diff --git "a/417.jsonl" "b/417.jsonl"
new file mode 100644--- /dev/null
+++ "b/417.jsonl"
@@ -0,0 +1,734 @@
+{"seq_id":"248253835","text":"import os\r\nimport numpy as np\r\nimport pandas as pd\r\nimport json\r\nimport re\r\nimport nltk\r\nfrom nltk.corpus import stopwords\r\nfrom nltk.stem import SnowballStemmer\r\nfrom nltk.tokenize import WordPunctTokenizer\r\nfrom gensim import models\r\nfrom sklearn.preprocessing import LabelEncoder\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.linear_model import LogisticRegression\r\nfrom sklearn.metrics import confusion_matrix, precision_recall_fscore_support \r\nimport dill\r\nimport pickle\r\nfrom sklearn.model_selection import cross_val_score\r\n\r\nclass classifier_logisticRegression:\r\n\tdef __init__(self, word2vec, json_file):\r\n\t\t# model = models.KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary=True)\r\n\t\tself.word2vec=models.KeyedVectors.load_word2vec_format(word2vec,binary=True)\r\n\t\tself.data=pd.read_json(json_file, lines=True)\r\n\t# def preprocessing_data(self, data):\r\n\tdef get_w2v_word(self, word):\r\n\t\treturn (self.word2vec[word])\r\n\tdef get_data_column(self, columns):\r\n\t\treturn (self.data[columns])\r\n\tdef preprocessing(self):\r\n\t\tself.data['text']=[\"title \"+ str(i)+\" \" for i in range(0,len(self.data['short_description']))]+self.data.short_description\r\n\tdef clean_text(self):\r\n\t\tstemmer = SnowballStemmer('english')\r\n\t\twords = stopwords.words('english')\r\n\t\tself.data['cleaned_text'] = self.data['text'].apply(lambda x:\" \".join([stemmer.stem(i) for i in re.sub(\"[^a-zA-Z]\",\" \",x).split() if i not in words]))\r\n\t\treturn self.data['cleaned_text']\r\n\tdef compute_doc_vec_single(self, clean_text):\r\n\t\tvec = np.zeros((self.word2vec.vector_size,), dtype=np.float32)\r\n\t\tn = 0\r\n\t\ttokenized_clean_text=nltk.word_tokenize(clean_text)\r\n\t\tfor word in tokenized_clean_text:\r\n\t\t\tif word in self.word2vec:\r\n\t\t\t\tvec += self.word2vec[word]\r\n\t\t\t\tn += 1\r\n\t\tif(n==0):\r\n\t\t\treturn (self.word2vec[\"Hello\"]*0)\r\n\t\telse:\r\n\t\t\treturn (vec/n)\r\n\tdef compute_doc_vec(self, clean_text):\r\n\t\treturn np.row_stack([self.compute_doc_vec_single(x) for x in clean_text])\r\n\r\n\tdef get_target_y(self):\r\n\t\ty_encoder=LabelEncoder()\r\n\t\ty=y_encoder.fit_transform(self.data['category'])\r\n\t\treturn y,y_encoder\r\n\tdef get_train_test(self, x, y):\r\n\t\ttrain_idx, test_idx = train_test_split(range(len(y)), test_size=0.1, stratify=y)\r\n\t\ttrain_x=x[train_idx, :]\r\n\t\ttrain_y=y[train_idx]\r\n\t\ttest_x=x[test_idx, :]\r\n\t\ttest_y=y[test_idx]\r\n\t\treturn train_x, train_y, test_x, test_y,test_idx\r\n\tdef get_LR_model(self, train_x, train_y):\r\n\t\tmodel=LogisticRegression(multi_class='multinomial',solver='lbfgs',max_iter=3000, n_jobs=-1)\r\n\t\tmodel.fit(train_x, train_y)\r\n\t\treturn (model)\r\n\tdef save_model(self, model, y_encoder):\r\n\t\toutput_dir=u'LR_output'\r\n\t\tif not os.path.exists(output_dir):\r\n\t\t\tos.mkdir(output_dir)\r\n\t\tmodel_file=os.path.join(output_dir, u'model.pkl')\r\n\t\twith open(model_file, 'wb') as outfile:\r\n\t\t\tpickle.dump({'y_encoder': y_encoder, 'lr': model}, outfile)\r\n\t\treturn model_file\r\n\t\r\n\tdef CV_predict(self,x,y):\r\n\t\tprint(\"10-fold cross validation...\")\r\n\t\tscore=cross_val_score(LogisticRegression(multi_class='multinomial', solver='lbfgs',max_iter=3000,verbose=2, n_jobs=-1), x,y, cv=10)\r\n\t\tprint(np.mean(score))\r\n\t\r\n\r\n\r\n\tdef tradition_predict(self, lr_model_file,test_idx, truth):\r\n\t\tprint(\"Traditional predict method...\")\r\n\t\tclf=Predictor(self.word2vec,lr_model_file)\r\n\t\tnew_y_pred = clf.predict(self.data['cleaned_text'][test_idx])\r\n\t\tdf=pd.DataFrame({u'test': new_y_pred, u'truth': truth[test_idx]})\r\n\t\ttest=list(map(str,df['test']))\r\n\t\ttruth=list(map(str,df['truth']))\r\n\t\tcount=0\r\n\t\tfor i in range(0, len(test)):\r\n\t\t\tif test[i]==truth[i]:\r\n\t\t\t\tcount+=1\r\n\t\tprint(count/len(test))\r\n\t\r\n\tdef process(self):\r\n\t\tprint(\"Preprocessing...\")\r\n\t\tself.preprocessing()\r\n\t\tprint(\"Cleaning text...\")\r\n\t\ttemp_clean_text=self.clean_text()\r\n\t\tprint(\"Getting the article vector...\")\r\n\t\tx=self.compute_doc_vec(temp_clean_text)\r\n\t\tprint(\"Getting train data and test data...\")\r\n\t\ty, y_encoder=self.get_target_y()\r\n\t\ttrain_x, train_y, test_x, test_y, test_idx=self.get_train_test(x,y)\r\n\t\tprint(\"Getting model...\")\r\n\t\tmodel=self.get_LR_model(train_x, train_y)\r\n\t\tprint(\"Saving model...\")\r\n\t\tmodel_file=self.save_model(model, y_encoder)\r\n\t\treturn model_file, test_idx, x,y\r\n\r\n\r\nclass Predictor(object):\r\n\tdef __init__(self, loaded_w2v, lr_model_file):\r\n\t\tself.w2v = loaded_w2v\r\n\t\twith open(lr_model_file, 'rb') as infile:\r\n\t\t\tself.model = pickle.load(infile)\r\n    \r\n\tdef predict(self, articles):\r\n\t\tx = self._compute_doc_vec(articles)\r\n\t\ty = self.model['lr'].predict(x)\r\n#         y_label = self.model1['y_encoder'].inverse_transform(y)\r\n\t\treturn y\r\n    \r\n\tdef _compute_doc_vec(self, clean_text):\r\n\t\treturn np.row_stack([self._compute_doc_vec_single(x) for x in clean_text])\r\n\r\n\tdef _compute_doc_vec_single(self, clean_text):\r\n\t\tvec = np.zeros((self.w2v.vector_size,), dtype=np.float32)\r\n\t\tn = 0\r\n\t\ttokenized_clean_text=nltk.word_tokenize(clean_text)\r\n\t\tfor word in tokenized_clean_text:\r\n\t\t\tif word in self.w2v:\r\n\t\t\t\tvec += self.w2v[word]\r\n\t\t\t\tn += 1\r\n\t\tif(n==0):\r\n\t\t\treturn (self.w2v[\"Hello\"]*0)\r\n\t\telse:\r\n\t\t\treturn (vec/n)\r\n\r\n\r\nif __name__==\"__main__\":\r\n\tprint(\"Loading the pre-trained word2vec file and the json file...\")\r\n\tLR_classifier=classifier_logisticRegression(\"GoogleNews-vectors-negative300.bin\", \"News_Category_Dataset_v2.json\")\r\n\tclf, test_idx, x,y=LR_classifier.process()\r\n\r\n\r\n\t#Two methods to get precision\r\n\t#Method1 tradition, the same as online test\r\n\t#**********************************************************\r\n\tLR_classifier.tradition_predict(clf, test_idx,y)\r\n\t#**********************************************************\r\n\t\r\n\t#Method2 10-fold cross validation\r\n\t#**********************************************************\r\n\t# LR_classifier.CV_predict(x,y)\r\n\t#**********************************************************\r\n\r\n\r\n","sub_path":"news_classifier/classifier_logisticRegression.py","file_name":"classifier_logisticRegression.py","file_ext":"py","file_size_in_byte":5716,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"254607490","text":"#!/usr/bin/env python\n\nfrom itertools import permutations\n\nwith open(\"inputs/day2.txt\", \"r\") as f:\n    nums = [list(map(int, l.split(\"\\t\"))) for l in f if l.strip() != \"\"]\n\nprint(sum(map(lambda r: max(r) - min(r), nums)))\nprint(sum([a // b for a, b in permutations(row, 2) if a % b == 0][0] for row in nums))\n\n","sub_path":"day2.py","file_name":"day2.py","file_ext":"py","file_size_in_byte":310,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"105343063","text":"# Some utility classes to represent a PDB structure\nimport numpy as np\nfrom itertools import combinations as combo\nimport pickle\n\nclass Atom:\n    \"\"\"\n    A simple class for an amino acid residue\n    \"\"\"\n\n    def __init__(self, type):\n        self.type = type\n        self.coords = (0.0, 0.0, 0.0)\n\n    # Overload the __repr__ operator to make printing simpler.\n    def __repr__(self):\n        return self.type\n\nclass Residue:\n    \"\"\"\n    A simple class for an amino acid residue\n    \"\"\"\n\n    def __init__(self, type, number):\n        self.type = type\n        self.number = number\n        self.atoms = []\n\n    # Overload the __repr__ operator to make printing simpler.\n    def __repr__(self):\n        return \"{0} {1}\".format(self.type, self.number)\n\nclass ActiveSite:\n    \"\"\"\n    A simple class for an active site\n    \"\"\"\n\n    def __init__(self, name):\n        self.name = name\n        self.residues = []\n        self.metrics = [None,None,None]\n\n    # Overload the __repr__ operator to make printing simpler.\n    def __repr__(self):\n        return self.name\n        \n    def get_raw_metrics(self):\n        if self.metrics[0] is None:\n            self._individual_metrics()\n        return self.metrics[0] \n    \n    def get_norm_metrics(self,redo_mean_dev=False):\n        if self.metrics[2] is None or redo_mean_dev:\n            self._individual_metrics()\n            self.metrics[1] = flatten_metrics(self.metrics[0])\n            self._normalize_metrics(redo_mean_dev)\n        return self.metrics[2]\n    \n    def _individual_metrics(self,printMe=None):\n        \"\"\"\n        Compute metrics of chemistry, morphology and homogeneity for an ActiveSite instance.\n\n        Input: ActiveSite instance\n        Output: a dictionary of metrics\n           \n        Metrics:\n            - Mean and Variance of total charge/polarity of residues\n            - Fraction of carbon / nitrogen / oxygen\n            - Total number of atoms / residues\n            - Mean distance between residue centers of mass\n        \"\"\"\n\n        chem_properties = {\n            \"GLY\": \"nonpolar\",\n            \"ALA\": \"nonpolar\",\n            \"VAL\": \"nonpolar\",\n            \"ILE\": \"nonpolar\",\n            \"LEU\": \"nonpolar\",\n            \"MET\": \"nonpolar\",\n            \"PHE\": \"nonpolar\",\n            \"TYR\": \"nonpolar\",\n            \"TRP\": \"nonpolar\",\n            \"PRO\": \"nonpolar\",\n            \"CYS\": \"nonpolar\",\n            \"SER\": \"polar\",\n            \"THR\": \"polar\",\n            \"ASN\": \"polar\",\n            \"GLN\": \"polar\",\n            \"ARG\": \"basic\",\n            \"HIS\": \"basic\",\n            \"LYS\": \"basic\",\n            \"ASP\": \"acidic\",\n            \"GLU\": \"acidic\",\n            }\n        \n        charge = {\"nonpolar\": 0, \"polar\": 0, \"basic\": 1, \"acidic\": -1}\n        polarity = {\"nonpolar\":0, \"polar\": 0.5, \"basic\": 1, \"acidic\": 1}\n        res_type = {\"nonpolar\":0, \"polar\": 0, \"basic\": 0, \"acidic\": 0}\n    \n        atom_type = {\"C\":0,\"N\":0,\"O\":0}\n    \n        metric_names = ['meanChar','meanPol','elemFracC',\n            'numAtoms','numRes','meanResDist',\n            'nonpolarFrac','acidicFrac','basicFrac']\n    \n        metrics = dict.fromkeys(metric_names)\n    \n        #first calculate mean of total charges/polarity of residues\n        residue_charge = np.ndarray(0)\n        residue_polarity = np.ndarray(0)\n        numRes = len(self.residues)\n    \n        for residue in self.residues:\n            res_type[chem_properties[residue.type.strip()]] += 1\n            residue_charge = np.append(residue_charge, charge[chem_properties[residue.type.strip()]])\n            residue_polarity = np.append(residue_polarity, polarity[chem_properties[residue.type.strip()]])\n    \n        for key in res_type.keys():\n            res_type[key] /= numRes\n        \n        self._update_dict(metrics,\n            ('meanChar','meanPol','nonpolarFrac','acidicFrac','basicFrac'),\n            (np.mean(residue_charge), np.mean(residue_polarity),res_type['nonpolar'],\n                res_type['acidic'],res_type['basic'])\n            )\n      \n        #now calculate the fraction of atoms of carbon in the active zone\n        numAtoms = 0\n        \n        for residue in self.residues:\n            for atom in residue.atoms:\n                numAtoms += 1\n                type = atom.type[0]\n                if type.lower()=='c':\n                    atom_type['C'] += 1\n    \n        for key in atom_type.keys():\n            atom_type[key] /= numAtoms\n    \n        self._update_dict(metrics,\n            ('elemFracC','numAtoms','numRes'),\n            (atom_type['C'],numAtoms,numRes)\n            )\n    \n        #now calculate the \"center of mass\" for each residue.\n        #then calculate the mean of the pairwise Euclidean distance between residue COMs.  \n\n        for residue in self.residues:\n            numAtoms = 0\n            centerOfMass = np.zeros(3)\n        \n            for atom in residue.atoms:\n                numAtoms += 1\n                centerOfMass += np.asarray(atom.coords)\n        \n            if not numAtoms==0:\n                centerOfMass /= numAtoms\n        \n            residue.com = tuple(centerOfMass)\n    \n        residue_dist = []\n    \n        for pair in combo(self.residues,2):\n            distance = np.linalg.norm(np.asarray(pair[0].com)-np.asarray(pair[1].com))\n            residue_dist.append(distance)  \n       \n        metrics['meanResDist'] = np.mean(residue_dist)\n    \n        if printMe:    \n            for metric in metrics.keys():\n                print('{0}\\n\\t\\t{1}\\n'.format(metric,metrics[metric]))\n        \n        #store\n        self.metrics[0] = metrics\n        \n        return self.metrics[0]\n    \n    \n    def _normalize_metrics(self,redo_mean_dev=False):\n        \"\"\"\n        Normalizes metric arrays to saved (or custom) dataset\n        \"\"\"\n    \n        #if you want to recalculate mean/devs\n        if redo_mean_dev:\n            io.gen_mean_dev_normalizations()\n    \n        means = load_pickle('means_arr')\n        devs = load_pickle('devs_arr')\n    \n        #normalize\n        \n        self.metrics[2] = (self.metrics[1] - means)/devs\n\n        return self.metrics[2]\n    \n    def _update_dict(self,dictionary,keys,values):\n        \"\"\"\n        Update dictionary given lists of keys and values\n    \n        Input: dictionary, keys, values\n        Output: None (dictionary updated automatically)\n        \"\"\"\n\n        dictionary.update(dict(zip(keys,values)))\n        \ndef save_pickle(obj, name):\n    with open('obj/'+ name + '.pkl', 'wb') as f:\n        pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)\n\ndef load_pickle(name):\n    with open('obj/' + name + '.pkl', 'rb') as f:\n        return pickle.load(f)\n\ndef flatten_metrics(metrics_dict):\n    \"\"\"\n    Flattens metric dictionary into a numpy array\n    \"\"\"\n\n    #NOTE: When converting into array, follows the metric_names list\n    #so that you can consistently compare numpy arrays element-wise\n\n    metric_names = ['meanChar','meanPol','elemFracC',\n        'numAtoms','numRes','meanResDist',\n        'nonpolarFrac','acidicFrac','basicFrac']\n\n    metric_arr = np.ndarray(0)\n\n    for metric in metric_names:\n        metric_arr = np.append(metric_arr,metrics_dict[metric])\n            \n    return metric_arr\n","sub_path":"clustering/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":7149,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"378451947","text":"import os\nfrom code.general import folder_management\nfrom code.general import request_file\nfrom code.general import run_multiprocessing\n\ndef download_files(list_of_enzyme_commission_numbers, list_of_databases, cpus):\n    \"\"\" Downloads a url into an HTML file\n        Creates data/raw path if does not exist\n        Creates a list of files to download, running multiprocessing for speed purposes\n        Multiprocessing function runs using a list of tupples, number of cpus and a function\n    \"\"\"\n    data_path = \"data/raw\"\n    folder_management.create_folder(data_path)\n    list_of_downloads = []\n    for enzyme in list_of_enzyme_commission_numbers:\n        for database in list_of_databases:\n            download_data = ((enzyme, database, data_path))\n            list_of_downloads.append(download_data)\n    run_multiprocessing.run_mp(list_of_downloads, cpus, download_manager)\n\ndef download_manager(enzyme, database, data_path):\n    \"\"\" Function that is being multiprocessed\n        Runs request_file script, who downloads the url into HTML file\n        download_path: in our case, it is conserved always with the same scheme\n        if the file that we are going to save does not exists, it makes the request\n    \"\"\"\n    download_path = \"https://www.genome.jp/dbget-bin/get_linkdb?-t+{}+ec:{}\".format(database, enzyme)\n    name_of_the_file_path = \"{}/{}_{}_raw.txt\".format(data_path, enzyme, database)\n    if not os.path.exists(name_of_the_file_path):\n        print(\"Downloading {} enzyme information from {} database. Path: {}\".format(enzyme, database, download_path))\n        request_file.download_file(download_path, name_of_the_file_path)\n","sub_path":"code/downloader/download_information.py","file_name":"download_information.py","file_ext":"py","file_size_in_byte":1646,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"527586701","text":"import random\n\nclass Fruit():\n\n    def __init__(self,tile_x,tile_y):\n        self._tile_x = tile_x\n        self._tile_y = tile_y\n        self._object = []\n    \n    def GetBody(self):\n        return self._object()\n\n    def SpawnFruit(self):\n        x = random.randint(1,self._tile_x-1)\n        y = random.randint(1,self._tile_y-1)\n        if (x,y) in self._player_1.GetBody() or (x,y) in self._player_2.GetBody():\n            self.SpawnFruit()\n        else:\n            self._fruit = [(x,y)]","sub_path":"fruit.py","file_name":"fruit.py","file_ext":"py","file_size_in_byte":490,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"411918235","text":"import vk,json,time,random\n\nclass PostToGroupOperation:\n    \"\"\"This class execute requests from users\n     to spam group via static methods implemented below\"\"\"\n\n    @staticmethod\n    def get_vk_api(token):\n        if None is not token:\n            session = vk.Session(access_token=token)\n            return vk.API(session=session)\n        return None\n\n    @staticmethod\n    def send_message(vk_api):\n        try:\n            print(vk_api.messages.send(domain = \"krupenin_pavel\", message = \"Hello\"))\n        except Exception as err:\n            print(err)\n\n\n    @staticmethod\n    def getFriends(vk_api):\n        print(vk_api.friends.get(fields = 'nickname'))\n\n    @staticmethod\n    def send_posts_to_groups(vk_api,list_of_posts,list_of_groups):\n            try:\n                for i in list_of_groups:\n                    print(vk_api.wall.post(owner_id = '-' + i,message = random.choice(list_of_posts)))\n                    time.sleep(5)\n            except Exception as err:\n                print(err)\n\n\n","sub_path":"Model/Logic/VkOperations/PostToGroup/PostToGroupOperation.py","file_name":"PostToGroupOperation.py","file_ext":"py","file_size_in_byte":1007,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"120111283","text":"# -*- coding: utf-8 -*-\n\"\"\"\nMétodo simple de infiltración de Green-Ampt\nde una capa. \n\nBasado en ecuaciones presentadas en \n\nChow, V.T., Maidment, D.R., Mays, L.W., 1988. Applied Hydrology, \n\tMcGraw-Hill series in water resources and environmental \n\tengineering. Tata McGraw-Hill Education.\n\nAutor: Alejandro Jaramillo\n\"\"\"\nimport numpy as np\nfrom scipy.optimize import fsolve\nimport matplotlib.pyplot as plt\nfrom matplotlib import animation\n\n### PARAMETROS INGRESADOS POR EL USUARIO ###\n\n#Porosidad(n=\\eta)\nn = 0.501\n#Porosidad efectiva (Qe=\\theta_e)\nQe = 0.486\n#Cabesa de succión del suelo en el frente mojado\n#(Psi=\\psi) en cm.\nPsi = 16.68 # cm\n#Conductividad hidráulica (K) en cm/hora \nK = 0.65 # cm/h\n#Saturación efectiva (Se)\nSe = 0.3 # cm\n# Horas de simulación\nSimT = 3 # Horas de similación\n### PARAMETROS INGRESADOS POR EL USUARIO ###\n\nDQ = (1.0-Se)*Qe\nt = np.linspace(0,SimT*60,SimT*60)\nt = t/60\nF = np.zeros(len(t))\n\n\n\ndef func(F,t):\n\tfun = F-(K*t)-(Psi*DQ*np.log(1.0+(F/(Psi*DQ))))\n\treturn fun\n\ni = 0\nfor tt in t:\n\tF[i] = fsolve(func,K*tt,args=tt)\n\ti += 1\n\nf = K*(((Psi*DQ)/F[1:])+1.0)\ntf = t[1:]\n\nFmax = F.max()\nfmax =  f.max()\n\nfig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 5))\nfig.suptitle('Modelo de Green-Ampt de 1 Capa \\n'+r'$\\eta=$'+str(n)+r'; $\\theta_e=$'+str(Qe)+r'; $\\psi=$'+str(Psi)+r'; $K=$'+str(K)+r'; $S_e=$'+str(Se))\nax1 = axes[0]\nax1.set_xlim([0, SimT])\nax1.set_ylim([0, Fmax])\nax1.set_xlabel('Tiempo [Horas]')\nax1.set_ylabel('F [cm]')\nax1.plot(t, F, lw=2,ls='-.',color='b')\nax2 = axes[1]\nax2.set_xlim([0, SimT])\nax2.set_ylim([0, fmax])\nax2.set_xlabel('Tiempo [Horas]')\nax2.set_ylabel('f [cm/Hora]')\nax2.plot(tf, f, lw=2,ls='-.',color='r')\nplt.savefig('Modelo_GreenAmpt.png',format='png')\n#plt.show()\nplt.close(1)\n\n\nfig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 5))\nfig.suptitle('Modelo de Green-Ampt de 1 Capa \\n'+r'$\\eta=$'+str(n)+r'; $\\theta_e=$'+str(Qe)+r'; $\\psi=$'+str(Psi)+r'; $K=$'+str(K)+r'; $S_e=$'+str(Se))\nax1 = axes[0]\nax1.set_xlim([0, SimT])\nax1.set_ylim([0, Fmax])\nax1.set_xlabel('Tiempo [Horas]')\nax1.set_ylabel('F [cm]')\nlineF, = ax1.plot([], [], lw=2,ls='-.',color='b')\nax2 = axes[1]\nax2.set_xlim([0, SimT])\nax2.set_ylim([0, fmax])\nax2.set_xlabel('Tiempo [Horas]')\nax2.set_ylabel('f [cm/Hora]')\nlinef, = ax2.plot([], [], lw=2,ls='-.',color='r')\n\ndef init():\n\tlineF.set_data([], [])\n\tlinef.set_data([], [])\n\treturn lineF,linef,\n\ndef animate(i):\n\t#print \"Frame = \" + str(i) \n\tlineF.set_data(t[0:i], F[0:i])\n\tlinef.set_data(tf[0:i], f[0:i])\n\treturn lineF,linef,\n\nanim = animation.FuncAnimation(fig, animate, init_func=init,\n                               frames=len(t)-2, interval=100, blit=True)\n\n# save the animation as an mp4.  This requires ffmpeg or mencoder to be\n# installed.  The extra_args ensure that the x264 codec is used, so that\n# the video can be embedded in html5.  You may need to adjust this for\n# your system: for more information, see\n# http://matplotlib.sourceforge.net/api/animation_api.html\nanim.save('GreenAmpt.mp4', fps=100, extra_args=['-vcodec', 'libx264'])\n\nplt.show()\n","sub_path":"Hidrologia/GreenAmpt_1LModel/GreenAmpt_1LModelV2.0.py","file_name":"GreenAmpt_1LModelV2.0.py","file_ext":"py","file_size_in_byte":3066,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"618019871","text":"import unittest\n\nfrom pyalink.alink import *\n\n\nclass TestPinjiu(unittest.TestCase):\n\n    def test_doc_hash_count_vectorizer_op(self):\n        import numpy as np\n        import pandas as pd\n        data = np.array([\n            [0, u'二手旧书:医学电磁成像'],\n            [1, u'二手美国文学选读（ 下册 ）李宜燮南开大学出版社 9787310003969'],\n            [2, u'二手正版图解象棋入门/谢恩思主编/华龄出版社'],\n            [3, u'二手中国糖尿病文献索引'],\n            [4, u'二手郁达夫文集（ 国内版 ）全十二册馆藏书']])\n        df = pd.DataFrame({\"id\": data[:, 0], \"text\": data[:, 1]})\n        inOp1 = BatchOperator.fromDataframe(df, schemaStr='id int, text string')\n        inOp2 = StreamOperator.fromDataframe(df, schemaStr='id int, text string')\n\n        segment = SegmentBatchOp().setSelectedCol(\"text\").linkFrom(inOp1)\n        train = DocHashCountVectorizerTrainBatchOp().setSelectedCol(\"text\").linkFrom(segment)\n        predictBatch = DocHashCountVectorizerPredictBatchOp().setSelectedCol(\"text\").linkFrom(train, segment)\n        [model, predict] = collectToDataframes(train, predictBatch)\n        print(model)\n        print(predict)\n","sub_path":"python/src/main/python/pyalink/alink/tests/examples/operator/batch/test_doc_hash_count_vectorizer.py","file_name":"test_doc_hash_count_vectorizer.py","file_ext":"py","file_size_in_byte":1213,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"282351668","text":"from keras.optimizers import Adam\nfrom keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau, TerminateOnNaN, CSVLogger\nfrom keras import backend as K\nfrom keras.models import load_model\n\nfrom models.keras_ssd7 import build_model\nfrom keras_loss_function.keras_ssd_loss import SSDLoss\nfrom keras_layers.keras_layer_AnchorBoxes import AnchorBoxes\nfrom keras_layers.keras_layer_DecodeDetections import DecodeDetections\nfrom keras_layers.keras_layer_DecodeDetectionsFast import DecodeDetectionsFast\n\nfrom ssd_encoder_decoder.ssd_input_encoder import SSDInputEncoder\nfrom ssd_encoder_decoder.ssd_output_decoder import decode_detections, decode_detections_fast\n\nfrom data_generator.object_detection_2d_data_generator import DataGenerator\nfrom data_generator.object_detection_2d_misc_utils import apply_inverse_transforms\nfrom data_generator.data_augmentation_chain_variable_input_size import DataAugmentationVariableInputSize\nfrom data_generator.data_augmentation_chain_constant_input_size import DataAugmentationConstantInputSize\nfrom data_generator.data_augmentation_chain_original_ssd import SSDDataAugmentation\n\nimport os\nimport csv\nimport cv2\nfrom math import ceil\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\nimg_height  = 300 # Height of the input images\nimg_width = 480 # Width of the input images\nimg_channels = 3 # Number of color channels of the input images\nintensity_mean = 127.5 # Set this to your preference (maybe `None`). The current settings transform the input pixel values to the interval `[-1,1]`.\nintensity_range = 127.5 # Set this to your preference (maybe `None`). The current settings transform the input pixel values to the interval `[-1,1]`.\nn_classes = 3 # Number of positive classes\nscales = [0.08, 0.16, 0.32, 0.64, 0.96] # An explicit list of anchor box scaling factors. If this is passed, it will override `min_scale` and `max_scale`.\naspect_ratios = [0.5, 1.0, 2.0] # The list of aspect ratios for the anchor boxes\ntwo_boxes_for_ar1 = True # Whether or not you want to generate two anchor boxes for aspect ratio 1\nsteps = None # In case you'd like to set the step sizes for the anchor box grids manually; not recommended\noffsets = None # In case you'd like to set the offsets for the anchor box grids manually; not recommended\nclip_boxes = False # Whether or not to clip the anchor boxes to lie entirely within the image boundaries\nvariances = [1.0, 1.0, 1.0, 1.0] # The list of variances by which the encoded target coordinates are scaled\nnormalize_coords = True # Whether or not the model is supposed to use coordinates relative to the image size\n\nbatch_size = 64\n\ndef loadModel(path_model):\n\tssd_loss = SSDLoss(neg_pos_ratio=3, alpha=1.0)\n\tmodel = load_model(path_model, custom_objects={'AnchorBoxes':AnchorBoxes,\n\t\t\t\t\t\t\t\t\t'compute_loss':ssd_loss.compute_loss})\n\treturn model\n\ndef buildModel(path_weights=None):\n\n\tmodel = build_model(image_size=(img_height, img_width, img_channels),\n\t\t\t\t\t\t\tn_classes=n_classes,\n\t\t\t\t\t\t\tmode='training',\n\t\t\t\t\t\t\tl2_regularization=0.0005,\n\t\t\t\t\t\t\tscales=scales,\n\t\t\t\t\t\t\taspect_ratios_global=aspect_ratios,\n\t\t\t\t\t\t\taspect_ratios_per_layer=None,\n\t\t\t\t\t\t\ttwo_boxes_for_ar1=two_boxes_for_ar1,\n\t\t\t\t\t\t\tsteps=steps,\n\t\t\t\t\t\t\toffsets=offsets,\n\t\t\t\t\t\t\tclip_boxes=clip_boxes,\n\t\t\t\t\t\t\tvariances=variances,\n\t\t\t\t\t\t\tnormalize_coords=normalize_coords,\n\t\t\t\t\t\t\tsubtract_mean=intensity_mean,\n\t\t\t\t\t\t\tdivide_by_stddev=intensity_range)\n\n\tif path_weights:\n\t\tmodel.load_weights(path_weights, by_name=True)\n\n\tadam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)\n\tssd_loss = SSDLoss(neg_pos_ratio=3, alpha=1.0)\n\tmodel.compile(optimizer=adam, loss=ssd_loss.compute_loss)\n\n\treturn model\n\ndef createModel(path_model=None, path_weights=None):\n\tif path_model:\n\t\tmodel = loadModel(path_model)\n\telse:\n\t\tmodel = buildModel(path_weights)\n\ndef createGenerator(dir_img, file_labels, file_h5=None, batch_size=64):\n\n\tdata_augmentation_chain = DataAugmentationConstantInputSize(\n\t\t\t\t\t\t\t\trandom_brightness=(-48, 48, 0.5),\n\t\t\t\t\t\t\t\trandom_contrast=(0.5, 1.8, 0.5),\n\t\t\t\t\t\t\t\trandom_saturation=(0.5, 1.8, 0.5),\n\t\t\t\t\t\t\t\trandom_hue=(18, 0.5),\n\t\t\t\t\t\t\t\trandom_flip=0.5,\n\t\t\t\t\t\t\t\trandom_translate=((0.03,0.5), (0.03,0.5), 0.5),\n\t\t\t\t\t\t\t\trandom_scale=(0.5, 2.0, 0.5),\n\t\t\t\t\t\t\t\tn_trials_max=3,\n\t\t\t\t\t\t\t\tclip_boxes=True,\n\t\t\t\t\t\t\t\toverlap_criterion='area',\n\t\t\t\t\t\t\t\tbounds_box_filter=(0.3, 1.0),\n\t\t\t\t\t\t\t\tbounds_validator=(0.5, 1.0),\n\t\t\t\t\t\t\t\tn_boxes_min=1,\n\t\t\t\t\t\t\t\tbackground=(0,0,0))\n\n\tpredictor_sizes = [model.get_layer('classes4').output_shape[1:3],\n\t\t\t\t\t\tmodel.get_layer('classes5').output_shape[1:3],\n\t\t\t\t\t\tmodel.get_layer('classes6').output_shape[1:3],\n\t\t\t\t\t\tmodel.get_layer('classes7').output_shape[1:3]]\n\n\tssd_input_encoder = SSDInputEncoder(img_height=img_height,\n\t\t\t\t\t\t\t\t\timg_width=img_width,\n\t\t\t\t\t\t\t\t\tn_classes=n_classes,\n\t\t\t\t\t\t\t\t\tpredictor_sizes=predictor_sizes,\n\t\t\t\t\t\t\t\t\tscales=scales,\n\t\t\t\t\t\t\t\t\taspect_ratios_global=aspect_ratios,\n\t\t\t\t\t\t\t\t\ttwo_boxes_for_ar1=two_boxes_for_ar1,\n\t\t\t\t\t\t\t\t\tsteps=steps,\n\t\t\t\t\t\t\t\t\toffsets=offsets,\n\t\t\t\t\t\t\t\t\tclip_boxes=clip_boxes,\n\t\t\t\t\t\t\t\t\tvariances=variances,\n\t\t\t\t\t\t\t\t\tmatching_type='multi',\n\t\t\t\t\t\t\t\t\tpos_iou_threshold=0.5,\n\t\t\t\t\t\t\t\t\tneg_iou_limit=0.3,\n\t\t\t\t\t\t\t\t\tnormalize_coords=normalize_coords)\n\n\n\n\tdatagen = DataGenerator(load_images_into_memory=False, hdf5_dataset_path=file_h5)\n\tdatagen.parse_csv(images_dir=dir_img,\n\t\t\t\t\t\t\tlabels_filename=file_labels,\n\t\t\t\t\t\t\tinput_format=['xmin', 'ymin', 'xmax', 'ymax', 'image_name','class_id'],\n\t\t\t\t\t\t\tinclude_classes='all')\n\n\tif not file_h5:\n\t\tdatagen.create_hdf5_dataset(file_path=file_h5,\n\t\t\t\t\t\t\t\t\tresize=False,\n\t\t\t\t\t\t\t\t\tvariable_image_size=False,\n\t\t\t\t\t\t\t\t\tverbose=True)\n\n\tprint('number of images: %d' %datagen.get_dataset_size())\n\n\tgenerator = datagen.generate(batch_size=batch_size,\n\t\t\t\t\t\t\t\t\tshuffle=True,\n\t\t\t\t\t\t\t\t\ttransformations=[data_augmentation_chain],\n\t\t\t\t\t\t\t\t\tlabel_encoder=ssd_input_encoder,\n\t\t\t\t\t\t\t\t\treturns={'processed_images',\n\t\t\t\t\t\t\t\t\t\t\t'encoded_labels'},\n\t\t\t\t\t\t\t\t\tkeep_images_without_gt=False)\n\n\treturn generator, datagen.get_dataset_size()\n\ndef createCallBacks(path_history):\n\tif not os.path.isdir(path_history):\n\t\tos.mkdir(path_history)\n\n\tfile_checkpoint = '{epoch:03d}_{loss:.4f}_{val_loss:.4f}.h5'\n\tfile_log = 'ssd7_training_log.csv'\n\n\tcheckpoint = ModelCheckpoint(filepath=os.path.join(path_history,file_checkpoint),\n\t\t\t\t\t\t\t\tmonitor='val_loss',\n\t\t\t\t\t\t\t\tverbose=1,\n\t\t\t\t\t\t\t\tsave_best_only=True,\n\t\t\t\t\t\t\t\tsave_weights_only=False,\n\t\t\t\t\t\t\t\tmode='auto',\n\t\t\t\t\t\t\t\tperiod=1)\n\n\tlogger = CSVLogger(filename=os.path.join(pathHistory,file_log),\n\t\t\t\t\t\t\tseparator=',',\n\t\t\t\t\t\t\tappend=True)\n\n\tearly_stopping = EarlyStopping(monitor='val_loss',\n\t\t\t\t\t\t\t\tmin_delta=0.0,\n\t\t\t\t\t\t\t\tpatience=10,\n\t\t\t\t\t\t\t\tverbose=1)\n\n\treduce_lr = ReduceLROnPlateau(monitor='val_loss',\n\t\t\t\t\t\t\t\t\t\tfactor=0.1,\n\t\t\t\t\t\t\t\t\t\tpatience=5,\n\t\t\t\t\t\t\t\t\t\tverbose=1,\n\t\t\t\t\t\t\t\t\t\tmin_delta=0.001,\n\t\t\t\t\t\t\t\t\t\tcooldown=0,\n\t\t\t\t\t\t\t\t\t\tmin_lr=0.00001)\n\n\tcallbacks = [checkpoint, logger, early_stopping, reduce_lr]\n\n\treturn callbacks\n\nif __name__ == \"__main__\":\n\tdir_img = '/DataSet/Crowdai300/' # image dir\n\tfile_labels_train = '/DataSet/train.csv' # file labels train\n\tfile_labels_val = '/DataSet/val.csv'\t# file labels val\n\tpath_history = '/DataSet/History'\t\t# dir contain results\n\t\n\tmodel = createModel()\n\n\tgenerator_train, nb_samples_train = createGenerator(dir_img, file_labels_train, batch_size=batch_size)\n\tgenerator_val, nb_samples_val = createGenerator(dir_img, file_labels_val, batch_size=batch_size)\n\n\tcallbacks = createCallBacks(path_history)\n\n\tini_epoch = 0\n\tnb_epochs = 200\n\tsteps = ceil(nb_samples_train/batch_size) \n\tsteps_val = ceil(nb_samples_val/batch_size) \n\n\tmodel.fit_generator(generator=generator_train,\n\t\t\t\t\t\tsteps_per_epoch=steps_per_epoch,\n\t\t\t\t\t\tepochs=nb_epochs,\n\t\t\t\t\t\tcallbacks=callbacks,\n\t\t\t\t\t\tvalidation_data=generator_val,\n\t\t\t\t\t\tvalidation_steps=steps_val,\n\t\t\t\t\t\tinitial_epoch=ini_epoch)\n\n","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":7797,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"131058160","text":"from DibujoSvg import Dibujo\nfrom Punto import Punto\nfrom FibonacciHeap import FH\nfrom random import randint\nfrom time import sleep, time\nimport matplotlib.pyplot as plt\n\n\n# iniciadores\ndef CrearPuntos(ancho, alto, numPuntos=50):  # Cambias el valor para decidir cuantos puntos habrá\n    # Crea un número N de puntos aleatorios y su matriz de adyacencia\n    dibujo = Dibujo(ancho, alto)\n    l = []  # lista de puntos\n    g = []  # matriz de adyacencia\n\n    for i in range(0, numPuntos):\n        l.append(Punto(randint(0, alto), randint(0, ancho), aleatorio=False))  # Se crea un punto aleatorio\n        dibujo.EscribirLinea(l[-1].GetSvg())  # Se dibuja en SVG el punto creado\n        # sleep(0.25)#Se espera\n    for j in range(len(l)):\n        g.append([0] * len(l))  # Se inicializan a 0 las distancias\n    for i in range(0, len(l)):\n        for j in range(0, len(l)):\n            if i == j:\n                g[i][j] = float(\n                    \"inf\")  # La distancia de un punto a él mismo se pone infinita para que no sea factible nunca\n            else:\n                g[i][j] = l[i].CalcularDistancia(l[j])  # Se rellena la matriz con las distancias a todos los puntos\n                g[j][i] = g[i][j]\n    return l, g, dibujo\n\n\ndef CrearCuadrícula(ancho, alto, numPuntos, dibujo):\n    l = []  # lista de puntos\n    g = []  # matriz de adyacencia\n    separación = 50\n    for i in range(0, numPuntos):\n        for j in range(0, numPuntos):\n            l.append(Punto(10 + i * separación, 10 + j * separación))  # Se crea un punto aleatorio\n            dibujo.EscribirLinea(l[-1].GetSvg())  # Se dibuja en SVG el punto creado\n            # sleep(0.25)#Se espera\n    for j in range(len(l)):\n        g.append([0] * len(l))\n    for i in range(0, len(l)):\n        print(i)\n        for j in range(0, len(l)):\n            if i == j:\n                g[i][j] = float(\"inf\")\n            else:\n                g[i][j] = l[i].CalcularDistancia(l[j])  # Se rellena la matriz con las distancias a todos los puntos\n                g[j][i] = g[i][j]\n    return l, g\n\n\n\ndef SacarAristas(g, elem, visitados, aristas=FH()):  # Devuelve en una lista todas las aristas del elemento indicado\n    for i in range(0, len(g[elem])):\n        if not visitados[i] and g[elem][i]!=float(\"inf\"):\n            aristas.añadir((elem, i, g[elem][i]))  # Se devuelve i,j,distancia\n    return aristas\n\ndef CrearLinea(p1, p2):\n    # Crea una línea entre dos puntos, además, deja un espacio vacío para que quede bonito\n    i1, j1 = p1.GetPuntos()\n    r1 = p1.GetRadio()\n    r2 = p2.GetRadio()\n    i2, j2 = p2.GetPuntos()\n    IncrementoJ1 = (j2 - j1)\n    IncrementoJ2 = -IncrementoJ1\n    IncrementoI1 = (i2 - i1)\n    IncrementoI2 = -IncrementoI1\n\n    h = (IncrementoI1 ** 2 + IncrementoJ1 ** 2) ** 0.5  # hipotenusa\n    return '<line x1=\"' + str(j1 + r1 * (IncrementoJ1) / h) + '\" y1=\"' + str(\n        i1 + r1 * (IncrementoI1) / h) + '\" x2=\"' + str(j2 + r2 * (IncrementoJ2) / h) + '\" y2=\"' + str(\n        i2 + r2 * (IncrementoI2) / h) + '\" style=\"stroke:rgb(240,240,240);stroke-width:2\" />'\n\n\ndef Prim(g, l, dibujo):\n    visitados = [False] * len(g)\n    visitados[0] = True\n    aristas = SacarAristas(g, 0, visitados)\n    while not all(visitados) and not aristas.IsEmpty():\n        linea = aristas.extractmin()\n        if not visitados[linea[1]]:\n            dibujo.EscribirLinea(CrearLinea(l[linea[0]], l[linea[1]]), True)  # Se dibuja la linea que une los puntos\n            #sleep(5)\n            dibujo.Mostrar()\n            visitados[linea[1]] = True\n            SacarAristas(g, linea[1], visitados, aristas)  # Se añaden las aristas del nuevo punto\n    dibujo.Terminar()\n\n\n\ndef UnirTodo(g, l, dibujo):\n    for i in range(0, len(g)):\n        for j in range(0, len(g)):\n            if not i == j:\n                dibujo.EscribirLinea(CrearLinea(l[i], l[j]), True)\n            else:\n                break\n    dibujo.Mostrar()\n\n\nancho = 1920\nalto = 1080\npuntos = 50\n\nsleep(1)\nl, g, d = CrearPuntos(ancho, alto, puntos)\n\"\"\"\np1=Punto(200,300)\np2=Punto(100,200)\nEscribirLinea(p1.GetSvg())\nEscribirLinea(p2.GetSvg())\nEscribirLinea(CrearLinea(p2,p1))\n\"\"\"\nd.Mostrar()\nPrim(g, l, d)\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4153,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"353659998","text":"array=[5,7,9,0,3,1,6,2,4,8]\n\ndef quick_sort(array,start,end):\n    if start>=end: # 원소가 1개인 경우 종료\n        return\n    pivot=start #피벗은 첫번째 원소\n    left=start+1\n    right =end\n    while left<=right:\n        while left <= end and array[left] <= array[pivot]:\n            left +=1\n        while right >start and array[right] >=array[pivot]:\n            right -=1\n\n        if left >right:\n            array[right],array[pivot]=array[pivot],array[right]\n        else:\n            array[left],array[right]=array[right],array[left]\n    quick_sort(array,start,right-1)\n    quick_sort(array,right+1,end)\n\nquick_sort(array,0,len(array)-1)\nprint(array)\n","sub_path":"정렬/QuickSort.py","file_name":"QuickSort.py","file_ext":"py","file_size_in_byte":673,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"600072868","text":"from inhere import myproject\nimport re\n\n\ninfo = {'Cid': '2019171064',\n        'Cname': '天津华电南疆热电环保信息化平台项目',\n        'Ccompany': '天津华电南疆热电有限公司',\n        'Caddress': '天津市塘沽区港塘路2657号 天津华电南疆热电有限公司 物资部',\n        'Cperson': '曾春',\n        'Cphone': '18874053395',\n        }\n\n\n\npath = r\"C:\\Users\\gao\\Desktop\\环保\\projects\"\nname = \"2019173067广东华电韶关热电有限公司环保信息化平台合同\"\nfilename = \"深圳坪山\"\n\na1 = myproject(path)\na1.makepro('天津南疆')\na1.makefy(info)\n\n\n\n\nid = re.match(r\"\\d*\",name)\norg = re.match(r\"[^0-9]*\",name)\n\n\n\n\n","sub_path":"Xiangmu/ProjectXG.py","file_name":"ProjectXG.py","file_ext":"py","file_size_in_byte":670,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"104707952","text":"#!/usr/bin/python\n\"\"\"\nTime series analysis of data\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pylab as ppl\n\nppl.close('all')\n\n# Load paramters\nimport ConfigDetailSingle\nreload(ConfigDetailSingle)\nfrom ConfigDetailSingle import *\n\nnT = 2       #Thermistor number\n\n# Chunks working best for 380-450 \nChunk1 = np.array([388, 404.5])\nChunk2 = np.array([408.5, 422.2])\n\n# For 290-350\n#Chunk1 = np.array([291.4, 306.5])\n#Chunk2 = np.array([312.1, 335.3])\n#Chunk2 = np.array([311.9, 315.2])\n#Chunk2 = np.array([307.4, 315.2])\n#Chunk2 = np.array([331.1, 341.5])\n\n#function to perform block averaging\nfrom BlockAverage import BlockAverage\n\nprint ('Load data...')\n\nfrom netCDF4 import Dataset as ncdf\nfile = ncdf(DetailDir+'Output_data.nc', mode='r', format='NETCDF4')\ntime = file.variables['Time'][:]\nT    = file.variables['Temp'][nT-1,np.nonzero((time>=Start)&(time<=End))[0]]\nZ    = file.variables['Depth'][nT-1]\nddt  = file.SkipData\nfile.close()\ntime = time[np.nonzero((time>=Start)&(time<=End))]\n\nprint ('Data loaded')\n\n# Make block average of data if subsampling is requested\nT    = BlockAverage(T, ddt, SubSamp)\ntime = BlockAverage(time, ddt, SubSamp)\n\n# Interpolate to regular time step\nintTime = np.arange(time[0], time[-1], SubSamp/86400.)\nData    = np.interp(intTime, time, T, left=np.nan, right=np.nan)\ntime    = intTime\n\n# For the FFT analysis\nfrom scipy.signal import detrend\nT1    = T[np.nonzero((time>Chunk1[0])&(time<=Chunk1[1]))]\nT1    = detrend(T1)\nT2    = T[np.nonzero((time>Chunk2[0])&(time<=Chunk2[1]))]\nT2    = detrend(T2)\ntime1 = time[np.nonzero((time>Chunk1[0])&(time<=Chunk1[1]))]\ntime2 = time[np.nonzero((time>Chunk2[0])&(time<=Chunk2[1]))]\n\nprint ('Fourier analysis')\n\n# Use Hanning window (see pag.445 Data Analysis Methods in Phys. Oc)\nfrom scipy.signal import hann\nT1w = T1*hann(T1.size)\nT2w = T2*hann(T2.size)\n# Use Kaiser window\n#from scipy.signal import kaiser\n#T1 *= kaiser(T1.size, 2)\n#T2 *= kaiser(T1.size, 2)\n\nprint ('Compute FFT')\n# Use matplotlib psd\n\nN1 = 2**int(np.log2(T1w.size)+1); N2 = 2**int(np.log2(T2w.size)+1)\n#N1 = 2**22; N2=2**22\n\n# 8/3 for hanning\n# N1/T1.size for compensate zero padding\nfft_pw1 = (N1/np.float(T1.size))*np.abs(((8./3.)**0.5)*np.fft.fft(T1w,N1)) ** 2     # FFT power spectrum\nfft_pw2 = (N2/np.float(T2.size))*np.abs(((8./3.)**0.5)*np.fft.fft(T2w,N2)) ** 2\nfreqs1 = np.fft.fftfreq(len(fft_pw1), SubSamp) # frequencies of FFT\nfreqs2 = np.fft.fftfreq(len(fft_pw2), SubSamp)\n\n# Normalization: Dt*abs(FFT)**2/N\n# this way Df*sum(power)/N = variance\nfft_power1 = SubSamp*fft_pw1[:fft_pw1.size/2]/N1\nfft_power1[1:] += SubSamp*fft_pw1[fft_pw1.size/2+1:]/N1\nfft_power2 = SubSamp*fft_pw2[:fft_pw2.size/2]/N2\nfft_power2[1:] += SubSamp*fft_pw2[fft_pw2.size/2+1:]/N2\nfreqs1 = freqs1[:freqs1.size/2]\nfreqs2 = freqs2[:freqs2.size/2]\n\nwin = 19 #width of smoothing window\nsm_fft_power1 = np.convolve(fft_power1,np.ones(win))\nsm_fft_power1 = sm_fft_power1[(win/2):-(win/2)]/np.float(win)\nsm_fft_power2 = np.convolve(fft_power2,np.ones(win))\nsm_fft_power2 = sm_fft_power2[(win/2):-(win/2)]/np.float(win)\n\n# significance levels for smoothed spectra (pag.454 Data Analysis Methods in Phys. Oc.)\nfrom scipy.stats import chi2\nslev = 0.05 # significance level\nnu1 = 2*win#T1.size/np.float(win) *8./3. # for a Hanning window!\nnu2 = 2*win#T2.size/np.float(win) *8./3.\nsig1 = np.diff([np.log10(nu1/chi2.ppf(1-slev/2,nu1)), np.log10(nu1/chi2.ppf(slev/2,nu1))])\nsig2 = np.diff([np.log10(nu2/chi2.ppf(1-slev/2,nu2)), np.log10(nu2/chi2.ppf(slev/2,nu2))])\n\nprint ('Wavelet analysis')\n\nfrom sys import path\n#Path to toolbox\npath.append('/home/cimatori/installed')\n\nimport wavelet\n\nstd = Data.std()                     # Standard deviation\nstd2 = std ** 2                      # Variance\nvar = (Data - Data.mean()) / std     # Calculating anomaly and normalizing\n#p = np.polyfit(time, Data, 1)        # Linear fit of the data\n#var = (Data - p[0]*time - p[1]) / std     # Calculating anomaly, normalizing and removing linear trend\n\nN = var.size                         # Number of measurements\n\nmother = wavelet.Morlet(6.)          # Morlet mother wavelet with wavenumber=6\n#mother = wavelet.Mexican_hat()       # Mexican hat wavelet, or DOG with m=2\n#mother = wavelet.Paul(4)             # Paul wavelet with order m=4\n#mother = wavelet.DOG(6)              # Derivative of the Gaussian, with m=6\n\n# perform the wavelet transform\nwave, scales, freqs, coi, fft, fftfreqs = wavelet.cwt(var, dt, dj, s0, J,\n                                                      mother)\n\npower = (np.abs(wave)) ** 2             # Normalized wavelet power spectrum\nfft_power = std2 * np.abs(fft) ** 2     # FFT power spectrum\nperiod = 1. / freqs\n\n# Calculates the global wavelet spectrum\nglbl_power = std2 * power.mean(axis=1)\n\n# Scale average between avg1 and avg2 periods and significance level\nsel = ppl.find((period >= avg1) & (period < avg2))\n#sel = np.ones(period.shape, dtype=int)\nCdelta = mother.cdelta\nscale_avg = (scales * np.ones((N, 1))).transpose()\n# As in Torrence and Compo (1998) equation 24\nscale_avg = power / scale_avg\nscale_avg = std2 * dj * dt / Cdelta * scale_avg[sel, :].sum(axis=0)\n\n# Compute time average of scale average in the chunks\nw_power1 = SubSamp*std2*\\\n           np.mean(power[:,np.nonzero((time>Chunk1[0])&(time<=Chunk1[1]))[0]],\n                   axis=1)\nw_power2 = SubSamp*std2*\\\n           np.mean(power[:,np.nonzero((time>Chunk2[0])&(time<=Chunk2[1]))[0]],\n                   axis=1)\n\nprint ('Done.\\n Plot data...')\n\n# The following routines plot the results in four different subplots containing\n# the original series anomaly, the wavelet power spectrum, the global wavelet\n# and Fourier spectra and finally the range averaged wavelet spectrum. In all\n# sub-plots the significance levels are either included as dotted lines or as\n# filled contour lines.\n#ppl.ion()\nfontsize = 'medium'\nparams = {'text.fontsize': fontsize,\n          'xtick.labelsize': fontsize,\n          'ytick.labelsize': fontsize,\n          'axes.titlesize': fontsize\n         }\nppl.rcParams.update(params)          # Plot parameters\n\n#\n# First plot, wavelet analysis\n#\n\nfigprops = dict(figsize=(11, 8))\nfig = ppl.figure(**figprops)\n\nunits='^{\\circ}C'\ntitle='Time series at depth {:.1f}m'.format(Z)\nlabel='Temp.'\n# First sub-plot, the original time series anomaly.\n\nax = fig.add_axes([0.1, 0.75, 0.65, 0.2])\nax.plot(time, Data, 'k', linewidth=1.5)\ntmin = Data.min()-Data.std(); tmax=Data.max()+Data.std()\nax.set_title('a) %s' % (title, ))\nif units != '':\n  ax.set_ylabel(r'%s [$%s$]' % (label, units,))\nelse:\n  ax.set_ylabel(r'%s' % (label, ))\nax.set_ylim(tmin,tmax)\n\n# Second sub-plot, the normalized wavelet power spectrum and significance level\n# contour lines and cone of influece hatched area.\nbx = fig.add_axes([0.1, 0.37, 0.65, 0.28], sharex=ax)\npmean = np.log2(power).mean(); pstd = np.log2(power).std()\nlevels = np.arange(int(pmean-2*pstd),int(pmean+2*pstd),1)\nbx.contourf(time, np.log2(period), np.log2(power), levels,\n            extend='both')\n#bx.contour(time, np.log2(period), sig95, [-99, 1], colors='k',\n#           linewidths=2.)\nbx.fill(np.concatenate([time[:1]-dt, time, time[-1:]+dt, time[-1:]+dt,\n        time[:1]-dt, time[:1]-dt]), np.log2(np.concatenate([[1e-9], np.fmax(coi,1e-9),\n        [1e-9], period[-1:], period[-1:], [1e-9]])), 'k', alpha='0.3',\n        hatch='x')\nbx.plot([time[0],time[-1]], [np.log2(Tf/24.),np.log2(Tf/24.)], '--',\n        color=[0.5,0.5,0.5], lw=2) # Inertial period\nbx.plot([time[0],time[-1]], [np.log2(avg1),np.log2(avg1)], 'w-') # Power averaging limits\nbx.plot([time[0],time[-1]], [np.log2(avg2),np.log2(avg2)], 'w-')\nbx.set_title('b) %s Wavelet Power Spectrum (%s)' % (label, mother.name))\nbx.set_ylabel('Period (hours)')\nYticks = 2 ** np.arange(np.ceil(np.log2(period.min())),\n                           np.ceil(np.log2(period.max())))\nbx.set_yticks(np.log2(Yticks))\nbx.set_yticklabels([\"%.3f\" % tick for tick in Yticks*24])\nbx.set_ylim(np.log2([period.min(), period.max()]))\nbx.invert_yaxis()\n\n# Third sub-plot, the global wavelet and Fourier power spectra and theoretical\n# noise spectra.\ncx = fig.add_axes([0.77, 0.37, 0.2, 0.28], sharey=bx)\n#cx.plot(np.log10(glbl_signif), np.log2(period), 'k--')\ncx.plot(np.log10(fft_power), np.log2(1./fftfreqs), '-', color=[0.7, 0.7, 0.7],\n        linewidth=1.)\ncx.plot(np.log10(glbl_power), np.log2(period), 'k-', linewidth=1.5)\ncx.plot([-100,100], [np.log2(Tf/24.),np.log2(Tf/24.)], '--',\n        color=[0.5,0.5,0.5], lw=2)\ncx.set_title('c) Global Wavelet Spectrum')\nif units != '':\n  cx.set_xlabel(r'log(Power) [$%s^2$]' % (units, ))\nelse:\n  cx.set_xlabel(r'log(Power)')\ncx.set_xlim([-9, np.log10(glbl_power.max())+0.9])\ncx.set_ylim(np.log2([period.min(), period.max()]))\ncx.set_yticks(np.log2(Yticks))\n#cx.set_yticklabels(Yticks)\nppl.setp(cx.get_yticklabels(), visible=False)\ncx.invert_yaxis()\n\n# Fourth sub-plot, the scale averaged wavelet spectrum as determined by the\n# avg1 and avg2 parameters\ndx = fig.add_axes([0.1, 0.07, 0.65, 0.2], sharex=ax)\n#dx.axhline(scale_avg_signif, color='k', linestyle='--', linewidth=1.)\ndx.plot(time, scale_avg, 'k-', linewidth=1.5)\ndx.set_title('d) ${}$-${}$ hour scale-averaged power' .format(24*avg1, 24*avg2))\ndx.set_xlabel('Time (yeardays)')\nif units != '':\n  dx.set_ylabel(r'Average variance [$%s$]' % (units, ))\nelse:\n  dx.set_ylabel(r'Average variance')\n#\nax.set_xlim([time.min(), time.max()])\n#\nppl.draw()\nppl.show()\nfig.savefig(OutDir+'WaveletAnalysis/'+\\\n            'Wavelet_analysis_nT_{}_day_{}_{}_SubSample_{}.png'.format(nT,Start,End,SubSamp))\n\n#\n# Second plot: comparison between fourier transform and wavelet power\n#\n\nfigprops = dict(figsize=(11, 6))\nfig = ppl.figure(**figprops)\nfig.subplots_adjust(wspace=0.5, hspace=0.5)\n\n#First subplot, time series\nax = ppl.subplot2grid((3,2), (0,0), colspan=3, rowspan=1)\nax.plot(time, Data, 'k', linewidth=1.5)\ntmin = Data.min()-Data.std(); tmax=Data.max()+Data.std()\nax.fill(np.hstack((Chunk1,Chunk1[::-1])), [tmin,tmin,tmax,tmax], 'b', alpha=0.5)\nax.fill(np.hstack((Chunk2,Chunk2[::-1])), [tmin,tmin,tmax,tmax], 'r', alpha=0.5)\nax.set_title('a) %s' % (title, ))\nif units != '':\n  ax.set_ylabel(r'%s [$%s$]' % (label, units,))\nelse:\n  ax.set_ylabel(r'%s' % (label, ))\nax.set_ylim(tmin,tmax)\n\n# First sub-plot, spectra of first chunk\nbx = ppl.subplot2grid((3,2), (1,0), colspan=1, rowspan=2)\n\n#Handy pointers for plotting spectra\nper1  = np.log10(1./freqs1)\nper2  = np.log10(1./freqs2)\ndata1 = np.log10(fft_power1) # data to be plotted\ndata2 = np.log10(fft_power2)\nsdata1 = np.log10(sm_fft_power1)\nsdata2 = np.log10(sm_fft_power2)\n\n#plot spectrum\nbx.plot(per1, data1,  'b-', linewidth=0.5, alpha=0.3)\nbx.plot(per1, sdata1, 'b-', linewidth=1.5, alpha=0.7)\nbx.plot(np.log10(period*86400), np.log10(w_power1), 'b-', linewidth=2)\nxsig = np.log10(60); ysig=1 #where to centre significance bar\nbx.errorbar(xsig,ysig, yerr=sig1, color='k', lw=2)\n\nbx.set_title('b) PS time interval: {}-{} days'.format(Chunk1[0],Chunk1[1]))\nbx.set_ylabel('$\\log_{10}$(Power) [$'+units+'^2 \\cdot Hz^{-1}$]')\n\n# Fifth sub-plot, spectra of second chunk\ncx = ppl.subplot2grid((3,2), (1,1), colspan=1, rowspan=2)\n\n#plot spectrum\ncx.plot(per2, data2,  'r-', linewidth=0.5, alpha=0.3)\ncx.plot(per2, sdata2, 'r-', linewidth=1.5, alpha=0.7)\ncx.plot(np.log10(period*86400), np.log10(w_power2), 'r-', linewidth=2)\ncx.errorbar(xsig,ysig, yerr=sig2, color='k', lw=2)\n\ncx.set_title('c) Time interval: {}-{} days'.format(Chunk2[0],Chunk2[1]))\n\n# Common settings for the spectra of the two chunks\n# plot reference lines\nxi = np.log10(np.array([86400, 3600])); xh = np.log10(np.array([2400, 180]))\nslopes = [3, 2, 5./3] # slopes to be plotted\nticks = np.array([60, 600, 3600, 86400]) #ticks to be used\nbx.plot(xi, xi-xi[0]+2, 'k-', lw=2)\nbx.text(xi[0], 2, '-1', fontsize='x-large')\n#cx.plot(xi, 2*(xi-xi[0])+3, 'k-', lw=2) # -2 slope\ncx.plot(xi, xi-xi[0]+2, 'k-', lw=2) # -1 slope\ncx.text(xi[0], 2, '-1', fontsize='x-large')\nfor axis in [bx,cx]:\n    for slope in slopes:\n        axis.plot(xh, slope*xh-slope*xh[1]-3, color='k', lw=2)\n    # plot inertial frequency\n    axis.plot(2*[np.log10(Tf*3600)], [-100, 100],\\\n              '--', color=[0.5,0.5,0.5], lw=2)\n    # settings common to spectra subplots\n    axis.set_xlabel('Period [hours]')\n    axis.set_xticks(np.log10(ticks))\n    axis.set_xticklabels([\"%.3f\" % member for member in ticks/3600.])\n    axis.set_xlim(np.log10(2.5*86400), np.log10(61))\nbx.set_ylim(sdata1.min(), sdata1.max()+1)\ncx.set_ylim(sdata2.min(), sdata2.max()+1)\n\nppl.draw()\nppl.show()\nfig.savefig(OutDir+'WaveletAnalysis/'+\\\n            'Wavelet_FFT_analysis_nT_{}_day_{}_{}_SubSample_{}.png'.format(nT,Start,End,SubSamp))\n\nprint ('Done.')\n","sub_path":"CanaryBasin/Wavelet_FFT_analysis.py","file_name":"Wavelet_FFT_analysis.py","file_ext":"py","file_size_in_byte":12627,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"246681021","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nfrom urllib.parse import unquote, unquote_plus\n\n\ndef main():\n    url1 = \"http://localhost:8080/~hellmann/\"\n    url2 = \"http%3A%2F%2Flocalhost%3A8080%2F%7Ehellmann%2F\"\n\n    print(\"unquote()     :\", unquote(url1))\n    print(\"unquote_plus():\", unquote_plus(url2))\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"standard/057.urllib.parse/encode_query_arguments/urllib_parse_unquote.py","file_name":"urllib_parse_unquote.py","file_ext":"py","file_size_in_byte":347,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"211262621","text":"dnaResult = []\nfileContent = \"\"\nfileContent1 = \"\"\nfileContent2 = \"\"\nmutateFile = \"\"\nnormalFile = \"\"\n\n#this function takes the users DNA sequence input and slpits it on every third character in the sequence (the code in line 2) and then runs a for loop for each set of 3 characters to test which Amino Acid the 3 characters form and adds the Acids accronym to the empty list which is returned at the end of the function\ndef translate(fileContent):\n   sequence = [fileContent[i:i+3] for i in range(0, len(fileContent), 3)]\n   for item in sequence:\n      if item == \"ATT\" or item == \"ATC\" or item == \"ATA\" : \n         dnaResult.append(\" I \")\n      elif item == \"CTT\" or item == \"CTC\" or item == \"CTA\" or item == \"CTG\" or item == \"TTA\" or item == \"TTG\" : \n         dnaResult.append(\" L \")\n      elif item == \"GTT\" or item == \"GTC\" or item == \"GTA\" or item == \"GTG\" :\n         dnaResult.append(\" V \")\n      elif item == \"TTT\" or item == \"TTC\" : \n         dnaResult.append(\" F \")\n      elif item == \"ATG\" : \n         dnaResult.append(\" M \")\n      else:\n         dnaResult.append(\" X \")\n   return dnaResult\n\n#i open the file and then read each line     \nfile = open(\"DNA.txt\",\"r\" )\nfileContent = file.read()\nfile.close()\n\n#then i make a for loop to go over each letter which was read from the file, if the latter is ‘a’ it mutates it ‘T’ and fix’s it ‘A’ and adds it to variables mutateFile and normalFile, if the letter is not ‘a’ it just adds the letter exactly as is to the same variables\nfor letter in fileContent:\n   if letter == \"a\":\n      mutateFile = (mutateFile + \"T\")\n      normalFile = (normalFile + \"A\")\n   else :\n      mutateFile = (mutateFile + letter)\n      normalFile = (normalFile + letter)\n\n#next 2 functions open 2 new files and writes to each file one of the different variabls mutateFile and normalFile\nfile = open(\"mutatedDNA.txt\",\"w\")\nfile.write(mutateFile)\nfile.close()\n\nfile = open(\"normalDNA.txt\",\"w\")\nfile.write(normalFile)\nfile.close()\n\n#the next 2 functions read from each file that was created above and runs them through the function “translate” to find the amino acids in each DNA sequence, i think this is where my error comes in, its when you call these to functions\nfile = open(\"mutatedDNA.txt\",\"r\")\nfileContent1 = file.read()\nprint(\"These are the folowing Amino Acids found in the DNA sequence's found in the mutated DNA text file : '\" + str(translate(fileContent1)))\nfile.close()\n\ndnaResult.clear()\n\nfile = open(\"normalDNA.txt\",\"r\")\nfileContent2 = file.read()\nprint(\"These are the folowing Amino Acids found in the DNA sequence's found in the normal DNA text file : '\" + str(translate(fileContent2)))\nfile.close()\n\ndnaResult.clear()\n","sub_path":"DNA_readfile.py","file_name":"DNA_readfile.py","file_ext":"py","file_size_in_byte":2685,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"126576279","text":"#  Copyright 2020 The Forte Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#      http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"\nTrain preprocessor helps doing data pre-processing during training.\n\"\"\"\nimport logging\nfrom typing import Optional, Dict, Type, Any, Union, Iterator, List\nfrom texar.torch.data import DataIterator, Batch\nimport torch\nfrom torch import device\n\nfrom forte.common.configuration import Config\nfrom forte.data.converter import Converter\nfrom forte.data.data_pack import DataPack\nfrom forte.data.data_pack_dataset import DataPackDataset, DataPackIterator\nfrom forte.data.base_extractor import BaseExtractor\nfrom forte.data.ontology.core import Entry\nfrom forte.data.ontology.core import EntryType\nfrom forte.utils import get_class\n\nlogger = logging.getLogger(__name__)\n\n__all__ = [\"TrainPreprocessor\"]\n\n\nclass TrainPreprocessor:\n    r\"\"\"\n    `TrainPreprocessor` provides the functionality of doing pre-processing work\n    including building vocabulary, extracting the features, batching and\n    padding (optional). The main functionality is provided by its method\n    :meth:`get_train_batch_iterator` which will return an `iterator` over the\n    batch of pre-processed data. Please refer to the documentation of\n    that method for how the pre-processing is done.\n\n    `TrainPreprocessor` will maintain a Config that stores all the configurable\n    parameters for various components.\n\n    `TrainPreprocessor` will also accept a user request. Internally it will\n    parse this user request and store the parsed result.\n\n    Args:\n        pack_iterator (Iterator[DataPack]): An iterator of\n            :class:`~forte.data.data_pack.DataPack`.\n        request (Dict): A request that specifies how to do train pre-processing.\n            Please refer to :meth:`request` for details.\n        config: A `Dict` or :class:`~forte.common.configuration.Config` that\n            configs this preprocessor. See :meth:`default_configs` for\n            the defaults.\n\n\n    .. note::\n        For parameters `request`, user does not necessarily need to provide\n        `converter`. If no `converter` is specified, a default converter of\n        type :class:`~forte.data.converter.Converter` will be picked.\n    \"\"\"\n\n    DATA_INPUT = 0\n    DATA_OUTPUT = 1\n\n    def __init__(self, pack_iterator: Iterator[DataPack]):\n        self._pack_iterator: Iterator[DataPack] = pack_iterator\n        self._cached_packs: List[DataPack] = []\n\n        self._user_request: Dict = {}\n        self._request: Dict = {}\n        self._request_ready: bool = False\n        self._vocab_ready: bool = False\n\n    def initialize(self, config: Optional[Union[Config, Dict]] = None):\n        # pylint: disable=attribute-defined-outside-init,unused-argument\n        self._config = Config(config, default_hparams=self.default_configs())\n        self._user_request = self._config.request\n        self._validate_config()\n        self._parse_request(self._user_request)\n        self._build_vocab()\n\n    @staticmethod\n    def default_configs():\n        r\"\"\"Returns a dictionary of default hyper-parameters.\n\n        .. code-block:: python\n\n            {\n                \"preprocess\": {\n                            \"device\": \"cpu\",\n                },\n                \"dataset\": DataPackDataset.default_hparams()\n            }\n\n        Here:\n\n        `\"preprocessor.device\"`:\n            The device of the produced batches. For GPU training,\n            set to current CUDA device.\n\n        `\"dataset\"`:\n            This contains all the configurable options same as\n            :class:`~forte.data.data_pack_dataset.DataPackDataset`.\n        \"\"\"\n\n        # Configs should be serializable\n        return {\n            \"preprocess\": {\n                \"device\": \"cpu\",\n            },\n            \"dataset\": DataPackDataset.default_hparams(),\n            \"request\": {\"scope\": None, \"feature_scheme\": None},\n        }\n\n    def _validate_config(self):\n        # Placeholder\n        pass\n\n    def _parse_request(self, request: Dict):\n        \"\"\"\n        This method has two responsibilities:\n        1. parse the given data request and stored it internally\n        2. validate if the given data request is valid\n        \"\"\"\n        parsed_request: Dict[str, Any] = {}\n\n        assert \"scope\" in request, \"Field not found for data request: `scope`\"\n        assert (\n            \"feature_scheme\" in request\n        ), \"Field not found for data request: `schemes`\"\n\n        parsed_request[\"scope\"] = get_class(request[\"scope\"])\n        parsed_request[\"schemes\"] = {}\n\n        # Used for check dependency between different extractors\n        scheme_group: Dict[str, Dict] = {\"dependent\": {}, \"dependee\": {}}\n\n        for tag, scheme in request[\"feature_scheme\"].items():\n            assert (\n                \"extractor\" in scheme\n            ), \"Field not found for data request scheme: `extractor`\"\n            parsed_request[\"schemes\"][tag] = {}\n\n            assert (\n                \"type\" in scheme\n            ), \"Field not found for data request scheme: `type`\"\n            assert scheme[\"type\"] in [\n                \"data_input\",\n                \"data_output\",\n            ], \"Type field must be either data_input or data_output.\"\n            if scheme[\"type\"] == \"data_input\":\n                parsed_request[\"schemes\"][tag][\n                    \"type\"\n                ] = TrainPreprocessor.DATA_INPUT\n            if scheme[\"type\"] == \"data_output\":\n                parsed_request[\"schemes\"][tag][\n                    \"type\"\n                ] = TrainPreprocessor.DATA_OUTPUT\n\n            extractor_class = scheme[\"extractor\"][\"class_name\"]\n            if not isinstance(get_class(extractor_class)(), BaseExtractor):\n                raise RuntimeError(\"Invalid extractor: \", scheme[\"extractor\"])\n\n            extractor: BaseExtractor = get_class(extractor_class)()\n            extractor.initialize(config=scheme[\"extractor\"][\"config\"])\n            parsed_request[\"schemes\"][tag][\"extractor\"] = extractor\n\n            # Track dependency\n            if hasattr(extractor, \"based_on\"):\n                if extractor.entry_type not in scheme_group[\"dependent\"]:\n                    scheme_group[\"dependent\"][extractor.entry_type] = set()\n                scheme_group[\"dependent\"][extractor.entry_type].add(extractor)\n            else:\n                if extractor.entry_type not in scheme_group[\"dependee\"]:\n                    scheme_group[\"dependee\"][extractor.entry_type] = set()\n                scheme_group[\"dependee\"][extractor.entry_type].add(extractor)\n\n            # Create default converter if there is no given converter\n            if \"converter\" not in scheme:\n                converter: Converter = Converter({})\n                parsed_request[\"schemes\"][tag][\"converter\"] = converter\n\n        # Check dependency\n        for _, dependent_extractors in scheme_group[\"dependent\"].items():\n            for dependent_extractor in dependent_extractors:\n                based_on: Entry = dependent_extractor.based_on\n                if based_on not in scheme_group[\"dependee\"]:\n                    raise ValueError(\n                        \"Extractor {} needs the entry {} to do extraction \"\n                        \"processing but it is not extracted by any other \"\n                        \"extractors given in request\".format(\n                            based_on, dependent_extractor.tag\n                        )\n                    )\n\n        self._request = parsed_request\n        self._request_ready = True\n\n    def _build_vocab(self):\n        scope: EntryType = self._request[\"scope\"]\n        schemes: Dict = self._request[\"schemes\"]\n\n        # TODO: clear vocab?\n\n        # Cached all data packs\n        for data_pack in self._pack_iterator:\n            self._cached_packs.append(data_pack)\n\n        for _, scheme in schemes.items():\n            extractor: BaseExtractor = scheme[\"extractor\"]\n            if extractor.vocab_method != \"raw\":\n                for data_pack in self._cached_packs:\n                    for instance in data_pack.get(scope):\n                        extractor.update_vocab(data_pack, instance)\n\n        self._vocab_ready = True\n\n    def _build_dataset_iterator(self) -> DataIterator:\n        scope: Type[EntryType] = self._request[\"scope\"]  # type: ignore\n        schemes: Dict[str, Dict[str, Any]] = self._request[\"schemes\"]\n\n        data_source = DataPackIterator(\n            pack_iterator=iter(self._cached_packs),\n            context_type=scope,\n            request={scope: []},\n        )\n\n        dataset = DataPackDataset(\n            data_source, schemes, self._config.dataset, self.device\n        )\n        iterator = DataIterator(dataset)\n\n        return iterator\n\n    @property\n    def request(self) -> Dict:\n        # pylint: disable=line-too-long\n        r\"\"\"A `Dict` containing all the information needed for doing the\n            pre-processing. This is obtained via parsing the input `request`\n\n            An example `request` is:\n\n            .. code-block:: python\n\n                request = {\n                    \"scope\": ft.onto.Sentence\n                    \"schemes\": {\n                        \"text_tag\": {\n                            \"extractor\": forte.data.extractor.AttributeExtractor,\n                            \"converter\": forte.data.converter.Converter,\n                            \"type\": TrainPreprocessor.DATA_INPUT,\n                        },\n                        \"char_tag\" {\n                            \"extractor\": forte.data.extractor.CharExtractor,\n                            \"converter\": forte.data.converter.Converter,\n                            \"type\": TrainPreprocessor.DATA_INPUT,\n                        }\n                        \"ner_tag\": {\n                            \"extractor\":\n                                forte.data.extractor.BioSeqTaggingExtractor,\n                            \"converter\": forte.data.converter.Converter,\n                            \"type\": TrainPreprocessor.DATA_OUTPUT,\n                        }\n                    }\n                }\n\n        Here:\n\n            `\"scope\"`: Entry\n                A class of type :class:`~forte.data.ontology.core.Entry` The\n                granularity to separate data into different examples. For example,\n                if `scope` is :class:`~ft.onto.base_ontology.Sentence`, then each\n                training example will represent the information of a sentence.\n\n            `\"schemes\"`: `Dict`\n                A `Dict` containing the information about doing the pre-processing.\n                The `key` is the tags provided by input `request`. The `value` is a\n                `Dict` containing the information for doing pre-processing for that\n                feature.\n\n            `\"schemes.tag.extractor\"`: Extractor\n                An instance of type :class:`~forte.data.extractor.BaseExtractor`.\n\n            `\"schemes.tag.converter\"`: Converter\n                An instance of type :class:`~forte.data.converter.Converter`.\n\n            `\"schemes.tag.type\"`: TrainPreprocessor.DATA_INPUT/DATA_OUTPUT\n                Denoting whether this feature is the input or output feature.\n        \"\"\"\n        if not self._request:\n            self._parse_request(self._request)\n        return self._request\n\n    @property\n    def device(self) -> device:\n        r\"\"\"The device of the produced batches. For GPU training,\n        set to current CUDA device.\n        \"\"\"\n        return torch.device(self._config.preprocess.device)\n\n    @property\n    def config(self) -> Config:\n        r\"\"\"A :class:`~forte.common.configuration.Config` maintaining all the\n        configurable options for this `TrainPreprocessor`.\n        \"\"\"\n        return self._config\n\n    def get_train_batch_iterator(self) -> Iterator[Batch]:\n        r\"\"\"\n        This method mainly has four steps:\n\n        1. Iterate over :class:`~forte.data.data_pack.DataPack`\n           via pack iterator\n        2. Extract :class:`~forte.data.converter.feature.Feature` from\n           :class:`~forte.data.data_pack.DataPack`\n        3. Batch :class:`~forte.data.converter.feature.Feature`\n        4. (optional) Pad a batch of\n           :class:`~forte.data.converter.feature.Feature`\n\n        It will return an `iterator` of a batch of pre-processed data.\n\n        Returns:\n            An `Iterator` of type :class:`~texar.torch.data.Batch`\n\n            Please refer to :meth:`collate` in\n            :class:`~forte.data.data_pack_dataset.DataPackDataset` for details\n            about its structure.\n        \"\"\"\n        if not self._request:\n            raise ValueError(\"Feature resource is not parsed\")\n\n        if not self._vocab_ready:\n            raise ValueError(\"Vocab is not built\")\n\n        dataset_iter = self._build_dataset_iterator()\n\n        return iter(dataset_iter)\n","sub_path":"forte/train_preprocessor.py","file_name":"train_preprocessor.py","file_ext":"py","file_size_in_byte":13243,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"417672064","text":"import ModelCreators.GANs\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nclass GAN:\n    def __init__(self, images, discriminator, generator, network_optimizer, trainer, noise_dim=100):\n        _, size = images.data.shape\n        if (images.image_width * images.image_height != size):\n            raise ValueError('Invalid image dimensions.')\n        self.images = images\n        \n        self.discriminator = discriminator\n        self.discriminator.trainable = False\n        self.discriminator_loss = []\n        \n        self.generator = generator\n        self.generator_loss = []\n                \n        self.network = ModelCreators.GANs.create_gan(generator, discriminator, network_optimizer, noise_dim)\n        \n        self.trainer = trainer\n        \n        self.noise_dim = noise_dim\n        \n    def train(self, epochs=1, batch_size=100):\n        self.trainer(self, epochs, batch_size)\n\n    def generateImages(self, number_images=100, dim=(10, 10), figsize=(10,10), filename=None):\n        noise_vector = np.random.normal(0, 1, size=[number_images, self.noise_dim])\n        \n        generated_images = self.generator.predict(noise_vector)\n        generated_images = generated_images.reshape(number_images,\n                                                    self.images.image_width, self.images.image_height)\n        \n        plt.figure(figsize=figsize)\n        \n        for image in range(number_images):\n            plt.subplot(dim[0], dim[1], image + 1)\n            plt.imshow(generated_images[image], interpolation='nearest', cmap='gray_r')\n            plt.axis('off')\n        \n        plt.tight_layout()\n        \n        if filename is not None:\n            plt.savefig(filename)\n        else:\n            plt.show()","sub_path":"YAGAN/GAN.py","file_name":"GAN.py","file_ext":"py","file_size_in_byte":1736,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"645937047","text":"# coding=utf-8\nfrom bs4 import BeautifulSoup\nimport requests\nimport re\nimport numpy as np\nimport pandas as pd\nfrom datetime import datetime\nimport calendar\nimport sys\n\n\ndef add_months(dt,months):\n    month = dt.month - 1 + months\n    year = dt.year + int(month / 12)\n    month = month % 12 + 1\n    day = min(dt.day, calendar.monthrange(year, month)[1])\n    return dt.replace(year=year, month=month, day=day)\n\ntoday = datetime.now().date()\ntoday=today.replace(year=2012,month=1,day=1)\n\nprint(today)\n\ntodaytime = datetime.now()\n# todaytime=todaytime.date(2014,1,1)\n# print (today.)\nprint(today.replace(2014).strftime('%Y%m'))\n\n# print(isinstance(demo, unicode))\n# with open('a.txt', 'w+', encoding=\"gb2312\") as f:\n#     f.write(wpage)\n\n\n# print(ss.find_all('tr'))\n# pd.dataframe\n\n# https://www.aqistudy.cn/historydata/daydata.php?city=%E6%9D%AD%E5%B7%9E&month=2014-12\nwith open('ZhouShan.csv', 'w+', encoding='utf-8') as f:\n    detail=today.strftime('%Y%m')\n    while detail<='201902':\n        \n        response = requests.get(\n        'http://qq.ip138.com/weather/zhejiang/ZhouShan/'+detail+'.htm')\n        wpage = response.text\n        wpage = wpage.encode('iso-8859-1').decode('gb2312')\n        soup = BeautifulSoup(wpage, 'html.parser')\n        if (soup.find_all('table', id='weatherHistory')!=[]):\n            ss = soup.find_all('table', id='weatherHistory')[0]\n        else :\n            ss = soup.find_all('table',class_='t12')[0]\n        month_sum=[]\n        for n, i in enumerate(ss):\n            if n != 0:\n                ii = i.find_all('td')\n                s = []\n                for item in ii:\n                    s.append(re.sub('[\\t\\n\\r]','',item.string))\n                month_sum.append(s)\n        month_sum=month_sum[::-1]\n        for m in month_sum:\n            f.writelines(','.join(m)+'\\n')\n                \n        today=add_months(today,1)\n        detail=today.strftime('%Y%m')\n        print(detail)\n\n","sub_path":"scrach.py","file_name":"scrach.py","file_ext":"py","file_size_in_byte":1925,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"546651406","text":"\"\"\"For parsing input/output files\"\"\"\nimport re\nimport os\nfrom copy import deepcopy\nfrom io import StringIO, IOBase\nfrom warnings import warn\n\nimport numpy as np\n\nfrom AaronTools.atoms import Atom\nfrom AaronTools.const import ELEMENTS, PHYSICAL, UNIT\n\nread_types = [\"xyz\", \"log\", \"com\", \"sd\"]\nwrite_types = [\"xyz\", \"com\"]\nfile_type_err = \"File type not yet implemented: {}\"\nfloat_num = re.compile(\"[-+]?\\d+\\.?\\d*\")\nNORM_FINISH = \"Normal termination\"\nERRORS = {\n    \"Convergence failure -- run terminated.\": \"CONV\",\n    \"Inaccurate quadrature in CalDSu\": \"CONV_CDS\",\n    \"Error termination request processed by link 9999\": \"CONV_LINK\",\n    \"FormBX had a problem\": \"FBX\",\n    \"NtrErr Called from FileIO\": \"CHK\",\n    \"Wrong number of Negative eigenvalues\": \"EIGEN\",\n    \"Erroneous write\": \"QUOTA\",\n    \"Atoms too close\": \"CLASH\",\n    \"The combination of multiplicity\": \"CHARGEMULT\",\n    \"Bend failed for angle\": \"REDUND\",\n    \"Unknown message\": \"UNKNOWN\",\n}\n\n\ndef is_alpha(test):\n    rv = re.search(\"^[a-zA-Z]+$\", test)\n    return bool(rv)\n\n\ndef is_int(test):\n    rv = re.search(\"^[+-]?\\d+$\", test)\n    return bool(rv)\n\n\ndef is_num(test):\n    rv = re.search(\"^[+-]?\\d+\\.?\\d*\", test)\n    return bool(rv)\n\n\ndef step2str(step):\n    if int(step) == step:\n        return str(int(step))\n    else:\n        return str(step).replace(\".\", \"-\")\n\n\ndef str2step(step_str):\n    if \"-\" in step_str:\n        return float(step_str.replace(\"-\", \".\"))\n    else:\n        return float(step_str)\n\n\nclass FileWriter:\n    @classmethod\n    def write_file(cls, geom, style=\"xyz\", append=False, outfile=None, *args, **kwargs):\n        \"\"\"\n        Writes file from geometry in the specified style\n\n        :geom: the Geometry to use\n        :style: the file type style to generate\n            Currently supported options: xyz, com\n        :append: for *.xyz, append geometry to the same file\n        :options: for *.com files, the computational options\n        \"\"\"\n        if style.lower() not in write_types:\n            raise NotImplementedError(file_type_err.format(style))\n\n        if style.lower() == \"xyz\":\n            cls.write_xyz(geom, append, outfile)\n        elif style.lower() == \"com\":\n            if \"theory\" in kwargs and \"step\" in kwargs:\n                step = kwargs[\"step\"]\n                theory = kwargs[\"theory\"]\n                del kwargs[\"step\"]\n                del kwargs[\"theory\"]\n                cls.write_com(geom, step, theory, outfile, **kwargs)\n            else:\n                raise TypeError(\n                    \"when writing com files, **kwargs must include: theory=Aaron.Theory(), step=int/float()\"\n                )\n\n    @classmethod\n    def write_xyz(cls, geom, append, outfile=None):\n        mode = \"a\" if append else \"w\"\n        fmt = \"{:3s} {: 10.5f} {: 10.5f} {: 10.5f}\\n\"\n        s = \"%i\\n\" % len(geom.atoms)\n        s += \"%s\\n\" % geom.comment\n        for atom in geom.atoms:\n            s += fmt.format(atom.element, *atom.coords)\n\n        if outfile is None:\n            #if no output file is specified, use the name of the geometry\n            with open(geom.name + \".xyz\", mode) as f:\n                f.write(s)\n        elif outfile is False:\n            #if no output file is desired, just return the file contents\n            return s.strip()\n        else:\n            #write output to the requested destination\n            with open(outfile, mode) as f:\n                f.write(s)\n\n        return\n\n    @classmethod\n    def write_com(cls, geom, step, theory, outfile=None, **kwargs):\n        has_frozen = False\n        fmt = \"{:<3s}\" + \" {:> 12.6f}\" * 3 + \"\\n\"\n        for atom in geom.atoms:\n            if atom.flag:\n                fmt = \"{:<3s}  {:> 2d}\" + \" {:> 12.6f}\" * 3 + \"\\n\"\n                has_frozen = True\n                break\n\n            s = theory.make_header(geom, step, **kwargs)\n            for atom in geom.atoms:\n                if has_frozen:\n                    s += fmt.format(a.element, -a.flag, *a.coords)\n                else:\n                    s += fmt.format(a.element, *a.coords)\n\n            s += theory.make_footer(geom, step)\n\n        if outfile is None:\n            #if outfile is not specified, name file in Aaron format\n            fname = \"{}.{}.com\".format(geom.name, step2str(step))\n            with open(fname, \"w\") as f:\n                f.write(s)\n        elif outfile is False:\n            return s\n        else:\n            with open(outfile, 'w') as f:\n                f.write(s)\n\n        return\n\n\nclass FileReader:\n    \"\"\"\n    Attributes:\n        name ''\n        file_type ''\n        comment ''\n        atoms [Atom]\n        other {}\n    \"\"\"\n\n    def __init__(self, fname, get_all=False, just_geom=True):\n        \"\"\"\n        :fname: either a string specifying the file name of the file to read\n            or a tuple of (str(name), str(file_type), str(content))\n        :get_all: if true, optimization steps are  also saved in\n            self.other['all_geom']; otherwise only saves last geometry\n        :just_geom: if true, does not store other information, such as\n            frequencies, only what is needed to construct a Geometry() obj\n        \"\"\"\n        # Initialization\n        self.name = \"\"\n        self.file_type = \"\"\n        self.comment = \"\"\n        self.atoms = []\n        self.other = {}\n        self.content = None\n        self.all_geom = None\n\n        # get file name and extention\n        if isinstance(fname, str):\n            fname = fname.rsplit(\".\", 1)\n            self.name = fname[0]\n            self.file_type = fname[1].lower()\n        elif isinstance(fname, (tuple, list)):\n            self.name = fname[0]\n            self.file_type = fname[1]\n            self.content = fname[2]\n        if self.file_type not in read_types:\n            raise NotImplementedError(file_type_err.format(self.file_type))\n\n        # Fill in attributes with geometry information\n        if self.content is None:\n            self.read_file(get_all, just_geom)\n        elif isinstance(self.content, str):\n            f = StringIO(self.content)\n        elif isinstance(self.content, IOBase):\n            f = self.content\n\n        if self.content is not None:\n            if self.file_type == \"log\":\n                self.read_log(f, get_all, just_geom)\n            elif self.file_type == \"sd\":\n                self.read_sd(f)\n            elif self.file_type == \"xyz\":\n                self.read_xyz(f)\n            elif self.file_type == \"com\":\n                self.read_com(f)\n\n    def read_file(self, get_all=False, just_geom=True):\n        \"\"\"\n        Reads geometry information from fname.\n        Parameters:\n            get_all     If false (default), only keep the last geom\n                        If true, self is last geom, but return list\n                            of all others encountered\n        \"\"\"\n        if os.path.isfile(self.name):\n            f = open(self.name)\n        else:\n            fname = '.'.join([self.name, self.file_type])\n            if os.path.isfile(fname):\n                f = open(fname)\n            else:\n                raise FileNotFoundError(\"Error while looking for %s: could not find %s or %s in %s\" % \\\n                        (self.name, fname, self.name, os.getcwd()))\n\n        if self.file_type == \"xyz\":\n            self.read_xyz(f, get_all)\n        elif self.file_type == \"log\":\n            self.read_log(f, get_all, just_geom)\n        elif self.file_type == \"com\":\n            self.read_com(f)\n        elif self.file_type == \"sd\":\n            self.read_sd(f)\n\n        f.close()\n\n        return\n\n    def skip_lines(self, f, n):\n        for i in range(n):\n            f.readline()\n        return\n\n    def read_xyz(self, f, get_all=False):\n        self.all_geom = []\n        # number of atoms\n        f.readline()\n        # comment\n        self.comment = f.readline().strip()\n        # atom info\n        for line in f:\n            line = line.strip()\n            if line == \"\":\n                continue\n            try:\n                int(line)\n                if get_all:\n                    self.all_geom += [\n                        (deepcopy(self.comment), deepcopy(self.atoms))\n                    ]\n                self.comment = f.readline().strip()\n                self.atoms = []\n            except ValueError:\n                line = line.split()\n                self.atoms += [Atom(element=line[0], coords=line[1:4])]\n                for i, a in enumerate(self.atoms):\n                    a.name = str(i + 1)\n        if get_all:\n            self.all_geom += [(deepcopy(self.comment), deepcopy(self.atoms))]\n\n    def read_sd(self, f, get_all=False):\n        self.all_geom = []\n        lines = f.readlines()\n        self.comment = lines[0]\n        counts = lines[3].split()\n        natoms = int(counts[0])\n        nbonds = int(counts[1])\n        self.atoms = []\n        for line in lines[4:4+natoms]:\n            atom_info = line.split()\n            self.atoms += [Atom(element=atom_info[3], coords = atom_info[0:3])]\n\n        for i, a in enumerate(self.atoms):\n            a.name = str(i + 1)\n\n    def read_log(self, f, get_all=False, just_geom=True):\n        def get_atoms(f, n):\n            rv = []\n            self.skip_lines(f, 4)\n            line = f.readline()\n            n += 5\n            while \"--\" not in line:\n                line = line.strip()\n                line = line.split()\n                for l in line:\n                    try:\n                        float(l)\n                    except ValueError:\n                        msg = \"Error detected with log file on line {}\"\n                        raise IOError(msg.format(n))\n                elem = ELEMENTS[int(line[1])]\n                flag = not bool(line[2])\n                rv += [Atom(element=elem, flag=flag, coords=line[3:])]\n                line = f.readline()\n                n += 1\n            return rv, n\n\n        self.all_geom = []\n        line = f.readline()\n        self.other[\"archive\"] = \"\"\n        found_archive = False\n        n = 1\n        while line != \"\":\n            # archive entry\n            if line.strip().startswith(\"1\\\\1\\\\\"):\n                found_archive = True\n                line = \"@\" + line.strip()[4:]\n            if found_archive and line.strip().endswith(\"@\"):\n                self.other[\"archive\"] = self.other[\"archive\"][:-2] + \"\\\\\\\\\"\n                found_archive = False\n            elif found_archive:\n                self.other[\"archive\"] += line.strip()\n\n            # geometry\n            if re.search(\"(Standard|Input) orientation:\", line):\n                if get_all and len(self.atoms) > 0:\n                    self.all_geom += [deepcopy(self.atoms)]\n                self.atoms, n = get_atoms(f, n)\n            if just_geom:\n                line = f.readline()\n                n += 1\n                continue\n            if \"Symbolic Z-matrix:\" in line:\n                line = f.readline()\n                n += 1\n                match = re.search(\n                    \"Charge\\s*=\\s*(\\d+)\\s*Multiplicity\\s*=\\s*(\\d+)\", line\n                )\n                if match is not None:\n                    self.other[\"charge\"] = int(match.group(1))\n                    self.other[\"multiplicity\"] = int(match.group(2))\n\n            # status\n            if NORM_FINISH in line:\n                self.other[\"finished\"] = True\n            if \"SCF Done\" in line:\n                self.other[\"energy\"] = float(line.split()[4])\n            if \"Molecular mass:\" in line:\n                self.other[\"mass\"] = float(float_num.search(line).group(0))\n                self.other[\"mass\"] *= UNIT.AMU_TO_KG\n\n            # Frequencies\n            if \"hpmodes\" in line:\n                self.other[\"hpmodes\"] = True\n            if \"Harmonic frequencies\" in line:\n                freq_str = line\n                while line != \"\\n\":\n                    line = f.readline()\n                    n += 1\n                    freq_str += line\n                if \"hpmodes\" not in self.other:\n                    self.other[\"hpmodes\"] = False\n                self.other[\"frequency\"] = Frequency(\n                    freq_str, self.other[\"hpmodes\"]\n                )\n\n            # Thermo\n            if \"Temperature\" in line:\n                self.other[\"temperature\"] = float(\n                    float_num.search(line).group(0)\n                )\n            if \"Rotational constants (GHZ):\" in line:\n                rot = float_num.findall(line)\n                rot = [\n                    float(r) * PHYSICAL.PLANK * (10 ** 9) / PHYSICAL.KB\n                    for r in rot\n                ]\n                self.other[\"rotational_temperature\"] = rot\n            if \"Sum of electronic and zero-point Energies=\" in line:\n                self.other[\"E_ZPVE\"] = float(float_num.search(line).group(0))\n            if \"Sum of electronic and thermal Enthalpies=\" in line:\n                self.other[\"enthalpy\"] = float(float_num.search(line).group(0))\n            if \"Sum of electronic and thermal Free Energies=\" in line:\n                self.other[\"free_energy\"] = float(\n                    float_num.search(line).group(0)\n                )\n            if \"Zero-point correction=\" in line:\n                self.other[\"ZPVE\"] = float(float_num.search(line).group(0))\n            if \"Rotational symmetry number\" in line:\n                self.other[\"rotational_symmetry_number\"] = int(\n                    re.search(\"\\d+\", line).group(0)\n                )\n\n            # Gradient\n            if re.search(\"Threshold\\s+Converged\", line) is not None:\n                line = f.readline()\n                n += 1\n                grad = {}\n\n                def add_grad(line, name, grad):\n                    line = line.split()\n                    grad[name] = {\n                        \"value\": line[2],\n                        \"threshold\": line[3],\n                        \"converged\": True if line[4] == \"YES\" else False,\n                    }\n                    return grad\n\n                while line != \"\":\n                    if \"Predicted change in Energy\" in line:\n                        break\n                    if re.search(\"Maximum\\s+Force\", line) is not None:\n                        grad = add_grad(line, \"Max Force\", grad)\n                    if re.search(\"RMS\\s+Force\", line) is not None:\n                        grad = add_grad(line, \"RMS Force\", grad)\n                    if re.search(\"Maximum\\s+Displacement\", line) is not None:\n                        grad = add_grad(line, \"Max Disp\", grad)\n                    if re.search(\"RMS\\s+Displacement\", line) is not None:\n                        grad = add_grad(line, \"RMS Disp\", grad)\n                    line = f.readline()\n                    n += 1\n                self.other[\"gradient\"] = grad\n\n            # capture errors\n            for err in ERRORS:\n                if err in line:\n                    self.other[\"error\"] = ERRORS[err]\n                    self.other[\"error_msg\"] = line.strip()\n                    break\n            else:\n                self.other[\"error\"] = None\n\n            line = f.readline()\n            n += 1\n\n        for i, a in enumerate(self.atoms):\n            a.name = str(i + 1)\n\n        if \"finished\" not in self.other:\n            self.other[\"finished\"] = False\n        if \"error\" not in self.other:\n            self.other[\"error\"] = None\n        if not self.other[\"finished\"] and not self.other[\"error\"]:\n            self.other[\"error\"] = ERRORS[\"Unknown message\"]\n            self.other[\"error_msg\"] = \"Unknown message\"\n        return\n\n    def read_com(self, f):\n        found_atoms = False\n        found_constraint = False\n        atoms = []\n        other = {}\n        for line in f:\n            # header\n            if line.startswith(\"%\"):\n                continue\n            if line.startswith(\"#\"):\n                method = re.search(\"^#([NnPpTt]\\s+?)(\\S+)|^#\\s*?(\\S+)\", line)\n                #route can be #n functional/basis ...\n                #or #functional/basis ...\n                #or # functional/basis ...\n                if method.group(3):\n                    other['method'] = method.group(3)\n                else:\n                    other['method'] = method.group(2)\n                if \"temperature=\" in line:\n                    other[\"temperature\"] = re.search(\n                        \"temperature=(\\d+\\.?\\d*)\", line\n                    ).group(1)\n                if \"solvent=\" in line:\n                    other[\"solvent\"] = re.search(\n                        \"solvent=(\\S+)\\)\", line\n                    ).group(1)\n                if \"scrf=\" in line:\n                    other[\"solvent_model\"] = re.search(\n                        \"scrf=\\((\\S+),\", line\n                    ).group(1)\n                if \"EmpiricalDispersion=\" in line:\n                    other[\"emp_dispersion\"] = re.search(\n                        \"EmpiricalDispersion=(\\S+)\", line\n                    ).group(1)\n                if \"int=(grid(\" in line:\n                    other[\"grid\"] = re.search(\"int=\\(grid(\\S+)\", line).group(1)\n                for _ in range(4):\n                    line = f.readline()\n                line = line.split()\n                other[\"charge\"] = line[0]\n                other[\"mult\"] = line[1]\n                found_atoms = True\n                continue\n            # constraints\n            if found_atoms and line.startswith(\"B\") and line.endswith(\"F\"):\n                found_constraint = True\n                if \"constraint\" not in other:\n                    other[\"constraint\"] = []\n                other[\"constraint\"] += [float_num.findall(line)]\n                continue\n            # footer\n            if found_constraint:\n                if \"footer\" not in other:\n                    other[\"footer\"] = \"\"\n                other[\"footer\"] += line\n                continue\n            # atom coords\n            nums = float_num.findall(line)\n            line = line.split()\n            if len(line) == 5 and is_alpha(line[0]) and len(nums) == 4:\n                if not is_int(line[1]):\n                    continue\n                a = Atom(element=line[0], coords=nums[1:], flag=nums[0])\n                atoms += [a]\n            elif len(line) == 4 and is_alpha(line[0]) and len(nums) == 3:\n                a = Atom(element=line[0], coords=nums)\n                atoms += [a]\n            else:\n                continue\n        self.atoms = atoms\n        self.other = other\n        return\n\n\nclass Frequency:\n    \"\"\"\n    ATTRIBUTES\n    :data: Data - contains frequencies, intensities, and normal mode vectors\n    :imaginary_frequencies: list(float)\n    :real_frequencies: list(float)\n    :lowest_frequency: float\n    :by_frequency: dict keyed by frequency containing intensities and vectors\n        {freq: {intensity: float, vector: np.array}}\n    :is_TS: bool - true if len(imaginary_frequencies) == 1, else False\n    \"\"\"\n\n    class Data:\n        \"\"\"\n        ATTRIBUTES\n        :frequency: float\n        :intensity: float\n        :vector: (2D array) normal mode vectors\n        \"\"\"\n\n        def __init__(self, frequency, intensity=None, vector=[], forcek=[]):\n            self.frequency = frequency\n            self.intensity = intensity\n            self.vector = np.array(vector)\n            self.forcek = np.array(forcek)\n\n    def __init__(self, data, hpmodes=None):\n        \"\"\"\n        :data: should either be a str containing the lines of the output file\n            with frequency information, or a list of Data objects\n        :hpmodes: required when data is a string\n        \"\"\"\n        self.data = []\n        self.imaginary_frequencies = None\n        self.real_frequencies = None\n        self.lowest_frequency = None\n        self.by_frequency = {}\n        self.is_TS = None\n\n        if isinstance(data[0], Frequency.Data):\n            self.data = data\n            self.sort_frequencies()\n            return\n        else:\n            if hpmodes is None:\n                raise TypeError(\n                    \"hpmode argument required when data is a string\"\n                )\n\n        lines = data.split(\"\\n\")\n        num_head = 0\n        for line in lines:\n            if \"Harmonic frequencies\" in line:\n                num_head += 1\n        if hpmodes and num_head != 2:\n            warn(\"Log file damaged, cannot get frequencies\")\n            return\n        self.parse_lines(lines, hpmodes)\n        self.sort_frequencies()\n        return\n\n    def parse_lines(self, lines, hpmodes):\n        num_head = 0\n        idx = -1\n        modes = []\n        for line in lines:\n            if \"Harmonic frequencies\" in line:\n                num_head += 1\n                if hpmodes and num_head == 2:\n                    # if hpmodes, want just the first set of freqs\n                    break\n                continue\n            if \"Frequencies\" in line and ((hpmodes and \"---\" in line) or \\\n                (\"--\" in line and not hpmodes)):\n                for i in float_num.findall(line):\n                    self.data += [Frequency.Data(float(i))]\n                    modes += [[]]\n                    idx += 1\n                continue\n\n            if (\"Force constants\" in line and \"---\" in line and hpmodes) or \\\n                (\"Frc consts\" in line and \"--\" in line and not hpmodes):\n                force_constants = float_num.findall(line)\n                for i in range(-len(force_constants), 0, 1):\n                    self.data[i].forcek = float(force_constants[i])\n                continue\n\n            if \"IR Inten\" in line and ((hpmodes and \"---\" in line) or \\\n                (not hpmodes and \"--\" in line)):\n                for i in float_num.findall(line):\n                    self.data[idx].intensity = float(i)\n                continue\n\n            if hpmodes:\n                match = re.search(\n                    \"^\\s+\\d+\\s+\\d+\\s+\\d+(\\s+[+-]?\\d+\\.\\d+)+$\", line\n                )\n                if match is None:\n                    continue\n                values = float_num.findall(line)\n                coord = int(values[0]) - 1\n                atom = int(values[1]) - 1\n                moves = values[3:]\n                for i, m in enumerate(moves):\n                    tmp = len(moves) - i\n                    mode = modes[-tmp]\n                    try:\n                        vector = mode[atom]\n                    except IndexError:\n                        vector = [0, 0, 0]\n                        modes[-tmp] += [[]]\n                    vector[coord] = m\n                    modes[-tmp][atom] = vector\n            else:\n                match = re.search(\"^\\s+\\d+\\s+\\d+(\\s+[+-]?\\d+\\.\\d+)+$\", line)\n                if match is None:\n                    continue\n                values = float_num.findall(line)\n                atom = int(values[0]) - 1\n                moves = np.array(values[2:], dtype=np.float)\n                n_moves = len(moves) // 3\n                for i in range(-n_moves, 0):\n                    modes[i].append(moves[3*n_moves+3*i:4*n_moves+3*i])\n\n        for mode, data in zip(modes, self.data):\n            data.vector = np.array(mode, dtype=np.float)\n        return\n\n    def sort_frequencies(self):\n        self.imaginary_frequencies = []\n        self.real_frequencies = []\n        for i, data in enumerate(self.data):\n            freq = data.frequency\n            if freq < 0:\n                self.imaginary_frequencies += [freq]\n            elif freq > 0:\n                self.real_frequencies += [freq]\n            self.by_frequency[freq] = {\n                \"intensity\": data.intensity,\n                \"vector\": data.vector,\n            }\n        self.lowest_frequency = self.data[0].frequency\n        self.is_TS = True if len(self.imaginary_frequencies) == 1 else False\n","sub_path":"fileIO.py","file_name":"fileIO.py","file_ext":"py","file_size_in_byte":23585,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"375921798","text":"import os  # isort:skip\nfrom decouple import config\nimport django_heroku\nimport dj_database_url\ngettext = lambda s: s\nDATA_DIR = os.path.dirname(os.path.dirname(__file__))\n\"\"\"\nDjango settings for mylaptop project.\n\nGenerated by 'django-admin startproject' using Django 1.11.25.\n\nFor more information on this file, see\nhttps://docs.djangoproject.com/en/1.11/topics/settings/\n\nFor the full list of settings and their values, see\nhttps://docs.djangoproject.com/en/1.11/ref/settings/\n\"\"\"\n\n\n# Build paths inside the project like this: os.path.join(BASE_DIR, ...)\nBASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n\n\n# Quick-start development settings - unsuitable for production\n# See https://docs.djangoproject.com/en/1.11/howto/deployment/checklist/\n\n\nSECRET_KEY = config('SECRET_KEY')\nDEBUG = config('DEBUG', default=False, cast=bool)\n\nALLOWED_HOSTS = ['coollaptop.herokuapp.com', '127.0.0.1']\n\n\n# Application definition\n\n\nROOT_URLCONF = 'mylaptop.urls'\n\n\n# Internationalization\n# https://docs.djangoproject.com/en/1.11/topics/i18n/\n\nLANGUAGE_CODE = 'en'\n\nTIME_ZONE = 'Europe/Kiev'\n\nUSE_I18N = False\n\nUSE_L10N = True\n\nUSE_TZ = True\n\n\n# Static files (CSS, JavaScript, Images)\n# https://docs.djangoproject.com/en/1.11/howto/static-files/\n\nSTATIC_URL = '/static/'\nMEDIA_URL = '/media/'\nMEDIA_ROOT = os.path.join(DATA_DIR, 'media')\nSTATIC_ROOT = os.path.join(DATA_DIR, 'mylaptop', 'static')\n\nSTATICFILES_DIRS = (\n    os.path.join(BASE_DIR, 'static'),\n)\nSITE_ID = 8\n\n\nTEMPLATES = [\n    {\n        'BACKEND': 'django.template.backends.django.DjangoTemplates',\n        'DIRS': [os.path.join(BASE_DIR, 'mylaptop', 'templates'),],\n        'OPTIONS': {\n            'context_processors': [\n                'django.contrib.auth.context_processors.auth',\n                'django.contrib.messages.context_processors.messages',\n                'django.template.context_processors.i18n',\n                'django.template.context_processors.debug',\n                'django.template.context_processors.request',\n                'django.template.context_processors.media',\n                'django.template.context_processors.csrf',\n                'django.template.context_processors.tz',\n                'sekizai.context_processors.sekizai',\n                'django.template.context_processors.static',\n                'cms.context_processors.cms_settings'\n            ],\n            'loaders': [\n                'django.template.loaders.filesystem.Loader',\n                'django.template.loaders.app_directories.Loader',\n            ],\n        },\n    },\n]\n\n\nMIDDLEWARE = [\n    'cms.middleware.utils.ApphookReloadMiddleware',\n    'django.contrib.sessions.middleware.SessionMiddleware',\n    'django.middleware.csrf.CsrfViewMiddleware',\n    'django.contrib.auth.middleware.AuthenticationMiddleware',\n    'django.contrib.messages.middleware.MessageMiddleware',\n    'django.middleware.locale.LocaleMiddleware',\n    'django.middleware.common.CommonMiddleware',\n    'django.middleware.clickjacking.XFrameOptionsMiddleware',\n    'cms.middleware.user.CurrentUserMiddleware',\n    'cms.middleware.page.CurrentPageMiddleware',\n    'cms.middleware.toolbar.ToolbarMiddleware',\n    'cms.middleware.language.LanguageCookieMiddleware',\n    'django.middleware.security.SecurityMiddleware',\n    'django_otp.middleware.OTPMiddleware',\n\n]\n\nINSTALLED_APPS = [\n    'djangocms_admin_style',\n    'django.contrib.auth',\n    'django.contrib.contenttypes',\n    'django.contrib.sessions',\n    'django.contrib.admin',\n    'django.contrib.sites',\n    'django.contrib.sitemaps',\n    'django.contrib.staticfiles',\n    'django.contrib.messages',\n    'cms',\n    'menus',\n    'sekizai',\n    'treebeard',\n    'djangocms_text_ckeditor',\n    'filer',\n    'easy_thumbnails',\n    'djangocms_column',\n    'djangocms_file',\n    'djangocms_link',\n    'djangocms_picture',\n    'djangocms_style',\n    'djangocms_snippet',\n    'djangocms_googlemap',\n    'djangocms_video',\n\n    'mylaptop',\n    'accounts',\n    'egs',\n    'questions',\n    'django_otp',\n    'django_otp.plugins.otp_static',\n    'django_otp.plugins.otp_totp',\n    'two_factor',\n\n]\n\n\nLANGUAGES = (\n    # Customize this\n    ('en', gettext('en')),\n)\nCMS_LANGUAGES = {\n    # Customize this\n    1: [\n        {\n            'code': 'en',\n            'name': gettext('en'),\n            'redirect_on_fallback': True,\n            'public': True,\n            'hide_untranslated': False,\n        },\n    ],\n    'default': {\n        'redirect_on_fallback': True,\n        'public': True,\n        'hide_untranslated': False,\n    },\n}\n\nCMS_TEMPLATES = (\n    # Customize this\n    ('fullwidth.html', 'Fullwidth'),\n    ('sidebar_left.html', 'Sidebar Left'),\n    ('sidebar_right.html', 'Sidebar Right'),\n    ('homepage.html', 'home'),\n\n)\n\nCMS_PERMISSION = True\n\nCMS_PLACEHOLDER_CONF = {}\n\nCMS_TOOLBAR_ANONYMOUS_ON = False\n\nDATABASES = {\n    'default': {\n        'CONN_MAX_AGE': 0,\n        'ENGINE': 'django.db.backends.postgresql_psycopg2',\n        'HOST': 'localhost',\n        'NAME': 'Laptop',\n        'PASSWORD': 'mortal99',\n        'PORT': '5432',\n        'USER': 'postgres'\n    }\n}\n\ndb_from_env = dj_database_url.config()\nDATABASES['default'].update(db_from_env)\n\nMIGRATION_MODULES = {\n    \n}\n\nTHUMBNAIL_PROCESSORS = (\n    'easy_thumbnails.processors.colorspace',\n    'easy_thumbnails.processors.autocrop',\n    'filer.thumbnail_processors.scale_and_crop_with_subject_location',\n    'easy_thumbnails.processors.filters'\n)\n\nEMAIL_USE_TLS = True\nEMAIL_USE_SSL = False\nEMAIL_HOST = 'smtp.gmail.com'\nEMAIL_HOST_USER = 'nineball.hustler.nine@gmail.com'\nDEFAULT_FROM_EMAIL = EMAIL_HOST_USER\nEMAIL_HOST_PASSWORD = 'mortal99kombat'\nEMAIL_PORT = 587\n\n\nLOGIN_URL = 'two_factor:login'\n\n\nSECURE_SSL_REDIRECT = True\nSESSION_COOKIE_SECURE = True\nCSRF_COOKIE_SECURE = True\nSECURE_PROXY_SSL_HEADER = ('HTTP_X_FORWARDED_PROTO', 'https')\n","sub_path":"mylaptop/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":5822,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"277183242","text":"\nimport time\nimport sys\n\nsys.path.append(\"..\")\nimport database.db_operator as db_operator\nimport log.custom_logger as custom_logger\n\nclass CommonDBOperation:\n    # 通用，常用的数据库操作\n\n    def __init__(self):\n        pass\n\n    def get_the_last_trading_date(self,day):\n        # 获取传入日期参数最近的交易日期, 即上一个交易日\n        # day: 交易日期，如 2021-06-09\n        # return: 如果存在最近的交易日期，则返回日期\n        #         如果不存在，则返回 0000-00-00\n\n        # 查询SQL\n        selecting_sql = \"SELECT trading_date FROM trading_days WHERE trading_date < '%s' ORDER BY \" \\\n                        \"ABS(DATEDIFF(trading_date, '%s')) ASC LIMIT 1\" % (day,day)\n\n        # 查询\n        selecting_result = db_operator.DBOperator().select_one(\"financial_data\", selecting_sql)\n\n        if selecting_result is not None:\n            return selecting_result[\"trading_date\"]\n        else:\n            # 日志记录\n            log_msg = \"无法获取 \"+day+\" 最近的交易日期\"\n            custom_logger.CustomLogger().log_writter(log_msg, 'error')\n            return \"0000-00-00\"\n\n\n\n\n\n\n\nif __name__ == '__main__':\n    go = CommonDBOperation()\n    last_trade_day = go.get_the_last_trading_date(\"2001-05-06\")\n    print(last_trade_day)","sub_path":"data_miner/common_db_operation.py","file_name":"common_db_operation.py","file_ext":"py","file_size_in_byte":1311,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"557199454","text":"import discord\nfrom discord.ext import commands\nimport random\nclass BAfun():\n\n    def __init__(ctx, bot):\n        ctx.bot = bot\n        ctx.toggle = False\n        ctx.nsword = ctx.nlove = ctx.nsquat = ctx.npizza = ctx.nbribe = ctx.ndad = ctx.ncalc \\\n            = ctx.nbutt = ctx.ncom = ctx.nflirt = ctx.nup = 0\n        \n@commands.command(name=\"8ball\")\nasync def _ball(self, ctx, *, question):\n        ': Ask me a question'\n        question = question\n        answers = random.randint(1, 20)\n\n        if question == \"\":\n            return\n\n        elif answers == 1:\n            await ctx.send(f\"\"\"\\U0001f3b1 Question by {ctx.author.name}: {question}``` It is certain```\"\"\")\n\n        elif answers == 2:\n            await ctx.send(f\"\"\"\\U0001f3b1 Question by {ctx.author.name}: {question}```  It is decidedly so```\"\"\")\n\n        elif answers == 3:\n            await ctx.send(f\"\"\"\\U0001f3b1 Question by {ctx.author.name}: {question}```  Without a doubt```\"\"\")\n\n        elif answers == 4:\n            await ctx.send(f\"\"\"\\U0001f3b1 Question by {ctx.author.name}: {question}``` Yes definitely```\"\"\")\n\n        elif answers == 5:\n            await ctx.send(f\"\"\"\\U0001f3b1 Question by {ctx.author.name}: {question}```  You may rely on it```\"\"\")\n\n        elif answers == 6:\n            await ctx.send(f\"\"\"\\U0001f3b1 Question by {ctx.author.name}: {question}```  As i see it, yes```\"\"\")\n\n        elif answers == 7:\n            await ctx.send(f\"\"\"\\U0001f3b1 Question by {ctx.author.name}: {question}```  Most likely```\"\"\")\n\n        elif answers == 8:\n            await ctx.send(f\"\"\"\\U0001f3b1 Question by {ctx.author.name}: {question}```  Outlook good```\"\"\")\n\n        elif answers == 9:\n            await ctx.send(f\"\"\"\\U0001f3b1 Question by {ctx.author.name}: {question}```  Yes```\"\"\")\n\n        elif answers == 10:\n            await ctx.send(f\"\"\"\\U0001f3b1 Question by {ctx.author.name}: {question}```  Signs point to yes```\"\"\")\n\n        elif answers == 11:\n            await ctx.send(f\"\"\"\\U0001f3b1 Question by {ctx.author.name}: {question}```  Reply hazy try again```\"\"\")\n\n        elif answers == 12:\n            await ctx.send(f\"\"\"\\U0001f3b1 Question by {ctx.author.name}: {question}```  Ask again later```\"\"\")\n\n        elif answers == 13:\n            await ctx.send(f\"\"\"\\U0001f3b1 Question by {ctx.author.name}: {question}```  Better not to tell you now```\"\"\")\n\n        elif answers == 14:\n            await ctx.send(f\"\"\"\\U0001f3b1 Question by {ctx.author.name}: {question}``` Cannot predict now```\"\"\")\n\n        elif answers == 15:\n            await ctx.send(f\"\"\"\\U0001f3b1 Question by {ctx.author.name}: {question}```  Concentrate and ask again```\"\"\")\n\n        elif answers == 16:\n            await ctx.send(f\"\"\"\\U0001f3b1 Question by {ctx.author.name}: {question}```  Don't count on it```\"\"\")\n\n        elif answers == 17:\n            await ctx.send(f\"\"\"\\U0001f3b1 Question by {ctx.author.name}: {question}```  My reply is no```\"\"\")\n\n        elif answers == 18:\n            await ctx.send(f\"\"\"\\U0001f3b1 Question by {ctx.author.name}: {question}```  My sources say no```\"\"\")\n\n        elif answers == 19:\n            await ctx.send(f\"\"\"\\U0001f3b1 Question by {ctx.author.name}: {question}```  Outlook not so good```\"\"\")\n\n        elif answers == 20:\n            await ctx.send(f\"\"\"\\U0001f3b1 Question by {ctx.author.name}: {question}```  Very doubtful```\"\"\")\n     \n@commands.command()\nasync def sword(ctx,  *, user: discord.Member):\n    \"\"\"Sword Duel!\"\"\"\n    author = ctx.message.author\n    if user.id == ctx.bot.user.id:\n        await ctx.send(\"I'm not the fighting kind\")\n    else:\n        await ctx.send(author.mention + \" and \" + user.mention + \" dueled for \" + str(randint(2, 120)) +\n                            \" gruesome hours! It was a long, heated battle, but \" +\n                            choice([author.mention, user.mention]) + \" came out victorious!\")\n    ctx.counter(n)\n\n@commands.command()\nasync def love(ctx, user: discord.Member):\n    \"\"\"Found your one true love?\"\"\"\n    author = ctx.message.author\n    if user.id == ctx.bot.user.id:\n        await ctx.send(\"I am not capable of loving like you can. I'm sorry.\" )\n    else:\n        await ctx.send(author.mention + \" is capable of loving \" + user.mention + \" a whopping \" +\n                            str(randint(0, 100)) + \"%!\")\n    ctx.counter(n)\n\n@commands.command()\nasync def squat(ctx):\n    \"\"\"How is your workout going?\"\"\"\n    author = ctx.message.author\n    await ctx.send(author.mention + \" puts on their game face and does \" + str(randint(2, 1000)) +\n                        \" squats in \" + str(randint(4, 90)) + \" minutes. Wurk it!\")\n    ctx.counter(n)\n\n@commands.command()\nasync def pizza(ctx):\n    \"\"\"How many slices of pizza have you eaten today?\"\"\"\n    author = ctx.message.author\n    await ctx.send(author.mention + \" has eaten \" + str(randint(2, 120)) + \" slices of pizza today.\")\n    ctx.counter(n)\n\n@commands.command()\nasync def bribe(ctx):\n    \"\"\"Find out who is paying under the table\"\"\"\n    author = ctx.message.author\n    await ctx.send(author.mention + \" has bribed \" + ctx.bot.user.mention + \" with \" +\n                        str(randint(10, 10000)) + \" dollars!\")\n    ctx.counter(n)\n\n@commands.command()\nasync def daddy(ctx):\n    \"\"\"Pass the salt\"\"\"\n    author = ctx.message.author\n    await ctx.send(\"I'm kink shaming you, \" + author.mention)\n    ctx.counter(n)\n\n@commands.command()\nasync def calculated(ctx):\n    \"\"\"That was 100% calculated!\"\"\"\n    await ctx.send(\"That was \" + str(randint(0, 100)) + \"% calculated!\")\n    ctx.counter(n)\n\n@commands.command()\nasync def butts(ctx):\n    \"\"\"butts\"\"\"\n    await ctx.send(\"ლ(́◉◞౪◟◉‵ლ)\")\n    ctx.counter(n)\n\n@commands.command()\nasync def _commands(ctx):\n    \"\"\"Command the bot\"\"\"\n    await ctx.send(\"Don't tell me what to do.\")\n    ctx.counter(n)\n\n@commands.command()\nasync def flirt(ctx):\n    \"\"\"Slide into DMs\"\"\"\n    await ctx.send(\" ;)) ))) hey b a b e ; ; ;))) ) ;)\")\n    ctx.counter(n)\n\n@commands.command()\nasync def updog(ctx):\n    \"\"\"This is updog\"\"\"\n    await ctx.send(\"What's updog?\")\n    ctx.counter(n)\n@commands.command(pass_context=True)\nasync def poke(ctx, member: discord.Member):\n    \"\"\"poke someone!\"\"\"\n    author = ctx.message.author.mention\n    mention = member.mention\n\n    poke = \"**{0} poked {1}!**\"\n\n    choices = ['https://pa1.narvii.com/6021/b50b8078fa1d8e8f6d2ebfb085f106c642141723_hq.gif',\n               'https://media1.tenor.com/images/8fe23ec8e2c5e44964e5c11983ff6f41/tenor.gif',\n               'https://media.giphy.com/media/WvVzZ9mCyMjsc/giphy.gif',\n               'https://media.giphy.com/media/pWd3gD577gOqs/giphy.gif',\n               'http://gifimage.net/wp-content/uploads/2017/09/anime-poke-gif-12.gif', 'https://i.gifer.com/S00v.gif',\n               'https://i.imgur.com/1NMqz0i.gif']\n\n    image = random.choice(choices)\n\n    embed = discord.Embed(description=poke.format(author, mention), colour=discord.Colour(0xba4b5b))\n    embed.set_image(url=image)\n\n    await ctx.send(embed=embed)\n\n@commands.command(pass_context=True)\nasync def hug(ctx, member: discord.Member):\n    \"\"\"hug someone!\"\"\"\n    author = ctx.message.author.mention\n    mention = member.mention\n\n    hug = \"**{0}Aww, I see you are lonely, take a hug <3{1}!**\"\n\n    choices = ['https://cdn.discordapp.com/attachments/447337220895145998/466226631778893824/hug-rk_6GyncG.gif',\n               'https://cdn.discordapp.com/attachments/447337220895145998/466227315110576129/hug-ry6o__7D-.gif',\n               'https://cdn.discordapp.com/attachments/447337220895145998/466227511165190175/hug-Bk5haAocG.gif',\n               'https://cdn.discordapp.com/attachments/447337220895145998/466228974326906891/hug-BkBs2uk_b.gif',\n               'https://cdn.discordapp.com/attachments/447337220895145998/466229286966394881/hug-HkfgF_QvW.gif'\n               'https://cdn.discordapp.com/attachments/447337220895145998/466230001872666635/hug-BkZngAYtb.gif'\n               'https://cdn.discordapp.com/attachments/447337220895145998/466230123209687040/hug-Bk5T2_1Ob.gif'\n               'https://cdn.discordapp.com/attachments/447337220895145998/466230234795212802/hug-Hy4hxRKtW.gif']\n\n\n    image = random.choice(choices)\n\n    embed = discord.Embed(description=hug.format(author, mention), colour=discord.Colour(0xba4b5b))\n    embed.set_image(url=image)\n\n    await ctx.send(embed=embed)\n\n@commands.command(pass_context=True)\nasync def slap(ctx, member: discord.Member):\n    \"\"\"Slap someone!\"\"\"\n    author = ctx.message.author.mention\n    mention = member.mention\n\n    slap = \"**{0}Hmm, why do you want this?Slaps.{1}!**\"\n\n    choices = ['https://cdn.discordapp.com/attachments/447337220895145998/466229677300908042/slap-rJYqQyKv-.gif',\n               'https://cdn.discordapp.com/attachments/447337220895145998/466229535059345408/slap-r1PXzRYtZ.gif',\n               'https://cdn.discordapp.com/attachments/447337220895145998/466229453236731904/slap-SkSCyl5yz.gif'\n               'https://cdn.discordapp.com/attachments/447337220895145998/466231429337055242/slap-B1-nQyFDb.gif',\n               'https://cdn.discordapp.com/attachments/447337220895145998/466231614352130048/slap-HkskD56OG.gif'\n               'https://cdn.discordapp.com/attachments/447337220895145998/466231875120267284/slap-By2iXyFw-.gif'\n               'https://cdn.discordapp.com/attachments/447337220895145998/466232154112917504/slap-SkKn-xc1f.gif'\n               'https://cdn.discordapp.com/attachments/447337220895145998/466232493889290241/slap-rJrnXJYPb.gif']\n\n\n    image = random.choice(choices)\n\n    embed = discord.Embed(description=slap.format(author, mention), colour=discord.Colour(0xba4b5b))\n    embed.set_image(url=image)\n\n    await ctx.send(embed=embed)\n\n\n@commands.command()\nasync def dog(ctx):\n    ''''sends cute dog pics'''\n    r = requests.get(\"https://dog.ceo/api/breeds/image/random\").json()\n    embed=discord.Embed()\n    embed.set_image(url=r[\"message\"])\n    await ctx.send(embed=embed)\n\n\n\n@commands.command(pass_context=True, no_pm=True)\nasync def insult(ctx, user : discord.Member=None):\n    \"\"\"Insult the user\"\"\"\n\n    msg =\" How original. No one else had thought of trying to get the bot to insult itself. I applaud your creativity. Yawn. Perhaps this is why you don't have friends. You don't add anything new to any conversation. You are more of a bot than me, predictable answers, and absolutely dull to have an actual conversation with.\"\n    if user != None:\n        if user.id == ctx.bot.user.id:\n            user = ctx.message.author\n            await ctx.send(user.mention + msg)\n        else:\n            await ctx.send(user.mention + msg + random.choice(msg))\n    else:\n        await ctx.send(ctx.message.author.mention + msg + random.choice(msg))\n\n@commands.command(pass_context=True)\nasync def bite(ctx, member: discord.Member):\n    \"\"\"bites  someone!\"\"\"\n    author = ctx.message.author.mention\n    mention = member.mention\n\n    bite = \"**{0}bites you.{1}!**\"\n\n    choices = ['https://cdn.discordapp.com/attachments/456701536912015361/466571069973856256/bite-HkutgeXob.gif',\n               'https://cdn.discordapp.com/attachments/456701536912015361/466571762339938304/bite-ry00lxmob.gif',\n               'https://cdn.discordapp.com/attachments/456701536912015361/466572007258193920/bite-H1_Jbemjb.gif'\n               'https://cdn.discordapp.com/attachments/456701536912015361/466572188372434964/bite-H1hige7sZ.gif',\n               'https://cdn.discordapp.com/attachments/456701536912015361/466572377233293322/bite-Hk1sxlQjZ.gif'\n               'https://cdn.discordapp.com/attachments/456701536912015361/466572552739880961/bite-rkakblmiZ.gif'\n               'https://cdn.discordapp.com/attachments/456701536912015361/466572804385669120/bite-BJXRmfr6-.gif'\n               'https://cdn.discordapp.com/attachments/456701536912015361/466573024078987284/bite-ry3pQGraW.gif']\n\n\n    image = random.choice(choices)\n\n    embed = discord.Embed(description=bite.format(author, mention), colour=discord.Colour(0xba4b5b))\n    embed.set_image(url=image)\n\n    await ctx.send(embed=embed)\n\n@commands.command(pass_context=True)\nasync def cuddle(ctx, member: discord.Member):\n    \"\"\"cuddle  someone!\"\"\"\n    author = ctx.message.author.mention\n    mention = member.mention\n\n    cuddle = \"**cuddles you.{1}!**\"\n\n    choices = ['https://cdn.discordapp.com/attachments/456701536912015361/466573538841591809/cuddle-SJn18IXP-.gif',\n               'https://cdn.discordapp.com/attachments/456701536912015361/466573996201082900/cuddle-r1s9RqB7G.gif',\n               'https://cdn.discordapp.com/attachments/456701536912015361/466574139805794306/cuddle-SJceIU7wZ.gif'\n               'https://cdn.discordapp.com/attachments/456701536912015361/466574279127859200/cuddle-r1XEOymib.gif',\n               'https://cdn.discordapp.com/attachments/456701536912015361/466574467070427156/cuddle-S1T91Att-.gif'\n               'https://cdn.discordapp.com/attachments/456701536912015361/466574644577697792/cuddle-BkZCSI7Pb.gif'\n               'https://cdn.discordapp.com/attachments/456701536912015361/466574850375548939/cuddle-Byd1IUmP-.gif'\n               'https://cdn.discordapp.com/attachments/456701536912015361/466575399862665216/cuddle-BkN0rIQDZ.gif']\n\n\n    image = random.choice(choices)\n\n    embed = discord.Embed(description=cuddle.format(author, mention), colour=discord.Colour(0xba4b5b))\n    embed.set_image(url=image)\n\n    await ctx.send(embed=embed)\n\n\n@commands.command(pass_context=True)\nasync def pat(ctx, member: discord.Member):\n    \"\"\"pat someone!\"\"\"\n    author = ctx.message.author.mention\n    mention = member.mention\n\n    pat = \"**you have been patted .{1}!**\"\n\n    choices = ['https://cdn.discordapp.com/attachments/456701536912015361/466577618771378176/pat-rktsca40-.gif',\n               'https://cdn.discordapp.com/attachments/456701536912015361/466577986209185812/pat-rkZbJAYKW.gif',\n               'https://cdn.discordapp.com/attachments/456701536912015361/466578464619626496/pat-SJva1kFv-.gif'\n               'https://cdn.discordapp.com/attachments/456701536912015361/466578677090484224/pat-BkJBQlckz.gif',\n               'https://cdn.discordapp.com/attachments/456701536912015361/466578825468182538/pat-H1s5hx0Bf.gif'\n               'https://cdn.discordapp.com/attachments/456701536912015361/466579159435706380/pat-rJMskkFvb.gif'\n               'https://cdn.discordapp.com/attachments/456701536912015361/466579338490544128/pat-rkBZkRttW.gif'\n               'https://cdn.discordapp.com/attachments/456701536912015361/466579500117917727/pat-Sk2FyQHpZ.gif']\n\n\n    image = random.choice(choices)\n\n    embed = discord.Embed(description=pat.format(author, mention), colour=discord.Colour(0xba4b5b))\n    embed.set_image(url=image)\n\n    await ctx.send(embed=embed)\n\n@commands.command(pass_context=True)\nasync def kiss(ctx, member: discord.Member):\n    \"\"\"kiss someone!\"\"\"\n    author = ctx.message.author.mention\n    mention = member.mention\n\n    kiss = \"**  kissed you.{1}!**\"\n\n    choices = ['https://cdn.discordapp.com/attachments/456701536912015361/466579840070582284/kiss-B1MJ2aODb.gif',\n               'https://cdn.discordapp.com/attachments/456701536912015361/466580423116324874/kiss-Hkt-nTOwW.gif',\n               'https://cdn.discordapp.com/attachments/456701536912015361/466581686591946763/kiss-r1VWnTuPW.gif'\n               'https://cdn.discordapp.com/attachments/456701536912015361/466582897755947017/kiss-BkUJNec1M.gif',\n               'https://cdn.discordapp.com/attachments/456701536912015361/466583102047780914/kiss-Sk1k3TdPW.gif'\n               'https://cdn.discordapp.com/attachments/456701536912015361/466583257341755392/kiss-BJv0o6uDZ.gif'\n               'https://cdn.discordapp.com/attachments/456701536912015361/466583404222087168/kiss-S1PCJWASf.gif'\n               'https://cdn.discordapp.com/attachments/456701536912015361/466583780736499712/kiss-SJ3dXCKtW.gif']\n\n\n    image = random.choice(choices)\n\n    embed = discord.Embed(description=kiss.format(author, mention), colour=discord.Colour(0xba4b5b))\n    embed.set_image(url=image)\n\n    await ctx.send(embed=embed)\n@commands.command(pass_context=True)\nasync def pepe(ctx, user: discord.Member = None):\n    \"\"\"kiss someone!\"\"\"\n    user = user or ctx.message.author\n\n    pepe = \"**  kissed you.{1}!**\"\n\n    choices = [\"http://i.imgur.com/vpIyEue.png\",\n               \"http://i.imgur.com/0koMC0v.jpg\",\n               \"http://i.imgur.com/9Q6KMZa.png\",\n               \"http://i.imgur.com/54xy6jr.png\",\n               \"http://i.imgur.com/QvCngiJ.jpg\",\n               \"http://i.imgur.com/ftWgrOE.jpg\",\n               \"http://i.imgur.com/rhDSqRv.jpg\",\n               \"http://i.imgur.com/89NZ3zM.jpg\",\n               \"http://i.imgur.com/I4cIH5b.png\",\n               \"http://i.imgur.com/GIFc4uX.png\",\n               \"http://i.imgur.com/bgShJpZ.png\",\n               \"http://i.imgur.com/jpfPLyn.png\",\n               \"http://i.imgur.com/pZeYoej.png\",\n               \"http://i.imgur.com/M8V9WKB.jpg\",\n               \"http://i.imgur.com/ZBzHxNk.jpg\",\n               \"http://i.imgur.com/xTyJ6xa.png\",\n               \"http://i.imgur.com/TOozxRQ.png\",\n               \"http://i.imgur.com/Eli5HdZ.png\",\n               \"http://i.imgur.com/pkikqcA.jpg\",\n               \"http://i.imgur.com/gMF8eo5.png\",\n               \"http://i.imgur.com/HYh8BUm.jpg\",\n               \"http://i.imgur.com/ZGVrRye.jpg\",\n               \"http://i.imgur.com/Au4F1px.jpg\",\n               \"http://i.imgur.com/gh36k9y.jpg\",\n               \"http://i.imgur.com/MHDoRuN.png\",\n               \"http://i.imgur.com/V3MJfyK.png\",\n               \"http://i.imgur.com/QGGTipc.jpg\",\n               \"http://i.imgur.com/PRFrTgz.png\",\n               \"http://i.imgur.com/9UBJrwM.jpg\",\n               \"http://i.imgur.com/WQY9Vhb.jpg\",\n               \"http://i.imgur.com/sIbQdou.jpg\",\n               \"http://i.imgur.com/LlUMg00.jpg\",\n               \"http://i.imgur.com/MmijlWa.png\",\n               \"http://i.imgur.com/i0CrtrX.png\",\n               \"http://i.imgur.com/Dfpudwp.jpg\",\n               \"http://i.imgur.com/hhg0wVF.gif\",\n               \"http://i.imgur.com/7VDiIHN.jpg\",\n               \"http://i.imgur.com/nxvXpNV.jpg\",\n               \"http://i.imgur.com/DZYEjrW.gif\",\n               \"http://i.imgur.com/mnyQ0Rh.jpg\",\n               \"http://i.imgur.com/aHawbbs.jpg\",\n               \"http://i.imgur.com/g8cCHV7.jpg\",\n               \"http://i.imgur.com/E2cMU7Y.jpg\",\n               \"http://i.imgur.com/PkmcgGF.jpg\",\n               \"http://i.imgur.com/7qLQ1xl.jpg\",\n               \"http://i.imgur.com/7qLQ1xl.jpg\",\n               \"http://i.imgur.com/arSsPwf.png\",\n               \"http://i.imgur.com/xcYh4iC.png\",\n               \"http://i.imgur.com/9692WND.jpg\",\n               \"http://i.imgur.com/diAK5Nu.jpg\",\n               \"http://i.imgur.com/zDs0tRW.jpg\",\n               \"http://i.imgur.com/PEM87nV.jpg\",\n               \"http://i.imgur.com/zlCzlND.jpg\",\n               \"http://i.imgur.com/n0OHxDl.jpg\",\n               \"http://i.imgur.com/TQRf1WH.png\",\n               \"http://i.imgur.com/zi9ad15.jpg\",\n               \"http://i.imgur.com/b8A6Qke.jpg\",\n               \"http://i.imgur.com/YuLapEu.png\",\n               \"http://i.imgur.com/fWFXkY1.jpg\",\n               \"http://i.imgur.com/i5vNvWU.png\",\n               \"http://i.imgur.com/oXwUwtJ.jpg\",\n               \"http://i.imgur.com/hadm4jV.jpg\",\n               \"http://i.imgur.com/gbCvkqo.png\",\n               \"http://i.imgur.com/wDiiWBG.jpg\",\n               \"http://i.imgur.com/Mvghx4V.jpg\",\n               \"http://i.imgur.com/SnTAjiJ.jpg\",\n               \"http://i.imgur.com/QvMYBnu.png\",\n               \"http://i.imgur.com/WkzPvfB.jpg\",\n               \"http://i.imgur.com/PfAm4ot.png\",\n               \"http://i.imgur.com/SIk4a45.png\",\n               \"http://i.imgur.com/aISFmQq.jpg\",\n               \"http://i.imgur.com/sMQkToE.png\",\n               \"http://i.imgur.com/7i3cBrP.png\",\n               \"http://i.imgur.com/1oMSz6e.png\",\n               \"http://i.imgur.com/nVCRnRv.png\",\n               \"http://i.imgur.com/FzWmxmi.jpg\",\n               \"http://i.imgur.com/rpUI20F.jpg\",\n               \"http://i.imgur.com/FDmnFDZ.jpg\",\n               \"http://i.imgur.com/40Z1Yyg.jpg\",\n               \"http://i.imgur.com/osy5Nu4.png\",\n               \"http://i.imgur.com/4w81MSS.jpg\",\n               \"http://i.imgur.com/qRXQFYa.png\",\n               \"http://i.imgur.com/A1af62j.jpg\",\n               \"http://i.imgur.com/wOc6fUe.jpg\",\n               \"http://i.imgur.com/Z6ILiJ4.jpg\",\n               \"http://i.imgur.com/537UpEJ.jpg\",\n               \"http://i.imgur.com/HDc6kko.png\",\n               \"http://i.imgur.com/oyLpuXq.jpg\",\n               \"http://i.imgur.com/iCmGtJS.jpg\",\n               \"http://i.imgur.com/MjpnlQm.png\",\n               \"http://i.imgur.com/c6MWRQ9.jpg\"]\n\n\n    image = random.choice(choices)\n\n    embed = discord.Embed(description=f\"\"\"{user.name}\"\"\", colour=discord.Colour(0xba4b5b))\n    embed.add_field(name=' Random', value=f''' ~~pepe~~''', inline=False)\n    embed.set_image(url=image)\n\n\n    await ctx.send(embed=embed)\n\n\n\n@commands.cooldown(1,120 , commands.BucketType.user)\n@commands.command(aliases= [\"s\"])\nasync def spawn(ctx, user: discord.Member = None): \n    user = user or ctx.message.author\n     \n\n\n    spawn = \"**  A wild .{1}!**\"\n\n    \n    choices = [\"http://www.pokestadium.com/sprites/xy/shiny/xerneas.gif\",\n                 \"http://www.pokestadium.com/sprites/xy/xerneas-active.gif\",\n                 \"http://www.pokestadium.com/sprites/xy/zekrom.gif\",\n                 \"http://www.pokestadium.com/sprites/xy/charizard.gif\",\n                 \"http://www.pokestadium.com/sprites/xy/shiny/charizard.gif\",\n                 \"http://www.pokestadium.com/sprites/xy/yveltal.gif\",\n                 \"http://www.pokestadium.com/sprites/xy/shiny/yveltal.gif\",\n                 \"http://www.pokestadium.com/sprites/xy/raikou.gif\",\n                 \"http://www.pokestadium.com/sprites/xy/shiny/raikou.gif\",\n                 \"https://cdn.discordapp.com/attachments/386324037552308224/503050326627319809/PokecordSpawn.jpg\",\n                 \"https://cdn.discordapp.com/attachments/386324037552308224/503050526603214848/PokecordSpawn.jpg\",\n                 \"https://cdn.discordapp.com/attachments/386324037552308224/503050157286227968/PokecordSpawn.jpg\",\n                 \"https://cdn.discordapp.com/attachments/482056981931098112/502861665180581908/PokecordSpawn.jpg\",\n                 \"https://img.nijimen.net/uploads/topic/wide_image/18879/78ea52a0-5e3c-48ee-bbe4-8ff6fe38764b.jpg\"\n                 \"https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRE1NX0EsJ0SUcP1LEdglNTN12UIatAoXfA1rxuz1fkL8Q8vWL9zQ\",\n                 \"https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTOENKjnLEl2H8OdMbOaVqJT0QWr0toBNsWfKa3wQWh_mj827UsLg\",\n                 \"https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQo8_G9cOLmqWNGNFh1BxSx_bkpNOxIW7sv2bqlClQgI_u9TiVx\"]\n      \n    image = random.choice(choices)\n\n    embed = discord.Embed(description=f\"\"\"{user.name}\"\"\", colour=discord.Colour(0xba4b5b))\n    embed.add_field(name=' A wild pokémon has appeared!', value=f''' ~~Guess the pokemon~~''', inline=False)\n    embed.set_image(url=image)\n\n\n\n    await ctx.send(embed=embed)\n\n\n@commands.command()\nasync def cat(ctx):\n        \"\"\"Get a random cat image!\n\n        **Usage:** `g_dog`\n\n        **Permission:** User\"\"\"\n        isVideo = True\n        while isVideo:\n            r = requests.get('https://random.dog/woof.json')\n            js = r.json()\n            if js['url'].endswith('.mp4'):\n                pass\n            else:\n                isVideo = False\n        colours = [0x1abc9c, 0x11806a, 0x2ecc71, 0x1f8b4c, 0x3498db, 0x206694, 0x9b59b6, 0x71368a, 0xe91e63, 0xad1457, 0xf1c40f, 0xc27c0e, 0xa84300, 0xe74c3c, 0x992d22, 0x95a5a6, 0x607d8b, 0x979c9f, 0x546e7a]\n        col = int(random.random() * len(colours))\n        content = [\":dog: Don't be sad! This doggy wants to play with you!\", \"You seem lonely, {0.mention}. Here, have a dog. They're not as nice as cats, but enjoy!\".format(ctx.message.author), \"Weuf, woof, woooooooooof. Woof you.\", \"Pupper!\", \"Meow... wait wrong animal.\"]\n        con = int(random.random() * len(content))\n        em = discord.Embed(color=colours[col])\n        em.set_image(url=js['url'])\n        await ctx.send(content=content[con], embed=em)\n\n@commands.command()\nasync def neko(ctx):\n    ''''sends cute dog pics'''\n    r = requests.get(\"https://nekos.life/api/neko\").json()\n\n    colours = [0x1abc9c, 0x11806a, 0x2ecc71, 0x1f8b4c, 0x3498db, 0x206694, 0x9b59b6, 0x71368a, 0xe91e63, 0xad1457, 0xf1c40f, 0xc27c0e, 0xa84300, 0xe74c3c, 0x992d22, 0x95a5a6, 0x607d8b, 0x979c9f, 0x546e7a]\n    col = int(random.random() * len(colours))\n    content = [\":neko: Don't be sad! This neko wants to play with you!\", \"You seem lonely, {0.mention}. Here, have a neko. They're not as nice , but enjoy!\".format(ctx.message.author), \"Weuf, woof, woooooooooof. Woof you.\", \"Pupper!\", \"Meow... wait its neko.\"]\n    con = int(random.random() * len(content))\n    embed=discord.Embed()\n    embed.set_image(url=r[\"neko\"])\n    await ctx.send(content=content[con],embed=embed)\n\n\n@commands.command(pass_context=True)\nasync def rps(ctx, choice):\n    \"\"\"\"\"\"\n    choices = [\"rock\", \"paper\", \"scissors\"]\n    await ctx.send(\"You chose {} | CPU chose {}\".format(choice, random.choice(choices)))\n\ndef setup(bot):\n    bot.add_cog(BAfun(bot))","sub_path":"fun.py","file_name":"fun.py","file_ext":"py","file_size_in_byte":25221,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"10581818","text":"\"\"\"\nWCH5Dataset update to apply normalization on the fly to the test dataset\n\"\"\"\n\n# PyTorch imports\nfrom torch.utils.data import Dataset\nimport h5py\n\nimport numpy as np\n\n# WatChMaL imports\nimport preprocessing.normalize_funcs as norm_funcs\n\nclass WCH5DatasetTest(Dataset):\n    \"\"\"\n    Dataset storing image-like data from Water Cherenkov detector\n    memory-maps the detector data from the hdf5 file\n    The detector data must be uncompresses and unchunked\n    labels are loaded into memory outright\n    No other data is currently loaded\n    \"\"\"\n\n    def __init__(self, test_dset_path, norm_params_path, chrg_norm=\"identity\", time_norm=\"identity\", shuffle=1, test_subset=None, seed=42):\n        \n        f = h5py.File(test_dset_path, \"r\")\n        \n        hdf5_event_data = f[\"event_data\"]\n        hdf5_labels = f[\"labels\"]\n        hdf5_energies = f[\"energies\"]\n        hdf5_positions = f[\"positions\"]\n        hdf5_angles = f[\"angles\"]\n        \n        assert hdf5_event_data.shape[0] == hdf5_labels.shape[0]\n        \n        assert hasattr(norm_funcs, chrg_norm) and hasattr(norm_funcs, time_norm), \"Functions \"+ chrg_norm + \" and/or \" + time_norm + \" are not implemented in normalize_funcs.py, aborting.\"\n        \n        # Create a memory map for event_data - loads event data into memory only on __getitem__()\n        self.event_data = np.memmap(test_dset_path, mode=\"r\", shape=hdf5_event_data.shape,\n                                    offset=hdf5_event_data.id.get_offset(), dtype=hdf5_event_data.dtype)\n        \n        # Load the contents which could fit easily into memory\n        self.labels = np.array(hdf5_labels)\n        self.energies = np.array(hdf5_energies)\n        self.positions = np.array(hdf5_positions)\n        self.angles = np.array(hdf5_angles)\n        \n        # Load the normalization parameters used by normalize_hdf5 methods\n        norm_params = np.load(norm_params_path, allow_pickle=True)\n        self.chrg_acc = norm_params[\"c_acc\"]\n        self.time_acc = norm_params[\"t_acc\"]\n        \n        self.chrg_func = getattr(norm_funcs, chrg_norm)\n        self.time_func = getattr(norm_funcs, time_norm)\n        \n        # Set the total size of the trainval dataset to use\n        self.reduced_size = test_subset\n        \n        self.test_indices = np.arange(len(self))\n        if self.reduced_size is not None:\n            assert len(self.test_indices)>=self.reduced_size\n            self.test_indices = np.random.choice(self.labels.shape[0], self.reduced_size)\n        \n        # Seed the pseudo random number generator\n        if seed is not None:\n            np.random.seed(seed)\n            \n        # Shuffle the indices\n        if shuffle:\n            np.random.shuffle(self.test_indices)\n            \n    def __getitem__(self, index):\n        return np.array(self.event_data[index,:]), np.concatenate([np.squeeze(self.chrg_func(np.expand_dims(self.event_data[index, :, :, :19], axis=0), self.chrg_acc, apply=True)), np.squeeze(self.time_func(np.expand_dims(self.event_data[index, :, :, 19:], axis=0), self.time_acc, apply=True))], axis=2), self.labels[index], self.energies[index], self.angles[index], index, self.positions[index]\n    \n    def __len__(self):\n        if self.reduced_size is None:\n            return self.labels.shape[0]\n        else:\n            return self.reduced_size","sub_path":"io_utils/data_handling_test.py","file_name":"data_handling_test.py","file_ext":"py","file_size_in_byte":3322,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"417744585","text":"from pymongo import MongoClient\nfrom modules.appleId_check import AppleId\nfrom config import Config\n\n\nclass ProfileExistence:\n\n    def __init__(self):\n\n        self.client = MongoClient(Config.MONGO_URI)\n        db = self.client.Apple_db\n        self.collection = db.account\n        self.dict = {}\n\n    def db_check(self, id):\n\n        # if database consists the profile\n        if self.collection.find_one({'profile_id': id}):\n\n            for doc in self.collection.find({'profile_id': id}):\n                # print(doc)\n                self.dict['profileExists'] = doc['profileExists']\n                self.dict['profile_id'] = doc['profile_id']\n\n                return self.dict\n\n        # when profile isn't in database\n        else:\n            obj = AppleId()\n            data_from_apple = obj.id_check(id)\n            # print(data_from_fb)\n            self.db_loader(data_from_apple)\n            return data_from_apple\n\n    def db_loader(self, data):\n\n        # print(data)\n        if data['profileExists'] is False:\n            pass\n        else:\n\n            d = {\"profileExists\": data['profileExists'],\n                 \"profile_id\": data['profile_id']\n                 }\n            self.collection.insert_one(d)\n\n            # close db connection\n            self.client.close()\n\n\nif __name__ == '__main__':\n    obj = ProfileExistence()\n    print(obj.db_check(\"jaqsoms43@gmail.com\"))\n\n","sub_path":"apple-api/modules/appleId_check_db.py","file_name":"appleId_check_db.py","file_ext":"py","file_size_in_byte":1398,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"100845173","text":"#!/usr/bin/python\n#\n# Derived from download_and_preprocess_flowers.sh\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# ==============================================================================\n\"\"\"\n Script to download and preprocess the flowers data set. This data set\n provides a demonstration for how to perform fine-tuning (i.e. tranfer\n learning) from one model to a new data set.\n\n This script provides a demonstration for how to prepare an arbitrary\n data set for training an Inception v3 model.\n\n We demonstrate this with the flowers data set which consists of images\n of labeled flower images from 5 classes:\n\n daisy, dandelion, roses, sunflowers, tulips\n\n The final output of this script are sharded TFRecord files containing\n serialized Example protocol buffers. See build_image_data.py for\n details of how the Example protocol buffer contains image data.\n\n usage:\n  ./download_and_preprocess_flowers.py [data-dir]\n\n\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom subprocess import call\nimport os\nimport os.path\nimport shutil\nimport sys\nimport random\n\n\nif __name__ == '__main__':\n  if len(sys.argv) < 2:\n    print('Usage: download_and_preprocess_flowers.sh [data dir]\\n')\n    sys.exit(-1)\ndata_dir = os.path.realpath(sys.argv[1])\n\n# Create the output and temporary directories.\nwork_dir = __file__ + \".runfiles/inception/inception\"\nscratch_dir = data_dir + '/raw-data/'\ntry:\n  os.makedirs(scratch_dir);\nexcept OSError as e:\n    print('Mkdir error: \"%s\" %s' % (scratch_dir,os.strerror(e.errno)))\n\n# Download the flowers data.\ndata_url = 'http://download.tensorflow.org/example_images/flower_photos.tgz'\nbase_dir = os.path.dirname(os.path.realpath(__file__))\ncurrent_dir = os.getcwd();\ntry:\n    os.chdir(data_dir)\nexcept OSError as e:\n    print(os.strerror(e.errno))\n    sys.exit(-1)\n\ntarball = \"flower_photos.tgz\"\nif not os.path.isfile(tarball):\n  print('Downloading flower data set.\\n')\n  call(['curl', '-o', tarball, data_url])\nelse:\n  print('Skipping download of flower data.')\n\n# Note the locations of the train and validation data.\ntrain_directory = scratch_dir + 'train/'\nvalidation_directory = scratch_dir + 'validation/'\n\n# process\nprint(\"Organizing the validation data into sub-directories.\")\nlabels_file = scratch_dir + 'labels.txt'\n\n# Expands the data into the flower_photos/ directory and rename it as the\n# train directory.\ncall(['tar', 'xf', 'flower_photos.tgz'])\n\nshutil.rmtree(train_directory, True)\nshutil.rmtree(validation_directory, True)\n\nos.rename('flower_photos', train_directory)\n\n# Generate a list of 5 labels: daisy, dandelion, roses, sunflowers, tulips\nlabels_file = scratch_dir + 'labels.txt'\n\nlabels = [d for d in os.listdir(train_directory) if os.path.isdir(os.path.join(train_directory, d))]\nlabels.sort()\nwith open(labels_file, 'w') as f:\n  [f.write(label+'\\n') for label in labels]\n\n\n# Generate the validation data set.\nos.makedirs(validation_directory)\nfor label in labels:\n  validation_dir_for_label = validation_directory + label\n  train_dir_for_label = train_directory + label\n  os.makedirs(validation_dir_for_label)\n\n  # Move the first randomly selected 100 images to the validation set.\n  images = [f for f in os.listdir(train_dir_for_label)]\n  random.shuffle(images)\n  validation_images = images[0:99]\n\n  for image in validation_images:\n    os.rename(train_dir_for_label + '/' + image, validation_dir_for_label + '/' + image)\n\n\n# Build the TFRecords version of the image data.\nos.chdir(current_dir)\nbuild_script = work_dir + \"/build_image_data\"\noutput_directory = data_dir\nos.system(build_script +\n  ' --train_directory=\"%s\"'\n  ' --validation_directory=\"%s\"'\n  ' --output_directory=\"%s\"'\n  ' --labels_file=\"%s\"' %\n  (train_directory, validation_directory, output_directory, labels_file));\n\n","sub_path":"inception/inception/data/download_and_preprocess_flowers.py","file_name":"download_and_preprocess_flowers.py","file_ext":"py","file_size_in_byte":4314,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"207873333","text":"def tic_tac_toe_check(board):\n    \n    col = []\n    cols = []\n    game_board = []\n    topL_to_bottomR = []\n    topR_to_bottomL = []\n    winner = []\n    with_empty = {'x','o',''}\n    without_empty = {'x','o'}\n    \n    #check that what is being passed in is not just a string or different data type\n    #Will catch things such as:\n    #test_board = 'x', 'x', '', 'x', 'x', 'x', 'x', 'x', 'x' (tuple)\n    if isinstance(board, list) is False:\n        #change to type error\n        raise TypeError('Element is of type \\'' + str(type(board)) + '\\'. A list must be passed in.')\n    #list is empty -> []\n    if not board:\n        return None\n        \n    #check that there are 9 spots\n    if len(board) != 9:\n        raise ValueError('Board does not have 9 spots. It has ' + str(len(board)) + ' spots.')\n\n    #check that each element is a string. \n    #Will catch things such as:\n    #test_board =  [['', '', '', '', '', '', '', '', '']] (list of lists)\n    for s in board:\n        if s:\n            if not isinstance(s, str):\n                raise ValueError('Element is not a string: ' + str(s))\n        if not s:\n            if not isinstance(s, str):\n                raise ValueError('Element is not a string: ' + str(s))\n\n    #All game pieces are the same. Either all 'x', 'o', or '' (empty string)\n    if len(set(board)) == 1:\n        if 'x' in board:\n            raise ValueError('Player \\'x\\' cannot win in more than one way.')\n        elif 'o' in board:\n            raise ValueError('Player \\'o\\' cannot win in more than one way.')\n        elif '' in board:\n            return None\n\n    #check if any elements are uppercase\n    for s in board:\n        if s.isupper():\n            raise ValueError('Uppercase is not allowed.')\n        \n    #check if a string in the list is more than one letter, must only be 'x' not 'xx'\n    for s in board:\n        if s != '':\n            if len(s) != 1:\n                raise ValueError('Element must be of length 1')\n                \n    #checking for invalid characters\n    if '' in board: #empty in board\n        if (set(board) != set(with_empty)):\n            if 'x' in board: #will allow only 'x' and ''\n                if((set(board) != set({'x',''}))):\n                    raise ValueError('Elements must be \\'x\\', \\'o\\', or \\'\\'')\n            elif 'o' in board: #will allow only 'o' and ''\n                if (set(board) != set({'o',''})):\n                    raise ValueError('Elements must be \\'x\\', \\'o\\', or \\'\\'')\n            else:\n                if set({''}) != set(board):\n                    raise ValueError('Elements must be \\'x\\', \\'o\\', or \\'\\'')\n    elif '' not in board: #no empty in board\n        if (set(board) != set(without_empty)):\n            #checks for board of either all 'x' or all 'o'\n            if(set(board) != set({'x'})) or (set(board) != set({'o'})):\n                raise ValueError('Elements must be \\'x\\', \\'o\\', or \\'\\'')\n    \n    #passed in as: board -> ['1', '2', '3', '4', '5', '6', '7', '8', '9']\n    #append as a list of lists -> [['1', '2', '3'], ['4', '5', '6'], ['7', '8', '9']]   \n    game_board.append(board[0:3]) #first row ie:  ['1', '2', '3']\n    game_board.append(board[3:6]) #second row ie: ['4', '5', '6']\n    game_board.append(board[6:9]) #third row ie:  ['7', '8', '9']\n    \n    i = 0\n    while i < 3:\n        \n        #get diagonal (top left -> bottom right)\n        topL_to_bottomR.append(game_board[i][i])\n        \n        j = 0       \n        while j < 3:\n            #get columns\n            col.append(game_board[j][i])\n            j += 1\n        i += 1\n\n    #diagonal (top right -> bottom left)\n    i = 0\n    j = 2\n    while i < 3:\n        topR_to_bottomL.append(game_board[i][j])\n        i += 1\n        j -= 1\n            \n    #append as a list of lists -> [['1', '4', '7'], ['2', '5', '8'], ['3', '6', '9']]\n    cols.append(col[0:3]) #first col ie:  ['1', '4', '7']\n    cols.append(col[3:6]) #second col ie: ['2', '5', '8']\n    cols.append(col[6:9]) #third col ie:  ['3', '6', '9']\n    \n    #Check for winners\n    #If string isn't empty, if set length is 1 add winner for row\n    for row in game_board:\n        if '' not in row:\n            if len(set(row)) == 1:\n                winner.append(row[0])\n                \n    #If string isn't empty, if set length is 1 add winner for col        \n    for col in cols:\n        if '' not in col:\n            if len(set(col)) == 1:\n                winner.append(col[0])\n            \n    #If string isn't empty, if set length is 1 add winner for Top L to Bottom R Diagonal\n    if len(set(topL_to_bottomR)) == 1:\n        if '' not in topL_to_bottomR:\n            winner.append(topL_to_bottomR[0])\n            \n    #If string isn't empty, if set length is 1 add winner for Top R to Bottom L Diagonal\n    if len(set(topR_to_bottomL)) == 1:\n        if '' not in topR_to_bottomL:\n            winner.append(topR_to_bottomL[0])\n  \n    winner_set = set(winner)\n    set_length = len(winner_set)\n\n    if len(winner) == 1 and '' not in winner_set:\n        return next(iter(winner_set)) #return tuple(winner_set)[0] #next(iter(winner_set))\n    else:\n        if set_length > 1:\n            raise ValueError('Two players are not allowed to win.')\n        elif len(winner) > 1:\n            raise ValueError('Player \\'' + next(iter(winner_set)) + '\\' cannot win in more than one way.')\n        else:\n            return None","sub_path":"640/impl-2.py","file_name":"impl-2.py","file_ext":"py","file_size_in_byte":5356,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"319866929","text":"# -*- coding: utf-8 -*-\n# @Time    : 2021-04-23 10:58\n# @Author  : zxl\n# @FileName: Offer015.py\n\n\nclass Solution:\n    def hammingWeight(self, n: int) -> int:\n\n        count = 0\n        while n:\n            count += n%2\n            n//=2\n        return count\n\n\n","sub_path":"Offer015.py","file_name":"Offer015.py","file_ext":"py","file_size_in_byte":260,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"335484083","text":"import itertools\nperms = itertools.permutations([1, 2, 3], 2)\nnext(perms) \n# (1, 2) , (1, 3), (2, 1) ...\n# 找排列组合的自带method， 太tm强了，这都有！！\n\n\nlist(itertools.product('ABC', '123'))\n#[('A', '1'), ('A', '2'), ('A', '3'), \n#('B', '1'), ('B', '2'), ('B', '3'), \n#('C', '1'), ('C', '2'), ('C', '3')]\n# itertools.product() function returns an iterator containing the Cartesian product of two sequences\n\nlist(itertools.combinations('ABC', 2))\n# [('A', 'B'), ('A', 'C'), ('B', 'C')]\n# itertools.combinations() function returns an iterator containing all the possible combinations of the given sequence of the given length","sub_path":"PythonWorkSpace/LearnPython/Advanced Iterators/itertools_showcase.py","file_name":"itertools_showcase.py","file_ext":"py","file_size_in_byte":643,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"323690747","text":"\"\"\"Bodega Custmize CLI class.\"\"\"\nfrom enum import Enum\nfrom bodega_utils import Utils\n\n\nclass Types(Enum):\n    \"\"\"Values TYPE can take in CUSTOMIZE command.\"\"\"\n\n    DEV_MACHINE = 'dev_machine'\n\n\nclass PositionalArguments(Enum):\n    \"\"\"POSITIONAL_ARGUMENTs for CUSTOMIZE command.\"\"\"\n\n    HOST_INFO = 'host_info'\n\n\nclass NamedArguments(Enum):\n    \"\"\"NAMED_ARGUMENTs for CUSTOMIZE command.\"\"\"\n\n    SD_DEV_MOUNTRC_FILE = 'sd_dev_mountrc_file'\n    PEM_FILE = \"pem_file\"\n    GIT_REPO_FILE = \"git_repo_file\"\n\n\nclass BodegaCustomize(object):\n    \"\"\"Sub-parser for the CUSTOMIZE command.\"\"\"\n\n    def __init__(self, sub_parser, bodega_commands):\n        \"\"\"Initialize BodegaCustomize class.\"\"\"\n        self.commands = bodega_commands\n\n        self.parser = sub_parser\n        self.subparsers = self.parser.add_subparsers()\n\n        self._init_subparsers()\n\n    def _init_subparsers(self):\n        self._init_dev_machine()\n\n    def _init_dev_machine(self):\n        description = \"\"\"Configure a existing dev machine\"\"\"\n        sub_parser = self.subparsers.add_parser(Types.DEV_MACHINE.value,\n                                                description=description,\n                                                epilog=Utils.epilog)\n        sub_parser.add_argument(PositionalArguments.HOST_INFO.value,\n                                type=Utils.parse_userhost,\n                                help='Host information <user>@<ipaddr>, ' +\n                                'username is optional')\n        sub_parser.add_argument(\n            Utils.get_short_arg_from_name(\n                NamedArguments.SD_DEV_MOUNTRC_FILE.value),\n            Utils.get_long_arg_from_name(\n                NamedArguments.SD_DEV_MOUNTRC_FILE.value),\n            default=Utils.get_default_sd_dev_file(),\n            help='sd_dev_mountrc file to use [default:%(default)s]',\n            metavar='FILE')\n        sub_parser.add_argument(\n            Utils.get_short_arg_from_name(\n                NamedArguments.PEM_FILE.value),\n            Utils.get_long_arg_from_name(\n                NamedArguments.PEM_FILE.value),\n            default=Utils.get_default_ubuntu_pem_key(),\n            help='pem file to use [default:%(default)s]',\n            metavar='FILE')\n        sub_parser.add_argument(\n            Utils.get_short_arg_from_name(\n                NamedArguments.GIT_REPO_FILE.value),\n            Utils.get_long_arg_from_name(\n                NamedArguments.GIT_REPO_FILE.value),\n            default=None,\n            help='git repo file to use, \" + \\\n            \"refer to conf/sd_dev_git_repos.defaults',\n            metavar='YML FILE')\n\n        sub_parser.set_defaults(func=self._exec_configure_dev_machine)\n\n    def _exec_configure_dev_machine(self, args_dict):\n        username, host = args_dict[PositionalArguments.HOST_INFO.value]\n        sync_file = args_dict[NamedArguments.SD_DEV_MOUNTRC_FILE.value]\n        pem_file = args_dict[NamedArguments.PEM_FILE.value]\n        git_repo_file = args_dict[NamedArguments.GIT_REPO_FILE.value]\n        self.commands.configure_dev_machine(username, host,\n                                            sync_file=sync_file,\n                                            git_repo_file=git_repo_file,\n                                            pem_file=pem_file)\n","sub_path":"lab/bodega/cli/bodega_customize.py","file_name":"bodega_customize.py","file_ext":"py","file_size_in_byte":3269,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"193815873","text":"#!/usr/bin/env python\n\"\"\"\n================================================================================\n:mod:`config` -- Configuration parser\n================================================================================\n\n.. module:: config\n   :synopsis: Read configuration file\n\n.. inheritance-diagram:: pymontecarlo.util.config\n\nConfiguration parser to read INI type files.\nThis parser differs slightly from those available in the :mod:`ConfigParser`\nmodule as the sections, options and values can be accessed using attributes\ninstead of getters and setters.\nWhile the access of the data is different, this class uses the\n:class:`SafeConfigParser` of the :mod:`ConfigParser` module to read and\nwrite configuration file.\n\n.. warning::\n\n   This parser does *not* support section or option starting with a number.\n\nLet's take the configuration file::\n\n  [section1]\n  option1=value1\n  option2=value2\n\n  [section2]\n  option3 = value3\n\nTo load the configuration file, use the :meth:`read` method::\n\n  >>> config = ConfigParser()\n  >>> with open(filepath, 'r') as fp:\n  ...     config.read(fp)\n\nTo get the value of ``option1`` in ``section1``::\n\n  >>> config.section1.option1\n  'value1'\n\nor of ``option3`` in ``section2``::\n\n  >>> config.section2.option3\n  'value3'\n\nTo check if a section is present, use the ``in`` statement::\n\n  >>> 'section3' in config\n  False\n  >>> 'option2' in config.section1\n  True\n\nTo iterate over all sections and options::\n\n  >>> for section, option, value in config:\n  ...     print section, option, value\n  'section1' 'option1' 'value1'\n  'section1' 'option2' 'value2'\n  'section2' 'option3' 'value3'\n\nNote that the order may changed.\n\nTo add a section or option::\n\n  >>> config.add_section('section3')\n  >>> 'section3' in config\n  True\n  >>> config.section3.option4 = 'value4'\n\nThe following syntax can be shorten as :meth:`add_section` method returns the\nadded section::\n\n  >>> config.add_section('section3').option4 = 'value4'\n\nIf the added section already exists, no section is added and the current one\nis returned::\n\n  >>> config.add_section('section1').option1\n  'value1'\n\nFinally, the configuration can be saved back to a file::\n\n  >>> with open(filepath, 'w') as fp:\n  ...     config.write(fp)\n\n\"\"\"\n\n# Script information for the file.\n__author__ = \"Philippe T. Pinard\"\n__email__ = \"philippe.pinard@gmail.com\"\n__version__ = \"0.1\"\n__copyright__ = \"Copyright (c) 2011 Philippe T. Pinard\"\n__license__ = \"GPL v3\"\n\n# Standard library modules.\nfrom configparser import ConfigParser as _ConfigParser\n\n# Third party modules.\n\n# Local modules.\n\n# Globals and constants variables.\n\nclass _Section(object):\n    def __init__(self, options={}):\n        self.__dict__.update(options)\n\n    def __iter__(self):\n        for option_name, value in self.__dict__.items():\n            yield option_name, value\n\n    def __contains__(self, option_name):\n        return option_name in self.__dict__\n\nclass ConfigParser(object):\n    \"\"\"\n    Configuration parser.\n    \"\"\"\n\n    def __contains__(self, section_name):\n        return section_name in self.__dict__\n\n    def __iter__(self):\n        for section_name, section in self.__dict__.items():\n            for option_name, value in section:\n                yield section_name, option_name, value\n\n    def add_section(self, section_name):\n        \"\"\"\n        Adds a new section if the specified section name doesn't exist.\n        Does nothing if the section already exists.\n\n        :return: section\n        \"\"\"\n        if section_name not in self.__dict__:\n            self.__dict__[section_name] = _Section()\n        return self.__dict__[section_name]\n\n    def read(self, fileobj, ignore_errors=False):\n        \"\"\"\n        Reads the configuration from the file object.\n\n        If ``ignore_errors`` is ``True`` and a section or option starts with\n        a number, a :exc:`IOError` exception is raised.\n        If not, these sections or options are skipped.\n\n        :arg fileobj: file object\n        :arg ignore_errors: whether to raise exception or skip invalid\n            section(s) and option(s)\n        \"\"\"\n        parser = _ConfigParser()\n        parser.read_file(fileobj)\n\n        for section in parser.sections():\n            if section[0].isdigit():\n                if ignore_errors:\n                    continue\n                raise IOError(\"Section name (%s) cannot start with a digit\" % section)\n\n            options = {}\n\n            for option in parser.options(section):\n                if option[0].isdigit():\n                    if ignore_errors:\n                        continue\n                    raise IOError(\"Option name (%s) cannot start with a digit\" % option)\n\n                options[option] = parser.get(section, option)\n\n            self.__dict__[section] = _Section(options)\n\n    def write(self, fileobj):\n        \"\"\"\n        Writes the configuration inside the file object.\n\n        :arg fileobj: file object\n        \"\"\"\n        parser = _ConfigParser()\n\n        # Add sections\n        for section_name in self.__dict__:\n            parser.add_section(section_name)\n\n        # Add options\n        for section_name, option_name, value in self:\n            parser.set(section_name, option_name, str(value))\n\n        parser.write(fileobj)\n","sub_path":"pymontecarlo/util/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":5233,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"429806656","text":"# 2675번: 문자열 반복\n# https://www.acmicpc.net/problem/2675\n# Version: Python 3.9.7\n\n\nfrom sys import stdin\n\nread = stdin.readline\n\nif __name__ == \"__main__\":\n    for _ in range(int(read())):\n        count, string = read().rstrip().split()\n        for char in string:\n            print(char * int(count), end=\"\")\n        print()\n\n","sub_path":"boj/bronze2/2675.py","file_name":"2675.py","file_ext":"py","file_size_in_byte":337,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"651403410","text":"class Graph:\n    def __init__(self, vertices=[], edges=[]):\n        self._vertices = vertices\n        self._edges = edges\n\n    def add_node(self, *vertices):\n        for vertex in vertices:\n            self._vertices.append(vertex)\n\n    def create_adj(self, *edges):\n        for edge in edges:\n            self._edges.append(edge)\n\n    def vertices(self): return self._vertices\n    def edges(self): return self._edges\n\n    def __str__(self):\n        traversal = \"\"\n        for i in self._edges:\n            vertex_from = self._vertices[i[0]]\n            vertex_to = self._vertices[i[1]]\n            traversal += str(vertex_from) + \" <-> \" + str(vertex_to) + \"\\n\"\n        return traversal\n\n\ndef read_int(prompt):\n    return int(input(prompt))\n\n\nif __name__ == '__main__':\n    g = Graph()\n\n    print(\"1. Add node to graph\")\n    print(\"2. Create adjacency\")\n    print(\"3. Print adjacencies\")\n\n    while True:\n        print(\"Vertices:\", g.vertices())\n        print(\"Edges\", g.edges())\n        inp = read_int(\"Your choice: \")\n        if inp == 1:\n            g.add_node(input(\" Enter node: \"))\n        elif inp == 2:\n            g.create_adj([read_int(\" Enter edge from: \"),\n                          read_int(\" Enter edge to: \")])\n        else:\n            print(g)\n","sub_path":"MCA/Data Structures/Graph_adj.py","file_name":"Graph_adj.py","file_ext":"py","file_size_in_byte":1262,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"133511127","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport django.core.validators\nimport uuidfield.fields\nimport django.utils.timezone\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='ManagrUser',\n            fields=[\n                ('password', models.CharField(max_length=128, verbose_name='password')),\n                ('last_login', models.DateTimeField(verbose_name='last login', default=django.utils.timezone.now)),\n                ('id', uuidfield.fields.UUIDField(editable=False, unique=True, primary_key=True, blank=True, serialize=False, max_length=32)),\n                ('email', models.EmailField(max_length=255, unique=True, verbose_name='email address')),\n                ('phone_number', models.CharField(unique=True, verbose_name='mobile phone number', default=None, null=True, validators=[django.core.validators.RegexValidator(regex='^\\\\+?1?\\\\d{9,15}$', message=\"Phone number must be entered in the format: '+999999999'. Up to 15 digits allowed.\")], blank=True, max_length=20)),\n                ('is_active', models.BooleanField(default=True)),\n                ('is_admin', models.BooleanField(default=False)),\n            ],\n            options={\n                'abstract': False,\n            },\n            bases=(models.Model,),\n        ),\n    ]\n","sub_path":"account/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":1412,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"265298686","text":"#!/usr/bin/python3\nimport numpy as np\nimport networkx as nx\nimport itertools\nimport scipy.stats as stats\nimport essentials as es\nimport random\nimport time\n#Scripted by David Vargas\n#---------------------------------------------\n#Convention if directed networks are introduced,\n#Aij=1 if there is an edge from j to i. This\n#means one would sum over row i to get the total\n#number of incoming edges to node i.\n#Here I am summing over rows.\n#Unfinished function - quantum assortativity\n#Next implement hierarchical clustering algorithm\n#described on page 388 of Newman's book on networks\n#Next implement fractal dimension calculation proposed\n#by Simona.\n\ndef strengths(mutualinformation):\n    #Return the strength of all nodes in the network in order.\n    return np.sum(mutualinformation, axis = 1)\n\ndef density(matrix):\n    #Calculates density, also termed connectance in some\n    #literature. Defined on page 134 of Mark Newman's book\n    #on networks.\n    l = len(matrix)\n    lsq = l*(l-1)\n    return np.sum(matrix)/lsq \n\n# def clustering(matrix):\n#     #Calculates the clustering coefficient\n#     #as it is defined in equation 7.39 of\n#     #Mark Newman's book on networks. Page 199.\n#     matrixcube = np.linalg.matrix_power(matrix, 3)\n#     matrixsq = np.linalg.matrix_power(matrix, 2)\n#     #Zero out diagonal entries. So we do not count edges as\n#     #connected triples.\n#     for i in range(len(matrixsq)):\n#         matrixsq[i][i] = 0\n#     denominator = np.sum(matrixsq)\n#     numerator = np.trace(matrixcube)\n#     #if there are no closed paths of length\n#     #three the clustering is automatically\n#     #set to zero.\n#     if numerator == 0.:\n#         return 0.\n#     else:\n#         return numerator/denominator\n\ndef clustering(matrix):\n    #Calculates the clustering coefficient\n    #as it is defined in equation 7.39 of\n    #Mark Newman's book on networks. Page 199.\n    matrixcube = np.linalg.matrix_power(matrix, 3)\n    numerator = np.trace(matrixcube)\n    #if there are no closed paths of length\n    #three the clustering is automatically\n    #set to zero.\n    if numerator == 0.:\n        return 0.\n    else:\n        matrixsq = np.linalg.matrix_power(matrix, 2)\n        denominator = np.sum(matrixsq) - np.trace(matrixsq)\n        return numerator/denominator\n\ndef localclustering(matrix):\n    l = len(matrix)\n    localclustering = []\n    matrixcube = np.linalg.matrix_power(matrix, 3)\n    for i in range(l):\n        matrix2 = np.outer(matrix[i,:], matrix[i,:])\n        numerator = matrixcube[i][i]\n        denominator = (np.sum(matrix2)-np.trace(matrix2))\n        if denominator != 0:\n            localclustering.append(numerator/denominator)\n        else:\n            localclustering.append(0)\n    # squaretmp = 0\n    # for i in range(l):\n    #     for j in range(l):\n    #         for k in range(j):\n    #             squaretmp += matrix[i][j]*matrix[i][k]\n    #     if squaretmp != 0:\n    #         localclustering.append(0.5*matrixcube[i][i]/squaretmp)\n    #     else:\n    #         localclustering.append(0)\n        # squaretmp = 0\n    return np.array(localclustering)\n\ndef rhotype(rho):\n    localtmp = 0\n    localtmp2 = 0\n    d = len(rho)\n    basisstate = np.zeros(d)\n    brastate = np.zeros(d)\n    #By construction I do not have to conjugate the elements to get the bra.\n    #This is because basis state is a vector with a single one in a list of zeros.\n    for i in range(d):\n        basisstate[i] = 1\n        brastate = basisstate\n        #Map basis state to basis state.\n        basisstate = np.dot(rho, basisstate)\n#        print 'basis',basisstate\n#        print 'bra',brastate,'basis',basisstate\n#        print 'rho',rho\n        expectation=np.dot(brastate, basisstate)\n        basisstate[i] = 0\n#        print tuple([expectation,i])\n        if expectation >= localtmp:\n            localtmp = expectation\n            localtmp2 = i\n    rhotype = localtmp2\n    return int(rhotype)\n\ndef rhotypes(rhos):\n    L = len(rhos)\n    rhoTypes = np.zeros(L, dtype = int)\n    for i in range(L):\n        rhoTypes[i] = rhotype(rhos[i])\n    return rhoTypes\n\ndef quantumassortativity(rhos,mutualinformation,typefunction):\n    L = len(rhos)\n    sites = np.array(range(L))\n    rhoTypes = typefunction(rhos)\n    types = np.unique(rhoTypes)\n    longtype = {}\n    for atype in types:\n        longtype[atype] = atype*np.ones(L, dtype = int)\n    ed = len(types)\n    eab = np.zeros((ed,ed))\n    sitesbytype = {}\n    sitesbytype2 = []\n    for atype in types:\n        abool = np.equal(rhoTypes,longtype[atype])\n        sitesbytype[atype] = list(sites[abool])\n    for atype in types:\n        for btype in types:\n            for i in sitesbytype[atype]:\n                for j in sitesbytype[btype]:\n                    eab[atype][btype] += mutualinformation[i][j]\n    eab = eab/np.sum(eab)\n    trace = np.trace(eab)\n    prodsum = np.sum(np.dot(eab,eab))\n    if 1. - prodsum != 0:\n        assortativity = (trace-prodsum)/(1.-prodsum)\n        return assortativity\n    else:\n        return nan\n  \n# def disparity(matrix):\n#     #Disparity defined on page 199 of doi:10.1016/j.physrep.2005.10.009 \n#     #Equation 2.39, Here I take the average of this quantity over the\n#     #entire network\n#     l = len(matrix)\n#     numerator = np.sum(matrix**2, axis = 1)/l\n#     denominator = np.sum(matrix, axis = 1)\n#     #Logical Check\n#     logos = denominator > 0\n#     numerator = numerator[logos]\n#     denominator = denominator[logos]\n#     denominator = denominator**2\n#     #Check for zero denominator.\n\n#     if sum(denominator) == 0.:\n#         return np.nan\n#     else: \n#         return sum(numerator/denominator)\n\ndef disparity(matrix):\n    #Disparity defined on page 199 of doi:10.1016/j.physrep.2005.10.009 \n    #Equation 2.39, Here I take the average of this quantity over the\n    #entire network\n    l = len(matrix)\n    numerator = np.sum(matrix**2, axis = 1)/l\n    denominator = np.sum(matrix, axis = 1)**2 + 1.E-16j\n    return np.sum(numerator/denominator).real# .real\n    # #Check for zero denominator.\n    # if sum(denominator) == 0.:\n    #     return np.nan\n    # else: \n    #     return sum(numerator/denominator)\n\ndef disparitylattice(matrix):\n    #Local disparity across the entire lattice.\n    numerator = np.sum(matrix**2, axis = 1)\n    denominator = np.sum(matrix, axis = 1)**2 + 1.E-16j\n    disparity = (numerator/denominator).real\n    return disparity\n\ndef strengthdist(mutualinformation, bins = np.linspace(0, 1, 11)):\n    #Compute the weighted analog of a degree distribution.\n    strens = strengths(mutualinformation)\n    # maxinfo=np.max(strens)\n    # mininfo=np.min(strens)\n    return np.histogram(strens,bins)\n\ndef pearson(mutualinformation):\n    L = len(mutualinformation)\n    pearsonR = np.zeros((L,L))\n    pvalues = np.zeros((L,L))\n    for i in range(L):\n        for j in range(L):\n            if i != j:\n                r,p = stats.pearsonr(mutualinformation[i], mutualinformation[j])\n                pearsonR[i][j] = r\n                pvalues[i][j] = p\n            else:\n                pearsonR[i][j] = 0.\n                pvalues[i][j] = 0.\n    return [pearsonR, pvalues]\n\ndef edgeweightdist(mutualinformation, bins):\n    L = len(mutualinformation)\n    edges = es.flatten(list(mutualinformation))\n    return np.histogram(edges,bins)[0]\n\ndef coarsegrainnetwork(mutualinformation, bins):\n    #Default behavior of histograms is to have the intervals\n    #Closed on the left and open on the right.\n    #The bin with the largest values is closed on both ends.\n    L = len(mutualinformation)\n    coarsematrix = np.zeros((L,L))\n    ll = len(bins)\n    for i in range(L):\n        for j in range(L):\n            for k in range(ll-2):\n                if bins[k] <= mutualinformation[i][j] < bins[k+1]:\n                    coarsematrix[i][j] = k\n            if bins[ll-2] <= mutualinformation[i][j] <= bins[ll-1]:\n                coarsematrix[i][j] = ll-2\n    return coarsematrix\n\n#NetworkX additions\n#---------------------------------------------\n\ndef distance(mutualinformation):\n    #Initialize array\n    length = len(mutualinformation)\n    thisdistance = np.zeros((length,length))\n    #If an element has value less than (10^-14) in absolute value,\n    #then treat it as of value (10^-16), set its distance\n    #to 10^16. Otherwise set it mij^(-1).\n    #The value (10^-14) must be adjusted in coordination with\n    #the cutoff applied to the weighted network of interest.\n    #The purpose of setting this value equal to 10^16 is\n    #so that the geodesic path length algorithm will ignore\n    #any edges less than the cutoff treating.\n    for i in range(length):\n        for j in range(length):\n            if np.abs(mutualinformation[i, j]) <= 1E-14:\n                thisdistance[i, j] = np.power(10.,16)\n            else:\n                thisdistance[i, j] = np.power(mutualinformation[i, j],-1)\n    return thisdistance\n\ndef geodesic(distance, i, j):\n    #Initialize networkx graph object\n    #NetworkX indexing starts at zero.\n    #Ends at N-1 where N is the number of nodes.\n    latticelength = len(distance)\n    G = nx.Graph(distance)\n    #Use networkx algorithm to compute the shortest path from\n    #the first lattice site to the last lattice site.\n    pathlength = nx.shortest_path_length(G, source = i-1, target = j-1, weight = 'weight')\n    #The pathlength is tested for an unreasonable value.\n    #An unreasonable value for the path length is a function of the cutoff\n    #applied to the weighted network under analysis and the value set in\n    #the distance function thisdistance[i,j]=np.power(10.,16)\n    if pathlength > np.power(10., 15):\n        return np.nan\n    return pathlength\n\ndef harmoniclength(distance):\n    #page 11, equation 2 The Structure and Function of Complex Networks\n    #If the geodesic distance between two nodes is a number then \n    #append it to alist to include it in the sum.\n    l = len(distance)\n#    print 'Node count: ', l\n    factor = 1./(0.5*l*(l-1))\n#    print 'Factor: ', factor\n    alist = []\n    for i in range(1, len(distance) + 1):\n        for j in range(i+1, len(distance) + 1):\n            geo = geodesic(distance, i, j)\n            if not np.isnan(geo):\n#                print '(i,j)',tuple([i,j])\n                alist.append(1./geo)\n#    print 'alist',alist\n#    print 'Harmonic Mean of Distances: ', 1./(factor*sum(alist))\n    return 1./(factor*sum(alist))\n\ndef eigenvectorcentralitynx0(mutualinformation):\n    #Uses the power method to find eigenvector of \n    #a weighted adjacency matrix.\n    G = nx.Graph(mutualinformation)\n    eigvcent = nx.eigenvector_centrality(G, weight='weight', max_iter=2000)\n    return eigvcent\n\ndef eigenvectorcentralitynx(mutualinformation, startingvector):\n    #Identical to eigenvectorcentralitynx0, but requires an additional argument startingvector.\n    #starting vector provides an initial guess for the eigen vector centrality of all nodes.\n    #startingvector must be a python dictionary. key = node, value = eigenvector centrality estimate.\n    G = nx.Graph(mutualinformation)\n    eigvcent = nx.eigenvector_centrality(G, weight = 'weight', max_iter = 2000, nstart = startingvector)\n    return eigvcent\n\n#Here I provide some test cases.  These test cases assume the weighted\n#networks being provided are quantum mutual information networks.\ntest=True\ndef main():\n    if test==True:\n        testrho = np.array([[0.1, 0], [0, 0.9]])\n        testrho2 = np.array([[0.7, 0], [0, 0.3]])\n        testrho3 = np.array([[0.5, 0.], [0.0, 0.5]])\n        testrhos = [testrho, testrho2, testrho3]\n        singlet = np.array([[0., 0., 1.], [0., 0., 0.], [1., 0., 0.]])\n        ghz = np.array([[0, 0.5, 0.5], [0.5, 0, 0.5], [0.5, 0.5, 0]])\n        w = np.array([[0, 0.636514, 0.636514],\n                    [0.636514, 0, 0.636514],\n                    [0.636514, 0.636514, 0]])\n        fourqubits = np.array([[0, 1, 0, 0], [1, 0, 1, 1],\n                             [0, 1, 0, 1], [0, 1, 1, 0]])\n        states = [singlet, ghz, w, fourqubits]\n        names = ['singlet', 'ghz', 'w', 'Four Qubits']\n        functions = [strengths, density, clustering, localclustering, disparity,\n                     disparitylattice, distance]#, eigenvectorcentralitynx0]\n        measurenames = ['Strengths', 'Density', 'Clustering', \n                        'Local Clustering', 'Average Disparity', \n                        'Nodal Disparity', 'Distances']#,\n#                        'Eigenvector Centrality']\n        for i in range(len(states)):\n            print(names[i])\n            for function in functions:\n                print(function, function(states[i]))\n        print('Rhos: ', testrho, '\\n', testrho2, '\\n', testrho3, '\\n')\n        print('Rho Types: ', rhotypes(testrhos))\n        print(quantumassortativity(testrhos, ghz, rhotypes))\n        #Counterintuitive result for pearson correlation of ghz state.\n        #It is negative. This is because we are arbitarily setting the \n        #diagonal of the mutual information matrix to zero.  \n        print(pearson(ghz))\n        \n        print(\"Edge Weight Distribution: \", edgeweightdist(ghz, [0, 0.5, 1]))\n        print(\"Full Matrix: \", ghz)\n        print(\"Coarse Matrix:\", coarsegrainnetwork(ghz, [0, 0.5, 1]))\n        \n        \n        # print(\"Local Clustering: \", localclustering(matrix))\n        \n        # total = 0\n        # total2 = 0\n        # for i in range(10000):\n        #     matrix = np.zeros((26, 26))\n        #     for i in range(26):\n        #         for j in range(26):\n        #             matrix[i][j] = random.random()\n        #     t0 = time.time()\n        #     clustering(matrix)\n        #     t1 = time.time()\n        #     clustering2(matrix)\n        #     t2 = time.time()\n        #     total += (t1-t0)/(t2-t1)\n        #     total2 += clustering2(matrix) - clustering(matrix)\n        # print(total/10000)\n        # print(total2/10000)\n        #This effect should go away in the thermodynamic limit. \n        #I can check this. Next I should check how robust the\n        #result is against the size of the numerical noise.\n        # def GHZ(L):\n        #     mutualinformation=np.zeros((L,L))\n        #     for i in range(L):\n        #         for j in range(L):\n        #             if i!=j:\n        #                 mutualinformation[i][j]=0.5#+0.01*(np.random.rand()-0.5)\n        #             else:\n        #                 mutualinformation[i][j]=0.\n        #     return mutualinformation\n        # for l in [4,10,20,50]:\n        #     print l,'\\n',pearson(GHZ(l))[0][l/2][l/2+1]\n        # a=1.E-14*np.ones((20,20))\n        # for i in range(len(a)):\n        #     a[i][i]=0.\n        # print 'disparity', disparity(a)\n        # print 'clustering', clustering(GHZ(3))\n        # print 0.2*GHZ(3)\n        # a=0.01*np.ones(100)*range(100)\n        # for i in a:\n        #     print clustering(i*GHZ(3))\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"networkmeasures.py","file_name":"networkmeasures.py","file_ext":"py","file_size_in_byte":14824,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"223273380","text":"import cv2\nimport numpy as np\n\nimg = cv2.imread('rb15.jpg')\n\n\"\"\"\nAffine transforms\n- scaling\n- rotation\n- translation\n- skewing\n- preserve collinearity and parallelism\n\nNon-affine transforms\n- also known as projective transformation, homography\n- does not preserve parallelism, length, and angle\n- does preserve collinearity and incidence\n\nTranslation Matrix = |1 0 Tx|\n                     |0 1 Ty|\nTx = shift along x-axis\nTy = shift along y-axis\nuse cv2.warpAffine to implement translations\n\nRotation Matrix:\nM = |cos(theta) -sin(theta)|\n    |sin(theta) cos(theta) |\n\ncv2.getRotationMatrix2D(rotation_center_x, rotation_center_y, angle of rotation, scale)\n\"\"\"\n\nheight, width = img.shape[:2]\nquarter_height, quarter_width = height/4, width/4\n\nT = np.float32([[1, 0, -quarter_width], [0, 1, -quarter_height]])\n\nimg_translation = cv2.warpAffine(img, T, (width, height))\ncv2.imshow('Translation', img_translation)\n\nrotation_matrix = cv2.getRotationMatrix2D((width/2, height/2), 90, 1)\nrotated_image = cv2.warpAffine(img, rotation_matrix, (width, height))\n\ncv2.imshow('Rotation', rotated_image)\n\nt_img = cv2.transpose(img)\ncv2.imshow(\"Transpose\", t_img)\n\nh_flipped = cv2.flip(image, 1)\nv_flipped = cv2.flip(image, -1)\n\n\ncv2.waitKey(0)\ncv2.destroyAllWindows()","sub_path":"translations, rotations, and transformations.py","file_name":"translations, rotations, and transformations.py","file_ext":"py","file_size_in_byte":1255,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"348595680","text":"# indexing a local csv file test.csv\nINDEX_NAME = 'titanic' # 数据库名字\nTYPE_NAME = 'passenger' # 表名字\nID_FIELD = 'passengerid' # 文档ID\n\nimport csv\nimport sys\n\nresponse = open(sys.argv[1], 'rt', encoding=\"utf-8\") # put this the same directory as test.csv\nfile_object = csv.reader(response)\nheader = file_object.__next__() # header得第一行 file_object去第一行\nheader = [item.lower() for item in header] # 第一行内容小写\n\nbulk_data = [] \n\nfor row in file_object:\n    data_dict = {}\n    for i in range(len(row)):\n        data_dict[header[i]] = row[i]\n    op_dict = {\n        \"index\": {\n            \"_index\": INDEX_NAME, \n            \"_type\": TYPE_NAME, \n            \"_id\": data_dict[ID_FIELD]\n        }\n    }\n    bulk_data.append(op_dict)\n    bulk_data.append(data_dict)\n\n# step1 : create ES client\nfrom elasticsearch import Elasticsearch\nesHost1 = {\"host\" : \"localhost\", \"port\" : 9200}\nes = Elasticsearch(hosts=[esHost1])\n\n# step2 : check\n# if the index already exist, delete it and create it again\nif es.indices.exists(INDEX_NAME):\n    print(\"deleting '%s' index...\" % (INDEX_NAME))\n    res = es.indices.delete(index = INDEX_NAME)\n    print(\" response: '%s'\" % (res))\n\n# step3 : settings\n# since we are running locally, use one shard and no replicas\n# Each index created can have specific settings associated with it.\nrequest_body = {\n    \"settings\" : {\n        \"index\" : {\n            \"number_of_shards\": 1,\n            \"number_of_replicas\": 0\n        }\n    }\n}\n# Default for number_of_shards is 5\n# Default for number_of_replicas is 1 (ie one replica for each primary shard)\n# The index settings can also be defined with JSON:\n'''\n\n$ curl -XPUT 'http://localhost:9200/twitter/' -d '{\n    \"settings\" : {\n        \"index\" : {\n            \"number_of_shards\" : 3,\n            \"number_of_replicas\" : 2\n        }\n    }\n}'\n\n'''\n# or more simplified\n'''\n\n$ curl -XPUT 'http://localhost:9200/twitter/' -d '{\n    \"settings\" : {\n        \"number_of_shards\" : 3,\n        \"number_of_replicas\" : 2\n    }\n}'\n\n'''\n# You do not have to explicitly specify index section inside the settings section.\n\n# step4 : create index\nprint(\"creating '%s' index...\" % (INDEX_NAME))\nres = es.indices.create(index = INDEX_NAME, body = request_body)\nprint(\" response: '%s'\" % (res))\n\n# bulk index the data\nprint(\"bulk indexing...\")\nres = es.bulk(index = INDEX_NAME, body = bulk_data, refresh = True)\n\n# sanity check\nres = es.search(index = INDEX_NAME, size=30, body={\"query\": {\"match_all\": {}}})\n# display the result immediately:\n# print(\" response: '%s'\" % (res)) \n\n# To display the results more clearly, we can loop through them:\nprint(\"results:\")\nfor hit in res['hits']['hits']:\n    print(\"\\n\") # str\n    print(hit[\"_source\"]) # dict\n\nresponse.close # close the file\n","sub_path":"es4.py","file_name":"es4.py","file_ext":"py","file_size_in_byte":2765,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"577984340","text":"#!/usr/bin/python\n\n\"\"\"\nUnit test for MetaData.\n\"\"\"\n\n# Get object to test \nimport sys\nsys.path.append('../')\nfrom MetaData import MetaData\n\n# Get base class to run tests off of\nimport unittest\n\n# Create derived class that has the tests.\nclass TestMetaData(unittest.TestCase):\n    def setUp(self):\n        self.metaData = MetaData({})\n\n    def test_meta_data_is_processed(self):\n        testStr  = '<!--\\n'\n        testStr += 'generated_by_MarkdownBlogger\\n'\n        testStr += 'generated_by_MarkdownBlogger blogId: myblog\\n'\n        testStr += 'generated_by_MarkdownBlogger postId: mypost\\n'\n        testStr += 'generated_by_MarkdownBlogger title: mytitle\\n'\n        testStr += 'generated_by_MarkdownBlogger tags: hello, world\\n'\n        testStr += '-->\\n'\n\n        self.metaData.process(testStr)\n        metaData = self.metaData.get()\n        self.assertEqual('myblog', metaData['blogId'])\n\n    def test_blank_meta_data_returns_none(self):\n        testStr = ''\n        self.metaData.process(testStr)\n        metaData = self.metaData.get()\n        self.assertEqual({}, metaData);\n\n\n\n\n# Main function will run all tests in classes derived from TestCase\nif __name__ == \"__main__\":\n    unittest.main()\n\n\n","sub_path":"unit_tests/MetaData.t.py","file_name":"MetaData.t.py","file_ext":"py","file_size_in_byte":1200,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"478601341","text":"#Create a function named exponents() that takes two lists as parameters named bases and powers. \n#Return a new list containing every number in bases raised to every number in powers.\n\n\ndef exponents(bases, powers):\n  new_ar = []\n  for a in bases:\n    for b in powers:\n      new_ar.append(a**b)\n\n  return new_ar\n\nprint(exponents([2, 3, 4], [1, 2, 3]))\n","sub_path":"6_1_Looping_Exponents.py","file_name":"6_1_Looping_Exponents.py","file_ext":"py","file_size_in_byte":351,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"506611961","text":"import snowboydecoder\nimport sys\nimport signal\n\ninterrupted = False\n\ndef signal_handler(signal, frame):\n    global interrupted\n    interrupted = True\n\ndef interrupt_callback():\n    global interrupted\n    return interrupted\n\nmodel = r'resources/snowboy.umdl'  # 修改model，指定其文件名\n\n# capture SIGINT signal, e.g., Ctrl+C\nsignal.signal(signal.SIGINT, signal_handler)\n\n# 唤醒词检测函数，调整sensitivity参数可修改唤醒词检测的准确性\ndetector = snowboydecoder.HotwordDetector(model, sensitivity=0.5)\nprint('Listening... Press Ctrl+C to exit')\n\n# main loop\n# 回调函数 detected_callback=snowboydecoder.play_audio_file \n# 修改回调函数可实现我们想要的功能\ndetector.start(detected_callback=snowboydecoder.play_audio_file,\n               interrupt_check=interrupt_callback,\n               sleep_time=0.03)\n\n# 释放资源\ndetector.terminate()\n","sub_path":"test1.py","file_name":"test1.py","file_ext":"py","file_size_in_byte":888,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"203364033","text":"import os \r\nfrom PIL import Image\r\n\r\nif not os.path.exists('../data/'):\r\n\tos.mkdir('../data')\r\nimgs = os.listdir()\r\nimgs = [i for i in imgs if '.jpg' in i]\r\nfor i in imgs:\r\n\timg = Image.open(i)\r\n\timg = img.resize((120,120))\r\n\timg.save(f'../data/{i}')\r\n\tprint(f'已完成第{i}张图片')\r\n","sub_path":"rawdata/datamake.py","file_name":"datamake.py","file_ext":"py","file_size_in_byte":289,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"577738248","text":"import Transactions\r\nimport Payments\r\nimport Customers\r\nimport Database as db\r\nimport Constants\r\n\r\n#name,prv_out_standing, payments_recvd, outstd_bal, old_points, points_added, new_points\r\nCustomers_test_data = [[\"Jhon\",30000,0,30000,100,0,100],\r\n                       [\"Ram\",50000,0,50000,200,0,200],\r\n                       [\"Raj\",450000,0,450000,600,0,600],\r\n                       [\"Ian\",26000,0,26000,400,0,400],\r\n                       [\"Sachin\",110000,0,110000,500,0,500]]\r\n\r\n#name, date, amount\r\nPayments_data = [[\"Jhon\",\"10-06-2016\",1000],\r\n                 [\"Jhon\",\"10-07-2016\",2000],\r\n                 [\"Jhon\",\"10-09-2016\",10000],\r\n                 [\"Ram\",\"10-11-2016\",11200],\r\n                 [\"Ram\",\"10-12-2016\",5000]]\r\n#name, trans_id, date, amount,trans_type\r\nTransactions_data = [[\"Jhon\",112,\"1-06-2016\",5000,Constants.TRANS_SCHD],\r\n                     [\"Jhon\",113,\"2-06-2016\",3000,Constants.TRANS_SWIPE],\r\n                     [\"Jhon\",114,\"3-06-2016\",4000,Constants.TRANS_ONLINE],\r\n                     [\"Ram\",1,\"1-06-2016\",5000,Constants.TRANS_ONLINE],\r\n                     [\"Ram\",2,\"2-06-2016\",7000,Constants.TRANS_SWIPE]]\r\n\r\n\r\n\r\ndef main():\r\n    print(\"Hello World!\")\r\n    for row in Customers_test_data:\r\n    #    print(row)\r\n        Customers.AddCustomer(row)\r\n    #db.printTable(Constants.CUSTOMER_TBL)\r\n\r\n    for row in Transactions_data:\r\n        print(\"Transaction:\", row)\r\n        Transactions.ProcessTrans(row)\r\n    #db.printTable(Constants.TRANS_TBL)\r\n    #db.printTable(Constants.CUSTOMER_TBL)\r\n\r\n    for row in Payments_data:\r\n        print(\"Payment : \", row)\r\n        Payments.ProcessPayments(row)\r\n    #db.printTable(Constants.TRANS_TBL)\r\n    #db.printTable(Constants.PAYMENTS_TBL)\r\n    #db.printTable(Constants.CUSTOMER_TBL)\r\n    Customers.generateStatment();\r\n\r\n    for row in Transactions_data:\r\n        print(\"Transaction:\",row)\r\n        Transactions.ProcessTrans(row)\r\n    #db.printTable(Constants.TRANS_TBL)\r\n    #db.printTable(Constants.CUSTOMER_TBL)\r\n\r\n    for row in Payments_data:\r\n        print(\"Payment : \", row)\r\n        Payments.ProcessPayments(row)\r\n    #db.printTable(Constants.TRANS_TBL)\r\n    #db.printTable(Constants.PAYMENTS_TBL)\r\n    #db.printTable(Constants.CUSTOMER_TBL)\r\n    Customers.generateStatment();\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    main()","sub_path":"Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":2308,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"589676043","text":"\"\"\"\nCreated on Aug 28, 2018\n\n@author: mjasnik\n\"\"\"\n\n# imports\nimport os\nimport getpass\nfrom os import geteuid\n\n# timekpr imports\nfrom timekpr.common.constants import constants as cons\nfrom timekpr.common.log import log\nfrom timekpr.client.interface.dbus.administration import timekprAdminConnector\nfrom timekpr.common.utils.config import timekprConfig\nfrom timekpr.common.constants import messages as msg\n\n\nclass timekprAdminClient(object):\n    \"\"\"Main class for holding all client logic (including dbus)\"\"\"\n\n    # --------------- initialization / control methods --------------- #\n\n    def __init__(self, pIsDevActive=False):\n        \"\"\"Initialize admin client\"\"\"\n        # dev\n        self._isDevActive = pIsDevActive\n\n        # get our connector\n        self._timekprAdminConnector = timekprAdminConnector(self._isDevActive)\n\n        # main object for GUI\n        self._adminGUI = None\n\n    def startTimekprAdminClient(self, *args):\n        \"\"\"Start up timekpr admin (choose gui or cli and start this up)\"\"\"\n        # check whether we need CLI or GUI\n        lastParam = args[len(args)-1]\n        timekprForceCLI = False\n\n        # check for script\n        if (\"/timekpra\" in lastParam or \"timekpra.py\" in lastParam):\n            # whether we have X running or wayland?\n            timekprX11Available = os.getenv(\"DISPLAY\") is not None\n            timekprWaylandAvailable = os.getenv(\"WAYLAND_DISPLAY\") is not None\n            timekprMirAvailable = os.getenv(\"MIR_SOCKET\") is not None\n\n            # if we are required to run graphical thing\n            if (timekprX11Available or timekprWaylandAvailable or timekprMirAvailable):\n                # save logging for later use in classes down tree\n                self._logging = {cons.TK_LOG_L: cons.TK_LOG_LEVEL_DEBUG, cons.TK_LOG_D: cons.TK_LOG_TEMP_DIR, cons.TK_LOG_W: (cons.TK_LOG_OWNER_ADMIN_SU if geteuid() == 0 else cons.TK_LOG_OWNER_ADMIN), cons.TK_LOG_U: getpass.getuser()}\n                # logging init\n                log.setLogging(self._logging)\n\n                # configuration init\n                _timekprConfigManager = timekprConfig(pIsDevActive=self._isDevActive, pLog=self._logging)\n                # load config\n                _timekprConfigManager.loadMainConfiguration()\n                # resource dir\n                _resourcePathGUI = os.path.join(_timekprConfigManager.getTimekprSharedDir(), \"client/forms\")\n\n                # use GUI\n                from timekpr.client.gui.admingui import timekprAdminGUI\n                # load GUI and process from there\n                self._adminGUI = timekprAdminGUI(cons.TK_VERSION, _resourcePathGUI, getpass.getuser(), self._isDevActive)\n            # nor X nor wayland are available\n            else:\n                # print to console\n                log.consoleOut(msg.getTranslation(\"TK_MSG_CONSOLE_GUI_NOT_AVAILABLE\") + \"\\n\")\n                # forced CLI\"\n                timekprForceCLI = True\n        else:\n            # CLI\n            timekprForceCLI = True\n\n        # for CLI connections\n        if timekprForceCLI:\n            # connect\n            self._timekprAdminConnector.initTimekprConnection(True)\n            # connected?\n            if self._timekprAdminConnector.isConnected()[1]:\n                # use CLI\n                # validate possible parameters and their values, when fine - execute them as well\n                self.checkAndExecuteAdminCommands(*args)\n\n    # --------------- parameter validation methods --------------- #\n\n    def checkAndExecuteAdminCommands(self, *args):\n        \"\"\"Init connection to timekpr dbus server\"\"\"\n        # initial param len\n        paramIdx = 0\n        paramLen = len(args)\n        adminCmdIncorrect = False\n        tmpIdx = 0\n\n        # determine parameter offset\n        for rArg in args:\n            # count offset\n            tmpIdx += 1\n            # check for script\n            if \"/timekpra\" in rArg or \"timekpra.py\" in rArg:\n                paramIdx = tmpIdx\n\n        # this gets the command itself (args[0] is the script name)\n        adminCmd = args[paramIdx] if paramLen > paramIdx else \"timekpra\"\n\n        # now based on params check them out\n        # this gets saved user list from the server\n        if adminCmd == \"--help\":\n            # fine\n            pass\n        # this gets saved user list from the server\n        elif adminCmd == \"--userlist\":\n            # check param len\n            if paramLen != paramIdx + 1:\n                # fail\n                adminCmdIncorrect = True\n            else:\n                # get list\n                result, message, userList = self._timekprAdminConnector.getUserList()\n\n                # process\n                if result == 0:\n                    # process\n                    self.printUserList(userList)\n                else:\n                    # log error\n                    log.consoleOut(message)\n\n        # this gets user configuration from the server\n        elif adminCmd == \"--userconfig\":\n            # check param len\n            if paramLen != paramIdx + 2:\n                # fail\n                adminCmdIncorrect = True\n            else:\n                # get user config\n                result, message, userConfig = self._timekprAdminConnector.getUserConfig(args[paramIdx+1])\n\n                # process\n                if result == 0:\n                    # process\n                    self.printUserConfig(args[paramIdx+1], userConfig)\n                else:\n                    # log error\n                    log.consoleOut(message)\n\n        # this sets allowed days for the user\n        elif adminCmd == \"--setalloweddays\":\n            # check param len\n            if paramLen != paramIdx + 3:\n                # fail\n                adminCmdIncorrect = True\n            else:\n                # set days\n                self.processSetAllowedDays(args[paramIdx+1], args[paramIdx+2])\n\n        # this sets allowed hours per specified day or ALL for every day\n        elif adminCmd == \"--setallowedhours\":\n            # check param len\n            if paramLen != paramIdx + 4:\n                # fail\n                adminCmdIncorrect = True\n            else:\n                # set days\n                self.processSetAllowedHours(args[paramIdx+1], args[paramIdx+2], args[paramIdx+3])\n\n        # this sets time limits per allowed days\n        elif adminCmd == \"--settimelimits\":\n            # check param len\n            if paramLen != paramIdx + 3:\n                # fail\n                adminCmdIncorrect = True\n            else:\n                # set days\n                self.processSetTimeLimits(args[paramIdx+1], args[paramIdx+2])\n\n        # this sets time limits per week\n        elif adminCmd == \"--settimelimitweek\":\n            # check param len\n            if paramLen != paramIdx + 3:\n                # fail\n                adminCmdIncorrect = True\n            else:\n                # set days\n                self.processSetTimeLimitWeek(args[paramIdx+1], args[paramIdx+2])\n\n        # this sets time limits per month\n        elif adminCmd == \"--settimelimitmonth\":\n            # check param len\n            if paramLen != paramIdx + 3:\n                # fail\n                adminCmdIncorrect = True\n            else:\n                # set days\n                self.processSetTimeLimitMonth(args[paramIdx+1], args[paramIdx+2])\n\n        # this sets whether to track inactive user sessions\n        elif adminCmd == \"--settrackinactive\":\n            # check param len\n            if paramLen != paramIdx + 3:\n                # fail\n                adminCmdIncorrect = True\n            else:\n                # set days\n                self.processSetTrackInactive(args[paramIdx+1], args[paramIdx+2])\n\n        # this sets time left for the user at current moment\n        elif adminCmd == \"--settimeleft\":\n            # check param len\n            if paramLen != paramIdx + 4:\n                # fail\n                adminCmdIncorrect = True\n            else:\n                # set days\n                self.processSetTimeLeft(args[paramIdx+1], args[paramIdx+2], args[paramIdx+3])\n\n        else:\n            # out\n            adminCmdIncorrect = True\n\n        # check whether command is supported\n        if (adminCmd not in cons.TK_USER_ADMIN_COMMANDS and adminCmd not in cons.TK_ADMIN_COMMANDS) or adminCmd == \"--help\" or adminCmdIncorrect:\n            # fail\n            if adminCmdIncorrect:\n                log.consoleOut(msg.getTranslation(\"TK_MSG_CONSOLE_COMMAND_INCORRECT\"), *args)\n\n            log.consoleOut(\"\\n\" + msg.getTranslation(\"TK_MSG_CONSOLE_USAGE_NOTES\"))\n            # initial order\n            cmds = [\"--help\", \"--userlist\", \"--userconfig\"]\n            # print initial commands as first\n            for rCmd in cmds:\n                log.consoleOut(\" \", rCmd, cons.TK_USER_ADMIN_COMMANDS[rCmd])\n\n            # put a little space in between\n            log.consoleOut(\"\")\n\n            # print help\n            for rCmd, rCmdDesc in cons.TK_USER_ADMIN_COMMANDS.items():\n                # do not print already known commands\n                if rCmd not in cmds:\n                    log.consoleOut(\" \", rCmd, rCmdDesc)\n\n    # --------------- parameter execution methods --------------- #\n\n    def printUserList(self, pUserList):\n        \"\"\"Format and print userlist\"\"\"\n        # print to console\n        log.consoleOut(msg.getTranslation(\"TK_MSG_CONSOLE_USERS_TOTAL\", len(pUserList)))\n        # loop and print\n        for rUser in pUserList:\n            log.consoleOut(rUser)\n\n    def printUserConfig(self, pUserName, pPrintUserConfig):\n        \"\"\"Format and print user config\"\"\"\n        # print to console\n        log.consoleOut(msg.getTranslation(\"TK_MSG_CONSOLE_CONFIG_FOR\") % (pUserName))\n        # loop and print the same format as ppl will use to set that\n        for rUserKey, rUserConfig in pPrintUserConfig.items():\n            # join the lists\n            if \"ALLOWED_W\" in rUserKey or \"LIMITS_P\" in rUserKey:\n                # print join\n                log.consoleOut(\"%s: %s\" % (rUserKey, \";\".join(list(map(str, rUserConfig)))))\n            # join the lists\n            elif \"ALLOWED_H\" in rUserKey:\n                # hrs\n                hrs = \"\"\n                # print join\n                if len(rUserConfig) > 0:\n                    for rUserHour in sorted(list(map(int, rUserConfig))):\n                        # get config per hr\n                        hr = \"%s\" % (rUserHour) if rUserConfig[str(rUserHour)][cons.TK_CTRL_SMIN] <= 0 and rUserConfig[str(rUserHour)][cons.TK_CTRL_EMIN] >= 60 else \"%s[%s-%s]\" % (rUserHour, rUserConfig[str(rUserHour)][cons.TK_CTRL_SMIN], rUserConfig[str(rUserHour)][cons.TK_CTRL_EMIN])\n                        # empty\n                        if hrs == \"\":\n                            hrs = \"%s\" % (hr)\n                        else:\n                            hrs = \"%s;%s\" % (hrs, hr)\n\n                log.consoleOut(\"%s: %s\" % (rUserKey, hrs))\n            elif \"TRACK_IN\" in rUserKey:\n                log.consoleOut(\"%s: %s\" % (rUserKey, bool(rUserConfig)))\n            else:\n                log.consoleOut(\"%s: %s\" % (rUserKey, str(rUserConfig)))\n\n    def processSetAllowedDays(self, pUserName, pDayList):\n        \"\"\"Process allowed days\"\"\"\n        # defaults\n        dayMap = ()\n        result = 0\n\n        # day map\n        try:\n            # try to parse parameters\n            dayMap = list(map(int, pDayList.split(\";\")))\n        except Exception as ex:\n            # fail\n            result = -1\n            message = msg.getTranslation(\"TK_MSG_PARSE_ERROR\") % (str(ex))\n\n        # preprocess successful\n        if result == 0:\n            # invoke\n            result, message = self._timekprAdminConnector.setAllowedDays(pUserName, dayMap)\n\n        # process\n        if result != 0:\n            # log error\n            log.consoleOut(message)\n\n    def processSetAllowedHours(self, pUserName, pDayNumber, pHourList):\n        \"\"\"Process allowed hours\"\"\"\n        # this is the dict for hour config\n        allowedHours = {}\n        result = 0\n\n        # allowed hours\n        try:\n            # check hours\n            for rHour in str(pHourList).split(\";\"):\n                # verify hour\n                hour = int(rHour.split(\"[\", 1)[0])\n                # if we have advanced config (minutes)\n                if \"[\" in rHour and \"]\" in rHour and \"-\" in rHour:\n                    # get minutes\n                    minutes = rHour.split(\"[\", 1)[1].split(\"]\")[0].split(\"-\")\n                    # verify minutes\n                    tmp = int(minutes[0])\n                    tmp = int(minutes[1])\n                    # get our dict done\n                    allowedHours[str(hour)] = {cons.TK_CTRL_SMIN: min(max(int(minutes[0]), 0), 60), cons.TK_CTRL_EMIN: min(max(int(minutes[1]), 0), 60)}\n                else:\n                    # get our dict done\n                    allowedHours[str(hour)] = {cons.TK_CTRL_SMIN: 0, cons.TK_CTRL_EMIN: 60}\n        except Exception as ex:\n            # fail\n            result = -1\n            message = msg.getTranslation(\"TK_MSG_PARSE_ERROR\") % (str(ex))\n\n        # preprocess successful\n        if result == 0:\n            # invoke\n            result, message = self._timekprAdminConnector.setAllowedHours(pUserName, pDayNumber, allowedHours)\n\n        # process\n        if result != 0:\n            # log error\n            log.consoleOut(message)\n\n    def processSetTimeLimits(self, pUserName, pDayLimits):\n        \"\"\"Process time limits for days\"\"\"\n        # defaults\n        dayLimits = ()\n        result = 0\n\n        # day limists\n        try:\n            # try to parse parameters\n            dayLimits = list(map(int, pDayLimits.split(\";\")))\n        except Exception as ex:\n            # fail\n            result = -1\n            message = msg.getTranslation(\"TK_MSG_PARSE_ERROR\") % (str(ex))\n\n        # preprocess successful\n        if result == 0:\n            # invoke\n            result, message = self._timekprAdminConnector.setTimeLimitForDays(pUserName, dayLimits)\n\n        # process\n        if result != 0:\n            # log error\n            log.consoleOut(message)\n\n    def processSetTimeLimitWeek(self, pUserName, pTimeLimitWeek):\n        \"\"\"Process time limits for week\"\"\"\n        # defaults\n        weekLimit = 0\n        result = 0\n\n        # week limit\n        try:\n            # try to parse parameters\n            weekLimit = int(pTimeLimitWeek)\n        except Exception as ex:\n            # fail\n            result = -1\n            message = msg.getTranslation(\"TK_MSG_PARSE_ERROR\") % (str(ex))\n\n        # preprocess successful\n        if result == 0:\n            # invoke\n            result, message = self._timekprAdminConnector.setTimeLimitForWeek(pUserName, weekLimit)\n\n        # process\n        if result != 0:\n            # log error\n            log.consoleOut(message)\n\n    def processSetTimeLimitMonth(self, pUserName, pTimeLimitMonth):\n        \"\"\"Process time limits for month\"\"\"\n        # defaults\n        monthLimit = 0\n        result = 0\n\n        # week limit\n        try:\n            # try to parse parameters\n            monthLimit = int(pTimeLimitMonth)\n        except Exception as ex:\n            # fail\n            result = -1\n            message = msg.getTranslation(\"TK_MSG_PARSE_ERROR\") % (str(ex))\n\n        # preprocess successful\n        if result == 0:\n            # invoke\n            result, message = self._timekprAdminConnector.setTimeLimitForMonth(pUserName, monthLimit)\n\n        # process\n        if result != 0:\n            # log error\n            log.consoleOut(message)\n\n    def processSetTrackInactive(self, pUserName, pTrackInactive):\n        \"\"\"Process track inactive\"\"\"\n        # defaults\n        trackInactive = None\n        result = 0\n\n        # check\n        if pTrackInactive not in [\"true\", \"True\", \"TRUE\", \"false\", \"False\", \"FALSE\"]:\n            # fail\n            result = -1\n            message =  msg.getTranslation(\"TK_MSG_PARSE_ERROR\") % (\"please specify true or false\")\n        else:\n            trackInactive = True if pTrackInactive in [\"true\", \"True\", \"TRUE\"] else False\n\n        # preprocess successful\n        if result == 0:\n            # invoke\n            result, message = self._timekprAdminConnector.setTrackInactive(pUserName, trackInactive)\n\n        # process\n        if result != 0:\n            # log error\n            log.consoleOut(message)\n\n    def processSetTimeLeft(self, pUserName, pOperation, pLimit):\n        \"\"\"Process time left\"\"\"\n        # defaults\n        limit = 0\n        result = 0\n\n        # limit\n        try:\n            # try to parse parameters\n            limit = int(pLimit)\n        except Exception as ex:\n            # fail\n            result = -1\n            message = msg.getTranslation(\"TK_MSG_PARSE_ERROR\") % (str(ex))\n\n        # preprocess successful\n        if result == 0:\n            # invoke\n            result, message = self._timekprAdminConnector.setTimeLeft(pUserName, pOperation, limit)\n\n        # process\n        if result != 0:\n            # log error\n            log.consoleOut(message)\n","sub_path":"client/admin/adminprocessor.py","file_name":"adminprocessor.py","file_ext":"py","file_size_in_byte":16989,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"109722134","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Feb  7 12:55:30 2018\n\n@author: Henry Tessier\n\"\"\"\n\nfrom bs4 import BeautifulSoup\nimport requests\nimport pandas as pd\nimport numpy as np\nimport re\nimport random\nfrom collections import Counter\nimport seaborn as sns\n\n\n# Base url, and a lambda func to return url for a given year\nbase_url = 'http://kenpom.com/index.php'\nurl_year = lambda x: '%s?y=%s' % (base_url, str(x) if x != 2018 else base_url)\n\n# Create a method that parses a given year and spits out a raw dataframe\ndef import_raw_year(year):\n    \n    #Imports raw data from a ken pom year into a dataframe\n    \n    f = requests.get(url_year(year))\n    soup = BeautifulSoup(f.text, \"lxml\")\n    table_html = soup.find_all('table', {'id': 'ratings-table'})\n\n    # Weird issue w/ <thead> in the html\n    # Prevents us from just using pd.read_html\n    # Let's find all the thead contents and just replace/remove them\n    # This allows us to easily put the table row data into a dataframe using panda\n    thead = table_html[0].find_all('thead')\n\n    table = table_html[0]\n    for x in thead:\n        table = str(table).replace(str(x), '')\n\n    df = pd.read_html(table)[0]\n    df['year'] = year\n    return df\n    \n\n# Import all the years into a singular dataframe\ndf = None\npickyear = input(\"Enter Year (2002 - 2018): \")\nprint(\"\")\nprint(\"Gathering most recent statistics...\")\ndf = pd.concat( (df, import_raw_year(pickyear)), axis=0) \n\n# Column rename based off of original website\ndf.columns = ['Rank', 'Team', 'Conference', 'W-L', 'AdjEM', \n             'AdjustO', 'AdjustO Rank', 'AdjustD', 'AdjustD Rank',\n             'AdjustT', 'AdjustT Rank', 'Luck', 'Luck Rank', \n             'SOS_Pyth', 'SOS Pyth Rank', 'SOS OppO', 'SOS OppO Rank',\n             'SOS OppD', 'SOS OppD Rank', 'NCSOS Pyth', 'NCSOS Pyth Rank', 'Year']\n             \n# Lambda that returns true if given string is a number and a valid seed number (1-16)\nvalid_seed = lambda x: True if str(x).replace(' ', '').isdigit() \\\n                and int(x) > 0 and int(x) <= 16 else False\n\n# Use lambda to parse out seed/team\ndf['Seed'] = df['Team'].apply(lambda x: x[-2:].replace(' ', '') \\\n                              if valid_seed(x[-2:]) else np.nan )\n\ndf['Team'] = df['Team'].apply(lambda x: x[:-2] if valid_seed(x[-2:]) else x)\n\n# Split W-L column into wins and losses\ndf['Wins'] = df['W-L'].apply(lambda x: int(re.sub('-.*', '', x)) )\ndf['Losses'] = df['W-L'].apply(lambda x: int(re.sub('.*-', '', x)) )\ndf.drop('W-L', inplace=True, axis=1)\n\n\ndf=df[[ 'Year', 'Rank', 'Team', 'Conference', 'Wins', 'Losses', 'Seed','AdjEM', \n             'AdjustO', 'AdjustO Rank', 'AdjustD', 'AdjustD Rank',\n             'AdjustT', 'AdjustT Rank', 'Luck', 'Luck Rank', \n             'SOS_Pyth', 'SOS Pyth Rank', 'SOS OppO', 'SOS OppO Rank',\n             'SOS OppD', 'SOS OppD Rank', 'NCSOS Pyth', 'NCSOS Pyth Rank']]\n             \ndf = df[['Team','Wins','Losses','AdjEM','SOS_Pyth']]\ndf.Team = df.Team.str.rstrip()\ndf.Team = df.Team.str.replace('\\s+', '_')\ndf.Team = df.Team.str.replace('.', '')\ndf.Team = df.Team.str.replace('&', '')\ndf.Team = df.Team.str.replace('\\'', '')\ndf['WLPercentage'] = df.Wins / (df.Losses + df.Wins)\ndf['Name'] = df.Team\ndf = df.set_index('Name')\n\n\n# ______________________Data Import Completed_________________________________\n\nprint(\"\")\nregion = {}\nx = 1\nwhile x < 17:    \n    region[\"Seed{0}\".format(x)] = input(\"Enter \"+str(x)+\" Seed Team: \")\n    if (region[\"Seed{0}\".format(x)]) not in df.Team:\n        print(x, \"seed team not found\")\n    else:\n        x += 1\n        \n\ndef sim_game(Team1, Team2):\n    #results = []\n    #for x in range(reps):    \n    ELO1 = ((df.loc[Team1].AdjEM + 40) + ((df.loc[Team1].SOS_Pyth + 15) * df.loc[Team1].WLPercentage))    \n    ELO2 = ((df.loc[Team2].AdjEM + 40) + ((df.loc[Team2].SOS_Pyth + 15) * df.loc[Team2].WLPercentage))\n    m = ELO1 - ELO2\n    A = (1/(1+10**(abs(m)/23)))\n    odds = (1-A)\n    if m < 0:\n        odds = (1-odds)\n    result = random.random() <= odds\n        \n    if result == True:\n        return(Team1)\n    else:\n        return(Team2)\n        \n\"\"\"    \n#For running multiple simulations of 1 game rather than multiple simulations of 1 region\n\n        results += [result]\n    if results.count(True) > results.count(False):\n        return(Team1)\n    elif results.count(False) > results.count(True):\n        return(Team2)\n    else:\n        if ELO1 > ELO2:\n            return(Team1)\n        else:\n            return(Team2)\n\"\"\"\n\nreps = int(input('Enter number of tournaments to simulate: '))\n\n\n###################################\n###NCAA Tourney Region Simulator###\n###################################\n\nWinners = []\nRound1W = []\nRound2W = []\nRound3W = []\n\nfor x in range(reps):\n#Round 1\n    Win1 = sim_game(region['Seed1'],region['Seed16'])\n    Win2 = sim_game(region['Seed8'],region['Seed9'])\n    Win3 = sim_game(region['Seed5'],region['Seed12'])\n    Win4 = sim_game(region['Seed4'],region['Seed13'])\n    Win5 = sim_game(region['Seed6'],region['Seed11'])\n    Win6 = sim_game(region['Seed3'],region['Seed14'])\n    Win7 = sim_game(region['Seed7'],region['Seed10'])\n    Win8 = sim_game(region['Seed2'],region['Seed15'])\n    Round1W += [Win1,Win2,Win3,Win4,Win5,Win6,Win7,Win8]\n\n#Round 2\n    Win2_1 = sim_game(Win1,Win2)\n    Win2_2 = sim_game(Win3,Win4)\n    Win2_3 = sim_game(Win5,Win6)\n    Win2_4 = sim_game(Win7,Win8)\n    Round2W += [Win2_1,Win2_2,Win2_3,Win2_4]\n\n#Round 3\n    Win3_1 = sim_game(Win2_1,Win2_2)\n    Win3_2 = sim_game(Win2_3,Win2_4)\n    Round3W += [Win3_1,Win3_2]\n\n#Round 4\n    WinFF = sim_game(Win3_1,Win3_2)\n    Winners += [WinFF]\n\n#Graph for final round winner\n\ntotwins = Counter(Winners)\nfor x in totwins:\n    totwins[x] = totwins[x]/reps\n\nresults = pd.DataFrame(list(totwins.items()), columns=['Team', 'Win Share %'])\nresults['Team'] = results['Team'].str.replace('_', ' ')\nresults[\"Win Share %\"] = (results[\"Win Share %\"] * 100)\nresults = results.sort_values([\"Win Share %\"], ascending = False)\nOutput = sns.barplot(x = results[\"Win Share %\"], y = results[\"Team\"])\nOutput.set(xlabel = \"Projected Win Shares (%)\")\n#print(results)\n\n######### Data for all previous rounds ##########\n\nround1wins = Counter(Round1W)\nround2wins = Counter(Round2W)\nround3wins = Counter(Round3W)\n\nfor x in round3wins:\n    round3wins[x] = round3wins[x]/reps\nfor x in round2wins:\n    round2wins[x] = round2wins[x]/reps\nfor x in round1wins:\n    round1wins[x] = round1wins[x]/reps\n\nresults3 = pd.DataFrame(list(round3wins.items()), columns=['Team', 'Win Share %'])\nresults3['Team'] = results3['Team'].str.replace('_', ' ')\nresults3[\"Win Share %\"] = (results3[\"Win Share %\"] * 100)\nresults3 = results3.sort_values([\"Win Share %\"], ascending = False)\n\nresults2 = pd.DataFrame(list(round2wins.items()), columns=['Team', 'Win Share %'])\nresults2['Team'] = results2['Team'].str.replace('_', ' ')\nresults2[\"Win Share %\"] = (results2[\"Win Share %\"] * 100)\nresults2 = results2.sort_values([\"Win Share %\"], ascending = False)\n\nresults1 = pd.DataFrame(list(round1wins.items()), columns=['Team', 'Win Share %'])\nresults1['Team'] = results1['Team'].str.replace('_', ' ')\nresults1[\"Win Share %\"] = (results1[\"Win Share %\"] * 100)\nresults1 = results1.sort_values([\"Win Share %\"], ascending = False)\n\nprint(\"\")\nprint(\"Make Final Four Probability:\")\nprint(results)\nprint(\"\")\nprint(\"Make Elite Eight Probability:\")\nprint(results3)\nprint(\"\")\nprint(\"Make Sweet 16 Probability:\")\nprint(results2)\nprint(\"\")\nprint(\"Make Second Round Probability:\")\nprint(results1)\n","sub_path":"BracketModel.py","file_name":"BracketModel.py","file_ext":"py","file_size_in_byte":7456,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"359603367","text":"import re\n\nimport pandas as pd\n\nfrom config.common import log\nfrom egovResearch.egovScDoc.spiders.docSpider.base_spider import *\nfrom pyQwSciLib import app\nfrom wSpider.egovernment.egov_spider_lite.extra.common_utils import *\n\n\n# app = create_app()\n\ndef filter_special_words():\n    \"\"\"\n    过滤特殊字符\n    :return:\n    \"\"\"\n    item_list = EGOVSCPolicyPaper.query.all()\n    for item in item_list:\n        title = item.title\n        title = title.replace(\"\\n\",\"\")\n        title = title.replace(\"\",\"\")\n        title = title.strip()\n        item.title = title\n        item.update()\n\n    pass\n\n\"\"\"读取和更新政策的年份信息\"\"\"\ndef get_year():\n    \"\"\"\n    读取年份\n    :return:\n    \"\"\"\n    item_list = db.session.query(\n        EGOVSCPolicyPaper.id,\n        EGOVSCPolicyPaper.release_time,\n        EGOVSCPolicyPaper.material_date,\n        EGOVSCPolicyPaper.year\n    ).filter(\n        EGOVSCPolicyPaper.year == None\n    ).all()\n    print(len(list(item_list)))\n    for elem in item_list:\n            item_id = elem[0]\n            release_time = elem[1]\n            material_date = elem[2]\n            year = elem[3]\n            if year is None:\n                item = EGOVSCPolicyPaper.query.filter(EGOVSCPolicyPaper.id==item_id).first()\n                if item.year is None:\n                    print(item.release_time,\" \",item.material_date,\" \",item.year)\n                    # if item.release_time is not None and item.material_date == \"\":\n                    #     item.material_date = item.release_time\n                    #     item.year = item.release_time.split(\"-\")[0]\n                    #     item.update()\n                    if item.release_time is not None:\n                        item.material_date = item.release_time\n                        item.year = item.material_date.split(\"-\")[0]\n                    item.update()\n\nwith open(\"key_words.txt\",\"r\",encoding=\"UTF-8\") as f:\n    key_words = f.read().split(\"\\n\")\ndef batch_judge_if_title_have_key_words(title):\n    for key_word in key_words:\n        if key_word in title:\n            return True\n    return False\n\n\"\"\"默认过滤是否和电子政务相关\"\"\"\ndef get_egov_related_title():\n    \"\"\"\n    过滤是否和电子政务相关\n    :return:\n    \"\"\"\n    item_list = EGOVSCPolicyPaper.query_by_city_year_and_flag(None,None,0)\n    for item in item_list:\n        elem = EGOVSCPolicyPaper.query.filter(\n            EGOVSCPolicyPaper.id == item.id\n        ).first()\n        if batch_judge_if_title_have_key_words(item.title):\n            elem.flag = EGOVSCPolicyPaper.__FILE_FLAG_BATCH_VALIDATED__\n            log.info(\"---------{}---------\".format(elem.title))\n        else:\n            if judge_if_title_have_key_words(item.title):\n                elem.flag = EGOVSCPolicyPaper.__FILE_FLAG_VALIDATED__\n                log.info(\"--{}--\".format(elem.title))\n            else:\n                elem.flag = EGOVSCPolicyPaper.__FILE_FLAG_DELETED__\n        elem.update()\n    pass\n\n\"\"\"二次过滤\"\"\"\ndef filter_egov_related_title():\n    \"\"\"\n    二次过滤是否和电子政务相关\n    :return:\n    \"\"\"\n    item_list = EGOVSCPolicyPaper.query_by_city_year_and_flag(\n        None,None,EGOVSCPolicyPaper.__FILE_FLAG_VALIDATED__)\n    print(len(item_list))\n    for item in item_list:\n        elem = EGOVSCPolicyPaper.query.filter(\n            EGOVSCPolicyPaper.id == item.id\n        ).first()\n        if batch_judge_if_title_have_key_words(item.title):\n            elem.flag = EGOVSCPolicyPaper.__FILE_FLAG_BATCH_VALIDATED__\n            log.info(\"---------{}---------\".format(elem.title))\n        elem.update()\n    pass\n\n\"\"\"查看模糊月份\"\"\"\ndef get_indefinite_month():\n    \"\"\"\n    查找月份模糊的File\n    :return:\n    \"\"\"\n    item_list = EGOVSCPolicyPaper.query.filter(\n        EGOVSCPolicyPaper.flag == EGOVSCPolicyPaper.__FILE_FLAG_ARCHIVED__\n    ).all()\n    for elem in item_list:\n        print(elem.material_date)\n    pass\n\n\"\"\"读取城市列表\"\"\"\ndef read_cities():\n    \"\"\"\n    初始化城市列表\n    :return:\n    \"\"\"\n    with open(\"cities.txt\",\"r\",encoding=\"utf-8\") as f:\n        city_list = f.read().split(\"\\n\")\n    for city in city_list:\n        item = EGOVSCPolicyPapersCompare()\n        city_item = EGOVProvinceCityInfo.query.filter(\n            EGOVProvinceCityInfo.city == city\n        ).first()\n        if city_item is None:\n            city_name = \"{}市\".format(city)\n            city_item = EGOVProvinceCityInfo.query.filter(\n                EGOVProvinceCityInfo.city == city_name\n            ).first()\n        item.city_id = city_item.id\n        item.city = city\n        item.city_name = city_item.city\n        item.save()\n\nsheets_name = [\n    \"省会城市政府网站服务能力.xlsx\",  # 0\n    \"省会城市政府微博服务能力.xlsx\",\n    \"省会城市政府微信服务能力.xlsx\",  # 2\n    \"省会城市政府APP服务能力.xlsx\",\n    \"省会城市政府双微服务能力.xlsx\",  # 4\n    \"省会城市政府新媒体服务能力.xlsx\",\n    \"省会城市电子政务综合服务能力.xlsx\",  # 6\n]\nsheet_name = sheets_name[5]\n\"\"\"读取测评报告数据\"\"\"\ndef read_egovsc_data():\n    \"\"\"\n    读取Excel中的测评数据结果\n    :return:\n    \"\"\"\n    base_dir = \"D:\\Documents\\QingwuDOC\\eGovernment\\省会城市政策文件分词\\Manuscript\\数据\\{}\"\n    data_sheet = pd.read_excel(base_dir.format(sheet_name))\n    for elem in data_sheet.iterrows():\n        elem = elem[1]\n        if not pd.isna(elem[\"省会城市\"]):\n            item = EGOVSCPolicyPapersCompare.query.filter(\n                EGOVSCPolicyPapersCompare.city == elem[\"省会城市\"]\n            ).first()\n            if item is None:\n                item = EGOVSCPolicyPapersCompare.query.filter(\n                    EGOVSCPolicyPapersCompare.city_name == elem[\"省会城市\"]\n                ).first()\n            print(item.city_name)\n            item.egovsc_new_media_2016 = elem[\"2016年\"] if not pd.isna(elem[\"2016年\"]) else 0\n            item.egovsc_new_media_2017 = elem[\"2017年\"] if not pd.isna(elem[\"2017年\"]) else 0\n            if float(\"%.2f\" % (item.egovsc_new_media_2017-item.egovsc_new_media_2016)) == float(\n                            \"%.2f\" % elem[\"变化情况\"]):\n                print(elem[\"变化情况\"])\n                item.egovsc_new_media_variation = elem[\"变化情况\"]\n            else:\n                raise Exception\n            item.update()\n\n\"\"\"政府网站元数据灰色关联排序\"\"\"\ndef update_website_metadata_gray_index():\n    \"\"\"\n    政府网站元数据灰色关联排序\n    :return:\n    \"\"\"\n    data_sheet = pd.read_excel(\"D:\\Desktop\\元数据data.xlsx\")\n    print(data_sheet)\n    for elem in data_sheet.iterrows():\n        elem = elem[1]\n        if not pd.isna(elem[\"省会城市\"]):\n            item = EGOVSCPolicyPapersCompare.query.filter(\n                EGOVSCPolicyPapersCompare.city_name == elem[\"省会城市\"]\n            ).first()\n            if item is not None:\n                print(item.city_name)\n                item.website_metadata_gray_index = elem[\"gray_index\"]\n                item.update()\n\n\"\"\"国家行政学院电子政务发展指数\"\"\"\ndef update_chinese_academy_of_governance_egdi():\n    \"\"\"\n    国家行政学院电子政务发展指数\n    :return:\n    \"\"\"\n    data_sheet = pd.read_excel(\n        \"D:\\Documents\\QingwuDOC\\eGovernment\\省会城市政策文件分词\\Manuscript\\数据\\中国行政学院报告\\中国城市电子政务报告.xlsx\",\n        sheet_name=\"2016\"\n    )\n    for elem in data_sheet.iterrows():\n        elem = elem[1]\n        if not pd.isna(elem[\"city_name\"]):\n            item = EGOVSCPolicyPapersCompare.query.filter(\n                EGOVSCPolicyPapersCompare.city_name == elem[\"city_name\"]\n            ).first()\n            if item is not None:\n                print(item.city_name)\n                item.ccps_egdi_online_service_2016 = elem[\"在线服务指数\"]\n                item.update()\n\n\"\"\"转移省会城市数据库文件\"\"\"\ndef transfer_city_egov_files():\n    \"\"\"\n    转移文件\n    :return:\n    \"\"\"\n    item_list = EGOVSCPolicyPaper.query.filter(\n        EGOVSCPolicyPaper.flag == EGOVSCPolicyPaper.__FILE_FLAG_ARCHIVED__\n    ).all()\n    for elem in item_list:\n        item = EGOVSCPolicyPaperAnalysis()\n        same_item = EGOVSCPolicyPaperAnalysis.query.filter(\n            EGOVSCPolicyPaperAnalysis.file_title.like(\n                elem.title\n            )\n        ).first()\n        if same_item is not None:\n            print(same_item.file_title)\n            continue\n        item.paper_id = elem.id\n        item.file_title = elem.title\n        item.file_content = elem.file_content\n        item.city_id = elem.city_id\n        item.city_name = elem.city_name\n\n        time_strs = elem.material_date.split(\"-\")\n        item.year_interval_str = \"{}.{}\".format(time_strs[0], int(time_strs[1]))\n        item.source_flag = 0\n        item.save()\n    pass\n\"\"\"转移中央数据库文件\"\"\"\ndef transfer_council_egov_files():\n    \"\"\"\n    转移中央文件\n    :return:\n    \"\"\"\n    from pyQwSciLib.models import EGOVPolicyPaper\n    from time import strftime,strptime\n    item_list = EGOVPolicyPaper.query.filter(\n        EGOVPolicyPaper.id >= 8560\n    ).all()\n    print(len(item_list))\n    for elem in item_list:\n        item = EGOVSCPolicyPaperAnalysis()\n        same_item = EGOVSCPolicyPaperAnalysis.query.filter(\n            EGOVSCPolicyPaperAnalysis.file_title.like(\n                elem.title\n            )\n        ).first()\n        if same_item is not None:\n            print(same_item.file_title)\n            continue\n        item.paper_id = elem.id\n        item.file_title = elem.title\n        file_content = str(elem.file_content, encoding='utf-8')\n\n        item.file_content = file_content\n        item.city_id = elem.city_id\n        item.city_name = elem.city_name\n\n        struct_time = strptime(elem.time,u\"%Y年%m月%d日\")\n        str_time = strftime(u\"%Y-%m-%d\",struct_time)\n        time_strs = str_time.split(\"-\")\n        item.year_interval_str = \"{}.{}\".format(time_strs[0], int(time_strs[1]))\n        print(item.year_interval_str)\n        item.source_flag = 0\n        item.org_flag = 1\n        item.save()\n    pass\n\"\"\"读取本地搜集省会城市文件\"\"\"\ndef read_city_files_from_local():\n    \"\"\"\n    读取本地文件存入数据库\n    :return:\n    \"\"\"\n    base_dir = \"D:\\Desktop\\修改政策\\\\2011.7-2016.6\\省会城市\"\n    city_list = os.listdir(base_dir)\n    for city in city_list:\n        city_item = EGOVProvinceCityInfo.query.filter(\n            EGOVProvinceCityInfo.city == \"{}市\".format(city)\n        ).first()\n        city_dir = \"{}\\{}\".format(base_dir,city)\n        paper_list = os.listdir(city_dir)\n        for paper in paper_list:\n            item = EGOVSCPolicyPaperAnalysis()\n            paper_file_name = paper.replace(\".txt\", \"\")\n            title_info = re.findall(\"(\\d+).(\\d+)\", paper_file_name)\n            year_interval_str = \"{}.{}\".format(title_info[0][0],title_info[0][1])\n            title = paper_file_name.replace(year_interval_str,\"\").strip()\n            # print(title_info)\n            # print(title)\n            # print(year_interval_str)\n            item.city_id = city_item.id\n            item.city_name = city_item.city\n            item.paper_id = 0\n\n            item.file_title = title\n            item.year_interval_str = year_interval_str\n            item.source_flag = 1\n            item.save()\n            with open(\"{}\\{}\".format(\n                city_dir, paper\n            ), \"r\", encoding=\"GBK\",errors=\"ignore\") as fc:\n                # print(fc.read())\n                item.file_content = fc.read()\n                res = item.update()\n                if res is False:\n                    print(paper)\n    pass\n\"\"\"读取本地搜集中央文件\"\"\"\ndef read_council_files_from_local():\n    \"\"\"\n    读取本地中央文件存入数据库\n    :return:\n    \"\"\"\n    # base_dir = \"D:\\Desktop\\修改政策\\\\2011.7-2016.6\\国务院\"\n    base_dir = \"D:\\Desktop\\修改政策\\\\2016.7-2017.6\\国务院\"\n    paper_list = os.listdir(base_dir)\n    for paper in paper_list:\n        item = EGOVSCPolicyPaperAnalysis()\n        paper_file_name = paper.replace(\".txt\", \"\")\n        title_info = re.findall(\"(\\d+).(\\d+)\", paper_file_name)\n        year_interval_str = \"{}.{}\".format(title_info[0][0],title_info[0][1])\n        title = paper_file_name.replace(year_interval_str,\"\").strip()\n\n        same_item = EGOVSCPolicyPaperAnalysis.query.filter(\n            EGOVSCPolicyPaperAnalysis.file_title.like(\n                title\n            )\n        ).first()\n        if same_item is not None:\n            print(same_item.file_title)\n            continue\n\n        item.city_id = 1\n        item.city_name = \"国务院\"\n        item.paper_id = 0\n\n        item.file_title = title\n        item.year_interval_str = year_interval_str\n        item.source_flag = 1\n        item.save()\n        with open(\"{}\\{}\".format(\n            base_dir, paper\n        ), \"r\", encoding=\"GBK\",errors=\"ignore\") as fc:\n            # print(fc.read())\n            item.file_content = fc.read()\n\n            res = item.update()\n            if res is False:\n                print(paper)\n    pass\n\nyear_intervals = [\n    \"2017.7之后\",  # 0\n    \"2016.7-2017.6\",  # 1\n    \"2015.7-2016.6\",  # 2\n    \"2014.7-2015.6\",  # 3\n    \"2013.7-2014.6\",  # 4\n    \"2012.7-2013.6\",  # 5\n    \"2011.7-2012.6\",  # 6   # \"2011.6之前\"  # 7\n    \"2010.7-2011.6\",  # 7\n    \"2009.7-2010.6\",  # 8\n    \"2009.6之前\",   # 9\n]\n\"\"\"更新年份区间\"\"\"\ndef update_year_interval():\n    \"\"\"\n    更新年份区间\n    :return:\n    \"\"\"\n\n    # id_list = db.session.query(EGOVSCPolicyPaper.id).all()\n    # print(len(id_list))\n    # for item_id in id_list:\n    #     item = EGOVSCPolicyPaper.query.filter(EGOVSCPolicyPaper.id == item_id).first()\n    #     print(item.id)\n    #     year_interval = item.material_date.split(\"-\")\n\n    item_list = EGOVSCPolicyPaperAnalysis.query.filter(\n        EGOVSCPolicyPaperAnalysis.org_flag == 1\n    ).all()\n    print(len(item_list))\n    for item in item_list:\n        year_interval = item.year_interval_str.split(\".\")\n        year = int(year_interval[0])\n        month = int(year_interval[1])\n        if year >= 2018:\n            item.year_interval = 0\n        elif year < 2009:\n            item.year_interval = 9\n        else:\n            if month <= 6:\n                item.year_interval = 2018 - year\n            else:\n                item.year_interval = 2018 - year - 1\n        item.update()\n\nconfigs = [\n    {\n        \"year\": 2014,\n        \"year_interval_start\": 3,\n        \"loop_minuend\": 10\n    },\n    {\n        \"year\": 2015,\n        \"year_interval_start\": 2,\n        \"loop_minuend\": 9\n    },\n    {\n        \"year\": 2016,\n        \"year_interval_start\": 1,\n        \"loop_minuend\": 8\n    },\n    {\n        \"year\": 2017,\n        \"year_interval_start\": 0,\n        \"loop_minuend\": 7\n    }\n]\ncount_configs = [\n    # {\n    #     \"count_str\":\"counts\"\n    # },\n    {\n        \"count_str\":\"total_counts\"\n    }\n]\n\n\"\"\"更新文件数量\"\"\"\ndef update_file_counts():\n    \"\"\"\n    更新文件数量（此处文件是指跟电子政务有关的文件）\n    更新文件总数，此处文件是指省会城市的所有文件\n    :return:\n    \"\"\"\n    for count_config in count_configs:\n        for config in configs:\n            if count_config[\"count_str\"] == \"counts\":\n                city_list = EGOVSCPolicyPaperAnalysis.query.all()\n                item_list = EGOVSCPolicyPaperAnalysis.query.filter(\n                    EGOVSCPolicyPaperAnalysis.year_interval > config[\"year_interval_start\"]\n                ).all()\n            else:\n                city_list = EGOVSCPolicyPaper.query.all()\n                print(len(city_list))\n                item_list = EGOVSCPolicyPaper.query.filter(\n                    EGOVSCPolicyPaper.year_interval > config[\"year_interval_start\"]\n                ).all()\n                print(len(item_list))\n\n            file_year_city_list = []\n            city_dicts = {}\n            for city in city_list:\n                city_dicts[city.city_id] = city.city_name\n            for i in range(1, 7):  # 一共5+1个回溯期\n                file_year_city_list.append({})\n                for city in city_list:\n                    file_year_city_list[i-1][city.city_id] = []\n\n            for item in item_list:\n                file_content = item.file_content\n                for i in range(config[\"loop_minuend\"]-item.year_interval):\n                    file_year_city_list[5-i][item.city_id].append(file_content)\n                if item.year_interval - config[\"year_interval_start\"] > 6:  # 1 2 3 4 5 6\n                    file_year_city_list[5][item.city_id].append(file_content)\n\n            for index, year_list in enumerate(file_year_city_list):\n                for city_id, file_list in year_list.items():\n                    egovsc_compare_item = EGOVSCPolicyPapersCompare.query.filter(\n                        EGOVSCPolicyPapersCompare.city_id == city_id\n                    ).first()\n                    if egovsc_compare_item is not None:\n                        if egovsc_compare_item.city_name == city_dicts[city_id]:\n                            print(city_dicts[city_id],\"recall:\",index,len(file_list))\n                            setattr(egovsc_compare_item,\n                                    \"policy_{}_{}_recall_{}\".format(\n                                        count_config[\"count_str\"],\n                                        config[\"year\"],\n                                        index+1\n                                    ),\n                                        len(file_list)\n                                    )\n                            egovsc_compare_item.update()\n                        else:\n                            raise Exception\n\n\"\"\"读取并输出文件数量\"\"\"\ndef output_file_counts():\n    \"\"\"\n    文件数量（此处文件是指跟电子政务有关的文件）\n    文件总数，此处文件是指省会城市的所有文件\n    :return:\n    \"\"\"\n    writer = pd.ExcelWriter(\"D:\\\\Desktop\\\\data.xlsx\")\n    city_list = EGOVSCPolicyPaperAnalysis.query.all()\n\n    city_dicts = {}\n    for city in city_list:\n        city_dicts[city.city_id] = city.city_name\n\n    def sum_file_counts(df,item_list):\n        for index, city in enumerate(city_dicts.items()):\n            df.loc[index, \"城市\"] = city[1]\n            for i in range(0,8):  # 一共8个年份\n                df.loc[index, \"{}\".format(i+2)] = 0\n        for item in item_list:\n            city_index = df[(df[\"城市\"] == item.city_name)].index[0]\n            df.loc[city_index,\"{}\".format(item.year_interval)] += 1\n        df.columns = [\"省会城市\"] + year_intervals[2:]\n\n    egov_item_list = EGOVSCPolicyPaperAnalysis.query.filter(\n        EGOVSCPolicyPaperAnalysis.year_interval > 1\n    ).all()\n    total_item_list = []\n    total_item_list_res = db.session.query(\n        EGOVSCPolicyPaper.id,\n        EGOVSCPolicyPaper.year_interval,\n        EGOVSCPolicyPaper.city_name\n    ).filter(\n        EGOVSCPolicyPaper.year_interval > 1\n    ).all()\n    print(len(total_item_list_res))\n    for elem in total_item_list_res:\n        item = EGOVSCPolicyPaper()\n        item.year_interval = elem[1]\n        item.city_name = elem[2]\n        total_item_list.append(item)\n    df_egov = pd.DataFrame()\n    df_total = pd.DataFrame()\n    sum_file_counts(df_egov,egov_item_list)\n    sum_file_counts(df_total,total_item_list)\n    df_egov.to_excel(writer,\"EGOV\",index=None)\n    df_total.to_excel(writer,\"TOTAL\",index=None)\n    writer.save()\n\nogd_key_words = [\n    # \"数据开放\",\n    \"政务公开\",\n    \"信息公开\",\n    # \"大数据行动\",\n    # \"大数据发展\",\n    \"申请公开\"\n]\ndef filter_ogd_papers():\n    item_list = EGOVSCPolicyPaper.query.filter(\n        EGOVSCPolicyPaper.flag == 6 ,EGOVSCPolicyPaper.year_interval > 0).all()\n    print(len(item_list))\n    item_list = EGOVSCPolicyPaper.query.filter(EGOVSCPolicyPaper.flag == 6).all()\n    counter = 0\n    for item in item_list:\n        for word in ogd_key_words:\n            if word in item.title:\n                print(item.title)\n                counter += 1\n                # item.flag = EGOVSCPolicyPaper.__FILE_FLAG_OGD__\n                # item.update()\n                break\n    print(len(item_list))\n    print(counter)\n    pass\n\nfield_list = [\n\n]\nif __name__ == '__main__':\n    with app.app_context():\n        # output_file_counts()\n\n        # query = db.session.query(EGOVSCPolicyPapersCompare)\n        # df = pd.read_sql(query.statement, query.session.bind)\n        # df.to_excel(\"D:\\\\Desktop\\\\xlsxdata.xlsx\")\n\n        filter_ogd_papers()\n        pass\n","sub_path":"egovResearch/egovScDoc/scripts.py","file_name":"scripts.py","file_ext":"py","file_size_in_byte":20574,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"49212279","text":"import json\nimport pickle\nimport shutil\n\nimport pandas as pd\nimport sklearn.datasets\nimport random\nimport os\nimport uuid\nimport uuid as myuuid\nfrom river import compose\nfrom river import linear_model\nfrom river import metrics\nfrom river import preprocessing\n\nimport time\nfrom confluent_kafka import Producer, Consumer, KafkaException, KafkaError, TopicPartition\nimport  certifi\n\ndef predict():\n    current_model_path = os.path.join(app_config['root_folder'], app_config['models_sub_folder'], app_config['latest_version'],\n                                      app_config['model_f_name'])\n\n    md=pd.read_pickle(current_model_path)\n    model =md['model']\n\n    consumer = get_kafka_consumer()\n    producer = get_kafka_producer()\n    while (True):\n        msg = consumer.poll(timeout=1.0)\n        if msg is None: continue\n\n\n        message = json.loads(msg.value().decode(\"utf-8\"))\n        message['y_hat']=model.predict_one(message['x'])\n        producer.produce(y_cap_topic,message)\n        producer.flush()\n\n        uuid = message['uuid']\n        with open(os.path.join(app_config['root_folder'],app_config['raw_data_folder'],app_config['data_sub_folder'],uuid),'w') as f:\n            f.write(json.dumps(message))\n\n\ndef get_kafka_consumer():\n    curdir = os.getcwd()\n    config_dir = curdir + '/../config/config.json'\n    kafka_config = json.load(open(config_dir))\n    kafka_consumer_config = kafka_config\n    kafka_consumer_config['group.id'] = 'prediction-x-1'\n    kafka_consumer_config['auto.offset.reset'] = 'earliest'\n    consumer = Consumer(kafka_consumer_config)\n\n    tls = [TopicPartition(x_topic, 0),TopicPartition(x_topic, 1),TopicPartition(x_topic, 2),TopicPartition(x_topic, 3)]\n    consumer.assign(tls)\n    return consumer\n\ndef get_kafka_producer():\n    curdir = os.getcwd()\n    config_dir = curdir + '/../config/config.json'\n    kafka_config = json.load(open(config_dir))\n    kafka_config['ssl.ca.location'] = certifi.where()\n    kafka_config['client.id'] = 'test-sw-1'\n    producer = Producer(kafka_config)\n    return producer\n\n\n\napp_config = json.load(open(\"../config/app_config.json\"))\nx_topic='X'\ny_cap_topic='X_Y_CAP'\n\nif __name__ == '__main__':\n    print('Start consuming x-data')\n    predict()\n    '''\n    data_folder = root_folder + data_sub_folder\n    truth_folder = root_folder + truth_sub_folder\n    datasets_folder = root_folder + datasets_sub_folder\n    dataset_metadata_config = root_folder + dataset_metadata_config_file\n    model_metadata_config = root_folder + model_metadata_config_file\n    print('Initializing')\n    initialize()\n    '''\n    #create_initial_data()\n    #gen_data(data_folder,truth_folder)\n    #create_versioned_data(dataset_metadata_config,truth_folder,data_folder,datasets_folder)\n    #create_versioned_model()","sub_path":"src/model_inference_api.py","file_name":"model_inference_api.py","file_ext":"py","file_size_in_byte":2759,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"380427632","text":"# -*- coding: utf-8 -*-\n# @Time    : 2018/5/21 14:03\n# @Author  : Gavin\n\nimport redis\nimport logging\nlogger = logging.getLogger('InsertRequest')\n\n\ndef InsertRequest(url, type):\n    try:\n        r = redis.Redis(host='127.0.0.1', port=6379, db=0)\n        if type == 0:\n            r.lpush('ZhiLianCrawl:requests', url)\n        if type == 1:\n            r.lpush('ZhuoPinCrawl:requests', url)\n        if type == 2:\n            r.lpush('BossCrawl:requests', url)\n        if type == 3:\n            r.lpush('ChinaHRCrawl:requests', url)\n    except:\n        logger.info(\"Redis open failed!!!\")\n\n","sub_path":"code/RecruitmentMaster/RecruitmentMaster/src/InsertRedis.py","file_name":"InsertRedis.py","file_ext":"py","file_size_in_byte":587,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"123726437","text":"def _pie(self, kwargs):\n\n\tkwargs['colors'] = kwargs.get('colors', self.colors)\n\tkwargs['colors'] = kwargs['colors'] + (len(kwargs['values'])-len(kwargs['colors']))*['#000000']\n\n\tdata = dict(\n\t\ttype = 'pie',\n\t\tvalues = kwargs['values'],\n\t\tlabels = kwargs.get('labels', ['']*len(kwargs['values'])),\n\t\ttextinfo = kwargs.get('textinfo', 'none'),\n\t\tmarker = dict(colors=kwargs['colors']),\n\t\thole = kwargs.get('hole', 0),\n\t\tdirection =kwargs.get('direction', 'clockwise'),\n\t\tsort = kwargs.get('sort', False),\n\t)\n\tself.data.append(data)\n\n\tkwargs['width'] = kwargs.get('width', 600)\n\t#self.layout.update(dict())\n\n\treturn kwargs","sub_path":"plotlywrapper/_pie.py","file_name":"_pie.py","file_ext":"py","file_size_in_byte":619,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"114832787","text":"# coding=utf-8\n\"\"\"\nInternal tools for NimLime development & testing.\n\"\"\"\nfrom pprint import pformat\n\nimport sublime\nfrom nimlime_core import configuration\n\ntry:\n    from cProfile import Profile\nexcept ImportError:\n    from profile import Profile\nfrom functools import wraps\nfrom pstats import Stats\n\ntry:\n    from StringIO import StringIO\nexcept ImportError:\n    from io import StringIO\n\ndebug_on = False\nif debug_on:\n    sublime.message_dialog('NimLime running in debug mode.')\n\n\n# Debug printer\ndef debug_print(*args):\n    \"\"\"\n    Print when debugging.\n    :type args: Any\n    \"\"\"\n    if configuration.in_debug_mode:\n        res = []\n        for o in args:\n            if isinstance(o, str):\n                res.append(o)\n            else:\n                res.append(pformat(o))\n        print(''.join(res))\n\n\n# Profiling functions\nprofiler = Profile()\nprofiler_running = False\n\n\ndef profile_func(func):\n    \"\"\"\n    Decorator which profiles a single function.\n    Call print_profile_data to print the collected data.\n    :type func: Callable\n    :rtype: Callable\n    \"\"\"\n\n    @wraps(func)\n    def _profile_wrapper(*args, **kwargs):\n        global profiler_running\n        if not profiler_running:\n            profiler_running = True\n            try:\n                profiler.enable()\n                return func(*args, **kwargs)\n            finally:\n                profiler.disable()\n                profiler_running = False\n\n    return _profile_wrapper\n\n\ndef print_profile_data():\n    \"\"\"\n    Print the collected profile data.\n    \"\"\"\n    stream = StringIO()\n    statistics = Stats(profiler, stream=stream)\n    statistics.sort_stats('cumulative')\n    statistics.print_stats()\n    print(stream.getvalue())\n","sub_path":"nimlime_core/utils/internal_tools.py","file_name":"internal_tools.py","file_ext":"py","file_size_in_byte":1708,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"394830621","text":"import os\nimport nibabel as nib\nimport numpy as np\nimport pickle\n\n#folders = [\"..\\\\Calgary_PS_DTI_Dataset\\\\\", \"..\\\\b2000\\\\\"]\nfolders = [\"../Calgary_PS_DTI_Dataset/\"]\nniiFiles = list()\nsNames = dict()\nfor folder in folders:\n    for dirpaths, dirs, files in os.walk(folder):\n        for file in files:\n            if file.endswith('.nii'):\n                filePath = os.path.join(dirpaths, file)\n                niiFiles.append(filePath)\n                sEnd = file.rfind('_')\n                if sEnd == -1:\n                    sEnd = len(file)-4\n                sName = file[0:sEnd]\n                sNames[filePath] = sName\n            \nmaxVals = dict()\ncount = 0\nfor file in niiFiles:\n    count+=1\n    sName = sNames[file]\n    print(file, sName, count)\n    nii = nib.load(file)\n    data = nii.get_fdata()\n    for vol in range(35):\n        maxVal = np.max(data[:,:,:,vol])\n        maxVals[sName, vol] = maxVal\n#%%\nwith open('maxVals.pickle', 'wb+') as f:\n    pickle.dump(maxVals, f)","sub_path":"CNN/MaxGenerator.py","file_name":"MaxGenerator.py","file_ext":"py","file_size_in_byte":981,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"111663054","text":"# DistRDF version of the RDataFrame benchmark df104\n# - Used DistRDF RDataFrame constructor for Spark\n# - Added npartitions parameter for Spark RDataFrame constructor\n# - Replaced generator by list in RDataFrame construction (the former is not supported by DistRDF yet)\n# - Removed RunGraphs execution option\n# - Replaced use of TH1DModel by a tuple (CppWorkflow does not support the former yet)\n\nimport os\nimport ROOT\n\ndistributed = ROOT.RDF.Experimental.Distributed\nRDataFrame = distributed.Spark.RDataFrame\n\ndef initialize():\n    # Compile a function to compute the invariant mass of the diphoton system\n    ROOT.gInterpreter.Declare(\n    \"\"\"\n    #ifndef df104_HiggsToTwoPhotons\n    #define df104_HiggsToTwoPhotons\n    using Vec_t = const ROOT::VecOps::RVec<float>;\n    float ComputeInvariantMass(Vec_t& pt, Vec_t& eta, Vec_t& phi, Vec_t& e) {\n        ROOT::Math::PtEtaPhiEVector p1(pt[0], eta[0], phi[0], e[0]);\n        ROOT::Math::PtEtaPhiEVector p2(pt[1], eta[1], phi[1], e[1]);\n        return (p1 + p2).mass() / 1000.0;\n    }\n    #endif\n    \"\"\")\n\ndef run(path, npartitions):\n    # Register initialization function\n    distributed.initialize(initialize)\n\n    # Create a ROOT dataframe for each dataset\n    df = {}\n    df[\"data\"] = RDataFrame(\"mini\", [os.path.join(path, \"data_{}.GamGam.root\".format(x)) for x in (\"A\", \"B\", \"C\", \"D\")]*10, npartitions=npartitions)\n    df[\"ggH\"] = RDataFrame(\"mini\", [os.path.join(path, \"mc_343981.ggH125_gamgam.GamGam.root\")]*10, npartitions=npartitions)\n    df[\"VBF\"] = RDataFrame(\"mini\", [os.path.join(path, \"mc_345041.VBFH125_gamgam.GamGam.root\")]*10, npartitions=npartitions)\n    processes = list(df.keys())\n\n    # Apply scale factors and MC weight for simulated events and a weight of 1 for the data\n    for p in [\"ggH\", \"VBF\"]:\n        df[p] = df[p].Define(\"weight\",\n                \"scaleFactor_PHOTON * scaleFactor_PhotonTRIGGER * scaleFactor_PILEUP * mcWeight\");\n    df[\"data\"] = df[\"data\"].Define(\"weight\", \"1.0\")\n\n    # Select the events for the analysis\n    for p in processes:\n        # Apply preselection cut on photon trigger\n        df[p] = df[p].Filter(\"trigP\")\n\n        # Find two good muons with tight ID, pt > 25 GeV and not in the transition region between barrel and encap\n        df[p] = df[p].Define(\"goodphotons\", \"photon_isTightID && (photon_pt > 25000) && (abs(photon_eta) < 2.37) && ((abs(photon_eta) < 1.37) || (abs(photon_eta) > 1.52))\")\\\n                     .Filter(\"Sum(goodphotons) == 2\")\n\n        # Take only isolated photons\n        df[p] = df[p].Filter(\"Sum(photon_ptcone30[goodphotons] / photon_pt[goodphotons] < 0.065) == 2\")\\\n                     .Filter(\"Sum(photon_etcone20[goodphotons] / photon_pt[goodphotons] < 0.065) == 2\")\n\n    # Define a new column with the invariant mass and perform final event selection\n    hists = {}\n    for p in processes:\n        # Make four vectors and compute invariant mass\n        df[p] = df[p].Define(\"m_yy\", \"ComputeInvariantMass(photon_pt[goodphotons], photon_eta[goodphotons], photon_phi[goodphotons], photon_E[goodphotons])\")\n\n        # Make additional kinematic cuts and select mass window\n        df[p] = df[p].Filter(\"photon_pt[goodphotons][0] / 1000.0 / m_yy > 0.35\")\\\n                     .Filter(\"photon_pt[goodphotons][1] / 1000.0 / m_yy > 0.25\")\\\n                     .Filter(\"m_yy > 105 && m_yy < 160\")\n\n        # Book histogram of the invariant mass with this selection\n        hists[p] = df[p].Histo1D(\n                (p, \"Diphoton invariant mass; m_{#gamma#gamma} [GeV];Events\", 30, 105, 160),\n                \"m_yy\", \"weight\")\n\n    # Run the event loop\n    # Time also event loops at this stage\n    watch = ROOT.TStopwatch()\n    ggh = hists[\"ggH\"].GetValue()\n    elapsed_ggh = watch.RealTime()\n    print(\"\\tEvent loop ggh:\", elapsed_ggh, \"s\")\n    watch.Start()\n    vbf = hists[\"VBF\"].GetValue()\n    elapsed_vbf = watch.RealTime()\n    print(\"\\tEvent loop vbf:\", elapsed_vbf, \"s\")\n    watch.Start()\n    data = hists[\"data\"].GetValue()\n    elapsed_data = watch.RealTime()\n    print(\"\\tEvent loop data:\", elapsed_data, \"s\")\n\n    # Create the plot\n\n    # Set styles\n    ROOT.gROOT.SetStyle(\"ATLAS\")\n\n    # Create canvas with pads for main plot and data/MC ratio\n    c = ROOT.TCanvas(\"c\", \"\", 700, 750)\n\n    upper_pad = ROOT.TPad(\"upper_pad\", \"\", 0, 0.35, 1, 1)\n    lower_pad = ROOT.TPad(\"lower_pad\", \"\", 0, 0, 1, 0.35)\n    for p in [upper_pad, lower_pad]:\n        p.SetLeftMargin(0.14)\n        p.SetRightMargin(0.05)\n        p.SetTickx(False)\n        p.SetTicky(False)\n    upper_pad.SetBottomMargin(0)\n    lower_pad.SetTopMargin(0)\n    lower_pad.SetBottomMargin(0.3)\n\n    upper_pad.Draw()\n    lower_pad.Draw()\n\n    # Fit signal + background model to data\n    upper_pad.cd()\n    fit = ROOT.TF1(\"fit\", \"([0]+[1]*x+[2]*x^2+[3]*x^3)+[4]*exp(-0.5*((x-[5])/[6])^2)\", 105, 160)\n    fit.FixParameter(5, 125.0)\n    fit.FixParameter(4, 119.1)\n    fit.FixParameter(6, 2.39)\n    fit.SetLineColor(2)\n    fit.SetLineStyle(1)\n    fit.SetLineWidth(2)\n    data.Fit(\"fit\", \"\", \"E SAME\", 105, 160)\n    fit.Draw(\"SAME\")\n\n    # Draw background\n    bkg = ROOT.TF1(\"bkg\", \"([0]+[1]*x+[2]*x^2+[3]*x^3)\", 105, 160)\n    for i in range(4):\n        bkg.SetParameter(i, fit.GetParameter(i))\n    bkg.SetLineColor(4)\n    bkg.SetLineStyle(2)\n    bkg.SetLineWidth(2)\n    bkg.Draw(\"SAME\")\n\n    # Draw data\n    data.SetMarkerStyle(20)\n    data.SetMarkerSize(1.2)\n    data.SetLineWidth(2)\n    data.SetLineColor(ROOT.kBlack)\n    data.Draw(\"E SAME\")\n    data.SetMinimum(1e-3)\n    data.SetMaximum(8e3)\n    data.GetYaxis().SetLabelSize(0.045)\n    data.GetYaxis().SetTitleSize(0.05)\n    data.SetStats(0)\n    data.SetTitle(\"\")\n\n    # Scale simulated events with luminosity * cross-section / sum of weights\n    # and merge to single Higgs signal\n    lumi = 10064.0\n    ggh.Scale(lumi * 0.102 / 55922617.6297)\n    vbf.Scale(lumi * 0.008518764 / 3441426.13711)\n    higgs = ggh.Clone()\n    higgs.Add(vbf)\n    higgs.Draw(\"HIST SAME\")\n\n    # Draw ratio\n    lower_pad.cd()\n\n    ratiobkg = ROOT.TF1(\"zero\", \"0\", 105, 160)\n    ratiobkg.SetLineColor(4)\n    ratiobkg.SetLineStyle(2)\n    ratiobkg.SetLineWidth(2)\n    ratiobkg.SetMinimum(-125)\n    ratiobkg.SetMaximum(250)\n    ratiobkg.GetXaxis().SetLabelSize(0.08)\n    ratiobkg.GetXaxis().SetTitleSize(0.12)\n    ratiobkg.GetXaxis().SetTitleOffset(1.0)\n    ratiobkg.GetYaxis().SetLabelSize(0.08)\n    ratiobkg.GetYaxis().SetTitleSize(0.09)\n    ratiobkg.GetYaxis().SetTitle(\"Data - Bkg.\")\n    ratiobkg.GetYaxis().CenterTitle()\n    ratiobkg.GetYaxis().SetTitleOffset(0.7)\n    ratiobkg.GetYaxis().SetNdivisions(503, False)\n    ratiobkg.GetYaxis().ChangeLabel(-1, -1, 0)\n    ratiobkg.GetXaxis().SetTitle(\"m_{#gamma#gamma} [GeV]\")\n    ratiobkg.Draw()\n\n    ratiosig = ROOT.TH1F(\"ratiosig\", \"ratiosig\", 5500, 105, 160)\n    ratiosig.Eval(fit)\n    ratiosig.SetLineColor(2)\n    ratiosig.SetLineStyle(1)\n    ratiosig.SetLineWidth(2)\n    ratiosig.Add(bkg, -1)\n    ratiosig.Draw(\"SAME\")\n\n    ratiodata = data.Clone()\n    ratiodata.Add(bkg, -1)\n    ratiodata.Draw(\"E SAME\")\n    for i in range(1, data.GetNbinsX()):\n        ratiodata.SetBinError(i, data.GetBinError(i))\n\n    # Add legend\n    upper_pad.cd()\n    legend = ROOT.TLegend(0.55, 0.55, 0.89, 0.85)\n    legend.SetTextFont(42)\n    legend.SetFillStyle(0)\n    legend.SetBorderSize(0)\n    legend.SetTextSize(0.05)\n    legend.SetTextAlign(32)\n    legend.AddEntry(data, \"Data\" ,\"lep\")\n    legend.AddEntry(bkg, \"Background\", \"l\")\n    legend.AddEntry(fit, \"Signal + Bkg.\", \"l\")\n    legend.AddEntry(higgs, \"Signal\", \"l\")\n    legend.Draw(\"SAME\")\n\n    # Add ATLAS label\n    text = ROOT.TLatex()\n    text.SetNDC()\n    text.SetTextFont(72)\n    text.SetTextSize(0.05)\n    text.DrawLatex(0.18, 0.84, \"ATLAS\")\n    text.SetTextFont(42)\n    text.DrawLatex(0.18 + 0.13, 0.84, \"Open Data\")\n    text.SetTextSize(0.04)\n    text.DrawLatex(0.18, 0.78, \"#sqrt{s} = 13 TeV, 10 fb^{-1}\");\n\n    # Save the plot\n    c.SaveAs(\"HiggsToTwoPhotons.pdf\");\n","sub_path":"benchmarks/df104_HiggsToTwoPhotons_10xdata.py","file_name":"df104_HiggsToTwoPhotons_10xdata.py","file_ext":"py","file_size_in_byte":7887,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"209477204","text":"# -*- coding: utf-8 -*-\nimport pandas as pd\nfrom matplotlib import pyplot as plt\n\n#importing datasets and executing basic operations on them\n\ndf1 = pd.read_csv(r\"C:\\Users\\user\\Downloads\\Datasets\\IMDB_title_basics\\data.tsv\", sep='\\t', \n                  dtype={'genres':str, 'startYear':str})\n\ndf2 = df1[['tconst', 'primaryTitle', 'genres', 'startYear','titleType']]\n\ndf3 = pd.read_csv(r\"C:\\Users\\user\\Downloads\\Datasets\\IMDB_title_ratings\\data.tsv\", delim_whitespace=True, \n                  dtype={'averageRating':float, 'numVotes':int})\n\ndf_SEnumbers = pd.read_csv(r\"C:\\Users\\user\\Downloads\\Datasets\\IMDB_TVSeries\\data.tsv\", delim_whitespace=True,\n                           dtype={'seasonNumber':str, 'episodeNumber':str})\ndfs = pd.merge(df2, df3)\ndf_without_na = dfs.dropna()\ndfs2 = pd.merge(dfs, df_SEnumbers)\n\n#creating dataframes for movies and tv episodes, filtering data\n\nmovies = df_without_na.drop(df_without_na[df_without_na.titleType != 'movie'].index)\nSciFiMovies = movies[movies.genres.str.contains('Sci-Fi') & (movies.numVotes >= 1000) & (movies.averageRating >= 7)]\npacked_SFM = SciFiMovies.drop(['tconst', 'genres', 'startYear', 'titleType'], axis=1)\nsortedSFM = packed_SFM.sort_values(by='averageRating', ascending=False)\nfinalSFM = sortedSFM.set_index('primaryTitle')\nSFM_rating_count = finalSFM['averageRating'].value_counts().sort_index()\n\ntv_df = pd.merge(df_without_na, df_SEnumbers)\ntv_df = tv_df.drop(tv_df[tv_df.titleType == 'tvMovie'].index)\ntv_df_without_na = tv_df.drop(tv_df[(tv_df.seasonNumber == r'\\N') | (tv_df.episodeNumber == r'\\N')].index)\nSciFiTV = tv_df_without_na[tv_df_without_na.genres.str.contains('Sci-Fi') & (tv_df_without_na.numVotes >= 1000) & \\\n          (tv_df_without_na.averageRating >= 7)]\nSciFiTV = SciFiTV.drop(['tconst', 'startYear', 'titleType', 'parentTconst', 'genres'], axis=1)\nsortedSFTV = SciFiTV.sort_values(by='averageRating', ascending=False)\nfinalSFTV = sortedSFTV.set_index('primaryTitle')\nSFTV_rating_count = finalSFTV['averageRating'].value_counts().sort_index()\n\n#gathering info to show it on plot\n\nSFM_text = \"\"\nfor i in range(10):\n    SFM_text += \"%i. %s avg.rate: %.1f \\n\" % (i+1, finalSFM.index[i], finalSFM['averageRating'][i])\nSFM_final_text = (\"Top 10 rated Sci-Fi movies ever:\\n\") + (SFM_text)\n\nSFTV_text = \"\"\nfor i in range(10):\n    SFTV_text += \"%i. %s (S%s E%s) avg.rate: %.1f \\n\" % (i+1, finalSFTV.index[i], finalSFTV['seasonNumber'][i], \n    finalSFTV['episodeNumber'][i], finalSFTV['averageRating'][i]) \nSFTV_final_text = (\"\\nTop 10 rated Sci-Fi episodes ever:\\n\") + (SFTV_text)\n\nfinal_text = SFM_final_text + SFTV_final_text\n\n#creating the plot\n\nfig = plt.figure()\nax = fig.add_subplot(111)\nx = [x * 0.1 for x in range(70,100)]\ny_m = SFM_rating_count\ny_tv = SFTV_rating_count\nax.bar(y_m.index, y_m, width=-0.025, align='edge', label='Movies')\nax.bar(y_tv.index, y_tv, width=0.025, align='edge', label='TV Episodes')\nax.legend(loc='upper left', fontsize=15)\nax.set_title(\"Sci-Fi Movies / TV Episodes from IMDB database with over 1 000 ratings\", fontsize=15)\nax.set_xlabel('User rating', fontsize=15)\nax.set_ylabel('Number of movies / TV Episodes which received the rating', fontsize =15)\nax.set_yticks(range(0,90,5))\nax.set_xticks([x*0.1 for x in range(70,100)])\nax.set_xlim(left=6.9, right=10.0)\nax.text(9.23, 38, final_text, color='black', bbox=dict(edgecolor='grey', facecolor='white', \n        boxstyle='round'), fontsize=11)\nax.yaxis.grid(True, linestyle='--')\nax.set_axisbelow(True)\n\nplt.show()\n\n#not used methods, helpful in different scenarios\n\n\"\"\"\nplt.gcf().subplots_adjust(bottom=1, top=1, left=1, right=1)\nymin, ymax = ax.get_ylim()\nxmin, xmax = ax.get_xlim()\nprint(xmin, xmax, ymin, ymax)\nfig.set_tight_layout(False)\n\"\"\"","sub_path":"comparison.py","file_name":"comparison.py","file_ext":"py","file_size_in_byte":3715,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"544762","text":"import numpy as np\nimport pywavefront\nfrom PIL import Image\nfrom pyrr import matrix44 as m44, Vector3 as v3\nfrom OpenGL.GL import *\n\nfrom shader import Shader\n\n\nclass LoadedObject:\n    def __init__(self, path: str, x: float = 0.0, y: float = 0.0, z: float = 0.0, scale: float = 1.0):\n        \"\"\"Object loaded from .obj and .mtl files, ready to be drawn.\"\"\"\n        self._path = path\n        self._wavefront = None\n        self.vaos = None\n        self._vbos = None\n        self.materials = []\n        self.textures = None\n        self.use_texture = False\n        self.lengths = []\n        # Set position and model\n        self.pos = m44.create_from_translation(v3([x, y, z]))\n        self.model = self.pos\n        self._scale = scale\n        self._scale_matrix: m44 = m44.create_from_scale(v3([self._scale] * 3))\n        # Load wavefront\n        self._load_obj()\n\n    def set_pos(self, pos: v3):\n        self.pos = m44.create_from_translation(pos)\n        self.model = self.pos\n\n    def _load_obj(self) -> None:\n        \"\"\"Loads wavefront obj and materials. Stores vertex data into VAOs and VBOs.\"\"\"\n        self._wavefront = pywavefront.Wavefront(self._path, collect_faces=True, create_materials=True)\n\n        # Generate buffers\n        materials_count = len(self._wavefront.materials)\n        self.vaos = glGenVertexArrays(materials_count)\n        self._vbos = glGenBuffers(materials_count)\n        self.textures = glGenTextures(materials_count)\n\n        if materials_count == 1:\n            # glGen* will return an int instead of an np.array if argument is 1.\n            self.vaos = np.array([self.vaos], dtype=np.uint32)\n            self._vbos = np.array([self._vbos], dtype=np.uint32)\n            self.textures = np.array([self.textures], dtype=np.uint32)\n\n        # For each material fill buffers and load a texture\n        ind = 0\n        for material in self._wavefront.materials.values():\n            vertex_size = material.vertex_size\n            scene_vertices = np.array(material.vertices, dtype=np.float32)\n            # Store length and materials for drawing\n            self.lengths.append(len(scene_vertices))\n            self.materials.append(material)\n            # Load texture by path (may break in some weird cases, I guess)\n            if material.texture is not None:\n                self._load_texture(material.texture.path, self.textures[ind])\n                self.use_texture = True\n\n            # Bind VAO\n            glBindVertexArray(self.vaos[ind])\n            # Fill VBO\n            glBindBuffer(GL_ARRAY_BUFFER, self._vbos[ind])\n            glBufferData(GL_ARRAY_BUFFER, scene_vertices.nbytes, scene_vertices, GL_STATIC_DRAW)\n\n            # Set attribute buffers\n            attr_format = {\n                \"T2F\": (1, 2),  # Tex coords (2 floats): ind=1\n                \"C3F\": (2, 3),  # Color (3 floats): ind=2\n                \"N3F\": (3, 3),  # Normal (3 floats): ind=3\n                \"V3F\": (0, 3),  # Position (3 floats): ind=0\n            }\n\n            cur_off = 0  # current start offset\n            for attr in material.vertex_format.split(\"_\"):\n                if attr not in attr_format:\n                    raise Exception(\"Unknown format\")\n\n                # Apply\n                attr_ind, attr_size = attr_format[attr]\n                glEnableVertexAttribArray(attr_ind)\n                glVertexAttribPointer(attr_ind, attr_size, GL_FLOAT, GL_FALSE, scene_vertices.itemsize * vertex_size,\n                                      ctypes.c_void_p(cur_off))\n                cur_off += attr_size * 4\n\n            # Unbind (Technically not necessary but used as a precaution)\n            glBindVertexArray(0)\n            ind += 1\n\n    @staticmethod\n    def _load_texture(path: str, texture: int) -> None:\n        \"\"\"\n        Loads texture into buffer by given path and tex buffer ID.\n\n        :param path: Texture path.\n        :param texture: Texture buffer ID.\n        \"\"\"\n        # For use with GLFW\n        glBindTexture(GL_TEXTURE_2D, texture)\n        # Set the texture wrapping parameters\n        glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_WRAP_S, GL_REPEAT)\n        glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_WRAP_T, GL_REPEAT)\n        # Set texture filtering parameters\n        glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MIN_FILTER, GL_LINEAR)\n        glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MAG_FILTER, GL_LINEAR)\n        # Load image\n        image = Image.open(path)\n        image = image.transpose(Image.FLIP_TOP_BOTTOM)\n        img_data = image.convert(\"RGBA\").tobytes()\n        glTexImage2D(GL_TEXTURE_2D, 0, GL_RGBA, image.width, image.height, 0, GL_RGBA, GL_UNSIGNED_BYTE, img_data)\n\n    def draw(self, shader: Shader, model=None) -> None:\n        \"\"\"Draws loaded object onto GL buffer with selected shader.\"\"\"\n        # shader.use_program()  # Not really sure if that's how you should do it\n        for vao, tex, length, mat in zip(self.vaos, self.textures, self.lengths, self.materials):\n            glBindVertexArray(vao)\n            glBindTexture(GL_TEXTURE_2D, tex)\n            if model is not None:\n                shader.set_model(model)\n            else:\n                shader.set_model(m44.multiply(self._scale_matrix, self.model))\n            shader.set_v3(\"material.ambient\", mat.ambient)\n            shader.set_v3(\"material.diffuse\", mat.diffuse)\n            shader.set_v3(\"material.specular\", mat.specular)\n            shader.set_float(\"material.shininess\", mat.shininess)\n            glDrawArrays(GL_TRIANGLES, 0, length)\n","sub_path":"loaded_object.py","file_name":"loaded_object.py","file_ext":"py","file_size_in_byte":5492,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"20101990","text":"\"\"\"\ntxt2psqldb.py\n处理 eh0(tcp)_8:58的数据，\n将数据打包成一个个请求，并且分类\n并存入数据库\n\"\"\"\n\nimport psycopg2\n\n\nclass FormatError(Exception):\n    pass\n\n\nclass TXT2PSQLDB:\n    \"\"\"\n    root_path: 类型：str。设置文件根目录，以 / 或 \\ 结尾，例如：\"/root/\"\n    file_name: 类型：str。设置文件名, 例如：\"demon.txt\"\n    \"\"\"\n\n    def __init__(self, header_amount=0, root_path=\"\", file_name=\"\"):\n        self.header_amount = header_amount\n        self.root_path = root_path\n        self.file_name = file_name\n\n    @property\n    def file_name(self):\n        return self.__file_name\n\n    @file_name.setter\n    def file_name(self, value):\n        self.__file_name = value\n\n    @property\n    def root_path(self):\n        return self.__root_path\n\n    @root_path.setter\n    def root_path(self, value):\n        if value[-1] not in (\"/\", \"\\\\\"):\n            raise FormatError(\"请按格式传参，root_path 例: '/root/'；file_name 例: 'demon.txt'\")\n        else:\n            self.__root_path = value\n\n    def __get_tar(self):\n        \"\"\"\n        将行数据分割好放到self.__tar中\n        :return:\n        \"\"\"\n        raise NotImplementedError\n\n    def __classify(self):\n        \"\"\"\n        把列分类好的数据放入self.__notes这个二维列表里面\n        :return:\n        \"\"\"\n        raise NotImplementedError\n\n\nclass MYTXT2PSQLDB(TXT2PSQLDB):\n    def __init__(self, header_amount=0, root_path=\"\", file_name=\"\"):\n        super().__init__(header_amount, root_path, file_name)\n        self.__tar = []\n        self.__notes = []\n        self.__list_info = []\n\n    def __get_tar(self):\n        fr = open(self.root_path + self.file_name, \"r\")\n        temp = \"\"\n        for line in fr:\n            if line[0:3] == \"No.\":\n                self.__tar.append(temp)\n                temp = \"\"\n            temp += line\n        fr.close()\n\n    def __classify(self):\n        for i in range(1, len(self.__tar)):\n            temp = []\n            str_info = self.__tar[i].split(\"\\n\")[1].strip()\n            list_info_temp = str_info.split(\" \")\n            self.__list_info = []\n            for j in range(len(list_info_temp)):\n                if list_info_temp[j] != \"\":\n                    self.__list_info.append(list_info_temp[j])\n            for k in range(self.header_amount - 2):\n                temp.append(self.__list_info[k])\n            temp.append(\" \".join(self.__list_info[6:]))\n            temp.append(\"\\n\".join(self.__tar[i].split(\"\\n\")[3:]))\n            self.__notes.append(temp)\n\n    def run(self):\n        # 打开数据库\n        db = psycopg2.connect(\"dbname=tar_data user=postgres password=toor host=localhost\")\n        cur = db.cursor()\n        # 以 No. 为标志进行分割，划分各个数据包\n        self.__get_tar()\n        self.__classify()\n        # 放入数据库\n        match = \"(\"+(\"%s,\" * self.header_amount)[0:-1] +\")\"\n        psql = \"insert into tcp values \" + match\n        for i in range(len(self.__notes)):\n            try:\n                cur.execute(psql, self.__notes[i])\n                db.commit()\n            except Exception as e:\n                print(e)\n                db.rollback()\n        cur.close()\n        db.close()\n","sub_path":"txt2psqldb.py","file_name":"txt2psqldb.py","file_ext":"py","file_size_in_byte":3223,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"23647490","text":"import mxnet as mx\r\nimport logging\r\nimport time\r\n\r\nlogging.getLogger().setLevel(logging.DEBUG)\r\nbatch_s = 8\r\nprint(\"Mariya config\")\r\ntrain_data = mx.io.ImageRecordIter(\r\n    path_imgrec=\"data.rec\",\r\n    data_shape=(3, 128, 128),\r\n    batch_size=batch_s)\r\ntest_data = mx.io.ImageRecordIter(\r\n    path_imgrec=\"test_data.rec\",\r\n    data_shape=(3, 128, 128),\r\n    batch_size=batch_s)\r\nprint(\"data - ok\")\r\n\r\n#First 'layer', if it can be put that way.\r\ndata = mx.sym.var('data')\r\ninput = mx.sym.flatten(data=data)\r\nfirst = mx.sym.FullyConnected(data=input, num_hidden=2000)\r\nfirstact = mx.sym.Activation(data=first, act_type='relu')\r\nsecond = mx.sym.FullyConnected(data=firstact, num_hidden=1000)\r\nsecondact = mx.sym.Activation(data=second, act_type='relu')\r\nthird = mx.sym.FullyConnected(data=secondact, num_hidden=500)\r\nthirdact = mx.sym.Activation(data=third, act_type='relu')\r\nfourth = mx.sym.FullyConnected(data=thirdact, num_hidden=250)\r\nfourthact = mx.sym.Activation(data=fourth, act_type='sigmoid')\r\nfc = mx.sym.FullyConnected(data=fourthact, num_hidden=2)\r\nsoftmax = mx.sym.SoftmaxOutput(data=fc, name='softmax')\r\n#softmax = mx.sym.LogisticRegressionOutput(data=fc, name='softmax')\r\n\r\nprint(\"layers - ok\")\r\nfcnn_net = mx.mod.Module(symbol=softmax, context=mx.gpu())\r\nstart_time = time.clock()\r\nfcnn_net.fit(train_data,\r\n             eval_data=test_data,\r\n             optimizer='sgd',\r\n             optimizer_params={'learning_rate': 0.001},\r\n             eval_metric='acc',\r\n             batch_end_callback=mx.callback.Speedometer(batch_s, 100),\r\n             num_epoch=10)\r\nfit_time = time.clock() - start_time\r\nprint(\"Fit time: %f\", fit_time)\r\nacc = mx.metric.Accuracy()\r\nfcnn_net.score(test_data, acc)\r\nprint(acc)\r\n","sub_path":"lab2/src/fcnn_rrrs1.py","file_name":"fcnn_rrrs1.py","file_ext":"py","file_size_in_byte":1722,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"496823084","text":"def delete_wire(coordinate_begin, itemnet, distances, gate_connections, allwires, blocked):\n    wires = []\n    x_coordinate_start = int(coordinate_begin[0])\n    y_coordinate_start = int(coordinate_begin[1])\n    z_coordinate_start = int(coordinate_begin[2])\n    coordinate = coordinate_begin\n    \n    # Switch order of gates\n    end_gate = itemnet[0]\n    start_gate = itemnet[1]\n    distances.append(((start_gate, end_gate), 2))\n\n    coordinates = gate_connections[itemnet]\n    print(\"BEFOREDEL: \", len(allwires), len(blocked))\n    # Delete wire from gate connections dictionary\n    del gate_connections[itemnet]\n   \n    # Delete blocking wire\n    for i in coordinates:\n        print(\"incoo: \", i, type(i))\n        if str(i) in blocked:\n            print(\"DELETEBLOCK\", i)\n            blocked.remove(str(i))\n    deletelist = []\n    # Delete blocking wire\n    for i, item2 in enumerate(allwires):\n        if item2.net == itemnet:\n            # print(\"DELETETOM\")\n            # print(allwires[i])\n            deletelist.append(allwires[i])\n        # print(\"DELETINGTHIS: \", str(deletelist))\n    for delete_wire in deletelist:  \n        allwires.remove(delete_wire)\n    print(\"AFTERDEL: \", len(allwires), len(blocked))\n\n    return wires, x_coordinate_start, y_coordinate_start, z_coordinate_start, coordinate, gate_connections, allwires, blocked","sub_path":"code/functions/astardelete.py","file_name":"astardelete.py","file_ext":"py","file_size_in_byte":1341,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"303778442","text":"#!/usr/bin/env python3\n\n\"\"\"\n/************************************************************/\n/* Author(s):         3JRS - Janeel, Jennie, Jess, Richie, Steph\n/* Major:             Information Technology\n/* Compiler:          Python 3.6                  \n/* Creation Date:     4/29/18\n/* Due Date:          12/12/18\n/* Course:            CSC354-020\n/* Professor Name:    Prof. Demarco\n/* Assignment:        Software Engineering Project (Prototype)\n/* Filename:          socket_communication.py\n/* Purpose:           To write a server that will communicate with a client (which will also be a server) via client/server communication\n/************************************************************/\n\"\"\"\nimport socket\nimport sys, os \n\ndef Main():\n    ipaddress = \"127.0.0.1\"\n    IPaddr = socket.gethostbyname(ipaddress) #translate hostname to ip address\n    message = \"TEST\"\n    bufferSize = 1024\n    port = 4444\n    serverInfo = ((IPaddr, port)) \n    \n    try: #Attempt to connect to server\n        clientSocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        clientSocket.connect(serverInfo)\n        print ('You have successfully connected')\n    except socket.error as err:\n        print('Socket connection error:', err)\n        sys.exit()\n        \n    try:\n        status = clientSocket.recv(bufferSize) #attempt to receive wait message \n        status = status.decode()\n    except socket.error as msg:\n        print ('Unable to receive message from server: ' + str(msg[0]))\n    \n    connection = True \n    while connection:\n        try:\n             final = clientSocket.recv(4) #Receieve GO from server\n             final = final.decode() \n        except Exception as s:\n            print ('Unable to send client input and READY message: ', s)\n            sys.exit(0)\n        if (final == \"TEST\"):\n            cInput(clientSocket)\n            connection = False\n            clientSocket.close()         \n\nif __name__ == '__main__': #ensures in current module Main \n    Main()\nelse:\n    sys.exit(0)","sub_path":"socket_communication.py","file_name":"socket_communication.py","file_ext":"py","file_size_in_byte":2006,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"114481584","text":"# -*- coding: utf-8 -*-\n# @Time    : 2018/9/21 11:34\n# @Author  : Burrows\n# @FileName: runTest.py\n\n'''封装公共测试流'''\n\nimport unittest\nimport time\nimport re\nimport json\n\nfrom jsonpath import jsonpath\n\nfrom base.runMethod import RunMethod\nfrom base.readLogger import ReadLogger\nfrom util.relyData import RelyData\nfrom base.aydException import *\n\nclass RunTest(unittest.TestCase, unittest.SkipTest, DataCheck):\n    def __init__(self, methodName = 'runTest'):\n        super(RunTest, self).__init__(methodName)\n\n        # 获取logger和run_log\n        read_logger = ReadLogger()\n        self.logger = read_logger.get_logger()\n        self.run_log_src = read_logger.get_run_log()\n\n        # 使用自封装requests\n        self.run_method = RunMethod()\n\n        # 使用自封装关联数据\n        self.rely_data = RelyData()\n\n        self.case_id = ''  # 用例id\n        self.desc = ''  # 用例描述\n        self.resp = None  # 响应数据\n        self.req_msg = {'request': {}, '\\nresponse': {}}  # 用例基本信息\n\n    def get_resp(self):\n        \"\"\"\n        获取响应信息\n        :return:  response data\n        \"\"\"\n        return self.resp\n\n    def skipTest(self, reason):\n        \"\"\"\n        过滤用例\n        :param reason:  过滤用例原因\n        :return:   unittest.SkipTest\n        \"\"\"\n        raise unittest.SkipTest\n\n    def getCasePro(self):\n        \"\"\"\n        获取用例基本信息\n        :return: case_id， desc\n        \"\"\"\n        return self.case_id, self.desc\n\n    def getCaseMsg(self):\n        \"\"\"\n        获取待执行用例详细信息\n        :return: req_msg = {'request': {}, '\\nresponse': {}}\n        \"\"\"\n        return self.req_msg\n\n    def start(self, url, uri, method, api_name, header, cookie, **kw):\n        \"\"\"\n        用例运行主入口\n        :param url:         请求地址\n        :param uri:         请求路径\n        :param method:      请求方法\n        :param api_name:    接口名称\n        :param header:      header设置\n        :param cookie:      cookie设置\n        :param kw:          其他参数\n        :return:            None\n        \"\"\"\n\n        # 获取case数据\n        options = kw.get('options', {})\n        skip = options.get('skip', '0')  # skip默认值为0，为1表示跳过该case\n        data_check = options.get('datacheck', None)  # 数据校验\n        toJsonData = options.get('jsonData', '0')  # jsonData默认值为0，表示请求数据不做序列化\n        self.case_id = kw.get('case_id')    # case id\n        self.desc = kw.get('desc')          # case描述\n        api_name = api_name                 # api名\n        url = url + uri     # 请求url\n        method = method     # 请求方法\n        header = header     # header设置\n        cookie = cookie     # cookie设置\n        data = kw.get('request_data', '')           # 请求数据，缺省为空\n        validators = kw.get('validators')       # 断言设置\n        relyDataExport = kw.get('relyDataExport')  # 关联输出，即该接口响应返回被其他接口关联字段\n        relyDataImport = kw.get('relyDataImport')  # 关联引入，即该接口运行所需的关联字段设置\n        start_time = time.strftime(\"%Y/%m/%d %H:%M:%S\")  # 开始时间\n        start_timestamp = time.time() * 1000  # 时间戳，ms\n\n        # 处理关联数据\n        if relyDataImport is not None:\n            for x in relyDataImport:\n                datatype = x.get('datatype')  # 获取目标关联数据替换源，可能是 request_data/header/cookie\n                relyDataSrc = x.get('relyDataSrc')  # 获取依赖字段，可能在request_data中为层级结构\n                relyDataDes = x.get('relyDataDes')  # 获取关联字段\n                rely_value = self.rely_data.get_rely_from_file(relyDataDes)  # 获取关联数据\n                # 替换依赖字段值为关联数据\n                if datatype in \"header\":\n                    header[relyDataSrc] = rely_value\n                elif datatype in \"cookie\":\n                    cookie[relyDataSrc] = rely_value\n                else:\n                    paths = jsonpath(data, '$..%s' % relyDataSrc, result_type='IPATH')  # 获取目标字段的位置的层级结构，返回list\n                    # 替换request_data中所有目标key的值为关联数据\n                    for path in paths:\n                        lens = len(path)\n                        for index in range(lens):\n                            if re.match(r'[0-9]', path[index]):\n                                path[index] = int(path[index])\n                        if lens == 1:\n                            data[path[0]] = rely_value\n                        elif lens == 2:\n                            data[path[0]][path[1]] = rely_value\n                        elif lens == 3:\n                            data[path[0]][path[1]][path[2]] = rely_value\n                        elif lens == 4:\n                            data[path[0]][path[1]][path[2]][path[3]] = rely_value\n                        elif lens == 5:\n                            data[path[0]][path[1]][path[2]][path[3]][path[4]] = rely_value\n                        elif lens == 6:\n                            data[path[0]][path[1]][path[2]][path[3]][path[4]][path[5]] = rely_value\n                        else:\n                            self.logger.debug('层级结构过长')\n\n        self.req_msg.get('request').setdefault('\\nreq.url', url)\n        self.req_msg.get('request').setdefault('\\nreq.method', method)\n        self.req_msg.get('request').setdefault('\\nreq.header', header)\n        self.req_msg.get('request').setdefault('\\nreq.cookie', cookie)\n        self.req_msg.get('request').setdefault('\\nreq.data', data)\n        self.req_msg.get('request').setdefault('\\nreq.validators', validators)\n        self.req_msg.get('request').setdefault('\\nreq.relyDataExport', relyDataExport)\n        self.req_msg.get('request').setdefault('\\nreq.relyDataImport', str(relyDataImport))\n        self.req_msg.get('request').setdefault('\\nreq.data_check', str(data_check) + '\\n')\n\n        if int(skip) == 1:\n            self.logger.debug('跳过用例   ：%s(%s)' % (self.case_id, self.desc))\n            self.skipTest('skip case')\n\n        try:\n            self.logger.debug('用例编号   ：%s ' % self.case_id)\n            self.logger.debug('接口名称   : %s' % api_name)\n            self.logger.debug('用例描述   : %s' % self.desc)\n            self.logger.debug('请求时间   : %s' % start_time)\n            self.logger.debug(' 请求信息 '.center(50, '-'))\n            self.logger.debug('url        : %s' % url)\n            self.logger.debug('method     : %s' % method)\n            self.logger.debug('header     : %s' % header)\n            self.logger.debug('cookie     : %s' % cookie)\n            self.logger.debug('请求数据   : %s' % data)\n            self.logger.debug('断言设置   : %s' % validators)\n            self.logger.debug('关联输出   : %s' % relyDataExport)\n            self.logger.debug('关联引入   : %s' % relyDataImport)\n            self.logger.debug('校验数据   : %s' % data_check)\n\n            # 发送请求\n            # 接口有异常的情况下，可能返回的不是json串，会报错\n            if toJsonData == 1:\n                resp = self.run_method.run_main(method, url, json.dumps(data), cookies=cookie, headers=header)\n            else:\n                resp = self.run_method.run_main(method, url, data, cookies=cookie, headers=header)\n            resp_headers = dict(resp.headers)           # 响应头\n            resp_status_code = resp.status_code     # 响应码\n            resp_cookie = dict(resp.cookies)        # 响应cookie\n            resp_text = resp.text                   # 响应文本\n\n            end_time = time.strftime(\"%Y/%m/%d %H:%M:%S\")\n            end_timestamp = time.time() * 1000  # ms\n            time_spend = '%.2f' % (end_timestamp - start_timestamp)  # ms精度\n\n            self.logger.debug(' 响应信息 '.center(50, '-'))\n            self.logger.debug('响应时间   : %s' % end_time)\n            self.logger.debug('响应耗时   : %s ms' % time_spend)\n            self.logger.debug('响应码     : %s' % resp_status_code)\n            self.logger.debug('响应头     : %s' % resp_headers)\n\n            self.req_msg.get('\\nresponse').setdefault('\\nresp.status_code', resp_status_code)\n            self.req_msg.get('\\nresponse').setdefault('\\nresp.headers', resp_headers)\n\n            # 查看是否包含 断言查看一次错误就停止，后面加入错误提示\n            self.assertEqual(resp_status_code, 200, msg='预计结果不符:http_code预期结果 200,实际结果【%s】' % (resp_status_code))\n\n            # 响应结果处理，可能是文本、网页、dict\n            if resp_text.startswith('{'):\n                resp = resp.json()\n                self.resp = resp\n                self.req_msg.get('\\nresponse').setdefault('\\nresp.json', str(resp) + '\\n')\n\n                # 保存被关联数据\n                if relyDataExport is not None:\n                    for x in relyDataExport:\n                        datatype = x.get('datatype')\n                        relyKey = x.get('relyKey')\n                        keepKey = x.get('keepKey')\n                        if datatype in \"header\":\n                            self.rely_data.write_rely_to_file(relyKey, keepKey, resp_headers)\n                        elif datatype in \"cookie\":\n                            self.rely_data.write_rely_to_file(relyKey, keepKey, resp_cookie)\n                        else:\n                            self.rely_data.write_rely_to_file(relyKey, keepKey, resp)\n\n                # 响应数据断言 json\n                for valid in validators:\n                    check = valid.get('check')  # 待检查字段\n                    expect = valid.get('expect')  # 预期值\n                    resp_data = resp.get(check)\n                    if isinstance(expect, int):\n                        self.assertEqual(expect, resp_data, msg='预计结果不符:【%s】字段预期结果【%s】,实际结果【%s】' % (check, expect, resp_data))\n                    if isinstance(expect, str):\n                        self.assertIn(expect, resp_data, msg='预计结果不符:【%s】字段预期结果【%s】,实际结果【%s】' % (check, expect, resp_data))\n\n            else:\n                resp = resp_text\n                resp = resp.replace(r\"<\", r\"<--\")  # 注释html标签，防止误识别\n                self.resp = resp\n                self.req_msg.get('\\nresponse').setdefault('\\nresp.text', str(resp) + '\\n')\n\n\n                # 保存被关联数据\n                if relyDataExport is not None:\n                    for x in relyDataExport:\n                        datatype = x.get('datatype')\n                        relyKey = x.get('relyKey')\n                        keepKey = x.get('keepKey')\n                        if datatype in \"header\":\n                            self.rely_data.write_rely_to_file(relyKey, keepKey, resp_headers)\n                        elif datatype in \"cookie\":\n                            self.rely_data.write_rely_to_file(relyKey, keepKey, resp_cookie)\n                        else:\n                            pass\n\n                # 响应数据断言 text\n                for valid in validators:\n                    expect = valid.get('expect')  # 预期值\n                    if isinstance(expect, str):\n                        self.assertIn(expect, resp_text, msg='预计结果不符:预期值【%s】在响应文本中不存在！' % expect)\n\n            self.logger.debug('响应内容   : %s' % resp)\n\n            # 数据校验\n            if data_check == 1:\n                raise DataCheck\n\n        except DataCheck:\n            raise\n        except Exception as e:\n            self.logger.debug('响应内容   : %s' % resp)\n            self.logger.error('错误信息   : %s' % e)\n            raise\n        # finally:\n        #     self.logger.debug('结束测试 %s ' % self.case_id)\n","sub_path":"src/common/runTest.py","file_name":"runTest.py","file_ext":"py","file_size_in_byte":12066,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"413153452","text":"# Get per capita personal income from CA1 table for SC counties for all years\nimport requests\nimport json\n\nkey = \"F414B54D-68CF-452A-BFD9-7B7BDA23AE7C\"\ndataset_name = \"RegionalIncome\"\nparameter_name = \"LineCode\"\ntable_name = \"CA1\"\nline_code = \"3\"\nyear = \"ALL\"\ngeo_fips = \"45000\"\nresult_format = \"json\"\nurl = \"https://apps.bea.gov/api/data?&UserID=\" + key + \\\n\"&method=GetData&datasetname=\" + dataset_name + \\\n\"&TableName=\" + table_name + \"&LineCode=\" + line_code + \\\n\"&Year=\" + year + \"&GeoFips=\" + geo_fips + \\\n\"&ResultFormat=\" + result_format\n\nresponse = requests.get(url)  # Get response from site\ndata = response.json() # put response in object\n\nwith open(\"ca1_per_capita_personal_income_sc.json\", \"w\") as jsondata:  # Write json response to file\n    jsondata.write(json.dumps(data, sort_keys=True, indent=2))\n\n#for item in data[\"BEAAPI\"][\"Results\"][\"Data\"]:  # Printing the info\n#    number_meaning = item[\"CL_UNIT\"]\n#    population = item[\"DataValue\"]\n#    table_code = item[\"Code\"]\n#    location_code = item[\"GeoFips\"]\n#    location_name = item[\"GeoName\"]\n#    year = item[\"TimePeriod\"]\n#    dollar_exponent = item[\"UNIT_MULT\"]\n#    print(year + \": \" + location_name + \"| \" + population)","sub_path":"regional_income/table_ca1/state/sc/ca1_per_capita_personal_income_sc.py","file_name":"ca1_per_capita_personal_income_sc.py","file_ext":"py","file_size_in_byte":1194,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"544576665","text":"#!-*- coding: utf-8 -*-\nfrom django.db import connection\nfrom django.template import Template, Context\n\nclass Middleware(object):\n    def process_response(self, request, response):\n        content_types = ('text/plain', 'text/html')\n        if request.META['CONTENT_TYPE'] not in content_types:\n            return response\n        time = sum([float(q['time']) for q in connection.queries])\n        template = Template(\"\"\"<div class=\"debug\">\n                <p>Number of requests: {{ count }}</p>\n                <p>Time: {{ time }} с.</p>\n            <div>\n            \"\"\")\n        renderred_template = template.render(Context(dict(time=time, count=len(connection.queries))))\n        content = response.content.decode('utf-8')\n        body = '</body>'\n        body_position = content.find(body)\n        content = content[:body_position] + renderred_template + content[body_position:]\n        response.content = content.encode('utf-8')\n        return  response","sub_path":"echo_ua_test/middleware.py","file_name":"middleware.py","file_ext":"py","file_size_in_byte":960,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"466481444","text":"#!/usr/bin/python3\n\nfrom sys import argv\nimport bs4 as bs\nimport re\nimport time\nfrom unidecode import unidecode\n\nscript, filename = argv\n\ninp = open(filename,'r')\nx=inp.read().split('</testcase>')\ninp.close()\n\n#write header\ncsv=\"Case Id;Title;Date;Priority;Planned duration (minutes);Test Areas;Average Duration;Tags;Objective;Test Data;Preconditions & Assumptions\"\nf = open(\"output.csv\",\"a\")\nf.write(csv)\nf.close()\n\n\n#get date\ndate=time.strftime(\"%Y-%d-%m\")\n\n\n#create soups\ny=0\nsoups=[]\nwhile y < len(x):\n    soups.append(bs.BeautifulSoup(x[y],'xml'))\n    y+=1\n\n#parse soup one by one\ns=0\nwhile s < len(soups)-1:\n\n    #parse name\n    name=\"\".join(str(soups[s].find('testcase')))\n\n    n=re.search('<testcase>\"(.*)\"&gt', name).group(1)\n\n    #parse preconditions\n    try:\n        preconditions=soups[s].preconditions.text.replace('\"', '').rstrip('\\n')\n    except AttributeError:\n        preconditions=\"\"\n\n\n    #parse actions\n    actions=soups[s].find_all('actions')\n    i = 0\n    a=[]\n    while i < len(actions):\n        a.append(actions[i].text.replace('\"', '').rstrip('\\n'))\n        i = i + 1\n\n\n\n    #parse expected results\n    expected_results=soups[s].find_all('expectedresults')\n    z = 0\n    exp=[]\n    while z < len(expected_results):\n        exp.append(expected_results[z].text.replace('\"', '').rstrip('\\n'))\n        z = z+1\n\n\n    #prepare csv\n    csv= \"\\nnew;%s;%s;\\\"\\\";\\\"\\\";\\\"\\\";\\\"\\\";\\\"\\\";\\\"\\\";\\\"\\\";%s\\n\\\"\\\";Step Id;Action;Result\" % (n, date, preconditions)\n    for i in range(len(actions)):\n        csv+=\"\"\"\\n\"\";new;%s;%s\"\"\" % (a[i], exp[i])\n\n\n    s+=1\n    #write to file\n    f = open(\"output.csv\",\"a\")\n    f.write(csv)\n    f.close()\n\n\n\n\n#replace line ending LF to CRLF, UTF8 --> ASCII encode\n\nwindows_line_ending = '\\r\\n'\nlinux_line_ending = '\\n'\n\n\n\nf = open(\"output.csv\", 'r')\ncontent = f.read()\ncontent = content.replace(linux_line_ending, windows_line_ending )\nencoded=unidecode(content)\nencoded.encode(\"ascii\")\nf.close()\n\nf = open(\"output.csv\", 'w')\nf.write(encoded)\nf.close()\n","sub_path":"tl2tr.py","file_name":"tl2tr.py","file_ext":"py","file_size_in_byte":1991,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"438104852","text":"#Ref: https://google.github.io/mediapipe/solutions/hands.html\n\n#imports\nimport cv2\nimport collections\nimport numpy as np\nimport mediapipe as mp\nimport time\nfrom sklearn import neighbors\nimport matplotlib\nfrom matplotlib import pyplot as plt \nimport pickle\nimport os as o\n\n#train data\nclass Train:\n    #init\n    def __init__(self, data_folder):\n        #KNN\n        self.clf = neighbors.KNeighborsClassifier(15)\n        #training x and y\n        t_x = []\n        t_y = []\n        #label\n        self.lb = []\n\n        #look for data\n        for i, df in enumerate(o.listdir(data_folder)):\n            #get path\n            with open(o.path.join(data_folder, df), 'rb') as f:\n                #load file\n                d = pickle.load(f)\n                #train path\n                for h in d:\n                    t_y.append(i)\n                    t_x.append((h - h[0]).flatten())\n                #CHECK THIS\n                self.lb.append(df[5:-2])\n        t_x = np.array(t_x)\n        self.clf.fit(t_x, t_y)\n        plt.switch_backend('Agg')\n\n    #get certain shape\n    def getshape(self, h):\n        pre = self.clf.predict(\n            np.expand_dims((h - h[0]).flatten(), 0))\n        return self.lb[int(pre)]\n\n\nclass MH:\n    #init\n    def __init__(self, buffer_size=None):\n        #get hands\n        self.hands = mp.solutions.hands.Hands(\n             min_tracking_confidence=0.9,\n             min_detection_confidence=0.75\n        )\n        #get\n        self.his = collections.deque(maxlen=None)\n\n        #thresh & num\n        self.tmt= 3\n        self.tmn= 0\n\n    #run img\n    def run(self, img):\n        #get img.shape\n        img_width, img_height, _ = img.shape\n        #input photo\n        inpi = cv2.cvtColor(cv2.flip(img, 1), cv2.COLOR_BGR2RGB)\n        inpi.flags.writeable = False\n        #set result\n        res = self.hands.process(cv2.cvtColor(inpi, cv2.COLOR_BGR2RGB))\n\n        #check result arr\n        if res.multi_hand_landmarks:\n            #set track missing num to 0\n            self.tmn = 0\n            res_arr = np.asarray([[pt.x, pt.y] for pt in res.multi_hand_landmarks[0].landmark])\n            self.his.append(res_arr)\n        #if track missing num smaller that track missing thresh\n        elif self.tmn < self.tmt:\n            self.tmn += 1\n        #otherwise clear history\n        else:\n            self.his.clear()\n            self.tmn = 0\n        plt.switch_backend('Agg')\n\n        return res\n\n    #draw\n    def drawlol(self, image, res):\n        image_width, image_height, _ = image.shape\n        #flip img\n        new_img = cv2.flip(image, 1) \n        #check for landmarks\n        if res.multi_hand_landmarks:\n            for hand_landmarks in res.multi_hand_landmarks:\n                #draww\n                mp.solutions.drawing_utils.draw_landmarks(\n                    new_img,\n                    hand_landmarks,\n                    mp.solutions.hands.HAND_CONNECTIONS\n                )\n                #get x and y coordinate\n                x = [landmark.x for landmark in hand_landmarks.landmark]\n                y = [landmark.y for landmark in hand_landmarks.landmark]\n\n                #get center\n                center = np.asarray([np.mean(x)*image_width*1.87, np.mean(y)*image_height*0.65]).astype('int32')\n                #draw circle and box\n                cv2.circle(new_img, tuple(center), 10, (225,150,255), 3)  #for checking the center \n                cv2.rectangle(new_img, (center[0]-200,center[1]-200), (center[0]+200,center[1]+200), (255,255,255), 5)\n                #put text\n                if self.his:\n                    hshape = Train(\"/Users/leonachen/mediapipe/CmdSpaceOX/Interface/data_folder\").getshape(self.his[-1])\n                    cv2.putText(new_img,hshape,(center[0]-250,center[1]-209), cv2.FONT_HERSHEY_COMPLEX,3, (225, 150, 255), 9, cv2.LINE_AA)\n\n        return new_img\n    def close(self):\n        self.hands.close()\n\n#get mp solution\nLM = mp.solutions.hands.HandLandmark\n\n#check function to determine what gesture\ndef check(h, a, w):\n    his = np.asarray(a)\n    if type(h) != list:\n        h = [h]\n    return all(hand in h for hand in his[-int(w):])\n\n#gesture state detector\nclass States:\n    #init\n    def __init__(self, win):\n        #set states\n        self.scrollh = -1 \n        self.state = \"none\"\n        self.ic = False\n        #get history\n        self.his = collections.deque(maxlen=100)\n        plt.switch_backend('Agg')\n\n    #run and set the states\n    def run(self, hand, landmarks, i_history):\n        #add hand to history\n        self.his.append(hand)\n        self.ic = False\n        #check fo specific gestures\n        if check(\"spiderman\", self.his, 8) and not self.state == \"scroll\":\n            self.state = \"volume\"\n        if check(\"peace\", self.his, 8)and not self.state == \"volume\":\n            self.state = \"scroll\"\n        elif check(\"call\", self.his, 5):\n            self.state = \"audio\"\n        \n        if self.state == \"scroll\" or self.state==\"volume\":\n            self.scrollh = 1 - landmarks[LM.MIDDLE_FINGER_MCP][1]\n            if check(\"palm-open\", self.his, 5):\n                self.scrollh = -1\n                self.state = \"cursor\"\n        elif self.state == \"audio\":\n            pass\n\n        else:\n            if check(\"palm-open\", self.his, 5):\n                self.state = \"cursor\"\n            elif check(\"fist\", self.his, 5):\n                self.ic = True\n            else:\n                self.state = \"none\"\n\n# if __name__ == \"__main__\":\n#     #get mph\n#     mph = MH(buffer_size=int(0.5 / 0.05))\n#     stated = States(6)\n#     handd = Train(\"/Users/leonachen/mediapipe/CmdSpaceOX/Interface/data_folder\")\n#     #open cam\n#     try:\n#         cap = cv2.VideoCapture(0)\n#         while cap.isOpened():\n#             success, image = cap.read()\n#             if not success:\n#                 print(\"Ignoring empty camera frame.\")\n#                 continue\n#             #get res\n#             res = mph.run(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n\n#             hs=None\n#             if mph.his: \n#                 hs = handd.getshape(\n#                     mph.his[-1])\n#                 stated.run(hs, mph.his[-1], mph.his)\n\n#             #draw\n#             img = mph.drawlol(image, res)\n#             cv2.imshow('cemera hehe', img)\n#             if cv2.waitKey(5) & 0xFF == 27:\n#                 break\n\n#             time.sleep(0.05)\n#     except:\n#         mph.close()\n#         cap.release()\n#         raise\n\n\n\n","sub_path":"Interface/gest.py","file_name":"gest.py","file_ext":"py","file_size_in_byte":6436,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"631050844","text":"##!/usr/bin/env python\n## encoding: utf-8\n#\"\"\"\n#Simple soundfile player used by 06_separated_threads.py example.\n\n#\"\"\"\n#from pyo import *\n\n#s = Server(duplex=0).boot()\n\n#a = SfPlayer('./Samples/test.aiff')\n#a.mix(2).out()\n\n\n#s.start()\n\n#!/usr/bin/env python\n# encoding: utf-8\n\"\"\"\nSimple soundfile player used by 06_separated_threads.py example.\n\n\"\"\"\nfrom pyo import *\n\ns = Server()\ns.setInOutDevice(2)\ns.boot()\n\na = SfPlayer('./Samples/kick.wav')\n#chor = Chorus(a, depth=[1.1,1.9], feedback=0.5, bal=0.5).out()\nchor = Chorus(a, depth=[1.1,1.9], bal=0.5).out()\na2 = chor.mix(2).out()\n\n\ns.start()","sub_path":"play_snd.py","file_name":"play_snd.py","file_ext":"py","file_size_in_byte":594,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"467937006","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 Twm(AutotoolsPackage, XorgPackage):\n    \"\"\"twm is a window manager for the X Window System.  It provides\n    titlebars, shaped windows, several forms of icon management,\n    user-defined macro functions, click-to-type and pointer-driven\n    keyboard focus, and user-specified key and pointer button bindings.\"\"\"\n\n    homepage = \"https://cgit.freedesktop.org/xorg/app/twm\"\n    xorg_mirror_path = \"app/twm-1.0.9.tar.gz\"\n\n    version(\"1.0.9\", sha256=\"1c325e8456a200693c816baa27ceca9c5e5e0f36af63d98f70a335853a0039e8\")\n\n    depends_on(\"libx11\")\n    depends_on(\"libxext\")\n    depends_on(\"libxt\")\n    depends_on(\"libxmu\")\n    depends_on(\"libice\")\n    depends_on(\"libsm\")\n\n    depends_on(\"xproto@7.0.17:\")\n    depends_on(\"bison\", type=\"build\")\n    depends_on(\"flex\", type=\"build\")\n    depends_on(\"pkgconfig\", type=\"build\")\n    depends_on(\"util-macros\", type=\"build\")\n","sub_path":"var/spack/repos/builtin/packages/twm/package.py","file_name":"package.py","file_ext":"py","file_size_in_byte":1094,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"521790072","text":"import pika, uuid, time, json, threading\nfrom datetime import datetime, timedelta\nimport os\nfrom src.entities import MesaageEntity, NotificationEntity, db_session, use_default_binding_settings\nfrom src.logger import LoggerProxy\n\ndef isoformat_to_datetime(dt_str):\n    dt, _, us= dt_str.partition(\".\")\n    dt= datetime.strptime(dt, \"%Y-%m-%dT%H:%M:%S\")\n    us= int(us.rstrip(\"Z\"), 10)\n    res = dt + timedelta(microseconds=us)\n    return res\n\nhost = os.environ.get('RABBIT_HOST', 'localhost')\nmax_retry_count = os.environ.get('MAX_RETRY_COUNT', 10)\ndefault_rabbit_config = { \n    # 'credentials': { 'username':'dev', 'password':'dev' },\n    'queue': { 'queue': 'notification_message_rpc', 'durable': True },\n    'exchange': { 'exchange': 'notification_rpc', 'exchange_type': 'fanout' },\n    # ----------------------\n    'prefetch_count': 2,\n    'routing_key': 'message',\n    'callback_queue': { 'queue': 'message_rpc_callback', 'durable': True, 'exclusive': False },\n    # 'port': 5672,\n    'host': host,\n    'no_ack': False\n    }\n\nclass MessageRpcClientError(Exception):\n    def __init__(self, message, inner=None):\n        self.message = message\n        self.inner = inner\n        Exception.__init__(inner)\n\nclass CallbackProcessingError(MessageRpcClientError):\n    def __init__(self, inner):\n        super().__init__(message=str(inner), inner=inner)\n\n    def __str__(self):\n        return 'inner({0}) - {1}'.format(type(self.inner).__name__, \n                                        str(self.inner))\n\nclass MessageRpcClient(object):\n    def __init__(self, rabbit_config=None, max_retry_c=None):\n        self.max_retry_count = max_retry_c if max_retry_c else max_retry_count\n        self.config = rabbit_config or default_rabbit_config.copy()\n        self.logger = LoggerProxy(self.__class__.__name__)\n\n    def process_notification(self, notification_id, \n                        is_test=None, include_faliled=False, all_unsuccess=False):\n        try:\n            if not isinstance(notification_id, uuid.UUID):\n                raise ValueError('MessageRpcClient - uuid is expected ofr param \"notification_id\"')\n\n            self.logger.info('send messages for notification \"{0}\". (is_test: {1}, include_faliled: {2}, all_unsuccess: {3})', \n                        notification_id, is_test, include_faliled, all_unsuccess)\n\n            created_messages_ids = []\n            with db_session:\n                n = NotificationEntity[notification_id]\n                tmp_list = n.messages.select(lambda m: (all_unsuccess and m.state_id in ['Error', 'Created']) or (include_faliled and m.state_id == 'Error') or m.state_id == 'Created')\n                tmp_id_list = [m.message_id for m in tmp_list]\n                created_messages_ids.extend(tmp_id_list)\n\n            if created_messages_ids and len(created_messages_ids) > 0:\n                for m_id in created_messages_ids:\n                    th = threading.Thread(target=RpcWorker(self.config.copy(), self.max_retry_count).send_message, args=[m_id, is_test])  # <- 1 element list\n                    th.start()\n\n            self.logger.info('{0} msgs was sended.', len(created_messages_ids))\n        except Exception as e:\n            raise MessageRpcClientError('process_notification({0}) error:\\n{1}'.format(notification_id, str(e)), e)\n\n# TODO: возможно стоит переделать на класс вида \"class CustomThread(threading.Thread)\"\nclass RpcWorker:\n    def __init__(self, rabbit_config, max_retry_c):\n        self.max_retry_count = max_retry_c\n        self.config = rabbit_config\n\n        self.routing_key = self.config.pop('routing_key') # required\n        self.callback_queue_params = self.config.pop('callback_queue') # required\n\n        self.no_ack = self.config.pop('no_ack', True)\n        self.prefetch_count = self.config.pop('prefetch_count', None)\n\n        self.queue_params = self.config.pop('queue', {})\n        self.exchange_params = self.config.pop('exchange', {})\n\n        # convert credentials dict to pika.PlainCredentials\n        _key = 'credentials'\n        if _key in self.config:\n            self.config[_key] = pika.PlainCredentials(**self.config[_key])\n\n        self.connection = None\n        self.channel = None\n\n        self.callback_queue = None\n        self.callback_queue_name = None\n\n        self.exchange = None\n        # self.exchange_name = None\n        self.queue = None\n        self.queue_name = None\n\n        self.response_received = False\n        self.log_prefix = ''\n        self.logger = LoggerProxy('{0}_{1}'.format(self.__class__.__name__, \n                                                datetime.strftime(datetime.utcnow(), '%Y%m%dT%H00')))\n\n    def log_info(self, message, *args, **kwargs):\n        self.logger.info('{0}{1}'.format(self.log_prefix, message), *args, **kwargs)\n\n    def _open_connection(self):\n        self.connection = None\n        _max_retry_count = self.max_retry_count\n        while not (self.connection and self.connection.is_open):\n            _max_retry_count -= 1\n            try:\n                self.connection = pika.BlockingConnection(pika.ConnectionParameters(**self.config))\n                self.log_info('connect to Rabbit - successful')\n            except pika.exceptions.AMQPError as e1:\n                self.log_info('[TRY RECONNECT: max_retry_count:{0}] AMQPError: {1}', self.max_retry_count, str(e1))\n                time.sleep(1)\n                self.connection = None\n                if self.max_retry_count <= 0:\n                    raise e1\n\n    def _check_connection_is_open(self, rabbit_connection: pika.BlockingConnection):\n        if not rabbit_connection or not rabbit_connection.is_open:\n            raise MessageRpcClientError('connection is closed.')\n\n    def _check_channel_is_open(self, rabbit_channel):\n        if not rabbit_channel or not rabbit_channel.is_open:\n            raise MessageRpcClientError('channel is closed.')\n\n    def _open_channel(self):\n        self._check_connection_is_open(self.connection)\n        self.channel = self.connection.channel()\n        if self.prefetch_count:\n            self.channel.basic_qos(prefetch_count=self.prefetch_count)\n\n    def _declare_callback_queue(self):\n        self._check_channel_is_open(self.channel)\n        self.callback_queue = self.channel.queue_declare(**self.callback_queue_params)\n        self.callback_queue_name = self.callback_queue.method.queue\n\n    def _main_queue_bind(self):\n        self._check_channel_is_open(self.channel)\n        if self.queue_params:\n            self.queue = self.channel.queue_declare(**self.queue_params)\n            self.queue_name = self.queue.method.queue\n        if self.exchange_params:\n            self.exchange = self.channel.exchange_declare(**self.exchange_params)\n            self.exchange_name = self.exchange_params['exchange']\n            self.exchange_type = self.exchange_params['exchange_type']\n\n        # (опциоанльно) биндим основную очередь и обменник, если их параметры указаны\n        if self.queue and self.exchange:\n            r_key = self.routing_key if self.exchange_type != 'fanout' else ''\n            self.channel.queue_bind(queue=self.queue_name, \n                                    exchange=self.exchange_name,\n                                    routing_key=r_key)\n\n    def _stop(self):\n        if self.channel:\n            self.channel.stop_consuming()\n            self.channel = None\n\n        if self.connection:\n            self.connection.close()\n            self.connection = None\n        self.log_info('was stopped.')\n\n    def on_response(self, ch, method, props, body):\n        try:\n            message_id = uuid.UUID(props.correlation_id)\n            self.log_info('receive callback for \"{0}\". body: {1}', message_id, body)\n\n            resp = json.loads(body)\n            error = resp.get('error')\n            date = resp.get('date')\n\n            # TODO: по хорошему MessageRpcClient не должен знать о сущностях в БД\n            # и сюда надо пробрасывать делегат\n            is_ok = False\n            with db_session:\n                m = MesaageEntity.get(message_id=message_id)\n                if m:\n                    if m.state_id != 'Sent':\n                        u_date = isoformat_to_datetime(date)\n                        if error:\n                            m.to_error_state(error_message=error, update_date=u_date)\n                        else:\n                            m.to_accepted_state(update_date=u_date)\n                    is_ok = True\n                else:\n                    self.log_info('message \"{0}\" not found', message_id)\n                    self.channel.basic_nack(delivery_tag=method.delivery_tag)\n\n            if is_ok:\n                if not self.no_ack:\n                    self.channel.basic_ack(delivery_tag=method.delivery_tag)\n                self.response_received = True\n        except Exception as ex:\n            if self.channel and self.channel.is_open:\n                self.channel.basic_nack(delivery_tag=method.delivery_tag)\n            raise CallbackProcessingError(ex)\n\n    def send_message(self, message_id, is_test=None):\n        try:\n            self.log_prefix = '{0} - '.format(message_id)\n            if isinstance(message_id, str):\n                message_id = uuid.UUID(message_id)\n                self.log_info('parse \"message_id\" from str to uuid', message_id)\n\n            self._open_connection()\n            self._open_channel()\n            self._declare_callback_queue()\n            self._main_queue_bind()\n\n            self.channel.basic_consume(consumer_callback=self.on_response, \n                                    no_ack=self.no_ack,\n                                    queue=self.callback_queue_name)\n\n            msg_dict = {}\n            with db_session:\n                m = MesaageEntity[message_id]\n                if m.state_id == 'Sent':\n                    raise ValueError('message [{0}] - is already processed'.format(message_id))\n                elif m.state_id == 'Processing':\n                    raise ValueError('message [{0}] - was is already sended to message broker'.format(message_id))\n                elif m.state_id in ['Error', 'Created']:\n                    m.to_processing()\n\n                msg_dict['recipient_type'] = m.recipient_type\n                msg_dict['recipients'] = m.recipient\n                msg_dict['subject'] = m.title\n                msg_dict['message'] = m.text\n                if is_test:\n                    msg_dict['is_test'] = True\n            body_str = json.dumps(msg_dict)\n            self.channel.basic_publish(exchange=self.exchange_name,\n                                    routing_key=self.routing_key,\n                                    properties=pika.BasicProperties(\n                                            reply_to=self.callback_queue_name,\n                                            correlation_id=str(message_id)\n                                            ),\n                                    body=body_str)\n            self.log_info('publish. sended data: {1}', message_id, body_str)\n\n            _cc = 0\n            while not self.response_received:\n                self.log_info('wait callback...{0}', str(_cc) if _cc > 0 else '')\n                _cc += 1\n                if _cc > 1:\n                    time.sleep(0.05)\n                self.connection.process_data_events(time_limit=None)\n\n        except MessageRpcClientError as rpc_err:\n            raise rpc_err\n        except Exception as e:\n            raise MessageRpcClientError('send_message({0}) error:\\n{1}'.format(message_id, str(e)), e)\n        finally:\n            try:\n                self._stop()\n            except:\n                pass\n","sub_path":"notification-service/src/message_rpc_client.py","file_name":"message_rpc_client.py","file_ext":"py","file_size_in_byte":11782,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"336333908","text":"from livewires import games, color\r\nimport pygame\r\nimport  random\r\nfrom flags import FLAGS\r\n\r\n\r\nclass RepairKit(games.Sprite):\r\n    \"\"\" repair kit for the player \"\"\"\r\n\r\n    FLAG = FLAGS[\"F_KIT\"]\r\n\r\n    image = games.load_image(\"textures\\\\kit.png\",\r\n                            transparent = False)\r\n\r\n    #kit parameters\r\n    LIFE_TIME = games.screen.fps*8\r\n    VALUE = 30\r\n\r\n    def __init__(self):\r\n        #random initial position\r\n        x = random.randint(20, games.screen.width - 20)\r\n        y = random.randint(20, games.screen.height - 20)\r\n        super(RepairKit, self).__init__(image = self.image,\r\n                                       x = x,\r\n                                       y = y)\r\n        self.lifetime = RepairKit.LIFE_TIME\r\n        self.value = RepairKit.VALUE\r\n\r\n    def update(self):\r\n        \"\"\" executed once per frame \"\"\"\r\n\r\n        if self.lifetime > 0:\r\n            self.lifetime -= 1\r\n        else:\r\n            self.destroy()\r\n\r\n    def collide(self):\r\n        \"\"\" repair kit destruction \"\"\"\r\n        self.destroy()","sub_path":"repair_kit.py","file_name":"repair_kit.py","file_ext":"py","file_size_in_byte":1050,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"542150823","text":"import unittest\n\n\ndef factorial(N):\n    if N == 0 or N ==1: return 1\n    ans = 2\n    for i in range(N, 2, -1):\n        ans *= i\n    return ans\n\n\ndef permutate(word):\n    if word == \"\":\n        return [\"\"]\n\n    else:\n        result = []\n        letter = word[0]\n\n        for w in permutate(word[1:]):  # for every word in the result from f(n-1)\n            for i in range(len(w) + 1):  # insert letter at all positions of the word\n                result.append(w[0:i] + letter + w[i:])\n\n        return result\n\n\nclass MyTestCase(unittest.TestCase):\n\n    def setUp(self):\n        self.data = [ \"A\", \"JOY\", \"VOICE\", \"IMPORTED\"]\n\n    def test_result_length(self):\n        for word in self.data:\n            result = permutate(word)\n            self.assertEqual(len(result), factorial(len(word)))\n\n    def test_word_length(self):\n        for word in self.data:\n            wlen = len(word)\n\n            for per in permutate(word):\n                self.assertTrue(len(per), wlen)\n\n                for ch in per:\n                    self.assertTrue(ch in word)\n\n    ## Letters must be unique for this test to pass\n    def test_no_duplicates(self):\n        for word in self.data:\n            dict = {}\n\n            for per in permutate(word):\n                self.assertTrue( per not in dict)\n                dict[per] = 1\n\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"class/Homework/6/hw6_task1_tester.py","file_name":"hw6_task1_tester.py","file_ext":"py","file_size_in_byte":1363,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"166196139","text":"from lxml import html\r\nimport requests\r\nimport mysql.connector\r\nimport string\r\n\r\na='http://www.realitica.com/?cur_page='\r\n\r\n\r\n\r\n\r\ncnx = mysql.connector.connect(user='asdadsad', password='wqewqeqe',\r\n                              host='localhost', port='3312',\r\n                              database='asdadsad')\r\n\r\n\r\n\r\nb = [\"Hrvatska\",\"Crna+Gora\",\"Srbija\",\"Bosna+i+Hercegovina\"]\r\nb1 = ['&for=Prodaja&pState=','&for=DuziNajam&pState=']\r\n\r\nfor counter in range(0,4):\r\n\r\n    for counter1 in range(0,2):\r\n        zemlja=b1[counter1] + (b[counter])\r\n\r\n        print (zemlja)\r\n\r\n\r\n        for x in range(0,5):\r\n\r\n            c = a + str(x)\r\n            c = c + zemlja\r\n            print (c)\r\n\r\n\r\n            page = requests.get(c)\r\n\r\n\r\n\r\n            tree = html.fromstring(page.text)\r\n\r\n\r\n            links = tree.xpath('//div[@class=\"thumb_div\"]/a/@href')\r\n\r\n            for j in range(0,len(links)):\r\n                new = requests.get(links[j])\r\n                tree1 = html.fromstring(new.text)\r\n\r\n                vrsta = tree1.xpath('//strong[text()=\"Vrsta\"]')\r\n                opis = tree1.xpath('//strong[text()=\"Opis\"]')\r\n\r\n                naslovi = tree1.xpath('//h3/text()')\r\n                lokacija = tree1.xpath('//strong[text()=\"Lokacija\"]')\r\n                podrucje = tree1.xpath('//strong[contains(text(),\"Podru\")]')\r\n                adresa = tree1.xpath('//strong[text()=\"Adresa\"]')\r\n\r\n                cijena = tree1.xpath('//strong[(text()=\"Cijena\") or (text()=\"Cena\")]')\r\n                godinaizgradnje = tree1.xpath('//strong[text()=\"Godina Gradnje\"]')\r\n                spavacihsoba = tree1.xpath('//strong[contains(text(),\" Soba\")]')\r\n                povrsina = tree1.xpath('//strong[contains(text(),\"Stambena Povr\")]')\r\n                zemljiste = tree1.xpath('//strong[contains(text(),\"Zemlji\")]')\r\n                datumunosa = tree1.xpath('//strong[(text()=\"Zadnja Promjena\") or (text()=\"Zadnja Promena\")]')\r\n                oglasio = tree1.xpath('//div[@id=\"aboutAuthor\"]/a/text()')\r\n                brojtel = tree1.xpath('//strong[text()=\"Mobitel\"]')\r\n\r\n\r\n                slika = tree1.xpath('//a[@class=\"fancybox\"]/@href')\r\n\r\n\r\n\r\n                for g in range(0, 1):\r\n\r\n                    opis[g] = opis[g].tail.encode('utf-8')[2:]\r\n                    vrsta[g] = vrsta[g].tail.encode('utf-8')[2:]\r\n                    if naslovi:\r\n                        naslovi[g] = naslovi[g].encode('utf-8')\r\n                    else:\r\n                        naslovi.append(\"\")\r\n                    print (naslovi[g])\r\n                    if lokacija:\r\n                        lokacija[g] = lokacija[g].tail.encode('utf-8')[2:]\r\n                    else:\r\n                        lokacija.append(\"\")\r\n                    if podrucje:\r\n                        podrucje[g] = podrucje[g].tail.encode('utf-8')[2:]\r\n                    else:\r\n                        naselje.append(\"\")\r\n                    if adresa:\r\n                        adresa[g] = adresa[g].tail.encode('utf-8')[2:]\r\n                    else:\r\n                        adresa.append(\"\")\r\n                    if cijena:\r\n                        cijena[g] = (cijena[g].tail.encode('utf-8')[2:])\r\n                    else:\r\n                        cijena.append(\"\")\r\n                    if godinaizgradnje:\r\n                        godinaizgradnje[g] = (godinaizgradnje[g].tail.encode('utf-8')[2:])\r\n                    else:\r\n                        godinaizgradnje.append(\"\")\r\n                    if spavacihsoba:\r\n                        spavacihsoba[g] = (spavacihsoba[g].tail.encode('utf-8')[2:])\r\n                    else:\r\n                        spavacihsoba.append(\"\")\r\n                    if povrsina:\r\n                        povrsina[g] = (povrsina[g].tail.encode('utf-8')[2:])\r\n                    else:\r\n                        povrsina.append(\"\")\r\n                    if zemljiste:\r\n                        zemljiste[g] = (zemljiste[g].tail.encode('utf-8')[2:])\r\n                    else:\r\n                        zemljiste.append(\"\")\r\n                    if datumunosa:\r\n                        datumunosa[g] = (datumunosa[g].tail.encode('utf-8')[2:])\r\n                    else:\r\n                        datumunosa.append(\"\")\r\n                    if oglasio:\r\n                        oglasio[g] = (oglasio[g].encode('utf-8'))\r\n                    else:\r\n                        oglasio.append(\"\")\r\n\r\n                    if brojtel:\r\n                        brojtel[g] = (brojtel[g].tail.encode('utf-8')[2:])\r\n                    else:\r\n                        brojtel.append(\"\")\r\n                    slika.append(\"\")\r\n                    slika.append(\"\")\r\n                    slika.append(\"\")\r\n                    slika.append(\"\")\r\n                    slika.append(\"\")\r\n\r\n                    if slika:\r\n\r\n                        for o in range(0,len(slika)):\r\n                                slika[o] = (slika[o].encode('utf-8'))\r\n\r\n\r\n\r\n\r\n                    cursor = cnx.cursor()\r\n\r\n                    if counter1 == 0:\r\n                        prodajailiizdavanje=\"Prodaja\"\r\n                    else:\r\n                        prodajailiizdavanje=\"Izdavanje\"\r\n\r\n\r\n                    cursor.execute(\"INSERT IGNORE INTO nekretnine (drzava,opis,vrsta,naslov,podrucje,lokacija,adresa,cijena,godinaizgradnje,spavacihsoba, \\\r\n                             povrsina,zemljiste,datumunosa,oglasio,brojtel,slika1,slika2,slika3,prodajaizdavanje) \\\r\n                             VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)\", (b[counter], opis[g], vrsta[g],naslovi[g],podrucje[g],\r\n                                                                                            lokacija[g],adresa[g],cijena[g],\r\n                                                                                            godinaizgradnje[g],spavacihsoba[g],\r\n                                                                                            povrsina[g],zemljiste[g],datumunosa[g],\r\n                                                                                            oglasio[g],brojtel[g],slika[0],slika[1],\r\n                                                                                            slika[2], prodajailiizdavanje))\r\n                    cursor.fetchone()\r\n                    print(\"affected rows = {}\".format(cursor.rowcount))\r\n\r\n                    print('\\n')\r\n                    cursor.close()\r\n\r\ncursor = cnx.cursor()\r\ncursor.execute(\"update nekretnine set datumunosa = replace(datumunosa, ',', '')\")\r\ncursor.execute(\"update nekretnine set drzava = replace(drzava, '+', ' ')\")\r\ncursor.close()\r\ncnx.close()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"scrap_ads.py","file_name":"scrap_ads.py","file_ext":"py","file_size_in_byte":6711,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"19621378","text":"import datetime\nimport sys\nimport torch\n\ndef printoneline(*argv):\n    s = ''\n    for arg in argv: s += str(arg) + ' '\n    s = s[:-1]\n    sys.stdout.write('\\r'+s)\n    sys.stdout.flush()\n\ndef dt():\n    return datetime.datetime.now().strftime('%H:%M:%S')\n\ndef freeze_model(model):\n#     model.train(False)\n    model.eval()\n    for params in model.parameters():\n        params.requires_grad = False\n\ndef unfreeze_model(model):\n    for params in model.parameters():\n        params.requires_grad = True\n\ndef KFold(n=6000, n_folds=10, shuffle=False):\n    folds = []\n    base = list(range(n))\n    for i in range(n_folds):\n        test = base[i*n//n_folds:(i+1)*n//n_folds]\n        train = list(set(base)-set(test))\n        folds.append([train,test])\n    return folds\n\ndef save_model(model, model_ckpt_path, multigpu_mode=False):\n    if multigpu_mode:\n        model_state = model.module.state_dict()\n    else:\n        model_state = model.state_dict()\n    torch.save(model_state, model_ckpt_path)\n    \ndef load_model(model, model_ckpt_path, multigpu_mode, use_cuda=True):\n    model_state_dict = torch.load(model_ckpt_path, map_location=lambda storage, loc: storage)\n    model.load_state_dict(model_state_dict)\n    if multigpu_mode:\n        model = torch.nn.DataParallel(model)\n    if use_cuda:\n        model = model.cuda()\n    return model","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1329,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"464129854","text":"# uncompyle6 version 3.7.4\n# Python bytecode 2.6 (62161)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build/bdist.linux-i686/egg/klog.py\n# Compiled at: 2011-12-03 15:03:14\nimport sys, time\nDEFAULT_FORMAT_STR = '%(info_fmt_start)s[%(time)s] %(logger)s > %(channel)s%(info_fmt_end)s %(msg_fmt_start)s%(level)s: %(msg)s%(msg_fmt_end)s'\n\nclass Output(object):\n    \"\"\"Formats the log messages and outputs them.\n  \n  .. note::\n    \n    This is an abstract class and should not be instantaniated. Instead,\n    subclasses should override the :method:`Output.log` method.\n  \n  Attributes:\n    `formatstr` (str):\n      The format string for the messages. It uses normal Python string\n      formatting syntax with keyword arguments. The available keywords\n      are:\n      \n      %(logger)s:\n        The name of the logger.\n      %(channel)s:\n        The name of the channel.\n      %(level)s:\n        The name of the level.\n      %(msg)s:\n        The actual log message.\n      %(time)s:\n        The current time, as returned by :func:\n  \"\"\"\n\n    def __init__(self, formatstr=DEFAULT_FORMAT_STR):\n        \"\"\"\n    Args:\n      `formatstr` (str):\n        The format string for the messages. Defaults to\n        DEFAULT_FORMAT_STR.\n    \"\"\"\n        self.formatstr = formatstr\n\n    def log(self, logger, channel, level, msg):\n        \"\"\"Logs a message. On the base class :class:`Output` this is just a\n    stub method.\n    \n    Args:\n      `logger` (:class:`Logger`):\n        The logger that sent the message.\n      `channel` (:class:`Channel`):\n        The channel that collected the message.\n      `level` (:class:`Level`):\n        The level of the message.\n      `msg` (str):\n        The log message\n    \"\"\"\n        pass\n\n    def format_msg(self, logger, channel, level, msg):\n        \"\"\"Formats a message using the `formatstr` attribute.\n    \n    Args:\n      `logger` (:class:`Logger`):\n        The logger that sent the message.\n      `channel` (:class:`Channel`):\n        The channel that collected the message.\n      `level` (:class:`Level`):\n        The level of the message.\n      `msg` (str):\n        The log message.\n    \n    Returns:\n      The formatted message.\n    \"\"\"\n        d = {'logger': logger.name, \n           'channel': channel.name, \n           'level': level.name, \n           'msg': msg, \n           'time': time.asctime(), \n           'info_fmt_start': level.info_fmt_start, \n           'info_fmt_end': level.info_fmt_end, \n           'msg_fmt_start': level.msg_fmt_start, \n           'msg_fmt_end': level.msg_fmt_end}\n        return self.formatstr % d\n\n\nclass StreamOutput(Output):\n    \"\"\"A subclass of :class:`Output` that sends output to a file-like\n  object.\n  \n  Attributes:\n    `formatstr` (str):\n      The format string for the messages. See the documentation for\n      :class:`Output` for information on its format.\n    `stream` (file-like object):\n      The object to send output to. It must have a `write()` method.\n    `use_ansi_escapes` (bool):\n      Whether to use ANSI escape codes to show visually formatted\n      messages. Only really useful if the output stream is stdout.\n  \n  \"\"\"\n\n    def __init__(self, stream=None, formatstr=DEFAULT_FORMAT_STR):\n        \"\"\"\n    \n    Args:\n      `stream` (file-like object):\n        The object to send output to. It must have a `write()` method.\n      `formatstr` (str):\n        The format string for the messages. Defaults to\n        DEFAULT_FORMAT_STR.\n      `use_ansi_escapes` (bool):\n        Whether to use ANSI escape codes to show visually formatted\n        messages. Only really useful if the output stream is stdout.\n      \n    \"\"\"\n        if stream is None:\n            stream = sys.stdout\n        self.formatstr = formatstr\n        self.stream = stream\n        return\n\n    def log(self, logger, channel, level, msg):\n        \"\"\"Logs a message.\n    \n    Args:\n      `logger` (:class:`Logger`):\n        The logger that sent the message.\n      `channel` (:class:`Channel`):\n        The channel that collected the message.\n      `level` (:class:`Level`):\n        The level of the message.\n      `msg` (str):\n        The log message.\n    \"\"\"\n        formatted = self.format_msg(logger, channel, level, msg)\n        self.stream.write(formatted + '\\n')\n\n\nclass FileOutput(StreamOutput):\n\n    def __init__(self, fname, formatstr=DEFAULT_FORMAT_STR):\n        self.formatstr = formatstr\n        self.stream = open(fname, 'w')\n\n    def __del__(self):\n        self.stream.close()\n\n\nclass Filter(object):\n\n    def __init__(self):\n        pass\n\n    def allow(self, logger, channel, level, msg):\n        return True\n\n\nclass LevelFilter(object):\n\n    def __init__(self, minlevel=0.0, maxlevel=1.0):\n        if isinstance(minlevel, str):\n            minlevel = LevelManager.get(minlevel)\n        if isinstance(minlevel, Level):\n            minlevel = minlevel.priority\n        if isinstance(maxlevel, str):\n            maxlevel = LevelManager.get(maxlevel)\n        if isinstance(maxlevel, Level):\n            maxlevel = maxlevel.priority\n        self.minlevel = minlevel\n        self.maxlevel = maxlevel\n\n    def allow(self, logger, channel, level, msg):\n        return level.priority >= self.minlevel and level.priority <= self.maxlevel\n\n\nclass Channel(object):\n\n    def __init__(self, name, outputs=None, filters=None):\n        if outputs is None:\n            outputs = [\n             StreamOutput()]\n        if filters is None:\n            filters = []\n        self.name = name\n        self.outputs = outputs\n        self.filters = filters\n        return\n\n    def add_output(self, output):\n        self.outputs.append(output)\n\n    def add_filter(self, filter):\n        self.filters.append(filter)\n\n    def log(self, logger, level, msg, channel=None):\n        if channel is None:\n            channel = self\n        for filter in self.filters:\n            if not filter.allow(logger, channel, level, msg):\n                return\n\n        for output in self.outputs:\n            output.log(logger, channel, level, msg)\n\n        return\n\n\nclass RedirectionChannel(Channel):\n\n    def __init__(self, name, dest):\n        self.name = name\n        if isinstance(dest, str):\n            dest = ChannelManager.get(dest)\n            if dest is None:\n                raise ValueError('Invalid channel')\n        self.dest = dest\n        return\n\n    def log(self, logger, level, msg):\n        self.dest.log(logger, level, msg, self)\n\n\nclass Level(object):\n\n    def __init__(self, name, priority):\n        self.name = name.upper()\n        self.priority = priority\n        self.info_fmt_start = ''\n        self.info_fmt_end = ''\n        self.msg_fmt_start = ''\n        self.msg_fmt_end = ''\n\n\ndef set_ansi_colour(level, clr):\n    level.msg_fmt_start = '\\x1b[3%dm' % clr\n    level.msg_fmt_end = '\\x1b[m'\n    return level\n\n\nclass ObjectManagerClass(object):\n\n    def __init__(self):\n        self.objects = {}\n\n    def add(self, obj):\n        self.objects[obj.name.lower()] = obj\n\n    def get(self, name):\n        return self.objects.get(name.lower(), None)\n\n\nChannelManager = ObjectManagerClass()\nChannelManager.add(Channel('main'))\nLevelManager = ObjectManagerClass()\nLevelManager.add(set_ansi_colour(Level('DEBUG', 0.1), 4))\nLevelManager.add(set_ansi_colour(Level('INFO', 0.3), 6))\nLevelManager.add(set_ansi_colour(Level('WARNING', 0.5), 3))\nLevelManager.add(set_ansi_colour(Level('ERROR', 0.8), 1))\nLevelManager.add(set_ansi_colour(Level('FATAL', 1.0), 5))\n\nclass Logger(object):\n\n    def __init__(self, name):\n        self.name = name\n\n    def log(self, channel, level, msg):\n        if isinstance(channel, str):\n            channel = ChannelManager.get(channel)\n            if channel is None:\n                raise ValueError('Invalid channel')\n        if isinstance(level, str):\n            level = LevelManager.get(level)\n            if level is None:\n                raise ValueError('Invalid level')\n        channel.log(self, level, msg)\n        return","sub_path":"pycfiles/KLog-0.4.0-py2.6/klog.py","file_name":"klog.py","file_ext":"py","file_size_in_byte":7957,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"524606016","text":"# -*- coding:utf8 -*-\nfrom django.conf import settings\nfrom django.conf.urls.defaults import *\nfrom django.views.generic.simple import direct_to_template\nfrom django.conf.urls.defaults import patterns, url\nfrom TripleMag.apps.mall.models import Category,CategoryFirst,CategorySecond,Product,mall_index,mall_ad\nfrom TripleMag.apps.management.models import notice_mall\nfrom TripleMag.apps.mall.views import *\nfrom TripleMag.apps.mall.forms import MallIndexForm,MallAdForm,NoticeMallForm\n\n\nurlpatterns = patterns('',\n    url(r'mall_management/$', index,{'template_name':'mall/mall_base.html'},'mall_management_index'),\n url(r'^category_change/$',change_category,name = \"change_category\"),\n    url(r'mall_management/category_index/$',category_index,{'template_name':'management/mall/mall_index_mag.html'},name='mall_category_index'),\n    url(r'^mall_management/delete_category_1st/(?P<c_id>.*)/$',delete_category,{'model':Category}),\n    url(r'^mall_management/delete_category_2nd/(?P<c_id>.*)/$',delete_category,{'model':CategoryFirst}),\n    url(r'^mall_management/delete_category_3rd/(?P<c_id>.*)/$',delete_category,{'model':CategorySecond}),\n    #删除产品\n    url(r'^mall_management/delete_product/(?P<p_id>.*)/$',delete_category,{'model':Product}),\n    #商城主页设置\n    url(r'^mall_home_mag/(?P<del_id>.*)$',mall_home_mag,{'model':mall_index,'form':MallIndexForm,'template_name':'management/mall/mall_home_mag.html'},name=\"mall_home_mag\"),\n    #商城推广设置\n    url(r'^mall_ad/(?P<del_id>.*)$',mall_home_mag,{'model':mall_ad,'form':MallAdForm,'template_name':\"management/mall/mall_ad.html\"},name=\"mall_ad\"),\n    #添加商城首页图片\n    url(r'add_home_mag/$',add_home_mag,name=\"add_home_mag\"),\n    #添加商城公告\n    url(r'add_mall_notice/$',add_mall_notice,name=\"add_mall_notice\"),\n    #添加商城推广\n    url(r'add_mall_ad/$',add_mall_ad,name=\"add_mall_ad\"),\n    \n    #商城公告设置\n    url(r'^mall_notice/(?P<del_id>.*)$',mall_home_mag,{'model':notice_mall,'form':NoticeMallForm,'template_name':\"management/mall/mall_new_Announcement.html\"},name=\"mall_notice\"),\n    url(r'^mall_management/product_list/$',product_list,{'template_name':'management/mall/product_list.html'},name='product_list'),\n    url(r'mall_management/category/(?P<style>.*)/$', category,name = \"mall_category\"),\n#    url(r'mall_management/category/add/(?P<style>.*)/$',add_category, name=\"mall_add_category\"),\n#    url(r'mall_management/delete_category/(?P<category_id>.*)$', delete_category, name=\"mall_delete_category\"),\n    url(r'manage_product/$',manage_product,name='manage_product'),\n    url(r'mall_collection/$',mall_collection,name='mall_collection'),\n    url(r'mall_register/$',register,name='mall_register'),\n    url(r'orders/$',order_management,name=\"order_management\"),\n    url(r'change_password/$',change_password,name=\"change_password\"),\n    #我的收藏界面\n    url(r'my_collections/$',my_collections,name=\"my_collections\"),\n    url(r'^comment/(?P<shop_record_id>.*)/$',comment,name=\"comment\"),\n    url(r'^search/$',search,name=\"search\"),\n    url(r'^change_mall_announcement/$',change_mall_announcement,name=\"change_mall_announcement\"),\n    url(r'^mall_notice_detail/$',mall_notice,name=\"mall_notice\"),\n    url(r'^order_cancle/$',cancle_order,name=\"cancle_order\"),\n)\n#urlpatterns = patterns('',\n\n#    url(r'^webshop/$','TripleMag.')\n\n\n#)\n","sub_path":"TripleMag/apps/mall/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":3364,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"444389599","text":"import sys\r\nimport json as JsonParser\r\nfrom bottle import route, run, get, post, request\r\n\r\n\r\n@route('/hello')\r\ndef hello():\r\n    return \"Hello World!\"\r\n\r\n@route('/')\r\ndef home():\r\n    return \"I am in the home page\"\r\n\r\n@post('/input') # or @route('/input', method='POST')\r\ndef input():\r\n    try:\r\n        dictData = request.json # This returns Dictionary Data\r\n        print(dictData)\r\n        for x in dictData:\r\n            print(x + \": \" + dictData[x])\r\n\r\n        return dictData\r\n    except:\r\n        return \"Error\"\r\n    \r\n\r\nrun(host='localhost', port=8080, debug=True)\r\n\r\n","sub_path":"Lab_Excercises/Prelabs/bottle-scripts/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":577,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"469000854","text":"\"\"\" Logging utilities. \"\"\"\nfrom pkg_resources import get_distribution\nfrom pkg_resources import resource_filename\nfrom pkg_resources import DistributionNotFound\nimport logging\nimport logging.config\nimport pprint\nfrom appdirs import AppDirs\nimport os\nfrom dautils import conf\nfrom dautils import options\nimport inspect\n\n\ndef env_logger():\n    \"\"\" Creates a configurable logger.\n    The environment variable **DAUTIL_LOGGER** controls\n    whether the logger is enabled.\n    If the value of the variable is *, then all loggers\n    will be enabled, otherwise the calling function name\n    is matched against `DAUTIL_LOGGER`.\n    Enabling/disabling the logger may be influenced by\n    an underlying framework.\n\n    :returns: A configurable logger.\n    \"\"\"\n    frame = inspect.stack()[1]\n    func_name = frame[3]\n    env_val = os.environ.get('DAUTIL_LOGGER')\n    log_enabled = False\n\n    if env_val:\n        if env_val == '*':\n            log_enabled = True\n        elif func_name in env_val:\n            log_enabled = True\n\n    logger = conf_logger(func_name)\n\n    if not log_enabled:\n        logger.addHandler(logging.NullHandler())\n        # Needed for ipython notebook sessions\n        logger.propagate = False\n\n    return logger\n\n\ndef get_logger(name):\n    \"\"\" Creates a logger using an internal configuration file.\n\n    :param name: The name of the logger.\n\n    :returns: The configured logger.\n    \"\"\"\n    log_config = resource_filename(__name__, 'log.conf')\n    logging.config.fileConfig(log_config)\n    logger = logging.getLogger(name)\n\n    return logger\n\n\ndef conf_logger(name):\n    \"\"\" Creates a logger using a configuration file\n    provided by the user in the current working directory.\n    If a configuration file is not found, dautils uses\n    basic configuration.\n\n    :param name: The name of the logger.\n\n    :returns: The configured logger.\n    \"\"\"\n    # TODO check for conf file in appropriate dir\n    conf_file = 'dautil_log.conf'\n\n    if conf.file_exists(conf_file):\n        logging.config.fileConfig(conf_file, disable_existing_loggers=False)\n    else:\n        logging.basicConfig()\n\n    return logging.getLogger(name)\n\n\ndef shorten(module_name):\n    \"\"\" Helper function which shortens a module name\n    using the first occurrence of a dot. For example\n    pandas.io.wb is shortened to pandas.\n\n    :param module_name: The name of the module.\n\n    :returns: The shortened name.\n\n    >>> from dautils import log_api\n    >>> log_api.shorten('pandas.io.wb')\n    'pandas'\n    \"\"\"\n    dot_i = module_name.find('.')\n\n    return module_name[:dot_i]\n\n\ndef log(modules, name):\n    \"\"\" Logs the versions of imported modules in a\n    best effort fashion. Some common modules are excluded.\n\n    :param modules: A list of modules as available in sys.modules.\n    :param name: The name of the logger.\n\n    :returns: A dictionary with modules as keys and versions as values.\n    \"\"\"\n    skiplist = ['pkg_resources', 'distutils', 'zmq']\n\n    logger = get_logger(name)\n    logger.debug('Inside the log function')\n    versions = {}\n\n    for k in modules.keys():\n        str_k = str(k)\n\n        if '.version' in str_k:\n            short = shorten(str_k)\n\n            if short in skiplist:\n                continue\n\n            try:\n                ver = get_distribution(short).version\n                logger.info('%s=%s' % (short, ver))\n                versions[short] = ver\n            except ImportError:\n                logger.warn('Could not import', short)\n            except DistributionNotFound as e:\n                logger.warn('Could not find distribution %s', e)\n\n    return versions\n\n\nclass VersionsLogFileHandler(logging.FileHandler):\n    \"\"\" A specialized file handler for the logging of software versions. \"\"\"\n    def __init__(self, fName):\n        dirs = AppDirs(options.APP_DIR, \"Ivan Idris\")\n        path = dirs.user_log_dir\n        print(path)\n\n        if not os.path.exists(path):\n            os.mkdir(path)\n\n        super(VersionsLogFileHandler, self).__init__(\n              os.path.join(path, fName))\n\n\nclass Printer():\n    \"\"\" Utility class for pretty printing.\n\n    :ivar nelems: Max number of elements in a list to print.\n    \"\"\"\n    def __init__(self, modules=None, name=None, nelems=-1):\n        if modules and name:\n            log(modules, name)\n\n        self.nelems = nelems\n\n    def print(self, *args, **kwargs):\n        \"\"\" Pretty prints a message. \"\"\"\n        for arg in args:\n            if self.nelems > 0 and isinstance(arg, list):\n                pprint.pprint(self.compress_mid(arg), **kwargs)\n            else:\n                pprint.pprint(arg, **kwargs)\n\n        print()\n\n    def compress_mid(self, alist, replace='...'):\n        \"\"\" Takes the head, mid and the tail of a list \\\n            and discards the rest. For example\n\n        >>> from dautils import log_api\n        >>> p = log_api.Printer(nelems=3)\n        >>> p.compress_mid(list(range(5)))\n        [0, '...', 2, '...', 4]\n\n        :param alist: A list.\n        :param replace: A string used to replace discarded elements.\n\n        :returns: The list with discarded values replaced.\n        \"\"\"\n        compressed = alist.copy()\n        N = len(alist)\n\n        if N > self.nelems:\n            half = self.nelems//2\n            mid = N//2\n            logger = env_logger()\n            compressed = alist[:half]\n\n            for i in range(half, N - half):\n                if i != mid and compressed[-1] != replace:\n                    compressed.append(replace)\n                    logger.debug('Compressed %s', compressed)\n                if i == mid:\n                    compressed.append(alist[mid])\n\n            compressed.extend(alist[-half:])\n\n        return compressed\n","sub_path":"dautils/log_api.py","file_name":"log_api.py","file_ext":"py","file_size_in_byte":5676,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"82809562","text":"import os, os.path\nimport numpy as np\n\n# Images Folder Path\nimageDir = \"objimages/\"\n\n# Images valid extensions\nvalid_image_extensions = [\".jpg\", \".jpeg\", \".png\"]\nvalid_image_extensions = [item.lower() for item in valid_image_extensions]\n\n# Helper function to get all the objects based on the images \ndef getObjects():\n    image_path_list = []\n    obj_list = []\n    obj_dict_list = []\n    \n    #create a list all files in directory and\n    #append files with a vaild extention to image_path_list\n    for file in os.listdir(imageDir):\n        filename = os.path.splitext(file)[0]\n        extension = os.path.splitext(file)[1]\n        if extension.lower() not in valid_image_extensions:\n            continue\n        image_path_list.append(os.path.join(imageDir, file))\n        obj_list.append(filename.split('_')[0])\n\n    # get only the unique objects\n    obj_list = np.unique(obj_list).tolist()\n\n    # get the objects name\n    for name in obj_list:\n        images = []\n        for image in image_path_list:\n            if(name == image.split('_')[0].split('/')[1]):\n                images.append(image)\n        \n        # create the dict with the obj name and image\n        obj_dict = {'name': name.replace('@', ' '), 'images': images}\n        obj_dict_list.append(obj_dict)\n    \n    return obj_dict_list\n\n# Helper function to check the objects folder is empty\ndef objectsExist():\n    obj_dict_list = getObjects()\n    return len(obj_dict_list) > 0\n\n# Helper function to clear the objects folder\ndef clearObjects():\n    for the_file in os.listdir(imageDir):\n        extension = os.path.splitext(the_file)[1]\n\n        if extension.lower() in valid_image_extensions:\n            file_path = os.path.join(imageDir, the_file)\n            try:\n                if os.path.isfile(file_path):\n                    os.unlink(file_path)\n            except Exception as e:\n                print(e)\n","sub_path":"app_oficial/getobjects.py","file_name":"getobjects.py","file_ext":"py","file_size_in_byte":1883,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"183892455","text":"from JumpScale import j\nimport time\n\n\nbase = j.tools.cuisine._getBaseClass()\n\n\nclass CuisinePortal(base):\n\n    def __init__(self, executor, cuisine):\n        self._executor = executor\n        self._cuisine = cuisine\n        self._config = None\n        self.portal_dir = j.sal.fs.joinPaths(self._cuisine.core.dir_paths[\"appDir\"], \"portals/\")\n        self.main_portal_dir = j.sal.fs.joinPaths(self.portal_dir, 'main')\n        self._cuisine.core.dir_ensure(self.main_portal_dir)\n        self.cfg_path = j.sal.fs.joinPaths(self.main_portal_dir, 'config.hrd')\n\n    def configure(self, mongodbip=\"127.0.0.1\", mongoport=27017, influxip=\"127.0.0.1\",\n                  influxport=8086, grafanaip=\"127.0.0.1\", grafanaport=3000, production=True):\n\n        # go from template dir which go the file above\n        content = self._cuisine.core.file_read('$tmplsDir/cfg/portal/config.hrd')\n\n        hrd = j.data.hrd.get(content=content, prefixWithName=False)\n\n        # ITS ALREADY THE DEFAULT IN THE CONFIG DIR\n        # hrd.set('param.cfg.appdir', j.sal.fs.joinPaths(self.portal_dir, 'portalbase'))\n\n        hrd.set('param.mongoengine.connection', {'host': mongodbip, 'port': mongoport})\n        hrd.set('param.cfg.influx', {'host': influxip, 'port': influxport})\n        hrd.set('param.cfg.grafana', {'host': grafanaip, 'port': grafanaport})\n        hrd.set('param.cfg.production', production)\n\n        if \"darwin\" in self._cuisine.platformtype.osname:\n            hrd.set('param.cfg.port', '8200')\n        self._config = hrd\n\n    def install(self, start=True, installdeps=False, branch='master'):\n        \"\"\"\n        grafanaip and port should be the external ip of the machine\n        Portal install will only install the portal and libs. No spaces but the system ones will be add by default.\n        To add spaces and actors, please use addSpace and addactor\n        \"\"\"\n        # set encoding to utf-8\n        self._cuisine.bash.environSet(\"LC_ALL\", \"C.UTF-8\")\n        self._cuisine.bash.environSet(\"LANG\", \"C.UTF-8\")\n\n        # install the dependencies if required\n        if installdeps:\n            self.installDeps()\n\n        # pull repo with required branch ; then link dirs and files in required places\n        self.getcode(branch=branch)\n        self.linkCode()\n\n        if start:\n            self.start()\n\n    def installDeps(self):\n        \"\"\"\n        make sure new env arguments are understood on platform\n        \"\"\"\n        self._cuisine.development.pip.ensure()\n\n        deps = \"\"\"\n        cffi==1.5.2\n        setuptools\n        aioredis\n        # argh\n        bcrypt\n        Beaker\n        blinker\n        blosc\n        # Cerberus\n        # certifi\n        # cffi\n        # click\n        # credis\n        # crontab\n        # Cython\n        decorator\n        # docker-py\n        # dominate\n        # ecdsa\n        # Events\n        # Flask\n        # Flask-Bootstrap\n        # Flask-PyMongo\n        # gitdb\n        gitlab3\n        # GitPython\n        greenlet\n        # hiredis\n        html2text\n        # influxdb\n        # ipdb\n        # ipython\n        # ipython-genutils\n        itsdangerous\n        Jinja2\n        # marisa-trie\n        MarkupSafe\n        mimeparse\n        mongoengine==0.10.6\n        msgpack-python\n        netaddr\n        # paramiko\n        # path.py\n        pathtools\n        # pexpect\n        # pickleshare\n        psutil\n        pyjwt\n        # psycopg2\n        # ptyprocess\n        # pycparser\n        # pycrypto\n        # pycurl\n        # pygo\n        # pygobject\n        pylzma\n        pymongo==3.2.1\n        pystache\n        # python-apt\n        python-dateutil\n        pytoml\n        pytz\n        PyYAML\n        # pyzmq\n        # redis\n        # reparted\n        requests\n        simplegeneric\n        simplejson\n        six\n        # smmap\n        # SQLAlchemy\n        traitlets\n        ujson\n        # unattended-upgrades\n        urllib3\n        visitor\n        watchdog\n        websocket\n        websocket-client\n        Werkzeug\n        wheel\n        # zmq\n        pillow\n        gevent\n        flask\n        Flask-Bootstrap\n        snakeviz\n        \"\"\"\n\n        if not \"darwin\" in self._cuisine.platformtype.osname:\n            self._cuisine.package.ensure('build-essential')\n            self._cuisine.package.ensure('libssl-dev')\n            self._cuisine.package.ensure('libffi-dev')\n            self._cuisine.package.ensure('python3-dev')\n\n        self._cuisine.development.pip.multiInstall(deps)\n\n        if \"darwin\" in self._cuisine.platformtype.osname:\n            self._cuisine.core.run(\"brew install libtiff libjpeg webp little-cms2\")\n            self._cuisine.core.run(\"brew install snappy\")\n            self._cuisine.core.run('CPPFLAGS=\"-I/usr/local/include -L/usr/local/lib\" pip3 install python-snappy')\n        else:\n            self._cuisine.package.multiInstall(['libjpeg-dev', 'libffi-dev', 'zlib1g-dev'])\n\n        # snappy install\n        if not \"darwin\" in self._cuisine.platformtype.osname:\n            self._cuisine.package.ensure('libsnappy-dev')\n            self._cuisine.package.ensure('libsnappy1v5')\n\n        self._cuisine.development.pip.install('python-snappy')\n\n    def getcode(self, branch='master'):\n        self._cuisine.development.git.pullRepo(\n            \"https://github.com/Jumpscale/jumpscale_portal8.git\", branch=branch)\n\n    def linkCode(self):\n        self._cuisine.bash.environSet(\"LC_ALL\", \"C.UTF-8\")\n\n        destjslib = self._cuisine.core.dir_paths['jsLibDir']\n\n        # _, destjslib, _ = self._cuisine.core.run(\"js --quiet 'print(j.do.getPythonLibSystem(jumpscale=True))'\",\n        #                                          showout=False)\n        #\n        # if \"darwin\" in self._cuisine.platformtype.osname:\n        #     # Needs refining,In osx destjslib='load dirs\\n/usr/local/lib/python3.5/site-packages/JumpScale/'\n        #     destjslib = destjslib.split(\"\\n\")[1]\n\n        if self._cuisine.core.file_exists(destjslib):\n            self._cuisine.core.file_link(\"%s/github/jumpscale/jumpscale_portal8/lib/portal\" % self._cuisine.core.dir_paths[\"codeDir\"],\n                                         \"%s/portal\" % destjslib, symbolic=True, mode=None, owner=None, group=None)\n\n        self._cuisine.core.run(\"js --quiet 'j.application.reload()'\", showout=False, die=False)\n\n        if not self.portal_dir.endswith(\"/\"):\n            self.portal_dir += '/'\n        self._cuisine.core.dir_ensure(self.portal_dir)\n\n        CODE_DIR = self._cuisine.core.dir_paths[\"codeDir\"]\n        self._cuisine.core.file_link(\"%s/github/jumpscale/jumpscale_portal8/jslib\" % CODE_DIR,\n                                     '%s/jslib' % self.portal_dir)\n        self._cuisine.core.dir_ensure(j.sal.fs.joinPaths(self.portal_dir, 'portalbase'))\n        self._cuisine.core.file_link(\"%s/github/jumpscale/jumpscale_portal8/apps/portalbase/system\" % CODE_DIR,\n                                     '%s/portalbase/system' % self.portal_dir)\n        self._cuisine.core.file_link(\"%s/github/jumpscale/jumpscale_portal8/apps/portalbase/wiki\" % CODE_DIR,\n                                     '%s/portalbase/wiki' % self.portal_dir)\n        self._cuisine.core.file_link(\"%s/github/jumpscale/jumpscale_portal8/apps/portalbase/macros\" %\n                                     CODE_DIR, '%s/portalbase/macros' % self.portal_dir)\n        self._cuisine.core.file_link(\"%s/github/jumpscale/jumpscale_portal8/apps/portalbase/templates\" %\n                                     CODE_DIR, '%s/portalbase/templates' % self.portal_dir)\n\n        self._cuisine.core.dir_ensure(self.main_portal_dir)\n\n        self._cuisine.core.dir_ensure('%s/base/home/.space' % self.main_portal_dir)\n        self._cuisine.core.file_ensure('%s/base/home/home.md' % self.main_portal_dir)\n\n        self._cuisine.core.dir_ensure('$tmplsDir/cfg/portal')\n        self._cuisine.core.file_copy(j.sal.fs.joinPaths(CODE_DIR, 'github/jumpscale/jumpscale_portal8/apps/portalbase/config.hrd'),\n                                     '$tmplsDir/cfg/portal/config.hrd')\n\n        # copy portal_start.py\n        self._cuisine.core.file_copy(j.sal.fs.joinPaths(CODE_DIR, 'github/jumpscale/jumpscale_portal8/apps/portalbase/portal_start.py'),\n                                     self.main_portal_dir)\n        self._cuisine.core.file_copy(\"%s/jslib/old/images\" % self.portal_dir,\n                                     \"%s/jslib/old/elfinder\" % self.portal_dir, recursive=True)\n        # link for ays\n        self._cuisine.core.file_link(source='$codeDir/github/jumpscale/jumpscale_portal8/apps/portalbase/AYS81',\n                                     destination='$appDir/portals/main/base/AYS81')\n\n    def addSpace(self, spacepath):\n        spacename = j.sal.fs.getBaseName(spacepath)\n        dest_dir = j.sal.fs.joinPaths(self._cuisine.core.dir_paths[\n            'appDir'], 'portals', 'main', 'base', spacename)\n        self._cuisine.core.file_link(spacepath, dest_dir)\n\n    def addactor(self, actorpath):\n        actorname = j.sal.fs.getBaseName(actorpath)\n        dest_dir = j.sal.fs.joinPaths(self._cuisine.core.dir_paths[\n            'appDir'], 'portals', 'main', 'base', actorname)\n        self._cuisine.core.file_link(actorpath, dest_dir)\n\n    def serviceconnect(self, hrd):\n        dest_cfg = j.sal.fs.joinPaths(self._cuisine.core.dir_paths['varDir'],\n                                      'cfg', \"portals\", \"main\", \"config.hrd\")\n        self._cuisine.core.file_write(dest_cfg, str(hrd))\n\n    def start(self, passwd=None):\n        \"\"\"\n        Start the portal\n        passwd : if not None, change the admin password to passwd after start\n        \"\"\"\n        dest_dir = j.sal.fs.joinPaths(self._cuisine.core.dir_paths['varDir'], 'cfg', 'portals', 'main')\n        self._cuisine.core.dir_ensure(dest_dir)\n\n        if not self._config:\n            self.configure()\n\n        self.serviceconnect(self._config)\n        cmd = \"jspython portal_start.py\"\n        self._cuisine.processmanager.ensure('portal', cmd=cmd, path=j.sal.fs.joinPaths(self.portal_dir, 'main'))\n\n        if passwd is not None:\n            self.set_admin_password(passwd)\n\n    def stop(self):\n        self._cuisine.processmanager.stop('portal')\n\n    def set_admin_password(self, passwd):\n        # wait for the admin user to be created by portal\n        timeout = 60\n        start = time.time()\n        resp = self._cuisine.core.run('jsuser list', showout=False)[1]\n        while resp.find('admin') == -1 and start + timeout > time.time():\n            try:\n                time.sleep(2)\n                resp = self._cuisine.core.run('jsuser list', showout=False)[1]\n            except:\n                continue\n\n        if resp.find('admin') == -1:\n            self._cuisine.core.run('jsuser add --data admin:%s:admin:admin@mail.com:cockpit' % passwd)\n        else:\n            self._cuisine.core.run('jsuser passwd -ul admin -up %s' % passwd)\n","sub_path":"lib/JumpScale/tools/cuisine/apps/CuisinePortal.py","file_name":"CuisinePortal.py","file_ext":"py","file_size_in_byte":10810,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"195542139","text":"#coding=utf-8  \nimport tornado.web\nimport tornado.ioloop\nimport tornado.httpserver\nimport tornado.options\nimport os\nimport datetime\nimport time\nimport json\nfrom tornado.web import RequestHandler\nfrom tornado.options import define, options\nfrom tornado.websocket import WebSocketHandler\n\n\n\n\ndefine(\"port\", default=1213, type=int)\n\nclass ChatHandler(WebSocketHandler):\n\n    #建立websocket连接\n    def open(self):\n        # self.write_message(\"connected\")\n        print(\"new websocket guest connected\")\n        pass\n\n    def data_cleaning(self,origin_data,data_type):\n        \n        info = origin_data.split(\"\\n\")\n        data_set = []\n        takeOrNot=False\n        for line in info:\n            if ('Output info' in line):\n                takeOrNot = True\n            elif ('Input info' in line):\n                takeOrNot = False    \n            elif ('timeStamp' in line):\n                if(takeOrNot):\n                    meta_data = int(line.split(':')[1].strip()[0:13])\n                    data_set.append([meta_data])\n            elif ('mtktscpu :' in line):\n                if(takeOrNot):\n                    meta_data = int(line.split(':')[1].strip())\n                    data_set[-1].append(meta_data/1000)\n            elif ('CPU#0:' in line):\n                if(takeOrNot):\n                    meta_data = int(line.split(':')[1].strip())\n                    data_set[-1].append(meta_data/10000)\n            elif ('CPU#4:' in line):\n                if(takeOrNot):\n                    meta_data = int(line.split(':')[1].strip())\n                    data_set[-1].append(meta_data/10000)\n            elif ('current power =' in line):\n                if(takeOrNot):\n                    meta_data = int(line.split('current power =')[1].strip()[0:4])\n                    data_set[-1].append(meta_data/10)\n            elif ('lane.alg_fps' in line):\n                if(takeOrNot):\n                    meta_data = line.split('lane.alg_fps')[1].strip()\n                    meta_data_arr = meta_data.split(' ')\n                    # 脏数据\n                    if(meta_data_arr[0] =='fps'):\n                        data_set.append([int(meta_data_arr[1][0:13]),float(meta_data_arr[2]),'lane_alg_fps'])\n                    else:\n                        data_set.append([int(meta_data_arr[0][0:13]),float(meta_data_arr[1]),'lane_alg_fps'])\n            elif ('lane.fps' in line):\n                if(takeOrNot):\n                    meta_data = line.split('lane.fps')[1].strip()\n                    meta_data_arr = meta_data.split(' ')\n                    data_set.append([int(meta_data_arr[0][0:13]),float(meta_data_arr[1]),'lane_fps'])\n                    # data_set.append((line.split('lane.fps')[1].strip().split(' ')))\n            elif ('vehicle.alg_fps' in line):\n                if(takeOrNot):\n                    meta_data = line.split('vehicle.alg_fps')[1].strip()\n                    meta_data_arr = meta_data.split(' ')\n                    data_set.append([int(meta_data_arr[0][0:13]),float(meta_data_arr[1]),'vehicle_alg_fps'])\n            elif ('v1.process_fps' in line):\n                if(takeOrNot):\n                    meta_data = line.split('v1.process_fps')[1].strip()\n                    meta_data_arr = meta_data.split(' ')\n                    try:\n                        data_set.append([int(meta_data_arr[0][0:13]),float(meta_data_arr[1]),'v1.process_fps'])     \n                    except:\n                        pass\n        data_set.sort()\n        mess = json.dumps(data_set,ensure_ascii=False)\n        if(data_type==\"sing\"):\n            self.write_message(\"sing\"+mess)\n        elif(data_type==\"mult\"):\n            self.write_message(\"mult\"+mess)\n\n    #websocket接受信息\n    def on_message(self, message):\n        if(message[0:4]==\"sing\"):\n            mess = message[4:]\n            self.data_cleaning(mess,'sing')\n    \n        elif(message[0:4]==\"mult\"):\n            mess = message[4:]\n            self.data_cleaning(mess,'mult')\n\n    def on_send(self,message):\n        self.write_message(message)\n\n    #关闭websocket    \n    def on_close(self):\n        print('connection closed')\n        pass\n\n    #允许跨域请求\n    def check_origin(self, origin):\n        return True  \n\nif __name__ == '__main__':\n    tornado.options.parse_command_line()\n    app = tornado.web.Application([\n            (r\"/chat\", ChatHandler)],\n        static_path = os.path.join(os.path.dirname(__file__), \"static\"),\n        template_path = os.path.join(os.path.dirname(__file__), \"template\"),\n        debug = True\n        )\n    http_server = tornado.httpserver.HTTPServer(app)\n    http_server.listen(options.port)\n    tornado.ioloop.IOLoop.current().start()\n","sub_path":".history/main_server_20180720193300.py","file_name":"main_server_20180720193300.py","file_ext":"py","file_size_in_byte":4671,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"632370176","text":"# tokenizer for a simple expression evaluator for\n# numbers and +,-,*,/\n# ------------------------------------------------------------\nimport lex\n\n# List of token names.   This is always required\ntokens = (\n    'NUMBER',\n    'OP',\n    'LPAREN',\n    'RPAREN',\n    'VARIABLE',\n    'TYPE',\n    'WHITE_SPACE'\n)\n\n# Regular expression rules for simple tokens\n\nt_LPAREN  = r'\\('\nt_RPAREN  = r'\\)'\n\n# A regular expression rule with some action code\ndef t_OP(t):\n    r'\\+|-|\\*|/'\n    return t\ndef t_WHITE_SPACE(t):\n    r' \\r|\\n|\\r\\n|\\t'\n    return t\ndef t_NUMBER(t):\n    r'\\d+'\n    t.value = int(t.value)\n    return t\ndef t_VARIABLE(t):\n    r'[a-zA-Z_][0-9a-zA-Z_]*'\n    t.value = str(t.value)\n    return t\n\n# Define a rule so we can track line numbers\ndef t_newline(t):\n    r'\\n+'\n    t.lexer.lineno += len(t.value)\n\n# A string containing ignored characters (spaces and tabs)\nt_ignore  = ' \\t'\n\n# Error handling rule\ndef t_error(t):\n    print(\"Illegal character '%s'\" % t.value[0])\n    t.lexer.skip(1)\n\n# Build the lexer\nlexer = lex.lex()\n\n# Test it out\ndata = '''\n3 + 4 * 10\n  + -20 *2\n  aaaVb\n  asdfDe\n  _dsf\n  1df\n'''\n\n# Give the lexer some input\nlexer.input(data)\nlexer.writetab(lextab='lextab')\n\n# Tokenize\nwhile True:\n    tok = lexer.token()\n    if not tok:\n        break      # No more input\n    print(tok)","sub_path":"compiler/lexical/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1305,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"190599472","text":"import Image\nimport pytesseract\nfrom subprocess import call  \na=pytesseract.image_to_string(Image.open('text.jpg'))\nprint(a)\nwith open('done.hl7', 'a') as the_file:\n    the_file.write(a.encode('utf8') + '\\n')\nfile = \"/home/pi/Dropbox-Uploader/dropbox_uploader.sh upload /home/pi/python-ocr-master/done.hl7 done.hl7\"   \ncall ([file], shell=True) \nwith open('done.txt', 'a') as the_file:\n    the_file.write(a.encode('utf8') + '\\n')\nfile1 = \"/home/pi/Dropbox-Uploader/dropbox_uploader.sh upload /home/pi/python-ocr-master/done.txt done.txt\"   \ncall ([file1], shell=True)\nfile2 = \"/home/pi/Dropbox-Uploader/dropbox_uploader.sh upload /home/pi/python-ocr-master/test.jpg test.jpg\"   \ncall ([file2], shell=True)\n","sub_path":"extract_text.py","file_name":"extract_text.py","file_ext":"py","file_size_in_byte":706,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"585538268","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri May 23 16:58:20 2014\n\n@author: richard\n\"\"\"\n\nimport scipy.ndimage\ngaussian = scipy.ndimage.filters.gaussian_filter(noisy_horizon, 3.0)\n \nplt.imshow(gaussian, aspect=0.5, vmin=vmin, vmax=vmax)\nplt.show()","sub_path":"mapSmoothing/untitled1.py","file_name":"untitled1.py","file_ext":"py","file_size_in_byte":244,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"137136538","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Sep 23 16:29:49 2019\n\n@author: pengning\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport scipy.special as sp\nimport mpmath\nfrom mpmath import mp\naxisfont = {'fontsize':'18'}\n\n#implement function that returns dipole field at given point\n\n#dipole is polarized in z direction, located at (xd,yd,zd)\n#the field at point (xp,yp,zp) is outputted as a numpy array with 3 elements\n#k is size of wavevector; dipole field not scale invariant\ndef zdipole_field(k,xd,yd,zd,xp,yp,zp):\n    x = xp-xd; y=yp-yd; z=zp-zd #apply formula with dipole at origin \n    r = np.sqrt(x**2+y**2+z**2)\n    kr = k*r\n    eikr = np.exp(1j*kr)\n    field = np.array([0.0j,0.0j,0.0j]) #(Ex,Ey,Ez) at (xp,yp,zp)\n    \n    field[0] = x*z*(-kr**2-3j*kr+3.0)*eikr / (4.0*np.pi*k**2*r**5)\n    field[1] = y*z*(-kr**2-3j*kr+3.0)*eikr / (4.0*np.pi*k**2*r**5)\n    field[2] = eikr/(4.0*np.pi*r)\n    field[2] += (1j*kr-1.0)*eikr/(4.0*np.pi*k**2*r**3) + z**2*(-kr**2-3j*kr+3.0)*eikr / (4.0*np.pi*k**2*r**5)\n\n    return field\n\ndef zdipole_field_array(k,xd,yd,zd,xp,yp,zp):\n    xp = np.atleast_3d(xp); yp = np.atleast_3d(yp); zp = np.atleast_3d(zp)\n    x = xp-xd; y=yp-yd; z=zp-zd #apply formula with dipole at origin \n    r = np.sqrt(x**2+y**2+z**2)\n    kr = k*r\n    eikr = np.exp(1j*kr)\n    #field = np.array([0.0j,0.0j,0.0j]) #(Ex,Ey,Ez) at (xp,yp,zp)\n    field = np.zeros(xp.shape + (3,),dtype=complex)\n    field[...,0] = x*z*(-kr**2-3j*kr+3.0)*eikr / (4.0*np.pi*k**2*r**5) #Ex\n    field[...,1] = y*z*(-kr**2-3j*kr+3.0)*eikr / (4.0*np.pi*k**2*r**5) #Ey\n    field[...,2] = eikr/(4.0*np.pi*r) #Ez\n    field[...,2] += (1j*kr-1.0)*eikr/(4.0*np.pi*k**2*r**3) + z**2*(-kr**2-3j*kr+3.0)*eikr / (4.0*np.pi*k**2*r**5)\n\n    return field\n\n#plot 3D vector part of dipole field, in box bounded by lx,ux,...... with dipole location (xd,yd,zd)\ndef plot_dipole_field_3D(k,xd,yd,zd,lx,ux,ly,uy,lz,uz,normal=False):\n    density = 4\n    xg = np.linspace(lx,ux,density); yg = np.linspace(ly,uy,density); zg = np.linspace(lz,uz,density)\n    [xg,yg,zg] = np.meshgrid(xg,yg,zg,indexing='ij')\n    \n    field = zdipole_field_array(k,xd,yd,zd,xg,yg,zg)\n    fig, ax = plt.subplots(1,1)\n    ax = fig.gca(projection='3d')\n    ax.quiver3D(xg,yg,zg,np.real(field[:,:,:,0]),np.real(field[:,:,:,1]),np.real(field[:,:,:,2]),normalize=normal)\n    plt.show()\n\n#plot a 2D streamplot with dipole in center so there are no out-of-plane fields\ndef plot_dipole_field_2Dstream(k,ly,uy,lz,uz):\n    dense = 50\n    y1d = np.linspace(ly,uy,dense); z1d = np.linspace(lz,uz,dense)\n    [xg,yg,zg] = np.meshgrid([0.0],y1d,z1d,indexing='ij') #note that meshgrid does xy indexing by default\n    field = zdipole_field_array(k,0,0,0,xg,yg,zg)\n    \n    plt.figure()\n    #all the transposing to deal with the issue that streamplot is hardwired with xy indexing, while we use ij indexing\n    plt.streamplot(np.transpose(yg[0,:,:]),np.transpose(zg[0,:,:]),np.real(np.transpose(field[0,:,:,1])),np.real(np.transpose(field[0,:,:,2])),density=2.5)\n    plt.xlim(ly,uy)\n    plt.ylim(lz,uz)\n    plt.show()\n\ndef xdipole_field(k,xd,yd,zd,xp,yp,zp):\n    x = xp-xd; y = yp-yd; z = zp-zd\n    r = np.sqrt(x**2+y**2+z**2)\n    kr = k*r\n    eikr = np.exp(1j*kr)\n    field = np.array([0.0j,0.0j,0.0j]) #(Ex,Ey,Ez) at (xp,yp,zp)\n    \n    factor = (-kr**2-3j*kr+3.0)*eikr / (4.0*np.pi*k**2*r**5)\n    field[2] = x*z*factor\n    field[1] = y*x*factor\n    field[0] = eikr/(4.0*np.pi*r)\n    field[0] += (1j*kr-1.0)*eikr/(4.0*np.pi*k**2*r**3) + x**2*factor\n\n    return field\n#################expansion of off origin dipole field in spherical waves########\n    \n#for zpolarized dipole along z axis only the RgNl0 waves are needed to represent dipole field\n#for region above dipole should use U^-, A^- in Kardar paper expression\n#normalization depends on given domain and will be treated in the calculation files for each domain, not here\n\nimport py3nj #small package for calculating the Wigner 3j symbols in expansion\n\ndef get_RgNl0_coeff_for_zdipole_field(k,R,dist,l):\n    ans = 0.0j\n    kd = k*(R+dist) #dist is distance between dipole and sphere surface, the translation distance d is between the two origins so dipole and sphere center\n #   norm = np.sqrt(rho_N(l,k*R)/k**3) #normalization\n    for nu in range(l-1,l+2):\n        tmp = (1j)**(1-nu-l)*0.5*(2+l*(l+1)-nu*(nu+1))*(2*nu+1)*np.sqrt(3*(2*l+1)/2/l/(l+1))\n        tmp *= py3nj.wigner3j(2*1,2*l,2*nu,0,0,0)**2 * (sp.spherical_jn(nu,kd)+1j*sp.spherical_yn(nu,kd))\n        ans += tmp\n #   print(ans)\n    ans *= 1j*k*np.sqrt((1.0/6/np.pi)) #normalization NOT included\n    return ans\n\n#mpmath based high precision spherical bessel\ndef mp_spherical_jn(l,z):\n    return mpmath.sqrt(mpmath.pi/2.0/z)*mpmath.besselj(l+0.5,z)\n\ndef mp_vec_spherical_jn(l,z):\n    vecfcn = np.vectorize(mp_spherical_jn)\n    return vecfcn(l,z)\n\ndef mp_spherical_yn(l,z):\n    return mpmath.sqrt(mpmath.pi/2.0/z)*mpmath.bessely(l+0.5,z)\n\ndef mp_vec_spherical_yn(l,z):\n    vecfcn = np.vectorize(mp_spherical_yn)\n    return vecfcn(l,z)\n\ndef mp_spherical_hn(l,z):\n    return mp_spherical_jn(l,z) + 1j*mp_spherical_yn(l,z)\n\ndef mp_spherical_djn(l,z):\n    return mpmath.sqrt(mpmath.pi/(2*z)) * (mpmath.besselj(l+0.5,z,1) - mpmath.besselj(l+0.5,z)/(2*z))\n\ndef mp_vec_spherical_djn(l,z):\n    vecfcn = np.vectorize(mp_spherical_djn)\n    return vecfcn(l,z)\n\ndef mp_spherical_dyn(l,z):\n    return mpmath.sqrt(mpmath.pi/(2*z)) * (mpmath.bessely(l+0.5,z,1) - mpmath.bessely(l+0.5,z)/(2*z))\n\ndef mp_vec_spherical_dyn(l,z):\n    vecfcn = np.vectorize(mp_spherical_dyn)\n    return vecfcn(l,z)\n\ndef mp_get_RgNl0_coeff_for_zdipole_field(k,R,dist,l):\n    ans = mpmath.mpc(0.0j)\n    kd = k*(R+dist) #dist is distance between dipole and sphere surface, the translation distance d is between the two origins so dipole and sphere center\n #   norm = np.sqrt(rho_N(l,k*R)/k**3) #normalization\n    for nu in range(l-1,l+2):\n        tmp = (1j)**(1-nu-l)*0.5*(2+l*(l+1)-nu*(nu+1))*(2*nu+1)*mpmath.sqrt(3*(2*l+1)/2/l/(l+1))\n        tmp *= py3nj.wigner3j(2*1,2*l,2*nu,0,0,0)**2 * mp_spherical_hn(nu,kd)\n        ans += tmp\n #   print(ans)\n    ans *= 1j*k*mpmath.sqrt((1.0/6/mpmath.pi)) #normalization NOT included\n    #ans *= 1j*k*mpmath.sqrt((1.0/6/mpmath.pi) * mp_rho_N(l,k*R)/k**3) #normalization included in sqrt\n    return ans\n\n\ndef cplxquad(func, intmin, intmax):\n    refunc = lambda x: np.real(func(x))\n    imfunc = lambda x: np.imag(func(x))\n    int_re, err = integrate.quad(refunc, intmin, intmax)\n    int_im, err = integrate.quad(imfunc, intmin, intmax)\n    return int_re + 1j*int_im\n\nfrom scipy import integrate\ndef check_G_on_spherical_RgM(n,r,R):\n    #numerically evaluate the radial integral involved in G dot RgM\n    int1, err = integrate.quad(lambda x: x**2 * (mp_spherical_jn(n,x))**2, 0,r)\n    int2 = cplxquad(lambda x: x**2*mp_spherical_hn(n,x)*mp_spherical_jn(n,x), r, R)\n    \n    radial = mp_spherical_hn(n,r)*int1 + mp_spherical_jn(n,r)*int2\n    print(radial)\n    print(mp_spherical_jn(n,r))\n    return radial / mp_spherical_jn(n,r)\n\ndef check_G_on_spherical_M(n,r,R):\n    #numerically evaluate the radial integral involved in G dot M\n    int1 = cplxquad(lambda x: x**2 * mp_spherical_jn(n,x) * mp_spherical_hn(n,x), 0,r)\n    int2 = cplxquad(lambda x: x**2*mp_spherical_hn(n,x)**2, r, R)\n    \n    radial = mp_spherical_hn(n,r)*int1 + mp_spherical_jn(n,r)*int2\n    print(radial)\n    print(mp_spherical_hn(n,r))\n    return radial / mp_spherical_hn(n,r)\n\n\ndef zdipole_field_xy2D_periodic_array(k0,L, xd,yd,zd, xp,yp,zp, sumtol=1e-12):\n    #field at coord (xp,yp,zp) of z-polarized dipoles in a square array in the z=czd plane\n    #generated by summing over discrete k-vectors; in evanescent region the summand decreases exponentially with increasing kp and abskz\n    #it seems that directly summing over individual dipole fields leads to convergence issues due to relatively slow (polynomial) decay of dipole fields\n    #order of summation in k-space: concentric squares, 0th square the origin, 1st square infinity-norm radius 1, 2nd square infinity-norm radius 2...\n    #since we are using the angular representation for consistency we insist that zp>zd\n    \"\"\"\n    if not (xp<L/2 and xp>-L/2 and yp<L/2 and yp>-L/2):\n        return 'target point out of Brillouin zone'\n    \"\"\"\n    if zp<=zd:\n        return 'need zp>zd'\n    \n    field = mp.zeros(3,1)\n    oldfield = mp.zeros(3,1)\n    deltak = 2*mp.pi/L\n    deltax = xp-xd; deltay = yp-yd; deltaz = zp-zd\n    \n    i = 1 #label of which square we are on\n    \n    prefac = -1j/(2 * L**2 * k0**2)\n    while True: #termination condition in loop\n        #sum over the i-th square\n        lkx = -i*deltak; rkx = i*deltak\n        for iy in range(-i,i+1):\n            ky = iy*deltak\n            kpsqr = lkx**2 + ky**2\n            kz = mp.sqrt(k0**2-kpsqr)\n            lphasefac = mp.expj(lkx*deltax + ky*deltay + kz*deltaz)\n            rphasefac = mp.expj(rkx*deltax + ky*deltay + kz*deltaz)\n            \n            field[0] += prefac*(lkx*lphasefac + rkx*rphasefac)\n            field[1] += prefac*(lphasefac+rphasefac)*ky\n            field[2] += prefac*(lphasefac+rphasefac)*(-kpsqr/kz)\n        \n        bky = -i*deltak; uky = i*deltak\n        for ix in range(-i+1,i):\n            kx = ix*deltak\n            kpsqr = kx**2 + bky**2\n            kz = mp.sqrt(k0**2-kpsqr)\n            bphasefac = mp.expj(kx*deltax + bky*deltay + kz*deltaz)\n            uphasefac = mp.expj(kx*deltax + uky*deltay + kz*deltaz)\n            \n            field[0] += prefac*(bphasefac+uphasefac)*kx\n            field[1] += prefac*(bphasefac*bky + uphasefac*uky)\n            field[2] += prefac*(bphasefac+uphasefac)*(-kpsqr/kz)\n            \n        if mp.norm(field-oldfield)<mp.norm(field)*sumtol:\n            break\n        \n        print('i',i)\n        #mp.nprint(field)\n        oldfield = field.copy()\n        i+=1\n    \n    return field\n\ndef zdipole_field_xy2D_periodic_array_from_individuals(k0,L, cxd,cyd,czd, xp,yp,zp, sumtol=1e-3):\n    field = zdipole_field(k0, cxd,cyd,czd, xp,yp,zp)\n    oldfield = field.copy()\n    \n    i=1 #we treat i=0 at beginning to avoid double counting\n    while True:\n        lxd = cxd-i*L; rxd = cxd+i*L\n        for iy in range(-i,i+1):\n            yd = cyd+iy*L\n            field += zdipole_field(k0, lxd,yd,czd, xp,yp,zp) + zdipole_field(k0,rxd,yd,czd, xp,yp,zp)\n        \n        byd = cyd-i*L; uyd = cyd+i*L\n        for ix in range(-i+1,i):\n            xd = cxd+ix*L\n            field += zdipole_field(k0,xd,byd,czd, xp,yp,zp) + zdipole_field(k0,xd,uyd,czd, xp,yp,zp)\n        \n        if np.linalg.norm(field-oldfield)<np.linalg.norm(field)*sumtol:\n            break\n        \n        print('i',i,'field',field)\n        oldfield = field.copy()\n        i+=1\n    return field","sub_path":"dualbound/Arnoldi/dipole_field.py","file_name":"dipole_field.py","file_ext":"py","file_size_in_byte":10696,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"148399537","text":"from robber import expect\nfrom robber.matchers.base import Base\n\n\nclass Equal(Base):\n    \"\"\"\n    expect(1).to.eq(1)\n    expect(1).to == 1\n    expect(1) == 1\n    \"\"\"\n\n    def matches(self):\n        return self.actual == self.expected\n\n    def failure_message(self):\n        message = 'Expected {actual_type}(\"{actual_value}\"){negative_message} ' \\\n                  'to equal {expected_type}(\"{expected_value}\")'\n\n        return message.format(\n            actual_type=type(self.actual).__name__,\n            actual_value=self.actual,\n            negative_message=self.negative_message,\n            expected_type=type(self.expected).__name__,\n            expected_value=self.expected,\n        )\n\n\nexpect.register('eq', Equal)\nexpect.register('__eq__', Equal)\n\nexpect.register('ne', Equal, is_negative=True)\nexpect.register('__ne__', Equal, is_negative=True)\n","sub_path":"robber/matchers/equal.py","file_name":"equal.py","file_ext":"py","file_size_in_byte":857,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"81365750","text":"from flask import Flask\n\nfrom app.models.base import db\n\n\ndef register_blueprints(apps):\n    # 将bp_v1版本的蓝图注册到核心对象上\n    from app.api.v1.book import book\n    from app.api.v1.user import user\n    from app.api.v1.client import client\n\n    apps.register_blueprint(book)\n    apps.register_blueprint(user)\n    apps.register_blueprint(client)\n\n\ndef create_app():\n    app = Flask(__name__)\n\n    # 注册配置选项\n    app.config.from_object('app.config.setting')\n    app.config.from_object('app.config.secure')\n\n    # 注册蓝图\n    register_blueprints(app)\n\n    # 初始化模型类\n    db.init_app(app)  # 将db注册到app上\n    with app.app_context():\n        db.create_all()  # 创建所有的表\n    return app\n","sub_path":"app/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":741,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"342215413","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# Jesse Rubin - project Euler\n\"\"\"\nPandigital prime sets\nProblem 118\nUsing all of the digits 1 through 9 and concatenating them freely to form\ndecimal integers, different sets can be formed. Interestingly with the set\n{2,5,47,89,631}, all of the elements belonging to it are prime.\n\nHow many distinct sets containing each of the digits one through nine exactly\nonce contain only prime elements?\n\"\"\"\nfrom collections import Counter\nfrom itertools import combinations, product, permutations\nfrom bib.maths import partitions_gen\nfrom bib.amazon_prime import is_prime\nfrom bib.decorations import Jasm\n\ntry: xrange\nexcept NameError: xrange = range\n\npandaprimespath = '../txt_files/panda_primes_set.txt'\n\n\n@Jasm(pandaprimespath)\ndef no_repeating_dijits_primes():\n    panda_primes = {}\n    for i in range(1, 9):  # dont have to go all the way up bc there are no pan dig primes\n        thing = []\n        for perm in permutations('123456789', i):\n            pn = int(\"\".join(perm))\n            if is_prime(pn):\n                thing.append(pn)\n        panda_primes[i] = thing\n    return panda_primes\n\n\ndef is_pandigital_set(st):\n    return True if len(set(dijit for dijit in \"\".join(str(n) for n in st))) == 9 else False\n\n\ndef p118():\n    norep_primes = {int(k):v for k, v in no_repeating_dijits_primes().items()}\n    okparts = [p for p in partitions_gen(9) if len(p) > 1 and sum(1 for n in p if n == 1) < 5]\n    pandigital_prime_sets = set()\n    for party in okparts:\n        c = Counter(p for p in party)\n        permutation_combinations = [[i for i in combinations(norep_primes[k], v)]\n                                    for k, v in c.items()]\n        for prod in product(*permutation_combinations):\n            a = tuple(sorted([n for combo in prod for n in combo]))\n            if is_pandigital_set(a):\n                pandigital_prime_sets.add(tuple(a))\n    return len(pandigital_prime_sets)\n\n\nif __name__ == '__main__':\n    ANSWER = p118()\n    print(\"# pandigital prime sets: {}\".format(ANSWER))","sub_path":"done/py/euler_118.py","file_name":"euler_118.py","file_ext":"py","file_size_in_byte":2041,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"21912184","text":"from django.urls import path\nfrom . import views\n\n# products url\nurlpatterns = [\n    path('', views.index, name=\"index\"),\n    path('search/', views.search, name=\"search\"),\n    path('save/<int:product_id>', views.save, name=\"save\"),\n    path('preferences/', views.preferences, name=\"preferences\"),\n    path('remove/<int:preference_id>', views.remove_preferences,\n         name=\"remove\"),\n    path('signup/', views.signup, name=\"signup\"),\n    path(\"contact/\", views.contact, name=\"contact\"),\n    path(\"legal/\", views.legal_notice, name=\"legal\"),\n    path(\"profile/\", views.account, name=\"profile\"),\n    path(\"details_products/<int:product_id>\",\n         views.details_products, name=\"details_products\"),\n    path(\"details_preferences/<int:product_id>\",\n         views.details_preferences, name=\"details_preferences\"),\n]\n","sub_path":"products/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":818,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"375110215","text":"\"\"\"Module with adaptors for processing downloaded feed files\"\"\"\nimport gzip\nimport os\n\nfrom netaddr import valid_ipv4, valid_ipv6\n\nfrom module_exceptions import NotSupportedFiletypeError, FileNotFoundError\nfrom module_exceptions import NotGzippedError\n\n\ndef valid_ip(address):\n    \"\"\"Validate v6 and v4 ip address. Return True if input is valid ipv6 or\n    ipv4 address.\n\n    :param address: String to be checked.\n\n    \"\"\"\n    if len(address.split()) == 1 and (\n        valid_ipv4(address) or valid_ipv6(address)\n    ):\n        return True\n    return False\n\n\ndef openbsd_adaptor(gz_file):\n    \"\"\"Adaptor for openbsd traplist. Read ips from gz file and return list of\n    ip addresses.\n\n    :param gz_file: a path to file to be processed.\n\n    \"\"\"\n    if not os.path.exists(gz_file):\n        raise FileNotFoundError(gz_file)\n    if os.path.splitext(gz_file)[1] != '.gz':\n        raise NotSupportedFiletypeError(gz_file, 'openbsd')\n    try:\n        raw_data = gzip.open(gz_file).read().split('\\n')\n    except IOError:\n        raise NotGzippedError(gz_file)\n    return [line for line in raw_data if valid_ip(line)]\n\n\ndef nixspam_adaptor(gz_file):\n    \"\"\"Adaptor for nixspam list. Read gz_file with ips and return list of ip\n    addresses.\n\n    :param gz_file: a path to file to be processed.\n\n    \"\"\"\n    if not os.path.exists(gz_file):\n        raise FileNotFoundError(gz_file)\n    if os.path.splitext(gz_file)[1] != '.gz':\n        raise NotSupportedFiletypeError(gz_file, 'openbsd')\n    try:\n        raw_data = gzip.open(gz_file).read().split('\\n')\n    except IOError:\n        raise NotGzippedError(gz_file)\n    filtered_data = []\n    for line in raw_data:\n        if line:\n            filtered = line.split()[1]\n            if valid_ip(filtered):\n                filtered_data.append(filtered)\n    return filtered_data\n\n\ndef psbl_adaptor(txt_file):\n    \"\"\"Adaptor for passive spam blocklist. Read txt file with ips and return\n    list of ip addresses.\n\n    :param txt_file: a path to file to be processed.\n\n    \"\"\"\n    if not os.path.exists(txt_file):\n        raise FileNotFoundError(txt_file)\n    with open(txt_file) as raw_file:\n        raw_data = raw_file.read().split('\\n')\n    return [line for line in raw_data if valid_ip(line)]\n\n\ndef chaosreigns_adaptor(txt_file):\n    \"\"\"Adaptor for chaosreigns whitelist. Read txt file with ips and return\n    list of ip addresses.\n\n    :param txt_file: a path to file to be processed.\n\n    \"\"\"\n    if not os.path.exists(txt_file):\n        raise FileNotFoundError(txt_file)\n    with open(txt_file) as raw_file:\n        filtered_data = []\n        for line in raw_file:\n            line = line.split()[0]\n            if (line.find('/')) != -1:\n                line = '::' + line.split('/')[0]\n            if valid_ip(line):\n                filtered_data.append(line)\n        return filtered_data\n\n\ndef generic_adaptor(filename):\n    \"\"\"Generic adaptor for parsing gzip or txt feed files.