diff --git "a/4044.jsonl" "b/4044.jsonl" new file mode 100644--- /dev/null +++ "b/4044.jsonl" @@ -0,0 +1,129 @@ +{"seq_id":"530303393","text":"import os\nfrom cinder.volume.drivers.sursen import common\nfrom cinder.openstack.common import log as logging\nLOG = logging.getLogger(__name__)\n\nclass InitiatorManager(object):\n def __init__(self,initpath,iexecute,i_helper):\n if initpath is None or iexecute is None or i_helper is None:\n raise NameError('InitiatorManager init error')\n if os.path.exists(initpath):\n pass\n else:\n icomm=common.CommonUtils()\n icomm.create_cinder_file(iexecute, i_helper, initpath)\n\n self.inicfg=common.INIConfig(initpath)\n if len(self.inicfg.get_sections())==0:\n self.inicfg.create_seciton('ISCSIDEFAULT')\n self.inicfg.create_seciton('INITNAMELIST')\n self.inicfg.op_execute()\n \n def add_vol_initname_pair(self,volumename,initname):\n self.inicfg.set('INITNAMELIST',volumename,initname)\n self.inicfg.op_execute()\n \n def remove_vol_initname_pair(self,volumename):\n self.inicfg.remove_key('INITNAMELIST',volumename)\n self.inicfg.op_execute()\n \n def get_vol_initname(self,volumename):\n return self.inicfg.get('INITNAMELIST', volumename)\n \n \n \n","sub_path":"iscsipatch.py","file_name":"iscsipatch.py","file_ext":"py","file_size_in_byte":1207,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"282260717","text":"from datetime import datetime as dt\n\nimport pandas as pd\n\nimport lib.helpers as helpers\nimport constants\n\n\ndef _get_acronyme_mesure(identifiant: str) -> str:\n code = identifiant.split(\"-\")[1]\n return constants.MESURES.get(code, code)\n\n\ndef prepare_df_mesures(df: pd.DataFrame, df_ruptures: pd.DataFrame):\n df = df.rename(\n columns={\n \"Etat\": \"etat_mesure\",\n \"Numéro Rupture\": \"numero\",\n \"Identifiant\": \"identifiant\",\n \"Description\": \"description\",\n \"Nom Produit\": \"nom\",\n \"Demande de mise en place\": \"date_demande\",\n \"Date mise en place\": \"date_mise_en_place\",\n \"Date de fin prévisionnelle\": \"date_previ_fin\",\n \"Date de clotûre\": \"date_cloture\",\n \"Justification\": \"justification\",\n }\n )\n df = df.where(pd.notnull(df), None)\n\n df.date_demande = df.date_demande.apply(\n lambda x: dt.strptime(x, \"%d/%m/%Y\") if x and not isinstance(x, dt) else x\n )\n df.date_mise_en_place = df.date_mise_en_place.apply(\n lambda x: dt.strptime(x, \"%d/%m/%Y\") if x and not isinstance(x, dt) else x\n )\n df.date_previ_fin = df.date_previ_fin.apply(\n lambda x: dt.strptime(x, \"%d/%m/%Y\") if x and not isinstance(x, dt) else x\n )\n df.date_cloture = df.date_cloture.apply(\n lambda x: dt.strptime(x, \"%d/%m/%Y\") if x and not isinstance(x, dt) else x\n )\n\n df[\"mesure\"] = df.identifiant.apply(_get_acronyme_mesure)\n df = df[~df.mesure.isin([\"REAP\", \"QST\", \"IMP\"])]\n df[\"annee\"] = df.numero.apply(lambda x: 2000 + int(x[:2]))\n helpers.serie_to_lowercase(df, [\"etat_mesure\", \"nom\"])\n df[\"avec_mesure\"] = \"Avec mesure\"\n\n numero_not_in_mesures = [\n num for num in df_ruptures.numero.unique() if num not in df.numero.unique()\n ]\n for num in numero_not_in_mesures:\n df = df.append(\n {\n \"etat_mesure\": \"pas de mesure\",\n \"numero\": num,\n \"identifiant\": num + \"-NOTHING\",\n \"description\": None,\n \"nom\": None,\n \"date_demande\": None,\n \"date_mise_en_place\": None,\n \"date_previ_fin\": None,\n \"date_cloture\": None,\n \"justification\": None,\n \"mesure\": \"Pas de mesure\",\n \"annee\": int(\"20\" + num[:2]),\n \"avec_mesure\": \"Sans mesure\",\n },\n ignore_index=True,\n )\n df = df[~df[\"numero\"].str.startswith(\"DRAFT\")]\n return df\n","sub_path":"datamed/populate/lib/transformers/mesures.py","file_name":"mesures.py","file_ext":"py","file_size_in_byte":2541,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"78140636","text":"# -*- coding: utf-8 -*-\n\nfrom __future__ import print_function\n\nfrom tfclass import TFClass\nfrom pack import Pack\nfrom weapon import Weapon\n\n\nclass TERRAIN:\n \"\"\"Terrain type.\"\"\"\n INACCESSIBLE = -1 # tf2class/item cannot be placed here\n NORMAL = 0\n WATER = 1\n LAVA = 2\n\n\nclass Location(object):\n \"\"\"Class representing an element on the board.\"\"\"\n\n def __init__(self, height, terrain, occupants, packs, weapons):\n # Type checks\n assert all(isinstance(x, TFClass) for x in occupants)\n assert all(isinstance(x, Pack) for x in packs)\n assert all(isinstance(x, Weapon) for x in weapons)\n\n self.__height = height\n self.__terrain = terrain\n self.__occupants = occupants\n self.__packs = packs\n self.__weapons = weapons\n\n @property\n def height(self):\n return self.__height\n\n @property\n def terrain(self):\n return self.__terrain\n\n","sub_path":"tf2boardgame/location.py","file_name":"location.py","file_ext":"py","file_size_in_byte":930,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"239861750","text":"import os\nimport csv\nimport requests\nfrom unidecode import unidecode\nfrom bs4 import BeautifulSoup\nimport math\nimport demjson\ncsv_columns = [\"id\",\"name\",\"category\",\"image\",\"model_number\",\"brand\",\"color_family\",\"link\",\"newprice\",\"oldprice\"]\ncurrentPath = os.getcwd()\ncsv_file = \"wadi_prod_scrape_sa.csv\"\nj=0\ndef WriteDictToCSV(dict_data):\n\tglobal csv_columns\n\tglobal csv_file\n\tglobal j\n\ttry:\n\t\twith open(csv_file, 'a+') as csvfile:\n\t\t\twriter = csv.DictWriter(csvfile, delimiter='|', lineterminator='\\n', fieldnames=csv_columns)\n\t\t\tif j == 0:\n\t\t\t\twriter.writeheader()\n\t\t\t\tj=1\n\t\t\t\n\t\t\tfor data in dict_data:\n\t\t\t\twriter.writerow(data)\n\t\t\t\t\n\t\treturn 1\n\texcept Exception as e:\n\t\tprint(str(e))\n\t\treturn 0\n\ndef deep_scrape(url,index,cat):\n\ttry:\n\t\tvar={}\n\t\tdatalist=list()\n\t\tresponse=requests.get(url)\n\t\tdata=demjson.decode(response.content)\n\t\tgeneral=data['data'][index]\n\t\titem_name=item_brand=item_newprice=item_oldprice=item_color=item_link=item_memory=item_ram=item_image=item_highlights=item_model=OS=comp=item_id=\" \"\n\t\tif general.get('sku'):\n\t\t\titem_id=general['sku']\n\t\tif general.get('name'):\n\t\t\titem_name=data['data'][index]['name']\n\t\tif general.get('link'):\n\t\t\titem_link='https://en-ae.wadi.com/' + data['data'][index]['link']\n\t\tif general['attributes'].get('color_family_en'):\n\t\t\titem_color=general['attributes']['color_family_en']\n\t\tif general.get('price'):\n\t\t\titem_oldprice=str(general['price'])\n\t\tif general.get('offerPrice'):\n\t\t\titem_newprice=str(general['offerPrice'])\n\t\tif general.get('internalMemory'):\n\t\t\titem_memory=general['internalMemory']\n\t\tif general.get('imageKey'):\n\t\t\titem_image='https://f.wadicdn.com/product/'+general['imageKey']+'/1-product.jpg'\n\t\tif general['attributes'].get('ram_size'):\n\t\t\titem_ram=general['attributes'].get('ram_size')\n\t\t# if general.get('highlights'):\n\t\t# \tfor highlight in general['highlights']:\n\t\t# \t\titem_highlights=item_highlights + ', ' + highlight\n\t\t# \titem_highlights=item_highlights[2:]\n\t\tif general.get('brand'):\n\t\t\titem_brand=general['brand']['name']\n\t\tif general['attributes'].get('model_number'):\n\t\t\titem_model=general['attributes']['model_number']\n\t\t# if general['attributes'].get('operating_system'):\n\t\t# \tOS=general['attributes']['operating_system']\n\t\t# if general['attributes'].get('compatibility'):\n\t\t# \tcomp=general['attributes']['compatibility']\n\t\tvar ['category']=cat[cat.rfind(\"/\")+1:]\n\t\tvar['id']=unidecode(item_id)\n\t\tvar['name']=unidecode(item_name)\n\t\t# var['model_name']=\"\"\n\t\t# if general['attributes'].get('model_name'):\n\t\t# \tvar['model_name']=unidecode(general['attributes']['model_name'])\n\t\tvar['brand']=unidecode(item_brand).strip()\n\t\tvar['model_number']=unidecode(item_model).strip()\n\t\tvar['link']=unidecode(item_link).strip()\n\t\tvar['oldprice']=unidecode(item_oldprice).strip()\n\t\tvar['newprice']=unidecode(item_newprice).strip()\n\t\tvar['color_family']=unidecode(item_color).strip()\n\t\t# var['internal_memory']=unidecode(item_memory).strip()\n\t\t# var['ram']=unidecode(item_ram).strip()\n\t\t# var['highlights']=unidecode(item_highlights).strip()\n\t\tvar['image']=unidecode(item_image).strip()\n\t\t# var['OS']=unidecode(OS).strip()\n\t\t# var['Compatible_with']=unidecode(comp).strip()\n\t\tdatalist.append(var.copy())\n\t\tWriteDictToCSV(datalist)\n\t\t#var['b']=unidecode(item_b)\n\t\tprint(var['name'])\n\texcept Exception as e:\n\t\tprint(str(e))\n\t\treturn\n\treturn\ndef get_scrape(url,cat,count):\n\ttry:\n\t\tprint(\"scrapping \" + url)\n\t\tresponse=requests.get(url)\n\t\tdata=demjson.decode(response.content)\n\t\tindex=0\n\t\tfor index in range(int(len(data['data']))):\n\t\t\tprint(\"On Page : \" + count)\n\t\t\tdeep_scrape(url,index,cat)\n\texcept Exception as e:\n\t\tprint(str(e))\n\t\treturn\n\treturn\ndef get_link(url , cat):\n\ttry:\n\t\turl='https://en-sa.wadi.com/api/sawa/v1/u' + url\n\t\t#print(url)\n\t\tresponse = requests.get(url)\n\t\tdata = demjson.decode(response.content)\n\t\titem_count=int(data['totalCount'])\n\t\tpage_count=math.ceil(item_count/30)\n\t\tpage_count=int(page_count)\n\t\tprint (\"Item count : \")\n\t\tprint(item_count)\n\t\tprint(\"Page Count : \")\n\t\tprint(page_count)\n\t\tcount=1\n\t\twhile (page_count>=0):\n\t\t\ttry:\n\t\t\t\tget_scrape(url + '&page=' + str(count),cat,str(count))\n\t\t\t\tcount=count+1\n\t\t\t\tpage_count=page_count-1\n\t\t\texcept Exception as e:\n\t\t\t\tprint(str(e))\n\t\t\t\treturn\n\texcept Exception as e:\n\t\tprint(str(e))\n\t\treturn\n\treturn\n\nwith open('wadi_sa_cat_Scrape-test.csv') as csvfile:\n\treader=csv.DictReader(csvfile, delimiter=\";\")\n\tfor row in reader:\n\t\tget_link(row['Url'][22:],row['Category'])\n\t\t","sub_path":"en-uae/wadi-prod.py","file_name":"wadi-prod.py","file_ext":"py","file_size_in_byte":4405,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"496545117","text":"from turtle import *\nfrom random import *\nfrom time import *\n\n#Your typical main\ndef main():\n #A nice little loading message\n print(\"Loading Turtle interface...\")\n #Open up turtle window\n Screen()\n Turtle()\n speed('fastest')\n #Welcome message\n print(\"Welcome to Turtle Draw!\\n\")\n #Start interactive environment\n commands()\n pensize(1)\n goDraw()\n\n#Will display commands for reference\ndef commands():\n print(\"\\n*****Commands*****\")\n print(\"***THE CURSOR ALWAYS TURNS RIGHT WHEN SHIFTING***\")\n print(\"*To change direction, type: turn(angle)\")\n print(\"*To move cursor with out drawing, type: move(length, angle)\")\n print(\"*To draw line, type: drawLine(length, angle)\")\n print(\"*To draw circle, type: drawCircle(radius)\")\n print(\"*To draw shape with X equal sides, type: drawESS(width, sides, offset)\")\n print(\"*To loop a shape, type: loopShape()\")\n print(\"*To increase the pen width, type: pensize(newSize)\")\n print(\"*To change pen color, type: pencolor('color')\")\n print(\"Type 'c' for command list at anytime\")\n print(\"Type 'o' for a list of menu options at anytime\")\n\n#Gets the input and calls the correct function\ndef goDraw():\n #Print commands and an extra line\n \n #Now we'll get the input\n command = input(\"\\n::\")\n if (command == \"c\"):\n commands()\n goDraw()\n elif(command == \"o\"):\n options()\n goDraw()\n try:\n eval(command)\n except NameError:\n pass\n print(\"\")\n goDraw()\n\ndef options():\n print(\"1. Save Drawing\")\n print(\"2. Clear Drawing\")\n print(\"3. Undo last stroke\")\n print(\"4. Exit\")\n selection = input(\"::\")\n if (selection == \"1\"):\n name = input(\"Name:\")\n name += \".eps\"\n drawing = getscreen()\n drawing.getcanvas().postscript(file=name)\n if (selection == \"2\"):\n resetscreen()\n goDraw()\n if (selection == \"3\"):\n undo()\n goDraw()\n if (selection == \"4\"):\n exit()\n\n#Turn turtle direction\ndef turn(angle):\n up()\n right(angle)\n down()\n return 0\n \n#Draws a line \ndef drawLine(length, angle):\n right(angle)\n forward(length)\n return 0\n\n#Draws a circle\ndef drawCircle(radius):\n circle(radius)\n return 0\n\n#Moves cursor anywhere the user wants\ndef move(length, angle):\n up()\n right(angle)\n forward(length)\n down()\n return(0)\n \n\n#Draws a shape with X number of equal lenght sides\ndef drawESS(width, sides, offset):\n left(offset)\n for i in range(sides):\n forward(width)\n right(360/sides)\n return 0\n\ndef loopShape():\n loop = input(\"Type function to loop with arguments: \")\n loops = input(\"Loop how many times?: \")\n for i in range(int(loops)):\n eval(loop)\n right(360/int(loops))\n return 0\n\n\n\n\n \n","sub_path":"Interactive Turtle.py","file_name":"Interactive Turtle.py","file_ext":"py","file_size_in_byte":2824,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"334937955","text":"# -*- coding: utf-8 -*-\nimport matplotlib.pyplot as plt\nimport matplotlib\nfrom matplotlib.ticker import FuncFormatter\nimport sys\nimport jsonpath\nimport json\nimport colorsTable as ctab\n\ndef add_percent(temp, position):\n return '%1.0f'%(temp) + '%'\n\nmatplotlib.rcParams['font.sans-serif'] = ['SimHei']\nmatplotlib.rcParams['axes.unicode_minus'] = False\nfig = plt.figure()\n\nax = plt.gca()\nax.spines['right'].set_color('none')\nax.spines['top'].set_color('none')\nax.spines['left'].set_color('none')\nax.spines['bottom'].set_position(('data', 0))\nax.yaxis.set_major_formatter(FuncFormatter(add_percent))\n\njsonStr = sys.argv[1]\njsonStr = jsonStr.replace('£','\"')\njsonData = json.loads(jsonStr)\n\nplotTile = jsonpath.jsonpath(jsonData,'title')\nx1=jsonData['months']\nevalueTypes = jsonData['evalueTypes']\nYY = jsonData['valueList']\nplotWidth = jsonData['width']\nplotHeight = jsonData['height']\n\nCOLORS = list(ctab.getLinesColors(len(YY)))\nMARKERS = list(ctab.getMarkerType(len(YY)))\n\n#plt.subplots_adjust(top = 0.9, bottom = 0.1, right = 0.9, left = 0.1, hspace = 0, wspace = 0)\n#plt.margins(0,0)\nfig.set_size_inches(plotWidth, plotHeight)\n\nfor i in range(len(YY)):\n plt.plot(x1,YY[i],label=evalueTypes[i],linewidth=2,color=COLORS[i],marker=MARKERS[i], markerfacecolor=COLORS[i],markersize=5)\n\nif plotTile:\n plt.title(plotTile[0])\n\nbox = ax.get_position()\nax.set_position([box.x0, box.y0, box.width , box.height*0.8])\nax.legend(loc='center', frameon=False, bbox_to_anchor=(0.5, -0.08),ncol=3)\n\nresultfilePath = sys.argv[2]+'.png'\nplt.savefig(resultfilePath, transparent=True, pad_inches = 0)\n\nprint(resultfilePath)\n","sub_path":"product/python/src/main/resources/python_script/plot_growingLines.py","file_name":"plot_growingLines.py","file_ext":"py","file_size_in_byte":1613,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"539143365","text":"import numpy as np\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\nfrom functools import lru_cache\nimport json\nfrom tqdm import tqdm\nfrom .util_classes import DimensionlessAnnotationUnit\n\nTQDM_MODE = True\n\n\nclass AcquisitionAggregation:\n def __init__(self, functions, dataset):\n self.functions = functions\n self.dataset = dataset\n\n def aggregation_step(self):\n # This is for learning aggregations, e.g. LSA bandit set up\n raise NotImplementedError\n\n def acquisition_aggregation(self, scores):\n raise NotImplementedError\n\n def score(self, i):\n scores = []\n for function in self.functions:\n scores.append(function.score(i))\n return self.acquisition_aggregation(scores).reshape(-1)\n\n def step(self):\n self.aggregation_step()\n for function in self.functions:\n if isinstance(function, UnitwiseAcquisition):\n pass\n elif isinstance(function, DataAwareAcquisition):\n function.step()\n else:\n raise NotImplementedError(f\"{type(function)} not a function type\")\n pass\n\n\nclass SimpleAggregation(AcquisitionAggregation):\n def __init__(self, functions, dataset, weighting):\n self.weighting = F.normalize(weighting)\n super(SimpleAggregation, self).__init__(functions, dataset)\n\n def acquisition_aggregation(self, scores):\n return self.weighting @ scores\n\n\nclass LearningAggregation(AcquisitionAggregation):\n def __init__(self, functions, dataset):\n super(LearningAggregation, self).__init__(functions, dataset)\n raise NotImplementedError\n\n def acquisition_aggregation(self, scores):\n raise NotImplementedError\n\n def aggregation_step(self):\n raise NotImplementedError\n\n\nclass DataAwareAcquisition:\n def __init__(self, dataset):\n self.dataset = dataset\n\n def score(self, i):\n raise NotImplementedError\n\n def step(self):\n raise NotImplementedError\n\n\nclass PredsKLAcquisition(DataAwareAcquisition):\n def __init__(self, dataset):\n super().__init__(dataset=dataset)\n self.scorer = nn.KLDivLoss(log_target=True)\n\n def score(self, i):\n preds = self.dataset.last_preds[i]\n previous_preds = self.dataset.last_preds.prev_attr[i]\n return self.scorer(preds, previous_preds)\n\n def step(self):\n pass\n\n\nclass EmbeddingMigrationAcquisition(DataAwareAcquisition):\n def __init__(self, dataset, embedding_name):\n super().__init__(dataset=dataset)\n self.embedding_name = embedding_name\n\n def score(self, i):\n embs = self.dataset.__getattr__(self.embedding_name)[i]\n previous_embs = self.dataset.__getattr__(self.embedding_name).prev_attr[i]\n return torch.cdist(embs, previous_embs)\n\n def step(self):\n pass\n\n\nclass UnitwiseAcquisition:\n def __init__(self, dataset):\n self.dataset = dataset\n\n def score(self, i):\n raise NotImplementedError\n\n\nclass RandomBaselineAcquisition(UnitwiseAcquisition):\n def __init__(self, dataset):\n super().__init__(dataset=dataset)\n self.score_shape = self.dataset.last_preds[0].max(axis=-1).shape\n\n def score(self, i):\n # Add an axis when batching\n return torch.randn(*self.scores_shape)\n\n\nclass LowestConfidenceAcquisition(UnitwiseAcquisition):\n def __init__(self, dataset):\n super().__init__(dataset=dataset)\n\n def score(self, i):\n return - self.dataset.last_preds[i].max(axis=-1)\n\n\nclass MaximumEntropyAcquisition(UnitwiseAcquisition):\n def __init__(self, dataset):\n super().__init__(dataset=dataset)\n\n def score(self, i):\n return torch.distributions.Categorical(logits=self.dataset.last_preds[i]).entropy()\n\n\nclass BatchAcquisition:\n \"\"\"No scores are used for this class of acquisition functions -\"\"\"\n\n def __init__(self, dataset):\n self.dataset = dataset\n\n def select_next_subset(self, candidate_windows, batchsize):\n raise NotImplementedError\n\n\nclass TFIDFFeatureFunctionBatchAcquisition(BatchAcquisition):\n \"\"\"\n SUBMODULAR SUBSET SELECTION FOR LARGE-SCALE SPEECH TRAINING DATA - Wei et al 2014\n --> set tfidf_feature to trigrams - remember to make them jsonable\n This is a very basic, one step coreset selection. Can offer base for other, sequential variants.\n This class DOES NOT currently support subinstance annotation - only works for unit data/sequences.\n \"\"\"\n\n def __init__(\n self,\n dataset,\n tfidf_feature,\n d_cache_path=\"d_cache.json\",\n index_log_path=\"submodular_log.log\",\n ):\n super(TFIDFFeatureFunctionBatchAcquisition, self).__init__(dataset)\n self.existing_mapper = {}\n self.d_cache = {}\n self.current_indices = []\n self.current_mu_mapper = {}\n self.d_cache_path = d_cache_path\n self.index_log_path = index_log_path\n self.tfidf_attribute = self.dataset.__getattr__(tfidf_feature)\n\n with open(d_cache_path, \"r\") as jfile:\n self.d_cache = json.load(jfile)\n\n with open(index_log_path, \"r\") as f:\n lines = f.read().split(\"\\n\")[:-1]\n if len(lines) > 0:\n all_indices = list(dataset.data.attr)\n self.load_previous_set([int(ind) for ind in lines], all_indices)\n\n def d(self, feature, all_windows):\n \"\"\"NEEDS TO BE TRIGGERED IF DATASET IS EXPANDED, MAINLY FOR ROUNDWISE ACQUISITION\"\"\"\n if feature in self.d_cache.keys():\n return self.d_cache[feature]\n else:\n d = 0\n for window in all_windows:\n ith_sequence_features = self.tfidf_attribute.get_attr_by_window(window)\n if feature in ith_sequence_features:\n d += 1\n self.d_cache[feature] = d\n self.save_d_cache()\n return d\n\n @staticmethod\n def g(x):\n return x ** 0.5\n\n @staticmethod\n def tf(feature, all_features):\n return all_features.count(feature)\n\n def idf(self, feature, all_windows):\n return np.log(self.V / self.d(feature, all_windows))\n\n def m_u(self, feature, all_features, all_windows):\n return self.tf(feature, all_features) * self.idf(feature, all_windows)\n\n @staticmethod\n def add_dictionaries(d1, d2):\n # THIS CAN BE SPED UP\n d3 = {}\n unadded_k = set(d2.keys())\n for k, v in d1.items():\n if k in d2:\n d3[k] = v + d2[k]\n unadded_k = unadded_k - {k}\n else:\n d3[k] = v\n for k in unadded_k:\n d3[k] = d2[k]\n return d3\n\n def get_mu_dict(self, window, all_windows):\n all_features = self.tfidf_attribute.get_attr_by_window(window)\n unique_features = []\n mu_dict = {}\n for fe in all_features:\n if fe not in unique_features:\n unique_features.append(fe)\n for fe in unique_features:\n mu_dict[fe] = self.m_u(fe, all_features, all_windows)\n return mu_dict\n\n def f_feature(self, current_windows, new_windows, all_windows):\n mu_scores = self.current_mu_mapper.copy()\n for window in new_windows:\n muj = self.get_mu_dict(window, all_windows)\n mu_scores = self.add_dictionaries(muj, mu_scores)\n return sum([self.g(m) for m in mu_scores.values()]), mu_scores\n\n def greedy_increment(self, candidate_windows):\n current_max = -np.inf\n chosen_idx = None\n next_mapper = None\n current_windows = [candidate_windows[j] for j in self.current_indices]\n for i, w in enumerate(candidate_windows):\n if i in self.current_indices:\n continue\n ith_score, candidate_mapper = self.f_feature(\n current_windows, [w], candidate_windows\n )\n if ith_score > current_max:\n current_max = ith_score\n chosen_idx = i\n next_mapper = candidate_mapper\n self.current_indices.append(chosen_idx)\n self.current_mu_mapper = next_mapper.copy()\n with open(self.index_log_path, \"a\") as f:\n print(chosen_idx, \"\\t\", current_max, \"\\n\", file=f)\n return current_max\n\n def load_previous_set(self, previous_indices, all_windows):\n self.V = len(all_windows)\n self.current_indices = previous_indices\n self.current_mu_mapper = {}\n print(\"loading previous progress!\")\n for i in tqdm(previous_indices, disable=not TQDM_MODE):\n self.current_mu_mapper = self.add_dictionaries(\n self.current_mu_mapper, self.get_mu_dict(all_windows[i], all_windows)\n )\n print(\"loading finished!\")\n\n def select_next_subset(self, candidate_windows, batchsize):\n self.V = len(candidate_windows)\n score_history = []\n for ra in tqdm(range(batchsize), disable=not TQDM_MODE):\n if len(self.current_indices) > ra:\n continue\n new_score = self.greedy_increment(candidate_windows)\n score_history.append(new_score)\n if len(self.current_indices) >= batchsize:\n print(\"DONE\")\n break\n return [candidate_windows[i] for i in self.current_indices]\n\n def save_d_cache(self):\n with open(self.d_cache_path, \"w\") as jfile:\n json.dump(self.d_cache, jfile)\n\n\nclass UncertaintyAugmentedTFIDFFeatureFunctionBatchAcquisition(\n TFIDFFeatureFunctionBatchAcquisition\n):\n def __init__(\n self,\n dataset,\n tfidf_feature,\n score_attribute,\n d_cache_path=\"d_cache.json\",\n index_log_path=\"submodular_log.log\",\n ):\n super(UncertaintyAugmentedTFIDFFeatureFunctionBatchAcquisition, self).__init__(\n dataset, tfidf_feature, d_cache_path, index_log_path\n )\n self.score_attribute = self.dataset.__getattr__(score_attribute)\n\n def get_mu_dict(self, window, all_windows):\n all_features = self.tfidf_attribute.get_attr_by_window(window)\n score = self.score_attribute.get_attr_by_window(window)\n score = np.mean(score)\n unique_features = []\n mu_dict = {}\n for fe in all_features:\n if fe not in unique_features:\n unique_features.append(fe)\n for fe in unique_features:\n mu_dict[fe] = self.m_u(fe, all_features, all_windows) * float(score)\n return mu_dict\n\n\nclass KMeansCentroidBatchAcquisition(BatchAcquisition):\n \"\"\"\n This is BADGE: https://arxiv.org/abs/1906.03671 if you set the relevant attribute to be made as:\n\n output = model(torch.tensor(batch).to('cuda'), False)\n hyp_preds = nn.functional.one_hot(output['last_preds'].argmax(axis = -1), 10)\n hyp_loss = (hyp_preds * output['last_preds']).sum()\n hyp_loss.backward()\n model.state_dict(keep_vars=True)['fc.weight'].grad.shape\n\n This would also require batch_size = 1 on the agent! This needs to be dealt with elsewhere\n \"\"\"\n\n def __init__(self, dataset, attribute_name, pca_comps=16):\n super(KMeansCentroidBatchAcquisition, self).__init__(dataset)\n self.attribute_name = attribute_name\n self.pca_comps = pca_comps\n\n def select_next_subset(self, candidate_windows, batchsize):\n mechanism = al.batch_querying.SequentialKMeansBatchQuerying(\n batchsize, self.attribute_name, pca_comps=self.pca_comps\n )\n chosen_indices = mechanism.init_round(candidate_windows, self.dataset)\n return [DimensionlessAnnotationUnit(i, ..., None) for i in chosen_indices]\n","sub_path":"active_learning/acquisition.py","file_name":"acquisition.py","file_ext":"py","file_size_in_byte":11689,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"581825411","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# CL Simplex 2015\n# https://CLSimplex.com\n#\n# Free Beer License.\n# 'As is'.\n# Don't expect anything from this. At all. Seriously.\n\nimport os\nimport tablib\nimport subprocess\nimport sqlite3 as sql\n\ndef sqlite3_table_to_xls(database_path, table_name, export_path):\n\t\"\"\"\n\tGiven an sqlite3 database file and a table_name, export it to a spreadsheet.\n\tNo sanity checks included.\n\t\"\"\"\n\n\tconnection = sql.connect( database_path )\n\tcursor = connection.cursor()\n\tcursor.execute( 'SELECT * FROM ' + str(table_name) )\n\n\tcol_names = [cn[0] for cn in cursor.description]\n\n\trows = cursor.fetchall()\n\n\tdata = tablib.Dataset()\n\n\tdata.headers = col_names\n\n\tfor item in rows:\n\t\tdata.append(item)\n\n\tfull_export_path = str(export_path) + str(table_name) + '_export.xls'\n\n\tprint('Creating xls sheet at: ' + str(full_export_path))\n\n\tfile_handler = open( full_export_path, 'wb+' )\n\tfile_handler.write(data.xls)\n\n\tfile_handler.close()\n\tconnection.close()\n\n\n\ndef sqlite3_table_entries_by_git_commit_history(repo_path, database_path, table_name, commits=20):\n\t\"\"\"\n\tGiven a repo, a database_path, and a table_name - display the number of entries in the table going back COMMITS commits.\n\tThis assumes you have GIT installed as well as a local repo of whatever you are attemping to look at.\n\n\tReturns a list of tuples of the form (COMMITS BACK, ROWS)\n\n\tThis uses checkout_output with shell=True so be careful with this one.\n\t\"\"\"\n\tnew_database_path = '/var/tmp/database.db'\n\n\toriginal_cwd = os.getcwd()\n\n\tos.chdir(repo_path)\n\n\tcommits_back = commits\n\n\tresults = []\n\n\twhile commits_back > -1:\n\t\tgit_command = 'git show HEAD~' + str( commits_back ) + ':.' + str( database_path )\n\n\t\t# Given git output, write a new, temporary file.\n\t\tfile_pointer = open( new_database_path, 'wb+')\n\n\t\toutput = subprocess.check_output( git_command, shell=True )\n\n\t\tfile_pointer.write( output )\n\t\tfile_pointer.close()\n\n\t\tconnection = sql.connect( new_database_path )\n\n\t\tcursor = connection.cursor()\n\n\t\tcursor.execute( 'SELECT * FROM ' + str( table_name ) )\n\n\t\trows = cursor.fetchall()\n\n\t\tresults.append((commits_back,rows))\n\t\n\t\tcommits_back = commits_back - 1\n\t\tconnection.close()\n\n\tos.chdir(original_cwd)\n\n\treturn results","sub_path":"sqlite3-tools.py","file_name":"sqlite3-tools.py","file_ext":"py","file_size_in_byte":2216,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"187879095","text":"import os\n\nimport matplotlib.pyplot as plt\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\nfrom torch import optim\nfrom tqdm import tqdm\n\nfrom torch.utils.tensorboard import SummaryWriter\n\nfrom utils import plot\n\n\nclass DistillationTrainer:\n def __init__(self,\n directory,\n student_model,\n teacher_model,\n trainloader,\n testloader,\n lr,\n ):\n # models\n # dataset ou dataloader ?\n self.logdir = directory\n # Optimizer\n self.student_model = student_model\n self.teacher_model = teacher_model\n self.teacher_model.eval()\n self.trainloader = trainloader\n self.testloader = testloader\n self.optimizer = optim.Adam(self.student_model.parameters(), lr=lr)\n self.writer = SummaryWriter(log_dir=directory)\n\n def do_epoch(self, device, train=True):\n dico = dict(total_loss=0.0,\n loss_classif=0.0,\n loss_logits=0.0,\n accuracy=0.0,\n )\n total = 0\n correct = 0\n\n loader = self.trainloader if train else self.testloader\n self.student_model.train(train)\n\n for i, (images, labels) in tqdm(enumerate(loader)):\n # Move tensors to the configured device\n images = images.to(device)\n labels = labels.to(device)\n\n # Forward pass\n with torch.set_grad_enabled(train):\n\n logits_teacher, f2, f4 = self.teacher_model(images)\n logits_student, fA, fB = self.student_model(images)\n\n loss_classif = self.classif_loss(logits_student, labels)\n loss_logits = self.logit_distillation_loss(logits_teacher, logits_student)\n\n total_loss=loss_classif+loss_logits\n\n dico['loss_classif'] += loss_classif.item()\n dico['loss_logits'] += loss_logits.item()\n dico['total_loss'] += total_loss.item()\n # dico['total_loss'] += total_loss.item()\n\n _, predicted_indices = torch.max(logits_student.data, 1)\n total += labels.size(0)\n correct += (predicted_indices == labels).sum().item()\n\n # Backprpagation and optimization\n if train:\n self.optimizer.zero_grad()\n total_loss.backward()\n self.optimizer.step()\n # fin de l'itération\n\n # fin de l'epoch\n dico = {\n key: (value / (i + 1))\n for key, value in dico.items()\n }\n\n dico['accuracy'] = correct / total\n return dico # average error for an iteration\n\n def classif_loss(self, logits, labels):\n return F.cross_entropy(logits, labels)\n\n def logit_distillation_loss(self, logits_teacher, logits_student, T=4):\n softmax_op = nn.Softmax(dim=1)\n mseloss_fn = nn.MSELoss()\n return mseloss_fn(softmax_op(logits_student / T), softmax_op(logits_teacher) / T)\n #\n # def feature_distillation_loss(self, fA, fB, f2, f4):\n # mseloss_fn = nn.MSELoss()\n # return mseloss_fn(fA, f2) + mseloss_fn(fB, f4)\n\n\n\n def train_model(self, num_epochs, device):\n\n for epoch in range(num_epochs):\n train_dico= self.do_epoch(device, train=True)\n print(\n f'TRAIN: Epoch[{epoch + 1}/{num_epochs}], Loss_Classif:{train_dico[\"loss_classif\"]:.4f}, Loss_Logits:{train_dico[\"loss_logits\"]:.4f}, Accuracy:{train_dico[\"accuracy\"]:.4f}')\n\n test_dico = self.do_epoch(device, train=False)\n print(f'TRAIN: Epoch[{epoch + 1}/{num_epochs}], Loss_Classif:{test_dico[\"loss_classif\"]:.4f}, Loss_Logits:{test_dico[\"loss_logits\"]:.4f}, Accuracy:{test_dico[\"accuracy\"]:.4f}')\n\n plot(self.writer, epoch, train_dico, test_dico)\n","sub_path":"training_distill_nofeature.py","file_name":"training_distill_nofeature.py","file_ext":"py","file_size_in_byte":3917,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"265436621","text":"import logging\nimport collections\nfrom datetime import timedelta\n\nlogged_scans = dict()\nSmallScan = collections.namedtuple('SmallScan', ['time', 'name'])\ngrace = timedelta(seconds=30)\n\n\ndef add_scan(result, when, meal):\n\t'''Add a result to the logged scans\n\n\tChecks to see if this Attendee has been scanned already. If they have\n\tthrow a ValueError after adding them to the list, but only if it's been\n\tlonger than the grace period. Uses a namedtuple in the list for easy\n\taccess to information when querying.\n\t'''\n\tdate = str(when.date())\n\tbnum = result['badge_num']\n\tif not date in logged_scans:\n\t\tlogged_scans[date] = dict()\n\tif not meal in logged_scans[date]:\n\t\tlogged_scans[date][meal] = dict()\n\tif result['badge_num'] in logged_scans[date][meal]:\n\t\tlogged_scans[date][meal][bnum] += [SmallScan(\n\t\t\twhen,\n\t\t\tresult['name']\n\t\t)]\n\t\tfirst_scan = logged_scans[date][meal][bnum][0]\n\t\tthis_scan = logged_scans[date][meal][bnum][-1]\n\t\tif (this_scan.time - first_scan.time) > grace:\n\t\t\traise ValueError(\n\t\t\t\t\"Badge #{} was already scanned (first {} ago)\".format(\n\t\t\t\t\tbnum,\n\t\t\t\t\tthis_scan.time - first_scan.time\n\t\t\t\t),\n\t\t\t\tfirst_scan,\n\t\t\t\tthis_scan\n\t\t\t)\n\telse:\n\t\tlogged_scans[date][meal][bnum] = [SmallScan(\n\t\t\twhen,\n\t\t\tresult['name']\n\t\t)]\n","sub_path":"backend/util/state.py","file_name":"state.py","file_ext":"py","file_size_in_byte":1237,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"112946030","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Oct 21 14:42:11 2018\n\n@author: hamed\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport colored_traceback; colored_traceback.add_hook()\n\n#%%\nfile_name = \"stats.npy\"\nstats = np.load(file_name) #stats = (\"mean_\", \"scale_\", \"min_\", \"max_\", \"var_\") * 1440\n\nmean_ = stats[0,:]\nscale_ = stats[1,:]\n\n\n\nprint(\"scale_ = \", scale_)\n\nplt.figure(1)\n\nplt.subplot(211)\nplt.title(\"mean\")\nplt.plot(mean_)\n\nplt.subplot(212)\nplt.title(\"scale\")\nplt.plot(scale_)\n\nplt.show()\n\n\n\n","sub_path":"dataset/analyze_stats.py","file_name":"analyze_stats.py","file_ext":"py","file_size_in_byte":514,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"44386146","text":"#!/usr/bin/env python\n# coding: utf-8\n\nimport pymongo\nfrom contextlib import contextmanager\n\nfrom mongolog.hooks import MongoLogCursor, MongoLogCollection\n\nSUPPORTED_PYMONGO_VERSIONS = ('2.6.3',)\n\nif pymongo.version not in SUPPORTED_PYMONGO_VERSIONS:\n raise NotImplementedError('\"mongolog\" supports only versions: %s pymongo' % SUPPORTED_PYMONGO_VERSIONS)\n\n\n@contextmanager\ndef patched_pymongo():\n \"\"\"\n Allow localy patch pymongo\n\n with patch_pymongo:\n coll.find({_id: '1'})\n \"\"\"\n original_cursor = pymongo.cursor.Cursor\n original_collection = pymongo.collection.Collection\n try:\n pymongo.collection.Cursor = MongoLogCursor\n pymongo.database.Collection = MongoLogCollection\n yield\n finally:\n pymongo.collection.Cursor = original_cursor\n pymongo.database.Collection = original_collection\n\n\ndef patch_pymongo():\n \"\"\"\n Patch pymongo\n \"\"\"\n pymongo.collection.Cursor = MongoLogCursor\n pymongo.database.Collection = MongoLogCollection\n","sub_path":"mongolog/monkey.py","file_name":"monkey.py","file_ext":"py","file_size_in_byte":1013,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"339796810","text":"\"\"\" 4.1 Cold merge\nhttps://polarion.engineering.redhat.com/polarion/#/project/RHEVM3/wiki/\nStorage_4_0/4_1_Storage_Cold_Merge\n\"\"\"\nfrom rhevmtests.storage import config\nfrom multiprocessing.dummy import Pool\nfrom multiprocessing import Process, Queue\n\nimport pytest\nfrom art.rhevm_api.utils.log_listener import watch_logs\nfrom art.rhevm_api.tests_lib.low_level import (\n vms as ll_vms,\n hosts as ll_hosts,\n jobs as ll_jobs,\n)\nimport remove_snapshot_base as basePlan\nfrom rhevmtests.storage.fixtures import remove_vm # noqa F401\nfrom fixtures import initialize_params, initialize_params_new_dc\nfrom rhevmtests.storage.fixtures import (\n create_dc, clean_dc, remove_hsm_host, delete_disks, create_vm,\n wait_for_disks_and_snapshots, prepare_disks_with_fs_for_vm,\n create_storage_domain, start_vm, init_vm_executor,\n)\nfrom art.test_handler.tools import polarion, bz\nfrom art.unittest_lib import (\n tier2,\n tier3,\n tier4,\n)\nfrom art.unittest_lib import testflow\n\nTEST_FILE_TEMPLATE = 'test_file_%s'\nREGEX_COLD_MERGE_CMD = r\"ColdMergeVDSCommand\\(HostName\\ =\\ (?P\\w+)\"\nREGEX_SDM_MERGE_CMD = \"sdm_merge\"\nTIMEOUT_COLD_MERGE_CMD = 60\n\n\nclass ColdMergeBaseClass(basePlan.BasicEnvironment):\n \"\"\"\n Set live merge parameter to False\n \"\"\"\n live_merge = False\n command_sdm_merge = None\n\n def remove_snapshot_with_verify_cold_merge(self, snapshot_idx):\n \"\"\"\n Removes the snapshot with `idx snapshot_idx` and checks on engine.log\n and on each host vdsm.log for the proper commands with regex\n\n Since all tests has multiple disks and multiple snapshots including\n all disks we have to assert that at least an HSM host executes the\n cold merge command\n \"\"\"\n self.hosts = {}\n for host in ll_hosts.get_cluster_hosts(config.CLUSTER_NAME):\n self.hosts[host] = ll_hosts.get_host_ip(host)\n\n def get_cold_merge_host_from_engine(q):\n \"\"\"\n Matches the REGEX_COLD_MERGE_CMD on the engine.log and returns\n it to find which host is actually executing the sdm_merge command\n \"\"\"\n found_regex, _ = watch_logs(\n files_to_watch=config.ENGINE_LOG, regex=REGEX_COLD_MERGE_CMD,\n time_out=TIMEOUT_COLD_MERGE_CMD,\n ip_for_files=config.ENGINE.host.ip,\n username=config.HOSTS_USER,\n password=config.VDC_ROOT_PASSWORD\n )\n if found_regex:\n q.put_nowait(found_regex.group('host_name'))\n return\n q.put_nowait(False)\n\n def get_cold_merge_host_from_vdsm(q):\n \"\"\"\n Create multiple processes to look for the merge command on all the\n hosts in the cluster\n \"\"\"\n pool = Pool(len(self.hosts))\n\n def check_vdsm(host_name):\n \"\"\"\n Matches REGEX_SDM_MERGE_CMD on the vdsm.log of `host_name`,\n and in case it is found executes the self.command_sdm_merge()\n \"\"\"\n found_regex, _ = watch_logs(\n files_to_watch=config.VDSM_LOG, regex=REGEX_SDM_MERGE_CMD,\n command_to_exec=self.command_sdm_merge,\n time_out=TIMEOUT_COLD_MERGE_CMD,\n ip_for_files=self.hosts[host_name],\n username=config.HOSTS_USER,\n password=config.VDC_ROOT_PASSWORD\n )\n if found_regex:\n return host_name\n return False\n q.put_nowait(pool.map(check_vdsm, self.hosts.keys()))\n\n q1, q2 = Queue(), Queue()\n testflow.step(\"Looking for expected regex on engine and VDSM logs\")\n p1 = Process(target=get_cold_merge_host_from_engine, args=(q1,))\n p2 = Process(target=get_cold_merge_host_from_vdsm, args=(q2,))\n p1.start()\n p2.start()\n try:\n testflow.step(\n \"Remove snapshot %s\", self.snapshot_list[snapshot_idx]\n )\n assert ll_vms.removeSnapshot(\n True, self.vm_name, self.snapshot_list[snapshot_idx],\n timeout=-1\n )\n host = q1.get(timeout=TIMEOUT_COLD_MERGE_CMD)\n hosts_executed_sdm_merge = filter(\n lambda w: w, q2.get(timeout=TIMEOUT_COLD_MERGE_CMD)\n )\n p1.join(timeout=TIMEOUT_COLD_MERGE_CMD)\n p2.join(timeout=TIMEOUT_COLD_MERGE_CMD)\n testflow.step(\n \"Hosts executed sdm_merge %s\", hosts_executed_sdm_merge\n )\n # The host that the engine.log reported was executing the\n # cold merge should be in list of hosts that executed the\n # cold merge\n assert host in hosts_executed_sdm_merge, (\n \"Couldn't find command %s executed in any of the hosts vdsm\"\n % REGEX_SDM_MERGE_CMD\n )\n # With the massive permutation of disks and snapshots for\n # the test cases at least one of the cold merge commands should\n # be executed in an HSM host\n assert any(\n ll_hosts.get_host_object(host).get_spm().get_status() == 'none'\n for host in hosts_executed_sdm_merge\n )\n finally:\n if self.command_sdm_merge:\n self.wait_for_command()\n ll_vms.wait_for_vm_snapshots(self.vm_name, config.SNAPSHOT_OK)\n\n def wait_for_command(self):\n \"\"\"\n Function to be instanciated which will execute instructions\n after the remove snapshot operations has started\n \"\"\"\n return\n\n\n@bz({'1509629': {}})\nclass TestCase18894(ColdMergeBaseClass, basePlan.TestCase6038):\n \"\"\"\n Basic offline delete and merge of snapshots\n\n https://polarion.engineering.redhat.com/polarion/#/project\n /RHEVM3/workitem?id=RHEVM3-6038\n \"\"\"\n __test__ = True\n test_case = '18894'\n\n\nclass TestCase18923(ColdMergeBaseClass, basePlan.TestCase16287):\n \"\"\"\n Basic offline delete and merge of a single snapshot's disk\n\n https://polarion.engineering.redhat.com/polarion/#/project\n /RHEVM/workitem?id=RHEVM3-16287\n \"\"\"\n __test__ = True\n test_case = '18923'\n\n\nclass TestCase18912(ColdMergeBaseClass, basePlan.TestCase12215):\n \"\"\"\n Deleting all snapshots\n\n https://polarion.engineering.redhat.com/polarion/#/project\n /RHEVM3/workitem?id=RHEVM3-12215\n \"\"\"\n __test__ = True\n test_case = '18912'\n\n\nclass TestCase18900(ColdMergeBaseClass, basePlan.TestCase6044):\n \"\"\"\n Offline delete and merge after deleting the base snapshot\n\n https://polarion.engineering.redhat.com/polarion/#/project\n /RHEVM3/workitem?id=RHEVM3-6044\n \"\"\"\n __test__ = True\n test_case = '18900'\n\n\nclass TestCase18901(ColdMergeBaseClass, basePlan.TestCase6045):\n \"\"\"\n Offline snapshot delete and merge with restart of vdsm\n\n https://polarion.engineering.redhat.com/polarion/#/project\n /RHEVM3/workitem?id=RHEVM3-6045\n \"\"\"\n __test__ = True\n test_case = '18901'\n\n\nclass TestCase18899(ColdMergeBaseClass, basePlan.TestCase6043):\n \"\"\"\n Offline delete and merge after deleting the last created snapshot\n\n https://polarion.engineering.redhat.com/polarion/#/project\n /RHEVM3/workitem?id=RHEVM3-6043\n \"\"\"\n __test__ = True\n test_case = '18899'\n\n\nclass TestCase18902(ColdMergeBaseClass, basePlan.TestCase6046):\n \"\"\"\n Offline delete and merge of snapshot while stopping the engine\n\n https://polarion.engineering.redhat.com/polarion/#/project\n /RHEVM3/workitem?id=RHEVM3-6046\n \"\"\"\n __test__ = True\n test_case = '18902'\n\n\nclass TestCase18904(ColdMergeBaseClass, basePlan.TestCase6048):\n \"\"\"\n Consecutive delete and merge of snapshots\n\n https://polarion.engineering.redhat.com/polarion/#/project\n /RHEVM3/workitem?id=RHEVM3-6048\n \"\"\"\n __test__ = True\n test_case = '18904'\n\n\nclass TestCase18906(ColdMergeBaseClass, basePlan.TestCase6050):\n \"\"\"\n Delete a 2nd offline snapshot during a delete and merge of another\n snapshot within the same VM\n\n https://polarion.engineering.redhat.com/polarion/#/project\n /RHEVM3/workitem?id=RHEVM3-6050\n \"\"\"\n __test__ = True\n test_case = '18906'\n\n\nclass TestCase18920(ColdMergeBaseClass, basePlan.TestCase12216):\n \"\"\"\n Basic offline merge after disk with snapshot is extended\n\n https://polarion.engineering.redhat.com/polarion/#/project\n /RHEVM3/workitem?id=12216\n \"\"\"\n __test__ = True\n test_case = '18920'\n\n\nclass TestCase18975(ColdMergeBaseClass):\n \"\"\"\n Cold Merge with SPM and several HSMs\n\n https://polarion.engineering.redhat.com/polarion/#/project\n /RHEVM3/workitem?id=RHEVM-18975\n \"\"\"\n __test__ = True\n test_case = '18975'\n\n @polarion(\"RHEVM-18975\")\n @tier2\n def test_basic_snapshot_cold_merge_sdm_merge_by_hsm(self):\n self.basic_flow()\n self.remove_snapshot_with_verify_cold_merge(1)\n self.verify_snapshot_files(\n self.snapshot_list[2], [TEST_FILE_TEMPLATE % i for i in range(3)]\n )\n\n\n@pytest.mark.usefixtures(\n remove_hsm_host.__name__,\n delete_disks.__name__,\n create_vm.__name__,\n start_vm.__name__,\n init_vm_executor.__name__,\n prepare_disks_with_fs_for_vm.__name__,\n initialize_params.__name__,\n wait_for_disks_and_snapshots.__name__,\n)\nclass TestCase18976(ColdMergeBaseClass):\n \"\"\"\n Verfiy that the new added HSM is used\n\n https://polarion.engineering.redhat.com/polarion/#/project\n /RHEVM3/workitem?id=RHEVM-18976\n \"\"\"\n __test__ = True\n test_case = '18976'\n\n @polarion(\"RHEVM-18976\")\n @tier2\n def test_basic_snapshot_merge_after_adding_hsm(self):\n self.basic_flow(4)\n self.remove_snapshot_with_verify_cold_merge(1)\n self.verify_snapshot_files(\n self.snapshot_list[2], [TEST_FILE_TEMPLATE % i for i in range(3)]\n )\n hosts_activate = []\n try:\n testflow.step(\"Deactivate all hsm hosts\")\n for host in ll_hosts.get_cluster_hosts(config.CLUSTER_NAME):\n host_obj = ll_hosts.get_host_object(host)\n if host_obj.get_spm().get_status() == 'none':\n assert ll_hosts.deactivate_host(True, host)\n hosts_activate.append(host)\n testflow.step(\"Add host %s back to the cluster\", self.hsm_host)\n assert ll_hosts.add_host(\n name=self.hsm_host, address=self.hsm_host_vds.fqdn,\n wait=True, cluster=config.CLUSTER_NAME,\n root_password=config.VDC_ROOT_PASSWORD,\n comment=self.hsm_host_vds.ip\n )\n self.remove_snapshot_with_verify_cold_merge(2)\n self.verify_snapshot_files(\n self.snapshot_list[3],\n [TEST_FILE_TEMPLATE % i for i in range(4)]\n )\n finally:\n for host in hosts_activate:\n assert ll_hosts.activate_host(True, host)\n\n\n@pytest.mark.usefixtures(\n create_dc.__name__,\n create_storage_domain.__name__,\n initialize_params_new_dc.__name__,\n clean_dc.__name__,\n delete_disks.__name__,\n create_vm.__name__,\n start_vm.__name__,\n init_vm_executor.__name__,\n prepare_disks_with_fs_for_vm.__name__,\n initialize_params.__name__,\n wait_for_disks_and_snapshots.__name__,\n)\nclass TestCase18932(basePlan.BaseTestCase):\n \"\"\"\n Cold Merge with previous compatibility version\n\n https://polarion.engineering.redhat.com/polarion/#/project\n /RHEVM3/workitem?id=RHEVM-18932\n \"\"\"\n __test__ = True\n clone_from_template = False\n test_case = '18932'\n dc_verison = \"4.0\"\n\n @polarion(\"RHEVM-18932\")\n @tier3\n def test_basic_flow_with_previous_compatibility_version(self):\n self.basic_flow()\n assert ll_vms.removeSnapshot(\n True, self.vm_name, self.snapshot_list[1],\n )\n self.verify_snapshot_files(\n self.snapshot_list[2], [TEST_FILE_TEMPLATE % i for i in range(3)]\n )\n\n\nclass TestCase18972(ColdMergeBaseClass):\n \"\"\"\n Cold merge with failure DURING PrepareMerge on SPM\n\n https://polarion.engineering.redhat.com/polarion/#/project\n /RHEVM3/workitem?id=RHEVM-18972\n \"\"\"\n __test__ = True\n test_case = '18972'\n\n @polarion(\"RHEVM-18972\")\n @tier4\n def test_basic_flow_restart_vdsm_during_prepare_merge(self):\n self.basic_flow()\n _, spm_dict = ll_hosts.get_host(\n True, config.DATA_CENTER_NAME, spm=True\n )\n self.spm = spm_dict['host']\n self.spm_ip = ll_hosts.get_host_ip(self.spm)\n testflow.step(\"Removing snapshot %s\", self.snapshot_list[1])\n assert ll_vms.removeSnapshot(\n True, self.vm_name, self.snapshot_list[1], timeout=-1\n )\n testflow.step(\"Waiting for PrepareMerge command\")\n found_regex, _ = watch_logs(\n config.ENGINE_LOG, regex='PrepareMerge',\n time_out=TIMEOUT_COLD_MERGE_CMD,\n command_to_exec=config.KILL_VDSM,\n ip_for_files=config.ENGINE.host.ip,\n username=config.HOSTS_USER, password=config.VDC_ROOT_PASSWORD,\n ip_for_execute_command=self.spm_ip,\n remote_username=config.HOSTS_USER,\n remote_password=config.HOSTS_PW\n )\n assert found_regex, (\n \"'PrepareImage' expression was not found on %s log\"\n % config.ENGINE_LOG\n )\n testflow.step(\"Waiting for the host and the data center for be UP\")\n assert ll_hosts.wait_for_hosts_states(True, self.spm), (\n \"Host %s failed to reach status UP\" % self.spm\n )\n assert ll_hosts.wait_for_spm(\n config.DATA_CENTER_NAME, config.WAIT_FOR_SPM_TIMEOUT,\n config.WAIT_FOR_SPM_INTERVAL\n )\n ll_jobs.wait_for_jobs([config.JOB_REMOVE_SNAPSHOT])\n ll_vms.wait_for_vm_snapshots(self.vm_name, config.SNAPSHOT_OK)\n snapshots = [\n snap.get_description() for snap in\n ll_vms.get_vm_snapshots(self.vm_name)\n ]\n if self.snapshot_list[1] in snapshots:\n testflow.step(\n \"Remove operation rolled back, removing snapshot %s\",\n self.snapshot_list[1]\n )\n assert ll_vms.removeSnapshot(\n True, self.vm_name, self.snapshot_list[1]\n )\n self.verify_snapshot_files(\n self.snapshot_list[2], [TEST_FILE_TEMPLATE % i for i in range(3)]\n )\n\n\nclass TestCase18974(ColdMergeBaseClass):\n \"\"\"\n Verify failure when HSM goes down after SDMMerge starts on HSM\n\n https://polarion.engineering.redhat.com/polarion/#/project\n /RHEVM3/workitem?id=RHEVM-18974\n \"\"\"\n __test__ = True\n test_case = '18974'\n command_sdm_merge = config.KILL_VDSM\n\n def wait_for_command(self):\n \"\"\"\n Vdsm restart could happen in any HSM host, wait for all hosts\n to be up\n \"\"\"\n for host in self.hosts:\n assert ll_hosts.wait_for_hosts_states(True, host), (\n \"Host %s failed to reach status UP\" % host\n )\n\n @polarion(\"RHEVM-18974\")\n @tier4\n def test_basic_flow_restart_vdsm_after_sdm_merge_starts(self):\n self.basic_flow()\n self.remove_snapshot_with_verify_cold_merge(1)\n ll_jobs.wait_for_jobs([config.JOB_REMOVE_SNAPSHOT])\n ll_vms.wait_for_vm_snapshots(self.vm_name, config.SNAPSHOT_OK)\n self.verify_snapshot_files(\n self.snapshot_list[2], [TEST_FILE_TEMPLATE % i for i in range(3)]\n )\n","sub_path":"art/tests/rhevmtests/storage/storage_remove_snapshots/test_cold_merge.py","file_name":"test_cold_merge.py","file_ext":"py","file_size_in_byte":15627,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"304768002","text":"import discord\nfrom discord.ext import commands, flags\nimport prettify_exceptions\n\nclass ErrorsCog(commands.Cog, name = \"Errors\"):\n #Error handler\n def __init__(self, bot):\n self.bot = bot\n\n @commands.Cog.listener()\n async def on_command_error(self, ctx, error):\n ignored_errors = (commands.CommandNotFound)\n stringed_errors = (commands.MissingPermissions, commands.MissingRequiredArgument, commands.BadArgument,\n commands.BotMissingPermissions,\n discord.NotFound, commands.CommandOnCooldown, commands.BadUnionArgument, flags.ArgumentParsingError)\n\n if isinstance(error, ignored_errors):\n return\n\n if isinstance(error, stringed_errors):\n await ctx.reply(embed=discord.Embed(title=str(error), color=discord.Color.red()))\n elif isinstance(error, commands.NotOwner):\n await ctx.reply(embed=discord.Embed(title=\"You do not own this bot.\", color=discord.Color.red()))\n else:\n prettify_exceptions.DefaultFormatter().theme['_ansi_enabled'] = False\n traceback = ''.join(prettify_exceptions.DefaultFormatter().format_exception(type(error), error, error.__traceback__))\n embed = discord.Embed(title = \"An error occurred!\",\n description = \"[Please report this to the bots GitHub with the codeblock's content.](https://github.com/ImVaskel/diabetes-discord-rank-bot)\",\n color = discord.Color.red(),\n timestamp = ctx.message.created_at)\n embed.add_field(name = \"Error Details: \",\n value = f\"```py\\n{error}```\")\n embed.set_footer(text = \"That above is a hyperlink to the github, click it!\")\n\n await ctx.send(embed = embed)\n print(traceback)\n\ndef setup(bot):\n bot.add_cog(ErrorsCog(bot))","sub_path":"cogs/errors.py","file_name":"errors.py","file_ext":"py","file_size_in_byte":1923,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"606874334","text":"# Technique: save and cut\n# Important: cut when meet repeat characters\n\nclass Solution:\n\t# @return an integer\n\tdef lengthOfLongestSubstring(self, s):\n\t\tmax_len = 0\n\t\tmax_substring = ''\n\t\trep = ''\n\t\tfor i in xrange(len(s)):\n\t\t\tif s[i] not in rep:\n\t\t\t\trep += s[i]\n\t\t\telse:\n\t\t\t\trep = rep.split(s[i])[1] + s[i]\n\t\t\tif len(rep) > max_len:\n\t\t\t\tmax_len = len(rep)\n\t\t\t\tmax_substring = rep\n\t\treturn max_len\n\ntemp = Solution()\ntemp.lengthOfLongestSubstring('abcabcbb')","sub_path":"Python/003_LongestSubstringWithoutRepeatingCharacters.py","file_name":"003_LongestSubstringWithoutRepeatingCharacters.py","file_ext":"py","file_size_in_byte":457,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"599666586","text":"import os\nimport sys\nimport errno\nimport cv2\n\ndef check_directory(path):\n if not (os.path.isdir(path)):\n print(\"no directory\")\n return -1\n else:\n return 1\n\ndef make_directory(base_path, success_dir=\"Yolo_data/\", fail_dir=\"No_Yolo_data/\"):\n # def make_directory(base_path, success_dir=\"success_img/\", fail_dir=\"fail_img/\", **kwargs):\n # '\\'를 문자열로 받을 시 문제 방지 위해 '/'로 변환\n base_path.replace('\\\\', '/')\n success_dir.replace('\\\\', '/')\n fail_dir.replace('\\\\', '/')\n # '/'가 마지막에 포함되지 않을 시 절대경로 위해 '/'를 넣어줌.\n if base_path[-1] != \"/\":\n base_path = base_path + \"/\"\n success_dir = base_path + success_dir\n fail_dir = base_path + fail_dir\n\n path_list = [success_dir, fail_dir]\n for path in path_list:\n if path != \"/\":\n path = path + \"/\"\n try:\n if not (os.path.isdir(path)):\n os.makedirs(os.path.join(path))\n except OSError as e:\n if e.errno != errno.EEXIST:\n print(\"Failed to create %s directory!!!!!\" % path)\n raise\n return path_list\n\n\ndef make_image_list(img_path, ext=\"jpg\"):\n img_path.replace('\\\\', '/')\n ext_list = [\"png\", \"jpg\", \"jpeg\", \"bmp\"]\n\n # ext(확장자명) 추가 코드\n if ext not in ext_list:\n ext_list.append(ext)\n ext = \", .\" + ext\n image_list = []\n img_dir = os.listdir(img_path)\n\n # ext 확인하여 ext_list에 있을 경우 img_list에 추가\n for file in img_dir:\n ext_check = file.split(\".\")[-1].lower()\n if ext_check in ext_list:\n image_list.append(file)\n\n # img_list가 없을 경우(확장자명이 일치하는 경우가 없을 경우)\n if len(image_list) == 0:\n if len(ext_list) == 4:\n print(\"There's no image file (.jpg, .png, .bmp)\")\n else:\n print(\"There's no image file (.jpg, .png, .bmp%s)\" % ext)\n return None\n return image_list\n\n\n\n\ndef read_yolo_data(save_dir_list, img_path, image_name):\n bbox_list = []\n img_path.replace('\\\\', '/')\n if img_path[-1] != \"/\":\n img_path = img_path + \"/\"\n img_dir = os.listdir(img_path)\n # image 정보 읽기 및 정보 유무 확인\n img = cv2.imread(img_path + image_name)\n\n\n if img is None:\n print(\"%s 이미지를 읽을 수 없습니다.\" % image_name)\n save_path = None\n # print(img)\n # bbox 정보, save path 저장\n for file in img_dir:\n if image_name[:-4]+\".txt\" == file:\n print(\"filename: \" + file)\n try:\n f = open(img_path+file, \"r\")\n except FileNotFoundError:\n print('%s 파일이 없습니다.' % file)\n break\n else:\n while True:\n line = f.readline()\n\n # line에 읽어온 정보가 없을 때 break\n if not line: break\n\n # line 끝의 '\\n' 와 ' '을 제외\n while True:\n if line[-1] == '\\n' or line[-1] == ' ':\n line = line[:-1]\n else:\n break\n # bbox가 5개로 구성되면 bbox_list에 추가\n if len(line.split(\" \")) == 5:\n line1 = line.split(\" \")\n for idx, value in enumerate(line1):\n line1[idx] = float(value)\n bbox_list.append(line1)\n save_path = save_dir_list[0]\n # print(bbox_list)\n # 요소가 부족한 경우 bbox None으로 반환\n else:\n save_path = save_dir_list[1]\n # bbox가 2개 이상인 경우, 1~(n-1)개는 5개요소이고, n번째는 5개 미만인 경우 잘못된 경우이므로, None으로 return 해줌.