args['play_number']) in list(data['play_number']):\n # evaluate strings of lists to lists !!! never put something like this in prod\n data['participating_players'] = data['participating_players'].apply(\n lambda x: ast.literal_eval(x)\n )\n # select our user\n play_data = data[data['play_number'] == args['play_number']]\n\n # update user's locations\n if len(play_data['participating_players']) == 0:\n play_data['participating_players'] = args['player']\n else:\n play_data['participating_players'] = play_data['participating_players'].values[0] \\\n .append(args['player'])\n \n \n # save back to CSV\n data.to_csv('plays.csv', index=False)\n # return data and 200 OK\n return {'data': data.to_dict()}, 200\n\n else:\n # otherwise the userId does not exist\n return {\n 'message': f\"'{args['play_number']}' not found.\"\n }, 404\n\n \n# class Players(Resource):\n# def get(self):\n# data = pd.read_csv('players.csv') # read local CSV\n# return {'data': data.to_dict()}, 200 # return data dict and 200 OK\n \n# def post(self):\n# parser = reqparse.RequestParser() # initialize parser\n# parser.add_argument('number', required=True, type=int) # add args\n# parser.add_argument('name', required=True)\n# args = parser.parse_args() # parse arguments to dictionary\n \n# # read our CSV\n# data = pd.read_csv('players.csv')\n \n# # check if location already exists\n# if args['number'] in list(data['number']):\n# # if locationId already exists, return 401 unauthorized\n# return {\n# 'message': f\"'{args['number']}' already exists.\"\n# }, 409\n# else:\n# # otherwise, we can add the new location record\n# # create new dataframe containing new values\n# new_data = pd.DataFrame({\n# 'number': [args['number']],\n# 'name': [args['name']]\n# })\n# # add the newly provided values\n# data = data.append(new_data, ignore_index=True)\n# data.to_csv('players.csv', index=False) # save back to CSV\n# return {'data': data.to_dict()}, 200 # return data with 200 OK\n \n \n\n\napi.add_resource(Plays, '/plays') # add endpoints\n#api.add_resource(Players, '/players')\n\nif __name__ == '__main__':\n app.run() # run our Flask app\n","sub_path":"API/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":5076,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"142479270","text":"from flask import render_template\n\nfrom app import app\n\n@app.route('/')\ndef index():\n\tuser = {'name':'World'}\n\tposts = [\n\t\t{\n\t\t 'author':{'name':'Tom'},\n\t\t 'body':'A nice day'\n\t\t},\n\t\t{\n\t\t 'author':{'name':'Jan'},\n\t\t 'body':'A good job'\n\t\t},\n\t]\n\treturn render_template('index.html', \n\t\ttitle='Welcome', \n\t\tuser=user,\n\t\tposts=posts)\n","sub_path":"app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":331,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"468632797","text":"import time\nimport rtmidi\nimport sys\n\nsignal_cutoff = 500\nplaying = False\n\nmidiout = rtmidi.MidiOut()\navailable_ports = midiout.get_ports()\nif available_ports:\n midiout.open_port(0)\nelse:\n midiout.open_virtual_port(\"My virtual output\")\n\ntime.sleep(.5)\nduration = .25\nif len(sys.argv) > 2:\n duration = float(sys.argv[2])\n\nnotes = {\n 'a' : 57,\n 'b' : 59,\n 'c' : 60,\n 'd' : 62,\n 'e' : 64,\n 'f' : 65,\n 'g' : 67\n }\n\ndef play_note(n):\n if n in notes:\n print(\"Playing\",n)\n note_on = [0x90, notes[n], 112] # channel 1, middle C, velocity 112\n note_off = [0x80, notes[n], 0]\n midiout.send_message(note_on)\n time.sleep(duration)\n midiout.send_message(note_off)\n\ndef start_c(velocity=112):\n print(\"Note ON\")\n global playing\n playing = True\n midiout.send_message([0x90, 60, velocity])\n\ndef stop_c():\n print(\"Note OFF\")\n global playing\n playing = False\n midiout.send_message([0x90, 60, 0])\n\n\nimport paho.mqtt.client as mqtt\n\n# The callback for when the client receives a CONNACK response from the server.\ndef on_connect(client, userdata, flags, rc):\n print(\"Connected with result code \"+str(rc))\n\n # Subscribing in on_connect() means that if we lose the connection and\n # reconnect then subscriptions will be renewed.\n # client.subscribe(\"$SYS/#\")\n client.subscribe(\"sensors/+\")\n\n\n# The callback for when a PUBLISH message is received from the server.\ncounter = 0\ncount_max = 50\nnote = 0\ndef on_message(client, userdata, msg):\n global counter\n # print(msg.topic+\" \"+str(msg.payload))\n # print(\"Playing:\",playing)\n signal_mag = int(msg.payload)\n print(signal_mag)\n if signal_mag > signal_cutoff and playing is True:\n stop_c()\n\n if playing is True:\n # counter = counter + 1\n # if counter > count_max:\n # counter = 0\n start_c((signal_cutoff - signal_mag) * 127 / signal_cutoff)\n\n if signal_mag < signal_cutoff and playing is False:\n start_c(min(signal_cutoff - signal_mag, 127))\n\n\nclient = mqtt.Client()\nclient.on_connect = on_connect\nclient.on_message = on_message\n\nclient.connect(\"manatee.local\", 1883, 60)\n\n# Blocking call that processes network traffic, dispatches callbacks and\n# handles reconnecting.\n# Other loop*() functions are available that give a threaded interface and a\n# manual interface.\nclient.loop_forever()\n\n\ndel midiout\n","sub_path":"PythonSketches/mqmidi2.py","file_name":"mqmidi2.py","file_ext":"py","file_size_in_byte":2476,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"88064659","text":"\nimport ast\nimport os\n\nimport pytest\n\nfrom bmd.bmd import BMD\nfrom bmd.avro_schema_resolver import AvroSchemaResolver\n\n\nTHIS_DIR = os.path.abspath(\n os.path.join(\n os.path.dirname(__file__)\n )\n)\nSCHEMAS_DIR = os.path.join(THIS_DIR, '..', '..', '..', 'schemas')\nFIXTURES_DIR = os.path.join(THIS_DIR, 'fixtures')\n\n\n@pytest.fixture\ndef bmd():\n schema_resolver = AvroSchemaResolver(SCHEMAS_DIR)\n schema_resolver.load_and_unify_schemas()\n bmd = BMD(schema_resolver)\n return bmd\n\n\n@pytest.fixture\ndef site_bmd_bytearray():\n room_bmd_file = os.path.join(FIXTURES_DIR, 'example.site')\n with open(room_bmd_file, 'rb') as f:\n return f.read()\n\n\n@pytest.fixture\ndef site_avro_model():\n site_model_file = os.path.join(FIXTURES_DIR, 'example.avro')\n with open(site_model_file, 'r') as f:\n return ast.literal_eval(f.read())\n\n\ndef test_bmd_to_model(bmd, site_bmd_bytearray, site_avro_model):\n avro_model = bmd.to_avro_model(site_bmd_bytearray, 'site')\n assert avro_model == site_avro_model\n\n\ndef test_model_to_bmd(bmd, site_bmd_bytearray, site_avro_model):\n bmd_bytearray = bmd.to_bmd_bytearray(site_avro_model, 'site')\n assert bmd_bytearray == site_bmd_bytearray\n","sub_path":"shared/python/bmd/tests/test_bmd.py","file_name":"test_bmd.py","file_ext":"py","file_size_in_byte":1211,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"175012301","text":"import sys\nimport MySQLdb as mdb\ndb = mdb.connect('localhost', 'ask-user', '19251925', 'ask_db',init_command='SET NAMES UTF8')\ncursor = db.cursor()\ncursor.execute('select id,date,rating,body from ask_answer order by rating desc limit 50' )\ntop = cursor.fetchall()\nprint('Top 50 answer')\nfor x in top:\n print ('id = {0} , date = {1} , rating = {2} , answer = {3}'.format(x[0],x[1],x[2],x[3]))\ndb.close()\n","sub_path":"scripts/answer_by_rating.py","file_name":"answer_by_rating.py","file_ext":"py","file_size_in_byte":406,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"344476413","text":"import sys, pygame, tiles, checkers\nfrom Gui import *\nfrom checkers.pieces import *\nfrom gameplay import *\nfrom Ai import *\n\nRESOLUTION = pygame.Rect(0, 0, 800, 600)\nBG_COLOR = (32, 32, 32)\n\n# Initialize everything\npygame.mixer.pre_init(22050, -16, 2, 512) # Small buffer for less sound lag\npygame.init()\npygame.display.set_caption(\"Checkers\")\nmain_gui = GUI(RESOLUTION, BG_COLOR)\nclock = pygame.time.Clock()\nargv = sys.argv[1:]\n\n# If a filename was given, load that level. Otherwise, load a default.\nlevel = \"CheckersBoard\"\nif len(argv) > 0:\n level = argv[0]\nnew_units = main_gui.load_level(\"GameBoard/\" + level + \".lvl\")\nfor i in new_units:\n\n Checkers = Pieces(i[0],i[1],i[2],i[3])\n\ngameplay = Gameplay(main_gui.map, Checkers.active_units)\nai = SmartAI(gameplay)\n\npath = None\n\n# The main game loop\nwhile 1:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n pygame.display.quit()\n sys.exit()\n # End if q is pressed\n elif (event.type == pygame.KEYDOWN and\n (event.key == pygame.K_q or event.key == pygame.K_ESCAPE)):\n pygame.display.quit()\n sys.exit()\n # Respond to clicks\n elif event.type == pygame.MOUSEBUTTONUP:\n tile_x,tile_y = main_gui.on_click(event)\n \n #Have we chosen heads or tails\n if main_gui.select_state == True:\n \n #Get position of click\n unitnew = gameplay.get_unit_at_pos((tile_x, tile_y))\n \n if not unitnew == None:\n print(gameplay.get_state())\n \n #This allows gui to outline selected unit\n main_gui.sel_unit = unitnew\n if unitnew.team == main_gui.current_team:\n\n #If there is a path available find it and send to Gui\n path = gameplay.set_path((tile_x, tile_y))\n main_gui.moveable_tiles = path\n \n #Sets highlights for the path\n main_gui.draw_path()\n \n #Need to know for movement\n unitold = unitnew\n \n elif not path == None:\n if (tile_x, tile_y) in path:\n #Move the active units, return remaining path\n path = gameplay.move((tile_x, tile_y), unitold, path)\n \n \n if path == [\"Done\"]:\n #Returns None so that there is no path\n path = main_gui.end_turn_processed() \n \n if main_gui.current_team == 1:\n #selects an active_unit on its team and finds its path\n unit, path = ai.find_path()\n \n #Special case where a unit is still alive but has no move available\n # moves.\n if path == [\"Over\"] and unit == None:\n path = main_gui.end_turn_processed()\n path = [\"Over\"]\n\n\n #If AI has a \"double-jump\" it requires unit to click between\n #jumps\n if not path == [] and not unit == None and not path == [\"Over\"]:\n path = gameplay.move(path[0], unit, path)\n\n if path == [\"Done\"]:\n path = main_gui.end_turn_processed()\n \n\n if path == [\"Over\"]:\n main_gui.win_team = main_gui.current_team\n main_gui.mode = Modes.GameOver\n main_gui.update()\n main_gui.draw(gameplay.active_units)\n clock.tick(60)\n","sub_path":"mainSmart.py","file_name":"mainSmart.py","file_ext":"py","file_size_in_byte":3646,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"120055160","text":"# neural network & xavier & dropout\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\nimport random\n\nfrom tensorflow.examples.tutorials.mnist import input_data\n\ntf.set_random_seed(777) # reproducibility\n#dropout (keep_prob) rate 0.7 on training, but should be 1 for testing\nkeep_prob = tf.placeholder(tf.float32)\n\n# Check out https://www.tensorflow.org/get_started/mnist/beginners for\n# more information about the mnist dataset\nmnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)\n\n# input place holders\nX = tf.placeholder(tf.float32, [None, 784])\nY = tf.placeholder(tf.float32, [None, 10])\n\n# weights & bias for nn layers\nW1 = tf.get_variable(\"W0001\",shape = [784, 512],initializer = tf.contrib.layers.xavier_initializer())\nb1 = tf.Variable(tf.random_normal([512]))\nlayer1 = tf.nn.relu(tf.matmul(X,W1) + b1)\nlayer1 = tf.nn.dropout(layer1, keep_prob = keep_prob)\n\nW2 = tf.get_variable(\"W0002\",shape = [512, 512],initializer = tf.contrib.layers.xavier_initializer())\nb2 = tf.Variable(tf.random_normal([512]))\nlayer2 = tf.nn.relu(tf.matmul(layer1,W2) + b2)\nlayer2 = tf.nn.dropout(layer2, keep_prob = keep_prob)\n\nW3 = tf.get_variable(\"W0003\",shape = [512, 512],initializer = tf.contrib.layers.xavier_initializer())\nb3 = tf.Variable(tf.random_normal([512]))\nlayer3 = tf.nn.relu(tf.matmul(layer2,W3) + b3)\nlayer3 = tf.nn.dropout(layer3, keep_prob = keep_prob)\n\nW4 = tf.get_variable(\"W004\",shape = [512, 512],initializer = tf.contrib.layers.xavier_initializer())\nb4 = tf.Variable(tf.random_normal([512]))\nlayer4 = tf.nn.relu(tf.matmul(layer3,W4) + b4)\nlayer4 = tf.nn.dropout(layer4, keep_prob = keep_prob)\n\nW5 = tf.get_variable(\"W0005\",shape = [512, 10],initializer = tf.contrib.layers.xavier_initializer())\nb5 = tf.Variable(tf.random_normal([10]))\nhypothesis = tf.matmul(layer4,W5) + b5\n\n# parameters\nlearning_rate = 0.001\nbatch_size = 100\nnum_epochs = 15\nnum_iterations = int(mnist.train.num_examples / batch_size)\n\n\n# define cost/loss & optimizer\ncost = tf.reduce_mean(\n tf.nn.softmax_cross_entropy_with_logits_v2(\n logits=hypothesis, labels=tf.stop_gradient(Y)\n )\n)\ntrain = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n\ncorrect_prediction = tf.equal(tf.argmax(hypothesis, axis=1), tf.argmax(Y, axis=1))\naccuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n\n# train my model\n# 기존의 mnist문제의 코드와 유사, epoch 추가\nwith tf.Session() as sess:\n # initialize\n sess.run(tf.global_variables_initializer())\n\n for epoch in range(num_epochs):\n avg_cost = 0\n\n for iteration in range(num_iterations):\n batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n _, cost_val = sess.run([train, cost], feed_dict={X: batch_xs, Y: batch_ys, keep_prob : 0.7})\n avg_cost += cost_val / num_iterations\n\n print(f\"Epoch:\", '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost))\n\n\n print(\"Learning Finished!\")\n\n # Test model and check accuracy (dropout : 1)\n print(\n \"Accuracy:\",\n sess.run(accuracy, feed_dict={X: mnist.test.images, Y: mnist.test.labels,keep_prob : 1}),\n )\n\n # Get one and predict\n r = random.randint(0, mnist.test.num_examples - 1)\n\n print(\"Label: \", sess.run(tf.argmax(mnist.test.labels[r : r + 1], axis=1)))\n print(\n \"Prediction: \",\n sess.run(\n tf.argmax(hypothesis, axis=1), feed_dict={X: mnist.test.images[r : r + 1],keep_prob : 1}\n ),\n )\n\n plt.imshow(\n mnist.test.images[r : r + 1].reshape(28, 28),\n cmap=\"Greys\",\n interpolation=\"nearest\",\n )\n plt.show()","sub_path":"ML-lab-10-5-mnist_nn_dropout.py","file_name":"ML-lab-10-5-mnist_nn_dropout.py","file_ext":"py","file_size_in_byte":3604,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"20194734","text":"board = [\n ['','',''],\n ['','',''],\n ['','','']\n]\n\nwhile True:\n try:\n row = int(input('Введите строку:'))\n column = int(input('Введиите столбик:'))\n print(row, column)\n print(0 == (row or column))\n if row == 0:\n print('Введите координаты от 1 до 3')\n continue\n elif column == 0:\n print('Введите координаты от 1 до 3')\n continue\n\n if board[row-1][column-1] == '':\n board[row-1][column-1] = 'x'\n break\n else:\n print('Там уже что-то есть, введите снова координаты')\n continue\n\n except ValueError:\n print('Вы ввели не число ')\n continue\n except IndexError:\n print('Мы вышли за пределы')\n continue\n","sub_path":"Lessons 8/x&o.py","file_name":"x&o.py","file_ext":"py","file_size_in_byte":920,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"252887806","text":"import torch\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nclass Average_Meter(object):\n \"\"\" computes and stores the average and current value \"\"\"\n def __init__(self):\n self.reset()\n\n def reset(self):\n self.val = 0\n self.avg = 0\n self.sum = 0\n self.count = 0\n\n def update(self, val, n):\n self.val = val\n self.sum += val*n\n self.count += n\n self.avg = self.sum/self.count\n\ndef mean_L2_error(true, pred):\n \"\"\" calculate mean L2 error \"\"\"\n return np.mean(np.sqrt(np.sum(np.square(true-pred), axis=-1) + 1e-8))\n\ndef calc_auc(error_vals, thresholds):\n \"\"\" calculate area under curve \"\"\"\n integral = np.trapz(error_vals, thresholds)\n norm = np.trapz(np.ones_like(error_vals), thresholds)\n return integral/norm\n\ndef percentage_frames_within_error_curve(true, pred, max_x=85,\n steps=5, plot=True):\n \"\"\" caclulate percentage of keypoints within a given error threshold and plot \"\"\"\n # (b, 21) error values for each individual keypoint\n data_21_pts = np.sqrt(np.sum(np.square(true-pred), axis=-1) + 1e-8)\n error_threshold = np.arange(0, max_x, steps)\n\n pck_curve_all = []\n for p_id in range(21):\n pck_curve = []\n for t in error_threshold:\n data_mean = np.mean((data_21_pts[:, p_id] <= t).astype('float32'))\n pck_curve.append(data_mean)\n pck_curve_all.append(pck_curve)\n pck_curve_all = np.mean(np.array(pck_curve_all), 0)\n\n if plot:\n fig, ax = plt.subplots()\n ax.plot(error_threshold, pck_curve_all)\n ax.set_xticks(np.arange(0, max_x, steps))\n ax.set_yticks(np.arange(0, 1.1, 0.1))\n plt.grid()\n ax.set_ylabel('Frames within threshold %')\n ax.set_xlabel('Error Threshold mm')\n plt.show()\n return pck_curve_all\n\ndef get_pck(error_21_pts, max_x=85, steps=5):\n \"\"\" caclulate percentage of keypoints within a given error threshold \"\"\"\n error_threshold = np.arange(0, max_x, steps)\n\n pck_curve_all = []\n for p_id in range(21):\n pck_curve = []\n for t in error_threshold:\n data_mean = np.mean((error_21_pts[:, p_id] <= t).astype('float32'))\n pck_curve.append(data_mean)\n pck_curve_all.append(pck_curve)\n pck_curve_all = np.mean(np.array(pck_curve_all), 0)\n return pck_curve_all\n","sub_path":"old_stuff/code/mlcv_exp_04/src/utils/eval.py","file_name":"eval.py","file_ext":"py","file_size_in_byte":2410,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"526436627","text":"import os\n\ncontext = ['Megascans', 'ThirdParty']\nfrom shutil import copyfile\ndef for_context(context):\n for root, dirs, files in os.walk(f\"../Content/{context}\", topdown=False):\n for name in files:\n # Compare it with twoassets\n p = os.path.join(root, name)\n d= f\"..\\\\twoassets\\\\{context}\\\\{name}\"\n try:\n f1 =os.path.getctime(p) > os.path.getctime(d)\n if (f1):\n print(f\"[MODIFIED]: {p}\")\n copyfile(p,d)\n except:\n print(f\"[NEW]: {p}\")\n copyfile(p, d)\n \n \nfor c in context:\n for_context(c)\n","sub_path":"Tools/update_twoassets.py","file_name":"update_twoassets.py","file_ext":"py","file_size_in_byte":670,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"68598761","text":"#!/usr/bin/env python3\r\n# -*- coding: utf-8 -*-\r\n\r\nimport argparse\r\nimport os\r\nimport shutil\r\n\r\n#############################################\r\n# Copy unlabeled samples into a temp folder.\r\n#############################################\r\n\r\nDIR_SAVE = \"/home/RaidDisk/new/nkrepo/search/samples/\"\r\nFILE_SHA256 = \"sha256.txt\"\r\nDIR_REPO = \"/home/RaidDisk/nkrepo/DATA/\"\r\nFILE_SCAN_RESULT = \"scan_result.csv\"\r\n\r\ndef copy_samples(f_scan_result, d_save):\r\n _n = 0\r\n list_sha256 = []\r\n\r\n with open(f_scan_result, \"r\") as f:\r\n list_scan_result = f.readlines()\r\n\r\n _n = 0\r\n for r in list_scan_result:\r\n sha256 = r.split(\",\")[0]\r\n src_file = DIR_REPO + sha256[0] + '/' + sha256[1] + '/' + sha256[2] + '/' + sha256[3] + '/' + sha256\r\n if not os.path.exists(src_file):\r\n continue\r\n src_json_file = src_file + \".json\" \r\n if not os.path.exists(src_json_file):\r\n continue\r\n #print(src_file)\r\n #print(src_json_file)\r\n dst_file = DIR_SAVE + sha256\r\n dst_json_file = DIR_SAVE + sha256 + \".json\"\r\n #print(dst_file)\r\n #print(dst_json_file)\r\n _n = _n + 1\r\n print(\"{}: {}\".format(_n, sha256))\r\n shutil.copyfile(src_file, dst_file)\r\n shutil.copyfile(src_json_file, dst_json_file)\r\n \r\ndef parse_args():\r\n parser = argparse.ArgumentParser(description = \"Search samples.\")\r\n parser.add_argument(\"-f\", \"--file_scan_result\", help=\"The file containing Kaspersky scan results.\", type=str, default=FILE_SCAN_RESULT)\r\n parser.add_argument(\"-d\", \"--dst_dir\", help=\"The directory to store the samples.\", type=str, default=DIR_SAVE)\r\n \r\n args = parser.parse_args()\r\n return args\r\n\r\n\r\ndef main():\r\n args = parse_args()\r\n copy_samples(args.file_scan_result, args.dst_dir)\r\n\r\nif __name__==\"__main__\":\r\n main()\r\n","sub_path":"search/cp_samples.py","file_name":"cp_samples.py","file_ext":"py","file_size_in_byte":1856,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"626126424","text":"import pygame\nimport time\nfrom threading import Thread\nfrom player_self import player_self\nfrom player_other import player_other\nfrom client import Client\nfrom loginscreen import LoginScreen\n\nfrom interface import Inventory, Chat, rightClick\nfrom combatinterfaces import statusbars, abilitybar\n\nimport sys\nsys.path.insert(1, '..//network')\nfrom NetworkConstants import login_status\nsys.path.insert(1, '..//game')\nfrom items import groundItem\nfrom wall import wall\n\n\n\n\nclass world:\n def __init__(self):\n self.displaysize=(300,300)#(920,680)#\n self.worldsize=(0,0)\n #view port\n self.viewport=[0,0]#[int(self.player.x+self.displaysize[0]/2),int(self.player.y+self.displaysize[1]/2)]\n self.viewboxdim=[50,50] #w, h\n self.FPS=200#600#60\n #objects\n self.walls=[]\n self.npcs=[]\n self.attacks=[]\n self.otherplayers=[]\n self.grounditems=[]\n \n #interfaces\n self.inventory=Inventory(self)\n self.chat=Chat(self)\n self.rightclick=rightClick(self)\n self.combatstatusbars=statusbars(self)\n self.abilitybar=abilitybar(self)\n #inputs\n self.keyspressed=[] #for key HOLD DOWN\n self.keyspress=[] #for key DOWN \n self.mouse_x=0\n self.mouse_y=0\n self.mouse_left=False\n self.mouse_left_up=False\n self.mouse_left_down=False\n \n self.mouse_right=False\n self.mouse_right_up=False\n self.mouse_right_down=False\n \n self.mouse_middle=False\n self.mouse_middle_up=False\n self.mouse_middle_down=False\n \n self.mouse_left_previous=False\n self.mouse_right_previous=False\n self.mouse_middle_previous=False\n \n #network\n self.client=Client(\"127.0.0.1\",1337, self).start()\n #wait for terrain to load from server\n \n \n \n #log in screen\n self.loggedin=False\n self.loginscreen=LoginScreen()\n while self.loggedin==False:\n self.loginscreen.start()\n \n #if the window is exitted, stop the connection to the server\n if self.loginscreen.running==False:\n self.client.stop()\n return\n sys.exit()\n #get the name, pass, and which button was pressed\n name=str.join(\"\",self.loginscreen.name_string)\n password=str.join(\"\",self.loginscreen.password_string)\n login=self.loginscreen.login\n \n #try to log in\n t=time.time()\n self.client.log(name, password, login)\n while self.client.loginStatus==login_status[\"wait\"]:\n if time.time()-t>10:\n time.sleep(1)\n print(\"connection to server timed out\")\n return\n print(self.client.loginStatus)\n if self.client.loginStatus==login_status[\"success\"]:\n self.loggedin=True\n self.loginscreen.stop()\n else:\n self.client.loginStatus=login_status[\"wait\"]\n \n\n \n \n \n \n #scene\n pygame.init()\n pygame.font.init()\n self.fontobject = pygame.font.Font(None,18)\n self.screen = pygame.display.set_mode(self.displaysize, pygame.HWSURFACE)\n #self.surface = pygame.Surface(self.displaysize, pygame.HWSURFACE | pygame.SRCALPHA)\n self.surface = pygame.Surface((150,150), pygame.HWSURFACE | pygame.SRCALPHA)\n self.clock=pygame.time.Clock()\n\n self.running=True\n \n\n \n #players\n self.player=player_self(self, name, self.client.pid, 100, 10).start()\n self.client.updatePlayerStart()\n \n\n\n def start(self):\n if self.loggedin==True:\n self.update()\n return self\n \n def update(self):\n while self.running:\n self.keyspress=[]#reset the tapped keys\n \n self.draw()\n \n #reset the mouse vars\n self.mouse_left_up=False\n self.mouse_left_down=False\n self.mouse_right_up=False\n self.mouse_right_down=False\n self.mouse_middle_up=False\n self.mouse_middle_down=False\n \n self.mouse_left_previous=self.mouse_left\n self.mouse_right_previous=self.mouse_right\n self.mouse_middle_previous=self.mouse_middle\n \n #mouse inputs\n mouse = pygame.mouse.get_pos() \n self.mouse_x=mouse[0]\n self.mouse_y=mouse[1]\n clicks = pygame.mouse.get_pressed()\n self.mouse_left=clicks[0]\n self.mouse_middle=clicks[1]\n self.mouse_right=clicks[2]\n \n if self.mouse_left and self.mouse_left_previous==False:\n self.mouse_left_down=True\n if self.mouse_right and self.mouse_right_previous==False:\n self.mouse_right_down=True\n if self.mouse_left==False and self.mouse_left_previous:\n self.mouse_left_up=True\n if self.mouse_right==False and self.mouse_right_previous:\n self.mouse_right_up=True\n if self.mouse_middle and self.mouse_middle_previous==False:\n self.mouse_middle_down=True\n if self.mouse_middle==False and self.mouse_middle_previous:\n self.mouse_middle_up=True\n \n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n self.stop()\n return\n #keyboard\n elif event.type == pygame.KEYDOWN:\n if event.key == pygame.K_ESCAPE:\n self.stop()\n return\n else:\n self.keyspressed.append(event.key)\n self.keyspress.append(event.key)\n elif event.type == pygame.KEYUP:\n if event.key in self.keyspressed:\n self.keyspressed.remove(event.key)\n\n \n #self.player.update()\n self.chat.update()\n self.inventory.update()\n \n #update the view port\n \n centerx=-(self.player.x-self.displaysize[0]/2)\n centery=-(self.player.y-self.displaysize[1]/2)\n if self.viewport[0]centerx+self.viewboxdim[0]:\n self.viewport[0]=centerx+self.viewboxdim[0]\n if self.viewport[1]centery+self.viewboxdim[1]:\n self.viewport[1]=centery+self.viewboxdim[1]\n #self.viewport[0]=-(self.player.x-self.displaysize[0]/2)\n #self.viewport[1]=-(self.player.y-self.displaysize[1]/2)\n \n '''\n if self.chat.chatting==False:\n if ord(\"g\") in self.keyspress or self.mouse_middle_down:\n self.mouse_drag_pos=(self.mouse_x,self.mouse_y)\n if ord(\"g\") in self.keyspressed or self.mouse_middle:\n self.viewport[0]+=self.mouse_x-self.mouse_drag_pos[0]\n self.viewport[1]+=self.mouse_y-self.mouse_drag_pos[1]\n self.mouse_drag_pos=(self.mouse_x,self.mouse_y)\n '''\n #print(self.viewport)\n \n \n \n def draw(self):\n self.screen.fill((255,255,255))\n self.surface.fill((0,0,0,0))\n \n #draw bounding box of world\n pygame.draw.line(self.screen, (0,0,0), (self.viewport[0],self.viewport[1]),(self.viewport[0],self.worldsize[1]+self.viewport[1]),1)\n pygame.draw.line(self.screen, (0,0,0), (self.viewport[0],self.viewport[1]),(self.worldsize[0]+self.viewport[0],self.viewport[1]),1)\n pygame.draw.line(self.screen, (0,0,0), (self.viewport[0],self.worldsize[1]+self.viewport[1]),(self.worldsize[0]+self.viewport[0],self.worldsize[1]+self.viewport[1]),1)\n pygame.draw.line(self.screen, (0,0,0), (self.worldsize[0]+self.viewport[0],self.viewport[1]),(self.worldsize[0]+self.viewport[0],self.worldsize[1]+self.viewport[1]),1)\n \n self.player.draw()\n \n for c in self.npcs:\n if (self.player.x-self.displaysize[0]0:\n c.draw()\n \n\n for c in self.otherplayers:\n if (self.player.x-self.displaysize[0] minister.ministerpower:\n minister.cursetimes += 1\n minister.ministerpower *= 0.95\n db.session.commit()\n\n# def singleStatusReply(characterid, ministerid):\n# '''单一大臣国力状态恢复\n# @param characterid: int 君王主键id\n# @param ministerid: int 大臣主键id\n# '''\n# character = Character.query.filter_by(characterid=characterid).first()\n# minister = Minister.query.filter_by(ministerid=ministerid).first()\n# if minister.cursetimes > 0:\n# if float(character.charactercoin) < STANDAR * pow(2, minister.cursetimes):\n# return {'result': False, 'reason': '元宝不足'}\n# else:\n# coins = float(character.charactercoin) - STANDAR * pow(2, minister.cursetimes)\n# Character.query.filter_by(characterid=characterid).update({\n# \"charactercoin\": str(coins)\n# })\n# power = round(minister.ministerpower / pow(0.95, minister.cursetimes))\n# Minister.query.filter_by(ministerid=ministerid).update({\n# \"ministerpower\": power,\n# \"cursetimes\": 0\n# })\n# db.session.commit()\n# setpower(characterid)\n# return {'result': True}\n# else:\n# return {'result': False, 'reason': '大臣很棒棒，没有被削弱'}\n#\n# def allStatusReply(characterid):\n# '''大臣群体国力状态恢复，K基因剔除\n# @param characterid: int 君王主键id\n# '''\n# character = Character.query.filter_by(characterid=characterid).first()\n# if int(character.miraclestate) == 1:\n# ministers = Minister.query.filter_by(characterid=characterid).all()\n# for minister in ministers:\n# if minister.cursetimes > 0:\n# power = round(minister.ministerpower / pow(0.95, minister.cursetimes))\n# Minister.query.filter_by(ministerid=minister.ministerid).update({\n# \"ministerpower\": power,\n# \"cursetimes\": 0\n# })\n# db.session.commit()\n# Minister.query.filter_by(characterid=characterid).update({\n# \"is_K\": \"N\"\n# })\n# db.session.commit()\n# setpower(characterid)\n#\n# def judegeGodMiracle(characterid):\n# '''超*N神迹清除所有新大臣的K基因\n# @param characterid: int 君王主键id\n# '''\n# character = Character.query.filter_by(characterid=characterid).first()\n# if character.miraclestate == \"1\":\n# ministers = Minister.query.filter_by(characterid=characterid).all()\n# for minister in ministers:\n# Minister.query.filter_by(ministerid=minister.ministerid).update({\"is_K\": 'N'})\n# db.session.commit()\n\n","sub_path":"app/core/calculate/Curse.py","file_name":"Curse.py","file_ext":"py","file_size_in_byte":4166,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"479293301","text":"import logging\nlogging.disable(logging.CRITICAL)\n\n# Standard library imports\nimport json\nimport os\nimport subprocess\nimport tempfile\n\n# Third party imports\nimport ipyparallel\nimport zmq.ssh\n\n# Local imports\nfrom .ssh_utils import setup_ssh\nfrom .utils import bash, on_hostname\n\n\ndef get_culler_cmd(profile='pbs', env_path=None, culler_args=None):\n python = 'python'\n if env_path:\n python = os.path.join(os.path.expanduser(env_path), 'bin', python)\n cmd = (f'nohup {python} -m hpc05_culler --logging=debug '\n f'--profile={profile} --log_file_prefix=culler.log {culler_args} &')\n return bash(cmd)\n\n\nclass Client(ipyparallel.Client):\n \"\"\"ipyparallel Client to connect to the hpc05.\n\n Parameters\n ----------\n hostname : str\n hostname, e.g. `hpc05.tudelft.net` or as in config.\n username : str\n user name at hostname\n password : str\n password for hpc05. NOT RECOMMENDED, use ssh-agent.\n profile : str\n profile name, default results in folder `profile_pbs`.\n culler : bool\n Controls starting of the culler. Default: True.\n culler_args : str\n Add arguments to the culler. e.g. '--timeout=200'\n env_path : str, default=None\n Path of the Python environment, '/path/to/ENV/' if Python is in /path/to/ENV/bin/python.\n Examples '~/miniconda3/envs/dev/', 'miniconda3/envs/dev', '~/miniconda3'.\n Defaults to the environment that is sourced in `.bashrc` or `.bash_profile`.\n\n Attributes\n ----------\n json_filename : str\n file name of tmp local json file with connection details.\n tunnel : pexpect.spawn object\n ssh tunnel for making connection to the hpc05.\n\n Notes\n -----\n You have to have a profile with PBS settings in your `.ipython`\n folder on the hpc05. You can generate this by running:\n hpc05.create_remote_pbs_profile(username, hostname)\n Then setup a ipcluster on the hpc05 by starting a screen and running\n ipcluster start --n=10 --profile=pbs\n \"\"\"\n\n def __init__(self, hostname='hpc05', username=None, password=None,\n profile='pbs', culler=True, culler_args=None, env_path=None,\n *args, **kwargs):\n\n if culler_args is None:\n culler_args = ''\n\n if on_hostname(hostname):\n # Don't connect over ssh if this is run on the hpc05.\n if culler:\n cmd = get_culler_cmd(profile, env_path, culler_args=culler_args)\n subprocess.Popen(cmd, shell=True,\n stdout=open('/dev/null', 'w'),\n stderr=open('logfile.log', 'a'))\n super(Client, self).__init__(profile=profile, *args, **kwargs)\n\n else:\n import pexpect\n # Create temporary file\n json_file, self.json_filename = tempfile.mkstemp()\n os.close(json_file)\n\n # Make ssh connection\n ssh = setup_ssh(hostname, username, password)\n\n # Open SFTP connection and get the ipcontroller-client.json\n with ssh.open_sftp() as sftp:\n remote_json = f\".ipython/profile_{profile}/security/ipcontroller-client.json\"\n try:\n sftp.get(remote_json, self.json_filename)\n except FileNotFoundError:\n raise Exception(\n f'Could not copy the json file: \"{remote_json}\"of the pbs '\n 'cluster, the `ipcluster` probably is not running or '\n 'you have no `profile_pbs`, create with '\n '`hpc05.pbs_profile.create_remote_pbs_profile()`')\n\n # Read the json file\n with open(self.json_filename) as json_file:\n json_data = json.load(json_file)\n\n keys = (\"control\", \"iopub\", \"mux\",\n \"notification\", \"registration\", \"task\")\n\n local_json_data = json_data.copy()\n local_json_data['location'] = 'localhost'\n\n # Select six random ports for the local machine\n local_ports = zmq.ssh.tunnel.select_random_ports(6)\n for port, key in zip(local_ports, keys):\n local_json_data[key] = port\n\n # Replace remote ports by local ports\n with open(self.json_filename, \"w\") as json_file:\n json.dump(local_json_data, json_file)\n\n ips = [\"{}:{}:{} \".format(local_json_data[key],\n json_data['location'],\n json_data[key])\n for key in keys]\n ssh_forward_cmd = (\"ssh -o ConnectTimeout=10 -N -L \" +\n \"-L \".join(ips) + hostname)\n self.tunnel = pexpect.spawn(ssh_forward_cmd)\n result = self.tunnel.expect([pexpect.TIMEOUT, pexpect.EOF,\n \"[Pp]assword\", \"passphrase\"],\n timeout=6)\n\n if culler:\n cmd = get_culler_cmd(profile, env_path, culler_args=culler_args)\n ssh.exec_command(cmd, get_pty=True)\n\n super(Client, self).__init__(self.json_filename, *args, **kwargs)\n\n if not on_hostname(hostname):\n def __del__(self):\n if self.tunnel:\n self.tunnel.close()\n","sub_path":"src/hpc05/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":5376,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"189960807","text":"import sqlite3\nimport struct\nfrom typing import Union, Tuple, Set, List\nfrom binascii import unhexlify\nfrom itertools import chain\nfrom decimal import Decimal\n\nfrom torba.server.db import DB\nfrom torba.server.util import class_logger\nfrom torba.client.basedatabase import query, constraints_to_sql\n\nfrom lbry.schema.url import URL, normalize_name\nfrom lbry.schema.tags import clean_tags\nfrom lbry.schema.mime_types import guess_stream_type\nfrom lbry.wallet.ledger import MainNetLedger, RegTestLedger\nfrom lbry.wallet.transaction import Transaction, Output\nfrom lbry.wallet.server.canonical import register_canonical_functions\nfrom lbry.wallet.server.trending import (\n CREATE_TREND_TABLE, calculate_trending, register_trending_functions\n)\n\n\nATTRIBUTE_ARRAY_MAX_LENGTH = 100\nCLAIM_TYPES = {\n 'stream': 1,\n 'channel': 2,\n}\nSTREAM_TYPES = {\n 'video': 1,\n 'audio': 2,\n 'image': 3,\n 'document': 4,\n 'binary': 5,\n 'model': 6\n}\n\n\ndef _apply_constraints_for_array_attributes(constraints, attr, cleaner):\n any_items = cleaner(constraints.pop(f'any_{attr}s', []))[:ATTRIBUTE_ARRAY_MAX_LENGTH]\n if any_items:\n constraints.update({\n f'$any_{attr}{i}': item for i, item in enumerate(any_items)\n })\n values = ', '.join(\n f':$any_{attr}{i}' for i in range(len(any_items))\n )\n constraints[f'claim.claim_hash__in#_any_{attr}'] = f\"\"\"\n SELECT DISTINCT claim_hash FROM {attr} WHERE {attr} IN ({values})\n \"\"\"\n\n all_items = cleaner(constraints.pop(f'all_{attr}s', []))[:ATTRIBUTE_ARRAY_MAX_LENGTH]\n if all_items:\n constraints[f'$all_{attr}_count'] = len(all_items)\n constraints.update({\n f'$all_{attr}{i}': item for i, item in enumerate(all_items)\n })\n values = ', '.join(\n f':$all_{attr}{i}' for i in range(len(all_items))\n )\n constraints[f'claim.claim_hash__in#_all_{attr}'] = f\"\"\"\n SELECT claim_hash FROM {attr} WHERE {attr} IN ({values})\n GROUP BY claim_hash HAVING COUNT({attr}) = :$all_{attr}_count\n \"\"\"\n\n not_items = cleaner(constraints.pop(f'not_{attr}s', []))[:ATTRIBUTE_ARRAY_MAX_LENGTH]\n if not_items:\n constraints.update({\n f'$not_{attr}{i}': item for i, item in enumerate(not_items)\n })\n values = ', '.join(\n f':$not_{attr}{i}' for i in range(len(not_items))\n )\n constraints[f'claim.claim_hash__not_in#_not_{attr}'] = f\"\"\"\n SELECT DISTINCT claim_hash FROM {attr} WHERE {attr} IN ({values})\n \"\"\"\n\n\nclass SQLDB:\n\n PRAGMAS = \"\"\"\n pragma journal_mode=WAL;\n \"\"\"\n\n CREATE_CLAIM_TABLE = \"\"\"\n create table if not exists claim (\n claim_hash bytes primary key,\n claim_id text not null,\n claim_name text not null,\n normalized text not null,\n txo_hash bytes not null,\n tx_position integer not null,\n amount integer not null,\n timestamp integer not null, -- last updated timestamp\n creation_timestamp integer not null,\n height integer not null, -- last updated height\n creation_height integer not null,\n activation_height integer,\n expiration_height integer not null,\n release_time integer not null,\n\n short_url text not null, -- normalized#shortest-unique-claim_id\n canonical_url text, -- channel's-short_url/normalized#shortest-unique-claim_id-within-channel\n\n claim_type integer,\n\n -- streams\n stream_type text,\n media_type text,\n fee_amount integer default 0,\n fee_currency text,\n\n -- claims which are channels\n public_key_bytes bytes,\n public_key_hash bytes,\n claims_in_channel integer,\n\n -- claims which are inside channels\n channel_hash bytes,\n channel_join integer, -- height at which claim got valid signature / joined channel\n signature bytes,\n signature_digest bytes,\n signature_valid bool,\n\n effective_amount integer not null default 0,\n support_amount integer not null default 0,\n trending_group integer not null default 0,\n trending_mixed integer not null default 0,\n trending_local integer not null default 0,\n trending_global integer not null default 0\n );\n\n create index if not exists claim_resolve_idx on claim (normalized, claim_id);\n create index if not exists claim_claims_in_channel_idx on claim (signature_valid, channel_hash, normalized);\n\n create index if not exists claim_id_idx on claim (claim_id);\n create index if not exists claim_normalized_idx on claim (normalized);\n create index if not exists claim_txo_hash_idx on claim (txo_hash);\n create index if not exists claim_channel_hash_idx on claim (channel_hash);\n create index if not exists claim_release_time_idx on claim (release_time);\n create index if not exists claim_timestamp_idx on claim (timestamp);\n create index if not exists claim_height_idx on claim (height);\n create index if not exists claim_activation_height_idx on claim (activation_height);\n create index if not exists claim_expiration_height_idx on claim (expiration_height);\n create index if not exists claim_public_key_hash_idx on claim (public_key_hash);\n\n create index if not exists claim_claim_type_idx on claim (claim_type);\n create index if not exists claim_stream_type_idx on claim (stream_type);\n create index if not exists claim_media_type_idx on claim (media_type);\n create index if not exists claim_fee_amount_idx on claim (fee_amount);\n create index if not exists claim_fee_currency_idx on claim (fee_currency);\n\n create index if not exists claim_signature_valid_idx on claim (signature_valid);\n\n create index if not exists claim_effective_amount_idx on claim (effective_amount);\n create index if not exists claim_trending_group_idx on claim (trending_group);\n create index if not exists claim_trending_mixed_idx on claim (trending_mixed);\n create index if not exists claim_trending_local_idx on claim (trending_local);\n create index if not exists claim_trending_global_idx on claim (trending_global);\n \"\"\"\n\n CREATE_SUPPORT_TABLE = \"\"\"\n create table if not exists support (\n txo_hash bytes primary key,\n tx_position integer not null,\n height integer not null,\n claim_hash bytes not null,\n amount integer not null\n );\n create index if not exists support_txo_hash_idx on support (txo_hash);\n create index if not exists support_claim_hash_idx on support (claim_hash, height);\n \"\"\"\n\n CREATE_TAG_TABLE = \"\"\"\n create table if not exists tag (\n tag text not null,\n claim_hash bytes not null,\n height integer not null\n );\n create index if not exists tag_tag_idx on tag (tag);\n create index if not exists tag_claim_hash_idx on tag (claim_hash);\n create index if not exists tag_height_idx on tag (height);\n \"\"\"\n\n CREATE_CLAIMTRIE_TABLE = \"\"\"\n create table if not exists claimtrie (\n normalized text primary key,\n claim_hash bytes not null,\n last_take_over_height integer not null\n );\n create index if not exists claimtrie_claim_hash_idx on claimtrie (claim_hash);\n \"\"\"\n\n CREATE_TABLES_QUERY = (\n PRAGMAS +\n CREATE_CLAIM_TABLE +\n CREATE_TREND_TABLE +\n CREATE_SUPPORT_TABLE +\n CREATE_CLAIMTRIE_TABLE +\n CREATE_TAG_TABLE\n )\n\n def __init__(self, main, path):\n self.main = main\n self._db_path = path\n self.db = None\n self.logger = class_logger(__name__, self.__class__.__name__)\n self.ledger = MainNetLedger if self.main.coin.NET == 'mainnet' else RegTestLedger\n\n def open(self):\n self.db = sqlite3.connect(self._db_path, isolation_level=None, check_same_thread=False)\n self.db.row_factory = sqlite3.Row\n self.db.executescript(self.CREATE_TABLES_QUERY)\n register_canonical_functions(self.db)\n register_trending_functions(self.db)\n\n def close(self):\n self.db.close()\n\n @staticmethod\n def _insert_sql(table: str, data: dict) -> Tuple[str, list]:\n columns, values = [], []\n for column, value in data.items():\n columns.append(column)\n values.append(value)\n sql = (\n f\"INSERT INTO {table} ({', '.join(columns)}) \"\n f\"VALUES ({', '.join(['?'] * len(values))})\"\n )\n return sql, values\n\n @staticmethod\n def _update_sql(table: str, data: dict, where: str,\n constraints: Union[list, tuple]) -> Tuple[str, list]:\n columns, values = [], []\n for column, value in data.items():\n columns.append(\"{} = ?\".format(column))\n values.append(value)\n values.extend(constraints)\n return f\"UPDATE {table} SET {', '.join(columns)} WHERE {where}\", values\n\n @staticmethod\n def _delete_sql(table: str, constraints: dict) -> Tuple[str, dict]:\n where, values = constraints_to_sql(constraints)\n return f\"DELETE FROM {table} WHERE {where}\", values\n\n def execute(self, *args):\n return self.db.execute(*args)\n\n def begin(self):\n self.execute('begin;')\n\n def commit(self):\n self.execute('commit;')\n\n def _upsertable_claims(self, txos: List[Output], header, clear_first=False):\n claim_hashes, claims, tags = [], [], []\n for txo in txos:\n tx = txo.tx_ref.tx\n\n try:\n assert txo.claim_name\n assert txo.normalized_name\n except:\n #self.logger.exception(f\"Could not decode claim name for {tx.id}:{txo.position}.\")\n continue\n\n claim_hash = sqlite3.Binary(txo.claim_hash)\n claim_hashes.append(claim_hash)\n claim_record = {\n 'claim_hash': claim_hash,\n 'claim_id': txo.claim_id,\n 'claim_name': txo.claim_name,\n 'normalized': txo.normalized_name,\n 'txo_hash': sqlite3.Binary(txo.ref.hash),\n 'tx_position': tx.position,\n 'amount': txo.amount,\n 'timestamp': header['timestamp'],\n 'height': tx.height,\n 'claim_type': None,\n 'stream_type': None,\n 'media_type': None,\n 'release_time': None,\n 'fee_currency': None,\n 'fee_amount': 0\n }\n claims.append(claim_record)\n\n try:\n claim = txo.claim\n except:\n #self.logger.exception(f\"Could not parse claim protobuf for {tx.id}:{txo.position}.\")\n continue\n\n if claim.is_stream:\n claim_record['claim_type'] = CLAIM_TYPES['stream']\n claim_record['media_type'] = claim.stream.source.media_type\n claim_record['stream_type'] = STREAM_TYPES[guess_stream_type(claim_record['media_type'])]\n if claim.stream.release_time:\n claim_record['release_time'] = claim.stream.release_time\n if claim.stream.has_fee:\n fee = claim.stream.fee\n if isinstance(fee.currency, str):\n claim_record['fee_currency'] = fee.currency.lower()\n if isinstance(fee.amount, Decimal):\n claim_record['fee_amount'] = int(fee.amount*1000)\n elif claim.is_channel:\n claim_record['claim_type'] = CLAIM_TYPES['channel']\n\n for tag in clean_tags(claim.message.tags):\n tags.append((tag, claim_hash, tx.height))\n\n if clear_first:\n self._clear_claim_metadata(claim_hashes)\n\n if tags:\n self.db.executemany(\n \"INSERT INTO tag (tag, claim_hash, height) VALUES (?, ?, ?)\", tags\n )\n\n return claims\n\n def insert_claims(self, txos: List[Output], header):\n claims = self._upsertable_claims(txos, header)\n if claims:\n self.db.executemany(\"\"\"\n INSERT OR IGNORE INTO claim (\n claim_hash, claim_id, claim_name, normalized, txo_hash, tx_position, amount,\n claim_type, media_type, stream_type, timestamp, creation_timestamp,\n fee_currency, fee_amount, height,\n creation_height, release_time, activation_height, expiration_height, short_url)\n VALUES (\n :claim_hash, :claim_id, :claim_name, :normalized, :txo_hash, :tx_position, :amount,\n :claim_type, :media_type, :stream_type, :timestamp, :timestamp,\n :fee_currency, :fee_amount, :height, :height,\n CASE WHEN :release_time IS NOT NULL THEN :release_time ELSE :timestamp END,\n CASE WHEN :normalized NOT IN (SELECT normalized FROM claimtrie) THEN :height END,\n CASE WHEN :height >= 137181 THEN :height+2102400 ELSE :height+262974 END,\n :claim_name||COALESCE(\n (SELECT shortest_id(claim_id, :claim_id) FROM claim WHERE normalized = :normalized),\n '#'||substr(:claim_id, 1, 1)\n )\n )\"\"\", claims)\n\n def update_claims(self, txos: List[Output], header):\n claims = self._upsertable_claims(txos, header, clear_first=True)\n if claims:\n self.db.executemany(\"\"\"\n UPDATE claim SET\n txo_hash=:txo_hash, tx_position=:tx_position, amount=:amount, height=:height,\n claim_type=:claim_type, media_type=:media_type, stream_type=:stream_type,\n timestamp=:timestamp, fee_amount=:fee_amount, fee_currency=:fee_currency,\n release_time=CASE WHEN :release_time IS NOT NULL THEN :release_time ELSE release_time END\n WHERE claim_hash=:claim_hash;\n \"\"\", claims)\n\n def delete_claims(self, claim_hashes: Set[bytes]):\n \"\"\" Deletes claim supports and from claimtrie in case of an abandon. \"\"\"\n if claim_hashes:\n binary_claim_hashes = [sqlite3.Binary(claim_hash) for claim_hash in claim_hashes]\n affected_channels = self.execute(*query(\n \"SELECT channel_hash FROM claim\", channel_hash__is_not_null=1, claim_hash__in=binary_claim_hashes\n )).fetchall()\n for table in ('claim', 'support', 'claimtrie'):\n self.execute(*self._delete_sql(table, {'claim_hash__in': binary_claim_hashes}))\n self._clear_claim_metadata(binary_claim_hashes)\n return set(r['channel_hash'] for r in affected_channels)\n return set()\n\n def _clear_claim_metadata(self, binary_claim_hashes: List[sqlite3.Binary]):\n if binary_claim_hashes:\n for table in ('tag',): # 'language', 'location', etc\n self.execute(*self._delete_sql(table, {'claim_hash__in': binary_claim_hashes}))\n\n def split_inputs_into_claims_supports_and_other(self, txis):\n txo_hashes = {txi.txo_ref.hash for txi in txis}\n claims = self.execute(*query(\n \"SELECT txo_hash, claim_hash, normalized FROM claim\",\n txo_hash__in=[sqlite3.Binary(txo_hash) for txo_hash in txo_hashes]\n )).fetchall()\n txo_hashes -= {r['txo_hash'] for r in claims}\n supports = {}\n if txo_hashes:\n supports = self.execute(*query(\n \"SELECT txo_hash, claim_hash FROM support\",\n txo_hash__in=[sqlite3.Binary(txo_hash) for txo_hash in txo_hashes]\n )).fetchall()\n txo_hashes -= {r['txo_hash'] for r in supports}\n return claims, supports, txo_hashes\n\n def insert_supports(self, txos: List[Output]):\n supports = []\n for txo in txos:\n tx = txo.tx_ref.tx\n supports.append((\n sqlite3.Binary(txo.ref.hash), tx.position, tx.height,\n sqlite3.Binary(txo.claim_hash), txo.amount\n ))\n if supports:\n self.db.executemany(\n \"INSERT OR IGNORE INTO support (\"\n \" txo_hash, tx_position, height, claim_hash, amount\"\n \") \"\n \"VALUES (?, ?, ?, ?, ?)\", supports\n )\n\n def delete_supports(self, txo_hashes: Set[bytes]):\n if txo_hashes:\n self.execute(*self._delete_sql(\n 'support', {'txo_hash__in': [sqlite3.Binary(txo_hash) for txo_hash in txo_hashes]}\n ))\n\n def validate_channel_signatures(self, height, new_claims, updated_claims, spent_claims, affected_channels, timer):\n if not new_claims and not updated_claims and not spent_claims:\n return\n\n sub_timer = timer.add_timer('segregate channels and signables')\n sub_timer.start()\n channels, new_channel_keys, signables = {}, {}, {}\n for txo in chain(new_claims, updated_claims):\n try:\n claim = txo.claim\n except:\n continue\n if claim.is_channel:\n channels[txo.claim_hash] = txo\n new_channel_keys[txo.claim_hash] = claim.channel.public_key_bytes\n else:\n signables[txo.claim_hash] = txo\n sub_timer.stop()\n\n sub_timer = timer.add_timer('make list of channels we need to lookup')\n sub_timer.start()\n missing_channel_keys = set()\n for txo in signables.values():\n claim = txo.claim\n if claim.is_signed and claim.signing_channel_hash not in new_channel_keys:\n missing_channel_keys.add(claim.signing_channel_hash)\n sub_timer.stop()\n\n sub_timer = timer.add_timer('lookup missing channels')\n sub_timer.start()\n all_channel_keys = {}\n if new_channel_keys or missing_channel_keys or affected_channels:\n all_channel_keys = dict(self.execute(*query(\n \"SELECT claim_hash, public_key_bytes FROM claim\",\n claim_hash__in=[\n sqlite3.Binary(channel_hash) for channel_hash in\n set(new_channel_keys) | missing_channel_keys | affected_channels\n ]\n )))\n sub_timer.stop()\n\n sub_timer = timer.add_timer('prepare for updating claims')\n sub_timer.start()\n changed_channel_keys = {}\n for claim_hash, new_key in new_channel_keys.items():\n if claim_hash not in all_channel_keys or all_channel_keys[claim_hash] != new_key:\n all_channel_keys[claim_hash] = new_key\n changed_channel_keys[claim_hash] = new_key\n\n claim_updates = []\n\n for claim_hash, txo in signables.items():\n claim = txo.claim\n update = {\n 'claim_hash': sqlite3.Binary(claim_hash),\n 'channel_hash': None,\n 'signature': None,\n 'signature_digest': None,\n 'signature_valid': None\n }\n if claim.is_signed:\n update.update({\n 'channel_hash': sqlite3.Binary(claim.signing_channel_hash),\n 'signature': sqlite3.Binary(txo.get_encoded_signature()),\n 'signature_digest': sqlite3.Binary(txo.get_signature_digest(self.ledger)),\n 'signature_valid': 0\n })\n claim_updates.append(update)\n sub_timer.stop()\n\n sub_timer = timer.add_timer('find claims affected by a change in channel key')\n sub_timer.start()\n if changed_channel_keys:\n sql = f\"\"\"\n SELECT * FROM claim WHERE\n channel_hash IN ({','.join('?' for _ in changed_channel_keys)}) AND\n signature IS NOT NULL\n \"\"\"\n for affected_claim in self.execute(sql, [sqlite3.Binary(h) for h in changed_channel_keys]):\n if affected_claim['claim_hash'] not in signables:\n claim_updates.append({\n 'claim_hash': sqlite3.Binary(affected_claim['claim_hash']),\n 'channel_hash': sqlite3.Binary(affected_claim['channel_hash']),\n 'signature': sqlite3.Binary(affected_claim['signature']),\n 'signature_digest': sqlite3.Binary(affected_claim['signature_digest']),\n 'signature_valid': 0\n })\n sub_timer.stop()\n\n sub_timer = timer.add_timer('verify signatures')\n sub_timer.start()\n for update in claim_updates:\n channel_pub_key = all_channel_keys.get(update['channel_hash'])\n if channel_pub_key and update['signature']:\n update['signature_valid'] = Output.is_signature_valid(\n bytes(update['signature']), bytes(update['signature_digest']), channel_pub_key\n )\n sub_timer.stop()\n\n sub_timer = timer.add_timer('update claims')\n sub_timer.start()\n if claim_updates:\n self.db.executemany(f\"\"\"\n UPDATE claim SET \n channel_hash=:channel_hash, signature=:signature, signature_digest=:signature_digest,\n signature_valid=:signature_valid,\n channel_join=CASE\n WHEN signature_valid=1 AND :signature_valid=1 THEN channel_join\n WHEN :signature_valid=1 THEN {height}\n END,\n canonical_url=CASE\n WHEN signature_valid=1 AND :signature_valid=1 THEN canonical_url\n WHEN :signature_valid=1 THEN\n (SELECT short_url FROM claim WHERE claim_hash=:channel_hash)||'/'||\n claim_name||COALESCE(\n (SELECT shortest_id(other_claim.claim_id, claim.claim_id) FROM claim AS other_claim\n WHERE other_claim.signature_valid = 1 AND\n other_claim.channel_hash = :channel_hash AND\n other_claim.normalized = claim.normalized),\n '#'||substr(claim_id, 1, 1)\n )\n END\n WHERE claim_hash=:claim_hash;\n \"\"\", claim_updates)\n sub_timer.stop()\n\n sub_timer = timer.add_timer('update claims affected by spent channels')\n sub_timer.start()\n if spent_claims:\n self.execute(\n f\"\"\"\n UPDATE claim SET\n signature_valid=CASE WHEN signature IS NOT NULL THEN 0 END,\n channel_join=NULL, canonical_url=NULL\n WHERE channel_hash IN ({','.join('?' for _ in spent_claims)})\n \"\"\", [sqlite3.Binary(cid) for cid in spent_claims]\n )\n sub_timer.stop()\n\n sub_timer = timer.add_timer('update channels')\n sub_timer.start()\n if channels:\n self.db.executemany(\n \"\"\"\n UPDATE claim SET\n public_key_bytes=:public_key_bytes,\n public_key_hash=:public_key_hash\n WHERE claim_hash=:claim_hash\"\"\", [{\n 'claim_hash': sqlite3.Binary(claim_hash),\n 'public_key_bytes': sqlite3.Binary(txo.claim.channel.public_key_bytes),\n 'public_key_hash': sqlite3.Binary(\n self.ledger.address_to_hash160(\n self.ledger.public_key_to_address(txo.claim.channel.public_key_bytes)))\n } for claim_hash, txo in channels.items()]\n )\n sub_timer.stop()\n\n sub_timer = timer.add_timer('update claims_in_channel counts')\n sub_timer.start()\n if all_channel_keys:\n self.db.executemany(f\"\"\"\n UPDATE claim SET\n claims_in_channel=(\n SELECT COUNT(*) FROM claim AS claim_in_channel\n WHERE claim_in_channel.signature_valid=1 AND\n claim_in_channel.channel_hash=claim.claim_hash\n )\n WHERE claim_hash = ?