\n\n    :param filename: a path of file to process\n    \"\"\"\n    ext = os.path.splitext(filename)[1]\n    if ext == '.txt':\n        raw_data = open(filename).read().split()\n    elif ext == '.gz':\n        raw_data = gzip.open(filename).read().split()\n    filtered_data = []\n    for item in raw_data:\n        if valid_ip(item):\n            filtered_data.append(item)\n    return filtered_data\n","sub_path":"src/python/feeds_processor/adaptors.py","file_name":"adaptors.py","file_ext":"py","file_size_in_byte":3311,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"374008458","text":"# -*- coding: utf-8 -*-\nimport pytest\nfrom unittest.mock import MagicMock, patch, mock_open, call\n\nfrom chaosaws.ec2_os.probes import describe_instance, describe_os_type, \\\n                                   ensure_tc_installed, ensure_tc_uninstalled\n\n\nCONFIG = {\n    \"aws\": {\n        \"aws_region\": \"cn-north-1\"\n    }\n}\n\nINSTANCE_IDS = [\"i-04cf7749ff48ca517\"]\n\nSECRETS = {\n    \"aws\": {\n        \"aws_access_key_id\": \"abcdefghijklmn\",\n        \"aws_secret_access_key\": \"opqrstuvwxyz\",\n        \"aws_session_token\": \"abcdefghijklmnopqrstuvwxyz\",\n    }\n}\n\nCMD_ID_RETURN = {\n    'Command': {\n        'CommandId': '6ff4cc59-dac6-4d2e-8952-8ac53930627a',\n        'DocumentName': 'AWS-RunShellScript',\n        'DocumentVersion': '',\n        'Comment': ''\n    }\n}\n\nINSTANCE_DESC = {\n    'Reservations': [{\n        'Instances': [{\n            'InstanceId': 'i-1234567890abcdef0',\n            'InstanceLifecycle': 'spot',\n            'SpotInstanceRequestId': 'sir-abcdef01'}]}]}\n\nINSTANCE_DESC_WINDOWS = {\n    'Reservations': [{\n        'Instances': [{\n            'InstanceId': 'i-1234567890abcdef0',\n            'Platform': 'Windows',\n            'InstanceLifecycle': 'spot',\n            'SpotInstanceRequestId': 'sir-abcdef01'}]}]}\n\nCMD_LIST_INVOCATION_RETURN = {\n    'CommandInvocations': [{\n        'CommandId': '6ff4cc59-dac6-4d2e-8952-8ac53930627a',\n        'InstanceId': 'i-04cf7749ff48ca517',\n        'InstanceName': '',\n        'Comment': '',\n        'DocumentName': 'AWS-RunShellScript',\n        'DocumentVersion': '',\n        'RequestedDateTime': None,\n        'Status': 'Success',\n        'StatusDetails': 'Success',\n        'StandardOutputUrl': '',\n        'StandardErrorUrl': '',\n        'CommandPlugins': [{\n            'Name': 'aws:runShellScript',\n            'Status': 'Success',\n            'StatusDetails': 'Success',\n            'ResponseCode': 0,\n            'ResponseStartDateTime': None,\n            'ResponseFinishDateTime': None,\n            'Output': '',\n            'StandardOutputUrl': '',\n            'StandardErrorUrl': '',\n            'OutputS3Region': 'cn-north-1',\n            'OutputS3BucketName': '',\n            'OutputS3KeyPrefix': ''\n        }],\n        'ServiceRole': '',\n        'NotificationConfig': {\n            'NotificationArn': '',\n            'NotificationEvents': [],\n            'NotificationType': ''\n        },\n        'CloudWatchOutputConfig':{\n            'CloudWatchLogGroupName': '',\n            'CloudWatchOutputEnabled': False\n        }\n    }],\n    'ResponseMetadata': {\n        'RequestId': 'b3f724e7-8ca2-4198-9483-d5b2dff3119a',\n        'HTTPStatusCode': 200,\n        'HTTPHeaders': {\n            'x-amzn-requestid': 'b3f724e7-8ca2-4198-9483-d5b2dff3119a',\n            'content-type': 'application/x-amz-json-1.1',\n            'content-length': '998',\n            'date': 'Wed, 23 Oct 2019 05:01:48 GMT'\n        },\n        'RetryAttempts': 0\n    }\n}\n\n\nclass AnyStringWith(str):\n    def __eq__(self, other):\n        return self in other\n\n\n@patch('chaosaws.ec2_os.probes.aws_client', autospec=True)\ndef test_describe_os_type(aws_client):\n    client = MagicMock()\n    aws_client.return_value = client\n    client.describe_instances.return_value = INSTANCE_DESC\n\n    os = describe_os_type(instance_id='i-1234567890abcdef0', secrets=SECRETS,\n                          configuration=CONFIG)\n    assert \"linux\" == os\n\n\n@patch('chaosaws.ec2_os.probes.aws_client', autospec=True)\ndef test_describe_os_type_windows(aws_client):\n    client = MagicMock()\n    aws_client.return_value = client\n    client.describe_instances.return_value = INSTANCE_DESC_WINDOWS\n\n    os = describe_os_type(instance_id='i-1234567890abcdef0', secrets=SECRETS,\n                          configuration=CONFIG)\n    assert \"Windows\" == os\n\n\n@patch('chaosaws.ec2_os.probes.aws_client', autospec=True)\ndef test_describe_instances_windows(aws_client):\n    client = MagicMock()\n    aws_client.return_value = client\n    client.describe_instances.return_value = INSTANCE_DESC_WINDOWS\n\n    response = describe_instance(instance_id='i-1234567890abcdef0',\n                                 secrets=SECRETS,\n                                 configuration=CONFIG)\n    assert INSTANCE_DESC_WINDOWS == response\n\n\n@patch('chaosaws.ec2_os.probes.aws_client', autospec=True)\ndef test_describe_instances(aws_client):\n    client = MagicMock()\n    aws_client.return_value = client\n    client.describe_instances.return_value = INSTANCE_DESC\n\n    response = describe_instance(instance_id='i-1234567890abcdef0',\n                                 secrets=SECRETS,\n                                 configuration=CONFIG)\n    assert INSTANCE_DESC == response\n\n\n@patch(\"builtins.open\", new_callable=mock_open, read_data=\"script\")\n@patch('chaosaws.ec2_os.probes.describe_os_type', autospec=True)\n@patch('chaosaws.ec2_os.probes.aws_client', autospec=True)\ndef test_ensure_tc_installed(aws_client, os_type, open):\n    client = MagicMock()\n    aws_client.return_value = client\n    client.send_command.return_value = CMD_ID_RETURN\n    client.list_command_invocations.return_value = CMD_LIST_INVOCATION_RETURN\n\n    os_type.return_value = \"Linux\"\n\n    list_res = ensure_tc_installed(instance_ids=INSTANCE_IDS,\n                                   configuration=CONFIG,\n                                   secrets=SECRETS)\n    open.assert_called_with(AnyStringWith(\"ensure_tc_installed.sh\"))\n    assert \"failed\" not in list_res[0]\n\n\n@patch(\"builtins.open\", new_callable=mock_open, read_data=\"script\")\n@patch('chaosaws.ec2_os.probes.describe_os_type', autospec=True)\n@patch('chaosaws.ec2_os.probes.aws_client', autospec=True)\ndef test_ensure_tc_uninstalled(aws_client, os_type, open):\n    client = MagicMock()\n    aws_client.return_value = client\n    client.send_command.return_value = CMD_ID_RETURN\n    client.list_command_invocations.return_value = CMD_LIST_INVOCATION_RETURN\n\n    os_type.return_value = \"Linux\"\n\n    list_res = ensure_tc_uninstalled(instance_ids=INSTANCE_IDS,\n                                     configuration=CONFIG,\n                                     secrets=SECRETS)\n    open.assert_called_with(AnyStringWith(\"ensure_tc_uninstalled.sh\"))\n    assert \"failed\" not in list_res[0]\n","sub_path":"tests/ec2_os/test_ec2_os_probes.py","file_name":"test_ec2_os_probes.py","file_ext":"py","file_size_in_byte":6171,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"206022815","text":"# -*- coding: utf-8 -*-\nfrom decorated.decorators.synchronized import MemoryLock, Synchronized, FileLock\nfrom threading import Thread\nfrom unittest2.case import TestCase\nimport time\n\nclass FileLockTest(TestCase):\n    def test(self):\n        # set up\n        def foo():\n            with FileLock('/tmp/lock'):\n                time.sleep(0.01)\n        class BlockThread(Thread):\n            def run(self):\n                foo()\n\n        # test\n        thread1 = BlockThread()\n        thread2 = BlockThread()\n        begin = time.time()\n        thread1.start()\n        thread2.start()\n        thread1.join()\n        thread2.join()\n        end = time.time()\n        self.assertGreater(end - begin, 0.02)\n\nclass SynchronizedTest(TestCase):\n    def test_simple(self):\n        lock = MemoryLock()\n        @Synchronized(lock)\n        def foo(a):\n            return a\n        result = foo(1)\n        self.assertEqual(1, result)\n\n    def test_normal(self):\n        # set up\n        lock = MemoryLock()\n        @Synchronized(lock)\n        def foo():\n            time.sleep(0.01)\n        class BlockThread(Thread):\n            def run(self):\n                foo()\n\n        # test\n        thread1 = BlockThread()\n        thread2 = BlockThread()\n        begin = time.time()\n        thread1.start()\n        thread2.start()\n        thread1.join()\n        thread2.join()\n        end = time.time()\n        self.assertGreater(end - begin, 0.02)\n\n    def test_error(self):\n        lock = MemoryLock()\n        @Synchronized(lock)\n        def foo():\n            raise Exception\n        with self.assertRaises(Exception):\n            foo()\n","sub_path":"src/decorated/test/decorators_test/synchronized_test.py","file_name":"synchronized_test.py","file_ext":"py","file_size_in_byte":1617,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"398167246","text":"import glob\nfrom os import path\nimport random\n\nfrom generators import *\n\n\nclass NameGenerator(object):\n    def __init__(self):\n        self.set = 0\n        self.case = 0\n\n    def next_case(self):\n        self.case = self.case+1\n\n    def next_set(self):\n        self.set = self.set+1\n        self.case = 0\n\n    def name(self):\n        if self.set == 0:\n            return path.join('test', '00.sample.%s' % (chr(ord('a')+self.case)))\n        return path.join('test', '%02d.%s' % (self.set, chr(ord('a')+self.case)))\n\nclass Writer(object):\n    def __init__(self):\n        self.G = NameGenerator();\n\n    def next_case(self):\n        self.G.next_case()\n\n    def next_set(self):\n        self.G.next_set()\n\n    def open(self):\n        print('Generujem: %s' % (self.G.name()))\n        self.file = open(self.G.name()+'.in','w')\n\n    def close(self):\n        self.file.close()\n\n    def write(self, line):\n        self.file.write(line)\n        self.file.write('\\n')\n\ndef w(data):\n    W = Writer()\n    for set in data:\n        for (N, colors, happy, vs) in set:\n            W.open()\n            W.write('%s' % N)\n            W.write(colors)\n            W.write(' '.join(map(str, happy)))\n            W.write(' '.join(map(str, vs)))\n            W.close()\n            W.next_case()\n        W.next_set()\n\n\ndata = [[]]\n\n# data with increasing N\nfor MIN_N, MAX_N in [(8000, 10000), (40000, 50000), (100000, 100000)]:\n    tc = []\n\n    if MIN_N == 8000:\n        tc.extend([\n            (2, 'BW', [5, -10], [1]),\n            (3, 'WBB', [-10, -20, 100], [1, 2]),\n            (5, 'BWWWW', [0, 1, 2, 3, 4], [1, 2, 3, 4]),\n            (5, 'BWWWW', [0, 1, 2, 3, 4], [1, 1, 1, 1])])\n    elif MIN_N == 100000:\n        N = 100000\n        tc.extend([\n            (N, gen_equal_random_colors_shuffle(N), gen_max_happy(N), gen_path_strict(N))])\n\n    for structure in gen_structure:\n        for colors in gen_colors:\n            N = random.randint(MIN_N, MAX_N)\n            tc.append((N, colors(N), gen_random_happy(N), structure(N)))\n    for structure in gen_structure:\n        N = random.randint(MIN_N, MAX_N)\n        tc.append((N, gen_random_colors(N), gen_random_happy(N, min_h=0, max_h=10**5), structure(N)))\n    data.append(tc)\n\nw(data)\n","sub_path":"tester/day-mix1/universities/gen-in.py","file_name":"gen-in.py","file_ext":"py","file_size_in_byte":2212,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"485115186","text":"from datetime import datetime\n\n#Results from NZB Searches\nclass Result(object):\n        def __init__(self, nzbName, link, dateAdded,source):\n                self.name = nzbName\n                self.link = link\n                self.provider = source\n                if source == 'NZB.SU':\n                        date =  datetime.strptime(dateAdded, \"%a, %d %b %Y %H:%M:%S\")\n                if source == 'nzbMatrix':\n                        date = datetime.strptime(dateAdded, '%Y-%m-%d %H:%M:%S')\n                today = datetime.now()\n                self.age = int((today - date).days)\n\n#GoogleBooks Reults\nclass Book(object):\n    def __init__(self, title, authors, isbn13):\n            self.title = title\n            self.authors = authors\n            self.isbn13 = isbn13\n","sub_path":"nightlight/classes.py","file_name":"classes.py","file_ext":"py","file_size_in_byte":776,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"91569986","text":"import requests\nimport re\nimport pandas as pd\n\nlis_name=['ABD', 'AEU', 'AWZ', 'AJK', 'ADU', 'PGU', 'BND', 'BDH', 'XBJ', 'BJB', 'BUZ', 'ZBR', 'DEF', 'GCH', 'GBT', 'HDM', 'IIL', 'IFN', 'JAR', 'JYR', 'KLM', 'KKS', 'KER', 'KSH', 'KHK', 'KHD', 'KHY', 'KIH', 'LFM', 'LRR', 'LVP', 'MRX', 'ACP', 'MHD', 'NSH', 'PFQ', 'GSM', 'RJN', 'RZR', 'RAS', 'AFZ', 'SDG', 'SRY', 'CQD', 'RUD', 'SYZ', 'SYJ', 'SXI', 'TCX', 'TBZ', 'THR', 'IKA', 'OMH', 'YES', 'AZD', 'ACZ', 'ZAH', 'JWN']\nn = 0\nll = []\n\n#错误机场代码\nerror_code = []\n#未来7天没有飞机起飞的机场代码\nnone_code = []\nfor i in lis_name:\n    try:\n        n += 1\n        url = 'https://www.flightradar24.com/data/airports/{}/routes'\n        new_url = url.format(i)\n        headers = {'User-Agent':'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.81 Safari/537.36'}\n        response = requests.get(url=new_url, headers=headers,verify=False)\n        html = response.text\n        L = re.search('arrRoutes=\\[(.*?)\\]', html, re.S).group(1)\n        # ER_fl出现错误的一些机场代码\n        ER_fl = ['AEU','BDH','BJB','GCH','JAR','KLM','KHY','ACP','PFQ','RJN', 'AFZ', 'SDG', 'CQD', 'SYJ', 'TCX', 'YES', 'JWN']\n        if i in ER_fl:\n            S = eval(L)\n        else:\n            S = eval(L)\n            dic = list(S)\n        for j in dic:\n            # 获取到达机场代码\n            flg_code = j['iata']\n            # 获取到达机场名称\n            # flg_name = j['name']\n            # 获取到达机场经度\n            flg_jingdu = j['lat']\n            # 获取到达机场纬度\n            flg_weidu = j['lon']\n            list1 = [i, flg_code, flg_jingdu, flg_weidu]\n            ll.append(list1)\n            print('打印次数：', n, '起飞机场代码：', i, '到达机场代码：', flg_code, '到达机场经度：', flg_jingdu, '到达机场纬度：', flg_weidu)\n    except AttributeError:\n        none_code.append(i)\n        print('起飞机场代码：', i, '{}此机场未来7天没有航信信息'.format(i))\n    except TypeError:\n        error_code.append(i)\n        print('错误机场代码：',i)\nprint(none_code)\n#写入csv文件中\n# name = ['F_code(起飞机场)', 'A_code（到达机场）', 'ar_lat（经度）', 'ar_lon（纬度）']\n# test = pd.DataFrame(columns=name,data=ll)\n# test.to_csv('D:\\伊朗机场连接数据.csv')","sub_path":"一代一路国家/伊朗/伊朗-机场.py","file_name":"伊朗-机场.py","file_ext":"py","file_size_in_byte":2373,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"74620097","text":"'''----------------------------------------------------------------------------------------------------------\nMODULE                  :       SettlementNettingRuleSND\nPURPOSE                 :       This module contains the SND Netting rule.\nDEPARTMENT AND DESK     :       OPS\nREQUASTER               :       Nicolette Burger\nDEVELOPER               :       Heinrich Cronje\nCR NUMBER               :       335129\n-------------------------------------------------------------------------------------------------------------\n\nHISTORY\n=============================================================================================================\nDate            Change no       Developer                       Description\n-------------------------------------------------------------------------------------------------------------\n2012-07-13      335129          Heinrich Cronje                 Initial Implementation\n2012-12-07      653119          Tesslyn Pillay                  Change to allow netting to take place within\n                                                                counterpiarties via trans ref\n-------------------------------------------------------------------------------------------------------------\n\nDESCRIPTION OF MODULE:\n\n    Settlements should be netted if the acquirer is STRUCT NOTES DESK or\n    if the acquirer is CREDIT DERIVATIVES DESK and the portfolio in one the following compound portfolios:\n    1574,1571,1573,2792,2793,2794,1572,3306,0057\n'''\n\nimport ael, acm\nfrom SettlementNettingHelperFunction import SettlementNettingHelperFunction as HelperUtils\nfrom SAGEN_IT_Functions import get_Port_Struct_from_Port\n\nclass SettlementNettingRuleSND():\n    def __init__(self, settlObj):\n        self.settlObj = settlObj\n        self.INVALID_SETTLEMENT_TYPES = ['Security Nominal']\n        self.ACQUIRER = [30301] #30301 = STRUCT NOTES DESK\n        self.ACQUIRER_FOR_VALID_PORTF = [6292]          #6292 = CREDIT DERIVATIVES DESK\n        self.VALID_PORTF_FOR_ACQUIRER = [1512, 1518, 1631, 1644, 1742, 1746, 1827, 1884, 1098]         #1512 = 1574,   1518 = 1571,    1631 = 1573,    1644 = 2792,    1742 = 2793,    1746 = 2794,    1827 = 1572,    1884 = 3306,    1098 = 0057\n        self.isUpdateCollision = ''\n        self.executeSNDNetting()\n        \n    def IsValidPortfolio(self, portfolio):\n        for p in self.VALID_PORTF_FOR_ACQUIRER:\n            if get_Port_Struct_from_Port(ael.Portfolio[str(portfolio.Name())], str(ael.Portfolio[p].prfid)):\n                return True\n        return False\n        \n    def IsSNDSettlement(self,overrideSettlement = None, override = False):\n        settlObj = self.settlObj\n        if overrideSettlement:\n            settlObj = overrideSettlement\n        validSNDSettlement = 0\n        if settlObj.RelationType() == 'None' and settlObj.Trade().TrxTrade() and (settlObj.Type() not in (self.INVALID_SETTLEMENT_TYPES)) and ((override) or (not override and not settlObj.Parent())):\n            acquirerOid = settlObj.Acquirer().Oid()\n            if acquirerOid in self.ACQUIRER:\n                validSNDSettlement = 1\n            elif acquirerOid in self.ACQUIRER_FOR_VALID_PORTF:\n                portfolio = HelperUtils.GetPortfolio(settlObj)\n                if portfolio:\n                    if self.IsValidPortfolio(portfolio):\n                        validSNDSettlement = 1\n\n        return validSNDSettlement\n\n    def TrxTradeCheck(self, s):\n        trxTrade = s.Trade().TrxTrade()\n        return s.RelationType() == 'None' and trxTrade and trxTrade.Oid() == self.settlObj.Trade().TrxTrade().Oid() and not s.Parent() and (self.settlObj.Type() not in (self.INVALID_SETTLEMENT_TYPES))\n\n    def TrxTradeCheckOnly(self, s, obj):\n        trxTrade = s.Trade().TrxTrade()\n        return trxTrade and trxTrade.Oid() == obj.Trade().TrxTrade().Oid() and (obj.Type() not in (self.INVALID_SETTLEMENT_TYPES))\n\n    def GetSNDSettlements(self):\n        valid_settl = acm.FArray()\n        if self.settlObj.Trade():\n            if not HelperUtils.IsPartOfNetting(self.settlObj):\n                settl_ACQUIRER_A = HelperUtils.SelectSettlementsCPRestriction(self.settlObj, 'Authorised', self.ACQUIRER[0], self.settlObj.RelationType(), self.settlObj.ValueDay())\n                \n                settlACQUIRER_FOR_VALID_PORTF_A = HelperUtils.SelectSettlementsCPRestriction(self.settlObj, 'Authorised', self.ACQUIRER_FOR_VALID_PORTF[0], self.settlObj.RelationType(), self.settlObj.ValueDay())\n\n                for s in settl_ACQUIRER_A:\n                    if self.TrxTradeCheck(s):\n                        valid_settl.Add(s)\n                \n                for s in settlACQUIRER_FOR_VALID_PORTF_A:\n                    if self.TrxTradeCheck(s):\n                        if self.IsSNDSettlement(s):\n                            valid_settl.Add(s)\n        return valid_settl\n\n    def GetParentSettlement(self, settlementsToNet):\n        s = settlementsToNet[0]\n        potentialParentsA = HelperUtils.SelectSettlementsCPRestriction(s, 'Authorised', self.ACQUIRER[0], 'Ad Hoc Net', s.ValueDay())\n\n        potentialParentsB = HelperUtils.SelectSettlementsCPRestriction(s, 'Authorised', self.ACQUIRER_FOR_VALID_PORTF[0], 'Ad Hoc Net', s.ValueDay())\n        \n        potentialParents = acm.FArray()\n        for p in potentialParentsA:\n            potentialParents.Add(p)\n        for p in potentialParentsB:\n            potentialParents.Add(p)\n        \n        for parent in potentialParents:\n            validParent = 1\n            for child in parent.Children():\n                if not self.IsSNDSettlement(child, True):        \n                    validParent = 0\n                elif not self.TrxTradeCheckOnly(child, s):\n                    validParent = 0\n            if validParent:\n                return parent\n        return None\n        \n    def executeSNDNetting(self):\n        if not HelperUtils.IsPartOfNetting(self.settlObj):\n            if self.IsSNDSettlement():\n                settlementsToNet = self.GetSNDSettlements()\n                parentSettlement = None\n                if settlementsToNet:\n                    parentSettlement = self.GetParentSettlement(settlementsToNet)\n                    self.isUpdateCollision = HelperUtils.executeNetting(self.settlObj, settlementsToNet, parentSettlement, 'SND_Auto_Netting')\n        self.isUpdateCollision =  False\n","sub_path":"Python modules/SettlementNettingRuleSND.py","file_name":"SettlementNettingRuleSND.py","file_ext":"py","file_size_in_byte":6329,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"27599119","text":"import time\nimport datetime\n\nfrom .sensor_type_info import APISensorTypeInfo\nimport pandas as pd\n\nclass Utils:\n    def __init__(self):\n        pass\n\n    @staticmethod\n    def now_ms():\n        \"\"\"\n        Utility function to get the time in milliseconds\n        :return:\n        \"\"\"\n        return int(time.time() * 1000)\n\n    @staticmethod\n    def convert_ts(ts):\n        \"\"\"\n        Convert a unix timestamp in milliseconds to a timestamp string (UTC)\n        :param ts:\n        :return:\n        \"\"\"\n        ts = ts / 1000\n        return str(datetime.datetime.utcfromtimestamp(int(ts)).strftime('%d-%m-%Y %H:%M:%S'))\n\n    @staticmethod\n    def get_dict_template(init_keys):\n        \"\"\"\n        returns a dict containing keys specified by init_keys, but each value set to None\n        :param init_keys:\n        :return:\n        \"\"\"\n        ret = dict()\n\n        for i in init_keys:\n            if i not in ret.keys():\n                ret[i] = None\n\n        return ret\n\n    # we construct an object to hold the *required* sensor data\n    # when it is \"full\" and time-aligned within a certain threshold, we call the model\n    @staticmethod\n    def is_full(data):\n        \"\"\"Ensure we have all the required types - the data dict has been preinitialized\"\"\"\n        for sensor_type in data:\n            if data[sensor_type] is None:\n                return False\n        return True\n\n    @staticmethod\n    def is_aligned(data, max_timestamp_diff:int = 30000):\n        \"\"\"ensure that all of the datums in the dict are within max_timestamp_diff of one another\"\"\"\n        timestamps = []\n        for sensor_type in data:\n            timestamps.append(data[sensor_type]['timestamp'])\n\n        min_ = min(timestamps)\n        max_ = max(timestamps)\n        diff = max_ - min_\n        if abs(diff) <= max_timestamp_diff:\n            return True\n        return False\n\n    @staticmethod\n    def is_full_and_aligned(data):\n        \"\"\"\n        Call is_aligned only after is_full passes since is_aligned tries to access the data[type]['timestamp'] index\n        and will by stymied when it is set to None by default.\n        There's also no point in checking it if the dict isn't full\n        :param data:\n        :return:\n        \"\"\"\n        is_aligned = False\n\n        is_full = Utils.is_full(data)\n        if is_full:\n            is_aligned = Utils.is_aligned(data)\n\n        return is_full and is_aligned\n\n    @staticmethod\n    def add_sensor_datum(sensor_datum: dict, mac: int, required_types, ingest_sensor_data: dict):\n        \"\"\"\n        Add a sensor_datum contents to the target dict only if\n        the type is already in required_types and the mac matches\n        :param sensor_datum:\n        :param mac:\n        :param required_types:\n        :param ingest_sensor_data:\n        :return:\n        \"\"\"\n        sensor_type = str(sensor_datum['type'])\n        datum_mac = sensor_datum['mac']\n        timestamp = sensor_datum['timestamp']\n        sensor_data = sensor_datum['data']\n\n        # ensure it matches our mac\n        if datum_mac == mac:\n            # ensure it's one of our required sensor types\n            if sensor_type in required_types:\n                ingest_sensor_data[sensor_type] = {\n                    'timestamp': timestamp,\n                    'data': sensor_data\n                }\n\n        return ingest_sensor_data\n\n    @staticmethod\n    def get_datum_df(data, sensor_type_info: APISensorTypeInfo):\n        \"\"\"\n            Get a structured dataframe for a single \"row\" of data\n            steps:\n            1. Get an average timestamp for the timestamp column\n            2. reshape the data into a matrix with one row [[col1, col2, col3, ...]]\n            3. prepend the timestamp\n            4. create a dataframe and set the columns\n        \"\"\"\n        timestamp_acc = 0\n        for sensor_type in data:\n            timestamp_acc += data[sensor_type]['timestamp']\n        timestamp_avg = int(timestamp_acc/len(data))\n\n        # the data comes out of the API sorted by key (sensortype) ascending\n        sorted_data = dict(sorted(data.items()))\n\n        sensor_types = [i for i in sorted_data]\n\n        sensor_data = [sorted_data[i]['data'] for i in sorted_data]\n\n        timestamp_str = Utils.convert_ts(timestamp_avg)\n        sensor_data.insert(0, timestamp_str)\n\n        sensor_labels = sensor_type_info.get_labels(sensor_types)\n        sensor_labels.insert(0, 'timestamp')\n\n        df_datum = pd.DataFrame([sensor_data], columns=sensor_labels)\n\n        return df_datum\n\n\n","sub_path":"python/aretasapiclient/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":4493,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"311865929","text":"from datetime import datetime\n\nfrom django.test import TestCase\nfrom django.urls import reverse\n\nfrom .models import Attendance\nfrom classroom.models import ClassRoom\nfrom staff.models import Staff\nfrom student.models import Student\n\n\ndt_string_format = \"%d/%m/%Y\"\n\n\nclass AttendanceTest(TestCase):\n    def create_attendance(self, date=datetime.strptime(\"04/04/2017\", \"%d/%m/%Y\")):\n        classroom = ClassRoom.objects.create(name='classroom1', capacity=30, web_lecture=True, shape='Oval')\n        student = Student.objects.create(current_status=\"Active\", registration_number=\"12132\", surname=\"xyz\", firstname=\"abc\", gender='Male')\n        teacher = Staff.objects.create(current_status='Active',surname='surname1',firstname='firstname1',other_name='other_name1',\n                     gender='Male',\n                     date_of_birth=datetime.strptime(\"04/04/2017\", \"%d/%m/%Y\"),\n                     date_of_admission=datetime.strptime(\"25/01/2018\", \"%d/%m/%Y\"),\n                     salary=10000,mobile_number='1234567890',address='address1',\n                     others='',takes_web_lecture=False)\n        return Attendance.objects.create(date=date,classroom=classroom,student=student,teacher=teacher)\n\n    def test_attendance_creation(self):\n        a = self.create_attendance()\n        self.assertTrue(isinstance(a, Attendance))\n        self.assertEqual(str(a), f'{a.classroom} {a.student} {a.teacher}')\n\n    def test_whatever_list_view(self):\n        s = self.create_attendance()\n        url = reverse(\"attendance-list\")\n        resp = self.client.get(url)\n        self.assertEqual(resp.status_code, 200)\n        self.assertIn(s.teacher.surname.encode(), resp.content)\n\n","sub_path":"attendance/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":1676,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"482562943","text":"# dataset settings\ndataset_type = 'CIFAR10'\nimg_norm_cfg = dict(\n    mean=[125.307, 122.961, 113.8575],\n    std=[51.5865, 50.847, 51.255],\n    to_rgb=True)\ntrain_pipeline = [\n    dict(type='RandomCrop', size=32, padding=4),\n    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),\n    dict(type='Normalize', **img_norm_cfg),\n    dict(type='ImageToTensor', keys=['img']),\n    dict(type='ToTensor', keys=['gt_label']),\n    dict(type='Collect', keys=['img', 'gt_label'])\n]\ntest_pipeline = [\n    dict(type='Normalize', **img_norm_cfg),\n    dict(type='ImageToTensor', keys=['img']),\n    dict(type='ToTensor', keys=['gt_label']),\n    dict(type='Collect', keys=['img', 'gt_label'])\n]\ndata = dict(\n    samples_per_gpu=16,\n    workers_per_gpu=2,\n    train=dict(\n        type=dataset_type, data_prefix='data/cifar10',\n        pipeline=train_pipeline),\n    val=dict(\n        type=dataset_type, data_prefix='data/cifar10', pipeline=test_pipeline),\n    test=dict(\n        type=dataset_type, data_prefix='data/cifar10', pipeline=test_pipeline))\n","sub_path":"configs/_base_/datasets/cifar10_bs16.py","file_name":"cifar10_bs16.py","file_ext":"py","file_size_in_byte":1042,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"65990464","text":"# -*- coding: UTF-8 -*-\n\nimport json\n\n\n\"\"\"\nFunción que sirve para corregir un problema de Python en el que se pide escribir\núnicamente un valor resultante de una invocación o expresión. \n\n - 'filename': \n     ruta absoluta donde almacenar el JSON con el resultados\n     \n - 'resultado':\n     resultado esperado que debe escribir el alumno\n     \n - 'ejecutor':\n     función Python que devuelve el valor que ha escrito el alumno     \n\"\"\"\ndef corrector_texto(filename, resultado, ejecutor):\n    dicc = {'isCorrect': resultado == ejecutor() }\n    if not dicc['isCorrect']:\n        dicc['Hints'] = [\"El valor {0} no coincide con el esperado\".format(ejecutor())]\n   \n    # Escribir el diccionario generado\n    with open(filename, 'w') as outfile:\n        json.dump(dicc, outfile)\n","sub_path":"temas/Python3/correctores/common/corrector_texto.py","file_name":"corrector_texto.py","file_ext":"py","file_size_in_byte":779,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"217439408","text":"# This Python file uses the following encoding: utf-8\nfrom __future__ import unicode_literals\nfrom collections import OrderedDict\nfrom TechuHelpers import *\n\n__version__ = \"1.1\"\n__all__ = ['QUERY_DEFAULT_OPTIONS', 'QUERY_ORDER_ASC', 'QUERY_ORDER_DESC', 'TechuQuery',]\n\n''' Constants '''\n\nQUERY_DEFAULT_OPTIONS = {\n    'agent_query_timeout' : 10000,\n    'boolean_simplify'    : 0,\n    'comment'             : '',\n    'cutoff'              : 0,\n    'field_weights'       : '',\n    'global_idf'          : '',\n    'idf'                 : 'normalized',\n    'index_weights'       : '',\n    'max_matches'         : 10000,\n    'max_query_time'      : 2,\n    'ranker'              : 'proximity_bm25',\n    'retry_count'         : 2,\n    'retry_delay'         : 100,\n    'reverse_scan'        : 0,\n    'sort_method'         : 'pq',\n}\nQUERY_ORDER_ASC = '1'\nQUERY_ORDER_DESC = '-1'\n\nclass TechuQuery(object):\n    DEBUG = False\n\n    def __init__(self, **kwargs):    \n        self.QUERY = kwargs.get('q', kwargs.get('query', ''))   \n        self.META = kwargs.get('meta', False)\n        self.ORDER_WITHIN_GROUP = OrderedDict()\n        self.GROUPBY = OrderedDict()\n        self.ORDERBY = OrderedDict()\n        self.WHERE   = {}    \n        self.FIELDS  = []\n        self.LIMIT   = {}\n        self.OPTIONS = {}\n        self.INDEXES = []\n\n    def add_filter(self, field, operator, value):\n        value = to_timestamp(value)\n        if not field in self.WHERE:\n            self.WHERE[field] = []\n        self.WHERE[field].append([ operator, value ])\n        return self\n\n    def __getattr__(self, __attr__, *args, **kwargs):\n        if __attr__ == \"eq\":\n            operator = '='\n        elif __attr__ == \"ne\":\n            operator = \"!=\"\n        elif __attr__ == \"lt\":\n            operator = \"<\"\n        elif __attr__ == \"gt\":\n            operator = \">\"  \n        elif __attr__ == \"ge\":\n            operator = \">=\"    \n        elif __attr__ == \"le\":\n            operator = \"<=\"      \n        elif __attr__ == \"include\":\n            operator = \"in\"\n        elif __attr__ == \"exclude\":\n            operator = \"not in\"\n        else:\n            operator = None\n        if not operator is None:\n            return self.add_filter(args[0], operator, args[1])\n\n    def add_query(self, query):\n        self.QUERY = query\n\n    def add_option(self, option, value):\n        options = listify(option)\n        values = listify(value)\n        if len(options) != len(values):\n            raise TechuOptionException('Different length of query options: %s and values: %s' % (str(options), str(values),))\n        for _ in options:\n            if not _ in QUERY_DEFAULT_OPTIONS:\n                raise TechuOptionDoesNotExistException('Invalid Query Option: %s' % _)\n        self.OPTIONS.update(dict(zip(options, values)))\n        return self\n\n    def add_field(self, fields):\n        for field in listify(fields):\n            if not field in self.FIELDS:\n                self.FIELDS.append(field)\n        return self\n\n    def groupby(self, field):\n        self.GROUPBY[field] = 1    \n        return self\n\n    def add_index(self, index):\n        self.INDEXES.extend(listify(index))\n        return self\n\n    def order(self, field, direction='1'):\n        self.ORDERBY[field] = direction\n\n    def group_order(self, field, direction='1'):\n        self.ORDER_WITHIN_GROUP[field] = direction\n        return self\n\n    def limit(self, **kwargs):\n        try:\n            self.LIMIT.update(dict(zip(kwargs.keys(), map(int, kwargs.values))))\n        except:\n            raise TechuLimitException\n        return self\n\n    def build(self):\n        query = {\n                'q' : self.QUERY,      \n        }\n        if self.LIMIT:\n            query['limit'] = {'offset': 0}      \n            query['limit'].update(self.LIMIT)\n        if self.ORDER_WITHIN_GROUP:\n            query['order_within_group'] = self.ORDER_WITHIN_GROUP.items()\n        if self.ORDERBY:\n            query['order_by'] = self.ORDERBY.items()\n        if self.INDEXES:\n            query['indexes'] = self.INDEXES\n        if self.GROUPBY:\n            query['group_by'] = self.GROUPBY.keys()\n        if self.FIELDS:\n            query['fields'] = self.FIELDS\n        else:\n            query['fields'] = '*'\n        query['where'] = self.WHERE\n        return query\n\n","sub_path":"TechuQuery.py","file_name":"TechuQuery.py","file_ext":"py","file_size_in_byte":4287,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"631700546","text":"\"\"\"\nTHIS IS NOW OUTDATED/NOT A REQUIRED FUNCTION\nDATABASE FRONTEND/VIEWING WILL BE CONDUCTED THROUGH PGADMIN\n\"\"\"\n#FOR PROTOTYPING ONLY, TO BE EXPANDED UPON FOR WEB INTEGRATION/POSTGRESQL\n#Author: Andrew Moreno\n#Date: Feb 8th\n\nfrom tkinter import *\nimport User_Profiles_DB\n\n\ndef get_selected_row(event):\n    try:\n        global selected_tuple\n        index=list1.curselection()[0]\n        selected_tuple=list1.get(index)\n        e1.delete(0, END)\n        e1.insert(END, selected_tuple[1])\n        e2.delete(0, END)\n        e2.insert(END, selected_tuple[2])\n        e3.delete(0, END)\n        e3.insert(END, selected_tuple[3])\n        e4.delete(0, END)\n        e4.insert(END, selected_tuple[4])\n        e5.delete(0, END)\n        e5.insert(END, selected_tuple[5])\n        e6.delete(0, END)\n        e6.insert(END, selected_tuple[6])\n        e7.delete(0, END)\n        e7.insert(END, selected_tuple[7])\n        e8.delete(0, END)\n        e8.insert(END, selected_tuple[8])\n        e9.delete(0, END)\n        e9.insert(END, selected_tuple[9])\n        e10.delete(0, END)\n        e10.insert(END, selected_tuple[10])\n        e11.delete(0, END)\n        e11.insert(END, selected_tuple[11])\n        e12.delete(0, END)\n        e12.insert(END, selected_tuple[12])\n        e13.delete(0, END)\n        e13.insert(END, selected_tuple[13])\n        e14.delete(0, END)\n        e14.insert(END, selected_tuple[14])\n        e15.delete(0, END)\n        e15.insert(END, selected_tuple[15])\n    except IndexError:\n        pass\n\n\ndef view_command():\n    list1.delete(0, END)\n    for row in User_Profiles_DB.view():\n        list1.insert(END,row)\n\ndef search_command():\n    list1.delete(0, END)\n    for row in User_Profiles_DB.search(firstname_text.get(), lastname_text.get(),\\\n        username_text.get(), email_text.get(), age_text.get(), \\\n        datejoined_text.get(), address_text.get(), city_text.get(), province_text.get(), \\\n        country_text.get(), postalcode_text.get(), distance_value.get(), txa_value.get(), \\\n        userrating_value.get(), status_text.get()):\n        list1.insert(END,row)\n\ndef newuser_command():\n    User_Profiles_DB.insert_newprofile_up_db(firstname_text.get(), lastname_text.get(),\\\n        username_text.get(), email_text.get(), age_text.get(), \\\n        address_text.get(), city_text.get(), province_text.get(), \\\n        country_text.get(), postalcode_text.get())\n    list1.delete(0, END)\n    view_command()\n\ndef delete_command():\n    User_Profiles_DB.delete(selected_tuple[0])\n    view_command()\n\ndef update_command():\n    User_Profiles_DB.update(firstname_text.get(), lastname_text.get(),\\\n        username_text.get(), email_text.get(), age_text.get(), \\\n        datejoined_text.get(), address_text.get(), city_text.get(), province_text.get(), \\\n        country_text.get(), postalcode_text.get(), txa_value.get(), \\\n        userrating_value.get(), status_text.get(), selected_tuple[0])\n\n\nwindow = Tk()\n\nl1 = Label(window, text=\"First Name\")\nl1.grid(row=0, column=0)\n\nl2 = Label(window, text=\"Last Name\")\nl2.grid(row=0, column=2)\n\nl3 = Label(window, text=\"Username\")\nl3.grid(row=0, column=4)\n\nl4 = Label(window, text=\"Email\")\nl4.grid(row=0, column=6)\n\nl5 = Label(window, text=\"Age\")\nl5.grid(row=0, column=8)\n\nl6 = Label(window, text=\"Date Joined\")\nl6.grid(row=1, column=0)\n\nl7 = Label(window, text=\"Address\")\nl7.grid(row=1, column=2)\n\nl8 = Label(window, text=\"City\")\nl8.grid(row=1, column=4)\n\nl9 = Label(window, text=\"Province\")\nl9.grid(row=1, column=6)\n\nl10 = Label(window, text=\"Country\")\nl10.grid(row=1, column=8)\n\nl11 = Label(window, text=\"Postal Code\")\nl11.grid(row=2, column=0)\n\nl12 = Label(window, text=\"Distance\")\nl12.grid(row=2, column=2)\n\nl13 = Label(window, text=\"TXA\")\nl13.grid(row=2, column=4)\n\nl14 = Label(window, text=\"User Rating\")\nl14.grid(row=2, column=6)\n\nl15 = Label(window, text=\"Status\")\nl15.grid(row=2, column=8)\n\n\nfirstname_text = StringVar()\ne1 = Entry(window, textvariable=firstname_text)\ne1.grid(row=0, column=1)\n\nlastname_text = StringVar()\ne2 = Entry(window, textvariable=lastname_text)\ne2.grid(row=0, column=3)\n\nusername_text = StringVar()\ne3 = Entry(window, textvariable=username_text)\ne3.grid(row=0, column=5)\n\nemail_text = StringVar()\ne4 = Entry(window, textvariable=email_text)\ne4.grid(row=0, column=7)\n\nage_text = StringVar()\ne5 = Entry(window, textvariable=age_text)\ne5.grid(row=0, column=9)\n\ndatejoined_text = StringVar()\ne6 = Entry(window, textvariable=datejoined_text)\ne6.grid(row=1, column=1)\n\naddress_text = StringVar()\ne7 = Entry(window, textvariable=address_text)\ne7.grid(row=1, column=3)\n\ncity_text = StringVar()\ne8 = Entry(window, textvariable=city_text)\ne8.grid(row=1, column=5)\n\nprovince_text = StringVar()\ne9 = Entry(window, textvariable=province_text)\ne9.grid(row=1, column=7)\n\ncountry_text = StringVar()\ne10 = Entry(window, textvariable=country_text)\ne10.grid(row=1, column=9)\n\npostalcode_text = StringVar()\ne11 = Entry(window, textvariable=postalcode_text)\ne11.grid(row=2, column=1)\n\ndistance_value = StringVar()\ne12 = Entry(window, textvariable=distance_value)\ne12.grid(row=2, column=3)\n\ntxa_value = StringVar()\ne13 = Entry(window, textvariable=txa_value)\ne13.grid(row=2, column=5)\n\nuserrating_value = StringVar()\ne14 = Entry(window, textvariable=userrating_value)\ne14.grid(row=2, column=7)\n\nstatus_text = StringVar()\ne15 = Entry(window, textvariable=status_text)\ne15.grid(row=2, column=9)\n\n\n\nlist1=Listbox(window, height=6, width=150)\nlist1.grid(row=3, column=0, rowspan=5, columnspan=8)\n\nsb1=Scrollbar(window, orient = VERTICAL)\nsb1.grid(row=3, column=14, rowspan=5, sticky=N+S+W)\n\nsb2=Scrollbar(window, orient = HORIZONTAL)\nsb2.grid(row=8, column=0, columnspan=8, sticky=W+E+N)\n\nlist1.configure(yscrollcommand=sb1.set)\nsb1.configure(command=list1.yview)\n\nlist1.configure(xscrollcommand=sb2.set)\nsb2.configure(command=list1.xview)\n\nlist1.bind('<<ListboxSelect>>', get_selected_row)\n\nb1=Button(window, text=\"View All\", width=12, command=view_command)\nb1.grid(row=3, column=9)\n\nb2=Button(window, text=\"Search Entry\", width=12, command=search_command)\nb2.grid(row=4, column=9)\n\nb3=Button(window, text=\"Add New User\", width=12, command=newuser_command)\nb3.grid(row=5, column=9)\n\nb4=Button(window, text=\"Update Selected\", width=12, command=update_command)\nb4.grid(row=6, column=9)\n\nb5=Button(window, text=\"Delete Selected\", width=12, command=delete_command)\nb5.grid(row=7, column=9)\n\nb6=Button(window, text=\"Close\", width=12, command=window.destroy)\nb6.grid(row=8, column=9)\n\nwindow.mainloop()\n","sub_path":"User_Profiles/User_Profiles_Frontend.py","file_name":"User_Profiles_Frontend.py","file_ext":"py","file_size_in_byte":6449,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"106103974","text":"from .logbase import LogBase\nfrom .thumbnail import THUMBNAIL_IMAGE\nfrom .seekablerequest import SeekableRequest\nfrom .responsestream import IteratorByteStream\nfrom requests import request\nfrom urllib.parse import urlencode\nimport httplib2\nimport json\n\nMIME_TYPE = \"application/tar\"\nFOLDER_MIME_TYPE = 'application/vnd.google-apps.folder'\nFOLDER_NAME = 'Hass.io Snapshots'\nDRIVE_VERSION = \"v3\"\nDRIVE_SERVICE = \"drive\"\n\nSELECT_FIELDS = \"id,name,appProperties,size,trashed,mimeType,modifiedTime,capabilities\"\nTHUMBNAIL_MIME_TYPE = \"image/png\"\nQUERY_FIELDS = \"nextPageToken,files(\" + SELECT_FIELDS + \")\"\nCREATE_FIELDS = SELECT_FIELDS\nURL_FILES = \"https://www.googleapis.com/drive/v3/files/\"\nURL_UPLOAD = \"https://www.googleapis.com/upload/drive/v3/files?uploadType=resumable\"\nPAGE_SIZE = 100\nCHUNK_SIZE = 5 * 262144\n\n\nclass DriveRequests(LogBase):\n    def __init__(self, creds, cred_storage):\n        self.creds = creds\n        self.cred_storage = cred_storage\n\n    def _getHeaders(self):\n        # TODO: this shoudl not use httplib2\n        if self.creds.access_token_expired:\n            self.creds.refresh(httplib2.Http())\n            self.cred_storage.put(self.creds)\n        return {\n            \"Authorization\": \"Bearer \" + json.loads(self.creds.to_json())['access_token']\n        }\n\n    def get(self, id):\n        q = {\n            \"fields\": SELECT_FIELDS\n        }\n        return self.retryRequest(\"GET\", URL_FILES + id + \"/?\" + urlencode(q), is_json=True)\n\n    def download(self, id, length_bytes):\n        iterator = self.retryRequest(\"GET\", URL_FILES + id, stream=True).iter_content(chunk_size=CHUNK_SIZE)\n        return IteratorByteStream(iterator, length_bytes)\n\n    def downloadSeekable(self, id, length_bytes):\n        return SeekableRequest(URL_FILES + id, self._getHeaders(), length_bytes)\n\n    def query(self, query):\n        # TODO: page size should be configurable\n        continuation = None\n        while True:\n            q = {\n                \"q\": query,\n                \"fields\": QUERY_FIELDS,\n                \"pageSize\": PAGE_SIZE\n            }\n            if continuation:\n                q[\"pageToken\"] = continuation\n            response = self.retryRequest(\"GET\", URL_FILES + \"?\" + urlencode(q), is_json=True)\n            for item in response['files']:\n                yield item\n            if \"nextPageToken\" not in response or len(response['nextPageToken']) <= 0:\n                break\n            else:\n                continuation = response['nextPageToken']\n\n    def update(self, id, update_metadata):\n        self.retryRequest(\"PATCH\", URL_FILES + id, json=update_metadata)\n\n    def delete(self, id):\n        self.retryRequest(\"DELETE\", URL_FILES + id)\n\n    def create(self, stream, metadata, mime_type):\n        # Upload logic is complicated. See https://developers.google.com/drive/api/v3/manage-uploads\n        total_size = stream.size()\n        metadata_bytes = json.dumps(metadata).encode(encoding='UTF-8')\n        headers = {\n            \"X-Upload-Content-Type\": mime_type,\n            \"X-Upload-Content-Length\": str(total_size),\n            \"Content-Length\": str(len(metadata_bytes)),\n            \"Content-Type\": \"application/json; charset=UTF-8\"\n        }\n        initial = self.retryRequest(\"POST\", URL_UPLOAD, headers=headers, data=metadata_bytes)\n        if 'Location' not in initial.headers:\n            raise Exception(\"Response from Google Drive included no upload location information\")\n        location = initial.headers['Location']\n        while True:\n            start = stream.tell()\n            data = stream.read(CHUNK_SIZE)\n            if len(data) == 0:\n                raise Exception(\"Snapshot file stream ended prematurely while uploading to Google Drive\")\n            headers = {\n                \"Content-Length\": str(len(data)),\n                \"Content-Range\": \"bytes {0}-{1}/{2}\".format(start, start + len(data) - 1, total_size)\n            }\n            partial = self.retryRequest(\"PUT\", location, headers=headers, data=data)\n            yield float(start + len(data)) / float(total_size)\n            if partial.status_code == 200 or partial.status_code == 201:\n                # upload completed, return the object json\n                yield partial.json()\n                break\n            elif partial.status_code == 308:\n                # upload partially complete, seek to the new requested position\n                if \"Range\" not in partial.headers or not partial.headers[\"Range\"].startswith(\"bytes=0-\"):\n                    raise Exception(\"Invalid range header from Google while uploading a snapshot to Google Drive.\")\n                try:\n                    position = int(partial.headers[\"Range\"][len(\"bytes=0-\"):])\n                    stream.seek(position + 1)\n                except ValueError:\n                    raise Exception(\"Invalid range header from Google while uploading a snapshot to Google Drive: \" + partial.headers[\"Range\"])\n            else:\n                raise Exception(\"Unexpected HTTP response while uploading a snapshot to Google Drive: \" + str(partial.status_code))\n\n    def retryRequest(self, method, url, headers=None, json=None, data=None, is_json=False, stream=False):\n        # TODO: This should *actually* retry on retryable errors\n        send_headers = self._getHeaders()\n        if headers:\n            send_headers.update(headers)\n        response = request(method, url, headers=send_headers, json=json, timeout=(30, 30), data=data, stream=stream)\n        response.raise_for_status()\n        if is_json:\n            return response.json()\n        else:\n            return response\n        # TODO: make timeout configurable\n","sub_path":"hassio-google-drive-backup/backup/driverequests.py","file_name":"driverequests.py","file_ext":"py","file_size_in_byte":5635,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"156945130","text":"import psycopg2\nimport logging, os\n\nfrom .models import User, Question, Answer\n\nlogging.basicConfig(level=logging.DEBUG)\n\nconnection_params = {\n    'database': os.environ.get('POSTGRES_DB') or 'default',\n    'user': os.environ.get('POSTGRES_USER') or 'admin',\n    'password': os.environ.get('POSTGRES_PASSWORD') or 'admin',\n    'host': 'db',\n    'port': '5432',\n}\n\nconnection = psycopg2.connect(**connection_params)\nlogging.debug('Successfully connected to database')\n\n\ndef add_user(email, username, password):\n    cur = connection.cursor()\n    cur.execute(\"INSERT INTO users (email, username, password) VALUES ('{0}', '{1}', '{2}');\"\n                .format(email, username, password))\n    connection.commit()\n\n\ndef add_question(user_id, question_text):\n    cur = connection.cursor()\n    cur.execute(\"INSERT INTO questions (user_id, question_text) VALUES ('{0}', '{1}');\"\n                .format(user_id, question_text))\n    connection.commit()\n\n\ndef add_answer(question_id, user_id, answer_text):\n    cur = connection.cursor()\n    cur.execute(\"INSERT INTO answers (question_id, user_id, answer_text) VALUES ('{0}', '{1}', '{2}');\"\n                .format(question_id, user_id, answer_text))\n    connection.commit()\n\n\ndef get_user(**kwargs):\n    cur = connection.cursor()\n\n    if 'email' in kwargs and 'username' in kwargs:\n        cur.execute(\"SELECT * FROM users WHERE email='{0}' AND username='{1}';\"\n                    .format(kwargs['email'], kwargs['username']))\n        res = cur.fetchall()\n        if res:\n            return User(res[0][0], res[0][1], res[0][2], res[0][3])\n        return None\n\n    if 'username' in kwargs:\n        cur.execute(\"SELECT * FROM users WHERE username='{0}';\".format(kwargs['username']))\n        res = cur.fetchall()\n        if res:\n            return User(res[0][0], res[0][1], res[0][2], res[0][3])\n        return None\n\n\ndef get_userID(**kwargs):\n    cur = connection.cursor()\n\n    if 'username' in kwargs:\n        cur.execute(\"SELECT id FROM users WHERE username='{0}';\".format(kwargs['username']))\n        res = cur.fetchall()\n        return res[0][0] if len(res) > 0 else None\n\n    return None\n\n\n\ndef get_questionID(**kwargs):\n    cur = connection.cursor()\n\n    if 'question' in kwargs:\n        cur.execute(\"SELECT id FROM questions WHERE question_text='{0}';\".format(kwargs['question']))\n        res = cur.fetchall()\n        return res[0][0] if len(res) > 0 else None\n    else:\n        return\n\n\ndef get_question(**kwargs):\n    cur = connection.cursor()\n\n    if 'id' in kwargs:\n        cur.execute(\"SELECT * FROM questions WHERE id='{0}';\".format(kwargs['id']))\n        res = cur.fetchall()\n        if res:\n            return Question(res[0][0], res[0][1], res[0][2])\n        return None\n\n\ndef get_questions(**kwargs):\n    cur = connection.cursor()\n\n    cur.execute(\"SELECT * FROM questions;\")\n    res = cur.fetchall()\n    if res:\n        return [Question(res[i][0], res[i][1], res[i][2]) for i in range(0, len(res))]\n    return []\n\n\ndef get_answers(**kwargs):\n    cur = connection.cursor()\n\n    if 'question_id' in kwargs:\n        cur.execute(\"SELECT * FROM answers WHERE question_id='{0}';\".format(kwargs['question_id']))\n        res = cur.fetchall()\n        if res:\n            return [Answer(res[i][0], res[i][1], res[i][2], res[i][3]) for i in range(0, len(res))]\n        return []","sub_path":"vulnerable/server/src/database.py","file_name":"database.py","file_ext":"py","file_size_in_byte":3329,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"23379670","text":"import requests\nfrom bs4 import BeautifulSoup\n\ndef express():\n\turl = \"https://indianexpress.com/section/technology\"\n\tpage = requests.get(url)\n\n\tsoup = BeautifulSoup(page.content, 'html.parser')\n\t\n\tdiv=soup.find(\"div\",class_='top-article')\n\theadings=div.findAll(\"li\")\n\t\n\tcount=0\n\tList = []\n\t\n\tfor heading in headings:\n\t\tcount+=1\n\t\tif count==15:\n\t\t\tbreak\n\n\t\t#if count==11:\n\t\t\t#List.append(\"\\n\\n🌐 Join @premiumcoursesdrive for daily tech news !\")\n\t\t\n\t\tList.append(\"\\n\\n🌐\")\n\t\tList.append(heading.h3.a.text)\n\n\treturn List\n\t\n","sub_path":"sites/indianexpress.py","file_name":"indianexpress.py","file_ext":"py","file_size_in_byte":525,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"466435818","text":"'''\r\n\r\nString Reduction\r\nHIDE QUESTION\r\nHave the function StringReduction(str) take the str parameter being passed and return the smallest number you can get through the following reduction method. The method is: Only the letters a, b, and c will be given in str and you must take two different adjacent characters and replace it with the third. For example \"ac\" can be replaced with \"b\" but \"aa\" cannot be replaced with anything. This method is done repeatedly until the string cannot be further reduced, and the length of the resulting string is to be outputted.\r\n\r\nFor example: if str is \"cab\", then \"ca\" can be reduced to \"b\" and you get \"bb\" (you can also reduce it to \"cc\"). The reduction is done so the output should be 2. If str is \"bcab\", \"bc\" reduces to \"a\", so you have \"aab\", then \"ab\" reduces to \"c\", and the final string \"ac\" is reduced to \"b\" so the output should be 1.\r\n\r\nUse the Parameter Testing feature in the box below to test your code with different arguments.\r\n\r\n\r\n\"[abcabc]\"\r\n\r\n'''\r\n\r\n\r\n\r\n\r\ndef StringReduction(str): \r\n    str = list(str)\r\n    cSet = set(['a','b','c'])\r\n    repeat = True\r\n    while repeat:\r\n      i = 0\r\n      repeat = False\r\n      print(len(str))\r\n      while i < len(str)-1: \r\n        print(i)\r\n        if str[i] != str[i+1]: # if next char is not THE SAME\r\n          print(list(cSet-set([str[i],str[i+1]])) )\r\n          str[i:i+2] = list(cSet-set([str[i],str[i+1]])) \r\n          repeat = True\r\n        else:\r\n          i += 1\r\n    return len(str)\r\n    \r\nprint(StringReduction(\"cccc\"))","sub_path":"Recursion/StringReduction.py","file_name":"StringReduction.py","file_ext":"py","file_size_in_byte":1529,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"371875591","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('dictionary', '0009_auto_20150408_0545'),\n    ]\n\n    operations = [\n        migrations.AlterModelOptions(\n            name='term',\n            options={'verbose_name': 'Term', 'verbose_name_plural': 'Terms'},\n        ),\n        migrations.AlterModelOptions(\n            name='translation',\n            options={'ordering': ['term__text'], 'verbose_name': 'Translation', 'verbose_name_plural': 'Translations'},\n        ),\n        migrations.AlterField(\n            model_name='term',\n            name='POS',\n            field=models.CharField(help_text='Term|POS|help_text', max_length=50, verbose_name='Term|POS', blank=True),\n        ),\n        migrations.AlterField(\n            model_name='term',\n            name='text',\n            field=models.CharField(help_text='Term|text|help_text', max_length=50),\n        ),\n        migrations.AlterField(\n            model_name='translation',\n            name='POS',\n            field=models.CharField(help_text='Translation|POS|help_text', max_length=50, verbose_name='Translation|POS', blank=True),\n        ),\n        migrations.AlterField(\n            model_name='translation',\n            name='sense',\n            field=models.CharField(help_text='Translation|sense|help_text', max_length=50, verbose_name='Translation|sense', blank=True),\n        ),\n        migrations.AlterField(\n            model_name='translation',\n            name='term',\n            field=models.ForeignKey(verbose_name='Term', to='dictionary.Term', help_text='Select the originating term to associate with this Translation'),\n        ),\n        migrations.AlterField(\n            model_name='translation',\n            name='to_lang',\n            field=models.ForeignKey(default=b'ngb', verbose_name='Translation|to_lang', to='dictionary.Language', help_text='Translation|to_lang|help_text'),\n        ),\n        migrations.AlterField(\n            model_name='translation',\n            name='to_word',\n            field=models.CharField(help_text='Translation|to_word|help_text', max_length=100, verbose_name='Translation|to_word'),\n        ),\n    ]\n","sub_path":"dictionary/migrations/0010_auto_20150408_2337.py","file_name":"0010_auto_20150408_2337.py","file_ext":"py","file_size_in_byte":2252,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"472856979","text":"#-*- coding:utf-8 _*-  \n\"\"\" \n@author:Administrator\n@file: 以小数据量为基准.py\n@time: 2018/7/13\n\"\"\"\n\nimport pandas as pd\nimport os\nimport re\n\n\n'''\n注意以drop的方式来删除行没有以赋值方式获取数据快；\n直接用new_data.loc[i] = data.loc[i] 要快点；\n'''\n\n\ndef read_csv_data(filename):\n    # 这种方式的文件读取方式仅限于py文件和dataset 文件属于同一级目录下\n    current_path = os.getcwd() #\n    file_name = '/dataset/{}.csv'.format(filename)\n    file_path = current_path + file_name\n    data = pd.read_csv(file_path)\n    return data\n\n\ndef to_csv_file(dataframe,filename):\n    # 把dataframe转换成csv文件并存储到dataset下,仅限于py文件和dataset文件属于同一级目录下\n    current_path = os.getcwd()  #\n    file_name = '/dataset/{}.csv'.format(filename)\n    file_path = current_path + file_name\n    dataframe.to_csv(file_path, index=False)\n\n\n\n\n# 以缺失值在%50以上，有效数据大于10000中的列为基准在有效数据大于10000上获取有效数据：\n\n# 获取列：\n\nmore_than_10000_data = read_csv_data('test_house_info')\n# more_10000_and_more_middle_data = read_csv_data('more_10000_and_more_middle')\n# based_column = more_10000_and_more_middle_data.columns\n# print(based_column[0])\n# contactFirstName\n\n# 还是要拿more_than_10000 数据，以10000有效数据到，缺失率大于%50 的列（不为空的）为基准获取样本数据；\n\n\n\n\ndef delete_row_based_on_column_null_fast_way(dataframe,column):\n    new_data = pd.DataFrame(columns=list_add)\n    data_length = len(dataframe)\n    for i in range(data_length):\n            row_i = dataframe.loc[i]\n            # 判断指定位置的数书否缺失\n            column_row_i = row_i[column]\n            if pd.notna(column_row_i):\n                new_data.loc[i] = dataframe.loc[i]\n                print('new_data_len_i', len(new_data))\n    return new_data\n# new_data = delete_row_based_on_column_null(data,'price')\n# print('datashape',data.shape)\n# print('new_data_shape',new_data.shape)\n\n\n\n\n\ndef delete_row_based_on_column_null(dataframe,column):\n    new_data = pd.DataFrame()\n    count = 0  # 第几次删除行，第一次就要用原始数据进行赋值，后面就不用了，直接inplace=True，为了保留原始文件；\n    for i in range(len(dataframe)):\n            row_i = dataframe.loc[i]\n            # 判断指定位置的数书否缺失\n            column_row_i = row_i[column]\n            if pd.isna(column_row_i):\n                count += 1\n                if count == 1:\n                    new_data = dataframe.drop(index=i)\n                    print('new_data_len_i', len(new_data))\n                if count > 1:\n                    new_data.drop(index=i, inplace=True)\n    return new_data\n\n\nnew_data = delete_row_based_on_column_null(more_than_10000_data,'price')\nprint(more_than_10000_data.shape)\nprint(new_data.shape)\n\n\n# # 循环处理 获取每一次处理之后的文件；\n# for column in more_10000_and_more_middle_data.columns:\n#     new_data = delete_row_based_on_ration(more_than_10000_data,column)\n#     print(column,':的形状',new_data.shape)\n#     new_data.tocsv(new_data,'{}'.format(column))","sub_path":"data_analysis/previous_process_way/以小数据量为基准.py","file_name":"以小数据量为基准.py","file_ext":"py","file_size_in_byte":3183,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"180392503","text":"#!C:\\Python27\\python.exe\n\n'''\nProgrammer: Vidyadhar Thatte\nID: vvt216\n\nfilename: rec09.py\n\nPurpose: A program for the family to create a shopping list.\nAssumptions: the user never enters the a different data type other than int in the menu.\n\n'''\nfrom __future__ import print_function\n\nFOODLIST = [\"milk\", \"chocolate covered cherries\", \"apple\", \"orange\", \"banana\",\n          \"corn on the cob\", \"kampyo sushi\", \"asparagus\", \"avacado\", \"alfalfa\",\n          \"acorn squash\", \"almond package\", \"arugala bunch\", \"artichoke\", \"applesauce\",\n          \"wasabi\", \"udon noodles\", \"tunafish can\", \"apple juice\", \"avocado sushi\",\n          \"bruscetta\", \"bagel\", \"barley\", \"bisque\", \"bluefish\", \"bread\", \"broccoli\",\n          \"buritto\", \"babaganoosh\", \"cabbage\", \"chocolate cake\",  \"red velvet cake\",\n          \"strawberry short cake\", \"carrots\", \"celery\", \"cheese\", \"catfish\", \"chowder\",\n          \"clams\", \"coffee\", \"corn\", \"crab\", \"curry\", \"cereal\", \"chimichanga\", \"dumpling\",\n          \"donut\", \"egg\", \"enchilada\", \"eggroll\", \"englishMuffin\", \"edimame\", \"eelSushi\",\n          \"oToroSashimi\", \"fajita\", \"falafel\", \"fondu\", \"french toast\", \"french dip\", \"garlic\",\n          \"ginger\", \"gnocchi\", \"granola\", \"grape\", \"green bean\", \"guancamole\", \"gumbo\", \"grits\",\n          \"graham crackers\", \"halibut\", \"honey\", \"huenos rancheros\", \"hash browns\", \"hummus\",\n          \"chocolate ice cream\", \"strawberryIceCream\", \"cherry garcia ice cream\", \"puri\",\n          \"veggie kurma\", \"jambalaya\",\"kale\", \"ketchup\", \"kiwi\", \"kidney beans pckg\",\n          \"great northern beans pckg\", \"lobster\",\"linguine\", \"lasagna\", \"pepperoni pizza\",\n          \"mushroom pizza\", \"pancakes\", \"quesadilla\",\"quiche\", \"spinach\", \"spaghetti\",\n          \"tater tots\", \"toast\", \"waffles\", \"walnuts\",\"peanuts\", \"hazelnuts\", \"cranberries\",\n          \"yogurt\", \"ziti\", \"zucchini\", \"canteloupe\", \"figs\", \"salt\", \"pepper\", \"nutmeg\", \"shichimi\"]\n\nSENTINEL = \"shop\"\n\nDO_NOT_BUY_DICT = { }\n\ndef getInput():\n    '''accepts input for shopping'''\n    item = raw_input( \"Enter the Item you would like to buy: \" )\n    return item\n\ndef putItemList ( shoppingList,availList ):\n    '''Adds all the items into the list'''\n    item= getInput().lower()\n    count = 0\n    while count==0:\n        if item in availList:\n            shoppingList.append( item )\n            count+=1\n        else:\n            print ( 'Item not Availble' )\n            item= getInput().lower()\n    shoppingList.sort()\n\n\ndef removeUnwanted( availList, familyList ):\n    '''Removes items from the main list from the do not buy list.'''\n    doNotBuyList = []\n    \n    name = raw_input( \"enter your name: \" )\n    while name not in familyList:\n        name = raw_input( \"wrong name!! re-enter\" )\n        \n    doNotGet = ( raw_input( \"Enter the Item you would like NOT to buy or Stop to end: \" ) ).lower()\n    while doNotGet != 'stop':\n        if doNotGet in FOODLIST:\n            doNotBuyList.append( doNotGet )\n            doNotGet=( raw_input( \"Enter the Item next you would like NOT to buy or Stop to end: \" ) ).lower()\n        else:\n            doNotGet=( raw_input( \"Invalid entry enter new item or stop to end:\" ) ).lower()\n    DO_NOT_BUY_DICT[ name ] = doNotBuyList\n    \ndef makeAvailList(availList):\n    '''makes a new  list of avalible Items'''\n    for var in FOODLIST:\n        var=var.lower()\n        availList.append( var )\n    availList.sort()\n\ndef printList(nicerList):\n    '''prints out the list'''\n    print( '''-- FAMILY SHOPPING  LIST --\\n\nFOOD       QUANTITY TO BUY\\n\n---------------------------\\n''' )\n    for listty in nicerList:\n        for num in range(2):\n            space=' '*abs( len( listty[num] )-20 )\n            print ( listty[num], end=space )\n        print()\n        \ndef makeBetter( shopping_list, nicer_list ):\n    '''displays the number of items related to the shopping item.'''\n    for item in shoppingList:\n        quanity = str( shoppingList.count( item ) )\n        if [ item, quanity ] not in nicerList:\n            nicerList.append( [ item, quanity ] )\n\ndef getMenuInput ():\n    '''accepts the menu input.'''\n    menu= int( raw_input( '''Enter your menu option: ''' ) )\n    return menu\n    \ndef showMenu():\n    '''displays the menu'''\n    print( \"Shopping List Program Menu\" )\n    print( \"1. create the DSper's I-won't-buy list\" )\n    print( \"2. add a food item to the shopping list\" )\n    print( \"3. display an alphabetic list of all available foods \" )\n    print( \"4. quit and print shopping list in alphabetical order\" )\n    print( \"5. Access Do not buy list\" )\n    print( \"6. Update Do not buy list\" )\n\ndef printAvail( available_list ):\n    '''prints availible list'''\n    for item in available_list:\n        print( item )\n\ndef doNotBuyListAccessor():\n    chooseKey = raw_input( \"type name of member whose list you want to access\" )\n    if chooseKey in DO_NOT_BUY_DICT:\n        print( DO_NOT_BUY_DICT[ chooseKey ] )\n    else:\n        print( \"wrong name?!?!\" )\n\ndef doNotBuyListUpdater():\n    chooseKey = raw_input( \"type name of member whose list you want to access\" )\n    chooseFunct = raw_input( \"R for remove A for add\" )\n\n    print( DO_NOT_BUY_DICT[ chooseKey ] )\n    if( chooseFunct == 'R' ):\n        eliminate = raw_input( \"type item you want to remove\" )\n        while eliminate not in DO_NOT_BUY_DICT[ chooseKey ]:\n            eliminate = raw_input( \"not in list. Please re-enter: \" )\n\n        DO_NOT_BUY_DICT[ chooseKey ].remove( eliminate )\n            \n\n    elif( chooseFunct == 'A' ):\n        appendIt = raw_input( \"type item you want to add\" )\n        while appendIt not in FOODLIST:\n            appendIt = raw_input( \"not in the main list. Try another item\" )\n\n        DO_NOT_BUY_DICT[ chooseKey ].append( appendIt )\n\n    else:\n        print(\"invalid\")\n                \n\n\ndef menuAssignment():\n    familyList = []\n    numberOfPeople = int(raw_input( \"Please enter the number of people: \" ))\n    for i in range(numberOfPeople):\n        name = raw_input(\"enter name: \")\n        familyList.append(name)\n    return familyList\n\ndef validation( inputValue, validateValue ):\n    '''validates any given set of inputs. Requires a first input statement'''\n    while( inputValue == validateValue ):\n        inputValue = raw_input( \"invalid! try again.\" )\n    return inputValue\n        \n\ndef main():\n    while True:\n        available_list=[]\n        makeAvailList(available_list)\n        shopping_list=[]\n        nicer_list=[]\n        familyList = menuAssignment()\n        showMenu()\n        menu=getMenuInput()\n\n        while( menu != 4 ):\n            if menu == 2:#add food item to the shopping list.\n                putItemList(shopping_list,available_list)\n                menu=getMenuInput()\n\n            elif menu == 1:#create Do not buy list.\n                removeUnwanted(available_list, familyList)\n                menu=getMenuInput()\n\n            elif menu == 3:#displays the available food items.\n                printAvail(available_list)\n                menu=getMenuInput()\n\n            elif menu == 5:#access the do not buy list.\n                doNotBuyListAccessor()\n                menu = getMenuInput()\n\n            elif menu == 6:#updates the do not buy list.\n                doNotBuyListUpdater()\n                menu = getMenuInput()\n\n            else:\n                showInvalid()\n                menu=getMenuInput()\n        print( \"you pressed quit. Bye Bye!\" )\n        break\n\n        makeBetter(shopping_list, nicer_list)\n        printList(nicer_list)\n\nif __name__ == '__main__':\n    main()\n        \n\n","sub_path":"Python/recitation/rec10.py","file_name":"rec10.py","file_ext":"py","file_size_in_byte":7477,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"80364500","text":"\"\"\"Test vandalism modules.\"\"\"\n#\n# (C) xqt, 2015-2021\n#\n# Distributed under the terms of the MIT license.\n#\nfrom __future__ import annotations\n\nimport re\nimport unittest\n\nfrom tests import utils  # noqa\nfrom vandalism import getAccuser, isIn  # noqa\n\n\nclass TestVandalismMethods(unittest.TestCase):\n\n    \"\"\"Test vandalism modules.\"\"\"\n\n    dry = True\n\n    def test_get_accuser(self):\n        \"\"\"Test getAccUser method.\"\"\"\n        self.assertEqual(getAccuser(''), ('', ''))\n        self.assertEqual(getAccuser(\n            'foo bar ([[Benutzer:xqt|xqbot]]) '\n            '11:46, 15. Nov. 2010 (CET) baz'),\n            ('xqt', '2010 Nov 15 11:46'))\n        self.assertEqual(getAccuser(\n            'foo bar ([[Benutzer:xqt|Xqt]]) '\n            '11:46, 15. Nov. 2011 (CET) baz'),\n            ('xqt', '2011 Nov 15 11:46'))\n        self.assertEqual(getAccuser(\n            'foo [[Benutzerin:xqt|Xqt]] bar '\n            '11:46, 15. Nov. 2012 (CET) baz'),\n            ('xqt', '2012 Nov 15 11:46'))\n        self.assertEqual(getAccuser(\n            'foo bar [[benutzerin:xqt|xqt]] '\n            '11:46, 15. Nov. 2013 (CET) baz'),\n            ('xqt', '2013 Nov 15 11:46'))\n        self.assertEqual(getAccuser(\n            'foo bar [[benutzerin:xqt|xqt]] '\n            '11:46, 15. Mai 2014 (CEST)'),\n            ('xqt', '2014 Mai 15 11:46'))\n        self.assertEqual(getAccuser(\n            'foo bar [[user:Xqt|xqt]] '\n            '11:46, 15. Apr. 2015 (CEST) baz'),\n            ('Xqt', '2015 Apr 15 11:46'))\n        self.assertEqual(getAccuser(\n            'foo bar [[User_talk:xqt|Xqt]] '\n            '11:46, 15. Nov. 2016 (CEST)'),\n            ('xqt', '2016 Nov 15 11:46'))\n        self.assertEqual(getAccuser(\n            'foo bar ([[Spezial:Beiträge/Xqt|xqt]]) '\n            '11:46, 15. Nov. 2017 (CEST) baz'),\n            ('Xqt', '2017 Nov 15 11:46'))\n        self.assertEqual(getAccuser(\n            'foo bar ([[Benutzer Diskussion:xqt|Diskussion]]) '\n            '11:46, 15. Nov. 2018 (CET) baz'),\n            ('xqt', '2018 Nov 15 11:46'))\n\n    def test_vmHeadlineRegEx(self):  # noqa: N802\n        \"\"\"Test vmHeadlineRegEx.\"\"\"\n        self.assertIsNotNone(isIn('== [[Benutzer:Xqt1]] ==',\n                                  re.escape('Xqt1')))\n        self.assertIsNotNone(isIn('== [[Benutzer:xqt2]] ==',\n                                  re.escape('Xqt2')))\n        self.assertIsNotNone(isIn('== [[Benutzerin:Xqt3]] ==',\n                                  re.escape('Xqt3')))\n        self.assertIsNotNone(isIn('== [[User:Xqt4]] ==',\n                                  re.escape('Xqt4')))\n        self.assertIsNotNone(isIn('== [[user:Xqt5]] ==',\n                                  re.escape('Xqt5')))\n        self.assertIsNotNone(isIn('== [[user:Xqt5]] ==',\n                                  re.escape('Xqt5')))\n        self.assertIsNotNone(isIn('== [[Benutzer:77.7.117.89]] ==',\n                                  re.escape('77.7.117.89')))\n        self.assertIsNotNone(isIn('== [[Benutzer:77.7.117.89]] ==',\n                                  re.escape('77.7.117.89')))\n        self.assertIsNotNone(isIn(\n            '== [[Benutzer:2003:D3:83C0:CB00:45F1:1850:1E89:1E22]] ==',\n            re.escape('2003:D3:83C0:CB00:45F1:1850:1E89:1E22')))\n        self.assertIsNotNone(isIn(\n            '== [[Benutzer:2003:d3:83c0:cb00:45f1:1850:1e89:1e22]] ==',\n            re.escape('2003:D3:83C0:CB00:45F1:1850:1E89:1E22')))\n\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"tests/vandalism_tests.py","file_name":"vandalism_tests.py","file_ext":"py","file_size_in_byte":3454,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"327299808","text":"def lcs(first, second):\n\n    if (len(first) == 0) or (len(second) == 0):\n        return 0\n\n    if first[0] == second[0]:\n        return 1 + lcs(first[1:], second[1:])\n    else:\n        left = lcs(first[1:], second)\n        right = lcs(first, second[1:])\n        return max(left, right)\nprint(lcs(\"man\",\"Man\"))\n\ndef ret_lcs(first, second):\n\n    if (len(first) == 0) or (len(second) == 0):\n        return 0, \"\"\n\n    if first[0] == second[0]:\n        total_c, yet = ret_lcs(first[1:], second[1:])\n        return total_c + 1, first[0] + yet\n\n    else:\n        left_c, left_yet = ret_lcs(first[1:], second)\n        right_c, right_yet = ret_lcs(first, second[1:])\n\n        if left_c > right_c:\n            return left_c, left_yet\n        else:\n            return right_c, right_yet\n\n\ndef ret_li_lcs(first, second):\n    if (len(first) == 0) or (len(second) == 0):\n        return 0, [\"\"]\n\n    if first[0] == second[0]:\n        total_c, yet = ret_li_lcs(first[1:], second[1:])\n        return total_c + 1, [first[0]+item for item in yet]\n\n    else:\n        left_c, left_yet = ret_li_lcs(first[1:], second)\n        right_c, right_yet = ret_li_lcs(first, second[1:])\n\n        if left_c > right_c:\n            return left_c, left_yet\n        elif left_c < right_c:\n            return right_c, right_yet\n        else:\n            return left_c, left_yet + right_yet\n\n\nmax_len, items = ret_li_lcs(\"manan\", \"aman\")\nprint(max_len, set(items))","sub_path":"lecture17/lcs.py","file_name":"lcs.py","file_ext":"py","file_size_in_byte":1425,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"264461489","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\n@author: hideyuki.takase\r\n\"\"\"\r\n\r\nimport psycopg2\r\nimport re\r\nfrom collections import defaultdict\r\nimport math\r\nimport MeCab\r\nimport matplotlib.pyplot as plt\r\nfrom wordcloud import WordCloud\r\n\r\nwords = set()\r\nwords_count = {}\r\nsegments_count = {}\r\nwords_count = defaultdict(lambda: 0)\r\nsegments_count = defaultdict(lambda: 0)\r\n\r\n### MeCab設定 ###\r\ntagger = MeCab.Tagger(\"-Ochasen\")\r\nwords = []\r\ndef morphological_Analysis(result_sql):\r\n    for row in result_sql:\r\n        node = tagger.parseToNode(row[3])\r\n\r\n        while node:\r\n            if node.feature.split(\",\")[0] == \"名詞\":\r\n                words.append(node.surface)\r\n            node = node.next\r\n    return words\r\n\r\n### データベースの設定 ###\r\nconnector = psycopg2.connect(\r\n    host='****', \r\n    database='****', \r\n    user='****', \r\n    password='****',\r\n)\r\ncursor = connector.cursor()\r\n# sql文作成\r\nsql = \"\"\"\r\nselect\r\n  ****\r\nfrom\r\n  ****\r\n\"\"\"\r\ncursor.execute(sql)\r\nresult = cursor.fetchall()\r\n\r\nseg = morphological_Analysis(result)\r\n\r\n### 終了処理 ###\r\ncursor.close()\r\nconnector.close()\r\n\r\n### 描画###\r\n# Stop Wordの設定もできる\r\nstop_words = [\"/\"]\r\n# フォントのパス指定\r\nfpath = \"C:/Windows/Fonts/meiryo.ttc\"\r\n\r\nwordcloud = WordCloud(background_color=\"white\", font_path=fpath, width=900, height=500, stopwords=stop_words).generate(\" \".join(seg))\r\nplt.figure(figsize=(15,12))\r\nplt.imshow(wordcloud)\r\nplt.axis(\"off\")\r\nplt.show()\r\n","sub_path":"wordcloud_mecab_postgres.py","file_name":"wordcloud_mecab_postgres.py","file_ext":"py","file_size_in_byte":1463,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"495784555","text":"from django.conf.urls import url,include\nfrom . import views\nfrom views import index, login, register, success, logout, delete\nurlpatterns = [\n    url(r'^$', index, name='index'),\n    url(r'^register/',register, name='register'),\n    url(r'^login/', login, name='login'),\n    url(r'^success$', success, name='success'),\n    # url(r'add/', include('apps.book_review.urls')),\n    url(r'^logout/', logout, name='logout'),\n    url(r'^delete/(?P<id>\\d+)$', delete, name='delete'),\n]\n","sub_path":"apps/login_registration/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":478,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"179146480","text":"#Copyright (c) 2018-2020 William Emerison Six\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 the following conditions:\n#\n#The above copyright notice and this permission notice shall be included in all\n#copies or substantial portions of the Software.\n#\n#THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n#IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n#FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n#AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n#LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n#OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n#SOFTWARE.\n\n\n# PURPOSE\n#\n# Learn about modelspace.\n\n\n\n# == Model-space\n\n# |=======================================\n# |Keyboard Input |Action\n# |w              |Move Left Paddle Up\n# |s              |Move Left Paddle Down\n# |i              |Move Right Paddle Up\n# |k              |Move Right Paddle Down\n# |=======================================\n\n# Normalized-device-coordinates are not a natural system of\n# numbers for use by humans.  Imagine that the paddles in the previous\n# chapters exist in real life, and are 20 meters wide and 60 meters tall.\n# The graphics programmer should be able to use those numbers directly;\n# they shouldn't have to manually transform the distances into normalized-device-coordinates.\n\n# Whatever a convenient numbering system is (i.e. coordinate system) for modeling objects\n# is called \"model-space\".  Since a paddle has four corners, which corner should be a\n# the origin (0,0)?  If you don't already know what you want at the origin, then\n# none of the corners should be; instead put the center of the object\n# at the origin (Because by putting the center of the object at the origin,\n# scaling and rotating the object are trivial).\n\n# eog ../images/modelspace.png\n\n# Modelspace - the coordinate system (origin plus axis), in which some object's\n# geometry is defined.\n\nimport sys\nimport os\nimport numpy as np\nimport math\nfrom OpenGL.GL import *\nimport glfw\n\nif not glfw.init():\n    sys.exit()\n\nglfw.window_hint(glfw.CONTEXT_VERSION_MAJOR,1)\nglfw.window_hint(glfw.CONTEXT_VERSION_MINOR,4)\n\nwindow = glfw.create_window(500,\n                            500,\n                            \"ModelViewProjection Demo 6\",\n                            None,\n                            None)\nif not window:\n    glfw.terminate()\n    sys.exit()\n\n# Make the window's context current\nglfw.make_context_current(window)\n\n# Install a key handler\ndef on_key(window, key, scancode, action, mods):\n    if key == glfw.KEY_ESCAPE and action == glfw.PRESS:\n        glfw.set_window_should_close(window,1)\nglfw.set_key_callback(window, on_key)\n\nglClearColor(0.0,\n             0.0,\n             0.0,\n             1.0)\n\n\nglMatrixMode(GL_PROJECTION);\nglLoadIdentity();\nglMatrixMode(GL_MODELVIEW);\nglLoadIdentity();\n\n\ndef draw_in_square_viewport():\n    # clear to gray.\n    glClearColor(0.2, #r\n                 0.2, #g\n                 0.2, #b\n                 1.0) #a\n    glClear(GL_COLOR_BUFFER_BIT)\n\n    width, height = glfw.get_framebuffer_size(window)\n    # figure out the minimum dimension of the window\n    min = width if width < height else height\n\n    # per pixel than just it's current color.\n    glEnable(GL_SCISSOR_TEST)\n    glScissor(int((width - min)/2.0),  #min x\n              int((height - min)/2.0), #min y\n              min,                     #width x\n              min)                     #width y\n\n    glClearColor(0.0, #r\n                 0.0, #g\n                 0.0, #b\n                 1.0) #a\n    # gl clear will only update the square to black values.\n    glClear(GL_COLOR_BUFFER_BIT)\n    # disable the scissor test, so now any opengl calls will\n    # happen as usual.\n    glDisable(GL_SCISSOR_TEST)\n\n    # But, we only want to draw within the black square.\n    # We set the viewport, so that the NDC coordinates\n    # will be mapped the the region of screen coordinates\n    # that we care about, which is the black square.\n    glViewport(int(0.0 + (width - min)/2.0),  #min x\n               int(0.0 + (height - min)/2.0), #min y\n               min,                           #width x\n               min)                           #width y\n\n\nclass Vertex:\n    def __init__(self,x,y):\n        self.x = x\n        self.y = y\n\n    def __repr__(self):\n        return f\"Vertex(x={repr(self.x)},y={repr(self.y)})\"\n\n    def translate(self, tx, ty):\n        return Vertex(x=self.x + tx, y=self.y + ty)\n\n    # NEW!\n    def scale(self, scale_x, scale_y):\n        return Vertex(x=self.x * scale_x, y=self.y * scale_y)\n\n\nclass Paddle:\n    def __init__(self,vertices, r, g, b, initial_position, input_offset_x=0.0, input_offset_y=0.0):\n        self.vertices = vertices\n        self.r = r\n        self.g = g\n        self.b = b\n        self.input_offset_x = input_offset_x\n        self.input_offset_y = input_offset_y\n        self.initial_position = initial_position\n        # global position is probably poorly named.\n        # it's the initial position for use if no inputs\n        # are inputs, and the offset x and y are the aggregate\n        # of the user's input.\n\n    def __repr__(self):\n        return f\"Paddle(vertices={repr(self.vertices)},r={repr(self.r)},g={repr(self.g)},b={repr(self.b)},initial_position={repr(self.initial_position)},input_offset_x={repr(self.input_offset_x)},input_offset_y={repr({self.input_offset_y})})\"\n\n# NEW! paddles are using modelspace coordinates instead of NDC\npaddle1 = Paddle(vertices=[Vertex(x=-10.0, y=-30.0),\n                           Vertex(x= 10.0, y=-30.0),\n                           Vertex(x= 10.0, y=30.0),\n                           Vertex(x=-10.0, y=30.0)],\n                 r=0.578123,\n                 g=0.0,\n                 b=1.0,\n                 initial_position=Vertex(-90.0,0.0))\n\npaddle2 = Paddle(vertices=[Vertex(x=-10.0, y=-30.0),\n                           Vertex(x= 10.0, y=-30.0),\n                           Vertex(x= 10.0, y=30.0),\n                           Vertex(x=-10.0, y=30.0)],\n                 r=1.0,\n                 g=0.0,\n                 b=0.0,\n                 initial_position=Vertex(90.0,0.0))\n\n\ndef handle_movement_of_paddles():\n    global paddle1, paddle2\n\n    if glfw.get_key(window, glfw.KEY_S) == glfw.PRESS:\n        paddle1.input_offset_y -= 10.0\n    if glfw.get_key(window, glfw.KEY_W) == glfw.PRESS:\n        paddle1.input_offset_y += 10.0\n    if glfw.get_key(window, glfw.KEY_K) == glfw.PRESS:\n        paddle2.input_offset_y -= 10.0\n    if glfw.get_key(window, glfw.KEY_I) == glfw.PRESS:\n        paddle2.input_offset_y += 10.0\n\n\nTARGET_FRAMERATE = 60 # fps\n\n# to try to standardize on 60 fps, compare times between frames\ntime_at_beginning_of_previous_frame = glfw.get_time()\n\n# Loop until the user closes the window\nwhile not glfw.window_should_close(window):\n    # poll the time to try to get a constant framerate\n    while glfw.get_time() < time_at_beginning_of_previous_frame +  1.0/TARGET_FRAMERATE:\n        pass\n    # set for comparison on the next frame\n    time_at_beginning_of_previous_frame = glfw.get_time()\n\n    # Poll for and process events\n    glfw.poll_events()\n\n    width, height = glfw.get_framebuffer_size(window)\n    glViewport(0, 0, width, height)\n    glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT)\n\n    # render scene\n    draw_in_square_viewport()\n    handle_movement_of_paddles()\n\n    # draw paddle1\n    glColor3f(paddle1.r,\n              paddle1.g,\n              paddle1.b)\n\n    glBegin(GL_QUADS)\n    for model_space in paddle1.vertices:\n\n        # Model-space to World-space.\n\n        # You can view the transformations from first transformation to last,\n        # where all transformations happen relative to the world-space origin.\n        # (this works well for world-space to camera-space,\n        # but not so well for model-space transformations)\n        # eog ../images/translationF.gif\n        # eog ../images/translation2F.gif\n\n        # Instead, for model-space to world-space transformations,\n        # it's easier to read the transformations backwards, where the transformations\n        # aren't relative to the global origin, instead it's from the local frame of reference.\n\n        # When reading the transformations backwards, I think it's best to think of it\n        # as moving the axises, and the plotting the data once the axies are in\n        # their final place.\n\n        # eog ../images/translationB.gif\n        # eog ../images/translation2B.gif\n\n        # Why do the two different views of the transformations matter?  In model-space\n        # to world-space transformations, especially once rotation and scaling of model-space\n        # is used, it allows the programmer to forget about most details, just specify\n        # where new objects are relative to that which you are already drawing.\n\n        # With that said, that doesn't mean that reading the transformations front to back\n        # has no value.  Front to back can useful when dealing with cameraspace transformations,\n        # introduced later.\n\n        # This will make more sense once rotation is involved.\n        world_space = model_space.translate(tx=paddle1.initial_position.x,\n                                            ty=paddle1.initial_position.y) \\\n                                 .translate(tx=paddle1.input_offset_x,\n                                            ty=paddle1.input_offset_y)\n\n        # === Scaling\n\n        # eog ../images/scale.png\n\n\n        # Similarly, we can expand or shrink the size of an object\n        # by \"scale\"ing each of the vertices of the object.\n        # Our global space is -100 to 100 in both dimesnions,\n        # and to get it into NDC, we need to scale by dividing by 100\n\n        ndc_space = world_space.scale(scale_x=1.0/100.0,\n                                      scale_y=1.0/100.0)\n        glVertex2f(ndc_space.x,\n                   ndc_space.y)\n\n        # The follwing diagrams shows the functions that transition between spaces.\n        # The arrow represents a function from one space to another.  All spaces\n        # will resolve to NDC.\n\n        # eog ../images/demo06.png\n\n    glEnd()\n\n    # draw paddle2\n    glColor3f(paddle2.r,\n              paddle2.g,\n              paddle2.b)\n\n    glBegin(GL_QUADS)\n    for model_space in paddle2.vertices:\n\n        # Do the same transformations to the second paddle\n\n        world_space = model_space.translate(tx=paddle2.initial_position.x,\n                                            ty=paddle2.initial_position.y) \\\n                                 .translate(tx=paddle2.input_offset_x,\n                                            ty=paddle2.input_offset_y)\n\n        ndc_space = world_space.scale(scale_x=1.0/100.0,\n                                      scale_y=1.0/100.0)\n        glVertex2f(ndc_space.x,\n                   ndc_space.y)\n    glEnd()\n\n\n    # done with frame, flush and swap buffers\n    # Swap front and back buffers\n    glfw.swap_buffers(window)\n\nglfw.terminate()\n","sub_path":"demo06/demo.py","file_name":"demo.py","file_ext":"py","file_size_in_byte":11393,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"445798828","text":"from GA.Utils import Info\nfrom SortingNetwork.GreenFilter import *\n\n\nclass SortedOutputsFitnessComputer(object):\n    def __init__(self):\n        self.nr_wire = Info.NR_WIRES\n        self.sorted_seq = []\n        self.max_sequence_to_check = 2 ** self.nr_wire\n\n    def set_seq_to_sort(self):\n        for i in range(0, self.nr_wire + 1):\n            bits = \"0\" * i + \"1\" * (self.nr_wire - i)\n            self.sorted_seq.append(int(bits, 2))\n\n    def get_fitness(self, sorting_network):\n        fails = 0\n        self.set_seq_to_sort()\n        for i in range(1, self.max_sequence_to_check):\n            if sorting_network.sort_binary_sequence(i) not in self.sorted_seq:\n                fails += 1\n\n        return 1 - (fails / self.max_sequence_to_check)\n\n    def get_fitness_using_green_filter(self, sorting_network):\n        fails = 0\n        self.set_seq_to_sort()\n        for i in Info.seq_to_check_after_using_green_filter:\n            if sorting_network.sort_binary_sequence(i) not in self.sorted_seq:\n                fails += 1\n\n        return 1 - (fails / len(Info.seq_to_check_after_using_green_filter))\n\n    def get_unsorted_binary_seq(self):\n        unsorted = []\n        sorting_network = GreenFilter().get_filter(Info.NR_WIRES)\n        self.set_seq_to_sort()\n        for i in range(1, self.max_sequence_to_check):\n            if sorting_network.sort_binary_sequence(i) not in self.sorted_seq:\n                unsorted.append(i)\n        return unsorted\n","sub_path":"GA/fitness/SortedOutputsFitnessComputer.py","file_name":"SortedOutputsFitnessComputer.py","file_ext":"py","file_size_in_byte":1460,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"170669305","text":"##############################################################################\n#\n# Copyright (c) 2007 Zope Foundation and Contributors.\n# All Rights Reserved.\n#\n# This software is subject to the provisions of the Zope Public License,\n# Version 2.1 (ZPL).  A copy of the ZPL should accompany this distribution.\n# THIS SOFTWARE IS PROVIDED \"AS IS\" AND ANY AND ALL EXPRESS OR IMPLIED\n# WARRANTIES ARE DISCLAIMED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED\n# WARRANTIES OF TITLE, MERCHANTABILITY, AGAINST INFRINGEMENT, AND FITNESS\n# FOR A PARTICULAR PURPOSE.\n#\n##############################################################################\n\"\"\"Hello World Message Interfaces\n\n$Id$\n\"\"\"\n__docformat__ = \"reStructuredText\"\nimport os\nimport zope.interface\nimport zope.schema\nfrom z3c.csvvocabulary import CSVVocabulary\n\nWhatVocabulary = CSVVocabulary(\n    os.path.join(os.path.dirname(__file__), 'what-values.csv'))\n\nclass IHelloWorld(zope.interface.Interface):\n    \"\"\"Information about a hello world message\"\"\"\n\n    who = zope.schema.TextLine(\n        title=u'Who',\n        description=u'Name of the person sending the message',\n        required=True)\n\n    when = zope.schema.Date(\n        title=u'When',\n        description=u'Date of the message sent.',\n        required=True)\n\n    what = zope.schema.Choice(\n        title=u'What',\n        description=u'What type of message it is.',\n        vocabulary=WhatVocabulary,\n        default=u'cool',\n        required=True)\n","sub_path":"src/z3c/formdemo/sqlmessage/interfaces.py","file_name":"interfaces.py","file_ext":"py","file_size_in_byte":1460,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"631545621","text":"from zope.interface import implements\nfrom Products.qPloneGoogleMaps.interfaces.markers import IMarkersListing\n\nclass MarkersListing(object):\n    \"\"\" simple markers adapter that prepare markers listing for view\n    \"\"\"\n    implements(IMarkersListing)\n    \n    def __init__(self, context):\n        \"\"\" init  \"\"\"\n        self.context = context\n    \n    def listMarkers(self):\n        \"\"\" return markers listing \"\"\"\n        if self.context.portal_type == \"Topic\":\n            contentsMethod = self.context.queryCatalog\n        elif self.context.portal_type in [\"Folder\", \"Large Plone Folder\"]:\n            contentsMethod = self.context.getFolderContents\n        else:\n             return None\n        markers = [marker for marker in contentsMethod() if marker.geoLocation]\n        return markers\n","sub_path":"qPloneGoogleMaps/tags/0.1.0/adapters/markers.py","file_name":"markers.py","file_ext":"py","file_size_in_byte":793,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"568863148","text":"# !/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom utils.db_handler import DB\nfrom action.get_rely import get_rely_data\nfrom utils.HttpClient import HttpClient\nfrom action.data_store import rely_data_store\nfrom action.check_result import CheckResult\nfrom utils.md5_encrypt import md5_encrypt\n\ndef main():\n    db = DB()\n    apiList = db.get_api_list()\n    print(apiList)\n    for api in apiList:\n        api_case_list = db.get_api_case(api[0])\n        print(api_case_list)\n        for case in api_case_list:\n            rely_lsit = case[3]\n            print(type(rely_lsit))\n            request_data = eval(case[2])\n            # 接下来进行数据依赖处理\n            if rely_lsit:\n                rely_lsit = eval(rely_lsit)\n                rely_case_id = rely_lsit[0].split(\".\")[2]\n                request_data = get_rely_data(rely_case_id, rely_lsit, request_data)\n            # 特殊处理一下密码加密\n            if api[2] == \"users_login\":\n                request_data[\"password\"] = md5_encrypt(request_data[\"password\"])\n            # 接下来进行接口请求，并获取响应body\n            responseObj = HttpClient.request(api[3], api[4], api[5], request_data)\n            print(responseObj.status_code)\n            # 接下来进行数据依赖存储\n            if responseObj.status_code == 200 and responseObj.json()[\"code\"] == \"00\" and case[6]:\n                rely_data_store(api[0], case[0], api[1], eval(case[6]), request_data, responseObj.json())\n            # 接下来进行接口响应结果验证\n            errorInfo = CheckResult.check(responseObj.json(), eval(case[7]))\n            print(errorInfo)\n            db.write_check_result(case[0], errorInfo, request_data)\n\n\nif __name__ == \"__main__\":\n    main()","sub_path":"interface_db/RunTest.py","file_name":"RunTest.py","file_ext":"py","file_size_in_byte":1751,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"388581906","text":"\nimport unittest\nimport logging\nimport warnings\nimport psycopg2\n\nfrom scribedb import scribedb,oracle,postgres\n\nclass TestCompare(unittest.TestCase):\n\n    def ignore_warnings(test_func):\n        def do_test(self, *args, **kwargs):\n            with warnings.catch_warnings():\n                warnings.simplefilter(\"ignore\")\n                test_func(self, *args, **kwargs)\n        return do_test\n\n    @ignore_warnings\n    def test_compare(self):\n        high_limit = 6\n        low_limit = 4\n        cxstring2 = \"hr/hr@localhost:1521/xe\"\n        cxstring1 = \"postgresql://postgres:postgres@localhost:5432/hr\"\n        cxrepo = \"postgresql://postgres:postgres@localhost:5432/hr\"\n        schema1 = \"hr\"\n        schema2 = \"hr\"\n        schemarepo = \"hrdiff2\"\n        qry_include_table = \"lower(table_name) not in ('employees')\"\n        repo = scribedb.Repo(cxrepo,\n                             schemarepo,\n                             cxstring1,\n                             cxstring2,\n                             schema1,\n                             schema2)\n        repo.dropSchema()\n        repo.create()\n        if cxstring1.startswith('postgresql'):\n            table1 = postgres.Table(cxstring1,schema1)\n        else:\n            table1 = oracle.Table(cxstring1,schema1)\n\n        if cxstring2.startswith('postgresql'):\n            table2 = postgres.Table(cxstring2,schema2)\n        else:\n            table2 = oracle.Table(cxstring2,schema2)\n\n        listTables = table1.get_tablelist(qry_include_table)\n        for table in listTables:\n            tablename = table[0]\n            table1.create(tablename,None)\n            table2.create(tablename,None)\n            repo.insert_table_diff(table1,table2)\n            step = 0\n            while step < 2:\n                step = step + 1\n                repo.split(table1,table2,step,high_limit,low_limit)\n                check_md5 = repo.compute_md5(table1,table2)\n                if check_md5.result == '':\n                    break\n                if check_md5.result == 'nok':\n                    if (step == 1 and check_md5.numrows < low_limit) \\\n                       or (step == 2):\n                        repo.compute_diffrowset(table1,table2)\n                        break\n            repo.update_table_result(table1)\n            table1.drop_view();\n            table2.drop_view();\n\n        sql = f\"\"\"select count(*) from {schemarepo}.tablediff\n        where result = 'nok'\"\"\"\n        conn = psycopg2.connect(cxrepo)\n        with conn:\n            with conn.cursor() as curs:\n                curs.execute(sql)\n                row = curs.fetchone()\n                result = row[0]\n\n        self.assertEqual(result, 0, f\"\"\"select count(*) from {schemarepo}.tablediff\n        where result = 'nok'  = > Should be 0\"\"\")\n\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"tests/test_scribedb.py","file_name":"test_scribedb.py","file_ext":"py","file_size_in_byte":2822,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"427683104","text":"import requests\n\ndef balance():\n    response = requests.get('http://127.0.0.1:8000/api/v1/accounting/client/all/')\n    response = response.json()\n    point = 0\n    for i in range(0, len(response)):\n        point = point + response[i]['points']\n    return point\n\ndef control_balance():\n    response = requests.get('http://127.0.0.1:8000/api/v1/accounting/client/all/')\n    response = response.json()\n    for i in range(0, len(response)):\n        if response[i]['points'] >=1000 or response[i]['points'] == 0:\n            print ('ID = ' + str(response[i]['id']) + ', \\\n                    Name = ' + str(response[i]['name']) + ', \\\n                    Points = ' + str(response[i]['points']))\n        else:\n            continue","sub_path":"StatisticManager/statistics/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":725,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"450641171","text":"import os\nimport sys\nimport time\nimport posixpath\nimport urllib\nimport BaseHTTPServer\nfrom SimpleHTTPServer import SimpleHTTPRequestHandler\nfrom threading import Thread\n\nfrom watchdog.observers import Observer\nfrom watchdog.events import PatternMatchingEventHandler\n\nfrom crotal import settings\nfrom crotal.core import Command\nfrom crotal import logger\nfrom crotal.version import __version__\n\nROUTES = (\n    ['', settings.PUBLISH_DIR],\n)\n\nLOGO = (\n        \"________________________________________\",\n        \"|   ____ ____   ___ _____  _    _      |\",\n        \"|  / ___|  _ \\\\ / _ \\\\_   _|/ \\\\  | |     |\",\n        \"| | |   | |_) | | | || | / _ \\\\ | |     |\",\n        \"| | |___|  _ <| |_| || |/ ___ \\\\| |___  |\",\n        \"|  \\____|_| \\_\\\\\\\\___/ |_/_/   \\\\_\\\\_____| |\",\n        \"|                                      |\",\n        \"|          Version: {:<6s}             |\".format(__version__),\n        \"|          Author: Dinever             |\",\n        \"|______________________________________|\",\n        )\n\n\nclass RequestHandler(SimpleHTTPRequestHandler):\n\n    def log_request(self, code='-', size='-'):\n        pass\n\n    def log_message(self, format, *args):\n        if args[0] == 404:\n            logger.yellow_text('404', message='Can not get file: {0}'.format(self.path))\n\n    def translate_path(self, path):\n\n        root = os.getcwd()\n\n        for pattern, rootdir in ROUTES:\n            if path.startswith(pattern):\n                path = path[len(pattern):]\n                root = rootdir\n                break\n\n        path = path.split('?', 1)[0]\n        path = path.split('#', 1)[0]\n        path = posixpath.normpath(urllib.unquote(path))\n        words = path.split('/')\n        words = filter(None, words)\n\n        path = root\n        for word in words:\n            drive, word = os.path.splitdrive(word)\n            head, word = os.path.split(word)\n            if word in (os.curdir, os.pardir):\n                continue\n            path = os.path.join(path, word)\n\n        return path\n\ndef test(HandlerClass,\n         ServerClass, protocol=\"HTTP/1.0\"):\n    \"\"\"Test the HTTP request handler class.\n\n    This runs an HTTP server on port 8000 (or the first command line\n    argument).\n\n    \"\"\"\n\n    if sys.argv[1:]:\n        port = int(sys.argv[1])\n    else:\n        port = 8000\n    server_address = ('', port)\n\n    HandlerClass.protocol_version = protocol\n    httpd = ServerClass(server_address, HandlerClass)\n\n    sa = httpd.socket.getsockname()\n    for line in LOGO:\n        logger.info(line)\n    print\n\n\n    HELP_INFO = (\n        \"Server started in `{0}`'\".format(settings.PUBLISH_DIR),\n        \"To see your site, visit http://localhost:{0}\".format(port),\n        \"To shut down Crotal, press <CTRL> + C at any time.\"\n    )\n    for line in HELP_INFO:\n        logger.info(line)\n    print\n    httpd.serve_forever()\n\nclass ServerThread(Thread):\n\n    def run(self):\n        del sys.argv[0]\n        test(RequestHandler, BaseHTTPServer.HTTPServer)\n\nclass ChangeHandler(PatternMatchingEventHandler):\n    patterns = [\"*.*\"]\n\n    def __init__(self, settings):\n        self.config = settings\n        super(ChangeHandler, self).__init__()\n\n    def process(self, event):\n        Command.generate(silent=True)\n\n    def on_modified(self, event):\n        logger.green_text('update', event.src_path)\n        self.process(event)\n\n    def on_created(self, event):\n        logger.green_text('[create] {0}'.format(event.src_path))\n        self.process(event)\n\n    def on_deleted(self, event):\n        logger.green_text('[remove] {0}'.format(event.src_path))\n        self.process(event)\n\ndef main(settings):\n    Command.generate(silent=True)\n    serverThread = ServerThread()\n    serverThread.daemon = True\n    serverThread.start()\n    observer = Observer()\n    observer.schedule(ChangeHandler(settings), path=settings.POSTS_DIR, recursive=True)\n    observer.schedule(ChangeHandler(settings), path=settings.PAGES_DIR, recursive=True)\n    observer.schedule(ChangeHandler(settings), path=settings.TEMPLATES_DIR, recursive=True)\n    observer.schedule(ChangeHandler(settings), path=settings.THEME_STATIC_DIR, recursive=True)\n    observer.start()\n    while True:\n        try:\n            time.sleep(1)\n        except KeyboardInterrupt:\n            logger.info('Server shutting down.')\n            sys.exit()\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"crotal/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":4336,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"438996169","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\"\"\"\n    zhouming 2016-8-1\n    dbaas模块逻辑层\n\"\"\"\nfrom app.bizs.constant import ApplicationNetType\nfrom app.service.dbaas.models import RmsOrdersDBaas\nfrom app.bizs.services import CmdbBzApplicationService\nfrom app.cmdb.openstack.models import CmdbOpenstackNetwork\nfrom app.cmdb.zone.models import CmdbTenantZoneMapping\nfrom app.user.tenant.models import CmdbTenantConfig\nfrom app.utils.approve import get_approve_from_policy, set_approver_in_policy_mailstamp\nfrom app.utils.format import FormatService\nfrom app.response import res\nfrom app.configs.code import ResponseCode\nfrom app.order.models import RmsOrder, RmsChildOrder\nfrom app.order.constant import OrderTypeCode\nfrom datetime import datetime\nfrom app.utils.helpers import str_now, json_dumps\n\n\nclass DBaasService(object):\n    \"\"\"\n        zhouming 2016-8-3\n        DBaas逻辑层\n    \"\"\"\n\n    def __init__(self):\n        pass\n\n    @staticmethod\n    def list(args):\n        page = args['page']\n        per_page = args['per_page']\n        start = (page - 1) * per_page\n        user_id = args['user_id']\n        logged_sulogin_user_id = args['logged_sulogin_user_id']\n        # 查询代理帐号或当前登录人\n        user_id = logged_sulogin_user_id if logged_sulogin_user_id else user_id\n\n        service_type = 'dbaas'\n        apache_list = RmsOrdersDBaas.list(start, per_page, user_id, service_type)\n        re = FormatService.format_result(apache_list)\n        return re\n\n    @staticmethod\n    def count(args):\n        user_id = args['user_id']\n        logged_sulogin_user_id = args['logged_sulogin_user_id']\n\n        # 查询代理帐号或当前登录人\n        user_id = logged_sulogin_user_id if logged_sulogin_user_id else user_id\n\n        service_type = 'dbaas'\n        total_count = RmsOrdersDBaas.count(user_id, service_type)\n        return total_count\n\n    @staticmethod\n    def get_create_preparation(args):\n        \"\"\"创建业务准备条件\"\"\"\n        user_id = args['user_id']\n        tenant_id = args['tenant_id']\n        logged_sulogin_user_id = args['logged_sulogin_user_id']\n\n        service_name = 'DBaaS'\n        result_dict = {'applist': '', 'network': '', 'usage': '', 'location': ''}\n\n        # 查看资源权限\n        service_list = CmdbTenantConfig.get_service_list(tenant_id, service_name)\n        if service_list is None:\n            error_msg = u\"您没有权限申请此种资源!\"\n            return res(ResponseCode.ERROR, error_msg)\n\n        # 查询代理帐号或当前登录人，申请资源可用业务列表\n        user_id = logged_sulogin_user_id if logged_sulogin_user_id else user_id\n\n        # 查询当前登陆人申请资源可用的归属业务列表\n        app_list = CmdbBzApplicationService.get_applications_belong(user_id)\n        result_dict['applist'] = app_list\n\n        # 网络列表\n        network_list = CmdbOpenstackNetwork.get_tenant_network(tenant_id)\n        result_dict['network'] = network_list\n        # 资源用途列表\n        resource_list = CmdbOpenstackNetwork.get_tenant_usage(tenant_id)\n        result_dict['usage'] = resource_list\n        # 地点列表\n        location_list = CmdbTenantZoneMapping.get_tenant_location(tenant_id)\n        result_dict['location'] = location_list\n        return result_dict\n\n    @staticmethod\n    def create_order(args):\n        \"\"\"生成订单记录\"\"\"\n        service_name = 'DBaaS'\n        user_id = args['user_id']\n        tenant_id = args['tenant_id']\n        logged_sulogin_user_id = args['logged_sulogin_user_id']\n\n        app_id = args['application']\n        if app_id:\n            # 1.判断当前登录人（或被代理人）是否是业务负责人\n            user_id = logged_sulogin_user_id if logged_sulogin_user_id else user_id\n\n            # 查看资源权限\n            service_list = CmdbBzApplicationService.get_applications_count(user_id, app_id)\n            if service_list is None:\n                error_msg = u\"您没有权限申请此种资源!\"\n                return res(ResponseCode.ERROR, error_msg)\n        else:\n            error_msg = u\"没有指定的业务!\"\n            return res(ResponseCode.ERROR, error_msg)\n\n        # 查询代理帐号或当前登录人，申请资源可用业务列表\n        user_id = logged_sulogin_user_id if logged_sulogin_user_id else user_id\n\n        # 计算审批策略，需要作为审批记录初始化在订单表中\n        policy = CmdbBzApplicationService.get_approve_resource(app_id)\n\n        status = '待审批'\n        if policy:\n            # 根据审批策略进行计算待审批人员，后续添加\n            approver_list_result = get_approve_from_policy(policy)\n            if approver_list_result[0]:\n                return None, approver_list_result[0]\n            elif not approver_list_result[1]:\n                return None, u\"系统异常，新订单审批人不能为空\"\n\n            # 给待审批人添加邮戳\n            policy_result = set_approver_in_policy_mailstamp(policy, approver_list_result[1])\n\n            if policy_result[0]:\n                return None, policy_result[0]\n            elif policy_result[1]:\n                policy = policy_result[1]\n        else:\n            status = '待接单'\n\n        # 开启savepoint事物\n        from app.extensions import db\n        db.session.begin_nested\n\n        # 保存订单元信息\n        order_id = RmsOrder.generator_id(pre=OrderTypeCode.apache_cluster)\n        rms_orders = RmsOrder(id=order_id,\n                              type=service_name,\n                              status=status,\n                              application=args['application'],\n                              applicant=user_id,\n                              apply_time=datetime.utcnow(),\n                              tenant_id=tenant_id,\n                              approve_json=policy)\n        rms_orders.save()\n\n        # 去除application业务字段数据，因为已经保存在rms_orders主表中\n        args.pop('application')\n        if 'status' in args:\n            args.pop('status')\n\n        # 保存订单具体参数\n        rms_orders_dbaas = RmsOrdersDBaas()\n        args['order_id'] = order_id\n        args[\"app_net\"] = ApplicationNetType.tradition.value\n        rms_orders_dbaas.create(**args)\n\n        # 生成订单的时候同时分配资源\n\n        return order_id\n\n    @staticmethod\n    def delete(args):\n        \"\"\"\n            删除业务\n        \"\"\"\n        # 权限判断\n        pass\n\n\nclass DBschemaService(object):\n    \"\"\"\n        zhouming 2016-8-3\n        DBschema逻辑层\n    \"\"\"\n\n    def __init__(self):\n        pass\n\n    @staticmethod\n    def list(args):\n        page = args['page']\n        per_page = args['per_page']\n        start = (page - 1) * per_page\n        user_id = args['user_id']\n        service_type = 'schema'\n        apache_list = RmsOrdersDBaas.list_dbschema(start, per_page, user_id, service_type)\n        re = FormatService.format_result(apache_list)\n        return re\n\n    @staticmethod\n    def count(args):\n        user_id = args['user_id']\n        service_type = 'schema'\n        total_count = RmsOrdersDBaas.count_dbschema(user_id, service_type)\n        return total_count\n\n    @staticmethod\n    def get_create_preparation(args):\n        \"\"\"创建业务准备条件\"\"\"\n        user_id = args['user_id']\n        tenant_id = args['tenant_id']\n        resource_id = args['resource_id']\n        logged_sulogin_user_id = args['logged_sulogin_user_id']\n\n        # 1.判断当前登录人（或被代理人）是否是业务负责人\n        user_id = logged_sulogin_user_id if logged_sulogin_user_id else user_id\n        service_name = 'DBaaSuserService'\n\n        result_dict = {'selective_resource_list': ''}\n        resource_list = DBschemaService.get_resource_list(user_id)\n        result_dict['selective_resource_list'] = resource_list\n        # 当指定了确定的资源的时候就无需用户再选择业务，否则需要业务负责人选择属于的业务\n        if resource_id > 0:\n            res = [r for r in result_dict['selective_resource_list'] if r['id'] == resource_id]\n            if res:\n                result_dict['application'] = res[0]['app_id']\n                result_dict['ip_default'] = res[0].get('res_name', None)\n                result_dict['selected_resource'] = res[0]\n        return result_dict\n\n    @staticmethod\n    def create_order(args):\n        \"\"\"生成订单记录\"\"\"\n        service_name = 'DBaaS'\n        user_id = args['user_id']\n        tenant_id = args['tenant_id']\n        zone_id = args['zone_id']\n        logged_sulogin_user_id = args['logged_sulogin_user_id']\n\n        args['status'] = u'执行中'\n        service_name = 'DBaaSuserService'\n\n        # 1.判断当前登录人（或被代理人）是否是业务负责人\n        user_id = logged_sulogin_user_id if logged_sulogin_user_id else user_id\n        app_id = args['application']\n\n        # 生成order_id\n        order_id = RmsOrder.generator_id(pre=OrderTypeCode.db_schema)\n\n        # 根据传入的参数列表生成子订单的详细参数字典，这里刨除了子订单字段\n        parameters = dict(\n            [(k, v) for k, v in args.items() if k not in ('status', 'order_id', 'resource_id', 'service')])\n\n        # 开启savepoint事物\n        from app.extensions import db\n        db.session.begin_nested\n\n        # 保存订单元信息,将用户填写订单信息存入rms_childorders\n        rms_child_orders = RmsChildOrder()\n        rms_child_orders.create(id=order_id,\n                                zone_id=zone_id,\n                                type=service_name,\n                                status=args['status'],\n                                service=args['service'],\n                                parameters=json_dumps(parameters),\n                                resource_id=args['resource_id'],\n                                applicant=user_id,\n                                apply_time=str_now()\n                                )\n\n        # 转换资源类型srv_name为中文名称\n\n        # 邮件通知接单人\n\n        # 对子订单直接发起后台任务,调用JCF开启后台任务执行\n\n        return order_id\n\n    @staticmethod\n    def delete(args):\n        \"\"\"\n            删除业务\n        \"\"\"\n        # 权限判断\n        pass\n\n    @staticmethod\n    def get_resource_list(user_id):\n        \"\"\"返回当前用户可以进行子服务操作的资源列表\"\"\"\n        resource_list = RmsOrdersDBaas.get_resource_list(user_id)\n        re = FormatService.format_result(resource_list)\n        return re\n","sub_path":"app/service/dbaas/services.py","file_name":"services.py","file_ext":"py","file_size_in_byte":10599,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"601108233","text":"# Copyright (C) 2014 SignalFuse, Inc.\n# Copyright (C) 2015 SignalFx, Inc.\n#\n# Docker container orchestration utility.\n\nimport getpass\nimport json\nimport requests\n\nfrom . import exceptions\nfrom .version import name as maestro_name\n\n\nclass BaseAuditor:\n    \"\"\"Base class for auditors that can save or notify about orchestration\n    plays being executed.\"\"\"\n\n    def _format_what(self, what):\n        if type(what) == list or type(what) == tuple:\n            return ', '.join(what)\n        return what\n\n    def _format_who(self, who=None):\n        return who or getpass.getuser()\n\n    def _format_action(self, what, action=None, who=None):\n        what = self._format_what(what)\n        who = self._format_who(who)\n\n        if action:\n            action = action if action is not 'stop' else 'stopp'\n            return '{} is {}ing {}.'.format(who, action, what)\n        return '{} is acting on {}.'.format(who, what)\n\n    def _format_success(self, what, action=None):\n        what = self._format_what(what)\n\n        if action:\n            return '{} of {} succeeded.'.format(action.title(), what)\n        return 'Action on {} succeeded.'.format(what)\n\n    def _format_error(self, what, action=None, message=None):\n        what = self._format_what(what)\n\n        if action:\n            s = 'Failed to {} {}!'.format(action, what)\n        else:\n            s = 'Failed action on {}!'.format(what)\n\n        if message:\n            s = '{} (message: {})'.format(s, message)\n        return s\n\n    def action(self, what, action=None, who=None):\n        raise NotImplementedError\n\n    def success(self, what, action=None):\n        raise NotImplementedError\n\n    def error(self, what, action=None, message=None):\n        raise NotImplementedError\n\n\nclass HipChatAuditor(BaseAuditor):\n    \"\"\"Auditor that sends notifications in a HipChat chat room.\"\"\"\n\n    def __init__(self, name, room, token):\n        if not room:\n            raise exceptions.InvalidAuditorConfigurationException(\n                'Missing HipChat room name!')\n        if not token:\n            raise exceptions.InvalidAuditorConfigurationException(\n                'Missing HipChat API token!')\n\n        self._name = name if name else maestro_name\n        self._room = room\n\n        import hipchat\n        self._hc = hipchat.HipChat(token)\n\n    def _message(self, params):\n        self._hc.message_room(**params)\n\n    def action(self, what, action=None, who=None):\n        self._message({\n            'room_id': self._room,\n            'message_from': self._name,\n            'message': self._format_action(what, action, who)\n        })\n\n    def success(self, what, action=None):\n        self._message({\n            'room_id': self._room,\n            'message_from': self._name,\n            'message': self._format_success(what, action),\n            'color': 'green',\n        })\n\n    def error(self, what, action=None, message=None):\n        self._message({\n            'room_id': self._room,\n            'message_from': self._name,\n            'message': self._format_error(what, action, message),\n            'color': 'red',\n            'notify': True,\n        })\n\n    @staticmethod\n    def from_config(cfg):\n        return HipChatAuditor(cfg.get('name'), cfg.get('room'),\n                              cfg.get('token'))\n\n\nclass LoggerAuditor(BaseAuditor):\n    \"\"\"Auditor that logs the notifications into a log file.\"\"\"\n\n    def __init__(self, filename):\n        if not filename:\n            raise exceptions.InvalidAuditorConfigurationException(\n                'Missing audit log filename!')\n\n        import logging\n        formatter = logging.Formatter(\n            fmt='%(asctime)s %(levelname)s: %(message)s')\n        handler = logging.FileHandler(filename)\n        handler.setFormatter(formatter)\n\n        self._logger = logging.getLogger('maestro')\n        self._logger.addHandler(handler)\n        self._logger.setLevel(logging.INFO)\n\n    def action(self, what, action=None, who=None):\n        self._logger.info(self._format_action(what, action, who))\n\n    def success(self, what, action=None):\n        self._logger.info(self._format_success(what, action))\n\n    def error(self, what, action=None, message=None):\n        self._logger.error(self._format_error(what, action, message))\n\n    @staticmethod\n    def from_config(cfg):\n        return LoggerAuditor(cfg.get('file'))\n\n\nclass WebHookAuditor(BaseAuditor):\n    \"\"\"Auditor that makes HTTP calls, webhooks-style, with JSON payload.\"\"\"\n\n    DEFAULT_TIMEOUT = 3\n    DEFAULT_HTTP_METHOD = 'POST'\n\n    def __init__(self, endpoint, payload=None, headers=None,\n                 method=DEFAULT_HTTP_METHOD, timeout=DEFAULT_TIMEOUT):\n        if not endpoint:\n            raise exceptions.InvalidAuditorConfigurationException(\n                'Missing webhook endpoint!')\n\n        self._endpoint = endpoint\n        self._payload = payload\n        self._headers = {'Content-Type': 'application/json; charset=utf-8'}\n        if headers:\n            self._headers.update(headers)\n\n        self._method = method.upper()\n        if self._method not in ['GET', 'POST']:\n            raise exceptions.InvalidAuditorConfigurationException(\n                'Invalid HTTP method {}!'.format(method))\n        self._timeout = timeout\n\n    def _prepare_payload(self, what, action, who, message):\n        what = self._format_what(what)\n        who = self._format_who(who)\n\n        def r(fn, on):\n            if type(on) == dict:\n                d = {}\n                for k, v in on.items():\n                    v2 = r(fn, v)\n                    if v2:\n                        d[k] = v2\n                return d\n            if type(on) == list or type(on) == tuple:\n                return filter(None, map(fn, on))\n            return fn(on)\n\n        return r(lambda s: s.format(what=what, action=action, who=who,\n                                    message=message),\n                 self._payload)\n\n    def action(self, what, action=None, who=None):\n        payload = self._prepare_payload(what, action, who,\n                                        self._format_action(what, action, who))\n        if not payload:\n            payload = None\n\n        method = getattr(requests, self._method.lower())\n        method(self._endpoint, headers=self._headers, data=json.dumps(payload),\n               timeout=self._timeout)\n\n    def success(self, what, action=None):\n        pass\n\n    def error(self, what, action=None, message=None):\n        pass\n\n    @staticmethod\n    def from_config(cfg):\n        return WebHookAuditor(\n            cfg['endpoint'],\n            cfg.get('payload', {}),\n            cfg.get('headers'),\n            cfg.get('method', WebHookAuditor.DEFAULT_HTTP_METHOD),\n            cfg.get('timeout', WebHookAuditor.DEFAULT_TIMEOUT))\n\n\nclass MultiplexAuditor(BaseAuditor):\n    \"\"\"Auditor multiplexer, to broadcast through multiple auditors.\"\"\"\n\n    def __init__(self, auditors):\n        self._auditors = auditors\n\n    def action(self, what, action=None, who=None):\n        for auditor in self._auditors:\n            try:\n                auditor.action(what, action, who)\n            except:\n                pass\n\n    def success(self, what, action=None):\n        for auditor in self._auditors:\n            try:\n                auditor.success(what, action)\n            except:\n                pass\n\n    def error(self, what, action=None, message=None):\n        for auditor in self._auditors:\n            try:\n                auditor.error(what, action, message)\n            except:\n                pass\n\n\nclass AuditorFactory:\n\n    AUDITORS = {\n        'hipchat': HipChatAuditor,\n        'log': LoggerAuditor,\n        'http': WebHookAuditor,\n    }\n\n    @staticmethod\n    def from_config(cfg):\n        auditors = set([])\n        for auditor in cfg:\n            if auditor['type'] not in AuditorFactory.AUDITORS:\n                raise exceptions.InvalidAuditorConfigurationException(\n                    'Unknown auditor type {}'.format(auditor['type']))\n            auditors.add(AuditorFactory.AUDITORS[auditor['type']]\n                         .from_config(auditor))\n        return MultiplexAuditor(auditors)\n","sub_path":"maestro/audit.py","file_name":"audit.py","file_ext":"py","file_size_in_byte":8115,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"129723252","text":"import cv2\nimport numpy as np\n'''\ngray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n'''\nimg = cv2.imread(\"./swordman.jpg\")\n#img = cv2.imread(\"./man.jpg\")\n#img = cv2.imread(\"./000260.png\")\nh, w = img.shape[:2]\nblured = cv2.medianBlur(img, 3) # medianBlur效果比blur好 blured = cv2.blur(img, (2,2))\n\nmask = np.zeros((h+2, w+2), np.uint8) #掩码长和宽都比输入图像多两个像素点，满水填充不会超出掩码的非零边缘\n#进行泛洪填充\ncv2.floodFill(blured, mask, (w-1,h-1), (255,255,255), (2,2,2),(3,3,3),8)\n#cv2.imshow(\"floodfill\", blured) \n\ngray = cv2.cvtColor(blured, cv2.COLOR_BGR2GRAY)\n#cv2.imshow(\"gray\", gray)\n\nkernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))\nopened = cv2.morphologyEx(gray, cv2.MORPH_OPEN, kernel)\nclosed = cv2.morphologyEx(opened, cv2.MORPH_CLOSE, kernel)\nret, binary = cv2.threshold(closed,100,255,cv2.THRESH_BINARY) \n#ret, binary = cv2.threshold(closed,0,255,cv2.THRESH_OTSU|cv2.THRESH_BINARY) \n#binary = cv2.Canny(closed,15,100)\n#cv2.imshow(\"img\", binary)\ncontours= cv2.findContours(binary,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)\ncontour_outer = contours[0]\n#创建白色幕布\n#temp = np.ones(binary.shape,np.uint8)*255\n#画出轮廓：temp是白色幕布，contours是轮廓，-1表示全画，然后是颜色，厚度\n#cv2.drawContours(temp,contour_outer,-1,(0,0,255),3)\ncv2.drawContours(img,contour_outer,-1,(0,0,255),3) \ncv2.imshow(\"img\", img)\ncv2.waitKey(0)\n#print (type(contours))\n#print (type(contours[0]))\n#print (len(contours))\n#print (len(contours[0]))","sub_path":"opencv/namecard_reg.py","file_name":"namecard_reg.py","file_ext":"py","file_size_in_byte":1525,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"501617089","text":"import os\nimport os.path\nimport tarfile\nimport tempfile\n\n\ndef make_tree(dirs, files):\n    base = tempfile.mkdtemp()\n\n    for path in dirs:\n        os.makedirs(os.path.join(base, path))\n\n    for path in files:\n        with open(os.path.join(base, path), 'w') as f:\n            f.write(\"content\")\n\n    return base\n\n\ndef simple_tar(path):\n    f = tempfile.NamedTemporaryFile()\n    t = tarfile.open(mode='w', fileobj=f)\n\n    abs_path = os.path.abspath(path)\n    t.add(abs_path, arcname=os.path.basename(path), recursive=False)\n\n    t.close()\n    f.seek(0)\n    return f\n\n\ndef untar_file(tardata, filename):\n    with tarfile.open(mode='r', fileobj=tardata) as t:\n        f = t.extractfile(filename)\n        result = f.read()\n        f.close()\n    return result\n","sub_path":"tests/helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":755,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"499443849","text":"__author__ = 'alex.taxter'\n'''\nhttps://www.reddit.com/r/dailyprogrammer/comments/3q9vpn/20151026_challenge_238_easy_consonants_and_vowels/\n'''\n\nimport random\n\ndef randword(cv):\n    randword = \"\"\n    keys = {'c': \"bcdfghjklmnpqrstvwxyz\", 'v': \"aeiou\"}\n    for i in cv:\n        if i.lower() in keys:\n            randword = randword + str(random.choice(keys[i.lower()]))\n        else:\n            break\n    else:\n        return randword\n    return None\n\nwhile True:\n    pattern = randword(input(\"Enter pattern, or type 'Quit' to quit: \"))\n    if not pattern is None:\n        print (pattern, \"\\n\")\n    else:\n        print(\"Please only enter c or v characters. \\n\")","sub_path":"Programming Challenges/[2015-10-26] Challenge #238 [Easy].py","file_name":"[2015-10-26] Challenge #238 [Easy].py","file_ext":"py","file_size_in_byte":660,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"223885326","text":"#!/usr/bin/python\nimport smbus\nimport time\nfrom ctypes import c_short\n\nclass bmp180():\n\n    DEVICE = 0x77  # Default device I2C address\n\n    bus = smbus.SMBus(1)  # Rev 2 Pi uses 1\n\n\n    def convertToString(self, data):\n        # Simple function to convert binary data into\n        # a string\n        return str((data[1] + (256 * data[0])) / 1.2)\n\n\n    def getShort(self, data, index):\n        # return two bytes from data as a signed 16-bit value\n        return c_short((data[index] << 8) + data[index + 1]).value\n\n\n    def getUshort(self, data, index):\n        # return two bytes from data as an unsigned 16-bit value\n        return (data[index] << 8) + data[index + 1]\n\n\n    def readBmp180(self, addr=DEVICE):\n        # Register Addresses\n        REG_CALIB = 0xAA\n        REG_MEAS = 0xF4\n        REG_MSB = 0xF6\n        REG_LSB = 0xF7\n        # Control Register Address\n        CRV_TEMP = 0x2E\n        CRV_PRES = 0x34\n        # Oversample setting\n        OVERSAMPLE = 3  # 0 - 3\n\n        # Read calibration data\n        # Read calibration data from EEPROM\n        cal = bmp180().bus.read_i2c_block_data(addr, REG_CALIB, 22)\n\n        # Convert byte data to word values\n        AC1 = bmp180().getShort(cal, 0)\n        AC2 = bmp180().getShort(cal, 2)\n        AC3 = bmp180().getShort(cal, 4)\n        AC4 = bmp180().getUshort(cal, 6)\n        AC5 = bmp180().getUshort(cal, 8)\n        AC6 = bmp180().getUshort(cal, 10)\n        B1 = bmp180().getShort(cal, 12)\n        B2 = bmp180().getShort(cal, 14)\n        MB = bmp180().getShort(cal, 16)\n        MC = bmp180().getShort(cal, 18)\n        MD = bmp180().getShort(cal, 20)\n\n        # Read temperature\n        bmp180().bus.write_byte_data(addr, REG_MEAS, CRV_TEMP)\n        time.sleep(0.005)\n        (msb, lsb) = bmp180().bus.read_i2c_block_data(addr, REG_MSB, 2)\n        UT = (msb << 8) + lsb\n\n        # Read pressure\n        bmp180().bus.write_byte_data(addr, REG_MEAS, CRV_PRES + (OVERSAMPLE << 6))\n        time.sleep(0.04)\n        (msb, lsb, xsb) = bmp180().bus.read_i2c_block_data(addr, REG_MSB, 3)\n        UP = ((msb << 16) + (lsb << 8) + xsb) >> (8 - OVERSAMPLE)\n\n        # Refine temperature\n        X1 = ((UT - AC6) * AC5) >> 15\n        X2 = (MC << 11) / (X1 + MD)\n        B5 = X1 + X2\n        temperature = int(B5 + 8) >> 4\n\n        # Refine pressure\n        B6 = B5 - 4000\n        B62 = int(B6 * B6) >> 12\n        X1 = (B2 * B62) >> 11\n        X2 = int(AC2 * B6) >> 11\n        X3 = X1 + X2\n        B3 = (((AC1 * 4 + X3) << OVERSAMPLE) + 2) >> 2\n\n        X1 = int(AC3 * B6) >> 13\n        X2 = (B1 * B62) >> 16\n        X3 = ((X1 + X2) + 2) >> 2\n        B4 = (AC4 * (X3 + 32768)) >> 15\n        B7 = (UP - B3) * (50000 >> OVERSAMPLE)\n\n        P = (B7 * 2) / B4\n\n        X1 = (int(P) >> 8) * (int(P) >> 8)\n        X1 = (X1 * 3038) >> 16\n        X2 = int(-7357 * P) >> 16\n        pressure = int(P + ((X1 + X2 + 3791) >> 4))\n\n        return (temperature / 10.0, pressure / 100.0)\n\n# while True:\n#     (temperature, pressure) = bmp180().readBmp180()\n#     print(\"Temperature : {0} C\".format(temperature))\n#     print(\"Pressure    : {0} mbar\".format(pressure))\n#     time.sleep(1)\n\n","sub_path":"PiScript/bmp180.py","file_name":"bmp180.py","file_ext":"py","file_size_in_byte":3123,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"538787231","text":"#!/usr/bin/env python\nimport os\nimport rospy\nimport time\nimport threading\nimport numpy as np\nimport pp_spline_interpolation as pcs                       #pycubicspline.py - class\nfrom tf.transformations import euler_from_quaternion as Q2E\nfrom tf.transformations import quaternion_from_euler as E2Q\n\nfrom nav_msgs.msg import Odometry\nfrom ctrl.msg import pwm_interface\nfrom ctrl.msg import pwm_controller\nfrom geometry_msgs.msg import PoseArray, Pose\nfrom std_msgs.msg import Float64\n\n\n#####################################################\n#         Define Threading Class                    #\n#####################################################\nclass ThreadedFunction(threading.Thread):\n\n    #####################################################\n    #          Initialize Object                        #\n    #####################################################\n    def __init__(self, fcn_to_thread):\n        threading.Thread.__init__(self)\n\n        self.runnable = fcn_to_thread\n        self.daemon = True\n\n    def run(self):\n        self.runnable()\n\n\n#####################################################\n#               Initialize Vehicle class            #\n#####################################################\nclass PurePursuitController:\n    def __init__(self):\n        #####################################################\n        #          Initialize Boolean States for System     #\n        #####################################################\n        self.PARAMETERS_INITIALIZED = False\n        self.PARAMETER_UPDATED = False\n        self.MAIN_INTERFACE_ACTIVE = False\n        self.ESSENTIAL_TOPICS_ALIVE = False\n        self.CONTROLLER_ACTIVE = False\n        self.EXPLORATION_MAPPING_COMPLETE = False\n        self.PATH_PUBLISHED = False\n        self.CHECKPOINT_ARRAY_PUBLISHED = False\n        self.REACTIVE_CONTROL_ACTIVATE = True\n        self.EXPLORATION_ACTIVATED = False\n        self.AUTONOMOUS_RACING_ACTIVATED = True\n        self.EMERGENCY_FLAG = False\n        self.GOAL_REACHED = False\n\n        #####################################################\n        #          Initialize Locks for Threaded Access     #\n        #####################################################\n        self.PUBLISH_ACTIVE = False\n        self.KEY_READ_ACTIVE = False\n        self.KEY = ''\n\n        #####################################################\n        #                Internal Parameters                #\n        #####################################################\n        self.wheel_base = 0.324\n        self.CTRL = False\n        self.dt_interface = 0.5\n        self.KEY = ''\n\n        #####################################################\n        #               Initialize State Variables          #\n        #####################################################\n        self.x = 0\n        self.y = 0\n        self.v = 0\n        self.yaw = 0\n        self.x_prev = 0\n        self.y_prev = 0\n        self.v_prev = 0\n        self.yaw_prev = 0\n        self.yaw_rate = 0\n\n        #####################################################\n        #         Initialize CTRL Tuning Variables          #\n        #####################################################\n        self.traj_x = []\n        self.traj_y = []\n        self.traj_k = []\n        self.traj_num_points = 0\n        self.path_length_prev = 0                                      # compare if path topic has been updated\n        self.LAH = 0.8\n        self.PPM = 5                                                    # Parts per Meter to be interpolated\n        self.nearest_point_index = 0\n        self.spline_step = 0.1\n\n        ##LOOK AHEAD PARAMS\n        # CURVATURE\n        self.LAH_max = 6\n        self.LAH_min = 1\n        self.LAH_step = 1                   # for 1 meter go 1 Meter ahead\n        self.LAH_ini = 1\n        self.LAH_phi_th = (10.0/180)*np.pi    # CHANGE CURVATURE -> LAH     # 15 deg change in path stops increase\n\n        # LATERAL DEVIATION\n        self.LAH_d2p_step = 13              # CHANGE LAT_DEVIATION -> LAH # 1m deviation from path = -0.5 m/s\n\n\n        ##VELOCITY PARAM\n        # LOOK AHEAD2VEL\n        self.LAH_to_VEL = 1               # CHANGE OVERALL LAH -> VELOCITY    # 1m = 0.1m/s\n\n        # STEERSAT compensation\n        self.STEER_SAT2_VEL = ( 1/10 ) * 0.1    # 10 deg over compensation corresponds to 0.2 m/s speed reduction\n\n        if self.REACTIVE_CONTROL_ACTIVATE:\n            self.vel_max = 0.3  ##TODO: limited by control output vel_PWM <=12 (in pwm_controller)\n            self.vel_min = 0.1\n            self.vel_stop = 0.0\n\n        else:\n            self.vel_max = 6                 ##TODO: limited by control output vel_PWM <=12 (in pwm_controller)\n            self.vel_min = 0.1\n            self.vel_stop = 0.0\n\n        #####################################################\n        #               Initialize Output Variables         #\n        #####################################################\n        self.steering_saturation_threshold = 30                                     # 45deg max steering input\n        self.steering = 0\n        self.velocity = 0\n        self.PWM_VEL_MIN = 0\n        self.PWM_VEL_MAX = 12                               ##TODO: REMOVE all sorts of limitations reg. actuation and includein low-level controller\n        self.SAT_STEER_MIN = -81\n        self.SAT_STEER_MAX = 81\n        self.REF_VEL_PWM = 0\n        self.REF_STEER_PWM = 0\n        self.SYSTEM_GAIN = 1.7 / 15\n\n        #####################################################\n        #         Initialize ROS Messages/Parameters        #\n        #####################################################\n        self.PARAMETERS_INITIALIZED = False\n        self.VELOCITY_CONTROLLER_ACTIVATED = True\n        self.PARAMETER_UPDATED = False\n        self.STATE_UPDATED = False\n\n        self.UPDATE_PARAMETERS_ACTIVE = False\n        self.PURE_PURSUIT_ACTIVE = False\n        self.COMPUTE_CONTROL_ACTIVE = False\n        self.SUBSCRIBER_ACTIVE = False\n        self.PUBLISHER_ACTIVE = False\n\n        p = Pose()\n        p.position.x = 0\n        p.position.y = 0\n\n        self.time_prev = 0\n        self.current_time = 0\n        self.dt = 1                                             # delta T to determine derivatives\n        self.UPDATE_PARAM_TIME = 0.2\n\n        self.PUBLISH_RATE = 10\n\n        self.FEEDBACK_IDLE = False\n        self.N_IDLE = 0\n        self.IDLE_TIME = 0.5\n        self.N_IDLE_THRESHOLD = 0.5/(1./self.PUBLISH_RATE)\n\n        self.PWM = pwm_interface()\n        self.REF = pwm_controller()\n        self.ODOM_feedback = Odometry()\n        self.GOAL_WP = PoseArray()\n        self.GOAL_WP.poses.append(p)\n        self.PP_LAH = Float64()\n        self.PP_LAH.data = 0\n        self.traj = PoseArray()\n        self.traj.poses.append(p)\n        self.frame_path = \"odom\"\n\n        self.vehicle_frame_id = \"base\"\n        self.global_frame_id = \"odom\"\n\n        #####################################################\n        #         Initialize ROS Subscriber/Publisher       #\n        #####################################################\n        rospy.init_node('ctrl_node', anonymous=True)\n        self.pub_pwm_vel = rospy.Publisher('pwm_interface', pwm_interface, queue_size=10)\n        self.pub_ref_vel = rospy.Publisher('pwm_controller', pwm_controller, queue_size=10)\n        self.pub_lah = rospy.Publisher('/PP_LAH', Float64, queue_size=10)\n        self.pub_gp = rospy.Publisher('/PP_GP', PoseArray, queue_size=1)\n        self.pub_traj = rospy.Publisher('/PP_TRAJ', PoseArray, queue_size=1)\n        self.rate = rospy.Rate(self.PUBLISH_RATE)\n\n    #####################################################\n    #                   Clear Screen                    #\n    #####################################################\n    def cls(self):\n        os.system(\"clear\")\n\n    #####################################################\n    #                   SET PARAMETER                   #\n    #####################################################\n    def set_f1vt18_parameter(self, parameter_name, value=False):\n        resolved_global_name = \"/f1vt18/\" + parameter_name\n        rospy.set_param(resolved_global_name, value)\n\n    #####################################################\n    #                   GET PARAMETER                   #\n    #####################################################\n    def get_f1vt18_parameter(self, parameter_name, value=False):\n        resolved_global_name = \"/f1vt18/\" + parameter_name\n        return rospy.get_param(resolved_global_name, value)\n\n    #####################################################\n    #   Initialize Control Interface for Terminal       #\n    #####################################################\n    def update_parameters(self):\n        while not rospy.is_shutdown():\n            self.UPDATE_PARAMETERS_ACTIVE = True\n            while not self.PARAMETERS_INITIALIZED:\n                self.PARAMETERS_INITIALIZED = self.get_f1vt18_parameter(\"PARAMETERS_INITIALIZED\")\n\n            self.PURE_PURSUIT_ACTIVE = True\n            self.set_f1vt18_parameter(\"PURE_PURSUIT_ACTIVE\",self.PURE_PURSUIT_ACTIVE)\n            #####################################################\n            #                   GET PARAMETER                   #\n            #####################################################\n            #self.AUTONOMOUS_RACING_ACTIVATED = self.get_f1vt18_parameter(\"AUTONOMOUS_RACING_ACTIVATED\")\n\n            #####################################################\n            #                   SET PARAMETER                   #\n            #####################################################\n\n            self.PARAMETER_UPDATED = True\n\n        self.UPDATE_PARAMETERS_ACTIVE = False\n        self.PURE_PURSUIT_ACTIVE = False\n\n    #####################################################\n    #               Initialize Subscriber               #\n    #####################################################\n    def initialize_subscriber(self):\n        while not rospy.is_shutdown():\n            while self.PURE_PURSUIT_ACTIVE:\n                self.SUBSCRIBER_ACTIVE = True\n                rospy.Subscriber('state_observer/odom', Odometry, self.update_state)            # for MOCAP experimental CTRL\n                rospy.Subscriber('path', PoseArray, self.generate_trajectory)\n                #rospy.Subscriber('qualisys/odom', Odometry, self.update_state)\n                #rospy.Subscriber('/odometry/filtered_map', Odometry, self.update_state)    # for Autonomous CTRL\n                rospy.spin()\n            self.SUBSCRIBER_ACTIVE = False\n\n    #####################################################\n    #              Update Vehicle States                #\n    #####################################################\n    def update_state(self, data):\n        if self.PURE_PURSUIT_ACTIVE:\n            self.N_IDLE = 0\n            self.FEEDBACK_IDLE = False\n\n            self.dt = rospy.get_time() - self.time_prev\n            self.x = data.pose.pose.position.x\n            self.y = data.pose.pose.position.y\n            self.yaw_prev = self.yaw\n            _, _, self.yaw = Q2E([data.pose.pose.orientation.x, data.pose.pose.orientation.y, data.pose.pose.orientation.z, data.pose.pose.orientation.w])\n            self.yaw_rate = (self.yaw - self.yaw_prev) / self.dt\n            self.v = ((self.x - self.x_prev) ** 2 + (self.y - self.y_prev) ** 2) ** 0.5 / self.dt\n\n            self.x_prev = self.x\n            self.y_prev = self.y\n            self.yaw_prev = self.yaw\n            self.time_prev = rospy.get_time()\n            self.STATE_UPDATED = True\n        else:\n            pass\n\n    #####################################################\n    #               Generate Trajectory                 #\n    #####################################################\n    def generate_trajectory(self, points):\n        if self.PURE_PURSUIT_ACTIVE:\n            ##TODO: Include differentiation between reactive control and mapped control in here (if closed loop path available then ...)\n\n            temp_path_length_curr = len(points.poses)\n            if temp_path_length_curr == self.path_length_prev:\n                pass\n            else:\n                ## Assume the closed loop path/center points are given\n                self.path_length_prev = temp_path_length_curr\n\n                if self.REACTIVE_CONTROL_ACTIVATE:\n                    wp_x = []\n                    wp_y = []\n                    for i in range(0, len(points.poses)):\n                        wp_x.append(points.poses[i].position.x)\n                        wp_y.append(points.poses[i].position.y)\n                else:\n                    wp_x = []\n                    wp_y = []\n\n                    for i in range(0, len(points.poses)):\n                        wp_x.append(points.poses[i].position.x)\n                        wp_y.append(points.poses[i].position.y)\n\n                    wp_x.append(points.poses[0].position.x)\n                    wp_y.append(points.poses[0].position.y)\n\n                    wp_x.append(points.poses[1].position.x)\n                    wp_y.append(points.poses[1].position.y)\n\n                self.traj = PoseArray()\n                self.traj_x = []\n                self.traj_y = []\n                self.traj_k = []\n\n                #############################################\n                ### Spline Interpolation of the waypoints_mapped ###\n                #############################################\n                spl = pcs.Spline2D(wp_x, wp_y)\n                s = np.arange(0, spl.s[-1], self.spline_step)\n                for i_s in s:\n                    p = Pose()\n                    ix, iy = spl.calc_position(i_s)\n                    self.traj_x.append(ix)\n                    self.traj_y.append(iy)\n                    self.traj_k.append(spl.calc_curvature(i_s))\n                    yaw = spl.calc_yaw(i_s)\n                    orientation = E2Q(0, 0, yaw)\n                    p.position.x = ix\n                    p.position.y = iy\n                    p.orientation.x = orientation[0]\n                    p.orientation.y = orientation[1]\n                    p.orientation.z = orientation[2]\n                    p.orientation.w = orientation[3]\n                    self.traj.poses.append(p)\n\n                self.traj_num_points = len(self.traj_x)\n                self.path_prev = len(points.poses)\n                print(\"GT:   PATH_UPDATED\\r\")\n\n                ''' \n                for index in range(1, len(wp_x)):\n                    dx_wp = wp_x[index] - wp_x[index - 1]\n                    dy_wp = wp_y[index] - wp_y[index - 1]\n                    dist_temp = (dx_wp ** 2 + dy_wp ** 2) ** 0.5\n\n                    num_points = int(dist_temp * float(self.PPM))\n                    yaw = np.arctan2(dy_wp, dx_wp)\n\n                    orientation = E2Q(0, 0, yaw)\n                    for num in range(0, num_points):\n                        p = Pose()\n                        dx = dx_wp * (float(num) / num_points)\n                        dy = dy_wp * (float(num) / num_points)\n                        temp_x = wp_x[index - 1] + dx\n                        temp_y = wp_y[index - 1] + dy\n                        self.traj_x.append(temp_x)\n                        self.traj_y.append(temp_y)\n\n                        p.position.x = temp_x\n                        p.position.y = temp_y\n                        p.orientation.x = orientation[0]\n                        p.orientation.y = orientation[1]\n                        p.orientation.z = orientation[2]\n                        p.orientation.w = orientation[3]\n                        self.traj.poses.append(p)\n\n                self.traj_num_points = len(self.traj_x)\n            '''\n            self.current_time = rospy.Time.now()\n            self.traj.header.stamp = self.current_time\n            if self.REACTIVE_CONTROL_ACTIVATE:\n                self.traj.header.frame_id = self.vehicle_frame_id\n            else:\n                self.traj.header.frame_id = self.global_frame_id\n            self.pub_traj.publish(self.traj)\n        else:\n            pass\n\n    def check_index_threshold(self, index):\n        if self.REACTIVE_CONTROL_ACTIVATE:\n            if index >= self.traj_num_points:\n                index = self.traj_num_points\n            else:\n                pass\n        else:\n            index = index % self.traj_num_points\n        return index\n    #####################################################\n    #             Execute Control Algorithm             #\n    #####################################################\n    def compute_control_input(self):\n        while not rospy.is_shutdown():\n            if self.PURE_PURSUIT_ACTIVE:\n                self.COMPUTE_CONTROL_ACTIVE = True\n\n                #print \"PP:   ACTIVE [x] \\r\"\n                D2P = 10000\n                min_index = 1\n                ref_index = 1\n                if self.traj_num_points != 0:\n                    for i in range(0, self.traj_num_points):\n                        temp_dist = np.sqrt((self.x - self.traj_x[i]) ** 2 + (self.y - self.traj_y[i]) ** 2)\n                        if temp_dist <= D2P:\n                            D2P = temp_dist\n                            min_index = i\n\n                    self.nearest_point_index = min_index\n\n                    #####################################################\n                    #               Adaptive Look Ahead                 #\n                    #####################################################\n                    k = self.nearest_point_index + 1\n                    k = self.check_index_threshold(k)\n                    length_path = 0\n\n                    dx_ref = self.traj_x[k] - self.traj_x[self.nearest_point_index]\n                    dy_ref = self.traj_y[k] - self.traj_y[self.nearest_point_index]\n                    phi_ref = np.arctan2(dy_ref, dx_ref)\n                    length_path += (dx_ref ** 2 + dy_ref ** 2) ** 0.5\n    \n                    phi_step = phi_ref\n                    temp_phi = 0\n                    while abs(temp_phi) <= self.LAH_phi_th:\n                        k += 1\n                        k = self.check_index_threshold(k)\n                        dx_step = self.traj_x[k] - self.traj_x[self.nearest_point_index]\n                        dy_step = self.traj_y[k] - self.traj_y[self.nearest_point_index]\n                        phi_step = np.arctan2(dy_step, dx_step)\n                        temp_phi += (phi_ref - phi_step)\n                        length_path += (dx_step ** 2 + dy_step ** 2) ** 0.5\n\n                    LAH_adapted = length_path/self.LAH_step\n                    self.LAH = min(max(LAH_adapted, self.LAH_min), self.LAH_max)\n                    #print('LAH adapted to curvature: {0}'.format(self.LAH))\n\n                    self.LAH -= self.LAH_d2p_step * D2P\n                    self.LAH = max(self.LAH, self.LAH_min)\n                    self.velocity = self.LAH * self.LAH_to_VEL\n                    self.velocity = min(max(self.velocity, self.vel_min), self.vel_max)\n\n                    ## TODO: included debugging publisher for LAH\n                    self.PP_LAH.data = self.LAH\n                    self.pub_lah.publish(self.PP_LAH)\n                    #print('LAH Curvature adaptive: {0}'.format(self.LAH))\n\n                    #####################################################\n                    #                  Find Goal Pose                   #\n                    #####################################################\n                    #print('PP:          DIST TO POINT {0} / {1} \\r'.format(D2P, self.LAH))\n                    if D2P > self.LAH:\n                        #print('PP:   WP GOOD\\r')\n                        ref_index = self.nearest_point_index\n                        self.CTRL = True\n                    else:\n                        #print('PP:      WP TOO CLOSE\\r')\n                        ref_index = self.nearest_point_index + int(self.LAH * self.PPM)\n                        ref_index = self.check_index_threshold(ref_index)\n                        #print('PP:   Ref-Index: {0}  |  Length: {1}\\r'.format(ref_index, self.traj_num_points))\n\n                        if ref_index > self.traj_num_points:\n                            #print('PP:          WP OUT OF RANGE --> STOP\\r')\n                            self.velocity = self.vel_stop  # Include Braking control sequence here\n                            self.CTRL = False\n                            #self.GOAL_REACHED = True\n                        else:\n                            #print('PP:          WP ADAPTED\\r')\n                            temp_new_dist = np.sqrt((self.x - self.traj_x[ref_index]) ** 2 + (self.y - self.traj_y[ref_index]) ** 2)\n                            #print('PP:          DIST TO POINT {0} / {1} \\r'.format(temp_new_dist, self.LAH))\n                            self.CTRL = True\n\n                    if self.CTRL:\n                        next_traj_point = self.check_index_threshold(ref_index+1)\n                        dx_gp = self.traj_x[next_traj_point] - self.traj_x[ref_index]\n                        dy_gp = self.traj_y[next_traj_point] - self.traj_y[ref_index]\n                        yaw = np.arctan2(dy_gp, dx_gp)\n                        orientation = E2Q(0, 0, yaw)\n                        ref_state = [self.traj_x[ref_index], self.traj_y[ref_index]]\n\n                        self.GOAL_WP.poses[0].position.x = ref_state[0]\n                        self.GOAL_WP.poses[0].position.y = ref_state[1]\n                        self.GOAL_WP.poses[0].orientation.x = orientation[0]\n                        self.GOAL_WP.poses[0].orientation.y = orientation[1]\n                        self.GOAL_WP.poses[0].orientation.z = orientation[2]\n                        self.GOAL_WP.poses[0].orientation.w = orientation[3]\n\n                        self.current_time = rospy.Time.now()\n                        self.GOAL_WP.header.stamp = self.current_time\n                        if self.REACTIVE_CONTROL_ACTIVATE:\n                            self.traj.header.frame_id = self.vehicle_frame_id\n                        else:\n                            self.traj.header.frame_id = self.global_frame_id\n                        self.pub_gp.publish(self.GOAL_WP)\n                        dx = ref_state[0] - self.x\n                        dy = ref_state[1] - self.y\n\n                        y_goal = -np.sin(self.yaw) * dx + np.cos(self.yaw) * dy\n\n                        distance_to_goal = np.sqrt(dx ** 2 + dy ** 2)\n                        self.steering = np.arctan(self.wheel_base * 2 * y_goal / distance_to_goal ** 2) * 180 / np.pi\n\n                        if abs(self.steering) > self.steering_saturation_threshold:\n                            self.velocity = self.velocity - abs(self.steering -self.steering_saturation_threshold)*self.STEER_SAT2_VEL\n                            self.velocity = min(max(self.velocity, self.vel_min), self.vel_max)\n                        else:\n                            pass\n                    else:\n                        #print \"PP:   --> No Control applied [-] --> Out of range condition\\r\"\n                        self.velocity = 0\n                        self.steering = 0\n                else:\n                    #print \"PP:   --> No Control applied [-]\\r\"\n                    self.steering = 0\n                    self.velocity = 0\n            else:\n                self.COMPUTE_CONTROL_ACTIVE = False\n        self.COMPUTE_CONTROL_ACTIVE = False\n\n    #####################################################\n    #                PUBLISH ACTUATION                  #\n    #####################################################\n    def publish_reference(self):\n        while not rospy.is_shutdown():\n            if self.PURE_PURSUIT_ACTIVE:\n                self.PUBLISHER_ACTIVE = True\n\n                self.REF_VEL_PWM = self.velocity * (1 / self.SYSTEM_GAIN)\n                self.REF_STEER_PWM = (self.steering / self.steering_saturation_threshold) * 100\n                self.REF_VEL_PWM = min(max(self.REF_VEL_PWM, self.PWM_VEL_MIN), self.PWM_VEL_MAX)\n                self.REF_STEER_PWM = -min(max(self.REF_STEER_PWM, self.SAT_STEER_MIN), self.SAT_STEER_MAX)\n\n                if self.N_IDLE >= self.N_IDLE_THRESHOLD:\n                    self.FEEDBACK_IDLE = True\n                else:\n                    pass\n\n                if self.FEEDBACK_IDLE:\n                    self.REF.velocity = 0\n                    self.REF.steering = 0\n                    self.PWM.velocity = 0\n                    self.PWM.steering = 0\n                else:\n                    self.N_IDLE += 1\n                    if self.VELOCITY_CONTROLLER_ACTIVATED:\n                        self.REF.velocity = self.velocity\n                        self.REF.steering = self.REF_STEER_PWM\n                        self.pub_ref_vel.publish(self.REF)\n                    else:\n                        self.PWM.velocity = self.REF_VEL_PWM\n                        self.PWM.steering = self.REF_STEER_PWM\n                        self.pub_pwm_vel.publish(self.PWM)\n            else:\n                self.PUBLISHER_ACTIVE = False\n            self.rate.sleep()\n\n    #####################################################\n    #   Initialize Control Interface for Terminal       #\n    #####################################################\n    def display_interface(self):\n        #####################################################\n        #               Show System States (Debugging)      #\n        #####################################################\n        while not rospy.is_shutdown():\n            print(\"----------------------------------\\r\")\n            print(\"System States: \")\n            print(\"----------------------------------\\r\")\n            print('PURE_PURSUIT_ACTIVE:   -- {0} --   \\r'.format(self.PURE_PURSUIT_ACTIVE))\n            print('VELOCITY_CONTROLLER:   -- {0} --   \\r'.format(self.VELOCITY_CONTROLLER_ACTIVATED))\n            print('SUBSCRIBER_ACTIVE      -- {0} --   \\r'.format(self.SUBSCRIBER_ACTIVE))\n            print('PUBLISHER_ACTIVE       -- {0} --   \\r'.format(self.PUBLISHER_ACTIVE))\n            print('UPDATE_PARAMETERS      -- {0} --   \\r'.format(self.UPDATE_PARAMETERS_ACTIVE))\n            print('COMPUTE_CONTROL        -- {0} --   \\r'.format(self.COMPUTE_CONTROL_ACTIVE))\n            print('FEEDBACK_IDLE          -- {0} --   \\r'.format(self.FEEDBACK_IDLE))\n            print(\"----------------------------------\\r\")\n            print(\"REFERENCE:\")\n            print(\"----------------------------------\\r\")\n            print('Steering: deg [PWM] {0} [{1}] \\r'.format(self.steering, self.REF_STEER_PWM))\n            print('Velocity: m/s [PWM] {0} [{1}]\\r'.format(self.velocity, self.REF_VEL_PWM))\n            time.sleep(self.dt_interface)\n            self.cls()\n        self.PURE_PURSUIT_ACTIVE = False\n\n\nif __name__ == '__main__':\n    try:\n        print(\"----------------------------------\\r\")\n        print(\"- PROGRAM HAS BEEN STARTED       -\\r\")\n        print(\"----------------------------------\\r\")\n        print(\"... initialize vehicle object     \\r\")\n        time.sleep(1)\n        pure_pursuit = PurePursuitController()\n        Subscriber = ThreadedFunction(pure_pursuit.initialize_subscriber)\n        Publisher = ThreadedFunction(pure_pursuit.publish_reference)\n        Parameter = ThreadedFunction(pure_pursuit.update_parameters)\n        Controller = ThreadedFunction(pure_pursuit.compute_control_input)\n\n        Subscriber.start()\n        Publisher.start()\n        Parameter.start()\n        Controller.start()\n\n        pure_pursuit.display_interface()\n        rospy.set_param('/f1vt18/PURE_PURSUIT_ACTIVE', False)\n\n    except rospy.ROSInterruptException:\n        print(\"----------------------------------\\r\")\n        print(\"- PROGRAM HAS BEEN INTERRUPTED   -\\r\")\n        print(\"----------------------------------\\r\")\n        rospy.set_param('/f1vt18/PURE_PURSUIT_ACTIVE', False)\n        pass\n        pass\n","sub_path":"ctrl/src/ctrl_pure_pursuit.py","file_name":"ctrl_pure_pursuit.py","file_ext":"py","file_size_in_byte":27796,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"525397663","text":"import json\nimport os\n\ndef save_json(file, path):\n    with open(path, 'w') as outfile:\n        json.dump(file, outfile)\n\n\ndef find_json_files(folder_path):\n    files = []\n    for file in os.listdir(folder_path):\n        if file.endswith(\".json\"):\n            files.append(os.path.join(folder_path, file))\n    return files\n\n\ndef read_json(path):\n    with open(path) as json_file:\n        return json.load(json_file)\n\n\ndef filter_list(l, filenames):\n    l_filtered = []\n    for p in l:\n        if p.split('/')[-1].split('.')[0] in filenames:\n            l_filtered.append(p)\n    return l_filtered\n\n\ndef filter_list_2(l, black_list):\n    l_filtered = []\n    for p in l:\n        if p.split('/')[-1].split('.')[0] not in black_list:\n            l_filtered.append(p)\n    return l_filtered\n\n\nclass InputFile:\n    def __init__(self, input_path, query_codes, query_code_descriptions):\n        self.init_file = read_json(input_path)\n        self.query_codes = query_codes\n        self.query_code_descriptions = query_code_descriptions\n        self.sampled_file_and_operation, self.key_list_and_operation = self.get_sampled_file_and_operation()\n        self.sampled_file_or_operation, self.key_list_or_operation = self.get_sampled_file_or_operation()\n\n\n    def get_sampled_file_and_operation(self):\n        sampled_dict = {}\n        key_list = []\n        for k1 in self.init_file.keys():\n            for k2 in self.init_file[k1].keys():\n                flag = 0\n                # Check if all the query codes exist in the record.\n                for i, q_c in enumerate(self.query_codes):\n                    q_c_descr = self.query_code_descriptions[i]\n                    if len(q_c_descr) == 1:\n                        if not(q_c in self.init_file[k1][k2][q_c_descr[0]]):\n                            flag = 1\n                    elif len(q_c_descr) == 2:\n                        if not(q_c in self.init_file[k1][k2][q_c_descr[0]][q_c_descr[1]]):\n                            flag = 1\n                if flag == 0:\n                    if k1 not in sampled_dict.keys():\n                        sampled_dict[k1] = {}\n                        sampled_dict[k1][k2] = self.init_file[k1][k2]\n                        key_list.append(k1 + '_' + k2)\n                    else:\n                        sampled_dict[k1][k2] = self.init_file[k1][k2]\n                        key_list.append(k1 + '_' + k2)\n        return sampled_dict, key_list\n\n    \n    def get_sampled_file_or_operation(self):\n        sampled_dict = {}\n        key_list = []\n        for k1 in self.init_file.keys():\n            for k2 in self.init_file[k1].keys():\n                flag = 0\n                # Check if at least one of the query codes exists in the record.\n                for i, q_c in enumerate(self.query_codes):\n                    q_c_descr = self.query_code_descriptions[i]\n                    if len(q_c_descr) == 1:\n                        if q_c in self.init_file[k1][k2][q_c_descr[0]]:\n                            flag = 1\n                            break\n                    elif len(q_c_descr) == 2:\n                        if q_c in self.init_file[k1][k2][q_c_descr[0]][q_c_descr[1]]:\n                            flag = 1\n                            break\n                if flag == 1:\n                    if k1 not in sampled_dict.keys():\n                        sampled_dict[k1] = {}\n                        sampled_dict[k1][k2] = self.init_file[k1][k2]\n                        key_list.append(k1 + '_' + k2)\n                    else:\n                        sampled_dict[k1][k2] = self.init_file[k1][k2]\n                        key_list.append(k1 + '_' + k2)\n        return sampled_dict, key_list\n\n\nclass save_results(object):\n    def __init__(self, filename, header=None):\n        self.filename = filename\n        if os.path.exists(filename):\n            os.remove(filename)\n        \n        if header is not None:\n            with open(filename, 'w') as out:\n                print(header, file=out)\n    \n    def save(self, info):\n        with open(self.filename, 'a') as out:\n            print(info, file=out)","sub_path":"kg-embedding/gnn-models/mimic/helper.py","file_name":"helper.py","file_ext":"py","file_size_in_byte":4084,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"411298194","text":"Miasto_A = input(\"Podaj Miasto A: \")\nMiasto_B = input(\"Podaj Miasto B: \")\n\nCena_paliwa = int(input(\"Podaj cenę paliwa: \"))\n\nSpalanie_na_100km = int(input(\"Podaj spalanie na 100 km: \"))\n\nDystans = int(input(\"Podaj odległość pomiędzy miastami: \"))\n\nkoszt_przejazdu = (Cena_paliwa * Spalanie_na_100km * (Dystans / 100))\n\nprint(\"cena za paliwo\", Cena_paliwa, 'średnie spalanie na 100 kilometrów', Spalanie_na_100km)\n\nprint(f'{koszt_przejazdu}')\n\n","sub_path":"zjazd_2/funkcje/przerobienie_zadan_na_funkcje/zadanie 4.py","file_name":"zadanie 4.py","file_ext":"py","file_size_in_byte":449,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"274452104","text":"from Tkinter import *\nimport sys\nimport time\nimport math\n#===========================================\nPATH = 'O'\nTRIED = 't'\nWALL = '%'\nDEAD_END = '-'\nGOAL = 'G'\nFOOD = '.'\nPathListAStar =[]\nDirection = [[1,0],[-1,0],[0,1],[0,-1]]\n#============================================\n#======== PRIORITY QUEUE ====================\nclass PriorityQueue:\n    def __init__(self):\n        self.queue= []\n    \n    def length(self):\n        return len(self.queue)\n    \n    def pop(self):\n        if self.length() != 0:\n            value = self.queue[0]\n            del self.queue[0]\n        else:\n            value = -1\n        return value\n\n    def is_empty(self):\n        if self.length() == 0:\n            return 1\n        else:\n            return 0\n\n    def push(self, x, y, prio_level):\n        i = 0\n        len_queue = self.length()\n        if len_queue == 0:\n            self.queue.append([x, y, prio_level])\n        else:\n            while i < len_queue and prio_level > self.queue[i][2] :\n                    i += 1\n                    if i == len_queue:\n                        break \n            len_queue = self.length()\n            self.queue.append([0, 0, 0]) # initialize first tuple\n            while i < len_queue:\n                self.queue[len_queue]=self.queue[len_queue-1]  \n                len_queue -= 1            \n            self.queue[len_queue] = [x, y, prio_level]\t        \n\n    \n#=============MAHATTAN FUNC====================\ndef Manhattan(x1,y1,x2,y2):\n    result = math.fabs(x1-x2) + math.fabs(y1-y2)\n    return result \n#============================================\nReadMazeList =[]\ncountGoal = 0\ninputlayout = sys.argv[2] + '.lay'\nf = open(inputlayout,\"r\")\ncountline = 0\n#xPacman = 0\n#yPacman = 0\nxGoal = -1\nyGoal = -1\nfor line in f:\n    countchar = 0\n    rowList = []\n    for char in line:\n        rowList.append(char)\n        if char == 'P':\n            xPacman = countchar\n            yPacman = countline\n        if char == 'G':\n            xGoal = countchar\n            yGoal = countline\n            countGoal += 1\n        countchar+=1\n    countline+=1\n    ReadMazeList.append(rowList)\n#==============================================\n#========= SET UP FOR CANVAS ==================\nSize = 0\nTypeforSize = 0\npick = 0\nexp1 = 1300 / countchar\nexp2 = 700 / countline\nif exp1 < exp2:\n    pick = exp1\nelse:\n    pick = exp2\n\nif pick >= 40:\n    Size = 40\nelse:\n    if pick >= 30:\n        Size = 30\n    else:\n        if pick >= 20:\n            Size = 20\n        else:\n            Size = 10\n\n#==============================================\n#============== DRAW CANVAS ===================\nmaster = Tk()\ncanvas_width = countchar * Size\ncanvas_height = countline * Size\nw = Canvas(master, \n    width=canvas_width,\n    height=canvas_height)\nw.pack()\n\n#======== ESC ===========\n#========NOT WORK =======\n\nif Size == 40:\n    img = PhotoImage(file='Pacman40.gif')\nif Size == 30:\n    img = PhotoImage(file='Pacman30.gif')\nif Size == 20:\n    img = PhotoImage(file='Pacman20.gif')\nif Size == 10:\n    img = PhotoImage(file='Pacman10.gif')\nLabel(master, text=\" PACMAN - AStar by Nguyen Trong Tuyen - 1453057\", fg = \"black\", font = \"Times\").pack()\n#==============================================\n#================== CLASS =====================\nclass Maze:\n    def __init__(self,mazeFileName):\n        self.cost = 0\n        self.mazelist = ReadMazeList\n        self.RowsInMaze = countline\n        self.ColumnsInMaze = countchar\n        if countGoal == 1:\n            self.goal = w.create_rectangle(xGoal*Size, yGoal*Size, (xGoal+1)*Size, (yGoal+1)*Size, fill = \"red\")\n        w.update()\n    \n    #==== DRAW MAZE ====\n    def DrawMaze(self):\n        for y in range(self.RowsInMaze):\n            for x in range(self.ColumnsInMaze):\n                if self.mazelist[y][x] == WALL:\n                    w.create_rectangle(x*Size,y*Size,(x+1)*Size, (y+1)*Size,fill=\"blue\")\n                if self.mazelist[y][x] == FOOD:\n                    w.create_oval(x*Size + Size / 4,y*Size+ Size / 4,(x+1)*Size- Size / 4, (y+1)*Size- Size / 4,fill=\"yellow\")\n        w.update()\n\n    def UpdateState(self,row,col,key=None):\n        if key:\n            self.mazelist[row][col] = key\n\n    def __getitem__(self,idx):\n        return self.mazelist[idx]\n    #=============== AStar for stay_left and stay_right ===============\n    def AStar(self, mRow,mCol,xPacman, yPacman,rMaze):\n        self.cost = 0\n        tempMaze = []\n        i = 0\n        while i < mRow:\n            j = 0\n            tempMaze.append([])\n            while j < mCol:\n                tempMaze[i].append(0)\n                j += 1\n            i += 1\n        tempMaze[xPacman][yPacman] = 1\n        tempPriority = PriorityQueue()\n        h = Manhattan(xPacman,yPacman,xGoal,yGoal)\n        tempPriority.push(xPacman,yPacman,h+1)\n        #Queue = []\n        #Queue.append([xPacman,yPacman])\n        Queue = []\n        \n        while tempPriority.is_empty() == 0 :\n            i = 0 \n            Queue = tempPriority.pop()\n            if Queue == -1:\n                break;\n            x = Queue[0]\n            y = Queue[1]\n            while i <=3 :# 4 direction\n                if (x+Direction[i][0] >= 0) and (x+Direction[i][0] < mRow) and (y+Direction[i][1] >= 0) and(y+Direction[i][1] < mCol):\n                    if (tempMaze[ x +Direction[i][0]][ y +Direction[i][1]] == 0) :\n                        if rMaze[ x +Direction[i][0]][ y +Direction[i][1]] == GOAL: # Goal found\n                            tempMaze [ x +Direction[i][0]] [ y +Direction[i][1]] = tempMaze[x][y] + 1\n                            changeX = x +Direction[i][0]\n                            changeY = y +Direction[i][1] \n                            i = 0\n                            while i <= 3: # 4 direction\n                                    if (changeX+Direction[i][0] >= 0) and (changeX+Direction[i][0] < mRow) and (changeY+Direction[i][1] >= 0) and(changeY+Direction[i][1] < mCol):\n                                        if (tempMaze[changeX+Direction[i][0]][changeY+Direction[i][1]] == tempMaze[changeX][changeY]-1 ) :\n                                            # update cost , function f(x) = x, + when turn left or right, remain the same when up or down\n                                            oldX = changeX\n                                            changeX = changeX + Direction[i][0]\n                                            changeY = changeY + Direction[i][1]\n                                            if oldX != changeX:\n                                                self.cost += changeX\n                                            PathListAStar.insert(0,[changeX,changeY])\n                                            if tempMaze[changeX][changeY] == 1:\n                                                return\n                                            i = -1;\n                                    i += 1\n                        if rMaze[ x +Direction[i][0]][ y +Direction[i][1]] != WALL :\n                            #Queue.append( [ x +Direction[i][0], y +Direction[i][1]] )\n                            #if (sys.argv[4] == 'ucs_stay_right' and i == 3) or  (sys.argv[4] == 'ucs_stay_left' and i == 2):\n                            #    tempPriority.push(x+Direction[i][0],y+Direction[i][1],0)                         \n                            #else:\n                            h = Manhattan(x+Direction[i][0],y+Direction[i][1],xGoal,yGoal)\n                            tempPriority.push(x+Direction[i][0],y+Direction[i][1],h+ tempMaze[x][y]+1)\n                            tempMaze[ x +Direction[i][0]] [ y +Direction[i][1]] = tempMaze[x][y] + 1\n                i = i + 1\n            del Queue[0]\n#===============================================\nmyMaze = Maze(inputlayout)\nmyMaze.DrawMaze()\n#================================================\n#================= FOR ASTAR ======================\nif sys.argv[4] == 'astar' :\n    if xGoal != -1 and yGoal !=-1:\n        myMaze.AStar(countline,countchar,yPacman, xPacman,ReadMazeList)\n    pacman = w.create_image(xPacman*Size, yPacman*Size, anchor=NW,image = img)\n    file = open(\"1453057.txt\", \"w\")\n    num = len(PathListAStar)\n    w.coords(pacman,xPacman*Size,yPacman*Size)\n    if num==0:\n        file.write(\"NO WAY AVAILABLE OR NO GOAL\")\n        file.close()\n    else:\n        file.write(\"%s\\n\" % (num))\n        file.write(\"%s\\n\" % (myMaze.cost))\n        if countGoal > 1:\n            toadox = PathListAStar[num-1][1] \n            toadoy = PathListAStar[num-1][0]\n            flag = 0\n            if myMaze.mazelist[toadoy-1][toadox] == 'G':\n                xGoal = toadox\n                yGoal = toadoy-1\n                flag = 1\n            if (myMaze.mazelist[toadoy+1][toadox] == 'G') and flag ==0:\n                xGoal = toadox\n                yGoal = toadoy+1\n                flag = 1\n            if (myMaze.mazelist[toadoy][toadox-1] == 'G') and flag ==0:\n                xGoal = toadox-1\n                yGoal = toadoy\n                flag = 1\n            if (myMaze.mazelist[toadoy][toadox+1] == 'G') and flag ==0:\n                xGoal = toadox+1\n                yGoal = toadoy\n                flag = 1\n            w.create_rectangle(xGoal*Size, yGoal*Size, (xGoal+1)*Size, (yGoal+1)*Size, fill = \"red\")\n        for i in (range(num)):\n            toadox = PathListAStar[i][1] \n            toadoy = PathListAStar[i][0]\n            w.coords(pacman,toadox*Size, toadoy*Size)\n            w.update()\n            time.sleep(0.2)\n            w.create_rectangle(toadox*Size,toadoy*Size,(toadox+1)*Size,(toadoy+1)*Size, fill = \"green\")\n            w.update()\n            if i < num - 1:\n                toadoxs = PathListAStar[i+1][1]\n                toadoys = PathListAStar[i+1][0]\n                if toadox == toadoxs and toadoy== toadoys - 1:\n                    file.write(\"D \")\n                if toadox ==  toadoxs and toadoy== toadoys + 1:\n                    file.write(\"U \")\n                if toadox == toadoxs - 1 and toadoy== toadoys:\n                    file.write(\"R \")\n                if toadox == toadoxs + 1 and toadoy== toadoys:\n                    file.write(\"L \")\n        toadox = PathListAStar[num-1][1] \n        toadoy = PathListAStar[num-1][0]\n        if toadox == xGoal and toadoy== yGoal - 1:\n            file.write(\"D \")\n        if toadox == xGoal and toadoy== yGoal + 1:\n            file.write(\"U \")\n        if toadox == xGoal - 1 and toadoy== yGoal:\n            file.write(\"R \")\n        if toadox == xGoal + 1 and toadoy== yGoal:\n            file.write(\"L \")\n        file.close()\n        w.coords(pacman,xGoal*Size,yGoal*Size)\n        w.create_image(xGoal*Size,yGoal*Size,anchor=NW,image = img)\nw.update()\nmainloop()","sub_path":"myPacman/myPacman.py","file_name":"myPacman.py","file_ext":"py","file_size_in_byte":10640,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"536506329","text":"import torch.nn as nn\nimport numpy as np\n\nclass Generator(nn.Module):\n    def __init__(self, configs, img_shape):\n        super().__init__()\n\n        def block(in_features, out_features, normalize=True):\n            layers = [nn.Linear(in_features, out_features)]\n            if normalize:\n                layers += [nn.BatchNorm1d(out_features, momentum=0.1)]\n            layers += [nn.LeakyReLU(0.2, inplace=True)]\n            return layers\n\n        self.model = nn.Sequential(\n            *block(configs.latent_dim, 128, normalize=False),\n            *block(128, 256),\n            *block(256, 512),\n            *block(512, 1024),\n            nn.Linear(1024, int(np.prod(img_shape))),\n            nn.Tanh()\n        )\n\n        self.img_shape = img_shape\n\n    def forward(self, z):\n        img = self.model(z)\n        img = img.view(img.size(0), self.img_shape[0], self.img_shape[1], self.img_shape[2])\n        return img\n\n\nclass Discriminator(nn.Module):\n    def __init__(self, configs, img_shape):\n        super().__init__()\n\n        self.model = nn.Sequential(\n            nn.Linear(int(np.prod(img_shape)), 512),\n            nn.LeakyReLU(0.2, inplace=True),\n            nn.Linear(512, 256),\n            nn.LeakyReLU(0.2, inplace=True),\n            nn.Linear(256, 1),\n            nn.Sigmoid()\n        )\n\n    def forward(self, img):\n        img_flattened = img.view(img.size(0), -1)\n        score = self.model(img_flattened)\n\n        return score","sub_path":"GANs/gan/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":1448,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"249023266","text":"from django.views.decorators.csrf import csrf_exempt\nfrom django.urls import path\nfrom .views import QueuesView, GetQueueInfoView, AddMemberToQueueView, EditQueueMember, DeleteMemberFromQueueView, ClearMemberships, ClearQueues, ClearTokens, ClearUsers\nfrom token_auth import views\n\nurlpatterns = [\n    path('auth', csrf_exempt(views.LoginView.as_view())),\n    path('users', csrf_exempt(views.SignUpView.as_view())),\n\n    path('queues', csrf_exempt(QueuesView.as_view())),\n    path('queues/<int:queue_id>', csrf_exempt(GetQueueInfoView.as_view())),\n    path('queue/<int:queue_id>/member/<int:member_id>', csrf_exempt(EditQueueMember.as_view())),\n    path('queues/<int:queue_id>/members', csrf_exempt(AddMemberToQueueView.as_view())),\n    path('queues/<int:queue_id>/members/<int:member_id>', csrf_exempt(DeleteMemberFromQueueView.as_view())),\n\n    path('clear/memberships', csrf_exempt(ClearMemberships.as_view())),\n    path('clear/users', csrf_exempt(ClearUsers.as_view())),\n    path('clear/queues', csrf_exempt(ClearQueues.as_view())),\n    path('clear/tokens', csrf_exempt(ClearTokens.as_view())),\n]","sub_path":"dj_queue/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1100,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"602982553","text":"import asyncio\nfrom jsonrpcserver.aio import methods\n\n@methods.add\nasync def ping():\n    return 'pong'\n\nasync def handle(request):\n    return await methods.dispatch(request)\n\nif __name__ == '__main__':\n    request = '{\"jsonrpc\": \"2.0\", \"method\": \"ping\", \"id\": 1}'\n    response = asyncio.get_event_loop().run_until_complete(handle(request))\n    print(str(response))\n","sub_path":"examples/asyncio_server.py","file_name":"asyncio_server.py","file_ext":"py","file_size_in_byte":365,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"324499147","text":"from spt3g import core, mapmaker, sptpol\nfrom spt3g.todfilter import ViewTimestreamPSD\n\nbolos_to_disp = ['C1.A1.1.X','C1.A1.1.Y', 'C1.A1.10.X', 'C1.A1.10.Y',\n                 'C1.A1.11.X', 'C1.A1.11.Y', 'C1.A1.12.X', 'C1.A1.12.Y']\n\npipe = core.G3Pipeline()\nidf_fn = mapmaker.get_test_files_path() + 'ra23h30dec-55_idf_20130430_003228_150ghz.h5'\npipe.Add(sptpol.DirectIdfReader, filename = idf_fn)\n\n# Low frequency cutoff to eliminate 1/f noise\nlow_f = 1.8*core.G3Units.Hz\n\n# With bolo_order present, plots each listed bolo in a separate subplot.\n# pipe.Add(ViewTimestreamPSD, bolo_order=bolos_to_disp, low_f=low_f)\npipe.Add(ViewTimestreamPSD, low_f=low_f)\n\npipe.Run()\n","sub_path":"examples/view_bolo_psd.py","file_name":"view_bolo_psd.py","file_ext":"py","file_size_in_byte":668,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"113428415","text":"from datetime import datetime\n\nfrom django.forms import ModelForm, CharField, Textarea, Form, ModelChoiceField, \\\n    ModelMultipleChoiceField, RadioSelect, CheckboxSelectMultiple\n\nfrom django.contrib.postgres.forms import SimpleArrayField\n\nfrom .models import Question, Survey, SurveyTemplate, Semester\n\n\nclass QuestionForm(ModelForm):\n    \"\"\"Question form; used by admin.\n\n    This form overrides the default one we'd get; this is used for the likert field to\n    show as a textarea with each answer on a line, rather than as a text input with each\n    answer separated by commas.\n    \"\"\"\n\n    likert_labels = SimpleArrayField(\n        CharField(max_length=25),\n        delimiter=\"\\r\",\n        widget=Textarea(attrs={'cols': 30, 'rows': 5}),\n        required=False,\n        help_text=\"Five labels, worst-to-best, each on a separate line. Some lines may be blank.\"\n    )\n\n    def clean_likert_labels(self):\n        \"\"\"There may be newlines left on the values; strip these off.\"\"\"\n\n        return [label.strip(\"\\n\") for label in self.cleaned_data['likert_labels']]\n\n    class Meta:\n        model = Question\n        exclude = []\n\n\nclass SurveyForm(ModelForm):\n    \"\"\"Make staff member not required.\n\n    This is a big of a hack; if we migrate to add blank=True in the model, the migration removes\n    existing data. Therefore, we just don't require it on the form. FIXME.\n    \"\"\"\n\n    def __init__(self, *args, **kwargs):\n        super(SurveyForm, self).__init__(*args, **kwargs)\n        self.fields['staff'].required = False\n        self.initial.setdefault('open_at', datetime.now())\n        if not self.initial.get('template'):\n            self.fields['template'].queryset = SurveyTemplate.objects.exclude(\n                semester__end_date__lt=datetime.now())\n\n    class Meta:\n        model = Survey\n        exclude = []\n\n\nclass SurveyTemplateCopyForm(Form):\n    \"\"\"Copy a survey template to a new semester.\"\"\"\n\n    templates = ModelMultipleChoiceField(\n        SurveyTemplate.objects.all(),\n        # widget=MultipleHiddenInput(),\n        widget=CheckboxSelectMultiple(),\n        label='Templates to Copy',\n    )\n\n    semester = ModelChoiceField(\n        Semester.objects.all(),\n        widget=RadioSelect(),\n        empty_label=None,\n        label='Semester to Copy Templates Into',\n    )\n\n    def is_valid(self): \n        \"\"\"Check for duplicates before we try to copy.\"\"\"\n\n        templates = SurveyTemplate.objects.filter(id__in=self.data['templates'])\n        semester = self.data['semester']\n\n        for template in templates:\n            if SurveyTemplate.objects.filter(name=template.name, semester=semester).exists():\n                self.add_error(None,\n                               \"Template already exists in that semester: %s\" % str(template))\n                return False\n\n        return super(SurveyTemplateCopyForm, self).is_valid()\n\n    def clone_templates(self):\n        \"\"\"Copy templates and questions. Return number of templates copied.\"\"\"\n\n        templates = SurveyTemplate.objects.filter(id__in=self.cleaned_data['templates'])\n        semester = self.cleaned_data['semester']\n\n        # Copy each template, first copying each question in the template.\n        # In Django. you can make a new version of an object by settings it's primary key\n        # to null and saving it--this will be interpreted as a new object.\n\n        for template in templates:\n            # Get list of questions from old template\n            old_questions = template.question_set.all()\n            new_questions = []\n\n            # Copy template\n            template.pk = None\n            template.semester = semester\n            template.save()\n\n            # Copy questions\n            for question in old_questions:\n                question.pk = None\n                question.save()\n                new_questions.append(question)\n\n            # Assign new list of questions to new template\n            template.question_set = new_questions\n            template.save()\n\n        return len(templates)\n","sub_path":"project/feedback/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":4008,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"591313339","text":"\n\nfrom xai.brain.wordbase.nouns._id import _ID\n\n#calss header\nclass _IDS(_ID, ):\n\tdef __init__(self,): \n\t\t_ID.__init__(self)\n\t\tself.name = \"IDS\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"id\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_ids.py","file_name":"_ids.py","file_ext":"py","file_size_in_byte":210,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"28050263","text":"# Copyright (c) 2019, DjaoDjin inc.\n# see LICENSE\nfrom __future__ import absolute_import\nfrom __future__ import unicode_literals\n\nimport logging\nfrom functools import wraps\n\nfrom django import http\nfrom django.contrib.auth import REDIRECT_FIELD_NAME\nfrom django.core.exceptions import PermissionDenied\nfrom django.db import DEFAULT_DB_ALIAS\nfrom django.db.models import Q\nfrom django.template.response import TemplateResponse\nfrom django.utils.decorators import available_attrs\nfrom django.utils import six\nfrom django.utils.translation import ugettext_lazy as _\nimport jinja2.exceptions\nfrom multitier.thread_locals import get_current_site\nfrom pages.locals import (enable_instrumentation, disable_instrumentation,\n    get_edition_tools_context_data)\nfrom rules.perms import NoRuleMatch, check_matched, redirect_or_denied\nfrom rules.utils import get_current_app\nfrom saas.decorators import (NORMAL, fail_authenticated, _fail_direct,\n    _fail_provider, _fail_provider_only, _fail_self_provider, _has_valid_access)\nfrom signup.models import Contact\nfrom signup.utils import has_invalid_password\nfrom saas.utils import get_role_model\n\nfrom .compat import reverse\nfrom .thread_locals import get_current_broker\nfrom .edition_tools import inject_edition_tools as _inject_edition_tools\n\n# This logger is really only useful for 'rules' in debug mode.\nLOGGER = logging.getLogger('rules')\n\nDEFAULT_PREFIXES = [\n    '/api/auth/', '/api/billing/', '/api/metrics/',\n    '/api/profile/', '/api/themes', '/api/users/',\n    '/billing/', '/metrics/', '/profile/', '/users/']\n\n\ndef inject_edition_tools(function=None):\n    \"\"\"\n    Inject the edition tools into the HTML response.\n    \"\"\"\n    def decorator(view_func):\n        @wraps(view_func, assigned=available_attrs(view_func))\n        def _wrapped_view(request, *args, **kwargs):\n            enable_instrumentation()\n            response = view_func(request, *args, **kwargs)\n            disable_instrumentation()\n            if isinstance(response, TemplateResponse):\n                # We could use ``SingleTemplateResponse`` to catch both\n                # django and restframework responses. Unfortunately\n                # the content_type on restframework responses is set\n                # very late (render), while at the same time django\n                # defaults it to text/html until then.\n                try:\n                    response.render()\n                except jinja2.exceptions.TemplateError as err:\n                    response = TemplateResponse(\n                        request=request,\n                        template=\"400.html\",\n                        context={'messages': [str(err)]},\n#                        using=view_func.template_engine,\n                        status=400)\n                    response.render()\n                soup = _inject_edition_tools(response, request,\n                    context=get_edition_tools_context_data())\n                if soup:\n                    # str(soup) instead of soup.prettify() to avoid\n                    # trailing whitespace on a reformatted HTML textarea\n                    response.content = str(soup)\n            return response\n        return _wrapped_view\n\n    if function:\n        return decorator(function)\n    return decorator\n\n\ndef requires_authenticated(function=None,\n                           redirect_field_name=REDIRECT_FIELD_NAME):\n    \"\"\"\n    Decorator for views that checks that the user is authenticated.\n\n    ``django.contrib.auth.decorators.login_required`` will automatically\n    redirect to the login page. We wante to redirect to the activation\n    page when required, as well as raise a ``PermissionDenied``\n    instead when Content-Type is showing we are dealing with an API request.\n    \"\"\"\n    def decorator(view_func):\n        @wraps(view_func, assigned=available_attrs(view_func))\n        def _wrapped_view(request, *args, **kwargs):\n            LOGGER.debug(\"Enters djaoapp.decorators.requires_authenticated\")\n            redirect_url = fail_authenticated(request)\n            if redirect_url:\n                verification_key = kwargs.get('verification_key', None)\n                if verification_key:\n                    contact = Contact.objects.filter(\n                        verification_key=verification_key).first()\n                    if not contact:\n                        # Not a `Contact`, let's try `Role`.\n                        role_model = get_role_model()\n                        try:\n                            role = role_model.objects.filter(\n                                Q(grant_key=verification_key)\n                                | Q(request_key=verification_key)).get()\n                            contact, _ = Contact.objects.update_or_create_token(\n                                role.user)\n                            verification_key = contact.verification_key\n                        except role_model.DoesNotExist:\n                            pass\n                    if contact and has_invalid_password(contact.user):\n                        redirect_url = request.build_absolute_uri(\n                            reverse('registration_activate',\n                                args=(verification_key,)))\n                return redirect_or_denied(request, redirect_url,\n                    redirect_field_name=redirect_field_name)\n            return view_func(request, *args, **kwargs)\n        return _wrapped_view\n\n    if function:\n        return decorator(function)\n    return decorator\n\n\ndef requires_direct(function=None, roledescription=None, strength=NORMAL,\n                    redirect_field_name=REDIRECT_FIELD_NAME):\n    \"\"\"\n    Same decorator as ``saas.decorators.requires_direct`` with the additional\n    look up for a ``Site`` account.\n    \"\"\"\n    def decorator(view_func):\n        @wraps(view_func, assigned=available_attrs(view_func))\n        def _wrapped_view(request, *args, **kwargs):\n            LOGGER.debug(\"Enters djaoapp.decorators.requires_direct\")\n            try:\n                app = get_current_app()\n                #pylint:disable=unused-variable\n                redirect_url, matched, session = check_matched(request, app,\n                    prefixes=DEFAULT_PREFIXES)\n                if redirect_url:\n                    if isinstance(redirect_url, six.string_types):\n                        return http.HttpResponseRedirect(redirect_url)\n                    raise PermissionDenied()\n            except NoRuleMatch:\n                slug = kwargs.get('charge', kwargs.get('organization', None))\n                redirect_url = _fail_direct(request, organization=slug,\n                        roledescription=roledescription, strength=strength)\n                if redirect_url:\n                    return redirect_or_denied(request, redirect_url,\n                        redirect_field_name=redirect_field_name,\n                        descr=_(\"%(auth)s is not a direct manager\"\\\n    \" of %(organization)s.\") % {'auth': request.user, 'organization': slug})\n\n            return view_func(request, *args, **kwargs)\n\n        return _wrapped_view\n\n    if function:\n        return decorator(function)\n    return decorator\n\n\ndef requires_provider(function=None, roledescription=None, strength=NORMAL,\n                    redirect_field_name=REDIRECT_FIELD_NAME):\n    \"\"\"\n    Same decorator as saas.requires_provider with the added permissions\n    that managers of the site database itself are also able to access\n    profiles of registered yet unsubscribed ``Organization``.\n    \"\"\"\n    def decorator(view_func):\n        @wraps(view_func, assigned=available_attrs(view_func))\n        def _wrapped_view(request, *args, **kwargs):\n            LOGGER.debug(\"Enters djaoapp.decorators.requires_provider\")\n            site = get_current_site()\n            organization = kwargs.get('organization', None)\n            if site.db_name and site.db_name != DEFAULT_DB_ALIAS:\n                # We have a separate database so it is OK for a manager\n                # of the site to access registered ``Organization`` which\n                # are not subscribed yet.\n                if _has_valid_access(\n                    request, [get_current_broker()], strength):\n                    return view_func(request, *args, **kwargs)\n            try:\n                app = get_current_app()\n                #pylint:disable=unused-variable\n                redirect_url, matched, session = check_matched(request, app,\n                    prefixes=DEFAULT_PREFIXES)\n                if redirect_url:\n                    if isinstance(redirect_url, six.string_types):\n                        return http.HttpResponseRedirect(redirect_url)\n                    raise PermissionDenied()\n            except NoRuleMatch:\n                # By default, we are looking for provider.\n                slug = kwargs.get('charge', organization)\n                redirect_url = _fail_provider(request, organization=slug,\n                    roledescription=roledescription, strength=strength)\n                if redirect_url:\n                    return redirect_or_denied(request, redirect_url,\n                        redirect_field_name=redirect_field_name,\n                        descr=_(\"%(auth)s is neither a manager of\"\\\n\" %(organization)s nor a manager of one of %(organization)s providers.\") % {\n    'auth': request.user, 'organization': slug})\n            return view_func(request, *args, **kwargs)\n\n        return _wrapped_view\n\n    if function:\n        return decorator(function)\n    return decorator\n\n\ndef requires_provider_only(function=None, roledescription=None,\n                           strength=NORMAL,\n                           redirect_field_name=REDIRECT_FIELD_NAME):\n    \"\"\"\n    Same decorator as saas.requires_provider with the added permissions\n    that managers of the site database itself are also able to access\n    profiles of registered yet unsubscribed ``Organization``.\n    \"\"\"\n    def decorator(view_func):\n        @wraps(view_func, assigned=available_attrs(view_func))\n        def _wrapped_view(request, *args, **kwargs):\n            LOGGER.debug(\"Enters djaoapp.decorators.requires_provider_only\")\n            site = get_current_site()\n            organization = kwargs.get('organization', None)\n            if site.db_name:\n                # We have a separate database so it is OK for a manager\n                # of the site to access registered ``Organization`` which\n                # are not subscribed yet.\n                if _has_valid_access(\n                    request, [get_current_broker()], strength):\n                    return view_func(request, *args, **kwargs)\n            try:\n                app = get_current_app()\n                #pylint:disable=unused-variable\n                redirect_url, matched, session = check_matched(request, app,\n                    prefixes=DEFAULT_PREFIXES)\n                if redirect_url:\n                    if isinstance(redirect_url, six.string_types):\n                        return http.HttpResponseRedirect(redirect_url)\n                    raise PermissionDenied()\n            except NoRuleMatch:\n                # By default, we are looking for provider.\n                slug = kwargs.get('charge', organization)\n                redirect_url = _fail_provider_only(request, organization=slug,\n                    roledescription=roledescription, strength=strength)\n                if redirect_url:\n                    return redirect_or_denied(request, redirect_url,\n                        redirect_field_name=redirect_field_name,\n                        descr=_(\"%(auth)s is not a manager of one of\"\\\n\" %(organization)s providers.\") % {'auth': request.user, 'organization': slug})\n            return view_func(request, *args, **kwargs)\n\n        return _wrapped_view\n\n    if function:\n        return decorator(function)\n    return decorator\n\n\ndef requires_self_provider(function=None, strength=NORMAL,\n                           redirect_field_name=REDIRECT_FIELD_NAME):\n    \"\"\"\n    Same decorator as saas.requires_self_provider with the added permissions\n    that managers of the site database itself are also able to access\n    profiles of registered yet unsubscribed ``Organization``.\n    \"\"\"\n    def decorator(view_func):\n        @wraps(view_func, assigned=available_attrs(view_func))\n        def _wrapped_view(request, *args, **kwargs):\n            LOGGER.debug(\"Enters djaoapp.decorators.requires_self_provider\")\n            site = get_current_site()\n            if site.db_name:\n                # We have a separate database so it is OK for a manager\n                # of the site to access profiles of ``User`` which\n                # are not subscribed yet.\n                if _has_valid_access(\n                    request, [get_current_broker()], strength):\n                    return view_func(request, *args, **kwargs)\n            try:\n                app = get_current_app()\n                #pylint:disable=unused-variable\n                redirect_url, matched, session = check_matched(request, app,\n                    prefixes=DEFAULT_PREFIXES)\n                if redirect_url:\n                    if isinstance(redirect_url, six.string_types):\n                        return http.HttpResponseRedirect(redirect_url)\n                    raise PermissionDenied()\n            except NoRuleMatch:\n                redirect_url = _fail_self_provider(\n                        request, user=kwargs.get('user', None),\n                        strength=strength)\n                if redirect_url:\n                    return redirect_or_denied(request, redirect_url,\n                        redirect_field_name=redirect_field_name,\n                        descr=_(\"%(auth)s has neither a direct\"\\\n\" relation to an organization connected to %(user)s nor a connection to one\"\\\n\" of the providers to such organization.\") % {\n    'auth': request.user, 'user': kwargs.get('user', None)})\n            return view_func(request, *args, **kwargs)\n        return _wrapped_view\n\n    if function:\n        return decorator(function)\n    return decorator\n","sub_path":"djaoapp/decorators.py","file_name":"decorators.py","file_ext":"py","file_size_in_byte":14023,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"202589225","text":"import numpy as np\nimport sys\nfrom scipy import integrate\nimport matplotlib.pyplot as plt\nimport UTILS.CALCULUS as calc\nimport UTILS.ALIMIT as al\n\n# Theoretical background https://arxiv.org/abs/1401.5176\n\n# Mocak, Meakin, Viallet, Arnett, 2014, Compressible Hydrodynamic Mean-Field #\n# Equations in Spherical Geometry and their Application to Turbulent Stellar #\n# Convection Data #\n\nclass ContinuityEquationWithMassFlux(calc.CALCULUS,al.ALIMIT,object):\n\n    def __init__(self,filename,ig,intc,data_prefix):\n        super(ContinuityEquationWithMassFlux,self).__init__(ig) \n\t\n        # load data to structured array\n        eht = np.load(filename)\t\t\n\n        # load grid\n        xzn0 = np.asarray(eht.item().get('xzn0')) \t\n        nx   = np.asarray(eht.item().get('nx')) \n\t\t\n        # pick equation-specific Reynolds-averaged mean fields according to:\n        # https://github.com/mmicromegas/ransX/blob/master/DOCS/ransXimplementationGuide.pdf\t\n\n        dd    = np.asarray(eht.item().get('dd')[intc])\n        ux    = np.asarray(eht.item().get('ux')[intc])\t\t\t\n        ddux  = np.asarray(eht.item().get('ddux')[intc])\t\t\n\t\t\n        # store time series for time derivatives\n        t_timec   = np.asarray(eht.item().get('timec'))\t\t\n        t_dd      = np.asarray(eht.item().get('dd')) \t\n\t\t\t\t\n        #t_mm    = np.asarray(eht.item().get('mm')) \t\t\n        #minus_dt_mm = -self.dt(t_mm,xzn0,t_timec,intc)\t\t\t\t\t\t\n        #fht_ux = minus_dt_mm/(4.*np.pi*(xzn0**2.)*dd)\t\t\t\t\n\t\t\t\t\n        # construct equation-specific mean fields\n        fht_ux = ddux/dd\t\t\t\n        fdd = ddux-dd*ux\t\n\t\n        ####################################\n        # CONTINUITY EQUATION WITH MASS FLUX\n        ####################################\n\t\t\t\t\t\t\t\t\n        # LHS -dq/dt \t\t\n        self.minus_dt_dd = -self.dt(t_dd,xzn0,t_timec,intc)\n\n        # LHS -fht_ux Grad dd\n        self.minus_fht_ux_grad_dd = -fht_ux*self.Grad(dd,xzn0)\n\t\t\t\t\n        # RHS -Div fdd\n        self.minus_div_fdd = -self.Div(fdd,xzn0)\n\t\t\t\t\n        # RHS +fdd_o_dd gradx dd\t\t\t\t\n        self.plus_fdd_o_dd_gradx_dd = +(fdd/dd)*self.Grad(dd,xzn0)\n\t\t\n        # RHS -dd Div ux \n        self.minus_dd_div_ux = -dd*self.Div(ux,xzn0) \n\n        # -res\n        self.minus_resContEquation = -(self.minus_dt_dd+self.minus_fht_ux_grad_dd+self.minus_div_fdd+\\\n                                       self.plus_fdd_o_dd_gradx_dd+self.minus_dd_div_ux)\n\t\t\n        ########################################\t\n        # END CONTINUITY EQUATION WITH MASS FLUX\n        ########################################\n\t\t\n        # assign global data to be shared across whole class\n        self.data_prefix = data_prefix\t\t\n        self.xzn0        = xzn0\n        self.dd          = dd\n        self.nx          = nx\n        self.ig          = ig\t\t\n\t\t\n\t\t\n    def plot_rho(self,LAXIS,xbl,xbr,ybu,ybd,ilg):\n        \"\"\"Plot rho stratification in the model\"\"\" \n\t\t\n        # load x GRID\n        grd1 = self.xzn0\n\t\n        # load DATA to plot\n        plt1 = self.dd\n\t\t\n        # create FIGURE\n        plt.figure(figsize=(7,6))\n\t\t\n        # format AXIS, make sure it is exponential\n        plt.gca().yaxis.get_major_formatter().set_powerlimits((0,0))\t\t\n\t\t\n        # set plot boundaries   \n        to_plot = [plt1]\t\t\n        self.set_plt_axis(LAXIS,xbl,xbr,ybu,ybd,to_plot)\t\n\t\t\n        # plot DATA \n        plt.title('density')\n        plt.plot(grd1,plt1,color='brown',label = r'$\\overline{\\rho}$')\n\n        # define and show x/y LABELS\n        if (self.ig == 1):\t\n            setxlabel = r'x (10$^{8}$ cm)'\t\n        elif (self.ig == 2):\t\n            setxlabel = r'r (10$^{8}$ cm)'\n        else:\n            print(\"ERROR: geometry not defined, use ig = 1 for CARTESIAN, ig = 2 for SPHERICAL, EXITING ...\")\n            sys.exit() \n\t\t\t\n        setylabel = r\"$\\overline{\\rho}$ (g cm$^{-3}$)\"\n\n        plt.xlabel(setxlabel)\n        plt.ylabel(setylabel)\n\t\t\n        # show LEGEND\n        plt.legend(loc=ilg,prop={'size':18})\n\n        # display PLOT\n        plt.show(block=False)\n\n        # save PLOT\n        plt.savefig('RESULTS/'+self.data_prefix+'mean_rho.png')\n\t\n    def plot_continuity_equation(self,LAXIS,xbl,xbr,ybu,ybd,ilg):\n        \"\"\"Plot continuity equation in the model\"\"\" \n\t\t\n        # load x GRID\n        grd1 = self.xzn0\n\n        lhs0 = self.minus_dt_dd\n        lhs1 = self.minus_fht_ux_grad_dd \n\n        rhs0 = self.minus_div_fdd\n        rhs1 = self.plus_fdd_o_dd_gradx_dd\t\t\n        rhs2 = self.minus_dd_div_ux\n\t\t\n        res = self.minus_resContEquation\n\t\t\n        # create FIGURE\n        plt.figure(figsize=(7,6))\n\t\t\n        # format AXIS, make sure it is exponential\n        plt.gca().yaxis.get_major_formatter().set_powerlimits((0,0))\t\t\n\t\t\n        # set plot boundaries   \n        to_plot = [lhs0,lhs1,rhs0,rhs1,rhs2,res]\t\t\n        self.set_plt_axis(LAXIS,xbl,xbr,ybu,ybd,to_plot)\n\t\t\n        # plot DATA \n        plt.title('continuity equation with mass flux')\n        if (self.ig == 1):\t\t\n            plt.plot(grd1,lhs0,color='g',label = r'$-\\partial_t (\\overline{\\rho})$')\n            plt.plot(grd1,lhs1,color='r',label = r'$-\\widetilde{u}_x \\partial_x (\\overline{\\rho})$')\n            plt.plot(grd1,rhs0,color='c',label = r\"$-\\nabla_x f_\\rho$\")\t\t\n            plt.plot(grd1,rhs1,color='m',label = r\"$+f_\\rho / \\overline{\\rho} \\partial_x \\overline{\\rho}$\")\n            plt.plot(grd1,rhs2,color='b',label = r'$-\\overline{\\rho} \\nabla_x (\\overline{u}_x)$')\n            plt.plot(grd1,res,color='k',linestyle='--',label='res')\n        elif (self.ig == 2):  \n            plt.plot(grd1,lhs0,color='g',label = r'$-\\partial_t (\\overline{\\rho})$')\n            plt.plot(grd1,lhs1,color='r',label = r'$-\\widetilde{u}_r \\partial_r (\\overline{\\rho})$')\n            plt.plot(grd1,rhs0,color='c',label = r\"$-\\nabla_r f_\\rho$\")\t\t\n            plt.plot(grd1,rhs1,color='m',label = r\"$+f_\\rho / \\overline{\\rho} \\partial_r \\overline{\\rho}$\")\n            plt.plot(grd1,rhs2,color='b',label = r'$-\\overline{\\rho} \\nabla_r (\\overline{u}_r)$')\n            plt.plot(grd1,res,color='k',linestyle='--',label='res')\n        else:\n            print(\"ERROR: geometry not defined, use ig = 1 for CARTESIAN, ig = 2 for SPHERICAL, EXITING ...\")\n            sys.exit()\t\t\n\t\t\n        # define and show x/y LABELS\n        if (self.ig == 1):\t\n            setxlabel = r'x (10$^{8}$ cm)'\t\n        elif (self.ig == 2):\t\n            setxlabel = r'r (10$^{8}$ cm)'\n        else:\n            print(\"ERROR: geometry not defined, use ig = 1 for CARTESIAN, ig = 2 for SPHERICAL, EXITING ...\")\n            sys.exit() \n\t\t\t\n        setylabel = r\"g cm$^{-3}$ s$^{-1}$\"\n        plt.xlabel(setxlabel)\n        plt.ylabel(setylabel)\n\t\t\n        # show LEGEND\n        plt.legend(loc=ilg,prop={'size':12})\n\n        # display PLOT\n        plt.show(block=False)\n\n        # save PLOT\n        plt.savefig('RESULTS/'+self.data_prefix+'continuityWithMassFlux_eq.png')\n\n    def plot_continuity_equation_integral_budget(self,laxis,xbl,xbr,ybu,ybd):\n        \"\"\"Plot integral budgets of continuity equation in the model\"\"\" \n\t\t\n        # load x GRID\n        grd1 = self.xzn0\n        nx = self.nx\n\n        term1 = self.minus_dt_dd\n        term2 = self.minus_fht_ux_grad_dd\n        term3 = self.minus_div_fdd\n        term4 = self.plus_fdd_o_dd_gradx_dd\n        term5 = self.minus_dd_div_ux\n        term6 = self.minus_resContEquation \n\t\t\n        # calculate INDICES for grid boundaries \n        if laxis == 1 or laxis == 2:\n            idxl, idxr = self.idx_bndry(xbl,xbr)\n        else:\n            idxl = 0\n            idxr = self.nx-1\n\t\t\n        term1_sel = term1[idxl:idxr]\n        term2_sel = term2[idxl:idxr]\n        term3_sel = term3[idxl:idxr]\n        term4_sel = term4[idxl:idxr]\n        term5_sel = term5[idxl:idxr]\n        term6_sel = term6[idxl:idxr]\n\t\t\n        rc = self.xzn0[idxl:idxr]\n\n        Sr = 4.*np.pi*rc**2\n\n        int_term1 = integrate.simps(term1_sel*Sr,rc)\n        int_term2 = integrate.simps(term2_sel*Sr,rc)\n        int_term3 = integrate.simps(term3_sel*Sr,rc) \n        int_term4 = integrate.simps(term4_sel*Sr,rc)     \n        int_term5 = integrate.simps(term5_sel*Sr,rc)\n        int_term6 = integrate.simps(term6_sel*Sr,rc)\n\t\t\n        fig = plt.figure(figsize=(7,6))\n    \n        ax = fig.add_subplot(1,1,1)\n        ax.yaxis.grid(color='gray', linestyle='dashed')\n        ax.xaxis.grid(color='gray', linestyle='dashed')\n\t\t\n        if laxis == 2:\t\t\n            plt.ylim([ybd,ybu])\t \t\t\t\n     \n        fc = 1.\n    \n        # note the change: I'm only supplying y data.\n        y = [int_term1/fc,int_term2/fc,int_term3/fc,int_term4/fc,int_term5/fc,int_term6/fc]\n\n        # Calculate how many bars there will be\n        N = len(y)\n \n        # Generate a list of numbers, from 0 to N\n        # This will serve as the (arbitrary) x-axis, which\n        # we will then re-label manually.\n        ind = range(N)\n \n        # See note below on the breakdown of this command\n        ax.bar(ind, y, facecolor='#0000FF',\n               align='center', ecolor='black')\n \n        #Create a y label\n        ax.set_ylabel(r'g s$^{-1}$')\n \n        # Create a title, in italics\n        ax.set_title('continuity with mass flux integral budget')\n \n        # This sets the ticks on the x axis to be exactly where we put\n        # the center of the bars.\n        ax.set_xticks(ind)\n \n        # Labels for the ticks on the x axis.  It needs to be the same length\n        # as y (one label for each bar)\n        group_labels = [r'$-\\partial_t (\\overline{\\rho})$',r'$-\\widetilde{u}_r \\partial_r (\\overline{\\rho})$',\\\n                        r\"$-\\nabla_r f_\\rho$\",r\"$+f_\\rho / \\overline{\\rho} \\partial_r \\overline{\\rho}$\",\\\n                        r'$-\\overline{\\rho} \\nabla_r (\\overline{u}_r)$','res']\n                         \n        # Set the x tick labels to the group_labels defined above.\n        ax.set_xticklabels(group_labels,fontsize=16)\n \n        # Extremely nice function to auto-rotate the x axis labels.\n        # It was made for dates (hence the name) but it works\n        # for any long x tick labels\n        fig.autofmt_xdate()\n        \n        # display PLOT\n        plt.show(block=False)\n\n        # save PLOT\n        plt.savefig('RESULTS/'+self.data_prefix+'continuityWithMassFlux_eq_bar.png')\t\t\n\t\t\n\t\t\n","sub_path":"EQUATIONS/ContinuityEquationWithMassFlux.py","file_name":"ContinuityEquationWithMassFlux.py","file_ext":"py","file_size_in_byte":10162,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"638459038","text":"#Useful links\n#https://docs.opencv.org/3.1.0/dd/d43/tutorial_py_video_display.html\n\nimport cv2\n\n#0 is for the 1st webcam in the device, 1 for the second webcam....\n#To load video file, specify name of the video file\nvideo_capture = cv2.VideoCapture(0)\n\n#Check if video capture object is initialized\nif(not(video_capture.isOpened())):\n    video_capture.open()\n\n#To write file\nfourcc = cv2.VideoWriter_fourcc(*'DIVX') #fourcc is a 4 byte code used to specify video codec. DIVX is for windows\nvideo_writer = cv2.VideoWriter('Recording.avi', fourcc, 20.0, (640,480))\n\nwhile True:\n    ret, frame = video_capture.read() #ret gets a true or false if feed was available, frame is stored in frame\n\n    #Convert color to grayscale\n    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n\n    #Write the frame to video\n    video_writer.write(frame)\n    \n    cv2.imshow('Video',frame)\n    cv2.imshow('Gray',gray)\n\n    #To end the video press q\n    if cv2.waitKey(1) & 0xFF == ord('q'):\n        break\n\n#Release the camera resource\nvideo_capture.release()\nvideo_writer.release()\n","sub_path":"Reading_Video/Example.py","file_name":"Example.py","file_ext":"py","file_size_in_byte":1060,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"604743417","text":"import os\nimport pydicom\nimport pandas as pd\nimport numpy as np\nimport matplotlib.image as image\n\n\ndata_dir = \"./AIMIN Challenge - Training Dataset/\"\npatients = sorted(os.listdir(data_dir))\nlabels = pd.read_excel('./AIMIN Challenge - Training Labels.xlsx', index_col=[0])\ntrain_images_dir = './White_Matter_Dataset/images/train'\ntest_images_dir = './White_Matter_Dataset/images/test'\nIMG_PIXEL_SIZE = 32\nSLICE_NUM = 7\ntrain_size = 0.9\n\nDWM_labels = []\nPVWM_labels = []\nfor i, patient in enumerate(patients[:np.int(train_size * len(patients))]):\n    label = labels.iloc[i, :3]\n    DWM_label = label['DWM']\n    PVWM_label = label['PVWM']\n    DWM_labels += [DWM_label]\n    PVWM_labels += [PVWM_label]\n    print(i)\n    path = data_dir + patient\n    p_dir = os.path.join(test_images_dir, str(i))\n    os.mkdir(p_dir)\n    for patient_dir, _, patient_file in os.walk(path):\n        for j, file in enumerate(sorted(patient_file)):\n            file_path = os.path.join(patient_dir, file)\n            data = pydicom.dcmread(file_path).pixel_array\n            image.imsave(os.path.join(p_dir, '{}.png'.format(j)), data, cmap='gray')\n\nfor i in range(SLICE_NUM):\n    np.savetxt(os.path.join('./White_Matter_Dataset/Targets/train', 'DWM_labels.txt'), np.array(DWM_labels), fmt=\"%d\")\n    np.savetxt(os.path.join('./White_Matter_Dataset/Targets/train', 'PVWM_labels.txt'), np.array(PVWM_labels), fmt=\"%d\")\n","sub_path":"build2D.py","file_name":"build2D.py","file_ext":"py","file_size_in_byte":1389,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"294007337","text":"from random import randint\nfrom time import sleep\ndef intro():\n    print('''Вы находитесь в землях, заселенных драконами.\n    Перед собой вы видите две пещеры.\n    В одной из них - дружелюбный дракон,\n    который готов поделиться с вами сокровищами.\n    Во второй - жадный и голо��ный дракон, который мигом вас съест.''')\n\n\ndef select_cave():\n    print('В какую пещеру вы войдете? (нажмите клавишу 1 или 2)')\n    selected_cave = int(input())\n    return selected_cave\n\n\ndef cheek_cave(selected_cave):\n    friendly_cave = randint(1, 2)\n    print('Вы приближаетесь к пещере...')\n    sleep(1)\n    print('Её темнота заставляет дрожать вас')\n    sleep(1)\n    print('Большой дракон выпрыгивает перед вами! Он раскрывает свою пасть и...')\n    sleep(1)\n    if selected_cave == friendly_cave:\n        print('Делится с вами своими сокровищами!')\n    else:\n        print('Моментально вас съедает!')\n\nplay_again = 'да'\nwhile play_again == 'да':\n    intro()\n    sleep(2)\n    cheek_cave(select_cave())\n    print('Сыграем  еще? (да или нет)')\n    play_again = input()\n\n","sub_path":"dragon_game.py","file_name":"dragon_game.py","file_ext":"py","file_size_in_byte":1448,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"487268534","text":"from tkinter import filedialog\r\nfrom tkinter import *\r\n\r\ndef mainscreen ():\r\n    print (\"Main Program\")\r\n\r\ngame = Tk()\r\ngame.title (\"Bouncing Ball\")\r\ngame.minsize (500,500)\r\n\r\nlblimage = PhotoImage(file=\"Basketball.png\")\r\nLabel(game, text=\"Welcome to Bouncing Ball\", bg=\"white\", fg=\"black\",\r\n      font=\"none 20 bold\"). grid(row=1, column =0, sticky=S)\r\nLabel(game, image=lblimage, bg=\"white\").grid(row = 6, column = 1, sticky=W)\r\n\r\n\r\n\r\n\r\n","sub_path":"Group 10.py","file_name":"Group 10.py","file_ext":"py","file_size_in_byte":439,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"621887837","text":"from django.urls import path,include\nfrom . import views\nfrom rest_framework.urlpatterns import format_suffix_patterns\nfrom .views import BigDataView\n\napp_name = \"parsed_data\"\n\nBigDataView_list = BigDataView.as_view({\n    'post': 'create',\n    'get': 'list'\n})\nBigDataView_detail = BigDataView.as_view({\n    'get': 'retrieve',\n    'put': 'update',\n    'patch': 'partial_update',\n    'delete': 'destroy',\n})\nurlpatterns = format_suffix_patterns([\n    path('auth/', include('rest_framework.urls', namespace='rest_framework')),\n    path('bigdata/', BigDataView_list, name='PortfolioView_list'),\n    path('', views.data, name='data'),\n    path('bigdata/<int:pk>/', BigDataView_detail, name='PortfolioView_detail'),\n])\n","sub_path":"parsed_data/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":714,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"367729149","text":"__all__ = ()\n\nfrom ...backend.utils import KeepType\nfrom ...discord.client import Client\n\nfrom .extension import EXTENSIONS, EXTENSION_STATE_LOADED\n\n@KeepType(Client)\nclass Client:\n\n    @property\n    def extensions(self):\n        \"\"\"\n        Returns a list of extensions added to the client. Added by the `extension_loader` extension.\n        \n        Returns\n        -------\n        extensions : `list` of ``Extension``\n        \"\"\"\n        extensions = []\n        for extension in EXTENSIONS.values():\n            if extension._state == EXTENSION_STATE_LOADED:\n                snapshot_difference = extension._snapshot_difference\n                if (snapshot_difference is not None):\n                    for client, client_snapshot_difference in snapshot_difference:\n                        if (self is client) and client_snapshot_difference:\n                            extensions.append(extension)\n                            break\n        \n        return extensions\n","sub_path":"hata/ext/extension_loader/client_extension.py","file_name":"client_extension.py","file_ext":"py","file_size_in_byte":970,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"472133041","text":"#Autor: Sławomir Kranc\n\nimport symbol_newtona as sn\n\nprint('-------------------------------------------------------------------')\nprint('To jest program zwracający wiersz n-tego rzędu z trójkąta Pascala. ')\nprint('-------------------------------------------------------------------')\n\n#To jest funckja zwracając elementy danego rzętu z trójkąta Pascala.\ndef trojkat_pascala(n):\n  return [sn.symbol_newtona(n,i) for i in range(n+1)]\n\n\n#To jest główna funkcja.\ndef main():\n  while True:\n    print('Podaj numer wiersza:')\n    n = sn.get_number()\n    print(trojkat_pascala(n))\n\n    while True:\n      decyzja = input('Czy chcesz wykonać jeszcze raz? (t/n) ')\n      if decyzja == 't':\n       break\n      elif decyzja == 'n':\n        print('Do zobaczenia!')\n        input('Kliknij dowolny klawisz, aby opuścić program ...')\n        return\n      else:\n        print('Proszę podj��ć decyzję!')\n\n\nif __name__ == \"__main__\":\n\n  main()\n","sub_path":"PYTHON_WARSZTATY/Warsztaty-2/zadanie_8_SK/zadanie_8_SK.py","file_name":"zadanie_8_SK.py","file_ext":"py","file_size_in_byte":941,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"346908443","text":"from __future__ import print_function\nfrom setuptools import setup, find_packages\nfrom setuptools.command.test import test as TestCommand\nimport io\nimport codecs\nimport os\nimport sys\n\n# set version\n__version__ = '0.0.9'\n\nsetup(\n    name='pyqm',\n    version=__version__,\n    url='https://github.com/rh-marketingops/pyqm',\n    license='GNU General Public License',\n    author='Jeremiah Coleman',\n    tests_require=['nose', 'mongomock>=3.6.0'],\n    install_requires=['pymongo>=3.3.0', 'pyexecjs>=1.4.0'],\n    author_email='colemanja91@gmail.com',\n    description='Bulk queue management in MongoDB',\n    #long_description=readme(),\n    packages=['pyqm'],\n    include_package_data=True,\n    platforms='any',\n    test_suite = 'nose.collector',\n    classifiers = [\n        'Programming Language :: Python',\n        'Development Status :: 4 - Beta',\n        'Natural Language :: English',\n        'Environment :: Web Environment',\n        'Intended Audience :: Developers',\n        'License :: OSI Approved :: Apache Software License',\n        'Operating System :: OS Independent',\n        'Topic :: Software Development :: Libraries :: Python Modules',\n        'Topic :: Software Development :: Libraries :: Application Frameworks'\n        ],\n    keywords = 'queue management mongo mongodb bulk'\n)\n","sub_path":"pypi_install_script/pyqm-0.0.9.tar/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1291,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"462031278","text":"#!/usr/bin/python3\nimport urllib3\nimport urllib\nimport sys\nfrom bs4 import BeautifulSoup\n\n# prepare \nbase_url = 'http://gol.gg/tournament/list/region-ALL/'\nstat_url = 'http://gol.gg/tournament'\nhttp = urllib3.PoolManager()\nrounds = []\n\ndef get_top_level_soup():\n    main = http.request('GET', base_url)\n    soup = BeautifulSoup(main.data,\"lxml\")\n    return soup\n\ndef parse_tournament_links_from_toplevel(soup):\n    links = []\n    for a in soup.find_all('a', href=True):\n        if \"tournament-stats\" in a[\"href\"]:\n            links += [a[\"href\"]]\n    return links\n\ndef get_tournament_table(link):\n    target = \"{}/{}\".format(stat_url,urllib.parse.quote( str(link[2:]) ) )\n    req = http.request(\"GET\",target)\n    score_table = None\n    soup_sub = BeautifulSoup(req.data,\"html5lib\")\n    tables = soup_sub.findAll(\"table\")\n    for t in tables:\n        if \"Score\" in t.get_text() and \"Blue Side\" in t.get_text():\n            return t\n\ndef get_rows_from_table(table):\n    if \"tbody\" not in table.get_text():\n      sct = table\n    else:\n      sct  = table.findChildren([\"tbody\"])[0]\n    rows = sct.findChildren([\"tr\"])\n    return rows[1:]\n\ndef parse_player_table(table):\n    rows = get_rows_from_table(table)\n    pnames = []\n    for row in rows:\n        cells = row.findChildren([\"td\"])\n        pname = cells[0].get_text()\n        pnames += [pname]\n    return pnames\n\ndef get_player_information_href(link_container):\n    plink = link_container.findAll(\"a\",href=True)[0][\"href\"]\n    plink = plink.replace(\"-game\",\"-summary\") # cause inconsitency\n    return \"http://gol.gg/{}\".format(plink.replace(\"../\",\"\"))\n\n\ndef get_player_information(href):\n    rss   = http.request(\"GET\", href)\n\n    psoup  = BeautifulSoup(rss.data,\"lxml\")\n    tables = psoup.findAll(\"table\")[-2:]\n    try: \n        left_team  = parse_player_table(tables[0])\n        right_team = parse_player_table(tables[1])\n    except IndexError:\n        return (None,None)\n    return (left_team,right_team)\n\ndef export_round(winner_team, winner_players, loser_team, loser_players):\n    line = \"{},{},{},{}\\n\".format( winner_team.get_text(),\"|\".join(winner_players),\\\n                                loser_team.get_text(), \"|\".join(loser_players ) \\\n                               )\n    print(line,end=\"\")\n    if True:\n        with open(\"out.ncsv\",\"a\") as f:\n            f.write(line)\n\nif __name__ == \"__main__\":\n    soup  = get_top_level_soup()\n    links = parse_tournament_links_from_toplevel(soup)\n\n\n    if len(sys.argv) > 1:\n        skips = int(sys.argv[1])\n    else:\n        skips = 0\n\n    try:\n        with open(\"linklist_done.csv\",\"r\") as f:\n            links_done_dict = dict((x.strip(),x.strip()) for x in f.readlines())\n    except IOError:\n        links_done_dict = dict()\n\n    for link in links:\n        if skips > 0:\n            skips -= 1\n            continue\n\n        if link in links_done_dict:\n            print(\"Skipping (in link-list): {}\".format(link))\n            continue\n\n        score_table = get_tournament_table(link)\n        if score_table == None:\n            print(\"no table\")\n            continue\n\n\n        rows = get_rows_from_table(score_table)\n\n        for row in rows:\n            link_container, left_team, score, right_team, week, date = row.findChildren([\"td\"])\n            \n            # continue if no data\n            score =  score.get_text()\n            if score.strip() == \"-\":\n                continue\n            \n            href = get_player_information_href(link_container)\n            players_left, players_right = get_player_information(href)\n            if players_right == None and players_left == None:\n                continue\n\n            # continue if already downloaded\n            if href in links_done_dict:\n                print(\"Skipping (in link-list): {}\".format(href))\n                continue\n            \n            # count down the rounds each team won as single games\n            left, right = score.strip().split(\"-\")\n            left  = int(left)\n            right = int(right)\n            \n            while left > 0:\n                left -= 1\n                export_round(left_team,players_left,right_team,players_right)\n            while right > 0:\n                right -= 1\n                export_round(right_team,players_right,left_team,players_left)\n            with open(\"linklist_done.csv\",\"a\") as f:\n                f.write(\"{}\\n\".format(href))\n        with open(\"linklist_done.csv\",\"a\") as f:\n            f.write(\"{}\\n\".format(link))\n","sub_path":"python/scrap_data/lolpro_scrapper.py","file_name":"lolpro_scrapper.py","file_ext":"py","file_size_in_byte":4465,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"584107761","text":"\"\"\"\nModule to define the configuration of the main program.\n\nClasses:\n    Config: Class to manipulate the configuration of the program.\n\"\"\"\n\nfrom collections import UserDict\nfrom ruamel.yaml import YAML\nfrom ruamel.yaml.scanner import ScannerError\n\nimport logging\nimport os\nimport sys\n\nlog = logging.getLogger(__name__)\n\n\nclass Config(UserDict):\n    \"\"\"\n    Class to manipulate the configuration of the program.\n\n    Arguments:\n        config_path (str): Path to the configuration file.\n            Default: ~/.local/share/pydo/config.yaml\n\n    Public methods:\n        get: Fetch the configuration value of the specified key.\n            If there are nested dictionaries, a dot notation can be used.\n        load: Loads configuration from configuration YAML file.\n        save: Saves configuration in the configuration YAML file.\n\n    Attributes and properties:\n        config_path (str): Path to the configuration file.\n        data(dict): Program configuration.\n    \"\"\"\n\n    def __init__(self, config_path='~/.local/share/pydo/config.yaml'):\n        self.config_path = os.path.expanduser(config_path)\n        self.load()\n\n    def get(self, key):\n        \"\"\"\n        Fetch the configuration value of the specified key. If there are nested\n        dictionaries, a dot notation can be used.\n\n        So if the configuration contents are:\n\n        self.data = {\n            'first': {\n                'second': 'value'\n            },\n        }\n\n        self.data.get('first.second') == 'value'\n\n        Arguments:\n            key(str): Configuration key to fetch\n        \"\"\"\n        keys = key.split('.')\n        value = self.data.copy()\n\n        for key in keys:\n            value = value[key]\n\n        return value\n\n    def load(self):\n        \"\"\"\n        Loads configuration from configuration YAML file.\n        \"\"\"\n\n        try:\n            with open(os.path.expanduser(self.config_path), 'r') as f:\n                try:\n                    self.data = YAML().load(f)\n                except ScannerError as e:\n                    log.error(\n                        'Error parsing yaml of configuration file '\n                        '{}: {}'.format(\n                            e.problem_mark,\n                            e.problem,\n                        )\n                    )\n                    sys.exit(1)\n        except FileNotFoundError:\n            log.error(\n                'Error opening configuration file {}'.format(self.config_path)\n            )\n            sys.exit(1)\n\n    def save(self):\n        \"\"\"\n        Saves configuration in the configuration YAML file.\n        \"\"\"\n\n        with open(os.path.expanduser(self.config_path), 'w+') as f:\n            yaml = YAML()\n            yaml.default_flow_style = False\n            yaml.dump(self.data, f)\n","sub_path":"pydo/configuration.py","file_name":"configuration.py","file_ext":"py","file_size_in_byte":2770,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"69119753","text":"#!/usr/bin/env python\n\nimport itertools\nimport json\nimport os\nimport re\nimport signal\nimport sys\nimport time\nfrom datetime import datetime\n\nimport click\nfrom send2trash import send2trash\n\n# Verify that external dependencies are present first, so the user gets a\n# more user-friendly error instead of an ImportError traceback.\nfrom elodie.dependencies import verify_dependencies\nif not verify_dependencies():\n    sys.exit(1)\n\nfrom elodie.config import Config\nfrom elodie import constants\nfrom elodie import log\nfrom elodie import utility\nfrom elodie.compatability import _decode\nfrom elodie.filesystem import FileSystem\nfrom elodie.manifest import Manifest\nfrom elodie.media.base import Base, get_all_subclasses\nfrom elodie.media.media import Media\nfrom elodie.media.text import Text\nfrom elodie.media.audio import Audio\nfrom elodie.media.photo import Photo\nfrom elodie.media.video import Video\nfrom elodie.result import Result\n\nfrom elodie.dependencies import get_exiftool\nfrom elodie.external.pyexiftool import ExifTool\n\n\nFILESYSTEM = FileSystem()\n\n\ndef import_file(file_path, config, manifest, metadata_dict, move=False, allow_duplicates=False, dryrun=False):\n\n    \"\"\"Set file metadata and move it to destination.\n    \"\"\"\n    if not os.path.exists(file_path):\n        log.warn('Import_file: Could not find %s' % file_path)\n        return\n\n    target = config[\"targets\"][0]\n    target_base_path = target[\"base_path\"]\n\n    # Check if the source, _file, is a child folder within destination\n    #   .... this is not the right time to be checking for that. Lots of unnecessary checks\n    # elif destination.startswith(os.path.abspath(os.path.dirname(_file))+os.sep):\n    #     print('{\"source\": \"%s\", \"destination\": \"%s\", \"error_msg\": \"Source cannot be in destination\"}' % (_file, destination))\n    #     return\n\n    # Creates an object of the right type, using the file extension ie .jpg -> photo\n    media = Media.get_class_by_file(file_path, get_all_subclasses())\n    if not media:\n        log.warn('Not a supported file (%s)' % file_path)\n        return\n\n    # if album_from_folder:\n    #     media.set_album_from_folder()\n\n    checksum = manifest.checksum(file_path)\n    is_duplicate = (checksum in manifest.entries)\n\n    # Merge it into the manifest regardless of duplicate entries, to record all sources for a given file\n    manifest_entry = FILESYSTEM.generate_manifest(file_path, target, metadata_dict, media)\n    manifest.merge({checksum: manifest_entry})\n\n    if (not allow_duplicates) and is_duplicate:\n        log.debug(\"[ ] File {} already present in manifest; allow_duplicates is false; skipping\".format(file_path))\n        return True\n\n    if dryrun:\n        log.info(\"Generated manifest: {}\".format(file_path))\n        return manifest_entry is not None\n    else:\n        result = FILESYSTEM.execute_manifest(file_path, manifest_entry, target_base_path, move_not_copy=move)\n        # if dest_path:\n        #     print('%s -> %s' % (_file, dest_path))\n        # if trash:\n        #     send2trash(_file)\n\n        return result\n\n\n@click.command('import')\n@click.option('--source', type=click.Path(file_okay=False),\n              help='Add an optional source directory to the configuration.')\n@click.option('-c', '--config', 'config_path', type=click.Path(file_okay=True),\n              required=True, help='Import configuration file.')\n@click.option('-m', '--manifest', 'manifest_path', type=click.Path(file_okay=True),\n              help='The database/manifest used to store file sync information.')\n@click.option('-i', '--indent-manifest', 'indent_manifest', is_flag=True,\n              help='Whether to indent the manifest for easier reading (roughly doubles file size)')\n@click.option('--no-overwrite-manifest', 'no_overwrite_manifest', is_flag=True,\n              help='Whether to overwrite the input manifest (default is overwrite)')\n# @click.option('--trash', default=False, is_flag=True,\n#               help='After copying files, move the old files to the trash.')\n@click.option('--allow-duplicates', default=False, is_flag=True,\n              help='Import the file even if it\\'s already been imported.')\n@click.option('--move', default=False, is_flag=True,\n              help='Move files rather than copying them. Faster within a drive.')\n@click.option('--dryrun', default=False, is_flag=True,\n              help=\"Don't move files or save the manifest; just print the manifest to terminal\")\n@click.option('--debug', default=False, is_flag=True,\n              help='Override the value in constants.py with True.')\n# @click.argument('paths', nargs=-1, type=click.Path())\ndef _import(source, config_path, manifest_path, allow_duplicates, dryrun, debug, move=False, indent_manifest=False, no_overwrite_manifest=False):\n    \"\"\"Import files or directories by reading their EXIF and organizing them accordingly.\n    \"\"\"\n    start_time = round(time.time())\n\n    constants.debug = debug\n    has_errors = False\n    result = Result()\n\n    # Load the configuration from the json file.\n    config = Config().load_from_file(config_path)\n\n    source = config[\"sources\"][0]  # For now, only one.\n    target = config[\"targets\"][0]  # For now, only one target allowed...but data structure allows more\n\n    source_file_path = source[\"file_path\"]\n\n    manifest = Manifest()\n\n    if manifest_path is not None:\n        manifest.load_from_file(manifest_path)\n\n    log_base_path, _ = os.path.split(manifest.file_path)\n    FILESYSTEM.create_directory(os.path.join(log_base_path, '.elodie'))\n    log_path = os.path.join(log_base_path, '.elodie', 'import_{}.log'.format(utility.timestamp_string()))\n\n    def signal_handler(sig, frame):\n        log.warn('[ ] Import cancelled')\n        log.write(log_path)\n        sys.exit(0)\n\n    signal.signal(signal.SIGINT, signal_handler)\n\n    original_manifest_key_count = len(manifest)\n\n    # destination = _decode(destination)\n    # destination = os.path.abspath(os.path.expanduser(destination))\n\n    exiftool_addedargs = [\n        # '-overwrite_original',\n        u'-config',\n        u'\"{}\"'.format(constants.exiftool_config)\n    ]\n\n    file_generator = FILESYSTEM.get_all_files(source_file_path, None)\n    source_file_count = 0\n\n    with ExifTool(addedargs=exiftool_addedargs) as et:\n        while True:\n            file_batch = list(itertools.islice(file_generator, constants.exiftool_batch_size))\n            if len(file_batch) == 0: break\n\n            # This will cause slight discrepancies in file counts: since elodie.json is counted but not imported,\n            #   each one will set the count off by one.\n            source_file_count += len(file_batch)\n            metadata_list = et.get_metadata_batch(file_batch)\n            if not metadata_list:\n                raise Exception(\"Metadata scrape failed.\")\n            # Key on the filename to make for easy access,\n            metadata_dict = dict((os.path.abspath(el[\"SourceFile\"]), el) for el in metadata_list)\n            for current_file in file_batch:\n                # Don't import localized config files.\n                if current_file.endswith(\"elodie.json\"):  # Faster than a os.path.split\n                    continue\n                try:\n                    result = import_file(current_file, config, manifest, metadata_dict, move=move, dryrun=dryrun, allow_duplicates=allow_duplicates)\n                except Exception as e:\n                    log.warn(\"[!] Error importing {}: {}\".format(current_file, e))\n                    result = False\n                has_errors = has_errors or not result\n        exiftool_waiting_time = et.waiting_time\n\n    manifest.write(indent=indent_manifest, overwrite=(not no_overwrite_manifest))\n\n    manifest_key_count = len(manifest)\n\n    try:\n        total_time = round(time.time() - start_time)\n        log.info(\"Statistics:\")\n        log.info(\"Source: File Count {}\".format(source_file_count))\n        log.info(\"Manifest: New Hashes {}\".format(manifest_key_count - original_manifest_key_count))\n        log.info(\"Manifest: Total Hashes {}\".format(manifest_key_count))\n        log.info(\"Time: Total {}s\".format(total_time))\n        log.info(\"Time: Files/sec {}\".format(round(source_file_count / total_time)))\n        log.info(\"Time: Waiting on ExifTool {}s\".format(round(exiftool_waiting_time)))\n    except Exception as e:\n        log.error(\"[!] Error generating statistics: {}\".format(e))\n\n    log.write(log_path)\n\n    if has_errors:\n        sys.exit(1)\n\n\n@click.command('analyze')\n@click.option('--debug', default=False, is_flag=True,\n              help='Override the value in constants.py with True.')\n@click.argument('manifest_path', nargs=1, required=True)\ndef _analyze(manifest_path, debug):\n    constants.debug = debug\n\n    manifest = Manifest()\n    manifest.load_from_file(manifest_path)\n    manifest_key_count = len(manifest)\n\n    duplicate_source_file_count = {}\n\n    # Could be made into a reduce, but I want more functionality here ( ie a list of the duplicated files )\n    for k, v in manifest.entries.items():\n        if len(v[\"sources\"]) > 1:\n            length = len(v[\"sources\"])\n            if length in duplicate_source_file_count:\n                duplicate_source_file_count[length] += 1\n            else:\n                duplicate_source_file_count[length] = 1\n\n    log.info(\"Statistics:\")\n    log.info(\"Manifest: Total Hashes {}\".format(manifest_key_count))\n    for k, v in duplicate_source_file_count.items():\n        log.info(\"Manifest: Duplicate (x{}) Source Files {}\".format(k, v))\n\n\n@click.command('merge')\n@click.option('-o', '--output', 'output_path', type=click.Path(file_okay=True),\n              required=True, help='The file path to save the merged manifest.')\n@click.argument('manifest_paths', nargs=-1, required=True)\n@click.option('--debug', default=False, is_flag=True,\n              help='Override the value in constants.py with True.')\ndef _merge(manifest_paths, output_path, debug):\n    constants.debug = debug\n\n    manifest = Manifest()\n    for manifest_path in manifest_paths:\n        manifest.load_from_file(manifest_path)\n\n    manifest.write(output_path, overwrite=False)\n\n    manifest_key_count = len(manifest)\n    log.info(\"Statistics:\")\n    log.info(\"Merged Manifest: Total Hashes {}\".format(manifest_key_count))\n\n\n@click.command('find')\n@click.option('-m', '--manifest', 'manifest_path', type=click.Path(file_okay=True),\n              help='The database/manifest used to store file sync information.', required=True)\n@click.option('-t', '--target-file-name', 'target_file_name', help='the name of the target file')\ndef _find(manifest_path, target_file_name):\n    manifest = Manifest().load_from_file(manifest_path)\n    for k, v in manifest.entries.items():\n        if v[\"target\"][\"name\"] == target_file_name:\n            print(\"Hash {}\".format(k))\n            print(json.dumps(v, indent=2))\n    print(\"Search complete.\")\n\n\n@click.command('generate-db')\n@click.option('--source', type=click.Path(file_okay=False),\n              required=True, help='Source of your photo library.')\n@click.option('--debug', default=False, is_flag=True,\n              help='Override the value in constants.py with True.')\ndef _generate_db(source, debug):\n    \"\"\"Regenerate the hash.json database which contains all of the sha256 signatures of media files. The hash.json file is located at ~/.elodie/.\n    \"\"\"\n    constants.debug = debug\n    result = Result()\n    source = os.path.abspath(os.path.expanduser(source))\n\n    if not os.path.isdir(source):\n        log.error('Source is not a valid directory %s' % source)\n        sys.exit(1)\n        \n    db = Manifest()\n    db.backup_hash_db()\n    db.reset_hash_db()\n\n    for current_file in FILESYSTEM.get_all_files(source):\n        result.append((current_file, True))\n        db.add_hash(db.checksum(current_file), current_file)\n        log.progress()\n    \n    db.update_hash_db()\n    log.progress('', True)\n    result.write()\n\n@click.command('verify')\n@click.option('--debug', default=False, is_flag=True,\n              help='Override the value in constants.py with True.')\ndef _verify(debug):\n    constants.debug = debug\n    result = Result()\n    db = Manifest()\n    for checksum, file_path in db.all():\n        if not os.path.isfile(file_path):\n            result.append((file_path, False))\n            log.progress('x')\n            continue\n\n        actual_checksum = db.checksum(file_path)\n        if checksum == actual_checksum:\n            result.append((file_path, True))\n            log.progress()\n        else:\n            result.append((file_path, False))\n            log.progress('x')\n\n    log.progress('', True)\n    result.write()\n\n\ndef update_location(media, file_path, location_name):\n    \"\"\"Update location exif metadata of media.\n    \"\"\"\n    location_coords = geolocation.coordinates_by_name(location_name)\n\n    if location_coords and 'latitude' in location_coords and \\\n            'longitude' in location_coords:\n        location_status = media.set_location(location_coords[\n            'latitude'], location_coords['longitude'])\n        if not location_status:\n            log.error('Failed to update location')\n            log.error(('{\"source\":\"%s\",' % file_path,\n                '\"error_msg\":\"Failed to update location\"}'))\n            sys.exit(1)\n    return True\n\n\ndef update_time(media, file_path, time_string):\n    \"\"\"Update time exif metadata of media.\n    \"\"\"\n    time_format = '%Y-%m-%d %H:%M:%S'\n    if re.match(r'^\\d{4}-\\d{2}-\\d{2}$', time_string):\n        time_string = '%s 00:00:00' % time_string\n    elif re.match(r'^\\d{4}-\\d{2}-\\d{2} \\d{2}:\\d{2}\\d{2}$', time_string):\n        msg = ('Invalid time format. Use YYYY-mm-dd hh:ii:ss or YYYY-mm-dd')\n        log.error(msg)\n        log.error('{\"source\":\"%s\", \"error_msg\":\"%s\"}' % (file_path, msg))\n        sys.exit(1)\n\n    time = datetime.strptime(time_string, time_format)\n    media.set_date_taken(time)\n    return True\n\n\n@click.command('update')\n@click.option('--album', help='Update the image album.')\n@click.option('--location', help=('Update the image location. Location '\n                                  'should be the name of a place, like \"Las '\n                                  'Vegas, NV\".'))\n@click.option('--time', help=('Update the image time. Time should be in '\n                              'YYYY-mm-dd hh:ii:ss or YYYY-mm-dd format.'))\n@click.option('--title', help='Update the image title.')\n@click.option('--debug', default=False, is_flag=True,\n              help='Override the value in constants.py with True.')\n@click.argument('paths', nargs=-1,\n                required=True)\ndef _update(album, location, time, title, paths, debug):\n    \"\"\"Update a file's EXIF. Automatically modifies the file's location and file name accordingly.\n    \"\"\"\n    constants.debug = debug\n    has_errors = False\n    result = Result()\n\n    files = set()\n    for path in paths:\n        path = os.path.expanduser(path)\n        if os.path.isdir(path):\n            files.update(FILESYSTEM.get_all_files(path, None))\n        else:\n            files.add(path)\n\n    for current_file in files:\n        if not os.path.exists(current_file):\n            has_errors = True\n            result.append((current_file, False))\n            log.warn('Could not find %s' % current_file)\n            log.error('{\"source\":\"%s\", \"error_msg\":\"Could not find %s\"}' % \\\n                (current_file, current_file))\n            continue\n\n        current_file = os.path.expanduser(current_file)\n\n        # The destination folder structure could contain any number of levels\n        #  So we calculate that and traverse up the tree.\n        # '/path/to/file/photo.jpg' -> '/path/to/file' ->\n        #  ['path','to','file'] -> ['path','to'] -> '/path/to'\n        current_directory = os.path.dirname(current_file)\n        destination_depth = -1 * len(FILESYSTEM.get_folder_path_definition())\n        destination = os.sep.join(\n                          os.path.normpath(\n                              current_directory\n                          ).split(os.sep)[:destination_depth]\n                      )\n\n        media = Media.get_class_by_file(current_file, get_all_subclasses())\n        if not media:\n            continue\n\n        updated = False\n        if location:\n            update_location(media, current_file, location)\n            updated = True\n        if time:\n            update_time(media, current_file, time)\n            updated = True\n        if album:\n            media.set_album(album)\n            updated = True\n\n        # Updating a title can be problematic when doing it 2+ times on a file.\n        # You would end up with img_001.jpg -> img_001-first-title.jpg ->\n        # img_001-first-title-second-title.jpg.\n        # To resolve that we have to track the prior title (if there was one.\n        # Then we massage the updated_media's metadata['base_name'] to remove\n        # the old title.\n        # Since FileSystem.get_file_name() relies on base_name it will properly\n        #  rename the file by updating the title instead of appending it.\n        remove_old_title_from_name = False\n        if title:\n            # We call get_metadata() to cache it before making any changes\n            metadata = media.get_metadata()\n            title_update_status = media.set_title(title)\n            original_title = metadata['title']\n            if title_update_status and original_title:\n                # @TODO: We should move this to a shared method since\n                # FileSystem.get_file_name() does it too.\n                original_title = re.sub(r'\\W+', '-', original_title.lower())\n                original_base_name = metadata['base_name']\n                remove_old_title_from_name = True\n            updated = True\n\n        if updated:\n            updated_media = Media.get_class_by_file(current_file,\n                                                    get_all_subclasses())\n            # See comments above on why we have to do this when titles\n            # get updated.\n            if remove_old_title_from_name and len(original_title) > 0:\n                updated_media.get_metadata()\n                updated_media.set_metadata_basename(\n                    original_base_name.replace('-%s' % original_title, ''))\n\n            dest_path = FILESYSTEM.process_file(current_file, destination,\n                updated_media, move=True, allowDuplicate=True)\n            log.info(u'%s -> %s' % (current_file, dest_path))\n            log.info('{\"source\":\"%s\", \"destination\":\"%s\"}' % (current_file,\n                dest_path))\n            # If the folder we moved the file out of or its parent are empty\n            # we delete it.\n            FILESYSTEM.delete_directory_if_empty(os.path.dirname(current_file))\n            FILESYSTEM.delete_directory_if_empty(\n                os.path.dirname(os.path.dirname(current_file)))\n            result.append((current_file, dest_path))\n            # Trip has_errors to False if it's already False or dest_path is.\n            has_errors = has_errors is True or not dest_path\n        else:\n            has_errors = False\n            result.append((current_file, False))\n\n    result.write()\n    \n    if has_errors:\n        sys.exit(1)\n\n\n@click.group()\ndef main():\n    pass\n\n\nmain.add_command(_analyze)\nmain.add_command(_import)\nmain.add_command(_merge)\nmain.add_command(_find)\nmain.add_command(_update)\nmain.add_command(_generate_db)\nmain.add_command(_verify)\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"elodie.py","file_name":"elodie.py","file_ext":"py","file_size_in_byte":19346,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"9780556","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Feb 22 13:35:59 2017\n\n@author: dbanco02\n\"\"\"\n\n## Init\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport numpy as np\nimport os\nimport RingModel as RM\nimport EllipticModels as EM\n\n\n\n# Data, Interpolation, Fitting Parameters\nload_step = 0\nimg_num = 35\n\nimg_file = 'polar_image_al7075_load_'+str(load_step)+'_img_'+str(img_num)+'.npy'\nimg_path = os.path.join('/home','dbanco','CHESS_data',img_file)\n \ndr = 30\nradius = 370\n \nnum_theta= 2048\nnum_rad = 2*dr\n\nnum_var_t = 15\nnum_var_r = 10\n\ndtheta = 2*np.pi/num_theta\ndrad = 1\n\nvar_theta = np.linspace((dtheta),(np.pi/32),num_var_t)**2\nvar_rad   = np.linspace(drad,3,num_var_r)**2\n\n# Initialize Ring Model\nringModel = RM.RingModel(load_step, img_num, radius-dr, radius+dr, \n    \t\t              num_theta, num_rad, dtheta, drad, var_theta, var_rad)\n\n# Compute basis matrix and interpolate ring to polar coordinates \nA0_stack = EM.unshifted_basis_matrix_stack(ringModel.var_theta,\n\t\t\t\t\t\t                               ringModel.var_rad,\n\t\t\t\t\t\t                               ringModel.dtheta,\n\t\t\t\t\t\t                               ringModel.drad,\n\t\t\t\t\t\t                               ringModel.num_theta, ringModel.num_rad)\n\nA0ft_svd_list = ringModel.generate_basis_matrices()\n\nringModel.polar_image = np.load(img_path)\nprint(ringModel.polar_image.shape)\nringModel.l1_ratio = 1\nringModel.max_iters = 500\nringModel.compute_lipschitz(A0_stack)\n\nringModel.fit_circulant_FISTA(A0ft_svd_list,positive=1,benchmark=1,verbose=1)\n\nnp.save('ringModel_0_35.npy',ringModel)\n\n","sub_path":"old_python/ringFit_2D_experiments.py","file_name":"ringFit_2D_experiments.py","file_ext":"py","file_size_in_byte":1571,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"517662452","text":"import cv2\r\ncam=cv2.VideoCapture(0)\r\nwhile True:\r\n \"\"\"   ret,frame=cam.read()\r\n    r,c,ch=frame.shape\r\n    x=int(r/2)\r\n    y=int(c/2)\r\n    cv2.rectangle(frame,(200,200),(100,100),(185,128,4),3)\r\n    cv2.circle(frame,(y,x),10,(185,128,4),-1)\r\n    cv2.imshow(\"img\",frame)\r\n# or we can use if cv2.waitKey(3)&0xFF==ord('q'):\r\n    if cv2.waitKey(3)==ord('a'):\r\n        break\"\"\"\r\ncam.release()\r\ncv2.destroyAllWindows()\r\n","sub_path":"caminterface.py","file_name":"caminterface.py","file_ext":"py","file_size_in_byte":414,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"132512634","text":"import csv\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\nimport scipy.signal as signal\r\n\r\ndef normalize_2d(l):\r\n    l = np.array(l)\r\n    minamp = np.amin(l)\r\n    maxamp = np.amax(l)\r\n    return (l-minamp)/(maxamp-minamp)\r\n\r\ndef spectrogram(s, sampFreq, fftsize=8192, overlap=0.25):\r\n    chunklen = fftsize/(1+overlap)\r\n    n_chunks = 0\r\n    window = signal.blackmanharris(fftsize)\r\n    chunks = []\r\n    t = []\r\n    a = 0\r\n    amax = len(s)-fftsize\r\n    while (a <= amax):\r\n        b = a+fftsize\r\n        t.append(a/sampFreq)\r\n        chunks.append(20*np.log10( np.maximum([0.004 for i in range(fftsize//2+1)],np.abs(np.fft.rfft(window*s[a:b])))))\r\n        n_chunks+=1\r\n        a = b - int(overlap*fftsize)\r\n    f = np.fft.rfftfreq(fftsize,1/sampFreq)\r\n    chunks = normalize_2d(chunks)\r\n    return np.array(t),np.array(f),np.array(chunks)\r\n\r\ndef running_mean(x, N):\r\n    cumsum = np.cumsum(np.insert(x, 0, 0))\r\n    return (cumsum[N:] - cumsum[:-N]) / float(N)\r\n\r\n\r\ntab = []\r\n\r\nwith open('D:/Yann/Desktop/stabien/8x20_60g (1).csv', newline='') as csvfile:\r\n    spamreader = csv.reader(csvfile, delimiter='\\t', quotechar='|')\r\n    for row in spamreader:\r\n        #print(', '.join(row))\r\n        tab.append(row)\r\n\r\nfor i in range(2,len(tab)):\r\n    for j in range(3):\r\n        tab[i][j]=float(tab[i][j].replace(',','.'))\r\n\r\ntab = np.array(tab[2:])\r\nvx = [(tab[i+1][1]-tab[i][1])/(tab[i+1][0]-tab[i][0]) for i in range(len(tab)-1)]\r\nvy = [(tab[i+1][2]-tab[i][2])/(tab[i+1][0]-tab[i][0]) for i in range(len(tab)-1)]\r\nv = [np.sqrt(vx[i]**2+vy[i]**2) for i in range(len(vx))]\r\n\r\n'''sos = signal.butter(10, 20, 'hp', fs=60, output='sos')\r\nsig = [ v[abs(i)] for i in range(-80,len(v))]\r\nfiltered = signal.sosfilt(sos, sig)[80:]'''\r\n\r\nmoy = running_mean( [v[abs(i)] if i<len(v) else v[len(v)-i-1] for i in range(-80,len(v)+80)], 80 )[80:len(v)+80]\r\nmoy2 = running_mean( [v[abs(i)] if i<len(v) else v[len(v)-i-1] for i in range(-80,len(v)+80)], 10 )[80:len(v)+80]\r\n\r\nplt.plot([x[0] for x in tab][:-1], v)\r\n#plt.plot([x[0] for x in tab][:-1], moy2-moy)\r\n#plt.plot([x[0] for x in tab][:-1], moy2)\r\n\r\nthresholded = [0.02 if moy2[i]-moy[i]>0.002 else 0 for i in range(len(moy))]\r\n#plt.plot([x[0] for x in tab][:-1], thresholded)\r\n\r\npks,_ = signal.find_peaks(moy2,height=0.002,distance=0.2/(tab[1][0]-tab[0][0]))\r\n#plt.scatter([tab[x][0] for x in pks], [moy2[x] for x in pks])\r\n\r\n\r\n#pks = signal.find_peaks_cwt(v,[2])\r\nthreshold=0.002\r\npks = [p for p in pks if moy2[p]>=threshold]\r\n#plt.scatter([tab[x][0] for x in pks], [v[x] for x in pks],color='red')\r\nperiode_apparente = [tab[pks[i+1]][0]-tab[pks[i]][0] for i in range(len(pks)-1)]\r\nplt.plot([tab[pks[i]][0] for i in range(len(periode_apparente))],periode_apparente)\r\n\r\nplt.grid()\r\nplt.show()","sub_path":"py/traitement03.py","file_name":"traitement03.py","file_ext":"py","file_size_in_byte":2737,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"420218241","text":"from __future__ import print_function\n\nimport ttfw_idf\n\n\n@ttfw_idf.idf_example_test(env_tag='Example_GENERIC')\ndef test_examples_system_console(env, extra_data):\n    dut = env.get_dut('console_example', 'examples/system/console', app_config_name='history')\n    print(\"Using binary path: {}\".format(dut.app.binary_path))\n    dut.start_app()\n    dut.expect(\"Command history enabled\")\n    env.close_dut(dut.name)\n\n    dut = env.get_dut('console_example', 'examples/system/console', app_config_name='nohistory')\n    print(\"Using binary path: {}\".format(dut.app.binary_path))\n    dut.start_app()\n    dut.expect(\"Command history disabled\")\n\n\nif __name__ == '__main__':\n    test_examples_system_console()\n","sub_path":"examples/system/console/example_test.py","file_name":"example_test.py","file_ext":"py","file_size_in_byte":698,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"247690127","text":"# WOrking with 2 players, tried\n# notw orking properly with 4 players, tried\n\nfrom board import D18\nfrom neopixel import NeoPixel\nfrom gpiozero import Button\nfrom time import sleep\nfrom random import choice, randint\nfrom Classes import Player\nfrom os import system\nfrom json import load\n\ncolors = {\n    'Game': (50, 40, 30),\n    'Black': (0, 0, 0),\n    'Wrong': (50, 255, 255),\n    'Goto': (0, 255, 255),\n    'Start': (200, 200, 200)\n}\n\nled_count = 16\npixels = NeoPixel(D18, led_count, auto_write=True)\n\n\ndef setup():\n    global total_players, player_names, led_count, pixels, i, colors, p1, p2,\\\n                p3, p4, switch, neigh_in_use\n    total_players = 0\n    player_names = []\n    neigh_in_use = []  # used in dices in order not to cause threading errors\n\n    i = 0\n    pname = ''\n    while total_players < 2 or total_players > 4:\n        total_players = int(input(\"Колко играча ще играете? - \"))\n\n    stlives = 0\n    while stlives < 1:\n        stlives = int(input('По колко живота ще има всеки? - '))\n\n    stmoney = 0\n    while stmoney < 100:\n        stmoney = int(input('С каква начална сума ще започнете? - '))\n\n    for _ in range(total_players):\n        sleep(0.3)\n        pixels.fill(colors['Game'])\n        sleep(0.3)\n        pixels.fill(colors['Wrong'])\n\n    for _ in range(total_players):\n        neigh_in_use.append(0)\n        pname = str(input(\"Как се казвате :)\"))\n        while player_names.count(pname) >= 1:\n            print(\"try again\")\n            pname = str(input(\"Опс, как се казвате :)\"))\n        else:\n            player_names.append(pname)\n\n    p0 = Player('Game', 10000000, 9999999999999, -1)\n    p1 = Player(player_names[0], stlives, stmoney, 0)\n    p2 = Player(player_names[1], stlives, stmoney, 1)\n    switch = {\n        'The game': p0,\n        0: p1,\n        1: p2\n    }\n\n    colors[switch[0].name] = (255, 250, 0)\n    colors[switch[1].name] = (0, 0, 255)\n\n    if total_players >= 3:\n        p3 = Player(player_names[2], stlives, stmoney, 2)\n        colors[player_names[2]] = (0, 255, 0)\n        switch[2] = p3\n    if total_players > 3:\n        p4 = Player(player_names[3], stlives, stmoney, 3)\n        colors[player_names[3]] = (255, 0, 0)\n        switch[3] = p4\n\n\njacks_diodes = {\n    12: 15,\n    9: 14,\n    13: 13,\n    22: 12,\n    6: 11,\n    8: 10,\n    3: 9,\n    10: 8,\n    20: 7,\n    27: 6,\n    19: 5,\n    24: 4,\n    23: 3,\n    25: 2,\n    16: 1,\n    11: 0\n}\n\njacks_repr = {\n    13: \"I\",\n    22: \"II\",\n    6: \"III\",\n    8: \"IV\",\n    3: \"V\",\n    10: \"VI\",\n    20: \"VII\",\n    27: \"VIII\",\n    19: \"IX\",\n    24: \"X\",\n    23: \"XI\",\n    25: \"XII\",\n    16: \"XIII\",\n    11: \"XIV\",\n    12: \"XV\",\n    9: \"XVI\",\n    # Buttons\n    2: \"Middle Button\",\n    26: \"Black Button\",\n    17: \"Red Button\"\n}\n\n\njacks_pins = [\n    13,\n    22,\n    6,\n    8,\n    3,\n    10,\n    20,\n    27,\n    19,\n    24,\n    23,\n    25,\n    16,\n    11,\n    12,\n    9\n]\n\nbuttons_pins = {\n    'Black': 26,\n    'Middle': 2,\n    'Red': 17\n}\n\nneighbours = []\n\n\n# LED Functions\ndef COLORISE():\n    # sets the LEDs for who they belong to\n    global pixels, switch, colors\n    pixels.fill(colors[\"Black\"])\n    for i in range(0, len(jacks_pins)):\n        for l in range(total_players):\n            if jacks_pins[i] in switch[l].owned_pos:\n                pixels[jacks_diodes[jacks_pins[i]]] = colors[switch[l].name]\n            elif jacks_pins[i] in switch['The game'].owned_pos:\n                pixels[jacks_diodes[jacks_pins[i]]] = colors['Game']\n\n\ndef LED_START(with_revercing=False, end='Start'):\n    global pixels\n    for _ in range(5):\n        for i in range(len(jacks_pins)):\n            pixels[jacks_diodes[jacks_pins[i]]] = choice(list(colors.values()))\n\n            sleep(0.01)\n    if with_revercing:\n        COLORISE()\n    else:\n        pixels.fill(colors[end])\n\n\n# Functions for beggining\ndef START_CHECK():\n    # checks if the board is clear from palms\n    global pixels, jacks_pins, jacks_diodes, colors\n    j = 0\n    while j is not len(jacks_pins):\n        j = 0\n        for i in jacks_pins:\n            try:\n                if Button(i).is_pressed:\n\n                    print(\"Извадете пионката на поле {}\".format(jacks_repr[i]))\n                    pixels[jacks_diodes[i]] = colors['Wrong']\n\n                    Button(i).wait_for_release()\n                    pixels[jacks_diodes[i]] = colors['Start']\n\n                j += 1\n            except RuntimeError as e:\n                print(e)\n\n\ndef DICE_1():\n    # returns a random pin number from jacks_pins list\n    global neighbours\n    print(\"Натиснете черния бутон за да хвърлите дигиталния зар\")\n    Button(buttons_pins['Black']).wait_for_press()\n    a = choice(jacks_pins)\n\n    if a in neighbours:\n        return DICE_1()\n    else:\n        return a\n\n\ndef CONDITION(player, all=False):\n    # returns the condition of the player\n    global total_players, switch\n    if not all:\n        return switch[player].condition\n    else:\n        for i in range(total_players):\n            if switch[i].condition:  # there is still a player with money\n                return -1\n        # all players are poors\n        return 0\n\n\ndef FATTEMPTS(player):\n    # returns the buy attempts of the player\n    global switch\n    return switch[player].failed_attempts\n\n\ndef BUY(player, pos, money_to_pay=100):\n    # gives <pos> over to <player> and\n    # changes <player>'s curr_position\n    # DO NOT try to improve with switch dictionary - already tried\n    if player == 0:\n        return p1.ADD_POSITION(pos, money_to_pay)\n    elif player == 1:\n        return p2.ADD_POSITION(pos, money_to_pay)\n    elif player == 2:\n        return p3.ADD_POSITION(pos, money_to_pay)\n    elif player == 3:\n        return p4.ADD_POSITION(pos, money_to_pay)\n\n\ndef LAST_POS(player):\n    # returns the player's position, before change\n    global switch\n    return switch[player].curr_position\n\n\ndef GO_BACK(player):\n    # waits for player to go back to its previous position\n    print(\"Нямате достатъчно пари да закупите тази позиция\\nМоля върнете се на\\\n{}\".format(jacks_repr[LAST_POS(player)]))\n    if Button(LAST_POS(player)).wait_for_press():\n        return 1\n    else:\n        return 0\n\n\ndef PLAYER_COLOR(player):\n    # returns the string representation for the color the player\n    global switch\n    return switch[player].name\n\n\ndef CURR_STATE():\n    # prints the current state of the game and players\n    global total_players, switch, jacks_repr\n    print('''Това е сегашното състояние:\\n\\\n    {} на {:3} позиция с {} лв. и {} живота\\n\\\n    {} на {:3} позиция с {} лв. и {} живота\\n\\\n    '''.format(\n        switch[0].name, jacks_repr[\n            switch[0].curr_position], switch[0].money, switch[0].lives,\n        switch[1].name, jacks_repr[\n            switch[1].curr_position], switch[1].money, switch[1].lives))\n\n    if total_players >= 3:\n        print(\"{} на {:3} позиция с {} лв. и {} живота\".format(\n            switch[2].name, jacks_repr[switch[2].curr_position],\n            switch[2].money, switch[2].lives))\n\n    if total_players > 3:\n        print(\"{} на {:3} позиция с {} лв. и {} живота\".format(\n            switch[3].name, jacks_repr[switch[3].curr_position], \n            switch[3].money, switch[3].lives))\n\n\ndef GIVE_ALL(player):\n    global neighbours, switch, jacks_pins, pixels\n    for i in range(len(jacks_pins)):\n        if jacks_pins[i] not in neighbours:\n            neighbours.append(jacks_pins[i])\n            switch[player].ADD_POSITION(jacks_pins[i], 100)\n            pixels[jacks_diodes[jacks_pins[i]]] = colors['Game']\n        else:\n            continue\n\n\ndef START():\n    START_CHECK()\n    global total_players, i, neigh_in_use, pixels\n    global neighbours, jacks_pins, switch, colors, jacks_diodes, jacks_repr\n    while len(neighbours) < len(jacks_pins):\n        for i in range(total_players):\n            if (len(neighbours) >= len(jacks_pins)):\n                break\n            try:\n                if CONDITION(i, True) == 0:\n                    GIVE_ALL('The game')\n                    print(\"Сега всички останали полета принадлежат на играта\")\n                elif CONDITION(i) and FATTEMPTS(i) < 1:\n                    place_to_go = DICE_1()\n                    system(\"clear\")\n                    print(\"{} отиди на {}\".format(PLAYER_COLOR(i), jacks_repr[\n                        place_to_go]))\n                    pixels[jacks_diodes[place_to_go]] = colors[\"Goto\"]\n                    sleep(0.4)\n                    if Button(place_to_go).wait_for_press():\n                        # Sometimes gives SegmentationFault Exactly here\n                        sleep(0.4)\n                        if BUY(i, place_to_go) != -1:\n                            neigh_in_use[i] = switch[i].curr_position\n                            neighbours.append(place_to_go)\n                            # so that DICE doesn't give u same pos twice\n                            pixels[jacks_diodes[place_to_go]] = colors[\n                                PLAYER_COLOR(i)]\n\n                        else:\n                            pixels[jacks_diodes[place_to_go]] = colors[\"Wrong\"]\n                            while not GO_BACK(i):\n                                GO_BACK(i)\n                            pixels[jacks_diodes[place_to_go]] = colors[\"Start\"]\n\n                    else:\n                        print(\"Error else - start anew\")\n\n                        exit(1)\n\n                else:\n                    # if both are with 0 money  its while True\n                    continue\n            except TypeError as e:\n                CURR_STATE()\n                print(\"ERROR: {}\".format(e))\n                continue\n# End of Functions for beggining\n\n\n# Start of Functions for main program\ndef DICE_2(player):\n    # returns a number between 1 and 6,\n    # warrant - the position it is pointing to not be a is_pressed jack\n    print(\"Натисни черния бутон за да хвърлиш дигиталния зар\")\n    Button(buttons_pins['Black']).wait_for_press()\n    global neigh_in_use, jacks_pins\n    move_to = 6\n    # move_to = choice(LAST_POS(player))\n    while Button(move_to).is_pressed:\n        rand = randint(1, 6)\n        if jacks_pins.index(6) + rand > len(jacks_pins):\n            move_to = jacks_pins[jacks_pins.index(6) + rand - len(jacks_pins)]\n        else:\n            move_to = jacks_pins[jacks_pins.index(6) + rand]\n    return move_to\n\n\ndef ENEMY(player, pos):\n    # if position in players arsenal\n    # - returns (-1) else returns the owner of the position\n    global total_players, switch\n    if pos in switch[player].owned_pos:\n        return -1\n    else:\n        for i in range(total_players):\n            if pos in switch[i].owned_pos:\n                return i\n            if pos in switch['The game'].owned_pos:\n                return 'The game'\n\n\ndef MQUESTION():\n    questions = load(open(\"/home/pi/Desktop/TUESFest2019/Comp_questions.json\\\n        \", \"r\"))\n    return list(questions.values())[randint(0, len(questions.values())-1)]\n\n\ndef PLAY_AGAIN():\n    print(\"Край на играта, искате ли да играете отново? (Натиснете черния\\\nбутон за да играете отначало, червения за да не играете отново)\")\n    while 1:\n        if Button(buttons_pins['Black']).is_pressed:\n            system('clear')\n            GAME()\n            break\n        elif Button(buttons_pins['Red']).is_pressed:\n            exit(1)\n            break\n        else:\n            continue\n\n\ndef WINNER(player):\n    global switch\n    CURR_STATE()\n    print(\"Поздравления {} ти си победител\".format(switch[player].name))\n    LED_START(end=switch[player].name)\n    PLAY_AGAIN()\n\n\ndef CHECK_ALL():\n    global switch, total_players\n    j = 0\n    i = 0\n    for i in range(total_players):\n        if switch[i].lives > 0:\n            j += 1\n    if j < 2:\n        WINNER(i)\n\n\ndef CHECK_P_STATUS(doer, taker):\n    global switch\n    if switch[taker].lives <= 0:\n        switch[doer].owned_pos.append(switch[taker].owned_pos)\n        COLORISE()\n        CHECK_ALL()\n\n\ndef TAKE(what_to_take, doer, taker, money=50, lives=1):\n    global switch\n    if what_to_take.lower() == 'hp':\n        switch[taker].lives -= lives\n        switch[doer].lives += lives\n        CHECK_P_STATUS(doer, taker)\n\n    elif what_to_take.lower() == 'money':\n        if switch[taker].money <= 0:\n            print(\"Съжалявам, но нямаш достатъчно пари да си платиш таксата =>\\\nедин от животите ти ще бъде отнет\")\n            TAKE('hp', doer, taker)\n        switch[taker].money -= money\n        switch[doer].money += money\n\n\ndef SENTENCE(to, frm='The game'):\n    # this part could be made with threads\n    # - gonna have better gameplay and less errors\n    if frm != 'The game':\n        print(\"{tfrm} натисни черния бутон за да оставиш {tto} безплатно\\n\\\n        Натисни средния бутон за да вземеш 50 лв. от {tto}.\\n\\\n        Натисни червения бутон за да вземеш 1 кръв на {tto}\".format(\n            tfrm=PLAYER_COLOR(frm), tto=PLAYER_COLOR(to)))\n    else:\n        print('{tfrm} зададе въпрос {tto} трябва да го отговори,\\n\\\n        натисни черния бутон за да оставиш {tto} безплатно\\n\\\n        Натисни средния бутон за да вземеш 50 лв. от {tto}.\\n\\\n        Натисни червения бутон за да вземеш 1 кръв на {tto}'.format(\n            tfrm=PLAYER_COLOR(frm), tto=PLAYER_COLOR(to)))\n    try:\n        while True:\n            if Button(buttons_pins['Middle']).is_pressed:  # Take money\n                TAKE('money', frm, to)\n                sleep(0.3)\n                break\n            sleep(0.01)\n            if Button(buttons_pins['Red']).is_pressed:  # Take HP\n                TAKE('HP', frm, to)\n                sleep(0.3)\n                break\n            sleep(0.01)\n            if Button(buttons_pins['Black']).is_pressed:  # Free to go\n                break\n                sleep(0.3)\n            sleep(0.01)\n    except RuntimeError as e:\n        print('RuntimeErroR: {}'.format(e))\n\n\ndef ASK(frm, to):\n\n    if frm == 'The game':\n        print(MQUESTION())\n        SENTENCE(to)\n    else:\n        print('\\n{} за да зададеш въпрос на {} натисни черния бутон\\n\\\nслед като отговориш трябва да решиш присъдата му\\n\\\nдали да го оставиш, дали да му вземеш пари или кръв\\n\\n\\\nРазбира се опция е и да оставиш компютъра да зададе въпрос - \\\nсамо натисни средния бутон'.format(PLAYER_COLOR(frm), PLAYER_COLOR(to)))\n        while 1:\n            if Button(buttons_pins['Middle']).is_pressed:  # MQuestion\n                sleep(0.1)\n                print(MQUESTION())\n                SENTENCE(to, frm)\n                break\n\n            elif Button(buttons_pins['Black']).is_pressed:  # I'll ask myself\n                sleep(0.1)\n                SENTENCE(to, frm)\n                break\n\n            else:  # Continue\n                continue\n\n\ndef MAIN():\n    global switch, neigh_in_use, colors, i, pixels  \n    # its global cos i is currently the player in order to move\n    while True:\n        dice_res = DICE_2(i)\n        # 100% there isn't another player - comes form DICE Function\n        i += 1\n        if i >= total_players:\n            i = 0\n\n        print(\"{} go To {}\".format(PLAYER_COLOR(i), jacks_repr[dice_res]))\n        pixels[jacks_diodes[dice_res]] = colors['Goto']\n\n        if Button(dice_res).wait_for_press():\n            neigh_in_use[i] = switch[i].curr_position\n            enemy = ENEMY(i, dice_res)\n            if enemy == -1:  # its my field, not enemie's\n                pixels[jacks_diodes[dice_res]] = colors[PLAYER_COLOR(i)]\n                continue\n            elif enemy == 'The game':  # has to be went on\n                print(\"Компютърът ще попита\")\n                ASK(enemy, i)\n                pixels[jacks_diodes[dice_res]] = colors['Game']\n            else:  # enemy field\n                pixels[jacks_diodes[dice_res]] = colors[PLAYER_COLOR(enemy)]\n                ASK(enemy, i)\n\n\ndef GAME():\n    LED_START(end='Wrong')\n    setup()\n    LED_START(end='Game')\n    print(\"Натисни средния бутон за да започне играта\")\n    Button(buttons_pins['Middle']).wait_for_press()\n    LED_START()\n    START()\n    CURR_STATE()\n    LED_START(True)\n    sleep(1)\n    MAIN()\n    PLAY_AGAIN()\n\n\nif __name__ == \"__main__\":\n    GAME()\n","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":17097,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"330664079","text":"\"\"\"\n    Run this script as follows: python process.py\n    It will output the result to files\n    input_text.txt contains input words\n    sans_stop_words.txt contains the input text after removing stop words\n    synset.txt contains the output of matched synset\n\"\"\"\nfrom googletrans import Translator\nimport pyiwn\n\n\"\"\" File initializations \"\"\"\ninput_text=open('input_text.txt','w') # Contains the initial input text\nsans_stop_words=open('sans_stop_words.txt','w') # Contains the input text after removing stop words\nsynset_output=open('synset.txt','w') # Contains the output of matched synset\n\n\"\"\" Sysnet initialization to Telugu\"\"\"\niwn = pyiwn.IndoWordNet(lang=pyiwn.Language.TELUGU)\n\n\"\"\" Translate input into Telugu \"\"\"\ntranslator = Translator()\ntranslated_input = translator.translate(\"silk saree is beautiful\",dest='te') # Enter input in this line\n\n\"\"\" Convert stop words as a list \"\"\"\nstop_words = []\nwith open('stop-words-tr.txt','r') as stop_word:\n    for line in stop_word:\n        stop_words.extend(line.split())\n\n\n\"\"\" Remove stop words \"\"\"\nprocessed_words = []\nwords = translated_input.text.split()\nfor word in words:\n    input_text.write(word+'\\n')\n    if word not in stop_words:\n        processed_words.append(word)\n        sans_stop_words.write(word+'\\n')\n\nfor word in processed_words:\n    synset_output.write('\\n'+str(iwn.synsets(word)))\n\n\n\n\n","sub_path":"process.py","file_name":"process.py","file_ext":"py","file_size_in_byte":1354,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"267155634","text":"import pandas as pd\nimport numpy as np\nfrom numpy.random import randn\n\nlabels = ['a', 'b', 'c']\ndat = [10, 20, 30]\narr = np.array(dat)\ndic = {'a': 10, 'b': 20, 'c':30}\n\n\n# Pandas series can be build on literally anything:\nser = pd.Series(data=dat, index=labels)\nser = pd.Series(data=arr, index=labels)\nser = pd.Series(dic)\n\nser1 = pd.Series([1, 2, 3, 4], ['USA', 'GB', 'RU', 'PL'])\nser2 = pd.Series([1, 2, 3, 4], ['GB', 'USA', 'PL', 'TT'])\n\n# Adding series is matched asing on labels. Not mached -> NaN\nser3 = ser1 +ser2\n\n# Data frames\n# DF is just a bunch of Series sharing the same label.\nnp.random.seed(101)  # For consistent results\n\ndf = pd.DataFrame(randn(5,4), ['a', 'b', 'c', 'd', 'e'], ['w', 'x', 'y', 'z'])\nw_and_z = df[['w', 'z']]\n\n# Derive new column from already existing:\ndf['new'] = df['x'] + df['y']\n\n# Remove column:\ndrp = df.drop('a', axis=0)  # axis = 0: rows, = 1: columns\ndrp = drp.drop('new', axis=1)\n\n# Remove 'In place':\ndrp.drop('z', axis=1, inplace=True)\n\n# Selecting rows by label\nrw = df.loc['a']  # Note: it is going to be displayed in column.\n\n# Selecting rows by numeric index:\nrw2 = df.iloc[2]\n\n# Selecting crossection:\ncs1 = df.loc['b', 'y']  # Single number\ncs2 = df.loc[['a', 'b'], ['w', 'y']]\n\nprint(cs2)\n","sub_path":"pandas_basic.py","file_name":"pandas_basic.py","file_ext":"py","file_size_in_byte":1241,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"32172657","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nimport sys\nfrom PyQt4 import QtGui\nfrom PyQt4 import QtCore\n#import serial\nimport sqlite3\nimport os\n#from time import strftime\nimport time\n#import threading\n#mport subprocess\n#import datetime\n#import urllib\n#from PyQt4 import QtCore as qtcore\n#from curses import ascii\n#import base64\n#import re\n#from func import funciones\n\n\n#retardo = 1\n#intervaloTiempoParaInFin = 5\n#nVuelta = 0\n\n\n\nfrom ClReloj import clReloj\nfrom ClSerial import clSerial\nfrom ClModem import clQuectel\nfrom ClDB import sqLite\nfrom ClMifare import clMifare\n\n\nclass mainWin(QtGui.QMainWindow):\n\n    stVersion = \"v1.28\"\n    flRFID = False\n    \n    def __init__(self):\n        super(mainWin, self).__init__()\n\n        '''\n            ############################################################\n                    Pantalla Principal del Sistema de Prepago\n\n            ############################################################\n        '''\n        if os.path.exists('/home/pi/innobusmx/innobus.log'):\n            os.remove('/home/pi/innobusmx/innobus.log')\n        self.logo = QtGui.QLabel(self)\n        self.logo.setScaledContents(True)\n        pathImgFondo = os.path.join('/home/pi/innobusmx/data/img/wall2.jpg')\n        self.logo.setPixmap(QtGui.QPixmap(pathImgFondo))\n        self.logo.move(0, 0)\n        self.logo.resize(800, 480)\n        \n        self.lblOperador = QtGui.QLabel(self)\n        self.lblOperador.setScaledContents(True)\n        self.lblOperador.move(90, 5)\n        self.lblOperador.resize(280, 368)\n\n        self.lblVersion = QtGui.QLabel('', self)\n        self.lblVersion.resize(self.width(), 50)\n        self.lblVersion.move(750, 42)\n        self.lblVersion.setStyleSheet('QLabel { font-size: 8pt; font-family: Arial; color:Gray}')\n        self.lblVersion.setText(self.stVersion)\n\n        self.lblNS = QtGui.QLabel('', self)\n        self.lblNS.resize(self.width(), 50)\n        self.lblNS.move(5, 435)\n        self.lblNS.setStyleSheet('QLabel { font-size: 8pt; font-family: Arial; color:White}')\n\n        self.lblNSFirmware = QtGui.QLabel('', self)\n        self.lblNSFirmware.resize(self.width(), 50)\n        self.lblNSFirmware.move(5, 450)\n        self.lblNSFirmware.setStyleSheet('QLabel { font-size: 8pt; font-family: Arial; color:White}')\n\n\n        self.lblUnidad = QtGui.QLabel('', self)\n        self.lblUnidad.resize(self.width(), 50)\n        self.lblUnidad.move(650, 120)\n        self.lblUnidad.setStyleSheet('QLabel { font-size: 30pt; font-family: Arial; color:White}')\n\n        self.lblNombreOperador = QtGui.QLabel('', self)\n        self.lblNombreOperador.resize(self.width(), 50)\n        self.lblNombreOperador.move(65, 380)\n        self.lblNombreOperador.setStyleSheet('QLabel { font-size: 20pt; font-family: Arial; color:White}')\n\n        self.lblRuta = QtGui.QLabel('', self)\n        self.lblRuta.resize(self.width(), 50)\n        self.lblRuta.move(620, 260)\n        self.lblRuta.setStyleSheet('QLabel { font-size: 30pt; font-family: Arial; color:White}')\n\n        self.lblVuelta = QtGui.QLabel('', self)\n        self.lblVuelta.resize(self.width(), 50)\n        self.lblVuelta.move(650, 190)\n        self.lblVuelta.setStyleSheet('QLabel { font-size: 30pt; font-family: Arial; color:White}')\n\n        self.lblVelocidad = QtGui.QLabel('', self)\n        self.lblVelocidad.resize(self.width(), 50)\n        self.lblVelocidad.move(670, 325)\n        self.lblVelocidad.setStyleSheet('QLabel { font-size: 30pt; font-family: Arial; color:White}')\n\n        self.lblHora = QtGui.QLabel('', self)\n        self.lblHora.resize(self.width(), 50)\n        self.lblHora.move(550, 440)\n        self.lblHora.setStyleSheet('QLabel { font-size: 14pt; font-family: Arial; color:White}')\n\n        self.lblMsg = QtGui.QLabel('', self)\n        self.lblMsg.resize(self.width(), 50)\n        self.lblMsg.move(10, 380)\n        self.lblMsg.setStyleSheet('QLabel { font-size: 8pt; font-family: Arial; color:Blue}')\n\n        self.lblMsg1 = QtGui.QLabel('', self)\n        self.lblMsg1.resize(self.width(), 50)\n        self.lblMsg1.move(10, 340)\n        self.lblMsg1.setStyleSheet('QLabel { font-size: 8pt; font-family: Arial; color:Blue}')\n\n        self.msg = QtGui.QLabel('', self)\n        self.msg.resize(self.width(), 50)\n        self.msg.move(135, 400)\n        self.msg.setStyleSheet('QLabel { font-size: 30pt; font-family: Arial; color:red}')\n\n        '''\n            ############################################################\n                Aqui estan las variables que necesito para mostrar\n                        la informacion de los aforo\n            ############################################################\n        '''\n\n        self.lblAlteTurno = QtGui.QLabel('', self)\n        self.lblAlteTurno.resize(self.width(), 50)\n        self.lblAlteTurno.move(200, 360)\n        self.lblAlteTurno.setStyleSheet('QLabel { font-size: 25pt; font-family: Arial; color:white}')\n\n\n        self.lblAlteTurnoDos = QtGui.QLabel('', self)\n        self.lblAlteTurnoDos.resize(self.width(), 50)\n        self.lblAlteTurnoDos.move(200, 400)\n        self.lblAlteTurnoDos.setStyleSheet('QLabel { font-size: 25pt; font-family: Arial; color:white}')\n\n        imgTarjetaAtras = \"/home/pi/innobusmx/data/img/imgTarjetas/Cl.jpg\"\n        imgUsuarioTarjeta = \"/home/pi/innobusmx/data/user/admin.Jpeg\"\n\n        self.no3G = QtGui.QLabel(self)\n        self.no3G.setPixmap(QtGui.QPixmap(\"/home/pi/innobusmx/data/img/no3G.Jpeg\"))\n        self.no3G.setScaledContents(True)\n        self.no3G.move(770,425)\n        self.no3G.resize(20, 20)\n\n        self.noGPS = QtGui.QLabel(self)\n        self.noGPS.setPixmap(QtGui.QPixmap(\"/home/pi/innobusmx/data/img/noGPSEncendido.png\"))\n        self.noGPS.setScaledContents(True)\n        self.noGPS.move(740,425)\n        self.noGPS.resize(20, 20)\n\n        self.noSocket = QtGui.QLabel(self)\n        self.noSocket.setPixmap(QtGui.QPixmap(\"/home/pi/innobusmx/data/img/noSocket.png\"))\n        self.noSocket.setScaledContents(True)\n        self.noSocket.move(710,425)\n        self.noSocket.resize(20, 20)\n\n        self.noFTP = QtGui.QLabel(self)\n        self.noFTP.setPixmap(QtGui.QPixmap(\"/home/pi/innobusmx/data/img/noFTP.Jpeg\"))\n        self.noFTP.setScaledContents(True)\n        self.noFTP.move(680,425)\n        self.noFTP.resize(20, 20)\n\n        self.noRDIF = QtGui.QLabel(self)\n        self.noRDIF.setPixmap(QtGui.QPixmap(\"/home/pi/innobusmx/data/img/noRFID.Jpeg\"))\n        self.noRDIF.setScaledContents(True)\n        self.noRDIF.move(650,425)\n        self.noRDIF.resize(20, 20)\n\n        self.noRed = QtGui.QLabel(self)\n        self.noRed.setPixmap(QtGui.QPixmap(\"/home/pi/innobusmx/data/img/noRed.Jpeg\"))\n        self.noRed.setScaledContents(True)\n        self.noRed.move(620,425)\n        self.noRed.resize(20, 20)\n\n        self.noSIM = QtGui.QLabel(self)\n        self.noSIM.setPixmap(QtGui.QPixmap(\"/home/pi/innobusmx/data/img/noSIM.png\"))\n        self.noSIM.setScaledContents(True)\n        self.noSIM.move(550,425)\n        self.noSIM.resize(32, 20)\n\n        \n        self.flFecha = False\n        self.flFTP = False\n        self.flRed = False\n        self.flSocket = False\n        self.flComm = True\n        self.iComm = 0\n        self.flRFID = False\n        self.flSIM = False\n        self.iccid = \"\"\n\n        self.flGPS = False\n        self.flGPSOK = False\n\n        self.serialNumber = \"\"\n        self.version = \"\"\n        self.TISC = \"\"\n        self.stImgTarjeta = \"\"\n        self.stSaldo = \"\"\n        self.stTarjeta = \"\"\n        self.stNombre = \"\"\n        self.stApellido = \"\"\n        self.stSaldoInsuficiente = \"\"\n        self.stMsg = \"\"\n\n        self.imgTarjeta = QtGui.QLabel(self)\n        self.imgTarjeta.setPixmap(QtGui.QPixmap(\"\"))\n        #self.imgTarjeta.setPixmap(QtGui.QPixmap(imgTarjetaAtras))\n        self.imgTarjeta.setScaledContents(True)\n        self.imgTarjeta.move(0,0)\n        self.imgTarjeta.resize(800, 480)\n\n        self.imgDefault = QtGui.QLabel(self)\n        self.imgDefault.setPixmap(QtGui.QPixmap(\"\"))\n        self.imgDefault.setScaledContents(True)\n        self.imgDefault.move(5,5)\n        self.imgDefault.resize(422,470)\n        #antigui tamano\n        #self.imgDefault.resize(335,345)\n\n        #self.lblNombreT = QtGui.QLabel('', self)\n        #self.lblNombreT.resize(self.width(), 50)\n        #self.lblNombreT.move(435, 100)\n        #self.lblNombreT.setStyleSheet('QLabel { font-size: 15pt; font-family: Arial; color:White}')\n        #self.lblNombreT.setText(\"NOMBRE\")\n        #self.lblNombreT.hide()\n\n        self.lblNombre = QtGui.QLabel('', self)\n        self.lblNombre.resize(376, 70)\n        self.lblNombre.move(424, 85)\n        self.lblNombre.setStyleSheet('QLabel { font-size: 40pt; font-family: Arial; color:White}')\n        self.lblNombre.setAlignment(QtCore.Qt.AlignCenter)\n        \n\n        #self.lblApellidoT = QtGui.QLabel('', self)\n        #self.lblApellidoT.resize(self.width(), 50)\n        #self.lblApellidoT.move(435, 200)\n        #self.lblApellidoT.setStyleSheet('QLabel { font-size: 15pt; font-family: Arial; color:White}')\n        #self.lblApellidoT.setText(\"APELLIDO\")\n        #self.lblApellidoT.hide()\n\n        self.lblApellido = QtGui.QLabel('', self)\n        self.lblApellido.resize(376, 50)\n        self.lblApellido.move(424, 150)\n        self.lblApellido.setStyleSheet('QLabel { font-size: 25pt; font-family: Arial; color:White}')\n        self.lblApellido.setAlignment(QtCore.Qt.AlignCenter)\n\n        #self.lblSaldoT = QtGui.QLabel('', self)\n        #self.lblSaldoT.resize(self.width(), 50)\n        #self.lblSaldoT.move(435, 300)\n        #self.lblSaldoT.setStyleSheet('QLabel { font-size: 15pt; font-family: Arial; color:White}')\n        #self.lblSaldoT.setText(\"SALDO\")\n        #self.lblSaldoT.hide()\n\n        self.lblSaldo = QtGui.QLabel('', self)\n        self.lblSaldo.resize(376, 75)\n        self.lblSaldo.move(424, 300)\n        self.lblSaldo.setStyleSheet('QLabel { font-size: 50pt; font-family: Arial; color:White}')\n        self.lblSaldo.setAlignment(QtCore.Qt.AlignCenter)\n\n        self.lblMsg = QtGui.QLabel('', self)\n        self.lblMsg.resize(self.width(), 75)\n        self.lblMsg.move(750, 385)\n        self.lblMsg.setStyleSheet('QLabel { font-size: 8pt; font-family: Arial; color:Red}')\n\n        self.lblSaldoInsuficiente = QtGui.QLabel('', self)\n        self.lblSaldoInsuficiente.resize(self.width(), 75)\n        self.lblSaldoInsuficiente.move(470, 410)\n        self.lblSaldoInsuficiente.setStyleSheet('QLabel { font-size: 35pt; font-family: Arial; color:Red}')\n\n        self.lblSS = QtGui.QLabel('', self)\n        self.lblSS.resize(self.width(), 50)\n        self.lblSS.move(270, 45)\n        self.lblSS.setStyleSheet('QLabel { font-size: 45pt; font-family: Arial; color:White}')\n\n        self.lblVAI = QtGui.QLabel('', self)\n        self.lblVAI.resize(self.width(), 50)\n        self.lblVAI.move(150, 45)\n        self.lblVAI.setStyleSheet('QLabel { font-size: 50pt; font-family: Arial; color:White}')\n\n        self.lblSSMsg = QtGui.QLabel('', self)\n        self.lblSSMsg.resize(self.width(), 50)\n        self.lblSSMsg.move(150, 350)\n        self.lblSSMsg.setStyleSheet('QLabel { font-size: 45pt; font-family: Arial; color:White}')\n\n        self.lblTarjetas = QtGui.QLabel('', self)\n        self.lblTarjetas.resize(self.width(), 50)\n        self.lblTarjetas.move(180, 220)\n        self.lblTarjetas.setStyleSheet('QLabel { font-size: 30pt; font-family: Arial; color:Yellow}')\n\n        self.lblNV = QtGui.QLabel('', self)\n        self.lblNV.resize(self.width(), 50)\n        self.lblNV.move(265, 45)\n        self.lblNV.setStyleSheet('QLabel { font-size: 45pt; font-family: Arial; color:White}')\n\n        self.lblInFinVuelta = QtGui.QLabel('', self)\n        self.lblInFinVuelta.resize(self.width(), 50)\n        self.lblInFinVuelta.move(200, 220)\n        self.lblInFinVuelta.setStyleSheet('QLabel { font-size: 40pt; font-family: Arial; color:red}')\n\n        self.setGeometry(-1, 0, 800, 480)\n        self.setWindowTitle('CytiBus '+self.stVersion)\n        self.setStyleSheet('''QMainWindow { background-color:#1D2556;}\n            QLabel{font-size: 15pt; font-family: Courier; color:black;\n            font-weight: bold}''')\n\n        conn = sqlite3.connect('/home/pi/innobusmx/data/db/aforo')\n        c = conn.cursor()\n        #conn.execute('UPDATE parametros SET urlAPN=\"innobusmx.itelcel.com\", userAPN=\"\", pwdAPN=\"\", urlSocket=\"192.168.2.175\", puertoSocket=5800, urlFTP=\"192.168.2.175\", puertoFTP=21')\n        conn.execute('UPDATE parametros SET urlAPN=\"internet.itelcel.com\", userAPN=\"webgprs\", pwdAPN=\"webgprs2002\", urlSocket=\"innovaslp.dyndns.org\", puertoSocket=11004, urlFTP=\"innovaslp.dyndns.org\", puertoFTP=21')\n        conn.commit()\n        c.execute(\"SELECT economico FROM parametros\")\n        data = c.fetchone()\n        if data is None:\n            self.lblUnidad.setText(\"\")\n        else:\n            self.lblUnidad.setText(str(data[0]))\n        c.execute('select fechaHora, ruta.nombre, csn, num_vuelta, tTurno.nombre, inicioFin from soloVuelta, ruta, tTurno WHERE soloVuelta.idRuta = ruta.Id AND soloVuelta.turno = tTurno.idTurno ORDER BY idSoloVuelta DESC LIMIT 1')\n        data = c.fetchone()\n        c.close()\n        if (data is None) or (data[5] == \"F\"):\n            self.lblOperador.setPixmap(QtGui.QPixmap(\"\"))\n            self.lblRuta.setText(\"\")\n            self.lblVuelta.setText(\"\")\n        else:\n            self.lblRuta.setText(data[1])\n            imgChofer = \"data/user/%s.Jpeg\"%data[2]\n            scriptChofer = os.path.join(self.dir, imgChofer)\n            self.lblOperador.setPixmap(QtGui.QPixmap(scriptChofer))\n            self.lblVuelta.setText(str(data[3]))\n        conn.close\n\n        #self.imgDefault.setPixmap(QtGui.QPixmap('/home/pi/innobusmx/data/user/04203/04203532545780.Jpeg'))\n        #self.imgTarjeta.setPixmap(QtGui.QPixmap('/home/pi/innobusmx/data/img/imgTarjetas/NO.jpg'))\n        #self.lblNombre.setText(\"Alfredo\")\n        #self.lblApellido.setText(\"Blanco Perez\")\n        #self.lblSaldo.setText(\"$100.00\")               \n\n\n        #self.lblOperador.setPixmap(QtGui.QPixmap(\"/home/pi/innobusmx/data/user/Shapes050.jpeg\"))\n        #thread = loadImageThread(file=\"/home/pi/innobusmx/data/img/noGPSEncendido.png\", w=512, h=512)\n        #self.connect(thread, QtCore.SIGNAL(\"showImage(QString)\"), self.showImage)\n        #thread.start()\n        \n        #thread = clReloj(self)\n        #self.connect(thread, QtCore.SIGNAL(\"showImage(QString)\"), self.showImage)\n        #thread.start()\n\n        #self.showMaximized()\n        #self.setWindowFlags(QtCore.Qt.CustomizeWindowHint)\n        #self.showFullScreen()\n        self.center()\n        self.show()\n\n        clreloj = clReloj(self)\n        clreloj.start()        \n      \n    def center(self): \n        frameGm = self.frameGeometry()\n        centerPoint = QtGui.QDesktopWidget().availableGeometry().center()\n        frameGm.moveCenter(centerPoint)\n        self.move(frameGm.topLeft())        \n\nclass Screen():\n    \n    def __init__(self, parent):\n        self.parent = parent\n        self.flG = False\n        self.red = False\n        self.ftp = False\n        self.flR = True\n        self.rfid = not self.parent.flRFID\n        self.icomm = -999\n        self.sim = False\n        self.tisc = \"\"\n        self.socket = False\n        self.show()\n\n    def show(self):\n\n        #clmodem.obtenerMensaje()\n        #print \"iComm:\",self.parent.iComm\n        if (self.icomm != self.parent.iComm):\n            if (self.parent.iComm == 0 or self.parent.iComm == 99):\n                self.parent.no3G.setPixmap(QtGui.QPixmap(\"/home/pi/innobusmx/data/img/no3G.Jpeg\"))\n            elif (self.parent.iComm == 1):\n                self.parent.no3G.setPixmap(QtGui.QPixmap(\"/home/pi/innobusmx/data/img/no3G.png\"))\n            elif (self.parent.iComm >= 2 and self.parent.iComm <= 10):\n                self.parent.no3G.setPixmap(QtGui.QPixmap(\"/home/pi/innobusmx/data/img/3G25.png\"))\n            elif (self.parent.iComm >= 11 and self.parent.iComm <= 20):\n                self.parent.no3G.setPixmap(QtGui.QPixmap(\"/home/pi/innobusmx/data/img/3G50.png\"))\n            elif (self.parent.iComm >= 21 and self.parent.iComm <= 30):\n                self.parent.no3G.setPixmap(QtGui.QPixmap(\"/home/pi/innobusmx/data/img/3G75.png\"))\n            elif (self.parent.iComm >= 31 and self.parent.iComm <= 98):\n                self.parent.no3G.setPixmap(QtGui.QPixmap(\"/home/pi/innobusmx/data/img/3G.png\"))\n            self.icomm = self.parent.iComm\n\n        if (self.parent.flRFID != self.rfid):\n            if (not self.parent.flRFID):\n                self.parent.noRDIF.setPixmap(QtGui.QPixmap(\"/home/pi/innobusmx/data/img/noRFID.Jpeg\"))\n            else:\n                self.parent.noRDIF.setPixmap(QtGui.QPixmap(\"\"))\n            self.rfid = self.parent.flRFID\n\n        if (self.parent.flSocket != self.socket):\n            if (not self.parent.flSocket):\n                self.parent.noSocket.setPixmap(QtGui.QPixmap(\"/home/pi/innobusmx/data/img/noSocket.png\"))\n            else:\n                self.parent.noSocket.setPixmap(QtGui.QPixmap(\"\"))\n            self.socket = self.parent.flSocket\n\n        if (self.parent.flRed != self.red):\n            if (not self.parent.flRed):\n                self.parent.noRed.setPixmap(QtGui.QPixmap(\"/home/pi/innobusmx/data/img/noRed.Jpeg\"))\n            else:\n                self.parent.noRed.setPixmap(QtGui.QPixmap(\"\"))\n            self.red = self.parent.flRed\n\n\n\n\n        if (self.parent.TISC != self.tisc):\n            if (self.parent.TISC != \"\"):\n                #print \"Saldo Insuficiente\",self.parent.stSaldoInsuficiente\n                #print \"Nombre\",self.parent.stNombre, self.parent.stApellido\n                self.parent.imgDefault.setPixmap(QtGui.QPixmap(self.parent.TISC))\n                self.parent.imgTarjeta.setPixmap(QtGui.QPixmap(self.parent.stImgTarjeta))\n                self.parent.lblNombre.setText(self.parent.stNombre)\n                self.parent.lblApellido.setText(self.parent.stApellido)\n                self.parent.lblSaldo.setText(self.parent.stSaldo)               \n                self.parent.lblSaldoInsuficiente.setText(self.parent.stSaldoInsuficiente)\n                self.parent.lblMsg.setText(self.parent.stMsg)\n                #if (self.parent.stSaldo != \"\"):\n                #    self.parent.lblSaldoT.show()\n                #if (self.parent.stNombre != \"\"):\n                #    self.parent.lblNombreT.show()\n                #    self.parent.lblApellidoT.show()                    \n            else:\n                self.parent.imgDefault.setPixmap(QtGui.QPixmap(\"\"))\n                self.parent.imgTarjeta.setPixmap(QtGui.QPixmap(\"\"))\n                self.parent.lblNombre.setText(\"\")\n                self.parent.lblApellido.setText(\"\")\n                self.parent.lblSaldo.setText(\"\")\n                self.parent.lblSaldoInsuficiente.setText(\"\")\n                self.parent.lblMsg.setText(\"\")\n                #self.parent.lblNombreT.hide()\n                #self.parent.lblApellidoT.hide()\n                #self.parent.lblSaldoT.hide()\n            self.tisc = self.parent.TISC\n\n\n        if (self.parent.flSIM != self.sim):\n            if (not self.parent.flSIM):\n                self.parent.noSIM.setPixmap(QtGui.QPixmap(\"/home/pi/innobusmx/data/img/noSIM.png\"))\n            else:\n                self.parent.noSIM.setPixmap(QtGui.QPixmap(\"\"))\n            self.sim = self.parent.flSIM\n\n        if (not self.parent.flGPSOK):\n            self.parent.noGPS.setPixmap(QtGui.QPixmap(\"/home/pi/innobusmx/data/img/noGPSEncendido.png\"))\n        else:\n            if (self.flG):\n                if (self.parent.flGPS):\n                    self.parent.noGPS.setPixmap(QtGui.QPixmap(\"/home/pi/innobusmx/data/img/GPS.png\"))\n                else:\n                    self.parent.noGPS.setPixmap(QtGui.QPixmap(\"/home/pi/innobusmx/data/img/noGPS.png\"))\n                self.flG = False\n            else:\n                self.parent.noGPS.setPixmap(QtGui.QPixmap(\"\"))\n                self.flG = True\n        '''\n        if (self.parent.flFTP != self.ftp):\n            if (self.parent.flFTP):\n                if (self.flftp):\n                    self.parent.noGPS.setPixmap(QtGui.QPixmap(\"/home/pi/innobusmx/data/img/noGPS.png\"))\n                    self.flG = False\n                else:\n                    self.parent.noFTP.setPixmap(QtGui.QPixmap(\"\"))\n                    self.flG = True\n            else:\n                self.parent.noFTP.setPixmap(QtGui.QPixmap(\"\"))\n            self.ftp = self.parent.flFTP\n        '''     \n            \n\n        QtCore.QTimer.singleShot(250, self.show)\n\n\n\ndef main():\n    app = QtGui.QApplication(sys.argv)\n    ex = mainWin()\n    #ex.start()\n    #self.modem = inicioGPS(self, self.ser, self.flGPS, self.noGPS)\n    #self.tresg = TresG(self, self.ser, self.modem)\n    #self.funcCore = funciones(self)\n    #self.funcCore.datosVuelta        \n    cldb = sqLite()\n\n\n\n    \n    #clStatus = Status(ex)\n    #clStatus.start()\n\n    clserial = clSerial(ex, cldb)\n#    serial.daemon = True\n#    clserial.start()\n\n    clmodem = clQuectel(ex, cldb, clserial)\n    clmodem.start()\n\n\n    clmifare = clMifare(ex, cldb, clserial, clmodem)\n    #clmifare.start()\n\n    clscreen = Screen(ex)\n    #clscreen.start()\n\n    sys.exit(app.exec_())    \n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"innobusmx/innobus.py","file_name":"innobus.py","file_ext":"py","file_size_in_byte":21223,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"312561000","text":"#!/usr/bin/env python\n# Copyright (c) 2016 Intel Corporation\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 the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in\n# all copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\n# -*- coding: utf-8 -*-\n\n\"\"\"\nexecute test runner on a node\n\nThis class is used by Multi Runner to execute and control a copy of Test Runner\non a remote node. The class uses its assigned node type classification to\ndetermine if it should to execute a request. The information about a request is\ncontained test_config map. The information with the test_config will be\nwrittern to the log directory for request and becomes the config file supplied\nto Test Runner. The class has methods for controlling the new Test Runner\nprocess.\n\n\"\"\"\n\n#pylint: disable=too-many-instance-attributes\n#pylint: disable=too-many-arguments\n\nimport os\nimport logging\nimport paramiko\nimport json\nfrom yaml import load\ntry:\n    from yaml import CLoader as Loader\nexcept ImportError:\n    from yaml import Loader\n\n\nclass RemoteTestRunner():\n    \"\"\"Simple node controller \"\"\"\n\n    def __init__(self, info, node, dir_path, scripts_dir, directives,\n                 node_type='all'):\n        self.info = info\n        self.node = node\n        self.node_type = node_type\n        self.dir_path = dir_path\n        self.scripts_dir = scripts_dir\n        self.test_directives = directives\n        self.logger = logging.getLogger(\"TestRunnerLogger\")\n        self.test_config = {}\n        self.test_name = \"\"\n        self.state = \"init\"\n        self.stdout = None\n        self.stderr = None\n        self.procrtn = 0\n        self.logfileout = \"\"\n        self.username = os.getlogin()\n        self.client = paramiko.SSHClient()\n        self.client.set_missing_host_key_policy(paramiko.AutoAddPolicy())\n\n    def match_type(self, node_type=\"all\"):\n        \"\"\" match the node requested node type to this nodes type \"\"\"\n        self.procrtn = 0\n        self.state = \"done\"\n        if node_type != self.node_type and node_type != 'all' and \\\n           self.node_type != 'all':\n            return False\n        return True\n\n    def connect(self):\n        \"\"\" connect to remote system \"\"\"\n        connect_args = {}\n        connect_args['hostname'] = self.node\n        connect_args['username'] = self.username\n        if self.test_directives.get('useKeyFile'):\n            connect_args['key_filename'] = \\\n                self.test_directives.get('useKeyFile')\n        connect_args['allow_agent'] = False\n        connect_args['look_for_keys'] = True\n        connect_args['timeout'] = 15\n        self.client.connect(**connect_args)\n\n    def launch_test(self, timeout=180):\n        \"\"\" Launch remote test runner \"\"\"\n        test_name = self.test_name\n        self.logger.info(\"TestRunner: start %s on %s\", test_name, self.node)\n        self.connect()\n        self.logger.debug(\"conf: %s\", self.test_config)\n        configfile = test_name + \"_config\"\n        log_path = self.test_config['log_base_path']\n        self.logger.info(\"log path: %s\", log_path)\n        test_config = os.path.abspath(os.path.join(log_path, configfile))\n        self.logger.debug(\"writing config: %s\", test_config)\n        with open(test_config, \"w\") as config_info:\n            json.dump(self.test_config, config_info, skipkeys=True, indent=4)\n\n        test_yml = os.path.join(self.scripts_dir, \"{!s}.yml\".format(test_name))\n        self.logfileout = os.path.join(log_path,\n                                       (\"{!s}.runout\".format(test_name)))\n        python_vers = self.test_directives.get('usePython', \"python3.4\")\n        cmdstr = \"{!s} {!s}/test_runner config={!s} {!s} 2>&1\".format(\n            python_vers, self.dir_path, test_config, test_yml)\n        self.logger.debug(\"cmd: %s\", cmdstr)\n        with open(self.logfileout, mode='w') as outfile:\n            outfile.write(\"{!s}\\n  Command: {!s} \\n{!s}\\n\".format(\n                (\"=\" * 40), cmdstr, (\"=\" * 40)))\n            outfile.flush()\n        # because the stderr was sent to stdout in the cmdstr, stderr should\n        # always be empty and can be ignored.\n        dummy_stdin, self.stdout, self.stderr = \\\n            self.client.exec_command(cmdstr, timeout=timeout)\n        dummy_stdin.close()\n        self.state = \"running\"\n        self.procrtn = None\n\n    def dump_data(self):\n        \"\"\" dump the output to a file \"\"\"\n        with open(self.logfileout, mode='a') as outfile:\n            outfile.write(\"Command complete within timeout.\")\n            for line in self.stdout.readlines():\n                outfile.write(line)\n        self.client.close()\n\n    def process_state(self):\n        \"\"\" poll remote processes for state \"\"\"\n        if self.state == \"running\":\n            if self.stdout.channel.exit_status_ready():\n                self.state = \"done\"\n                self.procrtn = self.stdout.channel.recv_exit_status()\n                self.dump_data()\n        return self.state\n\n    def process_rtn(self):\n        \"\"\" remote process exit code \"\"\"\n        return self.procrtn\n\n    def process_terminate(self):\n        \"\"\" terminate remote processes \"\"\"\n        if not self.stdout.channel.exit_status_ready():\n            # process terminate()\n            self.client.close()\n            self.state = \"terminate\"\n            self.procrtn = -1\n            with open(self.logfileout, mode='a') as outfile:\n                outfile.write(\"Command did not complete within timeout.\")\n\n        return self.procrtn\n\n    def match_testName(self):\n        \"\"\" match the name of the log to the testcase \"\"\"\n        subtest_results_file = os.path.join(self.test_config['log_base_path'],\n                                            \"subtest_results.yml\")\n        if not os.path.exists(subtest_results_file):\n            return\n        with open(subtest_results_file, 'r') as fd:\n            subtest_results_data = load(fd, Loader=Loader)\n        test_set_name = str(subtest_results_data[0]['name'])\n\n        for logfile in [self.logfileout]:\n            (path, name) = os.path.split(logfile)\n            namePlus = name.split('.')\n            log_type = \"console_{!s}\".format(namePlus[1])\n            new_name = os.path.join(path,\n                                    (\"{}.{}.{}.{}.log\".format(\n                                        test_set_name, test_set_name,\n                                        log_type, self.node\n                                        )))\n            os.rename(logfile, new_name)\n\n\n    def dump_files(self):\n        \"\"\" dump the log file \"\"\"\n        with open(self.logfileout, mode='r') as fd:\n            print(\"STDOUT/STDERR: %s\" % fd.read())\n\n    def dump_info(self):\n        \"\"\" print info about node \"\"\"\n        self.logger.info(\"node: %s  type: %s\", self.node, self.node_type)\n\n    def setup_config(self, name, logdir, node_type='all', configKeys=None,\n                     setFromConfig=None, directives=None):\n        \"\"\" setup base config \"\"\"\n        if node_type != self.node_type and node_type != 'all' and \\\n           self.node_type != 'all':\n            self.test_name = \"\"\n            return\n        self.test_name = name\n        self.test_config.clear()\n\n        copyList = self.test_directives.get('copyHostList', \"no\")\n        if copyList == \"yes\":\n            self.test_config['host_list'] = self.info.get_config('host_list')\n        build_path = self.info.get_config('build_path')\n        if not build_path:\n            build_path = os.path.dirname(os.getcwd())\n        self.test_config['build_path'] = build_path\n        self.test_config['log_base_path'] = logdir\n        self.test_config['node'] = self.node\n        self.test_config['node_type'] = self.node_type\n        self.test_config['hostlist'] = \\\n            \",\".join(self.info.get_config(keyname='host_list'))\n        if configKeys:\n            self.test_config.update(configKeys)\n        if setFromConfig:\n            self.test_config['setKeyFromConfig'] = {}\n            self.test_config['setKeyFromConfig'].update(setFromConfig)\n\n        # add testing directives\n        self.test_config['setDirectiveFromConfig'] = {}\n        if 'useKeyFile' in self.test_directives:\n            self.test_config['setDirectiveFromConfig']['useKeyFile'] = \\\n                self.test_directives.get('useKeyFile')\n        if directives:\n            self.test_config['setDirectiveFromConfig'].update(directives)\n        self.test_config['setDirectiveFromConfig']['renameTestRun'] = \"no\"\n        self.logger.debug(\"conf: %s\", str(self.test_config))\n","sub_path":"test_runner/RemoteTestRunner.py","file_name":"RemoteTestRunner.py","file_ext":"py","file_size_in_byte":9348,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"390544007","text":"from PyDictionary import PyDictionary\n\ndictionary = PyDictionary()\n\ndef process(words):\n    ret = ''\n    for word in words:\n        ret += '-' * 80 + '\\n'\n        ret += 'Word: %-15s' % word + '\\n'\n        ret += '-' * 80 + '\\n'\n        ret += '\\n Meanings: ' + str(dictionary.meaning(word))\n        ret += \"\\n\\t Synonyms: \" + str(dictionary.synonym(word))\n        ret += '\\n'\n    print(ret)\n    return ret\n","sub_path":"slack/modules/meow.py","file_name":"meow.py","file_ext":"py","file_size_in_byte":407,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"607266541","text":"import os\nimport  findspark\n\n\nfindspark.init()\n\n\nfrom pyspark.sql import SparkSession\nfrom pyspark.sql.functions import explode\nfrom pyspark.sql.functions import split\n\n\n\n\nos.environ['PYSPARK_SUBMIT_ARGS'] = '--packages org.apache.spark:spark-streaming-kafka-0-10_2.12:3.0.0-preview2,' \\\n                                    'org.apache.spark:spark-sql-kafka-0-10_2.12:3.0.0-preview2 pyspark-shell '\n\n\nspark = SparkSession \\\n    .builder \\\n    .appName(\"WordCount\") \\\n    .getOrCreate()\n\ndf = spark \\\n    .readStream \\\n    .format(\"kafka\") \\\n    .option(\"kafka.bootstrap.servers\", \"localhost:9092\") \\\n    .option(\"subscribe\", \"TOPIC_TEST\") \\\n    .option(\"startingOffsets\", \"earliest\") \\\n    .load()\n\n#df1 = df.selectExpr(\"CAST(key AS STRING)\", \"CAST(value AS STRING)\")\n#Split the lines into words\nwords = df.select(\n    #explode turns each item in an array into a separate row\n    explode(\n        split(df.value, ' ')\n    ).alias('word')\n)\n#Generate a running word count\nwordCounts = words.groupBy('word').count()\n\nquery =wordCounts  \\\n    .writeStream \\\n    .outputMode(\"complete\") \\\n    .format(\"console\") \\\n    .start()\n\nquery.awaitTermination()","sub_path":"Kafka/word_counts.py","file_name":"word_counts.py","file_ext":"py","file_size_in_byte":1148,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"247878406","text":"import pandas as pd\nimport seaborn as sns\nimport matplotlib\nimport matplotlib.pyplot as plt\nfrom os import listdir\nfrom os.path import isfile, join, isdir\nimport argparse\n#Get files from exp directory\nimport numpy as np\nimport matplotlib.ticker as ticker\nfrom matplotlib.ticker import FuncFormatter\n\nfrom matplotlib.ticker import PercentFormatter\n#current_palette_7 = sns.color_palette(\"hls\", 7)\n#sns.set_palette(current_palette_7)\nfrom sklearn.metrics import accuracy_score\n\n\ndef get_inc_mean_fact(input_path,out_path):\n    dirs= [dI for dI in listdir (input_path) if isdir (join (input_path , dI))]\n    datasets=[\"AMAZON_PRODUCTS\",\"NEWS_HEADLINES\",\"STANFORD_TREEBANK\"]\n    r=[1,2,3]\n    fig = plt.figure ()\n\n    for i in range (len (dirs)):\n        path_dirs_fact = join (input_path , dirs[i])\n        onlyfiles = [f for f in listdir (path_dirs_fact) if isfile (join (path_dirs_fact , f))]\n        means_fact_fact = 0\n        means_fact_opinion = 0\n        means_opinion_opinion = 0\n        meanopinion_fact1 = 0\n        meanopinion_fact2 = 0\n        dfi=pd.DataFrame(columns=[\"fact_fact\",\"sub_sub\",\"sub_fact\"],index=datasets)\n        facts=[]\n        subjectives=[]\n        for j in range (len (onlyfiles)):\n            path_file_fact = join (path_dirs_fact , onlyfiles[j])\n\n            df = pd.read_csv (path_file_fact , sep=\";\")\n\n            clusters1 = df.groupby ([\"Nature1\"])\n\n            clusters = df.groupby ([\"Nature1\" , \"Nature2\" , \"Inc\"]).agg ('count')\n            # clusters= list(clusters)\n\n            print(\"---------------------\")\n            clusters1=(list(clusters1))\n\n\n            fact= clusters1[0][1]\n            subjective = clusters1[1][1]\n            subjectives.append( accuracy_score(subjective[\"Golden\"],subjective[\"Pred1\"]))\n\n\n            facts.append ( accuracy_score(fact[\"Golden\"],fact[\"Pred1\"]))\n\n            res = [list (ele) for ele in clusters.index.values]\n\n            if [\"Fact\" , \"Fact\" , 1] in res:\n                means_fact_fact = (clusters.loc[\"Fact\" , \"Fact\" , 1][\"s1\"] / (\n                            clusters.loc[\"Fact\" , \"Fact\" , 1][\"s1\"] + clusters.loc[\"Fact\" , \"Fact\" , 0][\"s1\"]))\n\n            if [\"Subjective\" , \"Subjective\" , 1] in res:\n                means_opinion_opinion = (clusters.loc[\"Subjective\" , \"Subjective\" , 1][\"s1\"] / (\n                            clusters.loc[\"Subjective\" , \"Subjective\" , 1][\"s1\"] +\n                            clusters.loc[\"Subjective\" , \"Subjective\" , 0][\"s1\"]))\n\n            if [\"Subjective\" , \"Fact\" , 1] in res and [\"Subjective\" , \"Fact\" , 0] in res:\n                meanopinion_fact1 = clusters.loc[\"Subjective\" , \"Fact\" , 1][\"s1\"] / (\n                            clusters.loc[\"Subjective\" , \"Fact\" , 1][\"s1\"] + clusters.loc[\"Subjective\" , \"Fact\" , 0][\n                        \"s1\"])\n\n            else:\n                if [\"Subjective\" , \"Fact\" , 1] in res and [\"Subjective\" , \"Fact\" , 0] not in res:\n                    meanopinion_fact1 = 1\n\n            if [\"Fact\" , \"Subjective\" , 1] in res and [\"Fact\" , \"Subjective\" , 0] in res:\n                meanopinion_fact2 = clusters.loc[\"Fact\" , \"Subjective\" , 1][\"s1\"] / (\n                            clusters.loc[\"Fact\" , \"Subjective\" , 1][\"s1\"] + clusters.loc[\"Fact\" , \"Subjective\" , 0][\n                        \"s1\"])\n\n            else:\n                if [\"Fact\" , \"Subjective\" , 1] in res: meanopinion_fact2 = 1\n\n            means_fact_opinion = ((meanopinion_fact1 + meanopinion_fact2) / 2)\n            #print (means_fact_opinion , means_opinion_opinion , means_fact_fact)\n\n            means_fact_opinion= ((meanopinion_fact1 + meanopinion_fact2)/2)\n            dfi[\"fact_fact\"].iloc[j]= means_fact_fact\n            dfi[\"sub_sub\"].iloc[j]  = means_opinion_opinion\n            dfi[\"sub_fact\"].iloc[j] = means_fact_opinion\n\n        print(\"fact_acc\", facts)\n        print (\"fact_acc_mean\" , sum(facts)/len(facts))\n        print (\"subjective_acc\" , subjectives)\n        print (\"subjectives_acc_mean\" , sum (subjectives) / len (subjectives))\n        ax = fig.add_subplot (1 , 6, i + 1)\n        plt.xticks (fontsize=8)\n        plt.yticks (rotation=45 , fontsize=8)\n        ax.set_title (dirs[i])\n        # From raw value to percentage\n        print(dirs[i])\n        #print(dfi ,\"dfi\")\n        #print(dfi[\"fact_fact\"].mean(), \"fact_fact\")\n        #print (dfi[\"sub_sub\"].mean () , \"sub_sub\")\n        #print (dfi[\"sub_fact\"].mean () , \"sub_fact\")\n\n        totals = [h + z + k for h , z , k in zip (dfi['fact_fact'] , dfi['sub_sub'] , dfi['sub_fact'])]\n        #totals= [(lambda x: 1 if x==0 else x)(x) for x in totals]\n        greenBars = [h / z * 100 for h , z in zip (dfi['fact_fact'] , totals)]\n        orangeBars = [h / z * 100 for h , z in zip (dfi['sub_sub'] , totals)]\n        blueBars = [h / z * 100 for h , z in zip (dfi['sub_fact'] , totals)]\n        \"\"\"\n        greenBars=dfi[\"fact_fact\"]\n        orangeBars=dfi[\"sub_sub\"]\n        blueBars= dfi[\"sub_fact\"]\"\"\"\n        # plot\n        barWidth = 0.6\n        names = (\"AMAZON\\nREVIEWS\",\"NEWS\\nHEAD\",\"STS\")\n        # Create green Bars\n        plt.bar (r , greenBars , color='#FFC300' , edgecolor='white' , width=barWidth)\n        # Create orange Bars\n        plt.bar (r , orangeBars , bottom=greenBars , color='#FF5733' , edgecolor='white' , width=barWidth)\n        # Create blue Bars\n        plt.bar (r , blueBars , bottom=[k + z for k , z in zip (greenBars , orangeBars)] , color='#C70039' ,\n                 edgecolor='white' , width=barWidth)\n        plt.xticks (r , names)\n        ax.set_ylabel (\"% of inconsistencies\")\n\n        # Custom x axis\n\n\n        #bars = dfi.plot (kind='bar' , ax=ax , width=0.9 , linewidth=1.5 , edgecolor=\"black\" , alpha=0.8)\n        #ax.get_legend ().remove ()\n        #ax.set_ylabel (\"Mean inconsistency rate\" , fontsize=10)\n    plt.figlegend ([\"fact_fact\" , \"sub_sub\",\"sub_fact\"] , loc='upper center' , ncol=3 , labelspacing=0.)\n\n\n\n    plt.show ()\nif __name__ == '__main__':\n    ##model args\n    parser = argparse.ArgumentParser (description='Syntactically Controlled Paraphrase Transformer')\n\n\n    parser.add_argument ('--input_path' , type=str , default=\"D:/Users/wissam/Documents/These/these/papers_material/VLDB_submission/public_folder/experiments/logs/intool_inc_polar_fact\" ,\n                         help='input path of logs')\n\n    parser.add_argument ('--out_path' , type=str , default='./data/sentiment_dataset_sentic.csv' ,help='output of plots')\n\n    args = parser.parse_args ()\n    get_inc_mean_fact(args.input_path, args.out_path)\n","sub_path":"experiments/scripts/exp_inc_fact_opinion.py","file_name":"exp_inc_fact_opinion.py","file_ext":"py","file_size_in_byte":6475,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"225766008","text":"# Code copied and then modified from https://github.com/TeamHG-Memex/scrapy-rotating-proxies/blob/master/rotating_proxies/expire.py\n\n# Copyright (c) scrapy-rotating-proxies developers.\n# Copyright (C) 2013 by Kevin Glasson <kevinglasson+scrapyscylla@gmail.com> (Modifications)\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 the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in\n# all copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n# THE SOFTWARE.\n\nimport logging\nimport math\nimport random\nimport time\n\nimport attr\n\nlogger = logging.getLogger('scrapy-scylla-proxies.proxies')\n\n\nclass Proxies(object):\n    \"\"\"\n    Expiring proxies container.\n    A proxy can be in 3 states:\n    * good;\n    * dead;\n    * unchecked.\n    Initially, all proxies are in the 'unchecked' state.\n    When a request using a proxy is successful, this proxy moves to the 'good'\n    state. When a request using a proxy fails, proxy moves to the 'dead' state.\n    For crawling only 'good' and 'unchecked' proxies are used.\n    'Dead' proxies move to 'unchecked' after a timeout (they are called\n    'reanimated'). This timeout increases exponentially after each\n    unsuccessful attempt to use a proxy. Once a proxy has failed 3 times, it will be removed and replaced with a new proxy from Scylla.\n    \"\"\"\n\n    def __init__(self, proxy_list, backoff=None):\n        self.proxies = {url: ProxyState() for url in proxy_list}\n        self.unchecked = set(self.proxies.keys())\n        self.good = set()\n        self.dead = set()\n\n        if backoff is None:\n            backoff = exp_backoff_full_jitter\n        self.backoff = backoff\n\n    def get_random(self):\n        \"\"\" Return a random available proxy (either good or unchecked) \"\"\"\n\n        # Join the sets and create a list\n        available = list(self.unchecked | self.good)\n        if not available:\n            return None\n        return random.choice(available)\n\n    def get_proxy(self, proxy_address):\n        \"\"\"\n        Return complete proxy name associated with a hostport of a given\n        ``proxy_address``. If ``proxy_address`` is unkonwn or empty,\n        return None.\n        \"\"\"\n        if not proxy_address:\n            return None\n        return\n\n    def mark_dead(self, proxy, _time=None):\n        \"\"\" Mark a proxy as dead \"\"\"\n        if proxy not in self.proxies:\n            logger.warn(\"Proxy <%s> was not found in proxies list\" % proxy)\n            return\n\n        if proxy in self.good:\n            logger.debug(\"GOOD proxy became DEAD: <%s>\" % proxy)\n        else:\n            logger.debug(\"Proxy <%s> is DEAD\" % proxy)\n\n        self.unchecked.discard(proxy)\n        self.good.discard(proxy)\n        self.dead.add(proxy)\n\n        now = _time or time.time()\n        state = self.proxies[proxy]\n        state.backoff_time = self.backoff(state.failed_attempts)\n        state.next_check = now + state.backoff_time\n        state.failed_attempts += 1\n\n    def mark_good(self, proxy):\n        \"\"\" Mark a proxy as good \"\"\"\n        if proxy not in self.proxies:\n            logger.warn(\"Proxy <%s> was not found in proxies list\" % proxy)\n            return\n\n        if proxy not in self.good:\n            logger.debug(\"Proxy <%s> is GOOD\" % proxy)\n\n        self.unchecked.discard(proxy)\n        self.dead.discard(proxy)\n        self.good.add(proxy)\n        self.proxies[proxy].failed_attempts = 0\n\n    def reanimate(self, _time=None):\n        \"\"\"Move dead proxies to unchecked if a backoff timeout passes.\n        \"\"\"\n        n_reanimated = 0\n        now = _time or time.time()\n        for proxy in list(self.dead):\n            state = self.proxies[proxy]\n            assert state.next_check is not None\n            if state.next_check <= now:\n                self.dead.remove(proxy)\n                self.unchecked.add(proxy)\n                n_reanimated += 1\n        return n_reanimated\n\n    def reset(self):\n        \"\"\"Mark all dead proxies as unchecked.\n        \"\"\"\n        for proxy in list(self.dead):\n            self.dead.remove(proxy)\n            self.unchecked.add(proxy)\n\n    @property\n    def mean_backoff_time(self):\n        \"\"\"Calculate the mean backoff time of the current proxies.\n        \"\"\"\n        if not self.dead:\n            return 0\n        total_backoff = sum(self.proxies[p].backoff_time for p in self.dead)\n        return float(total_backoff) / len(self.dead)\n\n    @property\n    def reanimated(self):\n        return [p for p in self.unchecked if self.proxies[p].failed_attempts]\n\n    def __repr__(self):\n        n_reanimated = len(self.reanimated)\n        return \"Proxies(good: {}, dead: {}, unchecked: {}, reanimated: {}, \" \\\n               \"mean backoff time: {}s)\".format(\n                   len(self.good), len(self.dead),\n                   len(self.unchecked) - n_reanimated, n_reanimated,\n                   int(self.mean_backoff_time),\n               )\n\n\n@attr.s\nclass ProxyState(object):\n    \"\"\"Represent the state of a proxy.\n    \"\"\"\n\n    failed_attempts = attr.ib(default=0)\n    next_check = attr.ib(default=None)\n    backoff_time = attr.ib(default=None)  # for debugging\n\n\ndef exp_backoff(attempt, cap=3600, base=300):\n    \"\"\" Exponential backoff time.\n    \"\"\"\n    # this is a numerically stable version of\n    # min(cap, base * 2 ** attempt)\n    max_attempts = math.log(cap / base, 2)\n    if attempt <= max_attempts:\n        return base * 2 ** attempt\n    return cap\n\n\ndef exp_backoff_full_jitter(*args, **kwargs):\n    \"\"\" Exponential backoff time with Full Jitter.\n    \"\"\"\n    return random.uniform(0, exp_backoff(*args, **kwargs))\n","sub_path":"scrapy_scylla_proxies/future_potential_integration_code/proxies.py","file_name":"proxies.py","file_ext":"py","file_size_in_byte":6394,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"450861365","text":"\"\"\"Support for AVM FRITZ!Box update platform.\"\"\"\nfrom __future__ import annotations\n\nimport logging\nfrom typing import Any\n\nfrom homeassistant.components.update import UpdateEntity, UpdateEntityFeature\nfrom homeassistant.config_entries import ConfigEntry\nfrom homeassistant.core import HomeAssistant\nfrom homeassistant.helpers.entity_platform import AddEntitiesCallback\n\nfrom .common import AvmWrapper, FritzBoxBaseEntity\nfrom .const import DOMAIN\n\n_LOGGER = logging.getLogger(__name__)\n\n\nasync def async_setup_entry(\n    hass: HomeAssistant, entry: ConfigEntry, async_add_entities: AddEntitiesCallback\n) -> None:\n    \"\"\"Set up AVM FRITZ!Box update entities.\"\"\"\n    _LOGGER.debug(\"Setting up AVM FRITZ!Box update entities\")\n    avm_wrapper: AvmWrapper = hass.data[DOMAIN][entry.entry_id]\n\n    entities = [FritzBoxUpdateEntity(avm_wrapper, entry.title)]\n\n    async_add_entities(entities)\n\n\nclass FritzBoxUpdateEntity(FritzBoxBaseEntity, UpdateEntity):\n    \"\"\"Mixin for update entity specific attributes.\"\"\"\n\n    _attr_supported_features = UpdateEntityFeature.INSTALL\n    _attr_title = \"FRITZ!OS\"\n\n    def __init__(\n        self,\n        avm_wrapper: AvmWrapper,\n        device_friendly_name: str,\n    ) -> None:\n        \"\"\"Init FRITZ!Box connectivity class.\"\"\"\n        self._attr_name = f\"{device_friendly_name} FRITZ!OS\"\n        self._attr_unique_id = f\"{avm_wrapper.unique_id}-update\"\n        super().__init__(avm_wrapper, device_friendly_name)\n\n    @property\n    def installed_version(self) -> str | None:\n        \"\"\"Version currently in use.\"\"\"\n        return self._avm_wrapper.current_firmware\n\n    @property\n    def latest_version(self) -> str | None:\n        \"\"\"Latest version available for install.\"\"\"\n        if self._avm_wrapper.update_available:\n            return self._avm_wrapper.latest_firmware\n        return self._avm_wrapper.current_firmware\n\n    @property\n    def release_url(self) -> str | None:\n        \"\"\"URL to the full release notes of the latest version available.\"\"\"\n        return self._avm_wrapper.release_url\n\n    async def async_install(\n        self, version: str | None, backup: bool, **kwargs: Any\n    ) -> None:\n        \"\"\"Install an update.\"\"\"\n        await self._avm_wrapper.async_trigger_firmware_update()\n","sub_path":"homeassistant/components/fritz/update.py","file_name":"update.py","file_ext":"py","file_size_in_byte":2242,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"395424519","text":"# Definition for singly-linked list.\n# class ListNode:\n#     def __init__(self, val=0, next=None):\n#         self.val = val\n#         self.next = next\nclass Solution:\n    def insertionSortList(self, head: Optional[ListNode]) -> Optional[ListNode]:\n        dummy = ListNode()\n        dummy.next = head\n\n        curr = head\n        tail = dummy\n        while curr:\n            pre = dummy\n            pos = dummy.next\n            while pos != curr:\n                if pos.val <= curr.val:\n                    pre = pos\n                    pos = pos.next\n                else:\n                    break\n            if pos == curr:\n                tail = curr\n                curr = curr.next\n            else:\n                tail.next = curr.next\n                pre.next = curr\n                curr.next = pos\n                curr = tail.next\n\n        return dummy.next","sub_path":"147_InsertionSortList.py","file_name":"147_InsertionSortList.py","file_ext":"py","file_size_in_byte":868,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"219258170","text":"import tomodachi\n\n\n@tomodachi.service\nclass Service(tomodachi.Service):\n    name = 'example'\n    options = {\n        'http': {\n            'port': 8080\n        }\n    }\n\n    @tomodachi.http('GET', r'/')\n    async def index_endpoint(self, request):\n        return 'friends forever!'\n\n    @tomodachi.http('GET', r'/health')\n    async def health_check(self, request):\n        return 'healthy'\n","sub_path":"examples/docker_example/http_service/app/service.py","file_name":"service.py","file_ext":"py","file_size_in_byte":389,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"354774599","text":"from django.shortcuts import render, get_object_or_404, redirect\nfrom django.contrib import messages\nfrom django.contrib.auth import logout\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth.models import User\nfrom accounts.models import Volunteer, Visitor\nfrom initiatives.models import Initiative\nfrom accounts.forms import VisitorRegistrationForm, ProfileForm\nimport xlrd\nimport datetime\n\n@login_required\ndef volunteer_login_validation(request):\n    email = request.user.email\n    print(email)\n    if Volunteer.objects.filter(user = request.user).exists():\n        messages.success(request, 'Welcome, ' + request.user.volunteer.name + '!')\n        return redirect('internal_index')\n    elif Volunteer.objects.filter(bits_email = email).exists():\n        volunteer = Volunteer.objects.get(bits_email = email)\n        volunteer.user = request.user\n        volunteer.save()\n        messages.success(request, 'Welcome, ' + volunteer.name + '!')\n        return redirect('internal_index')\n    elif request.user.is_superuser:\n        messages.success(request, 'Welcome, Administrator!')\n        return redirect('internal_index')\n    else:\n        messages.error(request, 'Your account was not verified as a Nirmaan Volunteer Account. Please check your email.')\n        logout(request)\n        User.objects.get(email = email).delete()\n        return redirect('login')\n\n@login_required\ndef pl_dashboard(request):\n    if Volunteer.objects.filter(user = request.user).exists():       \n        volunteer = Volunteer.objects.filter(user = request.user)[0]\n        if volunteer.is_pl:\n            project = volunteer.project\n\n            messages.success(request, 'Welcome ' + volunteer.name + ', Project Leader of ' + project.name)\n            \n            context = {\n                'volunteers' : Volunteer.objects.filter(project = project).order_by('-is_pl','-year'),\n                'project' : project\n            }\n\n            return render(request, 'initiatives/pl_volunteers.htm', context)\n\n        else:\n            messages.error(request, 'You are not authorized to access that page!')\n            return redirect('internal_index')\n    else:\n        messages.error(request, 'Your account was not verified as a Nirmaan Volunteer Account. Please check your email.')\n        logout(request)\n        return redirect('login')  \n\ndef register_visitor(request):\n    if request.method == \"POST\":\n        form = VisitorRegistrationForm(request.POST)\n        if form.is_valid():\n            form.save()\n\n            messages.success(request, 'Thanks for filling out our guestbook! You can now recieve updates about Nirmaan through email.')\n            return redirect('internal_index')\n        \n    else:\n        form = VisitorRegistrationForm()\n\n    return render(request, 'initiatives/contact_form.htm', {'form' : form})\n\n@login_required\ndef profile(request):\n    if Volunteer.objects.filter(user = request.user).exists():       \n        volunteer = Volunteer.objects.filter(user = request.user)[0]\n        project = volunteer.project\n\n        if request.method == \"POST\":\n            form = ProfileForm(request.POST, request.FILES, instance = volunteer)\n            \n            if form.is_valid():\n                form.save()\n                return redirect('profile')\n\n        else:\n            form = ProfileForm(instance = volunteer)\n\n        context = {\n            'volunteer' : volunteer,\n            'project' : project,\n            'form' : form\n        }\n\n        return render(request, 'initiatives/profile.htm', context)\n\n    else:\n        messages.error(request, 'You are not authorized to access that page!')\n        return redirect('internal_index')\n\n        \n@login_required\ndef update_volunteers(request):\n    \n    if request.user.is_superuser:\n        projects = Initiative.objects.all()\n\n        for project in projects:\n            file = project.volunteers_file\n            wb = xlrd.open_workbook(file.path)\n            sheet = wb.sheet_by_index(0)\n            sheet.cell_value(0, 0)\n\n            rows = sheet.nrows\n            cols = sheet.ncols\n\n            for i in range(1, rows):\n                if sheet.cell_value(i, 0) == \"\" or sheet.cell_value(i, 0) == None:\n                    continue\n                \n                try: \n                    volunteer = Volunteer.objects.get(bits_id = sheet.cell_value(i, 1))\n                except:\n                    volunteer = Volunteer.objects.create(\n                        bits_id = sheet.cell_value(i, 1),\n                    )\n                \n                volunteer.name = str(sheet.cell_value(i, 0)).title()\n                phone = str(sheet.cell_value(i, 2))\n                if phone[-2:] == \".0\":\n                    phone = phone[:-2]\n                volunteer.phone = phone\n                volunteer.bits_email = sheet.cell_value(i, 3)\n\n                volunteer.project = project\n                volunteer.year = (datetime.datetime.now().year - int(str(sheet.cell_value(i, 1))[0:4]) + 1) if datetime.datetime.now().month >= 8 else (datetime.datetime.now().year - int(str(sheet.cell_value(i, 1))[0:4]))\n                volunteer.save()\n\n        return redirect('internal_index')\n           \n    else:\n        messages.error(request, 'You are not authorized to access that page!')\n        return redirect('internal_index')\n\n\n\n\n# Create your views here.\n","sub_path":"accounts/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5369,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"3769934","text":"from Specification import *\nimport Food\n\n\nclass Monster:\n    ################################################## CORE FUNCTIONS ##################################################\n    def __init__(self, app, pos, cell=None):\n        self.app = app\n        self.width = CELL_SIZE - 2\n        self.grid_pos = [pos[0], pos[1]]\n        self.pixel_pos = self.get_current_pixel_pos()\n        self.direction = 'up'\n\n        self.monster_left_image = pygame.image.load(MONSTER_LEFT_IMAGE)\n        self.monster_left_image = pygame.transform.scale(self.monster_left_image, (self.width, self.width))\n        self.monster_right_image = pygame.image.load(MONSTER_RIGHT_IMAGE)\n        self.monster_right_image = pygame.transform.scale(self.monster_right_image, (self.width, self.width))\n        self.monster_up_image = pygame.image.load(MONSTER_UP_IMAGE)\n        self.monster_up_image = pygame.transform.scale(self.monster_up_image, (self.width, self.width))\n        self.monster_down_image = pygame.image.load(MONSTER_DOWN_IMAGE)\n        self.monster_down_image = pygame.transform.scale(self.monster_down_image, (self.width, self.width))\n        self.black_background = pygame.image.load(BLACK_BG)\n        self.black_background = pygame.transform.scale(self.black_background, (CELL_SIZE, CELL_SIZE))\n        self.initial_cell = cell\n        self.cell = cell\n\n\n    def appear(self):\n        \"\"\"\n        Make the Monster appear on the screen.\n        \"\"\"\n        self.draw()\n\n\n    def move(self, new_grid_pos):\n        \"\"\"\n        Move the Monster to the new position (x, y) on the grid map.\n\n        :param new_grid_pos: new position (x, y) on the grid map\n        \"\"\"\n        self.update(new_grid_pos)\n        self.draw()\n\n\n    def get_around_cells_of_initial_cell(self, graph_map):\n        return graph_map[self.initial_cell]\n\n\n    def get_around_cells(self, graph_map):\n        return graph_map[self.cell]\n\n    ####################################################################################################################\n\n    def update_direction(self, new_grid_pos):\n        \"\"\"\n        Update the Monster's direction based on the `new_grid_pos`.\n        \n        :param new_grid_pos: new position (x, y) on the grid map\n        \"\"\"\n        if new_grid_pos[0] - self.grid_pos[0] == 1:\n            self.direction = 'right'\n        elif new_grid_pos[0] - self.grid_pos[0] == -1:\n            self.direction = 'left'\n        elif new_grid_pos[1] - self.grid_pos[1] == 1:\n            self.direction = 'down'\n        elif new_grid_pos[1] - self.grid_pos[1] == -1:\n            self.direction = 'up'\n\n\n    def update(self, new_grid_pos):\n        \"\"\"\n        Update the Monster's grid position\n\n        :param new_grid_pos: new position (x, y) on the grid map\n        \"\"\"\n        pygame.display.update(self.app.screen.blit(self.black_background, (self.pixel_pos[0], self.pixel_pos[1])))\n        self.update_direction(new_grid_pos)\n        self.grid_pos = new_grid_pos\n        self.pixel_pos = self.get_current_pixel_pos()\n\n\n    def get_current_pixel_pos(self):\n        \"\"\"\n        Get the current pixel position via the current grid position.\n\n        :return: the pixel position [x, y]\n        \"\"\"\n        return [self.grid_pos[0] * CELL_SIZE + CELL_SIZE // 2 - self.width // 2 + MAP_POS_X,\n                self.grid_pos[1] * CELL_SIZE + CELL_SIZE // 2 - self.width // 2 + MAP_POS_Y]\n\n\n    def draw(self):\n        \"\"\"\n        Draw the Monster onto the screen.\n        \"\"\"\n        if self.direction == 'up':\n            pygame.display.update(self.app.screen.blit(self.monster_up_image, (self.pixel_pos[0], self.pixel_pos[1])))\n        elif self.direction == 'down':\n            pygame.display.update(self.app.screen.blit(self.monster_down_image, (self.pixel_pos[0], self.pixel_pos[1])))\n        elif self.direction == 'left':\n            pygame.display.update(self.app.screen.blit(self.monster_left_image, (self.pixel_pos[0], self.pixel_pos[1])))\n        elif self.direction == 'right':\n            pygame.display.update(self.app.screen.blit(self.monster_right_image, (self.pixel_pos[0], self.pixel_pos[1])))\n","sub_path":"Source/Monster.py","file_name":"Monster.py","file_ext":"py","file_size_in_byte":4095,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"464441636","text":"import re\nfrom django.forms.extras.widgets import SelectDateWidget\nfrom django.forms.widgets import Widget, Select, MultiWidget\nfrom django.utils.safestring import mark_safe\n\n__all__ = ('SelectTimeWidget', 'SplitSelectDateTimeWidget')\n\n# Attempt to match many time formats:\n# Example: \"12:34:56 P.M.\"\tmatches:\n# ('12', '34', ':56', '56', 'P.M.', 'P', '.', 'M', '.')\n# ('12', '34', ':56', '56', 'P.M.')\n# Note that the colon \":\" before seconds is optional, but only if seconds are omitted\ntime_pattern = r'(\\d\\d?):(\\d\\d)(:(\\d\\d))? *([aApP]\\.?[mM]\\.?)?$'\n\nRE_TIME = re.compile(time_pattern)\n# The following are just more readable ways to access re.matched groups:\nHOURS = 0\nMINUTES = 1\nSECONDS = 3\nMERIDIEM = 4\n\nclass SelectTimeWidget(Widget):\n\t\"\"\"\n\thttp://djangosnippets.org/snippets/1202/\n\tmodified: took out seconds\n\t\n\t\n\tA Widget that splits time input into <select> elements.\n\tAllows form to show as 24hr: <hour>:<minute>:<second>, (default)\n\tor as 12hr: <hour>:<minute>:<second> <am|pm> \n\t\n\tAlso allows user-defined increments for minutes/seconds\n\t\"\"\"\n\thour_field = '%s_hour'\n\tminute_field = '%s_minute'\n\tsecond_field = '%s_second' \n\tmeridiem_field = '%s_meridiem'\n\ttwelve_hr = False # Default to 24hr.\n\t\n\tdef __init__(self, attrs=None, hour_step=None):\n\t\t\"\"\"\n\t\thour_step, minute_step, second_step are optional step values for\n\t\tfor the range of values for the associated select element\n\t\ttwelve_hr: If True, forces the output to be in 12-hr format (rather than 24-hr)\n\t\t\"\"\"\n\t\tself.attrs = attrs or {}\n\t\tself.hours = range(1, 25)\n\n\tdef render(self, name, value, attrs=None):\n\t\ttry: # try to get time values from a datetime.time object (value)\n\t\t\thour_val, minute_val, second_val = value.hour, value.minute, value.second\n\t\t\t\n\t\t\t#Round up hour if minutes past half hour\n\t\t\tif minute_val >= 30:\n\t\t\t\thour_val = hour_val + 1 if hour_val < 23 else 0\n\t\t\t\n\t\texcept AttributeError:\n\t\t\thour_val = minute_val = second_val = 0\n\t\t\tif isinstance(value, basestring):\n\t\t\t\tmatch = RE_TIME.match(value)\n\t\t\t\tif match:\n\t\t\t\t\ttime_groups = match.groups();\n\t\t\t\t\thour_val = int(time_groups[HOURS]) % 24 # force to range(0-24)\n\t\t\t\t\tminute_val = int(time_groups[MINUTES]) \n\t\t\t\t\tif time_groups[SECONDS] is None:\n\t\t\t\t\t\tsecond_val = 0\n\t\t\t\t\telse:\n\t\t\t\t\t\tsecond_val = int(time_groups[SECONDS])\n\t\t\t\t\t\n\t\t\t\t\t# check to see if meridiem was passed in\n\t\t\t\t\tif time_groups[MERIDIEM] is not None:\n\t\t\t\t\t\tself.meridiem_val = time_groups[MERIDIEM]\n\t\t\t\t\telse: # otherwise, set the meridiem based on the time\n\t\t\t\t\t\tif self.twelve_hr:\n\t\t\t\t\t\t\tif hour_val >= 12:\n\t\t\t\t\t\t\t\tself.meridiem_val = 'p.m.'\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tself.meridiem_val = 'a.m.'\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tself.meridiem_val = None\n\t\t\t\n\t\toutput = []\n\t\tif 'id' in self.attrs:\n\t\t\tid_ = self.attrs['id']\n\t\telse:\n\t\t\tid_ = 'id_%s' % name\n\n\t\t# For times to get displayed correctly, the values MUST be converted to unicode\n\t\t# When Select builds a list of options, it checks against Unicode values\n\t\thour_val = u\"%.2d\" % hour_val\n\t\t\n\t\thour_choices = [(None, '--'),]\n\t\tfor hour in self.hours:\n\t\t\th = hour % 12 if hour != 12 and hour !=24 else 12\n\t\t\tm = 'am' if hour < 12 or hour == 24 else 'pm'\n\t\t\thour_choices.append((\"%.2d\" % hour, \"%d %s\" % (h, m)))\n\t\t\n\t\tlocal_attrs = self.build_attrs(id=self.hour_field % id_)\n\t\t\n\t\tselect_html = Select(choices=hour_choices).render(self.hour_field % name, hour_val, local_attrs)\n\t\toutput.append(select_html)\n\n\t\treturn mark_safe(u'\\n'.join(output))\n\n\tdef id_for_label(self, id_):\n\t\treturn '%s_hour' % id_\n\tid_for_label = classmethod(id_for_label)\n\n\tdef value_from_datadict(self, data, files, name):\n\t\t# if there's not h:m:s data, assume zero:\n\t\th = data.get(self.hour_field % name, 0) # hour\n\t\tm = '00' # minute \n\t\ts = '00' # seconds\n\n\t\tmeridiem = data.get(self.meridiem_field % name, None)\n\n\t\t#NOTE: if meridiem is None, assume 24-hr\n\t\tif meridiem is not None:\n\t\t\tif meridiem.lower().startswith('p') and int(h) != 12:\n\t\t\t\th = (int(h)+12)%24 \n\t\t\telif meridiem.lower().startswith('a') and int(h) == 12:\n\t\t\t\th = 0\n\t\t\n\t\tif not h.isdigit(): return None\n\t\t\n\t\tif (int(h) == 0 or h) and m and s:\n\t\t\treturn '%s:%s:%s' % (h, m, s)\n\n\t\treturn data.get(name, None)\n\n\nfrom django import forms\nfrom django.db import models\nfrom django.template.loader import render_to_string\nfrom django.forms.widgets import Select, MultiWidget, DateInput, TextInput\nfrom time import strftime\n\nclass DateTimeWidget(MultiWidget):\n\t'''\n\thttp://www.copiesofcopies.org/webl/?p=81\n\tand\n\t\n\t'''\n\n\tdef __init__(self, attrs={'date_class':'datepicker','time_class':'timepicker'}, date_format=None, time_format=None):\n\t\tdate_class = attrs['date_class']\n\t\ttime_class = attrs['time_class']\n\t\tdel attrs['date_class']\n\t\tdel attrs['time_class']\n\n\t\ttime_attrs = attrs.copy()\n\t\ttime_attrs['class'] = time_class\n\t\tdate_attrs = attrs.copy()\n\t\tdate_attrs['class'] = date_class\n\n\t\twidgets = (\n\t\t\tDateInput(attrs=date_attrs, format=date_format),\n\t\t\tSelectTimeWidget(),\n\t\t)\n\n\t\tsuper(DateTimeWidget, self).__init__(widgets, attrs)\n\n\tdef decompress(self, value):\n\t\tif value:\n\t\t\td = strftime(\"%Y-%m-%d\", value.timetuple())\n\t\t\thour = strftime(\"%I\", value.timetuple())\n\t\t\tminute = strftime(\"%M\", value.timetuple())\n\t\t\tmeridian = strftime(\"%p\", value.timetuple())\n\t\t\treturn (d, value)\n\t\telse:\n\t\t\treturn (None, None, None, None)\n\n\tdef format_output(self, rendered_widgets):\n\t\t\"\"\"\n\t\tGiven a list of rendered widgets (as strings), it inserts an HTML\n\t\tlinebreak between them.\n\n\t\tReturns a Unicode string representing the HTML for the whole lot.\n\t\t\"\"\"\n\t\treturn \"Date: %s<br/>Time: %s\" % (rendered_widgets[0], rendered_widgets[1])\n\n\t'''\n\tclass Media:\n\t\tcss = {\n\t\t\t}\n\t\tjs = (\n\t\t\t\"js/jqsplitdatetime.js\",\n\t\t\t)\n\t'''\n\n\n","sub_path":"apps/images/widgets.py","file_name":"widgets.py","file_ext":"py","file_size_in_byte":5591,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"504232163","text":"## Gather required information to build document skeleton\nmainMenu = {}\nmainMenu['1'] = \"COMP5361\"\nmainMenu['2'] = \"COMP5461\"\n\ntypeMenu = {}\ntypeMenu['1'] = \"Theory Assignment\"\ntypeMenu['2'] = \"Programming Assignment\"\n\nauthor = \"Jonathan B. Schami - 40050610\"\n\nwhile True:\n\tprint(\"Create assignment selection for which course?\")\n\toptions = mainMenu.keys()\n\tfor entry in options:\n\t\tprint (entry, mainMenu[entry])\n\n\tselection = input(\"Select course:\")\n\tif selection == '1':\n\t\tcourseCode = \"COMP\\\\ 5361\"\n\t\tcourseID = \"COMP5361\"\n\t\tcourseInstructor = \"Grahne\"\n\t\tcourseTime = \"5:45\"\n\t\tbreak\n\telif selection == '2':\n\t\tcourseCode = \"COMP\\\\ 5461\"\n\t\tcourseID = \"COMP5461\"\n\t\tcourseInstructor = \"Goswami\"\n\t\tcourseTime = \"5:45\"\n\t\tbreak\n\telse:\n\t\tprint (\"Selection invalid\")\n\nwhile True:\n\tprint (\"What type of assignment would you like to create?\")\n\toptions = typeMenu.keys()\n\tfor entry in options:\n\t\tprint (entry, typeMenu[entry])\n\n\tselection = input(\"Select Assignment Type: \")\n\tif selection == '1':\n\t\tassignmentNumber = input(\"Assignment number: \")\n\t\tassignmentType = \"TA\"\n\t\tassingmentTitle = \"Theory Assignment\\\\ \\\\#%s\" % assignmentNumber\n\t\tbreak\n\telif selection == '2':\n\t\tassignmentNumber = input(\"Assignment number: \")\n\t\tassignmentType = \"PA\"\n\t\tassingmentTitle = \"Programming Assignment\\\\ \\\\#%s\" % assignmentNumber\n\t\tbreak\n\telse:\n\t\tprint (\"Selection invalid\")\n\nassignmentDueMonth = input(\"Assignment due month: \")\nassignmentDueDay = input(\"assignment due day: \")\nassignmentDueYear = input(\"Assignment due year: \")\nassignmentDueDate = \"%s\\\\ %s,\\\\ %s\" % (assignmentDueMonth, assignmentDueDay, assignmentDueYear)\n\nnumberOfProblems = input(\"Number of questions in assignment: \")\n\nnewFileName = \"%s_%s%s.tex\" % (courseID, assignmentType, assignmentNumber)\n\nwith open(newFileName, \"w\") as f1:\n\twith open(\"C:\\\\Scripts\\\\part1.tex\") as p1:\n\t\tfor line in p1:\n\t\t\tf1.write(line)\n\n\t\tf1.write(\"\\n\")\n\t\tf1.write(\"\\\\newcommand{\\\\hmwkTitle}{%s}\\n\" % assingmentTitle)\n\t\tf1.write(\"\\\\newcommand{\\\\hmwkDueDate}{%s}\\n\" % assignmentDueDate)\n\t\tf1.write(\"\\\\newcommand{\\\\hmwkClass}{%s}\\n\" % courseCode)\n\t\tf1.write(\"\\\\newcommand{\\\\hmwkClassTime}{%s}\\n\" % courseTime)\n\t\tf1.write(\"\\\\newcommand{\\\\hmwkClassInstructor}{%s}\\n\" % courseInstructor)\n\t\tf1.write(\"\\\\newcommand{\\\\hmwkAuthorName}{%s}\\n\" % author)\n\n\t\tp1.close()\n\n\twith open(\"C:\\\\Scripts\\\\part2.tex\") as p2:\n\t\tfor line in p2:\n\t\t\tf1.write(line)\n\t\tp2.close()\n\n\tfor x in range(0,int(numberOfProblems)):\n\t\tnewProblemName = \"%s_%s%s_Q%d.tex\" % (courseID, assignmentType, assignmentNumber,x+1)\n\t\twith open(newProblemName, \"w\") as question:\n\t\t\tquestion.write(\"\\\\begin{homeworkProblem}\\n\\n\")\n\t\t\tquestion.write(\"\\t \\\\lipsum \\n\")\n\t\t\tquestion.write(\"\\\\end{homeworkProblem}\")\n\t\t\tquestion.close()\n\t\tf1.write(\"\\n \\\\input{%s}\" %newProblemName)\n\n\tf1.write(\"\\n\\\\end{document}\")\n\tf1.close()\n\n\n\n","sub_path":"CreateTexAssignment/CreateTexAssignment.py","file_name":"CreateTexAssignment.py","file_ext":"py","file_size_in_byte":2792,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"159354486","text":"\"\"\"\nAuthor: Huynh Van Thong\nDepartment of AI Convergence, Chonnam Natl. Univ.\n\"\"\"\nimport argparse\nimport glob\nimport os.path\nimport pathlib\nimport shutil\nimport sys\n\nimport torch\nfrom pytorch_lightning.callbacks import ModelCheckpoint\nfrom torchvision import transforms\nfrom torch.utils.data import DataLoader\nfrom utils import EEVdataset, ToTensor, eev_collatefn\nimport pytorch_lightning as pl\nfrom models import EEVModel\nfrom pytorch_lightning.loggers import TensorBoardLogger\n\n\ndef get_dataloader(emotion_index=-1, feature='resnet'):\n    loaders = {}\n    for split in ['train', 'val', 'test']:\n        current_split = EEVdataset(root_path='/mnt/sXProject/EvokedExpression/', split=split,\n                                   feature=feature, emotion_index=emotion_index,\n                                   transforms=transforms.Compose([ToTensor()]))\n        shuffle = (split == 'train')\n        loaders[split] = DataLoader(current_split, batch_size=1, shuffle=shuffle, num_workers=20,  # pin_memory=True,\n                                    prefetch_factor=2, collate_fn=None)\n    #     for b in loaders[split]:\n    #         # print(b['effb0'].shape, b['resnet'].shape, b['audio'].shape, b['mask'].shape)\n    #         tmp = b['audio']\n    # #         # if tmp.shape[1] == 1:\n    # #         #     print(split, ' ', b['file_id'])\n    # # #\n    # sys.exit(0)\n    return loaders\n\n\ndef copyfiles(source_dir, dest_dir, ext='*.py'):\n    files = glob.iglob(os.path.join(source_dir, ext))\n    for file in files:\n        if os.path.isfile(file):\n            shutil.copy2(file, dest_dir)\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser(description='Evoked Expression')\n    parser.add_argument('--dir', type=str, default='./trial', help=\"Training directory\")\n    parser.add_argument('--lr_init', type=float, default=1e-3, help=\"Initial learning rate\")\n    parser.add_argument('--batch_size', type=int, default=32, help=\"Batch size\")\n    parser.add_argument('--seed', type=int, default=1, help=\"Random seed\")\n    parser.add_argument('--epoch', type=int, default=5, help=\"Number of epochs\")\n    parser.add_argument('--opt', type=str, default='sgd', help=\"Optimizer\")\n    parser.add_argument('--feature', type=str, default='resnet', help=\"Feature type (audio, resnet, effb0\")\n    parser.add_argument('--emotion', type=int, default=0, help=\"Emotion index to be learned (0-14) or all (-1)\")\n\n    args = parser.parse_args()\n\n    if args.feature not in ['resnet', 'audio', 'effb0']:\n        raise ValueError('Do not support {} at this time.'.format(args.feature))\n    if args.emotion not in range(-1, 15):\n        raise ValueError('Do not support emotion {} at this time.'.format(args.emotion))\n\n    tcn_in = {'resnet': 2048, 'audio': 2048, 'effb0': 1280}\n    if args.emotion == -1:\n        num_outputs = 15\n        emotion_index = -1\n    else:\n        num_outputs = 1\n        emotion_index = args.emotion\n\n    pl.seed_everything(args.seed)\n\n    emotions = ['amusement', 'anger', 'awe', 'concentration', 'confusion', 'contempt', 'contentment', 'disappointment',\n                'doubt', 'elation', 'interest', 'pain', 'sadness', 'surprise', 'triumph']\n\n    if num_outputs == 15:\n        save_dir = args.dir  # os.path.join(args.dir, 'emo_{}'.format(emotions[emo_ind]))\n        pathlib.Path(save_dir).mkdir(exist_ok=True, parents=True)\n        logger = TensorBoardLogger(save_dir, name='{}_emo'.format('full'), version=args.feature)\n        copyfiles('./', save_dir, ext='*.txt')\n        copyfiles('./', save_dir, ext='*.sh')\n        copyfiles('./', save_dir, ext='*.py')\n\n        if args.feature == 'effb0':\n            tcn_channels = (512, )\n        elif args.feature == 'audio':\n            tcn_channels = (512, 512, )\n        else:\n            tcn_channels = (128, )\n\n        model = EEVModel(num_outputs=15, tcn_in=tcn_in[args.feature] + 0, tcn_channels=tcn_channels, tcn_kernel_size=3,\n                         dropout=0.3, mtloss=False,\n                         opt=args.opt, lr=args.lr_init, use_norm=True, features_dropout=0., temporal_size=-1,\n                         num_dilations=4, features=args.feature, emotion_index=-1, warmup_steps=200,\n                         accum_grad=args.batch_size)\n        fast_dev_run = False\n        loaders = get_dataloader(emotion_index=-1, feature=args.feature)\n        checkpoint_callback = ModelCheckpoint(monitor='val_loss', mode=\"min\", save_top_k=1, save_last=True)\n        trainer = pl.Trainer(gpus=1, accumulate_grad_batches=args.batch_size, max_epochs=args.epoch,\n                             fast_dev_run=fast_dev_run,\n                             deterministic=True, callbacks=checkpoint_callback, num_sanity_val_steps=0,\n                             progress_bar_refresh_rate=0, logger=logger)\n        trainer.fit(model, loaders['train'], loaders['val'])\n        print('Best model scores: ', checkpoint_callback.best_model_score)\n        if not fast_dev_run:\n            ckpt_path = None  # checkpoint_callback.best_model_path\n            trainer.test(test_dataloaders=loaders['test'], ckpt_path=ckpt_path)\n","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5068,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"338299380","text":"import requests\nimport os\nimport io\nimport json\nfrom multiprocessing import Pool\nimport pandas as pd\nfrom Bio import Phylo\nimport itertools\n\n\nOUTPUT_DIR = \"./erv_algn/\"\nserver = \"https://rest.ensembl.org\"\n\n\ndef download_alignments(chr_table):\n\n    session = requests.Session()\n\n    for ridx, row in chr_table.iterrows():\n\n        reg_desc = \"%s:%d-%d\" % (row['chr'], row['start'], row['end'])\n        print(reg_desc)\n        id_ext = \"/alignment/region/homo_sapiens/\" + reg_desc\n\n        r = session.get(server + id_ext,\n                        headers={ \"Content-Type\" : \"application/json\"})\n\n        if not r.ok:\n            print(r.content)\n        else:\n            decoded = r.json()[0]\n            tr = Phylo.read(io.StringIO(decoded['tree']), \"newick\")\n            species = list(tr.get_terminals())\n\n            if len(species) > 10:\n                print(\"> 10\")\n                with open(os.path.join(OUTPUT_DIR, reg_desc + \".json\"), 'w', encoding='utf-8') as f:\n                    json.dump(decoded, f, ensure_ascii=False, indent=4)\n            else:\n                print(\" < 10\")\n\n\nif __name__ == '__main__':\n\n    ann_table = pd.read_csv(\"Hsap38.txt\", sep='\\t')\n    ann_table = ann_table.loc[(ann_table['N_cnt'] < 1) & (ann_table['AA_length'] > 100), ]\n\n    exclude = ['1', '2', '3', '4']\n\n    chrs = [ann_table.loc[ann_table['chr'] == c, ]\n            for c in list(set(ann_table['chr'])) if c not in exclude]\n\n    t_pool = Pool(20)\n    inference_results = t_pool.map(download_alignments, chrs)\n\n    t_pool.close()\n    t_pool.join()\n\n","sub_path":"eve_analysis/download_human_erv_alignments.py","file_name":"download_human_erv_alignments.py","file_ext":"py","file_size_in_byte":1549,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"492524865","text":"from swagger_server.models.array_property_model import ArrayPropertyModel\n\nfrom swagger_server.models.boolean_property_model import BooleanPropertyModel\nfrom swagger_server.models.number_property_model import NumberPropertyModel\nfrom swagger_server.models.string_list_property_model import StringListPropertyModel\nfrom swagger_server.models.string_property_model import StringPropertyModel\n\n\nclass PropertyHelp(object):\n    \n    @classmethod\n    def map_to_property_type(cls, _type, value=None):\n        \n        if _type == \"array\":\n            if len(value) > 0:\n                if type(value[0]) == str:\n                    return StringListPropertyModel\n            return ArrayPropertyModel\n        elif _type == \"string\":\n            return StringPropertyModel\n        elif _type == \"boolean\":\n            return BooleanPropertyModel\n        elif _type == \"number\":\n            return NumberPropertyModel\n    \n    @classmethod\n    def nested_properties_from_dict(cls, properties_list_of_dicts, set_on_model, set_to_attr):\n        properties = []\n        for p in properties_list_of_dicts:\n            property_type = PropertyHelp.map_to_property_type(p['_type'])\n            property = property_type.from_dict(p)\n            if type(property) is ArrayPropertyModel:\n                cls.nested_properties_from_dict(p['value'], property, 'value')\n            properties.append(property)\n        setattr(set_on_model, set_to_attr, properties)\n","sub_path":"kms_api/swagger_server/models/db_models/property_help.py","file_name":"property_help.py","file_ext":"py","file_size_in_byte":1446,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"547885705","text":"import glob\n\nimport requests\nimport re\nfrom bs4 import BeautifulSoup\nimport time\nfrom multiprocessing import Pool\nimport pandas as pd\n\noutput_columns = 'topic_id', 'topic', 'T/F', 'speecher', 'id', 'reference'\n\n# topic_id : 각 분야\ndef crawl(topic_id):\n    # flag 가 True 라면 최신팩트 url 형성\n    url = 'http://factcheck.snu.ac.kr/v2/facts?page=' + str(1) + '&topic_id=' + str(topic_id)\n\n    # 형성된 url 의 html 가져오기\n    res = requests.get(url)\n    soup = BeautifulSoup(res.content, 'html.parser')\n\n    # 각 분야의 주제에 해당하는 진위여부를 저장할 list\n    results = []\n\n    # 만들어진 url 과 html 이 제대로 만들어졌다면 계속 다음 page 로 가기\n    while url and res:\n\n        html = res.text\n\n        # script 에서 필요한 내용인 id 와 score 를 위한 정규식을 작성한다.\n        pattern = r'animate_meter\\(\\n +\\{\\n +\\'id\\': (.*?),\\n +\\'score\\': \\{\\n +\\'[a-z|A-Z|0-9|가-힣|(|)]+\\' : (.*?),\\n +\\},\\n +\\'under_debate\\':(.*?)\\n +\\}\\n +\\);'\n        # html 에서 pattern 과 일치하는 내용을 뽑아낸다.\n        matched = re.findall(pattern, html, re.S | re.MULTILINE)\n\n        # 일치하는 내용의 수는 각 페이지에 존재하는 주제의 수와 같다. 이를 이용하여 각 주제의 세부 내용을 뽑아낸다.\n        for i in range(0, len(matched)):\n            id = int(matched[i][0].replace('\\'', '').replace('id', '').strip())\n            scores = matched[i][1].replace('\\'', '').replace('\\n', '').replace(',', '').replace(':', '').split()\n\n            decided = True\n            tf = True\n\n            # score 값이 3이거나 0인 경우 진위여부가 판단되지 않았다 가정하여 decided 인자로 그것을 구분한다.\n            # 그 외의 값인 5, 4인 경우 True 1, 2인 경우 False 로 거짓진실을 변수에 저장한다.\n            for j in range(0, len(scores), 2):\n                currentScore = int(scores[j])\n                if currentScore == 0:\n                    decided = False\n                    break\n                elif currentScore < 3:\n                    tf = False\n                elif currentScore > 3:\n                    tf = True\n                else:\n                    decided = False\n\n            # 진위여부가 판단 된 경우 내용, 말한 이, 출처를 html 에서 selector 를 이용해서 뽑아낸다.\n            if decided:\n                topic = soup.select('#container > div > div.left_article > div > ul > li:nth-of-type(' + str(i + 1) + ') > div > div.fcItem_top.clearfix > div.prg.fcItem_li > p:nth-of-type(2) > a')\n                source = soup.select('#container > div > div.left_article > div > ul > li:nth-of-type(' + str(i + 1) + ') > div > div.fcItem_top.clearfix > div.person.fcItem_li > p')\n                reference = soup.select('#container > div > div.left_article > div > ul > li:nth-of-type(' + str(i + 1) + ') > div > div.fcItem_top.clearfix > div.prg.fcItem_li > a')\n\n                # 하나의 주제에서 발견한 모든 내용을 list 에 저장한다.\n                results.append(crawlCsv(topic_id, tf, id, topic, source, reference))\n\n        # 마지막 page 인 경우 next none 이라는 class 가 존재한다.\n        nextNone = soup.find('a', {'class': 'next none'})\n\n        # 단 하나의 page 인 경우 next 나 next none 이라는 class 둘다 존재 ��지 않는다.\n        tmpNext = soup.find('a', {'class': 'next'})\n\n        # 현재 마지막 page 인 경우 nextNone 이 존재하므로 해당 분야를 끝내도록 한다.\n        if nextNone:\n            break\n\n        # 하나의 page 만 있는 경우 다음 page 가 없으므로 해당 분야를 끝내도록 한다.\n        elif not tmpNext:\n            break\n\n        # 다음 page 가 있는 경우 다음 page 로 가도록 한다.\n        else:\n            next = tmpNext.get('href')\n            url = 'http://factcheck.snu.ac.kr' + next\n            res = requests.get(url)\n            soup = BeautifulSoup(res.content, 'html.parser')\n\n    df = pd.DataFrame(results,columns=['topic_id', 'topic', 'T/F', 'speecher', 'id', 'reference'])\n    df.to_csv(str(topic_id) + '.csv',index=False)\n\n\ndef crawlCsv(topic_id, tf, id, topic, source, reference):\n    if not source:\n        result_source = \"\"\n    else:\n        result_source = source[0].text.strip()\n\n    if not reference:\n        result_reference = \"\"\n    else:\n        result_reference = reference[0].text.strip()\n\n    if not topic:\n        result_topic = \"\"\n    else:\n        result_topic = topic[0].text.strip()\n\n    return [topic_id, result_topic, tf, result_source, id, result_reference]\n\n\nif __name__ == '__main__':\n    start_time = time.time()\n\n    # process 2개를 사용하여 crawling 을 진행한다.\n    pool = Pool(processes=2)\n    pool.starmap(crawl, zip(range(1, 8)))\n    pool.close()\n\n    finish_time = time.time()\n\n    filelist = glob.glob(\"*.csv\")\n    # crawling 진행 시간을 print 한다.\n    output = open(str(finish_time - start_time) + '.txt', 'w')\n    output.close()\n\n    OUTPUT = '서울대학교팩트체크_데이터.csv'\n\n    result = pd.concat([pd.read_csv(f) for f in filelist], ignore_index=True, axis=0)\n\n    if result.duplicated(subset=['id']).any():\n        result[result['id'].duplicated()].to_csv('서울대학교팩트체크_중복.csv')\n\n        result.drop_duplicates(subset='id',keep='first',inplace=True,ignore_index=True)\n \n        result.to_csv(OUTPUT,index=False,encoding='utf-8')\n\n    else:\n        result.to_csv(OUTPUT, index=False, encoding='utf-8')","sub_path":"snufactcheck/crawl.py","file_name":"crawl.py","file_ext":"py","file_size_in_byte":5551,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"48778738","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jun  6 12:04:19 2019\n\n@author: simon\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as animation\nfrom matplotlib.figure import Figure\nimport matplotlib.gridspec as gridspec\nfrom matplotlib.backends.backend_tkagg import FigureCanvasTkAgg\nimport tkinter as tk\nimport time\nfrom gpiozero import LED\nled = LED(17) #Platz 11\n\n\n\ndef button_f_callback():\n    print('!button pushed!')\n    led.on()\n    button_f.config(state = tk.DISABLED)\n    time.sleep(1)\n    led.off()\n    button_f.config(state = tk.NORMAL) \n\nclass FullScreenApp(object):\n    def __init__(self, master, **kwargs):\n        self.master=master\n        pad=3\n        self._geom='200x200+0+0'\n        master.geometry(\"{0}x{1}+0+0\".format(\n            master.winfo_screenwidth()-pad, master.winfo_screenheight()-pad))\n        master.bind('<Escape>',self.toggle_geom)\n    def toggle_geom(self,event):\n        geom=self.master.winfo_geometry()\n        print(geom,self._geom)\n        self.master.geometry(self._geom)\n        self._geom=geom\n\ndef func_ani(i):\n    hy_lim=20\n    hy_lim_min=0\n    glaettung=30\n    pullData = open(\"ReceivedData.txt\",\"r\").read()\n    dataArray = pullData.split('\\n')\n    xar = []\n    yar_height = []\n    yar_height_mean=[]\n    yar_pressure = []\n    yar_xa = []\n    yar_ya = []\n    yar_za = []\n    for eachLine in dataArray:\n        if len(eachLine)>1:\n            x,y1,y2,y3,y4,y5 = eachLine.split(';')\n            xar.append(float(x))\n            yar_height.append(float(y1))\n            yar_pressure.append(float(y2))\n            yar_xa.append(float(y3))\n            yar_ya.append(float(y4))\n            yar_za.append(float(y5))\n\n    ay_lim = [np.min([np.min(yar_xa), np.min(yar_ya), np.min(yar_za)]), np.max([np.max(yar_xa), np.max(yar_ya), np.max(yar_za)])]\n    #ay_lim = [1,2]\n    \n    #algorithm to filter the height measurements and mean it\n    sum=0\n    count=0\n    beg=0\n    end=glaettung\n    yar_height_mean=yar_height\n    #iterate through all values of the height list\n    for value in yar_height:\n        sum=sum+value\n        count=count+1\n        if(count==glaettung):\n            for i in range(count):\n                yar_height_mean[beg+i]=sum/count\n            beg=end\n            end=beg+glaettung\n            count=0\n            sum=0\n    \n    hy_min=np.min(yar_height_mean)\n    hy_max=np.max(yar_height_mean)\n            \n    #reassign the limit of the height\n    if(hy_min< hy_lim_min):\n        hy_lim_min=hy_min\n    \n            \n    #reassign the limit of the height\n    if(hy_max> hy_lim):\n        hy_lim=hy_max\n    \n    \n    #release para algorithm\n    release_para= False\n    #release parachute if the rocket is higher that 30 m\n    if(hy_max> 30):\n        release_para=True\n        \n    #get the max value of the last 100 values\n    hy_max_last=hy_max=np.max(yar_height_mean[-100:])\n    \n    #check if the current value is 2 meter lower than tha max value of the lest 100 vaulues\n    if((hy_max_last-yar_height_mean[-1])>=2):\n        release_para=True\n        \n    \n        \n    #if the release para variable i true release the parachute by use of the button callback function\n    if(release_para==True):\n        button_f_callback()\n        \n    \n        \n   \n    ax_height.cla()\n    ax_height.set(xlabel='Time', ylabel='Height in m', ylim=[hy_lim_min,hy_lim])\n    ax_height.plot(xar,yar_height_mean)\n    ax_pressure.cla()\n    ax_pressure.set(xlabel='Time', ylabel='Pressure in bar', ylim=[0,10])\n    ax_pressure.plot(xar,yar_pressure)\n    ax_xa.clear()\n    ax_xa.set(xlabel='Time', ylabel='x Acceleration in m/s^2', ylim = ay_lim)\n    ax_xa.plot(xar,yar_xa)\n    ax_ya.clear()\n    ax_ya.set(xlabel='Time', ylabel='y Acceleration in m/s^2', ylim = ay_lim)\n    ax_ya.plot(xar,yar_ya)\n    ax_za.clear()\n    ax_za.set(xlabel='Time', ylabel='z Acceleration in m/s^2', ylim = ay_lim)\n    ax_za.plot(xar,yar_za)\n\n\n\nfig = Figure(facecolor = \"white\")\nG = gridspec.GridSpec(3, 3)\n\n#Höhe\nax_height = fig.add_subplot(G[:, 0])\nax_height.set(xlabel='Time', ylabel='Height in m', ylim=[0,20])\nax_height.grid()\n\n#Druck\nax_pressure = fig.add_subplot(G[:, 1])\nax_pressure.set(xlabel='Time', ylabel='Pressure in bar', ylim=[0,8])\nax_pressure.grid()\n\n#Beschleunigung x\nax_xa = fig.add_subplot(G[0, 2])\nax_xa.set(xlabel='Time', ylabel='x Acceleration in m/s^2')\nax_xa.grid()\n\n#Beschleunigung y\nax_ya = fig.add_subplot(G[1, 2])\nax_ya.set(xlabel='Time', ylabel='y Acceleration in m/s^2')\nax_ya.grid()\n\n#Beschleunigung z\nax_za = fig.add_subplot(G[2, 2])\nax_za.set(xlabel='Time', ylabel='z Acceleration in m/s^2')\nax_za.grid()\n\n\nroot = tk.Tk()\napp=FullScreenApp(root)\nroot.title(\"Basestation\")\n\n#Fallschirm Button\nbutton_f = tk.Button(text='Fallschirm', width=25, command=button_f_callback)\n#button_f.pack(side = tk.BOTTOM, fill = tk.BOTH, expand = 0)\nbutton_f.pack()\n\ncanvas = FigureCanvasTkAgg(fig, master = root)\ncanvas._tkcanvas.pack(side = tk.TOP, fill = tk.BOTH, expand = 1)\n#canvas._tkcanvas.pack()\n\nani = animation.FuncAnimation(fig, func_ani, interval=300)\nplt.show()\n\nroot.mainloop()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"base_station/Receiver_GUI.py","file_name":"Receiver_GUI.py","file_ext":"py","file_size_in_byte":5082,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"147351328","text":"import threading\nimport wx\nimport sys\nimport random\nfrom twitchbot import bot\nfrom time import sleep\n\n\nclass MyFrame(wx.Frame):\n    def __init__(self, parent, title):\n        super(MyFrame, self).__init__(parent, title=title, size=(900, 900))\n\n        self.CreateStatusBar()\n        file_menu = wx.Menu()\n        menu_about = file_menu.Append(wx.ID_ABOUT, 'About', 'A twitch bot developed by twitch.tv/zerg3rr')\n        menu_exit = file_menu.Append(wx.ID_EXIT, 'Exit', 'Terminates the program')\n\n        menu_bar = wx.MenuBar()\n        menu_bar.Append(file_menu, 'File')\n        self.SetMenuBar(menu_bar)\n\n        self.Bind(wx.EVT_MENU, self.on_about, menu_about)\n        self.Bind(wx.EVT_MENU, self.on_exit, menu_exit)\n\n        panel = wx.Panel(self, wx.ID_ANY)\n        sizer = wx.BoxSizer(wx.VERTICAL)\n\n        style1 = wx.TE_MULTILINE|wx.TE_READONLY|wx.HSCROLL\n        log1 = wx.TextCtrl(panel, wx.ID_ANY, size=(900, 600), style=style1)\n        sizer.Add(log1, 1, wx.ALL|wx.ALIGN_LEFT, 5)\n\n        trivia_button = wx.Button(panel, wx.ID_ANY, 'Trivia Game')\n        self.Bind(wx.EVT_BUTTON, self.on_button, trivia_button)\n        sizer.Add(trivia_button, 4, wx.ALL|wx.ALIGN_CENTER, 5)\n\n        panel.SetSizer(sizer)\n        sys.stdout = log1\n\n    def on_button(self, e):\n        t2 = threading.Thread(target=bot.trivia)\n        t2.daemon = True\n        t2.start()\n\n    def on_about(self, e):\n        dlg = wx.MessageDialog(self, 'Testing')\n        dlg.ShowModal()\n        dlg.Destroy()\n\n    def on_exit(self, e):\n        self.Close(True)\n\n\ndef main():\n    app = wx.App(False)\n    frame = MyFrame(parent=None, title='Z3RG\\'s Twitch Bot').Show()\n    app.MainLoop()\n\n\nmain()\n","sub_path":"gui.py","file_name":"gui.py","file_ext":"py","file_size_in_byte":1674,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"453022902","text":"import json\nimport pytest\nimport responses\nfrom six import assertCountEqual, ensure_str\n\nfrom descarteslabs.client.exceptions import NotFoundError\n\nfrom .base import ClientTestCase\nfrom ..attributes import (\n    Attribute,\n    AttributeValidationError,\n    CatalogObjectReference,\n    DocumentState,\n)\nfrom ..catalog_base import (\n    CatalogClient,\n    CatalogObject,\n    DeletedObjectError,\n    UnsavedObjectError,\n)\n\n\nclass Foo(CatalogObject):\n    _doc_type = \"foo\"\n    _url = \"/foo\"\n    bar = Attribute()\n\n\nclass TestCatalogObject(ClientTestCase):\n    def test_constructor(self):\n        c = CatalogObject(id=\"id\")\n        assertCountEqual(\n            self,\n            list(c._attribute_types.keys()),\n            [\n                \"id\",\n                \"owners\",\n                \"writers\",\n                \"readers\",\n                \"created\",\n                \"modified\",\n                \"extra_properties\",\n                \"tags\",\n            ],\n        )\n        assert c.owners is None\n        assert c.is_modified\n\n    def test_constructor_no_id(self):\n        c = CatalogObject()\n        assert c.id is None\n        c.id = \"id\"\n        assert \"id\" == c.id\n        assert c.is_modified\n\n        with pytest.raises(AttributeValidationError):\n            c.id = \"oh no\"\n\n    def test_set_get(self):\n        c = CatalogObject(id={})\n        assert not c.is_modified\n\n        c.owners = [\"user\", \"org\"]\n        assert c.owners == [\"user\", \"org\"]\n        assert c.is_modified\n\n    def test_serialize(self):\n        c = CatalogObject(id=\"id\", owners=[\"user\", \"org\"], readers=[\"public\"])\n        assert c.readers == [\"public\"]\n        assert c.is_modified\n        assert {\"id\", \"owners\", \"readers\"} == c._modified\n\n        self.assertDictEqual(\n            c.serialize(), dict(owners=[\"user\", \"org\"], readers=[\"public\"])\n        )\n\n        self.assertDictEqual(\n            c.serialize(jsonapi_format=True),\n            dict(\n                data=dict(\n                    id=\"id\",\n                    type=None,\n                    attributes=dict(owners=[\"user\", \"org\"], readers=[\"public\"]),\n                )\n            ),\n        )\n\n    def test_clear_modified_attributes(self):\n        c = CatalogObject(\n            id=\"id\", owners=[\"user\", \"org\"], readers=[\"public\"], _saved=True\n        )\n        assert not c.is_modified\n        c.owners = [\"org\"]\n        assert c.is_modified\n        assert c.serialize(modified_only=True) == {\"owners\": [\"org\"]}\n\n        c._clear_modified_attributes()\n        assert not c.is_modified\n\n    @responses.activate\n    def test_get(self):\n        self.mock_response(\n            responses.GET,\n            {\n                \"jsonapi\": {\"version\": \"1.0\"},\n                \"data\": {\n                    \"type\": Foo._doc_type,\n                    \"id\": \"foo1\",\n                    \"attributes\": {\"bar\": \"baz\"},\n                },\n            },\n        )\n        self.mock_response(\n            responses.GET,\n            {\n                \"jsonapi\": {\"version\": \"1.0\"},\n                \"data\": {\n                    \"type\": Foo._doc_type,\n                    \"id\": \"foo1\",\n                    \"attributes\": {\"bar\": \"baz\"},\n                },\n            },\n        )\n\n        foo = Foo.get(\"foo1\", client=self.client)\n        assert foo is not None\n        assert foo.id == \"foo1\"\n        assert foo.bar == \"baz\"\n        assert foo.state == DocumentState.SAVED\n\n        CatalogClient.set_default_client(self.client)\n        foo = Foo.get(\"foo1\")\n        assert foo._client is not None\n\n    @responses.activate\n    def test_get_many(self):\n        self.mock_response(\n            responses.PUT,\n            {\n                \"data\": [\n                    {\n                        \"attributes\": {\"bar\": \"baz\"},\n                        \"id\": \"p1:foo\",\n                        \"type\": Foo._doc_type,\n                    },\n                    {\n                        \"attributes\": {\"bar\": \"qux\"},\n                        \"id\": \"p1:bar\",\n                        \"type\": Foo._doc_type,\n                    },\n                ],\n                \"jsonapi\": {\"version\": \"1.0\"},\n            },\n        )\n\n        with pytest.raises(NotFoundError):\n            foos = Foo.get_many([\"p1:foo\", \"p1:bar\", \"p1:missing\"], client=self.client)\n\n        foos = Foo.get_many(\n            [\"p1:foo\", \"p1:bar\", \"p1:missing\"], ignore_missing=True, client=self.client\n        )\n        assert [\"p1:foo\", \"p1:bar\"] == [f.id for f in foos]\n        assert [\"baz\", \"qux\"] == [f.bar for f in foos]\n\n    @responses.activate\n    def test_reload(self):\n        self.mock_response(\n            responses.GET,\n            {\n                \"jsonapi\": {\"version\": \"1.0\"},\n                \"data\": {\n                    \"type\": Foo._doc_type,\n                    \"id\": \"foo1\",\n                    \"attributes\": {\"bar\": \"baz\"},\n                },\n            },\n        )\n        self.mock_response(\n            responses.GET,\n            {\n                \"jsonapi\": {\"version\": \"1.0\"},\n                \"data\": {\n                    \"type\": Foo._doc_type,\n                    \"id\": \"foo1\",\n                    \"attributes\": {\"bar\": \"qux\"},\n                },\n            },\n        )\n\n        foo = Foo.get(\"foo1\", client=self.client)\n        assert foo is not None\n        assert foo.id == \"foo1\"\n        assert foo.bar == \"baz\"\n        assert foo.state == DocumentState.SAVED\n\n        foo.reload()\n        assert foo.id == \"foo1\"\n        assert foo.bar == \"qux\"\n        assert foo.state == DocumentState.SAVED\n\n    @responses.activate\n    def test_save_extra_attributes(self):\n        self.mock_response(\n            responses.POST,\n            {\n                \"jsonapi\": {\"version\": \"1.0\"},\n                \"data\": {\n                    \"type\": Foo._doc_type,\n                    \"id\": \"foo1\",\n                    \"attributes\": {\"bar\": \"baz\", \"foo\": \"bar\"},\n                },\n            },\n        )\n\n        foo = Foo(id=\"foo1\", client=self.client)\n        foo.bar = \"baz\"\n        foo.save(extra_attributes={\"foo\": \"bar\"})\n        assert foo.state == DocumentState.SAVED\n\n        body = json.loads(ensure_str(responses.calls[0].request.body))\n        assert {\"bar\": \"baz\", \"foo\": \"bar\"} == body[\"data\"][\"attributes\"]\n\n    @responses.activate\n    def test_save_update_extra_attributes(self):\n        self.mock_response(\n            responses.PATCH,\n            {\n                \"jsonapi\": {\"version\": \"1.0\"},\n                \"data\": {\n                    \"type\": Foo._doc_type,\n                    \"id\": \"foo1\",\n                    \"attributes\": {\"bar\": \"baz\", \"foo\": \"bar\"},\n                },\n            },\n        )\n\n        foo = Foo(id=\"foo1\", bar=\"baz\", client=self.client, _saved=True)\n        foo.save(extra_attributes={\"foo\": \"bar\"})\n        assert foo.state == DocumentState.SAVED\n\n        body = json.loads(ensure_str(responses.calls[0].request.body))\n        assert {\"foo\": \"bar\"} == body[\"data\"][\"attributes\"]\n\n    def test_equality(self):\n        assert Foo(id=\"foo2\") != Foo(id=\"foo1\")\n        assert CatalogObject(id=\"foo\") != Foo(id=\"foo\")\n        assert Foo(id=\"foo\") != Foo(id=\"foo\", _saved=True)\n\n        foo1 = Foo(id=\"foo\")\n        foo2 = Foo(id=\"foo\")\n        assert foo1 == foo2\n\n        foo2.bar = \"bar\"\n        assert foo1 != foo2\n        assert foo2 != foo1\n\n        class Bar(CatalogObject):\n            _doc_type = \"bar\"\n            _url = \"/bar\"\n            foo_id = Attribute()\n            foo = CatalogObjectReference(Foo)\n\n        bar1 = Bar(id=\"bar\", foo_id=\"foo\")\n        bar2 = Bar(id=\"bar\", foo_id=\"foo\")\n        assert bar1 == bar2\n\n        bar1.foo = Foo(id=\"foo\", _saved=True)\n        assert bar1 == bar2\n\n        bar2.foo = Foo(id=\"foo1\", _saved=True)\n        assert bar1 != bar2\n        assert bar2 != bar1\n\n    def test_hash(self):\n        with pytest.raises(TypeError):\n            hash(Foo(id=\"foo\"))\n\n    @responses.activate\n    def test_delete_classmethod_notfound(self):\n        self.mock_response(\n            responses.DELETE,\n            {\n                \"errors\": [\n                    {\n                        \"detail\": \"Object not found: nerp\",\n                        \"status\": \"404\",\n                        \"title\": \"Object not found\",\n                    }\n                ],\n                \"jsonapi\": {\"version\": \"1.0\"},\n            },\n            status=404,\n        )\n        with pytest.raises(NotFoundError):\n            Foo.delete(\"nerp\", client=self.client)\n\n        r = Foo.delete(\"nerp\", ignore_missing=True, client=self.client)\n        assert r is False\n\n    @responses.activate\n    def test_delete_classmethod(self):\n        self.mock_response(\n            responses.DELETE,\n            {\n                \"meta\": {\"message\": \"Object successfully deleted\"},\n                \"jsonapi\": {\"version\": \"1.0\"},\n            },\n        )\n\n        r = Foo.delete(\"nerp\", client=self.client)\n        assert r is True\n\n    @responses.activate\n    def test_delete_instancemethod(self):\n        self.mock_response(\n            responses.DELETE,\n            {\n                \"meta\": {\"message\": \"Object successfully deleted\"},\n                \"jsonapi\": {\"version\": \"1.0\"},\n            },\n        )\n        instance = Foo(id=\"merp\", client=self.client, _saved=True)\n        r = instance.delete()\n        assert r is True\n        assert instance.state == DocumentState.DELETED\n\n    def test_delete_not_saved(self):\n        foo = Foo(id=\"nerp\", client=self.client)\n        with pytest.raises(UnsavedObjectError):\n            foo.delete()\n\n    @responses.activate\n    def test_delete_instancemethod_notfound(self):\n        self.mock_response(\n            responses.DELETE,\n            {\n                \"errors\": [\n                    {\n                        \"detail\": \"Object not found: nerp\",\n                        \"status\": \"404\",\n                        \"title\": \"Object not found\",\n                    }\n                ],\n                \"jsonapi\": {\"version\": \"1.0\"},\n            },\n            status=404,\n        )\n        foo = Foo(id=\"nerp\", client=self.client, _saved=True)\n        with pytest.raises(NotFoundError):\n            foo.delete()\n\n        instance = Foo(id=\"nerp\", client=self.client, _saved=True)\n        r = instance.delete(ignore_missing=True)\n        assert r is False\n        assert instance.state != DocumentState.DELETED\n\n    @responses.activate\n    def test_prevent_operations_after_delete(self):\n        self.mock_response(\n            responses.DELETE,\n            {\n                \"meta\": {\"message\": \"Object successfully deleted\"},\n                \"jsonapi\": {\"version\": \"1.0\"},\n            },\n        )\n        instance = Foo(id=\"merp\", client=self.client, _saved=True)\n        instance.delete()\n\n        with pytest.raises(DeletedObjectError):\n            instance.reload()\n","sub_path":"descarteslabs/catalog/tests/test_catalog_base.py","file_name":"test_catalog_base.py","file_ext":"py","file_size_in_byte":10789,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"608902941","text":"import os\nimport warnings\n\nimport matplotlib\nimport tensorflow as tf\nmatplotlib.use('Agg')\nfrom matplotlib import pyplot as plt\nimport numpy as np\nimport scipy.signal\nfrom tensorflow import keras\nfrom tensorflow.keras import backend as K\n\n\n\n\n\n\nclass LossHistory(keras.callbacks.Callback):\n    def __init__(self, log_dir):\n        import datetime\n        curr_time = datetime.datetime.now()\n        time_str = datetime.datetime.strftime(curr_time, '%Y_%m_%d_%H_%M_%S')\n        self.log_dir = log_dir\n        self.time_str = time_str\n        self.save_path = os.path.join(self.log_dir, \"loss_\" + str(self.time_str))\n        self.losses = []\n        self.val_loss = []\n\n        os.makedirs(self.save_path)\n\n    def on_epoch_end(self, batch, logs={}):\n        self.losses.append(logs.get('loss'))\n        self.val_loss.append(logs.get('val_loss'))\n        with open(os.path.join(self.save_path, \"epoch_loss_\" + str(self.time_str) + \".txt\"), 'a') as f:\n            f.write(str(logs.get('loss')))\n            f.write(\"\\n\")\n        with open(os.path.join(self.save_path, \"epoch_val_loss_\" + str(self.time_str) + \".txt\"), 'a') as f:\n            f.write(str(logs.get('val_loss')))\n            f.write(\"\\n\")\n        self.loss_plot()\n\n    def loss_plot(self):\n        iters = range(len(self.losses))\n\n        plt.figure()\n        plt.plot(iters, self.losses, 'red', linewidth=2, label='train loss')\n        plt.plot(iters, self.val_loss, 'coral', linewidth=2, label='val loss')\n        try:\n            if len(self.losses) < 25:\n                num = 5\n            else:\n                num = 15\n\n            plt.plot(iters, scipy.signal.savgol_filter(self.losses, num, 3), 'green', linestyle='--', linewidth=2,\n                     label='smooth train loss')\n            plt.plot(iters, scipy.signal.savgol_filter(self.val_loss, num, 3), '#8B4513', linestyle='--', linewidth=2,\n                     label='smooth val loss')\n        except:\n            pass\n\n        plt.grid(True)\n        plt.xlabel('Epoch')\n        plt.ylabel('Loss')\n        plt.title('A Loss Curve')\n        plt.legend(loc=\"upper right\")\n\n        plt.savefig(os.path.join(self.save_path, \"epoch_loss_\" + str(self.time_str) + \".png\"))\n\n        plt.cla()\n        plt.close(\"all\")\n\n\nclass ExponentDecayScheduler(keras.callbacks.Callback):\n    def __init__(self,\n                 decay_rate,\n                 verbose=0):\n        super(ExponentDecayScheduler, self).__init__()\n        self.decay_rate = decay_rate\n        self.verbose = verbose\n        self.learning_rates = []\n\n    def on_epoch_end(self, batch, logs=None):\n        learning_rate = K.get_value(self.model.optimizer.lr) * self.decay_rate\n        K.set_value(self.model.optimizer.lr, learning_rate)\n        if self.verbose > 0:\n            print('Setting learning rate to %s.' % (learning_rate))\n\n\n\nclass ModelCheckpoint(keras.callbacks.Callback):\n    def __init__(self, filepath, monitor='val_loss', verbose=0,\n                 save_best_only=False, save_weights_only=False,\n                 mode='auto', period=1):\n        super(ModelCheckpoint, self).__init__()\n        self.monitor = monitor\n        self.verbose = verbose\n        self.filepath = filepath\n        self.save_best_only = save_best_only\n        self.save_weights_only = save_weights_only\n        self.period = period\n        self.epochs_since_last_save = 0\n\n        if mode not in ['auto', 'min', 'max']:\n            warnings.warn('ModelCheckpoint mode %s is unknown, '\n                          'fallback to auto mode.' % (mode),\n                          RuntimeWarning)\n            mode = 'auto'\n\n        if mode == 'min':\n            self.monitor_op = np.less\n            self.best = np.Inf\n        elif mode == 'max':\n            self.monitor_op = np.greater\n            self.best = -np.Inf\n        else:\n            if 'acc' in self.monitor or self.monitor.startswith('fmeasure'):\n                self.monitor_op = np.greater\n                self.best = -np.Inf\n            else:\n                self.monitor_op = np.less\n                self.best = np.Inf\n\n    def on_epoch_end(self, epoch, logs=None):\n        logs = logs or {}\n        self.epochs_since_last_save += 1\n\n        lr_t = self.model.optimizer._decayed_lr(tf.float32)\n        local_step = tf.cast(self.model.optimizer.iterations + 1, tf.float32)\n\n        if self.model.optimizer._initial_total_steps > 0:\n            total_steps = self.model.optimizer._get_hyper(\"total_steps\", tf.float32)\n            warmup_steps = total_steps * self.model.optimizer._get_hyper(\"warmup_proportion\", tf.float32)\n            min_lr = self.model.optimizer._get_hyper(\"min_lr\", tf.float32)\n            decay_steps = tf.maximum(total_steps - warmup_steps, 1)\n            decay_rate = (min_lr - lr_t) / decay_steps\n            lr_t = tf.where(\n                local_step <= warmup_steps,\n                lr_t * (local_step / warmup_steps),\n                lr_t + decay_rate * tf.minimum(local_step - warmup_steps, decay_steps),\n            )\n            if local_step <= warmup_steps:\n                print('The learning rate has warmed up to {:.2e}.'.format(lr_t.numpy()))\n            else:\n                print('The learning rate has decayed to {:.2e}'.format(lr_t.numpy()))\n\n        if self.epochs_since_last_save >= self.period:\n            self.epochs_since_last_save = 0\n            filepath = self.filepath.format(epoch=epoch + 1, **logs)\n            if self.save_best_only:\n                current = logs.get(self.monitor)\n                if current is None:\n                    warnings.warn('Can save best model only with %s available, '\n                                  'skipping.' % (self.monitor), RuntimeWarning)\n                else:\n                    if self.monitor_op(current, self.best):\n                        if self.verbose > 0:\n                            print('\\nEpoch %05d: %s improved from %0.5f to %0.5f,'\n                                  ' saving model to %s'\n                                  % (epoch + 1, self.monitor, self.best,\n                                     current, filepath))\n                        self.best = current\n                        if self.save_weights_only:\n                            self.model.save_weights(filepath, overwrite=True)\n                        else:\n                            self.model.save(filepath, overwrite=True)\n                    else:\n                        if self.verbose > 0:\n                            print('\\nEpoch %05d: %s did not improve' %\n                                  (epoch + 1, self.monitor))\n            else:\n                if self.verbose > 0:\n                    print('\\nEpoch %05d: saving model to %s' % (epoch + 1, filepath))\n                if self.save_weights_only:\n                    self.model.save_weights(filepath, overwrite=True)\n                else:\n                    self.model.save(filepath, overwrite=True)","sub_path":"utils/callbacks.py","file_name":"callbacks.py","file_ext":"py","file_size_in_byte":6875,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"609763180","text":"#\n# Module Imports\nfrom os.path import expanduser\nimport os\nimport pandas as pd\nhome = expanduser(\"~\")\n#\nclass TPC_Wrapper:\n    #\n    def __init__(self,\n                 ev_loader,\n                 logger,\n                 database_context):\n        self.