\n return img, [], save_path\n break\n # txt 파일은 있으나 비어 있는 경우 or 파일이 열리지 않은 경우 or 파일이 없는 경우\n if not len(bbox_list):\n save_path = save_dir_list[1]\n return img, [], save_path\n # bbox가 제대로 읽어진 경우\n else:\n return img, bbox_list, save_path\n\ndef SaveImage(img, save_path, name, bbox = []):\n name = name[:-4]\n cv2.imwrite(save_path + name + \".jpg\", img)\n\n if bbox != []:\n with open(save_path + name + \".txt\",'w') as f:\n for i in range(len(bbox)):\n bbox[i][0] = int(bbox[i][0])\n for j in range(5):\n f.write(str(bbox[i][j]) + \" \")\n\n f.write(\"\\n\")\n","sub_path":"pyqt_ver/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":4742,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"212150688","text":"from src.modules.reporting import *\nfrom src.modules.constants import *\nfrom matplotlib import pyplot as plt\nfrom src.modules.classes import SqliteFetcher\nfrom src.modules.thesis_plotting import *\nimport os\n\nsetup_pgf_plotting()\n\n# ============================================================================\n# IMPORT/MAKE DATA \n# ============================================================================\ndef make_data_old():\n models = {\n # https://app.wandb.ai/cubeml/cubeml/runs/2020-07-13-12.34.45\n '2020-07-13-12.34.45': {\n 'error': -1,\n 'N_pre': 0,\n 'N_enc': 2,\n 'N_dec': 4,\n 'Width': 256,\n 'enctype': 2,\n },\n \n # https://app.wandb.ai/cubeml/cubeml/runs/2020-07-13-12.35.23?workspace=user-bjoernmoelvig\n '2020-07-13-12.35.23': {\n 'error': -1,\n 'N_pre': 0,\n 'N_enc': 2,\n 'N_dec': 4,\n 'Width': 512,\n 'enctype': 2\n },\n\n # https://app.wandb.ai/cubeml/cubeml/runs/2020-07-13-22.03.25?workspace=user-bjoernmoelvig\n '2020-07-13-22.03.25': {\n 'error': -1,\n 'N_pre': 0,\n 'N_enc': 2,\n 'N_dec': 4,\n 'Width': 64,\n 'enctype': 2\n },\n\n # https://app.wandb.ai/cubeml/cubeml/runs/2020-07-13-22.03.33?workspace=user-bjoernmoelvig\n '2020-07-13-22.03.33': {\n 'error': -1,\n 'N_pre': 0,\n 'N_enc': 2,\n 'N_dec': 4,\n 'Width': 128,\n 'enctype': 2\n },\n\n # https://app.wandb.ai/cubeml/cubeml/runs/2020-07-14-04.11.36?workspace=user-bjoernmoelvig\n '2020-07-14-04.11.36': {\n 'error': -1,\n 'N_pre': 0,\n 'N_enc': 2,\n 'N_dec': 2,\n 'Width': 256,\n 'enctype': 2\n },\n\n # https://app.wandb.ai/cubeml/cubeml/runs/2020-07-14-04.33.22?workspace=user-bjoernmoelvig\n '2020-07-14-04.33.22': {\n 'error': -1,\n 'N_pre': 0,\n 'N_enc': 2,\n 'N_dec': 1,\n 'Width': 256,\n 'enctype': 2\n },\n\n # https://app.wandb.ai/cubeml/cubeml/runs/2020-07-14-09.07.30?workspace=user-bjoernmoelvig\n '2020-07-14-09.07.30': {\n 'error': -1,\n 'N_pre': 0,\n 'N_enc': 2,\n 'N_dec': 5,\n 'Width': 256,\n 'enctype': 2\n },\n\n # https://app.wandb.ai/cubeml/cubeml/runs/2020-07-14-09.15.21?workspace=user-bjoernmoelvig\n '2020-07-14-09.15.21': {\n 'error': -1,\n 'N_pre': 0,\n 'N_enc': 2,\n 'N_dec': 3,\n 'Width': 256,\n 'enctype': 2\n },\n\n # https://app.wandb.ai/cubeml/cubeml/runs/2020-03-02-19.02.57/overview?workspace=user-bjoernmoelvig\n '2020-03-02-19.02.57': {\n 'error': -1,\n 'N_pre': 1,\n 'N_enc': 2,\n 'N_dec': 4,\n 'Width': 256,\n 'enctype': 2\n },\n\n # https://app.wandb.ai/cubeml/cubeml/runs/2020-07-14-13.57.47/overview?workspace=user-bjoernmoelvig\n '2020-07-14-13.57.47': {\n 'error': -1,\n 'N_pre': 0,\n 'N_enc': 2,\n 'N_dec': 6,\n 'Width': 256,\n 'enctype': 2\n },\n\n # https://app.wandb.ai/cubeml/cubeml/runs/2020-03-20-11.41.40?workspace=user-bjoernmoelvig\n '2020-03-20-11.41.40': {\n 'error': -1,\n 'N_pre': 0,\n 'N_enc': 2,\n 'N_dec': 4,\n 'Width': 256,\n 'enctype': 1\n },\n\n # https://app.wandb.ai/cubeml/cubeml/runs/2020-02-28-12.16.24?workspace=user-bjoernmoelvig\n '2020-02-28-12.16.24': {\n 'error': -1,\n 'N_pre': 4,\n 'N_enc': 1,\n 'N_dec': 4,\n 'Width': 128,\n 'enctype': 2\n },\n\n # https://app.wandb.ai/cubeml/cubeml/runs/2020-02-29-14.10.52/overview?workspace=user-bjoernmoelvig\n '2020-02-29-14.10.52': {\n 'error': -1,\n 'N_pre': 5,\n 'N_enc': 1,\n 'N_dec': 4,\n 'Width': 128,\n 'enctype': 2\n },\n\n # https://app.wandb.ai/cubeml/cubeml/runs/2020-02-27-09.31.21?workspace=user-bjoernmoelvig\n '2020-02-27-09.31.21': {\n 'error': -1,\n 'N_pre': 0,\n 'N_enc': 6,\n 'N_dec': 4,\n 'Width': 64,\n 'enctype': 0\n },\n\n # https://app.wandb.ai/cubeml/cubeml/runs/2020-02-26-17.12.56?workspace=user-bjoernmoelvig\n '2020-02-26-17.12.56': {\n 'error': -1,\n 'N_pre': 0,\n 'N_enc': 5,\n 'N_dec': 4,\n 'Width': 32,\n 'enctype': 0\n },\n\n # https://app.wandb.ai/cubeml/cubeml/runs/2020-02-27-01.01.07?workspace=user-bjoernmoelvig\n '2020-02-27-01.01.07': {\n 'error': -1,\n 'N_pre': 0,\n 'N_enc': 5,\n 'N_dec': 4,\n 'Width': 64,\n 'enctype': 0\n },\n\n # https://app.wandb.ai/cubeml/cubeml/runs/2020-02-25-18.50.49/overview?workspace=user-bjoernmoelvig\n '2020-02-25-18.50.49': {\n 'error': -1,\n 'N_pre': 0,\n 'N_enc': 6,\n 'N_dec': 4,\n 'Width': 128,\n 'enctype': 0\n },\n\n # https://app.wandb.ai/cubeml/cubeml/runs/2020-02-24-02.47.22/overview?workspace=user-bjoernmoelvig\n '2020-02-24-02.47.22': {\n 'error': -1,\n 'N_pre': 0,\n 'N_enc': 3,\n 'N_dec': 4,\n 'Width': 128,\n 'enctype': 0\n },\n }\n\n for i_model, model in enumerate(models):\n model_path = locate_model(model)\n\n # for each model, load min error\n models[model]['error'] = min(\n pickle.load(open(\n model_path + '/data/val_error.pickle', 'rb'\n )\n )\n )\n \n return models\n\ndef make_data():\n data = {\n 'Preproc. Depth': [],\n 'Decode Depth': [],\n 'Width': [],\n 'Encode Depth': [],\n 'Error': [],\n }\n models = [\n 'https://app.wandb.ai/cubeml/cubeml/runs/2020-07-13-12.34.45?workspace=user-bjoernmoelvig',\n 'https://app.wandb.ai/cubeml/cubeml/runs/2020-07-13-12.35.23?workspace=user-bjoernmoelvig',\n 'https://app.wandb.ai/cubeml/cubeml/runs/2020-07-13-22.03.25?workspace=user-bjoernmoelvig',\n 'https://app.wandb.ai/cubeml/cubeml/runs/2020-07-13-22.03.33?workspace=user-bjoernmoelvig',\n 'https://app.wandb.ai/cubeml/cubeml/runs/2020-07-14-04.11.36?workspace=user-bjoernmoelvig',\n 'https://app.wandb.ai/cubeml/cubeml/runs/2020-07-14-04.33.22?workspace=user-bjoernmoelvig',\n 'https://app.wandb.ai/cubeml/cubeml/runs/2020-07-14-09.07.30?workspace=user-bjoernmoelvig',\n 'https://app.wandb.ai/cubeml/cubeml/runs/2020-07-14-09.15.21?workspace=user-bjoernmoelvig',\n 'https://app.wandb.ai/cubeml/cubeml/runs/2020-07-14-13.57.47?workspace=user-bjoernmoelvig',\n 'https://app.wandb.ai/cubeml/cubeml/runs/2020-07-15-16.36.14?workspace=user-bjoernmoelvig',\n 'https://app.wandb.ai/cubeml/cubeml/runs/2020-07-15-16.36.20?workspace=user-bjoernmoelvig',\n 'https://app.wandb.ai/cubeml/cubeml/runs/2020-07-15-21.32.25?workspace=user-bjoernmoelvig',\n 'https://app.wandb.ai/cubeml/cubeml/runs/2020-07-15-22.08.04?workspace=user-bjoernmoelvig',\n 'https://app.wandb.ai/cubeml/cubeml/runs/2020-07-16-02.48.49?workspace=user-bjoernmoelvig',\n 'https://app.wandb.ai/cubeml/cubeml/runs/2020-07-16-03.35.09?workspace=user-bjoernmoelvig',\n 'https://app.wandb.ai/cubeml/cubeml/runs/2020-07-16-08.11.08?workspace=user-bjoernmoelvig',\n 'https://app.wandb.ai/cubeml/cubeml/runs/2020-07-16-08.11.32?workspace=user-bjoernmoelvig',\n 'https://app.wandb.ai/cubeml/cubeml/runs/2020-07-16-12.21.44?workspace=user-bjoernmoelvig',\n 'https://app.wandb.ai/cubeml/cubeml/runs/2020-07-16-17.13.16?workspace=user-bjoernmoelvig',\n 'https://app.wandb.ai/cubeml/cubeml/runs/2020-07-16-22.59.26?workspace=user-bjoernmoelvig',\n # 'https://app.wandb.ai/cubeml/cubeml/runs/2020-07-17-01.52.16?workspace=user-bjoernmoelvig',\n 'https://app.wandb.ai/cubeml/cubeml/runs/2020-07-17-04.51.03?workspace=user-bjoernmoelvig',\n 'https://app.wandb.ai/cubeml/cubeml/runs/2020-07-17-07.23.17?workspace=user-bjoernmoelvig',\n 'https://app.wandb.ai/cubeml/cubeml/runs/2020-07-17-16.47.07?workspace=user-bjoernmoelvig',\n 'https://app.wandb.ai/cubeml/cubeml/runs/2020-07-17-16.47.12?workspace=user-bjoernmoelvig',\n 'https://app.wandb.ai/cubeml/cubeml/runs/2020-07-17-22.25.30?workspace=user-bjoernmoelvig',\n 'https://app.wandb.ai/cubeml/cubeml/runs/2020-07-18-00.28.56?workspace=user-bjoernmoelvig',\n 'https://app.wandb.ai/cubeml/cubeml/runs/2020-07-18-05.35.10?workspace=user-bjoernmoelvig'\n ]\n\n for i_model, model_full in enumerate(models):\n model = model_full.split('/')[-1].split('?')[0]\n model_path = locate_model(model)\n\n # for each model, load min error\n data['Error'].append(\n min(\n pickle.load(open(\n model_path + '/data/val_error.pickle', 'rb'\n )\n )\n )\n )\n\n arch = json.load(open(\n model_path + '/architecture_pars.json', 'r'\n )\n )['layers']\n\n if 'RnnBlock' in arch[0]:\n i_dec = 0\n data['Preproc. Depth'].append(0)\n elif 'ResBlock' in arch[0]:\n i_dec = 1\n data['Preproc. Depth'].append(len(arch[0]['ResBlock']['input_sizes'])-1)\n else:\n raise ValueError('Unknown first module')\n data['Width'].append(arch[i_dec]['RnnBlock']['n_out'])\n data['Encode Depth'].append(arch[i_dec]['RnnBlock']['num_layers'])\n \n if 'ResBlock' in arch[i_dec+1]:\n data['Decode Depth'].append(\n len(\n arch[i_dec+1]['ResBlock']['input_sizes']\n )\n )\n elif 'Linear' in arch[i_dec+1]:\n data['Decode Depth'].append(1)\n else:\n raise ValueError('Unknown decode module')\n \n \n return data\n\ndata_d = make_data()\nn_models = len(data_d[next(iter(data_d))])\nn_pars = len(data_d)\npar_names = [key for key in data_d]\n\ndata = np.empty(shape=(n_models, n_pars))\n\nfor i_var, varname in enumerate(data_d):\n data[:, i_var] = data_d[varname]\n\n# a+=1\n# for i_model, model in enumerate(data_d):\n# for i_par, par in enumerate(par_names):\n\n# data[i_model, i_par] = data_d[model][par]\n\nenctype = ['Attention', 'GRU', 'LSTM', 'Hybrid']\nd = {\n 'parallel_plot': data,\n 'names': par_names,\n 'color_index': 0,\n # 'labels': [None, None, None, None, None, enctype],\n 'grid': False\n}\nf = make_plot(d, for_thesis=True)\nax = f.gca()\n# update_ylabels(ax)\n\n# Standard ratio of width to height it 6.4/4.8\n# Standard figure: FOTW = 1.0\n# Subfigure 1/2: FOTW = 0.65. Remember to use a .5 cm of left and 0 cm of right\n# broad_figure: FOTW = 2.\n# single_fig, 2subfigs\nFOTW = get_frac_of_textwidth(keyword='broad_fig')\nwidth = get_figure_width(frac_of_textwidth=FOTW)\nw2 = get_figure_width(\n get_frac_of_textwidth(keyword='single_fig')\n)\nheight = get_figure_height(width=w2)\nf.set_size_inches(width, height)\n\n# ============================================================================\n# SAVE PGF AND PNG FOR VIEWING \n# ============================================================================\n\npath = Path(os.path.realpath(__file__))\nsave_thesis_pgf(path, f, save_pgf=True)\n","sub_path":"reports/thesis_plots/HPOParPlot/script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":11798,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"592637908","text":"import requests\nimport json\nfrom bs4 import BeautifulSoup as bs\n\n# 一小时内上传历史\ndef history():\n url = 'https://sm.ms/api/list' \n res = requests.get(url)\n \n jd = json.loads(res.text) # 将json格式转化为Python字典\n if jd['code'] == 'success':\n print(jd)\n # data = {'timestamp': jd['data'][0]['timestamp']}\n # print(data)\n return True\n\n# 上传图片 无法上传含中文名图片，会报错：No files were uploaded. 与request有关\ndef upload(img_path):\n url = 'https://sm.ms/api/upload' \n file = {'smfile': open(img_path, 'rb')} # smfile为表单名称。上传图片用到\n res = requests.post(url, files = file ) \n \n jd = json.loads(res.text) \n # print(jd)\n if jd['code'] == 'success': \n data = {'storename': jd['data']['storename'], # 存于服务器中的名字\n 'url': jd['data']['url'], # 可直接引用的地址链接\n 'hash': jd['data']['hash'], # 随机字符串，用于删除文件,删除链接为 https://sm.ms/api/delete/hash\n 'timestamp': jd['data']['timestamp'] # 上传的时间戳\n } \n # print(data)\n print('Upload Success.')\n else:\n print(jd['msg'])\n return True\n\n # requests.post('https://sm.ms/api/upload', files={'smfile': open(img_path, 'rb')})\n\n# 清除上传历史\ndef clear(): \n url = 'https://sm.ms/api/clear'\n res = requests.get(url)\n\n jd = json.loads(res.text) \n if jd['code'] == 'success':\n print(jd['msg'])\n return True\n\n\n# 删除某张图\ndef delete(hash):\n url = 'https://sm.ms/delete/' + hash \n res = requests.get(url)\n\n soup = bs(res.text, features ='html.parser')\n a = soup.find_all(\"div\", class_=\"bs-callout bs-callout-warning\")\n print(a[0].string)\n return True\n\nif __name__ == '__main__':\n #history()\n img_path = 'haha.jpg'\n #upload(img_path)\n # clear_all()\n hash = 'RhPKiASxmUloXLCsss'\n delete(hash)\n","sub_path":"sm.ms/sm.ms_app.py","file_name":"sm.ms_app.py","file_ext":"py","file_size_in_byte":2096,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"88672890","text":"from queue import Queue\n\n__author__ = 'Taejoon Byun '\n\n\nclass NodeVisitor: pass\n\n\nclass BstNode:\n \"\"\"\n The node class for a binary search tree,\n with an integer key and a string data.\n \"\"\"\n key = 0 # key value\n data = \"\" # data value\n parent = None # parent node\n left = None # left child\n right = None # right child\n\n def __init__(self, key: int, data='nodata', parent=None, left=None,\n right=None) -> None:\n \"\"\"\n :param key: the key value of this BST node\n :param data: the data in string\n :param parent: parent, BstNode\n :param left: left, BstNode\n :param right: right, BstNode\n :return: None\n \"\"\"\n self.key = key\n self.data = data\n self.parent = parent\n self.left = left\n self.right = right\n\n @classmethod\n def copy(cls, node: 'BstNode') -> 'BstNode':\n \"\"\"\n Copy constructor\n :param node: A node to copy from\n :return: a new node with the same key and data\n \"\"\"\n newNode = BstNode(node.key, node.data)\n return newNode\n\n @classmethod\n def node_equals(self, n1: 'BstNode', n2: 'BstNode') -> bool:\n\n \"\"\"\n :param node: Another node to compare\n :return: True if the key and value of the two nodes are equal\n \"\"\"\n if n1 is None and n2 is None:\n return True\n elif n1 is None or n2 is None:\n return False\n else:\n return (n1.key == n2.key) & (n1.data == n2.data)\n\n def search(self, key: int) -> 'BstNode':\n curNode = self # current node\n while curNode.key != key:\n if key < curNode.key:\n curNode = curNode.left\n elif key > curNode.key:\n curNode = curNode.right\n if curNode is None:\n # no such element\n return None\n return curNode\n\n def put(self, node: 'BstNode'):\n \"\"\"\n Insert a new node to a subtree from top to down\n :param node:\n :return: None\n :exception: when a node with the same key already exists\n \"\"\"\n if node.key > self.key:\n # when the key is bigger, put it on the right subtree\n if self.right is None:\n self.right = node\n node.parent = self\n else:\n self.right.put(node)\n elif node.key < self.key:\n # when the key is smaller, put it on the left subtree\n if self.left is None:\n self.left = node\n node.parent = self\n else:\n self.left.put(node)\n else:\n raise Exception(\"collision on key \" + str(node.key) + \" with \" +\n str(self.key))\n\n def remove(self, key: int) -> 'BstNode':\n \"\"\"\n Remove this node from the tree.\n Algorithm:\n * Case 1 - When the node to remove has no child:\n Erase the link to this node from the parent.\n * Case 2 - When the node to remove has one child:\n Link its parent to its child.\n * Case 3 - When the node to remove has two children:\n * Find a minimum value in the right subtree;\n * Replace value of the node to be removed with found minimum.\n Now, right subtree contains a duplicate!\n apply remove to the right subtree to remove a duplicate.\n :param key: The key of a node to search for.\n :return:\n \"\"\"\n # locate the node\n searchNode = self.search(key)\n if searchNode is None:\n raise Exception(\"No such element\")\n\n if searchNode.num_child() == 0:\n # Case 1\n if searchNode.key > searchNode.parent.key:\n searchNode.parent.right = None\n elif searchNode.key < searchNode.parent.key:\n searchNode.parent.left = None\n\n elif searchNode.num_child() == 1:\n # Case 2\n # cut the link to the searchNode\n if searchNode.parent.key > searchNode.key:\n searchNode.parent.left = None\n elif searchNode.parent.key < searchNode.key:\n searchNode.parent.right = None\n\n # connect the child of searchNode to its parent node\n if searchNode.left is not None:\n searchNode.parent.put(searchNode.left)\n elif searchNode.right is not None:\n searchNode.parent.put(searchNode.right)\n\n elif searchNode.num_child() == 2:\n # Case 3\n # copy the min node from the right subtree\n newNode = BstNode.copy(searchNode.right.min_node())\n # remove the duplicate in the right subtree\n searchNode.right.remove(newNode.key)\n newNode.left = searchNode.left\n if newNode.left is not None:\n newNode.left.parent = newNode\n newNode.right = searchNode.right\n if newNode.right is not None:\n newNode.right.parent = newNode\n\n # attach the new subtree to its parent\n if searchNode.parent is not None:\n if searchNode.parent.key < searchNode.key:\n searchNode.parent.right = newNode\n elif searchNode.parent.key > searchNode.key:\n searchNode.parent.left = newNode\n else:\n assert False # shall never happen\n return newNode\n\n return self\n\n def traverse(self, visitor: NodeVisitor):\n return visitor.visit(self)\n\n def num_child(self) -> int:\n \"\"\"\n :return: The number of children this node has.\n \"\"\"\n cnt = 0\n if self.left is not None:\n cnt += 1\n if self.right is not None:\n cnt += 1\n return cnt\n\n def min_node(self) -> 'BstNode':\n \"\"\"\n :return: A node with the minimum key value in this subtree.\n \"\"\"\n if self.left is None:\n return self\n else:\n return self.left.min_node()\n\n def __str__(self):\n return str(self.key) + \": \" + self.data + \" \"\n\n\nclass Bst:\n root = None\n\n @classmethod\n def print_node_list(cls, arr):\n result = \"\"\n for node in arr:\n result += node.__str__() + \" \"\n print(result)\n\n @classmethod\n def node_list_to_key_list(cls, nlist: list) -> list:\n lst = []\n for n in nlist:\n lst.append(n.key)\n return lst\n\n def insert(self, key: int, data=\"nodata\") -> 'Bst':\n \"\"\"\n Insert a node with a key and a data (optional).\n :param key: The key\n :param data: The data in string (optional)\n :return: itself. (method chaining)\n \"\"\"\n if self.root is None:\n self.root = BstNode(key, data)\n else:\n self.root.put(BstNode(key, data))\n return self\n\n def insert_node(self, node: BstNode) -> 'Bst':\n if self.root is None:\n self.root = node\n else:\n self.root.put(node)\n return self\n\n def remove(self, key: int) -> 'Bst':\n self.root = self.root.remove(key)\n return self\n\n def search(self, key: int) -> BstNode:\n if self.root is None:\n return None\n else:\n return self.root.search(key)\n\n def inorder(self):\n if self.root is not None:\n return self.root.traverse(Inorder())\n else:\n return None\n\n def preorder(self):\n if self.root is not None:\n return self.root.traverse(Preorder())\n else:\n return None\n\n def postorder(self):\n if self.root is not None:\n return self.root.traverse(Postorder())\n else:\n return None\n\n def levelorder(self):\n if self.root is not None:\n return self.root.traverse(LevelOrder())\n else:\n return None\n\n\nclass NodeVisitor:\n def __str__(self):\n return self.__class__.__name__\n\n def visit(self, node: BstNode) -> BstNode: pass\n\n\nclass Inorder(NodeVisitor):\n def visit(self, node: BstNode) -> list:\n \"\"\"\n In-order traversal.\n :return: A list of ``BstNode``s\n \"\"\"\n lst = []\n if node.left is not None:\n lst.extend(self.visit(node.left))\n lst.append(node)\n if node.right is not None:\n lst.extend(self.visit(node.right))\n return lst\n\n\nclass Preorder(NodeVisitor):\n def visit(self, node: BstNode) -> list:\n \"\"\"\n Pre-order traversal.\n :return: A list of nodes in this subtree, ordered in pre-order.\n \"\"\"\n lst = [node]\n if node.left is not None:\n lst.extend(self.visit(node.left))\n if node.right is not None:\n lst.extend(self.visit(node.right))\n return lst\n\n\nclass Postorder(NodeVisitor):\n def visit(self, node: BstNode) -> list:\n \"\"\"\n Post-order traversal.\n :return: A list of nodes in this subtree, ordered in post-order.\n \"\"\"\n lst = []\n if node.left is not None:\n lst.extend(self.visit(node.left))\n if node.right is not None:\n lst.extend(self.visit(node.right))\n lst.append(node)\n return lst\n\n\nclass LevelOrder(NodeVisitor):\n def visit(self, node: BstNode) -> list:\n \"\"\"\n Level-order traversal.\n :return: A list of nodes in this subtree, ordered in level-order (BFS).\n \"\"\"\n lst = [] # the list of visited nodes\n q = Queue() # a queue of nodes to visit next\n q.put(node) # start from the root node\n while not q.empty():\n # repeat BFS until the queue is empty\n node = q.get()\n lst.append(node)\n if node.left is not None:\n q.put(node.left)\n if node.right is not None:\n q.put(node.right)\n return lst\n","sub_path":"pybst.py","file_name":"pybst.py","file_ext":"py","file_size_in_byte":10010,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"1199349","text":"#!/usr/bin/python\nfrom sense_hat import SenseHat\n\nsense = SenseHat()\n\nsense.clear()\nsense.rotation = 90\n\nred = [255, 0, 0]\ngreen = [0, 255, 0]\nblue = [0, 0, 255]\nnewblue = [40, 54, 255]\nyellow = [255, 255, 0]\nwhite = [255, 255, 255]\nblack = [0, 0, 0]\n\nB = black\nY = yellow\nR = red\nG = green\n\nquestion_mark = [\nB, B, Y, Y, Y, Y, B, B,\nB, Y, Y, Y, Y, Y, Y, B,\nY, G, G, Y, G, G, Y, Y,\nY, G, G, Y, G, G, Y, Y,\nY, R, Y, Y, Y, Y, R, Y,\nY, R, R, R, R, R, R, Y,\nB, Y, R, R, R, R, Y, B,\nB, B, Y, Y, Y, Y, B, B\n]\n\nsense.set_pixels(question_mark)\n","sub_path":"server/modules/tick.py","file_name":"tick.py","file_ext":"py","file_size_in_byte":536,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"138707404","text":"import json\nimport os\nfrom argparse import ArgumentParser\nfrom collections import defaultdict\nfrom os import path as os_path\n\nimport torch\nfrom matplotlib import pyplot as plt\nfrom torch import autograd, cuda\nfrom torch import multiprocessing as mp\nfrom torch import optim\nfrom torch.nn import functional as F\nfrom torch.nn import utils as nn_utils\nfrom torch.utils.data import DataLoader\nfrom torchvision import datasets, transforms\nfrom tqdm import tqdm\n\nimport functions\nfrom datasets import CacheDataset, DelegateDataset\nfrom losses import MarginLoss, ReconLoss\nfrom networks import CapsDecoder, CapsNet, Cequential, FeatureExtractor\n\nif __name__ == \"__main__\":\n parser = ArgumentParser()\n\n convolution = parser.add_argument_group(\"convolution\")\n convolution.add_argument(\n \"-Cc\", \"--conv-channels\", type=int, nargs=\"+\", default=(1, 256, 32)\n )\n convolution.add_argument(\n \"-Ck\", \"--conv-kernels\", type=int, nargs=\"+\", default=(9, 9)\n )\n convolution.add_argument(\n \"-Cs\", \"--conv-strides\", type=int, nargs=\"+\", default=(1, 2)\n )\n\n capsule = parser.add_argument_group(\"capsule\")\n capsule.add_argument(\n \"-ch\", \"--caps-channels\", type=int, nargs=\"+\", default=(32 * 6 * 6, 16)\n )\n capsule.add_argument(\"-cc\", \"--caps-capsules\", type=int, nargs=\"+\", default=(8, 10))\n capsule.add_argument(\"-ci\", \"--caps-iters\", type=int, default=3)\n\n decoder = parser.add_argument_group(\"decoder\")\n decoder.add_argument(\n \"-dl\",\n \"--decoder-layers\",\n type=int,\n nargs=\"+\",\n default=(16 * 10, 512, 1024, 784),\n )\n\n augmentation = parser.add_argument_group(\"augmentation\")\n augmentation.add_argument(\"-am\", \"--aug-mean\", type=float, default=None)\n augmentation.add_argument(\"-as\", \"--aug-std\", type=float, default=None)\n augmentation.add_argument(\"-ar\", \"--aug-rotation\", type=float, default=0)\n augmentation.add_argument(\"-ab\", \"--aug-brightness\", type=float, default=0)\n augmentation.add_argument(\"-ac\", \"--aug-contrast\", type=float, default=0)\n augmentation.add_argument(\"-at\", \"--aug-saturation\", type=float, default=0)\n\n losses = parser.add_argument_group(\"losses\")\n losses.add_argument(\"-lt\", \"--Tc\", type=float, default=1)\n losses.add_argument(\"-ll\", \"--lmbda\", type=float, default=0.5)\n losses.add_argument(\"-lb\", \"--bound\", type=float, nargs=2, default=(1, 0.1))\n losses.add_argument(\"-lw\", \"--recon-weight\", type=float, default=1)\n\n parser.add_argument(\"-e\", \"--epochs\", type=int, default=100)\n parser.add_argument(\"-b\", \"--batch\", type=int, default=1000)\n parser.add_argument(\"-lr\", \"--learning-rate\", type=float, default=1e-3)\n parser.add_argument(\"-mg\", \"--max-grad\", type=float, default=None)\n parser.add_argument(\"-d\", \"--device\", type=str, default=\"cuda\")\n parser.add_argument(\"-P\", \"--processes\", type=int, default=0)\n parser.add_argument(\"-r\", \"--dataset-root\", type=str, default=\".\")\n parser.add_argument(\"-s\", \"--save\", type=int, default=None)\n parser.add_argument(\"-o\", \"--out\", type=str, default=\"\")\n parser.add_argument(\"-p\", \"--plot\", action=\"store_true\")\n parser.add_argument(\"-tnan\", \"--terminate-on-nan\", action=\"store_true\")\n parser.add_argument(\"-pm\", \"--print-module\", action=\"store_true\")\n\n flags = parser.parse_args()\n\n mp.set_start_method(method=\"spawn\")\n\n conv_channels = flags.conv_channels\n conv_kernels = flags.conv_kernels\n conv_strides = flags.conv_strides\n\n caps_channels = flags.caps_channels\n caps_capsules = flags.caps_capsules\n caps_iters = flags.caps_iters\n\n decoder_layers = flags.decoder_layers\n\n losses_Tc = flags.Tc\n losses_lmbda = flags.lmbda\n losses_bound = flags.bound\n losses_weight = flags.recon_weight\n\n aug_mean = flags.aug_mean\n aug_std = flags.aug_std\n aug_rotation = flags.aug_rotation\n aug_brightness = flags.aug_rotation\n aug_contrast = flags.aug_contrast\n aug_saturation = flags.aug_saturation\n\n epochs = flags.epochs\n batch = flags.batch\n learning_rate = flags.learning_rate\n max_grad = flags.max_grad\n device = flags.device if cuda.is_available() else \"cpu\"\n processes = flags.processes\n dataset_root = flags.dataset_root\n save = flags.save\n out = flags.out\n plot = flags.plot\n terminate_on_nan = flags.terminate_on_nan\n print_module = flags.print_module\n\n if out:\n os.makedirs(name=out, exist_ok=True)\n\n transform_list = []\n\n if any((aug_brightness, aug_contrast, aug_saturation)):\n transform_list.append(\n transforms.ColorJitter(\n brightness=aug_brightness,\n contrast=aug_contrast,\n saturation=aug_saturation,\n )\n )\n\n if aug_rotation:\n transform_list.append(transforms.RandomRotation(degrees=aug_rotation))\n\n transform_list.append(transforms.ToTensor())\n\n if all(((aug_mean is not None), (aug_std is not None))):\n transform_list.append(transforms.Normalize(mean=(aug_mean,), std=(aug_std,)))\n\n data_transform = transforms.Compose(transform_list)\n\n train_dataset = datasets.MNIST(\n root=dataset_root, train=True, download=True, transform=data_transform\n )\n eval_dataset = datasets.MNIST(\n root=dataset_root, train=False, download=True, transform=transforms.ToTensor()\n )\n\n categories = len(train_dataset.classes)\n assert categories == len(eval_dataset.classes)\n\n if len(transform_list) == 1:\n train_dataset = CacheDataset(dataset=train_dataset, device=device)\n eval_dataset = CacheDataset(dataset=eval_dataset, device=device)\n else:\n train_dataset = DelegateDataset(dataset=train_dataset, device=device)\n eval_dataset = DelegateDataset(dataset=eval_dataset, device=device)\n\n train_loader = DataLoader(\n dataset=train_dataset, batch_size=batch, shuffle=True, num_workers=processes\n )\n eval_loader = DataLoader(\n dataset=eval_dataset, batch_size=batch, shuffle=False, num_workers=processes\n )\n\n capsnet = CapsNet(\n feature_net=FeatureExtractor(\n conv_channels=conv_channels,\n kernel_sizes=conv_kernels,\n strides=conv_strides,\n out_features=caps_capsules[0],\n ),\n capsule_net=Cequential(\n channels=caps_channels, capsules=caps_capsules, num_iters=caps_iters\n ),\n decoder_net=CapsDecoder(layers=decoder_layers, categories=categories),\n ).to(device)\n optimizer = optim.Adam(params=capsnet.parameters(), lr=learning_rate)\n\n if print_module:\n print(capsnet)\n\n if terminate_on_nan:\n capsnet.apply(\n fn=lambda module: module.register_backward_hook(\n hook=functions.terminate_on_nan\n )\n )\n\n (margin_loss, recon_loss) = (\n MarginLoss(\n T_c=losses_Tc,\n lmbda=losses_lmbda,\n boundary=losses_bound,\n categories=categories,\n ),\n ReconLoss(),\n )\n\n history = defaultdict(list)\n for epoch in range(1, 1 + epochs):\n print(f\"epoch: {epoch:04d}/{epochs:04d}, training\")\n capsnet.train()\n (correct, total) = (0, 0)\n (total_margin_loss, total_recon_loss) = (0, 0)\n for (images, labels) in tqdm(train_loader):\n (predict, recon) = capsnet(images, labels)\n mar_loss = margin_loss(predict, labels)\n rec_loss = losses_weight * recon_loss(recon, images)\n loss = mar_loss + rec_loss\n optimizer.zero_grad()\n loss.backward()\n if max_grad:\n nn_utils.clip_grad_norm_(\n parameters=capsnet.parameters(), max_norm=max_grad\n )\n optimizer.step()\n\n correct += ((predict ** 2).sum(dim=1).argmax(dim=-1) == labels).sum().item()\n total_margin_loss += mar_loss.item()\n total_recon_loss += rec_loss.item()\n total += len(labels)\n\n history[\"train_accuracy\"].append(correct / total)\n history[\"train_margin_loss\"].append(total_margin_loss)\n history[\"train_recon_loss\"].append(total_recon_loss)\n\n print(\n f\"accuracy: {correct/total:4.3f}, \"\n f\"margin loss: {total_margin_loss:08.4f} \"\n f\"recon loss: {total_recon_loss:08.4f}\"\n )\n\n print(f\"epoch: {epoch:04d}/{epochs:04d}, testing\")\n capsnet.eval()\n with torch.no_grad():\n (correct, total) = (0, 0)\n (total_margin_loss, total_recon_loss) = (0, 0)\n for (images, labels) in tqdm(eval_loader):\n (predict, recon) = capsnet(images, labels)\n correct += (\n ((predict ** 2).sum(dim=1).argmax(dim=-1) == labels).sum().item()\n )\n total += len(labels)\n mar_loss = margin_loss(predict, labels)\n rec_loss = recon_loss(recon, images)\n total_margin_loss += mar_loss.item()\n total_recon_loss += rec_loss.item()\n\n history[\"eval_accuracy\"].append(correct / total)\n history[\"eval_margin_loss\"].append(total_margin_loss)\n history[\"eval_recon_loss\"].append(total_recon_loss)\n\n print(\n f\"accuracy: {correct/total:4.3f}, \"\n f\"margin loss: {total_margin_loss:08.4f} \"\n f\"recon loss: {total_recon_loss:08.4f}\"\n )\n\n if out:\n if (epoch % save) == 0:\n torch.save(\n obj=capsnet.state_dict(), f=os_path.join(out, f\"{epoch:04d}.pt\")\n )\n\n if out:\n json.dump(\n obj=history, fp=open(file=os_path.join(out, \"history.json\"), mode=\"w+\")\n )\n if plot:\n for (name, plot) in history.items():\n plt.clf()\n plt.plot(plot)\n plt.savefig(os_path.join(out, f\"{name}.png\"))\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":9957,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"438525154","text":"import sys\nfrom copy import copy,deepcopy\n\ndirs = [[1,0],[-1,0],[0,1],[0,-1],[-1,-1],[-1,1],[1,1],[1,-1]]\n\n#counting neighbours for part 2\ndef countneighbours2(board, row, cell):\n n = 0\n\n for d in dirs:\n \tdr = deepcopy(d)\n \twhile inbounds(board,row,cell,dr):\n \t\tif board[row + dr[0]][cell + dr[1]] == \"#\":\n \t\t\tn += 1\n \t\t\tbreak\n \t\telif board[row + dr[0]][cell+dr[1]] == \"L\":\n \t\t\tbreak\n \t\tdr[0] += d[0]\n \t\tdr[1] += d[1]\n \n return n\n\n\ndef inbounds(board,row,cell,d):\n\treturn ((0 <= row + d[0] < len(board)) and (0 <= cell + d[1] < len(board[row])))\n\n\n#counting neighbours for part 1\ndef countneighbours(board, row, cell):\n n = 0\n\n for d in dirs:\n \tif inbounds(board,row,cell,d):\n \t\tif board[row + d[0]][cell + d[1]] == \"#\":\n \t\t\tn += 1\n return n\n\n\ndef update(state):\n\tnew = []\n\tfor row in range(len(state)):\n\t\tr = []\n\t\tfor cell in range(len(state[row])):\n\t\t\tif state[row][cell] == \".\":\n\t\t\t\tr.append(\".\")\n\t\t\t\tcontinue\n\n\t\t\tn = countneighbours2(state,row,cell)\n\t\t\tif state[row][cell] == \"L\" and n == 0:\n\t\t\t\tr.append(\"#\")\n\t\t\telif state[row][cell] == \"#\" and n > 4:\n\t\t\t\tr.append(\"L\")\n\t\t\telse:\n\t\t\t\tr.append(state[row][cell])\n\t\tnew.append(r)\n\treturn new\n\n\ngrid = []\n\nfor line in sys.stdin:\n\tgrid.append([c for c in str(line.strip())])\n\n\nnums = 0\nstate = grid\nwhile True:\n\tnums +=1\n\tnew = update(deepcopy(state))\n\tif new == state:\n\t\tbreak\n\n\tstate = deepcopy(new)\n\nc = 0\nfor row in state:\n\tfor cell in row:\n\t\tif cell == \"#\":\n\t\t\tc += 1\n\nprint(c)","sub_path":"11.py","file_name":"11.py","file_ext":"py","file_size_in_byte":1486,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"427108313","text":"import torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\nimport torch.optim as optim\r\nimport numpy as np\r\nfrom nltk import tokenize\r\nfrom Tweet_to_OF import count_all, remove_untokenizable, get_backup_tweets\r\nfrom collections import Counter\r\n\r\n\r\ndef save_model(model):\r\n torch.save(model.state_dict(), \"./model.pth\")\r\n\r\n\r\ndef load_model():\r\n model = torch.load(\"./model.pth\")\r\n model.eval()\r\n return model\r\n\r\n\r\ndef prepare_sequence(seq, to_ix):\r\n idxs = [to_ix[w] for w in seq]\r\n return torch.tensor(idxs, dtype=torch.long)\r\n\r\n\r\ndef substitute_with_unk(data, n=1):\r\n count = Counter()\r\n for word in data:\r\n count += Counter(word)\r\n\r\n for i in range(len(count)):\r\n if count[data[i]] <= n:\r\n data[i] = \"\"\r\n return data\r\n\r\n\r\ndef get_twitter_data_tokenized(file=\"testtweets\"):\r\n with open(\".\\\\tweets\\\\\"+ file, \"r\", encoding=\"utf-8\") as f:\r\n return tokenize.word_tokenize(f.read())\r\n\r\n\r\ndef get_opin_data():\r\n return count_all()\r\n\r\n\r\nclass LSTMmodel(nn.Module):\r\n # Class that defines our model\r\n def __init__(self, embedding_dim, hidden_dim, vocab_size, tagset_size):\r\n super(LSTMmodel, self).__init__()\r\n self.hidden_dim = hidden_dim\r\n\r\n self.word_embeddings = nn.Embedding(vocab_size, embedding_dim)\r\n\r\n # The LSTM takes word embeddings as inputs, and outputs hidden states\r\n # with dimensionality hidden_dim.\r\n self.lstm = nn.LSTM(embedding_dim, hidden_dim)\r\n\r\n # The linear layer that maps from hidden state space to tag space\r\n self.hidden2tag = nn.Linear(hidden_dim, tagset_size)\r\n\r\n # This is the forward computation, which constructs the computation graph\r\n def forward(self, sentence):\r\n # Get the embeddings\r\n embeds = self.word_embeddings(sentence)\r\n # put them through the LSTM and get its output\r\n lstm_out, _ = self.lstm(embeds.view(len(sentence), 1, -1))\r\n # pass that output through the linnear layer\r\n tag_space = self.hidden2tag(lstm_out.view(len(sentence), -1))\r\n # convert the logits to a log probability distribution\r\n tag_scores = F.log_softmax(tag_space, dim=1)\r\n return tag_scores\r\n\r\n\r\nget_backup_tweets(\"sourcetesttweets\", \"testtweets\")\r\nremove_untokenizable(\"./tweets/sourcetesttweets\", \"./tweets/testtweets\")\r\ntrain_data_unk = substitute_with_unk(get_twitter_data_tokenized())\r\nprint(train_data_unk)\r\n\r\n\r\nword_to_ix = {}\r\nix_to_word = {}\r\ntag_to_ix = {}\r\nix_to_tag = {}\r\nfor sent, tags in train_data_unk:\r\n for word in sent:\r\n if word not in word_to_ix:\r\n word_to_ix[word] = len(word_to_ix)\r\n ix_to_word[word_to_ix[word]] = word\r\n for tag in tags:\r\n if tag not in tag_to_ix:\r\n tag_to_ix[tag] = len(tag_to_ix)\r\n ix_to_tag[tag_to_ix[tag]] = tag\r\n\r\nEMBEDDING_DIM = 32\r\nHIDDEN_DIM = 32\r\n# LAYERS =\r\n\r\n# Initialize the model\r\nmodel = LSTMmodel(EMBEDDING_DIM, HIDDEN_DIM, len(word_to_ix), len(tag_to_ix))\r\n# Loss function to use\r\nloss_function = nn.NLLLoss()\r\n# Optimizer to use during training\r\noptimizer = optim.SGD(model.parameters(), lr=0.1)\r\n\r\n# See what the scores are before training\r\n# Note that element i,j of the output is the score for tag j for word i.\r\n# Here we don't need to train, so the code is wrapped in torch.no_grad()\r\nwith torch.no_grad():\r\n inputs = prepare_sequence(train_data_unk[0][0], word_to_ix)\r\n tag_scores = model(inputs)\r\n print(tag_scores)\r\n for i, word in enumerate(train_data_unk[0][0]):\r\n j = int(np.argmax(tag_scores[i]))\r\n print(f\"\\t{word}|{ix_to_tag[j]}\")\r\n\r\n# Training loop\r\n\r\nfor epoch in range(20): # normally you would NOT do 100 epochs, it is toy data\r\n print(f\"Starting epoch {epoch}...\")\r\n loss_sum = 0\r\n correct = 0\r\n for sentence, tags in train_data_unk:\r\n # Step 1. Remember that Pytorch accumulates gradients.\r\n # We need to clear them out before each instance\r\n model.zero_grad()\r\n\r\n # Step 2. Get our inputs ready for the network, that is, turn them into\r\n # Tensors of word indices.\r\n # Eventually I suggest you use the DataLoader modules\r\n # The batching can take place here\r\n sentence_in = prepare_sequence(sentence, word_to_ix)\r\n targets = prepare_sequence(tags, tag_to_ix)\r\n\r\n # Step 3. Run our forward pass.\r\n tag_scores = model(sentence_in)\r\n # print(tag_scores)\r\n # Step 4. Compute the loss, gradients, and update the parameters by\r\n # calling optimizer.step()\r\n loss = loss_function(tag_scores, targets)\r\n loss_sum += loss.data.item()\r\n for i in range(len(tag_scores)):\r\n val = int(np.argmax(tag_scores[i].detach()))\r\n if ix_to_tag[val] in tags:\r\n correct += 1\r\n loss.backward()\r\n optimizer.step()\r\n print(\"Epoch {}, Loss: {:.3f}, Accuracy: {:.3f}\".format(epoch, loss_sum, correct / len(tag_scores)))\r\n","sub_path":"LSTMmodel.py","file_name":"LSTMmodel.py","file_ext":"py","file_size_in_byte":4974,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"316595549","text":"# Copyright 2013-2022 Lawrence Livermore National Security, LLC and other\n# Spack Project Developers. See the top-level COPYRIGHT file for details.\n#\n# SPDX-License-Identifier: (Apache-2.0 OR MIT)\n\nfrom spack.package import *\n\n\nclass Hdf5VolAsync(CMakePackage):\n \"\"\"This package enables asynchronous IO in HDF5.\"\"\"\n\n homepage = \"https://hdf5-vol-async.readthedocs.io\"\n git = \"https://github.com/hpc-io/vol-async.git\"\n\n maintainers = [\"hyoklee\", \"houjun\", \"jeanbez\"]\n\n tags = [\"e4s\"]\n\n version(\"develop\", branch=\"develop\")\n version(\"1.3\", tag=\"v1.3\")\n version(\"1.2\", tag=\"v1.2\")\n version(\"1.1\", tag=\"v1.1\")\n version(\"1.0\", tag=\"v1.0\")\n\n depends_on(\"mpi\")\n depends_on(\"argobots@main\")\n depends_on(\"hdf5@1.13: +mpi +threadsafe\")\n\n def setup_run_environment(self, env):\n env.set(\"HDF5_PLUGIN_PATH\", self.spec.prefix.lib)\n vol_connector = \"async under_vol=0;under_info=[]\"\n env.set(\"HDF5_VOL_CONNECTOR\", vol_connector)\n env.set(\"MPICH_MAX_THREAD_SAFETY\", \"multiple\")\n\n def cmake_args(self):\n \"\"\"Populate cmake arguments for HDF5 VOL.\"\"\"\n args = [\n self.define(\"CMAKE_C_COMPILER\", self.spec[\"mpi\"].mpicc),\n self.define(\"BUILD_SHARED_LIBS\", True),\n self.define(\"BUILD_TESTING\", self.run_tests),\n ]\n return args\n\n def check(self):\n if self.run_tests:\n with working_dir(self.build_directory):\n make(\"test\")\n","sub_path":"var/spack/repos/builtin/packages/hdf5-vol-async/package.py","file_name":"package.py","file_ext":"py","file_size_in_byte":1468,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"509746577","text":"# -*- coding: utf-8 -*-\n\n\nfrom dp_tornado.engine.controller import Controller as dpController\n\n\nclass ViewController(dpController):\n\n def get(self, product_id):\n self.render('front/product/view.html', {\n 'product': self.model.dao.product.get(product_id)\n })\n","sub_path":"controller/front/product/view.py","file_name":"view.py","file_ext":"py","file_size_in_byte":286,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"433518144","text":"def main():\n filename = 'hightemp.txt'\n\n with open(filename, mode='r', encoding='utf-8') as f:\n hightemp = [l.split() for l in f]\n\n for l in sorted(hightemp, key=lambda temp: temp[2], reverse=True):\n print('\\t'.join(l))\n\n\nif __name__ == '__main__':\n main()","sub_path":"nlp18.py","file_name":"nlp18.py","file_ext":"py","file_size_in_byte":282,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"466501234","text":"from behave import register_type\nfrom .utils import parse_py\n\nimport random\n\nregister_type(Py=parse_py)\n\n\n@when(u'clear trie tree') # noqa\ndef step_impl(context):\n context.trie.clear()\n\n\n@then(u'root will be blank') # noqa\ndef step_impl(context):\n assert context.trie.root == ''\n\n\n@given(u'pairs with keys: {keys:Py}') # noqa\ndef step_impl(context, keys):\n context.pairs = []\n for key in keys:\n value = range(random.randint(5, 40))\n random.shuffle(value)\n value = ''.join(str(x) for x in value)\n context.pairs.append((key, value))\n\n\n@when(u'insert pairs') # noqa\ndef step_impl(context):\n for (key, value) in context.pairs:\n context.trie.update(key, value)\n\n\n@then(u'for each pair, get with key will return the correct value') # noqa\ndef step_impl(context):\n for (key, value) in context.pairs:\n assert context.trie.get(key) == str(value)\n","sub_path":"features/steps/trie.py","file_name":"trie.py","file_ext":"py","file_size_in_byte":902,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"493311859","text":"# Harjutus 1 - Leia vahemik\n# Looge funktsioon, mis võtab sisendiks ühe arvu\n# Funktsioon peab kontrollima, kas antud arv kuulub vahemikku 0 kuni 100 või 101 kuni 1000\n # Kui kuulub vahemikku 0 kuni 100, siis tuleb printida tekst \"Arv on vahemikus 0st 100ni\".\n # Kui kuulub vahemikku 101 kuni 1000, siis tuleb printida tekst \"Arv on vahemikus 101st 1000ni\".\n\n\n\n# Harjutus 2 - prindi negatiivsed arvud\n# Ette on antud arvude list\narvud = [5, 9, 1, -2, 6, -15, -20]\n\n# Looge funktsioon, mis käib tsükliga kõik arvud listis läbi ning iga arvu puhul kontrollib, kas arv on negatiivne.\n # Kui arv on negatiivne, siis printige see välja\n\n\n# Harjutus 3 - boonus\n# Looge funktsioon, mis võtab sisendiks listi, mis koosneb 4st täis arvust.\n# Funktsioon peab iga listi elemendi puhul printima uuele reale nii mitu $ sümbolit kui elemendi väärtus ütleb\n\n# Näiteks listi sisend = [1, 3, 4, 3] puhul prinditakse järgnev tulemus\n#$\n#$$$\n#$$$$\n#$$$\n\n\n\n#ül.1\n\ndef arvud(võrreldavarv):\n if (võrreldavarv > 100 and võrreldavarv < 1000):\n print('arv on vahemikus 101st 1000ni')\n elif (võrreldavarv > 0 and võrreldavarv <= 100):\n print('arv on vahemikus 0st 100ni')\n\n\narvud(500)\n\n\n\n#ül.2\ndef negatiivnearv():\n\n list = [5, 9, 1, -2, 6, -15, -20]\n for arv in list:\n if arv < 0:\n print(arv)\n\n\nnegatiivnearv()\n\ndef täisarv():\n list2 = [1, 3, 4, 3]\n for x in list2:\n print (\"$\" * x)\ntäisarv()","sub_path":"harjutused.py","file_name":"harjutused.py","file_ext":"py","file_size_in_byte":1458,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"306668779","text":"import App\ndef CreateAI(pShip):\n\t#########################################\n\t# Creating PlainAI StationaryAttackPlayer at (29, 51)\n\tpStationaryAttackPlayer = App.PlainAI_Create(pShip, \"StationaryAttackPlayer\")\n\tpStationaryAttackPlayer.SetScriptModule(\"StationaryAttack\")\n\tpStationaryAttackPlayer.SetInterruptable(1)\n\tpScript = pStationaryAttackPlayer.GetScriptInstance()\n\tpScript.SetTargetObjectName(\"player\")\n\t# Done creating PlainAI StationaryAttackPlayer\n\t#########################################\n\t#########################################\n\t# Creating ConditionalAI ShortTimer at (30, 104)\n\t## Conditions:\n\t#### Condition TimerA\n\tpTimerA = App.ConditionScript_Create(\"Conditions.ConditionTimer\", \"ConditionTimer\", 15)\n\t## Evaluation function:\n\tdef EvalFunc(bTimerA):\n\t\tACTIVE = App.ArtificialIntelligence.US_ACTIVE\n\t\tDORMANT = App.ArtificialIntelligence.US_DORMANT\n\t\tDONE = App.ArtificialIntelligence.US_DONE\n\t\tif bTimerA:\n\t\t\treturn DONE\n\t\treturn ACTIVE\n\t## The ConditionalAI:\n\tpShortTimer = App.ConditionalAI_Create(pShip, \"ShortTimer\")\n\tpShortTimer.SetInterruptable(1)\n\tpShortTimer.SetContainedAI(pStationaryAttackPlayer)\n\tpShortTimer.AddCondition(pTimerA)\n\tpShortTimer.SetEvaluationFunction(EvalFunc)\n\t# Done creating ConditionalAI ShortTimer\n\t#########################################\n\t#########################################\n\t# Creating PreprocessingAI AtPlayerFire at (30, 152)\n\t## Setup:\n\timport AI.Preprocessors\n\tpFireScript = AI.Preprocessors.FireScript(\"player\")\n\tfor pSystem in [ pShip.GetPhaserSystem(), pShip.GetPulseWeaponSystem() ]:\n\t\tif pSystem:\n\t\t\tpFireScript.AddWeaponSystem( pSystem )\n\t## The PreprocessingAI:\n\tpAtPlayerFire = App.PreprocessingAI_Create(pShip, \"AtPlayerFire\")\n\tpAtPlayerFire.SetInterruptable(1)\n\tpAtPlayerFire.SetPreprocessingMethod(pFireScript, \"Update\")\n\tpAtPlayerFire.SetContainedAI(pShortTimer)\n\t# Done creating PreprocessingAI AtPlayerFire\n\t#########################################\n\t#########################################\n\t# Creating PreprocessingAI DeCloak at (29, 199)\n\t## Setup:\n\timport AI.Preprocessors\n\tpScript = AI.Preprocessors.CloakShip(0)\n\t## The PreprocessingAI:\n\tpDeCloak = App.PreprocessingAI_Create(pShip, \"DeCloak\")\n\tpDeCloak.SetInterruptable(1)\n\tpDeCloak.SetPreprocessingMethod(pScript, \"Update\")\n\tpDeCloak.SetContainedAI(pAtPlayerFire)\n\t# Done creating PreprocessingAI DeCloak\n\t#########################################\n\t#########################################\n\t# Creating PlainAI FlyWaypoints at (144, 51)\n\tpFlyWaypoints = App.PlainAI_Create(pShip, \"FlyWaypoints\")\n\tpFlyWaypoints.SetScriptModule(\"FollowWaypoints\")\n\tpFlyWaypoints.SetInterruptable(1)\n\tpScript = pFlyWaypoints.GetScriptInstance()\n\tpScript.SetTargetWaypointName(\"WarbirdWay1\")\n\t# Done creating PlainAI FlyWaypoints\n\t#########################################\n\t#########################################\n\t# Creating ConditionalAI Timer at (144, 104)\n\t## Conditions:\n\t#### Condition MediumTimer\n\tpMediumTimer = App.ConditionScript_Create(\"Conditions.ConditionTimer\", \"ConditionTimer\", 35)\n\t## Evaluation function:\n\tdef EvalFunc(bMediumTimer):\n\t\tACTIVE = App.ArtificialIntelligence.US_ACTIVE\n\t\tDORMANT = App.ArtificialIntelligence.US_DORMANT\n\t\tDONE = App.ArtificialIntelligence.US_DONE\n\t\tif bMediumTimer:\n\t\t\treturn DONE\n\t\treturn ACTIVE\n\t## The ConditionalAI:\n\tpTimer = App.ConditionalAI_Create(pShip, \"Timer\")\n\tpTimer.SetInterruptable(1)\n\tpTimer.SetContainedAI(pFlyWaypoints)\n\tpTimer.AddCondition(pMediumTimer)\n\tpTimer.SetEvaluationFunction(EvalFunc)\n\t# Done creating ConditionalAI Timer\n\t#########################################\n\t#########################################\n\t# Creating PreprocessingAI Cloak at (142, 155)\n\t## Setup:\n\timport AI.Preprocessors\n\tpScript = AI.Preprocessors.CloakShip(1)\n\t## The PreprocessingAI:\n\tpCloak = App.PreprocessingAI_Create(pShip, \"Cloak\")\n\tpCloak.SetInterruptable(1)\n\tpCloak.SetPreprocessingMethod(pScript, \"Update\")\n\tpCloak.SetContainedAI(pTimer)\n\t# Done creating PreprocessingAI Cloak\n\t#########################################\n\t#########################################\n\t# Creating PlainAI TorpRun at (164, 198)\n\tpTorpRun = App.PlainAI_Create(pShip, \"TorpRun\")\n\tpTorpRun.SetScriptModule(\"TorpedoRun\")\n\tpTorpRun.SetInterruptable(1)\n\tpScript = pTorpRun.GetScriptInstance()\n\tpScript.SetTargetObjectName(\"player\")\n\t# Done creating PlainAI TorpRun\n\t#########################################\n\t#########################################\n\t# Creating ConditionalAI ReallyShortTimer at (167, 249)\n\t## Conditions:\n\t#### Condition TimerC\n\tpTimerC = App.ConditionScript_Create(\"Conditions.ConditionTimer\", \"ConditionTimer\", 1)\n\t## Evaluation function:\n\tdef EvalFunc(bTimerC):\n\t\tACTIVE = App.ArtificialIntelligence.US_ACTIVE\n\t\tDORMANT = App.ArtificialIntelligence.US_DORMANT\n\t\tDONE = App.ArtificialIntelligence.US_DONE\n\t\tif bTimerC:\n\t\t\treturn DONE\n\t\treturn ACTIVE\n\t## The ConditionalAI:\n\tpReallyShortTimer = App.ConditionalAI_Create(pShip, \"ReallyShortTimer\")\n\tpReallyShortTimer.SetInterruptable(1)\n\tpReallyShortTimer.SetContainedAI(pTorpRun)\n\tpReallyShortTimer.AddCondition(pTimerC)\n\tpReallyShortTimer.SetEvaluationFunction(EvalFunc)\n\t# Done creating ConditionalAI ReallyShortTimer\n\t#########################################\n\t#########################################\n\t# Creating SequenceAI StartAttackSequence at (10, 280)\n\tpStartAttackSequence = App.SequenceAI_Create(pShip, \"StartAttackSequence\")\n\tpStartAttackSequence.SetInterruptable(1)\n\tpStartAttackSequence.SetLoopCount(1)\n\tpStartAttackSequence.SetResetIfInterrupted(1)\n\tpStartAttackSequence.SetDoubleCheckAllDone(0)\n\tpStartAttackSequence.SetSkipDormant(0)\n\t# SeqBlock is at (106, 286)\n\tpStartAttackSequence.AddAI(pDeCloak)\n\tpStartAttackSequence.AddAI(pCloak)\n\tpStartAttackSequence.AddAI(pReallyShortTimer)\n\t# Done creating SequenceAI StartAttackSequence\n\t#########################################\n\t#########################################\n\t# Creating PlainAI InterceptKaroon at (360, 11)\n\tpInterceptKaroon = App.PlainAI_Create(pShip, \"InterceptKaroon\")\n\tpInterceptKaroon.SetScriptModule(\"Intercept\")\n\tpInterceptKaroon.SetInterruptable(1)\n\tpScript = pInterceptKaroon.GetScriptInstance()\n\tpScript.SetTargetObjectName(\"Karoon\")\n\tpScript.SetInterceptDistance(228)\n\tpScript.SetInSystemWarpDistance(1000)\n\t# Done creating PlainAI InterceptKaroon\n\t#########################################\n\t#########################################\n\t# Creating CompoundAI WarbirdAttackKaroon at (472, 8)\n\timport AI.Compound.BasicAttack\n\tpWarbirdAttackKaroon = AI.Compound.BasicAttack.CreateAI(pShip, \"Karoon\", Difficulty = 0.31, UseCloaking = 1)\n\t# Done creating CompoundAI WarbirdAttackKaroon\n\t#########################################\n\t#########################################\n\t# Creating PriorityListAI PriorityList_2 at (284, 63)\n\tpPriorityList_2 = App.PriorityListAI_Create(pShip, \"PriorityList_2\")\n\tpPriorityList_2.SetInterruptable(1)\n\t# SeqBlock is at (415, 69)\n\tpPriorityList_2.AddAI(pInterceptKaroon, 1)\n\tpPriorityList_2.AddAI(pWarbirdAttackKaroon, 2)\n\t# Done creating PriorityListAI PriorityList_2\n\t#########################################\n\t#########################################\n\t# Creating ConditionalAI PlayerNotNearKaroon at (290, 113)\n\t## Conditions:\n\t#### Condition PlayerNearKaroon\n\tpPlayerNearKaroon = App.ConditionScript_Create(\"Conditions.ConditionInRange\", \"ConditionInRange\", 350, \"player\", \"Karoon\")\n\t## Evaluation function:\n\tdef EvalFunc(bPlayerNearKaroon):\n\t\tACTIVE = App.ArtificialIntelligence.US_ACTIVE\n\t\tDORMANT = App.ArtificialIntelligence.US_DORMANT\n\t\tDONE = App.ArtificialIntelligence.US_DONE\n\t\tif bPlayerNearKaroon:\n\t\t\treturn DORMANT\n\t\treturn ACTIVE\n\t## The ConditionalAI:\n\tpPlayerNotNearKaroon = App.ConditionalAI_Create(pShip, \"PlayerNotNearKaroon\")\n\tpPlayerNotNearKaroon.SetInterruptable(1)\n\tpPlayerNotNearKaroon.SetContainedAI(pPriorityList_2)\n\tpPlayerNotNearKaroon.AddCondition(pPlayerNearKaroon)\n\tpPlayerNotNearKaroon.SetEvaluationFunction(EvalFunc)\n\t# Done creating ConditionalAI PlayerNotNearKaroon\n\t#########################################\n\t#########################################\n\t# Creating CompoundAI WarbirdAttackPlayer at (335, 160)\n\timport AI.Compound.BasicAttack\n\tpWarbirdAttackPlayer = AI.Compound.BasicAttack.CreateAI(pShip, \"player\", Easy_Difficulty = 0.1, Easy_UseCloaking = 1, Difficulty = 0.2, UseCloaking = 1, Hard_Difficulty = 0.3, Hard_UseCloaking = 1)\n\t# Done creating CompoundAI WarbirdAttackPlayer\n\t#########################################\n\t#########################################\n\t# Creating PriorityListAI PriorityList at (261, 259)\n\tpPriorityList = App.PriorityListAI_Create(pShip, \"PriorityList\")\n\tpPriorityList.SetInterruptable(1)\n\t# SeqBlock is at (288, 220)\n\tpPriorityList.AddAI(pPlayerNotNearKaroon, 1)\n\tpPriorityList.AddAI(pWarbirdAttackPlayer, 2)\n\t# Done creating PriorityListAI PriorityList\n\t#########################################\n\t#########################################\n\t# Creating ConditionalAI TakenEnoughDamage at (198, 303)\n\t## Conditions:\n\t#### Condition CriticalSystemDamaged\n\tpCriticalSystemDamaged = App.ConditionScript_Create(\"Conditions.ConditionCriticalSystemBelow\", \"ConditionCriticalSystemBelow\", pShip.GetName(), 0.70)\n\t#### Condition KaroonInBeol\n\tpKaroonInBeol = App.ConditionScript_Create(\"Conditions.ConditionInSet\", \"ConditionInSet\", \"Karoon\", \"Beol4\")\n\t## Evaluation function:\n\tdef EvalFunc(bCriticalSystemDamaged, bKaroonInBeol):\n\t\tACTIVE = App.ArtificialIntelligence.US_ACTIVE\n\t\tDORMANT = App.ArtificialIntelligence.US_DORMANT\n\t\tDONE = App.ArtificialIntelligence.US_DONE\n\t\tif (bCriticalSystemDamaged) or (not bKaroonInBeol):\n\t\t\treturn DONE\n\t\treturn ACTIVE\n\t## The ConditionalAI:\n\tpTakenEnoughDamage = App.ConditionalAI_Create(pShip, \"TakenEnoughDamage\")\n\tpTakenEnoughDamage.SetInterruptable(1)\n\tpTakenEnoughDamage.SetContainedAI(pPriorityList)\n\tpTakenEnoughDamage.AddCondition(pCriticalSystemDamaged)\n\tpTakenEnoughDamage.AddCondition(pKaroonInBeol)\n\tpTakenEnoughDamage.SetEvaluationFunction(EvalFunc)\n\t# Done creating ConditionalAI TakenEnoughDamage\n\t#########################################\n\t#########################################\n\t# Creating PlainAI FleeFromPlayer at (373, 218)\n\tpFleeFromPlayer = App.PlainAI_Create(pShip, \"FleeFromPlayer\")\n\tpFleeFromPlayer.SetScriptModule(\"Flee\")\n\tpFleeFromPlayer.SetInterruptable(1)\n\tpScript = pFleeFromPlayer.GetScriptInstance()\n\tpScript.SetFleeFromGroup(\"player\")\n\tpScript.SetSpeed(1)\n\t# Done creating PlainAI FleeFromPlayer\n\t#########################################\n\t#########################################\n\t# Creating PreprocessingAI Cloak_2 at (378, 275)\n\t## Setup:\n\timport AI.Preprocessors\n\tpScript = AI.Preprocessors.CloakShip(1)\n\t## The PreprocessingAI:\n\tpCloak_2 = App.PreprocessingAI_Create(pShip, \"Cloak_2\")\n\tpCloak_2.SetInterruptable(1)\n\tpCloak_2.SetPreprocessingMethod(pScript, \"Update\")\n\tpCloak_2.SetContainedAI(pFleeFromPlayer)\n\t# Done creating PreprocessingAI Cloak_2\n\t#########################################\n\t#########################################\n\t# Creating ConditionalAI TimeBeforeWarp at (379, 314)\n\t## Conditions:\n\t#### Condition Timer\n\tpTimer = App.ConditionScript_Create(\"Conditions.ConditionTimer\", \"ConditionTimer\", 15, 0)\n\t## Evaluation function:\n\tdef EvalFunc(bTimer):\n\t\tACTIVE = App.ArtificialIntelligence.US_ACTIVE\n\t\tDORMANT = App.ArtificialIntelligence.US_DORMANT\n\t\tDONE = App.ArtificialIntelligence.US_DONE\n\t\tif bTimer:\n\t\t\treturn DONE\n\t\treturn ACTIVE\n\t## The ConditionalAI:\n\tpTimeBeforeWarp = App.ConditionalAI_Create(pShip, \"TimeBeforeWarp\")\n\tpTimeBeforeWarp.SetInterruptable(1)\n\tpTimeBeforeWarp.SetContainedAI(pCloak_2)\n\tpTimeBeforeWarp.AddCondition(pTimer)\n\tpTimeBeforeWarp.SetEvaluationFunction(EvalFunc)\n\t# Done creating ConditionalAI TimeBeforeWarp\n\t#########################################\n\t#########################################\n\t# Creating PlainAI WarpOutOfSet at (284, 360)\n\tpWarpOutOfSet = App.PlainAI_Create(pShip, \"WarpOutOfSet\")\n\tpWarpOutOfSet.SetScriptModule(\"Warp\")\n\tpWarpOutOfSet.SetInterruptable(1)\n\tpScript = pWarpOutOfSet.GetScriptInstance()\n\tpScript.WarpBlindlyNoCollisionsIfImpulseDisabled(bWarpBlindly = 1)\n\t# Done creating PlainAI WarpOutOfSet\n\t#########################################\n\t#########################################\n\t# Creating SequenceAI MainSequence at (15, 354)\n\tpMainSequence = App.SequenceAI_Create(pShip, \"MainSequence\")\n\tpMainSequence.SetInterruptable(1)\n\tpMainSequence.SetLoopCount(1)\n\tpMainSequence.SetResetIfInterrupted(1)\n\tpMainSequence.SetDoubleCheckAllDone(0)\n\tpMainSequence.SetSkipDormant(0)\n\t# SeqBlock is at (116, 364)\n\tpMainSequence.AddAI(pStartAttackSequence)\n\tpMainSequence.AddAI(pTakenEnoughDamage)\n\tpMainSequence.AddAI(pTimeBeforeWarp)\n\tpMainSequence.AddAI(pWarpOutOfSet)\n\t# Done creating SequenceAI MainSequence\n\t#########################################\n\t#########################################\n\t# Creating PreprocessingAI AvoidObstacles at (15, 409)\n\t## Setup:\n\timport AI.Preprocessors\n\tpScript = AI.Preprocessors.AvoidObstacles()\n\t## The PreprocessingAI:\n\tpAvoidObstacles = App.PreprocessingAI_Create(pShip, \"AvoidObstacles\")\n\tpAvoidObstacles.SetInterruptable(1)\n\tpAvoidObstacles.SetPreprocessingMethod(pScript, \"Update\")\n\tpAvoidObstacles.SetContainedAI(pMainSequence)\n\t# Done creating PreprocessingAI AvoidObstacles\n\t#########################################\n\treturn pAvoidObstacles\n","sub_path":"scripts/Maelstrom/Episode2/E2M1/E2M1_AI_Warbird.py","file_name":"E2M1_AI_Warbird.py","file_ext":"py","file_size_in_byte":13238,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"293592037","text":"text = \" I love apples very much \"\n\n# The number of characters in the text\ntext_size = len(text)\n\n# Initialize a pointer to the position of the first character of 'text'\npos = 0\n\n# This is a flag to indicate whether the character we are comparing\n# to is a white space or not\nis_space = text[0].isspace()\n\n# Start tokenization\nfor i, char in enumerate(text):\n\n # We are looking for a character that is the opposit of 'is_space'\n # if 'is_space' is True, then we want to find a character that is\n # not a space. and vice versa. This event marks the end of a token.\n is_current_space = char.isspace()\n if is_current_space != is_space:\n\n print(text[pos:i])\n\n if is_current_space:\n pos = i + 1\n else:\n pos = i\n\n # Update the character type of which we are searching\n # the opposite (space vs. not space).\n # prevent 'pos' from being out of bound\n if pos < text_size:\n is_space = text[pos].isspace()\n\n # Create the last token if the end of the string is reached\n if i == text_size - 1 and pos <= i:\n print(text[pos:])\n","sub_path":"syfertext/temp.py","file_name":"temp.py","file_ext":"py","file_size_in_byte":1125,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"559688227","text":"\"\"\"\nThis file has the purpose of taking a photo, resizing it, and then creating a vector\nrepresenting the color of each pixel\nAuthor: Alex Sutay\n\"\"\"\n\n\nimport numpy as np\nimport scipy.io as sio\nfrom PIL import Image\nimport os\nSIZE = 40 # How many pixels should it be?\n\n\ndef main():\n directory = input(\"Where are the photos?\")\n colors = {to_nums_r: \"red\", to_nums_g: \"green\", to_nums_b: \"blue\", to_nums_l: \"grayscale\"}\n for function in colors:\n outmatrix = None\n outdict = dict()\n print(\"Collecting data for \" + colors[function])\n for filename in os.listdir(directory):\n if filename.endswith((\".jpg\", \".png\", \".jpeg\")):\n im = Image.open(directory + \"/\" + filename, 'r')\n this_array = function(im, SIZE, SIZE)\n im.close()\n if outmatrix is None:\n outmatrix = this_array\n elif np.size(outmatrix) == SIZE**2:\n outmatrix = np.asarray([outmatrix, this_array])\n else:\n outmatrix = np.vstack([outmatrix, this_array])\n filename = directory + \"/\" + input(\"Where should I save the output?\") + \".mat\"\n outdict['X'] = outmatrix.astype(float)\n sio.savemat(filename, outdict)\n\n\ndef to_nums_r(image, height, width):\n image = image.resize((height, width))\n rgb_im = image.convert('RGB')\n r = (rgb_im.getdata(0))\n return np.asarray(list(r))\n\n\ndef to_nums_g(image, height, width):\n image = image.resize((height, width))\n rgb_im = image.convert('RGB')\n g = rgb_im.getdata(1)\n return np.asarray(list(g))\n\n\ndef to_nums_b(image, height, width):\n image = image.resize((height, width))\n rgb_im = image.convert('RGB')\n b = rgb_im.getdata(2)\n return np.asarray(list(b))\n\n\ndef to_nums_l(image, height, width):\n image = image.resize((height, width))\n l_im = image.convert('L')\n l = l_im.getdata() # L is the black and white version\n return np.asarray(list(l))\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"pic_to_vect.py","file_name":"pic_to_vect.py","file_ext":"py","file_size_in_byte":2035,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"313207922","text":"# Write a function that accepts a positive integer k and returns a list that contains the first five multiples of k.