\n \"\"\", [(sqlite3.Binary(channel_hash),) for channel_hash in all_channel_keys.keys()])\n sub_timer.stop()\n\n def _update_support_amount(self, claim_hashes):\n if claim_hashes:\n self.execute(f\"\"\"\n UPDATE claim SET\n support_amount = COALESCE(\n (SELECT SUM(amount) FROM support WHERE support.claim_hash=claim.claim_hash), 0\n )\n WHERE claim_hash IN ({','.join('?' for _ in claim_hashes)})\n \"\"\", claim_hashes)\n\n def _update_effective_amount(self, height, claim_hashes=None):\n self.execute(\n f\"UPDATE claim SET effective_amount = amount + support_amount \"\n f\"WHERE activation_height = {height}\"\n )\n if claim_hashes:\n self.execute(\n f\"UPDATE claim SET effective_amount = amount + support_amount \"\n f\"WHERE activation_height < {height} \"\n f\" AND claim_hash IN ({','.join('?' for _ in claim_hashes)})\",\n claim_hashes\n )\n\n def _calculate_activation_height(self, height):\n last_take_over_height = f\"\"\"COALESCE(\n (SELECT last_take_over_height FROM claimtrie\n WHERE claimtrie.normalized=claim.normalized),\n {height}\n )\n \"\"\"\n self.execute(f\"\"\"\n UPDATE claim SET activation_height = \n {height} + min(4032, cast(({height} - {last_take_over_height}) / 32 AS INT))\n WHERE activation_height IS NULL\n \"\"\")\n\n def _perform_overtake(self, height, changed_claim_hashes, deleted_names):\n deleted_names_sql = claim_hashes_sql = \"\"\n if changed_claim_hashes:\n claim_hashes_sql = f\"OR claim_hash IN ({','.join('?' for _ in changed_claim_hashes)})\"\n if deleted_names:\n deleted_names_sql = f\"OR normalized IN ({','.join('?' for _ in deleted_names)})\"\n overtakes = self.execute(f\"\"\"\n SELECT winner.normalized, winner.claim_hash,\n claimtrie.claim_hash AS current_winner,\n MAX(winner.effective_amount)\n FROM (\n SELECT normalized, claim_hash, effective_amount FROM claim\n WHERE normalized IN (\n SELECT normalized FROM claim WHERE activation_height={height} {claim_hashes_sql}\n ) {deleted_names_sql}\n ORDER BY effective_amount DESC, height ASC, tx_position ASC\n ) AS winner LEFT JOIN claimtrie USING (normalized)\n GROUP BY winner.normalized\n HAVING current_winner IS NULL OR current_winner <> winner.claim_hash\n \"\"\", changed_claim_hashes+deleted_names)\n for overtake in overtakes:\n if overtake['current_winner']:\n self.execute(\n f\"UPDATE claimtrie SET claim_hash = ?, last_take_over_height = {height} \"\n f\"WHERE normalized = ?\",\n (sqlite3.Binary(overtake['claim_hash']), overtake['normalized'])\n )\n else:\n self.execute(\n f\"INSERT INTO claimtrie (claim_hash, normalized, last_take_over_height) \"\n f\"VALUES (?, ?, {height})\",\n (sqlite3.Binary(overtake['claim_hash']), overtake['normalized'])\n )\n self.execute(\n f\"UPDATE claim SET activation_height = {height} WHERE normalized = ? \"\n f\"AND (activation_height IS NULL OR activation_height > {height})\",\n (overtake['normalized'],)\n )\n\n def _copy(self, height):\n if height > 50:\n self.execute(f\"DROP TABLE claimtrie{height-50}\")\n self.execute(f\"CREATE TABLE claimtrie{height} AS SELECT * FROM claimtrie\")\n\n def update_claimtrie(self, height, changed_claim_hashes, deleted_names, timer):\n r = timer.run\n binary_claim_hashes = [\n sqlite3.Binary(claim_hash) for claim_hash in changed_claim_hashes\n ]\n\n r(self._calculate_activation_height, height)\n r(self._update_support_amount, binary_claim_hashes)\n\n r(self._update_effective_amount, height, binary_claim_hashes)\n r(self._perform_overtake, height, binary_claim_hashes, list(deleted_names))\n\n r(self._update_effective_amount, height)\n r(self._perform_overtake, height, [], [])\n\n def get_expiring(self, height):\n return self.execute(\n f\"SELECT claim_hash, normalized FROM claim WHERE expiration_height = {height}\"\n )\n\n def advance_txs(self, height, all_txs, header, daemon_height, timer):\n insert_claims = []\n update_claims = []\n delete_claim_hashes = set()\n insert_supports = []\n delete_support_txo_hashes = set()\n recalculate_claim_hashes = set() # added/deleted supports, added/updated claim\n deleted_claim_names = set()\n delete_others = set()\n body_timer = timer.add_timer('body')\n for position, (etx, txid) in enumerate(all_txs):\n tx = timer.run(\n Transaction, etx.serialize(), height=height, position=position\n )\n # Inputs\n spent_claims, spent_supports, spent_others = timer.run(\n self.split_inputs_into_claims_supports_and_other, tx.inputs\n )\n body_timer.start()\n delete_claim_hashes.update({r['claim_hash'] for r in spent_claims})\n deleted_claim_names.update({r['normalized'] for r in spent_claims})\n delete_support_txo_hashes.update({r['txo_hash'] for r in spent_supports})\n recalculate_claim_hashes.update({r['claim_hash'] for r in spent_supports})\n delete_others.update(spent_others)\n # Outputs\n for output in tx.outputs:\n if output.is_support:\n insert_supports.append(output)\n recalculate_claim_hashes.add(output.claim_hash)\n elif output.script.is_claim_name:\n insert_claims.append(output)\n recalculate_claim_hashes.add(output.claim_hash)\n elif output.script.is_update_claim:\n claim_hash = output.claim_hash\n update_claims.append(output)\n recalculate_claim_hashes.add(claim_hash)\n delete_claim_hashes.discard(claim_hash)\n delete_others.discard(output.ref.hash) # claim insertion and update occurring in the same block\n body_timer.stop()\n\n skip_claim_timer = timer.add_timer('skip insertion of abandoned claims')\n skip_claim_timer.start()\n for new_claim in list(insert_claims):\n if new_claim.ref.hash in delete_others:\n insert_claims.remove(new_claim)\n skip_claim_timer.stop()\n\n expire_timer = timer.add_timer('recording expired claims')\n expire_timer.start()\n for expired in self.get_expiring(height):\n delete_claim_hashes.add(expired['claim_hash'])\n deleted_claim_names.add(expired['normalized'])\n expire_timer.stop()\n\n r = timer.run\n affected_channels = r(self.delete_claims, delete_claim_hashes)\n r(self.delete_supports, delete_support_txo_hashes)\n r(self.insert_claims, insert_claims, header)\n r(self.update_claims, update_claims, header)\n r(self.validate_channel_signatures, height, insert_claims,\n update_claims, delete_claim_hashes, affected_channels, forward_timer=True)\n r(self.insert_supports, insert_supports)\n r(self.update_claimtrie, height, recalculate_claim_hashes, deleted_claim_names, forward_timer=True)\n r(calculate_trending, self.db, height, self.main.first_sync, daemon_height)\n\n def get_claims(self, cols, join=True, **constraints):\n if 'order_by' in constraints:\n sql_order_by = []\n for order_by in constraints['order_by']:\n is_asc = order_by.startswith('^')\n column = order_by[1:] if is_asc else order_by\n if column not in self.ORDER_FIELDS:\n raise NameError(f'{column} is not a valid order_by field')\n if column == 'name':\n column = 'normalized'\n sql_order_by.append(\n f\"claim.{column} ASC\" if is_asc else f\"claim.{column} DESC\"\n )\n constraints['order_by'] = sql_order_by\n\n ops = {'<=': '__lte', '>=': '__gte', '<': '__lt', '>': '__gt'}\n for constraint in self.INTEGER_PARAMS:\n if constraint in constraints:\n value = constraints.pop(constraint)\n postfix = ''\n if isinstance(value, str):\n if len(value) >= 2 and value[:2] in ops:\n postfix, value = ops[value[:2]], value[2:]\n elif len(value) >= 1 and value[0] in ops:\n postfix, value = ops[value[0]], value[1:]\n if constraint == 'fee_amount':\n value = Decimal(value)*1000\n constraints[f'claim.{constraint}{postfix}'] = int(value)\n\n if constraints.pop('is_controlling', False):\n if {'sequence', 'amount_order'}.isdisjoint(constraints):\n join = True\n constraints['claimtrie.claim_hash__is_not_null'] = ''\n if 'sequence' in constraints:\n constraints['order_by'] = 'claim.activation_height ASC'\n constraints['offset'] = int(constraints.pop('sequence')) - 1\n constraints['limit'] = 1\n if 'amount_order' in constraints:\n constraints['order_by'] = 'claim.effective_amount DESC'\n constraints['offset'] = int(constraints.pop('amount_order')) - 1\n constraints['limit'] = 1\n\n if 'claim_id' in constraints:\n claim_id = constraints.pop('claim_id')\n if len(claim_id) == 40:\n constraints['claim.claim_id'] = claim_id\n else:\n constraints['claim.claim_id__like'] = f'{claim_id[:40]}%'\n\n if 'name' in constraints:\n constraints['claim.normalized'] = normalize_name(constraints.pop('name'))\n\n if 'public_key_id' in constraints:\n constraints['claim.public_key_hash'] = sqlite3.Binary(\n self.ledger.address_to_hash160(constraints.pop('public_key_id')))\n\n if 'channel' in constraints:\n channel_url = constraints.pop('channel')\n match = self._resolve_one(channel_url)\n if isinstance(match, sqlite3.Row):\n constraints['channel_hash'] = match['claim_hash']\n else:\n return [[0]] if cols == 'count(*)' else []\n if 'channel_hash' in constraints:\n constraints['claim.channel_hash'] = sqlite3.Binary(constraints.pop('channel_hash'))\n if 'channel_ids' in constraints:\n channel_ids = constraints.pop('channel_ids')\n if channel_ids:\n constraints['claim.channel_hash__in'] = [\n sqlite3.Binary(unhexlify(cid)[::-1]) for cid in channel_ids\n ]\n if 'not_channel_ids' in constraints:\n not_channel_ids = constraints.pop('not_channel_ids')\n if not_channel_ids:\n not_channel_ids_binary = [\n sqlite3.Binary(unhexlify(ncid)[::-1]) for ncid in not_channel_ids\n ]\n if constraints.get('has_channel_signature', False):\n constraints['claim.channel_hash__not_in'] = not_channel_ids_binary\n else:\n constraints['null_or_not_channel__or'] = {\n 'claim.signature_valid__is_null': True,\n 'claim.channel_hash__not_in': not_channel_ids_binary\n }\n if 'signature_valid' in constraints:\n has_channel_signature = constraints.pop('has_channel_signature', False)\n if has_channel_signature:\n constraints['claim.signature_valid'] = constraints.pop('signature_valid')\n else:\n constraints['null_or_signature__or'] = {\n 'claim.signature_valid__is_null': True,\n 'claim.signature_valid': constraints.pop('signature_valid')\n }\n elif constraints.pop('has_channel_signature', False):\n constraints['claim.signature_valid__is_not_null'] = True\n\n if 'txid' in constraints:\n tx_hash = unhexlify(constraints.pop('txid'))[::-1]\n nout = constraints.pop('nout', 0)\n constraints['claim.txo_hash'] = sqlite3.Binary(\n tx_hash + struct.pack(' Tuple[List, List, int, int]:\n assert set(constraints).issubset(self.SEARCH_PARAMS), \\\n f\"Search query contains invalid arguments: {set(constraints).difference(self.SEARCH_PARAMS)}\"\n total = self.get_claims_count(**constraints)\n constraints['offset'] = abs(constraints.get('offset', 0))\n constraints['limit'] = min(abs(constraints.get('limit', 10)), 50)\n if 'order_by' not in constraints:\n constraints['order_by'] = [\"height\", \"^name\"]\n txo_rows = self._search(**constraints)\n channel_hashes = set(txo['channel_hash'] for txo in txo_rows if txo['channel_hash'])\n extra_txo_rows = []\n if channel_hashes:\n extra_txo_rows = self._search(**{'claim.claim_hash__in': [sqlite3.Binary(h) for h in channel_hashes]})\n return txo_rows, extra_txo_rows, constraints['offset'], total\n\n def _resolve_one(self, raw_url):\n try:\n url = URL.parse(raw_url)\n except ValueError as e:\n return e\n\n channel = None\n\n if url.has_channel:\n query = url.channel.to_dict()\n if set(query) == {'name'}:\n query['is_controlling'] = True\n else:\n query['order_by'] = ['^height']\n matches = self._search(**query, limit=1)\n if matches:\n channel = matches[0]\n else:\n return LookupError(f'Could not find channel in \"{raw_url}\".')\n\n if url.has_stream:\n query = url.stream.to_dict()\n if channel is not None:\n if set(query) == {'name'}:\n # temporarily emulate is_controlling for claims in channel\n query['order_by'] = ['effective_amount']\n else:\n query['order_by'] = ['^channel_join']\n query['channel_hash'] = channel['claim_hash']\n query['signature_valid'] = 1\n elif set(query) == {'name'}:\n query['is_controlling'] = 1\n matches = self._search(**query, limit=1)\n if matches:\n return matches[0]\n else:\n return LookupError(f'Could not find stream in \"{raw_url}\".')\n\n return channel\n\n def resolve(self, urls) -> Tuple[List, List]:\n result = []\n channel_hashes = set()\n for raw_url in urls:\n match = self._resolve_one(raw_url)\n result.append(match)\n if isinstance(match, sqlite3.Row) and match['channel_hash']:\n channel_hashes.add(match['channel_hash'])\n extra_txo_rows = []\n if channel_hashes:\n extra_txo_rows = self._search(**{'claim.claim_hash__in': [sqlite3.Binary(h) for h in channel_hashes]})\n return result, extra_txo_rows\n\n\nclass LBRYDB(DB):\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.sql = SQLDB(self, 'claims.db')\n\n def close(self):\n super().close()\n self.sql.close()\n\n async def _open_dbs(self, *args, **kwargs):\n await super()._open_dbs(*args, **kwargs)\n self.sql.open()\n","sub_path":"lbry/lbry/wallet/server/db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":44089,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"337447335","text":"# -*- coding: utf-8 -*-\nfrom qgis.PyQt.QtCore import QSettings, QTranslator, QCoreApplication\nfrom qgis.PyQt.QtGui import QIntValidator, QDoubleValidator, QIcon\nfrom qgis.PyQt.QtWidgets import QAction, QFileDialog, QApplication, QMessageBox\n\nfrom qgis.core import QgsVectorLayer, QgsRasterLayer\nfrom qgis.core import QgsCoordinateReferenceSystem\nfrom qgis.gui import QgsMessageBar\n\n# Initialize Qt resources from file resources.py\nfrom . import resources\nfrom .forLim_dialog import forLimDialog\nfrom datetime import datetime\nfrom osgeo import ogr\nfrom .delaunay import delaunayMethod\nfrom processing import *\nimport os\nfrom uuid import uuid4 as uuid4\nfrom .delaunay.postProcessing import merge, clip\n\n\nclass forLim:\n \"\"\"QGIS Plugin Implementation.\"\"\"\n\n def __init__(self, iface):\n \"\"\"Constructor.\n\n :param iface: An interface instance that will be passed to this class\n which provides the hook by which you can manipulate the QGIS\n application at run time.\n :type iface: QgsInterface\n \"\"\"\n # Save reference to the QGIS interface\n self.iface = iface\n # initialize plugin directory\n self.plugin_dir = os.path.dirname(__file__)\n # initialize locale\n locale = QSettings().value('locale/userLocale')[0:2]\n locale_path = os.path.join(\n self.plugin_dir,\n 'forLim_{}.qm'.format(locale))\n\n if os.path.exists(locale_path):\n self.translator = QTranslator()\n self.translator.load(locale_path)\n\n if qVersion() > '5.6.0':\n QCoreApplication.installTranslator(self.translator)\n\n # Create the dialog (after translation) and keep reference\n self.dlg = forLimDialog()\n\n # Declare instance attributes\n self.actions = []\n self.menu = self.tr(u'&forLim')\n self.toolbar = self.iface.addToolBar(u'forLim')\n self.toolbar.setObjectName(u'forLim')\n\n def tr(self, message):\n \"\"\"Get the translation for a string using Qt translation API.\n\n We implement this ourselves since we do not inherit QObject.\n\n :param message: String for translation.\n :type message: str, QString\n\n :returns: Translated version of message.\n :rtype: QString\n \"\"\"\n return QCoreApplication.translate('forLim', message)\n\n def add_action(\n self,\n icon_path,\n text,\n callback,\n enabled_flag=True,\n add_to_menu=True,\n add_to_toolbar=True,\n status_tip=None,\n whats_this=None,\n parent=None):\n\n icon = QIcon(icon_path)\n action = QAction(icon, text, parent)\n action.triggered.connect(callback)\n action.setEnabled(enabled_flag)\n\n if status_tip is not None:\n action.setStatusTip(status_tip)\n\n if whats_this is not None:\n action.setWhatsThis(whats_this)\n\n if add_to_toolbar:\n self.toolbar.addAction(action)\n\n if add_to_menu:\n self.iface.addPluginToMenu(\n self.menu,\n action)\n\n self.actions.append(action)\n\n self.messageBar = self.iface.messageBar()\n\n return action\n\n def initGui(self):\n \"\"\"Create the menu entries and toolbar icons inside the QGIS GUI.\"\"\"\n\n # Input\n global last_path_input\n last_path_input = self.dlg.LE_input.text()\n\n self.dlg.PB_input.clicked.connect(self.select_input_files)\n self.dlg.LE_input.editingFinished.connect(self.check_input_path)\n\n # Output\n global last_path_output, output_message\n last_path_output = self.dlg.LE_output.text()\n output_message = True\n\n self.dlg.PB_output.clicked.connect(self.select_output_directory)\n self.dlg.LE_output.editingFinished.connect(self.check_output_path)\n self.dlg.PB_quit.clicked.connect(self.quit_plugin)\n self.dlg.PB_ok.clicked.connect(self.run)\n self.dlg.tabWidget.setCurrentIndex(0)\n\n # Set current line edit\n self.dlg.LE_input.setFocus()\n\n # Check validation\n self.dlg.LE_minHeightThres.setValidator(QIntValidator())\n self.dlg.LE_maxHeightThres.setValidator(QIntValidator())\n self.dlg.LE_DRFD.setValidator(QDoubleValidator())\n self.dlg.LE_DRPB.setValidator(QDoubleValidator())\n self.dlg.txt_triangle_peri.setValidator(QDoubleValidator())\n self.dlg.LE_minWidthThres.setValidator(QIntValidator())\n self.dlg.LE_minForSurfThres.setValidator(QIntValidator())\n self.dlg.LE_minClearingSurfThres.setValidator(QIntValidator())\n self.dlg.LE_gradConvDiameter.setValidator(QIntValidator())\n\n # Set add result to canevas as default\n self.dlg.CB_addLayer.setCheckState(2)\n\n # set progress bar settings\n self.dlg.progressBar.setMinimum(0)\n self.dlg.progressBar.setValue(0)\n\n icon_path = ':/plugins/forLim/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'forLim'),\n callback=self.run,\n parent=self.iface.mainWindow())\n\n def select_input_files(self):\n filenames = QFileDialog.getOpenFileNames(filter=\"Images (*.tif)\")\n files_to_process = ''\n if filenames:\n for f in filenames[0]:\n files_to_process += f + ';'\n files_to_process = files_to_process[:-1]\n self.dlg.LE_input.setText(files_to_process)\n\n def check_input_path(self):\n global last_path_input\n path_input = self.dlg.LE_input.text()\n if path_input and not path_input == last_path_input:\n last_path_input = path_input\n files = path_input.split(\";\")\n f_exist = True\n for f in files:\n if not os.path.exists(f):\n self.messageBar.pushMessage(\"Input\"\n \"Le champ '\" + f +\n \"' n'existe pas.\",\n QgsMessageBar.CRITICAL, 7)\n f_exist = False\n\n def select_output_directory(self):\n self.dlg.LE_output.setFocus()\n folder = QFileDialog.getExistingDirectory()\n if folder:\n if not os.path.exists(folder):\n self.messageBar.pushMessage(\"Output\",\n \"Le champ 'Dossier Destination '\" +\n \"est invalide.\",\n QgsMessageBar.CRITICAL, 7)\n else:\n self.dlg.LE_output.setText(folder)\n\n def check_output_path(self):\n path_output = self.dlg.LE_output.text()\n global last_path_output\n if not path_output == last_path_output:\n if os.path.exists(path_output):\n last_path_output = path_output\n elif path_output:\n self.messageBar.pushMessage(\"Attention\",\n \"Le champ 'Dossier Destination' \" +\n \"est invalide.\",\n QgsMessageBar.CRITICAL, 7)\n\n def quit_plugin(self):\n self.dlg.close()\n\n def unload(self):\n \"\"\"Removes the plugin menu item and icon from QGIS GUI.\"\"\"\n for action in self.actions:\n self.iface.removePluginMenu(\n self.tr(u'&forLim'),\n action)\n self.iface.removeToolBarIcon(action)\n del self.toolbar\n\n def run(self):\n \"\"\"Run method that performs all the real work\"\"\"\n self.dlg.show()\n ok_result = self.dlg.exec_()\n if ok_result:\n\n c = False\n error_msg = str()\n\n if not self.dlg.LE_input.text():\n error_msg = error_msg + \"Ajouter un chemin d'acces \" + \\\n \"au(x) fichier(s) source.\\n\\n\"\n c = True\n if not self.dlg.LE_output.text():\n error_msg = error_msg + \"Ajouter un chemin d'acces pour \" + \\\n \"le dossier de destination ou seront déposes \" + \\\n \"les fichiers produits.\\n\\n\"\n c = True\n\n if c:\n QMessageBox.warning(None, \"Erreur(s)\", error_msg)\n else:\n\n options = {\n # Chemin d'accès au Modèle Numérique de Canopée (entrée)\n \"Path_input\":\n str(self.dlg.LE_input.text()),\n # Chemin d'accès au dossier de sortie\n \"Path_output\":\n str(self.dlg.LE_output.text()),\n # Hauteur de la fenêtre de lissage\n \"WinRad\":\n int(self.dlg.LE_gradConvDiameter.text()),\n # Hauteur minimale des arbres\n \"MinHeightThres\":\n int(self.dlg.LE_minHeightThres.text()),\n # Hauteur maximale des arbres\n \"MaxHeightThres\":\n int(self.dlg.LE_maxHeightThres.text()),\n # Degré de recouvrement pour la foret dense\n \"forestRatio\":\n float(self.dlg.LE_DRFD.text()),\n # Degré de recouvrement pour le paturage boise\n \"woodenPastureRatio\":\n float(self.dlg.LE_DRPB.text()),\n # largeur minimale forêt\n \"WidthThres\":\n int(self.dlg.LE_minWidthThres.text()),\n # surface minimum forêt\n \"MinAreaThres\":\n int(self.dlg.LE_minForSurfThres.text()),\n # surface minimum clairière\n \"MaxAreaThres\":\n int(self.dlg.LE_minClearingSurfThres.text()),\n # Périmètre max des triangles de Delaunay\n \"MaxTrianglePerimeter\":\n float(self.dlg.txt_triangle_peri.text()),\n # Calcul selon la convolution uniquement\n \"onlyConvolution\":\n self.dlg.chkConvolution.isChecked(),\n \"AddLayer\":\n self.dlg.CB_addLayer.isChecked(),\n \"plugin\": True\n }\n # Set default values of process bar\n self.dlg.progressBar.reset()\n self.dlg.progressBar.setMinimum(0)\n self.dlg.progressBar.setMaximum(1000)\n\n # Print progress on user window\n self.dlg.label_printActualProcess.setText(\"ForLim...\")\n self.dlg.progressBar.setValue(0)\n QApplication.processEvents()\n\n now_time = datetime.now()\n\n name = \"forLim_\" + str(uuid4())\n options[\"Path_output\"] = \\\n os.path.join(options[\"Path_output\"], name)\n os.mkdir(options[\"Path_output\"])\n\n # Get file list\n path_input = options[\"Path_input\"]\n files = options[\"Path_input\"].split(\";\")\n nfiles = len(files)\n\n options['src'] = str(options['Path_input'])\n options['dst'] = str(options['Path_output'])\n f = open(options['Path_output'] + '/forlim_medatata.txt', 'w')\n f.write(str(options))\n f.close()\n\n i = 0\n for f in enumerate(files):\n i += 1\n self.dlg.label_printActualProcess \\\n .setText(\"Processing tile \" + str(i) + \"/\" +\n str(len((files))))\n options['Path_input'] = f[1]\n options['src'] = str(options['Path_input'])\n delaunayMethod.main(self, options, i)\n\n self.dlg.label_printActualProcess.setText(u'Calcul terminé')\n self.iface.mapCanvas().zoomToFullExtent()\n","sub_path":"forLim.py","file_name":"forLim.py","file_ext":"py","file_size_in_byte":12033,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"444149023","text":"#!/usr/bin/env python\n\nfrom misc.common import parse_play_args\nfrom misc.config import load_config\nfrom rl_server.server.agent import run_agent\nfrom rl_server.server.run_agents import get_algo_and_agent_config\n\nargs = parse_play_args()\nconfig = load_config(args.config)\n\nalgo_config, agent_config = get_algo_and_agent_config(\n config,\n args.algorithm_id,\n args.agent_id,\n args.seed\n)\n\nrun_agent(\n config,\n agent_config,\n checkpoint_path=args.checkpoint\n)\n","sub_path":"rl_server/server/play.py","file_name":"play.py","file_ext":"py","file_size_in_byte":476,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"544145722","text":"'''\nCreated on Jan 17, 2012\n\n@author: spengler\n'''\n\nfrom forms import FormCadastroDeUsuario\nfrom forms import FormCadastroDePerfil\nfrom forms import FormCadastroDeEndereco\nfrom forms import FormCadastroDeHabilidades\nfrom forms import FormCadastroDeProjeto\nfrom forms import FormConfiguracoesDeUsuario\nfrom forms import FormCadastroDeDescricao\nfrom forms import FormCadastroDePortfolio\n\nfrom models import Endereco\n\nfrom django.template import RequestContext\nfrom django.shortcuts import render_to_response\nfrom django.shortcuts import get_object_or_404\nfrom django.http import HttpResponseRedirect\nfrom django.contrib.auth.decorators import login_required\n\nfrom controle import Controle\nfrom utilidades.controle import Controle as UtilidadesControle #@UnresolvedImport\nfrom projeto.controle import Controle as ProjetoControle #@UnresolvedImport\n\nimport constantes #@UnresolvedImport\n\noControle= Controle()\n\ndef usuarios(vRequest):\n return render_to_response(\n 'usuarios/usuarios.html',\n locals(),\n context_instance= RequestContext(vRequest),)\n\ndef editar_usuario(vRequest, vClasseForm, vTitulo, vIDUsuario=None, vChamada=None):\n if vIDUsuario:\n iUsuario= get_object_or_404(vClasseForm._meta.model, pk= vIDUsuario)\n iUsuarioNovo= False\n else:\n iUsuario= None\n iUsuarioNovo= True\n if vRequest.method == 'POST':\n form = FormCadastroDeUsuario(vRequest.POST)\n if form.is_valid(): \n iUsuario = form.save(commit=False)\n iUsuario.tipo_usuario= oControle.obtemTipoDeUsuario(form.cleaned_data['tipos_de_usuario'])\n iUsuario.classe_usuario= oControle.obtemClasseDeUsuario(constantes.cntClasseUsuarioPadrao)\n iUsuario.save()\n UtilidadesControle().enviaEmailCadastroUsuario(iUsuario)\n return HttpResponseRedirect('/aviso/cadastro/')\n else:\n form = FormCadastroDeUsuario(vRequest.POST)\n else:\n form = vClasseForm(instance= iUsuario)\n \n return render_to_response(\n 'usuarios/editar_usuario.html',\n locals(),\n context_instance=RequestContext(vRequest))\n\n@login_required \ndef editar_perfil(vRequest, vClasseForm, vTitulo, vPossuiPerfil= None):\n iUser = vRequest.user\n if iUser:\n iUsuario= oControle.obterUsuario(iUser)\n iEndereco= oControle.obterEnderecoDoUsuario(iUsuario.id)\n iUsuarioNovo= False\n else:\n iUsuario= None\n iUsuarioNovo= True\n iEndereco= Endereco()\n \n iEmail= iUsuario.email\n \n if vRequest.method == 'POST':\n form = FormCadastroDePerfil(vRequest.POST, instance= iUsuario)\n form_endereco = FormCadastroDeEndereco(vRequest.POST)\n\n iEndereco.ddd= vRequest.POST.get('ddd')\n iEndereco.telefone= vRequest.POST.get('telefone')\n iEndereco.cep= vRequest.POST.get('cep')\n iEndereco.rua= vRequest.POST.get('rua')\n iEndereco.numero= vRequest.POST.get('numero')\n iEndereco.complemento= vRequest.POST.get('complemento')\n iEndereco.bairro= vRequest.POST.get('bairro')\n iEndereco.cidade= vRequest.POST.get('cidade')\n iEndereco.uf= vRequest.POST.get('uf')\n iEndereco.pais= vRequest.POST.get('pais')\n iEndereco.save()\n\n\n if form.is_valid() and form_endereco.is_valid() :\n iUsuario = form.save(commit=False)\n iUsuario.save()\n oControle.salvaEnderecoDoUsuario(iEndereco, iUsuario)\n return HttpResponseRedirect('/cadastro/usuario/resumo_perfil/0/')\n else:\n form= vClasseForm(instance= iUsuario)\n form_endereco= FormCadastroDeEndereco(instance= iEndereco)\n \n return render_to_response(\n 'perfil/editar_perfil.html',\n locals(),\n context_instance=RequestContext(vRequest),)\n\n@login_required \ndef editar_configuracoes(vRequest, vClasseForm, vTitulo):\n iUser = vRequest.user\n if iUser:\n iUsuario= oControle.obterUsuario(iUser)\n iUsuarioNovo= False\n else:\n iUsuario= None\n iUsuarioNovo= True\n if vRequest.method == 'POST':\n form = FormConfiguracoesDeUsuario(vRequest.POST, instance= iUsuario)\n if form.is_valid():\n iUsuario = form.save(commit=False)\n iUsuario.tipo_usuario= oControle.obtemTipoDeUsuario(form.cleaned_data['tipos_de_usuario'])\n iUsuario.save()\n return HttpResponseRedirect('/dashboard/projetos/meus_projetos/')\n else:\n form = vClasseForm(instance= iUsuario)\n form.fields['tipos_de_usuario'].initial = iUsuario.tipo_usuario\n \n return render_to_response(\n 'usuarios/editar_configuracoes.html',\n locals(),\n context_instance=RequestContext(vRequest),)\n\n@login_required \ndef editar_habilidades(vRequest, vClasseForm, vTitulo, vIDUsuario=None):\n if vIDUsuario:\n iUsuario= get_object_or_404(vClasseForm._meta.model, pk= vIDUsuario)\n iUsuarioNovo= False\n else:\n iUsuario= None\n iUsuarioNovo= True\n if vRequest.method == 'POST':\n form = FormCadastroDeHabilidades(vRequest.POST)\n iHabilidades= []\n iListaHabilidades= oControle.obterIDsHabilidades()\n for i in range(len(iListaHabilidades)):\n iHabilidade= vRequest.POST.getlist('habilidade_%s' % iListaHabilidades[i])\n if iHabilidade != []:\n iHabilidades.append(iHabilidade[0])\n iUsuario= oControle.obterUsuario(vRequest.user)\n oControle.salvaHabilidadesDoUsuario(iHabilidades, iUsuario)\n return HttpResponseRedirect('/cadastro/usuario/resumo_perfil/0/')\n else:\n form = FormCadastroDeHabilidades()\n iListaDeHabilidadesDoUsuario= oControle.obterIDsHabilidadesDoUsuario(vRequest.user)\n for i in range(len(iListaDeHabilidadesDoUsuario)):\n form.fields['habilidade_%s' % iListaDeHabilidadesDoUsuario[i]].initial = iListaDeHabilidadesDoUsuario[i]\n return render_to_response(\n 'perfil/editar_habilidades.html',\n locals(),\n context_instance=RequestContext(vRequest),)\n\n@login_required \ndef editar_descricao(vRequest, vClasseForm, vTitulo):\n iUser = vRequest.user\n if iUser:\n iUsuario= oControle.obterUsuario(iUser)\n iUsuarioNovo= False\n else:\n iUsuario= None\n iUsuarioNovo= True\n if vRequest.method == 'POST':\n form = FormCadastroDeDescricao(vRequest.POST, instance= iUsuario)\n if form.is_valid():\n iUsuario = form.save(commit=False)\n iUsuario.save()\n oControle.calculaPontuacaoDoUsuario(iUsuario.id)\n UtilidadesControle().verificaMensagemNoTermoDeUso(iUsuario.descricao, iUsuario,\n constantes.cntTipoViolacaoDescricao,\n iUsuario.id)\n return HttpResponseRedirect('/cadastro/usuario/resumo_perfil/0/')\n else:\n form = vClasseForm(instance= iUsuario)\n \n return render_to_response(\n 'perfil/editar_descricao.html',\n locals(),\n context_instance=RequestContext(vRequest),)\n\n@login_required \ndef editar_portfolio(vRequest, vClasseForm, vTitulo):\n iUser = vRequest.user\n if iUser:\n iUsuario= oControle.obterUsuario(iUser)\n iUsuarioNovo= False\n else:\n iUsuario= None\n iUsuarioNovo= True\n if vRequest.method == 'POST':\n form = FormCadastroDePortfolio(vRequest.POST)\n oControle.salvaPortfolioDoUsuario(iUser.id, \n vRequest.POST.getlist('gitHub')[0], \n vRequest.POST.getlist('linkedIn')[0], \n vRequest.POST.getlist('coderWall')[0], \n vRequest.POST.getlist('sitePessoal')[0], \n vRequest.POST.getlist('siteEmpresa')[0])\n return HttpResponseRedirect('/cadastro/usuario/resumo_perfil/0/')\n else:\n form = vClasseForm()\n iPortfolioAuxiliar= oControle.obtemPortfolioDoUsuario(vRequest.user.id)\n form.fields['gitHub'].initial = iPortfolioAuxiliar.usuario_gitHub\n form.fields['linkedIn'].initial = iPortfolioAuxiliar.usuario_linkedIn\n form.fields['coderWall'].initial = iPortfolioAuxiliar.usuario_coderWall\n form.fields['sitePessoal'].initial = iPortfolioAuxiliar.sitePessoal\n form.fields['siteEmpresa'].initial = iPortfolioAuxiliar.siteEmpresa\n \n return render_to_response(\n 'perfil/editar_portfolio.html',\n locals(),\n context_instance=RequestContext(vRequest),)\n\n@login_required \ndef resumo_perfil(vRequest, vClasseForm, vTitulo, vIDUsuario=None):\n if vIDUsuario == '0':\n iUsuario= oControle.obterUsuario(vRequest.user)\n else:\n iUsuario= oControle.obterUsuarioPeloID(vIDUsuario)\n iPortifolio= oControle.obtemPortfolioDoUsuario(iUsuario.id)\n iEmail= iUsuario.email\n iIDUsuario= iUsuario.id\n iListaFeedback= oControle.obtemListaFeedbacks(iUsuario)\n if len(iListaFeedback) > 0:\n iPossuiFeedback= True\n else:\n iPossuiFeedback= False\n iFeedbackMedias= oControle.obtemFeedbacksMedias(iUsuario)\n iNomeUsuario= oControle.obterNomeUsuario(iUsuario.id)\n iLocalUsuario= oControle.obterLocalUsuario(iUsuario.id)\n iListaHabilidades=oControle.obterListaHabilidadesDoUsuario(iUsuario)\n iGitHub= oControle.verificarURL(iPortifolio.gitHub)\n iLinkedIn= oControle.verificarURL(iPortifolio.linkedIn)\n iCoderWall= oControle.verificarURL(iPortifolio.coderWall)\n iSitePessoal= oControle.verificarURL(iPortifolio.sitePessoal)\n iSiteEmpresa= oControle.verificarURL(iPortifolio.siteEmpresa)\n if iUsuario.pontuacao == None:\n iPontuacao = 0.0\n else:\n iPontuacao= iUsuario.pontuacao\n iHabilidades=''\n for i in range(len(iListaHabilidades)):\n iHabilidades= iHabilidades + ' - ' + iListaHabilidades[i].descricao\n if iUsuario.descricao == None :\n iDescricao= ''\n else:\n iDescricao= iUsuario.descricao\n if iUsuario.cnpj == None or iUsuario.cnpj == '':\n iCNPJ = False\n else:\n iCNPJ = True\n return render_to_response(\n 'perfil/resumo_perfil.html',\n locals(),\n context_instance=RequestContext(vRequest),)\n\n#--------------------------PROJETOS------------------------------------------\n\ndef editar_projeto(vRequest, vClasseForm, vTitulo, vIDProjeto= None, vAcao= None):\n iUsuario= oControle.obterUsuario(vRequest.user)\n if not oControle.possuiPerfil(iUsuario.id):\n return HttpResponseRedirect('/cadastro/usuario/perfil/%s/' % constantes.cntOrigemPerfilProjeto)\n iPossuiHabilidade = True\n \n if vRequest.method == 'POST':\n form = FormCadastroDeHabilidades(vRequest.POST)\n form_projeto = FormCadastroDeProjeto(vRequest.POST)\n \n iHabilidades= []\n iListaHabilidades= oControle.obterIDsHabilidades()\n iPossuiHabilidade = False\n for i in range(len(iListaHabilidades)):\n iHabilidade= vRequest.POST.getlist('habilidade_%s' % iListaHabilidades[i])\n if iHabilidade != []:\n iHabilidades.append(iHabilidade[0])\n iPossuiHabilidade = True\n \n if form_projeto.is_valid() and iPossuiHabilidade:\n iProjeto = form_projeto.save(commit=False)\n if int(vAcao) == constantes.cntAcaoProjetoCopiar:\n iProjeto.id_projeto= None\n elif int(vAcao) == constantes.cntAcaoProjetoEditar:\n iProjeto.id_projeto= vIDProjeto\n iProjeto.id_proprietario= oControle.obterUsuario(vRequest.user)\n iProjeto.id_valor= oControle.obterValorDoProjeto(form_projeto.cleaned_data['lista_de_valores'])\n iProjeto.id_categoria= oControle.obterTipoDeHabilidade(form_projeto.cleaned_data['lista_de_categorias'])\n iProjeto.id_estado= oControle.obterEstadoDoProjeto(constantes.cntEstadoProjeto_Novo)\n iProjeto.save()\n UtilidadesControle().verificaMensagemNoTermoDeUso(iProjeto.descricao, iProjeto.id_proprietario,\n constantes.cntTipoViolacaoProjeto,\n iProjeto.id_proprietario.id)\n iPublicarAgora = vRequest.POST.getlist('publicar')\n oControle.sinalizaProjetoCriado(iProjeto, iPublicarAgora)\n \n oControle.salvaHabilidadesDoProjeto(iHabilidades, iProjeto)\n return HttpResponseRedirect('/aviso/projeto/')\n else:\n form = FormCadastroDeHabilidades()\n if int(vIDProjeto) == constantes.cntAcaoProjetoNovo:\n form_projeto = vClasseForm()\n iListaDeHabilidadesDoProjeto= oControle.obterIDsHabilidadesDoProjeto(None)\n else:\n iProjetoReal= ProjetoControle().obtemProjetoReal(vIDProjeto)\n form_projeto = FormCadastroDeProjeto(instance=iProjetoReal)\n iListaDeHabilidadesDoProjeto= oControle.obterIDsHabilidadesDoProjeto(vIDProjeto)\n form_projeto.fields['lista_de_valores'].initial = iProjetoReal.id_valor.id_valor\n form_projeto.fields['lista_de_categorias'].initial = iProjetoReal.id_categoria.id_tipo_habilidade\n for i in range(len(iListaDeHabilidadesDoProjeto)):\n form.fields['habilidade_%s' % iListaDeHabilidadesDoProjeto[i]].initial = iListaDeHabilidadesDoProjeto[i]\n \n return render_to_response(\n 'projetos/editar_projeto.html',\n locals(),\n context_instance=RequestContext(vRequest),)\n \n","sub_path":"PyProject_FreelaTI/src/PyProject_FreelaTI/cadastro/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":14078,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"506014476","text":"from graph_tools import *\n\n\nif __name__ == \"__main__\":\n text_file = open(\"06.dat\", \"r\")\n edges = [line.strip().split(')') for line in text_file.readlines()]\n\n graph = defaultdict(set)\n for (c, o) in edges:\n graph[c].add(o)\n graph[o].add(c)\n\n depths = dict()\n dfs(graph, 'COM', depths, 0)\n print(f\"Part 1: {sum(depths.values())}\")\n\n dists = dijkstra(graph, 'YOU')\n print(f\"Part 2: {dists['SAN'] - 2}\")\n","sub_path":"python/06.py","file_name":"06.py","file_ext":"py","file_size_in_byte":442,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"7670207","text":"# Copyright (c) Microsoft Corporation\n# Licensed under the MIT License.\n\nfrom .explanation_constants import \\\n ExplanationDashboardInterface, WidgetRequestResponseConstants\nfrom scipy.sparse import issparse\nfrom sklearn.feature_selection import mutual_info_classif\nimport numpy as np\nimport pandas as pd\nfrom lightgbm import LGBMClassifier\nfrom enum import Enum\nimport traceback\nfrom .constants import SKLearn\nfrom .error_handling import _format_exception\nfrom ._input_processing import _serialize_json_safe\n\n\nBIN_THRESHOLD = 8\nCATEGORY1 = \"category1\"\nCATEGORY2 = \"category2\"\nMATRIX = \"matrix\"\nVALUES = \"values\"\nFALSE_COUNT = \"falseCount\"\nCOUNT = \"count\"\nINTERVAL_MIN = \"intervalMin\"\nINTERVAL_MAX = \"intervalMax\"\nMETHOD = \"method\"\nMETHOD_GREATER = \"greater\"\nMETHOD_LESS_AND_EQUAL = \"less and equal\"\nMETHOD_RANGE = \"in the range of\"\nMETHOD_EXCLUDES = \"excludes\"\nMETHOD_INCLUDES = \"includes\"\nTRUE_Y = \"true_y\"\nDIFF = \"diff\"\nSPLIT_INDEX = \"split_index\"\nSPLIT_FEATURE = \"split_feature\"\nFEATURE_NAMES = ExplanationDashboardInterface.FEATURE_NAMES\nROW_INDEX = \"row_index\"\nLEAF_INDEX = \"leaf_index\"\nMETHOD_EQUAL = \"equal\"\n\n\nclass TreeSide(str, Enum):\n \"\"\"Provide model task constants.\n Can be 'classification', 'regression', or 'unknown'.\n\n By default the model domain is inferred if 'unknown',\n but this can be overridden if you specify\n 'classification' or 'regression'.\n \"\"\"\n\n RIGHT_CHILD = 'right_child'\n LEFT_CHILD = 'left_child'\n UNKNOWN = 'unknown'\n\n\nclass ErrorAnalysisDashboardInput:\n def __init__(\n self,\n explanation,\n model,\n dataset,\n true_y,\n classes,\n features,\n locale,\n categorical_features,\n true_y_dataset):\n \"\"\"Initialize the ErrorAnalysis Dashboard Input.\n\n :param explanation: An object that represents an explanation.\n :type explanation: ExplanationMixin\n :param model: An object that represents a model.\n It is assumed that for the classification case\n it has a method of predict_proba() returning\n the prediction probabilities for each\n class and for the regression case a method of predict()\n returning the prediction value.\n :type model: object\n :param dataset: A matrix of feature vector examples\n (# examples x # features), the same samples\n used to build the explanation.\n Will overwrite any set on explanation object already.\n Must have fewer than\n 10000 rows and fewer than 1000 columns.\n :type dataset: numpy.array or list[][] or pandas.DataFrame\n :param true_y: The true labels for the provided explanation.\n Will overwrite any set on explanation object already.\n :type true_y: numpy.array or list[]\n :param classes: The class names.\n :type classes: numpy.array or list[]\n :param features: Feature names.\n :type features: numpy.array or list[]\n :param categorical_features: The categorical feature names.\n :type categorical_features: list[str]\n :param true_y_dataset: The true labels for the provided dataset.\n Only needed if the explanation has a sample of instances from the\n original dataset. Otherwise specify true_y parameter only.\n :type true_y_dataset: numpy.array or list[]\n \"\"\"\n self._model = model\n original_dataset = dataset\n if isinstance(dataset, pd.DataFrame):\n self._dataset = dataset.to_json()\n else:\n self._dataset = dataset\n if true_y_dataset is None:\n self._true_y = true_y\n else:\n self._true_y = true_y_dataset\n self._categorical_features = categorical_features\n self._categories = []\n self._categorical_indexes = []\n self._is_classifier = model is not None\\\n and hasattr(model, SKLearn.PREDICT_PROBA) and \\\n model.predict_proba is not None\n self._dataframeColumns = None\n self.dashboard_input = {}\n # List of explanations, key of explanation type is \"explanation_type\"\n self._mli_explanations = explanation.data(-1)[\"mli\"]\n local_explanation = self._find_first_explanation(\n ExplanationDashboardInterface.MLI_LOCAL_EXPLANATION_KEY)\n global_explanation = self._find_first_explanation(\n ExplanationDashboardInterface.MLI_GLOBAL_EXPLANATION_KEY)\n ebm_explanation = self._find_first_explanation(\n ExplanationDashboardInterface.MLI_EBM_GLOBAL_EXPLANATION_KEY)\n dataset_explanation = self._find_first_explanation(\n ExplanationDashboardInterface.MLI_EXPLANATION_DATASET_KEY)\n\n if hasattr(explanation, 'method'):\n self.dashboard_input[\n ExplanationDashboardInterface.EXPLANATION_METHOD\n ] = explanation.method\n\n predicted_y = None\n feature_length = None\n if dataset_explanation is not None:\n if dataset is None or len(dataset) != len(true_y):\n dataset = dataset_explanation[\n ExplanationDashboardInterface.MLI_DATASET_X_KEY\n ]\n if true_y is None:\n true_y = dataset_explanation[\n ExplanationDashboardInterface.MLI_DATASET_Y_KEY\n ]\n elif len(dataset) != len(true_y):\n dataset = explanation._eval_data\n\n if isinstance(dataset, pd.DataFrame) and hasattr(dataset, 'columns'):\n self._dataframeColumns = dataset.columns\n try:\n list_dataset = self._convert_to_list(dataset)\n except Exception as ex:\n ex_str = _format_exception(ex)\n raise ValueError(\n \"Unsupported dataset type, inner error: {}\".format(ex_str))\n if dataset is not None and model is not None:\n try:\n predicted_y = model.predict(dataset)\n except Exception as ex:\n ex_str = _format_exception(ex)\n msg = \"Model does not support predict method for given\"\n \"dataset type, inner error: {}\".format(\n ex_str)\n raise ValueError(msg)\n try:\n predicted_y = self._convert_to_list(predicted_y)\n except Exception as ex:\n ex_str = _format_exception(ex)\n raise ValueError(\n \"Model prediction output of unsupported type,\"\n \"inner error: {}\".format(ex_str))\n\n if classes is None and hasattr(explanation, 'classes')\\\n and explanation.classes is not None:\n classes = explanation.classes\n if classes is not None:\n classes = self._convert_to_list(classes)\n self.dashboard_input[\n ExplanationDashboardInterface.CLASS_NAMES\n ] = classes\n class_to_index = {k: v for v, k in enumerate(classes)}\n\n if predicted_y is not None:\n # If classes specified, convert predicted_y to\n # numeric representation\n if classes is not None and predicted_y[0] in class_to_index:\n for i in range(len(predicted_y)):\n predicted_y[i] = class_to_index[predicted_y[i]]\n self.dashboard_input[\n ExplanationDashboardInterface.PREDICTED_Y\n ] = predicted_y\n row_length = 0\n if list_dataset is not None:\n row_length, feature_length = np.shape(list_dataset)\n if row_length > 100000:\n raise ValueError(\n \"Exceeds maximum number of rows\"\n \"for visualization (100000)\")\n if feature_length > 1000:\n raise ValueError(\"Exceeds maximum number of features for\"\n \" visualization (1000). Please regenerate the\"\n \" explanation using fewer features or\"\n \" initialize the dashboard without passing a\"\n \" dataset.\")\n self.dashboard_input[\n ExplanationDashboardInterface.TRAINING_DATA\n ] = _serialize_json_safe(list_dataset)\n self.dashboard_input[\n ExplanationDashboardInterface.IS_CLASSIFIER\n ] = self._is_classifier\n\n if true_y is not None and len(true_y) == row_length:\n list_true_y = self._convert_to_list(true_y)\n # If classes specified, convert true_y to numeric representation\n if classes is not None and list_true_y[0] in class_to_index:\n for i in range(len(list_true_y)):\n list_true_y[i] = class_to_index[list_true_y[i]]\n self.dashboard_input[\n ExplanationDashboardInterface.TRUE_Y\n ] = list_true_y\n\n if local_explanation is not None:\n try:\n local_explanation[\"scores\"] = self._convert_to_list(\n local_explanation[\"scores\"])\n if np.shape(local_explanation[\"scores\"])[-1] > 1000:\n raise ValueError(\"Exceeds maximum number of features for \"\n \"visualization (1000). Please regenerate\"\n \" the explanation using fewer features.\")\n local_explanation[\"intercept\"] = self._convert_to_list(\n local_explanation[\"intercept\"])\n # We can ignore perf explanation data.\n # Note if it is added back at any point,\n # the numpy values will need to be converted to python,\n # otherwise serialization fails.