__ev_loader = ev_loader\n        self.__logger = logger\n        self.__database_context = database_context\n        self.__supported_tpc_types = ('TPC-DS', 'TPC-E')\n    #\n    def generate_data(self, tpc_type=None):\n        \"\"\"\n        Method used to invoke respective TPC (TPC-DS/TPC-E) data generation tools\n        :param tpc_type: Triggers either TPC-E or TPC-DS logic\n        :return: None\n        \"\"\"\n        #\n        # Input validation\n        self.__validate_input(tpc_type=tpc_type)\n        #\n        # Invoke respective TPC tool\n        if tpc_type == self.__supported_tpc_types[0]:\n            # TPC-DS\n            dsdgen = self.__ev_loader.var_get('project_dir') + \"/data/TPC-DS/tools\"\n            #\n            # Navigates to tool directory\n            data_generated_path = self.__ev_loader.var_get('data_generated_directory') + \"/\" + \\\n                                  self.__supported_tpc_types[0] + \"/\" + self.__ev_loader.var_get(\"user\")\n            if not os.path.exists(data_generated_path):\n                os.makedirs(data_generated_path)\n            os.chdir(dsdgen)\n            #\n            if self.__ev_loader.var_get('parallel_degree') == 0:\n                sys = \"./dsdgen -scale \" + str(self.__ev_loader.var_get('data_size')) + \" -dir \" + data_generated_path + \" -FORCE\"\n            elif self.__ev_loader.var_get('parallel_degree') > 0:\n                sys = \"./dsdgen -f -scale \" + str(self.__ev_loader.var_get('data_size')) + \" -dir \" + data_generated_path \\\n                      + \" -parallel \" + str(self.__ev_loader.var_get('parallel_degree')) + \" -FORCE\"\n            else:\n                raise Exception(\"Parallel degree not supported!\")\n            self.__logger.log(\"Generating \" + self.__supported_tpc_types[0] + \" data for volumes of [\" +\n                       str(self.__ev_loader.var_get('data_size')) + \"]..\")\n            output = os.system(sys)\n            if output != 0:\n                raise Exception(\"Exception raised during generation of TPC files..Terminating process!\")\n            #\n            self.__logger.log(self.__supported_tpc_types[0] + \" data generated for [\" + str(self.__ev_loader.var_get('data_size'))\n                       + \"] Gigabytes using parallel degree [\" + str(self.__ev_loader.var_get('parallel_degree')) + \"]\")\n            #\n            # for file in self.get_data_file_list(tpc_type=tpc_type):\n            #     newfilename = self.__rename(file)\n            #     rename_cmd = \"mv \" + data_generated_path + \"/\" + file + \" \" + data_generated_path + \"/\" + newfilename\n            #     output = os.system(rename_cmd)\n            #     if output != 0:\n            #         raise Exception(\"Exception raised during renaming of TPC files..Terminating process!\")\n\n        elif tpc_type == self.__supported_tpc_types[1]:\n            raise NotImplementedError(\"TPC-E not supported yet!\")\n    #\n    def generate_sql(self, tpc_type=None):\n        \"\"\"\n        Method used to invoke respective TPC (TPC-DS/TPC-E) SQL generation tools.\n        The method is composed of dual functionality as follows:\n        1) Invokes the DSQGEN tool to create 'query_0.sql' (which will later be segmented into a number of seperate SQL\n           files) under the src/ directory\n        2) Moves Data Maintenance tasks (DML Logic) and moves it under the src directory\n\n        :param tpc_type: Triggers either TPC-E or TPC-DS logic\n        :return: None\n        \"\"\"\n        #\n        # Input validation\n        self.__validate_input(tpc_type=tpc_type)\n        #\n        if tpc_type == self.__supported_tpc_types[0]:\n            # TPC-DS - Query Generation\n            dsqgen = self.__ev_loader.var_get('project_dir')+\"/data/TPC-DS/tools\"\n            #\n            # Navigates to tool directory so as to invoke DSQGEN\n            sql_generated_path = self.__ev_loader.var_get('sql_generated_directory') + \"/\" + self.__supported_tpc_types[0] + \"/\" + self.__ev_loader.var_get(\"user\") + \"/Query\"\n            if not os.path.exists(sql_generated_path):\n                os.makedirs(sql_generated_path)\n            os.chdir(dsqgen)\n            #\n            sys = \"./dsqgen -DIRECTORY \" + self.__ev_loader.var_get('project_dir') + \"/data/TPC-DS/query_templates -INPUT \" + \\\n                  self.__ev_loader.var_get('project_dir') + \"/data/TPC-DS/query_templates/templates.lst -VERBOSE Y -QUALIFY Y \" \\\n                  \"-SCALE \" + str(self.__ev_loader.var_get('data_size')) + \" -DIALECT oracle -OUTPUT \" + sql_generated_path\n            output = os.system(sys)\n            if output != 0:\n                raise Exception(\"An exception arose during dsqgen invocation, raising error [\" + str(output) + \"] for the command [\" + sys + \"]..terminating process!\")\n                #\n            self.__logger.log(self.__supported_tpc_types[0] + \" SQLs generated for dataset of [\" + str(\n                self.__ev_loader.var_get('data_size')) + \"] Gigabytes\")\n            #\n            # TPC-DS - DML Generation\n            dml_data = self.__ev_loader.var_get('project_dir')+\"/data/TPC-DS/tests\"\n            dml_src = self.__ev_loader.var_get('src_dir')+\"/sql/Runtime/\" + self.__supported_tpc_types[0] + \"/\" + self.__ev_loader.var_get(\"user\") + \"/DML/\"\n            #\n            if not os.path.exists(dml_src):\n                os.makedirs(dml_src)\n            os.chdir(dml_data)\n            #\n            target_scripts = [] # Keeps reference of which DML scripts to move under src/\n            for filename in os.listdir(dml_data):\n                if filename.endswith(\".sql\"):\n                    target_scripts.append(filename)\n            #\n            for script in target_scripts:\n                cmd = \"cp \" + dml_data + \"/\" + script + \" \" + dml_src\n                output = os.system(cmd)\n                if output != 0:\n                    raise Exception(\"An exception arose during DML script migrations..terminating process!\")\n                else:\n                    self.__logger.log(\"Successfully migrated \" + str(script) + \" under src/ tree..\")\n            #\n            self.__logger.log(\"Successfully finished migrating DML scripts!\")\n        elif tpc_type == self.__supported_tpc_types[1]:\n            raise NotImplementedError(\"TPC-E not supported yet!\")\n    #\n    def split_tpc_sql_file(self, tpc_type=None):\n        \"\"\"\n        Parses TPC query_0.sql, composed of all TPC sql in a single sql file. Place each respective sql in a separate\n        SQL file for better handling.\n        :param tpc_type:\n        :return:\n        \"\"\"\n        #\n        # Input validation\n        self.__validate_input(tpc_type=tpc_type)\n        #\n        query0_path = self.__ev_loader.var_get('src_dir') + \"/sql/Runtime/TPC-DS/\" + self.__ev_loader.var_get(\"user\") + \"/Query/query_0.sql\"\n        #\n        if os.path.exists(query0_path) is False:\n            raise FileNotFoundError('Query_0.sql was not found! Ensure that schema type ['+tpc_type+'] SQL generation has occurred!')\n        else:\n            #\n            self.__logger.log(\"Starting \" + str(tpc_type) + \" sql splitting\")\n            #\n            # Read file into memory\n            with open(query0_path) as f:\n                read_data = f.read()\n            #\n            #read_data = read_data.replace(\"\\n\",\" \")\n            #\n            sql_list = read_data.split(\"\\n\\n\\n\\n\")\n            for i, sql in enumerate(sql_list):\n                with open(self.__ev_loader.var_get('src_dir') + \"/sql/Runtime/TPC-DS/\" + self.__ev_loader.var_get(\"user\") + \"/Query/query_\"+str(i+1)+\".sql\", \"w\") as f:\n                    #\n                    # Transform queries utilizing data logic so as to use 'to_date' instead of cast\n                    #sql = self.__convert_cast_to_date(sql=sql,tpc_type=tpc_type)\n                    #\n                    # Write to file\n                    f.write(sql)\n                self.__logger.log(\"Generated query_\" + str(i+1) + \".sql\")\n    #\n    def delete_data(self, tpc_type=None, file_name=None):\n        \"\"\"\n        Deletes datafile\n        :return:\n        \"\"\"\n        if file_name is None:\n            raise ValueError('Datafile/table-name must be declared!')\n        delete_cmd = \"rm \" + self.__ev_loader.var_get('data_generated_directory') + \"/\" + tpc_type + \"/\" + \\\n                     self.__ev_loader.var_get('user') + \"/\" + file_name\n        output = os.system(delete_cmd)\n        if output != 0:\n            raise Exception(\"Terminating process!\")\n        self.__logger.log('Deleted file ' + file_name)\n    #\n    def __validate_input(self, tpc_type=None):\n        \"\"\"\n        Validates that all input is configured as expected\n        :param tpc_type:\n        :return:\n        \"\"\"\n        if tpc_type is None:\n            raise Exception(\"TPC type not declared!\")\n        elif tpc_type not in self.__supported_tpc_types:\n            raise Exception(\"TPC type not supported!\")\n        if self.__ev_loader.var_get('data_generated_directory') is None:\n            raise Exception(\"No target data directory was declared!\")\n    #\n    def get_data_file_list(self, tpc_type=None):\n        \"\"\"\n        Returns list of all generated data files\n        :param tpc_type: Triggers either TPC-E or TPC-DS logic\n        :return:\n        \"\"\"\n        #\n        # Input validation\n        self.__validate_input(tpc_type=tpc_type)\n        #\n        file_list = os.listdir(self.__ev_loader.var_get('data_generated_directory') + \"/\" + tpc_type + \"/\" + self.__ev_loader.var_get('user'))\n        if file_list is None or len(file_list) < 1:\n            raise Exception(\"No data files where found!\")\n        return file_list\n    #\n    def get_file_extension_list(self, tpc_type=None):\n        \"\"\"\n        Iterates over all available files, and retrieves each respective file name + extension\n        :param tpc_type:\n        :return:\n        \"\"\"\n        file_names = []\n        file_extensions = []\n        for data_file_name in self.get_data_file_list(tpc_type=tpc_type):\n            datum = data_file_name.split(\".\")\n            if datum[0] is None or datum[1] is None or datum[0] == \"\" or datum[1] == \"\":\n                raise ValueError(\"An attempt was made to parse non eligible files!\")\n            file_names.append(datum[0])\n            file_extensions.append(datum[1])\n        if len(file_names) != len(file_extensions):\n            raise ValueError(\"File name list does not match list of file extensions!\")\n        return file_names, file_extensions\n    #\n    def __convert_cast_to_date(self, sql, tpc_type):\n        \"\"\"\n        This method attempts to fix SQL syntax produced from TPC-DS\n        :param sql:\n        :param tpc_type:\n        :return:\n        \"\"\"\n        if tpc_type == self.__supported_tpc_types[0]:\n            #sql = sql.replace(\"cast(d_date as date)\",\"to_char(to_date(d_date,'yyyy/mm/dd'),'yyyy-mm-dd')\")\n            sql = sql.replace(\" days)\",\"),'yyyy-mm-dd')\")\n            sql = sql.replace(\"as date) +\",\",'yyyy/mm/dd') +\")\n            sql = sql.replace(\"as date) -\",\",'yyyy/mm/dd') -\")\n            sql = sql.replace(\"as date)\", \",'yyyy/mm/dd'),'yyyy-mm-dd')\")\n            sql = sql.replace(\"(cast('\", \"to_char((to_date('\")\n            sql = sql.replace(\"cast('\",\"to_char(to_date('\")\n            sql = sql.replace(\"cast ('\",\"to_char(to_date('\")\n            sql = sql.replace(\"cast(d_date\",\"to_char(to_date(d_date\")\n        else:\n            raise NotImplementedError('This logic is not yet supported!')\n        return sql\n    #\n    def __rename(self, oldfilename):\n        \"\"\"\n        Renames file as follows:\n        warehouse_1_20.dat > warehouse.dat\n        :param oldfilename:\n        :return:\n        \"\"\"\n        counter = 0\n        delim_count = 0\n        delimeter = \"_\"\n        newfilename = \"\"\n        #\n        for i in oldfilename:\n            if delimeter == i:\n                delim_count += 1\n        #\n        for i in oldfilename:\n            if i == delimeter:\n                counter += 1\n            if delim_count == 3:\n                if counter > 1:\n                    break\n            elif delim_count == 2:\n                if counter > 0:\n                    break\n            newfilename += i\n        return newfilename + \".dat\"\n    #\n    def get_order_sequence(self, stream_identification_number=None, tpc_type=None, ev_loader=None):\n        \"\"\"\n        Parses order.csv into a pandas dataframe, and returns order sequence based on input stream number\n        :param stream_number:\n        :param tpc_type:\n        :return:\n        \"\"\"\n        #\n        # Input validation\n        self.__validate_input(tpc_type=tpc_type)\n        #\n        headers = [i for i in range(0,21)]\n        sequence_df = pd.read_csv(ev_loader.var_get('project_dir') + '/data/' + tpc_type.upper() + '/order.csv',\n                                  delimiter='\\t',\n                                  header=0,\n                                  names=headers)\n        idetification_numbers = list(sequence_df[stream_identification_number].values)\n        idetification_numbers = [int(i) for i in idetification_numbers]\n        return idetification_numbers","sub_path":"src/data/tpc.py","file_name":"tpc.py","file_ext":"py","file_size_in_byte":13264,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"140509717","text":"def cowsay(text):\n\tCOW = r'''\n\t\t\\   ^__^\n\t\t \\  (oo)\\_______\n\t\t\t(__)\\       )\\/\\\n\t\t\t\t||----w |\n\t\t\t\t||     ||\n'''\n\ttext = text.split(' ')\n\twhile '' in text:\n\t\ttext.remove('')\n\ttext = ' '.join(text)\n\tif len(text) <= 39:\n\t\ttop = ' ' + '_' * (len(text)+2)\n\t\tbot = ' ' + '-'* (len(text)+2)\n\t\tcontent2 = '< ' + text + ' >'\n\telse:\n\t\ttext = text.split(' ')\n\t\ti = 0\n\t\tcontent = []\n\t\twhile i < len(text):\n\t\t\tline = ''\n\t\t\twhile len(line) + len(text[i]) <= 39:\n\t\t\t\tline = line + text[i]+' '\n\t\t\t\ti += 1\n\t\t\t\tif i > len(text) - 1:\n\t\t\t\t\tbreak\n\t\t\tcontent.append(line)\n\t\tm = max(len(i) for i in content)\n\t\ttop = ' ' + '_' * (m+1)\n\t\tbot = ' ' + '-'* (m+1)\n\t\tcontent2 = []\n\t\tfor i in range(len(content)) :\n\t\t\tif i == 0:\n\t\t\t\t#line = '/ '\n\t\t\t\tcontent2.append('/ ' + content[i] + ' ' * (m-len(content[i])) + '\\\\' )\n\t\t\telif i == len(content)-1:\n\t\t\t\tcontent2.append('\\\\ ' + content[i] + ' ' * (m-len(content[i])) + '/' )\n\t\t\telse:\n\t\t\t\tcontent2.append('| ' + content[i] + ' ' * (m-len(content[i])) + '|' )\n\t\tcontent2 = '\\n'.join(content2)\n\treturn '\\n%s\\n%s\\n%s%s' %(top,content2,bot,COW)\n\t#return '%s'%COW\n\n\nif __name__ == '__main__':\n#These \"asserts\" using only for self-checking and not necessary for auto-testing\n    expected_cowsay_one_line = r'''\n ________________\n< Checkio rulezz >\n ----------------\n\t\t\\   ^__^\n\t\t \\  (oo)\\_______\n\t\t\t(__)\\       )\\/\\\n\t\t\t\t||----w |\n\t\t\t\t||     ||\n'''\n    expected_cowsay_two_lines = r'''\n ________________________________________\n/ A                                      \\\n\\ longtextwithonlyonespacetofittwolines. /\n ----------------------------------------\n\t\t\\   ^__^\n\t\t \\  (oo)\\_______\n\t\t\t(__)\\       )\\/\\\n\t\t\t\t||----w |\n\t\t\t\t||     ||\n'''\n\n    expected_cowsay_many_lines = r'''\n _________________________________________\n/ Lorem ipsum dolor sit amet, consectetur \\\n| adipisicing elit, sed do eiusmod tempor |\n| incididunt ut labore et dolore magna    |\n\\ aliqua.                                 /\n -----------------------------------------\n\t\t\\   ^__^\n\t\t \\  (oo)\\_______\n\t\t\t(__)\\       )\\/\\\n\t\t\t\t||----w |\n\t\t\t\t||     ||\n'''\n\n    cowsay_one_line = cowsay('Checkio rulezz')\n    assert cowsay_one_line == expected_cowsay_one_line, 'Wrong answer:\\n%s' % cowsay_one_line\n\n\n\t#assert cowsay_one_line == expected_cowsay_one_line, 'Wrong answer:\\n.%s.\\nRight answer:\\n.%s.\\n' % (cowsay_one_line, expected_cowsay_one_line)\n\n    cowsay_two_lines = cowsay('A longtextwithonlyonespacetofittwolines.')\n\t#assert cowsay_two_lines == expected_cowsay_two_lines, 'Wrong answer:\\n%s' % cowsay_two_lines\n    assert cowsay_two_lines == expected_cowsay_two_lines, 'Wrong answer:\\n.%s.\\nRight answer:\\n.%s.\\n' % (cowsay_two_lines, expected_cowsay_two_lines)\n\n    cowsay_many_lines = cowsay('Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do ''eiusmod tempor incididunt ut labore et dolore magna aliqua.')\n\t#assert cowsay_many_lines == expected_cowsay_many_lines, 'Wrong answer:\\n%s' % cowsay_many_lines\n    assert cowsay_many_lines == expected_cowsay_many_lines, 'Wrong answer:\\n.%s.\\nRight answer:\\n.%s.\\n' % (cowsay_many_lines, expected_cowsay_many_lines)\n\nprint(cowsay('                    h e     l l o w o r l d'))","sub_path":"checkio/cowsay.py","file_name":"cowsay.py","file_ext":"py","file_size_in_byte":3119,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"650547228","text":"# -*- coding: utf-8 -*-\n__author__ = 'xiaoxiaoming'\n\n\ndef bucket_sort(arr: list, bucket_size: int):\n    if len(arr) < 2: return\n    # 扫描最小值和最大值\n    min, max = arr[0], arr[0]\n    for i in range(0, len(arr)):\n        if arr[i] < min:\n            min = arr[i]\n        elif arr[i] > max:\n            max = arr[i]\n    bucket_count = (max - min + 1) // bucket_size  # 桶数量\n    buckets = [[]] * bucket_count\n    # 将数组中值分配到各个桶里\n    for i in range(len(arr)):\n        bucket_index = (arr[i] - min + 1) // bucket_size\n        buckets[bucket_index].append(arr[i])\n    # 对每个桶进行排序,时间复杂度小于O(nlogn)的排序算法都可以\n    k = 0\n    for bucket in buckets:\n        bucket.sort()\n        for data in bucket:\n            arr[k] = data\n            k += 1\n","sub_path":"07.sorts/bucket_sort.py","file_name":"bucket_sort.py","file_ext":"py","file_size_in_byte":816,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"363639277","text":"class HtmlUtils:\n\n    def __init__(self):\n        super()\n\n    def getLoginPage(self):\n        return '''\n<!DOCTYPE html>\n<html>\n\n<head>\n    <meta charset=\"utf-8\">\n    <meta name=\"viewport\" content=\"width=device-width\">\n    <title>Login or Register</title>\n</head>\n<br>\n\n<style>\ndiv{\nwidth: 500px;\nheight: 115px;\ntext-align: center;\nmargin-left:auto;\nmargin-right:auto;\nfont-size: 400%;\n}\n\nform{\nwidth: 500px;\nheight: 215px;\ntext-align: center;\nmargin-left:auto;\nmargin-right:auto;\nfont-size: 150%;\n}\n\n</style>\n\n\n<div class=\"highlight3\" style=\"background: linear-gradient(to bottom, lightblue 0%, white 80%)\">Play Tic Tac Toe!</div>\n\n<form class=\"form1\" action=\"/login\" style=\"background: linear-gradient(to bottom, #F493D1 0%, white 100%)\">\n    <h1>Login Below!</h1>\n    User:  <input type=\"text\" name=\"username\"><br>\n    Pass:  <input type=\"password\" name=\"password\"><br>\n    <input type=\"submit\" value=\"Log in\">\n</form><br>\n\n\n<form class=\"form2\" action=\"/register\" style=\"background: linear-gradient(to bottom, #D293F4 0%, white 100%)\">\n    <h1>Register Below!</h1>\n    User:  <input type=\"text\" name=\"username\"><br>\n    Pass:  <input type=\"password\" name=\"password\"><br>\n    <input type=\"submit\" value=\"Register\">\n</form></html>\n'''\n\n    def getNotFoundPage(self):\n        return 'Status 404: Resource not found'\n\n    def getNotLoginPage(self):\n        return 'Not logged in <a href=\"/\">Login</a>'\n\n    def getAccountPage(self, username, won, lost, join_game_id):\n        join_game_link = \"\"\n        if join_game_id != 0:\n            join_game_link = \"<a href='/game?gameId={}'>Join an existing game</a>\".format(join_game_id)\n\n        logout = \"<a href='/logout'>Log Out</a>\"\n\n        create_game = \"<a href='/create?username={}'>Create a new game</a>\".format(username)\n        page = self.getAccountPageStr()\n\n        page = page.format(username, join_game_link, create_game, logout)\n        #page = page.format(username, won, lost, join_game_link, create_game, logout)\n        page = page.replace('...', '}', 3)\n        page = page.replace('..', '{', 3)\n        return page\n\n    def getGamePage(self, gameId, user, u1, u2, win1, win2, nextTurn, gameBoard):\n\n        pos = gameBoard.getPositions()\n        links = [''] * 10\n        for i in range(1, 10):\n            if pos[i] == 'X':\n                links[i] = 'X'\n            elif pos[i] == 'O':\n                links[i] = 'O'\n            else:\n                if user == u1 and nextTurn == 'O' or user == u2 and nextTurn == 'X':\n                    links[i] = '_'\n                elif gameBoard.isBoardFull() == True or win1 == True or win2 == True:\n                    links[i] = '_'\n                else:\n                    links[i] = \"<a href='/game?gameId={}&move={}'>?</a>\".format(gameId, i)\n        # if  there  is a winner,  end  the game  and display message\n\n\n\n\n        page = self.getGamePageStrHead()\n\n        account = \"<a href='/account'>Go back to account screen.</a>\"\n        page = page.format(gameId, links[7] , links[8], links[9], links[4], links[5], links[6], links[1], links[2], links[3], u1, u2, account)\n\n        waitStr = self.getGamePageStrWait()\n        winStr = self.getGamePageStrWin()\n        loseStr = self.getGamePageStrLose()\n        tieStr = self.getGamePageStrTie()\n\n        if user == u1 and nextTurn == 'O' or user == u2 and nextTurn == 'X':\n            page = page + waitStr\n\n        if win1 == True and win2 == False:\n            winStr = winStr.format(u1)\n            loseStr = loseStr.format(u2)\n            page = page + winStr + loseStr\n\n        elif win1 == False and win2 == True:\n            winStr = winStr.format(u2)\n            loseStr = loseStr.format(u1)\n            page = page + winStr + loseStr\n\n        elif gameBoard.isBoardFull() == True and win1 == False and win2 == False:\n            page = page + tieStr\n\n        page = page + self.getGamePageStrTail()\n\n        page = page.replace('...', '}')\n        page = page.replace('..', '{')\n\n        return page\n\n    def getGamePageStrHead(self):\n        return '''\n<!DOCTYPE html>\n<html>\n<head>\n    <meta charset=\"utf-8\">\n    <meta name=\"viewport\" content=\"width=device-width\">\n    <meta http-equiv=\"refresh\" content=\"2\">\n    <title>Playing Tic Tac Toe!</title>\n</head>\n\n\n<br>\n<style>\ndiv..\nwidth: 500px;\nheight: 115px;\ntext-align: center;\nmargin-left:auto;\nmargin-right:auto;\nfont-size: 400%;\n...\n\ntable, td, th ..\n  border: 1px solid black;\n...\n\ntable ..\n  border-collapse: collapse;\n  height: 130px;\n  width: 13%;\n  margin-left:auto;\n  margin-right:auto;\n  font-size: 230%;\n...\n\n\ntd ..\n  text-align: center;\n...\n\nh2 ..\n  margin-left:auto;\n  margin-right:auto;\n  text-align: center;\n...\n\nbody..\n\ntext-align:center;\ncolor: black;\nbackground-color: white;\nalign:center;\n...\n\n</style>\n\n<body>\n\n<div style=\"background: linear-gradient(to bottom, lightblue 0%, white 80%\">Tic Tac Toe Game</div>\n<div style=\"background: linear-gradient(to bottom, yellow 0%, white 80%\">Game ID: {}</div>\n\n<table>\n  <tr>\n    <td>{}</td>\n    <td>{}</td>\n    <td>{}</td>\n  </tr>\n  <tr>\n    <td>{}</td>\n    <td>{}</td>\n    <td>{}</td>\n  </tr>\n  <tr>\n    <td>{}</td>\n    <td>{}</td>\n    <td>{}</td>\n  </tr>\n</table>\n<br>\n\n\n\n<h2 class=\"user1\">User 1: {}</h2>\n<h2 class=\"user2\">User 2: {}</h2>\n\n<h2>{}</h2>\n\n\n'''\n\n    def getGamePageStrWait(self):\n        return '<h2 style=\"background: linear-gradient(to bottom, lavendar 0%, white 70%\">Waiting for opponent\\'s move</h2>'\n\n    def getGamePageStrWin(self):\n        return '<div style=\"background: linear-gradient(to bottom, lightgreen 0%, white 70%\">{} wins!</div>'\n\n    def getGamePageStrLose(self):\n        return '<div style=\"background: linear-gradient(to bottom, tomato 0%, white 70%\">{} loses!</div>'\n\n    def getGamePageStrTie(self):\n        return '<div style=\"background: linear-gradient(to bottom, orange 0%, white 70%\">The game is a tie!</div>'\n\n\n\n    def getGamePageStrTail(self):\n        return '</body></html>'\n\n    def getAccountPageStr(self):\n        return '''\n<!DOCTYPE html>\n<html>\n<head>\n    <meta charset=\"utf-8\">\n    <meta name=\"viewport\" content=\"width=device-width\">\n    <meta http-equiv=\"refresh\" content=\"2\">\n    <title>Player's Account</title>\n</head>\n\n<style>\ndiv..\nwidth: 500px;\nheight: 115px;\ntext-align: center;\nmargin-left:auto;\nmargin-right:auto;\nfont-size: 400%;\n...\n\nh1..\nwidth:150px;\nheight: 55px;\nmargin-left:auto;\nmargin-right:auto;\nfont-size: 200%;\n...\n\nbody..\ntext-align:center;\ncolor: black;\nbackground-color: white;\nalign:center;\n...\n\n</style>\n\n<br>\n\n<div class=\"highlight2\" style=\"background: linear-gradient(to bottom, lightblue 0%, white 80%)\">Tic Tac Toe Lobby</div><br><br>\n\n<h1 class=\"highlight1\" style=\"background: linear-gradient(to bottom, yellow 0%, white 80%)\">User Info</h1>\n\n<body>\n<h2>Account: {}</h2><br>\n\n<h1 class=\"highlight1\" style=\"background: linear-gradient(to bottom, yellow 0%, white 80%)\">Play Now!</h1>\n\n<h2>{}</h2>\n<h2>{}</h2>\n<br>\n<h1 class=\"highlight1\" style=\"background: linear-gradient(to bottom, tomato 0%, white 80%)\">{}</h1>\n\n</body>\n\n</html>'''\n","sub_path":"HtmlUtils.py","file_name":"HtmlUtils.py","file_ext":"py","file_size_in_byte":7004,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"444905561","text":"class Joueur:\r\n    \"\"\"Classe définissant un joueur de roulette caractérisé par :\r\n    - son pseudo ;\r\n    - son porte monnaie ;\r\n    - un numéro misé\r\n    - une somme misée   \"\"\"\r\n\r\n    \r\n    def __init__(self, pseudo, porte_monnaie, numero_mise, somme_misee):\r\n        \"\"\"Constructeur de notre classe\"\"\"\r\n        self.pseudo = pseudo\r\n        self.porte_monnaie = porte_monnaie\r\n        self.numero_mise = numero_mise\r\n        self.somme_misee = somme_misee\r\n\r\n    def nom_joueur(self, pseudo):\r\n        \"\"\"Méthode permettant de vérifier que le nom du joueur n'est pas un chiffre et de l'afficher   \"\"\"\r\n        NomCorrect = False\r\n        while NomCorrect == False :\r\n            self.pseudo = input(\"Quel est votre nom ?\")\r\n            #self.pseudo = 'Anonyme' if len(self.pseudo) == 0 else self.pseudo\r\n            try:\r\n                int(self.pseudo) != False\r\n            except ValueError:\r\n                print(\"Bienvenue {} sur la table de la Roulette du Zcasino !\".format(self.pseudo))\r\n                NomCorrect = True\r\n            else :\r\n                print(\"Il y a une erreur, êtes vous certain de votre frappe ?\")\r\n\r\n\r\n\r\n    def argent_joueur(self, porte_monnaie):\r\n        \"\"\"Méthode permettant de vérifier que le joueur a de l'argent pour jouer\"\"\"\r\n        Argent = False\r\n        while Argent == False :\r\n            self.porte_monnaie = input(\"\\nAvec quelle somme d'argent souhaitez vous jouer ? \")\r\n            try:\r\n                int(self.porte_monnaie) != False\r\n            except ValueError:\r\n                print(\"Il y a une erreur, êtes vous certain de votre frappe ?\")\r\n            else :\r\n                self.porte_monnaie = int(self.porte_monnaie)\r\n                if self.porte_monnaie < 1 :\r\n                    print(\"Vous ne pouvez pas jouer sans argent !\")\r\n                else :\r\n                    Argent = True\r\n\r\n    def num_joueur(self, numero_mise):\r\n        \"\"\"Méthode permettant de vérifier que le joueur a bien misé sur un numéro de la roulette\"\"\"\r\n        Numero = False\r\n        while Numero == False :\r\n            self.numero_mise = input(\"\\nSur quel numéro souhaitez vous miser (entre 0 et 49) ? : \")\r\n            try:\r\n                int(self.numero_mise) != False\r\n            except ValueError:\r\n                print(\"Il y a une erreur, êtes vous certain de votre frappe ?\")\r\n            else :\r\n                self.numero_mise = int(self.numero_mise)\r\n                if self.numero_mise < 0 or self.numero_mise > 49:\r\n                    print(\"Le numéro choisi n'existe pas, veuillez en choisir un autre !\")\r\n                else :\r\n                    Numero = True\r\n    \r\n    def mise_joueur(self, somme_misee):\r\n        \"\"\" Methode permettant de vérifier que le joueur mise l'argent qu'il a dans son porte monnaie \"\"\"\r\n        Mise = False\r\n        while Mise == False :\r\n            self.somme_misee = input(\"\\nQuelle somme souhaitez vous miser sur ce numéro ? : \")\r\n            try:\r\n                int(self.somme_misee) != False\r\n            except ValueError :\r\n                print(\"Etes vous certain de votre frappe ?\")\r\n            else :\r\n                self.somme_misee = int(self.somme_misee)\r\n                if self.somme_misee < 0:\r\n                    print(\"Vous ne pouvez pas miser un nombre négatif !\")\r\n                elif self.somme_misee > self.porte_monnaie:\r\n                    print(\"Vous n'avez pas assez d'argent !\")\r\n                else :\r\n                    Mise = True\r\n\r\n\r\n","sub_path":"ZCasino/ZCasino_Orienté Objet/ZCasino_joueur.py","file_name":"ZCasino_joueur.py","file_ext":"py","file_size_in_byte":3503,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"266618182","text":"#!/usr/bin/env python\n\"\"\"\nS-curves have 7 phases\n- increase acceleration\n- constant acceleration\n- decrease acceleration\n- constant velocity\n- increase deceleration\n- constant deceleration\n- decrease deceleration\n\ncase 1\n - accel\n    v = v_s + acc * t\n    e = 0.5 * acc * t * t + v_s * t + s\ncase 2\n - accel cruise\n    e = 0.5 * acc * a_t * a_t + v_s * a_t + s + v_c * c_t\ncase 3\n - accel then decel\n    e = 0.5 * acc * a_t * a_t + v_s * a_t + s - 0.5 * dec * d_t * d_t - v_e * d_t\n - accel cruise then decel\n\ndon't enforce max accel\n\nfor polynomial paths\nhttp://www.springer.com/cda/content/document/cda_downloaddocument/9783540856283-c1.pdf?SGWID=0-0-45-615213-p173839603\n\"\"\"\n\nimport numpy\nimport pylab\n\n\napproximate = lambda v, t, e=0.001: abs(v - t) < e\n\n\ndef plot(ts, vs, xs, case, e, t, v_e):\n    pylab.subplot(211)\n    pylab.plot(ts, xs, color='b')\n    pylab.scatter(ts, xs, color='b')\n    pylab.axhline(e, color='b')\n    pylab.axvline(t, color='k')\n    xl = pylab.xlim()\n    pylab.xlim(xl[0], xl[1]+0.5)\n    yl = pylab.ylim()\n    pylab.ylim(yl[0], yl[1]+0.5)\n    pylab.title(\"Case %s: positon vs time\" % case)\n    pylab.subplot(212)\n    pylab.plot(ts, vs, color='g')\n    pylab.scatter(ts, vs, color='g')\n    pylab.axhline(v_e, color='g')\n    pylab.axvline(t, color='k')\n    xl = pylab.xlim()\n    pylab.xlim(xl[0], xl[1]+0.5)\n    yl = pylab.ylim()\n    pylab.ylim(yl[0], yl[1]+0.5)\n    pylab.title(\"Case %s: velocity vs time\" % case)\n    pylab.show()\n\n\ndef test(\n        s=0.0, e=3.0, t=2.0, dt=0.1,\n        v_s=0., v_e=1., a_s=0., a_e=0.,\n        a_max=10.0, d_max=10.0, v_max=10.0,\n        order=3):\n    xs, ts, vs, case = compute_motion_profile(\n        s, e, t, dt, v_s, v_e, a_s, a_e, a_max, d_max, v_max,\n        order)\n    #xs = numpy.cumsum(vs * dt)\n    plot(ts, vs, xs, case, e, t, v_e)\n\n\ndef polynomial_3(s, e, v_s, v_e, t):\n    a0 = s\n    a1 = v_s\n    a2 = (\n        (-3 * (s - e) - (2 * v_s + v_e) * t) /\n        (t * t))\n    a3 = (\n        (2 * (s - e) + (v_s + v_e) * t) /\n        (t * t * t))\n    return lambda dt: a0 + a1 * dt + a2 * dt ** 2. + a3 * dt ** 3.\n\n\ndef polynomial_5(s, e, v_s, v_e, a_s, a_e, t):\n    a0 = s\n    a1 = v_s\n    a2 = a_s * 0.5\n    a3 = (\n        (20 * (e - s) - (8 * v_e + 12 * v_s) * t - (3 * a_e - a_s) * t ** 2.) /\n        (2. * t ** 3.))\n    a4 = (\n        (\n            -30 * (e - s) + (14 * v_e + 16 * v_s) * t +\n            (3 * a_e - 2 * a_s) * t ** 2.) /\n        (2. * t ** 4.))\n    a5 = (\n        (12 * (e - s) - 6 * (v_e + v_s) * t + (a_e - a_s) * t ** 2.) /\n        (2 * t ** 5.))\n    return lambda dt: (\n        a0 + a1 * dt + a2 * dt ** 2. + a3 * dt ** 3. +\n        a4 * dt ** 4. + a5 * dt ** 5.)\n\n\ndef compute_point_velocities(pts, ts, v_s, v_e):\n    # TODO not sure if this works for vs[0] != 0 and vs[-1] != 0\n    ss = [v_s]\n    # first compute point-to-point slopes\n    for i in xrange(1, len(pts) - 1):\n        ss.append((pts[i] - pts[i - 1]) / (ts[i] - ts[i - 1]))\n    ss.append(v_e)\n    # then compute velocities\n    vs = []\n    for i in xrange(len(ss) - 1):\n        if numpy.sign(ss[i]) == numpy.sign(ss[i + 1]):\n            vs.append(0.5 * (ss[i] + ss[i + 1]))\n        else:\n            vs.append(0.)\n    vs[0] = v_s\n    vs.append(v_e)\n    return vs\n\n\ndef compute_motion_profile(\n        s=0.0, e=3.0, t=2.0, dt=0.1,\n        v_s=0., v_e=1., a_s=0., a_e=0.,\n        a_max=10.0, d_max=10.0, v_max=10.0,\n        order=5):\n    n = int(numpy.ceil(t / float(dt)))\n    #d = abs(e - s)\n\n    ts = numpy.linspace(0, t, n)\n    if order == 3:\n        p = polynomial_3(s, e, v_s, v_e, t)\n    elif order == 5:\n        p = polynomial_5(s, e, v_s, v_e, a_s, a_e, t)\n    pts = [p(tp) for tp in ts]\n    vs = compute_point_velocities(pts, ts, v_s, v_e)\n    #vs[0] = v_s\n    #vs[-1] = v_e\n    return pts, ts, vs, -1\n\n\ndef compute_motion_profile_old(\n        s=0.0, e=3.0, t=2.0, dt=0.1,\n        v_s=0., v_e=1.,\n        a_max=10.0, d_max=10.0, v_max=10.0):\n    \"\"\"\n    Compute a motion profile\n\n    Args:\n    ------\n        s : starting position\n        e : ending position\n        t : time to complete movement\n        dt : time resolution of movement\n        v_s : starting velocity\n        v_e : ending velocity\n        a_max : maximum acceleration\n        d_max : maximum deceleration\n        v_max : maximum velocity\n    \"\"\"\n    n = int(numpy.ceil(t / float(dt)))\n    d = abs(e - s)\n    v_avg = d / t\n\n    ts = numpy.linspace(0, t, n)\n    case = None\n    acc = abs(v_e - v_s) / t  # TODO cap acceleration\n    if acc > a_max:\n        raise Exception(\n            \"Movement requires acceleration[%s] over max [%s]\"\n            % (acc, a_max))\n\n    # case 1: accel\n    print(\"case 1?\")\n    ep = 0.5 * acc * t * t + v_s * t + s\n    print(\"ep, e: %s, %s\" % (ep, e))\n    if approximate(ep, e):\n        case = 1\n        vs = numpy.linspace(v_s, v_e, n)\n        return ts, vs, case\n\n    if v_s == v_e and s != e:\n        # accel then decel\n        raise NotImplementedError\n    # case 2: accel cruise\n    print(\"case 2?\")\n    acc_t = (e - s - v_e * t) / (0.5 * abs(v_e - v_s) + v_s - v_e)\n    print(\"acc_t: %s\" % acc_t)\n    if acc_t > 0.:\n        acc = abs(v_e - v_s) / acc_t\n        c_t = t - acc_t\n        if c_t > 0:\n            ep = c_t * v_e + 0.5 * acc * acc_t * acc_t + v_s * acc_t + s\n            if c_t > 0 and approximate(ep, e):\n                case = 2\n                vs = numpy.ones_like(ts) * v_e\n                am = ts < acc_t\n                vs[am] = numpy.linspace(v_s, v_e, sum(am))\n                return ts, vs, case\n\n    # case 3: accel then decel, accel cruise then decel\n    raise NotImplementedError\n\n\npylab.ion()\ntest(s=0.0, e=0.5, t=1.0, v_e=1.0, order=3)  # case 3\npylab.figure()\ntest(s=0.0, e=0.5, t=1.0, v_e=1.0, order=5)  # case 3\npylab.figure()\ntest(s=0.0, e=2.0, t=2.0, v_e=1.0, order=3)  # case 2\npylab.figure()\ntest(s=0.0, e=2.0, t=2.0, v_e=1.0, order=5)  # case 2\npylab.ioff()\npylab.show()\n","sub_path":"tests/motion_control.py","file_name":"motion_control.py","file_ext":"py","file_size_in_byte":5872,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"117745280","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Sep 21 20:21:47 2017\n\n@author: meyavuz\n\"\"\"\n\nclass Node(object):\n    \n    def __init__(self, key=None, left=None, right=None, parent=None, height=None):\n        self.key = key\n        self.left = left\n        self.right = right\n        self.parent = parent\n        self.height = height\n\n    def __repr__(self):\n#        return 'Node: key:{}, left:{}, right:{}, parent:{}'.format(self.key, self.left, self.right, self.parent)\n        return 'k:{}'.format(self.key)\n\n#class Tree(object):\nclass BST(object):\n    \n    def __init__(self, arr=None):\n        self.arr = arr\n    \ndef CreateSampleTree():\n#    /*   6\n#        /  \\\n#       10    2\n#      / \\   / \\\n#     1   3 7  12\n#     10 and 2 are swapped\n#    */    \n    # Swapped BST\n    root = Node(6, \n                Node(10, Node(1), Node(3)), \n                Node(2, Node(7), Node(12)) )\n    \n#    # Okay BST\n#    root = Node(6, \n#                Node(2, Node(1), Node(3)), \n#                Node(10, Node(7), Node(12)) )\n    \n    return root\n\ndef InOrderTRAVERSE(root):\n\n    if root.left:\n        InOrderTRAVERSE(root.left)\n    \n    print('{}, '.format(root.key), end='')\n        \n    if root.right:\n        root = InOrderTRAVERSE(root.right)\n    \n\ndef FindSwappedNodes(root, prev, first, second):\n\n    if first and second:\n        \n        first.key, second.key = second.key, first.key\n\n    \n    if root.left:\n        prev, first, second = FindSwappedNodes(root.left, prev, first, second)\n    \n    \n    if (prev and root.key < prev.key):\n        print('Problematic prev: {}'.format(prev))\n        if first and not second:\n            second = root\n        if not first:\n            first = prev\n    \n            \n    \n    print('current: {}\\t prev: {}'.format(root.key, prev))\n    prev = root\n        \n    if root.right:\n#        prev = root\n        prev, first, second = FindSwappedNodes(root.right, prev, first, second)\n    \n\n    return prev, first, second\n\ndef main():\n\n    root = CreateSampleTree()\n    print('Original BST InOrderTraversal:')\n    InOrderTRAVERSE(root)\n    print('\\n')\n\n    prev, first, second = FindSwappedNodes(root, None, None, None)\n    \n    print('\\nFirst : {}'.format(first))\n    print('Second: {}'.format(second))\n\n    print('After swapping BST InOrderTraversal:')\n    InOrderTRAVERSE(root)\n\n\nif __name__ == '__main__':\n    main()","sub_path":"mulakat/EPI15_BinarySearchTrees/swapnodesofBST2.py","file_name":"swapnodesofBST2.py","file_ext":"py","file_size_in_byte":2353,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"123866169","text":"from expand import expand\nimport heapq\n\n\n# PEP8 naming convention?\nclass OrderedPath:\n\n    # list of locations\n    def __init__(self):\n        self.loc = []\n\n    # add_loc :\n    # need to add location `start` to an ordered list\n    def add_loc(self, traveled, precedence):\n        return heapq.heappush(self.loc, (traveled, precedence))\n\n    # rem_loc :\n    # need to move location (by popping end of list) to `visited list`\n    def rem_loc(self):\n        return heapq.heappop(self.loc)\n\n    # list_not_empty : list -> boolean\n    # if the list is empty, return true\n    def list_not_empty(self):\n        return len(self.loc) > 0\n\n\ndef a_star_search(dis_map, time_map, start, end):\n    # steps:\n    # from start, calculate all adjacent locations' cost :\n    #   f(n) = time_to(n) + dis(n)\n\n    # initialize:\n    path = []  # list of strings ONLY\n    time_to_next_loc = {}\n    time_to_next_loc[start] = 0\n    loc_ordered = OrderedPath()\n    loc_ordered.add_loc(0, [start])  # precedence = 0, and travel = START\n    visited_list = set()  # UNORDERED, but doesn't matter\n\n    # function:\n    # while not empty, do things\n    # NOTE: loc_ordered is a list of paths\n    while loc_ordered.list_not_empty() == True:\n        # add removed to list of removed\n        just_removed = loc_ordered.rem_loc()\n\n        # if latest removed was not visited before, add to list of visited\n        if just_removed[-1][-1] not in visited_list:\n            visited_list.add(just_removed[-1][-1])\n\n            # if latest removed is the endpoint, this is the final path\n            if just_removed[-1][-1] == end:\n                path = just_removed[-1]\n                return path\n\n            else:\n                # for each adjacent location, add a time and distance (cost) measure\n                for loc in expand(just_removed[-1][-1], time_map):\n\n                    # g(n)\n                    gn = time_map[just_removed[-1][-1]][loc] + \\\n                         time_to_next_loc[just_removed[-1][-1]]\n\n                    # h(n)\n                    hn = dis_map[loc][end]\n\n                    # f(n)\n                    fn = gn + hn\n\n                    # add shortest to ordered list\n                    if loc not in time_to_next_loc.keys() or time_to_next_loc[loc] > gn:\n                        time_to_next_loc[loc] = gn\n                        append_loc = just_removed[-1].copy()\n                        append_loc.append(loc)\n                        loc_ordered.add_loc(fn, append_loc)\n    return path\n","sub_path":"Q2/MSAI 348 - Introduction to AI/HW/3-a-star-search/student_code.py","file_name":"student_code.py","file_ext":"py","file_size_in_byte":2496,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"414404392","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Apr  6 22:12:07 2018\n\n@author: Think\n\"\"\"\n\nclass Solution:\n    def setZeroes(self, matrix):\n        \"\"\"\n        :type matrix: List[List[int]]\n        :rtype: void Do not return anything, modify matrix in-place instead.\n        \"\"\"\n        m = len(matrix)\n        n = len(matrix[0])\n        if m == 0:\n            return\n        for i in range(m):\n            for j in range(n):\n                if matrix[i][j] == 0:\n                    for k in range(n):\n                        if matrix[i][k] != 0:\n                            matrix[i][k] = 'o'\n                    for k in range(m):\n                        if matrix[k][j] != 0:\n                            matrix[k][j] = 'o'\n                        \n        for i in range(m):\n            for j in range(n): \n                if matrix[i][j] == 'o':\n                    matrix[i][j] = 0\n    \nif __name__ == '__main__':\n    matrix = [[1,1,0],[1,1,1],[1,0,1]]\n    matrix = [[0]]\n    matrix = [[0,0,0,5],[4,3,1,4],[0,1,1,4],[1,2,1,3],[0,0,1,1]]\n    Solution().setZeroes(matrix)\n    print(matrix)","sub_path":"73.Set Matrix Zeroes.py","file_name":"73.Set Matrix Zeroes.py","file_ext":"py","file_size_in_byte":1088,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"375874391","text":"from django.conf.urls.defaults import patterns, url\nfrom django.contrib.auth.decorators import login_required\nfrom riot import views\n\n\nurlpatterns = patterns('riot.views',\n    url(r'^(?P<slug>[a-z0-9-]+)$', views.EventView.as_view(),\n        name='riot-event'),\n    url(r'^(?P<slug>[a-z0-9-]+)/preregister$',\n        views.PreregistrationView.as_view(),\n        name='riot-preregister'),\n    url(r'^(?P<slug>[a-z0-9-]+)/preregister/success$',\n        views.PreregisterSuccessView.as_view(),\n        name='riot-preregister-success'),\n    url(r'^(?P<slug>[a-z0-9-]+)/register$',\n        views.RegistrationView.as_view(),\n        name='riot-register'),\n    url(r'^(?P<slug>[a-z0-9-]+)/tournament-numbers$',\n        views.TournamentNumbersView.as_view(),\n        name='riot-tournament-numbers'),\n    url(r'^(?P<slug>[a-z0-9-]+)/equipment-numbers$',\n        views.EquipmentNumbersView.as_view(),\n        name='riot-equipment-numbers'),\n    url(r'^(?P<slug>[a-z0-9-]+)/volunteers$',\n        login_required(views.VolunteerListView.as_view()),\n        name='riot-equipment-numbers'),\n    url(r'^$', views.LatestEventView.as_view()),\n)\n","sub_path":"riot/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1127,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"510213616","text":"import re\n\n\ndef main():\n    data = {}\n    WorldValues = []\n    Rules = []\n    tophalf = []\n    Weights = []\n    values = []\n    ValueDict = []\n\n\n    file = input(\"Please enter file name:\")\n    openfile = open(file, \"r\")\n    openfile = (re.split(r'\\n\\n', openfile.read()))\n    CrispValues(Rules, openfile, data, WorldValues)\n    GetMembership(WorldValues, values, openfile, ValueDict)\n    GetFormula(openfile, ValueDict, tophalf, Weights)\n    print(\"The values at the top of the final equation are \" + str(tophalf))\n    print(\"The values on the bottom of the final equation are \" + str(Weights))\n\n\ndef GetFormula(openfile, ValueDict, tophalf, Weights):\n    RuleList = openfile[1]\n    Rules = RuleList.split(\"\\n\")\n    for i in range(0, len(Rules)):\n        SplitRules = Rules[i].split(\" \")\n        conditions = {SplitRules[4]: SplitRules[6], SplitRules[9]: SplitRules[11]}\n        output = {SplitRules[14]: SplitRules[17]}\n        RulesDict = {\"Rule\": SplitRules[1], \"Conditions\": conditions, \"Operator\": SplitRules[7],\n                     \"output_name\": SplitRules[14], \"Output\": SplitRules[17]}\n\n        PotentialWs = []\n        w1 = 0\n        for Entry in ValueDict:\n            if SplitRules[7] == 'and':\n                if Entry['name'] == SplitRules[4]:\n                    #Check if equals 0\n                    if Entry['Fuzzy Values'][SplitRules[6]] > 0:\n                        PotentialWs.append(Entry['Fuzzy Values'][SplitRules[6]])\n                    else:\n                        pass\n                elif Entry['name'] == SplitRules[9]:\n                    #Check if equals 0\n                    if Entry['Fuzzy Values'][SplitRules[11]] > 0:\n                        PotentialWs.append(Entry['Fuzzy Values'][SplitRules[11]])\n                    else:\n                        pass\n                if len(PotentialWs) > 1:\n                    w1 = min(PotentialWs)\n                else:\n                    pass\n\n\n\n            elif SplitRules[7] == 'or':\n                if Entry['name'] == SplitRules[4]:\n                    #Check if equals 0\n                    if Entry['Fuzzy Values'][SplitRules[6]] > 0:\n                        PotentialWs.append(Entry['Fuzzy Values'][SplitRules[6]])\n                    else:\n                        pass\n                elif Entry['name'] == SplitRules[9]:\n                    #Check if equals 0\n                    if Entry['Fuzzy Values'][SplitRules[11]] > 0:\n                        PotentialWs.append(Entry['Fuzzy Values'][SplitRules[11]])\n                    else:\n                        pass\n                if len(PotentialWs) > 0:\n                    w1 = max(PotentialWs)\n        variable = 0\n        counter = 0\n        MembershipName = []\n        MembershipTuples = []\n        for i in range(2, len(openfile) - 1, 2):\n            x = openfile[i].strip()\n            MembershipName.append(x)\n            y = openfile[i + 1].split(\"\\n\")\n            MembershipTuples.append(y)\n\n        Membership = dict(zip(MembershipName, MembershipTuples))\n        while counter < w1:\n            counter = 0\n            for val in (Membership[SplitRules[14]]):\n                if SplitRules[17] in val:\n                    goon = val.split(\" \")\n                    a = goon[1]\n                    b = goon[2]\n                    Alpha = goon[3]\n                    Beta = goon[4]\n                    e1 = curve(a, b, Alpha, Beta, 'name')\n                    counter = e1.membershipOf(variable)\n                    variable += 0.01\n        wz = variable * w1\n        if wz != 0:\n            tophalf.append(wz)\n        if w1 != 0:\n            Weights.append(w1)\n    return tophalf, Weights\n\ndef GetMembership(WorldValues, values, openfile, ValueDict):\n    for entry in WorldValues:\n        values.append(entry['name'])\n\n    MembershipName = []\n    MembershipTuples = []\n    for i in range(2, len(openfile) - 1, 2):\n        x = openfile[i].strip()\n        MembershipName.append(x)\n        y = openfile[i + 1].split(\"\\n\")\n        MembershipTuples.append(y)\n\n    Membership = dict(zip(MembershipName, MembershipTuples))\n    for j in range(0, len(WorldValues)):\n        x = WorldValues[j]\n        var = x['name']\n        Value = x['value']\n        Membervals = {}\n        for i in range(0, len(Membership)):\n            tuple = (Membership[var])\n            z = tuple[i].split(\" \")\n            a = int(z[1])\n            b = int(z[2])\n            Alpha = int(z[3])\n            Beta = int(z[4])\n            e1 = curve(a, b, Alpha, Beta, 'name')\n            Membervals[z[0]] = e1.membershipOf(Value)\n        ValueDict.append(dict({\"name\": var, \"Fuzzy Values\": Membervals}))\n    # print(ValueDict)\n    return ValueDict\n\n\ndef CrispValues(Rules, openfile, data, WorldValues):\n    RuleList = openfile[1]\n    Rules = RuleList.split(\"\\n\")\n\n    # Name of rulebase\n    ruleBaseName = openfile[0]\n    # Get crisp values\n    RealWorldValues = openfile[len(openfile) - 1]\n\n    RealWorldValues = RealWorldValues.split(\"\\n\")\n\n    for x in range(0, len(RealWorldValues)):\n        y = RealWorldValues[x].split(\" = \")\n        if y is not None:\n            WorldValues.append(dict({\"name\": y[0], \"value\": y[1]}))\n\n    data[\"CrispValues\"] = WorldValues\n    return Rules, data, WorldValues\n\n\nclass curve:\n    name = \"\"\n\n    def __init__(self, a, b, alpha, beta, name):\n        self.a = int(a)\n        self.b = int(b)\n        self.alpha = int(alpha)\n        self.beta = int(beta)\n        self.name = name\n\n    def CurveName(self):\n        return self.name\n\n    def membershipOf( self, value ):\n\n        value = int(value)\n\n        if(value < (self.a - self.alpha)):\n            return 0\n        elif(value in range (self.a - self.alpha, self.a)):\n            return (value - self.a + self.alpha) / self.alpha\n        elif(value in range(self.a,self.b)):\n            return 1\n        elif(value in range(self.b, self.b+self.beta)):\n            return (self.b + self.beta - value) / self.beta\n        elif(value > (self.b + self.beta)):\n            return 0\n\n        return\n\n\nif __name__ == '__main__':\n    main()","sub_path":"GetFormula.py","file_name":"GetFormula.py","file_ext":"py","file_size_in_byte":6032,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"270379477","text":"\r\ndef create_model(neurons=1, dropout=[0, 0 ], window_size=2,data_size=1,optimizer='nadam'):\r\n  \r\n  batch_size = 1\r\n  Input_Layer = Input(shape=( window_size, data_size ))\r\n  drop1 = Dropout(dropout[0])(Input_Layer)\r\n  lstm = LSTM(neurons,kernel_initializer='glorot_normal')(drop1)\r\n  drop2 = Dropout(dropout[1])(lstm)\r\n  dense = Dense(1)((drop2))\r\n  model = Model (inputs = Input_Layer, outputs = dense)                   \r\n  model.compile(loss='mse', optimizer=optimizer, metrics=[tf.keras.metrics.RootMeanSquaredError()])\r\n  \r\n  return model\r\n\r\n\r\ndef test_models(mode = 'basic'):\r\n\r\n  model_types = {'basic':['M','T','H'],\r\n                 'advanced':['MH','MTH']}\r\n\r\n  data_names = {'H':'total.npy', 'T':'TWEET_DATA.npy','M':'METEO_DATA.npy'}\r\n  params = {'H':[8,[0,0],4,2], 'T':[90,[0.1,0],4,226],'M':[60,[0,0],4,104]}\r\n  settings = params[model_type]\r\n  window_size = settings[2]\r\n  epochs = 50\r\n  for forecast_horizon in range(1,4):\r\n    for model_type in model_types:\r\n      \r\n      series_x = np.load(os.path.join(head_path, data_names[model_type]))\r\n      series_y = np.load(os.path.join(head_path,'HEALTH_DATA.npy'))\r\n      truth = np.load(os.path.join(head_path,'HEALTH_DATA.npy'))\r\n      truth = truth[forecast_horizon:]\r\n      series_x = series_x[:-forecast_horizon] \r\n      series_y =series_y[forecast_horizon:]\r\n      \r\n      directory = r'/content/drive/MyDrive/Final Thesis Fragkozidis Georgios/C:/Users/skote/Desktop/New Method/Final Method'\r\n      \r\n      for test_set in range(0,10):\r\n        model = create_model(settings[0],settings[1],settings[2],settings[3])\r\n        X, y = sliding_window(series_x,window_size), sliding_window(series_y,window_size)\r\n        train = list()\r\n        val = list()\r\n        X_train, y_train, X_test, y_test = get_test_partition(X, y, test_set)\r\n        \r\n        for i in range(epochs):\r\n          \r\n          if np.size(X_train[0]):\r\n            history = model.fit(X_train[0], y_train[0][:,-1,-1],shuffle=True,batch_size=1, epochs=1, verbose=0)\r\n          predict = model.predict(X_test)\r\n          model.reset_states()\r\n          if np.size(X_train[2]):\r\n            history = model.fit(X_train[2], y_train[2][:,-1,-1],shuffle=True,batch_size=1, epochs=1, verbose=0)\r\n            model.reset_states()\r\n          if i==49:\r\n            np.save(os.path.join(directory,'Results', model_type +'_'+ str(forecast_horizon)+'_'+str(2010+test_set) ),predict[-52:])\r\n            np.save(os.path.join(directory,'Results', 'Truth_'+ str(forecast_horizon)+'_'+str(2010+test_set) ),y_test[-52:,-1,-1])\r\n            print(model_type+'_'+str(forecast_horizon)+'_'+str(2010+test_set))\r\n            print(mean_squared_error(predict[-52:,-1],y_test[-52:,-1,-1]))\r\n            #plot_cases(predict,y_test, truth,forecast_horizon,window_size, test_set, directory, model_type)\r\n            #plot_learning(train,val)\r\n          \r\n          val.append(mean_squared_error(predict[-52:,-1],y_test[-52:,-1,-1]))\r\n          train.append((history.history['loss'][0]))#,history.history['root_mean_squared_error'][0] ))\r\n    \r\n        model.reset_states()\r\n        model.save(os.path.join(directory,'Models',model_type+str(forecast_horizon)+str(test_set)))\r\n\r\ntest_models()\r\n\r\nfrom keras.layers import Dense, TimeDistributed\r\n\r\n\r\ndef create_model(neurons=1, dropout=[0, 0 ], window_size=2,data_size=1,optimizer='nadam'):\r\n  batch_size = 1\r\n  Input_Layer = Input(shape=( window_size, data_size ))\r\n  lstm = LSTM(neurons,return_sequences=False,kernel_initializer='glorot_normal')(Input_Layer)\r\n  drop2 = Dropout(dropout[1])(lstm)\r\n  dense = Dense(1)((drop2))\r\n  model = Model (inputs = Input_Layer, outputs = dense)                   \r\n    \r\n  model.compile(loss='mse', optimizer=optimizer, metrics=[tf.keras.metrics.RootMeanSquaredError()])\r\n  return model\r\n\r\n\r\ndef merge_models(model1, model2):\r\n  \r\n  w1 = model1.get_weights()\r\n  w2 = model2.get_weights()\r\n  \r\n  m1 =  create_model(60,[0,0],4,104)  #M model\r\n  m2 =  create_model(8,[0,0],4,2)  #T model\r\n  \r\n  m1.set_weights(w1)\r\n  m2.set_weights(w2)\r\n  \r\n  \r\n\r\n  for layer in m1.layers[:]:\r\n    layer.trainable = False\r\n\r\n  for layer in model2.layers[:]:\r\n    layer.trainable = True  \r\n    #if hasattr(layer, 'rate'):\r\n    #  layer.rate = 0  \r\n  m1.compile(optimizer='nadam', loss = 'mse',metrics=['mse'])   \r\n  m2.compile(optimizer='nadam', loss = 'mse',metrics=['mse'])\r\n  input_data = [m1.input,model2.input]\r\n  concat = concatenate([(m1.layers[-2].output), model2.layers[-2].output])\r\n  drop = Dropout(0)(concat)\r\n  dense = Dense(1)(drop)\r\n  merged = Model(inputs=input_data, outputs=[dense])\r\n  merged.compile(optimizer='nadam', loss = 'mse',metrics=['mse'])    \r\n\r\n  return merged\r\n\r\n\r\ndef final_model(model1,model2):\r\n  mh = model1\r\n  \r\n  w2 = model2.get_weights()\r\n  t = create_model(90,[0.1,0],4,226)\r\n  t.set_weights(w2)\r\n  for layer in mh.layers[:]:\r\n    layer.trainable=True\r\n  for layer in t.layers[:]:\r\n    layer.trainable=True  \r\n  t.compile(optimizer='nadam', loss = 'mse',metrics=['mse'])\r\n  input_data = [mh.input,t.input]\r\n  concat = concatenate([ Dropout(0.1)(mh.layers[-2].output), Dropout(0.2)(t.layers[-2].output)])\r\n  drop = Dropout(0)(concat)\r\n  dense = Dense(1)(drop)\r\n  merged = Model(inputs=input_data, outputs=[dense])\r\n  merged.compile(optimizer='nadam', loss = 'mse',metrics=['mse'])   \r\n\r\n  return merged\r\ndef test_models():\r\n \r\n  window_size = 4 \r\n  merged_models = ['MTH']\r\n  model_types = ['M','H','T']\r\n  data_names = {'H':'total.npy', 'T':'TWEET_DATA.npy','M':'METEO_DATA.npy'}\r\n\r\n  epochs = 50\r\n  for forecast_horizon in range(3,4):\r\n    for model_type in  merged_models:\r\n      truth = np.load(os.path.join(head_path,'HEALTH_DATA.npy'))\r\n      truth = truth[forecast_horizon:]\r\n      series_x1 = np.load(os.path.join(head_path, data_names['M']))\r\n      series_x2 = np.load(os.path.join(head_path, data_names['H']))\r\n      series_x3 = np.load(os.path.join(head_path, data_names['T']))\r\n      series_x3[0:36] = series_x3[52:36+52]\r\n      series_y = np.load(os.path.join(head_path,'HEALTH_DATA.npy'))\r\n      scaler,series_x1,_ = scale(series_x1,series_x1)\r\n      scaler,series_x2,_ = scale(series_x2,series_x2)\r\n      scaler,series_x3,_ = scale(series_x3,series_x3)\r\n      series_x1 = series_x1[:-forecast_horizon] \r\n      series_x2 = series_x2[:-forecast_horizon] \r\n      series_x3 = series_x3[:-forecast_horizon] \r\n      series_y = series_y[forecast_horizon:]\r\n     \r\n      directory = r'/content/drive/MyDrive/Final Thesis Fragkozidis Georgios/C:/Users/skote/Desktop/New Method/Final Method'\r\n      for test_set in range(8,10):\r\n        if model_type=='MH':\r\n          model1 = tf.keras.models.load_model(os.path.join(directory,'Models','M'+str(forecast_horizon)+str(test_set)))\r\n          model2 = tf.keras.models.load_model(os.path.join(directory,'Models','H'+str(forecast_horizon)+str(test_set)))\r\n        y = sliding_window(series_y,window_size)\r\n        X1 = sliding_window(series_x1,window_size)\r\n        X2 = sliding_window(series_x2,window_size) \r\n        X_train1, y_train, X_test1, y_test = get_test_partition(X1, y, test_set)\r\n        X_train2, y_train, X_test2, y_test = get_test_partition(X2, y, test_set)\r\n        if model_type=='MTH':\r\n          model1 = tf.keras.models.load_model(os.path.join(directory,'Models','MH'+str(forecast_horizon)+str(test_set)))\r\n          model2 = tf.keras.models.load_model(os.path.join(directory,'Models','T'+str(forecast_horizon)+str(test_set)))\r\n          X3 = sliding_window(series_x3,window_size) \r\n          X_train3, y_train, X_test3, y_test = get_test_partition(X3, y, test_set)\r\n          model = final_model(model1, model2)\r\n        else:\r\n          model = merge_models(model1, model2)\r\n        train = list()\r\n        val = list()\r\n        for i in range(epochs):\r\n          if model_type=='MTH':\r\n            if np.size(X_train1[0]):\r\n              history = model.fit([[X_train1[0],X_train2[0]],X_train3[0]], y_train[0][:,-1,-1],shuffle=True,batch_size=1, epochs=1, verbose=0)\r\n            predict = model.predict([[X_test1,X_test2],X_test3])\r\n            if np.size(X_train1[2]):\r\n              history = model.fit([[X_train1[2],X_train2[2]],X_train3[2]], y_train[2][:,-1,-1],shuffle=True,batch_size=1, epochs=1, verbose=0)\r\n              model.reset_states()\r\n            #model.reset_states()\r\n            val.append(mean_squared_error(predict[-52:,-1],y_test[-52:,-1,-1]))\r\n            train.append((history.history['loss'][0]))#,history.history['val_root_mean_squared_error'][0]))  \r\n          else:\r\n            if np.size(X_train1[0]):\r\n              history = model.fit([X_train1[0],X_train2[0]], y_train[0][:,-1,-1],shuffle=True,batch_size=1, epochs=1, verbose=0)\r\n            predict = model.predict([X_test1,X_test2])\r\n            if np.size(X_train1[2]):\r\n              history = model.fit([X_train1[2],X_train2[2]], y_train[2][:,-1,-1],shuffle=True,batch_size=1, epochs=1, verbose=0)\r\n              model.reset_states()\r\n            \r\n            #model.reset_states()\r\n            val.append(mean_squared_error(predict[-52:,-1],y_test[-52:,-1,-1]))\r\n            train.append((history.history['loss'][0]))#,history.history['val_root_mean_squared_error'][0]))  \r\n          if i==(epochs-1):\r\n            model.save(os.path.join(directory,'Models',model_type+str(forecast_horizon)+str(test_set)))\r\n            np.save(os.path.join(directory,'Results', model_type +'_'+ str(forecast_horizon)+'_'+str(2010+test_set) ),predict)\r\n            print(mean_squared_error(predict[-52:,-1],y_test[-52:,-1,-1]))\r\n            plot_cases(predict,y_test, truth,forecast_horizon,window_size, test_set, directory, model_type)\r\n            plot_learning(train,val)\r\ntest_models()","sub_path":"model_testing.py","file_name":"model_testing.py","file_ext":"py","file_size_in_byte":9651,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"612459737","text":"import os\r\nimport torch\r\nfrom torch.utils.data import TensorDataset\r\n\r\n\r\nclass DataProcessor:\r\n    \"\"\"Base class for processor converters \"\"\"\r\n    def __init__(self,\r\n                 data_dir,\r\n                 logger,\r\n                 prefix='',\r\n                 truncate_label=False,\r\n                 **kwargs):\r\n        self.logger = logger\r\n        self.data_dir = data_dir\r\n        self.prefix = prefix\r\n        self.truncate_label = truncate_label\r\n\r\n        for key, value in kwargs.items():\r\n            setattr(self, key, value)\r\n\r\n    def get_train_examples(self, data_path):\r\n        \"\"\"Gets a collection of `InputExample`s for the train set.\"\"\"\r\n        return self.create_examples(self.read_data(data_path), 'train')\r\n\r\n    def get_dev_examples(self, data_path):\r\n        \"\"\"Gets a collection of `InputExample`s for the dev set.\"\"\"\r\n        return self.create_examples(self.read_data(data_path), 'dev')\r\n\r\n    def get_test_examples(self, data_path):\r\n        \"\"\"Gets a collection of `InputExample`s for the dev set.\"\"\"\r\n        return self.create_examples(self.read_data(data_path), 'test')\r\n\r\n\r\n    def get_labels(self):\r\n        \"\"\"Gets the list of labels for this processor set.\"\"\"\r\n        return None\r\n\r\n    def get_batch_keys(self):\r\n        '''\r\n         batch keys should be a list of (input_ids, attention_mask, *,*,*, labels) tuples...\r\n        '''\r\n        raise NotImplementedError\r\n\r\n    def convert_to_features(self, **kwargs):\r\n        raise NotImplementedError()\r\n\r\n    def create_examples(self, **kwargs):\r\n        raise NotImplementedError()\r\n\r\n    def read_data(self, input_file):\r\n        raise NotImplementedError()\r\n\r\n    def collate_fn(self, batch):\r\n\r\n        \"\"\"\r\n        batch should be a list of (input_ids, attention_mask, *,*,*, labels) tuples...\r\n        Returns a padded tensor of sequences sorted from longest to shortest,\r\n        \"\"\"\r\n        batch = list(map(torch.stack, zip(*batch)))\r\n        max_seq_len = torch.max(torch.sum(batch[1], 1)).item()\r\n        for i in range(len(batch) - 1):\r\n            if batch[i].size()[1] > max_seq_len:\r\n                batch[i] = batch[i][:, :max_seq_len]\r\n        if self.truncate_label:\r\n            batch[-1] = batch[-1][:, :max_seq_len]\r\n        return batch\r\n\r\n    def convert_to_tensors(self, features):\r\n        # In this method we'll make the assumption that all `features` in the batch\r\n        # have the same attributes.\r\n        # So we will look at the first element as a proxy for what attributes exist\r\n        # on the whole batch.\r\n        first = features[0]\r\n        # Special handling for labels.\r\n        # Ensure that tensor is created with the correct type\r\n        # (it should be automatically the case, but let's make sure of it.)\r\n        if hasattr(first, \"label\") and first.label is not None:\r\n            if type(first.label) is int:\r\n                labels = torch.tensor([f.label for f in features], dtype=torch.long)\r\n            else:\r\n                labels = torch.tensor([f.label for f in features], dtype=torch.float)\r\n            batch = {\"labels\": labels}\r\n        elif hasattr(first, \"label_ids\") and first.label_ids is not None:\r\n            if type(first.label_ids[0]) is int:\r\n                labels = torch.tensor([f.label_ids for f in features], dtype=torch.long)\r\n            else:\r\n                labels = torch.tensor([f.label_ids for f in features], dtype=torch.float)\r\n            batch = {\"labels\": labels}\r\n        else:\r\n            batch = {}\r\n        # Handling of all other possible attributes.\r\n        # Again, we will use the first element to figure out which key/values are not None for this model.\r\n        for k, v in vars(first).items():\r\n            if k not in (\"label\", \"label_ids\") and v is not None and not isinstance(v, str):\r\n                batch[k] = torch.tensor([getattr(f, k) for f in features], dtype=torch.long)\r\n        return batch\r\n\r\n    def load_from_cache(self, max_seq_length, data_name, mode):\r\n        '''\r\n        load feature cache\r\n        '''\r\n        # Load processor features from cache or dataset file\r\n        cached_features_file = f'cached_{mode}_{self.prefix}_{max_seq_length}'\r\n        cached_features_file = os.path.join(self.data_dir, cached_features_file)\r\n\r\n        if os.path.exists(cached_features_file):\r\n            self.logger.info(\"Loading features from cached file %s\", cached_features_file)\r\n            features = torch.load(cached_features_file)\r\n        else:\r\n            self.logger.info(\"Creating features from dataset file at %s\", str(self.data_dir))\r\n            label_list = self.get_labels()\r\n            if mode == 'train':\r\n                examples = self.get_train_examples(os.path.join(self.data_dir, data_name))\r\n            elif mode == 'dev':\r\n                examples = self.get_dev_examples(os.path.join(self.data_dir, data_name))\r\n            else:\r\n                examples = self.get_test_examples(os.path.join(self.data_dir, data_name))\r\n            features = self.convert_to_features(examples=examples, label_list=label_list, max_seq_length=max_seq_length)\r\n            self.logger.info(\"Saving features into cached file %s\", cached_features_file)\r\n            torch.save(features, cached_features_file)\r\n        return features\r\n\r\n    def create_dataset(self, max_seq_length, data_name, mode):\r\n        '''\r\n        dataset\r\n        '''\r\n        features = self.load_from_cache(max_seq_length=max_seq_length, data_name=data_name, mode=mode)\r\n        inputs = self.convert_to_tensors(features)\r\n        inputs_keys = self.get_batch_keys()\r\n        dataset = TensorDataset(*[inputs[key] for key in inputs_keys if inputs[key] is not None])\r\n        return dataset\r\n\r\n    def print_examples(self, **kwargs):\r\n        '''\r\n        打印一些样本信息\r\n        '''\r\n        inputs = {}\r\n        for key, value in kwargs.items():\r\n            if key not in inputs and value is not None:\r\n                inputs[key] = value\r\n        self.logger.info(\"*** Example ***\")\r\n        for key, value in inputs.items():\r\n            if isinstance(value, (float, int)):\r\n                value = str(value)\r\n            elif isinstance(value, list):\r\n                value = \" \".join([str(x) for x in value])\r\n            elif isinstance(value, str):\r\n                value = value\r\n            else:\r\n                raise ValueError(\"'value' value type: expected one of (float,int,str,list)\")\r\n            self.logger.info(\"%s: %s\" % (str(key), value))\r\n","sub_path":"torchblocks/processor/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":6472,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"546296127","text":"__author__ = 'KoicsD'\n\n\nclass PrimeFactor():\n    @staticmethod\n    def is_prime(n):\n        if n <= 1:\n            return False\n        for i in range(2, int(n ** 0.5) + 1):\n            if n % i == 0:\n                return False\n        return True\n\n    @staticmethod\n    def generate(n):\n        primes = []\n        if n > 1:\n            primes_to_check = [j for j in range(2, int(n ** 0.5) + 1) if PrimeFactor.generate(j)[0] == j]\n            for i in primes_to_check:\n                while n % i == 0:\n                    primes.append(i)\n                    n //= i\n            if len(primes) == 0:\n                primes.append(n)\n        return primes\n","sub_path":"prime_factors/prime_factor_rec.py","file_name":"prime_factor_rec.py","file_ext":"py","file_size_in_byte":659,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"79904167","text":"from sys import stdin\ninput = stdin.readline\n\nN = int(input())\ncnt = 0\nprime = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37]\na = [2, 3, 5, 7, 11]\nfor _ in range(N):\n    n = (int(input())*2)+1\n    if n < 9:\n        cnt += 1\n        continue\n    if n < 38:\n        if n in prime:\n            cnt += 1\n            continue\n    for i in a:\n        d = n - 1\n        ch = False\n        while d % 2 == 0:\n            if pow(i, d, n) == n - 1:\n                ch = True\n            d //= 2\n        tmp = pow(i, d, n)\n        if tmp == 1 or tmp == n-1:\n            ch = True\n        if not ch:\n            break\n    else:\n        cnt += 1\n\nprint(cnt)","sub_path":"210510/5615.py","file_name":"5615.py","file_ext":"py","file_size_in_byte":641,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"429869256","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\nProject Euler Problem 30\n=======================\n\nSurprisingly there are only three numbers that can be written as the sum\nof fourth powers of their digits:\n\n  1634 = 1^4 + 6^4 + 3^4 + 4^4\n  8208 = 8^4 + 2^4 + 0^4 + 8^4\n  9474 = 9^4 + 4^4 + 7^4 + 4^4\n\nAs 1 = 1^4 is not a sum it is not included.\n\nThe sum of these numbers is 1634 + 8208 + 9474 = 19316.\n\nFind the sum of all the numbers that can be written as the sum of fifth\npowers of their digits.\n\n\"\"\"\n\n\ndef main():\n    nice_nums = set()\n    for i in range(2, 1000000):\n        tot = 0\n        for c in str(i):\n            tot += int(c) ** 5\n        if tot == i:\n            nice_nums.add(i)\n\n    return sum(nice_nums)\n\n\nif __name__ == \"__main__\":\n    import ntpath\n    import time\n    from common.shared_functions import verify_solution\n\n    problem_number = int(ntpath.basename(__file__).replace(\"euler\", \"\").replace(\".py\", \"\"))\n    print(\"Retrieving my answer to Euler Problem {0} ...\".format(problem_number))\n\n    ts = time.time()\n    my_answer = main()\n    te = time.time()\n\n    print(\"My answer: {1}\".format(problem_number, my_answer))\n\n    verification_type = verify_solution(problem_number, my_answer)\n    print(\"Verification: {0}\".format(verification_type.name))\n    print(\"Took {0} seconds.\".format(te - ts))\n","sub_path":"project-euler/solvers/euler030.py","file_name":"euler030.py","file_ext":"py","file_size_in_byte":1322,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"652206957","text":"\nimport os\nimport sys\nimport logging\nimport warnings\nimport json\nfrom collections import defaultdict, Counter\ntry:\n    import yaml\nexcept ImportError:\n    print(\"Your python installation must have pyyaml installed.\")\n    sys.exit()\ntry:\n    import pokecat\nexcept ImportError:\n    print(\"Your python installation must have pokecat installed.\")\n    sys.exit()\n\n\nsets = []\noutfile = \"pbrpokemondb.json\"\n\nexisting_ids = {}\ngenders_per_species = defaultdict(set)\n\nillegal_chars = r\"[\\]^`|<>_{}\"\n\nname_replacements = {\"ᴹ\": \"M\", \"ɴ\": \"N\", \"×\": \"x\", \"’\": \"'\", \"”\": \"\\\"\", \"ᵖ\": \"P\", \"ᵏ\": \"K\", \" \": \" \", \"ᴾ\": \"P\"}\n\nallowed_characters = [\"\\u2640\", \"\\u2642\", \"â\", \"É\"]\n\nfor dir, _, files in os.walk(\".\"):\n    for file in files:\n        if not (file.endswith(\".yaml\") or file.endswith(\".yml\")) or file == outfile or file.startswith(\"_\"):\n            continue\n        filepath = os.path.join(dir, file)\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\n            try:\n                contents = list(yaml.load_all(f, Loader=yaml.FullLoader))\n                for pokeset in contents:\n                    if not pokeset:\n                        print(\"Skipping empty pokeset in {}\".format(filepath))\n                        continue\n                    identifier = \"{}:{} {}\".format(filepath, pokeset.get(\"species\"), pokeset.get(\"setname\"))\n                    with warnings.catch_warnings(record=True) as w:\n                        if \"ingamename\" in pokeset:\n                            fixed_ingamename = pokeset[\"ingamename\"].encode(\"ascii\", \"replace\").decode()\n                            for char in illegal_chars:\n                                fixed_ingamename = fixed_ingamename.replace(char, \"?\")\n                            temp = \"\"\n                            for i, char in enumerate(pokeset[\"ingamename\"]):\n                                if char in allowed_characters:\n                                    temp += char\n                                elif char in name_replacements:\n                                    temp += name_replacements[char]\n                                else:\n                                    temp += fixed_ingamename[i]\n                            fixed_ingamename = temp\n                            if pokeset[\"ingamename\"] != fixed_ingamename:\n                                print(\"Changed ingamename: {} -> {} to avoid encoding issues\"\n                                      .format(pokeset[\"ingamename\"], fixed_ingamename))\n                                pokeset[\"ingamename\"] = fixed_ingamename\n                        try:\n                            pokeset = pokecat.populate_pokeset(pokeset, skip_ev_check=True)\n                        except ValueError as ex:\n                            print(\"{}> ERROR: {}\".format(identifier, str(ex).encode(\"ascii\", \"replace\").decode()))\n                        else:\n                            for warning in w:\n                                text = str(warning.message).encode(\"ascii\", \"replace\").decode()\n                                if text != \"Set is shiny, but not hidden, which means it is publicly visible. Is this intended?\" and text != \"Set is shiny, but also biddable, which means it can be used in token matches. Is this intended?\" and not text.startswith(\"Sum of EV must not be larger than\") and \"is guaranteed to occupy multiple slots (possible stallmate due to PP-bug)\" not in text:\n                                    print(\"{}> WARNING: {}\".format(identifier, text))\n                            genders_this_species = genders_per_species[pokeset[\"species\"][\"id\"]]\n                            genders_this_species |= set(pokeset[\"gender\"])\n                            if None in genders_this_species and len(genders_this_species) > 1:\n                                print(\"{}> ERROR: Starting with this set, that species now has both genderless and genderful sets! \"\n                                      \"Stick to either genderless or genderful per species or PBR might crash!\"\n                                      .format(identifier))\n                                continue  # Crash risk, so do NOT add this pokeset\n                            id = (pokeset[\"species\"][\"id\"], pokeset[\"setname\"])\n                            if id in existing_ids:\n                                prev_identifier = existing_ids[id]\n                                print(\"{}> ERROR: combination of species {} ({}) and setname {} already exists ({}), but must be unique!\"\n                                      .format(identifier, id[0], pokeset[\"species\"][\"name\"], id[1], prev_identifier))\n                            else:\n                                existing_ids[id] = identifier\n                                sets += [pokeset]\n            except yaml.YAMLError as e:\n                print(\"Error reading file: {}/{}: {}\".format(dir, file, str(e).encode(\"ascii\", \"replace\").decode()))\n            except:\n                print(\"Error reading file: {}/{}\".format(dir, file))\n                raise\n\nprint(\"Writing compiled sets to {}...\".format(outfile))\nwith open(outfile, \"w+\", encoding=\"utf-8\") as f:\n    json.dump(sets, f, indent=\"    \", sort_keys=True)\n\n\ngroup_tags_file = \"group_tags.json\"\ngroup_tags = {}\nrunmons = Counter()\nanime = Counter()\npwt = Counter()\nfor set in sets:\n    tags = set['tags']\n    if 'runmon' in tags:\n        trainer_tag =\"setname+\" + set['setname']\n        runmons[trainer_tag] += 1\n    if 'anime' in tags:\n        trainer_tag = \"setname+\" + set['setname']\n        anime[trainer_tag] += 1\n    if 'in-game' in tags:\n        trainer_tag = list(filter(lambda tag: 'PWT' in tag, tags))\n        if trainer_tag:\n            pwt_set = trainer_tag[0]\n            pwt[pwt_set] += 1\n\ngroup_tags['pwt_versus_tags'] = [elem for (elem, cnt) in pwt.items() if cnt >= 4]\ngroup_tags['pwt_versus_tags'] += [elem for (elem, cnt) in runmons.items() if cnt >= 4]\n\nprint(\"Writing group tag data to {}...\".format(group_tags_file))\nwith open(group_tags_file, \"w+\", encoding=\"utf-8\") as f:\n    json.dump(group_tags, f, sort_keys=True, ensure_ascii=False)\n","sub_path":"compile.py","file_name":"compile.py","file_ext":"py","file_size_in_byte":6082,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"523094157","text":"from timeit import timeit\n\nimport numpy as np\nfrom numpy.random import rand\nfrom matplotlib import pyplot as plt\nfrom tqdm import tqdm\n\nfrom utils import covar, sd\n\n\ndef compute_correlation_matrix_loop(x):\n    feature_dim = x.shape[1]\n    z = np.zeros((feature_dim, feature_dim))\n    for i in range(feature_dim):\n        for j in range(feature_dim):\n            z[i][j] = covar(x, (i, j)) / (sd(x, i) * sd(x, j))\n    return z\n\n\ndef compute_correlation_matrix_ops(x):\n    num_vectors = x.shape[0]\n    y = np.subtract(x, np.divide(np.matmul(np.ones((num_vectors, num_vectors)), x), num_vectors))\n    vcv = np.divide(np.matmul(np.transpose(y), y), num_vectors)\n    d = np.linalg.inv(np.sqrt(np.diag(np.diag(vcv))))\n    corr_matrix = np.matmul(np.matmul(d, vcv), d)\n    return corr_matrix\n\n\ndef comparison(size=None):\n    if size is None:\n        size = int(input('Up to what size matrix (integer greater than 1) would you like to compare? '))\n\n    assert size > 1\n    sizes = [i for i in range(2, size + 1)]\n    loop_times = []\n    mat_ops_times = []\n\n    for i in tqdm(range(2, size + 1)):\n        r = rand(i, i)\n\n        loop_time = timeit(lambda: compute_correlation_matrix_loop(r), number=1)\n        mat_ops_time = timeit(lambda: compute_correlation_matrix_ops(r), number=1)\n\n        loop_times.append(loop_time)\n        mat_ops_times.append(mat_ops_time)\n\n    plt.xlabel('Matrix Size')\n    plt.ylabel('Running Times')\n    plt.plot(sizes, loop_times, label='Loop')\n    plt.plot(sizes, mat_ops_times, label='Matrix Operations')\n    plt.legend()\n    plt.savefig('comparison-problem-02.png')\n    plt.show()\n\n\nif __name__ == '__main__':\n    comparison()\n","sub_path":"problem-02.py","file_name":"problem-02.py","file_ext":"py","file_size_in_byte":1651,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"147445428","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Aug 18 17:05:10 2018\n\n@author: prasad\n@Roll No.: CMS1731\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\nfrom scipy.stats import multivariate_normal\nfrom sklearn import preprocessing,datasets\n\ndata = pd.DataFrame(datasets.load_iris().data)\nrandomShuf = np.random.choice(np.arange(len(data)),len(data),replace=True)\nshuf = np.random.choice(3,len(data),replace=True)\np = data.shape[1]\nclusterNum = 3\nitrmax = 100\nmu = []\nPriorProb = []\ncluster = []\nPostProb = []\n\nfor i in np.arange(clusterNum):\n    cluster1 = data.iloc[shuf == i]\n    pi = len(cluster1) / len(data)\n    PriorProb.append(pi)\n    cluster.append(cluster1)\n    mu = cluster1.mean(axis=0)\n    CovMat = cluster1.cov()\n    invCovMat = np.linalg.inv(CovMat)\n    norm = []\n    for j in np.arange(data.shape[0]):\n        ex = np.exp(-(0.5*float(np.mat(data.iloc[j] - mu) * np.mat(invCovMat) * np.mat(data.iloc[j]-mu).T)))\n        N = 1/(2*np.pi)**(p/2) * 1/(np.linalg.det(CovMat))**(1/2) * ex\n        norm.append(N)\n    PostProb.append(norm)\n\npi1 = len(data.iloc[shuf == 0]) / len(data)\npi2 = len(data.iloc[shuf == 1]) / len(data)\npi3 = len(data.iloc[shuf == 2]) / len(data)\nmu = [pi1,pi2,pi3]\n\nprobabilityMat = pd.DataFrame(PostProb).T\nitr = 0\nwhile(True):\n    W = []\n    for i in np.arange(data.shape[0]):\n        tp = probabilityMat.iloc[i] * PriorProb\n        w = []\n        for j in np.arange(clusterNum):\n            w.append(tp[j]/ sum(tp))\n        W.append(w)\n        \n    ric = (pd.DataFrame(W))\n    \n    phi = []\n    for i in np.arange(clusterNum):\n        phi.append(sum(ric[i])/data.shape[0])\n    \n    newMuList = []\n    for j in np.arange(ric.shape[1]):\n        tpMu = []    \n        for i in np.arange(p):\n            tpMu.append(sum(ric[j] * data[i])/sum(ric[j]))\n        newMuList.append(tpMu)\n    \n    newMu = pd.DataFrame(newMuList)\n    \n    SigmaList = []\n    for j in np.arange(ric.shape[1]):\n        sigma = 0\n        for i in np.arange(len(data)):\n            sigma = sigma + ric[j][i] * (np.mat(data.iloc[i] - newMu.iloc[j])).T * (np.mat(data.iloc[i] - newMu.iloc[j]))\n        SigmaList.append(sigma/sum(ric[j]))\n        \n    PriorProb = phi\n    CovMat = SigmaList.copy()\n    PostProb = []\n    for i in np.arange(clusterNum):\n        PostProb.append(multivariate_normal.pdf(data,newMu.iloc[i],CovMat[i]))\n        \n    probabilityMat = pd.DataFrame(PostProb).T\n    itr += 1\n    print(itr)\n    if abs(sum(mu) - sum(newMu)) < 0.0001:\n        break\n    if itr > itrmax:\n        break\n    mu = newMu","sub_path":"GMMC/GMM.py","file_name":"GMM.py","file_ext":"py","file_size_in_byte":2535,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"3490074","text":"from django.core.urlresolvers import reverse_lazy\nfrom django.http import HttpResponseRedirect\nfrom django.shortcuts import render, redirect\nfrom django.views.generic import UpdateView, ListView, CreateView\nfrom apps.student.forms import StudentForm, UsersForm\nfrom apps.course.models import Student\nfrom django.contrib.auth.models import User\n\n# Create your views here.\n\n#views\n\nclass StudentView(ListView):\n    model = Student\n    template_name = 'user/view_student.html'\n\nstudent_view = StudentView.as_view()\n\n#views form\n\nclass StudentForm(CreateView):\n    model = Student\n    template_name = 'user/view_form_student.html'\n    form_class = StudentForm\n    second_form_class = UsersForm\n    success_url = reverse_lazy('start:view_start')\n\n    def get_context_data(self, **kwargs):\n        context = super(StudentForm, self).get_context_data(**kwargs)\n        if 'form' not in context:\n            context['form'] = self.form_class(self.request.GET)\n        if 'form2' not in context:\n            context['form2'] = self.second_form_class(self.request.GET)\n        return context\n\n    def post(self, request, *args, **kwargs):\n        self.object = self.get_object\n        form = self.form_class(request.POST)\n        form2 = self.second_form_class(request.POST)\n        if form.is_valid() and form2.is_valid():\n            student = form.save(commit=False)\n            student.user = form2.save()\n            student.save()\n            return HttpResponseRedirect(self.get_success_url())\n        else:\n            return self.render_to_response(self.get_context_data(form=form, form2=form2))\n\nstudent_form = StudentForm.as_view()\n\n#views update\n\nclass StudentUpdate(UpdateView):\n    model = Student\n    second_model = User\n    template_name = 'user/update_student.html'\n    form_class = StudentForm\n    second_form_class = UsersForm\n\n    def get_context_data(self, **kwargs):\n        context = super(StudentUpdate, self).get_context_data(**kwargs)\n        pk = self.kwargs.get('pk', 0)\n        student = self.model.objects.get(id=pk)\n        user = self.second_model.objects.get(id=student.id)\n        if 'form' not in context:\n            context['form'] = self.form_class()\n        if 'form2' not in context:\n            context['form2'] = self.second_form_class(instance=user)\n        context['id'] = pk\n        return context\n\n\nstudent_update = StudentUpdate.as_view()\n\n#views delete\n\ndef student_delete(request, id_student):\n    student = Student.objects.get(id=id_student)\n    if request.method == 'POST':\n        student.delete()\n        return redirect('student:view_student')\n    return render(request, 'user/delete_student.html', {'student': student})\n\n","sub_path":"apps/student/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2666,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"70877614","text":"'''\n    MCTS solution to the AKQ game\n'''\n\n'''\n    Software design:\n\n        Player:\n\n            Props:\n\n                Range: {A,Q,K}\n\n                Chips: number of big blinds\n\n        Tree:\n\n            Props:\n\n                Root:\n\n                Struct: - The current structure of the tree. { a: {b,c}, b: {c,d} ...\n\n            Actions:\n\n                addNode\n\n                getNode\n\n                getChildren\n\n        Nodes:\n\n            Properties:\n\n                Pot: Size of the current pot\n\n                Player: Player whos action it is\n\n                Actions: Possible actions\n\n\n\n        Solver:\n\n            Props:\n\n                Type: - Type of solver it is. MCTS ect...\n\n                Tree: - The current tree\n\n                Strategy - The current strategy implemented on the current tree\n\n'''\n\nimport random\nimport numpy as np\nfrom Util.node import InfoNode,PokerNode\nfrom Util.tree import InfoTree\n#from RL.MCTS.Model import MCTS\nimport random\nimport gym\nimport numpy as np\nfrom collections import deque\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras.optimizers import Adam\n\n\nclass DQNAgent:\n\n    def __init__(self, state_size, action_size):\n\n        self.state_size = state_size\n        self.action_size = action_size\n        self.memory = deque(maxlen=2000)\n        self.gamma = 1  # discount rate\n        self.epsilon = 1.0  # exploration rate\n        self.epsilon_min = 0.01\n        self.epsilon_decay = 0.995\n        self.learning_rate = 0.1\n        self.model = self._build_model()\n\n    def _build_model(self):\n\n        # Neural Net for Deep-Q learning Model\n        model = Sequential()\n        model.add(Dense(48, input_dim=self.state_size, activation='relu'))\n        model.add(Dense(self.action_size, activation='linear'))\n        model.compile(loss='mse',\n                      optimizer=Adam(lr=self.learning_rate))\n        return model\n\n    def remember(self, state, action, reward, next_state, done):\n\n        self.memory.append((state, action, reward, next_state, done))\n\n    def act(self, state,valid_actions):\n\n        if np.random.rand() <= self.epsilon:\n\n            random_valid_action = valid_actions[random.randrange(len(valid_actions))]\n\n            return (random_valid_action,'random')\n\n        act_values = self.model.predict(state)\n\n        for i in range(self.action_size):\n\n            if i not in valid_actions:\n\n                act_values[0][i] = -100\n\n        return (np.argmax(act_values[0]),'max')  # returns action\n\n    def replay(self, batch_size):\n\n        minibatch = random.sample(self.memory, batch_size)\n\n        for state, action, reward, next_state, done in minibatch:\n\n            target = reward\n\n            if not done:\n\n                predict_next = self.model.predict(next_state)[0]\n\n                predict_next[2] = -100\n                predict_next[3] = -100\n\n                target = (reward + self.gamma *\n                          np.amax(predict_next))\n\n            target_f = self.model.predict(state)\n\n            target_f[0][action] = target\n\n            self.model.fit(state, target_f, epochs=1, verbose=0)\n\n        if self.epsilon > self.epsilon_min:\n\n            self.epsilon *= self.epsilon_decay\n\n    def load(self, name):\n        self.model.load_weights(name)\n\n    def save(self, name):\n        self.model.save_weights(name)\n\nclass ExtensiveFormMCTS(object):\n    '''\n    Outline of what a basic mcts algorith looks like for games of hidden info\n    as outlined by David Silver and heinrich\n    '''\n\n    def __init__(self):\n\n        pass\n\n    def search(game):\n\n        '''\n            While within budget\n                Sample initial game state\n                simulate(s_o)\n            end\n\n            return policy\n        '''\n        pass\n\n    def rollout(s):\n        '''\n            takes in a state\n            gets action based off of a rollout policy - i.e random actions, ect...\n            new state s' from G(s,a) - transition simulator\n            return simulate(s')\n        '''\n        pass\n\n    def simulate(s):\n        '''\n            Takes in a state\n\n            if state.terminal == True:\n                return reward\n\n            Player = player(s)\n            if Player.out_of_tree == True:\n                return rollout(s)\n            InfoState = information_function(s) maps state to info state\n            if InfoState not in PlayerTree:\n                Expand(PlayerTree,InfoState)\n                a = rollout_policy\n                Player.out_of_tree = True\n            else:\n                a = select(InfoState)\n            s' = G(s,a)\n            r = simulate(s')\n            update(InfoState,a,r)\n            return r\n        '''\n\n\n        pass\n\n    def select_uct(u_i):\n        '''\n            select action that maximizes\n            Q(u,a) + c sqrt( log(N(u))/N(u,a) )\n\n        '''\n        pass\n\n    def update(u_i, a, r):\n        '''\n        N(u_i) += 1\n        N(u,a) += 1\n        Q(u,a) += (r - Q(u,a)) / N(u,a)\n        '''\n        pass\n\nclass AKQPlayer(object):\n\n    def __init__(self,name,info_tree,starting_stack):\n\n        self.name = name\n\n        self.info_tree = info_tree\n\n        self.policy = {}\n\n        self.out_of_tree = False\n\n        self.current_hand = None\n\n        self.starting_stack = starting_stack\n\nclass AKQGameState(object):\n\n    '''\n        class used for simulating a simple AKQ poker game\n        The game state needs to deal random cards to each player\n        and to track the button\n    '''\n\n    def __init__(self,game_tree):\n\n        self.player1 = None\n\n        self.player2 = None\n\n        self.iter_count = 0\n\n        self.deck = [3,2,1]\n\n        self.game_tree = game_tree # the full game tree for for the information trees to reference\n\n        self.replay_data = []\n\n        self.adaptive_constant  = 0.8\n\n        self.behavior_policy = {\n            \"SB\": {},\n            \"BB\":{}\n        }\n\n        self.init_game()\n\n    def init_game(self):\n\n        SB_tree = InfoTree() # info tree\n\n        chance_node = PokerNode(player=\"chance\",SB_cip=0.0,BB_cip=0.0)\n\n        SB_tree.set_root(chance_node)\n\n        BB_tree = InfoTree() # info tree\n\n        game_root = self.game_tree.get_root()\n\n        BB_root_node = PokerNode(game_root.player,SB_cip=0,BB_cip=0)\n\n        BB_tree.set_root(BB_root_node)\n\n        self.player1 = AKQPlayer(name=\"SB\",info_tree=SB_tree,starting_stack=1)\n\n        self.player2 = AKQPlayer(name=\"BB\",info_tree=BB_tree,starting_stack=1)\n\n        self.player1.policy[0] = {}\n\n        self.player2.policy[0] = {}\n\n        for curr_node in self.game_tree.nodes:\n\n            if curr_node.player == 'Leaf':\n                continue\n\n            actions = [list(node.action.keys())[0] for node in curr_node.children]\n\n            self.behavior_policy[curr_node.player][curr_node.node_index] = {}\n\n            for action in actions:\n\n                self.behavior_policy[curr_node.player][curr_node.node_index][action] = {'A':0.0,'K':0.0,'Q':0.0}\n\n    def deal_hand(self):\n\n        return random.choice(self.deck)\n\n    def get_hand_string(self,hand):\n\n        if hand == 3:\n            return \"A\"\n\n        elif hand == 2:\n            return \"K\"\n\n        else:\n            return \"Q\"\n\n    def get_hero_villian(self,s):\n\n        '''\n        Take in the name of the owner of the current node and returns hero,villian\n        :param current_player:\n        :return: []\n        '''\n\n        if s.parent.player == \"SB\":\n\n            return [self.player1,self.player2]\n\n        else:\n\n            return [self.player2,self.player1]\n\n    def get_hero_villian_cip(self, s):\n\n        if s.parent.player == \"SB\":\n\n            return [s.SB_cip, s.BB_cip]\n\n        else:\n            return [s.BB_cip, s.SB_cip]\n\n    def get_info_state(self,current_player, s):\n\n        '''\n\n        info state for the AKG game is (actions,hand)\n        actions are all previous actions to this node\n\n        For the AKQ I dont think we need to keep track of\n        actions so the info stat is only going to have\n        the additional hand value\n\n        Append\n        :param s:\n        :return:\n\n        '''\n\n        if s.parent == None:\n            # this is a root node and the parent will be chance\n\n            NewInfoNode = InfoNode(current_player.current_hand, player=s.player,action=s.action, parent=current_player.info_tree.get_root(),\n                                   SB_cip=s.SB_cip, BB_cip=s.BB_cip,is_leaf=s.is_leaf)\n\n            return NewInfoNode\n\n        # need to make a copy of the parent cannot pass as reference\n\n        new_parent = PokerNode(s.parent.player,parent=s.parent.parent,SB_cip=s.parent.SB_cip,BB_cip=s.parent.BB_cip,action=s.parent.action)\n\n        NewInfoNode = InfoNode(current_player.current_hand,player=s.player ,action=s.action, parent=new_parent,\n                               SB_cip=s.SB_cip, BB_cip=s.BB_cip,is_leaf=s.is_leaf)\n\n        return NewInfoNode\n\n    def get_new_state(self,s,a):\n\n        node_children = s.children\n\n        for child in node_children:\n\n            if child.action == a:\n\n                return child\n\n            else:\n                continue\n\n        # we should not reach this line of code\n        # the function should always be able to return a new state\n\n        raise Exception(\"get_new_state was not able to find a child with the given action\")\n\n    def get_child_info(self,u_i,a):\n\n        for child in u_i.children:\n            if u_i.action == a:\n                return child\n\n        # should not reach this location\n\n        raise Exception(\"g_child_info parent does not have child with action: \" + str(a))\n\n    def reward(self,s):\n\n        '''\n\n        Takes in a leaf node and returns the reward to each player\n        :param s:\n        :return:\n\n        '''\n\n        r = {\"SB\":0,\"BB\":0}\n\n        hero, villian = self.get_hero_villian(s)\n\n        hero_cip, villian_cip = self.get_hero_villian_cip(s)\n\n        current_pot = 2.0 + s.SB_cip + s.BB_cip\n\n        action_type = list(s.action.keys())[0]\n\n        if action_type == \"fold\":\n            # the parent folded so the current player gets the pot\n            r[hero.name] = hero.starting_stack - hero_cip\n\n            r[villian.name] = current_pot + (villian.starting_stack - villian_cip)\n\n\n        elif action_type == \"check\":\n\n            # evaluate winner\n            if (hero.current_hand > villian.current_hand):\n                # SB wins\n                r[hero.name] = current_pot + (hero.starting_stack - hero_cip)\n\n                r[villian.name] = villian.starting_stack - villian_cip\n\n            else:\n\n                r[villian.name] = current_pot + (villian.starting_stack - villian_cip)\n\n                r[hero.name] = hero.starting_stack - hero_cip\n\n\n        elif action_type == \"call\": # same as check?\n\n            # evaluate winner\n            if (hero.current_hand > villian.current_hand):\n                # SB wins\n                r[hero.name] = current_pot + (hero.starting_stack - hero_cip)\n\n                r[villian.name] = villian.starting_stack - villian_cip\n\n            else:\n\n                r[villian.name] = current_pot + (villian.starting_stack - villian_cip)\n\n                r[hero.name] = hero.starting_stack - hero_cip\n\n        return r\n\n    def rollout(self,s):\n\n        '''\n            takes in a state\n            gets action based off of a rollout policy - i.e random actions, ect...\n            new state s' from G(s,a) - transition simulator\n            return simulate(s')\n        '''\n\n        new_action = self.rollout_policy(s)\n\n        new_state = self.get_new_state(s,new_action)\n\n        return self.simulate(new_state) # recursive call\n\n    def rollout_policy(self,s):\n\n        '''\n\n        Get a new action for the player based off the rollout policy\n        :param s:\n        :return:\n\n        '''\n\n        # for now just going to use a random rollout policy\n\n        #possible_actions = [child.action for child in s.children]\n\n        # just return the child node\n\n        try:\n\n            return random.choice(s.children).action\n\n        except Exception as e:\n            print(\"Error at rollout_policy: \" + str(e))\n\n    def select_uct(self,u_i):\n\n        '''\n            select action that maximizes\n\n            if random U [0,1] < mu\n\n                Q(u,a) + c sqrt( log(N(u))/N(u,a) )\n\n            else\n                policy = N(u,a) / N(u)\n                return a ~ p\n        '''\n\n        N_U = u_i.visit_count\n\n        if (np.random.random() < self.adaptive_constant):\n\n            if N_U == 0:\n                print(\"Visit count = 0!\")\n\n            current_max_action = None\n\n            current_max = -1\n\n            current_player = self.player1 if u_i.player == \"SB\" else self.player2\n\n            info_policy = current_player.policy[u_i.node_index]\n\n            for action in info_policy.keys():\n\n                child_ev_value = info_policy[action]['ev']\n\n                child_visit_count = info_policy[action]['count']\n\n                score = 0\n\n                if child_visit_count == 0:\n\n                    score = current_max + 1000\n\n                else:\n                    score = child_ev_value + 1.5*np.sqrt(np.log(N_U)/child_visit_count)\n\n                if score > current_max:\n\n                    current_max = score\n\n                    current_max_action = action\n\n            if current_max_action == \"check\" or current_max_action == \"fold\":\n                return {current_max_action: 0}\n            else:\n                return {current_max_action: 1}\n\n        else:\n\n            current_player = self.player1 if u_i.player == \"SB\" else self.player2\n\n            action_p = []\n\n            actions = []\n\n            for action in current_player.policy[u_i.node_index].keys():\n\n                n_a_count = current_player.policy[u_i.node_index][action]['count']\n\n                action_p.append(float(n_a_count/N_U))\n\n                actions.append(action)\n\n            choose_action = np.random.choice(actions,1,p=action_p)[0]\n\n\n            if choose_action == \"check\" or choose_action == \"fold\":\n\n                return {choose_action: 0}\n            else:\n                return {choose_action: 1}\n\n    def simulate(self,s):\n\n        self.iter_count += 1\n\n        '''\n\n            Takes in a state\n\n            if state.terminal == True:\n                return reward\n\n            Player = player(s)\n            if Player.out_of_tree == True:\n                return rollout(s)\n            InfoState = information_function(s) maps state to info state\n            if InfoState not in PlayerTree:\n                Expand(PlayerTree,InfoState)\n                a = rollout_policy\n                Player.out_of_tree = True\n            else:\n                a = select(InfoState)\n            s' = G(s,a)\n            r = simulate(s')\n            update(InfoState,a,r)\n            return r\n\n        '''\n\n        if s.is_leaf == True:\n\n            return self.reward(s)\n\n        current_player = self.player1 if s.player == \"SB\" else self.player2\n\n        if current_player.out_of_tree == True:\n\n            return self.rollout(s)\n\n        infostate = self.get_info_state(current_player,s)\n\n        action = None\n\n        action_select_type = \"uct\"\n\n        if not current_player.info_tree.node_in_tree(infostate):\n\n            current_player.info_tree.add_node(infostate)\n\n            action = self.rollout_policy(s)\n\n            action_select_type = \"rollout\"\n\n            current_player.out_of_tree = True\n\n            current_player.policy[infostate.node_index] = {}\n\n            for child in s.children:\n\n                new_action = list(child.action.keys())[0]\n\n                current_player.policy[infostate.node_index][new_action] = {}\n\n                current_player.policy[infostate.node_index][new_action]['count'] = 0\n\n                current_player.policy[infostate.node_index][new_action]['ev'] = 0\n\n        else:\n\n            infostate = current_player.info_tree.get_tree_node(infostate)\n\n            action = self.select_uct(infostate)\n\n\n\n        next_state = self.get_new_state(s,action)\n\n        ###############\n        # REPLAY DATA\n        ###############\n\n        action_type = list(action.keys())[0]\n\n        r = self.simulate(next_state)\n\n        replay_data = [current_player.name,s.node_index,self.get_hand_string(current_player.current_hand),action_type,r[current_player.name]]\n\n        self.replay_data.append(replay_data)\n\n        self.update(current_player,s,infostate,action,r)\n\n        return r\n\n    def update(self,current_player,s,u_i, a, r):\n\n        '''\n        N(u_i) += 1\n        N(u,a) += 1\n        Q(u,a) += (r - Q(u,a)) / N(u,a)\n        '''\n\n        u_i.visit_count += 1\n\n        player_reward = r[current_player.name]\n\n        action_type = list(a.keys())[0]\n\n        current_player.policy[u_i.node_index][action_type]['count'] += 1\n\n        current_count = current_player.policy[u_i.node_index][action_type]['count']\n\n        current_ev = current_player.policy[u_i.node_index][action_type]['ev']\n\n        update = (player_reward - current_ev)/current_count\n\n        current_player.policy[u_i.node_index][action_type]['ev'] += update\n\n        # update policy simply N(u,a) / N(u)\n\n        string_hand = self.get_hand_string(u_i.player_hand)\n\n        for action in list(current_player.policy[u_i.node_index].keys()):\n\n            N_U_A = current_player.policy[u_i.node_index][action]['count']\n\n            self.behavior_policy[current_player.name][s.node_index][action][string_hand] = N_U_A / float(u_i.visit_count)\n\n    def run(self,num_iterations):\n\n        for i in range(num_iterations):\n\n            #self.iter_count = i\n\n            self.deck = [3,2,1] # reshuffle the cards yo\n\n            self.player1.out_of_tree = False\n\n            self.player2.out_of_tree = False\n\n            # deals cards to each player\n\n            sb_card = self.deal_hand()\n\n            self.player1.current_hand = sb_card\n\n            self.deck.remove(sb_card)\n\n            bb_card = self.deal_hand()\n\n            self.player2.current_hand = bb_card\n\n            s0 = self.game_tree.get_root()\n\n            self.simulate(s0)\n\n\n        return [self.player1.policy,self.player2.policy]\n\n\n\n\n\n\n\n","sub_path":"Poker/mcts_akq.py","file_name":"mcts_akq.py","file_ext":"py","file_size_in_byte":18137,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"490296015","text":"import torch.nn.functional as F\nimport torch.optim as optim\nfrom torch.autograd import Variable\nfrom copy import deepcopy\nfrom CNN import CNN\n\nclass VanillaCNN(CNN):\n    def __init__(self, hidden_size, input_size, output_size, device):\n\n        super().__init__(hidden_size, input_size, output_size, device)\n\n    @classmethod\n    def from_existing_model(cls, m, new_hidden_size):\n\n        model = cls(new_hidden_size, m.input_size, m.output_size, m.device)\n\n        model.size_dictionary = deepcopy(m.size_dictionary)\n\n        model.task_post_training_weights = deepcopy(m.task_post_training_weights)\n\n        model.copy_weights_expanding(m)\n\n        return model\n\n\n    def train_model(self, args, train_loader, task_number, **kwargs):\n\n        # Set the module in \"training mode\"\n        # This is necessary because some network layers behave differently when training vs testing.\n        # Dropout, for example, is used to zero/mask certain weights during TRAINING to prevent overfitting.\n        # However, during TESTING (e.g. model.eval()) we do not want this to happen.\n        self.train()\n\n        self.reinitialize_output_weights()\n\n        # Set the optimization algorithm for the model- in this case, Stochastic Gradient Descent with/without\n        # momentum (depends on the value of args.momentum- default is 0.0, so no momentum by default).\n        #\n        # ARGUMENTS (in order):\n        #     params (iterable) - iterable of parameters to optimize or dicts defining parameter groups\n        #     lr (float) - learning rate\n        #     momentum (float, optional) - momentum factor (default: 0)\n        #\n        # NOTE on params:\n        #   model.parameters() returns an iterator over a list of the model parameters in the same order in\n        #   which they appear in the network when traversed input -> output\n        #   (e.g.\n        #       [weights b/w input and first hidden layer,\n        #        bias b/w input and hidden layer 1,\n        #        ... ,\n        #        weights between last hidden layer and output,\n        #        bias b/w hidden layer and output]\n        #   )\n        optimizer = optim.SGD(self.parameters(), lr=args.lr, momentum=args.momentum) # can use filter and requires_grad=False to freeze part of the network...\n        #optimizer = optim.Adadelta(self.parameters())\n\n\n        for epoch in range(1, args.epochs + 1):\n\n            running_loss = 0.0\n\n            # Enumerate will keep an automatic loop counter and store it in batch_idx.\n            # The (data, target) pair returned by DataLoader train_loader each iteration consists\n            # of an MNIST image data sample and an associated label classifying it as a digit 0-9.\n            #\n            # The image data for the batch is represented as a 4D torch tensor (see train_loader definition in main())\n            # with dimensions (batch size, 1, 28, 28)- containing a normalized floating point value for the color of\n            # each pixel in each image in the batch (MNIST images are 28 x 28 pixels).\n            #\n            # The target is represented as a torch tensor containing the digit classification labels for\n            # the training data as follows:\n            #       [ 3,  4,  2,  9,  7] represents ground truth labels for a 3, a 4, a 2, a 9, and a 7.\n            # NOTE:\n            # The indices are converted to one-hot label representations inside of the loss function:\n            #       [[0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n            #        [0, 0, 1, 0, 0, 0, 0, 0, 0, 0]]\n            # represents labels for a 5 and a 2, because 1's are at index 5 and 2 in rows 0 and 1, respectively.\n            #\n            # SOURCE:\n            # https://discuss.pytorch.org/t/why-does-the-minimal-pytorch-tutorial-not-have-mnist-images-be-onehot-for-logistic-regression/12562/6\n            for batch_idx, (data, target) in enumerate(train_loader, 0):\n\n                # The data needs to be wrapped in another tensor to work with our network,\n                # otherwise it is not of the appropriate dimensions... I believe these two statements effectively add\n                # a dimension.\n                #\n                # For an explanation of the meaning of these statements, see:\n                #   https://stackoverflow.com/a/42482819/9454504\n                #\n                # This code was used here in another experiment:\n                # https://github.com/kuc2477/pytorch-ewc/blob/4a75734ef091e91a83ce82cab8b272be61af3ab6/train.py#L35\n\n                #todo remove?\n                #data_size = len(data)\n\n                # todo remove from CNN?\n                # data = data.view(data_size, -1)\n\n                # wrap data and target in variables- again, from the following experiment:\n                #   https://github.com/kuc2477/pytorch-ewc/blob/4a75734ef091e91a83ce82cab8b272be61af3ab6/train.py#L50\n                #\n                # .to(device):\n                #   set the device (CPU or GPU) to be used with data and target to device variable (defined in main())\n\n                # todo may not need to wrap in variables - just to move to GPU if necessary\n                data, target = Variable(data).to(self.device), Variable(target).to(self.device)\n\n                #print(target) # todo remove- for debugging\n\n                # Gradients are automatically accumulated- therefore, they need to be zeroed out before the next backward\n                # pass through the network so that they are replaced by newly computed gradients at later training iterations,\n                # rather than SUMMED with those future gradients. The reasoning behind this approach and the need to zero\n                # gradients manually with each training minibatch is presented here in more detail:\n                # https://discuss.pytorch.org/t/why-do-we-need-to-set-the-gradients-manually-to-zero-in-pytorch/4903/9\n                #\n                # From PyTorch examples:\n                #   Before the backward pass, use the optimizer object to zero all of the\n                #   gradients for the variables it will update (which are the learnable\n                #   weights of the model). This is because by default, gradients are\n                #   accumulated in buffers( i.e, not overwritten) whenever .backward()\n                #   is called.\n                optimizer.zero_grad()\n\n                # forward pass: compute predicted output by passing data to the network\n                # NOTE: we have overridden forward() in class Net above, so this will call model.forward()\n                output = self(data)\n\n                # Define the training loss function for the model to be cross entropy loss based on predicted values\n                # and ground truth labels. This loss function only takes into account loss on the most recent task.\n                #\n                # NOTE: torch.nn.CrossEntropyLoss combines torch.nn.LogSoftmax() and torch.nn.NLLLoss() in one single class.\n                # apply the loss function to the predictions/labels for this batch to compute loss\n                loss = F.cross_entropy(output, target)\n\n                # Backward pass: compute gradient of the loss with respect to model\n                # parameters\n                loss.backward()\n\n                # Simplified abstraction provided by PyTorch which uses a single statement to update all model parameters\n                # according to gradients (with respect to the last loss function on which .backward() was called and\n                # optimization function's update rule.\n                # In the case of SGD (without momentum), essentially executes the following:\n                #\n                #       with torch.no_grad():\n                #           for param in model.parameters():\n                #               param -= learning_rate * param.grad\n                optimizer.step()\n\n                running_loss += loss.item()\n\n                # Each time the batch index is a multiple of the specified progress display interval (args.log_interval),\n                # print a message indicating progress AND which network (model) is reporting values.\n                if batch_idx % args.log_interval == args.log_interval - 1:\n                    print('{} Task: {} Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n                                                                                    'NoReg',\n                                                                                    task_number,\n                                                                                    epoch,\n                                                                                    batch_idx * len(data),\n                                                                                    args.train_dataset_size,\n                                                                                    100. * batch_idx / len(train_loader),\n                                                                                    running_loss / args.log_interval\n                                                                                    ))\n                    running_loss = 0.0\n\n\n        # update the model size dictionary\n        self.update_size_dict(task_number)\n\n        self.save_theta_stars(task_number)\n","sub_path":"VanillaCNN.py","file_name":"VanillaCNN.py","file_ext":"py","file_size_in_byte":9313,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"238354293","text":"import argparse  ##importing argparse\nparser=argparse.ArgumentParser()\nparser.add_argument(\"-v\", \"--verbosity\", help=\"increase output verbosity\", action=\"count\", default=0) ##Acts as a flag.  If you type python3 arg2.py verbose, it assumse verbose is true, otherwise false\n##                  inside here, you can use action=\"count\" such that -v acts like a flag, -v =1, -vv =2 and so on, and no -v means None, except when we fix bug now we need default=0\n##                  can also set it up such that the type of -v is an int, then put in 'choices=[0,1,2]' to limit what can be input, error message for all other numbers though\nparser.add_argument(\"square\", type=int, help=\"Display a square of a given number\")\nargs=parser.parse_args()\nanswer=args.square**2\n\n##bugfix, replace == with >= for the count part of -v to work correctly\nif args.verbosity >= 2:  ##display something when --verbosity is specified, nothing if not, if no argument is given this doesn't run\n    print(\"the square of {} equals {}\".format(args.square, answer))\nelif args.verbosity >= 1:\n    print(\"{}^2 == {}\".format(args.square, answer))\nelse:\n    print(answer)","sub_path":"arg2.py","file_name":"arg2.py","file_ext":"py","file_size_in_byte":1137,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"586032550","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# YuQi created\n__mtime__ = '2017/4/17'\nclass Solution(object):\n    def swap(self,nums,i,j):\n        nums[i],nums[j] = nums[j],nums[i]\n\n    def firstMissingPositive(self, nums):\n        for i in range(len(nums)):\n            if nums[i] <= 0 or nums[i] > len(nums):\n                continue\n            if nums[i] == i+1:\n                continue\n            while nums[i] != (i+1) and nums[i] <= len(nums) and nums[i] >0 and nums[i] != nums[nums[i]-1]:\n                self.swap(nums,i,nums[i]-1)\n        for i in range(len(nums)):\n            if nums[i] != i+1:\n                return i+1\n        return len(nums)+1\n\nso = Solution()\na = [3,-4,-1,1]\nres = so.firstMissingPositive(a)\nprint(res)\n","sub_path":"41-55/41. First Missing Positive.py","file_name":"41. First Missing Positive.py","file_ext":"py","file_size_in_byte":739,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"164052357","text":"import json\nfrom collections import OrderedDict\n\nfrom .h import *  # noqa\nfrom .messages import *\n\n\ndef addMdnPanels(doc):\n    if not doc.md.includeMdnPanels:\n        return\n\n    try:\n        filename = doc.md.vshortname + \".json\"\n        datafile = doc.dataFile.fetch(\"mdn\", filename, str=True)\n    except OSError:\n        try:\n            filename = doc.md.shortname + \".json\"\n            datafile = doc.dataFile.fetch(\"mdn\", filename, str=True)\n        except OSError:\n            if doc.md.includeMdnPanels == \"maybe\":\n                # if \"maybe\", failure is fine, don't complain\n                pass\n            else:\n                die(\n                    f\"Couldn't find the MDN data for '{doc.md.vshortname}' nor '{doc.md.shortname}'.\"\n                )\n            return\n    try:\n        data = json.loads(datafile, object_pairs_hook=OrderedDict)\n    except Exception as e:\n        die(f\"Couldn't load MDN Spec Links data for this spec.\\n{e}\")\n        return\n\n    panels = panelsFromData(doc, data)\n    if panels:\n        doc.extraScripts[\n            \"script-mdn-anno\"\n        ] = \"\"\"\n            document.body.addEventListener(\"click\", (e) => {\n                if(e.target.closest(\".mdn-anno-btn\")) {\n                    e.target.closest(\".mdn-anno\").classList.toggle(\"wrapped\");\n                }\n            });\n            \"\"\"  # noqa\n        doc.extraStyles[\n            \"style-mdn-anno\"\n        ] = \"\"\"\n            @media (max-width: 767px) { .mdn-anno { opacity: .1 } }\n            .mdn-anno { font: 1em sans-serif; padding: 0.3em; position: absolute; z-index: 8; right: 0.3em; background: #EEE; color: black; box-shadow: 0 0 3px #999; overflow: hidden; border-collapse: initial; border-spacing: initial; min-width: 9em; max-width: min-content; white-space: nowrap; word-wrap: normal; hyphens: none}\n            .mdn-anno:not(.wrapped) { opacity: 1}\n            .mdn-anno:hover { z-index: 9 }\n            .mdn-anno.wrapped { min-width: 0 }\n            .mdn-anno.wrapped > :not(button) { display: none; }\n            .mdn-anno > .mdn-anno-btn { cursor: pointer; border: none; color: #000; background: transparent; margin: -8px; float: right; padding: 10px 8px 8px 8px; outline: none; }\n            .mdn-anno > .mdn-anno-btn > .less-than-two-engines-flag { color: red; padding-right: 2px; }\n            .mdn-anno > .mdn-anno-btn > .all-engines-flag { color: green; padding-right: 2px; }\n            .mdn-anno > .mdn-anno-btn > span { color: #fff; background-color: #000; font-weight: normal; font-family: zillaslab, Palatino, \"Palatino Linotype\", serif; padding: 2px 3px 0px 3px; line-height: 1.3em; vertical-align: top; }\n            .mdn-anno > .feature { margin-top: 20px; }\n            .mdn-anno > .feature:not(:first-of-type) { border-top: 1px solid #999; margin-top: 6px; padding-top: 2px; }\n            .mdn-anno > .feature > .less-than-two-engines-text { color: red }\n            .mdn-anno > .feature > .all-engines-text { color: green }\n            .mdn-anno > .feature > p { font-size: .75em; margin-top: 6px; margin-bottom: 0; }\n            .mdn-anno > .feature > p + p { margin-top: 3px; }\n            .mdn-anno > .feature > .support { display: block; font-size: 0.6em; margin: 0; padding: 0; margin-top: 2px }\n            .mdn-anno > .feature > .support + div { padding-top: 0.5em; }\n            .mdn-anno > .feature > .support > hr { display: block; border: none; border-top: 1px dotted #999; padding: 3px 0px 0px 0px; margin: 2px 3px 0px 3px; }\n            .mdn-anno > .feature > .support > hr::before { content: \"\"; }\n            .mdn-anno > .feature > .support > span { padding: 0.2em 0; display: block; display: table; }\n            .mdn-anno > .feature > .support > span.no { color: #CCCCCC; filter: grayscale(100%); }\n            .mdn-anno > .feature > .support > span.no::before { opacity: 0.5; }\n            .mdn-anno > .feature > .support > span:first-of-type { padding-top: 0.5em; }\n            .mdn-anno > .feature > .support > span > span { padding: 0 0.5em; display: table-cell; }\n            .mdn-anno > .feature > .support > span > span:first-child { width: 100%; }\n            .mdn-anno > .feature > .support > span > span:last-child { width: 100%; white-space: pre; padding: 0; }\n            .mdn-anno > .feature > .support > span::before { content: ' '; display: table-cell; min-width: 1.5em; height: 1.5em; background: no-repeat center center; background-size: contain; text-align: right; font-size: 0.75em; font-weight: bold; }\n            .mdn-anno > .feature > .support > .chrome_android::before { background-image: url(https://resources.whatwg.org/browser-logos/chrome.svg); }\n            .mdn-anno > .feature > .support > .firefox_android::before { background-image: url(https://resources.whatwg.org/browser-logos/firefox.png); }\n            .mdn-anno > .feature > .support > .chrome::before { background-image: url(https://resources.whatwg.org/browser-logos/chrome.svg); }\n            .mdn-anno > .feature > .support > .edge_blink::before { background-image: url(https://resources.whatwg.org/browser-logos/edge.svg); }\n            .mdn-anno > .feature > .support > .edge::before { background-image: url(https://resources.whatwg.org/browser-logos/edge_legacy.svg); }\n            .mdn-anno > .feature > .support > .firefox::before { background-image: url(https://resources.whatwg.org/browser-logos/firefox.png); }\n            .mdn-anno > .feature > .support > .ie::before { background-image: url(https://resources.whatwg.org/browser-logos/ie.png); }\n            .mdn-anno > .feature > .support > .safari_ios::before { background-image: url(https://resources.whatwg.org/browser-logos/safari-ios.svg); }\n            .mdn-anno > .feature > .support > .nodejs::before { background-image: url(https://nodejs.org/static/images/favicons/favicon.ico); }\n            .mdn-anno > .feature > .support > .opera_android::before { background-image: url(https://resources.whatwg.org/browser-logos/opera.svg); }\n            .mdn-anno > .feature > .support > .opera::before { background-image: url(https://resources.whatwg.org/browser-logos/opera.svg); }\n            .mdn-anno > .feature > .support > .safari::before { background-image: url(https://resources.whatwg.org/browser-logos/safari.png); }\n            .mdn-anno > .feature > .support > .samsunginternet_android::before { background-image: url(https://resources.whatwg.org/browser-logos/samsung.svg); }\n            .mdn-anno > .feature > .support > .webview_android::before { background-image: url(https://resources.whatwg.org/browser-logos/android-webview.png); }\n            .name-slug-mismatch { color: red }\n            .caniuse-status:hover { z-index: 9; }\n\n            /* dt, li, .issue, .note, and .example are \"position: relative\", so to put annotation at right margin, must move to right of containing block */\n            .h-entry:not(.status-LS) dt > .mdn-anno, .h-entry:not(.status-LS) li > .mdn-anno, .h-entry:not(.status-LS) .issue > .mdn-anno, .h-entry:not(.status-LS) .note > .mdn-anno, .h-entry:not(.status-LS) .example > .mdn-anno { right: -6.7em; }\n            .h-entry p + .mdn-anno { margin-top: 0; }\n            h2 + .mdn-anno.after { margin: -48px 0 0 0; }\n            h3 + .mdn-anno.after { margin: -46px 0 0 0; }\n            h4 + .mdn-anno.after { margin: -42px 0 0 0; }\n            h5 + .mdn-anno.after { margin: -40px 0 0 0; }\n            h6 + .mdn-anno.after { margin: -40px 0 0 0; }\n            \"\"\"  # noqa\n\n\ndef createAnno(className, mdnButton, featureDivs):\n    return E.div({\"class\": className}, mdnButton, featureDivs)\n\n\ndef panelsFromData(doc, data):\n    mdnBaseUrl = \"https://developer.mozilla.org/en-US/docs/Web/\"\n\n    browsersProvidingCurrentEngines = [\"firefox\", \"safari\", \"chrome\"]\n    browsersWithBorrowedEngines = [\"opera\", \"edge_blink\"]\n    browsersWithRetiredEngines = [\"edge\", \"ie\"]\n    browsersForMobileDevices = [\n        \"firefox_android\",\n        \"safari_ios\",\n        \"chrome_android\",\n        \"webview_android\",\n        \"samsunginternet_android\",\n        \"opera_android\",\n    ]\n\n    # BCD/mdn-spec-links shortnames to full names\n    nameFromCodeName = {\n        \"chrome\": \"Chrome\",\n        \"chrome_android\": \"Chrome for Android\",\n        \"edge\": \"Edge (Legacy)\",\n        \"edge_blink\": \"Edge\",\n        \"firefox\": \"Firefox\",\n        \"firefox_android\": \"Firefox for Android\",\n        \"ie\": \"IE\",\n        \"nodejs\": \"Node.js\",\n        \"opera\": \"Opera\",\n        \"opera_android\": \"Opera Mobile\",\n        \"safari\": \"Safari\",\n        \"samsunginternet_android\": \"Samsung Internet\",\n        \"safari_ios\": \"iOS Safari\",\n        \"webview_android\": \"Android WebView\",\n    }\n\n    panels = False\n    for elementId, features in data.items():\n        isAnnoForHeadingContent = False\n        isAnnoForListItemOrTableContent = False\n        lessThanTwoEngines = 0\n        onlyTwoEngines = 0\n        allEngines = 0\n        featureDivs = []\n        targetElement = find(f\"[id='{elementId}']\", doc)\n        if targetElement is None:\n            warn(\n                f\"No '{elementId}' ID found, skipping MDN features that would target it.\"\n            )\n            continue\n\n        panels = True\n        if targetElement.tag in [\"h1\", \"h2\", \"h3\", \"h4\", \"h5\", \"h6\"]:\n            isAnnoForHeadingContent = True\n        else:\n            for ancestor in targetElement.iterancestors():\n                if ancestor.tag in [\n                    \"body\",\n                    \"main\",\n                    \"article\",\n                    \"aside\",\n                    \"nav\",\n                    \"section\",\n                    \"header\",\n                    \"footer\",\n                ]:\n                    break\n                targetElement = ancestor\n                if ancestor.tag in [\"h1\", \"h2\", \"h3\", \"h4\", \"h5\", \"h6\"]:\n                    isAnnoForHeadingContent = True\n                    break\n                if ancestor.tag in [\"td\", \"dt\", \"dd\", \"li\"]:\n                    isAnnoForListItemOrTableContent = True\n                    break\n                if ancestor.tag in [\"pre\", \"xmp\", \"p\"]:\n                    break\n        for feature in features:\n            if \"engines\" in feature:\n                engines = len(feature[\"engines\"])\n                if engines < 2:\n                    lessThanTwoEngines = lessThanTwoEngines + 1\n                elif engines == 2:\n                    onlyTwoEngines = onlyTwoEngines + 1\n                elif engines >= len(browsersProvidingCurrentEngines):\n                    allEngines = allEngines + 1\n            featureDivs.append(\n                mdnPanelFor(\n                    feature,\n                    mdnBaseUrl,\n                    nameFromCodeName,\n                    browsersProvidingCurrentEngines,\n                    browsersWithBorrowedEngines,\n                    browsersWithRetiredEngines,\n                    browsersForMobileDevices,\n                )\n            )\n\n        mdnButton = E.button({\"class\": \"mdn-anno-btn\"})\n        if lessThanTwoEngines > 0:\n            appendChild(\n                mdnButton,\n                E.b(\n                    {\n                        \"class\": \"less-than-two-engines-flag\",\n                        \"title\": \"This feature is in less than two current engines.\",\n                    },\n                    \"\\u26A0\",\n                ),\n            )\n        elif allEngines > 0 and lessThanTwoEngines == 0 and onlyTwoEngines == 0:\n            appendChild(\n                mdnButton,\n                E.b(\n                    {\n                        \"class\": \"all-engines-flag\",\n                        \"title\": \"This feature is in all current engines.\",\n                    },\n                    \"\\u2714\",\n                ),\n            )\n        appendChild(mdnButton, E.span(\"MDN\"))\n\n        className = \"mdn-anno wrapped\"\n        if isAnnoForListItemOrTableContent:\n            if targetElement.getchildren() and hasClass(\n                targetElement.getchildren()[0], \"mdn-anno\"\n            ):\n                # If there's already an annotation at the point where we want\n                # this, just re-use it (instead of creating another one).\n                appendChild(targetElement.getchildren()[0], featureDivs)\n            else:\n                # For elements we're annotating inside a dt, dd, li, or td, we\n                # prepend the annotation to the dt, dd, li, or td — because in\n                # cases where we have a long table or list, all the annotations\n                # for everything in it otherwise ends up being merged into a\n                # single annotation way up at the top of the table or list.\n                prependChild(\n                    targetElement, createAnno(className, mdnButton, featureDivs)\n                )\n        elif isAnnoForHeadingContent:\n            className = \"mdn-anno wrapped after\"\n            if (\n                targetElement.getnext() is not None\n                and targetElement.getnext().get(\"class\") == className\n            ):\n                # If there's already an annotation at the point where we want\n                # this, just re-use it (instead of creating another one).\n                appendChild(targetElement.getnext(), featureDivs)\n            else:\n                # For elements we're annotating inside an h1-h6 heading, we\n                # insert the annotation as the next sibling of the heading.\n                insertAfter(\n                    targetElement, createAnno(className, mdnButton, featureDivs)\n                )\n        else:\n            if (\n                targetElement.getprevious() is not None\n                and targetElement.getprevious().get(\"class\") == className\n            ):\n                # If there's already an annotation at the point where we want\n                # this, just re-use it (instead of creating another one) —\n                # unless it's a class=after annotation (following a heading).\n                appendChild(targetElement.getprevious(), featureDivs)\n            else:\n                # For elements we're annotating that aren't inside a table or\n                # list or heading, we insert the annotation as the previous\n                # sibling of whatever block-level element holds the element.\n                insertBefore(\n                    targetElement, createAnno(className, mdnButton, featureDivs)\n                )\n    return panels\n\n\ndef addSupportRow(browserCodeName, nameFromCodeName, support, supportData):\n    if browserCodeName not in support:\n        return\n    isEdgeLegacy = browserCodeName == \"edge\"\n    isIE = browserCodeName == \"ie\"\n    needsFlag = False\n    versionAdded = None\n    versionRemoved = None\n    minVersion = None\n    thisBrowserSupport = support[browserCodeName]\n    if isinstance(thisBrowserSupport, dict):\n        if \"version_added\" in thisBrowserSupport:\n            versionAdded = thisBrowserSupport[\"version_added\"]\n            if \"flags\" in thisBrowserSupport:\n                needsFlag = True\n            if (\n                \"prefix\" in thisBrowserSupport\n                or \"alternative_name\" in thisBrowserSupport\n                or \"partial_implementation\" in thisBrowserSupport\n            ):\n                versionAdded = False\n        if \"version_removed\" in thisBrowserSupport:\n            versionRemoved = thisBrowserSupport[\"version_removed\"]\n    if isinstance(thisBrowserSupport, list):\n        for versionDetails in thisBrowserSupport:\n            if \"version_removed\" in versionDetails:\n                versionRemoved = versionDetails[\"version_removed\"]\n                continue\n            if \"version_added\" in versionDetails:\n                if versionDetails[\"version_added\"] is False:\n                    versionAdded = False\n                    continue\n                if versionDetails[\"version_added\"] is None:\n                    versionAdded = None\n                    continue\n                if (\n                    \"prefix\" in versionDetails\n                    or \"alternative_name\" in versionDetails\n                    or \"partial_implementation\" in versionDetails\n                ):\n                    continue\n                if \"flags\" in thisBrowserSupport:\n                    needsFlag = True\n                versionAdded = versionDetails[\"version_added\"]\n                versionRemoved = None\n                break\n    statusCode = \"n\"\n    if versionAdded is None:\n        minVersion = \"?\"\n    elif versionAdded is False:\n        minVersion = \"None\"\n    elif versionAdded is True:\n        minVersion = \"Yes\"\n        statusCode = \"y\"\n    else:\n        if versionRemoved is None:\n            statusCode = \"y\"\n            minVersion = versionAdded + \"+\"\n            if isEdgeLegacy and versionAdded == \"18\":\n                minVersion = \"18\"\n            if isIE and versionAdded == \"11\":\n                minVersion = \"11\"\n        else:\n            statusCode = \"n\"\n            if versionAdded is not None:\n                minVersion = versionAdded + \"\\u2013\" + versionRemoved\n            else:\n                minVersion = \"None\"\n    browserFullName = nameFromCodeName[browserCodeName]\n    appendChild(\n        supportData,\n        browserCompatSpan(\n            browserCodeName, browserFullName, statusCode, minVersion, needsFlag\n        ),\n    )\n\n\ndef mdnPanelFor(\n    feature,\n    mdnBaseUrl,\n    nameFromCodeName,\n    browsersProvidingCurrentEngines,\n    browsersWithBorrowedEngines,\n    browsersWithRetiredEngines,\n    browsersForMobileDevices,\n):\n    featureDiv = E.div({\"class\": \"feature\"})\n    if \"slug\" in feature:\n        slug = feature[\"slug\"]\n        displaySlug = slug.split(\"/\", 1)[1]\n        title = feature.get(\"summary\", \"\")\n        mdnURL = mdnBaseUrl + slug\n        appendChild(\n            featureDiv, E.p({}, E.a({\"href\": mdnURL, \"title\": title}, displaySlug))\n        )\n    if \"engines\" in feature:\n        engines = len(feature[\"engines\"])\n        enginesPara = None\n        if engines == 0:\n            enginesPara = E.p(\n                {\"class\": \"less-than-two-engines-text\"}, \"In no current engines.\"\n            )\n        elif engines == 1:\n            enginesPara = E.p(\n                {\"class\": \"less-than-two-engines-text\"}, \"In only one current engine.\"\n            )\n        elif engines >= len(browsersProvidingCurrentEngines):\n            enginesPara = E.p({\"class\": \"all-engines-text\"}, \"In all current engines.\")\n        if enginesPara is not None:\n            appendChild(featureDiv, enginesPara)\n    supportData = E.div({\"class\": \"support\"})\n    appendChild(featureDiv, supportData)\n    support = feature[\"support\"]\n    for browserCodeName in browsersProvidingCurrentEngines:\n        addSupportRow(browserCodeName, nameFromCodeName, support, supportData)\n    appendChild(supportData, E.hr())\n    for browserCodeName in browsersWithBorrowedEngines:\n        addSupportRow(browserCodeName, nameFromCodeName, support, supportData)\n    appendChild(supportData, E.hr())\n    for browserCodeName in browsersWithRetiredEngines:\n        addSupportRow(browserCodeName, nameFromCodeName, support, supportData)\n    appendChild(supportData, E.hr())\n    for browserCodeName in browsersForMobileDevices:\n        addSupportRow(browserCodeName, nameFromCodeName, support, supportData)\n    if \"nodejs\" in support:\n        appendChild(supportData, E.hr())\n        addSupportRow(\"nodejs\", nameFromCodeName, support, supportData)\n    return featureDiv\n\n\ndef browserCompatSpan(\n    browserCodeName, browserFullName, statusCode, minVersion, needsFlag\n):\n    # browserCodeName: e.g. \"chrome\"\n    # browserFullName: e.g. \"Chrome for Android\"\n    minVersionAttributes = {}\n    flagSymbol = \"\"\n    if needsFlag:\n        flagSymbol = \"\\U0001f530 \"\n        minVersionAttributes[\n            \"title\"\n        ] = \"Requires setting a user preference or runtime flag.\"\n    statusClass = {\"y\": \"yes\", \"n\": \"no\"}[statusCode]\n    outer = E.span({\"class\": browserCodeName + \" \" + statusClass})\n    appendChild(outer, E.span({}, browserFullName))\n    appendChild(outer, E.span(minVersionAttributes, flagSymbol + minVersion))\n    return outer\n","sub_path":"bikeshed/mdnspeclinks.py","file_name":"mdnspeclinks.py","file_ext":"py","file_size_in_byte":20008,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"134719451","text":"import numpy as np\nimport torch \nimport torch.nn as nn\nimport torchvision.transforms as transform\nfrom torch.autograd.gradcheck import zero_gradients\nfrom PIL import Image\nfrom torchvision.models import resnet50\nimport torch.utils.model_zoo as model_zoo\nimport requests\nimport os\nfrom io import BytesIO\nimport sys\n\nmodel = resnet50(pretrained = False)\nmodel.load_state_dict(torch.load('fgsm_model.pth'))\nmodel.eval()\n\nlabel_idx = []\nf_file = \"hw5_data/labels.csv\"\nwith open(f_file) as file:\n    for line_id, line in enumerate(file):\n        if line_id != 0:\n            datas = line.split(',')\n            label = datas[3]\n            label_idx.append(int(label))\nlabel_idx = np.array(label_idx)\n\ndef threshhold(image, idx, trans_value):\n    for i in range(len(image[0])):\n        for j in range(len(image[0][i])):\n            for k in range(len(image[0][i][j])):\n                if idx[0][i][j][k] == 1:\n                    image[0][i][j][k] = trans_value * image[0][i][j][k].sign_()\n    return image\n\ndef clip(image, minvalue, maxvalue):\n    for i in range(len(image[0])):\n        for j in range(len(image[0][i])):\n            for k in range(len(image[0][i][j])):\n                if image[0][i][j][k] < minvalue:\n                    image[0][i][j][k] = minvalue\n                elif image[0][i][j][k] > maxvalue:\n                    image[0][i][j][k] = maxvalue\n    return image\n\nimg_file_path = sys.argv[1]\noutput_file = sys.argv[2]\ncriterion = nn.CrossEntropyLoss()\nepsilon = 0.007\n\n\nfor i in range(200):\n    \n    img = Image.open(img_file_path + (\"%03d\" % i) + \".png\")\n    trans = transform.Compose([transform.ToTensor()])\n    image = trans(img)\n    image = image.unsqueeze(0)\n\n    image.requires_grad = True\n    zero_gradients(image)\n\n    output = model(image)\n    \n    rank = output.argsort(-1)[0]\n    \n    if label_idx[i] != rank[-1]:\n        ori_label = label_idx[i]\n        target_label = rank[-1]\n    else:\n        ori_label = rank[-1]\n        target_label = rank[-2]\n        \n    target_label = [target_label]\n    target_label = torch.tensor(target_label)\n    \n    ori_label = [ori_label]\n    ori_label = torch.tensor(ori_label)\n#     print(ori_label, target_label)\n\n    loss1 = criterion(output, target_label)\n    loss2 = criterion(output, ori_label)\n    \n    loss = loss2 - loss1\n    loss.backward()\n    \n    deltaimage = image.grad.sign_()\n    \n#     deltaimage = image.grad.data\n#     idx = abs(deltaimage) > 0.01\n#     deltaimage = threshhold(deltaimage, idx, 0.01)\n    \n    \n    image = image + deltaimage * epsilon\n    \n#     deltaimage = image.grad.sign_()\n#     image = image + epsilon * deltaimage\n    \n#     image = image + epsilon * image.grad.sign_()\n    image = clip(image, 0, 1)\n    image_ad = transform.ToPILImage()(image[0])\n    image_ad.save(output_file + (\"%03d\" % i) + \".png\")\n    img.close()","sub_path":"hw5/hw5_fgsm.py","file_name":"hw5_fgsm.py","file_ext":"py","file_size_in_byte":2824,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"186407309","text":"from pyem import Em\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Instantiate an em\nmagnetization = np.array([1.,0,0])\nposition = np.array([0.,0,0])\nvelocity = np.array([0.,0,0])\ngyromagnetic_ratio = 1.0\nequilibrium_magnetization = 1.0\nem = Em(magnetization,position,velocity,gyromagnetic_ratio,equilibrium_magnetization)\n\n# Declare time step\ndelta_t = 0.01\nnum_steps = 500\n\n# Simulate precession and relaxation\nT1 = 1.0\nT2 = 1.0\nBz = 10.0\nmu = np.empty([num_steps+1,3])\nmu[0,:] = em.mu\nfor step_no in range(num_steps):\n    em.precess_and_relax(T1,T2,Bz,delta_t)\n    mu[step_no+1,:] = em.mu\nprint()\n\n# Plot\nt = delta_t*np.arange(num_steps+1)\nplt.plot(t,mu)\nplt.xlabel('Time (s)')\nplt.ylabel('Magnetization (a.u.)')\nplt.legend(('$\\mu_x$','$\\mu_y$','$\\mu_z$'))\nplt.savefig('free_precession_em_one.pdf')\n","sub_path":"notes/2019-11-28/free_precession_em_one.py","file_name":"free_precession_em_one.py","file_ext":"py","file_size_in_byte":809,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"57645725","text":"from src.CameraBuffer import CameraBuffer\nfrom scipy import signal\nfrom src.Util import *\nfrom matplotlib import pyplot as plt\nimport cv2\n\n\nclass OutputController(object):\n    def __init__(self, maxX, maxY, max_disp, timeRes, maxTimeSlot, cameraBuffer: CameraBuffer):\n        self.maxX = maxX\n        self.maxY = maxY\n        self.max_disp = max_disp\n        self.timeResolution = timeRes\n        self.cameraBuffer = cameraBuffer\n        self.maxTimeSlot = maxTimeSlot\n        self.WMI = np.zeros((maxY, maxX, max_disp))\n        # self.output = cv2.VideoWriter(\"output.avi\",  cv2.VideoWriter_fourcc(*'DIVX'), 5, (maxX, maxY))\n        self.image_idx=0\n        self.visual = np.zeros((self.maxY,self.maxX))\n\n    def refreshWMI(self, referenceEvent, candidateEvent: list):\n        #print(\"event from the left Buffer: {}\".format(referenceEvent))#\n        if True and len(candidateEvent) != 0 :\n            tmp_e = candidateEvent[0]\n            tmp_cost = 0\n\n        for e in candidateEvent:\n            dt = abs(referenceEvent[0] - e[0]) / self.timeResolution\n            cost = Util.calculateMatchingCosts(dt, self.maxTimeSlot)\n            #print(\"possible events for matching: {} \\n matching costs: {}\".format(\n            #    e, cost))\n            disp = int(abs(referenceEvent[1] - e[1]))\n            self.WMI[int(referenceEvent[2]), int(referenceEvent[1]), disp] = cost\n            if(tmp_cost < cost):\n                tmp_cost = cost\n                tmp_e = e\n        if False and len(candidateEvent) != 0:\n            self.visualizeMatching(referenceEvent,tmp_e)\n\n    def applyFilter(self, filter2d):\n        for i in range(self.max_disp):\n            self.WMI[:, :, i] = signal.convolve2d(self.WMI[:, :, i], filter2d, boundary='symm', mode='same', fillvalue=0)\n\n    def visualizeMatching(self, referenceEvents, matchingEvent):\n            self.visual = np.subtract(self.visual, 1)\n            self.visual = self.visual.clip(min=0)\n            self.visual[int(referenceEvents[2]),int(referenceEvents[1])] = 19\n            self.visual[int(referenceEvents[2]),int(matchingEvent[1])] = 19\n            plt.imshow(self.visual, cmap=\"gist_ncar_r\")\n            plt.show()\n\n\n    def evaluateAll(self):\n        \n        print(\"Process Start:\")\n        print(self.cameraBuffer.leftBuffer.shape[0])\n        print(str(self.cameraBuffer.leftBuffer.shape[0]) + \"event(s) in total.\")\n        filterAvg = np.ones((2, 2), dtype=np.float) / 4\n        #filtergauss = cv2.getGaussianKernel(3, 1.5)*cv2.getGaussianKernel(3, 1.5).transpose()\n        print(\"filter kernel: {}\".format(filterAvg))\n        for i in range(self.cameraBuffer.leftBuffer.shape[0]):\n            candidateEvent = self.cameraBuffer.searchCorrespondingEventsOnRight(self.cameraBuffer.leftBuffer[i])\n            self.refreshWMI(self.cameraBuffer.leftBuffer[i], candidateEvent)\n\n            if i % 10000 == 0:\n                #print(\"i: {}\".format(i))\n                self.applyFilter(filterAvg)\n                res = np.argmax(self.WMI[:,:,:], 2)\n                print(res)\n                \n                # Calculate Depth Map\n                # DepthMap = 3*20/res\n                # print(\"depth map: \", DepthMap)\n                \n                plt.imshow(res, cmap=\"binary\") #res\n                save_path = \"result_drone_2/\"\n                title = save_path+format(self.image_idx, '03d') + \".png\" #str(i) + \".png\"\n                self.image_idx += 1\n                plt.clim(0, 30)\n                plt.colorbar()\n                plt.savefig(title)\n                # plt.close()\n                plt.show()\n\n            if i% 100== 0:\n                self.WMI = np.subtract(self.WMI, 9)\n                self.WMI = self.WMI.clip(min=0)\n\n            #if i % 1000 == 0:\n            #    res = np.argmax(self.WMI, 2)*10\n            #    self.output.write(res)\n\n\n        # self.output.release()\n","sub_path":"src/OutputController.py","file_name":"OutputController.py","file_ext":"py","file_size_in_byte":3848,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"651380260","text":"\"\"\"\nThe in-pond data is very blocky - apply a bit of smoothing\nbefore it gets used in the dem\n\"\"\"\nimport matplotlib.pyplot as plt\nfrom stompy.spatial import field\n\n## \nfn='../../sbsprp/SbayPondBathy2005/merged_ponds.tif'\nponds_dem=field.GdalGrid(fn)\n\n# limit it to the parts we're actually using\nponds_crop=ponds_dem.extract_tile(xxyy=(579160, 592550, 4141950, 4146580),\n                                  res=2,interpolation='bilinear')\n# expand a bit\nponds_crop.fill_by_convolution(iterations=5,kernel_size=5)\n# Smooth it\nponds_crop.smooth_by_convolution(iterations=5)\n\n## \n            \nplt.figure(2).clf()\nfig,ax=plt.subplots(num=2)\nponds_crop.plot(ax=ax,interpolation='nearest')\n\n## \n\n# And looks like we need to convert to m, too.\nponds_crop.F *= 0.3048\n\n## \nponds_crop.write_gdal('../sources/merged_ponds_2m_smoothed.tif')\n","sub_path":"smooth_ponds.py","file_name":"smooth_ponds.py","file_ext":"py","file_size_in_byte":828,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"427951940","text":"tridays = {'monday','thursday','saturday'}\nsetdays = {'sunday','monday','tuesday','wednesday','thursday','friday','saturday'}\n\nflag = 0\ncount = len(tridays)\nprint (count)\nfor i in tridays:\n    for j in setdays: \n        if i == j: \n            flag = flag+1\n            # print(flag)\n        else:\n            flag = flag+0\n            # print(flag)\nif flag == count:\n    print (\"This is a subset\")\nelse:\n    print (flag ,\"This is not a subset\")\n\n\n        \n\n\n\n\n","sub_path":"submissions/sp_009_gayathri/week_12/day_4/session_2/check_subset.py","file_name":"check_subset.py","file_ext":"py","file_size_in_byte":461,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"482924421","text":"import os\nimport cv2\nimport numpy as np\nimport configparser\nfrom werkzeug.utils import secure_filename\n\ndef get_conf(key, value):\n    cf=configparser.ConfigParser()\n    cf.read('webApp/util/config.ini')\n    return cf.get(key, value)\n\ndef get_file_path(folder, filename):\n    return os.path.join(folder, filename)\n\ndef allowed_file(filename):\n    ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg', 'mp4', 'avi', 'dat', '3gp', 'mov', 'rmvb'}\n    return '.' in filename and \\\n           filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS\n\ndef save_file(file):\n    if allowed_file(file.filename):\n        filename = secure_filename(file.filename)\n        file.save(os.path.join('webApp/uploads', filename))\n        return 1\n    return 0\n\ndef toRGB(image):\n    return cv2.cvtColor(np.array(image), cv2.COLOR_BGR2RGB)\n\n\ndef rotate_bound(image, angle):\n    # grab the dimensions of the image and then determine the\n    # center\n    (h, w) = image.shape[:2]\n    (cX, cY) = (w // 2, h // 2)\n\n    # grab the rotation matrix (applying the negative of the\n    # angle to rotate clockwise), then grab the sine and cosine\n    # (i.e., the rotation components of the matrix)\n    M = cv2.getRotationMatrix2D((cX, cY), -angle, 1.0)\n    cos = np.abs(M[0, 0])\n    sin = np.abs(M[0, 1])\n\n    # compute the new bounding dimensions of the image\n    nW = int((h * sin) + (w * cos))\n    nH = int((h * cos) + (w * sin))\n\n    # adjust the rotation matrix to take into account translation\n    M[0, 2] += (nW / 2) - cX\n    M[1, 2] += (nH / 2) - cY\n\n    # perform the actual rotation and return the image\n    return cv2.warpAffine(image, M, (nW, nH))\n\nif __name__ == '__main__':\n    print('common function')","sub_path":"webApp/models/util/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1677,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"239920454","text":"# -*- coding: utf-8 -*-\n# @Time    : 2018/7/19 16:15\n# @Author  : Suzhenyu\n# @File    : AlexNet.py\n# @Email   : suzhenyu@qiyi.com\n\nfrom keras.models import Sequential\nfrom keras.layers.convolutional import Conv2D\nfrom keras.layers.convolutional import MaxPooling2D\nfrom keras.layers.core import Activation\nfrom keras.layers.core import Flatten\nfrom keras.layers.core import Dense\nfrom keras import backend as K\n\n\nclass LeNet:\n    @staticmethod\n    def build(FLAGS):\n        # initialize the model\n        model = Sequential()\n        inputShape = (FLAGS.normal_size,FLAGS.normal_size,FLAGS.channels)\n        model.add(Conv2D(20, (5, 5), padding=\"same\", input_shape=inputShape))\n        model.add(Activation(\"relu\"))\n        model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))\n        # second set of CONV => RELU => POOL layers\n        model.add(Conv2D(50, (5, 5), padding=\"same\"))\n        model.add(Activation(\"relu\"))\n        model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))\n        # first (and only) set of FC => RELU layers\n        model.add(Flatten())\n        model.add(Dense(500))\n        model.add(Activation(\"relu\"))\n\n        # softmax classifier\n        model.add(Dense(FLAGS.classes))\n        model.add(Activation(\"softmax\"))\n        model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n        # return the constructed network architecture\n        return model","sub_path":"src/netmodel/LeNet.py","file_name":"LeNet.py","file_ext":"py","file_size_in_byte":1421,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"56697221","text":"import math as mt\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom visual import *\n\n\n#Programma constantes\n#Positieve y-as is naar boven, positieve x-as is naar rechts (bewegingsrichting)\n\ng = 9.81\n\n#tijd\ndt = 0.01\nt_begin = 0\nt_eind = 20\nt = np.linspace(t_begin, t_eind, round(t_eind/dt+1))\n\n#robot constante eigenschappen\nT_max = 20 #Maximale koppel in Nm\narm_1 = 0.05 #Straal\nm = 1\nF_g = -g*m\n\n#Motor beweging voor constante vm_d\n\ntheta = np.empty(int(t_eind/dt+1))\n\n\nfor i in range(len(t)-1):\n\n    while true:\n        theta[i+1] = theta[i]+1\n        s = mt.sin(theta)\n\n\n\nplt.plot(s,t)","sub_path":"Beweging_simulatie.py","file_name":"Beweging_simulatie.py","file_ext":"py","file_size_in_byte":595,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"163427043","text":"import socket\r\nimport select\r\n\r\nIP_ADRESS = \"192.168.178.52\"\r\n\r\ns = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\r\ns.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\r\ns.bind((IP_ADRESS, 6969))\r\ns.listen(5)\r\n\r\nprint(\"Server up and running!\")\r\n\r\nsocket_list = [s]\r\nusers = {}\r\n\r\ndef receive_message(client_socket):  \r\n    try:\r\n        messageLen = client_socket.recv(8).decode(\"utf-8\")\r\n        message = client_socket.recv(int(messageLen))\r\n        return message.decode(\"utf-8\")\r\n    except:\r\n        return False\r\n\r\ndef send_message(client_socket, content):\r\n    try:\r\n        msg = str(content)\r\n        client_socket.send(bytes(f'{len(msg):<8}'+msg, \"utf-8\"))\r\n    except:\r\n        print(\"<Message Error>\")\r\n        socket_list.remove(client_socket)\r\n\r\nwhile True:\r\n    read_sockets, _, exception_sockets = select.select(socket_list, [], socket_list)\r\n\r\n    for client in read_sockets:\r\n        if client == s:\r\n            clientsocket, address = s.accept()\r\n            print(f\"Connection from {address} has been established.\")\r\n            clientsocket.send(bytes(\"Connected!\", \"utf-8\"))\r\n            username = clientsocket.recv(15)\r\n            users[clientsocket] = username.decode(\"utf-8\")\r\n            print(users[clientsocket])\r\n            socket_list.append(clientsocket)\r\n\r\n        else:\r\n            msg = receive_message(client)\r\n            if msg != False: \r\n                print(users[client]+\" > \"+msg)\r\n                for sentClient in socket_list:\r\n                    if sentClient != s:\r\n                        if sentClient != client:\r\n                            send_message(sentClient, users[client]+\" > \"+msg)\r\n\r\n    for client in exception_sockets:\r\n        socket_list.remove(client)\r\n\r\n        del users[client]\r\n                            \r\n\r\n","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1791,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"240063431","text":"import tkinter as tk\nwindow = tk.Tk()\nwindow.title(\"收银员1.1\")\nwindow.geometry(\"500x400\")\n# row为行，column为列\nlab0 = tk.Label(window, text=\"XiangYang STORE APPS\\n -----------------------------------\", font=('Arial', 18))\nlab0.grid(row=0, column=1)\n# 标签\nlab1 = tk.Label(window, text=\"项目(item)\")\nlab1.grid(row=1)\nlab2 = tk.Label(window, text=\"价格(Unit Price)\")\nlab2.grid(row=2)\nlab3 = tk.Label(window, text=\"数量(Quantity)\")\nlab3.grid(row=3)\nlab4 = tk.Label(window, text=\"折扣(Disconut)\")\nlab4.grid(row=4)\n# 输入框\n# padx:设置控件周围水平方向空白区域保留大小\n# pady:设置控件周围垂直方向空白区域保留大小\n# 项目输入框\nitem1 = tk.Entry(window, borderwidth=5)\nitem1.grid(row=1, column=1, padx=10, pady=5)\n# 价格输入框\nitem2 = tk.Entry(window, borderwidth=5)\nitem2.grid(row=2, column=1, padx=10, pady=5)\n# 数量输入框\nitem3 = tk.Entry(window, borderwidth=5)\nitem3.grid(row=3, column=1, padx=10, pady=5)\n# 折扣输入框\nitem4 = tk.Entry(window, borderwidth=5, width=10)\nitem4.grid(row=4, column=1, padx=10, pady=5)\n# 总折扣价显示标签和框\nshowToalDiscountname = tk.Label(window, text=\"总折扣\")\nshowToalDiscountname.grid(row=6, column=0, padx=10, pady=10)\n# 总付款显示标签和框\nshowToalPaidname = tk.Label(window, text=\"总付款\")\nshowToalPaidname.grid(row=7, column=0, padx=10, pady=10)\ndef ClickMe():\n    \"\"\"点击计算总折扣和总付款\"\"\"\n    # 获取价格输入框中的数据并转为int型\n    price = int(item2.get())\n    # 获取数量输入框中的数据\n    quantity = int(item3.get())\n    # 获取折扣输入框中的数据\n    discount = int(item4.get()) * 0.1\n    # 计算总折扣\n    TotalDiscount = (price - price * discount) * quantity\n    # 计算总付款价\n    TotalPaid = price * quantity - TotalDiscount\n    # 显示总折扣结果\n    showToalDiscountresult = tk.Label(window, text=TotalDiscount, foreground=\"#9932CC\", background=\"#ffb6c1\")\n    showToalDiscountresult.grid(row=6, column=1, padx=10, pady=10)\n    # 显示总付款结果\n    showToalPaidresult = tk.Label(window, text=TotalPaid, foreground=\"#9932CC\", background=\"#ffb6c1\")\n    showToalPaidresult.grid(row=7, column=1, padx=10, pady=10)\n\n\nbutt = tk.Button(window, text=\"处理(process)\", command=ClickMe)\nbutt.grid(row=5, column=1)\n# 循环显示\nwindow.mainloop()","sub_path":"xiangmu1/2.1收银员.py","file_name":"2.1收银员.py","file_ext":"py","file_size_in_byte":2353,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"492292306","text":"from __future__ import division\r\nimport os\r\nimport sys\r\nimport pygame\r\nfrom pygame.locals import *\r\n\r\n# from main.v00\r\nsubject = raw_input('Enter subject ID here: ')\r\nfilename = subject + '.csv'\r\nfilepath = os.path.join('data', filename)\r\nFILE = open(filepath, 'w')\r\nFILE.write('Subject: %s\\n' % subject)\r\n\r\n# from main.v01\r\npygame.init()\r\npygame.mixer.init()\r\npygame.event.set_grab(1)\r\nSCREEN = pygame.display.set_mode((800,600), 32) #, pygame.FULLSCREEN\r\nFONT = pygame.font.Font(None, 28)\r\nSCREEN.fill((86, 130, 160))\r\ntextimg = FONT.render('Hello World', 1, (0,0,0))\r\nSCREEN.blit(textimg, (10, 10))\r\npygame.display.flip()\r\nwait = True\r\nwhile wait:\r\n\tfor event in pygame.event.get():\r\n\t\tif (event.type == KEYDOWN and event.key == K_SPACE):\r\n\t\t\twait = False\r\n\r\n# new stuff\r\n# load two images from jpg files in ./img/\r\nimage1 = pygame.image.load(os.path.join('img', 'A.jpg'))\r\nimage2 = pygame.image.load(os.path.join('img', 'B.jpg'))\r\n\r\n# draw a blank screen, put image1 on it, update.\r\nSCREEN.fill((86, 130, 160))\r\nSCREEN.blit(image1, (50,50))\r\npygame.display.flip()\r\n\r\n# wait for 300 msec\r\npygame.time.wait(300)\r\n\r\n# draw a blank screen\r\nSCREEN.fill((86, 130, 160))\r\npygame.display.flip()\r\n\r\n# wait 200 msec\r\npygame.time.wait(200)\r\n\r\n# draw screen containing image2\r\nSCREEN.fill((86, 130, 160))\r\nSCREEN.blit(image2, (50,50))\r\npygame.display.flip()\r\n\r\n# wait 300 msec\r\npygame.time.wait(300)\r\n\r\n# draw blank screen\r\nSCREEN.fill((86, 130, 160))\r\npygame.display.flip()\r\n\r\nprint('thank you for your participation!')\r\nFILE.close()\r\nsys.exit(0)\r\n","sub_path":"tutorial/main.v02.py","file_name":"main.v02.py","file_ext":"py","file_size_in_byte":1541,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"339584559","text":"from wallace.db.base.attrs import DataType\nfrom wallace.db.base.errors import DoesNotExist, ValidationError\nfrom wallace.db.base.model import Base, Model\n\n\nclass _PKBase(Base):\n    def __new__(cls, name, bases, dct):\n        the_class = super(_PKBase, cls).__new__(cls, name, bases, dct)\n        the_class._cbs_primary_key_fields = cls._get_pk_fields(bases, dct)\n\n        if cls._is_proper_model(bases):\n            if not the_class._cbs_primary_key_fields:\n                raise TypeError('no primary keys set')\n\n        return the_class\n\n    @staticmethod\n    def _get_pk_fields(bases, dct):\n        pk_fields = set()\n\n        for base in bases:  # support model inheritance\n            for key in getattr(base, '_cbs_primary_key_fields', []):\n                pk_fields.add(key)\n\n        for key, val in dct.iteritems():\n            if isinstance(val, DataType) and val.is_pk:\n                pk_fields.add(key)\n            elif key in pk_fields:      # catch any superclass pk fields\n                pk_fields.remove(key)   # overridden here by a non-pk one\n\n        return tuple(pk_fields)\n\n    @staticmethod\n    def _is_proper_model(bases):\n        # Model hierarchy:\n        #     <model> -> <DB model wrapper (eg PostgresModel)> ->\n        #     RelationalModel -> Model -> object\n        # ergo, the hierarchy cardinality for any proper model subclass\n        # will be at least 4\n\n        base_tree = []\n        while bases:\n            base_tree.append(bases)\n            bases = map(lambda b: list(b.__bases__), bases)\n            bases = sum(bases, [])\n\n        return len(base_tree) > 3\n\n\ndef throw_null_pk_field_error(attr):\n    msg = 'primary key field \"%s\" cannot be null' % attr\n    raise ValidationError(msg)\n\n\nclass RelationalModel(Model):\n\n    __metaclass__ = _PKBase\n\n    @classmethod\n    def fetch(cls, **kwargs):\n        inst = cls.construct(new=False, **kwargs)\n        inst.pull()\n        return inst\n\n\n    def pull(self):\n        self._validate_pk()\n        return super(RelationalModel, self).pull()\n\n    def push(self, *a, **kw):\n        self._validate_pk()\n        return super(RelationalModel, self).push(*a, **kw)\n\n    def _validate_pk(self, silent=False):\n        for attr in self._cbs_primary_key_fields:\n            if getattr(self, attr, None) is None:\n                if silent:\n                    return False\n                throw_null_pk_field_error(attr)\n        return True\n\n\n    @property\n    def primary_key(self):\n        # The primary key CURRENTLY stored in the db.\n        # If a pk field is changed, this will continue to return the old\n        # value so updates can find the row.\n\n        if self.is_new:\n            raise DoesNotExist('new model')\n\n        pk = {}\n        for attr in self._cbs_primary_key_fields:\n            try:\n                pk[attr] = self._cbs_db_data[attr]\n            except KeyError:\n                throw_null_pk_field_error(attr)\n        return pk\n","sub_path":"wallace/db/base/relational.py","file_name":"relational.py","file_ext":"py","file_size_in_byte":2930,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"570624365","text":"import os\nimport re\nfrom . import CommandLineHandler\nfrom . import BookKeeping\n\n\nclass Managing:\n\n    def __init__(self, source_path_is, content_replace_keywords_with, dump_file_name_is, content_file_name_is, script_file_name_is, base_file_name_is='base.html', data_file_is=''):\n        self._dump_file_name = dump_file_name_is\n        self._data_file_name = data_file_is\n        self._content_file_name = content_file_name_is\n        self._script_file_name = script_file_name_is\n        self._base_file_name = base_file_name_is\n        self._path = source_path_is\n        self._content_populator = content_replace_keywords_with\n        self._book_keeping = BookKeeping.BookKeeping(time_stamp_directory_path=self.get_project_path())\n        self._command_line_handler = CommandLineHandler.CommandLineHandler(self.merge_and_dump, self.get_dump_file_name())\n\n    def get_data_file_name(self):\n        return self._data_file_name\n\n    def get_data_file_path(self):\n        if self._data_file_name != '':\n            return os.path.join(self._path, self._data_file_name)\n        else:\n            return ''\n\n    def get_dump_file_name(self):\n        return self._dump_file_name\n\n    def get_dump_file_path(self):\n        return os.path.join(self._path, self._dump_file_name)\n\n    def get_content_file_name(self):\n        return self._content_file_name\n\n    def get_content_file_path(self):\n        return os.path.join(self._path, self._content_file_name)\n\n    def get_script_file_name(self):\n        return self._script_file_name\n\n    def get_script_file_path(self):\n        return os.path.join(self._path, self._script_file_name)\n\n    def get_base_file_name(self):\n        return self._base_file_name\n\n    def get_base_file_path(self):\n        return os.path.join(self._path, self._base_file_name)\n\n    def get_project_path(self):\n        return self._path\n\n    def execute_as_main(self, parent_manage_script_is):\n        self._command_line_handler.main_exec_interface(\n            parent_manage_script=parent_manage_script_is,\n            book_keeping=self._book_keeping,\n            dump_file_path=self.get_dump_file_path(),\n            content_file_path=self.get_content_file_path(),\n            script_file_path=self.get_script_file_path(),\n            base_file_path=self.get_base_file_path(),\n            data_file_path=self.get_data_file_path()\n        )\n\n    def execute_as_module(self):\n        self._command_line_handler.sub_exec_interface(\n            book_keeping=self._book_keeping,\n            dump_file_path=self.get_dump_file_path(),\n            content_file_path=self.get_content_file_path(),\n            script_file_path=self.get_script_file_path(),\n            base_file_path=self.get_base_file_path(),\n            data_file_path=self.get_data_file_path()\n        )\n\n    def merge_and_dump(self):\n        with open(self.get_content_file_path(), 'r') as content, open(self.get_base_file_path(), 'r') as base:\n            base_structure = re.split('({{[\\s\\w]*}})', base.read())\n            contents_structure = re.split('({{[\\s\\w]*}})', content.read())\n\n            configurations = {}\n            configurations['title'] = ''\n            configurations['style'] = ''\n            configurations['hasLeft'] = False\n            configurations['hasBody'] = False\n            configurations['hasRight'] = False\n            configurations['left_content_left_tag'] = ''\n            configurations['left_content_right_tag'] = ''\n            configurations['right_content_left_tag'] = ''\n            configurations['right_content_right_tag'] = ''\n\n            self._content_populator(configurations, base_structure, contents_structure, self.get_project_path())\n            self.replaceBaseWithContent(configurations, base_structure, contents_structure)\n        with open(self.get_dump_file_path(), 'w') as target:\n            target.write(''.join(base_structure))\n            self._book_keeping.updateTimeStamp(\n                dump_file_path=self.get_dump_file_path(),\n                content_file_path=self.get_content_file_path(),\n                script_file_path=self.get_script_file_path(),\n                base_file_path=self.get_base_file_path(),\n                data_file_path=self.get_data_file_path()\n            )\n\n    def replaceBaseWithContent(self, configurations, base_structure, contents_structure):\n\n\n        base_structure[base_structure.index('{{PAGE_TITLE}}')] = configurations['title']\n        base_structure[base_structure.index('{{STYLE}}')] = configurations['style']\n\n        left_content_marking = base_structure.index('{{LEFT_CONTENT}}')\n        if configurations['hasLeft']:\n            left_content_start_idx = contents_structure.index('{{LEFT_CONTENT_START}}')\n            contents_structure[left_content_start_idx] = ''\n            left_content_end_idx = contents_structure.index('{{LEFT_CONTENT_END}}')\n            contents_structure[left_content_end_idx] = ''\n            base_structure[left_content_marking:left_content_marking + 1] = [configurations['left_content_left_tag']] + contents_structure[left_content_start_idx:left_content_end_idx + 1] + [configurations['left_content_right_tag']]\n        else:\n            base_structure[left_content_marking] = ''\n\n        body_content_marking = base_structure.index('{{BODY_CONTENT}}')\n        if configurations['hasBody']:\n            body_content_start_idx = contents_structure.index('{{BODY_CONTENT_START}}')\n            contents_structure[body_content_start_idx] = ''\n            body_content_end_idx = contents_structure.index('{{BODY_CONTENT_END}}')\n            contents_structure[body_content_end_idx] = ''\n            base_structure[body_content_marking:body_content_marking + 1] = contents_structure[body_content_start_idx:body_content_end_idx + 1]\n        else:\n            base_structure[body_content_marking] = ''\n\n        right_content_marking = base_structure.index('{{RIGHT_CONTENT}}')\n        if configurations['hasRight']:\n            right_content_start_idx = contents_structure.index('{{RIGHT_CONTENT_START}}')\n            contents_structure[right_content_start_idx] = ''\n            right_content_end_idx = contents_structure.index('{{RIGHT_CONTENT_END}}')\n            contents_structure[right_content_end_idx] = ''\n            base_structure[right_content_marking:right_content_marking + 1] = [configurations['right_content_left_tag']] + contents_structure[right_content_start_idx:right_content_end_idx + 1] + [configurations['right_content_right_tag']]\n        else:\n            base_structure[right_content_marking] = ''","sub_path":"Sources/utils/Managing.py","file_name":"Managing.py","file_ext":"py","file_size_in_byte":6529,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"516256541","text":"from models.token import separators\r\nimport re\r\n\r\nclass Scanner():\r\n    def __init__(self):\r\n        pass\r\n\r\n    def is_identifier(self,token):\r\n        return re.match(r'^[a-zA-Z]([a-zA-Z]|[0-9]|_){,256}$', token) is not None\r\n\r\n\r\n    def is_constant(self,token):\r\n        return re.match(r'^(0|[+-]?([0-9]*[.])?[0-9]+|[\\+\\-]?[1-9][0-9]*)$|^\\'\\\\.\\'$|^\\'.\\'$|^\\\".*\\\"$', token) is not None\r\n\r\n\r\n    def is_escaped_quotation_mark(self,line, index):\r\n        if index == 0:\r\n            return False\r\n        elif line[index - 1] == '\\\\':\r\n            return True\r\n        else:\r\n            return False\r\n\r\n\r\n    def get_string(self,line, index):\r\n        token = ''\r\n        number_of_quotation_mark = 0\r\n\r\n        while index < len(line) and number_of_quotation_mark < 2:\r\n            if line[index] == '\"' and not self.is_escaped_quotation_mark(line, index):\r\n                number_of_quotation_mark += 1\r\n            token += line[index]\r\n            index += 1\r\n\r\n        return token, index\r\n\r\n    def get_char(self,line, index):\r\n        token = ''\r\n        number_of_apostrophes = 0\r\n\r\n        while index < len(line) and number_of_apostrophes < 2:\r\n            if line[index] == '\\'' and not self.is_escaped_quotation_mark(line, index):\r\n                number_of_apostrophes += 1\r\n            token += line[index]\r\n            index += 1\r\n\r\n        return token, index\r\n\r\n\r\n    def token_generator(self,line, line_index):\r\n        tokens = []\r\n        token = ''\r\n        index = 0\r\n        while index < len(line):\r\n            if line[index] == '\"':\r\n                if token:\r\n                    tokens.append(token)\r\n                token, index = self.get_string(line, index)\r\n                if token[-1] != '\"':\r\n                    raise Exception(\r\n                        \"Syntax error, string is not closed! at line \" + str(line_index) + \" position \" + str(index) + \"\\n\")\r\n                tokens.append(token)\r\n                token = ''\r\n            elif line[index] == '\\'':\r\n                if token:\r\n                    tokens.append(token)\r\n                token, index = self.get_char(line, index)\r\n                if token[-1] != '\\'':\r\n                    raise Exception(\r\n                        \"Syntax error, character is not closed! at line \" + str(line_index) + \" position \" + str(index) + \"\\n\")\r\n                tokens.append(token)\r\n                token = ''\r\n            elif line[index] in separators:\r\n                if token:\r\n                    tokens.append(token)\r\n                tokens.append(line[index])\r\n                index += 1\r\n                token = ''\r\n            else:\r\n                token += line[index]\r\n                index += 1\r\n        if token:\r\n            tokens.append(token)\r\n        return tokens\r\n","sub_path":"lab3/models/Scanner.py","file_name":"Scanner.py","file_ext":"py","file_size_in_byte":2774,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"22247814","text":"def counting_rectangles(X, Y):\n    rects = 0\n    for m in range(1, X + 1):\n        for n in range(1, Y + 1):\n            rects += (X - m + 1) * (Y - n + 1)\n    return rects\n\n\ndef solution():\n    closestAreaRects = 0\n    closestArea = 0\n    target = 2 * 10e5\n    for x in range(1, 100):\n        for y in range(1, 100):\n            rects = counting_rectangles(x, y)\n            if abs(closestAreaRects - target) > abs(rects - target):\n                closestArea = x * y\n                closestAreaRects = rects\n            if rects > target:\n                break\n    return closestArea\n\n\ndef solution2():\n    for x in range(1, 100):\n        for y in range(1, 100):\n            t = counting_rectangles(x, y)\n            if 1999000 <= t <= 2100000:\n                return x * y\n\n\nprint(solution2())\n# 2772\n","sub_path":"competitiveprogramming-python/src/com/mounacheikhna/euler/85.Counting rectangles.py","file_name":"85.Counting rectangles.py","file_ext":"py","file_size_in_byte":804,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"535245082","text":"# import selenium drivers\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.options import Options\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.common.exceptions import TimeoutException\nimport os\nimport pandas as pd\nfrom datetime import datetime\nfrom wordcloud import WordCloud, STOPWORDS\nimport matplotlib.pyplot as plt\nfrom nltk.tokenize import word_tokenize\nfrom time import time\n\n\nclass NewsScraper():\n    '''Class to scrape news articles from \"www.energy.gov\"\n    '''\n\n    def __init__(self, webdriver_path: str, headless: bool, num_pages: int, save_path: str, url: str, delay: int):\n        '''\n        Parameters\n        ----------\n        webdriver_path: str, path to chrome webdriver\n        headless: bool, whther to run the scraper headlessly\n        num_pages: int, number of pages to scrape\n        save_path: str, dataset save location (include file name)\n        url: str, url to start scraping from\n        delay: int, number of seconds to wait for page to load\n        '''\n        # check parameter types\n        if type(webdriver_path) != str or type(headless) != bool or type(num_pages) != int or type(save_path) != str or type(url) != str or type(delay) != int:\n            print(\"Type Error: Invalid type in arguements\")\n            exit(0)\n        if not os.path.exists(webdriver_path):\n            print('Webdriver not found')\n            exit(0)\n\n        self.webdriver_path = webdriver_path\n        self.headless = headless\n        self.num_pages = num_pages\n        self.save_path = save_path\n        self.url = url\n        self.delay = delay\n\n    def init_record(self) -> dict:\n        return {'article_date': None, 'article_title': None, 'article_description': '', 'article_url': None}\n\n    def wait_for_element(self, classname: str) -> bool:\n        try:\n            element_present = EC.presence_of_element_located(\n                (By.CSS_SELECTOR, classname))\n            WebDriverWait(self.driver, self.delay).until(element_present)\n        except TimeoutException:\n            print(\"Timed out waiting for element\")\n            return False\n        return True\n\n    def get_news_urls(self, page: int) -> list:\n        if not self.wait_for_element('.search-result-title'):\n            return []\n        articles = self.driver.find_elements_by_css_selector(\n            '.search-result-title')\n        articles = [a.get_property('href') for a in articles]\n        print(f'Page: {page+1}    Num articles: {len(articles)}')\n        return articles\n\n    def make_url_clickable(self, url: str) -> str:\n        return f'<a href=\"{url}\">{url}</a>'\n\n    def get_record(self, article: str) -> dict:\n        record = self.init_record()\n        if not self.wait_for_element('.page-hero-date'):\n            return record\n\n        date = self.driver.find_element_by_css_selector('.page-hero-date').text\n        record['article_date'] = datetime.strptime(\n            date, '%B %d, %Y').strftime('%Y-%m-%d')\n        record['article_title'] = self.driver.find_element_by_css_selector(\n            '.page-title').text\n        record['article_url'] = article\n        desc = self.driver.find_element_by_css_selector(\n            'div.block.block-layout-builder.block-inline-blockbasic')\n        record['article_description'] += desc.find_element_by_tag_name(\n            'p').text\n        return record\n\n    def save_data(self, data: list) -> None:\n        df = pd.DataFrame(data)\n        df.style.format(self.make_url_clickable, subset=['article_url'])\n        df.to_excel(self.save_path, index=False)\n        print('Data saved successfully.')\n\n    def scrape(self) -> None:\n        start = time()\n        options = Options()\n        if self.headless:\n            options.add_argument('--headless')\n        self.driver = webdriver.Chrome(self.webdriver_path, options=options)\n\n        data = []\n\n        try:\n            # For each page\n            self.driver.get(self.url)\n            for i in range(self.num_pages):\n                cur_url = self.driver.current_url\n                articles = self.get_news_urls(i)\n                # For each article\n                for article in articles:\n                    # Open article\n                    self.driver.get(article)\n\n                    # Get details\n                    data.append(self.get_record(article))\n\n                self.driver.get(cur_url)\n                if not self.wait_for_element('li.pagination-item.pagination-next'):\n                    break\n                self.driver.find_element_by_css_selector(\n                    'li.pagination-item.pagination-next').click()\n                if self.driver.current_url == cur_url:\n                    print('No more articles found')\n                    break\n        finally:\n            # Close the webdriver\n            self.driver.quit()\n\n        print(f'Total articles scraped: {len(data)}')\n        self.save_data(data)\n        print(f'Time elapsed: {(time()-start): .2f} seconds')\n\n\ndef create_word_cloud(save_path):\n    df = pd.read_excel(save_path)\n    text = ''\n    for news in df.article_description.tolist():\n        tokens = word_tokenize(str(news))\n        for i in range(len(tokens)):\n            tokens[i] = tokens[i].lower()\n        text += ' '.join(tokens) + ' '\n\n    wordcloud = WordCloud(width=2000, height=2000,\n                          stopwords=set(STOPWORDS)).generate(text)\n\n    # plot the WordCloud image\n    plt.figure(figsize=(10, 7))\n    plt.imshow(wordcloud)\n    plt.axis(\"off\")\n    plt.tight_layout(pad=0)\n    plt.show()\n\n\nif __name__ == '__main__':\n    # Constants and Parameters\n    URL = 'https://www.energy.gov/listings/energy-news'\n    DRIVER_PATH = os.getcwd() + '/chromedriver'\n    SAVE_PATH = './energygov_selenium.xlsx'\n    HEADLESS = True\n    NUM_PAGES = 10\n    DELAY = 2\n\n    ns = NewsScraper(DRIVER_PATH, HEADLESS, NUM_PAGES, SAVE_PATH, URL, DELAY)\n    ns.scrape()\n\n    # Comment this if you don't want to plot wordcloud\n    create_word_cloud(SAVE_PATH)\n","sub_path":"Company/MindWorksGlobal/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6076,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"59348227","text":"#! /usr/bin/python\n# -*- coding: utf-8 -*-\n\nimport vkapi\n\n#topics = [{'id':val1, 'title':val2}]\n\nclass topics():\n    @staticmethod\n    def getAllTopics(token, group_id):#tested\n        assert (type(token) is str)\n        assert (type(group_id) is int)\n\n        return topics.getNewTopics(token, None, group_id)\n\n    @staticmethod\n    def getNewTopics(token, last_topic_id, group_id):#tested\n        assert (type(token) is str)\n        assert (type(last_topic_id) is int) or (last_topic_id is None)\n        assert (type(group_id) is int)\n\n        params = {}\n        params['group_id'] = group_id\n        params['offset'] = 0\n        params['count'] = 100\n\n        topics = []\n        offset = params['count']\n\n        while True:\n            data = vkapi.board.getTopics(token, params)['items']\n\n            if len(data) == 0:\n                break\n\n            data.sort(reverse=True, key=lambda x: x['id'])  # Самые свежие(большие) id должны быть наверху\n            ids = map(lambda x: x['id'], data)\n\n            if last_topic_id in ids:\n                last_post_key = ids.index(last_topic_id)\n                topics += data[0:last_post_key]\n                break\n            else:\n                topics += data\n\n            params['offset'] += offset\n\n        accepted_keys = ['id', 'created_by', 'title']\n\n        for topic in topics:\n            keys = topic.keys()\n            for key in keys:\n                if key not in accepted_keys:\n                    topic.pop(key, None)\n\n        return topics","sub_path":"vkparser/topics.py","file_name":"topics.py","file_ext":"py","file_size_in_byte":1547,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"126593212","text":"import typing  # noqa: F401\n\nfrom kubernetes import client  # noqa: F401\nfrom kuber import kube_api as _kube_api  # noqa: F401\n\nfrom kuber import definitions as _kuber_definitions  # noqa: F401\nfrom kuber import _types  # noqa: F401\nfrom kuber.v1_26.meta_v1 import Condition  # noqa: F401\nfrom kuber.v1_26.meta_v1 import DeleteOptions  # noqa: F401\nfrom kuber.v1_26.meta_v1 import LabelSelector  # noqa: F401\nfrom kuber.v1_26.meta_v1 import ListMeta  # noqa: F401\nfrom kuber.v1_26.meta_v1 import ObjectMeta  # noqa: F401\nfrom kuber.v1_26.meta_v1 import Status  # noqa: F401\nfrom kuber.v1_26.meta_v1 import StatusDetails  # noqa: F401\n\n\nclass Eviction(_kuber_definitions.Resource):\n    \"\"\"\n    Eviction evicts a pod from its node subject to certain\n    policies and safety constraints. This is a subresource of\n    Pod.  A request to cause such an eviction is created by\n    POSTing to .../pods/<pod name>/evictions.\n    \"\"\"\n\n    def __init__(\n        self,\n        delete_options: typing.Optional[\"DeleteOptions\"] = None,\n        metadata: typing.Optional[\"ObjectMeta\"] = None,\n    ):\n        \"\"\"Create Eviction instance.\"\"\"\n        super(Eviction, self).__init__(api_version=\"policy/v1\", kind=\"Eviction\")\n        self._properties = {\n            \"deleteOptions\": delete_options\n            if delete_options is not None\n            else DeleteOptions(),\n            \"metadata\": metadata if metadata is not None else ObjectMeta(),\n        }\n        self._types = {\n            \"apiVersion\": (str, None),\n            \"deleteOptions\": (DeleteOptions, None),\n            \"kind\": (str, None),\n            \"metadata\": (ObjectMeta, None),\n        }\n\n    @property\n    def delete_options(self) -> \"DeleteOptions\":\n        \"\"\"\n        DeleteOptions may be provided\n        \"\"\"\n        return typing.cast(\n            \"DeleteOptions\",\n            self._properties.get(\"deleteOptions\"),\n        )\n\n    @delete_options.setter\n    def delete_options(self, value: typing.Union[\"DeleteOptions\", dict]):\n        \"\"\"\n        DeleteOptions may be provided\n        \"\"\"\n        if isinstance(value, dict):\n            value = typing.cast(\n                DeleteOptions,\n                DeleteOptions().from_dict(value),\n            )\n        self._properties[\"deleteOptions\"] = value\n\n    @property\n    def metadata(self) -> \"ObjectMeta\":\n        \"\"\"\n        ObjectMeta describes the pod that is being evicted.\n        \"\"\"\n        return typing.cast(\n            \"ObjectMeta\",\n            self._properties.get(\"metadata\"),\n        )\n\n    @metadata.setter\n    def metadata(self, value: typing.Union[\"ObjectMeta\", dict]):\n        \"\"\"\n        ObjectMeta describes the pod that is being evicted.\n        \"\"\"\n        if isinstance(value, dict):\n            value = typing.cast(\n                ObjectMeta,\n                ObjectMeta().from_dict(value),\n            )\n        self._properties[\"metadata\"] = value\n\n    def create_resource(self, namespace: typing.Optional[\"str\"] = None):\n        \"\"\"\n        Creates the Eviction in the currently\n        configured Kubernetes cluster.\n        \"\"\"\n        names = [\"create_namespaced_eviction\", \"create_eviction\"]\n\n        _kube_api.execute(\n            action=\"create\",\n            resource=self,\n            names=names,\n            namespace=namespace,\n            api_client=None,\n            api_args={\"body\": self.to_dict()},\n        )\n\n    def replace_resource(self, namespace: typing.Optional[\"str\"] = None):\n        \"\"\"\n        Replaces the Eviction in the currently\n        configured Kubernetes cluster.\n        \"\"\"\n        names = [\"replace_namespaced_eviction\", \"replace_eviction\"]\n\n        _kube_api.execute(\n            action=\"replace\",\n            resource=self,\n            names=names,\n            namespace=namespace,\n            api_client=None,\n            api_args={\"body\": self.to_dict(), \"name\": self.metadata.name},\n        )\n\n    def patch_resource(self, namespace: typing.Optional[\"str\"] = None):\n        \"\"\"\n        Patches the Eviction in the currently\n        configured Kubernetes cluster.\n        \"\"\"\n        names = [\"patch_namespaced_eviction\", \"patch_eviction\"]\n\n        _kube_api.execute(\n            action=\"patch\",\n            resource=self,\n            names=names,\n            namespace=namespace,\n            api_client=None,\n            api_args={\"body\": self.to_dict(), \"name\": self.metadata.name},\n        )\n\n    def get_resource_status(self, namespace: typing.Optional[\"str\"] = None):\n        \"\"\"This resource does not have a status.\"\"\"\n        pass\n\n    def read_resource(self, namespace: typing.Optional[str] = None):\n        \"\"\"\n        Reads the Eviction from the currently configured\n        Kubernetes cluster and returns the low-level definition object.\n        \"\"\"\n        names = [\n            \"read_namespaced_eviction\",\n            \"read_eviction\",\n        ]\n        return _kube_api.execute(\n            action=\"read\",\n            resource=self,\n            names=names,\n            namespace=namespace,\n            api_client=None,\n            api_args={\"name\": self.metadata.name},\n        )\n\n    def delete_resource(\n        self,\n        namespace: typing.Optional[str] = None,\n        propagation_policy: str = \"Foreground\",\n        grace_period_seconds: int = 10,\n    ):\n        \"\"\"\n        Deletes the Eviction from the currently configured\n        Kubernetes cluster.\n        \"\"\"\n        names = [\n            \"delete_namespaced_eviction\",\n            \"delete_eviction\",\n        ]\n\n        body = client.V1DeleteOptions(\n            propagation_policy=propagation_policy,\n            grace_period_seconds=grace_period_seconds,\n        )\n\n        _kube_api.execute(\n            action=\"delete\",\n            resource=self,\n            names=names,\n            namespace=namespace,\n            api_client=None,\n            api_args={\"name\": self.metadata.name, \"body\": body},\n        )\n\n    @staticmethod\n    def get_resource_api(\n        api_client: typing.Optional[client.ApiClient] = None, **kwargs\n    ) -> \"client.PolicyV1Api\":\n        \"\"\"\n        Returns an instance of the kubernetes API client associated with\n        this object.\n        \"\"\"\n        if api_client:\n            kwargs[\"apl_client\"] = api_client\n        return client.PolicyV1Api(**kwargs)\n\n    def __enter__(self) -> \"Eviction\":\n        return self\n\n    def __exit__(self, exc_type, exc_val, exc_tb):\n        return False\n\n\nclass PodDisruptionBudget(_kuber_definitions.Resource):\n    \"\"\"\n    PodDisruptionBudget is an object to define the max\n    disruption that can be caused to a collection of pods\n    \"\"\"\n\n    def __init__(\n        self,\n        metadata: typing.Optional[\"ObjectMeta\"] = None,\n        spec: typing.Optional[\"PodDisruptionBudgetSpec\"] = None,\n        status: typing.Optional[\"PodDisruptionBudgetStatus\"] = None,\n    ):\n        \"\"\"Create PodDisruptionBudget instance.\"\"\"\n        super(PodDisruptionBudget, self).__init__(\n            api_version=\"policy/v1\", kind=\"PodDisruptionBudget\"\n        )\n        self._properties = {\n            \"metadata\": metadata if metadata is not None else ObjectMeta(),\n            \"spec\": spec if spec is not None else PodDisruptionBudgetSpec(),\n            \"status\": status if status is not None else PodDisruptionBudgetStatus(),\n        }\n        self._types = {\n            \"apiVersion\": (str, None),\n            \"kind\": (str, None),\n            \"metadata\": (ObjectMeta, None),\n            \"spec\": (PodDisruptionBudgetSpec, None),\n            \"status\": (PodDisruptionBudgetStatus, None),\n        }\n\n    @property\n    def metadata(self) -> \"ObjectMeta\":\n        \"\"\"\n        Standard object's metadata. More info:\n        https://git.k8s.io/community/contributors/devel/sig-\n        architecture/api-conventions.md#metadata\n        \"\"\"\n        return typing.cast(\n            \"ObjectMeta\",\n            self._properties.get(\"metadata\"),\n        )\n\n    @metadata.setter\n    def metadata(self, value: typing.Union[\"ObjectMeta\", dict]):\n        \"\"\"\n        Standard object's metadata. More info:\n        https://git.k8s.io/community/contributors/devel/sig-\n        architecture/api-conventions.md#metadata\n        \"\"\"\n        if isinstance(value, dict):\n            value = typing.cast(\n                ObjectMeta,\n                ObjectMeta().from_dict(value),\n            )\n        self._properties[\"metadata\"] = value\n\n    @property\n    def spec(self) -> \"PodDisruptionBudgetSpec\":\n        \"\"\"\n        Specification of the desired behavior of the\n        PodDisruptionBudget.\n        \"\"\"\n        return typing.cast(\n            \"PodDisruptionBudgetSpec\",\n            self._properties.get(\"spec\"),\n        )\n\n    @spec.setter\n    def spec(self, value: typing.Union[\"PodDisruptionBudgetSpec\", dict]):\n        \"\"\"\n        Specification of the desired behavior of the\n        PodDisruptionBudget.\n        \"\"\"\n        if isinstance(value, dict):\n            value = typing.cast(\n                PodDisruptionBudgetSpec,\n                PodDisruptionBudgetSpec().from_dict(value),\n            )\n        self._properties[\"spec\"] = value\n\n    @property\n    def status(self) -> \"PodDisruptionBudgetStatus\":\n        \"\"\"\n        Most recently observed status of the PodDisruptionBudget.\n        \"\"\"\n        return typing.cast(\n            \"PodDisruptionBudgetStatus\",\n            self._properties.get(\"status\"),\n        )\n\n    @status.setter\n    def status(self, value: typing.Union[\"PodDisruptionBudgetStatus\", dict]):\n        \"\"\"\n        Most recently observed status of the PodDisruptionBudget.\n        \"\"\"\n        if isinstance(value, dict):\n            value = typing.cast(\n                PodDisruptionBudgetStatus,\n                PodDisruptionBudgetStatus().from_dict(value),\n            )\n        self._properties[\"status\"] = value\n\n    def create_resource(\n        self, namespace: typing.Optional[\"str\"] = None\n    ) -> \"PodDisruptionBudgetStatus\":\n        \"\"\"\n        Creates the PodDisruptionBudget in the currently\n        configured Kubernetes cluster and returns the status information\n        returned by the Kubernetes API after the create is complete.\n        \"\"\"\n        names = [\n            \"create_namespaced_pod_disruption_budget\",\n            \"create_pod_disruption_budget\",\n        ]\n\n        response = _kube_api.execute(\n            action=\"create\",\n            resource=self,\n            names=names,\n            namespace=namespace,\n            api_client=None,\n            api_args={\"body\": self.to_dict()},\n        )\n\n        output = PodDisruptionBudgetStatus()\n        if response is not None:\n            output.from_dict(_kube_api.to_kuber_dict(response.status))\n        return output\n\n    def replace_resource(\n        self, namespace: typing.Optional[\"str\"] = None\n    ) -> \"PodDisruptionBudgetStatus\":\n        \"\"\"\n        Replaces the PodDisruptionBudget in the currently\n        configured Kubernetes cluster and returns the status information\n        returned by the Kubernetes API after the replace is complete.\n        \"\"\"\n        names = [\n            \"replace_namespaced_pod_disruption_budget\",\n            \"replace_pod_disruption_budget\",\n        ]\n\n        response = _kube_api.execute(\n            action=\"replace\",\n            resource=self,\n            names=names,\n            namespace=namespace,\n            api_client=None,\n            api_args={\"body\": self.to_dict(), \"name\": self.metadata.name},\n        )\n\n        output = PodDisruptionBudgetStatus()\n        if response is not None:\n            output.from_dict(_kube_api.to_kuber_dict(response.status))\n        return output\n\n    def patch_resource(\n        self, namespace: typing.Optional[\"str\"] = None\n    ) -> \"PodDisruptionBudgetStatus\":\n        \"\"\"\n        Patches the PodDisruptionBudget in the currently\n        configured Kubernetes cluster and returns the status information\n        returned by the Kubernetes API after the replace is complete.\n        \"\"\"\n        names = [\n            \"patch_namespaced_pod_disruption_budget\",\n            \"patch_pod_disruption_budget\",\n        ]\n\n        response = _kube_api.execute(\n            action=\"patch\",\n            resource=self,\n            names=names,\n            namespace=namespace,\n            api_client=None,\n            api_args={\"body\": self.to_dict(), \"name\": self.metadata.name},\n        )\n\n        output = PodDisruptionBudgetStatus()\n        if response is not None:\n            output.from_dict(_kube_api.to_kuber_dict(response.status))\n        return output\n\n    def get_resource_status(\n        self, namespace: typing.Optional[\"str\"] = None\n    ) -> \"PodDisruptionBudgetStatus\":\n        \"\"\"\n        Returns status information about the given resource within the cluster.\n        \"\"\"\n        names = [\n            \"read_namespaced_pod_disruption_budget\",\n            \"read_pod_disruption_budget\",\n        ]\n\n        response = _kube_api.execute(\n            action=\"read\",\n            resource=self,\n            names=names,\n            namespace=namespace,\n            api_client=None,\n            api_args={\"name\": self.metadata.name},\n        )\n\n        output = PodDisruptionBudgetStatus()\n        if response is not None:\n            output.from_dict(_kube_api.to_kuber_dict(response.status))\n        return output\n\n    def read_resource(self, namespace: typing.Optional[str] = None):\n        \"\"\"\n        Reads the PodDisruptionBudget from the currently configured\n        Kubernetes cluster and returns the low-level definition object.\n        \"\"\"\n        names = [\n            \"read_namespaced_pod_disruption_budget\",\n            \"read_pod_disruption_budget\",\n        ]\n        return _kube_api.execute(\n            action=\"read\",\n            resource=self,\n            names=names,\n            namespace=namespace,\n            api_client=None,\n            api_args={\"name\": self.metadata.name},\n        )\n\n    def delete_resource(\n        self,\n        namespace: typing.Optional[str] = None,\n        propagation_policy: str = \"Foreground\",\n        grace_period_seconds: int = 10,\n    ):\n        \"\"\"\n        Deletes the PodDisruptionBudget from the currently configured\n        Kubernetes cluster.\n        \"\"\"\n        names = [\n            \"delete_namespaced_pod_disruption_budget\",\n            \"delete_pod_disruption_budget\",\n        ]\n\n        body = client.V1DeleteOptions(\n            propagation_policy=propagation_policy,\n            grace_period_seconds=grace_period_seconds,\n        )\n\n        _kube_api.execute(\n            action=\"delete\",\n            resource=self,\n            names=names,\n            namespace=namespace,\n            api_client=None,\n            api_args={\"name\": self.metadata.name, \"body\": body},\n        )\n\n    @staticmethod\n    def get_resource_api(\n        api_client: typing.Optional[client.ApiClient] = None, **kwargs\n    ) -> \"client.PolicyV1Api\":\n        \"\"\"\n        Returns an instance of the kubernetes API client associated with\n        this object.\n        \"\"\"\n        if api_client:\n            kwargs[\"apl_client\"] = api_client\n        return client.PolicyV1Api(**kwargs)\n\n    def __enter__(self) -> \"PodDisruptionBudget\":\n        return self\n\n    def __exit__(self, exc_type, exc_val, exc_tb):\n        return False\n\n\nclass PodDisruptionBudgetList(_kuber_definitions.Collection):\n    \"\"\"\n    PodDisruptionBudgetList is a collection of\n    PodDisruptionBudgets.\n    \"\"\"\n\n    def __init__(\n        self,\n        items: typing.Optional[typing.List[\"PodDisruptionBudget\"]] = None,\n        metadata: typing.Optional[\"ListMeta\"] = None,\n    ):\n        \"\"\"Create PodDisruptionBudgetList instance.\"\"\"\n        super(PodDisruptionBudgetList, self).__init__(\n            api_version=\"policy/v1\", kind=\"PodDisruptionBudgetList\"\n        )\n        self._properties = {\n            \"items\": items if items is not None else [],\n            \"metadata\": metadata if metadata is not None else ListMeta(),\n        }\n        self._types = {\n            \"apiVersion\": (str, None),\n            \"items\": (list, PodDisruptionBudget),\n            \"kind\": (str, None),\n            \"metadata\": (ListMeta, None),\n        }\n\n    @property\n    def items(self) -> typing.List[\"PodDisruptionBudget\"]:\n        \"\"\"\n        Items is a list of PodDisruptionBudgets\n        \"\"\"\n        return typing.cast(\n            typing.List[\"PodDisruptionBudget\"],\n            self._properties.get(\"items\"),\n        )\n\n    @items.setter\n    def items(\n        self, value: typing.Union[typing.List[\"PodDisruptionBudget\"], typing.List[dict]]\n    ):\n        \"\"\"\n        Items is a list of PodDisruptionBudgets\n        \"\"\"\n        cleaned: typing.List[PodDisruptionBudget] = []\n        for item in value:\n            if isinstance(item, dict):\n                item = typing.cast(\n                    PodDisruptionBudget,\n                    PodDisruptionBudget().from_dict(item),\n                )\n            cleaned.append(typing.cast(PodDisruptionBudget, item))\n        self._properties[\"items\"] = cleaned\n\n    @property\n    def metadata(self) -> \"ListMeta\":\n        \"\"\"\n        Standard object's metadata. More info:\n        https://git.k8s.io/community/contributors/devel/sig-\n        architecture/api-conventions.md#metadata\n        \"\"\"\n        return typing.cast(\n            \"ListMeta\",\n            self._properties.get(\"metadata\"),\n        )\n\n    @metadata.setter\n    def metadata(self, value: typing.Union[\"ListMeta\", dict]):\n        \"\"\"\n        Standard object's metadata. More info:\n        https://git.k8s.io/community/contributors/devel/sig-\n        architecture/api-conventions.md#metadata\n        \"\"\"\n        if isinstance(value, dict):\n            value = typing.cast(\n                ListMeta,\n                ListMeta().from_dict(value),\n            )\n        self._properties[\"metadata\"] = value\n\n    @staticmethod\n    def get_resource_api(\n        api_client: typing.Optional[client.ApiClient] = None, **kwargs\n    ) -> \"client.PolicyV1Api\":\n        \"\"\"\n        Returns an instance of the kubernetes API client associated with\n        this object.\n        \"\"\"\n        if api_client:\n            kwargs[\"apl_client\"] = api_client\n        return client.PolicyV1Api(**kwargs)\n\n    def __enter__(self) -> \"PodDisruptionBudgetList\":\n        return self\n\n    def __exit__(self, exc_type, exc_val, exc_tb):\n        return False\n\n\nclass PodDisruptionBudgetSpec(_kuber_definitions.Definition):\n    \"\"\"\n    PodDisruptionBudgetSpec is a description of a\n    PodDisruptionBudget.\n    \"\"\"\n\n    def __init__(\n        self,\n        max_unavailable: typing.Optional[typing.Union[str, int, None]] = None,\n        min_available: typing.Optional[typing.Union[str, int, None]] = None,\n        selector: typing.Optional[\"LabelSelector\"] = None,\n        unhealthy_pod_eviction_policy: typing.Optional[str] = None,\n    ):\n        \"\"\"Create PodDisruptionBudgetSpec instance.\"\"\"\n        super(PodDisruptionBudgetSpec, self).__init__(\n            api_version=\"policy/v1\", kind=\"PodDisruptionBudgetSpec\"\n        )\n        self._properties = {\n            \"maxUnavailable\": max_unavailable if max_unavailable is not None else None,\n            \"minAvailable\": min_available if min_available is not None else None,\n            \"selector\": selector if selector is not None else LabelSelector(),\n            \"unhealthyPodEvictionPolicy\": unhealthy_pod_eviction_policy\n            if unhealthy_pod_eviction_policy is not None\n            else \"\",\n        }\n        self._types = {\n            \"maxUnavailable\": (_types.integer_or_string, None),\n            \"minAvailable\": (_types.integer_or_string, None),\n            \"selector\": (LabelSelector, None),\n            \"unhealthyPodEvictionPolicy\": (str, None),\n        }\n\n    @property\n    def max_unavailable(self) -> typing.Union[str, int, None]:\n        \"\"\"\n        An eviction is allowed if at most \"maxUnavailable\" pods\n        selected by \"selector\" are unavailable after the eviction,\n        i.e. even in absence of the evicted pod. For example, one\n        can prevent all voluntary evictions by specifying 0. This is\n        a mutually exclusive setting with \"minAvailable\".\n        \"\"\"\n        return typing.cast(\n            typing.Union[str, int, None],\n            self._properties.get(\"maxUnavailable\"),\n        )\n\n    @max_unavailable.setter\n    def max_unavailable(self, value: typing.Union[str, int, None]):\n        \"\"\"\n        An eviction is allowed if at most \"maxUnavailable\" pods\n        selected by \"selector\" are unavailable after the eviction,\n        i.e. even in absence of the evicted pod. For example, one\n        can prevent all voluntary evictions by specifying 0. This is\n        a mutually exclusive setting with \"minAvailable\".\n        \"\"\"\n        self._properties[\"maxUnavailable\"] = _types.integer_or_string(value)\n\n    @property\n    def min_available(self) -> typing.Union[str, int, None]:\n        \"\"\"\n        An eviction is allowed if at least \"minAvailable\" pods\n        selected by \"selector\" will still be available after the\n        eviction, i.e. even in the absence of the evicted pod.  So\n        for example you can prevent all voluntary evictions by\n        specifying \"100%\".\n        \"\"\"\n        return typing.cast(\n            typing.Union[str, int, None],\n            self._properties.get(\"minAvailable\"),\n        )\n\n    @min_available.setter\n    def min_available(self, value: typing.Union[str, int, None]):\n        \"\"\"\n        An eviction is allowed if at least \"minAvailable\" pods\n        selected by \"selector\" will still be available after the\n        eviction, i.e. even in the absence of the evicted pod.  So\n        for example you can prevent all voluntary evictions by\n        specifying \"100%\".\n        \"\"\"\n        self._properties[\"minAvailable\"] = _types.integer_or_string(value)\n\n    @property\n    def selector(self) -> \"LabelSelector\":\n        \"\"\"\n        Label query over pods whose evictions are managed by the\n        disruption budget. A null selector will match no pods, while\n        an empty ({}) selector will select all pods within the\n        namespace.\n        \"\"\"\n        return typing.cast(\n            \"LabelSelector\",\n            self._properties.get(\"selector\"),\n        )\n\n    @selector.setter\n    def selector(self, value: typing.Union[\"LabelSelector\", dict]):\n        \"\"\"\n        Label query over pods whose evictions are managed by the\n        disruption budget. A null selector will match no pods, while\n        an empty ({}) selector will select all pods within the\n        namespace.\n        \"\"\"\n        if isinstance(value, dict):\n            value = typing.cast(\n                LabelSelector,\n                LabelSelector().from_dict(value),\n            )\n        self._properties[\"selector\"] = value\n\n    @property\n    def unhealthy_pod_eviction_policy(self) -> str:\n        \"\"\"\n        UnhealthyPodEvictionPolicy defines the criteria for when\n        unhealthy pods should be considered for eviction. Current\n        implementation considers healthy pods, as pods that have\n        status.conditions item with type=\"Ready\",status=\"True\".\n\n        Valid policies are IfHealthyBudget and AlwaysAllow. If no\n        policy is specified, the default behavior will be used,\n        which corresponds to the IfHealthyBudget policy.\n\n        IfHealthyBudget policy means that running pods\n        (status.phase=\"Running\"), but not yet healthy can be evicted\n        only if the guarded application is not disrupted\n        (status.currentHealthy is at least equal to\n        status.desiredHealthy). Healthy pods will be subject to the\n        PDB for eviction.\n\n        AlwaysAllow policy means that all running pods\n        (status.phase=\"Running\"), but not yet healthy are considered\n        disrupted and can be evicted regardless of whether the\n        criteria in a PDB is met. This means perspective running\n        pods of a disrupted application might not get a chance to\n        become healthy. Healthy pods will be subject to the PDB for\n        eviction.\n\n        Additional policies may be added in the future. Clients\n        making eviction decisions should disallow eviction of\n        unhealthy pods if they encounter an unrecognized policy in\n        this field.\n\n        This field is alpha-level. The eviction API uses this field\n        when the feature gate PDBUnhealthyPodEvictionPolicy is\n        enabled (disabled by default).\n        \"\"\"\n        return typing.cast(\n            str,\n            self._properties.get(\"unhealthyPodEvictionPolicy\"),\n        )\n\n    @unhealthy_pod_eviction_policy.setter\n    def unhealthy_pod_eviction_policy(self, value: str):\n        \"\"\"\n        UnhealthyPodEvictionPolicy defines the criteria for when\n        unhealthy pods should be considered for eviction. Current\n        implementation considers healthy pods, as pods that have\n        status.conditions item with type=\"Ready\",status=\"True\".\n\n        Valid policies are IfHealthyBudget and AlwaysAllow. If no\n        policy is specified, the default behavior will be used,\n        which corresponds to the IfHealthyBudget policy.\n\n        IfHealthyBudget policy means that running pods\n        (status.phase=\"Running\"), but not yet healthy can be evicted\n        only if the guarded application is not disrupted\n        (status.currentHealthy is at least equal to\n        status.desiredHealthy). Healthy pods will be subject to the\n        PDB for eviction.\n\n        AlwaysAllow policy means that all running pods\n        (status.phase=\"Running\"), but not yet healthy are considered\n        disrupted and can be evicted regardless of whether the\n        criteria in a PDB is met. This means perspective running\n        pods of a disrupted application might not get a chance to\n        become healthy. Healthy pods will be subject to the PDB for\n        eviction.\n\n        Additional policies may be added in the future. Clients\n        making eviction decisions should disallow eviction of\n        unhealthy pods if they encounter an unrecognized policy in\n        this field.\n\n        This field is alpha-level. The eviction API uses this field\n        when the feature gate PDBUnhealthyPodEvictionPolicy is\n        enabled (disabled by default).\n        \"\"\"\n        self._properties[\"unhealthyPodEvictionPolicy\"] = value\n\n    def __enter__(self) -> \"PodDisruptionBudgetSpec\":\n        return self\n\n    def __exit__(self, exc_type, exc_val, exc_tb):\n        return False\n\n\nclass PodDisruptionBudgetStatus(_kuber_definitions.Definition):\n    \"\"\"\n    PodDisruptionBudgetStatus represents information about the\n    status of a PodDisruptionBudget. Status may trail the actual\n    state of a system.\n    \"\"\"\n\n    def __init__(\n        self,\n        conditions: typing.Optional[typing.List[\"Condition\"]] = None,\n        current_healthy: typing.Optional[int] = None,\n        desired_healthy: typing.Optional[int] = None,\n        disrupted_pods: typing.Optional[dict] = None,\n        disruptions_allowed: typing.Optional[int] = None,\n        expected_pods: typing.Optional[int] = None,\n        observed_generation: typing.Optional[int] = None,\n    ):\n        \"\"\"Create PodDisruptionBudgetStatus instance.\"\"\"\n        super(PodDisruptionBudgetStatus, self).__init__(\n            api_version=\"policy/v1\", kind=\"PodDisruptionBudgetStatus\"\n        )\n        self._properties = {\n            \"conditions\": conditions if conditions is not None else [],\n            \"currentHealthy\": current_healthy if current_healthy is not None else None,\n            \"desiredHealthy\": desired_healthy if desired_healthy is not None else None,\n            \"disruptedPods\": disrupted_pods if disrupted_pods is not None else {},\n            \"disruptionsAllowed\": disruptions_allowed\n            if disruptions_allowed is not None\n            else None,\n            \"expectedPods\": expected_pods if expected_pods is not None else None,\n            \"observedGeneration\": observed_generation\n            if observed_generation is not None\n            else None,\n        }\n        self._types = {\n            \"conditions\": (list, Condition),\n            \"currentHealthy\": (int, None),\n            \"desiredHealthy\": (int, None),\n            \"disruptedPods\": (dict, None),\n            \"disruptionsAllowed\": (int, None),\n            \"expectedPods\": (int, None),\n            \"observedGeneration\": (int, None),\n        }\n\n    @property\n    def conditions(self) -> typing.List[\"Condition\"]:\n        \"\"\"\n        Conditions contain conditions for PDB. The disruption\n        controller sets the DisruptionAllowed condition. The\n        following are known values for the reason field (additional\n        reasons could be added in the future): - SyncFailed: The\n        controller encountered an error and wasn't able to compute\n                      the number of allowed disruptions. Therefore\n        no disruptions are\n                      allowed and the status of the condition will\n        be False.\n        - InsufficientPods: The number of pods are either at or\n        below the number\n                            required by the PodDisruptionBudget. No\n        disruptions are\n                            allowed and the status of the condition\n        will be False.\n        - SufficientPods: There are more pods than required by the\n        PodDisruptionBudget.\n                          The condition will be True, and the number\n        of allowed\n                          disruptions are provided by the\n        disruptionsAllowed property.\n        \"\"\"\n        return typing.cast(\n            typing.List[\"Condition\"],\n            self._properties.get(\"conditions\"),\n        )\n\n    @conditions.setter\n    def conditions(\n        self, value: typing.Union[typing.List[\"Condition\"], typing.List[dict]]\n    ):\n        \"\"\"\n        Conditions contain conditions for PDB. The disruption\n        controller sets the DisruptionAllowed condition. The\n        following are known values for the reason field (additional\n        reasons could be added in the future): - SyncFailed: The\n        controller encountered an error and wasn't able to compute\n                      the number of allowed disruptions. Therefore\n        no disruptions are\n                      allowed and the status of the condition will\n        be False.\n        - InsufficientPods: The number of pods are either at or\n        below the number\n                            required by the PodDisruptionBudget. No\n        disruptions are\n                            allowed and the status of the condition\n        will be False.\n        - SufficientPods: There are more pods than required by the\n        PodDisruptionBudget.\n                          The condition will be True, and the number\n        of allowed\n                          disruptions are provided by the\n        disruptionsAllowed property.\n        \"\"\"\n        cleaned: typing.List[Condition] = []\n        for item in value:\n            if isinstance(item, dict):\n                item = typing.cast(\n                    Condition,\n                    Condition().from_dict(item),\n                )\n            cleaned.append(typing.cast(Condition, item))\n        self._properties[\"conditions\"] = cleaned\n\n    @property\n    def current_healthy(self) -> int:\n        \"\"\"\n        current number of healthy pods\n        \"\"\"\n        return typing.cast(\n            int,\n            self._properties.get(\"currentHealthy\"),\n        )\n\n    @current_healthy.setter\n    def current_healthy(self, value: int):\n        \"\"\"\n        current number of healthy pods\n        \"\"\"\n        self._properties[\"currentHealthy\"] = value\n\n    @property\n    def desired_healthy(self) -> int:\n        \"\"\"\n        minimum desired number of healthy pods\n        \"\"\"\n        return typing.cast(\n            int,\n            self._properties.get(\"desiredHealthy\"),\n        )\n\n    @desired_healthy.setter\n    def desired_healthy(self, value: int):\n        \"\"\"\n        minimum desired number of healthy pods\n        \"\"\"\n        self._properties[\"desiredHealthy\"] = value\n\n    @property\n    def disrupted_pods(self) -> dict:\n        \"\"\"\n        DisruptedPods contains information about pods whose eviction\n        was processed by the API server eviction subresource handler\n        but has not yet been observed by the PodDisruptionBudget\n        controller. A pod will be in this map from the time when the\n        API server processed the eviction request to the time when\n        the pod is seen by PDB controller as having been marked for\n        deletion (or after a timeout). The key in the map is the\n        name of the pod and the value is the time when the API\n        server processed the eviction request. If the deletion\n        didn't occur and a pod is still there it will be removed\n        from the list automatically by PodDisruptionBudget\n        controller after some time. If everything goes smooth this\n        map should be empty for the most of the time. Large number\n        of entries in the map may indicate problems with pod\n        deletions.\n        \"\"\"\n        return typing.cast(\n            dict,\n            self._properties.get(\"disruptedPods\"),\n        )\n\n    @disrupted_pods.setter\n    def disrupted_pods(self, value: dict):\n        \"\"\"\n        DisruptedPods contains information about pods whose eviction\n        was processed by the API server eviction subresource handler\n        but has not yet been observed by the PodDisruptionBudget\n        controller. A pod will be in this map from the time when the\n        API server processed the eviction request to the time when\n        the pod is seen by PDB controller as having been marked for\n        deletion (or after a timeout). The key in the map is the\n        name of the pod and the value is the time when the API\n        server processed the eviction request. If the deletion\n        didn't occur and a pod is still there it will be removed\n        from the list automatically by PodDisruptionBudget\n        controller after some time. If everything goes smooth this\n        map should be empty for the most of the time. Large number\n        of entries in the map may indicate problems with pod\n        deletions.\n        \"\"\"\n        self._properties[\"disruptedPods\"] = value\n\n    @property\n    def disruptions_allowed(self) -> int:\n        \"\"\"\n        Number of pod disruptions that are currently allowed.\n        \"\"\"\n        return typing.cast(\n            int,\n            self._properties.get(\"disruptionsAllowed\"),\n        )\n\n    @disruptions_allowed.setter\n    def disruptions_allowed(self, value: int):\n        \"\"\"\n        Number of pod disruptions that are currently allowed.\n        \"\"\"\n        self._properties[\"disruptionsAllowed\"] = value\n\n    @property\n    def expected_pods(self) -> int:\n        \"\"\"\n        total number of pods counted by this disruption budget\n        \"\"\"\n        return typing.cast(\n            int,\n            self._properties.get(\"expectedPods\"),\n        )\n\n    @expected_pods.setter\n    def expected_pods(self, value: int):\n        \"\"\"\n        total number of pods counted by this disruption budget\n        \"\"\"\n        self._properties[\"expectedPods\"] = value\n\n    @property\n    def observed_generation(self) -> int:\n        \"\"\"\n        Most recent generation observed when updating this PDB\n        status. DisruptionsAllowed and other status information is\n        valid only if observedGeneration equals to PDB's object\n        generation.\n        \"\"\"\n        return typing.cast(\n            int,\n            self._properties.get(\"observedGeneration\"),\n        )\n\n    @observed_generation.setter\n    def observed_generation(self, value: int):\n        \"\"\"\n        Most recent generation observed when updating this PDB\n        status. DisruptionsAllowed and other status information is\n        valid only if observedGeneration equals to PDB's object\n        generation.\n        \"\"\"\n        self._properties[\"observedGeneration\"] = value\n\n    def __enter__(self) -> \"PodDisruptionBudgetStatus\":\n        return self\n\n    def __exit__(self, exc_type, exc_val, exc_tb):\n        return False\n","sub_path":"kuber/v1_26/policy_v1.py","file_name":"policy_v1.py","file_ext":"py","file_size_in_byte":35916,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"449469489","text":"# pylint: disable=C0321,C0103,E1221,C0301,E1305,E1121,C0302,C0330\n# -*- coding: utf-8 -*-\n\"\"\"\n### Usage:\n  python tsampler.py  train_sampler        --config  config_sampler\n  python tsampler.py  transform_sampler    --config  config_sampler\n\n\n\n\"\"\"\nimport warnings, copy, os, sys\nwarnings.filterwarnings(\"ignore\")\n\n####################################################################################\n###### Path ########################################################################\nroot_repo      =  os.path.abspath(os.getcwd()).replace(\"\\\\\", \"/\") + \"/\"     ; print(root_repo)\nTHIS_FILEPATH  =  os.path.abspath(__file__) \n\nsys.path.append(root_repo)\nfrom source.util_feature import save,os_get_function_name\n\ndef global_pars_update(model_dict,  data_name, config_name):\n    print(\"config_name\", config_name)\n    dir_data  = root_repo + \"/data/\"  ; print(\"dir_data\", dir_data)\n\n    m                      = {}\n    m[\"config_path\"]       = THIS_FILEPATH  \n    m[\"config_name\"]       = config_name\n    m[\"model_file\"]        = \"model_sampler\"\n\n    #### peoprocess input path\n    m[\"path_data_preprocess\"] = dir_data + f\"/input/{data_name}/train/\"\n\n    #### train input path\n    dir_data_url              = \"https://github.com/arita37/dsa2_data/tree/master/\"  #### Remote Data directory\n    m[\"path_data_train\"]      = dir_data_url + f\"/input/{data_name}/train/\"\n    m[\"path_data_test\"]       = dir_data_url + f\"/input/{data_name}/test/\"\n    #m[\"path_data_val\"]       = dir_data + f\"/input/{data_name}/test/\"\n\n    #### train output path\n    m[\"path_train_output\"]    = dir_data + f\"/output/{data_name}/{config_name}/\"\n    m[\"path_train_model\"]     = dir_data + f\"/output/{data_name}/{config_name}/model/\"\n    m[\"path_features_store\"]  = dir_data + f\"/output/{data_name}/{config_name}/features_store/\"\n    m[\"path_pipeline\"]        = dir_data + f\"/output/{data_name}/{config_name}/pipeline/\"\n\n\n    #### predict  input path\n    m[\"path_pred_data\"]       = dir_data + f\"/input/{data_name}/test/\"\n    m[\"path_pred_pipeline\"]   = dir_data + f\"/output/{data_name}/{config_name}/pipeline/\"\n    m[\"path_pred_model\"]      = dir_data + f\"/output/{data_name}/{config_name}/model/\"\n\n    #### predict  output path\n    m[\"path_pred_output\"]     = dir_data + f\"/output/{data_name}/pred_{config_name}/\"\n\n    #####  Generic\n    m[\"n_sample\"]             = model_dict[\"data_pars\"].get(\"n_sample\", 5000)\n\n    model_dict[ \"global_pars\"] = m\n    return model_dict\n\n\n####################################################################################\n##### Params########################################################################\nconfig_default   = \"config_sampler\"    ### name of function which contains data configuration\n\n\ncols_input_type_1 = {\n     \"coly\"   :   \"Survived\"\n    ,\"colid\"  :   \"PassengerId\"\n    ,\"colcat\" :   [\"Sex\", \"Embarked\" ]\n    ,\"colnum\" :   [\"Pclass\", \"Age\",\"SibSp\", \"Parch\",\"Fare\"]\n    ,\"coltext\" :  []\n    ,\"coldate\" :  []\n    ,\"colcross\" : [ \"Name\", \"Sex\", \"Ticket\",\"Embarked\",\"Pclass\", \"Age\", \"SibSp\", ]\n}\n\n\n####################################################################################\ndef config_sampler() :\n    \"\"\"\n       ONE SINGLE DICT Contains all needed informations for\n       used for titanic classification task\n    \"\"\"\n    data_name    = \"titanic\"         ### in data/input/\n    model_class  = \"CTGAN\"  ### ACTUAL Class name for model_sklearn.py\n    n_sample     = 1000\n\n    def post_process_fun(y):   ### After prediction is done\n        return  int(y)\n\n    def pre_process_fun(y):    ### Before the prediction is done\n        return  int(y)\n\n    model_dict = {\n      \"model_pars\": {\n         \"model_class\": model_class\n        ,\"model_pars\" : { }\n        , \"post_process_fun\" : post_process_fun   ### After prediction  ##########################################\n        , \"pre_process_pars\" : {\n              \"y_norm_fun\" :  pre_process_fun ,  ### Before training  ##########################\n              ### Pipeline for data processing ##############################\n              \"pipe_list\": [\n                  #### coly target prorcessing\n                  {\"uri\": \"source/prepro.py::pd_coly\",                 \"pars\": {}, \"cols_family\": \"coly\",       \"cols_out\": \"coly\",           \"type\": \"coly\"         },\n\n                  {\"uri\": \"source/prepro.py::pd_colnum_bin\",           \"pars\": {}, \"cols_family\": \"colnum\",     \"cols_out\": \"colnum_bin\",     \"type\": \"\"             },\n                  {\"uri\": \"source/prepro.py::pd_colnum_binto_onehot\",  \"pars\": {}, \"cols_family\": \"colnum_bin\", \"cols_out\": \"colnum_onehot\",  \"type\": \"\"             },\n\n                  #### catcol INTO integer,   colcat into OneHot\n                  {\"uri\": \"source/prepro.py::pd_colcat_bin\",           \"pars\": {}, \"cols_family\": \"colcat\",     \"cols_out\": \"colcat_bin\",     \"type\": \"\"             },\n                  {\"uri\": \"source/prepro.py::pd_colcat_to_onehot\",     \"pars\": {}, \"cols_family\": \"colcat_bin\", \"cols_out\": \"colcat_onehot\",  \"type\": \"\"             },\n\n             ],\n                                  }\n      },\n\n      \"compute_pars\": { \"metric_list\": [\"accuracy_score\",\"average_precision_score\"]\n                        # ,\"mlflow_pars\" : {}   ### Not empty --> use mlflow\n                      },\n\n      \"data_pars\": { \n          \"n_sample\" : n_sample,\n          \"download_pars\" : None,\n          ### Filter data rows   ##################################################################\n          \"filter_pars\": { \"ymax\" : 2 ,\"ymin\" : -1 },\n\n          ### Raw data:  column input ##############################################################\n          \"cols_input_type\" : cols_input_type_1,\n\n\n          ### Model Input :  Merge family of columns   #############################################\n          \"cols_model_group\": [ \"colnum_bin\", \"colcat_bin\",]\n\n          #### Model Input : Separate Category Sparse from Continuous : Aribitrary name is OK (!)\n        ,'cols_model_type': {\n            'continuous'   : [ 'colnum',   ],\n            'sparse'       : [ 'colcat_bin', 'colnum_bin',  ],\n            'my_split_23'  : [ 'colnum_bin',   ],\n          }\n\n         }\n      }\n\n    ##### Filling Global parameters    ############################################################\n    model_dict        = global_pars_update(model_dict, data_name, config_name=os_get_function_name() )\n    return model_dict\n\n\n\n#####################################################################################\n########## Profile data #############################################################\nfrom core_run import  data_profile\n# def data_profile(path_data=\"\", path_output=\"\", n_sample= 5000):\n\n\n\n###################################################################################\n########## Preprocess #############################################################\n### def preprocess(config=\"\", nsample=1000):\nfrom core_run import preprocess\n\n\n\n\n##################################################################################\n########## Train #################################################################\n# def train_sampler(config=None, nsample=None):\nfrom core_run import train_sampler\n\n\n\ndef test_batch():\n   from core_run import  get_config_path, get_global_pars\n   mdict  = config_sampler()\n\n   nsample = 100\n   config  = \"\"\n   ll = [\n     ('CTGAN', { })\n   ]\n\n   for m in ll :\n    mdict['model_pars']['model_class'] = m[0]\n    mdict['model_pars']['model_pars']  = m[1]\n\n    config_uri, config_name = get_config_path(config)\n\n    mdict = get_global_pars(  config_uri)\n    m     = mdict['global_pars']\n    print(mdict)\n    from source import run_sampler\n    run_sampler.run_train(config_name     =  None,\n                        config_path       =  None,\n                        n_sample          =  nsample if nsample is not None else m['n_sample'],\n                        model_dict= mdict\n                        # use_mlmflow       =  False\n                        )\n\n\n####################################################################################\n####### Inference ##################################################################\n# predict(config=\"\", nsample=10000)\n# from core_run import transform_sampler\n\n\n\n\n\n###########################################################################################################\n###########################################################################################################\nif __name__ == \"__main__\":\n    import fire\n    fire.Fire()\n    \n\n\n\n","sub_path":"tsampler.py","file_name":"tsampler.py","file_ext":"py","file_size_in_byte":8441,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"242694813","text":"from django.db import models\nimport django.core.validators\nfrom django.core.exceptions import ValidationError\nimport Company.models\nfrom Registration.models import Account\nfrom math import ceil\nimport string\nfrom nltk.corpus import stopwords\nfrom nltk.stem.porter import *\nfrom random import *\nfrom datetime import date\n\n############################################# Helper Functions ##########################################\nGARYID = 2\n\n############################################# Models ###################################################\n\n\nclass Major(models.Model):\n    db_table = 'Majors',\n    major = models.CharField(max_length=50, primary_key=True)\n\n    @staticmethod\n    def getMajorList():\n        return [dic['major'] for dic in Major.objects.values('major')]\n\n\nclass Degree(models.Model):\n    db_table = 'Degrees'\n\n    # FIXME : Multiple choice field can't be set as primary key.\n    ''' \n    DEGREE_CHOICES = (\n        ('BS', 'BS'),\n        ('BA', 'BA'),\n        ('MS', 'MS'),\n        ('MA', 'MA'),\n        ('Ph.D', 'Ph.D'),\n    )'''\n    degree = models.CharField(max_length=15, primary_key=True)\n\n    @staticmethod\n    def getDegreeList():\n        return [dic['degree'] for dic in Degree.objects.values('degree')]\n\n\nclass User(models.Model):\n    db_table = 'user',\n    acc = models.OneToOneField(Account, on_delete=models.CASCADE, null=True)\n    F_Name = models.CharField(max_length=20)\n    L_Name = models.CharField(max_length=20)\n   # p_id = models.CharField(max_length=1500)\n    # ProfilePic = models.ImageField(upload_to='profile_image', blank=True, default='profile_image/File_Pikachu.png')\n    yr_graduation = models.IntegerField(validators=[django.core.validators.MaxValueValidator(2050, message='Year of graduation should be less than 2050!'),\n                                                    django.core.validators.MinValueValidator(1970, message='Year of graduation should be more than 1970!')])\n    major = models.ForeignKey(Major, null=True, on_delete=models.PROTECT)\n    degree = models.ForeignKey(Degree, null=True, on_delete=models.PROTECT)\n    contact_email = models.EmailField(verbose_name='email address', null=True, max_length=255, unique=True)\n    description = models.CharField(max_length=1500,\n                                   validators=[django.core.validators.MinLengthValidator(50, message='Description must be at least 50 characters!')])\n    referral_ability = models.BooleanField(default=False)\n    company = models.ForeignKey(Company.models.Company, on_delete=models.PROTECT, null=True, blank=True)\n    friend = models.ManyToManyField('User', symmetrical=True, blank=True)\n    recommended_users = models.ManyToManyField('User', symmetrical=False, blank=True, related_name='recommended')\n    picdata = models.CharField(max_length=256000)\n    search_result = models.ManyToManyField('User', symmetrical=False, blank=True, related_name='result')\n\n\n    ################################################################## Getters\n\n\n    # define the string representation of the user objects\n    def __str__(self):\n        return self.F_Name + \" \" + self.L_Name\n\n    # get the number of common friends between two users\n    # parameters user1 and user2 are user objects\n    @staticmethod\n    def get_common_friend_number(user1, user2):\n        if type(user1) != User or type(user2) != User:\n            return False\n        user1Friends = set(user1.friend.all())\n        user2Friends = set(user2.friend.all())\n        return len(user1Friends.intersection(user2Friends))\n\n    # get the referral list of a user\n    # parameter user is an automatically generated user object id\n    @staticmethod\n    def get_referral_list(userId):\n        try:\n            user = User.objects.get(id=userId)\n        except:\n            return False\n\n        referralList = []\n        for pal in user.friend.all():\n            if pal.referral_ability:\n                referralList.append(pal)\n        if user.referral_ability:\n            referralList.append(user)\n        return referralList\n\n    # get_user_by_name\n    @staticmethod\n    def get_user_by_name(fname, lname):\n        if type(fname) != str or type(lname) != str:\n            return False\n        else:\n            return User.objects.all.filter(F_Name=fname, L_Name=lname)\n\n    # get_user_by_graduation\n    @staticmethod\n    def get_user_by_graduation(yr_grad):\n        if type(yr_grad) != int or yr_grad < 1970 or yr_grad > 2050:\n            return False\n        else:\n            return User.objects.all.filter(yr_graduation=yr_grad)\n\n    # get_user_by_major\n    @staticmethod\n    def get_user_by_major(major):\n        if type(major) != Major:\n            return False\n        else:\n            return User.objects.all.filter(Major=major)\n\n    # get_user_by_degree\n    @staticmethod\n    def get_user_by_degree(degree):\n        if type(degree) != Degree:\n            return False\n        else:\n            return User.objects.all.filter(Degree=degree)\n\n    # get_user_by_company\n    @staticmethod\n    def get_user_by_company(company):\n        if type(company) != Company.models.Company:\n            return False\n        else:\n            return User.objects.all.filter(Company=company)\n\n\n    ################################################################## Setter\n\n\n    # set a user's 'friends who has common friends with the user' list\n    # parameter user is a user object\n    @staticmethod\n    def set_recommended_list(user):\n        if type(user) != User:\n            return False\n\n        for guy in User.objects.all():\n            if len(set(user.friend.all()).intersection(set(guy.friend.all()))) != 0:\n                if guy not in user.friend.all() and guy != user:\n                    user.recommended_users.add(guy)\n\n        num_of_users = User.objects.all().count()\n        # if the total number user is smaller than 5, add all users to his recommended list\n        if num_of_users < 6:\n            for pal in User.objects.all():\n                if pal != user:\n                    user.recommended_users.add(pal)\n        else:\n            while len(user.recommended_users.all()) < 6:\n                index = randint(0, num_of_users - 1)\n                user.recommended_users.add(User.objects.all()[index])\n        return True\n\n    # set_fname\n    def set_fname(self, fname):\n        if type(fname) != str:\n            return False\n        else:\n            self.F_Name = fname\n            return True\n\n    # set_lname\n    def set_lname(self, lname):\n        if type(lname) != str:\n            return False\n        else:\n            self.L_Name = lname\n            return True\n\n    # set_yr_grad\n    def set_yr_grad(self, yr_grad):\n        if type(yr_grad) != int or yr_grad < 1970 or yr_grad > 2050:\n            return False\n        else:\n            self.yr_graduation = yr_grad\n            return True\n\n    # set_major\n    def set_major(self, majr):\n        if type(majr) != Major:\n            return False\n        else:\n            self.major = majr\n            return True\n\n    # set_degree\n    def set_degree(self, deg):\n        if type(deg) != Degree:\n            return False\n        else:\n            self.degree = deg\n            return True\n\n    # set_email\n    def set_email(self, email):\n        try:\n            django.core.validators.validate_email(email)\n            self.contact_email = email\n\n        except ValidationError:\n            return False\n        return True\n\n    # set_description\n    def set_description(self, descrip):\n        if type(descrip) != str or len(descrip) > 300 or len(descrip) < 50:\n            return False\n        else:\n            self.description = descrip\n            return True\n\n    # add_company\n    def add_company(self, comp):\n        if type(comp) != Company.models.Company:\n            return False\n        else:\n            self.company.add(comp)\n            return True\n\n    # remove_company\n    def remove_company(self, comp):\n        if type(comp) != Company.models.Company:\n            return False\n        else:\n            self.company.remove(comp)\n            return True\n\n    # send_request\n    def send_request(self, target):\n        if type(target) != User:\n            return False\n        else:\n            if not Request.objects.filter(from_user=self).filter(to_user=target):\n                request = Request(from_user=self, to_user=target)\n                request.save()\n                if User.get_gary() is not None:\n                    if target == User.get_gary():\n                        target.accept_request(request)\n                return True\n            else:\n                return False\n\n    # accept_request\n    def accept_request(self, request):\n        if type(request) != Request:\n            return False\n        else:\n            if request.to_user == self:\n                self.friend.add(request.from_user)\n                request.delete()\n                return True\n            else:\n                return False\n\n    # deny_request\n    def deny_request(self, request):\n        if type(request) != Request:\n            return False\n        else:\n            request.delete()\n            return True\n\n    # remove_friend\n    def remove_friend(self, todelete):\n        if type(todelete) != User:\n            return False\n        elif todelete in self.friend.all():\n            self.friend.remove(todelete)\n            return True\n        else:\n            return False\n\n    # undo_request\n    def undo_request(self, request):\n        if type(request) != Request:\n            return False\n        else:\n            request.delete()\n            return True\n\n    # update_user_info\n    def update_user_info(self, major, degree, contact_email, description, year_graduation, company):\n        if major:\n            major_obj = list(Major.objects.filter(major=major))[0]\n            self.major = major_obj\n        if degree:\n            degree_obj = list(Degree.objects.filter(degree=degree))[0]\n            self.degree = degree_obj\n        if contact_email:\n            self.contact_email = contact_email\n        if description:\n            self.description = description\n        if year_graduation:\n            self.yr_graduation = year_graduation\n        if company:\n            company_obj = list(Company.models.Company.objects.filter(company_name=company))[0]\n            self.company = company_obj\n        self.save()\n        return True\n\n    # get_go_events\n    def get_go_events_today(self):\n        return self.go.filter(date=date.today()).order_by('time')\n\n    # clear_search_results\n    def clear_search_results(self):\n        self.search_result.clear()\n        return\n\n    # This function is for demonstration purposes\n    # get_gary\n    @staticmethod\n    def get_gary():\n        try:\n            User.objects.get(id=GARYID)\n            return User.objects.get(id=GARYID)\n        except:\n            return None\n\n    # gary_send_request\n    @staticmethod\n    def gary_send_request(user):\n        print('db')\n        gary = User.get_gary()\n        if gary is not None:\n            if gary.send_request(user):\n                return True\n            else:\n                return False\n        else:\n            return False\n\n\nclass Request(models.Model):\n    from_user = models.ForeignKey(User, on_delete=models.CASCADE, related_name='sent_request')\n    to_user = models.ForeignKey(User, on_delete=models.CASCADE, related_name='received_request')\n\n\nRELEVANT_COEFFICIENT = 0.5\nPUNCTUATIONS = set(string.punctuation)\nSTOPWORDS = set(stopwords.words('english'))\nSTEMMER = PorterStemmer()\nINFINITY = 999999\n\n\n# helper method - string pre-processing\n# return a list of stemmed words in the string in lowercase without punctuations, stop words, or repeated words\ndef string_preprocess(to_process):\n    if type(to_process) != str:\n        return []\n    else:\n        to_process = ''.join([char for char in to_process.lower() if char not in PUNCTUATIONS])\n        processed = set()\n        for word in to_process.split():\n            if word not in STOPWORDS:\n                word = STEMMER.stem(word)\n                processed.add(word)\n        return processed\n\n\n# search_by_Keywords\ndef search_By_Keywords(keywords):\n    if type(keywords) != str:\n        return False\n    else:\n        # keywords pre-processing:\n        keywords = string_preprocess(keywords)\n\n        relevant_usr = []\n        if len(keywords) == 0:\n            threshold = INFINITY\n        else:\n            threshold = ceil(RELEVANT_COEFFICIENT * len(keywords))\n\n        for usr in User.objects.all():\n\n            # description pre-processing\n            full_name = usr.__str__()\n            full_name = string_preprocess(full_name)\n\n            # comparing keywords with job descriptions\n            counter = 0\n            for word in keywords:\n                if word in full_name:\n                    counter += 1\n\n                if counter >= threshold:\n                    relevant_usr.append(usr)\n        return relevant_usr\n","sub_path":"Django_UCSD/User/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":12898,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"159671050","text":"from random import *\n\nclass Ball:\n\n\n    def __init__(self, canvas, x, y, color):\n\n        self.diameter = 10\n        self.color = color\n\n        self.canvas = canvas\n        self.ball = canvas.create_oval(x, y, x+self.diameter, self.diameter, fill=self.color)\n\n        self.corner = 1 if (randint(0, 1) > 0.5) else -1    # which corner ball is headed 1 = right -1 = left\n\n        # represents the direction in which the ball is going\n        # [1, 1] = SouthEast\n        # [1, -1] = NorthEast\n        # [-1, 1] = SouthWest\n        # [-1, -1] = NorthWest\n        self.direction = [self.corner, 1]\n\n        # we need this to allow ball to move away from the\n        # top of the screen (where it initially comes from)\n        # and not bounce off the top until it has moved away\n        self.has_moved = False\n\n\n        # we need this to keep track of which balls have\n        # moved off the screen (i.e. passed the bottom)\n        self.moved_off_screen = False\n\n\n\n    # move the ball and bounce off walls/paddle accordingly\n    # takes the only paddle in the game as input to \n    # check whether ball has touched the paddle and if\n    # it does then increment score\n    def move_ball(self, paddle, score):\n\n        ball_coords = self.canvas.coords(self.ball)\n        canvas_width = self.canvas.winfo_width()\n        canvas_height = self.canvas.winfo_height()\n        paddle_corner = paddle.return_coords()[0]\n        paddle_width = 100\n\n        x1 = ball_coords[0]\n        y1 = ball_coords[1]\n        x2 = ball_coords[2]\n        y2 = ball_coords[3]\n\n\n        # if a ball touches the left or right side of the screen\n        # bounce in the opposite horizontal direction\n        if x1 <= 0 or x2 >= canvas_width:\n\n            self.direction[0] *= -1\n            self.canvas.move(self.ball, self.direction[0]*2, self.direction[1]*2)\n\n\n        # if a ball touches the top of the screen AFTER\n        # it has already moved away from the top (because all\n        # balls are generated from top of screen) then bounce\n        if y1 <= 0 and self.has_moved:\n\n            self.direction[1] *= -1\n            self.canvas.move(self.ball, self.direction[0]*2, self.direction[1]*2)\n\n\n        # if a ball touches the top of the paddle then bounce and add 1 to score\n        if 450 <= y2 < 460 and paddle_corner <= x1 < paddle_corner+paddle_width-self.diameter:\n\n            self.direction[1] *= -1\n            self.canvas.move(self.ball, self.direction[0]*2, self.direction[1]*2)\n            score.update_score()\n\n        if y1 >= 500 and not self.moved_off_screen:\n            \n            score.update_lives()\n            self.moved_off_screen = True\n\n\n        # keep the ball moving\n        self.canvas.move(self.ball, self.direction[0]*2, self.direction[1]*2)\n        self.has_moved = True\n\n\n        # change the coordinates of the ball every \n        # 10 milliseconds simulating a moving ball\n        self.canvas.after(10, self.move_ball, paddle, score)\n\n\n\n        \n        \n        \n","sub_path":"Ball.py","file_name":"Ball.py","file_ext":"py","file_size_in_byte":2979,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"339698404","text":"from Cit_par import *\nimport control as ctr\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\n\nt_start = 0.\nt_end = 15.\ndt = 0.01\n\nt = np.arange(t_start, t_end + dt,dt)\n\n\nC1_s = np.array([[-2*muc*(c/(Vt0**2)), 0, 0, 0],\n                 [0, (CZadot -2*muc)*(c/Vt0), 0, 0],\n                 [0, 0, -(c/Vt0), 0],\n                 [0, Cmadot*(c/Vt0), 0, -2*muc*KY2*(c/Vt0)**2]])\n\nC2_s = np.array([[CXu*(1/Vt0), CXa, CZ0, CXq*(c/Vt0)],\n                 [CZu*(1/Vt0), CZa, -CX0, (CZq+2*muc)*(c/Vt0)],\n                 [0,0,0,(c/Vt0)],\n                 [Cmu*(1/Vt0), Cma, 0, Cmq*(c/Vt0)]])\n\nC3_s = np.array([[CXde],\n                 [CZde],\n                 [0],\n                 [Cmde]])\n\n\nC1_a = np.array([[(CYbdot - 2* mub) *(b/Vt0),0,0,0],\n                 [0,-0.5*(b/Vt0),0,0],\n                 [0,0,-2*mub*KX2*(b/Vt0)**2, 2*mub*KXZ*(b/Vt0)**2],\n                 [Cnbdot*(b/Vt0), 0 , 2*mub*KXZ*(b/Vt0)**2 , -2*mub*KZ2*(b/Vt0)**2]])\nC2_a = np.array([[CYb,CL,CYp*(b/(2*Vt0)), (CYr -4*mub)*(b/(2*Vt0))],\n                 [0,0,(b/(2*Vt0)),0],\n                 [Clb,0,Clp*(b/(2*Vt0)), Clr*(b/(2*Vt0))],\n                 [Cnb,0,Cnp*(b/(2*Vt0)), Cnr*(b/(2*Vt0))]])\nC3_a = np.array([[CYda,CYdr],\n                 [0,0],\n                 [Clda,Cldr],\n                 [Cnda,Cndr]])\n\nA_s = np.matmul(-np.linalg.inv(C1_s),C2_s)\nB_s = np.matmul(-np.linalg.inv(C1_s),C3_s)\nC_s = np.array([[1,0,0,0],\n       [0,1,0,0],\n       [0,0,1,0],\n       [0,0,0,1]])\nD_s = np.array([[0],\n       [0],\n       [0],\n       [0]])\n\nA_a = np.matmul(-np.linalg.inv(C1_a),C2_a)\nB_a = np.matmul(-np.linalg.inv(C1_a),C3_a)\nC_a = np.array([[1,0,0,0],\n       [0,1,0,0],\n       [0,0,1,0],\n       [0,0,0,1]])\nD_a = np.array([[0,0],\n       [0,0],\n       [0,0],\n       [0,0]])\n\nsys_s = ctr.ss(A_s, B_s, C_s, D_s)\nsys_a = ctr.ss(A_a, B_a, C_a, D_a)\n\ndef Symmetric_plot():\n    #for i in range(len(time)):\n    #    u_s[i]  =u_s[i]*0.5\n    t_s, y_s, xouts = ctr.forced_response(sys_s,time, u_s, X0=0)\n    damp_s = ctr.damp(sys_s)\n    for i in range(len(time)):\n        y_s[0][i]= y_s[0][i] + Vt0\n        y_s[1][i]= y_s[1][i] + alpha0\n        y_s[2][i]= y_s[2][i] + th0\n    \n    plt.subplot(221)\n    plt.plot(time, y_s[0], label = 'u')\n    plt.plot(time, u, label = 'u data')\n    plt.legend()\n    \n    plt.subplot(222)\n    plt.plot(time, y_s[1], label = 'alpha')\n    plt.plot(time, alpha, label = 'alpha data')\n    plt.legend()\n    \n    plt.subplot(223)\n    plt.plot(time, y_s[2], label = 'theta')\n    plt.plot(time, pitch, label = 'theta data')\n    plt.legend()\n    \n    plt.subplot(224)\n    plt.plot(time, y_s[3], label = 'pitch rate')\n    plt.plot(time, pitch_rate, label = 'pitch rate data')\n    plt.legend()\n    plt.show()\n    \ndef Asymmetric_plot():\n    u_a = []\n#    Rudder is wrongly defined, corrected with a minus sign\n    for i in range(len(time)):\n        delta_r[i]= -delta_r[i]\n        delta_a[i]= -delta_a[i]\n        roll[i] = roll[i]-roll0\n        roll_rate[i] = roll_rate[i]-roll_rate0\n        yaw_rate[i] = yaw_rate[i] - yaw_rate0\n        \n    u_a.append(delta_a)\n    u_a.append(delta_r)\n    \n    t_a, y_a, xout = ctr.forced_response(sys_a,time, u_a, X0=0.)\n    damp_a = ctr.damp(sys_a)\n    \n    plt.subplot(221)\n    plt.title(\"Side Slip\")\n    plt.plot(time, y_a[0], color = \"orange\" ,label = 'Side slip numerical model')\n    plt.plot(time,delta_r)\n    plt.plot(time,delta_a)\n    plt.xlabel(\"Time (s)\")\n    plt.ylabel('$\\\\beta (rad)$')\n    plt.grid()\n    \n    #plt.plot(time, u_data, label = 'side slip data')\n    plt.legend()\n    \n    plt.subplot(222)\n    plt.title(\"Roll\")\n    plt.plot(time, roll, color = \"darkblue\" ,label = 'Roll data')\n    plt.plot(time, y_a[1], color = \"orange\" ,label = 'Roll numerical model')\n    plt.xlabel(\"Time (s)\")\n    plt.ylabel('$\\\\phi$ (rad)')\n    plt.grid()\n    plt.legend()\n    \n    plt.subplot(223)\n    plt.title(\"Roll rate\")\n\n    plt.plot(time, roll_rate , color = \"darkblue\" , label = 'Roll rate data')\n    plt.plot(time, y_a[2], color = \"orange\" ,label = 'Roll rate numerical model')\n    plt.xlabel(\"Time (s)\")\n    plt.ylabel('$p$ (rad/s)')\n    plt.grid()\n    plt.legend()\n    \n    plt.subplot(224)\n    plt.title(\"Yaw rate\")\n    \n    plt.plot(time, yaw_rate,color = \"darkblue\" , label = 'Yaw rate data')\n    plt.plot(time, y_a[3], color = \"orange\" ,label = 'Yaw rate numerical model')\n    plt.xlabel(\"Time (s)\")\n    plt.ylabel(\"$r$ (rad/s)\")\n    plt.grid()\n    plt.legend()\n    plt.show()\n    \n\n\nAsymmetric_plot()\n\n\n","sub_path":"Numerical_model.py","file_name":"Numerical_model.py","file_ext":"py","file_size_in_byte":4428,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"423004292","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n#\n#   Date    :   15/01/07 19:21:42\n#   Desc    :\n#\n\nimport os\nimport subprocess\nimport logging\nimport yaml\nimport getpass\nimport ConfigParser\nfrom caliper.server.shared import error\nfrom caliper.server.shared import caliper_path\n\n\nclass SettingsError(error.AutoError):\n    pass\n\n\nclass SettingsValueError(SettingsError):\n    pass\n\nsettings_filename = 'project_config.cfg'\nshadow_config_filename = 'shadow_config.cfg'\n\nsettings_path_root = os.path.join(\n                        caliper_path.config_files.config_dir,\n                        settings_filename)\nconfig_in_root = os.path.exists(settings_path_root)\n\n# need to change\nif config_in_root:\n    DEFAULT_CONFIG_FILE = settings_path_root\n    RUNNING_STAND_ALONE_CLIENT = False\nelse:\n    DEFAULT_CONFIG_FILE = None\n    RUNNING_STAND_ALONE_CLIENT = False\n\nclass Settings(object):\n    _NO_DEFAULT_SPECIFIED = object()\n\n    config = None\n    config_file = DEFAULT_CONFIG_FILE\n    running_stand_alone_client = RUNNING_STAND_ALONE_CLIENT\n\n    def check_stand_alone_client_run(self):\n        return self.running_stand_alone_client\n\n    def set_config_files(self, config_file=DEFAULT_CONFIG_FILE):\n        self.config_file = config_file\n        self.config = self.parse_config_file()\n\n    def _handle_no_value(self, section, key, default):\n        if default is self._NO_DEFAULT_SPECIFIED:\n            msg = (\"Value '%s' not found in section '%s'\" % (key, section))\n            raise SettingsError(msg)\n        else:\n            return default\n\n    def get_section_values(self, sections):\n        \"\"\"\n        Return a config parser object containing a single section of the\n        global configuration, that can be written to a file object.\n\n        :param\n        section: Tuple with sections we want to turn into a config\n                        parser\n        :return: ConfigParser() onject containing all the contents of\n                        sections.\n        \"\"\"\n        self._ensure_config_parsed()\n\n        if isinstance(sections, str):\n            sections = [sections]\n        cfgparser = ConfigParser.ConfigParser()\n        for section in sections:\n            cfgparser.add_section(section)\n            for option, value in self.config.items(section):\n                cfgparser.set(section, option, value)\n        return cfgparser\n\n    def get_value(self, section, key, type=str, default=_NO_DEFAULT_SPECIFIED,\n                    allow_blank=False):\n        self._ensure_config_parsed()\n\n        try:\n            val = self.config.get(section, key)\n        except ConfigParser.Error:\n            return self._handle_no_value(section, key, default)\n\n        if not val.strip() and not allow_blank:\n            return self._handle_no_value(section, key, default)\n\n        return self._convert_value(key, section, val, type)\n\n    def override_value(self, section, key, new_value):\n        \"\"\"\n        Override a value from the config file with a new value.\n        \"\"\"\n        self._ensure_config_parsed()\n        self.config.set(section, key, new_value)\n\n    def reset_values(self):\n        \"\"\"\n        Reset all values to those found in the config files (undoes all\n        overrides).\n        \"\"\"\n        self.parse_config_file()\n\n    def _ensure_config_parsed(self):\n        if self.config is None:\n            self.parse_config_file()\n\n    def merge_configs(self, shadow_config):\n        # overwrite whats in config with whats in shadow_config\n        sections = shadow_config.sections()\n        for section in sections:\n            # add the section if needed\n            if not self.config.has_section(section):\n                self.config.add_section(section)\n\n            # now run through all options and set them\n            options = shadow_config.options(section)\n            for option in options:\n                val = shadow_config.get(section, option)\n                self.config.set(section, option, val)\n\n    def parse_config_file(self):\n        self.config = ConfigParser.ConfigParser()\n        if self.config_file and os.path.exists(self.config_file):\n            self.config.read(self.config_file)\n        else:\n            raise SettingsError('%s not found' % (self.config_file))\n\n    # the values pulled from ini are strings\n    # try to convert them to other types if needed.\n    def _convert_value(self, key, section, value, value_type):\n        sval = value.strip()\n\n        if len(sval) == 0:\n            if value_type == str:\n                return\n            elif value_type == bool:\n                return False\n            elif value_type == int:\n                return 0\n            elif value_type == float:\n                return 0.0\n            elif value_type == list:\n                return []\n            else:\n                return None\n\n        if value_type == bool:\n            if sval.lower() == \"false\":\n                return False\n            else:\n                return True\n\n        if value_type == list:\n            return [val.strip() for val in sval.split('.')]\n\n        try:\n            conv_val = value_type(sval)\n            return conv_val\n        except Exception:\n            msg = (\"Could not convert %s value in section %s to type %s\" %\n                    (key, section, value_type))\n            raise SettingsValueError(msg)\n\n    def read_config(self):\n        '''\n        :return: tool list and run case list\n        '''\n        config_files = os.path.join(caliper_path.config_files.config_dir, 'cases_config.json')\n        fp = open(config_files, 'r')\n        tool_list = []\n        run_case_list = []\n        case_list = yaml.load(fp.read())\n        for dimension in case_list:\n            for i in range(len(case_list[dimension])):\n                for tool in case_list[dimension][i]:\n                    for case in case_list[dimension][i][tool]:\n                        if case_list[dimension][i][tool][case][0] == 'enable':\n                            tool_list.append(tool)\n                            run_case_list.append(case)\n        sections = list(set(tool_list))\n        return sections, run_case_list\n\n    def get_config(self, option):\n        '''\n        :return: tool list and run case list\n        '''\n        config_files = os.path.join(caliper_path.config_files.config_dir, 'cases_config.json')\n        fp = open(config_files, 'r')\n        tool_list = []\n        run_case_list = []\n        case_list = yaml.load(fp.read())\n        dimension_list = case_list.keys()\n        tool_dic = {}\n        for key, value in case_list.items():\n            for va in value:\n                for toolkey, toolvalue in va.items():\n                    tool_dic[toolkey] = []\n                    for casekey, casevalue in toolvalue.items():\n                        tool_dic[toolkey].append(casekey)\n        sections = []\n        if option != 'all':\n            if option in dimension_list:\n                for i in range(len(case_list[option])):\n                    for tool in case_list[option][i]:\n                        for case in case_list[option][i][tool]:\n                            tool_list.append(tool)\n                            run_case_list.append(case)\n                sections = list(set(tool_list))\n            else:\n                if option in tool_dic:\n                    sections.append(option)\n                    run_case_list = tool_dic[option]\n                else:\n                    run_case_list.append(option)\n                    for tool in tool_dic:\n                        if option in tool_dic[tool]:\n                            sections.append(tool)\n        else:\n            sections, run_case_list = self.read_config()\n        return sections, run_case_list\n\n    def get_sections(self, dimension_list):\n        all_sections = []\n        config_files = os.path.join(caliper_path.config_files.config_dir, 'cases_config.json')\n        fp = open(config_files, 'r')\n        run_case_list = []\n        case_list = yaml.load(fp.read())\n        cf = ConfigParser.ConfigParser()\n        cf.read(DEFAULT_CONFIG_FILE)\n        devices = cf.options('FastTest')\n        for device in devices:\n            tool_list = yaml.load(cf.get('FastTest', device))\n            all_sections = all_sections + tool_list\n        sections = list(set(all_sections))\n        for section in sections:\n            for dimension in dimension_list:\n                for i in range(len(case_list[dimension])):\n                    try:\n                        for case in case_list[dimension][i][section]:\n                            run_case_list.append(case)\n                    except:\n                        pass\n        return sections, run_case_list\n\n    def get_run_sections(self, c_option, dimension_list, option):\n        if c_option == 1:\n            sections, run_case_list = self.get_sections(dimension_list)\n        else:\n            if 'all' in option:\n                sections, run_case_list = self.get_config('all')\n            else:\n                sections, run_case_list = self.get_config(option)\n        return sections, run_case_list\n\n    def push_ssh_key(self):\n        '''\n        use ansible push ssh key to test host\n        :return: \n        '''\n        TEST_CASE_DIR = caliper_path.config_files.config_dir\n        ssh_key_path = os.path.join(os.environ['HOME'], '.ssh', 'id_rsa.pub')\n        if not os.path.exists(ssh_key_path):\n            os.system(\"ssh-keygen -t rsa -P '' -f '%s/.ssh/id_rsa'\" % os.environ['HOME'])\n        try:\n            logging.info('Begining to push ssh key for the test host')\n            os.chdir(TEST_CASE_DIR)\n            cf = ConfigParser.ConfigParser()\n            cf.read(DEFAULT_CONFIG_FILE)\n            sections = cf.sections()\n            for section in sections:\n                if 'Device' not in section:\n                    continue\n                subprocess.call('ansible-playbook -i project_config.cfg push_sshkey.yml -e hosts=%s -u %s' % (\n                section, getpass.getuser()), stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)\n        except Exception as e:\n            logging.debug(e)\n            logging.info('push ssh key fail')\n            pass\n\n# insure the class is a singleton. Now the symbol settings will point to the\n# one and only one instance pof the class\nsettings = Settings()\n","sub_path":"server/shared/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":10256,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"135076538","text":"# coding: utf-8\n\nimport ujson\n\nimport time\n\nfrom .utils import rd, signer\n\n\nclass FoodCache(object):\n\n    KEY = \"foods:%s\"\n\n    def __init__(self, backend):\n        self.backend = backend\n        self._load()\n\n    def _load(self):\n        # load from cache\n        food_ids = self.backend.smembers(\"foods\")\n        food_stocks = self.backend.mget(\n            \"foods:stock:%s\" % i for i in food_ids)\n        food_prices = self.backend.mget(\n            \"foods:price:%s\" % i for i in food_ids)\n\n        self.cached_foods = {\n            int(i): {\"id\": int(i), \"stock\": int(s), \"price\": int(p)}\n            for i, s, p in zip(food_ids, food_stocks, food_prices)}\n\n        self.update_time = time.time()\n\n    def __contains__(self, item):\n        return item in self.cached_foods\n\n    def get_food(self, food_id):\n        return self.cached_foods[food_id]\n\n    def get_all(self, timeout=10):\n        if time.time() - self.update_time > timeout:\n            self._load()\n        return ujson.dumps(list(self.cached_foods.values()))\nfood_cache = FoodCache(rd)\n\n\nclass CartCache(object):\n\n    KEY = \"carts:%s\"\n    USER_KEY = \"carts:user:%s\"\n\n    # KEYS = [cart_id]\n    # ARGV = [*food_ids]\n    # language=Lua\n    LUA_ADD_FOOD = ' '.join(\"\"\"\n    local cart_key = 'carts:' .. KEYS[1]\n    local count = tonumber(redis.call('LLEN', cart_key))\n    local patch = tonumber(table.getn(ARGV))\n    if count + patch <= 3 then\n        redis.call('LPUSH', cart_key, unpack(ARGV))\n        return 0\n    else\n        return 1\n    end\n     \"\"\".split())\n\n    def __init__(self, backend):\n        self.backend = backend\n\n        self.add_food = backend.register_script(self.LUA_ADD_FOOD)\n\n    def get_cart(self, cart_id):\n        cart_str = self.backend.get(self.KEY % cart_id, force=True)\n        if cart_str:\n            return ujson.loads(cart_str)\n\n    def set_cart(self, user_id, cart):\n        self.backend.sadd(self.USER_KEY % user_id, cart[\"id\"])\n        self.backend.set(self.KEY % cart[\"id\"], ujson.dumps(cart), force=True)\n\n    def patch_cart(self, cart_id, food_id, count):\n        if count > 0:\n            return self.add_food(keys=[cart_id],\n                                 args=[str(food_id)] * count) == 0\n        else:\n            self.backend.lrem(self.KEY % cart_id, count, str(food_id))\n            return True\ncart_cache = CartCache(rd)\n\n\nclass UserCache(object):\n\n    KEY = \"users:%s\"\n    TOKEN_KEY = \"token:%s\"\n\n    def __init__(self, backend):\n        self.backend = backend\n        self._load()\n\n    def _load(self):\n        user_ids = self.backend.smembers(\"users\")\n        with self.backend.pipeline() as p:\n            for i in user_ids:\n                p.hgetall(self.KEY % i)\n            res = p.execute()\n\n        self.auth_map = {\n            u[\"name\"]: {\"id\": int(u[\"id\"]), \"password\": u[\"password\"]}\n            for u in res}\n\n        self.user_token = {int(i): signer.dumps(i) for i in user_ids}\n        self.token_user = {k: v for v, k in self.user_token.items()}\n\n    def login(self, username, password):\n        if username in self.auth_map and \\\n                self.auth_map[username][\"password\"] == password:\n            user_id = self.auth_map[username][\"id\"]\n            return user_id, self.user_token[user_id]\n        else:\n            return None, None\n\n    def auth(self, token):\n        return self.token_user.get(token)\nuser_cache = UserCache(rd)\n\n\nclass OrderCache(object):\n\n    # language=Lua\n    LUA_MAKE_ORDER = ' '.join(\"\"\"\n    local cart_key = 'carts:' .. KEYS[2]\n    local food_ids = redis.call('LRANGE', cart_key, 0, -1)\n    if table.getn(food_ids) == 0 then\n        return 1\n    end\n\n    for _, f in pairs(food_ids) do\n        if tonumber(redis.call('GET', 'foods:stock:' .. f)) < 1 then\n            return 2\n        else\n            redis.call('DECR', 'foods:stock:' .. f)\n        end\n    end\n\n    local user_key = 'orders:user:' .. KEYS[1]\n    local order_id = ARGV[1]\n    redis.call('RENAME', cart_key, user_key)\n    redis.call('LPUSH', user_key, order_id)\n    return 0\n    \"\"\".split())\n\n    ORDER_KEY = \"orders:user:%s\"\n\n    def __init__(self, backend):\n        self.backend = backend\n\n        self.make_order = backend.register_script(self.LUA_MAKE_ORDER)\n\n    def get_order(self, user_id):\n        return self.backend.lrange(self.ORDER_KEY % user_id, 0, -1)\n\n    def is_new(self, user_id):\n        return not self.backend.exists(self.ORDER_KEY % user_id)\n\n    def get_all(self):\n        order_keys = self.backend.keys(self.ORDER_KEY % '*')\n        with self.backend.pipeline() as p:\n            for k in order_keys:\n                p.lrange(k, 0, -1)\n            order_info = p.execute()\n        return {int(k.split(':')[-1]): info\n                for k, info in zip(order_keys, order_info)}\norder_cache = OrderCache(rd)\n","sub_path":"hackathon-py-lxyu/hackathon/cache.py","file_name":"cache.py","file_ext":"py","file_size_in_byte":4762,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"515644408","text":"import random\n\nfrom templates.text import TextTemplate\n\n\ndef process(input, entities=None):\n    greetings = [\n        'Salutations! Enter a command, user!',\n        'Yes? Tell me to do something, user.',\n        'Hello! How can I be of service?',\n        'At your service, user.',\n        'Oh hello, user!',\n        'Jarvis, reporting in. How can I help you today, user?',\n    ]\n    if entities is not None:\n        if 'sender' in entities and 'first_name' in entities['sender']:\n            sender_name = entities['sender']['first_name']\n            greetings = [greeting.replace('user', sender_name) for greeting in greetings]\n    output = {\n        'input': input,\n        'output': TextTemplate(random.choice(greetings)).get_message(),\n        'success': True\n    }\n    return output\n","sub_path":"modules/src/hello.py","file_name":"hello.py","file_ext":"py","file_size_in_byte":788,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"382458523","text":"from os import listdir\nfrom os.path import isfile, join\nimport numpy as np\n\nnp.set_printoptions(suppress=True)\n\ndataset_path = \"./SurveyLogs/\" # Keep trailing /\ndestination_path = \"./SFBased/\" # Keep trailing /\nfiles = [f for f in listdir(dataset_path) if isfile(join(dataset_path, f))]\nnumTxPkts = 30.\n\n\ndef splitSpread():\n    for fl in files:\n        with open(dataset_path+fl, 'r') as log_file:\n            logs = log_file.readlines()\n            name = logs[1].split(\",\")[0]\n            lat = logs[1].split(\",\")[1]\n            lon = logs[1].split(\",\")[2][:-1]\n            for row in logs[4:]:\n                new_row = row[:-1] + \",\" + name + \",\" + lat + \",\" + lon + \"\\n\"\n                sf = row.split(\",\")[2]\n                with open(sf+\".csv\", \"a\") as f:\n                    f.write(new_row)\n            \n           \ndef splitGeoCombine():\n    for fl in files:\n        sfbin = {} \n        #print(\"\\n\\n File, \" + fl)\n        with open(dataset_path+fl, 'r') as log_file:\n            logs = log_file.readlines()\n            lat = logs[1].split(\",\")[1]\n            lon = logs[1].split(\",\")[2][:-1]\n\n            for row in logs[4:]:\n                row = row.strip(\"\\n\").split(\",\")\n                sf = row[2]\n                if sf in sfbin.keys() :\n                    sfbin[sf] = np.concatenate((sfbin[sf], np.array([[float(row[0]), float(row[1])]])), axis=0)\n                else:\n                    sfbin[sf] = np.array([[float(row[0]), float(row[1])]])\n            for sf in sfbin:\n                rowStat = lat + \",\" + lon + \",\" + sf + \",\" + str(np.average(sfbin[sf][:,0])) + \",\" + str(np.average(sfbin[sf][:,1])) + \",\" + str(int(100*(numTxPkts-len(sfbin[sf]))/numTxPkts)) + \"\\n\"\n                with open(destination_path+sf+\".csv\", \"a\") as f:\n                    f.write(rowStat)\n            notEmptyFLag = 0\n\n\n\ndef main():\n    splitGeoCombine()\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"datasets/combine.py","file_name":"combine.py","file_ext":"py","file_size_in_byte":1898,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"211436026","text":"import socket\nimport os\nimport struct\nimport hashlib\nimport time\n\nserverIP = '10.0.2.15'\nserverPort = 3333\n\nclnt_sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\nclnt_sock.settimeout(3)\nprint(\"Sender Socket open...\")\nprint(\"Receiver IP = \", serverIP)\nprint(\"Receiver Port = \", serverPort)\n\nre_fileName = input(\"Input File Name : \")\nwhile os.path.exists(re_fileName) == 0:\n\tprint(\"No files found.\")\n\tre_fileName = input(\"Input File Name : \")\nfilePath = os.path.abspath(re_fileName) # file path\nre_fileSize = os.path.getsize(filePath) # file size\nfileName = re_fileName.encode()\t# byte file name\nfileSize = re_fileSize.to_bytes(4, byteorder = \"big\") # byte file size\n\nseqN = 0b0000\ntmpseqN = 0\nACK = 0b0000\ntmpACK = 0\nseqN = seqN << 4\nACK = ACK & 0b1111\nbNum = (seqN|ACK).to_bytes(1, byteorder =\"big\")\n\nh = hashlib.sha1()\nh.update(bNum + fileName + fileSize)\nmessage = h.digest() + bNum + fileName + fileSize # 36byte\nclnt_sock.sendto(message, (serverIP, serverPort)) # message info send\n\ncount = 0\ncurrentSize = 0\nflag = 0 # 초기상태\nbase = 0\nwindowSize = 4\nwindow = []\n\ndata, addr = clnt_sock.recvfrom(1)\nchk = struct.unpack(\"!b\", data[0:1])[0]\nif chk == 0:\n\tprint(\"정상\")\n\nfile = open(\"./\"+re_fileName, \"rb\")\nprint(\"Send File Info(file Name, file Size, seqNum) to Server...\")\nprint(\"Start File Send\")\n\nwhile True:\n\ttry:\n\t\tif currentSize == re_fileSize and len(window) == 0:\n\t\t\tbreak\t\t\n\t\t\n\t\tNAK = 0\n\t\tif currentSize !=0:\n\t\t\trecvData,addr = clnt_sock.recvfrom(1)\n\t\t\ttmpseqN = struct.unpack(\"!b\", recvData[0:1])[0]&0b11110000\n\t\t\ttmpACK = struct.unpack(\"!b\", recvData[0:1])[0]&0b1111\n\t\t\tif tmpACK == 15 and NAK == 0:           # NAK 받았을때\n\t\t\t\tfor i in range(len(window)):\n\t\t\t\t\tcurrentSize = currentSize - len(window[i][21:])\n\t\t\t\tprint(\"* Received NAK - Retransmit!\")\n\t\t\t\tfor i in range(len(window)):\n\t\t\t\t\tclnt_sock.sendto(window[i], addr)\n\t\t\t\t\tcurrentSize = currentSize + len(window[i][21:])\n\t\t\t\t\tprint(\"Retransmission : (current size / total size ) = \", currentSize,\"/\",re_fileSize,\" , \",round((currentSize/re_fileSize)*100, 3),\"%\")\n\t\t\t\t\tcontinue\n\t\t\t\t\n\t\t\tfor i in range(len(window)):\n\t\t\t\tif struct.unpack(\"!b\", window[i][20:21])[0]>>4 == tmpACK: #[0][1][받은 ACK가 있는 window][3]일때,  [0][1][2] 처리\n\t\t\t\t\tfor j in range(0, i+1):\n\t\t\t\t\t\twindow.pop(0)\n\t\t\t\t\t\tbase = base -1\n\t\t\t\t\tflag = 3 #받은 ACK를 정상적으로 처리한 경우\n\t\t\t\t\tbreak\n\n\t\t\tif flag == 2:\t#flagr가 3이 아닐경우 ACK가 정상적으로 처리되지 않은 경우\n\t\t\t\tprint(\"Data retransmission!\")\n\t\t\t\tfor i in range(len(window)):  # for문에 온 경우는 sender에서 보낸 data가 손실된 경우이다.  window의 data를 재전송한다.\n\t\t\t\t\tclnt_sock.sendto(window[i], addr)\n\t\t\tflag = 2\n\t\n\t\t\t\n\t\tif count == 283: # 바이트 조작\n\t\t\tdata = file.read(1024)\n\t\t\tif len(data) != 0:\n\t\t\t\tbase = base+1\n\t\t\tif seqN == 8:\n\t\t\t\tseqN = 0b0000\n\t\t\t\tACK = 0b0000\n\t\t\tsendData = ((seqN<<4)|(ACK&0b1111)).to_bytes(1,\"big\") + data\n\t\t\th = hashlib.sha1()\n\t\t\th.update(sendData)\n\t\t\tmessage = h.digest() + sendData\n\t\t\twindow.append(message)\n\t\t\tcurrentSize = currentSize + len(data)\n\t\t\tvar = \"var\"\n\t\t\ttemp = message + var.encode()\n\t\t\tclnt_sock.sendto(temp, addr)\n\t\t\tprint(\"(current size / total size) = \", currentSize,\"/\",re_fileSize,\" , \",round((currentSize/re_fileSize)*100, 3), \"%\")\n\t\t\tseqN = seqN +1\n\t\t\tACK = ACK +1\n\t\t\tflag = 2 # 바이트 조작\n\t\t\tcount = count+1\n\t\t\t\t\n\n\t\tif flag == 0:  # 처음 data 4개(windsize)만큼 전송, 이후에 사용x\n\t\t\tfor i in range(0, windowSize):\n\t\t\t\tbase = i + 1\n\t\t\t\tdata = file.read(1024)\n\t\t\t\tsendData = ((seqN<<4)|(ACK&0b1111)).to_bytes(1,\"big\") + data\n\t\t\t\th = hashlib.sha1()\n\t\t\t\th.update(sendData)\n\t\t\t\tmessage = h.digest() + sendData\n\t\t\t\tclnt_sock.sendto(message, addr)\n\t\t\t\tcurrentSize = currentSize + len(data)\n\t\t\t\tprint(\"(current size / total size) = \", currentSize,\"/\",re_fileSize,\" , \",round((currentSize/re_fileSize)*100, 3), \"%\")\n\t\t\t\twindow.append(message)\n\t\t\t\tseqN = seqN +1\n\t\t\t\tACK = ACK + 1\n\t\t\t\tcount = count+1\n\t\t\n\t\tflag = flag +1 #초기상태 벗어난 상태)\n\t\n\n\t\twhile flag != 0 and base < windowSize and currentSize < re_fileSize:\n\t\t\tdata = file.read(1024)\n\t\t\tif len(data) !=0:\n\t\t\t\tbase = base +1\n\t\t\tif seqN == 8:\n\t\t\t\tseqN = 0b0000\n\t\t\t\tACK = 0b0000\n\t\t\tsendData = ((seqN<<4)|(ACK&0b1111)).to_bytes(1,\"big\") + data\n\t\t\th = hashlib.sha1()\n\t\t\th.update(sendData)\n\t\t\tmessage = h.digest() + sendData\n\t\t\tclnt_sock.sendto(message, addr)\n\t\t\tcurrentSize = currentSize + len(data)\n\t\t\tprint(\"(current size / total size) = \", currentSize,\"/\",re_fileSize,\" , \",round((currentSize/re_fileSize)*100, 3),\"%\")\n\t\t\twindow.append(message)\n\t\t\tseqN = seqN +1\n\t\t\tACK = ACK +1\n\t\t\tcount = count+1\n\n\n\texcept socket.timeout:\n\t\tprint(\"* TimeOut!! ***\")\n\t\tfor i in range(len(window)):\n\t\t\tcurrentSize = currentSize - len(window[i][21:])\n\t\tfor i in range(len(window)):\n\t\t\tclnt_sock.sendto(window[i], addr)\n\t\t\tcurrentSize = currentSize + len(window[i][21:])\n\t\t\tprint(\"Retransmission : (current size / total size) = \", currentSize,\"/\",re_fileSize,\" , \",round((currentSize/re_fileSize)*100, 3), \"%\")\n\t\tcontinue\n\nprint(\"File Send End.\")\nfile.close()\nclnt_sock.close()\n\n","sub_path":"2018/Data Communications/DC_201402448_한진영_10/sender/201402448_sender.py","file_name":"201402448_sender.py","file_ext":"py","file_size_in_byte":5081,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"148569116","text":"'''\nInput:\n2\n20 20\n4 5\n13\nLLUUUUURRRRRR\n10 10\n3 4\n7\nUDUDDRR\n'''\nfor _ in range(int(input())):\n    m, n = [int(x) for x in input().split()]\n    rx, ry = [int(x) for x in input().split()]\n    nom  = int(input())\n    moves= input()\n    dx   = moves.count(\"L\")*-1 + moves.count(\"R\")\n    dy   = moves.count(\"U\") + moves.count(\"D\")*-1\n\n    if dx == rx and dy == ry:\n        print(\"REACHED\")\n    elif dx < 0 or dx > m or dy < 0 or dy > n:\n        print(\"DANGER\")\n    else:\n        print(\"SOMEWHERE\")\n\n","sub_path":"codechef/JReachedSafelyOrNot.py","file_name":"JReachedSafelyOrNot.py","file_ext":"py","file_size_in_byte":494,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"404914174","text":"# 909. 蛇梯棋\n\nclass Solution:\n    def snakesAndLadders(self, board: list[list[int]]) -> int:\n        # 转成一维数组\n        arr = [0]\n        row = 0\n        for i in range(len(board)-1, -1, -1):\n            if row & 1 == 0:\n                for j in range(len(board[0])):\n                    arr.append(board[i][j])\n            else:\n                for j in range(len(board[0])-1, -1, -1):\n                    arr.append(board[i][j])\n            row += 1\n        queue = [1]\n        visit = set([1])\n        target = len(arr)-1\n        res = 0\n        while queue:\n            temp = []\n            for i in queue:\n                for j in range(1, 7):\n                    pos = i + j\n                    if pos == target:\n                        return res + 1\n                    elif pos > target:\n                        continue\n                    if pos not in visit:\n                        if arr[pos] == target:\n                            return res + 1\n                        if arr[pos] != -1:\n                            temp.append(arr[pos])\n                        else:\n                            temp.append(pos)\n                        visit.add(pos)\n            queue = temp\n            res += 1\n        return -1\n\n\nif __name__ == '__main__':\n    print(Solution().snakesAndLadders([[-1,-1,19,10,-1],[2,-1,-1,6,-1],[-1,17,-1,19,-1],[25,-1,20,-1,-1],[-1,-1,-1,-1,15]]))\n    print(Solution().snakesAndLadders([[-1,-1,-1],[-1,9,8],[-1,8,9]]))\n","sub_path":"answers/snakesAndLadders.py","file_name":"snakesAndLadders.py","file_ext":"py","file_size_in_byte":1472,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"627424880","text":"\nversion = '0.0.5'\n__description__      = \"\"\"\n mclust for multi-resolution clustering\n\"\"\"\n\n__doc__             = __doc__ \n__version__          = version\n__author__             = [\"Gholamali Rahnavard\"]\n__contact__         = \"gholamali.rahnavard@gmail.com\"\n\n\nkeys_attribute = [\"__description__\", \"__version__\", \"__author__\", \"__contact__\", \"clustering\", \"multi-resolution\"]\n\n# default Parameters\nsimilarity_method = 'spearman'\ndiatance_metric = 'spearman' #euclidean'\ndata = None\nmetadata = None\nresolution = 'low'\noutput_dir = 'mclust_output'\nestimated_number_of_clusters = 2\nlinkage_method = 'average'\nplot = False\nsize_to_plot = 3\n\n#output directort\noutput_dir = './'","sub_path":"mclust/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":669,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"602424693","text":"from django import forms\r\n\r\nfrom crispy_forms.helper import FormHelper\r\nfrom crispy_forms.layout import Submit, Layout, Field\r\nfrom crispy_forms.bootstrap import (\r\n    PrependedText, PrependedAppendedText, FormActions)\r\n\r\n\r\nfrom .models import Paciente, Institucion, Examen\r\n\r\nclass PacienteModForm(forms.ModelForm):\r\n\r\n        class Meta:\r\n            model = Paciente\r\n            fields = '__all__'\r\n            #fields = (\r\n            #\t'nombre', 'apellidos', 'institucion', 'f_nac', 'sexo', 'domicilio', 'tel',\r\n            #\t'ci', 'seguro', 'procedencia', 'ocupacion', 'ec', 'enfermedad', 'sintomas',\r\n            #\t'signos', 'sindromes', 'a_personales', 'a_patologicos', 'a_nopatologicos',\r\n            #\t'a_hereditarios', 'a_presuntivo', 'd_definitivo', 'interconsulta'\r\n           #)\r\n\r\nclass InstitucionModForm(forms.ModelForm):\r\n\r\n\t\tclass Meta:\r\n\t\t\tmodel = Institucion\r\n\t\t\tfields = '__all__'\r\n\r\n\r\nclass ExamenModForm(forms.ModelForm):\r\n\r\n        class Meta:\r\n            model = Examen\r\n            fields = '__all__'\r\n\r\nExamenFormset = forms.modelformset_factory(Examen, form=ExamenModForm, can_delete=True)\r\n","sub_path":"cineantro/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":1123,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"448232438","text":"#------coding=utf-8--*---\n\nfrom urllib import request\nimport re\nimport os\nimport sys\nimport zipfile\nkeyword=sys.argv[1]\nprint(keyword)\nurl0=\"http://www.offensivecomputing.net/search.cgi?search=\"\nurl=url0+keyword\nurl1=\"http://www.offensivecomputing.net/\"\nprint(\"password = infected\")\nRequest =request.urlopen(url)\nresult = Request.read()\nmainhtml=keyword+\".html\"\nwith open(mainhtml,\"wb\") as fmain:\n    fmain.write(result)\nresult = result.decode('utf-8')\nreg=r'<a href=\"(.*?)\">Download Sample'\npattern = re.compile(reg)\ninfoList=pattern.findall(result)\nfor info in infoList:\n    print(info)\n    NowUrl=url1+info\n    exefile=request.urlopen(NowUrl).read()\n    regNow=r'id=(.*?)&auth'\n    patternNow = re.compile(regNow)\n    nameList = patternNow.findall(info)\n    name = keyword+\"_\"+nameList[0] + \".zip\"\n    with open(name,\"wb\") as f:\n        f.write(exefile)\nprint(\"OK\")\n","sub_path":"python/恶意代码历代/mal428.py","file_name":"mal428.py","file_ext":"py","file_size_in_byte":869,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"36046087","text":"#!/usr/bin/python\n# -*- codding: utf-8 -*-\nimport os\nimport sys\nsys.path.append(os.path.dirname(os.path.abspath(os.path.dirname(__file__))))\nfrom common.execute_command import write_one_parameter\n\n# url : https://awscli.amazonaws.com/v2/documentation/api/latest/reference/cloudwatch/disable-insight-rules.html\nif __name__ == '__main__':\n    \"\"\"\n\tdelete-insight-rules : https://awscli.amazonaws.com/v2/documentation/api/latest/reference/cloudwatch/delete-insight-rules.html\n\tdescribe-insight-rules : https://awscli.amazonaws.com/v2/documentation/api/latest/reference/cloudwatch/describe-insight-rules.html\n\tenable-insight-rules : https://awscli.amazonaws.com/v2/documentation/api/latest/reference/cloudwatch/enable-insight-rules.html\n\tput-insight-rule : https://awscli.amazonaws.com/v2/documentation/api/latest/reference/cloudwatch/put-insight-rule.html\n    \"\"\"\n\n    parameter_display_string = \"\"\"\n    # rule-names : An array of the rule names to disable. If you need to find out the names of your rules, use DescribeInsightRules .\n(string)\n    \"\"\"\n    add_option_dict = {}\n\n    #######################################################################\n    # parameter display string\n    add_option_dict[\"parameter_display_string\"] = parameter_display_string\n    # ex: add_option_dict[\"no_value_parameter_list\"] = \"--single-parameter\"\n    write_one_parameter(\"cloudwatch\", \"disable-insight-rules\", \"rule-names\", add_option_dict)\n\n\n\n\n\n","sub_path":"cloudwatch_write_1/insight-rule_disable.py","file_name":"insight-rule_disable.py","file_ext":"py","file_size_in_byte":1431,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"388972435","text":"import os\nimport re\nimport logging\nimport platform\nimport subprocess\n\nfrom distutils.version import LooseVersion\nfrom mcutk.apps.decorators import build\nfrom mcutk.apps.idebase import IDEBase, BuildResult\nfrom mcutk.elftool import transform_elf_basic\nfrom mcutk.apps.cmake import generate_build_cmdline\n\n\nclass APP(IDEBase):\n    \"\"\"GNU ARM GCC compiler.\n\n    CMake and ARM-GCC build explanation:\n        - Generate Makefile:\n        >>> cmake -DCMAKE_TOOLCHAIN_FILE={path}/armgcc.cmake -G \"{MinGW|Unix} Makefiles\" -DCMAKE_BUILD_TYPE=debug\n\n        - Start build with make tool or mingw32-make:\n        >>> make -C \"<path-to-makefile-directory>\" -j4\n        >>> mingw32-make -C \"<path-to-makefile-directory>\" -j4\n\n        - Compile. Armgcc compiler will be called to compile in makefile.\n\n    CMake is a cross-platform build system generator. Projects specify their build\n    process with platform-independent CMake listfiles included in each directory\n    of a source tree with the name CMakeLists.txt. Users build a project by using\n    CMake to generate a build system for a native tool on their platform.\n\n    GNU Make is a tool which controls the generation of executables and other non-source\n    files of a program from the program's source files. Make gets its knowledge of how to\n    build your program from a file called the makefile, which lists each of the non-source\n    files and how to compute it from other files. When you write a program, you should\n    write a makefile for it, so that it is possible to use Make to build and install the\n    program.\n\n\n    \"\"\"\n\n    OSLIST = [\"Windows\", \"Linux\", \"Darwin\"]\n\n\n    @property\n    def is_ready(self):\n        return os.path.exists(self.path)\n\n    @build\n    def build_project(self, project, target, logfile, **kwargs):\n        \"\"\"Return a command line string for armgcc. The build commands\n        are packaging into a shell/bat script.\n\n        Arguments:\n            project {armgcc.Project} -- armgcc project object\n            target {string} -- target name\n            logfile {string} -- log file path\n\n        Returns:\n            string -- commandline string.\n        \"\"\"\n        os.environ[\"ARMGCC_DIR\"] = self.path\n        return generate_build_cmdline(project, target, logfile)\n\n    def transform_elf(self, type, in_file, out_file):\n        \"\"\"Convert ELF to specific type.\n        Called <mcutk>/bin/arm-none-eabi-objcopy to do the job.\n\n        Supported types: bin, ihex, srec.\n\n        Arguments:\n            in_file {str} -- path to elf file.\n            out_file {str} -- output file\n            type {str} -- which type you want to convert.\n\n        Raises:\n            ReadElfError -- Unknown elf format will raise such error\n            Exception -- Convert failed will raise exception\n\n        Returns:\n            bool\n        \"\"\"\n        executor = os.path.join(self.path, 'bin/arm-none-eabi-objcopy')\n        if os.name == 'nt':\n            executor += '.exe'\n\n        return transform_elf_basic(type, in_file, out_file, executor)\n\n    @staticmethod\n    def verify(path):\n        '''\n        verify the path of compiler is avaliable\n        '''\n        return os.path.exists(path)\n\n    @staticmethod\n    def get_latest():\n        \"\"\"Search and return a latest armgcc instance from system.\n\n        Returns:\n            <armgcc.APP object>\n        \"\"\"\n        path, version = get_armgcc_latest()\n        if path:\n            return APP(path, version=version)\n        else:\n            return None\n\n    @staticmethod\n    def parse_build_result(exitcode, logfile):\n        \"\"\"GNU make exits with a status of zero if all makefiles were successfully\n        parsed and no targets that were built failed. A status of one will be\n        returned if the -q flag was used and  make  determines  that a target\n        needs to be rebuilt. A status of two will be returned if any errors\n        were encountered.\n        \"\"\"\n\n        if exitcode != 0:\n            return BuildResult.map(\"error\")\n\n        if not logfile:\n            return BuildResult.map(\"pass\")\n\n        p = re.compile(r'warning:', re.I)\n\n        with open(logfile) as f:\n            for line in f:\n                if p.search(line) != None:\n                    return BuildResult.map(\"warning\")\n\n        return BuildResult.map(\"pass\")\n\n\n    @staticmethod\n    def default_install_path():\n        default_path_table = {\n            \"Windows\": \"C:/Program Files (x86)/GNU Tools ARM Embedded\",\n            \"Linux\": \"/usr/local\",\n            \"Darwin\": \"/usr/local\",\n        }\n        osname = platform.system()\n        return default_path_table[osname]\n\ndef _scan_windows_register():\n    try:\n        import _winreg as winreg\n    except ImportError:\n        import winreg\n\n    root_key_path = r\"SOFTWARE\\\\Wow6432Node\\\\ARM\\\\GNU Tools for ARM Embedded Processors\"\n    try:\n        key_object = winreg.OpenKey(winreg.HKEY_LOCAL_MACHINE, root_key_path)\n        path = winreg.QueryValueEx(key_object, \"InstallFolder\")[0].replace('\\\\', '/')\n        version = path.split(\"/\")[-1].replace(\" \", \"-\")\n        winreg.CloseKey(key_object)\n        return path, version\n    except WindowsError:\n        pass\n\n    return None, None\n\ndef get_armgcc_latest():\n    \"\"\"Get the latest armgcc version\n\n    Returns:\n        tuple -- (path, version)\n    \"\"\"\n    path, version = None, None\n    osname = platform.system()\n\n    if osname == \"Windows\":\n        path, version = _scan_windows_register()\n        if path:\n            return path, version\n\n        logging.debug(\"Could not found ARM GCC installation in windows register!\"\\\n        \"Trying to scan in default installation directory!\")\n\n    root_path = APP.default_install_path()\n    if not os.path.exists(root_path):\n        return None, None\n\n    if osname == \"Windows\" and not os.path.exists(root_path):\n        root_path = \"C:/Program Files/GNU Tools ARM Embedded\"\n\n    return get_armgcc_latest_version(root_path)\n\n\ndef get_armgcc_latest_version(rootpath):\n    osname = platform.system()\n    versions_dict = {}\n    armgcc_rootfolders = []\n\n    if \"Windows\" == osname:\n        armgcc_rootfolders = os.listdir(rootpath)\n    else:\n        try:\n            s = subprocess.check_output(\"ls {0} | grep gcc-arm-none-eabi\".format(rootpath), shell=True)\n            armgcc_rootfolders = s.split('\\n')\n        except subprocess.CalledProcessError:\n            return None, None\n\n    for per_folder in armgcc_rootfolders:\n        if not os.path.isdir(os.path.join(rootpath, per_folder)) or '' == per_folder:\n            continue\n\n        version_content = os.popen(\"\\\"{}\\\" --version\".format(os.path.join(rootpath, per_folder, \"bin\", \"arm-none-eabi-gcc\"))).read()\n        ret = re.search(r\"\\d\\.\\d\\.\\d\", version_content)\n        if None != ret:\n            versions_dict[ret.group()] = os.path.join(rootpath, per_folder)\n\n    latest_v = sorted(versions_dict.keys(), key=lambda v:LooseVersion(v))[-1]\n    return versions_dict[latest_v], latest_v\n","sub_path":"mcutk/apps/armgcc/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":6911,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"69560640","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport random\nimport sys\nimport numpy as np\nfrom rl.core import Env\nfrom rl.core import Space\n\n# check up the environment, import python modules from $SUMO_HOME/tools directory\nif 'SUMO_HOME' in os.environ:\n    tools = os.path.join(os.environ['SUMO_HOME'], 'tools')\n    sys.path.append(tools)\nelse:\n    sys.exit(\"please declare environment variable 'SUMO_HOME' after installing sumo!\")\n\n# import python modules from sumo\nfrom sumolib import checkBinary\nimport traci\nimport traci.constants as tc\n\n\nclass SumoEnv(Env):\n    \"\"\"sumo environment definition, extend rl.core.Env\"\"\"\n    def __init__(self, cfg_file, route_file, router=None, observed_prob=1.0, nogui=False, log_out=\"log/inter\"):\n        self.cfg_file = cfg_file\n        self.route_file = route_file\n        self.router = router\n        if not self.router:\n            self.generate_route_file()\n        else:\n            self.router.generate_route_file(self.route_file)\n\n        self.observable_probability = observed_prob\n        self.observable_id = set()\n        self.rix = [0, 2, 1, 3]\n\n        if nogui:\n            self.sumoBinary = checkBinary('sumo')\n            self.cmd_lines = [self.sumoBinary, \"--start\", \"-c\", self.cfg_file]\n            self.load_cmd = [\"--start\", \"-c\", self.cfg_file]\n        else:\n            self.sumoBinary = checkBinary('sumo-gui')\n            # --start, --quit-on-end\n            self.cmd_lines = [self.sumoBinary, \"-c\", self.cfg_file]\n            self.load_cmd = [\"-c\", self.cfg_file]\n\n        self.log_out = log_out\n        if not os.path.exists(log_out):\n            os.makedirs(log_out)\n\n        self.log_file = os.path.join(log_out, 'metrics.log')\n        self.phases_file = os.path.join(log_out, 'phases.log')\n\n        # constants\n        self.road_length = 500  # m\n        self.margin = 14.65  # m\n        self.max_speed = 16.67  # m/s\n\n        # divide each lane into 32 cell\n        self.length = 256\n        self.eps = 2\n        self.veh_length = 5  # m\n        self.cell_length = 16  # m\n        self.max_n = 8.0\n        self.n_lanes = 4\n        self.n_sects = 4\n        self.region = self.length + self.margin - self.eps\n        self.height = self.length // self.cell_length\n        self.width = self.n_sects\n        self.index2green_phases = [0, 2, 4, 6]\n        self.green_phases2index = {0: 0, 2: 1, 4: 2, 6: 3}\n        self.phases_length = 8\n        self.max_green_time = 60  # s\n        self.min_green_time = 6  # s\n        self.action_space = PhasesSpace()\n        # max_simulation_time, ms\n        self.max_sim_time = 3600000\n\n        # the value need to be reset in reset()\n        self.epoch_num = 0\n        self.n_decision_step = 0\n        self.cycle = 0\n        self.phase_line = []\n        self.observable_id = set()\n        self.arrived_num = 0\n        self.total_waiting_time = 0\n        self.last_jam_vl = 0\n        self.last_green_time = 0\n\n        # this is the normal way of using traci. sumo is started as a\n        # subprocess and then the python script connects and runs\n        traci.start(self.cmd_lines)\n        # context subscriptions, subscribe vehicles near the intersection\n        traci.junction.subscribeContext(\"0\", tc.CMD_GET_VEHICLE_VARIABLE, self.region,\n                                        [tc.VAR_LANE_ID, tc.VAR_ROAD_ID,\n                                         tc.VAR_WAITING_TIME, tc.VAR_LANEPOSITION,\n                                         tc.VAR_SPEED])\n\n    def step(self, action):\n        # definition = traci.trafficlight.getCompleteRedYellowGreenDefinition('tlstatic')\n        self.n_decision_step += 1\n        current_phase = traci.trafficlight.getPhase('tlstatic')\n        current_index = self.green_phases2index[current_phase]  # type: int\n        next_phase = self.index2green_phases[(action + current_index) % len(self.index2green_phases)]\n        if next_phase == current_phase:\n            # current phase continues 2s\n            traci.trafficlight.setPhase('tlstatic', current_phase)\n            _ = self.simulate_one_step()\n            flag = self.simulate_one_step()\n\n            self.last_green_time += 2\n\n            if self.last_green_time >= self.max_green_time:\n                next_phase = self.index2green_phases[(current_index + 1)\n                                                     % len(self.index2green_phases)]\n                flag = self.change2phase(current_phase, next_phase)\n        else:\n            flag = self.change2phase(current_phase, next_phase)\n\n        cx_res = traci.junction.getContextSubscriptionResults(\"0\")\n        obs = self.get_observations(cx_res)\n        # remain modification\n        reward = self.get_reward(cx_res)\n\n        # if traci.simulation.getCurrentTime() > self.max_sim_time:  # ms\n        # print(\"MinExpected Number: %d\" % traci.simulation.getMinExpectedNumber())\n        if not flag:\n            with open(self.log_file, 'a') as log:\n                # write sim time /t arrived num /t aver waiting time /n\n                log.write('epoch: %d, time_step: %d, decision_step: %d, arrived_num: %d, awt: %.4f\\n' %\n                          (self.epoch_num, traci.simulation.getCurrentTime(), self.n_decision_step,\n                           self.arrived_num, float(self.total_waiting_time) / float(self.arrived_num)))\n            # returns a tuple (observation, reward, done, info). \"Done\" indicates whether the simulation has end.\n            self.epoch_num += 1\n            return obs, reward, True, {}\n        else:\n            return obs, reward, False, {}\n\n    def change2phase(self, current_phase, next_phase):\n        \"\"\"shift the current green phase to next green phase, with min duration time 6s\n        if current_phase == next_phase, reset the current_phase\"\"\"\n        if current_phase == next_phase:\n            traci.trafficlight.setPhase('tlstatic', current_phase)\n            return True\n        traci.trafficlight.setPhase('tlstatic', (current_phase + 1) % self.phases_length)\n        if current_phase == 0:\n            self.phase_line = ['epoch: %d, cycle: %d, green_phase: %d'\n                               % (self.epoch_num, self.cycle, self.last_green_time)]\n        elif current_phase == 6:\n            self.phase_line.append(str(self.last_green_time))\n            with open(self.phases_file, 'a') as ph_file:\n                if len(self.phase_line) == len(self.index2green_phases):\n                    ph_file.write(','.join(self.phase_line) + '\\n')\n            self.cycle += 1\n        else:\n            self.phase_line.append(str(self.last_green_time))\n        self.last_green_time = 0\n        # reset the traffic lights to the action green phases\n        while self.last_green_time < self.min_green_time:  # min duration time 6s\n            flag = self.simulate_one_step()\n            if not flag:\n                return False\n            if traci.trafficlight.getPhase('tlstatic') == next_phase:\n                self.last_green_time += 1\n            else:\n                self.last_green_time = 0\n        return True\n\n    def simulate_one_step(self):\n        if traci.simulation.getMinExpectedNumber() > 0:\n            traci.simulationStep()\n            for i in range(1, self.n_sects + 1):\n                for j in range(0, self.n_lanes):\n                    det = 'e2det_{}i_{}'.format(i, j)\n                    self.total_waiting_time += traci.lanearea.getLastStepHaltingNumber(det)\n\n            self.arrived_num += traci.simulation.getArrivedNumber()\n            id_list = traci.simulation.getDepartedIDList()\n            for ix in id_list:\n                if (ix not in self.observable_id) and random.uniform(0, 1) < self.observable_probability:\n                    self.observable_id.add(ix)\n            aid_list = traci.simulation.getArrivedIDList()\n            for aid in aid_list:\n                if aid in self.observable_id:\n                    self.observable_id.remove(aid)\n            return True\n        else:\n            return False\n\n    def get_observations(self, cx_res):\n        \"\"\"get vehicle position and speed matrix from context subscription, plus the current phase as one hot vector\"\"\"\n        current_phase = traci.trafficlight.getPhase('tlstatic')\n        if current_phase not in self.green_phases2index:\n            current_index = random.randint(0, len(self.index2green_phases) - 1)\n        else:\n            current_index = self.green_phases2index[current_phase]\n        phase = np.zeros(len(self.index2green_phases))\n        phase[current_index] = 1\n        position = np.zeros((self.height, self.width))\n        speed = np.zeros((self.height, self.width))\n        if not cx_res:\n            return np.array([position, speed, phase])\n        for vid, mes in cx_res.iteritems():\n            if (vid in self.observable_id) and mes[tc.VAR_LANE_ID].__contains__('i'):\n                rid, lid = [int(x) for x in mes[tc.VAR_LANE_ID].split('i_')]\n                cid = int((self.road_length - self.margin - mes[tc.VAR_LANEPOSITION]) / self.cell_length)\n                assert cid >= 0\n                assert cid < self.height\n                ix = self.rix[rid - 1]\n                position[cid][ix] += 1\n                speed[cid][ix] = speed[cid][ix] + (mes[tc.VAR_SPEED] - speed[cid][ix]) / position[cid][ix]\n        position /= self.max_n\n        speed /= self.max_speed\n        return np.array([position, speed, phase])\n\n    def get_reward(self, cx_res):\n        \"\"\"get change of the total queuing vehicle number between two state as reward.\"\"\"\n        if not cx_res:\n            reward = self.last_jam_vl\n            self.last_jam_vl = 0\n            return reward\n        jam_vl = 0\n        for i in range(1, self.n_sects + 1):\n            for j in range(0, self.n_lanes):\n                det = 'e2det_{}i_{}'.format(i, j)\n                jam_vl += traci.lanearea.getJamLengthVehicle(det)\n        reward = self.last_jam_vl - jam_vl\n        self.last_jam_vl = jam_vl\n        return reward\n\n    def reset(self):\n        # context subscriptions\n        # traci.close(wait=True)\n        # Reset before simulationOneStep\n        self.clear_metrics()\n        if not self.router:\n            self.generate_route_file()\n        else:\n            self.router.generate_route_file(self.route_file)\n\n        # traci.start(self.cmd_lines)\n        traci.load(self.load_cmd)\n        traci.junction.subscribeContext(\"0\", traci.constants.CMD_GET_VEHICLE_VARIABLE, self.region,\n                                        [traci.constants.VAR_LANE_ID, traci.constants.VAR_ROAD_ID,\n                                         traci.constants.VAR_WAITING_TIME, traci.constants.VAR_LANEPOSITION,\n                                         traci.constants.VAR_SPEED])\n        # reset the traffic lights to the action green phases\n        while self.last_green_time < self.min_green_time:\n            self.simulate_one_step()\n            if traci.trafficlight.getPhase('tlstatic') in self.green_phases2index:\n                self.last_green_time += 1\n            else:\n                self.last_green_time = 0\n        cx_res = traci.junction.getContextSubscriptionResults(\"0\")\n        return self.get_observations(cx_res)\n\n    def clear_metrics(self):\n        self.n_decision_step = 0\n        self.cycle = 0\n        self.phase_line = []\n        self.observable_id = set()\n        self.arrived_num = 0\n        self.total_waiting_time = 0\n        self.last_jam_vl = 0\n        self.last_green_time = 0\n\n    def close(self):\n        traci.close(wait=True)\n        self.epoch_num = 0\n        self.clear_metrics()\n\n    def generate_route_file(self, pns=1.0/4.0, pew=1.0/8.0, psn=1.0/4.0, pwe=1.0/8.0, plt=1.0/4.0, prt=1.0/4.0):\n        num_time_step = 3600  # number of time steps\n        routes = [{'route': 'n2s', 'prob': pns},\n                  {'route': 'n2e', 'prob': pns*plt},\n                  {'route': 'n2w', 'prob': pns*prt},\n                  {'route': 'e2w', 'prob': pew},\n                  {'route': 'e2s', 'prob': pew*plt},\n                  {'route': 'e2n', 'prob': pew*prt},\n                  {'route': 's2n', 'prob': psn},\n                  {'route': 's2w', 'prob': psn*plt},\n                  {'route': 's2e', 'prob': psn*prt},\n                  {'route': 'w2e', 'prob': pwe},\n                  {'route': 'w2n', 'prob': pwe*plt},\n                  {'route': 'w2s', 'prob': pwe*prt}]\n\n        head = ['<routes>',\n                '<vType id=\"stdSKVehicle\" accel=\"2.6\" decel=\"4.5\" sigma=\"0.5\" '\n                'length=\"5\" minGap=\"2.5\" maxSpeed=\"22.22\" guiShape=\"passenger\"/>',\n                '<route id=\"n2s\" edges=\"51i 1i 3o 53o\" />',\n                '<route id=\"n2e\" edges=\"51i 1i 2o 52o\" />',\n                '<route id=\"n2w\" edges=\"51i 1i 4o 54o\" />',\n                '<route id=\"e2w\" edges=\"52i 2i 4o 54o\" />',\n                '<route id=\"e2s\" edges=\"52i 2i 3o 53o\" />',\n                '<route id=\"e2n\" edges=\"52i 2i 1o 51o\" />',\n                '<route id=\"s2n\" edges=\"53i 3i 1o 51o\" />',\n                '<route id=\"s2w\" edges=\"53i 3i 4o 54o\" />',\n                '<route id=\"s2e\" edges=\"53i 3i 2o 52o\" />',\n                '<route id=\"w2e\" edges=\"54i 4i 2o 52o\" />',\n                '<route id=\"w2n\" edges=\"54i 4i 1o 51o\" />',\n                '<route id=\"w2s\" edges=\"54i 4i 3o 53o\" />']\n\n        tail = \"</routes>\"\n\n        # print(\"pns: %.2f, pew: %.2f, psn: %.2f, pwe: %.2f, plt: %.2f, prt: %.2f\" % (pns, pew, psn, pwe, plt, prt))\n        with open(self.route_file, \"w\") as output_file:\n            print(os.linesep.join(head), file=output_file)\n            cur_veh = 0\n            for i in range(num_time_step):\n                for route in routes:\n                    if random.uniform(0, 1) < route['prob']:\n                        print('<vehicle id=\"%s_%i\" type=\"%s\" route=\"%s\" depart=\"%i\" departLane=\"random\" '\n                              'departSpeed=\"random\"/>' % (route['route'], cur_veh, 'stdSKVehicle',\n                                                          route['route'], i), file=output_file)\n                        cur_veh += 1\n            print(tail, file=output_file)\n\n    def seed(self, seed=None):\n        if seed:\n            random.seed(seed)  # make tests reproducible\n\n    def render(self, mode='human', close=False):\n        pass\n\n    def configure(self, *args, **kwargs):\n        pass\n\n\nclass PhasesSpace(Space):\n\n    def __init__(self):\n        self.phases = [0, 1]\n\n    def sample(self, seed=None):\n        \"\"\"sample phase actions.\"\"\"\n        ix = random.randint(0, len(self.phases) - 1)\n        return self.phases[ix]\n\n    def contains(self, x):\n        return self.phases.__contains__(x)\n\n\nclass Router(object):\n\n    def __init__(self):\n        self.seed = None\n\n    def set_seed(self, seed=42):\n        if seed:\n            self.seed = seed\n            random.seed(seed)\n\n    def generate_route_file(self, output_file):\n        \"\"\"Generate the .rou.xml file.\"\"\"\n        raise NotImplementedError()\n","sub_path":"Code_Reference/sumocore.py","file_name":"sumocore.py","file_ext":"py","file_size_in_byte":14916,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"268502244","text":"import cv2\r\nimport numpy as  np \r\nimport pyautogui\r\nimport time\r\nct=0\r\nct1=0\r\ntime.sleep(5)\r\ncap = cv2.VideoCapture(0)\r\nhand_cascade = cv2.CascadeClassifier('palm.xml')\r\nwhile(True):\r\n    ret, frame = cap.read()\r\n    print(type(cap))\r\n    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\r\n    hand = hand_cascade.detectMultiScale(gray, 1.3, 5)\r\n    if len(hand) == 0:\r\n        print (\"No hand found\")\r\n    else:\r\n        sum = 0\r\n        print(\"detecting the number of hands\")\r\n        for (x,y,w,h) in hand:\r\n            cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)\r\n            sum = sum+1\r\n            #print(\"frame:\", len(frame))\r\n            if len(frame) > 0:\r\n                pyautogui.press('space')\r\n                print(\"1\")\r\n                sum = 1\r\n            else:\r\n                pass\r\n    cv2.imshow('frame', frame)\r\n    if cv2.waitKey(1) & 0xFF == ord('q'): \r\n        break\r\ncap.release()\r\ncv2.destroyAllWindows()\r\n","sub_path":"hand_detection_from_scratch/hand_detection_2.py","file_name":"hand_detection_2.py","file_ext":"py","file_size_in_byte":944,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"566825837","text":"from collections import namedtuple as struct\nimport pygame\nimport time\n\nBall = struct(\"Ball\", [\"pos_x\", \"pos_y\", \"width\", \"height\", \"speed_x\", \"speed_y\", \"image\"])\nPaddle = struct(\"Paddle\", [\"pos_x\", \"pos_y\", \"width\", \"height\", \"max_speed_y\", \"image\"])\nField = struct(\"Field\", [\"width\", \"height\", \"ball\", \"paddle\"])\nGame = struct(\"Game\", [\"field\", \"screen\"])\n\ndef ball_hits_paddle(ball, paddle):\n    \"\"\"Return True if the ball has hit the paddle.\"\"\"\n    return (ball.speed_x < 0 and \n        ball.pos_x >= paddle.pos_x + paddle.width / 2.0 and\n        ball.pos_x < paddle.pos_x + paddle.width and\n        ball.pos_y + ball.height > paddle.pos_y and\n        ball.pos_y <= paddle.pos_y + paddle.height)\n\ndef get_new_ball(ball, field, delta_t):\n    \"Return a new ball considering collisions (None if the ball went out of bounds)\"\n    new_pos_x = ball.pos_x + delta_t*ball.speed_x\n    new_pos_y = ball.pos_y + delta_t*ball.speed_y\n    new_ball = ball._replace(pos_x=new_pos_x, pos_y=new_pos_y)\n    \n    if (new_ball.pos_x + new_ball.width) < 0:\n        return\n    else:\n        if ball_hits_paddle(new_ball, field.paddle):\n            new_speed_x = abs(ball.speed_x)\n        elif ball.speed_x > 0 and (new_ball.pos_x + ball.width) >= field.width:\n            new_speed_x = -abs(ball.speed_x)\n        else:\n            new_speed_x = ball.speed_x\n            \n        if ball.speed_y < 0 and new_ball.pos_y < 0:\n            new_speed_y = abs(ball.speed_y)\n        elif ball.speed_y > 0 and (new_ball.pos_y + ball.height) >= field.height:\n            new_speed_y = -abs(ball.speed_y)\n        else:\n            new_speed_y = ball.speed_y\n\n        return new_ball._replace(speed_x=new_speed_x, speed_y=new_speed_y)\n\ndef get_new_paddle(paddle, field, keys, delta_t):\n    \"\"\"Return a new paddle (keys: UP/DOWN).\"\"\"\n    if keys[pygame.K_DOWN]:\n        paddle_speed_y = paddle.max_speed_y\n    elif keys[pygame.K_UP]:\n        paddle_speed_y = -paddle.max_speed_y\n    else:\n        paddle_speed_y = 0\n\n    new_paddle_pos_y = paddle.pos_y + delta_t*paddle_speed_y\n    if new_paddle_pos_y < 0 or (new_paddle_pos_y + paddle.height) >= field.height:\n        final_paddle_pos_y = paddle.pos_y\n    else:\n        final_paddle_pos_y = new_paddle_pos_y\n    return paddle._replace(pos_y=final_paddle_pos_y)\n\ndef get_new_field(field, keys, delta_t):\n    \"\"\"Return the new field (None if the game finished).\"\"\"\n    new_paddle = get_new_paddle(field.paddle, field, keys, delta_t)\n    new_ball = get_new_ball(field.ball, field, delta_t)\n    return (field._replace(ball=new_ball, paddle=new_paddle) if new_ball else None)\n\ndef update_game(game, keys, delta_t):\n    \"\"\"Return the new field (None if the game finished).\"\"\"\n    new_field = get_new_field(game.field, keys, delta_t)\n    return game._replace(field=new_field)\n\ndef draw_game(game):\n    \"\"\"Draw the game (field, ball and paddle) onto the game screen.\"\"\"\n    field = game.field\n    game.screen.fill((0, 0, 0))\n    game.screen.blit(field.ball.image, (field.ball.pos_x, field.ball.pos_y))\n    game.screen.blit(field.paddle.image, (field.paddle.pos_x, field.paddle.pos_y))\n    pygame.display.flip()\n      \ndef loop_game(game):\n    \"\"\"State loop of a game.\"\"\"\n    time0 = time.time()\n    while 1:  \n        draw_game(game)\n        \n        time1 = time.time()\n        events = pygame.event.get()\n        keys = pygame.key.get_pressed()\n        new_game = update_game(game, keys, delta_t=time1-time0)\n        \n        if any(event.type == pygame.QUIT for event in events):\n            break\n        elif not new_game.field:\n            break\n        else:\n            time0 = time1\n            game = new_game\n\ndef build_game(screen_size):\n    \"\"\"Load multimedia files and return the initial game object.\"\"\"\n    ball_image = pygame.image.load(\"media/ball.png\")\n    ball = Ball(image=ball_image, pos_x=100, pos_y=200, speed_x=550, speed_y=300, \n        width=ball_image.get_width(), height=ball_image.get_height())\n    \n    paddle_image = pygame.image.load(\"media/paddle.png\")\n    paddle = Paddle(image=paddle_image, pos_x=10, pos_y=100, max_speed_y=600, \n        width=paddle_image.get_width(), height=paddle_image.get_height())\n    \n    width, height = screen_size\n    field = Field(ball=ball, paddle=paddle, width=width, height=height)\n\n    screen = pygame.display.set_mode(screen_size)\n    return Game(field=field, screen=screen)\n\ndef run():\n    \"\"\"Run the loop game.\"\"\"\n    pygame.init()\n    game = build_game(screen_size=(640, 480))\n    loop_game(game)\n\nrun()\n","sub_path":"pong.py","file_name":"pong.py","file_ext":"py","file_size_in_byte":4488,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"197855075","text":"import sys\nimport os\nimport glob\n\nprint(\"Init Pre Caric\")\n\n\n#Estraggo info da file di properties\npathFileProp = \"dati\\\\FIX_CONFIG_FILE.txt\"\nfileConfigOut = \"dati\\\\CONFIGURATION_FILE.txt\"\n\naddedFile = [] #lista file aggiunti per controllo univocita caricamento\n\nwith open(pathFileProp) as f:\n\twith open(fileConfigOut,\"w\") as g:\n\t\tcontent = f.readlines()\n\t\tfor line in content:\n\t\t\tif not line.startswith(\"#\"):\n\t\t\t\tlineSpiltted = line.rstrip(\"\\n\").split(\",\")\n\t\t\t\t#print(lineSpiltted)\n\n\t\t\t\t#Estraggo nome file da directory dati\\ con filtro\n\t\t\t\t#print(\"filtro:\" + \"dati\\\\\"+lineSpiltted[1]+\"*\")\n\t\t\t\tfileCSVList = glob.glob(\"dati\\\\\"+lineSpiltted[1]+\"*\")\n\t\t\t\t#print(fileCSVList)\t\n\n\t\t\t\t#per ogni file trovati inserisco una entry nel file di configurazione \n\t\t\t\tfor entry in fileCSVList:\t\t\t\t\t\t\n\t\t\t\t\tif entry in addedFile:\n\t\t\t\t\t\tprint(\"###ERROR### file \"+entry[5:]+\" inserito più volte\")\t\n\t\t\t\t\t\tsys.exit()\n\t\t\t\t\telse:\n\t\t\t\t\t\t#print(\"write line:\" + lineSpiltted[0]+\",\"+entry[5:])\n\t\t\t\t\t\tg.write(lineSpiltted[0]+\",\"+entry[5:]+\"\\n\")\n\t\t\t\t\t\taddedFile.append(entry)\n\n\n\nallFileCSVList = glob.glob(\"dati\\\\*.csv\")\n#print(allFileCSVList)\n\nfor aFile in allFileCSVList:\n\tif not aFile in addedFile:\n\t\tprint(\"###ERROR###\"+aFile + \" non inserito in CONFIGURATION_FILE\")\n\t\t\nprint(\"End Pre Caric\")","sub_path":"Database Oracle/Sql loader script/preCaric.py","file_name":"preCaric.py","file_ext":"py","file_size_in_byte":1267,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"407925187","text":"import json\n\ndef textos_to_json():\n\tout = {}\n\tpuntos = {}\n\tfor i in range(7):\n\t\twith open(\"../data/output_punto_\"+str(i)+\".txt\", 'r') as f:\n\t\t\ttext = f.read()\n\t\t\titems = text.split('---')\n\n\t\t\tpunto_actual = {}\n\t\t\tresumen = {}\n\t\t\ttopicos = {}\n\n\t\t\tfor j in range(3):\n\t\t\t\tnivel = items[j+2].replace(\"nivel_\"+str(j+1)+\":\\n\", '')\n\t\t\t\ttopico = items[j+6].replace(\"topicos_\"+str(j+1)+\":\\n\", '')\n\t\t\t\tresumen[\"nivel_\"+str(j+1)] = nivel\n\t\t\t\ttopicos[\"topicos_\"+str(j+1)] = topico\n\n\t\t\tpunto_actual[\"resumen\"] = resumen\n\t\t\tpunto_actual[\"topicos\"] = topicos\n\t\t\tpuntos[\"punto_\"+str(i)] = punto_actual\n\n\tout[\"puntos\"] = puntos\n\n\twith open('../data/ejemplo.json', 'w', encoding='utf8') as json_file:\n\t    json.dump(out, json_file, ensure_ascii=False)\n\n\ndef frecuencias_to_json():\n\tout = {}\n\t\n\tpolitica = []\n\tjusticia = []\n\ttierras = []\n\tposconflicto = []\n\treparacion = []\n\n\twith open(\"../data/frecuencias.txt\", 'r') as f:\n\t\tlines = f.read().split(\"\\n\")\n\t\tpolitica = [float(x.split(\",\")[0]) for x in lines]\n\t\tjusticia = [float(x.split(\",\")[1]) for x in lines]\n\t\ttierras = [float(x.split(\",\")[2]) for x in lines]\n\t\tposconflicto = [float(x.split(\",\")[3]) for x in lines]\n\t\treparacion = [float(x.split(\",\")[4]) for x in lines]\n\n\tout[\"politica\"] = politica\n\tout[\"justicia\"] = justicia\n\tout[\"tierras\"] = tierras\n\tout[\"posconflicto\"] = posconflicto\n\tout[\"reparacion\"] = reparacion\n\n\twith open('../data/frecuencias.json', 'w', encoding='utf8') as json_file:\n\t    json.dump(out, json_file, ensure_ascii=False)\n\n\n\n\n\n\n\t\n\t\n\n\n","sub_path":"preprocesamiento/limpieza/to_json.py","file_name":"to_json.py","file_ext":"py","file_size_in_byte":1496,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"43346648","text":"#!/usr/bin/env python\n\n# Example GPIO code\n# Note difference between \"BOARD\" and \"BCM\" options\n\nimport time\nimport os\nimport RPi.GPIO as GPIO\n\n#PINS = (0, 1, 4, 14) # Specify pins to use, BCM mode\nPINS = (3, 5, 7, 8, 10, 11, 12, 13, 15, 16, 18, 19, 21, 22, 23, 24, 26) # Specify pins to use, Board mode\ndelay = 8 # time delay in seconds\n\ndef setup():\n\tGPIO.setmode(GPIO.BOARD)\n\tfor pin in PINS:\n\t\tGPIO.setup(pin, GPIO.OUT)\n\ndef applyvalues(values):\n\tfor pin, val in zip(PINS, values):\n\t\tprint(\"setting pin %s to %s\" % (pin, val))\n\t\tGPIO.output(pin, val)\n\ndef quit():\n\tfor pin in PINS:\n\t\tGPIO.output(pin, GPIO.LOW)\n\n# Set all pins high, then low.\n# Repeat until interrupted.\ndef main():\n\tsetup()\n\twhile True:\n\t\ttry:\n\t\t\tprint(\"High...\")\n\t\t\tapplyvalues([GPIO.HIGH]*len(PINS))\n\t\t\ttime.sleep(delay/2.0)\n\t\t\tprint(\"Low...\")\n\t\t\tapplyvalues([GPIO.LOW]*len(PINS))\n\t\t\ttime.sleep(delay)\n\t\texcept (KeyboardInterrupt, SystemExit):\n\t\t\tquit()\n\t\t\tbreak\n\t\tfinally:\n\t\t\tquit()\n\nif __name__ == \"__main__\":\n\tmain()\n","sub_path":"gpio_test.py","file_name":"gpio_test.py","file_ext":"py","file_size_in_byte":993,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"82398855","text":"import cv2\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\nimg = cv2.imread('../data/sample_image/sample_rib2.jpg')\nheight, width = img.shape[:2]\n\ngray_img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\neq_img = cv2.equalizeHist(gray_img)\nenhance_img = cv2.medianBlur(eq_img,3)\nplt.imshow(eq_img,'gray')\nplt.show()\n\nplt.imshow(enhance_img,cmap = 'gray')\nplt.show()\n\n\nstructuredEdgeModel = cv2.ximgproc.createStructuredEdgeDetection(\"../Materials/structuredEdgeModel.yml\")\nenhance_img2 = cv2.cvtColor(enhance_img,cv2.COLOR_GRAY2BGR)\nplt.imshow(enhance_img2,cmap = 'gray')\nplt.show()\n\nenhance_img2 = np.float32(enhance_img2)\nenhance_img2 = enhance_img2/255.0\nplt.imshow(enhance_img2,cmap = 'gray')\nplt.show()\n\nenhance_img3 = structuredEdgeModel.detectEdges(enhance_img2)\nplt.imshow(enhance_img3,cmap = 'gray')\nplt.show()\n\nenhance_img3 = enhance_img3*255\nenhance_img4 = enhance_img3>2\nenhance_img4\nlines = cv2.HoughLinesP(enhance_img4, 1, np.pi/180, 100)\nfor x in range(0, len(lines)):\n    for x1,y1,x2,y2 in lines[x]:\n        cv2.line(enhance_img4,(x1,y1),(x2,y2),(0,255,0),2)\nplt.imshow(enhance_img4,'gray')\nplt.show()\n\nenh_low_threshold = 120\nedge_img = cv2.Canny(enhance_img,enh_low_threshold, enh_low_threshold*3 )\nplt.imshow(edge_img,cmap = 'gray')\nplt.show()\n\n\n## Thoracic vertebrae detectionh\nvert_threshold_value = 100\nvert_max_BINARY_value = 255\nvert_threshold_type = 1\n\nret,vertebrae = cv2.threshold(img,vert_threshold_value, vert_max_BINARY_value, vert_threshold_type)\n\n## Draw Lines of each rib\nthreshold_value = 180\nmax_BINARY_value = 255\nthreshold_type = 1\nkernel = np.ones((5,5),np.uint8)\n\n#output  = cv2.Canny(inputImage,lowThreshold,lowThreshold*4);\nret2,binary_img = cv2.threshold(img,threshold_value, max_BINARY_value,threshold_type);\noutput = cv2.morphologyEx(binary_img, cv2.MORPH_CLOSE, kernel)\n\n# cv2.imshow(\"Thoracic Vertebrae\",vertebrae)\n# cv2.imshow(\"Output\",output)\nfig = plt.figure(figsize=(15,7))\ntitles = ['Original Image','BINARY','OUTPUT','VERTEBRAE']\nimages = [img, binary_img,output,vertebrae]\nfor i in range(len(titles)):\n    plt.subplot(1,4,i+1),plt.imshow(images[i])\n    plt.title(titles[i])\n    plt.xticks([]),plt.yticks([])\n    plt.show()\n# cv2.waitKey(0)\n","sub_path":"rib_counting_v1.py","file_name":"rib_counting_v1.py","file_ext":"py","file_size_in_byte":2194,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"257071215","text":"# 27 May 2018 Miroslav Gasparek\n# Python bootcamp, lesson 36: Exercise on testing and test-driven development\n\n# Import modules\nimport re\nimport pytest\n\nimport numpy as np\nimport pandas as pd\n\n\n### Practice 2 ###\n\ndef test_dissoc_equil():\n    \"\"\" Tests for dissoc_equil().\"\"\"\n\n    # Check for the validity of the response to the proper inputs\n    # Edge cases\n    assert np.allclose(dissoc_equil(1,0,0), np.array([0,0,0]))\n    assert np.allclose(dissoc_equil(0,0,0), np.array([0,0,0]))\n    assert np.allclose(dissoc_equil(0, 1, 1), np.array([0, 0, 1]))\n    assert np.allclose(dissoc_equil(0, 1, 2), np.array([0, 1, 1]))\n    assert np.allclose(dissoc_equil(0, 2, 1), np.array([1, 0, 1]))\n    assert np.allclose(dissoc_equil(np.inf,1,1), np.array([1,1,0]))\n\n    # Standard cases\n    # Check for the range of initial conditions on a log scale\n    # This will exhibit an error - due to the numerical stability issue\n    Kd_vals = np.logspace(-10, 1, 50)\n    ca0_vals = np.logspace(-5, 2, 50)\n    cb0_vals = np.logspace(-5, 2, 50)\n    for Kd in Kd_vals:\n        for ca0 in ca0_vals:\n            for cb0 in cb0_vals:\n                assert check_eq(Kd, ca0, cb0, *dissoc_equil(Kd, ca0, cb0)), \\\n                    'Kd = %g, ca0 = %g, cb0 = %g' % (Kd, ca0, cb0)\n    # Check for the validity of the user input\n    # Errors\n    pytest.raises(RuntimeError, \"dissoc_equil(-1,1,1)\")\n    pytest.raises(RuntimeError, \"dissoc_equil(1, -1, 1)\")\n    pytest.raises(RuntimeError, \"dissoc_equil(1, 1, -1)\")\n    pytest.raises(RuntimeError, \"dissoc_equil(1, np.inf, 1)\")\n    pytest.raises(RuntimeError, \"dissoc_equil(1, 1, np.inf)\")\n\n    return None\n\ndef dissoc_equil(Kd, ca0, cb0):\n    \"\"\" Compute equilibrium for dissociation reaction.\"\"\"\n\n    # Check input\n    if Kd < 0 or ca0 < 0 or cb0 < 0:\n        raise RuntimeError('All input must be nonnegative.')\n    if not (ca0 < np.inf and cb0 < np.inf):\n        raise RuntimeError('Input concentrations must be finite.')\n\n    # If we have infinite Kd\n    if Kd == np.inf:\n        return ca0, cb0, 0\n\n    # Compute cab from quadratic formula\n    b = ca0 + cb0 + Kd\n    cab = (b - np.sqrt(b**2 - 4*ca0*cb0))/2\n\n    # Compute ca and cb from conservation of mass\n    ca = ca0 - cab\n    cb = cb0 - cab\n    return ca, cb, cab\n\n\ndef check_eq(Kd, ca0, cb0, ca, cb, cab):\n    \"\"\"Verify equilibrium expressions hold.\"\"\"\n    eq = np.isclose(Kd, ca * cb / cab)\n    cons_mass_A = np.isclose(ca0, ca + cab)\n    cons_mass_B = np.isclose(cb0, cb + cab)\n\n    return eq and cons_mass_A and cons_mass_B\n\n# Test the function\ntest_dissoc_equil()\n","sub_path":"tdd_practice2.py","file_name":"tdd_practice2.py","file_ext":"py","file_size_in_byte":2552,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"287580834","text":"\"\"\"ecomerce_turing URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n    https://docs.djangoproject.com/en/2.2/topics/http/urls/\nExamples:\nFunction views\n    1. Add an import:  from my_app import views\n    2. Add a URL to urlpatterns:  path('', views.home, name='home')\nClass-based views\n    1. Add an import:  from other_app.views import Home\n    2. Add a URL to urlpatterns:  path('', Home.as_view(), name='home')\nIncluding another URLconf\n    1. Import the include() function: from django.urls import include, path\n    2. Add a URL to urlpatterns:  path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.contrib import admin\nfrom django.urls import include, path\nfrom .app import app, store\nfrom .app.api import departments, categories, attributes, products, customers, orders, cart, tax, shipping, stripe, base\n\ndepartmentPatterns = [\n    path('', departments.departments),\n    path('<int:department_id>', departments.departments),\n    path('<path:a>', base.url_error)\n]\n\ncategoryPatterns = [\n    path('', categories.categories),\n    path('<int:category_id>', categories.categories),\n    path('inProduct/<int:product_id>', categories.product),\n    path('inDepartment/<int:department_id>', categories.department),\n    path('<path:a>', base.url_error)\n]\n\nattributePatterns = [\n    path('', attributes.attributes),\n    path('<int:attribute_id>', attributes.attributes),\n    path('values/<int:attribute_id>', attributes.values),\n    path('inProduct/<int:product_id>', attributes.product),\n    path('<path:a>', base.url_error)\n]\n\nproductPatterns = [\n    path('', products.products),\n    path('<int:product_id>', products.products),\n    path('inCategory/<int:category_id>', products.products),\n    path('inDepartment/<int:category_id>', products.products),\n    path('inDepartment/', products.products),\n    path('search', products.search),\n    path('<int:product_id>/details', products.details),\n    path('<int:product_id>/locations', products.locations),\n    path('<int:product_id>/reviews', products.reviews),\n    path('<path:a>', base.url_error)\n]\n\ncustomerPatterns = [\n    path('', customers.register),\n    path('/login', customers.login),\n    path('/facebook', customers.login_fb),\n    path('/address', customers.update_address),\n    path('/creditCard', customers.update_creditcard),\n    path('/<path:a>', base.url_error)\n]\n\nordersPatterns = [\n    path('', orders.create),\n    path('/<int:order_id>', orders.details),\n    path('/inCustomer', orders.customer_data),\n    path('/shortDetail/<int:order_id>', orders.details_short),\n    path('/<path:a>', base.url_error)\n]\n\nshoppingCartPatterns = [\n    path('generateUniqueId', cart.get_id),\n    path('add', cart.add),\n    path('update/<int:item_id>', cart.update),\n    path('empty/<str:cart_id>', cart.empty),\n    path('moveToCart/<int:item_id>', cart.move),\n    path('totalAmount/<str:cart_id>', cart.amount),\n    path('saveForLater/<int:item_id>', cart.save),\n    path('getSaved/<str:cart_id>', cart.get_saved),\n    path('removeProduct/<int:item_id>', cart.remove),\n    path('clearOld', cart.clear_old_cart),\n    path('<str:cart_id>', cart.get_list),\n    path('<path:a>', base.url_error)\n]\n\ntaxPatterns = [\n    path('', tax.tax),\n    path('<int:tax_id>',tax.tax),\n    path('<path:a>', base.url_error)\n]\n\nshippingPatterns = [\n    path('regions', shipping.regions),\n    path('regions/<int:shipping_region_id>', shipping.region_data),\n    path('<path:a>', base.url_error)\n]\n\nstripePatterns = [\n    path('charge', stripe.charge),\n    path('webhooks', stripe.webhooks),\n    path('<path:a>', base.url_error)\n]\n\nurlpatterns = [\n    path('', app.main),\n    path('store/', store.main),\n    path('store/stripe_session/<int:order_id>', store.stripe_session),\n    path('admin/', admin.site.urls),\n    path('departments/', include(departmentPatterns)),\n    path('categories/', include(categoryPatterns)),\n    path('attributes/', include(attributePatterns)),\n    path('products/', include(productPatterns)),\n    path('customer', customers.customer),\n    path('customers', include(customerPatterns)),\n    path('orders', include(ordersPatterns)),\n    path('shoppingcart/',include(shoppingCartPatterns)),\n    path('tax/',include(taxPatterns)),\n    path('shipping/',include(shippingPatterns)),\n    path('stripe/',include(stripePatterns))\n]","sub_path":"ecomerce_turing/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":4316,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"37641655","text":"import numpy as np\nimport cv2\nfrom imutils.object_detection import non_max_suppression\nimport pytesseract\nimport os\nfrom matplotlib import pyplot as plt\n\nfrom imutils.video import FPS\nimport imutils\n\nfrom find_contour import thresh_callback\n\n\ndef load(img, newW, newH, **kwargs):\n    # resize the original image to new dimensions\n    image = cv2.resize(img, (newW, newH))\n    (H, W) = image.shape[:2]\n\n    # construct a blob from the image to forward pass it to EAST model\n    blob = cv2.dnn.blobFromImage(image, 1.0, (W, H), (123.68, 116.78, 103.94), swapRB=True, crop=False)\n\n    return blob\n\n\n# Returns a bounding box and probability score if it is more than minimum confidence\ndef predictions(prob_score, geo, args, **kwargs):\n    (numR, numC) = prob_score.shape[2:4]\n    boxes_list = []\n    confidence_val_list = []\n\n    # loop over rows\n    for y in range(0, numR):\n        scoresData = prob_score[0, 0, y]\n        x0 = geo[0, 0, y]\n        x1 = geo[0, 1, y]\n        x2 = geo[0, 2, y]\n        x3 = geo[0, 3, y]\n        anglesData = geo[0, 4, y]\n\n        # loop over the number of columns\n        for i in range(0, numC):\n            if scoresData[i] < args[\"min_confidence\"]:\n                continue\n\n            (offX, offY) = (i * 4.0, y * 4.0)\n\n            # extracting the rotation angle for the prediction and computing the sine and cosine\n            angle = anglesData[i]\n            cos = np.cos(angle)\n            sin = np.sin(angle)\n\n            # using the geo volume to get the dimensions of the bounding box\n            h = x0[i] + x2[i]\n            w = x1[i] + x3[i]\n\n            # compute start and end for the text pred bbox\n            endX = int(offX + (cos * x1[i]) + (sin * x2[i]))\n            endY = int(offY - (sin * x1[i]) + (cos * x2[i]))\n            startX = int(endX - w)\n            startY = int(endY - h)\n\n            boxes_list.append((startX, startY, endX, endY))\n            confidence_val_list.append(scoresData[i])\n\n    # return bounding boxes and associated confidence_val\n    return boxes_list, confidence_val_list\n\n\ndef decode_predictions(scores, geometry, min_confidence):\n    # grab the number of rows and columns from the scores volume, then\n    # initialize our set of bounding box rectangles and corresponding\n    # confidence scores\n    (numRows, numCols) = scores.shape[2:4]\n    rects = []\n    confidences = []\n    # loop over the number of rows\n    for y in range(0, numRows):\n        # extract the scores (probabilities), followed by the\n        # geometrical data used to derive potential bounding box\n        # coordinates that surround text\n        scoresData = scores[0, 0, y]\n        xData0 = geometry[0, 0, y]\n        xData1 = geometry[0, 1, y]\n        xData2 = geometry[0, 2, y]\n        xData3 = geometry[0, 3, y]\n        anglesData = geometry[0, 4, y]\n        # loop over the number of columns\n        for x in range(0, numCols):\n            # if our score does not have sufficient probability,\n            # ignore it\n            if scoresData[x] < min_confidence:\n                continue\n            # compute the offset factor as our resulting feature\n            # maps will be 4x smaller than the input image\n            (offsetX, offsetY) = (x * 4.0, y * 4.0)\n            # extract the rotation angle for the prediction and\n            # then compute the sin and cosine\n            angle = anglesData[x]\n            cos = np.cos(angle)\n            sin = np.sin(angle)\n            # use the geometry volume to derive the width and height\n            # of the bounding box\n            h = xData0[x] + xData2[x]\n            w = xData1[x] + xData3[x]\n            # compute both the starting and ending (x, y)-coordinates\n            # for the text prediction bounding box\n            endX = int(offsetX + (cos * xData1[x]) + (sin * xData2[x]))\n            endY = int(offsetY - (sin * xData1[x]) + (cos * xData2[x]))\n            startX = int(endX - w)\n            startY = int(endY - h)\n            # add the bounding box coordinates and probability score\n            # to our respective lists\n            rects.append((startX, startY, endX, endY))\n            confidences.append(scoresData[x])\n    # return a tuple of the bounding boxes and associated confidences\n    return rects, confidences\n\n\ndef read_video(args, vid_file, **kwargs):\n    # initialize the original frame dimensions, new frame dimensions,\n    # and ratio between the dimensions\n    (W, H) = (None, None)\n    (newW, newH) = (args[\"width\"], args[\"height\"])\n    (rW, rH) = (None, None)\n    # load the pre-trained EAST model for text detection\n    net = cv2.dnn.readNet(args[\"east\"])\n\n    # The following two layer need to pulled from EAST model for achieving this.\n    layerNames = [\"feature_fusion/Conv_7/Sigmoid\", \"feature_fusion/concat_3\"]\n\n    vs = cv2.VideoCapture(vid_file)\n\n    # start the FPS throughput estimator\n    fps = FPS().start()\n\n    # loop over frames from the video stream\n    while True:\n        # grab the current frame, then handle if we are using a\n        # VideoStream or VideoCapture object\n        frame = vs.read()\n        frame = frame[1]\n        # check to see if we have reached the end of the stream\n        if frame is None:\n            break\n        plt.imshow(frame)\n        plt.title('gray')\n        plt.show()\n        # resize the frame, maintaining the aspect ratio\n        frame = imutils.resize(frame, width=1000)\n        plt.imshow(frame)\n        plt.title('resize')\n        plt.show()\n        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n        blur = cv2.GaussianBlur(gray, (3, 3), 0)\n        #otsu\n        threshold = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]\n        invert = 255 - threshold\n        frame = np.dstack([invert] * 3)\n        # Invert the image\n        #threshold = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 11, 2)\n        plt.imshow(frame)\n        plt.title('invert')\n        plt.show()\n        # frame = np.dstack([gray, gray, gray])\n\n        kernel = np.ones((1, 1), np.uint8)\n        dilate = cv2.dilate(threshold, kernel, iterations=1)\n        erode = cv2.erode(dilate, kernel, iterations=1)\n\n        #thresholding\n\n        frame = np.dstack([invert] * 3)\n\n        plt.imshow(frame)\n        plt.title('Preprocessed')\n        plt.show()\n        orig = frame.copy()\n\n        # if our frame dimensions are None, we still need to compute the\n        # ratio of old frame dimensions to new frame dimensions\n        if W is None or H is None:\n            (H, W) = frame.shape[:2]\n            rW = W / float(newW)\n            rH = H / float(newH)\n        # resize the frame, this time ignoring aspect ratio\n        frame = cv2.resize(frame, (newW, newH))\n\n        # construct a blob from the frame and then perform a forward pass\n        # of the model to obtain the two output layer sets\n        blob = cv2.dnn.blobFromImage(frame, 1.0, (newW, newH),\n                                     (123.68, 116.78, 103.94), swapRB=True, crop=False)\n        net.setInput(blob)\n        (scores, geometry) = net.forward(layerNames)\n        # decode the predictions, then  apply non-maxima suppression to\n        # suppress weak, overlapping bounding boxes\n        (rects, confidences) = decode_predictions(scores, geometry, args['min_confidence'])\n        boxes = non_max_suppression(np.array(rects), probs=confidences)\n\n        # initialize the list of results\n        results = []\n        texts = []\n        # loop over the bounding boxes\n        for (startX, startY, endX, endY) in boxes:\n            # scale the bounding box coordinates based on the respective\n            # ratios\n            startX = int(startX * rW)\n            startY = int(startY * rH)\n            endX = int(endX * rW)\n            endY = int(endY * rH)\n\n            # extract the region of interest\n            r = orig[startY:endY, startX:endX]\n\n            # configuration setting to convert image to string.\n            configuration = (\"-l eng --oem 1 --psm 6\")\n            ##This will recognize the text from the image of bounding box\n            text = pytesseract.image_to_string(r, config=configuration)\n\n            # append bbox coordinate and associated text to the list of results\n            results.append(((startX, startY, endX, endY), text))\n\n            texts.append(text)\n\n            # Display the image with bounding box and recognized text\n            orig_image = orig.copy()\n\n            # Moving over the results and display on the image\n            for ((start_X, start_Y, end_X, end_Y), text) in results:\n                # display the text detected by Tesseract\n                print(\"{}\\n\".format(text))\n\n                # Displaying text\n                text = \"\".join([x if ord(x) < 128 else \"\" for x in text]).strip()\n                cv2.rectangle(orig_image, (start_X, start_Y), (end_X, end_Y),\n                              (0, 0, 255), 2)\n                cv2.putText(orig_image, text, (start_X, start_Y - 30),\n                            cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)\n\n            # draw the bounding box on the frame\n            # cv2.rectangle(orig, (startX, startY), (endX, endY), (0, 255, 0), 2)\n            # update the FPS counter\n            fps.update()\n\n            # show the output frame\n            plt.imshow(orig_image)\n            plt.title('Output')\n            plt.show()\n            concat = ','.join(texts)\n            key = cv2.waitKey(1) & 0xFF\n            # if the `q` key was pressed, break from the loop\n            if key == ord(\"q\"):\n                break\n        # stop the timer and display FPS information\n        fps.stop()\n        print(\"[INFO] elasped time: {:.2f}\".format(fps.elapsed()))\n        print(\"[INFO] approx. FPS: {:.2f}\".format(fps.fps()))\n\n        vs.release()\n\n        # close all windows\n        cv2.destroyAllWindows()\n\n\ndef read_frame(args, cropped, **kwargs):\n    # load the pre-trained EAST model for text detection\n    plt.imshow(cropped)\n    plt.title('Cropped')\n    plt.show()\n    threshold = cv2.threshold(cropped, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]\n    invert = 255 - threshold\n\n    cnts = cv2.findContours(invert, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)\n    cnts = cnts[0] if len(cnts) == 2 else cnts[1]\n    for contour in cnts:\n        (x, y, w, h) = cv2.boundingRect(contour)\n        cv2.rectangle(invert, (x, y), (x + w, y + h), (0, 255, 0), 2)\n    plt.imshow(invert)\n    plt.title('invert Image')\n    plt.show()\n    cnts = cv2.findContours(threshold, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n    cnts = cnts[0] if len(cnts) == 2 else cnts[1]\n    for c in cnts:\n        area = cv2.contourArea(c)\n        peri = cv2.arcLength(c, True)\n        approx = cv2.approxPolyDP(c, 0.01 * peri, True)\n        x, y, w, h = cv2.boundingRect(approx)\n        aspect_ratio = w / float(h)\n        if (aspect_ratio >= 2.5 or area < 75):\n            cv2.drawContours(threshold, [c], -1, (255, 255, 255), -1)\n\n    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (15, 3))\n    image = np.dstack([invert] * 3)\n    # kernel = np.ones((1, 1), np.uint8)\n    # image = cv2.dilate(image, kernel, iterations=1)\n    # image = cv2.erode(image, kernel, iterations=1)\n\n    plt.imshow(image)\n    plt.title('Preprocessed Image')\n    plt.show()\n\n    # Saving a original image and shape\n    orig = image.copy()\n    (origH, origW) = image.shape[:2]\n\n    # set the new height and width to default 320 by using args #dictionary.\n    (newW, newH) = (args[\"width\"], args[\"height\"])\n\n    # Forward pass the blob from the image to get the desired output layers\n    net.setInput(load(img=image, newW=newW, newH=newH))\n    (scores, geometry) = net.forward(layerNames)\n    # Find predictions and  apply non-maxima suppression\n    (boxes, confidence_val) = predictions(scores, geometry, args)\n    boxes = non_max_suppression(np.array(boxes), probs=confidence_val)\n\n    ##Text Detection and Recognition\n\n    # initialize the list of results\n    results = []\n\n    # Calculate the ratio between original and new image for both height and weight.\n    # This ratio will be used to translate bounding box location on the original image.\n    rW = origW / float(newW)\n    rH = origH / float(newH)\n\n    texts = []\n    # loop over the bounding boxes to find the coordinate of bounding boxes\n    for (startX, startY, endX, endY) in boxes:\n        # scale the coordinates based on the respective ratios in order to reflect bounding box on the original image\n        startX = int(startX * rW)\n        startY = int(startY * rH)\n        endX = int(endX * rW)\n        endY = int(endY * rH)\n\n        # extract the region of interest\n        r = orig[startY:endY, startX:endX]\n\n        # configuration setting to convert image to string.\n        configuration = (\"-l eng --oem 1 --psm 6\")\n        try:\n            ##This will recognize the text from the image of bounding box\n            text = pytesseract.image_to_string(r, config=configuration)\n\n            # append bbox coordinate and associated text to the list of results\n            results.append(((startX, startY, endX, endY), text))\n\n            texts.append(text)\n\n            # Display the image with bounding box and recognized text\n            orig_image = orig.copy()\n\n            # Moving over the results and display on the image\n            for ((start_X, start_Y, end_X, end_Y), text) in results:\n                # display the text detected by Tesseract\n                print(\"{}\\n\".format(text))\n\n                # Displaying text\n                text = \"\".join([x if ord(x) < 128 else \"\" for x in text]).strip()\n                cv2.rectangle(orig_image, (start_X, start_Y), (end_X, end_Y),\n                              (0, 0, 255), 2)\n                cv2.putText(orig_image, text, (start_X, start_Y - 30),\n                            cv2.FONT_HERSHEY_SIMPLEX, 1.2, (0, 0, 255), 2)\n\n                plt.imshow(orig_image)\n                plt.title('Output')\n                plt.show()\n                concat = ','.join(texts)\n        except Exception as e:\n            print(e)\n    return True\n\n\n# Press the green button in the gutter to run the script.\nif __name__ == '__main__':\n    user = \"consumingcouple\"\n    folder = f\"/Users/thomascho/code/tiktokvideos/{user}\"\n\n    default_args = {\n        \"east\": \"/Users/thomascho/code/frozen_east_text_detection.pb\",\n        \"min_confidence\": 0.5,\n        \"width\": 320,\n        \"height\": 320}\n\n    for root, dirs, files in os.walk(folder, topdown=False):\n        for index, file in enumerate(files):\n            if file.endswith('frame0.jpg'):\n                full_path = os.path.join(root, file)\n                net = cv2.dnn.readNet(default_args[\"east\"])\n\n                # The following two layer need to pulled from EAST model for achieving this.\n                layerNames = [\"feature_fusion/Conv_7/Sigmoid\", \"feature_fusion/concat_3\"]\n                image = cv2.imread(full_path)\n                plt.imshow(image)\n                plt.title('Image')\n                plt.show()\n                gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n                blur = cv2.GaussianBlur(gray, (3, 3), 0)\n                cropped = thresh_callback(100, blur)\n                for crop in cropped:\n                    read_frame(default_args, cropped=crop)\n                print(index)\n","sub_path":"Python/read_frames.py","file_name":"read_frames.py","file_ext":"py","file_size_in_byte":15279,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"215900302","text":"# coding=utf-8\nimport datetime\n\nfrom marvin.api.plugin import Plugin\n\nclass LastSeenPlugin(Plugin):\n    \"\"\"Remembers the last time people were online\"\"\"\n    alias = \"seen\"\n    online = {}\n    seen = {}\n\n    def __init__(self):\n        self.commands = {\n            \"seen\": self._seen\n        }\n\n    def on_joined(self, user):\n        name = user.name.lower()\n\n        self.online[name] = user.room\n        if name in self.seen:\n            del self.seen[name]\n\n    def on_left(self, user):\n        name = user.name.lower()\n\n        self.seen[name] = {\"room\": user.room, \"time\": datetime.datetime.now()}\n        if name in self.online:\n            del self.online[name]\n\n    def _seen(self, _, args):\n        \"\"\"Show the last time a user was online, or a room where they are online in\n        seen <user>\"\"\"\n        if not args:\n            return \"No args!\"\n\n        name = args[0].lower()\n\n        if name in self.online:\n            return \"{0} is online in {1}!\".format(args[0], self.online[name])\n        if name in self.seen:\n            return \"I last saw {0} in {1} at {2}!\".format(args[0], self.seen[name][\"room\"], self.seen[name][\"time\"])\n        return \"I haven't seen {0}!\".format(args[0])\n","sub_path":"marvin/plugins/seen.py","file_name":"seen.py","file_ext":"py","file_size_in_byte":1201,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"435589640","text":"import pickle\nfrom flask import Flask, request, render_template\n\napp = Flask(__name__)\nModel = pickle.load(open('Model.pkl','rb'))\n\n@app.route('/')\ndef index():\n    return render_template('CreditInfo.html') \n\n@app.route('/result', methods=['GET', 'POST'])\ndef creditPrediction():\n    if request.method == 'POST':\n        LoanAmount = request.form['loanAmount']\n        Age = request.form['age']\n        HasHouse = request.form['home']\n        CreditCount = request.form['creditCount']\n        HasPhone = request.form['phone']\n         \n        predict = Model.predict([[float(LoanAmount),\n                                    float(Age),\n                                    float(HasHouse),\n                                    float(CreditCount),\n                                    float(HasPhone),]])\n        creditResult = predict[0]\n        if(creditResult>=1):\n            creditState = \"Kredi verilebilir.\"\n        else:\n            creditState = \"Kredi verilemez.\"\n        \n        return render_template(\"result.html\",creditState = creditState)\n    else:\n        return render_template('CreditInfo.html')\n\nif __name__ == '__main__':\n    try:\n        app.run(debug=False)\n        print(\"Sunucu aktif!\")\n    except:\n        print(\"Sunucu hatası!\")","sub_path":"Credit_Service/services.py","file_name":"services.py","file_ext":"py","file_size_in_byte":1253,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"373067003","text":"__author__ = 'fferreira'\n\nfrom random import shuffle, random, choice\n\nimport psa\nfrom psa.util import argmax\n\n\n#___________________________________________________\n\n\nclass QLearning(object ):\n    def __init__(self): # ** deveriamos passar os parametros psa para anular as dependencais\n        self.alfa = 0.5   # se for 1 fica reativo\n        self.gama = 0.9   # e o elemento de previsao do futuro\n        self.epson = 0.010 # faz sentido ser pequeno senao nao aproveita so explora\n        self.Q= {}  #dicionario com par de estado accao com chave a que e associado o valor r\n        self.accoes= psa.dirmov()\n\n    def qval(self, s,a):\n        return self.Q.get((s,a), 0)\n\n    def max_accao(self, s):\n        # baralha a lista para garantir que nao corremos sempre a mesma lista\n        shuffle( self.accoes)\n        return argmax( self.accoes, lambda a: self.qval(s,a) )\n\n    def assimilar(self, s, a, r, sn):\n        amax = self.max_accao(sn)\n        qsa= self.Q.get( (s,a), 0)\n        qsnan= self.Q.get((sn,amax),0)\n        delta = r + self.gama * qsnan -qsa\n        self.Q[(s,a)] = qsa + self.alfa * delta\n\n\n\n    def seleccionar_accao(self, sn):\n        # se maior que ep\n        x = random()\n        if x < self.epson:\n            # vai explorar com um movimento aleatorio\n            accao = choice(self.accoes)\n        else:\n            accao = self.max_accao(sn)\n\n        return accao\n\n","sub_path":"src/lib/aprend_ref/q_learning.py","file_name":"q_learning.py","file_ext":"py","file_size_in_byte":1394,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"561529766","text":"# -*- coding:utf-8 -*-\n# json转换csv文件\n\nimport json\nimport csv\n\nimport sys\nreload(sys)\nsys.setdefaultencoding(\"utf-8\")\n\ndef json_to_csv():\n\n    # 1.读取json文件\n    json_file = open(\"5tencent.json\", \"r\")\n\n    # 2.创建csv文件\n    csv_file = open(\"6json.csv\", \"w\")\n\n    # 3.创建读写器\n    csv_writer = csv.writer(csv_file)\n\n    # 4.提取表头和正文内容\n    data = json.load(json_file)\n\n    # 表头\n    sheet_title = data[0].keys()\n    # 内容\n    content_list = []\n    for dic in data:\n        content_list.append(dic.values())\n\n    # content_list = [dic.values() for dic in data]\n\n    # 通过读写器 写入文件\n    csv_writer.writerow(sheet_title)\n    csv_writer.writerows(content_list)\n\n    # 关闭文件\n    json_file.close()\n    csv_file.close()\n\nif __name__ == '__main__':\n    json_to_csv()","sub_path":"day04/6jsontocsv.py","file_name":"6jsontocsv.py","file_ext":"py","file_size_in_byte":827,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"67331929","text":"\"\"\"zz URL Configuration\r\n\r\nThe `urlpatterns` list routes URLs to views. For more information please see:\r\n    https://docs.djangoproject.com/en/1.9/topics/http/urls/\r\nExamples:\r\nFunction views\r\n    1. Add an import:  from my_app import views\r\n    2. Add a URL to urlpatterns:  url(r'^$', views.home, name='home')\r\nClass-based views\r\n    1. Add an import:  from other_app.views import Home\r\n    2. Add a URL to urlpatterns:  url(r'^$', Home.as_view(), name='home')\r\nIncluding another URLconf\r\n    1. Import the include() function: from django.conf.urls import url, include\r\n    2. Add a URL to urlpatterns:  url(r'^blog/', include('blog.urls'))\r\n\"\"\"\r\nfrom django.conf.urls import url\r\nfrom myadmin import views\r\n\r\nurlpatterns = [\r\n    url(r'^$', views.index),\r\n    url(r'book/$', views.book),\r\n    url(r'book/add/$', views.book_add),\r\n    url(r'book/(?P<book_id>\\d+)/change/$', views.book_change),\r\n    url(r'publisher/$', views.publisher),\r\n    url(r'publisher/add/$', views.publisher_add),\r\n    url(r'publisher/(?P<publisher_id>\\d+)/change/$', views.publisher_change),\r\n    url(r'author/$', views.author),\r\n    url(r'author/add/$', views.author_add),\r\n    url(r'author/(?P<author_id>\\d+)/change/$', views.author_change),\r\n    url(r'(?P<the_table_name>\\w+)/(?P<the_id>\\d+)/delete/$', views.delete),\r\n]\r\n","sub_path":"day16/homework/zz/myadmin/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1303,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"267015493","text":"__author__ = 'ca1ek'\n\nimport argparse\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"file\", help='file to search in')\nparser.add_argument(\"text\", help='text to be searched in the file')\nparser.add_argument(\"-a\", \"--all\", help=\"show all occurrences of the word without any prompting\", action=\"store_true\")\nparser.add_argument(\"-s\", \"--simple\", help=\"returns only position of words separated with semicolons, works only with -a\", action=\"store_true\")\nargs = parser.parse_args()\n\ndef split_all_in_array(to_split, split_by):\n    words = []  # array of single words\n    for element in to_split:  # element is now a single line in the array\n        split = element.split(split_by)  # split is an array of words separated by split_by from to_split\n        for word in split:\n            words.append(word)\n    return words\n\ndef find_in_data(srch_in, srch_for, earlier_data):\n    if not earlier_data:\n        data = srch_in.read().splitlines()  # array split at lines\n        data = split_all_in_array(data, \" \")  # array split at words\n    if earlier_data:\n        data = earlier_data\n    position = data.index(srch_for)  # first position found\n    return position, data\n\ntry:\n\n    search_in = open(str(args.file))\n    search_for = str(args.text)\n\n    second_search = False\n\n    response = \"y\"\n    while response.upper() == \"Y\":\n        if second_search:\n            found_at, earlier_data = find_in_data(search_in, search_for, earlier_data)\n        else:\n            found_at, earlier_data = find_in_data(search_in, search_for, False)\n\n        if not args.simple:\n            print(\"Found, word #\" + str(found_at + 1))\n            del earlier_data[found_at]\n        elif args.simple:\n            print(str(found_at + 1) + \";\")\n            del earlier_data[found_at]\n\n        if not args.all:\n            print(\"Continue searching? (y/n)\")\n            response = str(raw_input())\n        else:\n            response = \"Y\"\n        if response.upper() == \"Y\":\n            second_search = True\n\n\nexcept ValueError:\n    if second_search and not args.all:\n        print(\"No more occurrences of that word found\")\n    elif not args.all:\n        print(\"Text was not found.\")\n    elif args.simple:\n        print(\"\")\n\nexcept IOError:\n    print(\"File does not exist, please check filename.\")","sub_path":"find_in_file.py","file_name":"find_in_file.py","file_ext":"py","file_size_in_byte":2282,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"583908151","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Sep  3 15:42:32 2020\n\n@author: joyce\n\"\"\"\n\n'''\nUma string S de letras inglesas minúsculas é fornecida. Queremos particionar esta string em tantas partes quanto possível, de \nmodo que cada letra apareça em no máximo uma parte e retornar uma lista de inteiros representando o tamanho dessas partes.\n\n'''\n\nclass Solution(object):\n    def partitionLabels(self, S):\n        \"\"\"\n        :type S: str\n        :rtype: List[int]\n        \"\"\"\n\n \n        ultima = dict() #criar um dicionário que armazenará os maiores indices de cada letra\n        unicas = set(S) #letras na ordem sem repetição\n        i = len(S)-1\n        res = []\n        start = 0\n        end = 0\n        \n        while unicas:\n            if S[i] in unicas:\n                ultima[S[i]] = i\n                unicas.remove(S[i])\n            i = i - 1\n        \n        #return ultima\n\n        for indice, valor in enumerate(S):\n            end = max(end, ultima[valor])\n            if end == indice: \n                res.append(end - start + 1)\n                start = indice + 1\n            \n        return res\n       \n        \ndef main():\n    \n    testlist = \"ababcbacadefegdehijhklij\" \n\n    print(\"Teste 1\")\n   \n    \n    s = Solution() #chamo a classe\n\n    print(s.partitionLabels(testlist)) #[9,7,8]\n    \n\n\n   \n    \nmain()\n                \n            ","sub_path":"leetcodePartitionLabels.py","file_name":"leetcodePartitionLabels.py","file_ext":"py","file_size_in_byte":1363,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"33075128","text":"from pymongo import MongoClient\nimport os\n\nclient = MongoClient(os.environ.get(\"MONGO_URL\", default = \"localhost\"), 27017)\n\ndef create_entrie(object, collection_name):\n\tcollection = client[collection_name]\n\tserialized_object = object.serialize()\n\tpost_id = collection.insert_one(serialized_object)\n\n\treturn post_id\n\ndef get_entries(collection_name, query, model):\n\tcollection = client[collection_name]\n\tentries = collection.find(query)\n\n\tif not entries: return []\n\n\tresult = []\n\tfor entry in entries:\n\t\tobj = model(serialized_object = entry)\n\t\tresult.append(obj)\n\n\treturn obj\n\ndef delete_entries(collection_name, query):\n\tcollection = client[collection_name]\n\treturn collection.delete_many(query)\n\ndef update_entries(query, obj):\n\tserialized_object = object.serialize()\n\tmycollection.update(query, {\"$set\": serialized_object}, upsert=False)","sub_path":"app/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":840,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"426232822","text":"# @Github : https://github.com/ermongroup/GraphScoreMatching\nimport numpy as np\nimport torch\n\n\ndef node_feature_to_matrix(x):\n    \"\"\"\n    :param x:  BS x N x F\n    :return:\n    x_pair: BS x N x N x 2F\n    \"\"\"\n    x_b = x.unsqueeze(-2).expand(-1, -1, x.size(1), -1)  # BS x N x N x F\n    x_b_t = x_b.transpose(1, 2)  # BS x N x N x F\n    x_pair = torch.cat([x_b, x_b_t], dim=-1)  # BS x N x N x 2F\n    return x_pair\n\n\ndef mask_adjs(adjs, node_flags):\n    \"\"\"\n    :param adjs:  B x N x N or B x C x N x N\n    :param node_flags: B x N\n    :return:\n    \"\"\"\n    if len(adjs.shape) == 4:\n        node_flags = node_flags.unsqueeze(1)  # B x 1 x N\n    adjs = adjs * node_flags.unsqueeze(-1)\n    adjs = adjs * node_flags.unsqueeze(-2)\n    return adjs\n\n\ndef pad_adjs(ori_adj, node_number):\n    a = ori_adj\n    ori_len = a.shape[-1]\n    if ori_len == node_number:\n        return a\n    if ori_len > node_number:\n        raise ValueError(f'ori_len {ori_len} > node_number {node_number}')\n    a = np.concatenate([a, np.zeros([ori_len, node_number - ori_len])],\n                       axis=-1)\n    a = np.concatenate([a, np.zeros([node_number - ori_len, node_number])],\n                       axis=0)\n    return a\n\n\ndef get_corrupt_k(min_k=0, max_k=None, p=0.5):\n    ret = np.random.geometric(p) + min_k - 1\n    if max_k is not None:\n        ret = min(ret, max_k)\n    return ret\n\n\ndef remove_self_loop_if_exists(adjs):\n    return (adjs - torch.eye(adjs.size()[-1]).unsqueeze(0).to(adjs.device)\n            ).clamp(min=0.0)\n\n\ndef add_self_loop_if_not_exists(adjs):\n    if len(adjs.shape) == 4:\n        return adjs + torch.eye(adjs.size()[-1]\n                                ).unsqueeze(0).unsqueeze(0).to(adjs.device)\n    return adjs + torch.eye(adjs.size()[-1]).unsqueeze(0).to(adjs.device)\n\n\ndef toggle_edge_np(adj, count=1):\n    \"\"\"\n    uniformly toggle `count` edges of the graph,\n    suppose that the vertex number is fixed\n\n    :param adj: N x N\n    :param count: int\n    :return: new adjs and node_flags\n    \"\"\"\n    count = min(count, adj.shape[-1])\n    x = np.random.choice(adj.shape[0], count)\n    y = np.random.choice(adj.shape[1], count)\n    change = 1. - adj[x, y]\n    adj[x, y] = change\n    adj[y, x] = change\n    return adj\n\n\ndef check_adjs_symmetry(adjs, do_check_adjs_symmetry=False):\n    if not do_check_adjs_symmetry:\n        return\n    tr_adjs = adjs.transpose(-1, -2)\n    assert (adjs - tr_adjs).abs().sum([0, 1, 2]) < 1e-2\n\n\ndef gen_list_of_data(train_x_b, train_adj_b, sigma_list):\n    \"\"\"\n    :param train_x_b: [bs, N, F_in], batch of feature vectors of nodes\n    :param train_adj_b: [bs, N, N], batch of original adjacency matrices\n    :param sigma_list: list of noise levels\n    :return:\n        train_x_b: [len(sigma_list) * bs, N, F_in],\n            batch of feature vectors of nodes (w.r.t. `train_noise_adj_b`)\n        train_noise_adj_b: [len(sigma_list) * bs, N, N],\n            batch of perturbed adjacency matrices\n        train_node_flag_b: [len(sigma_list) * bs, N], the flags for\n            the existence of nodes (w.r.t. `train_noise_adj_b`)\n        grad_log_q_noise_list: [len(sigma_list) * bs, N, N],\n            the ground truth gradient (w.r.t. `train_noise_adj_b`)\n    \"\"\"\n    assert isinstance(sigma_list, list)\n    train_noise_adj_b_list = []\n    grad_log_q_noise_list = []\n    for sigma_i in sigma_list:\n        train_noise_adj_b, grad_log_q_noise = add_gaussian_noise(\n            train_adj_b,\n            sigma=sigma_i\n        )\n        train_noise_adj_b_list.append(train_noise_adj_b)\n        grad_log_q_noise_list.append(grad_log_q_noise)\n    \n    train_noise_adj_b = torch.cat(train_noise_adj_b_list, dim=0)\n    train_x_b = train_x_b.repeat(len(sigma_list), 1, 1)\n    \n    return train_x_b, train_noise_adj_b, grad_log_q_noise_list\n\n\ndef add_gaussian_noise(adjs, sigma, is_half=False):\n    assert isinstance(adjs, torch.Tensor)\n    noise = torch.randn_like(adjs).triu(diagonal=1) * sigma\n    if is_half:\n        noise = noise.abs()\n    noise_s = noise + noise.transpose(-1, -2)\n    check_adjs_symmetry(noise_s)\n    grad_log_noise = - noise_s / (sigma ** 2)\n    ret_adjs = adjs + noise_s\n    \n    return ret_adjs, grad_log_noise\n\n\ndef add_feature_noise(feats, sigma=0.2):\n    assert isinstance(feats, torch.Tensor)\n    ret_feats, grad_log_noise = [], []\n    for i in range(36):\n        noise = torch.randn_like(feats[:, i, :]) * sigma\n        grad_log_noise.append(- noise / (sigma ** 2))\n        ret_feats.append(feats[:, i, :] + noise)\n    return torch.stack(ret_feats, dim=1), torch.stack(grad_log_noise, dim=1)\n\n\ndef add_feature_noise_v2(feats, sigma=0.2):\n    assert isinstance(feats, torch.Tensor)\n    noise = torch.randn_like(feats) * sigma\n    feats = feats + noise\n    grad_log_noise = - noise / (sigma ** 2)\n    return feats, grad_log_noise\n\n\ndef add_edge_noise(adjs, sigma=0.2):\n    noise = torch.zeros_like(adjs)\n    temp = torch.randn((adjs.shape[0], 630), device=adjs.device) * sigma\n    noise[torch.ones_like(adjs).triu(1) == 1] = temp.view(-1)\n    noise = noise + noise.transpose(-1, -2)\n    grad_log_noise = - noise / (sigma ** 2)\n    adjs = adjs + noise\n    return adjs, grad_log_noise\n\n\ndef add_edge_noise_v2(adjs, sigma=0.2):\n    assert isinstance(adjs, torch.Tensor)\n    noise = torch.randn_like(adjs).triu(diagonal=1) * sigma\n    noise = noise + noise.transpose(-1, -2)\n    grad_log_noise = - noise / (sigma ** 2)\n    adjs = adjs + noise\n    return adjs, grad_log_noise\n\n# def add_edge_noise(adjs, sigma=0.2):\n#     noise = torch.zeros_like(adjs)\n#     temp = torch.randn((adjs.shape[0], 630), device=adjs.device) * sigma\n#     noise[torch.ones_like(adjs).triu(1) == 1] = temp.view(-1)\n#\n#     noise = noise + noise.transpose(-1, -2)\n#     grad_log_noise = - noise / (sigma ** 2)\n#     adjs = adjs + noise\n#     adjs = torch.div(adjs, adjs.max(1)[0].unsqueeze(-1))\n#     return adjs, grad_log_noise\n","sub_path":"src/module/graph_utils.py","file_name":"graph_utils.py","file_ext":"py","file_size_in_byte":5854,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"480146417","text":"from selenium.webdriver import Chrome\nfrom selenium.webdriver.common.action_chains import ActionChains\nfrom selenium.webdriver.common.keys import Keys\nimport time\ndriver = Chrome(executable_path='F:\\TestingWorld\\SeleniumPython\\chromedriver.exe')\ndriver.get('http://www.thetestingworld.com')\ndriver.maximize_window()\n\n# Select all Ctrl+A\nact = ActionChains(driver)\nact.send_keys(Keys.CONTROL).send_keys(\"a\").perform()\n\n# Left Click\nact.click(driver.find_element_by_xpath(\"//a[text()='Login']\")).perform()\n# Right Click\nact.context_click(driver.find_element_by_xpath(\"//a[text()='Login']\")).perform()\n\n# Press TAB\ndriver.find_element_by_name('username')\nact = ActionChains(driver)\nact.send_keys(Keys.TAB).perform()\n\n# Mouse Hover\nact.move_to_element(driver.find_element_by_xpath(\"//a[text()='Login']\")).perform()","sub_path":"KeyBoardMouseOperation.py","file_name":"KeyBoardMouseOperation.py","file_ext":"py","file_size_in_byte":810,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"291821691","text":"#!/usr/bin/env python\nimport argparse\nfrom pathlib import Path\nfrom datetime import datetime\nfrom tqdm import tqdm\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom data import Data\nfrom model import Seq2Seq\nfrom tools import log, calc_bleu_score, Logging, batch_label2word, output_translation\n\nclass Classifier(nn.Module):\n    def __init__(self, model_s2t, model_t2s, constraint):\n        super(Classifier, self).__init__()\n        self.model_s2t = model_s2t\n        self.model_t2s = model_t2s\n        self.loss_fun = nn.CrossEntropyLoss(ignore_index=1)\n        self.constraint = constraint\n        self.mse_loss = nn.MSELoss()\n        \n    def forward(self, src, trg, train=True, max_length=60):\n        # train==False: Translation else: Train or Varidation\n        # Translation->trg is None, Train or Varidation->trg is not None\n        s_length = src[1]\n        src = src[0]\n        t_length = trg[1]\n        trg = trg[0]\n        src_sos = src[ :-1] if train else None\n        src_eos = src[1:  ]\n        trg_sos = trg[ :-1] if train else None\n        trg_eos = trg[1:  ]\n\n        # target2source \n        if train: max_length = len(src_eos)\n        labels_t2s, output_t2s, enc_outputs_t2s = self.model_t2s(\n            trg_eos, src_sos, src_lengths=t_length-1, length=max_length)\n\n        # sorce2target\n        if train: max_length = len(trg_eos)\n        labels_s2t, output_s2t, enc_outputs_s2t = self.model_s2t(\n            src_eos, trg_sos, src_lengths=s_length-1, length=max_length)\n\n        # translation\n        if not train:\n            return labels_s2t, labels_t2s\n\n        # constraint\n        if self.constraint is not None:\n            loss_c = self.constraint(enc_outputs_s2t, enc_outputs_t2s,\n                                     s_length, t_length, self.mse_loss)\n        else:\n            loss_c = None\n\n        # sorce2target loss\n        L, B, V = output_s2t.size()\n        output_s2t = output_s2t.view(L * B, V)\n        trg_eos = trg_eos.view(L * B)\n        loss_s2t = self.loss_fun(output_s2t, trg_eos)\n\n        # target2source loss\n        L, B, V = output_t2s.size()\n        output_t2s = output_t2s.view(L * B, V)\n        src_eos = src_eos.view(L * B)\n        loss_t2s = self.loss_fun(output_t2s, src_eos)\n\n        return labels_s2t, labels_t2s, loss_s2t, loss_t2s, loss_c\n\ndef main():\n    parser = argparse.ArgumentParser(description='Custom_loop PyTorch')\n    parser.add_argument('--mode', type=str,\n                        choices=['None', 'propose2', 'propose_soft', 'propose_hard'],\n                        default=None, const=None, nargs='+',\n                        help='None / propose2 / propose_soft, propose_hard')\n    parser.add_argument('--hiddensize', type=int, default=256,\n                        help='number of node in hidden layer')\n    parser.add_argument('--batchsize', '-b', type=int, default=50,\n                        help='Number of images in each mini-batch')\n    parser.add_argument('--epoch', '-e', type=int, default=20,\n                        help='Number of sweeps over the dataset to train')\n    parser.add_argument('--size', '-s', type=str, default='100k',\n                        help='Size of datasets')\n    parser.add_argument('--vocab-size', type=int, default=16000,\n                        help='Upper limit of dictionary size')\n    parser.add_argument('--freq', type=int, default=1,\n                        help='Lower limit of word frequency registered in dictionary')\n    parser.add_argument('--gradclip', type=int, default=1.0,\n                        help='Grad clipping Norm')\n    parser.add_argument('--no-bpe', action='store_true',\n                        help='If no use BPE, To enable this flag')\n    parser.add_argument('--lang', type=str, default='ja-en',\n                        help='Language of parallel corpus')\n    parser.add_argument('--gpu', '-g', type=int, default=-1,\n                        help='GPU ID (negative value indicates CPU)')\n    parser.add_argument('--out', '-o', type=str, default='result',\n                        help='Directory to output the result')\n    parser.add_argument('--load-last', action='store_true',\n                        help='load last.m, last.o')\n    parser.add_argument('--model', '-m', type=str, default=None,\n                        help='model')\n    parser.add_argument('--interval', '-i', type=int, default=None,\n                        help='Log interval')\n    args = parser.parse_args()\n\n    log('GPU: {}'.format(args.gpu))\n    log('# Minibatch-size: {}'.format(args.batchsize))\n    log('# epoch: {}'.format(args.epoch))\n\n    # Path to data\n    root = Path('Data')\n    Path(args.out).mkdir(parents=True, exist_ok=True)\n\n    # Log param\n    log(args)\n    log(args, file_name=Path(args.out) / 'param.txt')\n\n    # Setup a GPU\n    if args.gpu >= 0:\n        # Make a speciied GPU current\n        device = torch.device('cuda:{}'.format(args.gpu))\n    else:\n        device = torch.device('cpu')\n\n    # Load the dataset\n    data = Data(root, args.size, args.lang, args.batchsize, use_bpe=not args.no_bpe,\n                vocab_size=args.vocab_size, min_freq=args.freq, device=device)\n    data.make_dataset(Path(args.out), logger=log)\n    train_iter = data.train_iter\n    val_iter   = data.val_iter\n    test_iter   = data.test_iter\n    source_vocab_size = len(data.TEXT_src.vocab)\n    target_vocab_size = len(data.TEXT_trg.vocab)\n    log('vocab size source:{}, target:{}'.format(source_vocab_size, target_vocab_size))\n\n    # Setup a Seq2Seq\n    source2target = Seq2Seq(source_vocab_size, target_vocab_size, args.hiddensize)\n    target2source = Seq2Seq(target_vocab_size, source_vocab_size, args.hiddensize)\n\n    # Select constraint\n    args.mode = args.mode[0] if args.mode is not None else None\n    if args.mode == 'propose2':\n        from constraint import Map\n        constraint = Map()\n    elif args.mode == 'propose_soft':\n        from constraint import MapSoft\n        constraint = MapSoft()\n    elif args.mode == 'propose_hard':\n        from constraint import MapHard\n        constraint = MapHard()\n    else:#args.mode == 'None':\n        constraint = None\n\n    # Setup a model\n    model = Classifier(source2target, target2source, constraint)\n    if args.model is not None:\n        cp_path = Path(args.model)\n        if cp_path.exists():\n            check_point = torch.load(cp_path)\n            model.load_state_dict(check_point['model'], strict=False)\n    model.to(device=device)\n\n    # Setup an optimizer\n    optimizer_s2t = optim.Adam(source2target.parameters())\n    optimizer_t2s = optim.Adam(target2source.parameters())\n    if constraint is not None and args.mode != 'propose2':\n        optimizer_map = optim.Adam(model.constraint.parameters())\n    else:\n        optimizer_map = None\n\n    # Setup Logger\n    start_time = datetime.now()\n    train_loss = Logging()\n    train_tmp_loss = Logging()\n    val_loss   = Logging()\n    logger     = Logging()\n    start_epoch = 0\n    if args.load_last:\n        if logger.load_log(file_name=Path(args.out) / 'log.json'):\n            start_epoch = max(map(int, logger._log.keys()))\n            # Check pointからロード\n            cp_path = Path(args.out) / 'epoch_{}.cp'.format(start_epoch)\n            if cp_path.exists():\n                check_point = torch.load(cp_path)\n                model.load_state_dict(check_point['model'])\n                optimizer_s2t.load_state_dict(check_point['opt_s2t'])\n                optimizer_t2s.load_state_dict(check_point['opt_t2s'])\n                if optimizer_map is not None:\n                    optimizer_map.load_state_dict(check_point['opt_map'])\n            else:\n                # Check pointがない場合(互換性のため、あとで消す)\n                try:\n                    #直前のモデルをロード\n                    model.load_state_dict(\n                        torch.load(Path(args.out) / 'epoch_{}.m'.format(start_epoch)))\n                    optimizer_s2t.load_state_dict(\n                        torch.load(Path(args.out) / 'last_s2t_{}.o'.format(start_epoch)))\n                    optimizer_t2s.load_state_dict(\n                        torch.load(Path(args.out) / 'last_t2s_{}.o'.format(start_epoch)))\n                    if optimizer_map is not None:\n                        optimizer_map.load_state_dict(\n                            torch.load(Path(args.out) / 'last_map{}.o'.format(start_epoch)))\n                except:\n                    # saveに失敗している場合は,さらに一つ前のモデルをロード\n                    log(\"faild load epoch {}'s model\".format(start_epoch))\n                    start_epoch = start_epoch - 1\n                    model.load_state_dict(\n                        torch.load(Path(args.out) / 'epoch_{}.m'.format(start_epoch)))\n                    optimizer_s2t.load_state_dict(\n                        torch.load(Path(args.out) / 'last_s2t_{}.o'.format(start_epoch)))\n                    optimizer_t2s.load_state_dict(\n                        torch.load(Path(args.out) / 'last_t2s_{}.o'.format(start_epoch)))\n                    if optimizer_map is not None:\n                        optimizer_map.load_state_dict(\n                            torch.load(Path(args.out) / 'last_map{}.o'.format(start_epoch)))\n                # Seve the last model\n                epoch = start_epoch\n                status = {\n                    'epoch': epoch,\n                    'model': model.state_dict(),\n                    'opt_s2t': optimizer_s2t.state_dict(),\n                    'opt_t2s': optimizer_t2s.state_dict(),\n                }\n                if optimizer_map is not None:\n                    status['opt_map'] = optimizer_map.state_dict()\n\n                torch.save(status, Path(args.out) / 'epoch_{}.cp'.format(epoch))\n            log('Done load log.json: Start learning from epoch {}'.format(start_epoch+1))\n        else:\n            log('Not found log.json: Start learning from epoch 0')\n            start_epoch = 0\n\n    # Training loop\n    for e in range(start_epoch, args.epoch):\n        epoch = str(e + 1) #jsonのキーが文字列のため\n        # Train\n        model.train()\n        for i, batch in tqdm(enumerate(train_iter), desc='Iteration'):\n            iteration = i + 1\n            # Init Grad\n            optimizer_s2t.zero_grad()\n            optimizer_t2s.zero_grad()\n            if optimizer_map is not None: optimizer_map.zero_grad()\n            # Forward computation\n            _, _, loss_s2t, loss_t2s, loss_map = model(batch.src, batch.trg)\n            # Log\n            train_loss.log('loss_s2t', loss_s2t.item() / len(train_iter))\n            train_loss.log('loss_t2s', loss_t2s.item() / len(train_iter))\n            if optimizer_map is not None: train_loss.log('loss_map', loss_map.item() / len(train_iter))\n            if args.interval is not None:\n                train_tmp_loss.log('loss_s2t', loss_s2t.item() / args.interval)\n                train_tmp_loss.log('loss_t2s', loss_t2s.item() / args.interval)\n                if optimizer_map is not None:\n                    train_tmp_loss.log('loss_map', loss_map.item() / args.interval)\n                    \n            # Backword loss\n            if optimizer_map is not None:\n                loss_t2s.backward(retain_graph=True)\n                loss_s2t.backward(retain_graph=True)\n                loss_map.backward()\n            else:\n                loss_t2s.backward()\n                loss_s2t.backward()\n\n            # Grad cliping\n            nn.utils.clip_grad_norm_(source2target.parameters(), args.gradclip)\n            nn.utils.clip_grad_norm_(target2source.parameters(), args.gradclip)\n            if optimizer_map is not None: nn.utils.clip_grad_norm_(constraint.parameters(), args.gradclip)\n\n            # Update parameters\n            optimizer_s2t.step()\n            optimizer_t2s.step()\n            if optimizer_map is not None: optimizer_map.step()\n            if args.interval is not None and iteration % args.interval == 0:\n                # Log\n                train_tmp_loss.print_log(prefix='epoch: {}\\t'.format(epoch))\n                train_tmp_loss.reset(('loss_s2t', 'loss_t2s'))\n                if optimizer_map is not None: train_tmp_loss.reset('loss_map')\n\n        optimizer_s2t.zero_grad()\n        optimizer_t2s.zero_grad()\n\n        # Validation\n        model.eval()\n        for batch in val_iter:\n            # Forward computation with no_grad()\n            with torch.no_grad():\n                _, _, loss_s2t, loss_t2s, loss_map = model(batch.src, batch.trg)\n            # Log\n            val_loss.log('loss_s2t', loss_s2t.item() / len(val_iter))\n            val_loss.log('loss_t2s', loss_t2s.item() / len(val_iter))\n            if optimizer_map is not None: val_loss.log('loss_map', loss_map.item() / len(val_iter))\n\n        # BLEU (Val)\n        hyp_s2t, ref_s2t, hyp_t2s, ref_t2s = [], [], [], []\n        for batch in val_iter:\n            # Forward computation with no_grad()\n            with torch.no_grad():\n                labels_s2t, labels_t2s = model(batch.src, batch.trg, train=False)\n            # target2source\n            hyp_s2t.extend(batch_label2word(labels_s2t, data.TEXT_trg, args.no_bpe))\n            ref_s2t.extend(batch_label2word(batch.trg[0], data.TEXT_trg, args.no_bpe))\n            # target2source\n            hyp_t2s.extend(batch_label2word(labels_t2s, data.TEXT_src, args.no_bpe))\n            ref_t2s.extend(batch_label2word(batch.src[0], data.TEXT_src, args.no_bpe))\n\n        ref_s2t = [[line] for line in ref_s2t]\n        ref_t2s = [[line] for line in ref_t2s]\n        # Calcurate bleu\n        bleu_s2t = calc_bleu_score(hyp_s2t, ref_s2t)\n        bleu_t2s = calc_bleu_score(hyp_t2s, ref_t2s)\n\n        hyp_s2t, hyp_t2s = [], []\n        # BLEU (test)\n        for batch in test_iter:\n            # Forward computation with no_grad()\n            with torch.no_grad():\n                labels_s2t, labels_t2s = model(batch.src, batch.trg, train=False)\n            # target2source\n            hyp_s2t.extend(batch_label2word(labels_s2t, data.TEXT_trg, args.no_bpe))\n            # target2source\n            hyp_t2s.extend(batch_label2word(labels_t2s, data.TEXT_src, args.no_bpe))\n\n        # Output system translation\n        lang = args.lang.split('-')\n        output_translation(\n            hyp_s2t, file_name=Path(args.out) / 'output_epoch_{}.{}'.format(epoch, lang[1]))\n        output_translation(\n            hyp_t2s, file_name=Path(args.out) / 'output_epoch_{}.{}'.format(epoch, lang[0]))\n\n        # Log\n        logger.log('train/loss(s2t)', train_loss.get_log('loss_s2t'), epoch=epoch)\n        logger.log('train/loss(t2s)', train_loss.get_log('loss_t2s'), epoch=epoch)\n        if optimizer_map is not None: logger.log('train/loss(map)', train_loss.get_log('loss_map'), epoch=epoch)\n        logger.log('val/loss(s2t)', val_loss.get_log('loss_s2t'), epoch=epoch)\n        logger.log('val/loss(t2s)', val_loss.get_log('loss_t2s'), epoch=epoch)\n        if optimizer_map is not None: logger.log('val/loss(map)', val_loss.get_log('loss_map'), epoch=epoch)\n        logger.log('bleu(s2t)', bleu_s2t, epoch=epoch)\n        logger.log('bleu(t2s)', bleu_t2s, epoch=epoch)\n        current_time = datetime.now()\n        elapsed_time = (current_time - start_time).total_seconds()\n        logger.log('elapsed_time', elapsed_time, epoch=epoch)\n\n        # Reset log of loss\n        train_loss.reset()\n        val_loss.reset()\n\n        # Seve the last model\n        status = {\n            'epoch': epoch,\n            'model': model.state_dict(),\n            'opt_s2t': optimizer_s2t.state_dict(),\n            'opt_t2s': optimizer_t2s.state_dict(),\n        }\n        if optimizer_map is not None:\n            status['opt_map'] = optimizer_map.state_dict()\n\n        torch.save(status, Path(args.out) / 'epoch_{}.cp'.format(epoch))\n        \n        \"\"\"\n        with open(Path(args.out) / 'epoch_{}.m'.format(epoch), 'wb') as f:\n            torch.save(model.state_dict(), f)\n        with open(Path(args.out) / 'last_s2t_{}.o'.format(epoch), 'wb') as f:\n            torch.save(optimizer_s2t.state_dict(), f)\n        with open(Path(args.out) / 'last_t2s_{}.o'.format(epoch), 'wb') as f:\n            torch.save(optimizer_t2s.state_dict(), f)\n        if optimizer_map  is not None:\n            with open(Path(args.out) / 'last_map{}.o'.format(epoch), 'wb') as f:\n                torch.save(optimizer_map.state_dict(), f)\n        \"\"\"\n        # print log\n        logger.print_log(epoch=epoch)\n        # log file\n        logger.print_log(file_name=Path(args.out) / 'log.json')\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":16552,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"521019382","text":"# mira.py\n# -------\n# Licensing Information:  You are free to use or extend these projects for\n# educational purposes provided that (1) you do not distribute or publish\n# solutions, (2) you retain this notice, and (3) you provide clear\n# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.\n# \n# Attribution Information: The Pacman AI projects were developed at UC Berkeley.\n# The core projects and autograders were primarily created by John DeNero\n# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).\n# Student side autograding was added by Brad Miller, Nick Hay, and\n# Pieter Abbeel (pabbeel@cs.berkeley.edu).\n\n\n# Mira implementation\nimport util\nimport math\nPRINT = True\n\nclass MiraClassifier:\n    \"\"\"\n    Mira classifier.\n\n    Note that the variable 'datum' in this code refers to a counter of features\n    (not to a raw samples.Datum).\n    \"\"\"\n    def __init__( self, legalLabels, max_iterations):\n        self.legalLabels = legalLabels\n        self.type = \"mira\"\n        self.automaticTuning = False\n        self.C = 0.001\n        self.legalLabels = legalLabels\n        self.max_iterations = max_iterations\n        self.initializeWeightsToZero()\n\n    def initializeWeightsToZero(self):\n        \"Resets the weights of each label to zero vectors\"\n        self.weights = {}\n        for label in self.legalLabels:\n            self.weights[label] = util.Counter() # this is the data-structure you should use\n\n    def train(self, trainingData, trainingLabels, validationData, validationLabels):\n        \"Outside shell to call your method. Do not modify this method.\"\n\n        self.features = trainingData[0].keys() # this could be useful for your code later...\n\n        if (self.automaticTuning):\n            Cgrid = [0.002, 0.004, 0.008]\n        else:\n            Cgrid = [self.C]\n\n        return self.trainAndTune(trainingData, trainingLabels, validationData, validationLabels, Cgrid)\n\n    def calculate(self, validationData,validationLabels):\n        correctness1 = 0\n        correctness = 0\n        guesses = self.classify(validationData)\n        for i,j in enumerate(guesses):\n            correctness += (validationLabels[i] == j and 1.0 or 0.0)\n            acc = (correctness/len(guesses))\n        return acc\n\n\n    def compute_tau(self, W1, w2, y, c):\n        tau = min (c, (((W1-w2)*y + 1.0)/ (2.0 *(math.sqrt(y*y)))))\n        return tau \n\n    def changey(self, y, tau):\n        y.divideAll(1.0/tau)\n        return y\n          \n\n    def trainAndTune(self, trainingData, trainingLabels, validationData, validationLabels, Cgrid):\n        \"\"\"\n        This method sets self.weights using MIRA.  Train the classifier for each value of C in Cgrid,\n        then store the weights that give the best accuracy on the validationData.