\n# The multiples should be calculated starting from 1 to 5 (including both 1 and 5)\ndef multFive(k):\n theList = []\n for item in range(1, 6):\n theList.append(k * item)\n\n return theList\n\n\n# Write a function that accepts two positive integers a and b and returns a list of all the even numbers between a and b\n# (including a and not including b).\ndef evenBetween(a, b):\n evenList = []\n if a > b:\n maxNum = a\n minNum = b\n else:\n maxNum = b\n minNum = a\n for item in range(minNum, maxNum):\n if item % 2 == 0:\n evenList.append(item)\n\n return evenList\n\n\n# Write a function that accepts two positive integers a and b (a is smaller than b)and returns a list that contains all\n# the odd numbers between a and b (including a and including b if applicable) in descending order.\ndef oddBetween(a, b):\n oddList = []\n for item in range(b, (a - 1), -1):\n if item % 2 != 0:\n oddList.append(item)\n\n return oddList\n\n\n# Write a function that accepts a positive integer k and returns the ascending sorted list of cube root values of all\n# the numbers from 1 to k (including 1 and not including k). [if k is 1, your program should return an empty list]\ndef cubeRoot(k):\n cubeRootList = []\n for item in range(1, k):\n cubRoot = (item ** (1. / 3))\n cubeRootList.append(cubRoot)\n\n return cubeRootList\n\n# Write a function that accepts a positive integer k and returns the list of all the divisors of k.\n# Your list should include both 1 and k.\ndef divisors(k):\n divs = []\n for item in range (1,(k + 1)):\n if k % item == 0:\n divs.append(item)\n\n return divs\n","sub_path":"CSE1309x/listMethods.py","file_name":"listMethods.py","file_ext":"py","file_size_in_byte":1798,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"526073208","text":"#-*- coding: UTF-8 -*-\n'''\nTools for computing statistics about run time\ncompatibility: python 3.X\n'''\n\nimport sys\nimport os.path\nimport argparse\nimport subprocess\nfrom itertools import groupby\nimport numpy as np\nimport scipy.stats as spst\n\n#import cProfile\n\nVERSION = '0.1.0'\n\ndataKeywords = ('real','cpu','user')\n\ndef documentation():\n \"\"\"\n Print detailed documentation.\n \"\"\"\n print('\\n')\n print('DOCUMENTATION')\n print('============='+'\\n')\n print('Run & compute elapsed time statistics')\n print('-------------------------------------'+'\\n')\n print(' '*2+'SYNTAX')\n print(' '*4+'python pTimeStats.py command \"command_name arg1 arg2 ...\" NUM'+'\\n')\n print(' '*2+'DESCRIPTION')\n print(' '*4+'Runs the command \\'command_name\\' w/ the specified arguments (arg1, arg2) \\'NUM\\' times')\n print(' '*4+'and compute statistics related to:')\n print(' '*4+'* the real elapsed time')\n print(' '*4+'* the percentage of total CPU load (100% per core)')\n print(' '*4+'* the user elapsed time')\n print(' '*4+'The computed statistics are: min, mean, max and 90% confidence interval')\n print('\\n')\n\n print('Process a pTimeStats log file')\n print('-----------------------------'+'\\n')\n print(' '*2+'SYNTAX')\n print(' '*4+'python pTimeStats.py process-log filename'+'\\n')\n print(' '*2+'DESCRIPTION')\n print(' '*4+'Process the time logs the file \\'filename\\'. The log files are created when using command')\n print(' '*4+'pTimeStats.py command with the --log option. The data in these files stem from the ')\n print(' '*4+'output of the shell command: ')\n print(' '*4+'/usr/bin/time -f \"real:%E user:%U,%P cpu:%P\" ...')\n print()\n\n\ndef loadTxtFile(filename):\n \"\"\"\n Load data from a log file with one-line records\n \"\"\"\n if not(os.path.isfile(filename)):\n raise OSError('Function loadTxtFile(filename): impossible to load \\'{0}\\', there is no such file.'.format(filename))\n\n # Read and word split\n with open(filename,'r') as ioObj:\n splitLines = [line.split() for line in ioObj]\n\n return splitLines\n\ndef mapReduce(splitLines):\n # Map to key/value pairs\n def kwdMap(word):\n \"\"\"\n Map word to (keyword,value) pair\n \"\"\"\n wordSplit = word.split(':')\n if len(wordSplit)<2:\n return None\n kwd = wordSplit[0].lower()\n val = wordSplit[1:]\n if not(kwd in dataKeywords):\n return(None)\n elif kwd=='cpu':\n try:\n return( ('cpu',float(val[0].replace('%',''))) )\n except:\n return(None)\n else:\n return (kwd,val)\n\n\n mappedPairs = filter( lambda x: x!=None, (kwdMap(word) for words in splitLines for word in words) )\n\n # Reduce by keys\n sortedPairs = sorted(mappedPairs, key=(lambda t: t[0]))\n reducedValues = dict()\n for k,g in groupby(sortedPairs, key=lambda t: t[0]):\n reducedValues[k] = [kv[1] for kv in g]\n\n return reducedValues\n\n\n\ndef stats(samples):\n \"\"\"\n Compute usual statistics\n \"\"\"\n statistics = dict()\n\n def timeConverter(timeList):\n \"\"\"\n timeList = [[hours]:[minutes]:seconds]\n \"\"\"\n dim = len(timeList)\n return np.sum([float(txt)*60**(dim-1-pos) for pos,txt in enumerate(timeList)])\n\n def userConverter(val):\n \"\"\"\n e.g. val is ['7.08,188%']\n ['s.ss,xxx%'] to s.ss/x.xx\n \"\"\"\n splVal = val[0].split(',')\n return float(splVal[0])/float(splVal[1].replace('%',''))*100.\n\n def sampler(key,sample):\n \"\"\"\n Convert sample format for statistical estimator functions\n \"\"\"\n if key=='cpu':\n return np.array(sample, dtype=float)\n if key=='real':\n return np.array(list(map(timeConverter, iter(sample))), dtype=float)\n if key=='user':\n return np.array(list(map(userConverter, iter(sample))), dtype=float)\n\n\n def computeStatistics(smpl):\n \"\"\"\n Return a dict object with mean, [5percentile, 95percentile], min, max\n \"\"\"\n # (0.4,0.4) : approximately quantile unbiased (Cunnane)\n quantEst=spst.mstats.mquantiles(smpl, prob=[0.5, 0.95], alphap=0.4, betap=0.4) # 90%-confidence\n return {'mean':np.mean(smpl), '90%-conf':quantEst, 'min':np.min(smpl), 'max':np.max(smpl)}\n\n\n return dict( [ (key, computeStatistics(sampler(key,sample))) for (key,sample) in samples.items()] )\n\n\ndef displayStats(statistics):\n \"\"\"\n Display statistics values to the standard output\n \"\"\"\n title = {'real':'Elapsed real time',\\\n 'user':'Total number of CPU-seconds that the process spent in user mode divided by the CPU load',\\\n 'cpu':'Percentage of a CPU that this job got'}\n unit = {'real':'sec', 'user':'sec', 'cpu':'%'}\n\n for key in statistics.keys():\n print( (lambda k: title[k] if (k in title.keys()) else k)(key) )\n print('Min value: {value:.2f} {unit}'.format(value=statistics[key]['min'], unit=unit[key]))\n print('Mean value: {value:.2f} {unit}'.format(value=statistics[key]['mean'], unit=unit[key]))\n print('Max value: {value:.2f} {unit}'.format(value=statistics[key]['max'], unit=unit[key]))\n quant = statistics[key]['90%-conf']\n print('90% confidence: [{0:.2f} , {1:.2f}] {2}'.format(quant[0],quant[1], unit[key]))\n print()\n\n\ndef runSampling(command,runNum):\n \"\"\"\n \"\"\"\n subprocArgList = ['/usr/bin/time','-f', '\"real:%E user:%U,%P cpu:%P\"']+command.split()\n def generate_run(N):\n for n in range(N):\n proc = subprocess.Popen(subprocArgList, stderr=subprocess.PIPE)\n yield proc.stderr.read().decode('UTF-8').replace('\\\"','').split()\n\n return list(generate_run(int(runNum)))\n\n\nif __name__=='__main__':\n parser = argparse.ArgumentParser(description='Tools for computing statistics about run time')\n parser.add_argument('taskItem', nargs='*', help=\"\"\"Label specifying the tools to be called and tasks to be performed\n followed by the proper additional arguments. Use --doc option for more details.\"\"\")\n parser.add_argument('--doc', action='store_true', help='Show the documentation')\n parser.add_argument('--version', action='store_true', help='Show version')\n parser.add_argument('--log', action='store_true', help='Create a log file with time results')\n args = parser.parse_args()\n\n # Show documentation\n if args.doc:\n documentation()\n exit(0)\n\n # Show version\n if args.version:\n print('Version '+VERSION)\n esit(0)\n\n try:\n if args.taskItem[0]=='command':\n if len(args.taskItem)!=3:\n sys.stderr.write('Aborted. Three arguments required.'+'\\n'*2)\n sys.stderr.write('SYNTAX:\\n')\n sys.stderr.write(' pTimeStats.py command \"command_name arg1 arg2 ...\" runNum\\n')\n exit(1)\n command = args.taskItem[1]\n runNum = args.taskItem[2]\n # displayStats( stats( mapReduce( runSampling(command,runNum) ) ) )\n splitLines = runSampling(command,runNum)\n if args.log:\n with open('pTimeStats.log','w') as ioObj:\n for words in splitLines:\n ioObj.write(' '.join(words)+'\\n')\n displayStats( stats( mapReduce( splitLines ) ) )\n\n if args.taskItem[0]=='process-log':\n if len(args.taskItem)!=2:\n sys.stderr.write('Aborted. Two arguments required.'+'\\n'*2)\n sys.stderr.write('SYNTAX:\\n')\n sys.stderr.write(' pTimeStats.py process-log filename\\n')\n exit(1)\n displayStats( stats( mapReduce( loadTxtFile( args.taskItem[1]) ) ) )\n\n except OSError as err:\n print(err)\n","sub_path":"pTimeStats.py","file_name":"pTimeStats.py","file_ext":"py","file_size_in_byte":7792,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"432001917","text":"# Perceptron trained using Backpropagation\nfrom sklearn.base import BaseEstimator, ClassifierMixin\nfrom sklearn.utils import Parallel, delayed\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.utils.validation import check_is_fitted\nimport numpy as np\n\n\ndef _fit_binary_perceptron(X, y, pos_class, eta0=0.1, decay=0.01, max_iterations=1000):\n # Set positive class to 1 and the rest to -1\n y = np.where(y == pos_class, 1, -1)\n\n # Initial weight vector of size D\n w = np.random.rand(X.shape[1])\n\n has_converged = False\n n_misclassified = 0\n for iteration in range(max_iterations):\n # Compute the response of the decision function g()\n response = np.multiply(y, np.dot(X, w))\n\n # Construct chi; a matrix of misclassified samples\n misclassified_filter = response < 0\n chi = X[misclassified_filter, :]\n\n # Stop algorithm when all samples classified correctly\n n_misclassified = chi.shape[0]\n if n_misclassified == 0:\n has_converged = True\n break\n\n misclassified_y = y[misclassified_filter].reshape(-1, 1)\n update_w = np.sum(np.multiply(misclassified_y, chi), axis=0)\n learning_rate = eta0 * np.exp(-decay * iteration)\n # Perhaps faster alternative?\n # learning_rate = eta0 * (1. / (1. + decay * iteration))\n w = w + learning_rate * update_w\n\n if not has_converged:\n pass\n # print('Waring: Maximum number of iteration reached before convergence. '\n # 'Consider increasing max_iterations to improve the fit. '\n # 'Number of misclassified samples: ' + str(n_misclassified))\n return w\n\n\nclass Perceptron(BaseEstimator, ClassifierMixin):\n def __init__(self, eta0=0.1, decay=0.01, max_iterations=1000, n_jobs=-1, verbose=2):\n self.eta0 = eta0\n self.decay = decay\n self.max_iterations = max_iterations\n self.n_jobs = n_jobs\n self.verbose = verbose\n self.weights_ = None\n self.classes_ = None\n\n def _augment(self, X):\n ones = np.ones(X.shape[0]).reshape(-1, 1)\n return np.concatenate([ones, X], axis=1)\n\n def fit(self, X, y):\n # n_samples, n_features = X.shape\n X = self._augment(X)\n\n # Perform label encoding so label indicies start from zero\n le = LabelEncoder()\n encoded_y = le.fit_transform(y)\n self.classes_ = le.classes_\n n_classes = len(self.classes_)\n\n # Use the Parallel library to fit C binary classifiers in parallel\n results = Parallel(\n n_jobs=self.n_jobs, prefer='threads', verbose=self.verbose\n )(delayed(_fit_binary_perceptron)(X, encoded_y, c, self.eta0, self.decay, self.max_iterations)\n for c in range(n_classes))\n\n # Store final result for prediction\n self.weights_ = np.array(results)\n\n return self\n\n def predict(self, X):\n check_is_fitted(self, 'weights_')\n\n # Retrieved trained weights\n W = self.weights_\n\n # Augment X\n X = self._augment(X)\n\n # Compute distance between the C decision function\n # and each of the samples in the test set\n distances = np.dot(X, W.T)\n\n # Classify by taking the class with the largest distance\n return self.classes_[np.argmax(distances, axis=1)]\n\n\ndef get_classifier():\n return Perceptron()\n\n\ndef get_params_space(data_shape):\n return {\n 'eta0': [0.001, 0.01, 0.1, 1],\n # When decay=0 then learning rate is fixed to eta0\n 'decay': [0, 0.001, 0.01, 0.1, 1]\n }\n","sub_path":"Perceptron_multiclass_backpropagation.py","file_name":"Perceptron_multiclass_backpropagation.py","file_ext":"py","file_size_in_byte":3580,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"439959314","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Oct 12 15:05:16 2018\n\n@author: owen\n\"\"\"\n\n# Given two strings, write a method to decide if one is a permutation of the other.\n\n# 判断一个字符串是不是另一个的permutation\n\n# 两个字符串等长，和leetcode Permutation in String不一样\n\n# 类似于Find All Anagrams in a String的滑动窗口\n# 排序字符数组，从头比较, A = sorted(A) B = sorted(B) return A == B\n# 用两个哈希表存每个字符出现次数，比较哈希表\n\nclass Solution:\n \"\"\"\n @param A: a string\n @param B: a string\n @return: a boolean\n \"\"\"\n def Permutation(self, A, B):\n # write your code here\n # two pointers + sliding window, time / space O(n), same as find a anagram of A in B\n if not A and not B:\n return True\n \n n1, n2 = len(A), len(B)\n \n if n1 != n2:\n return False\n \n dmap = {}\n for c in A:\n dmap[c] = dmap.get(c, 0) + 1\n \n left, right = 0, 0\n cnt = len(dmap.keys()) # maintain a counter to check whether substring in the sliding window matches the target string.\n while right < n2: # 模板二\n if B[right] in dmap:\n dmap[B[right]] -= 1\n if dmap[B[right]] == 0:\n cnt -= 1\n right += 1\n \n while cnt == 0: # now this window can cover all chars, then increase pointer to make it invalid/valid again, and find the permutation of A\n if right - left == n1:\n return True\n if B[left] in dmap:\n dmap[B[left]] += 1\n if dmap[B[left]] > 0:\n cnt += 1\n left += 1\n \n return False\n \n \n \nclass Solution:\n \"\"\"\n @param A: a string\n @param B: a string\n @return: a boolean\n \"\"\"\n def Permutation(self, A, B):\n # write your code here\n dmap = collections.Counter(A)\n for c in B:\n dmap[c] -= 1\n \n for key, val in dmap.items():\n if val != 0:\n return False\n \n return True\n\n\n","sub_path":"String Permutation.py","file_name":"String Permutation.py","file_ext":"py","file_size_in_byte":2282,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"134223705","text":"import re\n\nimport scrapy\nfrom scrapy import Request\n\n\n\nclass HuuzFirst(scrapy.Spider):\n name = \"Huuz\"\n\n \n #handle_httpstatus_list = [301, 302]\n \n\n\n def start_requests(self):\n start_urls = [\n 'http://www.houzz.com/professionals/pools-and-spas/c/us',\n ]\n for url in start_urls:\n yield scrapy.Request(url=url,callback=self.parse)\n\n\n def parse(self,response):\n for x in response.xpath('//*[contains(@class,\"name-info\")]/a/@href').extract():\n yield scrapy.Request(response.urljoin(x), callback=self.parse_detail)\n\n \n \n\n # for next time try use response.xpath('//*[contains(@class,\"pagination\")]/li/a/@href').extract() loop for append list and use it for asyc pagination\n\n next_page = response.xpath('//*[@class=\"navigation-button next\"]/@href').extract_first()\n # print(next_page,'###############################################################################################1111111111')\n\n if next_page is not None:\n next_page = response.urljoin(next_page)\n #print(next_page,'##################################2222222222222#############################################################')\n #next_page = response.urljoin(next_page)\n yield scrapy.Request(next_page, self.parse) \n # r = re.search('(?<=t=)\\w+',''.join(response.xpath('//*[contains(@compid,\"fb\")]').extract()).strip())\n\n # re.search('(?<=t=)\\w+',''.join(response.xpath('//*[contains(@compid,\"fb_share\")]').extract()).strip()).groups()\n\n\n# response.xpath(\"//*[contains(text(), '@')]\").extract()\n# mailsrch = re.compile(r'[\\w\\-][\\w\\-\\.]+@[\\w\\-][\\w\\-\\.]+[a-zA-Z]{1,4}')\n #def pa(self, response):#\n # print('','@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@!!!!!!!!!!!!!!!!!!!!!!1')\n # email = response.xpath(\"//*[contains(text(), '@')]\").extract()\n # print(email,'@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@!!!!!!!!!!!!!!!!!!!!!!1')\n # mailsrch = re.compile(r'[\\w\\-][\\w\\-\\.]+@[\\w\\-][\\w\\-\\.]+[a-zA-Z]{1,4}')\n # yield mailsrch.findall(''.join(email))\n\n #print(mailsrch.findall(''.join(email)), '@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@!!!!!!!!!!!!!!!!!!!!!!1')\n\n\n\n\n\n\n def parse_detail(self,response):\n\n def re_data_fb(re_string):\n if re_string:\n clean_string = re.search('(?<=t=)\\w+', re_string)\n if clean_string:\n \n return 'https://www.facebook.com/%s' % clean_string.group()\n return ''\n\n \n def re_data_tw(re_string):\n if re_string:\n clean_string = re.search('(?<=text=)\\w+', re_string)\n if clean_string:\n \n return 'https://twitter.com/%s' % clean_string.group()\n return ''\n\n\n def re_data_number(re_string):\n if re_string:\n clean_string = re.search('(\\d+)', re_string)\n if clean_string:\n \n return clean_string.group()\n return ''\n\n \n \n def find_email_from_site(url_site):\n #res = Response(url_site)\n\n email = res.xpath(\"//*[contains(text(), '@')]\").extract()\n print(email,'@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@!!!!!!!!!!!!!!!!!!!!!!1')\n mailsrch = re.compile(r'[\\w\\-][\\w\\-\\.]+@[\\w\\-][\\w\\-\\.]+[a-zA-Z]{1,4}')\n yield mailsrch.findall(''.join(email))\n# d = Request(url_site, callback=self.pa) \n# yield d\n\n\n\n def join_lists(list_data):\n return ' '.join(list_data).strip()\n\n\n #op = self.pa(join_lists(response.xpath('//*[contains(@class,\"sidebar-item\")]/div/a/@href').extract()))\n #scrapy.Request(response.urljoin(x)\n\n # yield scrapy.Request(response.urljoin(join_lists(response.xpath('//*[contains(@class,\"sidebar-item\")]/div/a/@href').extract())), \n # callback=self.pa, \n # meta={'company_name':join_lists(response.xpath('//*[contains(@class,\"profile-title\")]/h1/a/text()').extract())})\n\n #! yield scrapy.Request(response.urljoin(re_data_fb(''.join(response.xpath('//*[contains(@compid,\"fb_share\")]').extract()).strip())), \n # callback=self.ne_pa, \n # meta={'company_name':join_lists(response.xpath('//*[contains(@class,\"profile-title\")]/h1/a/text()').extract())})\n y = (yield scrapy.Request(response.urljoin(join_lists(response.xpath('//*[contains(@class,\"sidebar-item\")]/div/a/@href').extract())), callback=self.ne_pa, meta={'email1':''})) \n #meta={'email1':join_lists(response.xpath('//*[contains(@class,\"profile-title\")]/h1/a/text()').extract())})\n\n\n\n \n\n\n\n\n print(y,'#########################################################',)\n \n\n #print(op,'#########################################################')\n yield {\n\n 'url':response.url,\n 'company_name':join_lists(response.xpath('//*[contains(@class,\"profile-title\")]/h1/a/text()').extract()),\n 'parent_category':join_lists(response.xpath('//*[contains(@class,\"hide\")]/a/span/text()').extract()),\n 'category':join_lists(response.css('div.info-list-text span a span::text').extract()),\n 'contact':''.join(response.xpath('/html/body/div[2]/div/div/div[2]/div/div/div[1]/div[2]/div/div[2]/div/div[3]/div[2]/div/text()').extract()).strip(),\n 'phone_number':join_lists(response.xpath('//*[contains(@class,\"sidebar-item\")]/div/span/text()').extract()),\n 'address': join_lists(response.xpath('.//*[@class=\"info-list-text\"]/span[@itemprop=\"streetAddress\"]/text()').extract()),\n 'city': join_lists(response.xpath('.//*[@class=\"info-list-text\"]/span[@itemprop=\"addressLocality\"]/a/text()').extract()),\n 'province': join_lists(response.xpath('.//*[@class=\"info-list-text\"]/span[@itemprop=\"addressRegion\"]/text()').extract()),\n 'postal_code':join_lists(response.xpath('.//*[@class=\"info-list-text\"]/span[@itemprop=\"postalCode\"]/text()').extract()),\n 'typical_job_cost':''.join(response.xpath('.//*[@class=\"info-list-label\"]/div[@class=\"info-list-text\"]/text()').extract()).strip(),\n 'website':join_lists(response.xpath('//*[contains(@class,\"sidebar-item\")]/div/a/@href').extract()),\n 'overview':join_lists((response.xpath('//*[contains(@class,\"profile-about\")]/div[1]/text()').extract())),\n 'service_provided':join_lists(response.xpath('//*[contains(@class,\"profile-about\")]/div[1]/text()').extract()),\n 'areas_served':join_lists(response.xpath('//*[contains(@class,\"profile-about\")]/div[5]/text()').extract()),\n 'certifications_and_awards': join_lists(response.xpath('.//div[contains(@class,\"leftSideBar\")]/div//*[@class=\"thumb-badge\"]/a/img/@title').extract()),\n 'numbers_of_reviews':join_lists(response.xpath('.//div[contains(@class,\"profile-info\")]//span[@itemprop=\"reviewCount\"]/text()').extract()),\n 'numbers_of_projects':re_data_number(join_lists(response.xpath('.//div[contains(@class,\"project-section\")]/div/a[1]/text()[1]').extract())),\n 'followers':join_lists(response.xpath('//*[contains(@class,\"follower\")]/span[@class=\"follow-count\"]/text()').extract()),\n 'following':join_lists(response.xpath('//*[contains(@class,\"following\")]/span[@class=\"follow-count\"]/text()').extract()),\n 'facebook':re_data_fb(''.join(response.xpath('//*[contains(@compid,\"fb_share\")]').extract()).strip()),\n 'twitter':re_data_tw(''.join(response.xpath('//*[contains(@compid,\"tw_share\")]').extract()).strip()),\n # 'linkedin':,\n # 'blog':,\n # 'google_plus':re.search('(?<=text=)\\w+',''.join(response.xpath('//*[contains(@compid,\"tw_share\")]').extract()).strip()).groups(),\n 'email1': y,\n # 'email2':,\n # 'email3':\n }\n def pa(self, response):#\n email = response.xpath(\"//*[contains(text(), '@')]\").extract()\n print(email,'@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@!!!!!!!!!!!!!!!!!!!!!!1')\n mailsrch = re.compile(r'[\\w\\-][\\w\\-\\.]+@[\\w\\-][\\w\\-\\.]+[a-zA-Z]{1,4}')\n \n # print('company_name','@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@!!!!!!!!!!!!!!!!!!!!!!1')\n #(response.meta['company_name'],mailsrch.findall(''.join(email)))\n t = response.meta['item'] = 'Scrapin from site @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@2'\n yield 'sdfsdfsfsdfsdfsdfsdfs'\n\n\n\n def ne_pa(self, response):#\n email = response.xpath(\"//*[contains(text(), '@')]\").extract()\n print(email,'@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@!!!!!!!!!!!!!!!!!!!!!!1')\n mailsrch = re.compile(r'[\\w\\-][\\w\\-\\.]+@[\\w\\-][\\w\\-\\.]+[a-zA-Z]{1,4}')\n \n print('NE_PA!!!!!!!!!!!!!!!!!!!!!!!!!!!!!1','@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@!!!!!!!!!!!!!!!!!!!!!!1')\n #print(response.meta['company_name'],mailsrch.findall(''.join(email)))\n #yield scrapy.Request(response.urljoin('https://mail.ru'), callback=self.pa)\n","sub_path":"bkp.py","file_name":"bkp.py","file_ext":"py","file_size_in_byte":9292,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"272114108","text":"import os\nimport sys\nimport bs4\nimport wget\nimport requests\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nfrom pdfminer.high_level import extract_text\n\nWEB_PAGE_ROOT = \"https://www.education.ie/en/Publications/Inspection-Reports-Publications/Whole-School-Evaluation-Reports-List/?pageNumber=\"\n\n\ndef PDFToText(path: str) -> str:\n try:\n extracted_report = extract_text(path)\n except (Exception) as e:\n print(f\"Error converting {path}: {e}\", file=sys.stderr)\n return \"\"\n return extracted_report\n\n\ndef DownloadPDF(numberOfPages: int, exportPath=\"./\") -> [str]:\n General_InspectionReports = pd.DataFrame(columns=['Date','School Roll No.','County','School Name','School Level','Inspection Type','Subject','URL'])\n\n for pageNumber in range(1, numberOfPages + 1):\n IrelandWebpage = requests.get(WEB_PAGE_ROOT + str(pageNumber))\n CleanIrelandWebpage = bs4.BeautifulSoup(IrelandWebpage.text, \"lxml\")\n InspectionReports = {}\n ID = 0\n Table = CleanIrelandWebpage.find('table', id=\"IRList\")\n for p in Table.find_all('tr'):\n if ID == 0:\n ID = ID + 1\n continue\n else:\n Date = p('td')[0].string[:2] + '_' + p('td')[0].string[3:5] + '_' + p('td')[0].string[6:]\n SchoolRoll = p('td')[1].string\n County = p('td')[2].string\n SchoolName = p('td')[3].string\n SchoolLevel = p('td')[4].string\n InspectionType = p('td')[5].string\n Subject = p('td')[6].string\n URL = p('td')[7]('a')[0].attrs['href'][86:]\n InspectionReports[ID] = {'Date': Date, 'School Roll No.': SchoolRoll, 'County': County, 'School Name': SchoolName, 'School Level': SchoolLevel, 'Inspection Type': InspectionType, 'Subject': Subject, 'URL': URL}\n ID = ID + 1\n\n df_InspectionReports = pd.DataFrame.from_dict(InspectionReports, orient='index')\n General_InspectionReports = pd.concat([General_InspectionReports,df_InspectionReports])\n\n print(f\"Number of reports to download: {len(General_InspectionReports)}\")\n\n PDFToConvert = []\n exported = []\n for index, row in General_InspectionReports.iterrows():\n DownloadURL = 'https://www.education.ie/en/Publications/Inspection-Reports-Publications/Whole-School-Evaluation-Reports-List/' + row['URL']\n if exportPath[-1] != '/': exportPath += '/'\n FileName = exportPath + row['School Roll No.'] + '_' + row['Date'] + '.pdf'\n print('\\tReport ' + row['School Roll No.'] + ' downloaded')\n wget.download(DownloadURL, FileName)\n exported.append(FileName)\n return exported\n\n\n\n\nif __name__ == \"__main__\":\n DOWNLOAD_PDF = False # If you want/need to download pdf, change this var to True\n NUMBER_OF_PAGES = 200\n PATH_TO_ALL_REPORTS = \"./Reports\"\n PATH_TO_PDF_REPORTS = PATH_TO_ALL_REPORTS + \"/pdf/\"\n PATH_TO_TEXT_REPORTS = PATH_TO_ALL_REPORTS + \"/plain_text\"\n\n General_InspectionReports = pd.DataFrame(columns=['Date','School Roll No.','County','School Name','School Level','Inspection Type','Subject','URL'])\n\n if os.path.exists(PATH_TO_ALL_REPORTS) == False: os.mkdir(PATH_TO_ALL_REPORTS)\n if os.path.exists(PATH_TO_PDF_REPORTS) == False: os.mkdir(PATH_TO_PDF_REPORTS)\n\n # Turn web table into Datafram\n if DOWNLOAD_PDF:\n for pageNumber in range(1, NUMBER_OF_PAGES + 1):\n IrelandWebpage = requests.get(WEB_PAGE_ROOT + str(pageNumber))\n CleanIrelandWebpage = bs4.BeautifulSoup(IrelandWebpage.text, \"lxml\")\n InspectionReports = {}\n ID = 0\n Table = CleanIrelandWebpage.find('table', id=\"IRList\")\n for p in Table.find_all('tr'):\n if ID == 0:\n ID = ID + 1\n continue\n else:\n Date = p('td')[0].string[:2] + '_' + p('td')[0].string[3:5] + '_' + p('td')[0].string[6:]\n SchoolRoll = p('td')[1].string\n County = p('td')[2].string\n SchoolName = p('td')[3].string\n SchoolLevel = p('td')[4].string\n InspectionType = p('td')[5].string\n Subject = p('td')[6].string\n URL = p('td')[7]('a')[0].attrs['href'][86:]\n InspectionReports[ID] = {'Date': Date, 'School Roll No.': SchoolRoll, 'County': County, 'School Name': SchoolName, 'School Level': SchoolLevel, 'Inspection Type': InspectionType, 'Subject': Subject, 'URL': URL}\n ID = ID + 1\n\n df_InspectionReports = pd.DataFrame.from_dict(InspectionReports, orient='index')\n General_InspectionReports = pd.concat([General_InspectionReports,df_InspectionReports])\n\n print(f\"Number of reports to download: {len(General_InspectionReports)}\")\n\n # Download PDF\n PDFToConvert = []\n for index, row in General_InspectionReports.iterrows():\n DownloadURL = 'https://www.education.ie/en/Publications/Inspection-Reports-Publications/Whole-School-Evaluation-Reports-List/' + row['URL']\n FileName = 'Reports/pdf/' + row['School Roll No.'] + '_' + row['Date'] + '.pdf'\n PDFToConvert.append('Reports/pdf/' + row['School Roll No.'] + '_' + row['Date'])\n print('\\tReport ' + row['School Roll No.'] + ' downloaded')\n wget.download(DownloadURL, FileName)\n\n # Converte PDF to text and remove useless data\n print(\"\\nProcessing data...\")\n\n\n else:\n PDFToConvert = os.listdir(PATH_TO_PDF_REPORTS)\n print(PDFToConvert[0:10])\n\n if os.path.exists(PATH_TO_TEXT_REPORTS) == False:\n os.mkdir(PATH_TO_TEXT_REPORTS)\n\n ConvertionCategories = {\"Properly processed\":0, \"Not in text format\":0, \"Cannot be processed\":0}\n FilesProperlyConverted = {}\n FilesNotConverted = []\n NUMBER_OF_PDF = len(PDFToConvert)\n\n for index, PDF in enumerate(PDFToConvert):\n print(f\"{index / NUMBER_OF_PDF * 100:.1f}%\\t-\\t{len(FilesNotConverted)}\\t-\\t{index}\\t-\\t{NUMBER_OF_PDF}\")\n try:\n extracted_report = PDFToText(f\"{PATH_TO_PDF_REPORTS + PDF}\")\n if \"ú\" in extracted_report :\n FilesNotConverted.append(PDF[len('Reports/pdf/'):])\n continue\n with open(f\"{PATH_TO_TEXT_REPORTS}/{PDF[:-4]}.txt\" ,\"w+\") as f:\n f.write(extracted_report)\n except (Exception) as e:\n ConvertionCategories[\"Cannot be processed\"] = ConvertionCategories[\"Cannot be processed\"] + 1\n FilesNotConverted.append(PDF[len('Reports/pdf/'):])\n print(PDF[len('Reports/pdf/'):] + f' could not be processed: {e}', file=sys.stderr)\n continue\n\n\n print(\"Data sucessfuly processed !\")\n print(f\"Number of errors during process: {len(FilesNotConverted)}\\t{len(FilesNotConverted) / NUMBER_OF_PDF * 100}%\")","sub_path":"dataExtraction.py","file_name":"dataExtraction.py","file_ext":"py","file_size_in_byte":6935,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"105183606","text":"\"\"\"InterviewBit.\n\nProgramming > Math > Excel Column Number\n\"\"\"\n\n\nclass Solution:\n \"\"\"Solution.\"\"\"\n\n # @param A : string\n # @return an integer\n def titleToNumber(self, A):\n \"\"\"Solution.\"\"\"\n A = [e for e in A]\n number = 0\n for i in range(len(A)):\n base = 26 ** i\n number += (ord(A[- 1 - i]) - 64) * base\n return number\n\n\ninputs = [\n \"A\",\n \"B\",\n \"C\",\n \"Z\",\n \"AA\",\n \"AB\",\n \"AZ\",\n \"ZZZ\",\n \"AAA\"\n]\n\nsolution = Solution()\nfor i in inputs:\n print(\"Input:\", i)\n print(\"Solution:\", solution.titleToNumber(*i))\n print('\\n' * 2)\n","sub_path":"interviewbit/Programming/Math/Excel Column Number/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":622,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"250305559","text":"import fnmatch\n\n\ndef acl_for(client, target=None):\n acl = client.config.acl.copy()\n\n if target is not None:\n if target in client.config.channels:\n channel_acl = client.config.channels[target].acl\n else:\n channel_acl = {}\n\n for hostmask, permissions in channel_acl.items():\n acl.setdefault(hostmask, set([])).update(permissions)\n\n return acl\n\n\ndef has_permission(client, hostmask, permission, target=None):\n for match_hostmask, permissions in acl_for(client, target).items():\n if fnmatch.fnmatch(hostmask, match_hostmask) and \\\n (permission in permissions or \"admin\" in permissions):\n return True\n return False\n\n\ndef requires_permission(permission):\n def _decorator(f):\n if not hasattr(f, \"permissions\"):\n f.permissions = set([])\n f.permissions.add(permission)\n\n return f\n return _decorator\n","sub_path":"kochira/auth.py","file_name":"auth.py","file_ext":"py","file_size_in_byte":926,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"63762216","text":"import math\r\n\r\nclass Matrix:\r\n def __init__(self, M1):\r\n self.M1 = M1\r\n self.n11 = len(self.M1)\r\n self.n12 = len(self.M1[0])\r\n\r\n def MultiplyMatrix(self, M2):\r\n self.M2 = M2\r\n self.n21 = len(self.M2)\r\n self.n22 = len(self.M2[0])\r\n if self.n21 != self.n12:\r\n print(\"Matrices cannot multiply!\")\r\n else:\r\n self.M3 = []\r\n for i in range(0, self.n11):\r\n self.M3.append([])\r\n for j in range(0, self.n22):\r\n sum = 0\r\n for k in range(0, self.n12):\r\n sum += self.M1[i][k] * self.M2[k][j]\r\n self.M3[-1].append(sum)\r\n return self.M3\r\n\r\n def MultiplyNumber(self, num):\r\n self.M3 = []\r\n for i in range(0, self.n11):\r\n self.M3.append([])\r\n for j in range(0, self.n12):\r\n self.M3[-1].append(num * self.M1[i][j])\r\n return self.M3\r\n\r\nclass StrainRossette:\r\n def __init__(self, StrainX, StrainS, StrainY, E, v):\r\n # StrainX: A list of horizontal strains\r\n # StrainY: A list of vertical strains\r\n # StrainS: A list of strains in 45 degree direction\r\n # E is Young's Modulus\r\n # v is Poisson's Ratio\r\n length1 = len(StrainX)\r\n length2 = len(StrainY)\r\n length3 = len(StrainS)\r\n if length1 != length2 or length2 != length3 or length1 != length3:\r\n print(\"Three Strain Components should have same length\")\r\n else:\r\n Angle = []\r\n Px = []\r\n Py = []\r\n VMStrain = []\r\n VMStress = []\r\n for i in range(0, length1):\r\n ShearStrain = -StrainX[i] + 2*StrainS[i] - StrainY[i]\r\n SquareMatrix = [[1, v, 0], [v, -1, 0], [0, 0, (1 - v)/2]]\r\n Step1 = Matrix(SquareMatrix).MultiplyNumber(float(E)/(1 - v**2))\r\n Step2 = Matrix(Step1).MultiplyMatrix([[StrainX[i]], [StrainY[i]], [ShearStrain]])\r\n VMStress.append((Step2[0][0]**2 - Step2[0][0]*Step2[1][0] + Step2[1][0]**2 + 3*Step2[2][0]**2)**0.5)\r\n Angle.append(math.atan(ShearStrain/(StrainX[i] - StrainY[i]))*0.5)\r\n p1 = (StrainX[i] + StrainY[i])/2\r\n p2 = (((StrainX[i] - StrainY[i])/2)**2 + (ShearStrain/2)**2)**0.5\r\n Px.append(p1 + p2)\r\n Py.append(p1 - p2)\r\n VMStrain.append(2.0**0.5/3*((2*p2)**2 + (p1 + p2)**2 + (p1 - p2)**2)**0.5)\r\n\r\n self.Px = Px\r\n self.Py = Py\r\n self.Angle = Angle\r\n self.VMStrain = VMStrain\r\n self.VMStress = VMStress\r\n\r\n def Px(self):\r\n # ============ Principal Strain 1 ================\r\n return self.Px\r\n\r\n def Py(self):\r\n # ============ Principal Strain 2 ===============\r\n return self.Py\r\n\r\n def Angle(self):\r\n return self.Angle\r\n\r\n def VMStrain(self):\r\n # ============== Equivalent Von-mises Strain =====================\r\n return self.VMStrain\r\n\r\n\r\n# ================ ImportFile =========================\r\nFile = open('/media/chenting/Work/ProgramCode/StrainRossette/SX1-1StrainGuages.txt')\r\nPx = []\r\nPy = []\r\nAngle = []\r\nn = 0\r\n\r\nfor line in File:\r\n content = line.split()\r\n if len(content) == 3:\r\n n = n + 1\r\n Px.append(float(content[2]))\r\n Py.append(float(content[1]))\r\n Angle.append(float(content[0]))\r\n\r\nFile.close()\r\n\r\nResult = StrainRossette(Px, Py, Angle, 205e-3, 0.3)\r\n\r\nFile = open('/media/chenting/Work/ProgramCode/StrainRossette/SX1-1StrainGuagesResult.txt', 'w')\r\nfor i in range(0, n):\r\n File.write('%f %f %f %f\\n' % (Result.Px[i], Result.Py[i], Result.Angle[i], Result.VMStress[i]))\r\n\r\nprint('2nd')\r\nr2 = StrainRossette([97], [455], [-122], 205e-3, 0.3)\r\nprint(r2.VMStress[0])\r\n\r\nFile.close()\r\n","sub_path":"StrainRossette.py","file_name":"StrainRossette.py","file_ext":"py","file_size_in_byte":3473,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"473896889","text":"import telebot\n\n# Подключение через прокси для обхода блокировки Телеграм:\n# from telebot import apihelper\n\n# apihelper.proxy = {'https': '51.68.172.7:3128'}\n\n# Тело бота\nbot = telebot.TeleBot(\"981933529:AAGP14DNhi7J5pFfp678r2a9lc0LVpH3EQA\")\n\n\n@bot.message_handler(content_types=['new_chat_members'])\ndef send_welcome(message):\n bot.send_message(message.chat.id,\n 'Добро пожаловать в чат ютуб канала Crypto Analysis! - ' + str(message.from_user.first_name))\n\n\nbot.polling(none_stop=True)","sub_path":"CryptoAnalysisBot1.py","file_name":"CryptoAnalysisBot1.py","file_ext":"py","file_size_in_byte":596,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"158566271","text":"import binascii\nfrom socket import *\nimport _thread\nimport binascii\nimport sys\nimport utils\n\ndef conectado(con,cliente):\n print('Conectado por', cliente)\n\n while True:\n quadro16 = con.recv(1024)\n quadro = (utils.decode16(quadro16)).decode()\n sync = 'dcc023c2dcc023c2'\n # Confirmar o sync\n if(quadro[0:16] == sync):\n if (utils.confirmChecksum(quadro)):\n # Enviar um ACK\n ack = utils.ack(quadro)\n con.send(ack)\n else:\n # Ignora quadro\n con.send(None)\n else:\n con.send(None)\n\n if not msg: break\n print(cliente, msg)\n\n print('Finalizando conexao do cliente', cliente)\n con.close()\n _thread.exit()\n\nhost = '127.0.0.1'\n# server_port = argv[1]\nserver_port = 5152\n\ns = socket(AF_INET, SOCK_STREAM)\n\norig = (host, server_port)\ns.bind(orig)\ns.listen(1)\n\nprint(\"Esperando conexao\")\nwhile True:\n con, cliente = s.accept()\n _thread.start_new_thread(conectado, tuple([con, cliente]))\ns.close()\n","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1065,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"533594467","text":"from flask import Flask, jsonify, render_template\nfrom flask_cors import CORS\nfrom flask_mongoengine import MongoEngine\nfrom flask_jwt_extended import JWTManager\nfrom flask_socketio import SocketIO, send, emit\nfrom app.decorators import ws_jwt_required\n\napp = Flask(__name__, static_folder='client')\napp.config.from_object('config')\n\nCORS(app)\ndb = MongoEngine(app)\njwt = JWTManager(app)\nsocketio = SocketIO(app)\n\n\n@app.errorhandler(405)\ndef method_not_allowed(error):\n return jsonify({'message': 'Method Not Allowed', 'code': 'E_NOALW'}), 405\n\n\n@app.errorhandler(500)\ndef server_error(error):\n return jsonify({'message': 'Server Error', 'code': 'E_SVRERR'}), 500\n\n\n# Importing views\nfrom app.auth.controller import auth\n# register blueprints\napp.register_blueprint(auth, url_prefix='/auth')\n\n\n@socketio.on('message')\ndef handle_message(message):\n print('received message: ' + message)\n message_back = f'Hey Back There!'\n emit('message back', message_back)\n return message_back\n\n\n@socketio.on('protected')\n@ws_jwt_required\ndef handle_protected(message):\n print('received message: ' + message)\n message_back = f'Hey Back There!'\n emit('protected back', message_back)\n return message_back\n\n\n@app.route('/')\ndef home():\n return render_template('index.html')\n\n\n@app.errorhandler(404)\ndef not_found(error):\n return render_template('index.html')\n\n\n# prevent cached responses\n@app.after_request\ndef add_header(r):\n \"\"\"\n This will help if you are using the client part, \n if not you can just delete this method\n \"\"\"\n r.headers[\"Cache-Control\"] = \"no-cache, no-store, must-revalidate, public, max-age=0\"\n r.headers[\"Pragma\"] = \"no-cache\"\n r.headers[\"Expires\"] = \"0\"\n return r\n","sub_path":"app/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1727,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"404364335","text":"\"\"\"\nScript for setting library version configuration used in installation.\nAutomatically called when running setup.py.\nVersion can be either dynamically determined through git information, or\npassed in as an argument.\n\"\"\"\n\nimport subprocess\nimport argparse\nimport re\nimport os\n\nCONFIG_FILE = os.path.join(os.path.abspath(os.path.dirname(__file__)),\"atom/config.py\")\n\n\ndef call_git_describe(abbrev):\n \"\"\"\n Returns latest git tag if available.\n \"\"\"\n p = subprocess.run(['git', 'describe', '--tags', '--abbrev=%d' % abbrev],\n stdout=subprocess.PIPE)\n line = str(p.stdout, 'utf-8').strip()\n line = line[1:] if line[0] == 'v' else line\n return line.strip('\\n')\n\n\ndef is_dirty():\n \"\"\"\n Returns True/False if there are commits since the last tag.\n \"\"\"\n try:\n p = subprocess.run([\"git\", \"diff-index\", \"--name-only\", \"HEAD\"],\n stdout=subprocess.PIPE)\n return len(str(p.stdout)) > 0\n except Exception:\n return False\n\n\ndef get_git_version(abbrev=7):\n \"\"\"\n Calls \"git describe\" to get current version; adds \"dirty\" if there have\n been extra commits since the latest tag. Will return empty list if no\n .git information is available.\n \"\"\"\n version = call_git_describe(abbrev)\n if is_dirty():\n version += \"-dirty\"\n\n return version\n\n\ndef get_existing_config_line():\n \"\"\"\n Returns configs and line of version in config.py;\n Returns None if version not present in config.py.\n \"\"\"\n with open(CONFIG_FILE, \"r\") as file:\n configs = file.readlines()\n\n version_bools = [x.startswith(\"VERSION =\") for x in configs]\n\n try:\n return configs, version_bools.index(True)\n except Exception:\n return configs, None\n\n\ndef replace_version_config(replacement):\n \"\"\"\n Replace existing configuration or add it if its not present\n \"\"\"\n configs, line_num = get_existing_config_line()\n\n if line_num:\n configs[line_num] = replacement\n else:\n configs.append(replacement)\n\n with open(CONFIG_FILE, \"w\") as file:\n file.writelines(configs)\n\n\ndef main(version=None):\n # use version argument if it is passed in; else find version from .git\n version = version if version else get_git_version()\n\n # replace version config if there's a new one\n if version:\n version_str = \"\\nVERSION = \\\"{}\\\"\\n\".format(version)\n replace_version_config(version_str)\n\n return version\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description='Replace library version config')\n parser.add_argument('--version', type=str,\n help='library version')\n\n args = parser.parse_args()\n\n main(version=args.version)\n","sub_path":"languages/python/version.py","file_name":"version.py","file_ext":"py","file_size_in_byte":2736,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"218801333","text":"'''\nCreated on Dec 7, 2011\n\n@author: mkiyer\n\nAssemblyLine: transcriptome meta-assembly from RNA-Seq\n\nCopyright (C) 2012 Matthew Iyer\n\nThis program is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or\n(at your option) any later version.\n\nThis program is distributed in the hope that it will be useful,\nbut WITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\nGNU General Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see .\n'''\nimport operator\nimport networkx as nx\n\nfrom base import Exon, TRANSCRIPT_IDS, NODE_SCORE, NODE_LENGTH, \\\n CHAIN_NODES, CHAIN_EDGES, CHAIN_DATA\n\ndef can_collapse(G,u,v):\n # see if edge nodes have degree larger than '1'\n if ((G.out_degree(u) > 1) or (G.in_degree(v) > 1)):\n return False\n return True\n\ndef can_collapse_contiguous(G,u,v):\n '''\n same as can_collapse but does not allow introns to be collapsed\n '''\n # see if edge nodes have degree larger than '1'\n if ((G.out_degree(u) > 1) or (G.in_degree(v) > 1)):\n return False\n # collapse non-intron edges \n if (u.end == v.start) or (v.end == u.start):\n return True\n return False\n\ndef get_chains(G, introns=True):\n \"\"\"\n group nodes into chains\n \n returns a dict mapping node -> chain, as well as a \n dict mapping chains to nodes\n \"\"\"\n if introns:\n can_collapse_func = can_collapse\n else:\n can_collapse_func = can_collapse_contiguous\n imin2 = lambda x,y: x if x<=y else y \n imax2 = lambda x,y: x if x>=y else y \n node_chain_map = {}\n chains = {}\n # initialize each node to be in a \"chain\" by itself\n for n in G.nodes_iter():\n node_chain_map[n] = n\n chains[n] = set((n,))\n for u,v in G.edges_iter():\n if not can_collapse_func(G,u,v):\n continue\n # get chains containing these nodes\n u_new = node_chain_map[u]\n u_chain = chains[u_new]\n del chains[u_new]\n v_new = node_chain_map[v]\n v_chain = chains[v_new]\n del chains[v_new]\n # merge chains \n merged_chain = u_chain.union(v_chain)\n merged_node = Exon(imin2(u_new.start, v_new.start),\n imax2(u_new.end, v_new.end))\n # point all nodes in chain to new parent\n for n in merged_chain:\n node_chain_map[n] = merged_node\n chains[merged_node] = merged_chain\n # sort chain nodes by genome position and store as list\n for parent in chains:\n chains[parent] = sorted(chains[parent], key=operator.attrgetter('start'))\n return node_chain_map, chains\n\ndef add_chains(G, chains, node_chain_map):\n H = nx.DiGraph()\n # add chain nodes\n for parent, nodes in chains.iteritems():\n # if nodes have already been collapsed they\n # might have chain attributes and these must\n # be preserved\n new_nodes = []\n chain_data = {}\n for n in nodes:\n d = G.node[n]\n if CHAIN_NODES in d:\n new_nodes.extend(d[CHAIN_NODES])\n if CHAIN_DATA in d:\n chain_data.update(d[CHAIN_DATA])\n new_nodes.extend(nodes)\n # sort nodes by genome position and find the min/max\n sorted_nodes = sorted(new_nodes, key=operator.attrgetter('start'))\n # add node attributes\n chain_data.update(dict((n, G.node[n]) for n in nodes))\n attr_dict = {CHAIN_NODES: sorted_nodes,\n CHAIN_DATA: chain_data,\n CHAIN_EDGES: []} \n H.add_node(parent, attr_dict=attr_dict)\n # add chain edges\n for u,v,d in G.edges_iter(data=True):\n u_chain_node = node_chain_map[u]\n v_chain_node = node_chain_map[v]\n if u_chain_node != v_chain_node:\n H.add_edge(u_chain_node, v_chain_node, attr_dict=d)\n else:\n # add internal chain edge attribute data\n # as 'chain_edges' attribute of parent node\n H.node[u_chain_node][CHAIN_EDGES].append((u,v,d))\n return H\n\ndef recalc_strand_specific_graph_attributes(G, transcript_map):\n \"\"\"\n computes score, length, and transcript ids after graph\n is divided into strand-specific subgraphs and collapsed\n \"\"\"\n for n,d in G.nodes_iter(data=True):\n chain_nodes = d[CHAIN_NODES]\n chain_data = d[CHAIN_DATA]\n transcript_ids = set()\n total_length = 0\n for cn in chain_nodes:\n total_length += (cn.end - cn.start)\n cattrs = chain_data[cn]\n transcript_ids.update(cattrs[TRANSCRIPT_IDS]) \n # new score is sum of scores of all transcripts\n # in the collapsed chain of nodes \n total_score = sum(transcript_map[t_id].score \n for t_id in transcript_ids)\n # set attributes\n d[TRANSCRIPT_IDS] = transcript_ids\n d[NODE_LENGTH] = total_length \n d[NODE_SCORE] = total_score\n\ndef collapse_strand_specific_graph(G, transcript_map, introns=True):\n \"\"\"\n find groups of nodes that have a single path through them\n and merges them into chains\n \n NOTE: assumes a strand-specific graph.\n \n returns new DiGraph object. each node has a 'chain' attribute \n containing the child nodes making up the chain. nodes also \n have 'chain_data' and 'chain_edges' attributes with node \n attribute data and edge data of child nodes \n \"\"\"\n # TODO: may not need transcript map here\n node_chain_map, chains = get_chains(G, introns)\n H = add_chains(G, chains, node_chain_map)\n recalc_strand_specific_graph_attributes(H, transcript_map)\n return H\n","sub_path":"assemblyline/assemblyline/deprecated/collapse_himem.py","file_name":"collapse_himem.py","file_ext":"py","file_size_in_byte":5912,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"569197334","text":"# -*- coding: utf-8 -*-\n# @Time : 2019/3/23 10:46\n# @Author: Tansy_Xiaoming\n# @Email : 279244228@qq.com\n# @File : common_path.py\nimport os\nimport time\n\ncurrent_time = time.strftime('%Y%m%d%H%M%S', time.localtime(time.time())) # 输出当前时间\nbase_path = os.path.split(os.path.split(os.path.realpath(__file__))[0])[0]\nexcel_path = os.path.join(base_path,'test_data','前程贷_用例.xlsx')\nlog_name = 'Log' + current_time + '.log'\nlog_path = os.path.join(base_path,'test_result','test_log',log_name)\nreport_name = current_time + '测试报告.html'\nreport_path = os.path.join(base_path,'test_result','test_report',report_name)\nconfig_path = os.path.join(base_path,'config_file','api_config.conf')\n\nif __name__ == '__main__':\n print(base_path)\n print(excel_path)\n print(log_path)\n print(report_path)\n print(config_path)","sub_path":"common/common_path.py","file_name":"common_path.py","file_ext":"py","file_size_in_byte":840,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"53723368","text":"import cv2 as cv\r\n\r\n#读取图片\r\nsrc = cv.imread('maliao.jpg')\r\nprint(src.shape)\r\n\r\n#图像缩放\r\nresult = cv.resize(src, None, fx=0.5, fy=0.5)\r\nprint(result.shape)\r\n\r\n#显示图像\r\ncv.imshow(\"src\", src)\r\ncv.imshow(\"result\", result)\r\n\r\n#等待显示\r\ncv.waitKey()\r\ncv.destroyAllWindows()","sub_path":"python-opencv/blog5-resize/demo-resize-fxfy.py","file_name":"demo-resize-fxfy.py","file_ext":"py","file_size_in_byte":291,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"364845720","text":"def rotate_a_matrix_by_90_degree(a): #2차원 리스트 90도 회전\n n=len(a)\n m=len(a[0])\n result = [[0]*n for _ in range(m)] #결과 리스트\n for i in range(n):\n for j in range(m):\n result[j][n-i-1] = a[i][j]\n return result\n\ndef check(new_lock): #자물쇠의 중간 부분이 모두 1인지 확인\n lock_length = len(new_lock)//3\n for i in range(lock_length,lock_length*2):\n for j in range(lock_length,lock_length*2):\n if new_lock[i][j]!=1:\n return False\n return True\n\ndef solution(key, lock):\n answer = True\n n=len(lock)\n m=len(key)\n \n new_lock = [[0]*(n*3) for _ in range(n*3)] #자물쇠의 크기를 기존의 3배로 변환\n for i in range(n): #새로운 자물쇠 중앙 부분에 기존 자물쇠 넣기\n for j in range(n):\n new_lock[i+n][j+n]=lock[i][j]\n \n for rotation in range(4): #4가지 방향에 대해서 확인\n key = rotate_a_matrix_by_90_degree(key) #열쇠 회전\n for x in range(n*2):\n for y in range(n*2):\n for i in range(m): #자물쇠에 열쇠 끼워넣기\n for j in range(m):\n new_lock[x+i][y+j]+=key[i][j]\n if check(new_lock)==True: #새로운 자물쇠에 열쇠가 정확히 들어맞는지 검사\n return True\n for i in range(m): #자물쇠에서 열쇠를 다시 빼기\n for j in range(m):\n new_lock[x+i][y+j] -=key[i][j]\n return False","sub_path":"level_3/lock_and_key.py","file_name":"lock_and_key.py","file_ext":"py","file_size_in_byte":1564,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"103717928","text":"\n\nfrom xai.brain.wordbase.nouns._atoll import _ATOLL\n\n#calss header\nclass _ATOLLS(_ATOLL, ):\n\tdef __init__(self,): \n\t\t_ATOLL.__init__(self)\n\t\tself.name = \"ATOLLS\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"atoll\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_atolls.py","file_name":"_atolls.py","file_ext":"py","file_size_in_byte":231,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"95791087","text":"# encoding uft-8\n\nimport requests\nfrom bs4 import BeautifulSoup\nimport json\n\n\ndef get_page():\n url = \"https://movie.douban.com/cinema/nowplaying/liaocheng/\"\n\n headers = {\n \"User-Agent\": \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Ubuntu \\\n Chromium/76.0.3809.100 Chrome/76.0.3809.100 Safari/537.36 \"\n }\n\n response = requests.get(url, headers=headers)\n text = response.text\n print(response.text)\n return text\n\n\ndef parse_page(text1):\n soup = BeautifulSoup(text1, 'html5lib')\n movies1 = []\n liList = soup.find_all(\"li\", attrs={\"data-category\": 'nowplaying'})\n\n for li in liList:\n movie = {}\n title = li['data-title']\n score = li['data-score']\n release = li['data-release']\n actors = li['data-actors']\n img = li.find('img')\n thumbnail = img['src']\n movie['title'] = title\n movie['score'] = score\n movie['release'] = release\n movie['actors'] = actors\n movie['thumbnail'] = thumbnail\n movies1.append(movie)\n return movies1\n\n\ndef save_data(data):\n with open('douban.json', 'w', encoding='utf-8') as fp:\n json.dump(data, fp, ensure_ascii=False)\n\n\nif __name__ == '__main__':\n text = get_page()\n movies = parse_page(text)\n save_data(movies)\n\n\n","sub_path":"demo/douban.py","file_name":"douban.py","file_ext":"py","file_size_in_byte":1322,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"121852477","text":"#!/usr/bin/python\nimport os\nimport datetime\n\n\nif __name__ == \"__main__\":\n src_foler = \"/home/frankie/work/crowd_conga/data_extractor/pvg/\";\n dst_folder = \"/home/frankie/work/crowd_conga/cc-staging/cc-business/private/car_data_json_files/\"\n date_time = datetime.datetime.now().strftime(\"%I%M%p_%B_%d_%Y\");\n\n os.chdir(dst_folder);\n #fst backup all old json: date_time.tar.gz, move to backup folder\n cmd = \"tar -cvzf \" + date_time + \".tar.gz *.json\";\n os.system(cmd);\n #del all old json file\n cmd = \"rm -f *.json\"\n os.system(cmd);\n #cp *.tar.gz to back_up folder\n cmd = \"mv *.tar.gz ./back_up\"\n os.system(cmd);\n #cp all new json file into it\n cmd = \"cp \" + src_foler + \"mak.json ./\";\n os.system(cmd);\n cmd = \"cp \" + src_foler + \"make_model.json ./\";\n os.system(cmd);\n cmd = \"cp \" + src_foler + \"all_feature_options.json ./\";\n os.system(cmd);\n cmd = \"cp \" + src_foler + \"mak_model_json_files/* ./\";\n os.system(cmd);\n","sub_path":"pvg/update_to_cc.py","file_name":"update_to_cc.py","file_ext":"py","file_size_in_byte":978,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"288379721","text":"# MIT License\n#\n# Copyright (c) 2017 Tom Runia\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to conditions.\n#\n# Author: Deep Learning Course | Fall 2018\n# Date Created: 2018-09-04\n################################################################################\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport numpy as np\nimport torch.utils.data as data\nfrom collections import Counter\nfrom sklearn.utils import shuffle\nimport torch\n\nclass LanguageDataset(data.Dataset):\n\n def __init__(self, filename, ref=None):\n data = open(filename, 'r', encoding='utf-8').read().split('\\n')\n self._inputs = []\n self._targets = []\n count = 0\n for line in data:\n if line != '':\n line_s = line.split('\\t')\n self._inputs.append(line_s[1])\n self._targets.append(line_s[0])\n\n print('{} paragraphs are too short.'.format(count))\n self._inputs, self._targets = shuffle(self._inputs, self._targets)\n self._offset = 0\n if ref is None:\n self._seq_length = 310\n self._langs = list(set(self._targets))\n self._chars = list(set(''.join(self._inputs)))\n self._data_size, self._vocab_size, self._n_langs = len(self._targets), len(self._chars), len(self._langs)\n print(\"Initialize dataset with {} characters {} langs.\".format(\n self._vocab_size, self._n_langs))\n self._char_to_ix = { ch:i for i,ch in enumerate(self._chars) }\n self._ix_to_char = { i:ch for i,ch in enumerate(self._chars) }\n\n self._lang_to_ix = { l:i for i,l in enumerate(self._langs) }\n self._ix_to_lang = { i:l for i,l in enumerate(self._langs) }\n else:\n self._langs = ref._langs\n self._seq_length = ref._seq_length\n self._chars = ref._chars\n self._data_size, self._vocab_size, self._n_langs = len(self._targets), len(self._chars), len(self._langs)\n print(\"Initialize dataset with {} characters {} langs.\".format(\n self._vocab_size, self._n_langs))\n self._char_to_ix = ref._char_to_ix\n self._ix_to_char = ref._ix_to_char\n self._lang_to_ix = ref._lang_to_ix\n self._ix_to_lang = ref._ix_to_lang\n def __getitem__(self, index):\n length = len(self._inputs[index])\n offset = length if length < self._seq_length else self._seq_length\n input = self._inputs[index][:offset]\n char_list = np.array([self._char_to_ix[char] for char in input])\n targets = np.array(self._lang_to_ix[self._targets[index]])\n pad_inputs = np.zeros(self._seq_length)\n for k,v in enumerate(char_list):\n pad_inputs[k] = v\n pad_inputs = torch.from_numpy(pad_inputs).long()\n inputs = torch.zeros((len(pad_inputs), self._vocab_size))\n inputs = inputs.scatter(1,pad_inputs.unsqueeze(-1),1)\n return inputs, targets\n # a simple custom collate function, just to show the idea\n\n def convert_to_string(self, char_ix):\n return ''.join(self._ix_to_char[ix] for ix in char_ix)\n\n def __len__(self):\n return self._data_size\n\n @property\n def vocab_size(self):\n return self._vocab_size\n\n @property\n def n_langs(self):\n return self._n_langs\n","sub_path":"rnn/dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":3773,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"620729621","text":"import csv\nfrom typing import TextIO, Optional\nfrom pathlib import Path\nfrom logging import INFO, Formatter, StreamHandler, FileHandler, Logger\n\nimport pandas as pd\nfrom sklearn.metrics import mean_absolute_error\n\nfrom transquest.algo.sentence_level.monotransquest.evaluation import (\n pearson_corr, spearman_corr, rmse\n)\n\n\ndef set_logger(logger: Logger, log_file: Optional[str] = None) -> Logger:\n logger.setLevel(INFO)\n formatter = Formatter('[%(asctime)s][%(levelname)s] %(message)s')\n if log_file is not None:\n f_handler = FileHandler(log_file)\n f_handler.setFormatter(formatter)\n logger.addHandler(f_handler)\n s_handler = StreamHandler()\n s_handler.setFormatter(formatter)\n logger.addHandler(s_handler)\n return logger\n\n\ndef find_data_file(base_dir: str, src: str, tgt: str) -> Optional[str]:\n try:\n data_dir = Path(base_dir + f'/{src}-{tgt}/')\n assert data_dir.exists(), FileNotFoundError(f'{data_dir}')\n data_f = next(iter(data_dir.glob('*.df.short.tsv')))\n return str(data_f)\n except AssertionError:\n return None\n\n\ndef read_annotated_file(path, index=\"index\"):\n indices = []\n originals = []\n translations = []\n z_means = []\n with open(path, mode=\"r\", encoding=\"utf-8-sig\") as csvfile:\n reader = csv.DictReader(csvfile, delimiter=\"\\t\",\n quoting=csv.QUOTE_NONE)\n for row in reader:\n indices.append(row[index])\n originals.append(row[\"original\"])\n translations.append(row[\"translation\"])\n z_means.append(float(row[\"z_mean\"]))\n\n return pd.DataFrame(\n {'index': indices, 'original': originals, 'translation': translations,\n 'z_mean': z_means}\n )\n\n\ndef read_test_file(path, index=\"index\"):\n indices = []\n originals = []\n translations = []\n with open(path, mode=\"r\", encoding=\"utf-8-sig\") as csvfile:\n reader = csv.DictReader(csvfile, delimiter=\"\\t\",\n quoting=csv.QUOTE_NONE)\n for row in reader:\n indices.append(row[index])\n originals.append(row[\"original\"])\n translations.append(row[\"translation\"])\n\n return pd.DataFrame(\n {'index': indices, 'original': originals, 'translation': translations}\n )\n\n\ndef stat_text(data_frame, label_column, prediction_column) -> str:\n data_frame = data_frame.sort_values(label_column)\n\n ground_trues = data_frame[label_column].tolist()\n predictions = data_frame[prediction_column].tolist()\n pearson = pearson_corr(ground_trues, predictions)\n spearman = spearman_corr(ground_trues, predictions)\n rmse_value = rmse(ground_trues, predictions)\n mae = mean_absolute_error(ground_trues, predictions)\n\n textstr = f'RMSE={rmse_value:.4f}\\n'\\\n f'MAE={mae:.4f}\\n'\\\n f'Pearson Correlation={pearson:.4f}\\n'\\\n f'Spearman Correlation={spearman:.4f}'\n return textstr\n\n\ndef format_submission(\n df: pd.DataFrame, language_pair: str, method: str, index: list, fp: TextIO,\n index_type=None\n):\n if index_type is None:\n index = index\n elif index_type == \"Auto\":\n index = range(0, df.shape[0])\n\n predictions = df['predictions']\n for number, prediction in zip(index, predictions):\n text = language_pair + \"\\t\" + method + \"\\t\" + \\\n str(number) + \"\\t\" + str(prediction)\n fp.write(\"%s\\n\" % text)\n","sub_path":"wmt21_qe/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3439,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"303000605","text":"N = int(input())\nW = [input() for _ in range(N)]\n\nres = True\n\ndic = {}\n\nlast = \"\"\n\nfor w in W:\n if last != \"\":\n if w in dic:\n res = False\n if not w[0] == last:\n res = False\n\n dic[w] = 1\n last = w[-1]\n\n\nif res:\n print(\"Yes\")\nelse:\n print(\"No\")","sub_path":"Python_codes/p03261/s185017981.py","file_name":"s185017981.py","file_ext":"py","file_size_in_byte":293,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"635286099","text":"# Jake Ledoux\n# KSP KRPC script intended to vertically soft-land a rocket\n\nimport jrpc, math\nfrom time import sleep\n\njrpc.connect(\"Landing Script\")\njrpc.gear(False)\njrpc.brakes(True)\njrpc.autopilot(False)\n\nlat = -0.09716278863190207\nlon = -74.55776901643725\n\nwhile True:\n\tvspeed = jrpc.speedVertical()*-1\n\thspeed = jrpc.speedHorizontal()\n\talt = jrpc.trueAltitude()\n\tmax_decel = (jrpc.maxThrust() / jrpc.mass()) - jrpc.gravity()\n\tburn_distance = vspeed**2 / (2*max_decel)\n\tttwr = jrpc.maxThrust() / (jrpc.mass()*jrpc.gravity())\n\thoverthrust = 1/ttwr\n\t# if hspeed > 2 and vspeed > 0:\n\t\t# jrpc.SAS(True)\n\t\t# jrpc.SASMode(\"retrograde\")\n\t\t# jrpc.throttle(hspeed/20)\n\t# else:\n\t\t# jrpc.throttle(0)\n\tif vspeed < 0:\n\t\tprint(\"Waiting...