\n local_explanation[\"perf\"] = None\n self.dashboard_input[\n ExplanationDashboardInterface.LOCAL_EXPLANATIONS\n ] = local_explanation\n except Exception as ex:\n ex_str = _format_exception(ex)\n raise ValueError(\n \"Unsupported local explanation type,\"\n \"inner error: {}\".format(ex_str))\n if list_dataset is not None:\n local_dim = np.shape(local_explanation[\"scores\"])\n if len(local_dim) != 2 and len(local_dim) != 3:\n raise ValueError(\n \"Local explanation expected to be a 2D or 3D list\")\n if len(local_dim) == 2 and (local_dim[1] != feature_length or\n local_dim[0] != row_length):\n raise ValueError(\n \"Shape mismatch: local explanation\"\n \"length differs from dataset\")\n if len(local_dim) == 3 and (local_dim[2] != feature_length or\n local_dim[1] != row_length):\n raise ValueError(\n \"Shape mismatch: local explanation\"\n \" length differs from dataset\")\n if classes is not None and len(classes) != local_dim[0]:\n raise ValueError(\"Class vector length mismatch:\"\n \"class names length differs from\"\n \"local explanations dimension\")\n if local_explanation is None and global_explanation is not None:\n try:\n global_explanation[\"scores\"] = self._convert_to_list(\n global_explanation[\"scores\"])\n if 'intercept' in global_explanation:\n global_explanation[\"intercept\"] = self._convert_to_list(\n global_explanation[\"intercept\"])\n self.dashboard_input[\n ExplanationDashboardInterface.GLOBAL_EXPLANATION\n ] = global_explanation\n except Exception as ex:\n ex_str = _format_exception(ex)\n raise ValueError(\"Unsupported global explanation type,\"\n \"inner error: {}\".format(ex_str))\n if ebm_explanation is not None:\n try:\n self.dashboard_input[\n ExplanationDashboardInterface.EBM_EXPLANATION\n ] = ebm_explanation\n except Exception as ex:\n ex_str = _format_exception(ex)\n raise ValueError(\n \"Unsupported ebm explanation type: {}\".format(ex_str))\n\n if features is None and hasattr(explanation, 'features')\\\n and explanation.features is not None:\n features = explanation.features\n if features is not None:\n features = self._convert_to_list(features)\n if feature_length is not None and len(features) != feature_length:\n raise ValueError(\"Feature vector length mismatch:\"\n \" feature names length differs\"\n \" from local explanations dimension\")\n self.dashboard_input[FEATURE_NAMES] = features\n if model is not None and hasattr(model, SKLearn.PREDICT_PROBA) \\\n and model.predict_proba is not None and dataset is not None:\n try:\n probability_y = model.predict_proba(dataset)\n except Exception as ex:\n ex_str = _format_exception(ex)\n raise ValueError(\"Model does not support predict_proba method\"\n \" for given dataset type,\"\n \" inner error: {}\".format(ex_str))\n try:\n probability_y = self._convert_to_list(probability_y)\n except Exception as ex:\n ex_str = _format_exception(ex)\n raise ValueError(\n \"Model predict_proba output of unsupported type,\"\n \"inner error: {}\".format(ex_str))\n self.dashboard_input[\n ExplanationDashboardInterface.PROBABILITY_Y\n ] = probability_y\n if locale is not None:\n self.dashboard_input[ExplanationDashboardInterface.LOCALE] = locale\n if self._categorical_features:\n category_dictionary = {}\n features = self.dashboard_input[FEATURE_NAMES]\n self._categorical_indexes = [features.index(feature) for feature\n in self._categorical_features]\n from sklearn.compose import ColumnTransformer\n from sklearn.preprocessing import OrdinalEncoder\n ordinal_enc = OrdinalEncoder()\n ct = ColumnTransformer([('ord', ordinal_enc,\n self._categorical_indexes)],\n remainder='drop')\n self.string_ind_data = ct.fit_transform(original_dataset)\n transformer_categories = ct.transformers_[0][1].categories_\n for category_arr, category_index in zip(transformer_categories,\n self._categorical_indexes):\n category_values = category_arr.tolist()\n self._categories.append(category_values)\n category_dictionary[category_index] = category_values\n self.dashboard_input[\n ExplanationDashboardInterface.CATEGORICAL_MAP\n ] = category_dictionary\n\n def build_cat_bounds_query(self, filter, colname, method):\n bounds = []\n if method == METHOD_EXCLUDES:\n operator = \" != \"\n else:\n operator = \" == \"\n for arg in filter['arg']:\n cat_idx = self._categorical_features.index(colname)\n arg_cat = \"'{}'\".format(str(self._categories[cat_idx][arg]))\n bounds.append(\"`{}`{}{}\".format(colname, operator, arg_cat))\n if method == METHOD_EXCLUDES:\n return ' & '.join(bounds)\n else:\n return ' | '.join(bounds)\n\n def build_query(self, filters):\n queries = []\n for filter in filters:\n if METHOD in filter:\n method = filter[METHOD]\n arg0 = str(filter['arg'][0])\n colname = filter['column']\n if method == METHOD_GREATER:\n queries.append(\"`\" + colname + \"` > \" + arg0)\n elif method == METHOD_LESS_AND_EQUAL:\n queries.append(\"`\" + colname + \"` <= \" + arg0)\n elif method == METHOD_RANGE:\n arg1 = str(filter['arg'][1])\n queries.append(\"`\" + colname + \"` >= \" + arg0 +\n ' & `' + colname + \"` <= \" + arg1)\n elif method == METHOD_INCLUDES or method == METHOD_EXCLUDES:\n query = self.build_cat_bounds_query(filter,\n colname,\n method)\n queries.append(query)\n elif method == METHOD_EQUAL:\n is_categorical = False\n if self._categorical_features:\n is_categorical = colname in self._categorical_features\n if is_categorical:\n cat_idx = self._categorical_features.index(colname)\n arg0i = filter['arg'][0]\n arg_cat = str(self._categories[cat_idx][arg0i])\n queries.append(\"`{}` == '{}'\".format(colname, arg_cat))\n else:\n queries.append(\"`\" + colname + \"` == \" + arg0)\n else:\n raise ValueError(\n \"Unsupported method type: {}\".format(method))\n else:\n cqueries = []\n for composite_filter in filter['compositeFilters']:\n cqueries.append(self.build_query([composite_filter]))\n if filter['operation'] == 'and':\n queries.append('(' + ') & ('.join(cqueries) + ')')\n else:\n queries.append('(' + ') | ('.join(cqueries) + ')')\n return '(' + ') & ('.join(queries) + ')'\n\n def apply_recursive_filter(self, df, filters):\n if filters:\n return df.query(self.build_query(filters))\n else:\n return df\n\n def filter_from_cohort(self, filters, composite_filters,\n feature_names, is_pandas, true_y):\n if is_pandas:\n df = pd.read_json(self._dataset)\n else:\n df = pd.DataFrame(self._dataset, columns=feature_names)\n df[TRUE_Y] = true_y\n df[ROW_INDEX] = np.arange(0, len(true_y))\n df = self.apply_recursive_filter(df, filters)\n df = self.apply_recursive_filter(df, composite_filters)\n return df\n\n def debug_ml(self, features, filters, composite_filters):\n try:\n interface = ExplanationDashboardInterface\n feature_names = self.dashboard_input[interface.FEATURE_NAMES]\n # Fit a surrogate model on errors\n surrogate = LGBMClassifier(n_estimators=1, max_depth=3)\n is_pandas = False\n if isinstance(self._dataset, str):\n is_pandas = True\n true_y = self._true_y\n filtered_df = self.filter_from_cohort(filters,\n composite_filters,\n feature_names,\n is_pandas,\n true_y)\n row_index = filtered_df[ROW_INDEX]\n true_y = filtered_df[TRUE_Y]\n input_data = filtered_df.drop(columns=[TRUE_Y, ROW_INDEX])\n if is_pandas:\n true_y = true_y.to_numpy()\n else:\n input_data = input_data.to_numpy()\n diff = self._model.predict(input_data) != true_y\n if not isinstance(diff, np.ndarray):\n diff = np.array(diff)\n indexes = []\n for feature in features:\n indexes.append(feature_names.index(feature))\n if is_pandas:\n input_data = input_data.to_numpy()\n cat_ind_reindexed = []\n categories_reindexed = []\n if self._categorical_features:\n # Inplace replacement of columns\n for idx, c_i in enumerate(self._categorical_indexes):\n input_data[:, c_i] = self.string_ind_data[row_index, idx]\n dataset_sub_features = input_data[:, indexes]\n dataset_sub_names = np.array(feature_names)[np.array(indexes)]\n dataset_sub_names = list(dataset_sub_names)\n if self._categorical_features:\n for c_index, feature in enumerate(self._categorical_features):\n try:\n index_sub = dataset_sub_names.index(feature)\n except ValueError:\n continue\n cat_ind_reindexed.append(index_sub)\n categories_reindexed.append(self._categories[c_index])\n surrogate.fit(dataset_sub_features, diff,\n categorical_feature=cat_ind_reindexed)\n else:\n surrogate.fit(dataset_sub_features, diff)\n filtered_indexed_df = pd.DataFrame(dataset_sub_features,\n columns=dataset_sub_names)\n filtered_indexed_df[DIFF] = diff\n model_json = surrogate._Booster.dump_model()\n tree_structure = model_json[\"tree_info\"][0]['tree_structure']\n max_split_index = self.get_max_split_index(tree_structure) + 1\n json_tree = self.traverse(filtered_indexed_df,\n tree_structure,\n max_split_index,\n (categories_reindexed,\n cat_ind_reindexed),\n [], dataset_sub_names)\n return {\n WidgetRequestResponseConstants.DATA: json_tree\n }\n except Exception as e:\n print(e)\n traceback.print_exc()\n return {\n WidgetRequestResponseConstants.ERROR:\n \"Failed to generate json tree representation\",\n WidgetRequestResponseConstants.DATA: []\n }\n\n def matrix(self, features, filters, composite_filters):\n try:\n if features[0] is None and features[1] is None:\n return {WidgetRequestResponseConstants.DATA: []}\n interface = ExplanationDashboardInterface\n feature_names = self.dashboard_input[interface.FEATURE_NAMES]\n is_pandas = False\n if isinstance(self._dataset, str):\n is_pandas = True\n true_y = self._true_y\n filtered_df = self.filter_from_cohort(filters,\n composite_filters,\n feature_names,\n is_pandas,\n true_y)\n true_y = filtered_df[TRUE_Y]\n input_data = filtered_df.drop(columns=[TRUE_Y, ROW_INDEX])\n if is_pandas:\n true_y = true_y.to_numpy()\n else:\n input_data = input_data.to_numpy()\n diff = self._model.predict(input_data) != true_y\n if not isinstance(diff, np.ndarray):\n diff = np.array(diff)\n indexes = []\n for feature in features:\n if feature is None:\n continue\n indexes.append(feature_names.index(feature))\n if is_pandas:\n input_data = input_data.to_numpy()\n dataset_sub_features = input_data[:, indexes]\n dataset_sub_names = np.array(feature_names)[np.array(indexes)]\n df = pd.DataFrame(dataset_sub_features, columns=dataset_sub_names)\n df_err = df.copy()\n df_err[DIFF] = diff\n df_err = df_err[df_err[DIFF]]\n # construct json matrix\n json_matrix = []\n if len(dataset_sub_names) == 2:\n feat1 = dataset_sub_names[0]\n feat2 = dataset_sub_names[1]\n unique_count1 = len(df[feat1].unique())\n unique_count2 = len(df[feat2].unique())\n f1_is_cat = False\n f2_is_cat = False\n if self._categorical_features is not None:\n f1_is_cat = feat1 in self._categorical_features\n f2_is_cat = feat2 in self._categorical_features\n if unique_count1 > BIN_THRESHOLD and not f1_is_cat:\n tabdf1, bins = pd.cut(df[feat1], BIN_THRESHOLD,\n retbins=True)\n tabdf1_err = pd.cut(df_err[feat1], bins)\n categories1 = tabdf1.cat.categories\n else:\n tabdf1 = df[feat1]\n tabdf1_err = df_err[feat1]\n categories1 = np.unique(tabdf1.to_numpy(),\n return_counts=True)[0]\n if unique_count2 > BIN_THRESHOLD and not f2_is_cat:\n tabdf2, bins = pd.cut(df[feat2], BIN_THRESHOLD,\n retbins=True)\n tabdf2_err = pd.cut(df_err[feat2], bins)\n categories2 = tabdf2.cat.categories\n else:\n tabdf2 = df[feat2]\n tabdf2_err = df_err[feat2]\n categories2 = np.unique(tabdf2.to_numpy(),\n return_counts=True)[0]\n matrix_total = pd.crosstab(tabdf1, tabdf2, rownames=[feat1],\n colnames=[feat2])\n matrix_error = pd.crosstab(tabdf1_err, tabdf2_err,\n rownames=[feat1], colnames=[feat2])\n json_matrix = self.json_matrix_2d(categories1, categories2,\n matrix_total, matrix_error)\n else:\n feat1 = dataset_sub_names[0]\n unique_count1 = len(df[feat1].unique())\n f1_is_cat = False\n if self._categorical_features is not None:\n f1_is_cat = feat1 in self._categorical_features\n if unique_count1 > BIN_THRESHOLD and not f1_is_cat:\n cutdf, bins = pd.cut(df[feat1], BIN_THRESHOLD,\n retbins=True)\n bin_range = range(BIN_THRESHOLD)\n catr = cutdf.cat.rename_categories(bin_range)\n catn, counts = np.unique(catr.to_numpy(),\n return_counts=True)\n # fix counts to include skipped categories\n fix_counts = []\n counts_idx = 0\n for idx, catdf in enumerate(cutdf.cat.categories):\n if idx not in catn:\n fix_counts.append(0)\n else:\n fix_counts.append(counts[counts_idx])\n counts_idx += 1\n counts = fix_counts\n cutdf_err = pd.cut(df_err[feat1], bins)\n catr_err = cutdf_err.cat.rename_categories(bin_range)\n val_err, counts_err = np.unique(catr_err.to_numpy(),\n return_counts=True)\n val_err = cutdf_err.cat.categories[val_err]\n json_matrix = self.json_matrix_1d(cutdf.cat.categories,\n val_err, counts,\n counts_err)\n else:\n values, counts = np.unique(df[feat1].to_numpy(),\n return_counts=True)\n val_err, counts_err = np.unique(df_err[feat1].to_numpy(),\n return_counts=True)\n json_matrix = self.json_matrix_1d(values, val_err, counts,\n counts_err)\n return {\n WidgetRequestResponseConstants.DATA: json_matrix\n }\n except Exception as e:\n print(e)\n traceback.print_exc()\n return {\n WidgetRequestResponseConstants.ERROR:\n \"Failed to generate json matrix representation\",\n WidgetRequestResponseConstants.DATA: []\n }\n\n def importances(self):\n try:\n interface = ExplanationDashboardInterface\n feature_names = self.dashboard_input[interface.FEATURE_NAMES]\n is_pandas = False\n if isinstance(self._dataset, str):\n is_pandas = True\n if is_pandas:\n input_data = pd.read_json(self._dataset)\n else:\n input_data = pd.DataFrame(self._dataset,\n columns=feature_names)\n diff = self._model.predict(input_data) != self._true_y\n if is_pandas:\n input_data = input_data.to_numpy()\n if self._categorical_features:\n # Inplace replacement of columns\n for idx, c_i in enumerate(self._categorical_indexes):\n input_data[:, c_i] = self.string_ind_data[:, idx]\n # compute the feature importances using mutual information\n scores = mutual_info_classif(input_data, diff).tolist()\n return {\n WidgetRequestResponseConstants.DATA: scores\n }\n except Exception as e:\n print(e)\n traceback.print_exc()\n return {\n WidgetRequestResponseConstants.ERROR:\n \"Failed to generate feature importances\",\n WidgetRequestResponseConstants.DATA: []\n }\n\n def json_matrix_2d(self, categories1, categories2, matrix_counts,\n matrix_err_counts):\n json_matrix = []\n json_category1 = []\n json_category1_min_interval = []\n json_category1_max_interval = []\n for row_index in range(len(categories1)):\n json_matrix_row = []\n cat1 = categories1[row_index]\n if isinstance(categories1, pd.IntervalIndex):\n json_category1_min_interval.append(cat1.left)\n json_category1_max_interval.append(cat1.right)\n json_category1.append(str(cat1))\n for col_index in range(len(categories2)):\n cat2 = categories2[col_index]\n index_exists_err = cat1 in matrix_err_counts.index\n col_exists_err = cat2 in matrix_err_counts.columns\n false_count = 0\n if index_exists_err and col_exists_err:\n false_count = int(matrix_err_counts.loc[cat1, cat2])\n index_exists = cat1 in matrix_counts.index\n col_exists = cat2 in matrix_counts.columns\n total_count = 0\n if index_exists and col_exists:\n total_count = int(matrix_counts.loc[cat1, cat2])\n json_matrix_row.append({\n FALSE_COUNT: false_count,\n COUNT: total_count\n })\n json_matrix.append(json_matrix_row)\n\n json_category2 = []\n json_category2_min_interval = []\n json_category2_max_interval = []\n for cat2 in categories2:\n if isinstance(categories2, pd.IntervalIndex):\n json_category2_min_interval.append(cat2.left)\n json_category2_max_interval.append(cat2.right)\n json_category2.append(str(cat2))\n category1 = {VALUES: json_category1,\n INTERVAL_MIN: json_category1_min_interval,\n INTERVAL_MAX: json_category1_max_interval}\n category2 = {VALUES: json_category2,\n INTERVAL_MIN: json_category2_min_interval,\n INTERVAL_MAX: json_category2_max_interval}\n return {MATRIX: json_matrix, CATEGORY1: category1,\n CATEGORY2: category2}\n\n def json_matrix_1d(self, categories, values_err, counts, counts_err):\n json_matrix = []\n json_matrix_row = []\n for col_idx in range(len(categories)):\n cat = categories[col_idx]\n false_count = 0\n if cat in values_err:\n index_err = list(values_err).index(cat)\n false_count = int(counts_err[index_err])\n json_matrix_row.append({\n FALSE_COUNT: false_count,\n COUNT: int(counts[col_idx])\n })\n json_matrix.append(json_matrix_row)\n json_category = []\n json_category_min_interval = []\n json_category_max_interval = []\n for cat in categories:\n if isinstance(categories, pd.IntervalIndex):\n json_category_min_interval.append(cat.left)\n json_category_max_interval.append(cat.right)\n json_category.append(str(cat))\n category1 = {VALUES: json_category,\n INTERVAL_MIN: json_category_min_interval,\n INTERVAL_MAX: json_category_max_interval}\n return {MATRIX: json_matrix, CATEGORY1: category1}\n\n def get_max_split_index(self, tree):\n if SPLIT_INDEX in tree:\n max_index = tree[SPLIT_INDEX]\n index1 = self.get_max_split_index(tree[TreeSide.LEFT_CHILD])\n index2 = self.get_max_split_index(tree[TreeSide.RIGHT_CHILD])\n return max(max(max_index, index1), index2)\n else:\n return 0\n\n def traverse(self, df, tree, max_split_index,\n categories, json, feature_names, parent=None,\n side=TreeSide.UNKNOWN):\n if SPLIT_INDEX in tree:\n nodeid = tree[SPLIT_INDEX]\n elif LEAF_INDEX in tree:\n nodeid = max_split_index + tree[LEAF_INDEX]\n else:\n nodeid = 0\n\n # write current node to json\n json, df = self.node_to_json(df, tree, nodeid, categories,\n json, feature_names, parent,\n side)\n\n # write children to json\n if 'leaf_value' not in tree:\n left_child = tree[TreeSide.LEFT_CHILD]\n right_child = tree[TreeSide.RIGHT_CHILD]\n json = self.traverse(df, left_child, max_split_index,\n categories, json, feature_names,\n tree, TreeSide.LEFT_CHILD)\n json = self.traverse(df, right_child, max_split_index,\n categories, json, feature_names,\n tree, TreeSide.RIGHT_CHILD)\n return json\n\n def node_to_json(self, df, tree, nodeid, categories, json,\n feature_names, parent=None,\n side=TreeSide.UNKNOWN):\n p_node_name = None\n condition = None\n arg = None\n method = None\n parentid = None\n if parent is not None:\n parentid = int(parent[SPLIT_INDEX])\n p_node_name = feature_names[parent[SPLIT_FEATURE]]\n parent_threshold = parent['threshold']\n parent_decision_type = parent['decision_type']\n if side == TreeSide.RIGHT_CHILD:\n if parent_decision_type == '<=':\n method = \"less and equal\"\n arg = float(parent_threshold)\n condition = \"{} <= {:.2f}\".format(p_node_name,\n parent_threshold)\n query = \"`\" + p_node_name + \"` <= \" + str(parent_threshold)\n df = df.query(query)\n elif parent_decision_type == '==':\n method = METHOD_INCLUDES\n arg = [float(i) for i in parent_threshold.split('||')]\n categorical_values = categories[0]\n categorical_indexes = categories[1]\n thresholds = []\n catcoli = categorical_indexes.index(parent[SPLIT_FEATURE])\n catvals = categorical_values[catcoli]\n for argi in arg:\n encoded_val = catvals[int(argi)]\n thresholds.append(encoded_val)\n threshold_str = \" | \".join(thresholds)\n condition = \"{} == {}\".format(p_node_name,\n threshold_str)\n query = []\n for argi in arg:\n query.append(\"`\" + p_node_name + \"` == \" + str(argi))\n df = df.query(\" | \".join(query))\n elif side == TreeSide.LEFT_CHILD:\n if parent_decision_type == '<=':\n method = \"greater\"\n arg = float(parent_threshold)\n condition = \"{} > {:.2f}\".format(p_node_name,\n parent_threshold)\n query = \"`\" + p_node_name + \"` > \" + str(parent_threshold)\n df = df.query(query)\n elif parent_decision_type == '==':\n method = METHOD_EXCLUDES\n arg = [float(i) for i in parent_threshold.split('||')]\n categorical_values = categories[0]\n categorical_indexes = categories[1]\n thresholds = []\n catcoli = categorical_indexes.index(parent[SPLIT_FEATURE])\n catvals = categorical_values[catcoli]\n for argi in arg:\n encoded_val = catvals[int(argi)]\n thresholds.append(encoded_val)\n threshold_str = \" | \".join(thresholds)\n condition = \"{} != {}\".format(p_node_name,\n threshold_str)\n query = []\n for argi in arg:\n query.append(\"`\" + p_node_name + \"` != \" + str(argi))\n df = df.query(\" & \".join(query))\n error = df[DIFF].values.sum()\n total = df.shape[0]\n success = total - error\n if SPLIT_FEATURE in tree:\n node_name = feature_names[tree[SPLIT_FEATURE]]\n else:\n node_name = None\n json.append({\n \"arg\": arg,\n \"badFeaturesRowCount\": 0,\n \"condition\": condition,\n \"error\": float(error),\n \"id\": int(nodeid),\n METHOD: method,\n \"nodeIndex\": int(nodeid),\n \"nodeName\": node_name,\n \"parentId\": parentid,\n \"parentNodeName\": p_node_name,\n \"pathFromRoot\": \"\",\n \"size\": float(success + error),\n \"sourceRowKeyHash\": \"hashkey\",\n \"success\": float(success)\n })\n return json, df\n\n def on_predict(self, data):\n try:\n if self._dataframeColumns is not None:\n data = pd.DataFrame(data, columns=self._dataframeColumns)\n if (self._is_classifier):\n model_pred_proba = self._model.predict_proba(data)\n prediction = self._convert_to_list(model_pred_proba)\n else:\n model_predict = self._model.predict(data)\n prediction = self._convert_to_list(model_predict)\n return {\n WidgetRequestResponseConstants.DATA: prediction\n }\n except Exception:\n return {\n WidgetRequestResponseConstants.ERROR:\n \"Model threw exception while predicting...\",\n WidgetRequestResponseConstants.DATA: []\n }\n\n def _convert_to_list(self, array):\n if issparse(array):\n if array.shape[1] > 1000:\n raise ValueError(\"Exceeds maximum number of \"\n \"features for visualization (1000)\")\n return array.toarray().tolist()\n if (isinstance(array, pd.DataFrame)):\n return array.values.tolist()\n if (isinstance(array, np.ndarray)):\n return array.tolist()\n return array\n\n def _find_first_explanation(self, key):\n interface = ExplanationDashboardInterface\n explanation_type_key = interface.MLI_EXPLANATION_TYPE_KEY\n new_array = [explanation for explanation\n in self._mli_explanations\n if explanation[explanation_type_key] == key]\n if len(new_array) > 0:\n return new_array[0][\"value\"]\n return None\n","sub_path":"raiwidgets/raiwidgets/error_analysis_dashboard_input.py","file_name":"error_analysis_dashboard_input.py","file_ext":"py","file_size_in_byte":41262,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"449640913","text":"from data_process import GREText\n\n# 导入数据\ndata_path=r'../data/rawdata_reading.txt'\ntxt=GREText(data_path)\n\n#输出排序后的单词\nprint(txt.sort_word())\n#查找单词出现频率\nprint(txt.lookup('success'))\n#保存数据\ntxt.save_json('../data/test.json')\ntxt.save_txt('../data/test.txt')\n#常用搭配\nprint(txt.text.collocations())\n#输出单词表\nprint(txt.get_word())\n#词频图\n#txt.plot_freq(30)\n#词云\ntxt.word_cloud()\n","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":439,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"117508461","text":"'''\nGiven an integer N, remove consecutive repeated digits from it.\n\nInput:\n\nThe first line of input contains an integer T denoting the number of test cases. Then T test cases follow. The first line of each test case contains the integer N.\n\nOutput:\n\nPrint the number after removing consecutive digits. Print the answer for each test case in a new line.\n\n\nConstraints:\n\n1<= T <=100\n\n1<= N <=1018\n\n\nExample:\n\nInput:\n\n1\n\n12224\n\nOutput:\n\n124\n'''\n\nfor _ in range(int(input())):\n \n t = input()\n s = list(map(int,t))\n n = len(s)\n ind1 = 0\n ind2 = 1\n \n for i in range(n-1):\n x = s[ind1]\n y = s[ind2]\n \n if x == y:\n s.pop(ind2)\n else:\n ind1 = ind2\n ind2 = ind2 + 1\n \n print(*s,sep=\"\")\n ","sub_path":"Geeks For Geeks/Stack/removeRepeatedDigits.py","file_name":"removeRepeatedDigits.py","file_ext":"py","file_size_in_byte":791,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"490460394","text":"import sys\nsys.path.append('../common-services-back-end')\n\nfrom database_util import *\nimport logging\nimport json\nfrom http.server import BaseHTTPRequestHandler, HTTPServer\nfrom http import HTTPStatus\nfrom urllib.parse import urlparse, parse_qs\n\n# Class Logger we can use for debugging our Python service. You can add an additional parameter here for\n# specifying a log file if you want to see a stream of log data in one file.\nlogging.basicConfig(level=logging.DEBUG)\n\n\n# BaseHTTPRequestHandler is a class from the http.server python module. http.server is a simple\n# module used for creating application servers. BaseHTTPRequestHandler will help us respond to requests that arrive\n# at our server, matching a specified hostname and port. For additional documentation on this module,\n# you can read: https://docs.python.org/3/library/http.server.html\nclass BaseAppService(BaseHTTPRequestHandler):\n # HTTP Response code dictionary constant we can reuse inside our responses back to the client. Typically this\n # would be in a configuration file where you store constants you repeatedly use throughout your services.\n HTTP_STATUS_RESPONSE_CODES = {\n 'OK': HTTPStatus.OK,\n 'FORBIDDEN': HTTPStatus.FORBIDDEN,\n 'NOT_FOUND': HTTPStatus.NOT_FOUND,\n }\n\n # Here's how you extract GET parameters from a URL entered by a client.\n def extract_GET_parameters(self):\n path = self.path\n parsedPath = urlparse(path)\n paramsDict = parse_qs(parsedPath.query)\n logging.info('GET parameters received: ' + json.dumps(paramsDict, indent=4, sort_keys=True))\n return paramsDict\n\n # Here's how we extract the POST body of data attached to the request by the client.\n def extract_POST_Body(self):\n # The content-length HTTP header is where our POST data will be in the request. So we'll need to\n # read the data using an IO input buffer stream built into the http.server module.\n postBodyLength = int(self.headers['content-length'])\n postBodyString = self.rfile.read(postBodyLength)\n postBodyDict = json.loads(postBodyString)\n logging.info('POST Body received: ' + json.dumps(postBodyDict, indent=4, sort_keys=True))\n return postBodyDict\n\n # The do_GET(self) function is how we respond to GET requests from clients.\n def do_GET(self):\n # self.path here will return us the http request path entered by the client.\n path = self.path\n # Extract the GET parameters associated with this HTTP request and store them in a python dictionary\n paramsDict = self.extract_GET_parameters()\n status = self.HTTP_STATUS_RESPONSE_CODES['NOT_FOUND'].value\n responseBody = {}\n\n # This is a root URI or block of code that will be executed when client requests the address:\n # http://localhost:8082.\n if path == '/':\n status = self.HTTP_STATUS_RESPONSE_CODES['OK'].value\n responseBody['data'] = 'We-Go Vehicle API'\n\n elif '/supply-back-end/vehicle_api/v1/vehicles' in path:\n database = DatabaseUtil()\n try:\n vehicles = database.read_vehicles()\n responseBody['data'] = vehicles\n responseBody['status'] = 'Success'\n status = self.HTTP_STATUS_RESPONSE_CODES['OK'].value\n except Exception as e:\n print(e)\n finally:\n database.close_connection()\n\n elif '/supply-back-end/vehicle_api/v1/vehicle' in path:\n vin = paramsDict['vin']\n database = DatabaseUtil()\n try:\n vehicle = database.read_vehicle(vin)\n responseBody['data'] = vehicle\n responseBody['status'] = 'Success'\n status = self.HTTP_STATUS_RESPONSE_CODES['OK'].value\n except Exception as e:\n print(e)\n finally:\n database.close_connection()\n\n elif '/supply-back-end/vehicle_api/v1/available_vehicle' in path:\n database = DatabaseUtil()\n vehicle = database.get_available_vehicle()\n if vehicle == \"\":\n responseBody['data'] = 'None'\n else:\n responseBody['data'] = vehicle\n responseBody['status'] = 'Success'\n status = self.HTTP_STATUS_RESPONSE_CODES['OK'].value\n\n elif '/supply-back-end/vehicle_api/v1/vehicle_location' in path:\n vin = paramsDict['vin']\n database = DatabaseUtil()\n try:\n location = database.read_vehicle_location(vin)\n responseBody['data'] = location\n responseBody['status'] = 'Success'\n status = self.HTTP_STATUS_RESPONSE_CODES['OK'].value\n except Exception as e:\n print(e)\n responseBody['status'] = 'Success'\n finally:\n database.close_connection()\n\n # This will add a response header to the header buffer. Here, we are simply sending back\n # an HTTP response header with an HTTP status code to the client.\n self.send_response(status)\n # This will add a header to the header buffer included in our HTTP response. Here we are specifying\n # the data Content-type of our response from the server to the client.\n self.send_header(\"Content-type\", \"text/html\")\n # The end_headers method will close the header buffer, indicating that we're not sending\n # any more headers back to the client after the line below.\n self.end_headers()\n # Convert the Key-value python dictionary into a string which we'll use to respond to this request\n response = json.dumps(responseBody)\n logging.info('Response: ' + response)\n # Fill the output stream with our encoded response string which will be returned to the client.\n # The wfile.write() method will only accept bytes data.\n byteStringResponse = response.encode('utf-8')\n self.wfile.write(byteStringResponse)\n\n # The do_POST(self) function is how we respond to POST requests from clients.\n def do_POST(self):\n path = self.path\n # Extract the POST body data from the HTTP request, and store it into a Python\n # dictionary we can utilize inside of any of our POST endpoints.\n postBody = self.extract_POST_Body()\n status = self.HTTP_STATUS_RESPONSE_CODES['NOT_FOUND'].value\n # 1. Access POST parameters using your postBody\n responseBody = {}\n if path == '/supply-back-end/vehicle_api/v1/vehicle':\n parameters = postBody\n database = DatabaseUtil()\n try:\n database.insert_vehicle(parameters)\n responseBody['status'] = 'Success'\n status = self.HTTP_STATUS_RESPONSE_CODES['OK'].value\n except Exception as e:\n responseBody['status'] = 'Failed'\n print(e)\n finally:\n database.close_connection()\n\n elif path == '/supply-back-end/vehicle_api/v1/vehicle_location':\n parameters = postBody\n print('Location post', parameters)\n vin = parameters['vin']\n\n database = DatabaseUtil()\n try:\n database.update_vehicle_location(vin, parameters['location'])\n responseBody['status'] = 'Success'\n responseBody['data'] = \"location updated for \" + str(vin)\n status = self.HTTP_STATUS_RESPONSE_CODES['OK'].value\n except Exception as e:\n responseBody['status'] = 'Failed'\n print(e)\n finally:\n database.close_connection()\n\n self.send_response(status)\n self.send_header(\"Content-type\", \"text/html\")\n self.end_headers()\n\n # When using the json.dumps() method, you may encounter data types which aren't easily serializable into\n # a string. When working with these types of data you can include an additional parameters in the dumps()\n # method, 'default=str' to let the serializer know to convert to a string when it encounters a data type\n # it doesn't automatically know how to convert.\n response = json.dumps(responseBody, indent=4, sort_keys=True, default=str)\n logging.info('Response: ' + response)\n byteStringResponse = response.encode('utf-8')\n self.wfile.write(byteStringResponse)\n\n\n# Turn the application server on at port 8082 on localhost and fork the process.\nif __name__ == '__main__':\n hostName = \"localhost\"\n # Ports are part of a socket connection made between a server and a client. Ports 0-1023 are\n # reserved for common TCP/IP applications and shouldn't be used here. Communicate with your\n # DevOps member to find out which port you should be running your application off of.\n serverPort = 8081\n appServer = HTTPServer((hostName, serverPort), BaseAppService)\n logging.info('Server started http://%s:%s' % (hostName, serverPort))\n\n # Start the server and fork it. Use 'Ctrl + c' command to kill this process when running it in the foreground\n # on your terminal.\n try:\n appServer.serve_forever()\n except KeyboardInterrupt:\n pass\n\n appServer.server_close()\n logging.info('Server stopped')\n","sub_path":"Supply-back-end/Supply-back-end/VehicleAPI.py","file_name":"VehicleAPI.py","file_ext":"py","file_size_in_byte":9352,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"47870574","text":"from argparse import Namespace\nimport os\nimport asyncio\nimport shutil\nimport csv\nimport sys\n\nfrom utils.timer import Timer\n\nfrom utils.check_requirements import check_requirements\n\n\nasync def init():\n check_requirements()\n\n # Imports need to be done after the requirements are checked / installed\n from utils.init_argparse import init_argparse\n from utils.utils import get_nodes\n from utils.colorstr import colorstr\n from utils.timer import Timer\n from utils.check_requirements import install_lastools\n from utils.clean_up import clean_up\n\n # LAStools is required to split pointclouds on devices with lower memory\n install_lastools()\n\n benchmark_timer = Timer(text=colorstr('yellow', 'bold',\n \"Benchmark completed in {:0.2f} seconds, results saved to benchmark.csv\"))\n benchmark_timer.start()\n parser = init_argparse()\n\n args = parser.parse_args()\n\n # Benchmarking will take all available nodes N and run the main loop for 1 to N nodes\n # It will also output the benchmarking results to a CSV file 'benchmark.csv' \n if args.benchmark:\n nodes = get_nodes()\n file = open('benchmark.csv', 'w', newline='')\n writer = csv.writer(file)\n writer.writerow([\"Node Count\", \"Remote Files\", \"Program Time\", \"Setup Time\",\n \"Split Time\", \"Transfer Time\", \"Processing Time\", \"Retrieval Time\", \"Merge Time\"])\n file.flush()\n for i in range(1, len(nodes)+1):\n print(colorstr('yellow', 'bold',\n f\"Starting benchmark loop {i} of {len(nodes)}\"))\n\n timers, file_count = await main(args, main_nodes=nodes[:i])\n writer.writerow([i, file_count, timers[\"program_timer\"], timers[\"setup_timer\"], timers[\"split_timer\"],\n timers[\"transfer_timer\"], timers[\"processing_timer\"], timers[\"retrieval_timer\"], timers[\"merge_timer\"]])\n file.flush()\n file.close()\n benchmark_timer.stop()\n\n else:\n await main(args)\n\n\nasync def main(args: Namespace, main_nodes=None):\n\n from utils.file_utils import file_split, process_files, merge, check_output_dir\n from utils.utils import get_nodes, transfer_files\n from utils.colorstr import colorstr\n from utils.clean_up import clean_up, async_clean_up\n import atexit\n\n timers = {}\n\n program_timer = Timer(text=colorstr(\n 'blue', 'bold', \"Done! Program completed in {:0.2f} seconds\"))\n program_timer.start()\n print(\"Starting program timer\\n\")\n\n files = process_files(args.files)\n\n if main_nodes is None:\n nodes = get_nodes()\n else:\n nodes = main_nodes\n\n if args.benchmark:\n overwrite_output = True\n else:\n args.output, overwrite_output = check_output_dir(args.output)\n\n setup_nodes = []\n\n setup_timer = Timer(\n text=colorstr('blue', \"Setup of nodes completed in {:.2f} seconds\"))\n setup_timer.start()\n results = await asyncio.gather(*[node.setup(use_cloud_compare=args.cloud_compare) for node in nodes])\n timers[\"setup_timer\"] = setup_timer.stop()\n\n for i in range(len(results)):\n if results[i]:\n setup_nodes.append(nodes[i])\n\n print(\"----------------------------------\")\n if len(setup_nodes) == 0:\n print(\n colorstr('red', 'bold', '\\nNo nodes where set up correctly, exiting\\n'))\n else:\n if len(setup_nodes) != len(nodes):\n if (args.benchmark):\n print(colorstr(\n 'red', 'bold', 'Cannot benchmark without all available nodes, please check your node setup'))\n sys.exit(1)\n print(\n f\"Not all nodes set up correctly,\\ncontinuing with {len(setup_nodes)} node(s): {[node.host for node in setup_nodes]}\\n\")\n\n print('Splitting files locally...')\n split_timer = Timer(text=colorstr(\n 'blue', \"File(s) split in {:0.2f} seconds\"))\n split_timer.start()\n paths = file_split(files, node_count=len(\n setup_nodes), tile_length=args.tile_length)\n timers[\"split_timer\"] = split_timer.stop()\n\n # Now that the splits have been completed, we want to register a cleanup function in case the function fails\n # If loops have occured previously we want to unregister the previous clean up function\n if args.benchmark and len(main_nodes) > 1:\n atexit.unregister(clean_up)\n atexit.register(clean_up, paths, setup_nodes)\n\n print('Copying files to remote nodes...')\n transfer_timer = Timer(\n text=colorstr('blue', \"Copying from local to nodes completed in {:.2f} seconds\"))\n transfer_timer.start()\n await transfer_files(files, args.pipeline, setup_nodes)\n timers[\"transfer_timer\"] = transfer_timer.stop()\n\n pipeline = args.pipeline.split('/')[-1]\n\n print(\"\\nStarting remote execution...\")\n processing_timer = Timer(text=colorstr(\n 'blue', \"All remote processing completed in {:0.2f} seconds\"))\n processing_timer.start()\n await asyncio.gather(*[node.remote_exec(pipeline) for node in setup_nodes])\n timers[\"processing_timer\"] = processing_timer.stop()\n\n # If the user has confirmed they want to overwrite output, the folder will be recursively removed\n # This is done at the end \n print(\"\\nRetrieving remote files...\")\n if overwrite_output:\n shutil.rmtree(args.output)\n\n # If the directory already exists, os.mkdir will silently fail\n os.mkdir(args.output)\n\n retrieval_timer = Timer(\n text=colorstr('blue', \"Copying from nodes to local completed in {:.2f} seconds\"))\n retrieval_timer.start()\n await asyncio.gather(*[node.get(args.output) for node in setup_nodes])\n timers[\"retrieval_timer\"] = retrieval_timer.stop()\n\n print(\"Merging...\")\n merge_timer = Timer(text=colorstr(\n 'blue', \"File(s) merged in {:0.2f} seconds\"))\n merge_timer.start()\n merge(args.output, files)\n timers[\"merge_timer\"] = merge_timer.stop()\n\n timers[\"program_timer\"] = program_timer.stop()\n\n # Cleanup in between runs instead of just at exit\n if args.benchmark:\n await async_clean_up(paths, nodes)\n \n return (timers, len(paths))\n\nif __name__ == '__main__':\n try:\n # An async functions is requried to wrap any async calls which is why we use an init functions instead of initialising here.\n asyncio.run(init())\n # Handling keyboard interupts prevents unwanted output when cancelling the operation.\n except KeyboardInterrupt:\n print('Cancelling')\n sys.exit(1)\n","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":6737,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"587695202","text":"#!/usr/bin/python\n# -*- coding: UTF-8 -*-\n\nimport socket\nimport os\ns = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n\ndef GetServerIP():\n\tServer_IP = input(\"The Server IP >>> \")\n\treturn Server_IP\ndef GetServerPort():\n\tServerPort = input(\"The Server Port >>> \")\n\treturn ServerPort\ndef ifMessage(me):\n\tif me == \"com exit\":\n\t\ts.close()\n\t\texit(1)\ndef SetClientName():\n\tname = input(\"What is you name >>> \")\n\treturn name\n\nif os.path.exists(r'Client.ini'):\n\tclient_name = fileini = open('Client.ini','r').read()\n\tb_client_name = bytes(client_name, 'utf-8')\nelse:\n\tfileini = open(\"Client.ini\",\"w\")\n\tcname = SetClientName()\n\tfileini.write(cname)\n\nip = GetServerIP()\nport = GetServerPort()\nnport = int(port)\nos.system(\"cls\")\n\nwhile True:\n\tdata = input(\"Messages >>> \")\n\tifMessage(data)\n\tsenddate = bytes(data, 'utf-8')\n\ts.sendto(b_client_name+b':'+senddate, (ip, nport))","sub_path":"Client.py","file_name":"Client.py","file_ext":"py","file_size_in_byte":864,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"291717652","text":"\"\"\"\nactor-criticのテスト\nグリッドワールドでの最適経路問題\n\"\"\"\n\nimport random\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport Task\nimport model\nimport sys\n\n# 設定\nNUM_ACTION = 5\n\nBOARD_ROW = 7\nBOARD_COL = 7\n\nSTART_STATE = 0\nREWARD_STATE = (BOARD_COL * BOARD_ROW) - 1\n\nSIMULAITON_TIMES = 1\nEPISODE_TIMES = 5000\nlearning_rate = 0.1\ndiscount_rate = 0.9\n\n\nenv = Task.GlidWorld(BOARD_ROW, BOARD_COL, START_STATE, REWARD_STATE)\n\nplayer = model.actor_critic(num_state=env.get_num_state(), num_action=env.get_num_action())\n\n\ndef play_task():\n reward_graph = np.zeros(EPISODE_TIMES)\n step_graph = np.zeros(EPISODE_TIMES)\n\n for n_simu in range(SIMULAITON_TIMES):\n player.init_params()\n\n # sys.stdout.write(\"\\r%s/%s\" % (str(n_simu), str(SIMULAITON_TIMES-1)))\n\n # GRCのトレーニング\n for n_epi in range(EPISODE_TIMES):\n # print(\"n_epi:{}\".format(n_epi))\n sys.stdout.write(\"\\r%s/%s\" % (str(n_epi), str(EPISODE_TIMES-1)))\n current_state = env.get_start_state()\n step = 0\n while True:\n \n current_action = player.serect_action(current_state)\n env.evaluate_next_state(current_action, current_state)\n next_state = env.get_next_state()\n\n reward = env.evaluate_reward(next_state)\n\n reward_graph[n_epi] += reward\n\n player.update(current_state, current_action, reward, next_state)\n\n current_state = next_state\n\n step += 1\n\n if step == 100 or next_state == env.get_goal_state():\n step_graph[n_epi] += step\n break\n \n print(\"シミュレーション完了\")\n\n print(\"Q値表示\")\n for n in range(env.get_num_state()):\n print(\"{}:{}\".format(n, player.critic.get_Q()[n]))\n\n print(\"TD誤差表示\")\n for n in range(env.get_num_state()):\n print(\"{}:{}\".format(n, player.critic.get_Q()[n]))\n \n print(\"weight表示\")\n for n in range(env.get_num_state()):\n print(\"{}:{}\".format(n, player.actor.get_weight()[n]))\n\n print(\"グラフ表示\")\n plt.plot(step_graph / SIMULAITON_TIMES, label=\"actor_critic\")\n plt.legend()\n plt.title(\"step time development\")\n plt.xlabel(\"episode\")\n plt.ylabel(\"step\")\n plt.show()\n\nplay_task()\n","sub_path":"Lec03/actor-critic/train_optpath.py","file_name":"train_optpath.py","file_ext":"py","file_size_in_byte":2371,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"56619466","text":"import discord\n\nfrom src.DbHandler import DbHandler\n\n\nclass CharacterV2:\n userName = None\n name = None\n job = None\n ev = None\n evMax = None\n ea = None\n eaMax = None\n courage = None\n intelligence = None\n charisme = None\n adresse = None\n force = None\n attaque = None\n parade = None\n pointsDeDestin = None\n competences = []\n piecesOr = None\n piecesArgent = None\n piecesBronze = None\n niveau = None\n sexe = None\n experience = None\n stuff = []\n weapons = []\n\n\nclass CharacterDBHandler:\n def __init__(self):\n self.dbHandler = DbHandler()\n self.data = self.dbHandler.data\n self.key = [\"RACE\", \"JOB\", \"EV\", \"EVMAX\", \"EA\", \"EAMAX\", \"COU\", \"INT\", \"CHA\", \"AD\", \"FO\", \"AT\", \"PRD\", \"DESTINY\", \"SKILLS\", \"GOLD\", \"SILVER\", \"BRONZE\", \"LEVEL\", \"SEX\", \"XP\", \"NAME\", \"STUFF\", \"WEAPONS\", \"PLAYER\"]\n self.mapping = {self.key[0]:\"Race\",\n self.key[1]:\"Métier\",\n self.key[2]:\"Energie vitale\",\n self.key[3]:\"Energie vitale maximale\",\n self.key[4]:\"Energie astrale\",\n self.key[5]:\"Energie astrale maximale\",\n self.key[6]:\"Courage\",\n self.key[7]:\"Intelligence\",\n self.key[8]:\"Charisme\",\n self.key[9]:\"Adresse\",\n self.key[10]:\"Force\",\n self.key[11]:\"Attaque\",\n self.key[12]:\"Parade\",\n self.key[13]:\"Points de destin\",\n self.key[14]:\"Compétences\",\n self.key[15]:\"Pièces d'or\",\n self.key[16]:\"Pièces d'argent\",\n self.key[17]:\"Pièces de bronze\",\n self.key[18]:\"Niveau\",\n self.key[19]:\"Sexe\",\n self.key[20]:\"Points d'expérience\",\n self.key[21]:\"Nom\",\n self.key[22]:\"Equipement\",\n self.key[23]:\"Armes\",\n self.key[23]:\"Joueur\"}\n\n def initialize(self):\n self.dbHandler.retrieve_game()\n\n def mappingCharStat(self, aKey, aChar):\n if aKey==self.key[0]:\n return aChar.race\n elif aKey==self.key[1]:\n return aChar.job\n elif aKey==self.key[2]:\n return aChar.ev\n elif aKey==self.key[3]:\n return aChar.evMax\n elif aKey==self.key[4]:\n return aChar.ea\n elif aKey==self.key[5]:\n return aChar.eaMax\n elif aKey==self.key[6]:\n return aChar.courage\n elif aKey==self.key[7]:\n return aChar.intelligence\n elif aKey==self.key[8]:\n return aChar.charisme\n elif aKey==self.key[9]:\n return aChar.adresse\n elif aKey==self.key[10]:\n return aChar.force\n elif aKey==self.key[11]:\n return aChar.attaque\n elif aKey==self.key[12]:\n return aChar.parade\n elif aKey==self.key[13]:\n return aChar.pointsDeDestin\n elif aKey==self.key[14]:\n return str(aChar.competences)\n elif aKey==self.key[15]:\n return aChar.piecesOr\n elif aKey==self.key[16]:\n return aChar.piecesArgent\n elif aKey==self.key[17]:\n return aChar.piecesBronze\n elif aKey==self.key[18]:\n return aChar.niveau\n elif aKey==self.key[19]:\n return aChar.sexe\n elif aKey==self.key[20]:\n return aChar.experience\n elif aKey==self.key[21]:\n return aChar.name\n elif aKey==self.key[22]:\n return str(aChar.stuff)\n elif aKey==self.key[23]:\n return str(aChar.weapons)\n elif aKey==self.key[24]:\n return str(aChar.userName)\n\n def readFileForCharacter(self,userId):\n for aPlayer in self.data['settings']['characters']:\n if aPlayer[\"PLAYER\"] == userId:\n return aPlayer\n\n def importCharacter(self, userName):\n values = self.readFileForCharacter(userName)\n aNewCharacter = CharacterV2()\n aNewCharacter.userName = values[\"PLAYER\"]\n aNewCharacter.name = values[\"NAME\"]\n aNewCharacter.name = values[\"NAME\"]\n aNewCharacter.race = values[\"RACE\"]\n aNewCharacter.job = values[\"JOB\"]\n aNewCharacter.ev = values[\"EV\"]\n aNewCharacter.evMax = values[\"EVMAX\"]\n aNewCharacter.ea = values[\"EA\"]\n aNewCharacter.eaMax = values[\"EAMAX\"]\n aNewCharacter.courage = values[\"COU\"]\n aNewCharacter.intelligence = values[\"INT\"]\n aNewCharacter.charisme = values[\"CHA\"]\n aNewCharacter.adresse = values[\"AD\"]\n aNewCharacter.force = values[\"FO\"]\n aNewCharacter.attaque = values[\"AT\"]\n aNewCharacter.parade = values[\"PRD\"]\n aNewCharacter.pointsDeDestin = values[\"DESTINY\"]\n aNewCharacter.competences = values[\"SKILLS\"]\n aNewCharacter.piecesOr = values[\"GOLD\"]\n aNewCharacter.piecesArgent = values[\"SILVER\"]\n aNewCharacter.piecesBronze = values[\"BRONZE\"]\n aNewCharacter.niveau = values[\"LEVEL\"]\n aNewCharacter.sexe = values[\"SEX\"]\n aNewCharacter.experience= values[\"XP\"]\n aNewCharacter.stuff = values[\"STUFF\"]\n aNewCharacter.weapons = values[\"WEAPONS\"]\n return aNewCharacter\n\n def convertKey(self, aKey):\n return self.mapping[aKey]\n\n def displayBasicInfos(self, aCharacter):\n return self.displayListOfInfos(0x00ff00, aCharacter, [\"RACE\",\"JOB\",\"SEX\",\"LEVEL\",\"XP\"], aCharacter.name)\n\n def displayBasicStats(self, aCharacter, displayName=False):\n return self.displayListOfInfos(0x00ff00, aCharacter, [\"EV\",\"EA\",\"DESTINY\",\"GOLD\",\"AT\",\"PRD\",\"AD\"], (aCharacter.name if displayName else None))\n\n def displayAttackInfo(self, aCharacter, displayName=False):\n return self.displayListOfInfos(0x00ff00, aCharacter, [\"AT\",\"PRD\",\"AD\"], (aCharacter.name if displayName else None))\n\n def displayMoneyInfos(self, aCharacter, displayName=False):\n return self.displayListOfInfos(0x00ff00, aCharacter, [\"GOLD\"], (aCharacter.name if displayName else None))\n\n def displaySkills(self, aCharacter, displayName=False):\n return self.displayListOfInfos(0x00ff00, aCharacter, [\"SKILLS\"], (aCharacter.name if displayName else None))\n\n def displayStuff(self, aCharacter, displayName=False):\n return self.displayListOfInfos(0x00ff00, aCharacter, [\"STUFF\"], (aCharacter.name if displayName else None))\n\n def displayWeapons(self, aCharacter, displayName=False):\n return self.displayListOfInfos(0x00ff00, aCharacter, [\"WEAPONS\"], (aCharacter.name if displayName else None))\n\n def displayListOfInfos(self, color, aCharacter, listOfStats, title=None):\n embedFormated = discord.Embed(color=color, title=title)\n for aStat in listOfStats:\n strValue = self.mappingCharStat(aStat, aCharacter)\n if(aStat == \"EA\" or aStat == \"EV\"):\n strValue = (self.mappingCharStat(aStat, aCharacter) + \"/\" + self.mappingCharStat(aStat+\"MAX\", aCharacter))\n elif aStat == \"GOLD\":\n strValue = (self.mappingCharStat(\"GOLD\", aCharacter) + \"/\" + self.mappingCharStat(\"SILVER\", aCharacter) + \"/\" + self.mappingCharStat(\"BRONZE\", aCharacter))\n embedFormated.add_field(\n name = self.mapping[aStat],\n value = strValue,\n inline = True)\n return embedFormated\n\n def displayMinimumInfoCharacter(self, aCharacter):\n embeds = [self.displayBasicStats(aCharacter,True)]\n return embeds\n\n def displayWeaponsCharacter(self, aCharacter):\n embeds = [self.displayWeapons(aCharacter, True)]\n return embeds\n\n def displayStuffCharacter(self, aCharacter):\n embeds = [self.displayStuff(aCharacter, True)]\n return embeds\n\n def displaySkillsCharacter(self, aCharacter):\n embeds = [self.displaySkills(aCharacter, True)]\n return embeds\n\n def displayInfoCharacter(self, aCharacter):\n embeds = [self.displayListOfInfos(0x00ff00, aCharacter, [\"RACE\",\"JOB\",\"SEX\",\"GOLD\",\"LEVEL\",\"XP\"]),\n self.displayListOfInfos(0x00ff00, aCharacter, [\"EV\",\"EA\",\"DESTINY\"]),\n self.displayListOfInfos(0x00ff00, aCharacter, [\"AT\",\"PRD\",\"AD\"]),\n self.displayListOfInfos(0x00ff00, aCharacter, [\"SKILLS\",\"STUFF\",\"WEAPONS\"])]\n return embeds\n\n def moneyOperation(self, userName, amount):\n theChar = self.readFileForCharacter(userName)\n print(theChar)\n operations = amount.split(\"/\")\n if len(operations) >= 1:\n tempOr = int(theChar[\"GOLD\"]) + int(operations[0])\n if(tempOr < 0):\n tempOr = 0\n theChar[\"GOLD\"] = str(tempOr)\n if len(operations) >= 2:\n tempArgent = int(theChar[\"SILVER\"]) + int(operations[1])\n if(tempArgent < 0):\n tempArgent = 0\n theChar[\"SILVER\"] = str(tempArgent)\n if len(operations) >= 3:\n tempBronze = int(theChar[\"BRONZE\"]) + int(operations[2])\n if(tempBronze < 0):\n tempBronze = 0\n theChar[\"BRONZE\"] = str(tempBronze)\n self.dbHandler.update_game()\n return theChar[\"GOLD\"], theChar[\"SILVER\"], theChar[\"BRONZE\"]\n\n def increaseEv(self, userName, amount):\n theCharSheet = self.readFileForCharacter(userName)\n tempEv = int(theCharSheet[\"EV\"]) + amount\n if(tempEv > int(theCharSheet[\"EVMAX\"])):\n tempEv = int(theCharSheet[\"EVMAX\"])\n if(tempEv < 0):\n tempEv = 0\n theCharSheet[\"EV\"] = str(tempEv)\n self.dbHandler.update_game()\n return theCharSheet[\"EV\"]\n\n def decreaseEv(self, userName, amount):\n return self.increaseEv(userName, (0-amount))\n\n def increaseEvGroup(self, amount):\n print(\"increase\")\n result = []\n for aPlayer in self.dbHandler.data['settings']['characters']:\n print(\"Decrease player\" + aPlayer[\"PLAYER\"])\n tempEv = int(aPlayer[\"EV\"]) + amount\n if(tempEv > int(aPlayer[\"EVMAX\"])):\n tempEv = int(aPlayer[\"EVMAX\"])\n if(tempEv < 0):\n tempEv = 0\n aPlayer[\"EV\"] = str(tempEv)\n result.append({'id': aPlayer[\"PLAYER\"], 'remainingLife': aPlayer[\"EV\"]})\n print('Save Changes')\n self.dbHandler.update_game()\n return result\n\n def decreaseEvGroup(self, amount):\n print(\"Decrease\")\n return self.increaseEvGroup((0-amount))\n","sub_path":"src/CharacterDBHandler.py","file_name":"CharacterDBHandler.py","file_ext":"py","file_size_in_byte":10726,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"528392021","text":"##from gpiozero import DigitalInputDevice\nfrom gpiozero import *\nfrom time import sleep\nfrom fonafuncs import *\n\n \ninputs = [DigitalInputDevice(4, pull_up=False),\n DigitalInputDevice(17, pull_up=False),\n DigitalInputDevice(27, pull_up=False),\n DigitalInputDevice(22, pull_up=False),\n DigitalInputDevice(23, pull_up=False)]\n\noutputs = [DigitalOutputDevice(5, active_high=True),\n DigitalOutputDevice(6, active_high=True),\n DigitalOutputDevice(13, active_high=True),\n DigitalOutputDevice(19, active_high=True),\n DigitalOutputDevice(26, active_high=True)]\n\ninputVal = [[\"top1\",\"top2\",None , None ,\"top3\"],\n [\"circ\",\"vol\",\"*\",\"#\",\"0\"],\n [\"bott\",\"cont\",\"7\",\"9\",\"8\"],\n [\"left\",\"right\",\"4\",\"6\",\"5\"],\n [\"top\",\"L\",\"1\",\"3\",\"2\"]]\n\ninputTyp = [[\"com\",\"com\", None, None, \"com\"],\n [\"com\",\"com\",\"spec\",\"spec\",\"num\"],\n [\"com\",\"com\",\"num\",\"num\",\"num\"],\n [\"com\",\"com\",\"num\",\"num\",\"num\"],\n [\"com\",\"com\",\"num\",\"num\",\"num\"]]\n\n\ndef dialIn(inter, interVal):\n i,j = 0,0\n while inter.value != interVal:\n for i in range(len(outputs)):\n outputs[i].on()\n for j in range (len(inputs)):\n if inputs[j].value:\n outputString =[inputVal [j][i],inputTyp[j][i]] \n print(i,\" \",j, \" \" , inputVal[j][i], \" \")\n if inputTyp[j][i] == \"spec\" or inputTyp[j][i] == \"num\":\n #pass\n CPTONE(inputVal[j][i])\n inputs[j].wait_for_inactive(timeout=2)\n outputs[i].off()\n sleep(0.2)\n return outputString\n outputs[i].off()\n \n for k in range(len(outputs)):\n outputs[k].off()\n return \"cancel\"\n\nif __name__ == \"__main__\":\n handsetSens = DigitalInputDevice(16, pull_up=False)\n #print(dialIn(handsetSens,1))\n while 1:\n dialIn(handsetSens,1)\n print(\"go\")\n","sub_path":"main_working/keyboardread.py","file_name":"keyboardread.py","file_ext":"py","file_size_in_byte":2024,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"270852299","text":"\r\nfname = input(\"Enter file name: \")\r\nif len(fname) < 1 : fname = \"mbox-short.txt\"\r\nfh = open(fname)\r\n\r\nhcount=dict()\r\nlst=[]\r\n\r\ncount = 0\r\nlst=[]\r\nfor lines in fh:\r\n if lines.startswith(\"From\") and len(lines.split())>2:\r\n words=lines.split()\r\n hour=words[5].split(':')\r\n hcount[hour[0]]=hcount.get(hour[0],0)+1\r\n else:\r\n continue\r\n\r\nfor k,v in hcount.items():\r\n lst.append((k,v))\r\n \r\n \r\nlst.sort()\r\nfor k,v in lst:\r\n print(k,v)","sub_path":"Coursera_solutions/coursera_6.py","file_name":"coursera_6.py","file_ext":"py","file_size_in_byte":482,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"320140403","text":"#coding=utf-8\nfrom __future__ import division\nimport string\nimport math\n\n'''\nCreated on 2016年3月19日\n\n@author: makao\n计算各个点基于目前关注关系的各自的影响力Ii\n使用趋同化方法将ti和ci相加\n'''\n\nif __name__ == '__main__':\n user_counter = 0\n user_list = []\n Ci_list=[]\n ci_square_sum = 0\n for line in open('/Users/makao/Yun/Workspace/lab/data/java_relation/ci.txt'):\n ci = string.atof(line.strip())\n ci_square_sum = ci*ci + ci_square_sum\n user_list.append([ci])\n user_counter = user_counter + 1\n Ti_list=[]\n ti_square_sum = 0\n line_counter = 0\n for line in open('/Users/makao/Yun/Workspace/lab/data/java_relation/ti.txt'):\n ti = string.atof(line.strip())\n ti_square_sum = ti*ti + ti_square_sum\n user_list[line_counter].append(ti)\n line_counter = line_counter + 1\n \n #for item in user_list:\n # print '[%s %s]' % (item[0],item[1])\n f = open('/Users/makao/Yun/Workspace/lab/data/java_relation/Ii.txt','w')\n ci_sqrt_sum = math.sqrt(ci_square_sum)\n ti_sqrt_sum = math.sqrt(ti_square_sum)\n for i in range(0,user_counter):\n ci = user_list[i][0]\n ti = user_list[i][1]\n Ii = ci/ci_sqrt_sum + ti/ti_sqrt_sum\n print >> f, '%s' % Ii\n f.close()","sub_path":"GenerateFromJava/calculateIi_7.py","file_name":"calculateIi_7.py","file_ext":"py","file_size_in_byte":1304,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"131460227","text":"#!/usr/bin/env python\n#\n# Copyright 2019 The Oppia Authors. All Rights Reserved.