\n\n        Use the provided self.weights[label] data structure so that\n        the classify method works correctly. Also, recall that a\n        datum is a counter from features to values for those features\n        representing a vector of values.\n        \"\"\"\n        \"*** YOUR CODE HERE ***\"\n        weights = util.Counter()\n        bestAccuracy = None\n        score = util.Counter()\n        for c in Cgrid:\n            weights = self.weights\n            for i in range(self.max_iterations):\n                for t,y in enumerate(trainingData):\n                    #print self.legalLabels\n                    for l1 in self.legalLabels:\n                        \n                        #print \"weight[c][l]\", weights[l1]\n                        #print \"print trainingData[t]\",trainingData[t]\n                        #print \"print trainingDLabels[t]\",trainingLabels[t]\n                        score[l1] = y*weights[l1]\n                    maxScore = score.argMax()\n                    if not (maxScore == trainingLabels[t]):     \n                        Wp = weights[maxScore]\n                        w = weights[trainingLabels[t]]\n                        \n                        tau = min(c, ((Wp-w)*y+1.0)/((y*y)*2))\n                        for f in self.features:\n                            weights[trainingLabels[t]][f] += tau*y[f]\n                            weights[maxScore][f] -= tau*y[f]\n\n                if self.calculate(validationData, validationLabels) > bestAccuracy or bestAccuracy is None:\n                    bestAccuracy = self.calculate(validationData, validationLabels)\n                    bestWeights = weights\n\n        self.weights = bestWeights\n\n    def classify(self, data ):\n        \"\"\"\n        Classifies each datum as the label that most closely matches the prototype vector\n        for that label.  See the project description for details.\n\n        Recall that a datum is a util.counter...\n        \"\"\"\n        guesses = []\n        for datum in data:\n            vectors = util.Counter()\n            for l in self.legalLabels:\n                vectors[l] = self.weights[l] * datum\n            guesses.append(vectors.argMax())\n        return guesses\n\n\n","sub_path":"chandrika2311-master/assignment5/classification/mira.py","file_name":"mira.py","file_ext":"py","file_size_in_byte":4937,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"19299114","text":"import os\nimport logging\nimport argparse\nimport time\nimport datetime\nimport random\n\nimport tensorflow as tf\nimport trfl\nfrom trfl import indexing_ops\nimport numpy as np\nimport pandas as pd\nimport torch\nimport matplotlib.pyplot as plt\n\nfrom utils import *\nfrom NextItNetModules import *\n\n\nclass Agent:\n\tdef __init__(self, args, name='Agent', dqda_clipping=None, clip_norm=False, max_action=1.0):\n\t\tself.args = args\n\t\tself.name = name\n\t\tself.hw = 10\n\t\tself.hidden_size = args.hidden_factor\n\t\tself.item_num = args.max_iid + 1\n\t\tself.is_training = tf.placeholder(tf.bool, shape=())\n\n\t\twith tf.variable_scope(self.name):\n\t\t\tself.all_embeddings = self.initialize_embeddings()\n\n\t\t\tself.inputs = tf.placeholder(tf.int32, [None, self.hw],name='inputs')\n\t\t\tself.len_state = tf.placeholder(tf.int32, [None],name='len_state')\n\t\t\tself.discount = tf.placeholder(tf.float32, [None] , name=\"discount\")\n\t\t\tself.reward = tf.placeholder(tf.float32, [None], name='reward')\n\t\t\tself.target = tf.placeholder(tf.float32, [None],name='target')\n\n\t\t\t# ranking model\n\t\t\tself.target_items = tf.placeholder(tf.int32, [None], name='target_items')\n\n\t\t\tmask = tf.expand_dims(tf.to_float(tf.not_equal(self.inputs, self.item_num)), -1)\n\n\t\t\t# self.input_emb=tf.nn.embedding_lookup(all_embeddings['state_embeddings'],self.inputs)\n\t\t\tself.model_para = {\n\t\t\t\t'dilated_channels': 64,  # larger is better until 512 or 1024\n\t\t\t\t'dilations': [1, 2, 1, 2, 1, 2, ],  # YOU should tune this hyper-parameter, refer to the paper.\n\t\t\t\t'kernel_size': 3,\n\t\t\t}\n\n\t\t\tcontext_embedding = tf.nn.embedding_lookup(self.all_embeddings['state_embeddings'],\n\t\t\t\t\t\t\t\t\t\t\t\t\t   self.inputs)\n\t\t\tcontext_embedding *= mask\n\n\t\t\tdilate_output = context_embedding\n\t\t\tfor layer_id, dilation in enumerate(self.model_para['dilations']):\n\t\t\t\tdilate_output = nextitnet_residual_block(dilate_output, dilation,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tlayer_id, self.model_para['dilated_channels'],\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.model_para['kernel_size'], causal=True, train=self.is_training)\n\t\t\t\tdilate_output *= mask\n\n\t\t\tself.state_hidden = extract_axis_1(dilate_output, self.len_state - 1)\n\t\t\tself.action_size = int(self.state_hidden.shape[-1])\n\n\t\t\t# ddpg\n\t\t\tactor = eval(args.actor_layers)\n\t\t\tactor.append(self.action_size)\n\t\t\twith tf.variable_scope(\"actor\"):\n\t\t\t\tself.actor_output = mlp(self.state_hidden, self.is_training, hidden_sizes=actor, \n\t\t\t\t\tdropout_rate=args.atten_dropout_rate, \n\t\t\t\t\tl2=tf.contrib.layers.l2_regularizer(args.weight_decay))\n\n\t\t\tself.actor_out_ = self.actor_output * max_action\n\t\t\t# learnable\n\t\t\t# self.alpha = tf.Variable(np.array([max_action for _ in range(self.action_size)], dtype=np.float32), name=\"a\")\n\t\t\t# self.alpha = tf.Variable(max_action, name=\"a\")\n\t\t\t# self.actor_out_ = self.actor_output * self.alpha\n\n\t\t\t# learnable A (MLP)\n\t\t\t# A = tf.contrib.layers.fully_connected(tf.concat([self.actor_output, self.state_hidden], axis=1), 1,\n\t\t\t# \tactivation_fn=None,\n\t\t\t# \tweights_regularizer=tf.contrib.layers.l2_regularizer(args.weight_decay))\n\t\t\t# self.actor_out_ = self.actor_output * A\n\n\t\t\tself.critic_input = tf.concat([self.actor_out_, self.state_hidden], axis=1)\n\t\t\tcritic = eval(args.critic_layers)\n\t\t\tcritic.append(1)\n\t\t\twith tf.variable_scope(\"critic\"):\n\t\t\t\tself.critic_output = mlp(self.critic_input, self.is_training, hidden_sizes=critic, \n\t\t\t\t\toutput_activation=None, dropout_rate=args.atten_dropout_rate, \n\t\t\t\t\tl2=tf.contrib.layers.l2_regularizer(args.weight_decay))\n\n\t\t\tself.dpg_return = trfl.dpg(self.critic_output, self.actor_out_, \n\t\t\t\tdqda_clipping=dqda_clipping, clip_norm=clip_norm)\n\n\t\t\tself.actor_loss = tf.reduce_mean(self.dpg_return.loss)\n\t\t\tself.actor_optim = tf.train.AdamOptimizer(args.alr).minimize(self.actor_loss)\n\n\t\t\tself.td_return = trfl.td_learning(tf.squeeze(self.critic_output), self.reward, \n\t\t\t\tself.discount, self.target)\n\t\t\tself.critic_loss = tf.reduce_mean(self.td_return.loss)\n\t\t\tself.critic_optim = tf.train.AdamOptimizer(args.clr).minimize(self.critic_loss)\n\n\t\t\t# NextItNet\n\t\t\t# self.actions = tf.placeholder(tf.float32, [None, self.action_size], name='actions')\n\t\t\t# self.ranking_model_input = self.actions + self.state_hidden\n\t\t\tself.ranking_model_input = self.actor_out_ + self.state_hidden\n\n\t\t\tself.logits = tf.contrib.layers.fully_connected(self.ranking_model_input, self.item_num,\n\t\t\t\tactivation_fn=None,\n\t\t\t\tweights_regularizer=tf.contrib.layers.l2_regularizer(args.weight_decay))\n\n\t\t\tself.ce_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=self.target_items,\n\t\t\t\tlogits=self.logits)\n\t\t\tself.ranking_model_loss = tf.reduce_mean(self.ce_loss)\n\t\t\tself.model_optim = tf.train.AdamOptimizer(args.mlr).minimize(self.ranking_model_loss)\n\n\tdef initialize_embeddings(self):\n\t\tall_embeddings = dict()\n\t\tstate_embeddings= tf.Variable(tf.random_normal([self.item_num+1, self.hidden_size], 0.0, 0.01),\n\t\t\tname='state_embeddings')\n\t\tall_embeddings['state_embeddings']=state_embeddings\n\t\treturn all_embeddings\n\n\tdef get_qnetwork_variables(self):\n\t\treturn [t for t in tf.trainable_variables() if t.name.startswith(self.name)]\n\n\nclass Run(object):\n\tdef __init__(self, args, main_agent, target_agent):\n\t\tsuper(Run, self).__init__()\n\t\tself.args = args\n\t\tself.main_agent = main_agent\n\t\tself.target_agent = target_agent\n\t\tself.target_network_update_ops = trfl.update_target_variables(target_agent.get_qnetwork_variables(), \n\t\t\tmain_agent.get_qnetwork_variables(), tau=args.tau)\n\t\tself.copy_weight = trfl.update_target_variables(target_agent.get_qnetwork_variables(), \n\t\t\tmain_agent.get_qnetwork_variables(), tau=1.0)\n\n\t\tself.topk = [int(x) for x in args.topk.split(',')]\n\t\tself.replay_buffer = pd.read_pickle(os.path.join(args.base_data_dir, 'replay_buffer.df'))\n\n\tdef sample_data(self):\n\t\tbatch = self.replay_buffer.sample(n=args.batch_size).to_dict()\n\t\tstate = list(batch['state'].values())\n\t\tlen_state = list(batch['len_state'].values())\n\t\tnext_state = list(batch['next_state'].values())\n\t\tlen_next_states = list(batch['len_next_states'].values())\n\t\ttarget_items = list(batch['action'].values())\n\t\tis_done = list(batch['is_done'].values())\n\t\tis_buy = list(batch['is_buy'].values())\n\t\treturn state, len_state, next_state, len_next_states, target_items, is_done, is_buy\n\n\tdef cal_rewards(self, logits, target_items, is_buy):\n\t\tlogits = torch.tensor(logits)\n\t\t_, rankings = logits.topk(self.args.reward_top)\n\t\trankings = rankings.tolist()\t# (batch, topk)\n\t\trewards = []\n\t\tfor target_iid, rec_list, buy in zip(target_items, rankings, is_buy):\n\t\t\tndcg = 0.0\n\t\t\tfor i, iid in enumerate(rec_list):\n\t\t\t\tif iid == target_iid:\n\t\t\t\t\tndcg = 1.0 / np.log2(i + 2.0).item()\n\t\t\t\t\tbreak\n\t\t\trewards.append(self.args.buy_weight * ndcg if buy == 1 else ndcg)\n\t\treturn rewards\n\n\tdef state_trans(self, rewards, state, next_state, len_state, len_next_states):\n\t\ttrue_next_state, true_next_state_len = [], []\n\t\tfor r, s, s_, sl, sl_ in zip(rewards, state, next_state, len_state, len_next_states):\n\t\t\tif r == 0:\n\t\t\t\ttrue_next_state.append(s)\n\t\t\t\ttrue_next_state_len.append(sl)\n\t\t\telse:\n\t\t\t\ttrue_next_state.append(s_)\n\t\t\t\ttrue_next_state_len.append(sl_)\n\t\treturn true_next_state, true_next_state_len\n\n\tdef train(self):\n\t\tnum_rows = self.replay_buffer.shape[0]\n\t\tnum_batches = int(num_rows / self.args.batch_size)\n\t\tmax_ndcg_and_epoch = [[0, 0, 0] for _ in self.args.topk.split(',')]\t# (ng_click, ng_purchase, step)\n\t\ttotal_step = 0\n\n\t\tgpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=self.args.mem_ratio)\n\t\twith tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:\n\t\t\tsess.run(tf.global_variables_initializer())\n\t\t\tsess.graph.finalize()\n\t\t\tsess.run(self.copy_weight)\t\t# copy weights\n\t\t\tdiscount = [self.args.gamma] * self.args.batch_size\n\t\t\tfor i_epoch in range(self.args.epoch):\n\t\t\t\tfor j in range(num_batches):\n\t\t\t\t\tstate, len_state, next_state, len_next_states, target_items, is_done, is_buy = self.sample_data()\n\t\t\t\t\tactions = sess.run(self.main_agent.actor_out_, feed_dict={\n\t\t\t\t\t\tself.main_agent.inputs: state, \n\t\t\t\t\t\tself.main_agent.len_state: len_state,\n\t\t\t\t\t\tself.main_agent.is_training: False})\n\n\t\t\t\t\t# add noise\n\t\t\t\t\tnoise = np.random.normal(0, self.args.noise_var, size=self.main_agent.action_size).clip(-self.args.noise_clip, self.args.noise_clip)\n\n\t\t\t\t\tactions = (actions + noise).clip(-self.args.max_action, self.args.max_action)\n\t\t\t\t\t# learnable\n\t\t\t\t\t# actions = (actions + noise).clip(-1, 1)\n\n\t\t\t\t\tce_loss, ranking_model_loss, _ = sess.run([\n\t\t\t\t\t\t# self.main_agent.logits, \n\t\t\t\t\t\tself.main_agent.ce_loss,\n\t\t\t\t\t\tself.main_agent.ranking_model_loss, \n\t\t\t\t\t\tself.main_agent.model_optim], \n\t\t\t\t\t\tfeed_dict={\n\t\t\t\t\t\tself.main_agent.inputs: state, \n\t\t\t\t\t\tself.main_agent.len_state: len_state,\n\t\t\t\t\t\tself.main_agent.actor_out_: actions,\n\t\t\t\t\t\t# self.main_agent.actions: actions,\n\t\t\t\t\t\tself.main_agent.target_items: target_items,\n\t\t\t\t\t\tself.main_agent.is_training: True})\n\n\t\t\t\t\tif self.args.reward == 'ndcg':\n\t\t\t\t\t\t# target logits\n\t\t\t\t\t\tlogits = sess.run(self.target_agent.logits,\n\t\t\t\t\t\t\tfeed_dict={\n\t\t\t\t\t\t\tself.target_agent.inputs: state, \n\t\t\t\t\t\t\tself.target_agent.len_state: len_state,\n\t\t\t\t\t\t\tself.target_agent.actor_out_: actions,\n\t\t\t\t\t\t\t# self.target_agent.actions: actions,\n\t\t\t\t\t\t\tself.target_agent.is_training: False})\n\t\t\t\t\t\trewards = self.cal_rewards(logits, target_items, is_buy)\n\t\t\t\t\t\t# true_next_state, true_next_state_len = self.state_trans(rewards, state, next_state, len_state, len_next_states)\n\t\t\t\t\telif self.args.reward == 'loss':\n\t\t\t\t\t\t# ce_loss = sess.run(self.target_agent.ce_loss, feed_dict={\n\t\t\t\t\t\t# \tself.target_agent.inputs: state, \n\t\t\t\t\t\t# \tself.target_agent.len_state: len_state,\n\t\t\t\t\t\t# \tself.target_agent.actor_out_: actions,\n\t\t\t\t\t\t# \t# self.target_agent.actions: actions,\n\t\t\t\t\t\t# \tself.target_agent.target_items: target_items,\n\t\t\t\t\t\t# \tself.target_agent.is_training: False})\n\t\t\t\t\t\trewards = loss_reward(ce_loss)\n\t\t\t\t\telif self.args.reward == 'hit':\n\t\t\t\t\t\t# target logits\n\t\t\t\t\t\tlogits = sess.run(self.target_agent.logits, feed_dict={\n\t\t\t\t\t\t\tself.target_agent.inputs: state, \n\t\t\t\t\t\t\tself.target_agent.len_state: len_state,\n\t\t\t\t\t\t\tself.target_agent.actor_out_: actions,\n\t\t\t\t\t\t\t# self.target_agent.actions: actions,\n\t\t\t\t\t\t\tself.target_agent.is_training: False})\n\t\t\t\t\t\trewards = hit_reward(self.args, logits, target_items)\n\t\t\t\t\t\t# true_next_state, true_next_state_len = self.state_trans(rewards, state, next_state, len_state, len_next_states)\n\n\t\t\t\t\ttarget_v = sess.run(self.target_agent.critic_output, feed_dict={\n\t\t\t\t\t\tself.target_agent.inputs: next_state,\n\t\t\t\t\t\tself.target_agent.len_state: len_next_states,\n\t\t\t\t\t\t# self.target_agent.inputs: true_next_state,\n\t\t\t\t\t\t# self.target_agent.len_state: true_next_state_len,\n\n\t\t\t\t\t\tself.target_agent.is_training: False})\n\t\t\t\t\ttarget_v = target_v.squeeze()\n\t\t\t\t\tfor index in range(self.args.batch_size):\n\t\t\t\t\t\tif is_done[index]:\n\t\t\t\t\t\t\ttarget_v[index] = 0.0\n\n\t\t\t\t\tcritic_loss, _ = sess.run([self.main_agent.critic_loss, \n\t\t\t\t\t\tself.main_agent.critic_optim], \n\t\t\t\t\t\tfeed_dict={self.main_agent.inputs:state, \n\t\t\t\t\t\tself.main_agent.len_state:len_state,\n\t\t\t\t\t\tself.main_agent.actor_out_: actions, \n\t\t\t\t\t\tself.main_agent.reward: rewards,\n\t\t\t\t\t\tself.main_agent.discount: discount,\n\t\t\t\t\t\tself.main_agent.target: target_v,\n\t\t\t\t\t\tself.main_agent.is_training: True})\n\t\t\t\t\tactor_loss, _ = sess.run([self.main_agent.actor_loss, self.main_agent.actor_optim],\n\t\t\t\t\t\tfeed_dict={self.main_agent.inputs: state, \n\t\t\t\t\t\tself.main_agent.len_state: len_state,\n\t\t\t\t\t\tself.main_agent.is_training: True})\n\t\t\t\t\tsess.run(self.target_network_update_ops)\t\t# update target net\n\n\t\t\t\t\ttotal_step += 1\n\t\t\t\t\tif (total_step == 1) or (total_step % 200 == 0):\n\t\t\t\t\t\taver_reward = round(np.array(rewards).mean().item(), 5)\n\t\t\t\t\t\tranking_model_loss, actor_loss, critic_loss = round(ranking_model_loss.item(), 5), round(actor_loss.item(), 5), round(critic_loss.item(), 5)\n\t\t\t\t\t\tinfo = f\"epoch:{i_epoch} Step:{total_step}, aver reward:{aver_reward}, ranking model loss:{ranking_model_loss}, actor loss:{actor_loss}, critic loss:{critic_loss}\"\n\t\t\t\t\t\tprint(info)\n\t\t\t\t\t\tlogging.info(info)\n\t\t\t\t\tif (total_step >= self.args.start_eval) and (total_step % self.args.eval_interval == 0):\n\t\t\t\t\t\tt1 = time.time()\n\t\t\t\t\t\t# debug\n\t\t\t\t\t\t# evaluate_with_actions(self.args, self.main_agent, sess, max_ndcg_and_epoch, total_step, logging)\n\t\t\t\t\t\tevaluate_multi_head(self.args, self.main_agent, sess, max_ndcg_and_epoch, total_step, logging)\n\t\t\t\t\t\tt2 = time.time()\n\t\t\t\t\t\tprint(f'Time:{t2 - t1}')\n\t\t\t\t\t\tlogging.info(f'Time:{t2 - t1}')\n\n\ndef main(args):\n\ttf.reset_default_graph()\n\tmain_agent = Agent(args, name='train', max_action=args.max_action)\n\ttarget_agent = Agent(args, name='target', max_action=args.max_action)\n\n\trun = Run(args, main_agent, target_agent)\n\trun.train()\n\n\ndef parse_args():\n\tbase_data_dir = '../../data/'\n\tparser = argparse.ArgumentParser(description=\"NextItNet DDPG\")\n\tparser.add_argument('--v', default=\"v\")\n\tparser.add_argument('--mode', default='valid')\n\tparser.add_argument('--seed', type=int, default=1)\n\tparser.add_argument('--base_log_dir', default=\"log/\")\n\tparser.add_argument('--base_data_dir', default=base_data_dir + 'kaggle-RL4REC')\n\tparser.add_argument('--topk', default='5,10,20')\n\n\tparser.add_argument('--epoch', type=int, default=100)\n\tparser.add_argument('--eval_interval', type=int, default=2000)\n\tparser.add_argument('--start_eval', type=int, default=2000)\n\tparser.add_argument('--eval_batch', type=int, default=10)\n\tparser.add_argument('--batch_size', type=int, default=256)\n\tparser.add_argument('--mlr', type=float, default=1e-3)\n\tparser.add_argument('--alr', type=float, default=5e-3)\n\tparser.add_argument('--clr', type=float, default=5e-3)\n\n\tparser.add_argument('--reward_top', type=int, default=20)\n\tparser.add_argument('--reward_click', type=float, default=1.0)\n\tparser.add_argument('--reward_buy', type=float, default=5.0)\n\n\tparser.add_argument('--max_iid', type=int, default=70851)\t# 0~70851\n\n\tparser.add_argument('--hidden_factor', type=int, default=64)\n\n\tparser.add_argument('--weight_decay', default=1e-4, type=float)\n\n\tparser.add_argument('--noise_var', type=float, default=0.01)\n\tparser.add_argument('--noise_clip', type=float, default=0.05)\n\tparser.add_argument('--tau', type=float, default=0.001)\n\tparser.add_argument('--gamma', type=float, default=0.5)\n\n\tparser.add_argument('--mem_ratio', type=float, default=0.2)\n\tparser.add_argument('--note', default=\"None......\")\n\tparser.add_argument('--cuda', default='0')\n\tparser.add_argument('--reward', default='ndcg')\n\n\tparser.add_argument('--atten_dropout_rate', type=float, default=0.1)\n\tparser.add_argument('--actor_layers', default=\"[]\")\n\tparser.add_argument('--critic_layers', default=\"[]\")\n\tparser.add_argument('--max_action', type=float, default=0.1)\n\n\tparser.add_argument('--init_r', type=float, default=-1.0)\n\tparser.add_argument('--buy_weight', type=float, default=5.0)\n\treturn parser.parse_args()\n\ndef init_log(args):\n\tif not os.path.exists(args.base_log_dir):\n\t\tos.makedirs(args.base_log_dir)\n\tstart = datetime.datetime.now()\n\tlogging.basicConfig(level = logging.INFO,\n\t\t\t\t\tfilename = args.base_log_dir + args.v + '-' + str(time.time()) + '.log',\n\t\t\t\t\tfilemode = 'a',\n\t\t\t\t\t)\n\tprint('start! '+str(start))\n\tlogging.info('start! '+str(start))\n\tlogging.info('Parameter:')\n\tlogging.info(str(args))\n\tlogging.info('\\n-------------------------------------------------------------\\n')\n\nif __name__ == '__main__':\n\targs = parse_args()\n\n\tos.environ['CUDA_VISIBLE_DEVICES'] = args.cuda\n\n\tif args.seed != -1:\n\t\trandom.seed(args.seed)\n\t\tnp.random.seed(args.seed)\n\t\ttf.set_random_seed(args.seed)\n\tinit_log(args)\n\tmain(args)","sub_path":"experiment/combineRL-TRFL/Kaggle/NexttNet_DDPG.py","file_name":"NexttNet_DDPG.py","file_ext":"py","file_size_in_byte":15396,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"462419649","text":"from django.shortcuts import render\nfrom django.db.models import Sum, Count\nfrom django.conf import settings\n\nfrom inventario.models import Articulos\nfrom inventario.choices import CATEGORIAS, ESTADO\n\nimport pygal \nimport os\n\ndef stats_dashboard(request):\n    if not Articulos.objects.all():\n        return render(request, 'reportes/stats_vacio.html')\n    else:\n        aggregate = Articulos.objects.aggregate(valor_sum=Sum('valor'), moi_sum=Sum('moi'))\n        total_art = Articulos.objects.all().count()\n        available = Articulos.objects.filter(disponible=True).count()\n        categories = Articulos.objects.values('categoria').annotate(Count('categoria')).order_by('-categoria__count')\n        categories_display = [{'cat':CATEGORIAS[c['categoria']][1], 'count': c['categoria__count']} for c in categories]\n        status = Articulos.objects.values('estado').annotate(Count('estado')).order_by('-estado__count')\n        status_display =  [{'status':ESTADO[s['estado']][1], 'count': s['estado__count']} for s in status]\n    \n        categories_chart = charts(categories_display) \n        status_chart = charts(status_display)\n\n        return render(request, 'reportes/stats_dashboard.html', {\n            'aggregate': aggregate,\n            'total_art': total_art,\n            'available': available,\n            'categories': categories_display,\n            'status': status_display,\n            'categories_chart': categories_chart,\n            'status_chart': status_chart,\n        })\n    \n\ndef charts(data):\n\n    config = pygal.Config()\n    css = os.path.join(settings.BASE_DIR, 'static/charts.css')\n    config.css.append('file://' + css)\n    pie_chart = pygal.Pie(disable_xml_declaration=True, config=config, legend_box_size=35, width=1100)\n    \n    for d in data:\n        values = list(d.values())\n        pie_chart.add(values[0], values[1])\n\n    chart = pie_chart.render()\n\n    return chart\n    ","sub_path":"reportes/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1909,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"43272403","text":"# coding: UTF-8\n\nimport lib_general as my_general\nimport customizer as my_cust\n\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout\nimport keras\n\n\nroot_path = my_general.root_path\ncurr_ticker = my_general.name_ticker\nspider = my_cust.spider\n\ncurr_path = root_path + 'Helper\\\\Classifier_politics_news\\\\'\n\n# ______________________________ Parser ______________________________\n\n\ndef get_html(url):\n    r = my_general.requests.get(url)\n    return r.text\n\n\ndef get_page_data(html, article_data):\n    soup = my_general.BeautifulSoup(html, 'lxml')\n    divs = soup.find('div', class_='list list-tags')\n    ads = divs.find_all('div', class_='list-item', limit=10)\n\n    article_data.clear()\n    title = ''\n    addit = ''\n    href = ''\n    time = ''\n    for ad in ads:\n        try:\n            div = ad.find('div', class_='list-item__content').find('a',\n                                                                   class_='list-item__title color-font-hover-only');\n            title = div.text;\n\n            href = div.get('href');\n\n            title_picture = ad.find('div', class_='list-item__content') \\\n                .find('a', class_='list-item__image') \\\n                .find('picture') \\\n                .find('img') \\\n                .get('title')\n            addit = title_picture;\n\n            time = ad.find('div', class_='list-item__info') \\\n                .find('div', class_='list-item__date').text\n            time = time.split(', ')\n            time = time[-1]\n\n            data = {'title': title, 'additionally': addit, 'href': href, 'time': time}\n            article_data.append(data)\n\n        except:\n            title = 'Error'\n            addit = 'Error'\n            href = 'Error'\n            time = 'Error'\n\n    return article_data\n\n\ndef main():\n    print(\"\\n__________________ Politic news __________________\\n\")\n\n    base_url = \"https://ria.ru/politics/\"\n    article_data = []\n    hash_news_p_n = []\n\n    url_gen = base_url\n    html = get_html(url_gen)\n    article_data = get_page_data(html, article_data)\n\n    # print(article_data.__len__())\n    file_name = 'politics_news'\n    my_general.write_data_json(article_data, curr_path, file_name)\n\n    # _________________________________________________________________________________\n\n    # Check on repeat\n\n    hash_news_p_n = my_general.read_data_json(curr_path, 'hash_news_p_n')\n\n    file_name = 'politics_news'\n    if my_general.md5(curr_path + file_name + '.json') == hash_news_p_n[0][\"hash\"]:\n        print(\"___ No the new politics news ___\")\n        return\n\n    # _________________________________________________________________________________\n\n    count_sentences = article_data.__len__()\n    count_words = 30\n    count_charters = 30\n\n    # _________________________________________________________________________________\n\n    # Creating list of news + to Lower Case + delete ',' and  '.'\n\n    file_name = 'politics_news'\n    news = my_general.read_data_json(curr_path, file_name)\n\n    listSpider_E_N = []\n    for item in news:\n        listSpider_E_N.append(item)\n\n    # print(listSpider_E_N)\n\n    reg = my_general.re.compile('[^а-яА-Я -]')\n\n    for obj in listSpider_E_N:\n        obj['title'] = obj['title'].lower()\n        obj['title'] = reg.sub('', obj['title'])\n        obj['additionally'] = obj['additionally'].lower()\n        obj['additionally'] = reg.sub('', obj['additionally'])\n        # print(obj.title, obj.additionally, obj.href, obj.time, sep=' ')\n\n    # _________________________________________________________________________________\n\n    # Deleting repeats hrefs\n\n    # print(listSpider_E_N[0].title,\n    #       listSpider_E_N[0].additionally,\n    #       listSpider_E_N[0].href,\n    #       listSpider_E_N[0].time,\n    #       sep=' ')\n\n    idx_1 = 0\n    idx_2 = 0\n    for idx_1 in range(1, len(listSpider_E_N) - 1):\n        ref_href = listSpider_E_N[idx_1]['href']\n        idx_2 = idx_1 + 1\n        for j in range(idx_2, len(listSpider_E_N) - 1):\n            if listSpider_E_N[j]['href'] == ref_href:\n                listSpider_E_N.remove(listSpider_E_N[j])\n\n    # print(listSpider_E_N[0].title,\n    #       listSpider_E_N[0].additionally,\n    #       listSpider_E_N[0].href,\n    #       listSpider_E_N[0].time,\n    #       sep=' ')\n\n    # _________________________________________________________________________________\n\n    # Normalization the list of news\n\n    morph = my_general.pymorphy2.MorphAnalyzer()\n\n    for obj in listSpider_E_N:\n        obj['title'] = (' '.join([morph.normal_forms(w)[0] for w in obj['title'].split()]))\n        obj['additionally'] = (' '.join([morph.normal_forms(w)[0] for w in obj['additionally'].split()]))\n\n    # _________________________________________________________________________________\n\n    # Read reference words from json file\n\n    # listParams_E_N = read_params_xlsx()\n    file_name = 'params'\n    listParams_E_N = my_general.read_data_json(curr_path, file_name)\n    # write_params_json(listParams_E_N)\n    # convert_json_to_xlsx()\n\n    # _________________________________________________________________________________\n\n    # Normalization reference words and rewrite json file\n    #\n    # morph = pymorphy2.MorphAnalyzer()\n    #\n    # newListParams_E_N = []\n    # for obj in listParams_E_N:\n    #     new_name = ' '.join([morph.normal_forms(w)[0] for w in obj.get('name').split()])\n    #     new_synonyms = ' '.join([morph.normal_forms(w)[0] for w in obj.get('synonyms').split()])\n    #     params = {'name': new_country, 'synonyms': new_synonyms, 'impact': item.get('impact')}\n    #     newListParams_E_N.append(params)\n    #\n    # write_params_json(newListParams_E_N)\n    # listParams_E_N = newListParams_E_N\n    #\n    # _________________________________________________________________________________\n\n    # Get only text information from title and additionally\n\n    newListSpider_E_N = []\n    time_news = []\n    for news in listSpider_E_N:\n        newListSpider_E_N.append(news['title'] + ' ' + news['additionally'])\n        time_news.append(news['time'])\n\n    listSpider_E_N = newListSpider_E_N\n\n    # _________________________________________________________________________________\n\n    # Transform to array words\n\n    listWords = []\n    for news in listSpider_E_N:\n        listWords.append(news.split())\n\n    # _________________________________________________________________________________\n\n    # Delete to array words\n\n    for sentence in listWords:\n        # print(sentence)\n        for word in sentence:\n            p = morph.parse(word)[0]\n            if (p.tag.POS == 'ADVB') or\\\n               (p.tag.POS == 'NPRO') or\\\n               (p.tag.POS == 'PRED') or\\\n               (p.tag.POS == 'PREP') or\\\n               (p.tag.POS == 'CONJ') or\\\n               (p.tag.POS == 'PRCL') or\\\n               (p.tag.POS == 'INTJ'):\n                sentence.remove(word)\n        # print(sentence)\n    # _________________________________________________________________________________\n\n    # Transform to digital mode\n\n    # print(listWords[0][0])\n\n    newListWords = []\n    listWordsToNN = my_general.np.zeros((count_sentences, count_words, count_charters))\n\n    idx_sentence = 0\n    for sentence in listWords:\n        idx_word = 0\n        for word in sentence:\n            new_word = []\n            idx_charter = 0\n            for charter in word:\n                idx = 0;\n                # numbers\n                for i in range(48, 57 + 1):\n                    if charter == chr(i):\n                        idx = i\n                        new_word.append(i)\n                # Latin uppers\n                for i in range(65, 90 + 1):\n                    if charter == chr(i):\n                        idx = i\n                        new_word.append(i)\n                # Latin downs\n                for i in range(97, 122 + 1):\n                    if charter == chr(i):\n                        idx = i\n                        new_word.append(i)\n                # Cyrillic\n                for i in range(1072, 1103 + 1):\n                    if charter == chr(i):\n                        idx = i\n                        new_word.append(i)\n\n                listWordsToNN[idx_sentence][idx_word][idx_charter] = idx\n                idx_charter = idx_charter + 1\n\n            idx_word = idx_word + 1\n            newListWords.append(new_word)\n\n        idx_sentence = idx_sentence + 1\n\n    # print(newListWords)\n\n    # print(listWordsToNN[0])\n\n    # _________________________________________________________________________________\n\n    # Prepare weights\n    # Finding reference words to array words\n\n    # _________________________________________________________________________________\n\n    # For Trainging NN\n\n    # _________________________________________________________________________________\n\n    # future_weigths = np.zeros(length_sentence, dtype=float)\n    list_future_weigths = my_general.np.zeros((len(listWords), count_words), dtype=float)\n\n    idx_word = 0\n    idx_sentence = 0\n    for header in listWords:\n        # print(header)\n        for obj in header:\n            # print(obj.lower())\n            for params in listParams_E_N:\n                if my_general.fuzz.ratio(params.get('name'), obj.lower()) > 90:\n                    # print(\"I found of name! --->>> \" + str(obj))\n                    list_future_weigths[idx_sentence][idx_word] = float(params.get('impact'))\n                    break\n                else:\n                    if len(params.get('synonyms')) >= 1:\n                        for it in params.get('synonyms'):\n                            if my_general.fuzz.ratio(str(it), str(obj.lower())) > 80:\n                                # print(\"I found of synonyms! --->>> \" + str(obj.lower()))\n                                list_future_weigths[idx_sentence][idx_word] = float(params.get('impact'))\n                                break\n            idx_word = idx_word + 1\n        idx_word = 0\n        idx_sentence = idx_sentence + 1\n\n    # print(list_future_weigths[len(listWords) - 2])\n    # print(list_future_weigths)\n    # _________________________________________________________________________________\n\n    # Appending feature of applicants to list to json file\n    # 1 day for remove from applicants.json\n    # 240 it's 50% <- 1 day - 24 hours - 48 query * 10 news\n    # 384 it's 80% <- 1 day - 24 hours - 48 query * 10 news\n    # 3 day for appending to params.json\n\n    border = 100\n\n    idx_word = 0\n    idx_sentence = 0\n    for header in listWords:\n        # print(header)\n        for obj in header:\n            if list_future_weigths[idx_sentence][idx_word] == 0:\n                file_name = 'applicants'\n                feature_list_applicants = my_general.read_data_json(curr_path, file_name)\n\n                # find to feature_list_applicants obj\n                success = 0\n                # Increase count\n                for item in feature_list_applicants:\n                    # print(item[\"name\"], item[\"count\"], sep=' ')\n                    if obj == item[\"name\"]:\n                        item[\"count\"] = item[\"count\"] + 1\n                        # print(\"I found of name! --->>> \" + str(item[\"count\"]))\n                        file_name = 'applicants'\n                        my_general.write_data_json(feature_list_applicants, curr_path, file_name)\n                        success = 1\n\n                        if item[\"count\"] >= border:\n                            rng = my_general.np.random.default_rng()\n                            file_name = 'params'\n                            list_params = my_general.read_data_json(curr_path, file_name)\n\n                            list_params.append({\"name\": item[\"name\"],\n                                                \"synonyms\": [\"\"],\n                                                \"impact\": (rng.random() - 0.5)\n                                                })\n                            file_name = 'params'\n                            my_general.write_data_json(list_params, curr_path, file_name)\n                            feature_list_applicants.remove(item)\n\n                            file_name = 'applicants'\n                            my_general.write_data_json(feature_list_applicants, curr_path, file_name)\n\n                        break\n                # Add new feature\n                if success == 0:\n                    new_feature_applicant = {\"name\": obj, \"count\": 1}\n                    feature_list_applicants.append(new_feature_applicant)\n                    file_name = 'applicants'\n                    my_general.write_data_json(feature_list_applicants, curr_path, file_name)\n                    # print(obj)\n\n            idx_word = idx_word + 1\n        idx_word = 0\n        idx_sentence = idx_sentence + 1\n\n\n    # feature_list_applicants.append()\n\n    # ______________________________ NN ______________________________\n\n    # logging.basicConfig(level=logging.DEBUG)\n\n    # curr_day = datetime.date(2020, 1, 1)\n    curr_day = my_general.datetime.date(my_general.datetime.datetime.now().year,\n                                    my_general.datetime.datetime.now().month,\n                                    my_general.datetime.datetime.now().day)\n    # print(curr_day)\n    exporter = my_general.Exporter()\n    data = exporter.lookup(name=curr_ticker, market=my_general.Market.ETF_MOEX)\n    # print(data.head())\n    stock = exporter.download(data.index[0], market=my_general.Market.ETF_MOEX, start_date=curr_day)\n    # print(stock.head())\n\n    file_name = curr_path + 'stocks_' + str(curr_ticker) + '.csv'\n    stock.to_csv(file_name)\n\n    time_value = stock.get('<TIME>')\n    open_value = stock.get('<OPEN>')\n    close_value = stock.get('<CLOSE>')\n    high_value = stock.get('<HIGH>')\n    low_value = stock.get('<LOW>')\n    volume_value = stock.get('<VOL>')\n\n    # plt.plot(time_value, low_value)\n    # close_value.plot()\n    # high_value.plot()\n    # low_value.plot()\n    # volume_value.plot()\n    # plt.show()\n\n    list_time_value = time_value.to_list()\n    list_open_value = open_value.to_list()\n    list_close_value = close_value.to_list()\n    list_high_value = high_value.to_list()\n    list_low_value = low_value.to_list()\n    list_volume_value = volume_value.to_list()\n\n    listOpenValuesToNN = []\n    listCloseValuesToNN = []\n    listHighValuesToNN = []\n    listLowValuesToNN = []\n    listVolumeValuesToNN = []\n    listTimePointsToNN = []\n    list_distances = []\n    list_distances.append(0)\n\n    for dt_news in time_news:\n        for dt in list_time_value:\n            regex = r\":00$\"\n            frame_minute = str(dt)\n            matches = my_general.re.findall(regex, frame_minute)\n            frame_minute = frame_minute.replace(matches[0], '')\n\n            if len(frame_minute) < 3:\n                frame_minute = frame_minute + ':00'\n\n            if dt_news == frame_minute:\n                listTimePointsToNN.append(dt)\n                listOpenValuesToNN.append(list_open_value[list_time_value.index(dt)])\n                listCloseValuesToNN.append(list_close_value[list_time_value.index(dt)])\n                listHighValuesToNN.append(list_high_value[list_time_value.index(dt)])\n                listLowValuesToNN.append(list_low_value[list_time_value.index(dt)])\n                listVolumeValuesToNN.append(list_volume_value[list_time_value.index(dt)])\n                break\n\n            # print(frame_minute)\n\n    listOpenValuesToNN.reverse()\n    listCloseValuesToNN.reverse()\n    listHighValuesToNN.reverse()\n    listLowValuesToNN.reverse()\n    listVolumeValuesToNN.reverse()\n    listTimePointsToNN.reverse()\n\n    time_point = list_time_value[0]\n    listOpenValuesToNN.insert(0, list_open_value[list_time_value.index(time_point)])\n    listCloseValuesToNN.insert(0, list_open_value[list_time_value.index(time_point)])\n    listHighValuesToNN.insert(0, list_open_value[list_time_value.index(time_point)])\n    listLowValuesToNN.insert(0, list_open_value[list_time_value.index(time_point)])\n    listVolumeValuesToNN.insert(0, list_open_value[list_time_value.index(time_point)])\n    listTimePointsToNN.insert(0, time_point)\n\n    # print(listWordsToNN)\n    # print(listOpenValuesToNN)\n\n    if len(listOpenValuesToNN) > 0:\n        # Morning\n        if len(listOpenValuesToNN) < 10:\n            size = 10 - len(listOpenValuesToNN)\n            firstValue = listOpenValuesToNN[0]\n            for item in range(0, size):\n                listOpenValuesToNN.insert(0, firstValue)\n\n            for idx in range(0, len(listTimePointsToNN) - 1):\n                curr_i = str(listTimePointsToNN[idx])\n                next_i = str(listTimePointsToNN[idx + 1])\n                list_distances.append(int(next_i.replace(':', '')) - int(curr_i.replace(':', '')))\n\n            # print(sum(list_distances))\n\n        # print(listOpenValuesToNN)\n        # print(len(listOpenValuesToNN))\n\n        listOpenValuesToNN.insert(0, listOpenValuesToNN[0])\n        listCloseValuesToNN.insert(0, listCloseValuesToNN[0])\n        listHighValuesToNN.insert(0, listHighValuesToNN[0])\n        listLowValuesToNN.insert(0, listLowValuesToNN[0])\n        listVolumeValuesToNN.insert(0, listVolumeValuesToNN[0])\n        listTimePointsToNN.insert(0, listTimePointsToNN[0])\n\n        listTrueValue = my_general.list_true_value(listOpenValuesToNN)\n        # print(listTrueValue)\n        # print(len(listTrueValue))\n\n        # задаем для воспроизводимости результатов\n        my_general.np.random.seed(2)\n        model_name = curr_path + 'NN_model.h5'\n\n        # создаем модели, добавляем слои один за другим\n        model = Sequential()\n        model.add(Dense(5 * count_words, input_dim=(count_words * count_charters), activation='relu'))  # 0\n        model.add(Dense(4 * count_words, activation='relu'))    # 1\n        model.add(Dense(3 * count_words, activation='tanh'))    # 2\n        model.add(Dense(2 * count_words, activation='tanh'))    # 3\n        model.add(Dense(count_words, activation='tanh'))        # 4\n        model.add(Dense(count_words - 10, activation='sigmoid'))\n        model.add(Dropout(0.2))\n        model.add(Dense(count_words - 20, activation='sigmoid'))\n        model.add(Dropout(0.2))\n        model.add(Dense(count_words - 25, activation='sigmoid'))\n        model.add(Dense(count_words - 27, activation='sigmoid'))\n        model.add(Dense(1, activation='sigmoid'))\n\n\n        number_layer_words = 5\n        native_weights = model.layers[number_layer_words].get_weights()[0]  # 0 - weights\n        native_biases = model.layers[number_layer_words].get_weights()[1]   # 1 - biases\n\n        # print(\"Old\")\n        # print(len(native_weights))\n\n        new_weights = my_general.np.zeros((len(native_weights), len(native_weights[0])), dtype=float)\n        for future_news in list_future_weigths:\n            idx_1 = 0\n            for weights in native_weights:\n                add = future_news[idx_1]\n                idx_2 = 0\n\n                for weight in weights:\n                    new_weights[idx_1][idx_2] = float(weight + add)\n                    idx_2 = idx_2 + 1\n\n                idx_1 = idx_1 + 1\n\n            # print(\"New\")\n            # print(len(new_weights))\n            keras_weights = [new_weights, native_biases]\n            model.layers[number_layer_words].set_weights(keras_weights)\n\n            # компилируем модель, используем градиентный спуск adam\n            model.compile(loss=\"mean_squared_error\", optimizer=\"rmsprop\", metrics=['accuracy'])\n\n            X = []\n\n            for news in listWordsToNN:\n                # разбиваем датасет на матрицу параметров (X) и вектор целевой переменной (Y)\n                one_sentence_news = news.ravel()\n\n                X.append(one_sentence_news)\n\n            X = my_general.np.asarray(X, dtype=my_general.np.float32)\n            Y = my_general.np.asarray(listTrueValue, dtype=my_general.np.float32)\n\n            if my_general.os.path.exists(model_name) != False:\n                # Recreate the exact same model\n                new_model = keras.models.load_model(model_name)\n            else:\n                new_model = model\n\n            try:\n                # обучаем нейронную сеть\n                history = new_model.fit(X, Y, epochs=1, batch_size=64)\n\n                # Export the model to a SavedModel\n                new_model.save(model_name)\n\n                # # evaluate the model\n                # scores = model.evaluate(X, Y)\n                # print(\"\\n%s: %.2f%%\" % (model.metrics_names[1], scores[1] * 100))\n\n                # оцениваем результат\n                scores = new_model.predict(X)\n                print(\"\\n%s: %.2f%%\" % (new_model.metrics_names[1], scores[1] * 100))\n\n                print(scores)\n                prediction = {\"score\": float(scores[-1])}\n                print(prediction)\n\n                file_name_prediction = 'prediction_p_n'\n\n                my_general.write_data_json(prediction, curr_path, file_name_prediction)\n\n            except:\n                print(\"Problem with – fit(C_P_N)!\")\n\n    hash_news_p_n = [{\"hash\": my_general.md5(curr_path + 'politics_news' + '.json')}]\n\n    file_name = 'hash_news_p_n'\n    my_general.write_data_json(hash_news_p_n, curr_path, file_name)\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"Classifier_politics_news/Classifier_p_n.py","file_name":"Classifier_p_n.py","file_ext":"py","file_size_in_byte":21356,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"596986289","text":"# -*- coding:utf-8 -*-\n\"\"\"\n    处理ip相关的逻辑\n    调用淘宝的api\n    返回：\n    {\n        code: 0,\n        data: {\n            country: \"中国\",\n            country_id: \"CN\",\n            area: \"华北\",\n            area_id: \"100000\",\n            region: \"北京市\",\n            region_id: \"110000\",\n            city: \"北京市\",\n            city_id: \"110000\",\n            county: \"\",\n            county_id: \"-1\",\n            isp: \"电信\",\n            isp_id: \"100017\",\n            ip: \"106.37.200.106\"\n    }\n}\n\"\"\"\n__author__ = 'liuzongrun'\n\n\nimport requests\nimport json\nfrom base.db_operation.main_db_op import get_ip_info_db, create_ip_obj_db, update_user_in_ip_info\nfrom base.tool.network import get_user_from_request\n\n\nIP_INFO_API = \"http://ip.taobao.com/service/getIpInfo.php?ip=%s\"\n\n\ndef get_ip_from_request(request):\n    \"\"\"\n        从request请求中提取访问ip\n    \"\"\"\n    x_forwarded_for = request.META.get('HTTP_X_FORWARDED_FOR')\n    if x_forwarded_for:\n        ip = x_forwarded_for.split(',')[-1].strip()\n    else:\n        ip = request.META.get('REMOTE_ADDR', '0.0.0.0')\n    return ip\n\n\ndef get_ip_info(ip):\n    \"\"\"\n        从淘宝api中获取IP信息\n    \"\"\"\n    ip_info = get_ip_info_db(ip)\n    if ip_info:\n        return ip_info\n    else:\n        url = IP_INFO_API % ip\n        ret = requests.get(url)\n        if ret.status_code == 200:\n            taobao_ip_info = json.loads(ret.content)['data']\n            return create_ip_obj_db(\n                ip=taobao_ip_info['ip'],\n                country=taobao_ip_info['country'],\n                area=taobao_ip_info['area'],\n                province=taobao_ip_info['region'],\n                city=taobao_ip_info['city'],\n                county=taobao_ip_info['county'],\n                isp=taobao_ip_info['isp'])\n        else:\n            return None\n\n\ndef get_city_from_request(request):\n    \"\"\"\n        根据访问请求里的ip获取城市信息\n    \"\"\"\n    ip = get_ip_from_request(request)\n    ip_info = get_ip_info(ip)\n    user = get_user_from_request(request)\n    ip_info = update_user_in_ip_info(ip_info, user)\n    return ip_info.city","sub_path":"base/backend/ip_handler.py","file_name":"ip_handler.py","file_ext":"py","file_size_in_byte":2130,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"15184473","text":"\"\"\"\r\nEvent loop module\r\n\r\nThe purpose of this module is creating a basic event loop for the chess game,\r\nwhich can also speed up the game\r\n\"\"\"\r\n\r\nimport itertools\r\n\r\nfrom tools import getcoords, getindex\r\nimport globalstate\r\n\r\n\r\nclass BasicEventHandler(object):\r\n    \"\"\"Basic event loop for ChessGame\"\"\"\r\n\r\n    def __init__(self, frame, board):\r\n        self.frame_handler = frame\r\n        self._prev_round = globalstate.turn\r\n        self.board = board\r\n\r\n    def round_event(self):\r\n        color = 'white' if globalstate.turn % 2 == 0 else 'black'\r\n        pieces = list()\r\n        for field in self.board:\r\n            if field is not None and field.color == color:\r\n                pieces.append(None for _ in field)\r\n        if sum(1 for piece in pieces for _ in piece):\r\n            if self.board.ischecked(*self.board.find_king(color), color=color):\r\n                ...\r\n            else:\r\n                ...\r\n        else:\r\n            return True\r\n\r\n\r\nclass EventHandler(BasicEventHandler):\r\n    \"\"\"Advanched event handler for ChessGame. Upgrades performance\r\n    when requesting lots of moves many times (Small performance upgrade\r\n    late in the game)\"\"\"\r\n    \r\n    def round_event(self):\r\n        color = 'white' if globalstate.turn % 2 == 0 else 'black'\r\n        pieces = dict()\r\n        pieces_ = list()\r\n        for field in self.board:\r\n            if field is not None and field.color == color:\r\n                moves_1, moves_2 = itertools.tee(\r\n                    ((x, y, action) for x, y, action in self.board.find_possible_moves(field))\r\n                )\r\n                pieces[getindex(field.x, field.y)] = moves_1\r\n                pieces_.append(moves_2)\r\n        if not sum(1 for piece in pieces_ for _ in piece):\r\n            # No possible moves\r\n            if self.board.ischecked(*self.board.find_king(color), color=color):\r\n                # Mat\r\n                return [1]\r\n            else:\r\n                # Pat\r\n                return [0]\r\n        else:\r\n            # Just regular moving, just return the possible moves\r\n            return pieces\r\n                        \r\n                \r\n        \r\n            \r\n","sub_path":"eventhandler.py","file_name":"eventhandler.py","file_ext":"py","file_size_in_byte":2158,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"131580728","text":"# seed the pseudorandom number generator\nfrom random import seed\nfrom random import randint\nimport time\nfrom os import system\n\nseed(1)\nbase_call = \"http://0.0.0.0:5000\"\n\ntry:\n    while True:\n        host_int = randint(0,8)\n        call_int = randint(0,4)\n        \n        if(host_int == 0):\n            host = \"Nils_Styger\"\n        elif(host_int == 1):\n            host = \"Lady_of_All_Sorrows\"\n        elif (host_int == 2):\n            host = \"Dr._Rory_Campbell\"\n        elif (host_int == 3):\n            host = \"Liz_Allan\"\n        elif (host_int == 4):\n            host = \"Bart_Gallows\"\n        elif (host_int == 5):\n            host = \"Thomas_Halloway\"\n        elif (host_int == 6):\n            host = \"Aaron_Nicholson\"\n        elif (host_int == 7):\n            host = \"Peter_Parker\"\n        elif (host_int == 8):\n            host = \"Ant-Man\"\n        else:\n            host =\"Squadron_Supreme\"\n            \n        \n        if(call_int == 0):\n            call = base_call + \"/join_a_guild/\" + str(randint(0,39))\n        elif(call_int == 1):\n            call = base_call + \"/kill_enemy/\" + str(randint(0,49))\n        elif(call_int == 2):\n            call = base_call + \"/take_damage/\" + str(randint(0,8))\n        elif(call_int == 3):\n            call = base_call + \"/accepted_a_quest/\"+ str(randint(0,14))\n        else:\n            call = base_call + \"/transaction/\" + str(randint(0,14))\n            \n            \n            \n        system(\"docker-compose exec mids ab -n 1 -H \" +\n               \"\\\"\" +host +  \"\\\" \" +\n               call)\n        \n        time.sleep(0.5)\n        \nexcept KeyboardInterrupt:\n    print('interrupted!')","sub_path":"project_3/api_call_script.py","file_name":"api_call_script.py","file_ext":"py","file_size_in_byte":1635,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"26136306","text":"#!/usr/bin/python\n\nimport sys\nimport re\nimport csv\nimport traceback\nimport operator\n\nimport sqlite3\n\nimport logging\n\nimport traceback\n\nimport argparse\n\nimport gotolong.cutil.cutil\n\nfrom prettytable import PrettyTable\n\nfrom gotolong.database.database import *\n\n\nclass Amfi(Database):\n    def __init__(self):\n        super(Amfi, self).__init__()\n        # isin number\n        self.amfi_isin_list = []\n        self.amfi_ticker_list = []\n        self.amfi_captype_list = []\n        # serial number\n        self.amfi_cname = {}\n        self.amfi_mcap = {}\n        self.amfi_rank = {}\n        self.amfi_captype = {}\n        self.amfi_ticker_isin_dict = {}\n        self.amfi_isin_ticker_dict = {}\n        self.debug_level = 0\n        self.amfi_table_truncate = False\n        self.amfi_table_name = \"global_amfi\"\n        self.amfi_table_dict = {\n            \"comp_rank\": \"int\",\n            \"comp_name\": \"text\",\n            \"comp_isin\": \"text\",\n            \"bse_symbol\": \"text\",\n            \"nse_symbol\": \"text\",\n            \"avg_mcap\": \"text\",\n            \"cap_type\": \"text\",\n        }\n\n    def set_log_level(self, log_level):\n        log_level_number = getattr(logging, log_level.upper(), None)\n        logging.basicConfig(level=log_level_number)\n\n    def amfi_table_reload(self, truncate=False):\n        self.amfi_table_truncate = truncate\n\n    def amfi_load_row(self, row):\n        try:\n            row_list = row\n            if len(row_list) == 0:\n                logging.info('ignored empty row %s', row_list)\n                return\n\n            comp_rank = row_list[0]\n            if comp_rank == 'Sr. No.' or comp_rank == \"sr_no\":\n                logging.info('skipped header line %s', row_list)\n                return\n\n            # mysql - automatically number\n            if self.config_db_type != 'mariadb':\n                comp_rank = gotolong.cutil.cutil.get_number(comp_rank)\n\n            comp_name = row_list[1]\n            isin_number = row_list[2]\n            bse_ticker = row_list[3].upper().strip()\n            nse_ticker = row_list[4]\n            if self.config_db_type == 'mariadb':\n                avg_mcap = row_list[5]\n            else:\n                avg_mcap = gotolong.cutil.cutil.get_number(row_list[5])\n            captype = row_list[6].strip()\n\n            comp_name = gotolong.cutil.cutil.normalize_comp_name(comp_name)\n\n            logging.debug('comp_rank : %s isin_number: %s bse_ticker : %s nse_ticker : %s avg_mcap : %s captype : %s'\n                          'comp_name : %s', comp_rank, isin_number, bse_ticker, nse_ticker, avg_mcap, captype,\n                          comp_name)\n\n            if nse_ticker == '':\n                if bse_ticker != '':\n                    self.amfi_rank[bse_ticker] = comp_rank\n                    self.amfi_ticker_isin_dict[bse_ticker] = isin_number\n                    self.amfi_isin_ticker_dict[isin_number] = nse_ticker\n                    self.amfi_cname[bse_ticker] = comp_name\n                    self.amfi_mcap[bse_ticker] = avg_mcap\n                    self.amfi_captype[bse_ticker] = captype\n                    self.amfi_isin_list.append(isin_number)\n                    self.amfi_ticker_list.append(bse_ticker)\n            else:\n                self.amfi_rank[nse_ticker] = comp_rank\n                self.amfi_ticker_isin_dict[nse_ticker] = isin_number\n                self.amfi_isin_ticker_dict[isin_number] = nse_ticker\n                self.amfi_cname[nse_ticker] = comp_name\n                self.amfi_mcap[nse_ticker] = avg_mcap\n                self.amfi_captype[nse_ticker] = captype\n                self.amfi_isin_list.append(isin_number)\n                self.amfi_ticker_list.append(nse_ticker)\n\n            if captype not in self.amfi_captype_list:\n                self.amfi_captype_list.append(captype)\n\n            logging.debug('comp_name : %s isin_number: %s', comp_name, isin_number)\n\n        except IndexError:\n            logging.error('except %s', row)\n        except:\n            logging.error('except %s', row)\n            traceback.print_exc()\n\n    def amfi_store_data_to_db(self, in_filename):\n        table = self.amfi_table_name\n\n        if self.amfi_table_truncate:\n            self.db_table_truncate(table)\n\n        # row_count = self.count_amfi_db(table)\n        row_count = self.db_table_count_rows(table)\n        if row_count == 0:\n            self.amfi_insert_data(in_filename)\n        else:\n            logging.info('amfi data already loaded in db %d', row_count)\n\n    def amfi_get_insert_row(self, line, row_bank):\n\n        # split on comma\n        line = re.sub(',\",', ',', line)\n        line = re.sub('\"', '', line)\n        row_list = line.split(',')\n\n        logging.debug('row_list %s', row_list)\n        logging.debug('len row_list %d', len(row_list))\n\n        (comp_rank, comp_name, comp_isin, bse_symbol, nse_symbol, avg_mcap, cap_type) = row_list\n\n        # remove new line character from end\n        cap_type = cap_type.strip('\\n')\n\n        if comp_rank == 'Sr. No.' or comp_rank == \"sr_no\":\n            logging.info('skipped header line %s', row_list)\n            logging.info('len row_list %d', len(row_list))\n            return\n\n        # remove any un-required stuff\n        new_row = (comp_rank, comp_name, comp_isin, bse_symbol, nse_symbol, avg_mcap, cap_type)\n        row_bank.append(new_row)\n\n    def amfi_insert_data(self, in_filename):\n\n        create_sql = gotolong.cutil.cutil.get_create_sql(self.amfi_table_name, self.amfi_table_dict)\n        insert_sql = gotolong.cutil.cutil.get_insert_sql(self.amfi_table_name, self.amfi_table_dict)\n\n        cursor = self.db_conn.cursor()\n        with open(in_filename, 'rt') as csvfile:\n            # future\n            # insert row\n            row_bank = []\n            for line in csvfile:\n                self.amfi_get_insert_row(line, row_bank)\n            logging.info('loaded entries %s', len(row_bank))\n            logging.info('from %s', in_filename)\n            # insert row\n            cursor.executemany(insert_sql, row_bank)\n            # commit db changes\n            self.db_conn.commit()\n\n    def amfi_load_data_from_db(self):\n        table = self.amfi_table_name\n        cursor = self.db_table_load(table)\n        for row in cursor.fetchall():\n            logging.debug('%s', row)\n            self.amfi_load_row(row)\n\n    def amfi_dump(self, pretty_dump, out_filename):\n\n        logging.info('output filename %s', out_filename)\n\n        pt = PrettyTable()\n        pt.field_names = [\"amfi_rank\", \"amfi_cname\", \"amfi_isin\", \"amfi_ticker\", \"amfi_mcap\", \"amfi_captype\"]\n\n        fh = open(out_filename, \"w\")\n\n        dump_first_rec = True\n        for ticker in sorted(self.amfi_rank, key=self.amfi_rank.__getitem__):\n            if dump_first_rec:\n                dump_first_rec = False\n                logging.debug('ticker  %s', ticker)\n\n            row_list = [str(self.amfi_rank[ticker])]\n            row_list.append(self.amfi_cname[ticker])\n            row_list.append(self.amfi_ticker_isin_dict[ticker])\n            row_list.append(ticker)\n            row_list.append(str(self.amfi_mcap[ticker]))\n            row_list.append(self.amfi_captype[ticker])\n\n            pt.add_row(row_list)\n\n        if pretty_dump:\n            fh.write(str(pt))\n            fh.close()\n\n    def amfi_export(self, export_to_file, out_filename):\n\n        logging.info('output filename %s', out_filename)\n\n        field_names = [\"amfi_rank\", \"amfi_cname\", \"amfi_isin\", \"amfi_ticker\", \"amfi_mcap\", \"amfi_captype\"]\n\n        if export_to_file:\n            fh = open(out_filename, \"w\")\n            p_hdr = ','.join(field_names)\n            p_hdr += '\\n'\n            fh.write(p_hdr)\n\n        dump_first_rec = True\n        for ticker in sorted(self.amfi_rank, key=self.amfi_rank.__getitem__):\n            if dump_first_rec:\n                logging.debug('ticker  %s', ticker)\n                dump_first_rec = False\n\n            row_list = [str(self.amfi_rank[ticker])]\n            row_list.append(self.amfi_cname[ticker])\n            row_list.append(self.amfi_ticker_isin_dict[ticker])\n            row_list.append(ticker)\n            row_list.append(str(self.amfi_mcap[ticker]))\n            row_list.append(self.amfi_captype[ticker])\n\n            p_str = ','.join(row_list)\n            p_str += '\\n'\n\n            if export_to_file:\n                fh.write(p_str);\n\n        if export_to_file:\n            fh.close()\n\n    def amfi_get_ticker_by_name(self, req_name):\n        if req_name is None:\n            print('req_name', ':', req_name, ':')\n            return '-none-'\n\n        req_name = re.sub('\\s+', ' ', req_name).strip()\n        for amfi_ticker in sorted(self.amfi_cname):\n            # try to find a matching company\n            comp_name = self.amfi_cname[amfi_ticker]\n            comp_name = comp_name.strip()\n            if re.match(req_name, comp_name) or req_name.upper() == amfi_ticker:\n                logging.debug('found match : name : %s', req_name)\n                return amfi_ticker\n\n        logging.debug('amfi : comp not found : req_name : %s', req_name)\n        return ''\n\n    def amfi_get_value_by_isin(self, isin, value_name):\n        # return ticker\n        if value_name == \"ticker\":\n            return self.amfi_get_ticker_by_isin(isin)\n        try:\n            ticker = self.amfi_isin_ticker_dict[isin]\n            if ticker:\n                self.amfi_get_value_by_ticker(self, isin, value_name)\n        except KeyError:\n            logging.error('KeyError ', isin)\n            traceback.print_exc()\n        return 'UNK_COMP_2'\n\n    def amfi_get_value_by_ticker(self, ticker, value_name):\n        try:\n            if ticker:\n                if ticker == 'UNK_TICKER':\n                    return 'UNK'\n                if value_name == \"cname\":\n                    if ticker in self.amfi_cname:\n                        return self.amfi_cname[ticker]\n                    else:\n                        return 'UNK_cname'\n                if value_name == \"mcap\":\n                    if ticker in self.amfi_mcap:\n                        return self.amfi_mcap[ticker]\n                    else:\n                        return 0\n                if value_name == \"rank\":\n                    if ticker in self.amfi_rank:\n                        return self.amfi_rank[ticker]\n                    else:\n                        return 0\n                if value_name == \"captype\":\n                    if ticker in self.amfi_captype:\n                        return self.amfi_captype[ticker]\n                    else:\n                        return 'UNK_captype'\n                if value_name == \"isin\":\n                    return self.amfi_get_isin_by_ticker(ticker)\n        except KeyError:\n            print('KeyError ', ticker)\n            traceback.print_exc()\n        except:\n            print('Except ', ticker)\n            traceback.print_exc()\n        if value_name == \"mcap\" or value_name == \"rank\":\n            return 0\n        else:\n            return 'UNK_COMP_E'\n\n    def amfi_get_ticker_by_isin(self, amfi_isin):\n        if amfi_isin in self.amfi_isin_list:\n            return self.amfi_isin_ticker_dict[amfi_isin]\n        return 'UNK_TICKER'\n\n    def amfi_get_isin_by_ticker(self, ticker):\n        try:\n            if ticker in self.amfi_ticker_isin_dict:\n                amfi_isin = self.amfi_ticker_isin_dict[ticker]\n                return amfi_isin\n        except KeyError:\n            print('KeyError ', ticker)\n            traceback.print_exc()\n        return 'UNK_ISIN'\n\n\ndef main():\n    print('in main')\n    print('inside amfi.py', sys.argv)\n\n    parser = argparse.ArgumentParser(description='Process arguments')\n    # dest= not required as option itself is the destination in args\n    parser.add_argument('-l', '--log_level', default='INFO', help='DEBUG|INFO|WARNING|ERROR|CRITICAL', type=str,\n                        choices=['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'])\n    parser.add_argument('-d', '--debug_level', default='0', help='debug level 0|1|2|3', type=int,\n                        choices=[0, 1, 2, 3])\n    parser.add_argument('-t', '--truncate_table', default='False', help='specify to truncate', action='store_true')\n    parser.add_argument('-i', '--in_files', required=True, nargs='+', dest='in_files', help='in files')\n    parser.add_argument('-o', '--out_files', required=True, nargs='+', dest='out_files', help='out files')\n    parser.add_argument('-s', '--save_to_db', default='True', help='save data to db', action='store_true')\n    parser.add_argument('-x', '--export_to_file', default='True', help='export data to file', action='store_true')\n    parser.add_argument('-p', '--pretty_dump', default='True', help='pretty dump', action='store_true')\n    args = parser.parse_args()\n\n    log_level = args.log_level\n    truncate_table = args.truncate_table\n    save_to_db = args.save_to_db\n    export_to_file = args.export_to_file\n    pretty_dump = args.pretty_dump\n\n    # dummy assignment\n    in_filename_phase = []\n    out_filename_phase = []\n    # use the argument as pattern\n    for index, filename in enumerate(args.in_files):\n        print('index = ', index, filename);\n        in_filename_phase.append(filename)\n\n    for index, filename in enumerate(args.out_files):\n        print('index = ', index, filename);\n        out_filename_phase.append(filename)\n\n    # Main caller\n    program_name = sys.argv[0]\n\n    if len(sys.argv) < 4:\n        print(\"usage: \" + program_name + \" <debug_level : 1-4> <amfi.csv> ... \")\n        sys.exit(1)\n\n    print('args :', len(sys.argv))\n\n    amfi = Amfi()\n\n    amfi.set_log_level(log_level)\n\n    if truncate_table:\n        amfi.amfi_table_reload(truncate_table)\n\n    if save_to_db:\n        amfi.amfi_store_data_to_db(in_filename_phase[0])\n\n    amfi.amfi_load_data_from_db()\n\n    amfi.amfi_export(export_to_file, out_filename_phase[0])\n\n    amfi.amfi_dump(pretty_dump, out_filename_phase[1])\n\n    print('end of main')\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"gotolong/amfi/amfi.py","file_name":"amfi.py","file_ext":"py","file_size_in_byte":13907,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"167639363","text":"'''\nunit test for cron monitor\n'''\n\nimport unittest\nfrom monitor.cron_monitor import cron_monitor as cm\n\n\n#global variables\nbase_path = '/tmp/'\ndb_name = 'cron_timestamp'\ndata_path = base_path + db_name\ntmp_path = '/tmp/tmp_cron'\n\nimport os.path\nclass Cron_Monitor_Test(unittest.TestCase):\n\t\t\n\tdef test_init_database(self):\n\t\tcm.init_database()\n\t\tself.assertEqual(os.path.exists(data_path), True)\n\t\t\n\t#register_entry(user, entry):\n\tdef test_register_entry(self):\n\t\tcm.register_entry('jasmine', '@@@ test cron @@@')\n\t\tref = cm.run_sql('select ref from crons where user=\"jasmine\" and cron=\"@@@ test cron @@@\"')\n\t\tcm.run_sql('delete from crons where user=\"jasmine\" and cron=\"@@@ test cron @@@\"')\n\t\tself.assertEqual(ref[0][0], 0)\n\t\t\t\n\t#increase_refnum(user, entry)\t\n\tdef test_increase_refnum(self):\t\n\t\tcm.register_entry('jasmine', '@@@ test cron @@@')\t\n\t\tcm.increase_refnum('jasmine', '@@@ test cron @@@')\n\t\tref = cm.run_sql('select ref from crons where user=\"jasmine\" and cron=\"@@@ test cron @@@\"')\n\t\tcm.run_sql('delete from crons where user=\"jasmine\" and cron=\"@@@ test cron @@@\"')\n\t\tself.assertEqual(ref[0][0], 1)\n\t\t\n\t#delete_old_entries(user):\n\tdef test_delete_old_entries(self):\n\t\tcm.run_sql('insert into crons(user, cron, ref) values(\"jasmine\", \"@@@ test cron @@@\", 24*30)')\n\t\tcm.delete_old_entries('jasmine')\n\t\tref = cm.run_sql('select ref from crons where user=\"jasmine\" and cron=\"@@@ test cron @@@\"')\n\t\tself.assertEqual(ref, [])\n\t\tcm.run_sql('insert into crons(user, cron, ref) values(\"jasmine\", \"@@@ test cron @@@\", 24*30-1)')\n\t\tcm.delete_old_entries('jasmine')\n\t\tref = cm.run_sql('select ref from crons where user=\"jasmine\" and cron=\"@@@ test cron @@@\"')\n\t\tcm.run_sql('delete from crons where user=\"jasmine\" and cron=\"@@@ test cron @@@\"')\n\t\tself.assertEqual(ref[0][0], 24*30-1)\n\t\t\n\t#get_users():\n\tdef test_get_users(self):\n\t\t#print cm.get_users()\n\t\tpass\n\t\t\n\t#refresh_entries(user):\t\n\tdef test_refresh_entries(self):\n\t\tpass\n\t\n\t#norm_cron_entry(cron_entry):\n\tdef test_norm_cron_entry(self):\n\t\ttentry = '*\t*\t*\t* * /tmp #jasmine'\n\t\tself.assertEqual(cm.norm_cron_entry(tentry), '* * * * * /tmp')\n\t\ttentry = '\t\t\t*\t*\t*\t* * /tmp #jasmine'\n\t\tself.assertEqual(cm.norm_cron_entry(tentry), '* * * * * /tmp')\n\t\ttentry = '#*\t*\t*\t* * /tmp #jasmine'\n\t\tself.assertEqual(cm.norm_cron_entry(tentry), '')\n\t\t\n\t\nif __name__ == '__main__':\n\tunittest.main()\n","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":2341,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"279414509","text":"#Team Kirkland Meeseeks\n#SoftDev pd1\n#P01 -- ArRESTed Development\n#2019-12-04\n\nimport sqlite3, urllib, json\n\nDB_FILE = \"trivia.db\"\n\ndef exec(cmd):\n    \"\"\"Executes a sqlite command\"\"\"\n    db = sqlite3.connect(DB_FILE)\n    c = db.cursor()\n    output = c.execute(cmd)\n    db.commit()\n    return output\n\ndef execmany(cmd, inputs):\n    \"\"\"Executes a sqlite command using ? placeholder\"\"\"\n    db = sqlite3.connect(DB_FILE)\n    c = db.cursor()\n    output = c.execute(cmd, inputs)\n    db.commit()\n    return output\n\n#==========================================================\ndef build_db():\n    \"\"\"Creates database if it does not yet exist with the necessary tables\"\"\"\n    print(\"Database is being created. It may take a while. Please stand by...\")\n    command = \"CREATE TABLE IF NOT EXISTS user_tbl (username TEXT, password TEXT, pic TEXT, coll TEXT, game_id TEXT, money INT, flag TEXT, stat TEXT, score INT)\"\n    exec(command)\n\n    command = \"SELECT * FROM user_tbl WHERE username='admin'\" #dummy account\n    data = exec(command).fetchone()\n    if (data is None):\n        u = urllib.request.urlopen(\"https://opentdb.com/api_category.php\")\n        response = json.loads(u.read())['trivia_categories']\n        allcategories = \"\"\n        for category in response:\n            allcategories += category['name'] + \"|0|0,\"\n        #print(allcategories)\n        allcategories = allcategories[0:-1]\n        command = \"INSERT OR IGNORE INTO user_tbl VALUES('admin', '123', 'https://restcountries.eu/data/usa.svg', 'https://restcountries.eu/data/usa.svg', '', 200, 'United States of America', '%s', 0)\" % allcategories\n        exec(command)\n\n    command = \"CREATE TABLE IF NOT EXISTS game_tbl (game_id TEXT, team1 TEXT, team2 TEXT, playing1 TEXT, playing2 TEXT, started INT, completed1 INT, completed2 INT, currentq1 TEXT, currentq2 TEXT)\"\n    exec(command)\n\n    command = \"CREATE TABLE IF NOT EXISTS question_tbl (category TEXT, question TEXT PRIMARY KEY, diff TEXT, choices TEXT, answer TEXT)\"\n    exec(command)\n\n    command = \"CREATE TABLE IF NOT EXISTS flags_tbl (country TEXT PRIMARY KEY, flag TEXT)\"\n    exec(command)\n\n    command = \"CREATE TABLE IF NOT EXISTS pic_tbl (category TEXT, pic TEXT PRIMARY KEY)\"\n    exec(command)\n\ndef build_flag():\n    \"\"\"Populates flag_tbl if it isn't already populated, makes a request to the Countries API\"\"\"\n    command = \"SELECT * FROM flags_tbl;\"\n    data = exec(command).fetchone()\n    if (data is None):\n        print(\"\\tBuilding flag cache...\")\n        u = urllib.request.urlopen(\"https://restcountries.eu/rest/v2/all?fields=name;flag\")\n        response = json.loads(u.read())\n        for country in response:\n            q = \"INSERT OR IGNORE INTO flags_tbl VALUES(?, ?)\"\n            inputs = (country['name'], country['flag'])\n            execmany(q, inputs)\n\ndef build_pic():\n    \"\"\"Populates pic_tbl if it isn't already populated\n    Makes a request to the PokeAPI, Rick and Morty API, and the Lorem Picsum API \"\"\"\n    command = \"SELECT * FROM pic_tbl;\"\n    data = exec(command).fetchone()\n    if (data is None):\n        print(\"\\tBuilding picture cache...\")\n        q = \"INSERT OR IGNORE INTO pic_tbl VALUES(?, ?)\"\n\n        #building Rick and Morty picture cache\n        for i in range(80):\n            i = i + 1\n            pic = \"https://rickandmortyapi.com/api/character/avatar/%d.jpeg\" % i\n            id = \"R\" + str(i)\n            inputs = (id, pic)\n            execmany(q, inputs)\n\n        #building lorem picsum picture cache\n        url = \"https://picsum.photos/v2/list?page=1&limit=80\"\n        u = urllib.request.urlopen(url)\n        response = json.loads(u.read())\n        for entry in response: #take every id\n            id = entry['id']\n            pic = \"https://picsum.photos/id/%s/250\" % id\n            id = \"M\" + id\n            inputs = (id, pic)\n            execmany(q, inputs)\n\n        #building pokemon picture cache\n        for i in range(80):\n            i = i + 1\n            pic = \"https://pokeres.bastionbot.org/images/pokemon/%d.png\" % i\n            id = \"P\" + str(i)\n            inputs = (id, pic)\n            execmany(q, inputs)\n\ndef build_question():\n    \"\"\"Populates question_tbl if it isn't already populated, makes a request to the OpenTrivia API\"\"\"\n    command = \"SELECT * FROM question_tbl;\"\n    data = exec(command).fetchone()\n    if (data is None):\n        print(\"\\tBuild question cache...\")\n        #get a token\n        url = \"https://opentdb.com/api_token.php?command=request\"\n        u = urllib.request.urlopen(url)\n        response = json.loads(u.read())\n        token = response['token']\n\n        #use token to retrieve questions\n        url = \"https://opentdb.com/api.php?amount=50&type=multiple&token=\" + token\n        u = urllib.request.urlopen(url)\n        response = json.loads(u.read())\n        while (response['response_code'] == 0): #while database has not been exhausted\n            for result in response['results']:\n                q = \"INSERT OR IGNORE INTO question_tbl VALUES(?, ?, ?, ?, ?)\"\n                choices = result['incorrect_answers']\n                choices.append(result['correct_answer'])\n                choices = \"~\".join(choices)\n                inputs = (result['category'], result['question'], result['difficulty'], choices, result['correct_answer'])\n                execmany(q, inputs)\n            url = \"https://opentdb.com/api.php?amount=50&type=multiple&token=\" + token\n            u = urllib.request.urlopen(url)\n            response = json.loads(u.read())\n","sub_path":"utl/db_builder.py","file_name":"db_builder.py","file_ext":"py","file_size_in_byte":5470,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"454555833","text":"from bin.NeuralNetwork import NeuralNetwork\nfrom bin.IOUtilities import IOUtilities\nimport pytest\nimport numpy as np\n\n\n@pytest.fixture\ndef data():\n    \"\"\"Inject data class to each test cases\"\"\"\n    return IOUtilities.read_data(\"../data/xor.txt\", ['x1', 'x2', 'Output'])\n\n\ndef test_xor(data):\n    x_train_mat = data[0]\n    y_train_mat = data[1]\n    nn = NeuralNetwork([2, 2, 1], 'tanh')\n    nn.fit(x_train_mat, y_train_mat)\n    test_cases = [([0, 0], 0), ([0, 1], 1), ([1, 0], 1), ([1, 1], 0)]\n    for test_case in test_cases:\n        np.testing.assert_almost_equal(test_case[1], nn.predict(np.asmatrix(test_case[0])), decimal=1)\n","sub_path":"tests/test_neural_network.py","file_name":"test_neural_network.py","file_ext":"py","file_size_in_byte":629,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"86382275","text":"import subprocess\r\n\r\ndef translate(url):\r\n    try:\r\n        ext = (url.split(\"/\")[-1]).split(\".\")[-1]\r\n        fileName = 'dataset/original.'+ext\r\n        subprocess.call([\"wget\", \"-O\", fileName, url])\r\n        subprocess.call([\"tesseract\", fileName, \"-l\", \"jpn\", \"dataset/out\"])\r\n        return \"dataset/out.txt\"\r\n    except Exception as e:\r\n        print (\"Exception Error: \", e)","sub_path":"translateImg2Txt.py","file_name":"translateImg2Txt.py","file_ext":"py","file_size_in_byte":381,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"118454523","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass LayoutNet(nn.Module):\n    def __init__(self):\n        super(LayoutNet, self).__init__()\n        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1, stride=1)\n        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1, stride=1)\n        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1, stride=1)\n        self.conv4 = nn.Conv2d(128, 256, kernel_size=3, padding=1, stride=1)\n        self.conv5 = nn.Conv2d(256, 512, kernel_size=3, padding=1, stride=1)\n        self.conv6 = nn.Conv2d(512, 1024, kernel_size=3, padding=1, stride=1)\n        self.conv7 = nn.Conv2d(1024, 2048, kernel_size=3, padding=1, stride=1)\n        self.deconv00 = nn.Conv2d(2048, 1024, kernel_size=3, padding=1, stride=1)\n        self.deconv0 = nn.Conv2d(1024*2, 512, kernel_size=3, padding=1, stride=1)\n        self.deconv1 = nn.Conv2d(512*2, 256, kernel_size=3, padding=1, stride=1)\n        self.deconv2 = nn.Conv2d(256*2, 128, kernel_size=3, padding=1, stride=1)\n        self.deconv3 = nn.Conv2d(128*2, 64, kernel_size=3, padding=1, stride=1)\n        self.deconv4 = nn.Conv2d(64*2, 32, kernel_size=3, padding=1, stride=1)\n        self.deconv5 = nn.Conv2d(32*2, 3, kernel_size=3, padding=1, stride=1)\n        self.deconv6_sf = nn.Sigmoid()\n        self.deconv00_c = nn.Conv2d(2048, 1024, kernel_size=3, padding=1, stride=1)\n        self.deconv0_c = nn.Conv2d(1024*3, 512, kernel_size=3, padding=1, stride=1)\n        self.deconv1_c = nn.Conv2d(512*3, 256, kernel_size=3, padding=1, stride=1)\n        self.deconv2_c = nn.Conv2d(256*3, 128, kernel_size=3, padding=1, stride=1)\n        self.deconv3_c = nn.Conv2d(128*3, 64, kernel_size=3, padding=1, stride=1)\n        self.deconv4_c = nn.Conv2d(64*3, 32, kernel_size=3, padding=1, stride=1)\n        self.deconv5_c = nn.Conv2d(32*3, 16, kernel_size=3, padding=1, stride=1)\n        self.deconv6_sf_c = nn.Sigmoid()\n        self.ref1 = nn.Linear(2048*4*4, 1024)\n        self.ref2 = nn.Linear(1024, 256)\n        self.ref3 = nn.Linear(256, 64)\n        self.ref4 = nn.Linear(64, 11)\n        self.relu = nn.ReLU(inplace=True)\n        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)\n\n\n    def forward(self, x):\n\n        ## ENCODER\n        conv1 = self.conv1(x)\n        conv1_relu = self.relu(conv1)\n        pool1 = self.pool(conv1_relu)\n        conv2 = self.conv2(pool1)\n        conv2_relu = self.relu(conv2)\n        pool2 = self.pool(conv2_relu)\n        conv3 = self.conv3(pool2)\n        conv3_relu = self.relu(conv3)\n        pool3 = self.pool(conv3_relu)\n        conv4 = self.conv4(pool3)\n        conv4_relu = self.relu(conv4)\n        pool4 = self.pool(conv4_relu)\n        conv5 = self.conv5(pool4)\n        conv5_relu = self.relu(conv5)\n        pool5 = self.pool(conv5_relu)\n        conv6 = self.conv6(pool5)\n        conv6_relu = self.relu(conv6)\n        pool6 = self.pool(conv6_relu)\n        conv7 = self.conv7(pool6)\n        conv7_relu = self.relu(conv7)\n        pool7 = self.pool(conv7_relu)\n\n        ## DECODER\n        unpool00 = F.interpolate(pool7, scale_factor=2)\n        deconv00 = self.deconv00(unpool00)\n        deconv00_relu = self.relu(deconv00)\n        unpool0_ = torch.cat((deconv00_relu, pool6), dim=1)\n        unpool0 = F.interpolate(unpool0_, scale_factor=2)\n        deconv0 = self.deconv0(unpool0)\n        deconv0_relu = self.relu(deconv0)\n        unpool1_ = torch.cat((deconv0_relu, pool5), dim=1)\n        unpool1 = F.interpolate(unpool1_, scale_factor=2)\n        deconv1 = self.deconv1(unpool1)\n        deconv1_relu = self.relu(deconv1)\n        unpool2_ = torch.cat((deconv1_relu, pool4), dim=1)\n        unpool2 = F.interpolate(unpool2_, scale_factor=2)\n        deconv2 = self.deconv2(unpool2)\n        deconv2_relu = self.relu(deconv2)\n        unpool3_ = torch.cat((deconv2_relu, pool3), dim=1)\n        unpool3 = F.interpolate(unpool3_, scale_factor=2)\n        deconv3 = self.deconv3(unpool3)\n        deconv3_relu = self.relu(deconv3)\n        unpool4_ = torch.cat((deconv3_relu, pool2), dim=1)\n        unpool4 = F.interpolate(unpool4_, scale_factor=2)\n        deconv4 = self.deconv4(unpool4)\n        deconv4_relu = self.relu(deconv4)\n        unpool5_ = torch.cat((deconv4_relu, pool1), dim=1)\n        unpool5 = F.interpolate(unpool5_, scale_factor=2)\n        deconv5 = self.deconv5(unpool5)\n        deconv6_sf = self.deconv6_sf(deconv5)\n\n        ## JOINT\n        deconv00_c = self.deconv00_c(unpool00)\n        deconv00_relu_c = self.relu(deconv00_c)\n        unpool0_c = torch.cat((deconv00_relu_c, unpool0_), dim=1)\n        unpool0_c = F.interpolate(unpool0_c, scale_factor=2)\n        deconv0_c = self.deconv0_c(unpool0_c)\n        deconv0_relu_c = self.relu(deconv0_c)\n        unpool1_c = torch.cat((deconv0_relu_c, unpool1_), dim=1)\n        unpool1_c = F.interpolate(unpool1_c, scale_factor=2)\n        deconv1_c = self.deconv1_c(unpool1_c)\n        deconv1_relu_c = self.relu(deconv1_c)\n        unpool2_c = torch.cat((deconv1_relu_c, unpool2_), dim=1)\n        unpool2_c = F.interpolate(unpool2_c, scale_factor=2)\n        deconv2_c = self.deconv2_c(unpool2_c)\n        deconv2_relu_c = self.relu(deconv2_c)\n        unpool3_c = torch.cat((deconv2_relu_c, unpool3_), dim=1)\n        unpool3_c = F.interpolate(unpool3_c, scale_factor=2)\n        deconv3_c = self.deconv3_c(unpool3_c)\n        deconv3_relu_c = self.relu(deconv3_c)\n        unpool4_c = torch.cat((deconv3_relu_c, unpool4_), dim=1)\n        unpool4_c = F.interpolate(unpool4_c, scale_factor=2)\n        deconv4_c = self.deconv4_c(unpool4_c)\n        deconv4_relu_c = self.relu(deconv4_c)\n        unpool5_c = torch.cat((deconv4_relu_c, unpool5_), dim=1)\n        unpool5_c = F.interpolate(unpool5_c, scale_factor=2)\n        deconv5_c = self.deconv5_c(unpool5_c)\n        deconv6_sf_c = self.deconv6_sf_c(deconv5_c)\n\n        ## REFINEMENT\n        ref0 = pool7.view(-1, 2048*4*4)\n        ref1 = self.ref1(ref0)\n        ref1_relu = self.relu(ref1)\n        ref2 = self.ref2(ref1_relu)\n        ref2_relu = self.relu(ref2)\n        ref3 = self.ref3(ref2_relu)\n        ref3_relu = self.relu(ref3)\n        ref4 = self.ref4(ref3_relu)\n\n        return deconv6_sf, deconv6_sf_c, ref4\n\n\nif __name__ == '__main__':\n    def count_parameters(model):\n        return sum(p.numel() for p in model.parameters() if p.requires_grad)\n    model = LayoutNet()\n    num_params = count_parameters(model)\n    print(num_params - 128187606)\n    x = torch.rand(2, 3, 512, 512)\n    #out = model(x)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\"\"\"\nclass LayoutNet(nn.Module):\n    def __init__(self):\n        super(LayoutNet, self).__init__()\n        self.enc1 = nn.Sequential(\n            nn.Conv2d(3, 32, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True)\n        )\n\n        self.enc2 = nn.Sequential(\n            nn.Conv2d(32, 64, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True)\n        )\n        \n        self.enc3 = nn.Sequential(\n            nn.Conv2d(64, 128, kernel_size=3, padding=1), \n            nn.ReLU(inplace=True)\n        )\n\n        self.enc4 = nn.Sequential(\n            nn.Conv2d(128, 256, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True)\n        )\n        self.enc5 = nn.Sequential(\n            nn.Conv2d(256, 512, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True)\n        )\n        self.enc6 = nn.Sequential(\n            nn.Conv2d(512, 1024, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True)\n        )\n        self.enc7 = nn.Sequential(\n            nn.Conv2d(1024, 2048, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True)\n        )        \n\n        self.dec1 = nn.Sequential(\n            nn.Conv2d(2048, 1024, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True)\n        )\n\n        self.dec2 = nn.Sequential(\n            nn.Conv2d(1024*2, 512, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True)\n        )\n        self.dec3 = nn.Sequential(\n            nn.Conv2d(512*2, 256, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True)\n        )\n        self.dec4 = nn.Sequential(\n            nn.Conv2d(256*2, 128, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True)\n        )\n        self.dec5 = nn.Sequential(\n            nn.Conv2d(128*2, 64, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True)\n        )        \n        self.dec6 = nn.Sequential(\n            nn.Conv2d(64*2, 32, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True)\n        )        \n        self.dec7 = nn.Sequential(\n            nn.Conv2d(32*2, 3, kernel_size=3, padding=1),\n            nn.Sigmoid()        #?\n        )        \n\n        self.jointdec2 = nn.Sequential(\n            nn.Conv2d(1024*3, 512, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True)\n        )\n        self.jointdec3 = nn.Sequential(\n            nn.Conv2d(512*3, 256, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True)\n        )\n        self.jointdec4 = nn.Sequential(\n            nn.Conv2d(256*3, 128, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True)\n        )\n        self.jointdec5 = nn.Sequential(\n            nn.Conv2d(128*3, 64, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True)\n        )        \n        self.jointdec6 = nn.Sequential(\n            nn.Conv2d(64*3, 32, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True)\n        )        \n        self.jointdec7 = nn.Sequential(\n            nn.Conv2d(32*3, 8, kernel_size=3, padding=1),\n            nn.Sigmoid()        #?\n        )        \n\n        \n        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)\n        #self.upsample = F.interpolate(scale_factor=2, mode='nearest')   # if below code runs ok, try using self.upsample\n        self.relu = nn.ReLU(inplace=True)\n\n        #ref0 = torch.reshape(2048 * 4 * 4)(encoder7_ds) #(pool7)\n        self.ref1 = nn.Linear(2048 * 4 * 4, 1024)\n        self.ref2 = nn.Linear(1024, 256)\n        self.ref3 = nn.Linear(256, 64)\n        self.roomtype = nn.Linear(64, 11)\n        \n    def forward(self, x):\n        print(\"x: \", x.shape)\n        ## ENCODER\n        encoder1 = self.enc1(x)\n        encoder1_ds = self.pool(encoder1)  # ds ==> downsample\n        encoder2 = self.enc2(encoder1_ds)\n        encoder2_ds = self.pool(encoder2) # pool2\n        encoder3 = self.enc3(encoder2_ds)\n        encoder3_ds = self.pool(encoder3) # pool3\n        encoder4 = self.enc4(encoder3_ds)\n        encoder4_ds = self.pool(encoder4) # pool4\n        encoder5 = self.enc5(encoder4_ds)\n        encoder5_ds = self.pool(encoder5) # pool5\n        encoder6 = self.enc6(encoder5_ds) # conv6_relu \n        encoder6_ds = self.pool(encoder6) # pool6\n        print('pool6 ', encoder6_ds.shape)\n        encoder7 = self.enc7(encoder6_ds) # conv7_relu \n        encoder7_ds = self.pool(encoder7) # pool7\n        print('pool7 shape: ', encoder7_ds.shape)\n\n\n        ## DECODER\n        \n        unpool00 = F.interpolate(encoder7_ds,  # AKA pool7\n                                    scale_factor=2, mode='nearest')\n        #print(unpool00.shape)\n        deconv00_relu = self.dec1(unpool00)\n        decoder1 = torch.cat([\n            deconv00_relu,  #deconv00_relu\n            encoder6_ds  # AKA pool6\n            ],  dim=1) # unpool0_\n        print('deconv00_relu ', self.dec1(unpool00).shape)\n        print('decoder1 shape: ', decoder1.shape)\n        print(\"upsample: \", F.interpolate(decoder1, #unpool0_\n                                    scale_factor=2, mode='nearest').shape)\n        deconv0_relu = self.dec2(F.interpolate(decoder1, #unpool0_\n                                    scale_factor=2, mode='nearest')\n                     )\n        decoder2 = torch.cat([\n            deconv0_relu, #deconv0_relu \n            encoder5_ds # pool5\n            ],  dim=1) #unpool1_ \n        decoder3 = torch.cat([\n            self.dec3(F.interpolate(decoder2, #unpool1_\n                                    scale_factor=2, mode='nearest')\n                     ), #deconv1_relu \n            encoder4_ds # pool4\n        ],  dim=1) #unpool2_ \n        decoder4 = torch.cat([\n            self.dec4(F.interpolate(decoder3, #unpool2_\n                                    scale_factor=2, mode='nearest')\n                     ), #deconv2_relu \n            encoder3_ds # pool3\n        ],  dim=1) #unpool3_ \n        decoder5 = torch.cat([\n            self.dec5(F.interpolate(decoder4, #unpool3_\n                                    scale_factor=2, mode='nearest'\n                                   )\n                      ), #deconv3_relu \n            encoder2_ds # pool2\n        ],  dim=1) #unpool4_ \n        decoder6 = torch.cat([\n            self.dec6(F.interpolate(decoder5, #unpool4_\n                                    scale_factor=2, mode='nearest'\n                                   )\n                      ), #deconv4_relu\n            encoder1_ds # pool1\n        ],  dim=1)  #unpool5_ \n        unpool5 = F.interpolate(decoder6, scale_factor=2, mode='nearest')\n        decoder6_sigmoid = self.dec7(unpool5)\n        \n        \n        ## JOINT\n        \n        joint1 =torch.cat([\n            self.dec1(unpool00), #deconv00_relu_c \n                           decoder1, # unpool0_ \n                          ],  dim=1) #unpool0_c\n        print(joint1.shape)\n        joint2  = torch.cat([\n            self.jointdec2(F.interpolate(joint1, scale_factor=2, mode='nearest')),\n                                decoder2, #unpool1_\n                               ],  dim=1) #unpool1_c\n        joint3  = torch.cat([\n            self.jointdec3(F.interpolate(joint2, scale_factor=2, mode='nearest')),\n                                decoder3, #unpool2_\n                               ],  dim=1) #unpool2_c\n        joint4  = torch.cat([\n            self.jointdec4(F.interpolate(joint3, scale_factor=2, mode='nearest')),\n                                decoder4, #unpool3_\n                               ],  dim=1) #unpool3_c\n        joint5  = torch.cat([\n            self.jointdec5(F.interpolate(joint4, scale_factor=2, mode='nearest')),\n                                decoder5, #unpool4_\n                               ],  dim=1) #unpool4_c\n        joint6  = torch.cat([\n            self.jointdec6(F.interpolate(joint5, scale_factor=2, mode='nearest')),\n                                decoder6, #unpool5_\n                               ],  dim=1) #unpool5_c\n        \n        \n        decoder6_sigmoid_corners = self.jointdec7(F.interpolate(joint6, scale_factor=2, mode='nearest'))\n        \n\n        ## REFINEMENT\n\n#        ref0 = encoder7_ds.flatten() #torch.reshape(2048 * 4 * 4)(encoder7_ds) #(pool7)\n        ref1 = self.ref1(encoder7_ds.view(-1, 2048 * 4 * 4))\n        print('ref1 ', ref1.shape)\n        ref1_relu = self.relu(ref1)\n        ref2 = self.ref2(ref1_relu)\n        ref2_relu = self.relu(ref2)\n        ref3 = self.ref3(ref2_relu)\n        ref3_relu = self.relu(ref3)\n        roomtype = self.roomtype(ref3_relu)\n\n\n        return decoder6_sigmoid, decoder6_sigmoid_corners, roomtype\n\n\n\n\n\n\n\n\n\n\n\ndef conv3x3(in_planes, out_planes):\n    return nn.Sequential(\n        nn.Conv2d(in_planes, out_planes, kernel_size=3, padding=1),\n        nn.ReLU(inplace=True))\n\n\ndef conv3x3_down(in_planes, out_planes):\n    return nn.Sequential(\n        conv3x3(in_planes, out_planes),\n        nn.MaxPool2d(kernel_size=2, stride=2))\n\n\nclass Encoder(nn.Module):\n    def __init__(self, in_planes=6):\n        super(Encoder, self).__init__()\n        self.convs = nn.ModuleList([\n            conv3x3_down(in_planes, 32),\n            conv3x3_down(32, 64),\n            conv3x3_down(64, 128),\n            conv3x3_down(128, 256),\n            conv3x3_down(256, 512),\n            conv3x3_down(512, 1024),\n            conv3x3_down(1024, 2048)])\n\n    def forward(self, x):\n        conv_out = []\n        for conv in self.convs:\n            x = conv(x)\n            conv_out.append(x)\n        return conv_out\n\n\nclass Decoder(nn.Module):\n    def __init__(self, skip_num=2, out_planes=3):\n        super(Decoder, self).__init__()\n        self.convs = nn.ModuleList([\n            conv3x3(2048, 1024),\n            conv3x3(1024 * skip_num, 512),\n            conv3x3(512 * skip_num, 256),\n            conv3x3(256 * skip_num, 128),\n            conv3x3(128 * skip_num, 64),\n            conv3x3(64 * skip_num, 32)])\n        self.last_conv = nn.Conv2d(\n            32 * skip_num, out_planes, kernel_size=3, padding=1)\n\n    def forward(self, f_list):\n        conv_out = []\n        f_last = f_list[0]\n        for conv, f in zip(self.convs, f_list[1:]):\n            f_last = F.interpolate(f_last, scale_factor=2, mode='nearest')\n            f_last = conv(f_last)\n            f_last = torch.cat([f_last, f], dim=1)\n            conv_out.append(f_last)\n            #print('f_last shape:', f_last.shape)\n        conv_out.append(self.last_conv(F.interpolate(\n            f_last, scale_factor=2, mode='nearest')))\n        return conv_out\n\n    \nclass Refinement(nn.Module):\n    def __init__(self, out_cat=11):  #or 8??\n        super(Refinement, self).__init__()\n        'torch.reshape(-1) #flatten(2048*4*4)'\n        self.refine = nn.sequential([\n            nn.linear(2048*4*4, 1024),\n            nn.ReLU(inplace=True),\n            nn.linear(1024, 256),\n            nn.ReLU(inplace=True),\n            nn.linear(256, 64),\n            nn.ReLU(inplace=True),\n            nn.linear(64, out_cat),\n            nn.ReLU(inplace=True),\n            ])\n\n    def forward(self, x):\n        # flatten here?\n        return self.refine(x)\n\nif __name__ == '__main__':\n\n    encoder = Encoder()\n    edg_decoder = Decoder(skip_num=2, out_planes=3)\n    cor_decoder = Decoder(skip_num=3, out_planes=1)\n    my_layoutnet = LayoutNet()\n\n    with torch.no_grad():\n        x_orig = torch.rand(2, 6, 512, 1024)\n        x = torch.rand(2, 3, 512, 512)\n        en_list = encoder(x_orig)\n        edg_de_list = edg_decoder(en_list[::-1])\n        cor_de_list = cor_decoder(en_list[-1:] + edg_de_list[:-1])\n        my_layoutnet_outputs = [decoder6_sigmoid, decoder6_sigmoid_corners, roomtype\n                               ] = my_layoutnet(x)\n    '''\n    for f in en_list:\n        print('encoder', f.size())\n    for f in edg_de_list:\n        print('edg_decoder', f.size())\n    for f in cor_de_list:\n        print('cor_decoder', f.size())\n    '''\n    for f in my_layoutnet_outputs:\n        print('my_layoutnet', f.size())\n\n\"\"\"\n\n","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":18236,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"30192873","text":"# Copyright 2015 Novo Nordisk Foundation Center for Biosustainability, DTU.\n\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n\n# http://www.apache.org/licenses/LICENSE-2.0\n\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom __future__ import absolute_import, print_function\n\nimport logging\nimport time\nimport re\nimport types\nfrom functools import reduce, partial\n\nimport inspyred\nfrom pandas import DataFrame\n\nfrom cameo import config\nfrom cameo.core.result import Result\nfrom cameo.core.solver_based_model import SolverBasedModel\nfrom cameo.exceptions import SolveError\nfrom cameo.flux_analysis.simulation import pfba, lmoma, moma, room\nfrom cameo.strain_design.heuristic.evolutionary import archives\nfrom cameo.strain_design.heuristic.evolutionary import decoders\nfrom cameo.strain_design.heuristic.evolutionary import generators\nfrom cameo.strain_design.heuristic.evolutionary import observers\nfrom cameo.strain_design.heuristic.evolutionary import plotters\nfrom cameo.strain_design.heuristic.evolutionary import variators\nfrom cameo.strain_design.heuristic.evolutionary import stats\nfrom cameo.strain_design.heuristic.evolutionary.decoders import KnockoutDecoder\nfrom cameo.strain_design.heuristic.evolutionary.objective_functions import ObjectiveFunction\nfrom cameo.strain_design.heuristic.evolutionary.processing import reactions2filter\nfrom cameo.util import RandomGenerator as Random\nfrom cameo.util import in_ipnb\nfrom cameo.util import partition, TimeMachine, memoize, ProblemCache\n\n__all__ = ['ReactionKnockoutOptimization', 'GeneKnockoutOptimization', 'CofactorSwapOptimization']\n\nREACTION_KNOCKOUT_TYPE = \"reaction\"\nSWAP_KNOCKOUT_TYPE = \"cofactor-swap\"\nGENE_KNOCKOUT_TYPE = \"gene\"\nNADH_NADPH = (['nad_c', 'nadh_c'], ['nadp_c', 'nadph_c'])\n\nSIZE = 'Size'\nBIOMASS = 'Biomass'\nKNOCKOUTS = 'Knockouts'\nREACTIONS = 'Reactions'\n\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.INFO)\n\nPRE_CONFIGURED = {\n    inspyred.ec.GA: [\n        [\n            variators.set_mutation,\n            variators.set_indel,\n            variators.set_n_point_crossover\n        ],\n        inspyred.ec.selectors.tournament_selection,\n        inspyred.ec.replacers.generational_replacement,\n        archives.BestSolutionArchive(),\n    ],\n    inspyred.ec.SA: [\n\n        [\n            variators.set_mutation,\n            variators.set_indel\n        ],\n        inspyred.ec.selectors.default_selection,\n        inspyred.ec.replacers.simulated_annealing_replacement,\n        archives.BestSolutionArchive()\n    ],\n    inspyred.ec.emo.NSGA2: [\n        [\n            variators.set_mutation,\n            variators.set_indel,\n            variators.set_n_point_crossover\n        ],\n        inspyred.ec.selectors.tournament_selection,\n        inspyred.ec.replacers.nsga_replacement,\n        inspyred.ec.archivers.population_archiver\n    ],\n    inspyred.ec.emo.PAES: [\n        [\n            variators.set_mutation,\n            variators.set_indel,\n            variators.set_n_point_crossover\n        ],\n        inspyred.ec.selectors.default_selection,\n        inspyred.ec.replacers.paes_replacement,\n        inspyred.ec.archivers.adaptive_grid_archiver\n    ]\n}\n\n\ndef set_distance_function(candidate1, candidate2):\n    return len(set(candidate1).symmetric_difference(set(candidate2)))\n\n\nclass HeuristicOptimization(object):\n    \"\"\"\n    Blueprint for any model optimization based on heuristic methods.\n\n\n    Attributes\n    ----------\n    model : SolverBasedModel\n        A constraint-based model.\n    heuristic_method : inspyred.ec.EvolutionaryComputation\n        An evolutionary algorithm.\n    objective_function : objective function or list(objective function)\n        The objectives for the algorithm to maximize.\n    seed : int\n        A seed for random. It is auto-generated if None is given.\n    termination : inspyred.ec.terminators\n        A termination criteria for the algorithm. The default is inspyred.ec.terminators.evaluation_termination\n\n\n    Methods\n    -------\n    run(view=config.default_view, maximize=True, **kwargs)\n\n\n    See Also\n    --------\n    *inspyred.ec\n    *cameo.config.default_view\n\n    \"\"\"\n\n    def __init__(self, model=None, heuristic_method=inspyred.ec.GA, objective_function=None, seed=None,\n                 termination=inspyred.ec.terminators.evaluation_termination, plot=True, progress=True,\n                 *args, **kwargs):\n        super(HeuristicOptimization, self).__init__(*args, **kwargs)\n        logger.debug(\"Seed: %s\" % seed)\n        self.plot = plot\n        self.progress = progress\n        self.observers = []\n        self.model = model\n        self._random = None\n        self.termination = termination\n        self._objective_function = objective_function\n        self._heuristic_method = None\n        self.heuristic_method = heuristic_method\n        self.heuristic_method.terminator = termination\n\n    @property\n    def archiver(self):\n        return self._heuristic_method.archiver\n\n    @archiver.setter\n    def archiver(self, archiver):\n        self._heuristic_method.archiver = archiver\n\n    @property\n    def objective_function(self):\n        return self._objective_function\n\n    @objective_function.setter\n    def objective_function(self, objective_function):\n        if self._heuristic_method.__module__ == inspyred.ec.ec.__name__ and isinstance(objective_function, list):\n            if len(objective_function) == 1:\n                self._objective_function = objective_function[0]\n            else:\n                raise TypeError(\"single objective heuristic do not support multiple objective functions\")\n        elif self._heuristic_method.__module__ == inspyred.ec.emo.__name__ and not isinstance(objective_function, list):\n            self._objective_function = [objective_function]\n        else:\n            self._objective_function = objective_function\n\n    @property\n    def random(self):\n        if self._random is None:\n            self._random = Random()\n        return self._random\n\n    @property\n    def heuristic_method(self):\n        return self._heuristic_method\n\n    @heuristic_method.setter\n    def heuristic_method(self, heuristic_method):\n        if heuristic_method.__module__ == inspyred.ec.emo.__name__ and not self.is_mo():\n            self._objective_function = [self.objective_function]\n        elif heuristic_method.__module__ == inspyred.ec.ec.__name__ and self.is_mo():\n            if len(self.objective_function) == 1:\n                self._objective_function = self.objective_function[0]\n            else:\n                raise TypeError(\"single objective heuristics do not support multiple objective functions\")\n        self._heuristic_method = heuristic_method(self.random)\n\n    def run(self, evaluator=None, generator=None, view=config.default_view, maximize=True, **kwargs):\n        if kwargs.get('seed', None) is None:\n            kwargs['seed'] = int(time.time())\n\n        self._heuristic_method._random.seed(kwargs['seed'])\n\n        for observer in self.observers:\n            observer.reset()\n        t = time.time()\n        print(time.strftime(\"Starting optimization at %a, %d %b %Y %H:%M:%S\", time.localtime(t)))\n        res = self.heuristic_method.evolve(generator=generator,\n                                           maximize=maximize,\n                                           evaluator=evaluator,\n                                           **kwargs)\n        for observer in self.observers:\n            observer.end()\n        runtime = time.time() - t\n        print(time.strftime(\"Finished after %H:%M:%S\", time.gmtime(runtime)))\n\n        return res\n\n    def is_mo(self):\n        return isinstance(self.objective_function, list)\n\n\nclass EvaluatorWrapper(object):\n    def __init__(self, view, evaluator):\n        if not hasattr(view, 'map'):\n            raise ValueError(\"View %s does not contain the required map function\")\n        if not (hasattr(evaluator, '__call__') or isinstance(evaluator, types.FunctionType)):\n            raise ValueError(\"evaluator %s must be a function or callable\")\n        self.view = view\n        self.evaluator = evaluator\n        self.__name__ = \"Wrapped %s\" % EvaluatorWrapper.__class__.__name__\n\n    def __enter__(self):\n        return self\n\n    def __exit__(self, exc_type, exc_val, exc_tb):\n        self.evaluator.reset()\n\n    def __call__(self, candidates, args):\n        population_chunks = (chunk for chunk in partition(candidates, len(self.view)))\n        try:\n            chunked_results = self.view.map(self.evaluator, population_chunks)\n        except KeyboardInterrupt as e:\n            self.view.shutdown()\n            raise e\n\n        fitness = reduce(list.__add__, chunked_results)\n\n        return fitness\n\n\nclass KnockoutEvaluator(object):\n    \"\"\"\n    Evaluator for knockouts. It gets a representation, decodes into knockouts and simulates\n    the model with a given method.\n\n    Attributes\n    ----------\n    model : SolverBasedModel\n        A constraint-based model\n    decoder : KnockoutDecoder\n        A decoder to convert the representation into knockouts\n    objective_function : objective_function or list(objective_function)\n        The objectives of the algorithm\n    simulation_method : see flux_analysis.simulation\n        The method use to simulate the knockouts\n    simulation_kwargs : dict\n        The extra parameters used by the simulation method\n\n    See Also\n    --------\n    cameo.strain_design.heuristic.objective_function\n\n    Methods\n    -------\n    __call__(population)\n        calling the object will evaluate a population (see inspyred)\n\n    \"\"\"\n\n    def __init__(self, model, decoder, objective_function, simulation_method, simulation_kwargs):\n        self.model = model\n        if not isinstance(decoder, KnockoutDecoder):\n            raise ValueError(\"Invalid decoder %s\" % decoder)\n        self.decoder = decoder\n\n        if isinstance(objective_function, list):\n            if not len(objective_function) > 0:\n                raise ValueError(\"list of objectives cannot be empty\")\n            invalid = []\n            for index, of in enumerate(objective_function):\n                if not isinstance(of, ObjectiveFunction):\n                    invalid.append((index, of))\n            if len(invalid) > 0:\n                raise ValueError(\"objectives %s must be instance of ObjectiveFunction (%s)\"\n                                 % (\",\".join(str(i[0]) for i in invalid), [i[1] for i in invalid]))\n        elif not isinstance(objective_function, ObjectiveFunction):\n            raise ValueError(\"'objective_function' must be instance of ObjectiveFunction (%s)\" % objective_function)\n\n        self.objective_function = objective_function\n        self.simulation_method = simulation_method\n        self.simulation_kwargs = simulation_kwargs\n        self.cache = ProblemCache(model)\n\n    def __call__(self, population):\n        return [self._evaluate_individual(tuple(i)) for i in population]\n\n    def reset(self):\n        self.cache.reset()\n\n    @property\n    def is_mo(self):\n        return isinstance(self.objective_function, list)\n\n    @memoize\n    def _evaluate_individual(self, individual):\n        \"\"\"\n        Evaluates a single individual.\n\n        Arguments\n        ---------\n\n        individual: set\n            The encoded representation of a single individual.\n\n\n        Returns\n        -------\n        fitness\n            A single real value or a Pareto, depending on the number of objectives.\n        \"\"\"\n        decoded = self.decoder(individual)\n        reactions = decoded[0]\n        with TimeMachine() as tm:\n            for reaction in reactions:\n                reaction.knock_out(time_machine=tm)\n            try:\n                solution = self.simulation_method(self.model,\n                                                  cache=self.cache,\n                                                  volatile=False,\n                                                  raw=True,\n                                                  reactions=reactions2filter(self.objective_function),\n                                                  **self.simulation_kwargs)\n                fitness = self._calculate_fitness(solution, decoded)\n            except SolveError as e:\n                logger.debug(e)\n                if self.is_mo:\n                    fitness = inspyred.ec.emo.Pareto(values=[0 for _ in self.objective_function])\n                else:\n                    fitness = 0.\n\n            return fitness\n\n    def _calculate_fitness(self, solution, decoded):\n        if self.is_mo:\n            logger.debug(\"evaluate multiobjective solution\")\n            return inspyred.ec.emo.Pareto(values=[of(self.model, solution, decoded) for of in self.objective_function])\n        else:\n            logger.debug(\"evaluate single objective solution\")\n            return self.objective_function(self.model, solution, decoded)\n\n\nclass SwapEvaluator(KnockoutEvaluator):\n    \"\"\" evaluate reaction swaps where we knock one reaction in favor of another \"\"\"\n\n    def __init__(self, *args, **kwargs):\n        super(SwapEvaluator, self).__init__(*args, **kwargs)\n\n    @memoize\n    def _evaluate_individual(self, individual):\n        decoded = self.decoder(individual)\n        swap_reactions = decoded[0]\n        with TimeMachine() as tm:\n            for reaction in swap_reactions:\n                self.model.swap_reaction(reaction.id, tm)\n            try:\n                solution = self.simulation_method(self.model,\n                                                  cache=self.cache,\n                                                  volatile=False,\n                                                  raw=True,\n                                                  reactions=reactions2filter(self.objective_function),\n                                                  **self.simulation_kwargs)\n                fitness = self._calculate_fitness(solution, decoded)\n            except SolveError as e:\n                logger.debug(e)\n                if self.is_mo:\n                    fitness = inspyred.ec.emo.Pareto(values=[0 for _ in self.objective_function])\n                else:\n                    fitness = 0.\n\n            return fitness\n\n\nclass KnockoutOptimization(HeuristicOptimization):\n    \"\"\"\n    Abstract class for knockout optimization.\n    \"\"\"\n\n    def __init__(self, simulation_method=pfba, wt_reference=None, knockout_evaluator=None,\n                 result_handler=None, *args, **kwargs):\n        \"\"\"\n        Class for generic optimization algorithms for knockout (or similar) strain design methods\n\n        Attributes\n        ----------\n        simulation_method: see flux_analysis.simulation\n        wt_reference: dict\n        simulation_method: method\n           the simulation method to use for evaluating results\n        knockout_evaluator: class\n           the class used to evaluate results\n        result_handler: clas\n           the class used to represent results, e.g. create a data frame representing solutions\n        \"\"\"\n        super(KnockoutOptimization, self).__init__(*args, **kwargs)\n        self._simulation_kwargs = dict()\n        self._simulation_kwargs['reference'] = wt_reference\n        self._simulation_method = None\n        self.simulation_method = simulation_method\n        self.representation = None\n        self.knockout_evaluator = knockout_evaluator or KnockoutEvaluator\n        self.result_handler = result_handler or KnockoutOptimizationResult\n        self._ko_type = None\n        self._decoder = None\n\n    @property\n    def simulation_method(self):\n        return self._simulation_method\n\n    @simulation_method.setter\n    def simulation_method(self, simulation_method):\n        if simulation_method in [lmoma, moma, room] and self._simulation_kwargs.get(\"reference\", None) is None:\n            logger.warning(\"No WT reference found, generating using pfba.\")\n            self._simulation_kwargs['reference'] = pfba(self.model).fluxes\n            logger.warning(\"Reference successfully computed.\")\n        self._simulation_method = simulation_method\n\n    @property\n    def simulation_kwargs(self):\n        return self._simulation_kwargs\n\n    @simulation_kwargs.setter\n    def simulation_kwargs(self, simulation_kwargs):\n        if self.simulation_method in [lmoma, moma, room] and simulation_kwargs.get(\"reference\", None) is None:\n            logger.warning(\"No WT reference found, generating using pfba.\")\n            simulation_kwargs['reference'] = pfba(self.model).fluxes\n            logger.warning(\"Reference successfully computed.\")\n        self._simulation_kwargs = simulation_kwargs\n\n    @HeuristicOptimization.heuristic_method.setter\n    def heuristic_method(self, heuristic_method):\n        HeuristicOptimization.heuristic_method.fset(self, heuristic_method)\n        self._set_observer()\n        try:\n            configuration = PRE_CONFIGURED[heuristic_method]\n            self._setup(*configuration)\n        except KeyError:\n            logger.warning(\"Please verify the variator is compatible with set representation\")\n\n    def _setup(self, variator, selector, replacer, archiver):\n        logger.debug(\"Setting up algorithm: %s\" % self.heuristic_method)\n        self.heuristic_method.variator = variator\n        self.heuristic_method.selector = selector\n        self.heuristic_method.replacer = replacer\n        self.heuristic_method.archiver = archiver\n        self.heuristic_method.terminator = self.termination\n\n    @HeuristicOptimization.objective_function.setter\n    def objective_function(self, objective_function):\n        HeuristicOptimization.objective_function.fset(self, objective_function)\n        self._set_observer()\n\n    def _set_observer(self):\n        self.observers = []\n\n        if in_ipnb() and self.plot:\n            if config.use_bokeh:\n                if self.is_mo():\n                    self.observers.append(plotters.IPythonBokehParetoPlotter(self.objective_function))\n                else:\n                    self.observers.append(plotters.IPythonBokehFitnessPlotter())\n\n        else:\n            if config.use_bokeh:\n                pass\n            else:\n                pass\n        if self.progress:\n            self.observers.append(observers.ProgressObserver())\n\n    def run(self, view=config.default_view, max_size=10, variable_size=True, **kwargs):\n        \"\"\"\n        Parameters\n        ----------\n        max_size: int\n            Maximum size of a solution, e.g., the maximum number of reactions or genes to knock-out or swap\n        variable_size: boolean\n            If true, the solution size can change meaning that the combination of knockouts can have different sizes up to\n            max_size. Otherwise it only produces knockout solutions with a fixed number of knockouts.\n\n        \"\"\"\n\n        if kwargs.get('seed', None) is None:\n            kwargs['seed'] = int(time.time())\n\n        self.heuristic_method.observer = self.observers\n        knockout_evaluator = self.knockout_evaluator(model=self.model,\n                                                     decoder=self._decoder,\n                                                     objective_function=self.objective_function,\n                                                     simulation_method=self._simulation_method,\n                                                     simulation_kwargs=self._simulation_kwargs)\n\n        with EvaluatorWrapper(view, knockout_evaluator) as evaluator:\n            super(KnockoutOptimization, self).run(distance_function=set_distance_function,\n                                                  representation=self.representation,\n                                                  evaluator=evaluator,\n                                                  generator=generators.set_generator,\n                                                  max_size=max_size,\n                                                  **kwargs)\n\n            return self.result_handler(model=self.model,\n                                       heuristic_method=self.heuristic_method,\n                                       simulation_method=self.simulation_method,\n                                       simulation_kwargs=self._simulation_kwargs,\n                                       solutions=self.heuristic_method.archive,\n                                       objective_function=self.objective_function,\n                                       ko_type=self._ko_type,\n                                       decoder=self._decoder,\n                                       seed=kwargs['seed'])\n\n\nclass KnockoutOptimizationResult(Result):\n    def __init__(self, model=None, heuristic_method=None, simulation_method=None, simulation_kwargs=None,\n                 solutions=None, objective_function=None, ko_type=None, decoder=None, seed=None, *args, **kwargs):\n        super(KnockoutOptimizationResult, self).__init__(*args, **kwargs)\n        self.seed = seed\n        self.model = model\n        self.heuristic_method = heuristic_method\n        self.simulation_method = simulation_method\n        self.simulation_kwargs = simulation_kwargs or {}\n        if isinstance(objective_function, list):\n            self.objective_functions = objective_function\n        else:\n            self.objective_functions = [objective_function]\n        self.ko_type = ko_type\n        self._decoder = decoder\n        self._solutions = self._decode_solutions(solutions)\n\n    def _decode_solutions(self, solutions):\n        decoded_solutions = DataFrame(columns=[\"reactions\", \"knockouts\", \"fitness\"])\n        for index, solution in enumerate(solutions):\n            reactions, knockouts = self._decoder(solution.candidate, flat=True)\n            if len(reactions) > 0:\n                decoded_solutions.loc[index] = [reactions, knockouts, solution.fitness]\n\n        return decoded_solutions\n\n    def __len__(self):\n        return len(self._solutions)\n\n    def __getstate__(self):\n        return {'model': self.model,\n                'decoder': self._decoder,\n                'simulation_method': self.simulation_method,\n                'simulation_kwargs': self.simulation_kwargs,\n                'heuristic_method.__class__': self.heuristic_method.__class__,\n                'heuristic_method.maximize': self.heuristic_method.maximize,\n                'heuristic_method.variator': self.heuristic_method.variator,\n                'heuristic_method.terminator': self.heuristic_method.terminator,\n                'heuristic_method.archiver': self.heuristic_method.archiver,\n                'heuristic_method.archive': self.heuristic_method.archive,\n                'heuristic_method.termination_cause': self.heuristic_method.termination_cause,\n                'heuristic_method._random': self.heuristic_method._random,\n                'heuristic_method.generator': self.heuristic_method.generator,\n                'heuristic_method._kwargs': self.heuristic_method._kwargs,\n                'objective_functions': self.objective_functions,\n                'ko_type': self.ko_type,\n                'solutions': self._solutions,\n                'seed': self.seed}\n\n    def __setstate__(self, d):\n        self.model = d['model']\n        self.simulation_method = d['simulation_method']\n        self.simulation_kwargs = d['simulation_kwargs']\n        self.seed = d['seed']\n        random = d['heuristic_method._random']\n        self.heuristic_method = d['heuristic_method.__class__'](random)\n        self.heuristic_method.maximize = d['heuristic_method.maximize']\n        self.heuristic_method.terminator = d['heuristic_method.terminator']\n        self.heuristic_method.termination_cause = d['heuristic_method.termination_cause']\n        self.heuristic_method.archiver = d['heuristic_method.archiver']\n        self.heuristic_method.archive = d['heuristic_method.archive']\n        self.heuristic_method._kwargs = d['heuristic_method._kwargs']\n        self.objective_functions = d['objective_functions']\n        self.ko_type = d['ko_type']\n        self._solutions = d['solutions']\n\n    def _repr_html_(self):\n\n        template = \"\"\"\n        <h4>Result:</h4>\n        <ul>\n            <li>model: %s</li>\n            <li>heuristic: %s</li>\n            <li>objective function: %s</li>\n            <li>simulation method: %s</li>\n            <li>type: %s</li>\n        <ul>\n        \"\"\"\n        model_id = self.model.id\n        heuristic = self.heuristic_method.__class__.__name__\n        of_string = \"<br/>\".join([o._repr_latex_() for o in self.objective_functions])\n        simulation = self.simulation_method.__name__\n        solutions = self.data_frame._repr_html_()\n\n        results = template % (model_id, heuristic, of_string, simulation, self.ko_type)\n        return results + solutions\n\n    def __iter__(self):\n        for _, row in self._solutions.iterrows():\n            yield [row['reactions'], row['knockouts'], row['fitness']]\n\n    def __iadd__(self, other):\n        if not isinstance(other, self.__class__):\n            raise AssertionError(\"Cannot merge result with %s\" % type(other))\n        if self.model.id != other.model.id:\n            raise AssertionError(\"Cannot merge results from different models\")\n        if self.ko_type != other.ko_type:\n            raise AssertionError(\"Cannot merge results with resulting from different strategies\")\n        if self.heuristic_method.__class__.__name__ != other.heuristic_method.__class__.__name__:\n            raise AssertionError(\"Cannot merge results from different heuristic methods\")\n\n        self._solutions = self._solutions.append(other._solutions, ignore_index=True)\n        self._solutions.drop_duplicates(subset=\"knockouts\", take_last=True, inplace=True)\n\n        return self\n\n    # TODO: find out how to plot an histogram (?) in bokeh\n    def stats(self):\n        stats_data = None\n        if in_ipnb():\n            if config.use_bokeh:\n                stats_data = stats.BokehStatsData(self)\n            elif config.use_matplotlib:\n                pass\n        else:\n            stats_data = stats.CLIStatsData(self)\n\n        stats_data.display()\n\n    def plot(self, grid=None, width=None, height=None, title=None):\n        raise NotImplementedError\n\n    @property\n    def data_frame(self):\n        return DataFrame(self._solutions)\n\n\nclass SwapOptimizationResult(KnockoutOptimizationResult):\n    def _decode_solutions(self, solutions):\n        decoded_solutions = DataFrame(columns=[\"reaction\", \"fitness\"])\n        for index, solution in enumerate(solutions):\n            reactions, _ = self._decoder(solution.candidate, flat=True)\n            if len(reactions) > 0:\n                decoded_solutions.loc[index] = [reactions, solution.fitness]\n        return decoded_solutions\n\n\nclass ReactionKnockoutOptimization(KnockoutOptimization):\n    \"\"\"\n    Knockout optimization using reactions.\n\n    Attributes\n    ----------\n    model : SolverBasedModel\n        A constraint-based model.\n    heuristic_method : inspyred.ec.EvolutionaryComputation\n        An evolutionary algorithm.\n    objective_function : objective function or list(objective function)\n        The objectives for the algorithm to maximize.\n    seed : int\n        A seed for random. It is auto-generated if None is given.\n    termination : inspyred.ec.terminators\n        A termination criteria for the algorithm. The default is inspyred.ec.terminators.evaluation_termination.\n    simulation_method: flux_analysis.simulation\n        The method used to simulate the model.\n    wt_reference: dict\n        A reference initial state for the optimization. It is required for flux_analysis.simulation.lmoma and\n        flux_analysis.simulation.room. If not given, it will be computed using flux_analysis.simulation.pfba\n    reactions: list\n        A list of valid reactions to knockout. If None, then all reactions in the model will be knockout candidates\n        except the ones defined in essential_reactions\n    essential_reactions: list\n        A list of reactions that cannot be knocked out. If None, then all essential reactions will be removed from\n        the valid reactions set.\n\n    Methods\n    -------\n    run(view=config.default_view, maximize=True, **kwargs)\n\n\n    See Also\n    --------\n    *inspyred.ec\n    *cameo.config.default_view\n\n    Examples\n    --------\n    >>> from cameo import models\n    >>> model = models.bigg.iJO1366\n    >>> from cameo.strain_design.heuristic.evolutionary.objective_functions import biomass_product_coupled_yield\n    >>> bpcy = biomass_product_coupled_yield(model.reactions.Ec_biomass_iJO1366_core_53p95,\n    >>>                                      model.reactions.EX_succ_e),\n    >>>                                      model.reactions.EX_glc__D_e)\n    >>> knockout_optimization = ReactionKnockoutOptimization(model=model, objective_function=bpcy,\n    >>>                                                      essential_reactions=[\"ATPM\"])\n    >>> knockout_optimization.run(max_evaluations=50000)\n\n\n    \"\"\"\n\n    def __init__(self, reactions=None, essential_reactions=None, *args, **kwargs):\n        super(ReactionKnockoutOptimization, self).__init__(*args, **kwargs)\n        if reactions is None:\n            self.reactions = set([r.id for r in self.model.reactions])\n        else:\n            self.reactions = reactions\n        logger.debug(\"Computing essential reactions...\")\n        if essential_reactions is None:\n            self.essential_reactions = set([r.id for r in self.model.essential_reactions()])\n        else:\n            self.essential_reactions = set([r.id for r in self.model.essential_reactions()] + essential_reactions)\n\n        exchange_reactions = set([r.id for r in self.model.exchanges])\n        self.representation = list(self.reactions.difference(self.essential_reactions).difference(exchange_reactions))\n        self._ko_type = REACTION_KNOCKOUT_TYPE\n        self._decoder = decoders.ReactionKnockoutDecoder(self.representation, self.model)\n\n\nclass GeneKnockoutOptimization(KnockoutOptimization):\n    \"\"\"\n    Knockout optimization using genes.\n\n    Attributes\n    ----------\n    model : SolverBasedModel\n        A constraint-based model.\n    heuristic_method : inspyred.ec.EvolutionaryComputation\n        An evolutionary algorithm.\n    objective_function : objective function or list(objective function)\n        The objectives for the algorithm to maximize.\n    seed : int\n        A seed for random. It is auto-generated if None is given.\n    termination : inspyred.ec.terminators\n        A termination criteria for the algorithm. The default is inspyred.ec.terminators.evaluation_termination.\n    simulation_method: flux_analysis.simulation\n        The method used to simulate the model.\n    wt_reference: dict\n        A reference initial state for the optimization. It is required for flux_analysis.simulation.lmoma and\n        flux_analysis.simulation.room. If not given, it will be computed using flux_analysis.simulation.pfba\n    genes: list\n        A list of valid genes to knockout. If None, then all genes in the model will be knockout candidates except the\n         ones defined in essential_genes\n    essential_genes: list\n        A list of genes that cannot be knocked out. If None, then all essential genes will be removed from the valid\n        genes set.\n\n    Methods\n    -------\n    run(view=config.default_view, maximize=True, **kwargs)\n\n\n    See Also\n    --------\n    *inspyred.ec\n    *cameo.config.default_view\n\n    Examples\n    --------\n    >>> from cameo import models\n    >>> model = models.bigg.iJO1366\n    >>> from cameo.strain_design.heuristic.evolutionary.objective_functions import biomass_product_coupled_yield\n    >>> bpcy = biomass_product_coupled_yield(model.reactions.Ec_biomass_iJO1366_core_53p95,\n    >>>                                      model.reactions.EX_succ_e),\n    >>>                                      model.reactions.EX_glc__D_e)\n    >>> knockout_optimization = GeneKnockoutOptimization(model=model, objective_function=bpcy)\n    >>> knockout_optimization.run(max_evaluations=50000)\n\n    \"\"\"\n\n    def __init__(self, genes=None, essential_genes=None, *args, **kwargs):\n        super(GeneKnockoutOptimization, self).__init__(*args, **kwargs)\n        if genes is None:\n            self.genes = set([g.id for g in self.model.genes])\n        else:\n            self.genes = genes\n        logger.debug(\"Computing essential genes...\")\n        if essential_genes is None:\n            self.essential_genes = set([g.id for g in self.model.essential_genes()])\n        else:\n            self.essential_genes = set([g.id for g in self.model.essential_genes()] + essential_genes)\n\n        self.representation = list(self.genes.difference(self.essential_genes))\n        self._ko_type = GENE_KNOCKOUT_TYPE\n        self._decoder = decoders.GeneKnockoutDecoder(self.representation, self.model)\n\n\nclass KnockinKnockoutEvaluator(KnockoutEvaluator):\n    def __init__(self, database, *args, **kwargs):\n        super(KnockinKnockoutEvaluator, self).__init__(*args, **kwargs)\n        self._database = database\n\n    def __call__(self, *args, **kwargs):\n        pass\n\n\nclass KnockinKnockoutOptimizationResult:\n    pass\n\n\nclass KnockinKnockoutOptimization(KnockoutOptimization):\n    def __init__(self, database=None, *args, **kwargs):\n        super(KnockinKnockoutOptimization, self).__init__(*args, **kwargs)\n        self._database = database\n\n    def _evaluator(self, candidates, args):\n        view = args.get('view')\n        population_chunks = (chunk for chunk in partition(candidates, len(view)))\n        func_obj = KnockinKnockoutEvaluator(self.model, self._decoder,\n                                            self.objective_function, self.simulation_method,\n                                            self.simulation_kwargs)\n        results = view.map(func_obj, population_chunks)\n        fitness = reduce(list.__add__, results)\n\n        return fitness\n\n    def run(self, **kwargs):\n        for observer in self.observers:\n            observer.reset()\n        self.heuristic_method.observer = self.observers\n        super(KnockoutOptimization, self).run(\n            keys=['knockout', 'knockin'],\n            knockout_representation=self.representation,\n            knockin_representation=self._database,\n            **kwargs)\n        return KnockinKnockoutOptimizationResult(model=self.model,\n                                                 heuristic_method=self.heuristic_method,\n                                                 simulation_method=self.simulation_method,\n                                                 solutions=self.heuristic_method.archive,\n                                                 objective_function=self.objective_function,\n                                                 ko_type=self._ko_type,\n                                                 decoder=self._decoder,\n                                                 product=kwargs.get('product', None))\n\n\n# TODO: implement a knockout knockin approach using a reaction db\nclass ReactionKnockinKnockoutOptimization(ReactionKnockoutOptimization, KnockinKnockoutOptimization):\n    def __init__(self, reaction_db=None, *args, **kwargs):\n        KnockinKnockoutOptimization.__init__(self, *args, **kwargs)\n        ReactionKnockoutOptimization.__init__(self, *args, **kwargs)\n\n    def run(self, **kwargs):\n        KnockinKnockoutOptimization.run(self, **kwargs)\n\n\nclass CofactorSwapOptimization(KnockoutOptimization):\n    \"\"\"\n    Optimize co-factor swapping\n\n    As suggested in [1]_, flux through a given reaction can sometimes be optimized by swapping complementary\n    co-factor. This class implements a search for reactions when swapped improve the given objective. Briefly,\n    the approach is to\n\n    - find reactions that have all the targeted co-factor pairs e.g. (nad_c -> nadp_c, nadh_c -> nadph_c)\n\n    - add reactions that have the co-factors swapped and then by a search algorithm switching one off in favor of the\n      other\n\n   The implementation here differs from that in [1]_ in that we use a general purpose search algorithm rather than\n   formulating the search as a mixed integer linear programming problem.\n\n   References\n   ----------\n   .. [1] King, Zachary A., and Adam M. Feist. \"Optimizing Cofactor Specificity of Oxidoreductase Enzymes for the\n      Generation of Microbial Production Strains - OptSwap.\" Industrial Biotechnology 9, no. 4 (August 1,\n      2013): 236-46. - doi:10.1089/ind.2013.0005.\n\n    Parameters\n    ----------\n    model : SolverBasedModel\n       the model to operator on\n    cofactor_id_swaps : tuple\n       a tuple of length 2 that defines two lists of metabolite identifiers that should be interchanged during the\n       swap optimization see e.g. `NADH_NADPH` which is also the default.\n    candidate_reactions : list\n       reactions to consider for co-factor swap - if not given then search for all reactions that include the given\n       cofactors\n    skip_reactions : list\n       reactions to not consider for co-factor swap, defaults to the objective function if not provided\n    args, kwargs : keyword arguments\n       passed on to super-classes, see in particular `objective_function`, `heuristic_method`, `termination`,\n       `simulation_method`, `wt_reference`, of `HeuristicOptimization` and `max_size` of `HeuristicOptimization.run`\n\n    Examples\n    --------\n    >>> from cameo import models\n    >>> from cameo.strain_design.heuristic.evolutionary.objective_functions import product_yield\n    >>> model = models.bigg.iJO1366\n    >>> model.objective = model.reactions.EX_thr__L_e\n    >>> model.reactions.BIOMASS_Ec_iJO1366_core_53p95M.lower_bound = 0.1\n    >>> py = product_yield(model.reactions.EX_thr__L_e, model.reactions.EX_glc__D_e)\n    >>> swap_optimization = CofactorSwapOptimization(model=model, objective_function=py)\n    >>> swap_optimization.run(max_evaluations=2000, max_size=2)\n    \"\"\"\n\n    def __init__(self, model, cofactor_id_swaps=NADH_NADPH, candidate_reactions=None,\n                 skip_reactions=None, *args, **kwargs):\n        super(self.__class__, self).__init__(model=model, knockout_evaluator=SwapEvaluator,\n                                             result_handler=SwapOptimizationResult, *args, **kwargs)\n        self.model = SwapperModel(model, cofactor_id_swaps, candidate_reactions, skip_reactions)\n        self._ko_type = SWAP_KNOCKOUT_TYPE\n        self.representation = list(self.model.swapped_reactions.keys())\n        self._decoder = decoders.ReactionKnockoutDecoder(self.representation, self.model)\n\n\nclass SwapperModel(SolverBasedModel):\n    \"\"\"An extension of the `SolverBasedModel` to add functions to easily add reactions that have co-factors swapped\n    etc.\n\n    Attributes\n    ----------\n    model : SolverBasedModel\n       the model to convert\n    cofactor_id_swaps : tuple\n       a tuple of length 2 that defines two lists of metabolite identifiers that should be interchanged during the\n       swap optimization see e.g. `NADH_NADPH` which is also the default.\n    candidate_reactions : list\n       reactions to consider for co-factor swap - if not given then search for all reactions that include the given\n       cofactors\n    skip_reactions : list\n       reactions to not consider for co-factor swap, defaults to the objective function if not provided\n    \"\"\"\n\n    def __init__(self, model, cofactor_id_swaps, candidate_reactions=None, skip_reactions=None):\n        super(SwapperModel, self).__init__()\n        self.__dict__ = model.copy().__dict__\n        check_swap = isinstance(cofactor_id_swaps, tuple) and len(cofactor_id_swaps) == 2\n        if not check_swap:\n            raise Exception('parameter cofactor_id_swap should be a tuple of length 2')\n        if skip_reactions is None:\n            skip_reactions = {reaction.id for reaction in model.reactions if reaction.objective_coefficient > 0}\n        self.cofactor_id_swap = cofactor_id_swaps\n        self.skip_reactions = skip_reactions\n        self.unswappable_reactions = []\n        self.swapped_reactions = {}\n        self.cofactor_swaps = [[self.metabolites.get_by_id(mid) for mid in self.cofactor_id_swap[0]],\n                               [self.metabolites.get_by_id(mid) for mid in self.cofactor_id_swap[1]]]\n        self.candidate_reactions = candidate_reactions\n        if candidate_reactions is None:\n            self.candidate_reactions = []\n            self.find_swappable_reactions()\n        for reaction_id in self.candidate_reactions:\n            self.add_swap_reaction(reaction_id)\n\n    def add_swap_reaction(self, reaction_id, time_machine=None):\n        \"\"\"\n        add a swap reaction to a model by adding a new reaction that has all metabolites swapped according to the\n        dictionary of replacements. The newly added reaction is knocked out.\n\n        Parameters\n        ----------\n        reaction_id : string\n            identifier for the reaction to swap metabolites in\n        time_machine : TimeMachine\n            enable undo for the added swap reaction\n        \"\"\"\n        parent = self.reactions.get_by_id(reaction_id)\n        if parent.check_mass_balance():\n            raise Exception('refusing to swap unbalanced reaction')\n        from_to = [all(metabolite in parent.metabolites for metabolite in self.cofactor_swaps[0]),\n                   all(metabolite in parent.metabolites for metabolite in self.cofactor_swaps[1])]\n        if any(from_to):\n            swapped = parent.clone(parent)\n            swapped.id = \"{}_swap\".format(swapped.id)\n            swapped.knock_out()\n            from_set = self.cofactor_swaps[from_to.index(True)]\n            to_set = self.cofactor_swaps[from_to.index(False)]\n            for index, metabolite in enumerate(to_set):\n                new_reactant = {metabolite: swapped.metabolites[from_set[index]]}\n                swapped.add_metabolites(new_reactant)\n            for metabolite in from_set:\n                metabolite._reaction.add(swapped)\n                swapped.pop(metabolite)\n            if swapped.check_mass_balance():\n                raise Exception('created reaction not mass balanced')\n            self.swapped_reactions[reaction_id] = swapped\n            if time_machine is not None:\n                time_machine(do=partial(self.add_reactions, [swapped]),\n                             undo=partial(self.remove_swap_reaction, reaction_id))\n            else:\n                self.add_reactions([swapped])\n\n    def remove_swap_reaction(self, reaction_id):\n        \"\"\"\n        remove a swapped reaction and update the dictionary of reactions swaps\n\n        Parameters\n        ----------\n        reaction_id : string\n           identifier of the swapped reaction, must be member of `swapped_reactions`\n        \"\"\"\n        self.remove_reactions([self.swapped_reactions[reaction_id]])\n        del self.swapped_reactions[reaction_id]\n\n    def swap_reaction(self, reaction_id, time_machine=None):\n        \"\"\"Knock-out the native reaction in and knock-in the reaction that has the co-factors swapped. Do it in\n        reverse if the native reaction is already knocker.\n\n        Parameters\n        ----------\n        reaction_id : string\n            the identifier of the reaction to swap\n        time_machine : TimeMachine\n            enable undo of the swap\n        \"\"\"\n        from_reaction = self.reactions.get_by_id(reaction_id)\n        to_reaction = self.swapped_reactions[reaction_id]\n        if abs(from_reaction.lower_bound) < 1e-6 and abs(from_reaction.upper_bound) < 1e-6:\n            from_reaction, to_reaction = to_reaction, from_reaction\n\n        def do_change(reaction_a, reaction_b, ub, lb):\n            reaction_b.upper_bound = ub\n            reaction_b.lower_bound = lb\n            reaction_a.knock_out()\n\n        if time_machine is not None:\n            time_machine(do=partial(do_change, from_reaction, to_reaction,\n                                    from_reaction.upper_bound, from_reaction.lower_bound),\n                         undo=partial(do_change, to_reaction, from_reaction,\n                                      from_reaction.upper_bound, from_reaction.lower_bound))\n        else:\n            do_change(from_reaction, to_reaction, from_reaction.upper_bound, from_reaction.lower_bound)\n\n    def find_swappable_reactions(self):\n        \"\"\"Get all reactions that can undergo co-factor swapping\n\n        find reactions that have one set of the cofactors targeted for swapping and are mass balanced and updates the\n        `candidate_reactions` attribute\n        \"\"\"\n\n        def swap_search(metabolites):\n            from_to = [all(metabolite in metabolites for metabolite in self.cofactor_swaps[0]),\n                       all(metabolite in metabolites for metabolite in self.cofactor_swaps[1])]\n            return sum(from_to) == 1\n\n        candidates = self.reactions.query(search_function=swap_search, attribute='metabolites')\n        for reaction in candidates:\n            if reaction.id in self.skip_reactions or \\\n                    reaction.check_mass_balance() or \\\n                    re.match(r'.*_swap$', reaction.id):\n                self.unswappable_reactions.append(reaction.id)\n            else:\n                self.candidate_reactions.append(reaction.id)\n","sub_path":"cameo/strain_design/heuristic/evolutionary/optimization.py","file_name":"optimization.py","file_ext":"py","file_size_in_byte":45344,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"32315197","text":"from EasySAV.EasySAV.Domain.Intervention import Intervention\nfrom typing import List\n\nclass Technicien(object):\n\n    def __init__(self, nom, prenom):\n        self.nom = nom\n        self.prenom = prenom\n        self.interventions: List[Intervention] = list()\n\n    def ajouter_intervention(self, intervention: Intervention):\n        self.interventions.append(intervention)\n\n\ninterv1 = Intervention(233,\"azze\", \"azerttt\", \"jjjjjjj\")\ninterv2 = Intervention(566,\"juju\", \"asdt\", \"aaaaa\")\ntech = Technicien(\"lnf\", \"bob\")\ntech.ajouter_intervention(interv1)\ntech.ajouter_intervention(interv2)\nfor i in tech.interventions:\n    print(i)","sub_path":"EasySAV/EasySAV/Domain/Technicien.py","file_name":"Technicien.py","file_ext":"py","file_size_in_byte":625,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"143093641","text":"from io import StringIO\nimport unittest\nfrom unittest import TestCase, mock\n\nfrom work import work_on, Helper, Worker, size_of, Pricer\n\n\nclass TestWorkMockingModule(TestCase):\n    def test_using_context_manager(self):\n        with mock.patch('work.os.getcwd', return_value='testing') as mocked_os:\n            assert work_on() == 'testing'\n\n\nclass TestWorkerModule(TestCase):\n\n    def test_patching_class(self):\n        with mock.patch('work.Helper', autospec=True) as MockHelper:\n            MockHelper.return_value.get_path.return_value = 'testing'\n            worker = Worker()\n            MockHelper.assert_called_once_with('db')\n            self.assertEqual(worker.work(), 'testing')\n            \n    def test_pathching_context_managers(self):\n        with mock.patch('work.open') as mock_open:\n            mock_open.return_value.__enter__.return_value = StringIO('testing')\n            self.assertEqual(size_of(), 7)\n\n    # Technology for mocking class attributes\n    def test_patch_incorrect_class_attribute(self):\n        with mock.patch.object(Pricer, 'PERCENTAGE', 1):\n            pricer = Pricer()\n            self.assertEqual(pricer.get_discounted_price(100), 100)\n\n    def test_patch_class_attribute(self):\n        with mock.patch.object(Pricer, 'DISCOUNT', 1):\n            pricer = Pricer()\n            self.assertEqual(pricer.get_discounted_price(100), 100)\n        self.assertAlmostEqual(pricer.get_discounted_price(100), 80)\n\n    # Mocking class Helpers\n    def test_delayed_import(self):\n        with mock.patch('work.COUNTRIES', ['GB']):\n            from work import CountryPricer\n            pricer = CountryPricer()\n            self.assertAlmostEqual(pricer.get_discount_price(100, 'GB'), 80)\n\n        pricer = CountryPricer()\n        self.assertAlmostEqual(pricer.get_discount_price(100, \"GB\"), 100)\n\n\nunittest.main()\n","sub_path":"mocks/lesson_1.py","file_name":"lesson_1.py","file_ext":"py","file_size_in_byte":1840,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"506272440","text":"# -*- encoding: utf-8 -*-\n# Copyright (c) 2015 b<>com\n#\n# Authors: Jean-Emile DARTOIS <jean-emile.dartois@b-com.com>\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\n# implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport os\n\nfrom lxml import etree\nfrom oslo_utils import uuidutils\nimport six\n\nfrom watcher.common import exception\nfrom watcher.decision_engine.model import element\nfrom watcher.decision_engine.model import model_root\nfrom watcher.tests import base\nfrom watcher.tests.decision_engine.model import faker_cluster_state\n\n\nclass TestModel(base.TestCase):\n\n    def load_data(self, filename):\n        cwd = os.path.abspath(os.path.dirname(__file__))\n        data_folder = os.path.join(cwd, \"data\")\n\n        with open(os.path.join(data_folder, filename), 'rb') as xml_file:\n            xml_data = xml_file.read()\n\n        return xml_data\n\n    def load_model(self, filename):\n        return model_root.ModelRoot.from_xml(self.load_data(filename))\n\n    def test_model_structure(self):\n        fake_cluster = faker_cluster_state.FakerModelCollector()\n        model = fake_cluster.build_scenario_1()\n\n        self.assertEqual(5, len(model.get_all_compute_nodes()))\n        self.assertEqual(35, len(model.get_all_instances()))\n        self.assertEqual(8, len(model.edges()))\n\n        expected_struct_str = self.load_data('scenario_1.xml')\n        parser = etree.XMLParser(remove_blank_text=True)\n        expected_struct = etree.fromstring(expected_struct_str, parser)\n        model_structure = etree.fromstring(model.to_string(), parser)\n\n        normalized_expected_output = six.BytesIO()\n        normalized_model_output = six.BytesIO()\n        expected_struct.getroottree().write_c14n(normalized_expected_output)\n        model_structure.getroottree().write_c14n(normalized_model_output)\n\n        normalized_expected_struct = normalized_expected_output.getvalue()\n        normalized_model_struct = normalized_model_output.getvalue()\n\n        self.assertEqual(normalized_expected_struct, normalized_model_struct)\n\n    def test_build_model_from_xml(self):\n        fake_cluster = faker_cluster_state.FakerModelCollector()\n\n        expected_model = fake_cluster.generate_scenario_1()\n        struct_str = self.load_data('scenario_1.xml')\n\n        model = model_root.ModelRoot.from_xml(struct_str)\n        self.assertEqual(expected_model.to_string(), model.to_string())\n\n    def test_add_node(self):\n        model = model_root.ModelRoot()\n        uuid_ = \"{0}\".format(uuidutils.generate_uuid())\n        node = element.ComputeNode(id=1)\n        node.uuid = uuid_\n        model.add_node(node)\n        self.assertEqual(node, model.get_node_by_uuid(uuid_))\n\n    def test_delete_node(self):\n        model = model_root.ModelRoot()\n        uuid_ = \"{0}\".format(uuidutils.generate_uuid())\n        node = element.ComputeNode(id=1)\n        node.uuid = uuid_\n        model.add_node(node)\n        self.assertEqual(node, model.get_node_by_uuid(uuid_))\n        model.remove_node(node)\n        self.assertRaises(exception.ComputeNodeNotFound,\n                          model.get_node_by_uuid, uuid_)\n\n    def test_get_all_compute_nodes(self):\n        model = model_root.ModelRoot()\n        for id_ in range(10):\n            uuid_ = \"{0}\".format(uuidutils.generate_uuid())\n            node = element.ComputeNode(id_)\n            node.uuid = uuid_\n            model.add_node(node)\n        all_nodes = model.get_all_compute_nodes()\n        for uuid_ in all_nodes:\n            node = model.get_node_by_uuid(uuid_)\n            model.assert_node(node)\n\n    def test_set_get_state_nodes(self):\n        model = model_root.ModelRoot()\n        uuid_ = \"{0}\".format(uuidutils.generate_uuid())\n        node = element.ComputeNode(id=1)\n        node.uuid = uuid_\n        model.add_node(node)\n\n        self.assertIn(node.state, [el.value for el in element.ServiceState])\n\n        node = model.get_node_by_uuid(uuid_)\n        node.state = element.ServiceState.OFFLINE.value\n        self.assertIn(node.state, [el.value for el in element.ServiceState])\n\n    def test_node_from_uuid_raise(self):\n        model = model_root.ModelRoot()\n        uuid_ = \"{0}\".format(uuidutils.generate_uuid())\n        node = element.ComputeNode(id=1)\n        node.uuid = uuid_\n        model.add_node(node)\n\n        uuid2 = \"{0}\".format(uuidutils.generate_uuid())\n        self.assertRaises(exception.ComputeNodeNotFound,\n                          model.get_node_by_uuid, uuid2)\n\n    def test_remove_node_raise(self):\n        model = model_root.ModelRoot()\n        uuid_ = \"{0}\".format(uuidutils.generate_uuid())\n        node = element.ComputeNode(id=1)\n        node.uuid = uuid_\n        model.add_node(node)\n\n        uuid2 = \"{0}\".format(uuidutils.generate_uuid())\n        node2 = element.ComputeNode(id=2)\n        node2.uuid = uuid2\n\n        self.assertRaises(exception.ComputeNodeNotFound,\n                          model.remove_node, node2)\n\n    def test_assert_node_raise(self):\n        model = model_root.ModelRoot()\n        uuid_ = \"{0}\".format(uuidutils.generate_uuid())\n        node = element.ComputeNode(id=1)\n        node.uuid = uuid_\n        model.add_node(node)\n        self.assertRaises(exception.IllegalArgumentException,\n                          model.assert_node, \"objet_qcq\")\n\n    def test_instance_from_uuid_raise(self):\n        fake_cluster = faker_cluster_state.FakerModelCollector()\n        model = fake_cluster.generate_scenario_1()\n        self.assertRaises(exception.InstanceNotFound,\n                          model.get_instance_by_uuid, \"valeur_qcq\")\n\n    def test_assert_instance_raise(self):\n        model = model_root.ModelRoot()\n        self.assertRaises(exception.IllegalArgumentException,\n                          model.assert_instance, \"valeur_qcq\")\n","sub_path":"python-watcher-1.0.1/watcher/tests/decision_engine/model/test_model.py","file_name":"test_model.py","file_ext":"py","file_size_in_byte":6178,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"206160588","text":"from save import *\n\n\ndef make_data(x, y, alpha, delta):\n    h = 0.01\n    xlist = []\n    ylist = []\n    for i in range(100000):\n        km1 = h * f(x, y, alpha)\n        lm1 = h * g(x, y, alpha, delta)\n        km2 = h * f(x + km1/2, y + lm1/2, alpha)\n        lm2 = h * g(x + km1/2, y + lm1/2, alpha, delta)\n        km3 = h * f(x + km2/2, y + lm2/2, alpha)\n        lm3 = h * g(x + km2/2, y + lm2/2, alpha, delta)\n        km4 = h * f(x + km3, y + lm3, alpha)\n        lm4 = h * g(x + km3, y + lm3, alpha, delta)\n        x1 = x + (km1 + 2 * km2 + 2 * km3 + km4)/6\n        y1 = y + (lm1 + 2 * lm2 + 2 * lm3 + lm4)/6\n        xlist.append(x1)\n        ylist.append(y1)\n        x = x1\n        y = y1\n    return xlist, ylist\n\n\ndef f(x, y, alpha):\n    return x - (x*y)/(1+alpha*x) - 0.01*x*x\n\n\ndef g(x, y, alpha, delta):\n    return -1*y + (x*y)/(1 + alpha*x)-delta*y*y\n\n\ndef make_data1(x, y, alpha, delta, h):\n    xlist = []\n    ylist = []\n    for i in range(100000):\n        km1 = h*f(x, y, alpha)\n        lm1 = h*g(x, y, alpha, delta)\n        km2 = h*f(x + km1/2, y + lm1/2, alpha)\n        lm2 = h*g(x + km1/2, y + lm1/2, alpha, delta)\n        km3 = h*f(x + km2/2, y + lm2/2, alpha)\n        lm3 = h*g(x + km2/2, y + lm2/2, alpha, delta)\n        km4 = h*f(x + km3, y + lm3, alpha)\n        lm4 = h*g(x + km3, y + lm3, alpha, delta)\n        x1 = x + (km1 + 2*km2 + 2*km3 + km4)/6\n        y1 = y + (lm1 + 2*lm2 + 2*lm3 + lm4)/6\n        xlist.append(x1)\n        ylist.append(y1)\n        x = x1\n        y = y1\n    return xlist, ylist\n\n\ndef main(alpha, delta):\n    x, y = make_data(1, 1, alpha, delta)\n    x1, y1 = make_data(60, 15, alpha, delta)\n    x2, y2 = make_data(110, 10, alpha, delta)\n\n    x3, y3 = make_data(20, 16, alpha, delta)\n    x4, y4 = make_data(5, 15, alpha, delta)\n    x5, y5 = make_data(41, 20, alpha, delta)\n\n    x6, y6 = make_data(10, 25, alpha, delta)\n    # sep1, sep2 = make_data1(0, 0.00001, alpha, delta, -0.01)\n    matlab_export(x, y, 'tr.txt')\n    matlab_export(x1, y1, 'tr1.txt')\n    matlab_export(x2, y2, 'tr2.txt')\n    matlab_export(x3, y3, 'tr3.txt')\n    matlab_export(x4, y4, 'tr4.txt')\n    matlab_export(x5, y5, 'tr5.txt')\n    matlab_export(x6, y6, 'tr6.txt')\n\n\nif __name__ == \"__main__\":\n    main(0.891, 0.0043)\n","sub_path":"Физтех/Model1.py","file_name":"Model1.py","file_ext":"py","file_size_in_byte":2228,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"93896678","text":"from __future__ import division\nimport sys\nsys.path.insert(0, '../..')\nimport Leap\nfrom pygameWindow import PYGAME_WINDOW\nimport pygame.image\nimport random\nimport constants\nimport pickle\nimport numpy as np\nfrom random import seed\nfrom random import randint\nimport time\nimport atexit\n\nclf = pickle.load(open('userData/classifier.p','rb'))\ntestData = np.zeros((1,30),dtype='f')\n\npygameWindow = PYGAME_WINDOW()\n\nx = 250\ny = 250\nxMin = -100.0\nxMax = 100.0\nyMin = -100.0\nyMax = 100.0\nprogramState = 0\nhandPos = 0\ncountForHandPos = 0\ncountForNumCorrect = 0\ncurrentNumCorrect = 1\nnumToSign = 0\nk = 0\nnumCounter = 0\nnumSuccesses = 0\nnumbersToDisplay = 3\nsuccessCount = 0\nfailureCount = 0\ntimeAllowedPerNumber = 40\nreducedTimeStage = 0\ncountAttempts = 0\ncountSuccesses = 0\npreviousPercentage = 0\ntoggleHandColor = 0\n\ndef Handle_Vector_From_Leap(v):\n    global xMin, xMax, yMin, yMax\n    x = int(v[0])\n    y = int(v[1])\n    z = int(v[2])\n\n    if(x < xMin):\n        xMin = x\n    if(x > xMax):\n        xMax = x\n    if(y < yMin):\n        yMin = y\n    if(y > yMax):\n        yMax = y\n\n    x = ScaleCoordinates(x, xMin, xMax, 0, constants.pygameWindowWidth/2)\n    y = ScaleCoordinates(y, yMin, yMax, 0, constants.pygameWindowDepth/2)\n    z = ScaleCoordinates(z, yMin, yMax, 0, constants.pygameWindowDepth/2)\n\n    return x, y, z\n\ndef Handle_Bone(bone):\n    global toggleHandColor\n    base = bone.prev_joint\n    tip = bone.next_joint\n    baseX, baseY, baseZ = Handle_Vector_From_Leap(base)\n    tipX, tipY, tipZ = Handle_Vector_From_Leap(tip)\n    pygameWindow.Draw_Black_Line(baseX, baseZ, tipX, tipZ, 3 - bone.type)\n    pygameWindow.Draw_Line(baseX, baseZ, tipX, tipZ, 3 - bone.type, toggleHandColor)\n    return tipX, tipY, tipZ\n\ndef Handle_Finger(finger):\n    global k\n    for b in range(0,4):\n        xTip, yTip, zTip = Handle_Bone(finger.bone(b))\n        if((b == 0) or (b == 3)):\n            testData[0,k] = xTip\n            testData[0,k+1] = yTip\n            testData[0,k+2] = zTip\n            k = k + 3\n\ndef Handle_Frame(frame):\n    hand = frame.hands[0]\n    fingers = hand.fingers\n    for finger in fingers:\n        Handle_Finger(finger)\n\ndef ScaleCoordinates(value, rangeOneLow, rangeOneHigh, rangeTwoLow, rangeTwoHigh):\n    diff = value - rangeOneLow\n    oldRange = rangeOneHigh - rangeOneLow\n    newRange = rangeTwoHigh - rangeTwoLow\n    if(oldRange == 0):\n        return value\n    oldFraction = diff / oldRange\n    newValue = oldFraction * newRange\n    return newValue\n\ndef CenterData(data):\n    # Array to pass back\n    newArray = np.zeros((1,30),dtype='f')\n\n    # x coordinates\n    xValues = data[0,::3]\n    xCount = 0\n    xTotal = 0\n    for x in xValues:\n        xCount = xCount + 1\n        xTotal = xTotal + x\n    mean = xTotal/xCount\n    newArray[0,::3] = data[0,::3] - mean\n\n    # y coordinates\n    yValues = data[0,1::3]\n    yCount = 0\n    yTotal = 0\n    for y in yValues:\n        yCount = yCount + 1\n        yTotal = yTotal + y\n    mean = yTotal/yCount\n    newArray[0,1::3] = data[0,1::3] - mean\n\n    # z coordinates\n    zValues = data[0,2::3]\n    zCount = 0\n    zTotal = 0\n    for z in zValues:\n        zCount = zCount + 1\n        zTotal = zTotal + z\n    mean = zTotal/zCount\n    newArray[0,2::3] = data[0,2::3] - mean\n\n    return newArray\n\ndef Handle_Hand_Position(frame):\n    global handPos, countForHandPos\n    hand = frame.hands[0]\n    fingers = hand.fingers\n    for finger in fingers:\n        tip = finger.bone(3).next_joint\n        x, y, z = Handle_Vector_From_Leap(tip)\n        if(x < 0):\n            pygameWindow.Draw_Instruction_Right()\n            handPos = 0\n            countForHandPos = 0\n        elif(x > 400):\n            pygameWindow.Draw_Instruction_Left()\n            handPos = 0\n            countForHandPos = 0\n        else:\n            if(y < 100):\n                pygameWindow.Draw_Instruction_Up()\n                handPos = 0\n                countForHandPos = 0\n            elif(y > 250):\n                pygameWindow.Draw_Instruction_Down()\n                handPos = 0\n                countForHandPos = 0\n            else:\n                if(z < 0):\n                    pygameWindow.Draw_Instruction_In()\n                    handPos = 0\n                    countForHandPos = 0\n                elif(z > 100):\n                    pygameWindow.Draw_Instruction_Out()\n                    handPos = 0\n                    countForHandPos = 0\n                else:\n                    pygameWindow.Draw_Instruction_Success()\n                    handPos = 1\n                    countForHandPos += 1\n\n\n\ndef HandleState0(frame):\n    global programState\n    pygameWindow.Draw_Instruction_Picture()\n    if HandOverDevice(frame):\n        programState = 1\n\ndef HandleState1(frame):\n    global programState, handPos, countForHandPos\n    Handle_Frame(frame)\n    if(countForHandPos >= 250):\n        programState = 2\n    Handle_Hand_Position(frame)\n    if not HandOverDevice(frame):\n        programState = 0\n\ndef HandleState2(frame, num):\n    global testData, clf, countForNumCorrect, currentNumCorrect, programState, numbersToDisplay, countAttempts, toggleHandColor\n    if(num == 1):\n        pygameWindow.Draw1()\n        if(numbersToDisplay != 9):\n            pygameWindow.Draw1Num()\n    elif(num == 2):\n        pygameWindow.Draw2()\n        if(numbersToDisplay != 9):\n            pygameWindow.Draw2Num()\n    elif(num == 3):\n        pygameWindow.Draw3()\n        if(numbersToDisplay != 9):\n            pygameWindow.Draw3Num()\n    elif(num == 4):\n        pygameWindow.Draw4()\n        if(numbersToDisplay != 9):\n            pygameWindow.Draw4Num()\n    elif(num == 5):\n        pygameWindow.Draw5()\n        if(numbersToDisplay != 9):\n            pygameWindow.Draw5Num()\n    elif(num == 6):\n        pygameWindow.Draw6()\n        if(numbersToDisplay != 9):\n            pygameWindow.Draw6Num()\n    elif(num == 7):\n        pygameWindow.Draw7()\n        if(numbersToDisplay != 9):\n            pygameWindow.Draw7Num()\n    elif(num == 8):\n        pygameWindow.Draw8()\n        if(numbersToDisplay != 9):\n            pygameWindow.Draw8Num()\n    elif(num == 9):\n        pygameWindow.Draw9()\n        if(numbersToDisplay != 9):\n            pygameWindow.Draw9Num()\n    Handle_Frame(frame)\n    testData = CenterData(testData)\n    predictedClass = clf.Predict(testData)\n    if(predictedClass == num):\n        toggleHandColor = 1\n        countForNumCorrect += 1\n    else:\n        toggleHandColor = 0\n        countForNumCorrect = 0\n    if(countForNumCorrect >= 10):\n        toggleHandColor = 0\n        currentNumCorrect = 1\n        programState = 3\n\ndef HandleState3(frame):\n    global programState, successCount, numSuccesses, countAttempts, countSuccesses\n    pygameWindow.Draw_Instruction_Success()\n    if(successCount >= 20):\n        numSuccesses += 1\n        programState = 2\n        countAttempts += 1\n        countSuccesses += 1\n    successCount += 1\n\ndef HandleState4(frame):\n    global programState, failureCount, currentNumCorrect, countAttempts\n    if(failureCount >= 20):\n        programState = 2\n        currentNumCorrect = 1\n        failureCount = 0\n        countAttempts += 1\n    failureCount += 1\n    pygameWindow.Draw_Instruction_Failure()\n\ndef HandOverDevice(frame):\n    if(len(frame.hands) > 0):\n        return True\n    return False\n\ndef exit_handler():\n    global database, userName, countAttempts, countSuccesses\n    userEntry = database[userName]\n    prevSuccessRate = 0\n    if countAttempts != 0:\n        prevSuccessRate = countSuccesses / countAttempts\n    if(countAttempts == 0):\n        userEntry['percentSuccess'] =  1.0\n    else:\n        userEntry['percentSuccess'] = prevSuccessRate\n    totalPercentage = userEntry['totalPercentage']\n    totalPercentage += prevSuccessRate\n    userEntry['totalPercentage'] = totalPercentage\n    pickle.dump(database, open('userData/database.p','wb'))\n\natexit.register(exit_handler)\n\ncontroller = Leap.Controller()\n\npygameX = 0\npygameY = 0\n\nseed(2)\n\ndatabase = pickle.load(open('userData/database.p','rb'))\n\nuserName = raw_input('Please enter your name: ')\n\ntotalPercentage = 1.0\ncountUsers = 0\ncurrUserRank = 1\n\nif userName in database:\n    print('welcome back ' + userName + '.')\n    userEntry = database[userName]\n    incrementMe = userEntry['logins']\n    incrementMe += 1\n    userEntry['logins'] = incrementMe\n    previousPercentage = userEntry['percentSuccess']\nelse:\n    database[userName] = {'logins': 1}\n    print('welcome ' + userName + '.')\n    userEntry = database[userName]\n    for i in range(1,10):\n        userEntry['digit'+str(i)+'attempted'] = 0\n    userEntry['totalPercentage'] = 0.0\n\ntotalPercentage = userEntry['totalPercentage']\ncurrUserTotalPercentageCalculated = totalPercentage / userEntry['logins']\nfor name in database:\n    countUsers += 1\n    otherUser = database[name]\n    otherUserTotalPercentageCalculated = otherUser['totalPercentage'] / otherUser['logins']\n    if otherUserTotalPercentageCalculated > currUserTotalPercentageCalculated:\n        currUserRank += 1\n\n\nwhile True:\n    pygameWindow.Prepare()\n    frame = controller.frame()\n    k = 0\n    if(len(frame.hands) < 1):\n        programState = 0\n    if programState == 0:\n        HandleState0(frame)\n    elif programState == 1:\n        HandleState1(frame)\n    elif programState == 2:\n        successCount = 0\n        if(numSuccesses >= 5 and reducedTimeStage == 0):\n            if(numbersToDisplay == 3):\n                numbersToDisplay = 4\n            elif(numbersToDisplay == 4):\n                numbersToDisplay = 5\n            elif(numbersToDisplay == 5):\n                numbersToDisplay = 6\n            elif(numbersToDisplay == 6):\n                numbersToDisplay = 7\n            elif(numbersToDisplay == 7):\n                numbersToDisplay = 8\n            elif(numbersToDisplay == 8):\n                numbersToDisplay = 9\n                reducedTimeStage = 1\n            numSuccesses = 0\n        if(numSuccesses >= 20 and reducedTimeStage == 1):\n            if(timeAllowedPerNumber == 40):\n                timeAllowedPerNumber = 35\n            if(timeAllowedPerNumber == 35):\n                timeAllowedPerNumber = 30\n            if(timeAllowedPerNumber == 30):\n                timeAllowedPerNumber = 25\n            numSuccesses = 0\n        if(numCounter >= timeAllowedPerNumber):\n            numCounter = 0\n            programState = 4\n        if(currentNumCorrect == 1):\n            numCounter = 0\n            numToSign = randint(1,numbersToDisplay)\n            currentNumCorrect = 0\n            countForNumCorrect = 0\n            userEntry = database[userName]\n            numToIncrement = userEntry['digit'+str(numToSign)+'attempted']\n            numToIncrement += 1\n            userEntry['digit'+str(numToSign)+'attempted'] = numToIncrement\n        HandleState2(frame,numToSign)\n        pickle.dump(database, open('userData/database.p','wb'))\n        pygameWindow.DrawDatabaseData(database, userName, countSuccesses, countAttempts, previousPercentage, currUserRank)\n        numCounter += 1\n    elif programState == 3:\n        HandleState3(frame)\n        pygameWindow.DrawDatabaseData(database, userName, countSuccesses, countAttempts, previousPercentage, currUserRank)\n    elif programState == 4:\n        HandleState4(frame)\n        pygameWindow.DrawDatabaseData(database, userName, countSuccesses, countAttempts, previousPercentage, currUserRank)\n    pygameWindow.Reveal()\n","sub_path":"Del10/Del10.py","file_name":"Del10.py","file_ext":"py","file_size_in_byte":11317,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"295667982","text":"# 1stPlot.py\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as mpatches\nplt.close()\n\npathin='../input/log.txt'\ndatainput=open(pathin,'r')\n\nNol=4400644   # 4400644\niplist=[]\nfor i in range(Nol):\n    line=datainput.readline()\n    linesplit=line.split()\n    host=linesplit[0]\n    iplist.append(host)\ndatainput.close()\n\nfrom collections import Counter\niplistcounter=Counter(iplist)\n\nipTop=200\n\nip_mostcommon=iplistcounter.most_common(ipTop)\n\npathout='../output/hosts.txt'\ndataout=open(pathout,'wb')\n\nfreqList=[]\nipList=[]\n\n\nfor i in range(ipTop):\n    a=ip_mostcommon[i]\n    ipa=a[0]; freqa=int(a[1])\n    dataout.write('%s,%d' % (ipa,freqa))\n    freqList.append(freqa)\n    ipList.append(ipa)\n    \ndataout.close() \n\nfig = plt.figure()\nwidth = .45\nind = np.arange(len(freqList))\nplt.subplot(2,1,1)\nplt.bar(ind, freqList, width=width)\n\nplt.title('Distribution of host activity')\n#plt.xticks(ind + width / 2, ipList)\n#fig.autofmt_xdate()\nplt.show()\nplt.savefig(\"1stplot.pdf\")\n\n\nipListF=[]\nfor i in range(np.size(freqList)):\n    ipF=float(freqList[i])\n    ipListF.append(ipF)\n    \n\nplt.subplot(2,1,2)\nplt.plot(ind,ipListF/np.max(ipListF),'b')\nred_patch = mpatches.Patch(color='blue', label='Normalized to unity')\nplt.legend(handles=[red_patch])\nplt.show()\nplt.savefig(\"1stplot.pdf\")\n\n\n\n\n\n","sub_path":"1stPlot/1stPlot.py","file_name":"1stPlot.py","file_ext":"py","file_size_in_byte":1310,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"516105078","text":"from django.contrib.auth.decorators import login_required\nfrom django.contrib.auth import login, authenticate, logout\nfrom django.contrib.auth.models import User\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.core.urlresolvers import reverse\nfrom django.core.validators import URLValidator\nfrom django.core.exceptions import ValidationError, ObjectDoesNotExist\nfrom django.shortcuts import *\nfrom django.forms.util import ValidationError, ErrorList\n\nimport random, logging, urllib\nfrom urlparse import urljoin\nfrom base64 import urlsafe_b64encode\n\nfrom google.appengine.api import mail, urlfetch, users\nfrom google.appengine.api.urlfetch import DownloadError \n\nfrom core.models import emailHooks, emailPostRequest, GoogleProfile\nfrom forms import AddHookForm\n\n#\n#  VIEW LOGIC\n#-----------------------------------------------------------------------------------\ndef home(request):\n\n    return render_to_response('home.html')\n\n\n#-----------------------------------------------------------------------------------\ndef faq(request):\n    \n    try:\n        return render_to_response('faq.html', {'email': request.user.email})\n    except:\n        return render_to_response('faq.html')\n\n\n#-----------------------------------------------------------------------------------\ndef log_user_out(request):\n\n    logout(request)\n    return HttpResponseRedirect(users.create_logout_url(reverse('core.views.home')))\n    \n\n#-----------------------------------------------------------------------------------\ndef googlelogin(request):\n\n    google_user = users.get_current_user()\n    \n    if google_user:\n        user = authenticate(request=request, google_user=google_user)\n        login(request, user)\n    else:\n        return HttpResponseRedirect(users.create_login_url(reverse('core.views.googlelogin')))\n    return redirect('core.views.viewWebhooks')\n\n\n#-----------------------------------------------------------------------------------\n@login_required\ndef viewWebhooks(request):\n    user_id = request.user.google_user_id               \n    hooks = emailHooks.objects.filter(google_user_id=user_id)\n    if len(hooks)<3:\n        canAddMore = True\n    else:\n        canAddMore = False\n    \n    return render_to_response('viewHooks.html', {'hooks': hooks,\n                                                 'canAddMore': canAddMore,\n                                                 'security_key': request.user.security_key,\n                                                 'email': request.user.email,\n                                                 'nickname': request.user.nickname})\n\n    \n#-----------------------------------------------------------------------------------\n@login_required\ndef details(request):\n    user_id = request.user.google_user_id\n    \n    recipient = request.GET.get('recipient')\n    script_url = request.GET.get('script_url')\n    if recipient is None or script_url is None:\n        return redirect('core.views.viewWebhooks')\n\n    else:             \n        try:\n            details = emailPostRequest.objects.filter(google_user_id=user_id,\n                                             recipient=request.GET['recipient'],\n                                             script_url=request.GET['script_url']).order_by('-created')[:20]\n            if len(details) == 0:\n                check = emailHooks.objects.filter(google_user_id=user_id,\n                                                  recipient=request.GET['recipient'],\n                                                  script_url=request.GET['script_url'])\n                if len(check) == 0:\n                    return redirect('core.views.viewWebhooks')\n\n            return render_to_response('details.html', {'details': details,\n                                                       'empty_details': False,\n                                                       'recipient': recipient,\n                                                       'script_url': script_url,\n                                                       'email': request.user.email})\n            \n        except emailPostRequest.DoesNotExist:\n            return redirect('core.views.viewWebhooks')\n    \n\n#-----------------------------------------------------------------------------------\n@login_required\ndef add(request):\n\n    #check again that they dont have more than 3\n    user_id = request.user.google_user_id\n    hooks = emailHooks.objects.filter(google_user_id=user_id)\n    if len(hooks)>=3:\n        return redirect('core.views.viewWebhooks')\n\n    #if clear, then show form and process\n    form = AddHookForm()\n    if request.method == 'POST':\n        form = AddHookForm(request.POST)\n        if form.is_valid():\n            recipient = form.cleaned_data['recipient']\n            script_url = form.cleaned_data['script_url']\n\n            #check to make sure no one else has that hook\n            if len(emailHooks.objects.filter(recipient=recipient)) >= 1:\n                form._errors['recipient'] = ErrorList(['This recipient is already taken :('])            \n                return render_to_response('add.html', {'form': form,\n                                                       'email': request.user.email})\n            \n            #if everything checks out, parse out domain root of the script location\n            #and get a rnd str for next step\n            #is this really important, maybe not\n            hostname = getHostname(script_url)\n            word = uri_b64encode(hostname)         \n            \n            return render_to_response('checkOwnership.html', {'recipient': recipient,\n                                                              'script_url': script_url,\n                                                              'rndstring': word,\n                                                              'domain_root': hostname,\n\t\t\t\t\t\t\t\t\t\t\t\t\t          'email': request.user.email})\n\n        else:\n            #else check this also to get it all in one pass\n            if len(emailHooks.objects.filter(recipient=request.POST['recipient'])) >= 1:\n                form._errors['recipient'] = ErrorList(['This recipient is already taken :('])            \n                return render_to_response('add.html', {'form': form,\n                                                       'email': request.user.email})\n                \n    return render_to_response('add.html', {'form': form,\n                                           'email': request.user.email})\n\n\n#-----------------------------------------------------------------------------------\n@login_required\ndef check(request):\n    form = AddHookForm()\n    if request.method == 'POST':\n        form = AddHookForm(request.POST)\n        if form.is_valid():\n            recipient = form.cleaned_data['recipient']\n            script_url = form.cleaned_data['script_url']\n            file_to_find = request.POST['rndstring']+'.html'\n            domain_root = request.POST['domain_root']\n           \n            validate = URLValidator(verify_exists=True)\n            try:\n                validate(domain_root+file_to_find)\n                                         \n                #if it works, run some final validation and save\n                user_id = request.user.google_user_id\n                hooks = emailHooks.objects.filter(google_user_id=user_id)\n                if len(hooks)>=3:\n                    return redirect('core.views.viewWebhooks')\n                if len(emailHooks.objects.filter(recipient=recipient)) >= 1 and request.POST['edit'] == False:\n                    return redirect('core.views.viewWebhooks')               \n                \n                #if this is an edit, delete old entry\n                if request.POST['edit'] == 'True':\n                    old_url = request.POST['old_url']\n                    old_recipient = request.POST['old_recipient']\n                    \n                    try:\n                        hook = emailHooks.objects.filter(google_user_id=user_id,\n                                                      recipient=old_recipient,\n                                                      script_url=old_url)\n                        hook.delete()\n                    except emailHooks.DoesNotExist:\n                        return redirect('core.views.viewWebhooks')\n                \n                #and save                \n                newHook = emailHooks(script_url=script_url,\n                                     google_user_id=user_id,\n                                     recipient=recipient)\n                newHook.save()\n                return redirect('core.views.viewWebhooks') \n\n            except ValidationError:\n                ownershipCheckFail = True            \n                return render_to_response('checkOwnership.html', {'recipient': recipient,\n                                     'script_url': script_url,\n                                     'rndstring': request.POST['rndstring'],\n                                     'domain_root':domain_root,\n                                     'ownershipCheckFail': ownershipCheckFail,\n                                     'email': request.user.email})\n    \n    return redirect('core.views.add')\n\n\n#-----------------------------------------------------------------------------------\n@login_required\ndef delete(request):\n\n    recipient = request.GET.get('recipient')\n    if recipient is None:\n        return redirect('core.views.viewWebhooks')\n        \n    user_id = request.user.google_user_id    \n    try:\n        hook = emailHooks.objects.filter(google_user_id=user_id,\n                                         recipient=request.GET['recipient'])\n        hook.delete()\n        return redirect('core.views.viewWebhooks') \n    except emailHooks.DoesNotExist:\n        return redirect('core.views.viewWebhooks')\n\n\n#-----------------------------------------------------------------------------------\n@login_required\ndef edit(request):\n\n    if request.method == 'POST':\n        form = AddHookForm(request.POST)\n        if form.is_valid():\n            recipient = form.cleaned_data['recipient']\n            script_url = form.cleaned_data['script_url']\n            old_recipient = request.POST['old_recipient']\n            old_url = request.POST['old_url']\n\n            new_domain = getHostname(script_url)\n            old_domain = getHostname(old_url)\n\n            if recipient != old_recipient:\n                #check to make sure no one else has that hook\n                if len(emailHooks.objects.filter(recipient=recipient)) >= 1:\n                    form._errors['recipient'] = ErrorList(['That recipient is already taken :('])            \n                    return render_to_response('edit.html', {'form': form,\n                                                            'old_recipient': old_recipient,\n                                                            'old_url': old_url,\n                                                            'email': request.user.email})\n            \n            if new_domain != old_domain:\n                \n                #send to check ownership again                \n                hostname = getHostname(script_url)\n                word = uri_b64encode(hostname)\n                return render_to_response('checkOwnership.html', {'recipient': recipient,\n                                                         'script_url': script_url,\n                                                         'rndstring': word,\n                                                         'domain_root': hostname,\n                                                         'edit': True,\n                                                         'old_recipient': old_recipient,\n                                                         'old_url': old_url,\n                                                         'email': request.user.email})\n            \n            #if all is good then delete the old one and save the new\n            try:\n                user_id = request.user.google_user_id  \n                hook = emailHooks.objects.filter(google_user_id=user_id,\n                                                 recipient=old_recipient,\n                                                 script_url=old_url)\n                hook.delete()\n            except emailHooks.DoesNotExist:\n                return redirect('core.views.viewWebhooks')\n                     \n            newHook = emailHooks(script_url=script_url,\n                                 google_user_id=user_id,\n                                 recipient=recipient)\n            newHook.save()\n            return redirect('core.views.viewWebhooks')\n\n        else:\n            return render_to_response('edit.html', {'form': form,\n                                  'old_recipient': request.POST['old_recipient'],\n                                  'old_url': request.POST['old_url'],\n                                  'email': request.user.email})          \n    \n    try:    \n        recipient=request.GET['recipient']        \n        try:\n            user_id = request.user.google_user_id\n            hookInstance = emailHooks.objects.get(google_user_id=user_id,\n                                                  recipient=recipient)\n            form = AddHookForm({'recipient': hookInstance.recipient,\n                                'script_url': hookInstance.script_url})\n\n            return render_to_response('edit.html', {'form': form,\n                                            'old_recipient': hookInstance.recipient,\n                                            'old_url': hookInstance.script_url,\n                                            'email': request.user.email})\n\n        except ObjectDoesNotExist:\n            return redirect('core.views.viewWebhooks')\n    except:\n        return redirect('core.views.viewWebhooks')\n  \n\n\n#\n# FCTs USED REPEATEDLY\n#-----------------------------------------------------------------------------------\ndef getHostname(script_url):\n    hostname = urljoin(script_url,' ').rstrip()\n    return hostname\n\ndef uri_b64encode(s):\n    return urlsafe_b64encode(s).strip('=')[:10]\n        \n\t\t\n\t\t\n#\n# EMAIL PROCESSING\n#-----------------------------------------------------------------------------------\n \ndef email_handler(request, catchall):\n    if request.POST:\n        message = mail.InboundEmailMessage(request.raw_post_data)        \n        \n        # pull out the emailization recipient from the\n\t\t# Delivered-To: _____ @emailization.com part of the original\n\t\t# This works because its being redirected from the catchall\n\t\t# otherwise it would fail\n        original = str(message.original)\n        start = original.index('Delivered-To: ') + len('Delivered-To: ')\n        end = original.index('@emailization.com', start)\n        recipient = original[start:end]\n        \t\t\n        hook = emailHooks.objects.get(recipient=recipient)\n        user = GoogleProfile.objects.get(google_user_id=hook.google_user_id)        \n\n        url = hook.script_url\n        logging.info('Received message for user: ' + user.email)\n        \n        security_key = user.security_key\n\n        plaintext_body = list(message.bodies(content_type='text/plain'))[0]\n        plaintext_body = plaintext_body[1].decode()\n        \t\t\n        html_bodies = message.bodies('text/html')\n        decoded_html = ''\n\n        for content_type, body in html_bodies:\n            decoded_html += body.decode()\n\n        try:\n            cc = message.cc\n        except:\n            cc = ''\n\n        try:\n            subject = message.subject\n        except:\n            subject = ''\n              \n        form_fields = {\n            'sender': message.sender,\n            'to': message.to,\n            'cc': cc,\n            'date': message.date,\n            'subject': subject,\n            'plaintext_body': plaintext_body,\n            'html_body': decoded_html,\n            'security_key': security_key\n        }\t\n\n        form_data = urllib.urlencode(form_fields)\n\t\t    \n        try:        \n            logging.info('saving...')\n            result = urlfetch.fetch(url=url,\n                            payload=form_data,\n                            method=urlfetch.POST,\n                            headers={'Content-Type': 'application/x-www-form-urlencoded'})\n\n            status_code = result.status_code\n            logging.info(status_code)\n\n            response = result.content\n\n            if response == '':\n                response = 'N/A'\n            else:            \n                response = (response[:50] + '..') if len(response) > 75 else response\n            \n            newRequestResponse = emailPostRequest(status_code = status_code,\n                                                  returned_response = response,\n                                                  sender = message.sender,\n                                                  subject = subject,\n                                                  google_date = message.date,\n                                                  script_url = url,\n                                                  google_user_id = hook.google_user_id,\n                                                  recipient = recipient)\n\n            newRequestResponse.save()          \n\n        except DownloadError:\n            logging.info('timeout!')\n\n            newRequestResponse = emailPostRequest(status_code = '408',\n                                                  returned_response = 'Request Timeout',\n                                                  sender = message.sender,\n                                                  subject = subject,\n                                                  google_date = message.date,\n                                                  script_url = url,\n                                                  google_user_id = hook.google_user_id,\n                                                  recipient = recipient)\n\n            newRequestResponse.save() \n            \n    return HttpResponse('done!')","sub_path":"core/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":17963,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"601106352","text":"\"\"\"\r\nMerge function for 2048 game.\r\n\"\"\"\r\n\r\ndef merge(line):\r\n    \"\"\"\r\n    Function that merges a single row or column in 2048.\r\n    \"\"\"\r\n    #input list must not be changed\r\n    line = list(line)\r\n    results = len(line)\r\n    \r\n    # Slide tiles to the front\r\n    while 0 in line:\r\n        line.remove(0)\r\n    \r\n    for tile in range(len(line) - 1):\r\n        if line[tile] == line[tile + 1]:\r\n            line[tile] *= 2\r\n            del line[tile + 1]\r\n            line.insert(tile + 1, 0)\r\n        # find the end of \"line\"\r\n        if tile + 2 > len(line):\r\n            break\r\n    # fill \"line\" up with zeros and slide to front\r\n    while 0 in line:\r\n        line.remove(0)\r\n    while len(line) != results:\r\n        line.append(0)\r\n    \r\n    \r\n    return line\r\n\r\n#print merge([2, 0, 2, 4])\r\n#print merge([0, 0, 2, 2])\r\n#print merge([2, 2, 0, 0])\r\n#print merge([2, 2, 2, 2])\r\n#print merge([4, 4, 4, 4])\r\n#print merge([8, 16, 16, 8])\r\n#print merge([0, 0, 0, 0])","sub_path":"Principles of Computing/Week_0_Merge function.py","file_name":"Week_0_Merge function.py","file_ext":"py","file_size_in_byte":961,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"619916580","text":"# -*- coding: utf-8 -*-\nimport changer\nimport unittest\nfrom lxml import etree\n\n\nclass TestGetFreePlanApplications(unittest.TestCase):\n    def test_bad_xml(self):\n        with self.assertRaises(etree.XMLSyntaxError):\n            changer.get_free_plan_applications(\"<spam>\", \"12345678\")\n\n    def test_good_xml(self):\n        file = open('test/applications_good.xml', 'r')\n        file_xml = file.read()\n        result = changer.get_free_plan_applications(file_xml, \"12345678\")\n        self.assertEqual(result, [\"12345\"])\n        file.close()\n\n    def test_good_xml_no_match(self):\n        file = open('test/applications_good.xml', 'r')\n        file_xml = file.read()\n        result = changer.get_free_plan_applications(file_xml, \"128401825\")\n        self.assertEqual(result, None)\n        file.close()\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"test/test_get_free_plan_applications.py","file_name":"test_get_free_plan_applications.py","file_ext":"py","file_size_in_byte":848,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"108153632","text":"import pygame\t# the main workhorse\nimport os\t\t# i KNOW I'll need to detect OS and handle paths, etc\nimport sys\t\t# for sys.exit\nimport math \t# for square roots on hex and other polygons\n\n# allows us to use the == QUIT later instead of pygame.locals.QUIT\nfrom pygame.locals import * \n\nWH = (255,255,255)\t# white\nBL = (0,0,0)\t\t# black\nGY = (128,128,128)\t# grey\nRE = (255,0,0)\t\t# red\n\n# we use legs as segments so it's all relative to any size of scale\nscale = 67\n\n# leg segment sizes\nleg1 = (8/7)\t\t# 1.1428571429\nleg2 = (8/7) * 2 \t# 2.2857142857\nleg3 = (8/7) * 3\t# 3.4285714286\nleg4 = (8/7) * 4\t# 4.5714285714\nleg5 = (8/7) * 5\t# \nleg6 = (8/7) * 6\t# \nleg7 = (8/7) * 7\t# \nctr  = (8/7) * 4\t# 4.5714285714, for code readability\n\n# line points\n# NW to SE\nnwx = ctr - .5 * leg3\nnwy = ctr - (math.sqrt(3)/2)*leg3\nsex = ctr + .5 * leg3\nsey = ctr + (math.sqrt(3)/2)*leg3\n \n# SW to NE\nswx = ctr - .5 * leg3\nswy = ctr + (math.sqrt(3)/2)*leg3\nnex = ctr + .5 * leg3\nney = ctr - (math.sqrt(3)/2)*leg3\n\n# relative game board size\nW  = int(ctr * scale * 2)\nH  = int(ctr * scale * 2)\n\n# Lines are xy start and xy stop coordinates, in inches/scaled\nlines = {\n\t1 : [nwx,nwy,sex,sey],\t# NW to SE\n\t2 : [swx,swy,nex,ney],\t# SW to NE\n\t3 : [leg1,leg4,leg7,leg4]# E to W\n}\n \n# Circles are x,y,size and fill\n# this is starting circles\ncircles = {\n\t1 : [ctr,ctr,leg1,0],\n\t2 : [ctr,ctr,leg2,0],\n\t3 : [ctr,ctr,leg3,0]\n}\n\ncounter = 1\ndots = {\n\tcounter : [ctr,ctr]\n}\nfor leg in [leg1,leg2,leg3]:\n\tdx1 = ctr - .5 * leg\n\tdy1 = ctr - (math.sqrt(3)/2)*leg\n\tdots[counter] = [dx1,dy1]\n\tcounter += 1\n\t\n\tdx1 = ctr + .5 * leg\n\tdy1 = ctr + (math.sqrt(3)/2)*leg\n\tdots[counter] = [dx1,dy1]\n\tcounter += 1\n \n\tdx1 = ctr - .5 * leg\n\tdy1 = ctr + (math.sqrt(3)/2)*leg\n\tdots[counter] = [dx1,dy1]\n\tcounter += 1\n \n\tdx1 = ctr + .5 * leg\n\tdy1 = ctr - (math.sqrt(3)/2)*leg;\n\tdots[counter] = [dx1,dy1]\n\tcounter += 1\n  \n# all of the dots on the center line\nfor leg in [leg1,leg2,leg3,leg4,leg5,leg6,leg7]:\n\tdots[counter] = [leg,ctr]\n\tcounter += 1\n\ndef main():\n\t# can't run pygame without init, just do it\n\tpygame.init()\n\n\t# clock required to limit fps\n\tFPS = pygame.time.Clock()\n\n\t# one of (possibly many) surfaces to draw on\n\tSURF = pygame.display.set_mode((W,H))\n\n\t# the title bar\n\tpygame.display.set_caption(\"Solomon's Game\")\n\n\t# default colors start at black\n\tr1,g1,b1 = (0,0,0)\n\tr2,g2,b2 = (0,0,0)\n\n\t# default x and y, until you click\n\tlw = 5            # line width of polygons\n\n\t# used to pulse color\n\tflip_r1,flip_g1,flip_b1 = (1,1,1)\n\tflip_r2,flip_g2,flip_b2 = (1,1,1)\n\n\t#Game loop begins\n\twhile True:\n\t\t# current color of pulse, r,g,b set at bottom of while \n\t\tr1,flip_r1 = get_pulse(flip_r1,r1,1) # mix and match your pulse, red\n\t\tb2,flip_b2 = get_pulse(flip_b2,b2,5) # mix and match your pulse, red\n\t\tpulse1 = (r1,g1,b1)\n\t\tpulse2 = (r2,g2,b2)\n\n\t\t# fill our surface with white\n\t\tSURF.fill(GY)\n\n\t\t# event section\n\t\tfor event in pygame.event.get():\n\t\t\tif event.type == pygame.MOUSEBUTTONDOWN:\n\n\t\t\t\t# update where we just clicked\n\t\t\t\t(x,y) = pygame.mouse.get_pos()\n\n\t\t\tif event.type == QUIT:\n\t\t\t\t# pygame has a buggy quit, do both\n\t\t\t\tpygame.quit()\n\t\t\t\tsys.exit()\n\n\t\tdraw_board(SURF,pulse1,pulse2,lw)\n\t\t# update the screen object itself\n\t\tpygame.display.update()\t# update entire screen if no surface passed\n\n\t\t# tick the fps clock\n\t\tFPS.tick(60)\n\n'''\nGive us some points and we draw a line\n'''\ndef draw_lines(surf,pulse,x1,y1,x2,y2,lw):\n\tpygame.draw.line(surf,pulse,(x1,y1),(x2,y2),lw)\n\n\n'''\nmoved out to clean up the main()\n'''\ndef draw_board(surf,pulse1,pulse2,lw):\n\t# update screen object with the lines\n\tfor xy in lines:\n\t\tdraw_lines(surf, pulse1,\n\t\t\tint(lines[xy][0] * scale),\n\t\t\tint(lines[xy][1] * scale),\n\t\t\tint(lines[xy][2] * scale),\n\t\t\tint(lines[xy][3] * scale),lw\n\t\t)\n\n\tfor xy in circles:\n\t\tpygame.draw.circle(surf, pulse1, (\n\t\t\t\tint(circles[xy][0] * scale), \n\t\t\t\tint(circles[xy][1] * scale)\n\t\t\t\t), \n\t\t\tint(circles[xy][2] * scale), lw\n\t\t)\n\n\tfor xy in dots:\n\t\tpygame.draw.circle(surf, pulse2, (\n\t\t\t\tint(dots[xy][0] * scale), \n\t\t\t\tint(dots[xy][1] * scale)\n\t\t\t\t), \n\t\t\t10, 0\n\t\t)\n\n'''\njust pulse 255 to 0 back to 255 repeat \nset boundaries so we don't get invalid rgb value\ninput: state of the flip, and current color code\nreturn: updated flip and color code\n'''\ndef get_pulse(flipped,c,step):\n\n\tif flipped:\n\t\tif c < 255: c += step\n\t\telse:\n\t\t\tc = 255\n\t\t\tflipped = 0\n\telse:\n\t\tif c > step: c -= step\n\t\telse:\n\t\t\tc = 0\n\t\t\tflipped = 1\n\n\tif c > 255: c = 255\n\tif c < 0: c = 0\n\n\treturn (c,flipped)\n\n\n\nif __name__ == '__main__':\n\t# capture ctrl c\n\ttry:\n\t\tmain()\n\texcept KeyboardInterrupt:\n\t\t# pygame has a buggy quit, do both\n\t\tpygame.quit()\n\t\tsys.exit()\n\n\n\n","sub_path":"pretwa.py","file_name":"pretwa.py","file_ext":"py","file_size_in_byte":4602,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"538017928","text":"def finder(files, queries):\n\n    \"\"\"\n    YOUR CODE HERE\n    \"\"\"\n    results = {}\n    for file in files:\n        short_name = file.split('/')[-1]\n        if short_name in results:\n            results[short_name].append(file)\n        else:\n            results[short_name] = [file]\n    result = []\n    for query in queries:\n        if query in results:\n            query_list = results[query]\n            for item in query_list:\n                result.append(item)\n    return result\n\n\nif __name__ == \"__main__\":\n    files = [\n        '/bin/foo',\n        '/bin/bar',\n        '/usr/bin/baz'\n    ]\n    queries = [\n        \"foo\",\n        \"qux\",\n        \"baz\"\n    ]\n    print(finder(files, queries))\n","sub_path":"hashtables/ex5/ex5.py","file_name":"ex5.py","file_ext":"py","file_size_in_byte":692,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"311989475","text":"# !/usr/bin/env python\n# _*_ coding:utf-8 _*_\n\n\nimport requests\n\n\ndef load_baidu_data():\n    # 1.目标url\n    url = 'https://www.so.com/'\n\n    # 2.发送请求\n    response = requests.get(url)\n\n    # 根据响应对象 取文本内容 str\n    data = response.text\n\n    # 3.验证数据\n    print(type(data))\n\n\nif __name__ == '__main__':\n    load_baidu_data()\n","sub_path":"scrapy_lian/laoshi01/01requestsbaseuse.py","file_name":"01requestsbaseuse.py","file_ext":"py","file_size_in_byte":360,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"327406016","text":"import os\nimport nstrace\nimport matplotlib.pyplot as plt\nimport random\nimport numpy as np\n\ndef drop_parser(file_name, v_name):\n    t1 = []\n\n    t2 = []\n\n    f = open(file_name, 'r')\n    Lines = f.readlines()\n\n    for i in range(len([line for line in Lines]) - 1):\n        try:\n            lne = Lines[i].split()\n            flag = lne[0]\n            if flag == v_name:\n                source_node = int(lne[7])\n                time = float(lne[1])\n\n                if source_node == 0:\n                    t1.append(time)\n\n                elif source_node == 1:\n                    t2.append(time)\n\n\n            else:\n                pass\n        except:\n            pass\n    \n    f.close()\n    return t1,t2\n\ndef make_v(t, max_time):\n    v = []\n    for time in range(max_time):\n        num = 0\n        for i in t:\n            if i < time:\n                pass\n            elif i > time + 1:\n                pass\n            else:\n                num += 1\n        v.append(num)\n\n    return v\n\n\nfilename = 'main.tcl'\nagents = ['Agent/TCP', 'Agent/TCP/Newreno', 'Agent/TCP/Vegas']\nftr_name = 'test.tr'\nfnm_name = 'test.nam'\nmany = 10\nmax_time = -1\nend_time = 1000\n\nplt.figure(figsize=(20, 20))\n\nfor agent in agents:\n\n    t0s = []\n\n    t1s = []\n\n    for i in range(many):\n        random.seed(5)\n        n23_d = str(int(random.uniform(5, 25))) + 'ms'\n        n46_d = str(int(random.uniform(5, 25))) + 'ms'\n\n        os.system(f'ns {filename} {agent} {n23_d} {n46_d} {ftr_name} {fnm_name} {end_time}')\n        t0, t1 = drop_parser(ftr_name, 'd')\n        t0s.append(t0)   \n        t1s.append(t1)\n\n    if t0s[0] == []:\n        plt.plot([0, end_time], [0, 0], label=f'flow0 :: {agent}')\n        plt.plot([0, end_time], [0, 0], label=f'flow1 :: {agent}')\n        continue\n    for i in t0s:\n        max_time = max(max_time, int(max(i)))\n\n\n    v0s = [make_v(t0s[i], max_time) for i in range(many)]\n    v1s = [make_v(t1s[i], max_time) for i in range(many)]\n    # print(v0s, v1s)\n    V0, V1 = [], []\n    for i in range(len(v0s[0])):\n        v = 0\n        for j in range(many): \n            v += v0s[j][i]\n        V0.append(v / many)\n\n\n    for i in range(len(v1s[0])):\n        v = 0\n        for j in range(many): \n            v += v1s[j][i]\n        V1.append(v / many)\n\n    # print(\"_______\")\n    # print(V0)\n    # print(V1)\n    V0 = [sum(V0[:i]) / i for i in range(1, len(V0))]\n    V1 = [sum(V1[:i]) / i for i in range(1, len(V1))]\n\n    plt.plot(V0, label=f'flow0 :: {agent}')\n    plt.plot(V1, label=f'flow1 :: {agent}')\n\nplt.title(f'Packet Loss Rate (avg on {many} times)')\nplt.grid()\nplt.legend()\nplt.xlabel('time(s)')\nplt.ylabel('avg packet loss')\nplt.show()\n    \n\n    ","sub_path":"codes/drop_graph.py","file_name":"drop_graph.py","file_ext":"py","file_size_in_byte":2657,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"379354634","text":"import logging\nfrom torch.nn.utils import clip_grad_norm_\nfrom torch.optim import Adam\nfrom ignite.engine import create_supervised_evaluator\nfrom ignite.engine.engine import Engine\n\nfrom ..Model import Model, _prepare_batch, init_params\nfrom .model import BSR_USER\nfrom ..data_loader import get_data_loaders\nfrom .losses import DSR_loss as loss_fn\nfrom ..metrics import MRR, Recall, CombinedLoss, RelevanceLoss, DiscreteDiversity, ILD\n\n\nclass BSR_USER_Manager(Model):\n    name = \"The BSR+USER uses the sequence of item interactions AND the user identifier to generate hidden representations.\" \\\n           \"Predictions are made by a MLP after obtaining the representations.\"\n\n    def __init__(self, args):\n        required_keys = [\"train_path\", \"valid_path\", \"test_path\", \"batch_size\",\n                         \"device\", \"num_item_units\",\n                         \"num_gru_units\", \"dropout_rate\", \"recommendation_len\",\n                         \"lr\", \"weight_decay\", \"properties_path\", \"metrics_save_path\"]\n\n        super(BSR_USER_Manager, self).__init__(required_keys, **vars(args))\n\n        logging.info(\"# Getting data loaders\")\n        item_num, user_num = self._set_dataloaders(self.train_path,\n                                                   self.valid_path,\n                                                   self.test_path,\n                                                   self.batch_size)\n        self.item_num = item_num\n        logging.info(f\"# Unique Items: {item_num}\")\n\n        model = BSR_USER(item_num,\n                    user_num,\n                    self.num_item_units,\n                    self.num_user_units,\n                    self.num_gru_units,\n                    self.dropout_rate,\n                    self.recommendation_len,\n                    self.device)\n        self._model = model\n        init_params(self._model)\n\n        logging.info(\"# Instantiating loss and optimizer\")\n        _loss_function = loss_fn\n\n        self._optimizer = Adam(model.parameters(), self.lr)\n\n        logging.info(\"# Instantiating trainer\")\n        self._set_trainer(model, self._optimizer, _loss_function, device=self.device)\n        self._set_evaluator(model, _loss_function, self.properties_path)\n\n    def _set_trainer(self, model, optimizer, loss_fn,\n                     scheduler=None,\n                     device='cpu', non_blocking=False,\n                     prepare_batch=_prepare_batch):\n        if device:\n            model.to(device)\n\n        def _update(engine, batch):\n            model.train()\n            optimizer.zero_grad()\n            x, y = prepare_batch(batch, device=device, non_blocking=non_blocking)\n            y_pred = model(x)\n\n            combined_loss, _ = loss_fn(y_pred, y)\n            combined_loss.backward()\n            clip_grad_norm_(model.parameters(), 1)\n            optimizer.step()\n\n            # Update LR\n            if scheduler is not None:\n                scheduler.step()\n\n            combined_loss = combined_loss.item()\n            return x, y, y_pred, combined_loss\n\n        self.trainer = Engine(_update)\n\n    def _set_evaluator(self, model, loss_fn, embeddings_path):\n        evaluator = create_supervised_evaluator(model, device=self.device,\n                                                metrics={\n                                                    'mrr': MRR(self.metrics_save_path),\n                                                    'recall': Recall(self.metrics_save_path),\n                                                    'combined_loss': CombinedLoss(loss_fn),\n                                                    'relevance_loss': RelevanceLoss(loss_fn),\n                                                    'ild': ILD(embeddings_path, self.item_num, self.metrics_save_path),\n                                                    'discrete': DiscreteDiversity(embeddings_path, self.item_num, self.metrics_save_path)\n                                                }, non_blocking=False)\n        self.evaluator = evaluator\n\n    def _set_dataloaders(self, train_path, valid_path, test_path, batch_size, num_workers=4):\n        train_loader, val_loader, test_loader, item_num, _, user_num = get_data_loaders(train_path,\n                                                                                     valid_path,\n                                                                                     test_path,\n                                                                                     batch_size,\n                                                                                     num_workers=num_workers,\n                                                                                     properties_path=self.properties_path)\n        self.train_loader = train_loader\n        self.val_loader = val_loader\n        self.test_loader = test_loader\n        self.item_num = item_num\n        self.user_num = user_num\n        return item_num, user_num\n","sub_path":"code/models/BSR_USER/manager.py","file_name":"manager.py","file_ext":"py","file_size_in_byte":4936,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"220573819","text":"import numpy as np\nimport scipy\nfrom scipy import ndimage\nimport scipy.signal as sps\nfrom scipy import signal\nx=4\ny=4\nz=3\ninput_3d = np.zeros((x, y, z))\npadded_input_3d=np.zeros((x+2,y+2,z))\ni=0\nj=0\nfor k in range(z):\n  input_3d[i][j][k]=1\n  padded_input_3d[i+1][j+1][k]=1\n\ni=0\nj=2\nfor k in range(z):\n  input_3d[i][j][k]=1\n  padded_input_3d[i+1][j+1][k]=1\n\ni=2\nj=1\nfor k in range(z):\n  input_3d[i][j][k]=1\n  padded_input_3d[i+1][j+1][k]=1\n\nfilter_3d = np.ones((3,3,z)) # 1 filer \n\noutput_2d=signal.convolve(padded_input_3d,filter_3d, mode='valid')\n\nprint(\"input_3d\",input_3d)\n#print(input_3d.shape)\n\n#submanifold operation: if input_3d[0 , 2 , (0,1,2) ]=1 , output_2d[0 , 2 , (0)]=1\nz1=0\nfor i in range(0, x):\n for j in range(0, y):\n  if input_3d[i][j][z1]!=1:\n     output_2d[i][j][z1]=0 # z1=0, it is 2d \n\nprint(\"output_2d\",output_2d)\n#print(output_2d.shape) #z=1, it is 2d\nprint(\"filter_3d\",filter_3d)\n#print(filter_3d.shape)\n\n","sub_path":"submanifold.py","file_name":"submanifold.py","file_ext":"py","file_size_in_byte":929,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"538622500","text":"# Copyright (c) Hu Zhiming jimmyhu@pku.edu.cn 2019/6/20 All Rights Reserved.\n\n\n#################### Libs ####################\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\n\nimport numpy as np\nimport math\n\n\n# Calculate the angular distance (visual angle) between 2 angular gaze position.\n# gth: the ground truth angular gaze position.\n# prd: the predicted angular gaze position.\ndef CalAngularDist(gth, prd):\n\t#the parameters of our Hmd (HTC Vive).\n\t#Vertical FOV.\n\tVerticalFov = math.pi*110/180;\n\t#Size of a half screen.\n\tScreenWidth = 1080;\n\tScreenHeight = 1200;\n\tScreenCenterX = 0.5*ScreenWidth\n\tScreenCenterY = 0.5*ScreenHeight\n\t#the pixel distance between eye and the screen center.\n\tScreenDist = 0.5* ScreenHeight/math.tan(VerticalFov/2);\n\t\n\t#transform the angular coords to screen coords.\n\tgth = AngularCoord2ScreenCoord(gth);\n\tprd = AngularCoord2ScreenCoord(prd);\n\t#transform the screen coords to pixel coords.\n\tgth[0] = gth[0]*ScreenWidth;\n\tgth[1] = gth[1]*ScreenHeight;\n\tprd[0] = prd[0]*ScreenWidth;\n\tprd[1] = prd[1]*ScreenHeight;\n\t\n\t#the distance between eye and gth.\n\teye2gth = np.sqrt(np.square(ScreenDist) + np.square(gth[0] - ScreenCenterX) + np.square(gth[1] - ScreenCenterY));\n\t#the distance between eye and prd.\n\teye2prd = np.sqrt(np.square(ScreenDist) + np.square(prd[0] - ScreenCenterX) + np.square(prd[1] - ScreenCenterY));\n\t#the distance between gth and prd.\n\tgth2prd = np.sqrt(np.square(prd[0] - gth[0]) + np.square(prd[1] - gth[1]));\n\t\n\t#the angular distance between gth and prd.\n\tangular_dist = 180/math.pi*math.acos((np.square(eye2gth) + np.square(eye2prd) - np.square(gth2prd))/(2*eye2gth*eye2prd));\n\treturn angular_dist\n\t\ndef AngularCoord2ScreenCoord(AngularCoord):\n\t# transform the angular coords ((0 deg, 0 deg) at screen center) to screen coords which are in the range of\n\t# 0-1. (0, 0) at Bottom-left, (1, 1) at Top-right\n\t\n\t# the parameters of our Hmd (HTC Vive).\n\t# Vertical FOV.\n\tVerticalFov = math.pi*110/180;\n\t# Size of a half screen.\n\tScreenWidth = 1080;\n\tScreenHeight = 1200;\n\t# the pixel distance between eye and the screen center.\n\tScreenDist = 0.5* ScreenHeight/math.tan(VerticalFov/2);\n\t\n\tScreenCoord = np.zeros(2)\n\t\n\t# the X coord.\n\tScreenCoord[0] = 0.5 + (ScreenDist * math.tan(math.pi*AngularCoord[0] / 180)) / ScreenWidth; \n\t# the Y coord.\n\tScreenCoord[1] = 0.5 + (ScreenDist * math.tan(math.pi*AngularCoord[1] / 180)) / ScreenHeight;\n\treturn ScreenCoord\n\t\n\t\nif __name__ == \"__main__\":\n\tdata = np.loadtxt('test/predictions.txt')\n\tsize = data.shape[0]\n\tdist = np.zeros(size)\n\tfor i in range(size):\n\t\t#print(data[i, :])\n\t\tdist[i] = CalAngularDist(data[i,0:2], data[i,2:4])\n\t\n\tprint(dist)\n\tprint(dist.mean())\n\tprint(dist.std())\n\t\n\t\n\t","sub_path":"DGaze/utils/CalAngularDist.py","file_name":"CalAngularDist.py","file_ext":"py","file_size_in_byte":2740,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"346965642","text":"from datadog import api, initialize\n\noptions = {\n  'api_key':'25af742dc74b5c5d1efd29459b5c7c08',\n  'app_key':'bcc64e922e922b0e92282821fd15e044d85aa46f',\n  'api_host': 'https://api.datadoghq.com'\n}\n\ninitialize(**options)\n\nemail = input(\"PLease enter the emailaddress of the user: \")\napi.User.create(handle=email, access_role='st')\n","sub_path":"datadoguserinvite.py","file_name":"datadoguserinvite.py","file_ext":"py","file_size_in_byte":330,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"79090482","text":"# -*- coding: utf-8 -*-\n\nimport sys\nimport os\nimport shutil\nfrom multiprocessing import Process\n\n'''\n    必须要先安装好相应的latex发行包，然后添加到环境变量\n'''\n\nCURRENT_DIR = os.path.split(__file__)[0]\nprint(CURRENT_DIR)\n\ndef run_create_html():\n    # 创建网页\n    os.system(\"sphinx-build -M {} {} {}\".format('html', os.path.join(CURRENT_DIR, 'source'), os.path.join(CURRENT_DIR, 'build')))\n    \n    print('html generate ok!')\n\ndef run_create_pdf():\n    # 生成tex文件\n    tex_file = ''\n    os.system(\"sphinx-build -M {} {} {}\".format('latex', os.path.join(CURRENT_DIR, 'source'), os.path.join(CURRENT_DIR, 'build')))\n    objdir = os.path.join(CURRENT_DIR, 'build', 'latex')\n    files = os.listdir(objdir)\n    os.chdir(objdir)\n\n    for file in files:\n        if file.endswith(\".tex\"):\n            tex_file = file\n            break\n\n    # 心须执行两次,第二次生成目录和参考文献\n    os.system(\"xelatex -interaction nonstopmode -f {}\".format(tex_file))\n    os.system(\"xelatex -interaction nonstopmode -f {}\".format(tex_file))\n\n    files = os.listdir(objdir)\n    for file in files:\n        if file.endswith(\".pdf\"):\n            dst = shutil.copy(os.path.join(objdir, file), os.path.join(objdir, '..', file))\n            print(dst)\n            print('generate ok!!!!!!!!!!!!!')\n            break\n    print('pdf generate ok!')\n\n\n\n\nif __name__ == \"__main__\":\n\n    p_html = Process(target = run_create_html)\n    #p_pdf = Process(target = run_create_pdf)\n    p_html.start()\n    #p_pdf.start()\n\n    p_html.join()\n    #p_pdf.join()\n    print(\"the build ok!\")\n","sub_path":"html_generate.py","file_name":"html_generate.py","file_ext":"py","file_size_in_byte":1595,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"361835699","text":"__author__ = 'Igor'\nimport os\nimport numpy as np\nfrom pandas.io.parsers import read_csv\nfrom sklearn.utils import shuffle\nfrom lasagne import layers\nfrom lasagne.updates import nesterov_momentum  # 基于牛顿方程式的随机梯度下降\nfrom nolearn.lasagne import NeuralNet\nimport matplotlib.pyplot as plt\nimport pickle\n\nF_TRAIN = 'data/training.csv'\nF_TEST = 'data/test.csv'\n\n\ndef load(test=False, cols=None):\n    '''\n    如果test为真则读取test数据，否则读取训练数据\n    '''\n    fname = F_TEST if test else F_TRAIN\n    df = read_csv(os.path.expanduser(fname))  # load pandas dataframe\n\n    # Image数据需要进行切分\n    df['Image'] = df['Image'].apply(lambda im: np.fromstring(im, sep=\" \"))\n\n    if cols:  # 获取子列\n        df = df[list[cols] + [\"Image\"]]\n\n    print(df.count())  # 打印每一列的数量\n    df = df.dropna()  # 删除有缺失值的行\n\n    X = np.vstack(df['Image'].values) / 255.\n    # normalize\n    X = X.astype(np.float32)\n\n    if not test:  # 只有训练集才有目标列\n        y = df[df.columns[:-1]].values\n        y = (y - 48) / 48\n        y = y.astype(np.float32)\n        # y = MinMaxScaler(feature_range=(-1, 1)).fit_transform(y)  # scale taget in [-1,1]\n        # 图像的像素大小是96*96\n        X, y = shuffle(X, y, random_state=42)  # 打乱训练集\n    else:\n        y = None\n\n    return X, y\n\n\nX, y = load()\nprint(\"X.shape == {}; X.min == {:.3f}; X.max == {:.3f}\".format(\n    X.shape, X.min(), X.max()))\nprint(\"y.shape == {}; y.min == {:.3f}; y.max == {:.3f}\".format(\n    y.shape, y.min(), y.max()))\n\nnet1 = NeuralNet(\n    layers=[  # 三层神经网络\n              ('input', layers.InputLayer),\n              ('hidden', layers.DenseLayer),\n              ('output', layers.DenseLayer)],\n\n    # layer 参数\n    input_shape=(None, 9216),  # 96*96 pxels\n    hidden_num_units=100,\n    output_nonlinearity=None,\n    output_num_units=30,  # 30 target values\n\n    # 最优化方法\n    update=nesterov_momentum,\n    update_learning_rate=0.01,\n    update_momentum=0.9,\n\n    regression=True,\n    max_epochs=400,\n    verbose=1,\n)\n\nnet1.fit(X, y)\nwith open(\"data/net1.pickle\", 'wb') as f:\n    pickle.dump(net1, f, -1)\nf.close()\n\n# net1保存了训练中的结果\ntrain_loss = np.array([i[\"train_loss\"] for i in net1.train_history_])\nvalid_loss = np.array([i[\"valid_loss\"] for i in net1.train_history_])\nplt.plot(train_loss, linewidth=3, label=\"train\")\nplt.plot(valid_loss, linewidth=3, label=\"valid\")\nplt.grid()\nplt.legend()\nplt.xlabel(\"epoch\")\nplt.ylabel(\"loss\")\nplt.ylim(1e-3, 1e-2)\nplt.yscale(\"log\")\n\n\ndef plot_sample(x, y, axis):\n    img = x.reshape(96, 96)\n    # 显示图片\n    axis.imshow(img, cmap=\"gray\")\n    # 画出检测点，一个参数是x抽，第二个参数是y轴\n    axis.scatter(y[0::2] * 48 + 48, y[1::2] * 48 + 48, marker=\"x\", s=10)\n\n\n# 导入训练集\nXtest, _ = load(test=True)\ny_pred = net1.predict(Xtest)\n\n# figsize图像大小，6×6,单位是英寸\nfig = plt.figure(figsize=(6, 6))\nfig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)\nfor i in range(16):\n    # subplot(行，列，索引)，即创建了一个4×4 16张子图，第三个参数是索引，按行\n    ax = fig.add_subplot(4, 4, i + 1, xticks=[], yticks=[])\n    plot_sample(X[i], y_pred[i], ax)\n","sub_path":"FacialKeypointsDetection/fkd.py","file_name":"fkd.py","file_ext":"py","file_size_in_byte":3289,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"508849043","text":"import random\nimport copy\n\ncodes = {}\n\nclass ChessError(Exception):\n    \"\"\"\n    Crée un nouveau type d'erreur, soit ChessError.\n    \"\"\"\n\nclass Gamestate:\n    \"\"\"\n    Classe qui donne accès aux informations nécessaires\n    aux bons fonctionnement d'une partie d'échecs:\n    positions, coups valides et etat de jeu\n    \"\"\"\n    def __init__(self):\n\n        self.etat = {\n                'black': {\n                    (1, 7): ['P'], (2, 7): ['P'], (3, 7): ['P'], (4, 7): ['P'],\n                    (5, 7): ['P'], (6, 7): ['P'], (7, 7): ['P'], (8, 7): ['P'],\n                    (1, 8): ['T'], (8, 8): ['T'], (2, 8): ['C'], (7, 8): ['C'],\n                    (3, 8): ['F'], (6, 8): ['F'], (5, 8): ['K'], (4, 8): ['Q']\n                        },\n                'white': {\n                    (1, 2): ['P'], (2, 2): ['P'], (3, 2): ['P'], (4, 2): ['P'],\n                    (5, 2): ['P'], (6, 2): ['P'], (7, 2): ['P'], (8, 2): ['P'],\n                    (1, 1): ['T'], (8, 1): ['T'], (2, 1): ['C'], (7, 1): ['C'],\n                    (3, 1): ['F'], (6, 1): ['F'], (4, 1): ['K'], (5, 1): ['Q']\n                        }\n                    }\n        self.value = {'K':1000, 'P':10, 'F':30, 'C':30, 'T':50, 'Q':90}\n        self.ate = {'black':[], 'white':[]}\n        self.unicode = {\n                    'black': {'P': '♙', 'C': '♘', 'F': '♗', 'Q': '♕', 'K': '♔', 'T': '♖'},\n                    'white': {'P': '♟', 'C': '♞', 'F': '♝', 'Q': '♛', 'K': '♚', 'T': '♜'}\n                    }\n        self.oppo = {'black':'white', 'white':'black'}\n\n    def etat_partie(self):\n        \"\"\"\n        :returns: l'état de partie sans être passé dans le valid_generator\n        \"\"\"\n        return self.etat\n\n    def state(self):\n        \"\"\"\n        Méthode permet de retourne l'état de partie actuelle\n        :returns: l'état de partie avec coups valides\n        \"\"\"\n        return self.valid_generator(self.etat)\n\n    def positions(self, etat):\n        \"\"\"\n        :returns: dico de la position des pions de chaque couleur,\n        les positions libres/vides, la position des rois et les positions\n        de pions 'pion' pour chaque couleur.\n        \"\"\"\n        # Positions des rois et des autres pions\n        pieces, king_pos, pions = {}, {}, {'black':[], 'white':[]}\n        for color, positions in etat.items():\n            pieces[color] = {position for position in positions.keys()}\n            for position, info in positions.items():\n                if info[0] == 'K':\n                    king_pos[color] = position\n                elif info[0] == 'P':\n                    pions[color].append(position)\n\n        # Cases libres/vides\n        positions_restantes = {(x, y) for x in range(1, 9) for y in range(1, 9)} - (pieces['black']|pieces['white'])\n\n        return {'all_pions':pieces, 'libres':positions_restantes, 'roi':king_pos, 'pions':pions}\n\n    def coups(self, etat):\n        \"\"\"\n        :returns: dico des deplacements et des captures possibles\n        pour chaque couleur\n        \"\"\"\n        moves, foods = {}, {}\n        for color, dico in etat.items():\n            moves[color], foods[color] = set(), set()\n            for info in dico.values():\n                # Positions de déplacement\n                for coup in info[1]:\n                    moves[color].add(coup)\n                # Positions de capture\n                for pos in info[2]:\n                    foods[color].add(pos)\n\n        return {'deplacement':moves, 'all_capture':foods}\n\n    def valid_generator(self, etat):\n\n        # Mise à jour des coups valides\n        pos_free = self.positions(etat)['libres']\n        pos_pions = self.positions(etat)['all_pions']\n\n        for color, positions in etat.items():\n            for position, liste in positions.items():\n                etat[color][position] = [etat[color][position][0], [], []]\n                x, y = position\n\n                if liste[0] == 'P':\n\n                    dico_pion = {\n                                    'black':[[(x, y-i) for i in range(1, 3)], (x, y-1), [(x+1, y-1), (x-1, y-1)]],\n                                    'white':[[(x, y+i) for i in range(1, 3)], (x, y+1), [(x-1, y+1), (x+1, y+1)]]\n                                }\n\n                    # Pion est sur la ligne de départ\n                    if (color, y) == ('black', 7) or (color, y) == ('white', 2):\n                        for i, pos in enumerate(dico_pion[color][0]):\n                            if pos not in pos_free:\n                                del dico_pion[color][0][i:]\n                                break\n                        etat[color][position][1] = dico_pion[color][0]\n\n                    # Pion n'est pas sur la ligne de départ et peut avancer\n                    elif dico_pion[color][1] in pos_free:\n                        etat[color][position][1] = [dico_pion[color][1]]\n\n                    # Pion ne peut pas avancer\n                    else:\n                        etat[color][position][1] = []\n\n                    # Positions que le pion peut aller pour bouffer\n                    coup_for_eat, attacks = [], dico_pion[color][2]\n                    for attack in attacks:\n                        if attack in pos_pions[self.oppo[color]]:\n                            coup_for_eat.append(attack)\n                    etat[color][position][2] = coup_for_eat\n\n                else:\n                    # Revoir pour simplifier les 'for i in range(1, 9)'\n                    dico_piece = {\n                                    'K':[\n                                            (x, y+1), (x, y-1), (x+1, y), (x-1, y),\n                                            (x+1, y+1), (x-1, y+1), (x+1, y-1), (x-1, y-1)\n                                        ],\n                                    'Q':[\n                                            [(x, y+i) for i in range(1, 9)], [(x, y-i) for i in range(1, 9)],\n                                            [(x+i, y) for i in range(1, 9)], [(x-i, y) for i in range(1, 9)],\n                                            [(x+i, y+i) for i in range(1, 9)], [(x-i, y+i) for i in range(1, 9)],\n                                            [(x+i, y-i) for i in range(1, 9)], [(x-i, y-i) for i in range(1, 9)]\n                                        ],\n                                    'F':[\n                                            [(x+i, y+i) for i in range(1, 9)], [(x+i, y-i) for i in range(1, 9)],\n                                            [(x-i, y+i) for i in range(1, 9)], [(x-i, y-i) for i in range(1, 9)]\n                                        ],\n                                    'T':[\n                                            [(x+i, y) for i in range(1, 9)], [(x-i, y) for i in range(1, 9)],\n                                            [(x, y+i) for i in range(1, 9)], [(x, y-i) for i in range(1, 9)]\n                                        ],\n                                    'C':[\n                                            (x+1, y+2), (x-1, y+2), (x+2, y+1), (x-2, y+1),\n                                            (x+2, y-1), (x-2, y-1), (x+1, y-2), (x-1, y-2)\n                                        ]\n                                    }\n\n                    # Positions fixes\n                    if liste[0] in ['K', 'C']:\n\n                        # Coups valides de déplacement\n                        etat[color][position][1] = list(set(dico_piece[liste[0]]) & pos_free)\n                        # Coups valides pour bouffer\n                        etat[color][position][2] = list(set(dico_piece[liste[0]]) & pos_pions[self.oppo[color]])\n\n                    # Positions longues portées\n                    else:\n\n                        capture = []\n                        for i, direction in enumerate(dico_piece[liste[0]]):\n                            for n, pos in enumerate(direction):\n                                if pos not in pos_free:\n                                    del dico_piece[liste[0]][i][n:]\n                                    if pos in pos_pions[self.oppo[color]]:\n                                        capture.append(pos)\n                                    break\n\n                        # Coups valides pour déplacement\n                        etat[color][position][1] = [move for direction in dico_piece[liste[0]] for move in direction]\n                        # Coups valides pour bouffer\n                        etat[color][position][2] = capture\n\n        return etat\n\n    def isCheckmate(self, color, etat):\n        \"\"\"\n        Méthode vérifiant si le roi adverse est en position\n        d'échec et mat\n        Si True,\n        :returns: le nom du gagnant\n        Autrement,\n        :returns: False\n        \"\"\"\n        player = {'black':'joueur noir', 'white':'joueur blanc'}\n        king_pos = self.positions(etat)['roi'][color]\n        deplacements, attacks = etat[color][king_pos][1], etat[color][king_pos][2]\n\n        # Situation d'échec\n        if not self.isCheck(color, etat):\n            return False\n\n        # Déplacement impossible\n        elif deplacements:\n            for deplacement in deplacements:\n                etat_futur = self.valid_generator(self.future_state(etat, color, king_pos, deplacement))\n                if deplacement not in self.coups(etat_futur)['all_capture'][self.oppo[color]]:\n                    return False\n\n        # Capture impossible\n        elif attacks:\n            for attack in attacks:\n                etat_futur = self.valid_generator(self.future_state(etat, color, king_pos, attack))\n                if attack not in self.coups(etat_futur)['all_capture'][self.oppo[color]]:\n                    return False\n\n        # Sacrifice impossible\n        sacrifice = []\n        for pos1, info in etat[color].items():\n            if info[1]:\n                for pos2 in info[1]:\n                    sacrifice.append((pos1, pos2))\n        for pos1, pos2 in sacrifice:\n            etat_futur = self.valid_generator(self.future_state(etat, color, pos1, pos2))\n            if not self.isCheck(color, etat_futur):\n                return False\n\n        return f\"Le gagnant est {player[self.oppo[color]]}!\"\n\n    def isCheck(self, color, etat):\n        \"\"\"\n        Vérifie si le roi adverse est en position d'échec\n        :returns: booleen\n        \"\"\"\n        echec = False\n        if self.positions(etat)['roi'][color] in self.coups(etat)['all_capture'][self.oppo[color]]:\n            echec = True\n\n        return echec\n\n    def exchange_pion(self, etat):\n        \"\"\"\n        Méthode qui permet d'échanger le pion par\n        le meilleur pion déjà manger\n        \"\"\"\n        endline = {'black':1, 'white':8}\n\n        for color, positions in self.positions(etat)['pions'].items():\n\n            # l'équipe 'color' s'est fait mangé au moins un pion\n            if self.ate[color]:\n                for position in positions:\n\n                    # Si un pion est sur endline\n                    if position[1] == endline[color]:\n                        piece = self.state()[color][position][0]\n                        del self.state()[color][position]\n\n                        # Ajout du pion échangé à la liste des bouffés\n                        self.ate[color][self.ate[color].index(max(self.ate[color]))] = (self.value[piece], piece)\n                        # Ajout de la pièce échangé sur l'échiquier\n                        etat[color][position] = [max(self.ate[color])[1], [], []]\n\n    def future_state(self, etat_now, color, pos1, pos2):\n        \"\"\"\n        Méthode qui permet de déplacer conditionnellement\n        des pions afin de pouvoir évaluer différentes situations\n        :returns: un etat de partie modifié\n        \"\"\"\n        etat_after = copy.deepcopy(etat_now)\n        piece = etat_after[color][pos1][0]\n\n        # un pion se trouve à 'pos2' et en est un adverse\n        if etat_after[self.oppo[color]].get(pos2):\n            del etat_after[self.oppo[color]][pos2]\n\n        del etat_after[color][pos1]\n        etat_after[color][pos2] = [piece, [], []]\n\n        return etat_after\n\nclass Optimize(Gamestate):\n    \"\"\"\n    Classe qui permet de faire l'optimisation du meilleur coup\n    en fonction de l'état de jeu courant\n    \"\"\"\n    def __init__(self):\n\n        super().__init__()\n        self.memo = {}\n\n    def material_value(self, etat):\n        \"\"\"\n        Méthode qui permet de générer la valeur de\n        chaque position pour chaque type de pion\n        :returns: dict = {color:{left:list, valeur:int}}\n        \"\"\"\n        dico = {}\n        for color, info in etat.items():\n            value = sum(self.value[info[0]] for info in etat[color].values())\n            dico[color] = {'left':len(info), 'valeur':value}\n\n        return dico\n\n    def evalf(self, etat, color):\n        \"\"\"\n        Méthode qui permet d'évaluer la valeur\n        d'un etat de jeu 'etat'\n        :returns: float\n        \"\"\"\n        eval = 0\n\n        # Material value\n        eval += sum(value for value in self.material_value(etat)[color].values())\n\n        # Mobility\n        # King safety\n        # Center control\n        # Pawn structure\n\n        return eval\n\n    def minimax(self, depth, alpha, beta, etat, color, MaximizePlayer):\n        \"\"\"\n        Méthode faisant l'optimisation du meilleur\n        coup possible pour les pions 'color'\n        :returns: (int, (pos1, pos2))\n        \"\"\"\n        if depth == 0 or self.isCheckmate(color, etat):\n            return self.evalf(etat, color), None\n\n        elif MaximizePlayer:\n            maxEval, maxPawn = -1000000, -1000000\n            coord, coord2 = None, None\n\n            for pos1, info in etat[color].items():\n\n                if info[1]+info[2]:\n                    for pos2 in info[1]+info[2]:\n                        etat_futur = self.valid_generator(self.future_state(etat, color, pos1, pos2))\n                        code = self.code_generator(etat_futur)\n\n                        # Memoization\n                        if code not in codes:\n                            codes[code] = etat_futur\n                            self.memo[code] = self.minimax(depth-1, alpha, beta, etat_futur, self.oppo[color], False)\n\n                        # Recherche du maximum et élagage alpha-beta\n                        reply = self.memo[code]\n                        alpha = max(alpha, reply[0])\n                        if beta <= alpha:\n                            continue\n                        if reply[0] > maxPawn:\n                            maxPawn, coord2 = reply[0], pos2\n\n                if maxPawn > maxEval:\n                    maxEval, coord = maxPawn, (pos1, coord2)\n\n            return maxEval, coord\n\n        else:\n            minEval, minPawn = 1000000, 1000000\n            coord, coord2 = None, None\n\n            for pos1, info in etat[color].items():\n\n                if info[1]+info[2]:\n                    for pos2 in info[1]+info[2]:\n                        etat_futur = self.valid_generator(self.future_state(etat, color, pos1, pos2))\n                        code = self.code_generator(etat_futur)\n\n                        # Memoization\n                        if code not in codes:\n                            codes[code] = etat_futur\n                            self.memo[code] = self.minimax(depth-1, alpha, beta, etat_futur, self.oppo[color], True)\n\n                        # Recherche du minimum et élagage alpha-beta\n                        reply = self.memo[code]\n                        beta = min(beta, reply[0])\n                        if beta <= alpha:\n                            continue\n                        if -reply[0] < minPawn:\n                            minPawn, coord2 = reply[0], pos2\n\n                if -minPawn < minEval:\n                    minEval, coord = -minPawn, (pos1, coord2)\n\n            return minEval, coord\n\n    def pawn_structure(self, etat):\n        \"\"\"\n        Analyse la structure des pions sur l'échiquier\n        Modifie les valeurs des positions si nécessaires\n        \"\"\"\n        pass\n\n    def mobility(self, etat):\n        \"\"\"\n        Méthode qui détermine à quel point\n        une équipe est \"mobile\".\n        On se base sur les paramètres suivants:\n        ...\n        \"\"\"\n        pass\n\n    def king_safety(self, etat):\n        \"\"\"\n        Évalue la sécurité du roi\n        On se base sur les paramètres suivants:\n        ...\n        \"\"\"\n        pass\n\n    def center_control(self, etat):\n        \"\"\"\n        Méthode qui évalue le controle central d'une équipe\n        On se base sur les paramètres suivants:\n        ...\n        \"\"\"\n        pass\n\n    def code_generator(self, etat):\n        \"\"\"\n        Méthode qui génère un code spécifique à un état\n        \"\"\"\n        code = list(color[0].upper()+str(pos1)+info[0] for color, dico in etat.items() for pos1, info in dico.items())\n        code.sort()\n\n        return tuple(code)\n\n    def constants(self):\n        \"\"\"\n        Méthode qui permet de déterminer la valeur des\n        constantes C pour la méthode evalf.\n        \"\"\"\n        pass\n\nclass Chess(Optimize):\n    \"\"\"\n    Classe gère une partie d'échecs\n    \"\"\"\n    def __init__(self, player1, player2='robot'):\n        \"\"\"\n        Initialiser un nouvel état de partie\n        :raise ChessError: si un des deux joueurs n'est pas une str\n        \"\"\"\n\n        if not isinstance(player1, str) or not isinstance(player2, str):\n            raise ChessError(\"Les deux joueurs doivent être des chaines de caractères.\")\n\n        super().__init__()\n\n        self.player1 = player1\n        self.player2 = player2\n\n    def __str__(self):\n\n        # Construction de l'équiquier\n        d1 = [['━' if y % 2 != 0 else ' ' for x in range(35)] for y in range(15)]\n        for i, ligne in enumerate(d1[::2]):\n            ligne[0] = str(8 - i)\n            for n in range(4, 35, 4):\n                ligne[n] = '.'\n\n        d2 = []\n        for ligne in d1:\n            ligne[2] = ligne[34] = '┃'\n            d2 += ligne + ['\\n']\n\n        # Position des joueurs\n        for color, dico in self.state().items():\n            for position, info in dico.items():\n                x, y = position\n                d2[36*(16-2*y)+4*x] = self.unicode[color][info[0]]\n\n        # Affiche de l'échiquier\n        title = \"♔  Chessgame ♚\"\n        debut = ['   ', '━'*31, '\\n']\n        end = ['━━┃', '━'*31, '\\n', '  ┃ ', '   '.join(str(n) for n in range(1, 9))]\n\n        return f\"{title:^37}\" + '\\n' + ''.join(debut + d2 + end) + '\\n' + \\\n        f\"White: {self.player1}\" + '\\n' + f\"Black: {self.player2}\"\n\n    def move(self, color, pos1, pos2):\n        \"\"\"\n        Méthode permettant de déplacer le pion 'piece' situé à\n        la position pos1 vers la position pos2.\n        :raise ChessError: si aucun pion ne se trouve à la position pos1\n        :raise ChessError: si pos2 n'est pas un coup valide pour le pion 'piece'\n        \"\"\"\n        pion = self.state()[color][pos1]\n\n        # Traitement d'erreurs\n        for x, y in [pos1, pos2]:\n            if not isinstance(x, int) or not isinstance(y, int):\n                raise ChessError(\"Veuillez entrer que des nombres entiers.\")\n\n        # pos1 n'est liée à aucun pion sur l'échiquier\n        if pos1 not in self.positions(self.etat)['all_pions'][color]:\n            raise ChessError(\"Aucun pion ne peut être déplacer.\")\n\n        # Déplacement invalide pour un pion\n        elif pion[0] == 'P' and pos2 not in pion[1] + pion[2]:\n            raise ChessError(\"Ce coup est invalide pour un pion.\")\n\n        # Déplacement invalide pour les autres pions\n        elif pion[0] in ['K', 'Q', 'F', 'C', 'T'] and pos2 not in pion[1]:\n            raise ChessError(\"Ce coup est invalide pour ce pion.\")\n\n        # Déplacement du pion 'piece'\n        piece = self.etat[color][pos1][0]\n        del self.etat[color][pos1]\n        self.etat[color][pos2] = [piece, [], []]\n\n    def autoplay(self, color):\n        \"\"\"\n        Méthode permettant de jouer un coup valide pour\n        l'état actuelle de jeu\n        :raise ChessError: si la couleur est ni 'black' ni 'white'\n        \"\"\"\n        # Traitement d'erreur\n        if color not in ['black', 'white']:\n            raise ChessError(\"Cette couleur n'existe pas aux échecs.\")\n\n        elif self.isCheckmate(color, self.state()):\n            raise ChessError(\"La partie est terminée.\")\n\n        self.exchange_pion(self.etat_partie())\n\n        alpha, beta = -1000000, 1000000\n        move = self.minimax(1, alpha, beta, self.state(), color, True)[1]\n        print(move)\n        if move[1] in self.positions(self.state())['all_pions'][self.oppo[color]]:\n            self.eat(color, move[0], move[1])\n        else:\n            self.move(color, move[0], move[1])\n\n    def formatted_state(self, demand=False):\n        \"\"\"\n        Permet d'afficher l'état de partie de façon plus clair\n        \"\"\"\n        total, message = set(), 'All on board'\n\n        print('='*100)\n\n        for color, info in self.state().items():\n            print(f\"{color}: value = {self.evalf(self.state(), color)}\")\n            for position, liste in info.items():\n                if demand:\n                    liste1 = ', '.join(map(str, (coord for coord in sorted(liste[1]))))\n                    liste2 = ', '.join(map(str, (coord for coord in sorted(liste[2]))))\n                    print(f\"{liste[0]}:{position} ⇒  moves = {liste1 if liste[1] else 'cannot move'}, food = {liste2 if liste[2] else 'None'}\")\n                total.add(position)\n\n        print(f\"{len(total)} pions sur l'échiquier\")\n\n        for color, pions in self.ate.items():\n            if pions:\n                print(f\"Pions bouffés pour team {color} (total = {len(pions)}): {', '.join([pion[1] for pion in pions])} \")\n            else:\n                print(f\"Pions bouffés pour team {color} (total = {len(pions)}) : {message}\")\n\n        print('='*100)\n\n    def eat(self, color, pos1, pos2):\n        \"\"\"\n        Méthode permettant de manger un pion adverse placé\n        à la position pos2\n        :raise ChessError: s'il n'y a aucun pion à la position pos2\n        :raise ChessError: si le pion à la position pos2 n'est pas un pion adverse\n        \"\"\"\n        # Traitement d'erreurs\n        for x, y in [pos1, pos2]:\n\n            # Une des deux coordonnées n'est pas un entier\n            if not isinstance(x, int) or not isinstance(y, int):\n                raise ChessError(\"Veuillez entrer que des nombres entiers.\")\n\n            # Une des deux coordonnées n'est pas sur l'échiquier\n            elif not 1 <= x <= 8 or not 1 <= y <= 8:\n                raise ChessError(\"Une position est hors de l'échiquier.\")\n\n        # pos1 n'est pas liée à un pion sur l'échiquier\n        if pos1 not in self.positions(self.etat)['all_pions'][color]:\n            raise ChessError(\"Aucun pion ne peut être déplacer.\")\n\n        # pos2 n'est pas liée à un pion sur l'échiquier\n        elif pos2 not in self.positions(self.etat)['all_pions'][self.oppo[color]]:\n            raise ChessError(\"Aucun pion ne se trouve à la position d'attaque.\")\n\n        # pos2 n'est pas un coup_for_eat valide\n        elif pos2 not in self.state()[color][pos1][2]:\n            raise ChessError(\"Coup d'attaque invalide.\")\n\n        # Déplacement et suppression des pions\n        piece = self.etat[self.oppo[color]][pos2][0]\n\n        if self.etat[color][pos1][0] == 'P':\n            self.move(color, pos1, pos2)\n            self.ate[self.oppo[color]].append((self.value[piece], piece))\n            del self.etat[self.oppo[color]][pos2]\n        else:\n            self.ate[self.oppo[color]].append((self.value[piece], piece))\n            del self.etat[self.oppo[color]][pos2]\n            self.move(color, pos1, pos2)\n\nclass Game:\n    \"\"\"\n    Classe qui run les games pour Chess\n    \"\"\"\n    def __init__(self, player1, player2='Robot'):\n\n        self.player1 = player1\n        self.player2 = player2\n\n    def autogame(self, n=0):\n\n        jeu = Chess(self.player1, self.player2)\n        jeu.formatted_state()\n        print(jeu)\n\n        if n > 0:\n            count = 0\n            while count < n:\n                try:\n                    jeu.autoplay('white')\n                    print(jeu.state())\n                    # jeu.formatted_state(demand=True)\n                    print(jeu)\n\n                    jeu.autoplay('black')\n                    print(jeu.state())\n                    # jeu.formatted_state(demand=True)\n                    print(jeu)\n\n                except ChessError as err:\n                    print(jeu)\n                    print(err)\n\n                count += 1\n\n        else:\n            while True:\n                try:\n                    jeu.autoplay('white')\n                    jeu.formatted_state(demand=False)\n                    print(jeu)\n\n                    jeu.autoplay('black')\n                    jeu.formatted_state(demand=False)\n                    print(jeu)\n\n                except ChessError as err:\n                    print(jeu)\n                    print(err)\n                    break\n\n    def handgame(self):\n\n        jeu = Chess(self.player1, self.player2)\n        print(jeu)\n\n        while True:\n            action = input(\"Type de coup (m, e): \")\n            pos1 = input(\"Position du pion xy: \")\n            pos2 = input(\"Position de déplacement xy: \")\n            try:\n                if action == 'm':\n                    jeu.move('white', (int(pos1[0]), int(pos1[1])), (int(pos2[0]), int(pos2[1])))\n                elif action == 'e':\n                    jeu.eat('white', (int(pos1[0]), int(pos1[1])), (int(pos2[0]), int(pos2[1])))\n                if jeu.isCheckmate('black', jeu.state()):\n                    print(jeu.isCheckmate('black', jeu.state()))\n                    break\n                jeu.formatted_state(demand=False)\n                print(jeu)\n\n                jeu.autoplay('black')\n                jeu.formatted_state(demand=False)\n                print(jeu)\n                if jeu.isCheckmate('white', jeu.state()):\n                    print(jeu.isCheckmate('white', jeu.state()))\n                    break\n\n            except ChessError as err:\n                print(err)\n                print(jeu)\n            except Exception:\n                print(jeu)\n                print(\"Coup invalide, réessayer.\")\n\nGame('joueur1').autogame(n=1)\n# Game('joueur1').handgame()\n","sub_path":"chessgame.py","file_name":"chessgame.py","file_ext":"py","file_size_in_byte":26277,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"76776616","text":"from datetime import date, time, datetime, timedelta\nimport typing\nimport csv\nimport copy\nimport re\n\n\ndef get_times_from_str(time_range: str) -> (time, time):\n\n    # match strings of form int.int-int.int\n    time_split_list = [\n        item\n        for item in re.split(\"(\\d\\d)\\.(\\d\\d)\\-(\\d\\d)\\.(\\d\\d)\", time_range)\n        if item != \"\"\n    ]\n\n    # extract hours and minutes\n    hour_from = int(time_split_list[0])\n    minute_from = int(time_split_list[1])\n    hour_to = int(time_split_list[2])\n    minute_to = int(time_split_list[3])\n\n    # create time objects from list entries\n    time_from = time(hour_from, minute_from)\n    time_to = time(hour_to, minute_to)\n\n    return time_from, time_to\n\n\ndef read_datetimes_from_csv(path: str):\n\n    datetime_list = []\n    # read in csv data\n    with open(\"results.csv\", mode=\"r\") as csv_file:\n        reader = csv.DictReader(csv_file, delimiter=\",\")\n        counter = 0\n        for row in reader:\n            if counter >= 1:\n                # dict to store extracted information\n                time_dict = {}\n                date_from = datetime.strptime(row[\"date\"], \"%d.%m.%Y\").date()\n                date_to = datetime.strptime(row[\"date\"], \"%d.%m.%Y\").date()\n\n                # split time range 17.00-18.00 at '-' to 17.00 and 18.00\n                assert re.search(\n                    \"\\d\\d\\.\\d\\d\\-\\d\\d\\.\\d\\d\", row[\"time\"]\n                ), f\"Input does not match the required scheme. Input was: {row['time']}\"\n                time_from, time_to = get_times_from_str(row[\"time\"])\n\n                # combine dates and times to datetimes\n                time_dict[\"from\"] = datetime.combine(date_from, time_from)\n                time_dict[\"to\"] = datetime.combine(date_to, time_to)\n                time_dict[\"name\"] = row[\"name\"]\n                # store datetime data\n                datetime_list.append(time_dict.copy())\n            counter += 1\n        return datetime_list\n\n\n# def sort_datetime_list(datetime_list: typing.List[datetime]) -> typing.List[datetime]:\n#     first_key_sorted = sorted(datetime_list, key=datetime_list[\"from\"])\n\n#     # get unique first datetime values\n#     unique_dts = set([dt[\"from\"] for dt in datetime_list])\n\n\n# def calc_dates_by_rhythm(\n#     start_dt: datetime,\n#     end_dt: datetime,\n#     rhythm_days: int,\n#     last_dt: datetime = datetime(datetime.now().year, 12, 31, 23, 59, 59),\n# ) -> typing.List[typing.Dict[str, datetime]]:\n#     \"\"\" Calculate datetimes by adding a rhythmic amount of days\n\n#     Arguments:\n#         start_dt {datetime} -- given start datetiime\n#         end_dt {datetime} -- given end datetime\n#         rhythm_days {int} -- rhythm in days to be added on start and end datetime\n\n#     Returns:\n#         typing.List[typing.Dict[str, datetime]] -- hd = get_holidays()\n#     # print(hd)Datetime ranges with rhythm time gap less than last_dt\n#     \"\"\"\n#     # rhythm_days == 0 is equal to a single event without repetition\n#     if rhythm_days == 0:\n#         result_list = [{\"from\": start_dt, \"to\": end_dt}]\n\n#     elif rhythm_days > 0:\n\n#         # how many times rhythm_days could be added to start_dt untill last_dt will be exceeded\n#         add_days_n_times = int(((last_dt - start_dt).days / rhythm_days))\n\n#         from_list = [\n#             start_dt + timedelta(days=i * rhythm_days) for i in range(add_days_n_times)\n#         ]\n#         to_list = [end_dt + timedelta(i * rhythm_days) for i in range(add_days_n_times)]\n\n#         result_list = [\n#             {\"from\": from_item, \"to\": to_item}\n#             for from_item, to_item in zip(from_list, to_list)\n#         ]\n\n#     # Exception for negative rhythm\n#     else:\n#         raise Exception(\"The parameter rhythm_days must be non-negetaive.\")\n\n#     return result_list\n\n\ndef test():\n    # # Test calculatiing the date range\n\n    # # Test case #1\n    # dt1 = datetime.now()\n    # dt2 = datetime.now() + timedelta(hours = 1)\n    # #dt_end = datetime(2020, 12, 31, 23, 59, 59)\n    # dt_end = datetime(2020, 12, 31, 23, 59, 59)\n    # rhythm = 0\n    # test_ls = calc_dates_by_rhythm(dt1, dt2, rhythm, dt_end)\n    # print(test_ls)\n\n    # # Test case #2\n    # dt1 = datetime.now()\n    # dt2 = datetime.now() + timedelta(hours = 1)\n    # #dt_end = datetime(2020, 12, 31# Test case #1\n    # dt1 = datetime.now()\n    # dt2 = datetime.now() + timedelta(hours = 1)\n    # #dt_end = datetime(2020, 12, 31, 23, 59, 59)\n    # dt_end = datetime(2020, 12, 31, 23, 59, 59)\n    # rhythm = 0\n    # test_ls = calc_dates_by_rhythm(dt1, dt2, rhythm, dt_end)\n    # print(test_ls)\n\n    # # Test case #2\n    # dt1 = datetime.now()\n    # dt2 = datetime.now() + timedelta(hours = 1)\n    # #dt_end = datetime(2020, 12, 31, 23, 59, 59)\n    # dt_end = datetime(2020, 12, 31, 23, 59, 59)\n    # rhythm = 7\n    # test_ls = calc_dates_by_rhythm(dt1, dt2, rhythm, dt_end)\n    # print(test_ls), 23, 59, 59)\n    # dt_end = datetime(2020, 12, 31, 23, 59, 59)\n    # rhythm = 7\n    # test_ls = calc_dates_by_rhythm(dt1, dt2, rhythm, dt_end)\n    # print(test_ls)\n\n    # #  Test get_holidays\n    # hd = get_holidays()\n    # print(hd)\n    datetime_list = read_datetimes_from_csv(\"results.csv\")\n    print(datetime_list)\n\n    # pass\n\n\nif __name__ == \"__main__\":\n\n    # full_sorted_list = sort_part_list(datetime_list)\n    # print(full_sorted_list)\n    test()\n","sub_path":"dates_scraping.py","file_name":"dates_scraping.py","file_ext":"py","file_size_in_byte":5299,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"324084322","text":"# -*- encoding: utf-8 -*-\n'''\n探针主节点服务升级逻辑\n'''\n\nimport fire\nimport subprocess\nimport os\nimport time\nimport struct\nimport delegator\n\nfrom app.constant import TASK_FREQUENCY_LIST\n\nROOT_DIR = os.path.dirname(os.path.abspath(__file__))\nSUPERVISOR_CONF = \"/etc/supervisor/conf.d/supervisor-app.conf\"\nUWSGI_CONFIG_FILE = os.path.join(ROOT_DIR, 'uwsgi.ini')\n\n\nUWSGI_CONFIG_TEMPLATE = \"\"\"\n[uwsgi]\nmaster=true      \nsocket=127.0.0.1:8000 \nhome={virtualenv_dir}\nprocesses=9      \nsocket-timeout=300\nreload-mercy=10\nvacuum=true\nmax-requests=1000\nlimit-as=1024\nlisten=1024\nbuffer-size=30000\nmemory-report=true\nchdir=/opt/master\nmodule=master.wsgi:application\n\"\"\"\nUWSGI_SUPERVISOR_TEMPLATE = \"\"\"\n[program:app-uwsgi]\ncommand = {uwsgi_path} --ini {config}\nstopsignal=QUIT\nredirect_stderr=true\nstdout_logfile = /var/log/uwsgi.log\nstdout_logfile_backups = 2\nstdout_logfile_maxbytes=20MB\nuser=root\n\"\"\"\n\nNGINX_SUPERVISOR_TEMPLATE = \"\"\"\n[program:nginx-app]\ncommand = /usr/sbin/nginx  -g 'daemon off;'\n\"\"\"\nNORMAL_TEMPLATE = \"\"\"\n[program:{frequency}_dispatcher]\ncommand={python} {project_dir}/manage.py {frequency}_dispatcher\ndirectory=/opt/master\nautostart = false\nautorestart = true\nstartsecs = 5\nstopwaitsecs = 10\nredirect_stderr=true\nstdout_logfile=/var/log/{frequency}_supervisor.log\nstdout_logfile_maxbytes=20MB\nuser=root\n\"\"\"\nRESET_TEMPLATE = \"\"\"\n[program:reset]\ncommand={python} {project_dir}/manage.py reset\ndirectory=/opt/master\nautostart = false\nautorestart = true\nstartsecs = 5\nstopwaitsecs = 10\nredirect_stderr=true\nstdout_logfile=/var/log/reset_supervisor.log\nstdout_logfile_maxbytes=20MB\nuser=root\n\"\"\"\n\n\ndef _log(msg):\n    print(msg)\n\n\ndef _run_supervisor_cmd(supervisor_cmd):\n    cmd = 'supervisorctl  {supervisor_cmd}'.format(\n        supervisor_cmd=supervisor_cmd\n    )\n    c = delegator.run(cmd)\n    print(c.out)\n\n\ndef _run_cmd(cmd):\n    c = delegator.run(cmd)\n    return c.out\n\n\ndef _get_process_cnt():\n    out = _run_cmd('ps aux |grep -v grep|grep manage.py | wc -l')\n    out_1 = _run_cmd('ps aux|grep \"supervisord\"|grep -v grep|wc -l')\n    out_2 = _run_cmd('ps aux|grep \"nginx\"|grep -v grep|wc -l')\n    out_3 = _run_cmd('ps aux|grep \"uwsgi\"|grep -v grep|wc -l')\n    return int(out) + int(out_1) + int(out_2) + int(out_3)\n\n\ndef get_python():\n    out = subprocess.check_output('pipenv run which python', shell=True)\n    return out.decode('utf-8').strip()\n\n\ndef get_uwsgi():\n    out = subprocess.check_output('pipenv run which uwsgi', shell=True)\n    return out.decode('utf-8').strip()\n\n\ndef update_django_res(python_path):\n    cmds = [\n        \"{} manage.py makemigrations\",\n        \"{} manage.py migrate\",\n        \"{} manage.py collectstatic --noinput\",\n        \"{} manage.py init\"\n    ]\n\n    for cmd in cmds:\n        out = subprocess.check_output(cmd.format(python_path), shell=True)\n        print(out)\n\n\ndef update_uwsgi_config(python_path):\n    \"\"\"\n    设置虚拟环境目录到uwsgi.ini,写入文件.\n    \"\"\"\n    with open(UWSGI_CONFIG_FILE, \"w\") as f:\n        f.write(\n            UWSGI_CONFIG_TEMPLATE.format(\n                virtualenv_dir=os.path.dirname(os.path.dirname(python_path))\n            )\n        )\n\n\ndef update_supervisor_config():\n    \"\"\"\n    将uwsgi和nginx写入supervisor配置.\n    \"\"\"\n    uwsgi_config = os.path.join(ROOT_DIR, 'uwsgi.ini')\n\n    configs = [NGINX_SUPERVISOR_TEMPLATE]\n    configs.append(UWSGI_SUPERVISOR_TEMPLATE.format(\n        config=uwsgi_config,\n        uwsgi_path=get_uwsgi()\n    ))\n    configs.append(RESET_TEMPLATE.format(\n        python=get_python(),\n        project_dir=ROOT_DIR,\n    ))\n    for frequency in TASK_FREQUENCY_LIST:\n        configs.append(NORMAL_TEMPLATE.format(\n            python=get_python(),\n            project_dir=ROOT_DIR,\n            frequency=frequency\n        ))\n\n    with open(SUPERVISOR_CONF, \"w\") as f:\n        f.write(\"\\n\\n\".join(configs))\n\n\ndef _check_process():\n    # 检查supervisord\n    out = subprocess.check_output('ps aux | grep \"supervisord\" | grep -v grep | wc -l', shell=True)\n    assert int(out.decode('utf-8')) == 1, 'supervisord process bad'\n\n    # 正常进程数字\n    process_count = 1  # supervisord进程\n    process_count += 8  # command\n    process_count += 5  # nginx\n    process_count += 10  # uwsgi\n\n    current_count = _get_process_cnt()\n    assert current_count == process_count, 'process shoudle has {process_count}, but only has {current_count}'.format(\n        process_count=process_count,\n        current_count=current_count)\n\n\ndef stop():\n    _run_supervisor_cmd('stop all')\n    _run_supervisor_cmd('shutdown')\n\n    if _get_process_cnt() > 0:\n        _run_cmd(\"ps aux |grep -v grep|grep manage.py |awk '{print $2}'|xargs kill -9\")\n        _run_cmd(\"ps aux |grep -v grep|grep supervisord |awk '{print $2}'|xargs kill -9\")\n        _run_cmd(\"ps aux |grep -v grep|grep nginx|awk '{print $2}'|xargs kill -9\")\n        _run_cmd(\"ps aux |grep -v grep|grep uwsgi|awk '{print $2}'|xargs kill -9\")\n        time.sleep(2)\n\n    if _get_process_cnt() != 0:\n        raise Exception('stop failed, process count not is 0')\n\n    if os.path.exists('/var/run/supervisor.sock'):\n        os.remove('/var/run/supervisor.sock')\n\n\ndef start():\n    if _get_process_cnt() != 0:\n        raise Exception('process count not is 0')\n\n    os.chdir(ROOT_DIR)\n    # 更新配置\n    python_path = get_python()\n    update_django_res(python_path)\n    update_uwsgi_config(python_path)\n    update_supervisor_config()\n\n    # 启动supervisor以及worker\n    subprocess.check_output('service supervisor start', shell=True)\n    time.sleep(1)\n    _run_supervisor_cmd('start all')\n    time.sleep(5)\n    # 检查进程是否正常\n    _check_process()\n\n\ndef restart():\n    stop()\n    start()\n\n\nif __name__ == '__main__':\n    fire.Fire({\n        'stop': stop,\n        'start': start,\n        'restart': restart,\n    })\n","sub_path":"master/service.py","file_name":"service.py","file_ext":"py","file_size_in_byte":5802,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"420179572","text":"# ===============================================================================\n# Copyright 2012 Jake Ross\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#   http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ===============================================================================\n\n# ============= enthought library imports =======================\n\nfrom numpy import asarray, sqrt, matrix, diagonal, array, exp\n# ============= standard library imports ========================\nfrom scipy import optimize\nfrom traits.api import Callable, List\n\nfrom pychron.core.regression.base_regressor import BaseRegressor\n\n\n# ============= local library imports  ==========================\n\n\nclass FitError(BaseException):\n    pass\n\n\nclass LeastSquaresRegressor(BaseRegressor):\n    fitfunc = Callable\n    initial_guess = List\n\n    _covariance = None\n\n    # def make_equation(self):\n    #     import inspect\n    #     eq = inspect.getsource(self.fitfunc).strip()\n    #\n    #     def func(match):\n    #         m = match.group(0)\n    #         idx = int(m[2:-1])\n    #         return floatfmt(self._coefficients[idx])\n    #\n    #     for ci in self._coefficients:\n    #         eq = re.sub(r'p\\[\\d]', func, eq)\n    #\n    #     h, t = eq.split(':')\n    #     return 'fitfunc={}'.format(t)\n\n    # def _fitfunc_changed(self):\n    #     self.calculate()\n\n    # def _initial_guess_changed(self):\n    #     self._degree = len(self.initial_guess) - 1\n\n    def calculate(self, filtering=False):\n        cxs = self.pre_clean_xs\n        cys = self.pre_clean_ys\n\n        if not self._check_integrity(cxs, cys):\n            # logger.debug('A integrity check failed')\n            # import traceback\n            # traceback.print_stack()\n            return\n\n        if not filtering:\n            # prevent infinite recursion\n            fx, fy = self.calculate_filtered_data()\n        else:\n            fx, fy = cxs, cys\n        try:\n            coeffs, cov = optimize.curve_fit(self.fitfunc, fx, fy, p0=self._calculate_initial_guess())\n            self._coefficients = list(coeffs)\n            self._covariance = cov\n            self._coefficient_errors = list(sqrt(diagonal(cov)))\n        except RuntimeError:\n            import os\n            if not os.getenv('TRAVIS_CI'):\n                from pyface.message_dialog import warning\n                warning(None, 'Exponential failed to converge. Choose a different fit')\n            raise FitError()\n\n    def _calculate_coefficients(self):\n        return self._coefficients\n\n    def _calculate_coefficient_errors(self):\n        return self._coefficient_errors\n\n    def predict(self, x):\n        return_single = False\n        if not hasattr(x, '__iter__'):\n            x = [x]\n            return_single = True\n\n        x = asarray(x)\n\n        fx = self.fitfunc(x, *self._coefficients)\n        if return_single:\n            fx = fx[0]\n\n        return fx\n\n    def predict_error(self, x, error_calc='sem'):\n        \"\"\"\n            returns percent error\n        \"\"\"\n        return_single = False\n        if not hasattr(x, '__iter__'):\n            x = [x]\n            return_single = True\n\n        sef = self.calculate_standard_error_fit()\n        r, _ = self._covariance.shape\n\n        def calc_error(xi):\n            Xk = matrix([xi, ] * r).T\n\n            varY_hat = (Xk.T * self._covariance * Xk)\n            if error_calc == 'sem':\n                se = sef * sqrt(varY_hat)\n            else:\n                se = sqrt(sef ** 2 + sef ** 2 * varY_hat)\n\n            return se[0, 0]\n\n        fx = array([calc_error(xi) for xi in x])\n        # fx = ys * fx / 100.\n\n        if return_single:\n            fx = fx[0]\n\n        return fx\n\n\nclass ExponentialRegressor(LeastSquaresRegressor):\n    def __init__(self, *args, **kw):\n        def fitfunc(x, a, b, c):\n            return a * exp(-b * x) + c\n\n        self.fitfunc = fitfunc\n        super(ExponentialRegressor, self).__init__(*args, **kw)\n\n    def _calculate_initial_guess(self):\n        if self.ys[0] > self.ys[-1]:\n            ig = 100, 0.1, -100\n        else:\n            ig = -10, 0.1, 10\n        return ig\n# ============= EOF =============================================\n","sub_path":"pychron/core/regression/least_squares_regressor.py","file_name":"least_squares_regressor.py","file_ext":"py","file_size_in_byte":4598,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"542230051","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\n\nfrom torch.autograd import Variable\nfrom torch.nn.parameter import Parameter\n\nimport math\nimport utils\nimport argparse\nimport data_loader\n\nimport uni_impute\nfrom sklearn import metrics\n\nSEQ_LEN = 36\nRNN_HID_SIZE = 64\n\n\nclass TemporalDecay(nn.Module):\n    def __init__(self, input_size):\n        super(TemporalDecay, self).__init__()\n        self.build(input_size)\n\n    def build(self, input_size):\n        self.W = Parameter(torch.Tensor(input_size, input_size))\n        self.b = Parameter(torch.Tensor(input_size))\n        self.reset_parameters()\n\n    def reset_parameters(self):\n        stdv = 1. / math.sqrt(self.W.size(0))\n        self.W.data.uniform_(-stdv, stdv)\n        if self.b is not None:\n            self.b.data.uniform_(-stdv, stdv)\n\n    def forward(self, d):\n        gamma = F.relu(F.linear(d, self.W, self.b))\n        gamma = torch.exp(-gamma)\n        return gamma\n\nclass Model(nn.Module):\n    def __init__(self):\n        super(Model, self).__init__()\n        self.build()\n\n    def build(self):\n        self.impute_f = uni_impute.Model()\n        self.impute_b = uni_impute.Model()\n\n    def forward(self, data):\n        ret_f = self.impute_f(data['forward'])\n        ret_b = self.reverse(self.impute_b(data['backward']))\n\n        ret = self.merge_ret(ret_f, ret_b)\n\n        return ret\n\n    def merge_ret(self, ret_f, ret_b):\n        g_loss_f = ret_f['g_loss']\n        g_loss_b = ret_b['g_loss']\n        g_loss_c = self.get_consistency_g_loss(ret_f['predictions'], ret_b['predictions'])\n\n#        g_loss = g_loss_f + g_loss_b + g_loss_c\n        g_loss = g_loss_f + g_loss_b\n\n        predictions = (ret_f['predictions'] + ret_b['predictions']) / 2\n\n        ret_f['g_loss'] = g_loss\n        ret_f['predictions'] = predictions\n\n        return ret_f\n\n    def get_consistency_g_loss(self, pred_f, pred_b):\n        g_loss = torch.pow(pred_f - pred_b, 2).mean()\n        return g_loss\n\n    def reverse(self, ret):\n        def reverse_tensor(tensor_):\n            if tensor_.dim() <= 1:\n                return tensor_\n            indices = range(tensor_.size()[1])[::-1]\n            indices = torch.LongTensor(indices)\n\n            if torch.cuda.is_available():\n                indices = indices.cuda()\n\n            return tensor_.index_select(1, indices)\n\n        for key in ret:\n            ret[key] = reverse_tensor(ret[key])\n\n        return ret\n\n    def run_on_batch(self, data, optimizer):\n        ret = self(data)\n\n        if optimizer is not None:\n            optimizer.zero_grad()\n            ret['g_loss'].backward()\n            optimizer.step()\n\n        return ret\n\n","sub_path":"Airquality/models/bi_impute.py","file_name":"bi_impute.py","file_ext":"py","file_size_in_byte":2676,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"434921816","text":"import re\nimport sys\nimport os\nimport requests\nfrom collections import deque\nfrom bs4 import BeautifulSoup\nfrom colorama import init, Fore, Style\n\n# Taking Command-line arguments\nargs = sys.argv\nif len(args) < 2:\n    directory = \"mydir\"\nelse:\n    directory = args[1]\n\n# Creating Directory if it does not exist\nif not os.path.exists(directory):\n    os.mkdir(directory)\n\n# Regex\nurlRegex = re.compile(r\"(https://)?(.*)\")\nfilenameRegex = re.compile(r\"^(https://)?(www.)?(.*)\\.(\\w\\w\\w)$\")\n\ndomain_names = (\".org\", \".com\")\ninit()  # Colorama Initialise\nstack = deque()\ncurrentPage = None\n\nwhile True:\n    mock_url = input()\n    mo = urlRegex.search(mock_url)\n\n    # Exit\n    if mock_url == \"exit\":\n        exit(0)\n\n    # Back\n    elif mock_url == \"back\":\n        if len(stack) == 1 and stack[0] is None:\n            pass\n        else:\n            currentPage = stack.pop()\n            print(currentPage)\n\n    # Display Page from internet\n    elif mock_url.endswith(domain_names):\n        # Adding the https:// part to the url if it does not contain it\n        if not mo.group(1):\n            mock_url = \"https://\" + mock_url\n\n        r = requests.get(mock_url)\n        if r:\n            stack.append(currentPage)\n            soup = BeautifulSoup(r.content, features='html.parser')\n            lines = []\n            soup.script.decompose()\n            for soupy in soup.find_all(['p', 'a', 'ul', 'ol', 'li']):\n                if soupy.has_attr('href'):\n                    print(Fore.BLUE + soupy.get_text().strip())\n                    print(Style.RESET_ALL, end=\"\")\n                else:\n                    print(soupy.get_text().strip())\n                lines.append(soupy.get_text().strip())\n\n            mo2 = filenameRegex.search(mock_url)\n            filename = mo2.group(3)\n            with open(f\"{directory}/{filename}.txt\", mode=\"w\", encoding=soup.encoding) as f:\n                for line in lines:\n                    f.write(line + \"\\n\")\n\n        else:\n            print(\"Error: Incorrect URL\")\n\n    # Display locally stored Page\n    else:\n        try:\n            with open(f\"{directory}/{mock_url}.txt\") as f:\n                stack.append(currentPage)\n                currentPage = f.read()\n                print(currentPage)\n        except FileNotFoundError:\n            print(\"Error: Incorrect URL\")\n","sub_path":"Medium Difficulty/browser.py","file_name":"browser.py","file_ext":"py","file_size_in_byte":2313,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"341240447","text":"\ndef stringlike(obj):\n    return isinstance(obj, (str, bytes))\n\ndef extend(tag, iterable):\n    '''given a tag, extend it in-place with iterable; this works as you would expect\neven if iterable contains strings. returns back same tag, modified.'''\n    if stringlike(tag):\n        raise TypeError('`tag` argument must be Element')\n    last_child = tag[-1] if len(tag) else None\n    for x in iterable:\n        if isinstance(x, (str, bytes)):\n            if last_child is None:\n                text = tag.text\n                tag.text = text+x if text else x\n            else:\n                tail = last_child.tail\n                last_child.tail = tail+x if tail else x\n        else:\n            tag.append(x)\n            last_child = x\n    return tag\n\ndef split(tag, *, del_children=True, separate_tails=True):\n    '''opposite of extend\n\nfor example, `split(E\"<a>A<child>B</child>C</a>\")` will return\n`[\"A\", E\"<child>B</child>\", \"C\"]`\n\nif `del_children` is true, `del tag[:]` is performed before returning\n\nif `separate_tails` is false, then we don't separate children's text tails,\ne.g. `split(E\"<a>A<child>B</child>C</a>\")` will return\n`[\"A\", E\"<child>B</child>C\"]`\n'''\n    r = []\n    if isinstance(tag, (str, bytes)):\n        return r\n    append = r.append\n    tag_text = tag.text\n    tag.text = None\n    if tag_text is not None:\n        append(tag_text)\n    if separate_tails:\n        for child in tag:\n            child_tail = child.tail\n            child.tail = None\n            append(child)\n            if child_tail is not None:\n                append(child_tail)\n    else:\n        r.extend(tag)\n    if del_children:\n        del tag[:]\n    return r\n\ndef unwrap(tag, *, del_children=True, separate_tails=True):\n    r = split(\n        tag, del_children=del_children, separate_tails=separate_tails)\n    if not stringlike(tag):\n        tail = tag.tail\n        if tail:\n            r.append(tail)\n    return r\n\ndef extend_with_children_of(target, source):\n    return extend(target, unwrap(source, separate_tails=False))\n\ndef regex_sub_str_iter(regex, string, repl):\n    i = 0\n    for m in regex.finditer(string):\n        k0, k1 = m.span(0)\n        if i != k0: yield string[i:k0]\n        yield from repl(m)\n        i = k1\n    k0 = len(string)\n    if i != k0: yield string[i:k0]\n\ndef regex_sub_e_iter(regex, repl, e):\n    if isinstance(e, (str, bytes)):\n        yield from regex_sub_str_iter(regex, e, repl)\n    else:\n        yield e\n\ndef transform_text(tag, *transformations):\n    src = split(tag)\n    r = []\n    for transformation in transformations:\n        for x in src:\n            if isinstance(x, (str, bytes)):\n                r.extend(transformation(x))\n            else:\n                r.append(x)\n        src, r = r, src\n        r.clear()\n    extend(tag, src)\n    return tag\n\ndef equal(x, y):\n    # TODO: don't use function stack\n    # TODO: support list as returned by split\n    return (x.tag    == y.tag    and\n            x.text   == y.text   and\n            x.tail   == y.tail   and\n            x.attrib == y.attrib and\n            len(x)   == len(y)   and\n            all(equal(a, b) for a, b in zip(x, y)))\n\n","sub_path":"venv/Lib/site-packages/etreetools/lib.py","file_name":"lib.py","file_ext":"py","file_size_in_byte":3127,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"15701626","text":"import itertools\nimport numpy as np\nimport matplotlib.pyplot as plot\n\n# Numerical calculation and visualisation of the change in position\n# and velocity of a spacecraft when it travels to the moon. Newtons \n# law of gravitation and second law of motion are used. Derivative is\n# evaluated with simple Euler method. This project was originally\n# done in Finnish, the variable names reflect that.\n\n# Define constants and vectors for position and velocity\ng = 6.674*(10**(-11))  # Gravitation constant\nMaa = 5.974*(10**24)   # Earths mass\nKuu = 7.348*(10**22)   # Moons mass\nX = 376084800          # Target distance, low orbit of moon (Wikipedia)\nn = 0                  # Keeps tab on how many time steps has gone\nr1 = 6578100           # Distance from Earths center in the beginning\nv1 = 12012             # Starting velocity + 10%\nV = np.array([v1])     # Vector that will house the values of velocity\nR = np.array([r1])     # Vector that will house the values of distance\n\ndt = 10                 # Length of time step in seconds\n\n# Iterate our way to the moon!\nfor i in itertools.count():\n    n = n+1\n    # New distance\n    r = r1+v1*dt   \n    R = np.append(R,r)\n    # Updated acceleration\n    b = Kuu/((3844*(10**5)-r1)**2) \n    c = Maa/(r1**2)\n    A = g*(b-c)\n    # New velocity\n    v = v1+A*dt\n    V = np.append(V,v)\n    # Update position and velocity\n    r1 = r\n    v1 = v\n    # When arriving to the moon\n    if r>=X:\n        break\n\nT = (n*dt)/3600        # Travel time in hours\n\nprint(T)\n\n# Plot the graphs of position and velocity as function of time\n\nplot.figure(1)\nplot.subplot(211)\nplot.plot(R)\nplot.xlabel('Aika, s')      # \"Aika\" means time\nplot.ylabel('Matka, m')     # \"Matka\" means distance\n\nplot.subplot(212)\nplot.plot(V)\nplot.xlabel('Aika, s')\nplot.ylabel('Nopeus, m/s')  # \"Nopeus\" means velocity\n\nplot.show()\n","sub_path":"kuumatka_xv.py","file_name":"kuumatka_xv.py","file_ext":"py","file_size_in_byte":1828,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"8343855","text":"from subprocess import check_output\nimport os\nimport re\nimport fnmatch\n\ngazebo_info = check_output('pkg-config gazebo --cflags', shell=True).decode('utf-8')\n\nhit = re.search(r'-I(\\S*gazebo\\S*).*', gazebo_info)\nif not hit:\n    sys.exit(\"Gazebo is not installed\")\n\ngazebo_include_dir = hit.group(1)\ngazebo_proto_dir = os.path.join(gazebo_include_dir, \"gazebo\", \"msgs\", \"proto\")\n\npackage_dir = 'revolve'\nproto_dir = 'msgs'\ngenerated_dir = os.path.join(package_dir, proto_dir)\n\n# generate protobuf python files\ncheck_output(\n    'protoc -I {GAZEBO_PROTO_DIR} -I ./ --python_out=./ {PKG_DIR}/{PROTO_DIR}/*.proto'\n    .format(\n        GAZEBO_INCLUDE_DIR = gazebo_include_dir,\n        GAZEBO_PROTO_DIR = gazebo_proto_dir,\n        PKG_DIR = package_dir,\n        PROTO_DIR = proto_dir),\n    shell=True)\n\n\n\n# fix bullshit include syntax in protobuf-generated python files\ngenerated_files = (f for f in check_output(\"ls {}\".format(generated_dir), shell=True)\n    .decode('utf-8').split() if fnmatch.fnmatch(f, \"*_pb2.py\"))\n\n# all imports of 'import foo_pb2' kind are assumed to be from pygazebo.msg\npatt = r'^import\\s+(\\w*_pb2)'\nreplace_with = r'from pygazebo.msg import \\1'\n\npygazebo_package_finder = re.compile(patt, re.MULTILINE)\n\nfor fname in generated_files:\n    fpath = os.path.join(generated_dir, fname)\n    with open(fpath, 'r') as stream:\n        text = stream.read()\n\n    text = re.sub(pygazebo_package_finder, replace_with, text)\n    with open(fpath, 'w') as stream:\n        stream.write(text)\n","sub_path":"tol/compile-proto.py","file_name":"compile-proto.py","file_ext":"py","file_size_in_byte":1494,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"437435141","text":"import os\nfrom elasticsearch import Elasticsearch\nimport re\nimport pandas as pd\n\n#workdir = \"/mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/opengwas-infpval-fix\"\nworkdir = \"temp\"\nlogdir = \"logs\"\n\nlocs = [x[0] for x in os.walk(workdir)][1:]\nidlist = [os.path.basename(x) for x in locs]\n\nes = Elasticsearch(\n\t[{'host': '140.238.83.192','port': '9200'}],\n)\n\n\n# o = es.search(index=\"met-d\", body={\"query\":{\"bool\":{\"must\":[{\"term\":{\"gwas_id\": \"DHA\"}}, {\"term\":{\"snp_id\":\"rs2524296\"}}, {\"term\": {\"p\": \"1\"}}]}}})\n# id = [x['_id'] for x in o['hits']['hits']]\n\n# [es.update(index=\"met-d\", doc_type=\"_doc\", id=x, body={\"doc\":{\"p\":1.0}}) for x in id]\n# [es.get(index=\"met-d\", id=x) for x in id]\n\n\n# 1. read list of SNPs with p-val problems\n# 2. lookup SNPs to get their document IDs\n# 3. Update the documents\n\n\ndef fixpval(id, logdir):\n\treg = r'^([\\w]+-[\\w]+)-([\\w]+)'\n\tindex,study = re.match(reg, id).groups()\n\tprint(id)\n\tprint(index)\n\tprint(study)\n\ta = pd.read_csv(logdir + \"/\" + id + \".out\", sep=\"\\t\", header=None)\n\tprint(a)\n\tcounter=0\n\tfor i in range(len(a)):\n\t\tprint(i)\n\t\tbody={\n\t\t\t\"query\":{\n\t\t\t\t\"bool\":{\n\t\t\t\t\t\"must\":\n\t\t\t\t\t[\n\t\t\t\t\t\t{\"term\":{\"gwas_id\": study}},\n\t\t\t\t\t\t{\"term\":{\"position\": a[1][i]}},\n\t\t\t\t\t\t{\"term\":{\"chr\": a[0][i]}},\n\t\t\t\t\t\t{\"term\":{\"p\": 1}}\n\t\t\t\t\t]\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\to = es.search(index=index, body=body)\n\t\tif o['hits']['total'] > 0:\n\t\t\tdocid = [x['_id'] for x in o['hits']['hits']]\n\t\t\tind = [x['_index'] for x in o['hits']['hits']]\n\t\t\t[es.update(index=x['_index'], doc_type=\"_doc\", id=x['_id'], body={\"doc\":{\"p\":0.0}}, wait_for_active_shards=\"all\") for x in o['hits']['hits']]\n\t\t\tprint([es.get(index=x['_index'], id=x['_id']) for x in o['hits']['hits']])\n\t\t\tcounter+=1\n\treturn counter\n\n\n# 4. Read in clump.txt rsid list\n\ndef update_tophits(id, workdir):\n\treg = r'^([\\w]+-[\\w]+)-([\\w]+)'\n\tindex,study = re.match(reg, id).groups()\n\tprint(id)\n\tprint(index)\n\tprint(study)\n\twith open(workdir + \"/\" + id + \"/\" + \"clump.txt\", \"r\") as f:\n\t\tclump = f.read().splitlines()\n\t# 5. Get summary data\n\tprint(len(clump))\n\to=[]\n\tfor rsid in clump:\n\t\tbody={\n\t\t\t\"query\":{\n\t\t\t\t\"bool\":{\n\t\t\t\t\t\"must\":\n\t\t\t\t\t[\n\t\t\t\t\t\t{\"term\":{\"gwas_id\":study}},\n\t\t\t\t\t\t{\"term\":{\"snp_id\":rsid}},\n\t\t\t\t\t\t{\"range\":{\"p\":{\"lt\":5e-8}}}\n\t\t\t\t\t]\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\tres = es.search(index=index, body=body)\n\t\tif res['hits']['total'] > 0:\n\t\t\to.append(res['hits']['hits'][0]['_source'])\n\t# 6. Delete the tophits for that dataset\n\tbody={\n\t\t\"query\":{\n\t\t\t\"term\":{\"gwas_id\":study}\n\t\t}\n\t}\n\tprint(es.count(index=index+\"-tophits\", body=body))\n\t# es.delete_by_query(index=index+\"-tophits\", body=body, request_timeout=600,conflicts='abort', wait_for_active_shards='all', slices=1,wait_for_completion=True)\n\tdel_current_tophits(id)\n\tes.indices.refresh(index+'-tophits')\n\tprint(es.count(index=index+\"-tophits\", body=body))\n\t# 7. Upload the summary data as new tophits\n\t[es.index(index=index+\"-tophits\", body=x) for x in o]\n\tes.indices.refresh(index+'-tophits')\n\tprint(es.count(index=index+\"-tophits\", body=body))\n\n\ndef del_current_tophits(id):\n\treg = r'^([\\w]+-[\\w]+)-([\\w]+)'\n\tindex,study = re.match(reg, id).groups()\n\tprint(id)\n\tprint(index)\n\tprint(study)\n\tpp.pprint(es.indices.get_settings(index + \"-tophits\"))\n\tes.indices.put_settings(index=index + \"-tophits\", body={'index.blocks.read_only_allow_delete': None})\n\tpp.pprint(es.indices.get_settings(index + \"-tophits\"))\n\tes.indices.refresh(index + \"-tophits\")\n\tbody={\n\t\t\"query\":{\n\t\t\t\"term\":{\"gwas_id\":study}\n\t\t}\n\t}\n\to = es.search(index=index+'-tophits', body=body, size=10000)\n\tprint(len(o['hits']['hits']))\n\t[es.delete(index=x['_index'], id=x['_id']) for x in o['hits']['hits']]\n\ncount=[]\nfix_success=[]\nfor id in idlist:\n\tprint(id)\n\ttry:\n\t\tc=fixpval(id, logdir)\n\t\tprint(c)\n\t\tcount.append({id:c})\n\t\tprint()\n\texcept:\n\t\tfix_success.append({id:False})\n\telse:\n\t\tfix_success.append({id:True})\n\n\nc={}\nfor d in count:\n\tprint(d)\n\tc.update(d)\n\nclump_success=[]\nfor id in idlist:\n\tprint(id)\n\tif id in c.keys():\n\t\tif c[id] > 0:\n\t\t\ttry:\n\t\t\t\tupdate_tophits(id, workdir)\n\t\t\texcept:\n\t\t\t\tclump_success.append({id:False})\n\t\t\telse:\n\t\t\t\tclump_success.append({id:True})\n\n\n\n","sub_path":"scripts/fix_es.py","file_name":"fix_es.py","file_ext":"py","file_size_in_byte":4054,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"348387172","text":"# -*- coding: UTF-8 -*-\n'''\nCreated on 2020/9/30 17:39\n@File  : get_final_result.py\n@author: ZL\n@Desc  :\n'''\n\nfrom commonfunc.change_data_type import ChangeDataType\nimport os\nimport json\nimport ast\n\ncurPath = os.path.abspath(os.path.dirname(__file__))\nrootPath = os.path.split(curPath)[0]\n\n\nclass GetFinalResult:\n    def get_expect_result_data(self, file):\n        temp = 401\n        relation_list, relation_l = [], []\n        entity_list, entity_l = [], []\n        dialog_list = []\n        test_list = []\n        expect_result = ChangeDataType.file_to_dict(rootPath + \"\\\\testdata\\\\knowledgegraph\\\\relationship\\\\\" + file)\n        entity_value_relationship = expect_result.entity_value_relationship.tolist()\n        dialog_id = expect_result.dialog_id.tolist()\n        for i in range(len(dialog_id)):\n            if dialog_id[i] != temp:\n                relation_list.append(relation_l)\n                entity_list.append(entity_l)\n                dialog_list.append(temp)\n                temp = dialog_id[i]\n                relation_l, entity_l = [], []\n            if \"无需标注\" not in entity_value_relationship[i]:\n                entitys = entity_value_relationship[i].split(\"#\")\n                relation = entitys[1]\n                entity_1 = [entitys[0].split(\"@\")[0], entitys[0].split(\"@\")[1]]\n                entity_2 = [entitys[2].split(\"@\")[0], entitys[2].split(\"@\")[1]]\n                relation_l.append(relation)\n                entity_l.append([entity_1, entity_2])\n                test_list.append([entity_1, entity_2])\n            else:\n                relation_l = []\n                entity_l = []\n            if i == len(dialog_id) - 1:\n                relation_list.append(relation_l)\n                entity_list.append(entity_l)\n                dialog_list.append(temp)\n                temp = dialog_id[i]\n                relation_l, entity_l = [], []\n        return dialog_list, relation_list, entity_list\n\n    def get_final_result(self, file1, file2):\n        dialog_list, relation_list, entity_list = GetFinalResult.get_expect_result_data(self, file1)\n\n        api_result = ChangeDataType.file_to_dict(rootPath + \"\\\\testdata\\\\knowledgegraph\\\\relationship\\\\\" + file2)\n        api_entity = api_result.e_list.tolist()\n        api_relation = api_result.r_list.tolist()\n        api_threshold = api_result.t_list.tolist()\n\n        new_re_list = []\n        for i in range(len(api_entity)):\n            new_relation_list = []\n            print(api_entity[i])\n            for j in ast.literal_eval(api_entity[i]):\n                print(j)\n                if j in entity_list[i]:\n                    new_relation_list.append(relation_list[i][entity_list[i].index(j)])\n                else:\n                    new_relation_list.append(\"other\")\n            new_re_list.append(new_relation_list)\n        print(len(new_relation_list))\n        print(new_relation_list)\n\n\nif __name__ == '__main__':\n    GetFinalResult().get_final_result(\"result.csv\", \"20_10_11-04_56_33api_result_1.csv\")\n","sub_path":"knowledgegraph/get_final_result.py","file_name":"get_final_result.py","file_ext":"py","file_size_in_byte":2999,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"101387488","text":"import random\n\ndef main():\n    # prob1()\n    # prob2()\n    prob3()\n    # prob4()\n\ndef prob1():\n    print(random.randint(0,9))\n\ndef prob2():\n    while 1==1:\n        userInput = input(\"Enter a word: \")\n        if userInput == \"q\":\n            print(\"User has Quit\")\n            break\n        else:\n            print(\"Again, Again\")\n\ndef prob3():\n    while 9==9:\n        number = int(input(\"Enter a positive number!: \"))\n        if number !=0:\n            i = 0\n            while i <= number:\n                print(i)\n                i += 1\n        else:\n            print(\"User Has Quit\")\n            break\n\ndef prob4():\n    cpuNum = random.randint(1,100)\n    while cpuNum <= 100:\n        userNum = int(input(\"The computer has picked a number between 1 - 100! Guess it!!!:\"))\n        if userNum == cpuNum:\n            print(\"You got the right number!\")\n            break\n        elif userNum > cpuNum:\n            print(\"Go lower\")\n        elif userNum < cpuNum:\n            print(\"Go Higher\")\n        else:\n            print(\"Try Again\")\n\nif __name__ == '__main__':\n    main()","sub_path":"Classwork.py","file_name":"Classwork.py","file_ext":"py","file_size_in_byte":1075,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"602509961","text":"import numpy as np\nimport operator\nimport time\n\n'''\n函数\n@描述:创建数据集\n@return:group-数据集,labels-分类标签\n'''\n\n\ndef createDataSet():\n    # 给出数据的四组二维特征\n    group = np.array([[1, 101], [5, 89], [108, 5], [115, 8]])\n\n    # 给出四组特征的标签\n    labels = ['爱情片', '爱情片', '动作片', '动作片']\n\n    return group, labels\n\n\n'''\n函数\n@描述:KNN算法,分类器\n@参数:inX-还未分类的数据,dataSet-用于训练的数据,labels-分类标签,k-KNN算法参数\n@returns:sortResult[0][0],分类结果\n'''\n\n\ndef classify(inX, dataSet, labels, k):\n    # 返回dataSet的行数\n    dataSetSize = dataSet.shape[0]\n\n    # 列向量方向上重复inX1次,行向量方向上重复inX功dataSetSize次\n    # np.tile(mat,(n,m)),在横向上将mat复制m次,在纵向上将mat复制n次,构成一个矩阵\n\n    # L2距离计算\n    # 这一步实现了(x1-x2),(y1-y2)\n    diffMat = np.tile(inX, (dataSetSize, 1)) - dataSet\n    # 二维特征相减后平方\n    # 这一步实现了(x1-x2)^2,(y1-y2)^2\n    sqDiffMat = diffMat ** 2\n    # sum()所有元素相加,sum(0)列相加,sum(1)行相加\n    sqDistances = sqDiffMat.sum(axis=1)\n    # 开方计算距离\n    distances = sqDistances ** 0.5\n    # 返回distances中元素从小到大排序后的索引值\n    # x.argsort(),x中的元素从小到大排序后,输出x中对应位置的元素的index(索引)\n    # argsort(x),x默认大于0,x取负值时,输出是大于0的结果的反向的索引\n    sortedDistIndices = distances.argsort()\n    # print(sortedDistIndices)\n    # 选择过程\n    # 定一个记录类别次数的字典\n    classCount = {}\n    for i in range(k):\n        # 获得前k个元素的类别\n        voteLabel = labels[sortedDistIndices[i]]\n        # print(voteLabel)\n        # dict.get(key,default=None),字典的get方法,返回指定键的值,如果值不再字典中返回默认值\n        # 计算类别的次数\n        classCount[voteLabel] = classCount.get(voteLabel, 0) + 1\n    # key=operator.itemgetter(1),根据字典的值进行排序\n    # key=operator.itemgetter(0),根据字典的键进行排序\n    # reverse降序排列字典\n    # print(classCount)\n    sortResult = sorted(classCount.items(),\n                        key=operator.itemgetter(1), reverse=True)\n    # 返回次数最多的类别\n    # print(sortResult)\n    return sortResult[0][0]\n\n\nif __name__ == '__main__':\n    # 创建数据集\n    group, labels = createDataSet()\n    # 测试数据\n    test = [101, 20]\n    start = time.clock()\n    # 获得KNN分类结果\n    test_class = classify(test, group, labels, 3)\n    end = time.clock()\n    print(test_class)\n    print(end - start)\n","sub_path":"KNN/KNN.py","file_name":"KNN.py","file_ext":"py","file_size_in_byte":2689,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"501353245","text":"class Solution(object):\n    def cmp(self, a, b):\n      if a[0] > b[0]:\n          return 1;\n      elif a[0] == b[0]:\n        return a[1] - b[1];\n      else:\n        return -1;\n    \n    def merge(self, intervals):\n        \"\"\"\n        :type intervals: List[List[int]]\n        :rtype: List[List[int]]\n        \"\"\"\n        intervals.sort(self.cmp);\n        result = [];\n        i = 0;\n        start = 0;\n        length = len(intervals);\n        while i < length:\n          right = intervals[start][1];\n          while i < length - 1 and (intervals[i][1] >= intervals[i+1][0] or right >= intervals[i+1][1] or right >= intervals[i+1][0]):\n            right = max(right, max(intervals[start][1], intervals[i+1][1]));\n            i += 1;\n          result.append([intervals[start][0], right]);\n          start = i + 1;\n          i += 1;\n        return result;","sub_path":"dataStructure/MergeInterval.py","file_name":"MergeInterval.py","file_ext":"py","file_size_in_byte":848,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"133525860","text":"# -*- coding:utf-8 -*-\n# @version: 1.0\n# @author: wuxikun\n# @date: '2020/8/19 10:31 AM'\n\n\ndef manacher(s:str) -> int:\n    s = '#' + '#'.join(s) + '#'\n    radius = [0 for _ in range(len(s))]\n    maxlen, pos, max_right = 0, 0, 0\n\n    for i in range(len(s)):\n        if i < max_right:\n            radius[i] = min(max_right - i, radius[pos * 2 - i])\n        else:\n            radius[i] = 1\n        while i - radius[i] >= 0 and i + radius[i] < len(s) and s[i - radius[i]] == s[i + radius[i]]:\n            radius[i] += 1\n        if i - 1 + radius[i] > max_right:\n            max_right = i - 1 + radius[i]\n            pos = i\n        maxlen = max(maxlen, radius[i])\n    return maxlen - 1\n\n\ndef get_next_list(s: str)->list:\n    next_list = [0 for _ in range(len(s))]\n    for i in range(len(s)):\n        if i == 0:\n            next_list[i] = 0\n        else:\n            cnt = 0\n            for k in range(0, i):\n                if s[0: k] == s[i - k: i]:\n                    cnt += 1\n                    next_list[i] = max(next_list[i], cnt)\n            # if next_list[i] == 0:\n            #     next_list[i] = 1\n    return next_list\n\n\n# 2. kmp匹配算法\ndef kmp_index_sub_list(src_str, target_str):\n    i, j = 0, 0\n    target_next = get_next_list(target_str)\n    while i < len(src_str) and j < len(target_str):\n        if src_str[i] == target_str[j]:\n            i += 1\n            j += 1\n        elif j != 0:\n            j = target_next[j - 1]\n        else:\n            i += 1\n\n    if j >= len(target_str):\n        return i - len(target_str)\n    else:\n        return -1\n\nif __name__ == '__main__':\n    print(manacher('caba'))\n\n    print(get_next_list(\"ABABAA\"))","sub_path":"ds_alg/manacher_alg.py","file_name":"manacher_alg.py","file_ext":"py","file_size_in_byte":1656,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"157487947","text":"import commands\nfrom art import text2art\ncmd = commands.Command()\n@cmd.event(command=\"textart\")\ndef textart(args):\n    assert args != None\n    assert len(args) >=2\n    font = args[0]\n    strtoart = ' '.join(args[1:])\n    return text2art(strtoart,font=font)","sub_path":"commands/textart.py","file_name":"textart.py","file_ext":"py","file_size_in_byte":256,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"185856488","text":"\"\"\"Pydantic-ish way to manage YAML configurations.\n\nThis module is heavily inspired in Pydantic's Settings management.\n\nDribia Data Research 2021\n\"\"\"\n\nfrom collections.abc import Mapping\nfrom pathlib import Path\nfrom typing import AbstractSet, Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport yaml\nfrom pydantic import BaseConfig, BaseModel, Extra\nfrom pydantic.fields import ModelField\nfrom pydantic.utils import deep_update\nfrom yaml import YAMLError\n\ntry:\n    from importlib.metadata import version  # type: ignore\nexcept ModuleNotFoundError:\n    from importlib_metadata import version  # type: ignore\n\n__version__ = version(__name__)\n\nconfig_file_sentinel = str(object())\nconfig_folder_sentinel = str(object())\n\nConfigsSourceCallable = Callable[[\"DriConfig\"], Dict[str, Any]]\n\n\nclass YAMLConfigError(YAMLError):\n    \"\"\"Error in YAML configuration file.\"\"\"\n\n    pass\n\n\nclass DriConfig(BaseModel):\n    \"\"\"Base class with for project configurations.\n\n    All of its values can be overridden by values found in a YAML\n    configuration file.\n\n    \"\"\"\n\n    def __init__(\n        __pydantic_self__,\n        _config_file_name: Union[str, None] = config_file_sentinel,\n        _config_folder: Union[Path, str, None] = config_folder_sentinel,\n        _config_file_encoding: Optional[str] = None,\n        **values: Any,\n    ) -> None:\n        \"\"\"Initialization parameters.\n\n        It allows to define on initialization the YAML config file name\n        and folder, and its encoding. However, those parameters should\n        be configured by overriding them in the internal Config class.\n\n        It also allows to provide any configuration value defined as a\n        class attribute. However, it would defeat the purpose, since\n        they should be defined in a YAML configuration file.\n\n        Args:\n            _config_file_name: YAML configuration file name.\n            _config_folder: YAML configuration folder.\n            _config_file_encoding: YAML configuration file encoding.\n            **values: Any config value.\n        \"\"\"\n        # Uses something other than `self` the first arg\n        # to allow \"self\" as a settable attribute\n        super().__init__(\n            **__pydantic_self__._build_values(\n                values,\n                _config_file_name=(\n                    _config_file_name\n                    if _config_file_name != config_file_sentinel\n                    else __pydantic_self__.__config__.config_file_name\n                ),\n                _config_folder=(\n                    _config_folder\n                    if _config_folder != config_folder_sentinel\n                    else __pydantic_self__.__config__.config_folder\n                ),\n                _config_file_encoding=(\n                    _config_file_encoding\n                    if _config_file_encoding is not None\n                    else __pydantic_self__.__config__.config_file_encoding\n                ),\n            )\n        )\n\n    def _build_values(\n        self,\n        init_kwargs: Dict[str, Any],\n        _config_file_name: Union[str, None] = None,\n        _config_folder: Union[Path, str, None] = None,\n        _config_file_encoding: Optional[str] = None,\n    ) -> Dict[str, Any]:\n        \"\"\"Build the configuration values based on init and YAML.\n\n        We prioritize initialization values over configured values\n        in the YAML file. However, the purpose of this class is to\n        defined the configuration values via a YAML configuration\n        file!\n\n        Args:\n            init_kwargs: Initialization arguments.\n            _config_file_name: YAML configuration file name.\n            _config_folder: YAML configuration folder.\n            _config_file_encoding: YAML configuration file encoding.\n\n        Returns: The value dictionary.\n\n        \"\"\"\n        init_config = InitConfigSource(init_kwargs=init_kwargs)\n        yaml_config = YamlConfigSource(\n            config_file_name=_config_file_name,\n            config_folder=_config_folder,\n            config_file_encoding=_config_file_encoding,\n        )\n        sources = self.__config__.customise_sources(\n            init_config=init_config, yaml_config=yaml_config\n        )\n        if sources:\n            return deep_update(*reversed([source(self) for source in sources]))\n        else:\n            return {}\n\n    class Config(BaseConfig):\n        \"\"\"Base internal Config class.\n\n        This class and its attributes can (should) be overridden\n        when creating a YAML configuration class.\n\n        \"\"\"\n\n        config_prefix = \"\"  # Only load YAML entries with given prefix.\n        config_folder = \".\"  # Folder where the YAML file is located.\n        config_file_name = None  # File name of the YAML file.\n        config_file_encoding = None  # Encoding of the YAML file.\n        validate_all = True\n        extra = Extra.forbid\n        arbitrary_types_allowed = True\n        case_sensitive = False\n\n        @classmethod\n        def prepare_field(cls, field: ModelField) -> None:\n            \"\"\"Prepare field for being loaded w/ the configured prefix.\n\n            Args:\n                field: Model field.\n\n            Returns: Prepared model field.\n\n            \"\"\"\n            config_names: Union[List[str], AbstractSet[str]]\n            config_names = {cls.config_prefix + field.name}\n            if not cls.case_sensitive:\n                config_names = config_names.__class__(n.lower() for n in config_names)\n            field.field_info.extra[\"config_names\"] = config_names\n\n        @classmethod\n        def customise_sources(\n            cls,\n            init_config: ConfigsSourceCallable,\n            yaml_config: ConfigsSourceCallable,\n        ) -> Tuple[ConfigsSourceCallable, ...]:\n            \"\"\"Join configurations from different sources.\n\n            Here we define the prioritization of initialization values\n            over YAML-configured values.\n\n            Args:\n                init_config: Initialization values.\n                yaml_config: Configured values in YAML.\n\n            Returns: Sorted configuration value sources.\n\n            \"\"\"\n            return init_config, yaml_config\n\n    __config__: Config  # type: ignore\n\n\nclass InitConfigSource:\n    \"\"\"Configuration source from the __init__ arguments.\"\"\"\n\n    __slots__ = (\"init_kwargs\",)\n\n    def __init__(self, init_kwargs: Dict[str, Any]):\n        \"\"\"Configurations passed.\"\"\"\n        self.init_kwargs = init_kwargs\n\n    def __call__(self, configs: DriConfig) -> Dict[str, Any]:\n        \"\"\"Obtain configurations.\"\"\"\n        return self.init_kwargs\n\n    def __repr__(self) -> str:\n        \"\"\"Class representation.\"\"\"\n        return f\"InitConfigSource(init_kwargs={self.init_kwargs!r})\"\n\n\nclass YamlConfigSource:\n    \"\"\"Configuration source from the a YAML configuration file.\"\"\"\n\n    __slots__ = (\"config_file\", \"config_file_encoding\")\n\n    def __init__(\n        self,\n        config_file_name: Union[str, None],\n        config_folder: Union[Path, str, None],\n        config_file_encoding: Optional[str],\n    ):\n        \"\"\"Initialize the YAML configurations source.\n\n        Args:\n            config_file_name: YAML configuration file name.\n            config_folder: YAML configuration folder.\n            config_file_encoding: YAML configuration encoding.\n\n        \"\"\"\n        self.config_file: Union[Path, None] = None\n        if config_file_name is not None and config_folder is not None:\n            self.config_file = Path(config_folder) / config_file_name\n        self.config_file_encoding: Optional[str] = config_file_encoding\n\n    def __call__(self, configs: DriConfig) -> Dict[str, Any]:\n        \"\"\"Build variables from the YAML configuration file.\"\"\"\n        d: Dict[str, Optional[str]] = {}\n        config_vars: Dict[str, Any] = {}\n        if self.config_file is not None:\n            config_path = Path(self.config_file).expanduser()\n            if config_path.is_file():\n                yaml_file = read_yaml_file(\n                    config_path,\n                    encoding=self.config_file_encoding,\n                    case_sensitive=configs.__config__.case_sensitive,\n                )\n                if not isinstance(yaml_file, Mapping):\n                    raise YAMLConfigError(\n                        f\"The YAML configuration file must be a \"\n                        f\"mapping and not a '{type(yaml_file).__name__}'.\"\n                    )\n                config_vars = {**yaml_file}\n\n        for field in configs.__fields__.values():\n            config_val: Optional[str] = None\n            for config_name in field.field_info.extra[\"config_names\"]:\n                config_val = config_vars.get(config_name)\n                if config_val is not None:\n                    break\n\n            if config_val is None:\n                continue\n\n            d[field.alias] = config_val\n        return d\n\n    def __repr__(self) -> str:\n        \"\"\"Class representation.\"\"\"\n        return (\n            f\"YamlConfigSource(config_file={str(self.config_file)!r}, \"\n            f\"config_file_encoding={self.config_file_encoding!r})\"\n        )\n\n\ndef read_yaml_file(\n    file_path: Path, *, encoding: str = None, case_sensitive: bool = False\n) -> Dict[str, Optional[str]]:\n    \"\"\"Parse a YAML configuration file.\n\n    Args:\n        file_path: YAML configuration file path.\n        encoding: YAML configuration file encoding.\n        case_sensitive: Whether read variables case-sensitively.\n\n    Returns: Parsed YAML configuration file.\n\n    \"\"\"\n    with open(file_path, \"r\", encoding=encoding or \"utf8\") as f:\n        file_vars: Dict[str, Any] = yaml.load(f, Loader=yaml.SafeLoader)\n    if not case_sensitive:\n        try:\n            return {k.lower(): v for k, v in file_vars.items()}\n        except AttributeError:\n            return file_vars\n    else:\n        return file_vars\n","sub_path":"driconfig/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":9799,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"39404365","text":"# coding=utf8\n# Copyright (c) 2016 CineUse\n\nfrom Qt import QtWidgets\n\n\nclass LoggerWidget(QtWidgets.QPlainTextEdit):\n    def __new__(cls, *args, **kwargs):\n        if not hasattr(cls, '_instance'):\n            orig = super(LoggerWidget, cls)\n            cls._instance = orig.__new__(cls, *args, **kwargs)\n            cls._instance.__initialized = False\n        return cls._instance\n\n    def __init__(self, parent=None):\n        # only initialize at first time\n        if self.__initialized:\n            return\n        self.__initialized = True\n\n        # do init\n        super(LoggerWidget, self).__init__(parent)\n\n        self.setReadOnly(True)\n\n\nif __name__ == \"__main__\":\n    LoggerWidget()\n","sub_path":"apps/strack_desktop/console_ui/LoggerWidget.py","file_name":"LoggerWidget.py","file_ext":"py","file_size_in_byte":694,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"564748325","text":"# -*- coding: utf-8 -*-\n##########################################################################\n#\n# pgAdmin 4 - PostgreSQL Tools\n#\n# Copyright (C) 2013 - 2020, The pgAdmin Development Team\n# This software is released under the PostgreSQL Licence\n#\n##########################################################################\nfrom unittest.mock import patch\n\nfrom pgadmin.utils.route import BaseTestGenerator\nfrom pgadmin.browser.server_groups.servers.databases.tests import utils as \\\n    database_utils\nfrom regression.python_test_utils import test_utils\nimport json\nfrom pgadmin.utils import server_utils\nimport random\n\n\nclass TestDownloadCSV(BaseTestGenerator):\n    \"\"\"\n    This class validates download csv\n    \"\"\"\n    scenarios = [\n        (\n            'Download csv URL with valid query',\n            dict(\n                sql='SELECT 1 as \"A\",2 as \"B\",3 as \"C\"',\n                init_url='/datagrid/initialize/query_tool/{0}/{1}/{2}/{3}',\n                donwload_url=\"/sqleditor/query_tool/download/{0}\",\n                output_columns='\"A\",\"B\",\"C\"',\n                output_values='1,2,3',\n                is_valid_tx=True,\n                is_valid=True,\n                download_as_txt=False,\n                filename='test.csv'\n            )\n        ),\n        (\n            'Download csv URL with wrong TX id',\n            dict(\n                sql='SELECT 1 as \"A\",2 as \"B\",3 as \"C\"',\n                init_url='/datagrid/initialize/query_tool/{0}/{1}/{2}/{3}',\n                donwload_url=\"/sqleditor/query_tool/download/{0}\",\n                output_columns=None,\n                output_values=None,\n                is_valid_tx=False,\n                is_valid=False,\n                download_as_txt=False,\n                filename='test.csv'\n            )\n        ),\n        (\n            'Download csv URL with wrong query',\n            dict(\n                sql='SELECT * FROM this_table_does_not_exist',\n                init_url='/datagrid/initialize/query_tool/{0}/{1}/{2}/{3}',\n                donwload_url=\"/sqleditor/query_tool/download/{0}\",\n                output_columns=None,\n                output_values=None,\n                is_valid_tx=True,\n                is_valid=False,\n                download_as_txt=False,\n                filename='test.csv'\n            )\n        ),\n        (\n            'Download as txt without filename parameter',\n            dict(\n                sql='SELECT 1 as \"A\",2 as \"B\",3 as \"C\"',\n                init_url='/datagrid/initialize/query_tool/{0}/{1}/{2}/{3}',\n                donwload_url=\"/sqleditor/query_tool/download/{0}\",\n                output_columns='\"A\";\"B\";\"C\"',\n                output_values='1;2;3',\n                is_valid_tx=True,\n                is_valid=True,\n                download_as_txt=True,\n                filename=None\n            )\n        ),\n        (\n            'Download as csv without filename parameter',\n            dict(\n                sql='SELECT 1 as \"A\",2 as \"B\",3 as \"C\"',\n                init_url='/datagrid/initialize/query_tool/{0}/{1}/{2}/{3}',\n                donwload_url=\"/sqleditor/query_tool/download/{0}\",\n                output_columns='\"A\",\"B\",\"C\"',\n                output_values='1,2,3',\n                is_valid_tx=True,\n                is_valid=True,\n                download_as_txt=False,\n                filename=None\n            )\n        ),\n    ]\n\n    def setUp(self):\n        self._db_name = 'download_csv_' + str(random.randint(10000, 65535))\n        self._sid = self.server_information['server_id']\n\n        server_con = server_utils.connect_server(self, self._sid)\n\n        self._did = test_utils.create_database(\n            self.server, self._db_name\n        )\n\n    def runTest(self):\n\n        db_con = database_utils.connect_database(self,\n                                                 test_utils.SERVER_GROUP,\n                                                 self._sid,\n                                                 self._did)\n        if not db_con[\"info\"] == \"Database connected.\":\n            raise Exception(\"Could not connect to the database.\")\n\n        # Initialize query tool\n        self.trans_id = str(random.randint(1, 9999999))\n        url = self.init_url.format(\n            self.trans_id, test_utils.SERVER_GROUP, self._sid, self._did)\n        response = self.tester.post(url)\n        self.assertEquals(response.status_code, 200)\n\n        # If invalid tx test then make the Tx id invalid so that tests fails\n        if not self.is_valid_tx:\n            self.trans_id = self.trans_id + '007'\n\n        # Check character\n        url = self.donwload_url.format(self.trans_id)\n        # Disable the console logging from Flask logger\n        self.app.logger.disabled = True\n        if self.filename is None:\n            if self.download_as_txt:\n                with patch('pgadmin.tools.sqleditor.blueprint.'\n                           'csv_field_separator.get', return_value=';'), patch(\n                        'time.time', return_value=1587031962.3808076):\n                    response = self.tester.post(url, data={\"query\": self.sql})\n                    headers = dict(response.headers)\n                    # when valid query\n                    self.assertEquals(response.status_code, 200)\n                    csv_data = response.data.decode()\n                    self.assertTrue(self.output_columns in csv_data)\n                    self.assertTrue(self.output_values in csv_data)\n                    self.assertIn('text/plain', headers['Content-Type'])\n                    self.assertIn('1587031962.txt',\n                                  headers['Content-Disposition'])\n            else:\n                with patch('time.time', return_value=1587031962.3808076):\n                    response = self.tester.post(url, data={\"query\": self.sql})\n                    headers = dict(response.headers)\n                    # when valid query\n                    self.assertEquals(response.status_code, 200)\n                    csv_data = response.data.decode()\n                    self.assertTrue(self.output_columns in csv_data)\n                    self.assertTrue(self.output_values in csv_data)\n                    self.assertIn('text/csv', headers['Content-Type'])\n                    self.assertIn('1587031962.csv',\n                                  headers['Content-Disposition'])\n\n        else:\n            response = self.tester.post(\n                url,\n                data={\"query\": self.sql, \"filename\": self.filename}\n            )\n            headers = dict(response.headers)\n            # Enable the console logging from Flask logger\n            self.app.logger.disabled = False\n            if self.is_valid:\n                # when valid query\n                self.assertEquals(response.status_code, 200)\n                csv_data = response.data.decode()\n                self.assertTrue(self.output_columns in csv_data)\n                self.assertTrue(self.output_values in csv_data)\n                self.assertIn('text/csv', headers['Content-Type'])\n                self.assertIn(self.filename, headers['Content-Disposition'])\n            elif not self.is_valid and self.is_valid_tx:\n                # When user enters wrong query\n                self.assertEquals(response.status_code, 200)\n                response_data = json.loads(response.data.decode('utf-8'))\n                self.assertFalse(response_data['data']['status'])\n                self.assertTrue(\n                    'relation \"this_table_does_not_exist\" does not exist' in\n                    response_data['data']['result']\n                )\n            else:\n                # when TX id is invalid\n                self.assertEquals(response.status_code, 500)\n\n        database_utils.disconnect_database(self, self._sid, self._did)\n\n    def tearDown(self):\n        main_conn = test_utils.get_db_connection(\n            self.server['db'],\n            self.server['username'],\n            self.server['db_password'],\n            self.server['host'],\n            self.server['port'],\n            self.server['sslmode']\n        )\n        test_utils.drop_database(main_conn, self._db_name)\n","sub_path":"web/pgadmin/tools/sqleditor/tests/test_download_csv_query_tool.py","file_name":"test_download_csv_query_tool.py","file_ext":"py","file_size_in_byte":8114,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"561243903","text":"km_travel=80\n#kilometers travelled in one day\nvehicle_avg=18\n#average of your vehicle fuel\nfuel_required=km_travel/vehicle_avg\n#required fuel \ncost=80*fuel_required\n#cost of one day fuel\n#cost of diesel per litre is 80 INR\nprint(\"Cost of driving per day to office is: \",cost)\n\n","sub_path":"Day01/ridecost_cal.py","file_name":"ridecost_cal.py","file_ext":"py","file_size_in_byte":277,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"39874200","text":"def min_palindrome_steps(txt):\n    if txt == txt[::-1]:\n        return 0\n    count = 0\n    letters = []\n    for c in txt:\n        letters[:0] = c\n        count += 1\n        chars = txt + ''.join(letters)\n        if chars == chars[::-1]:\n            break\n    return count\n","sub_path":"edabit/minimum_steps_to_a_palindrome.py","file_name":"minimum_steps_to_a_palindrome.py","file_ext":"py","file_size_in_byte":272,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"206249212","text":"import logging\nimport random\nimport numpy as np\nfrom argparse import ArgumentParser, SUPPRESS\nimport tensorflow_addons as tfa\nimport tensorflow as tf\nimport tables\nimport os\nimport sys\nfrom itertools import accumulate\n\nimport clair3.model as model_path\nfrom shared.utils import str2bool\n\nlogging.basicConfig(format='%(message)s', level=logging.INFO)\ntables.set_blosc_max_threads(512)\nos.environ['NUMEXPR_MAX_THREADS'] = '64'\nos.environ['NUMEXPR_NUM_THREADS'] = '8'\n\n\ndef get_label_task(label, label_shape_cum, task):\n    if task == 0:\n        return label[:label_shape_cum[task]]\n    elif task == len(label_shape_cum) - 1:\n        return label[label_shape_cum[task - 1]:]\n    else:\n        return label[label_shape_cum[task - 1]:label_shape_cum[task]]\n\n\ndef cal_class_weight(samples_per_cls, no_of_classes, beta=0.999):\n    effective_num = 1.0 - np.power(beta, samples_per_cls)\n    cls_weights = (1.0 - beta) / np.array(effective_num)\n    cls_weights = cls_weights / np.sum(cls_weights) * no_of_classes\n    return cls_weights\n\n\nclass FocalLoss(tf.keras.losses.Loss):\n    \"\"\"\n    updated version of focal loss function, for multi class classification, we remove alpha parameter, which the loss\n    more stable, and add gradient clipping to avoid gradient explosion and precision overflow.\n    \"\"\"\n\n    def __init__(self, label_shape_cum, task, effective_label_num=None, gamma=2):\n        super(FocalLoss, self).__init__()\n        self.gamma = gamma\n        self.cls_weights = None\n        if effective_label_num is not None:\n            task_label_num = get_label_task(effective_label_num, label_shape_cum, task)\n            cls_weights = cal_class_weight(task_label_num, len(task_label_num))\n            cls_weights = tf.constant(cls_weights, dtype=tf.float32)\n            cls_weights = tf.expand_dims(cls_weights, axis=0)\n            self.cls_weights = cls_weights\n\n    def call(self, y_true, y_pred):\n        y_pred = tf.clip_by_value(y_pred, clip_value_min=1e-9, clip_value_max=1 - 1e-9)\n        cross_entropy = -y_true * tf.math.log(y_pred)\n        weight = ((1 - y_pred) ** self.gamma) * y_true\n        FCLoss = cross_entropy * weight\n        if self.cls_weights is not None:\n            FCLoss = FCLoss * self.cls_weights\n        reduce_fl = tf.reduce_sum(FCLoss, axis=-1)\n        return reduce_fl\n\n\ndef get_chunk_list(chunk_offset, train_data_size, chunk_size):\n    \"\"\"\n    get chunk list for training and validation data. we will randomly split training and validation dataset,\n    all training data is directly acquired from various tensor bin files.\n\n    \"\"\"\n    all_shuffle_chunk_list = []\n    total_size = 0\n    offset_idx = 0\n    for bin_idx, chunk_num in enumerate(chunk_offset):\n        all_shuffle_chunk_list += [(bin_idx, chunk_idx) for chunk_idx in range(chunk_num)]\n    np.random.seed(0)\n    np.random.shuffle(all_shuffle_chunk_list)  # keep the same random validate dataset\n    for bin_idx, chunk_num in enumerate(chunk_offset):\n        if chunk_num * chunk_size + total_size >= train_data_size:\n            chunk_num = (train_data_size - total_size) // chunk_size\n            offset_idx += chunk_num\n            return np.array(all_shuffle_chunk_list[:offset_idx]), np.array(all_shuffle_chunk_list[offset_idx + 1:])\n        else:\n            total_size += chunk_num * chunk_size\n            offset_idx += chunk_num\n\n\ndef exist_file_prefix(exclude_training_samples, f):\n    for prefix in exclude_training_samples:\n        if prefix in f:\n            return True\n    return False\n\n\ndef train_model(args):\n    platform = args.platform\n    pileup = args.pileup\n    add_indel_length = args.add_indel_length\n    exclude_training_samples = args.exclude_training_samples\n    exclude_training_samples = set(exclude_training_samples.split(',')) if exclude_training_samples else set()\n    add_validation_dataset = args.validation_dataset\n    ochk_prefix = args.ochk_prefix if args.ochk_prefix is not None else \"\"\n    if pileup:\n        import shared.param_p as param\n        model = model_path.Clair3_P()\n    else:\n        import shared.param_f as param\n        model = model_path.Clair3_F(add_indel_length=add_indel_length)\n\n    tensor_shape = param.ont_input_shape if platform == 'ont' else param.input_shape\n    label_size, label_shape = param.label_size, param.label_shape\n    label_shape_cum = list(accumulate(label_shape))\n    batch_size, chunk_size = param.trainBatchSize, param.chunk_size\n    random.seed(param.RANDOM_SEED)\n    np.random.seed(param.RANDOM_SEED)\n    learning_rate = args.learning_rate if args.learning_rate else param.initialLearningRate\n    max_epoch = args.maxEpoch if args.maxEpoch else param.maxEpoch\n    task_num = 4 if add_indel_length else 2\n    TensorShape = (\n    tf.TensorShape([None] + tensor_shape), tuple(tf.TensorShape([None, label_shape[task]]) for task in range(task_num)))\n    TensorDtype = (tf.int32, tuple(tf.float32 for _ in range(task_num)))\n\n    bin_list = os.listdir(args.bin_fn)\n    # default we exclude sample hg003 and all chr20 for training\n    bin_list = [f for f in bin_list if '_20_' not in f and not exist_file_prefix(exclude_training_samples, f)]\n    logging.info(\"[INFO] total {} training bin files: {}\".format(len(bin_list), ','.join(bin_list)))\n    total_data_size = 0\n    table_dataset_list = []\n    validate_table_dataset_list = []\n    chunk_offset = np.zeros(len(bin_list), dtype=int)\n\n    effective_label_num = None\n    for bin_idx, bin_file in enumerate(bin_list):\n        table_dataset = tables.open_file(os.path.join(args.bin_fn, bin_file), 'r')\n        validate_table_dataset = tables.open_file(os.path.join(args.bin_fn, bin_file), 'r')\n        table_dataset_list.append(table_dataset)\n        validate_table_dataset_list.append(validate_table_dataset)\n        chunk_num = (len(table_dataset.root.label) - batch_size) // chunk_size\n        data_size = int(chunk_num * chunk_size)\n        chunk_offset[bin_idx] = chunk_num\n        total_data_size += data_size\n\n    train_data_size = total_data_size * param.trainingDatasetPercentage\n    validate_data_size = int((total_data_size - train_data_size) // chunk_size) * chunk_size\n    train_data_size = int(train_data_size // chunk_size) * chunk_size\n    train_shuffle_chunk_list, validate_shuffle_chunk_list = get_chunk_list(chunk_offset, train_data_size, chunk_size)\n\n    def DataGenerator(x, data_size, shuffle_chunk_list, train_flag=True):\n\n        \"\"\"\n        data generator for pileup or full alignment data processing, pytables with blosc:lz4hc are used for extreme fast\n        compression and decompression. random chunk shuffling and random start position to increase training model robustness.\n\n        \"\"\"\n\n        chunk_iters = batch_size // chunk_size\n        batch_num = data_size // batch_size\n        position_matrix = np.empty([batch_size] + tensor_shape, np.int32)\n        label = np.empty((batch_size, param.label_size), np.float32)\n\n        random_start_position = np.random.randint(0, batch_size) if train_flag else 0\n        if train_flag:\n            np.random.shuffle(shuffle_chunk_list)\n        for batch_idx in range(batch_num):\n            for chunk_idx in range(chunk_iters):\n                offset_chunk_id = shuffle_chunk_list[batch_idx * chunk_iters + chunk_idx]\n                bin_id, chunk_id = offset_chunk_id\n                position_matrix[chunk_idx * chunk_size:(chunk_idx + 1) * chunk_size] = x[bin_id].root.position_matrix[\n                        random_start_position + chunk_id * chunk_size:random_start_position + (chunk_id + 1) * chunk_size]\n                label[chunk_idx * chunk_size:(chunk_idx + 1) * chunk_size] = x[bin_id].root.label[\n                        random_start_position + chunk_id * chunk_size:random_start_position + (chunk_id + 1) * chunk_size]\n\n            if add_indel_length:\n                yield position_matrix, (\n                        label[:,                   :label_shape_cum[0]],\n                        label[:, label_shape_cum[0]:label_shape_cum[1]],\n                        label[:, label_shape_cum[1]:label_shape_cum[2]],\n                        label[:, label_shape_cum[2]:                  ]\n                    )\n            else:\n                yield position_matrix, (\n                        label[:,                   :label_shape_cum[0]],\n                        label[:, label_shape_cum[0]:label_shape_cum[1]]\n                    )\n\n\n    train_dataset = tf.data.Dataset.from_generator(\n        lambda: DataGenerator(table_dataset_list, train_data_size, train_shuffle_chunk_list, True), TensorDtype,\n        TensorShape).prefetch(buffer_size=tf.data.experimental.AUTOTUNE)\n    validate_dataset = tf.data.Dataset.from_generator(\n        lambda: DataGenerator(validate_table_dataset_list, validate_data_size, validate_shuffle_chunk_list, False), TensorDtype,\n        TensorShape).prefetch(buffer_size=tf.data.experimental.AUTOTUNE)\n\n    total_steps = max_epoch * train_data_size // batch_size\n\n    #RectifiedAdam with warmup start\n    optimizer = tfa.optimizers.Lookahead(tfa.optimizers.RectifiedAdam(\n        lr=learning_rate,\n        total_steps=total_steps,\n        warmup_proportion=0.1,\n        min_lr=learning_rate*0.75,\n    ))\n\n    loss_func = [FocalLoss(label_shape_cum, task, effective_label_num) for task in range(task_num)]\n    loss_task = {\"output_{}\".format(task + 1): loss_func[task] for task in range(task_num)}\n    metrics = {\"output_{}\".format(task + 1): tfa.metrics.F1Score(num_classes=label_shape[task], average='micro') for\n               task in range(task_num)}\n\n    model.compile(\n        loss=loss_task,\n        metrics=metrics,\n        optimizer=optimizer\n    )\n    early_stop_callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=10, mode=\"min\")\n    model_save_callbakck = tf.keras.callbacks.ModelCheckpoint(ochk_prefix + \".{epoch:02d}\", period=1, save_weights_only=False)\n\n    # Use first 20 element to initialize tensorflow model using graph mode\n    output = model(np.array(table_dataset_list[0].root.position_matrix[:20]))\n    logging.info(model.summary(print_fn=logging.info))\n\n    logging.info(\"[INFO] The size of dataset: {}\".format(total_data_size))\n    logging.info(\"[INFO] The training batch size: {}\".format(batch_size))\n    logging.info(\"[INFO] The training learning_rate: {}\".format(learning_rate))\n    logging.info(\"[INFO] Total training steps: {}\".format(total_steps))\n    logging.info(\"[INFO] Maximum training epoch: {}\".format(max_epoch))\n    logging.info(\"[INFO] Start training...\")\n\n    validate_dataset = validate_dataset if add_validation_dataset else None\n    if args.chkpnt_fn is not None:\n        model.load_weights(args.chkpnt_fn)\n\n    train_history = model.fit(x=train_dataset,\n                              epochs=max_epoch,\n                              validation_data=validate_dataset,\n                              callbacks=[early_stop_callback, model_save_callbakck],\n                              verbose=1,\n                              shuffle=False)\n\n    for table_dataset in table_dataset_list:\n        table_dataset.close()\n\n    for table_dataset in validate_table_dataset_list:\n        table_dataset.close()\n\n    # show the parameter set with the smallest validation loss\n    if 'val_loss' in train_history.history:\n        best_validation_epoch = np.argmin(np.array(train_history.history[\"val_loss\"])) + 1\n        logging.info(\"[INFO] Best validation loss at epoch: %d\" % best_validation_epoch)\n    else:\n        best_train_epoch = np.argmin(np.array(train_history.history[\"loss\"])) + 1\n        logging.info(\"[INFO] Best train loss at epoch: %d\" % best_train_epoch)\n\n\ndef main():\n    parser = ArgumentParser(description=\"Train a Clair3 model\")\n\n    parser.add_argument('--platform', type=str, default=\"ont\",\n                        help=\"Sequencing platform of the input. Options: 'ont,hifi,ilmn', default: %(default)s\")\n\n    parser.add_argument('--bin_fn', type=str, default=\"\", required=True,\n                        help=\"Binary tensor input generated by Tensor2Bin.py, support multiple bin readers using pytables\")\n\n    parser.add_argument('--chkpnt_fn', type=str, default=None,\n                        help=\"Input a model to resume training or for fine-tuning\")\n\n    parser.add_argument('--ochk_prefix', type=str, default=None, required=True,\n                        help=\"Prefix for model output after each epoch\")\n\n    # options for advanced users\n    parser.add_argument('--maxEpoch', type=int, default=None,\n                        help=\"Maximum number of training epochs\")\n\n    parser.add_argument('--learning_rate', type=float, default=1e-3,\n                        help=\"Set the initial learning rate, default: %(default)s\")\n\n    parser.add_argument('--validation_dataset', action='store_true',\n                        help=\"Use validation dataset when training, default: %(default)s\")\n\n    parser.add_argument('--exclude_training_samples', type=str, default=None,\n                        help=\"Define training samples to be excluded\")\n\n    # Internal process control\n    ## In pileup training mode or not\n    parser.add_argument('--pileup', action='store_true',\n                        help=SUPPRESS)\n\n    ## Add indel length for training and calling, default true for full alignment\n    parser.add_argument('--add_indel_length', type=str2bool, default=False,\n                        help=SUPPRESS)\n\n\n\n    args = parser.parse_args()\n\n    if len(sys.argv[1:]) == 0:\n        parser.print_help()\n        sys.exit(1)\n\n    train_model(args)\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"clair3/Train.py","file_name":"Train.py","file_ext":"py","file_size_in_byte":13485,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"114342680","text":"from operator import truediv\nfrom os import listdir, getcwd, path, environ\nimport yaml, re\n\ndef try_get_value(chart: dict, key: str) -> str | dict:\n    try:\n        value = chart[key]\n    except KeyError:\n        return \"\"\n    return value\n\ndef fail(value) -> bool:\n    print(f\">    Error: Chart value '{value}' is incorrect or not set!\")\n    return True\n\ndef validate_chart_file(chart_file) -> None:\n    failed = False\n    chart = yaml.safe_load(chart_file)\n    print(f\"> Validating chart {chart['name']}...\")\n\n    home = try_get_value(chart, \"home\")\n    description = try_get_value(chart, \"description\")\n    version = try_get_value(chart, \"version\")\n\n    maintainers = try_get_value(chart, \"maintainers\")\n    try:\n        maintainers = maintainers[0]\n    except IndexError:\n        maintainers = {}\n    maintainer_name = try_get_value(maintainers, \"name\")\n    maintainer_email = try_get_value(maintainers, \"email\")\n\n    if home != \"https://github.com/citizensadvice/helm-charts\":\n        failed = fail(\"home\")\n    \n    if description == \"\":\n        failed = fail(\"decription\")\n\n    if maintainer_name != \"CA Devops\":\n        failed = fail(\"maintainer name\")\n    \n    if maintainer_email != \"ca-devops@citizensadvice.org.uk\":\n        failed = fail(\"maintainer email\")\n\n    if not re.match(\"^\\d{1,3}.\\d{1,3}.\\d{1,3}$\", version):\n        failed = fail(\"version\")\n\n    if failed:\n        print(f\">\\n>  One or more checks for chart '{chart['name']}' have failed. Exiting 1.\")\n        exit(1)\n\ndef main() -> None:\n    try:\n        charts_dir = environ[\"CHARTS_DIR\"]\n    except KeyError:\n        print(\"CHARTS_DIR not defined! Exiting 1.\")\n        exit(1)\n\n    cwd = getcwd()\n    charts = listdir(path.join(cwd, charts_dir))\n\n    if len(charts) < 1:\n        print(f\"No directories found in {charts_dir}. Exiting.\")\n        exit(0)\n\n    for chart in charts:\n        try:\n            with open(path.join(cwd,charts_dir,chart,\"Chart.yaml\")) as f:\n                chart_file = f.read()\n                validate_chart_file(chart_file)\n        except NotADirectoryError:\n            continue\n    \n    print(\">\\n> All charts successfully validated\")\n\nif __name__ == \"__main__\":\n    main()","sub_path":"bin/validate.py","file_name":"validate.py","file_ext":"py","file_size_in_byte":2176,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"435755680","text":"import gensim\nimport urllib.request\nimport matplotlib.pyplot as plt\nimport networkx as nx\n\nurllib.request.urlretrieve('http://rusvectores.org/static/models/rusvectores4/RNC/ruscorpora_upos_skipgram_300_5_2018.vec.gz','ruscorpora_1_300_10.vec.gz')\nm = 'ruscorpora_1_300_10.vec.gz'\nif m.endswith('.vec.gz'):\n    model = gensim.models.KeyedVectors.load_word2vec_format(m, binary=False)\nelif m.endswith('.bin.gz'):\n    model = gensim.models.KeyedVectors.load_word2vec_format(m, binary=True)\nelse:\n    model = gensim.models.KeyedVectors.load(m)\nmodel.init_sims(replace=True)\n\nwords = ['шкатулка_NOUN', 'ларец_NOUN', 'комод_NOUN', 'сундук_NOUN']\n\nG = nx.Graph()\n\nfor word in words:\n    G.add_node(word)\n    for i in model.most_similar(positive=[word], topn=10):\n        if i[1]>=0.5:\n            G.add_node(i[0])\n            G.add_edge(i[0],word)\n\npos=nx.spring_layout(G)\n\nnx.draw_networkx_nodes(G, pos, node_color='red', node_size=50)\nnx.draw_networkx_edges(G, pos, edge_color='blue')\nnx.draw_networkx_labels(G, pos, font_size=8, font_family='Arial')\nplt.axis('off')\nplt.show()\n\ndeg = nx.degree_centrality(G)\nfor nodeid in sorted(deg, key=deg.get, reverse=True):\n    print('Самое центральное слово: ',nodeid)\n    break\n\nprint('Радиус графа: ',nx.radius(G))\nprint('Коэффициент кластеризации: ',nx.average_clustering(G))\n\n","sub_path":"semantic/Semantic_field.py","file_name":"Semantic_field.py","file_ext":"py","file_size_in_byte":1395,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"238362674","text":"import numpy as np\nimport math\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nimport matplotlib.gridspec as gridspec\nimport scipy.optimize\nfrom scipy.optimize import curve_fit\nplt.ioff()\n\n\n#####################\n### Define Functions\n#####################\ndef gauss_function(x, a, x0, sigma):\n    return a*np.exp(-(x-x0)**2/(2*sigma**2))\n\ndef txt2ratio_radial(txtfile,\n                     coeff,\n                     cent_x,\n                     cent_y,\n                     scale,\n                     pa,\n                     incl):\n    \"\"\"\n    \"\"\"\n    data = np.loadtxt(txtfile, usecols=(0,1,2,3,8,11,4))\n    dx, dy = data[:,0] - cent_x, data[:,1] - cent_y\n    pa_radi = math.radians(pa)\n    incl_radi = math.radians(90-incl)\n    dx2 = (dx*math.cos(pa_radi) - dy*math.sin(pa_radi)) \\\n          / math.sin(incl_radi)\n    dy2 = dx*math.sin(pa_radi) + dy*math.cos(pa_radi)\n    dval_tmp = data[:,3] / data[:,2] / coeff\n    drad_tmp = np.sqrt((dx2)**2 + (dy2)**2)\n    drad_tmp2 = drad_tmp * 3600 * scale\n    ddisp_tmp = data[:,4] * math.sin(incl_radi)\n    fw1_tmp = data[:,6]\n    dval = []\n    drad = []\n    ddisp = []\n    fco21 = []\n    galmask = []\n    for j in range(len(dval_tmp)):\n        if data[:,2][j] > 0:\n            if data[:,3][j] > 0:\n                if fw1_tmp[j] > 0:\n                    dval.append(dval_tmp[j])\n                    drad.append(drad_tmp2[j])\n                    ddisp.append(ddisp_tmp[j])\n                    fco21.append(data[:,3][j]) # 3==co21, 2==co10\n                    if data[:,5][j] > 0.5:\n                        galmask.append(1.0)\n                    else:\n                        galmask.append(0.0)\n\n    dval = np.array(dval)\n    drad = np.array(drad)\n    ddisp = np.array(ddisp)\n    #fco21 = mp.array(fco21)\n\n    return dval, drad, ddisp, fco21, galmask\n\n\ndef plotter(aperture,\n            drad,\n            dval,\n            ddisp,\n            fco21,\n            galmask,\n            histobin_ap,\n            savefig,\n            ylim_rval = [0,1.001]):\n    ### setup\n    figure = plt.figure(figsize=(16, 6))\n    plt.rcParams[\"font.size\"] = 18\n    gs = gridspec.GridSpec(nrows=6, ncols=13)\n    ax1 = plt.subplot(gs[0:5,12])\n    ax2 = plt.subplot(gs[0:5,0:4])\n    ax3 = plt.subplot(gs[5,0:4])\n    ax4 = plt.subplot(gs[0:5,4:8])\n    ax5 = plt.subplot(gs[5,4:8])\n    ax6 = plt.subplot(gs[0:5,8:12])\n    ax7 = plt.subplot(gs[5,8:12])\n    plt.subplots_adjust(left=0.06, right=0.93, bottom=0.11, top=0.94)\n\n    ax2.set_title(\"Deprojected Radius\")\n    ax4.set_title(\"log $S_{CO(2-1)}dv$\")\n    ax6.set_title(\"log $\\sigma$\")\n\n    ### ax1\n    edges_y = [0.05*x for x in range(40)]\n    hist_ax1 = ax1.hist(dval,\n                        bins=edges_y,\n                        alpha=0.4,\n                        histtype=\"stepfilled\",\n                        orientation=\"horizontal\",\n                        color=\"grey\")\n    ax1.set_xticks([])\n    ax1.set_yticks([])\n    ax1.yaxis.tick_right()\n    ax1.set_xlim([max(hist_ax1[0])*1.2,0])\n    ax1.set_ylim(ylim_rval)\n    ax1.invert_xaxis()\n    ax1b = ax1.twinx()\n    ax1b.set_xticks([])\n    ax1b.set_ylim(ylim_rval)\n    ax1b.set_ylabel(\"$R_{21}$\")\n    \n    # inside mask\n    dval_masked_tmp = np.array(dval) * np.array(galmask)\n    dval_masked = []\n    for i in range(len(dval_masked_tmp)):\n        if galmask[i] > 0.5:\n            dval_masked.append(dval_masked_tmp[i])\n\n    hist_ax1b = ax1.hist(dval_masked,\n                         bins=edges_y,\n                         alpha=0.4,\n                         linewidth=0,\n                         histtype=\"stepfilled\",\n                         orientation=\"horizontal\",\n                         color=\"red\")\n\n    # outside mask\n    dval_masked_tmp = np.array(dval) * abs(np.array(galmask)-1)\n    dval_masked_out = []\n    for i in range(len(dval_masked_tmp)):\n        if galmask[i] < 0.5:\n            dval_masked_out.append(dval_masked_tmp[i])\n\n    hist_ax1c = np.histogram(dval_masked_out,\n                             bins=edges_y)\n\n    #\n    popt, pcov = curve_fit(gauss_function,\n                           hist_ax1[1][2:],\n                           hist_ax1[0][1:],\n                           p0 = [150, 0.5, 0.1],\n                           maxfev = 100000)\n    x = np.linspace(hist_ax1[1][1], hist_ax1[1][-1], 50)\n    ax1.plot(gauss_function(x, *popt),\n             x,\n             \"-\",\n             c=\"black\",\n             lw=4)\n    ratio_peak = round(popt[1], 2)\n    ratio_disp = round(popt[2], 2)\n\n\n    ax1.hlines([ratio_peak],\n               max(hist_ax1[0])*1.2, 0,\n               \"black\",\n               linestyles=\"-\",\n               linewidth=3)\n    ax1.hlines([ratio_peak-ratio_disp],\n               max(hist_ax1[0])*1.2, 0,\n               \"black\",\n               linestyles=\"dashed\",\n               linewidth=3)\n    ax1.hlines([ratio_peak+ratio_disp],\n               max(hist_ax1[0])*1.2, 0,\n               \"black\",\n               linestyles=\"dashed\",\n               linewidth=3)\n\n    #\n    popt, pcov = curve_fit(gauss_function,\n                           hist_ax1b[1][2:],\n                           hist_ax1b[0][1:],\n                           p0 = [150, 0.5, 0.1],\n                           maxfev = 100000)\n    x = np.linspace(hist_ax1b[1][1], hist_ax1b[1][-1], 50)\n    ratio_peak_red = round(popt[1], 2)\n    ratio_disp_red = round(popt[2], 2)\n\n    ax1.hlines([ratio_peak_red],\n               max(hist_ax1[0])*1.2, 0,\n               \"red\",\n               linestyles=\"-\",\n               linewidth=3)\n\n    #\n    popt, pcov = curve_fit(gauss_function,\n                           hist_ax1c[1][2:],\n                           hist_ax1c[0][1:],\n                           p0 = [150, 0.5, 0.1],\n                           maxfev = 100000)\n    x = np.linspace(hist_ax1[1][1], hist_ax1c[1][-1], 50)\n    ratio_peak_blue = round(popt[1], 2)\n    ratio_disp_blue = round(popt[2], 2)\n    \n    ax1.hlines([ratio_peak_blue],\n               max(hist_ax1[0])*1.2, 0,\n               \"blue\",\n               linestyles=\"-\",\n               linewidth=3)\n    \n\n    ### ax2\n    ax2.scatter(drad,\n                dval,\n                alpha=0.4,\n                c=galmask,#'cornflowerblue',\n                cmap=\"bwr\",\n                linewidth=0,\n                s=180/(drad+0.5),\n                )#label = \"aperture = \"+str(aperture)+\"\\\"\")\n\n    ax2.set_xlim([-0.5,max(drad)+0.5])\n    ax2.set_ylim(ylim_rval)\n    ax2.set_xticks([])\n    ax2.set_ylabel(\"$R_{21}$\")\n\n    label = \"$R_{21}$ = \"+str(ratio_peak)+\" $\\pm$ \"+str(ratio_disp)\n    ax2.hlines([ratio_peak],\n               -0.5, max(drad)+0.5,\n               \"black\",\n               linestyles=\"-\",\n               linewidth=3,\n               label = label)\n    ax2.hlines([ratio_peak-ratio_disp],\n               -0.5, max(drad)+0.5,\n               \"black\",\n               linestyles=\"dashed\",\n               linewidth=3)\n    ax2.hlines([ratio_peak+ratio_disp],\n               -0.5, max(drad)+0.5,\n               \"black\",\n               linestyles=\"dashed\",\n               linewidth=3)\n\n    n, _ = np.histogram(drad,bins=6)\n    sy, _ = np.histogram(drad,bins=6,weights=dval)\n    sy2, _ = np.histogram(drad,bins=6,weights=dval*dval)\n    mean = sy / n\n    std = np.sqrt(sy2/n - mean*mean)\n    ax2.errorbar((_[1:] + _[:-1])/2,\n                 mean,\n                 yerr=std,\n                 c=\"grey\",\n                 ecolor=\"grey\",\n                 linewidth=5)\n    ax2.legend(loc=\"lower right\")\n\n    ### ax3\n    edges_x = [0.5*x for x in range(0,int(max(drad)*2)*2,1)]\n    hist_ax3 = ax3.hist(drad,\n                        bins=edges_x,\n                        alpha=0.4,\n                        histtype=\"stepfilled\",\n                        color=\"grey\")#\"cornflowerblue\")\n    ax3.set_xlim([-0.5,max(drad)+0.5])\n    ax3.set_ylim([0,max(hist_ax3[0])*1.2])\n    ax3.invert_yaxis()\n    ax3.set_yticks([])\n    ax3.set_xlabel(\"Deprojected Radius (kpc)\")\n\n    drad_masked_tmp = np.array(drad) * np.array(galmask)\n    drad_masked = []\n    for i in range(len(drad_masked_tmp)):\n        if galmask[i] > 0.5:\n            drad_masked.append(drad_masked_tmp[i])\n    \n    hist_ax3b = np.histogram(drad_masked,bins=edges_x)\n\n    ax3b = ax3.twinx()\n    ax3b.set_xlim([-0.5,max(drad)+0.5])\n    ax3b.set_yticks([])\n    ax3b.invert_yaxis()\n    frac_x = hist_ax3[1]\n    frac_y_red = np.array(hist_ax3b[0])/np.array(hist_ax3[0], dtype=\"float32\")\n    frac_y_blue = (np.array(hist_ax3[0])-np.array(hist_ax3b[0]))/np.array(hist_ax3[0], dtype=\"float32\")\n    ax3b.step(frac_x[1:], frac_y_red, c=\"red\", linewidth=2)\n    ax3b.step(frac_x[1:], frac_y_blue, c=\"blue\", linewidth=2)\n    \n\n    ### ax4\n    log_fco21 = np.log10(fco21)\n    log_xlim = [min(log_fco21)-0.1, max(log_fco21)*1.1]\n\n    ax4.scatter(log_fco21,\n                dval,\n                alpha=0.4,\n                c=galmask,#'cornflowerblue',\n                cmap=\"bwr\",\n                linewidth=0,\n                s=180/(drad+0.5),\n                )#label = \"aperture = \"+str(aperture)+\"\\\"\")\n\n    ax4.set_xlim(log_xlim)\n    ax4.set_ylim(ylim_rval)\n    ax4.set_xticks([])\n    ax4.set_yticks([])\n\n    ax4.hlines([ratio_peak],\n               log_xlim[0], log_xlim[1],\n               \"black\",\n               linestyles=\"-\",\n               linewidth=3)\n    ax4.hlines([ratio_peak-ratio_disp],\n               log_xlim[0], log_xlim[1],\n               \"black\",\n               linestyles=\"dashed\",\n               linewidth=3)\n    ax4.hlines([ratio_peak+ratio_disp],\n               log_xlim[0], log_xlim[1],\n               \"black\",\n               linestyles=\"dashed\",\n               linewidth=3)\n\n    n, _ = np.histogram(log_fco21,bins=6)\n    sy, _ = np.histogram(log_fco21,bins=6,weights=dval)\n    sy2, _ = np.histogram(log_fco21,bins=6,weights=dval*dval)\n    mean = sy / n\n    std = np.sqrt(sy2/n - mean*mean)\n    ax4.errorbar((_[1:] + _[:-1])/2,\n                 mean,\n                 yerr=std,\n                 c=\"gray\",\n                 ecolor=\"gray\",\n                 linewidth=5)\n    ax4.legend(loc=\"upper right\")\n\n    ### ax5\n    edges_x = [0.12*x for x in range(0,int(max(log_fco21*2))*10,1)]\n    hist_ax5 = ax5.hist(log_fco21,\n                        bins=edges_x,\n                        alpha=0.4,\n                        histtype=\"stepfilled\",\n                        color=\"grey\")#\"cornflowerblue\")\n    ax5.set_xlim(log_xlim)\n    ax5.set_ylim([0,max(hist_ax5[0])*1.2])\n    ax5.invert_yaxis()\n    ax5.set_yticks([])\n    ax5.set_xlabel(\"log ($S_{CO(2-1)}dv$ (Jy km s$^{-1}$)$^{-1}$)\")\n\n    log_fco21_masked_tmp = np.array(log_fco21) * np.array(galmask)\n    log_fco21_masked = []\n    for i in range(len(log_fco21_masked_tmp)):\n        if galmask[i] > 0.5:\n            log_fco21_masked.append(log_fco21_masked_tmp[i])\n    \n    hist_ax5b = np.histogram(log_fco21_masked, bins=edges_x)\n\n    ax5b = ax5.twinx()\n    ax5b.set_xlim(log_xlim)\n    ax5b.set_yticks([])\n    ax5b.invert_yaxis()\n    frac_x = hist_ax5[1]\n    frac_y_red = np.array(hist_ax5b[0])/np.array(hist_ax5[0], dtype=\"float32\")\n    frac_y_blue = (np.array(hist_ax5[0])-np.array(hist_ax5b[0]))/np.array(hist_ax5[0], dtype=\"float32\")\n    ax5b.step(frac_x[1:], frac_y_red, c=\"red\", linewidth=2)\n    ax5b.step(frac_x[1:], frac_y_blue, c=\"blue\", linewidth=2)\n\n    ### ax6\n    log_ddisp = np.log10(ddisp)\n    log_xlim = [min(log_ddisp)-0.1, max(log_ddisp)*1.2]\n    #log_xlim = [min(log_ddisp)-0.1, max(log_ddisp)*1.2]\n\n    ax6.scatter(log_ddisp,\n                dval,\n                alpha=0.4,\n                c=galmask,#'cornflowerblue',\n                cmap=\"bwr\",\n                linewidth=0,\n                s=180/(drad+0.5),\n                )#label = \"aperture = \"+str(aperture)+\"\\\"\")\n\n    ax6.set_xlim(log_xlim)\n    ax6.set_ylim(ylim_rval)\n    ax6.set_xticks([])\n    ax6.set_yticks([])\n\n    ax6.hlines([ratio_peak],\n               log_xlim[0], log_xlim[1],\n               \"black\",\n               linestyles=\"-\",\n               linewidth=3)\n    ax6.hlines([ratio_peak-ratio_disp],\n               log_xlim[0], log_xlim[1],\n               \"black\",\n               linestyles=\"dashed\",\n               linewidth=3)\n    ax6.hlines([ratio_peak+ratio_disp],\n               log_xlim[0], log_xlim[1],\n               \"black\",\n               linestyles=\"dashed\",\n               linewidth=3)\n\n    n, _ = np.histogram(log_ddisp,bins=6)\n    sy, _ = np.histogram(log_ddisp,bins=6,weights=dval)\n    sy2, _ = np.histogram(log_ddisp,bins=6,weights=dval*dval)\n    mean = sy / n\n    std = np.sqrt(sy2/n - mean*mean)\n    ax6.errorbar((_[1:] + _[:-1])/2,\n                 mean,\n                 yerr=std,\n                 c=\"gray\",\n                 ecolor=\"gray\",\n                 linewidth=5)\n    ax6.legend(loc=\"upper right\")\n\n    ### ax7\n    edges_x = [0.1*x for x in range(int(min(log_ddisp))*10,int(max(log_ddisp)*2)*10,1)]\n    hist_ax7 = ax7.hist(log_ddisp,\n                        bins=edges_x,\n                        alpha=0.4,\n                        histtype=\"stepfilled\",\n                        color=\"grey\")#\"cornflowerblue\")\n    ax7.set_xlim(log_xlim)\n    ax7.set_ylim([0,max(hist_ax7[0])*1.2])\n    ax7.invert_yaxis()\n    ax7.set_xticks([0,0.3,0.6,0.9,1.2,1.5,1.8,2.1])\n    ax7.set_yticks([])\n    ax7.set_xlabel(\"log ($\\sigma$ (km s$^{-1}$)$^{-1}$)\")\n\n    log_ddisp_masked_tmp = np.array(log_ddisp) * np.array(galmask)\n    log_ddisp_masked = []\n    for i in range(len(log_ddisp_masked_tmp)):\n        if galmask[i] > 0.5:\n            log_ddisp_masked.append(log_ddisp_masked_tmp[i])\n    \n    hist_ax7b = np.histogram(log_ddisp_masked, bins=edges_x)\n\n    ax7b = ax7.twinx()\n    ax7b.set_xlim(log_xlim)\n    ax7b.set_yticks([0,0.5,1])\n    ax7b.invert_yaxis()\n    frac_x = hist_ax7[1]\n    frac_y_red = np.array(hist_ax7b[0])/np.array(hist_ax7[0], dtype=\"float32\")\n    frac_y_blue = (np.array(hist_ax7[0])-np.array(hist_ax7b[0]))/np.array(hist_ax7[0], dtype=\"float32\")\n    ax7b.step(frac_x[1:], frac_y_red, c=\"red\", linewidth=2)\n    ax7b.step(frac_x[1:], frac_y_blue, c=\"blue\", linewidth=2)\n\n    plt.savefig(savefig,dpi=300)\n\n\n\n#####################\n### Main Procedure\n#####################\n### ngc4321\nhistobin = 50\nscale = 0.0736\npa = 157.8\nincl = 35.1\nra_dgr = 185.729\ndec_dgr = 15.8229\ndir_data = \"../../phangs/co_ratio/ngc4321/\"\n\ntxtfiles = glob.glob(dir_data + \"*_flux_wise7p5_*.txt\")\nfor i in range(len(txtfiles)):\n    aperture = float(txtfiles[i].split(\"_\")[-1].replace(\".txt\",\"\").replace(\"p\",\".\"))\n    histobin_ap = int(histobin*4 / aperture)\n    dval, drad, ddisp, fco21, galmask \\\n        = txt2ratio_radial(txtfiles[i], 4.,ra_dgr, dec_dgr,\n                           scale, pa, incl)\n    plotter(aperture, drad, dval, ddisp, fco21, galmask, histobin_ap,\n            savefig=txtfiles[i].replace(\".txt\",\".png\"),\n            ylim_rval = [0,1.001])\n\n\n\n### ngc0628\nhistobin = 50\nscale = 0.044\npa = 21.1\nincl = 8.7\nra_dgr = 24.1737\ndec_dgr = 15.7829\ndir_data = \"../../phangs/co_ratio/ngc0628/\"\n\ntxtfiles = glob.glob(dir_data + \"*_flux_wise7p5_*.txt\")\nfor i in range(len(txtfiles)):\n    aperture = float(txtfiles[i].split(\"_\")[-1].replace(\".txt\",\"\").replace(\"p\",\".\"))\n    histobin_ap = int(histobin*4 / aperture)\n    dval, drad, ddisp, fco21, galmask \\\n        = txt2ratio_radial(txtfiles[i], 4.,ra_dgr, dec_dgr,\n                           scale, pa, incl)\n    plotter(aperture, drad, dval, ddisp, fco21, galmask, histobin_ap,\n            savefig=txtfiles[i].replace(\".txt\",\".png\"),\n            ylim_rval = [0,1.401])\n\n\n\n### ngc3627\nhistobin = 50\nscale = 0.040\npa = 172.4\nincl = 56.2\nra_dgr = 170.063\ndec_dgr = 12.9916\ndir_data = \"../../phangs/co_ratio/ngc3627/\"\n\ntxtfiles = glob.glob(dir_data + \"*_flux_wise7p5_*.txt\")\nfor i in range(len(txtfiles)):\n    aperture = float(txtfiles[i].split(\"_\")[-1].replace(\".txt\",\"\").replace(\"p\",\".\"))\n    histobin_ap = int(histobin*4 / aperture)\n    dval, drad, ddisp, fco21, galmask \\\n        = txt2ratio_radial(txtfiles[i], 4.,ra_dgr, dec_dgr,\n                           scale, pa, incl)\n    plotter(aperture, drad, dval, ddisp, fco21, galmask, histobin_ap,\n            savefig=txtfiles[i].replace(\".txt\",\".png\"),\n            ylim_rval = [0,1.401])\n","sub_path":"mycasa_scripts_active/trashbox/scripts_phangs_veryold/myim022a_r21_vs_co.py","file_name":"myim022a_r21_vs_co.py","file_ext":"py","file_size_in_byte":15972,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"513118401","text":"import util\nfrom datetime import datetime\nfrom os import path\nfrom brokers import shoonya_option_data\nimport pandas as pd\n\nnow = datetime.now()\ntoday_date = now.strftime(\"%d-%m-%Y\")\ncurrent_time = now.strftime(\"%H:%M\")\nFILE_PATH = f'data/positions/nifty_{today_date}.pkl'\n\ndef fetch_data_shoonya():\n    if not position_file_exist():\n        util.log_data(\"PREPARE DATA SHOONYA: Fetching Nifty OI data for today\")\n        nifty_ltp, data = shoonya_option_data.get_nifty_100_atm_prem()\n        data[\"Date\"] = today_date\n        data[\"Time\"] = current_time\n        data.rename(columns = {'StrikePrice':'Strike', 'OptionType': 'Type', 'Price':'Premium'}, inplace=True)\n        save_position_file(data)\n        return data, nifty_ltp\n    else:\n        util.log_data(\"PREPARE DATA SHOONYA: Nifty data already exist for today\")\n        data = read_position_file()\n        return data, shoonya_option_data.get_nifty_atm()[0]\n\ndef save_position_file(data):\n    data.to_pickle(FILE_PATH)\n\ndef position_file_exist():\n    return path.exists(FILE_PATH)\n\ndef read_position_file():\n    util.log_data(\"NIFTY 100 PREM: Reading latest position file\")\n    data = pd.read_pickle(FILE_PATH)\n    return data\n\ndef get_file_path():\n    today_date, _ = util.get_current_date_time()\n    FILE_PATH = f'data/positions/nifty_{today_date}.pkl'\n    return FILE_PATH","sub_path":"strategy/nifty_strategy.py","file_name":"nifty_strategy.py","file_ext":"py","file_size_in_byte":1334,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"90608749","text":"import unittest\nimport json\nfrom app import createApp\nfrom app.api.database.migrations.migrations import migrate\n\n\nclass TestParties(unittest.TestCase):\n    def setUp(self):\n        self.app = createApp(\"testing\")\n        self.client = self.app.test_client()\n        self.endpoint = \"/api/v2/parties\"\n        self.partyID = 3\n        self.data = {\n          \"name\": \"Party Name\",\n          \"abbr\": \"Party Abbreviation\",\n          \"logo_url\": \"http://logo/url\",\n          \"hq_address\": \"Party HQ\"\n        }\n        self.dataUpdate = {\n          \"name\": \"Updated Party Name\",\n          \"abbr\": \"Updated Party Abbreviation\",\n          \"logo_url\": \"http://logo/url\",\n          \"hq_address\": \"Updated Party HQ\"\n        }\n        self.dataNoNameProperty = {\n          \"abbr\": \"Updated Party Abbreviation\",\n          \"logo_url\": \"http://logo/url\",\n          \"hq_address\": \"Updated Party HQ\"\n        }\n        self.dataEmptyValues = {\n          \"name\": \"\",\n          \"abbr\": \"\",\n          \"logo_url\": \"\",\n          \"hq_address\": \"\"\n        }\n        self.loginData = {\n          \"email\": \"admin@gmail.com\",\n          \"password\": \"adminpass\"\n        }\n\n    def tearDown(self):\n        migrate()\n\n    def loginUser(self):\n        response = self.client.post(path=\"/api/v2/auth/login\", data=json.dumps(self.loginData), content_type='application/json')\n        token = response.json[\"data\"][\"token\"]\n        return {\n            \"Authorization\": \"Bearer \" + token\n        }\n\n    def post(self, path, data):\n        return self.client.post(path=path, data=json.dumps(data), content_type='application/json', headers=self.loginUser())\n\n    def get(self, path):\n        return self.client.get(path=path, content_type='application/json', headers=self.loginUser())\n\n    def patch(self, path, data):\n        return self.client.patch(path=path, data=json.dumps(data), content_type='application/json', headers=self.loginUser())\n\n    def delete(self, path):\n        return self.client.delete(path=path, content_type='application/json', headers=self.loginUser())\n\n    def test_create_party(self):\n        response = self.post(self.endpoint, self.data)\n        self.assertEqual(response.status_code, 200, response)\n\n    def test_get_all_parties(self):\n        response = self.get(self.endpoint)\n        self.assertEqual(response.status_code, 200)\n\n    def test_get_specific_party(self):\n        postParty = self.post(self.endpoint, self.data)\n        response = self.get(self.endpoint + \"/\" + str(self.partyID))\n        self.assertEqual(response.status_code, 200)\n\n    def test_get_specific_party_not_found(self):\n        response = self.get(self.endpoint + \"/2000\")\n        self.assertEqual(response.status_code, 404)\n\n    def test_edit_specific_party(self):\n        postParty = self.post(self.endpoint, self.data)\n        response = self.patch(self.endpoint + \"/\" + str(self.partyID), self.dataUpdate)\n        self.assertEqual(response.status_code, 200)\n\n    def test_edit_specific_party_not_found(self):\n        response = self.patch(self.endpoint + \"/2000\", self.dataUpdate)\n        self.assertEqual(response.status_code, 404)\n\n    def test_delete_specific_party(self):\n        postParty = self.post(self.endpoint, self.data)\n        response = self.delete(self.endpoint + \"/\" + str(self.partyID))\n        self.assertEqual(response.status_code, 200)\n\n    def test_delete_specific_party_not_found(self):\n        response = self.delete(self.endpoint + \"/2000\")\n        self.assertEqual(response.status_code, 404)\n\n    def test_with_empty_values(self):\n        response = self.post(self.endpoint, self.dataEmptyValues)\n        self.assertEqual(response.status_code, 400)\n\n    def test_with_no_name_property(self):\n        response = self.post(self.endpoint, self.dataNoNameProperty)\n        self.assertEqual(response.status_code, 400)\n","sub_path":"tests/v2/parties/test_parties.py","file_name":"test_parties.py","file_ext":"py","file_size_in_byte":3812,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"591457421","text":"\"\"\"\nGiven an unsorted array nums, reorder it such that nums[0] < nums[1] > nums[2] < nums[3]....\n\nExample 1:\n\nInput: nums = [1, 5, 1, 1, 6, 4]\nOutput: One possible answer is [1, 4, 1, 5, 1, 6].\nExample 2:\n\nInput: nums = [1, 3, 2, 2, 3, 1]\nOutput: One possible answer is [2, 3, 1, 3, 1, 2].\n\"\"\"\n\nclass Solution:\n    def wiggleSort(self, nums: List[int]) -> None:\n        \"\"\"\n        Do not return anything, modify nums in-place instead.\n        \"\"\"\n        tmp = sorted(nums)\n        mid = (len(nums) + 1) // 2\n        nums[0::2] = tmp[:mid][::-1]\n        nums[1::2] = tmp[mid:][::-1]\n        return nums","sub_path":"python/0324.Wiggle Sort II.py","file_name":"0324.Wiggle Sort II.py","file_ext":"py","file_size_in_byte":603,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"144209692","text":"# Karuta Brawl Bot\r\n# By: BentoBot02\r\n# TODO: Tournament\r\n\r\nimport discord\r\nimport os\r\nfrom discord.ext import commands\r\nfrom dotenv import load_dotenv\r\n\r\nimport asyncio\r\nimport aiorwlock\r\nimport urllib.request\r\nfrom urllib.request import urlopen\r\nfrom PIL import Image\r\n\r\nimport json\r\nimport time\r\nimport math\r\n\r\nimport globalValues\r\nfrom setup import setup\r\nfrom host import host\r\nfrom submit import submit\r\nfrom close import close\r\nfrom open import open\r\nfrom start import start\r\nfrom winners import winners\r\nfrom edit import edit\r\nfrom delete import delete\r\nfrom submissions import submissions\r\nfrom participants import participants\r\nfrom eligible import eligible\r\nfrom mybrawls import mybrawls\r\n\r\nload_dotenv()\r\nTOKEN = os.getenv(\"BOT_TOKEN\")\r\nAWS_ACCESS_KEY_ID = os.getenv(\"AWS_ACCESS_KEY_ID\")\r\nAWS_SECRET_ACCESS_KEY = os.getenv(\"AWS_SECRET_ACCESS_KEY\")\r\n\r\nPREFIX = \"kb!\"\r\n\r\nbot = commands.Bot(command_prefix=['kb!', 'KB!', 'Kb!', 'kB!'], case_insensitive=True, help_command=commands.DefaultHelpCommand(no_category='Commands'))\r\n\r\n@bot.event\r\nasync def on_ready():\r\n    print(f'{bot.user} has connected to Discord!')\r\n    globalValues.init(bot, AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY)\r\n    await bot.change_presence(activity=discord.Activity(type=discord.ActivityType.watching, name=\"Karuta Brawls\"))\r\n\r\n\r\n@bot.command(\r\n    name=\"setup\",\r\n    help=\"Returns information on setting up the bot and the roles required to use commands.\",\r\n    brief=\"Returns info on the bot.\")\r\nasync def setup_command(ctx):\r\n\r\n    if ctx.author.guild_permissions.manage_guild:\r\n        await setup(ctx)\r\n        return\r\n\r\n    roles = ctx.author.roles\r\n\r\n    for role in roles:\r\n        if role.name == \"Brawls\":\r\n            await setup(ctx)\r\n            break\r\n\r\n@bot.command(\r\n    name=\"host\",\r\n    help=\"Creates a Karuta brawl or tournament.\\n\" +\r\n         \"eg. \" + PREFIX + \"host <brawl or tournament>\",\r\n    brief=\"Creates a Karuta brawl or tournament.\")\r\n@commands.has_role(\"Brawls\")\r\nasync def host_command(ctx, arg):\r\n    await host(ctx, arg)\r\n\r\n@bot.command(\r\n    name=\"start\",\r\n    help=\"Starts the Karuta brawl or tournament. Requires the name of the brawl or tournament. If the name is multiple words, surround the name in quotation marks.\\n\" + \r\n         \"eg. \" + PREFIX + \"start \\\"<name>\\\"\",\r\n    brief=\"Starts a Karuta brawl or tournament\")\r\n@commands.has_role(\"Brawls\")\r\nasync def start_command(ctx, arg):\r\n    await start(ctx, arg)\r\n\r\n@bot.command(\r\n    name=\"close\",\r\n    help=\"Closes a brawl or tournament to submissions.\\n\" +\r\n         \"eg. \" + PREFIX + \"close <brawl or tournament name>\",\r\n    brief=\"Closes a brawl or tournament\"\r\n    )\r\n@commands.has_role(\"Brawls\")\r\nasync def close_command(ctx, arg):\r\n    await close(ctx, arg)\r\n    \r\n@bot.command(\r\n    name=\"open\",\r\n    help=\"Opens a brawl or tournament to submissions after it has been closed.\\n\" +\r\n         \"eg. \" + PREFIX + \"open <brawl or tournament name>\",\r\n    brief=\"Opens a brawl or tournament\"\r\n    )\r\n@commands.has_role(\"Brawls\")\r\nasync def open_command(ctx, arg):\r\n    await open(ctx, arg)\r\n\r\n@bot.command(\r\n    name=\"edit\",\r\n    help=\"Opens the editor for saved brawls and tournaments.\\n\" +\r\n         \"eg. \" + PREFIX + \"edit <brawl or tournament name>\",\r\n    brief=\"Edits a saved brawl or tournament\")\r\n@commands.has_role(\"Brawls\")\r\nasync def edit_command(ctx, arg):\r\n    await edit(ctx, arg)\r\n            \r\n@bot.command(\r\n    name=\"delete\",\r\n    help=\"Deletes a saved brawl or tournament.\\n\" +\r\n         \"eg. \" + PREFIX + \"delete <brawl or tournament name>\",\r\n    brief=\"Deletes a saved brawl or tournament\")\r\n@commands.has_role(\"Brawls\")\r\nasync def delete_command(ctx, arg):\r\n    await delete(ctx, arg)\r\n\r\n@bot.command(\r\n    name=\"eligible\",\r\n    help=\"Adds a participant to the eligibility list of the given brawl or tournament.\\n\" +\r\n         \"eg. \" + PREFIX + \"eligible <brawl or tournament name> <id or @>\",\r\n    brief=\"Adds a person to the eligibility list\")\r\n@commands.has_role(\"Brawls\")\r\nasync def eligible_command(ctx, arg1, arg2):\r\n    await eligible(ctx, arg1, arg2)\r\n\r\n@bot.command(\r\n    name=\"mybrawls\",\r\n    help=\"Lists the saved brawls and tournaments that correspond to the server where the command is given.\\n\" +\r\n         \"eg. \" + PREFIX + \"mybrawls\",\r\n    brief=\"Lists your server's saved brawls and tournaments\")\r\n@commands.has_role(\"Brawls\")\r\nasync def mybrawls_command(ctx):\r\n    await mybrawls(ctx)\r\n\r\n@bot.command(\r\n    name=\"winners\",\r\n    help=\"Views the winners of the given brawl or tournament.\\n\" +\r\n         \"eg. \" + PREFIX + \"winners <brawl or tournament name>\",\r\n    brief=\"Views the winners\")\r\n@commands.has_role(\"Brawls\")\r\nasync def winners_command(ctx, arg):\r\n    await winners(ctx, arg)\r\n\r\n@bot.command(\r\n    name=\"submissions\",\r\n    help=\"Displays all of the cards in the brawl or tournament.\\n\" +\r\n         \"eg. kb!submissions <brawl or tournament name>\",\r\n    brief=\"Displaays all the participants' cards\")\r\n@commands.has_role(\"Brawls\")\r\nasync def submissions(ctx, arg):\r\n    await submissions(ctx, arg)\r\n\r\n@bot.command(\r\n    name=\"submit\",\r\n    help=\"Submits a card into a brawl or tournament. If the name is multiple words, surround it in quotation marks.\\n\" + \r\n         \"eg. kb!submit \\\"<name>\\\" <card code>\",\r\n    brief=\"Submits a card\")\r\nasync def submit_command(ctx, arg1, arg2):\r\n    await submit(ctx, arg1, arg2)\r\n\r\n@bot.command(\r\n    name=\"participants\",\r\n    help=\"Views the current participant list for the given brawl or tournament.\\n\" +\r\n         \"eg. \" + PREFIX + \"participants <brawl or tournament name>\",\r\n    brief=\"Views the participant list\")\r\nasync def participants_command(ctx, arg):\r\n    await participants(ctx, arg)\r\n\r\n\r\n@bot.event\r\nasync def on_guild_join(guild):\r\n\r\n    if not globalValues.guildLocks.get(str(guild.id)):\r\n        globalValues.guildLocks[str(guild.id)] = aiorwlock.RWLock()\r\n        if globalValues.savedCompetitions.get(str(guild.id)) == None:\r\n            globalValues.savedCompetitions[str(guild.id)] = {}\r\n\r\n    for channel in guild.text_channels:\r\n        if channel.permissions_for(guild.me).send_messages:\r\n            inviteChannel = bot.get_channel(INVITE_CHANNEL)\r\n            invite = await inviteChannel.create_invite()\r\n            await channel.send(\"Hey there! Thank you for inviting Karuta Brawl to your server. Type \\\"kb!setup\\\" to get started! \\nJoin the server to stay in the loop on updates and bug fixes.\")\r\n            await channel.send(invite)\r\n            break\r\n\r\n@bot.command()\r\n@commands.is_owner()\r\nasync def addBackground(ctx, arg1, arg2, arg3):\r\n    \r\n    num = int(arg1)\r\n    url = arg2\r\n    name = arg3\r\n\r\n    async with globalValues.backgroundLock.writer_lock:\r\n        backgroundList = globalValues.backgrounds.get(num)\r\n        backgroundList.append((arg2, arg3))\r\n\r\n        globalValues.backgrounds[num] = backgroundList\r\n\r\n        with open('backgrounds.txt', 'wb') as f:\r\n            pickle.dump(globalValues.backgrounds, f)\r\n\r\nbot.run(TOKEN)\r\n\r\n\r\n","sub_path":"KarutaBrawl.py","file_name":"KarutaBrawl.py","file_ext":"py","file_size_in_byte":6946,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"79404234","text":"from django.db import models\nimport django.utils.timezone as timezone\n\n\nclass Article_type(models.Model):\n    \"\"\"模块类型表\"\"\"\n    article_type_name = models.CharField(max_length=32, verbose_name=\"板块类型\")\n    article_type_alias_name = models.CharField(max_length=32, null=True, verbose_name=\"板块别名\")\n    create_time = models.DateTimeField(auto_now_add=True, verbose_name=\"创建时间\")\n    edit_time = models.DateTimeField(auto_now=True, verbose_name=\"修改时间\")\n\n\nclass User(models.Model):\n    \"\"\"用户表\"\"\"\n    login_name = models.CharField(max_length=32, verbose_name=\"登录账号\")\n    login_passwd = models.CharField(max_length=64, verbose_name=\"登录密码\")\n    name = models.CharField(max_length=32, null=True, default=None, verbose_name=\"用户昵称\")\n    email = models.EmailField(verbose_name=\"用户邮箱\")\n    integral = models.DecimalField(max_digits=10, decimal_places=2, verbose_name=\"积分\")\n    create_time = models.DateTimeField(auto_now_add=True, verbose_name=\"创建时间\")\n    edit_time = models.DateTimeField(auto_now=True, verbose_name=\"修改时间\")\n\n\nclass Central(models.Model):\n    \"\"\"地区表, 东北、华北、华中、华东、华南、西北、西南\"\"\"\n    central_name = models.CharField(max_length=32, verbose_name=\"地区名称\")\n    create_time = models.DateTimeField(auto_now_add=True, verbose_name=\"创建时间\")\n    edit_time = models.DateTimeField(auto_now=True, verbose_name=\"修改时间\")\n\n\nclass Provinces(models.Model):\n    \"\"\"省份表\"\"\"\n    central = models.ForeignKey(\n        Central,\n        on_delete=models.SET_NULL,\n        null=True,\n        related_name=\"cen_province\",\n        verbose_name=\"地区外键\"\n    )\n    provices_name = models.CharField(max_length=32, verbose_name=\"省份、直辖市名称\")\n    create_time = models.DateTimeField(auto_now_add=True, verbose_name=\"创建时间\")\n    edit_time = models.DateTimeField(auto_now=True, verbose_name=\"修改时间\")\n\n\nclass City(models.Model):\n    \"\"\"城市表\"\"\"\n    provinces = models.ForeignKey(\n        Provinces,\n        on_delete=models.SET_NULL,\n        null=True,\n        related_name=\"pro_city\",\n        verbose_name=\"省份外键\"\n    )\n    city_name = models.CharField(max_length=32, verbose_name=\"城市、直辖市区名称\")\n    create_time = models.DateTimeField(auto_now_add=True, verbose_name=\"创建时间\")\n    edit_time = models.DateTimeField(auto_now=True, verbose_name=\"修改时间\")\n\n\nclass Article(models.Model):\n    \"\"\"帖子表\"\"\"\n    title = models.CharField(max_length=128, verbose_name=\"标题\")\n    user = models.ForeignKey(\n        to=User,\n        on_delete=models.CASCADE,\n        related_name=\"act_user\",\n        verbose_name=\"用户外键\"\n    )\n    content = models.TextField(verbose_name=\"正文\")\n    view_num = models.IntegerField(default=0, verbose_name=\"浏览量\")\n    comment_num = models.IntegerField(default=0, verbose_name=\"评论数\")\n    like_num = models.IntegerField(default=0, verbose_name=\"点赞数\")\n    collection_num = models.IntegerField(default=0, verbose_name=\"收藏数\")\n    provinces = models.ForeignKey(\n        Provinces,\n        on_delete=models.SET_NULL,\n        null=True,\n        related_name=\"provinces\",\n        verbose_name=\"省份、直辖市\"\n    )\n    city = models.ForeignKey(\n        City,\n        on_delete=models.SET_NULL,\n        null=True,\n        related_name=\"city\",\n        verbose_name=\"市、直辖市区\"\n    )\n    article_type = models.ForeignKey(\n        to=Article_type,\n        on_delete=models.SET_NULL,\n        related_name='acticle_type',\n        null=True,\n        default=1,\n        verbose_name=\"article外键关联类型\"\n    )\n    main_photo_path = models.CharField(max_length=128, verbose_name=\"主图路径\", null=True)\n    create_time = models.DateTimeField(auto_now_add=True, verbose_name=\"创建时间\")\n    edit_time = models.DateTimeField(auto_now=True, verbose_name=\"修改时间\")\n    jc_num = models.CharField(max_length=16, null=True, verbose_name=\"jc数量\")\n    jc_age = models.CharField(max_length=16, null=True, verbose_name=\"jc年龄\")\n    jc_quality = models.CharField(max_length=16, null=True, verbose_name=\"jc素质\")\n    jc_shape = models.CharField(max_length=16, null=True, verbose_name=\"jc外形\")\n    jc_service = models.CharField(max_length=32, null=True, verbose_name=\"服务项目\")\n    jc_business_hours = models.CharField(max_length=32, null=True, verbose_name=\"营业时间\")\n    jc_environment = models.CharField(max_length=32, null=True, verbose_name=\"环境\")\n    jc_security = models.CharField(max_length=32, null=True, verbose_name=\"安全\")\n    jc_address = models.CharField(max_length=64, null=True, verbose_name=\"地址\")\n    jc_contact = models.CharField(max_length=64, null=True, verbose_name=\"联系方式\")\n    jc_detailed = models.CharField(max_length=128, null=True, verbose_name=\"详细介绍\")\n\n\nclass article_photo(models.Model):\n    \"\"\"信息-照片表\"\"\"\n    article = models.ForeignKey(\n        Article,\n        on_delete=models.CASCADE,\n        related_name=\"photo\",\n        verbose_name=\"帖子图片外键\"\n    )\n    photo_path = models.CharField(max_length=256, null=True, verbose_name=\"图片path\")\n    create_time = models.DateTimeField(auto_now_add=True, verbose_name=\"创建时间\")\n    edit_time = models.DateTimeField(auto_now=True, verbose_name=\"修改时间\")\n\n\nclass Comment(models.Model):\n    \"\"\"评论表，可以多级评论\"\"\"\n    content = models.CharField(max_length=2048, null=False, verbose_name=\"评论正文\")\n    article = models.ForeignKey(\n        Article,\n        on_delete=models.CASCADE,\n        related_name=\"comments\"\n    ),\n    father_comment_id = models.ForeignKey(to='self', on_delete=models.CASCADE, null=True)\n    user = models.ForeignKey(\n        User,\n        on_delete=models.CASCADE,\n        related_name='user_com'\n    )\n    create_time = models.DateTimeField(auto_now_add=True, verbose_name=\"创建时间\")\n    edit_time = models.DateTimeField(auto_now=True, verbose_name=\"修改时间\")\n\n    def __str__(self):\n        return self.content\n\n\nclass Attention(models.Model):\n    \"\"\"关注表\"\"\"\n    user = models.ForeignKey(\n        User,\n        on_delete=models.CASCADE,\n        related_name=\"att_user\"\n    )\n    att_user = models.ForeignKey(\n        User,\n        on_delete=models.CASCADE,\n        related_name=\"att_ed_user\"\n    )\n    create_time = models.DateTimeField(auto_now_add=True, verbose_name=\"创建时间\")\n    edit_time = models.DateTimeField(auto_now=True, verbose_name=\"修改时间\")\n\n\nclass Collect(models.Model):\n    \"\"\"收藏表\"\"\"\n    user = models.ForeignKey(\n        User,\n        on_delete=models.CASCADE,\n        related_name=\"col_user\"\n    )\n    col_art = models.ForeignKey(\n        Article,\n        on_delete=models.CASCADE,\n        related_name=\"art_user\"\n    )\n    create_time = models.DateTimeField(auto_now_add=True, verbose_name=\"创建时间\")\n    edit_time = models.DateTimeField(auto_now=True, verbose_name=\"修改时间\")\n\n\nclass Jc_html(models.Model):\n    \"\"\"老哥稳网站详情页编码\"\"\"\n    html_id = models.CharField(max_length=32, null=True)\n    create_time = models.DateTimeField(auto_now_add=True, verbose_name=\"创建时间\")\n    edit_time = models.DateTimeField(auto_now=True, verbose_name=\"修改时间\")\n","sub_path":"jcshare/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":7294,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"132654468","text":"import numpy as np\ntrain_origin = np.loadtxt('train.csv',delimiter=',',skiprows=1)\ntrain_x = train_origin[0:,1:]\ntrain_y = train_origin[0:,0]\n\nfrom sklearn.model_selection import train_test_split\ndata_train_x,data_test_x,data_train_y,data_test_y=train_test_split(train_x,train_y,train_size=0.7, random_state=0) #划分训练集和测试集\n\n#支持向量机分类\n# from sklearn import svm\n# clf = svm.SVC()\n# clf.fit(data_train_x,data_train_y)\n\n#决策树分类\n# from sklearn.tree import DecisionTreeClassifier\n# clf=DecisionTreeClassifier()\n# clf.fit(data_train_x,data_train_y)\n\nfrom sklearn.ensemble import RandomForestClassifier\n# from sklearn.grid_search import GridSearchCV \n# 调参数----最大迭代器个数【随机森林】\n# #首先对n_estimators进行网格搜索  \n# nestimators = [i for i in range(50,121,10)]\n# param_test1= {'n_estimators': nestimators}  \n# gsearch1= GridSearchCV(estimator = RandomForestClassifier(),param_grid =param_test1)\n# gsearch1.fit(data_train_x,data_train_y)  \n# print(gsearch1.grid_scores_,gsearch1.best_params_, gsearch1.best_score_) \n# exit(0)\n\n# 调参数----决策树最大深度【随机森林】\n# maxdepth = [i for i in range(25,40,2)]\n# param_test2= {'max_depth': maxdepth}  \n# gsearch1= GridSearchCV(estimator = RandomForestClassifier(n_estimators=100,oob_score=True),param_grid =param_test2)\n# gsearch1.fit(data_train_x,data_train_y)  \n# print(gsearch1.grid_scores_,gsearch1.best_params_, gsearch1.best_score_) \n# exit(0)\n\n\n#随机森林分类\nclf = RandomForestClassifier(n_estimators=100,oob_score=True,max_depth=35)\n# clf.fit(data_train_x,data_train_y)\n\n\n#交叉验证\n# from sklearn import cross_validation\n# scores = cross_validation.cross_val_score(clf, train_x,train_y,cv=5)\n# print(scores)\n# exit(0)\n#[ 0.96383105  0.96453648  0.96249554  0.96558295  0.96462601]\n\n\npred = clf.predict(data_test_x) \n\n#评估,输出评估结果\nfrom sklearn import metrics\ndef calculate_result(actual,pred):  \n    m_precision = metrics.precision_score(actual,pred,average='micro');  \n    print('predict info:' )\n    print('precision:{0:.3f}'.format(m_precision) )\n\ncalculate_result(data_test_y,pred)\nprint(metrics.classification_report(data_test_y,pred))","sub_path":"process/main_05181101.py","file_name":"main_05181101.py","file_ext":"py","file_size_in_byte":2199,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"510402463","text":"# uncompyle6 version 3.7.4\n# Python bytecode 3.7 (3394)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build/bdist.linux-x86_64/egg/kill_hogs/kill_hogs.py\n# Compiled at: 2019-09-03 07:37:17\n# Size of source mod 2**32: 9007 bytes\nfrom collections import defaultdict\nimport argparse, json, logging, os, psutil, re, requests, smtplib, subprocess, time, yaml\n\ndef post_to_slack(message: str, slack_url: str):\n    \"\"\"\n    Post a message to slack.\n\n    Args:\n        message (str): Message to post\n        slack_url (str): url to post message to\n    \"\"\"\n    data = json.dumps({'channel':'#peregrine-alerts', \n     'username':'kill-hogs', \n     'text':message, \n     'icon_emoji':':scales:'}).encode('utf-8')\n    response = requests.post(slack_url,\n      data=data, headers={'Content-Type': 'application/json'})\n    logging.info('Posting to slack')\n    logging.info(str(response.status_code) + str(response.text))\n\n\ndef send_message_to_terminals(user: str, message: str):\n    \"\"\"\n    Sends <message> to all terminals on which <user> is logged in.\n    \"\"\"\n    terminals = find_terminals_of_user(user)\n    for terminal in terminals:\n        subprocess.run('echo \"{message}\" | write {user} {terminal}'.format(message=message,\n          user=user,\n          terminal=terminal),\n          shell=True)\n\n\ndef find_terminals_of_user(user: str):\n    \"\"\"\n    Args:\n        user (str): The user who's terminals to return.\n    Returns:\n        list: A list of terminals (string)\n    \"\"\"\n    terminals = subprocess.run('w -s -h',\n      shell=True, stdout=(subprocess.PIPE), stderr=(subprocess.PIPE))\n    return [t.split()[1] for t in str(terminals.stdout).strip(\"b'\").strip('').split('\\\\n') if user in t]\n\n\ndef on_terminate(proc):\n    \"\"\"\n    Callback for terminate()\n    \"\"\"\n    logging.info('process {} terminated with exit code {}'.format(proc, proc.returncode))\n\n\ndef terminate(kill_list):\n    \"\"\"\n    Terminate processes. Kill if terminate is unsuccesful.\n\n    Args:\n        kill_list (list): List of processes to kill.\n    \"\"\"\n    for proc in kill_list:\n        proc.terminate()\n\n    gone, alive = psutil.wait_procs(kill_list,\n      timeout=3, callback=on_terminate)\n    for proc in alive:\n        logging.info('Killing {} with signal 9'.format(proc))\n        proc.kill()\n\n\ndef is_restricted(username: str, pattern: str='^(?!root).*'):\n    \"\"\"\n    Test if processes of username should be limited in their resources.\n    Bu default everybody except root is restricted. (this, of course, can be dangerous)\n\n    Args:\n        username (str): the username to test\n        pattern (str): a regular expression to filter the users on.\n    \"\"\"\n    return re.match(pattern, username) is not None\n\n\ndef kill_hogs(config: dict, memory_threshold, cpu_threshold, dummy: bool=False, slack: bool=False, email: bool=False, interval: float=0.3):\n    \"\"\"\n    Kill all processes of a user using more than <threshold> % of memory. And cpu.\n    For efficiency reasons only processes using more than .1 % of the available\n    resources are counted.\n\n    Args:\n        memory_threshold (float): Percentage of user resources above which to kill.\n        cpu_threshold (float): Percentage of user resources above which to kill.\n        dummy (bool): If true, do not actually kill processes.\n        slack (bool): send messages to slack.\n    \"\"\"\n    users = defaultdict(lambda : {'cpu_percent':0,  'memory_percent':0,  'processes':[]})\n    procs = list(psutil.process_iter())\n    for proc in procs:\n        try:\n            proc.cpu_percent()\n        except (psutil.NoSuchProcess, FileNotFoundError):\n            pass\n\n    time.sleep(interval)\n    for proc in procs:\n        try:\n            proc.cached_cpu_percent = proc.cpu_percent()\n            proc.cached_memory_percent = proc.memory_percent()\n            if (proc.uids().real == 0 or proc.cached_memory_percent) < 0.1:\n                if proc.cached_cpu_percent < 1:\n                    continue\n            username = proc.username()\n            if not is_restricted(username, config['user_pattern']):\n                continue\n            users[username]['memory_percent'] += proc.cached_memory_percent\n            users[username]['cpu_percent'] += proc.cached_cpu_percent\n            users[username]['processes'].append(proc)\n        except (psutil.NoSuchProcess, FileNotFoundError):\n            pass\n\n    for username, data in users.items():\n        if data['memory_percent'] > memory_threshold or data['cpu_percent'] > cpu_threshold:\n            message = [\n             'User {} uses \\n {:.2f} % of cpu. '.format(username, data['cpu_percent']),\n             '{:.2f} % of memory. '.format(data['memory_percent']),\n             'The following processes will be killed:']\n            for proc in data['processes']:\n                try:\n                    message.append('{} pid {} {} memory {:.2f}% cpu {:.2f}%'.format(proc.username(), proc.pid, proc.name(), proc.cached_memory_percent, proc.cached_cpu_percent))\n                except (psutil.NoSuchProcess, FileNotFoundError):\n                    pass\n\n            logging.info('\\n'.join(message))\n            send_message_to_terminals(proc.username(), config['terminal_warning'])\n            if slack:\n                post_to_slack('\\n'.join(message), config['slack_url'])\n        if email:\n            email_address = find_email(proc.username())\n            if email_address is not None:\n                email_message = config['mail_body']\n                email_message += '\\n'.join(message)\n                send_mail(config['from_address'], email_address, email_message, config['mail_server_port'])\n            dummy or terminate(data['processes'])\n\n\ndef find_email(username):\n    \"\"\"\n    Return the email adress of <username> as reported by finger.\n\n    Args:\n      username (string): the username of the account.\n\n    Returns:\n      string: email adress or None\n\n    \"\"\"\n    finger = subprocess.run(('finger {} -s -m'.format(username)),\n      shell=True,\n      stdout=(subprocess.PIPE),\n      stderr=(subprocess.PIPE))\n    try:\n        data = finger.stdout.decode('utf-8').split('\\n')\n        address = data[1].split()[1]\n    except IndexError:\n        return\n    else:\n        if re.match('[^@]+@[^@]+\\\\.[^@]+', address):\n            return address\n\n\ndef send_mail(sender: str, receiver: str, message: str, port: int=25):\n    \"\"\"\n    Send a message to a user whose processes have been killed.\n    \"\"\"\n    message = f'From: \"(Kill Hogs)\" <{sender}>\\nTo: <{receiver}>\\nSubject: Processes killed.\\n\\n{message}\\n    '\n    try:\n        smtpObj = smtplib.SMTP('localhost', port=port)\n        smtpObj.sendmail(sender, [receiver], message)\n        logging.info(f\"Successfully sent email to {receiver}.\")\n    except Exception as e:\n        try:\n            logging.error('Error: unable to send email.\\nThe error was:\\n{}'.format(e))\n        finally:\n            e = None\n            del e\n\n\ndef main():\n    logging.basicConfig(level=(logging.INFO))\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--memory_threshold',\n      type=float,\n      default=10,\n      help='memory percentage above which processes are killed')\n    parser.add_argument('--cpu_threshold',\n      type=float,\n      default=600,\n      help='cpu percentage above which processes are killed')\n    parser.add_argument('--cpu_interval',\n      type=float,\n      default=0.3,\n      help='Interval accross which to calculate cpu usage.')\n    parser.add_argument('--dummy',\n      action='store_true',\n      help='Only display what would be killed')\n    parser.add_argument('--email',\n      action='store_true',\n      help='Mail offenders when their processes are killed.')\n    parser.add_argument('--slack',\n      action='store_true', help='Post messages to slack')\n    parser.add_argument('--config_file',\n      type=str,\n      default=('{}/.kill_hogs/kill_hogs.yml'.format(os.environ['HOME'])),\n      help='Config file, default: ~/.kill_hogs/kill_hogs.yml')\n    args = parser.parse_args()\n    with open(args.config_file, 'r') as (f):\n        config = yaml.load((f.read()), Loader=(yaml.BaseLoader))\n    kill_hogs(config=config,\n      memory_threshold=(args.memory_threshold),\n      cpu_threshold=(args.cpu_threshold),\n      interval=(args.cpu_interval),\n      dummy=(args.dummy),\n      slack=(args.slack),\n      email=(args.email))\n\n\nif __name__ == '__main__':\n    main()","sub_path":"pycfiles/kill_hogs-0.1-py3.7/kill_hogs.cpython-37.py","file_name":"kill_hogs.cpython-37.py","file_ext":"py","file_size_in_byte":8375,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"42841345","text":"from docassemble.base.util import path_and_mimetype, Address, LatitudeLongitude, DAStaticFile, markdown_to_html, prevent_dependency_satisfaction, DAObject, DAList, DADict, log, space_to_underscore, DAContext\nimport pandas as pd\nimport os\nfrom typing import List, Union, Dict\n\n__all__ = ['DataLoader', 'conditions_with_help', 'ConditionsDict', 'tr_category']\n\nclass BaseDataLoader(DAObject):\n  \"\"\"\n  Object to hold some methods surrounding loading/filtering data.\n  \n  Built around Pandas dataframe.\n  \"\"\"\n  def filter_items(self, display_column:Union[List[str],str]='name', allowed_types: list=None, filter_column=None)->list:\n    \"\"\"\n    Return a subset of rows, with only the specified column and index.\n    \n    This is useful for showing a drop-down menu of choices.\n    \"\"\"\n    return self.filter(display_column=display_column, allowed_types=allowed_types, filter_column=filter_column).items()\n  \n  def filter(self, display_column:Union[List[str],str]='name', allowed_types: list=None, filter_column=None)->pd.DataFrame:\n    \"\"\"\n    Return the raw dataframe filtered to the specified column(s) and matching the specified condition(s).\n    \"\"\"\n    df = self._load_data()\n    if allowed_types and filter_column:\n      # Return only the names for matching values in the specified column\n      return df[df[filter_column].isin(allowed_types)][display_column]\n    else:\n      return df[display_column]\n  \n  def load_row(self, index:Union[int,str])->pd.DataFrame:\n    \"\"\"\n    Retrieve all of the data in a single row of the DataFrame\n    \"\"\"\n    df = self._load_data()\n    try:\n      row = df.loc[index]\n    except:\n      return pd.DataFrame()\n    return df\n    \n  def load_rows(self, loci:List[Union[int,str]])->pd.DataFrame:\n    \"\"\"\n    Retrieve a slice of the dataframe, using the provided loci (indexes) as the basis for \n    retrieval.\n    \"\"\"\n    df = self._load_data()\n    try:\n      rows = df.loc[loci]\n      return rows\n    except:\n      return pd.DataFrame()  \n  \n  def get_rows(self, allowed_types: list=None, filter_column=None)->pd.DataFrame:\n    \"\"\"\n    Return a subset of rows, but with all columns.\n    \"\"\"\n    df = self._load_data()\n    if allowed_types and filter_column:\n      # Return only the names for matching values in the specified column\n      return df[df[filter_column].isin(allowed_types)]\n      # return df[df[search_column].isin([category])]\n    else:\n      return df\n  \n  def _load_data(self)->pd.DataFrame:\n    \"\"\"\n    Return dataframe of the whole file's contents.\n    \"\"\"\n    if \"/\" in self.filename:\n      to_load = path_and_mimetype(self.filename)[0]\n    else:\n      to_load = path_and_mimetype(os.path.join(\"data/sources\", self.filename))[0]\n    \n    if self.filename.lower().endswith('.xlsx'):\n      df = pd.read_excel(to_load)\n    elif self.filename.lower().endswith('.csv'):\n      df = pd.read_csv(to_load)\n    elif self.filename.lower().endswith('.json'):\n      # TODO: we may need to normalize a JSON file\n      df = pd.read_json(to_load)\n    else:\n      raise Exception('The datafile must be a CSV, XLSX, or JSON file. Unknown file type: ' + to_load)\n    return df\n  \nclass DataLoader(BaseDataLoader):\n  def _load_data(self)->pd.DataFrame:\n    df = super()._load_data()\n    df.set_index('ID', inplace=True) # Our XLSX file has a column 'ID' with a text invariant identifier\n    return df\n  \nclass Condition(DAObject):\n  def init(self, *pargs, **kwargs):\n    super().init(*pargs, **kwargs)\n    self.initializeAttribute('details', DADict)\n    self.details.object_type = DAObject\n\nclass ConditionsDict(DADict):\n  def init(self, *pargs, **kwargs):\n    super().init(*pargs, **kwargs)\n    self.object_type = Condition\n    self.complete_attribute = 'complete'\n    \n  def count_conditions(self):\n    \"\"\"Return the total number of selected conditions\"\"\"\n    total = 0\n    if not len(self):\n      return 0\n    for category in self:\n      total += len(self[category].df)\n    return total      \n\n  def as_merged_list(self):\n    \"\"\"Merge condition details with original DF row\"\"\"\n    results = pd.concat([self[c].df for c in self])\n\ndef conditions_with_help(dataloader: DataLoader, category:str, search_column:str='Category', language=\"en\")->List[Dict]:\n  \"\"\"\n  Function that simplifies grabbing the row, interview description, and full description given\n  a specific category.\n  \"\"\"\n  df = dataloader._load_data()\n  filtered_df = df[df[search_column].isin([category])]\n  conditions = []\n  \n  for row in filtered_df.iterrows():\n    if language == \"es\":\n      conditions.append(\n        {\n          row[0]: row[1]['Description_ES'], \n          \"help\": row[1]['Help_ES']\n        })\n    else:\n      conditions.append(\n        {\n          row[0]: row[1]['Interview description'], \n          \"help\": row[1]['Help']\n        })        \n    \n  return conditions\n\ndef tr_category(category, available_buttons):\n  for cat in available_buttons:\n    if category == cat.get('value'):\n      return cat.get('label')\n  return category    \n      ","sub_path":"docassemble/HousingCodeChecklist/load_data.py","file_name":"load_data.py","file_ext":"py","file_size_in_byte":4958,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"199643186","text":"# TO DO : pickleで読み込む\n\nfrom group_closeness import gc\nfrom reaction_closeness import rc\nfrom mention_closeness import mc\n\nfrom user_mail import ums\n\ndef get_closeness(p1, p2):\n    gcv = 0\n    if (p1 in gc) and (p2 in gc[p1]) and (p2 in gc) and (p1 in gc[p2]) :\n        gcv = min(gc[p1][p2], gc[p2][p1])\n    rcv = 0\n    if (p1 in rc) and (p2 in rc[p1]) and (p2 in rc) and (p1 in rc[p2]) :\n        rcv = min(rc[p1][p2], rc[p2][p1])\n    mcv = 0\n    if (p1 in mc) and (p2 in mc[p1]) and (p2 in mc) and (p1 in mc[p2]) :\n        mcv = min(mc[p1][p2], mc[p2][p1])\n    return gcv + rcv + mcv\n\n# p1 : 技術の人, p2 : 営業の人, n : 人数\n# 2 <= n <= 10\ndef calc_team(p1, p2, n = 5):\n    # 親密度が高い順に並べる\n    d = {}\n    for um in ums:\n        if um != p1 and um != p2 :\n            d[um] = get_closeness(um, p1)\n    sorted_d = sorted(d.items(), key=lambda x:x[1], reverse=True)\n\n    # 候補になる人を選ぶ\n    m = 2 * (n - 2) # 残りの人の候補数\n    cand = []\n    i = 0\n    for um in sorted_d :\n        if i == m:\n            break\n        cand.append(um[0])\n        i += 1\n    #print(cand)\n\n    # bit全探索\n    tv = {}\n    for s in range(1<<m) :\n        # 人数確認\n        c = 0\n        for i in range(m) :\n            if (s>>i)&1 :\n                c += 1\n        if c != n - 2 :\n            continue\n\n        # チームの値を計算\n        v = 0\n        for i in range(m) :\n            if (s>>i)&1:\n                v += get_closeness(p1, cand[i])\n                v += get_closeness(p2, cand[i])\n        for i in range(m):\n            for j in range(m) :\n                if (s>>i)&(s>>j)&1 :\n                    v += get_closeness(cand[i], cand[j])\n        tv[s] = v\n    #print(tv)\n    \n    ans_s = max(tv, key=tv.get)\n    #print(ans_s)\n    team = [p1, p2]\n    for i in range(m) :\n        if (ans_s>>i)&1 :\n            team.append(cand[i])\n    #print(team)\n    return team    \n\nif __name__ == '__main__':\n    print(calc_team('',''))\n","sub_path":"yamada/team_maker.py","file_name":"team_maker.py","file_ext":"py","file_size_in_byte":1985,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"147046601","text":"import sys\nimport traceback\n\nfrom lean.commands import lean\nfrom lean.container import container\n\n\ndef main() -> None:\n    \"\"\"This function is the entrypoint when running a Lean command in a terminal.