\")\n\t\twhile vspeed < 0:\n\t\t\tvspeed = jrpc.speedVertical()*-1\n\t\tprint(\"Starting\")\n\tif burn_distance > alt-9:\n\t\tbreak\njrpc.throttle(1)\nprint(\"Burning.\")\nwhile jrpc.speedVertical() < -2:\n\tprint(jrpc.speedVertical())\n\tif jrpc.speedHorizontal() < .5:\n\t\tjrpc.SAS(True)\n\t\tjrpc.SASMode(\"radial_out\")\n\tcontinue\njrpc.throttle(0)\nprint(\"Burn complete\")\njrpc.SASMode(\"standard\")\n\nwhile jrpc.isLanded() == False:\n\tjrpc.gear(True)\n\tvspeed = jrpc.speedVertical()*-1\n\tjrpc.throttle(hoverthrust+(vspeed-1))\njrpc.throttle(0)\njrpc.autopilot(False)\nprint(\"Touchdown.\")","sub_path":"wip/hoverland.py","file_name":"hoverland.py","file_ext":"py","file_size_in_byte":1272,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"309089318","text":"import json\nimport urllib.request\n\ndef Question47():\n url = \"http://api.open-notify.org/astros.json\"\n text_data = urllib.request.urlopen(url).read().decode('utf-8')\n dict = json.loads(text_data)\n print(\"There are {0:d} people in space right now : \".format(len(dict['people'])))\n print(\"Name | Craft\")\n print(\"---------------------------------------------\")\n for index in dict['people']:\n print(\"{0:s} | {1:s}\".format(index['name'], index['craft']))\n ","sub_path":"CodingTraining/PythonStudy/Chapter09/Question47.py","file_name":"Question47.py","file_ext":"py","file_size_in_byte":462,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"238004175","text":"from flask import Flask, make_response\nimport redis\n\nimport os\n\napp = Flask(__name__)\n\n_redis_host = os.getenv('REDIS_HOST')\n_redis_port = os.getenv('REDIS_PORT')\n\nr = redis.Redis(host=_redis_host, port=_redis_port, db=0)\n\n\n@app.route('/api/')\ndef api(dataset):\n data = r.get(dataset)\n #data = \"hello \" + dataset\n response = make_response(data, 200)\n response.mimetype = \"text/plain\"\n return response\n\n\nif __name__ == '__main__':\n app.run()\n","sub_path":"dockerfiles/geoapi/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":468,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"476652540","text":"#!/usr/bin/env python\n# Copyright 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions\n# are met:\n# * Redistributions of source code must retain the above copyright\n# notice, this list of conditions and the following disclaimer.\n# * Redistributions in binary form must reproduce the above copyright\n# notice, this list of conditions and the following disclaimer in the\n# documentation and/or other materials provided with the distribution.\n# * Neither the name of NVIDIA CORPORATION nor the names of its\n# contributors may be used to endorse or promote products derived\n# from this software without specific prior written permission.\n#\n# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY\n# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR\n# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR\n# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,\n# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,\n# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR\n# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY\n# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\n# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\n# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n\nimport unittest\n\nimport tritonclient.http.aio as httpclient\nfrom tritonclient.utils import *\n\n\nclass TestHttpAioClient(unittest.IsolatedAsyncioTestCase):\n \"\"\"Test if aio rpc can reach the server\"\"\"\n\n async def asyncSetUp(self):\n self._triton_client = httpclient.InferenceServerClient(url=\"localhost:8000\")\n\n async def asyncTearDown(self):\n await self._triton_client.close()\n\n async def test_is_server_live(self):\n ret = await self._triton_client.is_server_live()\n self.assertEqual(ret, True)\n\n async def test_is_server_ready(self):\n ret = await self._triton_client.is_server_ready()\n self.assertEqual(ret, True)\n\n async def test_is_model_ready(self):\n ret = await self._triton_client.is_model_ready(\"simple\")\n self.assertEqual(ret, True)\n\n async def test_get_server_metadata(self):\n ret = await self._triton_client.get_server_metadata()\n self.assertEqual(ret[\"name\"], \"triton\")\n\n async def test_get_model_metadata(self):\n ret = await self._triton_client.get_model_metadata(\"simple\")\n self.assertEqual(ret[\"name\"], \"simple\")\n\n async def test_get_model_config(self):\n ret = await self._triton_client.get_model_config(\"simple\")\n self.assertEqual(ret[\"name\"], \"simple\")\n\n async def test_get_model_repository_index(self):\n ret = await self._triton_client.get_model_repository_index()\n self.assertEqual(len(ret), 7)\n\n async def test_load_model(self):\n with self.assertRaisesRegex(\n InferenceServerException,\n \"explicit model load / unload is not allowed if polling is enabled\",\n ):\n await self._triton_client.load_model(\"simple\")\n\n async def test_unload_model(self):\n with self.assertRaisesRegex(\n InferenceServerException,\n \"explicit model load / unload is not allowed if polling is enabled\",\n ):\n await self._triton_client.load_model(\"simple\")\n\n async def test_get_inference_statistics(self):\n await self._triton_client.get_inference_statistics()\n\n async def test_update_trace_settings(self):\n await self._triton_client.update_trace_settings()\n\n async def test_get_trace_settings(self):\n await self._triton_client.get_trace_settings()\n\n async def test_get_system_shared_memory_status(self):\n await self._triton_client.get_system_shared_memory_status()\n\n async def test_register_system_shared_memory(self):\n with self.assertRaisesRegex(InferenceServerException, \"\"):\n await self._triton_client.register_system_shared_memory(\"\", \"\", 0)\n\n async def test_unregister_system_shared_memory(self):\n await self._triton_client.unregister_system_shared_memory()\n\n async def test_get_cuda_shared_memory_status(self):\n await self._triton_client.get_cuda_shared_memory_status()\n\n async def test_register_cuda_shared_memory(self):\n with self.assertRaisesRegex(InferenceServerException, \"\"):\n await self._triton_client.register_cuda_shared_memory(\"\", b\"\", 0, 0)\n\n async def test_unregister_cuda_shared_memory(self):\n await self._triton_client.unregister_cuda_shared_memory()\n\n\nif __name__ == \"__main__\":\n unittest.main()\n","sub_path":"qa/L0_http/python_http_aio_test.py","file_name":"python_http_aio_test.py","file_ext":"py","file_size_in_byte":4823,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"346683007","text":"import sys\n\nR, C, K = map(int, sys.stdin.readline().split())\n\ngrid = [[0 for _ in range(C+1)] for _ in range(R+1)]\nfor _ in range(K):\n r, c, v = map(int, sys.stdin.readline().split())\n grid[r][c] = v\n\n# r行目、c列目にいるときに、その行でアイテムをi個取得している場合の最大価値\ndp0 = [[0 for _ in range(C+1)] for _ in range(R+1)]\ndp1 = [[0 for _ in range(C+1)] for _ in range(R+1)]\ndp2 = [[0 for _ in range(C+1)] for _ in range(R+1)]\ndp3 = [[0 for _ in range(C+1)] for _ in range(R+1)]\n# print(dp)\nfor r in range(1, R+1):\n for c in range(1, C+1):\n # 取らない\n dp0[r][c] = max((dp0[r][c-1], dp0[r-1][c], dp1[r-1][c], dp2[r-1][c], dp3[r-1][c]))\n dp1[r][c] = max(dp1[r][c-1], dp1[r][c])\n dp2[r][c] = max(dp2[r][c-1], dp2[r][c])\n dp3[r][c] = max(dp3[r][c-1], dp3[r][c])\n\n # 取る\n dp1[r][c] = max(dp0[r-1][c] + grid[r][c], dp1[r][c])\n dp1[r][c] = max(dp1[r-1][c] + grid[r][c], dp1[r][c])\n dp1[r][c] = max(dp2[r-1][c] + grid[r][c], dp1[r][c])\n dp1[r][c] = max(dp3[r-1][c] + grid[r][c], dp1[r][c])\n\n dp1[r][c] = max(dp0[r][c-1] + grid[r][c], dp1[r][c])\n dp2[r][c] = max(dp1[r][c-1] + grid[r][c], dp2[r][c])\n dp3[r][c] = max(dp2[r][c-1] + grid[r][c], dp3[r][c])\n\n\n# print(dp0)\n# print(dp1)\n# print(dp2)\n# print(dp3)\nprint(max((dp0[R][C], dp1[R][C], dp2[R][C], dp3[R][C])))","sub_path":"Python_codes/p02586/s799733961.py","file_name":"s799733961.py","file_ext":"py","file_size_in_byte":1404,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"571099622","text":"import FWCore.ParameterSet.Config as cms\n\nPhi2KKPAT = cms.EDProducer('Phi2KKPAT',\n kaons = cms.InputTag(\"oniaSelectedTracks\"),\n beamSpotTag = cms.InputTag(\"offlineBeamSpot\"),\n primaryVertexTag = cms.InputTag(\"offlinePrimaryVertices\"), \n OniaTag = cms.InputTag(\"onia2MuMuPAT\"), ## Use Onia2MuMu as seed for PV, only tracks in this PV are used, PV=0 is used otherwise\n higherPuritySelection = cms.string(\"\"), ## At least one kaon must pass this selection\n lowerPuritySelection = cms.string(\"\"), ## BOTH kaons must pass this selection\n dikaonSelection = cms.string(\"0.95 < mass && mass < 1.1 && charge==0 && userFloat('deltar') < 0.7\"), ## The dikaon must pass this selection before vertexing\n addCommonVertex = cms.bool(True), ## Embed the full reco::Vertex out of the common vertex fit\n resolvePileUpAmbiguity = cms.bool(True) ## Order PVs by their vicinity to the Phi vertex, not by sumPt \n)\n","sub_path":"python/Phi2KKPAT_cfi.py","file_name":"Phi2KKPAT_cfi.py","file_ext":"py","file_size_in_byte":1044,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"608440839","text":"import sys\n\nPRINT_BEEJ = 1\nHALT = 2\nPRINT_NUM = 3\nSAVE = 4 # save a value to a register\nPRINT_REGISTER = 5 # print the value in a register\nADD = 6 # add 2 registers, store the result in 1st reg\nPUSH = 7\nPOP = 8\n\nmemory = [0] * 256\n\ndef load_memory(filename):\n try:\n address = 0\n with open(sys.argv[1]) as f:\n # read all the lines\n for line in f:\n # parse out comments\n comment_split = line.strip().split(\"#\")\n \n # cast the numbers from strings to ints\n value = comment_split[0].strip()\n\n # ignore blank lines\n if value == \"\":\n continue\n\n num = int(value)\n memory[address] = num\n address += 1\n\n\n except FileNotFoundError:\n print(\"File not found\")\n sys.exit(2)\n\nregister = [0] * 8\n\n# program counter\npc = 0\n\n# stack pointer is R7\nsp = 7\n\nif len(sys.argv) != 2:\n print(\"ERROR: must have file name\")\n sys.exit(1)\n\nload_memory(sys.argv[1])\n\n# processor\nwhile True:\n command = memory[pc]\n\n if command == PRINT_BEEJ:\n print(\"Beej!\")\n pc += 1\n elif command == PRINT_NUM:\n num = memory[pc + 1]\n print(num)\n pc += 2\n elif command == SAVE:\n num = memory[pc + 1]\n reg = memory[pc + 2]\n register[reg] = num\n pc += 3\n elif command == PRINT_REGISTER:\n reg = memory[pc + 1]\n print(register[reg])\n pc += 2\n elif command == ADD:\n reg_a = memory[pc + 1]\n reg_b = memory[pc + 2]\n register[reg_a] += register[reg_b]\n pc += 3\n elif command == HALT:\n sys.exit(0)\n elif command == PUSH:\n # grab the register argument\n reg = memory[pc + 1]\n val = register[reg]\n # decrement stack pointer\n register[sp] -= 1\n # copy the value in the given register to the address pointed to by sp\n memory[register[sp]] = val\n pc += 2\n elif command == POP:\n # grab the balue from the top of the stack\n reg = memory[pc + 1]\n val = memory[register[sp]]\n # copy the value from the address pointed to by sp to the given register\n register[reg] = val\n # increment sp\n register[sp] += 1\n pc += 2\n else:\n print(f\"I did not understand that command: {command}\")\n sys.exit(1)\n","sub_path":"lecture/simple.py","file_name":"simple.py","file_ext":"py","file_size_in_byte":2431,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"54432113","text":"import numpy as np\nfrom scipy.ndimage import measurements\n\ndef find_threshold(arr, k):\n return k * np.median( np.abs(arr) ) / 0.6745\n\ndef peak_finder(x0, thresh):\n '''Simple peak finding algorithm.'''\n assert x0.ndim == 1\n clusters, ix = measurements.label(x0 > thresh)\n peak_loc = np.concatenate(measurements.maximum_position(x0, labels=clusters, index=np.arange(ix)+1))\n peak_mag = measurements.maximum(x0, labels=clusters, index=np.arange(ix)+1)\n return peak_loc, peak_mag\n\ndef peak_censor(times, loc, mag, min_dist=2e-3):\n\n while True:\n \n ## Compute differences.\n td = np.diff(times[loc])\n \n ## Identify violations.\n violations, = np.where(td < min_dist) \n if not np.any(violations): break\n \n ## Identify smallest difference.\n i = violations[td[violations].argmin()] \n \n ## Remove smaller peak.\n if mag[i] > mag[i+1]: \n loc = np.delete(loc, i+1)\n mag = np.delete(mag, i+1)\n else:\n loc = np.delete(loc, i)\n mag = np.delete(mag, i)\n \n return loc, mag \n","sub_path":"neu501b/sensory_coding/peak_detection.py","file_name":"peak_detection.py","file_ext":"py","file_size_in_byte":1139,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"459868859","text":"import os\nclass fileReader :\n def getDicData(path):\n\n COMMON_FILE_PATH = \"/Users/lunjm/PycharmProjects/ReviewClustering/dic/\"\n\n fileNames = []\n for root, dirs, files in os.walk(COMMON_FILE_PATH + path):\n for file in files:\n fileNames.append(file)\n\n dic = {}\n for fileName in fileNames:\n file = open(COMMON_FILE_PATH + path + \"/\" + fileName)\n score = int(file.readline())\n for x in file:\n x = x.replace('\\n', '')\n dic[x] = score\n\n file.close()\n return dic","sub_path":"util/fileReader.py","file_name":"fileReader.py","file_ext":"py","file_size_in_byte":595,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"165759644","text":"\"\"\"Handels the game setup\"\"\"\n\nfrom classes.playerc import playerclass as pc\nfrom testing import input_testing as inp\n\ndef start_setup():\n \"\"\"Plays intro and calls the player set up\n returns a list with the player objects\"\"\"\n \n intro()\n \n players = setup_player()\n \n world = setup_world()\n \n return players, world\n \n\ndef intro():\n print(\"\"\"Myths say this world was created by two almighty beings.\nThe people of this world call them LMNO and Piwo.\nThey may guide you on your adventures through the lands of ______.\"\"\")\n\n\n\ndef setup_player():\n \"\"\"sets up the players in the playerclass\n returns a list with player objects\"\"\"\n \n player_quantity = input(\"How many players are you? \")\n test = inp.test_int(player_quantity, \"None\")\n while test == False: \n player_quantity = input(\"Try again. How many want to play? \")\n test = inp.test_int(player_quantity, \"None\")\n \n player_quantity = int(player_quantity)\n players = []\n \n for i in range(1, player_quantity + 1):\n \n name = input(\"Player {} whats your name? \".format(i))\n name = name.capitalize()\n test = inp.test_string(name, 'name')\n while test == False:\n name = input(\"Pls try again. \")\n name = name.capitalize()\n test = inp.test_string(name, 'name')\n \n \n race = input(\"\"\"Hello {}, what race do you want to be?\n[human, orc] \"\"\".format(name))\n race = race.casefold()\n test = inp.test_string(race, ['human', 'orc'])\n while test == False:\n race = input(\"Pls try again. [human, orc] \")\n race = race.casefold()\n test = inp.test_string(race, ['human', 'orc'])\n \n alignment = input(\"\"\"And what will be your alignment?\n[fighter, mage] \"\"\")\n alignment = alignment.casefold()\n test = inp.test_string(alignment, ['fighter', 'mage'])\n while test == False:\n alignment = input(\"Pls try again. [fighter, mage] \")\n alignment = alignment.casefold()\n test = inp.test_string(alignment, ['fighter', 'mage'])\n \n p = pc.Player(name, race, alignment)\n players.append(p)\n \n return players\n\n\ndef setup_world():\n \"\"\"selects the questline\"\"\"\n \n q1 = \"The secret piwo project, jan didn't see :)\"\n q2 = \"Placeholder\"\n questlist = [q1, q2]\n \n print(\"You have to choose a quest.\")\n for i, val in enumerate(questlist):\n print(val, \"[{}]\".format(i+1))\n \n quest = input(\"Which of these quests you wanna play? [1,2] \")\n test = inp.test_int(quest, [1, 2])\n while test == False:\n print(\"The quests are:\")\n for i, val in enumerate(questlist):\n print(val, \"[{}]\".format(i+1))\n quest = input(\"Which of these quests you wanna play? [1,2] \")\n test = inp.test_int(quest, [1, 2])\n quest = int(quest)\n \n ","sub_path":"start_setup.py","file_name":"start_setup.py","file_ext":"py","file_size_in_byte":2937,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"104728531","text":"import datetime\nimport os\nimport praw\nimport pandas as pd\n\nfrom dataclasses import dataclass, asdict\nfrom decouple import config\nfrom tqdm import tqdm\n\nfrom reddit_extractor import Comment, Submission, submission_factory, comment_factory\n\n\ndate_asof = datetime.datetime.strftime(datetime.datetime.now(),\"%m-%d-%Y\")\ndate_file_format = datetime.datetime.strftime(datetime.datetime.now(),\"%m%d%Y\")\n\nreddit = praw.Reddit(\n client_id=config(\"REDDIT_KEY\"),\n client_secret=config(\"REDDIT_SECRET\"),\n user_agent=\"NA\"\n)\n\n\ndef extract_attributes_from_subreddit(subreddit):\n return {\n \"active_user_count\": subreddit.active_user_count,\n \"url\": subreddit.url,\n \"title\": subreddit.title,\n \"subscribers\": subreddit.subscribers,\n \"subreddit_type\": subreddit.subreddit_type,\n \"spoilers_enabled\": subreddit.spoilers_enabled,\n \"public_description\": subreddit.public_description,\n \"over18\": subreddit.over18,\n \"created\": subreddit.created,\n \"created_utc\": subreddit.created_utc,\n \"lang\": subreddit.lang,\n \"videos_allowed\": subreddit.allow_videos,\n \"images_allowed\": subreddit.allow_images\n }\n\n\ndef convert_timestamp(ts):\n datetime_obj = datetime.datetime.fromtimestamp(ts)\n date = datetime.datetime.strftime(datetime_obj,\"%m-%d-%Y\")\n return date\n\n\n\ndef get_submissions(subreddit_name: str, category: str, limit: int) -> list:\n '''\n Categories: hot, new or top\n \n >>> get_submissions('MrRobot', 'hot', 10)\n '''\n subreddit = reddit.subreddit(subreddit_name)\n if category == 'hot':\n submission_obj = subreddit.hot(limit=limit)\n elif category == 'new':\n submission_obj = subreddit.new(limit=limit)\n else:\n submission_obj = subreddit.top(limit=limit)\n \n return [sub for sub in submission_obj]\n\n\n\ndef get_comments(submission: praw.models.reddit.submission.Submission, body: bool=False) -> list:\n if body:\n return [comment.body for comment in submission.comments]\n return list(submission.comments)\n\n\n\ndef get_top_100_subreddits():\n # read table from the webiste\n top_100 = pd.read_html('https://frontpagemetrics.com/top')\n top_100_subreddits = top_100[0]\n\n # return subreddit names as list \n subreddit_names = [subreddit.split('/')[-1] for subreddit in top_100_subreddits.Reddit]\n return subreddit_names\n\n\ndef extract_attributes_from_subreddits(subreddit_names):\n # get subreddit objects\n subreddits = [reddit.subreddit(subreddit_name) for subreddit_name in subreddit_names]\n\n # return attributes from each subreddit list of dict\n subreddit_data = [extract_attributes_from_subreddit(subreddit) for subreddit in tqdm(subreddits)]\n return subreddit_data \n \n\ndef store_subreddits_to_csv(subreddit_data, subreddit_names):\n # store the result in dataframe\n subreddits_df = pd.DataFrame(subreddit_data)\n\n # add a few columns\n subreddits_df['created_date'] = subreddits_df.created.apply(convert_timestamp)\n subreddits_df['name'] = subreddit_names\n subreddits_df['asof'] = date_asof\n\n # save as csv\n subreddits_df.to_csv(f'output/top_100_subreddits_{date_file_format}.csv', index=False)\n\n \ndef store_submissions_to_csv(subreddit_names, category, num_subreddits=10, limit=10):\n \n print('Getting list of subreddits...')\n \n submissions = []\n \n for subreddit in tqdm(subreddit_names[:num_subreddits]):\n submission_objects = get_submissions(subreddit, category, limit)\n # add error handling\n submission_data = []\n for submission_obj in submission_objects:\n if submission_obj:\n submission_data.append(submission_factory(submission_obj))\n\n for submission in submission_data:\n if submission:\n try:\n submissions.append(asdict(submission))\n except TypeError as e:\n print(submission)\n print(e)\n continue\n \n print('Data extraction complete.')\n print('Storing data to csv...')\n \n pd.DataFrame(submissions).to_csv(f'output/{category}_submissions_{date_file_format}.csv', index=False)\n\n\n\nif __name__ == \"__main__\":\n # # subreddit extraction\n subreddit_names = get_top_100_subreddits()\n # subreddit_data = extract_attributes_from_subreddits(subreddit_names)\n # store_subreddits_to_csv(subreddit_data, subreddit_names)\n \n # submission extraction\n # for category in ['hot', 'new', 'top']:\n # store_submissions_to_csv(subreddit_names, category, num_subreddits=50, limit=10)\n\n # retry\n store_submissions_to_csv(subreddit_names, 'top', num_subreddits=50, limit=10)\n ","sub_path":"api_wrappers_samples/reddit/reddit_extract/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4705,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"405934598","text":"import pandas as pd\nimport numpy as np\nfrom matplotlib import pyplot as plt\nimport matplotlib\nimport seaborn as sns\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn import tree\nfrom sklearn.model_selection import ShuffleSplit\nfrom sklearn.model_selection import cross_val_score\n\nmatplotlib.rcParams[\"figure.figsize\"] = (20, 10)\n\ndf1=pd.read_csv(\"house_price.csv\")\n\n#remove unnecessary features\ndf2 = df1.drop(['Postcode', 'Bedroom2', 'BuildingArea'], axis='columns')\n\n#identify object datatype(numeric,boolean,char,date but not string)\n#print(df2.select_dtypes(['object']).columns)\n\n# Convert objects to categorical variables\nobj_cats = ['Suburb', 'Method', 'SellerG', 'Regionname', 'CouncilArea']\n\nfor colname in obj_cats:\n df2[colname] = df2[colname].astype('category')\n\ndf2['Date'] = pd.to_datetime(df2['Date'])\n\n#convert rows to column and column to rows(temporary)\n#print(df2.describe().transpose())\n\n\n#print(df2.isnull().sum()/len(df2)*100)\n#significant pc of buildingarea feature is missing so we will drop this column alltogeher in line 12\n#same for year built\n\ndf2=df2.dropna()\n\n#outlier removal\n\n#print(df2.select_dtypes(['category']).columns)\n\n# Abbreviate Regionname categories\ndf2['Regionname'] = df2['Regionname'].map({'Northern Metropolitan': 'N Metro',\n 'Western Metropolitan': 'W Metro',\n 'Southern Metropolitan': 'S Metro',\n 'Eastern Metropolitan': 'E Metro',\n 'South-Eastern Metropolitan': 'SE Metro',\n 'Northern Victoria': 'N Vic',\n 'Eastern Victoria': 'E Vic',\n 'Western Victoria': 'W Vic'})\n\n\n#boxplot for features containing less variables\n#graph for catagorical features using boxplot feature of seaborn\n#the line in the middle of the box in boxplot represents median\n\n# Suplots of categorical features v price\nsns.set_style('darkgrid')\nf, axes = plt.subplots(2, 2, figsize=(15, 15))\n\n# Plot [0,0]\nsns.boxplot(data=df2, x='Type', y='Price', ax=axes[0, 0])\naxes[0,0].set_xlabel('Type')\naxes[0,0].set_ylabel('Price')\naxes[0,0].set_title('Type v Price')\n\n# Plot [0,1]\nsns.boxplot(x='Method', y='Price', data=df2, ax=axes[0, 1])\naxes[0, 1].set_xlabel('Method')\naxes[0,1].set_ylabel('Price')\naxes[0,1].set_title('Method v Price')\n\n# Plot [1,0]\nsns.boxplot(x = 'Regionname', y = 'Price', data = df2, ax = axes[1,0])\naxes[1,0].set_xlabel('Regionname')\naxes[1,0].set_ylabel('Price')\naxes[1,0].set_title('Region Name v Price')\n\n# Plot [1,1]\nsns.boxplot(x = 'CouncilArea', y='Price', data = df2, ax=axes[1,1])\naxes[1, 1].set_xlabel('CouncilArea')\naxes[1, 1].set_ylabel('Price')\naxes[1, 1].set_title('CouncilArea v Price')\n\nplt.show()\n\n#scatter plot for features containing large number of variables\nsns.set_style('darkgrid')\nf, axes = plt.subplots(2,2, figsize = (20,30))\n\n# Plot [0,0]\naxes[0,0].scatter(x = 'Suburb', y = 'Price', data=df2, edgecolor = 'b')\naxes[0,0].set_xlabel('Suburb')\naxes[0,0].set_ylabel('Price')\naxes[0,0].set_title('Suburb v Price')\n\n# Plot [0,1]\naxes[0,1].scatter(x = 'Distance', y='Price', data=df2, edgecolor = 'b')\naxes[0,1].set_xlabel('Distance')\n# axes[0,1].set_ylabel('Price')\naxes[0,1].set_title('Distance v Price')\n\n# Plot [1,0]\naxes[1,0].scatter(x='Bathroom', y='Price', data = df2, edgecolor = 'b')\naxes[1,0].set_xlabel('Bathroom')\naxes[1,0].set_ylabel('Price')\naxes[1,0].set_title('Bathroom v Price')\n\n# Plot [1,1]\naxes[1,1].scatter(x = 'CouncilArea', y='Price', data=df2, edgecolor='b')\naxes[1,0].set_xlabel('CouncilArea')\naxes[1,1].set_ylabel('Price')\naxes[1,1].set_title('CouncilArea v Price')\n\n\nplt.show()\n\n#remove outlier\n\ndf3 = np.sort(df2['Price'])\n\nQ1 = np.percentile(df3, 25, interpolation='midpoint')\nQ3 = np.percentile(df3, 75, interpolation='midpoint')\nIQR = Q3-Q1\n\nprint(IQR)\n\noutlier =[]\nfor x in df3:\n if ((x> Q3 + 1.5 * IQR ) or (x= 0:\n x[loc_index] = 1\n\n return clf.predict([x])[0]\n\nprint(predict_price('Abbotsford',2,2.5,1,1))","sub_path":"cleaner.py","file_name":"cleaner.py","file_ext":"py","file_size_in_byte":5888,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"5590768","text":"'''\r\nCreated on 11.08.2016\r\n\r\n@author: Michael\r\n\r\n\"7777...8?!??!\", exclaimed Bob, \"I missed it again! Argh!\" Every time there's an interesting number coming up, he notices and then promptly forgets. \r\nWho doesn't like catching those one-off interesting mileage numbers?\r\nLet's make it so Bob never misses another interesting number. We've hacked into his car's computer, and we have a box hooked up that reads mileage numbers. \r\nWe've got a box glued to his dash that lights up yellow or green depending on whether it receives a 1 or a 2 (respectively).\r\n\r\nIt's up to you, intrepid warrior, to glue the parts together. Write the function that parses the mileage number input, and returns a 2 if the number is \"interesting\" (see below), \r\na 1 if an interesting number occurs within the next two miles, or a 0 if the number is not interesting.\r\n\r\nNote: In Haskell, we use No, Almost and Yes instead of 0, 1 and 2.\r\n\r\n\"Interesting\" Numbers\r\n\r\nInteresting numbers are 3-or-more digit numbers that meet one or more of the following criteria:\r\n\r\nAny digit followed by all zeros: 100, 90000\r\n Every digit is the same number: 1111\r\n The digits are sequential, incementing*: 1234\r\n The digits are sequential, decrementing**: 4321\r\n The digits are a palindrome: 1221 or 73837\r\n The digits match one of the values in the awesomePhrases array\r\n * For incrementing sequences, 0 should come after 9, and not before 1, as in 7890.\r\n ** For decrementing sequences, 0 should come after 1, and not before 9, as in 3210.\r\n\r\nSo, you should expect these inputs and outputs:\r\n\r\n# \"boring\" numbers\r\nis_interesting(3, [1337, 256]) # 0\r\nis_interesting(3236, [1337, 256]) # 0\r\n\r\n# progress as we near an \"interesting\" number\r\nis_interesting(11207, []) # 0\r\nis_interesting(11208, []) # 0\r\nis_interesting(11209, []) # 1\r\nis_interesting(11210, []) # 1\r\nis_interesting(11211, []) # 2\r\n\r\n# nearing a provided \"awesome phrase\"\r\nis_interesting(1335, [1337, 256]) # 1\r\nis_interesting(1336, [1337, 256]) # 1\r\nis_interesting(1337, [1337, 256]) # 2\r\nError Checking\r\n\r\nA number is only interesting if it is greater than 99!\r\nInput will always be an integer greater than 0, and less than 1,000,000,000.\r\nThe awesomePhrases array will always be provided, and will always be an array, but may be empty. (Not everyone thinks numbers spell funny words...)\r\nYou should only ever output 0, 1, or 2.\r\n'''\r\n\r\ndef interesting(number, awesome_phrases):\r\n if number < 100:\r\n return 0\r\n if number >= 1000000000:\r\n return 0\r\n n = str(number)\r\n \r\n interesting = True\r\n incrementing = True\r\n decrementing = True\r\n digit = n[0]\r\n for i in range(1,len(n)):\r\n if n[i] != \"0\":\r\n interesting = False\r\n if n[i] != digit:\r\n digit = \"-1\"\r\n if int(n[i-1]) == 0:\r\n incrementing = False\r\n decrementing = False\r\n if (int(n[i-1]) != 9 and int(n[i]) != int(n[i-1])+1) or (int(n[i-1]) == 9 and int(n[i]) != 0):\r\n incrementing = False\r\n if int(n[i]) != int(n[i-1])-1:\r\n decrementing = False\r\n \r\n if interesting:\r\n return 2\r\n if digit != \"-1\":\r\n return 2\r\n if incrementing:\r\n return 2\r\n if decrementing:\r\n return 2\r\n \r\n left = n[0:len(n)//2]\r\n left = left[::-1]\r\n if len(n) % 2 == 0:\r\n right = n[len(n)//2:len(n)]\r\n else:\r\n right = n[len(n)//2+1:len(n)]\r\n if left == right:\r\n return 2\r\n \r\n if number in awesome_phrases:\r\n return 2\r\n \r\n return 0\r\n\r\n\r\ndef is_interesting(number, awesome_phrases):\r\n result = interesting(number, awesome_phrases)\r\n if result == 2:\r\n return result\r\n number = number + 1\r\n result = interesting(number, awesome_phrases)\r\n if result == 2:\r\n return 1\r\n number = number + 1\r\n result = interesting(number, awesome_phrases)\r\n if result == 2:\r\n return 1\r\n return 0\r\n\r\n# \"boring\" numbers\r\nprint(is_interesting(3, [1337, 256])) # 0\r\nprint(is_interesting(3236, [1337, 256]))# 0\r\n\r\n# progress as we near an \"interesting\" number\r\nprint(is_interesting(11207, [])) # 0\r\nprint(is_interesting(11208, [])) # 0\r\nprint(is_interesting(11209, [])) # 1\r\nprint(is_interesting(11210, [])) # 1\r\nprint(is_interesting(11211, [])) # 2\r\n\r\n# nearing a provided \"awesome phrase\"\r\nprint(is_interesting(1335, [1337, 256])) # 1\r\nprint(is_interesting(1336, [1337, 256])) # 1\r\nprint(is_interesting(1337, [1337, 256])) # 2\r\n\r\n\r\n\r\n","sub_path":"Python_Programs/CatchingCarMileageNumbers.py","file_name":"CatchingCarMileageNumbers.py","file_ext":"py","file_size_in_byte":4476,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"34133561","text":"# rospy for the subscriber\nimport rospy\n# ROS Image message\nfrom sensor_msgs.msg import Image\nfrom geometry_msgs.msg import Twist\nfrom std_msgs.msg import Int32\n# ROS Image message -> OpenCV2 image converter\nfrom cv_bridge import CvBridge\nimport utils\nimport matplotlib.pyplot as plt\n\nimport cv2\n\nimport base64\n\nfrom std_msgs.msg import String\n\n# Deep Learning Libraries\nimport numpy as np\nnp.set_printoptions(suppress=True)\nfrom keras.models import load_model\n\nimport tensorflow as tf\ntf.compat.v1.disable_eager_execution()\nimport keras.backend as K\n\nimport argparse\n\nclass RosTensorFlow():\n def __init__(self):\n self.epsilon = 1\n self.graph = tf.compat.v1.get_default_graph()\n\n self.model = load_model(\"model.h5\")\n\n self.loss_right = K.mean(-self.model.output, axis=-1)\n self.grads_right = K.gradients(self.loss_right, self.model.input)\n self.delta_right = K.sign(self.grads_right[0])\n\n self.loss_left = K.mean(self.model.output, axis=-1)\n self.grads_left = K.gradients(self.loss_left, self.model.input)\n self.delta_left = K.sign(self.grads_left[0])\n\n self.sess = tf.compat.v1.keras.backend.get_session()\n\n self._cv_bridge = CvBridge()\n\n self._sub = None\n self._pub = rospy.Publisher('/cmd_vel', Twist, queue_size=1)\n self._attack_sub = rospy.Subscriber('/attack', Int32, self.attack_callback, queue_size=1)\n self.attack = 0\n\n self.raw_pub = rospy.Publisher('/raw_img', String, queue_size=10)\n self.input_pub = rospy.Publisher('/input_img', String, queue_size=10)\n self.perturb_pub = rospy.Publisher('/perturb_img', String, queue_size=10)\n self.adv_pub = rospy.Publisher('/adv_img', String, queue_size=10)\n\n def attack_callback(self, attack_msg):\n self.attack = attack_msg.data\n print(self.attack)\n\n def callback(self, image_msg):\n cv_image = self._cv_bridge.imgmsg_to_cv2(image_msg, \"bgr8\")\n cv_image = cv2.resize(cv_image, (320, 160), interpolation = cv2.INTER_AREA)\n\n _, buffer = cv2.imencode('.jpg', cv_image)\n image_as_str = base64.b64encode(buffer).decode('utf-8')\n self.raw_pub.publish(image_as_str)\n\n cv_image = utils.preprocess(cv_image) # apply the preprocessing\n _, buffer = cv2.imencode('.jpg', cv_image)\n image_as_str = base64.b64encode(buffer).decode('utf-8')\n self.input_pub.publish(image_as_str)\n # plt.imshow(cv_image)\n # plt.show()\n \n with self.graph.as_default():\n\n if self.attack > 0:\n if self.attack == 1:\n noise = self.epsilon * self.sess.run(self.delta_left, feed_dict={self.model.input:np.array([cv_image])})\n if self.attack == 2:\n noise = self.epsilon * self.sess.run(self.delta_right, feed_dict={self.model.input:np.array([cv_image])})\n\n noise = noise.reshape(160, 320, 3)\n\n _, buffer = cv2.imencode('.jpg', noise)\n image_as_str = base64.b64encode(buffer).decode('utf-8')\n self.perturb_pub.publish(image_as_str)\n\n _, buffer = cv2.imencode('.jpg', noise + cv_image)\n image_as_str = base64.b64encode(buffer).decode('utf-8')\n self.adv_pub.publish(image_as_str)\n\n angle = self.epsilon * self.sess.run(self.model.output, feed_dict={self.model.input:np.array([cv_image+noise])})\n no_angle = self.sess.run(self.model.output, feed_dict={self.model.input:np.array([cv_image])})\n print('{0} --> {1}'.format(no_angle[0][0], angle[0][0]))\n else:\n angle = self.sess.run(self.model.output, feed_dict={self.model.input:np.array([cv_image])})\n # print('{0} --> {1}'.format(no_angle[0][0], angle[0][0]))\n\n msg = Twist()\n msg.linear.x = 0.02\n msg.linear.y = 0\n msg.linear.z = 0\n msg.angular.z = angle[0][0]\n self._pub.publish(msg)\n\n\n def main(self):\n rospy.spin()\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='Line Following')\n parser.add_argument('--env', help='environment', choices=['gazebo', 'turtlebot'], type=str, required=True)\n args = parser.parse_args()\n\n rospy.init_node('ros_tensorflow')\n\n if args.env == 'gazebo':\n image_topic = \"/camera/rgb/image_raw\"\n if args.env == 'turtlebot': \n image_topic = \"/raspicam_node/image_raw\"\n\n tensor = RosTensorFlow()\n tensor._sub = rospy.Subscriber(image_topic, Image, tensor.callback, queue_size=1)\n tensor.main()\n","sub_path":"model/drive.py","file_name":"drive.py","file_ext":"py","file_size_in_byte":4637,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"349729739","text":"from flask import Flask, request, jsonify, g, render_template\nfrom flask_json import FlaskJSON, JsonError, json_response, as_json\nimport plotly.graph_objects as go\nfrom datetime import datetime\nimport requests\nfrom app import db, cache\nfrom app.models import *\nfrom app.api import bp\nimport pandas as pd\nimport io\nimport requests\n\n\ndef get_results():\n items = request.get_json()\n c = Covid.query.filter_by(province=\"Ontario\")\n df = pd.read_sql(c.statement, db.engine)\n case_count = df.groupby(\"date\").case_id.count().cumsum().reset_index()\n case_count = case_count.loc[case_count.case_id > 100]\n df = df.groupby(\"date\").case_id.count().reset_index()\n df['case_id'] = df['case_id']*0.05\n df['case_id'] = df['case_id'].rolling(min_periods=1, window=8).sum()\n df = df.loc[df.date.isin(case_count.date.values)].reset_index()\n provines_dict = {}\n province_dict = df['case_id'].to_dict()\n provines_dict[\"Ontario\"] = province_dict\n provinces = [\"Italy\", \"South Korea\", \"Singapore\"]\n for province in provinces:\n c = Comparison.query.filter_by(province=province)\n df = pd.read_sql(c.statement, db.engine)\n case_count = df['count'].cumsum()\n df['case_count'] = case_count\n df = df.loc[df['case_count'] > 100].reset_index()\n df['count'] = df['count']*0.05\n df['count'] = df['count'].rolling(min_periods=1, window=8).sum()\n df = df.reset_index()\n province_dict = df['count'].to_dict()\n provines_dict[province] = province_dict\n return jsonify(provines_dict)\n\n\ndef get_date():\n c = Covid.query.filter_by(province=\"Ontario\")\n df = pd.read_sql(c.statement, db.engine)\n case_count = df.groupby(\"date\").case_id.count().cumsum().reset_index()\n case_count = case_count.loc[case_count.case_id > 100]\n df = df.groupby(\"date\").case_id.count().reset_index()\n df['case_id'] = df['case_id']*0.05\n df['case_id'] = df['case_id'].rolling(min_periods=1, window=8).sum()\n df = df.loc[df.date.isin(case_count.date.values)].reset_index()\n provines_dict = {}\n province_dict = df['date'].to_dict()\n provines_dict[\"Ontario\"] = province_dict\n return jsonify(provines_dict)\n\n\n@as_json\ndef get_phus():\n c = Covid.query.filter_by(province=\"Ontario\")\n dfs = pd.read_sql(c.statement, db.engine)\n replace = {\"Algoma\":\"The District of Algoma Health Unit\", \"Brant\":\"Brant County Health Unit\", \"Chatham-Kent\":\"Chatham-Kent Health Unit\", \"Durham\":\"Durham Regional Health Unit\",\n \"Eastern\":\"The Eastern Ontario Health Unit\", \"Grey Bruce\":\"Grey Bruce Health Unit\", \"Haliburton Kawartha Pineridge\":\"Haliburton, Kawartha, Pine Ridge District Health Unit\",\n \"Halton\":\"Halton Regional Health Unit\", \"Hamilton\":\"City of Hamilton Health Unit\", \"Hastings Prince Edward\":\"Hastings and Prince Edward Counties Health Unit\",\n \"Huron Perth\":\"Huron County Health Unit\", \"Kingston Frontenac Lennox & Addington\":\"Kingston, Frontenac, and Lennox and Addington Health Unit\",\n \"Lambton\":\"Lambton Health Unit\", \"Middlesex-London\":\"Middlesex-London Health Unit\", \"Niagara\":\"Niagara Regional Area Health Unit\",\n \"North Bay Parry Sound\":\"North Bay Parry Sound District Health Unit\", \"Northwestern\":\"Northwestern Health Unit\", \"Ottawa\":\"City of Ottawa Health Unit\",\n \"Peel\":\"Peel Regional Health Unit\", \"Peterborough\":\"Peterborough County-City Health Unit\", \"Porcupine\":\"Porcupine Health Unit\", \"Simcoe Muskoka\":\"Simcoe Muskoka District Health Unit\",\n \"Sudbury\": \"Sudbury and District Health Unit\", \"Timiskaming\":\"Timiskaming Health Unit\", \"Toronto\":\"City of Toronto Health Unit\", \"Waterloo\":\"Waterloo Health Unit\",\n \"Wellington Dufferin Guelph\":\"Wellington-Dufferin-Guelph Health Unit\", \"Windsor-Essex\":\"Windsor-Essex County Health Unit\", \"York\":\"York Regional Health Unit\"}\n dfs.region = dfs.region.replace(replace)\n regions = dfs.region.unique()\n provines_dict = {}\n for region in regions:\n df = dfs.loc[dfs.region == region]\n df = df.groupby(\"date\").case_id.count().cumsum().reset_index()\n date = datetime.strptime(\"2020-02-28\",\"%Y-%m-%d\")\n df = df.loc[df.date > date]\n df['date_str'] = df['date'].astype(str)\n province_dict = df.set_index('date_str')['case_id'].to_dict()\n provines_dict[region] = province_dict\n\n df = pd.read_sql_table('covidtests', db.engine)\n date = datetime.strptime(\"2020-02-28\",\"%Y-%m-%d\")\n df = df.loc[df.date > date]\n df['date_str'] = df['date'].astype(str)\n province_dict = df.set_index('date_str')['positive'].to_dict()\n provines_dict[\"Ontario\"] = province_dict\n return provines_dict\n\n\n@as_json\ndef get_phunew():\n c = Covid.query.filter_by(province=\"Ontario\")\n dfs = pd.read_sql(c.statement, db.engine)\n regions = dfs.region.unique()\n provines_dict = {}\n for region in regions:\n df = dfs.loc[dfs.region == region]\n df = df.groupby(\"date\").case_id.count().reset_index()\n date = datetime.strptime(\"2020-02-28\",\"%Y-%m-%d\")\n df = df.loc[df.date > date]\n df['date_str'] = df['date'].astype(str)\n province_dict = df.set_index('date_str')['case_id'].to_dict()\n provines_dict[region] = province_dict\n\n df = dfs.groupby(\"date\").case_id.count().reset_index()\n date = datetime.strptime(\"2020-02-28\",\"%Y-%m-%d\")\n df = df.loc[df.date > date]\n df['date_str'] = df['date'].astype(str)\n province_dict = df.set_index('date_str')['case_id'].to_dict()\n provines_dict[\"Ontario\"] = province_dict\n return provines_dict\n\n\n@as_json\ndef get_growth():\n dfs = pd.read_sql_table('covid', db.engine)\n regions = dfs.province.unique()\n provines_dict = {}\n for region in regions:\n df = dfs.loc[dfs.province == region]\n df = df.groupby(\"date\").case_id.count().cumsum().reset_index()\n df = df.loc[df.case_id > 100].reset_index()\n province_dict = df['case_id'].to_dict()\n provines_dict[region] = province_dict\n\n df = dfs.groupby(\"date\").case_id.count().cumsum().reset_index()\n df = df.loc[df.case_id > 100].reset_index()\n province_dict = df['case_id'].to_dict()\n provines_dict[\"Canada\"] = province_dict\n\n dfs = pd.read_sql_table('internationaldata', db.engine)\n regions = dfs.country.unique()\n for region in regions:\n df = dfs.loc[dfs.country == region]\n df = df['cases'].cumsum().reset_index()\n df = df.loc[df['cases'] > 100].reset_index()\n province_dict = df['cases'].to_dict()\n provines_dict[region] = province_dict\n\n\n\n return provines_dict\n\n@bp.route('/api/viz', methods=['GET'])\n@cache.cached(timeout=50)\n@as_json\ndef get_api_viz():\n df = pd.read_sql_table('viz', db.engine)\n df = df.loc[df.viz != 'NaN']\n df = df.loc[df.visible == True]\n df = df.sort_values(by=['category', 'header'])\n data = []\n for index, row in df.iterrows():\n data.append({\"header\": row[\"header\"], \"category\": row[\"category\"],\n \"content\": row[\"content\"], \"text_top\": row[\"text_top\"], \"text_bottom\": row[\"text_bottom\"],\n \"viz\": row[\"viz\"], \"thumbnail\": row[\"thumbnail\"],\n \"mobileHeight\": row[\"mobileHeight\"],\"desktopHeight\": row[\"desktopHeight\"],\n \"viz_type\": row[\"viz_type\"], \"date\": row[\"date\"]})\n return data\n\n@bp.route('/api/plots', methods=['GET'])\n@cache.cached(timeout=50)\n@as_json\ndef get_api_plots():\n df = pd.read_sql_table('viz', db.engine)\n df = df.loc[df.html.notna()]\n df = df.loc[df.order > 0]\n df = df.loc[df.visible == True]\n df = df.sort_values(by=['order'])\n data = []\n for index, row in df.iterrows():\n data.append({\"header\": row[\"header\"], \"order\": row[\"order\"],\n \"tab\": row[\"content\"],\"tab_order\": row[\"tab_order\"],\n \"row\": 'span '+ str(row[\"row\"]), \"column\": 'span '+ str(row[\"column\"]),\n \"html\": row[\"html\"],\"category\": row[\"page\"], \"group\": row[\"category\"],\n \"phu\": row[\"phu\"], \"viz_title\": row[\"viz_title\"],\n \"text_top\": row[\"text_top\"], \"text_bottom\": row[\"text_bottom\"]})\n return data\n\n@bp.route('/api/source', methods=['GET'])\n@cache.cached(timeout=50)\n@as_json\ndef get_api_source():\n df = pd.read_sql_table('source', db.engine)\n df = df.sort_values(by=['name'])\n data = []\n for index, row in df.iterrows():\n data.append({\"region\": row[\"region\"],\"type\": row[\"type\"],\"name\": row[\"name\"], \"source\": row[\"source\"],\n \"description\": row[\"description\"], \"data_feed_type\": row[\"data_feed_type\"],\n \"link\": row[\"link\"], \"refresh\": row[\"refresh\"],\n \"contributor\": row[\"contributor\"],\"contact\": row[\"contact\"],\n \"download\": row[\"download\"]})\n return data\n\n@bp.route('/api/team', methods=['GET'])\n@cache.cached(timeout=50)\n@as_json\ndef get_api_team():\n df = pd.read_sql_table('members', db.engine)\n df = df.sort_values(by=['team_status','last_name'])\n data = []\n for index, row in df.iterrows():\n data.append({\"team\": row[\"team\"],\"title\": row[\"title\"],\n \"first_name\": row[\"first_name\"], \"last_name\": row[\"last_name\"],\n \"education\": row[\"education\"], \"affiliation\": row[\"affiliation\"],\n \"role\": row[\"role\"], \"team_status\": row[\"team_status\"]})\n return data\n\n@as_json\ndef get_testresults():\n df = pd.read_sql_table('covidtests', db.engine)\n date = datetime.strptime(\"2020-02-28\",\"%Y-%m-%d\")\n df = df.loc[df.date > date]\n df = df.sort_values('date')\n tests ={}\n\n deaths = {}\n investigations = {}\n negatives = {}\n positives = {}\n resolveds = {}\n totals = {}\n news = {}\n investigations_pct = {}\n negatives_pct = {}\n positives_pct = {}\n\n df['new'] = df.total.diff()\n\n for index, row in df.iterrows():\n date = str(row['date'].date())\n negative = row['negative']\n investigation = row['investigation']\n positive = row['positive']\n resolved = row['resolved']\n death = row['deaths']\n total = row['total']\n new = row['new']\n\n\n deaths[date] = death\n investigations[date] = investigation\n negatives[date] = negative\n positives[date] = positive\n resolveds[date] = resolved\n totals[date] = total\n if row['new']==row['new']:\n news[date] = new\n positives_pct[date] = positive/total\n negatives_pct[date] = negative/total\n investigations_pct[date] = investigation/total\n\n tests['Deaths'] = deaths\n tests['Under Investigation'] = investigations\n tests['Positive'] = positives\n tests['Negatives'] = negatives\n tests['Total tested'] = totals\n tests['New tests'] = news\n tests['Resolved'] = resolveds\n tests['Positive pct'] = positives_pct\n tests['Negative pct'] = negatives_pct\n tests['Investigation pct'] = investigations_pct\n\n return tests\n","sub_path":"app/api/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":10745,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"119631441","text":"i = 0\r\nnumbers = []\r\nj = input(\"Input your variable j:\")\r\n\r\ndef f1(i):\r\n for i in range(int(j)):\r\n print(\"At the top i is {}\".format(i))\r\n numbers.append(i)\r\n print(\"Numbers now:\", numbers)\r\n print(\"At the bottom i is {}\".format(i))\r\n\r\nf1(i)\r\n\r\nprint(\"The numbers:\")\r\n\r\nfor num in numbers:\r\n print(num)\r\n\r\n","sub_path":"ex33.py","file_name":"ex33.py","file_ext":"py","file_size_in_byte":340,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"420284726","text":"from __future__ import division\r\nfrom random import choice\r\ndim=3\r\ntrials=1000\r\nsteps=1000\r\ngothome=0\r\nfor i in range(trials):\r\n point=[0]*dim\r\n for step in range(steps):\r\n for j in range(dim):\r\n point[j]+=choice((-1,1))\r\n if point.count(0)==0:\r\n gothome+=1\r\n break\r\nprint (\"Fraction that got home=%f\"% (gothome/trials))\r\n","sub_path":"Random3DWalk.py","file_name":"Random3DWalk.py","file_ext":"py","file_size_in_byte":375,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"416663157","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\n@version: 3.5\n@author: morgana\n@license: Apache Licence \n@contact: vipmorgana@gmail.com\n@site: \n@software: PyCharm\n@file: LambdaMapNewDic.py\n@time: 2017/6/25 下午5:32\n用map来处理下述l，然后用list得到一个新的列表，列表中每个人的名字都是sb结尾\nl=[{'name':'alex'},{'name':'y'}]\n\"\"\"\nl=[{'name':'alex'},{'name':'y'}]\ny=map(lambda x:{'name':x['name']+'sb'},l)\n\nfor i in y:\n print(i)","sub_path":"Morgana/D20170624review/LambdaMapNewDic.py","file_name":"LambdaMapNewDic.py","file_ext":"py","file_size_in_byte":463,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"221051074","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom flask import Flask, request, jsonify, make_response\nfrom flask_restful import Resource, Api\nfrom sqlalchemy import create_engine\nimport codecs\nengine = create_engine('sqlite:///csalud.db')\n\napp = Flask(__name__)\napi = Api(app)\n\nclass Provincias(Resource):\n\n def get(self):\n '''()->json\n Devuelve un JSON que contiene las provincias que aparacen en la base de datos\n '''\n conexion = engine.connect()\n peticion = conexion.execute(\"select distinct PROVINCIA from csalud\")\n return make_response(jsonify({'provincias': \\\n [i[0] for i in peticion.cursor.fetchall()]}), 200)\n\nclass Centros(Resource):\n\n def get(self, provincia):\n '''(str)->json\n Devuelve un JSON con los centros que están en la provicia proporcionada\n '''\n conexion = engine.connect()\n peticion = conexion.execute(\"select * from csalud where Provincia='%s'\" \\\n % provincia.upper())\n result = {'datos': [dict(zip(tuple (peticion.keys()) ,i)) for i in peticion.cursor]}\n return make_response(jsonify(result), 200)\n\napi.add_resource(Provincias, '/provincias') \napi.add_resource(Centros, '/centros/')\n\nif __name__ == '__main__':\n app.run(debug = True)\n","sub_path":"restapi.py","file_name":"restapi.py","file_ext":"py","file_size_in_byte":1301,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"171833398","text":"import json\n\n\ndef insertionSort(arr):\n for i in range(1, len(arr)):\n key = arr[i]\n j = i - 1\n while j >= 0 and key.age < arr[j].age:\n arr[j + 1] = arr[j]\n j -= 1\n arr[j + 1] = key\n\n return arr\n\n\ndef pageToJSON(n, m):\n return json.dumps({\"current page\": str(n), \"total pages\": str(m)})\n","sub_path":"src/Helpers.py","file_name":"Helpers.py","file_ext":"py","file_size_in_byte":344,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"70304192","text":"from django.contrib.auth.decorators import login_required\nfrom django.shortcuts import render, render_to_response\nfrom easy.decorators import *\nfrom www.models import *\nimport forms\n\n@context_template_response\ndef home(request):\n start = int(request.REQUEST.get('start', 0))\n defaults = [\n 'funny',\n 'pics',\n 'askreddit',\n 'news',\n 'movies',\n 'books',\n 'programming',\n ]\n\n if request.user.is_authenticated():\n posts = Post.objects.filter(sub__in=request.user.subscribed_to.all()).order_by('-score')[start:start+25]\n else:\n posts = Post.objects.filter(sub__name__in=defaults).order_by('-score')[start:start+25]\n\n data = {\n 'form': forms.LoginForm(),\n 'all_subs': Sub.objects.all(),\n 'posts': posts,\n 'is_sub': False,\n 'start': start,\n 'next': \"/?start=%s\" % (start+25),\n 'prev': \"/?start=%s\" % (start-25),\n }\n return \"home.html\", data\n\ndef view_subreddit(request, subreddit):\n issubbed = request.user.is_authenticated() and Sub.objects.filter(name=subreddit, subscribers__id=request.user.id).exists()\n start = int(request.REQUEST.get('start', 0))\n\n data = {\n 'issubbed': issubbed,\n 'all_subs': Sub.objects.all(),\n 'form': forms.LoginForm(),\n 'posts': Post.objects.filter(sub=Sub.objects.get(name=subreddit))[start:start+25],\n 'is_sub': True,\n 'subscribers': Sub.objects.get(name=subreddit).subscribers.count(),\n 'subreddit': subreddit,\n 'start': start,\n 'next': \"/r/%s/?start=%s\" % (subreddit, start+25),\n 'prev': \"/r/%s/?start=%s\" % (subreddit, start-25),\n }\n\n return render_to_response(\n \"home.html\",\n data,\n context_instance=RequestContext(request)\n )\n\ndef view_post(request, subreddit, post_id):\n issubbed = request.user.is_authenticated() and Sub.objects.filter(name=subreddit, subscribers__id=request.user.id).exists()\n data = {\n 'issubbed': issubbed,\n 'all_subs': Sub.objects.all(),\n 'form': forms.LoginForm(),\n 'post': Post.objects.get(id=post_id),\n 'is_sub': True,\n 'subscribers': Sub.objects.get(name=subreddit).subscribers.count(),\n 'subreddit': subreddit,\n 'rootcommentform': forms.PostComment().set_parent_id(-1).set_post_id(post_id),\n }\n\n return render_to_response(\n \"post.html\",\n data,\n context_instance=RequestContext(request)\n )\n\n@context_template_response\ndef register(request):\n return \"register.html\", {'form': forms.Register()}\n\n@context_template_response\ndef submit(request):\n sub = request.REQUEST.get('sub')\n return \"submit.html\", {'form': forms.SubmitPost().set_sub(sub)}\n\n@context_template_response\ndef create_sub(request):\n return \"createsub.html\", {'form': forms.CreateSub()}\n\n@context_template_response\ndef _login(request):\n return \"login.html\", {'form': forms.LoginForm()}\n\ndef view_profile(request, username):\n data = {\n 'owner': User.objects.get(username=username),\n }\n return render_to_response(\n \"profile.html\",\n data,\n context_instance=RequestContext(request)\n )\n","sub_path":"minireddit/www/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3389,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"147673807","text":"lines = []\n\nwith open('../data/day_6.txt', 'r') as f:\n lines = [l.replace('\\n', ' ').strip() for l in f.read().split('\\n\\n')]\n\ncnt = 0\n\nfor line in lines:\n s = set()\n\n for ans in line.split(' '):\n s = s | set(ans)\n \n cnt += len(s)\n\nprint(cnt)","sub_path":"python/day_6_1.py","file_name":"day_6_1.py","file_ext":"py","file_size_in_byte":264,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"322915343","text":"import math\nwith open('model_file.txt') as text:\n probabilities = {}\n for line in text:\n words = line.split()\n probabilities[words[0]] = float(words[1])\n\nwith open('../../data/wiki-en-test.word') as text:\n W=0\n unk=0\n H=0\n for line in text:\n words = line.split()\n words.append(\"\")\n for word in words:\n W+=1\n P = 0.05/1000000\n if word in probabilities:\n P+=0.95*probabilities[word]\n else:\n unk+=1\n H+=math.log(P)*-1\nprint(\"entropy =\" +str(H/W))\nprint(\"coverage =\" +str((W-unk)/W))\n\n\n\n\n","sub_path":"arai/tutorial01/test-unigram.py","file_name":"test-unigram.py","file_ext":"py","file_size_in_byte":622,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"384707155","text":"# -*- coding: utf-8 -*-\n# @Time: 2021/8/29 21:13\n# @Author: yuyinghao\n# @FileName: auto_differential.py\n# @Software: PyCharm\n\nimport torch\n\nx = torch.ones(2, 2, requires_grad=True)\nprint(x)\n\ny = x + 2\nprint(y)\n\nprint(y.grad_fn)\n\nz = y * y * 3\nout = z.mean()\nprint(z, out)\n\na = torch.randn(2, 2) # 标准正态分布\na = a * 3 / (a - 1)\n\nprint(a.requires_grad)\na.requires_grad_(True)\n\nb = (a * a).sum()\nprint(b.grad_fn)\n\n\n######################梯度##########################\nv = torch.tensor([0.1, 1.0, 0.0001], dtype=torch.float)","sub_path":"base/auto_differential.py","file_name":"auto_differential.py","file_ext":"py","file_size_in_byte":532,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"534199884","text":"import sys, requests, webbrowser, re, time\nfrom bs4 import BeautifulSoup\n\nstart = time.time()\n\ndef actor_imdb_url():\n while True:\n actor = str(input('Please enter an actor: '))\n actor_link = actor.replace(' ','').lower()\n find = 'imdb'\n res = requests.get('http://google.com/search?q=' + actor_link)\n res.raise_for_status()\n soup = BeautifulSoup(res.text, 'lxml')\n \n try:\n links = soup.find_all('a', href=re.compile('imdb'))\n imdb_url = ('http://google.com'+(links[0].get('href')))\n return {'actor':actor,'url':imdb_url}\n break\n \n except:\n print('There is no imdb page for that actor, try another name.')\n\n\n\ndef movies(imdb):\n new_url= stuff.get('url')\n res = requests.get(new_url)\n res.raise_for_status()\n soup = BeautifulSoup(res.text,'lxml')\n titles = []\n year_str = []\n year = []\n \n \n for a in soup.find_all('div', id=re.compile('actor-tt')):\n for title in a.find('a'):\n titles.append(title)\n\n print('{}) {}'.format(str(len(titles)),title))\n\n\n print()\n print('{} has featured in {} movies.'.format(stuff.get('actor').title(),str(len(titles)))) \n \nstuff = actor_imdb_url()\nmovies(stuff.get('url'))\nend = time.time()\nprint(end-start)\n\n\n\n\n\n \n\n\n\n\n","sub_path":"ActorSearch.py","file_name":"ActorSearch.py","file_ext":"py","file_size_in_byte":1311,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"509616276","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"Tests for `ebr_trackerbot` package.