\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\n\"\"\"Script that performs task to wrap up the release.\"\"\"\n\nfrom __future__ import absolute_import # pylint: disable=import-only-modules\nfrom __future__ import unicode_literals # pylint: disable=import-only-modules\n\nimport os\nimport sys\n\nimport python_utils\nimport release_constants\nfrom scripts import common\n\n_PARENT_DIR = os.path.abspath(os.path.join(os.getcwd(), os.pardir))\n_PY_GITHUB_PATH = os.path.join(\n _PARENT_DIR, 'oppia_tools', 'PyGithub-%s' % common.PYGITHUB_VERSION)\nsys.path.insert(0, _PY_GITHUB_PATH)\n\nimport github # isort:skip pylint: disable=wrong-import-position\n\n\ndef remove_release_labels(repo):\n \"\"\"Removes PR: released labels from PRs.\n\n Args:\n repo: github.Repository.Repository. The PyGithub object for the repo.\n\n Raises:\n Exception. If any PR is not released.\n \"\"\"\n current_release_label = repo.get_label(\n release_constants.LABEL_FOR_CURRENT_RELEASE_PRS)\n released_label = repo.get_label(release_constants.LABEL_FOR_RELEASED_PRS)\n\n current_release_prs = list(repo.get_issues(\n state='all', labels=[current_release_label])) + list(\n repo.get_issues(\n state='open', labels=[released_label]))\n\n released_prs = repo.get_issues(state='closed', labels=[released_label])\n\n current_release_pr_numbers = [pr.number for pr in current_release_prs]\n released_pr_numbers = [pr.number for pr in released_prs]\n\n unreleased_pr_numbers = [\n pr_num for pr_num in current_release_pr_numbers if pr_num not in (\n released_pr_numbers)]\n if unreleased_pr_numbers:\n raise Exception(\n 'Following PRs are not released: %s.' % unreleased_pr_numbers)\n\n for pr in current_release_prs:\n pr.remove_from_labels(release_constants.LABEL_FOR_CURRENT_RELEASE_PRS)\n python_utils.PRINT('%s label removed from PR: #%s' % (\n release_constants.LABEL_FOR_CURRENT_RELEASE_PRS, pr.number))\n for pr in released_prs:\n pr.remove_from_labels(release_constants.LABEL_FOR_RELEASED_PRS)\n python_utils.PRINT('%s label removed from PR: #%s' % (\n release_constants.LABEL_FOR_RELEASED_PRS, pr.number))\n\n\ndef remove_blocking_bugs_milestone_from_issues(repo):\n \"\"\"Removes blocking bug milestone from issues.\n\n Args:\n repo: github.Repository.Repository. The PyGithub object for the repo.\n\n Raises:\n Exception. If there is an open issue with blocking bug milestone.\n \"\"\"\n blocking_bugs_milestone = repo.get_milestone(\n number=release_constants.BLOCKING_BUG_MILESTONE_NUMBER)\n if blocking_bugs_milestone.state == 'closed':\n raise Exception('The blocking bug milestone is closed.')\n if blocking_bugs_milestone.open_issues:\n common.open_new_tab_in_browser_if_possible(\n 'https://github.com/oppia/oppia/issues?q=is%3Aopen+'\n 'is%3Aissue+milestone%3A%22Blocking+bugs%22')\n raise Exception('%s blocking bugs are not resolved.' % (\n blocking_bugs_milestone.open_issues))\n issues = repo.get_issues(milestone=blocking_bugs_milestone, state='closed')\n for issue in issues:\n issue.edit(milestone=None)\n\n\ndef ask_user_to_remove_protection_rule():\n \"\"\"Asks the release co-ordinator to perform the steps for deletion of\n github protection rule for release branch.\n \"\"\"\n common.ask_user_to_confirm(\n 'Ask Sean to delete '\n 'the github protection rule by:\\n'\n '1. Going to this page: '\n 'https://github.com/oppia/oppia/settings/branches. '\n '(Note, this link will give a 404 since access is limited to Sean.)\\n'\n '2. Delete the github protection rule for %s branch but leave the '\n 'existing release-* rule as-is. This will cause the branch to fall '\n 'under the general protection rule for release branches\\n' % (\n common.get_current_branch_name()))\n\n\ndef ask_user_to_update_jobs_tracker():\n \"\"\"Asks the release co-ordinator to update the status of jobs run\n in the release.\n \"\"\"\n common.open_new_tab_in_browser_if_possible(\n release_constants.JOBS_SPREADSHEETS_URL)\n common.ask_user_to_confirm(\n 'Move all the jobs which are run (both successful & failed ones) to '\n 'the past jobs tab in the jobs tracker.\\n')\n common.ask_user_to_confirm(\n 'Send updates regarding each job to the job author '\n '(just the success/failure status, don\\'t include any data or '\n 'job output).\\n')\n\n\ndef main():\n \"\"\"Performs task to wrap up the release.\"\"\"\n if not common.is_current_branch_a_release_branch():\n raise Exception(\n 'This script should only be run from the latest release branch.')\n\n if not os.path.exists(release_constants.RELEASE_SUMMARY_FILEPATH):\n raise Exception(\n 'Release summary file %s is missing. Please run the '\n 'release_info.py script and re-run this script.' % (\n release_constants.RELEASE_SUMMARY_FILEPATH))\n personal_access_token = common.get_personal_access_token()\n g = github.Github(personal_access_token)\n repo = g.get_organization('oppia').get_repo('oppia')\n\n ask_user_to_remove_protection_rule()\n ask_user_to_update_jobs_tracker()\n remove_blocking_bugs_milestone_from_issues(repo)\n remove_release_labels(repo)\n\n\n# The 'no coverage' pragma is used as this line is un-testable. This is because\n# it will only be called when wrap_up_release.py is used as a script.\nif __name__ == '__main__': # pragma: no cover\n main()\n","sub_path":"scripts/release_scripts/wrap_up_release.py","file_name":"wrap_up_release.py","file_ext":"py","file_size_in_byte":6145,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"386087483","text":"import mock\n\n\nclass PeerHook(object):\n def before_feature(self, context, feature):\n from pyethereum.packeter import packeter\n import socket\n\n context.packeter = packeter\n packeter.configure(context.conf)\n context._connection = mock.MagicMock(spec=socket.socket)\n\n def mock_peer(self, context):\n from pyethereum.peer import Peer\n context.peer = Peer(context._connection, '127.0.0.1', 1234)\n peer = context.peer\n\n def side_effect():\n for i in range(3):\n peer.loop_body()\n\n peer.run = mock.MagicMock()\n peer.run.side_effect = side_effect\n\n def mock_connection_recv(self, context):\n received_packets = []\n\n def add_recv_packet(packet):\n received_packets.append(packet)\n\n def genarator(bufsize):\n for packet in received_packets:\n for i in range(0, len(packet), bufsize):\n yield packet[i: i + bufsize]\n yield ''\n\n status = dict()\n\n def side_effect(bufsize):\n if 'genarator' not in status:\n status.update(genarator=genarator(bufsize))\n\n try:\n return status['genarator'].next()\n except:\n return ''\n\n context._connection.recv.side_effect = side_effect\n\n context._connection.recv = mock.MagicMock()\n context.add_recv_packet = add_recv_packet\n context.add_recv_packet('')\n context.received_packets = received_packets\n\n def mock_connection_send(self, context):\n context.sent_packets = []\n\n def side_effect(packet):\n context.sent_packets.append(packet)\n return len(packet)\n\n context._connection.send = mock.MagicMock(side_effect=side_effect)\n\n def before_scenario(self, context, scenario):\n self.mock_peer(context)\n self.mock_connection_recv(context)\n self.mock_connection_send(context)\n\n time_sleep_patcher = mock.patch('time.sleep')\n time_sleep_patcher.start()\n context.time_sleep_patcher = time_sleep_patcher\n\n def after_scenario(self, context, scenario):\n context.time_sleep_patcher.stop()\n\n\nhook = PeerHook()\n","sub_path":"features/hooks/peer.py","file_name":"peer.py","file_ext":"py","file_size_in_byte":2289,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"275189045","text":"'''\n\tOne way of doing this is through DFS, kind of like I did in Problem 91? or 90.\n\n\tTo find a cycle, start at some point traverse through, either having a list containing if its within\n\tor having a list of boolean checks of visited. If any are visited, then we have a cycle.\n\n\tThis solution in the book uses a DFS approach coupled with basic looping to visit all neighbors.\n'''\n\ndef search(graph, vertex, visited, parent):\n\tvisited[vertex] = True\n\n\t# DFS approach, iterate through neighbors and span till we visited\n\tfor neighbor in graph[vertex]:\n\t\tif not visited[neighbor]:\n\t\t\tif search(graph, neighbor, visited, vertex):\n\t\t\t\treturn True\n\t\t# Somewhere else we cycled\n\t\telif parent != neighbor:\n\t\t\treturn True\n\n\treturn False\n\ndef has_cycle(graph):\n\t# Create list that contains visited locations/verticies\n\tvisited = {v: False for v in graph.keys()}\n\t\n\t# Check each vertex, check for cycle\n\tfor vertex in graph.keys():\n\t\tif not visited[vertex]:\n\t\t\tif search(graph, vertex, visited, None):\n\t\t\t\treturn True\n\n\treturn False\n","sub_path":"DailyProgrammingBookChallenges/Chapter_10/has_cycle.py","file_name":"has_cycle.py","file_ext":"py","file_size_in_byte":1022,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"608787470","text":"import random\n\nimport matplotlib.pyplot as plt\n\nfrom lab1_1 import draw_line, Point\nfrom lab1_2 import is_intersect\nfrom copy import deepcopy\n\ndef is_simple(original_points):\n points = deepcopy(original_points)\n points = points + [points[0]]\n # print(points)\n for i in range(0,len(points)):\n total = 0\n for j in range(0,len(points)):\n try:\n total += is_intersect(points[i],points[i+1],points[j],points[j+1])\n except:\n pass\n if total > 3:\n return False\n if total != 3 and total != 0:\n return False\n return True\n\ndef draw_figure(points, color=False):\n for ind in range(0,len(points)):\n try:\n draw_line(points[ind], points[ind + 1],color)\n except:\n draw_line(points[ind], points[0],color)\n\n\nif __name__==\"__main__\":\n points = []\n for i in range(0,4):\n points.append(Point(random.randint(0, 20), random.randint(0, 20)))\n print(\"Simple\", is_simple(points))\n draw_figure(points)\n plt.show()","sub_path":"Lab3_no/lab1_3.py","file_name":"lab1_3.py","file_ext":"py","file_size_in_byte":1070,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"446104010","text":"#!/usr/bin/env python\n#coding:utf-8\n#Created by fsrm\n\nimport sys\nreload(sys)\nsys.setdefaultencoding(\"utf-8\")\n\nimport os\n\ndef main():\n import pika\n connection = pika.BlockingConnection(pika.ConnectionParameters('rabbit'))\n channel = connection.channel()\n channel.queue_declare(queue='hello')\n\n channel.basic_publish(exchange='',\n routing_key='hello',\n body='Hello World!')\n print(\" [x] Sent 'Hello World!'\")\n connection.close()\n\n\nif __name__ == \"__main__\":\n main()\n\n","sub_path":"scripts/rabbitmq/1_start/send.py","file_name":"send.py","file_ext":"py","file_size_in_byte":539,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"27708747","text":"#定向爬虫，中国大学排名\r\nimport requests\r\nimport bs4\r\nfrom bs4 import BeautifulSoup as be\r\n\r\n\r\ndef gethtmltext(url):\r\n try:\r\n r=requests.get(url,timeout=30)\r\n r.raise_for_status()\r\n r.encoding=r.apparent_encoding()\r\n return r.text\r\n except:\r\n return \"\"\r\n \r\ndef fillunivlist(ulist,html):\r\n soup=be(html,\"html.parser\")\r\n for tr in soup.find('tbody').children:\r\n if isinstance(tr,bs4.element.Tag):\r\n tds=tr('td')\r\n ulist.append([tds[0].string, tds[1].string, tds[2].string])\r\n \r\ndef printlist(ulist,num):\r\n tplt=\"{0:^10}\\t{1:{3}^10}\\t{2:^10}\"\r\n print(tplt.format(\"排名\",\"学校名称\",\"总分\",chr(12288)))\r\n for i in range(num):\r\n print(tplt.format(ulist[0],ulist[1],ulist[2],chr(12288)))\r\n \r\n\r\n\r\ndef main():\r\n unifo=[]\r\n url='http://www.zuihaodaxue.cn/zuihaodaxuepaiming2016.html'\r\n html=gethtmltext(url)\r\n fillunivlist(unifo,html)\r\n printlist(unifo,20)\r\n\r\nmain()\r\n","sub_path":"Files from MOOC/爬虫/MOOC实例/实例一：大学排名定向爬虫.py","file_name":"实例一：大学排名定向爬虫.py","file_ext":"py","file_size_in_byte":1007,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"233507654","text":"from random import randint\nimport time\n\n'''\ndef quick_sort(data, draw_data, wait_time):\n # If the input array contains fewer than two elements,\n # then return it as the result of the function\n if len(data) < 2:\n return data\n\n low, same, high = [], [], []\n\n pivot = data[randint(0, len(data) - 1)] # Select your `pivot` element randomly\n draw_data(data, ['crimson' if _ == pivot else 'deep sky blue' for _ in range(len(data))])\n time.sleep(wait_time)\n for item in data:\n # Elements that are smaller than the `pivot` go to\n # the `low` list. Elements that are larger than\n # `pivot` go to the `high` list. Elements that are\n # equal to `pivot` go to the `same` list.\n if item < pivot:\n low.append(item)\n draw_data(data, ['yellow' if low else 'deep sky blue' for _ in range(len(data))])\n\n elif item == pivot:\n same.append(item)\n draw_data(data, ['crimson' if same else 'deep sky blue' for _ in range(len(data))])\n\n elif item > pivot:\n high.append(item)\n draw_data(data, ['black' if high else 'deep sky blue' for _ in range(len(data))])\n\n # The final result combines the sorted `low` list\n # with the `same` list and the sorted `high` list\n quick_sort(low, draw_data, wait_time) + same + quick_sort(high, draw_data, wait_time)\n return data\n'''\n\n\ndef partition(data, head, tail, draw_data, wait_time):\n border = head\n pivot = data[tail]\n\n draw_data(data, get_color_arr(len(data), head, tail, border, border))\n time.sleep(wait_time)\n\n for j in range(head, tail):\n if data[j] < pivot:\n # draw\n draw_data(data, get_color_arr(len(data), head, tail, border, j, True))\n time.sleep(wait_time)\n\n data[border], data[j] = data[j], data[border]\n border += 1\n # draw\n draw_data(data, get_color_arr(len(data), head, tail, border, j, True))\n time.sleep(wait_time)\n\n # swapping pivot with border value.\n data[border], data[tail] = data[tail], data[border]\n return border\n\n\ndef quick_sort(data, head, tail, draw_data, wait_time):\n if head < tail:\n partition_index = partition(data, head, tail, draw_data, wait_time)\n\n # Left Partition\n quick_sort(data, head, partition_index - 1, draw_data, wait_time)\n # Right Partition\n quick_sort(data, partition_index + 1, tail, draw_data, wait_time)\n\n\ndef get_color_arr(data_length, head, tail, border, current_index, is_swapping=False):\n color_array = []\n # base color\n for i in range(data_length):\n if head <= i <= tail:\n color_array.append('deep sky blue')\n else:\n color_array.append('gainsboro')\n\n if i == tail:\n color_array[i] = 'light coral'\n elif i == border:\n color_array[i] = 'crimson'\n elif i == current_index:\n color_array[i] = 'deep sky blue'\n\n if is_swapping:\n if i == border or i == current_index:\n color_array[i] = 'yellow'\n\n return color_array\n\n\n\n\n\n\n","sub_path":"SortingVisualizerProject/QuickSort.py","file_name":"QuickSort.py","file_ext":"py","file_size_in_byte":3126,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"340696920","text":"#!/usr/bin/python\n# -*- coding: utf8 -*-\n# Author: Antipin S.O. @RLDA\n\nimport devices\n\n\nclass device_settings(object):\n \"\"\"\n Настройки для исполнительных устройств\n device_type - идентификатор типа устройства (str)\n topic - корневой топик в брокере\n error_codes - коды ошибки значения датчика\n timeout - таймаут ответа\n \"\"\"\n def __init__(self, obj):\n self.device_type = \"\"\n self.topic = \"\"\n self.limits = []\n self.error_codes = []\n self.timeout = 0\n self.get_settings(obj)\n\n def get_settings(self, device):\n ''' Установить параметры в ��ависимости от типа устройства '''\n if isinstance(device, devices.relay):\n self.device_type = \"DVC_RELAY\"\n self.topic = \"oh/devices/relays/\"\n self.limits = [\"HIGH\", \"LOW\"]\n self.error_codes = [0xFFF, 0x00]\n self.timeout = 1\n elif isinstance(device, devices.dimmer):\n self.device_type = \"DVC_DIMMER\"\n self.topic = \"oh/devices/dimmers/trmrl\"\n self.limits = [0, 100]\n self.error_codes = [0xFFF, 0x00]\n self.timeout = 1\n","sub_path":"settings_devs.py","file_name":"settings_devs.py","file_ext":"py","file_size_in_byte":1339,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"333754435","text":"import json\r\nimport time\r\nimport threading\r\nfrom datetime import datetime\r\nimport pymysql\r\nimport requests\r\nimport websocket\r\n\r\n\r\nclass Spider(object):\r\n def __init__(self):\r\n self.conn = pymysql.connect(host='127.0.0.1', user='user', password='password', db='house', charset='utf8mb4')\r\n self.cursor = self.conn.cursor()\r\n self.ws = websocket.create_connection('wss://danmuproxy.douyu.com:8506')\r\n self.gift_dict = self.get_gift_dict()\r\n self.gift_dict_keys = self.gift_dict.keys()\r\n\r\n # 编码请求消息\r\n def dy_encode(self, msg):\r\n try:\r\n data_len = len(msg) + 9\r\n msg_byte = msg.encode('utf-8')\r\n len_byte = int.to_bytes(data_len, 4, 'little')\r\n send_byte = bytearray([0xb1, 0x02, 0x00, 0x00])\r\n end_byte = bytearray([0x00])\r\n data = len_byte + len_byte + send_byte + msg_byte + end_byte\r\n return data\r\n except:\r\n pass\r\n\r\n # 解码响应消息\r\n def dy_decode(self, msg_byte):\r\n try:\r\n pos = 0\r\n msg = []\r\n while pos < len(msg_byte):\r\n content_length = int.from_bytes(msg_byte[pos: pos + 4], byteorder='little')\r\n content = msg_byte[pos + 12: pos + 3 + content_length].decode(encoding='utf-8', errors='ignore')\r\n msg.append(content)\r\n pos += (4 + content_length)\r\n return msg\r\n except:\r\n pass\r\n\r\n # 解析响应消息\r\n def parse_msg(self, raw_msg):\r\n res = {}\r\n attrs = raw_msg.split('/')[0:-1]\r\n for attr in attrs:\r\n try:\r\n attr = attr.replace('@S', '/')\r\n attr = attr.replace('@A', '@')\r\n couple = attr.split('@=')\r\n res[couple[0]] = couple[1]\r\n except Exception as e:\r\n print(e)\r\n pass\r\n return res\r\n\r\n # 登录\r\n def login(self):\r\n \"\"\"\r\n 1.客户端向弹幕服务器发送登录请求\r\n 2.客户端收到登录成功消息后发送进入弹幕分组请求给弹幕服务器\r\n \"\"\"\r\n login_msg = \"type@=loginreq/roomid@=74751/dfl@=/username@=380054864=/uid@=380054864/ver@=20190610/aver@=218101901/ct@=0/\"\r\n try:\r\n self.ws.send(self.dy_encode(login_msg))\r\n print('登陆成功')\r\n except Exception as e:\r\n print(e)\r\n exit(1)\r\n\r\n # 加入分组，-9999为海量弹幕\r\n def join_group(self):\r\n join_group_msg = 'type@=joingroup/rid@=74751/gid@=-9999/'\r\n try:\r\n self.ws.send(self.dy_encode(join_group_msg))\r\n print('加入分组成功')\r\n except Exception as e:\r\n exit(1)\r\n\r\n # 获取消息\r\n def get_msg(self):\r\n print('开始接收消息')\r\n while True:\r\n try:\r\n msg_bytes = self.ws.recv()\r\n msg_arr = self.dy_decode(msg_bytes)\r\n msg = self.parse_msg(msg_arr[0])\r\n # 弹幕\r\n if msg['type'] == 'chatmsg':\r\n self.parse_chatmsg(msg)\r\n # 禁言\r\n if msg['type'] == 'newblackres':\r\n self.parse_newblackres(msg)\r\n # 小礼物\r\n if msg['type'] == 'dgb':\r\n self.parse_gift(msg)\r\n # 大礼物\r\n if msg['type'] == 'tsboxb':\r\n self.parse_big_gift(msg)\r\n except ConnectionError:\r\n exit(1)\r\n except Exception as e:\r\n with open('error.txt', 'a+') as f:\r\n f.write(json.dumps(msg_arr))\r\n print(e)\r\n print(msg_arr)\r\n pass\r\n\r\n # 提取弹幕内容\r\n def parse_chatmsg(self, msg):\r\n item = {}\r\n try:\r\n content = msg['txt']\r\n content = content.replace('\\\\', '')\r\n content = content.replace('\\'', '')\r\n content = content.replace('\"', '')\r\n item['nickname'] = msg['nn'] # 昵称\r\n item['content'] = content # 内容\r\n item['level'] = msg['level'] # 用户等级\r\n item['fan_card'] = msg['bnn'] # 粉丝牌\r\n item['send_time'] = datetime.now()\r\n item['fan_level'] = msg['bl'] # 粉丝牌等级\r\n self.add_dm_sql(item)\r\n except Exception as e:\r\n pass\r\n\r\n # 提取禁言信息\r\n def parse_newblackres(self, msg):\r\n item = {}\r\n try:\r\n item['snic'] = msg['snic'] # 禁言者昵称\r\n item['dnic'] = msg['dnic'] # 被禁言用户昵称\r\n item['otype'] = msg['otype'] # 禁言操作人类型 1房管 2主播 3超管\r\n item['endtime'] = msg['endtime'] # 禁言结束时间\r\n self.add_jy_sql(item)\r\n except Exception as e:\r\n pass\r\n\r\n # 提取小礼物信息\r\n def parse_gift(self, msg):\r\n item = {}\r\n try:\r\n item['nickname'] = msg['nn'] # 用户昵称\r\n item['uid'] = msg['uid'] # 用户id\r\n item['gift_id'] = msg['gfid'] # 礼物id\r\n if msg['gfid'] in self.gift_dict_keys:\r\n item['gift_name'] = self.gift_dict[msg['gfid']] # 礼物名称\r\n else:\r\n item['gift_name'] = '未知'\r\n item['level'] = msg['level'] # 用户等级\r\n item['gift_count'] = msg['gfcnt'] # 礼物个数\r\n item['fan_card'] = msg['bnn'] # 粉丝牌\r\n item['fan_level'] = msg['bl'] # 粉丝牌等级\r\n item['fan_card_room_id'] = msg['brid'] # 粉丝牌房间号\r\n item['gift_from'] = msg['from'] # 礼物来源 2-背包\r\n item['send_time'] = datetime.now()\r\n self.add_gift_sql(item)\r\n except Exception as e:\r\n pass\r\n\r\n # 提取大礼物\r\n def parse_big_gift(self, msg):\r\n item = {}\r\n try:\r\n item['nickname'] = msg['snk'] # 用户昵称\r\n if msg['rpt'] in self.gift_dict_keys:\r\n item['gift_name'] = self.gift_dict[msg['rpt']] # 礼物名称\r\n else:\r\n item['gift_name'] = '未知'\r\n item['gift_from'] = msg['from'] # 礼物来源 2-背包\r\n item['send_time'] = datetime.now()\r\n self.add_big_gift_sql(item)\r\n except Exception as e:\r\n print(e)\r\n print(msg)\r\n pass\r\n\r\n # 保存弹幕数据到数据库\r\n def add_dm_sql(self, obj):\r\n try:\r\n nickname = obj['nickname']\r\n content = obj['content']\r\n fan_card = obj['fan_card']\r\n fan_level = obj['fan_level']\r\n level = obj['level']\r\n send_time = obj['send_time']\r\n sql = \"insert into douyu_danmu (nickname,content,user_level,fan_card,fan_level,send_time) values ('%s','%s','%s','%s','%s','%s')\" % (\r\n nickname, content, level, fan_card, fan_level, send_time)\r\n self.cursor.execute(sql)\r\n self.conn.commit()\r\n except Exception as e:\r\n self.conn.ping(reconnect=True) # 检查连接是否断开，断开重连\r\n\r\n # 保存禁言数据到数据库\r\n def add_jy_sql(self, obj):\r\n try:\r\n snic = obj['snic']\r\n dnic = obj['dnic']\r\n otype = obj['otype']\r\n endtime = obj['endtime']\r\n timestamp = int(endtime)\r\n endtime = datetime.fromtimestamp(timestamp)\r\n sql = \"insert into douyu_jinyan (snic,dnic,otype,endtime) values ('%s','%s','%s','%s')\" % (\r\n snic, dnic, otype, endtime)\r\n self.cursor.execute(sql)\r\n self.conn.commit()\r\n except Exception as e:\r\n self.conn.ping(reconnect=True) # 检查连接是否断开，断开重连\r\n\r\n # 保存小礼物数据到数据库\r\n def add_gift_sql(self, obj):\r\n try:\r\n nickname = obj['nickname']\r\n uid = obj['uid']\r\n fan_card = obj['fan_card']\r\n fan_level = obj['fan_level']\r\n fan_card_room_id = obj['fan_card_room_id']\r\n user_level = obj['level']\r\n gift_id = obj['gift_id']\r\n gift_name = obj['gift_name']\r\n gift_count = obj['gift_count']\r\n gift_from = obj['gift_from']\r\n send_time = obj['send_time']\r\n sql = \"insert into douyu_gift (nickname,uid,user_level,fan_card,fan_level,fan_room_id,gift_id,gift_name,gift_count,gift_from,send_time) \" \\\r\n \"values ('%s','%s','%s','%s','%s','%s','%s','%s','%s','%s','%s')\" \\\r\n % (nickname, uid, user_level, fan_card, fan_level, fan_card_room_id, gift_id, gift_name, gift_count,\r\n gift_from, send_time)\r\n self.cursor.execute(sql)\r\n self.conn.commit()\r\n except Exception as e:\r\n print(e)\r\n self.conn.ping(reconnect=True) # 检查连接是否断开，断开重连\r\n\r\n # 保存大礼物到数据库\r\n def add_big_gift_sql(self, obj):\r\n try:\r\n nickname = obj['nickname']\r\n gift_name = obj['gift_name']\r\n gift_from = obj['gift_from']\r\n send_time = obj['send_time']\r\n sql = \"insert into douyu_biggift (nickname,gift_name,gift_from,send_time) values ('%s','%s','%s','%s')\" % (\r\n nickname, gift_name, gift_from, send_time)\r\n self.cursor.execute(sql)\r\n self.conn.commit()\r\n except Exception as e:\r\n print(e)\r\n self.conn.ping(reconnect=True) # 检查连接是否断开，断开重连\r\n\r\n # 心跳连接\r\n def keep_alive(self):\r\n print('建立初始心跳连接...')\r\n \"\"\"\r\n 客户端每隔 45 秒发送心跳信息给弹幕服务器\r\n \"\"\"\r\n while True:\r\n try:\r\n msg = 'type@=mrkl/'\r\n self.ws.send(self.dy_encode(msg))\r\n time.sleep(45)\r\n except ConnectionError:\r\n exit(1)\r\n except Exception:\r\n pass\r\n\r\n # 礼物id映射\r\n def get_gift_dict(self):\r\n gift_json = {}\r\n gift1 = requests.get('https://www.douyu.com/betard/74751').json()\r\n gift2 = requests.get(\r\n 'https://webconf.douyucdn.cn/resource/common/prop_gift_list/prop_gift_config.json').text\r\n gift3 = requests.get(\r\n 'https://webconf.douyucdn.cn/resource/common/gift/gift_template/20003.json').text\r\n gift_json2 = gift2.lstrip('DYConfigCallback(').rstrip(');')\r\n gift_json3 = gift3.lstrip('DYConfigCallback(').rstrip(');')\r\n gift_json1 = gift1['room_gift']['gift']\r\n gift_json2 = json.loads(gift_json2)['data']\r\n gift_json3 = json.loads(gift_json3)['data']\r\n for gift in gift_json1:\r\n gift_json[gift] = gift_json1[gift]['name']\r\n # 有子选项\r\n if gift_json1[gift].get('effect'):\r\n treasure_type = gift_json1[gift].get('effect')['treasure_type']\r\n name = gift_json1[gift].get('effect')['name']\r\n gift_json[treasure_type] = name\r\n for gift in gift_json2:\r\n gift_json[gift] = gift_json2[gift]['name']\r\n for gift in gift_json3:\r\n gift_json[str(gift['id'])] = gift['name']\r\n return gift_json\r\n\r\n\r\nif __name__ == '__main__':\r\n print('启动程序...', time.strftime('%Y-%m-%d %H:%M:%S'))\r\n dm = Spider()\r\n dm.login()\r\n dm.join_group()\r\n t2 = threading.Thread(target=dm.keep_alive)\r\n t1 = threading.Thread(target=dm.get_msg)\r\n t2.setDaemon(True)\r\n t1.start()\r\n t2.start()\r\n","sub_path":"danmu-big-gift.py","file_name":"danmu-big-gift.py","file_ext":"py","file_size_in_byte":11759,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"112735634","text":"import numpy as np\nimport time\nimport math\nfrom pylatex import Document, Section, Subsection, Tabular, Math, TikZ, Axis, \\\n Plot, Figure, Matrix, Alignat\nfrom pylatex.utils import italic\nimport subprocess\nimport os\n\n\ndef generateLatex(phrases, key, result, flag):\n\n geometry_options = {\"tmargin\": \"1cm\", \"lmargin\": \"3cm\", \"rmargin\": \"3cm\"}\n doc = Document(geometry_options=geometry_options)\n\n if(flag == \"E\"):\n with doc.create(Section('Encyption Input')):\n doc.append('Text: ' + \"\".join(phrases) + \"\\n\")\n doc.append('Key: ' +key)\n\n with doc.create(Section(\"Matrix Mltiplications\")):\n for phrase in phrases:\n M = createEncryptMatrix(key)\n messageMatrix = np.array([[getCapitalAlphaMod(phrase[0]),getCapitalAlphaMod(phrase[1]),getCapitalAlphaMod(phrase[2])]]).astype(\"float64\").T\n doc.append(Math(data=[r'1/' + str(26), Matrix(M), Matrix(messageMatrix), '=', Matrix(getModMatrix(M @ messageMatrix))]))\n doc.append(\"\\n\")\n doc.append(\"Encrypted chunk: \" + getStringFromMatrix(getModMatrix(M @ messageMatrix)))\n\n with doc.create(Section('Encyption Result')):\n doc.append('Cipher: ' + result)\n elif(flag == \"D\"):\n image_filename = './LookupHill.png'\n \n with doc.create(Section('Introduction')):\n doc.append('In this project, I implement a 3 × 3 Hill Cipher Machine in Python. This machine automatically generates LaTex reports to decipher user-input step by step. \\n')\n doc.append('We will be deciphering: ' + \"\".join(phrases) + ' using the key: ' + key + '. \\n')\n\n with doc.create(Section(\"Enryption Matrix\")):\n with doc.create(Figure(position='h!')) as lookup_hill:\n lookup_hill.add_image(image_filename, width='120px')\n lookup_hill.add_caption('Lookup Table of Hill Cipher')\n \n doc.append('We use the Lookup Table above to create the Encryption Matrix below. \\n')\n M = createEncryptMatrix(key)\n doc.append(Math(data=[Matrix(M)]))\n\n with doc.create(Section(\"Encryption Matrix Mod 26 Inverse\")):\n '''iM = np.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]])\n E21E31 = iM.copy()\n E21E31[(1,0)] = -1 * (augM[(1,0)]/augM[(0,0)])\n E21E31[(2,0)] = -1 * (augM[(2,0)]/augM[(0,0)])\n augM = E21E31.dot(augM)\n E32 = iM.copy()\n E32[(2,1)] = -1 * (augM[(2,1)]/augM[(1,1)])\n augM = E32.dot(augM)\n E23E13 = iM.copy()\n E23E13[(1,2)] = -1 * (augM[(1,2)]/augM[(2,2)])\n E23E13[(0,2)] = -1 * (augM[(0,2)]/augM[(2,2)])\n augM = E23E13.dot(augM)\n E12 = iM.copy()\n E12[(0,1)] = -1 * (augM[(0,1)]/augM[(1,1)])\n augM = E12.dot(augM)\n\n det = augM[(0,0)] * augM[(1,1)] * augM[(2,2)]\n if(det == 0 or math.isnan(det)):\n raise ValueError(\"Matrix Non-Invertible\")\n #print(\"Det: \" + str(det))\n if(egcd(int(round(det)), 26)[0] != 1):\n raise ValueError(\"Key Matrix determinent not co-prime with 26\")\n\n #print(\"Mod inv of det: \" + str(modinv(det, 26)))\n D = iM.copy()\n D[(0,0)] = 1/augM[(0,0)]\n D[(1,1)] = 1/augM[(1,1)]\n D[(2,2)] = 1/augM[(2,2)]\n augM = D.dot(augM)\n\n #Here are the additional steps needed to find the modular inverse of a matrix\n augM = augM * det\n augM = augM * modinv(int(round(det)), 26)\n\n modAugM = getModMatrix(augM[0:, 3:])\n\n return modAugM'''\n invMat = gaussianInverseMod26(createEncryptMatrixAug(key))\n doc.append(Math(data=[Matrix(invMat)]))\n doc.append(\"\\n\")\n \n with doc.create(Section(\"Matrix Multiplications\")):\n for phrase in phrases:\n M = invMat\n #print(M)\n messageMatrix = np.array([[getCapitalAlphaMod(phrase[0]),getCapitalAlphaMod(phrase[1]),getCapitalAlphaMod(phrase[2])]]).astype(\"float64\").T\n doc.append(Math(data=[Matrix(M), Matrix(messageMatrix), '=', Matrix(getModMatrix(M @ messageMatrix))]))\n doc.append(\"\\n\")\n doc.append(\"Decrypted chunk: \" + getStringFromMatrix(getModMatrix(M @ messageMatrix)))\n\n with doc.create(Section('Decryption Result')):\n doc.append('Plaintext: ' + result)\n\n doc.generate_pdf('full', clean_tex=False)\n\n subprocess.call(['open', 'full.pdf'])\n\ndef runHill(lOut = False):\n np.printoptions(precision=3, suppress=True)\n # 3-symbol hill cipher\n # key must be length 9\n # message must be multiples of 3\n\n choice = input(\"(E)ncrypt or (D)ecrypt?\\n\").upper()\n if choice == \"E\":\n text = input(\"Enter plaintext to encrypt\\n\")\n key = input(\"Enter key to use (must be 9 characters long)\\n\").upper()\n if(len(key) != 9):\n raise ValueError(\"Invalid key\")\n for i in range((3 - (len(text) % 3)) % 3):\n text += \"A\"\n threePhrases = getThreeLenPhrases(text)\n\n cipher = \"\"\n for phrase in threePhrases:\n cipher += encryptMessage(phrase, key)\n print(\"Encrypted text is: \" + cipher)\n \n if(lOut==True):\n generateLatex(threePhrases, key, cipher, \"E\")\n\n elif choice == \"D\":\n cipher = input(\"Enter encrypted text to decrypt\\n\")\n key = input(\"Enter key to use (must be 9 characters long)\\n\").upper()\n if(len(key) != 9):\n raise ValueError(\"Invalid key\")\n threePhrases = getThreeLenPhrases(cipher)\n text = \"\"\n for phrase in threePhrases:\n text += decryptCipher(phrase, key)\n print(\"Decrypted text is: \" + text)\n if(lOut==True):\n generateLatex(threePhrases, key, text, \"D\")\n else:\n print(\"Invalid input\\n\")\n \n\ndef gaussianInverseMod26(augM):\n iM = np.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]])\n E21E31 = iM.copy()\n E21E31[(1,0)] = -1 * (augM[(1,0)]/augM[(0,0)])\n E21E31[(2,0)] = -1 * (augM[(2,0)]/augM[(0,0)])\n augM = E21E31.dot(augM)\n E32 = iM.copy()\n E32[(2,1)] = -1 * (augM[(2,1)]/augM[(1,1)])\n augM = E32.dot(augM)\n E23E13 = iM.copy()\n E23E13[(1,2)] = -1 * (augM[(1,2)]/augM[(2,2)])\n E23E13[(0,2)] = -1 * (augM[(0,2)]/augM[(2,2)])\n augM = E23E13.dot(augM)\n E12 = iM.copy()\n E12[(0,1)] = -1 * (augM[(0,1)]/augM[(1,1)])\n augM = E12.dot(augM)\n \n det = augM[(0,0)] * augM[(1,1)] * augM[(2,2)]\n if(det == 0 or math.isnan(det)):\n raise ValueError(\"Matrix Non-Invertible\")\n #print(\"Det: \" + str(det))\n if(egcd(int(round(det)), 26)[0] != 1):\n raise ValueError(\"Key Matrix determinent not co-prime with 26\")\n \n #print(\"Mod inv of det: \" + str(modinv(det, 26)))\n D = iM.copy()\n D[(0,0)] = 1/augM[(0,0)]\n D[(1,1)] = 1/augM[(1,1)]\n D[(2,2)] = 1/augM[(2,2)]\n augM = D.dot(augM)\n \n #Here are the additional steps needed to find the modular inverse of a matrix\n augM = augM * det\n augM = augM * modinv(int(round(det)), 26)\n\n modAugM = getModMatrix(augM[0:, 3:])\n\n return modAugM\n\ndef getCapitalAlphaMod(letter):\n return ord(letter.upper()) % 65 \n \ndef getAlphaFromNum(num):\n return chr(65 + num)\n \ndef getModMatrix(m):\n m = m.round()\n m = m % 26\n return m\n\ndef getStringFromMatrix(m):\n s = \"\"\n for i in range(m.shape[0]):\n #print(m[i])\n s += getAlphaFromNum(int(m[i]))\n return s\n \ndef createEncryptMatrix(key):\n return np.array([[getCapitalAlphaMod(key[0]),getCapitalAlphaMod(key[1]),getCapitalAlphaMod(key[2])],\n [getCapitalAlphaMod(key[3]),getCapitalAlphaMod(key[4]),getCapitalAlphaMod(key[5])],\n [getCapitalAlphaMod(key[6]),getCapitalAlphaMod(key[7]),getCapitalAlphaMod(key[8])]]).astype(\"float64\")\n\ndef createEncryptMatrixAug(key):\n return np.array([[getCapitalAlphaMod(key[0]),getCapitalAlphaMod(key[1]),getCapitalAlphaMod(key[2]), 1, 0, 0],\n [getCapitalAlphaMod(key[3]),getCapitalAlphaMod(key[4]),getCapitalAlphaMod(key[5]), 0 ,1 ,0],\n [getCapitalAlphaMod(key[6]),getCapitalAlphaMod(key[7]),getCapitalAlphaMod(key[8]), 0, 0, 1]]).astype(\"float64\")\n\ndef encryptMessage(message, key):\n if len(message) != 3:\n raise ValueError(\"Invalid message size\")\n \n messageMatrix = np.array([getCapitalAlphaMod(message[0]),getCapitalAlphaMod(message[1]),getCapitalAlphaMod(message[2])]).astype(\"float64\")\n \n keyMatrix = createEncryptMatrix(key)\n \n keyMatrixAug = createEncryptMatrixAug(key)\n \n gaussianInverseMod26(keyMatrixAug) #To check whether inverse exists/key matrix is valid. Raises ValueError if not\n \n encryptedMatrix = getModMatrix(keyMatrix.dot(messageMatrix))\n \n return getStringFromMatrix(encryptedMatrix)\n \ndef decryptCipher(cipher, key):\n if len(cipher) != 3:\n raise ValueError(\"Invalid message size\")\n \n cipherMatrix = np.array([getCapitalAlphaMod(cipher[0]),getCapitalAlphaMod(cipher[1])\n ,getCapitalAlphaMod(cipher[2])]).astype(\"float64\")\n \n keyMatrixAug = createEncryptMatrixAug(key)\n \n #Find mod 26 inverse of KeyMatrix (use Gauss Jordan + mod methods)\n modInvMat = gaussianInverseMod26(keyMatrixAug)\n #print(modInvMat)\n #Multiply that by cipherMatrix to get decryptedMatrix\n decryptedMatrix = getModMatrix(modInvMat.dot(cipherMatrix))\n #print(decryptedMatrix)\n return getStringFromMatrix(decryptedMatrix)\n\ndef getThreeLenPhrases(text):\n if(len(text) % 3 != 0):\n raise ValueError(\"Text not multiple of 3\")\n\n threePhrArr = [text[::3], text[1::3], text[2::3]]\n threePhrases = []\n \n for i in range(len(threePhrArr[0])) :\n phrase = \"\"\n for j in range(len(threePhrArr)) :\n phrase += threePhrArr[j][i]\n threePhrases += [phrase]\n return threePhrases \n\n#Helper methods borrowed to calculate modular inverse of a number (https://stackoverflow.com/questions/4798654/modular-multiplicative-inverse-function-in-python)\ndef egcd(a, b):\n if a == 0:\n return (b, 0, 1)\n else:\n g, y, x = egcd(b % a, a)\n return (g, x - (b // a) * y, y)\n\ndef modinv(a, m):\n g, x, y = egcd(a, m)\n if g != 1:\n raise Exception('modular inverse does not exist')\n else:\n return x % m\n \nclass PDF(object):\n def __init__(self, pdf, size=(200,200)):\n self.pdf = pdf\n self.size = size\n\n def _repr_html_(self):\n return ''.format(self.pdf, self.size)\n\n def _repr_latex_(self):\n return r'\\includegraphics[width=1.0\\textwidth]{{{0}}}'.format(self.pdf)\n\nif __name__ == '__main__':\n runHill(True)\n #generateLatxex()","sub_path":"hill.py","file_name":"hill.py","file_ext":"py","file_size_in_byte":10826,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"186942638","text":"class Solution(object):\n def wordPattern(self, pattern, str):\n \"\"\"\n :type pattern: str\n :type str: str\n :rtype: bool\n \"\"\"\n\n words = str.split()\n\n if len(words) != len(pattern):\n return False\n\n p2w = {}\n w2p = {}\n for p, w in zip(pattern, words):\n if p not in p2w and w not in w2p:\n w2p[w] = p\n p2w[p] = w\n elif w not in w2p or w2p[w] != p:\n return False\n\n return True\n\nSolution().wordPattern(\"abba\", \"dog cat cat dog\")\n \n","sub_path":"word-pattern/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":564,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"133770667","text":"\"\"\"\nThis add-on allows to automatically create GitHub releases or prereleases\nand upload associated packages. It respectively provides a \"release\" and\na \"prerelease\" sub-command.\n\"\"\"\n\nimport argparse\nimport datetime as dt\nimport errno\nimport glob\nimport os\nimport platform\nimport sys\nimport subprocess\nimport textwrap\n\nfrom github_release import (\n gh_asset_delete,\n gh_asset_upload,\n get_refs,\n get_release_info,\n gh_release_create,\n gh_release_edit\n)\n\n\n#\n# Git\n#\n\nGITS = [\"git\"]\nif sys.platform == \"win32\":\n GITS = [\"git.cmd\", \"git.exe\"]\n\n\n# Copied from https://github.com/warner/python-versioneer/blob/master/src/subprocess_helper.py # noqa: E501\ndef run_command(commands, args, cwd=None, verbose=False, hide_stderr=False,\n env=None):\n \"\"\"Call the given command(s).\"\"\"\n assert isinstance(commands, list)\n for c in commands:\n dispcmd = str([c] + args)\n try:\n # remember shell=False, so use git.cmd on windows, not just git\n p = subprocess.Popen([c] + args, cwd=cwd, env=env,\n stdout=subprocess.PIPE,\n stderr=(subprocess.PIPE if hide_stderr\n else None))\n break\n except EnvironmentError:\n e = sys.exc_info()[1]\n if e.errno == errno.ENOENT:\n continue\n if verbose:\n print(\"unable to run %s\" % dispcmd)\n print(e)\n return None, None\n else:\n if verbose:\n print(\"unable to find command, tried %s\" % (commands,))\n return None, None\n stdout = p.communicate()[0].strip()\n if sys.version_info[0] >= 3:\n stdout = stdout.decode()\n if p.returncode != 0:\n if verbose:\n print(\"unable to run %s (error)\" % dispcmd)\n print(\"stdout was %s\" % stdout)\n return None, p.returncode\n return stdout, p.returncode\n\n\ndef get_tags(ref=\"HEAD\"):\n \"\"\"If any, return all tags associated with `ref`.\n\n Note that since git (<=2.5.0) does *NOT* provide a direct way to get\n these, we first get the sha of the HEAD tag (if any), then group the\n the tag by SHA and return the corresponding list.\n \"\"\"\n output, _ = run_command(\n GITS, [\"describe\", \"--tags\", \"--exact-match\", str(ref)],\n hide_stderr=True)\n if output is None:\n return []\n # Get tag's commit\n tag_sha, _ = run_command(GITS, [\"rev-list\", \"-n\", \"1\", output])\n # List all tags and associated SHAs\n tags, _ = run_command(GITS, [\"tag\", \"--list\"])\n # map of sha -> tags\n all_tags = {}\n for tag in tags.splitlines():\n sha, _ = run_command(\n GITS, [\"rev-list\", \"-n\", \"1\", \"refs/tags/%s\" % tag])\n if sha not in all_tags:\n all_tags[sha] = [tag]\n else:\n all_tags[sha].append(tag)\n return all_tags[tag_sha]\n\n\ndef get_current_date():\n now = dt.datetime.utcnow()\n return now.strftime(\"%Y-%m-%d %H:%m UTC\")\n\n\ndef get_commit_date(ref=\"HEAD\"):\n # 2017-06-14 22:53:31 -0400\n output, _ = run_command(\n GITS, [\"log\", \"-1\", \"--format=%ci\", str(ref)])\n # 2017-06-14 22:53:31\n output = \" \".join(output.split(\" \")[:2])\n # 20170614\n return dt.datetime.strptime(\n output, \"%Y-%m-%d %H:%M:%S\").strftime(\"%Y%m%d\")\n\n\ndef get_commit_short_sha(ref=\"HEAD\"):\n output, _ = run_command(\n GITS, [\"rev-parse\", \"--short=7\", str(ref)])\n return output\n\n\n#\n# Python\n#\n\ndef python_wheel_platform():\n this_platform = platform.system().lower()\n return {\n \"linux\": \"manylinux1\", \"darwin\": \"macosx\", \"windows\": \"win\"\n }.get(this_platform, this_platform)\n\n\n#\n# Mini-language for package selection\n#\n\ndef _substitute_package_selection_strings(package, what):\n tokens = {\n '': python_wheel_platform,\n '': get_commit_date,\n '': get_commit_short_sha\n }\n if any([token in package for token in tokens]):\n print(\"Updating %s [%s]\" % (what, package))\n for token, replace_func in tokens.items():\n if token in package:\n updated_value = replace_func()\n print(\" %s -> %s\" % (token, updated_value))\n package = package.replace(token, updated_value)\n return package\n\n\ndef _update_package_list(input_packages, what):\n if input_packages is None:\n return input_packages\n packages = []\n if isinstance(input_packages, str):\n return _substitute_package_selection_strings(input_packages, what)\n for package in input_packages:\n packages.append(\n _substitute_package_selection_strings(package, what))\n return packages\n\n\n#\n# Entry point\n#\n\ndef configure_parser(parser):\n parser.add_argument(\n \"repo_name\", type=str, metavar=\"ORG/PROJECT\",\n help=\"Name of the repository\"\n )\n # \"release\" arguments\n release_group = parser.add_argument_group('release')\n release_group.add_argument(\n \"--release-packages\", metavar=\"PATTERN\", type=str, nargs=\"*\",\n help=\"Path(s) and/or wildcard expression(s)\"\n )\n # \"prerelease\" arguments\n prerelease_group = parser.add_argument_group('prerelease')\n prerelease_group.add_argument(\n \"--prerelease-packages\", metavar=\"PATTERN\", type=str, nargs=\"*\",\n help=\"Path(s) and/or globbing pattern(s)\"\n )\n prerelease_group.add_argument(\n \"--prerelease-packages-clear-pattern\",\n type=str, metavar=\"PATTERN\",\n help=\"Globbing pattern selecting the set of packages to remove\"\n )\n prerelease_group.add_argument(\n \"--prerelease-packages-keep-pattern\",\n type=str, metavar=\"PATTERN\",\n help=\"Globbing pattern identifying the subset of packages to keep\"\n )\n prerelease_group.add_argument(\n \"--prerelease-tag\", type=str,\n help=\"Name of the tag to associate with the pre-release. \"\n \"If needed, tag is created or updated (default: latest)\"\n )\n prerelease_group.add_argument(\n \"--prerelease-name\", type=str,\n help=\"Name of the pre-release \"\n \"(default: 'TagName (updated on YYYY-MM-DD HH:MM UTC)'. \"\n \"Example: 'Latest (updated on 2017-02-12 14:42 UTC)')\"\n )\n prerelease_group.add_argument(\n \"--prerelease-sha\", type=str,\n help=\"Commit or branch name to associate with the pre-release \"\n \"(default: master)\"\n )\n # Common arguments\n parser.add_argument(\n \"--token\", type=str, metavar=\"GITHUB_TOKEN\",\n help=\"If not specified, expects GITHUB_TOKEN env. variable\"\n )\n parser.add_argument(\n \"--exit-success-if-missing-token\", action=\"store_true\",\n help=\"Gracefully exit if no GITHUB_TOKEN env. variable is found\"\n )\n parser.add_argument(\n \"--re-upload\", action=\"store_true\",\n help=\"If packages with same exist, delete and re-upload them\"\n )\n parser.add_argument(\n \"--display-python-wheel-platform\", action=\"store_true\",\n help=\"Display current python platform (manylinux1, macosx, or win)\"\n \" used for Python wheels.\"\n )\n parser.add_argument(\n \"--dry-run\", action=\"store_true\",\n help=\"don't create release, upload or erase packages \"\n \"but act like it was done\"\n )\n parser.set_defaults(\n token=os.environ.get(\"GITHUB_TOKEN\", None),\n prerelease_packages_clear_pattern=None,\n prerelease_packages_keep_pattern=None,\n prerelease_name=None,\n prerelease_sha=\"master\",\n prerelease_tag=\"latest\"\n )\n\n\ndef _delete_matching_packages(repo_name, tag, packages):\n release = get_release_info(repo_name, tag)\n asset_names = [asset['name'] for asset in release['assets']]\n for package in packages:\n for path in glob.glob(package):\n local_asset_name = os.path.basename(path)\n # XXX Asset uploaded on GitHub always have \"plus\" sign found\n # in their name replaced by \"dot\".\n local_asset_name = local_asset_name.replace(\"+\", \".\")\n if local_asset_name in asset_names:\n gh_asset_delete(repo_name, tag, local_asset_name)\n\n\ndef _upload_release(release_tag, args):\n assert release_tag is not None\n # Create release\n gh_release_create(\n args.repo_name,\n release_tag,\n publish=True, prerelease=False\n )\n # Remove existing assets matching selected ones\n if args.re_upload:\n _delete_matching_packages(\n args.repo_name, release_tag, args.release_packages)\n # Upload packages\n gh_asset_upload(\n args.repo_name, release_tag, args.release_packages, args.dry_run)\n return True\n\n\ndef _cancel_additional_appveyor_builds(prerelease_tag):\n \"\"\"\n This function is a workaround for\n https://github.com/scikit-build/scikit-ci-addons/issues/45\n \"\"\"\n appveyor = os.environ.get(\"APPVEYOR\", \"\").lower()\n if appveyor != \"true\":\n return\n print(\"\")\n script_name = \"cancel-queued-build.ps1\"\n script = os.path.join(os.path.dirname(__file__), \"../appveyor\", script_name)\n if not os.path.exists(script):\n print(\"skipping AppVeyor job cancellation: \"\n \"script %s not found\" % script)\n return\n\n tag_pattern = \"^%s(-tmp)?$\" % prerelease_tag\n\n subprocess.check_call([\n \"powershell\", \"-ExecutionPolicy\", \"Unrestricted\",\n \"-file\", script, \"-tag_pattern\", tag_pattern\n ])\n\n\ndef _upload_prerelease(args):\n # Set default prerelease name\n prerelease_name = args.prerelease_name\n if prerelease_name is None:\n prerelease_name = \"%s (updated on %s)\" % (\n args.prerelease_tag.title(), get_current_date()\n )\n # Create release\n gh_release_create(\n args.repo_name,\n args.prerelease_tag,\n name=prerelease_name,\n publish=True, prerelease=True\n )\n # Remove existing assets matching selected ones\n if args.re_upload:\n _delete_matching_packages(\n args.repo_name, args.prerelease_tag, args.prerelease_packages)\n # Upload packages\n gh_asset_upload(\n args.repo_name, args.prerelease_tag, args.prerelease_packages,\n args.dry_run\n )\n # Remove obsolete assets\n if args.prerelease_packages_clear_pattern is not None:\n gh_asset_delete(\n args.repo_name,\n args.prerelease_tag,\n args.prerelease_packages_clear_pattern,\n keep_pattern=args.prerelease_packages_keep_pattern,\n dry_run=args.dry_run\n )\n # If needed, update target commit\n sha = args.prerelease_sha\n if sha is not None:\n # If a branch name is specified, get associated commit\n refs = get_refs(args.repo_name, pattern=\"refs/heads/%s\" % sha)\n if refs:\n assert len(refs) == 1\n branch = sha\n sha = refs[0][\"object\"][\"sha\"]\n print(\"resolved '%s' to '%s'\" % (branch, sha))\n gh_release_edit(\n args.repo_name,\n args.prerelease_tag,\n target_commitish=sha\n )\n # If needed, update name associated with the release\n gh_release_edit(\n args.repo_name,\n args.prerelease_tag,\n name=prerelease_name\n )\n\n _cancel_additional_appveyor_builds(args.prerelease_tag)\n\n return True\n\n\ndef main(argv=None):\n parser = argparse.ArgumentParser(description=__doc__)\n configure_parser(parser)\n args = parser.parse_args(argv[1:] if argv else None)\n\n if args.display_python_wheel_platform:\n print(python_wheel_platform())\n exit(0)\n\n # Check required parameters\n upload_release = (len(args.release_packages) > 0\n if args.release_packages else False)\n upload_prerelease = (len(args.prerelease_packages) > 0\n if args.prerelease_packages else False)\n if not upload_release and not upload_prerelease:\n parser.print_usage()\n print(\"error: at least --release-packages or --prerelease-packages \"\n \"argument is required\")\n sys.exit(1)\n dual = upload_release and upload_prerelease\n\n if not args.token:\n print(\"-\" * 80)\n print(textwrap.dedent(\n r\"\"\"\n %s: A token is expected.\n\n Specify the --token parameter or set GITHUB_TOKEN environment variable.\n See https://help.github.com/articles/creating-an-access-token-for-command-line-use/\n \"\"\" % (\"skipping\" if args.exit_success_if_missing_token else \"error\") # noqa: E501\n ).lstrip())\n print(\"-\" * 80)\n parser.print_usage()\n exit(0 if args.exit_success_if_missing_token else 1)\n os.environ[\"GITHUB_TOKEN\"] = args.token\n\n # Update package arguments\n args.release_packages = _update_package_list(\n args.release_packages, \"release package\")\n args.prerelease_packages = _update_package_list(\n args.prerelease_packages, \"prerelease package\")\n args.prerelease_packages_clear_pattern = _update_package_list(\n args.prerelease_packages_clear_pattern,\n \"prerelease package clear pattern\")\n args.prerelease_packages_keep_pattern = _update_package_list(\n args.prerelease_packages_keep_pattern,\n \"prerelease package keep pattern\")\n\n msg = \"Checking if HEAD is a release tag\"\n print(msg)\n\n head_tags = get_tags()\n head_tags = list(filter(lambda tag: tag != args.prerelease_tag, head_tags))\n\n is_release = False\n release_tag = None\n\n if len(head_tags) > 0:\n is_release = True\n # If there are more than one tag (different from args.prerelease_tag)\n # associated with the HEAD, we use the first one.\n release_tag = head_tags[0]\n\n # Upload\n uploaded = False\n if upload_release:\n if is_release:\n print(\"%s - yes \"\n \"(found %s: creating release)\\n\" % (msg, release_tag))\n uploaded = _upload_release(release_tag, args)\n elif not dual:\n print(\"%s - no (skipping release upload)\\n\" % msg)\n\n if not uploaded and upload_prerelease:\n if not is_release:\n print(\"%s - no (creating prerelease)\\n\" % msg)\n _upload_prerelease(args)\n elif not dual:\n print(\"%s - yes (found %s: \"\n \"skipping prerelease upload)\\n\" % (msg, release_tag))\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"anyci/publish_github_release.py","file_name":"publish_github_release.py","file_ext":"py","file_size_in_byte":14394,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"167270314","text":"__all__ = [\n 'test_add',\n 'test_adda',\n 'test_cmp',\n 'test_cmpi',\n 'test_eor',\n 'test_jsr',\n 'test_lea',\n 'test_move',\n 'test_movea',\n 'test_neg',\n 'test_opcode_helper',\n 'test_or',\n 'test_ori',\n 'test_rts',\n 'test_sub',\n 'test_subq',\n 'test_trap',\n 'test_branches'\n]","sub_path":"tests/easier68k/core/opcodes/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":321,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"311169480","text":"#!/usr/bin/env python\n\n# ================================================================================================= \n# PRATICA SRL - www.praticasrl.com \n# Progetto: AGV - Veicolo a guida automatica per movimentazione pallet \n# Progetto cofinanziato dal Fondo POR FESR 14/20 Calabria \n# Autori: \n# Duardo Domenico \n# Chila' Antonino \n# Scimonelli Mattia \n# =================================================================================================\n\nimport socket\nimport json\nimport rospy\nimport std_msgs.msg \nimport geometry_msgs.msg \nimport math\nimport numpy as np\n\n#Impostazioni IP e Porta da dove riceve i dati (Server Sewio)\n#UDP_IP = \"10.0.0.5\" #IP pc fisso con RTLS Server in esecuzione\n#UDP_PORT = 5300\n\nUDP_IP = \"192.168.1.19\" #IP pc portatile con RTLS Server in esecuzione\nUDP_PORT = 5300\n\n\n#Apertura socket\nsock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) \nsock.bind((UDP_IP, UDP_PORT))\n\nif __name__==\"__main__\":\n\n\n\trtls_pub = rospy.Publisher(\"/pose_rtls\", geometry_msgs.msg.PoseWithCovarianceStamped, queue_size=10)\t\n\n\trospy.init_node('rtls_sewio')\n\n\trospy.loginfo(\"rtls_sewio: Nodo attivo! In attesa di dati da server sewio...