\"\"\"\n    try:\n        lean.main()\n    except Exception as exception:\n        logger = container.logger()\n\n        if logger.debug_logging_enabled:\n            logger.debug(traceback.format_exc().strip())\n        else:\n            logger.error(f\"Error: {exception}\")\n\n        sys.exit(1)\n","sub_path":"lean/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":486,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"532279896","text":"from pico2d import *\nopen_canvas()\ngrass = load_image('grass.png')\ncharacter = load_image('Cookie2_Slide.png')\n\ndef handle_events():\n    global running\n    events = get_events()\n    for event in events:\n        if event.type == SDL_QUIT:\n            running = False\n        elif event.type == SDL_KEYDOWN and event.key == SDLK_ESCAPE:\n            running = False\n\nx = 0\nframe = 0\nrunning = True\nwhile(running):\n    clear_canvas()\n    grass.draw(400, 30)\n    #Cookie2_Run\n    #character.clip_draw(frame * 48, 0, 48, 100, x, 90)\n    #frame = (frame + 1 ) % 3\n\n    # Cookie2_Dead\n    #character.clip_draw(frame * 60, 0, 60, 100, x, 90)\n    #frame = (frame + 1 ) % 2\n\n    # Cookie2_Jump\n    #character.clip_draw(frame * 52, 0, 52, 100, x, 90)\n    #frame = (frame + 1 ) % 3\n\n    # Cookie2_Slide\n    #character.clip_draw(frame * 70, 0, 70, 100, x, 90)\n    #frame = (frame + 1 ) % 2\n    update_canvas()\n    x += 10\n    delay(0.05)\n    handle_events()\n\nclose_canvas()\n\nclass Brave_Cookie:\n    image = None\n\n    def __init__(self, state):\n        self.x = 150\n        self.y = 200\n        self.frame = 0\n        self.map_size = 0\n        self.hp = 400.0\n        self.jump = 0\n        self.jump_gravity = 0\n        self.collision_time = 0\n        self.state = state\n        self.state = \"Run\"\n\n        if Brave_Cookie.image == None:\n            self.Brave_Cookie_run = load_image('Resource\\\\Character1\\\\cookie_run.png')\n            self.Brave_Cookie_dead = load_image('Resource\\\\Character1\\\\cookie_run_dead.png')\n            self.Brave_Cookie_slide = load_image('Resource\\\\Character1\\\\cookie_run_slide.png')\n            self.Brave_Cookie_jump1 = load_image('Resource\\\\Character1\\\\cookie_run_jump.png')\n            self.Brave_Cookie_jump2 = load_image('Resource\\\\Character1\\\\cookie_run_jump2.png')\n            self.Brave_Cookie_collide = load_image('Resource\\\\Character1\\\\cookie_run_collid.png')\n            self.Brave_Cookie_hp = load_image('Resource\\\\Item\\\\hp.png')\n\n    def bump(self, state):\n        self.state = state\n        self.hp -= 40\n        if self.collision_time < 3:\n            self.collision_time += 1\n            self.map_size += 0\n\n        else:\n            self.state = \"Run\"\n            self.collision_time = 0\n\n    def heal(self):\n        self.hp += 60\n\n    def update(self):\n        self.hp -= 0.1\n\n        if self.map_size > 1550:\n            self.map_size = 0\n\n        self.gravity()\n        if self.state == \"Run\":\n            self.map_size += 1\n            self.frame = (self.frame + 1) % 6\n        elif self.state == \"Dead\":\n            self.frame = (self.frame + 1) % 4\n        elif self.state == \"Jump\" and self.y <= 210:\n            self.state = \"Run\"\n        elif self.state == \"Slide\" or self.state == \"Jump\":\n            self.map_size += 1\n        elif self.state == \"Collide\":\n            self.bump(\"Collide\")\n\n        if self.state == \"Jump\" and (self.map_size >= 1440 and self.map_size <= 1550):\n            if (self.y - 40 - self.jump_gravity) > 210:\n                self.jump_gravity += 2\n                self.y -= self.jump_gravity / 2\n            else:\n                self.y = 250\n                self.jump_gravity = 0\n        print(\"map_size : \", self.map_size)\n\n    def gravity(self):\n        if (self.y - 40 - self.jump_gravity) > 160:\n            self.jump_gravity += 2\n            self.y -= self.jump_gravity / 2\n        else:\n            self.y = 200\n            self.jump_gravity = 0\n\n    def draw(self):\n        if self.state == \"Run\":\n            self.Brave_Cookie_run.clip_draw(self.frame * 75, 0, 75, 100, self.x, self.y)\n        elif self.state == \"Slide\":\n            self.Brave_Cookie_slide.draw(self.x, self.y - 30)\n        elif self.state == \"Collide\":\n            self.Brave_Cookie_collide.draw(self.x, self.y)\n        elif self.state == \"Jump\":\n            if self.jump % 2 == 1:\n                self.Brave_Cookie_jump1.draw(self.x, self.y)\n            elif self.jump % 2 == 0:\n                self.Brave_Cookie_jump2.draw(self.x, self.y)\n        self.Brave_Cookie_hp.draw_to_origin(0, 500, self.hp, 50)\n\n    def draw_bb(self):\n        draw_rectangle(*self.get_bb())\n\n    def get_bb(self):\n        if self.state == \"Run\":\n            return self.x - 20, self. y - 30, self.x + 15, self.y + 10\n        elif self.state == \"Slide\":\n            return self.x - 5 , self. y - 50, self.x + 25, self.y - 20\n        elif self.state == \"Jump\":\n            return self.x - 15, self. y - 10, self.x + 25, self.y + 10\n        elif self.state == \"Collide\":\n            return self.x - 0, self. y - 0, self.x + 0, self.y + 0\n\n    def handle_events(self, event):\n        events = get_events()\n\n        if event.type == SDL_QUIT:\n            game_framework.quit()\n\n        if event.type == SDL_KEYDOWN:\n            if event.key == SDLK_DOWN:\n                self.state = \"Slide\"\n\n            elif event.key == SDLK_UP:\n                self.state = \"Jump\"\n                self.jump += 1\n                if (self.y - 40) == 160:\n                    self.jump_gravity = -30\n\n                if (self.map_size >= 1440 and self.map_size <= 1550) and (self.y - 40) == 210:\n                    self.jump_gravity = -30\n\n\n        elif event.type == SDL_KEYUP:\n            if event.key == SDLK_DOWN:\n                self.state = \"Run\"","sub_path":"dist/Resource/Character2/Cookie2_Sprite_Sheet.py","file_name":"Cookie2_Sprite_Sheet.py","file_ext":"py","file_size_in_byte":5256,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"292681024","text":"from django.utils.html import strip_tags\nfrom django.utils.http import is_safe_url, urlunquote\nimport datetime\nimport math\nimport re\nfrom django.core.cache import cache\nfrom django.core.cache.utils import make_template_fragment_key\n\n\n\"\"\"\nNot Using\n\"\"\"\n\n\ndef get_word_count(html_string):\n    word_string = strip_tags(html_string)\n    matching_words = re.findall(r'\\w+', word_string)\n    count = len(matching_words)\n    return count\n\n\ndef get_read_time(html_string):\n    count = get_word_count(html_string)\n    read_time_minutes = math.ceil(count/200.0)\n    # read_time_seconds = read_time_minutes * 60\n    # read_time = str(datetime.timedelta(seconds=read_time_seconds))\n    # read_time = str(datetime.timedelta(seconds=read_time_minutes))\n    return int(read_time_minutes)\n\n    # in model:\n    # read_time = models.IntegerField(default=0)\n\n\n# for redirecting after login or something else\ndef get_next_url(request):\n    next = request.META.get('HTTP_REFERER')\n    if next:\n        next = urlunquote(next)  # HTTP_REFERER may be encoded.\n    if not is_safe_url(url=next, host=request.get_host()):\n        next = '/'\n    return next\n\n\n# Can be useful\ndef get_client_ip(request):\n    x_forwarded_for = request.META.get('HTTP_X_FORWARDED_FOR')\n    if x_forwarded_for:\n        ip = x_forwarded_for.split(',')[-1].strip()\n    else:\n        ip = request.META.get('REMOTE_ADDR')\n    return ip\n\n\ndef clear_template_cache_by_key(cache_name, lst=None):\n    key = make_template_fragment_key(cache_name, lst)\n    cache.delete(key)\n","sub_path":"articles/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1518,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"340823073","text":"import numpy as np\nimport VoigtFit\nimport matplotlib\nmatplotlib.use(\"GTKAgg\")\n\n\n# -- Add the redshift of the cloud doing the absorption\n#    Estimating 0 for everything.\nz = 0.0\n\ndataset = VoigtFit.DataSet(z)\ndataset.set_name(\"PKS1101-325-SiIII\")\ndataset.verbose = True\n\n\n# -- If log(NHI) is not known use:\nlogNHI = None\n\n\n# -- Rebin the data set by 3 pixels\n\ndef downsample_1d(myarr,factor):\n    \"\"\"\n    Downsample a 1D array by averaging over *factor* pixels.\n    Crops right side if the shape is not a multiple of factor.\n\n    Got this specific function from \"Adam Ginsburgs python codes\" on agpy\n    myarr : numpy array\n\n    factor : how much you want to rebin the array by\n    \"\"\"\n    xs = myarr.shape[0]\n    crarr = myarr[:xs-(xs % int(factor))]\n    dsarr = np.mean( np.concatenate(\n                     [[crarr[i::factor] for i in range(factor)] ]\n                     ),axis=0)\n\n    return dsarr\n\n\n# -- Load the COS data (G130M and G160M if available) in ASCII format:\nG130M_filename = \"/Users/efrazer/science/cosdata/PKS1101-325_spec-G130M\"\n# G130M_filename = \"/Users/efrazer/science/cosdata/PKS1101-325_spec-G130M-N\"\n\nres_g130m = 16000.\n\nwl_g130m, spec_g130m, err_g130m = np.loadtxt(G130M_filename, unpack=True)\n\nwl_g130m_rb = downsample_1d(wl_g130m, 3)\nspec_g130m_rb = downsample_1d(spec_g130m, 3)\nerr_g130m_rb = downsample_1d(err_g130m, 3)\n\ndataset.add_data(wl_g130m_rb, spec_g130m_rb, 299792.458/res_g130m, err=err_g130m_rb, normalized=False)\n\n\n# -- Change the width of velocity search region\ndataset.velspan = 800.0\n\n\n# -- Add the ions we want to fit\n\n# ION WAVELENGTH  F_VALUE\n# C II    1334.5323   0.127800\n# C IV    1548.2049   1.908E-01\n# C IV    1550.7785   9.522E-02\n# SiIV    1393.7550   5.280E-01\n# SiIV    1402.7700   2.620E-01\n# SiIII   1206.5000   1.660E+00\n# SiII    1260.4221   1.007E+00\n# SiII    1193.2897   4.991E-01\n# SiII    1190.4158   2.502E-01\n\n# dataset.add_line(\"SiII_1260\")\n# dataset.add_line(\"SiII_1193\")\n# dataset.add_line(\"SiII_1190\")\ndataset.add_line(\"SiIII_1206\")\n# dataset.add_line(\"OI_1302\")\n# dataset.add_line(\"SiIV_1393\")\n# dataset.add_line(\"SiIV_1402\")\n# dataset.add_line(\"SII_1253\")\n\n# NOTES ABOUT THE DETECTIONS:\n# log N(C II) is an upper limit. Line is blended.\n# log N(Si II) is an upper limit. Line is blended.\n# log N(Si III) is an upper limit. Line is blended.\n# log N(Si IV) is an upper limit. Line is blended.\n\n\n# OI\n# dataset.add_component(\"OI\",  0., 50.0, 15.18, var_z=1, var_b=1, var_N=1)\n# dataset.add_component_velocity(\"OI\",  120.0, 35.0, 15.18, var_z=1, var_b=1, var_N=1)\n# dataset.add_component_velocity(\"OI\",  175.0, 20.0, 14.18, var_z=1, var_b=1, var_N=1)\n\n# SiII\n# dataset.add_component(\"SiII\",  0., 50.0, 14.5, var_z=1, var_b=1, var_N=1)\n# dataset.add_component_velocity(\"SiII\",  120.0, 30.0, 14.1, var_z=1, var_b=1, var_N=1)\n# dataset.add_component_velocity(\"SiII\",  175.0, 35.0, 14.1, var_z=1, var_b=1, var_N=1)\n# dataset.add_component_velocity(\"SiII\",  260.0, 20.0, 13.5, var_z=1, var_b=1, var_N=1)\n\n# SiIII\ndataset.add_component(\"SiIII\",  0., 50.0, 14.5, var_z=1, var_b=1, var_N=1)\ndataset.add_component_velocity(\"SiIII\",  120.0, 30.0, 14.1, var_z=1, var_b=1, var_N=1)\ndataset.add_component_velocity(\"SiIII\",  175.0, 35.0, 14.1, var_z=1, var_b=1, var_N=1)\ndataset.add_component_velocity(\"SiIII\",  260.0, 20.0, 13.5, var_z=1, var_b=1, var_N=1)\n\n\n# SiIV\n# dataset.add_component(\"SiIV\",  0., 50.0, 13.18, var_z=1, var_b=1, var_N=1)\n# dataset.add_component(\"SiIV\",  0., 50.0, 13.18, var_z=1, var_b=1, var_N=1)\n# dataset.add_component_velocity(\"SiIV\",  120.0, 35.0, 13.18, var_z=1, var_b=1, var_N=1)\n# dataset.add_component_velocity(\"SiIV\",  175.0, 20.0, 13.18, var_z=1, var_b=1, var_N=1)\n# dataset.add_component_velocity(\"SiIV\",  -70.0, 35.0, 13.18, var_z=1, var_b=1, var_N=1)\n\n\n\n# SII\n# dataset.add_component(\"SII\",  0., 26.0, 15.18, var_z=1, var_b=1, var_N=1)\n# dataset.add_component_velocity(\"SII\",  120.0, 20.0, 13.8, var_z=1, var_b=1, var_N=1)\n# dataset.add_component_velocity(\"SII\",  185.0, 20.0, 13.8, var_z=1, var_b=1, var_N=1)\n\n\n# -- Prepare the dataset: This will prompt the user for interactive\n#    masking and normalization, as well as initiating the Parameters:\n\ndataset.cheb_order = 1\n\ndataset.prepare_dataset(norm=True, mask=True)\n\n\n# -- Fit the dataset:\npopt, chi2 = dataset.fit()\n\ndataset.plot_fit(filename=\"PKS1101-325-SiIII.pdf\")\n\n\n# -- Save the dataset to file: taken from the dataset.name\ndataset.save()\ndataset.save_parameters(\"PKS1101-325-SiIII.fit\")\ndataset.save_cont_parameters_to_file(\"PKS1101-325-SiIII.cont\")\ndataset.save_fit_regions(\"PKS1101-325-SiIII.reg\")\n\n","sub_path":"sightlines/PKS1101-325/vf_PKS1101-325.py","file_name":"vf_PKS1101-325.py","file_ext":"py","file_size_in_byte":4558,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"470723786","text":"# -*- coding: utf-8 -*-\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport scipy\nimport scipy.optimize\nimport scipy.constants\nfrom scipy import signal\nimport uncertainties.unumpy as unp\nfrom uncertainties.unumpy import (nominal_values as noms,\n                                  std_devs as stds)\n\nt1_lit = 3.09\nt2_lit = 1.52\nD_lit = 2.78 * 10 ** - 9\neta_lit = 1.0002 * 10 ** - 3\nr_H_lit = 1.2 #Angstrom\nr_O_lit = 1.52 #Angstrom\n\nprint('################################# c) ########################')\n\n\ndef Magnetisierung(t, M0, T2):\n    return M0 * np.exp(- t / T2)\n\n\ndef carr(t, A, T2, nu1, nu2, phi1, phi2, c):\n    return A * np.exp(- t / T2) * np.sin(2 * np.pi * nu1 * t + phi1) * np.sin(2 * np.pi * nu2 * t + phi2) + c\n\n\ndata_meiboom = pd.read_csv('meiboom_gill.csv', skiprows=[0, 1, 2], names=['t', 'Signal'])\ndata_carr = pd.read_csv('carr_purcell.csv', skiprows=[0, 1], names=['t', 'Signal'])\nt_meiboom = np.array(data_meiboom.t)\nSignal_meiboom = np.array(data_meiboom.Signal)\nt_carr = np.array(data_carr.t)\nSignal_carr = np.array(data_carr.Signal)\n# Peaks finden\npeaks_meiboom, _ = np.array(signal.find_peaks(-Signal_meiboom, height=0.05))\n# print(peaks_meiboom)\n\npopt, pcov = scipy.optimize.curve_fit(Magnetisierung, t_meiboom[peaks_meiboom], Signal_meiboom[peaks_meiboom], p0=[-0.6, 0.9])\nperr = np.sqrt(np.diag(pcov))\nM0, T2 = unp.uarray(popt, perr)\nprint('--- meiboom_gill ---')\nprint('M0:', M0)\nprint('T2:', T2)\nwith open('T2.tex', 'w') as f:\n    f.write(r'\\SI{{{:L}}}{{\\second}}'.format(T2))\n\nt = np.linspace(t_meiboom[0], t_meiboom[-1], 1000)\nplt.plot(t_meiboom, Signal_meiboom, 'k-', label='Daten')\nplt.plot(t_meiboom[peaks_meiboom], Signal_meiboom[peaks_meiboom], 'ro', label='Fitdaten')\nplt.plot(t, Magnetisierung(t, noms(M0), noms(T2)), 'r-', label='Fit')\nplt.xlabel(r'Zeit $t$ / s')\nplt.ylabel(r'Spannung $U$ / V')\nplt.legend(loc='best')\nplt.tight_layout()\nplt.savefig('meiboom_gill.pdf')\nplt.close()\n\n#peaks finden\n# boolean = [Signal_carr > 0]\n# Signal_carr_fit = Signal_carr[boolean]\n# t_carr_fit = t_carr[boolean]\n# peaks_carr, _ = np.array(signal.find_peaks(Signal_carr_fit))\n# temp = []\n# for element in peaks_carr:\n#     if element == peaks_carr[0]:\n#         temp.append(element)\n#     if element > peaks_carr[2] and element < peaks_carr[-2]:\n#         if Signal_carr_fit[element - 2] < Signal_carr_fit[element] and Signal_carr_fit[element + 2] < Signal_carr_fit[element]:\n#             temp.append(element)\n# print(temp)\n# print(peaks_carr)\n#\nt_carr_fit = t_carr[30:]\nSignal_carr_fit = Signal_carr[30:]\npopt, pcov = scipy.optimize.curve_fit(carr, t_carr_fit, Signal_carr_fit, p0=[-0.5, 0.07, 124, 80, 1+np.pi, 1, 0])\nperr = np.sqrt(np.diag(pcov))\nM0, T2_carr, nu1, nu2, phi1, phi2, c = unp.uarray(popt, perr)\n# print(M0, T2, w1, w2, phi1, phi2, c)\nprint('--- carr_purcell ---')\nprint('M0:', M0)\nprint('T2:', T2_carr)\nprint('nu1:', nu1)\nprint('nu2:', nu2)\nprint('phi1:', phi1)\nprint('phi2:', phi2)\nprint('c:', c)\nwith open('T2_carr.tex', 'w') as f:\n    f.write(r'\\SI{{{:L}}}{{\\second}}'.format(T2_carr))\n\nt = np.linspace(t_carr_fit[0], t_carr_fit[-1], 1000)\nplt.plot(t_carr, Signal_carr, 'k-', label='Daten')\nplt.plot(t, carr(t, *popt), 'r-', label='Fit')\n# plt.plot(t, carr(t, noms(-M0), noms(T2_carr), 0, 0, np.pi/2, np.pi/2, noms(c)), 'b-', label=u\"Einhüllende\")\nplt.plot(t, carr(t, noms(M0), noms(T2_carr), 0, 0, np.pi/2, np.pi/2, noms(c)), 'b-', label=u\"Einhüllende\")\n# plt.plot(t, Magnetisierung(t, noms(-M0), 0.1), 'b-', label=u\"Einhüllende\")\nplt.xlabel(r'Zeit $t$ / s')\nplt.ylabel(r'Spannung $U$ / V')\nplt.legend(loc='best')\nplt.tight_layout()\nplt.savefig('carr_purcell.pdf')\n#plt.show()\nplt.close()\nprint('t2_lit:', t2_lit)\nprint('Abweichung T2_meiboom:', 100 - abs(T2 * 100 / t2_lit))\nprint('Abweichung T2_carr:', 100 - abs(T2_carr * 100 / t2_lit))\nwith open('Abweichung_T2_meiboom.tex', 'w') as f:\n    f.write(r'\\num{{{:2.1f}}}'.format(float(noms(100 - abs(T2 * 100 / t2_lit)))))\nwith open('Abweichung_T2_carr.tex', 'w') as f:\n    f.write(r'\\num{{{:2.1f}}}'.format(float(noms(100 - abs(T2_carr * 100 / t2_lit)))))\n\nprint('################################# d) e) ########################')\n# Diffusionskoeffizient\nd = 4.4 * 10 ** - 3\nomegaL = 21.71935 * 10 ** 6\ngamma = 42.576 * 10 ** 6 * 2 * np.pi #gamma_n/2pi\nwith open('gamma.tex', 'w') as f:\n    f.write(r'\\SI{{{:.3f}}}{{\\mega\\hertz\\per\\tesla}}'.format(gamma * 1e-6))\n# gamma = 26.7513  * 10 ** 7 #gamma_n/2pi\nB0 = gamma * omegaL\nT2 = T2\n\ndef Gradient(t_halbe):\n    return (4 * 2.2) / (d * gamma * t_halbe)\n\n\ndef lnplustdurchT2 (t, Umax):\n    return np.log(Umax) + t / noms(T2)\n\n\ndef thoch3(t, a, b):\n    return a * t ** 3 + b\n\n\ntau, Umax, t_halbe = np.genfromtxt('data_d_e.txt', unpack=True)\nt = 2 * tau\nUmax = Umax * 10 ** - 3\nt_halbe = t_halbe * 10 ** - 6\nG = unp.uarray(np.mean(Gradient(t_halbe)), scipy.stats.sem(Gradient(t_halbe)))\nt_halbe_mean = unp.uarray(np.mean(t_halbe), scipy.stats.sem(t_halbe))\n# G_test = Gradient(noms(t_halbe_mean))\nprint('Gradient G = ', G)\n# print('Gradient G_test = ', G_test)\n# with open('r_Mittel.tex', 'w') as f:\n    # f.write(r'\\SI{{{:L}}}{{\\angstrom}}'.format(r_Mittel * 1e10))\nwith open('G.tex', 'w') as f:\n    f.write(r'\\SI{{{:L}}}{{\\milli\\tesla\\per\\meter}}'.format(G * 1e3))\nprint('t 1/2 = ', t_halbe_mean)\n\nFitdata = lnplustdurchT2(t, Umax)\npopt, pcov = scipy.optimize.curve_fit(thoch3, t, Fitdata)\nperr = np.sqrt(np.diag(pcov))\na, b = unp.uarray(popt, perr)\n\nD = - 12 * a / (gamma ** 2 * G ** 2)\nprint('a = ', a)\nprint('b = ', b)\nwith open('D.tex', 'w') as f:\n    f.write(r'{{{:L}}}\\si{{\\meter\\squared\\per\\second}}'.format(D))\nprint('Diffusionskonstante D = ', D)\n\nx = np.linspace(t[0], t[-1], 1000)\nplt.plot(t ** 3, Fitdata,'kx', label='Daten')\nplt.plot(x ** 3, thoch3(x, *popt), 'b-', label='Fit')\nplt.ticklabel_format(style='sci', axis='x', scilimits=(0,0))\nplt.xlabel(r'Zeit $t^3$ / s$^3$')\nplt.ylabel(r'$\\tilde{U}$')\nplt.legend(loc='best')\nplt.tight_layout()\nplt.savefig('Diffkonst.pdf')\nplt.close()\n\nprint('D_lit:', D_lit)\nprint('Abweichung D:', 100 - abs(D * 100 / D_lit))\nwith open('Abweichung_D.tex', 'w') as f:\n    f.write(r'\\num{{{:2.1f}}}'.format(float(noms(100 - abs(D * 100 / D_lit)))))\n\nprint('################################# f) ########################')\n# T1 Messung\n\n\ndef T1bestimmen(tau, M0, T1, U1):\n    return M0 * (1 - 2 * np.exp(- tau / T1)) + U1\n\n\ntau, Umax = np.genfromtxt('data_f.txt', unpack=True)\nUmax = Umax * 10 ** - 3\n\npopt, pcov = scipy.optimize.curve_fit(T1bestimmen, tau, Umax)\nperr = np.sqrt(np.diag(pcov))\nM0, T1, U1 = unp.uarray(popt, perr)\nprint('M0 = ', M0)\nprint('T1 = ', T1)\nprint('U1 = ', U1)\nwith open('T1.tex', 'w') as f:\n    f.write(r'\\SI{{{:L}}}{{\\second}}'.format(T1))\nwith open('U1.tex', 'w') as f:\n    f.write(r'\\SI{{{:L}}}{{\\volt}}'.format(U1))\nwith open('U0.tex', 'w') as f:\n    f.write(r'\\SI{{{:L}}}{{\\volt}}'.format(M0))\n\nx = np.linspace(tau[0], tau[-1], 1000)\nplt.plot(tau, Umax, 'kx', label='Daten')\nplt.plot(x, T1bestimmen(x, *popt), 'b-', label='Fit')\nplt.xlabel(u\"Verzögerungszeit\" r'$\\; \\tau$ / s')\nplt.ylabel(r'Spannung $U$ / V')\nplt.legend(loc='best')\nplt.xscale('log')\nplt.savefig('T1Messung.pdf')\nplt.tight_layout()\nplt.close()\n\nprint('t1_lit:', t1_lit)\nprint('Abweichung T1:', 100 - abs(T1 * 100 / t1_lit))\nwith open('Abweichung_T1.tex', 'w') as f:\n    f.write(r'\\num{{{:2.1f}}}'.format(float(noms(100 - abs(T1 * 100 / t1_lit)))))\n\n\nprint('################################# g) ########################')\n# Viskositaetsmessung in s\nt_visko = 15 * 60 + 21\nprint('t_visko =', t_visko)\nrho = 998 #kg/m^3\nalpha = 1.024 * 10 ** -9 #m^2/sec^2\n\n\ndef fit(x, m, b, c):\n    return c * np.exp(- x * m) + b\n\n\ndef Visko(delta):\n    return rho * alpha * (t_visko - delta)\n\n\nt, delta = np.genfromtxt('data_g.txt', unpack=True)\nparams = np.polyfit(t[-2:], delta[-2:], 1)\nprint('Paramter für lineare Interpolation b = %f, m = %f' %(params[1], params[0]))\ndelta_t_visko = params[1] + params[0] * t_visko\nprint('delta_t_visko =', delta_t_visko)\neta = Visko(delta_t_visko)\nprint('eta =', eta)\n\n# popt, pcov = scipy.optimize.curve_fit(fit, t, delta, p0=[0.001, 1, 1])\n# perr = np.sqrt(np.diag(pcov))\n# m, b, c = unp.uarray(popt, perr)\n# print('m, b, c:', m, b, c)\n#\n# delta_t_visko = fit(t_visko, *popt)\n# print('delta_t_visko =', delta_t_visko)\nwith open('delta.tex', 'w') as f:\n    f.write(r'\\SI{{{:2.2f}}}{{\\second}}'.format(delta_t_visko))\nwith open('eta.tex', 'w') as f:\n    f.write(r'\\SI{{{:2.2f}}}{{\\milli\\pascal\\second}}'.format(eta * 1e3))\n\nr = np.linspace(t[-2], t[-1], 1000)\nplt.plot(t, delta,'kx', label='Data')\nplt.plot(r, params[0] * r + params[1], 'b-', label=r'lineare Interpolation')\nplt.plot(t_visko, delta_t_visko,'ro', label=u'$\\delta$ für $t$ = %.0f$\\,$s' %t_visko)\n# plt.plot(r, fit(r, *popt), 'b-', label=r'Fit 18.582$\\cdot \\exp$(-0.005t)+0.360')\nplt.xlabel(r'Zeit $t$ / s')\nplt.ylabel(r'$\\delta$ / s')\nplt.legend(loc='best')\nplt.savefig('Visko.pdf')\nplt.tight_layout()\nplt.close()\n\nprint('eta_lit:', eta_lit)\nprint('Abweichung eta:', 100 - abs(eta * 100 / eta_lit))\nwith open('Abweichung_eta.tex', 'w') as f:\n    f.write(r'\\num{{{:2.1f}}}'.format(float(noms(100 - abs(eta * 100 / eta_lit)))))\n\n\nprint('################################# h) ########################')\n#Molekuelradius\nkB = scipy.constants.k\nT = 293.\nD = D\neta = eta\nprint('r_H_lit:', r_H_lit)\nprint('r_O_lit:', r_O_lit)\n\ndef Mradius(T):\n    return kB * T / (6 * np.pi * D * eta)\n\n\nr = Mradius(T)\nwith open('r_eta.tex', 'w') as f:\n    f.write(r'\\SI{{{:L}}}{{\\angstrom}}'.format(r * 1e10))\n\nprint('r_eta = ', r)\n\nprint('############## 1.')\nu = scipy.constants.value('atomic mass constant')\nm = 18. * u\nr1 = np.power(0.74 * m / (4. * np.pi * rho / 3.), 1. / 3.)\nwith open('r_Gewicht.tex', 'w') as f:\n    f.write(r'\\SI{{{:2.2f}}}{{\\angstrom}}'.format(r1 * 1e10))\nprint('r_Gewicht = ', r1)\n\nprint('############## 2.')\nTc = 647 #K\npc = 22 * 10 ** 6 #MPa\nr2 = np.power(3 * kB * Tc / (128 * np.pi * pc), 1. / 3.)\nwith open('r_Druck.tex', 'w') as f:\n    f.write(r'\\SI{{{:2.2f}}}{{\\angstrom}}'.format(r2 * 1e10))\nprint('r_Druck = ', r2)\n\nprint('############## Mittel')\nr_Mittel = unp.uarray(np.mean([noms(r), r1, r2]), scipy.stats.sem([noms(r), r1, r2]))\nprint('r_Mittel = ', r_Mittel)\nwith open('r_Mittel.tex', 'w') as f:\n    f.write(r'\\SI{{{:L}}}{{\\angstrom}}'.format(r_Mittel * 1e10))\n","sub_path":"MasterPraktikum/NMR/Messwerte/Auswertung.py","file_name":"Auswertung.py","file_ext":"py","file_size_in_byte":10279,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"47282707","text":"import socket\nimport sys\nimport unittest\n\nfrom mock import patch\n\nfrom slapos.recipe import free_port\n\nclass SocketMock():\n  def __init__(self, *args, **kw):\n    self.args = args\n    self.kw = kw\n    pass\n\n  def nothing_happen(self, *args, **kw):\n    pass\n\n  bind = close = nothing_happen\n\ndef useMock(function):\n  def withMock(function):\n    with patch('slapos.recipe.free_port.socket.socket', new=SocketMock):\n      return function\n  return withMock\n\nclass FreePortTest(unittest.TestCase):\n  def setUp(self):\n    SocketMock.bind = SocketMock.close = SocketMock.nothing_happen\n\n  def new_recipe(self, **kw):\n    buildout = {\n      'buildout': {\n        'bin-directory': '',\n        'find-links': '',\n        'allow-hosts': '',\n        'develop-eggs-directory': '',\n        'eggs-directory': '',\n        'python': 'testpython',\n        'installed': '.installed.cfg',\n        },\n       'testpython': {\n         'executable': sys.executable,\n       },\n       'slap-connection': {\n         'computer-id': '',\n         'partition-id': '',\n         'server-url': '',\n         'software-release-url': '',\n       }\n    }\n    options = {\n      'ip': '127.0.0.1',\n    }\n    options.update(kw)\n    return free_port.Recipe(buildout=buildout, name='free_port', options=options)\n\n  @useMock\n  def test_ifNoBusyPortThenMinPortIsAlwaysReturned(self):\n    recipe = self.new_recipe(minimum=2000)\n    self.assertEqual(recipe.options['port'], '2000')\n\n  @useMock\n  def test_iterateUntilFreePortIsFound(self):\n    def bindFailExceptOnPort2020(socket_instance, binding):\n      ip, port = binding\n      if port != 2020:\n        raise socket.error()\n    SocketMock.bind = bindFailExceptOnPort2020\n    recipe = self.new_recipe(minimum=2000)\n    self.assertEqual(recipe.options['port'], '2020')\n\n  @useMock\n  def test_returnsPort0IfNoPortIsFreeInRange(self):\n    def bindAlwaysFail(socket_instance, binding):\n      raise socket.error()\n    SocketMock.bind = bindAlwaysFail\n    recipe = self.new_recipe(minimum=2000, maximum=2100)\n    self.assertEqual(recipe.options['port'], '0')\n\n\nif __name__ == '__main__':\n  unittest.main()\n","sub_path":"slapos/test/recipe/test_free_port.py","file_name":"test_free_port.py","file_ext":"py","file_size_in_byte":2102,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"415231848","text":"from django.forms import modelform_factory\nfrom django.shortcuts import get_object_or_404, redirect\nfrom django.template import RequestContext\nfrom django.template.response import TemplateResponse\nfrom django.utils.timezone import now\n\nfrom pyconcz_2016.cfp.models import Proposal, Cfp\n\n\ndef proposal_create(request, *, slug):\n    cfp = get_object_or_404(Cfp, slug=slug)\n\n    context = RequestContext(request, {'cfp': cfp})\n\n    right_now = now()\n    if cfp.date_start > right_now:\n        return TemplateResponse(request, 'cfp/proposal_before.html', context)\n    elif cfp.date_end < right_now:\n        return TemplateResponse(request, 'cfp/proposal_after.html', context)\n\n    ProposalForm = modelform_factory(Proposal, exclude=['cfp', 'date'])\n\n    if request.method.lower() == 'post':\n        form = ProposalForm(request.POST)\n        form.instance.cfp = cfp\n\n        if form.is_valid():\n            form.save()\n            return redirect(to='cfp_success', slug=slug)\n\n    else:\n        form = ProposalForm()\n\n    context['form'] = form\n    return TemplateResponse(request, 'cfp/proposal_form.html', context)\n\n\ndef proposal_success(request, *, slug):\n    context = RequestContext(request, {'cfp_slug': slug})\n    return TemplateResponse(request, 'cfp/proposal_success.html', context)\n\n\ndef cfp_about(request, *, slug):\n    cfp = get_object_or_404(Cfp, slug=slug)\n    context = RequestContext(request, {'cfp': cfp})\n    return TemplateResponse(request, cfp.template_about, context)\n","sub_path":"pyconcz_2016/cfp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1484,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"321641627","text":"# -*- encoding: utf-8 -*-\n\nfrom app.exception import *\nfrom app.log import rlogger\nimport random\nimport string\nimport resource\nimport gevent\nfrom gevent.core import timer\nfrom datetime import datetime, timedelta\nfrom resource import *\n\n_letters_and_digits = string.ascii_letters + string.digits\n\nclass ResourceManger(object):\n\n   __resource_pool = {}\n   __resource_types = {}\n\n   DEFAULT_REFRESH_INTERVAL = 60 # 60 sec\n\n   def __init__(self):\n      self._timer = timer(gevent.get_hub().loop, 0, self.DEFAULT_REFRESH_INTERVAL)\n      self.__init_resource_index()\n\n   def __init_resource_index(self):\n      for s in resource.__all__:\n         m = resource.__dict__[s]\n         for k in m.__dict__.keys():\n            c = m.__dict__[k]\n            if k.endswith('Resource') and type(c) == type:\n               rtype = self.get_resource_type(c)\n               self.__resource_types[rtype] = c\n\n\n   def _refresh_resources(self):\n      for rid in self.__resource_pool.keys():\n         robj = self.__resource_pool[rid]\n         if datetime.now() > robj.rendtime:\n            del self.__resource_pool[rid]\n            rlogger.debug('Delete resource %s' % rid)\n\n\n   def schedule_resources(self):\n      self._timer.start(gevent.spawn, self._refresh_resources)\n\n\n   def cancel_schedule_resources(self):\n      if self._timer.active:\n         self._timer.stop()\n      self._refresh_resources()\n\n\n   @property\n   def resource_types(self):\n      return self.__resource_types.keys()\n\n   @classmethod\n   def get_resource_type(cls, rclass):\n      return '%s.%s' % (rclass.__base__.__name__, rclass.__name__)\n\n\n   def get_resource(self, rid_or_rclass, rinterval=None, **kwargs):\n      ''' Get specific resource\n      rid: The resource ID to identify specific resource\n      rclass: The resource class\n      rinterval: The interval minutes of using this resource, default None to use default interval of each type of resources\n      '''\n      if rid_or_rclass in self.__resource_pool:\n         robj = self.__resource_pool[rid_or_rclass]\n         if kwargs:\n            robj._rparams = kwargs\n         if rinterval:\n            robj._rinterval = rinterval\n         n = datetime.now()\n         i = robj.rinterval\n         t = robj.rstarttime + timedelta(minutes=(i*2/3))\n         if n >= t: # Renew resource interval if using 2/3 time\n            robj.rinterval = i\n      else: # Create new resource\n         rtype = None\n         rname = None\n         if type(rid_or_rclass) is type:\n            rtype = self.get_resource_type(rid_or_rclass)\n         elif type(rid_or_rclass) in (str, unicode):\n            ps = rid_or_rclass.split(':')\n            if len(ps) < 3:\n               rtype = ps[0]\n               if len(ps) == 2:\n                  rname = ps[1]\n                  rid = rid_or_rclass\n\n         if rtype and rtype in self.__resource_types:\n            RC = self.__resource_types[rtype]\n            while not rname:\n               random_name = '__%s' % ''.join(random.sample(_letters_and_digits, 8))\n               rid = '%s:%s' % (rtype, random_name)\n               if rid not in self.__resource_pool:\n                  rname = random_name\n\n            if rinterval:\n               robj = RC(rid, rinterval, **kwargs)\n            else:\n               robj = RC(rid, **kwargs)\n            self.__resource_pool[rid] = robj\n            rlogger.debug('Create resource %s' % rid)\n         else:\n            robj = None\n\n      if robj:\n         return robj\n      else:\n         raise ResourceError(WRONG_RESOURCE_ID_OR_TYPE, rid_or_rclass)\n\n\nrmanger = ResourceManger()","sub_path":"resource/manger.py","file_name":"manger.py","file_ext":"py","file_size_in_byte":3552,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"285015359","text":"\"\"\" Copyright (c) 2018, Juniper Networks, Inc\n    All rights reserved\n    This SOFTWARE is licensed under the LICENSE provided in the\n    ./LICENCE file. By downloading, installing, copying, or otherwise\n    using the SOFTWARE, you agree to be bound by the terms of that\n    LICENSE.\n\"\"\"\n\n# stdlib\nimport concurrent.futures\nimport datetime\nfrom ftplib import error_reply, error_temp, error_perm, error_proto\nimport getpass\nimport logging\nimport os\nimport re\nimport shutil\nfrom socket import timeout as socket_timeout\nfrom socket import create_connection\nimport sys\nimport tempfile\nimport traceback\nfrom contextlib import contextmanager\nfrom math import ceil\n\n# 3rd party\nfrom paramiko.ssh_exception import SSHException\n\n# local modules\nfrom splitcopy.paramikoshell import SSHShell\nfrom splitcopy.ftp import FTP\n\nlogger = logging.getLogger(__name__)\n\n\nclass SplitCopyShared:\n    \"\"\"\n    functions shared by both SplitCopyGet and SplitCopyPut classes\n    \"\"\"\n\n    def __init__(self, **kwargs):\n        \"\"\"\n        Initialise the SplitCopyShared class\n        \"\"\"\n        self.user = kwargs.get(\"user\")\n        self.host = kwargs.get(\"host\")\n        self.passwd = kwargs.get(\"passwd\")\n        self.ssh_key = kwargs.get(\"ssh_key\")\n        self.ssh_port = kwargs.get(\"ssh_port\")\n        self.remote_dir = kwargs.get(\"remote_dir\")\n        self.remote_file = kwargs.get(\"remote_file\")\n        self.remote_path = kwargs.get(\"remote_path\")\n        self.local_dir = kwargs.get(\"local_dir\")\n        self.get_op = kwargs.get(\"get\")\n        self.split_timeout = kwargs.get(\"split_timeout\")\n        self.command_list = []\n        self.rm_remote_tmp = False\n        self.local_tmpdir = None\n        self.remote_tmpdir = None\n        self.ss = None\n\n    def connect(self, **ssh_kwargs):\n        try:\n            self.ss = SSHShell(**ssh_kwargs)\n            sock = self.ss.socket_open()\n            self.ss.transport_open(sock)\n            if self.ss.main_thread_auth():\n                self.ss.channel_open()\n                self.ss.invoke_shell()\n                ssh_kwargs = self.ss.kwargs\n                logger.debug(f\"ssh_kwargs returned are: {ssh_kwargs}\")\n            else:\n                raise SSHException(\"authentication failed\")\n            self.ss.set_keepalive()\n            # remove the welcome message from the socket\n            self.ss.stdout_read(timeout=30)\n        except Exception as err:\n            logger.debug(\"\".join(traceback.format_exception(*sys.exc_info())))\n            self.ss.close()\n            raise SystemExit(\n                f\"{err.__class__.__name__} returned while connecting via ssh: {str(err)}\"\n            )\n        return self.ss, ssh_kwargs\n\n    def which_proto(self, copy_proto):\n        \"\"\"\n        verify that if FTP is selected as protocol, that authentication works\n        \"\"\"\n        passwd = self.ss.kwargs[\"password\"]\n        result = None\n        if copy_proto == \"ftp\" and self.ftp_port_check():\n            if passwd is None:\n                passwd = getpass.getpass(\n                    prompt=f\"{self.user}'s password: \", stream=None\n                )\n            try:\n                result = self.ftp_login_check(passwd)\n            except (error_reply, error_temp, error_perm, error_proto) as err:\n                print(\n                    f\"ftp login failed, switching to scp for transfer. Error was: {err}\"\n                )\n            except socket_timeout:\n                print(f\"ftp auth timed out, switching to scp for transfer\")\n\n            if not result:\n                copy_proto = \"scp\"\n        else:\n            copy_proto = \"scp\"\n\n        logger.info(f\"copy_proto == {copy_proto}\")\n        return copy_proto, passwd\n\n    def ftp_port_check(self):\n        \"\"\"\n        checks ftp port is open\n        :returns: bool\n        \"\"\"\n        result = False\n        print(\"attempting FTP authentication...\")\n        try:\n            with create_connection((self.host, 21), 10) as ftp_sock:\n                logger.info(\"ftp port is open\")\n                result = True\n        except socket_timeout:\n            print(f\"ftp socket timed out, switching to scp for transfer\")\n        except ConnectionRefusedError:\n            print(\"ftp connection refused, switching to scp for transfer\")\n\n        return result\n\n    def ftp_login_check(self, passwd):\n        \"\"\"\n        checks ftp login works on remote host\n        :param passwd: password\n        :type: string\n        :returns: bool\n        \"\"\"\n        result = False\n        kwargs = {\n            \"host\": self.host,\n            \"user\": self.user,\n            \"passwd\": passwd,\n            \"timeout\": 10,\n        }\n        with FTP(**kwargs) as ftp:\n            result = True\n        return result\n\n    def which_os(self):\n        \"\"\"\n        determine if host is JUNOS/EVO/*nix\n        no support for Windows OS running OpenSSH\n        :returns None:\n        \"\"\"\n        logger.info(\"entering which_os()\")\n        evo = False\n        bsd_version = float()\n        sshd_version = float()\n        self.ss.run(\"start shell\", exitcode=False)\n        result, stdout = self.ss.run(\"uname\")\n        if not result:\n            err = \"failed to determine remote host os, it must be *nix based\"\n            self.close(err_str=err)\n        host_os = stdout[0].split(\"\\n\")[0].rstrip()\n        if host_os == \"Linux\" and self.evo_os():\n            evo = True\n        else:\n            junos, bsd_version, sshd_version = self.junos_os()\n        logger.info(\n            f\"evo = {evo}, junos = {junos}, bsd_version = {bsd_version}, \"\n            f\"sshd_version = {sshd_version}\"\n        )\n        return junos, evo, bsd_version, sshd_version\n\n    def evo_os(self):\n        \"\"\"\n        determines if host is EVO\n        :returns result: True if OS is EVO\n        :type: boolean\n        \"\"\"\n        logger.info(\"entering evo_os()\")\n        result, stdout = self.ss.run(\"test -e /usr/sbin/evo-pfemand\")\n        return result\n\n    def junos_os(self):\n        \"\"\"\n        determines if host is JUNOS\n        :returns None:\n        \"\"\"\n        logger.info(\"entering junos_os()\")\n        junos = False\n        bsd_version = float()\n        sshd_version = float()\n        result, stdout = self.ss.run(\"uname -i\")\n        if not result:\n            self.close(err_str=\"failed to determine remote host os\")\n        uname = stdout.split(\"\\n\")[1]\n        if re.match(r\"JUNIPER\", uname):\n            junos = True\n            bsd_version = 6.0\n            sshd_version = self.which_sshd()\n        elif re.match(r\"JNPR\", uname):\n            junos = True\n            bsd_version = self.which_bsd()\n            sshd_version = self.which_sshd()\n        else:\n            sshd_version = self.which_sshd()\n        return junos, bsd_version, sshd_version\n\n    def which_bsd(self):\n        \"\"\"\n        determines the BSD version of JUNOS\n        :returns None:\n        \"\"\"\n        logger.info(\"entering which_bsd()\")\n        result, stdout = self.ss.run(\"uname -r\")\n        if not result:\n            self.close(err_str=\"failed to determine remote bsd version\")\n        uname = stdout.split(\"\\n\")[1]\n        bsd_version = float(uname.split(\"-\")[1])\n        return bsd_version\n\n    def which_sshd(self):\n        \"\"\"\n        determines the OpenSSH daemon version\n        :returns None:\n        \"\"\"\n        logger.info(\"entering which_sshd()\")\n        result, stdout = self.ss.run(\"sshd -v\", exitcode=False)\n        if not re.search(r\"OpenSSH_\", stdout):\n            self.close(err_str=\"failed to determine remote openssh version\")\n        output = stdout.split(\"\\n\")[2]\n        version = re.sub(r\"OpenSSH_\", \"\", output)\n        sshd_version = float(version[0:3])\n        return sshd_version\n\n    def req_binaries(self, get_op=False, junos=False, evo=False):\n        \"\"\"\n        ensures required binaries exist on remote host\n        :returns None:\n        \"\"\"\n        logger.info(\"entering req_binaries()\")\n        if not junos and not evo:\n            if get_op:\n                req_bins = [\"dd\", \"ls\", \"df\", \"rm\"]\n            else:\n                req_bins = [\"cat\", \"ls\", \"df\", \"rm\"]\n            for req_bin in req_bins:\n                result, stdout = self.ss.run(f\"which {req_bin}\")\n                if not result:\n                    self.close(\n                        err_str=(\n                            f\"required binary '{req_bin}' is missing from remote host\"\n                        )\n                    )\n\n    def second_elem(self, elem):\n        \"\"\"\n        used for key sort\n        :returns: the 2nd part of an element\n        \"\"\"\n        return elem[1]\n\n    def req_sha_binaries(self, sha_hash):\n        \"\"\"\n        ensures required binaries for sha hash creation exist on remote host\n        :returns None:\n        \"\"\"\n        logger.info(\"entering req_sha_binaries()\")\n        sha_bins = []\n        sha_bin = None\n        sha_len = None\n        if sha_hash.get(512):\n            bins = [(\"sha512sum\", 512), (\"sha512\", 512), (\"shasum\", 512)]\n            sha_bins.extend(bins)\n        if sha_hash.get(384):\n            bins = [(\"sha384sum\", 384), (\"sha384\", 384), (\"shasum\", 384)]\n            sha_bins.extend(bins)\n        if sha_hash.get(256):\n            bins = [(\"sha256sum\", 256), (\"sha256\", 256), (\"shasum\", 256)]\n            sha_bins.extend(bins)\n        if sha_hash.get(224):\n            bins = [(\"sha224sum\", 224), (\"sha224\", 224), (\"shasum\", 224)]\n            sha_bins.extend(bins)\n        if sha_hash.get(1):\n            bins = [(\"sha1sum\", 1), (\"sha1\", 1), (\"shasum\", 1)]\n            sha_bins.extend(bins)\n\n        sha_bins = sorted(set(sha_bins), reverse=True, key=self.second_elem)\n        logger.info(sha_bins)\n\n        for req_bin in sha_bins:\n            result, stdout = self.ss.run(f\"which {req_bin[0]}\")\n            if result:\n                sha_bin = req_bin[0]\n                sha_len = req_bin[1]\n                break\n        if not sha_bin:\n            self.close(\n                err_str=(\n                    \"required binary used to generate a sha \"\n                    \"hash on the remote host isn't found\"\n                )\n            )\n        return sha_bin, sha_len\n\n    def close(self, err_str=None, config_rollback=True, hard_close=False):\n        \"\"\"\n        Called when we want to exit the script\n        attempts to delete the remote temp directory and close the TCP session\n        If hard_close == False, contextmanager will rm the local temp dir\n        If not, we must delete it manually\n        :param err_str: error description\n        :type: string\n        :raises SystemExit: terminates the script gracefully\n        :raises os._exit: terminates the script immediately (even asyncio loop)\n        \"\"\"\n        if err_str:\n            print(err_str)\n        if self.rm_remote_tmp:\n            self.remote_cleanup()\n        if config_rollback and self.command_list:\n            self.limits_rollback()\n        print(\"closing device connection\")\n        self.ss.close()\n        if hard_close:\n            try:\n                shutil.rmtree(self.local_tmpdir)\n            except PermissionError:\n                # windows can throw this error, silence it for now\n                print(\n                    f\"{self.local_tmpdir} may still exist, please delete manually if so\"\n                )\n            raise os._exit(1)\n        else:\n            raise SystemExit(1)\n\n    def file_split_size(self, file_size, sshd_version, bsd_version, evo, copy_proto):\n        \"\"\"\n        The chunk size depends on the python version, cpu count,\n        the protocol used to copy, FreeBSD and OpenSSH version\n        :returns split_size:\n        :type int:\n        :returns executor:\n        :type concurrent.futures object:\n        \"\"\"\n        logger.info(\"entering file_split_size()\")\n\n        try:\n            cpu_count = os.cpu_count()\n        except NotImplementedError:\n            cpu_count = 1\n        max_workers = min(32, cpu_count * 5)\n\n        # each uid can have max of 64 processes\n        # modulate worker count to consume no more than 40 pids\n        if copy_proto == \"ftp\":\n            # ftp creates 1 user process per chunk, no modulation required\n            split_size = ceil(file_size / max_workers)\n        elif max_workers <= 10:\n            # 1 or 2 cpu cores, 5 or 10 workers will create 20-40 pids\n            # no modulation required\n            split_size = ceil(file_size / max_workers)\n        else:\n            # scp to FreeBSD 6 based junos creates 3 user processes per chunk\n            # scp to FreeBSD 10+ based junos creates 2 user processes per chunk\n            # +1 user process if openssh version is >= 7.4\n            max_pids = 40\n            if sshd_version >= 7.4 and bsd_version == 6.0:\n                pid_count = 4\n            elif sshd_version >= 7.4 and bsd_version >= 10.0:\n                pid_count = 3\n            elif bsd_version == 6.0:\n                pid_count = 3\n            elif bsd_version >= 10.0:\n                pid_count = 2\n            elif evo:\n                pid_count = 3\n            else:\n                pid_count = 4\n            max_workers = round(max_pids / pid_count)\n            split_size = ceil(file_size / max_workers)\n\n        # concurrent.futures.ThreadPoolExecutor can be a limiting factor\n        # if using python < 3.5.3 the default max_workers is 5.\n        # see https://github.com/python/cpython/blob/v3.5.2/Lib/asyncio/base_events.py\n        # hence defining a custom executor to normalize max_workers across versions\n        executor = concurrent.futures.ThreadPoolExecutor(max_workers=max_workers)\n        logger.info(\n            f\"max_workers = {max_workers}, cpu_count = {cpu_count}, split_size = {split_size}\"\n        )\n        return split_size, executor\n\n    def mkdir_remote(self):\n        \"\"\"\n        creates a tmp directory on the remote host\n        :returns self.remote_tmpdir:\n        :type string:\n        \"\"\"\n        logger.info(\"entering mkdir_remote()\")\n        time_stamp = datetime.datetime.strftime(datetime.datetime.now(), \"%y%m%d%H%M%S\")\n        if self.get_op:\n            remote_tmpdir = f\"/var/tmp/splitcopy_{self.remote_file}.{time_stamp}\"\n        else:\n            remote_tmpdir = (\n                f\"{self.remote_dir}/splitcopy_{self.remote_file}.{time_stamp}\"\n            )\n        result, stdout = self.ss.run(f\"mkdir -p {remote_tmpdir}\")\n        if not result:\n            err = (\n                \"unable to create the tmp directory on remote host.\"\n                f\"cmd output was:\\n{stdout}\"\n            )\n            self.close(err_str=err)\n        self.rm_remote_tmp = True\n        return remote_tmpdir\n\n    def storage_check_remote(self, file_size, split_size):\n        \"\"\"\n        checks whether there is enough storage space on remote node\n        :returns None:\n        \"\"\"\n        logger.info(\"entering storage_check_remote()\")\n        avail_blocks = 0\n        print(\"checking remote storage...\")\n        result, stdout = self.ss.run(f\"df -k {self.remote_dir}\")\n        if not result:\n            self.close(err_str=\"failed to determine remote disk space available\")\n        df_num = len(stdout.split(\"\\n\")) - 2\n        if re.match(r\"^ \", stdout.split(\"\\n\")[df_num]):\n            split_num = 2\n        else:\n            split_num = 3\n        try:\n            avail_blocks = stdout.split(\"\\n\")[df_num].split()[split_num].rstrip()\n        except Exception:\n            err = \"unable to determine available blocks on remote host\"\n            self.close(err_str=err)\n\n        avail_bytes = int(avail_blocks) * 1024\n        logger.info(f\"remote filesystem available bytes is {avail_bytes}\")\n        if self.get_op:\n            if file_size > avail_bytes:\n                err = (\n                    \"not enough storage on remote host in /var/tmp.\\nAvailable bytes \"\n                    f\"({avail_bytes}) must be > the original file size \"\n                    f\"({file_size}) because it has to store the file chunks\"\n                )\n                self.close(err_str=err)\n        else:\n            if file_size + split_size > avail_bytes:\n                err = (\n                    f\"not enough storage on remote host in {self.remote_dir}.\\n\"\n                    f\"Available bytes ({avail_bytes}) must be > \"\n                    f\"the original file size ({file_size}) + largest chunk size \"\n                    f\"({split_size})\"\n                )\n                self.close(err_str=err)\n\n    def storage_check_local(self, file_size):\n        \"\"\"\n        checks whether there is enough storage space on local node\n        :returns None:\n        \"\"\"\n        logger.info(\"entering storage_check_local()\")\n        print(\"checking local storage...\")\n        local_tmpdir = tempfile.gettempdir()\n        avail_bytes = shutil.disk_usage(local_tmpdir)[2]\n        logger.info(f\"local filesystem {local_tmpdir} available bytes is {avail_bytes}\")\n        if file_size > avail_bytes:\n            err = (\n                f\"not enough storage on local host in temp dir {local_tmpdir}.\\n\"\n                f\"Available bytes ({avail_bytes}) must be > the original file size \"\n                f\"({file_size}) because it has to store the file chunks\"\n            )\n            self.close(err_str=err)\n\n        if self.get_op:\n            avail_bytes = shutil.disk_usage(self.local_dir)[2]\n            logger.info(\n                f\"local filesystem {self.local_dir} available bytes is {avail_bytes}\"\n            )\n            if file_size > avail_bytes:\n                err = (\n                    f\"not enough storage on local host in {self.local_dir}.\\n\"\n                    f\"Available bytes ({avail_bytes}) must be > the \"\n                    f\"original file size ({file_size}) because it has to \"\n                    \"recombine the file chunks into a whole file\"\n                )\n                self.close(err_str=err)\n\n    @contextmanager\n    def change_dir(self, cleanup=lambda: True):\n        \"\"\"\n        cds into temp directory.\n        Upon script exit, changes back to original directory\n        and calls cleanup() to delete the temp directory\n        :returns None:\n        \"\"\"\n        prevdir = os.getcwd()\n        os.chdir(os.path.expanduser(self.local_tmpdir))\n        try:\n            yield\n        finally:\n            os.chdir(prevdir)\n            cleanup()\n\n    @contextmanager\n    def tempdir(self):\n        \"\"\"\n        creates a temp directory\n        defines how to delete directory upon script exit\n        :returns None:\n        \"\"\"\n        self.local_tmpdir = tempfile.mkdtemp()\n        logger.info(self.local_tmpdir)\n\n        def cleanup():\n            \"\"\"deletes temp dir\"\"\"\n            shutil.rmtree(self.local_tmpdir)\n\n        with self.change_dir(cleanup):\n            yield self.local_tmpdir\n\n    def return_tmpdir(self):\n        return self.local_tmpdir\n\n    def limit_check(self, copy_proto):\n        \"\"\"\n        Checks the remote hosts /etc/inetd file to determine whether there are any\n        ftp or ssh connection/rate limits defined. If found, these configuration lines\n        will be deactivated\n        :returns None:\n        \"\"\"\n        logger.info(\"entering limit_check()\")\n        config_stanzas = [\"groups\", \"system services\", \"system login\"]\n        limits = [\"services ssh connection-limit\", \"services ssh rate-limit\"]\n        if copy_proto == \"ftp\":\n            limits.append(\"services ftp connection-limit\")\n            limits.append(\"services ftp rate-limit\")\n        retry_options = \"system login retry-options\"\n\n        # check for presence of rate/connection limits\n        cli_config = \"\"\n        for stanza in config_stanzas:\n            result, stdout = self.ss.run(\n                f'cli -c \"show configuration {stanza} | display set | no-more\"'\n            )\n            cli_config += stdout\n        if cli_config:\n            conf_list = cli_config.split(\"\\r\\n\")\n        else:\n            return None\n\n        for limit in limits:\n            if re.search(rf\"^set .* {limit} [0-9]\", cli_config, re.MULTILINE) is None:\n                continue\n            for conf_statement in conf_list:\n                if re.match(rf\"set .* {limit} [0-9]\", conf_statement):\n                    conf_line = re.sub(\" [0-9]+$\", \"\", conf_statement)\n                    conf_line = re.sub(r\"^set\", \"deactivate\", conf_line)\n                    self.command_list.append(f\"{conf_line};\")\n        if re.search(rf\"^set .* {retry_options}\", cli_config, re.MULTILINE) is not None:\n            for conf_statement in conf_list:\n                if re.match(rf\"set .* {retry_options}\", conf_statement):\n                    conf_line = re.match(\n                        rf\"(set .* {retry_options})\", conf_statement\n                    ).group(1)\n                    conf_line = re.sub(r\"^set\", \"deactivate\", conf_line)\n                    if conf_line not in self.command_list:\n                        self.command_list.append(f\"{conf_line};\")\n\n        # if limits were configured, deactivate them\n        if self.command_list:\n            print(\"rate-limit/connection-limit/login retry-options configuration found\")\n            logger.info(self.command_list)\n            result, stdout = self.ss.run(\n                f'cli -c \"edit;{\"\".join(self.command_list)}commit and-quit\"',\n                exitcode=False,\n                timeout=60,\n            )\n            # cli always returns true so can't use exitcode\n            if re.search(r\"commit complete\\r\\nExiting configuration mode\", stdout):\n                print(\"configuration has been modified. deactivated the relevant lines\")\n                self.ss.run(\n                    \"logger 'splitcopy has made the following config changes: \"\n                    f\"{''.join(self.command_list)}'\",\n                    exitcode=False,\n                )\n            else:\n                err = (\n                    \"Error: failed to deactivate connection-limit/rate-limit/login retry-options\"\n                    f\"configuration. output was:\\n{stdout}\"\n                )\n                self.close(err_str=err)\n            return self.command_list\n        else:\n            return None\n\n    def limits_rollback(self):\n        \"\"\"\n        revert config change made to remote host\n        :returns None:\n        \"\"\"\n        logger.info(\"entering limits_rollback()\")\n        rollback_cmds = \"\".join(self.command_list)\n        rollback_cmds = re.sub(\"deactivate\", \"activate\", rollback_cmds)\n        result, stdout = self.ss.run(\n            f'cli -c \"edit;{rollback_cmds}commit and-quit\"',\n            exitcode=False,\n            timeout=60,\n        )\n        # cli always returns true so can't use exitcode\n        if re.search(r\"commit complete\\r\\nExiting configuration mode\", stdout):\n            print(\"configuration changes made have been reverted\")\n            self.ss.run(\n                \"logger 'splitcopy has reverted config changes'\",\n                exitcode=False,\n            )\n        else:\n            print(\n                \"Error: failed to revert the configuration changes. \"\n                f\"output was:\\n{stdout}\"\n            )\n\n    def remote_cleanup(self, remote_dir=None, remote_file=None, silent=False):\n        \"\"\"\n        delete tmp directory on remote host\n        :param silent: determines whether we announce the dir deletion\n        :type: bool\n        :returns None:\n        \"\"\"\n        if remote_dir:\n            self.remote_dir = remote_dir\n        if remote_file:\n            self.remote_file = remote_file\n        if not silent:\n            print(\"deleting remote tmp directory...\")\n        if self.remote_tmpdir is None:\n            if self.get_op:\n                self.ss.run(f\"rm -rf /var/tmp/splitcopy_{self.remote_file}.*\")\n            else:\n                self.ss.run(f\"rm -rf {self.remote_dir}/splitcopy_{self.remote_file}.*\")\n        else:\n            result, stdout = self.ss.run(f\"rm -rf {self.remote_tmpdir}\")\n            if not result and not silent:\n                print(\n                    f\"unable to delete the tmp directory {self.remote_tmpdir} on remote host, \"\n                    \"delete it manually\"\n                )\n        self.rm_remote_tmp = False\n","sub_path":"splitcopy/shared.py","file_name":"shared.py","file_ext":"py","file_size_in_byte":24019,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"35452982","text":"import numpy as np\r\nfrom numpy.linalg import inv\r\nimport pandas\r\n\r\ngnss = pandas.read_csv(\"gnss.csv\")\r\nodom = pandas.read_csv(\"odom.csv\")\r\n\r\nx_observations = []\r\nv_observations = []\r\nx = []\r\n\r\ndef prediction2d(x, v, t, a):\r\n    A = np.array([[1, t],\r\n                  [0, 1]])\r\n    X = np.array([[x],\r\n                  [v]])\r\n    B = np.array([[0.5 * t ** 2],\r\n                  [t]])\r\n    X_prime = A.dot(X) + B.dot(a)\r\n    return X_prime\r\n\r\n\r\ndef covariance2d(sigma1, sigma2):\r\n    cov1_2 = sigma1 * sigma2\r\n    cov2_1 = sigma2 * sigma1\r\n    cov_matrix = np.array([[sigma1 ** 2, cov1_2],\r\n                           [cov2_1, sigma2 ** 2]])\r\n    return np.diag(np.diag(cov_matrix))\r\n\r\n\r\nfor i in range(0, len(gnss[\"real_time\"])):\r\n    x_observations.append(gnss[\"position_x\"][i])\r\n    v_observations.append(odom[\"linear_x\"][i*5])\r\n\r\nprint(len(x_observations))\r\nprint(len(v_observations))\r\n\r\nfor i in range(0, len(x_observations) - 2):\r\n    print(i)\r\n    z = np.c_[x_observations[i:2+i], v_observations[i:2+i]]\r\n\r\n    # Initial Conditions\r\n    a = 1\r\n    v = 0.1\r\n    t = 1\r\n\r\n    # Process / Estimation Errors\r\n    error_est_x = 0.2  # Tuning param\r\n    error_est_v = 20   # Tuning param\r\n\r\n    # Observation Errors\r\n    error_obs_x = 0.1  # Tuning param\r\n    error_obs_v = 26   # Tuning param\r\n\r\n    # Initial Estimation Covariance Matrix\r\n    P = covariance2d(error_est_x, error_est_v)\r\n    A = np.array([[1, t],\r\n                  [0, 1]])\r\n\r\n    # Initial State Matrix\r\n    X = np.array([[z[0][0]],\r\n                  [v]])\r\n    n = len(z[0])\r\n\r\n    for data in z[1:]:\r\n        X = prediction2d(X[0][0], X[1][0], t, a)\r\n        P = np.diag(np.diag(A.dot(P).dot(A.T)))\r\n        H = np.identity(n)\r\n        R = covariance2d(error_obs_x, error_obs_v)\r\n        S = H.dot(P).dot(H.T) + R\r\n        K = P.dot(H).dot(inv(S))\r\n        Y = H.dot(data).reshape(n, -1)\r\n        X = X + K.dot(Y - H.dot(X))\r\n        P = (np.identity(len(K)) - K.dot(H)).dot(P)\r\n\r\n    print(\"Kalman Filter State Matrix:\\n\", X)\r\n    x.append(X[0][0])\r\n\r\ndata = {\r\n    'x': x}\r\n\r\ndf = pandas.DataFrame(data)\r\ndf.to_csv('x.csv')\r\n","sub_path":"x_ekf.py","file_name":"x_ekf.py","file_ext":"py","file_size_in_byte":2103,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"46004396","text":"from django.conf.urls import url, include\nfrom systemconfig.views import *\n\napp_name = 'systemconfig'\n\nurlpatterns = [\n    url(r'^admin/', include([\n\n        url(r'^addcity/', citytown.addCityTown.as_view(), name='city.admin.addcity'),\n        url(r'^savecity/', citytown.saveCityTown.as_view(), name='city.admin.savecity'),\n        url(r'^editcity/(?P<edit_id>.+)', citytown.editCityTown.as_view(), name='city.admin.editcity'),\n        url(r'^updatecity/', citytown.updateCityTown.as_view(), name='city.admin.updatecity'),\n\n        url(r'^addcountry/', country.addCountry.as_view(), name='country.admin.addcity'),\n        url(r'^savecountry', country.saveCountry.as_view(), name='country.admin.savecountry'),\n        url(r'^editcountry/(?P<edit_id>.+)', country.editCountry.as_view(), name='country.admin.editcountry'),\n        url(r'^updatecountry/', country.updateCountry.as_view(), name='country.admin.updatecountry'),\n\n        url(r'^addlanguage/', language.addLanguage.as_view(), name='language.admin.addcity'),\n        url(r'^savelanguage/', language.saveLanguage.as_view(), name='language.admin.savelanguage'),\n        url(r'^editlanguage/(?P<edit_id>.+)', language.editLanguage.as_view(), name='language.admin.editlanguage'),\n        url(r'^updatelanguage/', language.updateLanguage.as_view(), name='language.admin.updatelanguage'),\n\n        url(r'^addnationality/', nationality.addNationality.as_view(), name='nationality.admin.addcity'),\n        url(r'^savenationality/', nationality.saveNationality.as_view(), name='nationality.admin.savenationality'),\n        url(r'^editnationality/(?P<edit_id>.+)', nationality.editNationality.as_view(),\n            name='nationality.admin.editnationality'),\n        url(r'^updatenationality/', nationality.updateNationality.as_view(),\n            name='nationality.admin.updatenationality'),\n\n        url(r'^addoccupation/', occupation.addOccupation.as_view(), name='occupation.admin.addcity'),\n        url(r'^saveoccupation', occupation.saveOccupation.as_view(), name='occupation.admin.saveoccupation'),\n        url(r'^editoccupation/(?P<edit_id>.+)', occupation.editOccupation.as_view(),\n            name='occupation.admin.editoccupation'),\n        url(r'^updateoccupation/', occupation.updateOccupation.as_view(), name='occupation.admin.updateoccupation'),\n\n        url(r'^addrelationship/', relationship.addRelationship.as_view(), name='relationship.admin.addrelationship'),\n        url(r'^saverelationship', relationship.saveRelationship.as_view(), name='relationship.admin.saverelationship'),\n        url(r'^editrelationship/(?P<edit_id>.+)', relationship.editRelationship.as_view(),\n            name='relationship.admin.editrelationship'),\n        url(r'^updaterelationship/', relationship.updateRelationship.as_view(),\n            name='relationship.admin.updaterelationship'),\n\n        url(r'^addreligion/', religion.addReligion.as_view(), name='religion.admin.addreligion'),\n        url(r'^savereligion/', religion.saveReligion.as_view(), name='religion.admin.savereligion'),\n        url(r'^editreligion/(?P<edit_id>.+)', religion.editReligion.as_view(), name='religion.admin.editreligion'),\n        url(r'^updatereligion/', religion.updateReligion.as_view(), name='religion.admin.updatereligion'),\n\n        url(r'^addstate/', state.addState.as_view(), name='state.admin.addstate'),\n        url(r'^savestate/', state.saveState.as_view(), name='state.admin.savestate'),\n        url(r'^editstate/(?P<edit_id>.+)', state.editState.as_view(), name='state.admin.editstate'),\n        url(r'^updatestate/', state.updateState.as_view(), name='state.admin.updatestate'),\n\n        url(r'^addorganization/', organization.addOrganization.as_view(), name='organization.admin.addorganization'),\n        url(r'^saveorganization/', organization.saveOrganization.as_view(), name='organization.admin.saveorganization'),\n        url(r'^editorganization/(?P<edit_id>.+)', organization.editOrganization.as_view(),\n            name='organization.admin.editorganization'),\n        url(r'^updateorganization/', organization.updateOrganization.as_view(),\n            name='organization.admin.updateorganization'),\n\n        url(r'^addsmsconfig/', smsconfig.addSmsConfig.as_view(), name='smsconfig.admin.addsmsconfig'),\n        url(r'^savesmsconfig/', smsconfig.saveSmsConfig.as_view(), name='smsconfig.admin.savesmsconfig'),\n        url(r'^editsmsconfig/(?P<edit_id>.+)', smsconfig.editSmsConfig.as_view(), name='smsconfig.admin.editsmsconfig'),\n        url(r'^updatesmsconfig/', smsconfig.updateSmsConfig.as_view(), name='smsconfig.admin.updatesmsconfig'),\n\n        url(r'^addemailconfig/', emailconfig.addEmailConfig.as_view(), name='emailconfig.admin.addemailconfig'),\n        url(r'^saveemailconfig/', emailconfig.saveEmailConfig.as_view(), name='emailconfig.admin.saveemailconfig'),\n        url(r'^editemailconfig/(?P<edit_id>.+)', emailconfig.editEmailConfig.as_view(),\n            name='emailconfig.admin.editemailconfig'),\n        url(r'^updateemailconfig/', emailconfig.updateEmailConfig.as_view(),\n            name='emailconfig.admin.updateemailconfig'),\n\n    ]))\n]\n","sub_path":"systemconfig/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":5110,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"645470305","text":"from matplotlib import gridspec\n\nimport sys\n\nimport pandas as pd\nimport numpy as np\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\n\ndef parse_data(filename):\n\t# read input file\n\tdata = np.array(pd.read_csv(filename + '.csv', header = None)) # read input file\n\n\t# determine number of meta-learning iterations and feature names\n\titers = [int(i) for i in data[0][1:]]\n\tfeatures = [row[0] for row in data[1:]]\n\n\t# eliminate column of feature names from data\n\tdata = np.array([row[1:] for row in data[1:]], dtype=float)\n\n\treturn iters, features, data\n\ndef get_layers(num_values):\n\treturn [\n\t\t('Programs (P)', 2),\n\t\t('Conditions (C)', 2),\n\t\t('Base conditions (B)', 2),\n\t\t('Offsets (O)', 8),\n\t\t('Numbers (N)', 4),\n\t\t('Values (V)', num_values - 18)\n\t]\n\ndef plot_heatmap(data, xticklabels, yticklabels, layers, title):\n\t# set up figure\n\tfig = plt.figure(figsize = (7, 9))\n\tgs = gridspec.GridSpec(len(layers), 1, height_ratios = [layer[1] for layer in layers]) \n\n\t# iterate through every layer in grammar\n\tstart_index = 0\n\tfor i in range(len(layers)):\n\t\tlayer_name, num_features = layers[i]\n\n\t\t# obtain dataframe for layer\n\t\tend_index = start_index + num_features\n\t\tdataframe = pd.DataFrame(data[start_index : end_index])\n\n\t\t# determine label booleans\n\t\tfirst_layer = i == 0 # title = Nim, xaxis = top, xaxis = 'Programs'\n\t\tmiddle_layer = i == 3 # title = label, xaxis = None\n\t\tlast_layer = i == len(layers) - 1 # title = label, xaxis = 'Iters'\n\n\t\t# initialize subplot\n\t\taxes = plt.subplot(gs[i])\n\n\t\t# plot heatmap for layer\n\t\tplot_layer_heatmap(dataframe,\n\t\t\txticklabels if last_layer else [], # xticklabels\n\t\t\tyticklabels[start_index : end_index]) # yticklabels\n\n\t\tset_plot_labels(axes,\n\t\t\ttitle, \n\t\t\t'Iterations of\\nMeta-Learning', # xlabel\n\t\t\t'DSL Feature', # ylabel\n\t\t\tlayer_name,\n\t\t\tfirst_layer, middle_layer, last_layer)\n\n\t\tstart_index = end_index\n\ndef plot_layer_heatmap(dataframe, xticklabels, yticklabels):\n\tg = sns.heatmap(dataframe,\n\t\tannot = False, square = True,\n\t\txticklabels = xticklabels, yticklabels = yticklabels,\n\t\tcbar = False)\n\tg.set_yticklabels(g.get_yticklabels(), rotation = 0)\n\ndef set_plot_labels(axes, title, xlabel, ylabel, layer_name, first_layer, middle_layer, last_layer):\n\tif first_layer: # title is plot title, xlabel is layer name\n\t\tplt.title(title, pad = 30)\n\t\taxes.xaxis.set_label_position('top')\n\t\tplt.xlabel(layer_name)\n\telif last_layer: # title is layer name, xlabel is plot xlabel\n\t\tplt.title(layer_name, pad = 4, fontsize = 10)\n\t\tplt.xlabel(xlabel)\n\telse: # title is layer name, ylabel is plot ylabel\n\t\tplt.title(layer_name, pad = 4, fontsize = 10)\n\t\tplt.ylabel(ylabel if middle_layer else '', labelpad = 70)\n\nif __name__  == \"__main__\":\n    minigame = str(sys.argv[1])\n\n    # get plotting parameters\n    iters, features, data = parse_data(minigame)\n    layers = get_layers(len(features))\n\n    plot_heatmap(data, iters, features, layers, minigame)\n    plt.savefig(minigame + \"_heatmap.png\")\n","sub_path":"heatmap.py","file_name":"heatmap.py","file_ext":"py","file_size_in_byte":2931,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"411864514","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n\"\"\"\n@project=liaoxuefeng\n@file=file_handle\n@author=book\n@create_time=18-6-21 下午3:05\n\"\"\"\n# 写文件操作\nfpath = r'/home/book/PycharmProjects/liaoxuefeng/venv/io_tongbu_study/yi.txt'\n# 以'w'模式写入文件时，如果文件已存在，会直接覆盖\nf = open(fpath, 'w')\nf.write('hello,world!')\nf.close()\n\n# 追加到文件末尾怎么办？可以传入'a'以追加（append）模式写入\n# 但是每次都这么写实在太繁琐，所以，Python引入了with语句来自动帮我们调用close()方法\nwith open(fpath, 'a') as f:\n\tf.write('nihaoma!')\n\n# 读文件操作\n\"\"\"\"\"\n如果文件很小，read()一次性读取最方便；\n如果不能确定文件大小，反复调用read(size)比较保险；\n如果是配置文件，调用readlines()最方便：\n\"\"\"\nf = open(fpath, 'r')\ns = f.read()\nf.close()\nprint(s)\n\n\n\"\"\"\"\nfile-like Object\n\n像open()函数返回的这种有个read()方法的对象，在Python中统称为file-like Object。除了file外，\n还可以是内存的字节流，网络流，自定义流等等。file-like Object不要求从特定类继承，只要写个read()方法就行。\nStringIO就是在内存中创建的file-like Object，常用作临时缓冲。\n\"\"\"\n\"\"\"\n二进制文件\n前面讲的默认都是读取文本文件，并且是UTF-8编码的文本文件。\n要读取二进制文件，比如图片、视频等等，用'rb'模式打开文件即可：\n\"\"\"\nf = open(fpath, 'rb')\ns = f.read()\nf.close()\nprint(s)\n\n\n\"\"\"\n字符编码\n\n要读取非UTF-8编码的文本文件，需要给open()函数传入encoding参数，例如，读取GBK编码的文件：\n\n>>> f = open('/Users/michael/gbk.txt', 'r', encoding='gbk')\n>>> f.read()\n'测试'\n\n遇到有些编码不规范的文件，你可能会遇到UnicodeDecodeError，因为在文本文件中可能夹杂了一些非法编码的字符。遇到这种情况，open()函数还接收一个errors参数，表示如果遇到编码错误后如何处理。最简单的方式是直接忽略：\n\n>>> f = open('/Users/michael/gbk.txt', 'r', encoding='gbk', errors='ignore')\n\n\"\"\"","sub_path":"liaoxuefeng/venv/io_tongbu_study/file_handle.py","file_name":"file_handle.py","file_ext":"py","file_size_in_byte":2100,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"325410833","text":"# app/visitors/models.py\nfrom app import db\nfrom datetime import datetime\n\nclass Visitor(db.Model):\n\t__tablename__ = \"visitors\"\n\tid = db.Column(db.Integer, primary_key=True)\n\tprofile_uid = db.Column(db.Integer, db.ForeignKey('users.id'))\n\tvisit_uid = db.Column(db.Integer, db.ForeignKey('users.id'))\n\tvisit_count = db.Column(db.Integer)\n\tvisit_time = db.Column(db.DateTime)\n\n\t@classmethod\n\tdef create_visitor(cls, profile_id, visit_id, visit_time):\n\t\tvisitor = cls(\n\t\t\tprofile_uid = profile_id,\n\t\t\tvisit_uid = visit_id,\n\t\t\tvisit_count = 1,\n\t\t\tvisit_time = visit_time\n\t\t)\n\t\tdb.session.add(visitor)\n\t\tdb.session.commit()\n\t\treturn visitor\n\n\tdef update_visitor(self):\n\t\tself.visit_time = datetime.now()\n\t\tself.visit_count += 1\n\t\tdb.session.add(self)\n\t\tdb.session.commit()","sub_path":"app/visitors/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":767,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"494615134","text":"# -*- coding: utf-8 -*-\nfrom django.core.urlresolvers import reverse\nfrom django.core.paginator import Paginator, PageNotAnInteger, EmptyPage, InvalidPage\nfrom django.contrib.auth import login, logout\nfrom django.contrib.auth.decorators import login_required\nfrom django.http import HttpResponseRedirect, Http404\nfrom django.template.loader import render_to_string\nfrom annoying.decorators import render_to, ajax_request\n\nfrom models import *\nfrom forms import LoginForm\n\n__author__ = 'boo'\n\n\n@login_required\n@render_to('newsapp/home.html')\ndef home(request):\n    return news(request)\n\n\ndef get_news_paginated(request, page):\n    deleted_news = [dn['deleted_id'] for dn in DeletedNew.objects.filter(user=request.user).values('deleted_id')]\n    news_all = New.objects.exclude(id__in=deleted_news)\n\n    paginator = Paginator(news_all, 5)\n    try:\n        page = int(page)\n    except (ValueError, TypeError):\n        page = 1\n\n    try:\n        news = paginator.page(page)\n    except PageNotAnInteger:\n        news = paginator.page(1)\n    except (EmptyPage, InvalidPage):\n        news = paginator.page(paginator.num_pages)\n    return news\n\n\n@login_required\n@render_to('newsapp/news.html')\ndef news(request):\n    page = request.GET.get('page')\n    news = get_news_paginated(request, page)\n    return {\n        'news': news,\n    }\n\n\n@ajax_request\ndef news_paginated(request, p):\n    news = get_news_paginated(request, p)\n    render = render_to_string('newsapp/news_paginated.html', {'news': news})\n    return {\n        'content': render,\n    }\n\n\n@login_required\n@render_to('newsapp/news_view.html')\ndef news_view(request, news_id):\n    item = New.objects.get(pk=news_id)\n    if not item:\n        return Http404()\n\n    return {\n        \"n\": item,\n    }\n\n\n@render_to('login.html')\ndef login_view(request):\n    if request.method == 'POST':\n        form = LoginForm(request.POST)\n        if form.is_valid():\n            user = form.cleaned_data\n            login(request, user)\n            return HttpResponseRedirect(request.GET.get('next', None) or reverse('home'))\n    else:\n        form = LoginForm()\n\n    return {\n        'form': form,\n    }\n\n\ndef logout_view(request):\n    logout(request)\n    return HttpResponseRedirect(reverse('home'))\n\n\n@login_required()\n@ajax_request\ndef news_delete(request, new_id=None):\n    if request.method == \"POST\" and request.is_ajax():\n        new = New.objects.get(pk=new_id)\n        dn = DeletedNew(\n            user=request.user,\n            deleted_id=new,\n        )\n        dn.save(force_insert=True)\n\n        return {\n            'id': new_id,\n        }\n\n    return {\n        'id': 0,\n    }","sub_path":"newsproject/newsapp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2622,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"164989394","text":"import os\nimport sys\nimport argparse\nimport logging\nimport cProfile\n\nimport numpy as np\n\nfrom model.thin_snn_lasagne import launch_thin_snn_lasagne\nfrom model.lenet import launch_thin_cnn\nfrom model.thin_snn_conv import launch_thin_snn_conv\nfrom model.thin_snn_conv_att import launch_thin_snn_conv_att\nimport exp_config\nimport platform\n\n\ndef process_args(args, defaults):\n    parser = argparse.ArgumentParser()\n\n    parser.add_argument('--update-rule', dest=\"update_rule\",\n                        type=str, default=defaults.UPDATE_RULE,\n                        help=('rmsprop|sgd ' + '(default: %(default)s)'))\n    parser.add_argument('--batch-accumulator', dest=\"batch_accumulator\",\n                        type=str, default=defaults.BATCH_ACCUMULATOR,\n                        help=('sum|mean (default: %(default)s)'))\n    parser.add_argument('--learning-rate', dest=\"learning_rate\",\n                        type=float, default=defaults.LEARNING_RATE,\n                        help='Learning rate (default: %(default)s)')\n    parser.add_argument('--rms-decay', dest=\"rms_decay\",\n                        type=float, default=defaults.RMS_DECAY,\n                        help='Decay rate for rms_prop (default: %(default)s)')\n    parser.add_argument('--rms-epsilon', dest=\"rms_epsilon\",\n                        type=float, default=defaults.RMS_EPSILON,\n                        help='Denominator epsilson for rms_prop ' +\n                             '(default: %(default)s)')\n    parser.add_argument('--momentum', type=float, default=defaults.MOMENTUM,\n                        help=('Momentum term for Nesterov momentum. ' +\n                              '(default: %(default)s)'))\n\n    parser.add_argument('--weight-decay', dest=\"weight_decay\",\n                        type=float, default=defaults.WEIGHT_DECAY,\n                        help='Weight decay for L2 regularization (default: %(default)s)')\n    parser.add_argument('--l1-coeff', dest=\"l1_coeff\",\n                        type=float, default=defaults.L1_COEFF,\n                        help='Coefficient for L1 regularization (default: %(default)s)')\n\n    parser.add_argument('--hidden-layer-width', dest=\"hidden_layer_width\",\n                        type=int, default=defaults.HIDDEN_LAYER_WIDTH,\n                        help='Hidden layer width. (default: %(default)s)')\n\n    parser.add_argument('--batch-size', dest=\"batch_size\",\n                        type=int, default=defaults.BATCH_SIZE,\n                        help='Batch size. (default: %(default)s)')\n    parser.add_argument('--network-type', dest=\"network_type\",\n                        type=str, default=defaults.NETWORK_TYPE,\n                        help='thin_snn|snn (default: %(default)s)')\n\n    # IO related\n    parser.add_argument('--training-file', dest=\"training_file\",\n                        default=defaults.TRAINING_FILE,\n                        help='Training File (.npy)')\n    parser.add_argument('--test-file', dest=\"test_file\",\n                        default=defaults.TEST_FILE,\n                        help='Test File (.npy)')\n    parser.add_argument('--experiment-prefix', dest=\"experiment_prefix\",\n                        default=defaults.EXPERIMENT_PREFIX,\n                        help='Experiment name prefix '\n                             '(default is the name of the game)')\n    parser.add_argument('--nn-file', dest=\"nn_file\", type=str, default=defaults.NN_FILE,\n                        help='Model file containing trained net.')\n    parser.add_argument('--log-file', dest=\"log_file\", type=str, default=defaults.LOG_FILE,\n                        help='Log File.')\n\n    parameters = parser.parse_args(args)\n\n    if parameters.experiment_prefix is None:\n        name = os.path.splitext(os.path.basename(parameters.training_file))[0]\n        parameters.experiment_prefix = name\n\n    return parameters\n\n\ndef launch(args, defaults):\n    parameters = process_args(args, defaults)\n    logging.basicConfig(\n        level=logging.DEBUG,\n        format='%(asctime)-15s %(name)-5s %(levelname)-8s %(message)s',\n        filename=parameters.log_file)\n\n    if parameters.network_type == 'thin_snn_lasagne':\n        launch_thin_snn_lasagne(parameters)\n    elif parameters.network_type == 'thin_cnn':\n        launch_thin_cnn(parameters)\n    elif parameters.network_type == 'thin_snn_conv':\n        launch_thin_snn_conv(parameters)\n    elif parameters.network_type == 'thin_snn_conv_att':\n        launch_thin_snn_conv_att(parameters)\n    else:\n        assert (False)\n\n\nif __name__ == \"__main__\":\n    # launch(sys.argv[1:], exp_config.ThinCNNConfig)\n    # launch(sys.argv[1:], exp_config.ThinSNNConfig)\n    launch(sys.argv[1:], exp_config.ThinSNNConvConfig)\n    # launch(sys.argv[1:], exp_config.ThinSNNConvAttConfig)","sub_path":"src/launcher.py","file_name":"launcher.py","file_ext":"py","file_size_in_byte":4743,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"384823056","text":"import pymongo\nimport boto3\nimport pandas as pd\nfrom joblib import load\nimport numpy as np\nimport io\n\n\n# Main logic controller of cron job\ndef calculateScores():\n    #Load the classifier models\n    print(\"Loading classifier models\")\n    model = load('xgboost.model')\n    scaler = load('xgboost.scaler')\n\n    #Get all the users\n    users = getUsers()\n    print(\"Users found: \" + str(len(list(users.clone()))))\n\n    #Iterate users into model\n    for u in users:\n        print(\"Classifying user: \" + str(int(u[\"user_id\"])))\n        retrieveHealthStatus(u, model, scaler)\n\n\n\n# Retrieve a list of all users in the database\ndef getUsers():\n    #Connect to MongoDB\n    client = pymongo.MongoClient(\"mongodb://localhost:27017\")\n    db = client[\"novutree\"]\n    col = db[\"users\"]\n\n    return col.find() # Return list of all users\n\n\n\ndef retrieveHealthStatus(user, model, scaler):\n    ID = str(int(user[\"user_id\"]))\n    num_data_fitbit = 0\n    num_data_hourly = 0\n    avg_calories = 0\n    avg_calories_mets = 0\n    avg_steps = 0\n    avg_distance = 0\n    avg_floors = 0\n    avg_elevation = 0\n    avg_heart_rate = 0\n    avg_sleep_score = 0\n    result = 0\n\n    # Load access file information\n    file = open(\"aws.txt\")\n    text = file.readlines()\n    name=\"\"\n    reg_name=\"\"\n    access_key=\"\"\n    secret_key=\"\"\n\n    # Parse the file\n    for line in text:\n        line = line.rstrip(\"\\n\")\n        linetokens = list(line.split(\",\"))\n        sname=str(linetokens[0])\n        reg_name=str(linetokens[1])\n        access_key=str(linetokens[2])\n        secret_key=str(linetokens[3])\n\n\n    s3 = boto3.resource(\n        service_name=sname,\n        region_name=reg_name,\n        aws_access_key_id=access_key,\n        aws_secret_access_key=secret_key\n    )\n\n    # Create instance to bucket\n    bucket = s3.Bucket('mobilebucket')\n\n    for obj in bucket.objects.all():\n        key = obj.key\n        body = obj.get()['Body'].read()\n    \n        if(str(ID + '_fitbitdata') in key):\n            summary = pd.read_csv(io.BytesIO(body), encoding='utf8')\n            calories = summary.at[0,'caloriesOut']\n            sleep_score = summary.at[0,'efficiency']\n            # compute running total\n            num_data_fitbit = num_data_fitbit + 1\n            avg_calories = avg_calories + calories\n            avg_sleep_score = avg_sleep_score + sleep_score\n        \n        if(str(ID + '_hourlydata') in key):\n            hourly = pd.read_csv(io.BytesIO(body), encoding='utf8')\n            calories_mets = hourly['caloriesMets'].sum() / (96 * 15)\n            steps = hourly['steps'].sum()\n            distance = hourly['distance'].sum()\n            floors = hourly['floors'].sum()\n            elevation = hourly['elevation'].sum()\n            try: \n                heart_rate = hourly['heartRate'].sum() / 96\n            except:\n                heart_rate = 0\n\n            # compute running total\n            num_data_hourly = num_data_hourly + 1\n            avg_calories_mets = avg_calories_mets + calories_mets\n            avg_steps = avg_steps + steps\n            avg_distance = avg_distance + distance\n            avg_floors = avg_floors + floors\n            avg_elevation = avg_elevation + elevation\n            avg_heart_rate = avg_heart_rate + heart_rate\n\n    try:\n        avg_calories = avg_calories / num_data_fitbit\n        avg_calories_mets = avg_calories_mets / num_data_hourly\n        avg_steps = avg_steps / num_data_hourly\n        avg_distance = avg_distance / num_data_hourly\n        avg_floors = avg_floors / num_data_hourly\n        avg_elevation = avg_elevation / num_data_hourly\n        avg_heart_rate = avg_heart_rate / num_data_hourly\n        avg_sleep_score = avg_sleep_score / num_data_fitbit\n        \n\n        age = 31\n        # make the array\n        test = [age, avg_calories, avg_calories_mets, avg_steps, avg_distance, avg_floors, avg_elevation, avg_heart_rate, avg_sleep_score]\n    \n        x = np.array([test])\n\n        # compute the classification result\n        result = int(model.predict(scaler.transform(x)))\n    except:\n        result = 0\n        print(\"Error in processing\")\n    \n\n    print(\"           Score: \" + str(result))\n\n    # update or write score to ml_user_score file in s3: first load the file from s3, then edit the file, then upload it back to s3.\n    s3 = boto3.client( # note: boto3.client allows to do low level API calls whereas, boto3.resource allows only high level API calls\n        service_name=sname,\n        region_name=reg_name,\n        aws_access_key_id=access_key,\n        aws_secret_access_key=secret_key\n    )\n    obj = s3.get_object(Bucket = 'mobilebucket', Key = 'ml_user_scores.csv')\n    ml_scores = pd.read_csv(io.BytesIO(obj['Body'].read()), encoding='utf8')\n    if int(ID) in ml_scores.values:\n        d = {'userid':[int(ID)], 'score':[result]}\n        dfn = pd.DataFrame(data=d)\n        ml_scores.update(ml_scores[['userid']].merge(dfn, 'left'))\n    else:\n        ml_scores.loc[len(ml_scores.index)] = [int(ID), result]\n\n    # Save file back\n    ml_scores.to_csv('ml_user_scores.csv', sep=',', encoding='utf-8', index=False)\n    s3.upload_file('ml_user_scores.csv','mobilebucket','ml_user_scores.csv')\n\n\n\n\n\n\n\n\n# # # # # # # # # # # # # Start the script # # # # # # # # # # # # # # # # # #\ncalculateScores()","sub_path":"cron_jobs/calc_scores.py","file_name":"calc_scores.py","file_ext":"py","file_size_in_byte":5243,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"95742621","text":"import time, json, socket\nfrom Log import Log as Logger\n\nclass Penguin:\n    def __init__(self):\n        self.Socket = None\n        self.Logger = Logger()\n        self.Id = None\n        self.Username = None\n        self.Nickname = None\n        \n        from Spine import Spine as Spine\n        self.Socket = Spine('login', False).getSocket()\n\n    def sendPacket(self, Packet):\n        if self.Socket != None:\n            self.Socket.send(bytes(Packet + chr(0)))\n            self.Logger.writeLog(str(Packet), 'sent')\n        else:\n            self.Logger.writeLog('Socket not connected', 'error')\n","sub_path":"PufflePlus/Penguin.py","file_name":"Penguin.py","file_ext":"py","file_size_in_byte":595,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"297730169","text":"# -*- coding: utf-8 -*-\n# tente algo como\ndef index(): return dict(message=\"hello from contato.py\")\n\ndef Contato():\n    form = SQLFORM.factory(\n    Field('Nome', requires=IS_NOT_EMPTY()),\n    Field('Email', requires=IS_EMAIL()),\n    Field('Mensagem', 'text', requires=IS_NOT_EMPTY())\n    )\n    if form.process().accepted:\n        if mail.send(\n        to='contato@hubtec.com.br' ,\n        subject='Contato enviado pelo site Nuvemb2b %s' % form.vars.Nome,\n        reply_to = form.vars.Email,\n        message='%s \\n %s \\n %s' % (form.vars.Nome, form.vars.Email, form.vars.Mensagem)\n        ):\n            response.flash = 'Agradecemos o contato, mensagem enviada'\n            redirect(URL('index'))\n        else:\n            response.flash = 'Erro ao enviar formulário!'\n\n    elif form.errors:\n        response.flash = 'Erros no formulário!'\n\n    return dict(form=form)\n","sub_path":"Portal2/controllers/contato.py","file_name":"contato.py","file_ext":"py","file_size_in_byte":870,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"441180347","text":"import pdb\nimport json\nfrom math import log\nfrom collections import Counter\nimport tqdm\nimport argparse\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--out_dataset_name', type=str, default='newsqa',\n                    help='out domain dataset name. in domain is always squad.')\nparser.add_argument('--normalize_lm_score', action='store_true', help='normalize lm score.')\nparser.add_argument('--out_score_on', action='store_true', help='include scores from out domain.')\nparser.add_argument('--norm_score_sp', action='store_true', help='normalize lm score, but only for out domain.')\nparser.add_argument('--norm_score_better', action='store_true', \n\thelp='normalize lm score; norm for each out domain, and norm for in domain over all data.')\n\nargs = parser.parse_args()\n\ntest_mode = False\nout_dataset_name = args.out_dataset_name\n# lm scores\nif out_dataset_name=='newsqa':\n\tin_domain_score_f = 'lm_scores_s_n.json'\n\tout_domain_score_f = 'lm_scores_n_n.json'\nelse:\n\tin_domain_score_f = 'lm_scores_s_m.json'\n\tout_domain_score_f = 'lm_scores_m_m.json'\nin_data_path = '../data/multitask/san_addmarco_quefea/squad_train.json'\nout_data_path = '../data/multitask/san_addmarco_quefea/{}_train.json'.format(out_dataset_name)\nout_gold_path = '../data/{}/train.json'.format(out_dataset_name)\nanswer_score_on = True\nlm_score_on = True\nnormalize_lm_score = args.normalize_lm_score\nnorm_score_sp = args.norm_score_sp\nnorm_score_better = args.norm_score_better\nout_score_on = args.out_score_on\noutput_name = 'score_{}'.format(out_dataset_name)\n\nif answer_score_on:\n\toutput_name+='_a'\nif lm_score_on:\n\toutput_name+='_l'\nif normalize_lm_score:\n\toutput_name +='_norm'\nif norm_score_sp:\n\toutput_name +='_normsp'\nif norm_score_better:\n\toutput_name +='_normb2'\nif not out_score_on:\n\toutput_name +='_inonly'\n\n\n# output_name +='.json'\n\ndef load_gold_data(path, is_train=True):\n\tquestions = []\n\tanswers=[]\n\tuids = []\n\twith open(path, encoding=\"utf8\") as f:\n\t\tdata = json.load(f)['data']\n\tcnt = 0\n\tfor article in tqdm.tqdm(data, total=len(data)):\n\t\tfor paragraph in article['paragraphs']:\n\t\t\tcnt += 1\n\t\t\tfor qa in paragraph['qas']:\n\t\t\t\tuid, question = qa['id'], qa['question']\n\t\t\t\tanswer = [a['text'] for a in qa.get('answers', [])]\n\t\t\t\tquestions.append(question)\n\t\t\t\tanswers.append(answer)\n\t\t\t\tuids.append(uid)\n\tif test_mode:\n\t\tprint('using only first 200 data.')\n\t\tquestions=questions[:200]\n\t\tuids=uids[:200]\n\tq_dict = {str(uid):{'question':q, 'answer':a} for uid,q,a in zip(uids,questions,answers)}\n\treturn q_dict\n\ndef load(path, is_train=True):\n\twith open(path, 'r', encoding='utf-8') as reader:\n\t\t# filter\n\t\tdata = []\n\t\tcnt = 0\n\t\tanswer_counter=0\n\t\tsum_answer_tokens=0\n\t\tfor line in reader:\n\t\t\tsample = json.loads(line)\n\t\t\tcnt += 1\n\t\t\tif is_train:\n\t\t\t\tif sample['start'] is None or sample['end'] is None:\n\t\t\t\t\tcontinue\n\t\t\t\tanswer_counter+=1\n\t\t\tdata.append(sample)\n\t\t\tif test_mode and len(data)>=200:\n\t\t\t\tprint('use only 200 data.')\n\t\t\t\tbreak\n\t# if is_train and self.span_mode:\n\t\t# print('average answer:',sum_answer_tokens/answer_counter,'counter=',answer_counter)\n\tprint('Loaded {} samples out of {}'.format(len(data), cnt))\n\treturn data\n\nin_score = json.load(open(in_domain_score_f))\nout_score = json.load(open(out_domain_score_f))\n\nif normalize_lm_score or norm_score_sp or norm_score_better:\n\tdef norm_score(scores,min_score=None, max_score = None):\n\t\tif min_score is None:\n\t\t\tmin_score = min([v for v in scores.values()])\n\t\tif max_score is None:\n\t\t\tmax_score = max([v for v in scores.values()])\n\t\tfor k,v in scores.items():\n\t\t\tscores[k]=(v-min_score)/(max_score-min_score)\n\t\treturn scores\n\tif normalize_lm_score:\n\t\tin_score=norm_score(in_score)\n\telif norm_score_better:\n\t\tin_score = norm_score(in_score, min_score=1.0041244983673097, max_score=10.724374532699585)\n\t# print('lm in socre max:',max([v for v in in_score.values()]),'min:',min([v for v in in_score.values()]))\n\t# pdb.set_trace()\n\tout_score = norm_score(out_score)\n\t\t\n\n\nin_data = load(in_data_path)\nout_data = load(out_data_path)\n\nall_lengths_s = [sample['end']-sample['start']+1 for sample in in_data]\nall_lengths_t = [sample['end']-sample['start']+1 for sample in out_data]\n\n# find a way to count frequency\ncounter_in = Counter(all_lengths_s)\nfreq_in = {k:v/len(all_lengths_s) for k,v in counter_in.items()}\ncounter_out = Counter(all_lengths_t)\nfreq_out = {k:v/len(all_lengths_t) for k,v in counter_out.items()}\nfor k in freq_out:\n\tif k not in freq_in:\n\t\tfreq_in[k]=1/len(all_lengths_s)\n\n# pdb.set_trace()\nfinal_scores = {}\nq_dict = load_gold_data(out_gold_path)\n\nin_answer_score={}\nout_answer_score={}\nfor sample in out_data:\n\tsample['uid']=str(sample['uid'])\n\ttry:\n\t\tin_answer_score[sample['uid']] = -log(freq_in[sample['end']-sample['start']+1])\n\t\tout_answer_score[sample['uid']] = -log(freq_out[sample['end']-sample['start']+1])\n\texcept:\n\t\tpdb.set_trace()\nif norm_score_better:\n\tin_answer_score = norm_score(in_answer_score,max_score=11.38052486133421, min_score=1.163358596320627)\n\tout_answer_score = norm_score(out_answer_score)\n\tprint('answer in socre max:',max([v for v in in_answer_score.values()]),'min:',min([v for v in in_answer_score.values()]))\n\nfor sample in out_data:\n\tthis_score = 0\n\tin_lm_score = in_score[sample['uid']]\n\tout_lm_score = out_score[sample['uid']]\n\tif not out_score_on:\n\t\tlm_score = in_lm_score\n\t\tanswer_score = in_answer_score[sample['uid']]\n\telse:\n\t\tlm_score = in_lm_score - out_lm_score\n\t\tanswer_score = in_answer_score[sample['uid']] - out_answer_score[sample['uid']]\n\n\tif answer_score_on:\n\t\tthis_score+=answer_score\n\tif lm_score_on:\n\t\tthis_score+= lm_score\n\n\tfinal_scores[sample['uid']]={'answer_score':answer_score,\n\t'lm_score':lm_score,\n\t'overall_score':this_score\n\t}\n\tk=sample['uid']\n\t# print('question:',q_dict[k]['question'],'answers:',q_dict[k]['answer'],\n\t# \t'lm_score:',final_scores[k]['lm_score'],'answer_score:',final_scores[k]['answer_score'],\n\t# \t'in_lm_score:',in_lm_score,'out_lm_score:',out_lm_score)\n\t# pdb.set_trace()\n\ndataset_scores = {'marco':final_scores}\n\njson.dump(final_scores, open(output_name+'.json','w'))\n\nprint('finish')\npdb.set_trace()\nfor k in final_scores:\n\tprint('question:',q_dict[k]['question'],'answers:',q_dict[k]['answer'],\n\t\t'lm_score:',final_scores[k]['lm_score'],'answer_score:',final_scores[k]['answer_score'])\n\tinput()\n\n","sub_path":"generate_scores.py","file_name":"generate_scores.py","file_ext":"py","file_size_in_byte":6258,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"339955037","text":"import alles, amy, sys\n\n# Sorry I have to add this. getting loris to compile cleanly was a PITA. looking for a better one\nsys.path.append('/usr/local/lib/python3.8/site-packages')\nimport pydub\nimport loris\nimport time\nimport numpy as np\nfrom math import pi\nimport queue\n\n\n\ndef parse_wav_file(wav_filename):\n    # Use APS code to parse a wave file \n    # This is a forked version of wavdumper that saves these variables and works in py3\n    import wavdumper\n    w = wavdumper.Wav(wav_filename)\n    w.printInfo()\n    return (w.midiUnityNote, (w.loopstart, w.loopend), w.samples, w.channels, w.sampleRate)\n\ntests = [\n    \"/Users/bwhitman/sounds/aps/samples/ADVORCH1/BA LONG FF/BA LGFF#C3.wav\",\n    \"/Users/bwhitman/sounds/aps/samples/ADVORCH2/VIS LONG F/VIS LGF G2.wav\"\n]\n\ndef partial_analyze(filename=tests[0]):\n    # Take a wav file from APS, output partials + noise train?\n    import simpl\n    import sounddevice as sd\n    sd.default.samplerate = amy.SAMPLE_RATE\n    sd.default.channels = 1\n    (note, (loopstart, loopend), samples, channels, samplerate) = parse_wav_file(filename)\n    audio, sampling_rate = simpl.read_wav(filename)\n    r = simpl.SMSResidual()\n    p = simpl.SMSPartialTracking()\n\n    audio_out = r.synth(audio)\n    audio_out = np.asarray(audio_out * 32768, np.int16)\n    sd.play(audio_out)\n\n\ndef partial_scheduler(filename, len_s = 10, noise_ratio = 1, freq_res = 150, analysis_window = 100, freq_drift = 50, min_partial_len_s = 0.25, \\\n        every_n_partial=1, every_n_bp=1, time_ratio=1):\n    # Given a file, read it in, SWS analyze it, send it back out as an alles sequence\n\n    def list_from_py2_iterator(obj, how_many):\n        # Oof, the loris object uses some form of iteration that py3 doesn't like. \n        ret = []\n        it = obj.iterator()\n        for i in range(how_many):\n            ret.append(it.next())\n        return ret\n\n    # https://www.classes.cs.uchicago.edu/archive/2005/spring/29500-1/Computing_resources/Loris/docs/python_module.html#Breakpoint-phase \n    audio = pydub.AudioSegment.from_file(filename)[:len_s*1000]\n    y = np.array(audio.get_array_of_samples())\n    if audio.channels == 2:\n        y =y.reshape((-1, 2))\n    y = y[:,1]\n    y = np.float64(y) / 2**15\n    analyzer = loris.Analyzer(freq_res, analysis_window)\n    analyzer.setFreqDrift(freq_drift) \n    partials = analyzer.analyze(y, audio.frame_rate)\n    loris.scaleNoiseRatio(partials, noise_ratio)\n    sequence = []\n    print(\"%d partials\" % (partials.size()))\n    # TODO: every_n_partial may skip ones that are big enough, probably makes more sense to grab them all then slice them out\n    full_partial_list = list_from_py2_iterator(partials, partials.size())\n    long_enough_partials = [partial for partial in full_partial_list if partial.duration() > min_partial_len_s]\n    print(\"but only %d were long enough (%2.2f%%)\" % (len(long_enough_partials),len(long_enough_partials)/partials.size()*100.0 ))\n    for partial_idx, partial in enumerate(long_enough_partials[::every_n_partial]):\n        breakpoints = list_from_py2_iterator(partial, partial.numBreakpoints())\n        first_bp = breakpoints[0]\n        if(partial.duration() > min_partial_len_s):\n            # phase is in radians -- -2pi - 2pi, do phase = phase / 2pi, if < 0 then add 1\n            phase = first_bp.phase() / (2*pi)\n            if(phase < 0): phase = phase + 1\n            time_ms = int(first_bp.time() * 1000)*time_ratio\n            sequence.append( (time_ms, partial_idx, 0, first_bp.frequency(), first_bp.amplitude(), first_bp.bandwidth(), phase ) )\n            for bp_idx, bp in enumerate(breakpoints[1::every_n_bp]):\n                time_ms = int(bp.time() * 1000)*time_ratio\n                sequence.append( (time_ms, partial_idx, bp_idx+1, bp.frequency(), bp.amplitude(), bp.bandwidth(), -1) )\n            # Turn off osc at last one\n            sequence.append( (time_ms, partial_idx, bp_idx + 1, 0, -1, 0, -1) )\n    # Now sort by time\n    time_ordered = sorted(sequence, key=lambda x:x[0])\n    return time_ordered\n\n\n\ndef play_partial_sequence(sequence, amp_mult=1, max_oscs=amy.OSCS, show_cpu=False, **kwargs):\n    amy.reset()\n    amy.buffer()\n    (time_ms, partial_idx, bp_idx, freq, amp, bw, phase) = range(7)\n    q = queue.Queue(max_oscs)\n    for i in range(int(max_oscs/2)):\n        q.put(int(i))\n        q.put(int(i+amy.OSCS/2))\n    osc_map = {}\n    start = alles.millis()\n    offset = alles.millis() - sequence[0][time_ms]\n    m = 0\n    amy.send(debug=2)\n    for event in sequence:\n        event_time_ms = (event[time_ms] + offset)\n        while(event_time_ms - alles.millis() > 1):\n            pass\n        # If this is the start of a partial\n        if(event[phase]>-1):\n            if(not q.empty()):\n                osc_map[event[partial_idx]] = q.get()\n                m = m + 1\n                alles.note_on(osc = osc_map[event[partial_idx]], wave=alles.SINE, freq=event[freq], phase=event[phase], timestamp = event_time_ms, vel=event[amp]*amp_mult, **kwargs)\n            else:\n                # No oscs available for this partial, so skip it\n                continue\n        else:\n            if(event[partial_idx] in osc_map):\n                # If this is a normal breakpoint\n                if(event[amp]>-1):\n                    m = m + 1\n                    alles.send(osc = osc_map[event[partial_idx]], freq=event[freq], amp=event[amp]*amp_mult, timestamp = event_time_ms, **kwargs)\n                else:\n                    # partial is over, free the oscillator\n                    m = m + 1\n                    alles.send(osc = osc_map[event[partial_idx]], vel=0, timestamp = event_time_ms, **kwargs)\n                    q.put(osc_map[event[partial_idx]])\n        if(alles.millis()-start > 1000):\n            if(show_cpu): alles.send(debug=1)\n            start = alles.millis()\n    print(\"Voice allocator was able to send %d messages out of %d (%2.2f%%)\" % (m, len(sequence), float(m)/len(sequence)*100.0))\n    # Wait for the last bits in the latency buffer\n    time.sleep(1)\n    amy.send(debug=1)\n    amy.reset()\n    amy.buffer(size=0)\n\n\n\n# OK defaults here\ndef partial_test(   filename=\"/Users/bwhitman/sounds/billboard/0157/0157.mp4\", \\\n                    len_s=60, \\\n                    min_partial_len_s = 0.25, \\\n                    freq_res = 150, \\\n                    freq_drift = 50, \\\n                    analysis_window = 100, \\\n                    every_n_partial = 1, \\\n                    every_n_bp = 3, \\\n                    time_ratio = 1, \\\n                    max_oscs = 40, \\\n                    noise_ratio = 0,\\\n                    amp_mult = 4, \\\n                    **kwargs):\n    a = partial_scheduler(filename, len_s=len_s, min_partial_len_s=min_partial_len_s, freq_res=freq_res, noise_ratio=noise_ratio, \\\n        freq_drift=freq_drift, analysis_window=analysis_window, every_n_bp=every_n_bp, every_n_partial=every_n_partial, time_ratio=time_ratio)\n    print(\"Generated %d alles events\" % (len(a)))\n    play_partial_sequence(a, amp_mult=amp_mult, max_oscs=max_oscs, **kwargs)\n\n","sub_path":"partials.py","file_name":"partials.py","file_ext":"py","file_size_in_byte":7024,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"411253429","text":"import csv\nimport os.path\nfrom pathlib import Path\n\nfrom bidict import bidict\n\nTO_APPEND = \"-tags\"\nTO_APPEND_OLD = \"-sceneRect\"\n\nAUDIO_EXTENSIONS = [\".wav\", \".WAV\", \".flac\", \".FLAC\"]\n\n\n# COLUMNS_SAVE = bidict(\n#     {\n#         \"id\": \"id\",\n#         \"file\": \"file_name\",\n#         \"label\": \"tag\",\n#         \"timestamp\": \"timestamp\",\n#         \"nstep\": \"nstep\",\n#         \"nwin\": \"nwin\",\n#         \"start\": \"tag_start\",\n#         \"end\": \"tag_end\",\n#         \"min_freq\": \"MinimumFreq_Hz\",\n#         \"max_freq\": \"MaximumFreq_Hz\",\n#         \"max_amp\": \"MaxAmp\",\n#         \"min_amp\": \"MinAmp\",\n#         \"mean_amp\": \"MeanAmp\",\n#         \"amp_sd\": \"AmpSD\",\n#         \"area_datapoints\": \"LabelArea_DataPoints\",\n#         \"overlap\": \"overlap\",\n#         \"background\": \"background\",\n#         \"related\": \"Related\",\n#         \"noise\": \"noise\",\n#     }\n# )\n\nCOLUMNS = bidict(\n    {\n        \"id\": \"id\",\n        \"file_name\": \"Filename\",\n        \"tag\": \"Label\",\n        \"timestamp\": \"LabelTimeStamp\",\n        \"nstep\": \"Spec_NStep\",\n        \"nwin\": \"Spec_NWin\",\n        \"nfft\": \"nfft\",\n        \"hop_length\": \"hop_length\",\n        \"win_length\" : \"win_length\",\n        # \"x1\": \"Spec_x1\",\n        # \"y1\": \"Spec_y1\",\n        # \"x2\": \"Spec_x2\",\n        # \"y2\": \"Spec_y2\",\n        \"start\": \"LabelStartTime_Seconds\",\n        \"end\": \"LabelEndTime_Seconds\",\n        \"frequency_min\": \"MinimumFreq_Hz\",\n        \"frequency_max\": \"MaximumFreq_Hz\",\n        \"amplitude_max\": \"MaxAmp\",\n        \"amplitude_min\": \"MinAmp\",\n        \"amplitude_mean\": \"MeanAmp\",\n        \"amplitude_sd\": \"AmpSD\",\n        \"area_datapoints\": \"LabelArea_DataPoints\",\n        \"overlap\": \"overlap\",\n        \"background\": \"background\",\n        \"related\": \"Related\",\n        \"noise\": \"noise\",\n    }\n)\n\n\ndef check_tag_file(audio_file, folder):\n    file_name = create_tag_filename(audio_file, folder)\n    if file_name.exists():\n        return file_name\n    else:\n        file_name = create_tag_filename(audio_file, folder, to_append=TO_APPEND_OLD)\n        if file_name.exists():\n            return file_name\n    return \"\"\n\n\ndef create_tag_filename(file_name, folder, to_append=TO_APPEND, ext=\".csv\"):\n    file_name = Path(file_name)\n    if file_name.suffix not in AUDIO_EXTENSIONS:\n        raise RuntimeError(\"Unrecognized audio format\")\n    filename = folder / (file_name.stem + to_append + ext)\n\n    return filename\n\n\ndef save_csv(file, folder, tags, columns=COLUMNS, to_append=TO_APPEND):\n    filename = create_tag_filename(file, folder, to_append=to_append, ext=\".csv\")\n\n    if not os.path.exists(folder):\n        os.makedirs(folder)\n\n    with open(filename, \"w\", encoding=\"UTF8\") as f:\n        dest_columns = list(COLUMNS.keys())\n        writer = csv.DictWriter(f=f, fieldnames=dest_columns, dialect=\"excel\")\n        writer.writeheader()\n        for tag in tags:\n            lbl = {key: value for key, value in tag.items() if key in columns}\n            writer.writerow(lbl)\n\n\ndef load_csv(audio_file, folder, columns=COLUMNS):\n    res = []\n    file_name = check_tag_file(audio_file, folder)\n    if file_name:\n        with open(file_name, \"r\", encoding=\"UTF8\") as f:\n            reader = csv.DictReader(f, dialect=\"excel\")\n            for line in reader:\n                tmp = {}\n                for key, value in line.items():\n                    if key in columns.keys():\n                        tmp[key] = value\n                    elif key in columns.values():\n                        tmp[columns.inverse[key]] = value\n                res.append(tmp)\n\n    return res\n","sub_path":"AudioTagger/db_utils.py","file_name":"db_utils.py","file_ext":"py","file_size_in_byte":3514,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"394780291","text":"import requests\nimport datetime\nimport time\nimport os.path\nimport re\nimport six\n\n\nclass Client:\n    \"\"\"Client for RSpace API v1.\n    Most methods return a dictionary with fields described in the API documentation. The documentation can be found at\n    https://community.researchspace.com/public/apiDocs (or your own instance's /public/apiDocs).\n    For authentication, an API key must be provided. It can be found by logging in and navigating to 'My Profile' page.\n    \"\"\"\n    class ConnectionError(Exception):\n        pass\n\n    class AuthenticationError(Exception):\n        pass\n\n    class NoSuchLinkRel(Exception):\n        pass\n\n    class ApiError(Exception):\n        def __init__(self, error_message, response_status_code=None):\n            Exception.__init__(self, error_message)\n            self.response_status_code = response_status_code\n\n    API_VERSION = 'v1'\n\n    def __init__(self, rspace_url, api_key):\n        \"\"\"\n        Initializes RSpace client.\n        :param rspace_url: RSpace server URL (for example, https://community.researchspace.com)\n        :param api_key: RSpace API key of a user can be found on 'My Profile' page\n        \"\"\"\n        self.rspace_url = rspace_url\n        self.api_key = api_key\n\n    def _get_api_url(self):\n        \"\"\"\n        Returns an API server URL.\n        :return: string URL\n        \"\"\"\n        return '{}/api/{}'.format(self.rspace_url, self.API_VERSION)\n\n    def _get_headers(self, content_type='application/json'):\n        return {\n            'apiKey': self.api_key,\n            'Accept': content_type\n        }\n\n    @staticmethod\n    def _get_numeric_record_id(global_id):\n        \"\"\"\n        Gets numeric part of a global ID.\n        :param global_id: global ID (for example, SD123 or FM12)\n        :return: numeric record id\n        \"\"\"\n        if re.match(r'[a-zA-Z]{2}\\d+$', str(global_id)) is not None:\n            return int(global_id[2:])\n        elif re.match(r'\\d+$', str(global_id)) is not None:\n            return int(global_id)\n        else:\n            raise ValueError('{} is not a valid global ID'.format(global_id))\n\n    @staticmethod\n    def _get_formated_error_message(json_error):\n        return 'error message: {}, errors: {}'.format(json_error.get('message', ''),\n                                                      ', '.join(json_error.get('errors', [])) or 'no error list')\n\n    @staticmethod\n    def _responseContainsJson(response):\n        return 'Content-Type' in response.headers and 'application/json' in response.headers['Content-Type']\n\n    @staticmethod\n    def _handle_response(response):\n        # Check whether response includes UNAUTHORIZED response code\n        # print(\"status: {}, header: {}\".format(response.headers, response.status_code))\n        if response.status_code == 401:\n            raise Client.AuthenticationError(response.json()['message'])\n\n        try:\n            response.raise_for_status()\n\n            if Client._responseContainsJson(response):\n                return response.json()\n            else:\n                return response.text\n        except:\n            if 'application/json' in response.headers['Content-Type']:\n                error = 'Error code: {}, {}'.format(response.status_code,\n                                                    Client._get_formated_error_message(response.json()))\n            else:\n                error = 'Error code: {}, error message: {}'.format(response.status_code, response.text)\n            raise Client.ApiError(error, response_status_code=response.status_code)\n\n    def doDelete(self, path, resource_id):\n        \"\"\"\n        Performs a delete operation for a given resource\n        \"\"\"\n        numeric_id = self._get_numeric_record_id(resource_id)\n        return self.retrieve_api_results(self._get_api_url() + '/{}/{}'.format(path,numeric_id),\n                                          content_type=None, request_type='DELETE')\n        \n    \n    def retrieve_api_results(self, url, params=None, content_type='application/json', request_type='GET'):\n        \"\"\"\n        Makes the requested API call and returns either an exception or a parsed JSON response as a dictionary.\n        Authentication header is automatically added. In most cases, a specialised method can be used instead.\n        :param url: URL to retrieve\n        :param request_type: 'GET', 'POST', 'PUT', 'DELETE'\n        :param params: arguments to be added to the API request\n        :param content_type: content type\n        :return: parsed JSON response as a dictionary\n        \"\"\"\n        headers = self._get_headers(content_type)\n        try:\n            if request_type == 'GET':\n                response = requests.get(url, params=params, headers=headers)\n            elif request_type == 'PUT' or request_type == 'POST' or  request_type == 'DELETE':\n                response = requests.request(request_type, url, json=params, headers=headers)\n            else:\n                raise ValueError('Expected GET / PUT / POST / DELETE request type, received {} instead'.format(request_type))\n\n            return self._handle_response(response)\n        except requests.exceptions.ConnectionError as e:\n            raise Client.ConnectionError(e)\n\n    @staticmethod\n    def _get_links(response):\n        \"\"\"\n        Finds links part of the response. Most responses contain links section with URLs that might be useful to query\n        for further information.\n        :param response: response from the API server\n        :return: links section of the response\n        \"\"\"\n        try:\n            return response['_links']\n        except KeyError:\n            raise Client.NoSuchLinkRel('There are no links!')\n\n    def get_link_contents(self, response, link_rel):\n        \"\"\"\n        Finds a link with rel attribute equal to link_rel and retrieves its contents.\n        :param response: response from the API server\n        :param link_rel: rel attribute value to look for\n        :return: parsed response from the found URL\n        \"\"\"\n        return self.retrieve_api_results(self.get_link(response, link_rel))\n\n    def get_link(self, response, link_rel):\n        \"\"\"\n        Finds a link with rel attribute equal to link_rel.\n        :param response: response from the API server.\n        :param link_rel: rel attribute value to look for\n        :return: string URL\n        \"\"\"\n        for link in self._get_links(response):\n            if link['rel'] == link_rel:\n                return link['link']\n        raise Client.NoSuchLinkRel('Requested link rel \"{}\", available rel(s): {}'.format(\n            link_rel, (', '.join(x['rel'] for x in self._get_links(response)))))\n\n    def download_link_to_file(self, url, filename):\n        \"\"\"\n        Downloads a file from the API server.\n        :param url: URL of the file to be downloaded\n        :param filename: file path to save the file to\n        \"\"\"\n        headers = {\n            'apiKey': self.api_key,\n            'Accept': 'application/octet-stream'\n        }\n        with open(filename, 'wb') as fd:\n            for chunk in requests.get(url, headers=headers).iter_content(chunk_size=128):\n                fd.write(chunk)\n\n    def link_exists(self, response, link_rel):\n        \"\"\"\n        Checks whether there is a link with rel attribute equal to link_rel in the links section of the response.\n        :param response: response from the API server\n        :param link_rel: rel attribute value to look for\n        :return: True, if the link exists\n        \"\"\"\n        return link_rel in [x['rel'] for x in self._get_links(response)]\n\n    # Documents methods\n    def get_documents(self, query=None, order_by='lastModified desc', page_number=0, page_size=20):\n        \"\"\"\n        The Documents endpoint returns a paginated list of summary information about Documents in the RSpace workspace.\n        These can be individual documents or notebook entries. More information on\n        https://community.researchspace.com/public/apiDocs (or your own instance's /public/apiDocs).\n        :param (optional) query: Global search for a term, works identically to the simple \"All' search in RSpace\n        Workspace.\n        :param order_by: Sort order for documents.\n        :param page_number: For paginated results, this is the number of the page requested, 0 based.\n        :param page_size: The maximum number of items to retrieve.\n        :return: parsed response as a dictionary\n        \"\"\"\n        params = {\n            'orderBy': order_by,\n            'pageSize': page_size,\n            'pageNumber': page_number\n        }\n        if query is not None:\n            params['query'] = query\n\n        return self.retrieve_api_results(self._get_api_url() + '/documents', params)\n\n    def get_documents_advanced_query(self, advanced_query, order_by='lastModified desc', page_number=0, page_size=20):\n        \"\"\"\n        The Documents endpoint returns a paginated list of summary information about Documents in the RSpace workspace.\n        These can be individual documents or notebook entries. More information on\n        https://community.researchspace.com/public/apiDocs (or your own instance's /public/apiDocs).\n        :param advanced_query: JSON representation of a search query. This can be built using AdvancedQueryBuilder.\n        :param order_by: Sort order for documents.\n        :param page_number: For paginated results, this is the number of the page requested, 0 based.\n        :param page_size: The maximum number of items to retrieve.\n        :return: parsed response as a dictionary\n        \"\"\"\n        params = {\n            'advancedQuery': advanced_query,\n            'orderBy': order_by,\n            'pageSize': page_size,\n            'pageNumber': page_number\n        }\n        return self.retrieve_api_results(self._get_api_url() + '/documents', params)\n\n    def get_document(self, doc_id):\n        \"\"\"\n        Gets information about a document. More information on\n        https://community.researchspace.com/public/apiDocs (or your own instance's /public/apiDocs).\n        :param doc_id: numeric document ID or global ID\n        :return: a dictionary that includes: document metadata, field content, metadata about media items belonging to\n        this document, links to download the content of media files\n        \"\"\"\n        numeric_doc_id = self._get_numeric_record_id(doc_id)\n        return self.retrieve_api_results(self._get_api_url() + '/documents/{}'.format(numeric_doc_id))\n    \n    def delete_document(self, doc_id):\n        \"\"\"\n         Marks document as deleted.\n         :param doc_id: numeric document ID or global ID\n        \"\"\"\n        return self.doDelete( \"documents\", doc_id)\n        \n    def get_document_csv(self, doc_id):\n        \"\"\"\n        Gets information about a document. More information on\n        https://community.researchspace.com/public/apiDocs (or your own instance's /public/apiDocs).\n        :param doc_id: numeric document ID or global ID\n        :return: CSV that includes: document metadata, field content, metadata about media items belonging to\n        this document, links to download the content of media files\n        \"\"\"\n        numeric_doc_id = self._get_numeric_record_id(doc_id)\n        return self.retrieve_api_results(self._get_api_url() + '/documents/{}'.format(numeric_doc_id),\n                                         content_type='text/csv')\n\n\n    def create_document(self, name=None, parent_folder_id=None, tags=None, form_id=None, fields=None):\n\n        \"\"\"\n        Creates a new document in user's Api Inbox folder. More information on\n        https://community.researchspace.com/public/apiDocs (or your own instance's /public/apiDocs).\n        :param name: name of the document (can be omitted)\n        :param tags: list of tags (['tag1', 'tag2']) or comma separated string of tags ('tag1,tag2'); optional\n        :param form_id: numeric document ID or global ID' optional; defaults to BasicDocument\n        :param parent_folder_id: ID of workspace folder or subfolder; optional; defaults to ApiInbox folder\n        :param fields: list of fields (dictionaries of (optionally) ids and contents). For example,\n        [{'content': 'some example text'}] or [{'id': 123, 'content': 'some example text'}].\n        :return: parsed response as a dictionary\n        \"\"\"\n        data = {}\n\n        if name is not None:\n            data['name'] = name\n\n        if parent_folder_id is not None:\n            numeric_folder_id = self._get_numeric_record_id(parent_folder_id)\n            data['parentFolderId'] = numeric_folder_id\n\n        if tags is not None:\n            if isinstance(tags, list):\n                tags = ','.join(tags)\n            data['tags'] = tags\n\n        if form_id is not None:\n            numeric_form_id = self._get_numeric_record_id(form_id)\n            data['form'] = {\"id\": int(numeric_form_id)}\n\n        if fields is not None and len(fields) > 0:\n            data['fields'] = fields\n\n        return self.retrieve_api_results(self._get_api_url() + '/documents', request_type='POST', params=data)\n\n    def update_document(self, document_id, name=None, tags=None, form_id=None, fields=None):\n        \"\"\"\n        Updates a document with a given document id. More information on\n        https://community.researchspace.com/public/apiDocs (or your own instance's /public/apiDocs).\n        :param document_id: numeric document ID or global ID\n        :param name: name of the document (can be omitted)\n        :param tags: list of tags (['tag1', 'tag2']) or comma separated string of tags ('tag1,tag2') (can be omitted)\n        :param form_id: numeric document ID or global ID (should be left None or otherwise should match the form id\n        of the document)\n        :param fields: list of fields (dictionaries of (optionally) ids and contents). For example,\n        [{'content': 'some example text'}] or [{'id': 123, 'content': 'some example text'}]. (can be omitted)\n        :return:\n        \"\"\"\n        data = {}\n\n        if name is not None:\n            data['name'] = name\n\n        if tags is not None:\n            if isinstance(tags, list):\n                tags = ','.join(tags)\n            data['tags'] = tags\n\n        if form_id is not None:\n            numeric_form_id = self._get_numeric_record_id(form_id)\n            data['form'] = {\"id\": int(numeric_form_id)}\n\n        if fields is not None and len(fields) > 0:\n            data['fields'] = fields\n\n        numeric_doc_id = self._get_numeric_record_id(document_id)\n        return self.retrieve_api_results(self._get_api_url() + '/documents/{}'.format(numeric_doc_id),\n                                         request_type='PUT', params=data)\n\n    # Sharing methods\n    def shareDocuments(self, itemsToShare, groupId, sharedFolderId=None, permission=\"READ\"):\n        \"\"\"\n        Shares 1 or more notebooks or documents with 1 group. You can optionally\n         specify the id of a folder to share into. If not set will share into the \n          top level of the group shared folder.\n         with read permission into\n        :param itemsToShare: A list of document/notebook IDs to share\n        :param groupId: The ID of a group to share with\n        :param sharedFolderId: The ID of a subfolder of the group's shared folder.\n        :param permission: The permission to use, default is \"READ\", or \"EDIT\"\n        \n        \"\"\"\n        if len(itemsToShare) == 0:\n            raise ValueError(\"Must be at least 1 item to share\")\n        \n        sharePost = dict()\n        sharePost['itemsToShare']=itemsToShare\n        groupShare= {\"id\":groupId, \"permission\":permission}\n        if(sharedFolderId is not None):\n            groupShare['sharedFolderId']=sharedFolderId\n        sharePost[\"groups\"]=[groupShare]\n        return self.retrieve_api_results(self._get_api_url() \n                                         + '/share', request_type='POST', params=sharePost)\n    def unshareItem(self, sharingId):\n        return self.doDelete(\"share\", sharingId)\n    \n    def get_shared_items(self,query=None, order_by='name asc', page_number=0, page_size=20):\n        \"\"\"\n         Paginated listing of shared items; default ordering is by document/notebook name.\n        :param page_number: For paginated results, this is the number of the page requested, 0 based.\n        :param page_size: The maximum number of items to retrieve.\n        :param order_by: Sort order for sharedItems - either 'name' or 'sharee' - the name of user\n         or group item is shared with.\n         \n        \"\"\"\n        params = {\n            'orderBy': order_by,\n            'pageSize': page_size,\n            'pageNumber': page_number\n        }\n        if query is not None:\n            params['query'] = query\n        return self.retrieve_api_results(self._get_api_url() + '/share', params)\n    \n    # File methods\n\n    def get_files(self, page_number=0, page_size=20, order_by=\"lastModified desc\", media_type=\"image\"):\n        \"\"\"\n        Lists media items - i.e. content shown in the Gallery in RSpace web application. Note that this does not include\n        files linked from external file systems or 3rd party providers. More information on\n        https://community.researchspace.com/public/apiDocs (or your own instance's /public/apiDocs).\n        :param page_number: For paginated results, this is the number of the page requested, 0 based.\n        :param page_size: The maximum number of items to retrieve.\n        :param order_by: Sort order for documents.\n        :param media_type: can be 'image', 'av' (audio or video), 'document' (any other file)\n        :return: parsed response as a dictionary\n        \"\"\"\n        params = {\n            'pageNumber': page_number,\n            'pageSize': page_size,\n            'orderBy': order_by,\n            'mediaType': media_type\n        }\n        return self.retrieve_api_results(self._get_api_url() + '/files', params)\n\n    def get_file_info(self, file_id):\n        \"\"\"\n        Gets metadata of a single file by its id. More information on\n        https://community.researchspace.com/public/apiDocs (or your own instance's /public/apiDocs).\n        :param file_id: numeric document ID or global ID\n        :return: parsed response as a dictionary\n        \"\"\"\n        numeric_file_id = self._get_numeric_record_id(file_id)\n        return self.retrieve_api_results(self._get_api_url() + '/files/{}'.format(numeric_file_id))\n\n    def download_file(self, file_id, filename):\n        \"\"\"\n        Downloads file contents. More information on\n        https://community.researchspace.com/public/apiDocs (or your own instance's /public/apiDocs).\n        :param file_id: numeric document ID or global ID\n        :param filename: file path to save the file to\n        \"\"\"\n        numeric_file_id = self._get_numeric_record_id(file_id)\n        return self.download_link_to_file(self._get_api_url() + '/files/{}/file'.format(numeric_file_id), filename)\n\n    def upload_file(self, file, folder_id=None, caption=None):\n        \"\"\"\n        Upload a file to the gallery. More information on\n        https://community.researchspace.com/public/apiDocs (or your own instance's /public/apiDocs).\n        :param file: open file object\n        :param folder_id: folder id of the destination folder\n        :param caption: optional caption\n        :return: parsed response as a dictionary\n        \"\"\"\n        data = {}\n\n        if folder_id is not None:\n            numeric_folder_id = self._get_numeric_record_id(folder_id)\n            data['folderId'] = numeric_folder_id\n\n        if caption is not None:\n            data['caption'] = caption\n\n        response = requests.post(self._get_api_url() + '/files', files={\"file\": file}, data=data,\n                                 headers=self._get_headers())\n        return self._handle_response(response)\n    \n    def update_file(self, file, fileId):\n        \"\"\"\n        Upload a file to the gallery. More information on\n        https://community.researchspace.com/public/apiDocs (or your own instance's /public/apiDocs).\n        :param file: open file object\n        :param fileId: Id of the file to replace\n        :return: updated File response as a dictionary\n        \"\"\"\n        response = requests.post(self._get_api_url() + '/files/{}/file'.format(fileId), files={\"file\": file}, \n                                 headers=self._get_headers())\n        return self._handle_response(response)\n\n    # Activity methods\n    def get_activity(self, page_number=0, page_size=100, order_by=None, date_from=None, date_to=None, actions=None,\n                     domains=None, global_id=None, users=None):\n        \"\"\"\n        Returns all activity for a particular document. More information on\n        https://community.researchspace.com/public/apiDocs (or your own instance's /public/apiDocs).\n        :param page_number: for paginated results, this is the number of the page requested, 0 based.\n        :param page_size: the maximum number of items to retrieve.\n        :param order_by: sort order for activities.\n        :param date_from: yyyy-mm-dd string or datetime.date object. The earliest date to retrieve activity from.\n        :param date_to: yyyy-mm-dd string or datetime.date object. The latest date to retrieve activity from.\n        :param actions: a comma separated string or list of strings. Actions to restrict the query.\n        :param domains: a comma separated string or list of strings. Domains to restrict the query.\n        :param global_id: the global ID of a resource, e.g. SD12345\n        :param users: a comma separated string or list of strings. Users to restrict the query.\n        :return:\n        \"\"\"\n        params = {\n            'pageNumber': page_number,\n            'pageSize': page_size\n        }\n\n        if order_by is not None:\n            params['orderBy'] = order_by\n\n        if date_from is not None:\n            if isinstance(date_from, datetime.date):\n                params['dateFrom'] = date_from.isoformat()\n            elif isinstance(date_from, six.string_types):\n                params['dateFrom'] = date_from\n            else:\n                raise TypeError('Unexpected date_from type {}'.format(type(date_from)))\n\n        if date_to is not None:\n            if isinstance(date_to, datetime.date):\n                params['dateTo'] = date_to.isoformat()\n            elif isinstance(date_to, six.string_types):\n                params['dateTo'] = date_to\n            else:\n                raise TypeError('Unexpected date_from type {}'.format(type(date_to)))\n\n        if actions is not None:\n            if isinstance(actions, list):\n                params['actions'] = ','.join(actions)\n            elif isinstance(actions, six.string_types):\n                params['actions'] = actions\n            else:\n                raise TypeError('Unexpected actions type {}'.format(type(actions)))\n\n        if domains is not None:\n            if isinstance(domains, list):\n                params['domains'] = ','.join(domains)\n            elif isinstance(domains, six.string_types):\n                params['domains'] = domains\n            else:\n                raise TypeError('Unexpected domains type {}'.format(type(domains)))\n\n        if global_id is not None:\n            params['oid'] = str(global_id)\n\n        if users is not None:\n            if isinstance(users, list):\n                params['users'] = ','.join(users)\n            elif isinstance(users, six.string_types):\n                params['users'] = users\n            else:\n                raise TypeError('Unexpected users type {}'.format(type(users)))\n\n        return self.retrieve_api_results(self._get_api_url() + '/activity', params=params)\n\n    # Export\n    def start_export(self, export_format, scope, uid=None):\n        \"\"\"\n        Starts an asynchronous export of user's or group's records. Currently export of selections of documents is\n        unsupported.\n        :param format: 'xml' or 'html'\n        :param scope: 'user' or 'group'\n        :param id: id of a user or a group depending on the scope (current user or group will be used if not provided)\n        :return: job id\n        \"\"\"\n        if export_format != 'xml' and export_format != 'html':\n            raise ValueError('format must be either \"xml\" or \"html\", got \"{}\" instead'.format(export_format))\n\n        if scope != 'user' and scope != 'group':\n            raise ValueError('scope must be either \"user\" or \"group\", got \"{}\" instead'.format(scope))\n\n        if id is not None:\n            request_url = self._get_api_url() + '/export/{}/{}/{}'.format(export_format, scope, uid)\n        else:\n            request_url = self._get_api_url() + '/export/{}/{}'.format(export_format, scope)\n\n        return self.retrieve_api_results(request_url, request_type='POST')\n\n    def download_export(self, export_format, scope, file_path, uid=None, wait_between_requests=30):\n        \"\"\"\n        Exports user's or group's records and downloads the exported archive to a specified location.\n        :param format: 'xml' or 'html'\n        :param scope: 'user' or 'group'\n        :param file_path: can be either a directory or a new file in an existing directory\n        :param id: id of a user or a group depending on the scope (current user or group will be used if not provided)\n        :param wait_between_requests: seconds to wait between job status requests (30 seconds default)\n        :return: file path to the downloaded export archive\n        \"\"\"\n        job_id = self.start_export(format=export_format, scope=scope, uid=uid)['id']\n\n        while True:\n            status_response = self.get_job_status(job_id)\n\n            if status_response['status'] == 'COMPLETED':\n                download_url = self.get_link(status_response, 'enclosure')\n\n                if os.path.isdir(file_path):\n                    file_path = os.path.join(file_path, download_url.split('/')[-1])\n                self.download_link_to_file(download_url, file_path)\n\n                return file_path\n            elif status_response['status'] == 'FAILED':\n                raise Client.ApiError('Export job failed: ' +\n                                      self._get_formated_error_message(status_response['result']))\n            elif status_response['status'] == 'ABANDONED':\n                raise Client.ApiError('Export job was abandoned: ' +\n                                      self._get_formated_error_message(status_response['result']))\n            elif status_response['status'] == 'RUNNING' or status_response['status'] == 'STARTING' or \\\n                    status_response['status'] == 'STARTED':\n                time.sleep(wait_between_requests)\n                continue\n            else:\n                raise Client.ApiError('Unknown job status: ' + status_response['status'])\n\n    def get_job_status(self, job_id):\n        \"\"\"\n        Return a job status.\n        :param job_id: job id\n        :return: parsed response as a dictionary (most important field is 'status' which is supposed to one of:\n        'STARTED', 'STARTING', 'RUNNING', 'COMPLETED', 'FAILED', 'ABANDONED')\n        \"\"\"\n        return self.retrieve_api_results(self._get_api_url() + '/jobs/{}'.format(job_id))\n\n    # Form related methods\n    def get_forms(self, query=None, order_by='lastModified desc', page_number=0, page_size=20):\n        \"\"\"\n        Provides a paginated list of Forms. You can use this endpoint to retrieve the IDs of the forms from which you\n        want to create documents. More information on\n        https://community.researchspace.com/public/apiDocs (or your own instance's /public/apiDocs).\n        :param (optional) query: Whole or part of a Form's name or tag.\n        :param order_by: Sort order for Forms.\n        :param page_number: For paginated results, this is the number of the page requested, 0 based.\n        :param page_size: The maximum number of items to retrieve.\n        :return: parsed response as a dictionary\n        \"\"\"\n        params = {\n            'orderBy': order_by,\n            'pageSize': page_size,\n            'pageNumber': page_number\n        }\n        if query is not None:\n            params['query'] = query\n\n        return self.retrieve_api_results(self._get_api_url() + '/forms', params)\n\n    def create_form(self, name, tags=None, fields=None):\n        \"\"\"\n        Create a new Form, supplying the field definitions. More information on\n        https://community.researchspace.com/public/apiDocs (or your own instance's /public/apiDocs).\n        :param name: name of the form\n        :param tags: list of tags (['tag1', 'tag2']) or comma separated string of tags ('tag1,tag2') (optional)\n        :param fields: list of fields (dictionaries of 'name', 'type' and optionally other parameters). Currently,\n        supported types of Form fields are: 'String', 'Text', 'Number', 'Radio', 'Date'. More information can be found\n        on /public/apiDocs.\n        :return: parsed response as a dictionary\n        \"\"\"\n        data = {}\n\n        if name is None or len(name) == 0:\n            raise ValueError('Name is a required argument')\n        data['name'] = name\n\n        if tags is not None:\n            if isinstance(tags, list):\n                tags = ','.join(tags)\n            data['tags'] = tags\n\n        if fields is not None and len(fields) > 0:\n            data['fields'] = fields\n        else:\n            raise ValueError('There has to be at least one field')\n\n        return self.retrieve_api_results(self._get_api_url() + '/forms', request_type='POST', params=data)\n\n    def get_form(self, form_id):\n        \"\"\"\n        Gets information about a Form. More information on\n        https://community.researchspace.com/public/apiDocs (or your own instance's /public/apiDocs).\n        :param form_id: numeric form ID or global ID\n        :return: a dictionary that includes: form metadata, fields\n        \"\"\"\n        numeric_doc_id = self._get_numeric_record_id(form_id)\n        return self.retrieve_api_results(self._get_api_url() + '/forms/{}'.format(numeric_doc_id))\n\n    def delete_form(self, form_id):\n        \"\"\"\n         Deletes form by its ID, if it is in 'NEW' state or has not been deleted.\n         :param form_id: numeric Form ID or global ID\n        \"\"\"\n        return self.doDelete( \"forms\", form_id)\n    \n    def publish_form(self, form_id):\n        \"\"\"\n        A newly created form is not available to create documents from until it has been published. More information on\n        https://community.researchspace.com/public/apiDocs (or your own instance's /public/apiDocs).\n        :param form_id: numeric form ID or global ID\n        :return: a dictionary that includes: form metadata, fields\n        \"\"\"\n        numeric_doc_id = self._get_numeric_record_id(form_id)\n        return self.retrieve_api_results(self._get_api_url() + '/forms/{}/publish'.format(numeric_doc_id),\n                                         request_type='PUT')\n\n    def unpublish_form(self, form_id):\n        \"\"\"\n        Unpublishing a form hides it from being available to create documents. More information on\n        https://community.researchspace.com/public/apiDocs (or your own instance's /public/apiDocs).\n        :param form_id: numeric form ID or global ID\n        :return: a dictionary that includes: form metadata, fields\n        \"\"\"\n        numeric_doc_id = self._get_numeric_record_id(form_id)\n        return self.retrieve_api_results(self._get_api_url() + '/forms/{}/unpublish'.format(numeric_doc_id),\n                                         request_type='PUT')\n\n    def share_form(self, form_id):\n        \"\"\"\n        Shares this form with your groups. More information on\n        https://community.researchspace.com/public/apiDocs (or your own instance's /public/apiDocs).\n        :param form_id: numeric form ID or global ID\n        :return: a dictionary that includes: form metadata, fields\n        \"\"\"\n        numeric_doc_id = self._get_numeric_record_id(form_id)\n        return self.retrieve_api_results(self._get_api_url() + '/forms/{}/share'.format(numeric_doc_id),\n                                         request_type='PUT')\n\n    def unshare_form(self, form_id):\n        \"\"\"\n        Unshares this form with your groups. Only the owner of the Form (its creator) will be able to read or modify\n        this Form after this action is performed. More information on\n        https://community.researchspace.com/public/apiDocs (or your own instance's /public/apiDocs).\n        :param form_id: numeric form ID or global ID\n        :return: a dictionary that includes: form metadata, fields\n        \"\"\"\n        numeric_doc_id = self._get_numeric_record_id(form_id)\n        return self.retrieve_api_results(self._get_api_url() + '/forms/{}/unshare'.format(numeric_doc_id),\n                                         request_type='PUT')\n\n    # Folder / notebook methods\n\n    def create_folder(self, name, parent_folder_id=None, notebook=False):\n        \"\"\"\n        Creates containers to hold RSpace documents and notebook entries. You can create folders in your Workspace and\n        Gallery folders, and notebooks in your Workspace. More information on\n        https://community.researchspace.com/public/apiDocs (or your own instance's /public/apiDocs).\n        :param name: name of the folder or notebook\n        :param parent_folder_id: numeric form ID or global ID\n        :param notebook: True to create a notebook, False to create a folder\n        :return: metadata about the created notebook or folder\n        \"\"\"\n        data = {\n            'notebook': notebook\n        }\n\n        if name is None or len(name) == 0:\n            raise ValueError('Name is a required argument')\n        data['name'] = name\n\n        if parent_folder_id is not None:\n            numeric_folder_id = self._get_numeric_record_id(parent_folder_id)\n            data['parentFolderId'] = numeric_folder_id\n\n        return self.retrieve_api_results(self._get_api_url() + '/folders', request_type='POST', params=data)\n    \n    def delete_folder(self, folder_id):\n        \"\"\"\n         Deletes a folder or notebook by its ID.\n         :param form_id: numeric Folder/Notebook ID or global ID\n        \"\"\"\n        return self.doDelete( \"folders\", folder_id)\n    \n    def get_folder(self, folder_id):\n        \"\"\"\n        Getter for a Folder or notebook that you are authorised to view.\n        :param folder_id: numeric folder ID or global ID\n        :return: metadata about the folder or notebook\n        \"\"\"\n        numeric_folder_id = self._get_numeric_record_id(folder_id)\n        return self.retrieve_api_results(self._get_api_url() + '/folders/{}'.format(numeric_folder_id))\n\n    def list_folder_tree (self, folder_id=None, typesToInclude=[]):\n        \"\"\"\n         Lists contents of a folder by its ID.\n         :param folder_id. Optional folderId. If none, will return listing of Home Folder\n         :param typesToInclude: An optional list of any of 'folder', 'notebook' or 'document'. Results\n          will be restricted to these types\n         :return a paginated folder listing\n        \"\"\"\n        url = \"\"\n        if folder_id is not None:\n            url = self._get_api_url() + '/folders/tree/{}'.format(folder_id)\n        else:\n            url = self._get_api_url() + '/folders/tree'\n        params = {}\n        if len(typesToInclude) > 0:\n            if 'document' not in typesToInclude and 'notebook' not in typesToInclude and 'folder' not in typesToInclude:\n                raise ValueError('typesToInclude must be contain \"document\", \"notebook\" and/or \"folder\"')\n            params['typesToInclude'] =\",\".join(typesToInclude)\n        return self.retrieve_api_results(url, params)\n        \n                 \n    # Groups methods\n    def get_groups(self):\n        \"\"\" \n        Gets a list of groups that you belong to. May be empty if you are not\n        in any groups.\n        \"\"\"\n        return self.retrieve_api_results(self._get_api_url()+'/groups')\n    \n    # Import methods\n    def import_word(self, file, folder_id=None ,image_folder_id=None):\n        \"\"\"\n         Imports a Word file into RSpace and creates an RSpace document from it.\n         :param file: The Word file to import\n         :param folder_id: Optionally, the ID of a folder in which to create the\n          new document\n         :param folder_id: Optionally, the ID of a folder in the image gallery\n          into which images extracted from Word documents will be placed. By default, these\n           will be placed in the top-level of the Gallery. \n        \"\"\"\n        data = {}\n        if folder_id is not None:\n            numeric_folder_id = self._get_numeric_record_id(folder_id)\n            data['folderId'] = numeric_folder_id\n        if image_folder_id is not None:\n            numeric_imagefolder_id = self._get_numeric_record_id(image_folder_id)\n            data['imageFolderId'] = numeric_imagefolder_id\n            \n\n        response = requests.post(self._get_api_url() + '/import/word', files={\"file\": file}, data=data,\n                                 headers=self._get_headers())\n        return self._handle_response(response)\n    \n    # Miscellaneous methods\n    def get_status(self):\n        \"\"\"\n        Simple API call to check that API service is available. Throws an AuthenticationError if authentication fails.\n        More information on https://community.researchspace.com/public/apiDocs (or your own instance's /public/apiDocs).\n        :return: parsed response as a dictionary (most important field is 'message' which is supposed to be 'OK')\n        \"\"\"\n        return self.retrieve_api_results(self._get_api_url() + '/status')\n","sub_path":"rspace_client/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":37428,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"611360790","text":"import re\nfrom html import unescape\n\nwith open('09_dp.html') as f:\n    html = f.read()\n\nfor partial_html in re.findall(r'<td class=\"left\"><a.*?</td>', html, re.DOTALL):\n    url = re.search(r'<a href=\"(.*?)\">', partial_html).group(1)\n    url = 'http://www.hanbit.co.kr' + url\n    title = re.sub(r'<.*?>', '', partial_html)\n    title = unescape(title)\n    print('url:', url)\n    print('title:', title)\n    print('---')\n","sub_path":"002/11_scrape_re.py","file_name":"11_scrape_re.py","file_ext":"py","file_size_in_byte":417,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"308496557","text":"from django.conf.urls import url, include\nfrom django.views.generic import TemplateView\nfrom django.views.generic import RedirectView\nfrom . import views\nfrom django.contrib import admin\nfrom django.contrib.auth import views as auth_views\n\n\nurlpatterns = [\n    # General Functions\n    url(r'^login/$', auth_views.login, name='login'),\n    url(r'^logout/$', auth_views.logout,\n        {'next_page': 'login'}, name='logout'),\n    url(r'session_security/', include('session_security.urls')),\n    url(r'^$', views.buttons, name=\"buttons\"),\n    url(r'^clear_results/$', views.clear_results, name=\"clear_results\"),\n    url(r'^opt_out/$', views.opt_out, name=\"opt_out\"),\n    url(r'^opt_out/thanks/$',\n        TemplateView.as_view(template_name='functions/thanks.html'), name=\"thanks\"),\n\n    # General API Views\n    url(r'^Get_UCL/$', views.Get_UCL, name=\"Get_UCL\"),\n    url(r'^CIC_Sev_1_CSG_Internal_Call/$',\n        views.CIC_Sev_1_CSG_Internal_Call, name=\"CIC_Sev_1_CSG_Internal_Call\"),\n    url(r'^DRILL_CIC_CSG_Bridge_APAC/$', views.DRILL_CIC_CSG_Bridge_APAC,\n        name=\"DRILL_CIC_CSG_Bridge_APAC\"),\n\n\n    # APAC API Views\n    url(r'^T20_APAC/$', views.T20_APAC, name=\"T20_APAC\"),\n    url(r'^CIC_CSG_Bridge_APAC/$', views.CIC_CSG_Bridge_APAC,\n        name=\"CIC_CSG_Bridge_APAC\"),\n\n    # EMEA API Views\n    url(r'^T20_EMEA/$', views.T20_EMEA, name=\"T20_EMEA\"),\n    url(r'^CIC_CSG_Bridge_EMEA/$', views.CIC_CSG_Bridge_EMEA,\n        name=\"CIC_CSG_Bridge_EMEA\"),\n\n    # AMER API Views\n    url(r'^T20_AMER/$', views.T20_AMER, name=\"T20_AMER\"),\n    url(r'^CIC_CSG_Bridge_AMER/$', views.CIC_CSG_Bridge_AMER,\n        name=\"CIC_CSG_Bridge_AMER\"),\n\n\n]\n","sub_path":"API_calls/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1641,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"644576927","text":"import os\nimport subprocess\nfrom glob import glob\nfrom io import StringIO\nfrom typing import Iterable, Union\n\nimport pandas as pd\n\nfrom .errors import JanitorError\nfrom .utils import deprecated_alias, check\n\n\n@deprecated_alias(seperate_df=\"separate_df\", filespath=\"files_path\")\ndef read_csvs(\n    files_path: Union[str, Iterable[str]], separate_df: bool = False, **kwargs\n) -> Union[pd.DataFrame, dict]:\n    \"\"\"\n    Read multiple CSV files and return a dictionary of DataFrames, or\n    one concatenated DataFrame.\n\n    :param files_path: The filepath pattern matching the CSV files.\n        Accepts regular expressions, with or without `.csv` extension.\n        Also accepts iterable of file paths.\n    :param separate_df: If `False` (default), returns a single Dataframe\n        with the concatenation of the csv files.\n        If `True`, returns a dictionary of separate DataFrames\n        for each CSV file.\n    :param kwargs: Keyword arguments to pass into the\n        original pandas `read_csv`.\n    :returns: DataFrame of concatenated DataFrames or dictionary of DataFrames.\n    :raises JanitorError: if `None` provided for `files_path`.\n    :raises JanitorError: if length of `files_path` is `0`.\n    :raises ValueError: if no CSV files exist in `files_path`.\n    :raises ValueError: if columns in input CSV files do not match.\n    \"\"\"\n    # Sanitize input\n    if files_path is None:\n        raise JanitorError(\"`None` provided for `files_path`\")\n    if len(files_path) == 0:\n        raise JanitorError(\"0 length `files_path` provided\")\n\n    # Read the csv files\n    # String to file/folder or file pattern provided\n    if isinstance(files_path, str):\n        dfs_dict = {\n            os.path.basename(f): pd.read_csv(f, **kwargs)\n            for f in glob(files_path)\n        }\n    # Iterable of file paths provided\n    else:\n        dfs_dict = {\n            os.path.basename(f): pd.read_csv(f, **kwargs) for f in files_path\n        }\n    # Check if dataframes have been read\n    if len(dfs_dict) == 0:\n        raise ValueError(\"No CSV files to read with the given `files_path`\")\n    # Concatenate the dataframes if requested (default)\n    col_names = list(dfs_dict.values())[0].columns  # noqa: PD011\n    if not separate_df:\n        # If columns do not match raise an error\n        for df in dfs_dict.values():  # noqa: PD011\n            if not all(df.columns == col_names):\n                raise ValueError(\n                    \"Columns in input CSV files do not match.\"\n                    \"Files cannot be concatenated\"\n                )\n        return pd.concat(\n            list(dfs_dict.values()),\n            ignore_index=True,\n            sort=False,  # noqa: PD011\n        )\n    else:\n        return dfs_dict\n\n\ndef read_commandline(cmd: str, **kwargs) -> pd.DataFrame:\n    \"\"\"\n    Read a CSV file based on a command-line command.\n\n    For example, you may wish to run the following command on `sep-quarter.csv`\n    before reading it into a pandas DataFrame:\n\n    ```bash\n    cat sep-quarter.csv | grep .SEA1AA\n    ```\n\n    In this case, you can use the following Python code to load the dataframe:\n\n    ```python\n    import janitor as jn\n    df = jn.read_commandline(\"cat data.csv | grep .SEA1AA\")\n\n    This function assumes that your command line command will return\n    an output that is parsable using pandas.read_csv and StringIO.\n    We default to using pd.read_csv underneath the hood.\n    Keyword arguments are passed through to read_csv.\n    ```\n\n    :param cmd: Shell command to preprocess a file on disk.\n    :param kwargs: Keyword arguments that are passed through to pd.read_csv().\n    :raises JanitorError: If commandline command is malformed or invalid.\n    :returns: A pandas DataFrame parsed from the stdout of the underlying\n        shell.\n    \"\"\"\n\n    check(\"cmd\", cmd, [str])\n    outcome = subprocess.run(cmd, shell=True, capture_output=True, text=True)\n    if outcome.returncode != 0:\n        raise JanitorError(outcome.stderr)\n    else:\n        outcome = outcome.stdout\n    return pd.read_csv(StringIO(outcome), **kwargs)\n","sub_path":"janitor/io.py","file_name":"io.py","file_ext":"py","file_size_in_byte":4061,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"46766046","text":"import numpy as np\n\n\n\ndef coordinates_extract_transform(image_slice, image_slice_key, boxes, scores, score_criterion=0.1, enlarge_bndbox=True, enlarge_extent=0):\n\t\"\"\"\n\tinput::\n\tas sliced image path, and bounding boxes coordinates (0 ~ 1)\n\timage_path: the directory of each sliced image (patch) of an orignal large image\n\tboxes: a tuple containing 100 relative coordinates in each image, in form of (xmin, ymin, xmax, ymax)\n\tscores: iou value\n\n\toutput::\n\tas a numpy asrray, the bounding boxes coordinates in the orignal image\n\t\"\"\"\n\n\t# image = Image.open(image_path)\n\tim_height, im_width = np.shape(image_slice)[:2]\n\n\t# select boxes that satisfy the score_criterion\n\tselected_boxes = boxes[0][scores[0] > score_criterion]\n\n\tif len(selected_boxes) != 0:\n\n\t\tif enlarge_bndbox:\n\t\t\t# patch_bndbox_coordinates contains the coordinates of all bounding boxes in the input image\n\t\t\t# each row of patch_bndbox_coordinates contain four corners of a single bounding box\n\t\t\tpatch_bndbox_coordinates = np.asarray([map(int,(selected_boxes[i][0]*im_height-enlarge_extent, selected_boxes[i][1]*im_width-enlarge_extent, \\\n\t\t\t\tselected_boxes[i][2]*im_height+enlarge_extent, selected_boxes[i][3]*im_width+enlarge_extent)) for i in range(len(selected_boxes))])\n\t\telse:\n\t\t\tpatch_bndbox_coordinates = np.asarray([map(int,(selected_boxes[i][0]*im_height, selected_boxes[i][1]*im_width, \\\n\t\t\t\tselected_boxes[i][2]*im_height, selected_boxes[i][3]*im_width)) for i in range(len(selected_boxes))])\n\n\t\t# patch_idx = image_path.split('/')[-1].split('.')[0][-5:]\n\t\tpatch_idx = image_slice_key[-3:]\n\t\trow_id    = np.int(patch_idx[0])\n\t\tcol_id    = np.int(patch_idx[-1])\n\t\t# print 'patch_idx is:', patch_idx\n\t    \n\t\tif row_id == 1 & col_id == 1:\n\t\t\tpatch_bndbox_coordinates = patch_bndbox_coordinates\n\t\t\n\t\telse:\n\t\t\tpatch_bndbox_coordinates = np.asarray([patch_bndbox_coordinates[:, 0]+(row_id-1)*im_height, patch_bndbox_coordinates[:, 1]+(col_id-1)*im_width, \n\t\t\t\tpatch_bndbox_coordinates[:, 2]+(row_id-1)*im_height, patch_bndbox_coordinates[:, 3]+(col_id-1)*im_width]).T\n\n\telse:\n\t\tpass # there is no bounding boxes found in the image slice.\n\n\treturn patch_bndbox_coordinates\n\n","sub_path":"utils_/coordinates_extract_transform.py","file_name":"coordinates_extract_transform.py","file_ext":"py","file_size_in_byte":2143,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"585100905","text":"from peewee import SqliteDatabase, TextField, DoubleField, DateField, Model\n\n# create the database file\ndb = SqliteDatabase('albums.db')\n\n\nclass BaseModel(Model):\n    class Meta:\n        database = db\n\n\nclass Pitchfork(BaseModel):\n    url = TextField()\n    pubdate = DateField()\n    score = DoubleField()\n    year = DateField()\n    label = TextField()\n    genre = TextField()\n    artwork = TextField()\n    title = TextField()\n    artist = TextField()\n    album = TextField()\n\n\nif __name__ == \"__main__\":\n    db.connect()\n    db.create_tables([Pitchfork])\n    db.close()\n","sub_path":"models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":570,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"254278167","text":"# 테스트 케이스의 갯수 입력\nlines = int(input())\n# 각 테스트 케이스 값 입력\nnumbers = []\nfor line in range(lines):\n    numbers.insert(line, input().split(' '))\n# 입력받은 각 테스트 케이스의 값들을 비교\nfor bos in range(lines):\n    comp = numbers[bos]\n    if comp[0] > comp[1]:\n        ineq = '>'\n    elif comp[0] == comp[1]:\n        ineq = '='\n    else:\n        ineq = '<'\n    # #(테스트 케이스 번호) (부등호 or 등호) 출��\n    print(f'#{bos+1} {ineq}')\n","sub_path":"big_or_smal.py","file_name":"big_or_smal.py","file_ext":"py","file_size_in_byte":506,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"149319038","text":"from bluepy.btle import Scanner\nimport paho.mqtt.publish as publish\nimport redis\n\nimport config\n\n# scan for ble devices\nscanner = Scanner()\ndevices = scanner.scan(10.0)\nids = [dev.addr for dev in devices]\n\n# get old ids from redis\nr = redis.StrictRedis(host='localhost', port=6379, db=0)\nr_ids = r.get('ble:devices')\nif not r_ids:\n    r_ids = []\nelse:\n    r_ids = r_ids.split(',')\n\n# store new ids in redis\nr.set('ble:devices', ','.join(ids))\n\nadded_ids = [id for id in ids if id not in r_ids]\ndeleted_ids = [id for id in r_ids if id not in ids]\n\nif added_ids:\n    # publish list of added ids\n    msgs = [\n        {'topic': '{}/{}'.format(config.MQTT_TOPIC, id), 'payload': 'home'}\n        for id in added_ids\n    ]\n    publish.multiple(msgs, hostname=config.MQTT_HOST, port=config.MQTT_PORT)\n\nif deleted_ids:\n    # publish list of deleted ids\n    msgs = [\n        {'topic': '{}/{}'.format(config.MQTT_TOPIC, id), 'payload': 'not_home'}\n        for id in deleted_ids\n    ]\n    publish.multiple(msgs, hostname=config.MQTT_HOST, port=config.MQTT_PORT)\n","sub_path":"gateway.py","file_name":"gateway.py","file_ext":"py","file_size_in_byte":1050,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"144955707","text":"import codecs\nfrom setuptools import setup, find_packages\nimport os\nimport re\nimport sys\n\nfrom setuptools.command.test import test as TestCommand\n\nPY_33 = sys.version_info < (3, 4)\nPY_35 = sys.version_info >= (3, 5)\n\n\nclass PyTest(TestCommand):\n    user_options = [('pytest-args=', 'a', \"Arguments to pass to py.test\")]\n\n    def initialize_options(self):\n        TestCommand.initialize_options(self)\n        self.pytest_args = []\n\n    def finalize_options(self):\n        TestCommand.finalize_options(self)\n        self.test_args = []\n        self.test_suite = True\n\n    def run_tests(self):\n        # import here, cause outside the eggs aren't loaded\n        import pytest\n        errno = pytest.main(self.pytest_args)\n        sys.exit(errno)\n\n\nwith codecs.open(os.path.join(os.path.abspath(os.path.dirname(\n        __file__)), 'janus', '__init__.py'), 'r', 'latin1') as fp:\n    try:\n        version = re.findall(r\"^__version__ = '([^']+)'$\", fp.read(), re.M)[0]\n    except IndexError:\n        raise RuntimeError('Unable to determine version.')\n\n\ndef read(f):\n    return open(os.path.join(os.path.dirname(__file__), f)).read().strip()\n\n\ninstall_requires = []\n\nif PY_33:\n    install_requires.append('asyncio')\n\n# if not PY_35:\n#     install_requires.append('typing')\n\ntests_require = install_requires + ['pytest']\nextras_require = {}\n\n\nsetup(\n    name='janus',\n    version=version,\n    description=(\"Mixed sync-async queue to interoperate between \"\n                 \"asyncio tasks and classic threads\"),\n    long_description='\\n\\n'.join((read('README.rst'), read('CHANGES.rst'))),\n    classifiers=[\n        'License :: OSI Approved :: Apache Software License',\n        'Intended Audience :: Developers', 'Programming Language :: Python',\n        'Programming Language :: Python :: 3',\n        'Programming Language :: Python :: 3.3',\n        'Programming Language :: Python :: 3.4',\n        'Programming Language :: Python :: 3.5',\n        'Topic :: Software Development :: Libraries'\n    ],\n    author='Andrew Svetlov',\n    author_email='andrew.svetlov@gmail.com',\n    url='https://github.com/aio-libs/janus/',\n    license='Apache 2',\n    packages=find_packages(),\n    install_requires=install_requires,\n    tests_require=tests_require,\n    cmdclass={'test': PyTest},\n    include_package_data=True,\n    extras_require=extras_require)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":2334,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"110325737","text":"\"\"\"GDX Analytics Redshift class forms part of the shared module\n\"\"\"\nimport os\nimport sys\nimport logging\nimport psycopg2\n\n# Default RedShift host and port values for GDX Analytics\nHOST = 'redshift.analytics.gov.bc.ca'\nPORT = 5439\n\nclass RedShift:\n    'Common microservice operations for RedShift'\n\n    def print_psycopg2_exception(self, err):\n        'handles and parses psycopg2 exceptions'\n        # get details about the exception\n        err_type, err_obj, traceback = sys.exc_info()\n\n        # get the line number when exception occured\n        line_num = traceback.tb_lineno\n\n        # print the connect() error\n        self.logger.error(\"psycopg2 %s: %s\", err.__class__.__name__, err)\n        self.logger.debug(\"pgerror: %s\", err.pgerror)\n        self.logger.debug(\"pgcode: %s\", err.pgcode)\n        self.logger.debug(\"psycopg2 error on line number: %s\", line_num)\n        self.logger.debug(\"psycopg2 traceback: %s\", traceback)\n        self.logger.debug(\"psycopg2 error type: %s\", err_type)\n\n        '''\n        XX000 is the code for an InternalError Exception in psycopg2.\n        It is raised when the database encounters an error due to some\n        operation that cannot be completed for some reason.\n        \n        In these microservices, this can occur when some content of a structured\n        file is S3 being loaded to a Redshift table is either mismatched with the\n        datatype in the destination table, or is otherwise misstructured.\n\n        When Redshift encounters a database load errors, a row is written to\n        stl_load_errors describing it's details. The output to INFO here gives\n        direction on how to query that table to extract the error of concern.\n\n        reference:\n        https://www.psycopg.org/docs/module.html?highlight=internalerror#psycopg2.InternalError\n        https://docs.aws.amazon.com/redshift/latest/dg/r_STL_LOAD_ERRORS.html\n        '''\n        if str(err.pgcode) == 'XX000':\n            self.logger.info(\n                \"To begin investigating this database error, connect to the \"\n                \"%s database with adminitrative credentials, then execute:\\n\"\n                \"> SELECT TOP 1 * FROM stl_load_errors WHERE filename LIKE \"\n                \"'%%%s%%';\", self.dbname, self.batchfile)\n\n    def open_connection(self):\n        'opens a connection to the Redshift database using the provided'\n        # The basic parameters for the data source name value parsed by psyopg2\n        # https://www.postgresql.org/docs/current/libpq-connect.html#LIBPQ-PARAMKEYWORDS\n        connection_string = (\n            f\"dbname='{self.dbname}' \"\n            f\"host='{self.host}' \"\n            f\"port='{self.port}' \"\n            f\"user='{self.user}' \"\n            f\"password={self.password}\")\n\n        connection_string_log = (\n            f\"dbname='{self.dbname}' \"\n            f\"host='{self.host}' \"\n            f\"port='{self.port}' \"\n            f\"user='{self.user}'\")\n\n        try:\n            conn = psycopg2.connect(dsn=connection_string)\n            self.logger.debug(\n                \"Opened connection on connection string:\\n%s\",\n                connection_string_log)\n        except psycopg2.Error as err:\n            self.logger.error(\n                \"Failed to connect using connection string:\\n%s\",\n                connection_string_log)\n            self.print_psycopg2_exception(err)\n        return conn\n\n    def close_connection(self):\n        'closes the connection'\n        self.connection.close()\n        self.logger.debug('closed connection')\n\n    def query(self, query):\n        'Performs a query'\n        with self.connection as conn:\n            with conn.cursor() as curs:\n                try:\n                    curs.execute(query)\n                except psycopg2.Error as err:\n                    self.logger.error(\n                        \"Loading %s to RedShift failed.\", self.batchfile)\n                    self.print_psycopg2_exception(err)\n                    return False\n                else:\n                    self.logger.info(\n                        \"Loaded %s to RedShift successfully\", self.batchfile)\n                    return True\n\n    def __init__(self, batchfile, name=None, user=None, password=None):\n        'The constructor opens a RedShift connection based on the arguments'\n\n        self.logger = logging.getLogger(__name__)\n\n        self.batchfile = batchfile\n\n        self.dbname = name\n        self.host = HOST\n        self.port = PORT\n        self.user = user\n        self.password = password\n\n        self.connection = self.open_connection()\n\n\n    @classmethod\n    def snowplow(cls, batchfile):\n        'A factory constructor for the GDX Analytics Snowplow database'\n        return cls(\n            batchfile, 'snowplow', os.environ['pguser'], os.environ['pgpass'])\n","sub_path":"microservices/lib/redshift.py","file_name":"redshift.py","file_ext":"py","file_size_in_byte":4775,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"477910344","text":"import os\nimport json\nimport string\nimport random\nimport subprocess\nimport multiprocessing\nimport cv2\nimport numpy as np\nimport tqdm\n\n\ndef convert_gif_to_frames(gif):\n    # Initialize the frame number and create empty frame list\n    frame_num = 0\n    frame_list = []\n\n    # Loop until there are frames left\n    while True:\n        try:\n            # Try to read a frame. Okay is a BOOL if there are frames or not\n\n            okay, frame = gif.read()\n\n            # 30fps\n            # Append to empty frame list\n            frame_list.append(frame)\n            # Break if there are no other frames to read\n            if not okay:\n                break\n            # Increment value of the frame number by 1\n            frame_num += 1\n        except KeyboardInterrupt:  # press ^C to quit\n            break\n    return frame_list\n\n\ndef save_frames(frames, path):\n    print(path)\n    for i in tqdm.trange(len(frames)):\n        cv2.imwrite(path[:-4] + str(i) + \".jpg\", frames[i])\n\n\ninput_root = 'videos/clips/'\n# input_root = '../MLSPdata/clips/' # Harold's computer\ninput_clipname = \"\"  # folder name\ninput_folder = input_root + input_clipname\n\noutput_root = 'videos/frames/'\n# output_root = '../MLSPdata/frames/' # Harold's computer\noutput_path = output_root + input_clipname + '/'\noutput_folder = os.path.exists(output_path)\nprint(\"output: \" + output_path)\nif not output_folder:\n    os.makedirs(output_path)\n\nfiles = os.listdir(input_folder)\n\nfor filename in files:\n    if not os.path.isdir(filename):\n        if filename[-4:] != \".mp4\":\n            continue\n\n        f = input_folder + \"/\" + filename\n        print(\"source: \" + f)\n        clip = cv2.VideoCapture(f)\n        frame_list = convert_gif_to_frames(clip)\n        viedoName= filename.split(\".\")[0]\n        print(\"frame length: %d, each frame: %d x %d\" % (len(frame_list), len(frame_list[0]), len(frame_list[0][1])))\n        if not os.path.exists(output_path+\"/\"+viedoName):\n            os.makedirs(output_path+\"/\"+viedoName)\n        save_frames(frame_list, output_path +\"/\"+viedoName+\"/\"+ filename)\n\n\n\n","sub_path":"c2f_Folders.py","file_name":"c2f_Folders.py","file_ext":"py","file_size_in_byte":2063,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"211515911","text":"'''\nAuthor: Jamie Lea\nScript to softlink masks from a directory into a masks directory based on files in the images directory.\nThe intention is to provide a directory setup that helps Rodney LaLonde's SegCaps.\nThe LUNA16 dataset is huge, and divided into 10 subsets.  I wanted to run SegCaps on a single subset.\nSegCaps expects data stored as:\nroot_data_dir/\n\t\t/imgs\n\t\t/masks\n\nBut the LUNA16 dataset has all masks in a single archive.  SegCaps however, globs the masks.  I didn't\nwant to edit SegCaps or reorganize the dataset.  So:\n\nIntended usage:\n\n* extract a single subset (e.g subset0).  Now there is a folder called subset0.\n* extrat the masks. Now there is a folder called seg-lungs-LUNA16\n* create a root_data_dir\n* create a symlink called imgs to subset0 in the root_data_dir\n* place this script in the root data dir\n* run this script\n\nThis script:\t\n\t\t\t\t* creates the masks folder if it doesn't exist.\n\t\t\t\t* scans the imgs dir and collects the base file names\n\t\t\t\t* scans the all_masks_dir and collects the base file names\n\t\t\t\t* takes the set intersection of the two lets\n\t\t\t\t* creates a masks dir\n\t\t\t\t* creates a symlink from each file in the all_masks_dir that coresponds to a file in the imgs dir\n\nThis way the SegCaps data loader only lists masks corresponding to the image subset used.\n'''\n\nfrom os.path import splitext, split, join, exists\nfrom os import symlink, makedirs, remove\nfrom glob import glob\nimport argparse\n\narg_parser = argparse.ArgumentParser()\narg_parser.add_argument(\"--all_masks_dir\", type=str, action=\"store\", required=True,\n\thelp='''\n\tWhere all the masks are stored.\n\t''')\n\narg_parser.add_argument(\"--print_lists\", action=\"store_true\",\n\thelp='''\n\tDebug option to print the basenames list.\n\t''')\n\nargs = arg_parser.parse_args()\n\n# get image list\nthis_img_list = glob(\"imgs/*.raw\")\nthis_img_basename_list = [split(splitext(img_filename)[0])[-1] for img_filename in this_img_list]\n\n# get all masks list\nall_masks_list = []\n\nfor ext in [\"mhd\", \"zraw\"]:\n\tall_masks_list.extend(glob(join(args.all_masks_dir, \"*{}\".format(ext))))\n\n# intersect\nthis_masks_list = [mask_filename for mask_filename in all_masks_list if split(splitext(mask_filename)[0])[-1] in this_img_basename_list]\n\nif args.print_lists:\n\tprint(\"Image BaseNames:\")\n\tprint(\"\\n\".join(this_img_list))\n\tprint(\"\\nMask BaseNames:\")\n\tprint(\"\\n\".join(all_masks_list))\n\tprint(\"\\nNeeded Mask BaseNames:\")\n\tprint(\"\\n\".join(this_masks_list))\n\t\n\n# create symlinks\nmakedirs(\"masks\", exist_ok=True)\nfor mask in this_masks_list:\n\ttarget_name = join(\"masks\", split(mask)[-1])\n\tif exists(target_name):\n\t\tremove(target_name)\n\tprint(target_name)\n\tsymlink(mask, target_name)\n\n# done\nprint(\"Created symlinks.  Please check your directory to confirm.\")","sub_path":"collect_masks_and_softlink.py","file_name":"collect_masks_and_softlink.py","file_ext":"py","file_size_in_byte":2720,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"568052797","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[83]:\n\n\nget_ipython().run_line_magic('matplotlib', 'inline')\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\nimport sqlite3\nimport pandas as pd\nimport numpy as np\nimport nltk\nimport string\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.feature_extraction.text import TfidfTransformer\nfrom sklearn.feature_extraction.text import TfidfVectorizer\n\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn import metrics\nfrom sklearn.metrics import roc_curve, auc\nfrom nltk.stem.porter import PorterStemmer\n\nimport re\n# Tutorial about Python regular expressions: https://pymotw.com/2/re/\nimport string\nfrom nltk.corpus import stopwords\nfrom nltk.stem import PorterStemmer\nfrom nltk.stem.wordnet import WordNetLemmatizer\n\nfrom gensim.models import Word2Vec\nfrom gensim.models import KeyedVectors\nimport pickle\n\nfrom tqdm import tqdm\nimport os\n\nfrom plotly import plotly\nimport plotly.offline as offline\nimport plotly.graph_objs as go\noffline.init_notebook_mode()\nfrom collections import Counter\nimport chart_studio\nfrom scipy.sparse import hstack\n\n\n# In[84]:\n\n\ndata=pd.read_csv(\"preprocessed_data1.csv\")\ndata=data.drop(['project_tile'],axis=1)\n\n\n# In[85]:\n\n\ndata.head(2)\n\n\n# <h1>1. K Nearest Neighbor</h1>\n# \n\n# Here we will be applying knnn to the model but before that we need to transform all the paramters in dataset to structured format, so to do these we will be using BOW,TFIDF,W2V for text data and for numerical values we will be using normalizer and other text values we will be using One hot encoder ,\n# so we need to check every model accuracy using auc score , to find the best k value we can go with hyperparamter tunning or grid search tunning to find the best k value as you know auc score is determined by TPR and FPR \n\n# In[86]:\n\n\nessays=data['essay'].values\n\n\n# # BOW Representation\n\n# In[87]:\n\n\ny = data['project_is_approved'].values\nX = data.drop(['project_is_approved'], axis=1)\nX.head(1)\n\n\n# <h2> Splitting data into Train and cross validation(or test): Stratified Sampling</h2>\n# \n\n# In[88]:\n\n\n# train test split\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, stratify=y)\nX_train, X_cv, y_train, y_cv = train_test_split(X_train, y_train, test_size=0.33, stratify=y_train)\n\n\n# # Encoding of Essay\n\n# In[159]:\n\n\nnbbow=[]\n\nvectorizer = CountVectorizer()\nprint(X_train.shape, y_train.shape)\nprint(X_cv.shape, y_cv.shape)\nprint(X_test.shape, y_test.shape)\n\nprint(\"=\"*100)\n\n\nvectorizer = CountVectorizer(min_df=10,ngram_range=(1,4))# \n#min_df=10(The words which are found atlest in 10 of the rows)\n\nvectorizer.fit(X_train['essay'].values) # fit has to happen only on train data\n\n# we use the fitted CountVectorizer to convert the text to vector\nX_train_essay_bow = vectorizer.transform(X_train['essay'].values)\nX_cv_essay_bow = vectorizer.transform(X_cv['essay'].values)\nX_test_essay_bow = vectorizer.transform(X_test['essay'].values)\n\nprint(\"After vectorizations\")\nprint(X_train_essay_bow.shape, y_train.shape)\nprint(X_cv_essay_bow.shape, y_cv.shape)\nprint(X_test_essay_bow.shape, y_test.shape)\nprint(\"=\"*100)\nnbbow.extend(vectorizer.get_feature_names())\n\n\n# <h3> encoding categorical features: School State</h3>\n\n# In[160]:\n\n\n\nvectorizer = CountVectorizer()\nvectorizer.fit(X_train['school_state'].values) # fit has to happen only on train data\n\n# we use the fitted CountVectorizer to convert the text to vector\nX_train_state_ohe = vectorizer.transform(X_train['school_state'].values)\nX_cv_state_ohe = vectorizer.transform(X_cv['school_state'].values)\nX_test_state_ohe = vectorizer.transform(X_test['school_state'].values)\n\nprint(\"After vectorizations\")\nprint(X_train_state_ohe.shape, y_train.shape)\nprint(X_cv_state_ohe.shape, y_cv.shape)\nprint(X_test_state_ohe.shape, y_test.shape)\nprint(vectorizer.get_feature_names())\nprint(\"=\"*100)\nnbbow.extend(vectorizer.get_feature_names())\n\n\n# # For all the categorical features present below we are applying one hot encoders\n\n# <h3> encoding categorical features:teacher_prefix </h3>\n\n# In[161]:\n\n\nvectorizer = CountVectorizer()\nvectorizer.fit(X_train['teacher_prefix'].values) # fit has to happen only on train data\n\n# we use the fitted CountVectorizer to convert the text to vector\nX_train_teacher_ohe = vectorizer.transform(X_train['teacher_prefix'].values)\nX_cv_teacher_ohe = vectorizer.transform(X_cv['teacher_prefix'].values)\nX_test_teacher_ohe = vectorizer.transform(X_test['teacher_prefix'].values)\n\nprint(\"After vectorizations\")\nprint(X_train_teacher_ohe.shape, y_train.shape)\nprint(X_cv_teacher_ohe.shape, y_cv.shape)\nprint(X_test_teacher_ohe.shape, y_test.shape)\nprint(vectorizer.get_feature_names())\nprint(\"=\"*100)\nnbbow.extend(vectorizer.get_feature_names())\n\n\n# <h3> encoding categorical features:project_grade_category </h3>\n\n# In[162]:\n\n\nvectorizer = CountVectorizer()\nvectorizer.fit(X_train['project_grade_category'].values) # fit has to happen only on train data\n\n# we use the fitted CountVectorizer to convert the text to vector\nX_train_pgc_ohe = vectorizer.transform(X_train['project_grade_category'].values)\nX_cv_pgc_ohe = vectorizer.transform(X_cv['project_grade_category'].values)\nX_test_pgc_ohe = vectorizer.transform(X_test['project_grade_category'].values)\n\nprint(\"After vectorizations\")\nprint(X_train_pgc_ohe.shape, y_train.shape)\nprint(X_cv_pgc_ohe.shape, y_cv.shape)\nprint(X_test_pgc_ohe.shape, y_test.shape)\nprint(vectorizer.get_feature_names())\nprint(\"=\"*100)\nnbbow.extend(vectorizer.get_feature_names())\n\n\n# <h3> encoding categorical features:clean_categories </h3>\n\n# In[163]:\n\n\nvectorizer = CountVectorizer()\nvectorizer.fit(X_train['clean_categories'].values) # fit has to happen only on train data\n\n# we use the fitted CountVectorizer to convert the text to vector\nX_train_clca_ohe = vectorizer.transform(X_train['clean_categories'].values)\nX_cv_clca_ohe = vectorizer.transform(X_cv['clean_categories'].values)\nX_test_clca_ohe = vectorizer.transform(X_test['clean_categories'].values)\n\nprint(\"After vectorizations\")\nprint(X_train_clca_ohe.shape, y_train.shape)\nprint(X_cv_clca_ohe.shape, y_cv.shape)\nprint(X_test_clca_ohe.shape, y_test.shape)\nprint(vectorizer.get_feature_names())\nprint(\"=\"*100)\nnbbow.extend(vectorizer.get_feature_names())\n\n\n# <h3> encoding categorical features:clean_subcategories </h3>\n\n# In[164]:\n\n\nvectorizer = CountVectorizer()\nvectorizer.fit(X_train['clean_subcategories'].values) # fit has to happen only on train data\n\n# we use the fitted CountVectorizer to convert the text to vector\nX_train_clsc_ohe = vectorizer.transform(X_train['clean_subcategories'].values)\nX_cv_clsc_ohe = vectorizer.transform(X_cv['clean_subcategories'].values)\nX_test_clsc_ohe = vectorizer.transform(X_test['clean_subcategories'].values)\n\nprint(\"After vectorizations\")\nprint(X_train_clsc_ohe.shape, y_train.shape)\nprint(X_cv_clsc_ohe.shape, y_cv.shape)\nprint(X_test_clsc_ohe.shape, y_test.shape)\nprint(vectorizer.get_feature_names())\nprint(\"=\"*100)\nnbbow.extend(vectorizer.get_feature_names())\n\n\n# # For the numerical features we are using the normalization technique\n\n# <h3> encoding numerical feature:price </h3>\n\n# In[167]:\n\n\nfrom sklearn.preprocessing import Normalizer\nnormalizer = Normalizer()\n# normalizer.fit(X_train['price'].values)\n# this will rise an error Expected 2D array, got 1D array instead: \n# array=[105.22 215.96  96.01 ... 368.98  80.53 709.67].\n# Reshape your data either using \n# array.reshape(-1, 1) if your data has a single feature \n# array.reshape(1, -1)  if it contains a single sample.\nnormalizer.fit(X_train['price'].values.reshape(-1,1))\n\nX_train_price_norm = normalizer.transform(X_train['price'].values.reshape(-1,1))\nX_cv_price_norm = normalizer.transform(X_cv['price'].values.reshape(-1,1))\nX_test_price_norm = normalizer.transform(X_test['price'].values.reshape(-1,1))\n\n\n\nprint(\"After vectorizations\")\nprint(X_train_price_norm.shape, y_train.shape)\nprint(X_cv_price_norm.shape, y_cv.shape)\nprint(X_test_price_norm.shape, y_test.shape)\nprint(\"=\"*100)\nnbbow.extend('price')\n\n\n# <h3> encoding numerical feature:teacher_number_of_previously_posted_projects </h3>\n\n# In[166]:\n\n\nfrom sklearn.preprocessing import Normalizer\nnormalizer = Normalizer()\n# normalizer.fit(X_train['price'].values)\n# this will rise an error Expected 2D array, got 1D array instead: \n# array=[105.22 215.96  96.01 ... 368.98  80.53 709.67].\n# Reshape your data either using \n# array.reshape(-1, 1) if your data has a single feature \n# array.reshape(1, -1)  if it contains a single sample.\nnormalizer.fit(X_train['teacher_number_of_previously_posted_projects'].values.reshape(-1,1))\n\n\nX_train_tnpp_norm = normalizer.transform(X_train['teacher_number_of_previously_posted_projects'].values.reshape(-1,1))\nX_cv_tnpp_norm = normalizer.transform(X_cv['teacher_number_of_previously_posted_projects'].values.reshape(-1,1))\nX_test_tnpp_norm = normalizer.transform(X_test['teacher_number_of_previously_posted_projects'].values.reshape(-1,1))\n\n\n\nprint(\"After vectorizations\")\nprint(X_train_tnpp_norm.shape, y_train.shape)\nprint(X_cv_tnpp_norm.shape, y_cv.shape)\nprint(X_test_tnpp_norm.shape, y_test.shape)\nprint(\"=\"*100)\n\nnbbow.extend('teacher_number_of_previously_posted_projects')\n\n\n# #Here we will be concatenating all the features\n\n# In[97]:\n\n\nX_trbow = hstack((X_train_essay_bow, X_train_state_ohe, X_train_teacher_ohe, X_train_price_norm,X_train_tnpp_norm,X_train_clsc_ohe,X_train_clca_ohe,X_train_pgc_ohe)).tocsr()\nX_crbow = hstack((X_cv_essay_bow, X_cv_state_ohe, X_cv_teacher_ohe, X_cv_price_norm,X_cv_tnpp_norm,X_cv_clsc_ohe,X_cv_clca_ohe,X_cv_pgc_ohe)).tocsr()\nX_tebow = hstack((X_test_essay_bow, X_test_state_ohe, X_test_teacher_ohe, X_test_price_norm,X_test_tnpp_norm,X_test_clsc_ohe,X_test_clca_ohe,X_test_pgc_ohe)).tocsr()\n\n\n# In[98]:\n\n\nprint(\"Final Data matrix\")\nprint(X_trbow.shape, y_train.shape)\nprint(X_crbow.shape, y_cv.shape)\nprint(X_tebow.shape, y_test.shape)\nprint(\"=\"*100)\n\n\n# In[171]:\n\n\nnbbwords=nbbow.copy()\nnbtfidf=nbbow.copy()\n\n\n# <h3> After Applying BOW for text data,One hot encoder for categorical features and Normalizing the numerical features we have divided the dataset in the ratio of 77 train and 33 % test we also used cross validation to make the accuracy better so we divided the training set again into 77 % train and 33 % cv so after that we used hstack and concatenated the columns to the three paramters , Now to make sure the model doen't overfit or underfit we need to find the right value of k, so we will be using tunning the model using random search or hyperparamter search or using loops and plot a graph to visually find the best value of k  </h3>\n\n# We will be using simple loop first to get the best value of k and the K fold cross validation we are performing is 1 here\n\n# In[17]:\n\n\n#Basically I am creating a function to iterate and predict the probabilities batch wise.\n\ndef batch_predict(clf, data):\n    # roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class\n    # not the predicted outputs\n\n    y_data_pred = []\n    tr_loop = data.shape[0] - data.shape[0]%1000\n    #consider you X_tr shape is 49041, then your tr_loop will be 49041 - 49041%1000 = 49000(Here we are actually taking the remainder 49041/1000=41 and \n    # subracting it from 49041 to get the multiplrs of 1000'''') )\n    # in this for loop we will iterate unti the last 1000 multiplier\n    for i in range(0, tr_loop, 1000): #(0,49000,1000)  # so here the loop will run for 49 times \n        y_data_pred.extend(clf.predict_proba(data[i:i+1000])[:,1])\n    # we will be predicting for the last data points\n    if data.shape[0]%1000 !=0:\n        y_data_pred.extend(clf.predict_proba(data[tr_loop:])[:,1])\n    \n    return y_data_pred\n\n\n# Here in the below code i am using auc score to determine the best k value\n\n# In[18]:\n\n\n# The loop is here running 490 times \n\nimport matplotlib.pyplot as plt\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.metrics import roc_auc_score\n\"\"\"\ny_true : array, shape = [n_samples] or [n_samples, n_classes]\nTrue binary labels or binary label indicators.\n\ny_score : array, shape = [n_samples] or [n_samples, n_classes]\nTarget scores, can either be probability estimates of the positive class, confidence values, or non-thresholded measure of\ndecisions (as returned by “decision_function” on some classifiers). \nFor binary y_true, y_score is supposed to be the score of the class with greater label.\n\"\"\"\ntrain_auc = []\ncv_auc = []\nK = [3, 15, 25, 35,45,55,71,81,91,101] # we can alos use range(1,50) but due to memory cnstrains we are only selecting 10 k values to choose\nfor i in tqdm(K):\n    neigh = KNeighborsClassifier(n_neighbors=i, n_jobs=-1)\n    neigh.fit(X_trbow, y_train)\n\n    y_train_pred = batch_predict(neigh, X_trbow)    \n    y_cv_pred = batch_predict(neigh, X_crbow)\n\n    # roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class\n    # not the predicted outputs        \n    train_auc.append(roc_auc_score(y_train,y_train_pred))\n    cv_auc.append(roc_auc_score(y_cv, y_cv_pred))\n\nplt.plot(K, train_auc, label='Train AUC')\nplt.plot(K, cv_auc, label='CV AUC')\n\nplt.scatter(K, train_auc, label='Train AUC points')\nplt.scatter(K, cv_auc, label='CV AUC points')\n\nplt.legend()\nplt.xlabel(\"K: hyperparameter\")\nplt.ylabel(\"AUC\")\nplt.title(\"ERROR PLOTS\")\nplt.grid()\nplt.show()\n\n\n#  So from the above plot we can see the K=101 is the best we got as both the curves are converging at this point , we are selecting this point as both cvauc curve and  train auc curve converges at this point\n\n# Here in the below code we will be using random search cv and the cv=3 so we are dividing the train dataset into 3 parts and using auc score to fidn the best k value\n# \n\n# In[99]:\n\n\nfrom sklearn.model_selection import GridSearchCV\nfrom scipy.stats import randint as sp_randint\nfrom sklearn.model_selection import RandomizedSearchCV\n\nneigh = KNeighborsClassifier(n_jobs=-1)\nparameters = {'n_neighbors':[3, 15, 25, 51, 101]}\nclf = RandomizedSearchCV(neigh, parameters, cv=3, scoring='roc_auc',return_train_score=True)\nclf.fit(X_trbow, y_train)\n\nresults = pd.DataFrame.from_dict(clf.cv_results_)\nresults = results.sort_values(['param_n_neighbors'])\n\n\ntrain_auc= results['mean_train_score']\ntrain_auc_std= results['std_train_score']\ncv_auc = results['mean_test_score'] \ncv_auc_std= results['std_test_score']\nK =  results['param_n_neighbors']\n\n\nplt.plot(K, train_auc, label='Train AUC')\n# this code is copied from here: https://stackoverflow.com/a/48803361/4084039\n#plt.gca().fill_between(K, train_auc - train_auc_std,train_auc + train_auc_std,alpha=0.2,color='darkblue')\n\nplt.plot(K, cv_auc, label='CV AUC')\n# this code is copied from here: https://stackoverflow.com/a/48803361/4084039\n# plt.gca().fill_between(K, cv_auc - cv_auc_std,cv_auc + cv_auc_std,alpha=0.2,color='darkorange')\n\nplt.scatter(K, train_auc, label='Train AUC points')\nplt.scatter(K, cv_auc, label='CV AUC points')\n\n\nplt.legend()\nplt.xlabel(\"K: hyperparameter\")\nplt.ylabel(\"AUC\")\nplt.title(\"Hyper parameter Vs AUC plot\")\nplt.grid()\nplt.show()\n\nresults.head()\n\n\n# In[20]:\n\n\n# from the error plot we choose K such that, we will have maximum AUC on cv data and gap between the train and cv is less\n# Note: based on the method you use you might get different hyperparameter values as best one\n# so, you choose according to the method you choose, you use gridsearch if you are having more computing power and note it will take more time\n# if you increase the cv values in the GridSearchCV you will get more rebust results.\n\n#here we are choosing the best_k based on forloop results\nbest_k = 101\n\n\n# we have found out the best value of k so we will be plotting auc curve auc using TPR and FPR\n# Auc represents the measure of seperability and Receiver operating characteristics represents the probability\n\n# In[100]:\n\n\nfrom sklearn.metrics import roc_curve, auc\n\n\nneigh = KNeighborsClassifier(n_neighbors=best_k, n_jobs=-1)\nneigh.fit(X_trbow, y_train)\n# roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class\n# not the predicted outputs\n\ny_train_pred = batch_predict(neigh, X_trbow)    \ny_test_pred = batch_predict(neigh, X_tebow)\n\ntrain_fpr, train_tpr, tr_thresholds = roc_curve(y_train, y_train_pred)\ntest_fpr, test_tpr, te_thresholds = roc_curve(y_test, y_test_pred)\n\nplt.plot(train_fpr, train_tpr, label=\"train AUC =\"+str(auc(train_fpr, train_tpr)))\nplt.plot(test_fpr, test_tpr, label=\"test AUC =\"+str(auc(test_fpr, test_tpr)))\nplt.legend()\nplt.xlabel(\"K: hyperparameter\")\nplt.ylabel(\"AUC\")\nplt.title(\"ERROR PLOTS\")\nplt.grid()\nplt.show()\n\n\n# As we know auc curve and roc curve can be used for bimary classification\n\n# In[101]:\n\n\n# we are writing our own function for predict, with defined thresould\n# we will pick a threshold that will give the least fpr\ndef find_best_threshold(threshould, fpr, tpr):\n    t = threshould[np.argmax(tpr*(1-fpr))]\n    # (tpr*(1-fpr)) will be maximum if your fpr is very low and tpr is very high\n    print(\"the maximum value of tpr*(1-fpr)\", max(tpr*(1-fpr)), \"for threshold\", np.round(t,3))\n    return t\n\ndef predict_with_best_t(proba, threshould):\n    predictions = []\n    for i in proba:\n        if i>=threshould:\n            predictions.append(1)\n        else:\n            predictions.append(0)\n    return predictions\n\n\n# In[102]:\n\n\nprint(\"=\"*100)\nfrom sklearn.metrics import confusion_matrix\nbest_t = find_best_threshold(tr_thresholds, train_fpr, train_tpr)\nprint(\"Train confusion matrix\")\nprint(confusion_matrix(y_train, predict_with_best_t(y_train_pred, best_t)))\nprint(\"Test confusion matrix\")\nprint(confusion_matrix(y_test, predict_with_best_t(y_test_pred, best_t)))\n\n\n#  # Applying AW2V using KNN\n\n# In[24]:\n\n\n# stronging variables into pickle files python: http://www.jessicayung.com/how-to-use-pickle-to-save-and-load-variables-in-python/\n# make sure you have the glove_vectors file\nwith open('glove_vectors', 'rb') as f:\n    model = pickle.load(f)\n    glove_words =  set(model.keys())\n\n\n# In[25]:\n\n\n# average Word2Vec for train essay values\n# compute average word2vec for each review.\navg_w2v_vectors_train = []; # the avg-w2v for each sentence/review is stored in this list\nfor sentence in tqdm(X_train['essay'].values): # for each review/sentence\n    vector = np.zeros(300) # as word vectors are of zero length\n    cnt_words =0; # num of words with a valid vector in the sentence/review\n    for word in sentence.split(): # for each word in a review/sentence\n        if word in glove_words:\n            vector += model[word]\n            cnt_words += 1\n    if cnt_words != 0:\n        vector /= cnt_words\n    avg_w2v_vectors_train.append(vector)\n\nprint(len(avg_w2v_vectors_train))\nprint(len(avg_w2v_vectors_train[0]))\nprint(avg_w2v_vectors_train[0])\n\n\n# In[26]:\n\n\n\n# avg w2v for cv data values\navg_w2v_vectors_cv = []; # the avg-w2v for each sentence/review is stored in this list\nfor sentence in tqdm(X_cv['essay'].values): # for each review/sentence\n    vector = np.zeros(300) # as word vectors are of zero length\n    cnt_words =0; # num of words with a valid vector in the sentence/review\n    for word in sentence.split(): # for each word in a review/sentence\n        if word in glove_words:\n            vector += model[word]\n            cnt_words += 1\n    if cnt_words != 0:\n        vector /= cnt_words\n    avg_w2v_vectors_cv.append(vector)\n\n\n# In[27]:\n\n\n#avg w2v for test set.\navg_w2v_vectors_test = []; # the avg-w2v for each sentence/review is stored in this list\nfor sentence in tqdm(X_test['essay'].values): # for each review/sentence\n    vector = np.zeros(300) # as word vectors are of zero length\n    cnt_words =0; # num of words with a valid vector in the sentence/review\n    for word in sentence.split(): # for each word in a review/sentence\n        if word in glove_words:\n            vector += model[word]\n            cnt_words += 1\n    if cnt_words != 0:\n        vector /= cnt_words\n    avg_w2v_vectors_test.append(vector)\n\n\n# In[28]:\n\n\nX_tr = hstack((avg_w2v_vectors_train, X_train_state_ohe, X_train_teacher_ohe, X_train_price_norm,X_train_tnpp_norm,X_train_clsc_ohe,X_train_clca_ohe,X_train_pgc_ohe)).tocsr()\nX_cr = hstack((avg_w2v_vectors_cv, X_cv_state_ohe, X_cv_teacher_ohe, X_cv_price_norm,X_cv_tnpp_norm,X_cv_clsc_ohe,X_cv_clca_ohe,X_cv_pgc_ohe)).tocsr()\nX_te = hstack((avg_w2v_vectors_test,X_test_state_ohe, X_test_teacher_ohe, X_test_price_norm,X_test_tnpp_norm,X_test_clsc_ohe,X_test_clca_ohe,X_test_pgc_ohe)).tocsr()\n\n\n\n\n\nprint(\"Final Data matrix\")\nprint(X_tr.shape, y_train.shape)\nprint(X_cr.shape, y_cv.shape)\nprint(X_te.shape, y_test.shape)\nprint(\"=\"*100)\n\n\n# Hyper parameter tunning\n\n# In[29]:\n\n\ntrain_auc = []\ncv_auc = []\nK = [3, 15, 25, 51, 101]\nfor i in tqdm(K):\n    neigh = KNeighborsClassifier(n_neighbors=i, n_jobs=-1)\n    neigh.fit(X_tr, y_train)\n\n    y_train_pred = batch_predict(neigh, X_tr)    \n    y_cv_pred = batch_predict(neigh, X_cr)\n\n    # roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class\n    # not the predicted outputs        \n    train_auc.append(roc_auc_score(y_train,y_train_pred))\n    cv_auc.append(roc_auc_score(y_cv, y_cv_pred))\n\nplt.plot(K, train_auc, label='Train AUC')\nplt.plot(K, cv_auc, label='CV AUC')\n\nplt.scatter(K, train_auc, label='Train AUC points')\nplt.scatter(K, cv_auc, label='CV AUC points')\n\nplt.legend()\nplt.xlabel(\"K: hyperparameter\")\nplt.ylabel(\"AUC\")\nplt.title(\"ERROR PLOTS\")\nplt.grid()\nplt.show()\n\n\n# In[ ]:\n\n\n# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html#sklearn.metrics.roc_curve\nbest_k=101\n\n\nfrom sklearn.metrics import roc_curve, auc\n\n\nneigh = KNeighborsClassifier(n_neighbors=best_k, n_jobs=-1)\nneigh.fit(X_tr, y_train)\n# roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class\n# not the predicted outputs\n\ny_train_pred = batch_predict(neigh, X_tr)    \ny_test_pred = batch_predict(neigh, X_te)\n\ntrain_fpr, train_tpr, tr_thresholds = roc_curve(y_train, y_train_pred)\ntest_fpr, test_tpr, te_thresholds = roc_curve(y_test, y_test_pred)\n\nplt.plot(train_fpr, train_tpr, label=\"train AUC =\"+str(auc(train_fpr, train_tpr)))\nplt.plot(test_fpr, test_tpr, label=\"test AUC =\"+str(auc(test_fpr, test_tpr)))\nplt.legend()\nplt.xlabel(\"K: hyperparameter\")\nplt.ylabel(\"AUC\")\nplt.title(\"ERROR PLOTS\")\nplt.grid()\nplt.show()\n\n\n# In[ ]:\n\n\nprint(\"=\"*100)\nfrom sklearn.metrics import confusion_matrix\nbest_t = find_best_threshold(tr_thresholds, train_fpr, train_tpr)\nprint(\"Train confusion matrix\")\nprint(confusion_matrix(y_train, predict_with_best_t(y_train_pred, best_t)))\nprint(\"Test confusion matrix\")\nprint(confusion_matrix(y_test, predict_with_best_t(y_test_pred, best_t)))\n\n\n# # Naive Bayes Assignment\n\n# We will be using multinomial naive bayes here and use BOW and TFIDF for encoding Text Data.\n# One thing we need to remember if auc score is greater then 0.5 it means model is able to make good classification\n\n# In[144]:\n\n\n# we will be using the below parameters to find the best K value.\n\nfrom sklearn.model_selection import GridSearchCV\nfrom scipy.stats import randint as sp_randint\nfrom sklearn.model_selection import RandomizedSearchCV\nfrom sklearn.naive_bayes import MultinomialNB\nimport warnings\nimport math\nwarnings.filterwarnings(\"ignore\")\n\n\n\n\nparameters={'alpha':[0.00001,0.0005, 0.0001,0.1,0.5,1,5,51,101,1001]}\n\n\nnb = MultinomialNB(fit_prior=False)\n\nclf = RandomizedSearchCV(nb,parameters , cv=3, scoring='roc_auc',return_train_score=True)\nclf.fit(X_trbow, y_train)\nresults = pd.DataFrame.from_dict(clf.cv_results_)\n\n\ntrain_auc= results['mean_train_score']\n\ntrain_auc_std= results['std_train_score']\ncv_auc = results['mean_test_score'] \ncv_auc_std= results['std_test_score']\n\nparameters['alpha']=[math.log10(i) for i in parameters['alpha']]\nprint('Best score: ',clf.best_score_)\nprint('Alpha value with best score: ',clf.best_params_)\n\n\n# this code is copied from here: https://stackoverflow.com/a/48803361/4084039\n#plt.gca().fill_between(K, train_auc - train_auc_std,train_auc + train_auc_std,alpha=0.2,color='darkblue')\nplt.plot(parameters['alpha'], train_auc, label='CV AUC')\n\nplt.plot(parameters['alpha'], cv_auc, label='CV AUC')\n# this code is copied from here: https://stackoverflow.com/a/48803361/4084039\n# plt.gca().fill_between(K, cv_auc - cv_auc_std,cv_auc + cv_auc_std,alpha=0.2,color='darkorange')\n\nplt.scatter(parameters['alpha'], train_auc, label='Train AUC points')\nplt.scatter(parameters['alpha'], cv_auc, label='CV AUC points')\n\n\nplt.legend()\nplt.xlabel(\"Alpha: hyperparameter\")\nplt.ylabel(\"AUC\")\nplt.title(\"Hyper parameter Vs AUC plot\")\nplt.grid()\nplt.show()\n\nresults\n\n\n\n\n# \n# <br> We have used randomized search cv for finding best alpha so to avoid model overfitting and underfitting.<br>\n# We have used 3 fold validation,cv=3 and trained over model <br>\n# We have used log10(alpha) to make the plot simple and look good.<br>\n# In the above plot we have used only 10 values of alpha to get the best model using roc score which is given by auc curve ,here the best values of alpha we got is 1 with 0.68 \n# \n# \n\n# In[145]:\n\n\nfrom sklearn.metrics import roc_curve, auc\n\n\nnb = MultinomialNB(alpha=1,fit_prior=False)\nnb.fit(X_trbow, y_train)\n# roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class\n# not the predicted outputs\n\ny_train_pred = batch_predict(neigh, X_trbow)    \ny_test_pred = batch_predict(neigh, X_tebow)\n\ntrain_fpr, train_tpr, tr_thresholds = roc_curve(y_train, y_train_pred)\ntest_fpr, test_tpr, te_thresholds = roc_curve(y_test, y_test_pred)\n\nplt.plot(train_fpr, train_tpr, label=\"train AUC =\"+str(auc(train_fpr, train_tpr)))\nplt.plot(test_fpr, test_tpr, label=\"test AUC =\"+str(auc(test_fpr, test_tpr)))\nplt.legend()\nplt.xlabel(\"alpha: hyperparameter\")\nplt.ylabel(\"AUC\")\nplt.title(\"ERROR PLOTS\")\nplt.grid()\nplt.show()\n\n\n# In[146]:\n\n\ndef find_best_threshold(threshould, fpr, tpr):\n    t = threshould[np.argmax(tpr*(1-fpr))]\n    # (tpr*(1-fpr)) will be maximum if your fpr is very low and tpr is very high\n    print(\"the maximum value of tpr*(1-fpr)\", max(tpr*(1-fpr)), \"for threshold\", np.round(t,3))\n    return t\n\ndef predict_with_best_t(proba, threshould):\n    predictions = []\n    for i in proba:\n        if i>=threshould:\n            predictions.append(1)\n        else:\n            predictions.append(0)\n    return predictions\n\n\n# In[147]:\n\n\nfrom sklearn.metrics import confusion_matrix\nbest_t = find_best_threshold(tr_thresholds, train_fpr, train_tpr)\nprint(\"Train confusion matrix\")\ncmtrain =confusion_matrix(y_train, predict_with_best_t(y_train_pred, best_t))\nprint(cmtrain)\nprint(\"Test confusion matrix\")\ncmtest=confusion_matrix(y_test, predict_with_best_t(y_test_pred, best_t))\nprint(cmtest)\n\n\n# In[148]:\n\n\nimport seaborn as sns\nimport matplotlib.pyplot as plt     \nprint(\"Train confusion matrix\")\n\nax= plt.subplot();\nsns.heatmap(cmtrain, annot=True,cmap='Blues',ax=ax,fmt=\"d\");\n# labels, title and ticks\nax.set_xlabel('Predicted labels');ax.set_ylabel('True labels'); \nax.set_ylim(2.0, 0)\nax.set_title('Confusion Matrix');\nax.xaxis.set_ticklabels(['False','Correct']); \nax.yaxis.set_ticklabels(['False','Correct']);\n\n\n# In[149]:\n\n\nimport seaborn as sns\nimport matplotlib.pyplot as plt     \nprint(\"Test confusion matrix\")\n\nax= plt.subplot();\nsns.heatmap(cmtest, annot=True,cmap='Blues',ax=ax,fmt=\"d\");\n# labels, title and ticks\nax.set_xlabel('Predicted labels');ax.set_ylabel('True labels'); \nax.set_ylim(2.0, 0)\nax.set_title('Confusion Matrix');\nax.xaxis.set_ticklabels(['False','Correct']); \nax.yaxis.set_ticklabels(['False','Correct']);\n\n\n# In[175]:\n\n\npos_class_prob_sorted = nb.feature_log_prob_[1].argsort()\nprint(\"The top 10 positive features are\")\nprint(np.take(nbbwords,pos_class_prob_sorted[-10:]))\n\nnag_class_prob_sorted = nb.feature_log_prob_[0].argsort()\nprint(\"The top 10 nagative features are\")\nprint(np.take(nbbwords,nag_class_prob_sorted[-10:]))\n\n\n# In[ ]:\n\n\n\n\n","sub_path":"Donor choose part3 (1).py","file_name":"Donor choose part3 (1).py","file_ext":"py","file_size_in_byte":27992,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"340807359","text":"def copyFiles(f1, f2, f3):\n    \"\"\"\n    Copies f1 and f2 onto f3.\n\n    The function assumes that files f1 and f2 can be opened, and that\n    no error occurs in writing file f3.\n    Therefore, the function will always return 0.\n    (Error handling with file I/O will be part of next week's lecture.)\n    :param f1: 'in1.txt'\n    :param f2: 'in2.txt'\n    :param f3: 'out.txt'\n    :return: none\n    \"\"\"\n    # Make file output\n    file_out = open(f3, 'w')\n\n    # About file a\n\n    file_a = open(f1, 'r')\n\n    add = file_a.readline()\n    while add != '':\n        file_out.write(add)\n        add = file_a.readline()\n\n    file_a.close()\n\n    # About file b\n\n    file_b = open(f2, 'r')\n\n    add = file_b.readline()\n    while add != '':\n        file_out.write(add)\n        add = file_b.readline()\n\n    file_b.close()\n\n    # Close file output\n    file_out.close()\n\n    return 0    # Feedback : -5(20%) missing return 0\n\n    #\n    # Test code\n    #\n    # copyFiles('p2 in1.txt', 'p2 in2.txt', 'p2 out.txt')\n    #\n\n    # Feedback : -5(20%) missing return 0\n","sub_path":"Lab solutions/Lab10/lab10_p2 Feedback.py","file_name":"lab10_p2 Feedback.py","file_ext":"py","file_size_in_byte":1044,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"523984855","text":"import numpy as np\r\nimport cv2\r\nfrom PIL import Image, ImageEnhance\r\nimport sys\r\nimport scipy\r\nfrom scipy import ndimage\r\nimport math\r\nfrom matplotlib import pyplot as plt\r\npath=\"\"\r\ndef check_aspect_ratio(aspect_ratio):\r\n    if(aspect_ratio<=1.9 and aspect_ratio>=1/1.9):\r\n        return True\r\n    return False\r\ndef check_dimension_within_range(img_height,img_width,crop_img_height,crop_img_width):\r\n    if(img_height!=crop_img_height and img_width!=crop_img_width and min(crop_img_height,crop_img_width)>=min(img_width,img_height)/10.0):\r\n        return True\r\n    return False\r\n\r\ndef add_motion_blurr(img):\r\n    size = 15\r\n    #generating the kernel\r\n    kernel_motion_blur = np.zeros((size, size))\r\n    kernel_motion_blur[int((size-1)/2), :] = np.ones(size)\r\n    kernel_motion_blur = kernel_motion_blur / size\r\n    output = cv2.filter2D(img, -1, kernel_motion_blur)\r\n    return output\r\n\r\ndef enhance(img):\r\n    kernel3 = np.array([[1,4,6,4,1],[4,16,24,16,4],[6,24,-476,24,6],[4,16,24,16,4],[1,4,6,4,1]])\r\n    kernel3 = kernel3/(-256)\r\n    output = cv2.filter2D(img, -1, kernel3)\r\n    return output\r\ndef sharpen(img):\r\n    kernel2 = np.array([[-1,-1,-1],[-1,9,-1],[-1,-1,-1]])\r\n    output = cv2.filter2D(img,-1,kernel2)\r\n    return output\r\n\r\ndef adapatative_histogram_equalisation(img):\r\n    bgr=img\r\n    #convert image from rgb to lab - l:lightness, a:green-red and b-blue yellow, numerical values\r\n    lab = cv2.cvtColor(bgr, cv2.COLOR_BGR2LAB)\r\n    #split into l,a,b\r\n    lab_planes = cv2.split(lab)\r\n    #clahe on lightness\r\n    clahe = cv2.createCLAHE(clipLimit=2.0,tileGridSize=(8,8))\r\n    lab_planes[0] = clahe.apply(lab_planes[0])\r\n    #merge with image\r\n    lab = cv2.merge(lab_planes)\r\n    #convert back to bgr\r\n    bgr = cv2.cvtColor(lab, cv2.COLOR_LAB2BGR)\r\n    return bgr\r\n\r\ndef denoise(img):\r\n    return cv2.fastNlMeansDenoisingColored(img,None,10,10,7,21)\r\n\r\ndef read_image(title,type):\r\n    directory = r\"C:\\Users\\MyPC\\Desktop\\IA project Final\\Images\\CroppedImages\"\r\n    path = directory+title\r\n    import os\r\n    if not os.path.exists(path):\r\n        os.makedirs(path)\r\n    img = cv2.imread(r'C:\\Users\\MyPC\\Desktop\\IA project Final\\Images'+title+'.'+type)\r\n    return img,path\r\n\r\ndef detect_red(img):\r\n    img_hsv = cv2.cvtColor(crop_img, cv2.COLOR_BGR2HSV)\r\n    mask1 = cv2.inRange(img_hsv, (0,50,20), (5,255,255))\r\n    mask2 = cv2.inRange(img_hsv, (175,50,20), (180,255,255))\r\n    mask = cv2.bitwise_or(mask1, mask2 )\r\n    output = cv2.bitwise_and(crop_img,crop_img,mask=mask)\r\n    return output\r\ndef detect_blue(img):\r\n    img_hsv = cv2.cvtColor(crop_img, cv2.COLOR_BGR2HSV)\r\n    mask = cv2.inRange(img_hsv, (90,127,127), (130,255,255))\r\n    mask2 = cv2.inRange(img_hsv, (86,31,4), (220,88,50))\r\n    output = cv2.bitwise_and(crop_img,crop_img,mask=mask+mask2)\r\n    return output\r\n\r\ndef get_crop_image(img,x,y,w,h,wdth,ht):\r\n   \r\n    h1 = int(h+h*0.4)\r\n    w1 = int(w+0.4*w)\r\n    x1 = int(x-0.2*w)\r\n    y1 = int(y-0.2*h)\r\n    if(x1>=0):\r\n        x = x1\r\n    if(y1>=0):\r\n        y = y1\r\n    if(w<=wdth):\r\n        w = w1\r\n    if(h<=ht):\r\n        h = h1\r\n    crop_img = img[y:y+h, x:x+w]\r\n    return crop_img\r\n\r\n\r\n\r\n\r\n\r\n\r\nimg,path = read_image(r\"\\icy_road\",\"jpg\")\r\nimg = enhance(img)\r\nimg = adapatative_histogram_equalisation(img)\r\nimg = denoise(img)\r\n\r\n#covert to graysale \r\ngray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\r\n#do thresholding\r\nret,thresh = cv2.threshold(gray,127,255,cv2.THRESH_BINARY_INV)\r\nimage, contours, h = cv2.findContours(thresh,1,2)\r\nnumber=1\r\nfor i, cnt in enumerate (contours):\r\n    approx = cv2.approxPolyDP(cnt,0.01*cv2.arcLength(cnt,True),True)\r\n    \r\n    x, y, w, h = cv2.boundingRect(cnt)\r\n    wdth, ht, g = img.shape\r\n\r\n    crop_img = get_crop_image(img,x,y,w,h,wdth,ht)\r\n    output_red = detect_red(crop_img)\r\n    crop_img_width = crop_img.shape[0]\r\n    crop_img_height = crop_img.shape[1]\r\n    img_width = img.shape[0]\r\n    img_height = img.shape[1]\r\n\r\n    crop_img_num_red_pixels = np.count_nonzero(output_red)\r\n    crop_img_num_pixels = output_red.shape[0]*output_red.shape[1]\r\n\r\n    \r\n    aspect_ratio = float(crop_img_width/crop_img_height)\r\n    color_ratio_red = float(crop_img_num_red_pixels/crop_img_num_pixels)\r\n    \r\n\r\n    if(not np.all(output_red==0) and color_ratio_red>0.1 and check_aspect_ratio(aspect_ratio) and check_dimension_within_range(img_height,img_width,crop_img_height,crop_img_width)):\r\n       if len(approx)==3 or len(approx)==8:\r\n           print(approx)\r\n           break\r\n           cv2.imwrite(path+r\"\\Crop_img_\"+str(number)+\".png\",crop_img)\r\n           number+=1\r\n            \r\n    output_blue = detect_blue(crop_img)\r\n    crop_img_num_blue_pixels = np.count_nonzero(output_blue)\r\n    crop_img_num_pixels = output_blue.shape[0]*output_blue.shape[1]\r\n    color_ratio_blue = float(crop_img_num_blue_pixels/crop_img_num_pixels)\r\n\r\n    if(not np.all(output_blue==0) and color_ratio_blue>0.1 and check_aspect_ratio(aspect_ratio) and check_dimension_within_range(img_height,img_width,crop_img_height,crop_img_width)):\r\n       if len(approx) > 10 or len(approx)==3 or len(approx)==8:\r\n            cv2.imwrite(path+r\"\\Crop_img_\"+str(number)+\".png\",crop_img)\r\n            number+=1\r\n","sub_path":"Demo/ExtractROI.py","file_name":"ExtractROI.py","file_ext":"py","file_size_in_byte":5152,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"370475463","text":"from itertools import product\n\nn = int(input())\nal = list(map(int, input().split()))\nres = 0\nfor p in product([-1,0,1], repeat=n):\n    bl = [al[i]+p[i] for i in range(n)]\n    m = 1\n    for b in bl:\n        m *= b\n    if m%2==0:\n        res += 1\nprint(res)","sub_path":"work/code_festival_2017_qualc_b.py","file_name":"code_festival_2017_qualc_b.py","file_ext":"py","file_size_in_byte":255,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"445511400","text":"from django.urls import path\nfrom .views import *\n\nurlpatterns = [\n\tpath('about/',vista_about, name = 'vista_about'),\n\n\t#Listas\n\tpath('libros/', vista_libros, name='vista_libros'),\n\tpath('autores/', vista_autores, name='vista_autores'),\n\tpath('generos/', vista_generos, name='vista_generos'),\n\tpath('editoriales/', vista_editoriales, name='vista_editoriales'),\n\n\t#Agregar\n\tpath('agregar_libro/', vista_agregar_libro, name='vista_agregar_libro'),\n\tpath('agregar_autor/', vista_agregar_autor, name='vista_agregar_autor'),\n\tpath('agregar_genero/', vista_agregar_genero, name='vista_agregar_genero'),\n\tpath('agregar_editorial/', vista_agregar_editorial, name='vista_agregar_editorial'),\n\n\t#Ver\n\tpath('ver_libro/<int:id_lbr>/', vista_ver_libro, name='vista_ver_libro'),\n\tpath('ver_autor/<int:id_aut>/', vista_ver_autor, name='vista_ver_autor'),\n\tpath('ver_genero/<int:id_gen>/', vista_ver_genero, name='vista_ver_genero'),\n\tpath('ver_editorial/<int:id_edi>/', vista_ver_editorial, name='vista_ver_editorial'),\n\n\t#Editar\n\tpath('editar_libro/<int:id_lbr>/', vista_editar_libro, name='vista_editar_libro'),\n\tpath('editar_autor/<int:id_aut>/', vista_editar_autor, name='vista_editar_autor'),\n\tpath('editar_genero/<int:id_gen>/', vista_editar_genero, name='vista_editar_genero'),\n\tpath('editar_editorial/<int:id_edi>/', vista_editar_editorial, name='vista_editar_editorial'),\n\tpath('eliminar_libro/<int:id_lib>/', vista_eliminar_libro, name='vista_eliminar_libro'),\n\tpath('login/', vista_login, name= 'vista_login'),\t\n\tpath('logout/', vista_logout, name='vista_logout'),\n\tpath('registro/', vista_registro, name='vista_registro'),\n\tpath('ws/libros/', ws_libros_vista, name='ws_libros_vista'),\n\n]","sub_path":"mis_gustos/salida/home/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1684,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"166168439","text":"# modules\nimport numpy as np\nfrom scipy.ndimage.interpolation import rotate\n\nclass g_silpion3():\n    '''\n    '''\n\n    def __init__(self):\n        self.original = np.zeros([10,10,10])\n        for i in range(8):\n            self.original[1:-2, 1:-2, 5] = 1\n\n        self.xspeed = 0.1\n        self.yspeed = 0.1\n\n    def control(self, xspeed, yspeed, blub2):\n        self.xspeed = 5*xspeed+0.01\n        self.yspeed = 5*yspeed\n\n\n    def label(self):\n        return ['rotating speed 1', round(self.xspeed, 2),\n                'rotating speed 2', round(self.yspeed, 2),\n                'empty', 'empty']\n\n\n    def generate(self, step, dumpworld):\n        # create world\n        world = np.zeros([3, 10, 10, 10])\n\n        # rotate\n        newworld = rotate(self.original, step*self.xspeed,\n                          axes = (1,2), order = 1,\n\t                      mode = 'nearest', reshape = False)\n\n        newworld = rotate(newworld, step*self.yspeed,\n                          axes = (0,1), order = 1,\n\t                      mode = 'nearest', reshape = False)\n\n        # insert array\n        world[0, :, :, :] = newworld\n        world[1, :, :, :] = newworld\n        world[2, :, :, :] = newworld\n\n        return np.clip(world, 0, 1)\n","sub_path":"generators/g_silpion3.py","file_name":"g_silpion3.py","file_ext":"py","file_size_in_byte":1227,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"248788746","text":"import tensorflow as tf\nimport numpy as np\nfrom numpy import array\nfrom skimage.io import imread\nfrom skimage.transform import resize\nfrom skimage import color\nimport matplotlib.pyplot as plt\nimport CNN as g\nimport init\n\n\nf = open(init.dict['test_img_list'], 'r')\nX_ = tf.placeholder(tf.float32, [None, init.dict['col'], init.dict['row'], 3])\ntotal_cnt = 0\nacc = 0.\n\nwith tf.Session(graph=g.graph, config=g.config) as sess:\n    sess.run(tf.global_variables_initializer())\n    checkpoint = tf.train.latest_checkpoint('save')\n\n    if checkpoint:\n        print('LOAD..')\n        g.saver.restore(sess, checkpoint)\n\n    for line in f.readlines():\n        path = line.split(' ')[0]\n        label = line.split(' ')[1]\n        total_cnt += 1\n\n        try:\n            img = imread(path)\n            img = resize(img, (init.dict['col'], init.dict['row']),\n                              mode='constant').astype(np.float32)\n\n        except ValueError as e:\n            print(\"ValueError : {}\".format(e))\n            continue\n\n        X_ = array(img).reshape(1, 224, 224, 3)\n        pred = sess.run(g.logits, feed_dict={g.X: X_, g.IS_TRAIN: False})\n\n        if np.argmax(pred) == int(label):\n            print('correct')\n            print(pred, label)\n            acc += 1\n        else:\n            print('wrong')\n            print(pred, label)\n\n        print(acc/total_cnt)\n","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1363,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"28456698","text":"from tkinter import *\nfrom tkinter import ttk\n\nCOLOR1 = 'Red'\nCOLOR2 = 'Blue'\nCOLOR3 = 'Yellow'\nclass Window(Tk):\n    def __init__(self):\n        super(Window, self).__init__()\n\n        self.title(\"My Label\")\n        self.minsize(500,400)\n        # self.label = ttk.Label(self, text = 'application')\n        # self.label.grid(column = 1, row = 0)\n        self.create_combo()\n        self.create_radio()\n    def rad_event(self):\n        radSelected = self.radValues.get()\n        if radSelected == 1:\n            self.configure(background=COLOR1)\n        elif radSelected == 2:\n            self.configure(background=COLOR2)\n        elif radSelected == 3:\n            self.configure(background=COLOR3)\n       \n\n    def create_radio(self):\n        self.radValues = IntVar()\n        self.rad1 = ttk.Radiobutton(self,text = COLOR1, value = 1, variable = self.radValues , command = self.rad_event)\n        self.rad1.grid(column = 5, row = 5, sticky = W, columnspan = 3)\n\n        self.rad2 = ttk.Radiobutton(self,text = COLOR2, value = 2, variable = self.radValues , command = self.rad_event)\n        self.rad2.grid(column = 6, row = 6, sticky = W, columnspan = 3)\n\n        self.rad3 = ttk.Radiobutton(self,text = COLOR3, value = 3, variable = self.radValues , command = self.rad_event)\n        self.rad3.grid(column = 7, row = 7, sticky = W, columnspan = 3)\n\n\n    def create_combo(self):\n        self.languages = StringVar()\n        self.combobox = ttk.Combobox(self, width = 20, textvariable = self.languages)\n        self.combobox['values'] = ('python', 'java', 'C++', 'C#', 'Ruby', 'Php', 'CSS')\n        \n        self.combobox.grid(column = 0, row = 1)\n\n        self.label = ttk.Label(self, text = 'Select your language')\n        self.label.grid(column = 0, row = 0)\n\n        self.button = ttk.Button(self, text = 'click me', command = self.click_me)\n        self.button.grid(column = 2, row =0)\n\n    def click_me(self):\n        self.label.configure(text ='selected : ' + self.languages.get())\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n# ##############\n#         self.text_entry()\n    \n\n#     def click_me(self):\n#         self.label.configure(text = 'Hello ' + self.name.get())\n#     def text_entry(self):\n#         self.name = StringVar()\n#         self.label == ttk.Label(self, text ='enter your name')\n#         self.label.grid(column = 15, row = 0)\n\n#         self.textbox = ttk.Entry(self, width = 20,  textvariable = self.name)\n#         self.textbox.focus()\n#         self.textbox.grid(column = 15, row = 1)\n\n#         self.button = ttk.Button(self, text = 'click me', command = self.click_me)\n#         self.button.grid(column = 15, row = 2)\n\n\n\n\n\n#####################\n        #self.wm_iconbitmap(\"myicon.ico\")\n        # self.createLabel()\n        #self.create_layout()\n       ################ \n        \n\n\n        #  self.button = ttk.Button(self, text = 'Click me', command = self.click_me)\n        #  self.button.grid(column = 0, row = 0)\n\n    # def click_me(self):\n    #     self.label.configure(text = 'text is changed')\n    #     self.label.configure(foreground = 'red', background = 'yellow')\n    #     self.button.configure(text = 'button changed')\n##############\n    # def createLabel(self):\n    #     labelFont = ('times', 40 , 'bold')\n    #     label = Label(self, text = \"TKinter Label Creation\")\n    #     label.config(font = labelFont, bg ='black', fg ='blue')\n    #     label.grid(column = 0, row = 0)\n\n    # def create_layout(self):\n    #     Label(self, text = \"Pack layout example\").pack()\n    #     Button(self, text = 'button ').pack()\n    #     Button(self, text = 'expand = 1').pack(expand = 1)\n    #     Button(self, text = 'fill = x, expand = 1').pack(fill = X, expand = 1)\n\n    #     Button(self, text = 'LEFT').pack(side = LEFT)\n    #     Button(self, text = 'BOTTOM').pack(side = BOTTOM)\n    #     Button(self, text = 'RIGHT').pack(side = RIGHT)\n\n\nwindow = Window()\nwindow.mainloop()","sub_path":"Code/AshtonSmith/ashton_TKinter_demo.py","file_name":"ashton_TKinter_demo.py","file_ext":"py","file_size_in_byte":3890,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"15647926","text":"'''\r\nCreated on May 21, 2014\r\n\r\n@author: Wang Jinde\r\n'''\r\nimport unittest\r\nimport mock\r\n\r\nfrom mstr.cleartool import Cleartool\r\nfrom mstr.exception import AppException, CCCheckInError, CCCheckOutException\r\nfrom mstr.config import Config\r\n\r\nclass CleartoolTest(unittest.TestCase):\r\n\r\n    def setUp(self):\r\n        # won't really execute cleartool command.\r\n        self.config = Config('testcases.config')\r\n        self.ct = Cleartool(self.config.get('cleartool', 'cc_view_path'))\r\n\r\n    def tearDown(self):\r\n        pass\r\n\r\n    def __mocked_cc_exec(self, cmd, *args):\r\n        '''Mocked cc_exec implementation.'''\r\n        command_str = 'cleartool ' + ' '.join(cmd) + ' '.join(args)\r\n        print(command_str)\r\n        return command_str\r\n\r\n    # Note, we cannot move the patch decorator to setUp. \r\n    @mock.patch.object(Cleartool, 'cc_exec')\r\n    def test_transaction(self, mock_cc_exec):\r\n        # mock Cleartool.cc_exec\r\n        mock_cc_exec.side_effect = self.__mocked_cc_exec\r\n        \r\n        self.ct.co('a/a.h')\r\n        self.assertEqual(set(['a/a.h']), self.ct.checkedout)\r\n        \r\n        self.ct.co('b.h')\r\n        self.assertEqual(set(['a/a.h', 'b.h']), self.ct.checkedout)\r\n        \r\n        self.ct.unco('a/a.h')\r\n        self.assertEqual(set(['b.h']), self.ct.checkedout)\r\n        \r\n        self.ct.mkelem('c.h', False)\r\n        self.assertEqual(set(['b.h', 'c.h']), self.ct.checkedout)\r\n        \r\n        self.ct.rollback()\r\n        self.assertEqual(set(), self.ct.checkedout)\r\n        \r\n    def test_cc_exec_exceptions(self):\r\n        no_file = 'not_exist_file'\r\n        self.ct._add_checkedout('t1.txt')\r\n        self.assertRaises(CCCheckInError, self.ct.ci, 't1.txt', 'test')\r\n        self.assertRaises(CCCheckOutException, self.ct.co, no_file)\r\n        self.assertRaises(AppException, self.ct.co, 'a/a/a/a/a')\r\n\r\nif __name__ == \"__main__\":\r\n    #import sys;sys.argv = ['', 'Test.testName']\r\n    unittest.main()","sub_path":"test/mstr/test_cleartool.py","file_name":"test_cleartool.py","file_ext":"py","file_size_in_byte":1936,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"53194217","text":"\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\nclass EnergyMod():\n    def __init__(self):\n        pass\n\n    def modHistory(self, df, featList):\n\n        #featList.append(\"tick_volume\")\n        rawDf = df.copy()\n        nDiffs = 0\n        for i in range(nDiffs):\n            for feat in featList:\n                notShifted = rawDf[feat]\n                shiftedData = rawDf[feat].shift(periods=1)\n                rawDf[feat] = notShifted - shiftedData\n            iter = next(rawDf.iterrows())\n            rawDf = rawDf.drop(iter[0])\n        #featList.remove(\"tick_volume\")\n\n        #rawDf.reset_index(drop=True, inplace=True)\n        modDf = rawDf.copy()\n        #modDf.reset_index(drop=True, inplace=True)\n        nSteps = 0\n        enFeatDict = {}\n        for feat in featList:\n            enFeatDict[feat] = []\n        for currentRow in modDf.iterrows():\n            #currentRow = modDf.iloc[i].copy()\n            for feat in featList:\n                sign = 1\n                if currentRow[1][feat] < 0:\n                    sign = -1\n                enFeatDict[feat].append( sign * ((currentRow[1][feat] ** 2) / 2) * abs(currentRow[1][\"tick_volume\"]) )\n            nSteps += 1\n            #if nSteps % (modDf.shape[0] // 20) == 0:\n            #    print( \"Energy mod: {:.2%}\".format(nSteps / modDf.shape[0]) )\n        for feat in featList:\n            modDf[feat] = enFeatDict[feat]\n        #modDf.reset_index(drop=True, inplace=True)\n        colDict = {}\n        for feat in featList:\n            colDict[feat] = \"en\" + feat\n        modDf.rename(columns=colDict, inplace=True)\n        colToRemove = [\"open\", \"high\", \"low\", \"close\"]\n        for feat in featList:\n            colToRemove.remove(feat)\n        modDf.drop(colToRemove, axis=1, inplace=True)\n        #modDf.drop( [\"real_volume\", \"spread\", \"tick_volume\", \"datetime\"], axis=1, inplace=True )\n\n        rawDf = df.copy()\n        #rawDf.reset_index(drop=True, inplace=True)\n        rawDf = rawDf.drop(rawDf.index.values[0])\n        #rawDf.reset_index(drop=True, inplace=True)\n\n        for feat in featList:\n            rawDf[colDict[feat]] = modDf[colDict[feat]]\n\n        return rawDf\n\n    def checkQuality(self, df):\n\n        fig, ax = plt.subplots(nrows=2, ncols=1)\n\n        enOpen = df[\"enopen\"].values\n        x = [x for x in range(df.shape[0])]\n        ax[0].plot( x, enOpen )\n\n        rawOpen = df[\"open\"].values\n        ax[1].plot(x, rawOpen)\n\n        plt.show()\n        pass","sub_path":"monlan/mods/EnergyMod.py","file_name":"EnergyMod.py","file_ext":"py","file_size_in_byte":2450,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"114574908","text":"###\n# File: code_object.py\n# Created Date: 2020-10-29\n# Author: anddy.liu\n# Contact: <lqdflying@gmail.com>\n# \n# Last Modified: Thursday October 29th 2020 3:12:03 pm\n# \n# Copyright (c) 2020 personal\n# <<licensetext>>\n# -----\n# HISTORY:\n# Date      \t By\tComments\n# ----------\t---\t----------------------------------------------------------\n###\ndef make(n):\n    ret = []\n\n    for i in range(n):\n        def test(x, y=10): # test = make_function(code)\n            x += 10\n            print(x, y)\n        print(id(test), id(test.__code__))\n        ret.append(test)\n        \n    return ret\n\nif __name__ == \"__main__\":\n    make(3)","sub_path":"Learning_Python/Basic/Chapter14/code_object.py","file_name":"code_object.py","file_ext":"py","file_size_in_byte":622,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"171679472","text":"#!/usr/bin/env python\n\n#  Permission is hereby granted, free of charge, to any person \n#  obtaining a copy of this software and associated documentation \n#  files (the \"Software\"), to deal in the Software without restriction, \n#  including without limitation the rights to use, copy, modify, \n#  merge, publish, distribute, sublicense, and/or sell copies of the \n#  Software, and to permit persons to whom the Software is furnished \n#  to do so, subject to the following conditions:\n#\n#  The above copyright notice and this permission notice shall be \n#  included in all copies or substantial portions of the Software.\n#\n#  THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, \n#  EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES \n#  OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. \n#  IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR \n#  ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF \n#  CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION \n#  WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n\"\"\"\nTest script for kealib Neighbours python bindings\n\"\"\"\nfrom __future__ import print_function\nimport os\nimport numpy\nfrom osgeo import gdal\nfrom kealib import neighbours\n\nTESTFILE = 'test.kea'\nN_VALUES = 8\n\ndef setupFile():\n    \"\"\"\n    Sets up a test file we can use and creates a single RAT column.\n    Returns a GDAL dataset.\n    \"\"\"\n    if os.path.exists(TESTFILE):\n        os.remove(TESTFILE)\n\n    driver = gdal.GetDriverByName('KEA')\n    ds = driver.Create(TESTFILE, 100, 100, 1, gdal.GDT_Byte)\n    band = ds.GetRasterBand(1)\n    band.SetMetadataItem('LAYER_TYPE', 'thematic')\n\n    rat = band.GetDefaultRAT()\n    rat.CreateColumn('Values', gdal.GFT_Integer, gdal.GFU_Generic)\n    rat.SetRowCount(N_VALUES)\n    values = numpy.arange(N_VALUES, dtype=numpy.integer)\n    rat.WriteArray(values, 0)\n\n    return ds\n\ndef testList(ds):\n    \"\"\"\n    Tests the ability to read and write lists of neighbours\n    \"\"\"\n    neighbourData = [[2, 3, 1], [5], [8, 0, 2], [6, 7,2]]\n    neighbours.setNeighbours(ds, 1, 0, neighbourData)\n\n    readData = neighbours.getNeighbours(ds, 1, 0, 4)\n    if readData != neighbourData:\n        raise SystemExit(\"Data doesn't match\")\n\ndef testArray(ds):\n    \"\"\"\n    Tests the ability to read and write numpy masked arrays\n    \"\"\"\n    # create a masked array to test\n    numpyNeighboursData = numpy.array([[8, 7, 0], [9, 0, 0], [1, 4, 3], \n                        [7, 0, 0]])\n    numpyNeighboursMask = numpy.array([[False, False, True], [False, True, True], \n                    [False, False, False], [False, True, True]])\n    numpyNeighbours = numpy.ma.MaskedArray(numpyNeighboursData, \n                    mask=numpyNeighboursMask)\n    # set it\n    neighbours.setNeighbours(ds, 1, 4, numpyNeighbours)\n\n    # get it\n    readData, readMask = neighbours.getNeighbours(ds, 1, 4, 4, \n                    neighbours.NEIGHBOURS_ARRAY)\n\n    # turn back into a masked array\n    numpyNeighboursOut = numpy.ma.MaskedArray(readData, mask=readMask)\n    if not (numpyNeighbours == numpyNeighboursOut).all():\n        raise SystemExit(\"Array Data doesn't match\")\n\ndef doTests():\n    \"\"\"\n    Main function\n    \"\"\"\n    ds = setupFile()\n    testList(ds)\n    testArray(ds)\n","sub_path":"python/run_test.py","file_name":"run_test.py","file_ext":"py","file_size_in_byte":3288,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"234179759","text":"'''\nThis module will contain the models: random forest, adaboost, and convolutional neural network.\n\n'''\n\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.metrics import mean_absolute_error\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.model_selection import cross_val_predict\nimport numpy as np\n\n\n\t\ndef train_model(model, X_train, y_train):\n\t'''\n\tTrains a model with X features predicting y.\n\tThis function can use any scikit-learn regressor\n\t'''\n\tmodel.fit(X_train, y_train.ravel())\n\treturn model\n\t\n\ndef evaluate_model(model, X_test, y_test):\n\t'''Generates statistics using the train and test data, \n\tmuch faster than cross validation, but not as accurate.\n\t'''\n\ty_pred = model.predict(X_test)\n\tmse = mean_squared_error(y_test, y_pred)\n\tmae = mean_absolute_error(y_test, y_pred)\n\trmse = np.sqrt(mse)\t   \n\tprint('mse {}, rmse {}'.format(mse, rmse))\n\tprint('Coefficient of Determination R^2:', model.score(X_test, y_test))\n\tprint('Mean Absolute Error:', mae)\n\t\ndef cross_val(regressor, X_train, y_train):\n\t''' Generates ten fold cross validation MSE and RMSE values\n\tParameters\n\t------------\n\tregressor : any scikit-learn regressor\n\t\tRegressor object.\n\n\tX_train, y_train : ndarrays\n\t\tArrays generated by the generate_training_data function\n\n\tReturns\n\t-------\n\tMean squared error, standard deviation of MSE, and the root mean squared error\n\t'''\n\tscores = cross_val_score(regressor, X_train, y_train.ravel(),\n\t\t\t\t\t\t scoring=\"neg_mean_squared_error\", cv=10)\n\tprint(\"Mean MSE:\", abs(scores.mean()))\n\tprint(\"Standard deviation of MSE:\", scores.std())\n\tprint('Mean RMSE:', np.sqrt(abs(scores.mean())))\n\treturn scores\n\t\n\n\ndef generate_predictions(regressor, X_all, y_all):\n\t'''\n\tFunction uses all of the data to make predictions using cross validation\n\t'''\n\ty_predicted = cross_val_predict(regressor, X_all, y_all.ravel(), cv=10)\n\ty_predicted = y_predicted.reshape(-1,1)\n\treturn y_predicted","sub_path":"ML_Analysis/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1956,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"491245292","text":"from mpl_toolkits.mplot3d import Axes3D\nfrom mpl_toolkits.mplot3d.art3d import Poly3DCollection\nfrom matplotlib import cm\nimport matplotlib.pyplot as plt\nimport autograd.numpy as np\n\nplt.ion()\n\ndef fig3d():\n    '''Makes a new 3D figure with Matplotlib.\n\n    Input:\n        None\n    Output: fig, ax\n        fig: a matplotlib figure object\n        ax: a matplotlib axis object\n    '''\n    fig = plt.figure()\n    fig.figsize = (10, 10)\n    ax = fig.add_subplot(111, projection='3d')\n    ax.set_xlabel(\"X\")\n    ax.set_ylabel(\"Y\")\n    ax.set_zlabel(\"Z\")\n    return fig, ax\n\n\ndef set_axes_equal(ax):\n    '''Make axes of 3D plot have equal scale so that spheres appear as spheres,\n    cubes as cubes, etc..  This is one possible solution to Matplotlib's\n    ax.set_aspect('equal') and ax.axis('equal') not working for 3D.\n\n    Input\n      ax: a matplotlib axis, e.g., as output from plt.gca().\n    '''\n\n    x_limits = ax.get_xlim3d()\n    y_limits = ax.get_ylim3d()\n    z_limits = ax.get_zlim3d()\n\n    x_range = abs(x_limits[1] - x_limits[0])\n    x_middle = np.mean(x_limits)\n    y_range = abs(y_limits[1] - y_limits[0])\n    y_middle = np.mean(y_limits)\n    z_range = abs(z_limits[1] - z_limits[0])\n    z_middle = np.mean(z_limits)\n\n    # The plot bounding box is a sphere in the sense of the infinity\n    # norm, hence I call half the max range the plot radius.\n    plot_radius = 0.5 * max([x_range, y_range, z_range])\n\n    ax.set_xlim3d([x_middle - plot_radius, x_middle + plot_radius])\n    ax.set_ylim3d([y_middle - plot_radius, y_middle + plot_radius])\n    ax.set_zlim3d([z_middle - plot_radius, z_middle + plot_radius])\n\n    plt.tight_layout()\n\n\ndef point_cloud(input):\n    # Makes a point cloud plot out of an input array of 3d data. Input should be a Nx3 array.\n    fig, ax = fig3d()\n    ax.scatter(input[:, 0], input[:, 1], input[:, 2])\n","sub_path":"aerosandbox_legacy_v0/plotting.py","file_name":"plotting.py","file_ext":"py","file_size_in_byte":1837,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"457086564","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Oct 28 14:51:08 2020\r\n\r\n@author: agabh\r\n\"\"\"\r\n\r\n\r\n# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Oct 26 19:40:30 2020\r\n\r\n@author: agabh\r\n\"\"\"\r\n\r\nimport math\r\nimport random\r\nimport pygame\r\nimport tkinter as tk\r\nfrom tkinter import messagebox\r\nimport numpy as np\r\nfrom scipy.stats import bernoulli\r\nimport pandas as pd\r\n\r\nclass cube(object):\r\n    \r\n    def __init__(self,start,width, rows,dirnx=1,dirny=0,color=(255,0,0)):\r\n        self.pos = start\r\n        self.dirnx = 1\r\n        self.dirny = 0\r\n        self.color = color\r\n        self.w = width\r\n        self.rows = rows\r\n        \r\n        pos_x = self.pos[0]\r\n        pos_y = self.pos[1]\r\n        if pos_x >= self.rows:\r\n            pos_x = pos_x - self.rows\r\n        if pos_y >= self.rows:\r\n            pos_y = pos_y - self.rows\r\n        if pos_x < 0:\r\n            pos_x += self.rows\r\n        if pos_y < 0:\r\n            pos_y += self.rows\r\n        self.pos = (pos_x, pos_y)\r\n \r\n       \r\n    def move(self, dirnx, dirny):\r\n        self.dirnx = dirnx\r\n        self.dirny = dirny\r\n        pos_x = self.pos[0] + self.dirnx\r\n        pos_y = self.pos[1] + self.dirny\r\n        if pos_x >= self.rows:\r\n            pos_x = pos_x - self.rows\r\n        if pos_y >= self.rows:\r\n            pos_y = pos_y - self.rows\r\n        if pos_x < 0:\r\n            pos_x += self.rows\r\n        if pos_y < 0:\r\n            pos_y += self.rows\r\n        self.pos = (pos_x, pos_y)\r\n \r\n    def draw(self, surface, eyes=False):\r\n        dis = self.w // self.rows\r\n        i = self.pos[0]\r\n        j = self.pos[1]\r\n \r\n        pygame.draw.rect(surface, self.color, (i*dis+1,j*dis+1, dis-2, dis-2))\r\n        if eyes:\r\n            centre = dis//2\r\n            radius = 3\r\n            circleMiddle = (i*dis+centre-radius,j*dis+8)\r\n            circleMiddle2 = (i*dis + dis -radius*2, j*dis+8)\r\n            pygame.draw.circle(surface, (0,0,0), circleMiddle, radius)\r\n            pygame.draw.circle(surface, (0,0,0), circleMiddle2, radius)\r\n       \r\n \r\nclass snake(object):\r\n    body = []\r\n    turns = {}\r\n    def __init__(self, color, pos, width, rows, last_move):\r\n        self.color = color\r\n        self.head = cube(pos, width, rows)\r\n        self.body.append(self.head)\r\n        self.dirnx = 0\r\n        self.dirny = 1\r\n        self.last_move = last_move\r\n        self.width = width\r\n        self.rows = rows\r\n \r\n    def move(self, key):\r\n        for event in pygame.event.get():\r\n            if event.type == pygame.QUIT:\r\n                pygame.quit()\r\n\r\n        #key = random_move(len(self.body), self.last_move)\r\n        self.last_move = key\r\n \r\n        if key == -1 :\r\n            self.dirnx = -1\r\n            self.dirny = 0\r\n            self.turns[self.head.pos[:]] = [self.dirnx, self.dirny]\r\n \r\n        elif key == 1:\r\n            self.dirnx = 1\r\n            self.dirny = 0\r\n            self.turns[self.head.pos[:]] = [self.dirnx, self.dirny]\r\n \r\n        elif key == 10:\r\n            self.dirnx = 0\r\n            self.dirny = -1\r\n            self.turns[self.head.pos[:]] = [self.dirnx, self.dirny]\r\n \r\n        elif key == -10:\r\n            self.dirnx = 0\r\n            self.dirny = 1\r\n            self.turns[self.head.pos[:]] = [self.dirnx, self.dirny]\r\n\r\n \r\n        for i, c in enumerate(self.body):\r\n            p = c.pos[:]\r\n            if p in self.turns:\r\n                turn = self.turns[p]\r\n                c.move(turn[0],turn[1])\r\n                if i == len(self.body)-1:\r\n                    self.turns.pop(p)\r\n            else:\r\n                if c.dirnx == -1 and c.pos[0] <= 0: \r\n                    c.pos = (c.rows + c.pos[0] - 1, c.pos[1])\r\n                elif c.dirnx == 1 and c.pos[0] >= c.rows - 1: \r\n                    c.pos = (c.pos[0] - c.rows + 1, c.pos[1])\r\n                elif c.dirny == 1 and c.pos[1] >= c.rows - 1: \r\n                    c.pos = (c.pos[0], c.pos[1] - c.rows + 1)\r\n                elif c.dirny == -1 and c.pos[1] <= 0: \r\n                    c.pos = (c.pos[0], c.rows + c.pos[1] - 1)\r\n                else: \r\n                    c.move(c.dirnx,c.dirny)\r\n       \r\n \r\n    def reset(self, pos):\r\n        self.head = cube(pos, self.width, self.rows)\r\n        self.body = []\r\n        self.body.append(self.head)\r\n        self.turns = {}\r\n        self.dirnx = 0\r\n        self.dirny = 1\r\n \r\n \r\n    def addCube(self):\r\n        tail = self.body[-1]\r\n        dx, dy = tail.dirnx, tail.dirny\r\n\r\n        if dx == 1 and dy == 0:\r\n            if tail.pos[0] - 1 > 0 :\r\n                self.body.append(cube((tail.pos[0]-1,tail.pos[1]), self.width, self.rows))\r\n            else : \r\n                self.body.append(cube((tail.pos[0] - 1 + self.rows ,tail.pos[1]), self.width, self.rows))\r\n        elif dx == -1 and dy == 0:\r\n            if tail.pos[0] + 1 < self.rows :\r\n                self.body.append(cube((tail.pos[0]+1,tail.pos[1]), self.width, self.rows))\r\n            else :\r\n                self.body.append(cube((tail.pos[0] + 1 - self.rows, tail.pos[1]), self.width, self.rows))\r\n        elif dx == 0 and dy == 1:\r\n            if tail.pos[1] - 1 > 0 :\r\n                self.body.append(cube((tail.pos[0], tail.pos[1] - 1), self.width, self.rows))\r\n            else :\r\n                self.body.append(cube((tail.pos[0], tail.pos[1] - 1 + self.rows), self.width, self.rows))\r\n        elif dx == 0 and dy == -1:\r\n            if tail.pos[1] + 1 < self.rows : \r\n                self.body.append(cube((tail.pos[0], tail.pos[1] + 1), self.width, self.rows))\r\n            else :\r\n                self.body.append(cube((tail.pos[0], tail.pos[1] + 1 - self.rows), self.width, self.rows))\r\n \r\n        self.body[-1].dirnx = dx\r\n        self.body[-1].dirny = dy\r\n       \r\n \r\n    def draw(self, surface):\r\n        for i, c in enumerate(self.body):\r\n            if i ==0:\r\n                c.draw(surface, True)\r\n            else:\r\n                c.draw(surface)\r\n \r\n \r\ndef drawGrid(w, rows, surface):\r\n    sizeBtwn = w // rows\r\n \r\n    x = 0\r\n    y = 0\r\n    for l in range(rows):\r\n        x = x + sizeBtwn\r\n        y = y + sizeBtwn\r\n \r\n        pygame.draw.line(surface, (255,255,255), (x,0),(x,w))\r\n        pygame.draw.line(surface, (255,255,255), (0,y),(w,y))\r\n       \r\n \r\ndef redrawWindow(surface):\r\n    global rows, width, s, snack\r\n    surface.fill((0,0,0))\r\n    s.draw(surface)\r\n    snack.draw(surface)\r\n    drawGrid(width,rows, surface)\r\n    pygame.display.update()\r\n \r\n \r\ndef randomSnack(rows, item):\r\n \r\n    positions = item.body\r\n \r\n    while True:\r\n        x = random.randrange(rows)\r\n        y = random.randrange(rows)\r\n        if len(list(filter(lambda z:z.pos == (x,y), positions))) > 0:\r\n            continue\r\n        else:\r\n            break\r\n       \r\n    return (x,y)\r\n\r\n# in case if the snake's length is >1, there will be one less legal action to \r\n# choose from, depending on the current direction or last action\r\ndef random_move(snake_len, last_move):\r\n    actions = [1, -1, 10, -10]\r\n    if snake_len < 2:\r\n        move = random.choice(actions)\r\n    else:\r\n        actions2 = [x for x in actions if x != -1 * last_move]\r\n        move = random.choice(actions2)\r\n    return move\r\n\r\n\r\n#state to be defined as the direction in which the snake was moving as the result \r\n# of previous action,and the corresponding horizontal and vertical distance b/w current \r\n# pos and the snack\r\n#update_1 - the state should also include the info about the snake's body to avoid\r\n# the snake from hitting itself\r\n\r\n# the total no of states will be = n_rows * n_columns * 4(no. of possible directions \r\n# resulting from previous action)\r\n\r\n# states = np.zeros((rows, rows, 4))\r\n\r\ndef snake_mat(rows,snake):\r\n    mat = np.zeros((rows, rows))\r\n    for cube in snake.body:\r\n        mat[cube.pos[0]][cube.pos[1]] = 1\r\n    return mat\r\n\r\n#objective - to determine a kXk matrix around the snake's head to scan for body parts\r\n#function to return the x & y indices to scan for\r\n# def snake_surround(snake, k, rows):\r\n#     x = snake.body[0].pos[0]\r\n#     y = snake.body[0].pos[1]\r\n#     x_indices = np.arange(x - k, x + k + 1)\r\n#     y_indices = np.arange(y - k, y + k + 1)\r\n    \r\n#     x_2 = snake.body[1].pos[0]\r\n#     y_2 = snake.body[1].pos[0]\r\n#     dir_x = x - x_2\r\n#     dir_y = y - y_2\r\n    \r\n#     for x_i in x_indices:\r\n#         if x_i < 0:\r\n#             x_i += rows\r\n#         elif x_i >= rows:\r\n#             x_i -= rows\r\n#     for y_i in y_indices:\r\n#         if y_i < 0:\r\n#             y_i += rows\r\n#         elif y_i >= rows:\r\n#             y_i -= rows\r\n            \r\n#     x_indices = list(x_indices)\r\n#     y_indices = list(y_indices)\r\n    \r\n#     if abs(dir_x) > 0:\r\n#         x_indices.remove(x)\r\n#     if abs(dir_y) > 0:\r\n#         y_indices.remove(y)\r\n#     return x_indices, y_indices\r\n    \r\n#the objective is to evaluate the snake's head near surroundings to check for\r\n#self body blocks. this is broken into 3 parts - in the direction of snake's\r\n#movement, left and right to it.\r\n    \r\n# to achieve this 3 functions are made which will evaluate the nearby body parts\r\n    \r\n\r\ndef snake_gap_up(x_pos, y_pos, x_dir, y_dir, k, snake_mat):\r\n    if x_dir < 0:\r\n        indices = np.arange(x_pos - k, x_pos)\r\n    elif x_dir > 0:\r\n        indices = np.arange(x_pos + 1, x_pos + k + 1)\r\n    elif y_dir < 0:\r\n        indices = np.arange(y_pos - k, y_pos)\r\n    elif y_dir > 0:   \r\n        indices = np.arange(y_pos + 1, y_pos + k + 1)\r\n    \r\n    for i in range(0, len(indices)):\r\n        if indices[i] < 0:\r\n            indices[i] += rows\r\n        elif indices[i] >= rows:\r\n            indices[i] -= rows\r\n\r\n    gap_up = 0\r\n    if abs(x_dir) > 0 :\r\n        for i in indices:\r\n            if snake_mat[i][y_pos] > 0 and gap_up < 1:\r\n                gap_up += 1\r\n            else:\r\n                break\r\n    elif abs(y_dir) > 0:\r\n        for i in indices:\r\n            if snake_mat[x_pos][i] > 0 and gap_up < 1:\r\n                gap_up += 1\r\n            else:\r\n                break\r\n    return gap_up\r\n    \r\ndef snake_gap_left(x_pos, y_pos, x_dir, y_dir, k, snake_mat):\r\n    if x_dir < 0:\r\n        indices = np.arange(y_pos + 1, y_pos + k + 1)\r\n    elif x_dir > 0:\r\n        indices = np.arange(y_pos - k, y_pos)\r\n    elif y_dir < 0:\r\n        indices = np.arange(x_pos - k, x_pos)\r\n    elif y_dir > 0:   \r\n        indices = np.arange(x_pos + 1, x_pos + k + 1)\r\n       \r\n    for i in range(0, len(indices)):\r\n        if indices[i] < 0:\r\n            indices[i] += rows\r\n        elif indices[i] >= rows:\r\n            indices[i] -= rows\r\n    \r\n    gap_left = 0\r\n    if abs(x_dir) > 0 :\r\n        for i in indices:\r\n            if snake_mat[x_pos][i] > 0 and gap_left < 1:\r\n                gap_left += 1\r\n            else:\r\n                break\r\n    elif abs(y_dir) > 0:\r\n        for i in indices:\r\n            if snake_mat[i][y_pos] > 0 and gap_left < 1:\r\n                gap_left += 1\r\n            else:\r\n                break\r\n    return gap_left\r\n\r\ndef snake_gap_right(x_pos, y_pos, x_dir, y_dir, k, snake_mat):\r\n    if x_dir < 0:\r\n        indices = np.arange(y_pos - k, y_pos)\r\n    elif x_dir > 0:\r\n        indices = np.arange(y_pos + 1, y_pos + k + 1)\r\n    elif y_dir < 0:\r\n        indices = np.arange(x_pos + 1, x_pos + k + 1)\r\n    elif y_dir > 0:   \r\n        indices = np.arange(x_pos - k, x_pos)\r\n       \r\n    for i in range(0, len(indices)):\r\n        if indices[i] < 0:\r\n            indices[i] += rows\r\n        elif indices[i] >= rows:\r\n            indices[i] -= rows\r\n    \r\n    gap_right = 0\r\n    if abs(x_dir) > 0 :\r\n        for i in indices:\r\n            if snake_mat[x_pos][i] > 0 and gap_right < 1:\r\n                gap_right += 1\r\n            else:\r\n                break\r\n    elif abs(y_dir) > 0:\r\n        for i in indices:\r\n            if snake_mat[i][y_pos] > 0 and gap_right < 1:\r\n                gap_right += 1\r\n            else:\r\n                break\r\n    return gap_right\r\n\r\ndef current_state(prev_action, snake, snack, rows, k):\r\n    snake_head_pos = snake.body[0].pos\r\n    snack_pos = snack.pos\r\n    x_pos = snake_head_pos[0]\r\n    y_pos = snake_head_pos[1]\r\n    dis_x = np.abs(snake_head_pos[0] - snack_pos[0])\r\n    dis_y = np.abs(snake_head_pos[1] - snack_pos[1])\r\n    \r\n    dis_x = snake_dis_bucket(dis_x, dis_xs)\r\n    dis_y = snake_dis_bucket(dis_y, dis_ys)\r\n    \r\n    snake_len = len(snake.body)\r\n    snake_len_f = snake_len_bucket(snake_len)\r\n    gap_up = 0\r\n    gap_left = 0\r\n    gap_right = 0\r\n    \r\n    horz_gap = horizontal_gap_snake_snack(snake, snack_pos)\r\n    ver_gap = vertical_gap_snake_snack(snake, snack_pos)\r\n    \r\n    if len(snake.body) > 2:\r\n        dir_x = x_pos - snake.body[1].pos[0]\r\n        dir_y = y_pos - snake.body[1].pos[1]\r\n        \r\n        snake_pos_mat = snake_mat(rows, snake)\r\n        \r\n        gap_up = snake_gap_up(x_pos, y_pos, dir_x, dir_y, k, snake_pos_mat)\r\n        gap_left = snake_gap_left(x_pos, y_pos, dir_x, dir_y, k, snake_pos_mat)\r\n        gap_right = snake_gap_right(x_pos, y_pos, dir_x, dir_y, k, snake_pos_mat)\r\n        \r\n    return prev_action, dis_x, dis_y, gap_up, gap_left, gap_right, snake_len_f, horz_gap, ver_gap\r\n\r\n\r\ndef snake_len_bucket(snake_len) :\r\n    array = np.asarray(snake_len_v)\r\n    index = (np.abs(array - snake_len + 1)).argmin()\r\n    return array[index]\r\n\r\ndef horizontal_gap_snake_snack(snake, snack_pos) :\r\n    snake_head_pos = snake.body[0].pos\r\n    snake_head_x = snake_head_pos[0]\r\n    snack_head_x = snack_pos[0]\r\n    \r\n    if len(snake.body) < 2 :\r\n        return 0\r\n    \r\n    else :\r\n        index_list = [x.pos[0] for x in snake.body]\r\n        index_list.pop(0)\r\n        \r\n        sum1 = sum([(x > snack_head_x) and (x < snake_head_x) for x in index_list])\r\n        sum2 = sum([(x < snack_head_x) and (x > snake_head_x) for x in index_list])\r\n        \r\n        return (sum1 + sum2) > 0\r\n    \r\ndef vertical_gap_snake_snack(snake, snack_pos) :\r\n    snake_head_pos = snake.body[0].pos\r\n    snake_head_y = snake_head_pos[1]\r\n    snack_head_y = snack_pos[1]\r\n    \r\n    if len(snake.body) < 2 :\r\n        return 0\r\n    \r\n    else :\r\n        index_list = [y.pos[1] for y in snake.body]\r\n        index_list.pop(0)\r\n        \r\n        sum1 = sum([(y > snack_head_y) and (y < snake_head_y) for y in index_list])\r\n        sum2 = sum([(y < snack_head_y) and (y > snake_head_y) for y in index_list])\r\n        \r\n        return (sum1 + sum2) > 0\r\n    \r\ndef snake_dis_bucket(dis, array):\r\n    index = (np.abs(array - dis)).argmin()\r\n    return array[index]\r\n    \r\nglobal state_dict, prev_actions, width, rows, eps, state_counter, q_mat, dis_xs, dis_ys, snake_len_v\r\neps = 0.3\r\nalpha = 0.7\r\ngamma = 0.9\r\n\r\nstate_counter = 0\r\nstate_dict = {}\r\nrows = 10\r\nwidth = 200\r\n\r\nprev_actions = [1, -1, 10, -10]\r\ndis_xs = np.arange(0, rows, 1)\r\ndis_ys = np.arange(0, rows, 1)\r\ngaps_up = [0, 1]\r\ngaps_left = [0, 1]\r\ngaps_right = [0, 1]\r\nsnake_len_v = [0, 5, 10, 15, 20, 30, 50, 100]\r\nhorz_gap = [0, 1]\r\nvert_gap = [0, 1]\r\n\r\nfor a in prev_actions:\r\n    for b in dis_xs:\r\n        for c in dis_ys:\r\n            for d in gaps_up:\r\n                for e in gaps_left:\r\n                    for f in gaps_right:\r\n                        for g in snake_len_v:\r\n                            for h in horz_gap:\r\n                                for i in vert_gap: \r\n                                    state_dict[a,b,c,d,e,f,g,h,i] = state_counter\r\n                                    state_counter += 1\r\n            \r\nglobal q_mat\r\nq_mat = np.zeros((len(state_dict), len(prev_actions)))\r\n                        \r\ndef q_matrix_update(current_state, current_action, next_state, snack, dead):\r\n    state_counter = state_dict[current_state]\r\n    state_counter_next = state_dict[next_state]\r\n    current_action_index = prev_actions.index(current_action)\r\n    reward = reward_function(current_state, next_state, snack, dead)\r\n    max_reward_next_state = np.max(q_mat[state_counter_next])\r\n    delta = alpha * (reward + gamma * max_reward_next_state - q_mat[state_counter, current_action_index])\r\n    q_mat[state_counter, current_action_index] += delta\r\n    return q_mat\r\n\r\ndef reward_function(current_state, next_state, snack, dead):\r\n    old_dis_x = current_state[1]\r\n    old_dis_y = current_state[2]\r\n    new_dis_x = next_state[1]\r\n    new_dis_y = next_state[2]\r\n    \r\n    gap_up_current = current_state[3]\r\n    gap_left_current = current_state[4]\r\n    gap_righ_current = current_state[5]\r\n    \r\n    gap_up_next = next_state[3]\r\n    gap_left_next = next_state[4]\r\n    gap_right_next = next_state[5]\r\n    \r\n    old_dis = np.sqrt((old_dis_x**2 + old_dis_y**2))\r\n    new_dis = np.sqrt((new_dis_x**2 + new_dis_y**2))\r\n    \r\n    if old_dis < new_dis :\r\n        reward_dis = -100 * (new_dis - old_dis)\r\n    else :\r\n        reward_dis = 100 * (old_dis - new_dis)\r\n    \r\n    if snack == 1:\r\n        reward_snack = 1000\r\n    else :\r\n        reward_snack = 0\r\n        \r\n    if dead == 1 :\r\n        reward_dead = -10000\r\n    else :\r\n        reward_dead = 0\r\n        \r\n    # if gap_up_next + gap_left_next + gap_right_next == 2:\r\n    #     reward_gap_next = -100\r\n    # elif gap_up_next + gap_left_next + gap_right_next == 3:\r\n    #     reward_gap_next = -10000\r\n    # else:\r\n    #     reward_gap_next = 0\r\n    \r\n    return reward_dis + reward_snack + reward_dead #+ reward_gap_next\r\n    \r\ndef action(current_state, last_move, snake_len_actual):\r\n    explore = bernoulli.rvs(eps, size = 1)[0]\r\n    index_neg_last_action = prev_actions.index(-1 * last_move)\r\n    if current_state[6]:\r\n        snake_len = 2\r\n    else :\r\n        snake_len = 1\r\n    if explore :\r\n        # print('action1')\r\n        return random_move(snake_len_actual, current_state[0])\r\n        \r\n    else :\r\n        state_counter = state_dict[current_state]\r\n        action_array = np.where(q_mat[state_counter] == max(q_mat[state_counter]))[0]\r\n        \r\n        if snake_len_actual < 2 :\r\n            if len(action_array) > 1 :\r\n                action_choice = random.choice(action_array)\r\n                # print('action2')\r\n                return prev_actions[action_choice]\r\n            else:\r\n                # print('action3')\r\n                return prev_actions[action_array[0]]\r\n        else:\r\n            if index_neg_last_action in action_array :\r\n                action_list = list(action_array)\r\n                action_list.remove(index_neg_last_action)\r\n                if len(action_list) == 0 :\r\n                    lst = list(q_mat[state_counter])\r\n                    lst_cpy = set(lst)\r\n                    lst_cpy.remove(max(lst_cpy))\r\n                    new_max = max(lst_cpy)\r\n                    new_action_array = np.where(q_mat[state_counter] == new_max)[0]\r\n                    action_choice = random.choice(new_action_array)\r\n                    return prev_actions[action_choice]\r\n                else:\r\n                    # print('action4')\r\n                    action_choice = random.choice(action_list)\r\n                    return prev_actions[action_choice]\r\n            else:\r\n                # print('action5')\r\n                action_choice = random.choice(action_array)\r\n                return prev_actions[action_choice]\r\n \r\n\r\nglobal scores, steps\r\n\r\nscores_l = []\r\nsteps_l = []\r\n \r\ndef main():\r\n    global width, rows, s, snack, eps\r\n    eps = 0.1\r\n    win = pygame.display.set_mode((width, width))\r\n    current_move = 1\r\n    last_move = 1\r\n    s = snake((255,0,0), (10,10), width, rows, last_move)\r\n    snack = cube(randomSnack(rows, s), width = width, rows = rows, color=(0,255,0))\r\n    flag = True\r\n    steps = 0\r\n    episodes = 0\r\n    clock = pygame.time.Clock()\r\n    \r\n    while steps <= 10000 and episodes <= 10000:\r\n        pygame.time.delay(10)\r\n        clock.tick(5000000)\r\n        \r\n        # if episodes > 0 and episodes % 100 == 0 and eps > 0.1 :\r\n            # eps = eps - 0.02\r\n        \r\n        act_0, d_x_0, d_y_0, gap_up_0, gap_left_0, gap_right_0, snake_len_f_0, horz_gap_0, ver_gap_0 = current_state(last_move, s, snack, rows, 1)\r\n        lst_0 = (act_0, d_x_0, d_y_0, gap_up_0, gap_left_0, gap_right_0, snake_len_f_0, horz_gap_0, ver_gap_0)\r\n        key = action(lst_0, last_move, len(s.body))\r\n        s.move(key)\r\n        if last_move == -1* s.last_move and len(s.body) > 1:\r\n            print('Error')\r\n            print('actual snake length :', len(s.body))\r\n        last_move = s.last_move\r\n        act, d_x, d_y, gap_up, gap_left, gap_right, snake_len_f, horz_gap, ver_gap = current_state(s.last_move, s, snack, rows, 1)\r\n        lst = (act, d_x, d_y, gap_up, gap_left, gap_right, snake_len_f, horz_gap, ver_gap)\r\n\r\n        print('steps :', steps)\r\n        print('episodes :', episodes)\r\n        steps += 1\r\n        snack_curr = 0\r\n        if s.body[0].pos == snack.pos:\r\n            s.addCube()\r\n            snack_curr = 1\r\n            snack = cube(randomSnack(rows, s), width, rows, color=(0,255,0))\r\n        \r\n        dead = 0\r\n        \r\n        for x in range(len(s.body)):\r\n            if s.body[x].pos in list(map(lambda z:z.pos,s.body[x+1:])):\r\n                dead = 1\r\n                print('Score: ', len(s.body))\r\n                scores_l.append(len(s.body))\r\n                steps_l.append(steps)\r\n                s.reset((10,10))\r\n                episodes += 1\r\n                steps = 0\r\n                break\r\n        \r\n        q_mat = q_matrix_update(lst_0, last_move, lst, snack_curr, dead)\r\n           \r\n        redrawWindow(win)\r\n \r\n       \r\n    pass\r\n \r\n \r\n \r\nmain()\r\n\r\nscore_df = pd.DataFrame()\r\nscore_df['steps'] = steps_l\r\nscore_df['score'] = scores_l\r\nscore_df['avg_steps_per_point'] = score_df['steps']/score_df['score']\r\nscore_df.avg_steps_per_point.plot.line()\r\nscore_df.score.plot.line()\r\n\r\ndef play():\r\n    \r\n    return","sub_path":"Codes/snake_2_v2.py","file_name":"snake_2_v2.py","file_ext":"py","file_size_in_byte":21523,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"501993599","text":"import socket\nimport select\n\n\n#Sends message to all users execpt the client that sent the message\ndef broadcast(sckt, message):\n    for socket in CLIENT_CONNECTIONS:\n\n        if socket != server_socket and socket != sckt:\n            try:\n                socket.send(message)\n\n            except:\n                    socket.close()\n                    CLIENT_CONNECTIONS.remove(socket)\n                    print(\"The user %s disconected\" % socket)\n\nif __name__ == \"__main__\":\n    CLIENT_CONNECTIONS = []\n    RECV_BUF = 4096\n    PORT = 1234\n    HOST = socket.gethostname()\n    #HOST = \"0.0.0.0\"\n    print(HOST)\n\n    server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n    server_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n\n    #server_socket.bind((socket.gethostname(), PORT))\n    server_socket.bind((HOST, PORT))\n    server_socket.listen(5)\n\n\n    CLIENT_CONNECTIONS.append(server_socket)\n\n    print(\"Server started on port %s\" % PORT)\n\n\n    while True:\n\n        server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\n        read_sockets,write_sockets,errror_sockets = select.select(CLIENT_CONNECTIONS,[],[])\n\n        for sct in read_sockets:\n            #New user\n            if sct == server_socket:\n                client, addr = server_socket.accept()\n                CLIENT_CONNECTIONS.append(client)\n\n                print(\"New user (%s, %s) joined\" % addr)\n\n                broadcast(client, \"<[%s , %s] entered room \\n\" % addr)\n\n            else:\n\n                try:\n\n                    data = client.recv(RECV_BUF)\n                    print(data)\n\n                    if data:\n\n\n                            broadcast(sct, \"\\r\" + \"<\" + str(sct.getpeername()) + \">\" + str(data))\n\n                \n                except:\n                    broadcast(sct, \"Client (%s, %s) is offline\" % addr)\n                    print(\"Client (%s, %s) is offline\" % addr)\n                    sct.close()\n                    CLIENT_CONNECTIONS.remove(sct)\n                    \n                \n\n\n","sub_path":"server/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2033,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"288128618","text":"# -*- coding: utf-8 -*-\n\n\"\"\"\n@date: 2020/7/26 上午10:36\n@file: misc.py\n@author: zj\n@description: \n\"\"\"\n\nimport pnno\nimport cv2\nimport os\nimport glob\n\n\ndef get_version():\n    return pnno.__version__\n\n\ndef is_dir(src_dir):\n    \"\"\"\n    判断是否是目录\n    \"\"\"\n    if not os.path.isdir(src_dir):\n        raise ValueError('{}不是目录'.format(src_dir))\n\n\ndef get_img_name(img_path):\n    \"\"\"\n    解析图像名。比如/path/to/lena.jpg得到lena\n    \"\"\"\n    return os.path.splitext(os.path.split(img_path)[1])[0]\n\n\ndef parse_img_path(img_path):\n    \"\"\"\n    解析图像路径，得到图像所在文件夹，文件名，图像名和图像后缀\n    比如/path/to/input_data/lena.jpg，则返回\n    ['input_data', 'lena.jpg', 'lena', '.jpg']\n    \"\"\"\n    base_name = os.path.basename(img_path)\n    dir_name = os.path.dirname(img_path)\n\n    folder = os.path.basename(dir_name)\n    file_name = base_name\n    img_name, img_extension = os.path.splitext(base_name)\n\n    return [folder, file_name, img_name, img_extension]\n\n\ndef get_classmap(anno_data, classmap):\n    \"\"\"\n    从中间格式标注数据中找到classmap\n    :param anno_list: [{'size':(width, height), 'objects':[{'name': '', 'bndbox':[xmin,ymin,xmax,ymax]}, ...]}, ...]\n    :param classmap: 已有类名和数字对应图\n    :return: {'a': 0, 'b':1 ,...}\n    \"\"\"\n    for _, anno_obj in anno_data.items():\n        objects = anno_obj['objects']\n        for obj in objects:\n            name = obj['name']\n            if name not in classmap.keys():\n                classmap[name] = len(classmap.keys())\n    return classmap\n\n\ndef check_image_label(img_path_list, anno_path_list):\n    \"\"\"\n    检查图像文件和标注文件是否一一对应\n    \"\"\"\n    assert len(img_path_list) == len(anno_path_list), \\\n        '图像文件数：{} - 标注文件数：{}'.format(len(img_path_list), len(anno_path_list))\n\n    for img_path, anno_path in zip(img_path_list, anno_path_list):\n        img_name = get_img_name(img_path)\n        anno_name = get_img_name(anno_path)\n\n        if img_name != anno_name:\n            raise ValueError('{}和{}不对应'.format(img_path, anno_path))\n\n\ndef check_input_output_folder(dir: str, image_folder: str, label_folder: str, is_input=True):\n    \"\"\"\n    检查根目录以及图像和标签文件夹\n    检查输入目录\n    1. 检查根路径是否存在：如果不存在，抛出异常\n    2. 检查图像文件夹是否存在：如果不存在，抛出异常\n    3. 检查标签文件夹是否存在：如果不存在，抛出异常\n    检查输出目录\n    1. 检查根路径是否存在：如果不存在，则新建\n    2. 检查图像文件夹是否存在：如果存在，抛出异常\n    3. 检查标签文件夹是否存在：如果存在，抛出异常\n    :param dir: 根路径\n    :param image_folder: 图像文件夹名\n    :param label_folder: 标签文件夹名\n    :return: 返回图像文件夹路径以及标签文件夹路径\n    \"\"\"\n    assert isinstance(dir, str) and isinstance(image_folder, str) and isinstance(label_folder, str)\n\n    img_dir = os.path.join(dir, image_folder)\n    label_dir = os.path.join(dir, label_folder)\n\n    if is_input:\n        if not os.path.exists(dir) or not os.path.exists(img_dir) or not os.path.exists(label_dir):\n            raise ValueError('请检查输入数据是否正确')\n    else:\n\n        if not os.path.exists(dir):\n            os.mkdir(dir)\n\n        if os.path.exists(img_dir):\n            raise ValueError('{}已存在'.format(img_dir))\n        os.mkdir(img_dir)\n\n        if img_dir != label_dir:\n            if os.path.exists(label_dir):\n                raise ValueError('{}已存在'.format(label_dir))\n            os.mkdir(label_dir)\n\n    return img_dir, label_dir\n\n\ndef parse_classmap(classmap: dict, objects: list):\n    \"\"\"\n    解析类名，添加对应的数字\n    \"\"\"\n    assert isinstance(classmap, dict) and isinstance(objects, list)\n\n    for obj in objects:\n        name = obj['name']\n\n        if name not in classmap.keys():\n            classmap[name] = len(classmap.keys())\n","sub_path":"pnno/util/misc.py","file_name":"misc.py","file_ext":"py","file_size_in_byte":4055,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"383801476","text":"from mymodule import stats_word as sw \n# 后面没有冒号\n# from stats_word.py import stats_text  【注意这种错误和前述正确的区别】\n# 调用 stats_text(text),参数传入非字符串，验证参数检查功能是否生效。\n# 加上try...except 捕获异常，并print出方便自己调试的信息。\ntext=[1,2,3,4]\ntry:\n    sw.stats_text(text)\n    print(sw)\n\nexcept ValueError:\n    print('Not string. Try again...')\n\n","sub_path":"19100303/liyan2019/d08/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":437,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"261013834","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n#\n# Example program to receive packets from the radio link\n#\n\nimport RPi.GPIO as GPIO\nGPIO.setmode(GPIO.BCM)\nfrom lib_nrf24 import NRF24\nimport time\nimport spidev\nimport sys\n\n\npipes = [[0xe7, 0xe7, 0xe7, 0xe7, 0xe7], [0xC2, 0xC2, 0xC2, 0xC2, 0xC2]]\n\nradio2 = NRF24(GPIO, spidev.SpiDev())\nradio2.begin(0, 17)\n\nradio2.setRetries(15,0)\n\nradio2.setPayloadSize(32)\nradio2.setChannel(0x60)\nradio2.setDataRate(NRF24.BR_2MBPS)\nradio2.setPALevel(NRF24.PA_MIN)\n\nradio2.setAutoAck(False)\nradio2.enableDynamicPayloads()\nradio2.enableAckPayload()\n\nradio2.openWritingPipe(pipes[0])\nradio2.openReadingPipe(1, pipes[1])\n\nradio2.startListening()\nradio2.stopListening()\n\nradio2.printDetails()\n\nradio2.startListening()\n\n\npacket_id = []\npackets_received = 0\npacket_completed = True\npacket_data = []\nnext_packet = 0\ncounter = 0\n\nf = open(\"hola.txt\", \"w\")\n\nwhile packet_completed:\n    pipe = [0]\n    while not radio2.available(pipe):\n        time.sleep(10000/1000000.0)\n\n    #print(\"test\")\n\n    recv_buffer = []\n    packet_size = radio2.getDynamicPayloadSize()\n    radio2.read(recv_buffer, packet_size)\n    if packets_received == 0:\n        packet_id = [0] * recv_buffer[1]\n    if recv_buffer[0] == next_packet:\n        print(\"Received packet: \"),\n        print(recv_buffer[0])\n        f.write(bytearray(recv_buffer[2:packet_size]))\n        packets_received = packets_received + 1\n        next_packet = next_packet + 1\n    if packets_received == recv_buffer[1]:\n        print(\"File completed!\")\n        packet_completed = False\n    counter = counter + 1\n    if counter == 10000000000000000000000:\n        f.close()\n        sys.exit()\n\n#for readed_packet in packet_data:\n    #f.write(bytearray(readed_packet))\n\nf.close()\n","sub_path":"quickmode/quickmode_rx2.py","file_name":"quickmode_rx2.py","file_ext":"py","file_size_in_byte":1740,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"390016006","text":"from constantes import *\nfrom random import randrange\nimport sys\nimport time\n\nclass Entrainement():\n\n\tdef __init__(self, paquet, application):\n\t\tself.paquetEntrainement = paquet\n\t\tself.application = application\n\n\t\tself.listeCarteDifficulteFacile = self.triCarte()[1]\n\t\tself.listeCarteDifficulteNonDefini = self.triCarte()[0]\n\t\tself.listeCarteDifficulteMoyenne = self.triCarte()[2]\n\t\tself.listeCarteDifficulteDifficile = self.triCarte()[3]\n\t\tself.carteTireAuSort = self.tireAuSortCarte()\n\n\tdef getCarteTireAuSort(self):\n\t\treturn self.carteTireAuSort\n\n\tdef setCarteTireAuSort(self, carte):\n\t\tself.carteTireAuSort = carte\n\n\tdef getPaquetEntrainement(self):\n\t\treturn self.paquetEntrainement\n\n\tdef setPaquetEntrainement(self, paquetEntrainement):\n\t\tself.paquetEntrainement = paquetEntrainement\n\n\tdef getListeFacile(self):\n\t\treturn self.listeCarteDifficulteFacile\n\n\tdef setListeFacile(self, listeFacile):\n\t\tself.listeCarteDifficulteFacile = listeFacile\n\n\tdef getListeNonDefini(self):\n\t\treturn self.listeCarteDifficulteNonDefini\n\n\tdef setListeNonDefini(self, listeNonDefini):\n\t\tself.listeCarteDifficulteNonDefini = listeNonDefini\n\n\tdef getListeMoyen(self):\n\t\treturn self.listeCarteDifficulteMoyenne\n\n\tdef setListeMoyen(self, listeMoyen):\n\t\tself.listeCarteDifficulteMoyenne = listeMoyen\n\n\tdef getListeDifficile(self):\n\t\treturn self.listeCarteDifficulteDifficile\n\n\tdef setListeDifficile(self, listeDifficile):\n\t\tself.listeCarteDifficulteDifficile = listeDifficile\n\n\tdef getApplication(self):\n\t\treturn self.application\n\n\tdef setApplication(self, application):\n\t\tself.application = application\n\n\tdef afficheListe(self, liste):\n\t\tfor item in liste:\n\t\t\tprint(item)\n\n\tdef __str__(self):\n\t\ts = \"le paquet sur lequel on s'entraine est : \"\n\t\ts += self.getPaquetEntrainement().getNom()\n\n\tdef changeCarteTireAuSort(self):\n\t\tself.carteTireAuSort = self.tireAuSortCarte()\n\n\t# Tri les cartes en fonction de leur difficulte dans 4 listes\n\t# Permet de faire le tri qu'une seule fois\n\tdef triCarte(self):\n\t\tlisteFacile = []\n\t\tlisteMoyen = []\n\t\tlisteDifficile = []\n\t\tlisteNonDefini = []\n\n\t\tfor carte in self.getPaquetEntrainement():\n\t\t\tif(carte.getDifficulte() == NON_DEFINI):\n\t\t\t\tlisteNonDefini.append(carte)\n\t\t\telif(carte.getDifficulte() == FACILE):\n\t\t\t\tlisteFacile.append(carte)\n\t\t\telif(carte.getDifficulte() == MOYEN):\n\t\t\t\tlisteMoyen.append(carte)\n\t\t\telif(carte.getDifficulte() == DIFFICILE):\n\t\t\t\tlisteDifficile.append(carte)\n\n\t\treturn listeNonDefini, listeFacile, listeMoyen, listeDifficile\n\n\t# Tire au sort une carte parmis les listes de paquet\n\tdef tireAuSortCarte(self):\n\n\t\tif(self.getPaquetEntrainement().getNombreCarte() > 0):\n\t\t\t# Tant qu'il y a des cartes dans la liste des non définies, on les affiche\n\t\t\tif(len(self.getListeNonDefini()) != 0):\n\t\t\t\tcarte =  self.getListeNonDefini()[0]\n\t\t\t\tdel self.listeCarteDifficulteNonDefini[0]\n\t\t\t\treturn carte\n\n\t\t\t# 2 fois plus de chance de tomber sur une moyenne que facile\n\t\t\t# 4 fois plus de chance de tomber sur une difficile que moyenne\n\t\t\telse:\n\t\t\t\tcarteNonTrouve = 1\n\t\t\t\twhile(carteNonTrouve):\n\t\t\t\t\tprint(\"toto\")\n\t\t\t\t\tprint(\"len liste facile : \", len(self.getListeFacile()))\n\t\t\t\t\tprint(\"len liste moyen : \", len(self.getListeMoyen()))\n\t\t\t\t\tprint(\"len liste difficile : \", len(self.getListeDifficile()))\n\t\t\t\t\tnombreAleatoire = randrange(1, 22)\n\t\t\t\t\t# print(\"nombre aleatoire : {}\".format(nombreAleatoire))\n\t\t\t\t\tif(nombreAleatoire <= 2 and len(self.getListeFacile()) != 0):\n\t\t\t\t\t\tprint(\"1\")\n\t\t\t\t\t\t# Tire dans la liste facile\n\t\t\t\t\t\tindexCarteAleatoire = randrange(0, len(self.getListeFacile()))\n\t\t\t\t\t\tcarte = self.getListeFacile()[indexCarteAleatoire]\n\t\t\t\t\t\t# carte.setDifficulte(FACILE)\n\t\t\t\t\t\tdel self.getListeFacile()[indexCarteAleatoire]\n\t\t\t\t\t\tcarteNonTrouve = 0\n\t\t\t\t\t\treturn carte \n\n\t\t\t\t\telif(nombreAleatoire >= 3 and nombreAleatoire <= 6 and len(self.getListeMoyen()) != 0):\n\t\t\t\t\t\tprint(\"2\")\n\t\t\t\t\t\t# Tire dans la liste moyen\n\t\t\t\t\t\tindexCarteAleatoire = randrange(0, len(self.getListeMoyen()))\n\t\t\t\t\t\tcarte = self.getListeMoyen()[indexCarteAleatoire]\n\t\t\t\t\t\t# carte.setDifficulte(MOYEN)\n\t\t\t\t\t\tdel self.getListeMoyen()[indexCarteAleatoire]\n\t\t\t\t\t\tcarteNonTrouve = 0\n\t\t\t\t\t\treturn carte\n\n\t\t\t\t\telif(nombreAleatoire >= 7 and len(self.getListeDifficile()) != 0):\n\t\t\t\t\t\tprint(\"3\")\n\t\t\t\t\t\t# Tire dans la liste difficile\n\t\t\t\t\t\tindexCarteAleatoire = randrange(0, len(self.getListeDifficile()))\n\t\t\t\t\t\tcarte = self.getListeDifficile()[indexCarteAleatoire]\n\t\t\t\t\t\t# carte.setDifficulte(DIFFICILE)\n\t\t\t\t\t\tdel self.getListeDifficile()[indexCarteAleatoire]\n\t\t\t\t\t\tcarteNonTrouve = 0\n\t\t\t\t\t\treturn carte \n\t\t\t\t\tif(len(self.getListeFacile()) == 0 and len(self.getListeMoyen()) == 0 and len(self.getListeDifficile()) == 0):\n\t\t\t\t\t\tprint(\"liste de difficulte vide\")\n\t\t\t\t\t\tbreak\n\t\telse:\n\t\t\tprint(\"Paquet d'entrainement vide\")\n\n\t# Supprime le paquet d'entrainement, et le rempli avec les cartes dont la difficulte a ete modifie\n\t# Puis on sauvegarde le paquet\n\tdef reformerPaquetAvecCartesTrieEtDifficulte(self):\n\t\tself.getPaquetEntrainement().viderPaquet()\n\n\t\tfor carte in self.getListeNonDefini():\n\t\t\tself.getPaquetEntrainement().ajoutCarte(carte)\n\n\t\tfor carte in self.getListeFacile():\n\t\t\tself.getPaquetEntrainement().ajoutCarte(carte)\n\n\t\tfor carte in self.getListeMoyen():\n\t\t\tself.getPaquetEntrainement().ajoutCarte(carte)\n\n\t\tfor carte in self.getListeDifficile():\n\t\t\tself.getPaquetEntrainement().ajoutCarte(carte)\n\t\t\n\t\tprint(\"paquet non defini\")\n\t\tself.afficheListe(self.getListeNonDefini())\n\t\tprint(\"-----------\")\n\t\tprint(\"paquet facile\")\n\t\tself.afficheListe(self.getListeFacile())\n\t\tprint(\"-----------\")\n\t\tprint(\"paquet moyen\")\n\t\tself.afficheListe(self.getListeMoyen())\n\t\tprint(\"-----------\")\n\t\tprint(\"paquet difficile\")\n\t\tself.afficheListe(self.getListeDifficile())\n\t\tprint(\"-----------\")\n\n\t\t# print(\"paquet entrainement\")\n\t\t# # self.afficheListe(self.getPaquetEntrainement())\n\t\t# print(self.getPaquetEntrainement())\n\t\t\n\t\tself.getPaquetEntrainement().sauvegarde()\n\n\tdef gestionEntrainement(self, entreeUtilisateur):\n\n\t\t# Si il y au moins une carte dans le paquet d'entrainement\n\t\tif(self.getPaquetEntrainement().getNombreCarte() > 0):\n\n\t\t\tif(entreeUtilisateur == FACILE):\n\t\t\t\tself.getCarteTireAuSort().setDifficulte(FACILE)\n\t\t\t\tself.getListeFacile().append(self.getCarteTireAuSort())\n\t\t\t\tself.reformerPaquetAvecCartesTrieEtDifficulte()\n\t\t\telif(entreeUtilisateur == MOYEN):\n\t\t\t\tself.getCarteTireAuSort().setDifficulte(MOYEN)\n\t\t\t\tself.getListeMoyen().append(self.getCarteTireAuSort())\n\t\t\t\tself.reformerPaquetAvecCartesTrieEtDifficulte()\n\t\t\telif(entreeUtilisateur == DIFFICILE):\n\t\t\t\tself.getCarteTireAuSort().setDifficulte(DIFFICILE)\n\t\t\t\tself.getListeDifficile().append(self.getCarteTireAuSort())\n\t\t\t\tself.reformerPaquetAvecCartesTrieEtDifficulte()\n\t\t\t\t# elif(choix == \"a\"):\n\t\t\t\t#\tprint(\"nomber carte paquet entrainement : {}\".format(self.getPaquetEntrainement().getNombreCarte()))\n\n\t\t\tself.setCarteTireAuSort(self.tireAuSortCarte())\n\t\t\tself.getApplication().chargementPaquet(self.getApplication().getPaquetCourant().getNom())\n\t\telse:\n\t\t\tprint(\"Impossible de s'entrainer sur un paquet vide\")\n","sub_path":"entrainement.py","file_name":"entrainement.py","file_ext":"py","file_size_in_byte":7000,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"605437652","text":"\"\"\"\r\nTest script for scheduled_tasks/log_cleanup\r\n\"\"\"\r\n\r\nimport logging\r\nlogger = logging.getLogger('scheduled_tasks_log_cleanup_test')\r\n\r\nimport os\r\nimport sys\r\nimport shutil\r\nimport tempfile\r\nimport unittest\r\nfrom functools import partial\r\nfrom datetime import datetime, timedelta\r\nfrom unittest.mock import patch, MagicMock\r\n\r\nfrom tornado.testing import AsyncTestCase\r\nfrom statsd import TCPStatsClient as StatsClient\r\nfrom rest_tools.client import RestClient\r\n\r\nfrom tests.util import unittest_reporter, glob_tests\r\n\r\nfrom iceprod.server.modules.schedule import schedule\r\nfrom iceprod.server.scheduled_tasks import log_cleanup\r\nfrom iceprod.server.util import datetime2str, nowstr\r\n\r\nclass log_cleanup_test(AsyncTestCase):\r\n    def setUp(self):\r\n        super(log_cleanup_test,self).setUp()\r\n        self.test_dir = tempfile.mkdtemp(dir=os.getcwd())\r\n        def cleanup():\r\n            shutil.rmtree(self.test_dir)\r\n        self.addCleanup(cleanup)\r\n\r\n        self.cfg = {\r\n            'queue':{\r\n                'init_queue_interval':0.1,\r\n                'submit_dir':self.test_dir,\r\n                '*':{'type':'Test1','description':'d'},\r\n            },\r\n            'master':{\r\n                'url':False,\r\n            },\r\n            'site_id':'abcd',\r\n        }\r\n\r\n    @unittest_reporter\r\n    async def test_200_run(self):\r\n        rc = MagicMock(spec=RestClient)\r\n        logs = {}\r\n        async def client(method, url, args=None):\r\n            logger.info('REST: %s, %s', method, url)\r\n            if method == 'GET' and url.startswith('/logs'):\r\n                return logs\r\n            elif method == 'DELETE' and url.startswith('/logs'):\r\n                client.called = True\r\n            else:\r\n                raise Exception()\r\n        client.called = False\r\n        rc.request = client\r\n\r\n        await log_cleanup.run(rc, debug=True)\r\n        self.assertFalse(client.called)\r\n\r\n        client.called = False\r\n        logs['a'] = {'log_id':'a'}\r\n        await log_cleanup.run(rc, debug=True)\r\n        self.assertTrue(client.called)\r\n\r\n    @unittest_reporter(name='run() - error')\r\n    async def test_201_run(self):\r\n        rc = MagicMock(spec=RestClient)\r\n        async def client(method, url, args=None):\r\n            logger.info('REST: %s, %s', method, url)\r\n            raise Exception()\r\n        rc.request = client\r\n        with self.assertRaises(Exception):\r\n            await log_cleanup.run(rc, debug=True)\r\n\r\n        # check it normally hides the error\r\n        await log_cleanup.run(rc, debug=False)\r\n\r\n\r\ndef load_tests(loader, tests, pattern):\r\n    suite = unittest.TestSuite()\r\n    alltests = glob_tests(loader.getTestCaseNames(log_cleanup_test))\r\n    suite.addTests(loader.loadTestsFromNames(alltests,log_cleanup_test))\r\n    return suite\r\n","sub_path":"tests/server/scheduled_tasks/log_cleanup_test.py","file_name":"log_cleanup_test.py","file_ext":"py","file_size_in_byte":2777,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"288463157","text":"class Solution(object):\n    def maxArea(self, height):\n        \"\"\"\n        :type height: List[int]\n        :rtype: int\n        \"\"\"\n        #O(n)\n        i=0\n        j=len(height)-1\n        maxx=0\n        while (i!=j):\n            if (height[i]<=height[j]):\n                if (maxx<height[i]*(j-i)):\n                    maxx=height[i]*(j-i)\n                i+=1\n            else:\n                if (maxx<height[j]*(j-i)):\n                    maxx=height[j]*(j-i)\n                j-=1\n        return maxx\n","sub_path":"011.py","file_name":"011.py","file_ext":"py","file_size_in_byte":505,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"116068653","text":"# messages.py\n\ndef completeMessage(order, message):\n    newMessage = {}\n    newMessage['type'] = message['type'] # keep original message type\n    newMessage['sec'] = message['sec'] # keep original message sec\n    newMessage['nano'] = message['nano'] # keep original message nano\n    newMessage['event'] = message['event'] # keep original event (typically '.')\n    newMessage['buysell'] = order['buysell'] # buysell from original order\n    if message['type'] == 5:\n        newMessage['price'] = message['price'] # message price != order price\n    else:\n        newMessage['price'] = order['price'] # price from original order\n    newMessage['shares'] = order['shares'] # shares from original order\n    newMessage['refno'] = message['refno'] # refno from message\n    newMessage['newrefno'] = message['newrefno'] # newrefno from message\n    return newMessage\n\n","sub_path":"messages.py","file_name":"messages.py","file_ext":"py","file_size_in_byte":857,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"405323357","text":"from zope import interface\nfrom zope import interface, schema\n\nfrom raptus.mailcone.core.interfaces import IContainer\nfrom raptus.mailcone.core.interfaces import IContainerLocator\n\nfrom raptus.mailcone.mails import _\n\n\n\nclass IMailContainer(IContainer):\n    \"\"\" A container for all mails. Note that all\n        mails came from a sql db! this container\n        serve only as mount point for all mails\n    \"\"\"\n\n\nclass IMailContainerLocator(IContainerLocator):\n    \"\"\" interface for locate the mails folder.\n    \"\"\"\n\n\nclass IStringList(interface.Interface):\n    \"\"\" reprencent a list with string\n    \"\"\"\n    id =  schema.Int(title=_(u'Id'), required=True,)\n    value = schema.TextLine(title=_(u'Value of this list'), required=True,)\n\n\nclass IAttachment(interface.Interface):\n    \"\"\" Interface description for a Attachment bind on a mail.\n    \"\"\"\n    id =  schema.Int(title=_(u'Id'), required=True,)\n    mail_id = schema.Int(title=_(u'Id'), required=True,)\n    path =  schema.TextLine(title=_(u'Path to attachment'), required=True,)\n    filesize = schema.Int(title=_(u'File size'), required=True,)\n\n\n\nclass ITag(interface.Interface):\n    \"\"\" Interface description for a Tag bind on a mail.\n    \"\"\"\n    id =  schema.Int(title=_(u'Id'), required=True,)\n    name =  schema.TextLine(title=_(u'Tag name'), required=True,)\n\n\n\nclass IMail(interface.Interface):\n    \"\"\" Interface description for a single mail.\n    \"\"\"\n\n    id =  schema.Int(title=_(u'Id'), required=True,)\n    date =  schema.Datetime(title=_(u'Date'), required=True,)\n    mail_from = schema.TextLine(title=_(u'Mail from'), required=True, max_length=250)\n    mail_from_domain = schema.TextLine(title=_(u'Mail from Domain'), required=True, max_length=250)\n    organisation = schema.TextLine(title=_(u'Organisation'), required=True, max_length=250)\n    subject = schema.TextLine(title=_(u'Subject'), required=True, max_length=250)\n    mail_to = schema.List(title=_(u'Mail to'),\n                          required=True,\n                          value_type=schema.TextLine(title=_(u'Mail to'), required=True, max_length=250))\n    mail_to_domain = schema.List(title=_('Mail To Domain'),\n                                 required=True,\n                                 value_type=schema.TextLine(title=_(u'Mail To Domain'), required=True, max_length=250))\n    mail_cc = schema.List(title=_(u'Mail CC'),\n                          required=True,\n                          value_type=schema.TextLine(title=_(u'Mail CC'), required=True, max_length=250))\n    mail_cc_domain = schema.List(title=_('Mail CC Domain'),\n                                 required=True,\n                                 value_type=schema.TextLine(title=_(u'Mail CC Domain'), required=True, max_length=250))\n    mail_bbc = schema.List(title=_(u'Mail BBC'),\n                          required=True,\n                          value_type=schema.TextLine(title=_(u'Mail BBC'), required=True, max_length=250))\n    mail_bbc_domain = schema.List(title=_('Mail BBC Domain'),\n                                 required=True,\n                                 value_type=schema.TextLine(title=_(u'Mail BBC Domain'), required=True, max_length=250))\n    precedence = schema.TextLine(title=_(u'Precedence'), required=True, max_length=250)\n    received = schema.List(title=_(u'Received'),\n                          required=True,\n                          value_type=schema.TextLine(title=_(u'Received'), required=True, max_length=1000))\n    reply_to = schema.TextLine(title=_(u'Reply-To'), required=True, max_length=250)\n    sender = schema.TextLine(title=_(u'Sender'), required=True, max_length=250)\n    content = schema.Text(title=_(u'Content'), required=True,)\n    multiparts = schema.List(title=_(u'Multiparts'),\n                             required=True,\n                             value_type=schema.Text(title=_(u'MailParts'), required=True, max_length=250))\n\n    attachments = schema.List(title=_(u'Attachments'),\n                              required=True,\n                              value_type=schema.Object(schema=IAttachment))\n    tags = schema.List( title=_(u'Tags'),\n                        required=True,\n                        value_type=schema.Object(schema=ITag))\n    header = schema.Text(title=_(u'Header'), required=True,)\n    mime_version = schema.Text(title=_(u'Mime Version'), required=True,)\n    x_source_ip = schema.TextLine(title=_(u'X-SourceIP'), required=True, max_length=250)\n\n    original_path = schema.TextLine(title=_(u'Original mail path'), required=True, max_length=250)\n    hash = schema.TextLine(title=_(u'Raw data hash'), required=True, max_length=32)\n    parsing_date =  schema.Datetime(title=_(u'Parsing date'), required=True,)\n    processed_on = schema.Datetime(title=_('Processed on'), required=True,)\n\n\n\n\n","sub_path":"mailcone/project/raptus.mailcone.mails/trunk/raptus/mailcone/mails/interfaces.py","file_name":"interfaces.py","file_ext":"py","file_size_in_byte":4758,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"205672888","text":"import pandas as pd\nfrom sklearn.linear_model import LinearRegression\nfrom statsmodels.stats.outliers_influence import variance_inflation_factor\nfrom sklearn.preprocessing import StandardScaler\nfrom pandas_profiling import ProfileReport\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import Ridge,Lasso,RidgeCV,LassoCV,ElasticNet,ElasticNetCV,LinearRegression\nimport numpy as np\nimport pickle\n\nclass FirstLinearModel:\n    def __init__(self,link):\n        self.link =link\n\n    def Dataframe(self):\n        self.df = pd.read_csv(self.link)\n        return self.df\n\n    def X(self,columns):\n        obj = FirstLinearModel(self.link)\n        self.columns = columns\n        self.x = obj.Dataframe().drop(self.columns,axis=1)\n        return self.x\n\n    def Y(self,column):\n        obj = FirstLinearModel(self.link)\n        self.column = column\n        self.y = obj.Dataframe()[column]\n        return self.y\n\n    def standardization(self):\n        scaler = StandardScaler()\n        obj = FirstLinearModel(self.link)\n        x = obj.X(self.columns)\n        std = scaler.fit_transform(x)\n        return std\n\n    def Report(self):\n\n        obj = FirstLinearModel(self.link)\n        x = obj.X(self.columns)\n        std = obj.standardization()\n        std_df = pd.DataFrame(std, columns=x.columns)\n        pdf = ProfileReport(std_df)\n        pdf.to_widgets()\n        return pdf\n\n    def Multicolinearity(self):\n        obj = FirstLinearModel(self.link)\n        x = obj.X(self.columns)\n        std = obj.standardization()\n        vif_df = pd.DataFrame()\n        vif_df['vif'] = [variance_inflation_factor(std, i) for i in range(std.shape[1])]\n        vif_df['features'] = x.columns\n        return vif_df\n\n    def x_train(self):\n        obj = FirstLinearModel(self.link)\n        x = obj.X(self.columns)\n        y = obj.Y(self.column)\n        std = obj.standardization()\n        self.x_train = x\n        x,x_test,y_train,y_test = train_test_split(std,y,test_size=0.25,random_state=True)\n        return x\n\n    def y_train(self):\n        obj = FirstLinearModel(self.link)\n        x = obj.X(self.columns)\n        y = obj.Y(self.column)\n        std = obj.standardization()\n        self.y_train = y\n        self.x_train, self.x_test, y, self.y_test = train_test_split(std, y, test_size=0.25,\n                                                                                random_state=True)\n        return y\n\n    def x_test(self):\n        obj = FirstLinearModel(self.link)\n        x = obj.X(self.columns)\n        y = obj.Y(self.column)\n        std = obj.standardization()\n        self.x_test = x\n        x_train, x, y_train, y_test = train_test_split(std, y, test_size=0.25,random_state=True)\n        return x\n\n    def y_test(self):\n        obj = FirstLinearModel(self.link)\n        x = obj.X(self.columns)\n        y = obj.Y(self.column)\n        std = obj.standardization()\n        self.y_test = y\n        x_train, x_test, y_train, y = train_test_split(std, y, test_size=0.25,\n                                                                                random_state=True)\n        return y\n\n    def linearModel(self):\n        obj = FirstLinearModel(self.link)\n        x = obj.X(self.columns)\n        y = obj.Y(self.column)\n        x_t = obj.x_train()\n        y_t = obj.y_train()\n        lr=LinearRegression()\n        lr.fit(x_t,y_t)\n        return lr\n\n    def test_transform(self,values):\n        obj = FirstLinearModel(self.link)\n        x = obj.X(self.columns)\n        scaler = StandardScaler()\n        scaler.fit_transform(x)\n        self.values = values\n        test = scaler.transform(values)\n        return test\n\n    def predict(self,value):\n        obj = FirstLinearModel(self.link)\n        obj.X(self.columns)\n        obj.Y(self.column)\n        obj.standardization()\n        self.value = value\n        value = obj.test_transform(self.values)\n        lr = obj.linearModel()\n        return lr.predict(value)\n\n    def score(self):\n        obj = FirstLinearModel(self.link)\n        obj.X(self.columns)\n        obj.Y(self.column)\n        y = obj.y_test()\n        return obj.linearModel().score(obj.x_test(),y)\n\n    def Lasso(self):\n        lassocv = LassoCV(cv = 10,max_iter=2000000,normalize = True)\n        obj = FirstLinearModel(self.link)\n        obj.X(self.columns)\n        obj.Y(self.column)\n        x = obj.x_train()\n        y = obj.y_train()\n        lassocv.fit(x,y)\n        lasso = Lasso(alpha = lassocv.alpha_)\n        return lasso.fit(x,y)\n\n    def lasso_score(self):\n        obj = FirstLinearModel(self.link)\n        obj.X(self.columns)\n        obj.Y(self.column)\n        y = obj.y_test()\n        lasso = obj.Lasso()\n        return lasso.score(obj.x_test(),y)\n\n    def Ridge(self):\n        ridgecv = RidgeCV(alphas = np.random.uniform(0,10,15),cv=10,normalize=True)\n        obj = FirstLinearModel(self.link)\n        obj.X(self.columns)\n        obj.Y(self.column)\n        x = obj.x_train()\n        y = obj.y_train()\n        ridgecv.fit(x, y)\n        ridge = Ridge(alpha = ridgecv.alpha_)\n        return ridge.fit(x,y)\n\n    def Ridge_score(self):\n        obj = FirstLinearModel(self.link)\n        obj.X(self.columns)\n        obj.Y(self.column)\n        y = obj.y_test()\n        ridge = obj.Ridge()\n        return ridge.score(obj.x_test(), y)\n\n    def ElasticNEt(self):\n        elasticcv =ElasticNetCV(alphas = None,cv=10)\n        obj = FirstLinearModel(self.link)\n        obj.X(self.columns)\n        obj.Y(self.column)\n        x = obj.x_train()\n        y = obj.y_train()\n        elasticcv.fit(x,y)\n        elastic = ElasticNet(alpha = elasticcv.alpha_,l1_ratio=elasticcv.l1_ratio)\n        return elastic.fit(x,y)\n\n    def ElasticNEt_score(self):\n        obj = FirstLinearModel(self.link)\n        obj.X(self.columns)\n        obj.Y(self.column)\n        y = obj.y_test()\n        elas = obj.ElasticNEt()\n        return elas.score(obj.x_test(),y)\n\n    def dump_model(self):\n        obj = FirstLinearModel(self.link)\n        obj.X(self.columns)\n        obj.Y(self.column)\n        lr = obj.linearModel()\n        #pickle.dump(lr,open('predictive_maintenance_lr_model.pickle','wb'))\n\n    def load_model(self):\n        model = pickle.load(open('predictive_maintenance_lr_model.pickle','rb'))\n        return model\n\n    def adjusted_r_square(self,x,y):\n        obj = FirstLinearModel(self.link)\n        obj.X(self.columns)\n        obj.Y(self.column)\n        r2 = obj.load_model().score(x, y)\n        n = x.shape[0]\n        p = x.shape[1]\n        adjusted_r2 = 1 - (1 - r2) * (n - 1) / (n - p - 1)\n        return adjusted_r2\n","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":6538,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"598015948","text":"from django.contrib import admin\nfrom django.urls import path, re_path, include\nfrom rest_framework import permissions, routers\nfrom rest_framework_simplejwt.views import (\n    TokenObtainPairView,\n    TokenRefreshView,\n)\nfrom drf_yasg.views import get_schema_view\nfrom drf_yasg import openapi\n\nfrom user.views import get_current_user, CreateUserView\nfrom .views import index\nfrom trail.views import TrailViewSet\nfrom review.views import ReviewViewSet\nfrom django.conf.urls.static import static\nfrom django.conf import settings\n\n\nrouter = routers.DefaultRouter()\nrouter.register(r'trails', TrailViewSet)\nrouter.register(r'reviews', ReviewViewSet)\n\n\nschema_view = get_schema_view(\n   openapi.Info(\n      title='Little Foot Trails API',\n      default_version='v1',\n      description='All APIs used for Little Foot Trails',\n      terms_of_service='https://www.google.com/policies/terms/',\n      contact=openapi.Contact(email='rmiyazaki11@ucsbalum.com'),\n   ),\n   public=True,\n   permission_classes=(permissions.AllowAny,),\n)\n\n\nurlpatterns = [\n    path('', index, name='index'),\n    path('admin/doc/', include('django.contrib.admindocs.urls')),\n    path('admin/', admin.site.urls),\n    path('api/', include(router.urls)),\n    path('api_docs/', schema_view.with_ui(\n        'swagger', cache_timeout=0), name='schema-swagger-ui'),\n    path('api/token/',\n         TokenObtainPairView.as_view(), name='token_obtain_pair'),\n    path('api/token/refresh/',\n         TokenRefreshView.as_view(), name='token_refresh'),\n    path('current_user/', get_current_user),\n    path('user/create/', CreateUserView.as_view()),\n] + static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT) \\\n              + [re_path(r'^(?:.*)/?$', index, name='index')]\n","sub_path":"backend/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1733,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"535696725","text":"# _*_ coding: utf-8 _*_\n#\n# HSPyLib v0.11.1\n#\n# Package: main.hspylib.modules.cli.tui.menu\n\"\"\"Package initialization.\"\"\"\n\n__all__ = [\n  'menu', \n  'menu_entry', \n  'menu_factory', \n  'menu_item', \n  'menu_option', \n  'menu_ui', \n  'menu_utils'\n]\n","sub_path":"src/main/hspylib/modules/cli/tui/menu/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":246,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"374673255","text":"\"\"\"TravelApp URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n    https://docs.djangoproject.com/en/1.8/topics/http/urls/\nExamples:\nFunction views\n    1. Add an import:  from my_app import views\n    2. Add a URL to urlpatterns:  url(r'^$', views.home, name='home')\nClass-based views\n    1. Add an import:  from other_app.views import Home\n    2. Add a URL to urlpatterns:  url(r'^$', Home.as_view(), name='home')\nIncluding another URLconf\n    1. Add an import:  from blog import urls as blog_urls\n    2. Add a URL to urlpatterns:  url(r'^blog/', include(blog_urls))\n\"\"\"\nfrom django.conf.urls import include, url\nfrom django.contrib import admin\nfrom AppTravel.views import splash, feedback, offers, checkbus, checkabus, filterbus, ticketbook, ticketcancel, mytrips\n\nurlpatterns = [\n    url(r'^admin/', include(admin.site.urls)),\n    url(r'^api/splash/', splash),\n    url(r'^api/feedback/', feedback),\n    url(r'^api/offers/', offers),\n    url(r'^api/buscheck/', checkbus),\n    url(r'^api/getbusdetails/(?P<pk>[0-9]+)/$', checkabus),\n    url(r'^api/filter/', filterbus),\n    url(r'^api/ticketbook/', ticketbook),\n    url(r'^api/ticketcancel/', ticketcancel),\n    url(r'^api/mytrips/', mytrips),\n]\n","sub_path":"TravelApp/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1244,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"48235902","text":"T = int(input())\nfor tc in range(1, T+1):\n    S1 = input()\n    S2 = input()\n    for i in range(len(S1)-1, len(S2)):\n        cnt = 0\n        for j in range(len(S1)-1, -1, -1):\n            if S1[j] != S2[i-(len(S1)-1-j)]:\n                break\n            cnt += 1\n        if cnt == len(S1):\n            print('#{} 1'.format(tc))\n            break\n        if i == len(S2)-1:\n            print('#{} 0'.format(tc))","sub_path":"work/SW_expert_academy/4864.py","file_name":"4864.py","file_ext":"py","file_size_in_byte":410,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"613971570","text":"from kalaha import Kalaha\nimport networkx as nx\nfrom kalaha import KalahaGameEvent\nimport copy\nimport pdb\n\nclass KalahaBot():\n    def __init__(self, player_name):\n        self.debug = True\n        self.player_name = player_name\n        if self.player_name == 'A':\n            self.other_player = 'B'\n        else:\n            self.other_player = 'A'\n\n    def get_smart_move(self, kalaha_game):\n        recommended_step = self.smart_move_helper(kalaha_game,'max',1)\n        return recommended_step\n\n    def smart_move_helper(self,kalaha_game,function,counter):\n        maximal_tree_depth = 8\n        # If leaves are reached this, just return the heuristic of the current gamestate\n        if counter >= maximal_tree_depth:\n            return self.heuristic(kalaha_game)\n\n        if function == 'max':\n            result = -float('inf')\n        else:\n            result = float('inf')\n\n        for hollow_number in range(1,kalaha_game.amount_of_hollows+1):\n            if kalaha_game.hollows[self.player_name]['hollow_' + str(hollow_number)] > 0:\n                temp_game = copy.deepcopy(kalaha_game)\n\n                # Switch the function for the next generation\n                if function == 'max':\n                    next_function = 'min' \n                else:\n                    next_function = 'max'\n\n                try:\n                    # Switch the player at every generation - is it a 'max' or a 'min' generation?\n                    if function == \"max\":\n                        temp_game.choose_hollow(self.player_name,hollow_number)\n                    else:\n                        temp_game.choose_hollow(self.other_player,hollow_number)\n                except KalahaGameEvent as e:\n                    # If a player gets a second turn\n                    if 'turn again' in e.value:\n                        if function == 'max':\n                            next_function = 'max' \n                        else:\n                            next_function = 'min'\n\n                # Get recursive value of this choice\n                result_of_child = self.smart_move_helper(temp_game,next_function,counter+1)\n\n                # Calc the best step by max/min\n                previous_result = result\n                if function == 'max':\n                    result = max(result,result_of_child)\n                else:\n                    result = min(result,result_of_child)\n                # If 'best_step' changes, remember which step it was\n                if previous_result != result or result == -float('inf') or result == float('inf'):\n                    recommended_step = hollow_number\n        if counter != 1: #If this is not the root, return the best value\n            return result\n        else:\n            return recommended_step #If this is root, return the recommended step\n\n    def heuristic(self,kalaha_game):    \n        score_winning_hollows = (kalaha_game.get_winning_hollow_of_player(self.player_name) - kalaha_game.get_winning_hollow_of_player(self.other_player)) * 3\n        score_empty_hollows = kalaha_game.get_amount_empty_hollows(self.player_name)\n        if kalaha_game.game_is_over() and kalaha_game.compute_winner() == self.player_name:\n            score_I_win_dis_game = 10000000000\n        elif kalaha_game.game_is_over() and kalaha_game.compute_winner() == self.other_player:\n            score_I_win_dis_game = -10000000000\n        else:\n            score_I_win_dis_game = 0\n        return score_winning_hollows + score_empty_hollows + score_I_win_dis_game\n","sub_path":"IS/Aufgabe_4/kalahaBot.py","file_name":"kalahaBot.py","file_ext":"py","file_size_in_byte":3508,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"241664011","text":"\"\"\"\nA pronunciation dictionary: dict of {str: list of str}\n  - each key is a word (a str)\n  - each value is a list of phonemes for that word (a list of str)\n\"\"\"\n\n\ndef read_pronunciation(pronunciation_file):\n    \"\"\" (file open for reading) -> pronunciation dictionary\n\n    Read pronunciation_file, which is in the format of the CMU Pronouncing\n    Dictionary, and return the pronunciation dictionary.\n    \"\"\"\n\n    pronunciation_dict = dict()\n\n    for line in pronunciation_file:\n\n        # all comments start with ;;;\n        if ';;;' not in line:\n            # comments ended read file lines and make dictionary\n            word_list = line.strip().split()\n            word, phoneme_list = word_list[0], word_list[1:]\n            pronunciation_dict[word] = phoneme_list\n\n    return pronunciation_dict\n\n\ndef read_poetry_form_description(poetry_forms_file):\n    \"\"\" (file open for reading) -> poetry pattern\n\n    Precondition: we have just read a poetry form name from poetry_forms_file.\n\n    Return the next poetry pattern from poetry_forms_file.\n    \"\"\"\n    \n    line_syllables, line_rhyme = [], []\n    read_pattern = False\n    \n    while not read_pattern:\n        line = poetry_forms_file.readline()\n        \n        if line != '\\n' and line != '':\n            # if True means we are still in pattern\n            # get the syllables and rhyme scheme\n            line_char = line.strip().split()\n            line_syllables.append(int(line_char[0]))\n            line_rhyme.append(line_char[1])            \n        \n        else:\n            # line is newline or empty\n            # reached end of poetry form or file\n            read_pattern = True\n    \n    return (line_syllables, line_rhyme)\n\n\n\ndef read_poetry_form_descriptions(poetry_forms_file):\n    \"\"\" (file open for reading) -> dict of {str: poetry pattern}\n\n    Return a dictionary of poetry form name to poetry pattern for the\n    poetry forms in poetry_forms_file.\n    \"\"\"\n\n    poetry_forms_dict = dict()\n\n    while True:\n\n        # read first line to get name\n        name = poetry_forms_file.readline().strip()\n        \n        # eventually the looping will reach to end of file\n        # name will become an empty string that is when we return\n        if name == '':\n            return poetry_forms_dict\n        \n        # read pattern\n        poetry_pattern = read_poetry_form_description(poetry_forms_file)\n        poetry_forms_dict[name] = poetry_pattern\n","sub_path":"108A2/poetry_reader.py","file_name":"poetry_reader.py","file_ext":"py","file_size_in_byte":2421,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"187837255","text":"from django.test import TestCase, Client\n\nfrom .models import Pub, Beer, Price\n\nclass PubTest(TestCase):\n\n    def test_no_image(self):\n        pub = Pub.objects.create(\n                name=\"Test pub\",\n                address=\"Test address\",\n                latitude=0.33,\n                longitude=0.66,\n            )\n\nclass BeerTest(TestCase):\n    \n    def test_no_image(self):\n        beer = Beer.objects.create(\n                name=\"Test beer\",\n            )\n\nclass PriceTest(TestCase):\n\n    def setUp(self):\n        Pub.objects.create(\n                name=\"Test pub\",\n                address=\"Test address\",\n                latitude=0.33,\n                longitude=0.66,\n            )\n        Beer.objects.create(\n                name=\"Test beer\",\n            )\n\n    def test_create(self):\n        pub = Pub.objects.get(id=1)\n        beer = Beer.objects.get(id=1)\n        Price.objects.create(\n                pub=pub,\n                beer=beer,\n                price=2.50,\n                student_discount=False,\n            )\n\nclass StudentDiscountFilter(TestCase):\n\n    def setUp(self):\n        pub = Pub.objects.create(\n                name=\"Test pub\",\n                address=\"Test address\",\n                latitude=0.33,\n                longitude=0.66,\n            )\n        beer = Beer.objects.create(\n                name=\"Test beer\",\n            )\n        Price.objects.create(\n                beer=beer,\n                pub=pub,\n                price=2.30,\n                student_discount=False\n            )\n        Price.objects.create(\n                beer=beer,\n                pub=pub,\n                price=2.50,\n                student_discount=True\n            )\n        self.client = Client()\n\n    def test_no_student(self):\n        response = self.client.get('/api/pubs/')\n        self.assertTrue(bytes('2.50', 'utf-8') not in response.content)\n\n    def test_student(self):\n        response = self.client.get('/api/pubs/?student=1')\n        self.assertTrue(bytes('2.50', 'utf-8') in response.content)\n","sub_path":"server/pub/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":2030,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"615511824","text":"# coding:UTF-8\nimport urllib.parse\nfrom bs4 import BeautifulSoup\nimport re\n\n\nclass HtmlParser(object):\n    def parse(self, page_url, html_cont):\n        if page_url is None or html_cont is None:\n            return\n\n        soup = BeautifulSoup(html_cont, \"lxml\", from_encoding='UTF-8')\n        new_urls = self._get_new_urls(page_url, soup)\n        new_data = self._get_new_data(page_url, soup)\n        return new_urls, new_data\n\n    def _get_new_urls(self, page_url, soup):\n        new_urls = set()\n\n        for link in soup.find_all('a', class_='pg'):\n            new_url = link.get('href')\n            new_full_url = urllib.parse.urljoin(page_url, new_url)\n            # new_full_url = urllib.parse.urljoin('http://www.nxpic.org/module/forum/', new_url)\n            new_urls.add(new_full_url)\n        return new_urls\n\n    def _get_new_data(self, page_url, soup):\n        res_data = {}\n\n        # url\n\n        # <dd class=\"lemmaWgt-lemmaTitle-title\"> <h1 >Python</h1>\n        title_node = soup.find('a', class_=\"s xst\")\n        res_data['title'] = title_node.string\n\n        # <div class=\"lemma-summary\" label-module=\"lemmaSummary\">\n        summary_node = soup.find('a', class_='s xst')\n        res_data['summary'] = summary_node.get('href')\n\n        return res_data\n","sub_path":"test_spider/html_parser.py","file_name":"html_parser.py","file_ext":"py","file_size_in_byte":1268,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"30599350","text":"from typing import Union, List\n\nimport os\nos.environ[\"OMP_NUM_THREADS\"] = \"1\"\nos.environ[\"MKL_NUM_THREADS\"] = \"1\"\n\nimport gc  # noqa E402\nimport time  # noqa E402\nimport random  # noqa E402\nfrom datetime import datetime  # noqa E402\n\nimport torch  # noqa E402\ntorch.set_num_threads(1)\n\nfrom tensorboardX import SummaryWriter  # noqa E402\n\nfrom catalyst.utils.misc import set_global_seed  # noqa E402\nfrom catalyst.dl.utils import UtilsFactory  # noqa E402\nfrom catalyst.rl.utils import EpisodeRunner  # noqa E402\nfrom catalyst.rl.exploration import ExplorationHandler  # noqa E402\nfrom catalyst.rl.environments.core import EnvironmentSpec  # noqa E402\nfrom catalyst.rl.db.core import DBSpec  # noqa E402\nfrom catalyst.rl.agents.core import ActorSpec, CriticSpec  # noqa E402\n\n\n_BIG_NUM = int(2 ** 32 - 2)\n_SEED_RANGE = _BIG_NUM\n\n\nclass Sampler:\n    def __init__(\n        self,\n        agent: Union[ActorSpec, CriticSpec],\n        env: EnvironmentSpec,\n        db_server: DBSpec = None,\n        exploration_handler: ExplorationHandler = None,\n        logdir: str = None,\n        id: int = 0,\n        mode: str = \"infer\",\n        seeds: List = None,\n        max_trajectories_to_sample: int = None,\n        force_store: bool = False,\n        gc_period: int = 10,\n    ):\n        self._device = UtilsFactory.get_device()\n        self._seed = 42 + id\n        self._sampler_id = id\n\n        self._infer = mode == \"infer\"\n        self.seeds = seeds\n\n        # logging\n        self._prepare_logger(logdir, mode)\n\n        # environment, model, exploration & action handlers\n        self.env = env\n        self.agent = agent\n        self.exploration_handler = exploration_handler\n        self.episode_index = 0\n        self.episode_runner = EpisodeRunner(\n            env=self.env,\n            agent=self.agent,\n            device=self._device,\n            deterministic=self._infer\n        )\n\n        # synchronization configuration\n        self.db_server = db_server\n        self.episode_limit = max_trajectories_to_sample or _BIG_NUM\n        self._force_store = force_store\n        self._sampler_weight_mode = \"actor\"\n        self._gc_period = gc_period\n\n    def _prepare_logger(self, logdir, mode):\n        if logdir is not None:\n            current_date = datetime.now().strftime(\"%y-%m-%d-%H-%M-%S-%M-%f\")\n            logpath = f\"{logdir}/\" \\\n                f\"sampler-{mode}-{self._sampler_id}-{current_date}\"\n            os.makedirs(logpath, exist_ok=True)\n            self.logger = SummaryWriter(logpath)\n        else:\n            self.logger = None\n\n    def _to_tensor(self, *args, **kwargs):\n        return torch.Tensor(*args, **kwargs).to(self._device)\n\n    def load_checkpoint(\n        self,\n        *,\n        filepath: str = None,\n        db_server: DBSpec = None\n    ):\n        if filepath is not None:\n            checkpoint = UtilsFactory.load_checkpoint(filepath)\n            weights = checkpoint[f\"{self._sampler_weight_mode}_state_dict\"]\n            self.agent.load_state_dict(weights)\n        elif db_server is not None:\n            while not db_server.get_sample_flag():\n                time.sleep(1.0)\n            weights = db_server.load_weights(prefix=self._sampler_weight_mode)\n            weights = {k: self._to_tensor(v) for k, v in weights.items()}\n            self.agent.load_state_dict(weights)\n        else:\n            raise NotImplementedError\n\n        self.agent.to(self._device)\n        self.agent.eval()\n\n    def _store_trajectory(self):\n        if self.db_server is None:\n            return\n        if not self.db_server.get_sample_flag():\n            return\n\n        trajectory = self.episode_runner.get_trajectory()\n        self.db_server.push_trajectory(trajectory)\n\n    def _get_seed(self):\n        seed = self._seed + random.randrange(_SEED_RANGE)\n        set_global_seed(seed)\n        if self.seeds is None:\n            seed = random.randrange(_SEED_RANGE)\n        else:\n            seed = random.choice(self.seeds)\n        set_global_seed(seed)\n        return seed\n\n    def _log_to_console(\n        self,\n        *,\n        episode_reward,\n        num_steps,\n        elapsed_time,\n        seed\n    ):\n        print(\n            f\"--- episode {int(self.episode_index):05d}:\\t\"\n            f\"steps: {int(num_steps):05d}\\t\"\n            f\"reward: {episode_reward:9.4f}\\t\"\n            f\"time: {elapsed_time:9.4f}\\t\"\n            f\"seed: {seed}\"\n        )\n\n    def _log_to_tensorboard(self, *, episode_reward, num_steps, elapsed_time):\n        if self.logger is not None:\n            self.logger.add_scalar(\n                \"episode/num_steps\", num_steps, self.episode_index\n            )\n            self.logger.add_scalar(\n                \"episode/reward\", episode_reward, self.episode_index\n            )\n            self.logger.add_scalar(\n                \"time/episode per minute\", 60. / elapsed_time,\n                self.episode_index\n            )\n            self.logger.add_scalar(\n                \"time/steps per second\", num_steps / elapsed_time,\n                self.episode_index\n            )\n            self.logger.add_scalar(\n                \"time/episode time (sec)\", elapsed_time, self.episode_index\n            )\n            self.logger.add_scalar(\n                \"time/step time (sec)\", elapsed_time / num_steps,\n                self.episode_index\n            )\n\n    def run(self):\n        while True:\n            self.load_checkpoint(db_server=self.db_server)\n            seed = self._get_seed()\n            exploration_strategy = \\\n                self.exploration_handler.get_exploration_strategy() \\\n                if self.exploration_handler is not None \\\n                else None\n            self.episode_runner.reset(exploration_strategy)\n\n            start_time = time.time()\n            episode_info = self.episode_runner.run(\n                exploration_strategy=exploration_strategy)\n            elapsed_time = time.time() - start_time\n\n            if not self._infer or self._force_store:\n                self._store_trajectory()\n\n            self._log_to_console(\n                **episode_info,\n                elapsed_time=elapsed_time,\n                seed=seed)\n\n            self._log_to_tensorboard(\n                **episode_info,\n                elapsed_time=elapsed_time)\n\n            self.episode_index += 1\n            if self.episode_index % self._gc_period == 0:\n                gc.collect()\n            if self.episode_index >= self.episode_limit:\n                return\n","sub_path":"catalyst/rl/onpolicy/sampler.py","file_name":"sampler.py","file_ext":"py","file_size_in_byte":6450,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"154073460","text":"from src.players.BasePlayer import BasePlayer\nfrom src.cards.Deck import Deck\n\n\nclass Dealer(BasePlayer):\n    \"\"\"\n\n    \"\"\"\n    def first_draw(self, deck: Deck):\n        \"\"\"\n\n        :return:\n        \"\"\"\n        super().draw(deck, 2)\n\n    def show_hands(self):\n        \"\"\"\n\n        :return:\n        \"\"\"\n        show_list = []\n        for index, card in enumerate(self.hand):\n            if index != 1:\n                show_list.append(card.text)\n            else:\n                show_list.append(\"Unknown\")\n\n        return show_list\n\n    def need_to_draw(self):\n        if self.score >= 17:\n            return False\n        return True\n\n","sub_path":"python/src/players/Dealer.py","file_name":"Dealer.py","file_ext":"py","file_size_in_byte":637,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"65507443","text":"import sys\nsys.path.append('src/model')\nsys.path.append('src/checks')\n\nimport unittest\nfrom CheckDummy import CheckDummy\n\nclass TestCheckDummy(unittest.TestCase):\n    \"\"\"\n    A simple check that reports always a good return condition.\n    \"\"\"\n\n    def test_check(self):\n        check = CheckDummy()\n        self.assertFalse(check.do())\n\nif __name__ == '__main__':\n    unittest.main()","sub_path":"tests/TestCheckDummy.py","file_name":"TestCheckDummy.py","file_ext":"py","file_size_in_byte":383,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"259100623","text":"import time\nimport mx.DateTime\nfrom datetime import datetime\nfrom report import report_sxw\nimport netsvc\nimport locale\nfrom compiler.ast import Print\nfrom osv import osv, fields\nimport xlwt\nimport socket\nfrom tools import amount_to_text_en\nimport babel\n\nlogger = netsvc.Logger()\nresult_acc=[]\n     \nclass unpaidtransport_fee_challan_parser(report_sxw.rml_parse):\n \n    def __init__(self, cr, uid, name, context):\n \n        super(unpaidtransport_fee_challan_parser, self).__init__(cr, uid, name, context)\n        self.result_temp=[]\n        self.localcontext.update({\n                'get_today':self.get_today,\n                'get_challans':self.get_challans,\n                'get_user_name':self.get_user_name,\n                'get_vertical_lines': self.get_vertical_lines,\n                'get_vertical_lines_total': self.get_vertical_lines_total,\n                'get_challan_number':self.get_challan_number,\n                'get_candidate_info':self.get_candidate_info,\n                'get_on_accounts':self.get_on_accounts,\n                'get_total_amount':self.get_total_amount,\n                'get_amount_in_words':self.get_amount_in_words,\n                'get_due_date':self.get_due_date,\n                'get_class_group':self.get_class_group,\n                'get_challan_logo':self.get_challan_logo,\n                'get_challan_header_lineone':self.get_challan_header_lineone,\n                'get_challan_header_linetwo':self.get_challan_header_linetwo,\n                'get_challan_header_linethree':self.get_challan_header_linethree,\n                'get_challan_footer_one':self.get_challan_footer_one,\n                'get_challan_footer_two':self.get_challan_footer_two,\n                })\n        self.context = context\n     \n    def get_challan_logo(self):\n        rescompany_id = self.pool.get('res.company').search(self.cr, self.uid,[])\n        #-------------Handling Only one Company is There are multiple companies blank space will be returned----------------        \n        if len(rescompany_id)>1:\n            return ''\n        company_recs = self.pool.get('res.company').browse(self.cr, self.uid, rescompany_id)\n        for rec in company_recs:\n            logo = rec.company_clogo_trans\n        return logo\n\n    def get_challan_header_lineone(self):\n        rescompany_id = self.pool.get('res.company').search(self.cr, self.uid,[])\n        #-------------Handling Only one Company is There are multiple companies blank space will be returned----------------        \n        if len(rescompany_id)>1:\n            return ''\n        company_recs = self.pool.get('res.company').browse(self.cr, self.uid, rescompany_id)\n        for rec in company_recs:\n            fieldone = rec.company_cfieldone_trans\n        return fieldone\n\n    def get_challan_header_linetwo(self):\n        rescompany_id = self.pool.get('res.company').search(self.cr, self.uid,[])\n        #-------------Handling Only one Company is There are multiple companies blank space will be returned----------------        \n        if len(rescompany_id)>1:\n            return ''\n        company_recs = self.pool.get('res.company').browse(self.cr, self.uid, rescompany_id)\n        for rec in company_recs:\n            fieldtwo = rec.company_cfieldtwo_trans\n        return fieldtwo\n    \n    def get_challan_header_linethree(self):\n        rescompany_id = self.pool.get('res.company').search(self.cr, self.uid,[])\n        #-------------Handling Only one Company is There are multiple companies blank space will be returned----------------        \n        if len(rescompany_id)>1:\n            return ''\n        company_recs = self.pool.get('res.company').browse(self.cr, self.uid, rescompany_id)\n        for rec in company_recs:\n            fieldthree = rec.company_cfieldthree_trans\n        return fieldthree\n    \n    def get_challan_footer_one(self):\n        rescompany_id = self.pool.get('res.company').search(self.cr, self.uid,[])\n        #-------------Handling Only one Company is There are multiple companies blank space will be returned----------------        \n        if len(rescompany_id)>1:\n            return ''\n        company_recs = self.pool.get('res.company').browse(self.cr, self.uid, rescompany_id)\n        for rec in company_recs:\n            footerone = rec.company_cfieldfour_trans\n        return footerone\n    \n    def get_challan_footer_two(self):\n        rescompany_id = self.pool.get('res.company').search(self.cr, self.uid,[])\n        #-------------Handling Only one Company is There are multiple companies blank space will be returned----------------        \n        if len(rescompany_id)>1:\n            return ''\n        company_recs = self.pool.get('res.company').browse(self.cr, self.uid, rescompany_id)\n        for rec in company_recs:\n            footertwo = rec.company_cfieldfive_trans\n        return footertwo\n    \n    def get_today(self):\n        today = time.strftime('%d-%m-%Y')\n        return today \n\n    def get_due_date(self):\n        due_date = self.datas['form']['due_date']\n        due_date = datetime.strptime(due_date, '%Y-%m-%d').strftime('%d/%m/%Y')\n        return due_date \n     \n    def get_class_group(self, data):\n        student_id = self.datas['form']['student_id'][0]\n        stu_rec = self.pool.get('sms.student').browse(self.cr ,self.uid , student_id)\n        return stu_rec.current_class.group_id.name\n     \n    def get_challans(self, data):\n        challan_list = []\n        student_id = self.datas['form']['student_id'][0]\n        stu_rec = self.pool.get('sms.student').browse(self.cr ,self.uid , student_id)\n        self.pool.get('sms.transportfee.challan.book').check_transportfee_challans_issued(self.cr, self.uid ,stu_rec.current_class.id ,stu_rec.id)\n        challan_ids = self.pool.get('sms.transportfee.challan.book').search(self.cr, self.uid, [('student_class_id','=', stu_rec.current_class.id),\n                                                                                                ('student_id','=', stu_rec.id),\n                                                                                                ('state','=','fee_calculated')])\n        if challan_ids:\n            rec_challan_ids = self.pool.get('sms.transportfee.challan.book').browse(self.cr, self.uid,challan_ids)\n            for challan in rec_challan_ids:\n                challan_dict = {'challan_number':'','candidate_info':'','on_accounts':'','vertical_lines':'','total_amount':'','amount_in_words':'','amount_after_due_date':''}\n                challan_dict['challan_number'] = self.get_challan_number(challan.id)\n                challan_dict['candidate_info'] = self.get_candidate_info(challan.student_id.id)\n                challan_dict['on_accounts'] = self.get_on_accounts(challan.id)\n                challan_dict['vertical_lines'] = self.get_vertical_lines_total(challan.id)\n                challan_dict['total_amount'] = self.get_total_amount(challan.id)\n                challan_dict['amount_in_words'] = self.get_amount_in_words(challan.id)\n                if self.datas['form']['amount_after_due_date']:\n                    challan_dict['amount_after_due_date'] = challan_dict['total_amount'] + self.datas['form']['amount_after_due_date'] \n                else:\n                    challan_dict['amount_after_due_date'] = data['form']['amount_after_due_date']\n                challan_list.append(challan_dict)\n        return challan_list  \n     \n    def get_user_name(self):\n        user_name = self.pool.get('res.users').browse(self.cr, self.uid, self.uid, self.context).name\n        return   user_name\n \n    def get_vertical_lines(self, data):\n        line_dots = []\n        for num in range(1,30):\n            dict = {'line-style':'|'}\n            line_dots.append(dict)\n        return line_dots\n\n    def get_vertical_lines_total(self, data):\n        line_dots = []\n        lines_ids = self.pool.get('sms.transport.fee.challan.lines').search(self.cr, self.uid, [('receipt_book_id','=', data)])\n        if lines_ids:\n            challans = self.pool.get('sms.transport.fee.challan.lines').browse(self.cr, self.uid, lines_ids)\n            start = len(challans)\n            if start >=11:\n                dict = {'line-style':'|'}\n                line_dots.append(dict)\n            else:\n                for num in range(start,11):\n                    dict = {'line-style':'|'}\n                    line_dots.append(dict)\n        return line_dots    \n     \n    def get_banks(self):\n        banks_ids = self.pool.get('res.company').search(self.cr,self.uid,[])\n        banks_recs = self.pool.get('res.company').browse(self.cr,self.uid,banks_ids)\n        for rec in banks_recs:\n            if rec.bank_name1 and rec.bank_acctno1:\n                bank= str(rec.bank_name1) + ' - ' +str(rec.bank_acctno1)\n            else:\n                bank = ''        \n        return bank\n\n    def get_banks_2(self):\n        banks_ids = self.pool.get('res.company').search(self.cr,self.uid,[])\n        banks_recs = self.pool.get('res.company').browse(self.cr,self.uid,banks_ids)\n        for rec in banks_recs:\n            if rec.bank_name1 and rec.bank_acctno1:\n                bank= str(rec.bank_name2) + ' - ' +str(rec.bank_acctno2)\n            else:\n                bank = ''\n        return bank\n \n    def get_challan_number(self, data):\n        line_dots = []\n#         challan = self.pool.get('cms.challan').browse(self.cr,self.uid,form['challan_id'])\n#         challan_str = str(challan.challan_no)\n#         return challan_str.split(\"-\")[1] + \" (\" + challan_str.split(\"-\")[0]+ \")\"\n        return '12345'\n \n    def get_candidate_info(self, data):\n        info_list = []\n        student_id = self.datas['form']['student_id'][0]\n        stdrec = self.pool.get('sms.student').browse(self.cr ,self.uid , student_id)\n        info_dict = {'name':'','father_name':'','class':'','fee_month':'','reg_no':''}\n        info_dict['reg_no'] = stdrec.registration_no \n        info_dict['name'] = stdrec.name \n        info_dict['father_name'] = stdrec.father_name\n        info_dict['class'] = stdrec.current_class.class_id.name + \" - \" + stdrec.current_class.section_id.name + \" \" + stdrec.current_class.class_session\n        fee_month = self.datas['form']['due_date']\n        due_date = datetime.strptime(fee_month, '%Y-%m-%d')\n        str_date = due_date.strftime('%b %Y')\n        info_dict['fee_month'] = str_date\n        info_list.append(info_dict)\n        return info_list\n \n    def get_on_accounts(self, data):\n        result = []\n        challans = []\n        lines_ids = self.pool.get('sms.transport.fee.challan.lines').search(self.cr,self.uid, [('receipt_book_id','=',data)])\n        if lines_ids:\n            challans = self.pool.get('sms.transport.fee.challan.lines').browse(self.cr,self.uid,lines_ids)\n        counter = 1\n        for challan in challans:\n            if counter <7:\n                dict = {'head_name':challan.name,'head_amount':challan.fee_amount}\n                result.append(dict)\n            counter = counter + 1 \n        return result\n     \n    def get_total_amount(self, data):\n        receipt = self.pool.get('sms.transportfee.challan.book').browse(self.cr, self.uid, data)\n        if self.datas['form']['amount_after_due_date']:\n            total_amount_str = receipt.total_payables + self.datas['form']['amount_after_due_date'] \n        else:\n            total_amount_str = receipt.total_payables\n        return total_amount_str\n     \n    def get_amount_in_words(self,data):\n        amount = self.pool.get('sms.transportfee.challan.book').browse(self.cr,self.uid,data).total_payables        \n        user_id = self.pool.get('res.users').browse(self.cr, self.uid,[self.uid])[0]\n        cur = user_id.company_id.currency_id.name\n        amt_en = amount_to_text_en.amount_to_text(amount,'en',cur);\n        return_value=str(amt_en).replace('Cent','Paisa')\n        return return_value\n     \nreport_sxw.report_sxw('report.smstransport_stu_unpaidtransportfee_receipt', \n                      'sms.transport.fee.payments',\n                      'addons/smstransport/report/smstransport_print_one_student_per_page.rml', \n                      parser=unpaidtransport_fee_challan_parser, \n                      header=None)\n","sub_path":"smstransport/report/student_unpaid_transportfee_challan.py","file_name":"student_unpaid_transportfee_challan.py","file_ext":"py","file_size_in_byte":12168,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"54248016","text":"'''\nName:\t\tProblem 5\nProblem:\t2520 is the smallest number that can be divided by each of the numbers from 1 to 10 without any remainder.\n\nWhat is the smallest positive number that is evenly divisible by all of the numbers from 1 to 20?\n1*2*3*4*5*6*7*8*9*10\n1*2*4*5*7*9=2520\n1*2*3*5*7=210\n1*11*12*13*14*15*16*17*18*19*20\n1*2*3*4*5*6*7*8*9*10*11*12*13*14*15*16*17*18*19*20\n\n2^4*3^2*5*7*11*13*17*19=232792560\n\n1*6*7*8*9*10\n\n1*2*3*5*7*11*13*17*19=9699690\n4,6,8,9,12,14,15,16,18,20\n\n\n\n1. Perform prime factorization on each number\n2. Check to see if there are more primes from the current number (or a new one)\n3. Multiply greatest prime factors\n\n'''\nimport math\n\nprimes=[2,3,5,7,11,13,17,19]\nprimePowers={}\nproduct=1;\n\nfor p in primes:\n\tprimePowers[str(p)]=0\n\tprint(str(p))\n\tfor n in range(2,21):\n\t\tif(n>=p):\n\t\t\tprint(\"\\t\"+str(n))\n\t\t\tnum=n\n\t\t\tcount=0\n\t\t\twhile (num-p>=0):\n\t\t\t\tif (num%p==0):\n\t\t\t\t\tnum=num/p\n\t\t\t\t\tcount+=1\n\t\t\t\t\tprint(\"\\t\"+\"\\t\"+str(num)+\":\"+str(count))\n\t\t\t\telse:\n\t\t\t\t\tnum=-1\n\t\t\tif (primePowers[str(p)] < count):\n\t\t\t\tprimePowers[str(p)] = count\n\t\t\t\tprint(str(p)+\"-\"+str(primePowers[str(p)])+\":\"+str(count))\nfor key, value in primePowers.items():\n\tproduct=product*int(math.pow(int(key), value))\n\tprint(key+\": \"+str(value))\n\n\nprint(str(product))","sub_path":"problem5.py","file_name":"problem5.py","file_ext":"py","file_size_in_byte":1251,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"464279988","text":"from la_cli.Commands import command, assert_in_project\nfrom la_cli.Util import GetApp\n\nfrom LibApplication.Stock.Services.Data import DataService\nfrom LibApplication.Stock.Views.DataWindow import DataWindow\nfrom LibApplication.Stock.Views.MessageBox import MessageBox\n\nimport click\n\n@command\ndef browse():\n    # Can only check info if we are in the project\n    assert_in_project(\"browse data\")\n\n    # Get the app\n    app = GetApp.get_app()\n\n    # Declare the data window variable\n    data_window = None\n\n    def error(exception):\n        MessageBox(\"Unable to retreive data\", \"The database may not be initialised, or there may not be a root object to inspect.\").show_modal(data_window)\n\n    # Extend the app to run the data viewer instead of the main window\n    class viewer_app(app):\n        def prepare(self):\n            # Nothing to prepare\n            pass\n\n        def start(self):\n            nonlocal data_window\n            print(\"start\")\n\n            # Construct the data window\n            data_window = DataWindow()\n            data_window.show()\n\n        def ready(self):\n            nonlocal data_window\n\n            print(\"ready\")\n            # Query data service for root object\n            DataService.get_instance().get().subscribe(data_window.set_root, error)\n\n\n    # Run the app\n    viewer_app()\n\n\n","sub_path":"la_cli/Commands/Browse.py","file_name":"Browse.py","file_ext":"py","file_size_in_byte":1318,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"243218515","text":"from mantid.simpleapi import *\n\nname = 'output'\nIPTS = 19834\nrun = 122687\nbackground = None\nBackgroundScale = 1\nvanadium = 101567 # Run number or `None`\nvanadium_IPTS = 7776\nnormaliseBy='Monitor' # One on (None, Monitor, Time)\nunits = 'Theta' # One of (Theta, ElasticQ, ElasticDSpacing)\nBinning = '3,123,1200' # Min,Max,Number_of_bins\n\n# Time filter options\nTimeInterval = 3600\nStartTime = None\nStopTime = None\n\n###############################################################################\n\n# Load vandium if not already loaded.\nvanadium_ws = 'HB2C_{}'.format(vanadium)\n\nif vanadium_ws not in mtd:\n     LoadWAND(IPTS=vanadium_IPTS, RunNumbers=vanadium, OutputWorkspace=vanadium_ws)\n\n# Load background if needed and if not already loaded.\n\nif background is not None:\n     background_ws = 'HB2C_{}'.format(vanadium)\n     if background_ws not in mtd:\n          LoadWAND(IPTS=IPTS, RunNumbers=background, OutputWorkspace=background_ws)\nelse:\n     background_ws = None\n\n# Load data if not already loaded\nws = 'HB2C_{}'.format(run)\n\nif ws not in mtd:\n    LoadEventNexus(Filename='/HFIR/HB2C/IPTS-{}/nexus/HB2C_{}.nxs.h5'.format(IPTS, run), OutputWorkspace=ws,LoadMonitors=True)\n    # Mask detectors to be the same as vanadium\n    MaskDetectors(ws, MaskedWorkspace=vanadium_ws)\n\n\n# Filter events\n\nGenerateEventsFilter(InputWorkspace=ws, OutputWorkspace='filter', InformationWorkspace='info',\n                     TimeInterval=TimeInterval, StartTime=StartTime, StopTime=StopTime)\nFilterEvents(InputWorkspace=ws, SplitterWorkspace='filter', OutputWorkspaceBaseName=ws+'_filtered', InformationWorkspace='info',\n             GroupWorkspaces=True,FilterByPulseTime=True,OutputWorkspaceIndexedFrom1=True)\nFilterEvents(InputWorkspace=ws+'_monitors', SplitterWorkspace='filter', OutputWorkspaceBaseName=ws+'_filtered_mon', InformationWorkspace='info',\n             GroupWorkspaces=True,FilterByPulseTime=True,SpectrumWithoutDetector='Skip only if TOF correction',OutputWorkspaceIndexedFrom1=True)\n\n# Set the monitor count on filtered WS\nfor n in range(mtd[ws+'_filtered'].getNumberOfEntries()):\n    AddSampleLog(mtd[ws+'_filtered'].getItem(n), LogName=\"gd_prtn_chrg\",\n                 LogType='Number', NumberType='Double',LogText=str(mtd[ws+'_filtered_mon'].getItem(n).getNumberEvents()))\n\n# Run powder diffraction reduction\nxmin, xmax, bins = Binning.split(',')\nWANDPowderReduction(ws+'_filtered',\n                    CalibrationWorkspace=vanadium_ws,\n                    BackgroundWorkspace=background_ws, BackgroundScale=BackgroundScale,\n                    XMin=xmin, XMax=xmax, NumberBins=bins, Target=units,\n                    NormaliseBy=normaliseBy,\n                    OutputWorkspace=name)\n","sub_path":"filtering/FilterTime.py","file_name":"FilterTime.py","file_ext":"py","file_size_in_byte":2690,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"482686749","text":"from django.urls import path\nfrom django.contrib.staticfiles.urls import staticfiles_urlpatterns\n\nfrom . import views\n\nurlpatterns = [\n    path('', views.principal, name = 'cliente'),\n    path('crear_cliente/', views.crear),\n    path('modificar_cliente/',views.modificar),\n    path('eliminar_cliente/',views.eliminar),\n    path('ver_cuenta/',views.ver_cuenta),\n    path('clientes', views.ver_cliente),\n    path(r'deposito/(?P<numero>d+)/$', views.depositar, name = 'deposito'),\n    path(r'retirar/(?P<numero>d+)/$', views.retirar, name = 'retirar'),\n    path('agregar_cuenta/', views.agregar_cuenta),\n\n]","sub_path":"apps/cliente/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":603,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"379014626","text":"import pandas as pd\r\nimport numpy as np\r\nimport seaborn as sns\r\n\r\nadvertising = pd.read_csv(r'C:\\Users\\imanm\\Desktop\\MTA questions\\studies\\anjan\\003_Machine Learning\\Supervised_learning\\Regression\\Linear_Regreesion\\tvmarketing.csv')\r\nsns.pairplot(advertising,x_vars='TV',y_vars= 'Sales',size = 6.7,kind='scatter')\r\nx = advertising['TV']\r\ny = advertising['Sales']\r\n\r\nfrom sklearn.model_selection import train_test_split\r\nx_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.7 , random_state=100)\r\nx_train = x_train[:, np.newaxis]\r\nx_test = x_test[:, np.newaxis]\r\nprint(x_train.ndim)\r\nprint(y_train.ndim)\r\n\r\nfrom sklearn.linear_model import LinearRegression\r\nlr =LinearRegression()\r\nlr.fit(x_train,y_train)\r\n\r\ny_prediction = lr.predict(x_test)\r\nprint(lr.predict([[12]]))\r\n\r\n","sub_path":"Linear Regression.py","file_name":"Linear Regression.py","file_ext":"py","file_size_in_byte":790,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"97143880","text":"# Copyright (c) 2015, System Engineering Software Society\n# 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 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 the System Engineering Software Society nor the\n#       names of its contributors may be used to endorse or promote products\n#       derived from this software without specific prior written permission.\n#\n# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\n# ARE DISCLAIMED.\n# IN NO EVENT SHALL SYSTEM ENGINEERING SOFTWARE SOCIETY BE LIABLE FOR ANY\n# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES\n# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;\n# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND\n# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\n# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS\n# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\nimport unittest\n\nfrom sylib.figure.util import parse_configuration\nfrom sylib.figure.colors import COLORS, parse_color_to_mpl_color\n\n\nclass ParseStringToMPLColorTestCase(unittest.TestCase):\n    def test_mpl_colors(self):\n        for c in COLORS:\n            self.assertEqual(c, parse_color_to_mpl_color(c))\n\n    def test_hex_color(self):\n        text = '#ffffff'\n        result = [1., 1., 1.]\n        self.assertEqual(result, parse_color_to_mpl_color(text))\n\n    def test_hexa_color(self):\n        text = '#ffffffff'\n        result = [1., 1., 1., 1.]\n        self.assertEqual(result, parse_color_to_mpl_color(text))\n\n    def test_rgb_int_color(self):\n        test_cases = ['255, 255, 255',\n                      '1, 1, 1']\n        results = [[1., 1., 1.],\n                   [1/255., 1/255., 1/255.]]\n        for case, result in zip(test_cases, results):\n            self.assertEqual(result, parse_color_to_mpl_color(case))\n\n    def test_rgba_int_color(self):\n        test_cases = ['255, 255, 255, 255',\n                      '(255, 255, 255, 255)']\n        result = [1., 1., 1., 1.]\n        for case in test_cases:\n            self.assertEqual(result, parse_color_to_mpl_color(case))\n\n    def test_rgb_float_color(self):\n        test_cases = ['1., 1., 1.',\n                      '(1., 1., 1.)',\n                      '1., 1. 1.',\n                      '1 1 1.']\n        result = [1., 1., 1.]\n        for case in test_cases:\n            self.assertEqual(result, parse_color_to_mpl_color(case))\n\n    def test_rgba_float_color(self):\n        text = '1., 1., 1., 1.'\n        result = [1., 1., 1., 1.]\n        self.assertEqual(result, parse_color_to_mpl_color(text))\n\n    def test_wrong_imput(self):\n        test_cases = ['255, 255',\n                      '#ffes',\n                      '#ffeegg',\n                      '256, 1, 1']\n        for text in test_cases:\n            self.assertEqual(None, parse_color_to_mpl_color(text))\n\n\nclass ParseConfigurationTestCase(unittest.TestCase):\n    def test_prase_configuration(self):\n        test_case = [\n            ('figure.title', 'figure title'),\n            ('axes.id.xaxis', 'x1'),\n            ('axes.id.yaxis', 'y1'),\n            ('axes.id.grid.color', 'g'),\n            ('axes.id.legend.loc', 'upper left'),\n            ('line.l1.xdata', 'index'),\n            ('line.l1.ydata', 'signal'),\n            ('line.l1.axes', 'id')\n        ]\n        result = {'axes': {'id': {'xaxis': 'x1',\n                                  'yaxis': 'y1',\n                                  'grid': {'show': True,\n                                           'color': 'g'},\n                                  'legend': {'show': True,\n                                             'loc': 'upper left'}}},\n                  'line': {'l1': {'xdata': 'index',\n                                  'ydata': 'signal',\n                                  'axes': 'id',\n                                  'valid': True}},\n                  'figure': {'title': 'figure title'}}\n        self.assertEqual(result, parse_configuration(test_case))\n\n\ndef runModelTest(): # noqa\n    test_loader = unittest.TestLoader()\n    test_loader.loadTestsFromTestCase(ParseStringToMPLColorTestCase)\n    unittest.TextTestRunner(verbosity=2)\n","sub_path":"Library/Test/Unit/test_figure_util.py","file_name":"test_figure_util.py","file_ext":"py","file_size_in_byte":4834,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"400677872","text":"# -*- coding: UTF-8 -*-\n#! python3\n\n\"\"\"\n    Name:         Script to replace \"Saint-Lô Agglo\" metadatas associated specifications\n    Author:       Isogeo\n    Purpose:      Script using the migrations-toolbelt package to perform metadata migration.\n                Logs are willingly verbose.\n    Python:       3.7+\n\"\"\"\n\n# ##############################################################################\n# ########## Libraries #############\n\n# Standard Library\nimport logging\nfrom logging.handlers import RotatingFileHandler\nfrom os import environ\nfrom pathlib import Path\nfrom timeit import default_timer\nfrom datetime import datetime\nimport csv\nimport json\nfrom pprint import pprint\n# 3rd party\nfrom dotenv import load_dotenv\nfrom halo import Halo\n\n# Isogeo\nfrom isogeo_pysdk import Isogeo, Metadata\n\n# load .env file\nload_dotenv(\"./env/stLo.env\", override=True)\n\nHARD_MODE = int(environ.get(\"HARD_MODE\"))\n\nif HARD_MODE:\n    is_ok = input(\"Are you sure you want to proceed in HARD_MODE? (y/n)\")\n    if is_ok == \"y\":\n        print(\"OK let's go !\")\n    else:\n        print(\"Cancellation of processing\")\n        exit()\nelse:\n    pass\n\n# #############################################################################\n# ############ Functions ################\n# #######################################\n\n\n# Print iterations progress\ndef printProgressBar(iteration, total, prefix='', suffix='', decimals=1, length=100, fill='█', printEnd=\"\\r\"):\n    \"\"\"\n    Call in a loop to create terminal progress bar\n    @params:\n        iteration   - Required  : current iteration (Int)\n        total       - Required  : total iterations (Int)\n        prefix      - Optional  : prefix string (Str)\n        suffix      - Optional  : suffix string (Str)\n        decimals    - Optional  : positive number of decimals in percent complete (Int)\n        length      - Optional  : character length of bar (Int)\n        fill        - Optional  : bar fill character (Str)\n        printEnd    - Optional  : end character (e.g. \"\\r\", \"\\r\\n\") (Str)\n    \"\"\"\n    percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n    filledLength = int(length * iteration // total)\n    bar = fill * filledLength + '-' * (length - filledLength)\n    print(f'\\r{prefix} |{bar}| {percent}% {suffix}', end=printEnd)\n    # Print New Line on Complete\n    if iteration == total:\n        print(\"\\n\")\n\n\n# #############################################################################\n# ########## Main program ###############\n# #######################################\n\nif __name__ == \"__main__\":\n    # logs\n    logger = logging.getLogger()\n    # ------------ Log & debug ----------------\n    logging.captureWarnings(True)\n    logger.setLevel(logging.INFO)\n\n    log_format = logging.Formatter(\n        \"%(asctime)s || %(levelname)s \"\n        \"|| %(module)s - %(lineno)d ||\"\n        \" %(funcName)s || %(message)s\"\n    )\n\n    # debug to the file\n    log_file_handler = RotatingFileHandler(\n        Path(\"./scripts/stLo/clean/_logs/replace_specifications.log\"), \"a\", 5000000, 1\n    )\n    log_file_handler.setLevel(logging.DEBUG)\n    log_file_handler.setFormatter(log_format)\n\n    # info to the console\n    log_console_handler = logging.StreamHandler()\n    log_console_handler.setLevel(logging.INFO)\n    log_console_handler.setFormatter(log_format)\n\n    logger.addHandler(log_file_handler)\n    logger.addHandler(log_console_handler)\n\n    # Shortcuts\n    wg_uuid = environ.get(\"ISOGEO_ORIGIN_WORKGROUP\")\n    app_base_url = \"https://app.isogeo.com/groups/\" + wg_uuid + \"/resources/\"\n    # set specifications uuid matrix from infos stored into the issue (https://github.com/isogeo/migrations-toolbelt/issues/127)\n    spec_uuid_matrix = [\n        (\"ca64e2b471d347dabf7ab8fa5d48dc12\", \"337b9e83c8c7422c9c87112aaafa8076\"),\n        (\"28e5055aa7be4be9940cf4c7db14da7d\", \"302408a7610a4d1ab5fc1d9f66504467\"),\n        (\"66e8fb7bab97422a8fe6255e38c012a3\", \"95356563058541ae82f0dc1d28acf9e3\"),\n        (\"84dd3158b8934167bb179c2d4bed450e\", \"91aaa1639cfa4363aecd7e41b22dc61f\"),\n        (\"491d6a80a8254ee8bafc2c31f7a9e541\", \"5a7b2ce5479442848ce3adce8f61e91b\"),\n        (\"4e023ae136694f2cb1187adb708e45c5\", \"673ae8c539494bec9ee10408e3f74733\"),\n        (\"6d07456db45a46349acb821b9cb962ad\", \"137ec6de24684c6ebe620fdd058aa596\"),\n        (\"943d05549322495a84a00d44305f194e\", \"137ec6de24684c6ebe620fdd058aa596\"),\n        (\"257e044ab3d049fc80bab292f891a992\", \"fa08a1b566cd4be79ec8e28a47cd3208\"),\n        (\"749e1961b7564121a1a160b7c6579950\", \"de9deb9e0e9749aa96bfd2129aeb468f\"),\n    ]\n\n    # API client instanciation\n    auth_spinner = Halo(text='Authenticating to Isogeo API', spinner='dots')\n    auth_spinner.start()\n    isogeo = Isogeo(\n        client_id=environ.get(\"ISOGEO_API_USER_LEGACY_CLIENT_ID\"),\n        client_secret=environ.get(\"ISOGEO_API_USER_LEGACY_CLIENT_SECRET\"),\n        auto_refresh_url=\"{}/oauth/token\".format(environ.get(\"ISOGEO_ID_URL\")),\n        platform=environ.get(\"ISOGEO_PLATFORM\", \"qa\"),\n        auth_mode=\"user_legacy\",\n    )\n    isogeo.connect(\n        username=environ.get(\"ISOGEO_USER_NAME\"),\n        password=environ.get(\"ISOGEO_USER_PASSWORD\"),\n    )\n    auth_timer = default_timer()\n    auth_succeed_text = \"Authenticated to Isogeo API.\"\n    auth_spinner.succeed(auth_succeed_text)\n    logger.debug(auth_succeed_text)\n\n    # Retrieve dedicated catalog\n    parsed_cat = isogeo.catalog.get(wg_uuid, environ.get(\"ISOGEO_CATALOG_SPEC_CLEANING\"))\n\n    # build specifications matrix with specification objects retrieved from the API\n    getSpec_spinner = Halo(text='Retrieving specifications objects', spinner='dots')\n    getSpec_spinner.start()\n    spec_matrix = []\n    for tup in spec_uuid_matrix:\n        # get old specification\n        old_spec_uuid = tup[0]\n        try:\n            old_spec = isogeo.specification.get(old_spec_uuid)\n        except Exception as e:\n            logger.error(\"Unable to get '{}' specification from Isogeo API : {}\".format(\n                old_spec_uuid, e\n            ))\n            continue\n        # get new specification\n        new_spec_uuid = tup[1]\n        try:\n            new_spec = isogeo.specification.get(new_spec_uuid)\n        except Exception as e:\n            logger.error(\"Unable to get '{}' specification from Isogeo API : {}\".format(\n                new_spec_uuid, e\n            ))\n            continue\n\n        # store objects\n        if isinstance(old_spec, tuple) or isinstance(new_spec, tuple):\n            continue\n        else:\n            spec_matrix.append(\n                (\n                    old_spec, new_spec\n                )\n            )\n\n    # Check specification matrix content\n    if len(spec_matrix) == len(spec_uuid_matrix):\n        getSpec_succeed_text = \"All {} specifications objects tuples have been retrieved.\".format(len(spec_matrix))\n        getSpec_spinner.succeed(getSpec_succeed_text)\n        logger.debug(getSpec_succeed_text)\n    else:\n        getSpec_warn_text = \"{} specifications objects tuples haven't been retrieved.\".format(len(spec_uuid_matrix) - len(spec_matrix))\n        getSpec_spinner.warn(getSpec_warn_text)\n        logger.debug(getSpec_warn_text)\n\n        keep_processing = input(\"Do you still want to proceed ? (y/n)\")\n        if keep_processing == \"y\":\n            print(\"OK let's go !\")\n        else:\n            print(\"Cancellation of processing\")\n            exit()\n\n    # Let's search for all metadatas of the workgroup\n    search_spinner = Halo(text='Searching for whole {} workgroup metadatas'.format(wg_uuid), spinner='dots')\n    search_spinner.start()\n    whole_search = isogeo.search(\n        group=wg_uuid,\n        whole_results=True,\n        include=(\"specifications\", \"tags\")\n    )\n\n    search_succeed_text = \"{} metadatas retrieved from '{}' workgroup.\\n\".format(whole_search.total, wg_uuid)\n    search_spinner.succeed(search_succeed_text)\n    logger.debug(search_succeed_text)\n\n    # Let's start to replace specifications\n    report_content = []\n    for tup in spec_matrix:\n        # retrieved infos related to currently parsed specifications\n        old_spec = tup[0]\n        old_spec_label = \"{} ({})\".format(old_spec.name, old_spec._id)\n        old_spec_tag = old_spec._tag\n\n        new_spec = tup[1]\n        new_spec_label = \"{} ({})\".format(new_spec.name, new_spec._id)\n        new_spec_tag = new_spec._tag\n\n        # intiate new csv report line\n        report_line = [old_spec.name, old_spec._id, new_spec.name, new_spec._id]\n\n        # build the list of metadata associated to currently parsed old specification\n        li_md_to_parse = [md for md in whole_search.results if any(spec.get(\"specification\").get(\"_tag\") == old_spec_tag for spec in md.get(\"specifications\"))]\n\n        if len(li_md_to_parse):\n            logger.info(\"Let's start to replace '{}' with '{}' for {} metadatas.\".format(\n                old_spec_label, new_spec_label, len(li_md_to_parse)\n            ))\n        else:\n            continue\n\n        # browsing metadatas associated to currently parsed old specification\n        index = 0\n        for md in li_md_to_parse:\n            index += 1\n\n            logger.debug(\"------- Parsing metadata {}/{} -------\".format(index, len(li_md_to_parse)))\n            printProgressBar(\n                iteration=index,\n                total=len(li_md_to_parse),\n                prefix='Processing progress:',\n                length=100,\n                suffix=\"- {}/{} specification association(s) replaced\".format(index, len(li_md_to_parse))\n            )\n            # refresh token if needed\n            if default_timer() - auth_timer >= 6500:\n                logger.debug(\"Manually refreshing token\")\n                isogeo.connect(\n                    username=environ.get(\"ISOGEO_USER_NAME\"),\n                    password=environ.get(\"ISOGEO_USER_PASSWORD\"),\n                )\n                auth_timer = default_timer()\n            else:\n                pass\n\n            # build metadata to parse object\n            metadata = Metadata().clean_attributes(md)\n            # retrieve specification role to use for new specification association\n            spec_conformity = int([spec.get(\"conformant\") for spec in metadata.specifications if spec.get(\"specification\").get(\"_tag\") == old_spec_tag][0])\n            # build metadata to parse app_url\n            metadata_app_url = app_base_url + metadata._id + \"/quality\"\n\n            new_report_line = report_line + [spec_conformity, metadata._id, metadata_app_url]\n\n            if HARD_MODE:\n                isogeo.specification.dissociate_metadata(metadata=metadata, specification_id=old_spec._id)\n                already_associated = any(spec.get(\"specification\").get(\"_tag\") == new_spec_tag for spec in metadata.specifications)\n                if not already_associated:\n                    isogeo.specification.associate_metadata(metadata=metadata, specification=new_spec, conformity=spec_conformity)\n                else:\n                    pass\n                isogeo.catalog.associate_metadata(metadata, parsed_cat)\n            else:\n                pass\n\n            report_content.append(new_report_line)\n\n    isogeo.close()\n\n    # writing csv report\n    csv_report = Path(\"./scripts/stLo/clean/csv/replace_spec_report_{}.csv\".format(int(datetime.now().timestamp())))\n    with open(csv_report, \"w\", newline=\"\") as csvfile:\n        writer = csv.writer(csvfile, delimiter=\";\")\n        writer.writerow(\n            [\n                \"old_spec_name\",\n                \"old_spec_uuid\",\n                \"new_spec_name\",\n                \"new_spec_uuid\",\n                \"spec_conformant\",\n                \"metadata_uuid\",\n                \"metadata_app_url\"\n            ]\n        )\n        for data in report_content:\n            writer.writerow(data)\n\n    logger.info(\"\\n\")\n","sub_path":"scripts/stLo/clean/replace_specifications.py","file_name":"replace_specifications.py","file_ext":"py","file_size_in_byte":11743,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"424231436","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\"\"\"\nCreated on Thu Jul  9 14:55:51 2020\n\n@author: Vera Zheng \n\"\"\"\nimport matplotlib as mpl\nfrom mpl_toolkits.mplot3d import Axes3D\nimport scipy.stats as si\nfrom sympy.stats import Normal, cdf\nimport numpy as np\nfrom scipy.stats import norm\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom scipy.optimize import fsolve\nimport scipy.optimize as opt\nfrom scipy.special import ndtr\nclass Process:\n    \n    def __init__(self, sigma0, a,m,alpha,nu,rho,beta,S0,b,c):\n        \"\"\"\n        Parameters\n        ----------\n        sigma0 : float\n            initial vol if the dynamic is chosen to be \"stochastic\"\n        a : float\n            param in the fititatious spot price process in paper Smile Modelling\n        m : int\n            number of simulations\n        alpha : float\n            param in the vol process if the dynamic is chosen to be \"Stochastic\"\n            \n        rho : float between 0 and 1\n             correlation between vol and spot if the dynamic is chosen to be \"Stochastic\"\n        S0 : float\n              initial ficticious spot price\n        b : float\n            param in the vol process if the dynamic is chosen to be \"Quadratic\"\n        c : float\n           param in the vol process if the dynamic is chosen to be \"stochastic\"\n\n        Returns\n        -------\n      \n\n        \"\"\"\n        self._S0 =S0\n        self._sigma0 = sigma0\n        self._a = a\n        self._m = m \n        self._alpha = alpha\n        self._rho = rho\n        self._b = b\n        self._c = c\n        self._nu = nu\n        self._beta = beta\n    \n    def simulate_spot(self,T,dynamics):\n        \"\"\"\n        function to model the normalised fictitious spot price\n        Input:\n        dt: time interval\n        T: time to maturity\n        dynamics: param to determine the spot process\n        Output:\n        St: spot prices dynamics\n            \n        \"\"\"\n        n = int(T / dt)\n        a = self._a\n        m = self._m\n        b = self._b\n        c = self._c\n        S0 = self._S0\n        beta = self._beta\n        St = np.zeros((n+1,m))\n        Yt = np.zeros((n+1,m))\n        St[0,:] = S0\n        alpha = self._alpha\n        nu = self._nu\n        rho = self._rho\n        sigma = np.zeros((n+1,m))\n        sigma0 = self._sigma0\n        sqrt_dt = dt ** 0.5\n        mP=int(m*0.5)\n        dw1 = np.random.normal(size=(n,mP))* sqrt_dt \n        \n        if dynamics == \"Quadratic\":\n            sigma[0,:] = sigma0\n            for i in range (n-1):\n                for j in range(mP):\n                     vol = b * St[i,j] +c \n                     Yt[i+1,j]=Yt[i,j]+vol*  dw1[i,j]+a*dt*(1-St[i,j])/St[i,j]-0.5*vol**2*dt\n                     vol = b * St[i,j+mP] +c \n                     Yt[i+1,j+mP]=Yt[i,j+mP]+vol*  dw1[i,j]+a*dt*(1-St[i,j+mP])/St[i,j+mP]-0.5*vol**2*dt\n                     St[i+1,j]=np.exp(Yt[i+1,j])\n                     St[i+1,j+mP]=np.exp(Yt[i+1,j+mP])\n        if dynamics == \"Stochastic\":\n             sigma[0,:] = alpha\n             \n             dw2 = np.random.normal(size=(n,mP))* sqrt_dt \n             dw3 = rho * dw1 + np.sqrt(1- rho **2) *dw2\n             for i in range (n):\n                 for j in range(mP):\n                      sigma[i+1,j] = sigma[i,j] + nu *sigma[i,j] * dw3[i,j]\n                      eta=sigma[i,j]*St[i,j]**(beta-1)\n                      Yt[i+1,j]=Yt[i,j]+ eta*  dw1[i,j]+a*dt*(1-St[i,j])/St[i,j]-0.5* eta**2*dt\n                      \n                      sigma[i+1,j+mP] = sigma[i,j+mP] -  nu *sigma[i,j+mP] * dw3[i,j] \n                      eta=sigma[i,j+mP]*St[i,j+mP]**(beta-1)\n                     \n                      Yt[i+1,j+mP]=Yt[i,j+mP]- eta*dw1[i,j]+a*dt*(1-St[i,j+mP])/St[i,j+mP]-0.5* eta**2*dt\n                      St[i+1,j]=np.exp(Yt[i+1,j])\n                      St[i+1,j+mP]=np.exp(Yt[i+1,j+mP])  \n        return St\n   \n        \n \n   \n   \n    def FutureDynamics(self, T,dt,St,F0):\n        \"\"\"\n        function to model the normalised fictitious spot price\n        Input:\n        dt: time interval\n        T: maturity\n        F0: Future price at the begining of the contact\n        \n        Output:\n        Ft: spot prices dynamics size n X m \n            \n        \"\"\"\n        a = self._a\n       \n        #save only date of interest \n        St = St[::21]\n        \n        Ft = F0*(1-(1-St)*np.exp(a*(-T)))\n\n        return Ft\n   \n    def OptionPricing(self,S_T,K,r,T,F_T,a,method):\n        \"\"\"\n        function to price call options on the futures \n        Input:\n       \n        T: maturity\n       \n        \n        Output:\n        Call: Call price\n        Put: Put price\n            \n        \"\"\"\n        sigma = self._sigma0\n        \n        K= 1-np.exp(a*T)*(1-K/F_T)\n        \n        if method == \"MC\":\n           \n           call = np.maximum(0,S_T - K)*F_T\n        if method == \"bls\":   \n            \n                d1 = (np.log(S_T / K) + (r + 0.5 * sigma ** 2) * T) / (sigma * np.sqrt(T))\n                d2 = (np.log(S_T/ K) + (r - 0.5 * sigma ** 2) * T) / (sigma * np.sqrt(T))\n               \n                call = (S_T * si.norm.cdf(d1, 0.0, 1.0) - K * np.exp(-r * T) * si.norm.cdf(d2, 0.0, 1.0))*F_T\n\n        Call = np.exp(-r*T)* np.sum(call)/m *np.exp(-a*T)\n        \n        SE_call = np.sqrt(sum(call-Call)/(m*(m-1)))\n       \n        return Call,  SE_call\n    \n   \n\n     \n ###############main############\nclass AsianBasketOption:\n    def __init__(self,weight,a,b,c,alpha,dt,T):\n       \n        self._a = a\n        self._b = b\n        self._c = c\n        self._alpha = alpha\n        self._dt = dt\n        self._T = T\n        self._weight = weight\n   \n    def simulate_correlated_paths(self,corr,nSims,nAssets,dynamics,F0):\n        \"\"\" Inputs: S0 - stock price\n            mu - expected return\n            sig - volatility\n            corr - correlation matrix\n            dt - size of time steps\n            steps - number of time steps to calculate\n            nsims - number of simulation paths to generate\n\n            Output: F - a (steps+1)-by-nsims-by-nassets 3-dimensional matrix where\n            each row represents a time step, each column represents a\n            seperate simulation run and each 3rd dimension represents a\n             different asset.\n        \"\"\"\n        a = self._a\n        b = self._b\n        c = self._c\n        alpha = self._alpha\n        T = self._T\n        nSteps = T*365\n        \n        sigma = np.zeros((nSteps,nSims,nAssets))\n        St = np.zeros((nSteps,nSims,nAssets))\n        #if incoporate control variate simulation\n        # sigma1 = np.zeros((nSteps,nSims,nAssets))\n        # St1 = np.zeros((nSteps,nSims,nAssets))\n        R = np.linalg.cholesky(corr)\n        if dynamics == \"Quadratic\":\n            sigma[0,:,:] = b * S0 + c\n            for j in range (nSims):\n                dw = np.random.normal(size=(nSteps,nAssets))\n                eps = np.dot(dw , R)\n                for i in range(nSteps - 1):\n                   \n                        sigma[i+1,j,:] = b * St[i,j,:] +c \n                       # sigma1[i+1,j,:] = b * St1[i,j,:] +c \n                        #St1[i+1,j,:] = St1[i,j,:] + (1-St1[i,j,:])* a * dt + sigma1[i,j,:] * St1[i,j,:]*-eps[i,:]\n                        St[i+1,j,:] = St[i,j,:] + (1-St[i,j,:])* a * dt + sigma[i,j,:] * St[i,j,:]*eps[i,:]\n                       \n        \n        \n        if dynamics == \"Stochastic\":\n             dw1 = np.random.normal(size=(nSteps,nSims))\n             dw2 = np.random.normal(size=(nSteps,nSims))\n             sigma[0,:,:] = sigma0\n             dw3 = rho * dw1 + np.sqrt(1- rho **2) *dw2\n             for j in range (nSims):\n                 dw = np.random.normal(size=(nSteps,nAssets))\n                 eps = np.dot(dw ,R)\n                 for i in range(nSteps - 1):\n                     \n                         sigma[i+1,j,:] = sigma[i,j,:] +  alpha *sigma[i,j,:] * dw3[i,j]\n                         St[i+1,j,:] = St[i,j,:] + (1-St[i,j,:])* a * dt + sigma[i,j,:] * St[i,j,:]^0.5* eps[i,:]\n                        # sigma1[i+1,j,:] = sigma[i,j,:] +  alpha *sigma[i,j,:] * dw3[i,j]\n                        # St1[i+1,j,:] = St[i,j,:] + (1-St[i,j,:])* a * dt + sigma[i,j,:] * St[i,j,:]^0.5 * eps[i,:]\n       # St1 = St1[::21,:,:] \n        St = St[::21,:,:] \n       # StTot =  np.concatenate((St,St1),1)\n        Ft = F0*(1-(1-St)*np.exp(a*T)) \n        return Ft   \n    \n    def pricing(self,n1,n2,Ft,k):\n      \n        #[n1,n2]is the interval to take averge of the future prices\n        weight = self._weight\n        #k=np.dot(Ft[0,:,:],weight)\n   \n        Ftot = np.dot(np.mean(Ft[n1:n2,:,:],0),weight)\n   \n        V = np.maximum(Ftot - k,0)\n        avgV = np.mean(V)\n    \n         \n        return V, avgV\n\ndef BS(F,K,sigma,T,r):\n    d1 = (np.log(F / K) + (r + 0.5 * sigma ** 2) * T) / (sigma * np.sqrt(T))\n    d2 = d1 - sigma * np.sqrt(T)\n    call = F* ndtr(d1)- K * np.exp(-r * T) * ndtr(d2)\n    return call    \n# \ndef implied_vol(mkt_price, F, K, T_maturity, *args):\n        Max_iteration = 500\n        PRECISION = 1.0e-5\n        sigma = 0.4\n        for i in range(0, Max_iteration):\n            d1 = (np.log(F / K) + (0.5 * sigma ** 2) * T_maturity) / (sigma * np.sqrt(T_maturity))\n            d2 = d1 - sigma * np.sqrt(T_maturity)\n            bls_price = F * ndtr(d1) - np.exp(-r * T_maturity) * K * ndtr(d2)\n            vega = F * norm._pdf(d1) * np.sqrt(T_maturity)\n            diff = mkt_price - bls_price  \n            if (abs(diff) < PRECISION):\n                return sigma\n            sigma = sigma + diff/vega # f(x) / f'(x)\n        return sigma \n \n\n   \n\n\nclass calibration_quadratic:\n     def __init__(self,a,K,T_M,Future):\n     \n        self._K =K\n        self._T =T_M\n        self._a = a\n        self._F = Future\n     \n       \n     def sum_difference(self,K,F,T,market_price,param):\n        diff = np.ones(len(K))\n        for i in range(len(K)):\n            price_i=BS(F+param[0],K[i]+param[0],param[1],T,0)\n            diff[i]=abs(price_i- market_price[i])\n        \n            if abs(K[i]-F)<0.5:\n                diff[i]=10000*diff[i]\n            else:\n                diff[i]=diff[i]\n        total=sum(diff)  \n        \n        return total\n     \n     def optimise(self,K,F,T,market_price,param):\n         \"to get the constant param in DD process\"\n         start_params = np.array([0.01, 0.1])\n         \n         sum_diff = lambda param: self.sum_difference(K,F,T,market_price,param)\n         #bnds = ((0,1),(0,1))\n         all_results = opt.minimize(fun=sum_diff, x0=start_params,\n                                        method=\"Nelder-Mead\")#\"BFGS\"\n         if (self.sum_difference(K,F,T,market_price,all_results.x))>0.01:\n             all_results = opt.minimize(fun=sum_diff, x0=all_results.x,   method=\"BFGS\")  \n         return all_results.x\n          \n     def estimateSigma(self,gamma_sigma,param,K,F,T,N_E):\n       \n       \n        a= self._a\n        \n        #param=np.exp(param) #transform to restrict a to be postive \n        effective_K = 1-np.exp(-a*N_E)*(1-K/F)\n        f0 = F + gamma_sigma[0]\n        k0 = effective_K*F+gamma_sigma[0]\n        d1 = (np.log(f0 / k0) + (r + 0.5 * gamma_sigma[1] ** 2) * T) / (gamma_sigma[1] * np.sqrt(T))\n        d2 = d1 - gamma_sigma[1] * np.sqrt(T)\n        #call_price = np.exp(a*T)/F *(f0*norm.cdf(d1)-k0*norm.cdf(d2))\n       \n        f1 = -2*a*(effective_K -1)*ndtr(d2)*np.exp(a*N_E)\n        f2 = f1**2\n        f3 = (f0 * norm._pdf(d1)*np.exp(0.5*a*N_E)*effective_K) **2/(k0**2*T)\n        f4 = f0*norm._pdf(d1)/(F*np.sqrt(T))\n        estimate_sigma = (f1+np.sqrt(f2+f3*(param[0]*effective_K +param[1])**2))/f4\n        \n        return estimate_sigma\n    \n     def object_func(self,gamma_sigma_list,F,K,T,N_E,param):\n         \n         \n         n= np.shape(K)[0]\n         diff = np.ones(n)\n         for i in range (n):\n             diff[i]= abs(gamma_sigma_list[1]- self.estimateSigma(gamma_sigma_list,param,K[i],F,T,N_E))\n        \n             if abs(K[i]-F)<0.5:\n                diff[i]=10000*diff[i]\n             else:\n                diff[i]=diff[i]\n        \n         return  sum(diff)\n    \n     def find_param(self,gamma_sigma_list,F,K,T,N_E,param):\n         start_params = np.array([0.01, 0.1])\n         \n         difference = lambda param: self.object_func(gamma_sigma_list,F,K,T,N_E,param)\n         #bnds = ((0,1),(0,1))\n         all_results = opt.minimize(fun=difference, x0=start_params,\n                                        method=\"BFGS\")#Nelder-Mead bounds=bnds)\n         \n         error=self.object_func(gamma_sigma_list,F,K,T,N_E,all_results.x)\n         if (error)>0.001:\n             all_results = opt.minimize(fun=difference, x0=all_results.x,  method=\"Nelder-Mead\")\n         return all_results.x\n \n         \n    \n    \nclass Sabr:\n    \n    def SABR(self,alpha,rho,nu,beta,F,K,time,MKT):\n        \"\"\"\n        function to evaluate model implied vol at each strike with each tenor\n        Input:\n        F: Current Future price \n        Time: time to maturity\n        MKT: market vol\n        Output:\n        diff: difference between model implied vol and market implied vol\n            \n        \"\"\"\n        \n        if F == K: # ATM formula\n            V = (F*K)**((1-beta)/2.)\n            logFK = np.log(F/K)\n            A = 1 + ( ((1-beta)**2*alpha**2)/(24.*(V**2)) + (alpha*beta*nu*rho)/(4.*V)+ ((nu**2)*(2-3*(rho**2))/24.) ) * time\n            B = 1 + (1/24.)*(((1-beta)*logFK)**2) + (1/1920.)*(((1-beta)*logFK)**4) \n            VOL = (alpha/V)*A\n            diff = VOL - MKT\n        elif F != K: # not-ATM formula \n             V = (F*K)**((1-beta)/2.) \n             logFK = np.log(F/K)\n             z = (nu/alpha)*V*logFK\n             x =np.log( ( np.sqrt(1-2*rho*z+z**2) + z - rho ) / (1-rho) ) \n             A = 1 + ( ((1-beta)**2*alpha**2)/(24.*(V**2)) + (alpha*beta*nu*rho)/(4.*V) + ((nu**2)*(2-3*(rho**2))/24.) ) * time\n             B = 1 + (1/24.)*(((1-beta)*logFK)**2) + (1/1920.)*(((1-beta)*logFK)**4)\n             VOL = (nu*logFK*A)/(x*B) \n             diff = VOL - MKT\n        return diff\n  \n\n  \n\n\n    def smile(self,alpha,rho,nu,beta,F,K,time,MKT,i):\n        \"\"\"\n        The smile function computes the implied volatilities for\n        a given ”smile” pointed out by the index i. F, time and \n        the parameters are scalars; K and MKT are vectors.\n        \"\"\"\n        for j in range(len(K)):\n        \n            self.SABR(alpha,rho,nu,beta,F,K[j],time,MKT[j])\n\n\n\n    def SABR_vol_matrix(self,alpha,rho,nu,beta,F,K,time,MKT): \n        \"\"\"function computes the implied volatilities for all \n        combinations of swaptions. F, time and the parameters are vectors; \n        K and MKT are matrices.\n        \"\"\"\n        for i in range(len(F)):\n            self.smile(alpha[i],rho[i],nu[i],beta[i],F[i],K[i],time[i],MKT[i],i)\n\n\n    def objfunc(self,par,beta,F,K,time,MKT): \n        sum_sq_diff = 0\n        alpha,rho,nu=par\n        for j in range(len(K)):\n            diff = self.SABR(alpha,rho,nu,beta,F,K[j],time,MKT[j])  \n            sum_sq_diff = sum_sq_diff + diff**2\n        obj = np.sqrt(sum_sq_diff) \n        return obj\n    \n    \n    \n    \n    def calibration(self,starting_par,beta,F,K,time,MKT): \n        \"\"\"The function used to calibrate each smile \n        (different strikes within the same tenor\"\"\"\n        alpha = np.zeros(len(F))\n        nu = np.zeros(len(F))\n        rho = np.zeros(len(F))\n        for i in range(len(F)):\n            x0 = starting_par\n            bnds = ( (0.001,None) ,(-0.9999,0.9999), (0.001,None) ) \n            Diff = lambda param: self.objfunc(param,beta,F[i],K[i],time[i],MKT[i])\n            res = opt.minimize(Diff,x0, method=\"SLSQP\",bounds = bnds) \n            alpha[i] = res.x[0]\n            rho[i] = res.x[1]\n            nu[i] = res.x[2]\n\n        return alpha, rho,nu\n\n    \n\n###############################################################\n\n### start main\nif __name__ == \"__main__\":\n    output_path = \"/Users/veraisice/Desktop/Comodity-Market-Research/thesis_1/\"\n    input_path  = \"/Users/veraisice/Desktop/Comodity-Market-Research/Input/\"\n   \n    Future_prices = pd.read_excel(input_path+ \"TTFdata\"+\".xlsx\",sheet_name=\"Futures\")     \n   \n    \n    T = 1\n    dt = 1/365\n    S0 = 1\n   \n    sigma0 = 0.1\n    alpha = 0.5\n    rho = 0.5\n    m = 1000\n    K0 = 1\n    r =0.01\n    \n    a =0.01   \n    blist = np.linspace(0,1,5)\n    c = 0.3\n    \n    F10 = Future_prices.loc[Future_prices['Month']== '2020-08-25','1-Month Future'].values[0]\n    F20 = Future_prices.loc[Future_prices['Month']== '2020-09-25','1-Month Future'].values[0]\n    \n    \n    np.random.seed(123)\n    \n    #list of mean reverting speed of interest\n    alist=[0,0.5,1]\n    \n   \n\n    dynamics = \"Stochastic\"\n    avgS = np.zeros((int(T/dt),len(alist)))\n    avgF = np.zeros((T*12,len(alist)))\n    call = np.zeros(len(alist))\n    \n    SE_call = np.zeros(len(alist))\n   \n    for i in range(len(alist)):\n        S = Process(sigma0, alist[i],m, alpha, rho,S0,blist[i],c).simulate_spot(T, dt,dynamics)\n        F= Process(sigma0,a,m, alpha, rho,S0,blist[i],c).FutureDynamics(T,dt,S,F10)\n        S_T=S[-1,:]\n\n        F_T=F[-1,:]\n        call[i],SE_call[i] = Process(sigma0, a,m, alpha, rho,S0,blist[i],c).OptionPricing(S_T, K0, r, dt, T,F_T,a,\"bls\")\n        avgF[:,i] =  np.mean(F,1)\n        avgS[:,i] = np.mean(S,1)\n    print (\"Call option prices by MC method with \" +str(m)+ \" simulations and different MR speed are \" + str(call) +\\\n  \n           \"  with Standard Error for Call \" +str(SE_call))\n\n    \n    plt.figure(dpi=1000)\n    for i in range(len(blist)):\n        plt.plot(avgS[:,i],label=r\"Linear(a =\" + str(a)+ \", b = \"+str(blist[i])+\",c = \"+str(c)+\")\")\n    plt.xlabel('Time index')\n    plt.ylabel('Value')\n    plt.title(\"Realization of \" +str(dynamics) +\"vol spot prices dynamics\")\n    plt.legend(loc='best')\n    #plt.savefig(output_path + \"Figures/\"+str(dynamics)+\"linear_spot\")\n \n    ####plot of future process \n    plt.figure(dpi=1000)\n    for i in range(len(blist)):\n        plt.plot(avgF[:,i],label=r\"Stochastic(a =\" + str(a)+ \",b = \"+ str(blist[i])+\")\")\n    plt.xlabel('Time index')\n    plt.ylabel('Value')\n    plt.title(\"Realization of \" +str(dynamics) +\"  vol futures prices dynamics\")\n    plt.legend(loc='best')\n    plt.savefig(output_path + \"Figures/linear_futures\")       \n    \n    \n    \n    \n   \n    \n\n################linear vol #########################\n\n#plot spot and futures prices with linear vol\n    \n    \n    avgS = np.zeros((int(T/dt),len(blist)))\n    avgF = np.zeros((T*12,len(blist)))\n    call = np.zeros(len(blist))\n    SE_call = np.zeros(len(blist))\n   \n    for i in range(len(blist)):\n        S = Process(sigma0, a, m, alpha, rho,S0,blist[i],c).simulate_spot(T,dt,\"Linear\")\n        F= Process(sigma0,a,m, alpha,rho,S0,blist[i],c).FutureDynamics(T,dt,S,F10)\n        F_T = F[-1,:]\n        S_T = S[-1,:]\n        call[i],SE_call[i] = Process(sigma0, a,m, alpha, rho,S0,blist[2],c).OptionPricing(S_T, K0, r, dt, T,F_T,a,\"bls\")\n        avgF[:,i] =  np.mean(F,1)\n        avgS[:,i] = np.mean(S,1)\n   #plot of spot process with linear vol\n    plt.figure(dpi=1000) \n    for i in range(len(blist)):\n        plt.plot(avgS[:,i],label=r\"Linear(a =\" + str(a)+ \", b = \"+str(blist[i])+\",c = \"+str(c)+\")\")\n    plt.xlabel('Time index')\n    plt.ylabel('Value')\n    plt.title(\"Realization of linear vol spot prices dynamics\")\n    plt.legend(loc='best')\n    plt.savefig(output_path + \"Figures/linear_spot\")\n  #plot of future process with linear vol  \n    plt.figure(dpi=1000)\n    for i in range(len(blist)):\n        plt.plot(avgF[:,i],label=r\"Stochastic(a =\" + str(a)+ \",b = \"+ str(blist[i])+\")\")\n    plt.xlabel('Time index')\n    plt.ylabel('Value')\n    plt.title(\"Realization of linear vol futures prices dynamics\")\n    plt.legend(loc='best')\n    plt.savefig(output_path + \"Figures/linear_futures\")       \n  \n  #plot option prices vs b\n    plt.figure(dpi=1200)\n    plt.plot(blist,call) \n    plt.xlabel(\"b value\")\n    plt.ylabel(\"European Call Price\")\n    plt.title(\"European Call Price vs vol Coefficient b\")\n\n\n###################Asian Basket Option#########\n    m=50000\n    klist = np.linspace(0.5*np.mean(Future),1.5*np.mean(Future),10)\n    b=0.15\n    c=0.4\n    alpha = 0.1\n    alpha_list =[0.002,0.004,0.006,0.008,0.010,0.012,0.014,0.016,0.018,0.20]\n    a =0.1\n    alist=np.linspace(0.0,0.8,40)\n    blist=np.linspace(0.0,0.2,20)\n    clist=np.linspace(0,0.08,30)\n    corr = ([1,0.2],[0.2,1])#([1, 0.5,0.4],[0.5,1,0.5],[0.4,0.5,1]) #([1,0.5],[0.5,1])\n    corr_list = [[[1, 0.2,0.1],[0.2,1,0.2],[0.1,0.2,1]],[[1, 0.2,0.3],[0.3,1,0.3],[0.3,0.3,1]],[[1, 0.4,0.3],[0.4,1,0.4],[0.3,0.4,1]],[[1, 0.5,0.4],[0.5,1,0.5],[0.4,0.5,1]],[[1, 0.6,0.5],[0.6,1,0.6],[0.5,0.6,1]],[[1, 0.7,0.6],[0.7,1,0.7],[0.6,0.7,1]],[[1, 0.8,0.7],[0.8,1,0.8],[0.8,0.7,1]],[[1, 0.9,0.8],[0.9,1,0.9],[0.8,0.5,0.9]]]\n    nSims =10000\n    nSteps = int(T/dt)\n    F0 = [5.495, 5.805]#[5.495, 5.805, 6.49]\n    nAssets = 3 #2\n    dynamics = \"Quadratic\"\n    weight=[1/3 , 1/3 ,1/3]#[1/2,1/2] #[1/3 , 1/3 ,1/3] #[1/2,1/2]\n    avgV= np.zeros((len(clist)))\n    \n    T=1/3\n    for i in range(len(clist)):\n       \n       \n        S = Process(sigma0, a, m, alpha,nu,rho,S0,b,clist[i]).simulate_spot(T,dt,\"Quadratic\")\n        F1= np.ones(len(S))\n        F2=np.ones(len(S))\n        F3=np.ones(len(S))\n        F1= Process(sigma0,a,m, alpha,nu,rho,S0,b,c).FutureDynamics(T,dt,S,Future[0])\n        F2= Process(sigma0,a,m, alpha,nu,rho,S0,b,c).FutureDynamics(T,dt,S,Future[1])\n        F3= Process(sigma0,a,m, alpha,nu,rho,S0,b,c).FutureDynamics(T,dt,S,Future[2])\n        Ft = np.dstack((F1,F2,F3))\n        k=np.dot(Ft[0,:,:],weight)\n        V, avgV[i] = AsianBasketOption(weight,alist[i],b,c,alpha,dt,T).pricing( len(Ft)-3,len(Ft)-1, Ft,k)    \n    \n    plt.figure(dpi=1000)\n    plt.plot(alist,avgV,label=\"Asian Basket Price\")\n    #plt.plot(np.linspace(0.1,0.8,len(corr_list)),avgV,label=\"Asian Basket Price\")\n    plt.xticks(rotation=45, ha='right')\n    plt.xlabel(r\"$a$\")\n    plt.ylabel(\"Price\")\n    plt.title(r\"Effect of Parameter $a$ in the \" +str(dynamics) + \" Model\" )\n    plt.legend(loc= 'best')\n    plt.savefig(output_path+\"Figures/SLV_c\")\n    \n    \n    \n #for sabr \n    dynamics = \"stochastic\"\n    alpha =0.5\n    nu=0.4\n    rho=-0.1\n    nu_list = np.linspace(0,1,20)\n    rho_list = np.linspace(-0.5,0.5,20)\n    alpha_list = np.linspace(0,0.8,20)\n    weight=[1/3 , 1/3 ,1/3]#[1/2,1/2] #[1/3 , 1/3 ,1/3] #[1/2,1/2]\n   \n    avgV= np.zeros((len(nu_list)))\n   \n    for i in range(len(nu_list)):\n       \n       \n        S = Process(sigma0, a, m, alpha,nu_list[i],rho,S0,b,c).simulate_spot(T,dt,\"Stochastic\")\n        F1= np.ones(len(S))\n        F2=np.ones(len(S))\n        F3=np.ones(len(S))\n        F1= Process(sigma0,a,m, alpha,nu,rho,S0,b,c).FutureDynamics(T,dt,S,Future[0])\n        F2= Process(sigma0,a,m, alpha,nu,rho,S0,b,c).FutureDynamics(T,dt,S,Future[1])\n        F3= Process(sigma0,a,m, alpha,nu,rho,S0,b,c).FutureDynamics(T,dt,S,Future[2])\n        Ft = np.dstack((F1,F2,F3))\n        k=np.dot(Ft[0,:,:],weight)\n        V, avgV[i] = AsianBasketOption(weight,a,b,c,alpha,dt,T).pricing( len(Ft)-3,len(Ft)-1, Ft,k)    \n    \n    plt.figure(dpi=1000)\n    plt.plot(nu_list,avgV,label=\"Asian Basket Price\")\n    #plt.plot(np.linspace(0.1,0.8,len(corr_list)),avgV,label=\"Asian Basket Price\")\n    plt.xticks(rotation=45, ha='right')\n    plt.xlabel(r\"$\\nu$\")\n    plt.ylabel(\"Price\")\n    plt.title(r\"Effect of Parameter $\\nu$ in the \" +str(dynamics) + \" Model\" )\n    plt.legend(loc= 'best')\n    plt.savefig(output_path+\"Figures/SLV_nu\")\n    \n    \n    \n    \n    \n    avgV= np.zeros((len(rho_list)))\n   \n    for i in range(len(rho_list)):\n       \n       \n        S = Process(sigma0, a, m, alpha,nu,rho_list[i],S0,b,c).simulate_spot(T,dt,\"Stochastic\")\n        F1= np.ones(len(S))\n        F2=np.ones(len(S))\n        F3=np.ones(len(S))\n        F1= Process(sigma0,a,m, alpha,nu,rho,S0,b,c).FutureDynamics(T,dt,S,Future[0])\n        F2= Process(sigma0,a,m, alpha,nu,rho,S0,b,c).FutureDynamics(T,dt,S,Future[1])\n        F3= Process(sigma0,a,m, alpha,nu,rho,S0,b,c).FutureDynamics(T,dt,S,Future[2])\n        Ft = np.dstack((F1,F2,F3))\n        k=np.dot(Ft[0,:,:],weight)\n        V, avgV[i] = AsianBasketOption(weight,a,b,c,alpha,dt,T).pricing( len(Ft)-3,len(Ft)-1, Ft,k)    \n    \n    plt.figure(dpi=1000)\n    plt.plot(rho_list,avgV,label=\"Asian Basket Price\")\n    #plt.plot(np.linspace(0.1,0.8,len(corr_list)),avgV,label=\"Asian Basket Price\")\n    plt.xticks(rotation=45, ha='right')\n    plt.xlabel(r\"$\\rho$\")\n    plt.ylabel(\"Price\")\n    plt.title(r\"Effect of Parameter $\\rho$ in the \" +str(dynamics) + \" Model\" )\n    plt.legend(loc= 'best')\n    plt.savefig(output_path+\"Figures/SLV_rho\")\n    \n    avgV= np.zeros((len(alpha_list)))\n   \n    for i in range(len(alpha_list)):\n       \n       \n        S = Process(sigma0, a, m, alpha_list[i],nu,rho,S0,b,c).simulate_spot(T,dt,\"Stochastic\")\n        F1= np.ones(len(S))\n        F2=np.ones(len(S))\n        F3=np.ones(len(S))\n        F1= Process(sigma0,a,m, alpha,nu,rho,S0,b,c).FutureDynamics(T,dt,S,Future[0])\n        F2= Process(sigma0,a,m, alpha,nu,rho,S0,b,c).FutureDynamics(T,dt,S,Future[1])\n        F3= Process(sigma0,a,m, alpha,nu,rho,S0,b,c).FutureDynamics(T,dt,S,Future[2])\n        Ft = np.dstack((F1,F2,F3))\n        k=np.dot(Ft[0,:,:],weight)\n        V, avgV[i] = AsianBasketOption(weight,a,b,c,alpha,dt,T).pricing( len(Ft)-3,len(Ft)-1, Ft,k)    \n    \n    plt.figure(dpi=1000)\n    plt.plot(rho_list,avgV,label=\"Asian Basket Price\")\n    #plt.plot(np.linspace(0.1,0.8,len(corr_list)),avgV,label=\"Asian Basket Price\")\n    plt.xticks(rotation=45, ha='right')\n    plt.xlabel(r\"$\\alpha$\")\n    plt.ylabel(\"Price\")\n    plt.title(r\"Effect of Parameter $\\alpha$ in the \" +str(dynamics) + \" Model\" )\n    plt.legend(loc= 'best')\n    plt.savefig(output_path+\"Figures/SLV_alpha\")\n    \n\n\n\n\n ######plot of European Option against parameters#######\n    \n\n    call = np.zeros(len(alist))\n    \n    SE_call = np.zeros(len(alist))\n   \n    for i in range(len(alist)):\n        S = Process(sigma0, alist[i],m, alpha, rho,S0,b,c).simulate_spot(T, dt,\"Linear\")\n       \n        S_T = S[-1,:]\n        call[i],SE_call[i] = Process(sigma0, alist[i],m, alpha, rho,S0,b,c).OptionPricing(S_T, K0, r, dt, T,Future,a,\"bls\")\n    \n    plt.figure(dpi=1000)\n    plt.plot(alist,call,label=\"European Option Price\")\n    plt.xlabel(r\"$a$\")\n    plt.ylabel(\"Price\")\n    plt.title(r\"Effect of Parameter $\\alpha$ in the\" +str(dynamics) + \" Model\" )\n    plt.legend(loc= 'best')\n    plt.savefig(output_path+\"Figures/SLV_a_EU\")\n           \n\n    call = np.zeros(len(alist))\n    \n    SE_call = np.zeros(len(alist))\n   \n    for i in range(len(alist)):\n        S = Process(sigma0, a,m, alist[i], rho,S0,b,c).simulate_spot(T, dt,dynamics)\n       \n        S_T = S[-1,:]\n        call[i],SE_call[i] = Process(sigma0, a,m, alist[i], rho,S0,blist,c).OptionPricing(S_T, K0, r, dt, T,Future,a,\"bls\")\n    \n    plt.figure(dpi=1000)\n    plt.plot(alist,call,label=\"European Option Price\")\n    plt.xlabel(r\"$a$\")\n    plt.ylabel(\"Price\")\n    plt.title(r\"Effect of Parameter $a$ in the SV Model\"  )\n    plt.legend(loc= 'best')\n    plt.savefig(output_path+\"Figures/SV_a_EU\")\n\n\n\n #############implied vol##################### \n    Month = \"Sep\"\n    Option_Data = pd.read_excel(input_path+ \"TTFdata\"+\".xlsx\",sheet_name = Month)    #SEPopt\n    #strike \n    Strike_list = Option_Data[\"Strike\"]\n    #market option price\n    Call_list = Option_Data[\"Call\"]\n    #time to maturity\n    T_M = Option_Data[\"Time to Maturity\"].values[0]\n    #future price\n    Future = Option_Data[\"1-Month Future\"].values[0]\n    ones =np.ones( np.size(Call_list) )        \n    params = np.vstack((Call_list, Future*ones, Strike_list, T_M*ones, r*ones, sigma0*ones))\n    vols = list(map(implied_vol, *params))\n    \n    plt.figure(dpi=1000)\n    plt.plot(Strike_list,vols,label=\"Implied Volatilities\")\n    plt.xlabel(\"Strike\")\n    plt.ylabel(\"Implied Volatility\")\n    plt.title(\"Implied Volatilities of TTF Futures Options Expired in \" + str(Month)  )\n    plt.legend(loc= 'best')\n    plt.savefig(output_path+\"Figures/IV_sep\")\n    \n    \n    avgS = np.zeros((int(T/dt),len(Strike_list)))\n    avgF = np.zeros((T*12,len(Strike_list)))\n    call_bls = np.zeros(len(Strike_list))\n\n    SE_call_mc = np.zeros(len(Strike_list))\n    call_mc = np.zeros(len(Strike_list))\n\n    SE_call_bls = np.zeros(len(Strike_list))\n    \n    S = Process(sigma0, a, m, alpha, rho,S0,blist[2],c).simulate_spot(T,dt,dynamics)\n    # F= Process(sigma0,a,m, alpha,rho,S0,blist[2],c).FutureDynamics(T,dt,S,F10)\n\n    S_T = S[-1,:]\n    for i in range(len(Strike_list)):\n            call_bls[i],SE_call_bls[i] = Process(sigma0, a,m, alpha, rho,S0,blist[1],c).OptionPricing(S_T, Strike_list[i], r, dt, T,Future*np.ones((1000)),a,\"bls\")\n            call_mc[i],SE_call_mc[i] = Process(sigma0, a,m, alpha, rho,S0,blist[1],c).OptionPricing(S_T, Strike_list[i], r, dt, T,Future*np.ones((1000)),a,\"MC\")\n            avgF[:,i] =  np.mean(F,1)\n            avgS[:,i] = np.mean(S,1)\n    plt.figure(dpi=1200)\n    plt.plot(Strike_list,call_bls,\"-o\",label=\"Model Call Price (Black-Scholes)\")\n    plt.plot(Strike_list,call_mc,\"-o\",label=\"Model Call Price (Monte Carlo)\")\n    plt.plot(Strike_list,Call_list,\"-o\",label= \"Market Call Price\") \n    plt.legend(loc=\"best\")\n    plt.xlabel(\"Strike\")\n    plt.ylabel(\"European Call Price\")\n    plt.title(\"European Call Price vs Strike\")\n    plt.savefig(output_path + \"Figures/option_LV\")\n    \n    \n    \n    \n    \n    \n    \n    ####calibration##################\n\n    #\n    a=0\n    Month_List = np.array([\"October\",\"November\",\"December\"])\n    c= np.ones(len(Month_List))\n    b= np.ones(len(Month_List))\n    N_E = np.ones((len(Month_List)))\n    T_M = np.ones((len(Month_List)))\n    Future= np.ones((len(Month_List)))\n    market_price=np.zeros((len(Month_List),10), dtype=np.ndarray)\n    K=np.zeros((len(Month_List),10), dtype=np.ndarray)\n    gamma_sigma_list= np.zeros((len(Month_List),2), dtype=np.ndarray)\n    for i in range(len(Month_List)):\n        Option_Data = pd.read_excel(input_path+ \"TTFdata\"+\".xlsx\",sheet_name = Month_List[i])    #SEPopt\n    #strike \n        K[i] = Option_Data[\"Strike\"].values\n    #market option price\n        market_price[i] = Option_Data[\"Call\"].values\n    #time to maturity\n        T_M[i]= Option_Data[\"Time to Maturity\"].values[0]\n    #expiry to notification date\n        N_E[i]=Option_Data[\"N-E\"].values[0]\n    #future price\n        Future[i] = Option_Data[\"1-Month Future\"].values[0]\n        gamma_sigma_list[i]= calibration_quadratic(a, K, T_M, Future).optimise(np.asarray(K[i],float), Future[i],T_M[i],np.asarray(market_price[i],float),param=[])\n        print('gamma',gamma_sigma_list[i][0])\n        print('sigma',gamma_sigma_list[i][1])\n        print('error', calibration_quadratic(a, K, T_M, Future).sum_difference(K[i],Future[i],T_M[i],market_price[i],gamma_sigma_list[i]))\n \n    \n        b[i],c[i]=calibration_quadratic(a, K, T_M, Future).find_param(gamma_sigma_list[i],Future[i],K[i],T_M[i],N_E[i],param=[])\n        \n        print(\"Calibration Result for \"+str(Month_List[i])+ \" is b=\" +str(b[i])+\" and c=\" +str(c[i]))\n        print('Error',calibration_quadratic(a, K, T_M, Future).object_func(gamma_sigma_list[i],Future[i],K[i],T_M[i],N_E[i],[b[i],c[i]]))\n    #####calculate the model price######\n    Month =\"November\"#“November” “December”\n    nM=1\n    sigma0= gamma_sigma_list[nM][1]\n    \n    gamma0= gamma_sigma_list[nM][0]\n   \n    a=0\n    m = 100000\n    alpha = 0\n    rho =0\n    S0=1\n    #\n    \n    dt=1/365\n   \n    \n    \n    ones =np.ones(np.size(K[nM]))   \n    \n    \n   \n    S = Process(sigma0, a, m, alpha,nu, rho,S0,b[nM],c[nM]).simulate_spot(T[nM],N_E[nM],\"Quadratic\")\n   \n    S_T = S[-1,:]\n    \n    Call = market_price[nM]\n   \n    call = np.zeros((len(K[nM])))\n    SE_call = np.zeros((len(K[nM])))\n    market_price_dd=np.zeros((len(K[nM])))\n    for i in range(len(K[nM])):\n       call[i],SE_call[i] = Process(sigma0, a ,m, alpha,nu,rho,S0,b[nM],c[nM]).OptionPricing(S_T,K[nM][i],r,N_E[nM],Future[nM],a,\"MC\")#N_E\n       market_price_dd[i]=BS(Future[nM]+gamma0,K[nM][i]+gamma0,sigma0,T_M[nM],0) \n    \n    \n    params = np.vstack((call, Future[nM]*ones,K[nM], T_M[nM]*ones, r*ones))\n    vols = list(map(implied_vol, *params))\n   \n    effective_K = 1-np.exp(-a*N_E[nM])*(1-K[nM]/Future[nM])\n   \n    ###implied vol from the market prices\n    params_mkt = np.vstack((market_price_dd, Future[nM]*ones,K[nM], T_M[nM]*ones, r*ones))\n    vols_mkt = list(map(implied_vol, *params_mkt))\n   \n   \n    \n    plt.figure(dpi=1000)\n    plt.plot(effective_K,vols,'--b*',label=\"Model IV\")\n    plt.plot(effective_K,vols_mkt,'--r*',label=\"Market IV\")\n    plt.xlabel(\"Strike\")\n    plt.ylabel(\"Implied Volatility\")\n    plt.title(\"Implied Volatilities of TTF Future Style Option Expires in \" + str(Month)  )\n    plt.legend(loc= 'best')\n    plt.savefig(output_path + \"Figures/LV_model_mkt_vol\"+str(Month))\n    \n    \n    \n    plt.figure(dpi=1000)\n    plt.plot(effective_K,Call,'--b*',label=\"Market Prices VS Strikes\")\n    plt.plot(effective_K,market_price_dd,linestyle='None',color='g',marker='o',label=\"Model Prices VS Strikes\")\n    \n    plt.xlabel(\"Strike\")\n    plt.ylabel(\"Option Price\")\n    plt.title(\"Comparison of TTF Future Style Option Price Expired in \" + str(Month)  )\n    plt.legend(loc= 'best')\n    plt.savefig(output_path + \"Figures/LV_model_mkt_price\"+str(Month))\n\n\n\n\n\n\n###Calibration Sabr####\n    \n    Month_List = Month_List = [\"October\",\"November\",\"December\"]\n    n_month=len(Month_List)\n    N_E = np.ones((len(Month_List)))\n    T_M = np.ones((len(Month_List)))\n    Future= np.ones((len(Month_List)))\n    market_price=np.zeros((len(Month_List),10), dtype=np.ndarray)\n    K=np.zeros((len(Month_List),10), dtype=np.ndarray)\n    vols_mkt = np.zeros((len(Month_List),10), dtype=np.ndarray)\n    vols_mdl = np.zeros((len(Month_List),10), dtype=np.ndarray)\n    effective_K=np.zeros((len(Month_List),10), dtype=np.ndarray)\n    \n    #magic number\n    a =0.\n    m=500\n    dt=1/365\n    np.random.seed(111)\n    for i in range(len(Month_List)):\n        Option_Data = pd.read_excel(input_path+ \"TTFdata\"+\".xlsx\",sheet_name = Month_List[i])    #SEPopt\n    #strike \n        K[i] = Option_Data[\"Strike\"].values\n    #market option price\n        market_price[i] = Option_Data[\"Call\"].values\n    #time to maturity\n        T_M[i]= Option_Data[\"Time to Maturity\"].values[0]\n    #expiry to notification date\n        N_E[i]=Option_Data[\"N-E\"].values[0]\n    #future price\n        Future[i] = Option_Data[\"1-Month Future\"].values[0]\n    \n        effective_K[i] = 1-np.exp(-a*N_E[i])*(1-K[i]/Future[i])\n        ones= np.ones(np.size(K[i]))\n        params_mkt = np.vstack((market_price[i], Future[i]*ones,K[i], T_M[i]*ones))\n    \n        vols_mkt[i] = list(map(implied_vol, *params_mkt))\n       \n   \n\n\n    beta=0.5\n    # Future_Y = np.exp(-a*N_E)*Future\n    # K_Y=np.dot(K*np.exp(-a*N_E).reshape(3,1),1)\n    # starting_par = np.array([0.1,0.1,0.1])\n    # [alpha,rho,nu] = Sabr().calibration(starting_par,beta,Future_Y,K_Y,T_M,vols_mkt)\n    \n    # call = np.zeros((len(Month_List),10), dtype=np.ndarray)\n    # SE_call= np.zeros((len(Month_List),10), dtype=np.ndarray)\n    # market_price_dd= np.zeros((len(Month_List),10), dtype=np.ndarray)\n    # alpha_spot= alpha*(np.exp(-a*N_E)*Future)**(beta-1)\n    # nu_spot= nu*(np.exp(-a*N_E)*Future)**(beta-1)\n    Future_Y = np.exp(-a*N_E)*Future\n    K_Y=K/Future.reshape(3,1)\n    starting_par = np.array([0.1,0.1,0.1])\n    [alpha,rho,nu] = Sabr().calibration(starting_par,beta,np.ones(len(Future)),K_Y,T_M,vols_mkt)\n    \n    call = np.zeros((len(Month_List),10), dtype=np.ndarray)\n    SE_call= np.zeros((len(Month_List),10), dtype=np.ndarray)\n   \n    # alpha_spot= alpha*(np.exp(-a*N_E)*Future)**(beta-1)\n    # nu_spot= nu*(np.exp(-a*N_E)*Future)**(beta-1)\n    #dynamic of spot\n   \n    for i in range(len(Month_List)):\n        \n        St= Process(sigma0,a,m,alpha[i],nu[i],rho[i],beta,1,b,c).simulate_spot(T_M[i],\"Stochastic\")\n        S_T = St[-1,:]\n        for j in range(len(K[i])):\n               call[i,j],SE_call[i,j] = Process(sigma0, a,m, alpha, nu,rho,beta,S0,b,c).OptionPricing(S_T,K[i][j],r,T_M[i],Future[i],0.01,\"MC\")\n               market_price_dd[i,j]=BS(Future[i]+gamma_sigma_list[i][0],K[i][j]+gamma_sigma_list[i][0],gamma_sigma_list[i][1],T_M[i],0) \n        params_mdl = np.vstack((call[i,:], Future[i]*ones,K[i], T_M[i]*ones))\n       \n       \n   \n       \n        vols_mdl[i] = list(map(implied_vol, *params_mdl))\n       \n    for i in range(len(Month_List)): \n        plt.figure(dpi=1000)\n        plt.plot(effective_K[i],call[i],linestyle='None',color='g',marker='o',label=\"Model Prices VS Strikes\")\n        plt.plot(effective_K[i],market_price[i],'--r*',label=\"Market Prices VS Strikes\")\n        plt.xlabel(\"Strike\")\n        plt.ylabel(\"Option Price\")\n        plt.title(\"Comparison of TTF Futures Option Price Expired in \" + str(Month_List[i])  )\n        plt.legend(loc= 'best')\n        plt.savefig(output_path + \"Figures/price_mdl_mkt\"+str(Month_List[i]))\n    for i in range(len(Month_List)):\n         plt.figure(dpi=1000)\n         plt.plot(effective_K[i],vols_mdl[i],'--b*',label=\"Model IV\")\n         plt.plot(effective_K[i],vols_mkt[i],'--r*',label=\"Market IV\")\n         plt.xlabel(\"Strike\")\n         plt.ylabel(\"Implied Volatility\")\n         plt.title(\"Implied Volatilities of TTF Futures Options Expires in \" + str(Month_List[i])  )\n         plt.legend(loc= 'best')\n         plt.savefig(output_path + \"Figures/vol_mdl_mkt\"+str(Month_List[i]))\n         \n    ","sub_path":"Code/Comodity.py","file_name":"Comodity.py","file_ext":"py","file_size_in_byte":37269,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"261647064","text":"# -*- coding: utf-8 -*-\n\nfrom __future__ import unicode_literals\n\n\"\"\"\n    根据消息类型, 给文本渲染上对应的颜色\n\n    curl -s http://localhost:8000/api/monitor/color | python -m json.tool\n\"\"\"\n\nfrom mi.war_screen import web, init, success, api_exception_catcher\nfrom mi.war_screen import marker\n\napp = web.create_app()\n\n\n@app.route('/api/monitor/color')\n@api_exception_catcher\ndef api_get_color():\n    return success({\n        1: marker.render_msg('文本', marker.MSG_TYPE_PLAIN),\n        2: marker.render_msg('正常', marker.MSG_TYPE_OK),\n        3: marker.render_msg('警告', marker.MSG_TYPE_WARNING),\n        4: marker.render_msg('错误', marker.MSG_TYPE_CRITICAL),\n        # 常用的几个状态, 直接封装好了\n        5: marker.OK_MSG,\n        6: marker.WARNING_MSG,\n        7: marker.CRITICAL_MSG,\n        8: marker.render_msg(1111, marker.MSG_TYPE_CRITICAL)\n    })\n\n\"\"\"\n渲染后的结果\n\n{\n  \"message\": \"success\",\n  \"code\": 0,\n  \"data\": {\n    \"1\": \"文本\",\n    \"2\": \"正常\",\n    \"3\": \"{color:yellow}警告{color}\",\n    \"4\": \"{color:red}错误{color}\",\n    \"5\": \"正常\",\n    \"6\": \"{color:yellow}警告{color}\",\n    \"7\": \"{color:red}��误{color}\",\n    \"8\": \"{color:red}1111{color}\"\n  },\n  \"userMessage\": \"success\"\n}\n\"\"\"\n\n\nif __name__ == '__main__':\n    init()\n    app.run(port=8000, threaded=True)\n","sub_path":"warscreen_api/render_color.py","file_name":"render_color.py","file_ext":"py","file_size_in_byte":1333,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"255161486","text":"# -*- coding: utf-8 -*-\n#  Copyright (c) 2020. Distributed under the terms of the MIT License.