\"\"\"\n\nimport re\nfrom ebr_trackerbot.command.track import track_command\nfrom ebr_trackerbot.bot import register_storage, get_storage, config\n\n\ndef test_track_command(test_payload):\n \"\"\"\n Test track command\n \"\"\"\n config[\"storage_backend\"] = \"test\"\n register_storage(\"test\", save_record, \"load_for_user\", \"load_all\", \"delete_for_user\", \"clean_expired_tracks\")\n assert get_storage()[\"save\"] == save_record # pylint: disable=comparison-with-callable\n payload = test_payload(post_message_commands)\n text = \"track test for 10d2h5m10s\"\n result = re.match(\"^track ([^ ]+) for ((?:[0-9]+(?:s|m|h|d))+)$\", text, re.IGNORECASE)\n commands = []\n track_command(text, result, payload, {}, commands)\n\n\ndef post_message_commands(channel, text, thread_ts):\n \"\"\"\n Check slack message when track was successful\n \"\"\"\n assert re.match(r\"^Tracking started for test \\*test\\* for \\*10d2h5m10s\\*\", text)\n return {\"ok\": \"ok\"}\n\n\ndef save_record(user, data):\n \"\"\"\n Helper function\n \"\"\"\n return True\n","sub_path":"tests/unit/test_command_track.py","file_name":"test_command_track.py","file_ext":"py","file_size_in_byte":1114,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"298812112","text":"from abc import ABC, abstractmethod\nfrom collections import OrderedDict\n\nimport numpy as np\n\nimport torch\nfrom torch.autograd import Variable as torch_Variable\nimport torch.nn as nn\n\n\nSQRT2 = np.sqrt(2.0)\nLOG2PI = np.log(2 * np.pi)\nIDENTITY = \"identity\"\nPOSITIVE = \"positive\"\nA_FOR_ERF = 8.0 / (3.0 * np.pi) * (np.pi - 3.0) / (4.0 - np.pi)\n\ndef constrain_parameter(torch_free_param, free_to_constrained, distribution_name, constrained_name):\n if free_to_constrained == IDENTITY:\n torch_constrained_param = torch_free_param\n elif free_to_constrained == POSITIVE:\n torch_constrained_param = torch.exp(torch_free_param) #tf可以给变量起名字\n else:\n raise NotImplementedError(\"unknown free_to_constrained = %s\" % (free_to_constrained))\n\n return torch_constrained_param\n\ndef build_q_local(PARAMETERS, hidden, devs, conds, verbose, local_parameters,use_bias=True, stop_grad=False, plot_histograms=False):\n assert hasattr(PARAMETERS, \"l\"), \"require local parameters\"\n distribution_descriptions = PARAMETERS.l\n\n # make a distribution that has \"log_prob(theta)\" and \"sample()\"\n q_local = ChainedDistribution(name=\"q_local\")\n\n for distribution_name in distribution_descriptions.list_of_params:\n if verbose:\n print(\"- build_q_local::%s\" % distribution_name)\n description = getattr(distribution_descriptions, distribution_name)\n conditioning = description.defaults['c'] # <-- not a tensor\n params = OrderedDict()\n for free_name, constrained_name, free_to_constrained in zip(\n description.free_params, description.params, description.free_to_constrained):\n # when appropriate, concatenate the dependencies\n to_concat = [hidden]\n if conditioning is not None: # collect tensors to concat\n if conditioning['treatments']:\n to_concat.append(conds)\n if conditioning['devices']:\n to_concat.append(devs)\n hidden_conditioned = torch.cat(to_concat, 1) #(batchsize,57) 50+7(hidden_unit+device_depth)\n\n # filter to whatever we want to condition\n name = '%s_%s'%(distribution_name,free_name)\n #free_param = nn.Linear(57,1, bias = use_bias)\n free_param = local_parameters[name]\n torch_free_param = free_param(hidden_conditioned)\n if stop_grad:\n torch_free_param.detach() # eliminate score function term from autodiff\n torch_constrained_param = constrain_parameter(torch_free_param, free_to_constrained, distribution_name, constrained_name) #恒等变换，不用指数变换（对value是否大于0没有要求）\n params[free_name] = torch_free_param\n params[constrained_name] = torch_constrained_param\n\n for other_param_name, other_param_value in description.other_params.items():\n params[other_param_name] = other_param_value\n\n new_distribution = description.class_type(wait_for_assigned=True, variable=True)\n new_distribution.assign_free_and_constrained(**params) # *args **kwargs\n\n q_local.add_distribution(distribution_name, new_distribution)\n\n return q_local\n\ndef build_q_global_cond(PARAMETERS, devs, conds, verbose, global_cond_parameters, kernel_regularizer=None, use_bias=False, stop_grad=False, plot_histograms=False):\n # make a distribution that has \"log_prob(theta)\" and \"sample()\"\n q_global_cond = ChainedDistribution(name=\"q_global_cond\")\n\n if not hasattr(PARAMETERS, \"g_c\"):\n print(\"- Found no global conditional params\")\n return q_global_cond\n\n distribution_descriptions = PARAMETERS.g_c\n\n for distribution_name in distribution_descriptions.list_of_params:\n description = getattr(distribution_descriptions, distribution_name)\n\n conditioning = description.defaults['c'] # <-- not a tensor\n\n if verbose:\n print(\"- build_q_global_cond::%s\"%distribution_name)\n params = OrderedDict()\n for free_name, constrained_name, free_to_constrained in zip(\n description.free_params, description.params, description.free_to_constrained):\n to_concat = []\n if conditioning is not None: # collect tensors to concat\n if verbose:\n print(\"- Conditioning parameter %s.%s\" % (distribution_name, free_name))\n if conditioning['treatments']:\n to_concat.append(conds)\n if conditioning['devices']:\n to_concat.append(devs)\n\n mlp_inp = torch.cat(to_concat,1)\n name = '%s_%s' % (distribution_name, free_name)\n # map sample from prior with conditioning information through 1-layer NN\n #free_param = nn.Linear(7,1, bias=use_bias)#需要之后用字母代替\n free_param = global_cond_parameters[name]\n torch_free_param = free_param(mlp_inp)\n #tf_free_param = tf.layers.dense(mlp_inp, units=1, use_bias=use_bias, kernel_regularizer=kernel_regularizer,\n if stop_grad:\n torch_free_param.detach()\n #variable_summaries(tf_free_param, 'nn_%s'%name, plot_histograms)\n torch_constrained_param = constrain_parameter(torch_free_param, free_to_constrained, distribution_name, constrained_name)\n\n params[free_name] = torch_free_param\n params[constrained_name] = torch_constrained_param\n\n for other_param_name, other_param_value in description.other_params.items():\n params[other_param_name] = other_param_value\n\n new_distribution = description.class_type(wait_for_assigned=True, variable=True)\n new_distribution.assign_free_and_constrained(**params)\n\n q_global_cond.add_distribution(distribution_name, new_distribution)\n\n return q_global_cond\n\ndef build_q_global(PARAMETERS, verbose, global_parameters, stop_grad=False):\n # make a distribution that has \"log_prob(theta)\" and \"sample()\"\n q_global = ChainedDistribution(name=\"q_global\")\n\n if not hasattr(PARAMETERS, \"g\"):\n print(\"- Found no global parameters\")\n return q_global\n\n distribution_descriptions = PARAMETERS.g\n\n for distribution_name in distribution_descriptions.list_of_params:\n description = getattr(distribution_descriptions, distribution_name)\n if verbose:\n print(\"- build_q_global::%s\" % distribution_name)\n params = OrderedDict()\n for free_name, constrained_name, free_to_constrained, init_free in zip(\n description.free_params, description.params, description.free_to_constrained,\n description.init_free_params):\n\n torch_free_param = global_parameters['%s_%s'%(distribution_name, free_name)]\n if stop_grad:\n torch_free_param.detach()\n torch_constrained_param = constrain_parameter(\n torch_free_param, free_to_constrained, distribution_name, constrained_name)\n\n params[free_name] = torch_free_param #log_prec\n params[constrained_name] = torch_constrained_param #prec\n\n for other_param_name, other_param_value in description.other_params.items():\n params[other_param_name] = other_param_value\n\n new_distribution = description.class_type(wait_for_assigned=True, variable=False)\n new_distribution.assign_free_and_constrained(**params)\n\n q_global.add_distribution(distribution_name, new_distribution)\n\n return q_global\n\ndef build_q_constant(PARAMETERS, verbose, stop_grad=False):\n # make a distribution that has \"log_prob(theta)\" and \"sample()\"\n q_constant = ChainedDistribution(name=\"q_constant\")\n\n if not hasattr(PARAMETERS, \"c\"): #hasattr：是否拥有该参数\n print(\"- Found no constant parameters\")\n return q_constant\n\n distribution_descriptions = PARAMETERS.c\n\n for distribution_name in distribution_descriptions.list_of_params:\n description = getattr(distribution_descriptions, distribution_name)#获取parameters.c对象的属性具体的值（TfConstant）\n if verbose:\n print(\"- build_q_constant::%s\" % distribution_name)\n params = OrderedDict()\n for free_name, constrained_name, free_to_constrained, init_free in zip(\n description.free_params, description.params, description.free_to_constrained,\n description.init_free_params):\n\n torch_free_param = torch_Variable(torch.tensor(init_free), requires_grad = False) #赋予tf_free_param一个init_free的初值 如何添加名字？\n params[free_name] = torch_free_param #an OrderedDict,{'value':torch_Variable}\n\n for other_param_name, other_param_value in description.other_params.items():\n params[other_param_name] = other_param_value\n\n new_distribution = description.class_type(wait_for_assigned=True, variable=False)\n new_distribution.assign_free_and_constrained(**params)\n\n q_constant.add_distribution(distribution_name, new_distribution)\n\n return q_constant\n\ndef build_p_global(PARAMETERS, verbose, theta=None):\n # p_global: generative model with fixed distribution parameters; ie the top-level distributions\n\n # make a distribution that has \"log_prob(theta)\" and \"sample()\"\n p_global = ChainedDistribution(name=\"p_global\")\n\n if not hasattr(PARAMETERS, \"g\"):\n print(\"- Found no global parameters\")\n return p_global\n\n assert hasattr(PARAMETERS, \"g\"), \"require global parameters\"\n distribution_descriptions = PARAMETERS.g\n\n for distribution_name in distribution_descriptions.list_of_params:\n if verbose:\n print(\"- build_p_global::%s\"%distribution_name)\n\n description = getattr(distribution_descriptions, distribution_name)\n\n params = OrderedDict()\n slots = OrderedDict()\n # check for dependencies\n for (constrained_name, dependency) in zip(description.params, description.dependencies):\n if dependency is None:\n continue #继续下一个循环，该循环内之后的表达式都不用管了\n # TODO(dacart): arguments for format string missing\n #if verbose:\n # print(\"build_p_global: found dependency for %s = %s\" % )\n if theta is None:\n if verbose:\n print(\"build_p_global: empty slot for dependency!\")\n params[constrained_name] = None\n slots[constrained_name] = dependency\n else:\n params[constrained_name] = getattr(theta, dependency)\n\n # for each default param not already found via dependency, add to params\n for constrained_name, default_value in description.defaults.items():\n if constrained_name not in params:\n params[constrained_name] = default_value\n\n new_distribution = description.class_type(**params)\n p_global.add_distribution(distribution_name, new_distribution, slots)\n\n return p_global\n\ndef build_p_constant(PARAMETERS, verbose, theta=None):\n # p_global: generative model with fixed distribution parameters; ie the top-level distributions\n\n # make a distribution that has \"log_prob(theta)\" and \"sample()\"\n p_constant = ChainedDistribution(name=\"p_constant\")\n\n if not hasattr(PARAMETERS, \"c\"):\n print(\"- Found no constant parameters\")\n return p_constant\n\n assert hasattr(PARAMETERS, \"c\"), \"require constant parameters\"\n distribution_descriptions = PARAMETERS.c\n\n for distribution_name in distribution_descriptions.list_of_params:\n if verbose:\n print(\"- build_p_constant::%s\"%distribution_name)\n\n description = getattr(distribution_descriptions, distribution_name)\n\n params = OrderedDict()\n slots = OrderedDict()\n # check for dependencies\n for (constrained_name, dependency) in zip(description.params, description.dependencies):\n if dependency is None:\n continue\n # TODO(dacart): arguments for format string missing\n #if verbose:\n # print(\"build_p_constant: found dependency for %s = %s\" % )\n if theta is None:\n if verbose:\n print(\"build_p_constant: empty slot for dependency!\")\n params[constrained_name] = None\n slots[constrained_name] = dependency\n else:\n params[constrained_name] = getattr(theta, dependency)\n\n # for each default param not already found via dependency, add to params\n for constrained_name, default_value in description.defaults.items():\n if constrained_name not in params:\n params[constrained_name] = default_value\n\n new_distribution = description.class_type(**params)\n p_constant.add_distribution(distribution_name, new_distribution, slots)\n\n return p_constant\n\ndef build_p_global_cond(PARAMETERS, verbose, theta=None):\n p_global_cond = ChainedDistribution(name=\"p_global_cond\")\n\n if not hasattr(PARAMETERS, \"g_c\"):\n print(\"- Found no global conditional params\")\n return p_global_cond\n\n assert hasattr(PARAMETERS, \"g_c\"), \"require global conditioned parameters\"\n distribution_descriptions = PARAMETERS.g_c\n\n for distribution_name in distribution_descriptions.list_of_params:\n if verbose:\n print(\"- build_p_global_cond::%s\"%distribution_name)\n\n description = getattr(distribution_descriptions, distribution_name)\n #conditioning = description.defaults['c'] # <-- not a tensor\n\n params = OrderedDict()\n slots = OrderedDict()\n # check for dependencies\n for (constrained_name, dependency) in zip(description.params, description.dependencies):\n\n if dependency is not None:\n #if verbose:\n # print(\"build_p_global_cond: found dependency for %s = %s\" % )\n if theta is None:\n if verbose:\n print(\"build_p_global_cond: empty slot for dependency!\")\n params[constrained_name] = None\n slots[constrained_name] = dependency\n else:\n params[constrained_name] = getattr(theta, dependency)\n\n # for each default param not already found via dependency, add to params\n for constrained_name, default_value in description.defaults.items():\n if constrained_name not in params:\n params[constrained_name] = default_value\n\n new_distribution = description.class_type(**params)\n p_global_cond.add_distribution(distribution_name, new_distribution, slots)\n\n return p_global_cond\n\ndef build_p_local(PARAMETERS, verbose, theta=None):\n assert hasattr(PARAMETERS, \"l\"), \"require local parameters\"\n distribution_descriptions = PARAMETERS.l\n\n # make a distribution that has \"log_prob(theta)\" and \"sample()\"\n p_local = ChainedDistribution(name=\"p_local\")\n\n for distribution_name in distribution_descriptions.list_of_params:\n if verbose:\n print(\"- build_p_local::%s\"%distribution_name)\n\n description = getattr(distribution_descriptions, distribution_name)\n\n params = OrderedDict()\n slots = OrderedDict()\n\n # check for dependencies\n for (constrained_name, dependency) in zip(description.params, description.dependencies):\n\n if dependency is not None:\n print(\"- build_p_local: found dependency for %s = %s\" % (constrained_name, dependency))\n #params[constrained_name] = getattr(theta, dependency)\n if theta is None:\n print(\"- build_p_local: empty slot for dependency!\")\n params[constrained_name] = None\n slots[constrained_name] = dependency\n else:\n params[constrained_name] = getattr(theta, dependency)\n\n # for each default param not already found via dependency, add to params\n for constrained_name, default_value in description.defaults.items():\n if constrained_name not in params:\n params[constrained_name] = default_value\n\n new_distribution = description.class_type(**params)\n p_local.add_distribution(distribution_name, new_distribution, slots)\n\n return p_local\n\nclass DotOperatorSamples(object):\n def __init__(self):\n self.samples = OrderedDict()\n self.keys = []\n self.values = []\n\n def add(self, distribution_name, distribution_sample):\n assert not hasattr(self, distribution_name), \"DotOperatorSamples already has %s\" % distribution_name\n self.samples[distribution_name] = distribution_sample\n self.keys.append(distribution_name)\n self.values.append(distribution_sample)\n setattr(self, distribution_name, distribution_sample)\n\n def __str__(self):\n s = \"\"\n for distribution_name, distribution_sample in self.samples.items():\n s += \"%s = %s\\n\" % (distribution_name, distribution_sample)\n return s\n\n def get_n_batch(self):\n return torch.tensor(self.values[0].shape[0]) #torch.tensor(g.shape[1]) torch.tensor(self.values[0].shape[0])\n\n def get_n_samples(self):\n return torch.tensor(self.values[0].shape[1])\n\n def get_tensors(self):\n return self.values #[tensor for tensor in self.samples.values()]\n\n # Commented out as not called.\n #def set_tensors(self, tensor_list):\n # # TODO(dacart) what is going on here?\n # for _tensor in self.samples.values():\n # pass\n # # return [tensor for tensor in self.samples.values()]\n\nclass ChainedDistribution(object):\n def __init__(self, name=\"unknown\"):\n self.name = name\n self.distributions = OrderedDict()\n self.slot_dependencies = OrderedDict()\n\n def log_prob(self, theta, stop_grad=False): # wraps log_prob for individual distributions\n log_probs = []\n for distribution, sample_value in theta.samples.items(): # sample_value:(batch_size,n_iwae)\n if distribution in self.distributions:\n log_probs.append(self.distributions[distribution].log_prob(sample_value,\n stop_grad)) # 具体样本的值？ self.distributions[distribution].mu = TfNormal.mu -->(batch_size,1)\n\n # stacking issue?\n if log_probs:\n return torch.sum(torch.stack(log_probs, -1), -1) # (batch_size,n_iwae,14)-->(batch_size,n_iwae)\n # return 0.0 so we can broadcast empty logprob with another non-empty\n return 0.0\n\n def clip(self, theta, stddevs=3, skip=None):\n clipped_theta = DotOperatorSamples()\n for distribution, sample_value in theta.samples.items():\n skip_this = skip is not None and distribution in skip\n if not skip_this:\n clipped_value = self.distributions[distribution].clip(sample_value, stddevs=stddevs)\n else:\n clipped_value = sample_value\n clipped_theta.add(distribution, clipped_value)\n return clipped_theta\n\n def order_distributions(self):\n names = self.distributions.keys()\n slots = self.slot_dependencies.values()\n\n orders = OrderedDict()\n\n while len(orders) < len(names):#长度为14\n # add distribution if all depenedencies are in orders already\n for name, s in zip(names, slots):\n if name not in orders:\n\n # what are dependencies for this\n dependencies = s.values()\n\n all_dependencies = True\n for dependency in dependencies: #因为dependencies为空，所以直接跳过该for循环\n if dependency not in orders:\n print(\"%s is waiting for %s\" % (name, dependency))\n all_dependencies = False\n break\n\n if all_dependencies is True:\n orders[name] = len(orders)\n\n return orders\n\n def sample(self, list_of_u, verbose, stop_grad=False):\n distribution_id_order = self.order_distributions() #给不同的distribution排序编号\n\n assert list_of_u.shape[-1] == len(self.distributions), \\\n \"ChainedDistribution (%s #= %d):: must give a list of u's, one for each distribution.\" % (\n self.name, list_of_u.shape[-1])\n samples = DotOperatorSamples()\n\n idx = 0\n\n if verbose:\n print(distribution_id_order)\n for name, idx in distribution_id_order.items():\n distribution = self.distributions[name]\n\n if distribution.slots_are_pending():\n print(\"while sampling, found pending slot for %s\"%name)\n distribution.fill_slots(self.slot_dependencies[name], samples)\n assert distribution.slots_are_pending() is False, \"STILL pending slot for %s\"%name\n\n if name == \"dummy\":\n pdb.set_trace()\n theta = distribution.sample(list_of_u[:, :, idx], stop_grad)\n samples.add(name, theta)\n return samples\n\n def add_distribution(self, key, value, slots=None):\n assert hasattr(self, key) is False, \"ChainedDistribution (%s) already has %s\" % (self.name, key)\n self.distributions[key] = value\n setattr(self, key, value)\n self.slot_dependencies[key] = slots or {}\n\n def get_tensors(self):\n tensors = []\n for distribution in self.distributions.values():\n tensors.extend(distribution.get_tensors())\n return tensors\n\n def get_tensor_names(self):\n names = []\n #for name, distribution in zip(self.distributions.iterkeys(), self.distributions.itervalues()):\n for name, distribution in self.distributions.items():\n names.extend(distribution.get_tensor_names(name))\n\n return names\n\n def get_theta_names(self):\n return list(self.distributions.keys()) #把keys转化成list\n\n def __str__(self):\n return self.pretty_print()\n\n def pretty_print(self):\n s = \"\"\n for key in self.distributions:\n s += \"%s = %s slots=[%s]\\n\" % (key, getattr(self, key), str(self.slot_dependencies[key]))\n return s\n\nclass TfCrnDistribution(ABC): # ABC-->abstract basic class\n def __init__(self, variable: bool):\n self.variable = variable\n self.waiting_slots = {}\n\n def slots_are_pending(self):\n return any(self.waiting_slots.values()) # 若有一个为true,则返回true;若全部为false,才返回false\n\n def clip(self, sample, stddevs=3):\n return sample\n\n @abstractmethod\n def get_tensors(self):\n pass\n\n @abstractmethod\n def log_prob(self, x, stop_grad):\n pass\n\n @abstractmethod\n def sample(self, u, stop_grad):\n pass\n\n @abstractmethod\n def get_tensor_names(self, name):\n pass\n\n @abstractmethod\n def attach_summaries(self, name, plot_histograms):\n pass\n\n # TODO(dacart): make the arguments to assign_free_and_constrained consistent,\n # then it can be an abstract method too.\n #\n # @abstractmethod\n # def assign_free_and_constrained(...):\n # pass\n\n\nclass TfConstant(TfCrnDistribution):\n\n def __init__(self, c=None, value=None, wait_for_assigned=False, variable=False):\n super(TfConstant, self).__init__(variable)\n self.value = value\n self.nbr_params = 1\n self.param_names = [\"value\"]\n\n def assign_free_and_constrained(self, value):\n self.value = value\n if self.value is not None:\n self.waiting_slots[\"value\"] = False\n\n def fill_slots(self, slots, samples):\n if self.waiting_slots[\"value\"] is True:\n self.value = getattr(samples, slots['value'])\n self.waiting_slots[\"value\"] = False\n\n def sample(self, u, stop_grad): # TODO reshape\n return torch.zeros_like(u) + self.value #shift\n\n def log_prob(self, x, stop_grad):\n return torch.zeros_like(x)\n\n def __str__(self):\n s = \"%s \" % (self.__class__)\n for p_name in self.param_names:\n s += \"%s = %s \" % (p_name, getattr(self, p_name))\n\n return s\n\n def get_tensors(self):\n return [self.value]\n\n def attach_summaries(self, name, plot_histograms):\n ()\n # self._attach_summary_ops(self.prec, 'prec', name)\n\n def get_tensor_names(self, name):\n return [\"%s.value\" % name] # , \"%s.prec\" % name]\n\n\nclass TfNormal(TfCrnDistribution):\n\n def __init__(self, mu=None, c=None, sigma=None, prec=None, variable=True, wait_for_assigned=False):\n super(TfNormal, self).__init__(variable)\n\n self.waiting_slots[\"mu\"] = True\n self.waiting_slots[\"prec\"] = True\n if wait_for_assigned is True:\n self.mu = mu\n self.sigma = sigma\n self.prec = prec\n\n else:\n self.mu = mu\n if sigma is None:\n if prec is not None:\n sigma = 1.0 / torch.sqrt(prec)\n else:\n # a sigma param is passed in\n\n # if prec is not None:\n # #assert prec is None, \"Need sigma or precision, not both.\"\n prec = 1.0 / (sigma * sigma)\n\n self.sigma = sigma\n self.prec = prec\n\n # if we have values for slots, we arent waiting for them\n if self.prec is not None:\n self.waiting_slots[\"prec\"] = False\n if self.mu is not None:\n self.waiting_slots[\"mu\"] = False\n\n # TODO: remove these guys\n self.nbr_params = 2\n self.param_names = [\"mu\", \"prec\"]\n\n def assign_free_and_constrained(self, mu, log_prec, prec):\n self.mu = mu\n self.log_prec = log_prec\n self.prec = prec\n if prec is not None:\n self.sigma = (1.0/torch.sqrt(prec)).to(torch.float32)\n #self.sigma = tf.cast(1.0 / tf.sqrt(prec), tf.float32)\n\n # if we have values for slots, we arent waiting for them\n if self.prec is not None:\n self.waiting_slots[\"prec\"] = False\n if self.mu is not None:\n self.waiting_slots[\"mu\"] = False\n\n def fill_slots(self, slots, samples):\n if self.waiting_slots[\"mu\"] is True:\n self.mu = getattr(samples, slots['mu'])\n self.waiting_slots[\"mu\"] = False\n\n if self.waiting_slots[\"prec\"] is True:\n self.prec = getattr(samples, slots['prec'])\n self.waiting_slots[\"prec\"] = False\n self.sigma = tf.cast(1.0 / tf.sqrt(self.prec), tf.float32)\n\n def sample(self, u, stop_grad):\n if stop_grad == True:\n return self.mu.detach() + self.sigma.detach() * u\n return self.mu + self.sigma * u\n\n def clip(self, x, stddevs=3):\n lower = self.mu - stddevs * self.sigma\n upper = self.mu + stddevs * self.sigma\n x = torch.clamp(x, lower, upper) #可能会因为x不是torch.tensor而报错\n return x\n\n def log_prob(self, x, stop_grad):\n if stop_grad == True:\n prec = self.prec.detach() #不知道这样写对不对\n mu = self.mu.detach()\n else:\n prec = self.prec\n mu = self.mu\n return -LOG2PI + 0.5 * torch.log(torch.tensor(prec + 1e-12)) - 0.5 * prec * ((mu - x)**2) # mu:(batch_size,1); x:(batch_size,n_iwae) 采用广播机制， 返回（batch_size,n_iwae）\n # return -LOG2PI + 0.5*tf.log(self.prec + 1e-12) -0.5*self.prec*tf.square(self.mu-x)\n\n def __str__(self):\n s = \"%s \" % self.__class__\n for p_name in self.param_names:\n s += \"%s = %s \" % (p_name, getattr(self, p_name))\n return s\n\n def get_tensors(self):\n return [self.mu, self.prec]\n\n def attach_summaries(self, name, plot_histograms):\n if self.variable:\n variable_summaries(self.mu, name + '.mu', plot_histograms)\n variable_summaries(self.prec, name + '.prec', plot_histograms)\n else:\n with tf.name_scope(name):\n tf.summary.scalar('mu', tf.reduce_mean(self.mu))\n tf.summary.scalar('prec', tf.reduce_mean(self.prec))\n\n def get_tensor_names(self, name):\n return [\"%s.mu\" % name, \"%s.prec\" % name]\n\n\nclass TfLogNormal(TfNormal):\n\n def sample(self, u, stop_grad):\n log_sample = super(TfLogNormal, self).sample(u, stop_grad)\n return torch.exp(log_sample)\n\n def log_prob(self, x, stop_grad):\n log_x = torch.log(x + 1e-12)\n return super(TfLogNormal, self).log_prob(log_x, stop_grad) - log_x\n\n def clip(self, x, stddevs=3):\n lower = tf.exp(self.mu - stddevs * self.sigma)\n upper = tf.exp(self.mu + stddevs * self.sigma)\n x = tf.clip_by_value(x, lower, upper)\n return x\n\n\nclass TfTruncatedNormal(TfNormal):\n\n def __init__(self, mu=None, c=None, sigma=None, prec=None, a=None, b=None, wait_for_assigned=False):\n if wait_for_assigned:\n self.mu = mu\n self.sigma = sigma\n self.prec = prec\n self.a = a # left boundary\n self.b = b # right boundary\n\n else:\n self.mu = mu\n if sigma is None:\n assert prec is not None, \"Need sigma or precision\"\n sigma = 1.0 / tf.sqrt(prec)\n else:\n assert prec is None, \"Need sigma or precision, not both.\"\n prec = 1.0 / (sigma * sigma)\n\n self.sigma = sigma\n self.prec = prec\n\n if a is None:\n a = -np.inf\n if b is None:\n b = np.inf\n self.a = a\n self.b = b\n\n self.A = (self.a - self.mu) # / self.sigma\n self.B = (self.b - self.mu) # / self.sigma\n # tf.case([(tf.less(-np.inf, self.a), lambda: self.Phi(self.A))], default=lambda: tf.constant(0.0))\n self.PhiA = self.Phi(self.A)\n # tf.case([(tf.less(self.b, np.inf), lambda: self.Phi(self.B))], default=lambda: tf.constant(1.0))\n self.PhiB = self.Phi(self.B)\n self.PhiA = tf.cast(self.PhiA, tf.float32)\n self.PhiB = tf.cast(self.PhiB, tf.float32)\n\n self.Z = self.PhiB - self.PhiA\n self.logZ = tf.log(self.Z)\n\n # TODO: remove these guys\n self.nbr_params = 4\n self.param_names = [\"mu\", \"prec\", \"a\", \"b\"]\n\n def assign_free_and_constrained(self, mu, log_prec, prec, a, b):\n self.mu = mu\n self.log_prec = log_prec\n self.prec = prec\n self.sigma = 1.0 / tf.sqrt(prec)\n self.a = a\n self.b = b\n\n self.A = (self.a - self.mu) # / self.sigma\n self.B = (self.b - self.mu) # / self.sigma\n # tf.case([(tf.less(-np.inf, self.a), lambda: self.Phi(self.A))], default=lambda: tf.constant(0.0))\n self.PhiA = self.Phi(self.A)\n # tf.case([(tf.less(self.b, np.inf), lambda: self.Phi(self.B))], default=lambda: tf.constant(1.0))\n self.PhiB = self.Phi(self.B)\n self.PhiA = tf.cast(self.PhiA, tf.float32)\n self.PhiB = tf.cast(self.PhiB, tf.float32)\n\n # self.PhiA = tf.minimum(self.PhiA, 0.98)\n # self.PhiA = tf.cond(tf.less(-np.inf, self.A), lambda: tf.cast(self.Phi(self.A), tf.float32), lambda: 0.0)\n # self.PhiB = tf.cond(tf.less(self.B, np.inf), lambda: tf.cast(self.Phi(self.B), tf.float32), lambda: 0.0)\n # if self.A > -np.inf:\n # self.PhiA = tf.cast(self.Phi(self.A), tf.float32)\n # else:\n # self.PhiA = 0.0\n\n # if self.B < np.inf:\n # self.PhiB = tf.cast(self.Phi(self.B), tf.float32)\n # else:\n # self.PhiB = 1.0\n\n self.Z = self.PhiB - self.PhiA\n self.logZ = tf.log(self.Z)\n\n def sample(self, u, stop_grad):\n raise NotImplementedError(\"Sample for TfTruncatedNormal hasn't been implemented with stop_grad argument yet \")\n # phi_u = self.Phi(u)\n # standardized_u = self.PhiInverse(self.PhiA + phi_u*self.Z)\n # s = self.mu + self.sigma*standardized_u\n # print(\"TRUNCATED SAMPLES: \", s.shape, self.mu.shape, self.sigma.shape, u.shape, self.Z.shape,\n # self.A.shape, self.B.shape, self.PhiA.shape, self.PhiB.shape)\n # return tf.clip_by_value(s, self.a, self.b)\n\n def log_prob(self, x, stop_grad):\n raise NotImplementedError(\"log_prob for TfTruncatedNormal hasn't been implemented with stop_grad argument yet \")\n # log_prob_full_support = super(TfTruncatedNormal, self).log_prob(x)\n # return log_prob_full_support - self.logZ\n\n def Phi(self, eta):\n # return 0.5*(1 + tf.erf(eta / SQRT2))\n return 0.5 * (1 + erf_approx(eta / SQRT2))\n # return 0.5*(1 + erf_approx(eta / SQRT2))\n\n def PhiInverse(self, u):\n return SQRT2 * erfinv_approx(2 * u - 1.0)\n # return SQRT2*tf.erfc(u)\n\n def __str__(self):\n s = \"%s \" % (self.__class__)\n for p_name in self.param_names:\n s += \"%s = %s \" % (p_name, getattr(self, p_name))\n return s\n\n def get_tensors(self):\n return [self.mu, self.prec]\n\n def get_tensor_names(self, name):\n return [\"%s.mu\" % name, \"%s.prec\" % name]\n\n\nclass TfKumaraswamy(TfCrnDistribution):\n\n # TODO(dacart): set self.prec somehow, as it's needed below.\n def __init__(self, a=None, b=None, zmin=0.0, zmax=1.0, wait_for_assigned=False):\n if not wait_for_assigned:\n self.a = a\n self.b = b\n self.one_over_a = 1.0 / self.a\n self.one_over_b = 1.0 / self.b\n self.log_a = tf.log(self.a)\n self.log_b = tf.log(self.b)\n\n self.zmin = zmin # left boundary\n self.zmax = zmax # right boundary\n self.zrange = self.zmax - self.zmin\n\n # TODO: remove these guys\n self.nbr_params = 4\n self.param_names = [\"a\", \"b\", \"zmin\", \"zmax\"]\n\n def assign_free_and_constrained(self, log_a, log_b, a, b, zmin, zmax):\n self.a = tf.clip_by_value(a, 0.0001, 1.0 / 0.0001)\n self.b = tf.clip_by_value(b, 0.0001, 1.0 / 0.0001)\n self.one_over_a = 1.0 / self.a\n self.one_over_b = 1.0 / self.b\n self.log_a = tf.log(self.a)\n self.log_b = tf.log(self.b)\n self.zmin = zmin # left boundary\n self.zmax = zmax # right boundary\n self.zrange = self.zmax - self.zmin\n\n def standard_sample(self, u, stop_grad):\n raise NotImplementedError(\n \"standard_sample for TfKumaraswamy hasn't been implemented with stop_grad argument yet\")\n\n def sample(self, u, stop_grad):\n raise NotImplementedError(\"sample for TfKumaraswamy hasn't been implemented with stop_grad argument yet\")\n\n def log_prob(self, x, stop_grad):\n raise NotImplementedError(\"log_prob for TfKumaraswamy hasn't been implemented with stop_grad argument yet\")\n\n # convert\n def std_normal_2_uniform(self, eta):\n return 0.5 * (1 + tf.erf(eta / SQRT2))\n\n def PhiInverse(self, u):\n return SQRT2 * erfinv_approx(2 * u - 1.0)\n # return SQRT2*tf.erfc(u)\n\n def __str__(self):\n s = \"%s \" % (self.__class__)\n for p_name in self.param_names:\n s += \"%s = %s \" % (p_name, getattr(self, p_name))\n return s\n\n def get_tensors(self):\n raise NotImplementedError(\"Haven't determined how to set mu and prec yet\")\n # TODO(dacart): set self.mu and self.prec somewhere\n # return [self.mu, self.prec]\n\n def get_tensor_names(self, name):\n return [\"%s.mu\" % name, \"%s.prec\" % name]","sub_path":"vi-hds-master_Pytorch/src/distributions.py","file_name":"distributions.py","file_ext":"py","file_size_in_byte":35734,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"558233971","text":"#!/apps/base/python3/bin/python3\n\nimport os\nimport re\nimport sys\nimport glob\nimport tarfile\nimport argparse\nimport itertools\n\ndef tar_em():\n args = get_args()\n files = glob.glob(args.regex)\n ordered_files = sorted(files)\n\n\n try:\n cutfile_index = re.findall('\\d+', args.cutfile)\n print('cut file index = {}'.format(cutfile_index))\n except Exception:\n cutfile_index = False\n try:\n addfile_index = re.search('\\d+', args.addfile).group(0)\n addfile_pattern = args.addfile.split(' ')[1]\n print('add file index = {}\\nadd file pattern = {}'.format(addfile_index, addfile_pattern))\n except Exception:\n addfile_index = False\n addfile_pattern = False\n\n\n user_continue()\n\n if addfile_index and cutfile_index:\n for i, n in enumerate(cutfile_index):\n cutfile_index[i] = str(eval(n) + 1)\n\n\n for f in ordered_files:\n split_file = f.split(args.delimiter)\n file_name = ''\n\n if addfile_index:\n # this loop will add the pattern to the tar file\n for i, section in enumerate(split_file):\n # if we are at the index specified to add pattern \n if str(i) in addfile_index:\n if i == 0:\n file_name = '{}.{}'.format(addfile_pattern, split_file[i])\n elif i == len(split_file):\n file_name = '{}.{}'.format(file_name, addfile_pattern)\n else:\n # add pattern before the specified index so it will work at index 0\n file_name = '{}.{}.{}'.format(file_name, addfile_pattern, split_file[i])\n # if not at specified index then reconstruct the original file name\n else:\n # if first element then simple assignment\n if i == 0:\n file_name = split_file[i]\n # else construct name with . delimiter\n else:\n file_name = '{}.{}'.format(file_name, split_file[i])\n\n split_file = file_name.split('.')\n\n if cutfile_index:\n file_name = ''\n for i, section in enumerate(split_file):\n if str(i) not in cutfile_index:\n if len(file_name) == 0:\n file_name = split_file[i]\n else:\n file_name = '{}.{}'.format(file_name, split_file[i])\n if not addfile_index and not cutfile_index:\n file_name = f\n\n os.system('mv {} {}'.format(f, file_name))\n\n print('{} ---> {}'.format(f, file_name))\n\n\ndef user_continue():\n flag = True\n while flag:\n decision = input('Continue? (y or n) :')\n if decision == 'y' or decision == 'yes':\n flag = False\n elif decision == 'n' or decision == 'no':\n print('aborting strip file name script.')\n exit(0)\n\nhelp_description='''\nThis script will cut or add to file names in a directory.\\n\nBuild a list using the regular expression given,\\n\nAdding a pattern to a file will be in front of the \\n\n index specified. \"0\" would be before index \"0\" or\\n\n at the front of the file. Adding at the end of the\\n\n file gets a little buggy if you are also removing \\n\n indexes. Indexes start at 0, cutting the 0 element \\n\n will cut the first element of the filename. File elements \\n\n are defined by splitting the file name on '.'.\n'''\n\nexample ='''\nEXAMPLE: \\n\n strip_file_name.py -re 'sgpmetE33*' -cf '4' -af '1 Table2.'\\n\n strip_file_name.py -re '*201706*' -af '2 table1'\\n\n strip_file_name.py -re '*20170[56]*' -cf '1 3 4 5 6'\\n\n\nERRORS:\\n\n strip_file_name.py -c '1' --> must have -re argument\\n\n strip_file_name.py -re 'oliaos*' -c '1 3 4 5' -a '5 test' --> adding and removing is buggy, \\n\n lower add index to achive intended results\\n\n strip_file_name.py -re 'sgp*' -af 'Table2 1' --> will add \"1\" at index \"2\"\\n\n'''\n\ndef get_args():\n parser = argparse.ArgumentParser(description=help_description, epilog=example, formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n \n requiredArguments = parser.add_argument_group('required arguments.')\n requiredArguments.add_argument('-re', type=str, dest='regex', help='regular expression for getting list of files to rename', required=True)\n\n parser.add_argument('-cf', type=str, dest='cutfile', help='cut index from files')\n parser.add_argument('-af', type=str, dest='addfile', help='add argument to file names at index')\n parser.add_argument('-d','--delimiter', type=str, dest='delimiter', default='.', help='')\n\n args = parser.parse_args()\n return args\n\nif __name__ == '__main__':\n tar_em()\n\n","sub_path":"strip_file_name.py","file_name":"strip_file_name.py","file_ext":"py","file_size_in_byte":4745,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"2812555","text":"import itertools\nimport time\n\nfrom Query.Plan import Plan\nfrom Query.Operators.Join import Join\nfrom Query.Operators.TableScan import TableScan \nfrom Query.Operators.Project import Project\nfrom Query.Operators.Select import Select\nfrom Query.Operators.GroupBy import GroupBy\nfrom Utils.ExpressionInfo import ExpressionInfo\n\nclass Optimizer:\n \"\"\"\n A query optimization class.\n\n This implements System-R style query optimization, using dynamic programming.\n We only consider left-deep plan trees here.\n\n We provide doctests for example usage only.\n Implementations and cost heuristics may vary.\n\n >>> import Database\n >>> db = Database.Database()\n >>> import sys\n >>> from Catalog.Schema import DBSchema\n >>> try:\n ... db.createRelation('department', [('did', 'int'), ('eid', 'int')])\n ... db.createRelation('employee', [('id', 'int'), ('age', 'int')])\n ... db.createRelation('Iabc', [('a', 'int'), ('b', 'int'), ('c', 'int')])\n ... db.createRelation('Idef', [('d', 'int'), ('e', 'int'), ('f', 'int')])\n ... db.createRelation('Ighi', [('g', 'int'), ('h', 'int'), ('i', 'int')])\n ... db.createRelation('Ijkl', [('j', 'int'), ('k', 'int'), ('l', 'int')])\n ... db.createRelation('Imno', [('m', 'int'), ('n', 'int'), ('o', 'int')])\n ... db.createRelation('Ipqr', [('p', 'int'), ('q', 'int'), ('r', 'int')])\n ... db.createRelation('Istu', [('s', 'int'), ('t', 'int'), ('u', 'int')])\n ... db.createRelation('Ivwx', [('v', 'int'), ('w', 'int'), ('x', 'int')])\n ... except ValueError:\n ... pass\n\n >>> depSchema = self.db.relationSchema('department')\n >>> empSchema = self.db.relationSchema('employee')\n >>> iabcSchema = self.db.relationSchema('Iabc')\n >>> idefSchema = self.db.relationSchema('Idef')\n >>> ighiSchema = self.db.relationSchema('Ighi')\n >>> ijklSchema = self.db.relationSchema('Ijkl')\n \n >>> for tup in [empSchema.pack(empSchema.instantiate(i, 2*i+20, i % 2)) for i in range(self.numEmployees)]:\n self.db.insertTuple(empSchema.name, tup)\n\n >>> query = db.query().fromTable('Iabc').join( \\\n db.query().fromTable('Idef'), method='block-nested-loops', expr='a == d').join( \\\n db.query().fromTable('Ighi').join( \\\n db.query().fromTable('Ijkl'), method='block-nested-loops', expr='h == j'), method='block-nested-loops', \\\n expr='b == i and e == k').finalize()\n >>> result = db.optimizer.pickJoinOrder(query)\n >>> print(result.explain())\n\n\n >>> aggMinMaxSchema = DBSchema('minmax', [('minAge', 'int'), ('maxAge','int')])\n >>> keySchema = DBSchema('aKey', [('a', 'int')])\n >>> queryGroup = db.query().fromTable('Iabc').where('a < 20').join( \\\n db.query().fromTable('Idef'), method='block-nested-loops', expr='a == d').where('c > f').join( \\\n db.query().fromTable('Ighi').join( \\\n db.query().fromTable('Ijkl'), method='block-nested-loops', expr='h == j'), method='block-nested-loops', \\\n expr='b == i and e == k').where('a == h and d == 5').groupBy( \\\n groupSchema=DBSchema('aKey', [('a', 'int')]), \\\n aggSchema=aggMinMaxSchema, \\\n groupExpr=(lambda e: e.a % 2), \\\n aggExprs=[(sys.maxsize, lambda acc, e: min(acc, e.b), lambda x: x), \\\n (0, lambda acc, e: max(acc, e.b), lambda x: x)], \\\n groupHashFn=(lambda gbVal: hash(gbVal[0]) % 2) \\\n ).finalize()\n >>> result = db.optimizer.pickJoinOrder(queryGroup)\n >>> print(result.explain())\n\n>>> querySelect = db.query().fromTable('Iabc').where('a < 20').join( \\\n db.query().fromTable('Idef'), method='block-nested-loops', expr='a == d').where('c > f').join( \\\n db.query().fromTable('Ighi').join( \\\n db.query().fromTable('Ijkl'), method='block-nested-loops', expr='h == j'), method='block-nested-loops', \\\n expr='b == i and e == k').where('a == h and d == 5').finalize()\n >>> result = db.optimizer.pickJoinOrder(querySelect)\n >>> print(result.explain())\n\n\n # Join Order Optimization\n >>> query4 = db.query().fromTable('employee').join( \\\n db.query().fromTable('department'), \\\n method='block-nested-loops', expr='id == eid').finalize()\n >>> result = db.optimizer.pickJoinOrder(query4)\n >>> print(result.explain())\n\n # Pushdown Optimization\n >>> query5 = db.query().fromTable('employee').union(db.query().fromTable('employee')).join( \\\n db.query().fromTable('department'), \\\n method='block-nested-loops', expr='id == eid')\\\n .where('eid > 0 and id > 0 and (eid == 5 or id == 6)')\\\n .select({'id': ('id', 'int'), 'eid':('eid','int')}).finalize()\n\n\n # Pushdown Optimization\n # >>> query6 = db.query().fromTable('employee').union(db.query().fromTable('employee')).join( \\\n # db.query().fromTable('department'), \\\n # method='block-nested-loops', expr='id == eid')\\\n # .where('eid > 0 and id > 0 and (eid == 5 or id == 6)').finalize()\n # >>> print(db.optimizer.pickJoinOrder(query6).explain())\n\n \"\"\"\n\n def __init__(self, db):\n self.db = db\n\n # Caches the cost of a plan computed during query optimization.\n def addPlanCost(self, plan, cost):\n raise NotImplementedError\n\n # Checks if we have already computed the cost of this plan.\n def getPlanCost(self, plan):\n raise NotImplementedError\n\n# def removeUnaryPlan(self, plan):\n# fieldDict = {}\n# selectList = []\n# q = []\n# q.append((plan.root,None, \"\"))\n#\n# while len(q) > 0:\n# (currNode, pNode, sub) = q.pop()\n# if currNode.operatorType() == \"Select\":\n# selectList.append(currNode)\n# q.append((currNode.subPlan, currNode, \"only\"))\n# if sub == \"only\":\n# pNode.subPlan = currNode.subPlan\n# elif sub == \"left\":\n# pNode.lhsPlan = currNode.subPlan\n# elif sub == \"right\":\n# pNode.rhsPlan = currNode.subPlan\n# else:\n# plan.root = currNode.subPlan\n# elif currNode.operatorType() == \"Project\":\n# #TODO add implementation\n# continue\n# elif currNode.operatorType() == \"TableScan\":\n# for f in currNode.schema().fields:\n# fieldDict[f] = (pNode,sub)\n# continue\n# elif currNode.operatorType() == \"GroupBy\" or currNode.operatorType() == \"Sort\":\n# q.append((currNode.subPlan, currNode, \"only\"))\n# else: #join and union\n# q.append((currNode.lhsPlan, currNode, \"left\"))\n# q.append((currNode.rhsPlan, currNode, \"right\"))\n# \n# return (plan,selectList,fieldDict)\n\n def decompSelects(self,selectList):\n decompList = []\n\n for s in selectList:\n exprList = ExpressionInfo(s.selectExpr).decomposeCNF()\n for e in exprList:\n select = Select(None,e)\n decompList.append(select)\n return decompList\n # Given a plan, return an optimized plan with both selection and\n # projection operations pushed down to their nearest defining relation\n # This does not need to cascade operators, but should determine a\n # suitable ordering for selection predicates based on the cost model below.\n# def pushdownOperators(self, plan):\n# (removedPlan,selectList,fieldDict) = self.removeUnaryPlan(plan)\n# decompList = self.decompSelects(selectList)\n# \n# for s in decompList:\n# attrList = ExpressionInfo(s.selectExpr).getAttributes()\n#\n# if len(attrList) == 1: #TODO should really be number of sources, not num attributes\n# (pNode, sub) = fieldDict[attrList.pop()]\n# if sub == \"only\":\n# s.subPlan = pNode.subPlan\n# pNode.subPlan = s\n# elif sub == \"left\":\n# s.subPlan = pNode.lhsPlan\n# pNode.lhsPlan = s\n# elif sub == \"right\":\n# s.subPlan = pNode.rhsPlan\n# pNode.rhsPlan = s\n# else:\n# s.subPlan = removedPlan.root\n# removedPlan.root = s\n# else:\n# #TODO handle selects with multiple attributes (and dealing with projects)\n# s.subPlan = removedPlan.root\n# removedPlan.root = s\n# \n# return removedPlan\n \n def obtainFieldDict(self, plan):\n q = []\n q.append(plan.root)\n \n attrDict = {}\n\n while len(q) > 0:\n currNode = q.pop()\n\n if currNode.operatorType() == \"TableScan\":\n for f in currNode.schema().fields:\n attrDict[f] = currNode.relationId()\n\n for i in currNode.inputs():\n q.append(i)\n \n return attrDict \n\n def getExprDicts(self, plan, fieldDict):\n q = []\n q.append(plan.root)\n selectTablesDict = {} # mapping of relation list to list of exprs using them: [A,B] -> [a < b, etc]\n joinTablesDict = {} # same thing but for joins, not selects \n\n\n while len(q) > 0:\n currNode = q.pop()\n \n if (currNode.operatorType() == \"Select\"):\n selectExprList = ExpressionInfo(currNode.selectExpr).decomposeCNF()\n for selectExpr in selectExprList:\n attrList = ExpressionInfo(selectExpr).getAttributes()\n sourceList = [] \n for attr in attrList:\n source = fieldDict[attr]\n if source not in sourceList:\n sourceList.append(source)\n\n sourceTuple = tuple(sorted(sourceList))\n if sourceTuple not in selectTablesDict:\n selectTablesDict[sourceTuple] = []\n selectTablesDict[sourceTuple].append(selectExpr)\n \n elif \"Join\" in currNode.operatorType():\n joinExprList = ExpressionInfo(currNode.joinExpr).decomposeCNF()\n for joinExpr in joinExprList:\n attrList = ExpressionInfo(joinExpr).getAttributes()\n sourceList = [] \n for attr in attrList:\n source = fieldDict[attr]\n if source not in sourceList:\n sourceList.append(source)\n\n sourceTuple = tuple(sorted(sourceList))\n if sourceTuple not in joinTablesDict:\n joinTablesDict[sourceTuple] = []\n joinTablesDict[sourceTuple].append(joinExpr)\n \n if len(currNode.inputs()) > 1:\n q.append(currNode.lhsPlan)\n q.append(currNode.rhsPlan)\n elif len(currNode.inputs()) == 1:\n q.append(currNode.subPlan)\n\n return (joinTablesDict, selectTablesDict)\n\n\n # Returns an optimized query plan with joins ordered via a System-R style\n # dyanmic programming algorithm. The plan cost should be compared with the\n # use of the cost model below.\n def pickJoinOrder(self, plan):\n relations = plan.relations()\n fieldDict = self.obtainFieldDict(plan)\n (joinTablesDict, selectTablesDict) = self.getExprDicts(plan, fieldDict)\n # makes dicts that maps a list of relations to exprs involving that list\n # then in system R we will build opt(A,B) Join C using join exprs involving A,C and B,C\n # and on top of it the select exprs that involve 2 tables A,C or B,C\n\n isGroupBy = True if plan.root.operatorType() == \"GroupBy\" else False\n outputSchema = plan.schema() \n optDict = {}\n\n for npass in range(1, len(relations) + 1):\n if npass == 1:\n for r in relations:\n table = TableScan(r,self.db.relationSchema(r))\n if (r,) in selectTablesDict: \n selectExprs = selectTablesDict[(r,)]\n selectString = self.combineSelects(selectExprs)\n select = Select(table,selectString)\n optDict[(r,)] = Plan(root=select)\n else:\n optDict[(r,)] = Plan(root=table)\n else:\n combinations = itertools.combinations(relations,npass)\n for c in combinations:\n clist = sorted(c)\n bestJoin = None\n for rel in clist:\n temp = list(clist)\n temp.remove(rel)\n leftOps = optDict[tuple(temp)].root\n rightOps = optDict[(rel,)].root\n\n selectExpr = self.createExpression(temp, [rel], selectTablesDict)\n joinExpr = self.createExpression(temp, [rel], joinTablesDict)\n \n joinBnljOp = Join(leftOps, rightOps, expr=joinExpr, method=\"block-nested-loops\" )\n fullBnljOp = Select(joinBnljOp, selectExpr)\n\n if selectExpr == \"True\":\n joinBnlj = Plan(root=joinBnljOp)\n else:\n joinBnlj = Plan(root=fullBnljOp)\n \n joinBnlj.prepare(self.db)\n joinBnlj.sample(100)\n \n joinNljOp = Join(leftOps, rightOps, expr=joinExpr, method=\"nested-loops\" )\n fullNljOp = Select(joinNljOp, selectExpr)\n\n if selectExpr == \"True\":\n joinNlj = Plan(root=joinNljOp)\n else:\n joinNlj = Plan(root=fullNljOp)\n \n joinNlj.prepare(self.db)\n joinNlj.sample(100)\n\n if joinBnlj.cost(True) < joinNlj.cost(True):\n if bestJoin == None or joinBnlj.cost(True) < bestJoin.cost(True):\n bestJoin = joinBnlj\n else:\n if bestJoin == None or joinNlj.cost(True) < bestJoin.cost(True):\n bestJoin = joinNlj\n \n self.clearSampleFiles()\n\n optDict[tuple(clist)] = bestJoin\n \n # after System R algorithm\n newPlan = optDict[tuple(sorted(relations))]\n\n if isGroupBy:\n newGroupBy = GroupBy(newPlan.root, groupSchema=plan.root.groupSchema, \\\n aggSchema=plan.root.aggSchema, groupExpr=plan.root.groupExpr, \\\n aggExprs=plan.root.aggExprs, \\\n groupHashFn=plan.root.groupHashFn)\n newGroupBy.prepare(self.db)\n newPlan = Plan(root=newGroupBy)\n\n if set(outputSchema.schema()) != set(newPlan.schema().schema()):\n projectDict = {}\n\n for f, t in outputSchema.schema():\n projectDict[f] = (f, t) \n \n currRoot = newPlan.root\n project = Project(currRoot, projectDict)\n project.prepare(self.db)\n newPlan = Plan(root=project)\n \n return newPlan\n\n def createExpression(self, lList, rList, exprDict):\n \n lcombos = []\n lTemp = []\n rcombos = []\n rTemp = []\n for i in range(1, len(lList) + 1):\n lTemp.extend(itertools.combinations(lList,i))\n lcombos = [list(elem) for elem in lTemp]\n for i in range(1, len(rList) + 1):\n rTemp.extend(list(itertools.combinations(rList,i)))\n rcombos = [list(elem) for elem in rTemp]\n plist = list(itertools.product(lcombos,rcombos))\n \n masterlist = []\n\n for elem in plist:\n item1 = elem[0]\n item2 = elem[1]\n item1.extend(item2)\n masterlist.append(sorted(item1))\n \n exprString = \"\"\n \n for listc in masterlist:\n c = tuple(listc)\n if c in exprDict:\n for s in exprDict[c]:\n exprString += s + \" and \"\n \n if(exprString == \"\"):\n return \"True\"\n exprString = exprString[:len(exprString) - 5]\n \n return exprString\n\n def combineSelects(self,selectExprs):\n selectString = \"\"\n for s in selectExprs:\n selectString += s\n selectString += \" and \"\n\n selectString = selectString[:len(selectString) - 5]\n return selectString\n\n # Optimize the given query plan, returning the resulting improved plan.\n # This should perform operation pushdown, followed by join order selection.\n def optimizeQuery(self, plan):\n #pushedDown_plan = self.pushdownOperators(plan)\n #start = time.time()\n joinPicked_plan = self.pickJoinOrder(plan)\n #end = time.time()\n\n #bushyOutput = open(\"bushy12Tests.txt\", \"a\")\n #bushyOutput.write(\"join size\\tplan count\\telapsed seconds\\n\")\n #bushyOutput.write(str(len(joinPicked_plan.relations())) + \", \" + str(self.reportPlanCount) + \", \" + str(end-start) + \"\\n\")\n #bushyOutput.close()\n\n return joinPicked_plan\n\n\n def clearSampleFiles(self):\n temp_rels = filter(lambda rel: rel not in self.db.relations(), self.db.storage.relations())\n for rel in list(temp_rels):\n self.db.storage.removeRelation(rel) \n\n\nif __name__ == \"__main__\":\n import doctest\n doctest.testmod()\n\n\n\nclass BushyOptimizer(Optimizer):\n \n def __init__(self, db):\n super().__init__(db)\n\n def pickJoinOrder(self, plan):\n \n relations = plan.relations()\n fieldDict = self.obtainFieldDict(plan)\n \n\n (joinTablesDict, selectTablesDict) = self.getExprDicts(plan, fieldDict)\n # makes dicts that maps a list of relations to exprs involving that list\n # then in system R we will build opt(A,B) Join C using join exprs involving A,C and B,C\n # and on top of it the select exprs that involve 2 tables A,C or B,C\n\n isGroupBy = True if plan.root.operatorType() == \"GroupBy\" else False\n outputSchema = plan.schema() \n optDict = {}\n self.reportPlanCount = 0\n\n for npass in range(1, len(relations) + 1):\n if npass == 1:\n for r in relations:\n table = TableScan(r,self.db.relationSchema(r))\n if (r,) in selectTablesDict: \n selectExprs = selectTablesDict[(r,)]\n selectString = self.combineSelects(selectExprs)\n select = Select(table,selectString)\n optDict[(r,)] = Plan(root=select)\n else:\n optDict[(r,)] = Plan(root=table)\n self.reportPlanCount += 1\n else:\n combinations = itertools.combinations(relations,npass)\n for c in combinations:\n fullList = sorted(c)\n clist = self.getCombos(fullList)\n bestJoin = None\n for subcombo in clist:\n complement = self.getComplement(fullList, subcombo)\n \n leftOps = optDict[tuple(complement)].root\n rightOps = optDict[tuple(subcombo)].root\n\n selectExpr = self.createExpression(complement, subcombo, selectTablesDict)\n joinExpr = self.createExpression(complement, subcombo, joinTablesDict)\n \n joinBnljOp = Join(leftOps, rightOps, expr=joinExpr, method=\"block-nested-loops\" )\n fullBnljOp = Select(joinBnljOp, selectExpr)\n\n if selectExpr == \"True\":\n joinBnlj = Plan(root=joinBnljOp)\n else:\n joinBnlj = Plan(root=fullBnljOp)\n \n joinBnlj.prepare(self.db)\n joinBnlj.sample(100)\n \n joinNljOp = Join(leftOps, rightOps, expr=joinExpr, method=\"nested-loops\" )\n fullNljOp = Select(joinNljOp, selectExpr)\n\n if selectExpr == \"True\":\n joinNlj = Plan(root=joinNljOp)\n else:\n joinNlj = Plan(root=fullNljOp)\n \n joinNlj.prepare(self.db)\n joinNlj.sample(100)\n\n if joinBnlj.cost(True) < joinNlj.cost(True):\n if bestJoin == None or joinBnlj.cost(True) < bestJoin.cost(True):\n bestJoin = joinBnlj\n else:\n if bestJoin == None or joinNlj.cost(True) < bestJoin.cost(True):\n bestJoin = joinNlj\n\n self.reportPlanCount += 2\n self.clearSampleFiles()\n\n optDict[tuple(fullList)] = bestJoin\n \n # after System R algorithm\n newPlan = optDict[tuple(sorted(relations))]\n\n if isGroupBy:\n newGroupBy = GroupBy(newPlan.root, groupSchema=plan.root.groupSchema, \\\n aggSchema=plan.root.aggSchema, groupExpr=plan.root.groupExpr, \\\n aggExprs=plan.root.aggExprs, \\\n groupHashFn=plan.root.groupHashFn)\n newGroupBy.prepare(self.db)\n newPlan = Plan(root=newGroupBy)\n\n if set(outputSchema.schema()) != set(newPlan.schema().schema()):\n projectDict = {}\n\n for f, t in outputSchema.schema():\n projectDict[f] = (f, t) \n \n currRoot = newPlan.root\n project = Project(currRoot, projectDict)\n project.prepare(self.db)\n newPlan = Plan(root=project)\n \n return newPlan\n \n def getComplement(self,fullList,xList):\n newList = fullList[:]\n \n for x in xList:\n newList.remove(x)\n return newList\n \n def getCombos(self, cList):\n combos = []\n temp = []\n for i in range(1, len(cList)):\n temp.extend(itertools.combinations(cList,i))\n combos = [sorted(list(elem)) for elem in temp]\n\n return combos\n\nclass GreedyOptimizer(Optimizer):\n\n def __init__(self, db):\n super().__init__(db)\n\n def pickJoinOrder(self, plan):\n relations = plan.relations()\n fieldDict = self.obtainFieldDict(plan)\n (joinTablesDict, selectTablesDict) = self.getExprDicts(plan, fieldDict)\n # makes dicts that maps a list of relations to exprs involving that list\n # then in system R we will build opt(A,B) Join C using join exprs involving A,C and B,C\n # and on top of it the select exprs that involve 2 tables A,C or B,C\n\n isGroupBy = True if plan.root.operatorType() == \"GroupBy\" else False\n outputSchema = plan.schema() \n self.reportPlanCount = 0\n\n worklist = []\n for r in relations:\n table = TableScan(r,self.db.relationSchema(r))\n table.prepare(self.db)\n if (r,) in selectTablesDict: \n selectExprs = selectTablesDict[(r,)]\n selectString = self.combineSelects(selectExprs)\n select = Select(table,selectString)\n select.prepare(self.db)\n worklist.append(Plan(root=select))\n else:\n worklist.append(Plan(root=table))\n\n while(len(worklist) > 1):\n combos = itertools.combinations(worklist,2)\n bestJoin = None\n sourcePair = None\n\n for pair in combos:\n op1 = pair[0].root\n op2 = pair[1].root\n\n selectExpr = self.createExpression(pair[0].relations(), pair[1].relations(), selectTablesDict)\n joinExpr = self.createExpression(pair[0].relations(), pair[1].relations(), joinTablesDict)\n \n join1BnljOp = Join(op1, op2, expr=joinExpr, method=\"block-nested-loops\" )\n join2BnljOp = Join(op2, op1, expr=joinExpr, method=\"block-nested-loops\" )\n\n\n join1NljOp = Join(op1, op2, expr=joinExpr, method=\"nested-loops\" )\n join2NljOp = Join(op2, op1, expr=joinExpr, method=\"nested-loops\" )\n\n if selectExpr == \"True\":\n full1BnljOp = join1BnljOp\n full2BnljOp = join2BnljOp\n \n full1NljOp = join1NljOp\n full2NljOp = join2NljOp\n\n else:\n full1BnljOp = Select(join1BnljOp, selectExpr)\n full2BnljOp = Select(join2BnljOp, selectExpr)\n \n full1NljOp = Select(join1NljOp, selectExpr)\n full2NljOp = Select(join2NljOp, selectExpr)\n \n\n joinList = [full1BnljOp, full2BnljOp, full1NljOp, full2NljOp]\n\n for j in joinList:\n joinplan = Plan(root=j)\n joinplan.prepare(self.db)\n joinplan.sample(100)\n\n if bestJoin == None or joinplan.cost(True) < bestJoin.cost(True):\n bestJoin = joinplan\n sourcePair = pair\n\n self.reportPlanCount += 4\n self.clearSampleFiles()\n\n\n\n worklist.remove(sourcePair[0])\n worklist.remove(sourcePair[1])\n worklist.append(bestJoin)\n\n # after System R algorithm\n newPlan = worklist[0]\n\n if isGroupBy:\n newGroupBy = GroupBy(newPlan.root, groupSchema=plan.root.groupSchema, \\\n aggSchema=plan.root.aggSchema, groupExpr=plan.root.groupExpr, \\\n aggExprs=plan.root.aggExprs, \\\n groupHashFn=plan.root.groupHashFn)\n newGroupBy.prepare(self.db)\n newPlan = Plan(root=newGroupBy)\n\n if set(outputSchema.schema()) != set(newPlan.schema().schema()):\n projectDict = {}\n\n for f, t in outputSchema.schema():\n projectDict[f] = (f, t) \n \n currRoot = newPlan.root\n project = Project(currRoot, projectDict)\n project.prepare(self.db)\n newPlan = Plan(root=project)\n \n return newPlan\n\n\n","sub_path":"DB_HW3/dbsys-hw3/Query/Optimizer.py","file_name":"Optimizer.py","file_ext":"py","file_size_in_byte":23275,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"248553033","text":"#!