\")\n\n\t#Preparo messaggio con dati fissi\n\trtls_mex = geometry_msgs.msg.PoseWithCovarianceStamped(header=rospy.Header(frame_id=\"rtls\"))\n\trtls_mex.pose.covariance = np.diag ([20, 20, 1e6, 1e6, 1e6, 1e6]).ravel() #COVARIANZA Pose x y z - Orientazione x y z \n\n\twhile not rospy.is_shutdown():\n\t\t\n\t\ttry:\n\t\t\tdata, addr = sock.recvfrom(2048) # buffer size is 1024 bytes\t\n\t\t\tdata_div = data.split(\"\\\"\")\n\t\t\tx = float(data_div[17])\n\t\t\ty = float(data_div[29])\n\t\texcept (IndexError):\n\t\t\trospy.loginfo(\"rtls_sewio: Errore dati \")\n\t\t\tcontinue\n\n\t\t#Se dati ricevuti OK invio messaggio\n\t\trtls_mex.header.stamp = rospy.Time.now()\n\t\trtls_mex.pose.pose.position.x = x\n\t\trtls_mex.pose.pose.position.y = y\n\t\trtls_mex.pose.pose.position.z = 0\n\t\trtls_pub.publish(rtls_mex)\t\n\t\trospy.loginfo(\"rtls_sewio: X: \" + str(x) + \"y: \" + str(y)) #Stampa di debug da cancellare\n\n\t\t\n","sub_path":"RTLS Sewio/script/rtls_sewio.py","file_name":"rtls_sewio.py","file_ext":"py","file_size_in_byte":2504,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"233858316","text":"c = input()\ns = input()\nt = input()\n\ndef match(a, b):\n if len(a) != len(b): return False\n i = 0\n while i < len(a) and (a[i] == b[i] or a[i] == '*'):\n i += 1\n return i == len(a)\n\ni = 0 \nscore = 0\nwhile i < len(c):\n if match(c[i:i+len(s)], s):\n c = c[:i] + s + c[i+len(s):]\n score += 1\n if c[i:i+len(t)] == t:\n score -= 1\n i += 1\nprint(score)\n","sub_path":"codeforces/558-div2/q4.py","file_name":"q4.py","file_ext":"py","file_size_in_byte":390,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"295351293","text":"import subprocess\nimport pdb\nimport re\nimport os.path\nimport time\nimport datetime\n\nhost = \"google.com\"\nlista = [\"ping\", host, \"-c\", \"1\"]\ntiempo = time.time()\ntiempo = datetime.datetime.fromtimestamp(tiempo).strftime('%Y-%m-%d %H:%M:%S')\ntry:\n resultado = subprocess.check_output(lista)\n resultado = resultado.decode(\"utf-8\")\n regex = re.compile(\".*time=([0-9.]* [a-z][a-z])\")\n r = regex.findall(resultado)[0]\n resultado = r\nexcept Exception as e:\n resultado = \"NaN\"\n\narchivo = open('/home/tona/Documents/sexto/redes3/ping-logger/salida.txt', \"a\")\nresultado = \"{} [{}] {}\\n\".format(host, tiempo, resultado)\narchivo.write(resultado)\narchivo.close()\n\"\"\"\n tamanio del archivo tras un ping 40 bytes\n Se hace un ping cada 1 minutos\n En una hora se hacen 60 pings\n En un dia se hacen 1440 pings\n En un anio se hacen 525600\n Por lo que en un anio el tamaño del archivo sera 21024000 bytes = 21.045 MB\n\"\"\"\n","sub_path":"ping-poller/ping-logger-archivo.py","file_name":"ping-logger-archivo.py","file_ext":"py","file_size_in_byte":934,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"242890266","text":"import pandas as pd\n\ndef save(df, filename):\n writer = pd.ExcelWriter(filename)\n df.to_excel(writer, \"sheet1\")\n writer.save()\n\ndf = pd.read_json(\"./nonPayment.json\")\n\nprint(df.count())\n\nsave(df, \"nonPayment.xlsx\")\n\n","sub_path":"data_go_kr/02_to_excel.py","file_name":"02_to_excel.py","file_ext":"py","file_size_in_byte":224,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"87631498","text":"\"\"\"\nsome util functions\n\"\"\"\nimport numpy as np\nimport pandas as pd\n\n# ********************************************************\n# 1st level analysis utility functions\n# ********************************************************\n\ndef get_contrasts(task, regress_rt=True):\n contrast_list = []\n if task == 'ANT':\n # task\n c1 = ['task','T', ['task'], [1]]\n # contrasts vs baseline\n c2 = ['orienting_network','T', ['orienting_network'], [1]]\n c3 = ['conflict_network','T', ['conflict_network'], [1]]\n contrast_list = [c1,c2, c3]\n if regress_rt:\n c4 = ['response_time', 'T', ['response_time'], [1]]\n contrast_list.append(c4)\n elif task == 'CCTHot':\n # task\n c1 = ['task','T', ['task'], [1]]\n # contrasts vs baseline\n c2 = ['EV','T', ['EV'], [1]]\n c3 = ['risk','T', ['risk'], [1]]\n contrast_list = [c1,c2,c3]\n if regress_rt:\n c4 = ['response_time', 'T', ['response_time'], [1]]\n contrast_list.append(c4)\n elif task == 'discountFix':\n c1 = ['subjective_value','T', ['subjective_value'], [1]]\n c2 = ['LL_vs_SS','T', ['LL_vs_SS'], [1]]\n contrast_list = [c1,c2]\n if regress_rt:\n c3 = ['response_time', 'T', ['response_time'], [1]]\n contrast_list.append(c3)\n elif task == 'DPX':\n # contrasts vs baseline\n c1 = ['AX','T', ['AX'], [1]]\n c2 = ['AY','T', ['AY'], [1]]\n c3 = ['BX','T', ['BX'], [1]]\n c4 = ['BY','T', ['BY'], [1]]\n # contrasts \n c5 = ['BX-BY','T', ['BX','BY'], [1,-1]]\n c6 = ['AY-BY','T', ['AY','BY'], [1,-1]]\n c7 = ['AY-BX','T', ['AY','BX'], [1,-1]]\n c8 = ['BX-AY','T', ['BX','AY'], [1,-1]]\n contrast_list = [c1,c2,c3,c4,c5,c6,c7,c8]\n if regress_rt:\n c9 = ['response_time', 'T', ['response_time'], [1]]\n contrast_list.append(c9)\n elif task == 'motorSelectiveStop':\n # contrasts vs baseline\n c1 = ['crit_go','T', ['crit_go'], [1]]\n c2 = ['crit_stop_success','T', ['crit_stop_success'], [1]]\n c3 = ['crit_stop_failure','T', ['crit_stop_failure'], [1]]\n c4 = ['noncrit_signal','T', ['noncrit_signal'], [1]]\n c5 = ['noncrit_nosignal','T', ['noncrit_nosignal'], [1]]\n # contrasts\n c6 = ['crit_stop_success-crit_go', 'T', \n ['crit_stop_success', 'crit_go'], [1,-1]]\n c7 = ['crit_stop_failure-crit_go', 'T', \n ['crit_stop_failure', 'crit_go'], [1,-1]]\n c8 = ['crit_go-noncrit_nosignal', 'T', \n ['crit_go', 'noncrit_nosignal'], [1,-1]]\n c9 = ['noncrit_signal-noncrit_nosignal', 'T' ,\n ['noncrit_signal','noncrit_nosignal'], [1,-1]]\n c10 = ['crit_stop_success-crit_stop_failure','T',\n ['crit_stop_success', 'crit_stop_failure'], [1,-1]]\n c11 = ['crit_stop_failure-crit_stop_success','T', \n ['crit_stop_failure', 'crit_stop_success'], [1,-1]]\n c12 = ['crit_stop_success-noncrit_signal','T',\n ['crit_stop_success', 'noncrit_signal'], [1,-1]]\n c13 = ['crit_stop_failure-noncrit_signal','T',\n ['crit_stop_failure', 'noncrit_signal'], [1,-1]]\n contrast_list = [c1,c2,c3,c4,c5,c6,c7,c8,c9,c10,c11,c12,c13]\n elif task == 'stopSignal':\n # contrasts vs baseline\n c1 = ['go','T', ['go'], [1]]\n c2 = ['stop_success','T', ['stop_success'], [1]]\n c3 = ['stop_failure','T', ['stop_failure'], [1]]\n # contrasts\n c4 = ['stop_success-go','T', ['stop_success', 'go'], [1,-1]]\n c5 = ['stop_failure-go','T', ['stop_failure', 'go'], [1,-1]]\n c6 = ['stop_success-stop_failure','T', \n ['stop_success', 'stop_failure'], [1,-1]]\n c7 = ['stop_failure-stop_success','T', \n ['stop_failure', 'stop_success'], [1,-1]]\n contrast_list = [c1,c2,c3,c4,c5,c6,c7]\n elif task == 'stroop':\n c1 = ['task', 'T', ['task'], [1]]\n c2 = ['incongruent-congruent','T', ['incongruent-congruent'], [1]]\n contrast_list = [c1, c2]\n if regress_rt:\n c3 = ['response_time', 'T', ['response_time'], [1]]\n contrast_list.append(c3)\n elif task == 'surveyMedley':\n # contrasts vs baseline\n c1 = ['stim_duration','T', ['stim_duration'], [1]]\n c2 = ['movement','T', ['movement'], [1]]\n contrast_list = [c1,c2]\n if regress_rt:\n c4 = ['response_time', 'T', ['response_time'], [1]]\n contrast_list.append(c4)\n elif task == 'twoByTwo':\n c1 = ['task', 'T', ['task'], [1]]\n # contrasts\n c2 = ['cue_switch_cost_100','T', \n ['cue_switch_cost_100'], [1]]\n c3 = ['cue_switch_cost_900','T', \n ['cue_switch_cost_900'], [1]]\n c4 = ['task_switch_cost_100','T', \n ['task_switch_cost_100'], [1]]\n c5 = ['task_switch_cost_900','T', \n ['task_switch_cost_900'], [1]]\n contrast_list = [c1,c2,c3,c4, c5]\n if regress_rt:\n c6 = ['response_time', 'T', ['response_time'], [1]]\n contrast_list.append(c6)\n elif task == 'WATT3':\n # contrasts vs baseline\n c1 = ['plan_PA_with','T', ['plan_PA_with'], [1]]\n c2 = ['plan_PA_without','T', ['plan_PA_without'], [1]]\n # contrasts\n c3 = ['search_depth','T', ['plan_PA_with','plan_PA_without'], [1,-1]]\n # nuisance\n c4 = ['movement', 'T', ['response_time'], [1]]\n c5 = ['feedback', 'T', ['response_time'], [1]]\n contrast_list = [c1,c2,c3,c4,c5]\n if regress_rt:\n c6 = ['response_time', 'T', ['response_time'], [1]]\n contrast_list.append(c6)\n # base covers generic contrast that just looks at all trials\n elif task == 'base':\n c1 = ['trial', 'T', ['trial'], [1]]\n contrast_list = [c1]\n return contrast_list\n \n# functions to extract fmri events\n \ndef get_ev_vars(output_dict, events_df, condition_spec, col=None, \n amplitude=1, duration=0, subset=None, onset_column='onset'):\n \"\"\" adds amplitudes, conditions, durations and onsets to an output_dict\n \n Args:\n events_df: events file to parse\n condition_spec: string specfying condition name, or list of tuples of the fomr\n (subset_key, name) where subset_key groups the rows in col. If a list,\n col must be specified\n col: the column to be subset by the keys in conditions\n amplitude: either an int or string. If int, sets a constant amplitude. If\n string, amplitude is set to that column\n duration: either an int or string. If int, sets a constant duration. If\n string, duration is set to that column\n subset: pandas query string to subset the data before use\n onset_column: the column of timing to be used for onsets\n \n \"\"\"\n required_keys = set(['amplitudes','conditions','durations','onsets'])\n assert set(output_dict.keys()) == required_keys\n amplitudes = output_dict['amplitudes']\n conditions = output_dict['conditions']\n durations = output_dict['durations']\n onsets = output_dict['onsets']\n \n # if subset is specified as a string, use to query\n if subset is not None:\n events_df = events_df.query(subset)\n # if a column is specified, group by the values in that column\n if type(condition_spec) == list:\n assert (col is not None), \"Must specify column when condition_spec is a list\"\n group_df = events_df.groupby(col)\n for condition, condition_name in condition_spec:\n if type(condition) is not list:\n condition = [condition]\n # get members of group identified by the condition list\n c_dfs = [group_df.get_group(c) for c in condition \n if c in group_df.groups.keys()]\n if len(c_dfs)!=0:\n c_df = pd.concat(c_dfs)\n conditions.append(condition_name)\n onsets.append(c_df.loc[:,onset_column].tolist())\n if type(amplitude) in (int,float):\n amplitudes.append([amplitude])\n elif type(amplitude) == str:\n amplitudes.append(c_df.loc[:,amplitude].tolist())\n if type(duration) in (int,float):\n durations.append([duration])\n elif type(duration) == str:\n durations.append(c_df.loc[:,duration].tolist())\n elif type(condition_spec) == str:\n group_df = events_df\n conditions.append(condition_spec)\n onsets.append(group_df.loc[:,onset_column].tolist())\n if type(amplitude) in (int,float):\n amplitudes.append([amplitude])\n elif type(amplitude) == str:\n amplitudes.append(group_df.loc[:,amplitude].tolist())\n elif type(amplitude) == list:\n amplitudes.append(amplitude)\n if type(duration) in (int,float):\n durations.append([duration])\n elif type(duration) == str:\n durations.append(group_df.loc[:,duration].tolist())\n elif type(duration) == list:\n durations.append(duration)\n # ensure that each column added is all numeric\n for attr in [durations, amplitudes, onsets]:\n assert np.issubdtype(np.array(attr[-1]).dtype, np.number) \n assert pd.isnull(attr[-1]).sum() == 0\n \n\n# specific task functions\ndef get_ANT_EVs(events_df, regress_rt=True):\n output_dict = {\n 'conditions': [],\n 'onsets': [],\n 'durations': [],\n 'amplitudes': []\n }\n # task\n get_ev_vars(output_dict, events_df,\n condition_spec='task',\n duration='duration',\n subset='junk==False')\n # cue type\n cue_amplitudes = ((events_df.cue=='spatial')*2-1).tolist()\n get_ev_vars(output_dict, events_df, \n condition_spec='orienting_network',\n amplitude=cue_amplitudes,\n duration='duration',\n subset='junk==False')\n # conflict type\n conflict_amplitudes = ((events_df.flanker_type=='incongruent')*2-1).tolist()\n get_ev_vars(output_dict, events_df,\n condition_spec='conflict_network',\n amplitude=conflict_amplitudes,\n duration='duration',\n subset='junk==False')\n # nuisance regressors\n get_ev_vars(output_dict, events_df, \n condition_spec=[(True, 'junk')], \n col='junk', \n duration='duration')\n if regress_rt == True:\n get_ev_vars(output_dict, events_df, \n condition_spec='response_time', \n duration='duration', \n amplitude='response_time',\n subset='junk==False')\n return output_dict\n\n\ndef get_CCTHot_EVs(events_df, regress_rt):\n output_dict = {\n 'conditions': [],\n 'onsets': [],\n 'durations': [],\n 'amplitudes': []\n }\n # task\n get_ev_vars(output_dict, events_df,\n condition_spec='task',\n duration='duration',\n subset='junk==False')\n # add main parametric regressors: EV and risk\n get_ev_vars(output_dict, events_df, \n condition_spec='EV', \n duration='duration', \n amplitude='EV',\n subset='junk==False')\n get_ev_vars(output_dict, events_df, \n condition_spec='risk',\n duration='duration', \n amplitude='risk',\n subset='junk==False')\n # other regressors\n get_ev_vars(output_dict, events_df, \n condition_spec='num_click_in_round', \n duration='duration', \n amplitude='num_click_in_round',\n subset='junk==False')\n get_ev_vars(output_dict, events_df, \n condition_spec=[(1,'reward'), (0,'punishment')], \n col='feedback',\n subset='junk==False')\n # nuisance regressors\n get_ev_vars(output_dict, events_df, \n condition_spec=[(True, 'junk')], \n col='junk', \n duration='duration')\n if regress_rt == True:\n get_ev_vars(output_dict, events_df, \n condition_spec='response_time', \n duration='duration', \n amplitude='response_time',\n subset='junk==False')\n return output_dict\n\ndef get_discountFix_EVs(events_df, regress_rt=True):\n output_dict = {\n 'conditions': [],\n 'onsets': [],\n 'durations': [],\n 'amplitudes': []\n }\n # regressors of interest\n trial_type = events_df.query('junk == False').trial_type \\\n .replace({'larger_later': 1, 'smaller_sooner': -1}).tolist()\n get_ev_vars(output_dict, events_df, \n condition_spec='LL_vs_SS',\n amplitude=trial_type,\n duration='duration',\n subset='junk == False')\n get_ev_vars(output_dict, events_df, \n condition_spec='subjective_value', \n duration='duration', \n amplitude='subjective_value',\n subset='junk==False')\n # nuisance regressors\n get_ev_vars(output_dict, events_df, \n condition_spec=[(True, 'junk')],\n col='junk', \n duration='duration')\n if regress_rt == True:\n get_ev_vars(output_dict, events_df, \n condition_spec='response_time', \n duration='duration', \n amplitude='response_time',\n subset='junk==False')\n return output_dict\n\ndef get_DPX_EVs(events_df, regress_rt=True):\n output_dict = {\n 'conditions': [],\n 'onsets': [],\n 'durations': [],\n 'amplitudes': []\n }\n get_ev_vars(output_dict, events_df, \n condition_spec=[('AX','AX'), ('AY','AY'), ('BX', 'BX'), ('BY','BY')],\n col='condition', \n duration='duration',\n subset='junk==False')\n # nuisance regressors\n get_ev_vars(output_dict, events_df, \n condition_spec=[(True, 'junk')], \n col='junk', \n duration='duration') \n if regress_rt == True:\n get_ev_vars(output_dict, events_df, \n condition_spec='response_time', \n duration='duration', \n amplitude='response_time',\n subset='junk==False')\n return output_dict\n\ndef get_motorSelectiveStop_EVs(events_df):\n output_dict = {\n 'conditions': [],\n 'onsets': [],\n 'durations': [],\n 'amplitudes': []\n }\n get_ev_vars(output_dict, events_df, \n condition_spec=[('crit_go','crit_go'), \n ('crit_stop_success', 'crit_stop_success'), \n ('crit_stop_failure', 'crit_stop_failure'),\n ('noncrit_signal', 'noncrit_signal'),\n ('noncrit_nosignal', 'noncrit_nosignal')],\n col='trial_type', \n duration='duration')\n # nuisance regressors\n get_ev_vars(output_dict, events_df, \n condition_spec=[(True, 'junk')], \n col='junk', \n duration='duration') \n return output_dict\n\ndef get_stopSignal_EVs(events_df):\n output_dict = {\n 'conditions': [],\n 'onsets': [],\n 'durations': [],\n 'amplitudes': []\n }\n get_ev_vars(output_dict, events_df, \n condition_spec=[('go','go'), \n ('stop_success', 'stop_success'), \n ('stop_failure', 'stop_failure')],\n col='trial_type', \n duration='duration',\n subset='junk==False')\n # nuisance regressors\n get_ev_vars(output_dict, events_df, \n condition_spec=[(True, 'junk')], \n col='junk', \n duration='duration') \n return output_dict\n\ndef get_stroop_EVs(events_df, regress_rt=True):\n output_dict = {\n 'conditions': [],\n 'onsets': [],\n 'durations': [],\n 'amplitudes': []\n }\n # task regressor\n get_ev_vars(output_dict, events_df,\n condition_spec='task',\n duration='duration',\n subset='junk==False')\n # contrast regressor\n conflict_amplitudes = ((events_df.condition=='incongruent')*2-1).tolist()\n get_ev_vars(output_dict, events_df,\n condition_spec='incongruent-congruent',\n amplitude=conflict_amplitudes,\n duration='duration',\n subset='junk==False')\n \n # nuisance regressors\n get_ev_vars(output_dict, events_df, \n condition_spec=[(True, 'junk')], \n col='junk', \n duration='duration') \n if regress_rt == True:\n get_ev_vars(output_dict, events_df, \n condition_spec='response_time', \n duration='duration', \n amplitude='response_time',\n subset='junk==False')\n return output_dict\n\ndef get_surveyMedley_EVs(events_df, regress_rt=True):\n output_dict = {\n 'conditions': [],\n 'onsets': [],\n 'durations': [],\n 'amplitudes': []\n }\n # nuisance regressors\n get_ev_vars(output_dict, events_df, \n condition_spec='stim_duration', \n duration='stim_duration')\n get_ev_vars(output_dict, events_df, \n condition_spec='movement', \n onset_column='movement_onset')\n get_ev_vars(output_dict, events_df, \n condition_spec=[(True, 'junk')], \n col='junk', \n duration='duration') \n if regress_rt == True:\n get_ev_vars(output_dict, events_df, \n condition_spec='response_time', \n duration='duration', \n amplitude='response_time',\n subset='junk==False')\n return output_dict\n\n \ndef get_twoByTwo_EVs(events_df, regress_rt=True):\n output_dict = {\n 'conditions': [],\n 'onsets': [],\n 'durations': [],\n 'amplitudes': []\n }\n # task regressor\n get_ev_vars(output_dict, events_df,\n condition_spec='task',\n duration='duration',\n subset='junk==False')\n # cue switch contrasts\n events_df.cue_switch.fillna(1, inplace=True)\n cue_switches = events_df.cue_switch.replace({'switch':1,'stay':-1}).tolist()\n get_ev_vars(output_dict, events_df, \n condition_spec='cue_switch_cost_900',\n amplitude=cue_switches,\n duration='duration',\n subset=\"CTI==900 and task_switch=='stay' and junk==False\")\n get_ev_vars(output_dict, events_df, \n condition_spec='cue_switch_cost_100',\n amplitude=cue_switches,\n duration='duration',\n subset=\"CTI==100 and task_switch=='stay' and junk==False\")\n # task switch contrasts\n task_switches = events_df.task_switch.replace({'switch':1,'stay':-1}).tolist()\n get_ev_vars(output_dict, events_df, \n condition_spec='task_switch_cost_900',\n amplitude=task_switches,\n duration='duration',\n subset=\"CTI==900 and cue_switch!='stay' and junk==False\")\n get_ev_vars(output_dict, events_df, \n condition_spec='task_switch_cost_100',\n amplitude=task_switches,\n duration='duration',\n subset=\"CTI==100 and cue_switch!='stay' and junk==False\")\n # nuisance regressors\n get_ev_vars(output_dict, events_df, \n condition_spec=[(True, 'junk')], \n col='junk', \n duration='duration') \n if regress_rt == True:\n get_ev_vars(output_dict, events_df, \n condition_spec='response_time', \n duration='duration', \n amplitude='response_time',\n subset='junk==False')\n return output_dict\n\ndef get_WATT3_EVs(events_df, regress_rt=True):\n output_dict = {\n 'conditions': [],\n 'onsets': [],\n 'durations': [],\n 'amplitudes': []\n }\n # planning conditions\n get_ev_vars(output_dict, events_df, \n condition_spec=[('PA_with_intermediate','plan_PA_with'),\n ('PA_without_intermediate','plan_PA_without')],\n col='condition', \n duration='duration', \n subset=\"planning==1\")\n # nuisance regressors\n get_ev_vars(output_dict, events_df, \n condition_spec='movement', \n onset_column='movement_onset')\n get_ev_vars(output_dict, events_df, \n condition_spec='feedback', \n duration='duration',\n subset=\"trial_id=='feedback'\")\n \n if regress_rt == True:\n get_ev_vars(output_dict, events_df, \n condition_spec='response_time', \n duration='duration', \n amplitude='response_time',\n subset=\"trial_id != 'feedback'\")\n return output_dict\n\ndef get_base_EVs(events_df):\n output_dict = {\n 'conditions': [],\n 'onsets': [],\n 'durations': [],\n 'amplitudes': []\n }\n get_ev_vars(output_dict, events_df, \n condition_spec='trial',\n duration='duration')\n get_ev_vars(output_dict, events_df, \n condition_spec=[(True, 'junk')], \n col='junk', \n duration='duration') \n return output_dict\n\ndef get_beta_series(events_df, regress_rt=True):\n output_dict = {\n 'conditions': [],\n 'onsets': [],\n 'durations': [],\n 'amplitudes': []\n }\n for i, row in events_df.iterrows():\n if row.junk == False:\n output_dict['conditions'].append('trial_%s' % str(i+1))\n output_dict['onsets'].append([row.onset])\n output_dict['durations'].append([row.duration])\n output_dict['amplitudes'].append([1])\n # nuisance regressors\n get_ev_vars(output_dict, events_df, \n condition_spec=[(True, 'junk')], \n col='junk', \n duration='duration') \n if regress_rt == True:\n get_ev_vars(output_dict, events_df, \n condition_spec='response_time', \n duration='duration', \n amplitude='response_time',\n subset='junk==False')\n return output_dict\n \n# How to model RT\n# For each condition model responses with constant duration \n# (average RT across subjects or block duration)\n# RT as a separate regressor for each onset, constant duration, \n# amplitude as parameteric regressor (function of RT)\ndef parse_EVs(events_df, task, regress_rt=True):\n if task == \"ANT\":\n EV_dict = get_ANT_EVs(events_df, regress_rt)\n elif task == \"CCTHot\": \n EV_dict = get_CCTHot_EVs(events_df, regress_rt)\n elif task == \"discountFix\": \n EV_dict = get_discountFix_EVs(events_df, regress_rt)\n elif task == \"DPX\":\n EV_dict = get_DPX_EVs(events_df, regress_rt)\n elif task == \"motorSelectiveStop\": \n EV_dict = get_motorSelectiveStop_EVs(events_df)\n elif task == 'surveyMedley':\n EV_dict = get_surveyMedley_EVs(events_df, regress_rt)\n elif task == \"stopSignal\":\n EV_dict = get_stopSignal_EVs(events_df)\n elif task == \"stroop\":\n EV_dict = get_stroop_EVs(events_df, regress_rt)\n elif task == \"twoByTwo\":\n EV_dict = get_twoByTwo_EVs(events_df, regress_rt)\n elif task == \"WATT3\":\n EV_dict = get_WATT3_EVs(events_df, regress_rt)\n # covers generic conversion of events_df into trial design file\n elif task == 'beta':\n EV_dict = get_beta_series(events_df, regress_rt)\n return EV_dict\n\n \ndef process_confounds(confounds_file):\n \"\"\"\n scrubbing for TASK\n remove TRs where FD>.5, stdDVARS (that relates to DVARS>.5)\n regressors to use\n ['X','Y','Z','RotX','RotY','RotY','<-firsttemporalderivative','stdDVARs','FD','respiratory','physio','aCompCor0-5']\n junk regressor: errors, ommissions, maybe very fast RTs (less than 50 ms)\n \"\"\"\n confounds_df = pd.read_csv(confounds_file, sep = '\\t', \n na_values=['n/a']).fillna(0)\n excessive_movement = (confounds_df.FramewiseDisplacement>.5) & \\\n (confounds_df.stdDVARS>1.2)\n excessive_movement_TRs = excessive_movement[excessive_movement].index\n excessive_movement_regressors = np.zeros([confounds_df.shape[0], \n np.sum(excessive_movement)])\n for i,TR in enumerate(excessive_movement_TRs):\n excessive_movement_regressors[TR,i] = 1\n excessive_movement_regressor_names = ['rejectTR_%d' % TR for TR in \n excessive_movement_TRs]\n # get movement regressors\n movement_regressor_names = ['X','Y','Z','RotX','RotY','RotZ']\n movement_regressors = confounds_df.loc[:,movement_regressor_names]\n movement_regressor_names += ['Xtd','Ytd','Ztd','RotXtd','RotYtd','RotZtd']\n movement_deriv_regressors = np.gradient(movement_regressors,axis=0)\n # add additional relevant regressors\n add_regressor_names = ['FramewiseDisplacement', 'stdDVARS'] \n add_regressor_names += [i for i in confounds_df.columns if 'aCompCor' in i]\n additional_regressors = confounds_df.loc[:,add_regressor_names].values\n regressors = np.hstack((movement_regressors,\n movement_deriv_regressors,\n additional_regressors,\n excessive_movement_regressors))\n # concatenate regressor names\n regressor_names = movement_regressor_names + add_regressor_names + \\\n excessive_movement_regressor_names\n return regressors, regressor_names\n \ndef process_physio(cardiac_file, resp_file):\n cardiac_file = '/mnt/temp/sub-s130/ses-1/func/sub-s130_ses-1_task-stroop_run-1_recording-cardiac_physio.tsv.gz'\n resp_file = '/mnt/temp/sub-s130/ses-1/func/sub-s130_ses-1_task-stroop_run-1_recording-respiratory_physio.tsv.gz'\n \n \n","sub_path":"fmri_analysis/scripts/old_nipype/event_utils.py","file_name":"event_utils.py","file_ext":"py","file_size_in_byte":26702,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"628408602","text":"#%%\nimport cmath\nfrom math import e, pi, radians\nwith open('input.txt') as file: \n directions = [(line[0],int(line[1:])) for line in file.read().split('\\n')]\n\n#%% \n\ndef rotation(change_dir, prev_orien): \n rot = {'L': 1, 'R': -1}[change_dir[0]]\n new_orien = prev_orien*e**(1j*change_dir[1]*rot*pi/180)\n return complex(round(new_orien.real), round(new_orien.imag))\n\n#%% \n\ncoords = complex(0,0)\nlegend = {\n 'N': complex(0,1),\n 'S': complex(0,-1),\n 'E': complex(1,0),\n 'W': complex(-1,0)\n}\norientation = complex(1,0)\n\nfor dir in directions: \n if dir[0] in legend.keys(): \n coords += dir[1]*legend[dir[0]]\n elif dir[0] == 'F': \n coords += dir[1]*orientation\n elif dir[0] in ('L','R'):\n orientation = rotation(dir,orientation)\n \nprint(coords, abs(coords.real) + abs(coords.imag))\n\n\n# %%\nwaypoint_loc = complex(10,1)\nship_loc = complex(0,0)\n\nfor dir in directions: \n if dir[0] in legend.keys(): \n waypoint_loc += dir[1]*legend[dir[0]]\n elif dir[0] == 'F': \n ship_loc = ship_loc + dir[1]*waypoint_loc\n elif dir[0] in ('L','R'):\n waypoint_loc = rotation(dir,waypoint_loc)\n\nprint(ship_loc, abs(ship_loc.real) + abs(ship_loc.imag))\n\n","sub_path":"d12/day12.py","file_name":"day12.py","file_ext":"py","file_size_in_byte":1209,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"492109337","text":"import re\nimport sublime\nimport sublime_plugin\n\nLOG_TYPES = ['log', 'info', 'warn', 'error']\n\ndef get_indent(view, region):\n matches = re.findall(r'^(\\s*)[^\\s]', view.substr(region))\n indent_str = matches and len(matches) and matches[0] or ''\n indent_line = view.substr(find_next_line(view, region)).strip()\n need_indent = [True for i in ['{', '=', ':', '->', '=>'] if indent_line.endswith(i)]\n indent_line.lstrip('{}[]() \\t')\n if need_indent:\n indent_str += len(indent_str) and indent_str[0] == '\\t' and '\\t' or ' '\n return indent_str\n\ndef find_next_line(view, region):\n while 0 < region.a and region.b < view.size() and view.classify(region.a) is sublime.CLASS_EMPTY_LINE:\n region = view.line(region.a - 1)\n return region\n\ndef get_wrapper(view, var_text, indent_str):\n # text_escaped = var_text.replace(\"'\", \"\\\\'\")\n tmpl = \"\\n\" + indent_str\n\n tmpl += \"use Data::Dumper;\"\n tmpl += \"\\n\" + indent_str\n tmpl += \"print Dumper (%s);\" % (var_text)\n return tmpl\n\ndef strip_semicolon(text):\n if text[-1:] == \";\":\n text = text[:-1]\n return text\n\nclass DumperwrapCommand(sublime_plugin.TextCommand):\n def run(self, edit):\n view = self.view\n selections = view.sel()\n\n for selection in selections:\n line_region = view.line(selection)\n\n if selection.empty():\n selected_text = sublime.get_clipboard()\n else:\n selected_text = view.substr(selection)\n\n strip_semicolon(selected_text)\n\n if len(selected_text) == 0:\n sublime.status_message('Nothing is selected or copied to clipbiard.')\n else:\n indent_str = get_indent(view, line_region)\n text = get_wrapper(view, selected_text, indent_str)\n view.insert(edit, line_region.end(), text)\n\n\n\nclass DumperremoveCommand(sublime_plugin.TextCommand):\n def run(self, edit):\n self.get_selections()\n cursor = self.view.sel()[0]\n line_region = self.view.line(cursor)\n string = self.view.substr(line_region)\n newstring = re.sub(r\"(?m)^((?!//|/\\*).)*((use\\ Data\\:\\:Dumper\\;)|(print\\ Dumper.*))\", '', string)\n self.view.replace(edit, line_region, newstring)\n self.view.sel().clear()\n\n def get_selections(self):\n selections = self.view.sel()\n has_selections = False\n for sel in selections:\n if sel.empty() == False:\n has_selections = True\n if not has_selections:\n full_region = sublime.Region(0, self.view.size())\n selections.add(full_region)\n return selections\n","sub_path":"dumperwrap.py","file_name":"dumperwrap.py","file_ext":"py","file_size_in_byte":2671,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"239580820","text":"# encoding:utf-8\nimport datetime\nfrom functools import reduce\nfrom flask import Flask, jsonify, request\nfrom flask_cors import CORS\nimport json\nfrom mailboxcode import mail\nfrom Tdengine import connect_tdengine, create_stable, create_account_subtable, insert_account_subtable, \\\n create_equipment_subtable, insert_equipment_subtable, drop_equipment_subtable, alter_accountstable, \\\n alter_equipmentstable, select_account, select_equipment, select_table, time_change, data_change, \\\n select_table_Finaldata, data_changelist, mapaddress\n\napp = Flask(__name__)\n# 实现跨域访问\ncors = CORS(app, resources={r\"*\": {\"origins\": \"*\"}})\n\n\n@app.route('/login', methods=['POST'])\ndef login():\n # {'requirement': xxx 'mailbox': '2550294419@qq.com', 'password': '123abc'}\n json_data = request.get_json()\n if json_data['requirement'] == 'registercode':\n if select_table(conn, td, \"account\", json_data['mailbox']) != 0:\n return json.dumps(False)\n else:\n coderesult = mail(json_data['mailbox'])\n return json.dumps(coderesult)\n if json_data['requirement'] == 'forgetcode':\n if select_table(conn, td, \"account\", json_data['mailbox']) != 0:\n coderesult = mail(json_data['mailbox'])\n return json.dumps(coderesult)\n else:\n return json.dumps(False)\n if json_data['requirement'] == 'register':\n if create_account_subtable(conn, td, json_data['mailbox'], json_data['password']) == 1:\n return json.dumps(True)\n else:\n return json.dumps(False)\n if json_data['requirement'] == 'forget':\n if alter_accountstable(conn, td, json_data['mailbox'], 'password', json_data['password']) == 1:\n return json.dumps(True)\n else:\n return json.dumps(False)\n if json_data['requirement'] == 'login':\n result = select_account(conn, td, 'mailbox', json_data['mailbox'])\n if not result:\n return json.dumps(False)\n else:\n if json_data['password'] == result[0][4]:\n if result[0][6] == \"\":\n return json.dumps({'power': result[0][5], 'information': {}})\n else:\n return json.dumps({'power': result[0][5], 'information': eval(result[0][6])})\n else:\n return json.dumps(False)\n\n\n# 设备数据页\n@app.route('/equipmentmap', methods=['POST'])\ndef equipmentmap():\n # {'mailbox': 'xxx'}\n json_data = request.get_json()\n # return json.dumps([{'name': '北流市', 'lng': 110.35, 'lat': 22.72}])\n datacount = {}\n data = []\n result = select_account(conn, td, 'mailbox', json_data['mailbox'])\n if not result:\n return json.dumps(False)\n else:\n for count in result:\n datacount[count[1]] = count[2]\n # {'original': True, 'beiliulzx1': True, 'guilinlzx1': False, 'guilinlzx2': False, 'guilinlzx3': False, 'guilin1': True, 'beijing': True, 'beijin1': False, 'beijin2': True, 'beiliu1': True, 'beiliulzx2': True, 'beiliulzx10': True, 'beijin': False, 'beijin3': False}\n for datacountdata in datacount:\n if not datacount[datacountdata]:\n pass\n else:\n result1 = select_equipment(conn, td, \"tid\", datacountdata)\n if result1:\n result2 = mapaddress(result1[0][3])\n data.append({'address': result1[0][3], 'tid': datacountdata, 'finalresponsetime': time_change(0, result1[0][0]), 'lng': result2['lng'], 'lat': result2['lat']})\n else:\n pass\n return json.dumps(data)\n\n\n# 设备数据页\n@app.route('/equipmenthome', methods=['POST'])\ndef equipmenthome():\n # {'requirement':'onquery', 'tid': 'xxx'}\n json_data = request.get_json()\n if json_data['requirement'] == 'onquery':\n result = select_table_Finaldata(conn, td, json_data['tid'])\n if result is not None:\n result1 = select_equipment(conn, td, \"tid\", json_data['tid'])\n result1 = result1[0][3]\n data = data_change(result)[0]\n data['address'] = result1\n return json.dumps(data)\n else:\n return json.dumps(False)\n if json_data['requirement'] == 'ononeday':\n result = select_table(conn, td, 'equipmentday', json_data['tid'])\n datalist = []\n for i in range(0, len(result)):\n if i % 40 == 0:\n datalist.append(result[i])\n data = data_changelist(datalist)\n return json.dumps(data)\n if json_data['requirement'] == 'ononeweek':\n result = select_table(conn, td, 'equipmentweek', json_data['tid'])\n datalist = []\n for i in range(0, len(result)):\n if i % 240 == 0:\n datalist.append(result[i])\n data = data_changelist(datalist)\n return json.dumps(data)\n # if json_data['requirement'] == 'ononemonth':\n # # result = select_table(conn, td, 'equipment', json_data['tid'])[-(240*7):0:240]\n # result = [(datetime.datetime(2021, 4, 6, 17, 29, 44, 375000), '01,03,14,00,4a,00,14,00,2a,02,51,02,11,87,39'),\n # (datetime.datetime(2021, 4, 6, 17, 30, 44, 375000), '01,03,14,00,4b,00,20,00,2c,02,53,02,13,87,39'),\n # (datetime.datetime(2021, 4, 6, 17, 31, 44, 375000), '01,03,14,00,4c,00,16,00,2d,02,55,02,1c,87,39'),\n # (datetime.datetime(2021, 4, 6, 17, 32, 44, 375000), '01,03,14,00,41,00,18,00,2f,02,5c,02,1f,87,39'),\n # (datetime.datetime(2021, 4, 6, 17, 33, 44, 375000), '01,03,14,00,43,00,17,00,21,02,51,02,10,87,39'),\n # (datetime.datetime(2021, 4, 6, 17, 34, 44, 375000), '01,03,14,00,4a,00,19,00,25,02,5d,02,17,87,39'),\n # (datetime.datetime(2021, 4, 6, 17, 35, 44, 375000), '01,03,14,00,42,00,18,00,23,02,5c,02,14,87,39'),\n # (datetime.datetime(2021, 4, 6, 17, 36, 44, 375000), '01,03,14,00,4a,00,15,00,2a,02,5a,02,15,87,39'),\n # (datetime.datetime(2021, 4, 6, 17, 37, 44, 375000), '01,03,14,00,4d,00,14,00,27,02,5c,02,1a,87,39'),\n # (datetime.datetime(2021, 4, 6, 17, 38, 44, 375000), '01,03,14,00,4a,00,19,00,24,02,52,02,1d,87,39'),\n # (datetime.datetime(2021, 4, 6, 17, 39, 44, 375000), '01,03,14,00,46,00,20,00,21,02,55,02,14,87,39'),\n # (datetime.datetime(2021, 4, 6, 17, 40, 44, 375000), '01,03,14,00,41,00,19,00,2a,02,56,02,1c,87,39'),\n # (datetime.datetime(2021, 4, 6, 17, 41, 44, 375000), '01,03,14,00,42,00,19,00,23,02,57,02,1d,87,39'),\n # (datetime.datetime(2021, 4, 6, 17, 42, 44, 375000), '01,03,14,00,41,00,15,00,2a,02,51,02,11,87,39')]\n #\n # data = data_changelist(result)\n # return json.dumps(data)\n\n\n# 设备控制台\n@app.route('/consoleequipment', methods=['POST'])\ndef consoleequipment():\n json_data = request.get_json()\n datacount = {}\n timerdata = {'total': 0, 'running': 0, 'stopped': 0}\n data = []\n if json_data['requirement'] == 'onquery':\n # {'requirement':'onquery', 'mailbox': '578761295@qq.com'}\n # result = [{'tid': 'beiliu1', 'status': 1, 'address': '北流', 'finalresponsetime': '2021-4-3'},]\n result = select_account(conn, td, 'mailbox', json_data['mailbox'])\n if not result:\n return json.dumps(False)\n else:\n for count in result:\n datacount[count[1]] = count[2]\n # {'original': True, 'beiliulzx1': True, 'guilinlzx1': False, 'guilinlzx2': False, 'guilinlzx3': False, 'guilin1': True, 'beijing': True, 'beijin1': False, 'beijin2': True, 'beiliu1': True, 'beiliulzx2': True, 'beiliulzx10': True, 'beijin': False, 'beijin3': False}\n for datacountdata in datacount:\n if not datacount[datacountdata]:\n pass\n else:\n result1 = select_equipment(conn, td, \"tid\", datacountdata)\n if result1:\n data.append({'tid': datacountdata, 'status': 1, 'address': result1[0][3],\n 'finalresponsetime': time_change(0, result1[0][0])})\n else:\n data.append({'tid': datacountdata, 'status': 1, 'address': 'xxx',\n 'finalresponsetime': 'xxxx-xx-xx xx:xx:xx'})\n return json.dumps(data)\n if json_data['requirement'] == 'timer':\n # {'requirement':'onquery', 'mailbox': '578761295@qq.com'}\n # result = [{'tid': 'beiliu1', 'status': 1, 'address': '北流', 'finalresponsetime': '2021-4-3'},]\n result = select_account(conn, td, 'mailbox', json_data['mailbox'])\n if not result:\n return json.dumps(False)\n else:\n for count in result:\n datacount[count[1]] = count[2]\n # {'original': True, 'beiliulzx1': True, 'guilinlzx1': False, 'guilinlzx2': False, 'guilinlzx3': False, 'guilin1': True, 'beijing': True, 'beijin1': False, 'beijin2': True, 'beiliu1': True, 'beiliulzx2': True, 'beiliulzx10': True, 'beijin': False, 'beijin3': False}\n for datacountdata in datacount:\n timerdata['total'] += 1\n if datacount[datacountdata]:\n timerdata['running'] += 1\n else:\n timerdata['stopped'] += 1\n return json.dumps(timerdata)\n if json_data['requirement'] == 'onchange':\n # {'requirement': 'onchange', 'tid': 'beiliu2', 'address': '桂林'}\n result = alter_equipmentstable(conn, td, json_data['tid'], 'address', json_data['address'])\n return json.dumps(result)\n\n\n# 个人中心\n@app.route('/consolepersonal', methods=['POST'])\ndef consolepersonal():\n json_data = request.get_json()\n result = alter_accountstable(conn, td, json_data['mailbox'], 'information', str(json_data['data']))\n return json.dumps(True)\n\n\n# 管理员\n@app.route('/useradministrator', methods=['POST'])\ndef useradministrator():\n json_data = request.get_json()\n datacount = {}\n data = []\n if json_data['requirement'] == 'onquery':\n # {'requirement':'onquery', 'mailbox': 'xxx', 'password':'xxx'}\n result = select_account(conn, td, 'mailbox', json_data['mailbox'])\n if not result:\n return json.dumps(False)\n else:\n if result[0][4] != json_data['password']:\n return json.dumps(False)\n else:\n for count in result:\n datacount[count[1]] = count[2]\n for resultdata in result:\n if not datacount[resultdata[1]]:\n pass\n else:\n result1 = select_equipment(conn, td, \"tid\", resultdata[1])\n if result1:\n data.append({'tid': resultdata[1], 'address': result1[0][3], 'tkey': result1[0][4]})\n datas = lambda x, y: x if y in x else x + [y]\n return json.dumps(reduce(datas, [[], ] + data))\n # return json.dumps([{'tid': '123', 'address': '456', 'tkey': '789'}, {'tid': '987', 'address': '654', 'tkey': '321'}])\n if json_data['requirement'] == 'onadd':\n # {'requirement':'onadd', 'mailbox': 'xxx', 'tid':'xxx', 'address':'xxx', 'tkey':'xxx'}\n result = create_equipment_subtable(conn, td, json_data['tid'], json_data['address'], json_data['tkey'])\n result1 = insert_account_subtable(conn, td, json_data['mailbox'], json_data['tid'], 1)\n if result == 1 and result1 == 1:\n return json.dumps(True)\n else:\n return json.dumps(False)\n if json_data['requirement'] == 'deleteRow':\n # 删除设备等于在该用户的表里对该客户的tidflag取反\n # {'requirement':'onquery', 'mailbox': 'xxx', 'tid':'xxx'}\n result = insert_account_subtable(conn, td, json_data['mailbox'], json_data['tid'], 0)\n if result == 1:\n return json.dumps(True)\n else:\n return json.dumps(False)\n if json_data['requirement'] == 'ondel':\n # {'requirement': 'ondel', 'mailbox': 'xxx', 'data': [{'tid': '123', 'address': '456', 'tkey': '789'}, {'tid': '987', 'address': '654', 'tkey': '321'}]}\n # 删除设备等于在该用户的表里对该客户的tidflag取反\n for data in json_data['data']:\n result = insert_account_subtable(conn, td, json_data['mailbox'], data['tid'], 0)\n if result != 1:\n return json.dumps(False)\n return json.dumps(True)\n\n\n# 超级管理员\n@app.route('/superadministrator', methods=['POST'])\ndef superadministrator():\n json_data = request.get_json()\n if json_data['requirement'] == 'onquery':\n # {'requirement':'onquery', 'mailbox': '578761295@qq.com'}\n result = select_account(conn, td, 'mailbox', json_data['mailbox'])\n if not result:\n return json.dumps(False)\n else:\n data = {'mailbox': result[0][3], 'password': result[0][4], 'power': result[0][5]}\n return json.dumps(data)\n if json_data['requirement'] == 'onadddel':\n # {'requirement':'onadddel', 'mailbox': 'xxx','power':'xxx'}\n result = alter_accountstable(conn, td, json_data['mailbox'], 'power', json_data['power'])\n if result == 1:\n return json.dumps(True)\n else:\n return json.dumps(False)\n if json_data['requirement'] == 'onchange':\n # {'requirement':'onadddel', 'mailbox': 'xxx','password':'xxx'}\n result = alter_accountstable(conn, td, json_data['mailbox'], 'password', json_data['password'])\n if result == 1:\n return json.dumps(True)\n else:\n return json.dumps(False)\n\n\nif __name__ == '__main__':\n conn, td = connect_tdengine() # 连接数据库\n if conn == 0 and td == 0:\n exit(0)\n else:\n app.run(host='0.0.0.0', port=5000)\n","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":13962,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"282137983","text":"from nose.plugins.attrib import attr\n\nfrom marvin.cloudstackException import CloudstackAPIException\nfrom marvin.cloudstackTestCase import cloudstackTestCase\nfrom marvin.codes import FAILED\nfrom marvin.lib.base import (\n NATRule,\n FireWallRule,\n VirtualMachine,\n PublicIPAddress,\n Account\n)\nfrom marvin.lib.common import (\n list_nat_rules,\n list_public_ip,\n get_template,\n get_zone,\n get_domain,\n get_default_virtual_machine_offering\n)\nfrom marvin.lib.utils import cleanup_resources\nfrom marvin.utils.MarvinLog import MarvinLog\nfrom marvin.utils.SshClient import SshClient\n\n\nclass TestPortForwarding(cloudstackTestCase):\n @classmethod\n def setUpClass(cls):\n cls.logger = MarvinLog(MarvinLog.LOGGER_TEST).get_logger()\n\n testClient = super(TestPortForwarding, cls).getClsTestClient()\n cls.apiclient = testClient.getApiClient()\n cls.services = testClient.getParsedTestDataConfig()\n cls.hypervisor = testClient.getHypervisorInfo()\n # Get Zone, Domain and templates\n cls.domain = get_domain(cls.apiclient)\n cls.zone = get_zone(cls.apiclient, testClient.getZoneForTests())\n template = get_template(\n cls.apiclient,\n cls.zone.id\n )\n if template == FAILED:\n assert False, \"get_template() failed to return template with description %s\" % cls.services[\n \"ostype\"]\n\n # Create an account, network, VM and IP addresses\n cls.account = Account.create(\n cls.apiclient,\n cls.services[\"account\"],\n admin=True,\n domainid=cls.domain.id\n )\n cls.services[\"virtual_machine\"][\"zoneid\"] = cls.zone.id\n cls.service_offering = get_default_virtual_machine_offering(cls.apiclient)\n cls.virtual_machine = VirtualMachine.create(\n cls.apiclient,\n cls.services[\"virtual_machine\"],\n templateid=template.id,\n accountid=cls.account.name,\n domainid=cls.account.domainid,\n serviceofferingid=cls.service_offering.id\n )\n cls._cleanup = [\n cls.virtual_machine,\n cls.account\n ]\n\n @classmethod\n def tearDownClass(cls):\n try:\n cls.apiclient = super(\n TestPortForwarding,\n cls).getClsTestClient().getApiClient()\n cleanup_resources(cls.apiclient, cls._cleanup)\n except Exception as e:\n raise Exception(\"Warning: Exception during cleanup : %s\" % e)\n\n def setUp(self):\n self.apiclient = self.testClient.getApiClient()\n self.cleanup = []\n return\n\n def tearDown(self):\n cleanup_resources(self.apiclient, self.cleanup)\n return\n\n @attr(tags=['advanced'])\n def test_01_port_fwd_on_src_nat(self):\n \"\"\"Test for port forwarding on source NAT\"\"\"\n\n # Validate the following:\n # 1. listPortForwarding rules API should return the added PF rule\n # 2. attempt to do an ssh into the user VM through the sourceNAT\n\n src_nat_ip_addrs = list_public_ip(\n self.apiclient,\n account=self.account.name,\n domainid=self.account.domainid\n )\n\n self.assertEqual(\n isinstance(src_nat_ip_addrs, list),\n True,\n \"Check list response returns a valid list\"\n )\n src_nat_ip_addr = src_nat_ip_addrs[0]\n\n # Check if VM is in Running state before creating NAT rule\n vm_response = VirtualMachine.list(\n self.apiclient,\n id=self.virtual_machine.id\n )\n\n self.assertEqual(\n isinstance(vm_response, list),\n True,\n \"Check list VM returns a valid list\"\n )\n\n self.assertNotEqual(\n len(vm_response),\n 0,\n \"Check Port Forwarding Rule is created\"\n )\n self.assertEqual(\n vm_response[0].state,\n 'Running',\n \"VM state should be Running before creating a NAT rule.\"\n )\n # Open up firewall port for SSH\n FireWallRule.create(\n self.apiclient,\n ipaddressid=src_nat_ip_addr.id,\n protocol=self.services[\"natrule_ssh\"][\"protocol\"],\n cidrlist=['0.0.0.0/0'],\n startport=self.services[\"natrule_ssh\"][\"publicport\"],\n endport=self.services[\"natrule_ssh\"][\"publicport\"]\n )\n\n # Create NAT rule\n nat_rule = NATRule.create(\n self.apiclient,\n self.virtual_machine,\n self.services[\"natrule_ssh\"],\n src_nat_ip_addr.id\n )\n\n list_nat_rule_response = list_nat_rules(\n self.apiclient,\n id=nat_rule.id\n )\n self.assertEqual(\n isinstance(list_nat_rule_response, list),\n True,\n \"Check list response returns a valid list\"\n )\n\n self.assertNotEqual(\n len(list_nat_rule_response),\n 0,\n \"Check Port Forwarding Rule is created\"\n )\n self.assertEqual(\n list_nat_rule_response[0].id,\n nat_rule.id,\n \"Check Correct Port forwarding Rule is returned\"\n )\n # SSH virtual machine to test port forwarding\n try:\n self.logger.debug(\"SSHing into VM with IP address %s with NAT IP %s\" %\n (\n self.virtual_machine.ipaddress,\n src_nat_ip_addr.ipaddress\n ))\n\n self.virtual_machine.get_ssh_client(src_nat_ip_addr.ipaddress)\n vm_response = VirtualMachine.list(\n self.apiclient,\n id=self.virtual_machine.id\n )\n if vm_response[0].state != 'Running':\n self.fail(\n \"State of VM : %s is not found to be Running\" % str(\n self.virtual_machine.ipaddress))\n\n except Exception as e:\n self.fail(\n \"SSH Access failed for %s: %s\" %\n (self.virtual_machine.ipaddress, e)\n )\n\n try:\n nat_rule.delete(self.apiclient)\n except Exception as e:\n self.fail(\"NAT Rule Deletion Failed: %s\" % e)\n\n # NAT rule listing should fail as the nat rule does not exist\n with self.assertRaises(Exception):\n list_nat_rules(self.apiclient,\n id=nat_rule.id)\n\n # Check if the Public SSH port is inaccessible\n with self.assertRaises(Exception):\n self.logger.debug(\n \"SSHing into VM with IP address %s after NAT rule deletion\" %\n self.virtual_machine.ipaddress)\n\n SshClient(\n src_nat_ip_addr.ipaddress,\n self.virtual_machine.ssh_port,\n self.virtual_machine.username,\n self.virtual_machine.password,\n retries=2,\n delay=0\n )\n return\n\n @attr(tags=['advanced'])\n def test_02_port_fwd_on_non_src_nat(self):\n \"\"\"Test for port forwarding on non source NAT\"\"\"\n\n # Validate the following:\n # 1. listPortForwardingRules should not return the deleted rule anymore\n # 2. attempt to do ssh should now fail\n\n ip_address = PublicIPAddress.create(\n self.apiclient,\n self.account.name,\n self.zone.id,\n self.account.domainid,\n self.services[\"virtual_machine\"]\n )\n self.cleanup.append(ip_address)\n\n # Check if VM is in Running state before creating NAT rule\n vm_response = VirtualMachine.list(\n self.apiclient,\n id=self.virtual_machine.id\n )\n\n self.assertEqual(\n isinstance(vm_response, list),\n True,\n \"Check list VM returns a valid list\"\n )\n\n self.assertNotEqual(\n len(vm_response),\n 0,\n \"Check Port Forwarding Rule is created\"\n )\n self.assertEqual(\n vm_response[0].state,\n 'Running',\n \"VM state should be Running before creating a NAT rule.\"\n )\n # Open up firewall port for SSH\n FireWallRule.create(\n self.apiclient,\n ipaddressid=ip_address.ipaddress.id,\n protocol=self.services[\"natrule_ssh\"][\"protocol\"],\n cidrlist=['0.0.0.0/0'],\n startport=self.services[\"natrule_ssh\"][\"publicport\"],\n endport=self.services[\"natrule_ssh\"][\"publicport\"]\n )\n # Create NAT rule\n nat_rule = NATRule.create(\n self.apiclient,\n self.virtual_machine,\n self.services[\"natrule_ssh\"],\n ip_address.ipaddress.id\n )\n # Validate the following:\n # 1. listPortForwardingRules should not return the deleted rule anymore\n # 2. attempt to do ssh should now fail\n\n list_nat_rule_response = list_nat_rules(\n self.apiclient,\n id=nat_rule.id\n )\n self.assertEqual(\n isinstance(list_nat_rule_response, list),\n True,\n \"Check list response returns a valid list\"\n )\n self.assertNotEqual(\n len(list_nat_rule_response),\n 0,\n \"Check Port Forwarding Rule is created\"\n )\n self.assertEqual(\n list_nat_rule_response[0].id,\n nat_rule.id,\n \"Check Correct Port forwarding Rule is returned\"\n )\n\n try:\n self.logger.debug(\"SSHing into VM with IP address %s with NAT IP %s\" %\n (\n self.virtual_machine.ipaddress,\n ip_address.ipaddress.ipaddress\n ))\n self.virtual_machine.get_ssh_client(ip_address.ipaddress.ipaddress)\n except Exception as e:\n self.fail(\n \"SSH Access failed for %s: %s\" %\n (self.virtual_machine.ipaddress, e)\n )\n\n nat_rule.delete(self.apiclient)\n\n try:\n list_nat_rule_response = list_nat_rules(\n self.apiclient,\n id=nat_rule.id\n )\n except CloudstackAPIException:\n self.logger.debug(\"Nat Rule is deleted\")\n\n # Check if the Public SSH port is inaccessible\n with self.assertRaises(Exception):\n self.logger.debug(\n \"SSHing into VM with IP address %s after NAT rule deletion\" %\n self.virtual_machine.ipaddress)\n\n SshClient(\n ip_address.ipaddress.ipaddress,\n self.virtual_machine.ssh_port,\n self.virtual_machine.username,\n self.virtual_machine.password,\n retries=2,\n delay=0\n )\n return\n","sub_path":"cosmic-core/test/integration/smoke/test_port_forwarding.py","file_name":"test_port_forwarding.py","file_ext":"py","file_size_in_byte":10930,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"146565278","text":"r = open(\"input.txt\", \"r\").read().split(\"\\n\")\nN = int(r[0])\nmass = []\n\nfor i in range(N):\n mass.append(r[i + 1])\n\n\n\nfor i in range(N):\n temp = list(mass[i].lower())\n for j in range(len(temp)):\n \n\n \n if \"s\" == temp[j] and j != 0:\n if j + 1 == len(temp):\n temp[j] = \"th\"\n elif \"h\" != temp[j + 1]:\n temp[j] = \"th\"\n \n if temp[0] == \"e\":\n temp[0] = \"ae\"\n if temp[j] == \"o\" and temp[j + 1] == \"o\":\n temp[j] = \"o\"\n temp[j + 1] = \"u\"\n\n \n \n temp_list = \"\"\n for j in range(len(temp)):\n temp_list += temp[j]\n mass[i] = temp_list\n\nw = open(\"output.txt\", \"w\")\nfor i in range(len(mass)):\n w.write(mass[i]+\"\\n\")\nw.close()\nprint(mass)\n","sub_path":"olimp/olimp_informat_2016/Англійська від Степана.py","file_name":"Англійська від Степана.py","file_ext":"py","file_size_in_byte":816,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"266956807","text":"import unittest\nimport os\n\nfrom funpdbe_csv_exporter.residue_mapping import ResidueMap\nfrom funpdbe_csv_exporter.pipeline_logger import PipelineLogger\n\n\nclass TestResidueMap(unittest.TestCase):\n\n def test_residue_map(self):\n with open(\"tests/test_1.csv\", \"w\") as test:\n test.write(\"2fit_1_1,2fit_1_A,1,,MET,0.0,N,N,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,\")\n rm = ResidueMap(\"tests/test_1.csv\", PipelineLogger(\"test\"))\n self.assertEqual(rm.map_residues(), {\"2fit_1_1\": \"2fit_1_1\"})\n\n with open(\"tests/test_2.csv\", \"w\") as test:\n test.write(\"2asd_1_1,2asd_1_A,2,,MET,0.0,N,N,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,\")\n\n with open(\"tests/test_header.csv\", \"w\") as test:\n test.write(\"invalid\")\n\n rm = ResidueMap(\"tests/test*.csv\", PipelineLogger(\"test\"))\n self.assertEqual(rm.map_residues(), {\"2fit_1_1\": \"2fit_1_1\", \"2asd_1_2\": \"2asd_1_1\"})\n\n os.system(\"rm ./tests/test*.csv\")\n","sub_path":"tests/test_residue_mapping.py","file_name":"test_residue_mapping.py","file_ext":"py","file_size_in_byte":947,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"134641688","text":"import numpy as np\nimport torch\nimport torch.utils.data as data\nimport torchvision\nimport torchvision.transforms as transforms\nfrom PIL import Image\nfrom random import sample, random\nimport os\nfrom os.path import join, dirname\nimport re\nimport pdb\nimport cv2\n\nclass JigsawDataset(data.Dataset):\n def __init__(self, name, split='src', jig_classes=100,\n img_transformer=None, tile_transformer=None, patches=False, bias_whole_image=None, cv_split_id = [0]):\n\n data_path = join(dirname(__file__), '..', 'datasets', name)\n\n self.split = split\n\n if split == 'src': \n disp_msg = 'Supervised Train Loader :'\n elif split == 'tar':\n disp_msg = 'Unsupervised Train Loader :'\n else:\n disp_msg = 'Validation Loader :'\n\n # Create a dictionary of classes, videos and respective frames\n self.data_list = []\n\n # Assumed folder structure\n # UAH\n # |-SplitID (eg. Split0)\n # |- ClassName (eg. adenoma, hyper, serr)\n # |- ClassVideoIDMode (eg. adenoma1nbi)\n # |- FrameNum (eg. 0.png)\n \n # Iterate through all split folders\n for split_folder in os.listdir(data_path): \n # Split string into text & numbers \n # https://www.geeksforgeeks.org/python-splitting-text-and-number-in-string/\n temp = re.compile(\"([a-zA-Z]+)([0-9]+)\") \n split_id = temp.match(split_folder).groups() \n split_id = int(split_id[-1])\n \n # Check if the current split belongs to the required loader\n if split_id in cv_split_id:\n print(disp_msg + split_folder + ' used')\n\n split_path = os.path.join(data_path, split_folder)\n\n # Iterate through all class folders\n for class_name in os.listdir(split_path): \n # Iterate through all videos \n class_path = os.path.join(split_path, class_name)\n\n for video_name in os.listdir(class_path):\n video_path = os.path.join(class_path,video_name)\n\n if not os.path.isdir(video_path):\n continue\n label, lesion_id, mode = self.get_video_label_uah(video_name)\n\n # Create list of all frames in the video folder\n for frame_name in os.listdir(video_path):\n frame_path = os.path.join(video_path, frame_name)\n self.data_list.append(dict(lesion_id = lesion_id,\n frame_path = frame_path,\n label = label,\n mode = mode\n ))\n\n if len(self.data_list):\n print(\"{} total frames collected\".format(len(self.data_list)))\n else:\n print(\"No images were loaded from {}\".format(data_path))\n \n self.permutations = self.