\n\nfrom pymatgen.io.vasp import Vasprun, Outcar\nfrom vise.analyzer.dielectric_function import DieleFuncData, \\\n    kramers_kronig_trans\nfrom vise.analyzer.vasp.band_edge_properties import VaspBandEdgeProperties\n\nimport numpy as np\n\n\ndef make_diele_func(vasprun: Vasprun,\n                    outcar: Outcar,\n                    use_vasp_real: bool = True,\n                    ita: float = 0.01) -> DieleFuncData:\n\n    energies, real, imag = vasprun.dielectric_data[\"density\"]\n    imag = np.array(imag)\n    if use_vasp_real:\n        real = np.array(real)\n    else:\n        # When CSHIFT = 0.0, the first component becomes 99999.0\n        # <dielectricfunction>\n        # <imag>\n        # <array>\n        # <dimension dim=“1”>gridpoints</dimension>\n        # <field>energy</field>\n        # <field>xx</field>\n        # <field>yy</field>\n        # <field>zz</field>\n        # <field>xy</field>\n        # <field>yz</field>\n        # <field>zx</field>\n        # <set>\n        # <r>     0.0000     0.0000     0.0000    -0.0000 99999.0000 99999.0000 99999.0000 </r>\n        imag[0] = 0.0\n        real = kramers_kronig_trans(imag, energies, ita)\n    band_gap = VaspBandEdgeProperties(vasprun, outcar).band_gap\n    return DieleFuncData(energies, real.tolist(), imag.tolist(), band_gap)\n\n\n","sub_path":"vise/analyzer/vasp/make_diele_func.py","file_name":"make_diele_func.py","file_ext":"py","file_size_in_byte":1375,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"525986163","text":"import numpy as np\nimport graph_network as gn\nimport matplotlib.pyplot as plt\nfrom time import time, clock\nfrom tqdm import tqdm\nfrom mpl_toolkits.mplot3d import axes3d\nfrom matplotlib import lines\n\n# Question 3.2\n\n\n# Une fonction pour calculer Nmax pour l'algo couplage\ndef measure_size_limit_coupling(time_limit_seconds, sample_size=1000):\n    size_lim = 2\n    while True:\n        avg_time = 0.0\n        for _ in range(sample_size):\n            prob = np.random.rand(1)[0]\n            gr = gn.Graph(size_lim, prob)\n            time_for_alg = time()\n            cover = gr.vertex_cover_coupling()\n            time_for_alg = time() - time_for_alg\n            avg_time += time_for_alg\n        avg_time = avg_time / sample_size\n        if avg_time < time_limit_seconds:\n            size_lim += 1\n            print(size_lim)\n        else:\n            break\n    return size_lim\n\n\n# Une fonction pour calculer Nmax pour l'algo glouton\ndef measure_size_limit_greedy(time_limit_seconds, sample_size=1000):\n    size_lim = 2\n    while True:\n        avg_time = 0.0\n        for _ in range(sample_size):\n            prob = np.random.rand(1)[0]\n            gr = gn.Graph(size_lim, prob)\n            time_for_alg = time()\n            cover = gr.vertex_cover_greedy()\n            time_for_alg = time() - time_for_alg\n            avg_time += time_for_alg\n        avg_time = avg_time / sample_size\n        if avg_time < time_limit_seconds:\n            size_lim += 1\n            print(size_lim)\n        else:\n            break\n    return size_lim\n\n\nif __name__ == '__main__':\n\n    # Initialization\n    # Si le temps moyen d'exécution de l'algorithme a été supérieur \"time_lim_sec\" pour un graphe de taille (n+1) et\n    # inférieur \"time_lim_sec\" pour celui de taille n, on va dire que Nmax de l'algorithme est n\n    time_lim_sec = 1\n    # Pour calculer Nmax des algoritmes, décommentez le code ci-dessous\n    # print('Size limit for coupling: %d' % measure_size_limit_greedy(time_lim_sec, sample_size=10))\n    # print('Size limit for greedy: %d' % measure_size_limit_greedy(time_lim_sec, sample_size=10))\n    # Après les calculations on peut obtenir, que Nmax = 540\n    nmax = 540\n    # Des nbs de tailles et de probas qu'on veut observer\n    nsize, nprob = 10, 10\n    # Tailles et probs qu'on veut observer\n    sizes, probs = (np.arange(nsize)+1) * (nmax // nsize), np.linspace(0.1, 1.0, nprob)\n    # Pour chaque taille et chaque proba, on va créer \"sample_size\" de graphes pour calculer le temps d'exécution\n    # d'un algo plus objectivement\n    sample_size = 100\n    # Les objets de type np.ndarray où on va stocker le temps d'exécution de l'algorithme et la taille de solution\n    coupling_time, greedy_time = np.zeros((nsize, nprob)), np.zeros((nsize, nprob))\n    coupling_solution, greedy_solution = np.zeros((nsize, nprob), dtype=int), np.zeros((nsize, nprob), dtype=int)\n    # On va afficher uniquement 4 premiers chiffres après la virgule pour les données stockées dans les tableaux\n    np.set_printoptions(precision=4, suppress=True)\n    # On va afficher un graphe de dépendance de temps d'exécution de taille d'un graphe où p = prob_to_plot\n    prob_to_plot = 0.5\n    # Les figures affichiées va avoir la même taille ci-dessous\n    figsize = (10, 6)\n\n    # Calculations de temps d'exécution et de tailles de solutions\n    for i in tqdm(range(nsize)):\n        for j in range(nprob):\n            for _ in range(sample_size):\n                gr = gn.Graph(sizes[i], probs[j])\n\n                time_for_alg = time()\n                cover = gr.vertex_cover_coupling()\n                time_for_alg = time() - time_for_alg\n                coupling_time[i, j] += time_for_alg\n                coupling_solution[i, j] += cover.size\n\n                time_for_alg = time()\n                cover = gr.vertex_cover_greedy()\n                time_for_alg = time() - time_for_alg\n                greedy_time[i, j] += time_for_alg\n                greedy_solution[i, j] += cover.size\n\n    coupling_time = coupling_time / sample_size\n    greedy_time = greedy_time / sample_size\n    coupling_solution = coupling_solution / sample_size\n    greedy_solution = greedy_solution / sample_size\n\n    # L'affichage des résultats par matplotlib\n\n    print('Temps pour couplage\\n\\n{0}\\n\\nSolutions pour couplage\\n\\n{1}\\n\\n'\n          'Temps pour algo glouton\\n\\n{2}\\n\\nSolutions pour algo glouton\\n\\n{3}\\n\\n'.\n          format(coupling_time, coupling_solution, greedy_time, greedy_solution))\n\n    plt.figure(0, figsize=figsize)\n    plt.plot(sizes, coupling_time[:, probs == prob_to_plot], 'r-', label='Couplage, p = %f' % prob_to_plot)\n    plt.plot(sizes, greedy_time[:, probs == prob_to_plot], 'b-', label='Glouton, p = %f' % prob_to_plot)\n    plt.legend()\n    plt.xlabel('n : taille de graphe')\n    plt.ylabel('Temps d\\'exécution d\\'algorithme')\n    plt.xticks(sizes)\n\n    fig = plt.figure(1, figsize=figsize)\n    probs_grid, sizes_grid = np.meshgrid(probs, sizes)\n    ax1 = fig.add_subplot(111, projection='3d')\n    ax1.plot_surface(probs_grid, sizes_grid, coupling_time, color=(1, 0, 0, 1))\n    ax1.plot_surface(probs_grid, sizes_grid, greedy_time, color=(0, 0, 1, 1))\n    fake2Dline_coupling = lines.Line2D([0], [0], linestyle=\"none\", c='r', marker='s')\n    fake2Dline_greedy = lines.Line2D([0], [0], linestyle=\"none\", c='b', marker='s')\n    ax1.legend([fake2Dline_coupling, fake2Dline_greedy], ['Couplage', 'Glouton'], numpoints=1)\n    ax1.set_xticks(probs)\n    ax1.set_yticks(sizes)\n    ax1.set_xlabel('Probabilité d\\'avoir une arrête (i, j) dans le graphe')\n    ax1.set_ylabel('n : taille de graphe')\n    ax1.set_zlabel('Temps d\\'exécution d\\'algorithme (moyen)')\n    \n    fig = plt.figure(2, figsize=figsize)\n    probs_grid, sizes_grid = np.meshgrid(probs, sizes)\n    ax2 = fig.add_subplot(111, projection='3d')\n    ax2.plot_surface(probs_grid, sizes_grid, coupling_solution, color=(1, 0, 0, 1), label='Couplage')\n    ax2.plot_surface(probs_grid, sizes_grid, greedy_solution, color=(0, 0, 1, 1), label='Glouton')\n    fake2Dline_coupling = lines.Line2D([0], [0], linestyle=\"none\", c='r', marker='s')\n    fake2Dline_greedy = lines.Line2D([0], [0], linestyle=\"none\", c='b', marker='s')\n    ax2.legend([fake2Dline_coupling, fake2Dline_greedy], ['Couplage', 'Glouton'], numpoints=1)\n    ax2.set_xticks(probs)\n    ax2.set_yticks(sizes)\n    ax2.set_xlabel('Probabilité d\\'avoir une arrête (i, j) dans le graphe')\n    ax2.set_ylabel('n : taille de graphe')\n    ax2.set_zlabel('Taille d\\'une solution (moyen)')\n\n    plt.show()\n","sub_path":"greedy_coupling_test_q.py","file_name":"greedy_coupling_test_q.py","file_ext":"py","file_size_in_byte":6516,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"346422040","text":"# -*- coding: utf-8 -*-\n\nfrom openerp import api, fields, models, _\nfrom openerp.exceptions import UserError, RedirectWarning, ValidationError\n\n\nclass ResUsers(models.Model):\n    _inherit = 'res.users'\n\n    center_ids = fields.One2many(\n        'res.users.centers',\n        'user_id',\n        string='Center',\n    )\n    vessel_id = fields.Many2one(\n        'fishing.vessel',\n        ondelete='cascade',\n        string='Vessel',\n        index=True,\n    )\n    fisherman = fields.Boolean(\n        related='partner_id.fisherman',\n        string='Is a Fisherman',\n        track_visibility=True,\n    )\n    pi_ids = fields.One2many(\n        'pipo.document',\n        'user_id',\n        ondelete='set null',\n        domain=[('type', '=', 'pi'), ],\n        string='PI Records',\n    )\n    po_ids = fields.One2many(\n        'pipo.document',\n        'user_id',\n        ondelete='set null',\n        domain=[('type', '=', 'po'), ],\n        string='PO Records',\n    )\n    state = fields.Selection(\n        compute='_compute_state',\n        string='Status',\n        selection=[\n            ('new', 'Waiting for approval'),\n            ('active', 'Approved'),\n        ],\n        index=True,\n    )\n    pid = fields.Char(\n        string='Personal ID',\n        track_visibility=True,\n        size=13,\n    )\n    address = fields.Text(\n        string='Address',\n        track_visibility=True,\n    )\n    telephone = fields.Char(\n        string='Telephone',\n        track_visibility=True,\n    )\n\n    @api.depends('active')\n    def _compute_state(self):\n        for user in self:\n            if user.fisherman:\n                user.state = ('active' if user.active else 'new')\n            else:\n                user.state = ('active' if user.login_date else 'new')\n\n    # @api.one\n    # @api.constrains('center_ids')\n    # def _check_center_ids(self):\n    #     defaults = [x.default for x in self.center_ids]\n    #     if defaults.count(True) != 1:\n    #         raise ValidationError(_('A user must be in one default center'))\n\n    @api.multi\n    def action_user_approve(self):\n        self.active = True\n\n    @api.multi\n    def action_user_cancel(self):\n        self.active = False\n\n\nclass ResUsersCenters(models.Model):\n    _name = 'res.users.centers'\n\n    user_id = fields.Many2one(\n        'res.users',\n        string='User',\n        index=True,\n    )\n    center_id = fields.Many2one(\n        'pipo.center',\n        string='PIPO Center',\n        required=True,\n        index=True,\n    )\n    default = fields.Boolean(\n        string='Default',\n        default=False,\n    )\n","sub_path":"fi2/models/res_users.py","file_name":"res_users.py","file_ext":"py","file_size_in_byte":2552,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"357790731","text":"#-------------------------------------------------------------------------------\n# Name:        module1\n# Purpose:\n#\n# Author:      Karanjot\n#\n# Created:     07/06/2017\n# Copyright:   (c) Karanjot 2017\n# Licence:     <your licence>\n#-------------------------------------------------------------------------------\n#string formatting method\n\ndef main():\n    day,month,year = eval(input(\"enter day, month and year numbers: \"))\n\n    date1 = str(day) + \"/\" + str(month) + \"/\" + str(year)\n\n    months = [\"January\", \"Febuary\", \"March\", \"April\",\n     \"May\", \"June\", \"July\", \"August\", \"September\", \"October\",\n     \"November\", \"December\"]\n\n    monthStr = months[month-1]\n    date2 = \"{0:3}\".format(str(day)) + \"{0:9}\".format(monthStr) +  str(year)\n#8 characters because month is less than that.\n\n    print(\"The Date is: {0:<5}\".format(date1), \"or\", date2 + \".\")\n\nmain()","sub_path":"5.1.py","file_name":"5.1.py","file_ext":"py","file_size_in_byte":859,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"147347710","text":"import RPi.GPIO as GPIO\nimport time\n\nled_pin = 18\nGPIO.setmode(GPIO.BCM)\nGPIO.setup(led_pin, GPIO.OUT)\n\npwm_led = GPIO.PWM(led_pin, 500)\npwm_led.start(100)\n\nSPEED_MIN = 80\nSPEED_MAX = 100\nSPEED_STEP = 1\n\nspeed = SPEED_MIN\nstep = SPEED_STEP\n\nwhile True:\n        if speed >= SPEED_MAX:\n            speed = SPEED_MAX\n            step = -SPEED_STEP\n        elif speed < SPEED_MIN:\n            speed = SPEED_MIN\n            step = SPEED_STEP\n        speed += step\n        print(speed)\n        duty_s = speed\n        duty = int(duty_s)\n        pwm_led.ChangeDutyCycle(duty)\n        time.sleep(0.1)\n\n","sub_path":"python/2017-08-24_0106_Python_Test_Raspberry/machine_control.py","file_name":"machine_control.py","file_ext":"py","file_size_in_byte":593,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"15808124","text":"n = int(input())\narr = list(map(int, input().split()))\n\nindex = -1\n\nfor i in range(1, 10000 + 1):\n    check = True\n    for j in range(n):\n        if i == arr[j]:\n            check = False\n            break\n    if check:\n        index = i\n        break\n\nprint(index)\n","sub_path":"Green/Lesson_5/L05P08.py","file_name":"L05P08.py","file_ext":"py","file_size_in_byte":266,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"72999965","text":"#!/usr/bin/python\n\"\"\"\nGiven a string, sort it in decreasing order based on the frequency of characters.\nExample 1:\n\nInput:\n\"tree\"\n\nOutput:\n\"eert\"\n\nExplanation:\n'e' appears twice while 'r' and 't' both appear once.\nSo 'e' must appear before both 'r' and 't'. Therefore \"eetr\" is also a valid answer.\nExample 2:\n\nInput:\n\"cccaaa\"\n\nOutput:\n\"cccaaa\"\n\nExplanation:\nBoth 'c' and 'a' appear three times, so \"aaaccc\" is also a valid answer.\nNote that \"cacaca\" is incorrect, as the same characters must be together.\nExample 3:\n\nInput:\n\"Aabb\"\n\nOutput:\n\"bbAa\"\n\nExplanation:\n\"bbaA\" is also a valid answer, but \"Aabb\" is incorrect.\nNote that 'A' and 'a' are treated as two different characters.\n#451\n\"\"\"\nclass SortByFreq:\n    def __init__(self):\n        self.indices = range(128)\n        self.freq = [0]*128\n\n    def sortByFreq(self, s):\n        # build freq array\n        for char in s: self.freq[ord(char)] += 1\n        self.indices.sort(cmp=self.cmp)\n        res = ''\n        for i in self.indices:\n            if self.freq[i] == 0: break\n            res += chr(i)*self.freq[i]\n\n        return res\n\n    def cmp(self, a, b):\n        return self.freq[b] - self.freq[a]\n\ndef test1():\n    s = 'tree'\n    sol = SortByFreq()\n    print(sol.sortByFreq(s))\n\ndef test2():\n    s = 'Aabb'\n    sol = SortByFreq()\n    print(sol.sortByFreq(s))\n\nif __name__ == '__main__':\n    test1()\n    test2()\n","sub_path":"sort/sortCharByFrequency.py","file_name":"sortCharByFrequency.py","file_ext":"py","file_size_in_byte":1370,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"483859851","text":"'''\r\nCreated on 19-Aug-2015\r\n\r\n@author: Selvam\r\n'''\r\n\r\nimport wx\r\n\r\nclass ToolFrame(wx.Frame):\r\n    \r\n    def __init__(self):\r\n        wx.Frame.__init__(self, parent=None, title=\"ToolBar\")\r\n        #panel = wx.Panel(self)\r\n        #panel.SetBackgroundColour('White')\r\n        self.CreateStatusBar()\r\n        toolbar = self.CreateToolBar()\r\n        toolbar.Realize()\r\n        \r\n        menuBar = wx.MenuBar()\r\n        menu1 = wx.Menu()\r\n        menuBar.Append(menu1, \"&File\")\r\n        menu2 = wx.Menu()\r\n        menu2.Append(wx.NewId(), \"&Copy\", \"Copy in status bar\")\r\n        menu2.Append(wx.NewId(), \"C&ut\", \"\")\r\n        menu2.Append(wx.NewId(), \"Paste\", \"\")\r\n        menu2.AppendSeparator()\r\n        menu2.Append(wx.NewId(), \"&Options...\", \"Display Options\")\r\n        menuBar.Append(menu2, \"&Edit\")\r\n        self.SetMenuBar(menuBar)\r\n        \r\n        dlg = wx.MessageDialog(self, \"Is it the coolest thing ever!\", 'MessageDialog', wx.YES_NO | wx.ICON_QUESTION)\r\n        dlg.ShowModal()\r\n        dlg.Destroy()\r\n        \r\nif __name__ == '__main__':\r\n    app = wx.App(False)\r\n    frame = ToolFrame()\r\n    frame.Show()\r\n    app.MainLoop()","sub_path":"wxpythonCode/toolbar1.py","file_name":"toolbar1.py","file_ext":"py","file_size_in_byte":1136,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"307152337","text":"from time import *\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom DPLL import *\nfrom math import sqrt\n\n\ndef dames(n):\n\n    literals = [i for i in range(1, n**2 + 1)]\n    cnf = []\n\n    '''Au moins 1 par ligne'''\n    for i in range(n):\n        clause = []\n        for j in range(n):\n            clause.append(literals[i*n + j])\n        cnf.append(clause)\n\n    literals_sorted_line = []\n    for i in range(n):\n        literals_sorted_line.append([])\n        for j in range(n):\n            literals_sorted_line[-1].append(literals[j*n + i])\n\n    '''Au plus 1 par ligne'''\n    for i in range(n):\n        for j in range(n):\n            for k in range(n-j-1):\n                clause = [-literals_sorted_line[i][j], -literals_sorted_line[i][j + k + 1]]\n                cnf.append(clause)\n\n    '''Au plus 1 par diagonale'''\n\n    for i in range(n):\n\n        for j in range(n-i):\n            for k in range(n-i-j-1):\n                clause = [-literals[i+j + j*n], -literals[i+j+k+1 + (j+k+1)*n]]\n                cnf.append(clause)\n\n        for j in range(i):\n            for k in range(i-j):\n                clause = [-literals_sorted_line[i-j][j], -literals_sorted_line[i-j-k-1][j+k+1]]\n                cnf.append(clause)\n\n    for i in range(1, n-1):\n\n        for j in range(n-1):\n            for k in range(n-i-j-1):\n                clause = [-literals_sorted_line[i+j][n-1-j], -literals_sorted_line[i+j+k+1][n-1-j-k-1]]\n                cnf.append(clause)\n\n        for j in range(i):\n            for k in range(i-j):\n                clause = [-literals_sorted_line[i-j][n-1-j], -literals_sorted_line[i-j-k-1][n-1-j-k-1]]\n                cnf.append(clause)\n\n    return n, literals, cnf\n\n\ndef time_dames_once(n):\n\n    start = time()\n    a = dames(n)\n    end = time()\n    exe_time = end - start\n    print(len(a[2]))\n    return exe_time\n\n\ndef time_dames_plt(n, s=1):\n    x = [i for i in range(0, n, s)]\n    tm = []\n    for i in x:\n        tm.append(time_dames_once(i))\n    plt.plot(x, tm)\n    plt.xlabel('n')\n    plt.ylabel('Time (s)')\n    plt.show()\n    plt.loglog(x, tm)\n    plt.show()\n\n\ndef create_txt_dames(n):\n    filename = 'Dames ' + str(n)\n    file = open(filename, 'w+')\n    results = dames(n)\n    file.write(str(results[0]) + '\\n' + str(results[1]) + '\\n' + str(results[2]))\n    file.close()\n\n\ndef read(filepath):\n    file = open(filepath, 'r')\n    content = file.readlines()\n\n    n = int(content[0])\n\n    literals = content[1]\n\n    cnf_str = content[2]\n    cnf_str = cnf_str[1:]\n    cnf_str = cnf_str[:-1]\n    cnf = []\n    last = ''\n\n    i = 0\n    while i < len(cnf_str):\n\n        if cnf_str[i] == '[':\n            cnf.append([])\n            last = cnf_str[i]\n            i += 1\n            continue\n\n        if last == '[':\n            print('i', i)\n            print(cnf_str[i])\n            if cnf_str[i] != ' ' and cnf_str[i] != ',' and cnf_str[i] != '-' and cnf_str[i] != ']':\n                string = cnf_str[i]\n                j = i + 1\n                print('j', j)\n                while cnf_str[j] != ' ' and cnf_str[j] != ',' and cnf_str[j] != ']':\n                    string += cnf_str[j]\n                    j += 1\n                    print('oui')\n                if cnf_str[i - 1] == '-':\n                    cnf[-1].append(-int(string))\n                else:\n                    cnf[-1].append(int(string))\n                i += j-i\n            else:\n                i += 1\n\n        else:\n            i += 1\n\n    file.close()\n\n    return n, literals, cnf\n\n\ndef results_show(mod_tot):\n    for mod in mod_tot:\n        n = int(sqrt(len(mod)))\n        chessboard = np.zeros((n, n))\n        for i in range(len(mod)):\n            if mod[i] is True:\n                chessboard[int(i/n)][i % n] = 1\n        plt.spy(chessboard)\n        for i in range(n+1):\n            for t in range(n * 25):\n                plt.plot(i - 0.5, t / 25 - 0.5, '*', color='black')\n        for i in range(n+1):\n            for t in range(n * 25):\n                plt.plot(t/25 - 0.5, i-0.5,'*',color = 'black')\n        plt.show()\n\n\ndef time_dpll_dames_once(n, heuristic=1, want_mod=True):\n    cnf = dames(n)[2]\n    t = time()\n    DPLL(cnf, heuristic, want_mod)\n    t = time() - t\n    return t\n\n\ndef time_dpll_dames_range(n, s, heuristic=1, want_mod=True):\n    time_list = []\n    x = []\n    for k in range(0, n, s):\n        print(k)\n        x.append(k)\n        time_list.append(time_dpll_dames_once(k, heuristic, want_mod))\n    return x, time_list\n\n\n\na = dames(6)\nc = a[1]\nb = a[2]\nprint(len(a[1]))\na = DPLL(b,2,want_mod=True)\nprint(a[1])","sub_path":"Dames.py","file_name":"Dames.py","file_ext":"py","file_size_in_byte":4532,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"165288966","text":"import shutil, os\r\n\r\n# Moves to working directory desired folder\r\ninput_folder = 'C:/Users/andyl/Documents/'\r\nos.chdir(input_folder)\r\n\r\n# Print current working directory \r\nprint('The current directory is now ' + os.getcwd())\r\n\r\n# List of folders\r\nsorted_folders = []\r\n\r\n#Outputs working folder name, list of folders and list of files\r\nfor file in os.listdir(input_folder):\r\n\tif os.path.isfile(file):\r\n\t\t# Searches for file type\r\n\t\t\tfile_type = file[file.find('.')+1:]\r\n\t\t\t# Makes directory if none exists for file type\r\n\t\t\tif file_type not in sorted_folders: \r\n\t\t\t\tsorted_folders.append(file_type)\r\n\t\t\t\ttry:\r\n\t\t\t\t\tos.mkdir(file_type)\r\n\t\t\t\texcept OSError as error:\r\n\t\t\t\t\tprint(error)\t\r\n\t\t\t# Moves file into designated directory\r\n\t\t\tshutil.move(file,input_folder + file_type)\r\n\t","sub_path":"automate_files.py","file_name":"automate_files.py","file_ext":"py","file_size_in_byte":776,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"187488734","text":"# coding: utf-8\nfrom __future__ import print_function\nimport sys\nimport os\nimport getopt\nimport tempfile\n#from hanziconv import HanziConv\nfrom opencc import OpenCC\nfrom zipfile import ZipFile,ZipInfo\ncc = OpenCC('s2hk')  # convert from Simplified Chinese to Traditional Chinese\ndef __conv__(file,string):\n    try:\n        return cc.convert(string.decode('utf-8'))\n    except Exception:\n        print ('Warning: Fail to decode '+file+'!')\n    return string\ndef convName(file,verbose):\n    org_file=os.path.abspath(file)\n    new_file=os.path.dirname(org_file)+'/'+__conv__(file,os.path.basename(org_file))\n    os.rename(org_file,new_file)\n    return\ndef convText(file,verbose):\n    if(verbose):\n        print(file)\n    try:\n        f=open(os.path.abspath(file),'r+')\n        text=__conv__(file,f.read())\n        f.seek(0)\n        f.write(text)\n        f.truncate()\n        f.close()\n    except IOError:\n           print ('Warning: Fail to open '+str(file)+'!')\ndef convEpub(file,verbose):\n    if(verbose):\n        print(file)\n    ext=['.ncx',\".htm\",\".xhtml\",\".html\",\".opt\",\".ncx\"]\n    zin=ZipFile(file,\"r\")\n    update_contant=[((zipinfo.filename,__conv__(zipinfo.filename,zin.read(zipinfo.filename)))) \\\n            for zipinfo in zin.filelist if zipinfo.filename.endswith(tuple(ext))]\n    tmpfd, tmpname = tempfile.mkstemp(dir=os.path.dirname(os.path.abspath(zin.filename)))\n    zout=ZipFile(tmpname, 'w')\n    os.close(tmpfd)\n    zin.comment = zout.comment\n    # get all names from input zip except file in update list\n    update_name=[name for name,content in update_contant]\n    zin_filename=[item.filename for item in zin.filelist if item.filename not in update_name]\n    # add all files with its new data to new archive\n    [zout.writestr(name,contant) for name,contant in update_contant]    \n    [zout.writestr(name,zin.read(name)) for name in zin_filename]\n    # replace with th temp archive\n    os.remove(zin.filename)\n    os.rename(tmpname,zin.filename)\n    zin.close()\ndef main():\n    sim=False\n    name=False\n    verbose=False\n    try:\n        opts,args = getopt.getopt(sys.argv[1:],'nsv',['simplify','name','verbose'])\n    except getopt.GetoptError as err:\n        print (str(err))\n        sys.exit(2)\n    for o, a in opts:\n        if o in ('-s','--simplify'):\n            sim=True\n            cc = OpenCC('t2s')  # convert from Traditional Chinese to Simplified Chinese\n        elif o in ('-n','--name'):\n            name=True\n        elif o in ('-v', '--verbpse'):\n            verbose=True\n        else:\n            assert False, 'Unhandled option'\n    [convEpub(arg,verbose) for arg in args if arg.endswith('.epub')]\n    [convText(arg,verbose) for arg in args if arg.endswith('.txt')]\n    [convName(arg,verbose) for arg in args if name]\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"tradify.py","file_name":"tradify.py","file_ext":"py","file_size_in_byte":2789,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"199549051","text":"import pickle\nimport sqlite3\nimport pandas as pd\nfrom sklearn import preprocessing, cross_validation, svm, neighbors\nfrom sklearn.linear_model import LogisticRegression\nimport numpy as np\n\nconn = sqlite3.connect(\"game_data.sqlite\")\n\nsql = \"select * from Games\"\n\nresults = []\nteams = []\nlist_result = {1: (1, 0), 2: (2, 0), 3: (2, 1), 4: (3, 0), 5: (3, 1), 6: (\n    3, 2), -1: (0, 1), -2: (0, 2), -3: (1, 2), -4: (0, 3), -5: (1, 3), -6: (2, 3)}\n\ndf = pd.read_sql(sql, conn, index_col='id')\nwith open('team_order.pickle', 'rb') as f:\n    team_order = pickle.load(f)\n\n# print(df.head())\n# print(team_order.keys())\n\nX = np.array(df[['team1', 'team2']])\n\n# print(X)\n\ny = np.array(df['result'])\n\n# print(y)\n\nX_train, X_test, y_train, y_test = cross_validation.train_test_split(\n    X, y, test_size=0.2)\n\nclf = neighbors.KNeighborsClassifier()\n\nclf.fit(X_train, y_train)\naccuracy = clf.score(X_test, y_test)\n\nprint(accuracy)\n\nteama = input('team1 : ')\nteamb = input('team2 : ')\n\nteama = team_order[teama]\nteamb = team_order[teamb]\n\nprint(teama, teamb)\n\nexample_measures = np.array([teama, teamb])\n\nresult = clf.predict(example_measures)\nresult = np.array_str(result)\n\n# print(result[1])\n\nif result[1] == '-':\n    print(list_result[int(result[2])])\nelse:\n    print(list_result[int(result[1])])\n","sub_path":"Data_processing/KNN/knn.py","file_name":"knn.py","file_ext":"py","file_size_in_byte":1286,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"190769827","text":"# -*- coding: utf-8 -*-\nfrom hashlib import md5\n\nfrom hashlib import md5\n\n\nclass SimpleHash(object):\n    def __init__(self, cap, seed):\n        self.cap = cap\n        self.seed = seed\n\n    def hash(self, value):\n        ret = 0\n        for i in range(len(value)):\n            ret += self.seed * ret + ord(value[i])\n        return (self.cap - 1) & ret\n\n\nclass BloomFilter(object):\n    def __init__(self, blockNum=1, key=None):\n        \"\"\"\n        :param blockNum: one blockNum for about 90,000,000; if you have more strings for filtering, increase it.\n        :param key: the key's name in Redis\n        \"\"\"\n        self.server = None\n        self.bit_size = 1 << 31\n        # Redis的String类型最大容量为512M，现使用256M\n        self.seeds = [5, 7, 11]\n        self.key = key\n        self.blockNum = blockNum\n        self.hashfunc = []\n        for seed in self.seeds:\n            self.hashfunc.append(SimpleHash(self.bit_size, seed))\n\n    def isContains(self, str_input):\n        if not str_input:\n            return False\n        m5 = md5()\n        m5.update(str_input.encode(\"utf8\"))\n        str_input = m5.hexdigest()\n        ret = True\n        for f in self.hashfunc:\n            loc = f.hash(str_input)\n            ret = ret & self.server.getbit(self.key, loc)\n        return ret\n\n    def insert(self, str_input):\n        m5 = md5()\n        m5.update(str_input.encode(\"utf8\"))\n        str_input = m5.hexdigest()\n        for f in self.hashfunc:\n            loc = f.hash(str_input)\n            self.server.setbit(self.key, loc, 1)\n\n\nif __name__ == '__main__':\n    content = ''\n    bl = BloomFilter(key='monitor_project')\n    if not bl.isContains(content):\n        bl.insert(content)\n    else:\n        pass\n","sub_path":"WSSpiderV2/utils/bloom_filter.py","file_name":"bloom_filter.py","file_ext":"py","file_size_in_byte":1720,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"289303369","text":"\n# Native libraries\nimport requests\n# Own modules\n# from .Logger import maps_log as log\n\n\nMAPS_API_URL = \"https://maps.googleapis.com/maps/api/staticmap?center={lat},{lon}&zoom=17&scale=2&size={sizeX}x{sizeY}&maptype={maptype}&format=png&visual_refresh=true&markers=size:mid%7Ccolor:0x0ba037%7Clabel:%7C{lat},{lon}&key={key}&secret={secret}\"\nMAPTYPE_NORMAL = \"roadmap\"\nMAPTYPE_TERRAIN = \"hybrid\"\nMAPS_IMAGESIZE_X_HORIZONTAL = 600\nMAPS_IMAGESIZE_Y_HORIZONTAL = 300\nMAPS_IMAGESIZE_X_VERTICAL = 325\nMAPS_IMAGESIZE_Y_VERTICAL = 400\n\n\nclass GoogleMaps(object):\n    def __init__(self, db):\n        pass\n\n    @staticmethod\n    def _get_maps_live(self, stop, vertical, terrain, sizeX=None, sizeY=None):\n        \"\"\"Get a Google Maps image of the desired stop from GMaps API.\n        This function does not check if the Stop queried Streetview image was already fetched and saved in DB.\n        The stop must have a valid location (it is not checked here).\n        :param stop: Stop object to get maps from\n        :param vertical: if True, get vertical image; if False, get horizontal image\n        :param terrain: if True, get terrain image; if False, get normal map\n        :param sizeX: Horizontal size of image (default *)\n        :param sizeY: Vertical size of image (default *)\n        :return: Bytes object of the fetched StreetView image\n        * Both sizes use constant MAPS_IMAGESIZE_X/Y_HORIZONTAL/VERTICAL variables from the module as default values.\n        \"\"\"\n        if sizeX is None or sizeY is None:\n            if vertical:\n                sizeX = MAPS_IMAGESIZE_X_VERTICAL\n                sizeY = MAPS_IMAGESIZE_Y_VERTICAL\n            else:\n                sizeX = MAPS_IMAGESIZE_X_HORIZONTAL\n                sizeY = MAPS_IMAGESIZE_Y_HORIZONTAL\n        url = MAPS_API_URL.format(\n            sizeX=sizeX,\n            sizeY=sizeY,\n            lat=stop.lat,\n            lon=stop.lon,\n            maptype=MAPTYPE_TERRAIN if terrain else MAPTYPE_NORMAL\n        )\n        # log.info(\"Getting StreetView image from URL: \" + url)\n        return requests.get(url).content\n\n    def save_maps_db(self, stopid, fileid, vertical, terrain):\n        \"\"\"Save a Maps image of a Stop in local DB.\n        This method must be called from the Telegram module when a picture has been sent.\n        :param stopid: Stop ID/Number of the stop related with the StreetView image\n        :param fileid: FileID returned by Telegram when image was originally sent\n        :param vertical: set to True if image is vertical\n        :param terrain: set to True is map is terrain-view (satellite hybrid)\n        \"\"\"\n        # log.info(\"Saving Maps image of Stop #{} (vertical={}, terrain={}) in local DB (File ID: {})\".format(stopid, vertical, terrain, fileid))\n        self.db.write(\n            \"INSERT OR IGNORE INTO maps (stopid, fileid, vertical, terrain, created) VALUES (?,?,?,?,?)\",\n            (stopid, fileid, int(vertical), int(terrain), self.db.curdate())\n        )\n\n    def _search_maps_db(self, stopid, vertical, terrain):\n        \"\"\"Search for a Maps image of the desired stop in local DB.\n        :param stopid: Stop ID/Number to get StreetView image of\n        :param vertical: True to search vertical images, False for horizontal\n        :param terrain: True to search terrain/satellite images, False for normal map\n        :return: Telegram FileID, if stop was saved in DB\n        :return: None if no image was found in DB for that stopid\n        \"\"\"\n        # log.debug(\"Searching Maps image for Stop #{} (vertical={}, terrain={}) in local DB\".format(stopid, vertical, terrain))\n        result = self.db.read(\n            \"SELECT fileid FROM maps WHERE stopid=? AND vertical=? AND terrain=?\",\n            variables=(stopid, int(vertical), int(terrain)),\n            fetchall=False,\n            single_column=True\n        )\n        # if result:\n        #     log.info(\"Found Maps image for Stop #{} (vertical={}, terrain={}) in local DB. File ID: {}\".format(stopid, vertical, terrain, result))\n        # else:\n        #     log.info(\"Maps image for Stop #{} (vertical={}, terrain={}) NOT found in local DB\".format(stopid, vertical, terrain))\n        return result\n\n    def get_maps(self, stop, vertical=True, terrain=False):\n        \"\"\"Get a Google Maps image for the desired stop from local DB or GMaps API.\n        The function searches first on the DB for the SV image (Telegram File ID)\n        If it's not saved there, the image is fetched from GMaps API and returned as Bytes.\n        Telegram send_photo method used by the source module must accept FileID AND Bytes.\n        :param stop: Stop object to get Maps from (Must have Lat&Lon!)\n        :param vertical: if True, get vertical image; if False, get horizontal image (default=True - vertical)\n        :param terrain: if True, get terrain image; if False, get normal map (default=False - normal map)\n        :return: FileID if image is saved in DB\n        :return: Bytes if image is not saved in DB\n        \"\"\"\n        # log.info(\"Getting Maps image for Stop #{} (Vertical={}; Terrain={})\".format(stop.id, vertical, terrain))\n        try:\n            imageid = self._search_maps_db(stop.id, vertical, terrain)\n            if imageid is None:\n                return self._get_maps_live(stop, vertical, terrain)\n            else:\n                return imageid\n        except Exception:\n            # log.exception(\"Could not get Maps image for Stop #{}\".format(stop.id))\n            pass\n","sub_path":"pybuses/googlemaps.py","file_name":"googlemaps.py","file_ext":"py","file_size_in_byte":5448,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"203262406","text":"import multiprocessing\nimport sys\nimport weakref\nimport functools\nimport inspect\n\nfrom .loop import EventLoop, TimerLoop, LState\nfrom .serialize import SType\nfrom .method import Method, wrap_method\nfrom .base import weak_method\nfrom .device import Device, not_process_creatable\nfrom .deviceClient import DeviceClient\nfrom malcolm.core.socketInterface import ClientSocket, ServerSocket\nfrom .subscription import ServerSubscription\nfrom .attribute import Attribute\n\n\n@not_process_creatable\nclass Process(Device, multiprocessing.Process):\n\n    def __init__(self, server_strings, name, ds_name=\"DirectoryService\",\n                 ds_string=None, timeout=None):\n        \"\"\"name is process name\n        server_strings is list of \"zmq://tcp://172.23.122.23:5600\"\n        \"\"\"\n        super(Process, self).__init__(name, timeout)\n        # Add attributes\n        self.add_attributes(\n            server_strings=Attribute(\n                [str], \"List of server strings for server socks\"),\n            device_types=Attribute([str], \"Available device types\"),\n            local_devices=Attribute([str], \"Devices we are hosting\"),\n        )\n        # Store ds details\n        self.ds = None\n        self.ds_name = ds_name\n        self.ds_string = ds_string\n        # Local devices\n        self._device_servers = {}\n        # Weak references to DeviceClients for remote devices\n        self.DeviceClient = DeviceClient\n        self._device_clients = weakref.WeakValueDictionary()\n        # Server socks\n        self._server_socks = {}\n        # Weak references to Client socks\n        self._client_socks = weakref.WeakValueDictionary()\n        # List of spawned cothreads\n        self.spawned = []\n        # send function -> ServerSubscription\n        self.subscriptions = {}\n        # Make a router loop\n        router = EventLoop(name + \".router\")\n        router.add_event_handler(SType.Call, self.do_call)\n        router.add_event_handler(SType.Get, self.do_get)\n        router.add_event_handler(SType.Subscribe, self.do_subscribe)\n        router.add_event_handler(SType.Unsubscribe, self.do_unsubscribe)\n        router.error_handler = weak_method(self.do_error)\n        self.router = router\n        self.add_loop(router)\n        # populate server strings\n        self.server_strings = server_strings\n        # populate device types\n        self.device_types = []\n        device_types = Device.subclasses()\n        for d in device_types:\n            # Add it to the device_types attribute\n            self.device_types.append(d.__name__)\n            if d not in Device.not_process_creatable and not inspect.isabstract(d):\n                # Make a method to create an instance of it\n                self.make_create_device(d)\n        # Local devices\n        self.local_devices = []\n\n    def make_create_device(self, cls):\n        @functools.wraps(cls)\n        def f(process, name, **kwargs):\n            return process.create_device(cls, name, **kwargs)\n        # Set the name and create a Method wrapper for it\n        f.__name__ = \"create_{}\".format(cls.__name__)\n        class_attributes = getattr(cls, \"class_attributes\", {})\n        method = Method(f, args_from=cls.__init__)\n        self.add_method(method, name=Attribute(str, \"Device name\"),\n                        timeout=Attribute(str, \"Timeout for any process\"),\n                        **class_attributes)\n        setattr(self, f.__name__, method)\n\n    def create_device(self, cls, name, **kwargs):\n        device = cls(name, **kwargs)\n        device.create_device = weak_method(self.create_device)\n        device.get_device = weak_method(self.get_device)\n        self._device_servers[name] = device\n        self.add_loop(device)\n        self.update_devices()\n        return device\n\n    def register_devices(self):\n        for device in self.local_devices:\n            if device not in self.ds.Device_instances:\n                self.ds.register_device(device, self.server_strings)\n\n    def update_devices(self):\n        self.local_devices = sorted(self._device_servers)\n\n    def get_device(self, device, server_strings=None):\n        \"\"\"Create a weak reference to a new DeviceClient object (or existing)\n        \"\"\"\n        assert self.loop_state() == LState.Running, \\\n            \"Can't get device when process has not been started running\"\n        if device in self._device_clients:\n            return self._device_clients[device]\n        elif device in self._device_servers:\n            return self._device_servers[device]\n        # Calculate server strings if not given\n        if server_strings is None:\n            assert self.ds is not None, \"Can't lookup server string for {} \"\\\n                \"as no DirectoryService present\".format(device)\n            server_strings = self.ds.connection_string(device=device)\n        dc = self.DeviceClient(device, self.get_client_sock(server_strings))\n        # Don't add loop, we want it to go when no-one needs it\n        dc.loop_run()\n        self._device_clients[device] = dc\n        return dc\n\n    def get_client_sock(self, server_strings):\n        # If we already have a client sock, return that\n        # TODO: handle localhost conversion here?\n        for string in server_strings:\n            if string in self._client_socks:\n                return self._client_socks[string]\n        cs = ClientSocket.make_socket(string)\n        assert cs, \"Can't make socket for {}\".format(string)\n        self._client_socks[string] = cs\n        cs.loop_run()\n        return cs\n\n    def run(self, block=True):\n        \"\"\"Sets up everything and starts the event loops.\"\"\"\n        # If we are in a multiprocessing loop then check that cothread has not\n        # been inherited from the forked mainprocess. If it has then we can't\n        # use cothread as we will share the same scheduler!\n        if type(multiprocessing.current_process()) == type(self):\n            # We are in a multiprocessing child process\n            assert \"cothread\" not in sys.modules, \\\n                \"Cothread has already been imported, this will not work!\"\n        self.loop_run()\n        server_strings = []\n        for string in self.server_strings:\n            ss = ServerSocket.make_socket(string, self.router.inq)\n            assert ss is not None, \"Can't make socket for {}\".format(string)\n            self._server_socks[string] = ss\n            self.add_loop(ss)\n            server_strings.append(ss.name)\n        self.server_strings = server_strings\n        # Create a client to directory service\n        if self.ds_string is not None:\n            self.ds = self.get_device(self.ds_name, [self.ds_string])\n            # Make a timer to keep checking ds has our devices\n            self.add_loop(TimerLoop(self.name + \".dsupdate\",\n                                    self.register_devices, 10))\n        self.quitsig = self.cothread.Pulse()\n        if block:\n            self.quitsig.Wait()\n\n    @wrap_method()\n    def exit(self):\n        \"\"\"Stops the event loops and waits until they're done.\"\"\"\n        # Can't do super, as we've decorated it\n        Device.exit.function(self)\n        for ct in self.spawned:\n            ct.Wait()\n        self.quitsig.Signal()\n\n    def _get_device(self, endpoint):\n        if \".\" in endpoint:\n            devicename, ename = endpoint.split(\".\", 1)\n        else:\n            devicename, ename = endpoint, None\n        if devicename in self._device_servers:\n            device = self._device_servers[devicename]\n        elif devicename == self.name:\n            device = self\n        else:\n            raise AssertionError(\"Can't find device {}\".format(devicename))\n        return device, ename\n\n    def do_func(self, send, f, args):\n        try:\n            ret = f(**args)\n        except Exception as e:\n            self.log_exception(\"Error running {}({})\".format(f, args))\n            send(SType.Error, e)\n        else:\n            send(SType.Return, ret)\n\n    def do_call(self, send, endpoint, method, args={}):\n        device, ename = self._get_device(endpoint)\n        assert ename is None, \\\n            \"Must Call with endpoint=<device> and method=<method>. \" \\\n            \"Got endpoint={} and method={}\".format(endpoint, method)\n        endpoint = device.get_endpoint(\"methods.\" + method)\n        assert callable(endpoint), \"Expected function, got {}\".format(endpoint)\n        if ename == \"exit\":\n            self._device_servers.pop(device.name)\n            self.update_devices()\n        ct = self.cothread.Spawn(self.do_func, send, endpoint, args)\n        self.spawned.append(ct)\n\n    def do_get(self, send, endpoint):\n        device, ename = self._get_device(endpoint)\n        endpoint = device.get_endpoint(ename)\n        send(SType.Return, endpoint)\n\n    def do_subscribe(self, send, endpoint):\n        sub = ServerSubscription(*self._get_device(endpoint), send=send)\n        self.subscriptions[send] = sub\n        self.add_loop(sub)\n        return sub\n\n    def do_unsubscribe(self, send):\n        self.subscriptions.pop(send).loop_stop()\n\n    def do_error(self, error, send, *args, **kwargs):\n        EventLoop.error_handler(self.router, error, send, *args, **kwargs)\n        send(SType.Error, error)\n","sub_path":"malcolm/core/process.py","file_name":"process.py","file_ext":"py","file_size_in_byte":9131,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"287141129","text":"\nfrom __future__ import print_function\nfrom apiclient.discovery import build\nfrom httplib2 import Http\nfrom oauth2client import file, client, tools\n\nfrom pprint import pprint\nfrom googleapiclient import discovery\n\nimport random\nimport time\nimport copy\n\nimport account as account\n\nfrom linebot.models import (\n\tMessageEvent, TextMessage, TextSendMessage, ImageSendMessage\n)\n\nmy_database_sheet_ID = '1RaGPlEJKQeg_xnUGi1mlUt95-Gc6n-XF_czwudIP5Qk'\nauth_json_path = 'auth.json'\napril_ID='Udf8f28a8b752786fa7a6be7d8c808ec6'\n\nanswer = -1\nquestion_no = 0\nplayer_id = \"\"\nplayer_name = \"\"\nbet_money = 0\ntime_count_waiting = 0\n\ndef get_value_from_google_sheet(SPREADSHEET_ID,RANGE_NAME):\n\t# Setup the Sheets API\n\tSCOPES = 'https://www.googleapis.com/auth/spreadsheets.readonly'\n\tstore = file.Storage('credentials.json')\n\tcreds = store.get()\n\tif not creds or creds.invalid:\n\t\tflow = client.flow_from_clientsecrets('client_secret.json', SCOPES)\n\t\tcreds = tools.run_flow(flow, store)\n\tservice = build('sheets', 'v4', http=creds.authorize(Http()))\n\n\t# Call the Sheets API\n\tresult = service.spreadsheets().values().get(spreadsheetId=SPREADSHEET_ID,\n\t\t\t\t\t\t\t\t\t\t\t\t range=RANGE_NAME).execute()\n\treturn result.get('values', [])\n\ndef time_count_set(user_message,user_id):\n\tglobal time_count_waiting,player_id,player_name,start_time,end_time,bet_money\n\tif time_count_waiting == 0:\n\t\tuser_money = account.inquire_only_money(user_id)\t\t\n\t\ttry:\n\t\t\tsplit_result = user_message.split(' ',1)\n\t\t\tprint(split_result)\n\t\t\tif split_result[1] == \"?\":\n\t\t\t\treturn \"【請輸入您的賭金：】\\n範例：!tick 賭金\"\n\t\t\telif split_result[1] == \"all\":\n\t\t\t\tbet_money = user_money\n\t\t\telse:\n\t\t\t\ttry:\n\t\t\t\t\tbet_money = int(split_result[1])\n\t\t\t\texcept:\n\t\t\t\t\treturn \"【請輸入有效的賭金：】\\n範例：!tick 賭金\"\n\t\texcept:\n\t\t\treturn \"【請輸入您的賭金：】\\n範例：!tick 賭金\"\n\n\t\tif bet_money < 0 :\n\t\t\treturn \"【請不要偷偷黑我的錢！】\\n範例：!tick 賭金\"\n\n\t\tif user_money >= bet_money :\n\t\t\tpass\n\t\telif bet_money > 10000 :\n\t\t\treturn \"【 超過賭金上限！！！ 】\\n好孩子不可以賭太多錢哦～\"\n\n\t\ttime_count_waiting = 1\n\t\tstart_time = 0\n\t\tend_time = 0\n\t\tplayer_id = user_id\n\t\tplayer_name = account.get_name(user_id)\n\t\treturn \" - 【 關鍵10秒鐘 】 - \\n請再次輸入【!tick】開始計時\\n請在開始後，內心數10秒\\n並在10秒時輸入【!stop】\\n即可贏得賭博！\\n(判定範圍9.5 sec ~ 10.5 sec)\\n【誤差如果極小可獲得100萬】\"\n\telif time_count_waiting == 1:\n\t\tstart_time = time.time()\n\t\ttime_count_waiting = 2\n\t\treturn \" - 【 關鍵10秒鐘 】 - \\n【 現在開始計時！！！ 】\"\n\telif time_count_waiting == 2:\n\t\treturn \" 請稍候~ 現在有人正在計時哦~ \"\n\n\ndef time_count_end():\n\tglobal time_count_waiting,end_time,start_time,player_name,player_id,bet_money\n\tend_time = time.time()\n\ttime_record = round((end_time - start_time),3)\n\ttime_second = time_record\n\n\tif time_second < 10:\n\t\ttotal_record = \"0\"+str(round((time_second),3))\n\telse:\n\t\ttotal_record = str(round((time_second),3))\n\tmessage = \"【 計時結束！！！】\\n您讀的秒數為： \"\n\tmessage += str(total_record)\n\tmessage += \" sec\\n\"\n\tif time_record >= 9.995 and time_record <= 10.005:\n\t\tmessage += \"太猛了！！！誤差超小！！！\\n\"\n\t\tmessage += account.change_money(player_id,+1000000)\n\telif time_record >= 9.5 and time_record <= 10.5:\n\t\tmessage += \"恭喜！！！好厲害ㄛ！！！\\n\"\n\t\tmessage += account.change_money(player_id,+bet_money)\n\telse:\n\t\tmessage += \"ㄟㄟ~ 你484不會讀秒Ｒ？\\n\"\n\t\tmessage += account.change_money(player_id,-bet_money)\n\ttime_count_waiting = 0\n\treturn message\n\t\t\n\n\n\n\n\n\n","sub_path":"time_count.py","file_name":"time_count.py","file_ext":"py","file_size_in_byte":3644,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"534023971","text":"from werkzeug.datastructures import Authorization\nfrom app import db\nfrom app.models.goal import Goal\nfrom app.models.task import Task\nfrom flask import Blueprint, jsonify, make_response, request, abort\nfrom datetime import date \nimport requests\nfrom dotenv import load_dotenv\nimport os\n\nload_dotenv()\ntasks_bp = Blueprint(\"tasks\", __name__, url_prefix=\"/tasks\")\n\n@tasks_bp.route(\"\", methods=[\"POST\"])\ndef create_task():\n    request_data=request.get_json()\n    if \"title\" not in request_data or \"description\" not in request_data or \"completed_at\" not in request_data:\n        return jsonify({\"details\": \"Invalid data\"}),400\n    new_task=Task(title=request_data[\"title\"], description=request_data[\"description\"], completed_at=request_data[\"completed_at\"])\n    db.session.add(new_task)\n    db.session.commit()\n\n    return jsonify({\"task\": new_task.to_dict()}), 201\n\n@tasks_bp.route(\"\", methods=[\"GET\"])\ndef handle_tasks():\n    tasks_response = []\n    if request.args.get(\"sort\")==\"asc\":\n        tasks=Task.query.order_by(Task.title)\n    elif request.args.get(\"sort\")==\"desc\":\n        tasks=Task.query.order_by(Task.title.desc())\n   \n    else:\n        tasks = Task.query.all()\n    for task in tasks:\n        tasks_response.append(task.to_dict())\n    return jsonify(tasks_response), 200\n\n@tasks_bp.route(\"/<task_id>\", methods=[\"GET\", \"PUT\"])\ndef handle_task(task_id):\n    task_id=validate_id_int(task_id)\n    # task_id = int(task_id)\n    task = Task.query.get(task_id)\n    if not task:\n        return make_response(\"\", 404)\n    if request.method==\"GET\":\n        return jsonify({\"task\": task.to_dict()}), 200\n    elif request.method== \"PUT\":\n        request_data=request.get_json()\n        task.title=request_data[\"title\"]\n        task.description=request_data[\"description\"]\n    \n        db.session.commit()\n        return jsonify({\"task\": task.to_dict()}),200\n\n@tasks_bp.route(\"/<task_id>/mark_complete\", methods=[\"PATCH\"])\ndef complete_task(task_id):\n    task_id=validate_id_int(task_id)\n    task = Task.query.get(task_id)\n    patch_data=request.get_json()\n    # print(patch_data)\n    if not task:\n        return make_response(\"\", 404)\n    \n    else:\n        task.completed_at=date.today()\n\n        db.session.commit()\n        \n        my_token=os.environ.get(\"MY_TOKEN\")\n        message=f\"Someone just completed the task {task.title}\"\n        query={\"channel\": \"task-notifications\", \"text\": message}\n        my_headers={\"Authorization\": f\"Bearer {my_token}\"}\n        requests.post(\"https://slack.com/api/chat.postMessage\", data=query, headers= my_headers)\n    \n        \n        return jsonify({\"task\": task.to_dict()}),200\n \n\n@tasks_bp.route(\"/<task_id>/mark_incomplete\", methods=[\"PATCH\"])\ndef incomplete_task(task_id):\n    task_id=validate_id_int(task_id)\n    task = Task.query.get(task_id)\n    patch_data=request.get_json()\n    # print(patch_data)\n    if not task:\n        return make_response(\"\", 404)\n    \n    else:\n        task.completed_at= None\n\n        db.session.commit()\n        return jsonify({\"task\": task.to_dict()}),200\n\n@tasks_bp.route(\"/<task_id>\", methods=[\"DELETE\"])\ndef delete_task(task_id):\n    task_id=validate_id_int(task_id)\n    \n    task = Task.query.get(task_id)\n\n    if task:\n        db.session.delete(task)\n        db.session.commit()\n        return {\"details\": f'Task {task_id} \"{task.title}\" successfully deleted'}, 200\n    else:\n        abort (404)\n\ndef validate_id_int(task_id):\n    try:\n        task_id = int(task_id)\n        return (task_id)\n    except:\n        abort(400, \"Error: Task ID needs to be a number\")\n\ngoals_bp = Blueprint(\"goals\", __name__, url_prefix=\"/goals\")\n\n@goals_bp.route(\"\", methods=[\"POST\"])\ndef create_goal():\n    request_data=request.get_json()\n    if \"title\" not in request_data:\n        return jsonify({\"details\": \"Invalid data\"}),400\n    new_goal=Goal(title=request_data[\"title\"])\n    db.session.add(new_goal)\n    db.session.commit()\n\n    return jsonify({\"goal\": new_goal.goal_to_dict()}), 201\n\n\n@goals_bp.route(\"\", methods=[\"GET\"])\ndef get_all_goals():\n    all_goals_response = []    \n    goals = Goal.query.all()\n    for goal in goals:\n        all_goals_response.append(goal.goal_to_dict())\n    return jsonify(all_goals_response), 200\n\n@goals_bp.route(\"/<goal_id>\", methods=[\"GET\", \"PUT\"])\ndef handle_one_goal(goal_id):\n    goal_id=validate_goal_id_int(goal_id)\n    goal = Goal.query.get(goal_id)\n    if not goal:\n        return make_response(\"\", 404)\n    if request.method==\"GET\":\n        return jsonify({\"goal\": goal.goal_to_dict()}), 200\n    elif request.method== \"PUT\":\n        request_data=request.get_json()\n        goal.title=request_data[\"title\"]\n    \n        db.session.commit()\n        return jsonify({\"goal\": goal.goal_to_dict()}),200\n\n@goals_bp.route(\"/<goal_id>\", methods=[\"DELETE\"])\ndef delete_goal(goal_id):\n    goal_id=validate_goal_id_int(goal_id)\n    \n    goal = Goal.query.get(goal_id)\n\n    if goal:\n        db.session.delete(goal)\n        db.session.commit()\n        return {\"details\": f'Goal {goal_id} \"{goal.title}\" successfully deleted'}, 200\n    else:\n        abort (404)\n\n\ndef validate_goal_id_int(goal_id):\n    try:\n        goal_id = int(goal_id)\n        return (goal_id)\n    except:\n        abort(400, \"Error: Task ID needs to be a number\")\n\n\n@goals_bp.route(\"/<goal_id>/tasks\", methods=[\"POST\"])\ndef create_goal_tasks(goal_id):\n    request_data=request.get_json()\n    goal = Goal.query.get(goal_id)\n    \n    task_ids=request_data[\"task_ids\"]\n  \n    for id in task_ids:\n        new_task = Task.query.get(id)\n        goal.tasks.append(new_task)\n\n        db.session.commit()\n\n    return jsonify({\"id\": goal.goal_id, \"task_ids\":[task.task_id for task in goal.tasks]}), 200\n\n@goals_bp.route(\"/<goal_id>/tasks\", methods=[\"GET\"])\ndef get_goal_tasks(goal_id):\n    goal = Goal.query.get(goal_id)\n    if not goal:\n        return make_response(\"\", 404)\n\n    return jsonify({\"id\": goal.goal_id, \"title\":goal.title, \"tasks\":[task.to_dict() for task in goal.tasks]}), 200\n\n    \n    ","sub_path":"app/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":5957,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"595252453","text":"import argparse\nimport os\nimport random\nimport sys\nimport numpy as np\nimport torch\n\nsys.path.append('%s/../' % os.path.dirname(os.path.realpath(__file__)))\n\nfrom src.mnist_model import MNIST_CNN\nfrom src.resnet_model import ResNet18\nfrom src.train_mnist import MMA_MNIST_Training_Parameters\nfrom src.train_cifar10 import MMA_CIFAR10_Training_Parameters\nfrom src.get_datasets import get_mnist_train_validate_loader\nfrom src.get_datasets import get_cifar10_train_validate_loader\n\nfrom Denfenses.DefenseMethods.DEEPFOOL_MMA_MART import DEEPFOOLMMAMARTDefense\nfrom Denfenses.DefenseMethods.DEEPFOOL_MMA_MART import add_indexes_to_loader\n\ndef main(args):\n    os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_index\n    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n    torch.backends.cudnn.deterministic = True\n    torch.backends.cudnn.benchmark = False\n    torch.manual_seed(args.seed)\n    if torch.cuda.is_available():\n        torch.cuda.manual_seed(args.seed)\n    np.random.seed(args.seed)\n    random.seed(args.seed)\n\n    # dataset：数据集名称MNIST/CIFAR10\n    dataset = args.dataset.upper()\n    assert dataset == 'MNIST' or dataset == 'CIFAR10'\n    # 获取训练的超参数，获取模型，划分训练集和验证集\n    # training_parameters：训练超参数\n    # model_framework：网络\n    # train_loader：训练集\n    # valid_loader：验证集\n    if dataset == 'MNIST':\n        training_parameters = MMA_MNIST_Training_Parameters\n        model_framework = MNIST_CNN().to(device)\n        batch_size = training_parameters['batch_size']\n        train_loader, valid_loader = get_mnist_train_validate_loader(dir_name='../data/MNIST/', batch_size=batch_size, valid_size=0.1,\n                                                                     shuffle=True)\n    else:\n        training_parameters = MMA_CIFAR10_Training_Parameters\n        model_framework = ResNet18().to(device)\n        batch_size = training_parameters['batch_size']\n        train_loader, valid_loader = get_cifar10_train_validate_loader(dir_name='../data/CIFAR10/', batch_size=batch_size, valid_size=0.1,\n                                                                       shuffle=True)\n    # 对训练集/验证集的每个分组进行编号\n    add_indexes_to_loader(train_loader)\n    add_indexes_to_loader(valid_loader)\n\n    # defense_name：防御名称\n    # nat_params：对抗训练的参数\n    defense_name = 'DEEPFOOL_MMA_MART'\n    deepfoolmmamart_params = {\n        'nb_iter': args.nb_iter,\n        'max_iters': args.max_iters,\n        'overshoot': args.overshoot,\n        'test_eps': args.test_eps,\n        'test_eps_iter': args.test_eps_iter,\n        'clean_loss_fn': args.clean_loss_fn,\n        'attack_loss_fn': args.attack_loss_fn,\n        'hinge_maxeps': args.hinge_maxeps,\n        'lamda': args.lamda,\n        'disp_interval': args.disp_interval\n    }\n\n    # 将参数传入DEEPFOOLMMAMART防御中\n    deepfoolmmamart = DEEPFOOLMMAMARTDefense(loader=train_loader, dataname=\"train\", verbose=True, model=model_framework, defense_name=defense_name, dataset=dataset, training_parameters=training_parameters, device=device,\n                     **deepfoolmmamart_params)\n    # 进行对抗训练\n    deepfoolmmamart.test_defense(validation_loader=valid_loader)\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser(description='The DEEPFOOLMMAMART Defenses')\n    # dataset：数据集名称MNIST/CIFAR10\n    # gpu_index：gpu数量，默认为0\n    # seed：随机种子，默认为100\n    # **********选择不同数据集MNIST/CIFAR10**********\n    parser.add_argument('--dataset', type=str, default='MNIST', help='the dataset (MNIST or CIFAR10)')\n    parser.add_argument('--gpu_index', type=str, default='0', help=\"gpu index to use\")\n    parser.add_argument('--seed', type=int, default=100, help='the default random seed for numpy and torch')\n    # **********根据数据集不同进行参数选择**********\n    parser.add_argument('--nb_iter', type=int, default=40, help='the iterations of PGD attacks')\n    parser.add_argument('--max_iters', type=float, default=50, help='the max iterations of DeepFool attacks')\n    parser.add_argument('--overshoot', type=float, default=0.02, help='the overshoot of DeepFool attacks')\n    parser.add_argument('--test_eps', type=float, default=0.3, help='the init perturbation of PGD attacks test')\n    parser.add_argument('--test_eps_iter', type=float, default=0.01, help='the parameter for calculating the step length of PGD attacks test')\n    # **********自由选择，与数据集无关**********\n    parser.add_argument('--clean_loss_fn', type=str, default='xent', help='the loss function')\n    parser.add_argument('--attack_loss_fn', type=str, default='slm', help='the loss function')\n    parser.add_argument('--hinge_maxeps', type=float, help='the max ANPGD perturbation')\n    parser.add_argument('--lamda', type=float, default=6.0, help='the regularization coefficient')\n    parser.add_argument('--disp_interval', type=int, default=100, help='the output node')\n    arguments = parser.parse_args()\n    main(arguments)\n","sub_path":"venv/Denfenses/DEEPFOOL_MMA_MART_Test.py","file_name":"DEEPFOOL_MMA_MART_Test.py","file_ext":"py","file_size_in_byte":5095,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"300834333","text":"'''\r\nVarious functions to load and permute the fashion-mnist dataset.\r\n\r\nauthor: Felix Geilert\r\n'''\r\n\r\n\r\nimport numpy as np\r\nimport tensorflow as tf\r\nfrom tensorflow.keras import datasets\r\nfrom tensorflow.keras import utils\r\nfrom bunch import Bunch\r\n\r\nfrom argonaut.utils import Summary\r\n#from . import utils as ds_utils\r\n\r\ndef _mnist_load(channels=1, norm=True, one_hot=True):\r\n  '''Loads the mnist data.'''\r\n  # load through keras\r\n  (x_train, y_train), (x_test, y_test) = datasets.fashion_mnist.load_data()\r\n\r\n  # define base parameters\r\n  img_width = 28\r\n  img_height = 28\r\n\r\n  # normalize data\r\n  if norm == True:\r\n    x_train = x_train.astype(\"float32\")\r\n    x_test = x_test.astype(\"float32\")\r\n    x_train /= 255.0\r\n    x_test /= 255.0\r\n  else:\r\n    x_train = x_train.astype(\"int32\")\r\n    x_test = x_test.astype(\"int32\")\r\n\r\n  # reshape input data\r\n  x_train = x_train.reshape(x_train.shape[0], img_width, img_height, 1)\r\n  x_test = x_test.reshape(x_test.shape[0], img_width, img_height, 1)\r\n  if channels == 3:\r\n    x_train = np.tile(x_train, (1, 1, 1, 3))\r\n    x_test = np.tile(x_test, (1, 1, 1, 3))\r\n\r\n  # one hot encode outputs\r\n  if one_hot == True:\r\n    y_train = utils.to_categorical(y_train)\r\n    y_test = utils.to_categorical(y_test)\r\n    num_classes = y_test.shape[1]\r\n  else:\r\n    num_classes = np.unique(y_train).shape[0]\r\n    print(num_classes)\r\n\r\n  return x_train, y_train, x_test, y_test, num_classes\r\n\r\ndef fashion_mnist(channels=1, norm=True, one_hot=True, summary=None, name=None):\r\n  '''Loads fashion mnist.\r\n\r\n  Args:\r\n    norm (bool): Defines if the values should be normalized between 0 and 1 (floats)\r\n    channels (int): Defines the number of channels to use (options: 1 or 3)\r\n    one_hot (bool): Defines if output should be encoded as one_hot categoricals\r\n    summary (Summary): Summary item to insert the operation\r\n    name (str): Name of the operation (default: exclude)\r\n\r\n  Returns:\r\n    Bunch object that contains the following keywords:\r\n    - test: contains `x` and `y` for the output values\r\n    - train: contains `x` and `y` for the output values\r\n    - size: size of the images\r\n    - num_classes: Integer of the number of different classes\r\n    - class_names: Names of the classes if available\r\n  '''\r\n  if summary is None:\r\n    summary = Summary()\r\n  # load through keras\r\n  x_train, y_train, x_test, y_test, num_classes = _mnist_load(channels, norm, one_hot)\r\n\r\n  # add to summary\r\n  summary.add(\"data loading\", \"loaded fashion-mnist dataset\", {\"channels\": channels, \"one-hot\": one_hot, \"normalized\": norm})\r\n\r\n  # create bunch package\r\n  return Bunch({\r\n    'name': 'fashionmnist',\r\n    'test': Bunch({'x': x_test, 'y': y_test}),\r\n    'train': Bunch({'x': x_train, 'y': y_train}),\r\n    'type': 'preloaded',\r\n    'size': (28, 28, x_train.shape[-1]),\r\n    'num_classes': num_classes,\r\n    'class_names': None,\r\n    'unique_classes': np.unique(y_train, axis=0)\r\n  }), summary\r\n\r\n\r\n# define variables in global scope\r\ng_x_train = None\r\ng_y_train = None\r\ng_x_test = None\r\ng_y_test = None\r\n\r\ndef _mnist_gen(mode, channels, norm, one_hot, load_once=True):\r\n  '''Defines generator around data.'''\r\n  if load_once == False:\r\n    x_train, y_train, x_test, y_test, num_classes = _mnist_load(channels, norm, one_hot)\r\n  else:\r\n    # set variables in global scope to avoid reloading\r\n    global g_x_train, g_y_train, g_x_test, g_y_test\r\n    if g_x_train is None:\r\n      g_x_train, g_y_train, g_x_test, g_y_test, num_classes = _mnist_load(channels, norm, one_hot)\r\n    x_train, y_train, x_test, y_test = (g_x_train, g_y_train, g_x_test, g_y_test)\r\n  if not isinstance(mode, str):\r\n    mode = mode.decode('UTF-8')\r\n  i = 0\r\n  if mode == \"train\":\r\n    while i < x_train.shape[0]:\r\n      img = x_train[i, ...]\r\n      lbl = y_train[i, ...]\r\n      yield img, lbl\r\n      i += 1\r\n  if mode == \"test\":\r\n    while i < x_test.shape[0]:\r\n      img = x_test[i, ...]\r\n      lbl = y_test[i, ...]\r\n      yield img, lbl\r\n      i += 1\r\n\r\ndef fashion_mnist_generator(channels=1, norm=True, one_hot=True, load_once=True, summary=None):\r\n  '''Creates generators for the relevant data.'''\r\n  if summary is None:\r\n    summary = Summary()\r\n\r\n  # add to summary\r\n  summary.add(\"data loading\", \"loaded mnist (generator) dataset\", {\"channels\": channels, \"one-hot\": one_hot, \"normalized\": norm, \"load_once\": load_once})\r\n\r\n  # retrieve data\r\n  gen = _mnist_gen(\"train\", channels, norm, one_hot, load_once)\r\n  ucls = {}\r\n  for x, y in gen:\r\n    if str(y) not in ucls:\r\n      ucls[str(y)] = y\r\n\r\n  # create bunch output\r\n  return Bunch({\r\n    'name': 'fashionmnist',\r\n    'train': lambda: _mnist_gen(\"train\", channels, norm, one_hot, load_once),\r\n    'test': lambda: _mnist_gen(\"test\", channels, norm, one_hot, load_once),\r\n    'type': 'generator',\r\n    'size': (28, 28, channels),\r\n    'num_classes': 10,\r\n    'class_names': None,\r\n    'unique_classes': np.array(list(ucls.values()))\r\n  }), summary\r\n\r\ndef fashion_mnist_tfdata(channels=1, norm=True, one_hot=True, shuffle=None, summary=None):\r\n  '''Loads mnist as a tf.data dataset.'''\r\n  if summary is None:\r\n    summary = Summary()\r\n\r\n  # define size\r\n  img_width = 28\r\n  img_height = 28\r\n\r\n  # create generators\r\n  shape = () if one_hot == False else (10,)\r\n  ds_train = tf.data.Dataset.from_generator(_mnist_gen, args=[\"train\", channels, norm, one_hot], output_types=(tf.float32, tf.float32),\r\n    output_shapes=([img_width, img_height, channels], shape))\r\n  ds_test  = tf.data.Dataset.from_generator(_mnist_gen, args=[\"test\",  channels, norm, one_hot], output_types=(tf.float32, tf.float32),\r\n    output_shapes=([img_width, img_height, channels], shape))\r\n\r\n  # check for shuffle\r\n  if shuffle is not None:\r\n    ds_train = ds_train.shuffle(shuffle)\r\n    ds_test  = ds_test.shuffle(shuffle)\r\n\r\n  # add to summary\r\n  summary.add(\"data loading\", \"loaded mnist (tfdata) dataset\", {\"channels\": channels, \"one-hot\": one_hot, \"normalized\": norm, \"shuffle\": shuffle})\r\n\r\n  # create bunch package\r\n  return Bunch({\r\n    'train': ds_train,\r\n    'test': ds_test,\r\n    'type': 'tfdata',\r\n    'size': (img_width, img_height, channels),\r\n    #'steps': Bunch({'train': None, 'test': None}),\r\n    'num_classes': 10,\r\n    'class_names': None,\r\n    'unique_classes': [c.numpy() for c in ds_train.map(lambda x, y: tf.cast(y, tf.int32)).apply(tf.data.experimental.unique())]\r\n  }), summary\r\n","sub_path":"argonaut/datasets/fashion_mnist.py","file_name":"fashion_mnist.py","file_ext":"py","file_size_in_byte":6331,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"407865180","text":"\"\"\"Examples of dictionaries.\"\"\"\n\n# Establish a type with the following syntax\n# dict[key type, value type]\n# empty dictionary literal is { }\n\nplayers: dict[str, int] = {}\n\n# insert a new key-value pair\nplayers[\"Brooks\"] = 15\n# Reference keys inside subscription notation, associated \n# values are assigned on RHS of assignment operator \nplayers[\"Love\"] = 2\nplayers[\"Bacot\"] = 4\n\nplayers[\"Bacot\"] += 1\nprint(players)\n\nnew_dict: dict[str, str] = {\"Kaki\": \"blue\", \"Lasya\": \"Green\"}\nnew_dict[\"Matt\"] = \"Red\"\nprint(new_dict)\n\nfor key in new_dict:\n    print(f\"The key is {key} and the value is {new_dict[key]}\")\n\nfor val in new_dict.value():\n    print(f\"The value is {val}\")\n\n\ndef passes(students: dict[str, float]) -> list[str]:\n    \"\"\"Given student info, who passes?\"\"\"\n    result: list[str] = []\n    for student in students:\n        if students[student] > 60:\n            result.append(student)\n    return result \n\ns: dict[str, float] = {\"Andrew\": 100.0, \"Shaurik\": 89.0, \"Claire\": 40.0}\nprint(passes(s))\n\nteachers_pet: list[str] = [\"Claire\", \"Jasper\"]\n\nfor pet in teachers_pet:\n    # initalize or increment \n    if pet in s:\n        # increment since the pet key is valid, and exists in the s dictionary\n        s[pet] += 50.0\n    else:\n        # initialing bc the key is not yet in the dictionary\n        s[pet] = 65.0\nprint(s)\n        \nfrequency_table: dict[str, int] = {}\n# for each color in input dictionary\n# if the color is already in the table, increment its value\n# if not, add it to the table with a starting value of 1\n\na: list[int] = [1, 2, 3, 4, 5, 6]\nb: list[int] = [2, 4, 6]\n\nfor e in a:\n    if e in b:\n        print(f\"Yes {e} is in list b!\")\n\ndef max_algo(xs: list[int]) -> int:\n    # make the assumption that the list is not empty\n    result: int = xs[0]\n    for item in xs:\n        if item > result:\n            result = item\n    return result\n\n    \n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"lessons/ls18.py","file_name":"ls18.py","file_ext":"py","file_size_in_byte":1878,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"207866804","text":"import torch\nimport numpy as np \nfrom torch.utils.data import Sampler\nimport kaldiio\nfrom utils import pad_list\nfrom torch.utils.data import DataLoader\nimport configargparse\n\n    \ndef create_loader(\n    data: dict ,\n    params: configargparse.Namespace,\n    pin_memory: bool=False,\n    min_batch_size: int=1 ,\n    shortest_first: bool=False):\n    \"\"\"\n    Creates batches with different batch sizes which maximizes\n    the number of bins up to `batch_bins`.\n    :param Dict[str, Dict[str, Any]] sorted_data: dictionary loaded from data.json\n    :param int batch_bins: Maximum frames of a batch\n    :param int min_batch_size: minimum batch size (for multi-gpu)\n    :param bool shortest_first: Sort from batch with shortest samples\n        to longest if true, otherwise reverse\n    :returns: List[Tuple[str, Dict[str, List[Dict[str, Any]]]] list of batches\n    \"\"\"\n    batch_bins = params.batch_bins\n    print(\"Batch Bins : {}\".format(batch_bins))\n    sorted_data = sorted(\n            data.items(),\n            key=lambda data: int(data[1][\"input\"][0][\"shape\"][0]),\n            reverse=not shortest_first,\n        )\n    if batch_bins <= 0:\n        raise ValueError(f\"invalid batch_bins={batch_bins}\")\n    length = len(sorted_data)\n    idim = int(sorted_data[0][1][\"input\"][0][\"shape\"][1])\n    odim = int(sorted_data[0][1][\"output\"][0][\"shape\"][1]) if \"output\" in sorted_data[0][1].keys() else 0\n    minibatches = []\n    start = 0\n    n = 0\n    while True:\n        # Dynamic batch size depending on size of samples\n        b = 0\n        next_size = 0\n        max_olen = 0\n        while next_size < batch_bins and (start + b) < length:\n            ilen = int(sorted_data[start + b][1][\"input\"][0][\"shape\"][0]) * idim\n            olen = int(sorted_data[start + b][1][\"output\"][0][\"shape\"][0]) * odim if \"output\" in sorted_data[0][1].keys() else 0\n            if olen > max_olen:\n                max_olen = olen\n            next_size = (max_olen + ilen) * (b + 1)\n            if next_size <= batch_bins:\n                b += 1\n            elif next_size == 0:\n                raise ValueError(\n                    f\"Can't fit one sample in batch_bins ({batch_bins}): \"\n                    f\"Please increase the value\"\n                )\n        end = min(length, start + max(min_batch_size, b))\n        batch = [element[0] for element in sorted_data[start:end]]\n        if shortest_first:\n            batch.reverse()\n        minibatches.append(batch)\n        # Check for min_batch_size and fixes the batches if needed\n        i = -1\n        while len(minibatches[i]) < min_batch_size:\n            missing = min_batch_size - len(minibatches[i])\n            if -i == len(minibatches):\n                minibatches[i + 1].extend(minibatches[i])\n                minibatches = minibatches[1:]\n                break\n            else:\n                minibatches[i].extend(minibatches[i - 1][:missing])\n                minibatches[i - 1] = minibatches[i - 1][missing:]\n                i -= 1\n        if end == length:\n            break\n        start = end\n        n += 1\n    lengths = [len(x) for x in minibatches]\n    print(\" #Utts: {} | Created {} minibatches containing {} to {} samples, and on average {} samples\".format(len(sorted_data),len(minibatches),min(lengths),max(lengths),int(np.mean(lengths)) ) )\n    \n    dataset = ASRDataset(data,params)\n    loader = DataLoader(dataset=dataset,batch_sampler=minibatches,num_workers=params.nworkers,collate_fn=dataset.collate_function)\n\n    return dataset,loader,minibatches\n              \n    \n        \nclass ASRDataset(torch.utils.data.Dataset):\n    def __init__(self,data:dict,params: configargparse.Namespace):\n        \"\"\"\n        :param dict data- The json data file dictionary\n        :param Namespace params- Training options\n        \"\"\"\n        self.data = data\n        self.audio_pad = params.audio_pad \n        self.text_pad = params.text_pad\n    \n    def __len__(self):\n        \"\"\"\n        Returns the number of examples in the dataset\n        \"\"\"\n        return len(self.data.keys())\n\n    def __getitem__(self,idx: str):\n        \"\"\"\n        Retrieves an item from the dataset given the index\n        :param str idx- Utterance ID key\n        :returns np.array audio_feat- Speech features\n        :returns list feat_len- Speech sequence length\n        :returns np.array target- Output sequence \n        :returns list target_len- Token sequence length\n        :returns str idx- Utterance ID key\n        \"\"\"\n        audio_feat = kaldiio.load_mat(self.data[idx][\"input\"][0][\"feat\"])\n        feat_len = self.data[idx][\"input\"][0][\"shape\"][0]\n        target = np.array([int(x) for x in self.data[idx][\"output\"][0][\"tokenid\"].split(' ')]) if \"output\" in self.data[idx].keys() else None\n        target_len = len(target) if target is not None else 0\n        return (audio_feat,feat_len,target,target_len,idx)\n        \n    def collate_function(self,batch):\n        \"\"\"\n        Retrieves an item from the dataset given the index\n        :param generator batch- Batch of data\n        :returns np.array padded_feats- Speech features\n        :returns list feat_lens- Speech sequence length\n        :returns np.array padded_targets- Output sequence \n        :returns list target_lens- Token sequence length\n        :returns list utt_keys- Utterance ID key\n        \"\"\"\n        device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n        padded_feats = pad_list([torch.from_numpy(x[0]).float() for x in batch],self.audio_pad).to(device)\n        padded_targets = pad_list([torch.from_numpy(x[2]).long() for x in batch],self.text_pad).to(device) if batch[0][2] is not None else None\n        feat_lens = [x[1] for x in batch]\n        target_lens = [x[3] for x in batch] if batch[0][2] is not None else None\n        utt_keys = [x[4] for x in batch]\n        return padded_feats,feat_lens,padded_targets,target_lens,utt_keys\n","sub_path":"loader.py","file_name":"loader.py","file_ext":"py","file_size_in_byte":5851,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"525110079","text":"from config_learn import *\nfrom scm import *\n# from brain import *\nfrom brainbranched import *\nfrom brainnonbranch import *\n\nimport logging\nfrom logging import StreamHandler, DEBUG, Formatter, FileHandler, getLogger\n\nimport matplotlib.pyplot as plt\nimport copy\nimport os\nimport datetime\nfrom datetime import datetime\nimport csv\nimport pickle\nimport gc\nimport time\nimport psutil\nimport itertools\nimport numpy as np\n\n\nclass Agent():\n    def __init__(self,scenario, env,num_actions_per_product, state_real:bool, obs_only:bool,sample_val:bool,\n                 op_trans_alloc):\n        self.scenario = scenario\n        self.Ctrans = Ctrans\n        self.env = env\n        self.season = env.season\n        self.error_rate = env.error_rate\n        self.num_ret = env.num_ret\n        self.num_products = env.num_products\n        self.obs_only = obs_only\n        self.sample_val = sample_val\n        self.op_trans_alloc = op_trans_alloc\n\n        # state space\n        # print(state_real)\n        self.state_real = state_real\n        if self.state_real:\n            if obs_only:\n                self.num_states_per_product = 5\n            else:\n                self.num_states_per_product = 5\n        else:\n            if obs_only:\n                self.num_states_per_product = 5\n            else:\n                self.num_states_per_product = 7\n\n        print('num_states:', self.num_states_per_product)\n\n        self.num_states = self.num_states_per_product * self.num_products * self.num_ret\n        if ESTIMATE_OTHERS:\n            self.num_states += 1\n\n        # action space\n        self.num_actions_per_product = num_actions_per_product\n        self.num_actions = self.num_actions_per_product ** (self.num_products * self.num_ret)#商品数の数だけactionスペースは増える\n\n        self.num_time = env.num_time\n        self.version = \"\"\n        self.comp_at =  datetime.now().strftime(\"%Y%m%d%H%M%S\")\n        self.model_path = 'model/weight.pth'\n        self.scm_best_path = 'scm/best_scm.pth'\n        self.scm_val_path = 'scm/val_scm.pth'\n\n        self.op_trans_alloc = env.op_trans_alloc\n\n    def write_to_csv(self, out_list):\n        self.file = 'result/{}_{}.csv'.format(self.comp_at, self.version)\n        f = open(self.file, 'a')\n        writer = csv.writer(f, lineterminator='\\n')\n        writer.writerow(out_list)\n        f.close()\n\n    def write_to_csv_val(self, out_list):\n        self.file_val = 'result_val/{}_{}_val.csv'.format(self.comp_at, self.version)\n        f = open(self.file_val, 'a')\n        writer = csv.writer(f, lineterminator='\\n')\n        writer.writerow(out_list)\n        f.close()\n\n    def _set_logger(self):\n        # Logger Setting\n        self.comp_at = datetime.now().strftime(\"%Y%m%d%H%M%S\")\n\n        self.model_path = 'model/{}_{}_weight.pth'.format(self.comp_at, self.version)\n        self.scm_best_path = 'scm/{}_{}_best_scm.pth'.format(self.comp_at, self.version)\n        self.scm_val_path = 'scm/{}_{}_val_scm.pth'.format(self.comp_at, self.version)\n        self.LOG =  'log/{}_{}.log'.format(self.comp_at, self.version)\n\n        self.logger = getLogger(__name__)\n        log_fmt = Formatter('%(asctime)s %(lineno)d [%(levelname)s][%(funcName)s] %(message)s ')\n        handler = StreamHandler()\n        handler.setLevel(logging.INFO)\n        handler.setFormatter(log_fmt)\n        self.logger.addHandler(handler)\n        self.logger.setLevel(logging.DEBUG)\n        handler = FileHandler(self.LOG, 'a')\n        handler.setLevel(logging.DEBUG)\n        handler.setFormatter(log_fmt)\n        self.logger.addHandler(handler)\n        \n        self.logger.info(self.LOG)\n        \n    ''' Q-Leavrning Agent Functions '''\n    def update_q_function(self, episode):\n        '''Q関数を更新する'''\n        self.brain.replay(episode)\n\n    def memorize_multi(self, idx, episode, step, state, action, state_next, reward):\n        '''memoryオブジェクトに、state, action, state_next, rewardの内容を保存する'''\n        ''' multi-step learning用に変更'''\n        ''' RNN対応するためシーケンシャルに経験を貯めるためのepisode, stepを記憶'''\n        # self.brain.memory.push(state, action, state_next, reward)\n        self.brains[idx].add_memory(Transition(episode, step, state, action, state_next, reward))\n\n    def memorize(self, episode, step, state, action, state_next, reward, reward_tuple):\n        '''memoryオブジェクトに、state, action, state_next, rewardの内容を保存する'''\n        ''' multi-step learning用に変更'''\n        ''' RNN対応するためシーケンシャルに経験を貯めるためのepisode, stepを記憶'''\n        # self.brain.memory.push(state, action, state_next, reward)\n        self.brain.add_memory(Transition(episode, step, state, action, state_next, reward, reward_tuple))\n\n    def update_target_q_function(self):\n        '''Target Q-NetworkをMain Q-Networkと同じに更新'''\n        self.brain.update_target_q_network()\n        \n    def memorize_td_error(self, td_error):  # PrioritizedExperienceReplayで追加\n        '''TD誤差メモリにTD誤差を格納'''\n        self.brain.td_error_memory.push(td_error)\n        \n    def update_td_error_memory(self):  # PrioritizedExperienceReplayで追加\n        '''TD誤差メモリに格納されているTD誤差を更新する'''\n        if self.option_nn == 10:\n            for brain in self.brains:\n                brain.update_td_error_memory()\n        else:\n            self.brain.update_td_error_memory()\n    ''' Q-Learning Agent Functions <END> '''\n        \n    def load_weight(self, brain):\n        param = torch.load(PATH)\n        brain.main_q_network.load_state_dict(param)\n        brain.target_q_network.load_state_dict(param)\n\n    def sim_s_q_policy_memorize(self, with_forcst, pred_fresh, memorize, episode,trans_cost_mode):\n        # self.logger.info('Simulate static (s,Q)policy...  with Forecast : {}'.format(with_forcst))\n        self.env_sq = copy.deepcopy(self.env)\n        if pred_fresh:\n            self.env_sq.reflesh_pred()  # 予測値のリフレッシュ\n        self.env_sq.create_result_memory()  # 結果格納用のResultMemoryを作る\n        episode_reward = 0\n\n        observation = self.reset(self.num_states)\n        state = observation  # 観測をそのまま状態sとして使用\n        state = torch.from_numpy(state).type(torch.FloatTensor)\n        state = torch.unsqueeze(state, 0).to(device)\n\n        for step in range(self.num_time):\n            action = []\n            # actionの決定\n            for i, node in enumerate(self.env_sq.nodes):\n                # action.extend(node.simulate_s_q_policy(step, with_forcst))\n                action.extend(node.simulate_s_s_policy(step, with_forcst,trans_cost_mode))\n\n            if memorize:\n                action = np.clip(np.array(action), a_min=0,\n                                 a_max=self.num_actions_per_product - 1)  # num_action以上にはならないように制御する\n                action_origin = self.get_action_idx_from_list(action)\n                action_origin = torch.LongTensor([[action_origin]])\n                # print(action, action_origin)\n\n            action_converted = np.array(action).reshape(self.num_products, self.num_ret)\n\n            done, reward, _ = self.env_sq.step(action_converted, step)\n            if done:  # 最後\n                state_next = None\n            else:\n                state_next, e_state_next = self.get_next_state(step, self.env_sq.nodes, False)\n\n            if step > IGNORE_STEP:\n                episode_reward += reward\n                if memorize:\n                    self.memorize(\n                        episode,\n                        step,\n                        state,\n                        action_origin.to(device),\n                        state_next,\n                        - reward.to(device)\n                    )\n                    if step % 90 == 0:\n                        # print(episode, step)\n                        self.update_q_function(episode)\n\n            state = state_next\n            # 終了時の処理\n            if (done) and (memorize):\n                # TD誤差メモリの中身を更新する\n                self.update_td_error_memory()\n                # DDQNで追加、2試行に1度、Target Q-NetworkをMainと同じにコピーする\n                if (episode % 2 == 0):\n                    self.update_target_q_function()\n                break\n\n        if not memorize:\n            self.logger.info('EpisodeReward: %d' % (episode_reward))\n            self.env_sq.calc_episode_performance()\n            self.env_sq.episode_performance_print(self.logger)\n        if (memorize) and (episode % 50) == 0:\n            self.logger.info('(%d)EpisodeReward: %d  Loss: %d' % (\n            episode, episode_reward, (np.array(self.brain.td_error_memory.memory) ** 2).mean()))\n\n    def sim_s_q_policy(self, with_forcst,pred_fresh,memorize,episode,trans_cost_mode):\n        if memorize:\n            self.sim_s_q_policy_memorize(with_forcst,pred_fresh,memorize,episode,trans_cost_mode)\n\n        # self.logger.info('Simulate static (s,Q)policy...  with Forecast : {}'.format(with_forcst))\n        self.env_sq = copy.deepcopy(self.env)\n        if pred_fresh:\n            self.env_sq.reflesh_pred() #予測値のリフレッシュ\n        self.env_sq.create_result_memory() #結果格納用のResultMemoryを作る\n        episode_reward = 0\n\n        for step in range(self.num_time):\n            action = []\n            # actionの決定\n            for i, node in enumerate(self.env_sq.nodes):\n                # action.extend(node.simulate_s_q_policy(step, with_forcst))\n                action.extend(node.simulate_s_s_policy(step, with_forcst,trans_cost_mode))\n\n            action_converted = np.array(action).reshape(self.num_products,self.num_ret)\n            \n            done, reward ,_ = self.env_sq.step(action_converted, step)\n            if step > IGNORE_STEP:\n                episode_reward += reward\n\n        if not memorize:\n            self.logger.info(f'EpisodeReward: {episode_reward}')\n            self.env_sq.calc_episode_performance()\n            self.env_sq.episode_performance_print(self.logger)\n\n    def set_scs_params(self, params):\n        self.env_scs = copy.deepcopy(self.env)\n        for i, node in enumerate(self.env_scs.nodes):\n            node.set_scs_params(params[i])\n\n    def optimize_scs_ga(self):\n        import random\n        from deap import base\n        from deap import creator\n        from deap import tools\n\n        print_setting_scs(self.logger)\n        def evaluateInd(individual):\n            self.set_scs_params(np.array(individual).reshape(self.num_products,3))\n            val = self.sim_scs_policy()\n            return val\n\n        creator.create(\"FitnessMin\", base.Fitness, weights=(-1.0,))\n        creator.create(\"Individual\", list, fitness=creator.FitnessMin)\n        IND_SIZE = int(self.num_products * 3)\n        toolbox = base.Toolbox()\n        INT_MIN, INT_MAX = 0, 20\n        # s_MIN, s_MAX = 5, 10\n        # c_MIN, c_MAX = 5, 10\n        # S_MIN, S_MAX = 5, 10\n\n        # Attributeを生成をする関数を定義(0,1のランダムを選ぶ)\n        toolbox.register(\"attr_int\", random.randint, INT_MIN, INT_MAX)\n        # 個体を生成する関数を定義(Individualクラスでattr_boolの値を100個持つ)\n        toolbox.register(\"individual\", tools.initRepeat, creator.Individual,\n                         toolbox.attr_int, IND_SIZE)\n        # 集団を生成する関数を定義(個体を持つlist)\n        toolbox.register(\"population\", tools.initRepeat, list, toolbox.individual)\n\n        toolbox.register(\"mate\", tools.cxTwoPoint)\n        toolbox.register(\"mutate\", tools.mutGaussian, mu=0, sigma=1, indpb=0.2)\n        toolbox.register(\"select\", tools.selTournament, tournsize=3)\n        toolbox.register(\"evaluate\", evaluateInd)\n\n        # 初期集団を生成する\n        pop = toolbox.population(n=self.num_products*50)\n\n        print(\"進化開始\")\n        # 初期集団の個体を評価する\n        fitnesses = list(map(toolbox.evaluate, pop))\n        for ind, fit in zip(pop, fitnesses):  # zipは複数変数の同時ループ\n            # 適合性をセットする\n            ind.fitness.values = fit\n\n        print(\"  %i の個体を評価\" % len(pop))\n\n        # 進化計算開始\n        for g in range(NGEN):\n            print(\"-- %i 世代 --\" % g)\n            # 選択\n            # 次世代の個体群を選択\n            offspring = toolbox.select(pop, len(pop))\n            # 個体群のクローンを生成\n            offspring = list(map(toolbox.clone, offspring))\n\n            # 交叉\n            # 偶数番目と奇数番目の個体を取り出して交差\n            for child1, child2 in zip(offspring[::2], offspring[1::2]):\n                if random.random() < CXPB:\n                    toolbox.mate(child1, child2)\n                    # 交叉された個体の適合度を削除する\n                    del child1.fitness.values\n                    del child2.fitness.values\n\n            # 変異\n            for mutant in offspring:\n                if random.random() < MUTPB:\n                    toolbox.mutate(mutant)\n                    del mutant.fitness.values\n\n            # 適合度が計算されていない個体を集めて適合度を計算\n            invalid_ind = [ind for ind in offspring if not ind.fitness.valid]\n            fitnesses = map(toolbox.evaluate, invalid_ind)\n            for ind, fit in zip(invalid_ind, fitnesses):\n                ind.fitness.values = fit\n\n            print(\"  %i の個体を評価\" % len(invalid_ind))\n\n            # 次世代群をoffspringにする\n            pop[:] = offspring\n\n            # すべての個体の適合度を配列にする\n            fits = [ind.fitness.values[0] for ind in pop]\n\n            length = len(pop)\n            mean = sum(fits) / length\n            sum2 = sum(x * x for x in fits)\n            std = abs(sum2 / length - mean ** 2) ** 0.5\n\n            print(\"  Min %s\" % min(fits))\n            print(\"  Max %s\" % max(fits))\n            print(\"  Avg %s\" % mean)\n            print(\"  Std %s\" % std)\n\n        print(\"-- 進化終了 --\")\n\n        best_ind = tools.selBest(pop, 1)[0]\n        self.logger.info(\"最も優れていた個体: %s, %s\" % (best_ind, best_ind.fitness.values))\n\n        return best_ind\n\n    def optimize_scs_params(self, all_cond):\n        res = []\n        best_reward = float('inf')\n\n        all_params = []\n        for param in all_cond:\n            all_params.append(range(param['s']['lb'], param['s']['ub']))\n            all_params.append(range(param['c']['lb'], param['c']['ub']))\n            all_params.append(range(param['S']['lb'], param['S']['ub']))\n\n        tot = len(list(itertools.product(*all_params)))\n        print(f'total # of combinations : {tot}')\n\n        for idx, param in enumerate(itertools.product(*all_params)):\n            self.set_scs_params(np.array(param).reshape(2,3))\n            val = self.sim_scs_policy()\n            res.append(val)\n            if val < best_reward:\n                best_reward = val\n                best_param = param\n                print(f'({idx}/{tot}): current best: {best_reward}, param={best_param}')\n\n        return best_reward, best_param, res\n\n    def sim_scs_policy(self, draw_result=False):\n        ''' (S, c, s)方策をシミュレートする'''\n        self.env_scs.create_result_memory()  # 結果格納用のResultMemoryを作る\n        episode_reward = 0\n\n        for step in range(self.num_time):\n            action = []\n\n            # sを下回る商品が一つでもあれば必ず発注する\n            flg = False\n            for i, node in enumerate(self.env_scs.nodes):\n                for ret in node.iRet:\n                    if USE_LT_INV:\n                        if ret.inv_lt[step] <= ret.scs_params['s']:\n                            flg = True\n                            break\n                    else:\n                         if ret.inv_position[step] <= ret.scs_params['s']:\n                             flg = True\n                             break\n            # actionの決定\n            for i, node in enumerate(self.env_scs.nodes):\n                for ret in node.iRet:\n                    action.extend(node.simulate_scs_policy(step, flg))\n\n            action_converted = np.array(action).reshape(self.num_products, self.num_ret)\n\n            done, reward, _ = self.env_scs.step(action_converted, step)\n            if step > IGNORE_STEP:\n                episode_reward += reward\n\n        if draw_result:\n            self.logger.info(f'EpisodeReward: {episode_reward}')\n            self.env_scs.calc_episode_performance()\n            self.env_scs.episode_performance_print(self.logger)\n\n        return episode_reward\n\n    def reset(self, num_states):\n        return np.zeros(num_states)\n\n    def select_action(self, state):\n        state = torch.from_numpy(state).float()\n        probs, state_value = self.policy(state)\n        m = Categorical(probs)\n        action = m.sample()\n        self.policy.saved_actions.append(SavedAction(m.log_prob(action), state_value))\n        return action.item()\n\n    def finish_episode(self, gamma, optimizer):\n        ''' Actir-Critic '''\n        eps = np.finfo(np.float32).eps.item()\n        \n        R = 0\n        saved_actions = self.policy.saved_actions\n        policy_losses = []\n        value_losses = []\n        returns = []\n        for r in self.policy.rewards[::-1]:\n            R = r + gamma * R\n            returns.insert(0, R)\n        returns = torch.tensor(returns)\n        returns = (returns - returns.mean()) / (returns.std() + eps)\n        for (log_prob, value), R in zip(saved_actions, returns):\n            advantage = R - value.item()\n            policy_losses.append(-log_prob * advantage)\n            value_losses.append(F.smooth_l1_loss(value, torch.tensor([R])))\n        optimizer.zero_grad()\n        loss = torch.stack(policy_losses).sum() + torch.stack(value_losses).sum()\n        loss.backward()\n        optimizer.step()\n        del self.policy.rewards[:]\n        del self.policy.saved_actions[:]\n\n    def learn_policy_gradient(self,pred_fresh):\n        def discount_rewards(rewards, gamma=0.99):\n            r = np.array([gamma**i * rewards[i] \n                          for i in range(len(rewards))])\n            # Reverse the array direction for cumsum and then\n            # revert back to the original order\n            r = r[::-1].cumsum()[::-1]\n            return r - r.mean()\n        \n        self.num_states_per_product = 3 # state を変更したらこれも変更する\n        self.num_states = self.num_states_per_product * self.num_products * self.num_ret\n        self.num_actions_per_product = 3\n        self.num_actions =  self.num_actions_per_product ** (self.num_products * self.num_ret)#商品数の数だけactionスペースは増える\n    \n        # self.pe = ActorPolicy(self.num_states, self.num_actions)\n        self.policy = ActorCriticPolicy(self.num_states, self.num_actions)\n        optimizer = optim.Adam(self.policy.parameters(), lr=3e-2)\n\n        # logger\n        log_str = \"_AC_Nproducts{}_Nret{}_Nstates{}_Nactions{}\".format(self.num_products, self.num_ret,self.num_states, self.num_actions)\n        self._set_logger(log_str)\n        self.logger.info('')\n\n        '''シミュレーションの実行 '''\n        for episode in range(NUM_EPISODES):  # 最大試行数分繰り返す\n                    \n            episode_reward = 0\n            self.env_sim = copy.deepcopy(self.env) #RetailerとWHインスタンスの初期状態をコピーする\n            self.env_sim.create_result_memory() #結果格納用のResultMemoryを作る\n            if pred_fresh:\n                self.env_sim.reflesh_pred()\n\n            # 初期値を得る\n            observation = self.reset(self.num_states)            \n            state = observation  # 観測をそのまま状態sとして使用\n            state = np.array(state)\n            e_state = state\n\n            for step in range(MAX_STEPS):  # 1エピソードのループ\n                # print(state)\n                action_origin = self.select_action(state)\n                # action_probs = self.pe.predict(state).detach().numpy()\n                # action_origin = np.random.choice(self.num_actions, p=action_probs)\n                action = self.convert_action(action_origin, True) # Retailerレベルでの発注量を計算する                \n                done, reward = self.env_sim.step(action, step)\n                self.policy.rewards.append(-reward)\n\n                if done : #最後\n                    state_next = None\n                    break;\n                else:\n                    state_next, e_state_next = self.get_next_state(step,self.env_sim.nodes, True)\n\n                if step > IGNORE_STEP:\n                    episode_reward += reward\n\n                # 観測の更新\n                state, e_state = state_next,e_state_next\n                state = np.array(state)\n                e_state = np.array(e_state)\n\n            self.finish_episode(GAMMA, optimizer)\n            \n            if episode % 50 ==0:\n                # self.logger.info('(%d) EpisodeReward: %d Loss: %d' % (episode, episode_reward, (np.array(self.brain.td_error_memory.memory)**2).mean()))\n                self.logger.info('(%d) EpisodeReward: %d' % (episode, episode_reward))\n                self.env_sim.calc_episode_performance()\n                self.env_sim.episode_performance_print(self.logger)\n            \n    def learn_initialize(self, option_nn):\n        # logger\n        print('in initialize:', self.state_real)\n\n        if self.state_real:\n            str_pomdp = \"POMDP\"\n        else:\n            str_pomdp = \"notPOMDP\"\n        version = \"{}_OptionNN{}_InvCap{}_dFac{}_ObsOnly{}_SampleVal{}_Nproducts{}_Nret{}_Nstates{}_Nactions{}_Nsteps{}_Error{}_Season{}_TransAlloc{}_CTrans{}\".format(\n            self.scenario, option_nn, self.env.inv_cap, self.env.d_factor, self.obs_only,self.sample_val, self.num_products,\n            self.num_ret,self.num_states, self.num_actions_per_product, NSTEP, self.error_rate, self.season, self.op_trans_alloc, self.Ctrans)\n        self.version = version\n        self._set_logger()\n\n        self.logger.info(version)\n\n        # header of result csv\n        self.write_to_csv([\n            'episode', 'episode_reward', 'loss','c_inv','c_pel','c_trans',\n            'c_inv','c_pel','c_trans','trans_rate','ord_size'])\n        self.write_to_csv_val([\n            'episode', 'episode_reward', 'c_inv','c_pel','c_trans',\n            'c_inv','c_pel','c_trans','trans_rate','ord_size'])\n\n    def learn_multi(self, load_weights, option_nn, pred_fresh, imitation_learn, has_wh_const):\n        '''Q-Learningのための初期設定 '''\n        self.learn_initialize(option_nn)\n        self.logger.info('')\n        print_setting_learn(self.logger)\n\n        self.option_nn = option_nn\n\n        self.num_states = self.num_states_per_product\n\n        if ESTIMATE_OTHERS:\n            self.num_states += 1\n\n        # agentごとに固定して学習を進める\n        if USE_INDEPENDENT_LEARN:\n            LEARNING_FREQ = self.num_products\n        else:\n            LEARNING_FREQ = 1\n\n        # Benchmarkの計算\n        self.logger.info('Start Benchmarking...')\n        self.sim_s_q_policy(True, False, False, 0, 'container')\n        self.sim_s_q_policy(True, False, False, 0, 'palette')\n        self.logger.info('')\n\n        # NNインスタンスの作成\n        self.brains = [IndependentBrain(self.num_states_per_product, self.num_products, self.num_ret,\n                                    self.num_actions_per_product, option_nn, self.num_states) for _ in\n                      range(self.num_products)]\n        self.logger.info(self.brains[0].main_q_network)\n\n        '''シミュレーションの実行 '''\n        self.best_episode_reward = 1_000_000\n        self.best_episode_reward_valid = 1_000_000\n\n        for episode in range(NUM_EPISODES):  # 最大試行数分繰り返す\n            if EPSILON_CONSTANT:\n                epsilon = START_EPSILON\n            else:\n                epsilon = START_EPSILON * (1 / (episode // DECAY_FREQ + 1)) + FINAL_EPSILON\n\n            # validate every N episodes\n            if episode % FREQ_VALIDATION == 0:\n                self.validate_multi(episode)\n\n            if CLEAR_MEMORY:\n                if episode % TARGET_UPDATE_FREQUENCY == 0:\n                    print(f'({episode}) Before clear: {len(self.brains[0].memory)}')\n                    for brain in self.brains:\n                        brain.memory.clear_memory()\n                        brain.td_error_memory.clear_memory()\n\n            episode_reward = 0\n            self.env_sim = copy.deepcopy(self.env)  # RetailerとWHインスタンスの初期状態をコピーする\n            self.env_sim.create_result_memory()  # 結果格納用のResultMemoryを作る\n            if pred_fresh:\n                self.env_sim.reflesh_pred()\n\n            # 初期値を得る\n            observation = self.reset(self.num_states)\n            state = observation\n            state = torch.from_numpy(state).type(torch.FloatTensor)\n            state = torch.unsqueeze(state, 0).to(device)\n            states = [state] * self.num_products\n\n            for step in range(MAX_STEPS):  # 1エピソードのループ\n                random_joint_trial = False\n                if epsilon > np.random.uniform(0, 1):\n                    random_joint_trial = True\n                action_origin = [brain.decide_action(state, episode, joint_random=random_joint_trial)[0]\n                                 for state, brain in zip(states, self.brains)]\n                action = np.array(action_origin).reshape(self.num_products, self.num_ret)\n\n                if ESTIMATE_OTHERS:\n                    # replay memoryには，確定した発注量を格納する\n                    orders = [action_origin * node.iRet[0].lot for action_origin, node in\n                              zip(action_origin, self.env.nodes)]\n                    for i in range(self.num_products):\n                        states[i][0, self.num_states - 1] = sum(orders) - orders[i]\n\n                done, reward, reward_p = self.env_sim.step(action, step)\n\n                if done:  # 最後\n                    states_next = [None] * self.num_products\n                else:\n                    states_next, _ = self.get_next_state_multi(step, self.env_sim.nodes)\n\n                if step > IGNORE_STEP:\n                    episode_reward += reward\n\n                    # メモリに経験を追加\n                    for idx in range(self.num_products):\n                        if (not USE_INDEPENDENT_LEARN) or (episode // TARGET_UPDATE_FREQUENCY) % LEARNING_FREQ == idx:\n                            self.memorize_multi(idx, episode, step, states[idx],\n                                            action_origin[idx].to(device),\n                                            states_next[idx], -reward_p[idx].view(-1).to(device))\n\n                    # 全てのエージェントのQ関数を更新する\n                    # multi-agentのため個別に学習を進める\n                    if step % FREQ_LEARN == 0:\n                        for idx, brain in enumerate(self.brains):\n                            if (not USE_INDEPENDENT_LEARN) or (episode // 50) % LEARNING_FREQ == idx:\n                                brain.replay(episode)\n\n                # 観測の更新\n                states = copy.deepcopy(states_next)\n\n                # 終了時の処理\n                if done:\n                    if episode % 50 == 0:\n                        # TD誤差メモリの中身を更新する\n                        # self.update_td_error_memory()\n                        pass\n\n                    # DDQNで追加、2試行に1度、Target Q-NetworkをMainと同じにコピーする\n                    if episode % TARGET_UPDATE_FREQUENCY == 0:\n                        for brain in self.brains:\n                            brain.update_target_q_network()\n                    break\n\n            for brain in self.brains:\n                brain.scheduler.step()\n\n            # loss = (np.array(self.brain.td_error_memory.memory)**2).mean()\n            loss = 0\n            # file出力\n            self.env_sim.calc_episode_performance()\n            wi, ws, qi, qs, ic, pc, tc, trate, qo = self.env_sim.get_episode_performance()\n            self.write_to_csv([episode, episode_reward.item(), loss,\n                               np.array(ic).sum(), np.array(pc).sum(), np.array(tc).sum(),\n                               ic, pc, tc, list(np.round(np.array(trate), 2)), list(np.round(np.array(qo), 2))])\n\n            # Best Episode更新\n            if self.best_episode_reward > episode_reward.item():\n                self.best_episode_reward = episode_reward.item()\n                self.env_sim_best = copy.deepcopy(self.env_sim)\n                with open(self.scm_best_path, mode='wb') as f:\n                    pickle.dump(self.env_sim_best, f)\n\n            # logger出力\n            if episode % 10 == 0:\n                # self.logger.info('(%d) EpisodeReward: %d Loss: %d MemUsed: %d Best: %d' % (\n                self.logger.info('(%d) EpisodeReward: %d Epsilon: %f MemoryUsed:%d Best: %d' % (\n                    episode, episode_reward.item(),\n                    epsilon,\n                    # loss,\n                    psutil.virtual_memory().percent,\n                    self.best_episode_reward))\n\n            del self.env_sim\n            gc.collect()\n\n    def get_next_state_multi(self, step, nodes, is_validate=False):\n        step = step + 1\n        states = []\n\n        for node in nodes:\n            state = []\n            for i, Ret in enumerate(node.iRet):\n                # 1. 直近在庫\n                state.append(Ret.inv[step])\n                # 2. LT後までの入荷予定\n                state.append(Ret.repl_plan[step: step + Ret.lt].sum())\n                # 3. LT後在庫（予測）\n                state.append(Ret.inv_lt[step])\n\n                # 4. LT後までの累積需要予測\n                pred_cum = Ret.pred[step + 1: step + Ret.lt + 1].sum()\n                state.append(pred_cum)\n\n                # 5. LT後から4週の累積需要予測\n                pred_cum_2 = Ret.pred[step + Ret.lt:step + Ret.lt + 5].sum()\n                state.append(pred_cum_2)\n\n                if ESTIMATE_OTHERS:\n                    # 他商品の合計発注量\n                    state.append(0)\n\n                state = torch.from_numpy(np.array(state)).type(torch.FloatTensor)\n                state = torch.unsqueeze(state, 0).to(device)\n                states.append(state)\n\n        # 次週の自分以外の商品の発注量を予測する（実質ここで行動を決定している）\n        st = time.time()\n        if ESTIMATE_OTHERS:\n            # 全探索\n            if SELECT_OPTIMAL:\n                Qs_all = self.get_all_qs(states, is_validate)\n                ed_qs = time.time()\n                # best_action_exact, best_orders_exact, best_q_exact = self.decide_optimal_action_multi(Qs_all)\n            else:\n                Qs_all = None\n            # 近似解\n            best_action, best_orders, best_q = self.decide_approx_action_multi(states, step, nodes, Qs_all, is_validate)\n\n            for i in range(self.num_products):\n                states[i][0, self.num_states - 1] = sum(best_orders) - best_orders[i]\n\n        if step % 100 == 50:\n            if SELECT_OPTIMAL:\n                pass\n                # print(f'diff in best : {best_q_exact - best_q}, {best_action}, {best_action_exact}')\n                # print(f'optimal selection:{time.time()-st: .2f}sec, forward:{ed_qs-st:.2f}sec')\n\n        return states, best_action\n\n    def get_all_qs(self, states, is_validate):\n        # まずは全ての組み合わせに対して，先にNNからforward処理しておく\n        max_order = sum([(self.num_actions_per_product-1) * node.iRet[0].lot for idx, node in enumerate(self.env.nodes)])\n        states_all = [[copy.deepcopy(states[j]) for _ in range(max_order)] for j in range(self.num_products)]\n        for j in range(self.num_products):\n            for i in range(max_order):\n                states_all[j][i][0, self.num_states - 1] = i\n\n        # forward処理\n        Qs_all = []\n        for j in range(self.num_products):\n            if is_validate:\n                self.brains[j].main_q_network.eval()\n                Qs = self.brains[j].main_q_network(torch.cat(states_all[j]).to(device))\n            else:\n                self.brains[j].target_q_network.eval()\n                Qs = self.brains[j].target_q_network(torch.cat(states_all[j]).to(device))\n            Qs_all.append(Qs)\n\n        return Qs_all\n\n    def decide_approx_action_multi(self, states, step, nodes, Qs_all, is_validate):\n        def calc_total_q(orders):\n            if Qs_all is None:\n                q_this = 0\n                for j in range(self.num_products):\n                    states[j][0, self.num_states - 1] = sum(orders) - orders[j]\n                    q_this += self.brains[j].decide_action(state=states[j], episode=0,\n                                                           greedy=True, target=(not is_validate))[1]\n            else:\n                q_this = sum([Qs_all[j][sum(orders) - orders[j], actions[j]].item() for j in range(self.num_products)])\n            return q_this\n\n        # 発注量0が初期状態\n        orders = [0] * self.num_products\n        actions = [0] * self.num_products\n        best_q = calc_total_q(orders)\n        best_action = actions\n        best_orders = orders\n\n        # qty allocの場合は最初は初期値は平均的な発注量とする\n        if self.op_trans_alloc == 'qty':\n            for idx, node in enumerate(nodes):\n                orders[idx] = node.iRet[0].pred[step]\n\n        # ordersを初期解として探索する\n        for k in range(LEVEL_K):\n            actions = []\n            for j in range(self.num_products):\n                # 現在のorderに対して，自分のrewardが上がるのであれば変更する\n                num = math.ceil(sum(orders) - orders[j])\n                states[j][0, self.num_states - 1] = num\n                if Qs_all is None:\n                    action, _ = self.brains[j].decide_action(state=states[j], episode=0,\n                                                             greedy=True, target=(not is_validate))\n                else:\n                    action = Qs_all[j][num].argmax().item()\n                actions.append(action)\n\n            # 現在のjoin-action(orders)におけるq値を取得する\n            orders = [actions[i] * self.env.nodes[i].iRet[0].lot for i in range(self.num_products)]\n            q_this = calc_total_q(orders)\n\n            # このjoin-actionの評価\n            if q_this > best_q:\n                best_q = q_this\n                best_action = actions\n                best_orders = orders\n                best_k = k\n\n        return best_action, best_orders, best_q\n\n    def decide_optimal_action_multi(self, Qs_all):\n        candidates = self.num_actions_per_product ** self.num_products\n        cnt_non_target = 0\n\n        best_q = - float('inf')\n        # 全探索（探索空間は少し狭くしている）\n        for option in range(candidates):\n            s = str(np.base_repr(option, self.num_actions_per_product))\n            s = '0' * (self.num_products - len(s)) + s\n            action = [int(s) for s in list(s)]\n            orders = [action * self.env.nodes[idx].iRet[0].lot for idx, action in enumerate(action)]\n\n            # 何も発注しないか，積載率70％未満は探索対象外とする: 探索空間を狭める\n            fills = np.array([])\n            if sum(orders) > 0:\n                fill_rate = (sum(orders)/TRANS_CAPACITY)/(math.ceil(sum(orders)/TRANS_CAPACITY))\n                fills = np.append(fills, fill_rate)\n            else:\n                fill_rate = 0\n            if sum(orders) > 0 and fill_rate < 0.4:\n                cnt_non_target += 1\n                continue\n\n            val = True\n            tot = sum([Qs_all[j][sum(orders) - orders[j], action[j]].item() for j in range(self.num_products)])\n\n            if val and (tot > best_q):\n                best_q = tot\n                best_action = action\n                best_orders = orders\n\n        # print(best_orders)\n\n        return best_action, best_orders, best_q\n\n    def validate_multi(self, episode, draw=False):\n        episode_reward = 0\n        self.env_val = copy.deepcopy(self.env)  # RetailerとWHインスタンスの初期状態をコピーする\n        self.env_val.create_result_memory()  # 結果格納用のResultMemoryを作る\n\n        observation = self.reset(self.num_states)\n        state = observation  # 観測をそのまま状態sとして使用\n        state = torch.from_numpy(state).type(torch.FloatTensor)  # NumPy変数をPyTorchのテンソルに変換\n        state = torch.unsqueeze(state, 0).to(device)  # size 4をsize 1x4に変換\n        states = [state] * self.num_products\n        best_action = None\n\n        for step in range(MAX_STEPS):  # 1エピソードのループ\n            if best_action is None:\n                action_origin = [brain.decide_action(state, episode)[0] for state, brain in zip(states, self.brains)]\n            else:\n                action_origin = best_action\n            action = np.array(action_origin).reshape(self.num_products, self.num_ret)\n            done, reward, reward_p = self.env_val.step(action, step)\n\n            if done:  # 最後\n                states = [None] * self.num_products\n            else:\n                states, best_action = self.get_next_state_multi(step, self.env_val.nodes, is_validate=True)\n\n            if step > IGNORE_STEP:\n                episode_reward += reward\n\n        self.env_val.calc_episode_performance()\n        wi, ws, qi, qs, ic, pc, tc, trate, qo = self.env_val.get_episode_performance()\n        self.write_to_csv_val([episode, episode_reward.item(),\n                               np.array(ic).sum(), np.array(pc).sum(), np.array(tc).sum(),\n                               ic, pc, tc, list(np.round(np.array(trate), 2)), list(np.round(np.array(qo), 2))])\n\n        if self.best_episode_reward_valid > episode_reward.item():\n            self.best_episode_reward_valid = episode_reward.item()\n\n        if episode % 50 == 0:\n            self.logger.info(F'EpisodeReward: {episode_reward.item():.0f} BEST : {self.best_episode_reward_valid:.0f}')\n            self.env_val.episode_performance_print(self.logger)\n\n        if draw:\n            self.env_val.draw()\n        with open(self.scm_val_path, mode='wb') as f:\n            pickle.dump(self.env_val, f)\n\n        del self.env_val\n\n    def learn(self, load_weights,option_nn, pred_fresh, imitation_learn, has_wh_const):\n        '''Q-Learningのための初期設定 '''\n        if option_nn == 10:\n            self.learn_multi(load_weights,option_nn, pred_fresh, imitation_learn, has_wh_const)\n            return\n\n        self.learn_initialize(option_nn)\n        self.logger.info('')\n        self.option_nn = option_nn\n\n        # Benchmarkの計算\n        self.logger.info('Start Benchmarking...')\n        self.sim_s_q_policy(True, False, False,0, 'container')\n        self.sim_s_q_policy(True, False, False,0, 'palette')\n        # self.sim_s_q_policy(False, False,False,0)\n        self.logger.info('')\n        \n        ''' Brain の初期化'''\n        if option_nn in [3,4,5,6,7]:#Action Branching\n            self.brain = BranchedBrain(self.num_states_per_product, self.num_products, self.num_ret,\n                                       self.num_actions_per_product, option_nn, self.num_states)\n            self.logger.info(self.brain.main_q_network)\n        elif option_nn ==10:\n            self.brain = [BranchedBrain(self.num_states_per_product, self.num_products, self.num_ret,\n                                       self.num_actions_per_product, option_nn, self.num_states) for _ in range(self.num_products)]\n            self.logger.info(self.brain[0].main_q_network)\n        else:\n            self.brain = Brain(\n                self.num_states, self.num_actions, self.num_states_per_product, self.num_actions_per_product, self.num_products, self.num_ret,option_nn)          \n        if load_weights:\n            self.load_weight(self.brain)\n\n        '''Imitation Learning '''\n        if imitation_learn:\n            self.logger.info('Start Imitation...')\n            # Experience Memoryにプッシュする\n            for episode in range(int(NUM_EPISODES/100)): \n                self.sim_s_q_policy(True, True,True,episode) #予測は毎回リフレッシュする\n            self.logger.info('End Imitation...')\n            self.logger.info('')\n        \n        '''シミュレーションの実行 '''\n        self.best_episode_reward = 1_000_000\n        self.best_episode_reward_valid = 1_000_000\n        for episode in range(NUM_EPISODES):  # 最大試行数分繰り返す\n            # 全体学習モードと枝別学習モードを切り替える場合はメモリをクリアする\n            if USE_INDEPENDENT_LEARN:\n                if episode % (self.num_products*10) == 0:\n                    print(f'({episode}) Before clear: {len(self.brain.memory)}')\n                    # self.brain.memory.clear_memory()\n                    # self.brain.td_error_memory.clear_memory()\n\n                # 枝別の場合は学習対象枝ごとにメモリをクリアする\n                if ((episode // (self.num_products*10)) % 2 == 1) and (episode % BRANCH_SWITCHING_FREQ == 0):\n                    print(f'({episode}) Before clear: {len(self.brain.memory)}')\n                    # self.brain.memory.clear_memory()\n                    # self.brain.td_error_memory.clear_memory()\n\n            st = time.time()\n            time_learing = 0\n            time_action=0\n            time_step=0\n\n            episode_reward = 0\n            self.env_sim = copy.deepcopy(self.env) #RetailerとWHインスタンスの初期状態をコピーする\n            self.env_sim.create_result_memory() #結果格納用のResultMemoryを作る\n            if pred_fresh:\n                self.env_sim.reflesh_pred()\n\n            self.brain.lots = [node.iRet[0].lot for node in self.env_sim.nodes]\n\n            # 初期値を得る\n            observation = self.reset(self.num_states)\n            state = observation\n            state = torch.from_numpy(state).type(torch.FloatTensor)\n            state = torch.unsqueeze(state, 0).to(device)\n            if SAMPLING_LEARN:\n                e_state = [state for i in range(10)]\n            else:\n                e_state = state\n\n            for step in range(MAX_STEPS):  # 1エピソードのループ\n                temp = time.time()\n                # action_origin = self.brain.decide_action(e_state, episode, False, SAMPLING_LEARN) #beliefでactionを決める\n                # 現時点でのWH在庫を上限として発注量を算出する\n                action_origin = self.brain.decide_action(e_state, episode, False, SAMPLING_LEARN,\n                                                         has_wh_const, np.trunc(self.env_sim.get_wh_inv(step)))\n                if ESTIMATE_OTHERS:\n                    # memoryに入れる際は，実際の発注量を格納する\n                    palettes = self.brain.calc_palettes(action_origin[0])\n                    state[0, self.num_states - 1] = palettes\n\n                if option_nn in [3, 4, 5, 6, 7]:  # Action Branching\n                    action = np.array(action_origin).reshape(self.num_products,self.num_ret)\n                else:\n                    action = self.convert_action(action_origin, False) # Retailerレベルでの発注量を計算する\n                time_action += time.time() - temp\n\n                # print(\"({0}) 1) GET ACTION: {1}\".format(step, time.time() - start) + \"[sec]\")\n                # start = time.time()\n\n                # actionを送ってその日の在庫受払計算を行う\n                temp = time.time()\n                done, reward, reward_p = self.env_sim.step(action, step)\n                time_step += time.time() - temp\n\n                # print(\"({0}) 2) STEP: {1}\".format(step, time.time() - start) + \"[sec]\")\n\n                # IGNORE_STEPまでは無視する（計算しないしMemoryにpushしない）\n                if done : #最後\n                    state_next = None\n                else:\n                    # ESTIMATE_OTHERSのときは合計予想発注量tとして0をいれる\n                    state_next, e_state_next = self.get_next_state(step,self.env_sim.nodes, False, SAMPLING_LEARN)\n\n                if step > IGNORE_STEP:\n                    episode_reward += reward\n                    # print(self.get_action_idx_from_list(action.flatten()))\n\n                    # メモリに経験を追加\n                    # rewardはコストであり,最小化問題にする\n                    # メモリに入れるstateは真のstateとする\n                    self.memorize(\n                        episode,\n                        step,\n                        state,\n                        action_origin.to(device),\n                        state_next,\n                        - reward.to(device),\n                        - reward_p.to(device)\n                    )\n\n                    # TD誤差メモリにTD誤差を追加\n                    ''' multi step learning対応で， Experienve Memoryへのmemorizeと同時に実施するよう修正 / sglab'''\n                    # self.memorize_td_error(0)  # 本当はTD誤差を格納するが、0をいれておく\n\n                    # Q関数を更新する\n                    if step % FREQ_LEARN == 0:\n                        temp = time.time()\n                        self.update_q_function(episode)\n                        time_learing += time.time() - temp\n\n                    # print(\"({0}) 4) UPDATE Q FUNCTION: {1}\".format(step, time.time() - start) + \"[sec]\")\n                    # start = time.time()\n\n                # 観測の更新\n                state = state_next\n                e_state = e_state_next\n\n                # 終了時の処理\n                if done:\n                    if episode % 50 == 0:\n                        # TD誤差メモリの中身を更新する\n                        self.update_td_error_memory()\n\n                    # DDQNで追加、2試行に1度、Target Q-NetworkをMainと同じにコピーする\n                    if episode % TARGET_UPDATE_FREQUENCY == 0:\n                        self.update_target_q_function()\n                    break\n\n            self.brain.scheduler.step()\n\n            # loss = (np.array(self.brain.td_error_memory.memory)**2).mean()\n            loss = 0\n            # file出力\n            self.env_sim.calc_episode_performance()\n            wi, ws, qi, qs, ic, pc, tc , trate, qo= self.env_sim.get_episode_performance()\n            self.write_to_csv([episode, episode_reward.item(),loss,\n                               np.array(ic).sum(), np.array(pc).sum(),np.array(tc).sum(),\n                               ic,pc,tc,list(np.round(np.array(trate),2)), list(np.round(np.array(qo),2))])\n\n            # Best Episode更新\n            if self.best_episode_reward > episode_reward.item():\n                self.best_episode_reward = episode_reward.item()\n                self.env_sim_best = copy.deepcopy(self.env_sim)\n                with open(self.scm_best_path, mode='wb') as f:\n                    pickle.dump(self.env_sim_best, f)\n\n            # logger出力\n            if episode % 10 ==0:\n                for param_group in self.brain.optimizer.param_groups:\n                    self.logger.info(f'lr:{param_group[\"lr\"]}, epsilon:{START_EPSILON * (1 / (episode//100 + 1)) + FINAL_EPSILON}')\n                self.logger.info('(%d) EpisodeReward: %d Loss: %d MemUsed: %d Best: %d' % (\n                    episode, episode_reward.item(),loss, psutil.virtual_memory().percent,\n                    self.best_episode_reward))\n                # self.env_sim.episode_performance_print(self.logger)\n\n            # 学習済みパラメータを保存する\n            if episode % 50 ==0:\n                torch.save(self.brain.main_q_network.state_dict(), self.model_path)\n\n            # validate every N episodes\n            if episode % FREQ_VALIDATION ==0:\n                for i in range(1):\n                    self.validate(episode, False, has_wh_const, False)\n\n            del self.env_sim\n            gc.collect()\n\n            tot = time.time() - st\n            if episode < 5:\n                print(f'{tot:.2}sec: {time_learing/tot:.0%}, {time_action/tot:.0%}, {time_step/tot:.0%}')\n                print(f'{(episode)} target_branch:{(episode // BRANCH_SWITCHING_FREQ) % self.num_products}, current memory size:', len(self.brain.memory))\n\n    def validate(self, episode, pred_fresh, has_wh_const, draw):\n        episode_reward = 0\n        self.env_val = copy.deepcopy(self.env) #RetailerとWH��ンスタンスの初期状態をコピーする\n        self.env_val.create_result_memory() #結果格納用のResultMemoryを作る\n\n        # ValidationではBenchmarkと条件を合わせるために予測値は変更しない\n        # if pred_fresh:\n        #     self.env_val.reflesh_pred()\n\n        observation = self.reset(self.num_states)\n        state = observation  # 観測をそのまま状態sとして使用\n        state = torch.from_numpy(state).type(torch.FloatTensor)  # NumPy変数をPyTorchのテンソルに変換\n        state = torch.unsqueeze(state, 0).to(device)  # size 4をsize 1x4に変換\n        if self.sample_val:\n            e_state = [state for i in range(NUM_SAMPLING)]\n        else:\n            e_state = state\n\n        for step in range(MAX_STEPS):  # 1エピソードのループ\n            # action_origin = self.brain.decide_action(e_state, episode, True, self.sample_val)  # greedy policy\n            # 現時点でのWH在庫を上限として発注量を算出する\n            action_origin = self.brain.decide_action(e_state, episode, True, self.sample_val,\n                                                     has_wh_const, np.trunc(self.env_val.get_wh_inv(step)))\n            if self.option_nn in [3, 4, 5, 6, 7]:  # Action Branching\n                action = np.array(action_origin).reshape(self.num_products, self.num_ret)\n                # print('chosen action in greedy:', action)\n            else:\n                action = self.convert_action(action_origin, False)  # Retailerレベルでの発注量を計算する\n\n            done, reward, reward_p = self.env_val.step(action, step)\n\n            if done : #最後\n                state_next = None\n            else:\n                # USE_ESTIMATE_OTHERSのときは合計予想発注量tとして0をいれる\n                state_next, e_state_next = self.get_next_state(\n                    step,self.env_val.nodes, False, self.sample_val, is_validate=True)\n\n            if step > IGNORE_STEP:\n                episode_reward += reward\n            # 観測の更新\n            state = state_next\n            e_state = e_state_next\n\n        self.env_val.calc_episode_performance()\n        wi, ws, qi, qs, ic, pc, tc, trate, qo = self.env_val.get_episode_performance()\n        self.write_to_csv_val([episode, episode_reward.item(),\n                           np.array(ic).sum(), np.array(pc).sum(), np.array(tc).sum(),\n                           ic, pc, tc, list(np.round(np.array(trate), 2)), list(np.round(np.array(qo), 2))])\n\n        if self.best_episode_reward_valid > episode_reward.item():\n            self.best_episode_reward_valid = episode_reward.item()\n\n        if episode % 50 == 0:\n            self.logger.info(F'EpisodeReward: {episode_reward.item():.0f} BEST : {self.best_episode_reward_valid:.0f}')\n            self.env_val.episode_performance_print(self.logger)\n\n        if draw:\n            self.env_val.draw()\n        with open(self.scm_val_path, mode='wb') as f:\n            pickle.dump(self.env_val, f)\n\n        del self.env_val\n\n    def get_next_state(self, step, nodes, prob, sampling, is_validate=False):\n        # print(self.state_real)\n        step = step + 1\n\n        if not self.state_real:\n            state = []  # 真の状態:Experience Memoryに入れて学習させる\n            e_state = []  # 期待される状態:Actionを決める際に用いる\n            # training時はsamplingせずに期待値を用いる，inference時はsamplingする\n\n            for node in nodes:\n                for i, Ret in enumerate(node.iRet):\n                    # 1. 直近在庫\n                    state.append(Ret.inv[step])\n                    # 2. LT後までの入荷予定\n                    state.append(Ret.repl_plan[step: step + Ret.lt].sum())\n                    # 3. LT後在庫（予測）\n                    state.append(Ret.inv_lt[step])\n\n                    # 4. LT後までの累積需要予測\n                    pred_cum = Ret.pred[step + 1: step + Ret.lt + 1].sum()\n                    demand_cum = Ret.demand[step + 1: step + Ret.lt + 1].sum()\n                    state.append(pred_cum)\n\n                    # 5. LT後から4週の累積需要予測\n                    pred_cum_2 = Ret.pred[step + Ret.lt:step + Ret.lt + 5].sum()\n                    demand_cum_2 = Ret.demand[step + Ret.lt:step + Ret.lt + 5].sum()\n                    state.append(pred_cum_2)\n\n                    if not self.obs_only:\n                        # 6. LT後までの累積誤差(stateは実績の予測誤差）\n                        state.append(pred_cum - demand_cum)\n                        # 7. LT後からN週の累積誤差(stateは実績の予測誤差）\n                        state.append(pred_cum_2 - demand_cum_2)\n\n            # 予測誤差パラメータをもとにサンプリングする\n            if sampling:\n                for j in range(NUM_SAMPLING):\n                    e_state_unit = []\n\n                    for node in nodes:\n                        for i, Ret in enumerate(node.iRet):\n                            Ret.pred_temp = np.random.normal(loc=Ret.pred, scale=Ret.sd * Ret.error_rate, size=Ret.num_time + BUFFER)\n                            Ret.pred_temp = np.where(Ret.pred_temp < 0, 0, Ret.pred_temp)\n                            Ret.inv_lt_temp = Ret.inv[step] - Ret.pred_temp[step: step + Ret.lt + 1].sum() + Ret.repl_plan[step: step + Ret.lt].sum()\n\n                    # 予測をupdateしたnodes_tempについて，e_stateをそれぞれ求める\n                    for node in nodes:\n                        for i, Ret in enumerate(node.iRet):\n                            # 1. 直近在庫\n                            e_state_unit.append(Ret.inv[step])\n                            # 2. LT後までの入荷予定\n                            e_state_unit.append(Ret.repl_plan[step: step + Ret.lt].sum())\n\n                            if self.obs_only:\n                                # 3. LT後在庫\n                                e_state_unit.append(Ret.inv_lt_temp)\n\n                                pred_cum = Ret.pred_temp[step + 1: step + Ret.lt + 1].sum()\n                                pred_cum_2 = Ret.pred_temp[step + Ret.lt:step + Ret.lt + 5].sum()\n\n                                # 4. LT後までの累積需要\n                                e_state_unit.append(pred_cum)\n                                # 5. LT後から4週の累積需要\n                                e_state_unit.append(pred_cum_2)\n\n                            else:\n                                # 3. LT後在庫\n                                e_state_unit.append(Ret.inv_lt[step])\n                                pred_cum = Ret.pred[step + 1: step + Ret.lt + 1].sum()\n                                pred_cum_2 = Ret.pred[step + Ret.lt:step + Ret.lt + 5].sum()\n                                # 4. LT後までの累積需要\n                                e_state_unit.append(pred_cum)\n                                # 5. LT後から4週の累積需要\n                                e_state_unit.append(pred_cum_2)\n                                # 6. LT後までの累積誤差（誤差の予測：inference時は分布で与える）\n                                e_state_unit.append(pred_cum * np.random.normal(loc=0, scale= (Ret.sd_ini / Ret.myu) * self.error_rate, size=1)[0] / np.sqrt(Ret.lt))\n                                # 7. LT後からN週の累積誤差（誤差の予測：inference時は分布で与える）\n                                e_state_unit.append(pred_cum_2 * np.random.normal(loc=0, scale=(Ret.sd_ini / Ret.myu) * self.error_rate, size=1)[0] / np.sqrt(4))\n\n                    e_state_unit = torch.from_numpy(np.array(e_state_unit)).type(torch.FloatTensor)\n                    e_state_unit = torch.unsqueeze(e_state_unit, 0).to(device)\n                    e_state.append(e_state_unit)\n                    # print(e_state)\n\n            else:\n                for node in nodes:\n                    for i, Ret in enumerate(node.iRet):\n                        # 1. 直近在庫\n                        e_state.append(Ret.inv[step])\n                        # 2. LT後までの入荷予定\n                        e_state.append(Ret.repl_plan[step: step + Ret.lt].sum())\n\n                        # 3. LT後在庫\n                        e_state.append(Ret.inv_lt[step])\n\n                        pred_cum = Ret.pred[step + 1: step + Ret.lt + 1].sum()\n                        pred_cum_2 = Ret.pred[step + Ret.lt:step + Ret.lt + 5].sum()\n\n                        # 4. LT後までの累積需要(stateは実績)\n                        e_state.append(pred_cum)\n                        # 5. LT後から4週の累積需要(stateは実績)\n                        e_state.append(pred_cum_2)\n\n                        if not self.obs_only:\n                            # 6. LT後までの累積誤差(行動選択時には予測は合っている前提で行動を選択する）\n                            e_state.append(0)\n                            # 7. LT後からN週の累積誤差(行動選択時には予測は合っている前提で行動を選択する）\n                            e_state.append(0)\n\n                if ESTIMATE_OTHERS:\n                    e_state.append(0)\n                e_state = torch.from_numpy(np.array(e_state)).type(torch.FloatTensor)\n                e_state = torch.unsqueeze(e_state, 0).to(device)\n\n            if ESTIMATE_OTHERS:\n                state.append(0)\n            state = torch.from_numpy(np.array(state)).type(torch.FloatTensor)\n            state = torch.unsqueeze(state, 0).to(device)\n\n            # 学習する際には相手の予測はせずに，全員発注しない場合として作る\n            if ESTIMATE_OTHERS and is_validate:\n                palettes = self.brain.estimate_palettes(state)\n                state[0, self.num_states-1] = palettes\n                e_state[0, self.num_states-1] = palettes\n\n            return state, e_state\n\n        else:\n            # !! POMDP対���\n            # e_stateについては，予測誤差をパラメータとしてサンプリングする\n            # 真のstate\n            state = [] # 真の状態:Experience Memoryに入れて学習させる\n            for node in nodes:\n                for i, Ret in enumerate(node.iRet):\n                    # 1. 直近在庫\n                    state.append(Ret.inv[step])\n                    # 2. 入荷予定\n                    state.append(Ret.repl_plan[step: step + Ret.lt].sum())\n                    # 3. LT後在庫\n                    state.append(Ret.inv_lt_true[step])\n                    # 4. LT後までの累積需要(stateは実績)\n                    demand_cum = Ret.demand[step + 1: step + Ret.lt + 1].sum()\n                    state.append(demand_cum)\n                    # 5. LT後から4週の累積需要(stateは実績)\n                    demand_cum_2 = Ret.demand[step + Ret.lt:step + Ret.lt + 5].sum()\n                    state.append(demand_cum_2)\n\n            # belief state : b(s)\n            e_state = [] # 期待される状態:Actionを決める際に用いる\n\n            # 予測誤差パラメータをもとにサンプリングする\n            if sampling:\n                for j in range(NUM_SAMPLING):\n                    e_state_unit = []\n\n                    # 現在の予測値を予測の期待値として，上振れ下振れシナリオでシミュレーションする\n                    # そのために，現在の予測値に対して想定される予測誤差をパラメータとしてブレさせる\n                    for node in nodes:\n                        for i, Ret in enumerate(node.iRet):\n                            Ret.pred_temp = np.random.normal(loc=Ret.pred, scale=Ret.sd * Ret.error_rate, size=Ret.num_time + BUFFER)\n                            Ret.pred_temp = np.where(Ret.pred_temp < 0, 0, Ret.pred_temp)\n                            Ret.inv_lt_temp = Ret.inv[step] - Ret.pred_temp[step: step + Ret.lt + 1].sum() + Ret.repl_plan[step: step + Ret.lt].sum()\n\n                    # print(\"({0}) 2) reflesh {1}\".format(step, time.time() - start) + \"[sec]\")\n                    # start = time.time()\n\n                    # 予測をupdateしたnodes_tempについて，e_stateをそれぞれ求める\n                    for node in nodes:\n                        for i, Ret in enumerate(node.iRet):\n                            # 1. 直近在庫\n                            e_state_unit.append(Ret.inv[step])\n                            # 2. 入荷予定\n                            e_state_unit.append(Ret.repl_plan[step: step + Ret.lt].sum())\n                            # 3. LT後在庫\n                            e_state_unit.append(Ret.inv_lt_temp)\n                            # 4. LT後までの累積需要\n                            pred_cum = Ret.pred_temp[step + 1: step + Ret.lt + 1].sum()\n                            e_state_unit.append(pred_cum)\n                            # 5. LT後から4週の累積需要\n                            pred_cum_2 = Ret.pred_temp[step+Ret.lt:step+Ret.lt+5].sum()\n                            e_state_unit.append(pred_cum_2)\n                    e_state_unit = torch.from_numpy(np.array(e_state_unit)).type(torch.FloatTensor)\n                    e_state_unit = torch.unsqueeze(e_state_unit, 0).to(device)\n                    e_state.append(e_state_unit)\n\n                    # print(\"({0}) 3) e_state append {1}\".format(step, time.time() - start) + \"[sec]\")\n                    # start = time.time()\n\n            else:\n                for node in nodes:\n                    for i, Ret in enumerate(node.iRet):\n                        # 1. 直近在庫\n                        e_state.append(Ret.inv[step])\n                        # 2. 入荷予定\n                        e_state.append(Ret.repl_plan[step: step + Ret.lt].sum())\n                        # 3. LT後在庫\n                        e_state.append(Ret.inv_lt[step])\n                        # 4. LT後までの累積需要\n                        pred_cum = Ret.pred[step + 1: step + Ret.lt + 1].sum()\n                        e_state.append(pred_cum)\n                        # 5. LT後から4週の累積需要\n                        pred_cum_2 = Ret.pred[step + Ret.lt:step + Ret.lt + 5].sum()\n                        e_state.append(pred_cum_2)\n\n                e_state = torch.from_numpy(np.array(e_state)).type(torch.FloatTensor)\n                e_state = torch.unsqueeze(e_state, 0).to(device)\n\n            state = torch.from_numpy(np.array(state)).type(torch.FloatTensor)\n            state = torch.unsqueeze(state, 0).to(device)\n\n            return state, e_state\n                    \n    def convert_action(self, action, prob):\n        '''各stepにおいて，商品，拠点別の発注量を計算する '''\n        # actionはnumpyでindexをproduct_idとして取得できる\n        # action = self.brain.decide_action(state, episode)\n        if self.option_nn == 0:\n            # Branching Architectureではない場合\n            is_per_product= False\n        else:\n            # Branching Architectureの場合\n            is_per_product = True\n        if prob:\n            action = self.get_action_list_from_idx(action)\n        else:\n            action = self.get_action_list_from_idx(action.item(), is_per_product)\n        return np.array(action).reshape(self.num_products,self.num_ret)\n    \n    def get_action_list_from_idx(self, action_idx, is_per_product):\n        def Base_10_to_n(X, n):\n            if (int(X/n)):\n                return Base_10_to_n(int(X/n), n)+str(X%n)\n            return str(X%n)\n        # action.item()をaction_idxとして渡す\n        # 124のようなaction_indexを渡して， [4,4,4]のようなリストを得る\n        action_list = Base_10_to_n(action_idx, self.num_actions_per_product)\n        ret = [int(a)  for a in action_list]\n        if is_per_product:\n            # productごとにbranchを分岐する場合\n            while len(ret) < self.num_ret:\n                ret.insert(0, 0)\n        else:\n            # branchを分岐しない場合（全てのproduct/retailerについて一つの大きなNNで表現する場合\n            while len(ret) < self.num_ret * self.num_products:\n                ret.insert(0, 0)\n        return ret\n\n    def get_action_idx_from_list(self, action_list):\n        def Base_n_to_10(X,n):\n            out = 0\n            for i in range(1,len(str(X))+1):\n                out += int(X[-i])*(n**(i-1))\n            return out #int out\n    # [4,4,4]のようなactionを渡して， 124のようなactionインデックスを得る\n        s = \"\"\n        for i in action_list:\n            s = s + str(i)\n        return Base_n_to_10(s, self.num_actions_per_product)\n\nclass Logger():\n    ''' Agent ごとにloggerを生成する　'''\n    def __init__(self, log_dir=\"\", dir_name=\"\"):\n        self.log_dir = log_dir\n        if not log_dir:\n            self.log_dir = os.path.join(os.path.dirname(__file__), \"logs\")\n        if not os.path.exists(self.log_dir):\n            os.mkdir(self.log_dir)\n\n        if dir_name:\n            self.log_dir = os.path.join(self.log_dir, dir_name)\n            if not os.path.exists(self.log_dir):\n                os.mkdir(self.log_dir)\n\n        self._callback = tf.summary.FileWriter(self.log_dir)\n        # K.callbacks.TensorBoard(self.log_dir)\n\n    @property\n    def writer(self):\n        return self._callback#.writer\n\n    def set_model(self, model):\n        self._callback.set_model(model)\n\n    def path_of(self, file_name):\n        return os.path.join(self.log_dir, file_name)\n\n    def describe(self, name, values, episode=-1, step=-1):\n        mean = np.round(np.mean(values), 3)\n        std = np.round(np.std(values), 3)\n        desc = \"{} is {} (+/-{})\".format(name, mean, std)\n        if episode > 0:\n            print(\"At episode {}, {}\".format(episode, desc))\n        elif step > 0:\n            print(\"At step {}, {}\".format(step, desc))\n\n    def plot(self, name, values, interval=10):\n        indices = list(range(0, len(values), interval))\n        means = []\n        stds = []\n        for i in indices:\n            _values = values[i:(i + interval)]\n            means.append(np.mean(_values))\n            stds.append(np.std(_values))\n        means = np.array(means)\n        stds = np.array(stds)\n        plt.figure()\n        plt.title(\"{} History\".format(name))\n        plt.grid()\n        plt.fill_between(indices, means - stds, means + stds,\n                         alpha=0.1, color=\"g\")\n        plt.plot(indices, means, \"o-\", color=\"g\",\n                 label=\"{} per {} episode\".format(name.lower(), interval))\n        plt.legend(loc=\"best\")\n        plt.show()\n\n    def write(self, index, name, value):\n        summary = tf.Summary()\n        summary_value = summary.value.add()\n        summary_value.tag = name\n        summary_value.simple_value = value\n        self.writer.add_summary(summary, index)\n        self.writer.flush()\n\n    def write_image(self, index, frames):\n        # Deal with a 'frames' as a list of sequential gray scaled image.\n        last_frames = [f[:, :, -1] for f in frames]\n        if np.min(last_frames[-1]) < 0:\n            scale = 127 / np.abs(last_frames[-1]).max()\n            offset = 128\n        else:\n            scale = 255 / np.max(last_frames[-1])\n            offset = 0\n        channel = 1  # gray scale\n        tag = \"frames_at_training_{}\".format(index)\n        values = []\n\n        for f in last_frames:\n            height, width = f.shape\n            array = np.asarray(f * scale + offset, dtype=np.uint8)\n            image = Image.fromarray(array)\n            output = io.BytesIO()\n            image.save(output, format=\"PNG\")\n            image_string = output.getvalue()\n            output.close()\n            image = tf.Summary.Image(\n                        height=height, width=width, colorspace=channel,\n                        encoded_image_string=image_string)\n            value = tf.Summary.Value(tag=tag, image=image)\n            values.append(value)\n\n        summary = tf.Summary(value=values)\n        self.writer.add_summary(summary, index)\n        self.writer.flush()\n\nclass Environment():\n    # 1つの商品についての{WH, Retailer}の集合であるNodeを統合管理するクラス\n    # env.node[p]で，商品pに対するRetailer, Warehouseが取得できる\n\n    def __init__(self, k, num_time, num_products, myu, lt_retailer, lt_wh, lot_retailer, lot_wh, num_ret, inv_cap, d_factor, season, s_m, error_rate, op_trans_alloc, draw):\n        self.num_time = num_time\n        self.num_products = num_products        \n        self.num_ret = num_ret\n        self.error_rate = error_rate\n        self.s_m = s_m\n        self.season = season\n        self.op_trans_alloc = op_trans_alloc\n        self.inv_cap = inv_cap\n        self.d_factor = d_factor\n        \n        # 商品ごとにNodeを作成し，Nodeの中に拠点を配置し，　それぞれ需要やコスト原単価を設定する\n        self.nodes =  [Node(i, k, num_time, lt_retailer[i], lt_wh[i], lot_retailer[i], lot_wh[i], inv_cap) for i in range(num_products)]\n        for i, node in enumerate(self.nodes):\n            node.create_retailer(i, num_ret, myu[i], Cinv_ret, Cpel_ret, season, s_m, error_rate)\n            node.create_warehouse(i, Cinv_wh, Cpel_wh)\n            if draw == True:\n                node.draw_ini_ret()\n                \n        '''\n        # Warehouse 在庫関連データ\n        self.qty_inv_wh = np.zeros((num_products, num_time + BUFFER))\n        self.qty_short_wh = np.zeros((num_products, num_time + BUFFER))\n\n        # product / 拠点 / time ごとの在庫関連データ\n        self.c_inv = np.zeros((num_products, num_ret, num_time + BUFFER))\n        self.c_pel = np.zeros((num_products, num_ret, num_time + BUFFER))\n        self.qty_inv = np.zeros((num_products, num_ret, num_time + BUFFER))\n        self.qty_short = np.zeros((num_products, num_ret, num_time + BUFFER))\n        \n        # 拠点 / timeごとの輸送関連データ\n        self.qty_trans = np.zeros((num_ret, num_time + BUFFER))\n        self.c_trans = np.zeros((num_ret, num_time + BUFFER))\n        \n        # 合計コスト\n        self.reward   = np.zeros(num_time + BUFFER) #　t=stepにおける総reward\n        self.c_trans_all  = np.zeros(num_time + BUFFER) #　t=stepにおける輸送コスト\n        '''\n        \n    def set_num_actions(self, num_actions_per_product):\n        for node in self.nodes:\n            for ret in node.iRet:\n                ret.num_actions_per_product = num_actions_per_product\n\n    def create_result_memory(self):\n        self.result = ResultMemory(self.num_products, self.num_ret, self.num_time, self.op_trans_alloc, self.inv_cap)\n\n    '''\n    def draw(self):\n    for node in self.nodes:\n        node.draw()\n    '''\n\n    def draw(self):\n        fig, ax = plt.subplots(nrows=self.num_products, figsize=(12, self.num_products*3))\n        plt.style.use('fivethirtyeight')\n        for cnt, node in enumerate(self.nodes):\n            for i in range(self.num_ret):\n                ax[cnt].plot(node.iRet[i].inv, label='inventory', linewidth=1)\n                ax[cnt].plot(node.iRet[i].inv_lt, label='inventory_lt', linewidth=1)\n                # ax[cnt].plot(node.iRet[i].inv_position, label='inventory_position', linewidth=1)\n                ax[cnt].plot(node.iRet[i].demand, label='demand', linewidth=1)\n                ax[cnt].plot(node.iRet[i].short, label='short', linewidth=1)\n                ax[cnt].plot(node.iRet[i].ord, label='ord', linewidth=1)\n                # ax2 = ax[cnt].twinx()\n                # ax2.plot(node.iRet[i].scs_flg, label='scs flg', linewidth=1)\n                ax[cnt].set_title(f'{cnt}')\n                plt.grid()\n        plt.legend()\n\n    def get_episode_performance(self):\n        return self.result.get_episode_performance()\n\n    def calc_episode_performance(self):\n        self.result.calc_episode_performance(self.nodes)\n        \n    def calc_step_reward(self, step):\n        return self.result.calc_step_reward(self.nodes, step)\n        \n    def episode_performance_print(self, logger):\n        self.result.episode_performance_print(logger)\n\n    def get_wh_inv(self, step):\n        ''' stepにおける各商品のWH在庫を取得する '''\n        inv = [node.iWH.inv[step]/node.lot_retailer for node in self.nodes]\n\n        return np.array(inv)\n\n    def step(self, action, step):\n        ''' Retailerのactionを送って在庫の受払を計算する '''\n        done =False\n        \n        for p, node in enumerate(self.nodes) :#商品ごとのループ\n            # Retailer\n            self.reflect_action(action[p], node, step)# 1-1. RetailerのActionをord, repl_planに反映させる\n            # WH\n            self.reflect_action_wh(node.iWH.get_action_s_q_policy(step), node, step)# 1-2. WH在庫は(s,Q)ポリシーにてord, repl_planに反映させる\n            #Tentative ; WHでの在庫切れや納品遅れはないとする場合\n            self.fix_retailer_repl(node, step)\n\n            ''' 2. 在庫の受払を行う '''\n            self.update_inventory_at_retailer(node, step)# 2-1. Retailerの受払計算\n            self.update_inventory_at_warehouse(node, step)# 2-2. WHの受払計算\n\n        ''' Rewardの計算：全Retailer Nodeについて '''\n        reward, reward_p = self.calc_step_reward(step)\n        reward = torch.FloatTensor([reward])\n        reward_p = torch.FloatTensor(np.array(reward_p)).view(-1)\n        # reward_p = torch.unsqueeze(reward_p, 0)\n        \n        if step == MAX_STEPS - 1:\n            done =True\n            \n        # action branching向けに商品ごとのrewardを返す\n        # 輸送コストは発注した商品に対して按分する，もしくは発注量に応じて商品ごとに按分する\n        return done, reward, reward_p\n    \n    def reflect_action(self, action, node, step):\n        # actionをもとに，　発注を確定し， LT後に入荷するよう，入荷予定を更新する\n        for i, Ret in enumerate(node.iRet):\n            Ret.action[step] =action[i]\n            Ret.ord[step] =action[i]* Ret.lot\n            Ret.repl_plan[step + Ret.lt -1] = Ret.ord[step]# 入荷予定に入れる\n\n    def reflect_action_wh(self, action, node, step):\n        # actionをもとに，　発注を確定し， LT後に入荷（利用可能，つまりdemand(step+LT)に対して引き当てできるタイミングで，入荷予定を更新する\n        node.iWH.action[step] =action\n        node.iWH.ord[step] =action* node.iWH.lot\n        node.iWH.repl_plan[step + node.iWH.lt -1] = node.iWH.ord[step]# 入荷予定に入れる\n\n    def fix_retailer_repl(self, node, step):\n        # ''' tentative ''' 　#入荷予定はそのままの場合\n        for i, Ret in enumerate(node.iRet):\n            Ret.repl[step] =Ret.repl_plan[step]\n            node.iWH.ship[step] +=  Ret.repl[step]\n        \n    def update_inventory_at_retailer(self, node, step):\n        # 全ての拠点について在庫受払を計算する\n        qty_short_at_retailer = 0\n        for i, Ret in enumerate(node.iRet):\n            Ret.ship[step] = min(Ret.inv[step], Ret.demand[step]) #在庫を上限として，需要そのまま\n            Ret.short[step] = Ret.demand[step] - Ret.ship[step] #欠品\n            # qty_short_at_retailer = qty_short_at_retailer + Ret.short[step]\n            Ret.proceed(step)\n            \n    def update_inventory_at_warehouse(self, node, step):\n        # WHでは在庫遅れはないとする\n        node.iWH.repl[step] = node.iWH.repl_plan[step]\n        # 受払を計算\n        node.iWH.proceed(step)\n\n    def reflesh_pred(self):\n        ''' Retailerでの将来予測値を毎回書き換える'''\n        for node in self.nodes:\n            node.reflesh_pred()\n                \n","sub_path":"agent.py","file_name":"agent.py","file_ext":"py","file_size_in_byte":77123,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"498826330","text":"\nimport os\nimport xml.etree.ElementTree\nimport sys\nfrom GLTypeToNativeType import typemap\n\n\nclass VertexShaderCode:\n    def __init__(self, xmlPath):\n        self.vertexShaderXmlPath = xmlPath\n        self.root = xml.etree.ElementTree.parse(xmlPath).getroot()\n        self.vertexAttributeTemplate = \"struct alignas(sizeof(float)) VertexAttribute\\n{{\\n{content}\\n}};\\n\"\n        self.uniformBlockVariableTemplate = \"struct alignas(sizeof(float)) {name} \\n{{\\n{content}\\n}};\\n\"\n        self.vertexDeclTemplate = \\\n            'template<>\\n' \\\n            'struct VertexDecl<{num}>\\n' \\\n            '{{\\n' \\\n            '\\tstatic const int ComponentCount={componentCount};\\n' \\\n            '\\tstatic const int Size={size};\\n' \\\n            '\\tstatic const GLenum Type={type};\\n' \\\n            '\\tstatic const int Offset={offset};\\n' \\\n            '}};\\n'\n        self.setUniformVarDefinitionTemplate = \\\n            'void VertexShaderBase::Set{name}UniformVarData(const {type}& Data) \\n{{\\n VSProgram->SetUniformVarData(\\\"{name}\\\", Data); \\n }}\\n'\n        self.setUniformVarDeclTemplate = \\\n            'void Set{name}UniformVarData(const {type}& Data);\\n'\n        self.vertexShaderClassDecl = \\\n        'class VertexShaderBase\\n'\\\n        '{{\\n'\\\n        '\\tusing IsVertexShader = std::true_type;\\n'\\\n        '\\tpublic:\\n'\\\n        '\\tstd::shared_ptr<class ShaderProgram> VSProgram;\\n\\n'\\\n        '\\tVertexShaderBase();\\n\\n'\\\n        '\\tstatic inline const char* GetVSShaderPath() {{ return \\\"{shaderPath}\\\"; }}\\n\\n'\\\n        '\\tvoid InitVertexShaderUniformBlock();\\n\\n'\\\n        '\\tvoid VertexAttribBinding();\\n\\n'\\\n        '\\t{uniformBlockFunctionDecl}\\n\\n'\\\n        '\\t{uniformBlockVariableDecl}\\n\\n' \\\n        '\\t{uniformVariableFunctionDecl}\\n\\n'\\\n        '\\t}};\\n\\n'\\\n\n    def GetNamespace(self):\n        return self.root.attrib['namespace']\n\n    def GetClassDefinitionCode(self):\n        return self.GetUniformBufferInitCode() + self.GetConstructorCode() + self.GetUniformBufferDataCode() + self.GetVertexAttribBindingCode() + self.GetUniformVariableDefinitionCode()\n\n    def GetConstructorCode(self):\n        template='\\nVertexShaderBase::VertexShaderBase()\\n{{ {content} }}\\n\\n'\n        uniformBlocks = self.GetUniformBlockNames()\n        functionBody =''\n        for uniformVar in uniformBlocks:\n            functionBody += '{name}Buffer = std::make_unique<UniformBuffer>();'.format(name = uniformVar)\n        return template.format(content = functionBody)\n\n    def GetUniformBufferDataCode(self):\n        template= \\\n            '\\tvoid VertexShaderBase::Set{name}UniformBlockData(const {name}& Data)\\n{{\\n'\\\n            '\\tauto Loc = VSProgram->GetUniformBlockIndex(\"{name}\");\\n'\\\n            '\\t{name}Buffer->BindBufferBase(Loc);\\n'\\\n            '\\t{name}Buffer->BufferWholeData(Data);\\n\\n}}\\n\\n'\n        result = ''\n        uniformBlocks = self.GetUniformBlockNames()\n        for uniformVar in uniformBlocks:\n            result += template.format(name=uniformVar)\n        return result\n\n    def GetUniformBufferInitCode(self):\n        template=\\\n        'void VertexShaderBase::InitVertexShaderUniformBlock()\\n{{\\n {content} }}\\n\\n'\n        functionBody = ''\n        uniformBlocks = self.GetUniformBlockNames()\n        for uniformVar in uniformBlocks:\n            functionBody += '{name}Buffer->CreateBufferStorage<{name}>();\\n'.format(name=uniformVar)\n        return template.format(content = functionBody)\n\n    def GetHeaderCode(self):\n        return self.GetVertexAttributeCode() \\\n               + self.GetUniformBlockStructCode() \\\n               + self.vertexShaderClassDecl.format(uniformBlockFunctionDecl = self.GetUniformBlockFunctionDeclCode(),\n                                                   uniformBlockVariableDecl = self.GetUniformBlockVariableDeclCode(),\n                                                   uniformVariableFunctionDecl = self.GetUniformVariableDeclarationCode(),\n                                                   shaderPath = self.root.attrib['shaderPath']) + self.GetVertexDeclTemplateCode()\n\n\n    def GetUniformBlockNames(self):\n        result = [child.attrib['name'] for child in self.root.iter('UniformBlock')]\n        return result\n\n    def GetVertexAttributeCode(self):\n        internal = ''\n        for child in self.root.iter('VertexAttribute'):\n            typeName = child.attrib['type']\n            internal += '{type} {name};\\n'.format(type=typemap[typeName], name=child.attrib['name'])\n        return self.vertexAttributeTemplate.format(content=internal)\n\n    def GetUniformBlockStructCode(self):\n        internal = ''\n        varName = ''\n        count = 0\n        for child in self.root.iter('UniformBlock'):\n            varName = child.attrib['name']\n            count = count + 1\n            for variableNode in child.iter('VariableNode'):\n                typename = variableNode.attrib['type']\n                membername = variableNode.attrib['name']\n                internal += '{type} {name};\\n'.format(type=typemap[typename], name=membername)\n        if count > 0 :\n            return self.uniformBlockVariableTemplate.format(content = internal, name=varName)\n        else:\n            return ''\n\n    def GetVertexDeclTemplateCode(self):\n        count = 0;\n        offset = 0;\n        totalSize = 0;\n        vertexDeclTemplateBase = 'template<int> struct VertexDecl{};\\n'\n        result =''\n        for child in self.root.iter('VertexAttribute'):\n            result += self.vertexDeclTemplate.format(num=count, componentCount=child.attrib['componentCount'],size=child.attrib['size'], type='GL_FLOAT', offset=offset)\n            offset += int(child.attrib['size'])\n            count = count + 1\n        return vertexDeclTemplateBase + result + 'static const int Size={size};\\n'.format(size = offset)\n\n    def GetVertexAttribBindingCode(self):\n        count=0\n        function='void VertexShaderBase::VertexAttribBinding()\\n{{\\n {functionBody} }}\\n'\n        content = ''\n        for child in self.root.iter('VertexAttribute'):\n            content += 'glEnableVertexAttribArray( {count} );\\n'.format(count = count)\n            content += 'glVertexAttribPointer({count}, VertexDecl<{count}>::ComponentCount, VertexDecl<{count}>::Type, false, Size, reinterpret_cast<void*>(VertexDecl<{count}>::Offset));\\n'.format(count=count)\n            count = count + 1\n        return function.format(functionBody = content)\n\n    def GetUniformVariableDeclarationCode(self):\n        result =''\n        for child in self.root.iter('UniformVariable'):\n            if str(child.attrib['type']) in typemap:\n                if  '[' in child.attrib['name']:\n                    name = child.attrib['name']\n                    index = name.index('[')\n                    result += self.setUniformVarDeclTemplate.format(name = name[:index], type = 'std::vector<{0}>'.format(typemap[child.attrib['type']]))\n                else:\n                    name = child.attrib['name']\n                    result += self.setUniformVarDeclTemplate.format(name=name, type=typemap[child.attrib['type']])\n        return result\n\n    def GetUniformBlockFunctionDeclCode(self):\n        result=''\n        for child in self.root.iter('UniformBlock'):\n            varName = child.attrib['name']\n            result += 'void Set{name}UniformBlockData(const {name}& Data);\\n'.format(name=varName)\n        return result\n\n    def GetUniformBlockVariableDeclCode(self):\n        result=''\n        for child in self.root.iter('UniformBlock'):\n            varName = child.attrib['name']\n            result += 'std::unique_ptr<class UniformBuffer> {name}Buffer;\\n'.format(name=varName)\n        return result;\n\n    def GetUniformVariableDefinitionCode(self):\n        result =''\n        for child in self.root.iter('UniformVariable'):\n            if str(child.attrib['type']) in typemap:\n                if '[' in child.attrib['name']:\n                    name = child.attrib['name']\n                    index = name.index('[')\n                    result += self.setUniformVarDefinitionTemplate.format(name=name[:index], type='std::vector<{0}>'.format(\n                        typemap[child.attrib['type']]))\n                else:\n                    name = child.attrib['name']\n                    result += self.setUniformVarDefinitionTemplate.format(name=name, type=typemap[child.attrib['type']])\n        return result","sub_path":"PythonShaderCodeGenerator/VertexShaderCode.py","file_name":"VertexShaderCode.py","file_ext":"py","file_size_in_byte":8328,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"642427238","text":"# -*- encoding: utf-8 -*-\nimport sys\nimport os\n\n\nsys.path.append(os.path.abspath(\"Engine/Tools/BuildTool\"))\npwd = os.path.dirname(os.path.abspath(__file__))\nsource_root = os.path.join(pwd, \"Engine/Sources\")\nbuild_root = os.path.join(pwd, \"Engine/Build\")\nif not os.path.exists(build_root):\n    os.makedirs(build_root)\n\n\nif __name__ == \"__main__\":\n    import driver\n    platform = \"vs2019\" if len(sys.argv) <= 1 else sys.argv[1]\n    if platform == \"vs2019\":\n        driver.generate_vs_project(pwd, source_root, build_root, os.path.join(pwd, \"Yes.2019.sln\"))\n\n","sub_path":"GenerateProject.py","file_name":"GenerateProject.py","file_ext":"py","file_size_in_byte":557,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"538732358","text":"from django.db import models\n\nfrom bmcore.models.account import Account\n\nfrom bmcore.models.voucher import Voucher\n\n\n# Account Transaction manager\nclass AccountTransactionManager(models.Manager):\n\n    def create(self, voucher, **kwargs):\n\n        instance = self.model()\n\n        instance.voucher = voucher\n\n        instance.account = kwargs.get('account', None)\n\n        instance.debit = kwargs.get('debit', 0)\n\n        instance.credit = kwargs.get('credit', 0)\n\n        instance.debit_pend = kwargs.get('debit_pend', None)\n\n        instance.credit_pend = kwargs.get('credit_pend', None)\n\n        instance.account1 = kwargs.get('account1', None)\n\n        instance.narration = kwargs.get('narration', None)\n\n        instance.instrument_no = kwargs.get('instrument_no', None)\n\n        instance.instrument_date = kwargs.get('instrument_date', None)\n\n        instance.bank_name = kwargs.get('bank_name', None)\n\n        instance.save()\n\n        return instance\n\n    @staticmethod\n    def update(inst, voucher, **kwargs):\n\n        instance = inst\n\n        instance.voucher = voucher\n\n        instance.account = kwargs.get('account', None)\n\n        instance.debit = kwargs.get('debit', None)\n\n        instance.credit = kwargs.get('credit', None)\n\n        instance.debit_pend = kwargs.get('debit_pend', None)\n\n        instance.credit_pend = kwargs.get('credit_pend', None)\n\n        instance.account1 = kwargs.get('account1', None)\n\n        instance.narration = kwargs.get('narration', None)\n\n        instance.instrument_no = kwargs.get('instrument_no', None)\n\n        instance.instrument_date = kwargs.get('instrument_date', None)\n\n        instance.bank_name = kwargs.get('bank_name', None)\n\n        instance.save()\n\n        return instance\n\n\nclass AccountTransaction(models.Model):\n\n    voucher = models.ForeignKey(Voucher, related_name=\"account_transactions\", blank=True, null=True)\n\n    account = models.ForeignKey(Account, related_name=\"transaction_account\", blank=True, null=True)\n\n    debit = models.DecimalField(max_digits=20, decimal_places=2, default=0, blank=True, null=True)\n\n    credit = models.DecimalField(max_digits=20, decimal_places=2, default=0, blank=True, null=True)\n\n    debit_pend = models.DecimalField(max_digits=20, decimal_places=2, blank=True, null=True)\n\n    credit_pend = models.DecimalField(max_digits=20, decimal_places=2, blank=True, null=True)\n\n    account1 = models.ForeignKey(Account, related_name=\"cash_transfer_account\", blank=True, null=True)\n\n    narration = models.CharField(max_length=50, blank=True, null=True)\n\n    bwd = models.CharField(max_length=50, blank=True, null=True)\n\n    instrument_no = models.CharField(max_length=50, blank=True, null=True)\n\n    instrument_date = models.DateField(blank=True, null=True)\n\n    bank_name = models.CharField(max_length=50, blank=True, null=True)\n\n    timestamp = models.DateTimeField(auto_now=False, auto_now_add=True)\n\n    updated = models.DateTimeField(auto_now=True, auto_now_add=False)\n\n    objects = AccountTransactionManager()\n\n    class Meta:\n        db_table = 'tbl_acc_tran'\n        ordering = [\"-timestamp\", \"-updated\"]\n\n    def __unicode__(self):\n        return str(self.id)\n\n    def __str__(self):\n        return str(self.id)\n","sub_path":"bmcore/models/account_transaction.py","file_name":"account_transaction.py","file_ext":"py","file_size_in_byte":3214,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"435648041","text":"# Написать генератор списка, который итерируется по функции range от 1 до 100\n# и фильтрует по четным числам.\n# Пример:\n# [1, 2, 3, 4, 5, 6]\n# [2, 4, 6]\n# Требование:\n# Использовать list comprehension\n\n\nnums = list(range(1, 101))\nnums2 = [num for num in nums if num%2 == 0 and num>0]\nprint(nums2)","sub_path":"task1.py","file_name":"task1.py","file_ext":"py","file_size_in_byte":402,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"374149115","text":"#Atividade\n\n#Construir um algoritmo que contenha 3 listas\n\n#- Nomes de produtos\n#- Preços de cada produto\n#- Quantidades de cada produto\n\n#Construir uma função para imprimir um dos produtos da lista e uma\n#Função para retirar um dos produtos das listas\n\nNome_Produtos = [\"Refrigerante\", \"Salsicha\", \"Miojo\"]\nPreco_produtos = [\"5.5\", \"3.8\", \"1.5\"]\nQtd_Produtos = [\"9\", \"22\", \"6\"]\n\n\ndef imprimir_produto (Nome_Produtos, Preco_produtos, Qtd_Produtos):\n    print (\"Lista de Produtos: \")\n    print(\"\",Nome_Produtos)\n    produto = input(\"\\nQual Produto você deseja ver mais informacoes ? \")\n    posicao = Nome_Produtos.index(produto)\n    print (\"\\nProduto: \", Nome_Produtos[posicao])\n    print (\"Preco: \", Preco_produtos[posicao])\n    print (\"Quantidade: \", Qtd_Produtos[posicao])\n\ndef apagar_produto (Nome_Produtos, Preco_produtos, Qtd_Produtos):\n    print (\"\\nLista de Produtos: \")\n    print(\"\",Nome_Produtos)\n    produto = input(\"\\nQual Produto você deseja remover ? \")\n    posicao = Nome_Produtos.index(produto)\n    del (Nome_Produtos[posicao])\n    del (Preco_produtos[posicao])\n    del(Qtd_Produtos[posicao])\n    print(\"\\nProduto removido! \\n\", Nome_Produtos)\n\n\n#Imprimir Produto conforme solicitação do usuário\nimprimir_produto(Nome_Produtos, Preco_produtos, Qtd_Produtos)\n\n#Apagar produto conforme solicitação do usuário\napagar_produto(Nome_Produtos, Preco_produtos, Qtd_Produtos)\n\n\n\n\n\n","sub_path":"Aula 1/Atividade Aula 1.py","file_name":"Atividade Aula 1.py","file_ext":"py","file_size_in_byte":1400,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"177945430","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nfrom django.conf import settings\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('utils', '0002_auto_20141215_1443'),\n        ('auth', '0001_initial'),\n        ('common', '0001_initial'),\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='Profile',\n            fields=[\n                ('user', models.OneToOneField(primary_key=True, serialize=False, to=settings.AUTH_USER_MODEL)),\n                ('is_verified', models.BooleanField(default=False)),\n                ('abstractmodel_ptr', models.OneToOneField(parent_link=True, auto_created=True, to='common.AbstractModel')),\n                ('experience', models.ManyToManyField(related_name='experience', to='utils.Tag')),\n                ('github_link', models.ForeignKey(to='utils.Link')),\n                ('preferences', models.ManyToManyField(related_name='preferences', to='utils.Tag')),\n            ],\n            options={\n            },\n            bases=('common.abstractmodel',),\n        ),\n    ]\n","sub_path":"server/farm/profiles/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":1114,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"328308600","text":"from torch import FloatTensor\nfrom torch.autograd import Variable, Function\nimport torch.nn as nn\n\n\n\nclass GradReverse(Function):\n    def forward(self, x):\n        return x.view_as(x)\n\n    def backward(self, grad_output):\n        return (grad_output * -lambd)\n\ndef grad_reverse(x):\n    return GradReverse()(x)\n\nclass network(nn.Module):\n    def __init__(self):\n        super(network, self).__init__()\n        #eights = [Variable(FloatTensor([i]), requires_grad=True) \\\n                   #for i in (2, 5)]\n        self.w1 = nn.Parameter(Variable(FloatTensor([2]), requires_grad=True))\n        self.w2 = nn.Parameter(Variable(FloatTensor([5]), requires_grad=True))\n\n        #self.f1 = w1 * x\n        #self.f2 = w2 * x\n        #self.L = L = ( 10 - self.f2 ) + 2*self.f1\n\n    def forward(self, x):\n        #x = grad_reverse(x)\n        f1 = self.w1 * x # 8\n        f2 = self.w2 * f1 # 40\n        L = ( 10 - f2 ) + 2 * f1  # ( 10 - 40 ) + 2 * 8 = -30+16 = -14\n        #x = self.f1(x)\n        #x = self.f2(x)\n        #x = self.L(x)\n        return L\n\nnet = network()\n#print(net)\nparams = list(net.parameters())\n#print(params)\ninput = Variable(FloatTensor([4]))\nout = net(input)\nprint(out)\nnet.zero_grad()\nout.backward()\n\nfor index, weight in enumerate(params, start=1):\n    gradient, *_ = weight.grad.data\n    print(\"Gradient of w{} to L : {}\".format(index, gradient))\n\n\n\n\n\n\n#x = Variable(FloatTensor([4]))\n\n#model = network()\n\n#model_out = model(x)\n#print(model_out)\n\n#model_out.zero_grad()\n\n#model_out.backward()\n\n\n\n#for index, weight in enumerate(weights, start=1):\n    #gradient, *_ = weight.grad.data\n    #print(\"Gradient of w{} to L : {}\".format(index, gradient))\n\n'''\nweights = [Variable(FloatTensor([i]), requires_grad=True)\\\n           for i in (2,5)]\n\n#print(weights)\n\nw1, w2 = weights\n\nf1 = w1 * x\nGradReverse()\nf2 = w2 * x\n\nL = ( 10 - f2 ) + 2*f1\n\nL.register_hook(lambda grad : print(grad))\nf2.register_hook(lambda grad : print(grad))\nf1.register_hook(lambda grad : print(grad))\n\nL.backward()\n\nfor index, weight in enumerate(weights, start=1):\n    gradient, *_ = weight.grad.data\n    print(\"Gradient of w{} to L : {}\".format(index, gradient))\n\n'''","sub_path":"GRL_practice.py","file_name":"GRL_practice.py","file_ext":"py","file_size_in_byte":2152,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"409713985","text":"import torch\nfrom torch import nn\nimport torch.nn.functional as F\n\nfrom read_data import *\nimport time\nimport os\n\nEPOCHS = 10\nlearning_rate=1e-3\nMAX_LENGTH = 15\nres_path = '../result/'\n###=======================================================\n### Check for GPU\n###=======================================================\nif torch.cuda.is_available():\n\tprint('Availabel GPU numbers: ', torch.cuda.device_count())\n\n\n###=======================================================\n### Build Model\n###=======================================================\nclass EncoderRNN(nn.Module):\n\tdef __init__(self, hidden_size=256):\n\t\tsuper(EncoderRNN, self).__init__()\n\t\tself.hidden_size = hidden_size\n\n\t\t# self.fc1 = nn.Linear(4096, 500)\n\t\tself.lstm = nn.LSTM(4096, hidden_size, batch_first=True)\n\n\tdef forward(self, input, hidden):\n\t\toutput, hidden = self.lstm(input, hidden)\n\t\treturn output, hidden\n\n\tdef initHidden(self):\n\t\treturn None\n\nclass DecoderRNN(nn.Module):\n\tdef __init__(self, output_size, embed_size=500, hidden_size=256):\n\t\tsuper(DecoderRNN, self).__init__()\n\t\tself.hidden_size = hidden_size\n\t\tself.output_size = output_size\n\t\tself.embed_size = embed_size\n\n\t\tself.embedding = nn.Embedding(output_size, embed_size)\n\t\tself.lstm = nn.LSTM(embed_size, hidden_size, batch_first=True)\n\t\tself.out = nn.Linear(hidden_size, output_size)\n\t\t# self.softmax = nn.LogSoftmax(dim=1)\n\n\tdef forward(self, input, hidden):\n\t\toutput = self.embedding(input)\n\t\toutput = F.relu(output)\n\t\toutput, hidden = self.lstm(output, hidden)\n\t\tdecoded = self.out(output.view(output.shape[0]*output.shape[1], output.shape[2]))\n\t\treturn decoded.view(output.shape[0], output.shape[1], decoded.shape[1]), hidden\n\n\tdef initHidden(self):\n\t\treturn None\n\nclass AttnDecoderRNN(nn.Module):\n\tdef __init__(self, output_size, embed_size=500, hidden_size=256, encoder_size=80):\n\t\tsuper(AttnDecoderRNN, self).__init__()\n\t\tself.hidden_size = hidden_size\n\t\tself.output_size = output_size\n\t\tself.embed_size = embed_size\n\t\tself.encoder_size = encoder_size\n\n\t\tself.embedding = nn.Embedding(self.output_size, self.embed_size)\n\t\tself.attn = nn.Linear(self.hidden_size+self.embed_size, self.encoder_size)\n\t\tself.attn_combine = nn.Linear(self.hidden_size+self.embed_size, self.embed_size)\n\t\tself.lstm = nn.LSTM(self.embed_size, self.hidden_size, batch_first=True)\n\t\tself.out = nn.Linear(self.hidden_size, self.output_size)\n\n\n\tdef forward(self, input, hidden, encoder_outputs):\n\t\t##encoder_outputs: (N, T, D_hid)\n\t\tN = input.shape[0]\n\t\tT = self.encoder_size\n\t\t# print(N, T)\n\t\t# print(encoder_outputs.shape)\n\t\tembedded = self.embedding(input)  ##(N, T'=1, D_emb)\n\t\th_n, c_n = hidden  ##h_n(1, N, D_hid)\n\t\tattn_weights = F.softmax(self.attn(torch.cat((embedded[:,0,:], h_n[0]), 1)), dim=1)  #(N, T)\n\n\t\tattn_applied = torch.bmm(attn_weights.view(N,1,T), encoder_outputs).squeeze()  ##(N, D_hid)\n\t\toutput = torch.cat((embedded[:,0,:], attn_applied), 1)  ##(N, D_emb+D_hid)\n\t\toutput = self.attn_combine(output)  ##(N, D_emb)\n\n\t\toutput = F.relu(output).view(N, 1, self.embed_size)\n\t\toutput, hidden = self.lstm(output, hidden)\n\n\t\tdecoded = self.out(output.view(output.shape[0]*output.shape[1], output.shape[2]))\n\t\treturn decoded.view(output.shape[0], output.shape[1], decoded.shape[1]), hidden, attn_weights\n\n\n\n###=======================================================\n### Training the model\n###=======================================================\ndef train(input_tensor, target_tensor, encoder, decoder, encoder_optimizer,\n\tdecoder_optimizer, criterion, use_teacher=True, max_len=MAX_LENGTH):\n\n\tencoder_hidden = encoder.initHidden()\n\tbsz = input_tensor.shape[0]\n\n\tloss = 0\n\tencoder_optimizer.zero_grad()\n\tdecoder_optimizer.zero_grad()\n\n\tencoder_output, encoder_hidden = encoder(input_tensor, encoder_hidden)\n\n\tdecoder_hidden = encoder_hidden\n\ttarget_length = target_tensor.shape[1]  ##(N, T, D)\n\tdecoder_input = torch.ones(bsz, 1, dtype=torch.int64).cuda()\n\toutput_all = []\n\n\tif use_teacher:\n\t\tfor i in range(target_length-1):\n\n\t\t\t##normal part\n\t\t\tdecoder_output, decoder_hidden = decoder(decoder_input, decoder_hidden)\n\n\t\t\t##attention part\n\t\t\t# decoder_output, decoder_hidden, _ = decoder(decoder_input, decoder_hidden, encoder_output)\n\n\t\t\t# print(decoder_output.shape)\n\t\t\t# print(target_tensor[:, i+1].shape)\n\t\t\tloss += criterion(decoder_output.view(-1, decoder_output.shape[2]), target_tensor[:, i+1])\n\t\t\t_, topi = decoder_output.data.topk(1)\n\t\t\tpred = topi.cpu().numpy().reshape(bsz, 1)\n\t\t\toutput_all.append(pred)\n\t\t\tdecoder_input = target_tensor[:,i+1].view(bsz, 1)\n\n\telse:\n\t\tfor i in range(target_length-1):\n\t\t\tdecoder_output, decoder_hidden = decoder(decoder_input, decoder_hidden)\n\t\t\tloss += criterion(decoder_output.view(-1, decoder_output.shape[2]), target_tensor[:, i+1])\n\t\t\t_, topi = decoder_output.data.topk(1)\n\t\t\tpred = topi.numpy().reshape(bsz, 1)\n\t\t\toutput_all.append(pred)\n\t\t\tdecoder_input = torch.from_numpy(pred)\n\n\tpred_seq = np.hstack((x for x in output_all))\n\n\tloss.backward()\n\tencoder_optimizer.step()\n\tdecoder_optimizer.step()\n\n\treturn pred_seq, loss.data.cpu().numpy()\n\n\nif __name__ == '__main__':\n\n\t###=======================================================\n\t### Import data\n\t###=======================================================\n\ttrain_label = get_train_label()\n\tenglish = readLangs(train_label)\n\tenglish = get_spe_dict(english, 3)\n\tprint('total words numbers: ', english.n_words)\n\n\tid2index = id_index(train_label)\n\ttrain_id = get_train_id()\n\ttrain_feature, train_caption, pick_ids = get_train_batch(train_id, train_label, \n\t\tid2index, english, size=5, max_len=15)\n\tprint('='*80)\n\tprint(train_feature.shape)\n\tprint(train_caption.shape)\n\n\n\t###=======================================================\n\t### Build the model\n\t###=======================================================\n\ttry:\n\t\tencoder = torch.load(res_path+'encoder.pkl')\n\t\tdecoder = torch.load(res_path+'decoder.pkl')\n\t\tprint('Reload succussfully')\n\texcept:\n\t\tencoder = EncoderRNN()\n\t\tdecoder = DecoderRNN(output_size=english.n_words)\n\t\tprint('Start new network')\n\t# decoder = AttnDecoderRNN(output_size=english.n_words, )\n\n\tencoder.cuda()\n\tdecoder.cuda()\n\n\tprint(encoder)\n\tprint(decoder)\n\n\tencoder_optimizer = torch.optim.Adam(encoder.parameters(), lr=learning_rate)\n\tdecoder_optimizer = torch.optim.Adam(decoder.parameters(), lr=learning_rate)\n\tcriterion = nn.CrossEntropyLoss()\n\n\t###=======================================================\n\t### Start training\n\t###=======================================================\n\tif not os.path.exists(res_path):\n\t\tos.mkdir(res_path)\n\n\tstart = time.time()\n\tloss_hist = []\n\n\tfor iter in range(EPOCHS):\n\t\tstart_single = time.time()\n\t\ttrain_feature, train_caption, pick_ids = get_train_batch(train_id, train_label, id2index, english, size=5, max_len=15)\n\t\tinput_tensor = torch.tensor(train_feature, dtype=torch.float32).cuda()\n\t\ttarget_tensor = torch.tensor(train_caption, dtype=torch.int64).cuda()\n\n\t\toutput, loss = train(input_tensor, target_tensor, encoder, decoder, encoder_optimizer,decoder_optimizer, \n\t\t\tcriterion, use_teacher=True, max_len=MAX_LENGTH)\n\n\t\tif iter % 5 == 0:\n\t\t\tend_single = time.time()\n\t\t\tcost = end_single - start\n\t\t\t# print(cost)\n\t\t\tcost_m = cost // 60\n\t\t\tcost_s = cost % 60\n\n\t\t\tprint('='*100)\n\t\t\tprint('iter: %d/%d | loss: %.2f | cost: %dmin%ds' % (iter, EPOCHS, loss, cost_m, cost_s))\n\t\t\tprint('example id: ', pick_ids[0])\n\t\t\tprint('predict example:')\n\t\t\tprint(vec2seq(output[0], english))\n\t\t\tprint('ground truth:')\n\t\t\tprint(vec2seq(train_caption[0], english))\n\n\t\t\ttorch.save(encoder, res_path+'encoder.pkl')\n\t\t\ttorch.save(decoder, res_path+'decoder.pkl')\n\t\t\tprint('Model Save Successfully~')\n\n\n\n\n\n\n\n\n","sub_path":"LHY-ML/HW2-1-video-caption-generation(attn-RNN)/pytorch/_origin_model_train.py","file_name":"_origin_model_train.py","file_ext":"py","file_size_in_byte":7598,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"23411321","text":"import threading\nimport socket\nimport time\n\nimport requests\n\ntext_content = \"\"\"\nHTTP/1.x 200 OK\nContent-Type: text/html\n\n<head>\n    <title>WOW</title>\n</head>\n<html>\n    <p>Hello World!</p>\n</html>\n\"\"\"\n\ndef server():\n    HOST = \"127.0.0.1\"\n    PORT = 9000\n\n    server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n    server_socket.bind((HOST, PORT))\n    server_socket.listen(5)\n\n    while 1:\n        client_socket, _ = server_socket.accept()\n        t = threading.Thread(target=handle_request, args=(client_socket, text_content))\n        t.start()\n\ndef handle_request(client_socket, text_content):\n    client_socket.recv(1024)\n    try:\n        client_socket.sendall(text_content)\n    except socket.error:\n        client_socket.close()\n        return\n    client_socket.close()\n\n\nif __name__ == \"__main__\":\n    server()\n","sub_path":"blocking_server/thread_server.py","file_name":"thread_server.py","file_ext":"py","file_size_in_byte":832,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"464298195","text":"import numpy as np\nimport queries\n\n\ndef income_histogram(cursor, num_bins):\n    #    max_income = queries.query_max_income(cursor)\n    max_income = 4000000.0\n    min_income = queries.query_min_income(cursor)\n    bin_limits = np.linspace(min_income, max_income, num_bins + 1)\n    counts = []\n    middles = []\n    for i in range(num_bins):\n        curr_min = bin_limits[i]\n        curr_max = bin_limits[i + 1]\n        counts.append(queries.query_income_count(cursor, curr_min, curr_max))\n        middles.append(abs(curr_min - curr_max) * 0.5 + curr_min)\n    return middles, counts\n","sub_path":"plotcalc.py","file_name":"plotcalc.py","file_ext":"py","file_size_in_byte":579,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"25812302","text":"#!/usr/bin/env python\n\nfrom kzpy3.utils2 import *\nfrom kzpy3.vis2 import *\nimport roslib\nimport std_msgs.msg\nimport geometry_msgs.msg\nimport rospy\nimport cv2\nfrom cv_bridge import CvBridge,CvBridgeError\nfrom sensor_msgs.msg import Image\nimport kzpy3.data_analysis.Angle_Dict_Creator as Angle_Dict_Creator\nimport runtime_parameters as rp\nbridge = CvBridge()\n\nreload_timer = Timer(10)\n\nR = {}\nfor topic_ in [steer, motor, state, encoder,\n\tacc_x,acc_y,acc_z,\n\tgyro_x,gyro_y,gyro_z,\n\tgyro_heading_x,gyro_heading_y,gyro_heading_z,\n\tleft_image,right_image\n\t]:\n\tR[topic_] = {ts:[],vals:[]}\n\nn_ = 20\ndef steer__callback(msg):\n\tR[steer][ts].append(time.time())\n\tR[steer][vals].append(msg.data)\n\tif len(R[steer][ts]) > 1.5*n_:\n\t\tR[steer][ts] = \t\tR[steer][ts][-n_:]\n\t\tR[steer][vals] = \tR[steer][vals][-n_:]\n\ndef motor__callback(msg):\n\tR[motor][ts].append(time.time())\n\tR[motor][vals].append(msg.data)\n\tif len(R[motor][ts]) > 1.5*n_:\n\t\tR[motor][ts] = \t\tR[motor][ts][-n_:]\n\t\tR[motor][vals] = \tR[motor][vals][-n_:]\n\ndef state__callback(msg):\n\tR[state][ts].append(time.time())\n\tR[state][vals].append(msg.data)\n\tif len(R[state][ts]) > 1.5*n_:\n\t\tR[state][ts] = \t\tR[state][ts][-n_:]\n\t\tR[state][vals] = \tR[state][vals][-n_:]\n\ndef encoder__callback(msg):\n\tR[encoder][ts].append(time.time())\n\tR[encoder][vals].append(msg.data)\n\tif len(R[encoder][ts]) > 1.5*n_:\n\t\tR[encoder][ts] = \t\tR[encoder][ts][-n_:]\n\t\tR[encoder][vals] = \t\tR[encoder][vals][-n_:]\n\ndef acc__callback(msg):\n\tt_ = time.time()\n\tR[acc_x][ts].append(t_)\n\tR[acc_x][vals].append(msg.x)\n\tR[acc_y][ts].append(t_)\n\tR[acc_y][vals].append(msg.y)\n\tR[acc_z][ts].append(t_)\n\tR[acc_z][vals].append(msg.z)\n\tif len(R[acc_x][ts]) > 1.5*n_:\n\t\tR[acc_x][ts] = \t\tR[acc_x][ts][-n_:]\n\t\tR[acc_x][vals] = \tR[acc_x][vals][-n_:]\n\t\tR[acc_y][ts] = \t\tR[acc_y][ts][-n_:]\n\t\tR[acc_y][vals] = \tR[acc_y][vals][-n_:]\n\t\tR[acc_z][ts] = \t\tR[acc_z][ts][-n_:]\n\t\tR[acc_z][vals] = \tR[acc_z][vals][-n_:]\n\ndef gyro__callback(msg):\n\tt_ = time.time()\n\tR[gyro_x][ts].append(t_)\n\tR[gyro_x][vals].append(msg.x)\n\tR[gyro_y][ts].append(t_)\n\tR[gyro_y][vals].append(msg.y)\n\tR[gyro_z][ts].append(t_)\n\tR[gyro_z][vals].append(msg.z)\n\tif len(R[gyro_x][ts]) > 1.5*n_:\n\t\tR[gyro_x][ts] = \tR[gyro_x][ts][-n_:]\n\t\tR[gyro_x][vals] = \tR[gyro_x][vals][-n_:]\n\t\tR[gyro_y][ts] = \tR[gyro_y][ts][-n_:]\n\t\tR[gyro_y][vals] = \tR[gyro_y][vals][-n_:]\n\t\tR[gyro_z][ts] = \tR[gyro_z][ts][-n_:]\n\t\tR[gyro_z][vals] = \tR[gyro_z][vals][-n_:]\n\ndef gyro_heading__callback(msg):\n\tt_ = time.time()\n\tR[gyro_heading_x][ts].append(t_)\n\tR[gyro_heading_x][vals].append(msg.x)\n\tR[gyro_heading_y][ts].append(t_)\n\tR[gyro_heading_y][vals].append(msg.y)\n\tR[gyro_heading_z][ts].append(t_)\n\tR[gyro_heading_z][vals].append(msg.z)\n\tif len(R[gyro_heading_x][ts]) > 1.5*n_:\n\t\tR[gyro_heading_x][ts] = \tR[gyro_heading_x][ts][-n_:]\n\t\tR[gyro_heading_x][vals] = \tR[gyro_heading_x][vals][-n_:]\n\t\tR[gyro_heading_y][ts] = \tR[gyro_heading_y][ts][-n_:]\n\t\tR[gyro_heading_y][vals] = \tR[gyro_heading_y][vals][-n_:]\n\t\tR[gyro_heading_z][ts] = \tR[gyro_heading_z][ts][-n_:]\n\t\tR[gyro_heading_z][vals] = \tR[gyro_heading_z][vals][-n_:]\n\ndef left_image__callback(data):\n\tR[left_image][ts].append(time.time())\n\tR[left_image][vals].append( bridge.imgmsg_to_cv2(data,\"rgb8\") )\n\tR[left_image][ts] = R[left_image][ts][-2:]\n\tR[left_image][vals] = R[left_image][vals][-2:]\n\ndef right_image__callback(data):\n\tR[right_image][ts].append(time.time())\n\tR[right_image][vals].append( bridge.imgmsg_to_cv2(data,\"rgb8\") )\n\tR[right_image][ts] = R[right_image][ts][-2:]\n\tR[right_image][vals] = R[right_image][vals][-2:]\n\n\nrospy.init_node('listener',anonymous=True)\n\nrospy.Subscriber('/bair_car/steer', std_msgs.msg.Int32, callback=steer__callback)\nrospy.Subscriber('/bair_car/motor', std_msgs.msg.Int32, callback=motor__callback)\nrospy.Subscriber('/bair_car/state', std_msgs.msg.Int32, callback=state__callback)\nrospy.Subscriber('/bair_car/encoder', std_msgs.msg.Float32, callback=encoder__callback)\nrospy.Subscriber('/bair_car/acc', geometry_msgs.msg.Vector3, callback=acc__callback)\nrospy.Subscriber('/bair_car/gyro', geometry_msgs.msg.Vector3, callback=gyro__callback)\nrospy.Subscriber('/bair_car/gyro_heading', geometry_msgs.msg.Vector3, callback=gyro_heading__callback)\n#rospy.Subscriber(\"/bair_car/zed/right/image_rect_color\",Image,right_image__callback,queue_size = 1)\n#rospy.Subscriber(\"/bair_car/zed/left/image_rect_color\",Image,left_image__callback,queue_size = 1)\n\nrobot_in_charge_pub_ = rospy.Publisher('cmd/robot_in_charge', std_msgs.msg.Int32, queue_size=100)\n\nGRAPHICS = False\nif True:\n\tacc2rd_list = []\n\tmean_acc2rd_list = []\n\tif GRAPHICS:\n\t\tfigure(1)\n\nrobot_in_charge_ = 0\nrobot_in_charge_exit_timer_ = Timer(2) # gives time to get to normal acc values after car turn signal\n\nwhile not rospy.is_shutdown():\n\ttime.sleep(0.01)\n\tif robot_in_charge_exit_timer_.check():\n\t\ttry:\n\t\t\tif reload_timer.check(): # put in thread?\n\t\t\t\treload(rp)\n\t\t\t\treload_timer.reset()\n\n\t\t\tif not robot_in_charge_:\n\t\t\t\tif False:\n\t\t\t\t\tcamera_img_ = R[left_image][vals][-1].copy()\n\t\t\t\t\tangles_to_center, angles_surfaces, distances_marker, markers = Angle_Dict_Creator.get_angles_and_distance(camera_img_)\n\t\t\t\t\tif GRAPHICS:\n\t\t\t\t\t\tkey_ = mci(camera_img_,color_mode=cv2.COLOR_RGB2BGR,delay=33,title='topics')\n\t\t\t\tacc2rd = R[acc_x][vals][-1]**2+R[acc_z][vals][-1]**2\n\t\t\t\tif True:\n\t\t\t\t\tacc2rd_list.append(acc2rd)\n\t\t\t\t\tif len(acc2rd_list) > 100:\n\t\t\t\t\t\tacc2rd_list = acc2rd_list[-100:]\n\t\t\t\t\tif GRAPHICS:\n\t\t\t\t\t\tclf();xylim(0,100,0,20)\n\t\t\t\t\t\tplot(acc2rd_list);\n\t\t\t\t\t#print acc2rd\n\t\t\t\tif len(acc2rd_list) > 3:\n\t\t\t\t\tmean_acc2rd = np.array(acc2rd_list[-3:]).mean()\n\t\t\t\t\t#print(dp(mean_acc2rd))\n\t\t\t\t\tmean_acc2rd_list.append(mean_acc2rd)\n\t\t\t\t\tif len(mean_acc2rd_list) > 100:\n\t\t\t\t\t\tmean_acc2rd_list = mean_acc2rd_list[-100:]\n\t\t\t\t\tif GRAPHICS:\n\t\t\t\t\t\tplot(mean_acc2rd_list);\n\t\t\t\t\tif mean_acc2rd > rp.robot_acc2rd_threshold:\n\t\t\t\t\t\trobot_in_charge_ = 1\n\t\t\t\t\t\tacc2rd_list = []\n\t\t\t\t\t\tmean_acc2rd_list = []\n\t\t\t\t\t\tprint(\"robot_in_charge_ = 1\")\n\t\t\telse:\n\t\t\t\tprint(R[acc_y][vals][-1],rp.robot_acc_y_exit_threshold,robot_in_charge_)\n\t\t\t\tif R[acc_y][vals][-1] < rp.robot_acc_y_exit_threshold:\n\t\t\t\t\trobot_in_charge_ = 0\n\t\t\t\t\tprint(\"robot_in_charge_ = 0\")\n\t\t\t\t\trobot_in_charge_exit_timer_.reset()\n\t\t\t\telse:\n\t\t\t\t\ttime.sleep(0.1)\n\t\t\trobot_in_charge_pub_.publish(std_msgs.msg.Int32(robot_in_charge_))\n\n\t\t\tif GRAPHICS:\n\t\t\t\tplt.pause(0.0001)\t\t\n\t\texcept Exception as e:\n\t\t\tpass\n\t\t\t#print(\"********** Exception ***********************\")\n\t\t\t#print(e.message, e.args)\n\telse:\n\t\tprint(\"NOT robot_in_charge_exit_timer_.check()\")\n\t\ttime.sleep(0.25)\n\nstop_ros()\n","sub_path":"Cars/robot_car_6Aug2017/nodes/robot_node.py","file_name":"robot_node.py","file_ext":"py","file_size_in_byte":6504,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"392920845","text":"#!/usr/bin/python3.8\n# -*- coding: utf-8 -*-\nfrom typing import List, Optional\n\nfrom fastapi import APIRouter\nfrom pydantic import BaseModel\n\nfrom campaign_finance_api.utils import constants_sql, helpers\n\n\nrouter = APIRouter()\n\n\nclass Candidates(BaseModel):\n    filerid: str\n    committee_name: Optional[str]\n    candidate_firstname: Optional[str]\n    candidate_middlename: Optional[str]\n    candidate_lastname: Optional[str]\n    candidate_suffix: Optional[str]\n\n\n@router.get('/candidates', response_model=List[Candidates])\ndef get_all_candidates():\n    \"\"\"\n    Get all candidates.\n    \"\"\"\n    sql = constants_sql.GET_ALL_CANDIDATES_SQL\n    return helpers.extract_data(sql)\n\n\nclass Committees(BaseModel):\n    filerid: str\n    committee_name: str\n\n\n@router.get('/committees', response_model=List[Committees])\ndef get_all_committees():\n    \"\"\"\n    Get all committees.\n    \"\"\"\n    sql = constants_sql.GET_ALL_COMMITTEES_SQL\n    return helpers.extract_data(sql)\n","sub_path":"campaign_finance_api/routers/campaigns.py","file_name":"campaigns.py","file_ext":"py","file_size_in_byte":958,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"46901857","text":"import heapq\r\r\nclass Solution:\r\n    '''\r\n    @param {int[]} nums an integer array\r\n    @param {int} k an integer\r\n    @return {int[]} the top k largest numbers in array\r\n\n    Given an integer array, find the top k largest numbers in it.\n    Given [3,10,1000,-99,4,100] and k = 3.\n    Return [1000, 100, 10].\n\n    '''\r\n    def topk(self, nums, k):\r\n        # Write your code here\r\n        if k == 0:\r\n            return[]\r\n        if k == 1:\r\n            return max(nums)\r\n        \r\n        heap = [0 for i in range(0,k)]\r\n        heapq.heapify(heap)\r\n        #print(heap)\r\n        for i in nums:\r\n            heapq.heappushpop(heap,i)\r\n            #print(i,heap)\r\n        heap.sort(reverse = True)\r\n        return heap\n","sub_path":"Heaps/TopKLargestNumbers.py","file_name":"TopKLargestNumbers.py","file_ext":"py","file_size_in_byte":719,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"556394421","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# created by: lavenliu\n# created date: 2016-01-22\n\n\nimport os\nimport sys\n\nMINUTE = 60\nHOUR = MINUTE * 60\nDAY = HOUR * 24\n\n\ndef which(program):\n    def is_exe(fpath):\n        return os.path.isfile(fpath) and os.access(fpath, os.X_OK)\n    \n    fpath, fname = os.path.split(program)\n    if fpath:\n        if is_exe(program):\n            return program\n    else:\n        for path in os.environ[\"PATH\"].split(os.pathsep):\n            path = path.strip('\"')\n            exe_file = os.path.join(path, program)\n            if is_exe(exe_file):\n                return exe_file\n\n    return None\n\n\nclass bcolors:\n    HEADER = \"\\033[1;35m\"\n    OKBLUE = \"\\033[1;34m\"\n    OKGREEN = \"\\033[1;32m\"\n    WARNING = \"\\033[1;33m\"\n    FAIL = \"\\033[1;31m\"\n    ENDC = \"\\033[m\"\n    \n    def disable(self):\n        self.HEADER = \"\"\n        self.OKBLUE = \"\"\n        self.OKGREEN = \"\"\n        self.WARNING = \"\"\n        self.FAIL = \"\"\n        self.ENDC = \"\"\n\n\ndef purple_str(str):\n    return \"%s%s%s\" % (bcolors.HEADER, str, bcolors.ENDC)\n\n\ndef green_str(str):\n    return \"%s%s%s\" % (bcolors.OKGREEN, str, bcolors.ENDC)\n\n\ndef blue_str(str):\n    return \"%s%s%s\" % (bcolors.OKBLUE, str, bcolors.ENDC)\n\n\ndef yellow_str(str):\n    return \"%s%s%s\" % (bcolors.WARNING, str, bcolors.ENDC)\n\n\ndef red_str(str):\n    return \"%s%s%s\" % (bcolors.FAIL, str, bcolors.ENDC)\n\n\nMSG_OK = bcolors.OKGREEN + \"SUCCESS:%s\" + bcolors.ENDC\nMSG_INFO = bcolors.OKGREEN + \"INFO:%s\" + bcolors.ENDC\nMSG_WARN = bcolors.WARNING + \"WARNING:%s\" + bcolors.ENDC\nMSG_FAIL = bcolors.FAIL + \"FAIL:%s\" + bcolors.ENDC\n\n\ndef print_time(label, time):\n    days = float(time) / DAY\n    hours = (float(time) % DAY) / HOUR\n    minutes = (float(time) % HOUR) / MINUTE\n    seconds = (float(time) % MINUTE)\n\n    print(\"%s: [%d days] + [%d hours %d minutes %d seconds]\" % (label,\n                                                                int(days),\n                                                                int(hours),\n                                                                int(minutes),\n                                                                int(seconds)))\n\n\n# get the system kernel version\nwith open('/proc/sys/kernel/version') as f:\n    sys_version = f.readline()\n    \n\n# 获取主机名\nwith open('/proc/sys/kernel/hostname') as f:\n    hostname = f.readline()\n    \n\n# 获取内核版本号\nwith open('/proc/sys/kernel/osrelease') as f:\n    os_release = f.readline()\n\n\n# 获取启动启动时间\nwith open('/proc/uptime') as f:\n    uptime_second, idle_second = f.readline().split()\n\n\nif __name__ == '__main__':\n    print(sys_version)\n    print(os_release)\n    print(hostname)\n    print_time(\"uptime\", uptime_second)\n","sub_path":"os_soup/system_report.py","file_name":"system_report.py","file_ext":"py","file_size_in_byte":2714,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"578891394","text":"#!/usr/bin/env python\n#\n# author: scott olesen <swo@mit.edu>\n\nimport argparse, fileinput, itertools\n\ndef down_to(l):\n    '''[1,2,3] becomes [[1], [1,2], [1,2,3]]'''\n    return [l[0: i] for i in range(1, len(l) + 1)]\n\nclass Lineage():\n    standard_ranks = ['domain', 'phylum', 'class', 'order', 'family', 'genus']\n    down_to_standard_ranks = down_to(standard_ranks)[1:]\n    pretty_dtsr = [\"2\".join([l[0], l[-1]]) for l in down_to_standard_ranks]\n\n    def __init__(self, assignments, conf=None):\n        self.assignments = assignments\n\n        if conf is not None:\n            self.conf = conf\n            self.assignments = list(itertools.takewhile(lambda x: x[2] >= conf, self.assignments))\n\n        self._taxa_map = {x[1]: x[0] for x in assignments} # e.g., domain => Bacteria\n        self.standard_taxa = [self._taxa_map.get(rank, \"NA\") for rank in self.standard_ranks]\n        self.down_to_standard_taxa = self._get_down_to_standard_taxa()\n        self.pretty_dtst = [\";\".join(l) for l in self.down_to_standard_taxa]\n\n    def _get_down_to_standard_taxa(self):\n        # drop trailing NAs\n        nonna_taxa = list(reversed(list(itertools.dropwhile(lambda x: x == \"NA\", reversed(self.standard_taxa))))) \n\n        # replace internal NAs with _, make downto craziness\n        out = down_to([\"_\" if t == \"NA\" else t for t in nonna_taxa])\n\n        # append enough copies to fill out the end \n        out += out[-1] * (len(self.standard_taxa) - len(out))\n\n        # drop the first one, which is just domain\n        out.pop(0)\n\n        return out\n\n\ndef parse_line(line):\n    fields = line.rstrip().translate({ord('\"'): None}).split(\"\\t\")\n    entry_id = fields[0].split(\";\")[0]\n    assignments = [[x, y, float(z)] for [x, y, z] in zip(*[iter(fields[2:])] * 3)]\n    \n    return entry_id, assignments\n\n\nif __name__ == '__main__':\n    p = argparse.ArgumentParser(description=\"turn RDP fixrank into tabbed format\", formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n    p.add_argument('--min_confidence', '-m', metavar='CONF', type=float, default=0.8)\n    p.add_argument('input', nargs='*', metavar='FILE')\n    args = p.parse_args()\n\n    headers = ['id'] + Lineage.standard_ranks + Lineage.pretty_dtsr\n    print(*headers, sep=\"\\t\")\n\n    for line in fileinput.input(args.input):\n        entry_id, assignments = parse_line(line)\n        lineage = Lineage(assignments, conf=args.min_confidence)\n\n        out_fields = [entry_id] + lineage.standard_taxa + lineage.pretty_dtst\n        print(*out_fields, sep=\"\\t\")\n","sub_path":"fixrank2tab.py","file_name":"fixrank2tab.py","file_ext":"py","file_size_in_byte":2505,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"33264350","text":"#coding:utf8\nfrom mxnet import ndarray as nd\nfrom mxnet import autograd as ag\nfrom mxnet import gluon\nfrom utils import load_data_fashion_mnist,accuracy,evaluate_accuracy,Result\n\n\n#超参数\nbatch_size = 10\nnum_inputs = 28828\nnum_outputs = 10\nnum_hiddens = 256\nlearning_rate = .01\n\n#加载数据\ntrain_data ,test_data = load_data_fashion_mnist(batch_size)\n\n#定义模型\nnet = gluon.nn.Sequential()\nwith net.name_scope():\n    net.add(gluon.nn.Flatten())\n    net.add(gluon.nn.Dense(num_hiddens,activation=\"relu\"))\n    net.add(gluon.nn.Dense(num_outputs))\nnet.initialize()\n\n#损失函数\nsoftmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss()\n\n#训练\nresult= Result()\ntrainer = gluon.Trainer(net.collect_params(),'sgd',{\"learning_rate\":learning_rate})\nnum_batchs = len(train_data)\nfor epoch in range(10):\n    train_loss = .0\n    train_acc= .0\n    for data,label in train_data:\n        with ag.record() :\n            output = net(data)\n            loss = softmax_cross_entropy(output,label)\n        loss.backward()\n        trainer.step(batch_size)\n        train_loss += nd.mean(loss).asscalar()\n        train_acc += accuracy(output,label)\n    test_acc = evaluate_accuracy(test_data,net)\n    result.add(\"train_loss\",train_loss/num_batchs)\n    result.add(\"train_acc\",train_acc/num_batchs)\n    result.add(\"test_acc\",test_acc)\n    print(\"Epoch %d : train_loss:%f, train_acc:%f, test_acc:%f\"%(epoch,train_loss/num_batchs,train_acc/num_batchs,test_acc))\n\nresult.show()\n","sub_path":"supervisor_multi_layer_perception_gluon_dh.py","file_name":"supervisor_multi_layer_perception_gluon_dh.py","file_ext":"py","file_size_in_byte":1467,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"191915714","text":"# simara 70\n# 172386\n# 1367689\nfrom Network import Network\nimport networkx as nx\nimport copy\n\n# Create network with all nodes/edges read in from C. Elegans connectivity data\nnw = Network()\nnw.read_neuron_data()\n\nstarting_graph = nw.graph\n\n# base_graph is the graph containing entire neural network without extra sensors (nonsensors + ALML, ALMR, AVM sensors)\nbase_graph = copy.deepcopy(starting_graph)\n\n#nodes = [node for node in nw.nodes if not nw.neurons[node].is_muscle() and node != \"PVDR\" and node != \"VC06\"]\nmuscles = [node for node in nw.nodes if nw.neurons[node].is_muscle() and node != \"MVULVA\" and node != \"MANAL\"]  # Exclude MVULVA and MANAL muscles (vulva and anal muscles)\n\nsensors =[node for node in nw.nodes if  nw.neurons[node].is_sensor()] \noldsensors = copy.deepcopy(sensors)\noldsensors.remove(\"ALML\")\noldsensors.remove(\"ALMR\")\noldsensors.remove(\"AVM\")\nnonsensors =[node for node in nw.nodes if not nw.neurons[node].is_sensor()] \nsensors = [\"ALML\", \"ALMR\", \"AVM\"] # Sensors selected for the paper which yield z = 89\n\nbase_graph.remove_nodes_from(oldsensors) # base_graph now  = nonsensors + the sensors ALML/R, AVM (such a weird way to do this, but ok man i didn't write this thing)\n\nT = len(base_graph.nodes) - len(sensors)  # T = 297 = N + M + S - S\nlinking_graph = nx.DiGraph()\nlinking_graph.add_node(\"SOURCE\")\nlinking_graph.add_node(\"TARGET\")\n\n# V_A\n# Create 300 copies of each node (len(V_A) = (N + M) * (N + M))\nfor node in base_graph.nodes:  # Includes sensors and muscles as well\n    # In this case, node serves as i = 1, 2, ... N + M\n    for t in range(1, T + 1):\n        act_node = node + \"_A_\" + str(t)  # Create ~300 copies of each node and add them to the linking graph as ADAL_A_1, ADAL_A_2, ... ADAL_A_300\n        linking_graph.add_node(act_node)\n\n\nVB = []  # 300 copies of each sensor: sensor_B_#\n# V_B\nfor sensor in sensors:  # i.e. ALML, ALMR, AVM\n    # In this case, sensor serves as i = 1, 2, ... S\n    for t in range(1, (T + 1) - 1):\n        act_node = sensor + \"_B_\" + str(t)  # Add 300 copies of each sensor and add them to the linking graph as ALML_B_0, ALML_B_1, ... ALML_B_299\n        linking_graph.add_node(act_node)\n        VB.append(act_node)\n\n\nVC = []  # all muscles: muscle_C_\n# V_C\n# This is done correctly\nfor muscle in muscles:\n    # In this case, muscle serves as i = 1, 2, ... M\n    act_node = muscle + \"_C\"  # Just add _C on to every muscle and add it to the linking graph\n    linking_graph.add_node(act_node)\n    VC.append(act_node)\n\n# E_A\n# E_A is composed of edges between any node in the graph\nfor t in range(1, (T + 1) - 1):\n    # Checking to see if the edge is in the actual graph effectively checks if a_ij != 0\n    for edge in base_graph.edges:  # Makes it impossible for a_ij = 0\n        act_edge = (edge[0] + \"_A_\" + str(t), edge[1] + \"_A_\" + str(t + 1))  # Create v^A_j,t -> v^A_i,t+1 by creating node1_t -> node2_t+1\n        linking_graph.add_edges_from([act_edge])\n\n# E_B\n# E_B is composed of edges between sensors\nfor t in range(1, (T + 1) - 1):\n    for sensor in sensors:\n        act_edge = (sensor + \"_B_\" + str(t), sensor + \"_A_\" + str(t + 1))  # Create v^B_j,t -> v^A_i,t+1 by creating sensor1_t -> node2_t+1\n        linking_graph.add_edges_from([act_edge])\n\n# E_C\n# E_C is composed of edges between muscles\nfor muscle in muscles:  # Makes it impossible for c_ij = 0\n    act_edge = (muscle + \"_A_\" + str(T), muscle + \"_C\")  # Create v^A_j,t -> v^C_i by creating \n    linking_graph.add_edges_from([act_edge])\n\nfor node in VB:\n    # We're adding a SOURCE node for every sensor, so basically we control every sensor\n    linking_graph.add_edge(\"SOURCE\", node)  # Add an edge between SOURCE and every copy of every sensor\n\nfor node in VC:\n    # We're adding a TARGET node for every muscle, so we're checking the output at each muscle\n    linking_graph.add_edge(node, \"TARGET\")  # Add an edge between every muscle and TARGET\n\n\n\nlinking_size = nx.node_disjoint_paths(linking_graph, \"SOURCE\", \"TARGET\")  # Find all disjoint paths between SOURCE and TARGET\n# Disjoint paths are paths that only share their first and last nodes\nprint(\"upper bound: \" + str(len(list(linking_size))))","sub_path":"Celegans/hungarian dude's code/new_upper_bound.py","file_name":"new_upper_bound.py","file_ext":"py","file_size_in_byte":4144,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"314418320","text":"#! /usr/bin/python\n\nimport json\nimport urllib\nimport requests\n\nelasticsearch_url=\"http://127.0.0.1:9200\"\nkibana_url=\"http://127.0.0.1:5601\"\nsearch_result_size=100\nsaved_objects={ \"dashboard\": [], \"index-pattern\": [], \"search\": [], \"visualization\": [] }\n\nfor object_type, object_list in saved_objects.iteritems():\n   object_search_url = elasticsearch_url + \"/.kibana/_search?q=type:\" + object_type + \"&size=\" + str(search_result_size)\n   response = requests.get(url=object_search_url)\n   json_data = response.json()\n   object_search_data=json_data['hits']['hits']\n   for item in object_search_data:\n       object_list.append(item['_id'].split(\":\")[1])\n   for object_id in object_list:\n       object_url = kibana_url + \"/api/saved_objects/\" + object_type + \"/\" + object_id\n       response = urllib.urlopen(object_url)\n       object_data = json.loads(response.read())\n       object_data_file = object_type + \":\" + object_id + \".json\"\n       with open(object_data_file, 'w') as outfile:  \n           json.dump(object_data, outfile)\n","sub_path":"export.py","file_name":"export.py","file_ext":"py","file_size_in_byte":1028,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"201516779","text":"from __future__ import division\nfrom __future__ import print_function\n\nimport numpy as np\nimport matplotlib\n\nmatplotlib.use('TkAgg')\nimport matplotlib.pyplot as plt\n\nnp.random.seed(0)  # set the random seed for reproducibility\n\ndef generate_sample(xmin, xmax, sample_size):\n    x = np.linspace(start=xmin, stop=xmax, num=sample_size)\n    pix = np.pi * x\n    target = np.sin(pix) / pix + 0.1 * x\n    noise = 0.05 * np.random.normal(loc=0., scale=1., size=sample_size)\n    return x, target + noise\n\n\ndef calc_design_matrix(x, c, h):\n    return np.exp(-(x[None] - c[:, None]) ** 2 / (2 * h ** 2))\n\nif __name__ == '__main__':\n    # create sample\n    sample_size = 50\n    xmin, xmax = -3, 3\n    x, y = generate_sample(xmin=xmin, xmax=xmax, sample_size=sample_size)\n\n    h = 1 # Gauss kernel band-width\n    l = 30 # regularization term\n\n    # S-fold cross-validation\n    s = 5\n    test_data_size = int(sample_size/s)\n    error = np.zeros(s)\n    for i in range(s):\n        train_x = np.concatenate((x[:test_data_size*i],x[test_data_size*(i+1):]))\n        train_y = np.concatenate((y[:test_data_size*i],y[test_data_size*(i+1):]))\n        test_x = x[test_data_size*i:test_data_size*(i+1)]\n        test_y = y[test_data_size*i:test_data_size*(i+1)]\n\n        # calculate design matrix\n        k = calc_design_matrix(train_x, train_x, h)\n\n        # solve the least square problem\n\n        theta = np.linalg.solve(\n            k.T.dot(k) + l * np.identity(len(k)),\n            k.T.dot(train_y[:, None]))\n\n        # evaluate test_data\n        test_K = calc_design_matrix(train_x, test_x ,h)\n        prediction = test_K.dot(theta)\n        error[i] = (prediction.T+-1*test_y).dot((prediction.T + -1*test_y).T)\n    print(np.sum(error)/s)\n\n    # create data to visualize the prediction\n    X = np.linspace(start=xmin, stop=xmax, num=5000)\n    K = calc_design_matrix(train_x, X, h)\n    prediction = K.dot(theta)\n\n    # visualization\n    plt.clf()\n    plt.scatter(x, y, c='green', marker='o')\n    plt.plot(X, prediction)\n    plt.savefig('l'+str(l)+'h'+str(h)+'.png')\n","sub_path":"ada/cross-validation.py","file_name":"cross-validation.py","file_ext":"py","file_size_in_byte":2046,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"447328619","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport annoying.fields\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('program_manager', '0004_auto_20160211_2006'),\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='JunkRemovalSettings',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('delete_spam_records', models.BooleanField(default=True)),\n            ],\n        ),\n        migrations.AlterField(\n            model_name='program',\n            name='junk_removal',\n            field=models.BooleanField(default=False),\n        ),\n        migrations.AddField(\n            model_name='junkremovalsettings',\n            name='program',\n            field=annoying.fields.AutoOneToOneField(related_name='junk_removal_settings', null=True, blank=True, to='program_manager.Program'),\n        ),\n    ]\n","sub_path":"program_manager/migrations/0005_auto_20160211_2007.py","file_name":"0005_auto_20160211_2007.py","file_ext":"py","file_size_in_byte":1011,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"50955070","text":"# -*- coding:utf8 -*-\nimport random\nimport requests\nimport re\nimport tkinter.messagebox as tk\n\nlogin_html = ''\ntemp_html = ''\nclock_html = ''\n\n# 填写下面信息即可\ntem = 37 - random.randint(5, 10) / 10  # 正常温度：36° - 36.5°\nuser = '2018xxxxx'         # 你的学号\npassword = 'password'      # 你的密码\narea = 'xx省'              # 你的省份\nlocation = 'xx市xx县'      # 详细位置\n\ntry:\n\n    # 抓取网页上的__VIEWSTATE 等参数\n    def get__(target, html):\n        '''\n        抓取网页上的__VIEWSTATE 等参数用于post\n        :param target: 要抓取的参数名\n        :param html:   抓取参数所在的html文本\n        :return: 返回参数的值\n        '''\n        text = 'id=\"' + target + '\" value=\"'\n        res = re.search(text + '[/+=\\w]+\"', html)  # 查找含有类似于id=\"__VIEWSTATE\" value=\"/wsdijd....\"的字段\n        start = res.span()[0] + len(text)\n        end = res.span()[1] - 1\n        return html[start:end]  # 截取参数内容返回\n\n\n    # 打卡提交数据\n    data = {\n        # 两个标志是必要的\n        '__VIEWSTATE': '',\n        '__EVENTVALIDATION': '',\n\n        # 打卡表单内容\n        'ctl00$cph_right$e_area': area,\n        'ctl00$cph_right$e_location': location,\n        'ctl00$cph_right$e_observation': '无下列情况',\n        'ctl00$cph_right$e_health$0': '无不适',\n        'ctl00$cph_right$e_temp': str(tem),\n        'ctl00$cph_right$e_describe': '',\n        'ctl00$cph_right$e_survey01': '疫情期间未出国出境',\n        'ctl00$cph_right$e_submit': '提交保存'\n    }\n\n    # 登录Requests Header\n    login_head = {\n        'Origin': 'https://ssp.scnu.edu.cn',\n        'Referer': 'https://ssp.scnu.edu.cn/login.aspx',\n        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.163 Safari/537.36'\n    }\n\n    # 打卡Requests Header\n    clock_head = {\n        'Origin': 'https://ssp.scnu.edu.cn',\n        'Referer': '',\n        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.163 Safari/537.36',\n    }\n\n    # 登录界面\n    s = requests.session()\n    login_url = 'https://ssp.scnu.edu.cn/login.aspx'  # 登录url\n    login = s.get(login_url)\n    login_html = login.text\n\n    # 登录Requests  Data\n    login_data = {\n        # '__VIEWSTATEGENERATOR': 'C2EE9ABB',\n        '__EVENTTARGET': 'logon',\n        '__VIEWSTATE': get__('__VIEWSTATE', login_html),\n        '__EVENTVALIDATION': get__('__EVENTVALIDATION', login_html),\n        'log_username': user,\n        'log_password': password\n    }\n\n    # 向登录页面post数据\n    res = s.post(url=login_url, data=login_data, headers=login_head)\n\n    # 利用登录后网页源代码查找key\n    # 因为打卡页面上参数key每次登录都会变化\n    # 找到key就可以定位到打卡页面\n    temp_html = res.text\n    start = temp_html.find('opt_rc_jkdk.aspx?')\n    end = temp_html.find('>健康打卡') - 1\n    clock_site = 'https://ssp.scnu.edu.cn/' + temp_html[start:end]  # 截取key获得打卡网址\n    clock_head['Referer'] = clock_site  # 用打卡网址更新 Requests Header 的参数\n\n    clock_html = s.get(clock_site).text  # 用于中间Requests Data查找请求参数\n\n    # 中间Requests Data\n    temp_data = {\n        'ctl00$cph_right$e_ok': 'on',\n        'ctl00$cph_right$ok_submit': '开始填报',\n        # '__VIEWSTATEGENERATOR':'DC47EEF4',\n\n        '__VIEWSTATE': get__('__VIEWSTATE', clock_html),\n        '__EVENTVALIDATION': get__('__EVENTVALIDATION', clock_html)\n    }\n\n    # 进入打卡页面\n    res = s.post(url=clock_site, data=temp_data, headers=clock_head)\n    clock_html = res.text\n\n    # 更新打卡Data\n    data['__VIEWSTATE'] = get__('__VIEWSTATE', clock_html)\n    data['__EVENTVALIDATION'] = get__('__EVENTVALIDATION', clock_html)\n\n    # 提交打卡内容\n    res2 = s.post(url=clock_site, data=data, headers=clock_head)\n\n    if res2.text.find('打卡成功') == -1:\n        tk.showwarning('打卡失败！','请查看打卡内容是否变更！')\n    else:\n        tk.showinfo('打卡成功', '温度为：' + str(tem))\nexcept Exception as e:\n    tk.showerror('打卡失败！', '请查看error log')\n    with open('error log.txt','w') as f:\n        f.write(str(e))\n        f.write('====='*6 + '\\n')\n        f.write(login_html)\n        f.write('====='*6 + '\\n')\n        f.write(temp_html)\n        f.write('====='*6 + '\\n')\n        f.write(clock_html)\n","sub_path":"requests.py","file_name":"requests.py","file_ext":"py","file_size_in_byte":4497,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"605693656","text":"import argparse\nimport pathlib\nimport random\nimport numpy as np\nimport cv2\nfrom tqdm import tqdm\n\n\nparser = argparse.ArgumentParser(description='calc mean and std from given paths')\nparser.add_argument('paths', default=None, help=\"Path to the png\")\nparser.add_argument('sample_num', default=3, type=int)\n\n\nif __name__ == \"__main__\":\n    args = parser.parse_args()\n\n    path = pathlib.Path(args.paths)\n    image_list = list(path.glob(\"**/*.png\"))\n    print(\"find {} samples, going to sample {}\".format(len(image_list), args.sample_num))\n\n    random.shuffle(image_list)\n\n    image_list = image_list[:args.sample_num]\n\n    imgs = [np.expand_dims(cv2.imread(str(x)), -1) for x in image_list]\n    imgs = np.concatenate(imgs, -1)\n    imgs = imgs.astype(np.float32) / 255.\n\n    means, stdevs = [], []\n    for i in tqdm(range(3)):\n        pixels = imgs[:, :, i, :].ravel()  # flatten\n        means.append(np.mean(pixels))\n        stdevs.append(np.std(pixels))\n\n    # cv2 : BGR\n    means.reverse()  # BGR --> RGB\n    stdevs.reverse()\n\n    print(\"normMean = {}\".format(means))\n    print(\"normStd = {}\".format(stdevs))\n    print('transforms.Normalize(normMean = {}, normStd = {})'.format(means, stdevs))\n","sub_path":"extra/CAMELYON16/calc_mean_std.py","file_name":"calc_mean_std.py","file_ext":"py","file_size_in_byte":1193,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"298019116","text":"from app import api, db\nfrom util.helpers import *\nfrom util.models import *\nfrom flask_restplus import Resource, abort, reqparse, fields\nfrom flask import request\n\nthread = api.namespace('thread', description='Thread APIs')\n\n\n@thread.route('/', strict_slashes=False)\nclass Thread(Resource):\n    @thread.response(200, 'Success', thread_details)\n    @thread.response(403, 'Invalid Auth Token')\n    @thread.response(400, 'Malformed Request')\n    @thread.param('id', 'thread id')\n    def get(self):\n        thread_id = request.args.get('id', None)\n        query = 'select t.id, u.name, t.title, t.description, t.course, t.endorsed from threads t left join users u on t.author = u.username where t.id = \"' + thread_id + '\"'\n        thread = db.to_list(query, [])\n        return {\n            \"author\": thread[0][1],\n            \"title\": thread[0][2],\n            \"description\": thread[0][3],\n            \"course\": thread[0][4],\n            \"endorsed\": thread[0][5]\n        }\n\n    @thread.response(200, 'Success', thread_details)\n    @thread.response(403, 'Invalid Auth Token')\n    @thread.response(400, 'Malformed Request')\n    @thread.expect(thread_details)\n    def post(self):\n        (author, title, description, course) = unpack(\n            request.json, 'author', 'title', 'description', 'course')\n        thread = db.insert('THREADS').with_values(\n            author=author,\n            title=title,\n            description=description,\n            course=course\n        ).execute()\n        return {\n            'message': 'success'\n        }\n\n    @thread.response(200, 'Success')\n    @thread.response(403, 'Invalid Auth Token')\n    @thread.response(400, 'Malformed Request')\n    @thread.param('id', 'thread id')\n    def delete(self):\n        thread_id = request.args.get('id', None)\n        query = 'select id from comments where tid = \"' + thread_id + '\"'\n        cids = db.to_list(query, [])\n        for cid in cids:\n            db.delete('COMMENTS').where(id=cid[0]).execute()\n            db.delete('VOTES').where(cid=cid[0]).execute()\n        db.delete('THREADS').where(id=thread_id).execute()\n        return {\n            'message': 'success'\n        }\n\n    @thread.response(200, 'Success')\n    @thread.response(403, 'Invalid Auth Token')\n    @thread.response(400, 'Malformed Request')\n    @thread.param('id', 'thread id')\n    def put(self):\n        thread_id = request.args.get('id', None)\n        query = 'select endorsed from threads where id = \"' + thread_id + '\"'\n        r = db.to_list(query, [])\n        endorsed = r[0][0]\n        if endorsed == True:\n            endorsed = False\n        else:\n            endorsed = True\n        db.update('THREADS').set(endorsed=endorsed).where(\n            id=thread_id).execute()\n        return {\n            'message': 'success'\n        }\n\n\n@thread.route('/search', strict_slashes=False)\nclass ThreadSearch(Resource):\n    @thread.response(200, 'Success', thread_list_details)\n    @thread.response(403, 'Invalid Auth Token')\n    @thread.response(400, 'Malformed Request')\n    @thread.param('content', 'thread content')\n    @thread.param('course', 'course code')\n    def get(self):\n        thread_content = request.args.get('content', None)\n        course_code = request.args.get('course', None)\n        query = 'select t.id, u.name, t.title, t.description, t.course, t.endorsed from threads t left join users u on t.author = u.username where t.course = \"' + course_code + '\" and t.title like \"%' + thread_content + '%\" order by t.id desc'\n        threads = db.to_list(query, [])\n        return {\n            \"threads\": threads\n        }\n","sub_path":"backend/namespaces/thread.py","file_name":"thread.py","file_ext":"py","file_size_in_byte":3591,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"388075563","text":"import os\nfrom datetime import datetime\nfrom urllib.request import urlretrieve\nfrom zipfile import ZipFile\n\nfrom ndj_toolbox.fetch import (xml_df_internal, save_files)\n\nurl_base = 'https://www.al.sp.gov.br/repositorioDados/'\nurl_file = 'processo_legislativo/documento_autor.zip'\nurl = url_base + url_file\n\n\ndef main():\n    hoje = datetime.strftime(datetime.now(), '%Y-%m-%d')\n    DATA_DIR = f'data_{hoje}'\n\n    urlretrieve(url, f'{DATA_DIR}/documento_autor.zip')\n    zip_file = ZipFile(f'{DATA_DIR}/documento_autor.zip', 'r')\n    zip_file.extractall(f'{DATA_DIR}')\n    zip_file.close()\n    os.remove(f'{DATA_DIR}/documento_autor.zip')\n\n    xml_data = f'{DATA_DIR}/documento_autor.xml'\n    dataset = xml_df_internal(xml_data).process_data()\n    dataset = dataset[['IdAutor', 'IdDocumento', 'NomeAutor']]\n    dataset = dataset.rename(columns={\n        'IdDocumento': 'id_documento',\n        'IdAutor': 'id_autor',\n        'NomeAutor': 'nm_autor'\n    })\n    save_files(dataset, 'documentos_autores_indice')\n    os.remove(xml_data)\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"novedejulho/doc_autores_indice.py","file_name":"doc_autores_indice.py","file_ext":"py","file_size_in_byte":1068,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"158951927","text":"import tensorflow as tf\nimport pandas as pd\nimport numpy as np\nfrom tensorflow.contrib import rnn\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import f1_score, accuracy_score, recall_score, precision_score\n\ndata = pd.read_csv('creditcard.csv', skiprows=[0], header=None)\n\nfeatures = data.iloc[:, 1:30]\nlabels = data.iloc[:, -1]\n\nX_train,X_test,y_train,y_test = train_test_split(features, labels, test_size=0.2, shuffle=False, random_state=42)\n\nepochs = 8\nn_classes = 1\nn_units = 200\nn_features = 29\nbatch_size = 35\n\nx_placeholder= tf.placeholder('float', [None, n_features])\ny_placeholder = tf.placeholder('float')\n\n\ndef recurrent_neural_network_model():\n    layer ={ 'weights': tf.Variable(tf.random_normal([n_units, n_classes])),'bias': tf.Variable(tf.random_normal([n_classes]))}\n\n    x = tf.split(x_placeholder, n_features, 1)\n    print(x)\n\n    lstm_cell = rnn.BasicLSTMCell(n_units)\n    \n    outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32)\n   \n    output = tf.matmul(outputs[-1], layer['weights']) + layer['bias']\n\n    return output\n\ndef train_neural_network():\n    logit = recurrent_neural_network_model()\n    logit = tf.reshape(logit, [-1])\n\n    loss_op = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logit, labels=y_placeholder))\n    optimizer = tf.train.AdamOptimizer().minimize(loss_op)\n\n    with tf.Session() as sess:\n\n        tf.global_variables_initializer().run()\n        tf.local_variables_initializer().run()\n\n        for epoch in range(epochs):\n            epoch_loss = 0\n\n            i = 0\n            for i in range(int(len(X_train) / batch_size)):\n\n                start = i\n                end = i + batch_size\n\n                batch_x = np.array(X_train[start:end])\n                batch_y = np.array(y_train[start:end])\n                \n                _, loss = sess.run([optimizer, loss_op], feed_dict={x_placeholder: batch_x, y_placeholder: batch_y})\n                epoch_loss += loss\n                i += batch_size\n\n            print('Epoch', epoch, 'completed out of', epochs, 'loss: ', epoch_loss)\n\n        pred = tf.round(tf.nn.sigmoid(logit)).eval({x_placeholder: np.array(X_test), y_placeholder: np.array(y_test)})\n        \n        f1 = f1_score(np.array(y_test), pred, average='macro')\n        accuracy=accuracy_score(np.array(y_test), pred)\n        recall = recall_score(y_true=np.array(y_test), y_pred= pred)\n        precision = precision_score(y_true=np.array(y_test), y_pred=pred)\n\n        print(\"F1 Score: \", f1)\n        print(\"Accuracy Score: \",accuracy)\n        print(\"Recall: \", recall)\n        print(\"Precision: \", precision)\n\n\ntrain_neural_network()\n","sub_path":"experimental/card.py","file_name":"card.py","file_ext":"py","file_size_in_byte":2658,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"288128727","text":"import urllib2\n\nwiki = \"https://squareup.com/receipt/preview/fsEiegL6b3DmOtEqPNpdOyMF\"\n\npage = urllib2.urlopen(wiki)\n\nfrom bs4 import BeautifulSoup\n\nsoup = BeautifulSoup(page, \"html.parser\")\n\nall_tables=soup.find_all('table')\n\nright_table=soup.find('table', class_=\"table-container-section table-date-and-tenders\")\n\n\n\nmydivs = soup.findAll(\"div\", { \"class\" : \"p name_on_card\" })\n\n\ny = str(mydivs)\nz = y.replace('[<div class=\"p name_on_card\">',\"\")\ncust = z.replace('</div>]',\"\")\nprint(cust)\n","sub_path":"parseScript.py","file_name":"parseScript.py","file_ext":"py","file_size_in_byte":490,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"647186716","text":"import  requests, json\n\n\ndef decode_qr(qr_url):\n    host = 'http://qrapi.market.alicloudapi.com'\n    path = '/yunapi/qrdecode.html'\n    appcode = 'b50239aa446f477d8e2738e1c2f31bde'\n    bodys = {}\n    url = host + path\n\n    bodys['imgurl'] = qr_url\n    bodys['version'] = '''1.1'''\n\n    headers={\n        'Authorization':'APPCODE ' + appcode,\n        'Content-Type':\"application/x-www-form-urlencoded; charset=UTF-8\"\n    }\n\n    try:\n        html = requests.post(url=url,data=bodys,headers=headers)\n        return json.loads(html.text)['data']['raw_text']\n    except:\n        return None\n\nif __name__ == '__main__':\n\n\n    # 解析网络二维码\n    print(decode_qr('http://47.56.193.188:80/nginx_upload/qrcode/df844564c507c11caee4a8ee9d92d21d_00000003.jpeg'))","sub_path":"libs/utils/qrcode.py","file_name":"qrcode.py","file_ext":"py","file_size_in_byte":758,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"209481471","text":"def puzzle(papers, visited, puzzle_cnt):\n    global min_puzzle\n    s, e = visited\n    for a in range(s, 10):\n        for b in range(10):\n            if array[a][b] == 1:\n                for i in range(5, 0, -1):\n                    if papers[i] > 0 and change(a, b, i):\n                        papers[i] -= 1\n                        for aa in range(a, a+i):\n                            for bb in range(b, b+i):\n                                array[aa][bb] = 0\n                        puzzle(papers, (a, b), puzzle_cnt + 1)\n                        for aa in range(a, a+i):\n                            for bb in range(b, b+i):\n                                array[aa][bb] = 1\n                        papers[i] += 1\n                return\n    if min_puzzle > puzzle_cnt:\n        min_puzzle = puzzle_cnt\n    return\n\n\ndef change(aa, bb, size):\n    if aa + size > 10 or bb + size > 10:\n        return False\n\n    for da in range(size):\n        for db in range(size):\n            if array[aa + da][bb + db] != 1:\n                return False\n\n    return True\n\n\narray = [list(map(int, input().split())) for _ in range(10)]\nmin_puzzle = 999\npuzzle([0, 5, 5, 5, 5, 5], (0, 0), 0)\nif min_puzzle == 999:\n    min_puzzle = -1\n\nprint(min_puzzle)\n","sub_path":"Python/BAEKJOON/17136 색종이 붙이기/temp1.py","file_name":"temp1.py","file_ext":"py","file_size_in_byte":1232,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"166939030","text":"#<ImportModules>\nimport ShareYourSystem as SYS\n#</ImportModules>\n\n#<DefineLocals>\nBasingLocalTypeString=\"Classor\"\nBasedLocalClass=getattr(SYS,SYS.getClassStringWithTypeString(BasingLocalTypeString))\n#</DefineLocals>\n\n#<DefineFunctions>\n#</DefineFunctions>\n\n#<Define_Class>\nclass DeriverClass(BasedLocalClass):\n\n\tdef __init__(self,**_KwargVariablesDict):\n\n\t\t#<DefineSpecificDict>\n\t\tself.DerivedBaseClass=None\n\t\t#</DefineSpecificDict>\n\n\t\t#Call the parent init method\n\t\tBasedLocalClass.__init__(self,**_KwargVariablesDict)\n\n\tdef __call__(self,_Class):\n\n\t\t#Call the parent __call__ method\n\t\tBasedLocalClass.__call__(self,_Class)\n\n\t\t#Derive\n\t\tself.derive(_Class)\n\n\t\t#Return \n\t\treturn self.ClassingClass\n\n\tdef derive(self,_Class):\n\n\t\t#Set\n\t\tif len(self.ClassingClass.__bases__)>0:\n\n\t\t\t#Set the DerivedBaseClas\n\t\t\tself.DerivedBaseClass=self.ClassingClass.__bases__[0]\n\n\t\t\t#Append to the parent class \n\t\t\ttry:\n\t\t\t\tif hasattr(self.DerivedBaseClass,'DerivedClassesList'):\n\t\t\t\t\tself.DerivedBaseClass.DerivedBaseClass.DerivedClassesList.append(self.ClassingClass)\n\t\t\t\telse:\n\t\t\t\t\tself.DerivedBaseClass.DerivedBaseClass=[self.ClassingClass]\n\t\t\texcept TypeError:\n\t\t\t\t#print TypeError\n\t\t\t\tpass\n\n\t\t\t#Define a functed init method with the one of the parent base\n\t\t\t#@SYS.HookerClass(**{'HookingAfterTuplesList':[(self.DerivedBaseClass,self.DerivedBaseClass.__init__)]})\n\t\t\t#def init(_InstanceVariable,**_KwargVariablesDict):\n\t\t\t#\n\t\t\t#\t#Call the __init__ of the DerivedBaseClass\n\t\t\t#\tself.DerivedBaseClass.__init__(_InstanceVariable,**_KwargVariablesDict)\n\n\n#</Define_Class>\n","sub_path":"Modules/Init/Modules/_LastDrafts/01a_Deriver/Deriver.py","file_name":"Deriver.py","file_ext":"py","file_size_in_byte":1556,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"576981006","text":"\"\"\"\nProgram to extract DOI from pdf and create bibtex file.\n\n[Examples]\n\n    [Input]\n        pydoi2bib a.pdf b.pdf c.pdf d.pdf  # list of pdf files\n        pydoi2bib 10.1063/1.432868         # list of doi numbers\n        pydoi2bib a.txt                    # list of txt files\n        pydoi2bib a.pdf b.txt              # any combination\n        pydoi2bib -s                       # search for pdf files\n        pydoi2bib -r                       # search recursivly\n\n    [Output]\n        pydoi2bib a.pdf --bib              # bibtex file\n        pydoi2bib a.pdf --csv              # csv file\n        pydoi2bib a.pdf --open             # open in webbrowser\n\"\"\"\nimport collections\nimport warnings\nimport argparse\nimport logging\nimport codecs\nimport sys\nimport os\nimport re\n\ntry:\n    import pydoi2bib\nexcept ImportError:\n    mpath = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n    sys.path.append(mpath)\n    import pydoi2bib\n\n@pydoi2bib.logutils.trace()\ndef get_arguments(args=None):\n    \"\"\"\n    Get command line arguments.\n    \"\"\"\n    parser = argparse.ArgumentParser(description=__doc__,\n        formatter_class=argparse.RawDescriptionHelpFormatter)\n\n    # input\n    parser.add_argument(dest='objs', nargs='*', default=[],\n        help='list of pdfs, dois, or txt files')\n    parser.add_argument('-s', '--search', action='store_true',\n        help='search for pdf files')\n    parser.add_argument('-r', '--recursive', action='store_true',\n        help='search recursivly')\n    parser.add_argument('-f', '--filter', action='store_true',\n        help='remove duplicates')\n\n    # output\n    parser.add_argument('--stub', default='out', type=str,\n        help='output file stub')\n    parser.add_argument('--csv', action='store_true',\n        help='save csv file')\n    parser.add_argument('--bib', action='store_true',\n        help='save bib file')\n    parser.add_argument('--encoding', default='utf-8',\n        help='file encoding')\n    parser.add_argument('--keep', action='store_true',\n        help='keep temp dir')\n    parser.add_argument('--open', action='store_true',\n        help='open (first) doi link in webbrowser')\n\n    # pdftotext\n    parser.add_argument('--mode', default='pdftotext',\n        choices=['pdftotext', 'gocr', 'ocrad'],\n        help='pdf doi extraction method')\n    parser.add_argument('--page', type=int, default=0,\n        help='initial page')\n    parser.add_argument('--scan', type=int, default=0,\n        help='number of pages to convert after initial page')\n\n    # regex\n    parser.add_argument('--stop', action='store_true',\n        help='stop matching after first regex succeeds')\n\n    # crossref\n    parser.add_argument('--timeout', type=float, default=5.0,\n        help='timeout for crossref response')\n\n    # logging\n    group = parser.add_mutually_exclusive_group()\n    group.add_argument('-v', '--verbose', action='store_true',\n        help='verbose logging')\n    group.add_argument('-q', '--quiet', action='store_true',\n        help='silent logging')\n\n    opts = parser.parse_args(args)\n    opts = process_opts(opts)\n\n    return opts\n\n@pydoi2bib.logutils.trace()\ndef process_opts(opts):\n    \"\"\"\n    Process and amend top level options.\n    \"\"\"\n    # logging\n    if opts.verbose:\n        pydoi2bib.logutils.setDebug()\n    else:\n        if opts.quiet:\n            pydoi2bib.logutils.setError()\n        else:\n            pydoi2bib.logutils.setInfo()\n\n    if opts.recursive:\n        opts.search == True\n\n    if opts.search:\n        opts.objs.extend(pydoi2bib.find.pdfs(os.getcwd(), r=opts.recursive))\n\n    if not opts.objs:\n        raise RuntimeError('no input!')\n\n    if opts.filter:\n        opts.objs = list(collections.OrderedDict.fromkeys(opts.objs))\n\n    return opts\n\nclass ProcessTextFiles(object):\n    \"\"\"\n    Read txt file lines into list recursivly.\n    \"\"\"\n    max_calls, calls = 10, 0\n    trace = pydoi2bib.logutils.ClassTrace\n\n    @trace()\n    def __call__(self, objs):\n        self.calls += 1\n\n        if self.calls > self.max_calls:\n            raise RuntimeError('too many nested calls of %s'\n                % self.__class__.__name__)\n\n        loaded = []\n        for obj in objs:\n\n            # lookout for txt files\n            if os.path.isfile(obj):\n                stub, ext = os.path.splitext(obj)\n\n                # load lines from txt files\n                if ext == '.txt':\n                    lines = map(lambda x : x.strip(), open(obj, 'r').readlines())\n\n                    # recursive call\n                    loaded.extend(self(lines))\n\n                # hopefully its a pdf file name string\n                else:\n                    loaded.append(obj)\n\n            # hopefully its a doi string\n            else:\n                loaded.append(obj)\n\n        return loaded\n\nprocess_text_files = ProcessTextFiles()\n\n@pydoi2bib.logutils.trace()\ndef extract_doi_pdftotext(pdf, page=0, scan=0, clean=True, stop=True):\n    \"\"\"\n    Extract doi from pdf using pdftotext.\n    \"\"\"\n    # convert the pdf to pure text\n    text = pydoi2bib.pdftools.pdftotext(pdf, page=page, scan=scan, clean=clean)\n\n    if text:\n        # use regex to find doi\n        dois = pydoi2bib.doitools.scrape(text, stop=stop)\n\n        if dois:\n            # check to make sure all found doi match\n            if not (doi == dois[0] for doi in dois):\n                warnings.warn('not all matched doi agree')\n\n            return dois[0]\n\n    return ''\n\n@pydoi2bib.logutils.trace()\ndef extract_doi_ocr(pdf, mode='ocrad', page=0, scan=0, clean=True, stop=True, **kwargs):\n    \"\"\"\n    Extract doi from pdf using optical char recognition.\n    \"\"\"\n    pdf = os.path.abspath(pdf)\n    cwd = os.getcwd()\n\n    if page <= 0:\n        page = 1\n\n    dois = []\n    with pydoi2bib.common.tempdir(dir=cwd, chdir=True, clean=clean):\n\n        # loop over page range\n        for i in range(page, page + scan + 1):\n\n            # extract page and convert it to pbm\n            pdf_page = pydoi2bib.pdftools.extract(pdf, page=i)\n            pbm_page = pydoi2bib.ocrtools.convert(pdf_page, ext='pbm')\n\n            if mode.lower() == 'ocrad':\n                text = pydoi2bib.ocrtools.ocrad(pbm_page, **kwargs)\n\n            elif mode.lower() == 'gocr':\n                text = pydoi2bib.ocrtools.gocr(pbm_page, **kwargs)\n\n            else:\n                logging.error('unknown ocr mode: %s', mode)\n                text = ''\n\n            # use regex to find doi\n            dois.extend(pydoi2bib.doitools.scrape(text, stop=stop))\n\n    if dois:\n        # check to make sure all found doi match\n        if not (doi == dois[0] for doi in dois):\n            warnings.warn('not all matched doi agree')\n\n        return dois[0]\n\n    return ''\n\nclass Writer(pydoi2bib.logutils.LoggingObject):\n    \"\"\"\n    Write to file and log.\n    \"\"\"\n    def __init__(self, handle=None):\n        if isinstance(handle, str):\n            handle = open(handle, 'w')\n        self.handle = handle\n\n    def __call__(self, text, *args, **kwargs):\n        text = text.format(*args, **kwargs)\n        try:\n            self.handle.write(text + '\\n')\n            self.info(text)\n        except AttributeError:\n            self.info(text)\n\n@pydoi2bib.logutils.trace()\ndef main():\n    \"\"\"\n    Main program loop.\n    \"\"\"\n    work = os.getcwd()\n    opts = get_arguments()\n\n    # process text files\n    opts.objs = process_text_files(opts.objs)\n\n    # remove duplicates\n    if opts.filter:\n        opts.objs = list(collections.OrderedDict.fromkeys(opts.objs))\n\n    # process pdf files\n    dois = []\n    for obj in opts.objs:\n\n        # lookout for pdf files\n        if os.path.isfile(obj):\n            stub, ext = os.path.splitext(obj)\n\n            # scrape the pdf text for doi\n            if ext.lower() == '.pdf':\n                logging.debug('processing %s', obj)\n\n                if opts.mode == 'pdftotext':\n                    doi = extract_doi_pdftotext(obj, page=opts.page,\n                        scan=opts.scan, clean=not opts.keep, stop=opts.stop)\n\n                if opts.mode in ['ocrad', 'gocr']:\n                    doi = extract_doi_ocr(obj, mode=opts.mode, page=opts.page,\n                        scan=opts.scan, clean=not opts.keep, stop=opts.stop)\n\n                if doi:\n                    dois.append(doi)\n                else:\n                    dois.append(None)\n                    logging.error('failed to extract doi for %s', obj)\n\n            # somehow the wrong file type made it this far\n            else:\n                dois.append(None)\n                logging.error('invalid file type: %s', obj)\n\n        # hopefully its already a doi\n        else:\n            dois.append(obj)\n\n    # output bibtex file\n    if opts.bib:\n\n        bibdata = []\n        for obj, doi in zip(opts.objs, dois):\n            if doi:\n                entry = pydoi2bib.doitools.bibtex(doi, timeout=opts.timeout,\n                    encoding=opts.encoding)\n\n                if entry:\n                    bibdata.append(entry)\n\n        handle = '{}.bib'.format(opts.stub)\n\n        with codecs.open(handle, 'w', opts.encoding) as handle:\n            for entry in bibdata:\n                handle.write(entry + '\\n')\n\n    # output doi info\n    if opts.csv:\n        handle = '{}.csv'.format(opts.stub)\n    else:\n        handle = None\n\n    writer = Writer(handle)\n    for obj, doi in zip(opts.objs, dois):\n        writer('{}, {}', obj, doi)\n\n    # open link\n    if opts.open:\n        if dois:\n            pydoi2bib.doitools.webopen(dois[0])\n        else:\n            logging.error('can not open weblink')\n\nif __name__ == '__main__':\n    with pydoi2bib.logutils.capture():\n        main()\n","sub_path":"scripts/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":9566,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"317589720","text":"import numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom mpl_toolkits.mplot3d import Axes3D\r\n\r\ndef target_function(x,y):\r\n    J = x**2 + np.sin(y)**2\r\n    return J\r\n\r\ndef derivative_function(theta):\r\n    x = theta[0]\r\n    y = theta[1]\r\n    return np.array([2*x,2*np.sin(y)*np.cos(y)])\r\n\r\ndef show_3d_surface(x, y, z):\r\n    fig = plt.figure()\r\n    ax = Axes3D(fig)\r\n \r\n    u = np.linspace(-3, 3, 100)\r\n    v = np.linspace(-3, 3, 100)\r\n    X, Y = np.meshgrid(u, v)\r\n    R = np.zeros((len(u), len(v)))\r\n    for i in range(len(u)):\r\n        for j in range(len(v)):\r\n            R[i, j] = X[i, j]**2 + np.sin(Y[i, j])**2\r\n\r\n    ax.plot_surface(X, Y, R, cmap='rainbow')\r\n    plt.plot(x,y,z,c='black')\r\n    plt.show()\r\n\r\nif __name__ == '__main__':\r\n    theta = np.array([3,1])\r\n    eta = 0.1\r\n    error = 1e-2\r\n\r\n    X = []\r\n    Y = []\r\n    Z = []\r\n    for i in range(100):\r\n        print(theta)\r\n        x=theta[0]\r\n        y=theta[1]\r\n        z=target_function(x,y)\r\n        X.append(x)\r\n        Y.append(y)\r\n        Z.append(z)\r\n        print(\"%d: x=%f, y=%f, z=%f\" %(i,x,y,z))\r\n        d_theta = derivative_function(theta)\r\n        print(\"    \",d_theta)\r\n        theta = theta - eta * d_theta\r\n        if z < error:\r\n            break\r\n    show_3d_surface(X,Y,Z)\r\n","sub_path":"2020AutumnClass/第1次作业/201809040 王梦琪/p2.py","file_name":"p2.py","file_ext":"py","file_size_in_byte":1262,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"592590357","text":"class ProductOfNumbers:\n\n    def __init__(self):\n        self.nums = [1]\n    \n    def add(self, num: int) -> None:\n        if num == 0:\n            self.nums = [1]\n        else:\n            self.nums.append(self.nums[-1]*num)\n    def getProduct(self, k: int) -> int:\n        if k >= len(self.nums):\n            return 0\n        else:\n            return self.nums[-1] // self.nums[-k-1]\n\n\n\n# Your ProductOfNumbers object will be instantiated and called as such:\n# obj = ProductOfNumbers()\n# obj.add(num)\n# param_2 = obj.getProduct(k\n\n\n\n# Your ProductOfNumbers object will be instantiated and called as such:\n# obj = ProductOfNumbers()\n# obj.add(num)\n# param_2 = obj.getProduct(k\n\nproductOfNumbers = ProductOfNumbers();\nproductOfNumbers.add(3);        # [3]\nproductOfNumbers.add(0);        # [3,0]\nproductOfNumbers.add(2);        # [3,0,2]\nproductOfNumbers.add(5);        # [3,0,2,5]\nproductOfNumbers.add(4);        # [3,0,2,5,4]\nproductOfNumbers.getProduct(2); # 返回 20 。最后 2 个数字的乘积是 5 * 4 = 20\nproductOfNumbers.getProduct(3); # 返回 40 。最后 3 个数字的乘积是 2 * 5 * 4 = 40\nproductOfNumbers.getProduct(4); # 返回  0 。最后 4 个数字的乘积是 0 * 2 * 5 * 4 = 0\nproductOfNumbers.add(8);        # [3,0,2,5,4,8]\nproductOfNumbers.getProduct(2); # 返回 32 。最后 2 个数字的乘积是 4 * 8 = 32 \n\n","sub_path":"1352.py","file_name":"1352.py","file_ext":"py","file_size_in_byte":1345,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"453884150","text":"sum_len = 0\nfor d in data:\n\tsum_len = sum_len + len(d)\nprint('All comment average length', sum_len/len(data))\n\nnew = []\nfor d in data:\n\tif len(d) < 100:\n\t\tnew.append(d)\nprint('Have', len(new), 'comment less than 100 words')\nprint(new[0])\nprint(new[1])\n\ngood = [d for d in data if 'good' in d]\n\ngood = []\nfor d in data:\n\tif 'good'in d:\n\t\tgood.append(d)\nprint('Have', len(good), 'comment is a good comment')\nprint(good[0])\n\n\ndata = []\ncount = 0\nwith open('reviews.txt', 'r')as f:\n\tfor line in f:\n\t\tdata.append(line)\n\t\tcount += 1\n\t\tif count % 1000 == 0:\n\t\t\tprint(len(data))\nprint('All has been rad, Total', len(data), 'comment')\n\nprint(data[0])\n\n#文字技術\nwc = {}  #wordcount\nfor d in data:\n\twords = d.split()\n\tfor word in words:\n\t\tif word in wc:\n\t\t\twc[word] += 1\n\t\telse:\n\t\t\twc[word] = 1  #有等於就是新加key\n\nfor word in wc:\n\tif wc[word] > 1000000:\n\t\tprint(word, wc[word])\n\nprint(len(wc))\n\nwhile True:\n\tword = input('What do u want to search: ')\n\tif word == 'q':\n\t\tbreak\n\tif word in wc:\n\t\tprint(word, 'came out', wc[word], 'time')\n\telse:\n\t\tprint('I dont get this word, Try another one.')\nprint('Thanks For Your Using')","sub_path":"read.py","file_name":"read.py","file_ext":"py","file_size_in_byte":1126,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"239064906","text":"import math\nimport numpy as np\n\nclass Lut:\n    def __init__(self, size, image_size):\n        self.size = size\n        self.image_size = image_size\n        self.swatch_count = pow(self.size, 3)\n        self.columns = math.ceil(math.sqrt(self.swatch_count))\n        self.swatch_size = self.image_size / self.columns\n\n    def cell_center(self, i, size=1):\n        \"\"\"\n        Compute x/y center position for a 3D lattice node by index\n        \"\"\"\n        row, column = self.grid_coords(i)\n        x = int(column * self.swatch_size + self.swatch_size / 2)\n        y = int(row * self.swatch_size + self.swatch_size / 2)\n        if size == 1:\n            return (x, y)\n        else:\n            hs = size / 2\n            return (x-hs, y-hs, x+hs, y+hs)\n\n    def cell_bounds(self, i):\n        row, column = self.grid_coords(i)\n        x = column * self.swatch_size\n        y = row * self.swatch_size\n        return (x, y, x + self.swatch_size, y + self.swatch_size)\n\n    def grid_coords(self, i):\n        row = math.floor(i / self.columns)\n        column = i % self.columns\n        return (row,column)\n\n    def lattice_coords(self, i):\n        column = i % self.size\n        row = math.floor(i / self.size) % self.size\n        z = math.floor(i / pow(self.size, 2))\n        return (column, row, z)\n\n    def generate_colors(self):\n        \"\"\"\n        Generate and return the appropriate RGB colors for each node in a uniform lattice.\n        \"\"\"\n        steps = np.linspace(0, 255, self.size, dtype=np.uint8)\n        colors = np.array(\n                [[[(r,g,b) for r in steps] for g in steps] for b in steps]\n                ).reshape((self.swatch_count,3)).tolist()\n        return colors\n","sub_path":"lut_maker/lut.py","file_name":"lut.py","file_ext":"py","file_size_in_byte":1682,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"418248841","text":"\n\nfrom ..utils import Object\n\n\nclass SetAuthenticationPhoneNumber(Object):\n    \"\"\"\n    Sets the phone number of the user and sends an authentication code to the user. Works only when the current authorization state is authorizationStateWaitPhoneNumber\n\n    Attributes:\n        ID (:obj:`str`): ``SetAuthenticationPhoneNumber``\n\n    Args:\n        phone_number (:obj:`str`):\n            The phone number of the user, in international format \n        allow_flash_call (:obj:`bool`):\n            Pass true if the authentication code may be sent via flash call to the specified phone number \n        is_current_phone_number (:obj:`bool`):\n            Pass true if the phone number is used on the current deviceIgnored if allow_flash_call is false\n\n    Returns:\n        Ok\n\n    Raises:\n        :class:`telegram.Error`\n    \"\"\"\n    ID = \"setAuthenticationPhoneNumber\"\n\n    def __init__(self, phone_number, allow_flash_call, is_current_phone_number, extra=None, **kwargs):\n        self.extra = extra\n        self.phone_number = phone_number  # str\n        self.allow_flash_call = allow_flash_call  # bool\n        self.is_current_phone_number = is_current_phone_number  # bool\n\n    @staticmethod\n    def read(q: dict, *args) -> \"SetAuthenticationPhoneNumber\":\n        phone_number = q.get('phone_number')\n        allow_flash_call = q.get('allow_flash_call')\n        is_current_phone_number = q.get('is_current_phone_number')\n        return SetAuthenticationPhoneNumber(phone_number, allow_flash_call, is_current_phone_number)\n","sub_path":"pytglib/api/functions/set_authentication_phone_number.py","file_name":"set_authentication_phone_number.py","file_ext":"py","file_size_in_byte":1516,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"77692632","text":"import numpy\n# used to import sigmoid function\nimport scipy.special \nfrom tqdm import tqdm\nimport json\n\nclass NeuralOne():\n    \n    def __init__(self, learningRate=0.001, hiddenSize=1):\n        self.learningRate = learningRate # Learning Rate\n        self.currentLayers = 1\n        self.hiddenSize = hiddenSize # Number of nodes in hidden layer\n        self.netMap = {} # Holds weight mappings\n        # activation function\n        self.activation = lambda x: numpy.tanh(x)\n        self.reverseActivation = lambda x: scipy.special.logit(x)\n        pass\n\n    # Adds layers to netMap (Network Map)\n    def addLayer(self, inputSize=0, outputSize=0, inputLayer=False, outputLayer=False):\n        \n        if inputLayer:\n            # from input to hidden R x C 300 x 782, input = 782 x 1 \n            # Output to next layer = 300 x 1\n            inputHidden = (numpy.random.rand(self.hiddenSize, inputSize) - 0.5)\n            self.netMap[self.currentLayers] = {\"weights\": inputHidden}\n        elif outputLayer:\n            # from hidden to output R x C 10 x 300, input = 300 x 1\n            # Final out 10 x 1 \n            hiddenOutput = (numpy.random.rand(outputSize, self.hiddenSize) - 0.5)\n            self.netMap[self.currentLayers] = {\"weights\": hiddenOutput}\n        else:\n            # hidden sandwich layer R x C 300 x 300, input = 300 x 1,\n            # W * I - 300 x 300 * 300 x 1\n            # Output is 300 x 1\n            hiddenLayer = (numpy.random.rand(self.hiddenSize, self.hiddenSize) - 0.5)\n            self.netMap[self.currentLayers] = {\"weights\": hiddenLayer}\n\n        self.currentLayers = self.currentLayers + 1 # Increments layer\n\n    # Feeds forward through network, returns output\n    def feedForward(self, inputData):\n        # W * I - Inputs feed into Weights\n        inputData = numpy.array(inputData, ndmin=2).T\n        \n        for n in range(1, self.currentLayers):\n            layerOutput = numpy.dot(self.netMap[n][\"weights\"], inputData)\n            inputData = self.activation(layerOutput)\n            self.netMap[n][\"output\"] = inputData\n\n        finalOutput = inputData\n        return finalOutput\n\n    # Returns output matrix from neural net\n    def predict(self, inputData):\n        result = self.feedForward(inputData)\n        return result\n\n    # BackProp through network, updates weights\n    def backPropagation(self, targets, inputs):\n        \n        # changeWeight = -lr ( [Ek * Ok (1 - Ok) * Oj] )\n\n        finalOutput = self.netMap[self.currentLayers - 1][\"output\"]\n        targets = numpy.array(targets, ndmin=2).T\n        \n        # Gives us sign we need to update weights\n        newError = targets - finalOutput\n\n        for l in range(self.currentLayers - 1, 0, -1):\n\n            currentOutput = self.netMap[l][\"output\"]\n\n            if (l - 1) > 0:\n                prevOutput = self.netMap[l-1][\"output\"].T\n            else:\n                prevOutput = numpy.array(inputs, ndmin=2)\n\n            # Update weights based on graient descent, chain rule of sigmoid\n            # Adding because error gives us sign, (See Perceptron Training by Udacity on Youtube)\n            self.netMap[l][\"weights\"] += ((self.learningRate) * numpy.dot((newError * ( 1 - currentOutput**2 )) , prevOutput)) \n            \n            # Spread error to weigths\n            newError = numpy.dot(self.netMap[l][\"weights\"].T, newError)\n\n    # Used to analyze network, returns feedback signal to see what network sees\n    def feedBackward(self, target):\n        # W.T * T - Feeds Target to weights in reverse order\n        target = self.reverseActivation(numpy.array(target, ndmin=2).T)\n        \n        for i in range(self.currentLayers - 1, 0, -1):\n            layerOutput = numpy.dot(self.netMap[i][\"weights\"].T, target)\n            print(layerOutput.shape)\n            target = layerOutput\n        return target\n\n    # Trains network using input and targets\n    def trainNetwork(self, inputData, targetData, epochs):\n        for i in range(1, epochs):\n            print(\"EPOCH \", i)\n            for index, value in tqdm(enumerate(inputData)):\n                self.feedForward(value)\n                self.backPropagation(targetData[index], value)\n\n    # Tests network based on testInput and Target data\n    def testNetwork(self, inputData, targetData):\n        total_test = 0\n        correct = []\n        for index, value in enumerate(inputData):\n\n            output = self.feedForward(value)\n            \n            # correct index, argmax returns index of highest value\n            correctLabel = int(numpy.argmax(targetData[index]))\n\n            # Prediction from network, index of highest prob\n            prediction = numpy.argmax(output)\n\n            # if it matches, append correct list\n            if prediction == correctLabel:\n                correct.append(1)\n\n            total_test = total_test + 1\n        \n        print(\"Performance: {}\".format(len(correct)/total_test))\n        print(\"Out of {} tests, {} predictions were correct\".format(total_test, len(correct)))\n\n    # Saves network as json\n    def saveNeuralNet(self, name):\n\n        formatNet = dict(self.netMap)\n\n        for i, w in formatNet.items():\n\n            storeW = formatNet[i][\"weights\"].tolist()\n            storeO = formatNet[i][\"output\"].tolist()\n            \n            formatNet[i][\"weights\"] =  storeW\n            formatNet[i][\"output\"] =  storeO\n\n        with open(\"{}.json\".format(name), \"w\") as file:\n            json.dump({\"neuralNet\": formatNet}, file)\n\n        self.loadNeuralNet(name)\n    \n    # Loads saved network\n    def loadNeuralNet(self, name):\n        \n        with open(\"{}.json\".format(name), \"r\") as file:\n            data = json.load(file)\n            file.close()\n\n        jsonNet = data[\"neuralNet\"]\n        savedNet = {}\n        \n        for i, w in jsonNet.items():\n\n            newWeight = jsonNet[i][\"weights\"]\n            newOutput = jsonNet[i][\"output\"]\n\n            i = int(i)\n\n            savedNet[i] = {}\n            savedNet[i][\"weights\"] =  numpy.array(newWeight)\n            savedNet[i][\"output\"] =  numpy.array(newOutput)\n            \n            self.currentLayers = i + 1\n\n        self.netMap = savedNet\n        ","sub_path":"NTEST.py","file_name":"NTEST.py","file_ext":"py","file_size_in_byte":6159,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"478835344","text":"load(\"@bazel_tools//tools/build_defs/repo:http.bzl\", \"http_archive\", \"http_file\")\nload(\"@bazel_tools//tools/build_defs/repo:git.bzl\", \"git_repository\", \"new_git_repository\")\n\ndef bark_dependencies():\n    _maybe(\n    git_repository,\n    name = \"bark_project\",\n    branch = \"master\",\n    remote = \"https://github.com/bark-simulator/bark\"\n    )\n\n    _maybe(\n    git_repository,\n    name = \"com_github_rules_rss\",\n    commit = \"f28d98d75010204db6a96ab1949f8346ba7e3237\",\n    remote = \"https://github.com/bark-simulator/rules_rss\"\n    )\n\n    _maybe(\n    git_repository,\n    name = \"gtest\",\n    commit = \"703bd9caab50b139428cea1aaff9974ebee5742e\",\n    remote = \"https://github.com/google/googletest\"\n    )\n\n    _maybe(\n    http_archive,\n    name = \"pybind11\",\n    strip_prefix = \"pybind11-2.5.0\",\n    urls = [\"https://github.com/pybind/pybind11/archive/v2.5.0.zip\"],\n    build_file = \"@bark_project//tools/pybind11:pybind.BUILD\"\n    )\n\n    _maybe(\n    git_repository,\n    name = \"com_github_nelhage_rules_boost\",\n    branch = \"master\",\n    remote = \"https://github.com/nelhage/rules_boost\"\n    )\n\n    _maybe(\n    new_git_repository,\n    name = \"com_github_interaction_dataset_interaction_dataset\",\n    build_file = \"@bark_project//tools/interaction-dataset:interaction-dataset.BUILD\",\n    commit = \"8e53eecfa9cdcb2203517af2f8ed154ad40c2956\",\n    remote = \"https://github.com/interaction-dataset/interaction-dataset.git\",\n    shallow_since = \"1568028656 +0200\",\n    )\n\n\n    # Need Eigen 3.4 (which is under development) for STL-compatible iterators\n    _maybe(\n    http_archive,\n    name = \"com_github_eigen_eigen\",\n    build_file = \"@bark_project//tools/eigen:eigen.BUILD\",\n    sha256 = \"4b1120abc5d4a63620a886dcc5d7a7a27bf5b048c8c74ac57521dd27845b1d9f\",\n    strip_prefix = \"eigen-git-mirror-98e54de5e25aefc6b984c168fb3009868a93e217\",\n    urls = [\"https://github.com/eigenteam/eigen-git-mirror/archive/98e54de5e25aefc6b984c168fb3009868a93e217.zip\"],\n    )\n\n    _maybe(\n      git_repository,\n      name = \"com_github_gflags_gflags\",\n      commit = \"addd749114fab4f24b7ea1e0f2f837584389e52c\",\n      remote = \"https://github.com/gflags/gflags\"\n    )\n\n    _maybe(\n      new_git_repository,\n      name = \"com_github_google_glog\",\n      commit = \"195d416e3b1c8dc06980439f6acd3ebd40b6b820\",\n      remote = \"https://github.com/google/glog\",\n      build_file_content = \"\"\"\nload(\"//:bazel/glog.bzl\", \"glog_library\")\nglog_library(with_gflags=0)\n    \"\"\"\n    )\n\n    _maybe(\n    native.new_local_repository,\n    name = \"python_linux\",\n    path = \"./bark/python_wrapper/venv/\",\n    build_file_content = \"\"\"\ncc_library(\n    name = \"python-lib\",\n    srcs = glob([\"lib/libpython3.*\", \"libs/python3.lib\", \"libs/python36.lib\"]),\n    hdrs = glob([\"include/**/*.h\", \"include/*.h\"]),\n    includes = [\"include/python3.6m\", \"include\", \"include/python3.7m\", \"include/python3.5m\"], \n    visibility = [\"//visibility:public\"],\n)\n    \"\"\"\n    )\n\n    _maybe(\n      new_git_repository,\n      name = \"com_github_spline\",\n      commit = \"619c634ef5f6f2df1508c767f979eb4b7bf9c66a\",\n      remote = \"https://github.com/ttk592/spline\",\n      build_file_content = \"\"\"\ncc_library(\n    name = 'spline',\n    srcs = [],\n    includes = ['.'],\n    hdrs = [\"src/spline.h\"],\n    visibility = ['//visibility:public'],\n)\n    \"\"\"\n    )\n\n\n    _maybe(\n    git_repository,\n    name = \"rule_monitor_project\",\n    commit = \"d1d7830e53213bc69d77c80ba0cc8397b0971c9d\",\n    remote = \"https://github.com/bark-simulator/rule-monitoring.git\",\n    )\n\ndef _maybe(repo_rule, name, **kwargs):\n    if name not in native.existing_rules():\n        repo_rule(name = name, **kwargs)\n\n\n","sub_path":"tools/deps.bzl","file_name":"deps.bzl","file_ext":"bzl","file_size_in_byte":3608,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"227116311","text":"#!/usr/bin/env python\n# coding: utf-8\n\nimport pandas as pd\n\n\ndef predict(symDF, tokenSeq, startSym):\n    \"\"\" 结合预测分析表进行归约\n    Args:\n        symDF: dataframe 预测分析表\n        tokenSeq: list token序列\n        startSym: str 起始符号\n    \"\"\"\n    symStack = ['#', startSym]\n    strStack = tokenSeq + ['#']\n    strStack.reverse()\n\n    def stackFormat(st):\n        return ''.join(reversed(st))\n\n    \"\"\"start predicting\"\"\"\n    while True:\n        # over condition\n        if symStack == ['#'] and strStack == ['#']:\n            print('accept!')\n            break\n\n        sym, ch = symStack[-1], strStack[-1]\n        if sym == ch:\n            print('{0} {1}-{2} passed {0}'.format('-'*50, sym, ch))\n            print(' '.join(symStack))\n            symStack.pop()\n            strStack.pop()\n            continue\n        else:\n            try:\n                geneStr = symDF.loc[sym, ch]\n                if geneStr == 'err':\n                    print('unexcept sym {} and char {}'.format(sym, ch))\n                    break\n                print('{0} {1}-{2} {0}'.format('-'*50, sym, ch))\n                print('{:110}{} -> {}'.format(' '.join(symStack), sym, geneStr))\n                symStack.pop()\n                if geneStr != '$':\n                    symStack += reversed(geneStr.split(' '))\n            except Exception as err:\n                print('err: {}\\nunexcept sym {} and char {}'.format(err, sym, ch))\n                break\n\n\nif __name__ == '__main__':\n    df = pd.read_csv('testdata.csv', index_col=0)\n    strStack = list('i*i+i#')\n    strStack.reverse()\n    predict(df, strStack, 'E')\n","sub_path":"predicator.py","file_name":"predicator.py","file_ext":"py","file_size_in_byte":1628,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"352643343","text":"from ase.build import bulk\nfrom gpaw import GPAW, PW, FermiDirac\n\n# Perform standard ground state calculation (with plane wave basis)\nsi = bulk(\"Si\", \"diamond\", 5.43)\ncalc = GPAW(mode=PW(200),\n            xc=\"PBE\",\n            kpts=(8, 8, 8),\n            random=True,  # random guess (needed if many empty bands required)\n            occupations=FermiDirac(0.01),\n            txt=\"LOG_Si_gs.txt\")\nsi.calc = calc\nsi.get_potential_energy()\ncalc.write(\"Si_gs.gpw\")\n\n","sub_path":"gpaw/tutorials/04_Si_bandstructure/run_scf.py","file_name":"run_scf.py","file_ext":"py","file_size_in_byte":463,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"15074080","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\n\npath_nudging = './results_nudging/'\npath_rlearning = './results_rlearning/'\n\n\nrhos_q = np.load(path_nudging + 'rhos.npy')\nrhos_r = np.load(path_rlearning + 'rhos.npy')\n\n\nfig, (ax1) = plt.subplots(1, 1)\n\n\nsteps = np.arange(0,len(rhos_q))/1000\nrho_base = 0.4286224337994642\nax1.plot(steps, np.abs(rhos_q-rho_base), label='Optimal nudging', color = 'tab:blue')\nsteps = np.arange(0,len(rhos_r))/1000\nax1.plot(steps, np.abs(rhos_r-rho_base), label='R Learning', color='tab:red')\nax1.set_yscale('log')\nax1.legend()\nax1.set_xlabel(f'x10^6 steps')\nax1.set_ylabel(f'|ρ*-ρt|')\n\n\nplt.show()\n\n","sub_path":"RiverSwim/plot_results.py","file_name":"plot_results.py","file_ext":"py","file_size_in_byte":636,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"594577922","text":"# coding: utf8\n\n# 图片验证码的redis有效期 单位： 秒\nIMAGE_CODE_REDIS_EXPIRES = 300\n\n# 短信验证码有效期  单位： 秒\nSMS_CODE_REDIS_EXPIRES = 300\n\n# 短信验证码发送间隔  单位： 秒\nSEND_SMS_CODE_INTERVAL = 60\n\n# 登录错误尝试次数\nLOGIN_ERROR_MAX_TIMES = 5\n\n# 登录错误限制时间 单位：秒\nLOGIN_ERROR_FORBID_TIME = 600\n\n# 七牛的域名\nQINIU_URL_DOMAIN = 'http://pusmpnsit.bkt.clouddn.com/'\n\n# 地区的缓存时间  单位：秒\nAREA_INFO_REDIS_CACHE_EXPIRES = 7200\n\n# 首页展示最多的房屋数量\nHOME_PAGE_MAX_HOUSES = 5\n\n# 首页房屋数据的Redis缓存时间，单位：秒\nHOME_PAGE_DATA_REDIS_EXPIRES = 7200\n\n# 房屋详情页展示的评论最大数\nHOUSE_DETAIL_COMMENT_DISPLAY_COUNTS = 30\n\n# 房屋详情页面数据Redis缓存时间，单位：秒\nHOUSE_DETAIL_REDIS_EXPIRE_SECOND = 7200\n\n# 房屋列表页面每页数据容量\nHOUSE_LIST_PAGE_CAPACITY = 2\n\n# 房屋列表页面页数缓存时间，单位秒\nHOUES_LIST_PAGE_REDIS_CACHE_EXPIRES = 7200\n\n# 支付宝的网关地址（支付地址域名）\nALIPAY_URL_PREFIX = \"https://openapi.alipaydev.com/gateway.do?\"\n","sub_path":"ihome/constants.py","file_name":"constants.py","file_ext":"py","file_size_in_byte":1136,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"169575892","text":"\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.exc import UnboundExecutionError\nfrom sqlalchemy.orm.exc import UnmappedError\n\nfrom alchy import manager, model\n\nfrom tests.base import TestBase, TestQueryBase\nfrom tests import fixtures\nfrom tests.fixtures import Foo\n\n\nclass TestManager(TestBase):\n\n    def test_create_drop_all(self):\n        db = manager.Manager(Model=fixtures.Model, config=self.config)\n\n        db.create_all()\n\n        self.assertTrue(len(self.models) > 0)\n        self.assertModelTablesExist(db.engine)\n\n        db.drop_all()\n\n        self.assertModelTablesNotExists(db.engine)\n\n    def test_default_model_config(self):\n        db = manager.Manager(config=self.config)\n\n        self.assertTrue(issubclass(db.Model, model.ModelBase))\n\n    def test_create_all_exception(self):\n        # pass in dummy value for Model\n        db = manager.Manager(Model=False, config=self.config)\n        self.assertRaises(UnmappedError, db.create_all)\n\n    def test_drop_all_exception(self):\n        # pass in dummy value for Model\n        db = manager.Manager(Model=False, config=self.config)\n        self.assertRaises(UnmappedError, db.drop_all)\n\n\nclass TestManagerSessionExtensions(TestQueryBase):\n\n    def get_count(self, table='foo'):\n        return self.db.execute('select count(*) from {0}'.format(table)).scalar()\n\n    def test_add(self):\n        count = self.get_count()\n        self.db.add(Foo())\n        self.db.add(Foo(), Foo())\n        self.db.add([Foo(), Foo()])\n\n        self.assertEqual(self.db.execute('select count(*) from foo').scalar(), count)\n\n        self.db.commit()\n\n        self.assertEqual(self.get_count(), count + 5)\n\n    def test_add_commit(self):\n        count = self.get_count()\n\n        self.db.add_commit(Foo())\n        self.assertEqual(self.get_count(), count + 1)\n\n        self.db.add_commit(Foo(), Foo())\n        self.assertEqual(self.get_count(), count + 3)\n\n        self.db.add_commit([Foo(), Foo()])\n        self.assertEqual(self.get_count(), count + 5)\n\n    def test_delete(self):\n        count = self.get_count()\n        foos = Foo.query.all()\n\n        self.db.delete(foos[0])\n        self.db.delete(foos[1], foos[2])\n        self.db.delete([foos[3], foos[4]])\n\n        self.assertEqual(self.get_count(), count)\n\n        self.db.commit()\n\n        self.assertEqual(self.get_count(), count - 5)\n\n    def test_delete_commit(self):\n        count = self.get_count()\n        foos = Foo.query.all()\n\n        self.db.delete_commit(foos[0])\n        self.assertEqual(self.get_count(), count - 1)\n\n        self.db.delete_commit(foos[1], foos[2])\n        self.assertEqual(self.get_count(), count - 3)\n\n        self.db.delete_commit([foos[3], foos[4]])\n        self.assertEqual(self.get_count(), count - 5)\n","sub_path":"tests/test_manager.py","file_name":"test_manager.py","file_ext":"py","file_size_in_byte":2743,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"197645633","text":"# Copyright (c) 2018 Boris Popadiuk\n\n# Implementation of Kahn's Algorithm to yield topological sort of a DAG\n# Written for New Beginnings Algorithms Winter 2018, Portland State University\n\nclass GraphEL(object):\n    \"\"\"Edgelist graph class. Written by Bart Massey and shared with students\"\"\"\n\n    def __init__(self, nvertices, edges, directed=True):\n        \"Create an edge-list graph.\"\n        self.nvertices = nvertices\n        self.directed = directed\n        if directed:\n            self.edges = edges\n        else:\n            self.edges = set()\n            for v1, v2 in edges:\n                self.edges.add((v1, v2))\n                self.edges.add((v2, v1))\n            self.edges = list(self.edges)\n\n    def undirected(self):\n        \"Return the undirected version of a directed edge-list graph.\"\n        assert self.directed\n        return GraphEL(self.nvertices, self.edges, directed=False)\n\n    def toGraphAL(self):\n        \"Return the adjacency-list representation of an edge-list graph.\"\n        return GraphAL(self.nvertices, self.edges, self.directed)\n\n    def __repr__(self):\n        return \"GraphEL({}, {}, directed={})\".format(\n            self.nvertices,\n            self.edges,\n            self.directed\n        )\n\n    def __eq__(self, g):\n        if type(g) is GraphAL:\n            g = g.toGraphEL()\n        if self.directed != g.directed:\n            return False\n        if self.nvertices != g.nvertices:\n            return False\n        return set(self.edges) == set(g.edges)\n\ndef kahn_toposort(graph):\n    \"\"\"Create topological sort of graph, return as list.\n       Fail if cycle is detected.\"\"\"\n\n    edges = build_edge_dict(graph)\n    no_incoming = build_no_incoming(graph)\n    yes_incoming = [x for v in edges.values() for x in v]\n    L = []\n\n    while len(no_incoming) > 0:\n        n = no_incoming.pop()\n        L.append(n)\n        if n not in edges.keys():\n            continue\n        for i in edges[n]:\n            yes_incoming.remove(i)\n            if i not in yes_incoming:\n                no_incoming.add(i)\n    if len(yes_incoming) > 0:\n        print('ERROR: Cycle detected')\n                \n    return L \n\ndef build_edge_dict(graph):\n    \"\"\"Create adjacency list representation of graph\"\"\"\n\n    edge_dict = dict()\n    for i, j in graph.edges:\n        if i not in edge_dict:\n            edge_dict[i] = [j]\n        else:\n            edge_dict[i].append(j)\n    return edge_dict\n                \ndef build_no_incoming(graph):\n    \"\"\"Compile set of vertices with no incoming edges\"\"\"\n\n    no_incoming = set()\n    yes_incoming = set()\n    for i, j in graph.edges:\n        yes_incoming.add(j)\n    for i, j in graph.edges:\n        if i not in yes_incoming:\n            no_incoming.add(i)\n    return no_incoming\n\n\ncascadia_web_serv = GraphEL(11, [(1,2), (2,3), (2,10), (2,5), (9,3), (9,10), (9,5), (9,11), (7,5), (7,8), (3,4), (10,6), (5,6)])\ntopological_sort = kahn_toposort(cascadia_web_serv)\nprint('\\nGraph: ', cascadia_web_serv)\nprint('Topological Sort: ', topological_sort, '\\n')\n","sub_path":"topo_sort.py","file_name":"topo_sort.py","file_ext":"py","file_size_in_byte":3015,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"218359678","text":"from flask import (\n    request,\n    jsonify,\n    Blueprint,\n    current_app as app\n)\nfrom flask_jwt_extended import (\n    create_access_token,\n    create_refresh_token,\n    jwt_required,\n    jwt_refresh_token_required,\n    get_jwt_identity,\n    get_raw_jwt)\nfrom marshmallow import ValidationError\n\nfrom api.api.schemas import UserSchema\nfrom api.models import Customer, User\nfrom api.extensions import (\n    db,\n    apispec,\n    jwt,\n    pwd_context)\nfrom api.auth.helpers import (\n    add_token_to_database,\n    revoke_token,\n    is_token_revoked)\nfrom api.utils.models import save_to_db, ROLE_CUSTOMER\n\nblueprint = Blueprint(\"auth\", __name__, url_prefix=\"/auth\")\n\n\n@blueprint.route(\"/register\", methods=[\"POST\"])\ndef register_user():\n    if not request.is_json:\n        return jsonify({\"msg\": \"Missing JSON in request\"}), 400\n\n    if not request.json.get(\"username\"):\n        return jsonify({\"msg\": \"JSON missing username\"}), 400\n\n    username = request.json.get(\"username\")\n    user = User.query.filter_by(username=username).first()\n\n    if user:\n        return jsonify({\"msg\": \"Username already taken\"}), 400\n\n    request.json[\"active\"] = True\n    user_schema = UserSchema()\n    user = user_schema.load(request.json)\n    user.role = ROLE_CUSTOMER\n    save_to_db(db, user)\n    user_claims_ = {\"id\": user.id, \"role\": ROLE_CUSTOMER}\n    access_token = create_access_token(\n        identity=user.id,\n        user_claims=user_claims_)\n    refresh_token = create_refresh_token(\n        identity=user.id,\n        user_claims=user_claims_)\n    # create a customer, only for simple auth purposes\n    customer = Customer(\n        name=username,\n        email=user.email,\n        user_id=user.id)\n    save_to_db(db, customer)\n    resp = {\n        \"user\": user_schema.dump(user),\n        \"customer_id\": customer.id,\n        \"access_token\": access_token,\n        \"refresh_token\": refresh_token}\n    return jsonify(resp), 201\n\n\n@blueprint.route(\"/login\", methods=[\"POST\"])\ndef login():\n    if not request.is_json:\n        return jsonify({\"msg\": \"Missing JSON in request\"}), 400\n\n    username = request.json.get(\"username\")\n    password = request.json.get(\"password\")\n    if not (username or password):\n        return jsonify({\"msg\": \"Missing username or password\"}), 400\n\n    user = User.query.filter_by(username=username).first()\n    if not user or not pwd_context.verify(password, user.password):\n        return jsonify({\"msg\": \"Bad credentials\"}), 400\n\n    access_token = create_access_token(identity=user.id)\n    refresh_token = create_refresh_token(identity=user.id)\n    add_token_to_database(access_token, app.config[\"JWT_IDENTITY_CLAIM\"])\n    add_token_to_database(refresh_token, app.config[\"JWT_IDENTITY_CLAIM\"])\n\n    resp = {\"access_token\": access_token, \"refresh_token\": refresh_token}\n    return jsonify(resp), 200\n\n\n@blueprint.route(\"/refresh\", methods=[\"POST\"])\n@jwt_refresh_token_required\ndef refresh():\n    current_user = get_jwt_identity()\n    access_token = create_access_token(identity=current_user)\n    ret = {\"access_token\": access_token}\n    add_token_to_database(access_token, app.config[\"JWT_IDENTITY_CLAIM\"])\n    return jsonify(ret), 200\n\n\n@blueprint.route(\"/revoke_access\", methods=[\"DELETE\"])\n@jwt_required\ndef revoke_access_token():\n    jti = get_raw_jwt()[\"jti\"]\n    user_identity = get_jwt_identity()\n    revoke_token(jti, user_identity)\n    return jsonify({\"message\": \"token revoked\"}), 200\n\n\n@blueprint.route(\"/revoke_refresh\", methods=[\"DELETE\"])\n@jwt_refresh_token_required\ndef revoke_refresh_token():\n    jti = get_raw_jwt()[\"jti\"]\n    user_identity = get_jwt_identity()\n    revoke_token(jti, user_identity)\n    return jsonify({\"message\": \"token revoked\"}), 200\n\n\n@jwt.user_loader_callback_loader\ndef user_loader_callback(identity):\n    return User.query.get(identity)\n\n\n@jwt.token_in_blacklist_loader\ndef check_if_token_revoked(decoded_token):\n    return is_token_revoked(decoded_token)\n\n\n@blueprint.before_app_first_request\ndef register_views():\n    apispec.spec.path(view=login, app=app)\n    apispec.spec.path(view=refresh, app=app)\n    apispec.spec.path(view=register_user, app=app)\n    apispec.spec.path(view=revoke_access_token, app=app)\n    apispec.spec.path(view=revoke_refresh_token, app=app)\n\n\n@blueprint.errorhandler(ValidationError)\ndef handle_marshmallow_error(e):\n    return jsonify(e.messages), 400\n","sub_path":"api/auth/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4340,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"472652421","text":"from os import listdir\nfrom os import makedirs\nfrom shutil import copyfile\n\ndayFolders=[int(x.replace('Day ', '')) for x in listdir('./') if 'Day ' in x]\nlastDay=max(dayFolders) if dayFolders else 0\nnewDay='Day ' + str(lastDay+1)\nmakedirs(newDay)\ncopyfile('./template.py', './'+newDay+'/d'+str(lastDay+1)+'.py')\nopen(newDay+'/input.txt', 'a').close()\nprint('Added ' + newDay)\n","sub_path":"addDay.py","file_name":"addDay.py","file_ext":"py","file_size_in_byte":376,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"104528061","text":"# -*- coding: utf-8 -*-\nfrom sklearn import datasets\nfrom sklearn import model_selection\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef loss(X,y,w,C):\n    m = y.shape[0]\n    hinge = sum(list(map(lambda x:max(0,x[0]),(1-y.A*(X*w).A))))\n    w_2 = sum(w.A**2)[0]\n    return (0.5*w_2+C*hinge)/m\n    \ndef gradient(X,y,w,C):\n    m = y.shape[0]\n    dw = np.zeros((X.shape[1],1))\n    indicator = 1-y.A*((X*w).A)\n    for i in range(m):\n        if indicator[i]>=0:\n            dw += w - C*(y[i]*X[i]).T\n        else:\n            dw += w \n    # print(dw,db)\n    return dw/m\n\ndef gradientDecent(X,y,w,C,alpha,num_rounds,val_x,val_y): \n    train_loss_history = []\n    val_loss_history = []\n    train_loss_history.append(loss(X,y,w,C))\n    val_loss_history.append(loss(val_x,val_y,w,C))\n    print(\"GradientDecent begin\")   \n    \n    for i in range(num_rounds):\n        random = np.random.permutation(X.shape[0])[0:100]\n        gdx = X[random]\n        gdy = y[random]\n        \n        new_w = w - gradient(gdx,gdy,w,C)*alpha\n        w = new_w\n        train_loss_history.append(loss(gdx,gdy,w,C))\n        val_loss_history.append(loss(val_x,val_y,w,C))\n        \n    return w,train_loss_history,val_loss_history\n\ndef NAG(X,y,w,C,alpha,num_rounds,val_x,val_y):\n    train_loss_history = []\n    val_loss_history = []\n    train_loss_history.append(loss(X,y,w,C))\n    val_loss_history.append(loss(val_x,val_y,w,C))\n    print(\"NAG begin\")   \n    r = 0.9\n    vw = np.zeros(w.shape)\n    \n    for i in range(num_rounds):\n        random = np.random.permutation(X.shape[0])[0:100]\n        gdx = X[random]\n        gdy = y[random]\n        \n        _w = w - r*vw\n        gt = gradient(gdx,gdy,_w,C)\n        vw = r*vw + alpha*gt\n        w = w - vw\n\n        train_loss_history.append(loss(X,y,w,C))\n        val_loss_history.append(loss(val_x,val_y,w,C))\n        \n    return w,train_loss_history,val_loss_history\n\ndef RMSProp(X,y,w,C,alpha,num_rounds,val_x,val_y):\n    train_loss_history = []\n    val_loss_history = []\n    train_loss_history.append(loss(X,y,w,C))\n    val_loss_history.append(loss(val_x,val_y,w,C))\n    print(\"RMSProp begin\")\n    \n    r = 0.9\n    vw = np.zeros(w.shape)\n    e = 1e-8\n    \n    for i in range(num_rounds):\n        random = np.random.permutation(X.shape[0])[0:100]\n        gdx = X[random]\n        gdy = y[random]\n        \n        gt = gradient(gdx,gdy,w,C)\n        gt_2 = np.matrix(gt*gt)\n        vw = r*vw + (1-r)*gt_2\n        w = w - 0.01/np.sqrt(vw+e).A*(gt)\n\n        train_loss_history.append(loss(X,y,w,C))\n        val_loss_history.append(loss(val_x,val_y,w,C))\n        \n    return w,train_loss_history,val_loss_history\n\ndef AdaDelta(X,y,w,C,alpha,num_rounds,val_x,val_y):\n    train_loss_history = []\n    val_loss_history = []\n    train_loss_history.append(loss(X,y,w,C))\n    val_loss_history.append(loss(val_x,val_y,w,C))\n    print(\"AdaDelta begin\")\n    \n    r = 0.95\n    vw = np.zeros(w.shape)\n    e = 1e-4\n    tw = np.matrix(np.zeros(w.shape))\n    \n    for i in range(num_rounds):\n        random = np.random.permutation(X.shape[0])[0:100]\n        gdx = X[random]\n        gdy = y[random]\n        \n        gt = gradient(gdx,gdy,w,C)\n        gt_2 = np.matrix((gt)**2)\n        vw = r*vw + (1-r)*gt_2\n        dw = -(np.sqrt(tw+e)/np.sqrt(vw+e)).A*gt\n        w = w + dw\n        tw = r*tw+(1-r)*(tw.A*tw.A)\n\n        train_loss_history.append(loss(X,y,w,C))\n        val_loss_history.append(loss(val_x,val_y,w,C))\n        \n    return w,train_loss_history,val_loss_history\n\ndef Adam(X,y,w,C,alpha,num_rounds,val_x,val_y):\n    train_loss_history = []\n    val_loss_history = []\n    train_loss_history.append(loss(X,y,w,C))\n    val_loss_history.append(loss(X,y,w,C))\n    print(\"Adam begin\")\n    \n    r = 0.99\n    vw = np.zeros(w.shape)\n    e = 1e-6\n    mw = np.zeros(w.shape)\n    b1 = 0.9\n    \n    \n    for i in range(num_rounds):\n        random = np.random.permutation(X.shape[0])[0:100]\n        gdx = X[random]\n        gdy = y[random]\n        \n        gt = gradient(gdx,gdy,w,C)\n        gt_2 = np.matrix(gt**2)\n        vw = r*vw + (1-r)*gt_2\n        mw = b1*mw + (1-b1)*gt\n        alp = alpha*np.sqrt(1-r)/(1-b1)\n        w = w - alp*mw/np.sqrt(vw+e).A\n\n        train_loss_history.append(loss(X,y,w,C))\n        val_loss_history.append(loss(val_x,val_y,w,C))\n        \n    return w,train_loss_history,val_loss_history\n\n\n\ndef train(X,y,val_x,val_y):\n    m = X.shape[1]\n    init_w = np.matrix(np.random.randn(m,1))\n    print(\"begin to train\")\n    C = 5\n    alpha = 0.1\n    num_rounds=300\n    \n    print(\"default acc: %f\"%predict(val_x,val_y,init_w))\n    print(\"\")\n    \n    w,train_loss_history,loss_history = gradientDecent(X,y,init_w,C,alpha,num_rounds,val_x,val_y)\n    print(\"Gradient Decent acc: %f\"%predict(val_x,val_y,w))\n    print(\"\")\n    w,train_loss_history,NAG_loss_history = NAG(X,y,init_w,C,alpha,num_rounds,val_x,val_y)\n    print(\"NAG acc: %f\"%predict(val_x,val_y,w))\n    print(\"\")\n    w,train_loss_history,RMSProp_loss_history = RMSProp(X,y,init_w,C,alpha,num_rounds,val_x,val_y)\n    print(\"RMSProp acc: %f\"%predict(val_x,val_y,w))\n    print(\"\")\n    w,train_loss_history,AdaDelta_loss_history = AdaDelta(X,y,init_w,C,alpha,num_rounds,val_x,val_y)\n    print(\"AdaDelta acc: %f\"%predict(val_x,val_y,w))\n    print(\"\")\n    w,train_loss_history,Adam_loss_history = Adam(X,y,init_w,C,alpha,num_rounds,val_x,val_y)\n    print(\"Adam acc: %f\"%predict(val_x,val_y,w))\n    print(\"\")\n    plt.plot(np.arange(num_rounds+1),loss_history,label='GradientDecent loss')\n    plt.plot(np.arange(num_rounds+1),NAG_loss_history,label='NAG loss')\n    plt.plot(np.arange(num_rounds+1),RMSProp_loss_history,label='RMSProp loss')\n    plt.plot(np.arange(num_rounds+1),AdaDelta_loss_history,label='AdaDelta loss')\n    plt.plot(np.arange(num_rounds+1),Adam_loss_history,label='Adam loss')\n    plt.legend(loc=1)\n    plt.xlabel('number_of_rounds')\n    plt.ylabel('loss')\n    return w\n    \ndef predict(X,y,w):\n    pred = X*w\n    pred_y = list(map(lambda x:1 if x[0]>0 else -1,pred.A))\n    acc = (y.A1==pred_y).sum()/len(y.A)\n    return acc\n\n\ndef getData():\n    X,y = datasets.load_svmlight_file('./a9a',n_features=123)\n    X = np.matrix(X.toarray())\n    ones = np.matrix(np.ones((X.shape[0],1)))\n    train_x = np.concatenate((ones,X),axis=1)\n    train_y = np.matrix(y).T\n    \n    X,y = datasets.load_svmlight_file('./a9a.t',n_features=123)\n    X = np.matrix(X.toarray())\n    ones = np.matrix(np.ones((X.shape[0],1)))\n    test_x = np.concatenate((ones,X),axis=1)\n    test_y = np.matrix(y).T\n    return train_x,test_x,train_y,test_y\n    \n\ntrain_x,test_x,train_y,test_y = getData()\nw = train(train_x,train_y,test_x,test_y)\n\n\n# -*- coding: utf-8 -*-\n\n","sub_path":"linearClassificationGD.py","file_name":"linearClassificationGD.py","file_ext":"py","file_size_in_byte":6621,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"453850655","text":"import math\nimport cleaner\nimport os\n'''\n@class Calculator to compare the cosine similarities between a query that's passed and multiple documents\n'''\nclass CosSimCalc():\n    '''\n    @constructor Initializes the attributes\n\n    @param {dict} invIndex The inverted index of the files being searched\n    '''\n    def __init__(self, invIndex=None):\n        self.invIndex = invIndex\n        self.maxFreq = {}\n        self.docs = {}\n\n    def setInvIndex(self, invIndex):\n        self.invIndex = invIndex\n\n    '''\n    Returns a list of cosign similarities sorted by relevance\n\n    @param {list} text The query as a sanitized list\n    @return {list} List of touples with the url and cosine similarity\n    @see getWeight\n    @see getCosSim\n    '''\n    def getRanking(self, text):\n\n        #Remove any words not in any docs\n        text2 = []\n        for k in range(len(text)):\n            if text[k] in self.invIndex.keys():\n                text2.append(text[k])\n        text = text2\n        \n        #If no results\n        if len(text) == 0:\n            return [(\"No Results\", \"\", \"\", -1)]\n\n        #Compile list of all documents containing query word\n        idsWithWords = []\n        for k in text:\n            ids = [x[0] for x in self.invIndex[k]]\n            for id in ids:\n                if id not in idsWithWords:\n                    idsWithWords.append(id)\n        #Get query weight\n        qVect = []\n        for k in text:\n            w = self.getWeight(k, text, True)\n            qVect.append(w)\n        vect = {}\n        cosSims = []\n        for i in idsWithWords:\n            if i not in self.docs.keys():\n                self.docs[i] = cleaner.cleanFile(\"cranfield\" + str(i) + \".txt\", False)\n            if len(self.docs[i][2]) == 0:\n                continue\n            vect[i] = []\n            for k in text:\n                w = self.getWeight(k, i)\n                vect[i].append(w)\n            #calc cosSim\n            cossim = self.getCosSim(qVect, vect[i])\n            if cossim != 0:\n                cosSims.append([self.docs[i][0], self.docs[i][1], cossim])\n        cosSims.sort(key = lambda x: x[2], reverse=True)\n        return cosSims[:10]\n\n    '''\n    Calculates weight of term using the following formula:\n    weight = (frequency of term in query/document / frequency of most common term in query/documents) * idf\n\n    @param {string} k the term being calculated\n    @param {string} i document index\n    @param {boolean} isQuery if true, term is from a query\n    @return tf * idf (weight of term in query or document)\n    @see getTF\n    @see getIDF\n    '''\n    def getWeight(self, k,i, isQuery=False):\n        if isQuery:\n            #i is the query in this case\n            query = i\n            freq = len([x for x in query if x == k])\n            maxv = query.count(max(set(query), key = query.count))\n            tf = freq/maxv\n        else:\n            tf = self.getTF(k,i)\n        idf = self.getIDF(k)\n        return tf * idf\n\n    '''\n    Calculates the term frequency by this formula:\n    tf = f / Max(f)\n\n    @param {string} k The term being calculated\n    @param {int} i The index of the document\n    @return {float} The term frequency of k in docs[i][2]\n    @see getWeight\n    '''\n    def getTF(self, k, i):\n\n        #check if word is in invIndex\n        if k not in self.invIndex.keys():\n            return 0\n\n        freq = [x[1] for x in self.invIndex[k] if x[0] == i]\n        \n        #Word isn't in doc\n        if len(freq) == 0:\n            return 0\n\n        freq = freq[0]\n\n        #If max freq for doc isn't known, count it and add it to the dict \n        maxv = 0\n        if i not in self.maxFreq.keys():\n            maxv = self.docs[i][2].count(max(set(self.docs[i][2]), key = self.docs[i][2].count))\n            self.maxFreq[i] = maxv\n        \n        return freq/self.maxFreq[i]\n\n    '''\n    Calculates IDF using frequency of word from inverted index and total number of documents:\n    IDF = documents / word frequency\n\n    @param {string} k The term being calculated\n    @return {float} IDF\n    @see getWeight()\n    '''\n    def getIDF(self, k):\n        df = len(self.invIndex[k])\n        n = len(os.listdir(os.getcwd() + \"/separated\"))\n        return math.log10(n/df)\n\n    '''\n    Calculates the cosine similarity of 2 vectors \n\n    @param {list} q The query vector\n    @param {list} d The vector of a document\n    @return {float} The cosine similarity between q and d\n    @see getRanking\n    '''\n    def getCosSim(self, q, d):\n        dotProd = self.getDotProd(q,d)\n        qMag = self.getMagnitude(q)\n        dMag = self.getMagnitude(d)\n\n        if qMag == 0 or dMag == 0:\n            return 0    \n        else:\n            return dotProd/ (qMag*dMag)\n\n    '''\n    Calculates dot product of two vectors\n\n    @param {list} vec1 first vector to multiply\n    @param {list} vec2 second vectory to multiply\n    @see getCosSim\n    '''\n    def getDotProd(self, vec1, vec2):\n        #Make sure they're the same length\n        if len(vec1) != len(vec2):\n            print(\"Length Vec 1: %d\" % vec1)\n            print(\"Length Vec 2: %d\" % vec2)\n            return 0\n\n        prod = 0\n\n        for x in range(len(vec1)):\n            prod += vec1[x] * vec2[x]\n        return prod\n\n    '''\n    Calculates the magnitude of a vector with the following formula:\n    |v| = sqrt(w1**2 + w2**2 + w3**2 + etc.)\n\n    @param {list} v The vector that's magnitude is being calculated\n    @return {float} The magnitude of v\n    @see getCosSim\n    '''\n    def getMagnitude(self, v):\n        wTotal = 0\n        for w in v:\n            wTotal += w**2\n\n        return math.sqrt(wTotal)","sub_path":"analysis/cossim.py","file_name":"cossim.py","file_ext":"py","file_size_in_byte":5601,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"246088514","text":"'''\nFunctions to prepare the data\n'''\n\nimport numpy as np\nimport pandas as pd\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn_pandas import DataFrameMapper\n\n\ndef normed_data(df_train, df_test):\n    \"\"\" Define the structure of the neural network for a Cox-MLP (CC), CoxTime and  DeepHit\n    # Arguments\n        df_train: Training set of simulated data with 20 entry variables, survival status and survival time. \n        df_test: Test set of simulated data with 20 entry variables, survival status and survival time. \n    # Returns\n        x_train: dataframe with the normalized explanatory variables.\n        x_test: dataframe with the normalized explanatory variables.\n    \"\"\"\n    col_list = list(df_train.columns)\n    cols_standardize = [e for e in col_list if e not in ['yy', 'status']]\n    standardize = [([col], StandardScaler()) for col in cols_standardize]\n    x_mapper = DataFrameMapper(standardize)\n    x_train = x_mapper.fit_transform(df_train).astype('float32')\n    x_test = x_mapper.transform(df_test).astype('float32')\n    return x_train, x_test\n\ndef prepare_pseudobs(x_train, y_train, df_train, x_test, df_test, name):\n    \"\"\" Define the input and output sets formated to use for neural network model with pseudoobservations\n    # Arguments\n        x_train: dataframe with the normalized explanatory variables\n        y_train: survival time\n        df_train: Training set of simulated data with 20 entry variables, survival status and survival time\n        x_test: dataframe with the normalized explanatory variables\n        df_test: Test set of simulated data with 20 entry variables, survival status and survival time\n        name: name of the model\n    # Returns\n        x_train_all : input variables for the training set (one line for one patient at one timepoint)\n        y_train_all : pseudoobservation (one line for one patient at one timepoint)\n        x_test_all : input variables for the test set (one line for one patient at one timepoint)\n    \"\"\"\n    n_picktime = int(y_train[['s']].apply(pd.Series.nunique))\n    x_test_all = pd.concat([pd.DataFrame(x_test)]*n_picktime)\n    time_test = pd.DataFrame(np.repeat(np.unique(y_train[['s']]),len(x_test)))\n    time_test.columns = ['s']\n    x_test_all.reset_index(inplace=True, drop=True)\n    x_test_all = pd.concat([x_test_all, time_test], axis = 1)\n    x_train = pd.DataFrame(x_train)\n    if name != \"pseudo-discrete\":\n        x_train_all = pd.concat([x_train]*n_picktime)\n        x_train_all.reset_index(inplace=True, drop=True)\n        x_train_all = pd.concat([x_train_all, y_train[['s']]], axis = 1)\n        y_train_all = y_train[['pseudost']]\n    else:\n        x_train['id'] = np.arange(len(x_train)) + 1\n        x_train = x_train.merge(y_train, left_on='id', right_on='id')\n        x_train_all = x_train.drop(['id','pseudost'], axis = 1)\n        y_train_all = x_train['pseudost']\n    return x_train_all, y_train_all, x_test_all\n\n\n\n\n\n","sub_path":"model/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2923,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"30400421","text":"class Solution:\n    def sortColors(self, nums: List[int]) -> None:\n        \"\"\"\n        Do not return anything, modify nums in-place instead.\n        \"\"\"\n        \n        reds, whites = 0, 0\n        \n        for color in nums:\n            if color == 0: reds += 1\n            if color == 1: whites += 1\n                \n        for i in range(reds): nums[i] = 0\n        for i in range(reds, reds+whites): nums[i] = 1\n        for i in range(reds+whites, len(nums)): nums[i] = 2\n","sub_path":"0075 Sort Colors.py","file_name":"0075 Sort Colors.py","file_ext":"py","file_size_in_byte":476,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"177667544","text":"'''\n归并排序\n归并排序（MERGE-SORT）是利用归并的思想实现的排序方法，该算法采用经典的分治（divide-and-conquer）策略.\n\n每次合并操作的平均时间复杂度为O(n)，而完全二叉树的深度为|log2n|。总的平均时间复杂度为O(nlogn)。\n而且，归并排序的最好，最坏，平均时间复杂度均为O(nlogn)。\n'''\n\ndef merge_sort(nums, left, right):\n\tif left < right:\n\t\tmid = (left + right) // 2\n\t\tmerge_sort(nums, left, mid)\n\t\tmerge_sort(nums, mid + 1, right)\n\t\tmerge(nums, left, right, mid)\n\ndef merge(nums, left, right, mid):\n\ti = left\n\tj = mid + 1\n\twhile i < j and j <= right:\n\t\twhile i < j and nums[i] <= nums[j]:\n\t\t\ti += 1\n\t\tj_old = j\n\t\twhile j <= right and nums[j] < nums[i]:\n\t\t\tj += 1\n\t\tswapBlocks(nums, i, j_old - 1, j - 1)\n\t\ti += (j - j_old)\n\ndef swapBlocks(nums, left, mid, right):\n\tswap(nums, left, mid)\n\tswap(nums, mid + 1, right)\n\tswap(nums, left, right)\n\ndef swap(nums, left, right):\n\twhile left < right:\n\t\tnums[left], nums[right] = nums[right], nums[left]\n\t\tleft += 1\n\t\tright -= 1\n","sub_path":"sort/merge_sort.py","file_name":"merge_sort.py","file_ext":"py","file_size_in_byte":1058,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"206781615","text":"\"\"\"\nSearching for names with given scope and name. This is very central in Jedi and\nPython. The name resolution is quite complicated with descripter,\n``__getattribute__``, ``__getattr__``, ``global``, etc.\n\nIf you want to understand name resolution, please read the first few chapters\nin http://blog.ionelmc.ro/2015/02/09/understanding-python-metaclasses/.\n\nFlow checks\n+++++++++++\n\nFlow checks are not really mature. There's only a check for ``isinstance``.  It\nwould check whether a flow has the form of ``if isinstance(a, type_or_tuple)``.\nUnfortunately every other thing is being ignored (e.g. a == '' would be easy to\ncheck for -> a is a string). There's big potential in these checks.\n\"\"\"\n\nfrom parso.python import tree\nfrom parso.tree import search_ancestor\nfrom jedi import debug\nfrom jedi import settings\nfrom jedi.evaluate.context import AbstractInstanceContext\nfrom jedi.evaluate import compiled\nfrom jedi.evaluate import analysis\nfrom jedi.evaluate import flow_analysis\nfrom jedi.evaluate.arguments import TreeArguments\nfrom jedi.evaluate import helpers\nfrom jedi.evaluate.context import iterable\nfrom jedi.evaluate.filters import get_global_filters, TreeNameDefinition\nfrom jedi.evaluate.base_context import ContextSet\nfrom jedi.parser_utils import is_scope, get_parent_scope\n\n\nclass NameFinder(object):\n    def __init__(self, evaluator, context, name_context, name_or_str,\n                 position=None, analysis_errors=True):\n        self._evaluator = evaluator\n        # Make sure that it's not just a syntax tree node.\n        self._context = context\n        self._name_context = name_context\n        self._name = name_or_str\n        if isinstance(name_or_str, tree.Name):\n            self._string_name = name_or_str.value\n        else:\n            self._string_name = name_or_str\n        self._position = position\n        self._found_predefined_types = None\n        self._analysis_errors = analysis_errors\n\n    @debug.increase_indent\n    def find(self, filters, attribute_lookup):\n        \"\"\"\n        :params bool attribute_lookup: Tell to logic if we're accessing the\n            attribute or the contents of e.g. a function.\n        \"\"\"\n        names = self.filter_name(filters)\n        if self._found_predefined_types is not None and names:\n            check = flow_analysis.reachability_check(\n                context=self._context,\n                context_scope=self._context.tree_node,\n                node=self._name,\n            )\n            if check is flow_analysis.UNREACHABLE:\n                return ContextSet()\n            return self._found_predefined_types\n\n        types = self._names_to_types(names, attribute_lookup)\n\n        if not names and self._analysis_errors and not types \\\n                and not (isinstance(self._name, tree.Name) and\n                         isinstance(self._name.parent.parent, tree.Param)):\n            if isinstance(self._name, tree.Name):\n                if attribute_lookup:\n                    analysis.add_attribute_error(\n                        self._name_context, self._context, self._name)\n                else:\n                    message = (\"NameError: name '%s' is not defined.\"\n                               % self._string_name)\n                    analysis.add(self._name_context, 'name-error', self._name, message)\n\n        return types\n\n    def _get_origin_scope(self):\n        if isinstance(self._name, tree.Name):\n            scope = self._name\n            while scope.parent is not None:\n                # TODO why if classes?\n                if not isinstance(scope, tree.Scope):\n                    break\n                scope = scope.parent\n            return scope\n        else:\n            return None\n\n    def get_filters(self, search_global=False):\n        origin_scope = self._get_origin_scope()\n        if search_global:\n            position = self._position\n\n            # For functions and classes the defaults don't belong to the\n            # function and get evaluated in the context before the function. So\n            # make sure to exclude the function/class name.\n            if origin_scope is not None:\n                ancestor = search_ancestor(origin_scope, 'funcdef', 'classdef', 'lambdef')\n                lambdef = None\n                if ancestor == 'lambdef':\n                    # For lambdas it's even more complicated since parts will\n                    # be evaluated later.\n                    lambdef = ancestor\n                    ancestor = search_ancestor(origin_scope, 'funcdef', 'classdef')\n                if ancestor is not None:\n                    colon = ancestor.children[-2]\n                    if position < colon.start_pos:\n                        if lambdef is None or position < lambdef.children[-2].start_pos:\n                            position = ancestor.start_pos\n\n            return get_global_filters(self._evaluator, self._context, position, origin_scope)\n        else:\n            return self._context.get_filters(search_global, self._position, origin_scope=origin_scope)\n\n    def filter_name(self, filters):\n        \"\"\"\n        Searches names that are defined in a scope (the different\n        ``filters``), until a name fits.\n        \"\"\"\n        names = []\n        if self._context.predefined_names and isinstance(self._name, tree.Name):\n            node = self._name\n            while node is not None and not is_scope(node):\n                node = node.parent\n                if node.type in (\"if_stmt\", \"for_stmt\", \"comp_for\"):\n                    try:\n                        name_dict = self._context.predefined_names[node]\n                        types = name_dict[self._string_name]\n                    except KeyError:\n                        continue\n                    else:\n                        self._found_predefined_types = types\n                        break\n\n        for filter in filters:\n            names = filter.get(self._string_name)\n            if names:\n                if len(names) == 1:\n                    n, = names\n                    if isinstance(n, TreeNameDefinition):\n                        # Something somewhere went terribly wrong. This\n                        # typically happens when using goto on an import in an\n                        # __init__ file. I think we need a better solution, but\n                        # it's kind of hard, because for Jedi it's not clear\n                        # that that name has not been defined, yet.\n                        if n.tree_name == self._name:\n                            if self._name.get_definition().type == 'import_from':\n                                continue\n                break\n\n        debug.dbg('finder.filter_name %s in (%s): %s@%s',\n                  self._string_name, self._context, names, self._position)\n        return list(names)\n\n    def _check_getattr(self, inst):\n        \"\"\"Checks for both __getattr__ and __getattribute__ methods\"\"\"\n        # str is important, because it shouldn't be `Name`!\n        name = compiled.create_simple_object(self._evaluator, self._string_name)\n\n        # This is a little bit special. `__getattribute__` is in Python\n        # executed before `__getattr__`. But: I know no use case, where\n        # this could be practical and where Jedi would return wrong types.\n        # If you ever find something, let me know!\n        # We are inversing this, because a hand-crafted `__getattribute__`\n        # could still call another hand-crafted `__getattr__`, but not the\n        # other way around.\n        names = (inst.get_function_slot_names(u'__getattr__') or\n                 inst.get_function_slot_names(u'__getattribute__'))\n        return inst.execute_function_slots(names, name)\n\n    def _names_to_types(self, names, attribute_lookup):\n        contexts = ContextSet.from_sets(name.infer() for name in names)\n\n        debug.dbg('finder._names_to_types: %s -> %s', names, contexts)\n        if not names and isinstance(self._context, AbstractInstanceContext):\n            # handling __getattr__ / __getattribute__\n            return self._check_getattr(self._context)\n\n        # Add isinstance and other if/assert knowledge.\n        if not contexts and isinstance(self._name, tree.Name) and \\\n                not isinstance(self._name_context, AbstractInstanceContext):\n            flow_scope = self._name\n            base_node = self._name_context.tree_node\n            if base_node.type == 'comp_for':\n                return contexts\n            while True:\n                flow_scope = get_parent_scope(flow_scope, include_flows=True)\n                n = _check_flow_information(self._name_context, flow_scope,\n                                            self._name, self._position)\n                if n is not None:\n                    return n\n                if flow_scope == base_node:\n                    break\n        return contexts\n\n\ndef _check_flow_information(context, flow, search_name, pos):\n    \"\"\" Try to find out the type of a variable just with the information that\n    is given by the flows: e.g. It is also responsible for assert checks.::\n\n        if isinstance(k, str):\n            k.  # <- completion here\n\n    ensures that `k` is a string.\n    \"\"\"\n    if not settings.dynamic_flow_information:\n        return None\n\n    result = None\n    if is_scope(flow):\n        # Check for asserts.\n        module_node = flow.get_root_node()\n        try:\n            names = module_node.get_used_names()[search_name.value]\n        except KeyError:\n            return None\n        names = reversed([\n            n for n in names\n            if flow.start_pos <= n.start_pos < (pos or flow.end_pos)\n        ])\n\n        for name in names:\n            ass = search_ancestor(name, 'assert_stmt')\n            if ass is not None:\n                result = _check_isinstance_type(context, ass.assertion, search_name)\n                if result is not None:\n                    return result\n\n    if flow.type in ('if_stmt', 'while_stmt'):\n        potential_ifs = [c for c in flow.children[1::4] if c != ':']\n        for if_test in reversed(potential_ifs):\n            if search_name.start_pos > if_test.end_pos:\n                return _check_isinstance_type(context, if_test, search_name)\n    return result\n\n\ndef _check_isinstance_type(context, element, search_name):\n    try:\n        assert element.type in ('power', 'atom_expr')\n        # this might be removed if we analyze and, etc\n        assert len(element.children) == 2\n        first, trailer = element.children\n        assert first.type == 'name' and first.value == 'isinstance'\n        assert trailer.type == 'trailer' and trailer.children[0] == '('\n        assert len(trailer.children) == 3\n\n        # arglist stuff\n        arglist = trailer.children[1]\n        args = TreeArguments(context.evaluator, context, arglist, trailer)\n        param_list = list(args.unpack())\n        # Disallow keyword arguments\n        assert len(param_list) == 2\n        (key1, lazy_context_object), (key2, lazy_context_cls) = param_list\n        assert key1 is None and key2 is None\n        call = helpers.call_of_leaf(search_name)\n        is_instance_call = helpers.call_of_leaf(lazy_context_object.data)\n        # Do a simple get_code comparison. They should just have the same code,\n        # and everything will be all right.\n        normalize = context.evaluator.grammar._normalize\n        assert normalize(is_instance_call) == normalize(call)\n    except AssertionError:\n        return None\n\n    context_set = ContextSet()\n    for cls_or_tup in lazy_context_cls.infer():\n        if isinstance(cls_or_tup, iterable.Sequence) and cls_or_tup.array_type == 'tuple':\n            for lazy_context in cls_or_tup.py__iter__():\n                for context in lazy_context.infer():\n                    context_set |= context.execute_evaluated()\n        else:\n            context_set |= cls_or_tup.execute_evaluated()\n    return context_set\n","sub_path":"contrib/python/jedi/jedi/evaluate/finder.py","file_name":"finder.py","file_ext":"py","file_size_in_byte":11873,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"85851128","text":"from flask import Flask, render_template_string\nfrom wtforms import SelectMultipleField, Form\nfrom wtforms import widgets\n\napp = Flask(__name__)\n\ndata = [('value_a','Value A'), ('value_b','Value B'), ('value_c','Value C')]\n\n\nclass ExampleForm(Form):\n    example = SelectMultipleField(\n        'Pick Things!',\n        choices=data,\n        option_widget=widgets.CheckboxInput(),\n        widget=widgets.ListWidget(prefix_label=False)\n        )\n\n\n@app.route('/')\ndef home():\n    form = ExampleForm()\n    return render_template_string('<form>{{ form.example }}</form>',form=form)\n\nif __name__ == '__main__':\n    app.run(debug=True)","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":627,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"576186489","text":"# coding=utf-8\n# Copyright 2021 The TensorFlow Datasets Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"Utils to generate builders for D4RL datasets.\"\"\"\nfrom typing import Any, Dict, Iterable, List, Tuple, Union\n\nimport h5py\nimport numpy as np\nfrom tensorflow.io import gfile\n\n\ndef generate_examples(file_path: str):\n  \"\"\"Provides a common generate_examples method for D4RL datasets.\"\"\"\n  dataset_dict = read_d4rl_dataset(file_path)\n  if 'timeouts' not in dataset_dict:\n    raise ValueError('Only datasets with explicit timeouts are supported.')\n\n  done = [\n      terminal or timeout\n      for (terminal,\n           timeout) in zip(dataset_dict['terminals'], dataset_dict['timeouts'])\n  ]\n  # is_first corresponds to the done flag delayed by one step.\n  dataset_dict['is_first'] = [True] + done[:-1]\n\n  # TODO(sabela): Add extra keys for metadata (qpos, qval, goal) that is only\n  # present in some datasets.\n  dataset_dict = {\n      'observation': dataset_dict['observations'],\n      'action': dataset_dict['actions'],\n      'reward': dataset_dict['rewards'],\n      'discount': np.ones_like(dataset_dict['rewards']),\n      'is_terminal': dataset_dict['terminals'],\n      'is_first': dataset_dict['is_first'],\n  }\n  num_steps = len(dataset_dict['is_first'])\n  prev = 0\n  counter = 0\n  for pos in range(num_steps):\n    if dataset_dict['is_first'][pos] and pos > prev:\n      yield counter, _get_episode(dataset_dict, prev, pos)\n      prev = pos\n      counter += 1\n  if prev < num_steps:\n    yield counter, _get_episode(dataset_dict, prev, num_steps)\n\n\ndef _get_episode(steps: Dict[str, Any], begin: int, end: int) -> Dict[str, Any]:\n  \"\"\"Builds a full episode dict.\n\n  Args:\n      steps: a dict with all steps in a dataset\n      begin: defines a starting position of an episode\n      end: defines an ending position of an episode\n\n  Returns:\n     A dict with data specific to one episode, already broken into steps.\n  \"\"\"\n  # It's an initial step if the episode is empty.\n  episode = {}\n  episode['is_first'] = steps['is_first'][begin:end]\n  episode['observation'] = steps['observation'][begin:end]\n  episode['action'] = steps['action'][begin:end]\n  episode['reward'] = steps['reward'][begin:end]\n  episode['discount'] = steps['discount'][begin:end]\n  episode['is_terminal'] = [False] * (end - begin)\n  if steps['is_terminal'][end - 1]:\n    # If the step is terminal, then we propagate the information to a next\n    # state. This matches the definition in RLDS. See types.py.\n    episode['is_first'] = np.concatenate((episode['is_first'], [False]))\n    # Observation, action and reward are dummy.\n    episode['observation'] = np.concatenate(\n        (episode['observation'], [np.zeros_like(steps['observation'][0])]))\n    episode['action'] = np.concatenate(\n        (episode['action'], [np.zeros_like(steps['action'][0])]))\n    episode['reward'] = np.concatenate(\n        (episode['reward'], [np.zeros_like(steps['reward'][0])]))\n    episode['discount'][-1] = 0.0\n    episode['discount'] = np.array(\n        np.concatenate((episode['discount'], [0.0])), dtype=np.float32)\n    episode['is_terminal'] = np.concatenate((episode['is_terminal'], [True]))\n  return {'steps': _unpack_steps(episode)}\n\n\ndef _get_nested_field(data: Union[Dict[str, Any], Tuple[Any], List[Any]],\n                      index: int) -> Any:\n  \"\"\"Gets nested data and returns element at index respecting the shape.\n\n  It assumes that the most leaf type is a list.\n\n  If the input data is, for example:\n  data = {\n    'field_1': {\n        'nested': [1,2,3],\n    },\n    'field_2': [4, 5, 6],\n  }\n  index = 1\n  The output is:\n  {\n    'field_1':{\n      'nested': 2,\n    },\n    'field_2': 5,\n  }\n\n  Args:\n    data: data with nested shape, where the most inner type is a list.\n    index: index in the list to construct the returned element.\n\n  Returns:\n    Element i of data respecting the nested shape.\n\n  Raises:\n    ValueError if the input data type is not dict, tuple, list or np.ndarray.\n  \"\"\"\n  if isinstance(data, list) or isinstance(data, np.ndarray):\n    return data[index]\n  if isinstance(data, dict):\n    return {k: _get_nested_field(data[k], index) for k in data}\n  if isinstance(data, tuple):\n    return (_get_nested_field(data[k], index) for k in data)\n  raise ValueError(f'Data has to be list, dict or tuple and it is {type(data)}')\n\n\ndef _unpack_steps(steps: Dict[str, List[Any]]) -> Iterable[Dict[str, Any]]:\n  \"\"\"Gets a step represented as a dict[nested_list] and returns list[dict].\"\"\"\n  length = len(steps['is_first'])\n  for i in range(length):\n    step = {}\n    for k in steps:\n      step[k] = _get_nested_field(steps[k], i)\n    yield step\n\n\ndef _get_dataset_keys(h5file):\n  \"\"\"Gets the keys present in the D4RL dataset.\"\"\"\n  keys = []\n\n  def visitor(name, item):\n    if isinstance(item, h5py.Dataset):\n      keys.append(name)\n\n  h5file.visititems(visitor)\n  return keys\n\n\ndef read_d4rl_dataset(file_path: str):\n  \"\"\"Reads a D4RL dataset and returns the dataset as a dictionary.\"\"\"\n  with gfile.GFile(file_path, 'rb') as f:\n    dataset_file = h5py.File(f, 'r')\n    dataset_dict = {}\n    for k in _get_dataset_keys(dataset_file):\n      try:\n        # first try loading as an array\n        dataset_dict[k] = dataset_file[k][:]\n      except ValueError:  # try loading as a scalar\n        dataset_dict[k] = dataset_file[k][()]\n    dataset_file.close()\n    return dataset_dict\n","sub_path":"tensorflow_datasets/d4rl/dataset_utils.py","file_name":"dataset_utils.py","file_ext":"py","file_size_in_byte":5865,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"238919061","text":"import time\n# TO-DO: Complete the selection_sort() function below\nstart = time.time()\ndef selection_sort(arr):\n    # loop through n-1 elements\n    for i in range(0, len(arr) - 1):\n        cur_index = i\n        smallest_index = cur_index\n        # TO-DO: find next smallest element\n        # (hint, can do in 3 loc)\n        # Your code here\n        for j in range(i + 1, len(arr)):\n            if arr[j] < arr[smallest_index]:\n                smallest_index = j\n        # TO-DO: swap\n        # Your code here\n        arr[i], arr[smallest_index] = arr[smallest_index], arr[i]\n\n    return arr\n\nrandom_list_of_numbers = [1, 10, 7, 8, 2, 23, 6]\nselection_sort(random_list_of_numbers)\nend = time.time()\nprint(f\"This is my runtime {end - start} seconds\")\nprint(random_list_of_numbers)\n\n# TO-DO:  implement the Bubble Sort function below\nstart1 = time.time()\ndef bubble_sort(arr):\n    # We set swapped to true so the loop runs at least once\n    swapped = True\n    while swapped:\n        swapped = False\n        for i in range(0, len(arr) - 1):\n            if arr[i] > arr[i + 1]:\n                # Swap the elements\n                arr[i], arr[i + 1] = arr[i + 1], arr[i]\n                # Set the flag to true so we'll loop again\n                swapped = True\n    \n    return arr\n\n# Verify it works:\nrandom_list_of_numbers1 = [4, 7, 2, 30, 12, 10, 9, 22]\nbubble_sort(random_list_of_numbers1)\nend1 = time.time() - start1\nfinalTime = f\"{end1: .21f}\"\nprint(finalTime)\n#print(\"My processing time for bubble sort is =\", end1)\nprint(random_list_of_numbers1)\n\n\n'''\nSTRETCH: implement the Counting Sort function below\n\nCounting sort is a sorting algorithm that works on a set of data where\nwe specifically know the maximum value that can exist in that set of\ndata. The idea behind this algorithm then is that we can create \"buckets\"\nfrom 0 up to the max value. This is most easily done by initializing an\narray of 0s whose length is the max value + 1 (why do we need this \"+ 1\"?).\n\nEach buckets[i] then is responsible for keeping track of how many times \nwe've seen `i` in the input set of data as we iterate through it.\nOnce we know exactly how many times each piece of data in the input set\nshowed up, we can construct a sorted set of the input data from the \nbuckets. \n\nWhat is the time and space complexity of the counting sort algorithm?\n'''\ndef counting_sort(arr, maximum=None):\n    # Your code here\n\n\n    return arr\n","sub_path":"src/iterative_sorting/iterative_sorting.py","file_name":"iterative_sorting.py","file_ext":"py","file_size_in_byte":2409,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"376735629","text":"# Import from third library\nimport torch\nfrom eod.models.heads.utils.nms_wrapper import nms\nfrom eod.models.heads.utils.bbox_helper import (\n    clip_bbox,\n    filter_by_size,\n    offset2bbox\n)\nfrom eod.utils.general.fp16_helper import to_float32\nfrom eod.utils.general.registry_factory import ROI_MERGER_REGISTRY, ROI_PREDICTOR_REGISTRY\n\n__all__ = [\n    'RoiPredictor',\n    'RoiPredictorMultiCls',\n    'RpnMerger',\n    'RetinaMerger',\n    'RetinaMergerMultiCls'\n]\n\n\n@ROI_PREDICTOR_REGISTRY.register('base')\nclass RoiPredictor(object):\n    \"\"\"Predictor for the first stage\n    \"\"\"\n\n    def __init__(self,\n                 pre_nms_score_thresh,\n                 pre_nms_top_n,\n                 post_nms_top_n,\n                 roi_min_size,\n                 merger=None,\n                 nms=None):\n        self.pre_nms_score_thresh = pre_nms_score_thresh\n        # self.apply_score_thresh_above = apply_score_thresh_above\n        self.pre_nms_top_n = pre_nms_top_n\n        self.post_nms_top_n = post_nms_top_n\n        self.nms_cfg = nms\n        self.roi_min_size = roi_min_size\n        if merger is not None:\n            self.merger = build_merger(merger)\n        else:\n            self.merger = None\n\n    @torch.no_grad()\n    @to_float32\n    def predict(self, mlvl_anchors, mlvl_preds, image_info):\n        mlvl_resutls = []\n        for anchors, preds in zip(mlvl_anchors, mlvl_preds):\n            results = self.single_level_predict(anchors, preds, image_info)\n            mlvl_resutls.append(results)\n        if len(mlvl_resutls) > 0:\n            results = torch.cat(mlvl_resutls, dim=0)\n        else:\n            results = mlvl_anchors[0].new_zeros((1, 7))\n        if self.merger is not None:\n            results = self.merger.merge(results)\n        return {'dt_bboxes': results}\n\n    def regression(self, anchors, preds, image_info):\n        cls_pred, loc_pred = preds[:2]\n        B, K = cls_pred.shape[:2]\n        concat_anchors = torch.stack([anchors.clone() for _ in range(B)])\n        rois = offset2bbox(concat_anchors.view(B * K, 4), loc_pred.view(B * K, 4)).view(B, K, 4)  # noqa\n        return rois\n\n    def single_level_predict(self, anchors, preds, image_info):\n        \"\"\"\n        Arguments:\n            - anchors (FloatTensor, fp32): [K, 4]\n            - preds[0] (cls_pred, FloatTensor, fp32): [B, K, C], C[i] -> class i+1, background class is excluded\n            - preds[1] (loc_pred, FloatTensor, fp32): [B, K, 4]\n            - image_info (list of FloatTensor): [B, >=2] (image_h, image_w, ...)\n\n        Returns:\n            rois (FloatTensor): [N, >=7] (batch_ix, x1, y1, x2, y2, score, cls)\n        \"\"\"\n\n        rois = self.regression(anchors, preds, image_info)\n\n        cls_pred = preds[0]\n        B, K, C = cls_pred.shape\n        roi_min_size = self.roi_min_size\n        pre_nms_top_n = self.pre_nms_top_n\n        pre_nms_top_n = pre_nms_top_n if pre_nms_top_n > 0 else K\n        post_nms_top_n = self.post_nms_top_n\n        # if featmap size is too large, filter by score thresh to reduce computation\n        if K > 120:\n            score_thresh = self.pre_nms_score_thresh\n        else:\n            score_thresh = 0\n\n        batch_rois = []\n        for b_ix in range(B):\n            # clip rois and filter rois which are too small\n            image_rois = rois[b_ix]\n            image_rois = clip_bbox(image_rois, image_info[b_ix])\n            image_rois, filter_inds = filter_by_size(image_rois, roi_min_size)\n            image_cls_pred = cls_pred[b_ix][filter_inds]\n            if image_rois.numel() == 0:\n                continue  # noqa E701\n\n            for cls in range(C):\n                cls_rois = image_rois\n                scores = image_cls_pred[:, cls]\n                assert not torch.isnan(scores).any()\n                if score_thresh > 0:\n                    # to reduce computation\n                    keep_idx = torch.nonzero(scores > score_thresh).reshape(-1)\n                    if keep_idx.numel() == 0:\n                        continue  # noqa E701\n                    cls_rois = cls_rois[keep_idx]\n                    scores = scores[keep_idx]\n\n                # do nms per image, only one class\n                _pre_nms_top_n = min(pre_nms_top_n, scores.shape[0])\n                scores, order = scores.topk(_pre_nms_top_n, sorted=True)\n                cls_rois = cls_rois[order, :]\n                cls_rois = torch.cat([cls_rois, scores[:, None]], dim=1)\n\n                if self.nms_cfg is not None:\n                    cls_rois, keep_idx = nms(cls_rois, self.nms_cfg)\n                if post_nms_top_n > 0:\n                    cls_rois = cls_rois[:post_nms_top_n]\n\n                ix = cls_rois.new_full((cls_rois.shape[0], 1), b_ix)\n                c = cls_rois.new_full((cls_rois.shape[0], 1), cls + 1)\n                cls_rois = torch.cat([ix, cls_rois, c], dim=1)\n                batch_rois.append(cls_rois)\n\n        if len(batch_rois) == 0:\n            return anchors.new_zeros((1, 7))\n        return torch.cat(batch_rois, dim=0)\n\n\n@ROI_PREDICTOR_REGISTRY.register('base_multicls')\nclass RoiPredictorMultiCls(RoiPredictor):\n    \"\"\"Predictor for the first stage\n    \"\"\"\n\n    def __init__(self, pre_nms_score_thresh,\n                 pre_nms_top_n, post_nms_top_n, roi_min_size, merger=None, nms=None):\n        super().__init__(pre_nms_score_thresh, pre_nms_top_n, post_nms_top_n, roi_min_size, merger, nms)\n        self.INF = 4096\n\n    def single_level_predict(self, anchors, preds, image_info):\n        \"\"\"\n        Arguments:\n            - anchors (FloatTensor, fp32): [K, 4]\n            - preds[0] (cls_pred, FloatTensor, fp32): [B, K, C], C[i] -> class i+1, background class is excluded\n            - preds[1] (loc_pred, FloatTensor, fp32): [B, K, 4]\n            - image_info (list of FloatTensor): [B, >=2] (image_h, image_w, ...)\n\n        Returns:\n            rois (FloatTensor): [N, >=7] (batch_ix, x1, y1, x2, y2, score, cls)\n        \"\"\"\n\n        rois = self.regression(anchors, preds, image_info)\n\n        cls_pred = preds[0]\n        B, K, C = cls_pred.shape\n        roi_min_size = self.roi_min_size\n        pre_nms_top_n = self.pre_nms_top_n\n        pre_nms_top_n = pre_nms_top_n if pre_nms_top_n > 0 else K\n        # post_nms_top_n = self.post_nms_top_n\n        # if featmap size is too large, filter by score thresh to reduce computation\n        if K > 120:\n            score_thresh = self.pre_nms_score_thresh\n        else:\n            score_thresh = 0\n\n        batch_rois = []\n        for b_ix in range(B):\n            # clip rois and filter rois which are too small\n            image_rois = rois[b_ix]\n            image_rois = clip_bbox(image_rois, image_info[b_ix])\n            image_rois, filter_inds = filter_by_size(image_rois, roi_min_size)\n            image_cls_pred = cls_pred[b_ix][filter_inds]\n            if image_rois.numel() == 0:\n                continue  # noqa E701\n\n            image_cls_pred = image_cls_pred.flatten()\n            _pre_nms_top_n = min(pre_nms_top_n, image_rois.shape[0])\n            scores, topk_idxs = image_cls_pred.sort(descending=True)\n            scores = scores[:_pre_nms_top_n]\n            topk_idxs = topk_idxs[:_pre_nms_top_n]\n            if score_thresh > 0:\n                # to reduce computation\n                keep_idx = torch.nonzero(scores > score_thresh).reshape(-1)\n                if keep_idx.numel() == 0:\n                    continue  # noqa E701\n                scores = scores[keep_idx]\n                topk_idxs = topk_idxs[keep_idx]\n            anchor_idxs = topk_idxs // C\n            classes_idxs = ((topk_idxs % C) + 1).float().view(-1, 1)\n            cls_rois = image_rois[anchor_idxs]\n            scores = scores.view(-1, 1)\n\n            if self.nms_cfg is not None:\n                boxes = cls_rois + (classes_idxs * self.INF).expand_as(cls_rois)\n                _, keep_idx = nms(torch.cat((boxes, scores), dim=1), self.nms_cfg)\n                cls_rois = cls_rois[keep_idx]\n                scores = scores[keep_idx]\n                classes_idxs = classes_idxs[keep_idx]\n            ix = cls_rois.new_full((cls_rois.shape[0], 1), b_ix)\n            cls_rois = torch.cat([ix, cls_rois, scores, classes_idxs], dim=1)\n            batch_rois.append(cls_rois)\n        if len(batch_rois) == 0:\n            return anchors.new_zeros((1, 7))\n        return torch.cat(batch_rois, dim=0)\n\n\n@ROI_MERGER_REGISTRY.register('rpn')\nclass RpnMerger(object):\n    \"\"\"Concat results from all levels and keep topk\n    \"\"\"\n\n    def __init__(self, top_n, topk_in_a_batch=False):\n        self.top_n = top_n\n        self.topk_in_a_batch = topk_in_a_batch\n\n    @torch.no_grad()\n    @to_float32\n    def merge(self, mlvl_rois):\n        \"\"\"Merge rois from all levels by keeping topk rois\n        Arguments:\n            - mlvl_rois (FloatTensor): [N, >=7], (batch_idx, x1, y1, x2, y2, score, cls)\n        \"\"\"\n        merged_rois = []\n        # TODO: we choose topk in a batch when training to align performance with maskrcnn-benchmark.\n        # This might lead to ~0.5 mAP performace gain.\n        if self.topk_in_a_batch:\n            topk = min(self.top_n, mlvl_rois.shape[0])\n            scores, order = torch.topk(mlvl_rois[:, 5], k=topk, sorted=True)\n            return mlvl_rois[order]\n        else:\n            merged_rois = []\n            B = int(torch.max(mlvl_rois[:, 0]).item() + 1)\n            for b_ix in range(B):\n                rois = mlvl_rois[mlvl_rois[:, 0] == b_ix]\n                if rois.numel() == 0:\n                    continue  # noqa E701\n                topk = min(self.top_n, rois.shape[0])\n                scores, order = torch.topk(rois[:, 5], k=topk, sorted=True)\n                merged_rois.append(rois[order])\n            if len(merged_rois) > 0:\n                merged_rois = torch.cat(merged_rois, dim=0)\n            else:\n                merged_rois = mlvl_rois.new_zeros((1, 7))\n            return merged_rois\n\n\n@ROI_MERGER_REGISTRY.register('retina')\nclass RetinaMerger(object):\n    \"\"\"Merge results from multi-levels\n    1. concat all results\n    2. nms\n    3. keep topk\n    \"\"\"\n\n    def __init__(self, top_n, nms):\n        self.top_n = top_n\n        self.nms_cfg = nms\n\n    @torch.no_grad()\n    @to_float32\n    def merge(self, mlvl_rois):\n        \"\"\"\n        Merge rois from different levels together\n\n        Note:\n            1. do nms for each class when nms_iou_thresh > 0\n            2. keep top_n rois for each image, keep all when top_n <= 0\n\n        Arguments:\n            - mlvl_rois (FloatTensor): [N, >=7], (batch_index, x1, y1, x2, y2, score, cls)\n        \"\"\"\n        merged_rois = []\n        B = int(torch.max(mlvl_rois[:, 0]).item() + 1)\n        for b_ix in range(B):\n            img_rois = mlvl_rois[mlvl_rois[:, 0] == b_ix]\n            if img_rois.numel() == 0:\n                continue  # noqa E701\n\n            classes = torch.unique(img_rois[:, 6].int()).cpu().numpy().tolist()\n            all_cls_rois = []\n            for cls in classes:\n                cls_rois = img_rois[img_rois[:, 6] == cls]\n                if cls_rois.numel() == 0:\n                    continue  # noqa E701\n                _, indices = nms(cls_rois[:, 1:6], self.nms_cfg)\n                all_cls_rois.append(cls_rois[indices])\n            if len(all_cls_rois) == 0:\n                continue  # noqa E701\n            rois = torch.cat(all_cls_rois, dim=0)\n\n            if self.top_n < rois.shape[0]:\n                _, inds = torch.topk(rois[:, 5], self.top_n)\n                rois = rois[inds]\n            merged_rois.append(rois)\n        if len(merged_rois) > 0:\n            merged_rois = torch.cat(merged_rois, dim=0)\n        else:\n            merged_rois = mlvl_rois.new_zeros((1, 7))\n        return merged_rois\n\n\n@ROI_MERGER_REGISTRY.register('retina_multicls')\nclass RetinaMergerMultiCls(RetinaMerger):\n    \"\"\"Merge results from multi-levels\n    1. concat all results\n    2. nms\n    3. keep topk\n    \"\"\"\n\n    def __init__(self, top_n, nms):\n        super().__init__(top_n, nms)\n        self.INF = 4096\n\n    @torch.no_grad()\n    @to_float32\n    def merge(self, mlvl_rois):\n        \"\"\"\n        Merge rois from different levels together\n\n        Note:\n            1. do nms for each class when nms_iou_thresh > 0\n            2. keep top_n rois for each image, keep all when top_n <= 0\n\n        Arguments:\n            - mlvl_rois (FloatTensor): [N, >=7], (batch_index, x1, y1, x2, y2, score, cls)\n        \"\"\"\n        merged_rois = []\n        B = int(torch.max(mlvl_rois[:, 0]).item() + 1)\n        for b_ix in range(B):\n            img_rois = mlvl_rois[mlvl_rois[:, 0] == b_ix]\n            if img_rois.numel() == 0:\n                continue  # noqa E701\n            img_rois = img_rois[img_rois[:, 5].argsort(descending=True)]\n            c = img_rois[:, 6]\n            boxes = img_rois[:, 1:6].clone()\n            boxes[:, :4] += c.view(-1, 1) * self.INF\n            _, indices = nms(boxes, self.nms_cfg)\n            del boxes\n            rois = img_rois[indices]\n            if self.top_n < rois.shape[0]:\n                rois = rois[:self.top_n]\n            merged_rois.append(rois)\n        if len(merged_rois) > 0:\n            merged_rois = torch.cat(merged_rois, dim=0)\n        else:\n            merged_rois = mlvl_rois.new_zeros((1, 7))\n        return merged_rois\n\n\ndef build_merger(merger_cfg):\n    return ROI_MERGER_REGISTRY.build(merger_cfg)\n\n\ndef build_roi_predictor(predictor_cfg):\n    return ROI_PREDICTOR_REGISTRY.build(predictor_cfg)\n","sub_path":"eod/models/postprocess/predictor.py","file_name":"predictor.py","file_ext":"py","file_size_in_byte":13392,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"100580690","text":"\n# coding: utf-8\n\n# In[1]:\n\n\n#Eduardo Darrazão\n#Rodrigo Faria\n\n\n# In[1]:\n\n\nimport cv2\nimport time\nimport math\nimport numpy as np\n\nglobal JANELA\nJANELA = 5\n\n\n# In[2]:\n\n\ndef filtroIngenuo(img):\n    imagem = img.copy()\n    altura, largura, cor = img.shape[0], img.shape[1], img.shape[2]\n    janela = int(JANELA/2)\n    \n    for k in range(cor):\n        for y in range(janela, altura-janela):\n            for x in range(janela, largura-janela):\n                \n                soma = 0\n                for alturaJanela in range(y-janela, y+janela):\n                    for larguraJanela in range(x-janela, x+janela):\n                        soma += imagem[alturaJanela][larguraJanela][k]\n                        \n                img[y][x][k] = soma/np.power(janela*2, 2)\n    return img\n\n\n# In[3]:\n\n\ndef filtroSeparavel(img):\n    imagem = img.copy()\n    altura, largura, cor = img.shape[0], img.shape[1], img.shape[2]  \n    janela = int(JANELA/2)\n        \n    for k in range(cor):\n        for y in range(janela, altura - janela):\n            for x in range(janela, largura - janela):\n                \n                soma = 0\n                for larguraJanela in range(x-janela, x+janela):\n                    soma+=imagem[y][larguraJanela][k]\n                img[y][x][k] = soma/(janela*2)\n                \n    for k in range(cor):\n        for y in range(janela, altura - janela):\n            for x in range(janela, largura - janela):\n            \n                soma = 0\n                for alturaJanela in range(y-janela, y+janela):\n                    soma += img[alturaJanela][x][k]\n                imagem[y][x][k] = soma/(janela*2)\n    \n    return imagem\n\n\n# In[4]:\n\n\ndef filtroSeparavelAproveitando(img):\n    imagem = img.copy()\n    altura, largura, cor = img.shape[0], img.shape[1], img.shape[2]  \n    janela = int(JANELA/2)\n        \n    for k in range(cor):\n        for y in range(janela, altura - janela):\n            ind = 0\n            for x in range(janela, largura - janela):\n                if ind == 0:\n                    soma = 0\n                    for larguraJanela in range(x-janela, x+janela):\n                        soma+=imagem[y][larguraJanela][k]\n                    ind = 1\n                    \n                else:\n                    soma -= imagem[y][x-janela-1][k]\n                    soma += imagem[y][x+janela-1][k]\n                \n                img[y][x][k] = soma/(janela*2)\n                \n    for k in range(cor):\n        for x in range(janela, largura - janela):\n\n            ind = 0\n            for y in range(janela, altura - janela):\n                if ind == 0:\n                    soma = 0\n                    for alturaJanela in range(y-janela, y+janela):\n                        soma += img[alturaJanela][x][k] \n                    ind = 1\n                else:\n                    soma -= img[y-janela-1][x][k]\n                    soma += img[y+janela-1][x][k]\n                \n                imagem[y][x][k] = soma/(janela*2)\n                \n                \n    return imagem\n   \n\n\n# In[5]:\n\n\ndef imagemIntegral(imagem):\n    altura, largura, cor = img.shape[0], img.shape[1], img.shape[2]\n    \n    for k in range(cor):\n        indAltura = 1\n        \n        for y in range(altura):\n            indLargura = 1\n            \n            for x in range(largura):\n                if indLargura != 1 and indAltura != 1: \n                        imagem[y][x][k] += imagem[y-1][x][k]\n                        imagem[y][x][k] += imagem[y][x-1][k]    \n                        imagem[y][x][k] -= imagem[y-1][x-1][k]\n                else:\n                    if not(indLargura == 1 and indAltura == 1):\n                        if indLargura!=1:\n                             imagem[y][x][k] += imagem[y][x-1][k]\n                        elif indAltura!=1:\n                            imagem[y][x][k] += imagem[y-1][x][k]\n                        \n                indLargura = 0\n                \n            indAltura = 0\n        \n    return imagem\n\ndef mediaIntegral(img):\n    imagem = img.copy()\n    imagem = imagemIntegral(imagem)\n    altura, largura, cor = img.shape[0], img.shape[1], img.shape[2]  \n    janela = int(JANELA/2)\n    \n    for k in range(cor):\n        for y in range(janela, altura - janela):\n            for x in range(janela, largura - janela):\n                sigma = imagem[y+janela][x+janela][k] + imagem[y-janela][x-janela][k]\n                sigma -= imagem[y+janela][x-janela][k] + imagem[y-janela][x+janela][k]\n                \n                img[y][x][k] = sigma/np.power(janela*2, 2)\n    return img\n    \n\n\n# In[6]:\n\n\n##MAIN\ntry: \n    img = cv2.imread(\"lena.jpg\", cv2.IMREAD_COLOR)\n    img = cv2.normalize(img, None, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F)\nexcept:\n    print('Erro ao abrir imagem')\n    exit()    \n    \nstart = time.time()   \ningenuo = filtroIngenuo(img)\nend = time.time()\n\nprint (\"Filtro Ingenuo: \", end-start)\n\ncv2.imshow('image',ingenuo)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n\n######################\n\nstart = time.time()   \nseparavel = filtroSeparavel(img)\nend = time.time()\n\nprint(\"Filtro Separavel: \", end-start)\n\ncv2.imshow('image',separavel)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n\n######################\n\nstart = time.time()   \nsepAproveitando = filtroSeparavelAproveitando(img)\nend = time.time()\n\nprint(\"Filtro Separavel Aproveitando as Somas Anteriores: \", end-start)\n\ncv2.imshow('image',sepAproveitando)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n\n######################\n\nstart = time.time()   \nintegral = mediaIntegral(img)\nend = time.time()\n\nprint (\"Filtro integral: \", end-start)\n\ncv2.imshow('image',integral)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n\n","sub_path":"Filtro_Media/filtro_media.py","file_name":"filtro_media.py","file_ext":"py","file_size_in_byte":5663,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"16089643","text":"# Time Complexity : O(n), where n is the len of list.\n# Space Complexity : O(1)\n# Did this code successfully run on Leetcode : yes\n# Any problem you faced while coding this : no\n\n# Definition for singly-linked list.\n# class ListNode:\n#     def __init__(self, val=0, next=None):\n#         self.val = val\n#         self.next = next\n\n\nclass Solution:\n    def isPalindrome(self, head: ListNode) -> bool:\n\n        if head is None or head.next is None:\n            return True\n\n        slow = head\n        fast = head\n\n        while fast.next != None and fast.next.next != None:\n            slow = slow.next\n            fast = fast.next.next\n\n        head2 = slow.next\n        slow.next = None\n        prev = None\n        nextnew = None\n        curr = head2\n\n        while curr != None:\n            nextnew = curr.next\n            curr.next = prev\n            prev = curr\n            curr = nextnew\n\n        head2 = prev\n\n        while head != None and head2 != None:\n            if head.val == head2.val:\n                head = head.next\n                head2 = head2.next\n            else:\n                return False\n        return True\n","sub_path":"Problem1.py","file_name":"Problem1.py","file_ext":"py","file_size_in_byte":1135,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"330468390","text":"import iotbx.pdb\nfrom scitbx.array_family import flex\nfrom cctbx import uctbx\nfrom cctbx import crystal\nfrom libtbx.utils import null_out\nimport mmtbx\nimport mmtbx.monomer_library.server\nimport mmtbx.monomer_library.pdb_interpretation\nimport mmtbx.restraints\nfrom mmtbx import monomer_library\nfrom libtbx import group_args\nimport math\nimport iotbx.pdb.utils\n\nmon_lib_srv = mmtbx.monomer_library.server.server()\nener_lib    = mmtbx.monomer_library.server.ener_lib(\n  use_neutron_distances = True)\n\ndef create_super_sphere(pdb_hierarchy,\n                        crystal_symmetry,\n                        select_within_radius,\n                        link_min=1.0,\n                        link_max=2.0,\n                        r=None):\n  if(r is None):\n    # This is equivalent to (but much faster):\n    #\n    #r = grm.geometry.pair_proxies().nonbonded_proxies.\\\n    #  get_symmetry_interacting_indices_unique(\n    #    sites_cart = pdb_hierarchy.atoms().extract_xyz())\n    #\n    sites_cart = pdb_hierarchy.atoms().extract_xyz()\n    sst = crystal_symmetry.special_position_settings().site_symmetry_table(\n      sites_cart = sites_cart)\n    r = {}\n    from cctbx import crystal\n    cutoff = select_within_radius+1 # +1 is nonbonded buffer\n    conn_asu_mappings = crystal_symmetry.special_position_settings().\\\n      asu_mappings(buffer_thickness=cutoff)\n    conn_asu_mappings.process_sites_cart(\n      original_sites      = sites_cart,\n      site_symmetry_table = sst)\n    conn_pair_asu_table = crystal.pair_asu_table(\n        asu_mappings=conn_asu_mappings)\n    conn_pair_asu_table.add_all_pairs(\n        distance_cutoff=cutoff)\n    pair_generator = crystal.neighbors_fast_pair_generator(\n       conn_asu_mappings,\n       distance_cutoff=cutoff)\n    for pair in pair_generator:\n      rt_mx_i = conn_asu_mappings.get_rt_mx_i(pair)\n      rt_mx_j = conn_asu_mappings.get_rt_mx_j(pair)\n      rt_mx_ji = rt_mx_i.inverse().multiply(rt_mx_j)\n      if str(rt_mx_ji)==\"x,y,z\": continue\n      r.setdefault(pair.j_seq, []).append(rt_mx_ji)\n    for k,v in zip(r.keys(), r.values()): # remove duplicates!\n      r[k] = list(set(v))\n  c = iotbx.pdb.hierarchy.chain(id = \"SS\") # all symmetry related full residues\n  fm = crystal_symmetry.unit_cell().fractionalization_matrix()\n  om = crystal_symmetry.unit_cell().orthogonalization_matrix()\n  selection = r.keys()\n  for rg in pdb_hierarchy.residue_groups():\n    keep=False\n    for i in rg.atoms().extract_i_seq():\n      if(i in selection):\n        keep=True\n        break\n    if(keep):\n      ops = r[i]\n      for op in ops:\n        rg_ = rg.detached_copy()\n        xyz = rg_.atoms().extract_xyz()\n        new_xyz = flex.vec3_double()\n        for xyz_ in xyz:\n          t1 = fm*flex.vec3_double([xyz_])\n          t2 = op*t1[0]\n          t3 = om*flex.vec3_double([t2])\n          new_xyz.append(t3[0])\n        rg_.atoms().set_xyz(new_xyz)\n        rg_.link_to_previous=True\n        c.append_residue_group(rg_)\n  resseq = 0\n  for rg in c.residue_groups():\n    rg.resseq = \"%4d\" % resseq\n    resseq+=1\n  # Now we need to re-order residues and group by chains so that they can be\n  # linked by pdb interpretation.\n  super_sphere_hierarchy = pdb_hierarchy.deep_copy()\n  #\n  all_chains = iotbx.pdb.utils.all_chain_ids()\n  all_chains = [chid.strip() for chid in all_chains]\n  for cid in list(set([i.id for i in pdb_hierarchy.chains()])):\n    all_chains.remove(cid)\n  all_chains = iter(all_chains)\n  #\n  def get_atom(atoms, name):\n    for a in atoms:\n      if(a.name.strip().lower()==name.strip().lower()):\n        return a.xyz\n  residue_groups_i = list(c.residue_groups())\n  residue_groups_j = list(c.residue_groups())\n  residue_groups_k = list() # standard aa residues only\n  #\n  def dist(r1,r2):\n    return math.sqrt((r1[0]-r2[0])**2+(r1[1]-r2[1])**2+(r1[2]-r2[2])**2)\n  #\n  def grow_chain(residue_groups_j, chunk, ci):\n    n_linked = 0\n    for rgj in residue_groups_j:\n      nj = get_atom(atoms=rgj.atoms(), name=\"N\")\n      if(nj is None): continue\n      d_ci_nj = dist(ci,nj)\n      if(d_ci_nj<link_max and d_ci_nj>link_min):\n        n_linked += 1\n        chunk.append(rgj)\n        residue_groups_j.remove(rgj)\n        break\n    return n_linked, rgj\n  # Find all isolated single residues, and also save starters\n  starters = []\n  for rgi in residue_groups_i:\n    ci = get_atom(atoms=rgi.atoms(), name=\"C\")\n    ni = get_atom(atoms=rgi.atoms(), name=\"N\")\n    if(ci is None or ni is None): # collect non-proteins\n      c = iotbx.pdb.hierarchy.chain(id = all_chains.next())\n      c.append_residue_group(rgi)\n      super_sphere_hierarchy.models()[0].append_chain(c)\n      continue\n    residue_groups_k.append(rgi)\n    c_linked = 0\n    n_linked = 0\n    for rgj in residue_groups_j:\n      cj = get_atom(atoms=rgj.atoms(), name=\"C\")\n      nj = get_atom(atoms=rgj.atoms(), name=\"N\")\n      if(cj is None or nj is None): continue\n      d_ci_nj = dist(ci,nj)\n      d_ni_cj = dist(ni,cj)\n      if(d_ci_nj<link_max and d_ci_nj>link_min):\n        n_linked += 1\n      if(d_ni_cj<link_max and d_ni_cj>link_min):\n        c_linked += 1\n    assert c_linked in [1, 0], c_linked # Either linked or not!\n    assert n_linked in [1, 0], n_linked # Either linked or not!\n    if(c_linked==0 and n_linked==0): # isolated single residue\n      c = iotbx.pdb.hierarchy.chain(id = all_chains.next())\n      rgi.link_to_previous=True\n      c.append_residue_group(rgi)\n      super_sphere_hierarchy.models()[0].append_chain(c)\n    elif(c_linked==0): # collect c-terminal residues\n      starters.append(rgi)\n  # Grow continuous chains from remaining residues starting from c-terminals\n  for rgi in starters:\n    ci = get_atom(atoms=rgi.atoms(), name=\"C\")\n    chunk = [rgi]\n    n_linked=1\n    while n_linked==1:\n      n_linked, rgj = grow_chain(residue_groups_k, chunk, ci)\n      if(n_linked==1):\n        ci = get_atom(atoms=rgj.atoms(), name=\"C\")\n    c = iotbx.pdb.hierarchy.chain(id = all_chains.next())\n    for i, rg in enumerate(chunk):\n      rg.resseq = \"%4d\" % i\n      c.append_residue_group(rg)\n    super_sphere_hierarchy.models()[0].append_chain(c)\n  #\n  box = uctbx.non_crystallographic_unit_cell_with_the_sites_in_its_center(\n    sites_cart   = super_sphere_hierarchy.atoms().extract_xyz(),\n    buffer_layer = 10)\n  cs_super_sphere = box.crystal_symmetry()\n  return group_args(\n    hierarchy        = super_sphere_hierarchy,\n    crystal_symmetry = cs_super_sphere,\n    selection_r                = r)\n\nclass expand(object):\n  def __init__(self, pdb_hierarchy, crystal_symmetry, select_within_radius=15,\n               create_restraints_manager=True):\n    # fixed members\n    self.create_restraints_manager = create_restraints_manager\n    self.crystal_symmetry = crystal_symmetry\n    self.select_within_radius = select_within_radius\n    self.cs_p1 = crystal.symmetry(self.crystal_symmetry.unit_cell(), \"P1\")\n    self.sel_str_focus = \" or \".join([\n      \"chain %s\"%c.id for c in pdb_hierarchy.chains()])\n    # variable members (can change upon self.update() call) or by end of\n    # constructor execution\n    self.pdb_hierarchy = pdb_hierarchy\n    self.super_sphere_geometry_restraints_manager = None\n    self.ph_p1           = None\n    self.selection_keep  = None\n    self.ph_super_cell   = None\n    self.cs_box          = None\n    self.ph_super_sphere = None\n    #\n    self.update()\n\n  def update(self, sites_cart=None, selection_r=None):\n    if(sites_cart is not None):\n      self.pdb_hierarchy.atoms().set_xyz(sites_cart)\n    o = create_super_sphere(\n      pdb_hierarchy        = self.pdb_hierarchy,\n      crystal_symmetry     = self.crystal_symmetry,\n      select_within_radius = self.select_within_radius,\n      r                    = selection_r)\n    self.ph_super_sphere = o.hierarchy\n    self.ph_super_cell = self.ph_super_sphere.deep_copy() # nonsense, we don't have super-cell anymore\n    self.cs_box = o.crystal_symmetry\n    self.selection_r = o.selection_r\n    if(self.create_restraints_manager):\n      if(selection_r is None): self.update_super_sphere_geometry_restraints_manager()\n    return self\n\n  def update_xyz(self, sites_cart=None):\n    # keep the selection of super_sphere, update its coordinates\n    self.update(sites_cart=sites_cart, selection_r=self.selection_r)\n    return self\n\n  def update_super_sphere_geometry_restraints_manager(self):\n    # XXX Unify with process_model_file of qr.py\n    params = mmtbx.monomer_library.pdb_interpretation.master_params.extract()\n    params.use_neutron_distances = True\n    params.restraints_library.cdl = False\n    params.sort_atoms = False\n    processed_pdb_file = mmtbx.monomer_library.pdb_interpretation.process(\n      mon_lib_srv              = mon_lib_srv,\n      ener_lib                 = ener_lib,\n      params                   = params,\n      pdb_hierarchy            = self.ph_super_sphere,\n      strict_conflict_handling = False,\n      crystal_symmetry         = self.cs_box,\n      force_symmetry           = True,\n      log                      = null_out())\n    xrs = processed_pdb_file.xray_structure()\n    sctr_keys = xrs.scattering_type_registry().type_count_dict().keys()\n    has_hd = \"H\" in sctr_keys or \"D\" in sctr_keys\n    geometry = processed_pdb_file.geometry_restraints_manager(\n      show_energies                = False,\n      assume_hydrogens_all_missing = not has_hd,\n      plain_pairs_radius           = 5.0)\n    self.super_sphere_geometry_restraints_manager = mmtbx.restraints.manager(\n       geometry = geometry, normalization = False)\n\n  def write_super_cell_selected_in_sphere(self, file_name=\"super_sphere.pdb\"):\n    self.ph_super_sphere.write_pdb_file(file_name = file_name,\n      crystal_symmetry = self.cs_box) # box around super-sphere, not original cs\n                                      # or cs in p1.\n","sub_path":"super_cell.py","file_name":"super_cell.py","file_ext":"py","file_size_in_byte":9700,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"322231620","text":"#!/usr/bin/env python\n\n\"\"\"\nORTHO\nMakefile interFACE (makeface) command-line interface\nNote that you should debug with `make debug_imports`\n\"\"\"\n\nfrom __future__ import print_function\n\nimport os,sys,re,importlib,inspect\nfrom .dev import tracebacker\nfrom .misc import str_types,locate,treeview\nfrom .config import set_config,setlist,set_list,unset,config,set_dict,look\nfrom .config import config_fold,set_hook,config_hook_get\nfrom .environments import environ\nfrom .bootstrap import bootstrap\nfrom .imports import importer,glean_functions\nfrom .unit_tester import unit_tester\nfrom .reexec import interact\nfrom .documentation import build_docs\nfrom .background import backrun,screen_background\nfrom .handler import introspect_function\nfrom .packman import packs,github_install\nfrom .queue.simple_queue import launch\nfrom .replicator import pipeline,repl\n\n# any functions from ortho exposed to CLI must be noted here and imported above\nexpose_funcs = {'set_config','setlist','set_list','unset','set_dict','environ',\n\t'config','bootstrap','interact','unit_tester','import_check','locate',\n\t#! consider developing screen_background at some point to replace backrun?\n\t'targets','build_docs','look','config_fold','debug_imports','set_hook',\n\t'backrun','packs','github_install','launch','repl','pipeline'}\nexpose_aliases = {'set_config':'set','environ':'env'}\n\n# collect functions once\nglobal funcs,_ortho_keys_exposed\nfuncs = None\n\nmessage_debug_suggestion =  'python -uBttc \"import ortho;ortho.get_targets(verbose=True,strict=True)\"'\n\ndef collect_functions(verbose=False,strict=False):\n\t\"\"\"\n\tCollect available functions.\n\tNote that strict in this context only prevents \"glean_functions\" from being used, and is distinct from\n\tthe strict import scheme in ortho.imports which is used to perform standard pythonic imports.\n\t\"\"\"\n\tglobal funcs,funcs_directory\n\tfuncs,funcs_directory = {},{}\n\t# start with the basic utility functions specified at the top\n\tfuncs = dict([(expose_aliases.get(k,k),globals()[k]) for k in expose_funcs])\n\t#! add the original names for the aliases in case they are necessary\n\t#! this is important if you make an env with name env because the folder precludes make env\n\tfor key,val in expose_aliases.items(): funcs[key] = globals()[key]\n\tsources = conf.get('commands',[])  # pylint: disable=undefined-variable\n\t# accrue functions over sources sequentially\n\tfor source in sources:\n\t\tif os.path.isfile(source) or os.path.isdir(source):\n\t\t\ttry: \n\t\t\t\tif verbose: print('status','importing source %s'%source)\n\t\t\t\tmod = importer(source,verbose=verbose)\n\t\t\t# if importing requires env then it is not ready when we get makefile targets\n\t\t\texcept Exception as e:\n\t\t\t\t# debug imports during dev with `ortho.get_targets(verbose=True)`\n\t\t\t\tif verbose: print('exception',e)\n\t\t\t\tif os.path.isdir(source):\n\t\t\t\t\traise Exception('failed to import %s '%source+\n\t\t\t\t\t\t'and cannot glean functions because it is not a file')\n\t\t\t\telif not strict: \n\t\t\t\t\tgleaned = glean_functions(source)\n\t\t\t\t\tfuncs.update(**gleaned)\n\t\t\t\t\tfor key in gleaned: funcs_directory[key] = source\n\t\t\t\telse:\n\t\t\t\t\tfrom dev import tracebacker\n\t\t\t\t\ttracebacker(e)\n\t\t\telse:\n\t\t\t\tincoming = dict([(k,v) for k,v in mod.items() if callable(v)])\n\t\t\t\t# remove items if they are not in all\n\t\t\t\tmod_all = mod.get('__all__',[])\n\t\t\t\t# also allow __all__ to be temporarily blanked during development\n\t\t\t\tif '__all__' in mod or mod_all: \n\t\t\t\t\tincoming_exposed = dict([(k,v) for k,v in incoming.items() if k in mod_all])\n\t\t\t\telse: incoming_exposed = incoming\n\t\t\t\tif verbose: print('status','trimming source %s to __all__ = %s'%(\n\t\t\t\t\tsource,incoming_exposed.keys()))\n\t\t\t\tfuncs.update(**incoming_exposed)\n\t\t\t\tfor key in incoming_exposed: funcs_directory[key] = source\n\t\telse: raise Exception('cannot locate code source %s'%source)\n\t# note which core functions are exposed so we can filter the rest\n\tglobal _ortho_keys_exposed\n\t_ortho_keys_exposed = set(funcs.keys())\n\t# no return because we just refresh funcs in globals\n\treturn\n\ndef import_check():\n\t\"\"\"\n\tUtility which simulates debugging via:\n\tpython -c \"import ortho;ortho.get_targets(verbose=True,strict=True)\"\n\t\"\"\"\n\tcollect_functions(verbose=True,strict=True)\n\tprint('status','see logs above for import details')\n\tprint('status','imported functions: %s'%funcs.keys())\n\ndef get_targets(verbose=False,strict=False,silent=False,locations=True):\n\t\"\"\"\n\tAnnounce available function names.\n\tNote that any printing that happens during the make call to get_targets is hidden by make.\n\t\"\"\"\n\tglobal funcs,funcs_directory\n\tif not funcs: collect_functions(verbose=verbose,strict=strict)\n\ttargets = funcs\n\t# filter out utility functions from ortho\n\ttarget_names = list(set(targets.keys())-\n\t\t(set(_ortho_keys)-_ortho_keys_exposed))  # pylint: disable=undefined-variable\n\tif not silent: print(\"make targets: %s\"%(' '.join(sorted(target_names))))\n\tif locations: return ['%s (%s)'%(k,funcs_directory.get(k,\n\t\tos.path.relpath(__file__,os.getcwd()))) for k in sorted(targets)]\n\telse: return target_names\n\ndef run_program(_do_debug=False,_no_run=False):\n\t\"\"\"\n\tInterpret the command-line arguments.\n\t\"\"\"\n\tglobal funcs\n\tignore_flags = ['w','--','s','ws','sw']\n\targlist = [i for i in list(sys.argv) if i not in ignore_flags]\n\t# previously wrote the backend script i.e. makeface.py from the makefile via:\n\t#   @test -f makeface.py || echo \"$$MAKEFACE_BACKEND\" > $(makeface)\n\t#   however now we pipe the script in from the environment and chop off the -c flag below\n\t# incoming arguments include a command (previously a script, makeface.py) which we ignore\n\tif arglist[0]!='-c':\n\t\traise Exception('argument list must start with the command flag (`-c`) from makefile '\n\t\t\t'however we received %s'%arglist)\n\tif not arglist: raise Exception('no arguments to parse')\n\telse: arglist = arglist[1:]\n\tif arglist[0] in ['set','unset']: funcs = {'set':set_config,'unset':unset}\n\telif not funcs: collect_functions()\n\targs,kwargs = [],{}\n\targlist = list(arglist)\n\tfuncname = arglist.pop(0)\n\t# regex for kwargs. note that the makefile organizes the flags for us\n\tregex_kwargs = r'^([\\w]+)\\=(.*?)$'\n\twhile arglist:\n\t\targ = arglist.pop(0)\n\t\tif re.match(regex_kwargs,arg):\n\t\t\tparname,parval = re.findall(regex_kwargs,arg)[0]\n\t\t\tkwargs[parname] = parval\n\t\telse:\n\t\t\tif isinstance(funcs[funcname],str_types):\n\t\t\t\traise Exception('run_program received a string instead of a function, indicating that '+\n\t\t\t\t\t'we have gleaned function without importing them. this indicates an error in that '+\n\t\t\t\t\t'script. the script is: \"%s\". try the following command to debug:\\n%s'%(\n\t\t\t\t\t\tfuncs[funcname],message_debug_suggestion))\n\t\t\targspecs = introspect_function(funcs[funcname])\n\t\t\targspec_args = [k for k,v in argspecs['kwargs'].items() if isinstance(v,bool)]\n\t\t\tif arg in argspec_args: kwargs[arg] = True\n\t\t\telse: args.append(arg)\n\tfor k,v in kwargs.items():\n\t\ttry: kwargs[k] = eval(v)\n\t\texcept: pass\n\t# ignore python flag which controls python version\n\tkwargs.pop('python',None)\n\targs = tuple(args)\n\tif arglist != []: raise Exception('unprocessed arguments %s'%str(arglist))\n\t# call the function and report errors loudly or drop into the debugger\n\tcall_text = (' with '+' and '.join([str('%s=%s'%(i,j)) \n\t\tfor i,j in zip(['args','kwargs'],[args,kwargs]) if j]) \n\t\tif any([args,kwargs]) else '')\n\tprint('status','calling \"%s\"%s'%(funcname,call_text))\n\tif funcname not in funcs:\n\t\traise Exception('function `%s` is missing? %s'%(funcname,funcs.keys()))\n\tif _no_run: return dict(funcname=funcname,args=args,kwargs=kwargs)\n\t# detect ghosted functions and try to import\n\tif type(funcs[funcname]) in str_types:\n\t\tprint('error','the function `%s` from `%s` was only inferred and not imported. '\n\t\t\t'this is commonly due to a failure to import the module '\n\t\t\t'because you do not have the right environment'%(funcname,funcs[funcname]))\n\t\tprint('status','to investigate the problem we will now import the module so you can see the error. '\n\t\t\t'importing %s'%funcs[funcname])\n\t\ttry: mod = importer(funcs[funcname]) # pylint: disable=unused-variable\n\t\texcept Exception as e: \n\t\t\ttracebacker(e)\n\t\t\tprint('error','to continue you can correct the script. '\n\t\t\t\t'you may need to install packages or source add an environment via '\n\t\t\t\t'`make set activate_env=\"path/to/activate <env_name>\"')\n\t\t\tsys.exit(1)\n\t# we must check for ipdb here before we try the target function\n\ttry: import ipdb as pdb_this\n\texcept: import pdb as pdb_this\n\ttry:\n\t\tfuncs[funcname](*args,**kwargs)\n\t#? catch a TypeError in case the arguments are not formulated properly\n\texcept Exception as e: \n\t\tdocstring = funcs[funcname].__doc__\n\t\tif docstring:\n\t\t\tprint('note the docstring for this function: \"%s\"'%funcname)\n\t\t\tprint(docstring)\n\t\ttracebacker(e)\n\t\tif conf.get('auto_debug',_do_debug): # pylint: disable=undefined-variable\n\t\t\t_,value,tb = sys.exc_info()\n\t\t\tpdb_this.post_mortem(tb)\n\t\telse: sys.exit(1)\n\texcept KeyboardInterrupt:\n\t\tprint('warning','caught KeyboardInterrupt during traceback')\n\t\tsys.exit(1)\n\ndef targets():\n\t\"\"\"Print the make  targets.\"\"\"\n\ttargets = get_targets(silent=True)\n\tfrom collections import OrderedDict as odict\n\ttreeview(dict(targets=targets))\n\ndef debug_imports():\n\t\"\"\"Check imports.\"\"\"\n\tos.system('python -uBttc \"import ortho;ortho.get_targets(verbose=True,strict=True)\"')\n","sub_path":"ortho/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":9214,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"538874909","text":"from django.urls import path, re_path\nfrom . import views\nurlpatterns = [\n    path(\"exam/\",views.ExampleAPIView.as_view()),\n    path(\"students/\",views.StudentListView.as_view()),\n    path(\"students2/\",views.Student2ListView.as_view()),\n    path(\"students3/\",views.Student3ListView.as_view()),\n    path(\"students4/\",views.StudentAPIView.as_view()),\n    re_path(\"students5/(?P<pk>\\d+)/\",views.Student2APIView.as_view()),\n]\n","sub_path":"day82 drf 4/代码/drfdemo/four/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":421,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"101867688","text":"import csv\nimport sys\nfrom config import config\nfrom logger import Logger\nfrom helper import Helper\nfrom etl import ETL\nfrom db import DB\n\n\nTEMP_DIR   = config['DATA']['TEMP_DIR']\nRESULT_DIR = config['RESULT']['RESULT_DIR']\n\nlogger = Logger('TASK')\nhelper = Helper.getHelper()\ndb     = DB.getDB()\n\ncsv.register_dialect('Dialect', quoting=csv.QUOTE_ALL, skipinitialspace=True)\n\n\nclass Task:\n\n  def __init__(self, name, question, db_table, row_handler, answer_cql):\n    self._name       = name\n    self._question   = question\n    self._db_table   = db_table\n    self._answer_cql = answer_cql\n    self._etl        = ETL(\n      f\"{name} ETL\",\n      [helper.join_path(TEMP_DIR, 'staging.csv')],\n      {\n        \"target\"  : 'Cassandra',\n        \"table\"   : db_table[\"table_name\"],\n        \"is_file\" : False\n      },\n      [i[0] for i in db_table[\"cols\"]],\n      row_handler,\n    )\n\n    logger.info(f\"{self._name} - Question: {self._question}\")\n\n  def _create_table(self):\n    table_name, cols, key = self._db_table.values()\n    cql_col_types = ', '.join([f\"{col[0]} {col[1]}\" for col in cols])\n    \n    db.execute(f\"CREATE TABLE IF NOT EXISTS {table_name} ({cql_col_types}, PRIMARY KEY({key}))\")\n\n    logger.info(f\"{self._name} - Create '{table_name}' table\")\n\n  def _get_result(self):\n    result = db.fetch(self._answer_cql)\n\n    result_col_name = helper.get_str_between(self._answer_cql, 'SELECT', 'FROM')[0].strip().split(', ')\n\n    try:\n      with open(helper.join_path(RESULT_DIR, f'{self._name}.csv'), 'a') as target_file:\n        writer = csv.writer(target_file, dialect='Dialect')\n        writer.writerow(result_col_name)\n\n        for row in result:\n          writer.writerow(row)\n    except IOError as e:\n      logger.error(e)\n    except:\n      logger.error(f\"Unexpected error: {sys.exc_info()[0]}\")\n\n    logger.info(f\"{self._name} - Complete generate '{self._name}.csv' file\")\n\n  def run(self):\n    self._create_table()\n\n    self._etl.run()\n    logger.info(f\"{self._name} - Complete ETL process\")\n\n    self._get_result()\n","sub_path":"project/task/task.py","file_name":"task.py","file_ext":"py","file_size_in_byte":2025,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"210317160","text":"class Student:\n    def __init__(self, name, age, grades):\n        self.name = name\n        self.age = age\n        self.grades = grades\n\n    def get_avg(self):\n        num_grades = len(self.grades)\n        if num_grades == 0:\n            return 0\n        sum_grades  = 0\n        for g in self.grades:\n            sum_grades += g\n        return sum_grades/num_grades\n\ndef highest_avg(stud_lst):\n    stud_name = \"\"\n    max_avg = 0\n    for s in stud_lst:\n        s_avg = s.get_avg()\n        if s_avg > max_avg:\n            max_avg = s_avg\n            stud_name = s.name\n        elif s_avg == max_avg:\n            if s.name < stud_name:\n                stud_name = s.name\n    return stud_name","sub_path":"introduction_to_python/classes_new/student_highest_average/solution/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":687,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"415492011","text":"import cv2\nimport os\nfrom tkinter import *\n\npath = 'data/positives'\n\nfor f in os.listdir(path):\n    for i in os.listdir(f'{path}/{f}'):\n        window = Tk()\n        img = cv2.imread(f'{path}/{f}/{i}')\n\n        # cv2.imshow(f'{path}/{f}/{i}', img)\n        window.title(f'{path}/{f}/{i}')\n        btn=Button(window, text=\"This is Button widget\")\n        btn.place(x=80, y=100)\n        window.geometry(\"300x200+10+20\")\n        window.mainloop()\n\n        # if cv2.waitKey(0) & 0xff == ord('q'):\n        #     pass\n\n\ndef remove_image(path):\n    print(f'remove this {path}')\n","sub_path":"cleanpositives.py","file_name":"cleanpositives.py","file_ext":"py","file_size_in_byte":570,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"419500194","text":"import numpy as np\nimport pandas as pd\nimport json\nimport time\nimport matplotlib.pyplot as plt\nfrom scipy.sparse import csr_matrix\nfrom sklearn.metrics.pairwise import pairwise_distances\nfrom copy import copy\nfrom itertools import combinations\n\nPLOT_FLAG = False\n\n\ndef norm(x):\n    \"\"\"\n    Compute norm of a sparse vector.\n    :param x: sparse vector\n    :return: 2-norm of given sparse vector\n    \"\"\"\n    sum_sq = x.dot(x.T)\n    norm = np.sqrt(sum_sq)\n    return norm\n\n\ndef cosine_distance(x, y):\n    \"\"\"\n    Compute cosine distance of two vectors.\n    :param x: vector x\n    :param y: vector y\n    :return: cosine distance of vector x and vector y\n    \"\"\"\n    xy = x.dot(y.T)\n    dist = xy / (norm(x) * norm(y))\n    return 1 - dist[0, 0]\n\n\ndef load_sparse_csr(filename):\n    \"\"\"\n    Read a numpy data file (.npz) and return as a compressed sparse row matrix.\n    :param filename: data file name\n    :return: CSR matrix\n    \"\"\"\n    loader = np.load(filename)\n    data = loader['data']\n    indices = loader['indices']\n    indptr = loader['indptr']\n    shape = loader['shape']\n\n    return csr_matrix((data, indices, indptr), shape)\n\n\ndef generate_random_vectors(num_vector, dim):\n    \"\"\"\n    Generate a collection of random vectors from the standard Gaussian distribution.\n    :param num_vector: number of vectors will be generated     n\n    :param dim: dimension of vectors                           d\n    :return: generated random vectors                          n x d\n    \"\"\"\n    return np.random.randn(dim, num_vector)\n\n\ndef brute_force_query(vec, data, k):\n    \"\"\"\n    Run brute force search to find k nearest neighbors of query vector.\n    :param vec: query vector\n    :param data: dataset\n    :param k: number of nearest neighbors\n    :return: Dataframe of k nearest nearest neighbors\n    \"\"\"\n    num_data_points = data.shape[0]\n\n    # Compute distances for ALL data points in training set\n    nearest_neighbors = pd.DataFrame({\n        'id': pd.Series(range(num_data_points)),\n        'distance': pd.Series(pairwise_distances(data, vec, metric='cosine').flatten())\n    })\n\n    return nearest_neighbors.sort_values('distance').head(k)\n\n\ndef train_lsh(data, num_vector=16, seed=None):\n    \"\"\"\n    Train a Locality Sensitive Hashing model from given data\n    :param data: data to perform LSH\n    :param num_vector: hash size (bit representation)\n    :param seed: random seed using when generate random vectors\n    :return: LSH model\n    \"\"\"\n    dim = data.shape[1]\n    if seed is not None:\n        np.random.seed(seed)\n    random_vectors = generate_random_vectors(num_vector, dim)\n    powers_of_two = 1 << np.arange(num_vector - 1, -1, -1)\n\n    table = {}\n    # Partition data points into bins\n    bin_index_bits = (data.dot(random_vectors) >= 0)\n\n    # Encode bin index bits into integers\n    bin_indices = bin_index_bits.dot(powers_of_two)\n\n    # Update `table` so that `table[i]` is the list of document ids with bin index equal to i.\n    for data_index, bin_index in enumerate(bin_indices):\n        if bin_index not in table:\n            # If no list yet exists for this bin, assign the bin an empty list.\n            table[bin_index] = []\n        # Fetch the list of document ids associated with the bin and add the document id to the end.\n        table[bin_index].append(data_index)\n\n    model = {'data': data,\n             'bin_index_bits': bin_index_bits,\n             'bin_indices': bin_indices,\n             'table': table,\n             'random_vectors': random_vectors,\n             'num_vector': num_vector}\n\n    return model\n\n\ndef search_nearby_bins(query_bin_bits, table, search_radius=2, initial_candidates=set()):\n    \"\"\"\n    For a given query vector and trained LSH model, return all candidate neighbors for\n    the query among all bins within the given search radius.\n    :param query_bin_bits: query bin vector\n    :param table: table of bin vectors\n    :param search_radius: max number of bits that are different from query bits vector\n    :param initial_candidates: set that contains initial candidates\n    :return: set of nearby bin vectors\n    \"\"\"\n    num_vector = len(query_bin_bits)\n    powers_of_two = 1 << np.arange(num_vector - 1, -1, -1)\n\n    # Allow the user to provide an initial set of candidates.\n    candidate_set = copy(initial_candidates)\n\n    for different_bits in combinations(range(num_vector), search_radius):\n        # Flip the bits (n_1,n_2,...,n_r) of the query bin to produce a new bit vector.\n        # Hint: you can iterate over a tuple like a list\n        alternate_bits = copy(query_bin_bits)\n        for i in different_bits:\n            alternate_bits[i] = not alternate_bits[i]\n\n        # Convert the new bit vector to an integer index\n        nearby_bin = alternate_bits.dot(powers_of_two)\n\n        # Fetch the list of documents belonging to the bin indexed by the new bit vector.\n        # Then add those documents to candidate_set\n        # Make sure that the bin exists in the table!\n        # Hint: update() method for sets lets you add an entire list to the set\n        if nearby_bin in table:\n            candidate_set.update(table[nearby_bin])  # Update candidate_set with the documents in this bin.\n\n    return candidate_set\n\n\ndef query(vec, model, k, max_search_radius):\n    \"\"\"\n    Collect all candidates and compute their true distance to the query vector.\n    Then return k nearest neighbors of query vector.\n    :param vec: query vector\n    :param model: LSH model\n    :param k: number of nearest neighbors\n    :param max_search_radius: max number of bits that are different from query bits vector\n    :return: k nearest neighbors of query vector\n             number of candidates searched\n    \"\"\"\n    data = model['data']\n    table = model['table']\n    random_vectors = model['random_vectors']\n    num_vector = random_vectors.shape[1]\n\n    # Compute bin index for the query vector, in bit representation.\n    bin_index_bits = (vec.dot(random_vectors) >= 0).flatten()\n\n    # Search nearby bins and collect candidates\n    candidate_set = set()\n    for search_radius in range(max_search_radius + 1):\n        candidate_set = search_nearby_bins(bin_index_bits, table, search_radius, initial_candidates=candidate_set)\n\n    # Sort candidates by their true distances from the query\n    candidates = data[np.array(list(candidate_set)), :]\n    candidate_distance = pairwise_distances(candidates, vec, metric='cosine').flatten()\n    nearest_neighbors = pd.DataFrame({\n        'id': pd.Series(list(candidate_set)),\n        'distance': pd.Series(candidate_distance)\n    })\n\n    return nearest_neighbors.sort_values('distance').head(k), len(candidate_set)\n\n# Load data\nwiki = pd.read_csv(\"../Data/people_wiki.csv\")\nwiki['id'] = wiki.index\ncorpus = load_sparse_csr(\"../Data/people_wiki_tf_idf.npz\")\nwith open(\"../Data/people_wiki_map_index_to_word.json\") as json_data:\n    map_index_to_word = json.load(json_data)\n\n# Generate random vectors of the same dimensionality as our vocabulary size.\n# We generate 16 vectors, leading to a 16-bit encoding of the bin index for each document.\nvocab_size = corpus.shape[1]         # Should be 547979\nnp.random.seed(0)\nrandom_vectors = generate_random_vectors(num_vector=16, dim=vocab_size)\n\n# Decide which bin document should go using 16 bits representation\n# Each bit is given by the sign of dot product of random vector and document's TF-IDF vector.\nprint(\"Part 1: Putting data into bins\")\npowers_of_two = (1 << np.arange(15, -1, -1))\n\ndocument_hash_bits_1 = corpus[0, :].dot(random_vectors) >= 0\nprint(\"The sign of dot product between random vectors and first document vector, 1 is positive:\")\nprint(np.array(document_hash_bits_1, dtype=int))\nprint(\"The hash value of first document vector: {:d}\\n\"\n      .format(document_hash_bits_1.dot(powers_of_two).tolist()[0]))\n\ndocument_hash_bits_2 = corpus[1, :].dot(random_vectors) >= 0\nprint(\"The sign of dot product between random vectors and second document vector, 1 is positive:\")\nprint(np.array(document_hash_bits_2, dtype=int))\nprint(\"The hash value of first document vector: {:d}\\n\"\n      .format(document_hash_bits_2.dot(powers_of_two).tolist()[0]))\n\nindex_bits = corpus.dot(random_vectors) >= 0\nprint(\"The hash values of all documents:\")\nprint(index_bits.dot(powers_of_two))\nprint()\n\n# Check train_lsh\nprint(\"Checking train_lsh function:\")\nmodel = train_lsh(corpus, num_vector=16, seed=143)\ntable = model['table']\nif 0 in table and table[0] == [39583] and \\\n   143 in table and table[143] == [19693, 28277, 29776, 30399]:\n    print('Passed!')\nelse:\n    print('Check your code.')\nprint()\n\n# Part 2: Inspect Bins\nprint(\"Part 2: Inspect Bins\")\n\n# Compare Joe Biden and Wynn Normington Hugh-Jones with Obama\nobama_id = wiki[wiki['name'] == 'Barack Obama'].index[0]\nprint(\"Barack Obama's article ID is {:d}.\".format(obama_id))\nobama_bin_bits = model['bin_index_bits'][obama_id]\nobama_bin = model['bin_indices'][obama_id]\nprint(\"Barack Obama's article is in bin with index {:d}.\".format(obama_bin))\nprint(\"The bits representation of Obama's bin index is:\")\nprint(np.array(obama_bin_bits, dtype=int))\nprint()\n\nbiden_id = wiki[wiki['name'] == 'Joe Biden'].index[0]\nprint(\"Joe Biden's article ID is {:d}.\".format(biden_id))\nbiden_bin_bits = model['bin_index_bits'][biden_id]\nbiden_bin = model['bin_indices'][biden_id]\nprint(\"Joe Biden's article is in bin with index {:d}.\".format(biden_bin))\nprint(\"The bits representation of Biden's bin index is:\")\nprint(np.array(biden_bin_bits, dtype=int))\nprint(\"Biden's bin representation agrees with Obama's in {:d} out of 16 places.\"\n      .format(np.sum(obama_bin_bits == biden_bin_bits)))\nprint()\n\nwynn_id = wiki[wiki['name'] == 'Wynn Normington Hugh-Jones'].index[0]\nprint(\"Wynn Normington Hugh-Jones's article ID is {:d}.\".format(wynn_id))\nwynn_bin_bits = model['bin_index_bits'][wynn_id]\nwynn_bin = model['bin_indices'][wynn_id]\nprint(\"Wynn Normington Hugh-Jones's article is in bin with index {:d}.\".format(wynn_bin))\nprint(\"The bits representation of Hugh-Jones's bin index is:\")\nprint(np.array(wynn_bin_bits, dtype=int))\nprint(\"Hugh-Jones's bin representation agrees with Obama's in {:d} out of 16 places.\"\n      .format(np.sum(obama_bin_bits == wynn_bin_bits)))\nprint()\n\n# Get documents in the same bin as Obama\ndocs_in_obama_bin_ids = list(model['table'][model['bin_indices'][obama_id]])\ndocs_in_obama_bin_ids.remove(obama_id)        # display documents other than Obama\n\ndocs = wiki[wiki['id'].isin(docs_in_obama_bin_ids)]\nprint(\"Documents in the same bin as Obama's article:\")\nprint(docs[['name']])\nprint()\n\n# Compute cosine distances between Obama, with Biden and other persons in the same bin.\nobama_tf_idf = corpus[obama_id, :]\nbiden_tf_idf = corpus[biden_id, :]\n\nprint('Cosine distance from Barack Obama:')\nprint('Barack Obama - {:24s}: {:f}'\n      .format('Joe Biden', cosine_distance(obama_tf_idf, biden_tf_idf)))\n\nfor doc_id in docs_in_obama_bin_ids:\n    doc_tf_idf = corpus[doc_id, :]\n    print('Barack Obama - {:24s}: {:f}'\n          .format(wiki['name'][doc_id], cosine_distance(obama_tf_idf, doc_tf_idf)))\nprint()\n\n# Part 3: Query the LSH model\n# 1. Decide on the search radius r. This will determine the number of different bits between the two vectors.\n# 2. For each subset (n_1, n_2, ..., n_r) of the list [0, 1, 2, ..., num_vector-1], do the following:\n#    * Flip the bits (n_1, n_2, ..., n_r) of the query bin to produce a new bit vector.\n#    * Fetch the list of documents belonging to the bin indexed by the new bit vector.\n#    * Add those documents to the candidate set.\nprint('Part 3: Query the LSH model')\n\n# Check search_nearby_bins function\n# Check search_radius = 0\nprint('Checking search_nearby_bins function:')\ncandidate_set = search_nearby_bins(obama_bin_bits, model['table'], search_radius=0)\nif candidate_set == {35817, 21426, 53937, 39426, 50261}:\n    print('Passed!')\nelse:\n    print('Check your code.')\n\n# Check search_radius = 1\ncandidate_set = search_nearby_bins(obama_bin_bits, model['table'], search_radius=1, initial_candidates=candidate_set)\nif candidate_set == {39426, 38155, 38412, 28444, 9757, 41631, 39207, 59050, 47773, 53937, 21426, 34547,\n                     23229, 55615, 39877, 27404, 33996, 21715, 50261, 21975, 33243, 58723, 35817, 45676,\n                     19699, 2804, 20347}:\n    print('Passed!')\nelse:\n    print('Check your code.')\nprint()\n\nnn_obama, len_nn_obama_candidates = query(corpus[obama_id, :], model, k=10, max_search_radius=3)\nprint(\"10 Nearest Neighbor of Obama's article:\")\nprint(nn_obama.merge(wiki, on='id').sort_values('distance')[['id', 'distance', 'name']].to_string(index=False))\nprint()\n\n# Part 4: Experimenting with LSH\nprint(\"Part 4: Experimenting with LSH\")\n\n# run LSH multiple times, each with different radii for nearby bin search\nprint(\"Run LSH with different search radii:\")\nnum_candidates_history = []\nquery_time_history = []\nmax_distance_from_query_history = []\nmin_distance_from_query_history = []\naverage_distance_from_query_history = []\n\nbiden_min_radius = float('Inf')\nfor max_search_radius in range(17):\n    start = time.time()\n    # Perform LSH query using Barack Obama, with max_search_radius\n    result, num_candidates = query(corpus[obama_id, :], model, k=10,\n                                   max_search_radius=max_search_radius)\n    end = time.time()\n    query_time = end - start  # Measure time\n\n    print('Radius:', max_search_radius)\n    # Display 10 nearest neighbors, along with document ID and name\n    print(result.merge(wiki, on='id').sort_values('distance')[['id', 'distance', 'name']].to_string(index=False))\n    print()\n\n    biden_found = not result[result['id'] == biden_id].empty\n    if biden_found and max_search_radius < biden_min_radius:\n        biden_min_radius = max_search_radius\n\n    # Collect statistics on 10 nearest neighbors\n    average_distance_from_query = result['distance'][1:].mean()\n    max_distance_from_query = result['distance'][1:].max()\n    min_distance_from_query = result['distance'][1:].min()\n\n    num_candidates_history.append(num_candidates)\n    query_time_history.append(query_time)\n    average_distance_from_query_history.append(average_distance_from_query)\n    max_distance_from_query_history.append(max_distance_from_query)\n    min_distance_from_query_history.append(min_distance_from_query)\n\n# Plot statistics\nif PLOT_FLAG:\n    plt.figure(figsize=(7, 4.5))\n    plt.plot(num_candidates_history, linewidth=4)\n    plt.xlabel('Search radius')\n    plt.ylabel('# of documents searched')\n    plt.rcParams.update({'font.size': 16})\n    plt.tight_layout()\n\n    plt.figure(figsize=(7, 4.5))\n    plt.plot(query_time_history, linewidth=4)\n    plt.xlabel('Search radius')\n    plt.ylabel('Query time (seconds)')\n    plt.rcParams.update({'font.size': 16})\n    plt.tight_layout()\n\n    plt.figure(figsize=(7, 4.5))\n    plt.plot(average_distance_from_query_history, linewidth=4, label='Average of 10 neighbors')\n    plt.plot(max_distance_from_query_history, linewidth=4, label='Farthest of 10 neighbors')\n    plt.plot(min_distance_from_query_history, linewidth=4, label='Closest of 10 neighbors')\n    plt.xlabel('Search radius')\n    plt.ylabel('Cosine distance of neighbors')\n    plt.legend(loc='best', prop={'size': 15})\n    plt.rcParams.update({'font.size': 16})\n    plt.tight_layout()\n\n# Randomly choose 10 documents to run queries.\n# First compute the true 25 nearest neighbors, and then run LSH multiple times.\n# look at two metrics:\n# 1. Precision@10: the number the 10 neighbors given by LSH are among the true 25 nearest neighbors\n# 2. Average cosine distance of the neighbors from the query\nprint(\"Randomly choose 10 documents to run queries:\")\nmax_radius = 17\nprecision = {i: [] for i in range(max_radius)}\naverage_distance = {i: [] for i in range(max_radius)}\nquery_time = {i: [] for i in range(max_radius)}\n\nnp.random.seed(0)\nnum_queries = 10\nfor i, ix in enumerate(np.random.choice(corpus.shape[0], num_queries, replace=False)):\n    print('%s / %s' % (i+1, num_queries))\n\n    # Get the set of 25 true nearest neighbors\n    ground_truth = set(brute_force_query(corpus[ix, :], corpus, k=25)['id'])\n\n    for r in range(1, max_radius):\n        start = time.time()\n        result, num_candidates = query(corpus[ix, :], model, k=10, max_search_radius=r)\n        end = time.time()\n\n        query_time[r].append(end - start)\n        # precision = (# of neighbors both in result and ground_truth) / 10.0\n        precision[r].append(len(set(result['id']) & ground_truth) / 10.0)\n        average_distance[r].append(result['distance'][1:].mean())\nprint()\n\n# Plot\nif PLOT_FLAG:\n    plt.figure(figsize=(7, 4.5))\n    plt.plot(range(1, 17), [np.mean(average_distance[i]) for i in range(1, 17)], linewidth=4,\n             label='Average over 10 neighbors')\n    plt.xlabel('Search radius')\n    plt.ylabel('Cosine distance')\n    plt.legend(loc='best', prop={'size': 15})\n    plt.rcParams.update({'font.size': 16})\n    plt.tight_layout()\n\n    plt.figure(figsize=(7, 4.5))\n    plt.plot(range(1, 17), [np.mean(precision[i]) for i in range(1, 17)], linewidth=4, label='Precison@10')\n    plt.xlabel('Search radius')\n    plt.ylabel('Precision')\n    plt.legend(loc='best', prop={'size': 15})\n    plt.rcParams.update({'font.size': 16})\n    plt.tight_layout()\n\n    plt.figure(figsize=(7, 4.5))\n    plt.plot(range(1, 17), [np.mean(query_time[i]) for i in range(1, 17)], linewidth=4, label='Query time')\n    plt.xlabel('Search radius')\n    plt.ylabel('Query time (seconds)')\n    plt.legend(loc='best', prop={'size': 15})\n    plt.rcParams.update({'font.size': 16})\n    plt.tight_layout()\n\n# Change the number of random vectors in LSH algorithm (number of bits)\nprint(\"Change the number of random vectors in LSH algorithm (number of bits):\")\nprecision = {i: [] for i in range(5, 20)}\naverage_distance = {i: [] for i in range(5, 20)}\nquery_time = {i: [] for i in range(5, 20)}\nnum_candidates_history = {i: [] for i in range(5, 20)}\nground_truth = {}\n\nnp.random.seed(0)\nnum_queries = 10\ndocs = np.random.choice(corpus.shape[0], num_queries, replace=False)\n\nfor i, ix in enumerate(docs):\n    ground_truth[ix] = set(brute_force_query(corpus[ix, :], corpus, k=25)['id'])\n    # Get the set of 25 true nearest neighbors\n\nfor num_vector in range(5, 20):\n    print('num_vector = %s' % num_vector)\n    model = train_lsh(corpus, num_vector, seed=143)\n\n    for i, ix in enumerate(docs):\n        start = time.time()\n        result, num_candidates = query(corpus[ix, :], model, k=10, max_search_radius=3)\n        end = time.time()\n\n        query_time[num_vector].append(end - start)\n        precision[num_vector].append(len(set(result['id']) & ground_truth[ix]) / 10.0)\n        average_distance[num_vector].append(result['distance'][1:].mean())\n        num_candidates_history[num_vector].append(num_candidates)\nprint()\n\n# Plot\nif PLOT_FLAG:\n    plt.figure(figsize=(7, 4.5))\n    plt.plot(range(5, 20), [np.mean(average_distance[i]) for i in range(5, 20)], linewidth=4,\n             label='Average over 10 neighbors')\n    plt.xlabel('# of random vectors')\n    plt.ylabel('Cosine distance')\n    plt.legend(loc='best', prop={'size': 15})\n    plt.rcParams.update({'font.size': 16})\n    plt.tight_layout()\n\n    plt.figure(figsize=(7, 4.5))\n    plt.plot(range(5, 20), [np.mean(precision[i]) for i in range(5, 20)], linewidth=4, label='Precison@10')\n    plt.xlabel('# of random vectors')\n    plt.ylabel('Precision')\n    plt.legend(loc='best', prop={'size': 15})\n    plt.rcParams.update({'font.size': 16})\n    plt.tight_layout()\n\n    plt.figure(figsize=(7, 4.5))\n    plt.plot(range(5, 20), [np.mean(query_time[i]) for i in range(5, 20)], linewidth=4, label='Query time (seconds)')\n    plt.xlabel('# of random vectors')\n    plt.ylabel('Query time (seconds)')\n    plt.legend(loc='best', prop={'size': 15})\n    plt.rcParams.update({'font.size': 16})\n    plt.tight_layout()\n\n    plt.figure(figsize=(7, 4.5))\n    plt.plot(range(5, 20), [np.mean(num_candidates_history[i]) for i in range(5, 20)], linewidth=4,\n             label='# of documents searched')\n    plt.xlabel('# of random vectors')\n    plt.ylabel('# of documents searched')\n    plt.legend(loc='best', prop={'size': 15})\n    plt.rcParams.update({'font.size': 16})\n    plt.tight_layout()\n\n\n# QUIZ QUESTIONS:\nprint(\"Quiz Questions:\")\n# 1. What is the document id of Barack Obama's article?\nprint(\"1. The document ID of Barack Obama's article is {:d}.\\n\".format(obama_id))\n# 2. Which bin contains Barack Obama's article? Enter its integer index.\nprint(\"2. The bin contains Barack Obama's article has index {:d}.\\n\".format(obama_bin))\n# 3. Examine the bit representations of the bins containing Barack Obama and Joe Biden.\n#    In how many places do they agree?\nprint(\"3. Biden's bin representation agrees with Obama's in {:d} out of {:d} places.\\n\"\n      .format(np.sum(obama_bin_bits == biden_bin_bits), len(obama_bin_bits.tolist())))\n# 4. What was the smallest search radius that yields the correct nearest neighbor, namely Joe Biden?\nprint(\"4. The smallest search radius that yields the correct nearest neighbor Joe Biden is {:d}.\\n\"\n      .format(biden_min_radius))\n# 5. Suppose our goal was to produce 10 approximate nearest neighbors\n#    whose average distance from the query is within 0.01 of the average for the true 10 nearest neighbors.\n#    For Barack Obama, the true 10 nearest neighbors are on average about 0.77.\n#    What was the smallest search radius for Barack Obama that produced an average distance of 0.78 or better?\nprint(\"5. The smallest search radius that produces an average distance of <= 0.78 is {:d}.\"\n      .format(np.min(np.argwhere(np.array(average_distance_from_query_history) <= 0.78))))\n\nif PLOT_FLAG:\n    plt.show()\n","sub_path":"Clustering_and_Retrieval/Week_2_NN_Search/assignment_2.py","file_name":"assignment_2.py","file_ext":"py","file_size_in_byte":21580,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"583989583","text":"import os\n\nclass bcolor:\n    red = '\\033[91m'\n    reset = '\\033[0m'\n\nclass tela:\n    def __init__(self):\n        self.nome = 'YTDownloader'\n\n    def clearScreen(self):\n        if os.name == 'nt':\n            os.system('cls')\n        else:\n            os.system('clear')\n\n    def telaini(self):\n        self.clearScreen()\n        print(bcolor.red + r\"\"\"\n        \\\\  // ||||||   ||\\\\     //\\\\   ||   /\\   || |\\\\   ||\n         \\\\//    ||     || \\\\   //  \\\\  ||  //\\\\  || ||\\\\  ||\n          \\/     ||     ||  || ||    || || //  \\\\ || || \\\\ ||\n          ||     ||     || //   \\\\  //  ||//    \\\\|| ||  \\\\||\n          ||     ||     ||//     \\\\//   |//      \\\\| ||   \\\\|\n        \"\"\" + bcolor.reset + \"\"\"\n        Enter para iniciar\n        \"\"\")\n        input()\n        self.menu()\n        \n    def menu(self):\n        self.clearScreen()\n        print(\"\"\"\n        Digite:\n\n        p - Para baixar uma playlist\n        v - Para baixar um video \n        \"\"\")\n\n    def playlist(self):\n        self.clearScreen()\n        link = input(\"Link o link da Playlist: \")\n        return link\n\n    def video(self):\n        self.clearScreen()\n        link = input(\"Digite o Link do Video: \")\n        return link\n\n    def downConfirm(self, title):\n        baixar = input(f'Baixar \"{title}\"?