/usr/bin/python3\n__author__ = 'kumaran'\n\nimport copy\n\nclass Solution:\n def __init__(self):\n self.count = 0\n\n def isValidMove(self, board, row, col, size):\n # check only rows above this\n\n # check for col intersect\n for i in range(0, row):\n if board[i][col] == 'Q':\n return False\n\n\n # check diagonal to top right\n j = row - 1\n for i in range(col + 1, size):\n if j < 0:\n break\n if board[j][i] == 'Q':\n return False\n j -= 1\n\n # check diagonal to top left\n j = row - 1\n for i in range(col - 1, -1, -1):\n if j < 0:\n break\n if board[j][i] == 'Q':\n return False\n\n j -= 1\n\n return True\n\n\n def queenProblem(self, board, currRow, size):\n if currRow == size:\n self.count += 1\n temp = []\n for i in board:\n temp.append(\"\".join(i))\n self.output.append(temp[:])\n return\n\n for i in range(0, size):\n if self.isValidMove(board, currRow, i, size):\n board[currRow][i] = 'Q'\n self.queenProblem(board, currRow + 1, size)\n board[currRow][i] = '.'\n\n # @return an integer\n def solveNQueens(self, n):\n self.output = []\n board = []\n size = n\n for i in range(size):\n board.append(['.'] * size)\n self.queenProblem(board, 0, size)\n return self.output\n\n\nif __name__ == '__main__':\n s = Solution()\n print(s.solveNQueens(4))\n \n","sub_path":"Algos/Others/SolveNQueen.py","file_name":"SolveNQueen.py","file_ext":"py","file_size_in_byte":1641,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"184690594","text":"import unittest\nimport os\nfrom common.myddt import ddt, data\nfrom common.handel_excel import HandleExcel\nfrom common.handle_path import datadir\nfrom common.handle_log import logger\nfrom common.handle_requests import HandleRequests\nfrom common.handle_data import Data, set_dataclass_attr_from_resp, replace_case_with_re\nfrom common.handle_assert import HandleAssert\n\nexcel_path = os.path.join(datadir, \"api_cases.xlsx\")\nhe = HandleExcel(excel_path, \"recharge\")\ncases = he.read_all_data()\n\n\n@ddt\nclass TestRechargeApi(unittest.TestCase):\n @classmethod\n def setUpClass(cls) -> None:\n # 实例化HandleRequests类\n cls.hr = HandleRequests()\n # 调用登录接口，从响应结果中获取token member_id\n # resp = cls.hr.send_requests(\"post\",\n # \"member/login\",\n # {\"mobile_phone\": \"13770830372\", \"pwd\": \"1234567890\"})\n # # 转换成字典，提取\n # resp_dict = resp.json()\n # # cls.member_id = resp_dict[\"data\"][\"id\"]\n # # cls.token = resp_dict[\"data\"][\"token_info\"][\"token\"]\n # 通过jsonpath提取响应结果\n # cls.member_id = jsonpath(resp_dict, \"$..id\")[0]\n # cls.token = jsonpath(resp_dict, \"$..token\")[0]\n\n @data(*cases)\n def testRecharge(self, case):\n logger.info(\"*****************开始执行充值接口用例**************************\")\n # 替换\n case = replace_case_with_re(case)\n\n logger.info(\"当前测试用例为：\\n {}\".format(case))\n # 发起请求\n # 判断是否要传递token值\n if hasattr(Data, \"token\"):\n res = self.hr.send_requests(case[\"method\"], case[\"url\"], case[\"request_data\"], token=getattr(Data, \"token\"))\n else:\n res = self.hr.send_requests(case[\"method\"], case[\"url\"], case[\"request_data\"])\n\n # 如果有提取字段，那么需要从响应中提取对应数据,设置为Data.token\n if case[\"extract\"]:\n set_dataclass_attr_from_resp(res.json(), case[\"extract\"])\n\n # 如果有预期结果，需要把响应结果与预期结果进行比较\n if case[\"expected\"]:\n # 响应结果\n actual = res.json()\n logger.info(\"用例实际执行结果：\\n {}\".format(actual))\n expected = eval(case[\"expected\"])\n logger.info(\"用例预期结果：\\n {}\".format(expected))\n\n # 断言\n try:\n assert actual[\"code\"] == expected[\"code\"]\n assert actual[\"msg\"] == expected[\"msg\"]\n if expected.get(\"leave_amount\"):\n assert actual[\"data\"]['leave_amount'] == expected[\"leave_amount\"]\n except AssertionError:\n logger.exception(\"断言失败！\")\n raise # 把异常抛给unittest框架\n except Exception:\n logger.exception(\"除断言以外的异常报错！\")\n raise\n\n # 如果有数据库校验，则进行数据库校验\n if case[\"check_sql\"]:\n ha = HandleAssert()\n ha.assert_sql(case[\"check_sql\"])\n","sub_path":"testCases/test_recharge_api.py","file_name":"test_recharge_api.py","file_ext":"py","file_size_in_byte":3148,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"545346634","text":"# uncompyle6 version 3.7.4\n# Python bytecode 3.6 (3379)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /usr/local/lib/python3.6/dist-packages/pwclip/lib/system/envconf.py\n# Compiled at: 2020-03-20 08:07:42\n# Size of source mod 2**32: 186 bytes\nfrom os import environ\n\ndef envconf(srcdict):\n newdict = {}\n for k, v in srcdict.items():\n if k in environ.keys():\n newdict[v] = environ[k]\n\n return newdict","sub_path":"pycfiles/pwclip-1.7.11.linux-x86_64.tar/envconf.cpython-36.py","file_name":"envconf.cpython-36.py","file_ext":"py","file_size_in_byte":482,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"57682476","text":"from jinja2 import Environment, PackageLoader, BaseLoader, sandbox, select_autoescape\nfrom .markdown_styler import markdown_with_inline_styles\nfrom flask import current_app, url_for, abort\nfrom dmutils.email import EmailError\nfrom app.tasks.email import send_email\nimport six\nimport rollbar\nimport re\n\nDEFAULT_STYLES = {\n 'em': '''\n color: #6e6e6e;\n font-style: normal;\n ''',\n 'a': '''\n color: #17657a;\n text-decoration: underline;\n font-weight: bold;\n ''',\n 'blockquote':\n '''\n padding: 2rem; background: #def4f9;\n '''\n}\n\n\ntemplate_env = Environment(\n loader=PackageLoader('app.emails', 'templates'),\n autoescape=select_autoescape(['html', 'xml', 'md'])\n)\n\n\ndef fill_template(filename, **kwargs):\n template = template_env.get_template(filename)\n return template.render(**kwargs)\n\n\ndef render_email_template(filename, **kwargs):\n header = kwargs.pop('header', '')\n styles = kwargs.pop('styles', DEFAULT_STYLES)\n\n md = fill_template(filename, **kwargs)\n rendered = markdown_with_inline_styles(md, styles)\n template = template_env.get_template('master.html')\n rendered = template.render(header=header, body=rendered)\n return rendered\n\n\ndef render_email_from_string(template, **kwargs):\n header = kwargs.pop('header', '')\n styles = kwargs.pop('styles', DEFAULT_STYLES)\n\n sub = sandbox.SandboxedEnvironment(loader=BaseLoader).from_string(template)\n md = sub.render(**kwargs)\n rendered = markdown_with_inline_styles(md, styles)\n master = template_env.get_template('master.html')\n rendered = master.render(header=header, body=rendered)\n return rendered\n\n\ndef send_or_handle_error(*args, **kwargs):\n if not current_app.config['SEND_EMAILS']:\n return\n\n error_desc = kwargs.pop('event_description_for_errors', 'unspecified')\n\n try:\n send_email.delay(*args, **kwargs)\n\n except EmailError as e:\n rollbar.report_exc_info()\n current_app.logger.error(\n 'email failed to send for event: {}'.format(error_desc),\n 'error {error}',\n extra={\n 'error': six.text_type(e),\n }\n )\n abort(503, response='Failed to send email for event: {}'.format(error_desc))\n\n\ndef escape_token_markdown(token):\n token = re.sub(r'([_-])', r'\\\\\\1', token)\n return token\n\n\ndef escape_markdown(val):\n val = re.sub(r'([_\\-#\\[\\]\\(\\)`=>*\\\\])', r'\\\\\\1', val)\n val = re.sub(r'\\s', r' ', val)\n val = re.sub(r'[ ]+', r' ', val)\n return val\n","sub_path":"app/emails/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":2536,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"1402272","text":"from typing import Dict, Any\n\nfrom flask import jsonify, make_response, request\nfrom labml_db import Model, Index\nfrom labml_db.serializer.pickle import PickleSerializer\nfrom labml_db.serializer.yaml import YamlSerializer\n\nfrom app.logging import logger\nfrom app.enums import COMPUTEREnums\nfrom ..analysis import Analysis\nfrom ..series import SeriesModel, Series\nfrom ..series_collection import SeriesCollection\nfrom ..preferences import Preferences\n\n\n@Analysis.db_model(PickleSerializer, 'CPU')\nclass CPUModel(Model['CPUModel'], SeriesCollection):\n pass\n\n\n@Analysis.db_index(YamlSerializer, 'cpu_index.yaml')\nclass CPUIndex(Index['CPU']):\n pass\n\n\n@Analysis.db_model(PickleSerializer, 'cpu_preferences')\nclass CPUPreferencesModel(Model['CPUPreferencesModel'], Preferences):\n pass\n\n\n@Analysis.db_index(YamlSerializer, 'cpu_preferences_index.yaml')\nclass CPUPreferencesIndex(Index['CPUPreferences']):\n pass\n\n\nclass CPUAnalysis(Analysis):\n cpu: CPUModel\n\n def __init__(self, data):\n self.cpu = data\n\n def track(self, data: Dict[str, SeriesModel]):\n res: Dict[str, SeriesModel] = {}\n for ind, s in data.items():\n ind_type = ind.split('.')[0]\n if ind_type == COMPUTEREnums.CPU:\n res[ind] = s\n\n self.cpu.track(res)\n\n def get_tracking(self):\n res = []\n for ind, track in self.cpu.tracking.items():\n name = ind.split('.')\n series: Dict[str, Any] = Series().load(track).summary\n series['name'] = '.'.join(name)\n\n res.append(series)\n\n res.sort(key=lambda s: s['name'])\n\n return res\n\n @staticmethod\n def get_or_create(computer_uuid: str):\n cpu_key = CPUIndex.get(computer_uuid)\n\n if not cpu_key:\n c = CPUModel()\n c.save()\n CPUIndex.set(computer_uuid, c.key)\n\n return CPUAnalysis(c)\n\n return CPUAnalysis(cpu_key.load())\n\n\n@Analysis.route('GET', 'cpu/')\ndef get_cpu_tracking(computer_uuid: str) -> Any:\n track_data = []\n status_code = 400\n\n ans = CPUAnalysis.get_or_create(computer_uuid)\n if ans:\n track_data = ans.get_tracking()\n status_code = 200\n\n response = make_response(jsonify(track_data))\n response.status_code = status_code\n\n return response\n\n\n@Analysis.route('GET', 'cpu/preferences/')\ndef get_cpu_preferences(computer_uuid: str) -> Any:\n preferences_data = {}\n\n preferences_key = CPUPreferencesIndex.get(computer_uuid)\n if not preferences_key:\n return jsonify(preferences_data)\n\n cp: CPUPreferencesModel = preferences_key.load()\n preferences_data = cp.get_data()\n\n response = make_response(jsonify(preferences_data))\n\n return response\n\n\n@Analysis.route('POST', 'cpu/preferences/')\ndef set_cpu_preferences(computer_uuid: str) -> Any:\n preferences_key = CPUPreferencesIndex.get(computer_uuid)\n\n if not preferences_key:\n return jsonify({})\n\n cp = preferences_key.load()\n cp.update_preferences(request.json)\n\n logger.debug(f'update cpu preferences: {cp.key}')\n\n return jsonify({'errors': cp.errors})\n","sub_path":"server/app/analyses/computers/cpu.py","file_name":"cpu.py","file_ext":"py","file_size_in_byte":3159,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"159372660","text":"from Program import *\nclass C(Program):\n\tdef __init__(self, usn, course, assign, file_path, files, main_file, sem):\n\t\tProgram.__init__(self, usn, course, assign, file_path, files, main_file, sem);\n\n\tdef display(self):\n\t\tProgram.display(self);\n\n\tdef getFilePath(self):\n\t\treturn self.file_path;\n\t\n\tdef compile(self):\n\t\tcompiled = False;\n\t\toutput = \"\";\n\t\ttry:\n\t\t\tcmd = \"gcc \" + self.main_file + \" \";\n\t\t\tfor f in self.files:\n\t\t\t\tm = re.match(r'.*\\.h$', f);\n\t\t\t\tif( m ):\n\t\t\t\t\tpass\n\t\t\t\telse:\n\t\t\t\t\tcmd = cmd + f + \" \";\n\t\t\tcmd = cmd.strip();\n\t\t\tcmd = cmd + \" 2>compile_error.txt\";\n\t\t\toutput = check_output(cmd, shell=True);\n\t\t\tcompiled = True;\n\t\texcept Exception as e:\n\t\t\tcompiled = False;\n\t\tif( compiled ):\n\t\t\tos.remove(\"compile_error.txt\");\n\t\t\treturn \"COMPILED\";\n\t\telse:\n\t\t\tfd = open(\"compile_error.txt\", \"r\");\n\t\t\terror = \"compile_error\";\n\t\t\tline = fd.read();\n\t\t\twhile line:\n\t\t\t\terror = error + line;\n\t\t\t\tline = fd.read();\n\t\t\tos.remove(\"compile_error.txt\");\n\t\t\treturn error;\n\n\tdef staticAnalysis(self):\n\t\treturn \"\";\n\n\tdef execute(self ,submit):\n\t\tconn = pymysql.connect(host=\"localhost\", user=\"root\",passwd=database_password);\n\t\tcur = conn.cursor();\n\t\tcur.execute(\"use course_connect\");\n\t\tquery = \"SELECT * FROM programming_test_case WHERE course_code=\\'\" + self.course + \"\\' and assign_id=\" + str(self.assign);\n\t\tcur.execute(query);\n\t\t\n\t\ttest_data = cur.fetchall();\n\t\treturn_val = \"\";\n\n\t\tquery = \"SELECT * FROM programming_exercise WHERE course_code=\\'\" + self.course + \"\\' and id=\" + str(self.assign);\n\t\tcur.execute(query);\n\t\tdata = cur.fetchall();\n\t\tcompare_data = data[0];\n\t\tstudent_total_marks = 0;\n\n\n\t\tfor test in test_data:\n\t\t\ttry:\n\t\t\t\tcmd = \"./a.out 1>student_output.txt 2>runtime_error.txt\";\n\n\t\t\t\tfin = open(\"../../../../../questionData/sem\" + self.sem + \"/\" + self.course + \"/\" + str(self.assign) + \"/\" + test[3], \"r\");\n\t\t\t\t\n\t\t\t\tout = check_output(cmd, stdin=fin, shell=True, timeout=test[6]);\n\n\t\t\t\toutput = \"\";\n\t\t\t\toutput_path = \"questionData/sem\" + self.sem + \"/\" + self.course + \"/\" + str(self.assign) + \"/\" + test[4];\n\n\t\t\t\tferr = open(\"runtime_error.txt\", \"r\");\n\t\t\t\tfout = open(\"student_output.txt\", \"r\");\n\t\t\t\tferr_output = ferr.read();\n\t\t\t\tferr_output = ferr_output.strip();\n\t\t\t\tif( ferr_output != \"\" ):\n\t\t\t\t\toutput = \"RUNTIME_ERROR\";\n\t\t\t\t\toutput = output + fout.read();\n\t\t\t\t\toutput = output + ferr_output;\n\n\t\t\t\tstudent_output = fout.read();\n\t\t\t\tffout = open(\"../../../../../\" + output_path, \"r\");\n\t\t\t\texpected_output = ffout.read();\n\n\t\t\t\tordered = False;\n\t\t\t\tnoise = False;\n\t\t\t\tcase_sensitive = False;\n\t\t\t\tdelimiter = compare_data[6];\n\t\t\t\tfloat_diff = compare_data[7];\n\n\t\t\t\tif( compare_data[8] == 1 ):\n\t\t\t\t\tnoise = True;\n\t\t\t\tif( compare_data[9] == 1 ):\n\t\t\t\t\tcase_sensitive = True;\n\t\t\t\tif( compare_data[10] == 1 ):\n\t\t\t\t\tordered = True;\n\n\t\t\t\tif( compare_data[5] == 1 ):\n\t\t\t\t\tres = self.exact_compare(student_output, expected_output);\n\t\t\t\t\tif( res == True ):\n\t\t\t\t\t\toutput = student_output;\n\t\t\t\t\t\tstudent_total_marks = student_total_marks + test[5];\n\t\t\t\t\telse:\n\t\t\t\t\t\toutput = \"ERROR@#$@\" + student_output;\n\n\t\t\t\telif( compare_data[5] == 2 ):\n\t\t\t\t\tres = self.float_text_without_noise_words(expected_output, student_output, float_diff, ordered, case_sensitive, delimiter);\n\t\t\t\t\tif( res == True ):\n\t\t\t\t\t\toutput = student_output;\n\t\t\t\t\t\tstudent_total_marks = student_total_marks + test[5];\n\t\t\t\t\telse:\n\t\t\t\t\t\toutput = \"ERROR@#$@\" + student_output;\n\n\t\t\t\telif( compare_data[5] == 3 ):\n\t\t\t\t\tres = self.float_text_with_noise_words(expected_output, student_output, float_diff, ordered, case_sensitive, delimiter);\n\t\t\t\t\tif( res == True ):\n\t\t\t\t\t\toutput = student_output;\n\t\t\t\t\t\tstudent_total_marks = student_total_marks + test[5];\n\t\t\t\t\telse:\n\t\t\t\t\t\toutput = \"ERROR@#$@\" + student_output;\n\n\t\t\t\telif( compare_data[5] == 4 ):\n\t\t\t\t\tres = self.first_char_compare(expected_output, student_output, ordered, case_sensitive, delimiter);\n\t\t\t\t\tif( res == True ):\n\t\t\t\t\t\toutput = student_output;\n\t\t\t\t\t\tstudent_total_marks = student_total_marks + test[5];\n\t\t\t\t\telse:\n\t\t\t\t\t\toutput = \"ERROR@#$@\" + student_output;\n\n\t\t\t\telif( compare_data[5] == 5 ):\n\t\t\t\t\tres = self.number_any_base(expected_output, student_output, ordered, delimiter);\n\t\t\t\t\tif( res == True ):\n\t\t\t\t\t\toutput = student_output;\n\t\t\t\t\t\tstudent_total_marks = student_total_marks + test[5];\n\t\t\t\t\telse:\n\t\t\t\t\t\toutput = \"ERROR@#$@\" + student_output;\n\n\t\t\t\telif( compare_data[5] == 6):\n\t\t\t\t\tres = self.number_range_comare(expected_output, student_output, delimiter);\n\t\t\t\t\tif( res == True ):\n\t\t\t\t\t\toutput = student_output;\n\t\t\t\t\t\tstudent_total_marks = student_total_marks + test[5];\n\t\t\t\t\telse:\n\t\t\t\t\t\toutput = \"ERROR@#$@\" + student_output;\n\n\t\t\t\treturn_val = return_val + output;\n\t\t\t\t\n\t\t\t\treturn_val = return_val + \"@#$$#@\";\n\t\t\texcept TimeoutExpired as timeout_execption:\n\t\t\t\treturn_val = return_val + \"@#$@TIMEOUT\";\n\t\t\t\treturn_val = return_val + \"@#$$#@\";\n\t\t\texcept CalledProcessError as e:\n\t\t\t\tfout = open(\"student_output.txt\", \"r\");\n\t\t\t\tferr = open(\"runtime_error.txt\", \"r\");\n\t\t\t\treturn_val = return_val + \"RUNTIME_ERROR\";\n\t\t\t\treturn_val = return_val + fout.read();\n\t\t\t\treturn_val = return_val + ferr.read();\n\t\t\t\treturn_val = return_val + \"@#$$#@\";\n\t\t\texcept Exception as e:\n\t\t\t\tfout = open(\"student_output.txt\", \"r\");\n\t\t\t\tferr = open(\"runtime_error.txt\", \"r\");\n\t\t\t\treturn_val = return_val + \"RUNTIME_ERROR\";\n\t\t\t\treturn_val = return_val + fout.read();\n\t\t\t\treturn_val = return_val + ferr.read();\n\t\t\t\treturn_val = return_val + Exception.__str__(e);\n\t\t\t\treturn_val = return_val + \"@#$$#@\";\n\n\t\t\tos.remove(\"student_output.txt\");\n\t\t\tos.remove(\"runtime_error.txt\");\n\n\t\tif( submit == \"1\" ):\n\t\t\tquery = \"UPDATE student_programming SET marks=\" + str(student_total_marks) + \" WHERE usn='\" + self.usn + \"' and assign_id=\" + str(self.assign);\n\t\t\tcur.execute(query);\n\t\t\tconn.commit();\n\t\treturn return_val;\n\t\t#pexpect -- tool","sub_path":"python/C.py","file_name":"C.py","file_ext":"py","file_size_in_byte":5735,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"14790668","text":"# -*- coding: utf-8 -*-\n#IStandForFreedom\nfrom osv import osv, fields\n\n\nclass jmdconcepto(osv.Model):\n _name = \"hr.concepto\"\n _columns = {\n 'name': fields.char(string=\"Clave\", size=40),\n 'descripcion': fields.char(string=\"Descripción\", size=40),\n 'linea_ids': fields.one2many(\"hr.concepto.linea\", \"relation\",\n string=\"Lineas del Concepto\"),\n }\n\n\nclass jmdconceptolinea(osv.Model):\n _name = \"hr.concepto.linea\"\n _columns = {\n 'name': fields.char(string=\"Nombre\", size=40),\n 'relation': fields.many2one(\"hr.concepto\"),\n 'tipo': fields.selection([(\"dias\", \"Días\"), (\"monto\", \"Monto\")],\n string=\"\"),\n 'dias': fields.float(digits=(6, 2), string=\"Días\"),\n 'monto': fields.float(digits=(9, 2), string=\"Monto\")\n }","sub_path":"ea_jmd/conceptopago.py","file_name":"conceptopago.py","file_ext":"py","file_size_in_byte":856,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"613911643","text":"from __future__ import division\nimport os\nimport time\nfrom sliverinfo.lxc.cgroup import cgroup\nfrom sliverinfo import lxc\nimport sys\n\nimport urllib2\nimport json\nimport common\n\ninterval =1\n\n\ndef usage_percent(used, total, _round=None):\n \"\"\"Calculate percentage usage of 'used' against 'total'.\"\"\"\n\n try:\n ret = (used / total) * 100\n except ZeroDivisionError:\n ret = 0\n if _round is not None:\n return round(ret, _round)\n else:\n return ret\n\n\n\ndef getRunningContainers():\n containers, lxcdir = [], []\n\n if(os.path.exists(lxc.containerspath)):\n lxcdir=os.listdir(lxc.containerspath)\n\n else:\n return containers\n\n for entry in lxcdir:\n if os.path.isdir(os.path.join(lxc.containerspath, entry)):\n containers.append(entry)\n\n #ret = get_container_that_are_slivers(containers)\n\n return containers\n\n\ndef byte2MiByte(val):\n return val/1024/1024\n\ndef container_mem_usage(name):\n inst = cgroup('memory', name)\n\n memlimit = int(inst.getValue(\"memory.limit_in_bytes\"))\n memswlimit = int(inst.getValue(\"memory.memsw.limit_in_bytes\"))\n memused = int(inst.getValue(\"memory.usage_in_bytes\"))\n memswused = int(inst.getValue(\"memory.memsw.usage_in_bytes\"))\n\n mem_total = memlimit\n mem_used = memused\n mem_free = memlimit-memused\n mem_percent_used = usage_percent(mem_used, mem_total, _round=1)\n\n swap_total = memswlimit-memlimit\n swap_used = memswused-memused\n swap_free = swap_total -swap_used\n swap_percent_used = usage_percent(swap_used, swap_total, _round=1)\n\n total = memswlimit\n total_used = memswused\n total_free = memswlimit-memswused\n\n total_percent_used = usage_percent(total_used, total, _round=1)\n\n\n return {'memory':{'mem_total': mem_total, 'mem_used': mem_used, 'mem_free': mem_free, 'mem_percent_used': mem_percent_used,\n 'swap_total':swap_total, 'swap_used': swap_used, 'swap_free': swap_free, 'swap_percent_used': swap_percent_used,\n 'total': total, 'total_used': total_used, 'total_free': total_free, 'total_percent_used': total_percent_used}}\n\n\ndef container_cpu_usage( name):\n inst = cgroup('cpuacct', name)\n previous_cpu_usage = inst.getValue(\"cpuacct.usage\")\n time.sleep(interval)\n current_cpu_usage = inst.getValue(\"cpuacct.usage\")\n diff_cpu_usage = int(current_cpu_usage) - int(previous_cpu_usage)\n cpu_usage = float(diff_cpu_usage/(interval*1000000000))*100\n return {'cpu':{'cpu_usage': cpu_usage}}\n","sub_path":"sliverinfo/lxc/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2514,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"479764242","text":"\"\"\"Foyer\n\"\"\"\n\nfrom __future__ import print_function\n\nimport os\nimport sys\nfrom setuptools import setup, find_packages\nfrom setuptools.command.test import test as TestCommand\n\nimport foyer.version\n\nrequirements = [line.strip() for line in open('requirements.txt').readlines()]\n\nif sys.argv[-1] == 'publish':\n os.system('python setup.py sdist upload')\n sys.exit()\n\n\nclass PyTest(TestCommand):\n def finalize_options(self):\n TestCommand.finalize_options(self)\n self.test_args = []\n self.test_suite = True\n\n def run_tests(self):\n import pytest\n errcode = pytest.main(['foyer'])\n sys.exit(errcode)\n\nsetup(\n name='foyer',\n version=foyer.version.short_version,\n description=__doc__.split('\\n'),\n long_description=__doc__,\n author='Janos Sallai, Christoph Klein',\n author_email='janos.sallai@vanderbilt.edu, christoph.klein@vanderbilt.edu',\n url='https://github.com/iModels/foyer',\n download_url='https://github.com/iModels/foyer/tarball/{}'.format(\n foyer.version.short_version),\n packages=find_packages(),\n package_dir={'foyer': 'foyer'},\n include_package_data=True,\n install_requires=requirements,\n license=\"MIT\",\n zip_safe=False,\n keywords='foyer',\n classifiers=[\n 'Development Status :: 4 - Beta',\n 'Intended Audience :: Science/Research',\n 'Intended Audience :: Developers',\n 'License :: OSI Approved :: MIT License,'\n 'Natural Language :: English',\n 'Programming Language :: Python',\n 'Programming Language :: Python :: 3',\n 'Topic :: Scientific/Engineering :: Chemistry',\n 'Operating System :: Microsoft :: Windows',\n 'Operating System :: POSIX',\n 'Operating System :: Unix',\n 'Operating System :: MacOS',\n ],\n test_suite='tests',\n cmdclass={'test': PyTest,\n },\n extras_require={'utils': ['pytest'],\n },\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1916,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"148327196","text":"import binascii\nimport copy\nimport functools\nimport json\nimport logging\nimport os\nimport re\nimport sys\nimport threading\nimport time\nimport uuid\n\nfrom agent.tasks import execute, service, chef\nfrom agent.utils import taskutil\nfrom common.utils import exc, sysutil\nfrom common.utils.facts import fact\nfrom scalarizr import handlers\nfrom scalarizr.messaging import Messages, Queues\nfrom scalarizr.node import __node__\nimport common.types\n\n\nLOG = logging.getLogger(__name__)\n__node__['scripting_log_dir'] = \\\n os.path.join(__node__['log_dir'], 'scripting') \\\n if fact['os']['family'] == 'windows' \\\n else os.path.join(__node__['log_dir'], 'scalarizr', 'scripting')\n\ndef get_handlers():\n return [BaseBehaviorHandler()]\n\nclass BaseBehaviorHandler(handlers.Handler):\n def __init__(self):\n __node__['events'].on(start=self.on_start)\n\n def accept(self, message, queue, **kwds):\n return message.name not in (\n Messages.INT_SERVER_HALT,\n Messages.INT_SERVER_REBOOT)\n\n def __call__(self, message):\n if not __node__['base']['union_script_executor']:\n return\n evts = __node__['events']\n if 'scripts' in message.body:\n execute_scripts(message)\n if message.name == Messages.HOST_INIT_RESPONSE:\n if 'chef' in message.body:\n evts.on(before_host_up=functools.partial(setup_chef_automation, message))\n if 'host_init_response' not in evts._listeners:\n evts.on(host_init_response=install_behaviors)\n else:\n evts._listeners['host_init_response'].insert(0, install_behaviors)\n evts.on(host_init_response=handlers.check_supported_behaviors)\n\n\n\n def on_start(self, *args, **kwds):\n __node__['periodical_executor'].add_task(\n rotate_task_dirs, 3600,\n title='Rotate tasks data')\n __node__['periodical_executor'].add_task(\n rotate_scripting_logs, 3600,\n title='Rotate scripting logs')\n farm_role_params = __node__['queryenv'].list_farm_role_params(__node__['farm_role_id'])\n chef_data = farm_role_params['params'].get('chef')\n if chef_data and int(chef_data.get('daemonize', False)):\n try:\n service.start('chef-client')\n except:\n msg = \"Can't daemonize Chef: {}\".format(sys.exc_info()[1])\n LOG.warn(msg, exc_info=sys.exc_info())\n\n\ndef get_env(message):\n env = os.environ.copy()\n gv = dict(\n (kv['name'], kv['value'].encode('utf-8') if kv['value'] else '')\n for kv in message.body.get('global_variables') or [])\n if fact['os']['family'] == 'windows':\n gv = dict(\n (k.encode('ascii'), v.encode('ascii'))\n for k, v in gv.items())\n env.update(gv)\n return env\n\n_matches_url = re.compile(r'^http(s)?://').search\ndef execute_scripts(message, bollard_before=None, reraise=False):\n kwds_ = {\n 'env': get_env(message),\n 'return_stdout': True,\n 'return_stderr': True}\n\n for script in message.body.get('scripts') or []:\n kwds = kwds_.copy()\n if 'chef' in script:\n # Chef execute\n task, chef_data = prepare_chef_task(script['chef'])\n kwds.update(chef_data)\n else:\n # Script execute\n task = 'execute.script'\n kwds['action_type'] = 'script'\n if _matches_url(script.get('path', '')):\n kwds['url'] = script['path']\n elif script.get('path'):\n kwds['path'] = script['path']\n else:\n kwds['code'] = script['body']\n kwds['name'] = script['name']\n kwds['timeout'] = int(script['timeout'])\n kwds['run_as'] = script.get('run_as')\n\n event_name = message.event_name \\\n if message.name == Messages.EXEC_SCRIPT \\\n else message.name\n push_task = functools.partial(\n push_execute, event_name, message.body, script)\n if bollard_before:\n bollard_before(task, kwds)\n result = __node__['bollard'].apply_async(\n task,\n kwds={'action': kwds},\n callbacks={\n 'task.push': push_task,\n 'task.after': symlink_execute_logs,\n 'task.pull': pull_execute})\n try:\n result.get()\n except:\n if reraise or fail_init_maybe.yes:\n fail_init_maybe.yes = False\n raise\n\n\ndef push_execute(event_name, message_body, script, task, meta):\n meta['persistent'].update({\n 'script_name': script['name'],\n 'script_path': script.get('path'),\n 'run_as': script.get('run_as'),\n 'event_name': event_name,\n #'role_name': message_body.get('role_name', 'unknown_role'),\n 'event_server_id': message_body.get('server_id'),\n 'event_id': message_body.get('event_id'),\n 'execution_id': script['execution_id']})\n\n\ndef pull_execute(task, meta):\n send_exec_script_result(task, meta)\n fail_init_maybe(task, meta)\n\n\ndef send_exec_script_result(task, meta):\n if task['state'] == 'failed':\n if isinstance(task.exception, execute.ExecuteError):\n data = task.exception.__dict__\n else:\n data = {\n 'stdout': '',\n 'stderr': str(task.exception),\n 'return_code': 1}\n elif task.name == 'chef.bootstrap':\n data = task.result.executed_script.to_primitive()\n else:\n data = task.result.to_primitive()\n data['stdout'] = binascii.b2a_base64(data['stdout'])\n data['stderr'] = binascii.b2a_base64(data['stderr'])\n data.pop('interpreter', None)\n data.pop('path', None)\n data.pop('command', None)\n data.pop('success_codes', None)\n data.pop('stdout_total_bytes', None)\n data.pop('stderr_total_bytes', None)\n data.update(meta.get('persistent', {}))\n __node__['messaging'].send('ExecScriptResult', body=data, queue=Queues.LOG)\n\ndef symlink_execute_logs(task, meta):\n if meta['persistent']['execution_id']:\n name_prefix = '{script_name}.{event_name}.{execution_id}'.format(\n **meta['persistent'])\n else:\n name_prefix = '{script_name}.{event_name}.{role_name}.{}'.format(\n task.id, **meta['persistent'])\n\n if task['state'] == 'completed' or isinstance(task.exception, execute.ExecuteError):\n if task['state'] == 'completed':\n result = task.result['executed_script'] \\\n if task.name == 'chef.bootstrap' \\\n else task.result\n else:\n result = task.exception.__dict__\n # Symlink log files to old location\n sysutil.mkdir_p(__node__['scripting_log_dir'])\n for name in ('out', 'err'):\n os.symlink(\n result['std{}_log_path'.format(name)],\n os.path.join(\n __node__['scripting_log_dir'],\n '{}-{}.log'.format(name_prefix, name)))\n else:\n # Scalr will query for logs despeate of the fact that script wasn't executed.\n # We should make fake logs from exception message.\n log_file = os.path.join(__node__['scripting_log_dir'], '{}-err.log'.format(name_prefix))\n with open(log_file, 'w+') as fp:\n fp.write(str(task.exception))\n log_file = os.path.join(__node__['scripting_log_dir'], '{}-out.log'.format(name_prefix))\n open(log_file, 'w+').close()\n\n\ndef fail_init_maybe(task, meta):\n if task['state'] == 'failed' and \\\n __node__['state'] == 'initializing' and \\\n __node__['base']['abort_init_on_script_fail'] and \\\n meta['persistent']['event_server_id'] == __node__['server_id'] and \\\n meta['persistent']['event_name'] == 'BeforeHostUp':\n fail_init_maybe.yes = True\nfail_init_maybe.yes = False\n\n\ndef rotate_task_dirs():\n min_ctime = time.time() - __node__['base']['keep_scripting_logs_time']\n tasks_dir = taskutil.AgentTaskExtension.TASKS_DIR\n if not os.path.exists(tasks_dir):\n return\n for name in os.listdir(tasks_dir):\n path = os.path.join(tasks_dir, name)\n if os.stat(path).st_ctime < min_ctime:\n LOG.debug('Remove {}'.format(path))\n sysutil.rm_rf(path)\n\n\ndef rotate_scripting_logs():\n if not os.path.exists(__node__['scripting_log_dir']):\n return\n for name in os.listdir(__node__['scripting_log_dir']):\n path = os.path.join(__node__['scripting_log_dir'], name)\n if os.path.islink(path) and not os.path.exists(os.path.realpath(path)):\n LOG.debug('Remove {}'.format(path))\n os.unlink(path)\n\n\ndef setup_chef_automation(hir, hostup, *args):\n data = copy.deepcopy(hir.body['chef'])\n # execute chef bootstrap script\n hi_chef = lambda: None\n hi_chef.name = data.get('event_name') or 'HostInit'\n hi_chef.role_name = hir.role_name\n hi_chef.body = {\n 'server_id': __node__['server_id'],\n 'global_variables': hir.body['global_variables'],\n 'scripts': [{\n 'name': data.get('script_name') or '[Scalr built-in] Chef bootstrap',\n 'execution_id': data.get('execution_id') or str(uuid.uuid4()),\n 'asynchronous': 0,\n 'timeout': sys.maxint,\n # why? hostup timeout not passed,\n # but chef one should be greater then hostup gipotetic value\n 'chef': data}]}\n def bollard_before(task, kwds):\n # a callback to get evaluated node_name\n if task == 'chef.bootstrap':\n data['node_name'] = kwds['node_name']\n execute_scripts(hi_chef, bollard_before, reraise=True)\n hostup.body['chef'] = data\n\n\ndef prepare_chef_task(chef_data):\n kwds = {}\n if chef_data.get('ssl_verify_mode', 'chef_auto') != 'chef_auto':\n kwds['ssl_verify_mode'] = chef_data.get('ssl_verify_mode')\n if chef_data.get('log_level'):\n kwds['log_level'] = chef_data['log_level']\n run_list = extract_chef_run_list(chef_data)\n kwds['json_attributes'] = extract_chef_json_attributes(chef_data)\n if 'cookbook_url' in chef_data:\n task = 'chef.solo'\n kwds['action_type'] = 'chef.solo'\n kwds['solo_rb_template'] = chef_data.get('solo_rb_template')\n kwds['json_attributes']['run_list'] = run_list\n kwds['cookbooks'] = {}\n if chef_data.get('cookbook_url_type') == 'git':\n kwds['cookbooks']['source_type'] = 'repository'\n kwds['cookbooks']['repo'] = {\n 'url': chef_data['cookbook_url'],\n 'ssh_key': chef_data.get('ssh_private_key')}\n if chef_data.get('relative_path'):\n kwds['cookbooks']['relative_path'] = chef_data['relative_path']\n elif chef_data.get('cookbook_url_type') == 'http':\n if chef_data['cookbook_url'].startswith('file://'):\n kwds['cookbooks']['source_type'] = 'path'\n kwds['cookbooks']['path'] = chef_data['cookbook_url'][7:]\n else:\n kwds['cookbooks']['source_type'] = 'url'\n kwds['cookbooks']['url'] = chef_data['cookbook_url']\n else:\n raise exc.MalformedError('Chef cookbook_url_type is not valid: {}'.format(\n chef_data['cookbook_url_type']))\n else:\n if chef_data.get('server_url'):\n # Bootstrap\n kwds['action_type'] = 'chef.bootstrap'\n kwds['client_rb_template'] = chef_data.get('client_rb_template')\n kwds['json_attributes']['run_list'] = run_list\n kwds['server_url'] = chef_data['server_url']\n kwds['validation_key'] = chef_data['validator_key']\n kwds['validation_client_name'] = chef_data['validator_name']\n if chef_data.get('environment'):\n kwds['environment'] = chef_data['environment']\n kwds['node_name'] = chef_data.get('node_name') or default_chef_node_name()\n kwds['daemonize'] = bool(int(chef_data.get('daemonize', 0)))\n elif run_list:\n # Override run_list\n kwds['action_type'] = 'chef.override'\n kwds['run_list'] = run_list\n else:\n # Re-converge\n kwds['action_type'] = 'chef.reconverge'\n\n return kwds['action_type'], kwds\n\n'''\n # Chef-Solo\n - asynchronous: '0'\n chef:\n cookbook_url: https://github.com/Scalr/cookbooks.git\n cookbook_url_type: git\n json_attributes: '{\"dummy\": \"what?\", \"nested\": [\"o\", \"u\", \"yeah\"]}'\n relative_path: ./cookbooks\n run_list: '[\"recipe[apt]\"]'\n execution_id: 1f50d1de-2261-43ce-a874-2089e92dd8ac\n name: chef-0336\n timeout: '1200'\n\n # Override run_list\n scripts:\n - asynchronous: '0'\n chef:\n json_attributes: '{\"dummy\": \"what?\", \"nested\": [\"no\", \"o\", \"o\"]}'\n run_list: '[\"role[dummy_role]\"]'\n execution_id: 79f82164-b88d-4740-8cda-abcb8e8504b6\n name: chef-0234\n timeout: '1200'\n\n # Re-converge\n - asynchronous: '0'\n chef:\n json_attributes: ''\n run_list: ''\n execution_id: a5891b00-9e5a-4a31-a2e6-a54bb91fed62\n name: chef-0597\n timeout: '1200'\n'''\n\ndef default_chef_node_name():\n hostname = __node__['base'].get('hostname')\n if hostname:\n return re.sub('\\s+', '-', hostname)\n else:\n return '{0}-{1}-{2}'.format(\n __node__['platform'].name,\n __node__['platform'].get_public_ip(),\n time.time())\n\ndef extract_chef_run_list(chef_data):\n if chef_data.get('run_list'):\n try:\n return json.loads(chef_data['run_list'])\n except ValueError as e:\n raise exc.MalformedError(\"Chef run list is not a valid JSON: {0}\".format(e))\n elif chef_data.get('role'):\n return ['role[{}]'.format(chef_data['role'])]\n\ndef extract_chef_json_attributes(chef_data):\n \"\"\"\n Extract json attributes dictionary from scalr formatted structure\n \"\"\"\n try:\n return json.loads(chef_data.get('json_attributes') or \"{}\")\n except ValueError as e:\n raise exc.MalformedError(\"Chef attributes is not a valid JSON: {0}\".format(e))\n\n\n# Role-builer uses the same mapping\nBEHAVIORS_RECIPES_MAP = dict(\n apache=\"recipe[apache2]\",\n haproxy=\"recipe[haproxy]\",\n mariadb=\"recipe[mariadb]\",\n memcached=\"recipe[memcached]\",\n mysql2=\"recipe[mysql::server]\",\n mysql=\"recipe[mysql::server]\",\n nginx=\"recipe[nginx]\",\n percona=\"recipe[percona]\",\n rabbitmq=\"recipe[rabbitmq]\",\n redis=\"recipe[redis]\",\n tomcat=\"recipe[tomcat]\"\n)\nBEHAVIORS_RECIPES_MAP['www'] = BEHAVIORS_RECIPES_MAP['nginx']\nBEHAVIORS_RECIPES_MAP['app'] = BEHAVIORS_RECIPES_MAP['apache']\n\nSCALR_COOKBOOKS_GIT_URL = \"git://github.com/Scalr/cookbooks.git\"\n\n\ndef install_behaviors(hir_message):\n behaviors = hir_message.body.get('base', {}).get('install', {}).get('behaviors', [])\n if not behaviors:\n return\n\n unknown_bhs = set(behaviors).difference(set(BEHAVIORS_RECIPES_MAP.keys()))\n if unknown_bhs:\n raise Exception('Unknown behaviors: {}'.format(list(unknown_bhs)))\n\n LOG.info('Installing software for behaviors: {}'.format(behaviors))\n chef_action = {\n 'action_type': 'chef.solo',\n 'json_attributes': {\n 'run_list': [BEHAVIORS_RECIPES_MAP[b] for b in behaviors]\n },\n 'cookbooks': {\n 'source_type': 'repository',\n 'repo': {\n 'url': SCALR_COOKBOOKS_GIT_URL\n },\n 'relative_path': 'cookbooks'\n }\n }\n task = __node__['bollard'].apply_async\n task('chef.install', kwds={'version': 'auto'}).get()\n task('pkgmgr.updatedb').get()\n task('chef.solo', kwds={'action': chef_action}).get()\n","sub_path":"src/scalarizr/handlers/union/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":15711,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"273844909","text":"from otree.api import (\n models, widgets, BaseConstants, BaseSubsession, BaseGroup, BasePlayer,\n Currency as c, currency_range\n)\n\n\nauthor = 'Your name here'\n\ndoc = \"\"\"\nYour app description\n\"\"\"\n\n\nclass Constants(BaseConstants):\n name_in_url = 'public_goods'\n players_per_group = 3\n num_rounds = 1\n endowment = c(100)\n multiplier = 2\n\n\nclass Subsession(BaseSubsession):\n pass\n\n\nclass Group(BaseGroup):\n\n total_contribution = models.CurrencyField()\n individual_share = models.CurrencyField()\n\n def set_payoff(self):\n players = self.get_players()\n self.total_contribution = sum([player.contribution for player in players])\n self.individual_share = (\n self.total_contribution * Constants.multiplier / Constants.players_per_group)\n for player in players:\n player.payoff = Constants.endowment - player.contribution + self.individual_share\n\n\nclass Player(BasePlayer):\n\n contribution = models.CurrencyField(\n min=0, max=Constants.endowment)\n","sub_path":"oTree/public_goods/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1025,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"282111654","text":"import sqlite3\nfrom sqlite3 import Error\nimport random\n\n\ndef create_connection():\n conn = None\n try:\n conn = sqlite3.connect(\"hotel.db\")\n except Error as e:\n print(e)\n\n return conn\n\n\ndef create_room(conn, data):\n statement = \"insert into Rooms(Number,Type,Status,Rate)VALUES(?,?,?,?)\"\n cur = conn.cursor()\n cur.execute(statement, data)\n conn.commit()\n return cur.lastrowid\n\n\ndef get_type(conn, i):\n cur = conn.cursor()\n statement = \"select Type from Rooms where Number = {0}\".format(i)\n cur.execute(statement)\n data = cur.fetchall()\n return data[0][0]\n\n\ndef get_status(conn, i):\n cur = conn.cursor()\n statement = \"select Status from Rooms where Number = {}\".format(i)\n cur.execute(statement)\n data = cur.fetchall()\n return data[0][0]\n\n\ndef get_report_data():\n conn = create_connection()\n cur = conn.cursor()\n statement = \"SELECT B.Room_number, G.First_name|| ' '||G.Last_name, B.CheckIn, B.CheckOut, sum(P.Payment_amount) from Booking as B JOIN Guest as G on B.Guest_Id = G.Guest_Id JOIN Payment as P on P.Booking_id= B.Booking_id WHERE P.Payment_date = date('now') GROUP by P.Booking_id;\"\n cur.execute(statement)\n data = cur.fetchall()\n conn.close()\n return data\n\n\ndef get_housekeeping():\n conn = create_connection()\n cur = conn.cursor()\n statement = \"SELECT * from Housekeeping\"\n cur.execute(statement)\n data = cur.fetchall()\n return data\n\ndef get_availiable_rooms():\n conn = create_connection()\n cur = conn.cursor()\n statement = \"SELECT * from Rooms as R where R.Status = 'Available'\"\n cur.execute(statement)\n data = cur.fetchall()\n return data\n\ndef get_reservations():\n conn = create_connection()\n cur = conn.cursor()\n statement = \"SELECT G.First_name, G.Last_name, B.CheckIn, B.CheckOut, R.Type from Booking as B join Guest as G on B.Guest_Id = G.Guest_Id join Rooms as R on R.number = B.room_number\"\n cur.execute(statement)\n data = cur.fetchall()\n return data\n\n# Capability 5-6\ndef get_guest(Guest_Id):\n conn = create_connection()\n cur = conn.cursor()\n statement = \"SELECT * FROM Guest WHERE Guest_Id = \" + str(Guest_Id)\n cur.execute(statement)\n data = cur.fetchall()\n conn.close()\n return data\n\ndef update_guest(Guest_Id):\n conn = create_connection()\n cur = conn.cursor()\n statement = \"UPDATE Guest SET First_Name = :first, Last_Name = :last, Phone = :phone, State_Id = :state_id, License_plate = :license_plate, Email = :email Address = :address, WHERE Guest_Id =\" + str(Guest_Id)\n conn.commit()\n data = cur.fetchall()\n conn.close()\n return data\n\ndef get_booking():\n conn = create_connection()\n cur = conn.cursor()\n statement = \"SELECT * FROM Booking WHERE Guest_Id = 1\"\n cur.execute(statement)\n data = cur.fetchall()\n conn.close()\n return data\n\ndef get_guest_1():\n conn = create_connection()\n cur = conn.cursor()\n statement = \"SELECT * FROM Guest WHERE Guest_Id = 1\" \n cur.execute(statement)\n data = cur.fetchall()\n conn.close()\n return data\n\ndef get_payment():\n conn = create_connection()\n cur = conn.cursor()\n statement = \"SELECT * FROM Booking WHERE Booking_id= 2\" \n cur.execute(statement)\n data = cur.fetchall()\n conn.close()\n return data\n\ndef get_rooms():\n conn = create_connection()\n cur = conn.cursor()\n statement = \"SELECT * FROM Rooms WHERE Number= 6\" \n cur.execute(statement)\n data = cur.fetchall()\n conn.close()\n return data\n#CAP 7 & 8\ndef get_report_data():\n conn = create_connection()\n cur = conn.cursor()\n statement = \"SELECT B.Room_number, G.First_name|| ' '||G.Last_name, B.CheckIn, B.CheckOut, sum(P.Payment_amount) from Booking as B JOIN Guest as G on B.Guest_Id = G.Guest_Id JOIN Payment as P on P.Booking_id= B.Booking_id WHERE P.Payment_date = date('now') GROUP by P.Booking_id;\"\n cur.execute(statement)\n data = cur.fetchall()\n conn.close()\n return data\n\ndef search_data(FirstName,LastName,Room,Phone,Address,CheckIn,CheckOut):\n '''\n Select G.First_name, G.Last_name, B.Room_number, B.CheckIn, B.CheckOut from Booking as B\nJOIN Guest AS G ON B.Guest_Id = G.Guest_Id\nwhere G.First_name = 'Dhruv' and G.Last_name = 'Patel' and B.Room_number=6 and G.Phone = 1231231234 and G.Address Like '%CA%' and B.CheckIn='2021-05-02' and B.CheckOut='2021-05-03';\n '''\n statement = \"Select G.First_name, G.Last_name, B.Room_number, B.CheckIn, B.CheckOut,B.Guest_Id from Booking as B JOIN Guest AS G ON B.Guest_Id = G.Guest_Id \"\n if FirstName or LastName or Room or Phone or Address or CheckIn or CheckOut:\n statement+= \"where\"\n else:\n return None\n if FirstName:\n statement+= \" lower(G.First_name) = lower('\" +str(FirstName) + str(\"') and\")\n if LastName:\n statement+=\" G.Last_name = '\" +str(LastName) + str(\"' and\")\n if Room:\n statement+=\" B.Room_number = '\"+ str(Room) + str(\"' and\")\n if Phone:\n statement+=\" G.Phone = '\" +str(Phone)+ str(\"' and\")\n if Address:\n statement+=\" lower(G.Address) Like lower(\"+str(\"'%\")+str(Address)+str(\"%')\") + str(\" and\")\n if CheckIn:\n statement+=\" B.CheckIn = '\"+str(CheckIn) +str(\"' and\")\n if CheckOut:\n statement+=\" B.CheckOut = '\"+str(CheckOut) + str(\"' and\")\n if statement.endswith('and'):\n run_statement = statement[:len(statement)-3]\n run_statement+=' ;'\n conn = create_connection()\n cur = conn.cursor()\n cur.execute(run_statement)\n data = cur.fetchall()\n conn.close()\n return data\ndef main():\n size = [\"King\", \"Double Queen\", \"Double Queen with Kitchen\", \"Suite\"]\n key = [\"Available\", \"Unavailable/Occupied\", \"Unavailable/Dirty\", \"Unavailable/Maintenance\"]\n\n conn = create_connection()\n '''\n with conn:\n for i in range(1,21):\n data = (i,size[random.randint(0,3)],key[random.randint(0,3)],0.0)\n create_room(conn,data)\n '''\n \n\n\nif __name__ == '__main__':\n main()\n","sub_path":"filldb.py","file_name":"filldb.py","file_ext":"py","file_size_in_byte":5996,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"417407029","text":"from django.urls import path \nfrom . import views\nurlpatterns = [\n path('', views.root),\n path('blogs/', views.index),\n path('new/', views.new),\n path('creat/', views.creat),\n path('/', views.show),\n path('/edit/', views.edit),\n path('/delete/', views.delete)\n ]","sub_path":"django/django_intro/first_django_project/firstapp/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":314,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"61520298","text":"from solve_patterns_game import PN_search\nfrom patterns_games import Tic_tac_toe\nfrom data_magic import save_sets\nimport os\n\ndef do_a_task(task):\n if not os.path.exists(os.path.dirname(task[\"solver_args\"][\"prooffile\"])):\n os.makedirs(os.path.dirname(task[\"solver_args\"][\"prooffile\"]))\n game = Tic_tac_toe(**task[\"game_args\"])\n solver = PN_search(game,**task[\"solver_args\"])\n solver.pn_search()\n save_sets((solver.provenset,solver.prooffile),(solver.disprovenset,solver.disprooffile),\n (solver.endgame_provenset,solver.endgame_prooffile),(solver.endgame_disprovenset,solver.endgame_disprooffile))\n\ntasks = [\n {\n \"game_args\":{\n \"startpos\":[[0,0],True],\n \"zobrist_file\":\"zobrist.pkl\" \n },\n \"solver_args\":{\n \"endgame_depth\":0,\n \"drawproves\":False,\n \"prooffile\":\"proofsets/tic_tac_toe/proof.txt\",\n \"disprooffile\":\"proofsets/tic_tac_toe/disproof.txt\",\n \"endgame_prooffile\":\"proofsets/tic_tac_toe/endproof.txt\",\n \"endgame_disprooffile\":\"proofsets/tic_tac_toe/enddisproof.txt\"\n }\n }\n]\n\nfor task in tasks:\n do_a_task(task)","sub_path":"__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":1184,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"627940513","text":"import praw, requests, os, json\nfrom datetime import datetime\nfrom time import sleep\n\nclass SubredditWatchdog:\n def __init__(self, redditId, secret, slackurl, subredditName):\n self.id = redditId\n self.secret = secret\n self.slackurl = slackurl\n\n self.subredditName = subredditName\n\n self.reddit = praw.Reddit(\n client_id=self.id,\n client_secret=self.secret,\n user_agent=\"linux:me.aleksilassila.subredditwatchdog:v1 (by u/0x3A7F14)\")\n\n def sendMessage(self, title, content, time, link):\n requests.post(\n self.slackurl,\n data = json.dumps({\"text\": f\"{datetime.utcfromtimestamp(time).strftime('%d.%m %H:%M')}\\n{title}:\\n{content}\\nSource: {link}\\n\\n\"})\n )\n print(f\"[+] Sent notification\")\n\n def watch(self):\n while True:\n try:\n for postId in self.reddit.subreddit(self.subredditName).stream.submissions():\n print(f\"[+] Caught new post: {postId}\")\n\n submission = self.reddit.submission(postId)\n\n title = submission.title\n content = submission.selftext\n time = submission.created_utc\n link = submission.url\n\n self.sendMessage(title, content, time, link)\n except: sleep(60)\n\n\nif __name__ == \"__main__\":\n watchdog = SubredditWatchdog(os.environ.get(\"REDDIT_ID\"), os.environ.get(\"REDDIT_SECRET\"), os.environ.get(\"SURL\"), \"TweakBounty\")\n watchdog.watch()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1548,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"267042602","text":"# -*- coding:utf-8 -*-\n# author:Deer404\n# contact: 919187569.com\n# datetime:2019/7/27 20:48\n# software: PyCharm\n\n\"\"\"\n文件说明：\n 爬取QQ音乐某一首歌的评论\n\"\"\"\nimport selenium\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.options import Options\n# import requests\nfrom bs4 import BeautifulSoup\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nimport time\nimport pymysql\nimport warnings\nimport jieba\nfrom wordcloud import WordCloud,ImageColorGenerator\nimport numpy as np\nfrom PIL import Image\nimport jieba.analyse\n# import ipproxyPool\nimport multiprocessing\nclass Musiccoment:\n def __init__(self):\n self.options = Options()\n # self.options.add_argument('--headless')\n # self.proxy = \"--proxy-server={}\".format(ipproxyPool.get_proxy())\n # self.options.add_argument(self.proxy)\n self.browser = webdriver.Chrome(options=self.options)\n self.wait = WebDriverWait(self.browser, 10)\n self.db = pymysql.connect(\"localhost\", \"root\", \"123456\", \"qqmusic\")\n self.cursor = self.db.cursor()\n self.tablesql = \"create table if not exists comment( id int primary KEY auto_increment,name varchar(255) NOT NULL ,comment LONGTEXT NOT NULL,subcomment LONGTEXT )\"\n with warnings.catch_warnings():\n warnings.simplefilter('ignore')\n self.cursor.execute(self.tablesql)\n self.comments = \"\"\n\n def web(self, url,page):\n print(\"开始爬取\")\n self.browser.get(url)\n self.browser.refresh()\n commentmain = self.wait.until(\n EC.presence_of_element_located((By.XPATH, \"//ul[@class='comment__list js_hot_list']\")))\n html = self.browser.page_source\n self.soup_source(html)\n if page != 0:\n total = page\n else:\n total = self.wait.until(\n EC.element_to_be_clickable((By.XPATH, \"//a[@class='next js_pageindex']/preceding-sibling::a[1]\"))).text\n # nextbutton = self.wait.until(EC.element_to_be_clickable((By.XPATH,\"//a[@class='next js_pageindex']\")))\n for i in range((int(total) - 1)):\n try:\n self.next_page()\n except selenium.common.exceptions.StaleElementReferenceException:\n self.next_page()\n\n def soup_source(self, html):\n soup = BeautifulSoup(html, 'lxml')\n commentlist = soup.find(class_=\"comment__list js_all_list\").find_all('li')\n for comment in commentlist:\n comment_name = \"\"\n comment_text = \"\"\n subcomment = \"\"\n if comment.find(class_=\"c_tx_thin js_nick js_nick_only\") is not None:\n try:\n comment_name = comment.find(class_=\"c_tx_thin js_nick js_nick_only\").text\n except TypeError:\n comment_name = \"\"\n if comment.find(class_=\"c_tx_normal comment__text js_hot_text\") is not None:\n comment_text = comment.find(class_=\"c_tx_normal comment__text js_hot_text\").text\n if comment.find(class_=\"js_subcomment\") is not None:\n subcomment = comment.find(class_=\"js_subcomment\").text\n self.insert_to_db(comment_name,comment_text,subcomment)\n\n def insert_to_db(self,name,text,subtext):\n print(name+\",\"+text+\",\"+subtext)\n sql = \"insert into comment(name,comment,subcomment) VALUES (%s,%s,%s)\"\n self.cursor.execute(sql,(name,text,subtext))\n self.db.commit()\n\n def next_page(self):\n try:\n nextbutton = self.wait.until(\n EC.element_to_be_clickable((By.XPATH, \"//strong[@class='current']/following-sibling::a[1]\")))\n nextbutton.click()\n except selenium.common.exceptions.ElementClickInterceptedException:\n self.next_page()\n commentmain = self.wait.until(\n EC.presence_of_all_elements_located((By.XPATH, \"//div[@class='main']\")))\n self.soup_source(self.browser.page_source)\n\n def convert_wordcloud(self):\n print(\"开始生成词云\")\n sql = \"select * from comment\"\n self.cursor.execute(sql)\n result = self.cursor.fetchall()\n for item in result:\n comment = item[2]\n subcomment = item[3]\n self.comments = self.comments +comment+subcomment\n keys= jieba.analyse.textrank(self.comments, topK=300, withWeight=True)\n keywords = dict()\n for i in keys:\n keywords[i[0]] = i[1]\n if \"徐坤蔡\" in keywords.keys():\n keywords['蔡徐坤'] = keywords.pop(\"徐坤蔡\")\n # jieba.add_word(\"蔡徐坤\")\n # cut_text = \" \".join(jieba.cut(self.comments,cut_all=True))\n girl_img = np.array(Image.open('girl.png'))\n image_colors = ImageColorGenerator(girl_img)\n wordcloud = WordCloud(\n scale=10,\n mask=girl_img,\n font_path=\"C:/Windows/Fonts/simfang.ttf\",\n max_words=150,\n random_state=42,\n max_font_size=60,\n background_color=\"white\",\n width=1920,\n height=1080).generate_from_frequencies(keywords)\n wordcloud.recolor(color_func=image_colors)\n wordcloud.to_file(\"评论词云.png\")\n print(\"生成成功\")\n def run(self, url,page=0):\n starttime = time.time()\n self.web(url,page)\n self.convert_wordcloud()\n print(\"耗时\" + str(time.time() - starttime) + \"秒\")\n\n\nif __name__ == '__main__':\n music = Musiccoment()\n music.run(\"https://y.qq.com/n/yqq/song/004BxrBT3coQnC.html\",1)\n","sub_path":"music_comment.py","file_name":"music_comment.py","file_ext":"py","file_size_in_byte":5651,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"60048204","text":"# -*- coding: utf-8 -*-\n\nimport datetime\nfrom flask import render_template, abort, request, current_app\nfrom flask_migrate import upgrade\nfrom app.main import main\nfrom app.models import Post, Catalog, Tag\nfrom app.update import update_from_github\nimport sys\nimport logging\nimport os\nimport markdown\n\n\n# compatible with Python2\nif sys.version[0] == '2' and sys.getdefaultencoding() != 'utf-8':\n reload(sys)\n sys.setdefaultencoding('utf-8')\n\n\n@main.before_app_first_request\ndef before_first_request():\n try:\n upgrade()\n except BaseException as e:\n logging.exception(e)\n\n\n@main.route('/')\ndef index():\n page = request.args.get('page', 1, type=int)\n pagination = Post.query.order_by(Post.timestamp.desc()).paginate(page,\n per_page=current_app.config['POSTS_PER_PAGE'],\n error_out=True)\n posts = pagination.items\n return render_template('posts_stream.html',\n pagination=pagination, posts=posts, name='首页', endpoint='main.index')\n\n\n@main.route('/archive')\ndef all_archives():\n posts = Post.query.order_by(Post.timestamp.desc())\n return render_template('archives.html', posts=posts)\n\n\n@main.route('/catalog')\ndef all_catalogs():\n catalogs = Catalog.query.order_by(Catalog.name).all()\n return render_template('catalogs.html', catalogs=catalogs)\n\n\n@main.route('/tag')\ndef all_tags():\n tags = Tag.query.order_by(Tag.name).all()\n return render_template('tags.html', tags=tags)\n\n\n# @main.route('/project')\n# def project():\n# abort(500)\n\n\n@main.route('/about')\ndef about_me():\n about_basedir = os.path.join(current_app.root_path, 'about')\n if not os.path.exists(os.path.join(about_basedir, '_about.html')):\n with open(os.path.join(about_basedir, 'about.md'), mode='r') as f:\n md_content = f.read()\n with open(os.path.join(about_basedir, '_about.html'), mode='w') as f:\n f.write(markdown.markdown(md_content, output_format='html5'))\n with open(os.path.join(about_basedir, '_about.html'), mode='r') as f:\n html_content = f.read()\n return render_template('about.html', content=html_content)\n\n\n@main.route('/post/')\ndef show_post(permalink):\n post = Post.query.filter_by(permalink=permalink).first()\n if post is None:\n abort(404)\n return render_template('post.html', post=post)\n\n\n@main.route('/catalog/')\ndef show_catalog(permalink):\n page = request.args.get('page', 1, type=int)\n catalog = Catalog.query.filter_by(permalink=permalink).first()\n if catalog is None:\n abort(404)\n pagination = catalog.posts.order_by(Post.timestamp.desc()).paginate(page,\n per_page=current_app.config['POSTS_PER_PAGE'],\n error_out=True)\n posts = pagination.items\n return render_template('posts_stream.html',\n pagination=pagination, posts=posts, name=catalog.name, endpoint='main.show_catalog')\n\n\n@main.route('/tag/')\ndef show_tag(permalink):\n page = request.args.get('page', 1, type=int)\n tag = Tag.query.filter_by(permalink=permalink).first()\n if tag is None:\n abort(404)\n pagination = tag.posts.order_by(Post.timestamp.desc()).paginate(page,\n per_page=current_app.config['POSTS_PER_PAGE'],\n error_out=True)\n posts = pagination.items\n return render_template('posts_stream.html',\n pagination=pagination, posts=posts, name=tag.name, endpoint='main.show_tag')\n\n\n@main.route('/archive/')\ndef show_archive(archive_id):\n page = request.args.get('page', 1, type=int)\n year = archive_id // 100\n month = archive_id % 100\n if year not in range(2000, 2100) or month not in range(1, 13):\n abort(404)\n\n date_begin = datetime.date(year, month, 1)\n if month == 12:\n date_end = datetime.date(year+1, 1, 1)\n else:\n date_end = datetime.date(year, month+1, 1)\n\n pagination = Post.query.filter(Post.timestamp.between(date_begin, date_end))\\\n .order_by(Post.timestamp.desc()).paginate(page,\n per_page=current_app.config['POSTS_PER_PAGE'],\n error_out=True)\n\n posts = pagination.items\n return render_template('posts_stream.html',\n pagination=pagination,\n posts=posts,\n name='%s年%s月' % (year, month),\n endpoint='main.show_archive')\n\n\n@main.route('/update')\ndef update():\n \"\"\"\n this route is used for update posts from Github,\n please set the UPDATE_VERIFY_CODE in environment virables to limit it.