__retrieve_permutations(jig_classes)\n self.grid_size = 3\n self.bias_whole_image = bias_whole_image\n if patches:\n self.patch_size = 64\n self._image_transformer = img_transformer\n self._augment_tile = tile_transformer\n if patches:\n self.returnFunc = lambda x: x\n else:\n def make_grid(x):\n return torchvision.utils.make_grid(x, self.grid_size, padding=0)\n self.returnFunc = make_grid\n\n def get_tile(self, img, n):\n w = float(img.size[0]) / self.grid_size\n y = int(n / self.grid_size)\n x = n % self.grid_size\n tile = img.crop([x * w, y * w, (x + 1) * w, (y + 1) * w])\n tile = self._augment_tile(tile)\n return tile\n\n def get_image(self, index):\n frame_dict = self.data_list[index]\n framename = frame_dict['frame_path']\n label = frame_dict['label']\n\n img = Image.open(framename).convert('RGB')\n return self._image_transformer(img), label\n\n def __getitem__(self, index):\n img, class_label = self.get_image(index)\n\n n_grids = self.grid_size ** 2\n tiles = [None] * n_grids\n for n in range(n_grids):\n tiles[n] = self.get_tile(img, n)\n\n order = np.random.randint(len(self.permutations) + 1) # added 1 for class 0: unsorted\n if self.bias_whole_image:\n if self.bias_whole_image > random():\n order = 0\n if order == 0 or self.split != 'train':\n data = tiles\n else:\n data = [tiles[self.permutations[order - 1][t]] for t in range(n_grids)]\n\n data = torch.stack(data, 0)\n sample = {'images': self.returnFunc(data),\n 'aux_labels': int(order),\n 'class_labels': int(class_label)}\n return sample\n\n def __len__(self):\n return len(self.data_list)\n\n def __retrieve_permutations(self, classes):\n all_perm = np.load(join(dirname(__file__), 'permutations_%d.npy' % (classes)))\n # from range [1,9] to [0,8]\n if all_perm.min() == 1:\n all_perm = all_perm - 1\n\n return all_perm\n\n def get_video_label_uah(self, filename):\n #Get class label from video's filename \n #hyperplastic = 0, adenoma = 1, serrated = 2 \n #filename format : classFileNumberMode \n \n #Split filename \n #REF : https://stackoverflow.com/questions/430079/how-to-split-strings-into-text-and-number\n match = re.match(r\"([a-z]+)([0-9]+)([a-z]+)\", filename, re.I)\n if match:\n className, fileId, mode = match.groups()\n else:\n print(\"Issue spilting file {}\".format(filename))\t\t\t\n\n if className == 'hyper':\n class_label = 0\n elif className == 'adenoma':\n class_label = 1\n elif className == 'serr':\n class_label = 2\n\n return (class_label, fileId, mode)\n\nclass JigsawTestDataset(JigsawDataset):\n def __init__(self, *args, **xargs):\n super().__init__(*args, **xargs)\n\n def __getitem__(self, index):\n frame_dict = self.data_list[index]\n framename = frame_dict['frame_path']\n label = frame_dict['label']\n img = Image.open(framename).convert('RGB')\n \n sample = {'images': self._image_transformer(img),\n 'aux_labels': 0,\n 'class_labels': int(label)}\n return sample\n","sub_path":"data/jigsaw_dataset.py","file_name":"jigsaw_dataset.py","file_ext":"py","file_size_in_byte":6595,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"378664161","text":"# %%\n# Import the modules we will use\nimport os\nimport numpy as np\nimport pandas as pd\n\n# %%\n\nx1 = np.arange(1,11,1)\nprint(x1)\nx2 = 1.3\ndx1=x1//x2\nanswer=np.floor_divide(x1,x2)\nprint(answer)\nprint(dx1)\nx1_mx = max(dx1)\n\n# x1 = np.divide(np.arange(0,11,1),1.3).astype(int)\n\n# l=np.max()\n# %%\nx1 = [0,4,37,17]\nx2 = [1.2,3,4.6,7]\n\nanswer = np.round(np.divide(x1,x2),decimals=2)\n\n# %% \n# np.random, np.round,np.mean,np.std\n\n# %% PANDAS!!!!!\n\ndata = pd.Series([0.1,50,47,1.367],index=['a','b','c','d'])\ndata.values\nprint(data)\n# %%\n\nrng = np.random.RandomState(42) # This holds rand no constant\ndataframe = pd.DataFrame(rng.randint(0,10,(3,3)),columns=['b','a','c'],\n index=['row1','row2','row3'])\ndataframe.b\ndataframe.values\nprint(dataframe)\nm = np.mean(dataframe.b)\nn = np.mean(dataframe['b'])\ndataframe.columns\ndataframe['b']\n# let you find rows by their names\ndataframe.loc['row1','a']\n\n# let you find rows by their numbers\ndataframe.iloc[0,1]\n# %%\n","sub_path":"assignment_4/Ex_wk5_2.py","file_name":"Ex_wk5_2.py","file_ext":"py","file_size_in_byte":960,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"547833179","text":"# qlearningAgents.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\nfrom game import *\nfrom learningAgents import ReinforcementAgent\nfrom featureExtractors import *\n\nimport random,util,math\n\nclass QLearningAgent(ReinforcementAgent):\n \"\"\"\n Q-Learning Agent\n\n Functions you should fill in:\n - computeValueFromQValues\n - computeActionFromQValues\n - getQValue\n - getAction\n - update\n\n Instance variables you have access to\n - self.epsilon (exploration prob)\n - self.alpha (learning rate)\n - self.discount (discount rate)\n\n Functions you should use\n - self.getLegalActions(state)\n which returns legal actions for a state\n \"\"\"\n def __init__(self, **args):\n \"You can initialize Q-values here...\"\n ReinforcementAgent.__init__(self, **args)\n\n \"*** YOUR CODE HERE ***\"\n # initialize values to 0\n # if we haven't seen them before\n self.qValues = util.Counter() # a Counter is a dict with default 0\n\n def getQValue(self, state, action):\n \"\"\"\n Returns Q(state,action)\n Should return 0.0 if we have never seen a state\n or the Q node value otherwise\n \"\"\"\n \"*** YOUR CODE HERE ***\"\n \n \"\"\"\n Make sure that in your computeValueFromQValues and computeActionFromQValues\n functions, you only access Q values by calling getQValue . This abstraction\n will be useful for question 10 when you override getQValue to use features\n of state-action pairs rather than state-action pairs directly.\n \"\"\"\n # self.qValues are initialized to util.Counter\n # where values are 0 as default\n # so 0.0 is returned if we have never seen a state\n # or the Q node value otherwise\n return self.qValues[(state, action)]\n\n def computeValueFromQValues(self, state):\n \"\"\"\n Returns max_action Q(state,action)\n where the max is over legal actions. Note that if\n there are no legal actions, which is the case at the\n terminal state, you should return a value of 0.0.\n \"\"\"\n \"*** YOUR CODE HERE ***\"\n \n legalActions = self.getLegalActions(state)\n # return a value of 0.0 if you are in the terminal state\n # there are no legal actions in the terminal state\n if not legalActions:\n return 0.0\n \"\"\"\n Make sure that in your computeValueFromQValues and computeActionFromQValues\n functions, you only access Q values by calling getQValue . This abstraction\n will be useful for question 10 when you override getQValue to use features\n of state-action pairs rather than state-action pairs directly.\n Note: For computeActionFromQValues, you should break ties randomly for\n better behavior. The random.choice() function will help. In a particular\n state, actions that your agent hasn't seen before still have a Q-value,\n specifically a Q-value of zero, and if all of the actions that your agent\n has seen before have a negative Q-value, an unseen action may be optimal.\n \"\"\"\n # create list of qValus for each legal action\n qValues = [self.getQValue(state, action) for action in legalActions]\n \"\"\"\n got this idea for finding all positions of max value in a list\n from this Stack Overflow post:\n \n https://stackoverflow.com/questions/3989016/how-to-find-all-positions-of-the-maximum-value-in-a-list\n \"\"\"\n # break ties randomly for better behavior\n # the ranom.choice() function will help\n maxqValue = max(qValues)\n # find all indices where there is a max value\n maxIndices = [i for i, j in enumerate(qValues) if j == maxqValue]\n # use the random.choice function as recommended\n randomIndex = random.choice(maxIndices)\n # return the max qValue at that random index\n return qValues[randomIndex]\n \n \n\n def computeActionFromQValues(self, state):\n \"\"\"\n Compute the best action to take in a state. Note that if there\n are no legal actions, which is the case at the terminal state,\n you should return None.\n \"\"\"\n \"*** YOUR CODE HERE ***\"\n # if there are no legal actions\n # which is the case at the terminal state,\n # you should return None\n legalActions = self.getLegalActions(state)\n if not legalActions:\n return None\n \"\"\"\n Make sure that in your computeValueFromQValues and\n computeActionFromQValues functions, you only access Q values by calling\n getQValue . This abstraction will be useful for question 10 when you\n override getQValue to use features of state-action pairs rather than\n state-action pairs directly.\n \"\"\"\n # util.Counter() is dictionary with default value 0\n qActions = util.Counter()\n # for each possible legal action,\n # have dictionary where an action is the key\n # the qValues are the dictionary values\n # fill dictionary with qValue associated with that state and action\n for action in legalActions:\n qActions[action] = self.getQValue(state, action)\n # util.argMax() returns the key with the highest value.\n # in this case the action with highest Q Value\n return qActions.argMax()\n\n def getAction(self, state):\n \"\"\"\n Compute the action to take in the current state. With\n probability self.epsilon, we should take a random action and\n take the best policy action otherwise. Note that if there are\n no legal actions, which is the case at the terminal state, you\n should choose None as the action.\n\n HINT: You might want to use util.flipCoin(prob)\n HINT: To pick randomly from a list, use random.choice(list)\n \"\"\"\n # Pick Action\n legalActions = self.getLegalActions(state)\n action = None\n \"*** YOUR CODE HERE ***\"\n # action is initialized to None\n # if there are no legal actions,\n # which is the case at the terminal state,\n # return None as the action\n \"\"\"\n Complete your Q-learning agent by implementing epsilon-greedy action\n selection in getAction, meaning it chooses random actions an epsilon\n fraction of the time, and follows its current best Q-values otherwise. Note\n that choosing a random action may result in choosing the best action - that\n is, you should not choose a random sub-optimal action, but rather any\n random legal action.\n You can choose an element from a list uniformly at random by calling the\n random.choice function. You can simulate a binary variable with probability\n p of success by using util.flipCoin(p), which returns True with probability\n p and False with probability 1-p.\n \"\"\"\n if legalActions:\n # with probability self.epsilon,\n # take a random action and take the best policy action otherwise\n if util.flipCoin(self.epsilon):\n # to pick randomly from a list, use random.choice(list)\n action = random.choice(legalActions)\n # othewise ake the best policy action\n else:\n action = self.computeActionFromQValues(state)\n \n\n return action\n\n def update(self, state, action, nextState, reward):\n \"\"\"\n The parent class calls this to observe a\n state = action => nextState and reward transition.\n You should do your Q-Value update here\n\n NOTE: You should never call this function,\n it will be called on your behalf\n \"\"\"\n \"*** YOUR CODE HERE ***\"\n \n # alpha is the learning rate\n alpha = self.alpha\n # Q(state,action) stored in self.qValues\n oldQValue = self.getQValue(state, action)\n \n gamma = self.discount\n \n # max_action Q(nextState,action) where the max is over legal actions\n successorQValue = self.computeValueFromQValues(nextState)\n \n sample = reward + (gamma * successorQValue)\n self.qValues[(state, action)] = (1 - alpha) * oldQValue + alpha * sample \n \n\n def getPolicy(self, state):\n return self.computeActionFromQValues(state)\n\n def getValue(self, state):\n return self.computeValueFromQValues(state)\n\n\nclass PacmanQAgent(QLearningAgent):\n \"Exactly the same as QLearningAgent, but with different default parameters\"\n\n def __init__(self, epsilon=0.05,gamma=0.8,alpha=0.2, numTraining=0, **args):\n \"\"\"\n These default parameters can be changed from the pacman.py command line.\n For example, to change the exploration rate, try:\n python pacman.py -p PacmanQLearningAgent -a epsilon=0.1\n\n alpha - learning rate\n epsilon - exploration rate\n gamma - discount factor\n numTraining - number of training episodes, i.e. no learning after these many episodes\n \"\"\"\n args['epsilon'] = epsilon\n args['gamma'] = gamma\n args['alpha'] = alpha\n args['numTraining'] = numTraining\n self.index = 0 # This is always Pacman\n QLearningAgent.__init__(self, **args)\n\n def getAction(self, state):\n \"\"\"\n Simply calls the getAction method of QLearningAgent and then\n informs parent of action for Pacman. Do not change or remove this\n method.\n \"\"\"\n action = QLearningAgent.getAction(self,state)\n self.doAction(state,action)\n return action\n\n\nclass ApproximateQAgent(PacmanQAgent):\n \"\"\"\n ApproximateQLearningAgent\n\n You should only have to overwrite getQValue\n and update. All other QLearningAgent functions\n should work as is.\n \"\"\"\n def __init__(self, extractor='IdentityExtractor', **args):\n self.featExtractor = util.lookup(extractor, globals())()\n PacmanQAgent.__init__(self, **args)\n self.weights = util.Counter()\n\n def getWeights(self):\n return self.weights\n\n def getQValue(self, state, action):\n \"\"\"\n Should return Q(state,action) = w * featureVector\n where * is the dotProduct operator\n \"\"\"\n \"*** YOUR CODE HERE ***\"\n \n \"\"\"\n Note: Approximate Q-learning assumes the existence of a feature function\n f(s,a) over state and action pairs, which yields a vector f1(s,a) ..\n fi(s,a) .. fn(s,a) of feature values. We provide feature functions for you\n in featureExtractors.py. Feature vectors are util.Counter (like a\n dictionary) objects containing the non-zero pairs of features and values;\n all omitted features have value zero.\n By default, ApproximateQAgent uses the IdentityExtractor, which assigns a\n single feature to every (state,action) pair.\n \"\"\"\n \"\"\"\n Multiplying two counters gives the dot product of their vectors where\n each unique label is a vector element.\n Get Q(s,a) by multiplying the weight and feature counters together.\n \"\"\"\n return self.weights * self.featExtractor.getFeatures(state, action)\n\n def update(self, state, action, nextState, reward):\n \"\"\"\n Should update your weights based on transition\n \"\"\"\n \"*** YOUR CODE HERE ***\"\n \n \n \"\"\"\n Note that the difference term\n is the same as in normal Q-learning,\n and r is the experienced reward.\n \"\"\"\n \"\"\"\n In your code, you should implement the weight vector as a dictionary\n mapping features (which the feature extractors will return) to weight\n values. You will update your weight vectors similarly to how you updated\n Q-values:\n \"\"\"\n gamma = self.discount\n successorQValue = self.computeValueFromQValues(nextState)\n sample = reward + (gamma * successorQValue)\n \n qValue = self.getQValue(state, action)\n difference = sample - qValue\n # retrieve vector of feature values\n features = self.featExtractor.getFeatures(state, action)\n \n \n # alpha is the learning rate\n alpha = self.alpha\n # the weight vector is a dictionary mapping features to weight values\n for feature in features:\n \"\"\"\n To update weights, add to the current weight_i,\n alpha * difference * f_i(s,a)\n \"\"\"\n self.weights[feature] += alpha * difference * features[feature]\n \n\n def final(self, state):\n \"Called at the end of each game.\"\n # call the super-class final method\n PacmanQAgent.final(self, state)\n\n # did we finish training?\n if self.episodesSoFar == self.numTraining:\n # you might want to print your weights here for debugging\n \"*** YOUR CODE HERE ***\"\n #print(\"Weights\")\n print(f\"Weights: {self.weights}\")\n \n","sub_path":"reinforcement/qlearningAgents.py","file_name":"qlearningAgents.py","file_ext":"py","file_size_in_byte":13761,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"358653902","text":"\n# coding: utf-8\n\n# # Sea Level Rise data from Sweet et al (2017)\n# described in https://tidesandcurrents.noaa.gov/publications/techrpt83_Global_and_Regional_SLR_Scenarios_for_the_US_final.pdf\n\n# In[1]:\n\nget_ipython().magic('matplotlib inline')\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport re\nimport pandas as pd\n\n\n# In[2]:\n\ndf = pd.read_csv('techrpt083.csv', skiprows=15)\n\n\n# In[3]:\n\ndf.shape\n\n\n# In[4]:\n\ndf2 = df[df['Site'].str.contains(\"HALIFAX\")]\ndf2\n\n\n# In[5]:\n\ndf3 = df2[df2['Scenario'].str.contains(\"1.0 - HIGH\")]\ndf3\n\n\n# In[ ]:\n\n\n\n\n# In[6]:\n\ndf3.columns.values[0:6]\n\n\n# In[7]:\n\ndf4 = pd.melt(df3, id_vars=df3.columns.values[0:6], \n var_name=\"Date\", value_name=\"Value\")\ndf4\n\n\n# In[8]:\n\ndf5 = df4.copy(deep=True)\nfor scenario in df2['Scenario'].values:\n df3 = df2[df2['Scenario'].str.contains(scenario)]\n var = scenario.replace(' ','')\n df4 = pd.melt(df3, id_vars=df3.columns.values[0:6], \n var_name=\"Date\", value_name=var)\n df5[var] = df4[var]\n\n\n# In[9]:\n\ndf5['Date'] = [int(re.findall(r'\\d+', v)[0]) for v in df5['Date'].values]\n\n\n# In[10]:\n\ndf5.rename(columns = {'Site':'id'}, inplace = True)\n\n\n# In[11]:\n\ndef df_station(df, station):\n df2 = df[df['Site'].str.contains(station)]\n df3 = df2[df2['Scenario'].str.contains(\"1.0 - HIGH\")]\n df4 = pd.melt(df3, id_vars=df3.columns.values[0:6], \n var_name=\"Date\", value_name=\"Value\")\n df5 = df4.copy(deep=True)\n for scenario in df2['Scenario'].values:\n df3 = df2[df2['Scenario'].str.contains(scenario)]\n var = scenario.replace(' ','')\n df4 = pd.melt(df3, id_vars=df3.columns.values[0:6], \n var_name=\"Date\", value_name=var)\n df5[var] = df4[var]\n return df5\n\n \n\n\n# In[12]:\n\ndf5 = df_station(df, 'HALIFAX')\n\n\n# In[13]:\n\ndf2 = df[~df['Site'].str.contains('grid_')]\ndf3 = df2[~df2['Site'].str.contains('GMSL')]\ndf3\n\n\n# In[14]:\n\ndf3['Site'].unique()\n\n\n# In[15]:\n\ndfs = [df_station(df3,station) for station in df3['Site'].unique()]\n\n\n# In[16]:\n\ntype(dfs)\n\n\n# In[17]:\n\ndf6 = pd.concat(dfs)\n\n\n# In[18]:\n\ndel df6['Value']\ndel df6['Scenario']\ndel df6['PSMSL ID']\n\n\n# In[20]:\n\ndf6['Date'] = [int(re.findall(r'\\d+', v)[0]) for v in df6['Date'].values]\ndf6.rename(columns = {'Site':'id'}, inplace = True)\n\n\n# In[21]:\n\ndf6.to_csv('major_stations.csv', index=False)\n\n\n# In[19]:\n\ndf6\n\n\n# In[ ]:\n\n\n\n","sub_path":"sweet_SLR.py","file_name":"sweet_SLR.py","file_ext":"py","file_size_in_byte":2379,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"134057291","text":"from django.utils.deprecation import MiddlewareMixin\nfrom auth.baseauth import BaseAuth\n\n\nclass AuthMiddleware(MiddlewareMixin):\n def process_request(self, request):\n auth = BaseAuth(request=request)\n\n if auth.is_authenticated():\n user = auth.authenticate()\n request.user = user\n request.context = auth.generate_context()\n else:\n request.user = None\n request.context = dict()\n","sub_path":"auth/middleware.py","file_name":"middleware.py","file_ext":"py","file_size_in_byte":455,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"48963454","text":"import pickle\nimport os\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport csv\n\npickle_fn = 'good_results/compiled.pkl'\n\nvals = pickle.load(open(pickle_fn, \"rb\"))\n\n# Fixed number of points\nfnp = vals[vals['num points'] == 2000]\nfnp_small_box = fnp[fnp['box type'] == 'smallBoxes']\nfnpsb_ds = fnp_small_box.sort_values(['data structure', 'memory size', 'block size'])\nfnpsb_final = fnpsb_ds[['data structure', 'memory size', 'block size',\n\t\t\t\t\t\t'avg cell probes', 'std cell probes',\n\t\t\t\t\t\t'avg disk accesses', 'std disk accesses']]\nfnpsb_final.to_csv('good_results/fnpsb.csv')\n# fnp_small_box_csv = 'good_results/fnp_small_box.csv'\n# with open(fnp_small_box_csv, 'w') as csvfile:\n# \tfieldnames = ['memory size', 'block size', 'avg cell probes',\n# \t\t\t\t'std cell probes', 'avg disk accesses', 'std disk accesses',\n# \t\t\t\t'data structure']\n# \twriter = csv.DictWriter(csvfile, fieldnames=fieldnames)\n# \twriter.writeheader()\n# \trows_to_write = []\n\n# \t# make chart of DS vs memory_config\n# \tfor ds in fnp_small_box['data structure'].unique():\n# \t\ttemp_df = fnp_small_box[fnp_small_box['data structure'] == ds]\n# \t\tfor i in range(len(temp_df)):\n# \t\t\trow = temp_df.iloc[i]\n# \t\t\tprint(row['memory size'], row['block size'],\n# \t\t\t\trow['avg cell probes'], row['std cell probes'],\n# \t\t\t\trow['avg disk accesses'], row['std disk accesses'])\n# \t\t\trows_to_write.append({\n# \t\t\t\t'memory size': row['memory size'],\n# \t\t\t\t'block size': row['block size'],\n# \t\t\t\t'avg cell probes': row['avg cell probes'],\n# \t\t\t\t'std cell probes': row['std cell probes'],\n# \t\t\t\t'avg disk accesses': row['avg disk accesses'],\n# \t\t\t\t'std disk accesses': row['std disk accesses']\n# \t\t\t\t})\n# \t\tfor row in rows_to_write:\n# \t\t\twriter.writerow(row)\n\nfnp_big_box = fnp[fnp['box type'] == 'bigBoxes']\nfnpbb_ds = fnp_big_box.sort_values(['data structure', 'memory size', 'block size'])\nfnpbb_final = fnpbb_ds[['data structure', 'memory size', 'block size',\n\t\t\t\t\t\t'avg cell probes', 'std cell probes',\n\t\t\t\t\t\t'avg disk accesses', 'std disk accesses']]\nfnpbb_final.to_csv('good_results/fnpbb.csv')\n\n# Fixed Memory config\nfm = vals[(vals['block size'] == 8) & (vals['memory size'] == 32)]\nfm_small_box = fm[fm['box type'] == 'smallBoxes']\nfmsb_ds = fm_small_box.sort_values(['data structure', 'num points'])\nfmsb_final = fmsb_ds[['data structure', 'num points',\n\t\t\t\t\t\t'avg cell probes', 'std cell probes',\n\t\t\t\t\t\t'avg disk accesses', 'std disk accesses']]\nfmsb_final.to_csv('good_results/fmsb.csv')\n\nfm_big_box = fm[fm['box type'] == 'bigBoxes']\nfmbb_ds = fm_big_box.sort_values(['data structure', 'num points'])\nfmbb_final = fmbb_ds[['data structure', 'num points',\n\t\t\t\t\t\t'avg cell probes', 'std cell probes',\n\t\t\t\t\t\t'avg disk accesses', 'std disk accesses']]\nfmbb_final.to_csv('good_results/fmbb.csv')\n\n# for fixed memory, how does increasing points compare?\n# \tsmall vs big queries\n# \tgraphical representation (for each DS)\n","sub_path":"plots.py","file_name":"plots.py","file_ext":"py","file_size_in_byte":2878,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"466385646","text":"import numpy as np\nimport pandas as pd\nfrom pre_process import get_adj_map, PreProcessor, freeze_support, Pool\nimport tqdm\nimport time\nfrom sklearn.metrics.pairwise import cosine_similarity\nfrom pre_process import get_trainroad_adjoin, get_testroad_adjoin\n\n\nclass FeatureEn:\n def __init__(self, term='first'):\n self.adj_map = get_adj_map() # 对象与路网绑定\n self.prp = PreProcessor(term) # 数据管理器\n\n def extract_relevancy(self, roadId, d, dFlow):\n '''抽取车流量表相关度，作为训练集\n :param roadId: 路口\n :param d: 时间延迟\n :param dFlow: 车流量表，{roadId:pd.Series}\n :param adjMap:\n :return: X，相关度矩阵，每一列代表一个样本，即横向空间维度，纵向时间维度\n '''\n\n # 时空相关系数计算\n lAdjNode = self.adj_map[roadId]\n X = np.zeros((d, len(lAdjNode))) # 相关系数矩阵,问题**每个路口训练一个模型\n return X\n\n def extract_adjoin_by_col(self):\n '''某时段内路口流量为特征，其邻接路口为目标值,构建训练集和测试集（按列遍历，很快）'''\n # 先提取训练集\n adj_map = {} # {(flow, road): [{flow, roadID)}}\n data_dct = {'timestamp': [],\n 'crossroadID': [],\n 'mean_flow': [],\n 'flow': []}\n\n for road, adjoins in self.adj_map.items():\n adj_map[('flow', int(road))] = set(('flow', int(r)) for r in adjoins)\n flow_data = self.prp.load_buffer()\n if self.prp.term: # 复赛情况\n data_dct['direction'] = []\n flow_data.set_index(['timestamp', 'direction', 'crossroadID'], inplace=True)\n else:\n flow_data.set_index(['timestamp', 'crossroadID'], inplace=True)\n flow_data = flow_data.unstack()\n flow_data.drop(columns=flow_data.columns ^ (flow_data.columns & adj_map.keys()), inplace=True)\n for key, values in adj_map.items(): # 缩小邻接表， 邻接卡口仅保留数据集出现的\n adj_map[key] = flow_data.columns & values\n for col in flow_data.columns:\n flow_data_nn = flow_data[flow_data[col].notna()] # 去除空值\n if len(adj_map[col]):\n mean_flow = flow_data_nn[adj_map[col]].mean(axis=1).dropna()\n data_dct['crossroadID'].extend(col[1] for _ in range(len(mean_flow)))\n data_dct['mean_flow'].extend(mean_flow)\n data_dct['flow'].extend(flow_data_nn.loc[mean_flow.index, col])\n if self.prp.term: # 复赛情况\n for ts, dire in mean_flow.index:\n data_dct['direction'].append(dire)\n data_dct['timestamp'].append(ts)\n else:\n data_dct['timestamp'].extend(mean_flow.index)\n # 获取测试集合\n if self.prp.term:\n test_df = self.prp.get_submit()[['crossroadID', 'direction', 'timeBegin', 'date']]\n test_df['timestamp'] = test_df[['timeBegin', 'date']].apply(\n lambda x: f'2019-{x[\"date\"]} {x[\"timeBegin\"].rjust(5, \"0\")}:00', axis=1)\n test_df.set_index(['timestamp', 'direction'], inplace=True)\n else:\n test_df = self.prp.get_submit()[['crossroadID', 'timeBegin', 'date']]\n test_df['timestamp'] = test_df[['timeBegin', 'date']].apply(\n lambda x: f'2019-08-{x[\"date\"]} {x[\"timeBegin\"].rjust(5, \"0\")}:00', axis=1)\n test_df.set_index('timestamp', inplace=True)\n return test_df, flow_data\n ts_index = test_df['timestamp'].unique()\n return ts_index, flow_data.index\n for road, indexes in test_df.groupby('crossroadID'):\n if ('flow', road) in adj_map:\n return ts_index, flow_data\n test_df.loc[indexes, 'mean_flow'] = flow_data.loc[ts_index, adj_map[('flow', road)]].mean(axis=1)\n else:\n test_df.loc[indexes, 'mean_flow'] = list(0 for _ in range(len(indexes)))\n # 邻接关系\n return pd.DataFrame(data_dct), test_df\n\n def similarity_matrix(self):\n '''\n 计算卡口之间的相似度矩阵\n :return:相似度矩阵\n '''\n matrix, index = self.prp.get_roadflow_alltheday()\n cos = cosine_similarity(pd.DataFrame(np.array(matrix), index=index, columns=[\"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\"]))\n return cos, index\n\n def get_train_data(self):\n '''\n 得到训练集和测试集\n :return:训练集和测试集\n '''\n global timelist\n train = self.prp.load_train()\n predMapping, mapping = get_testroad_adjoin(self.prp)\n train_mapping = get_trainroad_adjoin(predMapping, mapping)\n # [[邻居1[n天各个方向车流1]，邻居2，邻居3，……],[]]\n timelist = []\n for i in range(1, 22): # 完整的时间列表\n timelist.extend(pd.date_range(f'2019/09/{i} 07:00', f'2019/09/{i} 18:55', freq='5min').tolist())\n # 修改train中的时间\n train[\"timestamp\"] = [pd.to_datetime(i, errors='coerce') for i in train[\"timestamp\"].tolist()]\n train[\"direction\"] = [eval(i) for i in train[\"direction\"].tolist()]\n # 整理数据\n train_x = []\n train_y = []\n for key in train_mapping.keys():\n a = []\n tdf = pd.DataFrame(timelist, columns=[\"timestamp\"]) # 生成时间戳df\n tdf.to_csv(\"./data/tdf.csv\")\n for i in train_mapping[key][:]: # 相邻卡口\n # if train[train[\"crossroadID\"] == i][\"direction\"].tolist(): # 若非空,存在a里面\n # mean = np.array(train[train[\"crossroadID\"] == i][\"direction\"].tolist()).mean(axis=0)\n # mean = [int(round(x)) for x in mean[:]]\n # input(mean)\n # result = pd.merge(tdf, train[train[\"crossroadID\"] == i], on='timestamp', how=\"left\").drop(\"crossroadID\", axis=1)\n # result_ = []\n # for y in result.fillna(str(mean))[\"direction\"].tolist():\n # if type(y) is str:\n # y = eval(y)\n # result_.append(y)\n #\n result_ = get_something(i, train, tdf)\n if result_:\n a.append(result_)\n if a: # 判断a中是否有内容\n train_x.append(a) # 存入训练集[[[时间1],[时间2]],[],[]]\n train_y.append(get_something(key, train, tdf)) # 把key也加进来\n text_save(\"x\", train_x)\n\n def get_text_data(self):\n train = self.prp.load_train()\n predMapping, mapping = get_testroad_adjoin(self.prp)\n test_x = []\n # [[邻居1[n天各个方向车流1]，邻居2，邻居3，……],[]]\n timelist = []\n keylst = []\n for i in range(1, 22): # 完整的时间列表\n timelist.extend(pd.date_range(f'2019/09/{i} 07:00', f'2019/09/{i} 18:55', freq='5min').tolist())\n # 修改train中的时间\n train[\"timestamp\"] = [pd.to_datetime(i, errors='coerce') for i in train[\"timestamp\"].tolist()]\n train[\"direction\"] = [eval(i) for i in train[\"direction\"].tolist()]\n for key in predMapping.keys():\n keylst.append(key)\n a = []\n tdf = pd.DataFrame(timelist, columns=[\"timestamp\"]) # 生成时间戳df\n for i in list(predMapping[key])[:]: # 相邻卡口\n result_ = get_something(i, train, tdf)\n if result_:\n a.append(result_)\n print(a)\n if a: # 判断a中是否有内容\n test_x.append(a) # 存入训练集[[[时间1],[时间2]],[],[]]\n text_save(\"test\", test_x)\n return keylst\n\n\ndef text_save(flag, data): # filename为写入CSV文件的路径，data为要写入数据列表.\n if flag == \"x\":\n filename = \"./data/train_x.csv\"\n s = []\n for i in range(len(data)): # n个卡口\n for j in range(len(data[i][0])): # 时间\n print(len(data[i][0]))\n a = []\n for k in range(len(data[i])): # n个邻居\n a.append(data[i][k][j])\n # a = str(a).replace(\"'\", '').replace(',', '') # 去除单引号，逗号，每行末尾追加换行符\n print(a)\n s.append(a)\n pd.DataFrame(s).to_csv(filename)\n print(\"train_x保存文件成功\")\n elif flag == \"y\":\n filename = \"./data/train_y.txt\"\n f = open(filename, \"a\")\n s = []\n for i in range(len(data)): # n个卡口\n for j in range(len(data[i])): # 所有时段\n s.append(data[i][j])\n f.write(str(s))\n f.close()\n print(\"train_y保存文件成功\")\n else:\n filename = \"./data/test_x.csv\"\n s = []\n for i in range(len(data)): # n个卡口\n for j in range(len(data[i][0])): # 时间\n print(len(data[i][0]))\n a = []\n for k in range(len(data[i])): # n个邻居\n a.append(data[i][k][j])\n # a = str(a).replace(\"'\", '').replace(',', '') # 去除单引号，逗号，每行末尾追加换行符\n print(a)\n s.append(a)\n pd.DataFrame(s).to_csv(filename)\n print(\"test保存文件成功\")\n\n\ndef get_something(i, train, tdf):\n \"\"\" 用均值填补缺失值，返回所有时间段的数据\n :param i: 某卡口\n :param train:训练集\n :param tdf:时间表\n :return: 各个时间段流量,result\n \"\"\"\n b = []\n if train[train[\"crossroadID\"] == i][\"direction\"].tolist(): # 若非空,存在a里面\n mean = np.array(train[train[\"crossroadID\"] == i][\"direction\"].tolist()).mean(axis=0)\n mean = [int(round(x)) for x in mean[:]]\n result = pd.merge(tdf, train[train[\"crossroadID\"] == i], on='timestamp', how=\"left\").drop(\"crossroadID\", axis=1)\n for y in result.fillna(str(mean))[\"direction\"].tolist():\n if type(y) is str:\n y = eval(y)\n b.append(y)\n return b\n\n\n","sub_path":"feature_en/feature_en.py","file_name":"feature_en.py","file_ext":"py","file_size_in_byte":10304,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"431632281","text":"import torch\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nsns.set()\n\nvariables = {0:'SSS', 1:'SST', 2:'Precip'}\n\ndef plotActualPreds(actual, preds, n=50, title=None):\n # Function plots actual values and predicted values for regression task\n # Takes in tensor of actual and predictions, and plots both\n fig,ax = plt.subplots()\n ax.plot(actual[-n:].numpy(), label='actual')\n ax.plot(preds[-n:].numpy(), label='preds')\n ax.set_title(title)\n ax.legend()\n return fig, ax\n\n\ndef plot_w(multi, dim, month=0, var=0):\n lead, n_vars = multi.model.w.shape[:2]\n # Function plots weight tensor\n data = multi.model.w.detach().cpu().numpy().reshape(lead,n_vars,*dim)[month,var,:,:]\n max_abs = np.max(np.abs(data))\n fig,ax=plt.subplots()\n cp = ax.imshow(np.flipud(data), cmap='cmo.balance', vmin=-max_abs, vmax=max_abs)\n cb = fig.colorbar(cp,orientation='horizontal')\n ax.set_title('Weights for {} in month {}'.format(variables[var], month))\n return fig, ax\n\n\ndef plot_loss(full_train_loss, full_val_loss, full_finegrain_epochs, \n low_train_loss = [], low_val_loss=[], low_finegrain_epochs=None):\n # Function for plotting training and validation loss for MRTL\n \n train_loss = full_train_loss + low_train_loss\n val_loss = full_val_loss+low_val_loss\n \n if low_finegrain_epochs is not None:\n finegrain_epochs = np.concatenate([np.cumsum(full_finegrain_epochs), \n len(full_train_loss)+np.cumsum(low_finegrain_epochs)])\n else:\n finegrain_epochs = np.cumsum(full_finegrain_epochs)\n \n # Plot overall loss curves\n fig, ax = plt.subplots()\n ax.plot(np.arange(1, len(train_loss)+1), train_loss, label='Train')\n ax.plot(np.arange(1, len(val_loss)+1), val_loss, label='Val')\n for epoch in finegrain_epochs:\n ax.axvline(x=epoch, linestyle='--', color='k')\n if low_finegrain_epochs is not None:\n ax.axvline(x=len(full_train_loss), linestyle='--', color='g', label='Begin low-rank')\n ax.legend()\n ax.set_xlabel('Epoch')\n ax.set_ylabel('MSE')\n ax.set_ylim([0,.15])\n return fig, ax","sub_path":"mrtl_multiOutput/plotter.py","file_name":"plotter.py","file_ext":"py","file_size_in_byte":2164,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"183805101","text":"from ..error import StorageException\nfrom ..error import ErrorType\nfrom ..iterator import Iterator\nfrom struct import unpack\nimport os\nfrom typing import Tuple\n\n\nclass SStableIterator(Iterator):\n INT_SIZE = 4\n TIMESTAMP_SIZE = 8\n INDICATOR_SIZE = 4\n\n def __init__(self, store_name: str,\n id: int, size: int, index_table, path, last_index) -> None:\n \"\"\"\n :param store_name: the name of store the sstable belongs to\n :param id: the id of the sstable\n :param size: the size of sstable file\n :param index_table: the index table stored in memory for this sstable\n :param path: the path the sstable file and index file store in\n :param last_index: the offset of last\n key-data-time set data in sstable file\n \"\"\"\n sstable_file_name = store_name + str(id) + \".ss\"\n index_file_name = store_name + str(id) + \".index\"\n self.cur = None\n sstable_dir = os.path.join(path, \"sstable\")\n index_dir = os.path.join(path, \"index\")\n self.sstable_file = open(os.path.join(sstable_dir, sstable_file_name),\n \"rb\")\n self.index_file = open(os.path.join(index_dir, index_file_name), \"rb\")\n self.index_table = index_table\n self.cur_offset = 0\n self.file_size = size\n self.last_index = last_index\n self.dummy_first = True\n\n def __del__(self) -> None:\n \"\"\"\n close sstable file and index file\n :return: None\n \"\"\"\n self.sstable_file.close()\n self.index_file.close()\n\n def seek(self, key: str) -> None:\n \"\"\"\n :param key: the key the iterator will point to\n :return:\n \"\"\"\n if self.file_size == 0:\n raise StorageException(ErrorType.NOT_FOUND,\n \"The key is not found in table.\")\n index_range = self._get_range(key)\n if index_range[0] == index_range[1]:\n offset = index_range[0]\n self.index_file.seek(offset)\n key_size = unpack(\"i\", self.index_file.read(self.INT_SIZE))[0]\n self.index_file.read(key_size)\n self.cur_offset = unpack(\"i\",\n self.index_file.read(self.INT_SIZE))[0]\n else:\n offset = self._get_offset(key, index_range[0], index_range[1])\n if offset is None:\n raise StorageException(ErrorType.NOT_FOUND,\n \"The key is not found in table.\")\n self.cur_offset = offset\n\n def seek_to_first(self) -> None:\n \"\"\"\n set the iterator point to the beginning\n :return: None\n \"\"\"\n self.cur_offset = 0\n self.dummy_first = True\n\n def valid(self) -> bool:\n \"\"\"\n :return: true if the iterator has next.\n Otherwise false. considering empty sstable\n \"\"\"\n if self.cur_offset < 0:\n return False\n elif self.cur_offset == 0 and self.file_size == 0:\n return False\n else:\n if self.cur_offset >= self.file_size:\n return False\n self.sstable_file.seek(self.cur_offset)\n indicator = unpack(\"i\", self.sstable_file.\n read(self.INDICATOR_SIZE))[0]\n if indicator == 0:\n key_size_str = self.sstable_file.read(self.INT_SIZE)\n key_size = unpack(\"i\", key_size_str)[0]\n self.sstable_file.read(key_size)\n self.sstable_file.read(self.TIMESTAMP_SIZE)\n offset = self.cur_offset + self.INDICATOR_SIZE \\\n + self.INT_SIZE \\\n + key_size + self.TIMESTAMP_SIZE\n else:\n key_size_str = self.sstable_file.read(self.INT_SIZE)\n key_size = unpack(\"i\", key_size_str)[0]\n self.sstable_file.read(key_size)\n val_size_str = self.sstable_file.read(self.INT_SIZE)\n val_size = unpack(\"i\", val_size_str)[0]\n self.sstable_file.read(val_size)\n offset = self.cur_offset + \\\n self.INDICATOR_SIZE + self.INT_SIZE \\\n + key_size + self.INT_SIZE + val_size + self.TIMESTAMP_SIZE\n if offset >= self.file_size:\n return False\n return True\n\n def next(self) -> None:\n \"\"\"\n if iterator has next, set the iterator points to next;\n otherwise, raise an exception\n :return: None\n \"\"\"\n if self.file_size == 0:\n raise StorageException(ErrorType.NONE_POINTER,\n \"There is no next\")\n elif self.cur_offset == 0 and self.dummy_first:\n self.cur_offset = 0\n self.dummy_first = False\n elif self.cur_offset >= 0:\n self.sstable_file.seek(self.cur_offset)\n indicator = unpack(\"i\", self.sstable_file.\n read(self.INDICATOR_SIZE))[0]\n if indicator == 0:\n key_size_str = self.sstable_file.read(self.INT_SIZE)\n key_size = unpack(\"i\", key_size_str)[0]\n self.sstable_file.read(key_size)\n self.sstable_file.read(self.TIMESTAMP_SIZE)\n self.cur_offset = self.cur_offset + \\\n self.INDICATOR_SIZE + self.INT_SIZE + key_size \\\n + self.TIMESTAMP_SIZE\n else:\n key_size_str = self.sstable_file.read(self.INT_SIZE)\n key_size = unpack(\"i\", key_size_str)[0]\n self.sstable_file.read(key_size)\n val_size_str = self.sstable_file.read(self.INT_SIZE)\n val_size = unpack(\"i\", val_size_str)[0]\n self.sstable_file.read(val_size)\n self.cur_offset = self.cur_offset + \\\n self.INDICATOR_SIZE + self.INT_SIZE + key_size \\\n + self.INT_SIZE + val_size + self.TIMESTAMP_SIZE\n if self.cur_offset >= self.file_size:\n raise StorageException(ErrorType.NONE_POINTER,\n \"There is no next\")\n\n def value(self) -> str:\n \"\"\"\n :return: the value of the key-value-time\n set data the iterator points to\n \"\"\"\n self.sstable_file.seek(self.cur_offset)\n indicator = unpack(\"i\", self.sstable_file.\n read(self.INDICATOR_SIZE))[0]\n if indicator == 1:\n key_size_str = self.sstable_file.read(self.INT_SIZE)\n key_size = unpack(\"i\", key_size_str)[0]\n self.sstable_file.read(key_size)\n val_size_str = self.sstable_file.read(self.INT_SIZE)\n val_size = unpack(\"i\", val_size_str)[0]\n value = self.sstable_file.read(val_size).decode()\n return value\n else:\n raise StorageException(ErrorType.NOT_FOUND,\n \"The node is removed.\")\n\n def key(self) -> str:\n \"\"\"\n :return: the key of the key-value-time set data the iterator points to\n \"\"\"\n self.sstable_file.seek(self.cur_offset)\n self.sstable_file.read(self.INDICATOR_SIZE)\n key_size_str = self.sstable_file.read(self.INT_SIZE)\n key_size = unpack(\"i\", key_size_str)[0]\n key = self.sstable_file.read(key_size).decode()\n return key\n\n def timestamp(self) -> int:\n \"\"\"\n :return: the time of the key-value-time\n set data the iterator points to\n \"\"\"\n self.sstable_file.seek(self.cur_offset)\n indicator = unpack(\"i\", self.sstable_file.\n read(self.INDICATOR_SIZE))[0]\n if indicator == 1:\n key_size_str = self.sstable_file.read(self.INT_SIZE)\n key_size = unpack(\"i\", key_size_str)[0]\n self.sstable_file.read(key_size)\n val_size_str = self.sstable_file.read(self.INT_SIZE)\n val_size = unpack(\"i\", val_size_str)[0]\n self.sstable_file.read(val_size)\n bin_time_stamp = self.sstable_file.read(self.TIMESTAMP_SIZE)\n else:\n key_size_str = self.sstable_file.read(self.INT_SIZE)\n key_size = unpack(\"i\", key_size_str)[0]\n self.sstable_file.read(key_size)\n bin_time_stamp = self.sstable_file.read(self.TIMESTAMP_SIZE)\n time_stamp = unpack(\"q\", bin_time_stamp)[0]\n return time_stamp\n\n def length(self) -> int:\n \"\"\"\n :return: the length of the key-value-time\n set data the iterator points to\n \"\"\"\n self.sstable_file.seek(self.cur_offset)\n indicator = unpack(\"i\", self.sstable_file.\n read(self.INDICATOR_SIZE))[0]\n if indicator == 1:\n key_size_str = self.sstable_file.read(self.INT_SIZE)\n key_size = unpack(\"i\", key_size_str)[0]\n self.sstable_file.read(key_size)\n val_size_str = self.sstable_file.read(self.INT_SIZE)\n val_size = unpack(\"i\", val_size_str)[0]\n length = self.INDICATOR_SIZE + self.INT_SIZE + key_size \\\n + self.INT_SIZE + val_size \\\n + self.TIMESTAMP_SIZE\n else:\n key_size_str = self.sstable_file.read(self.INT_SIZE)\n key_size = unpack(\"i\", key_size_str)[0]\n length = self.INDICATOR_SIZE + self.INT_SIZE + key_size \\\n + self.TIMESTAMP_SIZE\n return length\n\n def get_cur_offset(self) -> int:\n \"\"\"\n :return: the offset of data the iterator currently points to\n \"\"\"\n return self.cur_offset\n\n def contain(self, key: str) -> bool:\n offset_range = self._get_range(key)\n offset = self._get_offset(key, offset_range[0], offset_range[1])\n if offset is None:\n return False\n return True\n\n def is_removed(self) -> bool:\n self.sstable_file.seek(self.cur_offset)\n indicator = unpack(\"i\", self.sstable_file.\n read(self.INDICATOR_SIZE))[0]\n if indicator == 0:\n return True\n else:\n return False\n\n def _get_range(self, key: str) -> Tuple[int, int]:\n \"\"\"\n :param key: the key user intends to find\n :return: the index range for key according\n to the index table in memory\n \"\"\"\n start = -1\n end = -1\n index_table_len = len(self.index_table)\n left = 0\n right = index_table_len - 1\n while left <= right and right >= 0 and left < index_table_len:\n mid = int((left + right) / 2)\n cur_data = self.index_table[mid]\n if cur_data[0] == key:\n start = unpack(\"i\", cur_data[1])[0]\n end = unpack(\"i\", cur_data[1])[0]\n return start, end\n elif cur_data[0] < key:\n left = mid + 1\n start = unpack(\"i\", cur_data[1])[0]\n elif cur_data[0] > key:\n right = mid - 1\n end = unpack(\"i\", cur_data[1])[0]\n return start, end\n\n # _get_offset: find the offset of key in index file\n def _get_offset(self, key: str, start, end):\n \"\"\"\n :param key: the key user intends to find in sstable\n :param start: the start of the index range\n :param end: the end of the index range\n :return: the offset of the key;\n if the offset is not found, return None\n \"\"\"\n if start == -1:\n start = 0\n if end == -1:\n end = self.last_index\n self.index_file.seek(start)\n bin_key = key.encode()\n while start <= end:\n key_size_str = self.index_file.read(self.INT_SIZE)\n key_size = unpack(\"i\", key_size_str)[0]\n cur_key = self.index_file.read(key_size)\n if cur_key == bin_key:\n offset_str = self.index_file.read(self.INT_SIZE)\n offset = unpack(\"i\", offset_str)[0]\n return offset\n else:\n self.index_file.read(self.INT_SIZE)\n start += self.INT_SIZE + key_size + self.INT_SIZE\n return None\n","sub_path":"pydynamo/storage/disk/sstableiterator.py","file_name":"sstableiterator.py","file_ext":"py","file_size_in_byte":12191,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"533306460","text":"# Copyright 2017 Rackspace US, Inc.\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\n__title__ = 'tuvok'\n__version__ = '0.1.0'\n__license__ = 'Apache 2.0'\n__copyright__ = 'Copyright Rackspace US, Inc. 2018'\n\n\nimport hcl2\nimport importlib\nimport inspect\nimport logging\nimport pkgutil\n\nimport tuvok.plugins\n\n\nLOG = logging.getLogger()\nJSON_CACHE = {}\n\n\ndef iter_namespace(ns_pkg):\n return pkgutil.iter_modules(ns_pkg.__path__, ns_pkg.__name__ + \".\")\n\n\ndef not_abstract_class(o):\n return inspect.isclass(o) and not inspect.isabstract(o)\n\n\ndef hcl2json(f):\n if f not in JSON_CACHE:\n with(open(f, 'r')) as file:\n JSON_CACHE[f] = hcl2.load(file)\n return JSON_CACHE[f]\n\n\nplugin_modules = [\n importlib.import_module(name)\n for (finder, name, ispkg)\n in iter_namespace(tuvok.plugins)\n]\nif(len(plugin_modules) > 0):\n LOG.info('Loaded plugin modules %s', plugin_modules)\n\ntuvok_plugins = []\nfor module in plugin_modules:\n for clazz in inspect.getmembers(module, not_abstract_class):\n if clazz[1].__module__ is module.__name__:\n tuvok_plugins.append(clazz[1])\nif(len(tuvok_plugins) > 0):\n LOG.info('Loaded plugins %s', tuvok_plugins)\n\ntuvok_checks = []\nfor clazz in tuvok_plugins:\n for check in clazz().get_checks():\n tuvok_checks.append(check)\nif(len(tuvok_checks) > 0):\n LOG.info('Loaded checks %s', tuvok_checks)\n","sub_path":"tuvok/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1879,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"436255626","text":"#!/usr/bin/env python3\n# coding=utf-8\n# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.\n# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.\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\n\nimport torch as th\nimport torch.nn.functional as F\nfrom pytorch_transformers import GPT2Tokenizer\n\n# Load pre-trained model tokenizer (vocabulary)\ntokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")\n\n\ndef encode_text(text):\n # Encode a text inputs\n indexed_tokens = tokenizer.encode(text)\n # Convert indexed tokens in a PyTorch tensor\n return th.tensor([indexed_tokens])\n\n\ndef decode_tokens(tokens):\n return tokenizer.decode(tokens)\n\n\ndef decode_local_response(response):\n predictions = th.tensor(response[0])\n predicted_index = th.argmax(predictions[0, -1, :]).item()\n predicted_text = decode_tokens([predicted_index])\n return predicted_text\n\n\ndef decode_response(response):\n predictions = th.tensor(response)\n predicted_index = th.argmax(predictions[0, -1, :]).item()\n predicted_text = decode_tokens([predicted_index])\n return predicted_text\n\n\ndef top_k_top_p_filtering(logits, top_k=0, top_p=0.0, filter_value=-float(\"Inf\")):\n \"\"\" Filter a distribution of logits using top-k and/or nucleus (top-p) filtering\n\n This code is from: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317\n Author: Thomas Wolf.\n\n Args:\n logits: logits distribution shape (vocabulary size)\n top_k > 0: keep only top k tokens with highest probability (top-k filtering).\n top_p > 0.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).\n Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)\n \"\"\"\n assert (\n logits.dim() == 1\n ) # batch size 1 for now - could be updated for more but the code would be less clear\n top_k = min(top_k, logits.size(-1)) # Safety check\n if top_k > 0:\n # Remove all tokens with a probability less than the last token of the top-k\n indices_to_remove = logits < th.topk(logits, top_k)[0][..., -1, None]\n logits[indices_to_remove] = filter_value\n\n if top_p > 0.0:\n sorted_logits, sorted_indices = th.sort(logits, descending=True)\n cumulative_probs = th.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)\n\n # Remove tokens with cumulative probability above the threshold\n sorted_indices_to_remove = cumulative_probs > top_p\n # Shift the indices to the right to keep also the first token above the threshold\n sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()\n sorted_indices_to_remove[..., 0] = 0\n\n indices_to_remove = sorted_indices[sorted_indices_to_remove]\n logits[indices_to_remove] = filter_value\n return logits\n\n\ndef sample_sequence(\n length,\n context,\n model=None,\n worker=None,\n model_id=\"GPT-2\",\n temperature=1,\n top_k=0,\n top_p=0.0,\n device=\"cpu\",\n):\n # We expect a list of indexes as input\n if isinstance(context, th.Tensor):\n context = context.tolist()[0]\n\n context = th.tensor(context, dtype=th.long, device=device)\n context = context.unsqueeze(0).repeat(1, 1)\n\n predicted_indexes = []\n generated = context\n with th.no_grad():\n for _ in range(length):\n # Inference\n if worker and model_id:\n outputs = worker.run_remote_inference(model_id=model_id, data=generated)\n elif model:\n outputs = model(generated)\n else:\n raise ValueError(\n \"You should provide a worker and a model_id or a model in order to run this function.