(S/N) ').upper()\n        if baixar == 'S':\n            return True\n        else:\n            return False\n                    \ntelaa = tela()","sub_path":"src/tela.py","file_name":"tela.py","file_ext":"py","file_size_in_byte":1405,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"592707980","text":"from collections import defaultdict\nclass Solution(object):\n    def getHint(self, secret, guess):\n        \"\"\"\n        :type secret: str\n        :type guess: str\n        :rtype: str\n        \"\"\"\n        bull,cow=0,0\n        count=defaultdict(int)\n        for i in range(len(secret)):\n                if secret[i]==guess[i]:\n                    bull+=1\n                else:\n                    if count[secret[i]]<0:\n                        cow+=1\n                    if count[guess[i]]>0:\n                        cow+=1\n                    count[secret[i]]+=1;\n                    count[guess[i]]-=1;\n                \n                \n        return str(bull)+\"A\"+str(cow)+\"B\"\n\n\n'''\n\n    use hashmap and one pass to keep count of bull and cow\n    when it's 0 it means there's no overlap\n\n'''\n","sub_path":"BullsAndCows.py","file_name":"BullsAndCows.py","file_ext":"py","file_size_in_byte":791,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"406174144","text":"import csv\n\nwith open(\"../../res/out/dep.csv\", \"r\") as csv_file:\n    data = csv.reader(csv_file)\n    info = []\n    for (row) in data:\n        item = [row[0], row[1], row[14]]\n        info.append(item)\n\n    info.pop(0)\n    # print(info)\n    dic = {\n        \"510100000000\":{\n            'name':\"成都交警支队\",\n            \"code\":\"510100000000\",\n            \"children\":[]\n        }\n    }\n    for index in range(len(info)):\n        code = str(info[index][0])\n        name = info[index][1]\n        pid = str(info[index][2])\n        if index > 0:\n            # print(code, name, pid)\n            if pid in dic:\n                dic[pid][\"children\"].append(info[index])\n            else:\n                dic[pid] = {\n                    \"code\": code,\n                    \"name\": name,\n                    \"children\": []\n            }\n    print(dic)\n    file = open(\"../../res/out/dep.js\",\"w\")\n    file.write(str(dic))\n    file.close()\n\n","sub_path":"code/csvdeal/csvfile.py","file_name":"csvfile.py","file_ext":"py","file_size_in_byte":934,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"384114246","text":"from urllib.parse import urlparse\nfrom sanic import Sanic\nfrom sanic.response import text\nfrom sanic.testing import HOST, PORT\nfrom spf import SanicPluginsFramework, SanicPlugin\nfrom spf.plugins import contextualize\nimport pytest\n\nfrom spf.context import HierDict\n\ndef test_contextualize_plugin_route(spf):\n    app = spf._app\n    ctx = spf.register_plugin(contextualize)\n\n    async def handler(request, context):\n        assert isinstance(context, HierDict)\n        shared = context.get('shared', None)\n        assert shared is not None\n        shared_request = shared.get('request', None)\n        assert shared_request is not None\n        shared_request_ctx = shared_request.get(id(request), None)\n        assert shared_request_ctx is not None\n        assert isinstance(shared_request_ctx, HierDict)\n        r2 = shared_request_ctx.get('request', None)\n        assert r2 is not None\n        assert r2 == request\n\n        priv_request = context.get('request', None)\n        assert priv_request is not None\n        priv_request_ctx = priv_request.get(id(request), None)\n        assert priv_request_ctx is not None\n        assert isinstance(priv_request_ctx, HierDict)\n        r3 = priv_request_ctx.get('request', None)\n        assert r3 is not None\n        assert r3 == request\n        assert priv_request_ctx != shared_request_ctx\n        return text('OK')\n\n    ctx.route('/')(handler)\n\n    request, response = app.test_client.get('/')\n    assert response.text == \"OK\"\n\ndef test_contextualize_plugin_middleware(spf):\n    app = spf._app\n    ctx = spf.register_plugin(contextualize)\n\n    @ctx.middleware(attach_to='request')\n    async def mw1(request, context):\n        assert isinstance(context, HierDict)\n        shared = context.get('shared', None)\n        shared_request = shared.get('request', None)\n        assert shared_request is not None\n        shared_request_ctx = shared_request.get(id(request), None)\n        assert shared_request_ctx is not None\n        shared_request_ctx['middleware_ran'] = True\n\n    @ctx.route('/')\n    async def handler(request, context):\n        assert isinstance(context, HierDict)\n        shared = context.get('shared', None)\n        shared_request = shared.get('request', None)\n        shared_request_ctx = shared_request.get(id(request), None)\n        middleware_ran = shared_request_ctx.get('middleware_ran', False)\n        assert middleware_ran\n        return text('OK')\n\n    request, response = app.test_client.get('/')\n    assert response.text == \"OK\"\n","sub_path":"tests/test_contextualize_plugin.py","file_name":"test_contextualize_plugin.py","file_ext":"py","file_size_in_byte":2494,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"492729563","text":"# import library open cv\nimport cv2\n\n#load image (simpan image dalam 1 folder dengan source code)\nimg = cv2.imread('Politeknik-TEDC-Bandung.png',0)\n#tampilkan dalam satu windows utama\ncv2.imshow('Politeknik-TEDC-Bandung', img)\n#tunggu action dari user\ncv2.waitKey(0)\n#hapus semua windows (form) yang ada\ncv2.destroyAllWindows()","sub_path":"M3.py","file_name":"M3.py","file_ext":"py","file_size_in_byte":327,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"350762590","text":"class fab(object):\n\tdef __init__(self, max):\n\t\tself.max = max\n\t\tself.a = 0\n\t\tself.b = 0\n\t\tself.c = 1\n\n\tdef __iter__(self):\n\t\treturn self\n\n\tdef next(self):\n\t\tif self.c < self.max:\n\t\t\tself.a = self.b\n\t\t\tself.b = self.c\n\t\t\tself.c = self.a + self.b\n\t\t\treturn self.c\n\t\traise StopIteration()\n\t\n","sub_path":"python/fabico.py","file_name":"fabico.py","file_ext":"py","file_size_in_byte":288,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"112733437","text":"########################################################################\n# $Header: /var/local/cvsroot/4Suite/Ft/Lib/MessageSource.py,v 1.14 2005/03/06 06:39:32 mbrown Exp $\n\"\"\"\nMessages for Ft.Lib\n\nCopyright 2004 Fourthought, Inc. (USA).\nDetailed license and copyright information: http://4suite.org/COPYRIGHT\nProject home, documentation, distributions: http://4suite.org/\n\"\"\"\n\nfrom Ft import TranslateMessage as _\nfrom Ft.Lib import UriException\n\n# %r preferred for reporting URIs because the URI refs can be empty\n# strings or, if invalid, could contain characters unsafe for the error\n# message stream.\n#\nURI = {\n    UriException.INVALID_BASE_URI:\n        _(\"Invalid base URI: %(base)r cannot be used to resolve reference %(ref)r\"),\n    UriException.RELATIVE_BASE_URI:\n        _(\"Invalid base URI: %(base)r cannot be used to resolve reference %(ref)r;\"\n          \" the base URI must be absolute, not relative.\"),\n    UriException.NON_FILE_URI:\n        _(\"Only a 'file' URI can be converted to an OS-specific path; URI given was %r\"),\n    UriException.UNIX_REMOTE_HOST_FILE_URI:\n        _(\"A URI containing a remote host name cannot be converted to a path on posix;\"\n          \" URI given was %r\"),\n    UriException.RESOURCE_ERROR:\n        _(\"Error retrieving resource %(loc)r: %(msg)s\"),\n    UriException.UNSUPPORTED_PLATFORM:\n        _(\"Platform %r not supported by URI function %s\"),\n    UriException.SCHEME_REQUIRED:\n        _(\"Scheme-based resolution requires a URI with a scheme; \"\n          \"neither the base URI %(base)r nor the reference %(ref)r have one.\"),\n    UriException.INVALID_PUBLIC_ID_URN:\n        _(\"A public ID cannot be derived from URN %(urn)r\"\n          \" because it does not conform to RFC 3151.\"),\n    UriException.UNSUPPORTED_SCHEME:\n        _(\"The URI scheme %(scheme)s is not supported by resolver %(resolver)s\"),\n    UriException.IDNA_UNSUPPORTED:\n        _(\"The URI ref %(uri)r cannot be made urllib-safe on this version of Python (IDNA encoding unsupported).\"),\n    }\n","sub_path":"dependencies/src/4Suite-XML-1.0.2/Ft/Lib/MessageSource.py","file_name":"MessageSource.py","file_ext":"py","file_size_in_byte":2002,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"195375567","text":"#Gerekli Kütüphanelerin Kurulması\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom matplotlib import pyplot as plt\r\nimport seaborn as sns\r\nimport lightgbm as lgb\r\nimport warnings\r\n\r\n#Veri Seti Ayarları\r\npd.set_option('display.max_columns', None)\r\nimport time\r\npd.set_option('display.width', 500)\r\nwarnings.filterwarnings('ignore')\r\n\r\n\r\n#Veri Hakkında Kısa Bir Bigi\r\ndef check_df(dataframe, head=5):\r\n    print(\"##################### Shape #####################\")\r\n    print(dataframe.shape)\r\n    print(\"##################### Types #####################\")\r\n    print(dataframe.dtypes)\r\n    print(\"##################### Head #####################\")\r\n    print(dataframe.head(head))\r\n    print(\"##################### Tail #####################\")\r\n    print(dataframe.tail(head))\r\n    print(\"##################### NA #####################\")\r\n    print(dataframe.isnull().sum())\r\n    print(\"##################### Quantiles #####################\")\r\n    print(dataframe.quantile([0, 0.05, 0.50, 0.95, 0.99, 1]).T)\r\n\r\n\r\n# Verinin Yüklenmesi\r\n\r\ntrain = pd.read_csv('datasets/demand_forecasting/train.csv', parse_dates=['date'])\r\ntest = pd.read_csv('datasets/demand_forecasting/test.csv', parse_dates=['date'])\r\nsample_sub = pd.read_csv('datasets/demand_forecasting/sample_submission.csv')\r\ndf = pd.concat([train, test], sort=False)\r\n\r\n\r\n# EDA(Keşifçi Veri Analizi)\r\n\r\ndf[\"date\"].min(), df[\"date\"].max()\r\n\r\n\r\ncheck_df(train)\r\ncheck_df(test)\r\ncheck_df(sample_sub)\r\ncheck_df(df)\r\n\r\n# Satış dağılımı nasıl?\r\ndf[\"sales\"].describe([0.10, 0.30, 0.50, 0.70, 0.80, 0.90, 0.95, 0.99])\r\n\r\n# Kaç store var?\r\ndf[[\"store\"]].nunique()\r\n\r\n# Kaç item var?\r\ndf[[\"item\"]].nunique()\r\n\r\n# Her store'da eşit sayıda mı eşsiz item var?\r\ndf.groupby([\"store\"])[\"item\"].nunique()\r\n\r\n# Peki her store'da eşit sayıda mı sales var?\r\ndf.groupby([\"store\", \"item\"]).agg({\"sales\": [\"sum\"]})\r\n\r\n# mağaza-item kırılımında satış istatistikleri\r\ndf.groupby([\"store\", \"item\"]).agg({\"sales\": [\"sum\", \"mean\", \"median\", \"std\"]})\r\n\r\ndf.date.dt.dayofyear.describe().T\r\n\r\n# FEATURE ENGINEERING\r\n#####################################################\r\n\r\n########################\r\n# Date Features Engineers\r\n########################\r\n\r\ndef create_date_features(df):\r\n    df['month'] = df.date.dt.month  # Hangi ay\r\n    df['day_of_month'] = df.date.dt.day # Ayın hangi günü\r\n    df['day_of_year'] = df.date.dt.dayofyear # yılın hangi günü\r\n    df['week_of_year'] = df.date.dt.weekofyear # yılın hangi haftası\r\n    df['day_of_week'] = df.date.dt.dayofweek #Haftanın hanig günü\r\n    df['year'] = df.date.dt.year  # Hangi yıl\r\n    df[\"is_wknd\"] = df.date.dt.weekday // 4 # Haftasonu mu değil mi\r\n    df['is_month_start'] = df.date.dt.is_month_start.astype(int)  # Ayın başlangıcı mı\r\n    df['is_month_end'] = df.date.dt.is_month_end.astype(int) #Ayın bitişi mi maaş gibi olarak düşünülebilir.\r\n    return df\r\n\r\ndf = create_date_features(df)\r\n\r\ndf['day_of_week'] = df['day_of_week'] +1\r\n\r\n# Mevsim\r\ndf.loc[(df['month'] == 1 ) & (df['month'] == 2 ) & (df['month'] == 12 ), 'season'] = 1\r\ndf.loc[(df['month'] > 2 ) & (df['month'] <= 5 ), 'season'] = 2\r\ndf.loc[(df['month'] > 5 ) & (df['month'] <= 8 ), 'season'] = 3\r\ndf.loc[(df['month'] > 8 ) & (df['month'] <= 11 ), 'season'] = 4\r\ndf[\"season\"].unique()\r\ndf[\"month\"].unique()\r\ndf[df['month'] == 12].count()\r\n\r\n# Ayın son günlerine göre\r\n\r\ndf.loc[(df[\"day_of_month\"] == 31), \"fewer_days_than_others\"] = 1\r\ndf.loc[(df[\"day_of_month\"] == 30), \"fewer_days_than_others\"] = 1\r\ndf.loc[(df[\"day_of_month\"] == 29), \"fewer_days_than_others\"] = 1\r\ndf.loc[(df[\"day_of_month\"] < 29) , \"fewer_days_than_others\"] = 0\r\n\r\n# Yıllara göre satışta önem sıralaması\r\ndf.loc[(df[\"year\"] == 2013), \"year_imp_on_sale\"] = 1\r\ndf.loc[(df[\"year\"] == 2014), \"year_imp_on_sale\"] = 2\r\ndf.loc[(df[\"year\"] == 2015), \"year_imp_on_sale\"] = 3\r\ndf.loc[(df[\"year\"] == 2016), \"year_imp_on_sale\"] = 4\r\ndf.loc[(df[\"year\"] == 2017), \"year_imp_on_sale\"] = 5\r\n\r\n# Mağazaların satış rakamlarına göre\r\ndf.loc[(df[\"store\"] == 8) | (df[\"store\"] == 2), \"sale_amount\"] = 3\r\ndf.loc[(df[\"store\"] == 3) | (df[\"store\"] == 4), \"sale_amount\"] = 2\r\ndf.loc[(df[\"store\"] == 9) | (df[\"store\"] == 10)| (df[\"store\"] == 1), \"sale_amount\"] = 2\r\ndf.loc[(df[\"store\"] == 5) | (df[\"store\"] == 6)| (df[\"store\"] ==7), \"sale_amount\"] = 1\r\ndf.head()\r\n\r\n# Random Noise\r\n\r\ndef random_noise(dataframe):\r\n    return np.random.normal(scale=1.4, size=(len(dataframe),))\r\n\r\n# Lag/Shifted Features\r\ndf.sort_values(by=['store', 'item', 'date'], axis=0, inplace=True)\r\n\r\ndef lag_features(dataframe, lags):\r\n    for lag in lags:\r\n        dataframe['sales_lag_' + str(lag)] = dataframe.groupby([\"store\", \"item\"])['sales'].transform(\r\n            lambda x: x.shift(lag)) + random_noise(dataframe)\r\n    return dataframe\r\n\r\n# bu değerler 3 ay sonrasını tahmin edilmesi istendiği için 3 aydan öncesini tahmin etmek gerekir .\r\ndf = lag_features(df, [91, 98, 105, 112, 119, 126, 182, 250,364, 546, 728,821])\r\n\r\ndf[df[\"sales\"].isnull()]\r\n\r\n\r\n# Rolling Mean Features\r\n\r\ndef roll_mean_features(dataframe, windows):\r\n    for window in windows:\r\n        dataframe['sales_roll_mean_' + str(window)] = dataframe.groupby([\"store\", \"item\"])['sales']. \\\r\n                                                          transform(\r\n            lambda x: x.shift(1).rolling(window=window, min_periods=10, win_type=\"triang\").mean()) + random_noise(\r\n            dataframe)\r\n    return dataframe\r\n\r\n\r\ndf = roll_mean_features(df, [180,365,450, 546])\r\n\r\n\r\n# Exponentially Weighted Mean Features\r\n\r\ndef ewm_features(dataframe, alphas, lags):\r\n    for alpha in alphas:\r\n        for lag in lags:\r\n            dataframe['sales_ewm_alpha_' + str(alpha).replace(\".\", \"\") + \"_lag_\" + str(lag)] = \\\r\n                dataframe.groupby([\"store\", \"item\"])['sales'].transform(lambda x: x.shift(lag).ewm(alpha=alpha).mean())\r\n    return dataframe\r\n\r\nalphas = [0.99,0.95, 0.9, 0.8, 0.7, 0.5,0.4]\r\nlags = [91, 95,98, 105, 112,150, 180, 270, 365,456, 546, 728,821]\r\n\r\ndf = ewm_features(df, alphas, lags)\r\n\r\n# One-Hot Encoding\r\n\r\ndf = pd.get_dummies(df, columns=['store', 'item',\"season\",\"year_imp_on_sale\", 'day_of_week', 'month'])\r\n\r\n# Converting sales to log(1+sales)\r\ndf['sales'] = np.log1p(df[\"sales\"].values)\r\n\r\n#####################################################\r\n# Model\r\n\r\n# Custom Cost Function\r\ndef smape(preds, target):\r\n    n = len(preds)\r\n    masked_arr = ~((preds == 0) & (target == 0))\r\n    preds, target = preds[masked_arr], target[masked_arr]\r\n    num = np.abs(preds - target)\r\n    denom = np.abs(preds) + np.abs(target)\r\n    smape_val = (200 * np.sum(num / denom)) / n\r\n    return smape_val\r\n\r\n# Smape i lightgbm özelinde tanımlama yaptık\r\ndef lgbm_smape(preds, train_data):\r\n    labels = train_data.get_label()\r\n    smape_val = smape(np.expm1(preds), np.expm1(labels))\r\n    return 'SMAPE', smape_val, False\r\n\r\n\r\n# Time-Based Validation Sets\r\n# 2017'nin başına kadar (2016'nın sonuna kadar) train seti.\r\ntrain = df.loc[(df[\"date\"] < \"2017-01-01\"), :]\r\n# 2017'nin ilk 3'ayı validasyon seti.\r\nval = df.loc[(df[\"date\"] >= \"2017-01-01\") & (df[\"date\"] < \"2017-04-01\"), :]\r\ncols = [col for col in train.columns if col not in ['date', 'id', \"sales\", \"year\"]]\r\nY_train = train['sales']\r\nX_train = train[cols]\r\nY_val = val['sales']\r\nX_val = val[cols]\r\n\r\nY_train.shape, X_train.shape, Y_val.shape, X_val.shape\r\n\r\n# LightGBM Model\r\n\r\n# LightGBM parameters\r\nlgb_params = {'metric': {'mae'},\r\n              'num_leaves': 10,\r\n              'learning_rate': 0.02,\r\n              'feature_fraction': 0.8,\r\n              'max_depth': 5,\r\n              'verbose': 0,\r\n              'num_boost_round': 1000,\r\n              'early_stopping_rounds': 200,\r\n              'nthread': -1}\r\n\r\nlgbtrain = lgb.Dataset(data=X_train, label=Y_train, feature_name=cols)\r\nlgbval = lgb.Dataset(data=X_val, label=Y_val, reference=lgbtrain, feature_name=cols)\r\n\r\nmodel = lgb.train(lgb_params, lgbtrain,\r\n                  valid_sets=[lgbtrain, lgbval],\r\n                  num_boost_round=lgb_params['num_boost_round'],\r\n                  early_stopping_rounds=lgb_params['early_stopping_rounds'],\r\n                  feval=lgbm_smape, # yukarıdaki fonksiyonlardan çekiyor sale değişkenleri karşılaştırılması yapıyor\r\n                  verbose_eval=100)\r\n# Custom Coss fonksiyonu oluşturduk hatamızı hesaplıyor lgbm_smape\r\ny_pred_val = model.predict(X_val, num_iteration=model.best_iteration)\r\n\r\nsmape(np.expm1(y_pred_val), np.expm1(Y_val))\r\n\r\n\r\n# Değişken Önem Düzeyleri\r\n\r\n\r\ndef plot_lgb_importances(model, plot=False, num=10):\r\n\r\n    gain = model.feature_importance('gain')\r\n    feat_imp = pd.DataFrame({'feature': model.feature_name(),\r\n                             'split': model.feature_importance('split'),\r\n                             'gain': 100 * gain / gain.sum()}).sort_values('gain', ascending=False)\r\n    if plot:\r\n        plt.figure(figsize=(10, 10))\r\n        sns.set(font_scale=1)\r\n        sns.barplot(x=\"gain\", y=\"feature\", data=feat_imp[0:25])\r\n        plt.title('feature')\r\n        plt.tight_layout()\r\n        plt.show()\r\n    else:\r\n        print(feat_imp.head(num))\r\n\r\n\r\nplot_lgb_importances(model, num=30)\r\nplot_lgb_importances(model, num=30, plot=True)\r\n# Gain feature kazandırdığı kazanımdır. bölmeden önce ve bölmeden sonraki entropi değişimidir.\r\nlgb.plot_importance(model, max_num_features=20, figsize=(10, 10), importance_type=\"gain\")\r\nplt.show()\r\n\r\n# Final Model\r\n########################\r\n# Sales NA Olmayanlar\r\ntrain = df.loc[~df.sales.isna()]\r\n\r\nY_train = train['sales']\r\nX_train = train[cols]\r\n\r\ntest = df.loc[df.sales.isna()]\r\nX_test = test[cols]\r\n\r\n# earlystopping rounds ı getirmiyoruz. Çünkü işlem bitti.\r\nlgb_params = {'metric': {'mae'},\r\n              'num_leaves': 10,\r\n              'learning_rate': 0.02,\r\n              'feature_fraction': 0.8,\r\n              'max_depth': 5,\r\n              'verbose': 0,\r\n              'nthread': -1,\r\n              \"num_boost_round\": model.best_iteration}\r\n\r\n# LightGBM dataset veriyi oluşturuyoruz\r\nlgbtrain_all = lgb.Dataset(data=X_train, label=Y_train, feature_name=cols)\r\n\r\nmodel = lgb.train(lgb_params, lgbtrain_all, num_boost_round=model.best_iteration)\r\n\r\n\r\n# Submission Sonucunun Kaggle Yüklenmesi\r\ntest_preds = model.predict(X_test, num_iteration=model.best_iteration)\r\n\r\nsmape(np.expm1(test_preds), np.expm1(Y_val))","sub_path":"Dynamic_Pricing.py","file_name":"Dynamic_Pricing.py","file_ext":"py","file_size_in_byte":10365,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"81947365","text":"from django.conf import settings\nfrom django.conf.urls import include, url\nfrom django.conf.urls.static import static\nfrom django.contrib import admin\n\nfrom django.views.generic import TemplateView\n\n\nurlpatterns = [\n    url(r'^admin/', admin.site.urls),\n    url(r'^accounts/', include('allauth.urls')),\n    url(r'^accounts/', include('regex.accounts.urls')),\n    url(r'^invoices/', include('regex.invoices.urls')),\n    url(r'^portfolio/', include('regex.portfolio.urls')),\n    url(r'^work_entries/', include('regex.work_entries.urls')),\n    url(r'^', include('regex.homepage.urls')),\n] + static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT, show_indexes=True)\n\nif settings.DEBUG:\n    import debug_toolbar\n    urlpatterns += [\n        url(r'^typography/$', TemplateView.as_view(template_name='typography.html')),\n        url(r'^__debug__/', include(debug_toolbar.urls)),\n    ]\n","sub_path":"src/regex/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":885,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"511631511","text":"#!/usr/bin/env python\r\nimport os\r\nfrom flask import Flask, render_template, url_for, request, jsonify\r\napp = Flask(__name__)\r\n\r\ndef sleep():\r\n    os.system(\"rundll32.exe powrprof.dll,SetSuspendState 0,1,0\")\r\n\r\n@app.route('/', methods=['GET', 'POST'])\r\ndef hello_world():\r\n    if request.method == \"GET\":\r\n        return render_template(\"home.html\")\r\n    if request.method == \"POST\":\r\n        if request.form[\"command\"]:\r\n            command = request.form[\"command\"]\r\n            if command == \"sleep\":\r\n                sleep()\r\n        return render_template(\"home.html\")\r\n\r\nif __name__ == '__main__':\r\n    app.run(host=\"0.0.0.0\")\r\n","sub_path":"src/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":633,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"535861558","text":"from __future__ import print_function, unicode_literals\n\"\"\"\nLaunches a local Weblab instance which makes use of the launch_sample configuration, and runs\non it the WeblabWeb tests using PhantomJS. Once the tests are run, the instance is automatically\nstopped.\n\"\"\"\n\n\nimport os\nimport threading\nimport sys\nimport time\n\noriginal_path = os.path.abspath(os.curdir)\n\nweblab_path = \"../../server/launch/sample\"\nos.chdir(weblab_path)\nsys.path.append(\".\")\n\nfrom launch_sample import inner\n\ncondition = threading.Condition()\nevent_notifier = threading.Condition()\n\n\nclass Runner(threading.Thread):\n    def run(self):\n        self.launcher = inner(condition=condition, event_notifier=event_notifier)\n        self.launcher.launch()\n\n\nrunner = Runner()\nrunner.start()\n\nwith event_notifier:\n    event_notifier.wait()\n\nos.chdir(original_path)\n\nresult = os.system(\n    \"mocha-phantomjs -s web-security=no -s localToRemoteUrlAccessEnabled=true -s webSecurityEnabled=false testrunner.html\")\n\nos.chdir(weblab_path)\n\n\nwith condition:\n    condition.notify()\n\nsys.exit(result)\n","sub_path":"tools/sketches/testpjs/testweblab_launcher.py","file_name":"testweblab_launcher.py","file_ext":"py","file_size_in_byte":1055,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"568844391","text":"from unittest import TestCase, main\n\nimport nltk\n\nfrom feature_extractor import document_preprocessor\nfrom tests import data\n\n\nclass TestToken_processor(TestCase):\n    def test_token_processor(self):\n        for sentence in data['Intitul de la demande']:\n            tokens = [t for t in nltk.word_tokenize(document_preprocessor(sentence.lower()), language='french',\n                                                    preserve_line=False)]\n            print(tokens)\n        self.assertEqual(tokens, nltk.word_tokenize(document_preprocessor(sentence.lower()), language='french',\n                                                    preserve_line=False))\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"tests/test_token_processor.py","file_name":"test_token_processor.py","file_ext":"py","file_size_in_byte":693,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"424977215","text":"import numpy as np\nimport cv2\nfrom PIL import ImageGrab\n\nfourcc = cv2.VideoWriter_fourcc('X','V','I','D')\nvid = cv2.VideoWriter('record1.avi',fourcc, 8,(590,490))  #  아래 x,y, w,h  보고 계산\nwhile(True):\n    img = ImageGrab.grab(bbox=(10, 10, 600, 500))\n    img_np = np.array(img)\n    #frame = cv2.cvtColor(imp_np, cv2.COLOR_BGR2GRAY)\n    vid.write(img_np)\n    cv2.imshow(\"frame\", img_np)\n    key = cv2.waitKey(1)\n    if key == 27:\n        break\n\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n","sub_path":"test/cv2/screen_recode.py","file_name":"screen_recode.py","file_ext":"py","file_size_in_byte":493,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"456120138","text":"import logging\nfrom pathlib import Path\nfrom typing import List\n\nimport requests\n\nfrom skilletlib.snippet.base import Snippet\nfrom skilletlib.snippet.rest import RestSnippet\nfrom .base import Skillet\n\nlogger = logging.getLogger(__name__)\n\n\nclass RestSkillet(Skillet):\n\n    def __init__(self, metadata: dict):\n        super().__init__(metadata)\n        self.session = requests.Session()\n\n    def get_snippets(self) -> List[Snippet]:\n        snippet_path_str = self.skillet_dict['snippet_path']\n        snippet_path = Path(snippet_path_str)\n        snippet_list = list()\n        for snippet_def in self.snippet_stack:\n            operation = snippet_def.get('operation', 'get')\n            if operation == 'post' and 'payload' in snippet_def:\n                snippet_file = snippet_path.joinpath(snippet_def['payload'])\n                if snippet_file.exists():\n                    with open(snippet_file, 'r') as sf:\n                        snippet = RestSnippet(sf.read(), snippet_def, self.session)\n                        snippet_list.append(snippet)\n                else:\n                    logger.warning(f'Snippet file: {snippet_def[\"name\"]} was not found!')\n\n        return snippet_list\n","sub_path":"skilletlib/skillet/rest.py","file_name":"rest.py","file_ext":"py","file_size_in_byte":1194,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"449238524","text":"from django.shortcuts import render\nfrom django.contrib import messages\n\ndef show_msg(request):\n    add_msg(request)\n    return render(request,'show_msg.html',{})\n\n\ndef add_msg(request):\n    messages.add_message(request, messages.INFO, 'Hello world.')\n    messages.debug(request, '调试消息')\n    messages.info(request, '一般消息')\n    messages.success(request, '成功消息')\n    messages.warning(request, '警告消息')\n    messages.error(request, '错误消息')\n","sub_path":"myapp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":475,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"404672005","text":"from datetime import datetime\n\n\nname = input(\"What is your name?:\")\nage = input(\"What is your age?:\")\n\ntry:\n    age = int(age)\nexcept:\n    print (\"Please enter a number for the age!\")\n    exit()\n\nnow = datetime.now()\nyear = now.year\nhundrethYear = ((100-age)+year)\n\nprint (\"Hello {}, you're turning 100 in the year {}\".format(name,hundrethYear))\n\n","sub_path":"user_input.py","file_name":"user_input.py","file_ext":"py","file_size_in_byte":347,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"482099959","text":"import random\nimport os\nfrom art import logo\n\nclear = lambda: os.system('cls')\n\ndef deal_card():\n  cards = [11, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 10, 10]\n  return random.choice(cards)\n\n\ndef calculate_score(cards):\n  if len(cards) == 2 and sum(cards) == 21:\n    return 0\n  # for card in cards:\n  #   if sum(cards) > 21 and card == 11:\n  #     card = 1\n  if 11 in cards and sum(cards) > 21:\n    cards.remove(11)\n    cards.append(1)\n  return sum(cards)\n\ndef compare(user_score, computer_score):\n  if user_score == computer_score:\n    return \"It's a draw\"\n  elif computer_score == 0:\n    return \"Lose, the opponent has a blackjack\"\n  elif user_score == 0:\n    return \"Win, You have a blackjack\"\n  elif user_score > 21:\n    return \"Lose, You have more than 21\"\n  elif computer_score > 21:\n    return \"Win, computer busted\"\n  elif user_score > computer_score:\n    return \"You Win\"\n  else:\n    return f\"Lose\"\n\ndef play_game():\n\n  user_cards = []\n  computer_cards = []\n  isGameOver = False\n\n  print(logo)\n\n  for _ in range(2):\n    user_cards.append(deal_card())\n    computer_cards.append(deal_card())\n\n  while not isGameOver:\n\n    user_score = calculate_score(user_cards)\n    computer_score = calculate_score(computer_cards)\n\n    print(f\"Your cards: {user_cards}, your score: {user_score}\")\n    print(f\"Computers first card: {computer_cards[0]}\")\n\n    if user_score == 0 or computer_score == 0 or user_score > 21:\n      isGameOver = True\n    else:\n      getAnother = input(\"Would you like to get another card? 'yes' or 'no': \")\n      if getAnother == \"yes\":\n        user_cards.append(deal_card())\n      else:\n        isGameOver = True\n\n  while computer_score != 0 and computer_score < 17:\n    computer_cards.append(deal_card())\n    computer_score = calculate_score(computer_cards)\n\n  print(f\"User cards: {user_cards}, user score: {user_score}\")\n  print(f\"Computer cards: {computer_cards}, computer score: {computer_score}\")\n\n  print(compare(user_score, computer_score))\n\nwhile input(\"Would you like to play a game of blackjack? 'yes or 'no': \") == 'yes':\n  clear()\n  play_game()\n","sub_path":"100 days of code/day 11/blackjack.py","file_name":"blackjack.py","file_ext":"py","file_size_in_byte":2069,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"610796332","text":"import collections\n\nclass Solution(object):\n    def maxSlidingWindow(self, nums, k):\n        \"\"\"\n        :type nums: List[int]\n        :type k: int\n        :rtype: List[int]\n        \"\"\"\n        # use deque as stack + queue\n        d = collections.deque()\n        res = []\n        for i, n in enumerate(nums):\n            # maintain a max stack\n            while d and nums[d[-1]] < n:\n                d.pop()\n            d.append(i)\n            # maintain stack/queue with size <= k\n            if i - d[0] >= k: d.popleft()\n            # start sliding: store max num in queue\n            if i >= k - 1: res.append(nums[d[0]])\n            \n        return res\n\n\nsolution = Solution()\nprint(solution.maxSlidingWindow([1, 3, -1, -3, 5, 3, 6, 7], 3))","sub_path":"239_Sliding_Window_Maximum.py","file_name":"239_Sliding_Window_Maximum.py","file_ext":"py","file_size_in_byte":746,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"92610410","text":"class Draw:\n\n    color = \"red\"\n    def __init__(self, x, y):                                   #A constructor fnction doesn't return anything.\n        self.x = x\n        self.y = y\n    \n    def __str__(self):                                          #If we print just \"point\", it'll print that it's an object at a given memory. Including __str__ \n                                                                #method will help solve that. Now, it'll print whatever's returned.\n        return f\"({self.x}, {self.y}) Color: {self.color}\"\n        \n\npoint = Draw(2,4)\npoint.color = \"green\"\nprint(point)\n\n\npoint = Draw(5,6)\n\nprint(f\"({point.x}, {point.y}) Color: {point.color}\")\n\n","sub_path":"Classes/magic_methods.py","file_name":"magic_methods.py","file_ext":"py","file_size_in_byte":677,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"456920026","text":"import asyncio\nimport functions\n\nHOST = functions.HOST\nPORT = functions.PORT\ntrackers = []\n\nclass GpsServer(asyncio.Protocol):\n\n    def connection_made(self, transport):\n        self.transport = transport\n        self.addr = transport.get_extra_info('peername')\n        self.s = transport.get_extra_info('socket')\n        self._rest = b''\n        trackers.append(self)\n        print(\"Connection from {}\".format(self.addr))\n        print(trackers)\n\n    def data_received(self, data):\n        data = self._rest + data\n        (msgs, rest) = functions.parse_recvd_data(data)\n        #(msgs, rest) = functions.recv_msgs(self.s, data)\n        self._rest = rest\n        for msg in msgs:\n            msg = '{}: {}'.format(self.addr, msg.decode())\n            print (msg)\n\n    def connection_lost(self, ex):\n        print('Client {} disconnected'.format(self.addr))\n        trackers.remove(self)\n\nif __name__ == \"__main__\":\n    loop = asyncio.get_event_loop()\n    coroutine = loop.create_server(GpsServer,\n                                       host=HOST,\n                                       port=PORT)\n    server = loop.run_until_complete(coroutine)\n    for socket in server.sockets:\n        addr = socket.getsockname()\n        print('Listening on {}'.format(addr))\n    loop.run_forever()\n        \n","sub_path":"python tcp server/v1/TCP2.py","file_name":"TCP2.py","file_ext":"py","file_size_in_byte":1294,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"69185582","text":"#!/usr/bin/env python3\n\"\"\"Infoset ORM classes.\n\nManages connection pooling among other things.\n\n\"\"\"\n\n# Main python libraries\nimport sys\nimport os\nfrom pathlib import Path\n\n# Pip3 libraries\nimport yaml\nfrom sqlalchemy import create_engine\n\n# Infoset libraries\ntry:\n    from infoset.utils import log\nexcept:\n    print(\n        'You need to set your PYTHONPATH to include the '\n        'infoset-ng root directory')\n    sys.exit(2)\nfrom infoset.utils import configuration\nfrom infoset.utils import general\nimport infoset.utils\nfrom infoset.db.db_orm import BASE, Agent, Department, Device, Billcode\nfrom infoset.db.db_orm import Configuration, DeviceAgent, Datapoint\nfrom infoset.db import URL\nfrom infoset.db import db_configuration\nfrom infoset.db import db_billcode\nfrom infoset.db import db_department\nfrom infoset.db import db_device\nfrom infoset.db import db_agent\nfrom infoset.db import db_deviceagent\nfrom infoset.db import db_datapoint\nfrom infoset.db import db\n\n\ndef insert_datapoint():\n    \"\"\"Insert first datapoint in the database.\n\n    Args:\n        None\n\n    Returns:\n        None\n\n    \"\"\"\n    # Initialize key variables\n    reserved = '_SYSTEM_RESERVED_'\n\n    # Insert\n    if db_datapoint.idx_datapoint_exists(1) is False:\n        record = Datapoint(\n            id_datapoint=general.encode(reserved),\n            agent_label=general.encode(reserved),\n            agent_source=general.encode(reserved)\n        )\n        database = db.Database()\n        database.add(record, 1011)\n\n\ndef insert_department():\n    \"\"\"Insert first department in the database.\n\n    Args:\n        None\n\n    Returns:\n        None\n\n    \"\"\"\n    # Initialize key variables\n    reserved = '_SYSTEM_RESERVED_'\n\n    # Insert\n    if db_department.idx_department_exists(1) is False:\n        record = Department(\n            code=general.encode(reserved),\n            name=general.encode(reserved))\n        database = db.Database()\n        database.add(record, 1102)\n\n\ndef insert_billcode():\n    \"\"\"Insert first billcode in the database.\n\n    Args:\n        None\n\n    Returns:\n        None\n\n    \"\"\"\n    # Initialize key variables\n    reserved = '_SYSTEM_RESERVED_'\n\n    # Insert\n    if db_billcode.idx_billcode_exists(1) is False:\n        record = Billcode(\n            code=general.encode(reserved),\n            name=general.encode(reserved))\n        database = db.Database()\n        database.add(record, 1104)\n\n\ndef insert_agent_device():\n    \"\"\"Insert first agent and device in the database.\n\n    Args:\n        None\n\n    Returns:\n        None\n\n    \"\"\"\n    # Initialize key variables\n    reserved = '_SYSTEM_RESERVED_'\n    idx_agent = 1\n    idx_device = 1\n\n    # Add agent\n    if db_agent.idx_agent_exists(idx_agent) is False:\n        # Generate a UID and add a record in the database\n        record = Agent(\n            id_agent=general.encode(reserved),\n            name=general.encode(reserved))\n        database = db.Database()\n        database.add(record, 1109)\n\n    # Add device\n    if db_device.idx_device_exists(idx_device) is False:\n        record = Device(\n            description=general.encode(reserved),\n            devicename=general.encode(reserved)\n        )\n        database = db.Database()\n        database.add(record, 1106)\n\n    # Add to Agent / Device table\n    if db_deviceagent.device_agent_exists(idx_device, idx_agent) is False:\n        record = DeviceAgent(idx_device=idx_device, idx_agent=idx_agent)\n        database = db.Database()\n        database.add(record, 1107)\n\n\ndef insert_config():\n    \"\"\"Insert first config in the database.\n\n    Args:\n        None\n\n    Returns:\n        None\n\n    \"\"\"\n    # Initialize key variables\n    key_values = [('version', '0.0.0.0')]\n\n    # Cycle through all the key value pairs\n    for item in key_values:\n        key = item[0]\n        value = item[1]\n\n        # Check if value exists and insert if not\n        if db_configuration.config_key_exists(key) is False:\n            record = Configuration(\n                config_key=general.encode(key),\n                config_value=general.encode(value))\n            database = db.Database()\n            database.add(record, 1108)\n\n\ndef _server_setup():\n    \"\"\"Setup server.\n\n    Args:\n        None\n\n    Returns:\n        None\n\n    \"\"\"\n    # Initialize key variables\n    use_mysql = True\n    pool_size = 25\n    max_overflow = 25\n\n    # Get configuration\n    config = configuration.Config()\n\n    # Create DB connection pool\n    if use_mysql is True:\n        # Add MySQL to the pool\n        engine = create_engine(\n            URL, echo=True,\n            encoding='utf8',\n            max_overflow=max_overflow,\n            pool_size=pool_size, pool_recycle=3600)\n\n        # Try to create the database\n        print('Attempting to create database tables')\n        try:\n            sql_string = (\n                'ALTER DATABASE %s CHARACTER SET utf8mb4 '\n                'COLLATE utf8mb4_general_ci') % (config.db_name())\n            engine.execute(sql_string)\n        except:\n            log_message = (\n                'Cannot connect to database %s. '\n                'Verify database server is started. '\n                'Verify database is created. '\n                'Verify that the configured database authentication '\n                'is correct.') % (config.db_name())\n            log.log2die(1036, log_message)\n\n        # Apply schemas\n        print('Applying Schemas')\n        BASE.metadata.create_all(engine)\n\n        # Insert database entries\n        insert_agent_device()\n        insert_billcode()\n        insert_department()\n        insert_datapoint()\n        insert_config()\n\n\ndef _update_config():\n    \"\"\"Update the configuration with good defaults.\n\n    Args:\n        None\n\n    Returns:\n        None\n\n    \"\"\"\n    # Initialize key variables\n    valid = True\n    updated_list = []\n\n    # Read configuration into dictionary\n    directory = ('%s/etc') % (general.root_directory())\n    config = general.read_yaml_files([directory])\n\n    # Update log_directory and ingest_cache_directory\n    if isinstance(config, dict) is True:\n        if 'main' in config:\n            # Setup the log_directory to a known good default\n            (updated, config) = _create_directory_entries(\n                'log_directory', config)\n            updated_list.append(updated)\n\n            # Setup the ingest_cache_directory to a known good default\n            (updated, config) = _create_directory_entries(\n                'ingest_cache_directory', config)\n            updated_list.append(updated)\n        else:\n            valid = False\n    else:\n        valid = False\n\n    # Gracefully exit if things are not OK\n    if valid is False:\n        log_message = (\n            'Configuration file found in %s is invalid') % (directory)\n        log.log2die_safe(2000, log_message)\n        sys.exit(2)\n\n    # Update configuration file if required\n    if len(updated_list) == updated_list.count(True):\n        # Delete all YAML files in the directory\n        general.delete_yaml_files(directory)\n\n        # Write config back to directory\n        filepath = ('%s/config.yaml') % (directory)\n        with open(filepath, 'w') as outfile:\n            yaml.dump(config, outfile, default_flow_style=False)\n\n\ndef _create_directory_entries(key, config):\n    \"\"\"Update the configuration with good defaults for directories.\n\n    Args:\n        key: Configuration key related to a directory.\n        config: Configuration dictionary\n\n    Returns:\n        updated: True if we have to update a value\n\n    \"\"\"\n    # Initialize key variables\n    updated = False\n    dir_dict = {\n        'log_directory': 'log',\n        'ingest_cache_directory': 'cache',\n    }\n    directory = ('%s/etc') % (general.root_directory())\n\n    # Setup the key value to a known good default\n    if key in config['main']:\n        # Verify whether key value is empty\n        if config['main'][key] is not None:\n            # Create\n            if os.path.isdir(config['main'][key]) is False:\n                config['main'][key] = ('%s/%s') % (directory, dir_dict[key])\n                updated = True\n        else:\n            config['main'][key] = ('%s/%s') % (directory, dir_dict[key])\n            updated = True\n    else:\n        config['main'][key] = ('%s/%s') % (directory, dir_dict[key])\n        updated = True\n\n    # Return\n    return (updated, config)\n\n\ndef _python_valid():\n    \"\"\"Determine whether we are running the minimum python version.\n\n    Args:\n        None\n\n    Returns:\n        None\n\n    \"\"\"\n    # Initialize key variables\n    valid = True\n    major = 3\n    minor = 5\n    major_installed = sys.version_info[0]\n    minor_installed = sys.version_info[1]\n\n    # Exit if python version is too low\n    if major_installed < major:\n        valid = False\n    elif major_installed == major and minor_installed < minor:\n        valid = False\n    if valid is False:\n        log_message = (\n            'Required python version must be >= {}.{}. '\n            'Python version {}.{} installed'\n            ''.format(major, minor, major_installed, minor_installed))\n        log.log2die_safe(2000, log_message)\n\n\ndef main():\n    \"\"\"Process agent data.\n\n    Args:\n        None\n\n    Returns:\n        None\n\n    \"\"\"\n    # Determine whether version of python is valid\n    _python_valid()\n\n    # Update configuration if required\n    _update_config()\n\n    # Run server setup\n    _server_setup()\n\n    # Install required PIP packages\n    print('Installing required pip3 packages')\n    pip3 = infoset.utils.general.search_file('pip3')\n    if pip3 is None:\n        log_message = ('Cannot find python \"pip3\". Please install.')\n        log.log2die(1052, log_message)\n\n    utils_directory = infoset.utils.__path__[0]\n    requirements_file = ('%s/requirements.txt') % (\n        Path(utils_directory).parents[1])\n    script_name = (\n        'pip3 install --user --upgrade --requirement %s'\n        '') % (requirements_file)\n    infoset.utils.general.run_script(script_name)\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":9930,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"335522727","text":"from __future__ import print_function\nimport tensorflow as tf\nimport tensorflow_fold as td\nimport model_fold\nimport preprocessing\nimport spacy\nimport pickle\nimport pprint\nimport os\n\n# Replication flags:\ntf.flags.DEFINE_string('logdir', '/home/arne/tmp/tf/log',\n                       'Directory in which to write event logs.')\ntf.flags.DEFINE_string('model_path', '/home/arne/tmp/tf/log/model.ckpt-748',\n                       'model file')\ntf.flags.DEFINE_string('data_mapping_path', 'data/nlp/spacy/dict.mapping',\n                       'model file')\ntf.flags.DEFINE_string('master', '',\n                       'Tensorflow master to use.')\ntf.flags.DEFINE_integer('task', 0,\n                        'Task ID of the replica running the training.')\ntf.flags.DEFINE_integer('ps_tasks', 0,\n                        'Number of PS tasks in the job.')\nFLAGS = tf.flags.FLAGS\n\nPROTO_PACKAGE_NAME = 'recursive_dependency_embedding'\nPROTO_CLASS = 'SequenceNode'\n\ndef parse_iterator(sequences, parser, sentence_processor, data_maps):\n    pp = pprint.PrettyPrinter(indent=2)\n    for s in sequences:\n        seq_tree = preprocessing.build_sequence_tree_from_str(s, sentence_processor, parser, data_maps)\n        pp.pprint(seq_tree)\n        yield seq_tree.SerializeToString()\n\nlex_size = 1300000\nembedding_dim = 300\n\ndef main(unused_argv):\n    print('load spacy ...')\n    nlp = spacy.load('en')\n    nlp.pipeline = [nlp.tagger, nlp.parser]\n    print('load data_mapping from: '+FLAGS.data_mapping_path + ' ...')\n    data_maps = pickle.load(open(FLAGS.data_mapping_path, \"rb\"))\n\n    with tf.Graph().as_default():\n        with tf.device(tf.train.replica_device_setter(FLAGS.ps_tasks)):\n            embed_w = tf.Variable(tf.constant(0.0, shape=[lex_size, embedding_dim]),\n                                  trainable=True, name='embeddings')\n            embedder = model_fold.SequenceTreeEmbedding(embed_w)\n            tree_embeddings = embedder.tree_embeddings\n\n            # Add ops to save and restore all the variables.\n            saver = tf.train.Saver()\n\n            # Later, launch the model, use the saver to restore variables from disk, and\n            # do some work with the model.\n            with tf.Session() as sess:\n                # Restore variables from disk.\n                print('restore model ...')\n                saver.restore(sess, FLAGS.model_path)\n                # Do some work with the model\n                print('parse input ...')\n                batch = list(parse_iterator(['Hallo.', 'Hallo!', 'Hallo?', 'Hallo'],\n                                            nlp, preprocessing.process_sentence3, data_maps))\n                fdict = embedder.build_feed_dict(batch)\n                print('calculate tree embeddings ...')\n                batch_embeddings, = sess.run(tree_embeddings, feed_dict=fdict)\n                print(batch_embeddings)\n\n\nif __name__ == '__main__':\n    ROOT_DIR = os.path.dirname(os.path.abspath(__file__))\n    td.proto_tools.map_proto_source_tree_path('', ROOT_DIR)\n    td.proto_tools.import_proto_file('sequence_node.proto')\n    tf.app.run()\n","sub_path":"apply_model.py","file_name":"apply_model.py","file_ext":"py","file_size_in_byte":3078,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"472624719","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# 给定一个单词列表，每个单词可以写成每个字母对应摩尔斯密码的组合。例如，\"cab\"\n# 可以写成\"-.-..--...\"，(即 \"-.-.\" + \"-...\" + \".-\"字符串的结合)。我们将这样一个连接过程称作单词翻译。\n# 返回我们可以获得所有词不同单词翻译的数量。\n#\n# 例如:\n# 输入: words = [\"gin\", \"zen\", \"gig\", \"msg\"]\n# 输出: 2\n# 解释:\n# 各单词翻译如下:\n# \"gin\" -> \"--...-.\"\n# \"zen\" -> \"--...-.\"\n# \"gig\" -> \"--...--.\"\n# \"msg\" -> \"--...--.\"\n#\n# 共有  2 种不同翻译, \"--...-.\" 和 \"--...--.\".\n#\n# 注意: 单词列表words的长度不会超过100。\n# 每个单词words[i]的长度范围为[1, 12]。\n# 每个单词words[i]只包含小写字母。\n\n\nclass Solution(object):\n    def uniqueMorseRepresentations(self, words):\n        \"\"\"\n        :type words: List[str]\n        :rtype: int\n        \"\"\"\n        arr = [\".-\", \"-...\", \"-.-.\", \"-..\", \".\", \"..-.\", \"--.\", \"....\", \"..\", \".---\", \"-.-\", \".-..\", \"--\", \"-.\", \"---\",\n               \".--.\", \"--.-\", \".-.\", \"...\", \"-\", \"..-\", \"...-\", \".--\", \"-..-\", \"-.--\", \"--..\"]\n        # a = 97\n        # a = ord('a')\n        result = []\n        dic = {}\n        for i in range(26):\n            dic[chr(97 + i)] = arr[i]\n        print(dic)\n        for w in words:\n            s = \"\"\n            for c in w:\n                s += dic[c.lower()]\n            if s not in result:\n                result.append(s)\n        return len(result)\n\n\nif __name__ == \"__main__\":\n    words = [\"gin\", \"zen\", \"gig\", \"msg\"]\n    a = Solution().uniqueMorseRepresentations(words)\n    print(a)","sub_path":"other/easy/q14.py","file_name":"q14.py","file_ext":"py","file_size_in_byte":1621,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"372351348","text":"#!/usr/bin/env python3\n\n\"\"\"Draw the diagram of the Solar System.\n\n__author__ = \"Zhu Qi\"\n__pkuid__  = \"1800011808\"\n__email__  = \"1800011808@pku.edu.cn\"\n\"\"\"\n\nimport math as m\nimport turtle as t\n\n\nt.bgcolor('black')\n\nMer=t.Turtle()\nVen=t.Turtle()\nEar=t.Turtle()\nMar=t.Turtle()\nJup=t.Turtle()\nSat=t.Turtle()\nSun=t.Turtle()\n\nMer.shape(\"circle\")\nVen.shape(\"circle\")\nEar.shape(\"circle\")\nMar.shape(\"circle\")\nJup.shape(\"circle\")\nSat.shape(\"circle\")\n\nMer.color('#FFFFFF')\nVen.color('#00FF00')\nEar.color('#0000FF')\nMar.color('#FFFF00')\nJup.color('#FF00FF')\nSat.color('#00FFFF')\nSun.color('#FF0000')\n\nSun.turtlesize(2,2)\nMer.turtlesize(0.5,0.5)\nVen.turtlesize(0.85,0.85)\nEar.turtlesize(1,1)\nMar.turtlesize(0.65,0.65)\nJup.turtlesize(1.7,1.7)\nSat.turtlesize(1.3,1.3)\n\nMer.width(2)\nVen.width(2)\nEar.width(2)\nMar.width(2)\nJup.width(2)\nSat.width(2)\n\nMer.speed(0)\nVen.speed(0)\nEar.speed(0)\nMar.speed(0)\nJup.speed(0)\nSat.speed(0)\n\n\ndef ellipse(e,p,tur,ang):\n    \"\"\"Move to a point whose angle with pole and pole axis is \"ang\" on a ellipse with Eccentricity e and Focal distance p using turtle \"tur\".\n    \"\"\"\n    if ang==0:\n        tur.pu()\n        tur.goto(e * p / (1 - e), 0)\n        tur.pd()\n    else:\n        r = e * p / (1 - e * m.cos(ang / 100))\n        tur.goto(r * m.cos(ang / 100), r * m.sin(ang / 100))\n\n\ndef main():\n    \"\"\"main module\n    \"\"\"\n    Sun.shape('circle')\n    i=0\n    while 1:\n        ellipse(0.20563, 200, Mer, 5.68376 * i)\n        ellipse(0.0068, 11000, Ven, 4.22519 * i)\n        ellipse(0.016710, 7000, Ear, 3.36896 * i)\n        ellipse(0.0934, 1700, Mar, 2.72788 * i)\n        ellipse(0.004839, 46000, Jup, 1.91554 * i)\n        ellipse(0.054151, 5500, Sat, 1.0218 * i)\n        i = i + 1\n    t.mainloop()\n\n\nif __name__ == '__main__':\n    main()\n\n","sub_path":"pyassign1/planets.py","file_name":"planets.py","file_ext":"py","file_size_in_byte":1750,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"600429958","text":"# Prompt the user for hours and rate per hour to compute gross pay\r\nwork_hours = 40.00\r\nloading = 1.5\r\n# Capture, convert to float and determine if numbers.\r\nhours = input(\"Enter work Hours: \")\r\ntry:\r\n    worked_hours = float(hours)\r\nexcept:\r\n    print(\"Please only enter numbers\")\r\n#\r\nrate = input(\"Enter basic Rate/hour: \")\r\ntry:\r\n    basic_rate = float(rate)\r\nexcept:\r\n    print(\"Please only enter numbers\")\r\n# Determine if working greater than 40 hours, determine amount over and pay\r\nif worked_hours > work_hours:\r\n    overtime = worked_hours - work_hours\r\n    overtime_pay = overtime * basic_rate * loading\r\n    standard_pay = work_hours * basic_rate\r\n    tot_pay = overtime_pay + standard_pay\r\nelse:\r\n    tot_pay = worked_hours * basic_rate\r\n# this function will round and print the result\r\nprint(\"Total pay is \", tot_pay)\r\n#round(tot_pay,2)","sub_path":"Grosspaywithovertime.py","file_name":"Grosspaywithovertime.py","file_ext":"py","file_size_in_byte":848,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"356388775","text":"# -*- coding: utf_8 -*-\nimport math\nimport numpy as np\nimport matplotlib.pyplot as plt\n__author__ = 'Anastasia Bazhutina'\n\ndef alg_interp(func_param, func_value):\n    \"\"\"\n        :func_param: список точек - аргументов интерполируемой функции\n        :func_value: значение интерполируемой функции в каждой точке\n        :return:\n         column_a: столбец коэффициентов a кубического сплайна\n         column_b: столбец коэффициентов b кубического сплайна\n         column_c: столбец коэффициентов c кубического сплайна\n         column_d: столбец коэффициентов d кубического сплайна\n        \"\"\"\n    # число срезов\n    N = len(func_param)\n    # Ac = b система уравнений относительно коэффициентов c кубического сплайна\n    A = np.zeros((N-1, N-1))\n    #print A\n    b = np.zeros(N-1)\n    param = func_param\n    value = func_value\n    #заполнение первой строки матрицы А\n    A[0, 0] = 2 * (param[2] - param[0])\n    A[0, 1] = param[2] - param[1]\n    A[0, N-2] = param[1] - param[0]\n    b[0] = 3 * ((value[1] - value[0]) / (param[1] - param[0]) -\n                (value[N-1] - value[N-2]) / (param[N-1] - param[N-2]))\n\n    for i in xrange(1, N-2):\n        A[i, i] = 2 * (param[i + 2] - param[i])\n        A[i, i + 1] = param[i + 2] - param[i+1]\n        A[i, i - 1] = param[i+1] - param[i]\n        b[i] = 3 * ((value[i + 1] - value[i]) / (param[i + 1] - param[i]) -\n                        (value[i] - value[i-1]) / (param[i] - param[i-1]))\n    #заполнение последней строки матрицы А\n    A[N-2, N-2] = 2 * (param[N-1] - param[N-2] + param[1] - param[0])\n    A[N-2, N-3] = param[N-1] - param[N-2]\n    A[N-2, 0] = param[1] - param[0]\n    b[N-2] = 3 * ((value[N-1] - value[N-2]) / (param[N-1] - param[N-2]) -\n                (value[N-2] - value[N-3]) / (param[N-2] - param[N-3]))\n    #print A\n    #находим  n1..n-1 коэффициенты c кубического сплайна\n    c = np.linalg.solve(A, b)\n    #столбцы результатов\n    column_a = value\n    column_b = np.zeros(N-1)\n    column_c = np.zeros(N-1)\n    column_d = np.zeros(N-1)\n    # находим коэффициенты c кубического сплайна\n    #for i in xrange(len(c)):\n    #    column_c[i+1] = c[i]\n    column_c = c\n    # находим коэффициенты b, d кубического сплайна\n    for i in xrange(N-2):\n        column_b[i] = (value[i+1]-value[i])/(param[i+1]-param[i])-\\\n        (param[i + 1] - param[i])*(column_c[i+1]+2*column_c[i])/3\n        column_d[i] = (column_c[i+1]-column_c[i])/(3*(param[i+1]-param[i]))\n    column_b[N-2] = (value[0] - value[N-2]) / (param[N-1] - param[N-2]) - \\\n                      (param[N-1] - param[N-2]) * (column_c[0] + 2 * column_c[N-2]) / 3\n    column_d[N-2] = (column_c[0] - column_c[N-2]) / (3 * (param[N-1] - param[N-2]))\n\n    #print column_b\n    #print  column_d\n    return column_a, column_b, column_c, column_d","sub_path":"model/interpolation.py","file_name":"interpolation.py","file_ext":"py","file_size_in_byte":3252,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"184349253","text":"import StringIO\nfrom PIL import Image\nfrom mock import patch\n\nfrom django.contrib.auth.models import Permission\nfrom django.core.files.uploadedfile import InMemoryUploadedFile\nfrom django.core.urlresolvers import reverse\nfrom django.db.models import Q\n\nfrom rest_framework import status\nfrom rest_framework.test import APITestCase, APIClient\nfrom rest_framework_jwt import utils\n\nfrom .factories import PhotoFactory, GalleryFactory\nfrom .models import Gallery, Photo\n\nfrom accounts.tests.factories import AccountFactory, GroupFactory, UserFactory\n\n\nclass UserMixin(object):\n    \"\"\" Inject the UserFactories into the TestCases\"\"\"\n    def setUp(self):\n        \"\"\" set up the factories \"\"\"\n        admin_group = GroupFactory.create(name='admin')\n\n        permissions = Permission.objects.filter(\n            Q(codename__contains='photo') |\n            Q(codename__contains='gallery')\n        )\n        self.admin = UserFactory.create(\n            username='admin',\n            groups=(\n                admin_group.pk,\n            ),\n            user_permissions=permissions\n        )\n\n        for user in [self.admin]:\n            AccountFactory.create(user=user)\n        self.admin_auth = self._get_jwt_header(self.admin)\n\n    def _get_jwt_header(self, user):\n        \"\"\" help to get HTTP HTTP_AUTHORIZATION for DRF tests \"\"\"\n        payload = utils.jwt_payload_handler(user)\n        token = utils.jwt_encode_handler(payload)\n        return 'JWT {}'.format(token)\n\n\ndef get_temporary_image():\n    io = StringIO.StringIO()\n    size = (200, 200)\n    color = (255, 0, 0, 0)\n    image = Image.new(\"RGBA\", size, color)\n    image.save(io, format='JPEG')\n    image_file = InMemoryUploadedFile(\n        io, None, 'foo.jpg', 'jpeg', io.len, None\n    )\n    image_file.seek(0)\n    return image_file\n\n\ndef get_temporary_textfile():\n    io = StringIO.StringIO()\n    io.write(\"test test\")\n    text_file = InMemoryUploadedFile(io, None, 'foo.txt', 'txt', io.len, None)\n    text_file.seek(0)\n    return text_file\n\n\nclass PhotosTestCase(UserMixin, APITestCase):\n    def setUp(self):\n        super(PhotosTestCase, self).setUp()\n        self.c = APIClient(enforce_csrf_checks=True)\n\n    def tearDown(self):\n        \"\"\" remove all photos \"\"\"\n        for photo in Photo.objects.all():\n            photo.delete()\n\n    def test_create_gallery(self):\n        \"\"\" add a gallery \"\"\"\n        response = self.c.post(\n            reverse('photos:photo_api:gallery-list'),\n            {'name': 'test'},\n            format='json',\n            HTTP_AUTHORIZATION=self.admin_auth\n        )\n        self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n        self.assertEqual(Gallery.objects.all().count(), 1)\n\n    def test_create_gallery_nouser(self):\n        \"\"\" add a gallery \"\"\"\n        response = self.c.post(\n            reverse('photos:photo_api:gallery-list'),\n            {'name': 'test'},\n            format='json'\n        )\n        self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\n        self.assertEqual(Gallery.objects.all().count(), 0)\n\n    def test_delete_gallery(self):\n        gallery = GalleryFactory()\n        response = self.c.delete(\n            reverse('photos:photo_api:gallery-detail', args=(gallery.pk,)),\n            HTTP_AUTHORIZATION=self.admin_auth\n        )\n        self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT)\n\n        self.assertEqual(Gallery.objects.all().count(), 0)\n\n    def test_delete_gallery_nouser(self):\n        gallery = GalleryFactory()\n        response = self.c.delete(\n            reverse('photos:photo_api:gallery-detail', args=(gallery.pk,)),\n        )\n        self.assertEqual(Gallery.objects.all().count(), 1)\n        self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\n\n    def test_create_photo(self):\n        \"\"\" add a gallery \"\"\"\n        gallery = GalleryFactory()\n\n        tmp_file = get_temporary_image()\n        response = self.c.post(\n            reverse(\n                'photos:photo_api:photo-list'\n            ),\n            data={\n                'gallery_set': gallery.pk,\n                'name': 'test',\n                'image': tmp_file,\n            },\n            format='multipart',\n            HTTP_AUTHORIZATION=self.admin_auth\n        )\n\n        self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n        self.assertEqual(Photo.objects.all().count(), 1)\n        self.assertEqual(\n            gallery.photos.get_queryset().first(),\n            Photo.objects.first()\n        )\n\n        photo = Photo.objects.first()\n        response = self.c.delete(\n            reverse('photos:photo_api:photo-detail', args=(photo.pk,)),\n            HTTP_AUTHORIZATION=self.admin_auth\n        )\n        self.assertEqual(Photo.objects.all().count(), 0)\n        self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT)\n\n    def test_create_photo_nouser(self):\n        \"\"\" add a gallery \"\"\"\n        gallery = GalleryFactory()\n        tmp_file = get_temporary_image()\n        response = self.c.post(\n            reverse(\n                'photos:photo_api:photo-list'\n            ),\n            data={\n                'gallery_set': gallery.pk,\n                'name': 'test',\n                'image': tmp_file,\n            },\n            format='multipart'\n        )\n\n        self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\n        self.assertEqual(Photo.objects.all().count(), 0)\n\n    def test_create_photo_textfile(self):\n        \"\"\" add a gallery \"\"\"\n        gallery = GalleryFactory()\n\n        tmp_file = get_temporary_textfile()\n\n        response = self.c.post(\n            reverse('photos:photo_api:photo-list'),\n            {\n                'gallery_set': gallery.pk,\n                'name': 'test',\n                'image': tmp_file,\n            },\n            format='multipart'\n        )\n\n        self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\n        self.assertEqual(Photo.objects.all().count(), 0)\n\n    @patch.object(Photo, 'create_thumbnail')\n    @patch.object(Photo, 'shrink_image')\n    def test_delete_photo(self, create, shrink):\n        create.return_value = True\n        shrink.return_value = True\n        photo = PhotoFactory()\n        response = self.c.delete(\n            reverse('photos:photo_api:photo-detail', args=(photo.pk,)),\n            HTTP_AUTHORIZATION=self.admin_auth\n        )\n\n        self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT)\n        self.assertEqual(Photo.objects.all().count(), 0)\n\n    @patch.object(Photo, 'create_thumbnail')\n    @patch.object(Photo, 'shrink_image')\n    def test_delete_photo_nouser(self, create, shrink):\n        create.return_value = True\n        shrink.return_value = True\n        self.c.logout()\n        photo = PhotoFactory()\n        response = self.c.delete(\n            reverse('photos:photo_api:photo-detail', args=(photo.pk,))\n        )\n        self.assertEqual(Photo.objects.all().count(), 1)\n        self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\n","sub_path":"limelight/photos/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":7011,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"82049016","text":"\n\n#calss header\nclass _CAPLET():\n\tdef __init__(self,): \n\t\tself.name = \"CAPLET\"\n\t\tself.definitions = [u'a pill (= a small solid piece of medicine that you swallow) that is long and thin, with rounded ends ']\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'nouns'\n\n\n\tdef run(self, obj1 = [], obj2 = []):\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/nouns/_caplet.py","file_name":"_caplet.py","file_ext":"py","file_size_in_byte":381,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"403110637","text":"#!/usr/bin/env python\nfrom os.path import *\nfrom confdirnames import *\nfrom tag import *\nfrom userpath import *\n\ndef run():\n\trepos = confdirnames()\n\tscripts = []\n\tno_scripts = []\n\tfor repo in repos:\n\t\tdir = join(repo,\"scripts\")\n\t\tif exists(dir):\n\t\t\tprint(basename(repo))\n\t\t\tscripts.append(repo)\n\t\telse:\n\t\t\tno_scripts.append(repo)\n\ttag = \"scripts\"\n\tadd_tags(tag,*scripts)\n\tremove_tags(tag,*scripts)\n\tprint(\"\\n%s scripts\" % len(scripts))\n\nif __name__==\"__main__\":\n\trun()\n\n","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":470,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"159269981","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\nimport platform as plat\nimport os\n\nimport tensorflow as tf\nfrom keras.backend.tensorflow_backend import set_session\n\n\n# from Model0_spp import ModelSpeech\n# from Model1_general import ModelSpeech\n# from model_set.Model2_1smart import ModelSpeech\n# from modification.SpeechModelClear import ModelSpeech\n# from modification.AttentionModel import ModelSpeech\n# from Model30_5folds import ModelSpeech\nfrom Model44_trainSetMain import ModelSpeech\n# from model_set.Model22_fl import ModelSpeech\n# from Model41_bowel import ModelSpeech\n\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\"\nos.environ['TF_CPP_MIN_LOG_LEVEL']='2' #only display error and warning; for 1: all info; for 3: only error.\n# #进行配置，使用90%的GPU\nconfig = tf.ConfigProto()\n# config.gpu_options.per_process_gpu_memory_fraction = 0.9\nconfig.gpu_options.allow_growth=True   #不全部占满显存, 按需分配\nset_session(tf.Session(config=config))\n\n\ndatapath = ''\nmodelpath = 'model_set'\n\n\nif(not os.path.exists(modelpath)): # 判断保存模型的目录是否存在\n\tos.makedirs(modelpath) # 如果不存在，就新建一个，避免之后保存模型的时候炸掉\n\nsystem_type = plat.system() # 由于不同的系统的文件路径表示不一样，需要进行判断\nif(system_type == 'Windows'):\n\tdatapath = 'E:\\workspace\\stdenv\\individual_spp\\dataset\\segments'\n\tmodelpath = modelpath + '\\\\'\nelif(system_type == 'Linux'):\n\t# datapath = '/home/zhaok14/example/PycharmProjects/setsail/individual_spp/bowelsounds/standard'\n\tdatapath = '/home/zhaok14/example/PycharmProjects/setsail/5foldCNNdesign/dataset/constructed'\n\tmodelpath = modelpath + '/'\nelse:\n\tprint('*[Message] Unknown System\\n')\n\tdatapath = 'dataset'\n\tmodelpath = modelpath + '/'\n\nms = ModelSpeech(datapath)\n\n#ms.LoadModel(modelpath + 'speech_model24_e_0_step_327500.model')\nms.TrainModel(datapath, epoch = 20, batch_size = 32, load_weights = False)\n\n","sub_path":"debug/small_trial/train_mspeech.py","file_name":"train_mspeech.py","file_ext":"py","file_size_in_byte":1931,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"29977765","text":"#!/usr/bin/env python3\n\nwith open(\"../positions.txt\") as fp:\n    lines = fp.read()\n\npositions = lines.split(\",\")\npositions = list(map(int, positions))\n\ncostOptions = []\n\nfor p in range(min(positions), max(positions)+1):\n    distances = [abs(distance - p) for distance in positions]\n    costOption = sum([int(cost*(cost+1)/2) for cost in distances])\n    costOptions.append(costOption)\n\nprint(min(costOptions))","sub_path":"2021/8/first/calculate.py","file_name":"calculate.py","file_ext":"py","file_size_in_byte":408,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"409401057","text":"data = input().split(',')\ncity = int(data[0])\nroad = int(data[1])\ncount = 0\nget = []\ncitydata = [[100000]*city for i in range(city)]\nfor i in range (0,road):\n    cityroad = input().split(',')\n    citydata[int(cityroad[0])-1][int(cityroad[1])-1] = int(cityroad[2])\n    citydata[int(cityroad[1])-1][int(cityroad[0])-1] = int(cityroad[2])\ni = 1\nwhile True:\n    get.append(i)\n    if len(get) == city:\n        break\n    count = count + min(citydata[i-1])\n    for j in range(0,len(citydata)):\n        citydata[j][i-1] = 100000\n    i = citydata[i-1].index(min(citydata[i-1])) + 1\n    \nprint(count)\n","sub_path":"ds/shortroad.py","file_name":"shortroad.py","file_ext":"py","file_size_in_byte":591,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"310893762","text":"\"\"\"\n《邢不行-2019新版|Python股票量化投资课程》\nauthor：邢不行\n微信：xingbuxing0807\n\n更新于：2020-12-17\n主要是解决了之前版本无法成功运行的问题。\n\n课程中自定制的库已经删除，新版中使用\n- 最新版本的同花顺客户端：http://activity.ths123.com/html/free/150324/\n- Python3.8\n- 最新版本的easytrader：pip install easytrader -i https://pypi.tuna.tsinghua.edu.cn/simple\n\"\"\"\nimport easytrader  # 导入自动交易的库\nimport pandas as pd\n\npd.set_option('expand_frame_repr', False)\n\n\n# =====创建自动下单对象\n# 使用同花顺客户端进行下单\nths = easytrader.use('ths')\n# 填入交易客户端的路径。即安装目录下的xiadan.exe的地址\nths.connect(r'C:\\同花顺软件\\同花顺\\xiadan.exe')\nths.enable_type_keys_for_editor()  # 不设置这个模式会有问题，无法录入股票代码、价格和数量\n\n# =====获取账户资金状况\nbalance_info = ths.balance\nprint(balance_info)\nprint(balance_info['资金余额'])  # 返回结果是一个dict\n\n# =====获取持仓\n# position_info = pd.DataFrame(ths.position)\n# if position_info.empty:\n     # print('没有持仓')\n# else:\n     # print(position_info)  # 返回结果是一个DataFrame\n\n# =====下单交易\n# ===限价买单\n# order_info = ths.buy('600000', '9.78', '100')\n# print(order_info)  # 返回order_id，即为合同编号：{'entrust_no': '74671'}\n# order_num = order_info['entrust_no']\n# print('订单编号：', order_num)\n\n# ===下单特殊情况\n# 未上市、停牌、退市的股票无法操作，尝试600001，提交失败：-990221020[-990221020]无此证券代码!\n# 委托股数不对：easytrader.exceptions.TradeError: 提交失败：-150904070[-150904070] 委托数量必须是每手股(张)数的倍数。\n# 委托价格最多小数点后两位，精度太高，会四舍五入。例如 5.423会取整为5.42\n# 不能提交低于跌停价，高于涨停价的价格: 提交失败：-990265060[-990265060]委托价格超过涨停价格。\n# 资金余额不够，尝试600519：提交失败：-150906130[-150906130]资金可用数不足,尚需322944.07。\n# 不在交易时间，提交失败：-990297020[-990297020]当前时间不允许做该项业务。\n# 断网会直接报错\n\n# ===限价卖单，同买单一样\n# order_info = ths.sell('600000', '9.78', '100')\n\n# ===市价单，不需要填价格\n# order_info = ths.market_buy('600789', '100')\n# order_info = ths.market_sell('600789', '100')\n\n# =====撤单\n# order_num = '150576'\n# cancel_info = ths.cancel_entrust(order_num)\n# print(cancel_info)  # {'message': '您的撤单委托已成功提交，合同编号：104332。'}\n\n# =====查询订单\n# 查看今日所有的委托\nentrust_info = pd.DataFrame(ths.today_entrusts)\nif entrust_info.empty:\n    print('今日没有委托')\nelse:\n    print(entrust_info)\n\n# 查看今日所有订单\ntrade_info = pd.DataFrame(ths.today_trades)\nif trade_info.empty:\n    print('今日没有订单')\nelse:\n    print(trade_info)\n","sub_path":"program/自动交易/案例_下单接口介绍_新版.py","file_name":"案例_下单接口介绍_新版.py","file_ext":"py","file_size_in_byte":2992,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"404445996","text":"import six\n\n\nclass SymbolsMeta(type):\n    def __new__(cls, name, bases, attrs):\n        syms = {}\n        reverse = {}\n        for k, v in attrs.items():\n            if not k.startswith('_') and isinstance(v, int):\n                syms[k] = v\n                reverse[v] = k\n        attrs['_symbols'] = syms\n        attrs['_reverse_symbols'] = reverse\n        return type.__new__(cls, name, bases, attrs)\n\n\n@six.add_metaclass(SymbolsMeta)\nclass Symbols(object):\n    def __getitem__(self, k):\n        if isinstance(k, str):\n            return self._symbols[k]\n        elif isinstance(k, int):\n            return self._reverse_symbols[k]\n        raise AttributeError(k)\n","sub_path":"cpy/parser/grammar/symbols.py","file_name":"symbols.py","file_ext":"py","file_size_in_byte":667,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"63989135","text":"# coding: utf-8\nimport numpy as np\nimport matplotlib.image as mpimg\nimport matplotlib.pyplot as plt\nimport matplotlib.cm as cm\nimport png\n\nheight, width = 25,25\nTWOPI = np.pi * 2\nimg = np.random.uniform(0,1,height*width)\nimgplot = plt.imshow(img.reshape(height,width), cm.Greys_r, interpolation='none')\n\ndef angleToVec(theta):\n    return np.array([np.cos(theta*TWOPI), np.sin(theta*TWOPI)])\n\nangleToVec = np.frompyfunc(angleToVec, 1, 1)\ngradients = angleToVec(img).reshape(height,width)\n\ndef dotGridGradient(i, x):\n    ix = i[0]\n    iy = i[1]\n    g = gradients[ix][iy]\n    return np.dot(g, x-i)\n\ndef scurve(x, upper_left):\n    p = x - upper_left\n    s = 3*(p**2) - 2*(p**3)\n    return s\n\ndef better_according_to_perlin(x, x0):\n    p = x - x0\n    s = 6*(p**5) - 15*p**4 + 10*(p**3)\n    return s\n\ndef noise(x, avg_func=scurve):\n    # get grid coordinates\n    x0 = upper_left = np.floor(x)\n    x1 = lower_right = upper_left + 1\n    upper_right = np.array([x1[0], x0[1]])\n    lower_left = np.array([x0[0], x1[1]])\n\n    grid_points = np.array([upper_left, upper_right, lower_left, lower_right])\n\n    weights = np.apply_along_axis(dotGridGradient, 1, grid_points, x)\n\n    # s-curve\n    s = avg_func(x, upper_left)\n\n    def lerp(start, end, t):\n        return (1.0 - t)*start + t*end\n\n    val = lerp(lerp(weights[0], weights[1], s[0]),\n               lerp(weights[2], weights[3], s[0]),\n               s[1],\n              )\n\n    return val\n\ndef cartesian_product(x, y):\n    return np.transpose([np.tile(x, len(y)), np.repeat(y, len(x))])\n\ndots_per_square = 20\n\nxs = np.linspace(0,height-1, endpoint=False, num=width*dots_per_square)\npoints = cartesian_product(xs, xs)\nnoise_img = np.apply_along_axis(noise, 1, points, avg_func=scurve)\nnoiseplot = plt.imshow(noise_img.reshape(width*dots_per_square,width*dots_per_square), cm.Greys_r, interpolation='none')\n\nimg = noise_img.reshape(width*dots_per_square,width*dots_per_square)\n\n\ndef write_to_png(filename, img):\n    f = open(filename, 'wb')\n    w = png.Writer(len(img),len(img[0]), greyscale=True)\n    w.write(f, ((img-img.min())*(255.0/(img.max()-img.min()))).astype(int))\n    f.close();\n\nwrite_to_png('/tmp/perlin_noise_3rd_degree.png', img)\n","sub_path":"perlin_noise.py","file_name":"perlin_noise.py","file_ext":"py","file_size_in_byte":2186,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"220374844","text":"import subprocess\nimport sys\nimport pygame\nfrom threading import Thread \n### starting child java process (client)\np = subprocess.Popen([\"java\", \"-cp\", 'bin', 'uno.clientside.Player1'],stdout=subprocess.PIPE, stdin=subprocess.PIPE, bufsize=1, universal_newlines=True)\n\n\nlinebuffer = []\ndef reader(f, buffer):\n    while True:\n        line=f.readline()\n        if(line):\n            buffer.append(line)\n        else:\n            break\nthread_reader = Thread(target = reader, args=(p.stdout, linebuffer))\nthread_reader.daemon = True\nthread_reader.start()\n\npygame.init()\nHEIGHT = 600\nWIDTH = 600\n\nscreen = pygame.display.set_mode((WIDTH, HEIGHT))\n\n\ncards = pygame.image.load(\"data/UNO_cards.png\")\n\ncard_dim = (85,128)\n        \ncards_dict_of_pos = {\n        \"0-rouge\": (0,0),\n        \"1-rouge\": (85,0),\n        \"2-rouge\": (171,0),\n        \"3-rouge\": (256,0),\n        \"4-rouge\": (341,0),\n        \"5-rouge\": (427,0),\n        \"6-rouge\": (512,0),\n        \"7-rouge\": (597,0),\n        \"8-rouge\": (683,0),\n        \"9-rouge\": (768,0),\n        \"Passer-rouge\": (853,0),\n        \"Inversion-rouge\": (938,0),\n        \"+2-rouge\": (1024,0),\n        \"Joker\": (1109, 0),\n        \"Joker-rouge\": (1109, 0),\n        \"Joker-jaune\": (1109, 0),\n        \"Joker-bleu\": (1109, 0),\n        \"Joker-vert\": (1109, 0),\n        \"0-jaune\": (0,128),\n        \"1-jaune\": (85,128),\n        \"2-jaune\": (171,128),\n        \"3-jaune\": (256,128),\n        \"4-jaune\": (341,128),\n        \"5-jaune\": (427,128),\n        \"6-jaune\": (512,128),\n        \"7-jaune\": (597,128),\n        \"8-jaune\": (683,128),\n        \"9-jaune\": (768,128),\n        \"Passer-jaune\": (853,128),\n        \"Inversion-jaune\": (938,128),\n        \"+2-jaune\": (1024,128),\n        \"0-vert\": (0,256),\n        \"1-vert\": (85,256),\n        \"2-vert\": (171,256),\n        \"3-vert\": (256,256),\n        \"4-vert\": (341,256),\n        \"5-vert\": (427,256),\n        \"6-vert\": (512,256),\n        \"7-vert\": (597,256),\n        \"8-vert\": (683,256),\n        \"9-vert\": (768,256),\n        \"Passer-vert\": (853,256),\n        \"Inversion-vert\": (938,256),\n        \"+2-vert\": (1024,256),\n        \"0-bleu\": (0,384),\n        \"1-bleu\": (85,384),\n        \"2-bleu\": (171,384),\n        \"3-bleu\": (256,384),\n        \"4-bleu\": (341,384),\n        \"5-bleu\": (427,384),\n        \"6-bleu\": (512,384),\n        \"7-bleu\": (597,384),\n        \"8-bleu\": (683,384),\n        \"9-bleu\": (768,384),\n        \"Passer-bleu\": (853,384),\n        \"Inversion-bleu\": (938,384),\n        \"+2-bleu\": (1024,384),\n        \"+4\": (1109, 512),\n        \"\": (0, 512)\n        }\n\n\n\n\nplayer_hand = [] # list of strings\ntalon = ''\n\nplayer_hand_x = 100\nplayer_hand_y = 400\ntalon_pos = (0,0)\nit_is_our_turn = False\nrunning = True\nwhile(running):\n    if(linebuffer):\n        msg = linebuffer.pop()\n        #msg = p.stdout.readline()\n    \n        print(msg, sep='')\n\n        msg = msg.split(' ')\n\n        if(msg[0] == 'prends'):\n            print(\"prendre carte\")\n            player_hand.append(msg[1][:-1])\n        elif(msg[0] == 'nouveau-talon'):\n            print(\"new talon\")\n            talon = msg[1][:-1]\n        elif(msg[0] == '0:'):\n            print('your turn to play')\n            it_is_our_turn = True\n\n    \n   \n    events = pygame.event.get()\n    \n    for event in events:\n        if(event.type == pygame.QUIT):\n            running = False\n            break\n        if(event.type == pygame.MOUSEBUTTONUP):\n            mouse_pos_x, mouse_pos_y = pygame.mouse.get_pos()\n            print(mouse_pos_x, mouse_pos_y)\n            for i in range(len(player_hand)):\n                if(mouse_pos_x > player_hand_x + i*(card_dim[0]+10) and mouse_pos_x < player_hand_x + i*(card_dim[0]+10) + card_dim[0]\n                        and mouse_pos_y > player_hand_y and mouse_pos_y < player_hand_y + card_dim[1]):\n                    card_selected = str(i)\n                    print(\"select : \", i)\n                    if(it_is_our_turn):\n                        p.stdin.write(card_selected+'\\n')\n                        it_is_our_turn = False\n\n\n\n    screen.fill((255,255,255))\n    for i, card in enumerate(player_hand):\n        screen.blit(cards, (player_hand_x + i*(card_dim[0]+10), player_hand_y), area = cards_dict_of_pos[card] + card_dim)\n    screen.blit(cards, talon_pos, area = cards_dict_of_pos[talon] + card_dim)\n    pygame.display.flip()\n","sub_path":"gui.py","file_name":"gui.py","file_ext":"py","file_size_in_byte":4280,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"646114580","text":"#!/usr/bin/env python3\n# Copyright (c) Facebook, Inc. and its affiliates.\n#\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport importlib\nimport os\nimport subprocess\nimport tempfile\nfrom dataclasses import dataclass\nfrom itertools import chain\nfrom typing import Iterable, List, Mapping, Optional, Tuple, cast\n\nfrom antlir.common import get_logger\nfrom antlir.fs_utils import Path\nfrom antlir.nspawn_in_subvol.common import DEFAULT_PATH_ENV\nfrom antlir.vm.tap import VmTap\n\n\nDEFAULT_TIMEOUT_SEC = 60\n\nDEFAULT_ENV = {\"PATH\": DEFAULT_PATH_ENV}\n\nlogger = get_logger()\n\n\n@dataclass()\nclass GuestSSHConnection:\n    tapdev: VmTap\n    # pyre-fixme[8]: Attribute has type `Mapping[str, str]`; used as `None`.\n    options: Mapping[str, str] = None\n    # pyre-fixme[8]: Attribute has type `Path`; used as `None`.\n    privkey: Path = None\n\n    def __enter__(self):\n        self.privkey = Path(tempfile.NamedTemporaryFile(delete=False).name)\n        logger.debug(f\"Enter ssh context. Load private key: {self.privkey}\")\n        with self.privkey.open(mode=\"w\") as f:\n            f.write(importlib.resources.read_text(__package__, \"privkey\"))\n            f.flush()\n        return self\n\n    def __exit__(self, exc_type, exc_value, traceback):\n        logger.debug(f\"Exit ssh context.  Remove private key: {self.privkey}\")\n        try:\n            os.remove(self.privkey)\n        except Exception as e:  # pragma: no cover\n            logger.error(f\"Error removing privkey: {self.privkey}: {e}\")\n\n    async def run(\n        self,\n        cmd: Iterable[str],\n        timeout_ms: int,\n        env: Optional[Mapping[str, str]] = None,\n        check: bool = False,\n        cwd: Optional[Path] = None,\n        stdout=subprocess.PIPE,\n        stderr=subprocess.PIPE,\n    ) -> Tuple[int, bytes, bytes]:\n        \"\"\"\n        run a command inside the vm\n        \"\"\"\n        cmd = list(cmd)\n        run_env = DEFAULT_ENV.copy()\n        run_env.update(env or {})\n\n        cmd_pre = []\n        if cwd is not None:\n            cmd_pre.append(f\"cd {str(cwd)};\")\n        cmd_pre.append(\n            \" \".join(f\"{key}={val}\" for key, val in run_env.items()),\n        )\n\n        cmd = self.ssh_cmd(timeout_ms=timeout_ms) + [\"--\"] + cmd_pre + cmd\n\n        logger.debug(f\"Running {cmd} in vm at {self.tapdev.guest_ipv6_ll}\")\n        logger.debug(f\"{' '.join(cmd)}\")\n        res = subprocess.run(\n            cmd,\n            check=check,\n            stdout=stdout,\n            stderr=stderr,\n            # Future: handle stdin properly so that we can pipe input from\n            # the caller into a program being executing inside a VM\n            timeout=timeout_ms / 1000,\n        )\n        logger.debug(f\"res: {res.returncode}, {res.stdout}, {res.stderr}\")\n        return res.returncode, res.stdout, res.stderr\n\n    def ssh_cmd(self, timeout_ms: int, **kwargs) -> List[str]:\n        options = {\n            # just ignore the ephemeral vm fingerprint\n            \"UserKnownHostsFile\": \"/dev/null\",\n            \"StrictHostKeyChecking\": \"no\",\n            \"ConnectTimeout\": int(timeout_ms / 1000),\n            \"ConnectionAttempts\": 10,\n        }\n\n        if self.options:\n            logger.debug(f\"Additional options: {self.options}\")\n            options.update(self.options)\n\n        options = list(\n            chain.from_iterable([\"-o\", f\"{k}={v}\"] for k, v in options.items())\n        )\n        return cast(\n            List[str],\n            self.tapdev.netns.nsenter_as_user(\n                \"ssh\",\n                *options,\n                \"-i\",\n                str(self.privkey),\n                f\"root@{self.tapdev.guest_ipv6_ll}\",\n            ),\n        )\n","sub_path":"antlir/vm/guest_ssh.py","file_name":"guest_ssh.py","file_ext":"py","file_size_in_byte":3708,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"255733173","text":"# uncompyle6 version 3.7.4\n# Python bytecode 3.7 (3394)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /tmp/pip-install-pti7pv2_/pip/pip/_internal/network/xmlrpc.py\n# Compiled at: 2020-02-14 17:24:43\n# Size of source mod 2**32: 1597 bytes\n\"\"\"xmlrpclib.Transport implementation\n\"\"\"\nimport logging\nfrom pip._vendor import requests\nfrom pip._vendor.six.moves import xmlrpc_client\nimport pip._vendor.six.moves.urllib as urllib_parse\nlogger = logging.getLogger(__name__)\n\nclass PipXmlrpcTransport(xmlrpc_client.Transport):\n    __doc__ = 'Provide a `xmlrpclib.Transport` implementation via a `PipSession`\\n    object.\\n    '\n\n    def __init__(self, index_url, session, use_datetime=False):\n        xmlrpc_client.Transport.__init__(self, use_datetime)\n        index_parts = urllib_parse.urlparse(index_url)\n        self._scheme = index_parts.scheme\n        self._session = session\n\n    def request(self, host, handler, request_body, verbose=False):\n        parts = (\n         self._scheme, host, handler, None, None, None)\n        url = urllib_parse.urlunparse(parts)\n        try:\n            headers = {'Content-Type': 'text/xml'}\n            response = self._session.post(url, data=request_body, headers=headers,\n              stream=True)\n            response.raise_for_status()\n            self.verbose = verbose\n            return self.parse_response(response.raw)\n        except requests.HTTPError as exc:\n            try:\n                logger.critical('HTTP error %s while getting %s', exc.response.status_code, url)\n                raise\n            finally:\n                exc = None\n                del exc","sub_path":"pycfiles/smriprep-0.5.2-py3-none-any/xmlrpc.cpython-37.py","file_name":"xmlrpc.cpython-37.py","file_ext":"py","file_size_in_byte":1667,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"183331950","text":"#  Hint:  You may not need all of these.  Remove the unused functions.\nfrom hashtables import (HashTable,\n                        hash_table_insert,\n                        hash_table_remove,\n                        hash_table_retrieve,\n                        hash_table_resize)\n\n\nclass Ticket:\n    def __init__(self, source, destination):\n        self.source = source\n        self.destination = destination\n\n\ndef reconstruct_trip(tickets, length):\n    hashtable = HashTable(length)\n    route = [None] * length\n\n    your_ticket = None\n\n    # Add key, value to the has table for every ticket \n    for ticket in range(length):\n        hash_table_insert(hashtable, tickets[ticket].source, tickets[ticket].destination)\n        # Check if the ticket flight is None\n        if tickets[ticket].source == \"NONE\":\n            # Assign it to your_ticket\n            your_ticket = tickets[ticket].destination\n            # Assign your_ticket to first route array\n            route[0] = your_ticket\n\n    for ticket in range(1, length):\n        # Use hash_table_retrieve to find next destination\n        next_destination = hash_table_retrieve(hashtable, your_ticket)\n        # Assign next_destination to the route array\n        route[ticket] = next_destination\n        # Assing your_ticket to next destination\n        your_ticket = next_destination\n\n    return route\n","sub_path":"hashtables/ex2/ex2.py","file_name":"ex2.py","file_ext":"py","file_size_in_byte":1355,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"258499101","text":"## Author: Raphael Holzer\n## Organisation: Gymnase du Bugnon\n## Date: 4. 1. 2018\n## File: tnt.py\n\nfrom mcpi import minecraft\n\nmc = minecraft.Minecraft.create()\n\ntnt = 46\nd = 10\n\nmc.postToChat(\"TNT explosion\")\nx, y, z = mc.player.getPos()\nmc.setBlocks(x+1, y+1, z+1, x+d, y+d, z+d, tnt, 1)\n","sub_path":"tutorial/tnt.py","file_name":"tnt.py","file_ext":"py","file_size_in_byte":289,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"83849431","text":"import gym\nfrom gym import spaces\nfrom gym.envs.registration import EnvSpec\nimport numpy as np\nfrom multiagent.multi_discrete import MultiDiscrete\nfrom multiagent.scenarios.constants import D_LINE, O_LINE, Q_BACK\n\nNOT_DONE = 0\nQ_BACK_FIRST_DOWN_LINE = 1\nAGENT_OUT_OF_BOUNDS = 2\nD_LINE_REACHED_Q_BACK = 3\nQ_BACK_NOT_IN_BOUNDS = 4\nQ_BACK_THREW_BALL = 5\n\n# environment for all agents in the multiagent world\n# currently code assumes that no agents will be created/destroyed at runtime!\nclass MultiAgentEnv(gym.Env):\n    metadata = {\n        'render.modes' : ['human', 'rgb_array']\n    }\n\n    def __init__(self, world, reset_callback=None, reward_callback=None,\n                 observation_callback=None, info_callback=None,\n                 done_callback=None, shared_viewer=True):\n\n        self.world = world\n        self.agents = self.world.policy_agents\n        # set required vectorized gym env property\n        self.n = len(world.policy_agents)\n        # scenario callbacks\n        self.reset_callback = reset_callback\n        self.reward_callback = reward_callback\n        self.observation_callback = observation_callback\n        self.info_callback = info_callback\n        # self.done_callback = _done_callback\n        # environment parameters\n        self.discrete_action_space = True\n        # if true, action is a number 0...N, otherwise action is a one-hot N-dimensional vector\n        self.discrete_action_input = False\n        # if true, even the action is continuous, action will be performed discretely\n        self.force_discrete_action = world.discrete_action if hasattr(world, 'discrete_action') else False\n        # if true, every agent has the same reward\n        self.shared_reward = world.collaborative if hasattr(world, 'collaborative') else False\n        self.time = 0\n\n        # configure spaces\n        self.action_space = []\n        self.observation_space = []\n        for agent in self.get_agents():\n            total_action_space = []\n            # physical action space\n            if self.discrete_action_space:\n                u_action_space = spaces.Discrete(world.dim_p * 2 + 1)\n            else:\n                u_action_space = spaces.Box(low=-agent.u_range, high=+agent.u_range, shape=(world.dim_p,), dtype=np.float32)\n            if agent.movable:\n                total_action_space.append(u_action_space)\n            # communication action space\n            if self.discrete_action_space:\n                c_action_space = spaces.Discrete(world.dim_c)\n            else:\n                c_action_space = spaces.Box(low=0.0, high=1.0, shape=(world.dim_c,), dtype=np.float32)\n            if not agent.silent:\n                total_action_space.append(c_action_space)\n            # total action space\n            if len(total_action_space) > 1:\n                # all action spaces are discrete, so simplify to MultiDiscrete action space\n                if all([isinstance(act_space, spaces.Discrete) for act_space in total_action_space]):\n                    act_space = MultiDiscrete([[0, act_space.n - 1] for act_space in total_action_space])\n                else:\n                    act_space = spaces.Tuple(total_action_space)\n                self.action_space.append(act_space)\n            else:\n                self.action_space.append(total_action_space[0])\n            # observation space\n            obs_dim = len(observation_callback(agent, self.world))\n            self.observation_space.append(spaces.Box(low=-np.inf, high=+np.inf, shape=(obs_dim,), dtype=np.float32))\n            agent.action.c = np.zeros(self.world.dim_c)\n\n        # rendering\n        self.shared_viewer = shared_viewer\n        if self.shared_viewer:\n            self.viewer = None\n        else:\n            self.viewers = [None] * self.n\n        self._reset_render()\n\n    def get_agents(self):\n        return [agent if not agent.is_done else None for agent in self.agents]\n\n    def step(self, action_n):\n        obs_n = []\n        reward_n = []\n        done_n = []\n        info_n = {'n': []}\n        self.agents = self.world.policy_agents\n        # print(self.agents)\n        # set action for each agent\n        for i, agent in enumerate(self.agents):\n            self._set_action(action_n[i], agent, self.action_space[i])\n        # advance world state\n        self.world.step()\n        # record observation for each agent\n        # print(\"New step\")\n\n        chance_of_completion = np.random.uniform(0.0, 1.0)\n        made_throw = chance_of_completion < list(filter(lambda player: player.position == 'q_back', self.world.agents))[0].completion_percentage\n\n        for agent in self.agents:\n            obs_n.append(self._get_obs(agent))\n            reward = self._get_reward(agent)\n            is_done = self.done_callback(agent, self.world)\n            done_n.append(is_done)\n            # TODO: \n            # If done, I need to somehow indicate that so that no more actions are taken...\n            # if (agent.position == 'q_back'):\n                # print(agent.state.p_pos, self.world.line_of_scrimmage, agent.state.p_pos[1], self.world.line_of_scrimmage - agent.state.p_pos[1])\n            if is_done != NOT_DONE:\n                agent.is_done = True\n\n                # print(\"agent position\", agent.position)\n                # print(agent.state.p_pos)\n\n                additional_reward = self.get_final_reward(is_done, agent, made_throw)\n\n                # print(\"additional_reward\", additional_reward)\n                reward = reward + additional_reward\n\n            reward_n.append(reward)\n            info_n['n'].append(self._get_info(agent))\n\n        # all agents get total reward in cooperative case\n        reward = np.sum(reward_n)\n        if self.shared_reward:\n            reward_n = [reward] * self.n\n\n        return obs_n, reward_n, done_n, info_n\n\n\n\n    def get_final_reward(self, is_done, agent, made_throw):\n        # print(is_done, Q_BACK_NOT_IN_BOUNDS)\n\n        if (is_done == Q_BACK_FIRST_DOWN_LINE):\n            if (agent.position == O_LINE) or (agent.position == Q_BACK):\n                return 120\n            else:\n                return -120\n        elif (is_done == AGENT_OUT_OF_BOUNDS):\n            return -80\n        elif (is_done == D_LINE_REACHED_Q_BACK):\n            if (agent.position == O_LINE) or (agent.position == Q_BACK):\n                return -120 # TODO \n            else:\n                return 120\n        elif is_done == Q_BACK_NOT_IN_BOUNDS:\n            if (agent.position == O_LINE) or (agent.position == Q_BACK):\n                return -80\n            else:\n                return 80\n        elif is_done == Q_BACK_THREW_BALL:\n            if (agent.position == O_LINE) or (agent.position == Q_BACK):\n                return 80 if made_throw else -80\n            else:\n                return -80 if made_throw else 80\n\n\n    def reset(self):\n        # reset world\n        self.reset_callback(self.world)\n        # reset renderer\n        self._reset_render()\n        # record observations for each agent\n        obs_n = []\n        self.agents = self.world.policy_agents\n        for agent in self.get_agents():\n            obs = None\n            if agent:\n                obs = self._get_obs(agent)\n            obs_n.append(obs)\n        return obs_n\n\n    # get info used for benchmarking\n    def _get_info(self, agent):\n        if self.info_callback is None:\n            return {}\n        return self.info_callback(agent, self.world)\n\n    # get observation for a particular agent\n    def _get_obs(self, agent):\n        if self.observation_callback is None:\n            return np.zeros(0)\n        return self.observation_callback(agent, self.world)\n\n    # get dones for a particular agent\n    # unused right now -- agents are allowed to go beyond the viewing screen\n    # def _get_done(self, agent):\n    #     if self.done_callback is None:\n    #         return False\n    #     return self.done_callback(agent, self.world)\n\n    def done_callback(self, agent, world):    \n        # Use world.timeout, and see what's wrong\n        if world.time > world.timeout:\n            return Q_BACK_THREW_BALL\n\n        # Agent is done if it is out of bounds\n        if (not agent.in_bounds):\n            return AGENT_OUT_OF_BOUNDS\n\n        q_back = list(filter(lambda player: player.position == 'q_back', world.agents))[0]\n        d_line = list(filter(lambda player: player.position == 'd_line', world.agents))\n        line_of_scrimmage = world.line_of_scrimmage\n        q_pos = q_back.state.p_pos\n\n        # Quarterback is past line of scrimmage\n        if (q_pos[1] > (line_of_scrimmage + world.first_down_line)): \n            return Q_BACK_FIRST_DOWN_LINE\n\n        if (not q_back.in_bounds):\n            return Q_BACK_NOT_IN_BOUNDS\n\n        for d_player in d_line:\n            # Check if d_player is close to q_back (ie touching, look into how to find that out)\n            # If so return True\n            d_pos = d_player.state.p_pos\n            dist_min = q_back.size + d_player.size\n\n            # If the quarterback and defensive player are touching, set agents to done\n            if (((d_pos[0] - q_pos[0])**2 + (d_pos[1] - q_pos[1])**2)**0.5 < dist_min):\n                return D_LINE_REACHED_Q_BACK\n\n        return NOT_DONE\n\n\n    # get reward for a particular agent\n    def _get_reward(self, agent):\n        if self.reward_callback is None:\n            return 0.0\n        return self.reward_callback(agent, self.world)\n\n    # set env action for a particular agent\n    def _set_action(self, action, agent, action_space, time=None):\n        agent.action.u = np.zeros(self.world.dim_p)\n        agent.action.c = np.zeros(self.world.dim_c)\n        # process action\n        if isinstance(action_space, MultiDiscrete):\n            act = []\n            size = action_space.high - action_space.low + 1\n            index = 0\n            for s in size:\n                act.append(action[index:(index+s)])\n                index += s\n            action = act\n        else:\n            action = [action]\n\n        if agent.movable:\n            # physical action\n            if self.discrete_action_input:\n                agent.action.u = np.zeros(self.world.dim_p)\n                # process discrete action\n                if action[0] == 1: agent.action.u[0] = -1.0\n                if action[0] == 2: agent.action.u[0] = +1.0\n                if action[0] == 3: agent.action.u[1] = -1.0\n                if action[0] == 4: agent.action.u[1] = +1.0\n            else:\n                if self.force_discrete_action:\n                    d = np.argmax(action[0])\n                    action[0][:] = 0.0\n                    action[0][d] = 1.0\n                if self.discrete_action_space:\n                    agent.action.u[0] += action[0][1] - action[0][2]\n                    agent.action.u[1] += action[0][3] - action[0][4]\n                else:\n                    agent.action.u = action[0]\n            sensitivity = 5.0\n            if agent.accel is not None:\n                sensitivity = agent.accel\n            agent.action.u *= sensitivity\n            action = action[1:]\n        if not agent.silent:\n            # communication action\n            if self.discrete_action_input:\n                agent.action.c = np.zeros(self.world.dim_c)\n                agent.action.c[action[0]] = 1.0\n            else:\n                agent.action.c = action[0]\n            action = action[1:]\n        # make sure we used all elements of action\n        assert len(action) == 0\n\n    # reset rendering assets\n    def _reset_render(self):\n        self.render_geoms = None\n        self.render_geoms_xform = None\n\n    # render environment\n    def render_whole_field(self, mode='human'):\n        if mode == 'human':\n            alphabet = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'\n            message = ''\n            for agent in self.world.agents:\n                comm = []\n                for other in self.world.agents:\n                    if other is agent: continue\n                    if np.all(other.state.c == 0):\n                        word = '_'\n                    else:\n                        word = alphabet[np.argmax(other.state.c)]\n                    message += (other.name + ' to ' + agent.name + ': ' + word + '   ')\n\n        # for i in range(len(self.viewers)):\n        #     # create viewers (if necessary)\n        #     if self.viewers[i] is None:\n        #         # import rendering only if we need it (and don't import for headless machines)\n        #         #from gym.envs.classic_control import rendering\n        #         from multiagent import rendering\n        #         self.viewers[i] = rendering.Viewer(700,700)\n\n        if self.viewer is None:\n            from multiagent import rendering\n            self.viewer = rendering.Viewer(53*7, 120*7)\n\n        # create rendering geometry\n        if self.render_geoms is None:\n            # import rendering only if we need it (and don't import for headless machines)\n            #from gym.envs.classic_control import rendering\n            from multiagent import rendering\n            self.render_geoms = []\n            self.render_geoms_xform = []\n            for entity in self.world.entities:\n                size = 2*entity.size\n                # if entity.position == 'q_back':\n                #     size = 2*size\n                geom = rendering.make_circle(size)\n                xform = rendering.Transform()\n                if 'q_back' == entity.position:\n                    geom.set_color(0, 1, 0, alpha=0.5)\n                else:\n                    geom.set_color(*entity.color)\n                geom.add_attr(xform)\n                self.render_geoms.append(geom)\n                self.render_geoms_xform.append(xform)\n\n            self.viewer.geoms = []\n            for geom in self.render_geoms:\n                self.viewer.add_geom(geom)\n\n        line_of_scrimmage = self.world.line_of_scrimmage\n        first_down_line = line_of_scrimmage + self.world.first_down_line\n\n        self.viewer.draw_line((0, line_of_scrimmage), (53, line_of_scrimmage))\n        self.viewer.draw_line((0, first_down_line), (53, first_down_line))\n\n        results = []\n        from multiagent import rendering\n        # update bounds to center around agent\n        cam_range = 1\n        if self.shared_viewer:\n            pos = np.zeros(self.world.dim_p)\n        else:\n            pos = self.agents[i].state.p_pos\n        self.viewer.set_bounds(0, 53, 0, 120)\n        # update geometry positions\n        for e, entity in enumerate(self.world.entities):\n            self.render_geoms_xform[e].set_translation(*entity.state.p_pos)\n        # render to display or array\n        results.append(self.viewer.render(return_rgb_array = mode=='rgb_array'))\n\n        return results\n\n    # render environment\n    def render(self, mode='human'):\n        if mode == 'human':\n            alphabet = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'\n            message = ''\n            for agent in self.world.agents:\n                comm = []\n                for other in self.world.agents:\n                    if other is agent: continue\n                    if np.all(other.state.c == 0):\n                        word = '_'\n                    else:\n                        word = alphabet[np.argmax(other.state.c)]\n                    message += (other.name + ' to ' + agent.name + ': ' + word + '   ')\n\n        for i in range(len(self.viewers)):\n            # create viewers (if necessary)\n            if self.viewers[i] is None:\n                # import rendering only if we need it (and don't import for headless machines)\n                #from gym.envs.classic_control import rendering\n                from multiagent import rendering\n                self.viewers[i] = rendering.Viewer(700,700)\n\n        # create rendering geometry\n        if self.render_geoms is None:\n            # import rendering only if we need it (and don't import for headless machines)\n            #from gym.envs.classic_control import rendering\n            from multiagent import rendering\n            self.render_geoms = []\n            self.render_geoms_xform = []\n            for entity in self.world.entities:\n                geom = rendering.make_circle(entity.size)\n                xform = rendering.Transform()\n                if 'q_back' == entity.position:\n                    geom.set_color(0, 1, 0, alpha=0.5)\n                else:\n                    geom.set_color(*entity.color)\n                geom.add_attr(xform)\n                self.render_geoms.append(geom)\n                self.render_geoms_xform.append(xform)\n\n            # add geoms to viewer\n            for viewer in self.viewers:\n                viewer.geoms = []\n                for geom in self.render_geoms:\n                    viewer.add_geom(geom)\n\n        results = []\n        for i in range(len(self.viewers)):\n            from multiagent import rendering\n            # update bounds to center around agent\n            cam_range = 1\n            if self.shared_viewer:\n                pos = np.zeros(self.world.dim_p)\n            else:\n                pos = self.agents[i].state.p_pos\n            self.viewers[i].set_bounds(pos[0]-cam_range,pos[0]+cam_range,pos[1]-cam_range,pos[1]+cam_range)\n            # update geometry positions\n            for e, entity in enumerate(self.world.entities):\n                self.render_geoms_xform[e].set_translation(*entity.state.p_pos)\n            # render to display or array\n            results.append(self.viewers[i].render(return_rgb_array = mode=='rgb_array'))\n\n        return results\n\n    # create receptor field locations in local coordinate frame\n    def _make_receptor_locations(self, agent):\n        receptor_type = 'polar'\n        range_min = 0.05 * 2.0\n        range_max = 1.00\n        dx = []\n        # circular receptive field\n        if receptor_type == 'polar':\n            for angle in np.linspace(-np.pi, +np.pi, 8, endpoint=False):\n                for distance in np.linspace(range_min, range_max, 3):\n                    dx.append(distance * np.array([np.cos(angle), np.sin(angle)]))\n            # add origin\n            dx.append(np.array([0.0, 0.0]))\n        # grid receptive field\n        if receptor_type == 'grid':\n            for x in np.linspace(-range_max, +range_max, 5):\n                for y in np.linspace(-range_max, +range_max, 5):\n                    dx.append(np.array([x,y]))\n        return dx\n\n\n# vectorized wrapper for a batch of multi-agent environments\n# assumes all environments have the same observation and action space\nclass BatchMultiAgentEnv(gym.Env):\n    metadata = {\n        'runtime.vectorized': True,\n        'render.modes' : ['human', 'rgb_array']\n    }\n\n    def __init__(self, env_batch):\n        self.env_batch = env_batch\n\n    @property\n    def n(self):\n        return np.sum([env.n for env in self.env_batch])\n\n    @property\n    def action_space(self):\n        return self.env_batch[0].action_space\n\n    @property\n    def observation_space(self):\n        return self.env_batch[0].observation_space\n\n    def step(self, action_n, time):\n        obs_n = []\n        reward_n = []\n        done_n = []\n        info_n = {'n': []}\n        i = 0\n        for env in self.env_batch:\n            obs, reward, done, _ = env.step(action_n[i:(i+env.n)], time)\n            i += env.n\n            obs_n += obs\n            # reward = [r / len(self.env_batch) for r in reward]\n            reward_n += reward\n            done_n += done\n        return obs_n, reward_n, done_n, info_n\n\n    def reset(self):\n        obs_n = []\n        for env in self.env_batch:\n            obs_n += env.reset()\n        return obs_n\n\n    # render environment\n    def render(self, mode='human', close=True):\n        results_n = []\n        for env in self.env_batch:\n            results_n += env.render(mode, close)\n        return results_n\n","sub_path":"multiagent/environment.py","file_name":"environment.py","file_ext":"py","file_size_in_byte":19705,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"514262936","text":"import numpy as np\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport tensorflow as tf\nimport time\nimport matplotlib.patches as mpatches\nfrom sklearn.manifold import TSNE\nfrom sklearn.decomposition import PCA, TruncatedSVD\n\n\n#%%\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.svm import SVC\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.ensemble import RandomForestClassifier\nimport collections\n\n#%%\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.pipeline import make_pipeline\nfrom imblearn.pipeline import make_pipeline as imbalanced_make_pipeline\nfrom imblearn.over_sampling import SMOTE\nfrom imblearn.under_sampling import NearMiss\nfrom imblearn.metrics import classification_report_imbalanced\nfrom sklearn.metrics import precision_score, recall_score, f1_score,precision_recall_curve\nfrom collections import Counter\n\nfrom sklearn.model_selection import KFold,StratifiedKFold, StratifiedShuffleSplit\nfrom sklearn.preprocessing import StandardScaler, RobustScaler,OneHotEncoder\nfrom scipy.stats import norm\nfrom sklearn.model_selection import cross_val_score, cross_val_predict, RandomizedSearchCV\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.metrics import roc_auc_score, roc_curve,classification_report, average_precision_score\nfrom sklearn.metrics import confusion_matrix, accuracy_score\nfrom sklearn.preprocessing import LabelEncoder\n\nfrom mlxtend.evaluate import confusion_matrix\nfrom mlxtend.plotting import plot_confusion_matrix\n\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\n# load the dataset\ndf = pd.read_csv(\"data/training.csv\")\ntest = pd.read_csv(\"data/test.csv\")\n\n\n\n# df.head()\n\n\ndf.columns\ntest.columns\n\ntrain = df.drop(\"FraudResult\", axis=1)\nlabel = df[\"FraudResult\"]\ntrain.columns == test.columns\n\ntrain.head()\ntest.head()\n\nall_data = pd.concat([train, test],axis=0)\n\nall_data.tail()\n\n\n\nall_data[\"TransactionStartTime\"] = pd.to_datetime(all_data.TransactionStartTime)\n\nall_data.head(2)\n\n\n# check null amounts\nall_data.isnull().sum().max()\n\nprint(\"No Frauds\", round(label.value_counts()[0]/len(df)*100,2), \"% of the dataset\")\nprint(\"Frauds\", round(label.value_counts()[1]/len(df)*100,2), \"% of the dataset\")\n\n\n\ncolors = [\"blue\",\"red\"]\nsns.countplot(\"FraudResult\", data=all_data, palette=colors)\nplt.title(\"Class Distributions \\n (0: No Fraud || 1: Fraud)\", fontsize=14)\nplt.show()\n\n\n# Distribution of transaction\nt_value = all_data[\"Value\"].values\nt_amount = all_data[\"Amount\"].values\n\nsns.distplot(t_value, color=\"r\")\nplt.title(\"Ditribution of Transaction Value\", fontsize=14)\nplt.xlim([min(t_value), max(t_value)])\n\nsns.distplot(t_amount, color=\"b\")\nplt.title(\"Ditribution of Transaction Amount\", fontsize=14)\nplt.xlim([min(t_amount), max(t_amount)])\nplt.show()\n\n\n\n# Scale value and amount\n\nvalue_std_scaler = StandardScaler()\n# amount_std_scaler = StandardScaler()\nall_data[\"scaled_value\"] = value_std_scaler.fit_transform(all_data.Value.values.reshape(-1,1))\n# all_data[\"scaled_amount\"] = amount_std_scaler.fit_transform(all_data.Amount.values.reshape(-1,1))\nall_data.head()\n\n\n\n\nall_data.drop([\"Value\", \"Amount\"], axis=1, inplace=True)\n\nall_data.head(1)\n\n\n# remove id features\nall_data.columns\ncol_to_remove = ['BatchId', 'AccountId', 'SubscriptionId',\n                'CustomerId','ProviderId', 'ProductId','ProviderId', 'ProductId'\n                ,'ChannelId',\"CurrencyCode\",\"CountryCode\"]\n\n\nall_data.drop(col_to_remove, axis=1, inplace=True)\n\n# create features from datetime\nall_data[\"hour\"] = all_data[\"TransactionStartTime\"].dt.hour\nall_data[\"day\"] = all_data[\"TransactionStartTime\"].dt.day\nall_data[\"week\"] = all_data[\"TransactionStartTime\"].dt.week\nall_data[\"month\"] = all_data[\"TransactionStartTime\"].dt.month\nall_data[\"year\"] = all_data[\"TransactionStartTime\"].dt.year\n\nall_data.drop(['TransactionStartTime'], axis=1, inplace=True)\nall_data.head()\n\n# categorical distributions\nsns.countplot(\"CurrencyCode\", data=all_data, palette=colors)\nplt.title(\"CurrencyCode Distripution\")\nplt.show()\n\nsns.countplot(\"CountryCode\", data=df, palette=colors)\nplt.title(\"CountryCode Distribution\")\nplt.show()\n\ndf[\"ProductCategory\"].value_counts()\nplt.figure(figsize=(12,6))\nsns.countplot(\"ProductCategory\", data=df)\nplt.title(\"ProductCategory Distripution\")\nplt.show()\n\ndf[\"PricingStrategy\"].value_counts()\nplt.figure(figsize=(12,6))\nsns.countplot(\"PricingStrategy\", data=df)\nplt.title(\"PricingStrategy Distripution\")\nplt.show()\n\n# drop country code and CurrencyCode\n\n# Convert ProductCategory to numerical\np_encoder = LabelEncoder()\nall_data[\"ProductCategory\"] = p_encoder.fit_transform(np.array(all_data.ProductCategory).reshape(-1, 1))\n\nall_data.head()\n\nX = all_data[:len(train)]\ntest_data = all_data[len(test):]\n\ntest_data.tail()\n\ntest.tail()\ndf = X.drop(\"TransactionId\", axis=1)\n\ndf[\"FraudResult\"] = label\n# Splitting the dataset\ny = df[\"FraudResult\"]\n\n\nsss = StratifiedKFold(n_splits=5, random_state=None, shuffle=False)\n\nfor train_index, test_index in sss.split(X,y):\n    print(\"Train:\", train_index, \"Test:\", test_index)\n    original_Xtrain,original_Xest = X.iloc[train_index], X.iloc[test_index]\n    original_ytrain,original_ytest = y.iloc[train_index], y.iloc[test_index]\n\n# Turn into array\n\noriginal_Xtrain = original_Xtrain.values\noriginal_Xest = original_Xest.values\noriginal_ytrain = original_ytrain.values\noriginal_ytest = original_ytest.values\n\ntrain_unique_label, train_counts_label = np.unique(original_ytrain, return_counts=True)\ntest_unique_label, test_counts_label = np.unique(original_ytest, return_counts=True)\nprint(\"-\"*100)\n\nprint(\"LabeL Distributions: \\n\")\nprint(train_counts_label/ len(original_ytrain))\nprint(test_counts_label/ len(original_ytest))\n\ndf = df.sample(frac=1)\n\nlen(df.loc[df[\"FraudResult\"] == 1])\n\nfraud_df = df.loc[df[\"FraudResult\"] == 1]\nnon_fraud_df = df.loc[df[\"FraudResult\"]==0][:193]\n\n\nnormal_distributed_df = pd.concat([fraud_df, non_fraud_df])\n\nnew_df = normal_distributed_df.sample(frac=1,random_state=42)\n\nnew_df.head()\n\n\nprint(\"Distribution of FraudResult in the sample dataset\")\nprint(new_df[\"FraudResult\"].value_counts()/len(new_df))\n\nsns.countplot(\"FraudResult\", data=new_df, palette=colors)\nplt.title(\"Equally distributed Classes\")\nplt.show()\n\nf, (ax1,ax2) = plt.subplots(2,1, figsize=(24,20))\n\n# Entire Dataframe\ncorr = df.corr()\nsns.heatmap(corr, cmap=\"coolwarm_r\", annot_kws = {\"size\":20}, ax=ax1)\nax1.set_title(\"Imbalanced Correlation Matrix\",fontsize=14)\n\nsub_sample_corr = new_df.corr()\nsns.heatmap(sub_sample_corr, cmap=\"coolwarm_r\", annot_kws = {\"size\":20}, ax=ax2)\nax2.set_title(\"Subsample Correlation Matrix\", fontsize=14)\nplt.show()\n\n# ProductCategory, scaled_value, scaled_amount, hour and year have positive Correlation\n# PricingStrategy, day,week and month have negative Correlation\n\n# remove scalled_amount, stroongly positively relted to scaled value\n#new_df.drop(['scaled_amount'], axis=1, inplace=True)\n# Box plots\nf, axes = plt.subplots(ncols=4, figsize=(20,4))\n\nsns.boxplot(x=\"FraudResult\", y=\"ProductCategory\", data=new_df, palette=colors, ax=axes[0])\naxes[0].set_title(\"ProductCategory vs Fraud Positive Correlation\")\n\nsns.boxplot(x=\"FraudResult\", y=\"scaled_value\", data=new_df, palette=colors, ax=axes[1])\naxes[1].set_title(\"scaled_value vs Fraud Positive Correlation\")\n\n# sns.boxplot(x=\"FraudResult\", y=\"scaled_amount\", data=new_df, palette=colors, ax=axes[2])\n# axes[2].set_title(\"scaled_amount vs Fraud Positive Correlation\")\n\nsns.boxplot(x=\"FraudResult\", y=\"hour\", data=new_df, palette=colors, ax=axes[3])\naxes[3].set_title(\"hour vs Fraud Positive Correlation\")\n\nplt.show()\n\n\nf, axes = plt.subplots(ncols=4, figsize=(20,4))\n\nsns.boxplot(x=\"FraudResult\", y=\"PricingStrategy\", data=new_df, palette=colors, ax=axes[0])\naxes[0].set_title(\"PricingStrategy vs Fraud negative Correlation\")\n\nsns.boxplot(x=\"FraudResult\", y=\"day\", data=new_df, palette=colors, ax=axes[1])\naxes[1].set_title(\"day vs Fraud negative Correlation\")\n\nsns.boxplot(x=\"FraudResult\", y=\"week\", data=new_df, palette=colors, ax=axes[2])\naxes[2].set_title(\"week vs Fraud negative Correlation\")\n\nsns.boxplot(x=\"FraudResult\", y=\"month\", data=new_df, palette=colors, ax=axes[3])\naxes[3].set_title(\"month vs Fraud negative Correlation\")\n\nplt.show()\n\n# Anomaly detection\n# f, (ax1,ax2) = plt.subplots(1,2, figsize=(20,4))\n#\n#\n# scaled_value_dist  = new_df['scaled_value'].loc[new_df[\"FraudResult\"]==1].values\n# sns.distplot(scaled_value_dist,ax=ax1, fit=norm, color=\"red\")\n# ax1.set_title(\"scaled_value Distribution \\n (Fraud Transactions)\")\n#\n# scaled_amount_dist  = new_df['scaled_amount'].loc[new_df[\"FraudResult\"]==1].values\n# sns.distplot(scaled_amount_dist,ax=ax2, fit=norm, color=\"blue\")\n# ax2.set_title(\"scaled_amount Distribution \\n (Fraud Transactions)\")\n#\n# plt.show()\n#\n#\n# scaled_value_fraud = new_df['scaled_value'].loc[new_df[\"FraudResult\"]==1].values\n# q25, q75 = np.percentile(scaled_value_fraud, 25), np.percentile(scaled_value_fraud, 75)\n# print(\"Quartile 25: {} | Quartile 75: {}\".format(q25,q75))\n# scaled_value_1qr = q75 - q25\n# print(\"iqr: {}\".format(scaled_value_1qr))\n#\n# scaled_value_cut_off = scaled_value_1qr*1.5\n# scaled_value_lower, scaled_value_upper = q25-scaled_value_cut_off, q75-scaled_value_cut_off\n#\n# outliers = [x for x in scaled_value_fraud if x < scaled_value_lower or x > scaled_value_upper]\n# print(\"Feature Scaled value for fraud cases: {}\".format(len(outliers)))\n# print(\"Scaled Value outliers:{}\".format(outliers))\n#\n# new_df = new_df.drop(new_df[(new_df[\"scaled_value\"] > scaled_value_upper) | (new_df[\"scaled_value\"] < scaled_value_upper)].index)\n# print(\"===\"*45)\n\nnew_df.head(1)\n# clasifiers under_sampling\nX = new_df.drop(\"FraudResult\", axis=1).values\ny = new_df[\"FraudResult\"].values\n\n\nX_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.2, random_state=42)\n\nclassifiers = {\n        \"LogisticRegression\":LogisticRegression(),\n        \"KNeighborsClassifier\": KNeighborsClassifier(),\n        \"Support Vector Classifier\": SVC(),\n        \"DecisionTreeClassifier\": DecisionTreeClassifier()\n\n        }\n\nfor key, classifier in classifiers.items():\n    classifier.fit(X_train, y_train)\n    train_score = cross_val_score(classifier, X_train, y_train)\n    test_score = cross_val_score(classifier, X_test, y_test)\n    print(\"Classifiers: \", classifier.__class__.__name__, \"Has Training Score of\", round(train_score.mean()))\n    print(\"Classifiers: \", classifier.__class__.__name__, \"Has Testing Score of\", round(test_score.mean()))\n\n\n# Prepare the test set\nsub = pd.read_csv(\"submission/ample_submission.csv\")\n# test_set.head()\n\n# sub.head()\n\n# test_set.columns\nid = test_data[\"TransactionId\"]\ntest_data = test_data.drop([\"TransactionId\"],axis=1)\ntest_data.head(2)\n\n\n\nlog_reg = LogisticRegression()\nlog_reg.fit(X, y)\n\ny_pred = log_reg.predict(X)\nprint(classification_report(y, y_pred))\n\npred = log_reg.predict(test_data)\n\nsubmission = pd.DataFrame(data=id,columns=[\"TransactionId\"])\nsubmission[\"FraudResult\"] = pred\n\n\n\nsubmission.head(2)\n\nsubmission.to_csv(\"initial_submit.csv\",index=False)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n# Random under_sampling\n","sub_path":"code/.ipynb_checkpoints/fraud_detection-checkpoint.py","file_name":"fraud_detection-checkpoint.py","file_ext":"py","file_size_in_byte":11107,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"407796486","text":"import socket\nimport random\n\n# Step i: create the TCP socket\n# Try to create a TCP socket. If it fails, let the user know and exit\ntry:\n    s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nexcept socket.error:\n    print('Failed to create socket')\n    sys.exit()\n\n# Step ii: get the destination IP address and port number ready to use\n# If we reached this point of the code, the socket was successfully created.\ndestIPAddr = '137.99.11.9'\ndestPort = 3300\n\n# Step iii: connect the socket to the destination server\ntry:\n    s.connect((destIPAddr, destPort))    # Connect the socket to the remote server\n    print(\"Connected\\n\")\nexcept socket.error:\n    print(\"Failed to connect\")\n    sys.exit()\n\n# Step iv: form the request string\nusernum = random.randint(1000, 8000)\nclientIPAddr = s.getsockname()[0]     # s.getsockname() outputs a tuple (ip_addr, port) so get ip_addr\nclientPort = s.getsockname()[1]        # get client port from that same tuple\nrequestType = 'ex0'\nusername = 'V.A.Chov'\nconnection_spec = destIPAddr + '-' + str(destPort) + ' ' + clientIPAddr + '-' + str(clientPort)\nrequestStr = requestType + ' ' + connection_spec + ' ' + str(usernum) + ' ' + username + ' ' + '\\n'\nprint(\"The request string we're sending is: \" + requestStr)\n\n# Step v: write the request string to the client\ns.send(requestStr.encode())\n\n# Step vi: read data from the socket until a 2nd newline character is recvd\n# Read the data from the socket and organize it\ndata = s.recv(1024)                                 # Grab data from the socket\ndataSplit_n = (data.decode()).split('\\n')           # Form a set where \\n is the delimiter after decoding it\nline2Split = (dataSplit_n[1]).split()               # Form a set where   is the delimiter for the 2nd line\nexpectedOK = line2Split[0]                          # The first word on the 2nd line is expected to be \"OK\"\nrecvUsernum = line2Split[1]                         # The second word on the 2nd line is expected to be usernum + 1\nservernum = line2Split[-1]                          # The last word on the 2nd line is expected to be servernum\n\n# check that the 1st word on the 2nd line is \"OK\" and usernum+1\nif expectedOK == 'OK' and recvUsernum == str(usernum + 1):\n    print(\"Message received: \" + dataSplit_n[0] + '\\n' + dataSplit_n[1] + '\\n')  # Output the first 2 lines\nelse:\n    print('Error in receiving response.  Received message: ' + data.decode())   # Output everything and quit\n    sys.exit()\n\n# Step vii: Construct ack string and write it to the socket\nusernum2 = int(usernum) + 2\nservernum1 = int(servernum) + 1\nackString = requestType + ' ' + str(usernum2) + ' ' + str(servernum1) + '\\n'\nprint(\"The ACK message we're sending is \" + ackString)\ns.send(ackString.encode())     # Encode the ackString and then send it\n\n# Step viii: read data from the socket until 1 newline character is recvd\ndata = s.recv(1024)             # Grab data from the socket\n\n# If the input to the server is wrong, you can get an out of bounds exception trying to organize its output\ntry:\n    dataSplit_n = (data.decode()).split('\\n')          # Form a set where \\n is the delimiter after decoding it\n    line1Split = (dataSplit_n[0]).split()              # Form a set where   is the delimiter for the 1st line\n    expectedOK = line1Split[0]                         # The first word on the 1st line is expected to be \"OK\"\n    recvServernum = line1Split[-1]                     # The last word on the 2nd line is expected to be servernum + 1\nexcept:\n    print(\"Unexpected input from the server: \" + data.decode() )\n    sys.quit\n\n# check that the 1st word on the 2nd line is \"OK\" and usernum+1\nservernum1 = int(servernum) + 1\nif ('OK' in line1Split) and (recvServernum == str(servernum1) ):\n    print(\"Message received: \" + dataSplit_n[0] + '\\n')  # Output the first line\nelse:\n    print('Error in receiving response.  Received message: ' + data.decode())   # Output everything and quit\n    sys.exit()\n\n# Step ix: Close the socket\ns.close()\nprint(\"Disconnected.\\n\")\n\nprint(\"servernum1: \" + servernum)\nprint(\"servernum2: \" + recvServernum)\n","sub_path":"ex0.py","file_name":"ex0.py","file_ext":"py","file_size_in_byte":4067,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"473530333","text":"#Filename: /perfeerapp/models/message.py\n# -*- coding:utf-8 -*-\n\nimport dateutil.parser\nimport sys\nimport time\n\nfrom django.db import models\n\nfrom perfeerapp.models.config.MongoConnFactory import *\n\nreload(sys)\nsys.setdefaultencoding('utf-8')\n\nmongoConn = MongoConnFactory()\ndb = mongoConn.getDB()\n\nclass MessageModel(models.Model):\n    title = models.CharField(max_length=20,help_text=u'标题')\n    content = models.TextField(max_length=200, help_text=u'留言内容')\n\n    def getMessages(self):\n        message_info = db['suggestions'].find().sort([('time',-1)])\n        return message_info\n\n    def messageScore(self, data ,user):\n        title = data.get('title','');\n        content = data.get('content','');\n        message_time = time.strftime(\"%Y-%m-%d %H:%M:%S\",time.localtime(time.time()));\n        bson_message_time = dateutil.parser.parse(str(message_time))\n\n        db['suggestions'].save(\n            {\"title\":title,\n             \"content\":content,\n             \"user\":user,\n             \"time\":bson_message_time\n             })\n\n\n","sub_path":"perfeersystem/web/perfeerapp/models/settings/message.py","file_name":"message.py","file_ext":"py","file_size_in_byte":1047,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"195051785","text":"from room import Room\nfrom player import Player\nfrom world import World\nimport collections\nfrom ast import literal_eval\n\n\n# Load world\nworld = World()\n\n# You may uncomment the smaller graphs for development and testing purposes.\n# map_file = \"maps/test_line.txt\"\n# map_file = \"maps/test_cross.txt\"\n# map_file = \"maps/test_loop.txt\"\n# map_file = \"maps/test_loop_fork.txt\"\nmap_file = \"maps/main_maze.txt\"\n\n# Loads the map into a dictionary\nroom_graph=literal_eval(open(map_file, \"r\").read())\n\nworld.load_graph(room_graph)\n\n# Print an ASCII map\nworld.print_rooms()\n\nplayer = Player(world.starting_room)\n\ntraversal_path = []\nreverse = {\"n\": \"s\", \"s\": \"n\", \"e\": \"w\", \"w\": \"e\"}\nmaze_graph = {}\n\ndef starting_room():\n    direction_dict = {}\n    room = player.current_room\n    exits = player.current_room.get_exits()\n    for way_out in exits:\n        direction_dict[way_out] = \"?\"\n    maze_graph[room.id] = direction_dict\n    print(maze_graph)\n\ndef explore(direction):\n    direction_dict = {}\n    current = player.current_room.id\n    room = player.current_room.get_room_in_direction(direction)\n    player.travel(direction)\n    \n    maze_graph[current][direction] = room.id\n    if room.id not in maze_graph:\n        for way_out in room.get_exits():\n            some_room = room.get_room_in_direction(way_out)\n            if some_room.id not in maze_graph:\n                direction_dict[way_out] = \"?\"\n            elif way_out is reverse[direction]:\n                direction_dict[way_out] = current\n            elif maze_graph[some_room.id][reverse[way_out]] == \"?\":\n                maze_graph[some_room.id][reverse[way_out]] = room.id\n        maze_graph[room.id] = direction_dict\n    traversal_path.append(direction)\n\ndef closest_unexplored_path(room_id):\n    queue = collections.deque([])\n    path = []\n    queue.append([(room_id, None)])\n    checked_rooms = set()\n    while queue:\n        path = queue.popleft()\n        room_tuple = path[-1]\n        for direction, room in maze_graph[room_tuple[0]].items():\n            if room not in checked_rooms:\n                shortest = list(path)\n                shortest.append((room, direction))\n                checked_rooms.add(room)\n                queue.append(shortest)\n                if room == \"?\":\n                    shortest.pop(0)\n                    path = [item[1] for item in shortest]\n                    print(f\"Players Current Room: {player.current_room.id}\")\n                    return path\n    return []\n\ndef complete_maze(start):\n    stack = collections.deque([])\n    stack.append(start)\n    starting_room()\n    while stack:\n        direction = stack.pop()\n        room = player.current_room\n        next_room = room.get_room_in_direction(direction)\n        print(f\"Players Current Room: {player.current_room.id}\")\n        exits = set(room.get_exits())\n        if next_room:\n            explore(direction)\n            print(f\"Players Current Room: {player.current_room.id}\")\n            stack.append(direction)\n        if not stack:\n            for way_out in exits:\n                if maze_graph[room.id][way_out] == \"?\":\n                    explore(way_out)\n                    print(f\"Players Current Room: {player.current_room.id}\")\n                    stack.append(way_out)\n                    break\n        if not stack:\n            if len(maze_graph.keys()) is len(room_graph.keys()):\n                return \n            path_to_unexplored = closest_unexplored_path(player.current_room.id)\n            for i in range(len(path_to_unexplored)):\n                explore(path_to_unexplored[i])\n                if i is len(path_to_unexplored) - 1:\n                    stack.appendleft(path_to_unexplored[i])\n    print(f\"Length of maze graph: {len(maze_graph.keys())}\")\ncomplete_maze(\"n\")\n\n\n# TRAVERSAL TEST\nvisited_rooms = set()\nplayer.current_room = world.starting_room\nvisited_rooms.add(player.current_room)\n\nfor move in traversal_path:\n    player.travel(move)\n    visited_rooms.add(player.current_room)\n\nif len(visited_rooms) == len(room_graph):\n    print(f\"TESTS PASSED: {len(traversal_path)} moves, {len(visited_rooms)} rooms visited\")\nelse:\n    print(\"TESTS FAILED: INCOMPLETE TRAVERSAL\")\n    print(f\"{len(room_graph) - len(visited_rooms)} unvisited rooms\")\n\n\n\n#######\n# UNCOMMENT TO WALK AROUND\n#######\n# player.current_room.print_room_description(player)\n# while True:\n#     cmds = input(\"-> \").lower().split(\" \")\n#     if cmds[0] in [\"n\", \"s\", \"e\", \"w\"]:\n#         player.travel(cmds[0], True)\n#     elif cmds[0] == \"q\":\n#         break\n#     else:\n#         print(\"I did not understand that command.\")","sub_path":"projects/adventure/adv.py","file_name":"adv.py","file_ext":"py","file_size_in_byte":4572,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"221261659","text":"from aws.services.v4.auth import AWS4SignerBase, AWS4SignerForAuthorizationHeader\nimport binascii\n\n\nfrom aws.services.util.http import HttpUtils\n\nclass ObjectManager:\n    \"\"\"\n    Collection of S3 operations for S3 object management\n    \"\"\"\n\n    _awsAccessKey = None\n    _awsSecretKey = None\n    _bucketName = None\n    _regionName = None\n\n    _objectContent = \"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nunc tortor metus, sagittis eget augue ut,\\n\" \\\n            + \"feugiat vehicula risus. Integer tortor mauris, vehicula nec mollis et, consectetur eget tortor. In ut\\n\" \\\n            + \"elit sagittis, ultrices est ut, iaculis turpis. In hac habitasse platea dictumst. Donec laoreet tellus\\n\" \\\n            + \"at auctor tempus. Praesent nec diam sed urna sollicitudin vehicula eget id est. Vivamus sed laoreet\\n\" \\\n            + \"lectus. Aliquam convallis condimentum risus, vitae porta justo venenatis vitae. Phasellus vitae nunc\\n\" \\\n            + \"varius, volutpat quam nec, mollis urna. Donec tempus, nisi vitae gravida facilisis, sapien sem malesuada\\n\" \\\n            + \"purus, id semper libero ipsum condimentum nulla. Suspendisse vel mi leo. Morbi pellentesque placerat congue.\\n\" \\\n            + \"Nunc sollicitudin nunc diam, nec hendrerit dui commodo sed. Duis dapibus commodo elit, id commodo erat\\n\" \\\n            + \"congue id. Aliquam erat volutpat.\\n\"\n\n    def __init__(self,\n                 awsAccessKey,\n                 awsSecretKey,\n                 bucketName,\n                 regionName):\n        \"\"\"\n\n        :param awsAccessKey: AWS Access Key\n        :param awsSecretKey: AWS Secret Key\n        :param bucketName: S3 Bucket Name\n        :param regionName: S3 Region Name\n        \"\"\"\n\n        self._awsAccessKey = awsAccessKey\n        self._awsSecretKey = awsSecretKey\n        self._bucketName = bucketName\n        self._regionName = regionName\n\n    def pubS3Object(self):\n\n        \"\"\"\n        Generate authorisation header and signed message then upload file to S3 storage\n\n        :return: Returns response body content from S3 API\n        \"\"\"\n\n        if self._regionName == \"us-east-1\":\n            endpoint = \"https://s3.amazonaws.com/\" + self._bucketName + \"/ExampleObject.txt\"\n        else:\n            endpoint = \"https://s3-\" + self._regionName + \".amazonaws.com/\" + self._bucketName + \"/ExampleObject.txt\"\n\n        hashedBinaryContent = AWS4SignerBase.hash(bytearray(self._objectContent,\"utf-8\"))\n        hashedHexContent = binascii.hexlify(hashedBinaryContent)\n        headers = {\n                    \"x-amz-content-sha256\": hashedHexContent,\n                    \"content-length\": str(len(self._objectContent)),\n                    \"x-amz-storage-class\": \"REDUCED_REDUNDANCY\"\n                    }\n\n        signer = AWS4SignerForAuthorizationHeader(endpoint, \"PUT\", \"s3\", self._regionName)\n        authorization = signer.computeSignature(headers,\n                                                None, # no query parameters\n                                                hashedHexContent,\n                                                self._awsAccessKey,\n                                                self._awsSecretKey)\n\n        headers['Authorization'] = authorization\n\n        resp = HttpUtils.invokeHttpRequest(endpoint=endpoint,\n                          method='PUT',\n                          headers=headers,\n                          body=self._objectContent)\n        return resp\n\n\n","sub_path":"aws/services/s3/manage_objects.py","file_name":"manage_objects.py","file_ext":"py","file_size_in_byte":3444,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"382548468","text":"# -*- coding: utf-8 -*-\n\n## \\package ui.checklist\n\n# MIT licensing\n# See: docs/LICENSE.txt\n\n\nimport wx\n\nfrom dbr.language       import GT\nfrom dbr.log            import Logger\nfrom globals.strings    import GS\nfrom globals.strings    import TextIsEmpty\nfrom ui.button          import ButtonCancel\nfrom ui.button          import ButtonClear\nfrom ui.button          import ButtonConfirm\nfrom ui.layout          import BoxSizer\n\n\nclass CheckList(wx.Panel):\n    def __init__(self, parent, ID=wx.ID_ANY, pos=wx.DefaultPosition, size=wx.DefaultSize,\n            style=wx.TAB_TRAVERSAL, name=wx.PanelNameStr):\n        wx.Panel.__init__(self, parent, ID, pos, size, style=style|wx.BORDER_THEME, name=name)\n        \n        self.SetBackgroundColour(u'white')\n        \n        self.scrolled_panel = wx.ScrolledWindow(self, style=wx.VSCROLL|wx.BORDER_NONE)\n        self.scrolled_panel.SetBackgroundColour(self.GetBackgroundColour())\n        self.scrolled_panel.SetScrollbars(20, 20, 50, 50)\n        \n        self.layout_scrolled = BoxSizer(wx.VERTICAL)\n        \n        self.scrolled_panel.SetSizer(self.layout_scrolled)\n        self.scrolled_panel.SetAutoLayout(True)\n        \n        layout_main = BoxSizer(wx.VERTICAL)\n        layout_main.Add(self.scrolled_panel, 1, wx.EXPAND)\n        \n        self.SetSizer(layout_main)\n        self.SetAutoLayout(True)\n        self.Layout()\n    \n    \n    def AddItem(self, label, checked=False):\n        # Yield for progress dialog pulse updates\n        wx.Yield()\n        \n        # FIXME: Static lines are counted\n        item_index = self.GetItemCount()\n        \n        #Logger.Debug(__name__, GT(u'Lintian tag: {}; Set checked: {}').format(label, checked))\n        \n        self.layout_scrolled.Add(wx.CheckBox(self.scrolled_panel, label=label), 1, wx.EXPAND)\n        self.layout_scrolled.Add(wx.StaticLine(self.scrolled_panel, style=wx.LI_HORIZONTAL), 0, wx.EXPAND)\n        \n        if checked:\n            check_box = self.GetItem(item_index)\n            \n            if isinstance(check_box, wx.CheckBox):\n                check_box.SetValue(True)\n        \n        self.scrolled_panel.Layout()\n        self.Layout()\n    \n    \n    def AddItems(self, labels, checked=False):\n        for l in labels:\n            Logger.Debug(__name__, u'Adding item: {} (checked={})'.format(l, checked))\n            \n            self.AddItem(l, checked)\n    \n    \n    def Clear(self):\n        for C in self.scrolled_panel.GetChildren():\n            if isinstance(C, wx.CheckBox) and C.GetValue():\n                C.SetValue(False)\n    \n    \n    def GetCheckedCount(self):\n        checked_count = 0\n        for C in self.scrolled_panel.GetChildren():\n            if isinstance(C, wx.CheckBox) and C.GetValue():\n                checked_count += 1\n        \n        return checked_count\n    \n    \n    def GetCheckedLabels(self):\n        checked_list = []\n        \n        for C in self.scrolled_panel.GetChildren():\n            if isinstance(C, wx.CheckBox) and C.IsChecked():\n                Logger.Debug(__name__, GT(u'Retrieving checked label: {}').format(C.GetLabel()))\n                \n                checked_list.append(C.GetLabel())\n        \n        return tuple(checked_list)\n    \n    \n    def GetItem(self, index):\n        return self.scrolled_panel.GetChildren()[index]\n    \n    \n    def GetItemCount(self):\n        if wx.MAJOR_VERSION > 2:\n            return self.layout_scrolled.GetItemCount()\n        \n        return len(self.layout_scrolled.GetChildren())\n    \n    \n    def LabelExists(self, label):\n        for C in self.scrolled_panel.GetChildren():\n            if isinstance(C, wx.CheckBox):\n                if label == C.GetLabel():\n                    return True\n        \n        return False\n    \n    \n    def ScrollToEnd(self):\n        self.scrolled_panel.SetScrollPos(wx.VERTICAL, self.scrolled_panel.GetScrollLines(wx.VERTICAL))\n        self.scrolled_panel.Refresh()\n        self.Refresh()\n    \n    \n    ## TODO: Define & Doxygen\n    def SetItemCheckedByLabel(self, label, checked=True):\n        # FIXME: Should use a more efficient method to index & retrieve items\n        for C in self.scrolled_panel.GetChildren():\n            if isinstance(C, wx.CheckBox) and C.GetLabel() == label:\n                C.SetValue(checked)\n                \n                break\n\n\n\nclass CheckListDialog(wx.Dialog):\n    def __init__(self, parent, ID=wx.ID_ANY, title=wx.EmptyString, pos=wx.DefaultPosition,\n            size=wx.DefaultSize, style=wx.DEFAULT_DIALOG_STYLE, name=wx.DialogNameStr,\n            allow_custom=False):\n        wx.Dialog.__init__(self, parent, ID, title, pos, size, style=style|wx.RESIZE_BORDER,\n                name=name)\n        \n        self.check_list = CheckList(self)\n        \n        # *** Buttons *** #\n        \n        layout_buttons = BoxSizer(wx.HORIZONTAL)\n        \n        btn_clear = ButtonClear(self)\n        btn_confirm = ButtonConfirm(self)\n        btn_cancel = ButtonCancel(self)\n        \n        btn_clear.Bind(wx.EVT_BUTTON, self.OnClearList)\n        \n        # FIXME: Correct button order?\n        layout_buttons.Add(btn_clear, 0, wx.LEFT, 5)\n        layout_buttons.AddStretchSpacer(1)\n        layout_buttons.Add(btn_confirm, 0, wx.RIGHT, 5)\n        layout_buttons.Add(btn_cancel, 0, wx.RIGHT, 5)\n        \n        # *** Layout *** #\n        \n        layout_main = BoxSizer(wx.VERTICAL)\n        \n        layout_main.Add(self.check_list, 1, wx.EXPAND|wx.ALL, 5)\n        layout_main.Add(layout_buttons, 0, wx.EXPAND|wx.BOTTOM, 5)\n        \n        if allow_custom:\n            btn_add_custom = wx.Button(self, label=GT(u'Add custom'))\n            btn_add_custom.Bind(wx.EVT_BUTTON, self.OnAddCustom)\n            \n            self.input_add_custom = wx.TextCtrl(self)\n            \n            layout_add_custom = BoxSizer(wx.HORIZONTAL)\n            layout_add_custom.Add(btn_add_custom, 0, wx.LEFT|wx.RIGHT, 5)\n            layout_add_custom.Add(self.input_add_custom, 1, wx.RIGHT, 5)\n            \n            layout_main.Insert(1, layout_add_custom, 0, wx.EXPAND)\n        \n        self.SetSizer(layout_main)\n        self.SetAutoLayout(True)\n        self.Layout()\n        \n        self.CenterOnParent()\n    \n    \n    def AddItem(self, label, checked=False):\n        self.check_list.AddItem(label, checked)\n    \n    \n    def GetCheckedCount(self):\n        return self.check_list.GetCheckedCount()\n    \n    \n    def GetCheckedLabels(self):\n        return self.check_list.GetCheckedLabels()\n    \n    \n    def InitCheckList(self, labels, checked=False):\n        self.check_list.AddItems(labels, checked)\n    \n    \n    def OnAddCustom(self, event=None):\n        custom_label = GS(self.input_add_custom.GetValue()).strip(u' ').replace(u' ', u'_')\n        if not TextIsEmpty(custom_label) and not self.check_list.LabelExists(custom_label):\n            self.check_list.AddItem(custom_label, True)\n            self.check_list.ScrollToEnd()\n    \n    \n    def OnClearList(self, event=None):\n        if self.GetCheckedCount():\n            warn_dialog = wx.MessageDialog(self, GT(u'Clear Lintian overrides list?'), GT(u'Warning'),\n                    style=wx.YES_NO|wx.NO_DEFAULT|wx.ICON_WARNING)\n            warn_dialog.CenterOnParent()\n            \n            if warn_dialog.ShowModal() == wx.ID_YES:\n                self.check_list.Clear()\n    \n    \n    def SetItemCheckedByLabel(self, label, checked=True):\n        self.check_list.SetItemCheckedByLabel(label, checked)\n    \n    \n    ## TODO: Doxygen\n    def ShowModal(self):\n        # wx 2.8 doesn't automatically center on parent\n        if self.Parent:\n            self.CenterOnParent()\n        \n        return wx.Dialog.ShowModal(self)\n","sub_path":"ui/checklist.py","file_name":"checklist.py","file_ext":"py","file_size_in_byte":7641,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"590832219","text":"'''\n    factcheck.py\n'''\n\n\nfrom nltk import word_tokenize, sent_tokenize\nimport pickle\nfrom nltk.classify import ClassifierI\nfrom statistics import mode\n\n\nclass VoteClassifier(ClassifierI):\n\n    def __init__(self, *classifiers):\n        self._classifiers = classifiers\n\n    def classify(self, features):\n        votes = []\n        for c in self._classifiers:\n            v = c.classify(features)\n            votes.append(v)\n\n        return mode(votes)\n\n\n\n\ndocumentsFile = open('saved_documents.pickle', 'rb')\ndocuments = pickle.load(documentsFile)\ndocumentsFile.close()\n\n\nwordFeaturesFile = open('saved_word_features.pickle', 'rb')\nwordFeatures = pickle.load(wordFeaturesFile)\nwordFeaturesFile.close()\n\n\ndef findFeatures(document):\n    words = word_tokenize(document)\n    features = {}\n    for w in wordFeatures:\n        features[w] = (w in words)\n\n    return features\n\n\nfeatureSetsFile = open(\"saved_feature_set.pickle\", \"rb\")\nfeatureSets = pickle.load(featureSetsFile)\nfeatureSetsFile.close()\n\n# Training and testing set\nopen_file = open(\"mnb.pickle\", \"rb\")\nMultinomialNBClassifier = pickle.load(open_file)\nopen_file.close()\n\nopen_file = open(\"bnb.pickle\", \"rb\")\nBernoulliNBClassifier = pickle.load(open_file)\nopen_file.close()\n\n\nopen_file = open(\"lreg.pickle\", \"rb\")\nLogisticRegressionClassifier = pickle.load(open_file)\nopen_file.close()\n\n\nopen_file = open(\"lsvc.pickle\", \"rb\")\nLinearSVCClassifier = pickle.load(open_file)\nopen_file.close()\n\n\nopen_file = open(\"sgd.pickle\", \"rb\")\nStochasticGradientDescentClassifier = pickle.load(open_file)\nopen_file.close()\n\n# Vote Classifier\nvoteClassifier = VoteClassifier(MultinomialNBClassifier,\n                                BernoulliNBClassifier,\n                                LogisticRegressionClassifier,\n                                StochasticGradientDescentClassifier,\n                                LinearSVCClassifier)\n\ndef factAnalysis(text):\n    text = sent_tokenize(text)\n    trueCount = 0.00\n    falseCount = 0.00\n    sentenceClass = []\n    for sentence in text:\n        features = findFeatures(sentence)\n        if voteClassifier.classify(features):\n            trueCount += 1\n            sentenceClass.append(True)\n        else:\n            falseCount += 1\n            sentenceClass.append(False)\n    if trueCount > falseCount:\n        return True, 1.00 - falseCount/trueCount\n    else:\n        return False, 1.00 - trueCount/falseCount\n","sub_path":"factcheck/factcheck.py","file_name":"factcheck.py","file_ext":"py","file_size_in_byte":2402,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"357392211","text":"#\n# Copyright (C) 2011, The Locker Project\n# All rights reserved.\n#\n# Please see the LICENSE file for more information.\n#\n\n#!/usr/bin/env python\n# encoding: utf-8\n\"\"\"\nBasic google data interface for contacts\n\n12-06-2010 Simon Murtha-Smith \n    * Original creation\n3-1-2011 Thomas \"temas\" Muldowney\n    * Moved it into a class for use by the Connector\n\"\"\"\n\nimport atom\nimport gdata.contacts\nimport gdata.contacts.service\nimport json\n# import hashlib\nimport sys\nimport os\nimport lockerfs\nfrom datetime import datetime\nfrom datetime import tzinfo\nimport time\nimport threading\nimport signal\n\n\n\n\ndef testCredentials(username, password):\n    me = lockerfs.loadMeData()\n    gd_client = gdata.contacts.service.ContactsService()\n    gd_client.email = username\n    gd_client.password = password\n    gd_client.source = 'locker-0.1'\n    try:\n        gd_client.ProgrammaticLogin()\n        return True\n    except Exception:\n        return False\n\n\nclass gPhotoThread(threading.Thread):\n    \"\"\"Will download a photo.\"\"\"\n    def __init__(self, gd_client, entries):\n        super(gPhotoThread, self).__init__()\n        self.gd_client = gd_client\n        self.entries = entries\n        self.shouldStop = False\n        # this feels like a memory leak to me!\n        signal.signal(signal.SIGINT, self.signal_handler)\n    \n    def run(self):    \n        for i, entry in enumerate(self.entries):\n            try:\n                indexOfSlash = entry.id.text.rfind(\"/\")\n                id = entry.id.text[indexOfSlash+1:]\n                # sys.stderr.write(\"%s\" % dir(entry))\n                sys.stderr.write(\"photo link for id %s:%s\" % (id, entry.GetPhotoLink()))\n                sys.stderr.flush()\n                hosted_image_binary = self.gd_client.GetPhoto(entry)\n                #print hosted_image_binary\n                if hosted_image_binary:\n                    image_file = open('photos/{0}.jpg'.format(id), 'wb')\n                    image_file.write(hosted_image_binary)\n                    image_file.close()\n            except gdata.service.RequestError:\n                pass\n            if self.shouldStop is True:\n                break\n    \n    def signal_handler(self, signal, frame):\n        self.shouldStop\n        sys.exit(0);\n                    \nclass GoogleDataContacts:\n    def __init__(self, email, password):\n        statusData = lockerfs.loadJsonFile(\"status.json\")\n        if \"lastUpdate\" in statusData:\n            self.lastUpdate = datetime.fromtimestamp(int(statusData[\"lastUpdate\"]))\n        else:\n            self.lastUpdate = datetime.fromtimestamp(0)\n        self.gd_client = gdata.contacts.service.ContactsService()\n        self.gd_client.email = email\n        self.gd_client.password = password\n        self.gd_client.source = 'locker-0.1'\n        # m = hashlib.sha1()\n        # m.update(self.gd_client.email)\n        # self.uid = m.hexdigest()\n\n    def updateAll(self):\n        try:\n            os.makedirs(\"photos\")\n        except OSError:\n            pass\n        sys.stdout.write(\"Checking for updates since %s\" % (str(self.lastUpdate.isoformat())))\n        sys.stdout.flush()\n        self.gd_client.ProgrammaticLogin()\n        self.write_groups_feed_to_file()\n        return self.write_feed_to_file()\n\n    def write_groups_feed_to_file(self):\n        feed = self.gd_client.GetGroupsFeed()\n        if len(feed.entry) <= 0:\n            return\n\n        jsonFile = open('groups.json', 'w')\n        for i, entry in enumerate(feed.entry):\n            jsonObject = {}\n            indexOfSlash = entry.id.text.rfind(\"/\")\n            jsonObject[\"id\"] = entry.id.text[indexOfSlash+1:]\n            jsonObject[\"name\"] = entry.title.text\n            jsonFile.write(json.dumps(jsonObject) + '\\n')\n\n\n    def write_feed_to_file(self):\n        \n        # get all contacts and write overwrite the current.json file\n        current = open('current.json', 'w')\n        allQuery = gdata.contacts.service.ContactsQuery()\n        allQuery.max_results = 3000\n        self.write_query_to_file(allQuery, current, False)\n        current.close()\n        \n        # get only updates since the last time and append to the all.json file\n        allFile = open('all.json', 'a')\n        updatesQuery = gdata.contacts.service.ContactsQuery()\n        updatesQuery['updated-min'] = self.lastUpdate.isoformat()\n        updatesQuery['showdeleted'] = 'true'\n        updatesQuery['sortorder'] = 'ascending'\n        updatesQuery.orderby = 'lastmodified'\n        updatesQuery.max_results = 3000\n        self.lastUpdate = datetime.utcnow()\n        numUpdated = self.write_query_to_file(updatesQuery, allFile, True)\n        allFile.close()\n        \n        lockerfs.saveJsonFile(\"status.json\", {\"lastUpdate\":time.mktime(self.lastUpdate.timetuple())})\n        return numUpdated\n\n    def write_query_to_file(self, query, a_file, isHistorical):\n        query.max_results = 3000\n        feed = self.gd_client.GetContactsFeed(query.ToUri())\n        if len(feed.entry) <= 0:\n            return\n        if isHistorical is True:\n            photoThread = gPhotoThread(self.gd_client, feed.entry)\n            photoThread.start()\n        for i, entry in enumerate(feed.entry):\n            self.write_entry_to_file(a_file, i, entry, isHistorical)\n        # sys.stderr.write(\"updated %s\" % (feed.updated))\n        # sys.stderr.flush()\n        \n        return len(feed.entry)\n\n    def write_entry_to_file(self, a_file, i, entry, isHistorical):\n        rawJSONObject = self.convert_to_json(entry)\n\n        jsonObject = {};\n        if isHistorical == True:\n            jsonObject['data'] = rawJSONObject\n            jsonObject['timeStamp'] = time.mktime(datetime.utcnow().timetuple())\n            # sys.stderr.write(\"obj %s\" % (entry.updated))\n            # sys.stderr.flush()\n        else :\n            jsonObject = rawJSONObject\n\n        a_file.write(json.dumps(jsonObject) + '\\n')\n        \n    def convert_to_json(self, entry):\n        jsonObject = {}\n        indexOfSlash = entry.id.text.rfind(\"/\")\n        jsonObject[\"id\"] = entry.id.text[indexOfSlash+1:]\n\n        if entry.title.text: jsonObject[\"name\"] = entry.title.text\n        if entry.nickname: jsonObject[\"nickname\"] = entry.nickname\n\n        # Display the primary email address for the contact.\n        if entry.email:\n            jsonObject[\"email\"] = []\n            for email in entry.email:\n                jsonEmail = {}\n                jsonEmail[\"value\"] = email.address\n                label = email.rel or email.label\n                indexOfHash = label.find(\"#\")\n                label = label[indexOfHash+1:]\n                if label != 'other': jsonEmail[\"type\"] = label\n                jsonObject[\"email\"].append(jsonEmail)\n\n        if entry.phone_number:\n            jsonObject[\"phone\"] = []\n            for phone in entry.phone_number:\n                jsonPhone = {}\n                jsonPhone[\"value\"] = phone.text\n                label = phone.rel or phone.label\n                indexOfHash = label.find(\"#\")\n                label = label[indexOfHash+1:]\n                if label != 'other': jsonPhone[\"type\"] = label\n                jsonObject[\"phone\"].append(jsonPhone)\n\n        if entry.postal_address:\n            jsonObject[\"address\"] = []\n            for postalAddress in entry.postal_address:\n                jsonAddress = {}\n                jsonAddress[\"value\"] = postalAddress.text\n                label = postalAddress.rel or postalAddress.label\n                indexOfHash = label.find(\"#\")\n                label = label[indexOfHash+1:]\n                if label != 'other': jsonAddress[\"type\"] = label\n                jsonObject[\"address\"].append(jsonAddress)\n\n        if entry.gender:\n            sys.stderr.write(\"gender %s\" % (entry.gender))\n            sys.stderr.flush()\n            \n        if entry.GetPhotoLink():\n            jsonObject[\"photo\"] = True\n        else :\n            jsonObject[\"photo\"] = False\n\n#        if entry.birthday:\n#            print entry.birthday\n#        print entry.system_group\n        if entry.group_membership_info:\n            jsonObject[\"groups\"] = []\n            for group in entry.group_membership_info:\n                jsonObject[\"groups\"].append(group.href[-16:])\n\n        return jsonObject\n\n","sub_path":"Connectors/GoogleContacts/gcontacts.py","file_name":"gcontacts.py","file_ext":"py","file_size_in_byte":8181,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"503576623","text":"##############\n# Exercise 2.6\n##############\n\nfrom collections import Counter\nfrom itertools import groupby\n\nclass AADist:\n    \"\"\"\n    The class provides a method to read fasta files and to calculate certain statistics on the read sequences.\n    \"\"\"\n    \n    def __init__(self, filepath):\n        self.__sequences = []\n        self.read_fasta(filepath)\n        \n\n    def get_counts(self):\n        return int(len(self.__sequences))\n\n\n    def get_average_length(self):\n        total_length = 0.0\n        \n        for seq in self.__sequences:\n            total_length += len(seq)\n\n        average_length = float(total_length/self.get_counts())\n\n        return float(average_length)\n\n    def read_fasta(self, path):\n        # f = open(path, \"r\")\n\n        # groups = groupby(f, key=lambda line: not(line.startswith(\">\") or line.startswith(\";\")))\n        # for key, group in groups:\n        #     if not key:\n        #         seq = list(group)[0].strip(), ''.join(map(str.rstrip, next(groups)[1],'')).rstrip('*')\n        #         self.__sequences.append(seq)\n\n        with open(path, \"r\") as f:\n            seq = \"\"\n            sequence_started = False\n            for line in f:\n                if line.startswith(\">\") or line.startswith(\";\"):\n                    if sequence_started:\n                        self.__sequences.append(seq.rstrip('*'))\n                        seq = \"\"\n                        sequence_started = False\n                    continue\n                sequence_started = True\n                seq += line.strip()\n            self.__sequences.append(seq.rstrip('*'))\n\n\n    def get_abs_frequencies(self):\n        # return number of occurences not normalized by length\n\n        proteins = \"\"\n        for seq in self.__sequences:\n            proteins += seq\n\n        occurrences = Counter(proteins)\n        abs_frequencies = dict(occurrences.most_common())\n\n        return abs_frequencies\n\n    def get_av_frequencies(self):\n        # return number of occurences normalized by length\n\n        total_length = 0.0\n        for seq in self.__sequences:\n            total_length += len(seq)\n\n        aa_av_frequencies = self.get_abs_frequencies()\n        \n        for aa, count in aa_av_frequencies.items():\n            #average = float(1/(count/total_length))\n            average = float(count/total_length)\n            aa_av_frequencies[aa] = average\n\n        return aa_av_frequencies\n\n\n# def main():\n#     dist = AADist(\"./tests/tests.fasta\")\n\n#     print(\"get_counts(): \" + str(dist.get_counts()))\n    \n#     print(\"get_average_length(): \" + str(dist.get_average_length()))\n    \n#     print(\"get_abs_frequencies(): \")\n#     print(dist.get_abs_frequencies())\n    \n#     print(\"get_av_frequencies(): \")\n#     print(dist.get_av_frequencies())\n\n# main()\n","sub_path":"codechecker/repos/1/collected_files/aa_dist/ga47qon.py","file_name":"ga47qon.py","file_ext":"py","file_size_in_byte":2765,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"616512793","text":"from deployer.node import required_property\nfrom deployer.contrib.nodes.config import Config\nfrom deployer.utils import esc1\n\n\nclass Variants(Config):\n    \"\"\"\n    Simple server-side persistent key-value list of attributes.\n\n    Mostly useful for an installation of a service on a server: what version is\n    installed with what options? Do we need to install it again or extend it\n    with new options for this extra service?\n\n    Similar to variants for MacPorts. Variants are conditional modifications\n    of installations. They are flags, saved on the target system. If we detect\n    that the variants don't match with those that we expect, then we know that\n    we have to reinstall the service.\n    This is useful, for when some services are installed system-wide from\n    several set-ups. Each set-up can add their own variants. If some variants\n    are already in place, and our service adds another variant, then we should\n    probably reinstall the service, combining all these variants.\n\n    Variants can be specified as a list/set or as a dict, but the helper\n    attributes convert them to dicts. This allows you to use keys and values,\n    which can be compared (like version numbers). It is possible that we will\n    drop list support in the future.\n\n    Example:\n\n    variants = ('version:1.2', 'plugin_foo')\n\n    Equivalent:\n\n    variants = {'version': '1.2', 'plugin_foo': True}\n\n    If the server would already contain ('version:1.1', 'plugin_bar'), you know\n    you will have to install Version 1.2 with plugins foo and bar to satisfy\n    your own service and other services on the same host that depend on it.\n    \"\"\"\n    # Override\n    variants = set()\n    slug = required_property()\n\n    @property\n    def remote_path(self):\n        return '/etc/variants/%s' % self.slug\n\n    @property\n    def content(self):\n        final_variants = self._as_list(self.variants_final)\n\n        # Return result\n        return ' '.join(final_variants)\n\n    def _as_dict(self, var_list):\n        if isinstance(var_list, dict):\n            return var_list\n        var_dict = {}\n        for var in var_list:\n            var_parts = var.split(':')\n            if len(var_parts) == 1:\n                var_dict[var_parts[0]] = True\n            elif len(var_parts) > 2:\n                raise Exception('Variant %s contains more than one colon' % var)\n            else:\n                var_dict[var_parts[0]] = var_parts[1]\n        return var_dict\n\n    def _as_list(self, var_dict):\n        if isinstance(var_dict, list):\n            return var_dict\n        var_list = []\n        for v, s in var_dict.iteritems():\n            if not isinstance(s, bool):\n                var_list.append('%s:%s' % (v, s))\n            else:\n                var_list.append(v)\n        var_list.sort()\n        return var_list\n\n    @property\n    def variants_installed(self):\n        \"\"\"\n        The currently installed variants.\n        \"\"\"\n        if self.host.exists(self.remote_path):\n            return self.current_content.split()\n        return {}\n\n\n    @property\n    def variants_final(self):\n        \"\"\"\n        Merge variants installed with requested.\n\n        If you (re-)install a service, use this as the guide of what to install.\n        \"\"\"\n        vars_installed = self._as_dict(self.variants_installed)\n        vars_installed.update(self.variants_to_update)\n        return vars_installed\n\n    @property\n    def variants_to_update(self):\n        \"\"\"\n        Compare the installed variants with the variants requested by this service.\n\n        This will return the variants that need to change, with their final\n        version. You can check this property to determine whether you need to\n        reinstall the service. If you decide to reinstall, use variants_final\n        as a guide of what to install, because variants_to_update will not\n        include variants that have not changed.\n        \"\"\"\n        vars_installed = self._as_dict(self.variants_installed)\n        vars_requested = self._as_dict(self.variants)\n        vars_to_update = {}\n        for var_req, var_spec_req in vars_requested.iteritems():\n            if var_req not in vars_installed:\n                # The variant is not yet installed\n                # Install the requested version\n                vars_to_update[var_req] = var_spec_req\n            else:\n                # The variant is already installed\n                if isinstance(var_spec_req, bool):\n                    # We do not request a specific version\n                    # No need to update\n                    pass\n                else:\n                    var_spec_cur = vars_installed[var_req]\n                    # We request a specific version\n                    if isinstance(var_spec_cur, bool):\n                        # We currently have an unspecified version\n                        # Go to the specific version\n                        vars_to_update[var_req] = var_spec_req\n                    else:\n                        # Comparison time!\n                        from distutils.version import LooseVersion\n                        if LooseVersion(var_spec_cur) < LooseVersion(var_spec_req):\n                            # We currently have a lower version, install ours\n                            vars_to_update[var_req] = var_spec_req\n        return vars_to_update\n\n    @property\n    def clear(self):\n        \"\"\"\n        Clear (reset) the variants file.\n        \"\"\"\n        self.host.sudo(\"rm '/etc/variants/%s'\" % esc1(self.remote_path))\n\n    def setup(self):\n        self.host.sudo('mkdir -p /etc/variants')\n        Config.setup(self)\n","sub_path":"deployer/contrib/nodes/variants.py","file_name":"variants.py","file_ext":"py","file_size_in_byte":5585,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"382166064","text":"import json\nimport os\nimport uuid\n\nfrom Bio.Seq import Seq\nfrom Bio.Alphabet import IUPAC\n\nfrom public_interface.models import Vouchers\nfrom public_interface.models import Sequences\nfrom public_interface.models import Genes\nfrom core import utils\n\n\nclass Results(object):\n    \"\"\"Returns:\n\n    Attributes:\n        * List of items with accession numbers that will not be included in FASTA or protein files (code, gene_code, accession).\n        * FASTA dataset as string.\n        * Protein dataset as string.\n\n    Usage:\n\n        >>> res = Results(voucher_codes, gene_codes)\n        >>> res.get_datasets()\n        >>> fasta_dataset = res.fasta\n        >>> protein_dataset = res.protein\n        >>> items_with_accession = res.items_with_accession\n\n    \"\"\"\n    def __init__(self, voucher_codes, gene_codes):\n        self.voucher_codes = voucher_codes\n        self.gene_codes = gene_codes\n        self.items_with_accession = []\n        self.guid = self.make_guid()\n        self.fasta = ''\n        self.protein = ''\n        self.cwd = os.path.dirname(__file__)\n        self.fasta_file = os.path.join(self.cwd,\n                                       'fasta_files',\n                                       'fasta_' + self.guid + '.fasta',\n                                       )\n        self.protein_file = os.path.join(self.cwd,\n                                         'fasta_files',\n                                         'prot_' + self.guid + '.fasta',\n                                         )\n\n    def get_datasets(self):\n        \"\"\"Queries sequences and creates FASTA, protein strings and list of\n        items with accession number (code, gene_code, accession).\n        \"\"\"\n        for code in self.voucher_codes:\n            try:\n                v = Vouchers.objects.get(code=code)\n            except Vouchers.DoesNotExist:\n                continue\n\n            for gene_code in self.gene_codes:\n                if v:\n                    try:\n                        s = Sequences.objects.get(code=v, gene_code=gene_code)\n                    except Sequences.DoesNotExist:\n                        continue\n\n                    try:\n                        g = Genes.objects.get(gene_code=gene_code)\n                    except Genes.DoesNotExist:\n                        continue\n\n                    if s.accession.strip() != '':\n                        self.items_with_accession.append(\n                            {\n                                'voucher_code': code,\n                                'gene_code': gene_code,\n                                'accession': s.accession,\n                            },\n                        )\n                    else:\n                        seq_id = v.genus + '_' + v.species + '_' + code\n                        seq_description = '[org=' + v.genus + ' ' + v.species + ']'\n                        seq_description += ' [Specimen-voucher=' + v.code + ']'\n                        seq_description += ' [note=' + g.description + ' gene, partial cds.]'\n                        seq_description += ' [Lineage=]'\n                        seq_seq = s.sequences\n\n                        # # DNA sequences\n                        seq_seq = utils.strip_question_marks(seq_seq)[0]\n                        if '?' in seq_seq or 'N' in seq_seq.upper():\n                            seq_obj = Seq(seq_seq, IUPAC.ambiguous_dna)\n                        else:\n                            seq_obj = Seq(seq_seq, IUPAC.unambiguous_dna)\n\n                        self.fasta += '>' + seq_id + ' ' + seq_description + '\\n'\n                        self.fasta += str(seq_obj) + '\\n'\n\n                        # # Protein sequences\n                        seq_seq, removed = utils.strip_question_marks(s.sequences)\n\n                        if int(g.reading_frame) == 1:\n                            if removed % 3 == 0:\n                                start_translation = 0\n                            if removed % 3 == 1:\n                                start_translation = 2\n                            if removed % 3 == 2:\n                                start_translation = 1\n\n                        if int(g.reading_frame) == 2:\n                            if removed % 3 == 0:\n                                start_translation = 1\n                            if removed % 3 == 1:\n                                start_translation = 0\n                            if removed % 3 == 2:\n                                start_translation = 2\n\n                        if int(g.reading_frame) == 3:\n                            if removed % 3 == 0:\n                                start_translation = 2\n                            if removed % 3 == 1:\n                                start_translation = 1\n                            if removed % 3 == 2:\n                                start_translation = 0\n\n                        if '?' in seq_seq or 'N' in seq_seq.upper():\n                            seq_obj = Seq(seq_seq[start_translation:], IUPAC.ambiguous_dna)\n                        else:\n                            seq_obj = Seq(seq_seq[start_translation:], IUPAC.unambiguous_dna)\n                        prot_sequence = seq_obj.translate(table=g.genetic_code)\n\n                        self.protein += '>' + seq_id + ' ' + seq_description + '\\n'\n                        self.protein += str(prot_sequence) + '\\n'\n\n        with open(self.fasta_file, 'w') as handle:\n            handle.write(self.fasta)\n\n        with open(self.protein_file, 'w') as handle:\n            handle.write(self.protein)\n\n    def make_guid(self):\n        return uuid.uuid4().hex\n","sub_path":"voseq/genbank_fasta/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":5586,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"421985490","text":"\n\n#+---------------------------------------------------------------+#\n#+                                                               +#\n#+                      Ultimate windows mess                    +#\n#+                                                               +#\n#+                       by khaled                               +#\n#+                                                               +#\n#+---------------------------------------------------------------+#\n\n\n# only work on windows\n\nimport os\n\nfrom random import randrange\n\ndef randm():\n\tsum = ''\n\tch = 'ABCDEFGHLMOPQRSTVW0123456789!$#'\n\tfor i in range(1,10):\n\t\tx = randrange(31)\n\t\tsum = sum + str(ch[x])\n\treturn sum\n\ndef mess(s):\n\tfor t in range(0,900):\n\t\tos.system(s[t])\n\nsoldiers = []\n\nprint(\"[~] loading ....... \")\n\nfor c in range(1,1500):\n\tname = ''\n\tname = str(randm)+'.cmd'\n\tfich = open(name , 'w')\n\tfor z in range(1,1000):\n\t\tfich.write('msg * hhhhhhhhhhhhhhhhhh \\n')\n\t\tfich.write('start \\n')\n\tfich.close\n\tsoldiers.append(name)\n\nprint(\"[+] Forgive me !\")\nmess(soldiers)\n","sub_path":"Winmess.py","file_name":"Winmess.py","file_ext":"py","file_size_in_byte":1048,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"550167033","text":"from django.shortcuts import render, get_object_or_404\nfrom django.utils import timezone\nfrom .models import Car\nfrom .forms import CarForm\nfrom django.shortcuts import redirect\nfrom django.contrib.auth.decorators import login_required\nfrom .forms import UserForm\nfrom django.contrib.auth import authenticate, login\nfrom django.contrib.auth.models import User\n\n\ndef post_cars(request):\n    cars = Car.objects.all()\n    return render(request, 'car_pool/post_cars.html', {'cars': cars})\n\ndef car_detail(request, pk):\n    car = get_object_or_404(Car, pk=pk)\n    return render(request, 'car_pool/car_detail.html', {'car': car})\n\ndef car_booked(request, pk):\n    car = get_object_or_404(Car, pk=pk)\n    car.book_seat()\n    return render(request, 'car_pool/car_booked.html', {'car': car})\n\n@login_required\ndef new_car(request):\n    if request.method == \"POST\":\n        form = CarForm(request.POST)\n        if form.is_valid():\n            car = form.save(commit=False)\n            car.driver = request.user\n            car.save()\n            return redirect('car_detail', pk=car.pk)\n    else:\n        form = CarForm()\n    return render(request, 'car_pool/car_edit.html', {'form': form})\n\ndef new_user(request):\n    if request.method == \"POST\":\n        form = UserForm(request.POST)\n        if form.is_valid():\n            new_user = User.objects.create_user(**form.cleaned_data)\n            authenticate(username=new_user.username, password=new_user.password)\n            #redirect, or however you want to get to the main view\n            return redirect('post_cars')\n    else:\n        form = UserForm()\n    return render(request, 'car_pool/adduser.html', {'form': form})\n\n@login_required\ndef car_edit(request, pk):\n    car = get_object_or_404(Car, pk=pk)\n    if request.method == \"POST\":\n        form = CarForm(request.POST, instance=car)\n        if form.is_valid():\n            car = form.save(commit=False)\n            car.driver = request.user\n            car.save()\n            return redirect('car_detail', pk=car.pk)\n    else:\n        form = CarForm(instance=car)\n    return render(request, 'car_pool/car_edit.html', {'form': form})\n","sub_path":"car_pool/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2133,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"334466250","text":"from .pupil import pupil\nfrom .aberrations import wavefront_aberrations\nfrom .coronagraphs import metis_modes\nfrom .detector import detector\n\nimport numpy as np\nfrom astropy.io import fits \nfrom multiprocessing import Pool\nfrom copy import deepcopy\nimport time\nimport multiprocessing as mpro\nfrom functools import partial\nfrom sys import platform\nimport os\n\ndef check_dim(input):\n    r = 0\n    try:\n        r = input.ndim\n    except: AttributeError\n    return r\n\ndef propagation_test(a,b):\n    if check_dim(a)==3 or check_dim(b)==2:\n        out = 'multi'\n    else:\n        out = 'single'\n    return out\n\ndef temp_prop(conf):\n    wf = pupil(conf) \n    metis_modes(wf, conf)\n    return \n\ndef multi_cube(atm_screen,tip_tilt,conf,wf_start,ncpa_scaling,iter):\n    wf = deepcopy(wf_start)\n    print(iter)\n    if ((isinstance(atm_screen, (list, tuple, np.ndarray)) == True)):\n        if (atm_screen.ndim == 3):\n            atm_screen_iter = atm_screen[iter,:,:]\n            scaling = ncpa_scaling[iter]\n        else:\n            atm_screen_iter = atm_screen\n    if (tip_tilt.ndim == 2):\n        tip_tilt_iter = tip_tilt[iter,:]\n    else:\n        tip_tilt_iter = tip_tilt\n    conf['tip_tilt'] = tip_tilt_iter\n    wavefront_aberrations(wf, scaling, atm_screen=atm_screen_iter, **conf)\n    metis_modes(wf, conf)\n    psf = detector(wf, conf)\n    return psf\n\ndef metis_hci(atm_screen, ncpa_scaling, **conf):\n    atm_screen = np.array(np.float32(atm_screen), ndmin=3)\n    tip_tilt = np.array(conf['tip_tilt'])\n    if (propagation_test(atm_screen,tip_tilt) == 'single'):\n        wf = pupil(conf) \n        conf['tip_tilt'] = tip_tilt\n        wavefront_aberrations(wf, ncpa_scaling, atm_screen=atm_screen, **conf)\n        metis_modes(wf, conf)\n        psf = detector(wf, conf)\n        psf_cube = np.array(psf, ndmin=3)\n    else:\n        temp_prop(conf) # takes care of calibration files, if not present\n        if (atm_screen.ndim == 3):\n            length_cube = atm_screen.shape[0]\n        if (tip_tilt.ndim == 2):\n            length_cube = tip_tilt.shape[0]\n        wf_start = pupil(conf)\n        \n        if conf['cpucount'] != 1 and platform in ['linux', 'linux2', 'darwin']:\n            if conf['cpucount'] == None:\n                conf['cpucount'] = mpro.cpu_count() - 1\n            print('%s: simulation starts using %s cores.'\\\n                    %(time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime()), conf['cpucount']))\n            p = Pool(conf['cpucount'])\n            func = partial(multi_cube, atm_screen, tip_tilt, conf, wf_start, ncpa_scaling)\n            psf_cube = np.array(p.map(func, range(length_cube)))\n        else:\n            psf_cube = np.zeros((length_cube, conf['ndet'], conf['ndet']))\n            for i in range(length_cube):\n                psf_cube[i,:,:] = multi_cube(atm_screen,tip_tilt,conf,wf_start,i)\n    \n    return psf_cube\n","sub_path":"heeps/metis_hci.py","file_name":"metis_hci.py","file_ext":"py","file_size_in_byte":2848,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"44737554","text":"\nimport librosa\nimport numpy\nimport matplotlib.pyplot as plt\n\n# Normalized and not\ndef plot_melfilters(sr=16000, n_fft=512, n_mels=10, fmin=0, fmax=None):\n    \n    mel = librosa.filters.mel(sr=sr, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax, norm=None)\n    mel_norm = librosa.filters.mel(sr=sr, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax, norm=1)\n    freqs = librosa.fft_frequencies(sr=sr, n_fft=n_fft)\n    \n    fig, (base_ax, norm_ax) = plt.subplots(2, figsize=(16,8))\n\n    base_ax.plot(freqs, mel.T)\n    base_ax.set_title('Mel filters')\n    \n    norm_ax.plot(freqs, mel_norm.T)\n    norm_ax.set_title('Mel filters normalized (area=1)')\n    \n    base_ax.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)    \n    norm_ax.set_xlabel('Frequency (Hz)')    \n    \n    return fig\n\nif __name__ == '__main__':\n\n    path = 'img/melfilters.png'\n    f = plot_melfilters()\n    f.savefig(path, bbox_inches='tight', pad_inches=0)\n    print('wrote {}'.format(path))\n","sub_path":"europython2019/img/melfilters.py","file_name":"melfilters.py","file_ext":"py","file_size_in_byte":991,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"512896733","text":"import os\nfrom lib import baseclass, logclass\n\nclass ProjectPrepare(baseclass.BaseClass):\n\n    folders = [ \"config\", \"dwca\", \"images\", \"lists\", \"log\", \"models\" ]\n\n    def __init__(self):\n        super().__init__()\n\n    def make_folders(self):\n        for folder in self.folders:\n            f = os.path.join(self.project_root, folder)\n            self._make_folder(f)\n\n    def make_tensorboard_path(self):\n        self._make_folder(os.path.join(self.project_root, \"log\", \"logs_keras\"))\n\n\n    def _make_folder(self,f):\n        if not os.path.exists(f):\n            os.mkdir(f)\n            os.chmod(f,0o777)\n            self.logger.info(\"created folder \\\"{}\\\"\".format(f))\n        else:\n            self.logger.info(\"folder \\\"{}\\\" already exists\".format(f))\n\n\nif __name__ == \"__main__\":\n\n    prepare = ProjectPrepare()\n    prepare.set_project(os.environ)\n    prepare.make_folders()\n    prepare.make_tensorboard_path()\n    \n","sub_path":"code/project_prepare.py","file_name":"project_prepare.py","file_ext":"py","file_size_in_byte":920,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"512020544","text":"# run instruction: python run_arc.py\n\nimport pygame\nimport numpy\nimport sys\nimport os\n\nimport time\nimport numpy as np\nimport random\nimport datetime\n\nimport h5py\n\nimport scipy\n\nimport threading\nfrom threading import Thread\n\nfrom aux import *\n\nDEBUG = len(sys.argv)>1 and sys.argv[1]=='dummy'\nif DEBUG:\n    import robot.dummy as robot # for debug/development\nelse:\n    import robot.interface as robot # for real testing\n\nimport subprocess\n\nPYTHON3 = (sys.version_info > (3, 0))\nif PYTHON3: # this really is tested on Python 3 so please use that\n    from tkinter import *\n    from tkinter import messagebox as tkMessageBox\n    from tkinter import filedialog\n    import tkinter.simpledialog as tksd\n    from tkinter import messagebox\n    import tkinter.ttk as ttk\n\nelse: # python2\n    print(\"## WARNING: don't use Python 2. This code is designed for Python 3.\")\n    from Tkinter import * # use for python2\n    import tkMessageBox\n    from Tkinter import filedialog\n    import tkSimpleDialog as tksd\n    \nimport json\nimport pandas as pd\nfrom pandas.api.types import is_string_dtype\n\n\n\nfrom mouse import *\n\n\n# The little control window\nCONTROL_WIDTH,CONTROL_HEIGHT= 600,700 #1000,800 #450,400 # control window dimensions\nCONTROL_X,CONTROL_Y = 500,50 # controls where on the screen the control window appears\n\n\nEXPERIMENT = \"recognitionrecall\"\n\n\n\nnumpy.random.seed() # make sure we're really random\nrandom.seed()\n\n# This is a global dict that holds all the configuration options. \n# Using a single variable for them makes it easier to keep track.\nglobal conf\nconf = {}\n\n\nconf['N_ROBOT_LOG'] = 13 # how many columns go in the robot log file\n\n\nconf['n_center_capture']=100 # how many samples to collect when capturing the center\nconf['center_capture_period']=.01 # how long to wait between capturing samples for establishing the center\n\n\nconf['fullscreen']=False\n\n# The background of the screen\nconf[\"bgcolor\"] = (25,25,25) #(0,0,0)\n\n# The size of the window (for pygame)\nconf[\"screensize\"] = (1920,1080) # inmotion screen\nconf[\"screenpos\"] = (1600,0) # the offset (allows you to place the subject screen \"on\" the second monitor)\n\n\n\n# Read the screen-to-robot calibration from file\nconf['calibfile']='calib.json'\nif not os.path.exists(conf['calibfile']):\n    print(\"## ERROR: cannot find screen calibration. You may want to run screencalib.py and save the calibration as %s\"%conf['calibfile'])\n    sys.exit(-1)\n_,conf[\"calib\"] = json.load(open(conf['calibfile'],'r')) #, encoding='latin1') if PYTHON3 else pickle.load(open('calib.pickle27','rb'))\n\n\n\n\n# The sizes of various objects\nconf[\"cursor_radius\"] = .0035 # in robot coordinates (m)\n\n# this is the display size of the target, not the size of the target area used for determining whether subjects are long enough \"within\" the target.\nconf[\"target_radius\"] = .0075\nconf[\"target_colour\"]  = (50,50,50) #(100,100,100)\n\n# These are the colours for the target, when the subject is too slow, too fast, or correct\nconf[\"target_slow_colour\"]    =(0,0,255)\nconf[\"target_correct_colour\"] =(0,255,0)\nconf[\"target_fast_colour\"]    =(255,0,0)\n\n# Cutoffs on the peak velocity for determining whether the movement was too slow\nconf['too_slow_vmax']=.35\nconf['too_fast_vmax']=.45\n\n# the marker for the center\nconf['center_marker_radius']=.0075\nconf['center_marker_colour']= conf[\"target_colour\"] #(50,50,50) #75,75,75) #(100,100,100)\n\n# The cursor colour\nconf[\"active_cursor_colour\"]  = (25,90,25)  # (0,255,0)\nconf[\"passive_cursor_colour\"] = (255,0,0)\n\n\n# The radius of the movement\nconf['movement_radius']=.15 # meters\nconf['min_movement_radius']=.75*conf['movement_radius'] # meters; the minimum distance a subject must have travelled before we can call the end of the movement based on peak velocity\n\nconf['target_overshoot_SD']=.01 # in meters, the SD of the overshoot/undershoot of the target in passive trials\nconf['target_overshoot_ask_p']=.25 # percentage of passive trials where we will randomly ask whether the cursor overshot or not\n\n\nconf['robot_center']= (0,-.05) # robot center position\nconf['robot_center_x'],conf['robot_center_y']=conf[\"robot_center\"]  # just because we often need these separately as well\n\n\n# The extent of the arc being displayed on the screen when the\n# subject selects the placement of the hand\n# This is in angles, degrees, with 0 = straight ahead, and positive angles are counterclockwise\nconf['arc_range']= (-45,45)\n\nconf['arc_draw_segments'] = 100 # how many segments to draw the arc (higher=more precision but takes more resources)\nconf['arc_thickness']     = .002 # how thick to draw the 'selector' arc\nconf['arc_colour']        = (50,50,50) # colour of the arc selector\n\n\nconf['selector_radius']=.002 # the radius of the selector ball that is controlled by the joystick (in m)\nconf['selector_colour']=(255,0,0)\nconf['selector_length']=.01 # the half length of the selector line (in m)\nconf['selector_width']=4 # the width of the selector (in pixels)\n\n\n# How long to take for the return movement\nconf['return_duration']=1.5 # seconds\n\n# How long to take for the passive movements\nconf['passive_duration']=1.0 # seconds\n\nconf['stay_duration']=1 # how long to stay \"out there\" in between forward and backward movement\n\n\n# The review panel that shows you the subject's movement\nconf['review_trajectory_colour'] =(255,255,255)\nconf['review_rotated_colour']    =(0,255,0)\nconf['review_linewidth']         =3\nconf['review_force_scale']       =.01 # scale from N to m, just for display purposes\nconf['review_force_colour']      =(255,0,0)\nconf['review_force_width']       =3\nconf['review_passive_width']     =3 # width of the line of the passive point\nconf['review_passive_colour']    =(150,150,150)\n\n\n# How much curl to use\nconf['force_field_curl'] = 15 # N/(m/s)\n\nconf['channel_stiffness'] = -4000.0\nconf['channel_damping']   = 30.0\n\n\n# Range of the joystick values\n# anything outside this range is snapped to the edges\nconf['use_joystick']=False\nconf['max_joystick']= 1\nconf['min_joystick']= 0\n\n\n\nconf['use_mouse']=False\n#conf['mouse_device']='/dev/input/by-id/usb-Kensington_Kensington_USB_PS2_Orbit-mouse'\nconf['mouse_device']='/dev/input/by-id/usb-Microsoft_Microsoft_5-Button_Mouse_with_IntelliEye_TM_-mouse'\nconf['mouse_selector_tick']=-.0005 # how much to change the selector (range 0..1) for one mouse 'tick' (this determines the maximum precision)\n\n\nconf['pause_duration']=.5 # how long to hold at start when returning from the previous trial.\nconf['recog_pause_duration']=1.5 # how long to hold at start in between recognition passive movements\n\n\n# The controller that can be used for the fade duration\nconf['fade_controller'] = 5\nconf['fade_duration']=.5 # how long to fade when holding at the starting point\nconf['fade_cue_colour']=(255,0,0) # the colour of the cursor while holding still (fading forces)\n\n#conf['move_cue_colour']=(0,255,0) # the colour of the cursor when ready to move\nconf['move_controller'] = 6\n\n\n# What is the cursor behaviour when rotation=NA?\n#  if na.cursor.show = False, the cursor will be hidden when rotation=NA and the cursor is outside the starting zone;\n#  if it is True, the cursor will be shown, but visually error clamped to zero\nconf['na.cursor.show'] = False\n\n# Show a \"horizontal\" bar moving along with the subject in the rotation=NA condition?\nconf['na.bar.show'] = True\n\nconf['bar.color']=(255,255,0)\nconf['bar.width']=3 # px\nconf['bar.minx']=-.2\nconf['bar.maxx']= .2\nconf['bar.as.arc']=True # whether to show the bar as an arc\n\n\n# The range of angles to show when we show an arc as a visual display\nconf['recognition_display_arc']= True # whether to show an arc (rather than a bar) during the recognition test\nconf['cursor_arc_range']=(-60,60)\nconf['cursor_arc_segments']=15\nconf['cursor_arc_thickness']=.01 # in robot coordinates (m)\nconf['cursor_arc_colour']=(255,255,0) ## DEPRECATED -- close-to-zero chance of actually having any effect on anything\n\n\nconf['motorcopy_forward_colour']=(180,180,180)\n\n\n# Note that these phase numbers are not necessarily incremental...\nconf['phases']=['init',\n                'return',\n                'pause',\n                'forward',\n                'backward',\n                'completed',\n                'move', \n                'endmove',\n                'select',\n                'fade',\n                'stay',\n                'hold',\n                'ask'\n]\n\nconf['python']=[sys.executable,sys.version,sys.prefix,sys.exec_prefix]\n\n# Data that is specific to this trial\ntrialdata = {}\n\n\n\n\n\ndef conv_ang(a):\n    \"\"\" Angles in this experiment are generally given in degrees\n    w.r.t. straight ahead, just because that's simpler.\n    But then sin and cos like to have them in radians, relative\n    to straight right. So here we do that translation.\"\"\"\n    return ((a+90)/180)*np.pi\n    \n\n\n    \n\ndef draw_arc_selector(surf):\n    \"\"\" Draw the arc on the screen along which the subjects can\n    choose the felt position of the hand.\"\"\"\n\n    surf.fill(conf['bgcolor'])\n\n    mna,mxa = conf['arc_range']\n    minang,maxang = conv_ang(mna),conv_ang(mxa) # convert into usable angles\n    angs = np.linspace(minang,maxang,conf['arc_draw_segments'])\n\n    # Now make an inner and outer arc\n    cx,cy = conf['robot_center']\n    points = []\n    for sgn in [-.5,.5]:\n        rad = conf['movement_radius']-sgn*conf['arc_thickness']\n        p = [ (cx+rad*np.cos(a),cy+rad*np.sin(a)) for a in angs ]\n        if sgn<0: p.reverse()\n        points += p\n\n\n    # And then plot a polygon!\n    poly = [ robot_to_screen(ry,rz,conf) for (ry,rz) in points ]\n    pygame.draw.polygon( surf,conf['arc_colour'],poly)\n\n    \n    if 'selector_angle' in trialdata:\n        #print(conf['selector_position'])\n        sx,sy=position_from_angle(conv_ang(trialdata['selector_angle']),conf['movement_radius']-conf['selector_length'])\n        shortpos = robot_to_screen(sx,sy,conf)\n        lx,ly=position_from_angle(conv_ang(trialdata['selector_angle']),conf['movement_radius']+conf['selector_length'])\n        longpos  = robot_to_screen(lx,ly,conf)\n        pygame.draw.line(surf,conf['selector_colour'],shortpos,longpos,conf['selector_width'])\n        #pos = \n        #draw_ball(conf['screen'],pos,conf['selector_radius'],conf['selector_colour'])\n        \n\n\n\ndef draw_bar(surface,ypos):\n    \"\"\"\n    Let's draw a bar that moves along with the subject on the screen.\n    The \"vertical\" position of the bar is indicated by ypos.\n    \"\"\"\n    fromc = robot_to_screen(conf['bar.minx'],ypos,conf)\n    toc   = robot_to_screen(conf['bar.maxx'],ypos,conf)\n\n    pygame.draw.line(surface,conf['bar.color'],fromc,toc,conf['bar.width'])\n    \n    \n\n\n\n\ndef draw_arc(surf,dist,color):\n    \"\"\"\n    Let's draw a bar that moves along with the subject on the screen.\n    The \"vertical\" position of the bar is indicated by ypos.\n    \"\"\"\n    mna,mxa = conf['cursor_arc_range']\n    minang,maxang = conv_ang(mna),conv_ang(mxa) # convert into usable angles\n    angs = np.linspace(minang,maxang,conf['cursor_arc_segments'])\n\n    # Now make an inner and outer arc\n    cx,cy = conf['robot_center']\n    points = []\n    for sgn in [-.5,.5]:\n        rad = dist-sgn*conf['arc_thickness']\n        p = [ (cx+rad*np.cos(a),cy+rad*np.sin(a)) for a in angs ]\n        if sgn<0: p.reverse()\n        points += p\n\n    # And then plot a polygon!\n    poly = [ robot_to_screen(ry,rz,conf) for (ry,rz) in points ]\n    pygame.draw.polygon( surf,color,poly)\n\n    \n    \n\n\n\n\n    \n    \ndef draw_ball(surface,pos,radius,colour):\n    \"\"\"\n    Draw one of the two target \"balls\" (at the edges of the arc)\n\n    Arguments\n    sgn is whether this is the left or right direction.\n    colour is the draw colour\n    \"\"\"\n\n    # Draw the two target balls\n    #for sgn in [-1,1]:\n    #tx,ty = conf[\"ARC_BASE_X\"]+sgn*conf[\"ARC_RADIUS\"],conf[\"ARC_BASE_Y\"]\n    tx,ty=pos\n    x1,y1 = robot_to_screen(tx-radius,ty-radius, conf)\n    x2,y2 = robot_to_screen(tx+radius,ty+radius, conf)\n    minx=min(x1,x2)\n    maxx=max(x1,x2)\n    miny=min(y1,y2)\n    maxy=max(y1,y2)\n    pygame.draw.ellipse(surface,colour,#conf[\"ARC_COLOUR\"],\n                        [minx,miny,maxx-minx,maxy-miny])\n\n\n\n\n    \ndef pinpoint():\n    \"\"\" Select a position on the screen using the joystick.\"\"\"\n    # Start the main loop\n    trialdata['redraw']=True\n    launch_mainloop()\n\n\n\n\n\ndef hold_fade():\n    \"\"\" Hold and fade the robot handle \"\"\"\n    robot.wshm('traj_count',          0) # ensure that we start recording at the beginning of the trajectory buffer\n    robot.wshm('fvv_workspace_enter', 0) # initialise: signal that the subject has not yet entered the workspace\n    robot.wshm('fvv_robot_center_x', conf['robot_center_x'])\n    robot.wshm('fvv_robot_center_y', conf['robot_center_y'])\n    robot.wshm('plg_p1x',            conf['robot_center_x']) # the position for the initial hold\n    robot.wshm('plg_p2x',            conf['robot_center_y'])\n    robot.wshm(\"fvv_force_fade\",      1.0) # This starts at 1.0 but exponentially decays to infinitely small\n    robot.wshm(\"plg_stiffness\",       robot.stiffness)\n    robot.wshm(\"plg_damping\",         robot.damping)\n\n    robot.controller(conf['fade_controller'])\n    \n\n\n    \n    \ndef launch_mainloop():\n    #mainloop()\n    conf['thread']=Thread(target=mainloop)\n    conf['thread'].start()\n    \n    \n\n\n\ndef get_selector_prop(pos):\n    \"\"\" Map the joystick position to a position of a selector on the screen.\"\"\"\n\n    # Determine the joystick position on a range from 0 to 1\n    joy = pos[0]\n    joyrel = (joy-conf['min_joystick'])/(conf['max_joystick']-conf['min_joystick'])\n    if joyrel<0: joyrel=0\n    if joyrel>1: joyrel=1\n    #print(joyrel)\n\n    return joyrel\n     \n\n\n\ndef update_selector(dpos):\n    \"\"\" Given a mouse move event (change in x, change in y),\n    update the selector value.\"\"\"\n    dchange = sum(dpos)*conf['mouse_selector_tick']\n    p = trialdata['selector_prop']+dchange\n    p = p%1\n    trialdata['selector_prop']=p\n    selector_to_angle()\n\n\n\ndef selector_to_angle():\n    \"\"\" Map a value in the selector range (0..1) to an actual angle (in deg) \"\"\"\n    prop = trialdata.get('selector_prop',None)\n    if prop:\n        mn,mx = conf['arc_range']\n        a = mn+(mx-mn)*prop\n        trialdata['selector_angle']=a\n\n        ## Add this to the history of selections\n        hist = trialdata.get('selector_history',[])\n        if len(hist)==0 or hist[-1]!=a:\n            hist.append(a)\n        trialdata['selector_history']=hist\n    \n    \n\n    \n\ndef get_joystick():\n    \"\"\" Get the current position of the joystick.\"\"\"\n    joystick = conf['joystick']\n    pos = [ joystick.get_axis( i ) for i in range(2) ] # get the first two axes\n    if 'joystick_history' in trialdata:\n        if len(trialdata['joystick_history'])==0 or pos!=trialdata['joystick_history'][-1]:\n            trialdata['joystick_history'].append(pos) # TODO: make sure we clear the joystick history also!\n    trialdata['selector_prop']=get_selector_prop(pos) # update the selector position\n    selector_to_angle()\n    return pos\n    \n\n\n\n\ndef current_schedule():\n    # Return the item that is currently scheduled\n    if 'current_schedule' not in trialdata or trialdata['current_schedule']==-1: return None\n    return trialdata['schedule'][trialdata['current_schedule']]\n\n\ndef phase_is(p):\n    # Return whether the current phase is p\n    return trialdata.get('phase',None)==p\n\ndef phase_in(phases):\n    # Return whether the current phase is p\n    return trialdata.get('phase',None)in phases\n\n\n\n\ndef start_new_trial():\n    \"\"\" Initiate a new trial. \"\"\"\n    # Okay, if that wasn't the case, we can safely start our new trial\n    trialdata['current_schedule']+=1\n\n    gui['progress']['maximum'] =len(trialdata['schedule'])\n    gui['progress']['value']   =trialdata['current_schedule']\n    #gui['progress'].update()\n\n    if trialdata['current_schedule']==len(trialdata['schedule']):\n        # Done the experiment!\n        print(\"## BLOCK COMPLETED ##\")\n        robot.move_to(conf['robot_center_x'],conf['robot_center_y'],conf['return_duration'])\n        while not robot.move_is_done():\n            time.sleep(.1)\n        robot.stay() # just fix the handle wherever it is\n        next_phase('completed')\n        gui['keep_going'] = False # this will bail out of the main loop\n        gui['running'] = False\n        time.sleep(1) # wait until some last commands may have stopped\n        close_logs()\n        update_ui()\n        # Now ask the experimenter for observations\n        #obsv = tksd.askstring('Please record any observations', 'Experimenter, please write down any observations.\\nAny irregularities?\\nDid the subject seem concentrated or not?\\nWere things unclear or clear?\\nAnything else that is worth noting?')\n        #with open(conf['obsvlog'],'w') as f:\n        #    f.write(obsv)\n\n        gui['trialinfo'].set('Completed block')\n        tkMessageBox.showinfo(\"Robot\", \"Block completed! Yay!\")\n        return\n\n\n\n    sched = current_schedule() # Retrieve the current schedule\n    #print(sched)\n    trialdata['timestamp']       =time.time()\n    trialdata['schedule.number'] =trialdata['current_schedule']\n    trialdata['trial']           =sched['trial'] # trial number\n    trialdata['type']            =sched['type']\n    trialdata['target.direction']=sched['target.direction']  # display angle of the target\n    trialdata['mov.direction']   =sched['mov.direction']     # physical angle of movement\n    trialdata['movement_position']=None\n    trialdata['cursor.rotation'] =sched['cursor.rotation']   # rotation applied to cursor before display\n    trialdata['force.field']     =sched['force.field']       # the force field (if any)\n    trialdata['captured']        =[] # nothing captured\n    trialdata['review.shown']    =False\n    #trialdata['position_history']=[] # start with a clean position history (we'll fill this up during active trials only)\n\n    for v in ['vmax_x','vmax_y','final_x','final_y']:\n        trialdata[v]=None\n\n    \n    trialinfo =  \"trial %d %s   \"%(trialdata['trial'],trialdata['type'])\n    trialinfo += (\"targ: %.2f   mov: %.2f   rot: %.2f deg;   ff: %s\\n\"%(trialdata['target.direction'],trialdata['mov.direction'],trialdata['cursor.rotation'],trialdata['force.field']))\n    print('\\n\\n\\n### TRIAL %d %s ####'%(trialdata['trial'],trialdata['type']))\n    print(trialinfo)\n    gui['trialinfo'].set(trialinfo)\n    robot.wshm('fvv_trial_no',     trialdata['trial'])\n\n    ## Return the robot to the center\n    sx,sy = conf['robot_center']\n    robot.move_to(sx,sy,conf['return_duration'])\n    next_phase('return') # return to the starting point to start the trial\n    \n    \n\n\n\ndef record_position(x,y):\n    # trialdata['position_history'].append((x,y)) # let's not do this -- we can use the info captured from the robot itself\n    pass\n\n\n\ndef position_from_angle(a,radius=None):\n    \"\"\" Return the position based on the angle, relative to the starting point\n    and assuming a movement of a standard radius.\"\"\"\n    if radius==None:\n        radius = conf['movement_radius']        \n    cx,cy = conf['robot_center']\n    pos = (cx+radius*np.cos(a),cy+radius*np.sin(a))\n    return pos\n\n\n\ndef start_move_controller(trialdata):\n    \"\"\" Start the move phase \"\"\"\n\n    # The variables below are set so that we can determine when the movement ends, namely\n    # by looking at the peak velocity profile.\n    robot.wshm('fvv_robot_center_x',conf['robot_center_x'])\n    robot.wshm('fvv_robot_center_y',conf['robot_center_y'])\n    robot.wshm('fvv_move_done',0) # we'll set this to one once the movement is completed\n    robot.wshm('fvv_min_dist',conf['min_movement_radius'])\n    robot.wshm('fvv_max_vel',0)\n    robot.wshm('fvv_vmax_x',0)\n    robot.wshm('fvv_vmax_y',0)\n    robot.wshm('fvv_vel_low_timer',0)\n    robot.wshm('fvv_subject_move_phase',1) # signal that this is the subject move phase\n    robot.wshm('traj_count',0)\n\n    if trialdata['force.field']=='none':\n        robot.controller(conf['move_controller'])\n\n    if trialdata['force.field']=='channel':\n        # This will be a channel trial between the start point and the target\n        robot.wshm('plg_p1x',conf['robot_center_x'])\n        robot.wshm('plg_p1y',conf['robot_center_y'])\n        tx,ty= trialdata['target_position']\n        robot.wshm('plg_p2x',tx)\n        robot.wshm('plg_p2y',ty)\n        robot.wshm(\"plg_channel_width\", 0.0)\n        robot.wshm(\"plg_stiffness\", conf['channel_stiffness'])\n        robot.wshm(\"plg_damping\", conf['channel_damping'])\n        robot.controller(15)\n\n    if trialdata['force.field']=='curl':\n        #print(\"Activating curl controller, curl=%.2f\"%ffval)\n        ffval = conf['force_field_curl']\n        if ffval > 18: ffval = 18 # for safety\n        robot.wshm(\"curl\",ffval)\n        robot.controller(17)\n    \n\n\n\n\ndef draw_line(surf,fromp,top,col,width):\n    \"\"\" Draw a line from a particular point to another point, in robot coordinates.\"\"\"\n    fromr = robot_to_screen(fromp[0],fromp[1],conf)\n    tor   = robot_to_screen(top[0],  top[1]  ,conf)\n    pygame.draw.line(surf,col,fromr,tor,width)\n\n    \n\n        \ndef review_plot():\n    \"\"\" Make a plot of the trajectory we recorded from the robot.\"\"\"\n\n    if 'review.shown' in trialdata and trialdata['review.shown']:\n        return # Review has already been shown for this trial\n    \n    #trajx,trajy = zip(*traj) # unzip!\n    plot = pygame.Surface(conf['screensize'])\n    plot.fill(conf['bgcolor'])\n\n    # Draw start position\n    draw_ball(plot,conf['robot_center'],conf['center_marker_radius'],conf['center_marker_colour'])\n    # Draw target\n    if 'target_position' in trialdata and not trialdata['type']=='pinpoint': # We show a target always, only not if this is a pinpoint trial\n        col = get_target_colour()\n        draw_ball(plot,trialdata['target_position'],conf['target_radius'],col)\n\n    if trialdata['type']=='pinpoint':\n        draw_arc_selector(plot)\n\n    if 'movement_position' in trialdata and trialdata['movement_position']: # this is where the subject was moved to\n        draw_ball(plot,trialdata['movement_position'],conf['cursor_radius'],conf['review_passive_colour'])\n        if trialdata['type'] in ['passive','pinpoint']:\n            draw_line(plot,conf['robot_center'],trialdata['movement_position'],conf['review_passive_colour'],conf['review_passive_width'])\n\n        rotpos = rotate(trialdata['movement_position'],deg2rad(trialdata['cursor.rotation']),conf['robot_center'])\n        draw_ball(plot,rotpos,conf['cursor_radius'],conf['passive_cursor_colour'])\n        if trialdata['type'] in ['passive','pinpoint']:\n            draw_line(plot,conf['robot_center'],rotpos,conf['passive_cursor_colour'],conf['review_passive_width'])\n            \n        \n    if 'captured' in trialdata and len(trialdata['captured'])>0: # if there is actually something captured\n\n        traj = [ (x,y,fx,fy,fz) for (x,y,fx,fy,fz) in trialdata['captured'] ] # make a copy, which seems to be a good idea here\n        if len(traj)==0:\n            print(\"Captured 0 samples\")\n            return\n        # Just resample a little bit, so that the drawing will consume less resources\n\n        #print(traj)\n        traj = traj[ ::10 ]\n\n        # Draw the forces also (how cool is that)\n        for (x,y,fx,fy,_) in traj[::3]: # further subsampling also\n            sx,sy = robot_to_screen(x,y,conf)\n            tx,ty = robot_to_screen(x+conf['review_force_scale']*fx,\n                                    y+conf['review_force_scale']*fy,\n                                    conf)\n            pygame.draw.line(plot,conf['review_force_colour'],(sx,sy),(tx,ty),conf['review_force_width'])\n\n        # Draw the actual positions\n        points = [ robot_to_screen(x,y,conf) for x,y,_,_,_ in traj ]\n        if len(points):\n            pygame.draw.lines(plot,conf['review_trajectory_colour'],False,points,conf['review_linewidth'])\n\n        # Draw the corresponding cursor display\n        if trialdata['cursor.rotation']!=0 and not np.isnan(trialdata['cursor.rotation']):\n            rottraj = [ rotate((x,y),deg2rad(trialdata['cursor.rotation']),conf['robot_center']) for x,y,_,_,_ in traj ]\n            points = [ robot_to_screen(x,y,conf) for (x,y) in rottraj ]\n            if len(points):\n                pygame.draw.lines(plot,conf['review_rotated_colour'],False,points,conf['review_linewidth'])\n\n    show_review(plot,trialdata)\n\n\n\n\ndef show_review(plot,trialdata):\n    \"\"\" Given a pygame surface, show it in the review window.\"\"\"\n    global gui\n    fname = '.sneak_peek.bmp'\n    jpg = '.tmp.jpg'\n    gif = '.screenshot.gif'\n    pygame.image.save(plot,fname)\n    subprocess.call(['convert',fname,\n                     '-crop','800x500+550+100',\n                     '-flip',\n                     '-resize','480x300',\n                     '+repage',\n                     gif])\n    #subprocess.call(['convert',jpg,gif])\n    trialdata['saved']=True\n    gui[\"photo\"]=PhotoImage(file=gif)\n    gui[\"photolabel\"].configure(image=gui[\"photo\"])\n\n    trialdata['review.shown'] = True # mark that we've shown the review for this trial, no need to do that again\n    \n    \n\n\n\n\n\n        \ndef recognition_review(trialdata,hist):\n    \"\"\" Make a plot of the trajectory we recorded from the robot.\"\"\"\n\n    plot = pygame.Surface(conf['screensize'])\n    plot.fill(conf['bgcolor'])\n\n    # Draw start position\n    cx,cy = conf['robot_center']\n    draw_ball(plot,(cx,cy),conf['center_marker_radius'],conf['center_marker_colour'])\n\n    # For reference, draw straight ahead as well\n    tpos =position_from_angle(conv_ang(0))\n    draw_ball(plot,tpos,conf['center_marker_radius'],conf['center_marker_colour'])\n\n    # Draw the two movement directions\n    #colors = {'A':(255,0,0),'B':(0,255,0)}\n    col = (255,255,0)\n    pos = hist['direction.xy']\n    points = [ robot_to_screen(x,y,conf) for (x,y) in [(cx,cy),pos]]\n    pygame.draw.lines(plot,col,False,points,3)\n    draw_ball(plot,pos,conf['target_radius'],col)\n    \n    show_review(plot,trialdata)\n\n\n\n    \n    \n\n\ndef get_target_colour():\n    \"\"\" After a trial is completed, get the target colour.\"\"\"\n    vmax = trialdata.get('max_vel',None) #robot.rshm('fvv_max_vel')\n    if not vmax: # if we don't have a reading\n        return conf['target_colour']\n\n    if vmax<conf['too_slow_vmax']:\n        return conf['target_slow_colour']\n    if vmax>conf['too_fast_vmax']:\n        return conf['target_fast_colour']\n    return conf['target_correct_colour']\n\n\n\n\ndef visual_error_clamp( pos, start, target):\n    \"\"\" \n    Return the cursor display position in a visual error clamp trial.\n\n    pos : (x,y) tuple denoting current position\n    start : (x,y) tuple denoting the starting position\n    target : (x,y) tuple denoting the target position\n    \"\"\"\n\n    pos    = np.array(pos)\n    start  = np.array(start)\n    target = np.array(target)\n\n    def d(a,b): return np.sqrt( sum(pow(a-b,2)) )\n    \n    # How far have we traveled from the starting point?\n    dist = d(pos,start) \n\n    # TODO: if we are moving *away* from the target, we will want to make dist negative\n    if pos[1]<start[1]:\n        dist=-dist\n    #dpostarg   = d(pos,target)\n    dstarttarg = d(start,target)\n    #if dpostarg>dstarttarg:\n    #    dist=-dist # count this as moving away\n    \n    # Unit vector pointing from target to start\n    targvect = (target-start)/dstarttarg\n\n    # Now take that amount and apply it to the vector start-to-target\n    return start + dist*targvect , dist\n    \n\n\n\n\n\n\ndef in_start_zone(trialdata):\n    \"\"\" Tell us whether the robot handle is in the starting zone.\"\"\"\n\n    # Compute how far we are from the starting zone\n    dstart = np.sqrt(pow(conf['robot_center_x']-trialdata['robot_x'],2)+pow(conf['robot_center_y']-trialdata['robot_y'],2))\n    return dstart<.5*conf['center_marker_radius']\n    \n\n\n\n    \ndef mainloop():\n\n    gui['running']=True\n    gui['keep_going']=True # be an optimist!\n    trialdata['redraw']=True\n    trialdata['first.t']=time.time()\n    pygame.event.clear() # Make sure there is no previous events in the pipeline\n    if conf['use_mouse']:\n        conf['mouse'].purgeEvents()\n\n    while gui['keep_going']:\n\n        time.sleep(.0001) # add a little breath\n        trialdata[\"t.absolute\"] = time.time()\n        trialdata[\"t.current\"] = trialdata[\"t.absolute\"]-trialdata['first.t']\n        schedule = current_schedule()\n\n\n        ##\n        ## INPUT PHASE\n        ##\n        \n        # Read the current position\n        trialdata['robot_x'],trialdata['robot_y'] = robot.rshm('x'),robot.rshm('y')\n        if phase_in(['fade','move']):\n            record_position(trialdata['robot_x'],trialdata['robot_y'])\n        \n        # Get any waiting events (joystick, but maybe other events as well)\n        if conf['use_joystick']:\n            evs = pygame.event.get()\n            if phase_is('select'):\n                for ev in evs:\n                    if ev.type==pygame.JOYAXISMOTION:\n                        trialdata['joystick.pos']=get_joystick() # update the joystick position\n                        trialdata['redraw']=True\n                    if ev.type==pygame.JOYBUTTONDOWN:\n                        trialdata['selection_made']=True\n\n        if conf['use_mouse']:\n            ev = conf['mouse'].getEvent()\n            if ev and phase_is('select'): # Update the selector position based on this movement\n                x,y=ev[0],ev[1]\n                update_selector((x,y))\n                trialdata['redraw']=True\n\n                for i in range(2,len(ev)-1):\n                    if ev[i]: trialdata['selection_made']=True\n\n\n                \n\n\n        ##\n        ## CONTROL FLOW\n        ##\n        if phase_is('init'):\n            # Start a new trial\n            start_new_trial()\n\n        if phase_is('return'):\n            if robot.move_is_done(): # if we are back at the starting point\n                robot.stay()\n                next_phase('pause')\n                trialdata['pause.until.t']=trialdata['t.absolute']+conf['pause_duration']\n\n        if phase_is('pause'):\n            if trialdata['t.absolute']>trialdata.get('pause.until.t',0): # if the hold time is expired\n                if schedule['type'] in ['passive','pinpoint','active']:\n\n                    # Determine the angle of the display target\n                    angle = conv_ang(schedule['target.direction'])\n                    trialdata['target.display.angle']=angle\n                    trialdata['target_position']=position_from_angle(angle)\n                    \n                    if schedule['type'] in ['passive','pinpoint']:\n\n                        angle = conv_ang(schedule['mov.direction'])\n                        trialdata['movement_angle']=angle\n                        radius = conf['movement_radius']\n                        if schedule['type']=='passive': # In passive trials, we add a little undershoot/overshoot\n                            o = np.random.normal(0,conf['target_overshoot_SD'])\n                            trialdata['movement_overshoot']=o\n                            radius+=o\n                        trialdata['movement_position']=position_from_angle(angle,radius)\n                        tx,ty = trialdata['movement_position']\n                        robot.move_to(tx,ty,conf['passive_duration'])\n                        next_phase('forward')\n                    \n                    if schedule['type']=='active': # active movement\n                        hold_fade()\n                        robot.background_capture()\n                        # the above line  will activate position capturing in the background,\n                        # so that we can later pull the captured trajectory out\n                        next_phase('fade')\n                        trialdata['hold.until.t']=trialdata['t.absolute']+conf['fade_duration']\n\n                        if DEBUG: # in the debug mode, we will simulate a subject's movement (because we are not connected to the physical robot)\n                            x,y=trialdata['target_position']\n                            robot.preprogram_move_to(x,y,1.5)\n                            robot.future.append({\"fvv_max_vel\":.46}) # send a max_vel value for testing\n                            robot.future.append({\"fvv_move_done\":1}) # this will happen in the future when the entire trajectory has finished\n                        \n                    trialdata['redraw']=True\n\n        if phase_is('fade'):\n            if trialdata['t.absolute']>trialdata.get('hold.until.t',0): # if the hold time is expired\n                start_move_controller(trialdata)\n                next_phase('move')\n                robot.wshm('fvv_trial_phase',5) # signal that we are moving\n                trialdata['redraw']=True\n                trialdata['t.move.start']=trialdata['t.absolute']\n\n        if phase_is('move'):\n            if robot.rshm('fvv_move_done'): # in active functioning mode\n                #if trialdata['t.absolute']>trialdata['t.move.start']+2 : # DEBUG ONLY robot.rshm('fvv_move_done'): # this is the signal from the move controller that the subject has stopped moving\n                robot.stay()\n                capt = robot.stop_background_capture() # stop capturing and return what we have captured\n                trialdata['captured'] = [ (x,y,fx,fy,fz) for (x,y,fx,fy,fz) in capt ] # make a copy, which seems to be a good idea here, otherwise I had issues when saving to pickle for example\n                \n                # Read the position at peak velocity\n                for vr in ['vmax_x','vmax_y','final_x','final_y','max_vel']:\n                    trialdata[vr]=robot.rshm('fvv_%s'%vr)\n                \n                next_phase('hold')\n                trialdata['hold.until.t']=trialdata['t.absolute']+conf['stay_duration']\n\n        if phase_is('hold'):\n            if trialdata['t.absolute']>trialdata.get('hold.until.t',0):\n                next_phase('completed')\n                \n                    \n        if phase_is('forward'):\n            if robot.move_is_done():\n                if schedule['type'] in ['passive','pinpoint']:\n                    robot.stay()\n                    trialdata['stay.until.t']=trialdata['t.absolute']+conf['stay_duration']\n                    next_phase('stay')\n\n        if phase_is('stay'):\n            if trialdata['t.absolute']>trialdata.get('stay.until.t',0):\n                if schedule['type'] in ['passive','pinpoint']:\n                    sx,sy = conf['robot_center']\n                    robot.move_to(sx,sy,conf['passive_duration'])\n                    next_phase('backward')\n                    \n        if phase_is('backward'):\n            if robot.move_is_done():\n                if schedule['type'] in ['passive']:\n                    robot.stay()\n                    next_phase('ask')\n\n                if schedule['type']=='pinpoint':\n                    robot.stay()\n\n                    r = random.random()\n                    trialdata['selector_prop']   =r\n                    trialdata['selector_initial']=r # mark that this was the first angle, in case we need to report this later on\n                    trialdata['selection_made']=False\n                    next_phase('select')\n\n\n        if phase_is('ask'):\n            r = numpy.random.uniform()\n            if r<conf['target_overshoot_ask_p']:\n                review_plot()\n                answer = messagebox.askyesno(\"Question\",\"Did the cursor overshoot (YES) or undershoot (NO) the center of the target?\")\n                trialdata['overshoot_answer']='overshoot' if answer else 'undershoot'\n            next_phase('completed')\n                    \n                    \n        if phase_is('select'):\n            if trialdata['selection_made']:\n                print(\"Selected angle %.2f deg (%.2f)\"%(trialdata['selector_angle'],trialdata['selector_prop']))\n                next_phase('completed') # this will automatically wrap up the trial\n\n\n        if phase_is('completed'):\n            review_plot()\n            write_logs()\n            start_new_trial()\n            \n        \n\n\n\n        #\n        #       DISPLAY \n        # Possibly update the display\n        #\n        if trialdata['redraw']:\n\n            conf['screen'].fill(conf['bgcolor'])\n            \n            clamp_trial = np.isnan(trialdata['cursor.rotation']) # Decide whether this is an error clamp trial\n\n            # Draw start position\n            draw_ball(conf['screen'],conf['robot_center'],conf['center_marker_radius'],conf['center_marker_colour'])\n\n            if 'target_position' in trialdata and not trialdata['type']=='pinpoint': # We show a target always, only not if this is a pinpoint trial\n\n                # Determine the colour\n                col = get_target_colour() if phase_is('hold') else conf['target_colour']\n\n                # Now draw the target.\n                # If we want to draw the target as an arc...\n                if schedule and schedule.get('target.type',None)=='arc':\n                    #if clamp_trial and conf['na.bar.show'] and conf['bar.as.arc']:\n                    draw_arc(conf['screen'],conf['movement_radius'],col)\n                else: #  or draw the target as a ball\n                    draw_ball(conf['screen'],trialdata['target_position'],conf['target_radius'],col)\n                    \n            if phase_is('select'): # If we are in the select phase\n                if 'selector_prop' in trialdata: selector_to_angle()\n                draw_arc_selector(conf['screen'])\n\n            # For debug only: show veridical robot position\n            if DEBUG:\n                draw_ball(conf['screen'],(trialdata['robot_x'],trialdata['robot_y']),conf['cursor_radius'],(100,100,100)) # only for debug: show the real robot position\n\n            if phase_in(['forward','stay','fade','move']):\n\n                # Show a cursor\n                if trialdata['type'] in ['passive','active']:\n\n                    # Determine the colour for the cursor (point cursor or arc cursor)\n                    if phase_is('fade'):\n                        colour=conf['fade_cue_colour']\n                    elif phase_is('move'):\n                        colour = conf['active_cursor_colour']\n                    else:\n                        colour = conf['passive_cursor_colour']\n\n                    if clamp_trial:\n                        if conf['na.bar.show']:\n                            trialdata['cursor_position']=(trialdata['robot_x'],trialdata['robot_y'])\n                            trialdata['bar_position'],trialdata['bar_distance']=visual_error_clamp( (trialdata['robot_x'],trialdata['robot_y']),\n                                                                                                    conf['robot_center'],\n                                                                                                    trialdata['target_position'] )\n                        else:\n                            assert False # not implemented, ask me this another day\n                            \n                    else:\n                        # Rotate the cursor by a specified amount\n                        trialdata['cursor_position']=rotate((trialdata['robot_x'],trialdata['robot_y']),\n                                                            deg2rad(trialdata['cursor.rotation']),conf['robot_center'])\n                    # OLD -- We show the cursor, but if the rotation is NA (i.e. no-feedback trial) then we only show it as long\n                    # as it's in the target zone.\n                    #if showcursor or ( (trialdata['type']=='active') and in_start_zone(trialdata)): # or (trialdata['type']=='active' and phase_is('fade')):\n                    showcursor = True # not np.isnan(trialdata['cursor.rotation']) # the signal to hide the cursor is setting cursor.rotation to NA\n                    if (not conf['na.cursor.show']) and clamp_trial:\n                        # Show the cursor only in the starting zone, hide it when it's outside\n                        showcursor = (trialdata['type']=='active') and in_start_zone(trialdata)\n\n                    # Decide whether we want to show a \"horizontal\" bar moving along with the subject...\n                    if clamp_trial and conf['na.bar.show'] and not in_start_zone(trialdata):\n\n                        if conf['bar.as.arc']:\n                            draw_arc(conf['screen'],trialdata['bar_distance'],colour)\n                        else:\n                            _,cursor_y = trialdata['bar_position']\n                            draw_bar(conf['screen'],cursor_y)\n                        \n                    if showcursor:\n                        # Always show the cursor\n                        draw_ball(conf['screen'],trialdata['cursor_position'],conf['cursor_radius'],colour)\n\n\n                        \n\n                \n            pygame.display.flip()\n\n            \n    print(\"Bailed out of main loop.\")\n    gui['running']=False\n    \n\n\n    \n\n\ndef next_phase(p):\n    \"\"\" Set the current phase to a given value. \"\"\"\n    print(p)\n    trialdata['phase']=p\n\n    # Track the start of this phase\n    k = trialdata.get('t.phase',{})\n    k[p]=time.time()\n    trialdata['t.phase']=k\n\n    # Mark on the robot that this phase started too\n    if p in conf['phases']:\n        robot.wshm('fvv_trial_phase',conf['phases'].index(p))\n    else:\n        print(\"## WARNING, unknown phase %s\"%p)\n\n\n\n\n\n\n\n#\n#\n#  Stuff relating to logging\n#\n#\n\n\n\ndef init_logs():\n    #logfile = open(\"data/exampledata.txt\",'w')\n\n    basedir = './data/%s'%conf['participant']\n    if not os.path.exists(basedir):\n        os.makedirs(basedir)\n    \n    timestamp = datetime.datetime.now().strftime(\"%d_%m.%Hh%Mm%S\")\n    schedulestem = os.path.splitext(os.path.basename(conf['schedulefile']))[0]\n    basename = '%s/%s_%s_%s--%s--'%(basedir,conf['participant'],EXPERIMENT,timestamp,schedulestem)\n        \n    trajlog = '%strajectory.bin'%basename\n    robot.start_log(trajlog,conf['N_ROBOT_LOG'])\n    gui[\"logging\"]=True\n\n    conf['captured'] = []\n    #capturelog = '%scaptured.pickle27'%basename\n    ##trajlog.write('participant experiment trial x y dx dy t t.absolute\\n')\n    \n    dumplog = '%sdump.pickle'%basename\n\n    ## Also dump the configuration parameters, this will make it easier to debug in the future\n    conflog = open('%sparameters.json'%basename,'w')\n    params = {}\n    for key in sorted(conf):\n        if key not in ['joystick','screen','mouse','thread','triallog']: # don't dump that stuff: not serialisable\n            params[key]=conf[key]\n    params['schedule']=trialdata['schedule']\n    #print(params)\n    json.dump(params,conflog)\n    conflog.close()\n\n    conf['obsvlog']     = '%sobservations.txt'%basename # where we will write the experimenter observations\n\n    triallog = '%strials.txt'%basename\n    conf['triallog'] = open(triallog,'w')\n    conf['triallog'].write(trial_header())\n\n    conf['trajlog']     = trajlog\n    conf['dumpf']       = dumplog\n    #conf['capturelog']  = capturelog\n\n\n\n\n\ndef write_logs():\n    \"\"\" At the end of a trial, write into the log. \"\"\"\n    hist = {}\n    for k in ['type','target.direction','mov.direction','cursor.rotation','target_position','cursor_position','t.phase','selector_angle','selector_prop','selector_history','selector_initial','captured','overshoot_answer','movement_overshoot','trial','schedule.number']:\n        v = trialdata.get(k,np.nan)\n        if isinstance(v,np.ndarray):\n            v = v.tolist()\n        hist[k]=v\n    conf['trialhistory'].append(hist)\n    pickle.dump(conf['trialhistory'],open(conf['dumpf'],'wb')) # this will overwrite the previous file\n    #h5f = h5py.File(conf['dumpf'], 'w')\n    #for h in conf['trialhistory']:\n    #    h5f.create_dataset(h['trial'], data=h)\n    #h5f.close()\n\n    conf['triallog'].write(trial_log())\n    conf['triallog'].flush()\n    \n\n\nLOG_COLUMNS = ['trial','type','mov.direction','cursor.rotation','selector_angle','selector_prop','max_vel','vmax_x','vmax_y','final_x','final_y','movement_overshoot','overshoot_answer','timestamp']\ndef trial_header():\n    return \" \".join(LOG_COLUMNS)+\"\\n\"\n    \ndef trial_log():\n    return \" \".join([str(trialdata.get(v,np.nan)) for v in LOG_COLUMNS])+\"\\n\"\n\n\n\ndef close_logs():\n    if gui[\"logging\"]:\n        if 'triallog' in conf:\n            conf['triallog'].close()\n        robot.stop_log()\n    gui[\"logging\"]=False\n\n\n\n\n\ndef read_schedule_file():\n    \"\"\" \n    Read a schedule file which tells us the parameters of each trial.\n    \"\"\"\n\n    print(\"Reading trial schedule file %s\"%conf['schedulefile'])\n\n    s = pd.read_csv(conf['schedulefile'],sep=',')\n    for c in ['trial','type','target.direction','mov.direction','cursor.rotation','force.field','target.type']:\n        if not c in s.columns:\n            print(\"## ERROR: missing column %s in schedule.\"%c)\n            return False\n\n    if not is_string_dtype(s['force.field']):\n        print(\"## ERROR: force.field column has to be a string.\")\n        return False\n\n    ttypes = list(set(s['target.type']))\n    for tt in ttypes:\n        if tt not in ['arc','point']:\n            print(\"## ERROR: unknown target type {}, has to be arc or point even according to Indian standards.\".format(tt))\n            return False\n            \n\n    # I don't trust pandas so I prefer a simple data structure, list of dicts            \n    schedule = []\n    for i,row in s.iterrows():\n        row = dict(row)\n        #print(row['force.field'])\n        row['force.field']=row['force.field'].strip().lower()\n        if not row['force.field'] in ['none','curl','channel']:\n            print(\"## ERROR: unknown force field value %s\"%row['force.field'])\n            return False\n        schedule.append(dict(row))\n\n    trialdata['schedule'] = schedule\n    print(\"Finished reading %i trials\"%(len(trialdata['schedule'])))\n    return True\n\n\n    \n\n\n\ndef update_ui():\n    global gui\n    gui[\"runb\"].configure(state=DISABLED)\n    gui[\"recogb\"].configure(state=DISABLED)\n    gui[\"mcb\"].configure(state=DISABLED)\n    gui[\"capturecenter\"].configure(state=DISABLED)\n    gui[\"holdcenter\"].configure(state=DISABLED)\n\n    if gui[\"loaded\"]:\n        gui[\"loadb\"].configure(state=DISABLED)\n\n        if not gui[\"running\"]:\n            gui[\"runb\"].configure(state=NORMAL)\n            gui[\"recogb\"].configure(state=NORMAL)\n            gui[\"mcb\"].configure(state=NORMAL)\n            gui[\"capturecenter\"].configure(state=NORMAL)\n            gui[\"holdcenter\"].configure(state=NORMAL)\n\n    else: # not loaded\n        gui[\"loadb\"].configure(state=NORMAL)\n\n    \n\n        \ndef load_robot():\n    \"\"\" Launches the robot process. \"\"\"\n    global gui\n    tkMessageBox.showinfo(\"Robot\", \"We will now load the robot.\\n\\nLook at the terminal because you may have to enter your sudo password there.\")\n\n    robot.load() # launches the robot C++ script\n    if DEBUG: robot.popup(master)\n    # Then do zero FT\n    tkMessageBox.showinfo(\"Robot\", \"Now we will zero the force transducers.\\nAsk the subject to let go of the handle.\" )\n    robot.zeroft()\n    bias = robot.bias_report()\n    global trialdata\n    trialdata['bias']=bias\n    tkMessageBox.showinfo(\"Robot\", \"Robot FT bias calibration done.\\nYou can tell the subject to hold the handle again.\\n\\nRobot bias settings:\\n\\n\" +\" \".join([ str(b) for b in bias ]))\n    gui[\"loaded\"]=True\n    update_ui()\n\n\n\n\n\n\ndef stop_running():\n    print(\"Currently {} threads still active.\".format(threading.active_count()))\n    gui['keep_going'] = False # this will bail out of the main loop\n    time.sleep(1) # wait until some last commands may have stopped\n    close_logs()\n    if 'thread' in conf and conf['thread']:\n        #conf['thread'].stop()\n        pass # cannot kill thread unfortunately...\n\n    if gui['loaded']:\n        robot.unload()\n        gui[\"loaded\"]=False\n        \n\n    \ndef end_program():\n    \"\"\" When the user presses the quit button. \"\"\"\n    stop_running()\n    ending()\n    master.destroy()\n    print(\"Just before killing, {} thread(s) still active.\".format(threading.active_count()))\n    sys.exit(0)\n\n\ndef runrecog():\n    \"\"\" Run the recognition task\"\"\"\n    if gui[\"running\"]:\n        return\n    \n    global conf\n\n    participant=gui[\"subject.id\"].get().strip()\n    if participant==\"\":\n        tkMessageBox.showinfo(\"Error\", \"You need to enter a participant ID.\")\n        return\n    conf['participant'] = participant\n\n    # Ask to open a schedule file\n    fn = filedialog.askopenfilename(filetypes = ((\"CSV files\", \"*.csv\")\n                                                 ,(\"All files\", \"*.*\") ))\n    if not fn or not len(fn):\n        print(\"No file selected.\")\n        return\n    schedulef = fn\n\n    print(\"Reading schedule file {}.\".format(schedulef))\n    # Read the recognition script\n    s = pd.read_csv(schedulef,sep=',')\n    for c in ['trial','direction','type','target.direction']:\n        if not c in s.columns:\n            print(\"## ERROR: missing column %s in schedule file - is this really a yes/no recognition task schedule file?\"%c)\n            return False\n\n    schedule = []\n    for i,row in s.iterrows():\n        row = dict(row)\n        schedule.append(row)\n    trialdata['schedule']=schedule\n\n    # Read the robot center\n    if not get_center(): return\n    robot.wshm('fvv_robot_center_x',conf['robot_center_x'])\n    robot.wshm('fvv_robot_center_y',conf['robot_center_y'])\n\n    # Let's go!\n    print(\"Launching recognition yes/no test: {} trials.\".format(len(schedule)))\n\n    # TODO: empty display\n    conf['screen'].fill(conf['bgcolor'])\n    pygame.display.flip()\n    \n    gui[\"running\"]=True\n    update_ui()\n\n    conf['thread']=Thread(target=recognitiontest)\n    conf['thread'].start()\n        \n\n\nclass NpEncoder(json.JSONEncoder):\n    def default(self, obj):\n        if isinstance(obj, np.integer):\n            return int(obj)\n        elif isinstance(obj, np.floating):\n            return float(obj)\n        elif isinstance(obj, np.ndarray):\n            return obj.tolist()\n        else:\n            return super(NpEncoder, self).default(obj)\n\n\n\ndef runmotorcopy():\n    \"\"\" Run the motor copy version of the recognition task \"\"\"\n    if gui[\"running\"]:\n        return\n    \n    global conf\n\n    participant=gui[\"subject.id\"].get().strip()\n    if participant==\"\":\n        tkMessageBox.showinfo(\"Error\", \"You need to enter a participant ID.\")\n        return\n    conf['participant'] = participant\n\n    # Ask to open a schedule file\n    fn = filedialog.askopenfilename(filetypes = ((\"CSV files\", \"*.csv\")\n                                                 ,(\"All files\", \"*.*\") ))\n    if not fn or not len(fn):\n        print(\"No file selected.\")\n        return\n    schedulef = fn\n\n    print(\"Reading schedule file {}.\".format(schedulef))\n    # Read the recognition script\n    s = pd.read_csv(schedulef,sep=',')\n    for c in ['trial','direction','type','target.direction']:\n        if not c in s.columns:\n            print(\"## ERROR: missing column %s in schedule file - is this really a yes/no recognition task schedule file?\"%c)\n            return False\n\n    schedule = []\n    for i,row in s.iterrows():\n        row = dict(row)\n        row['type']=str(row['type'])\n        schedule.append(row)\n    trialdata['schedule']=schedule\n\n    # Read the robot center\n    if not get_center(): return\n    robot.wshm('fvv_robot_center_x',conf['robot_center_x'])\n    robot.wshm('fvv_robot_center_y',conf['robot_center_y'])\n\n    # Initialise log information\n    basedir = './data/%s'%conf['participant']\n    if not os.path.exists(basedir):\n        os.makedirs(basedir)\n    timestamp = datetime.datetime.now().strftime(\"%d_%m.%Hh%Mm%S\")\n    basename = '%s/%s_%s_%s--motorcopy--'%(basedir,conf['participant'],EXPERIMENT,timestamp)\n    jsonf    = basename+\"parameters.json\"   # where we will save the data\n    capturef = basename+\"dump.pickle\"       # where we will save the data\n    conf['basename']       = basename\n    conf['jsonf']          = jsonf\n    conf['capturef']       = capturef\n    conf['trialhistory']   = []\n    conf['capturehistory'] = []\n    motor_copy_json_log()\n    motor_copy_capture_log()\n\n    trialdata['current_schedule']=-1 # start at the beginning\n    trialdata['phase']='init'\n\n    gui['progress']['maximum'] =len(trialdata['schedule'])\n    gui['progress']['value']   =0\n\n\n    # Open the logs\n    trajlog = '%strajectory.bin'%basename\n    robot.start_log(trajlog,conf['N_ROBOT_LOG'])\n    gui[\"logging\"]=True\n\n    \n    # Let's go!\n    print(\"Launching motor copy test: {} trials.\".format(len(schedule)))\n\n    conf['screen'].fill(conf['bgcolor'])\n    pygame.display.flip()\n    \n    gui[\"running\"]=True\n    update_ui()\n\n    conf['thread']=Thread(target=mainloopmotorcopy)\n    conf['thread'].start()\n        \n\n\n\n\ndef motor_copy_json_log():\n    # Write the JSON log for the motor copy test\n    dat = {\n            'robot_center':(conf['robot_center_x'],conf['robot_center_y']),\n            'schedule':conf['trialhistory']\n        }\n    #print(dat)\n    with open(conf['jsonf'],'w') as f:\n        json.dump(dat,f,cls=NpEncoder)\n\n\n\ndef motor_copy_capture_log():\n    # Write the (pickle) log for the motor copy test, the log that tracks the captured data\n    with open(conf['capturef'],'wb') as f:\n        pickle.dump(conf['capturehistory'],f)\n    \n        \n\n\ndef start_motor_copy_trial():\n\n    trialdata['current_schedule']+=1\n\n    gui['progress']['maximum'] =len(trialdata['schedule'])\n    gui['progress']['value']   =trialdata['current_schedule']\n\n    if trialdata['current_schedule']==len(trialdata['schedule']):\n        # Done the experiment!\n        print(\"## MOTOR COPY COMPLETED ##\")\n        robot.move_to(conf['robot_center_x'],conf['robot_center_y'],conf['return_duration'])\n        while not robot.move_is_done():\n            time.sleep(.1)\n        robot.stay() # just fix the handle wherever it is\n        next_phase('completed')\n        gui['keep_going'] = False # this will bail out of the main loop\n        gui['running'] = False\n        time.sleep(1) # wait until some last commands may have stopped\n        robot.stop_log()\n        gui[\"logging\"]=False\n        update_ui()\n        # Now ask the experimenter for observations\n        #obsv = tksd.askstring('Please record any observations', 'Experimenter, please write down any observations.\\nAny irregularities?\\nDid the subject seem concentrated or not?\\nWere things unclear or clear?\\nAnything else that is worth noting?')\n        #with open(conf['obsvlog'],'w') as f:\n        #    f.write(obsv)\n\n        gui['trialinfo'].set('Completed block')\n        tkMessageBox.showinfo(\"Robot\", \"Motor copy completed! Yay!\")\n        return\n\n    sched = current_schedule() # Retrieve the current schedule\n    trialdata['timestamp']       =time.time()\n    trialdata['schedule.number'] =trialdata['current_schedule']\n    trialdata['trial']           =sched['trial'] # trial number\n    trialdata['type']            =sched['type']\n    trialdata['movement.direction']=sched['direction']         # where subjects will be moved passivly\n    trialdata['movement.position'] =position_from_angle(conv_ang(sched['direction'])) # where subjects will be moved passively\n    trialdata['target.direction']=sched['target.direction']  # display angle of the target\n    trialdata['target.position'] =position_from_angle(conv_ang(sched['target.direction']))  # display angle of the target\n    trialdata['movement_position']=None\n    trialdata['force.field']     ='none'\n    trialdata['captured']        =[] # nothing captured\n    trialdata['review.shown']    =False\n    #trialdata['position_history']=[] # start with a clean position history (we'll fill this up during active trials only)\n\n    for v in ['vmax_x','vmax_y','final_x','final_y']:\n        trialdata[v]=None\n\n    #trialinfo =  \"trial %d %s   \"%(trialdata['trial'],trialdata['type'])\n    trialinfo = (\"targ: %.2f \\n\"%(trialdata['movement.direction']))\n    print('\\n\\n\\n### MOTOR COPY TRIAL %d TYPE %s ####'%(trialdata['trial'],trialdata['type']))\n    print(trialinfo)\n    gui['trialinfo'].set(trialinfo)\n    robot.wshm('fvv_trial_no',     trialdata['trial'])\n\n    ## Return the robot to the center\n    sx,sy = conf['robot_center']\n    robot.move_to(sx,sy,conf['return_duration'])\n    next_phase('return') # return to the starting point to start the trial\n\n\n\n\ndef mainloopmotorcopy():\n    \n    gui['running']=True\n    gui['keep_going']=True # be an optimist!\n    \n    trialdata['redraw']=True\n    trialdata['first.t']=time.time()\n    pygame.event.clear() # Make sure there is no previous events in the pipeline\n\n    while gui['keep_going']:\n\n        time.sleep(.0001) # add a little breath\n        trialdata[\"t.absolute\"] = time.time()\n        trialdata[\"t.current\"] = trialdata[\"t.absolute\"]-trialdata['first.t']\n        schedule = current_schedule()\n\n\n        ##\n        ## INPUT PHASE\n        ##\n        \n        # Read the current position\n        trialdata['robot_x'],trialdata['robot_y'] = robot.rshm('x'),robot.rshm('y')\n        if phase_in(['fade','move']):\n            record_position(trialdata['robot_x'],trialdata['robot_y'])\n\n\n        ##\n        ## CONTROL FLOW\n        ##\n        if phase_is('init'):\n            # Start a new trial\n            start_motor_copy_trial()\n\n        if phase_is('return'):\n            if robot.move_is_done(): # if we are back at the starting point\n                robot.stay()\n                next_phase('pause')\n                trialdata['pause.until.t']=trialdata['t.absolute']+conf['recog_pause_duration']\n\n        if phase_is('pause'):\n            if trialdata['t.absolute']>trialdata.get('pause.until.t',0): # if the hold time is expired\n\n                # Now start a forward movement to the given target direction\n                angle = conv_ang(schedule['direction'])\n                trialdata['movement_angle']=angle\n                radius = conf['movement_radius']\n                trialdata['movement_position']=position_from_angle(angle,radius)\n                tx,ty = trialdata['movement_position']\n                robot.move_to(tx,ty,conf['passive_duration'])\n                next_phase('forward')\n\n        if phase_is('forward'):\n            if robot.move_is_done():\n                robot.stay()\n                trialdata['stay.until.t']=trialdata['t.absolute']+conf['stay_duration']\n                next_phase('stay')\n\n        if phase_is('stay'):\n            if trialdata['t.absolute']>trialdata.get('stay.until.t',0):\n                sx,sy = conf['robot_center']\n                robot.move_to(sx,sy,conf['passive_duration'])\n                next_phase('backward')\n                    \n        if phase_is('backward'):\n            if robot.move_is_done():\n                \n                hold_fade()\n                robot.background_capture()\n                # the above line  will activate position capturing in the background,\n                # so that we can later pull the captured trajectory out\n                next_phase('fade')\n                trialdata['hold.until.t']=trialdata['t.absolute']+conf['fade_duration']\n                \n                if DEBUG: # in the debug mode, we will simulate a subject's movement (because we are not connected to the physical robot)\n                    x,y=trialdata['target.position']\n                    robot.preprogram_move_to(x,y,1.5)\n                    robot.future.append({\"fvv_max_vel\":.46}) # send a max_vel value for testing\n                    robot.future.append({\"fvv_move_done\":1}) # this will happen in the future when the entire trajectory has finished\n                    \n                trialdata['redraw']=True\n                \n        if phase_is('fade'):\n            if trialdata['t.absolute']>trialdata.get('hold.until.t',0): # if the hold time is expired\n                start_move_controller(trialdata) ### TODO: change this somehow, this should probably not refer to this normal loop function\n                next_phase('move')\n                robot.wshm('fvv_trial_phase',5) # signal that we are moving \n                trialdata['redraw']=True\n                trialdata['t.move.start']=trialdata['t.absolute']\n\n        if phase_is('move'):\n            if robot.rshm('fvv_move_done'): # in active functioning mode\n                #if trialdata['t.absolute']>trialdata['t.move.start']+2 : # DEBUG ONLY robot.rshm('fvv_move_done'): # this is the signal from the move controller that the subject has stopped moving\n                robot.stay()\n                capt = robot.stop_background_capture() # stop capturing and return what we have captured\n                trialdata['captured'] = [ (x,y,fx,fy,fz) for (x,y,fx,fy,fz) in capt ] # make a copy, which seems to be a good idea here, otherwise I had issues when saving to pickle for example\n                \n                # Read the position at peak velocity\n                for vr in ['vmax_x','vmax_y','final_x','final_y','max_vel']:\n                    trialdata[vr]=robot.rshm('fvv_%s'%vr)\n                \n                next_phase('hold')\n                trialdata['hold.until.t']=trialdata['t.absolute']+conf['stay_duration']\n\n        if phase_is('hold'):\n            if trialdata['t.absolute']>trialdata.get('hold.until.t',0):\n                next_phase('completed')\n                \n\n        if phase_is('completed'):\n            motorcopy_review_plot()\n\n            # Write the log\n            thistrial = {}\n            for col in ['trial','movement.direction','movement.position','target.direction','target.position','type','timestamp','vmax_x','vmax_y','final_x','final_y']:\n                thistrial[col] = trialdata[col]\n            conf['trialhistory'].append(thistrial)\n            motor_copy_json_log()\n\n            # Write the captured trajectory log\n            capt = {'trial'     :trialdata['trial'],\n                    'timestamp' :trialdata['timestamp'],\n                    'direction' :trialdata['movement.direction'],\n                    'captured'  :list(trialdata['captured'])}\n            conf['capturehistory'].append(capt)\n            motor_copy_capture_log()\n            \n            ## write_logs() TODO this has to be done but for motor copy\n\n            ## launch the next trial\n            start_motor_copy_trial()\n            \n        \n\n        #\n        #       DISPLAY \n        # Possibly update the display\n        #\n        if trialdata['redraw']:\n\n            conf['screen'].fill(conf['bgcolor'])\n            \n            # Draw start position\n            draw_ball(conf['screen'],conf['robot_center'],conf['center_marker_radius'],conf['center_marker_colour'])\n\n            # Draw the target\n            ### Determine the colour\n            col = get_target_colour() if phase_is('hold') else conf['target_colour']\n            \n            ### Now draw the target.\n            ### If we want to draw the target as an arc...\n            draw_arc(conf['screen'],conf['movement_radius'],col)\n                    \n            # For debug only: show veridical robot position\n            if DEBUG:\n                draw_ball(conf['screen'],(trialdata['robot_x'],trialdata['robot_y']),conf['cursor_radius'],(100,100,100)) # only for debug: show the real robot position\n\n            if phase_in(['forward','stay','fade','move']):\n\n                # Show a cursor\n                \n                # Determine the colour for the cursor (point cursor or arc cursor)\n                if phase_is('forward') or phase_is('stay'):\n                    colour=conf['motorcopy_forward_colour']\n                elif phase_is('fade'):\n                    colour=conf['fade_cue_colour']\n                elif phase_is('move'):\n                    colour = conf['active_cursor_colour']\n\n                # Determine the cursor position\n                trialdata['cursor_position']=(trialdata['robot_x'],trialdata['robot_y'])\n                _,trialdata['bar_distance']=visual_error_clamp( (trialdata['robot_x'],trialdata['robot_y']),\n                                                                conf['robot_center'],\n                                                                trialdata['target.position'] )\n\n                # Now actually draw the cursor\n                cursor_is_arc = not in_start_zone(trialdata)\n                in_start_zone(trialdata)\n                \n                if cursor_is_arc:\n                    draw_arc(conf['screen'],trialdata['bar_distance'],colour)\n                else:\n                    # Always show the cursor\n                    draw_ball(conf['screen'],trialdata['cursor_position'],conf['cursor_radius'],colour)\n                \n            pygame.display.flip()\n\n            \n    print(\"Bailed out of main motor copy loop.\")\n    gui['running']=False\n    \n\n\n\n\n\n\n\n        \ndef motorcopy_review_plot():\n    \"\"\" Make a plot of the passive movement and following active movement.\"\"\"\n\n    plot = pygame.Surface(conf['screensize'])\n    plot.fill(conf['bgcolor'])\n\n    # Draw start position\n    cx,cy = conf['robot_center']\n    draw_ball(plot,(cx,cy),conf['center_marker_radius'],conf['center_marker_colour'])\n\n    # For reference, draw straight ahead as well\n    tpos =position_from_angle(conv_ang(0))\n    draw_ball(plot,tpos,conf['center_marker_radius'],conf['center_marker_colour'])\n\n    # Draw the movement direction\n    #colors = {'A':(255,0,0),'B':(0,255,0)}\n    col = (255,255,0)\n    pos = trialdata['movement.position']\n    points = [ robot_to_screen(x,y,conf) for (x,y) in [(cx,cy),pos]]\n    pygame.draw.lines(plot,col,False,points,3)\n    draw_ball(plot,pos,conf['target_radius'],col)\n\n    # Draw the subject movement\n    if 'captured' in trialdata and len(trialdata['captured'])>0: # if there is actually something captured\n\n        traj = [ (x,y,fx,fy,fz) for (x,y,fx,fy,fz) in trialdata['captured'] ] # make a copy, which seems to be a good idea here\n        if len(traj)==0:\n            print(\"Captured 0 samples\")\n            return\n        # Just resample a little bit, so that the drawing will consume less resources\n\n        #print(traj)\n        traj = traj[ ::10 ]\n\n        # Draw the forces also (how cool is that)\n        for (x,y,fx,fy,_) in traj[::3]: # further subsampling also\n            sx,sy = robot_to_screen(x,y,conf)\n            tx,ty = robot_to_screen(x+conf['review_force_scale']*fx,\n                                    y+conf['review_force_scale']*fy,\n                                    conf)\n            pygame.draw.line(plot,conf['review_force_colour'],(sx,sy),(tx,ty),conf['review_force_width'])\n\n        # Draw the actual positions\n        points = [ robot_to_screen(x,y,conf) for x,y,_,_,_ in traj ]\n        if len(points):\n            pygame.draw.lines(plot,conf['review_trajectory_colour'],False,points,conf['review_linewidth'])\n\n    show_review(plot,trialdata)\n\n\n\n    \n\n    \n\n\ndef get_center():\n    \"\"\" Get the center X coordinate from the text entry box.\"\"\"\n    centerx=gui[\"centerv\"].get().strip()\n    try:\n        centerx=float(centerx)\n    except:\n        tkMessageBox.showinfo(\"Error\", \"The center X location you entered is invalid: %s.\\n\\nIt has to be a floating number.\"%centerx)\n        return\n    conf['robot_center_x'] = centerx\n    conf['robot_center']=(conf['robot_center_x'],conf['robot_center_y'])\n    return True\n    \n\n    \ndef move_until_done(x,y,t):\n    robot.move_to(x,y,t)\n    while not robot.move_is_done():\n        time.sleep(.1)\n    return\n\n\ndef askoptions(message,options):\n    \"\"\" Display a window that asks for options \"\"\"\n    win = Toplevel(master)\n    win.title('Question')\n    Label(win, text=message).pack()\n    global selected\n    selected = None\n    def finish(option):\n        global selected\n        print('selected {}'.format(option))\n        selected = option\n    for option in options:\n        Button(win, text=option, command=lambda option=option: finish(option)).pack()\n    while not selected:\n        time.sleep(.1)\n    win.destroy()\n    return selected\n\n\n\n\n\n\ndef recognition_display(trialdata,markersonly=False):\n    # Draw the subject display screen for use during the recognition task\n    conf['screen'].fill(conf['bgcolor'])\n\n    trialdata['robot_x']=robot.rshm('x')\n    trialdata['robot_y']=robot.rshm('y')\n    \n    # Draw start position\n    draw_ball(conf['screen'],conf['robot_center'],conf['center_marker_radius'],conf['center_marker_colour'])\n\n    # Draw the target (either arc or point)\n    if conf['recognition_display_arc']:\n        draw_arc(conf['screen'],conf['movement_radius'],conf['target_colour'])\n    else:\n        draw_ball(conf['screen'],trialdata['target_position'],conf['target_radius'],conf['target_colour'])\n\n    # Draw a cursor\n    if not markersonly: # if we also show a marker indicating the subject position somehow (could be a bar or arc or cursor)\n        trialdata['bar_position'],dist=visual_error_clamp( (trialdata['robot_x'],trialdata['robot_y']),\n                                                           conf['robot_center'],\n                                                           trialdata['target_position'] )\n        if conf['recognition_display_arc']:\n            draw_arc(conf['screen'],dist,conf['active_cursor_colour'])\n        else: # this stuff below is so obscene we don't do it anymore\n            _,cursor_y = trialdata['bar_position']\n            draw_bar(conf['screen'],cursor_y)\n        \n    pygame.display.flip()\n\n\n\ndef move_and_display(targetx,targety,movet,displayfunction):\n    # Show a display while moving to a given location in a given time\n    robot.move_to(targetx,targety,movet)\n    while not robot.move_is_done():\n        displayfunction()\n        time.sleep(.001)\n\n\n    \ndef recognitiontest():\n    \"\"\" The main procedure for the recognition test.\"\"\"\n    gui['running']=True\n    gui['keep_going']=True # be an optimist!\n\n    t0 = time.time()\n    \n    # Initialise log information\n    basedir = './data/%s'%conf['participant']\n    if not os.path.exists(basedir):\n        os.makedirs(basedir)\n    timestamp = datetime.datetime.now().strftime(\"%d_%m.%Hh%Mm%S\")\n    basename = '%s/%s_%s_%s'%(basedir,conf['participant'],EXPERIMENT,timestamp)\n    jsonf = basename+\"recognition_yesno.json\" # where we will save the data\n\n    cx,cy = conf['robot_center_x'],conf['robot_center_y']\n    move_until_done(cx,cy,conf['return_duration'])\n    robot.stay_at(cx,cy)\n    time.sleep(conf['recog_pause_duration'])\n\n    showvis = bool(gui['recognitionvisuals'])\n    \n    current_schedule = 0\n    history = []\n    gui['progress']['maximum'] =len(trialdata['schedule'])\n    for i,schedule in enumerate(trialdata['schedule']):\n        gui['progress']['value']   =i\n        gui['progress'].update()\n        trialdata['trial']=schedule['trial']\n        trialdata['target_position'] = position_from_angle(conv_ang(schedule['target.direction']),conf['movement_radius']) # this is where we will display the target\n        \n        hist = {'trial':schedule['trial'],'start.t':time.time()-t0}\n        print(\"\\n\\n### Trial {} ###\".format(trialdata['trial']))\n\n        angle_deg = schedule['direction'] # this is where we will move the hand to\n        angle = conv_ang(angle_deg) # Compute conventional angle\n        tx,ty = position_from_angle(angle,conf['movement_radius'])\n        print(\"Moving to direction : {:.2f} deg -> ({:.2f},{:.2f})\".format(angle_deg,tx,ty))\n        trialdata['movement']=tx,ty\n        move_and_display(tx,ty,conf['passive_duration'], lambda : recognition_display(trialdata)) # move out to that direction\n        robot.stay_at(tx,ty)\n        time.sleep(conf['stay_duration'])\n        recognition_display(trialdata,True)\n        move_until_done(cx,cy,conf['return_duration']) # return to the center\n        robot.stay_at(cx,cy)\n        #time.sleep(conf['recog_pause_duration'])\n        hist['direction'    ]     =angle_deg\n        hist['direction_rad']     =angle\n        hist['display_direction'] =schedule['target.direction']\n        hist['display_position']  =trialdata['target_position']\n        hist['type'         ]     =schedule['type']\n        hist['direction.xy' ]     =tx,ty\n        \n        recognition_review(trialdata,hist)\n        answer = askoptions(\"Was this the movement you produced?\",['yes','no'])\n        hist['answer']=answer #'A' if answer=='first' else 'B'\n        hist['end.t']=time.time()-t0\n        history.append(hist)\n        savejson(history,jsonf)\n\n        if not gui['keep_going']: return # This is if the user has pressed exit somehow\n\n    print(history)\n    savejson(history,jsonf)\n    print(\"Results written to {}.\".format(jsonf))\n    print (\"Completed recognition test.\")\n    \n    gui['running']=False\n    update_ui()\n\n\n\ndef savejson(history,jsonf):\n    \"\"\" Write the history of the recognition task\n    to the json file.\"\"\"\n    def default(o): # A hack so that we can save int64\n        if isinstance(o, numpy.int64): return int(o)  \n        raise TypeError\n    with open(jsonf,'w') as f:\n        json.dump(history,f,default=default)\n    \n\n    \n\ndef run():\n    \"\"\" Do one run of the experiment. \"\"\"\n    if gui[\"running\"]:\n        return\n\n    global conf\n\n    participant=gui[\"subject.id\"].get().strip()\n    if participant==\"\":\n        tkMessageBox.showinfo(\"Error\", \"You need to enter a participant ID.\")\n        return\n    conf['participant'] = participant\n\n    \n    schedulefile=gui[\"schedulef\"].get().strip()\n    if schedulefile==\"\":\n        tkMessageBox.showinfo(\"Error\", \"You need to enter a schedule file name.\")\n        return\n    if not os.path.exists(schedulefile):\n        tkMessageBox.showinfo(\"Error\", \"The schedule file name you entered does not exist.\")\n        return\n    conf['schedulefile'] = schedulefile\n\n\n    centerx=gui[\"centerv\"].get().strip()\n    try:\n        centerx=float(centerx)\n    except:\n        tkMessageBox.showinfo(\"Error\", \"The center X location you entered is invalid: %s.\\n\\nIt has to be a floating number.\"%centerx)\n        return\n    conf['robot_center_x'] = centerx\n    conf['robot_center']=(conf['robot_center_x'],conf['robot_center_y'])\n    robot.wshm('fvv_robot_center_x',conf['robot_center_x'])\n    robot.wshm('fvv_robot_center_y',conf['robot_center_y'])\n\n    \n    if not read_schedule_file():\n        tkMessageBox.showinfo(\"Error\", \"Error reading the schedule file. Please look at the terminal for the error message.\")\n        return\n\n    print(\"Running the experiment.\")\n    gui[\"running\"]=True\n    update_ui()\n    \n\n\n    conf['trialhistory']=[] # this is where we will keep info from all previous trials.\n\n    trialdata['participant'] =conf['participant']\n    trialdata['schedulefile']=conf['schedulefile']\n    init_logs()\n\n    trialdata['current_schedule']=-1 # start at the beginning\n    trialdata['phase']='init'\n    \n    launch_mainloop()\n\n\n    \n\ndef select_schedule():\n    \"\"\" Show a dialog that lets the user select a file. \"\"\"\n    \n    fn = filedialog.askopenfilename(filetypes = ((\"CSV files\", \"*.csv\")\n                                                 ,(\"All files\", \"*.*\") ))\n    if not fn or not len(fn):\n        print(\"No file selected.\")\n    else:\n        gui[\"schedulef\"].set(fn)\n\n\n\n\n\n\n\ndef capture_center():\n    \"\"\" Capture the center position from the robot.\"\"\"\n    xs = []\n    for i in range(conf['n_center_capture']):\n        xs.append(robot.rshm('x'))\n        time.sleep(conf['center_capture_period'])\n    #print(xs)\n    mean_x = np.mean(xs)\n    gui['centerv'].set(\"%.3f\"%mean_x)\n        \n\n\ndef hold_center():\n    \"\"\" Hold the robot handle at the center position.\"\"\"\n    if not get_center(): return\n    cx,cy = conf['robot_center_x'],conf['robot_center_y']\n    print(\"Holding at {},{}\".format(cx,cy))\n    robot.move_to(cx,cy,conf['return_duration'])\n    while not robot.move_is_done():\n        time.sleep(.1)\n    robot.stay_at(cx,cy)\n    print(\"Holding indefinitely.\")\n    return\n    \n\n        \n\ndef init_tk():\n    global gui\n    gui = {}\n    \n    master = Tk()\n    master.geometry('%dx%d+%d+%d' % (CONTROL_WIDTH, CONTROL_HEIGHT, CONTROL_X, CONTROL_Y))\n    master.configure(background='black')\n\n    f = Frame(master,background='black')\n    loadb   = Button(f, text=\"Load robot\",                background=\"green\",foreground=\"black\", command=load_robot)\n    runb    = Button(f, text=\"Run\",                       background=\"blue\", foreground=\"white\", command=run)\n    recogb  = Button(f, text=\"Recognition yes/no\",        background=\"purple\",foreground=\"white\", command=runrecog)\n    mcb     = Button(f, text=\"Motor copy\",                background=\"orange\",foreground=\"black\", command=runmotorcopy)\n    quitb   = Button(f, text=\"Quit\",                      background=\"red\",foreground=\"black\", command=end_program)\n    \n    gui[\"subject.id\"] = StringVar()\n    l      = Label(f, text=\"subject ID\",             fg=\"white\", bg=\"black\")\n    subjid = Entry(f, textvariable=gui[\"subject.id\"],fg=\"white\", bg=\"black\",insertbackground='yellow')\n\n    row  = 0\n    f.grid         (row=row,padx=10,pady=10)\n    row += 1\n    loadb.grid     (row=row,column=0,sticky=W,padx=10,pady=10)\n\n    row += 1\n    l.grid         (row=row,column=0,sticky=W,pady=10)\n    subjid.grid    (row=row,column=1,sticky=W,padx=10)\n\n    row +=1\n    gui[\"schedulef\"]  = StringVar()\n    gui[\"schedulef\"].set(\"schedule.csv\")\n    l      = Label(f, text=\"schedule file\",          fg=\"white\", bg=\"black\")\n    e      = Entry(f, textvariable=gui[\"schedulef\"], fg=\"white\", bg=\"black\",insertbackground='yellow')\n    l.grid(row=row,column=0,sticky=W,pady=10)\n    e.grid(row=row,column=1,sticky=W,padx=10)\n    b   = Button(f, text=\"select\",                background=\"gray\",foreground=\"black\", command=select_schedule)\n    b.grid(row=row,column=2,sticky=W,padx=10)\n\n    row +=1\n    gui[\"centerv\"]  = StringVar()\n    gui[\"centerv\"].set(\"0.0\")\n    l      = Label(f, text=\"center X\",             fg=\"white\", bg=\"black\")\n    e      = Entry(f, textvariable=gui[\"centerv\"], fg=\"white\", bg=\"black\",insertbackground='yellow')\n    l.grid(row=row,column=0,sticky=W,pady=10)\n    e.grid(row=row,column=1,sticky=W,padx=10)\n    b   = Button(f, text=\"capture\",                background=\"gray\",foreground=\"black\", command=capture_center)\n    b.grid(row=row,column=2,sticky=W,padx=10)\n    gui['capturecenter']=b\n    b   = Button(f, text=\"hold\",                   background=\"gray\",foreground=\"black\", command=hold_center)\n    b.grid(row=row,column=3,sticky=W,padx=10)\n    gui['holdcenter']=b\n    \n    row += 1\n    runb.grid      (row=row,column=0,sticky=W,padx=10)\n    recogb.grid    (row=row,column=1,padx=10)\n    mcb.grid       (row=row,column=2,padx=10)\n    \n    b   = Button(f, text=\"pinpoint\",             background=\"purple\",foreground=\"black\", command=pinpoint)\n    #b.grid(row=row,column=1,sticky=W,padx=10)\n    \n    row += 1\n    gui['recognitionvisuals']=IntVar()\n    gui['recognitionvisuals'].set(1)\n    #c = Checkbutton(f, text=\"Show visuals during recogn.\", variable=gui['recognitionvisuals'], background='black',foreground='green',highlightcolor='black',highlightthickness=0)\n    #c.grid(row=row,column=1)\n    #Label(f,text=\"\")\n    quitb.grid     (row=row,sticky=W,padx=10,pady=10)\n\n    row +=1\n    l = Label(f,text='Progress',fg='white',bg='black')\n    l.grid(row=row,column=0)\n    p = ttk.Progressbar(f, orient=HORIZONTAL,mode='determinate',value=0,maximum=100)\n    p['value']=0\n    p.grid(row=row,column=1,columnspan=2,sticky=W+N+E+S)\n    gui['progress'] = p\n\n    row +=1\n    gui[\"trialinfo\"]  = StringVar()\n    gui[\"trialinfo\"].set(\"Not running\")\n    l = Label(f,textvariable=gui['trialinfo'],fg='yellow',bg='black')\n    l.grid(row=row,column=0,columnspan=3,sticky=W,pady=10,padx=10)\n\n    row += 1\n    gui[\"photo\"]=PhotoImage(file='screenshot_base.gif')\n    l = Label(f,image=gui['photo'],borderwidth=0,highlightthickness=0)\n    l.grid(row=row,column=0,columnspan=5,sticky=W)\n    gui[\"photolabel\"]=l\n    \n    # Make some elements available for the future\n    gui[\"loadb\"]     =loadb\n    gui[\"runb\"]      =runb\n    gui['recogb']    =recogb\n    gui['mcb']       =mcb\n    gui[\"quitb\"]     =quitb\n    gui[\"keep_going\"]=False\n    gui[\"loaded\"]    =False\n    gui[\"running\"]   =False\n    gui[\"logging\"]   =False\n    \n    master.bind()\n\n    return master\n    \n\n\n\n\n\n\ndef init_mouse():\n    if conf['use_mouse']:\n        if not os.path.exists(conf['mouse_device']):\n            print(\"## ERROR: mouse device %s does not exist\"%conf['mouse_device'])\n            sys.exit(-1)\n        mouse = MouseInput(conf['mouse_device'])\n        conf['mouse']=mouse\n\n\n\n\n## Initialise everything\nconf['screen'],conf['mainfont'] = init_interface(conf)\ninit_mouse()\npygame.display.flip()\n\n\nmaster = init_tk()\nupdate_ui()\nmaster.mainloop()\n\n\n\n\n","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":79874,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"646229840","text":"import tensorflow as tf\r\nimport numpy as np\r\n\r\n\r\nclass TextCNN(object):\r\n    \"\"\"\r\n    A CNN for text classification.\r\n    Uses an embedding layer, followed by a convolutional, max-pooling and softmax layer.\r\n    \"\"\"\r\n\r\n    def __init__(\r\n            self, sequence_length, num_classes, filter_sizes, num_filters, embedding, l2_reg_lambda=0.0):\r\n        self.sequence_length = sequence_length\r\n        self.num_classes = num_classes\r\n        self.filter_sizes = filter_sizes\r\n        self.num_filters = num_filters\r\n        self.embedding = embedding\r\n        self.l2_reg_lambda = l2_reg_lambda\r\n\r\n    def model_fn(self, input_x, input_y, dropout_keep_prob, mode, optimizer=tf.train.AdamOptimizer()):\r\n        # Keeping track of l2 regularization loss (optional)\r\n        l2_loss = tf.constant(0.0)\r\n\r\n        # Embedding layer\r\n        with tf.device('/cpu:0'), tf.name_scope(\"embedding\"):\r\n            W = tf.Variable(self.embedding, name=\"W\")\r\n            embedded_chars = tf.nn.embedding_lookup(W, input_x)\r\n            embedded_chars_expanded = tf.expand_dims(embedded_chars, -1)\r\n            embedding_size = tf.shape(W)[1]\r\n\r\n        # Create a convolution + maxpool layer for each filter size\r\n        pooled_outputs = []\r\n        for i, filter_size in enumerate(self.filter_sizes):\r\n            with tf.name_scope(\"conv-maxpool-%s\" % filter_size):\r\n                # Convolution Layer\r\n                filter_shape = [filter_size, embedding_size, 1, self.num_filters]\r\n                W = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1), name=\"W\")\r\n                b = tf.Variable(tf.constant(0.1, shape=[self.num_filters]), name=\"b\")\r\n                conv = tf.nn.conv2d(\r\n                    embedded_chars_expanded,\r\n                    W,\r\n                    strides=[1, 1, 1, 1],\r\n                    padding=\"VALID\",\r\n                    name=\"conv\")\r\n                # Apply nonlinearity\r\n                h = tf.nn.elu(tf.nn.bias_add(conv, b), name=\"elu\")\r\n                # Maxpooling over the outputs\r\n                pooled = tf.nn.max_pool(\r\n                    h,\r\n                    ksize=[1, self.sequence_length - filter_size + 1, 1, 1],\r\n                    strides=[1, 1, 1, 1],\r\n                    padding='VALID',\r\n                    name=\"pool\")\r\n                pooled_outputs.append(pooled)\r\n\r\n        # Combine all the pooled features\r\n        num_filters_total = self.num_filters * len(self.filter_sizes)\r\n        h_pool = tf.concat(pooled_outputs, 3)\r\n        h_pool_flat = tf.reshape(h_pool, [-1, num_filters_total])\r\n\r\n        # Add dropout\r\n        with tf.name_scope(\"dropout\"):\r\n            h_drop = tf.nn.dropout(h_pool_flat, dropout_keep_prob)\r\n\r\n        # Final (unnormalized) scores and predictions\r\n        with tf.name_scope(\"output\"):\r\n            W = tf.get_variable(\r\n                \"W\",\r\n                shape=[num_filters_total, self.num_classes],\r\n                initializer=tf.contrib.layers.xavier_initializer())\r\n            b = tf.Variable(tf.constant(0.1, shape=[self.num_classes]), name=\"b\")\r\n            l2_loss += tf.nn.l2_loss(W) + tf.nn.l2_loss(b)\r\n            logits = tf.nn.xw_plus_b(h_drop, W, b, name=\"scores\")\r\n            predictions = {\r\n                'classes': tf.argmax(input=logits, axis=1, name='classes'),\r\n                'probabilities': tf.nn.softmax(logits, name='probabilities')\r\n            }\r\n\r\n            # predict\r\n        if mode == tf.estimator.ModeKeys.PREDICT:\r\n            return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)\r\n\r\n        # Calculate mean cross-entropy loss\r\n        with tf.name_scope(\"loss\"):\r\n            losses = tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits, labels=input_y)\r\n            loss = tf.reduce_mean(losses) + self.l2_reg_lambda * l2_loss\r\n\r\n        accuracy, update_op = tf.metrics.accuracy(labels=input_y, predictions=predictions['classes'], name='accuracy')\r\n        batch_acc = tf.reduce_mean(tf.cast(tf.equal(tf.cast(input_y, tf.int64), predictions['classes']), tf.float32))\r\n        tf.summary.scalar('batch_acc', batch_acc)\r\n        tf.summary.scalar('streaming_acc', update_op)\r\n\r\n        # train\r\n        if mode == tf.estimator.ModeKeys.TRAIN:\r\n            update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\r\n            with tf.control_dependencies(update_ops):\r\n                train_op = optimizer.minimize(loss=loss, global_step=tf.train.get_global_step())\r\n            return tf.estimator.EstimatorSpec(mode=tf.estimator.ModeKeys.TRAIN, loss=loss, train_op=train_op)\r\n\r\n        # validate\r\n        if mode == tf.estimator.ModeKeys.EVAL:\r\n            return tf.estimator.EstimatorSpec(mode=tf.estimator.ModeKeys.EVAL, loss=loss,\r\n                                              eval_metric_ops={'accuracy': (accuracy, tf.get_collection(update_op))})\r\n","sub_path":"textcnn/text_cnn.py","file_name":"text_cnn.py","file_ext":"py","file_size_in_byte":4832,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"342422637","text":"import streamlit as st\r\nfrom sklearn.preprocessing import MinMaxScaler\r\nimport yfinance as yf\r\nimport pickle\r\nimport time\r\nimport datetime\r\nimport pytz\r\nimport io\r\nimport requests\r\nfrom sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\r\nimport numpy as np\r\nimport pandas as pd\r\n\r\nst.set_page_config(\r\n    page_title=\"Stock Analysis App\",\r\n    page_icon=\"🤑\",\r\n    layout=\"wide\",\r\n    initial_sidebar_state=\"expanded\",\r\n)\r\n\r\nst.title('CLOSING PRICE ANALYSIS')\r\n\r\nst.sidebar.image('https://www.fool.com.au/wp-content/uploads/2017/07/time-to-buy.jpg', width=270)\r\nst.sidebar.write('')\r\n\r\ndataset_name = st.sidebar.radio(\r\n    'SELECT STOCK',\r\n    ('Apple', 'Google', 'Microsoft', 'Tesla', 'IBM', 'Accenture')\r\n)\r\n\r\nst.markdown(\r\n    \"\"\"\r\n    <style>\r\n    .selector-rect {\r\n        fill: grey  !important; \r\n    }\r\n    img{\r\n    margin-top: 10px;\r\n    margin-left: 14px;\r\n    }\r\n    div.row-widget.stRadio > div[role='radiogroup'] > label[data-baseweb='radio'] > div:first-child{\r\n    background-color: #5e829f;\r\n    }\r\n    .st-ce{\r\n    background-color: #5e829f ;\r\n    }\r\n    .st-ch {\r\n    background-color: #5e829f;\r\n    }\r\n    .st-cc{\r\n       font-size: 17px;\r\n       font-weight: 510;\r\n       }\r\n    .css-vfskoc{\r\n    font-size: 20px;\r\n    color: white;\r\n    font-weight: 900;\r\n    }\r\n    .st-bj {\r\n    margin-top: 11px;\r\n    }\r\n    #MainMenu {visibility : hidden;}\r\n    footer {visibility : hidden;}\r\n    footer:after {\r\n    font-size: 15px;\r\n    content: 'Made By: Namrata Patel, Bansi Shah, Darshi Shah, Vishal Shah';\r\n    visibility: visible;\r\n    position: relative;\r\n    display: block;\r\n    background-color: #5e829f;\r\n    color: white;\r\n    text-align: center;\r\n    }\r\n    h3{\r\n    font-size: 18px;\r\n    font-weight: 700;\r\n    color: #2cab18;\r\n    }\r\n    h1{\r\n      text-align: center;}\r\n    </style>\r\n    \"\"\",\r\n    unsafe_allow_html=True\r\n)\r\n\r\ndef get_model(name):\r\n    repo = 'Stock_Market_Analysis'\r\n    if name == 'Apple':\r\n        ticker_symbol = 'AAPL'\r\n        url = \"https://github.com/VishalShah1999/\" + repo + \"/blob/main/\" + ticker_symbol + \".p?raw=true\"\r\n        byte_content = requests.get(url).content\r\n        model = io.BytesIO(byte_content)\r\n        reg = pickle.load(model)\r\n        return ticker_symbol, reg\r\n    elif name == 'Google':\r\n        ticker_symbol = 'GOOG'\r\n        url = \"https://github.com/VishalShah1999/\" + repo + \"/blob/main/\" + ticker_symbol + \".p?raw=true\"\r\n        byte_content = requests.get(url).content\r\n        model = io.BytesIO(byte_content)\r\n        reg = pickle.load(model)\r\n        return ticker_symbol, reg\r\n    elif name == 'Microsoft':\r\n        ticker_symbol = 'MSFT'\r\n        url = \"https://github.com/VishalShah1999/\" + repo + \"/blob/main/\" + ticker_symbol + \".p?raw=true\"\r\n        byte_content = requests.get(url).content\r\n        model = io.BytesIO(byte_content)\r\n        reg = pickle.load(model)\r\n        return ticker_symbol, reg\r\n    elif name == 'Tesla':\r\n        ticker_symbol = \"TSLA\"\r\n        url = \"https://github.com/VishalShah1999/\" + repo + \"/blob/main/\" + ticker_symbol + \".p?raw=true\"\r\n        byte_content = requests.get(url).content\r\n        model = io.BytesIO(byte_content)\r\n        reg = pickle.load(model)\r\n        return ticker_symbol, reg\r\n    elif name == 'IBM':\r\n        ticker_symbol = \"IBM\"\r\n        url = \"https://github.com/VishalShah1999/\" + repo + \"/blob/main/\" + ticker_symbol + \".p?raw=true\"\r\n        byte_content = requests.get(url).content\r\n        model = io.BytesIO(byte_content)\r\n        reg = pickle.load(model)\r\n        return ticker_symbol, reg\r\n    elif name == 'Accenture':\r\n        ticker_symbol = \"ACN\"\r\n        url = \"https://github.com/VishalShah1999/\" + repo + \"/blob/main/\" + ticker_symbol + \".p?raw=true\"\r\n        byte_content = requests.get(url).content\r\n        model = io.BytesIO(byte_content)\r\n        reg = pickle.load(model)\r\n        return ticker_symbol, reg\r\n\r\n\r\nnow = datetime.datetime.now(tz=pytz.timezone('US/Eastern'))\r\nst.subheader('Actual Closing Price for ' + str(now.date()) + ' : ')\r\noriginal = st.empty()\r\nst.subheader('Predicted Closing Price for ' + str(now.date()) + ' : ')\r\npredicted = st.empty()\r\nst.subheader(\"Line Chart (Scaled) :\")\r\nline_chart_scaled = st.empty()\r\nst.subheader(\"Line Chart (Transformed) :\")\r\nline_chart_transformed = st.empty()\r\nst.subheader(\"Errors (Scaled): \")\r\nmae_scaled = st.empty()\r\nmse_scaled = st.empty()\r\nrmse_scaled = st.empty()\r\nr2_scaled = st.empty()\r\naccuracy_scaled = st.empty()\r\nst.subheader(\"Errors (Transformed): \")\r\nmae_transformed = st.empty()\r\nmse_transformed = st.empty()\r\nrmse_transformed = st.empty()\r\nr2_transformed = st.empty()\r\naccuracy_transformed = st.empty()\r\n\r\nwhile (True):\r\n    ticker, reg = get_model(dataset_name)\r\n\r\n    yf_data = yf.download(tickers=ticker, period='1d', interval='1m')\r\n    yf_data.reset_index(inplace=True, drop=False)\r\n    X_test = yf_data[['Open', 'High', 'Low', 'Volume']]\r\n    y_test = yf_data[['Close']]\r\n    size = y_test.size\r\n\r\n    scaler = MinMaxScaler()\r\n    scaler = scaler.fit(X_test)\r\n    Xtest_scaled = scaler.transform(X_test)\r\n\r\n    scaler = MinMaxScaler()\r\n    scaler = scaler.fit(y_test)\r\n    ytest_scaled = scaler.transform(y_test)\r\n\r\n    y_pred = reg.predict(Xtest_scaled)\r\n    y_pred_transform = scaler.inverse_transform(y_pred.reshape(size, 1))\r\n    now = datetime.datetime.now(tz=pytz.timezone('US/Eastern'))\r\n    #st.write(str(y_test.tail(1)).split(\" \"))\r\n    statement_for_actual = str(now.hour) + str(':') + str(now.minute) + str(' -- ') + str(y_test.tail(1).to_numpy()[0][0])\r\n    original.write(statement_for_actual)\r\n    statement_for_predicted = str(now.hour) + str(':') + str(now.minute) + str(' -- ') + str(y_pred_transform[-1][0])\r\n    predicted.write(statement_for_predicted)\r\n\r\n    ytest_scaled = ytest_scaled.reshape(size)\r\n\r\n    y_pred = y_pred.reshape(size)\r\n    y_test = y_test.to_numpy()\r\n    y_test = y_test.reshape(size)\r\n    y_pred_transform = y_pred_transform.reshape(size)\r\n\r\n    df = pd.DataFrame({'Actual': ytest_scaled, 'Predicted': y_pred})\r\n    line_chart_scaled.line_chart(df, width=900, height=500)\r\n\r\n    df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred_transform})\r\n    line_chart_transformed.line_chart(df, width=900, height=500)\r\n\r\n    mae_scaled.write('Mean Absolute Error: '+ str(mean_absolute_error(ytest_scaled, y_pred)))\r\n    mse_scaled.write('Mean Squared Error: '+ str(mean_squared_error(ytest_scaled, y_pred)))\r\n    rmse_scaled.write('Root Mean Squared Error: '+ str(np.sqrt(mean_squared_error(ytest_scaled, y_pred))))\r\n    r2_scaled.write('R2 Score: '+ str(r2_score(ytest_scaled, y_pred)))\r\n    accuracy_scaled.write('Accuracy Score: '+ str((1 - (mean_absolute_error(ytest_scaled, y_pred) / ytest_scaled.mean())) * 100))\r\n\r\n    mae_transformed.write('Mean Absolute Error: '+ str(mean_absolute_error(y_test, y_pred_transform)))\r\n    mse_transformed.write('Mean Squared Error: '+ str(mean_squared_error(y_test, y_pred_transform)))\r\n    rmse_transformed.write('Root Mean Squared Error: '+ str(np.sqrt(mean_squared_error(y_test, y_pred_transform))))\r\n    r2_transformed.write('R2 Score: '+ str(r2_score(y_test, y_pred_transform)))\r\n    # st.write('Accuracy Score: ', str((1 - (mean_absolute_error(y_test.to_numpy(), y_pred_transform) / y_test.to_numpy().mean())) * 100))\r\n    accuracy_transformed.write('Accuracy Score: '+ str((1 - (mean_absolute_error(y_test, y_pred_transform) / y_test.mean())) * 100))\r\n\r\n    time.sleep(60)\r\n\r\n","sub_path":"closing_price_module_final.py","file_name":"closing_price_module_final.py","file_ext":"py","file_size_in_byte":7429,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"138117548","text":"import numpy as np\nimport tensorflow as tf\n\niters_num = 100\nplot_interval = 10\n\n# データを生成\nn = 100\nx = np.random.rand(n)\nd = x *  3 + 2\n\n# ノイズを加える\nnoise = 0.3\nd = d + noise * np.random.randn(n) \n\n\n# 最適化の対象の変数を初期化\nW = tf.Variable(tf.zeros([1]), name='W')\nb = tf.Variable(tf.zeros([1]), name='b')\n\n# モデル\ny = W * x + b\n\n# 誤差関数\nloss = tf.reduce_mean(tf.square(y - d))\noptimizer = tf.train.GradientDescentOptimizer(0.5)\ntrain = optimizer.minimize(loss)\n\n# 初期化\nsaver = tf.train.Saver()\ninit = tf.global_variables_initializer()\nsess = tf.Session()\nsess.run(init)\n\n# トレーニング\nfor i in range(iters_num):\n    sess.run(train)\n    if (i+1) % plot_interval == 0:\n        loss_val = sess.run(loss) \n        print('Generation: ' + str(i+1) + '. 誤差 = ' + str(loss_val))\n\n# saver.save(sess, 'model.ckpt')        \nsaver.save(sess, 'model/test_model')\n\nprint('W:', sess.run(W))\nprint('b:', sess.run(b))\n\nsess.close()","sub_path":"Exercise/RC_DNN_code/lesson_4/4_2_save_load/4_2_save.py","file_name":"4_2_save.py","file_ext":"py","file_size_in_byte":981,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"606571048","text":"import mainTrain\nimport argparse\nfrom util.LFUtil import *\n\n# args  = [\n#     '--epochs', 50 ,\n#     '--valEvery', 0.25,\n#     '--imagesToUse', [ 0, 1, 2, 3, 4, 5],\n#     '--batchSize', 16,\n#     '--validationSplit', 0.1 ,\n#     '--biasVal', 0.1,\n#     '--learningRate', 0.005,\n#     '--useBias', True ,\n#     '--useSkipCon', False,\n#     '--fovInput', 9,\n#     '--neighShape', 3 ,\n#     '--useShallowUnet', True,\n#     '--ths', 0.03 ,\n#     '--datasetPath', \"C:/Users/LabOldenbourg/Desktop/BrainImagesJosuePage/brainSubset/brainSubset_1_1.h5\",\n#     '--outputPath', \"runs/\" ,\n#     '--outputPrefix', \"\"\n# ]\ndef main():\n    # Arguments\n    parser = argparse.ArgumentParser()\n    # Number of epochs\n    parser.add_argument('--epochs', type=int, default=100)  # can be 1000\n    # Validate every n percentage of the data\n    parser.add_argument('--valEvery', type=float, default=0.25)\n    # Image indices to use for training and validation\n    parser.add_argument('--imagesToUse', nargs='+', type=int, default=list(range(0,5,1)))\n    # List of GPUs to use: 0 1 2 for example\n    parser.add_argument('--GPUs', nargs='+', type=int, default=[0])\n    # Batch size\n    parser.add_argument('--batchSize', type=int, default=32)  # 128\n    # Perentage of the data to use for validation, from 0 to 1\n    parser.add_argument('--validationSplit', type=float, default=0.1)\n    # Bias initialization value\n    parser.add_argument('--biasVal', type=float, default=0.1)\n    # Learning rate\n    parser.add_argument('--learningRate', type=float, default=0.002) # 0.001\n    # Use bias flag\n    parser.add_argument('--useBias', type=str2bool, default=True)\n    # Use skip connections flag\n    parser.add_argument('--useSkipCon', type=str2bool, default=False)\n    # User selected random seed\n    parser.add_argument('--randomSeed', type=int, default=None) \n    # fov of input or neighboarhood around lenslet to reconstruct\n    parser.add_argument('--fovInput', type=int, default=9)\n    # nT number of lenslets to reconstruct simultaneously use at training time\n    parser.add_argument('--neighShape', type=int, default=3)\n    # Flag to use shallow or large U-net\n    parser.add_argument('--useShallowUnet', type=str2bool, default=True)\n    # Lower threshold of GT stacks, to get rid of autofluorescence\n    parser.add_argument('--ths', type=float, default=0.03)\n    # Path to dataset\n    parser.add_argument('--datasetPath', nargs='?', default= \"C:/Users/LabOldenbourg/Desktop/BrainImagesJosuePage/brainSubset/brainSubset_1_1.h5\")\n    # Path to directory where models and tensorboard logs are stored\n    parser.add_argument('--outputPath', nargs='?', default=\"runs/\")\n    # Prefix for current output folder\n    parser.add_argument('--outputPrefix', nargs='?', default=\"\")\n    # Path to model in case of continuing a training\n    parser.add_argument('--checkpointPath', nargs='?', default=None)\n\n    args = parser.parse_args()\n    # import os, sys\n    # currentdir = os.path.dirname(os.path.realpath(__file__))\n    # grandparentdir = os.path.dirname(os.path.dirname(currentdir))\n    # sys.path.append(grandparentdir)\n\n    mainTrain.main(args)\n\n\nif __name__ == '__main__':\n    main()\n    ","sub_path":"runTrain.py","file_name":"runTrain.py","file_ext":"py","file_size_in_byte":3163,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"270996193","text":"# -*- coding: utf-8 -*-\n# author: ronniecao\nimport json\nimport os\nimport shutil\nimport codecs\nimport random\nimport math\nimport numpy\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nfrom scipy import misc\nimport platform\nimport sys\nfrom pyltp import Segmentor\n\ncolors = {\n    'word': [50, 50, 50], # 灰色\n    'date': [50, 50, 50], # 蓝色\n    'digit': [50, 50, 50], # 绿色\n    'line': [50, 50, 50], # 黑色\n    'page': [50, 50, 50], # 紫色\n    'table': [50, 50, 50], # 红色\n    'picture': [50, 50, 50], # 黄色\n}\n\ndef read_word_vector(path):\n    word_dict, word_vector, n = {}, [], 0\n\n    with codecs.open(path, 'r', 'utf8') as fo:\n        for line in fo:\n            [word, vector] = line.strip().split('\\t')\n            vector = [float(t) for t in vector.split(' ')]\n            word_dict[n] = word\n            n += 1\n            word_vector.append(vector)\n\n    for j in range(3):\n        col_min = min([word_vector[i][j] for i in range(n)])\n        col_max = max([word_vector[i][j] for i in range(n)])\n        for i in range(n):\n            word_vector[i][j] = 1.0 * (word_vector[i][j] - col_min) / (col_max - col_min)\n\n    new_word_dict = {}\n    for i in range(n):\n        new_word_dict[word_dict[i]] = word_vector[i]\n\n    return new_word_dict\n\ndef load_source(maindir, word_dict):\n    n_processed = 0\n    contents_dict = {}\n    segmentor = Segmentor()\n    segmentor.load('/home/caory/github/table-detection/data/table-v5/ltp_data/cws.model')\n\n    dirlist = os.listdir(maindir)\n    for docid in dirlist:\n        n_processed += 1\n        print('Load Source: doc: %s, rate: %.2f%%' % (\n            docid, 100.0 * n_processed / len(dirlist)))\n        sys.stdout.flush()\n        contents_dict[docid] = {}\n\n        json_path = os.path.join(maindir, docid, 'pages_with_tables')\n        if not os.path.exists(json_path):\n            continue\n\n        data = read_json(json_path)\n        for pageid in data:\n            contents_dict[docid][pageid] = {}\n            size = data[pageid]['size']\n            texts, curves, others, tables = [], [], [], []\n\t\t\t\n            # 获取表格框\n            pad, offset = 2, 5\n            for box in data[pageid]['tables']:\n                left = max(offset, int(math.floor(float(box[0])) - pad))\n                right = min(int(math.ceil(float(box[2])) + pad), size[0]-offset)\n                top = max(offset, int(math.floor(float(size[1]-box[3])) - pad))\n                bottom = min(int(math.ceil(float(size[1]-box[1])) + pad), size[1]-offset)\n                if 0 <= left <= right < size[0] and 0 <= top <= bottom < size[1]:\n                    tables.append({'position': [left, right, top, bottom]})\n\t\t\t\n            # 获取文本框\n            for text in data[pageid]['texts']:\n                # 获取每一个字符的位置\n                chars = []\n                for char in text['chars']:\n                    left = int(math.floor(float(char['box'][0])))\n                    right = int(math.floor(float(char['box'][2])))\n                    top = int(math.floor(float(size[1]-char['box'][3])))\n                    bottom = int(math.floor(float(size[1]-char['box'][1])))\n                    if 0 <= left <= right < size[0] and 0 <= top <= bottom < size[1]:\n                        chars.append({'position': [left, right, top, bottom], 'sentence': char['text'].strip()})\n                \n                # 对于距离近的字符进行合并\n                for char in chars:\n                    merged = False\n                    for i in range(len(texts)):\n                        box = texts[i]\n                        if char['position'][2] == texts[i]['position'][2] and \\\n                            char['position'][3] == texts[i]['position'][3] and \\\n                            text['type'] == texts[i]['type']:\n                            if abs(char['position'][0] - texts[i]['position'][1]) <= 5:\n                                texts[i]['position'][1] = char['position'][1]\n                                merged = True\n                                break\n                            elif abs(char['position'][1] - texts[i]['position'][0]) <= 5:\n                                texts[i]['position'][0] = char['position'][0]\n                                merged = True\n                                break\n                    if not merged:\n                        texts.append({'position': char['position'], 'type': text['type'],\n                            'sentence': text['text'].strip()})\n\t\t\t\n            # 对于页码进行特殊识别\n            for i in range(len(texts)):\n                top = texts[i]['position'][2]\n                bottom = texts[i]['position'][3]\n                if 1.0 * top / size[1] <= 0.85:\n                    continue\n                is_page = True\n\n                for j in range(len(texts)):\n                    if j == i:\n                        continue\n                    other_top = texts[j]['position'][2]\n                    other_bottom = texts[j]['position'][3]\n                    if other_bottom >= top:\n                        is_page = False\n                        break\n                \n                if is_page:\n                    texts[i]['type'] = 5\n\t\t\t\n            # 将下划线文本框改为表格框\n            new_texts = []\n            for text in texts:\n                isline = True\n                if 'sentence' in text and text['type'] == 2:\n                    for s in text['sentence']:\n                        if s != '_':\n                            isline = False\n                    if isline and len(text['sentence']) >= 3:\n                        pos = [text['position'][0], text['position'][1], \n                            text['position'][3]-1, text['position'][3]]\n                        curves.append({'position': pos, 'type': 1})\n                    else:\n                        new_texts.append(text)\n                else:\n                    new_texts.append(text)\n            texts = new_texts\n\t\t\t\n            # 获取其他框（图片等）\n            for other in data[pageid]['others']:\n                left = int(math.floor(float(other['box'][0])))\n                right = int(math.floor(float(other['box'][2])))\n                top = int(math.floor(float(size[1]-other['box'][3])))\n                bottom = int(math.floor(float(size[1]-other['box'][1])))\n                if 0 <= left <= right < size[0] and 0 <= top <= bottom < size[1]:\n                    others.append({'position': [left, right, top, bottom], 'type': other['type']})\n\t\t\t\n            # 获取每一个线条的位置\n            curves = []\n            curve_width = 2\n            for curve in data[pageid]['curves']:\n                left = int(math.floor(float(curve['box'][0])))\n                right = int(math.floor(float(curve['box'][2])))\n                top = int(math.floor(float(size[1]-curve['box'][3])))\n                bottom = int(math.floor(float(size[1]-curve['box'][1])))\n                if right - left <= curve_width and bottom - top > curve_width:\n                    right = left\n                    line = {'position': [left, right, top, bottom], 'type': curve['type']}\n                elif right - left > curve_width and bottom - top <= curve_width:\n                    bottom = top\n                    line = {'position': [left, right, top, bottom], 'type': curve['type']}\n                if line:\n                    if 0 <= line['position'][0] <= line['position'][1] < size[0] and \\\n                        0 <= line['position'][2] <= line['position'][3] < size[1]:\n                        curves.append(line)\n            \n            contents_dict[docid][pageid] = {\n                'texts': texts, 'size': size, 'tables': tables,\n                'others': others, 'curves': curves}\n\n    return contents_dict\n\ndef draw_image(contents_dict, maindir):\n    n_processed = 0\n    for docid in contents_dict:\n        n_processed += 1\n        print('Draw Images: docid: %s, rate: %.2f%%' % (docid, 100.0 * n_processed / len(contents_dict)))\n        sys.stdout.flush()\n\n        if not os.path.exists(os.path.join(maindir, 'Images')):\n            os.mkdir(os.path.join(maindir, 'Images'))\n        if not os.path.exists(os.path.join(maindir, 'Images', docid)):\n            os.mkdir(os.path.join(maindir, 'Images', docid))\n        if not os.path.exists(os.path.join(maindir, 'Images', docid, 'png_notable')):\n            os.mkdir(os.path.join(maindir, 'Images', docid, 'png_notable'))\n        if not os.path.exists(os.path.join(maindir, 'Images', docid, 'png_line_table')):\n            os.mkdir(os.path.join(maindir, 'Images', docid, 'png_line_table'))\n        if not os.path.exists(os.path.join(maindir, 'Images', docid, 'png_noline_table')):\n            os.mkdir(os.path.join(maindir, 'Images', docid, 'png_noline_table'))\n        if not os.path.exists(os.path.join(maindir, 'Images', docid, 'png_line_notable')):\n            os.mkdir(os.path.join(maindir, 'Images', docid, 'png_line_notable'))\n        if not os.path.exists(os.path.join(maindir, 'Images', docid, 'png_noline_notable')):\n            os.mkdir(os.path.join(maindir, 'Images', docid, 'png_noline_notable'))\n        for pageid in contents_dict[docid]:\n            shape = contents_dict[docid][pageid]['size']\n            if len(contents_dict[docid][pageid]['tables']) == 0:\n                # 如果没有表格，则画图并保存在no_table文件夹中 \n                image = numpy.zeros((shape[1], shape[0], 3), dtype='uint8') + 255\n\n                for text in contents_dict[docid][pageid]['others']:\n                    if text['type'] == 100:\n                        image[text['position'][2]:text['position'][3],\n                            text['position'][0]:text['position'][1], :] = colors['picture']\n\n                for text in contents_dict[docid][pageid]['texts']:\n                    if text['type'] == 2:\n                        image[text['position'][2]:text['position'][3],\n                            text['position'][0]:text['position'][1], :] = colors['word']\n\n                for text in contents_dict[docid][pageid]['texts']:\n                    if text['type'] == 5:\n                        image[text['position'][2]:text['position'][3],\n                            text['position'][0]:text['position'][1], :] = colors['page']\n\n                for text in contents_dict[docid][pageid]['texts']:\n                    if text['type'] == 3:\n                        image[text['position'][2]:text['position'][3],\n                            text['position'][0]:text['position'][1], :] = colors['date']\n\n                for text in contents_dict[docid][pageid]['texts']:\n                    if text['type'] == 4:\n                        image[text['position'][2]:text['position'][3],\n                            text['position'][0]:text['position'][1], :] = colors['digit']\n\n                for text in contents_dict[docid][pageid]['curves']:\n                    if text['type'] == 1:\n                        image[text['position'][2]:text['position'][3],\n                            text['position'][0]:text['position'][1], :] = colors['line']\n\n                image_path = os.path.join(\n                    maindir, 'Images', docid, 'png_notable', '%s_%s_notable.png' % (docid, pageid))\n                misc.imsave(image_path, image)\n            else:\n                image_line = numpy.zeros((shape[1], shape[0], 3), dtype='uint8') + 255\n                image_noline = numpy.zeros((shape[1], shape[0], 3), dtype='uint8') + 255\n\n                for text in contents_dict[docid][pageid]['others']:\n                    if text['type'] == 100:\n                        image_line[text['position'][2]:text['position'][3],\n                            text['position'][0]:text['position'][1], :] = colors['picture']\n                        image_noline[text['position'][2]:text['position'][3],\n                            text['position'][0]:text['position'][1], :] = colors['picture']\n                            \n                for text in contents_dict[docid][pageid]['texts']:\n                    if text['type'] == 2:\n                        image_line[text['position'][2]:text['position'][3],\n                            text['position'][0]:text['position'][1], :] = colors['word']\n                        image_noline[text['position'][2]:text['position'][3],\n                            text['position'][0]:text['position'][1], :] = colors['word']\n\n                for text in contents_dict[docid][pageid]['texts']:\n                    if text['type'] == 5:\n                        image_line[text['position'][2]:text['position'][3],\n                            text['position'][0]:text['position'][1], :] = colors['page']\n                        image_noline[text['position'][2]:text['position'][3],\n                            text['position'][0]:text['position'][1], :] = colors['page']\n\n                for text in contents_dict[docid][pageid]['texts']:\n                    if text['type'] == 3:\n                        image_line[text['position'][2]:text['position'][3],\n                            text['position'][0]:text['position'][1], :] = colors['date']\n                        image_noline[text['position'][2]:text['position'][3],\n                            text['position'][0]:text['position'][1], :] = colors['date']\n\n                for text in contents_dict[docid][pageid]['texts']:\n                    if text['type'] == 4:\n                        image_line[text['position'][2]:text['position'][3],\n                            text['position'][0]:text['position'][1], :] = colors['digit']\n                        image_noline[text['position'][2]:text['position'][3],\n                            text['position'][0]:text['position'][1], :] = colors['digit']\n\n                for text in contents_dict[docid][pageid]['curves']:\n                    if text['type'] == 1:\n                        image_line[text['position'][2]:text['position'][3],\n                            text['position'][0]:text['position'][1], :] = colors['line']\n\n                image_line_path = os.path.join(\n                    maindir, 'Images', docid, 'png_line_notable',\n                    '%s_%s_linenotable.png' % (docid, pageid))\n                misc.imsave(image_line_path, image_line)\n                image_noline_path = os.path.join(\n                    maindir, 'Images', docid, 'png_noline_notable',\n                    '%s_%s_nolinenotable.png' % (docid, pageid))\n                misc.imsave(image_noline_path, image_noline)\n\n                for table in contents_dict[docid][pageid]['tables']:\n                    image_line[table['position'][2]:table['position'][3],\n                        table['position'][0]-1:table['position'][0]+1, :] = colors['table']\n                    image_line[table['position'][2]:table['position'][3],\n                        table['position'][1]-1:table['position'][1]+1, :] = colors['table']\n                    image_line[table['position'][2]-1:table['position'][2]+1,\n                        table['position'][0]:table['position'][1], :] = colors['table']\n                    image_line[table['position'][3]-1:table['position'][3]+1,\n                        table['position'][0]:table['position'][1], :] = colors['table']\n                    image_noline[table['position'][2]:table['position'][3],\n                        table['position'][0]-1:table['position'][0]+1, :] = colors['table']\n                    image_noline[table['position'][2]:table['position'][3],\n                        table['position'][1]-1:table['position'][1]+1, :] = colors['table']\n                    image_noline[table['position'][2]-1:table['position'][2]+1,\n                        table['position'][0]:table['position'][1], :] = colors['table']\n                    image_noline[table['position'][3]-1:table['position'][3]+1,\n                        table['position'][0]:table['position'][1], :] = colors['table']\n\n                image_line_path = os.path.join(\n                    maindir, 'Images', docid, 'png_line_table',\n                    '%s_%s_linetable.png' % (docid, pageid))\n                misc.imsave(image_line_path, image_line)\n                image_noline_path = os.path.join(\n                    maindir, 'Images', docid, 'png_noline_table', \n                    '%s_%s_nolinetable.png' % (docid, pageid))\n                misc.imsave(image_noline_path, image_noline)\n\t\t\ndef create_labels(contents_dict, maindir):\n    dataset = []\n    \n    n_processed = 0\n    for docid in contents_dict:\n        n_processed += 1\n        print('Write Labels: docid: %s, rate: %.2f%%' % (docid, 100.0 * n_processed / len(contents_dict)))\n        sys.stdout.flush()\n\n        if not os.path.exists(os.path.join(maindir)):\n            os.mkdir(os.path.join(maindir))\n        if not os.path.exists(os.path.join(maindir, 'Labels')):\n            os.mkdir(os.path.join(maindir, 'Labels'))\n        if not os.path.exists(os.path.join(maindir, 'Labels', docid)):\n            os.mkdir(os.path.join(maindir, 'Labels', docid))\n        if not os.path.exists(os.path.join(maindir, 'Labels', docid, 'png_notable')):\n            os.mkdir(os.path.join(maindir, 'Labels', docid, 'png_notable'))\n        if not os.path.exists(os.path.join(maindir, 'Labels', docid, 'png_line_notable')):\n            os.mkdir(os.path.join(maindir, 'Labels', docid, 'png_line_notable'))\n        if not os.path.exists(os.path.join(maindir, 'Labels', docid, 'png_noline_notable')):\n            os.mkdir(os.path.join(maindir, 'Labels', docid, 'png_noline_notable'))\n        \n        for pageid in contents_dict[docid]:\n            shape = contents_dict[docid][pageid]['size']\n            if len(contents_dict[docid][pageid]['tables']) == 0:\n                labels = []\n                image_path = os.path.join(maindir, 'Images', docid, 'png_notable', \\\n                    '%s_%s_notable.png' % (docid, pageid))\n                label_path = os.path.join(maindir, 'Labels', docid, 'png_notable', \\\n                    '%s_%s_notable.txt' % (docid, pageid))\n                \n                with open(label_path, 'w') as fw:\n                    for label in labels:\n                        fw.writelines((label + '\\n').encode('utf8'))\n            else:\n                labels = []\n                for table in contents_dict[docid][pageid]['tables']:\n                    left = int(table['position'][0])\n                    right = int(table['position'][1])\n                    top = int(table['position'][2])\n                    bottom = int(table['position'][3])\n                    x = 1.0 * (left + right) / (2.0 * shape[0])\n                    y = 1.0 * (top + bottom) / (2.0 * shape[1])\n                    w = 1.0 * (right - left) / shape[0]\n                    h = 1.0 * (bottom - top) / shape[1]\n                    x = min(max(0.0, x), 0.9999)\n                    y = min(max(0.0, y), 0.9999)\n                    w = min(max(0.0, w), 0.9999)\n                    h = min(max(0.0, h), 0.9999)\n                    labels.append(' '.join([str(t) for t in [0, x, y, w, h]]))\n\n                # noline\n                image_path = os.path.join(maindir, 'Images', docid, 'png_noline_notable', \\\n                    '%s_%s_nolinenotable.png' % (docid, pageid))\n                label_path = os.path.join(maindir, 'Labels', docid, 'png_noline_notable', \\\n                    '%s_%s_nolinenotable.txt' % (docid, pageid))\n\n                with open(label_path, 'w') as fw:\n                    for label in labels:\n                        fw.writelines((label + '\\n').encode('utf8'))\n\n                # line\n                image_path = os.path.join(maindir, 'Images', docid, 'png_line_notable', \\\n                    '%s_%s_linenotable.png' % (docid, pageid))\n                label_path = os.path.join(maindir, 'Labels', docid, 'png_line_notable', \\\n                    '%s_%s_linenotable.txt' % (docid, pageid))\n                \n                with open(label_path, 'w') as fw:\n                    for label in labels:\n                        fw.writelines((label + '\\n').encode('utf8'))\n\n            if os.path.exists(image_path) and os.path.exists(label_path):\n                dataset.append(image_path)\n\n    random.shuffle(dataset)\n\n    train_list, valid_list, test_list = [], [], []\n    train_list.extend(dataset[0: int(len(dataset) * 0.9)])\n    valid_list.extend(dataset[int(len(dataset) * 0.9): int(len(dataset) * 0.95)])\n    test_list.extend(dataset[int(len(dataset) * 0.95):])\n\t\n    def _writes(data_list, fname):\n        with open(os.path.join(maindir, fname), 'w') as fw:\n            for label in data_list:\n                fw.writelines('%s\\n' % (label))\n\t\t\t\t\t\n    _writes(train_list, 'train.txt')\n    _writes(valid_list, 'valid.txt')\n    _writes(test_list, 'test.txt')\n\ndef read_json(path):\n    with open(path, 'r') as fo:\n        contents_dict = json.load(fo, encoding='utf8')\n        return contents_dict\n\ndef write_json(contents_dict, path):\n    with open(path, 'w') as fw:\n        fw.write(json.dumps(contents_dict, indent=4))\n\n\nif 'Windows' in platform.platform():\n    contents_dict = load_table_json('E:\\\\Temporal\\Python\\darknet-master\\datasets\\\\table-png\\JPEGImages')\n    write_json(contents_dict, 'E:\\\\Temporal\\Python\\darknet-master\\datasets\\\\table-png\\\\texts.json')\n    contents_dict = read_json('E:\\\\Temporal\\Python\\darknet-master\\datasets\\\\table-png\\\\texts.json')\n    draw_image(contents_dict, 'E:\\\\Temporal\\Python\\darknet-master\\datasets\\\\table-png\\JPEGImages')\n    create_labels(contents_dict, 'E:\\\\Temporal\\Python\\darknet-master\\datasets\\\\table-png\\labels')\n    create_datasets(contents_dict, 'E:\\\\Temporal\\Python\\darknet-master\\datasets\\\\table-png')\n    uint_test1('E:\\\\Temporal\\Python\\darknet-master\\datasets\\\\table-png')\n    uint_test2('E:\\\\Temporal\\Python\\darknet-master\\datasets\\\\table-png')\n    uint_test3('E:\\\\Temporal\\Python\\darknet-master\\datasets\\\\table-png')\nelif 'Linux' in platform.platform():\n    # word_dict = read_word_vector('/home/caory/github/table-detection/data/table-v5/word_vector_3.txt')\n    # contents_dict = load_source('/home/wangxu/data/pdf2jpg_v4/output', word_dict)\n    # write_json(contents_dict, '/home/caory/github/table-detection/data/table-v5/texts.json')\n    contents_dict = read_json('/home/caory/github/table-detection/data/table-v5/texts.json')\n    draw_image(contents_dict, '/home/caory/github/darknet-master/datasets/table-v4')\n    # create_labels(contents_dict, '/home/caory/github/darknet-master/datasets/table-v1/')\n","sub_path":"exps/table-v6/datasets-v4.py","file_name":"datasets-v4.py","file_ext":"py","file_size_in_byte":22433,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"124101418","text":"# uncompyle6 version 3.7.4\n# Python bytecode 3.5 (3351)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build/bdist.macosx-10.6-intel/egg/exactly_lib/instructions/setup/utils/instruction_utils.py\n# Compiled at: 2019-12-27 10:07:41\n# Size of source mod 2**32: 1867 bytes\nfrom typing import Sequence\nfrom exactly_lib.test_case.phases.common import InstructionEnvironmentForPostSdsStep, InstructionEnvironmentForPreSdsStep\nfrom exactly_lib.test_case.phases.setup import SetupPhaseInstruction\nfrom exactly_lib.test_case.result import svh\nfrom exactly_lib.test_case_utils.path_check import PathCheck, pre_sds_validate, pre_or_post_sds_validate\n\nclass InstructionWithFileRefsBase(SetupPhaseInstruction):\n\n    def __init__(self, path_check_list: Sequence[PathCheck]):\n        \"\"\"\n        :param path_check_list: Sequence of files to be validates\n        \"\"\"\n        self.path_check_list = path_check_list\n\n    def validate_pre_sds(self, environment: InstructionEnvironmentForPreSdsStep) -> svh.SuccessOrValidationErrorOrHardError:\n        for path_check in self.path_check_list:\n            assert isinstance(path_check, PathCheck)\n            path = path_check.path_sdv.resolve(environment.symbols)\n            if path.exists_pre_sds():\n                result = pre_sds_validate(path_check, environment.path_resolving_environment)\n                if not result.is_success:\n                    return result\n\n        return svh.new_svh_success()\n\n    def validate_post_setup(self, environment: InstructionEnvironmentForPostSdsStep) -> svh.SuccessOrValidationErrorOrHardError:\n        for path_check in self.path_check_list:\n            assert isinstance(path_check, PathCheck)\n            path = path_check.path_sdv.resolve(environment.symbols)\n            if not path.exists_pre_sds():\n                result = pre_or_post_sds_validate(path_check, environment.path_resolving_environment_pre_or_post_sds)\n                if not result.is_success:\n                    return result\n\n        return svh.new_svh_success()","sub_path":"pycfiles/exactly-0.12.0-py3.5/instruction_utils.cpython-35.py","file_name":"instruction_utils.cpython-35.py","file_ext":"py","file_size_in_byte":2065,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"648645018","text":"import numpy as np\nimport pandas as pd\nimport torch\nimport pickle as pkl\nimport os\nfrom os import system\nimport fnmatch\nimport shutil\nimport tarfile\nimport random\n\nfrom torch_geometric.utils import remove_self_loops\nfrom torch_geometric.data import Data\n\n\nfrom . import utils\nfrom .. import config\n\n\n\n\ndef prepare_adjacency_matrix(data_path:str, \n                             adjacency_matrix_save_path:str,\n                            gene_mapping_save_path:str,\n                            binay_adjacency_matrix_save_path:str, \n                            threshold:float= 0.8):\n    '''Create the Adjacency matrix from data matrix\n    \n    data_path: the path of mranseq data text file\n    adjacency_matrix_save_path: the path where the adjacency matrix will be saved as npy format. the path contains .npy extension\n    gene_mapping_save_path: the gene name(key) to index(value) mapping file save path\n    binay_adjacency_matrix_save_path: the path where the biary adjacency matrix will be saved as npy format. the path contains .npy extens\n    threshold: the threshold value for correlation coefficient matrix\n    '''\n    \n    df = pd.read_csv(data_path,sep=\"\\t\")\n    \n    #the first row is the duplication of columns names\n    df=df.drop(0,axis=0)\n    \n    # set the row index to the gene name\n    df.set_index('Hybridization REF',inplace= True)\n    \n    # make the genes as columns\n    df= df.T\n    \n    #convert the string to float data type\n    df = df.astype('float32')\n    \n    ## removing out the genes whose have no expression in any samples\n    df_con= df.apply(lambda x: x.sum()==0.0,axis=0)\n    \n    #how many genes have no value in any sample\n    df_con[df_con==True].shape\n    print(f\"The number of genes which have no expression in any sample in the data set is {len(df_con[df_con==True])}\")\n    \n    #get the filtered df\n    df_final = df.loc[:,~df_con.values]\n    \n    del(df)\n    \n    #mapping of columns\n    col_index_dic = {col:i for i,col in enumerate(df_final.columns)}\n    \n    print(f\"Number of genes in final data is {len(col_index_dic)}\")\n    \n    #convert to numpy array\n    np_data= df_final.values\n    \n    \n    \n    #correlation coefficient matrix\n    np_data = torch.from_numpy(np_data)\n    cor_np = utils.cov(np_data)\n    \n    cor_np = cor_np.detach().numpy()\n    \n    np.save(adjacency_matrix_save_path , cor_np)\n    \n    with open(gene_mapping_save_path,'wb') as f:\n        pkl.dump(col_index_dic,f)\n        \n    # setting 1/0 based on thresholding\n    \n    cor_np = np.where(cor_np>=threshold, 1, 0)\n\n    #in 2,num_edge_format\n    cor_np = np.array(np.where(cor_np>0)) # this [2,#edges]: contians self loops\n    cor_np= torch.from_numpy(cor_np)\n\n    #remove the self loop\n    #cor_np = remove_self_loops(cor_np)[0]\n    print(f\"Now the edge_index has the shape of {cor_np.shape}\")\n\n    \n    np.save(binay_adjacency_matrix_save_path, cor_np)\n    \n    print(\"Complete\")\n    \n\ndef get_dataset(dataset_path:str):\n    '''Get the dataset in which only final genes are present\n    \n    dataset_path: the path of dataset which will be transformed\n    '''\n    pass\n\n\n\ndef process_each_sample(x,**kwargs):\n    '''Process a single sample, return the Data Object'''\n    \n    x = x.values\n    x = x[:,np.newaxis]\n    x = np.log2(x+1)\n    label = torch.tensor(kwargs['label'])\n    edge_index = kwargs['edge_index']\n\n    \n\n    return Data(x=torch.from_numpy(x),edge_index=edge_index, y=label)  \n\ndef prepare_label_dict(dataset_dir,prefix):\n    '''Make the label dictionary for a specific modal'''\n\n    src_files = os.listdir(dataset_dir)\n\n    all_rawfile = set([s.split(\".\")[0] for s in src_files])\n\n    label_dict = {label:i for i,label in enumerate(set(all_rawfile))}\n\n    save_path = f\"{prefix}_label_dict\"\n    save_path = os.path.join(config.MODEL_RELATED_DATA_PATH,save_path)\n\n    with open(save_path,'wb') as f:\n        pkl.dump(label_dict,f)\n\n    print(\"Label is completed\")\n\ndef transform_df(csv_path):\n    ''' Return the df in which rows are samples and columns are gene\n    \n    '''\n    df = pd.read_csv(csv_path,sep=\"\\t\")\n    df = df.drop(0,axis= 0)\n    df.index = df['Hybridization REF']\n    df.drop('Hybridization REF',axis = 1, inplace=True)\n    df =df.T\n    \n    return df\n\ndef get_sample_part(id):\n    '''This will return the sample part of the sample id.\n    \n    TCGA-3C-AAAU-01A-11R-A41B-07 => 01\n    '''\n    \n    return int(id.split(\"-\")[3][:2])\n\ndef remove_normal_samples(df):\n    '''Remove the normal samples from dataframe of one specific cancer type.\n       The index of df should be the sample IDs\n    \n\n\n    \n    '''\n    normal_sample=list(range(10,20))    \n    disease_sample_indexes = [get_sample_part(id) not in normal_sample for id in df.index]\n    normal_sample_indexes = [get_sample_part(id) in normal_sample for id in df.index]\n\n    #normal samples df\n    if sum(normal_sample_indexes)>0:\n        nomral_samples_df = df.iloc[normal_sample_indexes,:]\n    else:\n        nomral_samples_df = None\n        \n    # diseased samples df    \n    df = df.iloc[disease_sample_indexes,:]\n    \n    return df,nomral_samples_df\n\ndef process_csv(csv_path,mapping_dict):\n    '''Return the dataframe in which sampels are in rows, genes are in columns, all the normal samples are removed.\n        \n      Returns:\n      df : diseasesed_samples dataframe\n      nomral_samples_df: Normal samples dataframe\n      \n    '''\n    \n    df =transform_df(csv_path)\n    df,nomral_samples_df = remove_normal_samples(df)\n    \n    \n    #select only selected genes\n    df = df.loc[:,list(mapping_dict.keys())]\n    df = df.astype('float32')\n    \n    \n    if nomral_samples_df is not None:\n        nomral_samples_df = nomral_samples_df.loc[:,list(mapping_dict.keys())]\n        nomral_samples_df = nomral_samples_df.astype('float32')\n\n\n    \n    \n    return df,nomral_samples_df\n\n\n\n\ndef prepared_data(dataset_path:str, save_dir:str,save_dir_normal:str, prefix:str,extension:str):\n    \n    \n    '''Prepared Pygeometric.data.Data object from each sample.\n    \n    save_name: the name of the pickle file to which the list of Data object will be saved\n    \n    \n    \n    '''\n    mapping = os.path.join(config.MODEL_RELATED_DATA_PATH,\"gene_mapping.pkl\")\n    edge_index_path = os.path.join(config.MODEL_RELATED_DATA_PATH,\"binay_adjacency_mat.npy\")\n    \n\n    label_dict_path = os.path.join(config.MODEL_RELATED_DATA_PATH,f\"{prefix}_label_dict\")\n    \n    if not os.path.exists(label_dict_path):\n        print(\"Label dict does not exist. Creating One\")\n        prepare_label_dict(dataset_path,prefix)\n    \n    with open(mapping,'rb') as f:\n        mapping_dict = pkl.load(f)\n    \n    with open(label_dict_path,'rb') as f:\n        label_dict = pkl.load(f)\n\n    edge_index_ = torch.from_numpy(np.load(edge_index_path))\n\n    #process unprocessed datasets\n    src_files = os.listdir(dataset_path)\n    dest_files = os.listdir(save_dir)\n\n    already_proceesd = set([s.split(\".\")[0] for s in dest_files])\n    all_rawfile = set([s.split(\".\")[0] for s in src_files])\n\n    print(f\"The file already processed {already_proceesd}\")\n    print()\n\n    #need to be processed\n    need_to_be_processed = all_rawfile.difference(already_proceesd)\n    print(f\"The files to be processed {need_to_be_processed}\")\n    #now process each unprocess one\n\n    for fs in need_to_be_processed:\n        print(f\"Now working on {fs}\")\n        file_path = os.path.join(dataset_path,fs+\".txt\")\n\n\n        deseased_df,nomral_samples_df = process_csv(file_path,mapping_dict) \n        '''\n        # start from here\n        df = pd.read_csv(file_path,sep=\"\\t\")\n        \n        #the first row is the duplication of columns names\n        df=df.drop(0,axis=0)\n\n        # set the row index to the gene name\n        df.set_index('Hybridization REF',inplace= True)\n            \n        # make the genes as columns\n        df= df.T\n        # upto this\n\n        df = df.loc[:,list(mapping_dict.keys())]\n        \n        #convert the string to float data type\n        df = df.astype('float32')\n\n        '''\n\n        ds = [process_each_sample(deseased_df.iloc[i], label= label_dict[fs], edge_index = edge_index_ ) for i in range(len(deseased_df))]\n        \n        if nomral_samples_df is not None:\n            \n            ds_normal = [process_each_sample(nomral_samples_df.iloc[i], label= label_dict[fs], edge_index = edge_index_ ) for i in range(len(nomral_samples_df))]\n        else:\n            print(f\"There is no Normal Samples for {label_dict[fs]}\")\n\n        #saving the files\n        #save_path = os.path.join(config.data_path(),'PROCESSED_DATA','MRNASEQ')\n\n        os.makedirs(save_dir,exist_ok= True)\n        os.makedirs(save_dir_normal,exist_ok= True)\n\n        with open(os.path.join(save_dir,f\"{prefix}_{fs}.{extension}\"),'wb') as f:\n            pkl.dump(ds,f)\n\n        if nomral_samples_df is not None:\n            with open(os.path.join(save_dir_normal,f\"{prefix}_{fs}_NORMAL_RNASEQ.{extension}\"),'wb') as f:\n                \n                pkl.dump(ds_normal,f)        \n\n        print(f\"Completed Processing of {fs}\")\n\n\ndef big_file_name_rename(file_name:str):\n    '''From the very big file name, get the abbrebiated Cancer Name'''\n\n    name_list = file_name.split(\"_\")\n    abbr_name_ls = [s.split(\".\")[0]  for s in name_list if len(s.split(\".\"))==2 and s.split(\".\")[1]==\"Merge\"]\n\n    if len(abbr_name_ls)>1:\n        dest = abbr_name_ls[0]+\".tar.gz\"\n        os.rename(file_name,dest)\n    else:\n        print(\"Did not find single Abbrebiated Name\")\n\ndef rename_tar_gzfile(folder_path):\n    '''Rename the very big file names in this directory'''\n    pass\n\n\n\ndef process_raw_tar_file(dir_path:str):\n\n    '''Process the tar.gz file to extract the mrnaseq files to PROCESSED_DATA directory'''\n\n    os.chdir(dir_path)\n    #find all tar.gz file\n    all_gz_files =[s for s in os.listdir(dir_path) if s.find(\".tar.gz\")>0]\n\n    #process each_gz_files \n    for gz_file in all_gz_files:\n        gz_file_full_path = os.path.join(dir_path,gz_file)  \n        \n        #extract it\n        system(f\" tar -xvf {gz_file_full_path}\")\n\n\n        new_folder_name = \".\".join(gz_file.split(\".\")[:-2])\n        temp_path = os.path.join(dir_path,new_folder_name)\n\n        #find the .data.data.txt file\n        for ft in os.listdir(temp_path):\n            \n            if ft.find(\".data.txt\")>0:\n                \n                #rename the file\n                \n                abbr_name = ft.split(\".\")[0]\n\n                dest_file_name = abbr_name+\".txt\"\n\n                os.rename(os.path.join(temp_path,ft),os.path.join(temp_path,dest_file_name))\n\n                target_file = os.path.join(temp_path,dest_file_name)\n                dest_folder = os.path.join(config.MRNAS_DATASET_PATH,dest_file_name)\n\n                \n\n                shutil.move(target_file,dest_folder)\n                print(f\"Complete moving {dest_file_name} to {dest_folder}\")\n\n        #delete the extracted file\n        system(f\"rm -r {temp_path}\")        \ndef get_stratified_train_test_dataset(dataset_dir:str, test_split:float, modal=\"MRNASEQ\"):\n    ''' Get the balanced dataset for training and testing. Balanced means that, if \n    we set 80 % for triaining, then 80% of each class will be in train set.\n    other 20% will be in the test set.\n    \n    dataset_dir : the directory in which the processed pygeometric.data.Data for each\n    cohort are present\n    test_split: fraction of samples of each cohort that should be in test set\n    \n    '''\n    print(f\"Creating Train and test dataset for {modal}\")\n    all_chorort_files = os.listdir(dataset_dir)\n\n    grand_train_ls = []\n    grand_test_ls = []\n\n\n    #process each cohort\n    for  cohort in all_chorort_files:\n        full_path = os.path.join(dataset_dir,cohort)\n\n        with open(full_path,'rb') as f:\n            data = pkl.load(f)\n        random.shuffle(data)\n\n        tot= len(data)\n        temp_train_data = data[:int(tot*(1-test_split))]\n        temp_test_data = data[int(tot*(1-test_split)):]\n\n        grand_train_ls = grand_train_ls + temp_train_data\n        grand_test_ls = grand_test_ls + temp_test_data\n\n    #saveing\n\n    if modal == \"MRNASEQ\":\n        print(\"Saving in MRNASEQ data modality\")\n\n        train_save_name = f\"train_{modal}.pkl\"\n        with open(os.path.join(config.TRAIN_MRNASEQ_PATH,train_save_name),'wb') as f:\n            pkl.dump(grand_train_ls,f)\n\n        test_save_name = f\"test_{modal}.pkl\"\n        with open(os.path.join(config.TEST_MRNASEQ_PATH,test_save_name),'wb') as f:\n            pkl.dump(grand_test_ls,f)\n        \n        print(\"Saving the train and test dataset for MRNASEQ\")\n    else:\n\n        print(\"There is no modality match. So, no saving is committed\")","sub_path":"course/Project/graphUnet/src/data_preparations.py","file_name":"data_preparations.py","file_ext":"py","file_size_in_byte":12585,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"64764746","text":"# --------------------------------------------------------------------------------------------\n# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License. See License.txt in the project root for license information.\n# --------------------------------------------------------------------------------------------\n\nfrom __future__ import unicode_literals, print_function\n\nimport datetime\nimport json\nimport math\nimport os\nimport re\nimport subprocess\nimport sys\nfrom threading import Thread\n\nfrom six.moves import configparser\nfrom knack.log import get_logger\nfrom knack.util import CLIError\nfrom azure.cli.core._profile import _SUBSCRIPTION_NAME, Profile\nfrom azure.cli.core._session import ACCOUNT, CONFIG, SESSION\nfrom azure.cli.core.api import get_config_dir\nfrom azure.cli.core.util import handle_exception\n\n# pylint: disable=import-error\nimport jmespath\nfrom prompt_toolkit.auto_suggest import AutoSuggestFromHistory\nfrom prompt_toolkit.buffer import Buffer\nfrom prompt_toolkit.document import Document\nfrom prompt_toolkit.enums import DEFAULT_BUFFER\nfrom prompt_toolkit.filters import Always\nfrom prompt_toolkit.history import FileHistory\nfrom prompt_toolkit.interface import Application, CommandLineInterface\nfrom prompt_toolkit.shortcuts import create_eventloop\n# pylint: enable=import-error\n\nfrom . import VERSION\nfrom .az_completer import AzCompleter\nfrom .az_lexer import get_az_lexer, ExampleLexer, ToolbarLexer\nfrom .configuration import Configuration, SELECT_SYMBOL\nfrom .frequency_heuristic import DISPLAY_TIME, frequency_heuristic\nfrom .gather_commands import add_new_lines, GatherCommands\nfrom .key_bindings import InteractiveKeyBindings\nfrom .layout import LayoutManager\nfrom .progress import progress_view\nfrom . import telemetry\nfrom .threads import LoadCommandTableThread\nfrom .util import get_window_dim, parse_quotes, get_os_clear_screen_word\n\n\nNOTIFICATIONS = \"\"\nPART_SCREEN_EXAMPLE = .3\nSTART_TIME = datetime.datetime.utcnow()\nCLEAR_WORD = get_os_clear_screen_word()\n_ENV_ADDITIONAL_USER_AGENT = 'AZURE_HTTP_USER_AGENT'\n\nlogger = get_logger(__name__)\n\n\ndef space_toolbar(settings_items, empty_space):\n    \"\"\" formats the toolbar \"\"\"\n    counter = 0\n    for part in settings_items:\n        counter += len(part)\n\n    if len(settings_items) == 1:\n        spacing = ''\n    else:\n        spacing = empty_space[\n            :int(math.floor((len(empty_space) - counter) / (len(settings_items) - 1)))]\n\n    settings = spacing.join(settings_items)\n\n    empty_space = empty_space[len(NOTIFICATIONS) + len(settings) + 1:]\n    return settings, empty_space\n\n\n# pylint: disable=too-many-instance-attributes\nclass AzInteractiveShell(object):\n\n    def __init__(self, cli_ctx, style=None, completer=None,\n                 lexer=None, history=None,\n                 input_custom=sys.stdin, output_custom=None,\n                 user_feedback=False, intermediate_sleep=.25, final_sleep=4):\n\n        from .color_styles import style_factory\n\n        self.cli_ctx = cli_ctx\n        self.config = Configuration(cli_ctx.config, style=style)\n        self.config.set_style(style)\n        self.style = style_factory(self.config.get_style())\n        try:\n            gathered_commands = GatherCommands(self.config)\n            self.completer = completer or AzCompleter(self, gathered_commands)\n            self.completer.initialize_command_table_attributes()\n            self.lexer = lexer or get_az_lexer(gathered_commands)\n        except IOError:  # if there is no cache\n            self.completer = AzCompleter(self, None)\n            self.lexer = None\n        self.history = history or FileHistory(os.path.join(self.config.get_config_dir(), self.config.get_history()))\n        if os.environ.get(_ENV_ADDITIONAL_USER_AGENT):\n            os.environ[_ENV_ADDITIONAL_USER_AGENT] += ' AZURECLISHELL/' + VERSION\n        else:\n            os.environ[_ENV_ADDITIONAL_USER_AGENT] = 'AZURECLISHELL/' + VERSION\n\n        # OH WHAT FUN TO FIGURE OUT WHAT THESE ARE!\n        self._cli = None\n        self.layout = None\n        self.description_docs = u''\n        self.param_docs = u''\n        self.example_docs = u''\n        self.last = None\n        self.last_exit = 0\n        self.user_feedback = user_feedback\n        self.input = input_custom\n        self.output = output_custom\n        self.config_default = \"\"\n        self.default_command = \"\"\n        self.threads = []\n        self.curr_thread = None\n        self.spin_val = -1\n        self.intermediate_sleep = intermediate_sleep\n        self.final_sleep = final_sleep\n        self.command_table_thread = None\n\n        # try to consolidate state information here...\n        # Used by key bindings and layout\n        self.example_page = 1\n        self.is_prompting = False\n        self.is_example_repl = False\n        self.is_showing_default = False\n        self.is_symbols = True\n\n    def __call__(self):\n\n        if self.cli_ctx.data[\"az_interactive_active\"]:\n            logger.warning(\"You're in the interactive shell already.\")\n            return\n\n        if self.config.BOOLEAN_STATES[self.config.config.get('DEFAULT', 'firsttime')]:\n            self.config.firsttime()\n\n        if not self.config.has_feedback() and frequency_heuristic(self):\n            print(\"\\n\\nAny comments or concerns? You can use the \\'feedback\\' command!\" +\n                  \" We would greatly appreciate it.\\n\")\n\n        self.cli_ctx.data[\"az_interactive_active\"] = True\n        self.run()\n        self.cli_ctx.data[\"az_interactive_active\"] = False\n\n    @property\n    def cli(self):\n        \"\"\" Makes the interface or refreshes it \"\"\"\n        if self._cli is None:\n            self._cli = self.create_interface()\n        return self._cli\n\n    def handle_cd(self, cmd):\n        \"\"\"changes dir \"\"\"\n        if len(cmd) != 2:\n            print(\"Invalid syntax: cd path\", file=self.output)\n            return\n        path = os.path.expandvars(os.path.expanduser(cmd[1]))\n        try:\n            os.chdir(path)\n        except OSError as ex:\n            print(\"cd: %s\\n\" % ex, file=self.output)\n\n    def on_input_timeout(self, cli):\n        \"\"\"\n        brings up the metadata for the command if there is a valid command already typed\n        \"\"\"\n        document = cli.current_buffer.document\n        text = document.text\n\n        text = text.replace('az ', '')\n        if self.default_command:\n            text = self.default_command + ' ' + text\n\n        param_info, example = self.generate_help_text()\n\n        self.param_docs = u'{}'.format(param_info)\n        self.example_docs = u'{}'.format(example)\n\n        self._update_default_info()\n\n        cli.buffers['description'].reset(\n            initial_document=Document(self.description_docs, cursor_position=0))\n        cli.buffers['parameter'].reset(\n            initial_document=Document(self.param_docs))\n        cli.buffers['examples'].reset(\n            initial_document=Document(self.example_docs))\n        cli.buffers['default_values'].reset(\n            initial_document=Document(\n                u'{}'.format(self.config_default if self.config_default else 'No Default Values')))\n        self._update_toolbar()\n        cli.request_redraw()\n\n    def restart_completer(self):\n        command_info = GatherCommands(self.config)\n        if not self.completer:\n            self.completer.start(command_info)\n        self.completer.initialize_command_table_attributes()\n        if not self.lexer:\n            self.lexer = get_az_lexer(command_info)\n        self._cli = None\n\n    def _space_examples(self, list_examples, rows, section_value):\n        \"\"\" makes the example text \"\"\"\n        examples_with_index = []\n\n        for i, _ in list(enumerate(list_examples)):\n            if len(list_examples[i]) > 1:\n                examples_with_index.append(\"[\" + str(i + 1) + \"] \" + list_examples[i][0] +\n                                           list_examples[i][1])\n\n        example = \"\".join(exam for exam in examples_with_index)\n        num_newline = example.count('\\n')\n\n        page_number = ''\n        if num_newline > rows * PART_SCREEN_EXAMPLE and rows > PART_SCREEN_EXAMPLE * 10:\n            len_of_excerpt = math.floor(float(rows) * PART_SCREEN_EXAMPLE)\n\n            group = example.split('\\n')\n            end = int(section_value * len_of_excerpt)\n            begin = int((section_value - 1) * len_of_excerpt)\n\n            if end < num_newline:\n                example = '\\n'.join(group[begin:end]) + \"\\n\"\n            else:\n                # default chops top off\n                example = '\\n'.join(group[begin:]) + \"\\n\"\n                while ((section_value - 1) * len_of_excerpt) > num_newline:\n                    self.example_page -= 1\n            page_number = '\\n' + str(section_value) + \"/\" + str(int(math.ceil(num_newline / len_of_excerpt)))\n\n        return example + page_number + ' CTRL+Y (^) CTRL+N (v)'\n\n    def _update_toolbar(self):\n        cli = self.cli\n        _, cols = get_window_dim()\n        cols = int(cols)\n\n        empty_space = \" \" * cols\n\n        delta = datetime.datetime.utcnow() - START_TIME\n        if self.user_feedback and delta.seconds < DISPLAY_TIME:\n            toolbar = [\n                ' Try out the \\'feedback\\' command',\n                'If refreshed disappear in: {}'.format(str(DISPLAY_TIME - delta.seconds))]\n        elif self.command_table_thread.is_alive():\n            toolbar = [\n                ' Loading...',\n                'Hit [enter] to refresh'\n            ]\n        else:\n            toolbar = self._toolbar_info()\n\n        toolbar, empty_space = space_toolbar(toolbar, empty_space)\n        cli.buffers['bottom_toolbar'].reset(\n            initial_document=Document(u'{}{}{}'.format(NOTIFICATIONS, toolbar, empty_space)))\n\n    def _toolbar_info(self):\n        sub_name = \"\"\n        try:\n            profile = Profile(cli_ctx=self.cli_ctx)\n            sub_name = profile.get_subscription()[_SUBSCRIPTION_NAME]\n        except CLIError:\n            pass\n\n        curr_cloud = \"Cloud: {}\".format(self.cli_ctx.cloud.name)\n\n        tool_val = 'Subscription: {}'.format(sub_name) if sub_name else curr_cloud\n\n        settings_items = [\n            \" [F1]Layout\",\n            \"[F2]Defaults\",\n            \"[F3]Keys\",\n            \"[Ctrl+D]Quit\",\n            tool_val\n        ]\n        return settings_items\n\n    def generate_help_text(self):\n        \"\"\" generates the help text based on commands typed \"\"\"\n        param_descrip = example = \"\"\n        self.description_docs = u''\n\n        rows, _ = get_window_dim()\n        rows = int(rows)\n\n        param_args = self.completer.leftover_args\n        last_word = self.completer.unfinished_word\n        command = self.completer.current_command\n        new_command = ' '.join([command, last_word]).strip()\n\n        if not self.completer.complete_command and new_command in self.completer.command_description:\n            command = new_command\n\n        # get command/group help\n        if self.completer and command in self.completer.command_description:\n            self.description_docs = u'{}'.format(self.completer.command_description[command])\n\n        # get parameter help if full command\n        if self.completer and command in self.completer.command_param_info:\n            param = param_args[-1] if param_args else ''\n            param = last_word if last_word.startswith('-') else param\n\n            if param in self.completer.command_param_info[command] and self.completer.has_description(\n                    command + \" \" + param):\n                param_descrip = ''.join([\n                    param, \":\", '\\n', self.completer.param_description.get(command + \" \" + param, '')])\n\n            if command in self.completer.command_examples:\n                string_example = []\n                for example in self.completer.command_examples[command]:\n                    for part in example:\n                        string_example.append(part)\n                ''.join(string_example)\n                example = self._space_examples(\n                    self.completer.command_examples[command], rows, self.example_page)\n\n        return param_descrip, example\n\n    def _update_default_info(self):\n        try:\n            defaults_section = self.cli_ctx.config.defaults_section_name\n            self.config_default = \"\"\n            if hasattr(self.cli_ctx.config, 'config_parser'):\n                options = self.cli_ctx.config.config_parser.options(defaults_section)\n            else:\n                return\n            for opt in options:\n                self.config_default += opt + \": \" + self.cli_ctx.config.get(defaults_section, opt) + \"  \"\n        except configparser.NoSectionError:\n            self.config_default = \"\"\n\n    def create_application(self, full_layout=True):\n        \"\"\" makes the application object and the buffers \"\"\"\n        layout_manager = LayoutManager(self)\n        if full_layout:\n            layout = layout_manager.create_layout(ExampleLexer, ToolbarLexer)\n        else:\n            layout = layout_manager.create_tutorial_layout()\n\n        buffers = {\n            DEFAULT_BUFFER: Buffer(is_multiline=True),\n            'description': Buffer(is_multiline=True, read_only=True),\n            'parameter': Buffer(is_multiline=True, read_only=True),\n            'examples': Buffer(is_multiline=True, read_only=True),\n            'bottom_toolbar': Buffer(is_multiline=True),\n            'example_line': Buffer(is_multiline=True),\n            'default_values': Buffer(),\n            'symbols': Buffer(),\n            'progress': Buffer(is_multiline=False)\n        }\n\n        writing_buffer = Buffer(\n            history=self.history,\n            auto_suggest=AutoSuggestFromHistory(),\n            enable_history_search=True,\n            completer=self.completer,\n            complete_while_typing=Always()\n        )\n\n        return Application(\n            mouse_support=False,\n            style=self.style,\n            buffer=writing_buffer,\n            on_input_timeout=self.on_input_timeout,\n            key_bindings_registry=InteractiveKeyBindings(self).registry,\n            layout=layout,\n            buffers=buffers,\n        )\n\n    def create_interface(self):\n        \"\"\" instantiates the interface \"\"\"\n        return CommandLineInterface(\n            application=self.create_application(),\n            eventloop=create_eventloop())\n\n    def set_prompt(self, prompt_command=\"\", position=0):\n        \"\"\" writes the prompt line \"\"\"\n        self.description_docs = u'{}'.format(prompt_command)\n        self.cli.current_buffer.reset(\n            initial_document=Document(\n                self.description_docs,\n                cursor_position=position))\n        self.cli.request_redraw()\n\n    def set_scope(self, value):\n        \"\"\" narrows the scopes the commands \"\"\"\n        if self.default_command:\n            self.default_command += ' ' + value\n        else:\n            self.default_command += value\n        return value\n\n    def handle_example(self, text, continue_flag):\n        \"\"\" parses for the tutorial \"\"\"\n        cmd = text.partition(SELECT_SYMBOL['example'])[0].rstrip()\n        num = text.partition(SELECT_SYMBOL['example'])[2].strip()\n        example = \"\"\n        try:\n            num = int(num) - 1\n        except ValueError:\n            print(\"An Integer should follow the colon\", file=self.output)\n            return \"\"\n        if cmd in self.completer.command_examples:\n            if num >= 0 and num < len(self.completer.command_examples[cmd]):\n                example = self.completer.command_examples[cmd][num][1]\n                example = example.replace('\\n', '')\n            else:\n                print('Invalid example number', file=self.output)\n                return '', True\n\n        example = example.replace('az', '')\n\n        starting_index = None\n        counter = 0\n        example_no_fill = \"\"\n        flag_fill = True\n        for word in example.split():\n            if flag_fill:\n                example_no_fill += word + \" \"\n            if word.startswith('-'):\n                example_no_fill += word + \" \"\n                if not starting_index:\n                    starting_index = counter\n                flag_fill = False\n            counter += 1\n\n        return self.example_repl(example_no_fill, example, starting_index, continue_flag)\n\n    def example_repl(self, text, example, start_index, continue_flag):\n        \"\"\" REPL for interactive tutorials \"\"\"\n        if start_index:\n            start_index = start_index + 1\n            cmd = ' '.join(text.split()[:start_index])\n            example_cli = CommandLineInterface(\n                application=self.create_application(\n                    full_layout=False),\n                eventloop=create_eventloop())\n            example_cli.buffers['example_line'].reset(\n                initial_document=Document(u'{}\\n'.format(\n                    add_new_lines(example)))\n            )\n            while start_index < len(text.split()):\n                if self.default_command:\n                    cmd = cmd.replace(self.default_command + ' ', '')\n                example_cli.buffers[DEFAULT_BUFFER].reset(\n                    initial_document=Document(\n                        u'{}'.format(cmd),\n                        cursor_position=len(cmd)))\n                example_cli.request_redraw()\n                answer = example_cli.run()\n                if not answer:\n                    return \"\", True\n                answer = answer.text\n                if answer.strip('\\n') == cmd.strip('\\n'):\n                    continue\n                else:\n                    if len(answer.split()) > 1:\n                        start_index += 1\n                        cmd += \" \" + answer.split()[-1] + \" \" +\\\n                               u' '.join(text.split()[start_index:start_index + 1])\n            example_cli.exit()\n            del example_cli\n        else:\n            cmd = text\n\n        return cmd, continue_flag\n\n    # pylint: disable=too-many-statements\n    def _special_cases(self, cmd, outside):\n        break_flag = False\n        continue_flag = False\n        args = parse_quotes(cmd)\n        cmd_stripped = cmd.strip()\n\n        if not cmd_stripped and cmd:\n            # add scope if there are only spaces\n            cmd = self.default_command + \" \" + cmd\n        elif cmd_stripped in (\"quit\", \"exit\"):\n            break_flag = True\n        elif cmd_stripped == \"clear-history\":\n            continue_flag = True\n            self.reset_history()\n        elif cmd_stripped == CLEAR_WORD:\n            outside = True\n            cmd = CLEAR_WORD\n        elif cmd_stripped[0] == SELECT_SYMBOL['outside']:\n            cmd = cmd_stripped[1:]\n            outside = True\n            if cmd.strip() and cmd.split()[0] == 'cd':\n                self.handle_cd(parse_quotes(cmd))\n                continue_flag = True\n            telemetry.track_outside_gesture()\n\n        elif cmd_stripped[0] == SELECT_SYMBOL['exit_code']:\n            meaning = \"Success\" if self.last_exit == 0 else \"Failure\"\n\n            print(meaning + \": \" + str(self.last_exit), file=self.output)\n            continue_flag = True\n            telemetry.track_exit_code_gesture()\n        elif SELECT_SYMBOL['query'] in cmd_stripped and self.last and self.last.result:\n            continue_flag = self.handle_jmespath_query(args)\n            telemetry.track_query_gesture()\n        elif not args:\n            continue_flag = True\n        elif args[0] == '--version' or args[0] == '-v':\n            try:\n                continue_flag = True\n                self.cli_ctx.show_version()\n            except SystemExit:\n                pass\n        elif SELECT_SYMBOL['example'] in cmd:\n            cmd, continue_flag = self.handle_example(cmd, continue_flag)\n            telemetry.track_ran_tutorial()\n        elif SELECT_SYMBOL['scope'] == cmd_stripped[0:2]:\n            continue_flag, cmd = self.handle_scoping_input(continue_flag, cmd, cmd_stripped)\n            telemetry.track_scope_changes()\n        else:\n            # not a special character; add scope and remove 'az'\n            if self.default_command:\n                cmd = self.default_command + \" \" + cmd\n            elif cmd.split(' ', 1)[0].lower() == 'az':\n                cmd = ' '.join(cmd.split()[1:])\n            if \"|\" in cmd or \">\" in cmd:\n                # anything I don't parse, send off\n                outside = True\n                cmd = \"az \" + cmd\n            telemetry.track_cli_commands_used()\n\n        return break_flag, continue_flag, outside, cmd\n\n    def handle_jmespath_query(self, args):\n        \"\"\" handles the jmespath query for injection or printing \"\"\"\n        continue_flag = False\n        query_symbol = SELECT_SYMBOL['query']\n        symbol_len = len(query_symbol)\n        try:\n            if len(args) == 1:\n                # if arguments start with query_symbol, just print query result\n                if args[0] == query_symbol:\n                    result = self.last.result\n                elif args[0].startswith(query_symbol):\n                    result = jmespath.search(args[0][symbol_len:], self.last.result)\n                print(json.dumps(result, sort_keys=True, indent=2), file=self.output)\n            elif args[0].startswith(query_symbol):\n                # print error message, user unsure of query shortcut usage\n                print((\"Usage Error: \" + os.linesep +\n                       \"1. Use {0} stand-alone to display previous result with optional filtering \"\n                       \"(Ex: {0}[jmespath query])\" +\n                       os.linesep + \"OR:\" + os.linesep +\n                       \"2. Use {0} to query the previous result for argument values \"\n                       \"(Ex: group show --name {0}[jmespath query])\").format(query_symbol), file=self.output)\n            else:\n                # query, inject into cmd\n                def jmespath_query(match):\n                    if match.group(0) == query_symbol:\n                        return str(self.last.result)\n                    query_result = jmespath.search(match.group(0)[symbol_len:], self.last.result)\n                    return str(query_result)\n\n                def sub_result(arg):\n                    escaped_symbol = re.escape(query_symbol)\n                    # regex captures query symbol and all characters following it in the argument\n                    return json.dumps(re.sub(r'%s.*' % escaped_symbol, jmespath_query, arg))\n                cmd_base = ' '.join(map(sub_result, args))\n                self.cli_execute(cmd_base)\n            continue_flag = True\n        except (jmespath.exceptions.ParseError, CLIError) as e:\n            print(\"Invalid Query Input: \" + str(e), file=self.output)\n            continue_flag = True\n        return continue_flag\n\n    def handle_scoping_input(self, continue_flag, cmd, text):\n        \"\"\" handles what to do with a scoping gesture \"\"\"\n        default_split = text.partition(SELECT_SYMBOL['scope'])[2].split()\n        cmd = cmd.replace(SELECT_SYMBOL['scope'], '')\n\n        continue_flag = True\n\n        if not default_split:\n            self.default_command = \"\"\n            print('unscoping all', file=self.output)\n\n            return continue_flag, cmd\n\n        while default_split:\n            if not text:\n                value = ''\n            else:\n                value = default_split[0]\n\n            tree_path = self.default_command.split()\n            tree_path.append(value)\n\n            if self.completer.command_tree.in_tree(tree_path):\n                self.set_scope(value)\n                print(\"defaulting: \" + value, file=self.output)\n                cmd = cmd.replace(SELECT_SYMBOL['scope'], '')\n            elif SELECT_SYMBOL['unscope'] == default_split[0] and self.default_command.split():\n\n                value = self.default_command.split()[-1]\n                self.default_command = ' ' + ' '.join(self.default_command.split()[:-1])\n\n                if not self.default_command.strip():\n                    self.default_command = self.default_command.strip()\n                print('unscoping: ' + value, file=self.output)\n\n            elif SELECT_SYMBOL['unscope'] not in text:\n                print(\"Scope must be a valid command\", file=self.output)\n\n            default_split = default_split[1:]\n        return continue_flag, cmd\n\n    def reset_history(self):\n        history_file_path = os.path.join(self.config.get_config_dir(), self.config.get_history())\n        os.remove(history_file_path)\n        self.history = FileHistory(history_file_path)\n        self.cli.buffers[DEFAULT_BUFFER].history = self.history\n\n    def cli_execute(self, cmd):\n        \"\"\" sends the command to the CLI to be executed \"\"\"\n\n        try:\n            args = parse_quotes(cmd)\n\n            if args and args[0] == 'feedback':\n                self.config.set_feedback('yes')\n                self.user_feedback = False\n\n            azure_folder = get_config_dir()\n            if not os.path.exists(azure_folder):\n                os.makedirs(azure_folder)\n            ACCOUNT.load(os.path.join(azure_folder, 'azureProfile.json'))\n            CONFIG.load(os.path.join(azure_folder, 'az.json'))\n            SESSION.load(os.path.join(azure_folder, 'az.sess'), max_age=3600)\n\n            invocation = self.cli_ctx.invocation_cls(cli_ctx=self.cli_ctx,\n                                                     parser_cls=self.cli_ctx.parser_cls,\n                                                     commands_loader_cls=self.cli_ctx.commands_loader_cls,\n                                                     help_cls=self.cli_ctx.help_cls)\n\n            if '--progress' in args:\n                args.remove('--progress')\n                execute_args = [args]\n                thread = Thread(target=invocation.execute, args=execute_args)\n                thread.daemon = True\n                thread.start()\n                self.threads.append(thread)\n                self.curr_thread = thread\n\n                progress_args = [self]\n                thread = Thread(target=progress_view, args=progress_args)\n                thread.daemon = True\n                thread.start()\n                self.threads.append(thread)\n                result = None\n            else:\n                result = invocation.execute(args)\n\n            self.last_exit = 0\n            if result and result.result is not None:\n                if self.output:\n                    self.output.write(result)\n                    self.output.flush()\n                else:\n                    formatter = self.cli_ctx.output.get_formatter(self.cli_ctx.invocation.data['output'])\n                    self.cli_ctx.output.out(result, formatter=formatter, out_file=sys.stdout)\n                    self.last = result\n\n        except Exception as ex:  # pylint: disable=broad-except\n            self.last_exit = handle_exception(ex)\n        except SystemExit as ex:\n            self.last_exit = int(ex.code)\n\n    def progress_patch(self, *args, **kwargs):\n        \"\"\" forces to use the Shell Progress \"\"\"\n        from .progress import ShellProgressView\n        self.cli_ctx.progress_controller.init_progress(ShellProgressView())\n        return self.cli_ctx.progress_controller\n\n    def run(self):\n        \"\"\" starts the REPL \"\"\"\n        from .progress import ShellProgressView\n        self.cli_ctx.get_progress_controller().init_progress(ShellProgressView())\n        self.cli_ctx.get_progress_controller = self.progress_patch\n\n        self.command_table_thread = LoadCommandTableThread(self.restart_completer, self)\n        self.command_table_thread.start()\n\n        from .configuration import SHELL_HELP\n        self.cli.buffers['symbols'].reset(\n            initial_document=Document(u'{}'.format(SHELL_HELP)))\n        # flush telemetry for new commands and send successful interactive mode entry event\n        telemetry.set_success()\n        telemetry.flush()\n        while True:\n            try:\n                document = self.cli.run(reset_current_buffer=True)\n                text = document.text\n                if not text:\n                    # not input\n                    self.set_prompt()\n                    continue\n                cmd = text\n                outside = False\n\n            except AttributeError:\n                # when the user pressed Control D\n                break\n            except (KeyboardInterrupt, ValueError):\n                # CTRL C\n                self.set_prompt()\n                continue\n            else:\n                self.history.append(text)\n                b_flag, c_flag, outside, cmd = self._special_cases(cmd, outside)\n\n                if b_flag:\n                    break\n                if c_flag:\n                    self.set_prompt()\n                    continue\n\n                self.set_prompt()\n\n                if outside:\n                    subprocess.Popen(cmd, shell=True).communicate()\n                else:\n                    telemetry.start()\n                    self.cli_execute(cmd)\n                    if self.last_exit and self.last_exit != 0:\n                        telemetry.set_failure()\n                    else:\n                        telemetry.set_success()\n                    telemetry.flush()\n        telemetry.conclude()\n","sub_path":"src/interactive/azext_interactive/azclishell/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":29112,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"108769866","text":"import copy\nimport json\nimport logging\nimport os\nimport uuid\n\nfrom pykube import exceptions as pykube_exc\nimport yaml\n\nfrom fuel_ccp.common import jinja_utils\nfrom fuel_ccp.common import utils\nfrom fuel_ccp import config\nfrom fuel_ccp.config import images as config_images\nfrom fuel_ccp import exceptions\nfrom fuel_ccp import kubernetes\nfrom fuel_ccp import templates\n\n\nCONF = config.CONF\n\nLOG = logging.getLogger(__name__)\n\nRESTART_POLICY_ALWAYS = \"always\"\nRESTART_POLICY_NEVER = \"never\"\n\n\nclass Action(object):\n    def __init__(self, **kwargs):\n        self.name = kwargs.pop(\"name\")\n        self.component = kwargs.pop(\"component\")\n        self.component_dir = kwargs.pop(\"component_dir\")\n        self.image = kwargs.pop(\"image\")\n        self.command = kwargs.pop(\"command\")\n        self.dependencies = kwargs.pop(\"dependencies\", ())\n        self.files = kwargs.pop(\"files\", ())\n        self.parameters = kwargs.pop(\"parameters\", ())\n        self.restart_policy = kwargs.pop(\"restart_policy\",\n                                         RESTART_POLICY_NEVER)\n\n        if kwargs:\n            key_names = \", \".join(kwargs.keys())\n            raise ValueError(\"Invalid keys '%s' for '%s' action\" % (\n                             key_names, self.name))\n\n    @property\n    def k8s_name(self):\n        if not hasattr(self, \"_k8s_name\"):\n            self._k8s_name = \"%s-%s\" % (self.name, str(uuid.uuid4())[:8])\n        return self._k8s_name\n\n    def validate(self):\n        pass\n\n    def run(self, user_parameters=None):\n        if user_parameters:\n            self.user_parameters = user_parameters\n        else:\n            self.user_parameters = ()\n        self._process_dependencies()\n        self._create_configmap()\n        self._create_action()\n        return self.k8s_name\n\n    def _process_dependencies(self):\n        services_map = utils.get_deploy_components_info()\n        deps_map = utils.get_dependencies_map(services_map)\n        new_deps = []\n        for dep in self.dependencies:\n            new_deps.extend(utils.extend_dependency(dep, deps_map, {}, {}))\n        self.dependencies = new_deps\n\n    # configmap methods\n\n    def _create_configmap(self):\n        CONF.configs._update(action_parameters=self._get_custom_parameters())\n        data = {\n            \"config\": CONF.configs._json(sort_keys=True),\n            \"secret-config\": CONF.secret_configs._json(sort_keys=True),\n            \"nodes-config\": utils.get_nodes_config(CONF.nodes),\n            \"workflow\": self._get_workflow()\n        }\n        data.update(self._get_file_templates())\n\n        cm = templates.serialize_configmap(self.k8s_name, data)\n        kubernetes.process_object(cm)\n\n    def _get_workflow(self):\n        wf = {\n            \"name\": self.name,\n            \"dependencies\": self.dependencies,\n            \"job\": {\n                \"command\": self.command\n            },\n            \"files\": []\n        }\n        for f in self.files:\n            wf[\"files\"].append({\n                \"name\": f[\"content\"],\n                \"path\": f[\"path\"],\n                \"perm\": f.get(\"perm\"),\n                \"user\": f.get(\"user\")\n            })\n        return json.dumps({\"workflow\": wf})\n\n    def _get_custom_parameters(self):\n        parameters = {}\n        # add defaults\n        for param in self.parameters:\n            parameters[param[\"key\"]] = param[\"default_value\"]\n        # process user params\n        for param in self.user_parameters:\n            key, sep, value = param.partition(\"=\")\n            if key in parameters:\n                parameters[key] = value\n            else:\n                raise Exception(\"Parameter %s is not allowed for %s action\",\n                                key, self.name)\n        return parameters\n\n    def _get_file_templates(self):\n        exports_map = utils.get_repositories_exports()\n        exports_template = jinja_utils.generate_jinja_imports(exports_map)\n        data = {}\n        for f in self.files:\n            template_path = os.path.join(self.component_dir,\n                                         \"service\", \"files\",\n                                         f[\"content\"])\n            with open(template_path) as filedata:\n                data[f[\"content\"]] = exports_template + \"\\n\" + filedata.read()\n        for ex_item in exports_map.values():\n            data[ex_item[\"name\"]] = ex_item[\"body\"]\n        return data\n\n    # job methods\n\n    def _create_action(self):\n        cont_spec = {\n            \"name\": self.k8s_name,\n            \"image\": config_images.image_spec(self.image),\n            \"imagePullPolicy\": CONF.kubernetes.image_pull_policy,\n            \"command\": templates.get_start_cmd(self.name),\n            \"volumeMounts\": [\n                {\n                    \"name\": \"config-volume\",\n                    \"mountPath\": \"/etc/ccp\"\n                },\n                {\n                    \"name\": \"start-script\",\n                    \"mountPath\": \"/opt/ccp_start_script/bin\"\n                }\n            ],\n            \"env\": templates.serialize_env_variables({}),\n            \"restartPolicy\": \"Never\"\n        }\n        config_volume_items = [\n            {\n                \"key\": \"config\",\n                \"path\": \"globals/globals.json\"\n            },\n            {\n                \"key\": \"secret-config\",\n                \"path\": \"global-secrets/global-secrets.json\"\n            },\n            {\n                \"key\": \"nodes-config\",\n                \"path\": \"nodes-config/nodes-config.json\"\n            },\n            {\n                \"key\": \"workflow\",\n                \"path\": \"role/%s.json\" % self.name\n            }\n        ]\n        for f in self.files:\n            config_volume_items.append({\n                \"key\": f[\"content\"],\n                \"path\": \"files/%s\" % f[\"content\"]\n            })\n        for ex_item in utils.get_repositories_exports().values():\n            config_volume_items.append({\n                \"key\": ex_item[\"name\"],\n                \"path\": \"exports/%s\" % ex_item[\"name\"]\n            })\n        pod_spec = {\n            \"metadata\": {\n                \"name\": self.k8s_name,\n                \"labels\": {\n                    \"app\": self.name,\n                    \"ccp\": \"true\",\n                    \"ccp-action\": \"true\",\n                    \"ccp-component\": self.component\n                }\n            },\n            \"spec\": {\n                \"containers\": [cont_spec],\n                \"restartPolicy\": \"Never\",\n                \"volumes\": [\n                    {\n                        \"name\": \"config-volume\",\n                        \"configMap\": {\n                            \"name\": self.k8s_name,\n                            \"items\": config_volume_items\n                        }\n                    },\n                    {\n                        \"name\": \"start-script\",\n                        \"configMap\": {\n                            \"name\": templates.SCRIPT_CONFIG,\n                            \"items\": [\n                                {\n                                    \"key\": templates.SCRIPT_CONFIG,\n                                    \"path\": \"start_script.py\"\n                                }\n                            ]\n                        }\n                    }\n                ]\n            }\n        }\n        if self.restart_policy == RESTART_POLICY_NEVER:\n            self._create_pod(pod_spec)\n        elif self.restart_policy == RESTART_POLICY_ALWAYS:\n            self._create_job(pod_spec)\n        else:\n            raise ValueError(\"Restart policy %s is not supported\" % (\n                self.restart_policy))\n\n    def _create_pod(self, pod_spec):\n        spec = copy.deepcopy(pod_spec)\n        spec[\"metadata\"].setdefault(\"labels\", {})\n        spec.update({\n            \"kind\": \"Pod\",\n            \"apiVersion\": \"v1\"})\n        kubernetes.process_object(spec)\n\n    def _create_job(self, pod_spec):\n        job_spec = templates.serialize_job(\n            name=self.k8s_name,\n            spec=pod_spec,\n            component_name=self.component,\n            app_name=self.name)\n        job_spec[\"metadata\"][\"labels\"].update({\"ccp-action\": \"true\"})\n        if kubernetes.process_object(job_spec):\n            LOG.info('%s: action \"%s\" has been successfully run',\n                     self.component, self.k8s_name)\n\n\nclass ActionStatus(object):\n\n    @classmethod\n    def get_actions(cls, action_name=None):\n        selector = \"ccp-action=true\"\n        if action_name:\n            selector += \",\" + \"app=%s\" % action_name\n        actions = []\n        job_names = []\n        for job in kubernetes.list_cluster_jobs(selector=selector):\n            actions.append(cls(job))\n            job_names.append(job.name)\n        for pod in kubernetes.list_cluster_pods(selector=selector):\n            job_name = pod.labels.get(\"job-name\")\n            if job_name and job_name not in job_names:\n                actions.append(cls(pod, job_name))\n            elif not job_name:\n                actions.append(cls(pod))\n        return actions\n\n    def __init__(self, k8s_spec, overridden_name=None):\n        self._spec = k8s_spec\n        self.name = k8s_spec.name\n        if overridden_name:\n            self.name = overridden_name\n        self.component = k8s_spec.labels[\"ccp-component\"]\n        self.date = k8s_spec.obj[\"metadata\"][\"creationTimestamp\"]\n        self.terminating = k8s_spec.obj[\"metadata\"].get(\"deletionTimestamp\",\n                                                        False)\n        if k8s_spec.kind == \"Job\":\n            self.restarts = k8s_spec.obj[\"status\"].get(\"failed\", 0)\n            self.active = k8s_spec.obj[\"status\"].get(\"active\", 0)\n            self.failed = False\n        else:\n            phase = k8s_spec.obj[\"status\"][\"phase\"]\n            self.restarts = 0\n            self.active = 1 if phase not in {\"Failed\",\n                                             \"Completed\",\n                                             \"Succeeded\"} else 0\n            self.failed = phase == \"Failed\"\n\n    @property\n    def status(self):\n        if self.terminating:\n            return \"terminating\"\n        if self.failed:\n            return \"fail\"\n        if self.active:\n            return \"wip\"\n        return \"ok\"\n\n    def log(self):\n        if self._spec.kind == \"Pod\":\n            return self._spec.logs()\n        else:\n            pod_selector = \"job-name=%s\" % self._spec.name\n            pods = kubernetes.list_cluster_pods(raw_selector=pod_selector)\n            for pod in pods:\n                if pod.obj['status']['phase'] == \"Failed\":\n                    continue\n                return pod.logs()\\\n\n\n    @classmethod\n    def delete(cls, action_name):\n        delete_configmap_status = False\n        delete_action_status = cls.delete_action(action_name)\n        if delete_action_status:\n            delete_configmap_status = cls.delete_configmap(action_name)\n        return {'action_status': delete_action_status,\n                'configmap_status': delete_configmap_status}\n\n    @staticmethod\n    def delete_action(action_name):\n        try:\n            action = kubernetes.list_cluster_jobs(name=action_name)\n        except pykube_exc.ObjectDoesNotExist:\n            try:\n                action = kubernetes.list_cluster_pods(name=action_name)\n            except pykube_exc.ObjectDoesNotExist:\n                LOG.error('Action with name %s not found', action_name)\n                return False\n        try:\n            action.delete()\n        except pykube_exc.HTTPError as ex:\n            LOG.error(ex.message)\n            return False\n        LOG.info('Action %s is terminating', action_name)\n        return True\n\n    @staticmethod\n    def delete_configmap(action_name):\n        try:\n            configmap = kubernetes.get_configmap(action_name)\n            configmap.delete()\n        except pykube_exc.ObjectDoesNotExist:\n            pass\n        except pykube_exc.HTTPError as ex:\n            LOG.error(ex.message)\n            return False\n        return True\n\n\ndef list_actions():\n    \"\"\"List of available actions.\n\n    :returns: list -- list of all available actions\n    \"\"\"\n    actions = []\n    for repo in utils.get_repositories_paths():\n        component_name = utils.get_component_name_from_repo_path(repo)\n        action_path = os.path.join(repo, \"service\", \"actions\")\n        if not os.path.isdir(action_path):\n            continue\n        for filename in os.listdir(action_path):\n            if filename.endswith(\".yaml\"):\n                action_file = os.path.join(action_path, filename)\n                conf = utils.get_rendering_config()\n                data = jinja_utils.jinja_render(action_file, conf._dict)\n                for action_dict in yaml.load(data).get(\"actions\", ()):\n                    actions.append(Action(component=component_name,\n                                          component_dir=repo,\n                                          **action_dict))\n    return actions\n\n\ndef get_action(action_name):\n    \"\"\"Get action by name.\n\n    :returns: Action -- action object\n    :raises: fuel_ccp.exceptions.NotFoundException\n    \"\"\"\n    for action in list_actions():\n        if action_name == action.name:\n            return action\n    raise exceptions.NotFoundException(\"Action with name '%s' not found\" % (\n                                       action_name))\n\n\ndef run_action(action_name, user_parameters=None):\n    \"\"\"Run action.\n\n    :returns: str -- action name\n    :raises: fuel_ccp.exceptions.NotFoundException\n    \"\"\"\n    action = get_action(action_name)\n    action.validate()\n    return action.run(user_parameters)\n\n\ndef list_action_status(action_type=None):\n    return ActionStatus.get_actions(action_type)\n\n\ndef get_action_status_by_name(action_name):\n    for action in list_action_status():\n        if action.name == action_name:\n            return action\n    raise exceptions.NotFoundException(\"Action with name \\\"%s\\\" not found\" % (\n                                       action_name))\n\n\ndef get_action_statuses_by_names(action_names):\n    action_names = set(action_names)\n    actions = []\n    for action in list_action_status():\n        if action.name in action_names:\n            action_names.remove(action.name)\n            actions.append(action)\n    if action_names:\n        raise exceptions.NotFoundException(\n            \"Action(s) with name(s) %s not found\" % (\n                \", \".join(action_names)))\n    return actions\n\n\ndef delete_action(action_names):\n    \"\"\"Delete action.\n\n    :raises: fuel_ccp.exceptions.NotFoundException\n    \"\"\"\n    not_removed = []\n    configmap_not_removed = []\n    for action_name in action_names:\n        action_status = ActionStatus.delete(action_name)\n        if not action_status.get('action_status'):\n            not_removed.append(action_name)\n        if not action_status.get('configmap_status'):\n            configmap_not_removed.append(action_name)\n    if not_removed:\n        raise exceptions.NotFoundException(\n            'The following actions were not removed: %s'\n            % ','.join(not_removed)\n        )\n    if configmap_not_removed:\n        raise exceptions.NotFoundException(\n            'Configmaps for the following actions were not removed: %s'\n            % ','.join(configmap_not_removed)\n        )\n","sub_path":"fuel_ccp/action.py","file_name":"action.py","file_ext":"py","file_size_in_byte":15137,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"610760294","text":"import json \nimport matplotlib.pyplot as plt\nimport sys\n\n# Opening JSON file \nwith open(sys.argv[1], 'r') as openfile: \n    result = json.load(openfile) \n\ntime = []\nprob = []\n\nfor pair in result[\"slipping\"]:\n    time.append(pair[0])\n    prob.append(pair[1])\n\nplt.plot(time, prob, 'o-')\nplt.title(\"Prediction Timeline for Sample\")\nplt.ylabel('Probability')\nplt.ylim(0,1)\nplt.xlabel('time (sec.)')\nplt.savefig(sys.argv[2])\n","sub_path":"plot.py","file_name":"plot.py","file_ext":"py","file_size_in_byte":421,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"103436250","text":"# coding=utf-8\n\nimport bencoding\nimport hashlib\nimport os\nimport sys\nimport shutil\n\n# 在UT下载完成运行程序中填入（路径自行替换） \"C:\\Program Files\\Python37\\python.exe\" \"C:\\Plugin\\uTorrent_1\\ut_copy_torrent.py\" \"-%D-\" \"-%N-\" \"-%T-\" \"-%I-\"\ntorrent_folder = r'C:\\\\Plugin\\\\uTorrent_1\\\\'  # 种子文件存放目录\n\nD = sys.argv[1][1:-1]\nDx = D.replace('\\\\', '\\\\\\\\')\nN = sys.argv[2][1:-1]\nT = sys.argv[3][1:-1]\nI = sys.argv[4][1:-1]\n# print('-------------')\n# print(D)\n# print(N)\n# print(T)\n# print(I)\n\n\ndef search(path, name):\n    for file in os.listdir(path):\n        if os.path.isfile(path + '\\\\' + file):\n            if name in file:\n                list1.append(file)\n        else:\n            search(path + '\\\\' + file, name)\n\n\ndef hash(filename):\n    for file_fuck in filename:\n        torrent_file = f'{torrent_folder}{file_fuck}'\n        objTorrentFile = open(torrent_file, 'rb')\n        decodedDict = bencoding.bdecode(objTorrentFile.read())\n        objTorrentFile.close()\n        info_hash = hashlib.sha1(bencoding.bencode(\n            decodedDict[b\"info\"])).hexdigest()\n        list2.append(f'{info_hash}==>{torrent_file}')\n\n\ndef copy(infohash):\n    for hash in infohash:\n        if I.lower() in hash:\n            if T == \"\":\n                shutil.copy(hash.split('==>', 1)[1], f'{Dx}\\\\{N}.{I[0:6].lower()}.torrent')\n            else:\n                shutil.copy(hash.split('==>', 1)[1], f'{Dx}\\\\{N}.{T.split(\"/\")[2].split(\":\")[0]}.{I[0:6].lower()}.torrent')\n\nif __name__ == \"__main__\":\n    list1 = []\n    list2 = []\n    search(os.path.abspath(torrent_folder), N)\n    hash(list1)\n    copy(list2)\n","sub_path":"uTorrent/ut_copy_torrent.py","file_name":"ut_copy_torrent.py","file_ext":"py","file_size_in_byte":1625,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"412295350","text":"import json\n\n\nclass Node:\n    def __init__(self, id, **extra_data):\n        self.id = id\n        self.data = extra_data\n\n    def serialize(self):\n        data = {\n            'id': str(self.id)\n        }\n        data.update({str(key): str(value) for key, value in self.data.items()})\n        return data\n\n\nclass Edge:\n    def __init__(self, id, source, target, classes=[], **extra_data):\n        self.id = str(id)\n        self.source = str(source)\n        self.target = str(target)\n        self.classes = classes\n        self.data = extra_data\n\n    def serialize(self):\n        data = {\n            'id': str(self.id),\n            'source': str(self.source),\n            'target': str(self.target),\n        }\n\n        if self.classes:\n            data[classes] = \" \".join(self.classes)\n\n        data.update({str(key): str(value) for key, value in self.data.items()})\n        return data\n\n\nclass Style:\n    def __init__(self, selector, css):\n        self.selector = selector\n        self.css = css if css else {}\n\n    def serialize(self):\n        data = {\n            'selector': str(self.selector),\n            'css': {str(key): str(value) for key, value in self.css.items()},\n        }\n        return data\n\n\nclass Graph:\n    default_edge_extra_data = {}\n    default_node_extra_data = {}\n\n    def __init__(self, container=None, layout='cose', layout_padding=20,\n                 options=None):\n        self.edges = {}\n        self.nodes = {}\n        self.styles = {}\n        self.container = container\n        self.layout = layout\n        self.layout_padding = layout_padding\n        self.options = options or {}\n\n    def serialize(self):\n        data = {\n            'container': self.container,\n            'layout': {\n                'name': str(self.layout),\n                'padding': str(self.layout_padding)\n            },\n            'style': [style.serialize() for _, style in self.styles.items()],\n            'elements': {\n                'nodes': [node.serialize() for _, node in self.nodes.items()],\n                'edges': [edge.serialize() for _, edge in self.edges.items()]\n            }\n        }\n        data.update(self.options)\n        return data\n\n    def dumps(self, *args, **kwargs):\n        return json.dumps(self.serialize(), *args, **kwargs)\n\n    def add(self, instance):\n        if instance is None:\n            return instance\n\n        if isinstance(instance, Node):\n            self.nodes[instance.id] = instance\n        elif isinstance(instance, Edge):\n            self.edges[instance.id] = instance\n        elif isinstance(instance, Style):\n            self.styles[instance.selector] = instance\n        else:\n            raise TypeError(\n                \"instance must be a Node, Edge or Style, got %r\",\n                instance.__class__)\n\n        return instance\n\n    def add_edge(self, id, source, target, classes=[], **extra_data):\n        _extra_data = {}\n\n        if not classes and 'classes' in self.default_edge_extra_data:\n            classes = self.default_edge_extra_data.pop('classes')\n\n        _extra_data.update(self.default_edge_extra_data)\n        _extra_data.update(extra_data)\n\n        edge = Edge(id, source, target, classes=[], **_extra_data)\n        self.edges[edge.id] = edge\n        return edge\n\n    def add_node(self, id, **extra_data):\n        _extra_data = {}\n        _extra_data.update(self.default_edge_extra_data)\n        _extra_data.update(extra_data)\n        node = Node(id, **_extra_data)\n        self.nodes[node.id] = node\n        return node\n\n    def add_style(self, selector, css=None, update=False):\n        style = Style(selector, css.copy() if css else {})\n        self.add(style)\n        return style\n\n    def update_style(self, selector, css=None):\n        if selector not in self.styles:\n            return self.add_style(selector, css)\n        self.styles[selector].css.update(css or {})\n        return self.styles[selector]\n","sub_path":"network_graph/graph.py","file_name":"graph.py","file_ext":"py","file_size_in_byte":3899,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"40513921","text":"from Testing import ZopeTestCase as ztc\nimport doctest\nimport unittest\n\nOPTIONFLAGS = (doctest.ELLIPSIS |\n               doctest.NORMALIZE_WHITESPACE)\n\n\ndef test_suite():\n    return unittest.TestSuite([\n        ztc.ZopeDocFileSuite(\n            'tests/coverage.txt',\n            package='collective.mcp',\n            optionflags=OPTIONFLAGS),\n        ztc.ZopeDocFileSuite(\n            'tests/sorted_list.txt',\n            package='collective.mcp',\n            optionflags=OPTIONFLAGS),\n        doctest.DocTestSuite(\n            module='collective.mcp.sorted_list',\n            optionflags=OPTIONFLAGS),\n    ])\n","sub_path":"collective/mcp/tests/test_unit.py","file_name":"test_unit.py","file_ext":"py","file_size_in_byte":610,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"122394044","text":"import gc\nimport os\nimport sys\nimport argparse\nimport warnings\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport lightgbm as lgb\nimport matplotlib.pyplot as plt\nfrom scipy.sparse import csr_matrix\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.preprocessing import LabelEncoder\nfrom pandas.plotting import register_matplotlib_converters\nfrom scipy.sparse import csr_matrix\n\nimport tqdm\nimport logging\nimport sys\nimport pprint\nimport uuid\nimport datetime\nfrom utils import *\nimport make_data\nfrom load_data import *\nfrom losses import *\nfrom sklearn.model_selection import GroupKFold, KFold\n\n\n# 2011-01-29 ~ 2016-04-24 : d_1    ~ d_1913\n# 2016-04-25 ~ 2016-05-22 : d_1914 ~ d_1941 (public)\n# 2016-05-23 ~ 2016-06-19 : d_1942 ~ d_1969 (private)\n\n\nwarnings.filterwarnings(\"ignore\")\nseed_everything()\nNUM_ITEMS = 30490\nDAYS_PRED = 28\nYEARS = 1\nweights = 0 #global var\nweights_test = 0 #global var\nroll_mat_csr = 0 #global var\n\nexclude_features = [\n            'id',\n            'wm_yr_wk',\n            'date',\n            'demand',\n            'part',\n            'd',\n            # 'wts',\n        ]\n\n\ndef load_rollup_matrix():\n    global roll_mat_csr\n    roll_mat_df = pd.read_pickle('data/roll_mat_df.pkl')\n    roll_mat_csr = csr_matrix(roll_mat_df.values)\n    del roll_mat_df; gc.collect() \n\ndef rollup(v):\n    global roll_mat_csr\n    return roll_mat_csr*v \n\ndef custom_asymmetric_train(y_pred, y_true):\n    y_true = y_true.get_label()\n    #grad\n    # residual = (y_true - y_pred).astype(\"float\").reshape(-1, NUM_ITEMS) * roll_mat_csr.T\n    residual = rollup((y_true - y_pred).astype(\"float\").reshape(NUM_ITEMS, -1))\n    # print(residual.flatten()[:10])\n    # print(\"\\n\")\n    # grad = np.where(residual < 0, -2 * residual, -2 * residual * 1.15) * roll_mat_csr\n    grad = roll_mat_csr.T * (np.where(residual < 0, -2 * residual, -2 * residual * 1.15))\n    grad = grad.flatten()\n    # print(grad[:10])\n    #hess\n    # residual = (y_true - y_pred).astype(\"float\")\n    # hess = np.where(residual < 0, 2, 2 * 1.15) * roll_mat_csr\n    hess = roll_mat_csr.T * (np.where(residual < 0, 2, 2 * 1.15))\n    hess = hess.flatten()\n    # print(hess[:10])\n    # print(grad[:10])\n    # print(len(grad))\n    # print(len(y_pred))\n    return 1e-3*grad, hess\n\n# def custom_asymmetric_train(y_pred, y_true):\n#     y_true = y_true.get_label()\n#     residual = (y_true - y_pred).astype(\"float\")\n#     grad = np.where(residual < 0, -2 * residual, -2 * residual * 1.15)\n#     hess = np.where(residual < 0, 2, 2 * 1.15)\n#     print(grad[:10])\n#     return grad, hess\n\n\n# define custom evaluation metric\n#WORKING:-\n# def custom_asymmetric_valid(y_pred, y_true):\n#     y_true = y_true.get_label()\n#     residual = ((y_true - y_pred).astype(\"float\").reshape(-1, NUM_ITEMS) * roll_mat_csr.T).flatten()\n#     # loss = np.where(residual < 0, (residual ** 2) , (residual ** 2) * 1.15) \n#     # return \"custom_asymmetric_eval\", np.mean(loss), False\n#     return \"custom_asymmetric_eval\", np.sqrt(np.mean(np.square(residual)))/12, False\n\ndef custom_asymmetric_valid(y_pred, y_true):\n    y_true = y_true.get_label()\n    residual = ((y_true - y_pred).astype(\"float\").reshape(-1, NUM_ITEMS) * roll_mat_csr.T).flatten()\n    loss = np.where(residual < 0, (residual ** 2) , (residual ** 2) * 1.15) \n    # return \"custom_asymmetric_eval\", np.mean(loss), False\n    return \"custom_asymmetric_eval\", np.sqrt(np.mean(loss))/12, False\n\ndef weighted_rmse(y_pred, y_true):\n    global weights_test\n    global weights\n\n    if len(y_pred)==len(weights_test):\n        return 'weighted rmse', np.sqrt(np.sum(weights_test*np.square(y_pred-y_true.get_label()))/np.sum(weights_test)), False    \n    return 'weighted rmse', np.sqrt(np.sum(weights*np.square(y_pred-y_true.get_label()))/np.sum(weights)), False    \n\ndef train_lgb(bst_params, fit_params, X, y, X_val, cv,\n              drop_when_train=None, categorical_features=[],\n              use_custom_loss=False, get_score=False, random_fold=False,\n              nf=1,\n              ):\n    \n    global weights\n    global weights_test\n\n    models = []\n    score = 0\n\n    logger = logging.getLogger(\"global\")\n\n    if drop_when_train is None:\n        drop_when_train = []\n\n    dates = X.date.unique()\n    kf = KFold(nf, shuffle=True)\n\n    if True:\n        print(f\"Training...\")\n\n        # train_dates = dates[trn_idx]\n        # val_dates = dates[val_idx] \n        # X_train = X[X['date'].isin(train_dates)]\n        # X_val = X[X['date'].isin(val_dates)]\n\n        train_set = lgb.Dataset(X_train.drop(drop_when_train, axis=1).values,\n                                label=X_train.demand.values, \n                                categorical_feature=categorical_features)\n        train_set.raw_data = None                                \n        print(\"Created train set\")                        \n        \n        val_set = lgb.Dataset(X_val.drop(drop_when_train, axis=1).values,\n                                label=X_val.demand.values, \n                                categorical_feature=categorical_features)\n        val_set.raw_data = None       \n        print(\"Created valid set\")                        \n        gc.collect()                         \n\n        if use_custom_loss:\n            print(f\"Using custom loss!!\")\n            feval = custom_asymmetric_valid\n            # feval = None\n            fobj = custom_asymmetric_train\n        else:\n            print(f\"Not using custom loss.\")\n            fobj = None\n            feval = None    \n      \n        model = lgb.train(\n            bst_params,\n            train_set,\n            valid_sets=[train_set, val_set],\n            valid_names=[\"train\", \"valid\"],\n            fobj=fobj,\n            feval=feval,\n            # learning_rates=lambda iter: 0.0007 if iter>400 else 0.00007,\n            **fit_params,\n            )\n\n        models.append(model)\n        \n        # del X_train, X_val\n        gc.collect()\n\n\n        \n    # for idx_fold, (idx_trn, idx_val) in enumerate(cv.split(X, y, group)):\n\n    #     print(f\"\\n----- Fold: ({idx_fold + 1} / {nf}) -----\\n\")\n\n    #     # X_trn, X_val = X.iloc[idx_trn], X.iloc[idx_val]\n    #     # y_trn, y_val = y.iloc[idx_trn], y.iloc[idx_val]\n\n    #     train_set = lgb.Dataset(X.iloc[idx_trn].drop(drop_when_train, axis=1).values,\n    #                             label=y.iloc[idx_trn].values, \n    #                             categorical_feature=categorical_features)\n    #     train_set.raw_data = None                                \n    #     print(\"Created train set\")                        \n        \n    #     val_set = lgb.Dataset(X.iloc[idx_val].drop(drop_when_train, axis=1).values,\n    #                             label=y.iloc[idx_val].values, \n    #                             categorical_feature=categorical_features)\n    #     val_set.raw_data = None       \n    #     gc.collect()                         \n\n    #     print(\"Created valid set\")                        \n\n    #     if use_custom_loss:\n    #         print(f\"Using custom loss!!\")\n            \n    #         # weights = X.iloc[idx_trn]['wts']  \n    #         # weights_test = X.iloc[idx_val]['wts'] \n\n    #         feval = custom_asymmetric_valid\n    #         fobj = custom_asymmetric_train\n\n    #     else:\n    #         print(f\"Not using custom loss.\")\n    #         fobj = None\n    #         feval = None    \n      \n    #     model = lgb.train(\n    #         bst_params,\n    #         train_set,\n    #         valid_sets=[train_set, val_set],\n    #         valid_names=[\"train\", \"valid\"],\n    #         fobj=fobj,\n    #         feval=feval,\n    #         **fit_params,\n    #         )\n\n    #     if get_score:\n    #         y_preds = model.predict(X.iloc[idx_val].drop(drop_when_train,\n    #                                 axis=1).values)\n    #         score += rmse(y.iloc[idx_val].values, y_preds)/nf\n    #     models.append(model)\n        \n\n    #     del idx_trn, idx_val\n    #     gc.collect()\n\n    return score if get_score else models\n\ndef predict_and_f_importance(models, X_test, n_splits=5, imp_type = \"gain\",\n                            exclude_features=None, combine_by='mean'):\n    if exclude_features is None:\n        exclude_features = []\n\n    X_test = X_test.drop(exclude_features, axis=1)\n    print(X_test.shape)\n    importances = np.zeros(X_test.shape[1])\n    preds = pd.DataFrame()\n\n\n    for i, model in enumerate(models):\n        preds[f'model_{i}'] = model.predict(X_test)\n        importances += model.feature_importance(imp_type)\n\n    weights = []\n    if combine_by == 'corr':\n        for i, col in enumerate(preds.columns):\n            cur = preds[col]\n            others = preds.loc[:, preds.columns != col].mean(axis=1)\n            corr = np.corrcoef(cur, others)[0][1]\n            weights.append(1/corr)\n    else:\n        weights = [1 for x in range(len(models))]\n\n    finalpreds = np.zeros(X_test.shape[0])\n    for w, col in zip(weights, preds.columns):\n        finalpreds += w*preds[col]\n\n    finalpreds /= sum(weights)\n    importances /= n_splits\n\n    return finalpreds, importances\n\ndef predict_recursive(models, X_test, n_splits=5, imp_type = \"gain\",\n                            exclude_features=None, combine_by='mean'):\n    if exclude_features is None:\n        exclude_features = []\n\n    # X_test = X_test.drop(exclude_features, axis=1)\n    print(X_test.shape)\n    preds = pd.DataFrame()\n\n    fday = datetime.datetime(2016,4, 25)\n    max_lags = int(28*2)+2\n    alpha = 1\n\n    for tdelta in tqdm.tqdm(range(0, 28)):\n    # for tdelta in tqdm.tqdm(range(0, 1)):\n        day = fday + datetime.timedelta(days=tdelta)\n        tst = X_test[(X_test.date >= day - datetime.timedelta(days=max_lags)) & (X_test.date <= day)].copy()\n        tst = make_data.add_demand_features_minimal(tst, recursive=True)\n        tst = tst.drop(exclude_features, axis=1).loc[tst.date == day]\n        for model in models:\n            X_test.loc[X_test.date == day, \"demand\"] += alpha*model.predict(tst)/len(models) #alpha is the **magic** factor, I dont trust it. So I just set it to 1.\n\n    return X_test[X_test.date>=fday]['demand'].values\n\n\ndef str2bool(v):\n    if isinstance(v, bool):\n       return v\n    if v.lower() in ('yes', 'true', 't', 'y', '1'):\n        return True\n    elif v.lower() in ('no', 'false', 'f', 'n', '0'):\n        return False\n    else:\n        raise argparse.ArgumentTypeError('Boolean value expected.')\n\n\ndef make_submission(test, fname=\"submission.csv\"):\n    submission = pd.read_csv(f\"data/sample_submission.csv\")\n    preds = test[[\"id\", \"date\", \"demand\"]]\n    preds = preds.pivot(index=\"id\", columns=\"date\", values=\"demand\").reset_index()\n    preds.columns = [\"id\"] + [\"F\" + str(d + 1) for d in range(DAYS_PRED)]\n\n    evals = submission[submission[\"id\"].str.endswith(\"evaluation\")]\n    vals = submission[[\"id\"]].merge(preds, how=\"inner\", on=\"id\")\n    final = pd.concat([vals, evals])\n\n    assert final.drop(\"id\", axis=1).isnull().sum().sum() == 0\n    assert final[\"id\"].equals(submission[\"id\"])\n\n    final.to_csv(fname, index=False)\n    return final\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser(description='M5 accuracy')\n\n    parser.add_argument(\"-data\", \"--d\", type=str, \n                        default=\"/home/timetraveller/Work/m5data/features.hdf\")\n    parser.add_argument(\"-outdir\", \"--o\", type=str, \n                        default=\"/home/timetraveller/Work/m5data/output\")\n    parser.add_argument(\"-logfname\", \"--l\", type=str, \n                        default=\"m5acc.log\")   \n    parser.add_argument(\"-modelname\", \"--m\", type=str, \n                        default=\"model.pkl\")  \n    parser.add_argument(\"-num_boost_round\", \"--nbr\", type=int, \n                        default=10000)   \n    parser.add_argument(\"-num_folds\", \"--nf\", type=int, \n                        default=5)     \n    parser.add_argument(\"-early_stopping_rounds\", \"--esr\", type=int, \n                        default=50)  \n    parser.add_argument(\"-params\", \"--pr\", type=str,\n                        default=\"p2\")    \n    parser.add_argument(\"-kaggle_message\", \"--km\", type=str,\n                        default=\"gambatte!