\n \"\"\"\n verify_code = request.args.get('vc')\n force_update = request.args.get('f', False, type=bool)\n if verify_code != current_app.config['UPDATE_VERIFY_CODE']:\n abort(403)\n return update_from_github(force_update)\n","sub_path":"app/main/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5292,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"83148499","text":"import unittest\nfrom random import randrange\nfrom unittest import mock\nfrom tests.mock_server import get_free_port, start_mock_server\nfrom сlasses.vk_api_classes import VKinderClient\nfrom сlasses.vk_api_client import VkApiClient\nfrom сlasses.vkinder_db_client import VKinderDb\n\n\nclass TestVKinderDb(unittest.TestCase):\n\n @classmethod\n def setUpClass(cls):\n cls.db = VKinderDb('test', 'test', 'test', debug_mode=True)\n cls.mock_server_port = get_free_port()\n start_mock_server(cls.mock_server_port)\n\n def test_client_save_load(self):\n assert self.db.is_initialized\n mock_users_url = 'http://localhost:{port}/'.format(port=self.mock_server_port)\n with mock.patch('сlasses.vk_api_client.VkApiClient.API_BASE_URL', new_callable=mock.PropertyMock) as mock_f:\n mock_f.return_value = mock_users_url\n self.api = VkApiClient(token='', app_id='', user_id='1', debug_mode=True)\n assert self.api.is_initialized\n users = self.api.get_users('1')\n assert len(users) == 2\n user = users[0]\n client = VKinderClient(user)\n self.db.save_client(client)\n client_db = self.db.load_client_from_db('1')\n assert client_db\n client = VKinderClient(client_db)\n assert client.vk_id == '1'\n assert client.fname == 'Павел'\n assert client.lname == 'Дуров'\n\n def test_users_save_load(self):\n mock_users_url = 'http://localhost:{port}/'.format(port=self.mock_server_port)\n with mock.patch('сlasses.vk_api_client.VkApiClient.API_BASE_URL', new_callable=mock.PropertyMock) as mock_f:\n mock_f.return_value = mock_users_url\n self.api = VkApiClient(token='', app_id='', user_id='1', debug_mode=True)\n assert self.api.is_initialized\n users = self.api.get_users(['1', '5', 1])\n client_1 = VKinderClient(users[0])\n assert client_1\n client_2 = VKinderClient(users[1])\n assert client_2\n\n self.db.save_client(client_1)\n self.db.save_client(client_2)\n\n for i in range(self.db.search_history_limit + 1):\n client_1.reset_search()\n client_2.reset_search()\n\n client_1.search.sex_id = randrange(0, 2, 1)\n client_1.search.status_id = randrange(1, 8, 1)\n client_1.search.city_id = 1\n client_1.search.city_name = 'Москва'\n client_1.search.min_age = randrange(0, 60, 1)\n client_1.search.max_age = randrange(client_1.search.min_age, 127, 1)\n client_1.rating_filter = 0\n\n client_2.search.sex_id = randrange(0, 2, 1)\n client_2.search.status_id = randrange(1, 8, 1)\n client_2.search.city_id = 2\n client_2.search.city_name = 'Санкт-Петербург'\n client_2.search.min_age = randrange(0, 60, 1)\n client_2.search.max_age = randrange(client_2.search.min_age, 127, 1)\n client_2.rating_filter = 0\n\n self.db.save_search(client_1)\n self.db.save_search(client_2)\n\n client_1.found_users = self.api.search_users()\n client_2.found_users = self.api.search_users(q='babych')\n assert len(client_1.found_users) > 0\n assert len(client_2.found_users) > 0\n\n self.db.save_users(client_1)\n self.db.save_users(client_2)\n\n\n\n\n","sub_path":"tests/test_vkinder_db_client.py","file_name":"test_vkinder_db_client.py","file_ext":"py","file_size_in_byte":3576,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"389642488","text":"\"\"\"Support for Magic Home select.\"\"\"\nfrom __future__ import annotations\n\nfrom flux_led.aio import AIOWifiLedBulb\nfrom flux_led.protocol import PowerRestoreState\n\nfrom homeassistant import config_entries\nfrom homeassistant.components.select import SelectEntity\nfrom homeassistant.const import CONF_NAME\nfrom homeassistant.core import HomeAssistant, callback\nfrom homeassistant.helpers.entity import EntityCategory\nfrom homeassistant.helpers.entity_platform import AddEntitiesCallback\n\nfrom .const import DOMAIN\nfrom .coordinator import FluxLedUpdateCoordinator\nfrom .entity import FluxBaseEntity\n\n\nasync def async_setup_entry(\n hass: HomeAssistant,\n entry: config_entries.ConfigEntry,\n async_add_entities: AddEntitiesCallback,\n) -> None:\n \"\"\"Set up the Flux selects.\"\"\"\n coordinator: FluxLedUpdateCoordinator = hass.data[DOMAIN][entry.entry_id]\n async_add_entities([FluxPowerState(coordinator.device, entry)])\n\n\ndef _human_readable_option(const_option: str) -> str:\n return const_option.replace(\"_\", \" \").title()\n\n\nclass FluxPowerState(FluxBaseEntity, SelectEntity):\n \"\"\"Representation of a Flux power restore state option.\"\"\"\n\n _attr_should_poll = False\n\n def __init__(\n self,\n device: AIOWifiLedBulb,\n entry: config_entries.ConfigEntry,\n ) -> None:\n \"\"\"Initialize the power state select.\"\"\"\n super().__init__(device, entry)\n self._attr_entity_category = EntityCategory.CONFIG\n self._attr_name = f\"{entry.data[CONF_NAME]} Power Restored\"\n if entry.unique_id:\n self._attr_unique_id = f\"{entry.unique_id}_power_restored\"\n self._name_to_state = {\n _human_readable_option(option.name): option for option in PowerRestoreState\n }\n self._attr_options = list(self._name_to_state)\n self._async_set_current_option_from_device()\n\n @callback\n def _async_set_current_option_from_device(self) -> None:\n \"\"\"Set the option from the current power state.\"\"\"\n restore_states = self._device.power_restore_states\n assert restore_states is not None\n assert restore_states.channel1 is not None\n self._attr_current_option = _human_readable_option(restore_states.channel1.name)\n\n async def async_select_option(self, option: str) -> None:\n \"\"\"Change the power state.\"\"\"\n await self._device.async_set_power_restore(channel1=self._name_to_state[option])\n self._async_set_current_option_from_device()\n self.async_write_ha_state()\n","sub_path":"homeassistant/components/flux_led/select.py","file_name":"select.py","file_ext":"py","file_size_in_byte":2512,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"504213778","text":"import re\n\ndef isInteger(value):\n return re.match(r\"^\\d+$\", value) is not None\n\ndef isAlphaNumeric(value):\n \"\"\"\n 半角英数字チェック\n :param value: チェック対象の文字列\n :rtype: チェック対象文字列が、全て半角英数字の場合 True\n \"\"\"\n return re.match(r\"^\\w+$\", value) is not None\n\ndef isAlpha(value):\n \"\"\"\n 半角英字チェック\n :param value: チェック対象の文字列\n :rtype: チェック対象文字列が、全て半角英字の場合 True\n \"\"\"\n return re.match(r\"^[a-zA-Z]+$\", value) is not None\n\ndef isenglish(value):\n \"\"\"\n 半角英分チェック\n :param value: チェック対象の文字列\n :rtype: チェック対象文字列が、全て半角英字と　' . ␣ の場合 True\n \"\"\"\n return re.match(r\"^[A-Za-z]+[\\s'.-]\", value) is not None\n\ndef isephone(value):\n \"\"\"\n 電話番号チェック\n :param value: チェック対象の文字列\n :rtype: チェック対象文字列が、全て半角英字と　' . ␣ の場合 True\n \"\"\"\n return re.match(r\"[0-9]+[0-9-]+[0-9]\", value) is not None\n\ncomment = input(\"文字を入力してください\")\n\n#すべてが半角数字（第1条件）\nint_result = isInteger(comment)\nalfanum_result = isAlphaNumeric(comment)\neibuncheck_result = isenglish(comment)\nphonechek_result = isephone(comment)\n\nif int_result == True:\n print(\"The character strings are all half-width numbers.\")\n\n#すべてが半角英数字（第2条件）\nelif alfanum_result == True:\n print(\"The character strings are all alphanumeric characters.\")\n\n#半角英文　or ␣　or . or '　(第3条件)\nelif eibuncheck_result == True:\n print(\"The character string is half-width English.\")\n\n#電話番号　半角数字 or - and 最初と最後は数字\nelif phonechek_result == True:\n print(\"The string is a phone number.\")\n\n#それ以外\nelse:\n print(\"The string is neither.\")","sub_path":"k204_04_2.py","file_name":"k204_04_2.py","file_ext":"py","file_size_in_byte":1935,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"102439084","text":"import os\n\nfrom tuscloudos import config as conf\nfrom tuscloudos import utils\n\nCONF = conf.CONF\n\n\ndef config():\n with open(\"/var/front/manager/hawk/config/env/proc.json\", \"w+\") as fout:\n fout.write(conf.env.get_template(\"UI/proc.json\").render(\n virtual_ip=CONF.virtual_ip))\n\n with open(\"/var/front/manager/config/env.js\", \"w+\") as fout:\n fout.write(conf.env.get_template(\"UI/env.js\").render(\n virtual_ip=CONF.virtual_ip,\n controllers=CONF.controllers))\n utils.execute(\"mkdir -p /var/front/manager/log\")\n\n\ndef start():\n os.chdir(\"/var/front/manager\")\n utils.call(\"pm2 start start.json\")\n utils.call(\"pm2 startup centos\")\n utils.call(\"pm2 save\")\n utils.call(\"chattr +i /root/.pm2/dump.pm2\")\n\n\ndef main():\n config()\n start()\n","sub_path":"others/src/cloud/ui/start_ui.py","file_name":"start_ui.py","file_ext":"py","file_size_in_byte":827,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"586416868","text":"#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Mar 25 13:11:47 2017\n\n@author: blaire\n\"\"\"\nfrom matplotlib import pyplot as plt\ndef dataplot(var1,var2,xlabel,ylabel,title,p):\n plt.plot(var1,var2,'s',label=title)\n plt.xlabel(xlabel)\n plt.ylabel(ylabel)\n plt.title(title)\n plt.legend()\n plt.savefig(p+title+'.png')\n plt.close()\n \n","sub_path":"INSTRON/INSTRON_analysis/plotdata.py","file_name":"plotdata.py","file_ext":"py","file_size_in_byte":369,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"158567259","text":"class OAITransformer:\n PHASE_PRE = \"pre\"\n PHASE_POST = \"post\"\n PROCESSED = \"ok\"\n NO_HANDLER_CALLED = \"no_handler_called\"\n\n def __init__(self, rules: dict = None, unhandled_paths: set = None, **options):\n if rules is None:\n rules = {}\n if unhandled_paths is None:\n unhandled_paths = set()\n self.rules = rules\n self.options = options\n self.unhandled_paths = unhandled_paths\n\n def transform(self, record):\n result = {}\n if self.iter_json(el=record, paths=[\"\"], results=[result]) is not OAITransformer.PROCESSED:\n raise Exception(\"Top level handler returned unexpected result\") # pragma: no cover\n return result\n\n def iter_json(self, el, paths, results):\n \"\"\"\n\n \"\"\"\n # print(\" \" * 4 * len(paths), f\"Iter element {repr(el)[:100]}\")\n # List items call themselves\n if isinstance(el, (list, tuple)):\n for _ in el:\n if self.iter_json(_, paths, results) is not OAITransformer.PROCESSED:\n raise ValueError( # pragma: no cover\n f\"Path {paths} has not been processed by any handler {_}\")\n return OAITransformer.PROCESSED\n\n # Dict\n elif isinstance(el, dict):\n result = self.call_handlers(paths=paths, el=el, results=results,\n phase=OAITransformer.PHASE_PRE)\n if result == OAITransformer.PROCESSED:\n return result\n for k, v in el.items():\n child_paths = [*(f\"{path}/{k}\" for path in paths), k]\n if result != OAITransformer.NO_HANDLER_CALLED:\n child_results = [*results, result]\n else:\n child_results = results\n if self.iter_json(v, child_paths,\n child_results) is not OAITransformer.PROCESSED:\n raise ValueError(\n f\"Path {child_paths} has not been processed by any handler {v}\")\n result = self.call_handlers(paths=paths, el=el, results=results,\n phase=OAITransformer.PHASE_POST)\n if result is OAITransformer.NO_HANDLER_CALLED:\n return OAITransformer.PROCESSED\n return result\n\n # string\n elif isinstance(el, (str, int, float)):\n result = self.call_handlers(paths=paths, el=el, results=results,\n phase=OAITransformer.PHASE_PRE)\n if result == OAITransformer.PROCESSED:\n return result\n else:\n raise ValueError(f\"Path {paths} has not been processed by any handler {el}\")\n else:\n raise ValueError(\n f\"Path with simple value {paths} has not been processed by any handler {el}\")\n\n def call_handlers(self, paths, el, results, phase, **kwargs):\n paths = set(paths)\n intersec = paths.intersection(self.unhandled_paths)\n if intersec:\n return OAITransformer.PROCESSED\n for path in paths:\n if path not in self.rules:\n continue\n handler = self.rules[path]\n if phase not in handler:\n continue\n ret = handler[phase](paths=paths, el=el, results=results, phase=phase,\n **self.options)\n assert ret is not None, f\"Handler {handler[phase]} must not return None\"\n return ret\n return OAITransformer.NO_HANDLER_CALLED\n","sub_path":"invenio_oarepo_oai_pmh_harvester/transformer.py","file_name":"transformer.py","file_ext":"py","file_size_in_byte":3590,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"526052487","text":"from flask import Flask,request,jsonify\nfrom flask_cors import CORS\n\nfrom models import db\nfrom models import Alumno\n\nfrom schemas import ma\nfrom schemas import alumno_schema\nfrom schemas import alumnos_schema\n\napp = Flask(__name__)\n#Configurando CORS\nCORS(app)\n\napp.config['SQLALCHEMY_DATABASE_URI'] = 'mysql://root:Pwsqladmin1@localhost/libreria'\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS']=False\n\n@app.route('/')\ndef home():\n return 'Holav'\n\n\n\n#Ruta para obtener y agregar tareas\n@app.route('/api/alumnos',methods=['GET','POST'])\ndef manage_alumnos():\n if request.method == 'POST':\n nombre = request.json['nombre']\n apellido = request.json['apellido']\n codigo = request.json['codigo']\n nueva_tarea = Alumno(nombre,apellido, codigo)\n db.session.add(nueva_tarea)\n db.session.commit()\n return alumno_schema.jsonify(nueva_tarea)\n elif request.method == 'GET':\n todos = Alumno.query.all()\n result = alumnos_schema.dump(todos)\n return jsonify(result)\n\n@app.route('/api/alumnos/',methods=['GET','DELETE','PUT'])\ndef manage_alumno(id):\n if request.method == 'GET':\n tarea = Alumno.query.get(id)\n return alumno_schema.jsonify(tarea)\n elif request.method == 'PUT':\n tarea = Alumno.query.get(id)\n nombre = request.json['nombre']\n apellido = request.json['apellido']\n codigo = request.json['codigo']\n tarea.nombre = nombre\n tarea.apellido = apellido\n tarea.codigo = codigo\n db.session.commit()\n return alumno_schema.jsonify(tarea)\n elif request.method == 'DELETE':\n tarea = Alumno.query.get(id)\n db.session.delete(tarea)\n db.session.commit()\n return alumno_schema.jsonify(tarea)\n\nif __name__ == '__main__': \n db.init_app(app)\n ma.init_app(app) \n with app.app_context(): \n db.create_all()\n app.run(port=5000,debug=True)\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1804,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"554152367","text":"import pandas as pd\nimport os\nfrom time import strftime\n\ndef get_folder(nclass):\n folderdf = pd.read_excel('classes.xlsx',sheet_name='Folders')\n folderdf.index = folderdf.iloc[:,0]\n return folderdf.loc[nclass]\n\ndef get_class(wkd,tm):\n classdf = pd.read_excel('classes.xlsx',sheet_name='Classes')\n if tm in list(classdf.Time):\n print('Found!')\n nclass = classdf[(classdf.Time == tm)].loc[:,wkd]\n return list(nclass)[0]\n else:\n return 0\n\ndef open_folder_scheduled():\n while True:\n tm = strftime('%H:%M')\n wkd = strftime('%a')\n nclass = get_class(wkd,tm)\n if nclass:\n folder = get_folder(nclass)\n os.system('explorer '+folder[1])\n print('folder opened')\n break\nopen_folder_scheduled()\n \n\n","sub_path":"get_folder.py","file_name":"get_folder.py","file_ext":"py","file_size_in_byte":809,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"402351285","text":"# import maya\n# maya.cmds.loadPlugin(\"eulerDiff.py\")\n# maya.cmds.createNode(\"eulerDiff\")\n\nimport math, sys\n\nimport maya.OpenMaya as OpenMaya\nimport maya.OpenMayaMPx as OpenMayaMPx\n\nkPluginNodeTypeName = \"eulerDiff\"\n\neulerNodeId = OpenMaya.MTypeId(0x2029000a)\n\n# Node definition\nclass eulerNode(OpenMayaMPx.MPxNode):\n\t# class variables\n\tfrom_space = OpenMaya.MObject()\n\tto_space = OpenMaya.MObject()\n\tinTwist = OpenMaya.MObject()\n\toutputX = OpenMaya.MObject()\n\toutputY = OpenMaya.MObject()\n\toutputZ = OpenMaya.MObject()\n\tdef __init__(self):\n\t\tOpenMayaMPx.MPxNode.__init__(self)\n\tdef compute(self,plug,dataBlock):\n\t\tif ( plug == eulerNode.outputX or plug == eulerNode.outputY or plug == eulerNode.outputZ ):\n\n\t\t\ttwist = dataBlock.inputValue( eulerNode.inTwist ).asFloat()\n\t\t\t\n\t\t\tnrev = math.floor( twist/ 360.0 )\n\t\t\tfrev = twist/ 360.0 - nrev\n\t\t\t\n\t\t\tif(frev > 0.5):\n\t\t\t\tnrev = nrev + 1\n\t\t\t\n\t\t\t#print \"tw \", twist\n\t\t\t\n\t\t\tspaceA = dataBlock.inputValue( eulerNode.from_space ).asMatrix()\n\t\t\tspaceB = dataBlock.inputValue( eulerNode.to_space ).asMatrix()\n\t\t\t\n\t\t\tspaceA = spaceA * spaceB.inverse()\n\t\t\t\n\t\t\ttm = OpenMaya.MTransformationMatrix( spaceA )\n\t\t\teul = tm.eulerRotation()\n\t\t\t\n\t\t\t#qua = eul.asQuaternion()\n\t\t\t\n\t\t\t#axis = OpenMaya.MVector()\n\t\t\t\n\t\t\t#scriptUtil = OpenMaya.MScriptUtil()\n\t\t\t\n\t\t\t#angptr = scriptUtil.asDoublePtr()\n\t\t\t\n\t\t\t#qua.getAxisAngle( axis, angptr)\n\t\t\t\n\t\t\t#ang = scriptUtil.getDouble(angptr)\n\t\t\t\n\t\t\teul.reorder(OpenMaya.MEulerRotation.kZYX)\n\t\t\t\n\t\t\t#print \"x \", eul.x, \" y \", eul.y , \" z \", eul.z\n\t\t\t\n\t\t\tresultX = -eul.x * 180.0 / 3.14159269 + nrev * 360.0\n\t\t\toutputHandle = dataBlock.outputValue( eulerNode.outputX )\n\t\t\toutputHandle.setFloat( resultX )\n\t\t\t\n\t\t\tresultY = eul.y * 180.0 / 3.14159269\n\t\t\toutputHandle = dataBlock.outputValue( eulerNode.outputY )\n\t\t\toutputHandle.setFloat( resultY )\n\t\t\t\n\t\t\tresultZ = eul.z * 180.0 / 3.14159269\n\t\t\toutputHandle = dataBlock.outputValue( eulerNode.outputZ )\n\t\t\toutputHandle.setFloat( resultZ )\n\t\t\t\n\t\t\tdataBlock.setClean( plug )\n\n\t\treturn OpenMaya.kUnknownParameter\n\n# creator\ndef nodeCreator():\n\treturn OpenMayaMPx.asMPxPtr( eulerNode() )\n\n# initializer\ndef nodeInitializer():\n\t# input\n\tnAttr = OpenMaya.MFnNumericAttribute()\n\teulerNode.inTwist = nAttr.create( \"inputTwist\", \"tiw\", OpenMaya.MFnNumericData.kFloat, 0.0 )\n\tnAttr.setStorable(1)\n\t\n\tmAttr = OpenMaya.MFnMatrixAttribute()\n\teulerNode.from_space = mAttr.create( \"orginSpace\", \"orim\" )\n\teulerNode.to_space = mAttr.create( \"destinationSpace\", \"destm\" )\n\t\n\t# output\n\tnAttr = OpenMaya.MFnNumericAttribute()\n\teulerNode.outputX = nAttr.create( \"rotateX\", \"rx\", OpenMaya.MFnNumericData.kFloat, 0.0 )\n\tnAttr.setStorable(1)\n\tnAttr.setWritable(1)\n\t\n\teulerNode.outputY = nAttr.create( \"rotateY\", \"ry\", OpenMaya.MFnNumericData.kFloat, 0.0 )\n\tnAttr.setStorable(1)\n\tnAttr.setWritable(1)\n\t\n\teulerNode.outputZ = nAttr.create( \"rotateZ\", \"rz\", OpenMaya.MFnNumericData.kFloat, 0.0 )\n\tnAttr.setStorable(1)\n\tnAttr.setWritable(1)\n\t\n\t# add attributes\n\teulerNode.addAttribute( eulerNode.inTwist )\n\teulerNode.addAttribute( eulerNode.from_space )\n\teulerNode.addAttribute( eulerNode.to_space )\n\teulerNode.addAttribute( eulerNode.outputX )\n\teulerNode.addAttribute( eulerNode.outputY )\n\teulerNode.addAttribute( eulerNode.outputZ )\n\teulerNode.attributeAffects( eulerNode.inTwist, eulerNode.outputX )\n\teulerNode.attributeAffects( eulerNode.from_space, eulerNode.outputX )\n\teulerNode.attributeAffects( eulerNode.to_space, eulerNode.outputX )\n\teulerNode.attributeAffects( eulerNode.from_space, eulerNode.outputY )\n\teulerNode.attributeAffects( eulerNode.to_space, eulerNode.outputY )\n\teulerNode.attributeAffects( eulerNode.from_space, eulerNode.outputZ )\n\teulerNode.attributeAffects( eulerNode.to_space, eulerNode.outputZ )\n\t\n# initialize the script plug-in\ndef initializePlugin(mobject):\n\tmplugin = OpenMayaMPx.MFnPlugin(mobject, \"Zhang Jian\", \"0.1\")\n\ttry:\n\t\tmplugin.registerNode( kPluginNodeTypeName, eulerNodeId, nodeCreator, nodeInitializer )\n\texcept:\n\t\tsys.stderr.write( \"Failed to register node: %s\" % kPluginNodeTypeName )\n\t\traise\n\n# uninitialize the script plug-in\ndef uninitializePlugin(mobject):\n\tmplugin = OpenMayaMPx.MFnPlugin(mobject, \"Zhang Jian\", \"0.1\")\n\ttry:\n\t\tmplugin.deregisterNode( eulerNodeId )\n\texcept:\n\t\tsys.stderr.write( \"Failed to deregister node: %s\" % kPluginNodeTypeName )\n\t\traise\n\t\n","sub_path":"mayaeuler/eulerDiff.py","file_name":"eulerDiff.py","file_ext":"py","file_size_in_byte":4294,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"169075712","text":"#!/usr/bin/env python\n\nimport sys\nfrom tkinter import filedialog\nfrom tkinter import *\nfrom collections import Counter\n\ndef filepath():\n myfile = Tk()\n myfile.filename = filedialog.askopenfilename(initialdir = \"/\",\n title = \"Select file\",\n filetypes = ((\"text files\",\"*.txt\"),))\n \n return myfile.filename\n \ndef scan_text():\n file = open(filename, 'r')\n countwords = Counter(file.read().split())\n for item in countwords.items(): print (\"{}\\t{}\".format(*item))\n file.close();\n while True:\n print ('Would you like to write information to file?')\n answer = input()\n if answer in ['yes', 'y', 'yeah']:\n print ('Alright, writing to file')\n print ('Program will exit upon scan completion.')\n #a = file\n with open('wordscan.txt', 'w+') as wc:\n print(countwords, file=wc)\n break\n elif answer in ['no', 'n']:\n print ('Okay, exiting now..')\n sys.exit()\n break\n else:\n print ('Please enter a yes or no value')\n\n\nfilename = filepath()\nprint (filename)\n\n\n\nfile = scan_text()\n","sub_path":"WordScanner.py","file_name":"WordScanner.py","file_ext":"py","file_size_in_byte":1275,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"216614356","text":"#!/usr/bin/env python3\r\n\r\n#days until birthday program using date and datetime objects\r\n\r\nfrom datetime import date, datetime\r\n\r\ndef main():\r\n print(\"Birthday Countdown Program!\")\r\n while True:\r\n print()\r\n #get birthday from user\r\n birthday_input = input(\"Enter date of birth (MM/DD/YYYY): \")\r\n print()\r\n\r\n #parse input into a date object\r\n birthday = datetime.strptime(birthday_input, \"%m/%d/%Y\")\r\n\r\n #print as a formatted string\r\n print( \"Your birthday is \", birthday.strftime(\"%B %d, %Y\"))\r\n print()\r\n\r\n #take the month and day from the user and convert them into a date for this year's birthday\r\n today = date.today()\r\n birth_month = birthday.month\r\n birth_day = birthday.day\r\n birthday_date = date(today.year, birth_month, birth_day)\r\n\r\n #determine if thier birthday has passed, is coming up or is today\r\n if today > birthday_date:\r\n print(\"Your birthday has already passed this year!\")\r\n elif today < birthday_date:\r\n countdown = (birthday_date - today).days\r\n print(\"Only \", countdown, \" days until your birthday!\")\r\n elif today == birthday_date:\r\n print(\"Happy Birthday!\")\r\n \r\n #ask if they want do another countdown\r\n print()\r\n another = input(\"Would you like countdown the days until a different birthday? (y/n): \")\r\n print()\r\n if another.lower() !=\"y\" or another.upper() !=\"Y\": #make sure they can put in upper or lower case\r\n print(\"Thank you for using the Birthday Countdown!\")\r\n break\r\n \r\n\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n\r\n\r\n\r\n","sub_path":"birthday_countdown.py","file_name":"birthday_countdown.py","file_ext":"py","file_size_in_byte":1705,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"548082463","text":"\n\n#6__________Don't Pass Null.__________________________________________________________________________\nclass Cat:\n def __init__(self, name, color):\n self.name = name\n self.hairColor = color\n\ndef main():\n Sphynx = Cat(\"Sphynx\", None)\n FelisCatus = Cat(\"Felis Catus\", \"Bicolor\")\n print(childCatHairColor(Sphynx , FelisCatus))\n\ndef childCatHairColor(cat1, cat2):\n return (cat1.hairColor + cat2.hairColor)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n#5______________________Don't Return Null.______________________________________________________________\ndef demo5():\n emp1 = Employee(\"Employee 1\",18)\n emp2 = Employee(\"Employee 2\",25)\n emp3 = Employee(\"Employee 3\",30)\n\n company1 = Company(\"Company1\", [emp1,emp2,emp3])\n company2 = Company(\"Company2\", None)\n\n employees = getEmployees(company1)\n for employee in employees:\n print(employee.name) \n\nclass Employee:\n def __init__(self, name, age):\n self.name = name\n self.age = age\n \nclass Company:\n def __init__(self, name, employees):\n self.name = name\n self.employees = employees\n \ndef getEmployees(company):\n try:\n return list(company.employees)\n except:\n return []\n\n\n\n\n\n\n\n\n\n\n#4_____________Define the Normal Flow___________________\ndef demo4():\n box1 = [\"cake1\",\"cake2\",\"cake3\"]\n box2 = 10\n box3 = [\"cake1\",\"cake2\",\"cake3\",\"cake4\",\"cake5\"]\n sumCake = 0\n sumCake += getCakeCount(box1)\n print(f\"Sum cakes count after check box 1: {sumCake}\")\n sumCake += getCakeCount(box2)\n print(f\"Sum cakes count after check box 2: {sumCake}\")\n sumCake += getCakeCount(box3)\n print(f\"Sum cakes count after check box 3: {sumCake}\")\n\ndef getCakeCount(box):\n try:\n return len(box)\n except:\n return 0\n\n\n\n\n\n\n\n\n\n#2___________Extract Try/Catch blocks._________________________________________________________________________\nimport datetime\n\ndef updateFile(filePath, newContent):\n f = open(filePath, \"a\")\n f.write(f\"{newContent} \\n\")\n f.close()\n\ndef printFileContent(filePath):\n f = open(filePath, \"r\")\n content = f.read()\n f.close()\n print(content)\n\ndef demo2():\n existFilePath = \"demo/cleanCode.txt\"\n notExistFilePath = \"demo/myGirlFriendInfo.txt\"\n path = existFilePath\n\n newContent = datetime.datetime.now()\n try:\n printFileContent(path) # Print current file content.\n updateFile(path, newContent) # Update new content.\n printFileContent(path) # Print new file content.\n except Exception as e:\n print(f\"Error: {e}\")\n\n\n\n\n\n\n\n\n\n#1________________Use Exceptions Rather Than Return Codes____________________________________________________________________\nimport enum\n\ndef demo1():\n num1 = \"10\" \n num2 = \"It is string.\"\n num3 = \"0\"\n num4 = \"5\"\n # result = divisionUseReturnCode(num1, num3)\n result = divisionUseException(num1, num3)\n print(result)\n\nclass errorHandler(enum.Enum):\n DIVISION_BY_ZERO_ERROR = \"divideByZero\"\n NOT_NUMBER_ERROR = \"notNumber\"\n\ndef divisionUseReturnCode(number1, number2):\n if not number1.isnumeric():\n return errorHandler.NOT_NUMBER_ERROR.value\n if not number2.isnumeric():\n return errorHandler.NOT_NUMBER_ERROR.value\n if int(number2) is 0:\n return errorHandler.DIVISION_BY_ZERO_ERROR.value\n return int(number1) / int(number2) \n\ndef divisionUseException(number1, number2):\n try:\n return int(number1) / int(number2)\n except Exception as e:\n return e\n\nif __name__ == '__main__':\n main()\n print(\"Program ended!\")\n\n\n\n\n\n \n\n\n","sub_path":"cleanCode.py","file_name":"cleanCode.py","file_ext":"py","file_size_in_byte":3592,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"504328710","text":"#!/usr/local/bin/python\n\"\"\" Created by: Erik Goodman\n Section Leader: Katherine Fackrell\n Date: 29 APR, 2015\n ISTA 350 Hw10\n\n This program serves as an introduction to tree sort.\n\"\"\"\n\n\nclass Node:\n def __init__(self, datum):\n \"\"\" This method creates a new node.\n\n Args:\n datum -- (int) node value\n \"\"\"\n self.left=None\n self.right=None\n self.datum=datum\n self.entries=1\n\n def insert(self, datum):\n \"\"\" This method adds a new node to the tree. If the node exists,\n it increments 'entries'. Else it recursively moves down the\n appropriate side until the node is either placed or entries is incremented\n\n Args:\n datum -- (int) node value\n \"\"\"\n if self.datum == datum:\n self.entries += 1\n elif self.datum > datum:\n if self.left is None:\n self.left=Node(datum)\n else:\n self.left.insert(datum)\n elif self.datum < datum:\n if self.right is None:\n self.right=Node(datum)\n else:\n self.right.insert(datum)\n\n def sort(self, lst):\n \"\"\"\n This is method takes a list as an argument and alters it in\n place, in the course of an inorder traversal of the tree. If there\n is a left subtree, recursively calls sort on it. Append datum to\n the list 'entries' times. Take care of the right subtree.\n\n Args:\n lst -- (list)\n \"\"\"\n if self.left:\n self.left.sort(lst)\n for i in range(self.entries):\n lst.append(self.datum)\n if self.right:\n self.right.sort(lst)\n\n ##* provided helper methods\n def __repr__(self):\n ''' reverse inorder traversal '''\n result = (repr(self.datum) + ' ') * self.entries\n if self.right:\n result = repr(self.right) + result\n if self.left:\n result += repr(self.left)\n return result\n\n def fill_list_2D(self, lsts, level, column):\n '''\n This is a helper method for filling a pre-initalized\n 2-dimensional list of lists of strings with the elements of a tree.\n '''\n lsts[level][column] = (str(self.datum) + '(' + str(self.entries)+\\\n ')').zfill(6)\n if self.left:\n self.left.fill_list_2D(lsts, level + 1, 2 * column)\n if self.right:\n self.right.fill_list_2D(lsts, level + 1, 2 * column + 1)\n\n def height(self):\n # does not adjust for empty nodes\n if self.left and self.right:\n return 1 + max(self.left.height(), self.right.height())\n if self.left:\n return 1 + self.left.height()\n if self.right:\n return 1 + self.right.height()\n return 0\n\nclass BST:\n def __init__(self, lst=None):\n \"\"\" The initializer takes one list argument with a default\n argument of None. The tree contains one field, a Node called\n root, initialized to None. If the list argument contains data, it\n is inserted into the tree in the order in which it occurs in the list.\n\n Args:\n lst -- (list)\n \"\"\"\n self.root=None\n if lst:\n for i in lst:\n self.insert(i)\n\n def insert(self, item):\n \"\"\" This method takes one argument, the item to be inserted. If\n root exists, call Node's insert method on it. Otherwise, assign\n a new Node containing the item to root.\n\n Args:\n item -- (int) value to be inserted\n \"\"\"\n if self.root:\n self.root.insert(item)\n else:\n self.root=Node(item)\n\n def sort(self):\n \"\"\" Initialize a list. If root exists, call Node's sort method\n on it. Return the list.\n\n returns:\n a sorted list\n \"\"\"\n lst = []\n if self.root:\n self.root.sort(lst)\n return lst\n\n ##* provided helper methods\n def __repr__(self):\n ''' Works if all entries < 10, all data are ints and < 1000 and > -99. '''\n if not self.root:\n return 'Empty tree'\n h = self.height()\n w = 2**h # max possible nodes in the highest level\n tree_list = []\n for i in range(h + 1):\n tree_list.append(['******'] * 2**i)\n #tree_list = ['******'] * (2 * w - 1) # for 1D list: max nodes = 2**(h + 1) - 1\n self.root.fill_list_2D(tree_list, 0, 0)\n return BST._repr(tree_list, w * 6 + (w - 1) * 2, h) # width of the longest line\n\n @staticmethod\n def _repr(tree_list, w, h):\n for i in range(len(tree_list)):\n num_items = 2**i # elements in this level's inner list\n leading = 2**(h - i + 2) - 4\n if i == 0:\n tree_list[0] = ' ' * leading + tree_list[0][0]\n elif i < len(tree_list) - 1:\n spacer = ' ' * (round((w - num_items * 6 - leading * 2) // (num_items - 1)))\n tree_list[i] = ' ' * leading + spacer.join(tree_list[i])\n else:\n tree_list[-1] = ' '.join(tree_list[-1])\n return '\\n'.join(tree_list)\n\n def height(self):\n if self.root:\n return self.root.height()\n return 0","sub_path":"HW/10/hw10.py","file_name":"hw10.py","file_ext":"py","file_size_in_byte":5298,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"641895542","text":"import xml.etree.ElementTree as ET\nfrom os import getcwd\nimport os\nfrom PIL import Image\n\nclasses = []\nwith open(\"../model_data/bkseeing_classes.txt\") as f:\n classes = list(line.replace('\\n', '') for line in f)\nprint (classes)\n\n\ndef convert_annotation(image_id, list_file):\n # try:\n # print(\"Trying\")\n # Image.open(\"BKSeeing/data-ver-1.2/\"+image_id+\".jpg\")\n # in_file = open(\"BKSeeing/data-ver-1.2/%s.xml\"%(image_id))\n # except:\n # return\n in_file = open(\"../../../data/BKSeeing/data-ver-1.2/%s.xml\"%(image_id))\n # print(\"Success\")\n tree=ET.parse(in_file)\n root = tree.getroot()\n\n for obj in root.iter('size'):\n w = int(obj.find('width').text)\n h = int(obj.find('height').text)\n\n for obj in root.iter('object'):\n difficult = obj.find('difficult').text\n cls = obj.find('name').text\n if cls not in classes or int(difficult)==1:\n continue\n cls_id = classes.index(cls)\n xmlbox = obj.find('bndbox')\n b = (int(xmlbox.find('xmin').text) + 1, int(xmlbox.find('ymin').text) + 1, int(xmlbox.find('xmax').text) + 1, int(xmlbox.find('ymax').text) + 1)\n # if (b[0] not in range(1,w) or b[2] not in range(1,w)):\n # continue\n # if (b[1] not in range(1,h) or b[3] not in range(1,h)):\n # continue\n for i in range(4):\n if b[i] < 1:\n b[i] = 1\n elif (i == 0 or i == 2) and (b[i] > w):\n b[i] = w\n elif (i == 1 or i == 3) and (b[i] > h):\n b[i] = h\n if b[0] > b[2]:\n temp = b[2]\n b[2] = b[0]\n b[0] = temp\n if b[1] > b[3]:\n temp = b[1]\n b[1] = b[3]\n b[3] = temp\n list_file.write(\"../../../data/BKSeeing/data-ver-1.9/%s.jpg\"%(image_id) + \" \" + \",\".join([str(a) for a in b]) + ',' + str(cls_id) + '\\n')\n\nlist_file = open(\"../train.txt\", \"w\")\nimages_list = open(\"../ID.csv\", \"r\")\n\nprint(\"Checkpoint_1\")\n\nimages_id = list(line.replace('\\n','') for line in images_list)\n\nprint(\"Checkpoint_2\")\n\nfor image_id in images_id:\n print(\"/%s.jpg\"%(image_id))\n convert_annotation(image_id, list_file)\nlist_file.close()\n","sub_path":"Model/ext_tool/convert_annotation.py","file_name":"convert_annotation.py","file_ext":"py","file_size_in_byte":2216,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"146075375","text":"import os\n\nfilename = \"hello word.txt\" # 檔案名稱\n\"\"\"\nif os.path.exists(filename): # 確認檔案是否存在\n os.remove(filename) # 檔案存在，就移除\nelse:\n# 檔案不存在, 就用with open()新增檔案 w代表寫入 as f代表檔案的別名\n with open(filename, \"w\", encoding='UTF-8') as f:\n f.write(\"hello word!!!!\")\n\nwith open(filename, 'r', encoding='UTF-8') as ha:\n for line in ha:\n print(line.strip())\n\"\"\"\n# 如檔案中有中文，讀取時也要加註encoding='UTF-8'\nwith open(filename, \"a\") as f:\n f.write(\"\\nhello word!!!!\")\n with open(filename, \"w\") as f:\n f.write(\"hello word!!!!\")\n\n\n\"\"\"\nr 開啟檔案準備讀取，檔案流一開始會在檔案的開頭。\nr+ 開啟檔案準備讀取與寫入，檔案流一開始會在檔案的開頭。\nw 複寫既有的檔案準備讀取與寫入，如果沒有檔案會出錯，檔案流一開始會在檔案的開頭。\nw+ 複寫既有的檔案準備寫入，有檔案就複寫，沒有檔案就開啟一個新的檔案，檔案流一開始會在檔案的開頭。\na 開啟檔案準備讀取，檔案不存在就會開啟新檔，檔案流一開始會在檔案的結束地方，後續寫入該文件將永遠\n 停留在文件當時的結束的地方，即使中間經過類似 fseek的任何動作。\na+ 開啟檔案準備讀取與寫入，有檔案就複寫，沒有檔案就開啟一個新的檔案，檔案流一開始會在檔案的結束地\n 方，後續寫入該文件將永遠停留在文件當時的結束的地方，即使中間經過類似 fseek的任何動作。\n\"\"\"\n","sub_path":"test_file/index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":1600,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"509566264","text":"__author__ = 'jjzhu'\nimport jieba\nimport jieba.posseg as pseg\nfrom sklearn.linear_model import Perceptron, SGDClassifier\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.feature_extraction.text import TfidfVectorizer, HashingVectorizer, CountVectorizer\nfrom sklearn.cross_validation import cross_val_predict, ShuffleSplit, cross_val_score\nfrom sklearn import metrics\nfrom sklearn.svm import LinearSVC\nfrom sklearn.naive_bayes import BernoulliNB\nfrom sklearn.naive_bayes import MultinomialNB\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.linear_model import PassiveAggressiveClassifier\nimport logging\nimport logging.config\nfrom sklearn.neighbors import KNeighborsClassifier, NearestCentroid\nfrom sklearn.linear_model import RidgeClassifier\ndef logger_conf():\n import platform\n import os\n if platform.system() is 'Windows':\n logging.config.fileConfig(os.path.abspath('./')+'\\\\conf\\\\logging.conf')\n elif platform.system() is 'Linux':\n logging.config.fileConfig(os.path.abspath('./')+'/conf/logging.conf')\n logger = logging.getLogger('simpleLogger')\n return logger\n\n\nclass SougouNBC():\n def __init__(self, save_file_name='./data/sougou/result/sougou_result_clf_no_seg.csv'):\n # self.seg_need = ['n', 'v', 'e', 'j', 'l']\n self.my_logger = logger_conf()\n self.my_logger.info('init SougouNBC')\n self.train_file_name = './data/user_tag_query.2W.TRAIN'\n self.test_file_name = './data/user_tag_query.2W.TEST'\n self.stop_word_file_name = './extra_dict/stop_words_ch.txt'\n self.stop_word_file_name = './extra_dict/stop_words_ch.txt'\n self.age_model_save_path = './model/age_ber_no_seg.model'\n self.gender_model_save_path = './model/gender_ber_no_seg.model'\n self.edu_model_save_path = './model/edu_ber_no_seg.model'\n self.save_file_name = save_file_name\n self.my_logger.info('start get train data from %s' % self.train_file_name)\n self.all_words = set()\n self.stop_words = []\n self.load_stop_word()\n self.age_input, self.gender_input, self.edu_input = self.get_data(self.train_file_name)\n\n self.age_mul_nbc = Pipeline([('vect', TfidfVectorizer()), ('clf', MultinomialNB(alpha=1.0)), ])\n self.gender_mul_nbc = Pipeline([('vect', TfidfVectorizer()), ('clf', MultinomialNB(alpha=1.0)), ])\n self.edu_mul_nbc = Pipeline([('vect', TfidfVectorizer()), ('clf', MultinomialNB(alpha=1.0)), ])\n\n self.age_ber_nbc = Pipeline([('vect', TfidfVectorizer()), ('clf', BernoulliNB(alpha=.01)), ])\n self.gender_ber_nbc = Pipeline([('vect', TfidfVectorizer()), ('clf', BernoulliNB(alpha=.01)), ])\n self.edu_ber_nbc = Pipeline([('vect', TfidfVectorizer()), ('clf', BernoulliNB(alpha=.01)), ])\n\n self.age_gs_nbc = Pipeline([('vect', TfidfVectorizer()), ('clf', GaussianNB()), ])\n self.gender_gs_nbc = Pipeline([('vect', TfidfVectorizer()), ('clf', GaussianNB()), ])\n self.edu_gs_nbc = Pipeline([('vect', TfidfVectorizer()), ('clf', GaussianNB()), ])\n\n def load_stop_word(self):\n with open(self.stop_word_file_name, 'r', encoding='utf-8') as s_w_file:\n for line in s_w_file:\n self.stop_words.append(line.strip())\n\n def get_data(self, train_f_n):\n\n age_input = []\n gender_input = []\n edu_input = []\n temp_list = []\n rewrite_file_name = './data/sougou/rewrite4.csv'\n unused_words_file_name = './data/sougou/unused_words4.csv'\n rewrite_file = open(rewrite_file_name, 'w', encoding='utf-8')\n unused_file = open(unused_words_file_name, 'w', encoding='utf-8')\n rewrite_context = []\n unused_context = []\n with open(train_f_n, mode='r', encoding='utf-8') as train_file:\n for line in train_file:\n line = line.strip()\n split_r = line.split('\\t')\n pesg_words = jieba.cut(','.join(split_r[4:]), HMM=True)\n for pesg_word in pesg_words:\n if pesg_word not in self.stop_words \\\n and len(pesg_word) > 1:\n temp_list.append(str(pesg_word))\n else:\n unused_context.append('%s \\n' % pesg_word)\n all_word = ' '.join(temp_list)\n if split_r[2] != '0':\n gender_input.append((all_word, split_r[2]))\n no_sex = False\n if split_r[1] != '0' and not no_sex:\n age_input.append((all_word, split_r[2]+'_'+split_r[1]))\n\n if split_r[3] != '0' and not no_sex:\n edu_input.append((all_word, split_r[2]+'_'+split_r[3]))\n rewrite_context.append(' '.join(temp_list) + '\\n')\n temp_list.clear()\n self.my_logger.info('rewrite word cut result to file: %s' % rewrite_file_name)\n rewrite_file.writelines(rewrite_context)\n self.my_logger.info('rewrite complete')\n self.my_logger.info('rewrite unused words to file: %s' % unused_words_file_name)\n unused_file.writelines(unused_context)\n self.my_logger.info('rewrite complete')\n return age_input, gender_input, edu_input\n\n def get_train_data(self, data_input):\n train_data_ = [elem[0] for elem in data_input]\n train_target_ = [elem[1] for elem in data_input]\n return train_data_, train_target_\n\n def train(self):\n self.my_logger.info('training age classify model')\n age_train_data, age_train_target = self.get_train_data(self.age_input)\n self.age_ber_nbc.fit(age_train_data, age_train_target)\n self.my_logger.info('train completed')\n self.my_logger.info('training gender classify model')\n gender_train_data, gender_train_target = self.get_train_data(self.gender_input)\n self.gender_ber_nbc.fit(gender_train_data, gender_train_target)\n self.my_logger.info('train completed')\n self.my_logger.info('training edu classify model')\n edu_train_data, edu_train_target = self.get_train_data(self.edu_input)\n self.edu_ber_nbc.fit(edu_train_data, edu_train_target)\n self.my_logger.info('model train completed')\n\n def classify(self):\n self.my_logger.info('start classify')\n pre_data = []\n temp_list = []\n result = []\n\n with open(self.test_file_name, mode='r', encoding='utf-8') as test_file:\n for line in test_file:\n split_r = line.strip().split('\\t')\n words = jieba.cut(','.join(split_r[1:]), HMM=True)\n for w in words:\n if w not in self.stop_words: # and len(w) > 1\n temp_list.append(str(w))\n if len(temp_list) > 0:\n pre_data.append((split_r[0], ' '.join(temp_list)))\n else:\n result.append('%s %s %s %s\\n' % (str(split_r[0]), '0', '0', '0'))\n self.my_logger.warn('%s %s %s %s\\n\\t%s' % (str(split_r[0]), '0', '0', '0', str(words)))\n temp_list.clear()\n user_ids = [elem[0] for elem in pre_data]\n input_data = [elem[1] for elem in pre_data]\n age_predict = self.age_ber_nbc.predict(input_data)\n gender_predict = self.gender_ber_nbc.predict(input_data)\n edu_predict = self.edu_ber_nbc.predict(input_data)\n self.my_logger.info('classify complete')\n self.my_logger.info('start save predict result')\n\n result_file = open(self.save_file_name, 'w', encoding='utf-8')\n for id_, age, gender, edu in zip(user_ids, age_predict, gender_predict, edu_predict):\n result.append('%s %s %s %s\\n' % (str(id_), str(age), str(gender), str(edu)))\n if len(result) > 1000:\n result_file.writelines(result)\n result_file.flush()\n self.my_logger.info('write result, total %d' % len(result))\n result.clear()\n if len(result) != 0:\n result_file.writelines(result)\n result_file.flush()\n self.my_logger.info('write result, total %d' % len(result))\n result.clear()\n result_file.close()\n self.my_logger.info('predict result saved ')\n\n def validation(self):\n self.my_logger.info('start validation')\n self.my_logger.info('age validation')\n result_hyp_file = open('./data/sougou/result/result_hyp.txt', 'a', encoding='utf-8')\n age_models = (('Perceptron', Pipeline([('vect', TfidfVectorizer()), ('clf', Perceptron(n_iter=50))])),\n ('SGDClassifier', Pipeline([('vect', TfidfVectorizer()),\n ('clf', SGDClassifier(alpha=.0001, n_iter=50, penalty=\"elasticnet\"))])),\n ('NearestCentroid', Pipeline([('vect', TfidfVectorizer()), ('clf', NearestCentroid())])),\n ('LinerSVC', Pipeline([('vect', TfidfVectorizer()),\n ('feature_selection', LinearSVC(penalty=\"l1\", dual=False, tol=1e-3)),\n ('classification', LinearSVC())])),\n ('KNeighborsClassifier', Pipeline([('vect', TfidfVectorizer()),\n ('clf', KNeighborsClassifier(n_neighbors=10)), ])),\n ('RidgeClassifier', Pipeline([('vect', TfidfVectorizer()),\n ('clf', RidgeClassifier(tol=1e-2, solver=\"sag\")), ])),\n ('PassiveAggressiveClassifier', Pipeline([('vect', TfidfVectorizer()),\n ('clf', PassiveAggressiveClassifier(n_iter=50)), ])))\n gender_models = (('Perceptron', Pipeline([('vect', TfidfVectorizer()), ('clf', Perceptron(n_iter=50))])),\n ('SGDClassifier', Pipeline([('vect', TfidfVectorizer()),\n ('clf', SGDClassifier(alpha=.0001, n_iter=50, penalty=\"elasticnet\"))])),\n ('NearestCentroid', Pipeline([('vect', TfidfVectorizer()), ('clf', NearestCentroid())])),\n ('LinerSVC', Pipeline([('vect', TfidfVectorizer()),\n ('feature_selection', LinearSVC(penalty=\"l1\", dual=False, tol=1e-3)),\n ('classification', LinearSVC())])),\n ('KNeighborsClassifier', Pipeline([('vect', TfidfVectorizer()),\n ('clf', KNeighborsClassifier(n_neighbors=10)), ])),\n ('RidgeClassifier', Pipeline([('vect', TfidfVectorizer()),\n ('clf', RidgeClassifier(tol=1e-2, solver=\"sag\")), ])),\n ('PassiveAggressiveClassifier', Pipeline([('vect', TfidfVectorizer()),\n ('clf', PassiveAggressiveClassifier(n_iter=50)), ])))\n edu_models = (('Perceptron', Pipeline([('vect', TfidfVectorizer()), ('clf', Perceptron(n_iter=50))])),\n ('SGDClassifier', Pipeline([('vect', TfidfVectorizer()),\n ('clf', SGDClassifier(alpha=.0001, n_iter=50, penalty=\"elasticnet\"))])),\n ('NearestCentroid', Pipeline([('vect', TfidfVectorizer()), ('clf', NearestCentroid())])),\n ('LinerSVC', Pipeline([('vect', TfidfVectorizer()),\n ('feature_selection', LinearSVC(penalty=\"l1\", dual=False, tol=1e-3)),\n ('classification', LinearSVC())])),\n ('KNeighborsClassifier', Pipeline([('vect', TfidfVectorizer()),\n ('clf', KNeighborsClassifier(n_neighbors=10)), ])),\n ('RidgeClassifier', Pipeline([('vect', TfidfVectorizer()),\n ('clf', RidgeClassifier(tol=1e-2, solver=\"sag\")), ])),\n ('PassiveAggressiveClassifier', Pipeline([('vect', TfidfVectorizer()),\n ('clf', PassiveAggressiveClassifier(n_iter=50)), ])))\n # age_temp_model = Pipeline([('vect', TfidfVectorizer()), ('clf', KNeighborsClassifier(n_neighbors=10)), ])\n for model_name, model in age_models:\n age_train_data, age_train_target = self.get_train_data(self.age_input)\n try:\n age_result = cross_val_score(model, age_train_data, age_train_target, cv=5)\n self.my_logger.info('use:%s age:%s' % (model_name, str(age_result)))\n result_hyp_file.write('use:%s age:%s\\n' % (model_name, str(age_result)))\n except ValueError:\n self.my_logger.error(model_name)\n result_hyp_file.flush()\n self.my_logger.info('age validation')\n\n for model_name, model in gender_models:\n # gender_temp_model = Pipeline([('vect', TfidfVectorizer()), ('clf', KNeighborsClassifier(n_neighbors=10)), ])\n gender_train_data, gender_train_target = self.get_train_data(self.gender_input)\n gender_result = cross_val_score(model, gender_train_data, gender_train_target, cv=5)\n self.my_logger.info('use:%s gender:%s' % (model_name, str(gender_result)))\n result_hyp_file.write('use:%s gender:%s\\n' % (model_name, str(gender_result)))\n self.my_logger.info('gender validation')\n result_hyp_file.flush()\n for model_name, model in edu_models:\n # edu_temp_model = Pipeline([('vect', TfidfVectorizer()), ('clf', KNeighborsClassifier(n_neighbors=10)), ])\n edu_train_data, edu_train_target = self.get_train_data(self.edu_input)\n edu_result = cross_val_score(model, edu_train_data, edu_train_target, cv=5)\n self.my_logger.info('use:%s edu:%s' % (model_name, str(edu_result)))\n result_hyp_file.write('use:%s edu:%s\\n' % (model_name, str(edu_result)))\n result_hyp_file.flush()\n result_hyp_file.close()\n '''\n ('KNeighborsClassifier', Pipeline([('vect', TfidfVectorizer()),\n ('clf', KNeighborsClassifier(n_neighbors=10)), ])),\n ('RidgeClassifier', Pipeline([('vect', TfidfVectorizer()),\n ('clf', RidgeClassifier(tol=1e-2, solver=\"sag\")), ])),\n ('PassiveAggressiveClassifier', Pipeline([('vect', TfidfVectorizer()),\n ('clf', PassiveAggressiveClassifier(n_iter=50)), ])\n import numpy as np\n self.my_logger.info('start cross validation')\n clf_name = ['MultinomialNB', 'BernoulliNB']\n cv = ShuffleSplit(n=len(age_train_data), n_iter=10, test_size=0.3, random_state=0)\n self.my_logger.info('validation age models')\n # age_models = {str(1/10**i): Pipeline([('vect', TfidfVectorizer()), ('clf', BernoulliNB(alpha=1/10**i)), ])\n # for i in range(5)}\n # for alp, age_model in age_models.items():\n # age_result = cross_val_score(age_model, age_train_data, age_train_target, cv=5)\n # self.my_logger.info('use:%s alpha:%s age:%s' % (clf_name[1], alp, str(age_result)))\n self.my_logger.info('validation gender models')\n gender_models = {str(1/10**i): Pipeline([('vect', TfidfVectorizer()), ('clf', BernoulliNB(alpha=1/10**i)), ])\n for i in range(5)}\n for alp, gender_model in gender_models.items():\n gender_result = cross_val_score(gender_model, gender_train_data, gender_train_target, cv=5)\n self.my_logger.info('use:%s alpha:%s gender:%s' % (clf_name[1], alp, str(gender_result)))\n self.my_logger.info('validation edu models')\n edu_models = {str(1/10**i): Pipeline([('vect', TfidfVectorizer()), ('clf', BernoulliNB(alpha=1/10**i)), ])\n for i in range(5)}\n for alp, edu_model in edu_models.items():\n age_result = cross_val_score(edu_model, edu_train_data, edu_train_target, cv=5)\n self.my_logger.info('use:%s alpha:%s age:%s' % (clf_name[1], alp, str(age_result)))\n # mean_result = []\n # for a, g, e in zip(age_result, gender_result, edu_result):\n # mean_result.append(np.mean([a, g, e]))\n # self.my_logger.info('mean result: %s' % str(mean_result))\n '''\n self.my_logger.info('end')\n\n def test_gender(self):\n gender_train_data, gender_train_target = self.get_train_data(self.gender_input)\n gender_result = cross_val_score(self.gender_gs_nbc, gender_train_data, gender_train_target, cv=5)\n self.my_logger.info(str(gender_result))\n\n def model_save(self):\n from sklearn.externals import joblib\n self.my_logger.info('save age model, target path： %s' % self.age_model_save_path)\n joblib.dump(self.age_ber_nbc, self.age_model_save_path)\n self.my_logger.info('save gender model, target path： %s' % self.gender_model_save_path)\n joblib.dump(self.gender_ber_nbc, self.gender_model_save_path)\n self.my_logger.info('save edu model, target path： %s' % self.edu_model_save_path)\n joblib.dump(self.edu_ber_nbc, self.edu_model_save_path)\n\n def start(self):\n self.train()\n self.model_save()\n # self.classify()\n\nif __name__ == '__main__':\n sougou = SougouNBC()\n sougou.validation()\n # sougou.start()","sub_path":"sougou/sougou_clf_hyp.py","file_name":"sougou_clf_hyp.py","file_ext":"py","file_size_in_byte":17702,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"582536790","text":"import json\r\n\r\nimport requests\r\nfrom selenium import webdriver\r\nfrom selenium.common.exceptions import WebDriverException\r\nfrom selenium.webdriver.common.by import By\r\nfrom selenium.webdriver.support import expected_conditions as EC\r\nfrom selenium.webdriver.support.ui import WebDriverWait\r\nfrom store import MongoStore\r\nfrom models import Session\r\nfrom utils import *\r\n\r\n\r\nclass BeiJingWcdmaSpider:\r\n \"\"\"\r\n 北京联通爬虫\r\n \"\"\"\r\n\r\n def __init__(self, **kwargs):\r\n self.name = kwargs.get('name', '[北京联通爬虫]')\r\n self.mobile = kwargs.get('mobile', None)\r\n self.password = kwargs.get('password', None)\r\n self.province = kwargs.get('province', None)\r\n self.vendor = kwargs.get('vendor', None)\r\n self.sms_code = kwargs.get('sms_code', None)\r\n self.session_id = kwargs.get('session_id', None)\r\n self.session = requests.session()\r\n self._chrome_options = webdriver.ChromeOptions()\r\n self._chrome_options.add_argument('--disable-gpu')\r\n\r\n def pre_login(self):\r\n \"\"\"\r\n 获取登陆页面, 模拟发送验证码后保存cookie\r\n :return: 需要的cookie\r\n \"\"\"\r\n login_page_url = 'https://uac.10010.com/portal/mallLogin.jsp?redirectURL=http://www.10010.com'\r\n browser = webdriver.Chrome(chrome_options=self._chrome_options)\r\n try:\r\n browser.get(login_page_url)\r\n wait = WebDriverWait(browser, 300)\r\n wait.until(EC.frame_to_be_available_and_switch_to_it((By.CLASS_NAME, 'login_iframe')))\r\n browser.find_element_by_id(\"userName\").send_keys(self.mobile)\r\n browser.find_element_by_id(\"userPwd\").click()\r\n browser.find_element_by_id(\"userPwd\").send_keys(self.password)\r\n wait.until(EC.element_to_be_clickable((By.ID, 'randomCKCode')))\r\n browser.find_element_by_id('randomCKCode').click()\r\n return True, {'captcha': None, 'sms_code': True}\r\n except WebDriverException as e:\r\n log.err(e)\r\n return False, ''\r\n finally:\r\n browser.close()\r\n\r\n def login(self):\r\n \"\"\"\r\n 登陆\r\n :return 返回是否登陆成功 & 登陆后需要的额外参数\r\n \"\"\"\r\n login_page_url = 'https://uac.10010.com/portal/mallLogin.jsp?redirectURL=http://www.10010.com'\r\n browser = webdriver.Chrome(chrome_options=self._chrome_options)\r\n try:\r\n browser.get(login_page_url)\r\n wait = WebDriverWait(browser, 300)\r\n wait.until(EC.frame_to_be_available_and_switch_to_it((By.CLASS_NAME, 'login_iframe')))\r\n browser.find_element_by_id(\"userName\").send_keys(self.mobile)\r\n browser.find_element_by_id(\"userPwd\").click()\r\n browser.find_element_by_id(\"userPwd\").send_keys(self.password)\r\n wait.until(EC.visibility_of_element_located((By.ID, 'userCK'))).send_keys(self.sms_code)\r\n wait.until(EC.element_to_be_clickable((By.ID, 'login1')))\r\n browser.find_element_by_id('login1').click()\r\n wait.until(lambda x: x.current_url != login_page_url)\r\n tmp_cookies = {}\r\n for cookie in browser.get_cookies():\r\n tmp_cookies[cookie['name']] = cookie['value']\r\n\r\n log.info('cookies size: {}, cookies: {}'.format(len(tmp_cookies), tmp_cookies))\r\n\r\n # 调用checklogin获取完整cookies\r\n check_login_url = 'http://iservice.10010.com/e3/static/check/checklogin/'\r\n\r\n check_login_params = {'_': str(current_timestamp())}\r\n\r\n check_login_headers = {'X-Requested-With': 'XMLHttpRequest'}\r\n\r\n check_login_resp = self.session.post(check_login_url, headers=check_login_headers,\r\n params=check_login_params, cookies=tmp_cookies)\r\n\r\n log.info(check_login_resp.text)\r\n\r\n result = json.loads(check_login_resp.text)\r\n\r\n tmp_cookies.update(check_login_resp.cookies.get_dict())\r\n\r\n if bool(result['isLogin']):\r\n # 保存会话信息，返回session_id，一小时过期\r\n session = Session(self.province, self.vendor, tmp_cookies)\r\n return True, set_session(session).session_id\r\n else:\r\n return False, '登陆失败，请检查网络'\r\n except WebDriverException as e:\r\n log.err(e)\r\n return False, None\r\n finally:\r\n browser.close()\r\n\r\n def check_billing_histories(self):\r\n \"\"\"\r\n 检测是否需要验证\r\n :return:\r\n \"\"\"\r\n return True, {'auto': True, 'captcha': None, 'sms_code': None}\r\n\r\n def fetch_billing_histories(self, session):\r\n \"\"\"\r\n 抓取历史账单\r\n :return:\r\n \"\"\"\r\n\r\n if session is None or session.get('cookies', None) is None:\r\n raise ValueError('会话已经过期，请重新授权')\r\n\r\n cookies = session.get('cookies', None)\r\n\r\n query_billing_history_url = 'http://iservice.10010.com/e3/static/query/queryHistoryBill'\r\n\r\n headers = {\r\n 'Accept': 'application/json, text/javascript, */*; q=0.01',\r\n 'Accept-Encoding': 'gzip, deflate',\r\n 'Accept-Language': 'zh-CN,zh;q=0.9,en-US;q=0.8,en;q=0.7,ja;q=0.6,zh-TW;q=0.5',\r\n 'Cache-Control': 'no-cache',\r\n 'Pragma': 'nocache',\r\n 'X-Requested-With': 'XMLHttpRequest',\r\n 'Host': 'iservice.10010.com',\r\n 'Content-Type': 'application/x-www-form-urlencoded; charset=UTF-8',\r\n 'Connection': 'keep-alive',\r\n 'Referer': 'http://iservice.10010.com/e4/query/basic/history_list.html?menuId=000100020001',\r\n 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_6) '\r\n 'AppleWebKit/537.36 (KHTML, like Gecko) Chrome/62.0.3202.94 Safari/537.36'\r\n }\r\n\r\n params = {\r\n 'accessURL': 'http://iservice.10010.com/e4/skip.html?menuCode=000100020001&menuid=000100020001',\r\n 'menuCode': '000100020001',\r\n '_': str(current_timestamp())\r\n }\r\n\r\n fn = lambda bill_date: self.session.post(query_billing_history_url,\r\n headers=headers,\r\n params=params,\r\n data={\r\n 'querytype': '0001',\r\n 'querycode': '0001',\r\n 'flag': '2',\r\n 'billdate': bill_date\r\n },\r\n cookies=cookies).text\r\n # 抓取最近6个月账单\r\n raw_bills = {'raw_bills': [fn(bill_date) for bill_date in latest_six_month()]}\r\n\r\n MongoStore.collection('raw_bills').insert(raw_bills)\r\n\r\n return raw_bills\r\n\r\n def check_call_records(self, session):\r\n \"\"\"\r\n 抓取通话记录，检验是否需要验证码\r\n :param session_id:\r\n :return:\r\n \"\"\"\r\n if session is None or session.get('cookies', None) is None:\r\n raise ValueError('会话已经过期，请重新授权')\r\n\r\n cookies = session.get('cookies', None)\r\n\r\n check_call_record_url = 'http://iservice.10010.com/e3/static/query/sendRandomCode'\r\n\r\n params = {\r\n '_': current_timestamp(),\r\n 'accessURL': 'http://iservice.10010.com/e4/query/bill/call_dan-iframe.html?'\r\n 'menuCode=000100030001&menuid=000100030001'\r\n }\r\n\r\n data = {'menuId': '000100030001'}\r\n\r\n # {\"issuccess\":true,\"sendcode\":true}\r\n check_call_record_resp = self.session.post(check_call_record_url, params=params, cookies=cookies, data=data)\r\n\r\n result = json.loads(check_call_record_resp.text)\r\n\r\n return bool(result['issuccess']), {'sms_code': result['sendcode'], 'captcha': None}\r\n\r\n def fetch_call_records(self, session):\r\n \"\"\"\r\n 抓取通话记录\r\n :return:\r\n \"\"\"\r\n if session is None or session.get('cookies', None) is None:\r\n raise ValueError('会话已经过期，请重新授权')\r\n\r\n cookies = session.get('cookies', None)\r\n\r\n verification_url = 'http://iservice.10010.com/e3/static/query/verificationSubmit'\r\n verification_params = {\r\n '_': str(current_timestamp()),\r\n 'accessURL': 'http://iservice.10010.com/e4/query/bill/call_dan-iframe.html?'\r\n 'menuCode=000100030001&menuid=000100030001'\r\n }\r\n verification_data = {\r\n 'inputcode': self.sms_code,\r\n 'menuId': '000100030001'\r\n }\r\n\r\n verification_resp = self.session.post(verification_url,\r\n params=verification_params,\r\n cookies=cookies,\r\n data=verification_data)\r\n\r\n verification_result = json.loads(verification_resp.text)\r\n\r\n log.info('通话记录验证成功? {}'.format(verification_result['flag'] == '00'))\r\n\r\n call_record_url = 'http://iservice.10010.com/e3/static/query/callDetail'\r\n\r\n call_record_params = {\r\n '_': str(current_timestamp()),\r\n 'menuid': '000100030001',\r\n 'accessURL': 'http://iservice.10010.com/e4/query/bill/call_dan-iframe.html?'\r\n 'menuCode=000100030001&menuid=000100030001'\r\n }\r\n\r\n fn = lambda start_date, end_date: self.session.post(call_record_url,\r\n params=call_record_params,\r\n cookies=cookies,\r\n data={\r\n 'pageNo': 1,\r\n 'pageSize': 100,\r\n 'beginDate': start_date,\r\n 'endDate': end_date\r\n }).json()\r\n\r\n raw_call_records = {\r\n 'raw_call_records': [fn(start_date, end_date) for start_date, end_date in latest_six_month_time_pair()]\r\n }\r\n\r\n return raw_call_records\r\n\r\n def check_message_records(self, session):\r\n \"\"\"\r\n 抓取短信记录检验，模拟发送短信验证码\r\n :return:\r\n \"\"\"\r\n if session is None or session.get('cookies', None) is None:\r\n raise ValueError('会话已经过期，请重新授权')\r\n\r\n cookies = session.get('cookies', None)\r\n\r\n check_msg_record_url = 'http://iservice.10010.com/e3/static/query/sendRandomCode'\r\n\r\n params = {\r\n '_': current_timestamp(),\r\n 'menuid': '000100030002',\r\n 'accessURL': 'http://iservice.10010.com/e4/query/calls/call_sms-iframe.html?'