\"\n )\n\n # Applying Filter\n if model:\n next_token_logits = outputs[0][0, -1, :] / temperature\n else:\n next_token_logits = outputs[0, -1, :] / temperature\n filtered_logits = top_k_top_p_filtering(\n next_token_logits, top_k=top_k, top_p=top_p\n )\n\n next_token = th.multinomial(\n F.softmax(filtered_logits, dim=-1), num_samples=1\n )\n\n # Update context shifting tokens\n generated = th.cat(\n (th.tensor([generated[0][1:].tolist()]), next_token.unsqueeze(0)), dim=1\n )\n\n # Save predicted word\n predicted_indexes.append(next_token.item())\n\n return decode_tokens(predicted_indexes)\n","sub_path":"examples/Serving and Querying models on Grid/gpt2_utils.py","file_name":"gpt2_utils.py","file_ext":"py","file_size_in_byte":5075,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"378266370","text":"# -*- coding: utf-8 -*-\r\nfrom copy import copy, deepcopy\r\nfrom math import sqrt\r\n\r\nfrom Popul import Popul\r\nfrom Indiv import Indiv\r\nimport numpy as np\r\nfrom collections import defaultdict\r\nfrom random import shuffle, randint, sample\r\nimport itertools\r\n\r\n\r\n\r\nclass EvolAlgorithm:\r\n \r\n def __init__ (self, popsize, numits, noffspring,blocks,mat):\r\n self.popsize = popsize\r\n self.numits = numits\r\n self.noffspring = noffspring\r\n self.blocks = blocks\r\n self.matdist = mat\r\n\r\n def generate_indvs(self,block):\r\n \"\"\"\r\n generate the individuals for the first generation using the previously built blocks\r\n \"\"\"\r\n res = []\r\n for i in range(self.popsize):\r\n x = block.copy()\r\n shuffle(x)\r\n indv = Indiv(self.matdist, list(itertools.chain.from_iterable(x)))\r\n res.append(indv)\r\n return res\r\n \r\n def initPopul(self):\r\n \"\"\"\r\n create the first generation of individuals in each island\r\n \"\"\"\r\n self.populs=[]\r\n for block in self.blocks:\r\n indivs = self.generate_indvs(block)\r\n self.populs.append(Popul(self.popsize,indivs))\r\n \r\n def iteration(self,mode):\r\n \"\"\"\r\n iterative process depending on the mode for each generation and further selection, recombination\r\n and reinsertion\r\n param mode: mode 1 will perform a random mutation in populations being the default one;\r\n mode 2 will be activated when the best fitness of the populations remains the same for the last 100 iterations.\r\n This will perform a random mutations in the 5th to the 9th individual with the best fitness and\r\n random mutations in the worst 70% individuals of the population, also in the 2nd to the 14th individual with\r\n the best fitness the mutation type 4 is performed;\r\n mode 3 is activated when the best fitness remains the\r\n same for the previous 50 iterations and performs random mutations in the worst 50% of the population;\r\n mode 4 is activated when the best fitness remains the same for the previous 150 iterations. This mode will\r\n perform a type 3 mutation in the individual with best fitness, random mutations in the worst 50% of the\r\n population and will promote migrations between islands\r\n \"\"\"\r\n for i in range(len(self.populs)):\r\n if mode == 1:\r\n self.populs[i].random_mutations()\r\n pass\r\n elif mode==2:\r\n best_indexes2 = self.populs[i].getRanking()[4:10]\r\n for j in best_indexes2:\r\n self.populs[i].getIndiv(j[0]).random_mutations_indiv()\r\n self.populs[i].updateRanking()\r\n self.populs[i].random_mutations(0.7)\r\n best_indexes2 = self.populs[i].getRanking()[1:15]\r\n for j in best_indexes2:\r\n self.populs[i].getIndiv(j[0]).mutation(4)\r\n self.populs[i].updateRanking()\r\n\r\n elif mode==3:\r\n self.populs[i].random_mutations(0.5)\r\n\r\n elif mode==4 and i>0:\r\n n=randint(0,1)\r\n self.populs[i].random_mutations(0.5)\r\n self.populs[i].updateRanking()\r\n\r\n self.populs[i].migration(self.populs[i-1])\r\n self.populs[i-1].migration(self.populs[i])\r\n\r\n\r\n\r\n parents = self.populs[i].selection(self.noffspring)\r\n shuffle(parents)\r\n offspring = self.populs[i].recombination(parents, self.noffspring)\r\n self.populs[i].reinsertion(offspring)\r\n\r\n def run(self):\r\n \"\"\"\r\n Will start the iteration process generation a population and retrieving the best fitness for each iteration.\r\n It will further select the modes for the iteration process and retrieve at the end the best sequence and\r\n respective fitness value\r\n \"\"\"\r\n self.initPopul()\r\n self.bestsol = []\r\n self.bestfit = self.populs[0].getFitnesses()[0]\r\n l=1\r\n bs, bf = self.populs[0].getIndiv(self.populs[0].getRanking()[0][0]), self.populs[0].getIndiv(\r\n self.populs[0].getRanking()[0][0]).getFitness()\r\n\r\n previous=bf\r\n for i in range(self.numits+1):\r\n if l%50==0 and l%100!=0 and l%150!=0:\r\n self.iteration(3)\r\n print(\"---------Random mutations in the worst 50 % ------------\")\r\n elif l%100==0:\r\n self.iteration(2)\r\n print(\"---------Random mutations in the worst 70 % and type 3 mutation in the best 3 to 9 + block swap------------\")\r\n elif l%150==0:\r\n self.iteration(4)\r\n print(\"---------Random mutations in the worst 50 % and type 3 mutation in the best and migration between islands------------\")\r\n l=1\r\n else:\r\n self.iteration(1)\r\n\r\n\r\n for popul in self.populs:\r\n newfit = popul.getIndiv(\r\n popul.getRanking()[0][0]).getFitness()\r\n if bf > newfit:\r\n bs, bf = popul.getIndiv(popul.getRanking()[0][0]), popul.getIndiv(popul.getRanking()[0][0]).getFitness()\r\n\r\n\r\n if bf!=previous:\r\n l=1\r\n previous=bf\r\n else:\r\n l+=1\r\n\r\n if bf < self.bestfit:\r\n self.bestfit = bf\r\n self.bestsol = deepcopy(bs)\r\n\r\n # if i % 100 == 0:\r\n # print(bs.getGenes())\r\n # print(self.bestsol.getGenes())\r\n print(\"Iteration:\", i, \" \", \"Best: \", self.bestfit)\r\n print(\"Best solution: \", self.bestsol.getGenes())\r\n print(\"Best fitness: \", self.bestfit)\r\n\r\ndef parser(file):\r\n \"\"\"\r\n creates a dictionary with the node id as keys and the corresponding coordinates as values\r\n param file: text file with the node id and respective coordinates\r\n \"\"\"\r\n with open(file) as f:\r\n lines=f.readlines()\r\n res={}\r\n go=False\r\n for line in lines:\r\n if line.strip(\"\\n\") == \"EOF\":\r\n go=False\r\n if go:\r\n line=line.strip(\"\\n\")\r\n l=line.split(' ')\r\n res[int(l[0])]=(float(l[1]), float(l[2]))\r\n if line.strip(\"\\n\")==\"NODE_COORD_SECTION\":\r\n go=True\r\n return res\r\n\r\ndef distmat(dic):\r\n \"\"\"\r\n creates a distance matrix calculating the distance between each node\r\n param dic: dictionary with the node id as keys and respective coordinates as values\r\n \"\"\"\r\n res= np.zeros((len(dic.keys()),(len(dic.keys()))))\r\n for i in dic.keys():\r\n for j in range(i+1,len(dic.keys())+1):\r\n d=dist(i,j,dic)\r\n res[i-1][j-1]=d\r\n res[j-1][i-1]= d\r\n return res\r\n\r\ndef dist(i,j,dic):\r\n \"\"\"\r\n calculates the euclidean distance between each node\r\n param i: line of the matrix\r\n param j: column of the matrix\r\n param dic: dictionary with the node id as keys and respective coordinates as values\r\n \"\"\"\r\n return int(sqrt((dic[i][0]-dic[j][0])**2+(dic[i][1]-dic[j][1])**2))\r\n\r\ndef generate_blocks(mat,perc):\r\n \"\"\"\r\n generates blocks of close nodes further merging the blocks with common nodes between them. Returns a list of blocks\r\n param mat: distance matrix\r\n param perc: percentage of blocks to be kept for individual generation\r\n \"\"\"\r\n res = []\r\n for i in range(len(mat)):\r\n min=None\r\n for j in range(i+1,len(mat)):\r\n if min==None:\r\n min = (i+1,j+1,mat[i][j])\r\n elif min[2] > mat[i][j]:\r\n min = (i+1,j+1,mat[i][j])\r\n if min is not None:\r\n res.append(min)\r\n n = len(res)\r\n for i in range(n):\r\n for j in range(0, n - i - 1):\r\n if res[j][2] > res[j + 1][2]:\r\n res[j], res[j + 1] = res[j + 1], res[j]\r\n n_blocks = int(len(res)*perc)\r\n filtered = res[:n_blocks]\r\n blocks = [ [r[0],r[1]] for r in filtered ]\r\n merged = merge_common(blocks)\r\n merged = list(merged)\r\n merged_blocks = list(itertools.chain.from_iterable(merged))\r\n for g in [i for i in range(1,len(mat[0])+1)]:\r\n if g not in merged_blocks:\r\n merged.append([g])\r\n return merged\r\n\r\ndef merge_common(lists):\r\n \"\"\"\r\n merge function to merge all sublist having common elements\r\n param lists: lists of blocks\r\n \"\"\"\r\n neigh = defaultdict(set)\r\n visited = set()\r\n for each in lists:\r\n for item in each:\r\n neigh[item].update(each)\r\n\r\n def comp(node, neigh=neigh, visited=visited, vis=visited.add):\r\n nodes = set([node])\r\n next_node = nodes.pop\r\n while nodes:\r\n node = next_node()\r\n vis(node)\r\n nodes |= neigh[node] - visited\r\n yield node\r\n\r\n for node in neigh:\r\n if node not in visited:\r\n yield sorted(comp(node))\r\n\r\n\r\n\r\n\r\n\r\nif __name__==\"__main__\":\r\n dic = parser(\"qa194.tsp\")\r\n mat = distmat(dic)\r\n blocks=[]\r\n for i in range(3):\r\n blocks.append(generate_blocks(mat, 0.86+(i/100)))\r\n ea = EvolAlgorithm(40, 10000, 20,blocks,mat)\r\n ea.run()\r\n\r\n\r\n\r\n","sub_path":"EvolAlgorithm.py","file_name":"EvolAlgorithm.py","file_ext":"py","file_size_in_byte":9286,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"250472493","text":"# Plotting Code for Triangle Wave\n\nimport numpy as np\nimport math\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nfrom scipy import signal\nimport scipy.fftpack\nmpl.style.use('classic')\n\n\ndef FS_Triangle(k, tt):\n '''\n k, waveNumbers, is a list of integer wave numbers\n tt is a list of time stamps\n returns the aggregated triangle wave\n '''\n Cn = np.pi / 2.0 # constant term\n return Cn + np.sum([(4.0 / np.pi) * (np.cos(k*t) / k**2 ) for t in tt], axis=1) # sum of waves\n\ntt = np.arange(0, 2*np.pi + 0.01, 0.01) # time scale \ntt_fs = np.arange(-np.pi, np.pi + 0.01, 0.01) # time scale \n\n# Plot saw triangle wave\nk_max = 10001 # max wave number\nk_odd = np.arange(1.0,k_max,2) # list of odd wave numbers\n\ny_tri10001 = FS_Triangle(k_odd, tt_fs)\nplt.figure(1)\nplt.title(\"Triangle Wave\")\nplt.ylabel(\"Magnitude\")\nplt.xlabel(\"Time (seconds)\")\nplt.plot(tt_fs, y_tri10001, 'black')\nplt.xlim(-1, 7)\nplt.show()\n\n# Fourier Series\nk_max3 = 3\nk_odd3 = np.arange(1.0,k_max3,2) # list of odd wave numbers\ny_tri3 = FS_Triangle(k_odd3, tt_fs)\n\nk_max5 = 5\nk_odd5 = np.arange(1.0,k_max5,2) # list of odd wave numbers\ny_tri5 = FS_Triangle(k_odd5, tt_fs)\n\nk_max7 = 7\nk_odd7 = np.arange(1.0,k_max7,2) # list of odd wave numbers\ny_tri7 = FS_Triangle(k_odd7, tt_fs)\n\nk_max9 = 9\nk_odd9 = np.arange(1.0,k_max9,2) # list of odd wave numbers\ny_tri9 = FS_Triangle(k_odd9, tt_fs)\n\n\nplt.figure(2)\nplt.title(\"Triangle Wave\"\n \"\\n\"\n \"Fourier Series\")\nplt.ylabel(\"Magnitude\")\nplt.xlabel(\"Time\")\nplt.plot(tt_fs, y_tri3)\nplt.plot(tt_fs, y_tri5)\nplt.plot(tt_fs, y_tri7)\nplt.plot(tt_fs, y_tri9)\nplt.show()\n\n# Plot Fourier Transform\n# Number of sample points\nN = 600\n# sample spacing\nT = 1.0 / 800.0\nx = np.linspace(0.0, N*T, N)\n\ny = signal.sawtooth(2 * np.pi * 1.33 * x, width=.5)\nyf = scipy.fftpack.fft(y)\nxf = np.linspace(-1.0 / (T), 1.0 / (T), N//2)\n\nplt.figure(6)\nplt.title(\"Triangle Wave\"\n \"\\n\"\n \"Fourier Transform\")\nplt.ylabel(\"Magnitude\")\nplt.xlabel(\"Frequency (Hz)\")\nplt.plot(xf, 2.0/N * np.abs(yf[0:N//2]))\nplt.ylim(0., 0.6)\nplt.show() \n\n","sub_path":"ArtNet_Supporting_Documentation/01_Fourier/CodeForImages/FourierSeries_TriangleWave.py","file_name":"FourierSeries_TriangleWave.py","file_ext":"py","file_size_in_byte":2075,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"318431482","text":"# ELSE AND FINALLY IN TRY EXCEPT.\r\nf1 = open(\"HarryF.txt\")\r\n\r\ntry:\r\n f = open(\"does2.txt\") # File doesnt exist.\r\n a = open(\"does.txt\")\r\n\r\nexcept IOError as e: # If try fails then it goes to except else it is skipped. The error is printed. It is handled.\r\n print(\"IO error Occured.\", e)\r\n # More than one except can be given.\r\nexcept EOFError as e:\r\n print(\"EOF error Occured.\", e)\r\n\r\n# If except is run then else is not run and vice-versa.\r\nelse:\r\n print(\"This will run only if except is not running.\")\r\n\r\nfinally: # This is run anyway. If the exception occurs or not.\r\n # This is used to cleanup the code.\r\n print(\"Run this anyway.\")\r\n # f.close() # This file doesnt exist.\r\n f1.close()\r\n\r\nprint(\"important Stuff.\")\r\n","sub_path":"Code With Harry/Python Tuts/tut62 (Else and Finally in Try Except).py","file_name":"tut62 (Else and Finally in Try Except).py","file_ext":"py","file_size_in_byte":752,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"199415593","text":"from setuptools import setup\n\nNAME = \"em_tools\"\nVERSION = \"0.0.1\"\nDESCR = \"Set of routines used in every day Connectomics research.\"\nREQUIRES = ['numpy', 'opencv-python', 'h5py', 'scipy', 'neuroglancer', 'imageio']\n\nAUTHOR = \"Donglai Wei, Matin Raayai Ardakani\"\nEMAIL = \"weiddoonngglai@gmail.com, raayai.matin@gmail.com\"\nLICENSE = \"Apache 2.0\"\nSRC_DIR = \"em_tools\"\nURL = ''\nPACKAGES = [SRC_DIR]\n\nif __name__ == \"__main__\":\n # python setup.py develop install\n setup(install_requires=REQUIRES,\n packages=PACKAGES,\n zip_safe=False,\n url=URL,\n name=NAME,\n version=VERSION,\n description=DESCR,\n author=AUTHOR,\n author_email=EMAIL,\n license=LICENSE\n )\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":742,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"130471017","text":"from ConfigSpace.configuration_space import ConfigurationSpace\r\nfrom ConfigSpace.hyperparameters import UniformFloatHyperparameter, \\\r\n UniformIntegerHyperparameter, CategoricalHyperparameter, \\\r\n UnParametrizedHyperparameter, Constant\r\nfrom ConfigSpace.conditions import EqualsCondition\r\nfrom automl.utl import json_utils\r\n\r\ncs = ConfigurationSpace()\r\n\r\n# Parameterized Hyperparameters\r\nmax_depth = UniformIntegerHyperparameter(\r\n name=\"max_depth\", lower=1, upper=20, default_value=3\r\n)\r\nlearning_rate = UniformFloatHyperparameter(\r\n name=\"learning_rate\", lower=0.001, upper=1, default_value=0.1,\r\n log=True,\r\n)\r\nn_estimators = UniformIntegerHyperparameter(\r\n name=\"n_estimators\", lower=50, upper=500, default_value=100\r\n)\r\nbooster = CategoricalHyperparameter(\r\n \"booster\", [\"gbtree\", \"dart\"]\r\n)\r\nsubsample = UniformFloatHyperparameter(\r\n name=\"subsample\", lower=0.01, upper=1.0, default_value=1.0, log=False\r\n)\r\nmin_child_weight = UniformIntegerHyperparameter(\r\n name=\"min_child_weight\", lower=0,\r\n upper=20, default_value=1, log=False\r\n)\r\ncolsample_bytree = UniformFloatHyperparameter(\r\n name=\"colsample_bytree\", lower=0.1, upper=1.0, default_value=1,\r\n)\r\ncolsample_bylevel = UniformFloatHyperparameter(\r\n name=\"colsample_bylevel\", lower=0.1, upper=1.0, default_value=1,\r\n)\r\nreg_alpha = UniformFloatHyperparameter(\r\n name=\"reg_alpha\", lower=1e-10, upper=1e-1, log=True,\r\n default_value=1e-10)\r\nreg_lambda = UniformFloatHyperparameter(\r\n name=\"reg_lambda\", lower=1e-10, upper=1e-1, log=True,\r\n default_value=1e-10)\r\n\r\n# Unparameterized Hyperparameters\r\n# https://xgboost.readthedocs.io/en/latest//parameter.html\r\n# minimum loss reduction required to make a further partition on a\r\n# leaf node of the tree\r\ngamma = Constant(\r\n name=\"gamma\", value=0)\r\n# absolute regularization (in contrast to eta), comparable to\r\n# gradient clipping in deep learning - according to the internet this\r\n# is most important for unbalanced data\r\nmax_delta_step = Constant(\r\n name=\"max_delta_step\", value=0)\r\nbase_score = Constant(\r\n name=\"base_score\", value=0.5)\r\nscale_pos_weight = Constant(\r\n name=\"scale_pos_weight\", value=1)\r\n\r\ncs.add_hyperparameters([\r\n # Active\r\n max_depth, learning_rate, n_estimators, booster,\r\n subsample, colsample_bytree, colsample_bylevel,\r\n reg_alpha, reg_lambda,\r\n # Inactive\r\n min_child_weight, max_delta_step, gamma,\r\n base_score, scale_pos_weight\r\n])\r\n\r\njson_utils.write_cs_to_json_file(cs, \"XGBRegressor\")\r\n\r\n","sub_path":"examples/components_json_generator/regression_json_generator/xgb_regressor.py","file_name":"xgb_regressor.py","file_ext":"py","file_size_in_byte":2507,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"279702914","text":"import pandas as pd\r\nimport numpy as np\r\nimport math\r\n\r\n# Calculates day weightings according to the formula exp(tbV/10^9)\r\ndef volumeBasedWeights(length, b, volumes):\r\n weights = []\r\n for t in range(0, length):\r\n weights.append(math.exp(t*b*(volumes[t] / 1000000000)))\r\n weightSum = sum(weights)\r\n for t in range(0, length):\r\n weights[t] = weights[t] / weightSum\r\n return weights\r\n\r\n\r\ndef movingAverage(shortTermLength, LongTermLength, volumeFactor):\r\n\r\n # Loading initial variables\r\n shortMovingAverage = 0\r\n longMovingAverage = 0\r\n workingCapital = 1000000\r\n cashAvailable = workingCapital\r\n cash = []\r\n numTrades = 0\r\n numUnits = 0\r\n\r\n # Converting price and volume information to a list\r\n prices = data['Price'].to_list()\r\n volumes = data['Volume'].to_list()\r\n\r\n # Looping through the time period\r\n for i in range(LongTermLength, len(prices)):\r\n # Calculate the weights for both periods\r\n shortTermWeights = volumeBasedWeights(shortTermLength, volumeFactor, volumes[i - shortTermLength:i])\r\n longTermWeights = volumeBasedWeights(LongTermLength, volumeFactor, volumes[i - LongTermLength:i])\r\n\r\n # Calclate the averages from the weights and prices\r\n longMovingAverage = (np.dot(prices[i - LongTermLength:i], longTermWeights))\r\n shortMovingAverage = (np.dot(prices[i - shortTermLength:i], shortTermWeights))\r\n\r\n difference = shortMovingAverage - longMovingAverage\r\n\r\n # Buy and selling conditions\r\n if( difference > 0):\r\n if(numUnits == 0):\r\n numUnits = math.floor(cashAvailable / (prices[i]))\r\n cashAvailable -= numUnits * (prices[i])\r\n numTrades += 1\r\n if( difference < 0 or i == len(prices) - 1):\r\n if(numUnits != 0):\r\n cashAvailable += numUnits * (prices[i])\r\n numUnits = 0\r\n numTrades += 1\r\n # Collect portfolio value information\r\n cash.append(numUnits * (prices[i]) + cashAvailable)\r\n # Calculate total return\r\n totalReturn = (cashAvailable - workingCapital) / workingCapital\r\n # Calculate benchmark returns\r\n bmark = (data['Price'][len(prices) - 1] - data['Price'][LongTermLength]) / data['Price'][LongTermLength]\r\n print(bmark)\r\n print(totalReturn)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n data = pd.read_csv('data/2017-2020-withVolume.csv')\r\n movingAverage(12, 26, 2.88)\r\n\r\n ","sub_path":"EMACD.py","file_name":"EMACD.py","file_ext":"py","file_size_in_byte":2476,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"366260479","text":"# -*- coding: utf-8 -*-\n\n\"\"\"\nA number is prime if it is only divisible by 1 and itself.\n\nSo for example 2, 3, 5, 79, 311 and 1931 are all prime, while 21 is not prime because it is divisible by 3 and 7.\nTo find if a number n is prime we could simply check if it divides any numbers below it.\nWe can use the modulus (%) operator to check for divisibility:\n\n- ref: http://community.topcoder.com/tc?module=Static&d1=tutorials&d2=math_for_topcoders\n\"\"\"\n\nclass IsPrime(object):\n\n def check_primality_in_brute_force(self, num):\n \"\"\"\n Return True if the given number is prime. Otherwise, return False\n\n >>> IsPrime().check_primality_in_brute_force(2)\n True\n >>> IsPrime().check_primality_in_brute_force(3)\n True\n >>> IsPrime().check_primality_in_brute_force(4)\n False\n >>> IsPrime().check_primality_in_brute_force(35)\n False\n \"\"\"\n for divider in xrange(2, num):\n if num % divider == 0:\n return False\n return True\n\n def check_primality_with_optimization(self, num):\n \"\"\"\n Return True if the given number is prime. Otherwise, return False\n\n 1. if num is less than equal to 1, it is not a prime number.\n 3. if num is 2, it is a prime number.\n 2. if num is greater than 2 and num is even number, it is not a prime number.\n 4. If num = a*b is composite(합성수), (with a and b <> 1), one of the factors a or b is necessarily at most sqrt(num).\n\n - 합성수(composite)는 1과 자기 자신이 아닌 다른 자연수의 곱으로 나타낼 수 있는 자연수를 의미한다. 1보다 큰 모든 정수는 소수이거나 합성수이다.\n - A composite number is a positive integer that has at least one positive divisor other than one or the number itself.\n In other words, a composite number is any positive integer greater than one that is not a prime number.\n\n >>> IsPrime().check_primality_with_optimization(2)\n True\n >>> IsPrime().check_primality_with_optimization(3)\n True\n >>> IsPrime().check_primality_with_optimization(4)\n False\n >>> IsPrime().check_primality_with_optimization(35)\n False\n \"\"\"\n\n import math\n\n if num <= 1:\n return False\n if num == 2:\n return True\n if num % 2 == 0:\n return False\n\n m = int(math.sqrt(num))\n for divider in xrange(3, m+1, 2):\n if num % divider == 0:\n return False\n\n return True\n\n\n def get_prime_numbers(self, num):\n \"\"\"\n\n Return list of prime number which are less than the given num\n\n Sieve of Eratosthenes\n The Sieve of Eratosthenes will generate all the primes from 2 to a given number n.\n It begins by assuming that all numbers are prime.\n It then takes the first prime number and removes all of its multiples.\n\n >>> IsPrime().get_prime_numbers(5)\n [2, 3, 5]\n >>> IsPrime().get_prime_numbers(10)\n [2, 3, 5, 7]\n \"\"\"\n import math\n\n prime = {x:True for x in xrange(0, num+1)}\n prime[0] = False\n prime[1] = False\n\n m = int(math.sqrt(num))\n for n in xrange(2, m+1):\n if prime[n]:\n k = n*n\n while k <= num:\n prime[k] = False\n k = k + n\n\n return [x for x in prime if prime[x] == True]\n\n\nif __name__ == \"__main__\":\n import doctest\n doctest.testmod()\n","sub_path":"tobebest/algorithms/is_prime.py","file_name":"is_prime.py","file_ext":"py","file_size_in_byte":3553,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"147655811","text":"# try:\nimport unittest\n\nimport pygame\n\nfrom vec2d import Vec2D\nfrom collision import collide\nfrom abilities import Skill, SkillManager\nfrom character import Character\n\n# except ImportError as message:\n # raise SystemExit(message)\n\n\nclass CollisionTest(unittest.TestCase):\n\n \"\"\"The pictures are rectangular, taken the smallest\n bounding rect. So if two sides of two recs completele overlap\n then its for sure that the two images have overlapped.\n\n \"\"\"\n\n def setUp(self):\n # pygame.display.set_mode()\n\n self.p1 = Character(1, \"itachi\")\n self.p1.sound_effects.set_volume(0.0)\n self.p2 = Character(2, \"sasuke\")\n self.p2.sound_effects.set_volume(0.0)\n\n def tearDown(self):\n # pygame.display.quit()\n\n del self.p1\n del self.p2\n\n def test_no_collision_char_with_char(self):\n self.p1.rect.right = 0\n self.p2.rect.left = self.p1.rect.w\n\n result = collide(self.p1, self.p2)\n self.assertFalse(result)\n\n result = collide(self.p2, self.p1)\n self.assertFalse(result)\n\n def test_no_collision_char_with_skill(self):\n self.p1.rect.midright = (100, 100)\n self.p2.action = \"throw\"\n self.p2.rect.midleft = (200 + self.p2.rect.w, 100)\n skill = Skill(self.p2)\n\n result = collide(self.p1, skill)\n self.assertFalse(result)\n\n result = collide(skill, self.p1)\n self.assertFalse(result)\n\n def test_char_with_char_stance(self):\n self.p1.action = \"stance\"\n self.p1.rect.midright = (100, 100)\n self.p2.action = \"stance\"\n self.p2.rect.midleft = (90, 100)\n\n result = collide(self.p1, self.p2)\n self.assertTrue(result)\n self.assertEqual(self.p1.rect.right, 100)\n self.assertEqual(self.p2.rect.left, 90)\n\n result = collide(self.p2, self.p1)\n self.assertTrue(result)\n self.assertEqual(self.p1.rect.right, 100)\n self.assertEqual(self.p2.rect.left, 90)\n\n def test_char_with_char_normal_attack_block(self):\n self.p1.rect.midright = (100, 100)\n self.p1.action = \"normal_attack\"\n self.p2.rect.midleft = (90, 100)\n self.p2.action = \"move_right\"\n\n collide(self.p1, self.p2)\n self.assertEqual(self.p2.action, \"block\")\n self.assertTrue(\"block\" in self.p2.skills)\n self.assertTrue(pygame.mixer.get_busy())\n self.assertTrue(self.p2.hud.get_health(), self.p2.hud.health.capacity)\n self.assertTrue(self.p1.hud.get_mana(), self.p2.hud.mana.capacity)\n\n def test_char_with_char_normal_attack_take_damage(self):\n self.p1.rect.midright = (100, 100)\n self.p1.action = \"normal_attack\"\n self.p2.rect.midleft = (90, 100)\n self.p2.action = \"stance\"\n\n collide(self.p1, self.p2)\n self.assertEqual(self.p2.action, \"take_damage\")\n self.assertTrue(\"take_damage\" in self.p2.skills)\n self.assertTrue(pygame.mixer.get_busy())\n self.assertTrue(self.p2.hud.get_health(),\n self.p2.hud.health.capacity - self.p1.damage)\n self.assertTrue(self.p1.hud.get_mana(), self.p2.hud.mana.capacity)\n\n def test_char_with_skill_block(self):\n self.p1.rect.midright = (100, 100)\n self.p1.action = \"move_left\"\n\n self.p2.rect.midleft = (100, 100)\n self.p2.action = \"special_2\"\n skill = Skill(self.p2)\n skill.rect.midleft = (95, 100)\n\n collide(self.p1, skill)\n self.assertTrue(pygame.mixer.get_busy())\n self.assertTrue(skill.cooldown)\n self.assertEqual(self.p1.action, \"block\")\n self.assertEqual(self.p1.hud.get_health(), self.p1.hud.health.capacity)\n\n def test_char_with_skill_take_damage(self):\n self.p1.rect.midright = (100, 100)\n self.p1.action = \"move_right\"\n\n self.p2.rect.midleft = (100, 100)\n self.p2.action = \"special_2\"\n skill = Skill(self.p2)\n skill.rect.midleft = (95, 100)\n\n collide(skill, self.p1)\n self.assertTrue(pygame.mixer.get_busy())\n self.assertTrue(skill.cooldown)\n self.assertEqual(self.p1.action, \"take_damage\")\n self.assertEqual(self.p1.hud.get_health(),\n self.p1.hud.health.capacity - skill.damage)\n\n def test_char_with_many_skills(self):\n self.p1.rect.midright = (100, 100)\n self.p1.action = \"move_right\"\n\n self.p2.rect.midleft = (100, 100)\n self.p2.action = \"special_2\"\n skill1 = Skill(self.p2)\n skill1.rect.midleft = (95, 100)\n\n self.p2.action = \"throw\"\n skill2 = Skill(self.p2)\n skill2.rect.midleft = (95, 100)\n\n self.p2.action = \"special_1\"\n skill3 = Skill(self.p2)\n skill3.rect.midleft = (95, 100)\n\n skills = SkillManager(skill2, skill3, skill1)\n\n collide(self.p1, skills)\n self.assertTrue(pygame.mixer.get_busy())\n self.assertEqual(self.p1.action, \"take_damage\")\n self.assertEqual(self.p1.hud.get_health(),\n self.p1.hud.health.capacity - skill2.damage)\n self.assertTrue(skill2.cooldown)\n self.assertTrue(skill1.cooldown)\n self.assertTrue(skill3.cooldown)\n\n def test_jump(self):\n self.p1.rect.midright = (100, 100)\n self.p1.action = \"jump\"\n self.p1.movements[\"jump\"].current_frame = self.p1.movements[\"jump\"].\\\n max_frame\n\n self.p2.rect.midleft = (80, 100)\n\n collide(self.p1, self.p2)\n\n self.assertTrue(self.p1.rect.left, self.p2.rect.right)\n\n def test_jump_precise(self):\n self.p1.rect.midright = (100, 100)\n self.p1.position = Vec2D(self.p1.rect.topleft)\n self.p1.action = \"jump\"\n self.p1.movements[\"jump\"].current_frame = self.p1.movements[\"jump\"].\\\n max_frame\n\n self.p2.rect.midleft = (80, 100)\n self.p2.position = Vec2D(self.p2.rect.topleft)\n\n collide(self.p1, self.p2)\n\n self.assertTrue(self.p1.rect.left, self.p2.rect.right)\n\n\nif __name__ == \"__main__\":\n unittest.main()\n","sub_path":"tests/test_collision.py","file_name":"test_collision.py","file_ext":"py","file_size_in_byte":6044,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"566354313","text":"# SPDX-License-Identifier: Apache-2.0\n\"\"\"\nPython Package for controlling Tesla API.\n\nFor more details about this api, please refer to the documentation at\nhttps://github.com/zabuldon/teslajsonpy\n\"\"\"\nfrom teslajsonpy.battery_sensor import Battery, Range\nfrom teslajsonpy.binary_sensor import (\n ChargerConnectionSensor,\n OnlineSensor,\n ParkingSensor,\n)\nfrom teslajsonpy.charger import ChargerSwitch, ChargingSensor, RangeSwitch\nfrom teslajsonpy.climate import Climate, TempSensor\nfrom teslajsonpy.controller import Controller\nfrom teslajsonpy.exceptions import TeslaException, UnknownPresetMode\nfrom teslajsonpy.gps import GPS, Odometer\nfrom teslajsonpy.lock import Lock\nfrom teslajsonpy.sentry_mode import SentryModeSwitch\nfrom teslajsonpy.trunk import TrunkLock, FrunkLock\n\nfrom .__version__ import __version__\n\n__all__ = [\n \"Battery\",\n \"Range\",\n \"ChargerConnectionSensor\",\n \"ChargingSensor\",\n \"OnlineSensor\",\n \"ParkingSensor\",\n \"ChargerSwitch\",\n \"RangeSwitch\",\n \"Climate\",\n \"TempSensor\",\n \"Controller\",\n \"TeslaException\",\n \"UnknownPresetMode\",\n \"GPS\",\n \"Odometer\",\n \"Lock\",\n \"SentryModeSwitch\",\n \"TrunkLock\",\n \"FrunkLock\",\n \"__version__\",\n]\n","sub_path":"teslajsonpy/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1213,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"105088061","text":"import pygame\nimport random\n\n\nclass Fruit:\n def __init__(self):\n self.color = (255, 0, 0)\n crash = True\n while crash:\n self.x = random.randrange(0, scr_width, scale)\n self.y = random.randrange(0, scr_height, scale)\n crash = False\n for j in snake.location:\n if j[0]==self.x and j[1]==self.y:\n crash = True\n\n def ate(self):\n crash = True\n while crash:\n self.x = random.randrange(0, scr_width, scale)\n self.y = random.randrange(0, scr_height, scale)\n crash = False\n for j in snake.location:\n if j[0]==self.x and j[1]==self.y:\n crash = True\n\n\nclass Snake:\n def __init__(self):\n self.x = scale\n self.y = scale\n self.speedx = speed\n self.speedy = 0\n self.location=[]\n self.location.append([self.x, self.y])\n self.ate = False\n self.color = (255,255,255)\n self.die = False\n\n def update(self):\n self.x += self.speedx\n self.y += self.speedy\n\n # constrain x and y\n if self.x > scr_width - scale:\n # self.x = scr_width - scale\n self.die = True\n if self.y > scr_height - scale:\n # self.y = scr_height - scale\n self.die = True\n if self.x < 0:\n # self.x = 0\n self.die = True\n if self.y < 0:\n # self.y = 0\n self.die = True\n for loc in self.location:\n if self.x == loc[0] and self.y == loc[1]:\n self.die = True\n\n self.location.append([self.x, self.y])\n if not self.ate:\n self.location = self.location[1:]\n else:\n fruit.ate()\n self.ate = False\n\n def eat(self):\n self.ate = True\n\n\nscale = 30\nspeed = 1*scale\nscr_width = scale*scale\nscr_height = scale*scale\ndt = pygame.time.Clock()\ndone = False\npygame.init()\nscreen = pygame.display.set_mode((scr_width, scr_height))\nsnake = Snake()\nfruit = Fruit()\n\nwhile not done:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n done = True\n if event.type == pygame.KEYDOWN and event.key == pygame.K_UP:\n if snake.speedy == 0:\n snake.speedx = 0\n snake.speedy = -speed\n if event.type == pygame.KEYDOWN and event.key == pygame.K_DOWN:\n if snake.speedy == 0:\n snake.speedx = 0\n snake.speedy = speed\n if event.type == pygame.KEYDOWN and event.key == pygame.K_RIGHT:\n if snake.speedx == 0:\n snake.speedx = speed\n snake.speedy = 0\n if event.type == pygame.KEYDOWN and event.key == pygame.K_LEFT:\n if snake.speedx == 0:\n snake.speedx = -speed\n snake.speedy = 0\n\n # eat fruit\n if snake.x == fruit.x and snake.y == fruit.y:\n snake.eat()\n\n snake.update()\n done = snake.die\n # draw rect\n screen.fill((0,0,0))\n for i in snake.location:\n pygame.draw.rect(screen, snake.color, pygame.Rect(i[0], i[1], scale, scale))\n pygame.draw.rect(screen, fruit.color, pygame.Rect(fruit.x, fruit.y, scale, scale))\n\n pygame.display.flip()\n\n dt.tick(10)\n","sub_path":"snake.py","file_name":"snake.py","file_ext":"py","file_size_in_byte":3315,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"487168488","text":"#!/usr/bin/env python3\n\nfrom computer import Intcode\n\nclass Day2:\n def __init__(self, program):\n self.codes = [int(x) for x in program.split(',')]\n\n def set_noun_verb(self, computer, noun, verb):\n computer.memory[1] = noun\n computer.memory[2] = verb\n\n def part1(self):\n computer = Intcode(self.codes)\n self.set_noun_verb(computer, 12, 2)\n computer.run()\n return computer.memory[0]\n\n def part2(self):\n OUTPUT = 19690720\n for noun in range(0, 100):\n for verb in range(0, 100):\n computer = Intcode(self.codes)\n self.set_noun_verb(computer, noun, verb)\n computer.run()\n if computer.memory[0] == OUTPUT:\n return 100 * noun + verb\n\ndef main():\n program = open('day2.txt').read().strip()\n day2 = Day2(program)\n print(f'Part 1: {day2.part1()}')\n print(f'Part 2: {day2.part2()}')\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"2019/day2.py","file_name":"day2.py","file_ext":"py","file_size_in_byte":987,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"118449905","text":"from numpy import ndarray\nimport numpy as np\nfrom typing import Callable\nfrom scipy.stats import t as student_t_dist\n\n\nTWO_SIGMA = 0.6826\n\n\ndef var(func: Callable, x_data: ndarray, y_data: ndarray,\n params: ndarray) -> float:\n \"\"\"\n Calculate the variance of the data: sum_i (f(x_i) - y_i)^2\n :param func: the fitting function\n :param x_data: the x data\n :param y_data: the y data\n :param params: the fitting parameters\n :return the variance\n \"\"\"\n return np.sum(pow(func(x_data, *params) - y_data, 2))\n\n\ndef res(func, x_data, y_data, e_data, param):\n \"\"\"\n Calculate the residuals of the data: sum_i ((f(x_i) - y_i)/e_i)^2\n :param func: the fitting function\n :param x_data: the x data\n :param y_data: the y data\n :param e_data: the e data\n :param params: the fitting parameters\n :return the residuals\n \"\"\"\n tmp = (func(x_data, *param) - y_data)/e_data\n return np.sum(tmp*tmp)\n\n\ndef log10_hessian_det(covar: ndarray) -> float:\n \"\"\"\n Calculate the log base 10 of the determinant\n of the Hessian matrix\n :param covar: the covarience matrix\n :return the log of the determinant of the\n Hessian matrix\n \"\"\"\n hessian = np.linalg.inv(covar)\n det = np.linalg.det(hessian)\n # cannot have a negative value in log\n if det <= 0:\n det = 1.e-9\n return np.log10(det)\n\n\ndef chi_squared(x_data: ndarray, y_data: ndarray, e_data: ndarray,\n fit: ndarray, params: ndarray) -> float:\n \"\"\"\n Method for calculating the reduced chi squared value\n :param x_data: x data to fit\n :param y_data: y data to fit\n :param e_data: error data for fit\n :param fit: the fit\n :param params: the fit parameters\n :return the chi squared value\n \"\"\"\n chisq = np.sum(((y_data - fit)/e_data)**2)\n chisq /= (len(x_data) - len(params))\n return chisq\n\n\ndef param_errors(covar: ndarray) -> ndarray:\n \"\"\"\n Get the errors for the parameters\n :param covar: the covarience matrix\n :return the errors for the parameters\n \"\"\"\n return np.sqrt(np.diag(covar))\n\n\ndef derivative(x_data: ndarray, params: ndarray, func: Callable) -> ndarray:\n \"\"\"\n Get numerical derivative for a function\n :param x_data: the x data\n :param params: the paramaters\n :param func: the function\n :return numerical derivatives (with respect to fitting parameter)\n \"\"\"\n df_by_dp = []\n N = len(params)\n for j in range(N):\n # only want to change one parameter at a time\n dparams = np.zeros(N)\n # small (0.1%) change in parameter value\n dparams[j] = params[j]*0.001\n # forward difference\n df_by_dp.append((func(x_data, *(params + dparams)) -\n func(x_data, *(params)))/np.sum(dparams))\n return df_by_dp\n\n\ndef fit_errors(x_data: ndarray, params: ndarray, fit: ndarray,\n covar: ndarray, df_by_dp: ndarray) -> ndarray:\n \"\"\"\n Generate the errors for the fit line\n :param x_data: the x data\n :param params: the parameters for the function\n :param fit: the y data values from the fit\n :param covar: the covarience matrix\n :param df_by_dp: the derivatives\n :return the error values\n \"\"\"\n confidence = TWO_SIGMA\n\n prob = 0.5 + confidence/2. # even distribution above and below data point\n N = len(params)\n M = len(x_data)\n dof = M - N\n tval = student_t_dist.ppf(prob, dof)\n\n df_sq = np.zeros(M)\n for j in range(N):\n for k in range(N):\n df_sq += df_by_dp[j]*df_by_dp[k]*covar[j, k]\n df = np.sqrt(df_sq)\n\n return tval*df\n","sub_path":"src/quickBayes/fit_engines/fit_utils.py","file_name":"fit_utils.py","file_ext":"py","file_size_in_byte":3590,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"368109203","text":"import dash\nimport dash_bootstrap_components as dbc\nimport dash_html_components as html\nimport dash_core_components as dcc\nimport dash_table as dt\nfrom dash.dependencies import Input, Output, State\n\nimport pandas as pd\nimport plotly.graph_objs as go\nfrom plotly.graph_objs import Figure\n\nimport app\nF1_LOGO = \"https://logodownload.org/wp-content/uploads/2016/11/formula-1-logo-7.png\"\n\n# Dataset Processing\n\npath = 'datasets/f1_2020_drivers.xlsx'\ndf = pd.read_excel(path)\n\ndf_constructors = pd.read_csv('datasets/f1_2020_constructors.csv', error_bad_lines=False)\ndf_drivers = pd.read_csv('datasets/f1_2020_drivers_fred.csv', error_bad_lines=False)\n\ndf_drivers['date'] = pd.to_datetime(df_drivers['date'])\ndf_drivers = df_drivers.sort_values(by='date')\npistas = df_drivers.name_x.unique()\n\nfl = df_drivers[['name_x', 'fastestLapTime', 'surname', 'Team']]\npistas_fl = fl.groupby('name_x', as_index=False)['fastestLapTime'].min()\n\ndriver = []\nfor time in pistas_fl.fastestLapTime:\n driver.append(fl.loc[fl['fastestLapTime'] == time, 'surname'])\n\nDriver = ['Hamilton', 'Ricciardo', 'Norris', 'Verstappen', 'Ricciardo', 'Verstappen', 'Verstappen',\n 'Hamilton', 'Hamilton', 'Hamilton', 'Hamilton', 'Bottas', 'Russell', 'Bottas', 'Sainz',\n 'Norris', 'Hamilton']\n\npistas_fl = pistas_fl.assign(Driver=Driver)\n\nfl = fl.sort_values('name_x')\n\ndef selected_driver(name):\n times = fl.loc[fl['surname'] == name, 'fastestLapTime'].reset_index(drop=True)\n return times\n\n\ndef selected_driver_diff(name):\n time = fl.loc[fl['surname'] == name, 'fastestLapTime'].reset_index(drop=True)\n time_diff = time.subtract(pistas_fl['fastestLapTime'])\n return time_diff\n\ndriver_radio = dcc.RadioItems(\n id='radio-items',\n options=[\n {'label': 'Ocon ', 'value': 'Ocon'},\n {'label': 'Räikkönen ', 'value': 'Räikkönen'},\n {'label': 'Albon ', 'value': 'Albon'},\n {'label': 'Hamilton ', 'value': 'Hamilton'},\n {'label': 'Gasly ', 'value': 'Gasly'},\n {'label': 'Latifi ', 'value': 'Latifi'},\n {'label': 'Sainz ', 'value': 'Sainz'},\n {'label': 'Vettel ', 'value': 'Vettel'},\n {'label': 'Giovinazzi ', 'value': 'Giovinazzi'},\n {'label': 'Norris ', 'value': 'Norris'},\n {'label': 'Russell ', 'value': 'Russell'},\n {'label': 'Magnussen ', 'value': 'Magnussen'},\n {'label': 'Grosjean ', 'value': 'Grosjean'},\n {'label': 'Leclerc ', 'value': 'Leclerc'},\n {'label': 'Stroll ', 'value': 'Stroll'},\n {'label': 'Ricciardo ', 'value': 'Ricciardo'},\n {'label': 'Bottas ', 'value': 'Bottas'},\n {'label': 'Verstappen ', 'value': 'Verstappen'},\n {'label': 'Kvyat ', 'value': 'Kvyat'},\n {'label': 'Hülkenberg ', 'value': 'Hülkenberg'},\n {'label': 'Fittipaldi ', 'value': 'Fittipaldi'},\n {'label': 'Pérez ', 'value': 'Pérez'},\n {'label': 'Aitken ', 'value': 'Aitken'}\n ],\n value='Ocon', labelStyle={'display': 'inline-block'},\n inputStyle={\"margin-right\": \"2px\", \"margin-left\": \"12px\"},\n persistence=True,\n persistence_type='session'\n)\n\ndriver_options = [\n dict(label=' ' + driver, value=driver)\n for driver in df['surname'].unique()]\n\n\ndropdown_driver = dcc.Dropdown(\n id='driver_drop',\n options=driver_options,\n value=['Bottas', 'Vettel'],\n multi=True,\n persistence=True,\n persistence_type='session'\n )\n\n\nlayout = dbc.Container([\n dbc.Row([\n dbc.Col([\n html.H1('Drivers', className=\"text-left\")\n ],width=12)\n ]),\n dbc.Row([\n dbc.Col([\n html.H4('Choose the drivers to compare:', className=\"text-left\")\n ], width=12)\n ]),\n dbc.Row([\n dbc.Col([\n dropdown_driver,\n ], width={'size':12})\n ]),\n dbc.Row([\n dbc.Col([\n html.H5('Cumulative Points per Race', className=\"text-center\")\n ],width=6),\n dbc.Col([\n html.H5('Final Position per Race', className=\"text-center\")\n ],width=6)\n ],className=\"mt-2\"),\n dbc.Row([\n dbc.Col([\n dbc.Card(\n dcc.Graph(id='graph_pointsperrace', style={'height':570}),\n body=True, color=\"#31343b\"\n )\n ],width={'size':6}, className='my-2'),\n dbc.Col([\n dbc.Card(\n dcc.Graph(id='graph_rank', style={'height': 570}),\n body=True, color=\"#31343b\"\n )\n ], width={'size': 6}, className='my-2'),\n ], className=\"mb-2\"),\n dbc.Row([\n dbc.Col([\n html.H6(\"* if the Driver didn't participate in the race, no information will be presented\", className=\"text-center\")\n ], width=6),\n dbc.Col([\n html.H6('* if rank=0, then the Driver Did Not Finished (DNF)', className=\"text-center\")\n ], width=6)\n ], className=\"mt-2 mb-4\"),\n dbc.Row([\n dbc.Col([\n html.H5('Lap Time Difference in seconds between selected Driver and Fastest Lap', className=\"text-center\")\n ],width=12),\n ],className=\"mt-2\"),\n dbc.Row([\n dbc.Col([\n html.Div(driver_radio, className=\"text-center\"),\n ], width={'size':12})\n ]),\n dbc.Row([\n dbc.Col([\n dbc.Card(\n dcc.Graph(id='graph_timedelta', style={'height':570}),\n body=True, color=\"#31343b\"\n )\n ],width={'size':12})\n ], className=\"mb-2\"),\n dbc.Row([\n dbc.Col([\n html.H6('* if time=200s then the Driver DNF (Did Not Finished)'\n ' or was declassified by the 107% rule.', className=\"text-left\"),\n ], width=12),\n ], className=\"mt-2\"),\n dbc.Row([\n dbc.Col([\n dbc.Card(\n dbc.CardImg(src=\"/assets/logos_horizontal.png\", top=True),\n )\n ], width=12)\n ], className=\"mb-2\"),\n\n ], fluid=True)\n\n\n@app.app.callback(\n Output('graph_pointsperrace', 'figure'),\n Input('driver_drop', 'value')\n)\ndef update_graph(driver):\n scatter_data = []\n\n for drive in driver:\n df_s = df.loc[df['surname'] == drive]\n temp_data = dict(\n type='scatter',\n y=df_s['Pointsinday'],\n x=df_s['name_x'],\n name=drive\n )\n scatter_data.append(temp_data)\n scatter_layout = dict(xaxis=dict(title='Race'),\n yaxis=dict(title='Points'),\n paper_bgcolor='rgba(255,255,255)',\n plot_bgcolor='rgba(0,0,0,0)'\n )\n\n fig = go.Figure(data=scatter_data, layout=scatter_layout)\n fig.update_xaxes(showline=True, linewidth=1, linecolor='black')\n fig.update_yaxes(showline=True, linewidth=1, linecolor='black')\n return fig\n\n@app.app.callback(\n Output('graph_rank', 'figure'),\n Input('driver_drop', 'value')\n)\ndef update_graph2(driver):\n scatter_data = []\n\n for drive in driver:\n df_s = df.loc[df['surname'] == drive]\n temp_data = dict(\n type='scatter',\n y=df_s['rank'],\n x=df_s['name_x'],\n name=drive\n )\n scatter_data.append(temp_data)\n scatter_layout = dict(xaxis=dict(title='Race'),\n yaxis=dict(title='Drivers Ranks'),\n paper_bgcolor='rgba(255,255,255)',\n plot_bgcolor='rgba(0,0,0,0)'\n )\n\n fig = go.Figure(data=scatter_data, layout=scatter_layout)\n fig.update_xaxes(showline=True, linewidth=1, linecolor='black')\n fig.update_yaxes(showline=True, linewidth=1, linecolor='black', autorange=\"reversed\")\n return fig\n\n@app.app.callback(\n Output('graph_timedelta', 'figure'),\n Input('radio-items', 'value')\n)\ndef graph2(driver):\n fig = go.Figure()\n fig.add_trace(go.Bar(\n y=pistas_fl.name_x.unique(),\n x=pistas_fl['fastestLapTime'],\n name='Fastest Lap Recorded',\n orientation='h',\n marker=dict(\n color='#009246',\n line=dict(color='rgba(0,146,70,0.3)', width=3)\n )\n ))\n fig.add_trace(go.Bar(\n y=pistas_fl.name_x.unique(),\n x=selected_driver_diff(driver),\n name=driver,\n orientation='h',\n marker=dict(\n color='#ce2b37',\n line=dict(color='rgba(206,43,55,0.3)', width=3)\n ),\n ))\n fig.update_layout(barmode='stack',paper_bgcolor='rgba(255,255,255)',plot_bgcolor='rgba(0,0,0,0)', xaxis_title=\"Fastest Lap Time in Seconds\",\n yaxis_title=\"Race\")\n fig.update_xaxes( gridcolor='black', showgrid=True, gridwidth=0.5)\n\n return fig","sub_path":"drivers.py","file_name":"drivers.py","file_ext":"py","file_size_in_byte":9243,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"67229450","text":"import pandas as pd\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport os\n\nos.chdir('/home/sameen/maltrail/new')\nfile_chdir = os.getcwd()\n\n\nfilecsv_list = []\nfor root, dirs, files in os.walk(file_chdir):\n for file in files:\n if os.path.splitext(file)[1] == '.csv':\n filecsv_list.append(file)\n\ndata = pd.DataFrame()\ndatas=pd.DataFrame()\ndata=pd.read_csv(filecsv_list[0],names=[\"ip\",\"info\",\"reference\"])\ndel filecsv_list[0]\nlabel_list =[]\nsize=[]\ncount=0\nfor csv in filecsv_list:\n data.append(pd.read_csv(csv,names=[\"ip\",\"info\",\"reference\"]),ignore_index=True)\n data.drop_duplicates()\n\ndatas=data.groupby(\"info\").size()\ndatas.columns=[\"info\",\"number\"]\nprint(datas)\nfor row in datas.iteritems():\n\n if row[1]>10000:\n label_list.append(row[0])\n size.append(row[1])\n\nplt.figure(figsize=(6, 6))\n\nplt.pie(size, labels=label_list, autopct='%1.1f%%')\n\nplt.show()\n\n","sub_path":"shibo/ipinfo.py","file_name":"ipinfo.py","file_ext":"py","file_size_in_byte":904,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"403389082","text":"from .kps_login import doLogin, TOKEN, KPS, Kps\nfrom enum import Enum\n\n\nclass ApplicationType(Enum):\n PAPER = 'CNOTE_APPLICATION_TYPE_PAPER'\n ELECTRONIC = 'CNOTE_APPLICATION_TYPE_ELECTRONIC'\n FORM = 'CNOTE_APPLICATION_TYPE_FORM'\n\n\nclass SmgsConsignor:\n def __init__(self):\n\n # Vai nosūtītājs ir fiziskā persona\n self.IsPhysicalPersonConsignor = False\n # Kravas nosūtītāja KLR ID\n self.ConsignorCustomerId = ''\n # Kravas nosūtītāja adrese\n self.ConsignorAddress = ''\n # Kravas nosūtītāja adrese: Iela, nams\n self.ConsignorStreetAddress = ''\n # Kravas nosūtītāja adrese: Pilsēta\n self.ConsignorCity = ''\n # Kravas nosūtītāja adrese: Pasta indekss\n self.ConsignorPostalCode = ''\n # Kravas nosūtītāja tālrunis.\n self.ConsignorPhone = ''\n # Nosūtītāja atzīmes, nesaistošas dzelzceļam. Teksta brīva ievade.\n self.ConsignorNotes = ''\n\n def consignor(self):\n cnsgnr = {}\n for key in self.__dict__:\n if self.__dict__[key] != '':\n cnsgnr[key] = self.__dict__[key]\n return cnsgnr\n\n\nclass SmgsConsignee:\n def __init__(self):\n self.IsPhysicalPersonConsignee = False\n # Kravas saņēmēja klienta ID\n self.ConsigneeCustomerId = ''\n # Kravas saņēmēja kods. Teksta brīva ievade.\n self.ConsigneeCode = ''\n # Kravas saņēmēja nosaukums. Teksta brīva ievade.\n self.ConsigneeName = ''\n # Kravas saņēmēja tālrunis. Teksta brīva ievade.\n self.ConsigneePhone = ''\n # Kravas saņēmēja adrese - valsts. Tiek aizpildīts automātiski no klientu klasifikatora.\n self.ConsigneeCountryCode = ''\n # Kravas saņēmēja adrese: Iela, nams\n self.ConsigneeStreetAddress = ''\n # Kravas saņēmēja adrese: Pilsēta\n self.ConsigneeCity = ''\n # Kravas saņēmēja adrese: Pasta indekss\n self.ConsigneePostalCode = ''\n # Saņēmēja uzņēmuma reģ.nr, ja fiziska persona - pers.kods. Teksta brīva ievade.\n self.ConsigneeRegNumber = ''\n\n def consignee(self):\n cnsgnee = {}\n for key in self.__dict__:\n if self.__dict__[key] != '':\n cnsgnee[key] = self.__dict__[key]\n return cnsgnee\n\n\nclass FromToStations:\n def __init__(self):\n # Nosūtīšanas stacijas kods\n self.DepartureStationCode = ''\n # Nosūtīšanas stacijas kods\n self.DeliveryStationCode = ''\n\n def stations(self):\n sts = {}\n for key in self.__dict__:\n sts[key] = self.__dict__[key]\n return sts\n\n\nclass SmgsDraftMainData:\n def __init__(self):\n # Pavadzīmes iesniegšanas veids - papīra, elektroniski\n self.ApplicationTypeCode = 'CNOTE_APPLICATION_TYPE_PAPER'\n\n # Vai vērtība nav noteikta\n self.IsCargoValueUnknown = True\n\n def main_data(self):\n m_data = {}\n for key in self.__dict__:\n m_data[key] = self.__dict__[key]\n return m_data\n\n\nclass SmgsDraft:\n def __init__(self):\n self.main_data = SmgsDraftMainData()\n self.consignor = SmgsConsignor()\n self.consignee = SmgsConsignee()\n self.stations = FromToStations()\n\n def smgsDraft(self):\n smgs_draft = {}\n smgs_draft.update(self.main_data.main_data())\n smgs_draft.update(self.consignor.consignor())\n smgs_draft.update(self.consignee.consignee())\n smgs_draft.update(self.stations.stations())\n return smgs_draft\n\n\nclient = KPS.client_cnotes\nservice = client.bind('Cnotes', 'cnotes')\n\n\ndef saveSmgsDraft():\n\n if TOKEN['Cookie'] == '':\n doLogin()\n\n draft = SmgsDraft()\n draft.stations.DeliveryStationCode = '700007'\n draft.stations.DepartureStationCode = '090609'\n draft.consignor.ConsignorCustomerId = 51224\n draft.consignee.IsPhysicalPersonConsignee = True\n draft.consignee.ConsigneeName = 'Джанибек Абдуллаев'\n draft.consignee.ConsigneeCity = \"Алматы\"\n draft.consignee.ConsigneeStreetAddress = \"ул. дом 23 \"\n draft.consignee.ConsigneeCode = \"1000\"\n draft.consignee.ConsigneeCountryCode = 'KZ'\n draft.consignee.ConsigneePostalCode = '123456'\n\n data = draft.smgsDraft()\n print(data)\n res = service.saveSmgsDraft(Smgs=data,\n _soapheaders={'Accept-Language': 'RU', 'UserId': 14716077, 'CompanyId': 51223})\n\n print(res.__dict__)\n","sub_path":"rest/kps_service/cnotes.py","file_name":"cnotes.py","file_ext":"py","file_size_in_byte":4525,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"532269145","text":"# -*- coding: utf-8 -*-\n# @Time : 2018/5/15 10:27\n# @Author : didi_order_check\n# @Email : xup@shinemo.com\n# @File : plandispose.py\n# @Software: PyCharm Community Edition\n\nimport configparser\nimport mysql.connector\nimport sys\nfrom global_func.global_log import logger\n\nlog_config = './config/dbconfig.conf'\nclass MysqlFunction:\n\n def __init__(self,DBSelection):\n #生成config对象\n conf = configparser.ConfigParser()\n #读配置文件\n conf.read(log_config,encoding='utf-8')\n # 指定section，option读取值\n try:\n #取host\n self.host = conf.get(DBSelection,'host')\n # 取端口\n self.port = conf.get(DBSelection, 'port')\n # 取数据库用户名\n self.user = conf.get(DBSelection, 'user')\n # 取数据库密码\n self.passwd = conf.get(DBSelection, 'passwd')\n # 取数据库table名称\n self.dbname = conf.get(DBSelection, 'dbname')\n # 取encodeing\n self.charset = conf.get(DBSelection, 'charset')\n except Exception as e:\n logger.error('初始化数据参数失败：%s' % e)\n sys.exit()\n try:\n # 打开数据库连接\n self.dbconnect = mysql.connector.connect(host=self.host, port=self.port, user=self.user, password=self.passwd, database=self.dbname, charset=self.charset)\n except Exception as e:\n logger.error('初始化数据连接失败：%s' % e)\n sys.exit()\n def getDBConnect(self):\n return self.dbconnect\n def getHost(self):\n return self.host\n def getPort(self):\n return self.port\n def getUser(self):\n return self.user\n def getPasswd(self):\n return self.passwd\n def getDbname(self):\n return self.dbname\n def getCharset(self):\n return self.charset\n #关闭数据库\n def close(self):\n self.dbconnect.close\n #创建数据库表\n def createSqlTable(self, sqlTable):\n try:\n # 使用cursor()方法获取操作游标\n dbCursor = self.dbconnect.cursor()\n #使用execute方法执行SQL语句\n dbCursor.execute(sqlTable)\n #提交保存\n dbCursor.execute('commit')\n #关闭\n dbCursor.close()\n return True\n except Exception as e:\n logger.error('创建数据库表操作失败：%s' % e)\n dbCursor.execute('rollback')\n dbCursor.close()\n exit()\n #更新数据库表\n def updateSqlTable(self, query, data):\n query = query % data\n try:\n dbCursor = self.dbconnect.cursor()\n dbCursor.execute(query)\n dbCursor.execute('commit')\n dbCursor.close()\n return ('',True)\n except Exception as e:\n logger.error('执行数据库更新操作失败：%s' % e)\n dbCursor.execute('rollback')\n dbCursor.close()\n return (e, False)\n #插入数据库数据\n def insertSqlTable(self, query, data):\n try:\n dbCursor = self.dbconnect.cursor()\n dbCursor.execute(query, data)\n dbCursor.execute('commit')\n dbCursor.close()\n return True\n except Exception as e:\n logger.error('执行数据库插入操作失败：%s' % e)\n dbCursor.execute('rollback')\n dbCursor.close()\n exit()\n def selectOneRecord(self, query, data=\"\"):\n '''返回结果只包含一条记录'''\n try:\n dbCursor = self.dbconnect.cursor()\n if data:\n dbCursor.execute(query, data)\n else:\n dbCursor.execute(query)\n query_result = dbCursor.fetchone()\n dbCursor.close()\n return (query_result,True)\n except Exception as e:\n logger.error('执行数据库查询操作失败：%s' % e)\n dbCursor.close()\n return(e,False)\n\n def selectMoreRecord(self, query, data=\"\"):\n '''返回结果只包含多条记录'''\n try:\n dbCursor = self.dbconnect.cursor()\n if data:\n dbCursor.execute(query, data)\n else:\n dbCursor.execute(query)\n query_result = dbCursor.fetchall()\n dbCursor.close()\n return query_result\n except Exception as e:\n logger.error('执行数据库查询操作失败：%s' % e)\n dbCursor.close()\n exit()\n\n\na = [{'1':1},2,3,4]\nprint(a[0]['1'])","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":4631,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"494253772","text":"\"\"\"\n챕터: day6\n주제: class\n문제: 정수 집합 클래스\n1. intSet 클래스는 정수들의 집합이다. 정수들을 관리하는 리스트 self.vals를 애트리튜브로 가진다.\n2. 새로운 정수 e를 추가하는 insert 메쏘드, 해당 요소가 이미 있다면 추가하지 않음\n3. e가 정수집합에 포함되어 있는지 확인하는 메쏘드인 involve정의(True,False 반환)\n4. e를 제거하는 remove 메쏘드\n5. 단, e가 해당 집합에 없다면 적당한 오류 메세지를 출력\n6. 집합 형식의 문자열로 변환시켜주는 __str__메쏘드. 단, 정수들은 정렬되어 반환되어야 한다.\n\n작성자: 김은정\n작성일: 2018.11.1\n\"\"\"\n\nclass intSet():\n #생성자 메쏘드(초기화)\n def __init__(self):\n self.vals = [] #정수들을 관리하는 리스트\n\n #새로운 정수 e를 추가하는 메쏘드\n def insert(self,e):\n if e in self.vals: #e가 리스트 안에 있는지 검사\n return self.vals #있다면 e를 포함하지 않은 vals리스트 반환\n else: #만약 e가 없다면\n return self.vals.append(e) #리스트에 e를 추가 후 반환\n\n #e가 정수집합에 포함되어 있는지 확인하는 메쏘드\n def involve(self,e):\n if e in self.vals: #e가 리스트 안에 존재하면\n return True #True 반환\n else: #아니면\n return False #False반환\n\n #e를 제거하는 메쏘드\n def remove(self,e):\n if e in self.vals: #e가 리스트에 존재한다면\n self.vals.remove(e) #리스트 안에 e를 지우고\n return self.vals #지워진 리스트를 반환\n else: #존재하지 않을 경우\n return print(\"해당 집합에 %d가 존재하지 않습니다.\" %e) #오류메세지 반환\n\n #집합 형식의 문자열로 변환시켜주는 메쏘드\n def __str__(self):\n self.vals.sort() #리스트 정렬\n v= \"\" #문자열로 만들기 위한 변수 v 정의\n for i in range(0,len(self.vals)): #for문을 리스트안에 숫자만큼 돌려서\n v += str(self.vals[i])+\" \" #v에 i번째 리스트 값을 문자열화\n return v #만들어진 문자열v를 반환\n\n\n#실행부분\n\"\"\"\nA. intSet을 저장하는 변수 s를 정의\nB. s에 insert를 이용하여 하나씩 5,3,7을 삽입\nC. s를 정렬하여 출력\nD. s에 8이 있는지 결과 출력\nE. s에 3이 있는지 결과 출력\nF. s에서 3을 제거\nG. s에서 4를 제거\nH. s를 정렬하여 출력\n\"\"\"\n\ns = intSet() #intSet을 저장하는 변수 s를 정의\n\ns.insert(5) #s에 insert를 이용하여 5를 삽입\ns.insert(3) #s에 insert를 이용하여 3,7를 삽입\ns.insert(7) #s에 insert를 이용하여 7를 삽입\nprint(s) #s를 정렬하여 출력\n\nprint(s.involve(8)) #s에 8이 있는지 결과 출력\nprint(s.involve(3)) #s에 3이 있는지 결과 출력\n\ns.remove(3) #s에서 3을 제거\ns.remove(4) #s에서 4를 제거\n\nprint(s) #s를 정렬하여 출력\n","sub_path":"python/lab11_1.py","file_name":"lab11_1.py","file_ext":"py","file_size_in_byte":3015,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"388416865","text":"from django.urls import path\n\nfrom jobs_portal.payments.views import *\n\nurlpatterns = [\n path('pricing-list/', PricingListView.as_view(), name='pricing-list'),\n path('landing-checkout//', LandingCheckoutView.as_view(), name='landing-checkout'),\n path('success-checkout/', SuccessCheckoutView.as_view(), name='success-checkout'),\n path('cancel-checkout/', CancelCheckoutView.as_view(), name='cancel-checkout'),\n path('create-checkout-session//', CreateCheckoutSessionView.as_view(), name='create-checkout-session'),\n path('cancel-subscription', cancel_subscription, name='cancel-subscription'),\n path('stripe-webhook/', my_webhook_view, name='webhook')\n]\n","sub_path":"jobs_portal/payments/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":691,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"610211616","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# Jesse Rubin - project Euler\n\"\"\"\nCriss Cross\nProblem 166\nA 4x4 grid is filled with digits d, 0 ≤ d ≤ 9.\n\nIt can be seen that in the grid\n\n6 3 3 0\n5 0 4 3\n0 7 1 4\n1 2 4 5\n\nthe sum of each row and each column has the value 12. Moreover the sum of each diagonal is also 12.\n\nIn how many ways can you fill a 4x4 grid with the digits d, 0 ≤ d ≤ 9 so that each row, each column, and both diagonals have the same sum?\n\"\"\"\n# INDICES\n# [0, 1, 2, 3,\n# 4, 5, 6 7,\n# 8, 9, 10, 11,\n# 12, 13, 14, 15]\nfrom collections import defaultdict\nfrom itertools import permutations, combinations, combinations_with_replacement\n\nbslash = [0, 5, 10, 15]\nfslash = [3, 6, 9, 12]\n\n\ndef flatten(l):\n return [item for sublist in l for item in sublist]\n\n\ndef rowi(r):\n return [i for i in range(r*4, r*4+4)]\n\n\ndef coli(c):\n return [i for i in range(c, 16, 4)]\n\n\n# def is_criss_cross(arr):\n# firstrowsum = sum(arr[i] for i in rowi(0))\n# for ci in range(4):\n# if firstrowsum != sum(arr[i] for i in coli(ci)):\n# return False\n# # return 1 == len((set.union({sum(arr[i] for i in coli(i)) for i in range(4)},\n# # {sum(arr[i] for i in rowi(i)) for i in range(4)},\n# # {sum(arr[i] for i in bslash)},\n# # {sum(arr[i] for i in fslash)})))\n# if sum(arr[i] for i in bslash)!=firstrowsum or firstrowsum!=sum(arr[i] for i in fslash):\n# return False\n# return True\n\n\nsum_combos = defaultdict(set)\nfor combo in combinations_with_replacement([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 4):\n # sum_combos[sum(combo)].add(tuple(p for p in permutations(combo)]))\n csum = sum(combo)\n for perm in permutations(combo):\n sum_combos[csum].add(perm)\n\nthirty_five = sum_combos[35]\nthirty_three = sum_combos[33]\n\n\n# for k, v in sum_combos.items():\n# print(k, len(v))\n# print(sum_combos)\n# print(thirty_five)\n\n# for cr in combinations_with_replacement(thirty_five, 4):\n# print(cr)\n\ndef p166():\n pass\n\n\ndictionary = {}\n\nfor k, v in sum_combos.items():\n # print(v)\n dictionary[k] = {}\n for i in range(1, 10):\n dictionary[k][i] = set(n for n in v if n[0] == i)\n\n# things_test= 0\n#\n# for i in range(33, 35):\n# for k, v in dictionary[i].items():\n# if len(v)>0:\n# for co in combinations_with_replacement(v, 2):\n# print(co)\n# co2 = (co[1], co[0])\n#\n# things_test += 1\n#\n#\n#\n# print(things_test)\n#\n\ndef is_criss_cross2(arr):\n firstrowsum = sum(arr[i] for i in rowi(0))\n for ci in range(4):\n if tuple((arr[i] for i in coli(ci))) not in sum_combos[firstrowsum]:\n return False\n # return 1 == len((set.union({sum(arr[i] for i in coli(i)) for i in range(4)},\n # {sum(arr[i] for i in rowi(i)) for i in range(4)},\n # {sum(arr[i] for i in bslash)},\n # {sum(arr[i] for i in fslash)})))\n if tuple((arr[i] for i in bslash)) not in sum_combos[firstrowsum]:\n return False\n if tuple((arr[i] for i in fslash)) not in sum_combos[firstrowsum]:\n return False\n return True\n# ncc = 0\n# for sumthing in range(35):\n# print(\"\")\n# print(sumthing)\n# sumthingtot = 0\n# for comb in combinations_with_replacement(sum_combos[sumthing], 4):\n# # print(comb)\n# flat = flatten(comb)\n# # print(flat)\n# if is_criss_cross2(flat):\n# sumthingtot+= 1\n# # print(flat)\n# # print(is_criss_cross(flat))\n# print(sumthingtot)\n\n# 0\n# 1\n#\n# 1\n# 0\n#\n# 2\n# 4\n#\n# 3\n# 9\n#\n# 4\n# 35\n#\n# 5\n# 79\n#\n# 6\n# 219\n#\n# 7\n# 447\n\ncc = [6, 3, 3, 0,\n 5, 0, 4, 3,\n 0, 7, 1, 4,\n 1, 2, 4, 5]\nscc = ['6', '3', '3', '0',\n '5', '_', '4', '_',\n '0', '7', '_', '_',\n '1', '_', '_', '_']\n\ncc = [6, 3, 3, 0,\n 5, 0, 4, 3,\n 0, 7, 1, 4,\n 1, 2, 4, 5]\ndef triangle_cc(tcc):\n print(tcc)\n ccsum = sum(int(tcc[n]) for n in range(4))\n print(ccsum)\n\n\ndef upleft_tri(top, left, fs, ccsum):\n print(top, left, fs, ccsum)\n\n\ntriangle_cc(scc)\n\ndef criss_cross(total = 3):\n sumset = sum_combos[total]\n starts = defaultdict(set)\n for s in sumset:\n starts[s[0]].add(s)\n print(sumset, starts)\n\n arr = [0]*16\n for first_n, toopset in starts.items():\n print(first_n)\n for comba, combb in combinations_with_replacement(toopset, 2):\n print(\"\")\n print(comba, combb)\n sss = set()\n for n in sumset:\n if n[0]== comba[3] and n[3] == combb[3]:\n sss.add(n)\n if n[3]== comba[3] and n[0] == combb[3]:\n sss.add(n)\n\n\n print(sss)\n for diag in sss:\n upleft_tri(comba, combb,diag, ccsum=total)\n\n\n\n\n\n\n\ncriss_cross()\n\n\n\n# for k, v in sum_combos.items():\n# print(k, len(v))\n\nif __name__ == '__main__':\n cc = [6, 3, 3, 0,\n 5, 0, 4, 3,\n 0, 7, 1, 4,\n 1, 2, 4, 5]\n assert is_criss_cross2(cc)\n p166()\n","sub_path":"not_done/euler_166.py","file_name":"euler_166.py","file_ext":"py","file_size_in_byte":5203,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"3973957","text":"import cv2\n\nRED = (0,0,255)\nGREEN = (0,255,0)\nBLACK = (0,0,0)\nPURPLE = (128, 0, 128) \n\nclass Draw:\n def __init__(self,video):\n self.history = [[]]\n self.line_i = 0\n self.color= GREEN\n video.bind(\"\",self.draw)\n video.bind(\"\",self.handle_drawing)\n\n def clear_screen(self):\n self.history = [[]]\n self.line_i = 0\n\n def handle_drawing(self,event):\n self.history.append([])\n self.line_i += 1 \n\n def draw(self,event):\n self.history[self.line_i].append((event.x,event.y))\n\n def set_color(self,color):\n if color==\"RED\":\n self.color = RED\n elif color ==\"GREEN\":\n self.color = GREEN\n elif color ==\"BLACK\":\n self.color = BLACK\n elif color ==\"PURPLE\":\n self.color = PURPLE\n\n def draw_lines(self,frame):\n for i in enumerate(self.history):\n for j in enumerate(i[1]):\n try:\n cv2.line(frame,self.history[i[0]][j[0]],self.history[i[0]][j[0]+1],self.color,thickness=2)\n except:\n pass\n","sub_path":"draw.py","file_name":"draw.py","file_ext":"py","file_size_in_byte":1139,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"5532119","text":"import numpy as np\nfrom sklearn.model_selection import StratifiedKFold\nfrom sklearn.decomposition import PCA\nfrom sklearn.metrics import accuracy_score\nfrom multiprocessing import Pool\nimport tqdm\nimport time\nimport view\n\nles, one, low, high = [], np.array([]), np.array([]), np.array([])\n\n\ndef ReadData(filename):\n ant, con = [], []\n with open(file=filename, mode='r') as f:\n for i in f:\n e = list(map(float, i.split()))\n ant.append(e[:-1]), con.append(e[-1])\n global one, les, low, high\n one, les = np.ptp(ant, axis=0) * 0.1, list(set(con))\n low, high = np.min(ant, axis=0), np.max(ant, axis=0)\n for i in range(len(con)):\n o = np.zeros((len(les),))\n o[les.index(con[i])] = 1.0\n con[i] = o\n return np.array(ant), np.array(con)\n\n\ndef EvidentialReasoing(a, ant, con, rw):\n w = np.exp(-0.5 * np.sum((ant-a)*(ant-a)/(one*one), axis=1)) * rw\n sw, b = np.sum(w), np.random.uniform(size=len(les))\n if np.max(w) == sw:\n return [b/np.sum(b), con[np.argmax(w)]][int(sw != 0.0)]\n b = np.prod(np.transpose(con) * w / (sw - w) + 1, axis=1) - 1\n return b / np.sum(b)\n\n\ndef GradientDescent(ta, tc, bs, mua, muc, muw, ba, bc, bw):\n bi = np.arange(len(ta)-len(ta) % bs)\n np.random.shuffle(bi)\n pb = tqdm.tqdm(total=len(bi)/bs)\n for i, bm in enumerate(np.split(bi, len(bi)/bs)):\n t = i % len(ba)\n nx, ny, nz = np.zeros(ba[t].shape), np.zeros(bc[t].shape), 0.0 # (T),(N),()\n for a, c in zip(ta[bm], tc[bm]):\n theta = 1 / (1 + np.exp(-bw)) # (L)\n alpha = np.exp(-np.sum((ba-a)*(ba-a)/(one*one), axis=1) / 2) # (L)\n w = alpha * theta # (L)\n sw = np.sum(w)\n if np.max(w) == sw:\n continue\n B = np.transpose(np.exp(bc) / np.sum(np.exp(bc), axis=1)[:, None]) # (N,L)\n Bi = B * w / (sw - w) + 1 # (N,L)\n B_ = np.prod(Bi, axis=1) - 1 # (N)\n pc = B_ / np.sum(B_) # (N)\n dBB_ = (np.sum(B_) - B_) / (np.sum(B_) * np.sum(B_)) # (N)\n\n bn1 = - theta[t] * B * w / (sw - w) / (sw - w) * (np.prod(Bi, axis=1)[:, None] / Bi) # (N,L)\n bn2 = theta[t] * B[:, t] / (sw - w[t]) * np.prod(Bi, axis=1) / Bi[:, t] # (N)\n dB_a = np.sum(bn1, axis=1) - bn1[:, t] + bn2 # (N)\n dax = (a - ba[t]) / one / one * np.exp(-np.sum((a - ba[t])*(a - ba[t]) / one / one) / 2) # (T)\n nx += np.sum((pc - c) * dBB_ * dB_a) * dax\n\n dB_b = w[t] / (sw - w[t]) * np.prod(Bi, axis=1) / Bi[:, t] # (N)\n dby = np.exp(bc[t]) * (np.sum(np.exp(bc[t])) - np.exp(bc[t])) / (np.sum(np.exp(bc[t])) * np.sum(np.exp(bc[t]))) # (N)\n ny += (pc - c) * dBB_ * dB_b * dby\n\n bn3 = - alpha[t] * B * w / ((sw - w) * (sw - w)) * (np.prod(Bi, axis=1)[:, None] / Bi) # (N,L)\n bn4 = alpha[t] * B[:, t] / (sw - w[t]) * np.prod(Bi, axis=1) / Bi[:, t] # (N)\n dB_t = np.sum(bn3, axis=1) - bn3[:, t] + bn4 # (N)\n dtz = theta[t] * (1.0 - theta[t])\n nz += np.sum((pc - c) * dBB_ * dB_t * dtz)\n\n ba[t] -= mua * nx / bs\n bc[t] -= muc * ny / bs\n bw[t] -= muw * nz / bs\n pb.update()\n pb.close()\n return ba, bc, bw\n\n\ndef Evaluating(ant, con, ba, bc, bw):\n bc = np.exp(bc) / np.sum(np.exp(bc), axis=1)[:, None]\n bw = 1 / (1 + np.exp(-bw))\n pc = np.array([EvidentialReasoing(a, ba, bc, bw) for a in ant])\n err = np.sum((con - pc)*(con - pc))\n acc = accuracy_score(np.argmax(con, axis=1), np.argmax(pc, axis=1))\n return acc, err\n\n\ndef main():\n ant, con = ReadData(\"../data/glass_rev.data\")\n cnt, bn = 5000, 50\n\n skf = StratifiedKFold(10, True)\n for train_mask, test_mask in skf.split(ant, np.argmax(con, axis=1)):\n train_ant, train_con = ant[train_mask], con[train_mask]\n test_ant, test_con = ant[test_mask], con[test_mask]\n\n # pred_con=np.array([EvidentialReasoing(a, train_ant, train_con) for a in train_ant])\n # print(accuracy_score(np.argmax(test_con, axis=1), np.argmax(pred_con, axis=1)))\n\n base_ant = np.random.uniform(low, high, (bn, ant.shape[1]))\n base_con = np.random.uniform(-1.0, 1.0, (bn, con.shape[1]))\n base_wei = np.random.uniform(-1.0, 1.0, (bn,))\n\n # acc, err = [], []\n sc = 0.0\n for _ in range(cnt):\n base_ant, base_con, base_wei = GradientDescent(train_ant, train_con, 8,\n 1e-3*(1-sc), 1e-3*(1-sc), 1e-3*(1-sc),\n base_ant, base_con, base_wei)\n\n a1, e1 = Evaluating(train_ant, train_con, base_ant, base_con, base_wei)\n a2, e2 = Evaluating(test_ant, test_con, base_ant, base_con, base_wei)\n print(a1, e1, a2, e2)\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"code/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4874,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"422031029","text":"from pico2d import *\n\nRIGHT_DOWN, RIGHT_UP, LEFT_DOWN, LEFT_UP = range(4)\n\nkey_event_table ={\n (SDL_KEYDOWN, SDLK_RIGHT) : RIGHT_DOWN,\n (SDL_KEYDOWN, SDLK_LEFT) : LEFT_DOWN,\n (SDL_KEYUP, SDLK_RIGHT) : RIGHT_UP,\n (SDL_KEYUP, SDLK_LEFT) : LEFT_UP\n}\n\nclass IdleState:\n @staticmethod\n def enter(boy, event):\n if event == RIGHT_DOWN:\n boy.velocity += 1\n elif event == LEFT_DOWN:\n boy.velocity -= 1\n elif event == RIGHT_UP:\n boy.velocity -= 1\n elif event == LEFT_UP:\n boy.velocity += 1\n boy.timer = 1000\n\n @staticmethod\n def exit(boy, event):\n pass\n\n @staticmethod\n def do(boy):\n boy.frame = (boy.frame + 1) % 8\n boy.timer -= 1\n\n @staticmethod\n def draw(boy):\n if boy.dir == 1:\n boy.image.clip_draw(boy.frame * 100, 300, 100, 100, boy.x, boy.y)\n else:\n boy.image.clip_draw(boy.frame * 100, 200, 100, 100, boy.x, boy.y)\n\nclass RunState:\n @staticmethod\n def enter(boy, event):\n if(event == RIGHT_DOWN):\n boy.velocity += 1\n elif(event == RIGHT_UP):\n boy.velocity -= 1\n elif(event == LEFT_DOWN):\n boy.velocity -= 1\n elif(event == LEFT_UP):\n boy.velocity += 1\n boy.dir = boy.velocity\n\n @staticmethod\n def exit(boy, event):\n pass\n\n @staticmethod\n def do(boy):\n boy.frame = (boy.frame + 1) % 8\n boy.timer -= 1\n boy.x += boy.velocity\n boy.x = clamp(25, boy.x, 800-25)\n\n @staticmethod\n def draw(boy):\n if boy.velocity == 1:\n boy.image.clip_draw(boy.frame * 100, 100, 100, 100, boy.x, boy.y)\n else:\n boy.image.clip_draw(boy.frame * 100, 0, 100, 100, boy.x, boy.y)\n\n\nnext_state_table = {\n IdleState: {RIGHT_UP: RunState, LEFT_UP:RunState,\n RIGHT_DOWN : RunState, LEFT_DOWN:RunState},\n RunState: {RIGHT_UP :IdleState, LEFT_UP:IdleState,\n RIGHT_DOWN: IdleState, LEFT_DOWN : IdleState}\n}\n\nclass Boy:\n\n def __init__(self):\n self.x, self.y = 800//2, 90\n self.image = load_image('animation_sheet.png')\n self.dir = 1\n self.velocity = 0\n self.frame = 0\n self.timer = 0\n self.event_que = []\n self.cur_state = IdleState\n self.cur_state.enter(self, None)\n\n def add_event(self, event):\n self.event_que.insert(0, event)\n\n def update(self):\n self.cur_state.do(self)\n if len(self.event_que) > 0:\n event = self.event_que.pop()\n self.cur_state.exit(self, event)\n self.cur_state = next_state_table[self.cur_state][event]\n self.cur_state.enter(self. event)\n\n def handle_event(self, event):\n if(event.type, event.key) in key_event_table:\n key_event = key_event_table[(event.type, event.key)]\n self.add_event(key_event)","sub_path":"Drills/Drill 09/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":2931,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"432818664","text":"# uncompyle6 version 3.7.4\n# Python bytecode 2.7 (62211)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build/bdist.linux-x86_64/egg/booki/portal/templatetags/booki_paginator.py\n# Compiled at: 2012-02-14 23:34:00\nfrom django import template\nregister = template.Library()\nLEADING_PAGE_RANGE_DISPLAYED = TRAILING_PAGE_RANGE_DISPLAYED = 10\nLEADING_PAGE_RANGE = TRAILING_PAGE_RANGE = 8\nNUM_PAGES_OUTSIDE_RANGE = 2\nADJACENT_PAGES = 4\n\ndef booki_paginator(context, pages):\n in_leading_range = in_trailing_range = False\n pages_outside_leading_range = pages_outside_trailing_range = range(0)\n current = 2\n if pages.paginator.num_pages <= LEADING_PAGE_RANGE_DISPLAYED + NUM_PAGES_OUTSIDE_RANGE + 1:\n in_leading_range = in_trailing_range = True\n page_numbers = [ n for n in range(1, pages.paginator.num_pages + 1) if n > 0 and n <= pages.paginator.num_pages ]\n elif pages.number <= LEADING_PAGE_RANGE:\n in_leading_range = True\n page_numbers = [ n for n in range(1, LEADING_PAGE_RANGE_DISPLAYED + 1) if n > 0 and n <= pages.paginator.num_pages ]\n pages_outside_leading_range = [ n + pages.paginator.num_pages for n in range(0, -NUM_PAGES_OUTSIDE_RANGE, -1) ]\n elif pages.number > pages.paginator.num_pages - TRAILING_PAGE_RANGE:\n in_trailing_range = True\n page_numbers = [ n for n in range(pages.paginator.num_pages - TRAILING_PAGE_RANGE_DISPLAYED + 1, pages.paginator.num_pages + 1) if n > 0 and n <= pages.paginator.num_pages ]\n pages_outside_trailing_range = [ n + 1 for n in range(0, NUM_PAGES_OUTSIDE_RANGE) ]\n else:\n page_numbers = [ n for n in range(pages.number - ADJACENT_PAGES, pages.number + ADJACENT_PAGES + 1) if n > 0 and n <= pages.paginator.num_pages ]\n pages_outside_leading_range = [ n + pages.paginator.num_pages for n in range(0, -NUM_PAGES_OUTSIDE_RANGE, -1) ]\n pages_outside_trailing_range = [ n + 1 for n in range(0, NUM_PAGES_OUTSIDE_RANGE) ]\n return {'previous': pages.previous_page_number(), \n 'has_previous': pages.has_previous(), \n 'next': pages.next_page_number(), \n 'has_next': pages.has_next(), \n 'page': pages.number, \n 'results_per_page': 50, \n 'page_numbers': page_numbers, \n 'in_leading_range': in_leading_range, \n 'in_trailing_range': in_trailing_range, \n 'pages_outside_leading_range': pages_outside_leading_range, \n 'pages_outside_trailing_range': pages_outside_trailing_range}\n\n\nregister.inclusion_tag('booki_paginator.html', takes_context=True)(booki_paginator)","sub_path":"pycfiles/Booktype-1.5-py2.7/booki_paginator.py","file_name":"booki_paginator.py","file_ext":"py","file_size_in_byte":2606,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"361621258","text":"import numpy as np\nimport vtk\n\n\n\ndef main():\n cols = vtk.vtkNamedColors()\n cols.SetColor(\"lightbrown\", [77, 51, 26, 255])\n cols.SetColor(\"man\", [255, 255, 0, 255])\n\n reader_house = vtk.vtkOBJReader()\n reader_house.SetFileName(\"house.obj\")\n\n\n mapper_house = vtk.vtkPolyDataMapper()\n mapper_house.SetInputConnection(reader_house.GetOutputPort())\n\n\n actor_house = vtk.vtkActor()\n actor_house.SetMapper(mapper_house)\n actor_house.GetProperty().SetColor(cols.GetColor3d(\"lightbrown\"))\n\n\n reader_legoman = vtk.vtkOBJReader()\n reader_legoman.SetFileName(\"lego_man.obj\")\n\n\n trans = vtk.vtkTransform()\n trans.Scale(10, 10, 10)\n trans.Translate(-17, 0, -1)\n trans.RotateY(-90)\n\n transFilter = vtk.vtkTransformPolyDataFilter()\n transFilter.SetInputConnection(reader_legoman.GetOutputPort())\n transFilter.SetTransform(trans)\n transFilter.Update()\n\n\n mapper_legoman = vtk.vtkPolyDataMapper()\n mapper_legoman.SetInputConnection(transFilter.GetOutputPort())\n\n\n\n actor_legoman = vtk.vtkActor()\n actor_legoman.SetMapper(mapper_legoman)\n actor_legoman.GetProperty().SetColor(cols.GetColor3d(\"man\"))\n\n\n\n renderer = vtk.vtkRenderer()\n\n\n\n\n rendererWindow= vtk.vtkRenderWindow()\n rendererWindow.AddRenderer(renderer)\n rendererWindow.SetSize(1024, 768)\n\n\n interactor = vtk.vtkRenderWindowInteractor()\n interactor.SetRenderWindow(rendererWindow)\n\n intStyle = vtk.vtkInteractorStyleTrackballCamera()\n interactor.SetInteractorStyle(intStyle)\n\n\n renderer.AddActor(actor_house)\n renderer.AddActor(actor_legoman)\n\n\n rendererWindow.Render()\n\n interactor.Start()\n\nif __name__ == \"__main__\":\n main()","sub_path":"Projekt 1/Exercise 7/Exercise_7_part3.py","file_name":"Exercise_7_part3.py","file_ext":"py","file_size_in_byte":1681,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"614587652","text":"from asyncio import Future, get_event_loop, coroutine, iscoroutinefunction\nfrom functools import wraps\nimport json\n\nfrom again.utils import unique_hex\nfrom aiohttp.web import Response\n\nfrom .utils.ordered_class_member import OrderedClassMembers\n\n# Service Client decorators\n\n\ndef make_request(func, self, args, kwargs, method):\n params = func(self, *args, **kwargs)\n entity = params.pop('entity', None)\n app_name = params.pop('app_name', None)\n self = params.pop('self')\n response = yield from self._send_http_request(app_name, method, entity, params)\n return response\n\n\ndef get_decorated_fun(method, path, required_params):\n def decorator(func):\n @wraps(func)\n def f(self, *args, **kwargs):\n if isinstance(self, HTTPServiceClient):\n return (yield from make_request(func, self, args, kwargs, method))\n elif isinstance(self, HTTPApplicationService):\n if required_params is not None:\n req = args[0]\n query_params = req.GET\n params = required_params\n if not isinstance(required_params, list):\n params = [required_params]\n missing_params = list(filter(lambda x: x not in query_params, params))\n if len(missing_params) > 0:\n return Response(status=400, content_type='application/json',\n body=json.dumps({'error': 'Required params {} not found'.format(\n ','.join(missing_params))}).encode())\n wrapped_func = func\n if not iscoroutinefunction(func):\n wrapped_func = coroutine(func)\n return (yield from wrapped_func(self, *args, **kwargs))\n\n f.is_http_method = True\n f.method = method\n f.paths = path\n if not isinstance(path, list):\n f.paths = [path]\n return f\n\n return decorator\n\n\ndef get(path=None, required_params=None):\n return get_decorated_fun('get', path, required_params)\n\n\ndef head(path=None, required_params=None):\n return get_decorated_fun('head', path, required_params)\n\n\ndef options(path=None, required_params=None):\n return get_decorated_fun('options', path, required_params)\n\n\ndef patch(path=None, required_params=None):\n return get_decorated_fun('patch', path, required_params)\n\n\ndef post(path=None, required_params=None):\n return get_decorated_fun('post', path, required_params)\n\n\ndef put(path=None, required_params=None):\n return get_decorated_fun('put', path, required_params)\n\n\ndef trace(path=None, required_params=None):\n return get_decorated_fun('put', path, required_params)\n\n\ndef delete(path=None, required_params=None):\n return get_decorated_fun('delete', path, required_params)\n\n\ndef subscribe(func):\n \"\"\"\n use to listen for publications from a specific endpoint of a service,\n this method receives a publication from a remote service\n \"\"\"\n\n @wraps(func)\n def wrapper(*args, **kwargs):\n coroutine_func = func\n if not iscoroutinefunction(func):\n coroutine_func = coroutine(func)\n return (yield from coroutine_func(*args, **kwargs))\n\n wrapper.is_subscribe = True\n return wrapper\n\n\ndef request(func):\n \"\"\"\n use to request an api call from a specific endpoint\n \"\"\"\n\n @wraps(func)\n def wrapper(self, *args, **kwargs):\n params = func(self, *args, **kwargs)\n self = params.pop('self', None)\n entity = params.pop('entity', None)\n app_name = params.pop('app_name', None)\n request_id = unique_hex()\n params['request_id'] = request_id\n future = self._send_request(app_name, endpoint=func.__name__, entity=entity, params=params)\n return future\n\n wrapper.is_request = True\n return wrapper\n\n\ndef message_sub(func):\n \"\"\"\n use to listen for publications from a specific endpoint of a service,\n this method receives a publication from a remote service\n \"\"\"\n\n @wraps(func)\n def wrapper(self, *args, **kwargs):\n params = func(self, *args, **kwargs)\n entity = params.pop('entity')\n app_name = params.pop('app_name', None)\n self._send_message_sub(app_name, endpoint=func.__name__, entity=entity)\n\n wrapper.is_directed_subscribe = True\n return wrapper\n\n\n# Service Host Decorators\n\ndef publish(func):\n \"\"\"\n publish the return value of this function as a message from this endpoint\n \"\"\"\n\n @wraps(func)\n def wrapper(self, *args, **kwargs): # outgoing\n payload = func(self, *args, **kwargs)\n payload.pop('self', None)\n self._publish(func.__name__, payload)\n return None\n\n wrapper.is_publish = True\n\n return wrapper\n\n\ndef message_pub(func):\n \"\"\"\n publish the return value of this function as a message from this endpoint\n \"\"\"\n\n @wraps(func)\n def wrapper(self, *args, **kwargs): # outgoing\n payload = func(self, *args, **kwargs)\n entity = payload.pop('entity')\n self._message(func.__name__, payload, entity)\n return None\n\n wrapper.is_publish = True\n\n return wrapper\n\n\ndef api(func): # incoming\n \"\"\"\n provide a request/response api\n receives any requests here and return value is the response\n all functions must have the following signature\n - request_id\n - entity (partition/routing key)\n followed by kwargs\n \"\"\"\n\n @coroutine\n @wraps(func)\n def wrapper(*args, **kwargs):\n self = args[0]\n rid = kwargs.pop('request_id')\n entity = kwargs.pop('entity')\n from_id = kwargs.pop('from_id')\n wrapped_func = func\n result = None\n error = None\n if not iscoroutinefunction(func):\n wrapped_func = coroutine(func)\n try:\n if len(kwargs):\n result = yield from wrapped_func(self, **kwargs)\n else:\n result = yield from wrapped_func(self)\n except BaseException as e:\n error = str(e)\n\n return self._make_response_packet(request_id=rid, from_id=from_id, entity=entity, result=result, error=error)\n\n wrapper.is_api = True\n return wrapper\n\n\nclass _Service:\n _PUB_PKT_STR = 'publish'\n _REQ_PKT_STR = 'request'\n _RES_PKT_STR = 'response'\n _MSG_PUB_PKT_STR = 'message_pub'\n _MSG_SUB_PKT_STR = 'message_sub'\n\n def __init__(self, service_name, service_version):\n self._service_name = service_name\n self._service_version = service_version\n self._bus = None\n\n @property\n def name(self):\n return self._service_name\n\n @property\n def version(self):\n return self._service_version\n\n @property\n def properties(self):\n return self.name, self.version\n\n @property\n def bus(self):\n return self._bus\n\n @bus.setter\n def bus(self, bus):\n self._bus = bus\n\n @staticmethod\n def time_future(future:Future, timeout:int):\n def timer_callback(f):\n if not f.done() and not f.cancelled():\n f.set_exception(TimeoutError())\n\n get_event_loop().call_later(timeout, timer_callback, future)\n\n\nclass TCPServiceClient(_Service):\n REQUEST_TIMEOUT_SECS = 10\n\n def __init__(self, service_name, service_version):\n super(TCPServiceClient, self).__init__(service_name, service_version)\n self._pending_requests = {}\n\n def _send_request(self, app_name, endpoint, entity, params):\n packet = self._make_packet(app_name, _Service._REQ_PKT_STR, endpoint, params, entity)\n future = Future()\n request_id = params['request_id']\n self._pending_requests[request_id] = future\n self._bus.send(packet)\n _Service.time_future(future, TCPServiceClient.REQUEST_TIMEOUT_SECS)\n return future\n\n def _send_message_sub(self, app_name, endpoint, entity):\n packet = self._make_packet(app_name, _Service._MSG_SUB_PKT_STR, endpoint, None, entity)\n self._bus.send(packet)\n\n def process_packet(self, packet):\n if packet['type'] == _Service._RES_PKT_STR:\n self._process_response(packet)\n elif packet['type'] == _Service._PUB_PKT_STR:\n self._process_publication(packet)\n else:\n print('Invalid packet', packet)\n\n def _process_response(self, packet):\n payload = packet['payload']\n request_id = payload['request_id']\n has_result = 'result' in payload\n has_error = 'error' in payload\n future = self._pending_requests.pop(request_id)\n if has_result:\n future.set_result(payload['result'])\n elif has_error:\n exception = RequestException()\n exception.error = payload['error']\n future.set_exception(exception)\n else:\n print('Invalid response to request:', packet)\n\n def _process_publication(self, packet):\n endpoint = packet['endpoint']\n func = getattr(self, endpoint)\n func(**packet['payload'])\n\n def _make_packet(self, app_name, packet_type, endpoint, params, entity):\n packet = {'pid': unique_hex(),\n 'app': app_name,\n 'service': self.name,\n 'version': self.version,\n 'entity': entity,\n 'endpoint': endpoint,\n 'type': packet_type,\n 'payload': params}\n return packet\n\n\nclass _ServiceHost(_Service):\n def __init__(self, service_name, service_version, host_ip, host_port):\n super(_ServiceHost, self).__init__(service_name, service_version)\n self._ip = host_ip\n self._port = host_port\n\n def is_for_me(self, service, version):\n return service == self.name and int(version) == self.version\n\n @property\n def socket_address(self):\n return self._ip, self._port\n\nclass _TCPServiceHost(_ServiceHost):\n def __init__(self, service_name, service_version, host_ip, host_port):\n # TODO: to be multi-tenant make app_name a list\n super(_TCPServiceHost, self).__init__(service_name, service_version, host_ip, host_port)\n\n def _publish(self, endpoint, payload):\n self._bus.publish(self.name, self.version, endpoint, payload)\n\n def _make_response_packet(self, request_id: str, from_id: str, entity: str, result: object, error: object):\n if error:\n payload = {'request_id': request_id, 'error': error}\n else:\n payload = {'request_id': request_id, 'result': result}\n\n packet = {'pid': unique_hex(),\n 'to': from_id,\n 'entity': entity,\n 'type': _Service._RES_PKT_STR,\n 'payload': payload}\n return packet\n\nclass RequestException(Exception):\n pass\n\n\nclass _HTTPServiceHost(_ServiceHost, metaclass=OrderedClassMembers):\n def __init__(self, service_name, service_version, host_ip, host_port, ssl_context=None, allow_cross_domain=False):\n super(_HTTPServiceHost, self).__init__(service_name, service_version, host_ip, host_port)\n self._ssl_context = ssl_context\n self._allow_cross_domain = allow_cross_domain\n\n @property\n def ssl_context(self):\n return self._ssl_context\n\n @property\n def cross_domain_allowed(self):\n return self._allow_cross_domain\n\n def pong(self, request):\n return Response()\n\n\nclass TCPApplicationService(_TCPServiceHost):\n pass\n\n\nclass TCPDomainService(_TCPServiceHost):\n pass\n\n\nclass TCPInfraService(_TCPServiceHost):\n pass\n\n\nclass HTTPApplicationService(_HTTPServiceHost):\n pass\n\n\nclass HTTPDomainService(_HTTPServiceHost):\n pass\n\n\nclass HTTPInfraService(_HTTPServiceHost):\n pass\n\n\nclass HTTPServiceClient(_Service):\n def __init__(self, service_name, service_version):\n super(HTTPServiceClient, self).__init__(service_name, service_version)\n\n def _send_http_request(self, app_name, method, entity, params):\n response = yield from self._bus.send_http_request(app_name, self.name, self.version, method, entity,\n params)\n return response\n","sub_path":"vyked/services.py","file_name":"services.py","file_ext":"py","file_size_in_byte":12199,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"576509668","text":"import logging\n\nfrom cliff.lister import Lister\nfrom cliff.show import ShowOne\nfrom atmosphere.api import AtmosphereAPI\nfrom atmosphere.utils import ts_to_isodate\n\n\nclass AllocationSourceList(Lister):\n \"\"\"\n List allocation sources for a user.\n \"\"\"\n\n log = logging.getLogger(__name__)\n\n def take_action(self, parsed_args):\n column_headers = ('uuid', 'name', 'compute_allowed', 'compute_used', 'global_burn_rate', 'start_date')\n api = AtmosphereAPI(self.app_args.auth_token, base_url=self.app_args.base_url, timeout=self.app_args.api_server_timeout, verify=self.app_args.verify_cert)\n data = api.get_allocation_sources()\n allocation_sources = []\n if data.ok:\n for source in data.message['results']:\n start_date = ts_to_isodate(source['start_date'])\n allocation_sources.append((\n source['uuid'],\n source['name'],\n source['compute_allowed'],\n source['compute_used'],\n source['global_burn_rate'],\n start_date\n ))\n\n return (column_headers, tuple(allocation_sources))\n\n\nclass AllocationSourceShow(ShowOne):\n \"\"\"\n Show details for an allocation source.\n \"\"\"\n\n log = logging.getLogger(__name__)\n\n def get_parser(self, prog_name):\n parser = super(AllocationSourceShow, self).get_parser(prog_name)\n parser.add_argument('id', help='the allocation source uuid')\n return parser\n\n def take_action(self, parsed_args):\n column_headers = ('id',\n 'uuid',\n 'name',\n 'renewal_strategy',\n 'compute_allowed',\n 'compute_used',\n 'global_burn_rate',\n 'user_compute_used',\n 'user_burn_rate',\n 'start_date',\n 'end_date')\n api = AtmosphereAPI(self.app_args.auth_token, base_url=self.app_args.base_url, timeout=self.app_args.api_server_timeout, verify=self.app_args.verify_cert)\n data = api.get_allocation_source(parsed_args.id)\n allocation_source = ()\n if data.ok:\n message = data.message\n start_date = ts_to_isodate(message['start_date'])\n end_date = ''\n if message['end_date']:\n end_date = ts_to_isodate(message['end_date'])\n allocation_source = (\n message['id'],\n message['uuid'],\n message['name'],\n message['renewal_strategy'],\n message['compute_allowed'],\n message['compute_used'],\n message['global_burn_rate'],\n message['user_compute_used'],\n message['user_burn_rate'],\n start_date,\n end_date\n )\n\n return (column_headers, allocation_source)\n","sub_path":"atmosphere/allocation_source.py","file_name":"allocation_source.py","file_ext":"py","file_size_in_byte":3022,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"66295565","text":"#!/usr/bin/env python\n# coding: utf-8\n# Copyright 2013 The Font Bakery Authors. All Rights Reserved.\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#\n# See AUTHORS.txt for the list of Authors and LICENSE.txt for the License.\nfrom __future__ import print_function\n\nimport os\nimport sys\nimport yaml\n\nfrom fontaine.font import FontFactory\nfrom fontaine.cmap import Library\n\nfrom bakery_cli.utils import UpstreamDirectory\n\n\nclass Widgets(object):\n\n def __init__(self, app):\n self.app = app\n self.commit = urwid.Edit(edit_text=app.commit)\n self.ttfautohint = urwid.Edit(edit_text=app.ttfautohint)\n self.newfamily = urwid.Edit(edit_text=app.newfamily)\n self.pyftsubset = urwid.Edit(edit_text=app.pyftsubset)\n self.notes = urwid.Edit(edit_text=app.notes)\n self.afdko_parameters = urwid.Edit(edit_text=app.afdko)\n\n self.compiler = []\n self.licenses = []\n self.process_files = []\n self.subset = []\n\n def on_checkbox_state_change(self, widget, state, user_data):\n self.app.config[user_data['name']] = state\n\n def on_subset_state_change(self, widget, state, user_data):\n if 'subset' not in self.app.config:\n self.app.config['subset'] = []\n try:\n index = self.app.config['subset'].index(user_data['name'])\n if user_data['name'] == 'devanagari':\n self.ttfautohint.edit_text = self.ttfautohint.edit_text.replace('-f deva', '')\n self.ttfautohint.edit_text = self.ttfautohint.edit_text.replace('-D deva', '')\n self.ttfautohint.edit_text = self.ttfautohint.edit_text.strip()\n self.ttfautohint.edit_text = self.ttfautohint.edit_text.replace(' ', ' ')\n del self.app.config['subset'][index]\n except ValueError:\n if user_data['name'] == 'devanagari':\n self.ttfautohint.edit_text += ' -f deva -D deva'\n self.app.config['subset'].append(user_data['name'])\n\n def create_checkbox(self, title, name, state=False):\n return urwid.CheckBox(title, user_data={'name': name}, state=state,\n on_state_change=self.on_checkbox_state_change)\n\n def create_process_file(self, filepath):\n try:\n state = [x.lstrip('./') for x in self.app.process_files].index(filepath) >= 0\n except ValueError:\n state = False\n\n widget = urwid.CheckBox(filepath, state=state)\n self.process_files.append(widget)\n return widget\n\n def create_subset_widget(self, subsetname, coverage):\n widget = urwid.CheckBox('{0} ({1})'.format(subsetname, coverage),\n state=bool(subsetname in app.subset),\n user_data={'name': subsetname},\n on_state_change=self.on_subset_state_change)\n self.subset.append(widget)\n return widget\n\n\nclass App(object):\n\n commit = 'HEAD'\n process_files = []\n subset = []\n compiler = 'fontforge'\n ttfautohint = '-l 7 -r 28 -G 50 -x 13 -w \"G\"'\n afdko = ''\n downstream = True\n optimize = True\n license = ''\n pyftsubset = '--notdef-outline --name-IDs=* --hinting'\n notes = ''\n newfamily = ''\n fontcrunch = False\n config = {}\n configfile = 'bakery.yaml'\n\n def __init__(self, directory):\n\n os.chdir(directory)\n\n if os.path.exists('bakery.yaml'):\n self.configfile = 'bakery.yaml'\n self.config = yaml.load(open('bakery.yaml'))\n elif os.path.exists('bakery.yml'):\n self.config = yaml.load(open('bakery.yml'))\n self.configfile = 'bakery.yml'\n\n self.commit = self.config.get('commit', 'HEAD')\n self.process_files = self.config.get('process_files', [])\n self.subset = self.config.get('subset', [])\n self.compiler = self.config.get('compiler', 'fontforge')\n self.ttfautohint = self.config.get('ttfautohint', '-l 7 -r 28 -G 50 -x 13 -w \"G\"')\n self.afdko = self.config.get('afdko', '')\n\n self.license = self.config.get('license', '')\n self.pyftsubset = self.config.get('pyftsubset',\n '--notdef-outline --name-IDs=* --hinting')\n self.notes = self.config.get('notes', '')\n self.newfamily = self.config.get('newfamily', '')\n\n self.widgets = Widgets(self)\n\n def save(self, *args, **kwargs):\n if os.path.exists(self.configfile):\n print('{} exists...'.format(self.configfile))\n\n self.configfile = '{}.new'.format(self.configfile)\n while os.path.exists(self.configfile):\n self.configfile = '{}.new'.format(self.configfile)\n\n self.config['commit'] = self.widgets.commit.get_edit_text()\n if not self.config['commit']:\n del self.config['commit']\n\n self.config['ttfautohint'] = self.widgets.ttfautohint.get_edit_text()\n self.config['newfamily'] = self.widgets.newfamily.get_edit_text()\n if not self.config['newfamily']:\n del self.config['newfamily']\n\n self.config['pyftsubset'] = self.widgets.pyftsubset.get_edit_text()\n\n self.config['process_files'] = [w.get_label()\n for w in self.widgets.process_files\n if w.get_state()]\n\n self.config['compiler'] = ', '.join([w.get_label()\n for w in self.widgets.compiler\n if w.get_state()])\n\n self.config['license'] = ', '.join([w.get_label().replace(' (exists)', '')\n for w in self.widgets.licenses\n if w.get_state()])\n\n self.config['notes'] = self.widgets.notes.get_edit_text()\n if not self.config['notes']:\n del self.config['notes']\n\n self.config['afdko'] = self.widgets.afdko_parameters.get_edit_text()\n if not self.config['afdko']:\n del self.config['afdko']\n\n yaml.safe_dump(self.config, open(self.configfile, 'w'))\n print('Wrote {}'.format(self.configfile))\n sys.exit(0)\n\n\ndef get_subsets_coverage_data(source_fonts_paths):\n \"\"\" Return dict mapping key to the corresponding subsets coverage\n\n {'subsetname':\n {'fontname-light': 13, 'fontname-bold': 45},\n 'subsetname':\n {'fontname-light': 9, 'fontname-bold': 100}\n }\n \"\"\"\n library = Library(collections=['subsets'])\n subsets = {}\n for fontpath in source_fonts_paths:\n try:\n font = FontFactory.openfont(fontpath)\n except AssertionError:\n continue\n for info in font.get_orthographies(_library=library):\n\n subsetname = info.charmap.common_name.replace('Subset ', '')\n if subsetname not in subsets:\n subsets[subsetname] = {}\n\n subsets[subsetname][fontpath] = info.coverage\n return subsets\n\n\ndef generate_subsets_coverage_list():\n directory = UpstreamDirectory('.')\n\n source_fonts_paths = []\n # `get_sources_list` returns list of paths relative to root.\n # To complete to absolute paths use python os.path.join method\n # on root and path\n for p in directory.ALL_FONTS:\n source_fonts_paths.append(p)\n return get_subsets_coverage_data(source_fonts_paths)\n\n\nimport argparse\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument('directory')\n\n\nargs = parser.parse_args()\n\n\ndirectory = UpstreamDirectory(args.directory)\nprocess_files = directory.ALL_FONTS\n\nimport urwid.curses_display\nimport urwid.raw_display\nimport urwid.web_display\nimport urwid\n\ndef show_or_exit(key):\n if key in ('q', 'Q', 'esc'):\n raise urwid.ExitMainLoop()\n\nheader = urwid.Text(\"Fontbakery Setup. Q exits.\")\n\n\napp = App(args.directory)\n\n\nwidgets = []\nif os.path.exists('.git/config'):\n githead = urwid.Text(u\"Build a specific git commit, or HEAD? \")\n widgets.append(urwid.AttrMap(githead, 'key'))\n widgets.append(urwid.LineBox(app.widgets.commit))\n widgets.append(urwid.Divider())\n\n\nwidgets.append(urwid.AttrMap(urwid.Text('Which files to process?'), 'key'))\nfor f in process_files:\n widgets.append(app.widgets.create_process_file(f))\n\nwidgets.append(urwid.Divider())\n\nwidgets.append(urwid.AttrMap(\n urwid.Text('License filename?'), 'key'))\n\nfor f in ['OFL.txt', 'LICENSE.txt', 'LICENSE']:\n if os.path.exists(f):\n widgets.append(urwid.RadioButton(app.widgets.licenses, f + ' (exists)',\n state=bool(f == app.license)))\n else:\n widgets.append(urwid.RadioButton(app.widgets.licenses, f,\n state=bool(f == app.license)))\n\nwidgets.append(urwid.Divider())\nwidgets.append(\n urwid.AttrMap(\n urwid.Text('What subsets do you want to create?'), 'key'))\n\nsubsets = generate_subsets_coverage_list()\nfor s in sorted(subsets):\n ll = ', '.join(set(['{}%'.format(subsets[s][k])\n for k in subsets[s] if subsets[s][k]]))\n if ll:\n widgets.append(app.widgets.create_subset_widget(s, ll))\n\n\nwidgets.append(urwid.Divider())\n\nwidgets.append(urwid.AttrMap(\n urwid.Text('ttfautohint command line parameters?'), 'key'))\n\nwidgets.append(urwid.LineBox(app.widgets.ttfautohint))\n\nwidgets.append(urwid.Divider())\n\nwidgets.append(urwid.AttrMap(\n urwid.Text(('New font family name (ie, replacing repo'\n ' codename with RFN)?')), 'key'))\n\nwidgets.append(urwid.LineBox(app.widgets.newfamily))\n\n\nwidgets.append(urwid.Divider())\n\nwidgets.append(urwid.AttrMap(app.widgets.create_checkbox('Use FontCrunch?', 'fontcrunch', app.fontcrunch), 'key'))\n\nwidgets.append(urwid.Divider())\n\nwidgets.append(urwid.AttrMap(app.widgets.create_checkbox('Run tests?', 'downstream', app.downstream), 'key'))\n\nwidgets.append(urwid.Divider())\n\nwidgets.append(urwid.AttrMap(app.widgets.create_checkbox('Run optimize?', 'optimize', app.optimize), 'key'))\n\nwidgets.append(urwid.Divider())\n\nwidgets.append(urwid.AttrMap(\n urwid.Text('pyftsubset defaults parameters?'), 'key'))\n\nwidgets.append(urwid.LineBox(app.widgets.pyftsubset))\n\nwidgets.append(urwid.Divider())\n\nwidgets.append(urwid.AttrMap(\n urwid.Text('Which compiler to use?'), 'key'))\n\nwidgets.append(urwid.Divider())\nquote = ('By default, bakery uses fontforge to build fonts from ufo.'\n ' But some projects use automake, or their own build system'\n ' and perhaps the AFDKO.')\nwidgets.append(urwid.Padding(urwid.Text(quote), left=4))\nwidgets.append(urwid.Divider())\n\nchoices = ['fontforge', 'afdko', 'make', 'build.py']\nfor choice in choices:\n widgets.append(urwid.RadioButton(app.widgets.compiler, choice,\n state=bool(choice == app.compiler)))\n\nwidgets.append(urwid.Divider())\n\nwidgets.append(urwid.AttrMap(\n urwid.Text('afdko default command line parameters?'), 'key'))\n\nwidgets.append(urwid.LineBox(app.widgets.afdko_parameters))\n\nwidgets.append(urwid.Divider())\nwidgets.append(urwid.AttrMap(\n urwid.Text('Notes to display on Summary page?'), 'key'))\n\nwidgets.append(urwid.LineBox(app.widgets.notes))\n\nwidgets.append(urwid.Button(u'Save and Exit', on_press=app.save))\n\nheader = urwid.AttrWrap(header, 'header')\nlw = urwid.SimpleListWalker(widgets)\n\nlistbox = urwid.ListBox(lw)\nlistbox = urwid.AttrWrap(listbox, 'listbox')\ntop = urwid.Frame(listbox, header)\n\npalette = [('header', 'black', 'dark cyan', 'standout'),\n ('key', 'white', 'dark blue', 'bold'),\n ('listbox', 'light gray', 'black')]\nloop = urwid.MainLoop(top, palette, unhandled_input=show_or_exit)\nloop.run()\n","sub_path":"tools/fontbakery-setup.py","file_name":"fontbakery-setup.py","file_ext":"py","file_size_in_byte":12105,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}
+{"seq_id":"124965026","text":"import numpy as np\nfrom scipy.optimize import leastsq\nfrom scipy import stats\nimport matplotlib.pyplot as plt\n\ndef lorentzian(x, p):\n temp = p[2]*(p[0]**2)/(( x - (p[1]) )**2 + p[0]**2) \n #print np.isnan(temp), np.isinf(temp)\n return temp\n\n\ndef within_bounds(x, p):\n if p[1] < x.min():\n return 0\n elif p[1] > x.max():\n return 0\n else:\n return 1\n\n\ndef residuals(p,y,x, func):\n if within_bounds(x, p):\n err = y - func(x,p)\n #print np.isnan(err), np.isinf(err)\n return err\n else:\n return 1e6\n\ndef gaussian_fit(data, min, max):\n wv, counts = data\n fits = fit(wv, counts, min, max)\n return np.array(fits)\n\ndef param_fit(data, min, max):\n wv, counts = data\n \n mid = (min+max)/2\n hwhm = (max - min)/6 \n minIdx = (np.abs(wv-min)).argmin()\n maxIdx = (np.abs(wv-max)).argmin()\n midIdx = (np.abs(wv-mid)).argmin()\n\n results = []\n real = []\n fits = []\n best_parameters = []\n\n func = lorentzian\n for iter in xrange(len(counts)):\n x = wv[minIdx:maxIdx]\n y = counts[iter][minIdx:maxIdx]\n x = x.astype(np.float64)\n y = y.astype(np.float64)\n \n #p is the best guess params, hwhm, mid peak pos, and height.\n p = [0.1, wv[midIdx], 0]\n\n pbest = leastsq(residuals, p, args=(y, x, func), full_output=1)\n best_p = pbest[0]\n best_parameters.append([best_p])\n x = wv\n fit = func(x, np.fabs(best_p))\n fits.append([x, fit])\n real.append([x,counts[iter]])\n return [np.array(fits), np.array(real)], np.array(best_parameters)\n\n\ndef fit(wv, counts, min, max):\n mid = (min+max)/2\n hwhm = (max - min)/6 \n minIdx = (np.abs(wv-min)).argmin()\n maxIdx = (np.abs(wv-max)).argmin()\n midIdx = (np.abs(wv-mid)).argmin()\n\n results = []\n real = []\n fits = []\n best_parameters = []\n\n func = lorentzian\n for iter in xrange(len(counts)):\n x = wv[minIdx:maxIdx]\n y = counts[iter][minIdx:maxIdx]\n x = x.astype(np.float64)\n y = y.astype(np.float64)\n \n #p is the best guess params, hwhm, mid peak pos, and height.\n p = [0.1, wv[midIdx], 0]\n\n pbest = leastsq(residuals, p, args=(y, x, func), full_output=1)\n best_p = pbest[0]\n best_parameters.append([best_p])\n x = wv\n fit = func(x, np.fabs(best_p))\n fits.append([x, fit])\n real.append([x,counts[iter]])\n #fits is now the original x axis, and smooth fitted y points.\n return np.array(fits), np.array(real)\n\n\ndef integrate(data, min, max):\n import numpy as np\n from scipy import integrate\n #data is a 2d array of wavelength vs counts\n minIdx = (np.abs(data[0, 0]-min)).argmin()\n maxIdx = (np.abs(data[0, 0]-max)).argmin()\n final = np.array([])\n for d in data:\n temp = integrate.trapz(d[1, minIdx:maxIdx], dx=1)\n final = np.append(final, temp ) \n return final\n\n\ndef fix_filters(ex_wv, front_filter):\n # the OD of the filters is a function of wavelength, here are 2nd degree poly fits\n original_front_filter = front_filter\n filter_80 = -0.98116 + (ex_wv)*(0.00494) + (ex_wv**2)*(-3.37766e-6)\n filter_70 = -0.31781 + (ex_wv)*(0.00335) + (ex_wv**2)*(-2.70064e-6)\n filter_63 = -2.6378 + (ex_wv)*(0.00907) + (ex_wv**2)*(-6.18428e-6)\n filter_50 = -3.86338 + (ex_wv)*(0.01209) + (ex_wv**2)*(-8.24043e-6)\n filter_40 = -3.35289 + (ex_wv)*(0.01) + (ex_wv**2)*(-6.49817e-6)\n filter_30 = -3.1768 + (ex_wv)*(0.0093) + (ex_wv**2)*(-6.02818e-6)\n filter_25 = -3.40178 + (ex_wv)*(0.00972) + (ex_wv**2)*(-6.30865e-6)\n filter_10 = -2.54988 + (ex_wv)*(0.00708) + (ex_wv**2)*(-4.60732e-6)\n filter_5 = -1.82088 + (ex_wv)*(0.00498) + (ex_wv**2)*(-3.21651e-6)\n filter_1 = -0.53984 + (ex_wv)*(0.00142) + (ex_wv**2)*(-8.7237e-7)\n\n front_filter_80 = 0.41597 + (ex_wv)*(0.00134) + (ex_wv**2)*(-1.18794e-6)\n front_filter_70 = -0.04073 + (ex_wv)*(0.00254) + (ex_wv**2)*(-2.15215e-6)\n front_filter_63 = -0.71643 + (ex_wv)*(0.00387) + (ex_wv**2)*(-2.77242e-6)\n\n if front_filter == 0.8:\n front_filter = front_filter_80\n elif front_filter == 0.7:\n front_filter = front_filter_70\n elif front_filter == 0.63:\n front_filter = front_filter_63\n elif front_filter == 0.4:\n front_filter = filter_40\n\n # for measurement Jan-2013\n filters = np.array([1.0, filter_1, filter_5, filter_10, filter_25, filter_30, filter_40, filter_50, filter_63, filter_70, filter_80, 1.0])\n \n # original, incorrect filters for all wavelengths\n # filters = np.array([1.0, 0.01, 0.05, 0.1, 0.25, 0.3, 0.4, 0.5, 0.63, 0.7, 0.8, 1])\n # front_filter = original_front_filter \n\n #for measurements in Dec-2012\n #filters = np.array([0.0001, 0.001, filter_1, filter_5, filter_10, filter_25, filter_30, 1, filter_50, 1, filter_80, 1])\n #original, incorrect filters for all wavelengths\n #filters = np.array([0.0001, 0.001, 0.01, 0.05, 0.1, 0.25, 0.3, 1, 0.5, 1, 0.8, 1])\n \n \n return filters*front_filter\n\n","sub_path":"controllers/fitting.py","file_name":"fitting.py","file_ext":"py","file_size_in_byte":5076,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"83"}