\r\n 'menuCode=000100030002'\r\n }\r\n\r\n data = {'menuId': '000100030002'}\r\n\r\n # {\"issuccess\":true,\"sendcode\":true}\r\n check_msg_record_resp = self.session.post(check_msg_record_url, params=params, cookies=cookies, data=data)\r\n\r\n result = json.loads(check_msg_record_resp.text)\r\n\r\n return bool(result['issuccess']), {'sms_code': result['sendcode'], 'captcha': None}\r\n\r\n def fetch_message_records(self, session):\r\n \"\"\"\r\n 抓取短信记录，提交短信验证码验证\r\n :return:\r\n \"\"\"\r\n if session is None or session.get('cookies', None) is None:\r\n raise ValueError('会话已经过期，请重新授权')\r\n\r\n cookies = session.get('cookies', None)\r\n\r\n verification_url = 'http://iservice.10010.com/e3/static/query/verificationSubmit'\r\n\r\n verification_params = {\r\n '_': str(current_timestamp()),\r\n 'menuid': '000100030002',\r\n 'accessURL': 'http://iservice.10010.com/e4/query/calls/call_sms-iframe.html?menuCode=000100030002'\r\n }\r\n verification_data = {\r\n 'inputcode': self.sms_code,\r\n 'menuId': '000100030002'\r\n }\r\n\r\n verification_resp = self.session.post(verification_url,\r\n params=verification_params,\r\n cookies=cookies,\r\n data=verification_data)\r\n\r\n verification_result = json.loads(verification_resp.text)\r\n\r\n log.info('短信验证成功? {}'.format(verification_result['flag'] == '00'))\r\n\r\n msg_record_url = 'http://iservice.10010.com/e3/static/query/sms'\r\n\r\n msg_record_params = {\r\n '_': str(current_timestamp()),\r\n 'menuid': '000100030002',\r\n 'accessURL': 'http://iservice.10010.com/e4/query/calls/call_sms-iframe.html?menuCode=000100030002'\r\n }\r\n\r\n fn = lambda begin_date, end_date: self.session.post(msg_record_url,\r\n params=msg_record_params,\r\n cookies=cookies,\r\n data={\r\n 'pageNo': 1,\r\n 'pageSize': 100,\r\n 'begindate': begin_date,\r\n 'enddate': end_date\r\n }).json()\r\n\r\n raw_msg_records = {\r\n 'raw_msg_records': [fn(start_date, end_date)] for start_date, end_date in latest_six_month_time_pair()\r\n }\r\n return raw_msg_records\r\n","sub_path":"operator-spider/spiders/beijing_wcdma_spider.py","file_name":"beijing_wcdma_spider.py","file_ext":"py","file_size_in_byte":14103,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"83081628","text":"import os\nimport numpy as np\nimport pandas as pd\nimport random\nimport math\nfrom PIL import Image\nimport torch\nfrom torchvision import transforms, models\nfrom torch.cuda.amp import GradScaler\nimport torch.nn as nn\nfrom torch.utils.data import DataLoader, Dataset\nimport torch.optim as optim\nimport torch.optim.lr_scheduler as lr_scheduler\nimport torch.nn.functional as F\nfrom transformers import BertTokenizer, BertModel\nfrom nltk.translate.bleu_score import sentence_bleu\nfrom tqdm import tqdm\nfrom PIL import Image\nfrom random import choice\nimport matplotlib.pyplot as plt\nfrom transformers import AutoTokenizer, AutoModel\n\ndef seed_everything(seed):\n random.seed(seed)\n os.environ['PYTHONHASHSEED'] = str(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n random.seed(seed)\n torch.cuda.manual_seed(seed)\n torch.backends.cudnn.deterministic = True\n torch.backends.cudnn.benchmark = True\n\ndef make_df(file_path):\n paths = os.listdir(file_path)\n df_list = []\n for p in paths:\n df = pd.read_csv(os.path.join(file_path, p), sep='|', names = ['img_id', 'question', 'answer'])\n df['category'] = p.split('_')[1]\n df['mode'] = p.split('_')[2][:-4]\n df_list.append(df)\n return pd.concat(df_list)\n\ndef load_data(args, remove = None):\n\n traindf = pd.read_csv(os.path.join(args.data_dir, 'traindf.csv'))\n valdf = pd.read_csv(os.path.join(args.data_dir, 'valdf.csv'))\n testdf = pd.read_csv(os.path.join(args.data_dir, 'testdf.csv'))\n\n if remove is not None:\n traindf = traindf[~traindf['img_id'].isin(remove)].reset_index(drop=True)\n\n # resolution = args.data_dir.split('/')[-1].split('_')[-1]\n print('res',args.data_dir)\n traindf['img_id'] = traindf['img_id'].apply(lambda x: os.path.join(args.data_dir, f'Images_Resized', str(x) + '.jpg'))\n valdf['img_id'] = valdf['img_id'].apply(lambda x: os.path.join(args.data_dir, f'Images_Resized', str(x) + '.jpg'))\n testdf['img_id'] = testdf['img_id'].apply(lambda x: os.path.join(args.data_dir, f'Images_Resized', str(x) + '.jpg'))\n # testdf['img_id'] = testdf['img_id'].apply(lambda x: os.path.join(args.data_dir, x + '.jpg'))\n\n traindf['category'] = traindf['category'].str.lower()\n valdf['category'] = valdf['category'].str.lower()\n testdf['category'] = testdf['category'].str.lower()\n\n\n traindf['answer'] = traindf['answer'].str.lower()\n valdf['answer'] = valdf['answer'].str.lower()\n testdf['answer'] = testdf['answer'].str.lower()\n\n traindf = traindf.sample(frac = args.train_pct)\n valdf = valdf.sample(frac = args.valid_pct)\n testdf = testdf.sample(frac = args.test_pct)\n\n\n return traindf, valdf, testdf\n\n#Utils\ndef gelu(x):\n return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))\n\ndef encode_text(caption,tokenizer, args):\n part1 = [0 for _ in range(5)]\n #get token ids and remove [CLS] and [SEP] token id\n part2 = tokenizer.encode(caption)[1:-1]\n\n tokens = [tokenizer.cls_token_id] + part1 + [tokenizer.sep_token_id] + part2[:args.max_position_embeddings-8] + [tokenizer.sep_token_id]\n segment_ids = [0]*(len(part1)+2) + [1]*(len(part2[:args.max_position_embeddings-8])+1)\n input_mask = [1]*len(tokens)\n n_pad = args.max_position_embeddings - len(tokens)\n tokens.extend([0]*n_pad)\n segment_ids.extend([0]*n_pad)\n input_mask.extend([0]*n_pad)\n\n return tokens, segment_ids, input_mask\n\ndef onehot(size, target):\n vec = torch.zeros(size, dtype=torch.float32)\n vec[target] = 1.\n return vec\n\n\nclass LabelSmoothing(nn.Module):\n def __init__(self, smoothing = 0.1):\n super(LabelSmoothing, self).__init__()\n self.confidence = 1.0 - smoothing\n self.smoothing = smoothing\n\n def forward(self, x, target):\n if self.training:\n x = x.float()\n target = target.float() ####\n logprobs = torch.nn.functional.log_softmax(x, dim = -1)\n\n nll_loss = -logprobs * target\n nll_loss = nll_loss.sum(-1)\n\n smooth_loss = -logprobs.mean(dim=-1)\n\n loss = self.confidence * nll_loss + self.smoothing * smooth_loss\n #print(loss)\n\n return loss.mean()\n else:\n return torch.nn.functional.cross_entropy(x, target)\n\ndef crop(img):\n c_y, c_x = img.shape[:2]\n c_y = c_y // 2\n c_x = c_x // 2\n shorter = min(img.shape[:2])\n if img.shape[0] <= img.shape[1]:\n img = img[c_y - shorter // 2: c_y + (shorter - shorter // 2) - 20, c_x - shorter // 2: c_x + (shorter - shorter // 2), :]\n else:\n img = img[c_y - shorter // 2: c_y + (shorter - shorter // 2), c_x - shorter // 2: c_x + (shorter - shorter // 2), :]\n\n return img\n\n\ndef map_and_dropnans(df, ans2idx):\n df['answer'] = df['answer'].map(ans2idx)\n df = df.dropna().reset_index(drop=True)\n df['answer'] = df['answer'].astype(int)\n return df\n\ndef map_answer_2_ids(train_df,val_df,test_df,args):\n\n #combine answers from all data splits and have the output neuron size equal to all possible answers\n if args.map_answers == 'combine':\n df = pd.concat([train_df, val_df, test_df]).reset_index(drop=True)\n ans2idx = {ans:idx for idx,ans in enumerate(df['answer'].unique())}\n idx2ans = {idx:ans for ans,idx in ans2idx.items()}\n df['answer'] = df['answer'].map(ans2idx).astype(int)\n \n train_df = df[df['mode']=='train'].reset_index(drop=True)\n val_df = df[df['mode']=='val'].reset_index(drop=True)\n test_df = df[df['mode']=='test'].reset_index(drop=True)\n\n #output neuron size equal to 1000 top most frequent answer and have a neuron for the rest\n elif args.map_answers == 'top1000':\n answers=list(train_df['answer'].value_counts()[:1000].index)\n ans2idx = {ans:idx for idx,ans in enumerate(answers)}\n idx2ans = {idx:ans for ans,idx in ans2idx.items()}\n\n #top-1000 labels from training set from 0 to 999, drop the rest\n train_df = map_and_dropnans(train_df, ans2idx)\n val_df = map_and_dropnans(val_df, ans2idx)\n test_df = map_and_dropnans(test_df, ans2idx)\n \n # val_df['answer'] = val_df['answer'].map(ans2idx).fillna(1000).astype(int) \n \n return train_df, val_df, test_df, ans2idx, idx2ans\n\nclass VQAMed(Dataset):\n def __init__(self, df, imgsize, tfm, args, mode): #mode = 'train'\n self.df = df\n self.tfm = tfm\n self.size = imgsize\n self.args = args\n if args.task == 'MLM':\n self.tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n elif args.task == 'distillation':\n self.tokenizer = AutoTokenizer.from_pretrained(args.clinicalbert)\n self.mode = mode\n\n if self.mode == 'train':\n cats = self.df.category.unique()\n self.cats2ans = {c:i for i,c in enumerate(cats)}\n\n def __len__(self):\n return len(self.df)\n\n def __getitem__(self, idx):\n path = self.df.loc[idx,'img_id']\n question = self.df.loc[idx, 'question']\n\n answer = self.df.loc[idx, 'answer']\n\n # if self.mode == 'eval':\n # tok_ques = self.tokenizer.tokenize(question)\n\n # if self.args.smoothing:\n # answer = onehot(self.args.num_classes, answer)\n\n img = Image.open(path).convert('RGB')#cv2.imread(path)\n\n if self.tfm:\n img = self.tfm(img)\n\n tokens, segment_ids, input_mask= encode_text(question, self.tokenizer, self.args)\n\n if self.mode == 'train':\n cat = self.cats2ans[self.df.loc[idx, 'category']]\n return img, torch.tensor(tokens, dtype = torch.long), torch.tensor(segment_ids, dtype = torch.long), torch.tensor(input_mask, dtype = torch.long), torch.tensor(answer, dtype = torch.long), path, torch.tensor(cat, dtype = torch.long)\n else:\n return img, torch.tensor(tokens, dtype = torch.long), torch.tensor(segment_ids, dtype = torch.long), torch.tensor(input_mask, dtype = torch.long), torch.tensor(answer, dtype = torch.long), path\n\n\n\n\ndef calculate_bleu_score(preds,targets, idx2ans):\n bleu_per_answer = np.asarray([sentence_bleu([idx2ans[target].split()],idx2ans[pred].split(), weights = [1]) for pred,target in zip(preds,targets)])\n return np.mean(bleu_per_answer)\n\n\n\n\ndef train_one_epoch(loader, model, optimizer, criterion, device, scaler, args, idx2ans):\n\n model.train()\n train_loss = []\n IMGIDS = []\n PREDS = []\n TARGETS = []\n bar = tqdm(loader, leave = False)\n for (img, question_token,segment_ids,attention_mask,target, imgid, category) in bar:\n\n img, question_token,segment_ids,attention_mask,target,category = img.to(device), question_token.to(device), segment_ids.to(device), attention_mask.to(device), target.to(device), category.to(device)\n question_token = question_token.squeeze(1)\n attention_mask = attention_mask.squeeze(1)\n loss_func = criterion\n optimizer.zero_grad()\n\n if args.mixed_precision:\n with torch.cuda.amp.autocast():\n logits, _, _ = model(img, question_token, segment_ids, attention_mask)\n loss = loss_func(logits, target)\n else:\n logits, _, _ = model(img, question_token, segment_ids, attention_mask)\n if args.smoothing:\n loss = loss_func(logits, target, category)\n else:\n loss = loss_func(logits, target)\n if args.mixed_precision:\n scaler.scale(loss)\n loss.backward()\n\n if args.clip:\n nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n\n scaler.step(optimizer)\n scaler.update()\n else:\n loss.backward()\n\n if args.clip:\n nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n\n optimizer.step()\n\n # if args.smoothing:\n # TARGETS.append(target.argmax(1))\n # else:\n TARGETS.append(target)\n\n pred = logits.softmax(1).argmax(1).detach()\n PREDS.append(pred)\n IMGIDS.append(imgid)\n\n loss_np = loss.detach().cpu().numpy()\n train_loss.append(loss_np)\n bar.set_description('train_loss: %.5f' % (loss_np))\n\n PREDS = torch.cat(PREDS).cpu().numpy()\n TARGETS = torch.cat(TARGETS).cpu().numpy()\n IMGIDS = [i for sub in IMGIDS for i in sub]\n\n acc = (PREDS == TARGETS).mean() * 100.\n bleu = calculate_bleu_score(PREDS,TARGETS,idx2ans)\n\n return np.mean(train_loss), PREDS, acc, bleu, IMGIDS\n\n#calc mock results for testing/debugging\ndef calc_MOCK_acc_and_bleu(val_df, PREDS, TARGETS,idx2ans,data_split):\n total_acc = 0.85 * 100.\n \n cats=val_df['category'].unique()\n accs = {}\n bleu = {}\n\n for c in cats:\n accs[c] = 0.80 * 100.\n bleu[c] = 0.70 * 100.\n\n \n final_accs = {}\n final_accs[f'{data_split}_total_acc'] = np.round(total_acc,4)\n\n final_bleus = {}\n total_bleu = 0.85\n final_bleus[f'{data_split}_total_bleu'] = np.round(total_bleu,4)\n\n for k,v in accs.items():\n final_accs[f'{data_split}_{k}_acc']=np.round(v,4)\n final_bleus[f'{data_split}_{k}_bleu']=np.round(v,4)\n return final_accs, final_bleus\n\ndef calc_acc_and_bleu(val_df, PREDS, TARGETS,idx2ans,data_split):\n\n total_acc = (PREDS == TARGETS).mean() * 100.\n \n cats=val_df['category'].unique()\n accs = {}\n bleu = {}\n\n for c in cats:\n accs[c] = (PREDS[val_df['category']==c] == TARGETS[val_df['category']==c]).mean() * 100.\n bleu[c] = calculate_bleu_score(PREDS[val_df['category']==c],TARGETS[val_df['category']==c],idx2ans)\n\n \n final_accs = {}\n final_accs[f'{data_split}_total_acc'] = np.round(total_acc,4)\n\n final_bleus = {}\n total_bleu = calculate_bleu_score(PREDS,TARGETS,idx2ans)\n final_bleus[f'{data_split}_total_bleu'] = np.round(total_bleu,4)\n\n for k,v in accs.items():\n final_accs[f'{data_split}_{k}_acc']=np.round(v,4)\n final_bleus[f'{data_split}_{k}_bleu']=np.round(v,4)\n return final_accs, final_bleus\n\ndef validate(loader, model, criterion, device, scaler, args, val_df, idx2ans):\n\n model.eval()\n criterion.eval()\n val_loss = []\n\n PREDS = []\n TARGETS = []\n bar = tqdm(loader, leave=False)\n\n with torch.no_grad():\n for (img, question_token,segment_ids,attention_mask,target, _) in bar:\n\n img, question_token,segment_ids,attention_mask,target = img.to(device), question_token.to(device), segment_ids.to(device), attention_mask.to(device), target.to(device)\n question_token = question_token.squeeze(1)\n attention_mask = attention_mask.squeeze(1)\n\n\n if args.mixed_precision:\n with torch.cuda.amp.autocast():\n logits, _, _ = model(img, question_token, segment_ids, attention_mask)\n loss = criterion(logits, target)\n else:\n logits, _ , _= model(img, question_token, segment_ids, attention_mask)\n if args.smoothing:\n loss = criterion(logits, target,0)\n else:\n loss = criterion(logits, target)\n\n\n loss_np = loss.detach().cpu().numpy()\n\n pred = logits.softmax(1).argmax(1).detach()\n\n PREDS.append(pred)\n\n # if args.smoothing:\n # TARGETS.append(target.argmax(1))\n # else:\n TARGETS.append(target)\n\n val_loss.append(loss_np)\n\n bar.set_description('val_loss: %.5f' % (loss_np))\n\n val_loss = np.mean(val_loss)\n\n PREDS = torch.cat(PREDS).cpu().numpy()\n TARGETS = torch.cat(TARGETS).cpu().numpy()\n\n # Calculate total and category wise accuracy\n if args.category:\n acc = (PREDS == TARGETS).mean() * 100.\n bleu = calculate_bleu_score(PREDS,TARGETS,idx2ans)\n return val_loss, PREDS, acc, bleu \n else:\n final_accs, final_bleus = calc_acc_and_bleu(val_df, PREDS, TARGETS,idx2ans,data_split='val')\n\n return val_loss, PREDS, final_accs, final_bleus \n\ndef test(loader, model, criterion, device, scaler, args, val_df,idx2ans):\n\n model.eval()\n criterion.eval()\n\n PREDS = []\n TARGETS = []\n\n #test_loss = []\n #c= 0\n\n with torch.no_grad():\n for (img,question_token,segment_ids,attention_mask,target, _) in tqdm(loader, leave=False):\n\n img, question_token, segment_ids, attention_mask, target = img.to(device), question_token.to(device), segment_ids.to(device), attention_mask.to(device), target.to(device)\n question_token = question_token.squeeze(1)\n attention_mask = attention_mask.squeeze(1)\n\n if args.mixed_precision:\n with torch.cuda.amp.autocast():\n logits, _, _ = model(img, question_token, segment_ids, attention_mask)\n #loss = criterion(logits, target)\n else:\n logits, _, _ = model(img, question_token, segment_ids, attention_mask)\n # if args.smoothing:\n # loss = criterion(logits, target,0)\n # else:\n # loss = criterion(logits, target)\n\n\n #loss_np = loss.detach().cpu().numpy()\n\n #test_loss.append(loss_np)\n\n pred = logits.softmax(1).argmax(1).detach()\n\n PREDS.append(pred)\n\n # if args.smoothing:\n # TARGETS.append(target.argmax(1))\n # else:\n TARGETS.append(target)\n\n # if c ==3:\n # break\n # c+=1\n \n\n #test_loss = np.mean(test_loss)\n\n PREDS = torch.cat(PREDS).cpu().numpy()\n TARGETS = torch.cat(TARGETS).cpu().numpy()\n\n if args.category:\n acc = (PREDS == TARGETS).mean() * 100.\n bleu = calculate_bleu_score(PREDS,TARGETS,idx2ans)\n return PREDS, acc, bleu\n #return test_loss, PREDS, acc, bleu\n else:\n final_accs, final_bleus = calc_acc_and_bleu(val_df, PREDS, TARGETS,idx2ans,data_split='test')\n\n return PREDS, final_accs, final_bleus\n #return test_loss, PREDS, final_accs, final_bleus\n\ndef final_test(loader, all_models, device, args, val_df, idx2ans):\n\n PREDS = []\n\n with torch.no_grad():\n for (img,question_token,segment_ids,attention_mask,target) in tqdm(loader, leave=False):\n\n img, question_token, segment_ids, attention_mask, target = img.to(device), question_token.to(device), segment_ids.to(device), attention_mask.to(device), target.to(device)\n question_token = question_token.squeeze(1)\n attention_mask = attention_mask.squeeze(1)\n\n for i, model in enumerate(all_models):\n if args.mixed_precision:\n with torch.cuda.amp.autocast():\n logits, _, _ = model(img, question_token, segment_ids, attention_mask)\n else:\n logits, _, _ = model(img, question_token, segment_ids, attention_mask)\n\n if i == 0:\n pred = logits.detach().cpu().numpy()/len(all_models)\n else:\n pred += logits.detach().cpu().numpy()/len(all_models)\n\n PREDS.append(pred)\n\n PREDS = np.concatenate(PREDS)\n\n return PREDS\n\ndef test2020(loader, model, device, args):\n\n model.eval()\n\n PREDS = []\n\n with torch.no_grad():\n for (img, question_token, segment_ids, attention_mask) in tqdm(loader, leave=False):\n\n img, question_token, segment_ids, attention_mask = img.to(device), question_token.to(device), segment_ids.to(device), attention_mask.to(device)\n question_token = question_token.squeeze(1)\n attention_mask = attention_mask.squeeze(1)\n\n if args.mixed_precision:\n with torch.cuda.amp.autocast():\n logits, _, _ = model(img, question_token, segment_ids, attention_mask)\n # logits = model(img)\n else:\n logits, _, _ = model(img, question_token, segment_ids, attention_mask)\n # logits = model(img)\n\n\n pred = logits.softmax(1).argmax(1).detach()\n\n PREDS.append(pred)\n\n\n PREDS = torch.cat(PREDS).cpu().numpy()\n\n\n return PREDS\n\n\ndef val_img_only(loader, model, criterion, device, scaler, args, val_df, idx2ans):\n\n model.eval()\n val_loss = []\n\n PREDS = []\n TARGETS = []\n bar = tqdm(loader, leave=False)\n\n with torch.no_grad():\n for (img, question_token,segment_ids,attention_mask,target, _) in bar:\n\n img, question_token,segment_ids,attention_mask,target = img.to(device), question_token.to(device), segment_ids.to(device), attention_mask.to(device), target.to(device)\n # question_token = question_token.squeeze(1)\n # attention_mask = attention_mask.squeeze(1)\n\n\n if args.mixed_precision:\n with torch.cuda.amp.autocast():\n logits = model(img)\n loss = criterion(logits, target)\n else:\n logits = model(img)\n loss = criterion(logits, target)\n\n\n loss_np = loss.detach().cpu().numpy()\n\n pred = logits.softmax(1).argmax(1).detach()\n\n PREDS.append(pred)\n\n if args.smoothing:\n TARGETS.append(target.argmax(1))\n else:\n TARGETS.append(target)\n\n val_loss.append(loss_np)\n\n bar.set_description('val_loss: %.5f' % (loss_np))\n\n val_loss = np.mean(val_loss)\n\n PREDS = torch.cat(PREDS).cpu().numpy()\n TARGETS = torch.cat(TARGETS).cpu().numpy()\n\n acc = (PREDS == TARGETS).mean() * 100.\n bleu = calculate_bleu_score(PREDS,TARGETS,idx2ans)\n\n\n\n return val_loss, PREDS, acc, bleu\n\ndef test_img_only(loader, model, criterion, device, scaler, args, test_df, idx2ans):\n\n model.eval()\n TARGETS = []\n PREDS = []\n test_loss = []\n\n with torch.no_grad():\n for (img, question_token, segment_ids, attention_mask, target, _) in tqdm(loader, leave=False):\n\n img, question_token, segment_ids, attention_mask, target = img.to(device), question_token.to(device), segment_ids.to(device), attention_mask.to(device), target.to(device)\n # question_token = question_token.squeeze(1)\n # attention_mask = attention_mask.squeeze(1)\n\n if args.mixed_precision:\n with torch.cuda.amp.autocast():\n logits = model(img)\n loss = criterion(logits, target)\n else:\n logits = model(img)\n loss = criterion(logits, target)\n\n\n pred = logits.softmax(1).argmax(1).detach()\n loss_np = loss.detach().cpu().numpy()\n\n PREDS.append(pred)\n TARGETS.append(target)\n test_loss.append(loss_np)\n\n test_loss = np.mean(test_loss)\n\n PREDS = torch.cat(PREDS).cpu().numpy()\n TARGETS = torch.cat(TARGETS).cpu().numpy()\n\n acc = (PREDS == TARGETS).mean() * 100.\n bleu = calculate_bleu_score(PREDS,TARGETS,idx2ans)\n\n return test_loss, PREDS, acc, bleu\n\n\n\ndef train_img_only(loader, model, optimizer, criterion, device, scaler, args, idx2ans):\n\n model.train()\n train_loss = []\n PREDS = []\n TARGETS = []\n IMGIDS = []\n bar = tqdm(loader, leave = False)\n for (img, question_token,segment_ids,attention_mask,target, imgid) in bar:\n\n img, question_token,segment_ids,attention_mask,target = img.to(device), question_token.to(device), segment_ids.to(device), attention_mask.to(device), target.to(device)\n # question_token = question_token.squeeze(1)\n # attention_mask = attention_mask.squeeze(1)\n loss_func = criterion\n optimizer.zero_grad()\n\n if args.mixed_precision:\n with torch.cuda.amp.autocast():\n logits = model(img)\n loss = loss_func(logits, target)\n else:\n logits = model(img)\n loss = loss_func(logits, target)\n\n if args.mixed_precision:\n scaler.scale(loss)\n loss.backward()\n\n if args.clip:\n nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n\n scaler.step(optimizer)\n scaler.update()\n else:\n loss.backward()\n\n if args.clip:\n nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n\n optimizer.step()\n\n if args.smoothing:\n TARGETS.append(target.argmax(1))\n else:\n TARGETS.append(target)\n\n pred = logits.softmax(1).argmax(1).detach()\n PREDS.append(pred)\n\n IMGIDS.append(imgid)\n loss_np = loss.detach().cpu().numpy()\n train_loss.append(loss_np)\n bar.set_description('train_loss: %.5f' % (loss_np))\n\n PREDS = torch.cat(PREDS).cpu().numpy()\n TARGETS = torch.cat(TARGETS).cpu().numpy()\n IMGIDS = [i for sub in IMGIDS for i in sub]\n\n acc = (PREDS == TARGETS).mean() * 100.\n bleu = calculate_bleu_score(PREDS,TARGETS,idx2ans)\n\n return np.mean(train_loss), PREDS, acc, bleu, IMGIDS\n\nclass LabelSmoothByCategory(nn.Module):\n def __init__(self, train_df, num_classes, device,smoothing = 0.1):\n super(LabelSmoothByCategory, self).__init__()\n self.confidence = 1.0 - smoothing\n self.smoothing = smoothing\n self.train_df = train_df\n self.num_classes = num_classes\n self.device = device\n self.cross_entropy_criterion = nn.CrossEntropyLoss()\n\n self.computeCategoryTensors()\n\n\n def forward(self, x, target, category):\n if self.training:\n vecs = [self.idx2vector[xi.item()].clone().detach() for xi in category]\n v = []\n for i, xi in enumerate(vecs):\n idx = target[i]\n #print('idx', idx)\n xi[idx] = self.confidence\n v.append(xi.unsqueeze(dim=0))\n soft_targets = torch.cat(v,0).to(self.device)\n #a = torch.cat([self.idx2vector[xi.item()].unsqueeze(dim=0) for xi in category],0)\n #a[target[:, 0], target[:, 1]] = self.confidence\n self.a=soft_targets\n return self.cross_entropy(x,soft_targets)\n else:\n #return torch.nn.functional.cross_entropy(x, target)\n # import IPython; IPython.embed(); exit(1)\n return self.cross_entropy_criterion(x, target)\n\n def computeCategoryTensors(self):\n #binary\n idx = self.train_df[self.train_df['category'] == 'binary']['answer'].unique()\n self.binary_tensor = torch.zeros(self.num_classes)\n self.binary_tensor[idx] = self.smoothing / len(idx)\n\n #plane\n idx = self.train_df[self.train_df['category'] == 'plane']['answer'].unique()\n self.plane_tensor = torch.zeros(self.num_classes)\n self.plane_tensor[idx] = self.smoothing / len(idx)\n\n #modality\n idx = self.train_df[self.train_df['category'] == 'modality']['answer'].unique()\n self.modality_tensor = torch.zeros(self.num_classes)\n self.modality_tensor[idx] = self.smoothing / len(idx)\n\n #organ\n idx = self.train_df[self.train_df['category'] == 'organ']['answer'].unique()\n self.organ_tensor = torch.zeros(self.num_classes)\n self.organ_tensor[idx] = self.smoothing / len(idx)\n\n #abnormality\n idx = self.train_df[self.train_df['category'] == 'abnormality']['answer'].unique()\n self.abnorm_tensor = torch.zeros(self.num_classes)\n self.abnorm_tensor[idx] = self.smoothing / len(idx)\n\n self.idx2vector = {0: self.plane_tensor, 1: self.modality_tensor, 2: self.binary_tensor,\n 3: self.organ_tensor, 4: self.abnorm_tensor} #order by train_df.category.unique()\n\n\n def cross_entropy(self,pred, soft_targets):\n logsoftmax = nn.LogSoftmax(dim=1)\n a = - soft_targets * logsoftmax(pred)\n #print('a',a)\n return torch.mean(torch.sum(a, 1))\n\n","sub_path":"rsvqa/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":25895,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"209877430","text":"import itertools\nfrom time import time\nfrom fun import prog\nalphabet = \"qwertyuiopasdfghjklzxcvbnm\" #lazy\n\n####For basic Virgin Media based WIFI listing.\n\npattack = []\ntick = 0\nrep = 8\noutOf = (len(alphabet)) ** (rep)\nref = 5 # Refresh rate\nstart = time() + ref\nfor x in itertools.product(alphabet, repeat=rep):\n pattack.append(list(x))\n tick += 1\n if start <= time():\n start = time() + ref\n print ( prog(tick, outOf, size=60) )\n","sub_path":"GeneratePassesWithRules.py","file_name":"GeneratePassesWithRules.py","file_ext":"py","file_size_in_byte":452,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"383092234","text":"import ast\nimport inspect\nimport os\nfrom datetime import datetime as dt\nfrom typing import Any, Callable, Union\n\nimport pendulum\nimport yaml\nfrom expandvars import expand\n\nfrom ..config.interface import ConfigInterface, mapped_config\nfrom ..config.mapping import config_map\nfrom ..core.exceptions import ExecutionException\nfrom ..core.settings import get_settings\nfrom ..engine import Engine\nfrom ..execution.schedule import Schedule\nfrom ..experiment.experiment import Experiment\nfrom ..experiment.parser import parse_experiment\nfrom ..filesystem import open_fs\nfrom ..index import Index\nfrom ..project import Project\nfrom ..project.export import Export\nfrom ..registration import Registration\nfrom ..storage import Storage\nfrom ..storage.experiment import StorageExperiment\nfrom ..utils.dicts import update_dict\nfrom ..utils.formatting import exception_to_str, msg\nfrom ..utils.host import get_host_info\nfrom ..utils.identifiers import (\n decode_experiment_id,\n encode_experiment_id,\n generate_experiment_id,\n)\nfrom ..utils.importing import resolve_instance\nfrom ..utils.traits import Jsonable\n\n\ndef to_color(experiment_id):\n return \"\".join(\n [encode_experiment_id(decode_experiment_id(c) % 16) for c in experiment_id]\n )\n\n\n_latest = [None]\n\n\nclass Execution(Jsonable):\n def __init__(\n self,\n experiment: Union[Experiment, Any],\n storage: Union[dict, str] = None,\n engine: Union[Engine, str, dict, None] = None,\n index: Union[Index, str, dict, None] = None,\n project: Union[Project, Callable, str, dict] = None,\n seed: Union[int, None, str] = None,\n ):\n self.function = None\n\n self.experiment = None\n self.storage = Storage()\n self.engine = None\n self.index = None\n self.project = None\n self.seed = None\n\n self.timestamp = None\n self.schedule = Schedule()\n self.code_backup = None\n self.code_version = None\n self.started_at = None\n\n if not isinstance(experiment, Experiment) and (\n inspect.isclass(experiment) or callable(experiment)\n ):\n # decorator use\n if None not in (storage, engine, index, project, seed):\n raise ValueError(\n \"Execution decorator takes no arguments; \"\n \"call the decorated object with arguments instead.\"\n )\n self.function = experiment\n return\n\n self.set_project(project)\n self.set_storage(storage)\n self.set_experiment(experiment)\n self.set_engine(engine)\n self.set_index(index)\n self.set_seed(seed)\n\n self._behavior = {\"raise_exceptions\": False}\n self.failures = 0\n\n def __call__(\n self, experiment, storage=None, engine=None, index=None, project=None, seed=None\n ):\n self.set_project(project)\n self.set_storage(storage)\n self.set_experiment(experiment)\n self.set_engine(engine)\n self.set_index(index)\n self.set_seed(seed)\n if self.function is not None:\n # decorator invocation\n self.project.default_component = self.function\n return self\n\n @classmethod\n def latest(cls):\n return _latest[0]\n\n @classmethod\n def set_latest(cls, latest):\n _latest[0] = latest\n\n @classmethod\n def create(self, args):\n if isinstance(args, Execution):\n return args\n\n if args is None:\n return Execution()\n\n resolved = resolve_instance(args, Execution, \"executions\")\n if resolved is not None:\n return resolved\n\n if isinstance(args, dict):\n return Execution(**args)\n\n if isinstance(args, (list, tuple)):\n return Execution(*args)\n\n raise ValueError(f\"Invalid argument: {args}\")\n\n def set_experiment(self, experiment):\n self.experiment = Experiment.create(experiment)\n\n return self\n\n def set_storage(self, storage):\n if storage is None:\n storage = get_settings()[\"default_storage\"]\n\n self.storage = Storage.create(storage)\n\n return self\n\n def set_engine(self, engine):\n if engine is None:\n engine = get_settings()[\"default_engine\"]\n\n self.engine = Engine.create(engine)\n\n return self\n\n def set_index(self, index):\n if index is None:\n index = get_settings()[\"default_index\"]\n\n self.index = Index.get(index)\n\n return self\n\n def set_project(self, project):\n self.project = Project.create(project)\n\n return self\n\n def set_seed(self, seed):\n if isinstance(seed, str):\n seed = decode_experiment_id(seed, or_fail=True)\n elif isinstance(seed, int):\n seed = generate_experiment_id(random_state=seed, with_encoding=False)\n else:\n seed = generate_experiment_id(with_encoding=False)\n\n self.seed = seed\n\n self.schedule.set_seed(seed)\n\n return self\n\n def set_schedule(self, schedule=None):\n if schedule is not None:\n self.schedule = schedule\n return self\n\n self.schedule = Schedule(seed=self.seed)\n\n config = ConfigInterface(\n self.project.parse_config(),\n self.experiment.specification[\"version\"],\n default_class=self.project.default_component,\n )\n\n for index, (node, components, resources) in enumerate(\n parse_experiment(self.experiment, seed=self.seed)\n ):\n component_config, components_config = config.get(node, components)\n\n # compute resources\n if isinstance(self.engine, Engine) and not self.engine.supports_resources():\n if resources is not None:\n msg(\n \"Engine does not support resource specification. Skipping ...\",\n level=\"warn\",\n color=\"header\",\n )\n resources = None\n elif callable(resources):\n resources = resources(\n engine=self.engine,\n component=mapped_config(component_config),\n components=[\n mapped_config(component) for component in components_config\n ],\n )\n elif resources is None:\n resources = Registration.get().default_resources(\n engine=self.engine,\n component=mapped_config(component_config),\n components=[\n mapped_config(component) for component in components_config\n ],\n )\n\n if \"tune\" in self.experiment.specification:\n self.schedule.add_tune(\n component=component_config,\n components=components_config,\n resources=resources,\n **self.experiment.specification[\"tune\"][\"arguments\"],\n )\n else:\n self.schedule.add_execute(\n component=component_config,\n components=components_config,\n resources=resources,\n )\n\n return self\n\n def set_checkpoint(self, checkpoint):\n def transformation(i, component, element):\n element[1][\"flags\"][\"CHECKPOINT\"] = checkpoint\n return element\n\n self.schedule.transform(transformation)\n\n return self\n\n def set_version(self, config=None):\n if isinstance(config, str):\n config = ast.literal_eval(config)\n elif callable(config):\n self.schedule.transform(config)\n return self\n\n if not isinstance(config, dict):\n raise ValueError(\"Version must be a dictionary or callable\")\n\n def transformation(i, component, element):\n element[1][\"args\"] = update_dict(element[1][\"args\"], config)\n return element\n\n self.schedule.transform(transformation)\n\n return self\n\n def set_behavior(self, **settings):\n unknown_options = set(settings.keys()) - {\"raise_exceptions\"}\n if len(unknown_options) > 0:\n raise ValueError(f\"Invalid options: {unknown_options}\")\n self._behavior.update(settings)\n return self\n\n def is_submitted(self):\n try:\n with open_fs(self.storage.config[\"url\"]) as filesystem:\n return filesystem.isdir(\n os.path.join(self.storage.config[\"directory\"], self.experiment_id)\n )\n except (FileNotFoundError, AttributeError, KeyError, TypeError, ValueError):\n pass\n return False\n\n def submit(self):\n if Registration.get().on_before_submit(self) is False:\n return False\n\n self.failures = 0\n self.storage.config[\"experiment\"] = self.experiment_id\n\n # auto-name experiment if imported via @ directive\n if self.experiment.specification[\"name\"] is None:\n if hasattr(self.experiment, \"_resolved_module_name\"):\n self.experiment.name(self.experiment._resolved_module_name)\n\n # expand variables in storage directory\n if isinstance(self.storage.config[\"directory\"], str):\n # % variables\n variables = dict()\n\n variables[\"PROJECT\"] = (\n self.project.name.replace(\".\", \"/\")\n if self.project.name is not None\n else None\n )\n\n variables[\"EXPERIMENT\"] = (\n self.experiment.specification[\"name\"].replace(\".\", \"/\")\n if self.experiment.specification[\"name\"] is not None\n else \"\"\n )\n\n self.storage.config[\"directory\"] = expand(\n self.storage.config[\"directory\"], environ=variables, var_symbol=\"&\"\n )\n # strftime variables\n try:\n self.storage.config[\"directory\"] = dt.now().strftime(\n self.storage.config[\"directory\"]\n )\n except ValueError:\n pass\n self.storage.config[\"directory\"] = self.storage.config[\"directory\"].strip(\n \"/\"\n )\n\n # check if URL is an existing experiment\n derived_from = None\n try:\n with open_fs(self.storage.config[\"url\"]) as filesystem:\n experiment_id = filesystem.load_file(\"execution.json\")[\"experiment_id\"]\n # register URL as parent storage and rewrite to experiments/ subdirectory\n self.storage.config[\"ancestor\"] = {\n \"url\": self.storage.config[\"url\"],\n \"experiment\": experiment_id,\n }\n self.storage.config[\"url\"] = os.path.join(\n self.storage.config[\"url\"], \"experiments\"\n )\n derived_from = self.storage.config[\"ancestor\"][\"url\"]\n except (ValueError, KeyError, FileNotFoundError):\n pass\n\n is_submitted = self.is_submitted()\n if not is_submitted:\n if len(self.schedule) == 0:\n self.set_schedule()\n\n def set_derived_from_flag(i, component, element):\n element[1][\"flags\"][\"DERIVED_FROM_STORAGE\"] = derived_from\n return element\n\n self.schedule.transform(set_derived_from_flag)\n storage = self.engine.storage_middleware(self.storage.config)\n\n now = pendulum.now()\n self.timestamp = now.timestamp()\n self.started_at = str(now)\n # do not backup on mem:// filesystem unless explicitly set to True\n code_backup = self.code_backup\n if code_backup is None and not self.storage.config[\"url\"].startswith(\n \"mem://\"\n ):\n code_backup = True\n\n # collect and write execution data\n url = os.path.join(\n storage.get(\"url\", \"mem://\"),\n storage.get(\"directory\", \"\"),\n storage[\"experiment\"],\n )\n data = {\n \"code.json\": {\n \"resolvers\": {\n \"experiment\": getattr(\n self.experiment, \"_resolved_by_expression\", None\n ),\n \"storage\": getattr(\n self.storage, \"_resolved_by_expression\", None\n ),\n \"engine\": getattr(self.engine, \"_resolved_by_expression\", None),\n },\n \"code_backup\": code_backup,\n \"code_version\": self.project.get_code_version(),\n },\n \"code.diff\": self.project.get_diff(),\n \"execution.json\": self.serialize(),\n \"schedule.json\": self.schedule.serialize(),\n \"host.json\": get_host_info(),\n }\n\n with open_fs({\"url\": url, \"create\": True}) as filesystem:\n if code_backup:\n self.project.backup_source_code(opener=filesystem.open)\n\n for k, v in data.items():\n filesystem.save_file(name=k, data=v)\n\n self.index.add(StorageExperiment(url, data))\n\n msg(\n f\"{self.experiment_id} <{url}> ({self.started_at})\\n\",\n level=\"info\",\n color=\"header\",\n )\n\n Registration.get().on_submit(self, is_submitted)\n\n return self.engine.submit(self)\n\n def set_result(self, result, index=None, echo=True):\n if index is None:\n index = len(self.schedule._result)\n if isinstance(result, ExecutionException):\n self.failures += 1\n if self._behavior[\"raise_exceptions\"]:\n raise result\n if echo or echo == \"success\":\n msg(\n f\"\\nComponent <{self.components[index]}> of experiment {self.experiment_id} failed \"\n f\"({index + 1}/{len(self.schedule)})\\n\"\n f\"{exception_to_str(result)}\",\n level=\"error\",\n color=\"header\",\n )\n else:\n if echo or echo == \"failure\":\n msg(\n f\"\\nComponent <{self.components[index]}> of experiment {self.experiment_id} completed \"\n f\"({index + 1}/{len(self.schedule)})\\n\",\n level=\"info\",\n color=\"header\",\n )\n self.schedule.set_result(result, index)\n\n @classmethod\n def from_storage(cls, url):\n with open_fs(url) as filesystem:\n serialized = filesystem.load_file(\"execution.json\")\n execution = cls.from_json(serialized)\n schedule = filesystem.load_file(\"schedule.json\", default=None)\n if schedule:\n execution.set_schedule(Schedule.from_json(schedule))\n return execution\n\n def export(self, path=None, overwrite=False):\n \"\"\"Exports the execution\n\n Converts the execution into a plain Python project\n that can be executed without machinable.\n\n ::: warning\n This feature may not work reliably in all circumstances and project use cases\n :::\n\n # Arguments\n path: String, directory where exported execution will be stored. If None defaults to 'exports' in the\n current working directory\n overwrite: Boolean, whether to overwrite an existing export.\n \"\"\"\n if path is None:\n path = os.path.join(os.getcwd(), \"exports\")\n\n if len(self.schedule) == 0:\n self.set_schedule()\n\n for (\n index,\n (execution_type, component, components, resources, args, kwargs),\n ) in enumerate(self.schedule):\n if execution_type != \"execute\":\n msg(\"Not supported\", level=\"error\", color=\"fail\")\n continue\n\n # if more than one job, write into subdirectories\n if len(self.schedule) > 1:\n path = os.path.join(path, str(index))\n\n export_path = os.path.abspath(path)\n\n if os.path.exists(export_path) and not overwrite:\n raise FileExistsError(\n f\"Export directory '{export_path}' exists. To overwrite, set overwrite=True\"\n )\n\n msg(f\"\\nExporting to {export_path}\", color=\"yellow\")\n\n export = Export(self.project.directory_path, export_path)\n\n # instantiate targets\n nd = component[\"class\"](config=component[\"args\"], flags=component[\"flags\"])\n comps = [\n c[\"class\"](config=c[\"args\"], flags=c[\"flags\"], node=nd)\n for c in components\n ]\n\n def get_class_name(cls):\n try:\n return cls.__name__\n except AttributeError:\n return cls.name()\n\n # export config\n export.write(\n \"config.json\",\n {\n \"component\": {\n \"args\": nd.config.toDict(evaluate=True),\n \"flags\": nd.flags.toDict(evaluate=True),\n },\n \"components\": [\n {\n \"args\": comps[i].config.toDict(evaluate=True),\n \"flags\": comps[i].flags.toDict(evaluate=True),\n \"class\": get_class_name(components[i][\"class\"]),\n }\n for i in range(len(comps))\n ],\n \"storage\": {\"url\": \"./results\"},\n },\n meta=True,\n )\n\n # export components and node\n export.module(component[\"class\"])\n for c in components:\n export.module(c[\"class\"])\n\n # export mixins\n mixins = component[\"args\"].get(\"_mixins_\", [])\n for c in components:\n mixins.extend(c[\"args\"].get(\"_mixins_\", []))\n for mixin in mixins:\n export.module(\n mixin[\"origin\"].replace(\"+.\", \"vendor.\"), from_module_path=True\n )\n\n export.write(\"__init__.py\")\n\n export.machinable()\n\n export.entry_point(component, components)\n\n export.echo()\n\n msg(\n f\"\\nExporting finished. Run as 'cd {export_path} && python run.py'\",\n color=\"yellow\",\n )\n\n def serialize(self):\n return {\n \"experiment_id\": self.experiment_id,\n \"seed\": self.seed,\n \"timestamp\": self.timestamp,\n \"started_at\": self.started_at,\n \"components\": self.components,\n \"project_name\": self.project.name if self.project is not None else None,\n \"experiment_name\": self.experiment.specification[\"name\"]\n if self.experiment is not None\n else None,\n }\n\n @classmethod\n def unserialize(cls, serialized):\n if not isinstance(serialized, dict):\n raise ValueError(f\"Invalid execution: {serialized}\")\n execution = cls(None)\n execution.seed = serialized[\"seed\"]\n execution.timestamp = serialized[\"timestamp\"]\n execution.started_at = serialized[\"started_at\"]\n return execution\n\n @property\n def unique_id(self):\n return self.experiment_id + \"_\" + self.components[0]\n\n @property\n def experiment_id(self):\n return encode_experiment_id(self.seed)\n\n def summary(self):\n if len(self.schedule) == 0:\n self.set_schedule()\n\n msg(\n f\"\\n{self.experiment_id}\\n------\", color=\"header\",\n )\n msg(\n f\"{repr(self.experiment)}\", color=\"blue\",\n )\n msg(\n f\"{repr(self.storage)}\", color=\"blue\",\n )\n msg(f\"{repr(self.engine)}\", color=\"blue\")\n msg(f\"{repr(self.index)}\", color=\"blue\")\n msg(f\"{repr(self.project)}\", color=\"blue\")\n msg(f\"Seed <{self.seed}>\", color=\"blue\")\n\n def _flags(flags):\n filtered = {\n k: v\n for k, v in flags.items()\n if k\n not in {\n \"GLOBAL_SEED\",\n \"EXPERIMENT_ID\",\n \"SEED\",\n \"COMPONENT_ID\",\n \"REPEAT_SEED\",\n \"REPEAT_TOTAL\",\n \"REPEAT_NUMBER\",\n \"NAME\",\n \"MODULE\",\n }\n }\n if len(filtered) == 0:\n return \"\"\n return yaml.dump(filtered, default_flow_style=False)\n\n for (\n index,\n (execution_type, component, components, resources, args, kwargs),\n ) in enumerate(self.schedule):\n # only print first and last one\n shortened = len(self.schedule) > 3 and 0 < index < len(self.schedule) - 1\n if shortened:\n if index == 1:\n msg(\n f\"[ + {len(self.schedule) - 2} other components ]\",\n color=\"header\",\n )\n continue\n\n msg(\n f\"\\nComponent: {component['name']} <{self.components[index]}> ({index + 1}/{len(self.schedule)})\",\n color=\"green\",\n )\n\n if len(component[\"versions\"]) > 0:\n msg(f\">> {', '.join(component['versions'])}\", color=\"green\")\n msg(_flags(component[\"flags\"]))\n msg(yaml.dump(component[\"args\"], default_flow_style=False), color=\"blue\")\n\n for c in components:\n if c:\n msg(f\"\\t{c['name']}\", color=\"yellow\")\n if len(c[\"versions\"]) > 0:\n msg(f\"\\t>> {', '.join(c['versions'])}\", color=\"green\")\n msg(_flags(c[\"flags\"]))\n msg(\n yaml.dump(c[\"args\"], default_flow_style=False), color=\"blue\",\n )\n\n msg(\"------\\n\", color=\"header\")\n\n return self\n\n def filter(self, callback=None):\n self.schedule.filter(callback)\n\n @property\n def components(self):\n return self.schedule.components\n\n def __repr__(self):\n experiment_id = self.experiment_id if isinstance(self.seed, int) else \"None\"\n return f\"Execution <{experiment_id}> ({self.storage})\"\n\n def __str__(self):\n return self.__repr__()\n","sub_path":"src/machinable/execution/execution.py","file_name":"execution.py","file_ext":"py","file_size_in_byte":22716,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"540493445","text":"import pygame\nimport random, os.path\n\nfrom player import Player\nfrom obstacle import *\nfrom pygame.locals import *\n\n#Here is the behaviour of the controller\nclass Agent:\n #Main agent constants\n #If you need to create a variable or a data structure, do it here\n speed = 2\n\n player = Player()\n clock = pygame.time.Clock()\n \n #Init agent\n def __init__(self):\n self.clock.tick(60)\n\n #The main agent code\n #Here is the behaviour\n #For example, here is a basic key controlling algorythm.\n #You can find examples of pre-made codes in the \"examples folder\"\n def action(self):\n pygame.init()\n\n pressed = pygame.key.get_pressed()\n keystate = pygame.key.get_pressed()\n\n #Set the direction\n direction = keystate[K_RIGHT] - keystate[K_LEFT]\n\n #Take player input\n pygame.display.update()\n if(pressed[pygame.K_UP]):\n self.player.moveUp(self.speed)\n if(pressed[pygame.K_DOWN]):\n self.player.moveDown(self.speed)\n if(pressed[pygame.K_LEFT]):\n self.player.moveLeft(self.speed)\n if(pressed[pygame.K_RIGHT]):\n self.player.moveRight(self.speed)\n self.player.move(direction)","sub_path":"agent.py","file_name":"agent.py","file_ext":"py","file_size_in_byte":1226,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"86115600","text":"'''from threading import *\r\nimport speak_now\r\n#import jervis\r\nimport takevo\r\nimport time\r\ndef new():\r\n #speak_now.speakf(\"jchgc\")\r\n for x in range(10):\r\n speak_now.speakf(\"jchgc\")\r\n print(\"scnbacj\")\r\n time.sleep(1)\r\ndef hi():\r\n\r\n for x in range(10):\r\n takevo.takevoice()\r\n time.sleep(1)\r\nt1=Thread(target=new)\r\nt2=Thread(target=hi)\r\nt1.start()\r\nt2.start()\r\nprint(\"hbjb\")'''\r\nimport os\r\nos.add_dll_directory(r'C:\\Program Files (x86)\\VideoLAN\\VLC')\r\nimport vlc\r\nplayer=\"\"\r\ndef play():\r\n global player\r\n c = vlc.MediaPlayer(\"C:/songs/WhatsApp Audio 2020-02-13 at 12.15.48 PM (1).mpeg\")\r\n c.play()\r\ndef pause():\r\n global player\r\n player.pause()\r\ndef stop():\r\n global player\r\n player.stop\r\n\r\n\r\nplay()","sub_path":"thread.py","file_name":"thread.py","file_ext":"py","file_size_in_byte":760,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"351627465","text":"#!/usr/bin/env python\n\"\"\"Extract fingerprints from the specified JSON file.\"\"\"\nimport argparse\nimport json\nimport logging\nimport re\nimport sys\n\nimport selectolax.lexbor\nimport selectolax.parser\n\nimport fingerprints\n\n_LOGGER = logging.getLogger(__name__)\n_LOGGER.addHandler(logging.NullHandler())\n\n\nHTML_ENCODING_REGEX = re.compile(r\"\"\"\n \\ ?\n (\n encoding=\n [\"'](?P[-\\w\\d]+)[\"']\n |\n charset=[\"'](?P[-\\w\\d]+)[\"']\n |\n charset=(?P[-\\w\\d]+)\n )\n \"\"\", re.VERBOSE)\n\"\"\"Detects XML encoding declarations. Encoding appears in tags like::\n\n \n\nCharset appears in tags like::\n\n \"\"\"\n\n\ndef _strip_xml_encoding(html: str) -> str:\n \"\"\"lxml won't parse Unicode strings that contain encoding information.\n This function strips that encoding information so that the string can be\n parsed with lxml.\"\"\"\n return HTML_ENCODING_REGEX.sub(\"\", html)\n\n\ndef parse_html(html_string):\n \"\"\"Parse a HTML string.\n\n Returns:\n A tree or None if parsing failed.\"\"\"\n html_string = _strip_xml_encoding(html_string)\n parser = selectolax.lexbor.LexborHTMLParser\n # parser = selectolax.parser.HTMLParser\n return parser(html_string)\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument('inputfile')\n args = parser.parse_args()\n\n if args.inputfile == '-':\n fetched = json.load(sys.stdin)\n else:\n with open(args.inputfile) as fin:\n fetched = json.load(fin)\n\n def has_fragment(url_fragment):\n for req in fetched['all_net_reply']:\n if url_fragment in req:\n return True\n return False\n\n tree = parse_html(fetched['html'])\n if tree is None:\n sys.stderr.write('failed to retrieve or parse the page\\n')\n return\n\n for fp in fingerprints.ALL_FINGERPRINTS:\n if fp(fetched['html'], tree, fetched['headers'], has_fragment):\n d = {'category': fp.category, 'name': fp.name}\n sys.stdout.write(json.dumps(d) + '\\n')\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"extract.py","file_name":"extract.py","file_ext":"py","file_size_in_byte":2235,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"416714743","text":"\nimport pandas as pd\nimport numpy as np\n\nuser_usage = pd.read_csv(\"./data/user_usage.csv\")\nuser_device = pd.read_csv(\"./data/user_device.csv\")\ndevices = pd.read_csv(\"./data/android_devices.csv\")\n\nprint(\"user_usage 데이터프레임: \\n\", user_usage)\nprint(\"user_device 데이터프레임: \\n\",user_device)\nprint(\"devices 데이터프레임: \\n\",devices)\n\nresult = None\n### 코드 구현 ######\ndevices.rename(columns={'Retail Branding':'manufacturer'}, inplace=True)\nprint(devices)\nresult = pd.merge(user_usage, user_device[['use_id', 'platform', 'device']], how=\"inner\", on=\"use_id\")\n### 코드 구현 ######\nprint(result.head())\n\n","sub_path":"workshop5/m_question01_제공파일.py","file_name":"m_question01_제공파일.py","file_ext":"py","file_size_in_byte":633,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"104395769","text":"# -*- coding: utf-8 -*-\n# File @ mathematical_evaluation.py\n# Create @ 2018/2/28 17:49\n# Author @ 819070918@qq.com\n\n\nfrom __future__ import unicode_literals\nimport jieba\nfrom chatterbot.logic import LogicAdapter\nfrom chatterbot.conversation import Statement\nfrom smart_voice.utils import mathparse\n\n\nclass MathematicalEvaluation(LogicAdapter):\n \"\"\"\n The MathematicalEvaluation logic adapter parses input to determine\n whether the user is asking a question that requires math to be done.\n If so, the equation is extracted from the input and returned with\n the evaluated result.\n\n For example:\n User: 'What is three plus five?'\n Bot: 'Three plus five equals eight'\n\n :kwargs:\n * *language* (``str``) --\n The language is set to 'ENG' for English by default.\n \"\"\"\n\n def __init__(self, **kwargs):\n super(MathematicalEvaluation, self).__init__(**kwargs)\n\n self.language = kwargs.get('language', 'CHS')\n self.calculate = kwargs.get('calculate', [\n '+',\n '-',\n '*',\n '/',\n '加',\n '减',\n '乘',\n '除',\n '平方',\n '立方',\n '开方',\n '平方根',\n ])\n\n def can_process(self, statement):\n \"\"\"\n Determines whether it is appropriate for this\n adapter to respond to the user input.\n \"\"\"\n is_can = False\n\n for item in self.calculate:\n if item in statement.text:\n is_can = True\n\n return is_can\n\n def process(self, statement):\n \"\"\"\n Takes a statement string.\n Returns the equation from the statement with the mathematical terms solved.\n \"\"\"\n\n # Getting the mathematical terms within the input statement\n\n input_text = \" \".join([item for item in jieba.cut(statement.text)])\n expression = mathparse.extract_expression(input_text, language=self.language)\n expression = mathparse.trans_expression(expression)\n\n response = Statement(text=expression)\n\n try:\n response.text += ' = ' + str(mathparse.parse(expression, language=self.language))\n\n # The confidence is 1 if the expression could be evaluated\n response.confidence = 1\n except Exception as EX:\n response.confidence = 0\n\n return response\n","sub_path":"smart_voice/logic_adapter/mathematical_evaluation.py","file_name":"mathematical_evaluation.py","file_ext":"py","file_size_in_byte":2406,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"336884345","text":"#!/usr/bin/env python3\n# vim: set fileencoding=utf-8\n\n\"\"\"Ukázka polymorfismu (třída jako rozhraní).\"\"\"\n\n\nclass Printable:\n def display(self):\n print(self)\n\n\nclass Person(Printable):\n def __init__(self, first_name, surname):\n \"\"\"Konstruktor objektu.\"\"\"\n print(\"Person.__init__\")\n self._first_name = first_name\n self._surname = surname\n\n def __str__(self):\n \"\"\"Speciální metoda pro převod objektu na řetězec.\"\"\"\n return \"**Person** Full name: {name} {surname}\".format(\n name=self._first_name, surname=self._surname\n )\n\n\nclass Student(Person):\n def __init__(self, first_name, surname):\n \"\"\"Konstruktor objektu.\"\"\"\n print(\"Student.__init__\")\n super().__init__(first_name, surname)\n\n def __str__(self):\n \"\"\"Speciální metoda pro převod objektu na řetězec.\"\"\"\n return \"**Student** Full name: {name} {surname}\".format(\n name=self._first_name, surname=self._surname\n )\n\n\nclass Employee(Person):\n def __init__(self, first_name, surname, salary):\n \"\"\"Konstruktor objektu.\"\"\"\n print(\"Employee.__init__\")\n super().__init__(first_name, surname)\n self._salary = salary\n\n def __str__(self):\n \"\"\"Speciální metoda pro převod objektu na řetězec.\"\"\"\n return \"**Employee** Full name: {name} {surname}   Salary: {salary}\".format(\n name=self._first_name, surname=self._surname, salary=self._salary\n )\n\n\npeople = [\n Person(\"Eda\", \"Wasserfall\"),\n Person(\"Přemysl\", \"Hájek\"),\n Student(\"John\", \"Doe\"),\n Employee(\"Eric\", \"Iverson\", 10000),\n]\n\nfor p in people:\n p.display()\n","sub_path":"Python2/examples/OOP/polymorphism2.py","file_name":"polymorphism2.py","file_ext":"py","file_size_in_byte":1685,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"146583280","text":"# coding: utf-8\n\nimport os,sys\nimport time\nfrom operator import itemgetter, attrgetter\nimport threading\nimport win32file\nimport shutil\nsys.path.append('../')\nfrom common.uty_log import log\n\ndef get_fs_info(caption = \"D:\"):\n '''\n 获取磁盘信息\n '''\n try:\n # 有可能不在 d:\n sectorsPerCluster, bytesPerSector, numFreeClusters, totalNumClusters = win32file.GetDiskFreeSpace(caption)\n freespace = (numFreeClusters * sectorsPerCluster * bytesPerSector) /(1024 * 1024 * 1024)\n except:\n log(\"can't stat disk space of %s\" % caption, project = 'recording', level = 2)\n freespace = 16\n\n log('get_fs_info ret free space %d' % freespace, project='recording', level=4)\n\n if freespace < 15:#小于10G的时候开始清理空间\n return True\n else:\n return False\n\ndef sort_cmp(a,b):\n '''\n 比较函数\n '''\n return int(a['time'] - b['time'])\n\ndef dir_list_file(path = 'D:\\RecordFile'):\n '''\n 获取指定目录下的文件夹\n '''\n dir_list = []\n if os.path.exists(path):\n for dir in os.listdir(path):\n dir_path = path + '\\\\' + dir\n if os.path.isdir(dir_path):\n dir_info = {}\n dir_info['path'] = dir_path\n dir_info['time'] = os.path.getmtime(dir_path)\n dir_list.append(dir_info)\n\n dir_list.sort(cmp = sort_cmp)\n return dir_list\n\ndef del_dir_schedule():\n thread = threading.Timer(600, del_dir_schedule) #10分钟执行一次\n thread.start()\n if get_fs_info():\n dir_list = dir_list_file()\n while get_fs_info():\n if len(dir_list)==0:\n return\n try:\n log('del dir: %s' % dir_list[0]['path'], project = 'recording', level = 2)\n shutil.rmtree(dir_list[0]['path'],True)\n except Exception as error:\n log('del dir: %s exception?: %s' % (dir_list[0]['path'], str(error)), project='recording', level=1)\n dir_list.remove(dir_list[0])\n\n","sub_path":"zkdm/python/package/recording/DiskManagement.py","file_name":"DiskManagement.py","file_ext":"py","file_size_in_byte":2038,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"227505795","text":"def hangman():\r\n import os\r\n import random\r\n os.system('cls')\r\n word = ['hello','Austria','Lamborghini','Python']\r\n word = (random.choice(word)).casefold()\r\n turns = 5\r\n guesses=''\r\n while turns > 0:\r\n flag = 0\r\n print(\"Turns:: \",turns)\r\n for char in word:\r\n if char in guesses:\r\n print(char,end='')\r\n else:\r\n print(\"-\",end='')\r\n flag += 1\r\n print()\r\n if flag == 0:\r\n print(\"You Won\")\r\n input()\r\n break\r\n guess = input(\"Enter guess:: \")\r\n guess = guess.casefold()\r\n guesses += guess\r\n if guess not in word:\r\n turns -= 1\r\n os.system('cls')\r\n if turns == 0:\r\n print(\"You Lose\")\r\n input()","sub_path":"hangman.py","file_name":"hangman.py","file_ext":"py","file_size_in_byte":819,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"} +{"seq_id":"159790643","text":"# POST请求的交互方式\r\nfrom urllib import request, parse\r\nimport json\r\n\r\n\r\nyoudaoURL = \"http://fanyi.youdao.com/translate?smartresult=dict&smartresult=rule\"\r\nheaders = {\"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:83.0) Gecko/20100101 Firefox/83.0\",\r\n \"X-Requested-With\": \"XMLHttpRequest\",\r\n \"Accept\": \"application/json, text/javascript, */*; q=0.01\",\r\n \"Content-Type\": \"application/x-www-form-urlencoded; charset=UTF-8\"}\r\n\r\nwhile True:\r\n key = input(\"请输入您要翻译的英文，输入closeMe则退出：\")\r\n if key == \"closeMe\":\r\n break\r\n formdata = {\"i\": key,\r\n \"from\": \"AUTO\",\r\n \"to\": \"AUTO\",\r\n \"smartresult\": \"dict\",\r\n \"client\": \"fanyideskweb\",\r\n \"salt\": \"16068304951076\",\r\n \"sign\": \"c12605cf87278ac11991702ceed38121\",\r\n \"lts\": \"1606830495107\",\r\n \"bv\": \"e2a78ed30c66e16a857c5b6486a1d326\",\r\n \"doctype\": \"json\",\r\n \"version\": \"2.1\",\r\n \"keyfrom\": \"fanyi.web\",\r\n \"action\": \"FY_BY_REALTlME\"}\r\n # 做一次urlencode\r\n data = parse.urlencode(formdata).encode(\"utf-8\")\r\n response = request.urlopen(youdaoURL, data)\r\n info = response.read().decode(\"utf-8\") # byte -> json str\r\n\r\n jsonLoads = json.loads(info)\r\n print(\"翻译如下:%s\" % jsonLoads[\"translateResult\"][0][0][\"tgt\"])\r\n\r\n# D:/学习资料/python/网络爬虫/day04/testCopyFiles2\r\n","sub_path":"aid1805/spider/day04/youdaoTest2.py","file_name":"youdaoTest2.py","file_ext":"py","file_size_in_byte":1510,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"22"}