\")      \n    \n    #Boolean args:-\n    parser.add_argument(\"-target_encoding\", \"--te\", type=str2bool, nargs='?',\n                        const=True, default=False)\n    parser.add_argument(\"-justpredict\", \"--j\", type=str2bool, nargs='?',\n                        const=True, default=False)\n    parser.add_argument(\"-submit_kaggle\", \"--ks\", type=str2bool, nargs='?',\n                        const=True, default=True)\n    parser.add_argument(\"-random_fold\", \"--rf\", type=str2bool, nargs='?',\n                        const=True, default=True)\n    parser.add_argument(\"-dropna\", \"--dn\", type=str2bool, nargs='?',\n                        const=True, default=False)\n    parser.add_argument(\"-use_custom_loss\", \"--cl\", type=str2bool, nargs='?',\n                        const=True, default=False)        \n    parser.add_argument(\"-recursive\", \"--rec\", type=str2bool, nargs='?',\n                        const=True, default=False)    \n    parser.add_argument(\"-calc_wrmsse\", \"--wrmsse\", type=str2bool, nargs='?',\n                        const=True, default=False)                                                                                                                                                                        \n                                                                                                                                                                                                                                               \n\n  \n    \n    args = parser.parse_args()\n    # project_name = str(uuid.uuid4())\n    \n\n    num_boost_round = args.nbr\n    submit_kaggle = args.ks\n    just_predict = args.j\n    path = args.d\n    logfname = args.l\n    outdir = args.o\n    modelname = args.m\n    nf = args.nf\n    kaggle_message = args.km\n    target_encoding = args.te\n    random_fold = args.rf\n    params = args.pr\n    dropna = args.dn\n    use_custom_loss = args.cl\n    early_stopping_rounds = args.esr\n    recursive = args.rec \n    calc_wrmsse = args.wrmsse\n\n\n\n    print(f\"submit_kaggle: {submit_kaggle}\")\n    print(f\"use_custom_loss: {use_custom_loss}\")\n    print(f\"recursive: {recursive}\")\n    print(f\"random_fold: {random_fold}\")\n    print(f\"calc_wrmsse: {calc_wrmsse}\")\n    print(f\"num_folds: {nf}\")\n\n    \n    project_name = str(datetime.datetime.today()).replace(' ', '_')\n    make_dir(os.path.join(outdir, project_name))\n\n    logfile = os.path.join(outdir, project_name, logfname)   \n    stdoutfile = os.path.join(outdir, project_name, \"stdout.txt\")   \n        \n\n    fhandler = logging.FileHandler(logfile)\n    fhandler.setLevel(logging.INFO)\n    logging.basicConfig(format='%(asctime)s %(message)s',\n                        datefmt='%m/%d/%Y %I:%M:%S %p',\n                        level=logging.INFO,\n            )\n                                \n    logger = logging.getLogger(\"global\")\n    logger.addHandler(fhandler)\n    logger.info(f\"Training on: {args.d}\")\n    logger.info(f\"submit_kaggle: {submit_kaggle}\")\n    logger.info(f\"use_custom_loss: {use_custom_loss}\")\n    logger.info(f\"recursive: {recursive}\")\n    logger.info(f\"random_fold: {random_fold}\")\n    logger.info(f\"num_folds: {nf}\")\n\n    data = pd.read_hdf(path).pipe(reduce_mem_usage)\n    \n    if dropna:\n        bef = data.shape[0]\n        print(f\"#Rows before: {bef}\")\n        data.dropna(inplace = True)\n        print(f\"#Rows after: {data.shape[0]} ({(bef-data.shape[0])/bef}% reduction)\")\n\n    if use_custom_loss:\n        load_rollup_matrix()   \n\n    cat_cols = [\n        \"item_id\",\n        \"dept_id\",\n        \"cat_id\",\n        \"store_id\",\n        \"state_id\",\n        \"event_name_1\",\n        \"event_type_1\",\n        \"event_name_2\",\n        \"event_type_2\",\n        \"snap_CA\",\n        \"snap_TX\",\n        \"snap_WI\",\n    ]\n    cat_cols = [] #Memory issues\n\n    train_end = datetime.datetime(2016, 4, 24)\n    # train_start = datetime.datetime(2014, 2, 24)\n    train_start = datetime.datetime(2014, 4, 24)\n    test_start = train_end - datetime.timedelta(days=28)\n\n    # mask = data[\"date\"] <= \"2016-04-24\"  #19727030\n    # mask2 = data[\"date\"] >= \"2014-04-24\" #FUCK LARGE DATA AND MY SMALL RAM\n\n    # train_mask = data[\"date\"] <= test_start \n    train_mask = (data[\"date\"] <= test_start) & (data[\"date\"] > train_start) #FUCK LARGE DATA AND MY SMALL RAM\n    val_mask = (data[\"date\"] <= train_end) & (data[\"date\"] > test_start)\n    test_mask = data[\"date\"] > train_end\n\n\n\n    X_train = data[train_mask].reset_index(drop=True)\n    y_train = data[train_mask][\"demand\"].reset_index(drop=True)\n\n\n    X_val = data[val_mask].reset_index(drop=True) #For oof WRMSSE\n    \n    X_test = data[test_mask].reset_index(drop=True)\n    id_date = data[test_mask][[\"id\", \"date\"]].reset_index(drop=True)\n    \n\n    logger.info(f\"X_train shape: {X_train.shape}\")\n    logger.info(f\"X_test shape: {X_test.shape}\")   \n    logger.info(list(X_train.columns))\n\n    del data, train_mask, val_mask, test_mask\n    # del X_test, y_train\n    gc.collect()\n    \n\n    if random_fold:\n        cv = GroupKFold(nf)\n    else:   \n        cv = TimeSeriesCV(n_splits=nf, predict_days=28, date_col=\"date\")\n\n    if just_predict:\n        model_pth = \"something\"\n        models = [load_model(model_pth)[-1]]\n\n        # finalpreds, importances = predict_and_f_importance(models, X_test,\n        #                         n_splits=nf, exclude_features=exclude_features)\n\n        finalpreds = predict_recursive(models, pd.concat([X_train, X_test]),\n                                n_splits=nf, exclude_features=exclude_features)                        \n        make_submission(id_date.assign(demand=finalpreds))\n        sys.exit()\n\n\n    if params == 'p1':\n        # bst_params = {\n        #             'boosting_type': 'gbdt',\n        #             'subsample': 0.70,\n        #             'subsample_freq': 1,\n        #             'learning_rate': 0.09,\n        #             'num_leaves': 2**11-1,\n        #             'min_data_in_leaf': 2**12-1,\n        #             'feature_fraction': 0.5,\n        #             'max_bin': 120,\n        #             'boost_from_average': False,\n        #             'verbose': -1,\n        # }   \n        \n        bst_params = {\n            'boosting_type': 'gbdt',\n            'n_jobs': -1,\n            'seed': 42,\n            'learning_rate': 0.00007,\n            # 'num_leaves': 200,\n            'num_leaves': 50,\n            # 'num_leaves': 15,\n            # 'max_bin': 16,\n            'max_bin': 20,\n            'bagging_fraction': 0.85,\n            'bagging_freq': 1, \n            'colsample_bytree': 0.65,\n            'colsample_bynode': 0.65,\n            'min_data_per_leaf': 45,\n            'lambda_l1': 0.75,\n            'lambda_l2': 0.75,\n            'metric':'rmse',\n        }\n\n        if not use_custom_loss:\n            bst_params['objective']='tweedie'\n            bst_params['tweedie_variance_power']=1.1\n            bst_params['metric']='rmse'\n\n        fit_params = {\n                'num_boost_round': num_boost_round,\n                'verbose_eval': 100,\n                'early_stopping_rounds': early_stopping_rounds,\n        } \n    else:    \n\n        # bst_params = {\n        #     'boosting_type': 'gbdt',\n        #     \"objective\" : \"tweedie\",\n        #     'n_jobs': -1,\n        #     'seed': 42,\n        #     # 'learning_rate': 0.00007,\n        #     'learning_rate': 0.08,\n        #     'num_leaves': 200,\n        #     'bagging_fraction': 0.35,\n        #     'bagging_freq': 1, \n        #     'colsample_bytree': 0.75,\n        #     'colsample_bynode': 0.75,\n        #     'min_data_per_leaf': 35,\n        #     'lambda_l1': 0.5,\n        #     'lambda_l2': 0.5,\n        #     'metric':'rmse',\n        # }\n\n        bst_params = {\n        \"objective\" : \"tweedie\",\n        \"metric\" :\"rmse\",\n        \"force_row_wise\" : True,\n        \"learning_rate\" : 0.075,\n        \"sub_feature\" : 0.8,\n        \"sub_row\" : 0.75,\n        \"bagging_freq\" : 1,\n        \"lambda_l2\" : 0.1,\n        \"metric\": [\"rmse\"],\n        'verbosity': 1,\n        # 'num_iterations' : num_boost_round,\n        'num_leaves': 128,\n        \"min_data_in_leaf\": 104,\n        }\n        \n        # if not use_custom_loss:\n        #     bst_params['objective']='tweedie'\n        #     bst_params['metric']='rmse'\n\n\n        fit_params = {\n                'num_boost_round': num_boost_round,\n                # 'verbose_eval': 100,\n                'verbose_eval': 100,\n                'early_stopping_rounds': early_stopping_rounds,\n        }\n\n    # bst_params = clean_params(bst_params)\n    logger.info(bst_params)\n    logger.info(fit_params)\n\n    day_col = 'd'\n    logger.info(f\"Custom loss: [{use_custom_loss}]\")\n    \n    models = train_lgb(\n        bst_params, fit_params, X_train, y_train, X_val, cv, \n        drop_when_train=exclude_features, categorical_features=cat_cols,\n        use_custom_loss=use_custom_loss,\n        random_fold=random_fold, nf=nf,\n    )\n\n\n    model_pth = os.path.join(outdir, project_name, modelname)\n\n    save_model(models, model_pth)\n    logger.info(f\"Model saved in [{model_pth}]\")\n\n    if calc_wrmsse:\n        wrmsse, wrmsse_matrix = get_wrmsse(X_val, models, exclude_features)\n        logger.info(f\"WRMSSE: {wrmsse}\")\n        \n        matrix_pth = os.path.join(outdir, project_name, \"wrmsse_matrix.csv\")\n        pd.DataFrame(wrmsse_matrix).to_csv(matrix_pth, index=False)\n\n        logger.info(f\"WRMSSE Matrix saved in {matrix_pth}\")\n\n    if recursive:\n        idx = int(NUM_ITEMS * 30) \n        finalpreds = predict_recursive(models, pd.concat([X_train.iloc[-idx:], X_test]),\n                                n_splits=nf, exclude_features=exclude_features) \n    else:    \n        finalpreds, importances = predict_and_f_importance(models, X_test,\n                                n_splits=nf, exclude_features=exclude_features)\n    \n    subfname=\"submission.csv\"    \n    \n    del X_train, y_train\n    gc.collect()\n    \n    subfname = os.path.join(outdir, project_name, f\"{project_name}-submission.csv\")\n\n    make_submission(id_date.assign(demand=finalpreds), subfname)\n    logger.info(f\"Submission file created: {subfname}\")\n\n    if submit_kaggle:\n        os.system(f\"kaggle competitions submit -f {subfname} -m \\\"{kaggle_message}\\\" -c m5-forecasting-accuracy\")  \n        logger.info(f\"Submitted to kaggle!\")\n      \n\n# [100]\ttrain's rmse: 3.52426\ttrain's custom_asymmetric_eval: 4.49295\tvalid's rmse: 3.58432\tvalid's custom_asymmetric_eval: 5.3514\n# [200]\ttrain's rmse: 3.50969\ttrain's custom_asymmetric_eval: 4.44281\tvalid's rmse: 3.56896\tvalid's custom_asymmetric_eval: 5.2686\n# 0.65\n\n\n","sub_path":"lightgbm/train_1.py","file_name":"train_1.py","file_ext":"py","file_size_in_byte":22541,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"575632150","text":"import numpy\r\ndata=list(map(int, input().split()))\r\nn=data[0]\r\nm=data[1]\r\nmatrix=[]\r\nfor i in range(n):\r\n    row=list(map(float, input().split()))\r\n    matrix.append(row)\r\nprint(numpy.mean(matrix, axis=1))\r\nprint(numpy.var(matrix, axis=0))\r\nprint(round(numpy.std(matrix, axis=None),11))\r\n","sub_path":"Python/Mean, Var, and Std.py","file_name":"Mean, Var, and Std.py","file_ext":"py","file_size_in_byte":288,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"605776085","text":"\"\"\"Small program to find the sum of all multiples of 3 and 5.\n    Danielle Rosenfeld-Lovell, August 2019\"\"\"\n\ndef three_and_five():\n\n    # First define the upper limit on multiples and the sum value initialised to 0\n    max = 1000\n    sum_multiples = 0\n\n    # Then loop over all values less than or equal to 1000 to determine if a multiple\n    for num in range(3, max):\n        if (num % 3 == 0 or num % 5 == 0):\n            sum_multiples+=num\n            # Otherwise just keep looping until we reach 1000\n    \n    return sum_multiples\n\n\nthree_and_five()","sub_path":"3and5.py","file_name":"3and5.py","file_ext":"py","file_size_in_byte":553,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"309129576","text":"import discord\nimport asyncio\nimport json\n\nfrom discord.ext import commands\nfrom discord.utils import get\nfrom discord.ext.commands import has_permissions\n\n# ------------------------ COGS ------------------------ #  \n\nclass AntiProfanityCog(commands.Cog, name=\"change setting from anti nudity command\"):\n    def __init__(self, bot):\n        self.bot = bot\n\n# ------------------------------------------------------ #  \n\n    @commands.command(name = 'antiprofanity', \n                        aliases= [\"profanity\"],\n                        usage=\"<true/false>\",\n                        description=\"Enable or disable the anti profanity.\")\n    @has_permissions(administrator = True)\n    @commands.cooldown(1, 3, commands.BucketType.member)\n    @commands.guild_only()\n    async def antiprofanity (self, ctx, antiProfanity):\n\n        antiProfanity = antiProfanity.lower()\n\n        if antiProfanity == \"true\":\n            # Edit configuration.json\n            with open(\"configuration.json\", \"r\") as config:\n                data = json.load(config)\n                # Add modifications\n                data[\"antiProfanity\"] = True\n                newdata = json.dumps(data, indent=4, ensure_ascii=False)\n                \n            embed = discord.Embed(title = f\"**ANTI PROFANITY WAS ENABLED**\", description = f\"The anti profanity was enabled.\", color = 0x2fa737) # Green\n            await ctx.channel.send(embed = embed)\n        else:\n            # Edit configuration.json\n            with open(\"configuration.json\", \"r\") as config:\n                data = json.load(config)\n                # Add modifications\n                data[\"antiProfanity\"] = False\n                newdata = json.dumps(data, indent=4, ensure_ascii=False)\n                \n            embed = discord.Embed(title = f\"**ANTI PROFANITY WAS DISABLED**\", description = f\"The anti profanity was disabled.\", color = 0xe00000) # Red\n            await ctx.channel.send(embed = embed)\n            \n        with open(\"configuration.json\", \"w\") as config:\n            config.write(newdata)\n\n# ------------------------ BOT ------------------------ #  \n\ndef setup(bot):\n    bot.add_cog(AntiProfanityCog(bot))","sub_path":"Cogs/profanity.py","file_name":"profanity.py","file_ext":"py","file_size_in_byte":2164,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"425694103","text":"from django.shortcuts import render, redirect, reverse, get_object_or_404\nfrom django.db.models import Q\n\nfrom .models import Song\nfrom .forms import SearchSongsForm, CategoryForm, SearchForm\n\nimport logging\nfrom urllib.parse import urlencode\nfrom random import choice\n\ndef index(request):\n    search_form = SearchSongsForm(request.GET)\n    category_form = CategoryForm(request.GET)\n    if category_form.is_valid():\n        query = urlencode({'category': category_form.cleaned_data['category']})\n        return redirect(reverse('random') + '?' + query)\n    elif search_form.is_valid():\n        query_key = search_form.cleaned_data['key']\n        query_val = search_form.cleaned_data['name']\n        query = urlencode({query_key: query_val})\n        return redirect(reverse('random') + '?' + query)\n    else:\n        category_form = CategoryForm()\n        search_form = SearchSongsForm()\n        return render(request, 'music/index.html',\n                {'search_form': search_form, 'category_form': category_form})\n\ndef search(request):\n    form = SearchForm(request.GET)\n    if form.is_valid():\n        search = form.cleaned_data['search']\n        results = Song.objects.filter(\n                Q(name__icontains = search)\n                | Q(artist__icontains = search)\n                | Q(album__icontains = search)\n                | Q(genre__icontains = search)\n        )\n    else:\n        form = SearchForm()\n        results = []\n    return render(request, 'music/search.html',\n            {'form': form, 'songs': results})\n\ndef random(request):\n    candidates = Song.objects.all()\n\n    title = request.GET.get(\"title\", None)\n    if title:\n        candidates = candidates.filter(title__contains=title)\n\n    artist = request.GET.get(\"artist\", None)\n    if artist:\n        candidates = candidates.filter(artist__contains=artist)\n\n    category = request.GET.get(\"category\", None)\n    if category:\n        candidates = candidates.filter(genre__iexact=category)\n\n    source = request.GET.get(\"source\", None)\n    if source:\n        candidates = candidates.filter(album__contains=source)\n\n    if len(candidates) == 0:\n        return render(request, 'music/search_failed.html')\n    else:\n        url = choice(candidates).get_absolute_url()\n        if len(request.GET) > 0:\n            url += '?' + request.GET.urlencode()\n        return redirect(url)\n\ndef song(request, song_id):\n    song = get_object_or_404(Song, pk=song_id)\n    filepath = 'music/songs/' + song.filename()\n    logging.info('Serving song %s' % song)\n    return render(request, \"music/song.html\",\n            {'song': song, 'filepath': filepath})\n","sub_path":"views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2612,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"95823704","text":"#!/usr/bin/env python3\n\n# Copyright (c) 2009-2018, Mario Vilas\n# 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 are met:\n#\n#     * Redistributions of source code must retain the above copyright notice,\n#       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 the copyright holder nor the names of its\n#       contributors may be used to endorse or promote products derived from\n#       this software without specific prior written permission.\n#\n# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\n# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE\n# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR\n# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF\n# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS\n# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN\n# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)\n# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\n# POSSIBILITY OF SUCH DAMAGE.\n\n\"\"\"\nCommon definitions.\n\"\"\"\n\nimport ctypes\nimport functools\n\n# =============================================================================\n# This is used later on to calculate the list of exported symbols.\n_ALL = None\n_ALL = set(vars().keys())\n# =============================================================================\n\n# -----------------------------------------------------------------------------\n\n# Some stuff from ctypes we'll be using very frequently.\nBY_REF = ctypes.byref\nSIZE_OF = ctypes.sizeof\nPOINTER = ctypes.POINTER\nWINDLL = ctypes.windll\n\nclass Structure(ctypes.Structure):\n    \"\"\"\n    Automatically disable padding of structs and unions on 32 bits.\n    \"\"\"\n    if SIZE_OF(ctypes.c_void_p) == 4:\n        _pack_ = 1\n\nclass Union(ctypes.Union):\n    \"\"\"\n    Automatically disable padding of structs and unions on 32 bits.\n    \"\"\"\n    if SIZE_OF(ctypes.c_void_p) == 4:\n        _pack_ = 1\n\n# -----------------------------------------------------------------------------\n\ndef raise_if_zero(result, _func=None, _arguments=()):\n    \"\"\"\n    Error checking for most Win32 API calls.\n    The function is assumed to return an integer, which is C{0} on error.\n    In that case the C{WindowsError} exception is raised.\n    \"\"\"\n    if not result:\n        raise ctypes.WinError()\n    return result\n\nclass GuessStringType():\n    \"\"\"\n    Decorator that guesses the correct version (A or W) to call\n    based on the types of the strings passed as parameters.\n    Calls the B{ANSI} version if the only string types are ANSI.\n    Calls the B{Unicode} version if Unicode or mixed string types are passed.\n    The default if no string arguments are passed depends on the value of the\n    L{t_default} class variable.\n    @type fn_ansi: function\n    @ivar fn_ansi: ANSI version of the API function to call.\n    @type fn_unicode: function\n    @ivar fn_unicode: Unicode (wide) version of the API function to call.\n    @type t_default: type\n    @cvar t_default: Default string type to use.\n        Possible values are:\n         - type(b'') for ANSI\n         - type('') for Unicode\n    \"\"\"\n\n    # ANSI and Unicode types\n    t_ansi = type(b'')\n    t_unicode = type('')\n\n    # Default is Unicode for Python 3.x\n    t_default = t_unicode\n\n    def __init__(self, fn_ansi, fn_unicode):\n        \"\"\"\n        @type  fn_ansi: function\n        @param fn_ansi: ANSI version of the API function to call.\n        @type  fn_unicode: function\n        @param fn_unicode: Unicode (wide) version of the API function to call.\n        \"\"\"\n        self.fn_ansi = fn_ansi\n        self.fn_unicode = fn_unicode\n\n        # Copy the wrapped function attributes.\n        try:\n            self.__name__ = self.fn_ansi.__name__[:-1]  # remove the A or W\n        except AttributeError:\n            pass\n        try:\n            self.__module__ = self.fn_ansi.__module__\n        except AttributeError:\n            pass\n        try:\n            self.__doc__ = self.fn_ansi.__doc__\n        except AttributeError:\n            pass\n\n    def __call__(self, *argv, **argd):\n        # Get the types of all arguments for the function\n        v_types = [type(item) for item in argv]\n        v_types.extend([type(value) for (key, value) in argd.items()])\n\n        # Get the appropriate function for the default type\n        if self.t_default == self.t_unicode:\n            function = self.fn_unicode\n        else:\n            function = self.fn_ansi\n\n        # If at least one argument is a Unicode string...\n        if self.t_unicode in v_types:\n\n            # If al least one argument is an ANSI string,\n            # convert all ANSI strings to Unicode\n            if self.t_ansi in v_types:\n                argv = list(argv)\n                for index, argument in enumerate(argv):\n                    if v_types[index] == self.t_ansi:\n                        argv[index] = str(argument)\n                for (key, value) in argd.items():\n                    if isinstance(value, self.t_ansi):\n                        argd[key] = str(value)\n\n            # Use the W version\n            function = self.fn_unicode\n\n        # If at least one argument is an ANSI string,\n        # but there are no Unicode strings...\n        elif self.t_ansi in v_types:\n\n            # Use the A version\n            function = self.fn_ansi\n\n        # Call the function and return the result\n        return function(*argv, **argd)\n\nclass DefaultStringType():\n    \"\"\"\n    Decorator that uses the default version (A or W) to call\n    based on the configuration of the L{GuessStringType} decorator.\n    @see: L{GuessStringType.t_default}\n    @type fn_ansi: function\n    @ivar fn_ansi: ANSI version of the API function to call.\n    @type fn_unicode: function\n    @ivar fn_unicode: Unicode (wide) version of the API function to call.\n    \"\"\"\n\n    def __init__(self, fn_ansi, fn_unicode):\n        \"\"\"\n        @type  fn_ansi: function\n        @param fn_ansi: ANSI version of the API function to call.\n        @type  fn_unicode: function\n        @param fn_unicode: Unicode (wide) version of the API function to call.\n        \"\"\"\n        self.fn_ansi = fn_ansi\n        self.fn_unicode = fn_unicode\n\n        # Copy the wrapped function attributes.\n        try:\n            self.__name__ = self.fn_ansi.__name__[:-1]  # remove the A or W\n        except AttributeError:\n            pass\n        try:\n            self.__module__ = self.fn_ansi.__module__\n        except AttributeError:\n            pass\n        try:\n            self.__doc__ = self.fn_ansi.__doc__\n        except AttributeError:\n            pass\n\n    def __call__(self, *argv, **argd):\n\n        # Get the appropriate function based on the default.\n        if GuessStringType.t_default == GuessStringType.t_ansi:\n            function = self.fn_ansi\n        else:\n            function = self.fn_unicode\n\n        # Call the function and return the result\n        return function(*argv, **argd)\n\ndef make_wide_version(function):\n    \"\"\"\n    Decorator that generates a Unicode (wide) version of an ANSI only API call.\n    @type  function: callable\n    @param function: ANSI version of the API function to call.\n    \"\"\"\n    @functools.wraps(function)\n    def wrapper(*argv, **argd):\n        t_ansi = GuessStringType.t_ansi\n        t_unicode = GuessStringType.t_unicode\n        v_types = [type(item) for item in argv]\n        v_types.extend([type(value) for (key, value) in argd.items()])\n        if t_unicode in v_types:\n            argv = list(argv)\n            for index, _ in enumerate(argv):\n                if issubclass(v_types[index], t_unicode):\n                    argv[index] = t_ansi(argv[index], 'utf-8')\n            for key, value in argd.items():\n                if issubclass(value, t_unicode):\n                    argd[key] = t_ansi(value, 'utf-8')\n        return function(*argv, **argd)\n    return wrapper\n\n# --- Types -------------------------------------------------------------------\n# http://msdn.microsoft.com/en-us/library/aa383751(v=vs.85).aspx\n\n# Map of basic C types to Win32 types\nBOOL = ctypes.c_int32\nBYTE = ctypes.c_ubyte\nCHAR = ctypes.c_char\nDOUBLE = ctypes.c_double\nDWORD = ctypes.c_uint32\nFLOAT = ctypes.c_float\nINT = ctypes.c_int32\nLONG = ctypes.c_int32\nLONGLONG = ctypes.c_int64\nLPSTR = ctypes.c_char_p\nLPWSTR = ctypes.c_wchar_p\nLPVOID = ctypes.c_void_p\nQWORD = ctypes.c_uint64\nSBYTE = ctypes.c_byte\nSHORT = ctypes.c_int16\nUINT = ctypes.c_uint32\nULONG = ctypes.c_uint32\nULONGLONG = ctypes.c_uint64  # c_ulonglong\nUSHORT = ctypes.c_uint16\nWCHAR = ctypes.c_wchar\nWORD = ctypes.c_uint16\n\n# Map size_t to SIZE_T\ntry:\n    SIZE_T = ctypes.c_size_t\n    SSIZE_T = ctypes.c_ssize_t\nexcept AttributeError:\n    # Size of a pointer\n    SIZE_T = {1:BYTE, 2:WORD, 4:DWORD, 8:QWORD}[SIZE_OF(LPVOID)]\n\n# Other Win32 types, more may be added as needed\nPVOID = LPVOID\nHANDLE = LPVOID\nHMODULE = HANDLE\nHMONITOR = HANDLE\nHWND = HANDLE\nPHANDLE = POINTER(HANDLE)\nLPHANDLE = PHANDLE\nLPDWORD = POINTER(DWORD)\nPDWORD = LPDWORD\nPLONGLONG = POINTER(LONGLONG)\n# XXX ANSI by default?\nTCHAR = CHAR\n\n# --- Constants ---------------------------------------------------------------\n\nINFINITE = -1\nNULL = None\n\n# Invalid handle value is -1 casted to void pointer.\ntry:\n    INVALID_HANDLE_VALUE = ctypes.c_void_p(-1).value #-1 #0xFFFFFFFF\nexcept TypeError:\n    if SIZE_OF(ctypes.c_void_p) == 4:\n        INVALID_HANDLE_VALUE = 0xFFFFFFFF\n    elif SIZE_OF(ctypes.c_void_p) == 8:\n        INVALID_HANDLE_VALUE = 0xFFFFFFFFFFFFFFFF\n    else:\n        raise\n\nMAX_MODULE_NAME32 = 255\nMAX_PATH = 260\n\n# Error codes\nERROR_SUCCESS = 0\nERROR_INVALID_ENVIRONMENT = 10\nERROR_NO_MORE_FILES = 18\nERROR_BAD_LENGTH = 24\nERROR_PARTIAL_COPY = 299\nERROR_IO_PENDING = 997\n\n# =============================================================================\n# This calculates the list of exported symbols.\n_ALL = set(vars().keys()).difference(_ALL)\n__all__ = [_x for _x in _ALL if not _x.startswith('_')]\n__all__.sort()\n# =============================================================================\n","sub_path":"win32/defines.py","file_name":"defines.py","file_ext":"py","file_size_in_byte":10549,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"459182411","text":"from __future__ import annotations\n# venus parts common to regular venus and multiple mutant version.\n\n\nfrom ..uniprot_data import *\n# ProteinCore organism human uniprot2pdb\nfrom michelanglo_protein import ProteinAnalyser, Mutation, ProteinCore, Structure\n\nfrom ...models import User, Page  ##needed solely for log.\nfrom ..common_methods import is_malformed, notify_admin, get_pdb_block_from_request\nfrom ..user_management import permission\nfrom .. import custom_messages\n\nfrom typing import Optional, Any, List, Union, Tuple, Dict\nimport random, traceback, sys\nfrom pyramid.view import view_config, view_defaults\nfrom pyramid.renderers import render_to_response\nfrom ..common_methods import notify_admin\nimport pyramid.httpexceptions as exc\n\nimport json, os, logging, operator\n\nlog = logging.getLogger(__name__)\n# from pprint import PrettyPrinter\n# pprint = PrettyPrinter().pprint\n\n### This is a weird way to solve the status 206 issue.\nfrom michelanglo_app.views.buffer import system_storage\n\nfrom .venus_base import VenusException, VenusBase\n\n\n############################### Analyse the mutation\n@view_defaults(route_name='venus')\nclass Venus(VenusBase):\n\n    @property\n    def handle(self):\n        return self.request.params['uniprot'] + self.request.params['mutation']\n\n    ############################### server main page\n    @view_config(renderer=\"../../templates/venus/venus_main.mako\")\n    def main_view(self):\n        return {'user': self.request.user, 'mutation_mode': 'main', **self.generic_data}\n\n    ############################### Give a random view that will give a protein\n    @view_config(route_name='venus_random', renderer=\"json\")\n    def random_view(self):\n        while True:\n            name = random.choice(list(human.keys()))\n            uniprot = human[name]\n            protein = ProteinCore(taxid=9606, uniprot=uniprot).load()\n            if protein.pdbs:\n                pdb = random.choice(protein.pdbs)\n            elif protein.swissmodel:\n                pdb = random.choice(protein.swissmodel)\n            else:\n                continue\n            try:\n                i = random.randint(pdb.x, pdb.y)\n                # the to_resn cannot be the same as original or *\n                to_resn = random.choice(list(set(Mutation.aa_list) - {'*', protein.sequence[i - 1]}))\n                return {'name': name, 'uniprot': uniprot, 'taxid': '9606', 'species': 'human',\n                        'mutation': f'p.{protein.sequence[i - 1]}{i}{to_resn}'}\n            except IndexError:\n                log.error(f'Impossible... pdb.x out of bounds in unicode for gene {uniprot}')\n                continue\n\n    @view_config(route_name='venus_debug', renderer=\"../../templates/venus/venus_debug.mako\")\n    def debug(self):\n        return {'user': self.request.user, 'mutation_mode': 'main', **self.generic_data}\n\n    ############################### Analyse\n    @view_config(route_name='venus_analyse', renderer=\"json\")\n    def analyse(self):\n        \"\"\"\n        View that does the analysis. Formerly returned html now returns json.\n        The request has to contain each of following 'step', 'uniprot', 'species', 'mutation' fields.\n        /venus_analyse?species=9606&uniprot=Q96C12&mutation=p.V123F&step=protein\n        step can be protein\n        Species is taxid. Human is 9606.\n\n        :param request:\n        :return: self.reply\n        \"\"\"\n        ### check valid\n        try:\n            self.assert_malformed('uniprot', 'species', 'mutation')\n            if 'step' not in self.request.params:\n                step = None\n            else:\n                step = self.request.params['step']\n            return self.do_step(step)\n        except VenusException as err:\n            log.info(err)\n            return self.reply\n        except Exception as err:\n            if self.reply['status'] != 'error':\n                # this is a new one.\n                self.reply['status'] = 'error'\n                self.reply['error'] = 'analysis'\n                self.reply['msg'] = str(err)\n            log.warning(f'Venus error {err.__class__.__name__}: {err}')\n            notify_admin(f'Venus error {err.__class__.__name__}: {err}')\n            log.debug(traceback.format_exc())\n            return self.reply\n\n    def has(self, key: Optional[str] = None) -> bool:\n        # checks whether the protein object has a filled value (not the reply!)\n        if self.handle not in system_storage:  # this is already done\n            return False\n        elif key is None:\n            return True\n        elif not hasattr(system_storage[self.handle], key):\n            return False\n        elif getattr(system_storage[self.handle], key) is None:\n            return False\n        else:\n            return True\n\n    @property\n    def steps(self):\n        return {'protein': self.protein_step,\n                'mutation': self.mutation_step,\n                'structural': self.structural_step,\n                'ddG': self.ddG_step,\n                'ddG_gnomad': self.ddG_gnomad_step}\n\n    def do_step(self, step):\n        # a likely API call\n        if step is None:\n            log.info(f'Full analysis requested by {User.get_username(self.request)}')\n            map(lambda f: f(), self.steps)  # run all steps\n            return self.reply\n        # ajax\n        elif step in self.steps.keys():\n            # these have replies that stack\n            self.steps[step]()\n            return self.reply\n        elif step == 'fv':\n            ## this is the same as get_uniprot but does not redundantly redownload the data.\n            if not self.has():\n                self.protein_step()\n            protein = system_storage[self.handle]\n            return render_to_response(os.path.join(\"..\", \"..\", \"templates\", \"results\", \"features.js.mako\"),\n                                      {'protein': protein, 'featureView': '#fv', 'include_pdb': False}, self.request)\n        elif step == 'extra':\n            self.assert_malformed('extra', 'algorithm')\n            self.extra_step(mutation=self.request.params['extra'], algorithm=self.request.params['algorithm'])\n            return self.reply\n        elif step == 'phosphorylate':\n            self.phospho_step()\n            return self.reply\n        elif step == 'customfile':\n            self.assert_malformed('pdb', 'filename')\n            pdb = get_pdb_block_from_request(self.request)\n            params = self.save_params()\n            self.change_to_file(pdb, self.request.params['filename'], params)\n            return self.reply\n        else:\n            self.request.response.status = 422\n            return {'status': 'error', 'error': 'Unknown step'}\n\n    ### STEP 1\n    def protein_step(self):\n        \"\"\"\n        Check mutations are valid\n        \"\"\"\n        self.start_timer()\n        if self.has():  # this is already done (very unlikely/\n            protein = system_storage[self.handle]\n        else:\n            log.info(f'Step 1 analysis requested by {User.get_username(self.request)}')\n            uniprot = self.request.params['uniprot']\n            taxid = self.request.params['species']\n            mutation_text = self.request.params['mutation']\n            ## Do analysis\n            mutation = Mutation(mutation_text)\n            protein = ProteinAnalyser(uniprot=uniprot, taxid=taxid)\n            protein.load()\n            protein.mutation = mutation\n        # assess\n        if not protein.check_mutation():\n            log.info('protein mutation discrepancy error')\n            discrepancy = protein.mutation_discrepancy()\n            self.reply = {**self.reply, 'error': 'mutation', 'msg': discrepancy, 'status': 'error'}\n            raise VenusException(discrepancy)\n        else:\n            system_storage[self.handle] = protein\n            self.reply['protein'] = self.jsonable(protein)\n        self.stop_timer()\n\n    ### STEP 2\n    def mutation_step(self):\n        \"\"\"\n        Runs protein.predict_effect()\n        \"\"\"\n        self.start_timer()\n        log.info(f'Step 2 analysis requested by {User.get_username(self.request)}')\n        ## has the previous step been done?\n        if not self.has():\n            self.protein_step()\n        # if protein.mutation has already run??\n        # no shortcut useful.\n        protein = system_storage[self.handle]\n        protein.predict_effect()\n        featpos = protein.get_features_at_position(protein.mutation.residue_index)\n        featnear = protein.get_features_near_position(protein.mutation.residue_index)\n        pos_percent = round(protein.mutation.residue_index / len(protein) * 100)\n        self.reply['mutation'] = {**self.jsonable(protein.mutation),\n                                  'features_at_mutation': featpos,\n                                  'features_near_mutation': featnear,\n                                  'position_as_protein_percent': pos_percent,\n                                  'gnomAD_near_mutation': protein.get_gnomAD_near_position()}\n        self.stop_timer()\n\n        ### STEP 3\n\n    def structural_step(self, structure=None):\n        \"\"\"\n        runs protein.analyse_structure() iteratively until it works.\n        \"\"\"\n        self.start_timer()\n        log.info(f'Step 3 analysis requested by {User.get_username(self.request)}')\n        # previous done?\n        if not self.has():\n            self.mutation_step()\n        protein = system_storage[self.handle]\n        if not self.has('structural'):\n            if structure is not None:\n                protein.analyse_structure(structure)\n            else:\n                # do not use the stored values of pdbs, but get the swissmodel ones:\n                try:\n                    protein.retrieve_structures_from_swissmodel()\n                except Exception as error:\n                    msg = f'Swissmodel retrieval failed: {error.__class__.__name__}: {error}'\n                    log.critical(msg)\n                    notify_admin(msg)\n                    self.reply['warnings'].append('Retrieval of latest PDB data failed (admin notified). ' +\n                                                  'Falling back onto stored data.')\n                try:\n                    protein.analyse_structure()\n                except Exception as error:  # ConnectionError: # failed to download model  # deubg\n                    broken_structure = best = protein.get_best_model()\n                    # ---- remove\n                    if protein.swissmodel.count(broken_structure) != 0:\n                        i = protein.swissmodel.index(broken_structure)\n                        del protein.swissmodel[i]\n                        source = 'Swissmodel'\n                    elif protein.pdbs.count(broken_structure) != 0:\n                        i = protein.pdbs.index(broken_structure)\n                        del protein.pdbs[i]\n                        source = 'RCSB PDB'\n                    else:\n                        raise ValueError('structure from mystery source')\n                    # ---- logging\n                    if isinstance(error, ConnectionError):\n                        msg = f'{source} {broken_structure} could not be downloaded'\n                        log.info(msg)\n                        self.reply['warnings'].append(msg)\n                    elif isinstance(error, ValueError):\n                        msg = f'Residue missing in structure ({source}): {broken_structure.code} ({error})'\n                        log.info(msg)\n                        self.reply['warnings'].append(msg)\n                    else:  # this should not happen in step 3.\n                        msg = f'Major issue ({error.__class__.__name__}) with model {broken_structure.code} ({error})'\n                        self.reply['warnings'].append(msg)\n                        log.critical(msg)\n                        notify_admin(msg)\n                    # ---- repeat\n                    self.structural_step()\n        if protein.structural:\n            self.reply['structural'] = self.jsonable(protein.structural)\n            self.reply['has_structure'] = True\n        else:\n            log.info('No structural data available')\n            self.reply['status'] = 'terminated'\n            self.reply['error'] = 'No crystal structures or models available.'\n            self.reply['msg'] = 'Structrual analyses cannot be performed.'\n            self.reply['has_structure'] = False\n            raise VenusException(self.reply['msg'])\n        self.stop_timer()\n\n    ### Step 4\n    def ddG_step(self):\n        self.start_timer()\n        log.info(f'Step 4 analysis requested by {User.get_username(self.request)}')\n        # ------- get protein\n        if self.handle not in system_storage:\n            status = self.protein_step()\n            if 'error' in status:\n                return status\n            status = self.mutation_step()\n            if 'error' in status:\n                return status\n        protein = system_storage[self.handle]\n        # -------- analyse\n        if hasattr(protein, 'energetics') and protein.energetics is not None:\n            analysis = protein.energetics\n        else:\n            analysis = protein.analyse_FF()\n        if 'error' in analysis:\n            self.log_if_error('pyrosetta step', analysis)\n        else:\n            self.reply['ddG'] = analysis\n            # {ddG: float, scores: Dict[str, float], native:str, mutant:str, rmsd:int}\n        self.stop_timer()\n\n    ### Step 5\n    def ddG_gnomad_step(self):\n        log.info(f'Step 5 analysis requested by {User.get_username(self.request)}')\n        if self.handle not in system_storage:\n            status = self.protein_step()\n            if 'error' in status:\n                return status\n            status = self.mutation_step()\n            if 'error' in status:\n                return status\n        protein = system_storage[self.handle]\n        if hasattr(protein, 'energetics_gnomAD') and protein.energetics_gnomAD is not None:\n            analysis = protein.energetics_gnomAD\n        else:\n            analysis = protein.analyse_gnomad_FF()\n        if 'error' in analysis:  # it is a failure.\n            self.log_if_error('ddG_gnomad_step', analysis)\n            self.reply['status'] = 'error'\n            self.reply['error'] = 'pyrosetta step'\n            self.reply['msg'] = analysis['error']\n        else:\n            self.reply['gnomAD_ddG'] = analysis\n\n    ### STEP EXTRA\n    def extra_step(self, mutation, algorithm):\n        if self.has():\n            self.ddG_step()\n        protein = system_storage[self.handle]\n        log.info(f'Extra analysis ({algorithm}) requested by {User.get_username(self.request)}')\n        self.reply = {**self.reply,\n                      **protein.analyse_other_FF(mutation=mutation, algorithm=algorithm, spit_process=True)}\n        self.log_if_error('extra_step')\n\n    ### STEP EXTRA2\n    def phospho_step(self):\n        if self.has():\n            self.ddG_step()\n        protein = system_storage[self.handle]\n        log.info(f'Phosphorylation requested by {User.get_username(self.request)}')\n        coordinates = protein.phosphorylate_FF(spit_process=True)\n        if isinstance(coordinates, str):\n            self.reply['coordinates'] = coordinates\n        elif isinstance(coordinates, dict):\n            self.reply = {**self.reply, **coordinates}  # it is an error msg!\n        else:\n            self.reply = {**self.reply, 'status': 'error', 'error': 'Unknown', 'msg': 'No coordinates returned'}\n        self.log_if_error('phospho_step')\n\n    ### CHANGE STEP\n    def change_to_file(self, block, name, params: List[str] = ()):\n        # params is either None or a list of topology files\n        if self.has():\n            self.mutation_step()\n        protein = system_storage[self.handle]\n        protein.structural = None\n        protein.energetics = None\n        protein.rosetta_params_filenames = params\n        title, ext = os.path.splitext(name)\n        structure = Structure(title, 'Custom', 0, 9999, title,\n                              type='custom', chain=\"A\", offset=0, coordinates=block)\n        structure.is_satisfactory(protein.mutation.residue_index)\n        self.structural_step(structure=structure)\n        return self.reply\n\n    def save_params(self) -> List[str]:\n        \"\"\"\n        Confusingly, by params I mean Rosetta topology files\n        saves params : str to params as filenames\n        \"\"\"\n        if 'params' in self.request.params:\n            params = self.request.params['params']\n        elif 'params[]' in self.request.params:\n            params = self.request.params.getall('params')\n        else:\n            params = []\n        temp_folder = os.path.join('michelanglo_app', 'temp')\n        if not os.path.exists(temp_folder):\n            os.mkdir(temp_folder)\n        files = []\n        for param in params:  # it should be paramses\n            n = len(os.listdir(temp_folder))\n            file = os.path.join(temp_folder, f'{n:0>3}.params')\n            with open(file, 'w') as w:\n                w.write(param)\n            files.append(file)\n        return files\n","sub_path":"michelanglo_app/views/venus/venus_proper.py","file_name":"venus_proper.py","file_ext":"py","file_size_in_byte":16986,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"241190151","text":"from collections import namedtuple\n\nimport gym\nimport math\nimport numpy as np\n\nfrom utils import is_maze_valid, oob\n\nRectangle = namedtuple('Rectangle', 'xmin ymin xmax ymax')\n\n\nclass Point(gym.Env):\n    def __init__(self, start=[0.0, 0.0], end=[0.5, 0.5], eps=0.05, obstacle=None, random_starts=False):\n        self.start = np.array(start)\n        self.end = np.array(end)\n        self.current = np.array(start)\n        self.obstacle = obstacle  # obstacle should be a size 4 array specifying top left and bottom right\n        self.random_starts = random_starts\n        self.eps = eps\n\n    def reset(self):\n        if self.random_starts:\n            self.current = np.random.uniform(-1, 1, (2))\n        else:\n            self.current = self.start\n        # print(\"Reset \", self.current)\n        self.counter = 0\n        self.add_timstep = np.random.randint(0, 30)\n        return self.current\n\n    def is_overlapping(self, a, b):  # returns None if rectangles don't intersect\n        dx = min(a.xmax, b.xmax) - max(a.xmin, b.xmin)\n        dy = min(a.ymax, b.ymax) - max(a.ymin, b.ymin)\n        if (dx >= 0) and (dy >= 0):\n            return True\n        else:\n            return False\n\n    def is_step_valid(self, curr_pos, next_pos):\n        top, left, bottom, right = self.obstacle\n\n        ra = Rectangle(left, bottom, right, top)\n        rb = Rectangle(min(curr_pos[0], next_pos[0]),\n                       min(curr_pos[1], next_pos[1]),\n                       max(curr_pos[0], next_pos[0]),\n                       max(curr_pos[1], next_pos[1]))\n\n        if self.is_overlapping(ra, rb):\n            return False\n        else:\n            return True\n\n    def step(self, action):\n        self.counter += 1\n        # Scale down action from range (-1, 1) to (-0.05, 0.05)\n        action = action / max(np.abs(action).max(), 1)\n        action = action / 20.\n        info = {}\n\n        # action = np.clip(action, -0.05, 0.05)\n        temp = self.current + action\n        if self.obstacle is not None:\n            if self.is_step_valid(self.current, temp):\n                self.current = temp\n            else:\n                pass\n        else:\n            self.current = temp\n\n        self.current = np.clip(self.current, -1, 1)\n        observation = self.current\n\n        dist = np.abs(self.current - self.end).sum()\n        reward = -1 * dist\n\n        if dist < self.eps or self.counter > 100 + self.add_timstep:\n            done = True\n        else:\n            done = False\n\n        return observation, reward, done, info\n\n    def seed(self, seed):\n        np.random.seed(seed)\n\n\nclass Maze(gym.Env):\n    def __init__(self, start=[0.1, 0.0], end=[0.7, -0.8], eps=0.01, obstacle=[0.1, 0.1], random_starts=False):\n        self.start = np.array(start)\n        self.end = np.array(end)\n        self.current = np.array(start)\n        self.random_starts = random_starts\n\n        self.eps = eps\n\n    def reset(self):\n        if self.random_starts:\n            self.current = np.random.uniform(-1, 1, (2))\n            if not is_maze_valid(self.current[None, :])[0]:\n                self.reset()\n        else:\n            self.current = self.start\n\n        print(\"Reset: \", self.current)\n        self.counter = 0\n\n        return self.current\n\n    def step(self, action):\n        # Scale down action from range (-1, 1) to (-0.05, 0.05)\n        self.counter += 1\n        action = action / max(np.abs(action).max(), 1)\n        action = action / 20.\n\n        reward = 0\n        info = {}\n\n        # action = np.clip(action, -0.05, 0.05)\n        temp = self.current + action\n        if is_maze_valid(temp[None, :])[0]:\n            self.current = temp\n        else:\n            pass\n\n        self.current = np.clip(self.current, -1, 1)\n        observation = self.current\n\n        dist = np.abs(self.current - self.end).sum()\n        reward = -1 * dist\n\n        if dist < self.eps:\n            done = True\n        elif np.any(oob(temp[None, :]), axis=1)[0]:\n            done = True\n        else:\n            done = False\n\n        return observation, reward, done, info\n\n    def seed(self, seed):\n        np.random.seed(seed)\n\n\nclass Ball(gym.Env):\n    def __init__(self, start=[0.1, 0.1], end=[1.0, 1.0], a=[0.0, 0.0], eps=0.05, cor=1.0, random_starts=False, phy_type=None):\n        self.start = np.array(start)\n        self.end = np.array(end)\n        self.random_starts = random_starts\n\n        # range of container\n        self.low = 0.0\n        self.high = 1.0\n\n        # initial velocity\n        self.v = np.array([0.0, 0.0])\n\n        # random acceleration; a physical parameter to be inferred\n        self.a = np.array(a)\n\n        # coefficient of restitution, delta v'/ delta v; a physical parameter to be inferred\n        self.cor = cor\n\n        self.phy_type = phy_type\n        if phy_type == 'a':\n            self.start = np.concatenate((self.start, self.a))\n            self.end = np.concatenate((self.end, self.a))\n        elif phy_type == 'cor':\n            self.start = np.append(self.start, self.cor)\n            self.end = np.append(self.end, self.cor)\n\n        self.current = self.start\n\n        self.eps = eps\n\n    def reset(self):\n        if self.random_starts:\n            self.current[:2] = np.random.uniform(0.0, 1.0, (2))\n        else:\n            self.current = self.start\n\n        print(\"Reset ball position to: \", self.current)\n\n        return self.current\n\n    def collide(self, current, action):\n        # free movement for one time step\n\n        # print(\"Start position\", current)\n        # print(\"Start velocity\", action)\n\n        action += self.a   # add random force (~air resistance/wind)\n\n        end = []\n\n        # check for collisions with container's wall\n        for i, vi in list(zip(current, action)):\n            if i <= self.low:\n                i = -i\n                vi = -vi\n                vi *= self.cor\n            elif i >= self.high:\n                i = 2 * self.high - i\n                vi = -vi\n                vi *= self.cor\n            else:\n                pass\n\n            end.append((i, vi))\n\n        # unzip\n        current, v = zip(*end)\n        current = np.array(current)\n        v = np.array(v)\n\n        # print(\"Final position\", current)\n        # print(\"Final velocity\", v)\n\n        self.v = v\n\n        # return updated position\n        return current\n\n    def move(self):\n        # step without action\n        start = self.current\n\n        self.current, self.v = self.collide(self.current, self.v)\n\n        # calculate the \"action\" (i.e. reverse dynamics)\n        action = self.current - start\n\n        return action\n\n    def step(self, action):\n        # Scale down action from range (-1, 1) to (-0.05, 0.05)\n        action = action / max(np.abs(action).max(), 1)\n        action = action / 20.\n        reward = 0\n        info = {}\n\n        # action = np.clip(action, -0.05, 0.05)\n        self.current[:2] = self.collide(self.current[:2], action)\n\n        dist = np.abs(self.current[:2] - self.end[:2]).sum()\n        reward = -1 * dist\n\n        if dist < self.eps:\n            done = True\n        else:\n            done = False\n\n        observation = self.current\n\n        return observation, reward, done, info\n\n    def seed(self, seed):\n        np.random.seed(seed)\n\n\nclass ManyBalls(gym.Env):\n    def __init__(self, num_balls=10, cor=None):\n        self.num_balls = num_balls\n        self.radius = 0.05\n        self.pos = [np.random.uniform(low=0.0, high=5.0, size=(2,)) for _ in range(self.num_balls)]\n        self.v = [np.random.uniform(low=-1.0, high=1.0, size=(2,)) for _ in range(self.num_balls)]\n\n        # assume all balls are of the same mass\n        self.mass = [1. for _ in range(self.num_balls)]\n\n        if cor is None:\n            self.cor = np.random.uniform()   # [0.0, 1.0] range\n        elif 0.0 <= cor <= 1.0:\n            self.cor = cor\n        else:\n            raise ValueError(\"coefficient of restitution must be in [0., 1.] range.\")\n\n    def reset(self):\n        self.pos = [np.random.uniform(low=0.0, high=10.0, size=(2,)) for _ in range(self.num_balls)]\n\n    def dist(self, i, j):\n        return np.abs(self.pos[i] - self.pos[j]).sum()\n\n    def collide(self, i, j):\n        vi, vj = self.v[i], self.v[j]\n\n        # solve collision dynamics\n        # (1) conversation of momentum\n        # (2) coefficient of restitution\n        vii = (vi + vj + self.cor * (vj - vi)) / 2.\n        vjj = vi + vj - vii\n        self.v[i] = vii\n        self.v[j] = vjj\n\n    def step(self, action):\n        # irrelevant in this env\n        reward = 0\n        info = {}\n        done = False\n\n        for i in range(self.num_balls):\n            for j in range(self.num_balls):\n                if self.dist(i, j) <= self.radius:\n                    # calculate velocity after collision\n                    self.collide(i, j)\n\n        for i in range(self.num_balls):\n            # update position\n            self.pos[i] += self.v[i]\n\n        observation = self.pos[:]\n\n        return observation, reward, done, info\n\n\nclass Reacher(gym.Env):\n    def __init__(self, end=[0.7, 0.5], eps=0.01, pretrain_eval=False):\n        self.env = gym.make(\"Reacher-v2\")\n\n        # Internal variable for computing reward\n        self.target = np.array(end)\n\n        # Visible variable indicating the goal state\n        self.end = self.target\n\n        if pretrain_eval:\n            self.cross_preprocess(end)\n\n        self.eps = eps\n        self.pretrain_eval = pretrain_eval\n\n        self.mean = np.array([-0.00414585, -0.00524412, -0.0067701 , -0.00752602])\n        self.std = np.array([3.51472521, 1.72293722, 8.62294938, 6.39224953])\n\n    def cross_preprocess(self, end):\n        end[:2] = end[:2] * np.pi + np.pi\n        end[2:4] = end[2:4] * 10.0\n\n        target = (end - self.mean) / self.std\n        self.end = end\n\n    def reset(self):\n        self.env.reset()\n        ob = self._get_obs()\n\n        return ob\n\n    def step(self, action):\n        # Scale down action from range (-1, 1) to (-0.05, 0.05)\n        reward = 0\n\n        _, _, done, info = self.env.step(action)\n        obs = self._get_obs()\n\n        reward = self.reward()\n        dist = -1 * reward\n\n        if dist < self.eps:\n            done = True\n        else:\n            done = done\n\n        return obs, reward, done, info\n\n    def seed(self, seed):\n        np.random.seed(seed)\n\n    def reward(self):\n        qpos = self.env.unwrapped.sim.data.qpos.copy()\n        qvel = self.env.unwrapped.sim.data.qvel.copy()\n        obs = np.concatenate([qpos[:2], qvel[:2]], axis=0)\n        obs[:2] = ((obs[:2] % (2 * np.pi)) - np.pi) / np.pi\n        obs[2:4] = obs[2:4] / 10.0\n\n        dist = np.minimum(np.minimum(np.abs(obs[:2] - self.target), np.abs(self.target + 2 - obs[:2])), np.abs(self.target - 2 -obs[:2])).sum()\n        reward = -1 * dist\n\n        return reward\n\n    def _get_obs(self):\n        qpos = self.env.unwrapped.sim.data.qpos.copy()\n        qvel = self.env.unwrapped.sim.data.qvel.copy()\n        obs = np.concatenate([qpos[:2], qvel[:2]], axis=0)\n\n        if self.pretrain_eval:\n            obs = (obs - mean) / std\n        else:\n            obs[:2] = ((obs[:2] % (2 * np.pi)) - np.pi) / np.pi\n            obs[2:4] = obs[2:4] / 10.0\n\n        return obs\n","sub_path":"envs.py","file_name":"envs.py","file_ext":"py","file_size_in_byte":11178,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"556975502","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport django.db.models.deletion\nfrom django.conf import settings\n\n\nclass Migration(migrations.Migration):\n    dependencies = [\n        ('usr', '0001_initial'),\n    ]\n\n    operations = [\n        migrations.AlterField(\n            model_name='profile',\n            name='user',\n            field=models.ForeignKey(related_name='members',\n                                    on_delete=django.db.models.deletion.SET_DEFAULT, default=None,\n                                    blank=True, to=settings.AUTH_USER_MODEL, null=True),\n        ),\n    ]\n","sub_path":"src/usr/migrations/0002_auto_20150618_1512.py","file_name":"0002_auto_20150618_1512.py","file_ext":"py","file_size_in_byte":648,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"162571812","text":"from skimage.measure import compare_ssim\nimport imutils\nimport cv2\nimport numpy as np\n\n# load the two input images\nimageA = cv2.imread(\"./survey1.jpg\")\nimageB = cv2.imread(\"./survey1_checked.jpg\")\n\nprint(imageA.shape)\nprint(imageB.shape)\nH = imageA.shape[0]\nW = imageA.shape[1]\n\nimageB = cv2.resize(imageB, (W, H))\n\nprint(imageA.shape)\nprint(imageB.shape)\n\n#convert the images to gray scale\ngrayA = cv2.cvtColor(imageA, cv2.COLOR_BGR2GRAY)\ngrayB = cv2.cvtColor(imageB, cv2.COLOR_BGR2GRAY)\n\n#compute the Structural similarity index(SSIM) between the\n#images, ensuring that the difference image is returned\n'''\nThe score represents SSIM between two images, a floating type number in the range of [-1, 1] where 1 being perfect match\nDiff variable is a matrix of floating point numbers that stores the actual differences, we change the number to\nunsigned integer in the range of [0, 255]\n'''\n\n(score, diff) = compare_ssim(grayA, grayB, full=True)\ndiff = (diff*255).astype(\"uint8\")\nprint(\"SSIM: {}\".format(score))\n\n'''\ndiff = cv2.subtract(grayA, grayB)\nthresh = not np.any(diff)\ncv2.imwrite(\"result.jpg\", diff)\n\n\n# threshold the difference image, followed by finding contour\n# obtain the regions of the two input images that differ\ndiff = cv2.imread(\"./result.jpg\")\nthresh = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY)\n'''\n\nthresh = cv2.threshold(diff, 0, 255,\n                           cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]\nfor i in range(1000):\n    thresh = cv2.medianBlur(thresh, 5)\n\ncnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,\n                        cv2.CHAIN_APPROX_SIMPLE)\ncnts = cnts[0] if imutils.is_cv2() else cnts[1]\n\n\n# loop over the contours\nfor c in cnts:\n    # compute the bounding box of the contour and then draw the\n    # bounding box on both input images to represent where the images\n    # differ\n    (x, y, w, h) = cv2.boundingRect(c)\n    cv2.rectangle(imageA, (x, y), (x + w, y + h), (0, 0, 255))\n    cv2.rectangle(imageB, (x, y), (x + w, y + h), (0, 0, 255))\n\n# show the output images\ncv2.imwrite(\"./Original.jpg\", imageA)\ncv2.imwrite(\"./checked.jpg\", imageB)\ncv2.imwrite(\"./Diff.jpg\", diff)\ncv2.imwrite(\"./Thresh.jpg\", thresh)\n","sub_path":"Practicals/image_diff.py","file_name":"image_diff.py","file_ext":"py","file_size_in_byte":2162,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"373219951","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.animation import FuncAnimation\n\nfig = plt.figure()\nax = fig.add_subplot(111)\n\ndef animate(fig, ax):\n    xdata, ydata = [], []\n    ln, = ax.plot([], [], 'ro')\n\n    def init():\n        nonlocal ax\n        ax.set_xlim(0, 2*np.pi)\n        ax.set_ylim(-1, 1)\n        return ln,\n\n    def update(frame):\n        nonlocal xdata, ydata, ln\n        if xdata:\n            xdata.pop()\n        if ydata:\n            ydata.pop()\n        xdata.append(frame)\n        ydata.append(np.sin(frame))\n        ln.set_data(xdata, ydata)\n        return ln,\n\n    FuncAnimation(fig, update, frames=np.linspace(0, 2*np.pi, 128),\n                  init_func=init, blit=True)\n    plt.show()\n\n\nanimate(fig, ax)\n","sub_path":"algorithms/tips/animation/simple.py","file_name":"simple.py","file_ext":"py","file_size_in_byte":747,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"112971911","text":"# get all planets and return no records \ndef test_get_all_planets_with_no_records(client):\n    # Act \n    response = client.get(\"/planets\")\n    response_body = response.get_json() \n\n    # Assert \n    assert response.status_code == 200\n    assert response_body == []\n\ndef test_get_all_planets(client, two_saved_planets):\n    # Act \n    response = client.get(\"/planets\")\n    response_body = response.get_json() \n\n    # Assert \n    assert response.status_code == 200\n    assert response_body[0][\"name\"] == \"Venus\"\n\n# get one planet by id \ndef test_get_one_planet(client, two_saved_planets):\n    # Act\n    response = client.get(\"/planets/1\")\n    response_body = response.get_json()\n\n    # Assert\n    assert response.status_code == 200\n    assert response_body == {\n        \"id\": 1,\n        \"name\": \"Venus\",\n        \"description\": \"Goddess of love and beauty\",\n        \"founder\": \"Heidi and Manu\"\n    }\n\n# get one planet by id with no fixture\ndef test_get_one_planet_no_fixture(client): \n    # Act \n    planet_id = 1\n    response = client.get(f\"/planets/{planet_id}\")\n    response_body = response.get_json()\n\n    # Assert\n    assert response.status_code == 404 \n    assert response_body[\"message\"] == f\"Planet with id {planet_id} was not found\"\n\ndef test_create_one_planet(client, planet_data): \n    response = client.post(\"/planets\", json=planet_data)\n    response_body = response.get_json() \n\n    assert response.status_code == 201 \n    assert response_body[\"message\"] == f\"Planet {planet_data['name']} successfully created\"  \n\n","sub_path":"tests/test_routes.py","file_name":"test_routes.py","file_ext":"py","file_size_in_byte":1525,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"79945882","text":"#VARIANT 2 Siim Kustassoo\n\n#Esimene ülesanne\ndef elements_sum(n):\n    #meetod mis võtab sisse täisarvu ning arvutab kõikide talle eelnevate arvude summa\n    summa = 0\n    #commit test\n    i = 1\n    \n    for x in range(1,n+1):\n        summa= summa+i\n        i=i+1\n        \n    print(summa)\n    \n  \n\n\n#Teine ülesanne\ndef time_convert(sisend):\n    #meetod mis võtab sisse muutuja string ning arvutab tunnid ja minutit summaarselt minutiteks\n    tykk = sisend.split(\":\") #splitin inputi : pealt\n    elementlist = [] # teeks kaks elementi, tunnid ja minutid\n    counter = 0 # loeb for jaoks repeatide arvu\n    vastus=0\n    vastusSone=\"\"\n    \n    for x in tykk: # lisan sisendi numbritena arraysse\n        elementlist.append(tykk[counter])\n        counter = counter+1\n        \n    vastus = 60*(int(elementlist[0])) + int(elementlist[1])\n    \n    print(vastus)\n\n\n#Kolmas ülesanne\ndef sort_values(string):\n    #meetod, mis võtab sisse string ja loob selle põhjal väljundi, kus on numbrid ja tähed järjestatud ning eraldi ridadel\n    nimekiri = [\"a\",\"b\",\"c\",\"d\",\"e\",\"f\",\"g\",\"h\",\"i\",\"j\",\"k\",\"l\",\"m\",\"n\",\"o\",\"p\",\"q\",\"r\",\"s\",\"š\",\"t\",\"u\",\"v\",\"w\",\"x\",\"y\",\"z\",\"ž\",\"ö\",\"ä\",\"ü\",\"õ\"]\n    sone=\"\"\n    tahed=\"\"\n    numbrid=\"\"\n    tykk = string.split(\",\") #sjagan inputi iga koma tagant\n    elementlist = [] # elementide list kuhu kirjutatakse string osadeks\n    counter = 0 # loeb for jaoks repeatide arvu\n    \n    for x in tykk: # elementide viimine sõnest -> list\n        elementlist.append(tykk[counter])\n        counter = counter+1\n        \n    elementlist.sort() # elementide nimekiri on tähestikulises ning numbrlises järjekorras    \n    counter = 0 # Panen lugeja uuesti 0\n\n    \n    for x in elementlist: # kui element on täht, siis lisada tähtede sõnesesse\n        if elementlist[counter] in nimekiri:\n            tahed=tahed+\" \"+elementlist[counter]\n            counter = counter+1\n        else: #teisel juhul on number, lisatakse numbrite sõnesse\n            numbrid=numbrid+\" \"+elementlist[counter]\n            counter = counter+1\n            \n    sone = numbrid +\"\\n\" +tahed #prinditakse numbrid, reavahetus ning tahed\n    \n    print(sone) \n\n\n","sub_path":"KT_Siim_Kustassoo.py","file_name":"KT_Siim_Kustassoo.py","file_ext":"py","file_size_in_byte":2160,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"417444056","text":"# encoding: utf-8\n\nfrom __future__ import division\n\nfrom collections import defaultdict\n\nfrom sqlalchemy.orm import (contains_eager)\nfrom sqlalchemy.orm import (joinedload, joinedload_all, subqueryload, subqueryload_all)\nfrom sqlalchemy.orm.exc import NoResultFound\nimport pyramid.httpexceptions as exc\n\nimport pokedex.db.tables as t\n\nfrom .. import db\nfrom . import caching\n\ndef type_list(request):\n    c = request.tmpl_context\n\n    c.types = db.pokedex_session.query(t.Type) \\\n        .join(t.Type.names_local) \\\n        .filter(t.Type.damage_efficacies.any()) \\\n        .order_by(t.Type.names_table.name) \\\n        .options(contains_eager(t.Type.names_local)) \\\n        .options(joinedload('damage_efficacies')) \\\n        .all()\n\n    if 'secondary' in request.params:\n        try:\n            c.secondary_type = db.get_by_name_query(\n                    t.Type, request.params['secondary'].lower()) \\\n                .filter(t.Type.damage_efficacies.any()) \\\n                .options(joinedload('target_efficacies')) \\\n                .one()\n        except NoResultFound:\n            raise exc.HTTPNotFound()\n\n        c.secondary_efficacy = dict(\n            (efficacy.damage_type, efficacy.damage_factor)\n            for efficacy in c.secondary_type.target_efficacies\n        )\n    else:\n        c.secondary_type = None\n        c.secondary_efficacy = defaultdict(lambda: 100)\n\n    # Count up a relative score for each type, both attacking and\n    # defending.  Normal damage counts for 0; super effective counts for\n    # +1; not very effective counts for -1.  Ineffective counts for -2.\n    # With dual types, x4 is +2 and x1/4 is -2; ineffective is -4.\n    # Everything is of course the other way around for defense.\n    attacking_score_conversion = {\n        400: +2,\n        200: +1,\n        100:  0,\n         50: -1,\n         25: -2,\n          0: -2,\n    }\n    if c.secondary_type:\n        attacking_score_conversion[0] = -4\n\n    c.attacking_scores = defaultdict(int)\n    c.defending_scores = defaultdict(int)\n    for attacking_type in c.types:\n        for efficacy in attacking_type.damage_efficacies:\n            defending_type = efficacy.target_type\n            factor = efficacy.damage_factor * \\\n                c.secondary_efficacy[attacking_type] // 100\n\n            c.attacking_scores[attacking_type] += attacking_score_conversion[factor]\n            c.defending_scores[defending_type] -= attacking_score_conversion[factor]\n\n    return {}\n\ndef type_view(request):\n    name = request.matchdict.get('name')\n    c = request.tmpl_context\n\n    try:\n        c.type = db.get_by_name_query(t.Type, name).one()\n    except NoResultFound:\n        raise exc.HTTPNotFound()\n\n    ### Prev/next for header\n    c.prev_type, c.next_type = db.prev_next(\n        table=t.Type,\n        current=c.type,\n        language=c.game_language,\n    )\n\n    ### Cache\n    caching.cache_content(\n        request=request,\n        key=c.type.identifier,\n        do_work=_do_type,\n    )\n\n    return {}\n\ndef _do_type(request, cache_key):\n    c = request.tmpl_context\n\n    # Eagerload a bit of type stuff\n    db.pokedex_session.query(t.Type) \\\n        .filter_by(id=c.type.id) \\\n        .options(\n            subqueryload('damage_efficacies'),\n            joinedload('damage_efficacies.target_type'),\n            subqueryload('target_efficacies'),\n            joinedload('target_efficacies.damage_type'),\n\n            # Move stuff\n            subqueryload('moves'),\n            joinedload('moves.damage_class'),\n            joinedload('moves.generation'),\n            joinedload('moves.move_effect'),\n            joinedload('moves.type'),\n\n            # Pokémon stuff\n            subqueryload('pokemon'),\n            joinedload('pokemon.abilities'),\n            joinedload('pokemon.hidden_ability'),\n            joinedload('pokemon.species'),\n            subqueryload('pokemon.species.egg_groups'),\n            joinedload('pokemon.types'),\n            joinedload('pokemon.stats'),\n        ) \\\n        .one()\n","sub_path":"splinext/pokedex/views/types.py","file_name":"types.py","file_ext":"py","file_size_in_byte":3989,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"58382448","text":"import numpy as np\nimport os\nimport torch\nimport argparse\nimport wandb\n\nfrom train import VaeAlg, TruncCVar, AdaCVar\nfrom datasets import (\n    GaussianDataSet,\n    MNIST,\n    ImbalancedMNIST,\n    CelebA,\n    CIFAR10,\n    ImbalancedCIFAR10,\n)\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--dataset_nb\", type=int, default=0)\nparser.add_argument(\"--dataset\", default=\"celeba\")\nparser.add_argument(\"--model\", type=str, default=\"ada\", choices=[\"vae\", \"ada\", \"trunc\"])\nparser.add_argument(\"--alpha\", type=float, default=0.3)\nparser.add_argument(\"--epochs\", type=int, default=1)\nparser.add_argument(\"--early_stop\", type=int, default=100)\nparser.add_argument(\"--batch_size\", type=int, default=128)\nparser.add_argument(\"--beta\", type=float, default=1.0)\nparser.add_argument(\"--optim\", type=str, default=\"adam\", choices=[\"adam\", \"sgd\"])\nparser.add_argument(\"--learning_rate\", type=float, default=0.0001)\nparser.add_argument(\"--momentum\", type=float, default=0.0)\nparser.add_argument(\"--exp3_gamma\", type=float, default=0.2)\nparser.add_argument(\"--save_model\", action=\"store_true\", default=False)\nparser.add_argument(\"--seed\", type=int, default=0)\nargs = parser.parse_args()\nwandb.init(project=\"cvar-generative\", entity=\"yooyoo9\", config=args)\n\nnp.random.seed(args.seed)\ntorch.manual_seed(args.seed)\n\n# Create directories for the output if they do not exist\nif args.dataset == \"gaussian\":\n    path_data = \"experiments/synthetic/input/X10000.npy\"\n    path_model = os.path.join(\"experiments/synthetic/vae/model/\", str(args.dataset_nb))\n    path_out = os.path.join(\"experiments/synthetic/vae/output\", str(args.dataset_nb))\n    for cur in [path_model, path_out]:\n        if not os.path.exists(cur):\n            os.makedirs(cur)\n    path_out = os.path.join(\n        \"experiments/synthetic/vae/output\", str(args.dataset_nb), args.model + \".npy\"\n    )\nelse:\n    path_data = os.path.join(\"experiments\", args.dataset, \"input\")\n    path_model = os.path.join(\n        \"experiments\", args.dataset, \"vae\", \"model\", str(args.seed)\n    )\n    for cur_dir in [path_data, path_model]:\n        if not os.path.exists(cur_dir):\n            os.makedirs(cur_dir)\n\noptim_param = {\n    \"name\": args.optim,\n    \"lr\": args.learning_rate,\n    \"momentum\": args.momentum,\n}\ncriterion = torch.nn.MSELoss(reduction=\"none\")\n\n# Generate data\nif args.dataset == \"gaussian\":\n    vae_model = \"VAE\"\n    train_set = GaussianDataSet(path_data, args.dataset_nb, train=0)\n    valid_set = GaussianDataSet(path_data, args.dataset_nb, train=1)\n    test_set = GaussianDataSet(path_data, args.dataset_nb, train=2)\n    model_param = {\n        \"x_dim\": 2,\n        \"hidden_dims\": [64, 64],\n        \"z_dim\": 2,\n    }\nelse:\n    vae_model = \"VaeImg\"\n    if args.dataset == \"mnist\" or args.dataset == \"mnist_imb\":\n        dataset = MNIST if args.dataset == \"mnist\" else ImbalancedMNIST\n        img_size = 28\n        model_param = {\n            \"n_channel\": 1,\n            \"hidden_dims\": [512, 512],\n            \"z_dim\": 2,\n            \"img_size\": img_size,\n        }\n    else:\n        if args.dataset == \"cifar\":\n            dataset = CIFAR10\n        elif args.dataset == \"cifar_imb\":\n            dataset = ImbalancedCIFAR10\n        else:\n            dataset = CelebA\n        img_size = 64\n        model_param = {\n            \"n_channel\": 3,\n            \"hidden_dims\": [512, 256, 128],\n            \"z_dim\": 128,\n            \"img_size\": img_size,\n        }\n\n    train_set = dataset(path_data, 0, img_size)\n    valid_set = dataset(path_data, 1, img_size)\n    test_set = dataset(path_data, 2, img_size)\n\nif args.model == \"vae\":\n    model = VaeAlg(\n        vae_model,\n        path_model,\n        model_param,\n        train_set,\n        valid_set,\n        test_set,\n        args.batch_size,\n        optim_param,\n        criterion,\n        args.alpha,\n        args.beta,\n    )\nelif args.model == \"trunc\":\n    model = TruncCVar(\n        vae_model,\n        path_model,\n        model_param,\n        train_set,\n        valid_set,\n        test_set,\n        args.batch_size,\n        optim_param,\n        criterion,\n        args.alpha,\n        args.beta,\n    )\nelif args.model == \"ada\":\n    exp3_param = {\"gamma\": args.exp3_gamma, \"beta\": 0.0, \"eps\": 0.0, \"iid_batch\": False}\n    model = AdaCVar(\n        vae_model,\n        path_model,\n        model_param,\n        exp3_param,\n        train_set,\n        valid_set,\n        test_set,\n        args.batch_size,\n        optim_param,\n        criterion,\n        args.alpha,\n        args.beta,\n    )\n\nmodel.train(args.epochs, args.early_stop, args.save_model)\n\nif args.dataset == \"gaussian\":\n    test_loss, test_mean, test_worst, test_cvar = model.evaluate(\n        val=False, output_path=path_out\n    )\n    wandb.log(\n        {\n            \"test_loss\": test_loss,\n            \"test_mean\": test_mean,\n            \"test_worst\": test_worst,\n            \"test_cvar\": test_cvar,\n        }\n    )\n","sub_path":"algorithms/vae/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4870,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"326447230","text":"import math\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.feature_extraction.text import TfidfTransformer\n\ndef extract_keywords(raw_documents, topn=2):\n    vectorizer = CountVectorizer(max_features=math.ceil(len(raw_documents) * 0.8))\n    transformer = TfidfTransformer()\n\n    X = vectorizer.fit_transform(raw_documents)\n    X_new = transformer.fit_transform(X)\n    X_arr = X_new.toarray()\n    feature_names = vectorizer.get_feature_names()\n\n    lines = []\n    for idx1,val1 in enumerate(X_arr):\n        line = []\n        for idx2,val2 in enumerate(val1):\n            if val2 != 0:\n                line.append((feature_names[idx2], val2))\n\n        line_sorted = sorted(line, key=lambda x: -x[1])\n        line_topn = line_sorted[:topn]\n        lines.append(line_topn)\n\n    return lines\n\n\nimport os\ndef read_file(file):\n    if os.path.exists(file):\n        with open(file, mode='r', encoding='utf-8') as f:\n            return f.read()\n    else:\n        return None\n\n\nlines = read_file('../test/wbz-07.csv_result_tokens_clean.txt').split('\\n')\nlines = [line.strip() for line in lines if line.strip() != '']\n\nprint(extract_keywords(lines))\n","sub_path":"NLP/Feature Extraction/TF-IDF/tf-idf.py","file_name":"tf-idf.py","file_ext":"py","file_size_in_byte":1163,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"533444635","text":"import dml\nimport prov.model\nimport datetime\nimport uuid\nimport pandas as pd\nimport json\n\nclass getAlcLicenses(dml.Algorithm):\n    contributor = 'ferrys'\n    reads = []\n    writes = ['ferrys.alc_licenses']\n\n    @staticmethod\n    def execute(trial = False):\n        startTime = datetime.datetime.now()\n\n        # Set up the database connection.\n        client = dml.pymongo.MongoClient()\n        repo = client.repo\n        repo.authenticate('ferrys', 'ferrys')\n\n        url = 'https://data.boston.gov/dataset/df9987bb-3459-4594-9764-c907b53f2abe/resource/9e15f457-1923-4c12-9992-43ba2f0dd5e5/download/all-section-12-alcohol-licenses.csv'\n        license_dict = pd.read_csv(url).to_dict(orient='records')\n        \n        repo.dropCollection(\"alc_licenses\")\n        repo.createCollection(\"alc_licenses\")\n        repo['ferrys.alc_licenses'].insert_many(license_dict)\n        repo['ferrys.alc_licenses'].metadata({'complete':True})\n        print(repo['ferrys.alc_licenses'].metadata())\n\n        repo.logout()\n        endTime = datetime.datetime.now()\n\n        return {\"start\":startTime, \"end\":endTime}\n    \n    @staticmethod\n    def provenance(doc = prov.model.ProvDocument(), startTime = None, endTime = None):\n        '''\n        Create the provenance document describing everything happening\n        in this script. Each run of the script will generate a new\n        document describing that invocation event.\n        '''\n\n        # Set up the database connection.\n        client = dml.pymongo.MongoClient()\n        repo = client.repo\n        repo.authenticate('ferrys', 'ferrys')\n        doc.add_namespace('alg', 'http://datamechanics.io/algorithm/') # The scripts are in <folder>#<filename> format.\n        doc.add_namespace('dat', 'http://datamechanics.io/data/') # The data sets are in <user>#<collection> format.\n        doc.add_namespace('ont', 'http://datamechanics.io/ontology#') # 'Extension', 'DataResource', 'DataSet', 'Retrieval', 'Query', or 'Computation'.\n        doc.add_namespace('log', 'http://datamechanics.io/log/') # The event log.\n        doc.add_namespace('bdp', 'https://data.boston.gov/dataset/')\n\n        this_script = doc.agent('alg:ferrys#getAlcLicenses', {prov.model.PROV_TYPE:prov.model.PROV['SoftwareAgent'], 'ont:Extension':'py'})\n        resource = doc.entity('bdp:all-section-12-alcohol-licenses', {'prov:label':'Alcohol License Data', prov.model.PROV_TYPE:'ont:DataResource', 'ont:Extension':'csv'})\n        get_licenses = doc.activity('log:uuid'+str(uuid.uuid4()), startTime, endTime)\n        doc.wasAssociatedWith(get_licenses, this_script)\n        doc.usage(get_licenses, resource, startTime, None,\n                  {\n                    prov.model.PROV_TYPE:'ont:Retrieval'\n                  })\n\n\n        licenses = doc.entity('dat:ferrys#alc_licenses', {prov.model.PROV_LABEL:'alc_licenses', prov.model.PROV_TYPE:'ont:DataSet'})\n        doc.wasAttributedTo(licenses, this_script)\n        doc.wasGeneratedBy(licenses, get_licenses, endTime)\n        doc.wasDerivedFrom(licenses, resource, get_licenses, get_licenses, get_licenses)\n\n        repo.logout()\n                  \n        return doc\n\n#getAlcLicenses.execute()\n#doc = getAlcLicenses.provenance()\n#print(doc.get_provn())\n#print(json.dumps(json.loads(doc.serialize()), indent=4))\n\n## eof\n\n","sub_path":"ferrys/getAlcLicenses.py","file_name":"getAlcLicenses.py","file_ext":"py","file_size_in_byte":3282,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"297037334","text":"from django import forms\nfrom .widgets import CustomClearableFileInput\nfrom .models import Blog, Keyword, Reply\n\n\nclass BlogForm(forms.ModelForm):\n    class Meta:\n        model = Blog\n        fields = '__all__'\n        exclude = ['author']\n        labels = {\n            'keywords': 'Keywords ',\n            'title': 'Title ',\n            'body': 'Body ',\n            'teaser': 'Teaser ',\n        }\n        widgets = {\n            'keywords': forms.CheckboxSelectMultiple,\n        }\n    image = forms.ImageField(label='Image',\n                             required=False,\n                             widget=CustomClearableFileInput)\n\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        keywords = Keyword.objects.all()\n        friendly_names = [(k.id, k.get_friendly_name()) for k in keywords]\n        # update form keywords field to friendly name\n        self.fields['keywords'].choices = friendly_names\n\n\nclass ReplyForm(forms.ModelForm):\n    class Meta:\n        model = Reply\n        exclude = ['blog', 'author']\n\n    def __init__(self, *args, **kwargs):\n        \"\"\"\n        Add placeholders and classes, remove auto-generated\n        labels and set autofocus on first field\n        \"\"\"\n        super().__init__(*args, **kwargs)\n        placeholders = {\n            'body': 'Your comment'\n        }\n\n        for field in self.fields:\n            if field != 'author':\n                if self.fields[field].required:\n                    placeholder = f'{placeholders[field]} *'\n                else:\n                    placeholder = placeholders[field]\n                self.fields[field].widget.attrs['placeholder'] = placeholder\n            # add class for styling\n            self.fields[field].label = False\n","sub_path":"blog/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":1754,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"171527642","text":"from __future__ import print_function\n\nimport json\nimport os\nimport requests\n\nprint('Loading function')\n\ndef lambda_handler(event, context):\n\n    status_opts = {\n        0: 'Operational', \n        1: 'Under Maintenance',\n        2: 'Degraded Performance',\n        3: 'Partial Outage',\n        4: 'Major Outage'\n    }\n\n    kwargs = {\n        'api_endpoint': os.environ['API_ENDPOINT'],\n        'component_id': 'BBmI7qnHfrwn',\n        'api_key': os.environ['API_KEY']\n    }\n\n    r = requests.get(\"https://tasks.hotosm.org/api/health-check\", headers={'Accept':'application/json'})\n\n    if not r.ok:\n    \tr = requests.patch(\"https://{api_endpoint}/api/v0/components/{component_id}\".format(**kwargs), json={'status': status_opts[4]}, headers={'x-api-key': kwargs['api_key']})\n    \treturn r.text\n    else:\n        r = requests.patch(\"https://{api_endpoint}/api/v0/components/{component_id}\".format(**kwargs), json={'status': status_opts[0]}, headers={'x-api-key': kwargs['api_key']})\n        return r.text","sub_path":"tm3-outage.py","file_name":"tm3-outage.py","file_ext":"py","file_size_in_byte":999,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"197093369","text":"# -*- coding:utf-8 -*-\nimport os\nimport config\nimport queue\nimport threading\nfrom collections import OrderedDict\nfrom util.ins_util import *\nfrom util.ins_log_util import *\nfrom util.time_util import *\nfrom threading import Semaphore\nfrom threading import Thread \n\nEXTERNAL_DEV = '_external_dev'\nEXTERNAL_ENTRIES ='entries'\nSD1 = 'sd1'\nSD2 = 'sd2'\nUSB = 'usb'\nBATTERY = '_battery'\nID_RES = '_idRes'\nTL_INFO = '_tl_info'\nLEFT_INFO = '_left_info'\nREC_LEFT_INFO = '_rec_left_sec'\nLIVE_REC_LEFT_INFO = '_live_rec_left_sec'\nREC_INFO = '_rec_sec'\nLIVE_REC_INFO = '_live_rec_sec'\nTL_REC_INFO = '_tl_left'\n\nINTERVAL = 10000\n\nSTORAGE_POS = 1\nCAM_STATE = '_cam_state'\nGPS_STATE = '_gps_state'\nSND_STATE = '_snd_state'\nSYS_TEMP  = '_sys_temp'\n\nsem_vfs = Semaphore()\n\n\nclass DiskCheckThread(Thread):\n    def __init__(self, name, parent):\n        super().__init__()\n        Info('----------------> constructor ComThread obj')\n        self.name = name\n        self.parent_obj = parent\n\n    def run(self):\n        while True:\n            # Info('----------------> diskcheck run parent func obj')\n            self.parent_obj.diskInfoUpdateFunc()\n            time.sleep(2)\n\nclass osc_state(threading.Thread):\n    def __init__(self, queue):\n        threading.Thread.__init__(self)\n        self._queue = queue\n        self._exit = False\n\n\n        # 小卡信息字典列表\n        self._tf_info = []      # 外部TF设备列表\n        self._local_dev = []    # 本地存储设备列表\n        self._rec_left = 0\n        self._live_rec_left = 0\n        self._rec_sec = 0\n        self._live_rec_sec = 0\n        self._time_lapse_left = 0\n\n        self.poll_info = OrderedDict({BATTERY: {},\n                                 ID_RES: [],\n                                 EXTERNAL_DEV: {EXTERNAL_ENTRIES: [],'save_path':None},\n                                 TL_INFO: {},\n                                 CAM_STATE: config.STATE_IDLE,\n                                 GPS_STATE:0,\n                                 LEFT_INFO:{REC_LEFT_INFO:0, REC_INFO:0, LIVE_REC_LEFT_INFO:0, LIVE_REC_INFO:0, TL_REC_INFO:0},\n                                 SND_STATE:{}})\n\n        self.sem = Semaphore()\n        self.stateSem = Semaphore()\n\n        self._cur_save_dev_info = {'free':0, 'total':0, 'test':False}\n\n\n    def run(self):\n        self.func = OrderedDict({\n            osc_state_handle.CLEAR_TL_COUNT:            self.clear_tl_count,\n            osc_state_handle.RM_RES_ID:                 self.rm_res_id,\n            osc_state_handle.SET_DEV_SPEED_SUC:         self.set_dev_speed_test_suc,\n            osc_state_handle.SET_TL_COUNT:              self.set_tl_count,\n            osc_state_handle.ADD_RES_ID:                self.add_res_id,\n            osc_state_handle.HAND_SAVE_PATH:            self.handle_save_path_change,\n            osc_state_handle.HANDLE_BAT:                self.handle_battery,\n            osc_state_handle.HANDLE_DEV_NOTIFY:         self.handle_dev_notify_action,\n            osc_state_handle.SET_TF_INFO:               self.set_tf_info,\n            osc_state_handle.CLEAR_TF_INFO:             self.clear_tf_info,\n            osc_state_handle.TF_STATE_CHANGE:           self.change_tf_info,\n            osc_state_handle.TF_FORMAT_CLEAR_SPEED:     self.clear_tf_speed_flag,\n            osc_state_handle.UPDATE_REC_LEFT_SEC:       self.set_rec_left_sec,\n            osc_state_handle.UPDATE_TIME_LAPSE_LEFT:    self.update_timelapse_left,\n            osc_state_handle.UPDATE_SYS_TEMP:           self.updateSysTemp,\n        })\n\n        # 创建检测磁盘信息的线程\n        DiskCheckThread('disk_info', self).start()\n\n        while self._exit is False:\n            try:\n                req = self._queue.get()\n                msg_what = req['msg_what']\n                self.aquire_sem()\n                if check_dic_key_exist(req, 'args'):\n                    self.func[msg_what](req['args'])\n                else:\n                    self.func[msg_what]()\n                self.release_sem()\n            except Exception as e:\n                Err('monitor_fifo_write2 e {}'.format(e))\n                self.release_sem()\n\n\n    def diskInfoUpdateFunc(self):\n        division  = {\n            'M':1024 *1024,\n            'K':1024,\n            'G':1024 *1024 * 1024,\n            }\n\n        diskPath = self.poll_info[EXTERNAL_DEV]['save_path']\n        if diskPath != None:\n            vfs = self.get_vfs(diskPath)\n            if vfs is not None:\n                self._cur_save_dev_info['free'] = vfs.f_bsize * vfs.f_bfree / division['M']\n                self._cur_save_dev_info['total'] = vfs.f_bsize * vfs.f_blocks / division['M']\n            else:\n                self._cur_save_dev_info['free'] = 0\n                self._cur_save_dev_info['total'] = 0    \n\n\n            # 如果该路径下含有/.pro_suc文件，表示已经通过了测速\n            if file_exist(join_str_list((diskPath, \"/.pro_suc\"))):\n                self._cur_save_dev_info['test'] = True\n            else:\n                self._cur_save_dev_info['test'] = False\n\n        else:\n            pass\n            # Info('locak disk not exist')\n\n\n    # 获取文件\n    def get_vfs(self, name):\n        vfs = None\n        sem_vfs.acquire()\n\n        try:\n            if os.path.exists(name) and os.path.isdir(name):\n                vfs = os.statvfs(name)\n        except OSError as e:\n            Err('get_vfs OSError {} name {}'.format(e, name))\n        except Exception as e:\n            Err('get_vfs exception {}'.format(e, name))\n        sem_vfs.release()\n        return vfs\n\n\n    # get_tf_storage_info\n    # 获取TF卡信息\n    def get_tf_storage_info(self, path, dev_type, dev_name, index, total, free, speed_test):\n        \n        info = OrderedDict() \n\n        info['type']  = 'sd'   # 现在所有的设备类型都是USB，将该字段改为区分大卡和小卡\n        info['mounttype'] = 'module'    \n        info['path']  = path\n        info['name']  = dev_name\n        info['index'] = index               # 对于本地卡，只支持一张的模式\n\n        info['free']  = free\n        info['total'] = total\n\n        if speed_test == 0:\n            info['test']  = False          # 没有进行速度测试，默认\n        else:\n            info['test']  = True          # 没有进行速度测试，默认\n\n        # Print('external info {}'.format(info))\n        return info\n\n\n    def get_dev_info_detail(self, dev_list):\n        dev_info = []\n        try:\n            for dev in dev_list:\n                info = self.get_local_storage_info(dev['path'], dev_type = dev['dev_type'], dev_name = dev['name'])\n                dev_info.append(info)\n        except Exception as e:\n            Info('get_dev_info_detail exception {}'.format(str(e)))\n        return dev_info\n\n\n\n    # 对于大卡: 需要手动计算出卡的容量\n    # 对于小卡: 直接使用查询到存储数据来填充卡信息\n    # 卡信息:\n    #   path   - 卡的挂载路径(对于外部TF卡，该项为NULL)\n    #   type   - 表示是设备的类型(本地还是模组的))\n    #   index  - 索引号(表示是第几号卡)\n    #   totoal - 卡的容量\n    #   free   - 卡的剩余空间\n    # 查询本地卡的信息\n    def get_local_storage_info(self, path, dev_type, dev_name, unit='M'):\n        info = OrderedDict()\n\n        info['type'] = dev_type   # 现在所有的设备类型都是USB，将该字段改为区分大卡和小卡\n        info['mounttype'] = 'nv'\n        info['path'] = path\n        info['name'] = dev_name\n        info['index'] = 0           # 对于本地卡，只支持一张的模式\n\n        info['free'] = self._cur_save_dev_info['free']\n        info['total'] = self._cur_save_dev_info['total']\n        info['test'] = self._cur_save_dev_info['test']\n\n        return info\n\n\n    def update_timelapse_left(self, param):\n        self._time_lapse_left = param['tl_left']\n\n\n    def updateSysTemp(self, param):\n        self._sys_temp = param\n\n\n    def set_tf_info(self, dev_infos):\n        self._tf_info = dev_infos['module']\n        # for dev_info in self._tf_info:\n        #     Info('tfcard dev info {}'.format(dev_info))\n\n    # 将所有TF卡的速度测试标志设置为False\n    def clear_tf_speed_flag(self):\n        for dev_info in self._tf_info:\n            dev_info['pro_suc'] = 0\n\n\n    # 方法名称: set_rec_left_sec\n    # 功能: 设置录像,直播的剩余时间（单位为秒）\n    # 参数: params - 有变化的卡的信息(dict类型)\n    # 返回值: 无\n    # {'rec_left': 3999, 'live_rec_left': 0}\n    def set_rec_left_sec(self, param):\n        self._rec_left = param['rec_left_sec']\n        self._live_rec_left = param['live_rec_left_sec']\n        self._rec_sec = param['rec_sec']\n        self._live_rec_sec = param['live_rec_sec']\n\n\n    # 方法名称: change_tf_info\n    # 功能: 处理TF卡状态变化\n    # 参数: params - 有变化的卡的信息(dict类型)\n    # 返回值: 无\n    def change_tf_info(self, params):        \n        for tmp_dev in self._tf_info:\n            if tmp_dev['index'] == params['index']:\n                tmp_dev['storage_total'] = params['storage_total']\n                tmp_dev['storage_left'] = params['storage_left']\n                if check_dic_key_exist(params, 'pro_suc'):\n                    tmp_dev['pro_suc'] = params['pro_suc']\n        \n\n    def clear_tf_info(self):\n        self._tf_info.clear()\n\n    # vendor specifix: _usb_in -- usb inserted; _sd_in --sdcard inserted\n    def get_osc_state(self, bStitch):\n        state = OrderedDict()\n        state['state'] = self.get_poll_info(bStitch)\n        return dict_to_jsonstr(state)\n\n\n    def set_external_info(self, dev_info):\n        try:\n            self._local_dev = dev_info\n        except Exception as e:\n            Err('set_external_info exception {}'.format(e))\n\n\n    def set_save_path(self, content):\n        Info('----- set_save_path: {}'.format(content))\n        try:\n            self.poll_info[EXTERNAL_DEV]['save_path'] = content['path']\n        except Exception as e:\n            Err('set_save_path exception {}'.format(e))\n\n\n    # 设置电池信息\n    def set_battery_info(self, info):\n        try:\n            self.poll_info[BATTERY] = info\n        except Exception as e:\n            Err('set_battery_info exception {}'.format(e))\n\n\n    # 方法名称: handle_dev_notify_acttion\n    # 功能: 处理存储设备变化通知\n    # 参数: content - 存储设备列表\n    # 返回值: 无\n    def handle_dev_notify_action(self, content = None):\n        dev_list = []\n        dev_info = []\n        # Info('----- handle_dev_notify_action: {}'.format(content))\n\n        if content['dev_list'] is not None:\n            dev_list = content['dev_list']\n            dev_info = self.get_dev_info_detail(dev_list)\n        self.set_external_info(dev_info)\n\n\n    def set_dev_speed_test_suc(self, param):\n        try:\n            # TODO: 访问_tf_info全局变量需要加锁访问\n            # 更新大卡的测试结果\n            # Print('ins_osc_state: set_dev_speed_test_suc ret {}'.format(param))\n\n            test_extern_list = param['module']\n            for _test_dev in test_extern_list:\n                for tmp_dev in self._tf_info:\n                    if _test_dev['index'] == tmp_dev['index']:\n                        if _test_dev['result'] == True:\n                            tmp_dev['pro_suc'] = 1\n                        else:\n                            tmp_dev['pro_suc'] = 0\n\n        except Exception as e:\n            Err('set_dev_speed_test_suc exception {}'.format(e))\n\n\n    def handle_save_path_change(self, content):\n        self.set_save_path(content)\n\n    def handle_battery(self, content):\n        self.set_battery_info(content)\n\n    def aquire_sem(self):\n        self.sem.acquire()\n\n    def release_sem(self):\n        self.sem.release()\n\n\n    # 方法名称: check_storage_space\n    # 功能：检查系统的存储空间（缓存的）\n    # 参数：无\n    # 返回值：无\n    def check_storage_space(self):\n        new_dev_info = []\n        \n        # 查询内部卡（大卡）\n        for internal_dev_info in self._local_dev:            \n            new_dev_info.append(self.get_local_storage_info(internal_dev_info['path'], dev_type = internal_dev_info['type'], dev_name = internal_dev_info['name']))\n\n        # 查询外部卡（小卡）path, dev_type, dev_name, index, total, free\n        for extern_dev_info in self._tf_info:\n            new_dev_info.append(self.get_tf_storage_info('null', 'external', 'tfcard', extern_dev_info['index'], extern_dev_info['storage_total'], extern_dev_info['storage_left'], extern_dev_info['pro_suc']))\n\n        # 更新整个存储部分信息(大卡 + 小卡)\n        self.poll_info[EXTERNAL_DEV][EXTERNAL_ENTRIES] = new_dev_info\n\n\n    # 方法名称: get_poll_info\n    # 功能：获取轮询信息（作为心跳包的数据）\n    # 参数：bStitch - 是否Stitch状态\n    # 返回值: 轮询信息（结构为字典）\n    def get_poll_info(self, bStitch):\n\n        self.aquire_sem()\n        try:\n            # 获取Camera当前的状态\n            st = self.poll_info[CAM_STATE]\n\n            # 去掉模组的温度\n            # self.poll_info[SYS_TEMP] = self._sys_temp\n            self.poll_info[LEFT_INFO][REC_LEFT_INFO] = self._rec_left\n            self.poll_info[LEFT_INFO][LIVE_REC_LEFT_INFO] = self._live_rec_left\n            self.poll_info[LEFT_INFO][REC_INFO] = self._rec_sec\n            self.poll_info[LEFT_INFO][LIVE_REC_INFO] = self._live_rec_sec\n            self.poll_info[LEFT_INFO][TL_REC_INFO] = self._time_lapse_left\n\n            # 检查存储空间\n            self.check_storage_space()\n        except Exception as e:\n            Err('get_poll_info exception {}'.format(e))\n\n        info = self.poll_info\n        self.release_sem()\n        return info\n\n\n    def add_res_id(self, id):\n        try:\n            # Info('add res is {}'.format(id))\n            self.poll_info[ID_RES].append(id)\n            # Info('poll_info {}'.format(self.poll_info))\n        except Exception as e:\n            Err('add_res_id exception {}'.format(e))\n\n    def set_tl_count(self, count):\n        try:\n            self.poll_info[TL_INFO]['tl_count'] = count\n        except Exception as e:\n            Err('add_res_id exception {}'.format(e))\n\n    def clear_tl_count(self):\n        try:\n            # Info('clear_tl_count tl_info')\n            self.poll_info[TL_INFO] = {}\n        except Exception as e:\n            Err('add_res_id exception {}'.format(e))\n\n    def rm_res_id(self, id):\n        try:\n            Info('rm res is {}'.format(id))\n            self.poll_info[ID_RES].remove(id)\n        except Exception as e:\n            Err('rm_res_id exception {}'.format(e))\n\n    def get_cam_state(self):\n        self.stateSem.acquire()\n        try:\n            state = self.poll_info[CAM_STATE]\n        except Exception as e:\n            Err('get_cam_state exception {}'.format(e))\n            state = config.STATE_EXCEPTION\n        self.stateSem.release()\n        return state\n\n    def get_save_path(self):\n        self.aquire_sem()\n        try:\n            path = self.poll_info[EXTERNAL_DEV]['save_path']\n        except Exception as e:\n            Err('get_cam_state exception {}'.format(e))\n            path = None\n        self.release_sem()\n        return path\n\n    def get_rec_info(self):\n        rec_info = OrderedDict()\n        self.aquire_sem()\n        try:\n            rec_info['_rec_sec'] = self._rec_sec\n            rec_info['_rec_left_sec'] = self._rec_left\n        except Exception as e:\n            Err('get_rec_info exception {}'.format(e))\n        self.release_sem()\n        return rec_info\n\n    def get_live_rec_info(self):\n        live_rec_info = OrderedDict()\n        self.aquire_sem()\n        try:\n            live_rec_info['_live_rec_sec'] = self._live_rec_sec\n            live_rec_info['_live_rec_left_sec'] = self._live_rec_left\n        except Exception as e:\n            Err('get_rec_info exception {}'.format(e))\n        self.release_sem()\n\n        return live_rec_info\n\n    def get_live_rec_pass_time(self):\n        return self._live_rec_sec\n\n    def get_live_rec_left_time(self):\n        return self._live_rec_left\n\n    def get_rec_pass_time(self):\n        return self._rec_sec\n\n    def get_rec_left_time(self):\n        return self._rec_left\n\n    # set_cam_state\n    # 设置服务器的状态\n    # \n    def set_cam_state(self, state):\n        self.stateSem.acquire()\n        try:\n            self.poll_info[CAM_STATE] = state\n        except Exception as e:\n            Err('set_cam_state exception {}'.format(e))\n        Info('2set cam state {}'.format(state))\n        self.stateSem.release()\n\n    def set_gps_state(self, state):\n        try:\n            self.poll_info[GPS_STATE] = state\n        except Exception as e:\n            Err('set_gps_state exception {}'.format(e))\n\n\n    # 设置_snd_state\n    def set_snd_state(self, param):\n        try:\n            # 将字符串转换为json对象, 2018年7月26日（修复BUG）\n            self.poll_info[SND_STATE] = param\n            #self.poll_info[SND_STATE] = json.loads(param)\n        except Exception as e:\n            Err('set_snd_state exception {}'.format(e))\n\n    def stop(self):\n        Print('stop osc state')\n        self._exit = True\n\n\n\nOSC_STATE_QUEUE_SIZE = 20\nclass osc_state_handle:\n    _queue = queue.Queue(OSC_STATE_QUEUE_SIZE)\n    _osc_state = osc_state(_queue)\n    CLEAR_TL_COUNT = 0\n    RM_RES_ID = 1\n    SET_DEV_SPEED_SUC = 2\n    SET_TL_COUNT = 3\n    ADD_RES_ID = 4\n    HAND_SAVE_PATH = 5\n    HANDLE_BAT = 6\n    HANDLE_DEV_NOTIFY = 7\n    SET_TF_INFO = 8\n    CLEAR_TF_INFO = 9\n    TF_STATE_CHANGE = 10\n    TF_FORMAT_CLEAR_SPEED = 11\n    UPDATE_REC_LEFT_SEC = 12\n    UPDATE_TIME_LAPSE_LEFT = 13\n    UPDATE_SYS_TEMP = 14\n\n    @classmethod\n    def start(cls):\n        cls._osc_state.start()\n\n    @classmethod\n    def stop(cls):\n        cls._osc_state.stop()\n        cls._osc_state.join()\n\n    @classmethod\n    def get_osc_state(cls,bStitch):\n        return cls._osc_state.get_osc_state(bStitch)\n\n    @classmethod\n    def get_cam_state(cls):\n        return cls._osc_state.get_cam_state()\n\n    @classmethod\n    def set_cam_state(cls,st):\n        cls._osc_state.set_cam_state(st)\n\n    @classmethod\n    def set_gps_state(cls,st):\n        cls._osc_state.set_gps_state(st)\n\n    @classmethod\n    def set_snd_state(cls,st):\n        cls._osc_state.set_snd_state(st)\n\n    @classmethod\n    def get_save_path(cls):\n        return cls._osc_state.get_save_path()\n\n    @classmethod\n    def get_rec_info(cls):\n        return cls._osc_state.get_rec_info()\n\n    @classmethod\n    def get_live_rec_info(cls):\n        return cls._osc_state.get_live_rec_info()\n\n    @classmethod\n    def get_live_rec_pass_time(cls):\n        return cls._osc_state.get_live_rec_pass_time()\n\n    @classmethod\n    def get_live_rec_left_time(cls):\n        return cls._osc_state.get_live_rec_left_time()\n\n    @classmethod\n    def get_rec_pass_time(cls):\n        return cls._osc_state.get_rec_pass_time()\n\n    @classmethod\n    def get_rec_left_time(cls):\n        return cls._osc_state.get_rec_left_time()\n\n    @classmethod\n    def make_req(cls, cmd, args=None):\n        dic = OrderedDict()\n        dic['msg_what'] = cmd\n        if args is not None:\n            dic['args'] = args\n        return dic\n\n    @classmethod\n    def send_osc_req(cls, req):\n        # Info('osc req {}'.format(req))\n        if cls._queue._qsize() < OSC_STATE_QUEUE_SIZE:\n            cls._queue.put(req)\n        else:\n            # avoid full\n            Warn('osc_state_handle write queue exceed')","sub_path":"web_server/osc_protocol/ins_osc_state.py","file_name":"ins_osc_state.py","file_ext":"py","file_size_in_byte":19528,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"561120581","text":"from Voicelab.pipeline.Node import Node\nfrom Voicelab.toolkits.Voicelab.VoicelabNode import VoicelabNode\n\n\nimport crepe\nimport os\nimport pandas as pd\nimport parselmouth\nfrom scipy.io import wavfile\nfrom parselmouth.praat import call\n\nclass MeasurePitchCrepeNode(VoicelabNode):\n\n    def __init__(self, *args, **kwargs):\n        \"\"\"\n        Args:\n            *args:\n            **kwargs:\n        \"\"\"\n        super().__init__(*args, **kwargs)\n\n        # initialize with default arguments\n        self.args = {\n\n        }\n\n    def process(self):\n        try:\n            voice = self.args[\"voice\"]\n            voice.save(\"tmp.wav\", \"WAV\")\n            filename = \"tmp.wav\"\n            sr, audio = wavfile.read(filename)\n            time, frequency, confidence, activation = crepe.predict(audio, sr, viterbi=True)\n            data = (list(zip(time, frequency, confidence)))\n            df = pd.DataFrame(data, columns=[\"time\", \"frequency\", \"confidence\"])\n            pitch_data = df[df.confidence > 0.7]\n            mean_pitch = pitch_data.frequency.mean().item()\n            min_pitch = pitch_data.frequency.min().item()\n            max_pitch = pitch_data.frequency.max().item()\n            stdev_pitch = pitch_data.frequency.std().item()\n            os.remove(\"tmp.wav\")\n            return{\n                \"Mean Pitch Crepe\": mean_pitch,\n                \"Minimum Pitch Crepe\": min_pitch,\n                \"Maximum Pitch Crepe\": max_pitch,\n                \"Standard Deviation Pitch Crepe\": stdev_pitch,\n            }\n        except:\n            return{\n                \"Mean Pitch Crepe\": \"Measure Crepe Failed\",\n                \"Minimum Pitch Crepe\": \"Measure Crepe Failed\",\n                \"Maximum Pitch Crepe\": \"Measure Crepe Failed\",\n                \"Standard Deviation Pitch Crepe\": \"Measure Crepe Failed\",\n            }\n","sub_path":"Voicelab/toolkits/Voicelab/MeasurePitchCrepeNode.py","file_name":"MeasurePitchCrepeNode.py","file_ext":"py","file_size_in_byte":1822,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"486444588","text":"import math\n\nclass Point3:\n    def move(self,x,y):\n        self.x = x\n        self.y = y\n\n    def reset(self):\n        self.x = 0\n        self.y = 0\n\n    def calculate_distance(self, other_point):\n        return math.sqrt(\n            (self.x - other_point.x)**2 +\n            (self.y - other_point.y) ** 2\n        )\n\n\nif __name__ == \"__main__\":\n    print('main called')\n","sub_path":"chapter_2/snippet_03/class_file_wrapped.py","file_name":"class_file_wrapped.py","file_ext":"py","file_size_in_byte":371,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"611781438","text":"import numpy as np \nimport matplotlib.pyplot as plt\nimport csv\nimport datetime\nimport time\nfrom sklearn import linear_model\nfrom scipy import stats\nfrom collections import Counter\n\n# clf = svm.SVC()\n #Definimos las multiples variables que han de ser tomadas en cuenta para nuestra regresión lineal\nx_1 = [] #Cod_calendario\nx_2 = [] #conteo_restaurantes\nx_3 = [] #temp_maxima\nx_4 = [] #temp_minima\nx_5 = [] #precipitacion \nx_6 = [] #eventos\nx_0 = [] #fecha del evento\ny = [] #Esta es la variable a la cual se le hace la regresión y la que queremos predecir\n\n#Se lee el archivo csv y se extraen las respectivas columnas \nwith open('training_set.csv', newline='') as csvfile:\n\tnext(csvfile)\n\treader = csv.reader(csvfile, delimiter=',', quotechar='|')\n\tfor row in reader:\n\t\tx_0.append(row[0])\n\t\tx_1.append(float(row[1]))\n\t\tx_2.append(int(row[3]))\n\t\tx_3.append(float(row[4]))\n\t\tx_4.append(float(row[5]))\n\t\tx_5.append(float(row[6]))\n\t\tx_6.append(row[7])\n\t\ty.append(int(row[2]))\n\ndef DiaSemana(array):\n\tdiassemana=[]\n\t#Sacamos el dia de la semana\n\tfor j in range(0, len(array)):\n\t\tdt = array[j]\n\t\tdias = datetime.datetime.strptime(dt, \"%Y-%m-%d\")\n\t\tdtt= dias.weekday()\n\t\tdiassemana.append(dtt)\n\treturn diassemana\n\nx_0 = DiaSemana(x_0)\n\n#Contamos el número de eventos\nprint(Counter(x_6))\n#Ahora se les asigna un número según el evento del día\ndef saberEvento(eventos):\n\tfor i in range(0,len(eventos)):\n\t\tif eventos[i] == 'Ninguno':\n\t\t\teventos[i] = 0\n\t\telif eventos[i] == 'Lluvia':\n\t\t\teventos[i] = 0\n\t\telif eventos[i] == 'Nieve':\n\t\t\teventos[i] = 1\n\t\telif eventos[i] == 'Lluvia-Nieve':\n\t\t\teventos[i] = 1\n\t\telif eventos[i] == 'Niebla-Lluvia':\n\t\t\teventos[i] = 0\n\t\telif eventos[i] == 'Lluvia-Tormenta':\n\t\t\teventos[i] = 0\n\t\telif eventos[i] == 'Niebla-Nieve':\n\t\t\teventos[i] = 1\n\t\telif eventos[i] == 'Niebla':\n\t\t\teventos[i] = 0\n\t\telif eventos[i] == 'Niebla-Lluvia-Nieve':\n\t\t\teventos[i] = 1\n\treturn eventos\n\nx_6 = saberEvento(x_6)\n\n#Se pasa todo a arreglos de numpy en vez de listas\nfecha = np.array(x_0)\nCod_calendario = np.array(x_1)\nconteo_restaurantes = np.array(x_2)\ntemp_maxima = np.array(x_3)\ntemp_minima = np.array(x_4)\nprecipitacion = np.array(x_5)\neventos = np.array(x_6)\n\n\n#Así pues, el numero de variables a considerar son n=7\n\nX_train = np.zeros((len(eventos),6))\nfor i in range(0,len(eventos)):\n\tX_train[i][0], X_train[i][1], X_train[i][2], X_train[i][3], X_train[i][4], X_train[i][5] = fecha[i], Cod_calendario[i], conteo_restaurantes[i], temp_maxima[i], temp_minima[i], eventos[i] \n#print(X_train)\n\nY_train = np.array(y)\n\n\n#Ahora se hace un fit con los datos del training usando el paquete de sklearn \nregr = linear_model.Ridge (alpha = .5)\nregr.fit(X_train, Y_train)\nprint('Coeficientes: \\n', regr.coef_)\n\n\n##-------------------------------------------------------------------------------------\n## DATOS TEST \n##-------------------------------------------------------------------------------------\n#Se abre ahora el paquete de los datos test con los que se quiere hacer las predicciones \nx_1_test = [] #Cod_calendario\nx_2_test = [] #conteo_restaurantes\nx_3_test = [] #temp_maxima\nx_4_test = [] #temp_minima\nx_5_test = [] #precipitacion \nx_6_test = [] #eventos\nx_0_test = [] #fecha test \nwith open('test_set.csv', newline='') as csvfile1:\n\tnext(csvfile1)\n\treader = csv.reader(csvfile1, delimiter=',', quotechar='|')\n\tfor row in reader:\n\t\tx_1_test.append(float(row[1]))\n\t\tx_2_test.append(int(row[2]))\n\t\tx_3_test.append(float(row[3]))\n\t\tx_4_test.append(float(row[4]))\n\t\tx_5_test.append(float(row[5]))\n\t\tx_6_test.append(row[6])\n\t\tx_0_test.append((row[0]))\n\nx_6_test = saberEvento(x_6_test)\nx_0_test1 = DiaSemana(x_0_test)\n\nfechas_test = np.array(x_0_test1)\nCod_calendario_test = np.array(x_1_test)\nconteo_restaurantes_test = np.array(x_2_test)\ntemp_maxima_test = np.array(x_3_test)\ntemp_minima_test = np.array(x_4_test)\nprecipitacion_test = np.array(x_5_test)\neventos_test = np.array(x_6_test)\n\nX_test = np.zeros((len(eventos_test),6))\nfor i in range(0,len(eventos_test)):\n\tX_test[i][0], X_test[i][1], X_test[i][2], X_test[i][3], X_test[i][4], X_test[i][5] = fechas_test[i], Cod_calendario_test[i], conteo_restaurantes_test[i], temp_maxima_test[i], temp_minima_test[i], eventos_test[i] \n# print(X_train)\n\nprediccion = regr.predict(X_test)\nprint('Arreglo con los valores de las predicciones: \\n ', prediccion)\n\nwith open('conteo_ordenes_prediccion.csv', 'w', newline='') as f:\n\tfieldnames = ['fecha', 'conteo_ordenes']\n\twriter = csv.DictWriter(f, fieldnames=fieldnames)\n\twriter.writeheader()\n\tfor i in range(0, len(prediccion)):\n\t\twriter.writerow({ 'fecha' :x_0_test[i], 'conteo_ordenes' : prediccion[i]})\n\n\n# Plot outputs\nplt.subplot(3,2,1)\nplt.scatter(conteo_restaurantes_test, prediccion,  color='OrangeRed')\nslope, intercept, r_value, p_value, std_err = stats.linregress(conteo_restaurantes_test, prediccion)\ntendencia = intercept + slope *conteo_restaurantes_test\nplt.title('Conteo Restaurantes')\nplt.plot(conteo_restaurantes_test, tendencia, color='Moccasin')\n\nplt.subplot(3,2,2)\nplt.scatter(fechas_test, prediccion,  color='Crimson')\n# slope1, intercept1, r_value1, p_value1, std_err1 = stats.linregress(fechas_test, prediccion)\n# tendencia1 = intercept1 + slope1*fechas_test\nplt.title('Fechas')\n# plt.plot(fechas_test, tendencia1, color='Aquamarine')\n\nplt.subplot(3,2,3)\nplt.scatter(Cod_calendario_test, prediccion,  color='OliveDrab')\n# slope2, intercept2, r_value2, p_value2, std_err2 = stats.linregress(Cod_calendario_test, prediccion)\n# tendencia2 = intercept2 + slope2*Cod_calendario_test\nplt.title('Dias Festivos')\n# plt.plot(Cod_calendario_test, tendencia2, color='Sienna')\n\nplt.subplot(3,2,4)\nplt.scatter(temp_minima_test, prediccion,  color='ForestGreen')\nslope3, intercept3, r_value3, p_value3, std_err3 = stats.linregress(temp_minima_test, prediccion)\ntendencia3 = intercept3 + slope3*temp_minima_test\nplt.title('Temp Minima')\nplt.plot(temp_minima_test, tendencia3, color='Peru')\n\nplt.subplot(3,2,5)\nplt.scatter(temp_maxima_test, prediccion,  color='Maroon')\nslope4, intercept4, r_value4, p_value4, std_err4 = stats.linregress(temp_maxima_test, prediccion)\ntendencia4 = intercept4 + slope4*temp_maxima_test\nplt.title('Temp Maxima')\nplt.plot(temp_maxima_test, tendencia4, color='SteelBlue')\n\nplt.subplot(3,2,6)\nplt.scatter(eventos_test, prediccion,  color='Maroon')\n# slope4, intercept4, r_value4, p_value4, std_err4 = stats.linregress(eventos_test, prediccion)\n# tendencia4 = intercept4 + slope4*eventos_test\nplt.title('Evento del dia')\n# plt.plot(eventos_test, tendencia4, color='SteelBlue')\n\nplt.show()\n","sub_path":"PrediccionConteos.py","file_name":"PrediccionConteos.py","file_ext":"py","file_size_in_byte":6526,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"336242042","text":"# uncompyle6 version 3.6.7\n# Python bytecode 2.4 (62061)\n# Decompiled from: Python 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)]\n# Embedded file name: build/bdist.linux-i686/egg/pocoo/pkg/pony/plugins.py\n# Compiled at: 2006-12-26 17:18:07\n__doc__ = '\\n    pocoo.pkg.pony.plugins\\n    ~~~~~~~~~~~~~~~~~~~~~~\\n\\n    Display information about packages and components.\\n\\n    :copyright: 2006 by Armin Ronacher, Georg Brandl.\\n    :license: GNU GPL, see LICENSE for more details.\\n'\nfrom os.path import abspath\nfrom pocoo import Component\nfrom pocoo.application import RequestHandler\nfrom pocoo.http import Response, TemplateResponse\n\nclass PluginPony(RequestHandler):\n    __module__ = __name__\n    handler_regexes = [\n     'pony/plugins/$']\n\n    def handle_request(self, req):\n        pkgname = req.args.pop('pkg', '')\n        compname = req.args.pop('comp', '')\n        if pkgname and compname:\n            return Response(pkgname)\n        elif pkgname:\n            return Response(compname + 'x')\n        else:\n            packages = []\n            for pkgname in sorted(self.ctx.pkgmanager.pkgs):\n                pkg = {'name': pkgname, 'loaded': pkgname in self.ctx.packages, 'path': abspath(self.ctx.pkgmanager.pkgs[pkgname])}\n                if pkg['loaded']:\n                    pkg.update(self.ctx.packagemeta[pkgname])\n                packages.append(pkg)\n\n            plugintypes = []\n            for comp_type in Component.__subclasses__():\n                plugintypes.append({'name': comp_type.__name__, 'module': comp_type.__module__.replace(self.ctx.pkg_prefix, 'pocoo.pkg'), \n                   'plugins': [ comp.__module__.split('.')[2] + '::' + comp.__class__.__name__ for comp in self.ctx.get_components(comp_type) ], 'doc': comp_type.__doc__})\n\n            plugintypes.sort(key=lambda x: x['name'].lower())\n            plugins = {}\n            for comp_type in Component.__subclasses__():\n                for comp in self.ctx.get_components(comp_type):\n                    plugins[comp] = {'name': comp.__class__.__name__, 'package': comp.__module__.split('.')[2], 'types': [ c.__name__ for c in comp.__class__.comptypes ], 'doc': comp.__class__.__doc__}\n\n            plugins = plugins.values()\n            plugins.sort(key=lambda x: x['name'].lower())\n            regexes = []\n            for comp in self.ctx.get_components(RequestHandler):\n                for regex in comp.handler_regexes:\n                    if not isinstance(regex, basestring):\n                        (regex, _) = regex\n                    regexes.append({'regex': regex, 'component': comp.__class__.__name__, 'package': comp.__module__.split('.')[2]})\n\n            regexes.sort(key=lambda x: x['regex'].lower())\n            return TemplateResponse('plugins.html', packages=packages, plugins=plugins, regexes=regexes, plugintypes=plugintypes)","sub_path":"pycfiles/pocoui-1.0.79.tar/plugins.py","file_name":"plugins.py","file_ext":"py","file_size_in_byte":2866,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"222096728","text":"import copy\nimport datetime\ntry:\n    import cPickle as pickle\nexcept ImportError:\n    import pickle\n\nfrom holidays import Holidays\nfrom days import Days\nfrom time_cls import Time\nfrom util import next_weekday\n\n\nclass Business(object):\n    CLOSEST_LAST = 0\n    CLOSEST_NEXT = 1\n\n    def __init__(self, days, holidays=None, timezone=None):\n        if isinstance(holidays, (list, tuple)):\n            holidays = Holidays(holidays)\n        elif not holidays:\n            holidays = Holidays()\n        elif isinstance(holidays, Holidays):\n            raise ValueError('The holidays parameter must be an array of \\DateTime objects, an instance of Business\\Holidays or null.')\n\n        self.set_days(days)\n        self.holidays = holidays\n        self.timezone = timezone\n\n    def closest(self, date, mode=CLOSEST_NEXT):\n        tmp_date = copy.copy(date)\n        if self.CLOSEST_LAST == mode:\n            return self.get_closest_date_before(tmp_date)\n        return self.get_closest_date_after(tmp_date)\n\n    def within(self, date):\n        tmp_date = copy.copy(date)\n        if self.holidays.is_holiday(tmp_date):\n            day = self.get_day(tmp_date.isoweekday())\n            if day is not None:\n                return day.is_time_within_opening_hours(Time.fromDate(tmp_date), tmp_date)\n        return False\n\n    def timeline(self, start, end, interval):\n        if start >= end:\n            raise ValueError('The start date must be before the end date.')\n        tmp_start = copy.copy(start)\n        tmp_end = copy.copy(end)\n        dates = []\n        last_date = tmp_start\n        while True:\n            date = self.get_closest_date_after(last_date)\n            if date > tmp_end:\n                break\n            dates.append(date)\n            last_date += datetime.timedelta(days=interval)\n        return dates\n\n    def timedelta(self, start, end):\n        \"\"\"\n        Returns a datetime.timedelta with the number of full business days\n        and business time between d1 and d2\n        \"\"\"\n        time = datetime.timedelta()\n        if start >= end:\n            return time\n\n        # within the same day\n        if start.date() == end.date():\n            if self.holidays.is_holiday(start):\n                return time\n\n            day_of_week = start.isoweekday()\n            day = self.get_day(day_of_week)\n            # we doesn't work at `start` date (may be it's Sat or Sun)\n            if day is None:\n                return time\n            start_time = Time.fromDate(start)\n            end_time = Time.fromDate(end)\n            time = day.timedelta(start_time, end_time)\n        else:\n            dates = []\n            if end.date() - start.date() == datetime.timedelta(days=1):\n                start_day = self.get_day(start.isoweekday())\n                end_day = self.get_day(end.isoweekday())\n                dates = [\n                    [start, Time.fromDate(start), start_day.get_closing_time(start)],\n                    [end, end_day.get_opening_time(end), Time.fromDate(end)],\n                ]\n            else:\n                start_day = self.get_day(start.isoweekday())\n                end_day = self.get_day(end.isoweekday())\n                dates = [\n                    [start, Time.fromDate(start), start_day.get_closing_time(start)],\n                    [end, end_day.get_opening_time(end), Time.fromDate(end)],\n                ]\n                last_date = start\n                while True:\n                    date = self.get_closest_date_after(last_date)\n                    if date.date() >= end.date():\n                        break\n                    day = self.get_day(date.isoweekday())\n                    dates.append([date, day.get_opening_time(date), day.get_closing_time(date)])\n                    last_date += datetime.timedelta(days=1)\n\n            for date, start_time, end_time in dates:\n                if self.holidays.is_holiday(date):\n                    continue\n                day_of_week = date.isoweekday()\n                day = self.get_day(day_of_week)\n                # we doesn't work at `date` day (may be it's Sat or Sun)\n                if day is None:\n                    continue\n                time += day.timedelta(start_time, end_time)\n\n        return time\n\n    def serialize(self):\n        return pickle.dumps([self.days, self.holidays, self.timezone], 2)\n\n    def unserialize(self, serialized):\n        data = pickle.loads(serialized)\n        self.days = data[0]\n        self.holidays = data[1]\n        self.timezone = data[2]\n\n    def get_closest_date_before(self, date):\n        tmp_date = copy.copy(date)\n        day_of_week = tmp_date.isoweekday()\n        time = Time.fromDate(tmp_date)\n        if self.holidays.is_holiday(tmp_date):\n            day = self.get_day(day_of_week)\n            if day is not None:\n                closest_time = day.get_closest_opening_time_before(time, tmp_date)\n                if closest_time is not None:\n                    return tmp_date.replace(hour=closest_time.get_hours(), minute=closest_time.get_minutes())\n        tmp_date = self.get_date_before(tmp_date)\n        while self.holidays.is_holiday(tmp_date):\n            tmp_date = self.get_date_before(tmp_date)\n        closest_day = self.get_closest_day_before(tmp_date.isoweekday())\n        closing_time = closest_day.get_closing_time(tmp_date)\n        return tmp_date.replace(hour=closing_time.get_hours(), minute=closing_time.get_minutes())\n\n    def get_date_before(self, date):\n        tmp_date = copy.copy(date)\n        tmp_date -= datetime.timedelta(days=1)\n        day_of_week = tmp_date.isoweekday()\n        closest_day = self.get_closest_day_before(day_of_week)\n        if closest_day.get_day_of_week() != day_of_week:\n            #php: $tmpDate->modify(sprintf('last %s', Days::toString($closestDay->getDayOfWeek())));\n            tmp_date = prev_weekday(tmp_date, closest_day.get_day_of_week())\n        return tmp_date\n\n    def get_closest_date_after(self, date):\n        tmp_date = copy.copy(date)\n        day_of_week = tmp_date.isoweekday()\n        time = Time.fromDate(tmp_date)\n        if self.holidays.is_holiday(tmp_date):\n            day = self.get_day(day_of_week)\n            if day is not None:\n                closest_time = day.get_closest_opening_time_after(time, tmp_date)\n                if closest_time is not None:\n                    return tmp_date.replace(hour=closest_time.get_hours(), minute=closest_time.get_minutes())\n        tmp_date = self.get_date_after(tmp_date)\n        while self.holidays.is_holiday(tmp_date):\n            tmp_date = self.get_date_after(tmp_date)\n        closest_day = self.get_closest_day_before(tmp_date.isoweekday())\n        closing_time = closest_day.get_opening_time(tmp_date)\n        return tmp_date.replace(hour=closing_time.get_hours(), minute=closing_time.get_minutes())\n\n    def get_date_after(self, date):\n        tmp_date = copy.copy(date)\n        tmp_date += datetime.timedelta(days=1)\n        day_of_week = tmp_date.isoweekday()\n        closest_day = self.get_closest_day_after(day_of_week)\n        if closest_day.get_day_of_week() != day_of_week:\n            #php: $tmpDate->modify(sprintf('next %s', Days::toString($closestDay->getDayOfWeek())));\n            tmp_date = next_weekday(tmp_date, closest_day.get_day_of_week())\n        return tmp_date\n\n    def get_closest_day_before(self, day_number):\n        day = self.get_day(day_number)\n        if day is not None:\n            return day\n        return self.get_day_before(day_number)\n\n    def get_closest_day_after(self, day_number):\n        day = self.get_day(day_number)\n        if day is not None:\n            return day\n        return self.get_day_after(day_number)\n\n    def get_day_before(self, day_number):\n        tmp_day_number = day_number\n        for i in xrange(0, 6):\n            if tmp_day_number == Days.MONDAY:\n                tmp_day_number = Days.SUNDAY\n            else:\n                tmp_day_number -= 1\n            day = self.get_day(tmp_day_number)\n            if day is not None:\n                return day\n        return self.get_day(day_number)\n\n    def get_day_after(self, day_number):\n        tmp_day_number = day_number\n        for i in xrange(0, 6):\n            if Days.SUNDAY == tmp_day_number:\n                tmp_day_number = Days.MONDAY\n            else:\n                tmp_day_number += 1\n            day = self.get_day(tmp_day_number)\n            if day is not None:\n                return day\n        return self.get_day(day_number)\n\n    def get_day(self, day_number):\n        return self.days.get(day_number)\n\n    def add_day(self, day):\n        self.days[day.get_day_of_week()] = day\n\n    def set_days(self, days):\n        self.days = {}\n        for day in days:\n            self.add_day(day)\n","sub_path":"businesstime/business.py","file_name":"business.py","file_ext":"py","file_size_in_byte":8734,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"396407322","text":"import cv2\nimport numpy as np\n#from task1_utils import segmentBackground, predictBoundingBox\nfrom task1_utils import segmentBackground_v2 as segmentBackground\n#from task1_utils import predictBoundingBox, predictBlueBoundingBox\nfrom task1_utils_v2 import predictBlueTSBoundingBox, predictBrownBoundingBox, predictRedTSBoundingBox, predictYellowTSBoundingBox, predictLargeBoundingBox\nimport matplotlib.pyplot as plt\n\ndef generateBboxes(image):\n    clasterNum      = 2\n    n_init          = 50\n    max_iter        = 300\n\n    lower_factor    = 1.5\n    upper_factor    = 1.5\n    kernel          = 5\n    iterations      = 4\n\n    cannyLowerThres = 20\n    cannyUpperThres = 300\n\n    imgHSV              = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\n    \n    combinedMask        = segmentBackground(imageHSV = imgHSV, clasterNum = clasterNum, n_init = n_init, max_iter = max_iter, lower_factor = lower_factor, upper_factor = upper_factor, kernel = kernel, iterations = iterations)\n    \n    combinedMask = np.uint8(combinedMask)\n    \n    edges = cv2.Canny(combinedMask,cannyLowerThres,cannyUpperThres)\n    #edges = cv2.dilate(edges , np.ones((kernel,kernel)), iterations = 1)\n    #edges = cv2.morphologyEx(edges, cv2.MORPH_OPEN, np.ones((kernel,kernel)), iterations=1)\n    cv2.imshow('CANNY', edges)\n\n    '''\n    kernelOpenClose = np.ones((kernel,kernel))\n    edges = cv2.Canny(combinedMask,cannyLowerThres,cannyUpperThres)\n    edgesClose = cv2.morphologyEx(edges, cv2.MORPH_CLOSE, kernelOpenClose, iterations = 1)\n    edgesOpen = cv2.morphologyEx(edgesClose, cv2.MORPH_OPEN, kernelOpenClose, iterations = 1)\n\n    edgesCopy = np.copy(edges)\n    edgesContours, edgesHierarchy = cv2.findContours(edgesCopy, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)\n\n    edgesOpenCopy = np.copy(edgesOpen)\n    edgesOpenContours, edgesOpenHierarchy = cv2.findContours(edgesOpenCopy, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)\n    cv2.drawContours(image, edgesContours, -1, (0, 255, 0), 3)\n    cv2.imshow('CANNY', edges)\n    cv2.imshow('CANNYOPEN', edgesOpen)\n    cv2.imshow('CONTOUR', image)\n    '''\n    '''\n    #combinedMask[:80][:]        = 255\n\n    imageIdx            =(combinedMask == 255)\n    maskedImg           = np.copy(image)\n    maskedImg[imageIdx] = 0\n\n    maskedImgHSV        = cv2.cvtColor(maskedImg, cv2.COLOR_BGR2HSV)\n\n    edgesMask, edgesCloseMask, edgesImageMask, edgesCloseImageMask, origMaskImg, origMaskImgClose, bboxes = predictBoundingBox(origImg = image, imageHSV = maskedImgHSV, kernel = 5, iterations = iterations, cannyLowerThres = cannyLowerThres, cannyUpperThres = cannyLowerThres)\n    '''\n    return combinedMask#, origMaskImgClose, bboxes\n\nimport glob\n\nrgbImagePath1 \t= glob.glob('../CVS_HW_FILES/g1/rgb/*.jpg')\t\t# 20 db\nrgbImagePath2 \t= glob.glob('../CVS_HW_FILES/g2/rgb/*.jpg')\t\t# 16 db\nrgbImagePath3 \t= glob.glob('../CVS_HW_FILES/g3/rgb/*.jpg')\t\t# 19 db\nrgbImagePath4 \t= glob.glob('../CVS_HW_FILES/g4/rgb/*.jpg')\t\t# 15 db\n\ntrainPath \t\t= rgbImagePath1 + rgbImagePath2 + rgbImagePath3\t# 55 db || eltérő fényviszonyos képek indexei: 18, 34, 53\nvalPath\t\t\t= rgbImagePath4\t\t\t\t\t\t\t\t\t# 15 db || eltérő fényviszonyos képek indexei: 12\n\n\nimage = cv2.imread(trainPath[35])#25   #35-nél nem kell nms                       20!!!!\n\n\nclasterNum      = 2\nn_init          = 50\nmax_iter        = 300\n\nlower_factor    = 1.5\nupper_factor    = 1.5\nkernel          = 5\niterations      = 4\ncannyLowerThres = 10\ncannyUpperThres = 50\n\n\nbrown_lower = np.array([10,100,20], dtype=np.uint8)\nbrown_upper = np.array([20,255,200], dtype=np.uint8)\ngreen_lower = np.array([40, 52, 72], dtype=np.uint8)\ngreen_upper = np.array([80, 255, 255], dtype=np.uint8)\n\nyellow_lower = np.array([20, 100, 100], dtype = np.uint8)\nyellow_upper = np.array([30, 255, 255], dtype = np.uint8)\nred_lower1 = np.array([0,70,50], dtype=np.uint8)\nred_upper1 = np.array([10, 255, 255], dtype=np.uint8)\nred_lower2 = np.array([170,70,50], dtype=np.uint8)\nred_upper2 = np.array([180, 255, 200], dtype=np.uint8)\nblue_lower = np.array([70,40,0],np.uint8) #100, 150\nblue_upper = np.array([140,255,255],np.uint8)\n\n#blueBB = predictBlueTSBoundingBox(image, blue_lower, blue_upper, cannyLowerThres, cannyUpperThres)\n#redBB = predictRedTSBoundingBox(image, blueBB, red_lower1, red_upper1, red_lower2, red_upper2, cannyLowerThres, cannyUpperThres)\n#yellowBB = predictYellowTSBoundingBox(image, blueBB, redBB, yellow_lower, yellow_upper, cannyLowerThres, cannyUpperThres)\n\n#img = predictLargeBoundingBox(image, blueBB, yellowBB, yellow_lower, yellow_upper, cannyLowerThres, cannyUpperThres)\n\n\n\n'''\n#combinedMask = generateBboxes(image)\nimgHSV              = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\n#print(imgHSV[176][300][:])\nh,s,v = cv2.split(imgHSV)                \n\n#h = cv2.equalizeHist(h)\n#s = cv2.equalizeHist(s)\n#v = cv2.equalizeHist(v)\nimgHSV = cv2.merge([h,s,v])\n\n\nblueMask = cv2.inRange(imgHSV, blue_lower, blue_upper)\n#blueMask = cv2.dilate(blueMask , np.ones((5,5)), iterations = 1)\n#blueMask = cv2.morphologyEx(blueMask, cv2.MORPH_CLOSE, np.ones((3,3)), iterations = 3)\n#blueMask = cv2.morphologyEx(blueMask, cv2.MORPH_OPEN, np.ones((2,2)), iterations = 1)\n#blueMask = cv2.dilate(blueMask , np.ones((4,4)), iterations = 3)\n#blueMask = cv2.erode(blueMask , np.ones((3,3)), iterations = 1)\nblueMask = np.uint8(blueMask)\nblue_edges = cv2.Canny(blueMask,cannyLowerThres,cannyUpperThres)\n#blue_edges_close = cv2.morphologyEx(blue_edges, cv2.MORPH_CLOSE, np.ones((5,5)), iterations = 3)\n#blue_edges_open = cv2.morphologyEx(blue_edges, cv2.MORPH_OPEN, np.ones((5,5)), iterations = 1)\n\n\n#blueBB = predictBlueBoundingBox(image, blue_lower, blue_upper, cannyLowerThres, cannyUpperThres)\n\nmask, firstLine = predictBrownBoundingBox(image, brown_lower, brown_upper, cannyLowerThres, cannyUpperThres)\n#combinedMask = segmentBackground(imageHSV = imgHSV, firstLine = firstLine, clasterNum = clasterNum, n_init = n_init, max_iter = max_iter, lower_factor = lower_factor, upper_factor = upper_factor, kernel = kernel, iterations = iterations)\n\nmaskIdx               = (mask == 255)\ninvMask               = np.ones((mask.shape[0], mask.shape[1]), dtype=np.uint8) * 255\ninvMask[maskIdx]      = 0\ninvMask               = np.clip(invMask, 0, 1)\n\n#productMask           = invMask * combinedMask\n\n#productMask           = np.clip(productMask, 0, 255)\n'''\n\n\n'''\nmask1 = cv2.inRange(imgHSV, red_lower1, red_upper1)\nmask2 = cv2.inRange(imgHSV, red_lower2, red_upper2)\nmask3 = cv2.inRange(imgHSV, brown_lower, brown_upper)\nmask4 = cv2.inRange(imgHSV, yellow_lower, yellow_upper)\nmask5 = cv2.inRange(imgHSV, green_lower, green_upper)\nmask = mask1 + mask2 + mask3# + mask4# + mask5\n\nmask = np.clip(mask, 0, 255)\n\nmaskIdx               = (mask == 255)\ninvMask               = np.ones((mask.shape[0], mask.shape[1]), dtype=np.uint8) * 255\ninvMask[maskIdx]      = 0\ninvMask               = np.clip(invMask, 0, 1)\n\n#productMask           = invMask * combinedMask\n\n#productMask           = np.clip(productMask, 0, 255)\n\n\nhHist = cv2.calcHist(imgHSV,[0],None,[180],[0,180]) # kép, channel, mask, bin, range\nsHist = cv2.calcHist(imgHSV,[1],None,[255],[0,255])\nvHist = cv2.calcHist(imgHSV,[2],None,[255],[0,255])\n#print(np.amax(hist))\n#print(type(hist))\n\nhsv = cv2.merge([h,s,v])\n#bgr = cv2.cvtColor(hsv,cv2.COLOR_HSV2BGR)\n\n#plt.plot(hHist)\n#plt.plot(sHist)\n#plt.plot(vHist)\n#plt.xlim([0,500])\n#plt.show()\n'''\n\n\n'''\nfor i in range(len(trainPath)):\n    image = cv2.imread(trainPath[i])\n    blueBB = predictBlueTSBoundingBox(image, blue_lower, blue_upper, cannyLowerThres, cannyUpperThres)\n    for box in blueBB:\n        x1, y1, x2, y2 = box[0], box[1], box[2], box[3]        \n        cv2.rectangle(image, (x1,y1), (x2,y2), (0,0,255), 2)\n    cv2.imwrite('../PredictionsBlue/{}.jpg'.format(i), image)\n'''\n'''\nfor i in range(len(trainPath)):\n    image = cv2.imread(trainPath[i])\n    mask, firstLine = predictBrownBoundingBox(image, brown_lower, brown_upper, cannyLowerThres, cannyUpperThres)\n    \n    cv2.imwrite('../PredictionsRedTest/{}.jpg'.format(i), mask)\n'''\n'''\nfor i in range(len(trainPath)):\n    image = cv2.imread(trainPath[i])\n    blueBB = predictBlueBoundingBox(image, blue_lower, blue_upper, cannyLowerThres, cannyUpperThres)\n    img, redBB = predictRedTSBoundingBox(image, blueBB, red_lower1, red_upper1, red_lower2, red_upper2, cannyLowerThres, cannyUpperThres)\n    \n    cv2.imwrite('../PredictionsRedBlueTest/{}.jpg'.format(i), img)\n'''\n'''\nfor i in range(len(trainPath)):\n    image = cv2.imread(trainPath[i])\n    blueBB = predictBlueTSBoundingBox(image, blue_lower, blue_upper, cannyLowerThres, cannyUpperThres)\n    redBB = predictRedTSBoundingBox(image, blueBB, red_lower1, red_upper1, red_lower2, red_upper2, cannyLowerThres, cannyUpperThres)\n    img, yellowBB = predictYellowTSBoundingBox(image, blueBB, redBB, yellow_lower, yellow_upper, cannyLowerThres, cannyUpperThres)\n    \n    cv2.imwrite('../PredictionsYellowTest/{}.jpg'.format(i), img)\n'''\n'''\nfor i in range(len(trainPath)):\n    image = cv2.imread(trainPath[i])\n    blueBB = predictBlueTSBoundingBox(image, blue_lower, blue_upper, cannyLowerThres, cannyUpperThres)\n    redBB = predictRedTSBoundingBox(image, blueBB, red_lower1, red_upper1, red_lower2, red_upper2, cannyLowerThres, cannyUpperThres)\n    yellowBB = predictYellowTSBoundingBox(image, blueBB, redBB, yellow_lower, yellow_upper, cannyLowerThres, cannyUpperThres)\n    img = predictLargeBoundingBox(i, image, blueBB, yellowBB, yellow_lower, yellow_upper, cannyLowerThres, cannyUpperThres)\n    \n    cv2.imwrite('../PredictionsLargeRed/{}.jpg'.format(i), img)\n'''\n\nfor i in range(len(trainPath)):\n    image = cv2.imread(trainPath[i])\n    blueBB = predictBlueTSBoundingBox(image, blue_lower, blue_upper, cannyLowerThres, cannyUpperThres)\n    redBB = predictRedTSBoundingBox(image, blueBB, red_lower1, red_upper1, red_lower2, red_upper2, cannyLowerThres, cannyUpperThres)\n    yellowBB = predictYellowTSBoundingBox(image, blueBB, redBB, yellow_lower, yellow_upper, cannyLowerThres, cannyUpperThres)\n    img = predictLargeBoundingBox(image, blueBB, redBB, yellowBB, yellow_lower, yellow_upper, cannyLowerThres, cannyUpperThres)\n    \n    cv2.imwrite('../PredictionsLargeTest/{}.jpg'.format(i), img)\n\n\n\n#cv2.imshow('COLOR BASED MASK', mask)\n#cv2.imshow('COLOR BASED INVERTED MASK', invMask*255)\n#cv2.imshow('BLUE BB', blueBB)\n#cv2.imshow('BLUE MASK', blueMask)\n#cv2.imshow('BLUE CANNY', blue_edges)\n#cv2.imshow('BLUE CANNY CLOSE', blue_edges_close)\n#cv2.imshow('BLUE CANNY OPEN', blue_edges_open)\n#cv2.imshow('ORIGINAL', image)\n#cv2.imshow('CLUSTER BASED MASK', combinedMask)\n#cv2.imshow('PRODUCT MASK', productMask)\n#cv2.imshow('BBOX', origMaskImgClose)\n#cv2.waitKey(0)","sub_path":"task1_final.py","file_name":"task1_final.py","file_ext":"py","file_size_in_byte":10608,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"602327256","text":"import allure\nfrom appium.webdriver.common.mobileby import MobileBy\n\nfrom UIframework.page.logger import log\n\n\ndef handle_black(func):\n    def run(*args,**kwargs):\n        # 弹框出现后，进行处理，\n        black_list = [\"//*[@resource-id='com.xueqiu.android:id/iv_close']\"]\n        #相当于self\n        instance = args[0]\n        #args = args[]\n        try:\n            log.debug(\"find\" + args[2])\n            return func(*args,**kwargs)\n        except Exception:\n            # instance.screenshot()\n            # with open(\"./tmp.png\",\"rb\") as f:\n            #     allure.attach(f.read(),attachment_type=allure.attachment_type.PNG)\n\n            allure.attach(instance.screenshot(), attachment_type=allure.attachment_type.PNG)\n\n            # 捕获异常(自动化时出现的各种弹窗)\n            for ele_xpath in black_list:\n                # 查看异常是否存在\n                eles = instance.finds(MobileBy.XPATH, ele_xpath)\n                # 存在异常就杀掉\n                if len(eles) > 0:\n                    eles[0].click()\n                    return func(*args,**kwargs)\n    return run\n","sub_path":"UIframework/page/handle_black_list.py","file_name":"handle_black_list.py","file_ext":"py","file_size_in_byte":1124,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"52924496","text":"import os\n\nfrom flask import session, Flask, request, abort, redirect\nfrom flask_session import Session\nfrom tempfile import mkdtemp\nfrom werkzeug.exceptions import default_exceptions\nimport random, string\nfrom Commons.helpers import apology, login_required\n\n# import pymysql\n# from flask_sqlalchemy import SQLAlchemy\n\n# driver = 'mysql'\n# db_username = 'root'\n# db_passwd = 'red@1night'\n# db_host = 'localhost'\n# db_name = 'project-k'\n\n# pymysql.install_as_MySQLdb()\n# app = Flask(__name__)\n# app.secret_key = \"SOSECRET\"\n# app.config['SQLALCHEMY_DATABASE_URI'] = '{}://{}:{}@{}/{}'.format(driver, db_username, db_passwd, db_host, db_name)\n# app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = True\n# app.config['MINIFY_PAGE'] = True\n# db = SQLAlchemy(app)\n\n\n#Configure application\napp = Flask(__name__)\napp.secret_key = b'_5#y2L\"F4Q8z\\n\\xec]/'\n\n#Ensure template are auto-reloaded\napp.config[\"TEMPLATES_AUTO_RELOAD\"] = True\n\n@app.before_request\ndef csrf_protect():\n    if request.method == \"POST\":\n        token = session.pop('_csrf_token', None)\n        if not token or token != request.form.get('_csrf_token'):\n            return abort(403)\n\n\ndef generate_csrf_token():\n    if '_csrf_token' not in session:\n        session['_csrf_token'] = ''.join(\n            random.SystemRandom().choice(string.ascii_uppercase + string.digits) for _ in range(30))\n    return session['_csrf_token']\n\n\napp.jinja_env.globals['csrf_token'] = generate_csrf_token\napp.jinja_env.globals['csrf_token'] = generate_csrf_token\n\n#Ensure responses arent cache\n@app.after_request\ndef after_request(response):\n\tresponse.headers[\"Cache-Control\"] = \"no-cache, no-store, must-revalidate\"\n\tresponse.headers[\"Expires\"] = 0\n\tresponse.headers[\"Pragma\"] = \"no-cache\"\n\treturn response\n\n#Configure Session to use filesystem\napp.config[\"SESSION_FILE_DIR\"] = mkdtemp()\napp.config[\"SESSION_PERMANENT\"] = False\napp.config[\"SESSION_TYPE\"] = \"filesystem\"\n\n# @app.route(\"/\")\n# @login_required\n# def index():\n#     pass\ndef errorhandler(e):\n    \"\"\"Handle error\"\"\"\n    return apology(e.name, e.code)\n\nfrom Core import Router\nfrom Core import Model\n\n# listen for errors\n# for code in default_exceptions:\n#     app.errorhandler(code)(errorhandler)\n#     print(code)","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2212,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"136605349","text":"'''\nCreated on Mar 10, 2015\n\n@author: Adrian\n'''\n\nimport requests\nimport sys\nimport Constants\n\nfrom imgurpython import ImgurClient\nfrom UsefulFunctions import getJson\nfrom UsefulFunctions import sendPost\n\n\n\nclient_id = Constants.CLIENT_ID\nclient_secret = Constants.CLIENT_SECRET\naccess_token = Constants.ACCESS_TOKEN\nrefresh_token = Constants.REFRESH_TOKEN\n\nobj = getJson('http://localhost:3000/get_pages_for_imgur')\n\nif len(obj)>0:\n    \n    # Double check and make sure the site is down\n    url = obj[0]['url']\n    headers = {'User-Agent':'Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2228.0 Safari/537.36'}\n    r = requests.get(url, headers=headers)\n    \n    if r.status_code == 200:\n        temp_obj = {'status':'U','post_id':obj[0]['post_id']}\n        sendPost('http://localhost:3000/update_site_status', temp_obj)\n        sys.exit()\n    \n    # If it didn't return a 200 code, post to imgur.\n    client = ImgurClient(client_id, client_secret, access_token, refresh_token)\n    image_location = Constants.BASE_LOCATION_OF_IMAGES + str(obj[0]['post_id']) + '.jpg'\n    resp = client.upload_from_path(image_location, config=None, anon=False)\n    sendPost('http://localhost:3000/post_imgur_url',{'post_id':obj[0]['post_id'],'imgur_url':resp['link']})","sub_path":"PostOnImgur.py","file_name":"PostOnImgur.py","file_ext":"py","file_size_in_byte":1283,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"289605634","text":"\n# diagram.py\n\n# diagrams docs - https://diagrams.mingrammer.com/\n\nfrom diagrams import Cluster, Diagram\nfrom diagrams.k8s.compute import Pod\nfrom diagrams.k8s.podconfig import CM\nfrom diagrams.k8s.storage import PersistentVolume, PersistentVolumeClaim\n\nwith Diagram(\"wizter\", show=False):\n\n    \n    with Cluster(\"cluster\"):\n        pv = PersistentVolume(\"mongo-pv\")\n        with Cluster(\"wizards namespace\"):\n            client = Pod(\"client\") \n            client_cm = CM(\"client\")\n            server = Pod(\"server\") \n            server_cm = CM(\"server\")\n            mongo = Pod(\"mongo\")\n            pvc = PersistentVolumeClaim(\"mongo-pvc\")\n\n    client >> server >> mongo >> pvc\n    pvc >> pv\n    client >> client_cm\n    server >> server_cm","sub_path":"diagram.py","file_name":"diagram.py","file_ext":"py","file_size_in_byte":741,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"374014701","text":"\"\"\"empty message\n\nRevision ID: 2cba13042716\nRevises: \nCreate Date: 2020-03-29 12:38:46.578988\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '2cba13042716'\ndown_revision = None\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.create_table('departments',\n    sa.Column('id', sa.Integer(), nullable=False),\n    sa.Column('name', sa.String(length=200), nullable=True),\n    sa.PrimaryKeyConstraint('id')\n    )\n    op.create_table('permission_groups',\n    sa.Column('id', sa.Integer(), nullable=False),\n    sa.Column('name', sa.String(length=40), nullable=True),\n    sa.Column('description', sa.String(length=200), nullable=True),\n    sa.PrimaryKeyConstraint('id')\n    )\n    op.create_table('sessions',\n    sa.Column('id', sa.Integer(), nullable=False),\n    sa.Column('session_id', sa.String(length=255), nullable=True),\n    sa.Column('data', sa.LargeBinary(), nullable=True),\n    sa.Column('expiry', sa.DateTime(), nullable=True),\n    sa.PrimaryKeyConstraint('id'),\n    sa.UniqueConstraint('session_id')\n    )\n    op.create_table('staff_skills',\n    sa.Column('id', sa.Integer(), nullable=False),\n    sa.Column('name', sa.String(length=100), nullable=True),\n    sa.PrimaryKeyConstraint('id')\n    )\n    op.create_table('users',\n    sa.Column('id', sa.Integer(), nullable=False),\n    sa.Column('username', sa.String(length=40), nullable=True),\n    sa.Column('password_hash', sa.String(length=128), nullable=True),\n    sa.Column('is_authorised', sa.Boolean(), nullable=True),\n    sa.Column('is_icu_trained', sa.Boolean(), nullable=True),\n    sa.PrimaryKeyConstraint('id')\n    )\n    op.create_index(op.f('ix_users_username'), 'users', ['username'], unique=True)\n    op.create_table('user_departments',\n    sa.Column('user_id', sa.Integer(), nullable=True),\n    sa.Column('department_id', sa.Integer(), nullable=True),\n    sa.ForeignKeyConstraint(['department_id'], ['departments.id'], ondelete='CASCADE'),\n    sa.ForeignKeyConstraint(['user_id'], ['users.id'], ondelete='CASCADE')\n    )\n    op.create_index(op.f('ix_user_departments_department_id'), 'user_departments', ['department_id'], unique=False)\n    op.create_index(op.f('ix_user_departments_user_id'), 'user_departments', ['user_id'], unique=False)\n    op.create_table('user_permissions',\n    sa.Column('user_id', sa.Integer(), nullable=True),\n    sa.Column('permission_id', sa.Integer(), nullable=True),\n    sa.ForeignKeyConstraint(['permission_id'], ['permission_groups.id'], ondelete='CASCADE'),\n    sa.ForeignKeyConstraint(['user_id'], ['users.id'], ondelete='CASCADE')\n    )\n    op.create_index(op.f('ix_user_permissions_permission_id'), 'user_permissions', ['permission_id'], unique=False)\n    op.create_index(op.f('ix_user_permissions_user_id'), 'user_permissions', ['user_id'], unique=False)\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_index(op.f('ix_user_permissions_user_id'), table_name='user_permissions')\n    op.drop_index(op.f('ix_user_permissions_permission_id'), table_name='user_permissions')\n    op.drop_table('user_permissions')\n    op.drop_index(op.f('ix_user_departments_user_id'), table_name='user_departments')\n    op.drop_index(op.f('ix_user_departments_department_id'), table_name='user_departments')\n    op.drop_table('user_departments')\n    op.drop_index(op.f('ix_users_username'), table_name='users')\n    op.drop_table('users')\n    op.drop_table('staff_skills')\n    op.drop_table('sessions')\n    op.drop_table('permission_groups')\n    op.drop_table('departments')\n    # ### end Alembic commands ###\n","sub_path":"migrations/versions/2cba13042716_.py","file_name":"2cba13042716_.py","file_ext":"py","file_size_in_byte":3704,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"440086963","text":"from requests import request\nfrom oauthlib.oauth1.rfc5849 import Client\n\nfrom .MKMClient import MKMClient\nfrom . import exceptions\nfrom .utils import get_mkm_app_token, get_mkm_app_secret, get_mkm_access_token, get_mkm_access_token_secret\nfrom .MKMOAuth1 import MKMOAuth1\n\n\nclass Api:\n    def __init__(self, base_endpoint):\n        \"\"\"\n        Initializes the endpoint used for requests\n        \"\"\"\n        self.base_endpoint = base_endpoint\n\n    def request(self, url, method, auth_params=None, **kwargs):\n        \"\"\"\n        Sends requests to the server with parameters passed\n\n        Params:\n            `url`: URL where request is submitted\n            `method`: Method used for the requests\n            `kwargs`: Optional additional parameters used for the request\n\n        Return:\n            `response`: Returns the response received from the server\n        \"\"\"\n\n        complete_url = \"{}{}\".format(self.base_endpoint, url)\n\n        if auth_params is None:\n            auth_params = {}\n\n        auth = self.create_auth(complete_url, **auth_params)\n\n        # Some MKM endpoints might return a 3xx status code but they're not meant to be followed\n        # so disable auto follow of redirections.\n        # For more info see the official MKM documentation:\n        # https://www.mkmapi.eu/ws/documentation/API_1.1:Main_Page#307_Temporary_Redirect\n        response = request(method=method, url=complete_url, auth=auth, allow_redirects=False, **kwargs)\n        return self.handle_response(response)\n\n    def create_auth(self, url, app_token=None, app_secret=None, access_token=None, access_token_secret=None):\n        \"\"\"\n        Create authorization with MKMOAuth1, if Access Token and Access Token Secret\n        are not found a custom Client is used.\n        This is done because MKM expects an authorization header with certain parameters\n        even if they're empty strings.\n\n        Params:\n            `url`: URL where request is submitted\n            `app_token`: use this app token instead of the one in env vars\n            `app_secret`: use this app secret instead of the one in env vars\n            `access_token`: use this access token instead of the one in env vars\n            `access_token_secret`: use this access token secret instead of the one in env vars\n\n        Return:\n            `auth`: Returns an instance of `MKMOAuth1` with `url` as realm\n        \"\"\"\n\n        app_token = app_token if app_token is not None else get_mkm_app_token()\n        app_secret = app_secret if app_secret is not None else get_mkm_app_secret()\n        access_token = access_token if access_token is not None else get_mkm_access_token()\n        access_token_secret = access_token_secret if access_token_secret is not None else get_mkm_access_token_secret()\n\n        # If access_token and access_token_secret are empty strings a personalized OAuth1 Client is used.\n        # This is done because that would mean the user is using a Widget Application and having empty strings\n        # as tokens causes issues with the default Client\n        if not access_token and not access_token_secret:\n            client = MKMClient\n        else:\n            client = Client\n\n        return MKMOAuth1(\n            app_token,\n            client_secret=app_secret,\n            resource_owner_key=access_token,\n            resource_owner_secret=access_token_secret,\n            client_class=client,\n            realm=url,\n        )\n\n    def handle_response(self, response):\n        \"\"\"\n        Check the HTTP response\n\n        Params:\n            `response`: Response received from the server\n        Return:\n            `response`: Returns the response received if positive or raise exception if negative\n        \"\"\"\n\n        # We don't automatically follow redirects so accept those responses\n        if 200 <= response.status_code <= 399:\n            return response\n        else:\n            raise exceptions.ConnectionError(response)\n","sub_path":"mkmsdk/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":3931,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"194490582","text":"# coding:utf-8\nimport os\nimport time\n\nfrom tornado.escape import json_encode, json_decode\nfrom tornado.web import asynchronous, authenticated\nfrom tornado.gen import Task, coroutine\n\nfrom core.files import Folder\nfrom handlers.base_handler import AppHandler\n\n\nclass List(AppHandler):\n    \"\"\"\n    JSON Request content\n\n    { \"params\": {\n        \"mode\": \"list\",\n        \"onlyFolders\": false,\n        \"path\": \"/public_html\"\n    }}\n\n    JSON Response\n\n    { \"result\": [\n      {\n        \"name\": \"joomla\",\n        \"rights\": \"drwxr-xr-x\",\n        \"size\": \"4096\",\n        \"date\": \"2015-04-29 09:04:24\",\n        \"type\": \"dir\"\n      }, {\n        \"name\": \"magento\",\n        \"rights\": \"drwxr-xr-x\",\n        \"size\": \"4096\",\n        \"date\": \"17:42\",\n        \"type\": \"dir\"\n      }, {\n        \"name\": \"index.php\",\n        \"rights\": \"-rw-r--r--\",\n        \"size\": \"549923\",\n        \"date\": \"2013-11-01 11:44:13\",\n        \"type\": \"file\"\n      }\n    ]}\n    \"\"\"\n    @authenticated\n    def post(self):\n        params = json_decode(self.request.body)\n        only_folders = params['params']['onlyFolders']\n        path = self.s3_path(params['params']['path'])\n        f = self.get_model(path, _type='dir')\n        self.render_json({'result': f.list(only_folders)})\n\n\nclass Upload(AppHandler):\n    \"\"\"\n    Http post request payload\n\n    ------WebKitFormBoundaryqBnbHc6RKfXVAf9j\n    Content-Disposition: form-data; name=\"destination\"\n    /\n\n    ------WebKitFormBoundaryqBnbHc6RKfXVAf9j\n    Content-Disposition: form-data; name=\"file-0\"; filename=\"github.txt\"\n    Content-Type: text/plain\n\n    JSON Response\n\n    { \"result\": { \"success\": true, \"error\": null } }\n\n    Unlimited file items to upload, each item will be enumerated as file-0, file-1, etc.\n\n    For example, you may retrieve the file in PHP using:\n\n    $destination = $_POST['destination'];\n    $_FILES['file-0'] or foreach($_FILES)\n    \"\"\"\n    @authenticated\n    @asynchronous\n    @coroutine\n    def post(self):\n        path = self.get_argument('destination')\n        s3_path = self.s3_path(path)\n        result = {\"result\": {\"success\": True, \"error\": None}}\n\n        for datas in self.request.files.values():\n            name = os.path.join(s3_path, datas[0]['filename'])\n\n            obj_info = {\n                'name': datas[0]['filename'],\n                'size': len(datas[0]['body']),\n                'date': time.strftime('%Y-%m-%d %H:%M:%S'),\n                '_type': 'file'\n            }\n\n            obj = self.get_model(name, **obj_info)\n            self._identify_space(obj.size_diff)\n\n            yield Task(self.s3.upload, name, datas[0]['body'])\n            yield obj.save()\n\n        self.render_json(result)\n\n\nclass Rename(AppHandler):\n    \"\"\"\n    JSON Request content\n\n    { \"params\": {\n        \"mode\": \"rename\",\n        \"path\": \"/public_html/index.php\",\n        \"type\": \"file\",\n        \"newPath\": \"/public_html/index2.php\"\n    }}\n\n    JSON Response\n\n    { \"result\": { \"success\": true, \"error\": null } }\n    \"\"\"\n    @authenticated\n    @asynchronous\n    @coroutine\n    def post(self):\n        result = {\"result\": {\"success\": True, \"error\": None}}\n\n        params = json_decode(self.request.body)\n        _type = params['params']['type']\n        src_path = self.s3_path(params['params']['path'], _type == 'file')\n        dist_path = self.s3_path(params['params']['newPath'], _type == 'file')\n\n        obj = self.get_model(src_path, _type=_type)\n        yield obj.rename(dist_path)\n\n        self.render_json(result)\n\n\nclass Copy(AppHandler):\n    \"\"\"\n    JSON Request content\n\n    { \"params\": {\n        \"mode\": \"copy\",\n        \"path\": \"/public_html/index.php\",\n        \"newPath\": \"/public_html/index-copy.php\"\n    }}\n\n    JSON Response\n\n    { \"result\": { \"success\": true, \"error\": null } }\n    \"\"\"\n    @authenticated\n    @asynchronous\n    @coroutine\n    def post(self):\n        result = {\"result\": {\"success\": True, \"error\": None}}\n\n        params = json_decode(self.request.body)\n        src_path = self.s3_path(params['params']['path'], True)\n        dist_path = self.s3_path(params['params']['newPath'], True)\n\n        obj = self.get_model(src_path, _type='file')\n        yield obj.copy(dist_path)\n\n        self.render_json(result)\n\n\nclass Remove(AppHandler):\n    \"\"\"\n    JSON Request content\n\n    { \"params\": {\n        \"mode\": \"delete\",\n        \"type\": \"file\",\n        \"path\": \"/public_html/index.php\",\n    }}\n\n    JSON Response\n\n    { \"result\": { \"success\": true, \"error\": null } }\n    \"\"\"\n    @authenticated\n    @asynchronous\n    @coroutine\n    def post(self):\n        result = {\"result\": {\"success\": True, \"error\": None}}\n\n        params = json_decode(self.request.body)\n        _type = params['params']['type']\n        path = self.s3_path(params['params']['path'], _type == 'file')\n        obj = self.get_model(path, _type=_type)\n        yield obj.delete()\n\n        self.render_json(result)\n\n\nclass Edit(AppHandler):\n    \"\"\"\n    JSON Request content\n\n    { \"params\": {\n        \"mode\": \"savefile\",\n        \"content\": \"<?php echo random(); ?>\",\n        \"path\": \"/public_html/index.php\",\n    }}\n\n    JSON Response\n\n    { \"result\": { \"success\": true, \"error\": null } }\n    \"\"\"\n    @authenticated\n    @asynchronous\n    @coroutine\n    def post(self):\n        result = {\"result\": {\"success\": True, \"error\": None}}\n        params = json_decode(self.request.body)\n        path = self.s3_path(params['params']['path'], True)\n        content = params['params']['content']\n\n        yield Task(self.s3.upload, path, content)\n        obj = self.get_model(\n            path,\n            size=len(content),\n            date=time.strftime('%Y-%m-%d %H:%M:%S'),\n            _type='file'\n        )\n        yield obj.save()\n\n        self.render_json(result)\n\n\nclass GetContent(AppHandler):\n    \"\"\"\n    JSON Request content\n\n    { \"params\": {\n        \"mode\": \"editfile\",\n        \"path\": \"/public_html/index.php\"\n    }}\n\n    JSON Response\n\n    { \"result\": \"<?php echo random(); ?>\" }\n    \"\"\"\n    @authenticated\n    @asynchronous\n    @coroutine\n    def post(self):\n        result = {'result': ''}\n        params = json_decode(self.request.body)\n        path = self.s3_path(params['params']['path'], True)\n        result['result'] = yield Task(self.s3.get, path)\n\n        self.render_json(result)\n\n\nclass CreateFolder(AppHandler):\n    \"\"\"\n    JSON Request content\n\n    { \"params\": {\n        \"mode\": \"addfolder\",\n        \"name\": \"new-folder\",\n        \"path\": \"/public_html\"\n    }}\n\n    JSON Response\n\n    { \"result\": { \"success\": true, \"error\": null } }\n    \"\"\"\n    @authenticated\n    @asynchronous\n    @coroutine\n    def post(self):\n        result = {\"result\": {\"success\": True, \"error\": None}}\n\n        params = json_decode(self.request.body)\n        path = params['params']['path']\n        name = params['params']['name']\n        s3_new_folder = self.s3_path(os.path.join(path, name))\n        obj = self.get_model(\n            s3_new_folder,\n            name=name,\n            date=time.strftime('%Y-%m-%d %H:%M:%S'),\n            _type='dir'\n        )\n        yield obj.save()\n\n        self.render_json(result)\n\n\nclass DownloadFile(AppHandler):\n    \"\"\"\n    Http query params\n\n    [fileManagerConfig.downloadFileUrl]?mode=download&preview=true&path=/public_html/image.jpg\n\n    Response\n\n    -File content\n\n    Errors / Exceptions\n\n    Any backend error should be with an error 500 HTTP code.\n\n    Btw, you can also report errors with a 200 response both using this json structure\n\n    { \"result\": {\n        \"success\": false,\n        \"error\": \"Access denied to remove file\"\n    }}\n    \"\"\"\n    @authenticated\n    def get(self):\n        path = self.s3_path(self.get_argument('path'), True)\n        s3_url = self.s3.generate_url(path)\n        self.redirect(s3_url)\n\n\nclass Compress(AppHandler):\n    \"\"\"\n    JSON Request content\n\n    { \"params\": {\n        \"mode\": \"compress\",\n        \"path\": \"/public_html/compressed.zip\",\n        \"destination\": \"/public_html/backups\"\n    }}\n\n    JSON Response\n\n    { \"result\": { \"success\": true, \"error\": null } }\n    \"\"\"\n    @authenticated\n    def post(self):\n        pass\n\n\nclass Extract(AppHandler):\n    \"\"\"\n    JSON Request content\n\n    { \"params\": {\n        \"mode\": \"extract\",\n        \"destination\": \"/public_html/extracted-files\",\n        \"path\": \"/public_html/compressed.zip\",\n        \"sourceFile\": \"/public_html/compressed.zip\"\n    }}\n\n    JSON Response\n\n    { \"result\": { \"success\": true, \"error\": null } }\n    \"\"\"\n    @authenticated\n    def post(self):\n        pass\n\n\nclass Permissions(AppHandler):\n    \"\"\"\n    JSON Request content\n\n    { \"params\": {\n        \"mode\": \"changepermissions\",\n        \"path\": \"/public_html/index.php\",\n        \"perms\": \"653\",\n        \"permsCode\": \"rw-r-x-wx\",\n        \"recursive\": false\n    }}\n\n    JSON Response\n\n    { \"result\": { \"success\": true, \"error\": null } }\n    \"\"\"\n    @authenticated\n    def post(self):\n        pass\n\n\nclass Search(AppHandler):\n    \"\"\"\n    Search objects that contains given keyword.\n    JSON Request content\n\n    { \"params\": {\n        \"mode\": \"search\",\n        \"onlyFolders\": false,\n        \"keywords\": \"good job\",\n        \"path\": \"/\",\n    }}\n\n    JSON Response\n\n    { \"result\": [\n      {\n        \"name\": \"joomla\",\n        \"rights\": \"drwxr-xr-x\",\n        \"size\": \"4096\",\n        \"date\": \"2015-04-29 09:04:24\",\n        \"type\": \"dir\"\n      }, {\n        \"name\": \"magento\",\n        \"rights\": \"drwxr-xr-x\",\n        \"size\": \"4096\",\n        \"date\": \"17:42\",\n        \"type\": \"dir\"\n      }, {\n        \"name\": \"index.php\",\n        \"rights\": \"-rw-r--r--\",\n        \"size\": \"549923\",\n        \"date\": \"2013-11-01 11:44:13\",\n        \"type\": \"file\"\n      }\n    ]}\n    \"\"\"\n    @authenticated\n    def post(self):\n        params = json_decode(self.request.body)\n        path = self.s3_path(params['params']['path'])\n        keywords = params['params']['keywords']\n        only_folders = params['params']['onlyFolders']\n        items = Folder.filter(\n            path, keywords, s3=self.s3,\n            db=self.db, current_user=self.current_user,\n            only_folders=only_folders\n        )\n\n        self.render_json({'result': items})\n\n\nclass Share(AppHandler):\n    \"\"\"\n    Share object with other user, be shared user can download this object.\n    JSON Request content\n\n    { \"params\": {\n        \"mode\": \"share\",\n        \"path\": \"/root/test.txt\",\n        \"dist_user\": \"username\",\n        \"expire\": \"30\"\n    }}\n\n    JSON Response\n\n    { \"result\": { \"shared_url\": \"http://localhost/abc.txt?xxx\" } }\n    \"\"\"\n    @authenticated\n    def post(self):\n        params = json_decode(self.request.body)\n        source = params[\"params\"][\"path\"]\n        # expire = params[\"params\"][\"expire\"]\n        s3_path = self.s3_path(source, True)\n        shared_url = self.s3.generate_url(s3_path, expires_in=3600)\n\n        result = {\"result\": {\"shared_url\": shared_url}}\n        self.render_json(result)\n","sub_path":"handlers/action_handlers.py","file_name":"action_handlers.py","file_ext":"py","file_size_in_byte":10801,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"180715814","text":"import sys\n#sys.path.append(\"/home/zishan/Documents/\")\nsys.path.append(\"/home/azishan/EnergyBoost/\")\n \n#import gym\nimport itertools\nimport matplotlib\nimport numpy as np\nimport pandas as pd\nimport sys\nimport tensorflow as tf\nimport collections\nimport csv\nimport os\n\nfrom NewBoost.env.environment import EnergyEnvironment\nfrom NewBoost.lib import plotting\n# from environment import EnergyEnvironment\n#\nimport matplotlib.pyplot as plt\nimport sklearn.pipeline\nimport sklearn.preprocessing\n#\n# if \"../\" not in sys.path:\n#   sys.path.append(\"../\")\n# # from lib.envs.cliff_walking import CliffWalkingEnv\n# from lib import plotting\n#\nfrom sklearn.kernel_approximation import RBFSampler\n\nmatplotlib.style.use('ggplot')\n\n#GLOBAL_VARIABLES\nMAX_CHARGE_RATE = float(sys.argv[3])\nACTION_BOUND = [-MAX_CHARGE_RATE, MAX_CHARGE_RATE]\n#print(\"0187230981723897\",ACTION_BOUND)\ncurrent_bill = 0\ncurrent_soc = float(sys.argv[2]) * 0.5\n\n# our environment\nenv = EnergyEnvironment(MAX_CHARGE_RATE)\n\n# Feature Preprocessing: Normalize to zero mean and unit variance\n# We use a few samples from the observation space to do this\nobservation_examples = pd.read_csv(sys.argv[4])[0:8640][['use','ac','hour','month','is_weekday']]\n#observation_examples = np.array([env.state[5]])\nobservation_examples = observation_examples.values\nscaler = sklearn.preprocessing.StandardScaler()\nscaler.fit(observation_examples)\n\n# Used to converte a state to a featurizes represenation.\n# We use RBF kernels with different variances to cover different parts of the space\nfeaturizer = sklearn.pipeline.FeatureUnion([\n     (\"rbf1\", RBFSampler(gamma=5.0, n_components=200)),\n     (\"rbf2\", RBFSampler(gamma=2.0, n_components=200)),\n     (\"rbf3\", RBFSampler(gamma=1.0, n_components=200)),\n     (\"rbf4\", RBFSampler(gamma=0.5, n_components=200))\n     ])\nfeaturizer.fit(scaler.transform(observation_examples))\n\n\ndef featurize_state(state):\n    \"\"\"\n    RBF feature representation of a given state\n    :param state: current state\n    :return: state with new feature representation\n    \"\"\"\n    scaled = scaler.transform([state])\n    featurized = featurizer.transform(scaled)\n    return featurized[0]\n\nclass PolicyEstimator():\n    \"\"\"\n    Policy Function approximator.\n    \"\"\"\n\n    def __init__(self, learning_rate=0.01, scope=\"policy_estimator\"):\n        with tf.variable_scope(scope):\n            self.state = tf.placeholder(tf.float32, [800], \"state\")\n            self.target = tf.placeholder(dtype=tf.float32, name=\"target\")\n\n            # This is just linear classifier\n            self.mu = tf.contrib.layers.fully_connected(\n                inputs=tf.expand_dims(self.state, 0),\n                num_outputs=1,\n                activation_fn=None,\n                weights_initializer=tf.zeros_initializer)\n            self.mu = tf.squeeze(self.mu)\n\n            self.sigma = tf.contrib.layers.fully_connected(\n                inputs=tf.expand_dims(self.state, 0),\n                num_outputs=1,\n                activation_fn=None,\n                weights_initializer=tf.zeros_initializer)\n\n            self.sigma = tf.squeeze(self.sigma)\n            self.sigma = tf.nn.softplus(self.sigma) + 1e-5\n            self.lower = ACTION_BOUND[0]\n            self.higher = ACTION_BOUND[1]\n            self.normal_dist = tf.contrib.distributions.Normal(self.mu, self.sigma)\n            self.action = self.normal_dist._sample_n(1)\n            #print(\"----------------read here------------------\")\n            self.action = tf.clip_by_value(self.action, self.lower, self.higher)\n\n            # Loss and train op\n            self.loss = -self.normal_dist.log_prob(self.action) * self.target\n            # Add cross entropy cost to encourage exploration\n            self.loss -= 1e-1 * self.normal_dist.entropy()\n\n            self.optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n            self.train_op = self.optimizer.minimize(\n                self.loss, global_step=tf.contrib.framework.get_global_step())\n\n    def predict(self, state, sess=None):\n        \"\"\"\n        predict the action given a state\n        :param state: current state\n        :param sess: tensorflow session\n        :return: Updated session\n        \"\"\"\n        sess = sess or tf.get_default_session()\n        state = featurize_state(state)\n        return sess.run(self.action, { self.state: state })\n\n    def update(self, state, target, action, sess=None):\n        \"\"\"\n        Update the policy function\n        :param state: current state\n        :param target: td target\n        :param action: learned action\n        :param sess: tensorflow session\n        :return: loss\n        \"\"\"\n        sess = sess or tf.get_default_session()\n        state = featurize_state(state)\n        feed_dict = { self.state: state, self.target: target, self.action: action  }\n        _, loss = sess.run([self.train_op, self.loss], feed_dict)\n        return loss\n\n\nclass ValueEstimator():\n    \"\"\"\n    Value Function approximator.\n    \"\"\"\n\n    def __init__(self, learning_rate=0.1, scope=\"value_estimator\"):\n        with tf.variable_scope(scope):\n            self.state = tf.placeholder(tf.float32, [800], \"state\")\n            self.target = tf.placeholder(dtype=tf.float32, name=\"target\")\n\n            # This is just linear classifier\n            self.output_layer = tf.contrib.layers.fully_connected(\n                inputs=tf.expand_dims(self.state, 0),\n                num_outputs=1,\n                activation_fn=None,\n                weights_initializer=tf.zeros_initializer)\n\n            self.value_estimate = tf.squeeze(self.output_layer)\n            self.loss = tf.squared_difference(self.value_estimate, self.target)\n\n            self.optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n            self.train_op = self.optimizer.minimize(\n                self.loss, global_step=tf.contrib.framework.get_global_step())\n\n    def predict(self, state, sess=None):\n        \"\"\"\n        predict the value given a state\n        :param state: current state\n        :param sess: tensorflow session\n        :return: Updated session\n        \"\"\"\n        sess = sess or tf.get_default_session()\n        state = featurize_state(state)\n        return sess.run(self.value_estimate, { self.state: state })\n\n    def update(self, state, target, sess=None):\n        \"\"\"\n        Update the policy function\n        :param state: current state\n        :param target: td target\n        :param action: learned action\n        :param sess: tensorflow session\n        :return: loss\n        \"\"\"\n        sess = sess or tf.get_default_session()\n        state = featurize_state(state)\n        feed_dict = { self.state: state, self.target: target }\n        _, loss = sess.run([self.train_op, self.loss], feed_dict)\n        return loss\n\n\ndef actor_critic(env, month_var, battery_var, estimator_policy, estimator_value, num_episodes, discount_factor=1.0):\n    \"\"\"\n    Actor Critic Algorithm. Optimizes the policy\n    function approximator using policy gradient.\n    :param env: OpenAI environment.\n    :param month_var: index of month\n    :param battery_var: current battery SoC\n    :param estimator_policy: Policy Function to be optimized\n    :param estimator_value: Value function approximator, used as a critic\n    :param num_episodes: Number of episodes to run for\n    :param discount_factor: Time-discount factor\n    :return: An EpisodeStats object with two numpy arrays for episode_lengths and episode_rewards.\n    \"\"\"\n\n    # #pretrain\n    # ACTION_BOUND = [-MAX_CHARGE_RATE,MAX_CHARGE_RATE]\n    # for i_episode in range(10):\n    #\n    #     state = env.reset()\n    #     ep_reward = 0\n    #\n    #     # One step in the environment\n    #     for t in range(len(env.state)-1):\n    #\n    #         ACTION_BOUND = [-min(env.state[env.current_index][8], MAX_CHARGE_RATE), min((env.maximum_battery - env.state[env.current_index][8]), MAX_CHARGE_RATE)]\n    #\n    #\n    #         # Take a step\n    #         action_learn = estimator_policy.predict(state)\n    #         action = np.clip(action_learn,*ACTION_BOUND)\n    #\n    #\n    #\n    #         #print(\"this is action\",action)\n    #         tng, next_state, reward, done = env.step(action)\n    #\n    #         #update stats\n    #         ep_reward +=reward\n    #\n    #         # Calculate TD Target\n    #         value_next = estimator_value.predict(next_state)\n    #         td_target = reward + discount_factor * value_next\n    #         td_error = td_target - estimator_value.predict(state)\n    #\n    #         # Update the value estimator\n    #         estimator_value.update(state, td_target)\n    #\n    #         # Update the policy estimator\n    #         # using the td error as our advantage estimate\n    #         estimator_policy.update(state, td_error, action)\n    #\n    #         # Print out which step we're on, useful for debugging.\n    #         print(\"\\rStep {} @ Episode {}/{}\".format(t, i_episode + 1, 10))\n    #\n    #         if t== (len(env.state)-2):\n    #             break\n    #\n    #         state = next_state\n    # print(\"pretrain finished\")\n\n    # Keeps track of useful statistics\n    stats = plotting.EpisodeStats(\n        episode_lengths=np.zeros(num_episodes),\n        episode_rewards=np.zeros(num_episodes))\n\n    Transition = collections.namedtuple(\"Transition\", [\"state\", \"action\", \"reward\", \"next_state\", \"done\"])\n    env.month_starter = month_var\n    env.battery_starter = battery_var\n    global current_bill\n    best_reward = 0\n    best_battery = 0\n    best_actions = []\n    best_bill = []\n    env.ulist=[]\n    env.alist=[]\n    #print(\"======the updated action bound is========098q20938109\", ACTION_BOUND)\n\n    for i_episode in range(num_episodes):\n        # Reset the environment and pick the fisrst action\n        # print(\"month_starter\",env.month_starter)\n        # print(\"battery_starter\",env.battery_starter)\n        # print(\"current battery\",env.state[env.current_index][8])\n        # print(\"current state\",env.state[env.current_index])\n        # print(\"MAX_CHARGE_RATE\",MAX_CHARGE_RATE)\n        # print(\"======the updated action bound is========\", ACTION_BOUND)\n        #print(\"\\n=================================\")\n        state = env.reset()\n        #print(\"The state is\\n\",env.state)\n        #print(\"current index\",env.current_index)\n        ep_reward = 0\n        actions = []\n        total_bill = []\n\n        #print(\"\\n\")\n        # print(state)\n        # print(\"---------------------\")\n\n        episode = []\n\n        # One step in the environment\n        for t in itertools.count():\n            # print(\"In episode:\",i_episode)\n            # print(\"The step\",t)\n            #print(\"state current_index\",env.current_index)\n            # print(\"The state is \",env.state)\n            # env.render()\n\n            ACTION_BOUND = [-min(env.state[env.current_index][8], env.state[env.current_index][5], MAX_CHARGE_RATE), min((env.maximum_battery - env.state[env.current_index][8]), MAX_CHARGE_RATE)]\n            # estimator_policy.lower = ACTION_BOUND[0]\n            # estimator_policy.higher = ACTION_BOUND[1]\n            # if t==0:\n            #     #print(\"==========================================\")\n            #     print(\"month_starter\",env.month_starter)\n            #     print(\"battery_starter\",env.battery_starter)\n            #     print(\"current battery\",env.state[env.current_index][8])\n            #     print(\"current state\",env.state[env.current_index])\n            #     print(\"======the updated action bound is========\", ACTION_BOUND)\n\n\n\n            # Take a step\n            action_learn = estimator_policy.predict(state)\n            # print(\"policy learned action\",action_learn)\n            action = np.clip(action_learn,*ACTION_BOUND)\n            # print(\"real action\",action)\n\n            #print(\"this is action\",action)\n            actions.append(action[0])\n            tng, next_state, reward, done = env.step(action)\n            # print(\"tng is\", tng)\n            # print(\"next_state is\", next_state)\n            # print(\"reward is\", reward)\n            # print(\"it is done or not\", done)\n\n            # Keep track of the transition\n            episode.append(Transition(\n              state=state, action=action, reward=reward, next_state=next_state, done=done))\n\n            # Update statistics\n            stats.episode_rewards[i_episode] += reward\n            ep_reward +=reward\n            stats.episode_lengths[i_episode] = t\n\n            # Calculate TD Target\n            value_next = estimator_value.predict(next_state)\n            td_target = reward + discount_factor * value_next\n            td_error = td_target - estimator_value.predict(state)\n\n            # Update the value estimator\n            estimator_value.update(state, td_target)\n\n            # Update the policy estimator\n            # using the td error as our advantage estimate\n            estimator_policy.update(state, td_error, action)\n\n            # Print out which step we're on, useful for debugging.\n            #print(\"\\rStep {} @ Episode {}/{} ({})\".format(\n            #        t, i_episode + 1, num_episodes, stats.episode_rewards[i_episode - 1]))\n\n            state = next_state\n            total_bill.append(-ep_reward)\n            if done or t==MAX_EP_STEPS-1:\n                break\n\n        if i_episode == 0 or ep_reward > best_reward:\n            best_actions = actions[:]\n            best_reward = ep_reward\n            best_bill = total_bill[:]\n            best_battery = env.state[env.current_index][8]\n\n        if i_episode == num_episodes - 1:\n            for i in range(len(best_actions)):\n                # print(\"this is index---------\",i)\n                # print(\"action\",best_actions[i])\n                # print(\"bill\",best_bill[i])\n                writer.writerow([month_var+i,best_actions[i],current_bill+best_bill[i]])\n\n            current_bill = current_bill+(-best_reward)\n            current_soc = best_battery\n\n\n\n    return stats\n\nif __name__ == '__main__':\n\n    tf.reset_default_graph()\n\n    global_step = tf.Variable(0, name=\"global_step\", trainable=False)\n    policy_estimator = PolicyEstimator(learning_rate=0.001)\n    value_estimator = ValueEstimator(learning_rate=0.1)\n    #MAX_EP_STEPS = 24*7*4*3 #season\n    #MAX_EP_STEPS = 24*7 #week\n    MAX_EP_STEPS = 24 #Day\n    #MAX_EP_STEPS = 1 #Hour\n    env.sell_back = float(sys.argv[1])\n    env.maximum_battery = float(sys.argv[2])\n    env.battery_starter = env.maximum_battery * 0.5\n    env.charge_mode = \"TOU\"\n    env.datafile = sys.argv[4]\n    homeid= sys.argv[4].split(\".\")[2].split(\"_\")[2]\n    print(\"Sell back price is\",env.sell_back)\n    print(\"battery size is\",env.maximum_battery)\n    env.init_ground_truth()\n    #env.init_price()\n    #print out the initial state\n    #print(\"inistial state\",env.state)\n    directory=\"result_{}\".format(homeid)\n    if not os.path.exists(directory):\n        os.makedirs(directory)\n    csvfile = open(directory+\"/sb\".format(homeid)+str(int(float(sys.argv[1])*100))+\"b\"+str(int(float(sys.argv[2])*10))+\".csv\", 'w', newline='')\n    #csvfile = open(directory+\"{}_{}_{}_{}\".format(str(int(float(sys.argv[1])*100)), str(int(float(sys.argv[2])*10)), str(int(sys.argv[5])), str(int(sys.argv[6])))+\".csv\", 'w', newline='')\n    writer = csv.writer(csvfile, delimiter=',')\n    writer.writerow([\"Hour\", \"Best_Action\", \"Best_Bill\"])\n    #start_point = int(sys.argv[5])\n    #end_point = int(sys.argv[6])\n    start_point = 0\n    end_point = 8616\n\n    day_count = 1\n    with tf.Session() as sess:\n        #sess.run(tf.initialize_all_variables())\n        # Note, due to randomness in the policy the number of episodes you need varies\n        # TODO: Sometimes the algorithm gets stuck, I'm not sure what exactly is happening there.\n        for i in range (start_point,end_point,MAX_EP_STEPS):\n            sess.run(tf.initialize_all_variables())\n            stats = actor_critic(env, i, current_soc, policy_estimator, value_estimator, 50, discount_factor=1)\n            print(day_count)\n            day_count += 1\n\n    print(\"this is best bill\",current_bill)\n    sell_back_round=int(float(sys.argv[1])*100)\n    battery_round=int(float(sys.argv[2])*10)\n    # plot the stat results\n    #plotting.plot_episode_stats(stats,homeid,sell_back_round,battery_round,name=directory+'/A2C_'+str(homeid)+\"_\"+str(sell_back_round)+\"_\"+str(battery_round), smoothing_window=10)\n","sub_path":"NewBoost/A2C/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":16244,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"55379468","text":"#!/usr/bin/python2.7\n# Copyright 2012 JatLeGo Inc. All Rights Reserved.\n# Author: andyzh1314@gmail.com (Andy Zhau)\n#\n# TODO(andyzhau): Comments here.\n\nimport base\nimport math\nimport random\n\nfrom stock.ann import ann_pb2\n\nflags, FLAGS = base.flags, base.FLAGS\n\n\nclass Generation(object):\n\n  def __init__(self, generation_number=0, generation_id=0,\n               generation_proto=None):\n    if generation_proto is not None:\n      self.generation_number = generation_proto.generation_number\n      self.generation_id = generation_proto.generation_id\n    else:\n      self.generation_number = generation_number\n      self.generation_id = generation_id\n\n\nclass ActivationFunction(object):\n\n  def __init__(self,\n               type=ann_pb2.ANNProto.ActivationFunction.SQUASHING_FUNCTION,\n               a=-1, b=2, d=0.5, activation_function_proto=None):\n    if activation_function_proto is not None:\n      self.type = activation_function_proto.type\n      self.a = activation_function_proto.a\n      self.b = activation_function_proto.b\n      self.d = activation_function_proto.d\n    else:\n      self.type = type\n      self.a = a\n      self.b = b\n      self.d = d\n\n  def Calc(self, x):\n    if self.type == ann_pb2.ANNProto.ActivationFunction.SQUASHING_FUNCTION:\n      if x < -100: x = -100\n      if x > 100: x = 100\n      return self.a + self.b / (1 + math.exp(-self.d * x))\n    return x\n\n\nclass ANN(object):\n\n\n  def __init__(self, ann_type=ann_pb2.ANNProto.MP_ANN, number_of_inputs=0,\n               number_of_outputs=0, weights=None, generation_number=0,\n               generation_id=0, number_of_hidden_layers=1,\n               number_of_neuron_per_hidden_layer=1, bias=1,\n               activation_function_type=0, activation_function_a=-1,\n               activation_function_b=2, activation_function_d=0.5,\n               learn_speed=0.7, momentum=0.9,\n               ann_proto=None):\n    if ann_proto is not None:\n      self.ann_type = ann_proto.ann_type\n      self.number_of_inputs = ann_proto.number_of_inputs\n      self.number_of_outputs = ann_proto.number_of_outputs\n      self.generation = Generation(generation_proto=ann_proto.generation)\n      self.number_of_hidden_layers = ann_proto.number_of_hidden_layers\n      self.number_of_neuron_per_hidden_layer = (\n          ann_proto.number_of_neuron_per_hidden_layer)\n      self.bias = ann_proto.bias\n      self.activation_function = ActivationFunction(\n          activation_function_proto=ann_proto.activation_function)\n      self.weights = ann_proto.weights\n      self.learn_speed = ann_proto.learn_speed\n      self.momentum = ann_proto.momentum\n    else:\n      self.ann_type = ann_type\n      self.number_of_inputs = number_of_inputs\n      self.number_of_outputs = number_of_outputs\n      self.generation = Generation(generation_number=generation_number,\n                                   generation_id=generation_id)\n      self.number_of_hidden_layers = number_of_hidden_layers\n      self.number_of_neuron_per_hidden_layer = number_of_neuron_per_hidden_layer\n      self.bias = bias\n      self.activation_function = ActivationFunction(\n          type=activation_function_type,\n          a=activation_function_a,\n          b=activation_function_b,\n          d=activation_function_d)\n      self.learn_speed = learn_speed\n      self.momentum = momentum\n\n      if weights is not None:\n        self.weights.extend(weights)\n      else:\n        self.weights = [random.uniform(-1.0, 1.0)\n                        for i in range(self.NumberOfWeights())]\n\n  def NumberOfWeights(self):\n    if self.ann_type == ann_pb2.ANNProto.MP_ANN:\n      return (self.number_of_neuron_per_hidden_layer * (\n          self.number_of_inputs + 1) +  # First hidden layer.\n\n              (self.number_of_hidden_layers - 1) *\n              (self.number_of_neuron_per_hidden_layer + 1) *\n              (self.number_of_neuron_per_hidden_layer + 1) +\n              # Interal hidden layer\n\n              (self.number_of_neuron_per_hidden_layer + 1) *\n              self.number_of_outputs  # Output layer.\n              )\n    return 0\n\n  def NumberOfNeuron(self):\n    if self.ann_type == ann_pb2.ANNProto.MP_ANN:\n      return (self.number_of_neuron_per_hidden_layer *\n              self.number_of_hidden_layers + self.number_of_outputs)\n\n  def Update(self, inputs, full_output=False):\n    if self.ann_type == ann_pb2.ANNProto.MP_ANN:\n      return self._UpdateMPAnn(inputs, full_output=full_output)\n\n  def _UpdateMPAnn(self, inputs, full_output=False):\n    weight_index = [-1]\n    def NextWeight():\n      weight_index[0] += 1\n      return self.weights[weight_index[0]]\n\n    if full_output:\n      full_outputs = []\n    for i in range(self.number_of_hidden_layers + 1):\n      outputs = []\n\n      if i == self.number_of_hidden_layers:\n        num_of_neuron = self.number_of_outputs\n      else:\n        num_of_neuron = self.number_of_neuron_per_hidden_layer\n      for j in range(num_of_neuron):\n        num_inputs = len(inputs)\n        new_output = 0.0\n        for k in range(num_inputs):\n          new_output += float(inputs[k]) * NextWeight()\n        new_output += self.bias * NextWeight()\n        outputs.append(self.activation_function.Calc(new_output))\n      inputs = outputs\n      if full_output:\n        full_outputs.extend(outputs)\n    assert weight_index[0] == len(self.weights) - 1\n    if full_output:\n      return full_outputs\n    return outputs\n\n  def BPTrain(self, inputs, stdoutputs):\n    self.errors = [0 for i in range(self.NumberOfNeuron())]\n    outputs = self.Update(inputs, full_output=True)\n\n    id = lambda i, j: i * self.number_of_neuron_per_hidden_layer + j\n\n    sqr_error = 0\n    for i in range(self.number_of_outputs):\n      o = outputs[id(self.number_of_hidden_layers, i)]\n      e = stdoutputs[i] - o\n      sqr_error += e * e\n      self.errors[id(self.number_of_hidden_layers, i)] = e * o * (1 - o)\n\n    weight_index = [len(self.weights)]\n    def NextWeight():\n      weight_index[0] -= 1\n      return self.weights[weight_index[0]]\n\n    for l in range(self.number_of_hidden_layers - 1, -1, -1):\n      if l == self.number_of_hidden_layers - 1:\n        num_of_neuron = self.number_of_outputs\n      else:\n        num_of_neuron = self.number_of_neuron_per_hidden_layer\n      for i in range(num_of_neuron - 1, -1, -1):\n        NextWeight()  # scape the bias weight.\n        for j in range(self.number_of_neuron_per_hidden_layer - 1, -1, -1):\n          self.errors[id(l, j)] += self.errors[id(l + 1, i)] * NextWeight()\n      for j in range(self.number_of_neuron_per_hidden_layer - 1, -1, -1):\n        self.errors[id(l, j)] *= outputs[id(l, j)] * (1 - outputs[id(l, j)])\n\n    delta_weights = [0 for x in self.weights]\n\n    weight_index = len(self.weights)\n    for l in range(self.number_of_hidden_layers, -1, -1):\n      if l == self.number_of_hidden_layers:\n        num_of_neuron = self.number_of_outputs\n      else:\n        num_of_neuron = self.number_of_neuron_per_hidden_layer\n      for i in range(num_of_neuron - 1, -1, -1):\n        #  Bias.\n        weight_index -= 1\n        delta_weights[weight_index] = self.learn_speed * self.errors[id(l, i)]\n\n        if l:\n          for j in range(self.number_of_neuron_per_hidden_layer - 1, -1, -1):\n            weight_index -= 1\n            delta_weights[weight_index] = self.learn_speed * (\n                self.errors[id(l, i)] * outputs[id(l - 1, j)])\n        else:\n          for j in range(self.number_of_inputs - 1, -1, -1):\n            weight_index -= 1\n            delta_weights[weight_index] = self.learn_speed * (\n                self.errors[id(l, i)] * inputs[j])\n    assert weight_index == 0\n    if hasattr(self, \"delta_weights\"):\n      for i in range(len(self.weights)):\n        self.weights[i] += self.delta_weights[i] * self.momentum + delta_weights[i]\n    else:\n      for i in range(len(self.weights)):\n        self.weights[i] += delta_weights[i]\n    self.delta_weights = delta_weights\n    return sqr_error\n\n  def Serialize(self):\n    ann_proto = ann_pb2.ANNProto()\n    ann_proto.ann_type = self.ann_type\n    ann_proto.number_of_inputs = self.number_of_inputs\n    ann_proto.number_of_outputs = self.number_of_outputs\n    ann_proto.generation.generation_number = self.generation.generation_number\n    ann_proto.generation.generation_id = self.generation.generation_id\n    ann_proto.weights.extend(self.weights)\n    ann_proto.number_of_hidden_layers = self.number_of_hidden_layers\n    ann_proto.number_of_neuron_per_hidden_layer = (\n        self.number_of_neuron_per_hidden_layer)\n    ann_proto.bias = self.bias\n    ann_proto.activation_function.type = self.activation_function.type\n    ann_proto.activation_function.a = self.activation_function.a\n    ann_proto.activation_function.b = self.activation_function.b\n    ann_proto.activation_function.d = self.activation_function.d\n    ann_proto.learn_speed = self.learn_speed\n    ann_proto.momentum = self.momentum\n    return ann_proto\n\n  def Branch(self, id):\n    a = ANN(ann_proto=self.Serialize())\n    a.generation.generation_number += 1\n    a.generation.generation_id = id\n    return a\n\n  def ClearErrors(self):\n    for i in range(len(self.errors)):\n      self.errors[i] = self.gen_errors[i] = 0\n","sub_path":"stock/ann/ann.py","file_name":"ann.py","file_ext":"py","file_size_in_byte":9133,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"345681387","text":"#!/usr/bin/env python\r\n# -*- coding=utf-8 -*-\r\nimport pandas as pd\r\n\r\nfrom conf.datafile_path import CHECK_AND_DESIGN_FILE as cd\r\nfrom conf.datafile_path import AUTONOMY_TASK_FILE as at\r\n\r\nCHECK_DESIGN_DF = pd.read_excel(\r\n    cd['path'],\r\n    header=cd['header'],\r\n    index_col=cd['index_col']\r\n)\r\nAUTONOMY_TASK_DF = pd.read_excel(\r\n    at['path'],\r\n    header=at['header'],\r\n    sheet_name=at['sheet']\r\n)\r\n\r\nPD_DATETIME = pd.datetime  # 用于判断 数据是不是datetime对象\r\n\r\nif __name__ == '__main__':\r\n    print(AUTONOMY_TASK_DF)\r\n","sub_path":"data/dataframe.py","file_name":"dataframe.py","file_ext":"py","file_size_in_byte":543,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"21589363","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Nov 16 08:34:04 2020\n@author: Ajit Johnson Nirmal\nOmero ROI to Scimap\n\"\"\"\n\n# Library\nimport pandas as pd\nimport numpy as np\nimport re\nimport matplotlib.path as mpath\nfrom joblib import Parallel, delayed\n\n\ndef add_roi_omero (adata, roi, x_coordinate='X_centroid',y_coordinate='Y_centroid',label='ROI'):\n    \n    \"\"\"\n    \n    Parameters\n    ----------\n    adata : AnnData object\n\n    roi : DataFrame\n        Pandas dataframe of ROI's that have been extracted from Omero using the following script: https://gist.github.com/Yu-AnChen/58754f960ccd540e307ed991bc6901b0.\n        Please note that the function currently does not handle overlapping ROI's and so make sure the ROI's are mutually exclusive.\n    x_coordinate : float, required\n        Column name containing the x-coordinates values. The default is 'X_centroid'.\n    y_coordinate : float, required\n        Column name containing the y-coordinates values. The default is 'Y_centroid'.\n    label : string, optional\n        Key for the returned data, stored in `adata.obs`. The default is 'ROI'.\n\n    Returns\n    -------\n    adata\n        Modified AnnData object. Check `adata.obs` for an additional column.\n    \n    Example\n    -------\n    roi = pd.read_csv('ROI/ROI_Z147_1_750.csv')\n    adata = sm.hl.add_roi_omero (adata, roi, label='aj_ROI')\n\n    \"\"\"\n    \n    # create data matrix that has the co-ordinates\n    data = pd.DataFrame(adata.obs)[[x_coordinate, y_coordinate]]\n    \n    def add_roi_internal (roi_id):\n        roi_subset = roi[roi['Id'] == roi_id]\n        \n        # Make a list of all ROI's from the ROI table\n        all_points = [\n        np.array(re.findall(r'\\d+\\.\\d+', s))\n            .astype(np.float64)\n            .reshape(-1, 2)\n        for s in roi_subset['all_points']\n        ]\n        \n        # Identify the cells within the ROI\n        path = mpath.Path(all_points[0], closed=True)\n        \n        # the mask to query cells within one ROI\n        inside = data[path.contains_points(data)]\n        inside['ROI'] = roi_subset['Name'].iloc[0]\n\n        # return\n        return inside\n    \n    # Apply function to all rows of the ROI dataframe\n    roi_list = roi['Id'].unique()\n    final_roi = Parallel(n_jobs=-1)(delayed(add_roi_internal)(roi_id=i) for i in roi_list)   \n    \n    # Merge all into a single DF\n    final_roi = pd.concat(final_roi)[['ROI']]\n    \n    # Add the list to obs\n    result = pd.merge(data, final_roi, left_index=True, right_index=True, how='outer')\n    \n    # Reindex\n    result = result.reindex(adata.obs.index)\n    result['ROI'] = result['ROI'].fillna('Other')\n    \n    # Add to adata\n    adata.obs[label] = result['ROI']\n    \n    # return\n    return adata\n    \n","sub_path":"scimap/helpers/_add_roi_omero.py","file_name":"_add_roi_omero.py","file_ext":"py","file_size_in_byte":2733,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"255036470","text":"from django.db import models\n\n\nclass Snippet(models.Model):\n    class Meta:\n        app_label = \"codekeeper\"\n\n    title = models.CharField(max_length=256, blank=True, null=True)\n    snippet = models.TextField()\n    tags = models.ManyToManyField(\"codekeeper.Tag\", blank=True)\n    creator = models.ForeignKey(\"codekeeper.Person\")\n    language = models.ForeignKey(\"codekeeper.Language\")\n\n    created = models.DateTimeField(auto_now_add=True)\n    updated = models.DateTimeField(auto_now=True)\n\n    def __str__(self):\n        return \"{0}\".format(self.title)","sub_path":"codekeeper/models/snippet.py","file_name":"snippet.py","file_ext":"py","file_size_in_byte":552,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"157767705","text":"import nltk\nimport re\nimport string\nimport csv\nimport operator\nfrom nltk import sent_tokenize\nfrom nltk.corpus import stopwords\nfrom nltk.stem import *\nfrom collections import Counter\n\n\n\n#---------------Question 1----------------------\ndef split_text(text):\n\twords = text.split()\n\tprint(\"-----------Question 1---------\")\n\tfor word in words:\n\t\tprint(word)\n\ndef extract_second(text):\n\twords = text.split()\n\tw = \"\"\n\tfor word in words:\n\t\tw+=word[1]\n\tprint(w)\n\ndef list_phrases(text):\n\twords = text.split()\n\tphrases = []\n\tfor word in words:\n\t\tphrases.append(word)\n\tindex = phrases.index(\"sleep\")\n\tprint(phrases[:index])\n\treturn phrases[:index]\n\ndef list_phrases_join(words_list):\n\tprint (\" \".join(words_list))\n\ndef alphabetical(text):\n\twords = text.split()\n\tphrases = []\n\tfor word in words:\n\t\tphrases.append(word)\n\tphrases.sort()\n\tlist_phrases_join(phrases)\n\nfunny = \"colorless green ideas sleep furiously\"\nsplit_text(funny)\nextract_second(funny)\nphrases_list = list_phrases(funny)\nlist_phrases_join(phrases_list)\nalphabetical(funny)\n#-----------------------------------------------\n\n#----------------Question 2---------------------\ndef wordfrequency(text):\n\twordlist = []\n\tfreqDict = {}\n\twords = text.split()\n\tprint(\"\\n\\n-----------Question 2---------\")\n\tfor word in words:\n\t\twordlist.append(words.count(word))\n\t\tfreqDict[word] = words.count(word)\n\tprint(freqDict)\nsentence = input(\"Enter a Sentence:\")\nwordfrequency(sentence)\n#-----------------------------------------------\n\n#---------------Question 3----------------------\nprint(\"\\n\\n---------------Question 3----------------------\")\nprint(\"3a. \\\\b(a|an|the)\\\\b\")\nprint(\"3b. ([-+]?[0-9]*\\.?[0-9]+[\\/\\+\\-\\*])+([-+]?[0-9]*\\.?[0-9]+)\")\n\n#-----------------------------------------------\n\n#---------------Question 4----------------------\nimport re\n\ndef parseLogin(text):\n\tprint(\"\\n\\n-----------Question 4---------\")\n\tids = re.findall(r\"[a-zA-Z0-9.]+@[a-zA-Z.]+[.][a-z]+\", text)\n\tprint(ids)\n\nfile_content = \"austen-emma.txt:hart@vmd.cso.uiuc.edu (internet) hart@uiucvmd (bitnet)austen-emma.txt:Internet(72600.2026@compuserve.com); TEL: (212-254-5093)austen-persuasion.txt:Editing by Martin Ward (Martin.Ward@uk.ac.durham)blake-songs.txt:Prepared by David Price, email ccx074@coventry.ac.uk\"\n\nparseLogin(file_content)\n#-----------------------------------------------\n\n#---------------Question 5----------------------\nprint(\"\\n\\n-----------Question 5---------\")\nfileRead = input(\"Q5.Enter File Name(use existing input.txt if needed):\")\ninfilename = fileRead\noutfilename = \"output.txt\" \nlines_seen = set() # holds lines already seen\noutfile = open(outfilename, \"w\")\nfor line in open(infilename, \"r\"):\n    if line not in lines_seen: # not a duplicate\n        outfile.write(line)\n        lines_seen.add(line)\nprint(\"\\n\\nOpen generated outputfile.txt to check for duplicate lines removed\\n\")\noutfile.close()\n\n#-----------------------------------------------\n\n\n#---------------Question 6----------------------\nprint(\"\\n\\n-----------Question 6---------\")\nprint(\"The code iterates over the given input file one line after the other. Program will break when it encounters an empty line or a line which starts with # symbol. Further it replaces the symbol '(' with '(.' and ')' with '.)', after which it returns everythiong with brackets '()'.\")\n#-----------------------------------------------\n\n#---------------Question 7----------------------\nprint(\"\\n\\n-----------Question 7---------\")\ninputFile = open(\"debate.txt\", \"r\")\ntext = inputFile.read()\nbracketRegex = '\\(\\w+\\)'\n\t\n\n\nsentences = nltk.sent_tokenize(text[293:-78])\n#print(\"\\n\\n\",sentences, \"\\n\\n\")\n\n\nlehrer = []\nobama = []\nromney = []\nprevSpkr = ''\nfor sentence in sentences:\n\tif re.search(\"\\(\\w+\\)\", sentence):\n\t\tcontinue\n\t#print(sentence)\n\tif re.search(\"^[LEHRER]+:\", sentence):\t\n\t\tprevSpkr = 'lehrer'\n\t\tlehrer.append(sentence[8:])\n\telif re.search(\"^[OBAMA]+:\", sentence):\n\t\tprevSpkr = 'obama'\n\t\tobama.append(sentence[7:])\n\telif re.search(\"^[ROMNEY]+:\", sentence):\n\t\tprevSpkr = 'romney'\n\t\tromney.append(sentence[8:])\n\telif prevSpkr == 'lehrer':\n\t\tlehrer.append(sentence)\n\telif prevSpkr == 'obama':\n\t\tobama.append(sentence)\n\telif prevSpkr == 'romney':\n\t\tromney.append(sentence)\n\n\n\nlehrer_tokenised = []\nobama_tokenised = []\nromney_tokenised = []\n\ntranslator = str.maketrans('','',string.punctuation)\n\nlehrer_tokenised_stopwords = []\nobama_tokenised_stopwords = []\nromney_tokenised_stopwords = []\n\nfor sents in lehrer:\n\ttemp_sent = sents.translate(translator)\n\ttemp_sent = temp_sent.lower()\n\tlehrer_tokenised.append(nltk.word_tokenize(temp_sent))\n\t\n\nfor sents in obama:\n\ttemp_sent = sents.translate(translator)\n\ttemp_sent = temp_sent.lower()\n\tobama_tokenised.append(nltk.word_tokenize(temp_sent)) \n\nfor sents in romney:\n\ttemp_sent = sents.translate(translator)\n\ttemp_sent = temp_sent.lower()\n\tromney_tokenised.append(nltk.word_tokenize(temp_sent)) \n\nfor stpwords in lehrer_tokenised:\n\tfor w in stpwords:\n\t\tif w not in stopwords.words(\"english\"):\n\t\t\tlehrer_tokenised_stopwords.append(w)\n\nfor stpwords in obama_tokenised:\n\tfor w in stpwords:\n\t\tif w not in stopwords.words(\"english\"):\n\t\t\tobama_tokenised_stopwords.append(w)\n\nfor stpwords in romney_tokenised:\n\tfor w in stpwords:\n\t\tif w not in stopwords.words(\"english\"):\n\t\t\tromney_tokenised_stopwords.append(w)\n\n#print(lehrer_tokenised_stopwords)\n\n\n#apply stemmers\n#for Lehrer\npstemmer = PorterStemmer()\nlehrer_tokenised_psingles = [pstemmer.stem(plural) for plural in lehrer_tokenised_stopwords]\n\nsstemmer = SnowballStemmer(\"english\")\nlehrer_tokenised_ssingles = [sstemmer.stem(plural) for plural in lehrer_tokenised_stopwords]\n\nlstemmer = LancasterStemmer()\nlehrer_tokenised_lsingles = [sstemmer.stem(plural) for plural in lehrer_tokenised_stopwords]\n\n#print (lehrer_tokenised_lsingles)\n\n#for Obama\npstemmer = PorterStemmer()\nobama_tokenised_psingles = [pstemmer.stem(plural) for plural in obama_tokenised_stopwords]\n\nsstemmer = SnowballStemmer(\"english\")\nobama_tokenised_ssingles = [sstemmer.stem(plural) for plural in obama_tokenised_stopwords]\n\nlstemmer = LancasterStemmer()\nobama_tokenised_lsingles = [sstemmer.stem(plural) for plural in obama_tokenised_stopwords]\n\n#print (obama_tokenised_lsingles)\n\n#for Romney\npstemmer = PorterStemmer()\nromney_tokenised_psingles = [pstemmer.stem(plural) for plural in romney_tokenised_stopwords]\n\nsstemmer = SnowballStemmer(\"english\")\nromney_tokenised_ssingles = [sstemmer.stem(plural) for plural in romney_tokenised_stopwords]\n\nlstemmer = LancasterStemmer()\nromney_tokenised_lsingles = [sstemmer.stem(plural) for plural in romney_tokenised_stopwords]\n\n#print (romney_tokenised_lsingles)\n\n#top 10 lehrer\ncounts_pstem_lehrer = dict(Counter(lehrer_tokenised_psingles))\nsorted_counts_pstem_lehrer = sorted(counts_pstem_lehrer.items(), key=operator.itemgetter(1))\nprint(\"Porter stemmer output for Lehrer:\\t\",sorted_counts_pstem_lehrer[-10:])\n\ncounts_sstem_lehrer = dict(Counter(lehrer_tokenised_ssingles))\nsorted_counts_sstem_lehrer = sorted(counts_sstem_lehrer.items(), key=operator.itemgetter(1))\nprint(\"Snowball stemmer output for Lehrer:\\t\",sorted_counts_sstem_lehrer[-10:])\n\ncounts_lstem_lehrer = dict(Counter(lehrer_tokenised_lsingles))\nsorted_counts_lstem_lehrer = sorted(counts_lstem_lehrer.items(), key=operator.itemgetter(1))\nprint(\"Lancaster stemmer output for Lehrer:\\t\",sorted_counts_lstem_lehrer[-10:])\n\n#top 10 obama\ncounts_pstem_obama = dict(Counter(obama_tokenised_psingles))\nsorted_counts_pstem_obama = sorted(counts_pstem_obama.items(), key=operator.itemgetter(1))\nprint(\"Porter stemmer output for Obama:\\t\",sorted_counts_pstem_obama[-10:])\n\ncounts_sstem_obama = dict(Counter(obama_tokenised_ssingles))\nsorted_counts_sstem_obama = sorted(counts_sstem_obama.items(), key=operator.itemgetter(1))\nprint(\"Snowball stemmer output for Obama:\\t\",sorted_counts_sstem_obama[-10:])\n\ncounts_lstem_obama = dict(Counter(obama_tokenised_lsingles))\nsorted_counts_lstem_obama = sorted(counts_lstem_obama.items(), key=operator.itemgetter(1))\nprint(\"Lancaster stemmer output for Obama:\\t\",sorted_counts_lstem_obama[-10:])\n\n#top 10 romney\ncounts_pstem_romney = dict(Counter(romney_tokenised_psingles))\nsorted_counts_pstem_romney = sorted(counts_pstem_romney.items(), key=operator.itemgetter(1))\nprint(\"Porter stemmer output for Romney:\\t\",sorted_counts_pstem_romney[-10:])\n\ncounts_sstem_romney = dict(Counter(romney_tokenised_ssingles))\nsorted_counts_sstem_romney = sorted(counts_sstem_romney.items(), key=operator.itemgetter(1))\nprint(\"Snowball stemmer output for Romney:\\t\",sorted_counts_sstem_romney[-10:])\n\ncounts_lstem_romney = dict(Counter(romney_tokenised_lsingles))\nsorted_counts_lstem_romney = sorted(counts_lstem_romney.items(), key=operator.itemgetter(1))\nprint(\"Lancaster stemmer output for Romney:\\t\",sorted_counts_lstem_romney[-10:])\n\ndef positiveWords():\n\tinFile = open(\"positive.txt\", \"r\")\n\tpositiveFile = inFile.read()\n\tpstemmer = PorterStemmer()\n\twords = positiveFile.split()\n\tposWords = []\n\tfor word in words:\t\n\t\tposWords.append(pstemmer.stem(word))\n\treturn posWords\n\nposwrds = positiveWords()\n\n\n#-------LEHER----------\nlehrer_topwords = list(Counter(lehrer_tokenised_psingles))\n#print(lehrer_topwords)\n\nlehrer_top_positivewords = set(lehrer_topwords).intersection(poswrds)\n#print(lehrer_top_positivewords)\n\n#-------OBAMA----------\nobama_topwords = list(Counter(obama_tokenised_psingles))\n#print(obama_topwords)\n\nobama_top_positivewords = set(obama_topwords).intersection(poswrds)\n#print(obama_top_positivewords)\n\n#-------ROMNEY----------\nromney_topwords = list(Counter(romney_tokenised_psingles))\n#print(romney_topwords)\n\nromney_top_positivewords = set(romney_topwords).intersection(poswrds)\n#print(romney_top_positivewords)\n\n\nlehrerPositiveWords = {}\nobamaPositiveWords = {}\nromneyPositiveWords = {}\n\n#lehrer top positive words\nlehrer_poswordcount = 0\nfor key,value in counts_pstem_lehrer.items():\n\tfor x in lehrer_top_positivewords:\n\t\tif key == x:\n\t\t\tlehrer_poswordcount = lehrer_poswordcount+1\n\t\t\tlehrerPositiveWords[key] = value\n\nsortedLehrerPosWords = sorted(lehrerPositiveWords.items(), key=operator.itemgetter(1))\nprint(\"Lehrer positive words: \" , sortedLehrerPosWords[-10:], \"total:\", lehrer_poswordcount)\n\n#obama top positive words\nobama_poswordcount = 0\nfor key,value in counts_pstem_obama.items():\n\tfor x in obama_top_positivewords:\n\t\tif key == x:\n\t\t\tobama_poswordcount = obama_poswordcount + 1 \n\t\t\tobamaPositiveWords[key] = value\n\nsortedobamaPosWords = sorted(obamaPositiveWords.items(), key=operator.itemgetter(1))\nprint(\"Obama positive words:\" , sortedobamaPosWords[-10:] , \"total:\", obama_poswordcount)\n\n#romney top positive words\nromney_poswordcount = 0\nfor key,value in counts_pstem_romney.items():\n\tfor x in romney_top_positivewords:\n\t\tif key == x:\n\t\t\tromney_poswordcount = romney_poswordcount + 1 \n\t\t\tromneyPositiveWords[key] = value\n\nsortedromneyPosWords = sorted(romneyPositiveWords.items(), key=operator.itemgetter(1))\nprint(\"Romney positive words:\" , sortedromneyPosWords[-10:] , \"total:\", romney_poswordcount)\n\n\nwith open('topPositiveWords.csv', 'w', newline='') as csvfile:\n    Pwriter = csv.writer(csvfile, delimiter=' ',\n                            quotechar='|', quoting=csv.QUOTE_MINIMAL)\n    l = \"Lehrer\"+str(lehrer_poswordcount)\n    o = \"Obama\"+ str(obama_poswordcount)\n    r = \"Romney\"+ str(romney_poswordcount)\n    Pwriter.writerow(\" \")\n    Pwriter.writerow(l)\n    Pwriter.writerow(o)\n    Pwriter.writerow(r)\n\n\nprint(\"Total Positive words of every speaker is logged into topPositiveWords.csv\")\n\n\n\n","sub_path":"assignment1/assignment1 (copy)/debateAnalysis.py","file_name":"debateAnalysis.py","file_ext":"py","file_size_in_byte":11427,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"406639820","text":"from django.shortcuts import render\nfrom wrr import models\n# Create your views here.\ndef movie(request):\n    mov_list=models.Movie.objects.all()\n    print(mov_list)\n    context={\n        'mov_list':mov_list\n    }\n    return render(request,'show.html',context=context)\n","sub_path":"wrr/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":268,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"619811298","text":"import discord\r\nimport random\r\nimport os\r\nimport json\r\nfrom discord.ext import commands,tasks\r\nfrom discord.ext.commands import CommandNotFound\r\n\r\n\r\ntoken=os.getenv('TOKEN')\r\n\r\ndef get_prefix(bot,message):\r\n    with open ('prefixes.json','r') as f:\r\n        prefixes=json.load(f)\r\n\r\n    return [prefixes[str(message.guild.id)]]\r\n\r\nbot=commands.Bot(command_prefix=get_prefix)\r\n\r\n@bot.event\r\nasync def on_guild_join(guild):#add server's prefix from json file\r\n    with open('prefixes.json','r') as f:\r\n        prefixes=json.load(f)\r\n\r\n    prefixes[str(guild.id)]='.'\r\n\r\n    with open('prefixes.json','w') as f:\r\n        json.dump(prefixes,f,indent=4)\r\n\r\n@bot.event\r\nasync def on_guild_remove(guild):#remove server's prefix from json file\r\n        with open('prefixes.json','r') as f:\r\n            prefixes=json.load(f)\r\n\r\n        prefixes.pop(str(guild.id))\r\n\r\n        with open('prefixes.json','w') as f:\r\n            json.dump(prefixes,f,indent=4)\r\n\r\n@bot.command()\r\nasync def change_prefix(ctx,prefix):\r\n    with open('prefixes.json','r') as f:\r\n        prefixes=json.load(f)\r\n\r\n    prefixes[str(ctx.guild.id)]=prefix\r\n\r\n    with open('prefixes.json','w') as f:\r\n        json.dump(prefixes,f,indent=4)\r\n\r\n    await ctx.send(f'Prefix changed to {prefix}')\r\n\r\n\r\n@bot.event\r\nasync def on_command_error(ctx, error):\r\n    if isinstance(error, commands.CommandNotFound):\r\n        await ctx.send('Invalid command')\r\n@bot.event\r\nasync def on_ready():\r\n    #name of the loop.start\r\n    await bot.change_presence(status=discord.Status.idle , activity=discord.Game('with cogs'))\r\n    print('Bot is online.')\r\n\r\n@bot.command()\r\n@commands.has_permissions(manage_messages=True)\r\nasync def clear(ctx,amount=5):\r\n    await ctx.channel.purge(limit=amount)\r\n\r\n@clear.error\r\nasync def clear_error(ctx,error):\r\n    if isinstance(error,commands.MissingRequiredArgument):\r\n        await ctx.send('Specify how many messages to clear')\r\n    if isinstance(error,commands.MissingPermissions):\r\n        await ctx.send('NOT')\r\n\r\n@bot.command()\r\nasync def load(ctx,extension):\r\n    bot.load_extension(f'cogs.{extension}')\r\n\r\n@bot.command()\r\nasync def unload(ctx,extension):\r\n    bot.unload_extension(f'cogs.{extension}')\r\n\r\n@bot.command()\r\nasync def reload(ctx,extension):\r\n    bot.unload_extension(f'cogs.{extension}')\r\n    bot.load_extension(f'cogs.{extension}')\r\n\r\n#@tasks.loop(seconds=10) start a loop - name of the loop is the name of the function\r\n#async def\r\n\r\nfor filename in os.listdir('./cogs'):#iterates through all the files in cogs folder\r\n    if filename.endswith('.py'):\r\n        bot.load_extension(f'cogs.{filename[:-3]}')\r\n\r\n\r\n\r\nbot.run(token)\r\n","sub_path":"discord_bot/bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":2634,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"24567466","text":"\"\"\"\nDjango settings for myblog project.\n\nFor more information on this file, see\nhttps://docs.djangoproject.com/en/1.7/topics/settings/\n\nFor the full list of settings and their values, see\nhttps://docs.djangoproject.com/en/1.7/ref/settings/\n\"\"\"\n\n# Build paths inside the project like this: os.path.join(BASE_DIR, ...)\nimport os\nfrom django.conf.global_settings import TEMPLATE_CONTEXT_PROCESSORS as TCP\nBASE_DIR = os.path.dirname(os.path.dirname(__file__))\n\n\n# Quick-start development settings - unsuitable for production\n# See https://docs.djangoproject.com/en/1.7/howto/deployment/checklist/\n\n# SECURITY WARNING: keep the secret key used in production secret!\nSECRET_KEY = ')!tt(+7wb*g2fn-@8nk4v$peb9@u+#ji@s7_srn*g+*wrf(lzb'\n\n# SECURITY WARNING: don't run with debug turned on in production!\nDEBUG = False\n\nTEMPLATE_DEBUG = False\n\nALLOWED_HOSTS = ['*']\n\n\n# Application definition\n\nINSTALLED_APPS = (\n    'ckeditor',            \n    'suit',\n    'django.contrib.admin',\n    'django.contrib.auth',\n    'django.contrib.contenttypes',\n    'django.contrib.sessions',\n    'django.contrib.messages',\n    'django.contrib.staticfiles',\n    'blog',\n)\n\nMIDDLEWARE_CLASSES = (\n    'django.contrib.sessions.middleware.SessionMiddleware',\n    'django.middleware.common.CommonMiddleware',\n    'django.middleware.csrf.CsrfViewMiddleware',\n    'django.contrib.auth.middleware.AuthenticationMiddleware',\n    'django.contrib.auth.middleware.SessionAuthenticationMiddleware',\n    'django.contrib.messages.middleware.MessageMiddleware',\n    'django.middleware.clickjacking.XFrameOptionsMiddleware',\n)\n\nROOT_URLCONF = 'myblog.urls'\n\nWSGI_APPLICATION = 'myblog.wsgi.application'\n\n\n# Database\n# https://docs.djangoproject.com/en/1.7/ref/settings/#databases\n\nDATABASES = {\n    'default': {\n        'ENGINE': 'django.db.backends.mysql',\n        'NAME': 'myblog',\n        'USER': 'root',                      # Not used with sqlite3.\n        'PASSWORD': 'yaolin878',                  # Not used with sqlite3.\n        'HOST': '',                      # Set to empty string for localhost. Not used with sqlite3.\n        'PORT': '',    \n    }\n}\n\n# Internationalization\n# https://docs.djangoproject.com/en/1.7/topics/i18n/\n\nLANGUAGE_CODE = 'en-us'\n\nTIME_ZONE = 'Asia/Shanghai'\n\nUSE_I18N = True\n\nUSE_L10N = True\n\nUSE_TZ = True\n\n\n# Static files (CSS, JavaScript, Images)\n# https://docs.djangoproject.com/en/1.7/howto/static-files/\n\nSTATIC_URL = '/static/'\n\n\nSTATIC_ROOT = os.path.join(BASE_DIR, \"static\")\n\nMEDIA_URL = \"/media/\"\nMEDIA_ROOT = os.path.join(BASE_DIR, \"media\")\nCKEDITOR_UPLOAD_PATH = \"uploads\"\n\n\nTEMPLATE_DIRS = [os.path.join(BASE_DIR, 'templates')]\n\nTEMPLATE_CONTEXT_PROCESSORS = (\n    \"django.contrib.auth.context_processors.auth\",\n    \"django.core.context_processors.debug\",\n    \"django.core.context_processors.i18n\",\n    \"django.core.context_processors.media\",\n    \"django.core.context_processors.static\",\n    \"django.core.context_processors.tz\",\n    \"django.contrib.messages.context_processors.messages\"\n)\n\nTEMPLATE_CONTEXT_PROCESSORS = TCP + (\n    'django.core.context_processors.request',\n)\n\nPER_PAGE = 5\n\nCKEDITOR_CONFIGS = {\n    'default': {\n        'toolbar': (\n            ['div','Source','-','Save','NewPage','Preview','-','Templates'], \n            ['Cut','Copy','Paste','PasteText','PasteFromWord','-','Print','SpellChecker','Scayt'], \n            ['Undo','Redo','-','Find','Replace','-','SelectAll','RemoveFormat'], \n            ['Form','Checkbox','Radio','TextField','Textarea','Select','Button', 'ImageButton','HiddenField'], \n            ['Bold','Italic','Underline','Strike','-','Subscript','Superscript'], \n            ['NumberedList','BulletedList','-','Outdent','Indent','Blockquote'], \n            ['JustifyLeft','JustifyCenter','JustifyRight','JustifyBlock'], \n            ['Link','Unlink','Anchor'], \n            ['Image','Flash','Table','HorizontalRule','Smiley','SpecialChar','PageBreak'], \n            ['Styles','Format','Font','FontSize'], \n            ['TextColor','BGColor'], \n           \n        ),\n        'height': 600,\n        'width': 750,\n    }\n}\n\nSUIT_CONFIG = {\n    # header\n     'ADMIN_NAME': 'Scarecrow admin',\n    # 'HEADER_DATE_FORMAT': 'l, j. F Y',\n    # 'HEADER_TIME_FORMAT': 'H:i',\n\n    # forms\n    'SHOW_REQUIRED_ASTERISK': True,  # Default True\n    'CONFIRM_UNSAVED_CHANGES': True, # Default True\n\n    # menu\n    # 'SEARCH_URL': '/admin/auth/user/',\n    # 'MENU_ICONS': {\n    #    'sites': 'icon-leaf',\n    #    'auth': 'icon-lock',\n    # },\n    # 'MENU_OPEN_FIRST_CHILD': True, # Default True\n    # 'MENU_EXCLUDE': ('auth.group',),\n    # 'MENU': (\n    #     'sites',\n    #     {'app': 'auth', 'icon':'icon-lock', 'models': ('user', 'group')},\n    #     {'label': 'Settings', 'icon':'icon-cog', 'models': ('auth.user', 'auth.group')},\n    #     {'label': 'Support', 'icon':'icon-question-sign', 'url': '/support/'},\n    # ),\n\n    # misc\n    # 'LIST_PER_PAGE': 15\n}\n\n","sub_path":"myblog/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":4894,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"301494188","text":"#strip digits.test (i.e. leave only the label. For easier matlab processing)\nimport os, shutil, subprocess, sys, re;\n\nos.chdir('D:\\\\Documents\\\\Dropbox\\\\MLProj\\\\p4\\\\')\n\nfiler = open(\"arxiv\\\\arxiv.norm.test\")\nfilew = open(\"arxiv\\\\arxiv.unnorm.stripped.test\",\"w\")\n\nwhile 1:\n    line = filer.readline()\n    if not line:\n        break\n    stemp = line.split(' ')\n\n    filew.write(stemp[0]+'\\n'); \n    \nfilew.close()\nfiler.close()\n","sub_path":"p4/submit/src/Scripts/threea_d2.py","file_name":"threea_d2.py","file_ext":"py","file_size_in_byte":425,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"649200256","text":"from tkinter import *\nfrom window_menu import Window\n\n\nHEIGHT = 700\nWIDTH = 1000\nBGCOLOUR = '#535353'\nEDITORHEIGHT = 300\ncursor_mouse_position = 100\n\ndef move_cursor(position: int):\n    for i in range(cursor_mouse_position-3, cursor_mouse_position+3):\n        if i == position:\n            cursor_mouse_position = position\n\n\n\n# create the tkinter object\nroot = Tk()\nroot.title(\"camera robot controller\")\nroot.minsize(800, 600)\n\n# creating a canvas to define height, width\ncanvas = Canvas(root, height=HEIGHT, width=WIDTH)\ncanvas.pack()\n\n\nbackground = Window(root)\nbackground.configure(background=BGCOLOUR)\nbackground.place(relwidth=1, relheight=1)\n\nmenu_bar = Frame(background)\nmenu_bar.configure(background=BGCOLOUR)\nmenu_bar.place(anchor='n', x=0, y=0, height=30, relwidth=2, bordermode='inside')\n\n\neditor = Frame(background)\neditor.configure(background=BGCOLOUR)\neditor.place(relx=0, rely=0.5, relheight=1, relwidth=1)\n\npitch_axis = Button(editor, text=\"Pitch Axis\", bg=BGCOLOUR, activebackground=BGCOLOUR, activeforeground=BGCOLOUR)\npitch_axis.place(x=10, y=10)\n\ny_axis = Button(editor, text=\"Yaw Axis  \", bg=BGCOLOUR, activebackground=BGCOLOUR, activeforeground=BGCOLOUR)\ny_axis.place(x=10, y=40)\n\n\ntimeline = Canvas(\n    editor, height=300, width=1000, scrollregion=(0, 0, 800, 0), bg=BGCOLOUR)\ntimeline.place(x=100, y=0, relwidth=0.9, relheight=0.5)\n\n\ndef get_timeline_mouse_position():\n    root.winfo_rootx()\n\n\ncursor = timeline.create_line(cursor_mouse_position, 0, cursor_mouse_position, 400)\n\ntimeline_scale = Scrollbar(editor, orient='horizontal')\ntimeline_scale.place(anchor='w', x=100, rely=0.49, relwidth=0.9)\n\ntimeline.yscrollcommand = timeline_scale.set\n\n# run to periodically update the programme interface\nroot.mainloop()\nroot.after(2, move_cursor(get_timeline_mouse_position()))\n\n\n","sub_path":"GUI/root/main_frame.py","file_name":"main_frame.py","file_ext":"py","file_size_in_byte":1801,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"495077741","text":"# -*- coding: utf-8 -*-\nimport urllib\nimport re\n#1.url规律\n#    第一页：https://www.neihan8s.com/njjzw/\n#    第二页：https://www.neihan8s.com/njjzw//index_2.html\n#2.用正则匹配出内容\n#    内容：\t<div class=\"text-column-item box box-790\">\n#            <h3><a href=\"/njjzw/164041.html\" class=\"title\" title=\"1234567890哪个数字最勤劳，哪个数字最懒惰?\">1234567890哪个数字最勤劳，哪个数字最懒惰?</a></h3>\n#            <div class=\"desc\"> 　　1懒惰;2勤劳。(1不做2不休)</div>\n#    正则：<div class=\"text-column-item box box-790\">.*?title=\"(.*?)\".*?<div class=\"desc\">(.*?)</div>\n#3.写代码\nclass NeihanSpider():\n    def __init__(self):\n        self.baseurl = \"https://www.neihan8s.com/njjzw/\"\n        self.headers={\"User-Agent\":\"Mozilla/5.0\"}\n        self.page = 2\n        \n    #获取页面\n    def getPage(self,url):\n        req = urllib.request.Request(url,headers=self.headers)\n        res = urllib.request.urlopen(req)\n        html = res.read().decode(\"utf-8\")\n        #调用解析函数\n        self.parsePage(html)\n        \n    #解析页面\n    def parsePage(self,html):\n        p = re.compile('<div class=\"text-column-item box box-790\">.*?title=\"(.*?)\".*?<div class=\"desc\">(.*?)</div>',re.S)\n        #匹配结果[(\"问题\":\"答案\"),...]\n        rlist=p.findall(html)\n        self.writePage(rlist)\n    \n    #保存数据\n    def writePage(self,rlist):\n        for rtuple in rlist:\n            with open(\"内涵.txt\",\"a\") as f:\n                f.write(rtuple[0].strip()+\"\\n\") \n                f.write(rtuple[1].strip()+\"\\n\\n\")\n    \n    #主函数\n    def workOn(self):\n        self.getPage(self.baseurl)\n        while True:\n            c = input(\"成功，是否继续(y/n):\")\n            if c.strip().lower() == \"y\":\n                url = self.baseurl + \"index_\" + str(self.page) + \".html\"\n                self.getPage(url)\n                self.page += 1\n            else:\n                print(\"爬取结束\")\n                break\n    \nif __name__==\"__main__\":\n    spider = NeihanSpider()\n    spider.workOn()","sub_path":"linux/language/python/code/spider/spider08_neihan.py","file_name":"spider08_neihan.py","file_ext":"py","file_size_in_byte":2077,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"572443204","text":"import os\nimport sys\nfrom configparser import ConfigParser\nfrom shutil import copyfile\n\n\nclass Config:\n    # ---- global config ----\n    game_name = \"\"\n    repeat_count_max = -1\n    device_type = \"\"\n    screenshot_to_disk = False\n    screenshot_to_disk_file_name = None\n\n    # ---- azurelane config ----\n    language = \"zh-cn\"\n    battle_no = \"\"\n    team_switch = False\n    team_switch_threshold = 5\n    enable_custom_enemy_search = False\n\n    # ---- arknights config ----\n    use_pharmacy_max = 0\n    use_stone_max = 0\n\n    # ---- girlsfrontline ----\n    max_like_dormitory = 0\n\n    def __init__(self):\n        # 从配置文件读取配置\n        cfg = self.__load_configuration_file()\n        self.device_type = cfg.get('global', 'device_type')\n        self.game_name = cfg.get('global', 'game_name')\n        self.repeat_count_max = cfg.getint('global', 'repeat_count_max')\n        self.server_chan_enable = cfg.getboolean('global', 'server_chan_enable')\n        self.server_chan_secret = cfg.get('global', 'server_chan_secret')\n        self.screenshot_to_disk = cfg.getboolean('global', 'screenshot_to_disk')\n        self.screenshot_to_disk_file_name = cfg.get('global', 'screenshot_to_disk_file_name')\n\n        self.use_stone_max = cfg.getint('arknights', 'use_stone_max')\n        self.use_pharmacy_max = cfg.getint('arknights', 'use_pharmacy_max')\n\n        self.language = cfg.get('azurelane', 'language')\n        self.battle_no = cfg.get('azurelane', 'battle_no')\n        self.team_switch = cfg.getboolean('azurelane', 'team_switch')\n        self.team_switch_threshold = cfg.getint('azurelane', 'team_switch_threshold')\n        self.enable_custom_enemy_search = cfg.getboolean('azurelane', 'enable_custom_enemy_search')\n\n        self.max_like_dormitory = cfg.getint('girlsfrontline', 'max_like_dormitory')\n        if self.game_name == 'arknights':\n            self.battle_no = '(无)'\n\n        # 如果有命令行参数，更新覆盖配置\n        self.__override_from_command_line()\n\n    @staticmethod\n    def __load_configuration_file():\n        cfg = ConfigParser()\n        config_file_name = 'conf/config.ini'\n        if not os.path.exists(config_file_name):\n            # see reference:https://stackoverflow.com/questions/123198/how-do-i-copy-a-file-in-python\n            copyfile('conf/default-config.ini', config_file_name)\n        cfg.read(config_file_name, encoding=\"utf-8\")\n        return cfg\n\n    def __override_from_command_line(self):\n        args = sys.argv\n        args_count = len(args)  # always >=1, first is main entry file path.\n        if args_count == 1:\n            return\n        elif args_count > 1 and args[1] == '-h' or args[1] == '--help':\n            print('  -h,  --help         查看所有支持的参数')\n            print('  -g,  --game         游戏名称，枚举:azurelane, arknights, girlsfrontline')\n            print('  -L,  --language     语言')\n            print('  -r,  --repeat       复读次数')\n            print('  -s,  --save         屏幕截图是否保存到本地磁盘')\n            print('  -sf, --save-file    屏幕截图保存到本地磁盘的路径')\n            print('  -azlb               碧蓝航线(azurelane)复读的关卡名称,如: 3_1, 7_3')\n            print('  -arkp               明日方舟(arknights)药剂使用数量')\n            print('  -arks               明日方舟(arknights)源石使用数量')\n            exit(0)\n        else:\n            if (args_count - 1) % 2 != 0:\n                raise AttributeError('请检查输入的命令行参数')\n            pairs = (args_count - 1) // 2\n            offset = 1\n            for i in range(pairs):\n                name_index = i * 2 + offset\n                value_index = name_index + 1\n                if args[name_index] == '-g' or args[name_index] == '--game':\n                    self.game_name = args[value_index]\n                elif args[name_index] == '-r' or args[name_index] == '--repeat':\n                    self.repeat_count_max = int(args[value_index])\n                elif args[name_index] == '-L' or args[name_index] == '--language':\n                    self.language = int(args[value_index])\n                elif args[name_index] == '-s' or args[name_index] == '--save':\n                    self.screenshot_to_disk = bool(args[value_index])\n                elif args[name_index] == '-sf' or args[name_index] == '--save-file':\n                    self.screenshot_to_disk_file_name = args[value_index]\n                elif args[name_index] == '-azlb':\n                    self.battle_no = args[value_index]\n                elif args[name_index] == '-arkp':\n                    self.use_pharmacy_max = int(args[value_index])\n                elif args[name_index] == '-arks':\n                    self.use_stone_max = int(args[value_index])\n\n    def __str__(self):\n        _str = '\\n'\n        _str += '屏幕截图位置：{0}\\n'.format(self.screenshot_to_disk_file_name if self.screenshot_to_disk else '(None)')\n        _str += '当前复读游戏：{0}\\n'.format(self.game_name)\n        _str += '最多复读次数: {0}\\n'.format(self.repeat_count_max)\n        if self.game_name == 'arknights':\n            _str += ' - 理智不足时\\n'\n            _str += '  - 自动使用药剂:{0}\\n'.format(self.use_pharmacy_max)\n            _str += '  - 自动使用石头:{0}\\n'.format(self.use_stone_max)\n        elif self.game_name == 'azurelane':\n            _str += ' - 服务器: {}\\n'.format(self.language)\n            _str += ' - 关卡: {}\\n'.format(self.battle_no)\n        _str += '\\n'\n        return _str\n\n\nclass Context:\n    # ---- global ----\n    game = \"\"\n    screen_width = 1136\n    screen_height = 640\n\n    repeated_count = 0  # 复读次数\n    flag_start_printed = False\n\n    # ---- azurelane ----\n    search_enemy = False  # 进入索敌界面\n    team_switched = False\n    swipe_hor_times = 0    # 画面向上移动次数\n    swipe_hor_positive = True\n    swipe_ver_times = 0    # 画面向左移动次数\n    swipe_ver_positive = True\n    round_count = 0\n\n    # ---- arknights ----\n    pharmacy_used = 0  # 已使用的药剂数量，初识为0\n    stone_used = 0  # 已使用的源石数量，初识为0\n\n    # ---- girlsfrontline ----\n    like_friend_dormitory_count = 0\n","sub_path":"common/runtime.py","file_name":"runtime.py","file_ext":"py","file_size_in_byte":6249,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"337508882","text":"# import thư viện:\nfrom flask import Flask, jsonify\nimport newspaper\nfrom newspaper import Article\nimport sqlite3\n\nfrom  pyvi import ViTokenizer as tach_tu\nfrom Stopword import *\ndef match_data(keyword_format): # hàm trả về kết quả các bài báo liên quan tới key word\n    conn = sqlite3.connect(\"data/crawlingDB.db\")\n    query = \"\"\"SELECT * FROM news where description like ? or subject like ?\"\"\"\n    data = conn.execute(query, (keyword_format,keyword_format)).fetchall()\n    conn.commit()\n    return data\n\n\ndef creat_word_count_dict(data):\n    # tạo thư viện chứa các từ\n    words = \"\"\n    for item in data:\n        for i in item:\n            words += i\n\n    words = tach_tu.tokenize(words)\n    #print(\"Nội dung sau khi word segmentation: \",str(words))\n    words = words.split()\n    words_dict = set(words)\n    #print(\"Các từ trong nội dung: \",words_dict)\n    words_count_dict = dict.fromkeys(words_dict, 0)\n\n    # đếm số lượng từ xuất hiện trong thư viện\n    for word in words:\n        words_count_dict[word] += 1\n    return dict(words_count_dict)\n\ndef compute_TF(word_count_dict, bow):\n    tf_dict = {}\n    bow_count = len(bow)\n    for word, count in word_count_dict.items():\n        tf_dict[word] = round(count/float(bow_count),2)\n    #print(\"compute_TF\", tf_dict) ###\n    return tf_dict\n\n\ndef compute_IDF(doc_list):\n    import math\n    idf_dict = {}\n    N = len(doc_list)\n\n    idf_dict = dict.fromkeys(doc_list.keys(), 0)\n\n    for word, count in doc_list.items():\n        if count > 0:\n            idf_dict[word] += 1\n\n    for word, count in idf_dict.items():\n        idf_dict[word] = round(math.log(N / float(count)),2)\n    #print(\"compute_IDF\", idf_dict)  ###\n    return idf_dict\n\ndef compute_TFIDF(tf_bow, idfs):\n    tfidf = {}\n    for word, val in tf_bow.items():\n        tfidf[word] = round((val*idfs[word]),2)\n    #print(\"compute_TFIDF\", tfidf)  ###\n    return tfidf\n\n\n\n#define function get all from database\n#docstr: tạo conn để connect tới DB, thực thi query để lấy dữ liệu từ DB, đóng conn.\n# \\fetchall để lấy tất cả dữ liệu\n\ndef get_all(query):\n    conn = sqlite3.connect(\"DATA/crawlingDB.db\")\n    data = conn.execute(query).fetchall()\n    conn.close()\n\n    return data\n\n# define function get news from database by id of news\n# lấy 1 tin nên dùng fetchone(), truyền news_id ở dạng tuple\ndef get_news_id(news_id):\n    conn = sqlite3.connect(\"DATA/crawlingDB.db\")\n    query = \"\"\"\n    SELECT N.id, N.subject, N.description, N.image, N.original_url, C.name, C.url\n    FROM news N INNER JOIN category C on N.category_id = C.id\n    WHERE N.id=?\n    \"\"\"\n    news = conn.execute(query,(news_id, )).fetchone()\n    conn.close()\n    return news\n\n\n#define functin Add bài báo được crawling từ web vào server sử dụng newspaper3k\n#tạo query để insert bài báo craw được vào table news, gọi conn.commit() để add vào database\ndef add_news(conn,url,category_id):\n    query = \"\"\"\n    INSERT INTO news(subject,description,image,original_url,category_id)\n    VALUES(?,?,?,?,?)\n    \"\"\"\n    article = Article(url)\n    article.download()\n    article.parse()\n\n    conn.execute(query, (article.title,article.text,article.top_image,article.url,category_id))\n    conn.commit()\n\n\n# API lấy danh mục bài báo sử dụng newspaper3k\n#cats: gọi function get_all để lấy tất cả danh mục web có trong category\n# tạo connection tới database\n# với mỗi category được lấy, sẽ tiến hành lấy id, url, sau đó gọi method build của newspaper\\\n# để build link.\n# với mỗi link sẽ gọi function add_news để add nội dung bài báo craw được vào database,\n# sử dụng try - except để bỏ qua một số trường hợp không thể parse.\ndef get_news_url():\n    cats = get_all(\"SELECT * FROM category\") # lấy danh mục web từ DB\n    conn = sqlite3.connect(\"DATA/crawlingDB.db\") # tạo connect tới DB\n    for cat in cats: # với mỗi trang trong DB\n        cat_id = cat[0] # lấy id\n        url = cat[2] # lấy link gốc\n        cat_paper = newspaper.build(url) # dùng phương thức build của newspaper tạo ra các link cần craw\n        for article in cat_paper.articles:\n            try:\n                print(\"===\",article.url)\n                add_news(conn,article.url,cat_id)# gọi add_news để add link dl vào DB\n            except Exception as ex:\n                print(\"ERROR:\" + str(ex))\n                pass\n\n    conn.close() # đóng kết nối\n\n# define function get news from database by str of news\n# lấy 1 tin nên dùng fetchone(), truyền news_str ở dạng tuple\ndef get_news_by_kw(keywords = None):\n    conn = sqlite3.connect(\"DATA/crawlingDB.db\")\n    if keywords is not None:\n        #news = get_all(\"SELECT * FROM news\")\n        news_by_kw = [n for n in get_all(\"SELECT N.*, C.name FROM news N,category C where N.category_id = C.id\") if n[1].lower().find(keywords.lower()) >= 0]\n        data =[]\n        for n in news_by_kw:\n            data.append(get_news_id(n[0]))\n        #print(data)\n    conn.close()\n    return data\n\n#define function search theo keyword truyền vào form\ndef search_by_keywords_cuathay(keywords = None):\n    conn = sqlite3.connect(\"DATA/crawlingDB.db\")\n    if keywords is not None:\n        # news = get_all(\"SELECT * FROM news\")\n        search_news_by_kw = [n for n in get_all(\"SELECT * FROM news\") if n[1].lower().find(keywords.lower()) >= 0]\n        data = []\n        for n in search_news_by_kw:\n            data.append(get_news_id(n[0]))\n        # print(data)\n    conn.close()\n    return data\n\n\n\n\n\n\n\n\n\n\ndef get_news_by_kw(keywords = None):\n    conn = sqlite3.connect(\"DATA/crawlingDB.db\")\n    data = []\n    if keywords is not None:\n        #news = get_all(\"SELECT * FROM news\")\n        keyword_format = \"%{}%\".format(keywords)\n        keyword_tokennize = tach_tu.tokenize(keywords)\n        print(\"Từ khoá: \", keyword_tokennize)\n\n        # output\n        bow = str(keyword_tokennize).split(' ')\n        print(\"bow tìm kiếm :\", bow)\n        print(\"------------------------------------------------------------\")\n\n        # xử lí\n        news_match = 1  # đánh dấu số lượng bài viết match với keyword\n        list_news_match = []  # list chứa if_idf và link bài viết\n        sort_new_by_tf_idf = []  # list chứa tf_idf của các bài viết match với keyword -> dùng để sắp xếp theo thứ tự\n        for row in match_data(keyword_format):  # với mỗi bài báo liên quan tới keyword, tiến hành tính tf-idf\n            data_match = loai_bo_stopword(str(row[2]))  # loại bỏ stopword\n            word_dict = creat_word_count_dict(data_match)  # tạo bag of word đếm số lần xuất hiện\n            # print(\"Số lần xuất hiện :\",word_dict)\n            # tính TF\n            tf = compute_TF(word_dict, bow)\n            # print(\"Kết quả tf:\", tf)\n            # Tính IDF\n            idf = compute_IDF(word_dict)\n            # print(\"Kết quả idf:\", idf)\n            # Cuối cùng: Tính TF-IDF Từ kết quả TF và IDF phía trên chỉ cần nhân lại là xong\n            tf_idf = compute_TFIDF(tf, idf)\n            #print(tf_idf)\n            for key, value in tf_idf.items():  # tạo vòng lặp tìm ra keyword và tf_idf của nó,  -> tf_idf = value\n                if (key == keyword_tokennize):\n                    #print(\"****Bài số \", news_match)\n                    news_match += 1  # tăng số lượng bài viết lên\n                    #print(\"tf-idf(\" + str(key) + \"):\", value)\n                    #print(\"id :\", row[0])\n                    #print(\"Mô tả: \", row[1])\n                    #print(\"Link:\", row[2])\n\n                    data.append(get_news_id(row[0]))\n\n                    #new = {value: row[1]}\n                    #list_news_match.append(new)  # chứa tf_idf của keyword và link bài viết chứa keyword\n                    #sort_new_by_tf_idf.append(value)  # thêm tf_idf vào list\n\n                    #print(\"--------------------------------------------------------------------------------\")\n        # sau khi lặp xong\n        # -> sắp sếp giảm dần\n        #  -> hiện kết quả tìm kiếm\n        #sort_new_by_tf_idf.sort(reverse=True)\n        #print(sort_new_by_tf_idf)\n        #print(list_news_match)\n\n        #print(data)\n    conn.close()\n    return data\n\n\n\n\n\n\nif __name__ == \"__main__\":\n    #print(get_news_id(13)[1])\n    #print(get_news_url())\n    print(get_news_by_kw(\"SUV\"))","sub_path":"DOAN/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":8568,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"540616236","text":"import logging\nimport requests\nfrom requests.exceptions import RequestException\nfrom statuspageio import Client\nfrom statuspageio.errors import RequestError\nfrom .statuspage_helper import compose_incident_data, \\\n    need_update, \\\n    get_incident_status_and_severity\nfrom ..config import Config\nfrom ..discord_bot import publish_sanbase_webapp_alert\nfrom ..constants import ENVIRONMENT, \\\n    SANBASE_WEBAPP_HOST, \\\n    STATUSPAGE_API_KEY, \\\n    STATUSPAGE_PAGE_ID, \\\n    STATUSPAGE_COMPONENT_ID, \\\n    STATUSPAGE_INCIDENT_NAME, \\\n    STATUSPAGE_INCIDENT_BODY\n\n\ndef run():\n    config = Config(ENVIRONMENT)\n    message = None\n    logging.info('Testing Sanbase webapp...')\n\n    try:\n        message = test_sanbase_webapp()\n    except (RequestError, RequestException) as e:\n        message = str(e)\n        logging.error(message)\n    else:\n        logging.info('Success')\n\n    if config.getboolean('send_discord_notification') and message:\n        logging.info('Sending discord notification...')\n        publish_sanbase_webapp_alert(message)\n    else:\n        logging.info('Skipping discord notification')\n\n\ndef test_sanbase_webapp():\n    client = Client(api_key=STATUSPAGE_API_KEY,\n                    page_id=STATUSPAGE_PAGE_ID[ENVIRONMENT])\n    message = None\n    broken_webpages = {}\n    severity_ratio = 0.0\n    for webpage in SANBASE_WEBAPP_HOST[ENVIRONMENT]:\n        logging.info(f'Testing webpage: {webpage}')\n        response = requests.get(SANBASE_WEBAPP_HOST[ENVIRONMENT][webpage],\n                                timeout=60)\n        if not response.ok:\n            broken_webpages[webpage] = response.text\n            severity_ratio += 0.15\n\n    incidents = list(filter(filter_open_sanbase_webapp_incident,\n                     client.incidents.list_unresolved()))\n    (status, severity) = get_incident_status_and_severity(severity_ratio)\n    if len(incidents) > 0:\n        if len(broken_webpages) > 0:\n            message = STATUSPAGE_INCIDENT_BODY['webapp']\n            for webpage in broken_webpages:\n                message += f'\\n{webpage}: {broken_webpages[webpage]}'\n        logging.info(\n            f'Has {len(broken_webpages)} broken webpages')\n        for incident in incidents:\n            new_incident = compose_incident_data(\n                STATUSPAGE_INCIDENT_NAME['sanbase_webapp'], status, message,\n                STATUSPAGE_COMPONENT_ID[ENVIRONMENT]['sanbase_webapp'], severity)\n            if need_update(new_incident, incident):\n                logging.info('Updating incident')\n                client.incidents.update(incident['id'], **new_incident)\n            else:\n                logging.info('Incident doesnt need an update')\n    else:\n        if len(broken_webpages) > 0:\n            logging.info(\n                f'Has {len(broken_webpages)} broken webpages, creating incident')\n            message = STATUSPAGE_INCIDENT_BODY['webapp']\n            for webpage in broken_webpages:\n                message += f'\\n{webpage}: {broken_webpages[webpage]}'\n            new_incident = compose_incident_data(\n                STATUSPAGE_INCIDENT_NAME['sanbase_webapp'], status, message,\n                STATUSPAGE_COMPONENT_ID[ENVIRONMENT]['sanbase_webapp'], severity)\n            client.incidents.create(**new_incident)\n\n    return message\n\n\ndef filter_open_sanbase_webapp_incident(incident):\n    '''\n    Filter open incidents that affect sanbase webapp\n    '''\n    component_ids = [component['id'] for component in incident['components']]\n    return STATUSPAGE_COMPONENT_ID[ENVIRONMENT]['sanbase_webapp'] in component_ids\n","sub_path":"api_tests/statuspage/sanbase_webapp.py","file_name":"sanbase_webapp.py","file_ext":"py","file_size_in_byte":3555,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"357806297","text":"#Mooc.fi - Python-kurssin tehtävä kaupunkipyöristä: lasketaan annettujen asemien välinen suurin etäisyys\n\nimport math\n\ndef hae_asematiedot(tiedosto:str):\n    asemat={}\n    with open(tiedosto) as tiedostona:\n        for rivi in tiedostona:\n            \n            osat=rivi.split(\";\")\n            if osat[0]==\"Longitude\":\n                continue\n            asemat[osat[3]]=(float(osat[0]),float(osat[1]))\n    return(asemat)\n        \ndef etaisyys(asemat: dict, asema1:str, asema2:str):\n    longitude1=asemat[asema1][0]\n    \n    latitude1 = asemat[asema1][1]\n    longitude2=asemat[asema2][0]\n    latitude2=asemat[asema2][1]\n    \n    x_kilometreina=(longitude1 - longitude2)*55.26\n    y_kilometreina = (latitude1-latitude2)*111.2\n    etaisyys = math.sqrt(x_kilometreina**2 + y_kilometreina**2)\n    return etaisyys\ndef suurin_etaisyys(asemat:dict):\n    #lasketaan kaikkien asemien etäisyys toisiinsa\n    \n    #palauttaa tuplen(asema1, asema2, etaisyys)\n    suurin=0\n    mista=\"\"\n    mihin=\"\"\n    for asema1 in asemat:\n        for asema2 in asemat:\n            ero=etaisyys(asemat, asema1, asema2)\n            if ero > suurin:\n                suurin=ero\n                mista=asema1\n                mihin=asema2\n    return mista, mihin, suurin\n            \n\n\nif __name__ == \"__main__\":\n    asemat=hae_asematiedot(\"stations1.csv\")\n    asema1, asema2,suurin=suurin_etaisyys(asemat)\n\n    #e=etaisyys(asemat, \"Designmuseo\", \"Hietalahdentori\")\n    print(asema1, asema2, (round(suurin,2)))","sub_path":"kaupunkipyorat.py","file_name":"kaupunkipyorat.py","file_ext":"py","file_size_in_byte":1487,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"120219480","text":"from sklearn.linear_model import LogisticRegression\nfrom sklearn.svm import SVC\n\nlr = {\n        'model':LogisticRegression()\n        , 'grid':{\n            'C':[1e-6, 1e-3, 1]\n            ,'penalty':['l1', 'l2']\n        }\n    }\n\n\nsvc = {\n        'model':SVC()\n        , 'grid':{\n            'C':[1e-6, 1e-3, 1]\n        }\n    }","sub_path":"models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":326,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"135121284","text":"import unittest\n\nfrom selenium import webdriver\n\nfrom pages.seleniumkurs_home_page import SeleniumKursHomePage\nfrom pages.seleniumkurs_login_page import SeleniumKursLoginPage\nfrom pages.seleniumkurs_test_app_page import SeleniumKursTestAppPage\nfrom pages.seleniumkurs_testform1_page import SeleniumKursTestForm1Page\n\n\nclass TestForm1SeleniumKursChrome(unittest.TestCase):\n\n    def setUp(self):\n        print(\"Initialisiere WebDriver für den Test\")\n        self.driver = webdriver.Chrome()\n        self.driver.implicitly_wait(2)\n        self.driver.maximize_window()\n        self.driver.get(\"https://seleniumkurs.codingsolo.de\")\n\n    def tearDown(self):\n        print(\"Nach dem Test. Ich räume auf\")\n        self.driver.close()\n        self.driver.quit()\n\n    def test_form1(self):\n        print(\"Starte test_testform1 \")\n\n        ##Arrange\n\n        # Login\n        loginPage = SeleniumKursLoginPage(self.driver)\n        loginPage.zugangsdaten_eingeben(\"selenium101\", \"codingsolo\")\n        loginPage.login_button_anklicken()\n        # Navigation durch das Portal\n        homePage = SeleniumKursHomePage(self.driver)\n        homePage.hauptmenu_aufrufen()\n        homePage.selenium_test_app_anklicken()\n\n        testAppPage = SeleniumKursTestAppPage(self.driver)\n        testAppPage.test_form1_anklicken()\n\n        # Starte Formular\n        testForm1Page = SeleniumKursTestForm1Page(self.driver)\n        testForm1Page.betreff_eingeben(\"Automatischer Test\")\n        testForm1Page.name_eingeben(\"Dieter\")\n\n        testForm1Page.kurs_auswaehlen(\"Java Grundlagen Kurs mit Dieter\")\n\n        testForm1Page.firma_in_box1_auswaehlen([2, 4, 6])\n        testForm1Page.firmen_uebernehmen()\n\n        testForm1Page.firma_in_box2_auswaehlen([2])\n\n        testForm1Page.ausgewaehlte_firmen_nach_oben_verschieben()\n\n        ##Act\n\n        testForm1Page.formular_speichern()\n\n        ##Assert\n\n        erfolgsmeldung = testForm1Page.statusmeldung_auslesen()\n        self.assertTrue(\"Java Grundlagen Kurs\" in erfolgsmeldung)\n\n        erstesElement = testForm1Page.erstes_lsitenelement_auslesen()\n        self.assertEqual(erstesElement, \"Magazzini Alimentari Riuniti\")\n\n    if __name__ == '__main__':\n        unittest.main()\n","sub_path":"tests/test_4form1_selenium_kurs_chrome.py","file_name":"test_4form1_selenium_kurs_chrome.py","file_ext":"py","file_size_in_byte":2205,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"128468281","text":"import socket\r\nfrom random import *\r\n\r\nTCP_IP = '192.168.137.101'\r\nTCP_PORT = 5000\r\nBUFFER_SIZE = 1024\r\nMESSAGE = \"Test!\"\r\n\r\ns = socket.socket(socket.AF_INET,socket.SOCK_DGRAM)\r\ns.connect((TCP_IP,TCP_PORT))\r\n\r\nfor i in range(0,99):\r\n\tif i < 60:\r\n\t\tval = str(i + uniform(-2,2))\r\n\telse:\r\n\t\tval = str(i + .01*i*i)\r\n\ttest = bytes(val, 'utf-8')\r\n\ts.send(test)\r\n\r\ns.close()","sub_path":"Hardware/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":367,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"585554523","text":"#!/usr/bin/env python\n\n# Note that ucsc.so must be compiled depending on machines\n# C-extension can be integrated using distutils.\n\nimport sas\na=sas.load_nst(\"Allpairs.H.nst\")\ndir(a)\na.get_all_SAT_ID()\nsat = a.get_all_SAT_ID('SAT04694')\nsat = a.get_SAT('SAT04694')\nsat.strand()\nsat.get_strand()\nc = sat.get_cluster(0)\nc = sat.get_cluster(1)\n\n\n\n","sub_path":"csplugins/trunk/ucsd/rsaito/rs_Progs/rs_Python/rs_Python_Pack/tags/rs_Python_Pack090515/SAT_Packages/SAT44K_Info/README.py","file_name":"README.py","file_ext":"py","file_size_in_byte":344,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"36054107","text":"# 목적:\r\n# WPCN에서 HAP(=drone)의 위치와 충전 시간 할당 방법을 최적화한다.\r\n# 논문: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7337464\r\n#       Placement Optimization of Energy and Information Access Points in Wireless Powered Communication Networks\r\n#       Suzhi Bi, Member, IEEE, and Rui Zhang, Senior Member, IEEE\r\n#       Using Algorithm 5. Greedy algorithm for HAP placement optimization\r\n\r\n# map.txt의 구성:\r\n# (맵 세로길이) (맵 가로길이) (wireless device의 개수) (train 개수) (test 개수)\r\n\r\n# convex/concave function이므로 위치가 바뀜에 따라 THROUGHPUT의 값이 convex/concave하게 바뀜\r\n\r\nimport math\r\nimport random\r\nimport sys\r\nimport WPCN_helper_REAL\r\n\r\n# 진행\r\nif __name__ == '__main__':\r\n\r\n    # 사용자 입력\r\n    problemNo = int(input('0->sum throughtput maximization, 1->common throughtput maximization'))\r\n    readOrWrite = int(input('0->read files, 1->create new files'))\r\n\r\n    # 0. 파일을 읽어서 size 값과 train 개수, test 개수 구하기\r\n    # 파일 형식 : (맵 세로길이) (맵 가로길이) (wireless device의 개수) (train 개수) (test 개수)\r\n    # train 개수: 트레이닝할 데이터(맵)의 개수\r\n    # test 개수 : 테스트할 데이터(맵)의 개수\r\n    file = open('map.txt', 'r')\r\n    read = file.readlines()\r\n    readSplit = read[0].split(' ')\r\n    deviceName = input('device name (for example, cpu:0 or gpu:0)')\r\n    \r\n    height = int(readSplit[0]) # 맵의 세로길이 (= 가로길이 = size)\r\n    size = height\r\n    numTrain = int(readSplit[3]) # 트레이닝할 데이터(맵)의 개수 (실제로는 트레이닝하지 않으며 파일 인덱싱에 사용됨)\r\n    numTest = int(readSplit[4]) # 테스트할 데이터(맵)의 개수\r\n    \r\n    file.close()\r\n    \r\n    # 입력 정보\r\n    originalScreen = [] # 각 맵의 스크린\r\n    wdList = [] # 각 맵에서 wireless device의 위치를 저장한 배열, [[wd0Y, wd0X], [wd1Y, wd1X], ..., [wd(n-1)Y, wd(n-1)X]]\r\n\r\n    # 평가 결과 저장\r\n    RL = []\r\n\r\n    # 1. 테스트용 맵 생성\r\n    # 맵의 구성: .(공간), H(HAP), W(wireless device)\r\n    if readOrWrite == 1: # 맵 파일을 쓰고 그 맵으로 테스트\r\n        print('generating maps...')\r\n        for i in range(numTest):\r\n            initScreen_ = WPCN_helper_REAL.initScreen(size, False)\r\n            \r\n            originalScreen.append(initScreen_[0])\r\n            wdList.append(initScreen_[2])\r\n\r\n            # 맵 파일 작성\r\n            f = open('originalMaps/DL_WPCN_' + ('0' if i < 1000 else '') + ('0' if i < 100 else '') + ('0' if i < 10 else '') + str(i) + '.txt', 'w')\r\n            for j in range(size):\r\n                for k in range(size):\r\n                    if originalScreen[i][j][k] == -1: f.write('W') # wireless device\r\n                    elif originalScreen[i][j][k] == 0: f.write('.') # space\r\n                    elif originalScreen[i][j][k] == 1: f.write('H') # HAP (not exist in the original map)\r\n                f.write('\\n')\r\n            f.close()\r\n            \r\n    else: # 기존 맵 파일 읽어서 기존 맵으로 테스트\r\n        print('reading maps...')\r\n        for i in range(numTrain, numTrain+numTest):\r\n            f = open('originalMaps/DL_WPCN_' + ('0' if i < 1000 else '') + ('0' if i < 100 else '') + ('0' if i < 10 else '') + str(i) + '.txt', 'r')\r\n            map_ = f.readlines() # 맵을 나타낸 배열\r\n            f.close()\r\n            \r\n            for j in range(len(map_)): map_[j] = map_[j].replace('\\n', '') # 각 줄마다 개행문자 제거\r\n\r\n            # originalScreen과 wdList 읽어오기\r\n            wdListTemp = []\r\n            thisScreen = [[0] * size for j in range(size)] # originalScreen에 추가할 맵(스크린)\r\n            for j in range(size):\r\n                for k in range(size):\r\n                    if map_[j][k] == 'W':\r\n                        thisScreen[j][k] = -1 # wireless device\r\n                        wdListTemp.append([j, k]) # wdListTemp에 추가\r\n                    elif map_[j][k] == '.': thisScreen[j][k] = 0 # space\r\n                    elif map_[j][k] == 'H': thisScreen[j][k] = 1 # HAP (not exist in the original map)\r\n                    \r\n            originalScreen.append(thisScreen)\r\n            wdList.append(wdListTemp)\r\n\r\n    # 2~3. 논문에 나온 방법대로 실험 및 테스트\r\n    while True:\r\n\r\n        optiInfo = open('optiInfoForMap/optiInfoForMap_' + str(problemNo) + '_200519.txt', 'r')\r\n        optiInformation = optiInfo.readlines()\r\n        optiInfo.close()\r\n\r\n        savePrint = ''\r\n\r\n        print('testing...')\r\n\r\n        # 2. 실험 및 테스트\r\n        sumTestThroughput = 0.0 # test throughput의 합계\r\n        sumCorrectMaxThroughput = 0.0 # 정답의 throughput의 합계 (training data를 생성할 때와 같은 방법으로 최대 throughput 확인)\r\n\r\n        for i in range(numTest):\r\n\r\n            # 2-0. HAP의 좌표를 wdList의 평균으로 지정\r\n            wdList_ = wdList[i]\r\n\r\n            y = 0.0\r\n            x = 0.0\r\n            for j in range(len(wdList_)):\r\n                y += wdList_[j][0]\r\n                x += wdList_[j][1]\r\n            y /= len(wdList_)\r\n            x /= len(wdList_)\r\n\r\n            print('wdList_ = ' + str(wdList_))\r\n            print('y, x = ' + str(y) + ', ' + str(x))\r\n            \r\n            opti = [y, x]\r\n            optiY_test = y\r\n            optiX_test = x\r\n\r\n            # 2-1. 테스트 결과 받아오기\r\n            (throughput, HAPtime) = WPCN_helper_REAL.getThroughput(wdList_, opti, size, problemNo)\r\n            sumTestThroughput += throughput\r\n            testScreen = originalScreen[i] # 테스트할 스크린\r\n\r\n            # 2-2. testScreen과 optiScreen 중 일부 출력\r\n            if i < 10: # 처음 10개의 map에 대해서만 출력\r\n                # testScreen 출력\r\n                print(' --- test input screen for map ' + str(i) + ' ---')\r\n                toPrint = ''\r\n                for j in range(size):\r\n                    toPrint += '['\r\n                    for k in range(size):\r\n                        if testScreen[j][k] == -1: toPrint += 'W  '\r\n                        else: toPrint += '.  '\r\n                    toPrint += ']\\n'\r\n                print(toPrint)\r\n\r\n            # 2-3. 정답과 비교\r\n            # 최적의 HAP의 위치 및 할당 시간 찾기\r\n            # HAP의 위치 및 할당 시간에 따른 throughput의 최댓값\r\n            correctMaxThroughput = float(optiInformation[(numTrain + i) * (size + 1)].split(' ')[5])\r\n            sumCorrectMaxThroughput += correctMaxThroughput\r\n\r\n            # 정답과 비교\r\n            printForThis = ('test throughput for map ' + str(i) + ' [Y=' + str(optiY_test) + ' X=' + str(optiX_test)\r\n                            + ' HT=' + str(HAPtime) + '] : ' + str(round(throughput, 6)) + ' / ' + str(round(correctMaxThroughput, 6)) + ' ('\r\n                            + str(round(100.0 * throughput / correctMaxThroughput, 6)) + ' %)')\r\n            print(printForThis)\r\n            savePrint += printForThis + '\\n'\r\n            if i < 10: print('')\r\n                \r\n        # 3. 평가\r\n        percentage = round(100.0 * sumTestThroughput / sumCorrectMaxThroughput, 6)\r\n        print('size:' + str(size) + ' test:' + str(numTest) + ' loc(y,x):' + str(y) + ',' + str(x)\r\n                + ' problem(0=Sum,1=Common):' + str(problemNo))\r\n        print('total test throughput: ' + str(round(sumTestThroughput, 6)))\r\n        print('total max  throughput: ' + str(round(sumCorrectMaxThroughput, 6)))\r\n        print('percentage           : ' + str(percentage) + ' %')\r\n\r\n        RL.append([size, numTest, str(y) + ',' + str(x), problemNo, percentage])\r\n\r\n        # 현재까지의 평가 결과 출력\r\n        print('\\n <<< ESTIMATION RESULT >>>')\r\n        print('No.\\tsize\\ttest\\tproblem\\tpercentage')\r\n\r\n        saveResult = '\\n <<< ESTIMATION RESULT >>>\\nNo.\\tsize\\ttest\\tloc_yx\\tproblem\\tpercentage\\n'\r\n                    \r\n        for i in range(len(RL)):\r\n            toPrint = (str(i) + '\\t' + str(RL[i][0]) + '\\t' + str(RL[i][1]) + '\\t' + str(RL[i][2]) + '\\t'\r\n                    + str(RL[i][3]) + '\\t' + (' ' if round(RL[i][4], 4) < 10.0 else '') + str(round(RL[i][4], 4)))\r\n            print(toPrint)\r\n            saveResult += (toPrint + '\\n')\r\n        print('\\n')\r\n\r\n        # 평가 결과 저장\r\n        fSave = open('DL_WPCN_result_' + str(problemNo) + '_avg_ver200519.txt', 'w')\r\n        fSave.write(saveResult)\r\n        fSave.close()\r\n\r\n        fSavePrint = open('DL_WPCN_result_' + str(problemNo) + '_avg_ver200519_print.txt', 'w')\r\n        fSavePrint.write(savePrint)\r\n        fSavePrint.close()\r\n\r\n        # 학습 및 테스트 종료\r\n        break\r\n","sub_path":"WPCN/WPCN_avgLocation.py","file_name":"WPCN_avgLocation.py","file_ext":"py","file_size_in_byte":8766,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"481559890","text":"\nfrom importlib import reload\nimport sys\nimport os\n#sys.path.insert(1, '../tools/')\nimport copy\ncdc = copy.deepcopy\nimport numpy as np\nimport scipy.stats\nimport matplotlib.pyplot as plt\nimport pandas as pd\nfrom sklearn.mixture import GaussianMixture\nimport sklearn.metrics\nimport matplotlib.patheffects as path_effects\nfrom matplotlib import cm\nfrom sklearn.decomposition import PCA\nfrom sklearn.manifold import TSNE\nfrom sklearn.cluster import AgglomerativeClustering\nfrom tools import GeneNameConverter, MakeMarkerDict\nreload(GeneNameConverter)\nreload(MakeMarkerDict)\nfrom scipy.cluster.hierarchy import linkage, dendrogram\n\n\n\nprint ('\\n================\\nPackages loaded!\\n================')\n\nclass DigitalCellSorter:\n    \n    # Normalize df_expr data\n    # Params - df_expr: expression data\n    #          sigma_over_sigma: threshold when keeping only genes with large enough standard deviation\n    #          gnc: gene name converter\n    def Normalize(self, df_expr, sigma_over_mean_sigma, gnc):\n        print ('Pre-filter size: %s genes, %s cells' % df_expr.shape)\n        # Keep only cells with at least one expressed gene\n        df_expr = df_expr[df_expr.columns[np.sum(df_expr,axis=0)>0]]\n        #Keep only genes expressed in at least one cell\n        df_expr = df_expr.iloc[(np.sum(df_expr,axis=1)>0).values,:]\n        print ('Post-filter size: %s genes, %s cells' % df_expr.shape)\n\n        # Scale all cells\n        median = np.median(np.sum(df_expr,axis=0))*1.0 # sum of all columns/cells\n        df_expr = df_expr.apply(lambda q: q*median/np.sum(q),axis=0)\n\n        # Replace zeros with minimum value\n        MIN = np.min(df_expr.values[df_expr.values>0])\n        df_expr = df_expr.replace(0,MIN)\n\n        # Take log2 of expression\n        df_expr = np.log2(df_expr)\n        df_expr -= np.min(df_expr.values)\n\n        # Keep only those genes with large enough standard deviation\n        df_expr = df_expr.iloc[np.where(np.std(df_expr,axis=1)/np.mean(np.std(df_expr.values))>sigma_over_mean_sigma)[0]]\n        print ('Post-sigma cut size: %s genes, %s cells' % df_expr.shape)\n\n        # Convert gene names from aliases to hugos when possible\n        #df_expr.index = gnc.Convert(list(df_expr.index),'alias','hugo',returnUnknownString=False)\n\n        # Sort rows by gene name for the heck of it\n        df_expr = df_expr.sort_index()\n\n        print ('\\n=========================\\nDone processing raw data!\\n=========================')\n        return df_expr\n    \n    # Project df_expr to lower dimensions\n    # Params - df_expr: expression data\n    #          n_components_pca: number of components for PCA dimension reduction\n    def Project(self, df_expr, n_components_pca):\n        print ('Performing PC projection from %s to %s features...' % (df_expr.shape[0],n_components_pca))\n        X_pca = PCA(n_components=n_components_pca).fit_transform(df_expr.values.T).T\n        # X_pca_2D = PCA(n_components=2).fit_transform(X_pca.T).T\n        print ('Performing tSNE projection from %s to %s features...' % (n_components_pca,2))\n        X_tsne = TSNE(n_components=2).fit_transform(X_pca.T).T\n        \n        print ('\\n==================\\nDone transforming!\\n==================')\n        return X_pca, X_tsne\n    \n    # Cluster df_expr\n    # Params - X_pca: expression data after pca\n    #          n_clusters: number of clusters\n    #          clustering_f: clustering function, if not provided then AgglomerativeClustering is used, otherwise should have .fit method and same input and output\n    def Cluster(self, X_pca, n_clusters, clustering_f=None):\n        if clustering_f is None:\n            ac = AgglomerativeClustering(n_clusters=n_clusters)\n            cellClusterIndexLabel = ac.fit(X_pca.T).labels_\n        else:\n            cellClusterIndexLabel = clustering_f.fit(X_pca.T).labels_\n        possible_cluster_labels = np.sort(np.unique(cellClusterIndexLabel))\n\n        print ('\\n================\\nDone clustering!\\n================')\n        return cellClusterIndexLabel, possible_cluster_labels\n    \n    # Produce cluster voting results\n    # Params - df_markers_cluster_centroids: Y_mc\n    #          markerDict: markers data\n    #          zscore_cutoff: zscore cutoff when calculating Z_mc\n    #          votingScheme: voting function. If None then our voting function from the paper will be used. \n    #          NOTE: If you want to use your own custom voting function, make sure the output has the same format as out DCSVotingScheme function as below. You can modify inputs if needed. \n    #          dataName: name used in output files\n    #          saveDir: directory for output files\n    # Return - votingResults: a dictionary in form of {clusterLabel: predicted cellType}\n    def Vote(self, df_markers_cluster_centroids, markerDict, zscore_cutoff, cellClusterIndexLabel, votingScheme, dataName=None, saveDir=None):\n        # use the voting scheme from the paper if no other scheme is given\n        if votingScheme is None:\n            votingResults = self.DCSVotingScheme(df_markers_cluster_centroids, markerDict, zscore_cutoff, cellClusterIndexLabel, dataName, saveDir)\n        else:\n            votingResults = votingScheme(df_markers_cluster_centroids, markerDict, dataName, saveDir)\n        return votingResults\n    \n    # Produce cluster voting results and write it to an excel file is saveDir is given\n    # Params - df_markers_cluster_centroids: Y_mc\n    #          markerDict: markers data\n    #          zscore_cutoff: zscore cutoff when calculating Z_mc\n    #          dataName: name used in output files\n    #          saveDir: directory for output files\n    # Return - votingResults: a dictionary in form of {clusterLabel: predicted cellType}\n    def DCSVotingScheme(self, df_markers_cluster_centroids, markerDict, zscore_cutoff, cellClusterIndexLabel, dataName=None, saveDir=None):\n        def f(x,zscore_cutoff):\n            return (scipy.stats.zscore(x) > zscore_cutoff)*1\n        \n        markers = np.intersect1d(df_markers_cluster_centroids.index,list(markerDict.keys()))\n        cellTypes = np.sort(np.unique([item for sublist in markerDict.values() for item in sublist]))\n        \n        # Build marker/cell type weight matrix\n        # This is the marker/cell type matrix (M^T)_mk (the transpose of M_km as defined in the paper)\n        df_marker_cellType = pd.DataFrame(np.zeros([len(markers),len(cellTypes)]),index=markers,columns=cellTypes)\n        for marker in markers:\n            for cellType in markerDict[marker]:\n                df_marker_cellType.loc[marker,cellType] = 1\n\n        # Normalize columns (cell types) so that the absolute number of known markers in a given cell type is irrelevant\n        df_marker_cellType = df_marker_cellType.apply(lambda q: q/np.sum(q),axis=0)\n\n        # Normalize rows (markers) by the number of cell types expressing that marker (i.e. the \"specificity\")\n        df_marker_cellType = df_marker_cellType.apply(lambda q:q/np.sum(q>0),axis=1)\n\n        print ('\\n======================================\\nDone building marker/cell type matrix!\\n======================================')\n\n        # This is Z_mc\n        df_marker_hits = df_markers_cluster_centroids.apply(lambda q: f(q,zscore_cutoff),axis=1)\n        df_marker_hits=pd.DataFrame.from_dict(dict(zip(df_marker_hits.index, df_marker_hits.values))).T\n\n        fig,ax = plt.subplots(figsize=(5,3));\n        ax.hist(np.sum(df_marker_hits,axis=0),10);\n        ax.set_xlabel('Number of markers passing z-score threshold');\n        ax.set_ylabel('Number of clusters');\n        if saveDir is not None: fig.savefig(saveDir+'marker_count.pdf')\n\n\n        # This is the vote matrix V_kc\n        df_votes = df_marker_cellType.T.dot(df_marker_hits).apply(lambda q: q/np.sum(q),axis=0)\n\n        # This is the cell type vector T_c\n        argmaxes = np.argmax(df_votes.values,axis=0)\n\n        # This is the cell type vector T_c (with cell type names instead of integers)\n        cellTypes = pd.Series(df_votes.index[argmaxes],\n                              index = df_votes.columns)\n        cellTypes[df_votes.isnull().any()]='Unknown'\n        \n        # This is analogous to the percentage of the popular vote in a first-past-the-post political system\n        scores = pd.Series([df_votes.iloc[i,j] for i,j in zip(argmaxes,range(len(argmaxes)))],\n                            index = df_votes.columns)\n\n        allMarkersList = [df_marker_hits.index[df_marker_hits[c]>0] \n                          for c in df_votes.columns] # important markers in each cluster based on Zmc\n        supportingMarkersList = [np.intersect1d(allMarkers,df_marker_cellType.index[df_marker_cellType[c]>0]) \n                                 if len(allMarkers)!=0 else [] for allMarkers,c in zip(allMarkersList,cellTypes)]\n        allMarkers = pd.Series([' // '.join(i) for i in allMarkersList],\n                               index = df_votes.columns)\n        supportingMarkers = pd.Series([' // '.join(i) for i in supportingMarkersList],\n                                      index = df_votes.columns)\n\n        # Build a table to save voting results to an excel file\n        df_votingResults = pd.DataFrame(index=list(range(df_markers_cluster_centroids.shape[1])))\n        df_votingResults['Cluster label'] = list(range(df_markers_cluster_centroids.shape[1]))\n        df_votingResults = df_votingResults.set_index('Cluster label')\n        df_votingResults['Winning score'] = scores\n        df_votingResults['# cells in cluster'] = [np.sum(cellClusterIndexLabel==label) for label in df_votingResults.index]\n        df_votingResults['Supporting markers'] = supportingMarkers\n        df_votingResults['All markers'] = allMarkers\n        df_scoresToAppend = df_votes.T\n        df_scoresToAppend.columns = ['%s score'%i for i in df_scoresToAppend.columns]\n        df_votingResults = pd.concat((df_votingResults,df_scoresToAppend),axis=1)\n\n\n        # Append numbers if cell types are repeated (e.g. \"Erythrocyte #1', \"Erythrocyte #2', ...)\n        for cellType in set(cellTypes):\n            instances = np.where(cellTypes.values==cellType)[0]\n            if len(instances)>1:\n                for counter,instance in enumerate(instances,1):\n                    cellTypes[instances[counter-1]] = \\\n                       cellTypes[instances[counter-1]] + ' #' + str(counter).zfill(len(str(len(instances))))\n        df_votingResults['Predicted cell type'] = cellTypes\n        \n        # Build voting results dictionary with cluster labels as keys and predicted celltypes as values\n        votingResults = dict(zip(range(df_markers_cluster_centroids.shape[1]), cellTypes))\n    \n        if saveDir is not None: df_votingResults.to_excel('%s/%s_voting.xlsx'%(saveDir,dataName))\n\n        print ('\\n============\\nDone voting!\\n============')\n        return votingResults\n    \n    # Save processed data to a zip file\n    # Params - df_expr: expression data\n    #          votingResults: voting results dictionary\n    #          cellClusterIndexLabel: cluster labels for all cells\n    #          dataName: name used in output files\n    #          saveDir: directory for output files\n    def SaveProcessedData(self, df_expr, votingResults, cellClusterIndexLabel, dataName, saveDir):\n        df_labeled = cdc(df_expr)\n        df_labeled.loc['_TRUE_LABEL'] = [list(votingResults.values())[i] for i in cellClusterIndexLabel]\n        df_labeled = df_labeled.sort_values(df_labeled.last_valid_index(),axis=1)\n        fileName = '%s/%s_expression_labeled.tar.gz' % (saveDir,dataName)\n        print ('Saving %s...' % fileName.split('/')[-1])\n        df_labeled.to_csv(fileName, compression='gzip')\n        print ('Final array size written to disk: %s genes, %s cells' % df_labeled.shape)\n        print ('\\n=========================\\nDone saving labeled data!\\n=========================')\n        \n        \n    # Produce image on genes and their expression on all clusters\n    # Params - votingResults: voting results dictionary\n    #          X_markers_cluster_means: Y_mc, mean expression of all markers in all clusters\n    #          df_markers: markers and their expression data\n    #          df_markers_cluster_centroids: Y_mc\n    #          zscore_cutoff: zscore cutoff when calculating Z_mc\n    #          dataName: name used in output files\n    #          saveDir: directory for output files\n    def MakeMarkerExpressionPlot(self, votingResults, X_markers_cluster_means, df_markers, df_markers_cluster_centroids, zscore_cutoff, dataName, saveDir):\n        def f(x,zscore_cutoff):\n            return (scipy.stats.zscore(x) > zscore_cutoff)*1\n        \n        # Y_mc.T\n        X_markers_cluster_means_transpose = X_markers_cluster_means.T\n\n        # normalization\n        for i in range(X_markers_cluster_means_transpose.shape[1]):\n            X_markers_cluster_means_transpose[:,i] -= np.min(X_markers_cluster_means_transpose[:,i])\n            X_markers_cluster_means_transpose[:,i] /= np.max(X_markers_cluster_means_transpose[:,i])\n\n\n        Z = linkage(X_markers_cluster_means_transpose, 'ward')\n        ORDER = dendrogram(Z,no_plot=True,get_leaves=True)['leaves']\n\n        Z = linkage(X_markers_cluster_means_transpose.T, 'ward')\n        ORDER2 = dendrogram(Z,no_plot=True,get_leaves=True)['leaves']\n\n        X_markers_cluster_means_sorted = X_markers_cluster_means_transpose[ORDER,:][:,ORDER2]\n\n        df_marker_hits = df_markers_cluster_centroids.apply(lambda q: f(q,zscore_cutoff),axis=1)\n        df_marker_hits=pd.DataFrame.from_dict(dict(zip(df_marker_hits.index, df_marker_hits.values))).T\n        \n        X_marker_hits = df_marker_hits.values.T[ORDER,:][:,ORDER2]\n\n        fig,ax = plt.subplots(\n            figsize=\\\n              np.float_(X_markers_cluster_means_transpose.shape[::-1])/\\\n                        np.max(X_markers_cluster_means_transpose.shape)*20.0+2.0\n        )\n        ax.imshow(X_markers_cluster_means_sorted,cmap='Blues',interpolation='None')\n        ax.set_aspect('auto')\n\n        i_list,j_list = np.where(X_marker_hits.T>0)\n        ax.plot(i_list,j_list,'k*',mec='w')\n\n        ax.set_xticks(range(X_markers_cluster_means_transpose.shape[1]))\n        ax.set_yticks(range(X_markers_cluster_means_transpose.shape[0]))\n\n        xtickslabels = np.array(df_markers.index[ORDER2])\n\n        ax.set_xticklabels(xtickslabels,rotation=90)\n        ax.set_yticklabels([list(votingResults.values())[i]+' ('+str(i)+')' for i in ORDER])#,rotation=24,ha='right',va='top')\n\n        ax.set_xlim([-0.5,X_markers_cluster_means_transpose.shape[1]-0.5])\n        ax.set_ylim([-0.5,X_markers_cluster_means_transpose.shape[0]-0.5])\n\n        fig.tight_layout()\n        if saveDir is not None: fig.savefig('%s/%s_voting.png'%(saveDir,dataName),dpi=100)\n\n        print ('\\n================================\\nDone plotting marker expression!\\n================================')\n\n    \n    # Produce subplots on each marker and its expression on all clusters\n    # Params - df_expr: expression data\n    #          votingResults: voting results dictionary\n    #          X_tsne: expression data after tSNE projection\n    #          markers: list of markers\n    #          cellClusterIndexLabel: cluster labels for all cells\n    #          hugo_cd_dict: dictionary that converts gene names from aliases to hugos\n    #          dataName: name used in output files\n    #          saveDir: directory for output files\n    def MakeMarkerSubplot(self, df_expr, votingResults, X_tsne, markers, cellClusterIndexLabel, hugo_cd_dict, dataName, saveDir):\n        maxs = np.max(X_tsne,axis=1)\n        mins = np.min(X_tsne,axis=1)\n        maxDiffs = maxs - mins\n        deltas = maxDiffs*0.05\n        XLIM = [mins[0]-deltas[0],maxs[0]+deltas[0]]\n        YLIM = [mins[1]-deltas[1],maxs[1]+deltas[1]]\n\n        directory = '%s/marker_subplots/' % saveDir\n        if not os.path.exists(directory):\n            os.makedirs(directory)\n\n        q = int(np.ceil(np.sqrt(len(markers))))\n\n        fig,ax = plt.subplots(figsize=(8,8))\n\n        for counter,marker in enumerate(markers):\n                ax.cla()\n                if hugo_cd_dict[marker] == marker:\n                    label = marker\n                else:\n                    label = '%s (%s)' % (hugo_cd_dict[marker],marker)\n                ax.plot(np.nan,np.nan,'*',markersize=15,c=cm.seismic(1.0),label=label)\n                circleIndices = np.where(df_expr.loc[marker].values==0)[0] # cells that don't have this marker\n                starIndices = np.where(df_expr.loc[marker].values>0)[0] # cells that have this marker\n                starIndices = starIndices[np.argsort(df_expr.loc[marker].values[starIndices])]\n                args1 = [X_tsne[0,circleIndices],\n                         X_tsne[1,circleIndices]]\n                kwargs1 = {'marker':'o',\n                           'c':'b',\n                           'alpha':0.1,\n                           's':6*3,\n                           'linewidth':0,}\n                args2 = [X_tsne[0,starIndices],\n                         X_tsne[1,starIndices]]\n                kwargs2 = {'marker':'*',\n                           'c':cm.seismic(df_expr.loc[marker].values[starIndices]/np.max(df_expr.loc[marker].values[starIndices])),\n                           's':30*4,\n                           'linewidth':0.0,}\n                ax.scatter(*args1,**kwargs1)\n                ax.scatter(*args2,**kwargs2)\n                for label in set(cellClusterIndexLabel):\n                    # cells with this label\n                    X_tsne_cluster = X_tsne[:,cellClusterIndexLabel==label]\n                    x_mean = np.mean(X_tsne_cluster[0,:])\n                    y_mean = np.mean(X_tsne_cluster[1,:])\n                    ax.text(x_mean,y_mean,\n                            (list(votingResults.values())[label]).\n                                replace('-','-\\n').replace(' ','\\n').\n                                replace('T\\n','T ').replace('B\\n','B ').\n                                replace('\\n#',' #').replace('/','/\\n').\n                                replace('NK\\n','NK ').replace('Stem\\n','Stem '),\n                            fontsize=10,\n                            ha='center',va='center',#alpha=0.75,\n                            ).set_path_effects([path_effects.Stroke(linewidth=3, foreground='white'),path_effects.Normal()])\n                    radius = np.sqrt(X_tsne_cluster.shape[1])*300.0\n                    ax.scatter(x_mean,y_mean,s=radius*1,facecolors='none',edgecolors='k')\n                ax.set_xlim(XLIM)\n                ax.set_ylim(YLIM)\n                ax.legend(loc='best',numpoints=1,fontsize=12)\n                ax.set_xticks([]); ax.set_yticks([]); fig.tight_layout()\n                if saveDir is not None: \n                    fig.savefig('%s/marker_subplots/%s_%s_%s.png' % (saveDir,dataName,hugo_cd_dict[marker],marker),dpi=300)\n                    print ('\\n=========================\\nDone Saving marker subplot %s_%s labeled data!\\n=========================' % (hugo_cd_dict[marker],marker))\n                    \n                    \n    # Produce plot on clusters\n    # Params - df_votingResults: voting results dictionary\n    #          X_tsne: expression data after tSNE projection\n    #          cellClusterIndexLabel: cluster labels for all cells\n    #          dataName: name used in output files\n    #          saveDir: directory for output files\n    def MakeClusterPlot(self, votingResults, X_tsne, cellClusterIndexLabel, dataName, saveDir):\n        fig,ax = plt.subplots(figsize=(8,8))\n        for label in set(cellClusterIndexLabel):\n            # cells in that cluster, 2*number of cells\n            X_tsne_cluster = X_tsne[:,cellClusterIndexLabel==label]\n            # X_tsne_cluster = X_pca_2D[:,cellClusterIndexLabel==label]\n\n            ax.plot(X_tsne_cluster[0,:],X_tsne_cluster[1,:],'o',\n                    color=cm.jet(label*1.0/len(set(cellClusterIndexLabel))),mew=0.5,alpha=0.3,markeredgecolor='k')\n            x_mean = np.mean(X_tsne_cluster[0,:])\n            y_mean = np.mean(X_tsne_cluster[1,:])\n            # draw the line to the centroid\n            for i in range(X_tsne_cluster.shape[1]):\n                ax.plot([x_mean,X_tsne_cluster[0,i]],[y_mean,X_tsne_cluster[1,i]],'k-',alpha=0.03,zorder=-np.inf)\n            ax.text(x_mean,y_mean,\n                            list(votingResults.values())[label].\n                                replace('-','-\\n').replace(' ','\\n').\n                                replace('T\\n','T ').replace('B\\n','B ').\n                                replace('\\n#',' #').replace('/','/\\n').\n                                replace('NK\\n','NK ').replace('Stem\\n','Stem '),\n                    fontsize=10,\n                    ha='center',va='center',\n                    ).set_path_effects([path_effects.Stroke(linewidth=3, foreground='white'),path_effects.Normal()])\n\n        ax.set_xticks([])\n        ax.set_yticks([])\n\n\n        maxs = np.max(X_tsne,axis=1)\n        mins = np.min(X_tsne,axis=1)\n        maxDiffs = maxs - mins\n        deltas = maxDiffs*0.05\n        XLIM = [mins[0]-deltas[0],maxs[0]+deltas[0]]\n        YLIM = [mins[1]-deltas[1],maxs[1]+deltas[1]]\n        ax.set_xlim(XLIM)\n        ax.set_ylim(YLIM)\n\n        fig.tight_layout() \n        if saveDir is not None: fig.savefig('%s/%s_clusters.png'%(saveDir,dataName),dpi=300)\n        print ('\\n================================\\nDone plotting cluster image!\\n================================')\n    \n        \n    # Main function\n    # Params - df_expr: expression data\n    #          dataName: name used in output files\n    #          sigma_over_mean_sigma: threshold when keeping only genes with large enough standard deviation\n    #          n_clusters: number of clusters\n    #          n_components_pca: number of pca components\n    #          zscore_cutoff: zscore cutoff when calculating Z_mc\n    #          saveDir: directory for output files, if None then output not saved\n    #          marker_expression_plot: whether to produce marker expression plot\n    #          tSNE_cluster_plot: whether to produce cluster plot\n    #          save_processed_data: whether to save processed data\n    #          marker_subplot: whether to make subplots on markers\n    def Process(self, df_expr, dataName, sigma_over_mean_sigma=0.3, n_clusters=11, n_components_pca=100, zscore_cutoff=0.3, saveDir=None, marker_expression_plot=True, tSNE_cluster_plot=True, save_processed_data=True, marker_subplot=True, votingScheme=None):\n        print (\"MARK\")\n        np.random.seed(0)\n        gnc = GeneNameConverter.GeneNameConverter(dictDir='tools/pickledGeneConverterDict/ensembl_hugo_entrez_alias_dict.pythdat')\n        \n        if not os.path.exists(saveDir):\n            os.makedirs(saveDir)\n            \n        ##############################################################################################\n        # Normalize\n        ##############################################################################################\n        df_expr = self.Normalize(df_expr, sigma_over_mean_sigma, gnc)\n        \n        try:\n            if savedName != dataName: asdf\n            print ('\\n====================\\nAlready transformed!\\n====================')\n        except NameError:\n            X_pca, X_tsne = self.Project(df_expr, n_components_pca)\n            savedName = cdc(dataName)\n  \n\n        ##############################################################################################\n        # Cluster\n        ##############################################################################################\n        cellClusterIndexLabel, possible_cluster_labels = self.Cluster(X_pca, n_clusters)\n        \n        \n        ##############################################################################################\n        # Get dictionary to map from markers to cell types\n        ##############################################################################################\n        markerDict,hugo_cd_dict = MakeMarkerDict.MakeMarkerDict('geneLists/cd_marker_handbook2.xlsx',gnc=gnc)\n\n        print ('\\n=========================\\nDone loading marker data!\\n=========================')\n        \n        \n        ##############################################################################################\n        # Compute mean expression of each marker in each cluster\n        ##############################################################################################\n        markers = np.intersect1d(df_expr.index,list(markerDict.keys()))\n        df_markers = df_expr.loc[markers]\n        df_markers=df_markers.groupby(level=0).mean()\n        X_markers = df_markers.values\n\n        means = []\n        for cluster in possible_cluster_labels:\n            means.append( np.mean(X_markers[:,cellClusterIndexLabel==cluster],axis=1) )\n\n        X_markers_cluster_means = np.vstack(means).T\n\n        # This is the marker/centroid matrix Y_mc\n        df_markers_cluster_centroids = pd.DataFrame(X_markers_cluster_means,\n                                                    index=markers,\n                                                    columns=possible_cluster_labels)\n\n        print ('\\n=================================\\nDone identifying cluster markers!\\n=================================')\n        \n        \n        ##############################################################################################\n        # Vote on cell type\n        ##############################################################################################\n        votingResults = self.Vote(df_markers_cluster_centroids, markerDict, zscore_cutoff, cellClusterIndexLabel, votingScheme, dataName, saveDir)\n        \n        \n        ##############################################################################################\n        # Plot mean marker expression for each cluster\n        ##############################################################################################\n        if marker_expression_plot:\n            self.MakeMarkerExpressionPlot(votingResults, X_markers_cluster_means, df_markers, df_markers_cluster_centroids, zscore_cutoff, dataName, saveDir)\n        \n        \n        ##############################################################################################\n        # tSNE picture of final clustering and cell types\n        ##############################################################################################\n        if tSNE_cluster_plot:\n            self.MakeClusterPlot(votingResults, X_tsne, cellClusterIndexLabel, dataName, saveDir)\n           \n        \n        ##############################################################################################\n        # Save labelled expression data to disk\n        ##############################################################################################\n        if save_processed_data:\n            self.SaveProcessedData(df_expr, votingResults, cellClusterIndexLabel, dataName, saveDir)\n        \n        \n        ##############################################################################################\n        # Make a bunch of tSNE plots showing relative expression of different markers\n        ##############################################################################################\n        if marker_subplot:\n            self.MakeMarkerSubplot(df_expr, votingResults, X_tsne, markers, cellClusterIndexLabel, hugo_cd_dict, dataName, saveDir)\n            \n        return cellClusterIndexLabel","sub_path":"scripts/DigitalCellSorter.py","file_name":"DigitalCellSorter.py","file_ext":"py","file_size_in_byte":27354,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"139540047","text":"#!/usr/bin/env python\r\n# pylint: disable=C0116\r\n# This program is dedicated to the public domain under the CC0 license.\r\n\r\n\"\"\"\r\nSimple Bot to reply to Telegram messages.\r\nFirst, a few handler functions are defined. Then, those functions are passed to\r\nthe Dispatcher and registered at their respective places.\r\nThen, the bot is started and runs until we press Ctrl-C on the command line.\r\nUsage:\r\nBasic Echobot example, repeats messages.\r\nPress Ctrl-C on the command line or send a signal to the process to stop the\r\nbot.\r\n\"\"\"\r\n\r\nimport logging\r\nfrom time import gmtime, strftime\r\nfrom telegram import Update, ForceReply, InlineKeyboardButton\r\nfrom telegram.ext import Updater, CommandHandler, MessageHandler, Filters, CallbackContext\r\nfrom googletrans import Translator  #pip install googletrans==3.1.0a0\r\n\r\n\r\n# Enable logging\r\nlogging.basicConfig(\r\n    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', level=logging.INFO\r\n)\r\n\r\nlogger = logging.getLogger(__name__)\r\n\r\n\r\n# Define a few command handlers. These usually take the two arguments update and\r\n# context.\r\ndef start(update: Update, _: CallbackContext) -> None:\r\n    \"\"\"Send a message when the command /start is issued.\"\"\"\r\n    user = update.effective_user\r\n    update.message.reply_markdown_v2(\r\n        fr'Hi {user.mention_markdown_v2()}\\!',\r\n        \r\n    )\r\n    update.message.reply_text(\"Send me text in any language and I will translate and send this in Hindi with pronunciation.😌😎\",\r\n        reply_markup=ForceReply(selective=True)\r\n    )\r\n\r\n    update.message.reply_text(\"Please visit my Youtube channel and Subscirbe it.!!😚\\nhttps://t.ly/2PrB\")\r\n\r\n\r\ndef time_cmd(update: Update, _: CallbackContext) -> None:\r\n    \"\"\"Send a current time when the command /time is issued.\"\"\"\r\n    \r\n    c_time = strftime(\"%H:%M:%S\", gmtime())\r\n    t_date = strftime(\"%d-%m-%Y\",gmtime())\r\n    update.message.reply_text(f\"Today's Date📅 : {t_date}\\nCurrent Time⏲ : {c_time}\")\r\n\r\ndef stop(update: Update, _:CallbackContext) -> None:\r\n    \"\"\"Send a message when the command /help is issued.\"\"\"\r\n    update.message.reply_text(\"Hey dude!\\nWhy are you stopping this bot?\\nThis is so useful bot.🤓😉\")\r\n\r\ndef help_command(update: Update, _: CallbackContext) -> None:\r\n    \"\"\"Send a message when the command /help is issued.\"\"\"\r\n    update.message.reply_text(\"Contact me at💬: @mr_vaibhavmaheshwari_chatbot\")\r\n\r\ndef contact(update, context):\r\n    \"\"\"Send a message when the command /contact is issued.\"\"\"\r\n    update.message.reply_text(\"Contact me at💬: @mr_vaibhavmaheshwari_chatbot \\nOr \\nClick Here: /help\")\r\n\r\n\r\ndef translate_cmd(update: Update, _: CallbackContext) -> None:\r\n\r\n    \"\"\"Echo the user message.\"\"\"\r\n    language_1 = update.message.text\r\n    cl = \"hi\"\r\n    tr = Translator()\r\n    output = tr.translate(language_1, dest=cl)\r\n    msg_out = output.text\r\n    proun = output.pronunciation\r\n    if proun == None:\r\n        prounc = \"Not Available!!\"\r\n    else:\r\n        prounc = proun\r\n\r\n    update.message.reply_text(f\"From : English\\nTo : Hindi \\nText : {language_1}\\nTranslate : {msg_out}\\nProunciation : {prounc}\")\r\n    update.message.reply_text(\"Keep Smiling, Be Happy!!😇😇\")\r\n\r\n\r\ndef main() -> None:\r\n    \"\"\"Start the bot.\"\"\"\r\n    # Create the Updater and pass it your bot's token.\r\n    api_token = \"Paste your Api token here\" #Go to botfather and get your api.\r\n    updater = Updater(api_token)\r\n\r\n    # Get the dispatcher to register handlers\r\n    dispatcher = updater.dispatcher\r\n\r\n    # on different commands - answer in Telegram\r\n    dispatcher.add_handler(CommandHandler(\"start\", start))\r\n    dispatcher.add_handler(CommandHandler(\"help\", help_command))\r\n    dispatcher.add_handler(CommandHandler(\"contact\",contact))\r\n    dispatcher.add_handler(CommandHandler(\"stop\",stop))\r\n    dispatcher.add_handler(CommandHandler(\"time\",time_cmd))\r\n\r\n    # on non command i.e message - translate the message on Telegram\r\n    dispatcher.add_handler(MessageHandler(Filters.text & ~Filters.command, translate_cmd))\r\n\r\n    # Start the Bot\r\n    updater.start_polling()\r\n\r\n    # Run the bot until you press Ctrl-C or the process receives SIGINT,\r\n    # SIGTERM or SIGABRT. This should be used most of the time, since\r\n    # start_polling() is non-blocking and will stop the bot gracefully.\r\n    updater.idle()\r\n\r\n\r\nif __name__ == '__main__':\r\n    main()\r\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4336,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"575857660","text":"import numpy as np\nfrom verification_tools import calc_limits\n\nconfigs = [{'aperture':'lw','filter':'f250m','disperser':'grismr','bounds':(2.421,2.581)},\n           {'aperture':'lw','filter':'f277w','disperser':'grismr','bounds':(2.421,3.09)},\n           {'aperture':'lw','filter':'f300m','disperser':'grismr','bounds':(2.848,3.137)},\n           {'aperture':'lw','filter':'f322w2','disperser':'grismr','bounds':(2.451,3.958)},\n           {'aperture':'lw','filter':'f335m','disperser':'grismr','bounds':(3.207,3.502)},\n           {'aperture':'lw','filter':'f356w','disperser':'grismr','bounds':(3.152,3.942)},\n           {'aperture':'lw','filter':'f360m','disperser':'grismr','bounds':(3.442,3.777)},\n           {'aperture':'lw','filter':'f410m','disperser':'grismr','bounds':(3.914,4.257)},\n           {'aperture':'lw','filter':'f430m','disperser':'grismr','bounds':(4.195,4.367)},\n           {'aperture':'lw','filter':'f444w','disperser':'grismr','bounds':(3.929,4.949)},\n           {'aperture':'lw','filter':'f460m','disperser':'grismr','bounds':(4.543,4.713)},\n           {'aperture':'lw','filter':'f480m','disperser':'grismr','bounds':(4.693,4.921)}]\n\napertures = np.array([5,5,5,5,5,5,5,5,5,5,5,5])*0.0648\nidt_fluxes = np.array([1e-2, 1e-2,1e-2,1e-2,1e-2,1e-2,1e-2,1e-2,1e-2,1e-2,1e-2,1e-2])\nskyfacs = [2,2,2,2,2,2,2,2,2,2,2,2]\nobsmode = {\n           'instrument': 'nircam',\n           'mode': 'wfgrism',\n           'filter': 'f090w',\n           'aperture': 'lw',\n           'disperser': 'grismr'\n           }\nexp_config = {\n              'subarray': 'full',\n              'readout_pattern': 'deep8',\n              'ngroup': 5,\n              'nint': 1,\n              'nexp': 10\n              }\nstrategy = {\n            'method': 'specapphot',\n            'aperture_size': 0.15,\n            'sky_annulus': [0.16,0.5],\n            'background_subtraction': False,\n            'target_xy': [0.0, 0.0],\n            \"units\": \"arcsec\"\n            }\n\noutputs_regular, outputs_one = calc_limits.calc_limits(configs,apertures,idt_fluxes,obsmode=obsmode,scanfac=10000000,skyfacs=skyfacs,nflx=80,\n                                 exp_config=exp_config,strategy=strategy,background='minzodi12')\n\nnp.savez('../../outputs/nircam_wfgrism_sensitivity.npz',\n    wavelengths=outputs_regular['wavelengths'], sns=outputs_regular['sns'], lim_fluxes=outputs_regular['lim_fluxes'], sat_limits=outputs_regular['sat_limits'], configs=outputs_regular['configs'])\n\nnp.savez('../../outputs/nircam_wfgrism_sensitivity_one.npz',\n    wavelengths=outputs_one['wavelengths'], sns=outputs_one['sns'], lim_fluxes=outputs_one['lim_fluxes'], sat_limits=outputs_one['sat_limits'], configs=outputs_one['configs'])\n","sub_path":"tests/nircam/nircam_sensitivity_wfgrism.py","file_name":"nircam_sensitivity_wfgrism.py","file_ext":"py","file_size_in_byte":2680,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"555261230","text":"from typing import List\n\n\nclass Solution:\n    def kLengthApart(self, nums: List[int], k: int) -> bool:\n\n        if len(nums) <= 1:\n            return True\n        cnt = 0\n        try:\n            index = nums.index(1)\n        except:\n            return True\n        for i, v in enumerate(nums[index + 1:]):\n            if v == 0:\n                cnt += 1\n            else:\n                if cnt < k:\n                    return False\n                cnt = 0\n            # print(cnt, i, v)\n        return True\n\n\nif __name__ == '__main__':\n    S = Solution()\n    print(S.kLengthApart([1, 0, 0, 0, 1, 0, 0, 1], 2))\n    print(S.kLengthApart([1, 1, 1, 0], 3))\n","sub_path":"187/2.py","file_name":"2.py","file_ext":"py","file_size_in_byte":655,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"607393334","text":"import errno\nimport json\nimport logging\nimport os\nimport psutil\nimport re\nimport signal\nimport socket\nimport stat\nimport subprocess\nimport sys\nimport time\nfrom tempfile import NamedTemporaryFile\nimport threading\nimport yaml\nimport numbers\nimport inspect\n\nimport click\nfrom shortuuid import ShortUUID\nimport six\nfrom six.moves import queue\nimport requests\nfrom watchdog.observers import Observer\nfrom watchdog.events import PatternMatchingEventHandler\nimport webbrowser\n\nimport wandb\nfrom wandb.apis.file_stream import BinaryFilePolicy, CRDedupeFilePolicy, DefaultFilePolicy, OverwriteFilePolicy\nfrom wandb import env\nfrom wandb import Error\nfrom wandb import io_wrap\nfrom wandb import jsonlfile\nfrom wandb import file_pusher\nfrom wandb import meta\nfrom wandb.core import START_TIME\nimport wandb.rwlock\nfrom wandb import sparkline\nfrom wandb import stats\nfrom wandb import streaming_log\nfrom wandb import util\nfrom wandb import wandb_config as config\nfrom wandb import wandb_run\nfrom wandb import wandb_socket\n\n\nlogger = logging.getLogger(__name__)\n\n\nOUTPUT_FNAME = 'output.log'\nDEBUG_FNAME = 'wandb-debug.log'\n\n\nclass LaunchError(Error):\n    \"\"\"Raised when there's an error starting up.\"\"\"\n\n\nclass FileTailer(object):\n    def __init__(self, path, on_read_fn, binary=False, seek_end=False):\n        self._path = path\n        mode = 'r'\n        if binary:\n            mode = 'rb'\n        self._file = open(path, mode)\n        if seek_end:\n            self._file.seek(0, 2)  # seek to 0 bytes from end (2 means end)\n        self._on_read_fn = on_read_fn\n        self.running = True\n        self._thread = threading.Thread(target=self._thread_body)\n        self._thread.start()\n\n    def _thread_body(self):\n        while self.running:\n            where = self._file.tell()\n            data = self._file.read(1024)\n            if not data:\n                time.sleep(1)\n                # required for to get python2 working (Issue #50)\n                self._file.seek(where)\n            else:\n                self._on_read_fn(data)\n\n    def stop(self):\n        self._file.seek(0)\n        self.running = False\n        self._thread.join()\n\n\nclass FileEventHandler(object):\n    def __init__(self, file_path, save_name, api):\n        self.file_path = file_path\n        self.save_name = save_name\n        self._api = api\n\n    def on_created(self):\n        pass\n\n    def on_modified(self):\n        pass\n\n    def on_renamed(self, new_path, new_name):\n        self.file_path = new_path\n        self.save_name = new_name\n\n    def finish(self):\n        pass\n\n\nclass FileEventHandlerOverwrite(FileEventHandler):\n    def __init__(self, file_path, save_name, api, file_pusher, *args, **kwargs):\n        super(FileEventHandlerOverwrite, self).__init__(\n            file_path, save_name, api, *args, **kwargs)\n        self._file_pusher = file_pusher\n\n    def on_created(self):\n        self.on_modified()\n\n    def on_modified(self):\n        # Tell file_pusher to copy the file, we want to allow the user to modify the\n        # original while this one is uploading (modifying while uploading seems to\n        # cause a hang somewhere in the google upload code, until the server times out)\n        self._file_pusher.file_changed(\n            self.save_name, self.file_path, copy=True)\n\n\nclass FileEventHandlerOverwriteDeferred(FileEventHandler):\n    def __init__(self, file_path, save_name, api, file_pusher, *args, **kwargs):\n        super(FileEventHandlerOverwriteDeferred, self).__init__(\n            file_path, save_name, api, *args, **kwargs)\n        self._file_pusher = file_pusher\n\n    def finish(self):\n        self._file_pusher.file_changed(self.save_name, self.file_path)\n\n\nclass FileEventHandlerConfig(FileEventHandler):\n    \"\"\"Set the config instead of uploading the file\"\"\"\n    RATE_LIMIT_SECONDS = 30\n\n    def __init__(self, file_path, save_name, api, file_pusher, run, *args, **kwargs):\n        self._api = api\n        super(FileEventHandlerConfig, self).__init__(\n            file_path, save_name, api, *args, **kwargs)\n        self._last_sent = time.time() - self.RATE_LIMIT_SECONDS\n        self._file_pusher = file_pusher\n        self._run = run\n        self._thread = None\n\n    def on_created(self):\n        self._eventually_update()\n\n    def on_modified(self):\n        self._eventually_update()\n\n    def _eventually_update(self):\n        if self._thread:\n            # assume the existing thread will catch this update\n            return\n\n        if time.time() - self._last_sent >= self.RATE_LIMIT_SECONDS:\n            self._update()\n        else:\n            self._thread = threading.Timer(\n                self.RATE_LIMIT_SECONDS, self._thread_update)\n            self._thread.start()\n\n    def _thread_update(self):\n        try:\n            self._update()\n        finally:\n            self._thread = None\n\n    def _update(self):\n        try:\n            config_dict = yaml.load(open(self.file_path))\n        except yaml.parser.ParserError:\n            wandb.termlog(\n                \"Unable to parse config file; probably being modified by user process?\")\n            return\n\n        # TODO(adrian): ensure the file content will exactly match Bucket.config\n        # ie. push the file content as a string\n        self._api.upsert_run(id=self._run.storage_id, config=config_dict)\n        self._file_pusher.file_changed(\n            self.save_name, self.file_path, copy=True)\n        self._last_sent = time.time()\n\n    def finish(self):\n        if self._thread:\n            self._thread.join()\n            self._thread = None\n\n        self._update()\n\n\nclass FileEventHandlerSummary(FileEventHandler):\n    \"\"\"Read the file and add to the file push api\"\"\"\n\n    def __init__(self, file_path, save_name, api, file_pusher, run, *args, **kwargs):\n        super(FileEventHandlerSummary, self).__init__(\n            file_path, save_name, api, *args, **kwargs)\n        self._api = api\n        self._file_pusher = file_pusher\n\n    def on_created(self):\n        self.on_modified()\n\n    def on_modified(self):\n        self._api.get_file_stream_api().push(self.save_name, open(self.file_path).read())\n\n    def finish(self):\n        self._file_pusher.file_changed(self.save_name, self.file_path)\n\n\nclass FileEventHandlerTextStream(FileEventHandler):\n    def __init__(self, *args, **kwargs):\n        self._seek_end = kwargs.pop('seek_end', None)\n        super(FileEventHandlerTextStream, self).__init__(*args, **kwargs)\n        self._tailer = None\n\n    def on_created(self):\n        if self._tailer:\n            logger.error(\n                'Streaming file created twice in same run: %s', self.file_path)\n            return\n        self._setup()\n\n    def on_modified(self):\n        if self._tailer:\n            return\n        self._setup()\n\n    def _setup(self):\n        fsapi = self._api.get_file_stream_api()\n        pusher = streaming_log.TextStreamPusher(fsapi, self.save_name)\n\n        def on_read(data):\n            pusher.write_string(data)\n\n        self._tailer = FileTailer(\n            self.file_path, on_read, seek_end=self._seek_end)\n\n    def finish(self):\n        if self._tailer:\n            self._tailer.stop()\n            self._tailer = None\n\n\nclass FileEventHandlerBinaryStream(FileEventHandler):\n    def __init__(self, *args, **kwargs):\n        super(FileEventHandlerBinaryStream, self).__init__(*args, **kwargs)\n        self._tailer = None\n\n    def on_created(self):\n        if self._tailer:\n            logger.error(\n                'Streaming file created twice in same run: %s', self.file_path)\n            return\n        self._setup()\n\n    def on_modified(self):\n        if self._tailer:\n            return\n        self._setup()\n\n    def _setup(self):\n        fsapi = self._api.get_file_stream_api()\n\n        def on_read(data):\n            fsapi.push(self.save_name, data)\n\n        self._tailer = FileTailer(self.file_path, on_read, binary=True)\n\n\nclass WriteSerializingFile(object):\n    \"\"\"Wrapper for a file object that serializes writes.\n    \"\"\"\n\n    def __init__(self, f):\n        self.lock = threading.Lock()\n        self.f = f\n\n    def write(self, *args, **kargs):\n        self.lock.acquire()\n        try:\n            self.f.write(*args, **kargs)\n            self.f.flush()\n        finally:\n            self.lock.release()\n\n\nclass Process(object):\n    \"\"\"Represents a running process with an interface that\n    mimics Popen's.\n\n    Only works on Unix-y systems.\n\n    TODO(adrian): probably rewrite using psutil.Process\n    \"\"\"\n\n    def __init__(self, pid):\n        self.returncode = None\n        self.pid = pid\n\n    def poll(self):\n        if self.returncode is None:\n            try:\n                os.kill(self.pid, 0)\n            except OSError as err:\n                if err.errno == errno.ESRCH:\n                    # ESRCH == No such process\n                    # we have no way of getting the real return code, so just set it to 0\n                    self.returncode = 0\n                elif err.errno == errno.EPERM:\n                    # EPERM clearly means there's a process to deny access to\n                    pass\n                else:\n                    # According to \"man 2 kill\" possible error values are\n                    # (EINVAL, EPERM, ESRCH)\n                    raise\n\n        return self.returncode\n\n    def wait(self):\n        while self.poll() is None:\n            time.sleep(1)\n\n    def interrupt(self):\n        os.kill(self.pid, signal.SIGINT)\n\n    def terminate(self):\n        os.kill(self.pid, signal.SIGTERM)\n\n    def kill(self):\n        os.kill(self.pid, signal.SIGKILL)\n\n\nclass RunManager(object):\n    \"\"\"Manages a run's process, wraps its I/O, and synchronizes its files.\n    \"\"\"\n\n    def __init__(self, api, run, project=None, tags=[], cloud=True, job_type=\"train\", output=True, port=None):\n        self._api = api\n        self._run = run\n        self._cloud = cloud\n        self._port = port\n\n        self._project = project if project else api.settings(\"project\")\n        self._tags = tags\n        self._watch_dir = self._run.dir\n\n        self._config = run.config\n        self.job_type = job_type\n        self.url = self._run.get_url(api)\n\n        # We lock this when the backend is down so Watchdog will keep track of all\n        # the file events that happen. Then, when the backend comes back up, we unlock\n        # it so all the outstanding events will get handled properly. Watchdog's queue\n        # only keeps at most one event per file.\n        # Counterintuitively, we use the \"reader\" locking to guard writes to the W&B\n        # backend, and the \"writer\" locking to indicate that the backend is down. That\n        # way, users of the W&B API won't block each other, but can all be\n        # blocked by grabbing a \"writer\" lock.\n        self._file_event_lock = wandb.rwlock.RWLock()\n        # It starts acquired. We release it when we want to allow the events to happen.\n        # (ie. after the Run is successfully created)\n        self._file_event_lock.writer_enters()\n\n        self._event_handlers = {}\n\n        self._handler = PatternMatchingEventHandler()\n        self._handler.on_created = self._on_file_created\n        self._handler.on_modified = self._on_file_modified\n        self._handler.on_moved = self._on_file_moved\n        self._handler._patterns = [\n            os.path.join(self._watch_dir, os.path.normpath('*'))]\n        # Ignore hidden files/folders and output.log because we stream it specially\n        self._handler._ignore_patterns = [\n            '*/.*',\n            '*.tmp',\n            os.path.join(self._run.dir, OUTPUT_FNAME),\n            os.path.join(self._run.dir, DEBUG_FNAME)\n        ]\n        for glob in self._api.settings(\"ignore_globs\"):\n            self._handler._ignore_patterns.append(\n                os.path.join(self._run.dir, glob))\n\n        self._observer = Observer()\n        self._observer.schedule(self._handler, self._watch_dir, recursive=True)\n\n        self._stats = stats.Stats()\n        # Calling .start() on _meta and _system_stats will spin a thread that reports system stats every 30 seconds\n        self._system_stats = stats.SystemStats(run, api)\n        self._meta = meta.Meta(api, self._run.dir)\n        self._meta.data[\"jobType\"] = job_type\n        if self._run.program:\n            self._meta.data[\"program\"] = self._run.program\n        self._file_pusher = file_pusher.FilePusher(self._push_function)\n\n        self._socket = wandb_socket.Client(self._port)\n\n        logger.debug(\"Initialized sync for %s/%s\", self._project, self._run.id)\n        # Manually initialize the debug handler\n        self._get_handler(os.path.join(\n            self._run.dir, DEBUG_FNAME), DEBUG_FNAME).on_created()\n\n        if self._cloud:\n            self._observer.start()\n\n            self._api.save_patches(self._watch_dir)\n\n            if output:\n                wandb.termlog(\"Syncing %s\" % self.url)\n                wandb.termlog(\"Run `wandb off` to turn off syncing.\")\n                wandb.termlog(\"Local directory: %s\" % os.path.relpath(run.dir))\n\n            self._api.get_file_stream_api().set_file_policy(\n                OUTPUT_FNAME, CRDedupeFilePolicy())\n\n    \"\"\" FILE SYNCING / UPLOADING STUFF \"\"\"\n\n    # TODO: limit / throttle the number of adds / pushes\n    def _on_file_created(self, event):\n        logger.info('file/dir created: %s', event.src_path)\n        if os.path.isdir(event.src_path):\n            return None\n        save_name = os.path.relpath(event.src_path, self._watch_dir)\n        self._file_event_lock.await_readable()\n        self._get_handler(event.src_path, save_name).on_created()\n\n    def _on_file_modified(self, event):\n        logger.info('file/dir modified: %s', event.src_path)\n        if os.path.isdir(event.src_path):\n            return None\n        save_name = os.path.relpath(event.src_path, self._watch_dir)\n        self._file_event_lock.await_readable()\n        self._get_handler(event.src_path, save_name).on_modified()\n\n    def _on_file_moved(self, event):\n        logger.info('file/dir moved: %s -> %s', event.src_path, event.dest_path)\n        if os.path.isdir(event.dest_path):\n            return None\n        old_save_name = os.path.relpath(event.src_path, self._watch_dir)\n        new_save_name = os.path.relpath(event.dest_path, self._watch_dir)\n        self._file_event_lock.await_readable()\n\n        # We have to move the existing file handler to the new name, and update the stats\n        handler = self._get_handler(event.src_path, old_save_name)\n        self._event_handlers[new_save_name] = handler\n        del self._event_handlers[old_save_name]\n        self._stats.rename_file(event.src_path, event.dest_path)\n\n        handler.on_renamed(event.dest_path, new_save_name)\n\n    def _get_handler(self, file_path, save_name):\n        if not save_name.startswith(\"media/\") and save_name not in [\n                'wandb-history.jsonl', 'wandb-events.jsonl', 'wandb-summary.json']:\n            # Don't show stats on media files\n            self._stats.update_file(file_path)\n        if save_name not in self._event_handlers:\n            if save_name == 'wandb-history.jsonl':\n                self._event_handlers['wandb-history.jsonl'] = FileEventHandlerTextStream(\n                    file_path, 'wandb-history.jsonl', self._api)\n            elif save_name == 'wandb-events.jsonl':\n                self._event_handlers['wandb-events.jsonl'] = FileEventHandlerTextStream(\n                    file_path, 'wandb-events.jsonl', self._api)\n            # Don't try to stream tensorboard files for now.\n            # elif 'tfevents' in save_name:\n            #    # TODO: This is hard-coded, but we want to give users control\n            #    # over streaming files (or detect them).\n            #    self._api.get_file_stream_api().set_file_policy(save_name,\n            #                                                    BinaryFilePolicy())\n            #    self._event_handlers[save_name] = FileEventHandlerBinaryStream(\n            #        file_path, save_name, self._api)\n            # Overwrite handler (non-deferred) has a bug, wherein if the file is truncated\n            # during upload, the request to Google hangs (at least, this is my working\n            # theory). So for now we defer uploading everything til the end of the run.\n            # TODO: send wandb-summary during run. One option is to copy to a temporary\n            # file before uploading.\n            elif save_name == config.FNAME:\n                self._event_handlers[save_name] = FileEventHandlerConfig(\n                    file_path, save_name, self._api, self._file_pusher, self._run)\n            elif save_name == 'wandb-summary.json':\n                # Load the summary into the syncer process for meta etc to work\n                self._run.summary.load()\n                self._api.get_file_stream_api().set_file_policy(save_name, OverwriteFilePolicy())\n                self._event_handlers[save_name] = FileEventHandlerSummary(\n                    file_path, save_name, self._api, self._file_pusher, self._run)\n            elif save_name.startswith('media/'):\n                # Save media files immediately\n                self._event_handlers[save_name] = FileEventHandlerOverwrite(\n                    file_path, save_name, self._api, self._file_pusher)\n            else:\n                self._event_handlers[save_name] = FileEventHandlerOverwriteDeferred(\n                    file_path, save_name, self._api, self._file_pusher)\n        return self._event_handlers[save_name]\n\n    def _finish_handlers(self):\n        # TODO: there was a case where _event_handlers was getting modified in the loop.\n        for handler in list(self._event_handlers.values()):\n            handler.finish()\n\n    def _push_function(self, save_name, path):\n        try:\n            with open(path, 'rb') as f:\n                self._api.push(self._project, {save_name: f}, run=self._run.id,\n                               progress=lambda _, total: self._stats.update_progress(path, total))\n        except (OSError, IOError):\n            pass\n\n    \"\"\" RUN MANAGEMENT STUFF \"\"\"\n\n    def mirror_stdout_stderr(self):\n        \"\"\"Simple STDOUT and STDERR mirroring used by _init_jupyter\"\"\"\n        # TODO: Ideally we could start collecting logs without pushing\n        fs_api = self._api.get_file_stream_api()\n        io_wrap.SimpleTee(sys.stdout, streaming_log.TextStreamPusher(\n            fs_api, OUTPUT_FNAME, prepend_timestamp=True))\n        io_wrap.SimpleTee(sys.stderr, streaming_log.TextStreamPusher(\n            fs_api, OUTPUT_FNAME, prepend_timestamp=True, line_prepend='ERROR'))\n\n    def _get_stdout_stderr_streams(self):\n        \"\"\"Sets up STDOUT and STDERR streams. Only call this once.\"\"\"\n        if six.PY2 or \"buffer\" not in dir(sys.stdout):\n            stdout = sys.stdout\n            stderr = sys.stderr\n        else:  # we write binary so grab the raw I/O objects in python 3\n            try:\n                stdout = sys.stdout.buffer.raw\n                stderr = sys.stderr.buffer.raw\n            except AttributeError:\n                # The testing environment and potentially others may have screwed with their\n                # io so we fallback to raw stdout / err\n                stdout = sys.stdout.buffer\n                stderr = sys.stderr.buffer\n\n        output_log_path = os.path.join(self._run.dir, OUTPUT_FNAME)\n        self._output_log = WriteSerializingFile(open(output_log_path, 'wb'))\n\n        stdout_streams = [stdout, self._output_log]\n        stderr_streams = [stderr, self._output_log]\n\n        if self._cloud:\n            # Tee stdout/stderr into our TextOutputStream, which will push lines to the cloud.\n            fs_api = self._api.get_file_stream_api()\n            self._stdout_stream = streaming_log.TextStreamPusher(\n                fs_api, OUTPUT_FNAME, prepend_timestamp=True,\n                lock_function=self._file_event_lock.reader_enters)\n            self._stderr_stream = streaming_log.TextStreamPusher(\n                fs_api, OUTPUT_FNAME, line_prepend='ERROR',\n                prepend_timestamp=True,\n                lock_function=self._file_event_lock.reader_enters)\n\n            stdout_streams.append(self._stdout_stream)\n            stderr_streams.append(self._stderr_stream)\n\n        return stdout_streams, stderr_streams\n\n    def _close_stdout_stderr_streams(self, exitcode):\n        self._output_log.f.close()\n        self._output_log = None\n\n        # Close output-capturing stuff. This also flushes anything left in the buffers.\n        if self._stdout_tee.tee_file is not None:\n            # we don't have tee_file's in headless mode\n            self._stdout_tee.tee_file.close()\n            # TODO(adrian): we should close these even in headless mode\n            # but in python 2 the read thread doesn't stop on its own\n            # for some reason\n            self._stdout_tee.close_join()\n        if self._stderr_tee.tee_file is not None:\n            self._stderr_tee.tee_file.close()\n            self._stderr_tee.close_join()\n\n        if self._cloud:\n            # not set in dry run mode\n            self._stdout_stream.close()\n            self._stderr_stream.close()\n            self._api.get_file_stream_api().finish(exitcode)\n            # Ensures we get a new file stream thread\n            self._api._file_stream_api = None\n\n    def _setup_resume(self, resume_status):\n        # write the tail of the history file\n        try:\n            history_tail = json.loads(resume_status['historyTail'])\n            jsonlfile.write_jsonl_file(os.path.join(self._run.dir, wandb_run.HISTORY_FNAME),\n                                       history_tail)\n        except ValueError:\n            print(\"warning: couldn't load recent history\")\n\n        # write the tail of the events file\n        try:\n            events_tail = json.loads(resume_status['eventsTail'])\n            jsonlfile.write_jsonl_file(os.path.join(self._run.dir, wandb_run.EVENTS_FNAME),\n                                       events_tail)\n        except ValueError:\n            print(\"warning: couldn't load recent events\")\n\n        # Note: these calls need to happen after writing the files above. Because the access\n        # to self._run.events below triggers events to initialize, but we need the previous\n        # events to be written before that happens.\n\n        # output.log\n        self._api.get_file_stream_api().set_file_policy(\n            OUTPUT_FNAME, CRDedupeFilePolicy(resume_status['logLineCount']))\n\n        # history\n        self._api.get_file_stream_api().set_file_policy(\n            wandb_run.HISTORY_FNAME, DefaultFilePolicy(\n                start_chunk_id=resume_status['historyLineCount']))\n        self._event_handlers[wandb_run.HISTORY_FNAME] = FileEventHandlerTextStream(\n            self._run.history.fname, wandb_run.HISTORY_FNAME, self._api, seek_end=True)\n\n        # events\n        self._api.get_file_stream_api().set_file_policy(\n            wandb_run.EVENTS_FNAME, DefaultFilePolicy(\n                start_chunk_id=resume_status['eventsLineCount']))\n        self._event_handlers[wandb_run.EVENTS_FNAME] = FileEventHandlerTextStream(\n            self._run.events.fname, wandb_run.EVENTS_FNAME, self._api, seek_end=True)\n\n    def init_run(self, env=None):\n        self._system_stats.start()\n        self._meta.start()\n        self._api.get_file_stream_api().start()\n        if self._cloud:\n            storage_id = None\n            if self._run.resume != 'never':\n                resume_status = self._api.run_resume_status(project=self._api.settings(\"project\"),\n                                                            entity=self._api.settings(\n                                                                \"entity\"),\n                                                            name=self._run.id)\n                if resume_status == None and self._run.resume == 'must':\n                    raise LaunchError(\n                        \"resume='must' but run (%s) doesn't exist\" % self._run.id)\n                if resume_status:\n                    print('Resuming run: %s' % self._run.get_url(self._api))\n                    self._setup_resume(resume_status)\n                    storage_id = resume_status['id']\n\n            if not self._upsert_run(False, storage_id, env):\n                self._upsert_run_thread = threading.Thread(\n                    target=self._upsert_run, args=(True, storage_id, env))\n                self._upsert_run_thread.daemon = True\n                self._upsert_run_thread.start()\n\n    def shutdown(self, exitcode=0):\n        \"\"\"Stops system stats, streaming handlers, and uploads files without output, used by wandb.monitor\"\"\"\n        self._system_stats.shutdown()\n        self._meta.shutdown()\n        self._finish_handlers()\n        self._file_pusher.shutdown()\n        self._api.get_file_stream_api().finish(exitcode)\n        # Ensures we get a new file stream thread\n        self._api._file_stream_api = None\n\n    def _upsert_run(self, retry, storage_id, env):\n        \"\"\"Upsert the Run (ie. for the first time with all its attributes)\n\n        Arguments:\n            retry: (bool) Whether to retry if the connection fails (ie. if the backend is down).\n                False is useful so we can start running the user process even when the W&B backend\n                is down, and let syncing finish later.\n        Returns:\n            True if the upsert succeeded, False if it failed because the backend is down.\n        Throws:\n            LaunchError on other failures\n        \"\"\"\n        if retry:\n            num_retries = None\n        else:\n            num_retries = 0  # no retries because we want to let the user process run even if the backend is down\n\n        try:\n            upsert_result = self._run.save(\n                id=storage_id, num_retries=num_retries, job_type=self.job_type, api=self._api)\n        except wandb.apis.CommError as e:\n            # TODO: Get rid of str contains check\n            if self._run.resume == 'never' and 'exists' in str(e):\n                raise LaunchError(\n                    \"resume='never' but run (%s) exists\" % self._run.id)\n            else:\n                if isinstance(e.exc, (requests.exceptions.HTTPError,\n                                      requests.exceptions.Timeout,\n                                      requests.exceptions.ConnectionError)):\n                    wandb.termerror(\n                        'Failed to connect to W&B. Retrying in the background.')\n                    return False\n\n                raise LaunchError(\n                    'Launch exception: {}, see {} for details.  To disable wandb set WANDB_MODE=dryrun'.format(e, util.get_log_file_path()))\n\n        self._run.set_environment(environment=env)\n\n        # unblock file syncing and console streaming, which need the Run to have a .storage_id\n        self._file_event_lock.writer_leaves()\n\n        return True\n\n    def run_user_process(self, program, args, env):\n        \"\"\"Launch a user process, capture its output, and sync its files to the backend.\n\n        This returns after the process has ended and syncing is done.\n        Captures ctrl-c's, signals, etc.\n        \"\"\"\n        stdout_streams, stderr_streams = self._get_stdout_stderr_streams()\n\n        if sys.platform == \"win32\":\n            # PTYs don't work in windows so we use pipes.\n            self._stdout_tee = io_wrap.Tee.pipe(*stdout_streams)\n            self._stderr_tee = io_wrap.Tee.pipe(*stderr_streams)\n            # Seems like the following actually isn't necessary on Windows\n            # TODO(adrian): we may need to do the following if we use pipes instead of PTYs\n            # because Python on Unix doesn't like writing UTF-8 to files\n            # tell child python interpreters we accept utf-8\n            # env['PYTHONIOENCODING'] = 'UTF-8'\n        else:\n            self._stdout_tee = io_wrap.Tee.pty(*stdout_streams)\n            self._stderr_tee = io_wrap.Tee.pty(*stderr_streams)\n\n        self._stdout_stream.write_string(\" \".join(psutil.Process(\n            os.getpid()).cmdline()) + \"\\n\\n\")\n\n        command = [program] + list(args)\n        runner = util.find_runner(program)\n        if runner:\n            command = runner + command\n        command = ' '.join(six.moves.shlex_quote(arg) for arg in command)\n\n        try:\n            self.proc = subprocess.Popen(\n                command,\n                env=env,\n                stdout=self._stdout_tee.tee_file,\n                stderr=self._stderr_tee.tee_file,\n                shell=True,\n            )\n            self._run.pid = self.proc.pid\n        except (OSError, IOError):\n            raise Exception('Could not find program: %s' % command)\n\n        self._sync_etc()\n\n    def wrap_existing_process(self, pid, stdout_read_fd, stderr_read_fd, port=None):\n        \"\"\"Do syncing, etc. for an already-running process.\n\n        This returns after the process has ended and syncing is done.\n        Captures ctrl-c's, signals, etc.\n        \"\"\"\n        stdout_read_file = os.fdopen(stdout_read_fd, 'rb')\n        stderr_read_file = os.fdopen(stderr_read_fd, 'rb')\n        stdout_streams, stderr_streams = self._get_stdout_stderr_streams()\n        self._stdout_tee = io_wrap.Tee(stdout_read_file, *stdout_streams)\n        self._stderr_tee = io_wrap.Tee(stderr_read_file, *stderr_streams)\n\n        self.proc = Process(pid)\n        self._run.pid = pid\n\n        try:\n            self.init_run()\n        except LaunchError as e:\n            wandb.termerror(str(e))\n            self._socket.launch_error()\n            return\n\n        # Signal the main process that we're all hooked up\n        self._socket.ready()\n\n        self._sync_etc(headless=True)\n\n    def _sync_etc(self, headless=False):\n        # Ignore SIGQUIT (ctrl-\\). The child process will # handle it, and we'll\n        # exit when the child process does.\n        #\n        # We disable these signals after running the process so the child doesn't\n        # inherit this behaviour.\n        try:\n            signal.signal(signal.SIGQUIT, signal.SIG_IGN)\n        except AttributeError:  # SIGQUIT doesn't exist on windows\n            pass\n\n        if self._api.update_available:\n            wandb.termlog(\n                \"An update is available!  To upgrade, please run:\\n $ pip install wandb --upgrade\")\n        # Add a space before user output\n        wandb.termlog()\n\n        if env.get_show_run():\n            webbrowser.open_new_tab(self._run.get_url(self._api))\n\n        exitcode = None\n        try:\n            while True:\n                res = bytearray()\n                try:\n                    res = self._socket.recv(2)\n                except socket.timeout:\n                    pass\n                if len(res) == 2 and res[0] == 2:\n                    exitcode = res[1]\n                    break\n                elif len(res) > 0:\n                    wandb.termerror(\n                        \"Invalid message received from child process: %s\" % str(res))\n                    break\n                else:\n                    exitcode = self.proc.poll()\n                    if exitcode is not None:\n                        break\n                    time.sleep(1)\n        except KeyboardInterrupt:\n            exitcode = 255\n            if headless:\n                wandb.termlog('Ctrl-c pressed.')\n            else:\n                wandb.termlog(\n                    'Ctrl-c pressed; waiting for program to end. Press ctrl-c again to kill it.')\n                try:\n                    while self.proc.poll() is None:\n                        time.sleep(0.1)\n                except KeyboardInterrupt:\n                    pass\n\n                if self.proc.poll() is None:\n                    wandb.termlog('Program still alive. Killing it.')\n                    try:\n                        self.proc.kill()\n                    except OSError:\n                        pass\n\n        \"\"\"TODO(adrian): garbage that appears in the logs sometimes\n\n        Exception ignored in: <bound method Popen.__del__ of <subprocess.Popen object at 0x111adce48>>\n        Traceback (most recent call last):\n          File \"/Users/adrian/.pyenv/versions/3.6.0/Python.framework/Versions/3.6/lib/python3.6/subprocess.py\", line 760, in __del__\n        AttributeError: 'NoneType' object has no attribute 'warn'\n        \"\"\"\n\n        if exitcode is None:\n            exitcode = 254\n            wandb.termlog(\n                'Killing program failed; syncing files anyway. Press ctrl-c to abort syncing.')\n        else:\n            if exitcode == 0:\n                wandb.termlog('Program ended.')\n            else:\n                wandb.termlog(\n                    'Program failed with code %d. Press ctrl-c to abort syncing.' % exitcode)\n        #termlog('job (%s) Process exited with code: %s' % (program, exitcode))\n\n        self._meta.data[\"exitcode\"] = exitcode\n        if exitcode == 0:\n            self._meta.data[\"state\"] = \"finished\"\n        elif exitcode == 255:\n            self._meta.data[\"state\"] = \"killed\"\n        else:\n            self._meta.data[\"state\"] = \"failed\"\n\n        self._meta.shutdown()\n        self._system_stats.shutdown()\n\n        if exitcode != 0 and time.time() - START_TIME < 30:\n            wandb.termlog(\"Process crashed early, not syncing files\")\n            sys.exit(exitcode)\n\n        # TODO: these can be slow to complete\n        self._close_stdout_stderr_streams(exitcode)\n\n        # If we're not syncing to the cloud, we're done\n        if not self._cloud:\n            sys.exit(exitcode)\n\n        # Show run summary/history\n        self._run.summary.load()\n        summary = self._run.summary._summary\n        if len(summary):\n            wandb.termlog('Run summary:')\n            max_len = max([len(k) for k in summary.keys()])\n            format_str = '  {:>%s} {}' % max_len\n            for k, v in summary.items():\n                # arrays etc. might be too large. for now we just don't print them\n                if isinstance(v, six.string_types):\n                    if len(v) >= 20:\n                        v = v[:20] + '...'\n                    wandb.termlog(format_str.format(k, v))\n                elif isinstance(v, numbers.Number):\n                    wandb.termlog(format_str.format(k, v))\n\n        self._run.history.load()\n        history_keys = self._run.history.keys()\n        if len(history_keys):\n            wandb.termlog('Run history:')\n            max_len = max([len(k) for k in history_keys])\n            for key in history_keys:\n                vals = util.downsample(self._run.history.column(key), 40)\n                if any((not isinstance(v, numbers.Number) for v in vals)):\n                    continue\n                line = sparkline.sparkify(vals)\n                format_str = u'  {:>%s} {}' % max_len\n                wandb.termlog(format_str.format(key, line))\n\n        if self._run.has_examples:\n            wandb.termlog('Saved %s examples' % self._run.examples.count())\n\n        wandb.termlog('Waiting for final file modifications.')\n        # This is a a heuristic delay to catch files that were written just before\n        # the end of the script.\n        # TODO: ensure we catch all saved files.\n        # TODO(adrian): do we need this?\n        time.sleep(2)\n        try:\n            # avoid hanging if we crashed before the observer was started\n            if self._observer.is_alive():\n                self._observer.stop()\n                self._observer.join()\n        # TODO: py2 TypeError: PyCObject_AsVoidPtr called with null pointer\n        except TypeError:\n            pass\n        # TODO: py3 SystemError: <built-in function stop> returned a result with an error set\n        except SystemError:\n            pass\n\n        self._finish_handlers()\n        self._file_pusher.finish()\n\n        wandb.termlog('Syncing files in %s:' %\n                      os.path.relpath(self._watch_dir))\n        for file_path in self._stats.files():\n            wandb.termlog('  %s' % os.path.relpath(file_path, self._watch_dir))\n        step = 0\n        spinner_states = ['-', '\\\\', '|', '/']\n        stop = False\n        self._stats.update_all_files()\n        while True:\n            if not self._file_pusher.is_alive():\n                stop = True\n            summary = self._stats.summary()\n            line = (' %(completed_files)s of %(total_files)s files,'\n                    ' %(uploaded_bytes).03f of %(total_bytes).03f bytes uploaded\\r' % summary)\n            line = spinner_states[step % 4] + line\n            step += 1\n            wandb.termlog(line, newline=False)\n            if stop:\n                break\n            time.sleep(0.25)\n            #print('FP: ', self._file_pusher._pending, self._file_pusher._jobs)\n        # clear progress line.\n        wandb.termlog(' ' * 79)\n\n        # Check md5s of uploaded files against what's on the file system.\n        # TODO: We're currently using the list of uploaded files as our source\n        #     of truth, but really we should use the files on the filesystem\n        #     (ie if we missed a file this wouldn't catch it).\n        # This polls the server, because there a delay between when the file\n        # is done uploading, and when the datastore gets updated with new\n        # metadata via pubsub.\n        wandb.termlog('Verifying uploaded files... ', newline=False)\n        error = False\n        mismatched = None\n        for delay_base in range(4):\n            mismatched = []\n            download_urls = self._api.download_urls(\n                self._project, run=self._run.id)\n            for fname, info in download_urls.items():\n                if fname == 'wandb-history.h5' or OUTPUT_FNAME:\n                    continue\n                local_path = os.path.join(self._watch_dir, fname)\n                local_md5 = util.md5_file(local_path)\n                if local_md5 != info['md5']:\n                    mismatched.append((local_path, local_md5, info['md5']))\n            if not mismatched:\n                break\n            wandb.termlog('  Retrying after %ss' % (delay_base**2))\n            time.sleep(delay_base ** 2)\n\n        if mismatched:\n            print('')\n            error = True\n            for local_path, local_md5, remote_md5 in mismatched:\n                wandb.termerror(\n                    '%s (%s) did not match uploaded file (%s) md5' % (\n                        local_path, local_md5, remote_md5))\n        else:\n            print('verified!')\n\n        if error:\n            wandb.termerror('Sync failed %s' % self.url)\n        else:\n            wandb.termlog('Synced %s' % self.url)\n        sys.exit(exitcode)\n","sub_path":"wandb/run_manager.py","file_name":"run_manager.py","file_ext":"py","file_size_in_byte":38441,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"70606799","text":"from flask import Flask, render_template, request, redirect, url_for, abort, flash\nfrom passlib.hash import sha256_crypt\nfrom flask_fontawesome import FontAwesome\nimport datetime\nfrom source.ORM_relations import Car, Driver, Wish, Services\nfrom source.tryconn import d,c, g, create_plot\n\nuser_logged= False\n\nuser_name = \"Not Logged\"\niddd = 0\ncurrent_page = \"home1\"\n\napp = Flask(__name__)\napp.secret_key = b'_5#y2L\"F4Q8z\\n\\xec]/'\nfa = FontAwesome(app)\n\n\n# if request.method == 'POST':\n#     name = request.form.get('name')\n#     username = request.form.get('username')\n#     password = request.form.get('password')\n#     confirm = request.form.get('confirm')\n#     secure_password = sha256_crypt.encrypt(str(password))\n\n@app.route('/')\ndef home_page():\n    current_page ='home1'\n    return render_template('home1.html', user_logged=user_logged, user_name=user_name, iddd = iddd)\n@app.route('/services')\ndef services():\n    current_page = 'services'\n    return render_template('services.html', result = g, user_logged=user_logged, user_name=user_name, iddd = iddd)\n\n@app.route('/services/find', methods = ['POST', 'GET'])\ndef services_find():\n    if (request.method == 'POST'):\n        find_serv_price = Services.search_service(request.form['namee'])\n        print(find_serv_price)\n        if find_serv_price==False:\n            flash('No matches found')\n            return render_template('services.html', result = list(zip(d,c)), user_logged=user_logged, user_name=user_name, iddd = iddd)\n        else:\n            g = find_serv_price\n            return render_template(\"services.html\", result=g, user_logged=user_logged, user_name=user_name,\n                                   iddd=iddd)\n    if (request.method == 'GET'):\n        return redirect(url_for('services'))\n\n\n@app.route('/contacts')\ndef contacts():\n    current_page = 'contacts'\n    return render_template('contacts.html', user_logged=user_logged, user_name=user_name, iddd = iddd)\n\n@app.route('/reserve')\ndef reserve():\n    current_page = 'reserve'\n    return render_template('reserve.html', result = g, user_logged=user_logged, user_name=user_name, iddd = iddd)\n\n@app.route('/reserve/make', methods = ['POST', 'GET'])\ndef reserve_make():\n    if (request.method == 'POST'):\n        date_diff = Wish.difference_between_dates(datetime.datetime.strptime(request.form['datetime'], '%Y-%m-%dT%H:%M'))\n        if user_logged==False or iddd == 0:\n            flash('Log in for reserving')\n            return redirect(url_for('login'))\n        elif date_diff == False:\n            flash('You have to reserve at least one hour before coming')\n            return render_template('reserve.html', result = g, user_logged=user_logged, user_name=user_name, iddd = iddd)\n        else:\n            date_is_busy = Wish.free_date(datetime.datetime.strptime(request.form['datetime'], '%Y-%m-%dT%H:%M'))\n            if date_is_busy==False:\n                Wish.add(request.form['service_name'], int(Wish.get_price(request.form['service_name'])),\n                         datetime.datetime.strptime(request.form['datetime'], '%Y-%m-%dT%H:%M'), request.form['car_type'], iddd)\n            else:\n                msg = 'Date is taken by another driver'\n                flash(msg)\n                redirect(url_for('reserve'))\n\n\n        return render_template(f\"{current_page}.html\", result = g, user_logged=user_logged, user_name=user_name, iddd = iddd)\n    if(request.method == 'GET'):\n        return redirect(url_for('reserve'))\n\n\n@app.route('/register', methods = ['POST', 'GET'])\ndef register():\n    return render_template('register.html', user_logged=user_logged, user_name=user_name, iddd = iddd)\n\n@app.route('/register/submit', methods = ['POST', 'GET'])\ndef register_submit():\n    global user_logged\n    if(request.method == 'POST'):\n        if request.form['password'] == request.form['confirm']:\n            Driver.add(request.form['email'],request.form['first_name'],request.form['second_name'],\n                       request.form['user_login'],request.form['password'],\n                       datetime.datetime.strptime(request.form['birthday'], '%Y-%m-%d'), request.form['telephone'])\n            Car.add(request.form['car_license_plate'],request.form['car_name'],str(request.form['car_type']),\n                    request.form['car_color'])\n        else:\n            msg = 'You entered incorrect confirm password'\n            flash(msg)\n            return render_template('register.html', user_logged=user_logged, user_name=user_name, iddd = iddd)\n\n        global current_page\n        user_logged = True\n        return render_template(f\"{current_page}.html\", user_logged=user_logged, user_name=request.form['user_login'], iddd = iddd)\n\n    if(request.method == 'GET'):\n        return redirect(url_for('register'))\n\n@app.route('/login')\ndef login():\n    return render_template('login.html', user_logged=user_logged, user_name=user_name, iddd = iddd)\n@app.route('/login/submit', methods = ['POST', 'GET'])\ndef login_submit():\n    if(request.method == 'POST'):\n        global current_page\n        global user_logged\n\n        user_id = Driver.authorisation(request.form['login'],request.form['password'])\n        if user_id == 0:\n            # TODO Add notification\n            msg = 'You didnt sign up yet'\n            flash(msg)\n            return redirect(url_for('login'))\n        global iddd\n        iddd = user_id\n        user_logged = True\n        global user_name\n        user_name = Driver.get_username(user_id)\n        return render_template(f\"{current_page}.html\",user_logged=user_logged, user_name=user_name,iddd = iddd)\n    if(request.method == 'GET'):\n        return redirect(url_for('login'), user_logged=user_logged, user_name=user_name, iddd = iddd)\n\n\n@app.route('/logout', methods = ['POST', 'GET'])\ndef Logout():\n    global current_page\n    global user_logged\n    user_logged = False\n    global user_name\n    global iddd\n    iddd = 0\n    user_name = \"Not Logged\"\n    return render_template(f\"{current_page}.html\",user_logged=user_logged, user_name=user_name, iddd = iddd)\n@app.route('/graph')\ndef graph():\n    bar = create_plot()\n    return render_template('graph.html', plot = bar, user_logged=user_logged, user_name=user_name, iddd = iddd)\n\n@app.route('/<path>', methods = [ 'GET'])\ndef get(path):\n\n   if path == \"service\":\n      return redirect(url_for('services'))\n\n   elif path == \"contact\":\n      return redirect(url_for(\"contacts\"))\n\n   elif path == \"reservation\" or \"book\":\n      return redirect(url_for(\"reserve\"))\n   else:\n      return abort(404)\n\n\n\n\nif __name__ == '__main__':\n    service_discr = {\n        'name' : 'Детейлинг мойка',\n        'price_1_class': 30,\n        'price_2_class': 70,\n        'price_3_class': 100\n    }\n    driver = {\n        'name' : 'Sasha',\n        'birth_date': '12.02.1996',\n        'telefon' : 23345676,\n        'auto': 'Toyota Camry',\n        'type_of_auto': '2_class',\n        'color': 'black'\n    }\n    app.run(debug='True', port = 5003)","sub_path":"Borozniuk_Dmytro/workshop5/source/first_progr.py","file_name":"first_progr.py","file_ext":"py","file_size_in_byte":6963,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"525754889","text":"import re\r\nimport hashlib\r\n\r\nfrom Crypto.Cipher import AES\r\n\r\n#: default jenkins magic\r\nMAGIC = b'::::MAGIC::::'\r\n#: by default\r\nBLOCK_SIZE = 16\r\n#: appears to be a random seed, but jenkins knows a drill\r\nIV = '\\x00' * BLOCK_SIZE\r\n\r\n\r\ndef pad(text: bytes, block_size=BLOCK_SIZE):\r\n    size = (block_size - len(text) % block_size)\r\n    return text + b'\\x0c' * size\r\n\r\n\r\nclass JenkinsCipher(object):\r\n    def __init__(self, master_key: bytes, hudson_secret_key: bytes):\r\n        \"\"\"\r\n        Initiate jenkins cipher object with master and hudson secret keys\r\n        for further encryption / decryption procedures\r\n\r\n        :param master_key: jenkins master key\r\n        :param hudson_secret_key: hudson secret key\r\n        :raises AssertionError:\r\n            - if MAGIC was not found in processed data\r\n        \"\"\"\r\n        hashed_master_key = hashlib.sha256(master_key).digest()[:BLOCK_SIZE]\r\n        cipher = AES.new(hashed_master_key, AES.MODE_ECB)\r\n        result = cipher.decrypt(hudson_secret_key)\r\n        assert MAGIC in result\r\n\r\n        key = result[:-16]\r\n        self.cipher_key = key[:16]\r\n\r\n    def decrypt(self, encrypted_text: bytes) -> str:\r\n        \"\"\"\r\n        Decrypts jenkins ``password_hash`` given in base64 encoded string to\r\n        plain password\r\n\r\n        :param encrypted_text: base64 encoded password hash\r\n            .. note::\r\n\r\n                password hash should be in bytes form not in base64 encoded\r\n                string format.\r\n\r\n        :return: plain password\r\n        :raises AssertionError:\r\n            - if MAGIC was not found in decrypted data taken from\r\n              ``encrypted_text``\r\n        \"\"\"\r\n        cipher = AES.new(self.cipher_key, AES.MODE_ECB)\r\n        raw = cipher.decrypt(encrypted_text)\r\n        assert MAGIC in raw\r\n\r\n        return re.split(MAGIC.decode('utf-8'), raw.decode('utf-8'), 1)[0]\r\n\r\n    def encrypt(self, plain_text: str) -> bytes:\r\n        \"\"\"\r\n        Encrypt ``plain`` string with jenkins hudson secret key\r\n\r\n        :param plain_text: plain text to encrypt\r\n        :return: encrypted text\r\n\r\n        .. note::\r\n\r\n            encrypted text are not encoded to base64, so please pack it after\r\n        \"\"\"\r\n        cipher = AES.new(self.cipher_key, AES.MODE_ECB, IV)\r\n        message = pad(plain_text.encode('utf-8') + MAGIC)\r\n        return cipher.encrypt(message)\r\n","sub_path":"control/control/utils/jenkins.py","file_name":"jenkins.py","file_ext":"py","file_size_in_byte":2353,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"561841336","text":"#!/usr/bin/env python\n\nfrom __future__ import print_function\nimport chess\nimport optparse\nimport json\nimport difflib\nimport progressbar\n\nparser = optparse.OptionParser()\nparser.add_option('-f', '--file',\n                  dest='filename', help='Move database, obtained from apollo3 perft',\n                  metavar='FILE')\n\ndef run_position(position):\n    fen = position['fen']\n    moves = sorted(position['moves'])\n    pychess_board = chess.Board(fen)\n    pychess_moves = sorted([x.uci() for x in pychess_board.legal_moves])\n    if moves != pychess_moves:\n        print('Move generation divergence (fen {0}):'.format(fen))\n        print('\\n'.join(difflib.unified_diff(pychess_moves, moves)))\n\n\ndef run_all_positions(db):\n    bar = progressbar.ProgressBar();\n    for position in bar(db):\n        run_position(position)\n\ndef run(filename):\n    with open(filename, 'r+') as f:\n        db = json.loads(f.read())\n        run_all_positions(db)\n\ndef main():\n    (ops, args) = parser.parse_args()\n    if ops.filename is None:\n        print('error: move database file is required')\n        exit(1)\n\n    run(ops.filename)\n    print('done!')\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"scripts/movegen_diff.py","file_name":"movegen_diff.py","file_ext":"py","file_size_in_byte":1172,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"562845819","text":"# slicing\n\n# my_list[start_index:end_index]\n# my_list[:]  # This would be all of the items in my_list\n# my_list[:5] # This would be the items from index 0 to 4\n# my_list[5:] # This would be the items from index 5 to the end of the list\n\n# guided\n\n\"\"\"\nGiven an array of integers `nums`, define a function that returns the \"pivot\" index of the array.\nThe \"pivot\" index is where the sum of all the numbers on the left of that index is equal to the sum of all the numbers on the right of that index.\nIf the input array does not have a \"pivot\" index, then the function should return `-1`. If there are more than one \"pivot\" indexes, then you should return the left-most \"pivot\" index.\nExample 1:\nInput: nums = [1,7,3,6,5,6]\nOutput: 3\nExplanation:\nThe sum of the numbers to the left of index 3 (1 + 7 + 3 = 11) is equal to the sum of numbers to the right of index 3 (5 + 6 = 11).\nAlso, 3 is the first index where this occurs.\nExample 2:\nInput: nums = [1,2,3]\nOutput: -1\nExplanation:\nThere is no index that satisfies the conditions in the problem statement.\n\"\"\"\n\nnums = [1, 7, 3, 6, 5, 6]\n\n\n# nums = [1,2,3]\n\n#  O(N^2) solution\n# def pivot_index(nums):\n#     # iterate array starting at index 1\n#     # get sum of items on left of i and compare to sum of items on right of i\n#     # if they are equal return i else keep going to the next i\n#     for i in range(len(nums)):\n#         left = sum(nums[:i])\n#         right = sum(nums[i + 1:])\n#         if left == right:\n#             return i\n#     return -1\n\n\n# O(N) solution\ndef pivot_index(nums):\n    if len(nums) <= 1:\n        return -1\n    left = 0\n    right = sum(nums)\n    for i in range(len(nums)):\n        right -= nums[i]\n        if right == left:\n            return i\n\n        left += nums[i]\n\n\n# print('def pivot_index:', pivot_index(nums))\n\n\"\"\"\nYou are given a non-empty array that represents the digits of a non-negative integer.\nWrite a function that increments the number by 1.\nThe digits are stored so that the digit representing the most significant place value is at the beginning of the array. Each element in the array only contains a single digit.\nYou will not receive a leading 0 in your input array (except for the number 0 itself).\nExample 1:\nInput: [1,3,2]\nOutput: [1,3,3]\nExplanation: The input array represents the integer 132. 132 + 1 = 133.\nExample 2:\nInput: [3,2,1,9]\nOutput: [3,2,2,0]\nExplanation: The input array represents the integer 3219. 3219 + 1 = 3220.\nExample 3:\nInput: [9,9,9]\nOutput: [1,0,0,0]\nExplanation: The input array represents the integer 999. 999 + 1 = 1000.\n\"\"\"\n\n\n# def plus_one(digits):\n# turn the numbers in the array into an integer\n# # The repr() function returns a printable representation of the given object.\n# number = (''.join(repr(int(n)) for n in digits))\n# result = []\n# for num in number:\n#     result.append(num)\n# print(int(number) + 1)\n\ndef plus_one(digits):\n    # check the last digit if its not 9 we just add 1\n    # if it is a 9 make it 0\n    # go left check 2nd to last if its not a 9 add 1\n    # if it is a 9\n\n    index = len(digits) - 1\n    while index >= 0 and digits[index] == 9:\n        digits[index] = 0\n        index -= 1\n\n    if index == -1:\n        digits.insert(0, 1)\n    else:\n        digits[index] += 1\n\n    return digits\n\n\n# print(plus_one([1, 1, 1]))\n\n\"\"\"\nYou are given the prices of a stock, in the form of an array of integers, prices. Let's say that prices[i] is the price of the stock on the ith day (0-based index). Assuming that you are allowed to buy and sell the stock only once, your task is to find the maximum possible profit (the difference between the buy and sell prices).\n\nNote: You can assume there are no fees associated with buying or selling the stock.\n\nExample\n\nFor prices = [6, 3, 1, 2, 5, 4], the output should be buyAndSellStock(prices) = 4.\n\nIt would be most profitable to buy the stock on day 2 and sell it on day 4. Thus, the maximum profit is prices[4] - prices[2] = 5 - 1 = 4.\n\nFor prices = [8, 5, 3, 1], the output should be buyAndSellStock(prices) = 0.\n\nSince the value of the stock drops each day, there's no way to make a profit from selling it. Hence, the maximum profit is 0.\n\nFor prices = [3, 100, 1, 97], the output should be buyAndSellStock(prices) = 97.\n\nIt would be most profitable to buy the stock on day 0 and sell it on day 1. Thus, the maximum profit is prices[1] - prices[0] = 100 - 3 = 97.\n\"\"\"\n\n# prices = [6, 3, 1, 2, 5, 4]\n# prices = [8, 5, 3, 1]\nprices = [3, 100, 1, 97]\n\n#\n# prices = []\n# prices = [61, 91, 6, 15, 28, 30, 39, 69, 78, 81, 62, 38, 56, 69, 22, 95, 47, 82,\n#           52, 64, 74, 97, 60, 68, 5, 23, 45, 55, 66, 57, 26, 4, 21, 65, 55, 50,\n#           41, 88, 39, 84, 77, 5, 76, 11, 3, 51, 96, 100, 13, 26, 79, 98, 84, 66,\n#           93, 65, 98, 60, 57, 35, 12, 40, 83, 62, 46, 60, 26, 94, 59, 29, 70,\n#           34, 83, 98, 89, 57, 71, 44, 23, 43, 55, 1, 70, 29, 44, 10, 70, 83, 95,\n#           96, 97, 84, 23, 16, 34, 55, 59, 73, 17, 73]\n\n# def buyAndSellStock(prices):\n#     # iterate array, for each item after subtract and store the amount as\n#     # highest profit.. replace if needed with a higher profit\n#     if prices == sorted(prices, reverse=True) or len(prices) < 2:\n#         return 0\n#     highest_profit = 0\n#     for indx1 in range(len(prices)):\n#         rest = prices[indx1 + 1:]\n#         for price in range(len(rest)):\n#             if highest_profit >= max(rest):\n#                 return highest_profit\n#             if rest[price] - prices[indx1] > highest_profit:\n#                 highest_profit = rest[price] - prices[indx1]\n#     if highest_profit < 0:\n#         return 0\n#     else:\n#         return highest_profit\n\nprices = [6, 3, 1, 2, 5, 4]\nprices = [8, 5, 3, 1]\nprices = [3, 100, 1, 97]\n\n\ndef buyAndSellStock(prices):\n    length = len(prices)\n    # iterate array, for each item after subtract and store the amount as\n    # highest profit.. replace if needed with a higher profit\n    if prices == sorted(prices, reverse=True) or len(prices) < 2:\n        return 0\n    highest_profit = prices[1] - prices[0]\n    smallest_number = prices[0]\n    for i in range(1, length):\n        # only check if the selected number minus the smallest number before\n        # it is larger than the highest profit to avoid unnecessary checks\n        if prices[i] - smallest_number > highest_profit:\n            highest_profit = prices[i] - smallest_number\n        if prices[i] < smallest_number:\n            smallest_number = prices[i]\n\n    return highest_profit\n\n\n# print(buyAndSellStock(prices))\n\n\"\"\"\nGiven a string, your task is to replace each of its characters by the next one in the English alphabet; i.e. replace a with b, replace b with c, etc (z would be replaced by a).\n\nExample\n\nFor inputString = \"crazy\", the output should be alphabeticShift(inputString) = \"dsbaz\".\n\"\"\"\n\ninputString = \"crazy\"\n\n\ndef alphabeticShift(inputString):\n    new_string = ''\n    for letter in inputString:\n        new_letter = ord(letter) + 1\n        if new_letter == 123:\n            new_letter = 97\n        new_string += chr(new_letter)\n    return new_string\n\n\n# print(alphabeticShift(inputString))\n\n\n\"\"\"\nYou are given a parentheses sequence, check if it's regular.\n\nExample\n\nFor s = \"()()(())\", the output should be\nvalidParenthesesSequence(s) = true;\nFor s = \"()()())\", the output should be\nvalidParenthesesSequence(s) = false\n\"\"\"\n\ns = \"()()(())\"\n\ns = \"()()())\"\n\n\ndef validParenthesesSequence(s):\n    check = []\n    if s == '':\n        return True\n    if s[0] == ')':\n        return False\n    for paren in s:\n        if paren == '(':\n            check.append(paren)\n        else:\n            if len(check) == 0:\n                return False\n            else:\n                check.pop()\n    if check != []:\n        return False\n    return True\n\n\n# print(validParenthesesSequence(s))\n\n\n# Guided\n\n\"\"\"\nGiven only a reference to a specific node in a linked list, delete that node from a singly-linked list.\nExample:\nThe code below should first construct a linked list (x -> y -> z) and then delete `y` from the linked list by calling the function `delete_node`. It is your job to write the `delete_node` function.\n```python\nclass LinkedListNode():\n    def __init__(self, value):\n        self.value = value\n        self.next  = None\nx = LinkedListNode('X')\ny = LinkedListNode('Y')\nz = LinkedListNode('Z')\nx.next = y\ny.next = z\ndelete_node(y)\n```\n*Note: We can do this in O(1) time and space! But be aware that our solution will have some side effects...*\n\"\"\"\n\n\nclass LinkedListNode():\n    def __init__(self, value):\n        self.value = value\n        self.next = None\n\n\ndef delete_node(node_to_delete):\n    next = node_to_delete.next\n    next.next = None\n    node_to_delete.value = next.value\n    node_to_delete.next = next.next\n\n\nx = LinkedListNode('X')\ny = LinkedListNode('Y')\nz = LinkedListNode('Z')\n\nx.next = y\ny.next = z\n\n# print(delete_node(y))\n# print(x.next.value)\n\n\n\"\"\"\nGiven a reference to the head node of a singly-linked list, write a function\nthat reverses the linked list in place. The function should return the new head\nof the reversed list.\nIn order to do this in O(1) space (in-place), you cannot make a new list, you\nneed to use the existing nodes.\nIn order to do this in O(n) time, you should only have to traverse the list\nonce.\n*Note: If you get stuck, try drawing a picture of a small linked list and\nrunning your function by hand. Does it actually work? Also, don't forget to\nconsider edge cases (like a list with only 1 or 0 elements).*\n\"\"\"\n\n\nclass LinkedListNode():\n    def __init__(self, value):\n        self.value = value\n        self.next = None\n\n\ndef reverse(head_of_list):\n    current_node = head_of_list\n    previous_node = None\n    next_node = None\n\n    # Until we have 'fallen off' the end of the list\n    while current_node:\n        # Copy a pointer to the next element\n        # before we overwrite current_node.next\n        next_node = current_node.next\n\n        # Reverse the 'next' pointer\n        current_node.next = previous_node\n\n        # Step forward in the list\n        previous_node = current_node\n        current_node = next_node\n\n    return previous_node\n\n\n# Singly-linked lists are already defined with this interface:\nclass ListNode(object):\n    def __init__(self, x):\n        self.value = x\n        self.next = None\n\n\n[1, 3, 4, 6]\n\n\"\"\"\nNote: Your solution should have O(n) time complexity, where n is the number of elements in l, since this is what you will be asked to accomplish in an interview.\n\nYou have a singly linked list l, which is sorted in strictly increasing order, and an integer value. Add value to the list l, preserving its original sorting.\n\nNote: in examples below and tests preview linked lists are presented as arrays just for simplicity of visualization: in real data you will be given a head node l of the linked list\n\nExample\n\nFor l = [1, 3, 4, 6] and value = 5, the output should be\ninsertValueIntoSortedLinkedList(l, value) = [1, 3, 4, 5, 6];\nFor l = [1, 3, 4, 6] and value = 10, the output should be\ninsertValueIntoSortedLinkedList(l, value) = [1, 3, 4, 6, 10];\nFor l = [1, 3, 4, 6] and value = 0, the output should be\ninsertValueIntoSortedLinkedList(l, value) = [0, 1, 3, 4, 6].\n\"\"\"\n\n\ndef insertValueIntoSortedLinkedList(l, value):\n    # create a new node with the value\n    node = ListNode(value)\n    # if there is no list return the new node\n    if l == None:\n        return node\n    else:\n        # else if the list.value (first item in the list) > the new value\n        if l.value > value:\n            # set new values as the first item in the list\n            node.next = l\n            return node\n        else:\n            # else create a temp value for the current list item and the previous set to None\n            temp, prev = l, None\n            # while the there is a next item and current item value is less\n            # than the value iterate\n            while temp.next and temp.value <= value:\n                # set previous to temp and temp to next\n                prev = temp\n                temp = temp.next\n            # check if temp.next is None (last item) and still not larger than the value\n            if temp.next == None and temp.value <= value:\n                # if so add the value as the last item in the list since it is the largest\n                temp.next = node\n            else:\n                # else if the next item is larger than the value set the next item as the next item of the new value\n                node.next = prev.next\n                # and set the previous item to point to the new value next\n                prev.next = node\n            # return the list\n            return l\n\n\n\"\"\"\nNote: Your solution should have O(l1.length + l2.length) time complexity, since this is what you will be asked to accomplish in an interview.\n\nGiven two singly linked lists sorted in non-decreasing order, your task is to merge them. In other words, return a singly linked list, also sorted in non-decreasing order, that contains the elements from both original lists.\n\nExample\n\nFor l1 = [1, 2, 3] and l2 = [4, 5, 6], the output should be\nmergeTwoLinkedLists(l1, l2) = [1, 2, 3, 4, 5, 6];\nFor l1 = [1, 1, 2, 4] and l2 = [0, 3, 5], the output should be\nmergeTwoLinkedLists(l1, l2) = [0, 1, 1, 2, 3, 4, 5].\n\"\"\"\n\n\n# Singly-linked lists are already defined with this interface:\nclass ListNode(object):\n    def __init__(self, x):\n        self.value = x\n        self.next = None\n\n\ndef mergeTwoLinkedLists(l1, l2):\n    # create empty node to hold the new merged list\n    merged_node = ListNode(0)\n    # end will hold the end node\n    end = merged_node\n    while True:\n        # if either list becomes empty join lists\n        if l1 is None:\n            end.next = l2\n            break\n        if l2 is None:\n            end.next = l1\n            break\n        # merge the smaller list to the end of the larger and create a head from the merged list\n        if l1.value <= l2.value:\n            end.next = l1\n            l1 = l1.next\n        else:\n            end.next = l2\n            l2 = l2.next\n        # iterate the end node\n        end = end.next\n    return merged_node.next\n\n\n\"\"\"\nNote: Your solution should have O(n) time complexity, where n is the number of elements in l, and O(1) additional space complexity, since this is what you would be asked to accomplish in an interview.\n\nGiven a linked list l, reverse its nodes k at a time and return the modified list. k is a positive integer that is less than or equal to the length of l. If the number of nodes in the linked list is not a multiple of k, then the nodes that are left out at the end should remain as-is.\n\nYou may not alter the values in the nodes - only the nodes themselves can be changed.\n\nExample\n\nFor l = [1, 2, 3, 4, 5] and k = 2, the output should be\nreverseNodesInKGroups(l, k) = [2, 1, 4, 3, 5];\nFor l = [1, 2, 3, 4, 5] and k = 1, the output should be\nreverseNodesInKGroups(l, k) = [1, 2, 3, 4, 5];\nFor l = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11] and k = 3, the output should be\nreverseNodesInKGroups(l, k) = [3, 2, 1, 6, 5, 4, 9, 8, 7, 10, 11].\n\"\"\"\n\n\n# Singly-linked lists are already defined with this interface:\nclass ListNode(object):\n    def __init__(self, x):\n        self.value = x\n        self.next = None\n\n\ndef reverseNodesInKGroups(l, k):\n    # create an empty node to hold the new list\n    new_node = ListNode(0)\n    # set the next value to the list\n    new_node.next = l\n    # set the previous node to the new list\n    prev = new_node\n\n    while True:\n        # set the start to the new node next\n        start = prev.next\n        # set the end to the prev\n        end = prev\n        # iterate k times\n        for i in range(0, k):\n            # set end to the value its pointing to\n            end = end.next\n            # if the last node\n            if end == None:\n                # return\n                return new_node.next\n        # set the new reverse list at the end of the last reversed list\n        new_reversed = end.next\n        # call the reverse_list function passing in the start and end\n        reverse_list(start, end)\n        # set pointer of prev to end\n        prev.next = end\n        # set pointer of start to the new list\n        start.next = new_reversed\n        # set prev value to the start value\n        prev = start\n\n\n# # function to reverse the list\ndef reverse_list(start, end):\n    # set the last reversed group to the new start\n    old_reversed = start\n    # set the new current to the start\n    current = start\n    # set next node to the node start is pointing to\n    next_node = start.next\n    # while the current node is not the last node\n    while current != end:\n        # iterate\n        current = next_node\n        next_node = next_node.next\n        # set the current pointer to the last reversed\n        current.next = old_reversed\n        # set the last reversed to the current\n        old_reversed = current\n\n\n\"\"\"\nRefactor the Queue class below by adding an `is_empty` method. After writing this method, refactor your other methods to use this method in your other methods.\n\"\"\"\n\n\nclass LinkedListNode:\n    def __init__(self, data):\n        self.data = data\n        self.next = None\n\n\nclass Queue:\n    def __init__(self):\n        self.front = None\n        self.rear = None\n\n    def enqueue(self, item):\n        new_node = LinkedListNode(item)\n        # check if queue is empty\n        if self.is_empty():\n            self.front = new_node\n            self.rear = new_node\n        else:\n            # add new node to rear\n            self.rear.next = new_node\n            # reassign rear to new node\n            self.rear = new_node\n\n    def dequeue(self):\n        # check if queue is empty\n        if not self.is_empty():\n            # keep copy of old front\n            old_front = self.front\n            # set new front\n            self.front = old_front.next\n\n        # check if the queue is now empty\n        if self.is_empty():\n            # make sure rear is also None\n            self.rear = None\n\n        return old_front\n\n    # my code to check if the queue is empty\n    def is_empty(self):\n        return self.front is None and self.rear is None\n\n\n\"\"\"\nAdd a peek method to the Stack class. The peek method should return the value of the top item in the stack without actually removing it from the stack.\n\"\"\"\n\n\nclass Stack:\n    def __init__(self):\n        self.data = []\n\n    def push(self, item):\n        self.data.append(item)\n\n    # my code for peek method\n    def peek(self, item):\n        return self.data[-1]\n\n    def pop(self):\n        if len(self.data) > 0:\n            return self.data.pop()\n        return \"The stack is empty\"\n\n\n\"\"\"\nAdd a peek method to the Stack class below. The peek method should return the value of the node that is at the top of the stack without actually removing it from the stack.\n\"\"\"\n\n\nclass LinkedListNode:\n    def __init__(self, data):\n        self.data = data\n        self.next = None\n\n\nclass Stack:\n    def __init__(self):\n        self.top = None\n\n    def push(self, data):\n        # create new node with data\n        new_node = LinkedListNode(data)\n        # set current top to new node's next\n        new_node.next = self.top\n        # reset the top pointer to the new node\n        self.top = new_node\n\n    def pop(self):\n        # make sure stack is not empty\n        if self.top is not None:\n            # store popped node\n            popped_node = self.top\n            # reset top pointer to next node\n            self.top = popped_node.next\n            # return the value from the popped node\n            return popped_node.data\n\n    # my code for peek method\n    def peek(self):\n        if self.top is not None:\n            return self.top\n\n\n\"\"\"\nQueues and stacks guided\n\"\"\"\n\n\"\"\"\nYou've encountered a situation where you want to easily be able to pull the\nlargest integer from a stack.\nYou already have a `Stack` class that you've implemented using a dynamic array.\nUse this `Stack` class to implement a new class `MaxStack` with a method\n`get_max()` that returns the largest element in the stack. `get_max()` should\nnot remove the item.\n*Note: Your stacks will contain only integers. You should be able to get a\nruntime of O(1) for push(), pop(), and get_max().*\n\"\"\"\n\n\nclass Stack(object):\n    def __init__(self):\n        \"\"\"Initialize an empty stack\"\"\"\n        self.items = []\n\n    def push(self, item):\n        \"\"\"Push a new item onto the stack\"\"\"\n        self.items.append(item)\n\n    def pop(self):\n        \"\"\"Remove and return the last item\"\"\"\n        # If the stack is empty, return None\n        # (it would also be reasonable to throw an exception)\n        if not self.items:\n            return None\n\n        return self.items.pop()\n\n    def peek(self):\n        \"\"\"Return the last item without removing it\"\"\"\n        if not self.items:\n            return None\n        return self.items[-1]\n\n\nclass MaxStack(object):\n    def __init__(self):\n        # Your code here\n        self.stack = Stack()\n        # self.head = []\n        self.max_stack = Stack()\n\n    def push(self, item):\n        \"\"\"Add a new item onto the top of our stack.\"\"\"\n        # Your code here\n        # self.head.append(item)\n        # self.max_value = max(max(self.head), item)\n        current_max = self.get_max()\n        if current_max is None or current_max < item:\n            self.max_stack.push(item)\n        self.stack.push(item)\n\n    def pop(self):\n        \"\"\"Remove and return the top item from our stack.\"\"\"\n        # Your code here\n        # if not self.head:\n        #     return None\n        # self.head.pop()\n        # self.max_value = max(self.head)\n        item = self.stack.pop()\n        self.max_stack.pop()\n        return item\n\n    def get_max(self):\n        \"\"\"The last item in maxes_stack is the max item in our stack.\"\"\"\n        # Your code here\n        return self.max_stack.peek()\n\n\nmax_stack = MaxStack()\nmax_stack.push(1)\nmax_stack.push(2)\nmax_stack.push(5)\nmax_stack.pop()\n# print(max_stack.get_max())\n\n\"\"\"\nYour goal is to define a `Queue` class that uses two stacks. Your `Queue` class\nshould have an `enqueue()` method and a `dequeue()` method that ensures a\n\"first in first out\" (FIFO) order.\nAs you write your methods, you should optimize for time on the `enqueue()` and\n`dequeue()` method calls.\nThe Stack class that you will use has been provided to you.\n\"\"\"\n\n\nclass Stack:\n    def __init__(self):\n        self.data = []\n\n    def push(self, item):\n        self.data.append(item)\n\n    def pop(self):\n        if len(self.data) > 0:\n            return self.data.pop()\n        return \"The stack is empty\"\n\n\nclass QueueTwoStacks:\n    def __init__(self):\n        # Your code here\n        self.stack1 = Stack()\n        self.stack2 = Stack()\n\n    def enqueue(self, item):\n        # Your code here\n        self.stack1.push(item)\n\n    def dequeue(self):\n        # Your code here\n        if len(self.stack1.data) != 0:\n            shifted = self.stack1.pop()\n            self.stack2.push(shifted)\n            return self.dequeue()\n        else:\n            if len(self.stack2.data) != 0:\n                last_item = self.stack2.pop()\n                return last_item\n\n\n# queue = QueueTwoStacks()\n# queue.enqueue(1)\n# queue.enqueue(3)\n# queue.enqueue(5)\n# print('deque:', queue.dequeue())\n# print('stack1:', queue.stack1.data)\n# print('stack2:', queue.stack2.data)\n\n\"\"\"\nA colleague of yours keeps including incorrect JavaScript code in pull\nrequests. You are getting tired of parsing through their code to make sure\nthey've included correct brackets, braces, and parentheses.\nIn order to save yourself time, you decide to write a function that can parse\nthe code to make sure it includes the correct amount of opening and closing\ncharacters.\nWe will call `(`, `{`, and `[` \"openers\".\nWe will call `)`, `}`, and `]` \"closers\".\nYour input will be a string and your function needs to make sure that there is\na correct number of openers and closers and that they are properly nested.\nExamples:\n`\"{ [ ] ( ) }\"` should return `True`\n`\"{ [ ( ] ) }\"` should return `False`\n`\"{ [ }\"` should return `False`\nYou should be able to do this in one pass with O(n) time and O(n) space\ncomplexity.\n*Note: Remember that just making sure that each opener has a corresponding\ncloser is not enough. You also have to confirm that they are ordered and nested\ncorrectly. For example, \"{ [ ( ] ) }\" should return False, even though each\nopener can be matched to a closer.*\n\"\"\"\n\ncode = \"{ [ ] ( ) }\"\n\ncode = \"{ [ ( ] ) }\"\n\ncode = \"{ [ }\"\n\n\ndef is_valid(code):\n    check = []\n    if code == '':\n        return True\n    if code[0] == ')' or code[0] == '}' or code[0] == ']':\n        return False\n\n    for paren in code:\n        if paren == ' ':\n            continue\n        if paren == '(' or paren == '{' or paren == '[':\n            print('paren:', paren)\n            check.append(paren)\n            print('append:', check)\n        elif paren == ')' and check[-1] == '(' or paren == '}' and check[-1] \\\n                == '{' or paren == ']' and check[-1] == '[':\n\n            if len(check) == 0:\n                print('len:', len(check))\n                return False\n            else:\n                print('pop:', paren)\n                check.pop()\n        else:\n            print('else check:', paren, check)\n            return False\n\n    if check:\n        print('check:', check)\n        return False\n    return True\n\n\n# print(is_valid(code))\n\n\"\"\"\nImplement the missing code, denoted by ellipses. You may not modify the pre-existing code.\nImplement a queue using two stacks.\n\nYou are given an array of requests, where requests[i] can be \"push <x>\" or \"pop\". Return an array composed of the results of each \"pop\" operation that is performed.\n\nExample\n\nFor requests = [\"push 1\", \"push 2\", \"pop\", \"push 3\", \"pop\"], the output should be\nqueueOnStacks(requests) = [1, 2].\n\nAfter the first request, the queue is {1}; after the second it is {1, 2}. Then we do the third request, \"pop\", and add the first element of the queue 1 to the answer array. The queue becomes {2}. After the fourth request, the queue is {2, 3}. Then we perform \"pop\" again and add 2 to the answer array, and the queue becomes {3}.\n\"\"\"\n\nrequests = [\"push 1\", \"push 2\", \"pop\", \"push 3\", \"pop\"]\n\n\nclass Stack:\n    def __init__(self):\n        self.items = []\n\n    def isEmpty(self):\n        return self.items == []\n\n    def push(self, item):\n        self.items.append(item)\n\n    def pop(self):\n        return self.items.pop()\n\n\ndef queueOnStacks(requests):\n    left = Stack()\n    right = Stack()\n\n    def insert(x):\n        left.push(x)\n        print('queue:', left.items)\n\n    def remove():\n        if len(right.items) == 0:\n            while len(left.items) > 0:\n                shifted = left.pop()\n                right.push(shifted)\n        return right.items.pop()\n\n    ans = []\n    for request in requests:\n        req = request.split(\" \")\n        if req[0] == 'push':\n            insert(int(req[1]))\n        else:\n            ans.append(remove())\n            print('add to ans:', ans)\n\n    return ans\n\n\n# print(queueOnStacks(requests))\n\n\"\"\"\nGiven a string sequence consisting of the characters '(', ')', '[', ']', '{', and '}'. Your task is to determine whether or not the sequence is a valid bracket sequence.\n\nThe Valid bracket sequence is defined in the following way:\n\nAn empty bracket sequence is a valid bracket sequence.\nIf S is a valid bracket sequence then (S), [S] and {S} are also valid.\nIf A and B are valid bracket sequences then AB is also valid.\nExample\n\nFor sequence = \"()\", the output should be validBracketSequence(sequence) = true;\nFor sequence = \"()[]{}\", the output should be validBracketSequence(sequence) = true;\nFor sequence = \"(]\", the output should be validBracketSequence(sequence) = false;\nFor sequence = \"([)]\", the output should be validBracketSequence(sequence) = false;\nFor sequence = \"{[]}\", the output should be validBracketSequence(sequence) = true.\n\"\"\"\nsequence = \"()\"\n\n\ndef validBracketSequence(sequence):\n    pairs = dict(zip('(,[,{', '),],}'))\n    stack = []\n    for item in sequence:\n        if item in pairs:\n            stack.append(pairs[item])\n        elif not (stack and item == stack.pop()):\n            return False\n    return not stack\n\n\n# print(validBracketSequence(sequence))\n\n\n\"\"\"\nFor a given positive integer n determine if it can be represented as a sum of two Fibonacci numbers (possibly equal).\n\nExample\n\nFor n = 1, the output should be\nfibonacciSimpleSum2(n) = true.\n\nExplanation: 1 = 0 + 1 = F0 + F1.\n\nFor n = 11, the output should be\nfibonacciSimpleSum2(n) = true.\n\nExplanation: 11 = 3 + 8 = F4 + F6.\n\nFor n = 60, the output should be\nfibonacciSimpleSum2(n) = true.\n\nExplanation: 11 = 5 + 55 = F5 + F10.\n\nFor n = 66, the output should be\nfibonacciSimpleSum2(n) = false\n\"\"\"\n\n\ndef fibonacciSimpleSum2(n):\n    # if 0 is less than n and n is less than 5 then we know we can return\n    # true because n will be 1-4 which can be created with 2 fib numbers\n    if 0 < n < 5:\n        return True\n\n    # first get fibonacci sequence up to n\n    seq = [0, 1]\n    # starting from 2 and ending at n\n    for i in range(2, n):\n        # add seq at i - 2 (0 to start) and seq at i - 1 (1 to start)\n        fib = seq[i - 2] + seq[i - 1]\n        # if n is greater than fib\n        if n >= fib:\n            # we can append fib to the sequence\n            seq.append(fib)\n            # if fib is greater than or equal to n we can stop\n        else:\n            break\n    print(seq)\n\n    # The check I googled\n    # for i, number in enumerate(seq[:-1]):\n    #     paired = n - number\n    #     if paired in seq[i + 1:]:\n    #         return True\n\n    # check if any 2 of the numbers in seq add up to n\n    # My check\n    for i in range(len(seq) - 1):  # O(n^2)\n        j = 0\n        while (seq[i] + seq[j]) != n:\n            if j == len(seq) - 1:\n                break\n            else:\n                j += 1\n        if seq[i] + seq[j] == n:\n            return True\n\n    return False\n\n\nprint(fibonacciSimpleSum2(5))\n\n\"\"\"\nGiven a sorted (in ascending order) integer array nums of n elements and a target value, write a function to search for target in nums. If target exists, then return its index, otherwise, return -1.\n\nExample 1:\nInput: nums = [-1,0,3,5,9,12], target = 9\nOutput: 4\nExplanation: 9 exists in nums and its index is 4\n\nExample 2:\nInput: nums = [-1,0,3,5,9,12], target = 2\nOutput: -1\nExplanation: 2 does not exist in nums so return -1\n\nNote:\n\nAll elements in nums are unique.\nThe length of nums will be <= 100\nThe value of each element in nums will be in the range [1, 10000]\n\"\"\"\n\n\ndef csBinarySearch(nums, target):\n    min = 0\n    max = len(nums) - 1\n    while not max < min:\n        guess = (max + min) // 2\n\n        if nums[guess] == target:\n            return guess\n        elif nums[guess] < target:\n            min = guess + 1\n        else:\n            max = guess - 1\n\n    return -1\n\n\n# print(csBinarySearch([-1, 0, 3, 5, 9, 12], 9))\n\n\n\"\"\"\nGiven an integer array nums sorted in ascending order, and an integer target.\n\nSuppose that nums is rotated at some pivot unknown to you beforehand (i.e., [0,1,2,4,5,6,7] might become [4,5,6,7,0,1,2]).\n\nYou should search for target in nums and if found return its index, otherwise return -1.\n\nExample 1:\nInput: nums = [6,7,0,1,2,3,4,5], target = 0\nOutput: 2\n\nExample 2:\nInput: nums = [6,7,0,1,2,3,4,5], target = 3\nOutput: 5\n\nExample 3:\nInput: nums = [1], target = 0\nOutput: -1\n\nNote:\n\n1 <= nums.length < 100\n1 <= nums[i] <= 100\nAll values of nums are unique.\n\"\"\"\n\n\ndef csSearchRotatedSortedArray(nums, target):\n    min = 0\n    max = len(nums) - 1\n\n    while not max < min:\n        guess = (max + min) // 2\n        # print(f'min: {nums[min]} max: {nums[max]} guess:{nums[guess]} target:'\n        #       f' {target}')\n        # if the guess is the target we got it and return the guess\n        if nums[guess] == target:\n            # print('guessed the target')\n            return guess\n        # if min is less than or equal to the guess\n        elif nums[min] <= nums[guess]:\n            # print('min less than guess')\n            # if min is less than or equal to the target and less than the guess\n            if nums[min] <= target < nums[guess]:\n                # print('min less than or equal to target and less than guess')\n                # we can set max to the guess because nothing past the guess\n                # can be the target\n                max = guess\n                # else we can set min to guess + 1 because nothing before it\n                # can be the target\n            else:\n                # print('min is greater than target and greater than or equal '\n                #       'to guess')\n                min = guess + 1\n        # else if min is greater than the guess\n        else:\n            print('min is greater than or equal to guess')\n            # if max - 1 is greater than the target and greater than the guess\n            if nums[max - 1] >= target > nums[guess]:\n                # print('max - 1 greater than or equal to target and greater '\n                #       'than guess')\n                # we can set min to guess plus one because nothing before it\n                # can be the target\n                min = guess + 1\n            else:\n                # print('max -1 less than target and less than or equal to guess')\n                # else we set max equal to guess because nothing after it can\n                # be the target\n                max = guess\n\n    return -1\n\n# print(f'search rotate'\n#       f'd array: '\n#       f''\n#       f'{csSearchRotatedSortedArray([45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], 48)}')\n\n\"\"\"\nChallenge\nWrite a logarithmic expression that is identical to this exponential expression:\n\n2^n = 64\nlog_2 64 = 6\n\nWrite an exponential expression that is identical to this logarithmic expression:\n\nlog_2 128 = n\n2^7 = 128\n\nWhat keywords should you look out for that might alert you that logarithms are involved?\ndoubles, halves\n\"\"\"\n\n\"\"\"\nRewrite the implementation of linear search below so that the algorithm searches from the end of the list to the beginning.\n\"\"\"\n\n\ndef linear_search(arr, target):\n    # loop through each item in the input array\n    i = len(arr) - 1\n    for idx in range(len(arr)):\n        # check if the item at the current index is equal to the target\n        if arr[i] == target:\n            # return the current index as the match\n            return i\n        i -= 1\n    # if we were able to loop through the entire array, the target is not present\n    return -1\n\n\narr = [1, 2, 3, 4, 5, 6]\ntarget = 3\n# print(linear_search(arr, target))\n\n\n\"\"\"\nWrite a recursive search function that receives as input an array of integers and a target integer value. This function should return True if the target element exists in the array, and False otherwise.\nWhat would be the base case(s) we'd have to consider for implementing this function?\nHow should our recursive solution converge on our base case(s)?\n\nIn your own words, write out the three rules for recursion and how you can identify when a problem is amenable to using a recursive method.\n- problem has an obvious base case\n- the data changes predictably on the way to the base case \n- the function must call itself\n\"\"\"\n\n\ndef recursive_search(arr, target):\n    if arr[0] == target:\n        return True\n    elif len(arr[1:]) > 1:\n        return recursive_search(arr[1:], target)\n    return False\n\n\n# print(recursive_search(arr, target))\n\n\n\"\"\"\nBinary Search\n\"\"\"\n\n\ndef binary_search(arr, target):\n    # 1. Declare min = 0 and max = length of array - 1\n    min = 0\n    max = len(arr) - 1\n    while not max < min:\n        # 2. Figure out the guess value by getting the middle integer between min and max\n        guess = (max + min) // 2\n        # 3. if array[guess] equals the target, we found the element, return the index\n        if arr[guess] == target:\n            return guess\n        # 4. if the guess was too low, reset min to be one more than the guess\n        elif arr[guess] < target:\n            min = guess + 1\n        # 5. if the guess was too high, reset max to be one less than the guess\n        else:\n            max = guess - 1\n    # no match was found\n    return -1\n\n\n# target = 5\n# print(binary_search(arr, target))\n\n\"\"\"\nWhat is the time complexity of our binary_search function above?\n- logN\nCan you turn the function above into a recursive function? Any variables tracked/updated in the while loop will have to become parameters for the recursive function.\n\"\"\"\narr = [1, 2, 3, 4, 5, 6]\ntarget = 55\n\n\ndef binary_recursive_search(arr, target, min_index, max_index):\n    if min_index >= max_index:\n        return -1\n    guess = (max_index + min_index) // 2\n    if arr[guess] == target:\n        return guess\n    elif target < arr[guess]:\n        return binary_recursive_search(arr, target, min_index, guess - 1)\n    else:\n        return binary_recursive_search(arr, target, guess + 1, max_index)\n\n\n# print(binary_recursive_search(arr, target, 0, len(arr)))\n\n\"\"\"\nSearching-recursion guided\n\"\"\"\n\n\"\"\"\nI was bored one day and decided to look at last names in the phonebook for my\narea.\nI flipped open the phonebook to a random page near the middle and started\nperusing. I wrote each last name that I was unfamiliar with down on paper in\nincreasing order. When I got to the end of the phonebook, I was having so much\nfun I decided to start from the beginning and keep going until I reached the\npage where I had started.\nWhen I was finished, I had a list of interesting last names that were mostly\nalphabetical. The problem was that my list starts somewhere near the middle of\nthe alphabet, reaches the end, and then starts from the beginning of the\nalphabet. In other words, my list of names is sorted, but it is \"rotated.\"\nExample:\nsurnames = [\n    'liu',\n    'mcdowell',\n    'nixon',\n    'sparks',\n    'zhang',\n    'ahmed',  # <-- rotates here!\n    'brandt',\n    'davenport',\n    'farley',\n    'glover',\n    'kennedy',\n]\nWrite a function that finds the index of the \"rotation point\". The \"rotation\npoint\" is where I started working from the beginning of the phone book. The\nlist I came up was absolutely huge, so make sure your solution is efficient.\n*Note: you should be able to come up with a solution that has O(log n) time\ncomplexity.*\n\"\"\"\n\nsurnames = [\n    'sparks',\n    'zhang',\n    'liu',\n    'ahmed',  # <-- rotates here!\n    'brandt',\n    'davenport',\n    'farley',\n    'glover',\n    'kennedy',\n    'mcdowell',\n    'nixon',\n]\n\n\n# works w numbers not names\ndef find_rotation_point(surnames):\n    # Your code here\n    min = 0\n    max = len(surnames) - 1\n    while not max < min:\n        guess = (min + max) // 2\n        if surnames[guess] < surnames[guess + 1] and surnames[guess] < surnames[\n            guess - 1]:\n            return guess\n        else:\n            if surnames[guess] > surnames[0]:\n                min = guess + 1\n            elif surnames[guess] < surnames[0]:\n                max = guess - 1\n\n\n# print('rotation', find_rotation_point([6, 7, 8, 9, 10, 0, 1, 2, 3, 4, 5]))\n\ndef find_rotation_point(surnames):\n    # Your code here\n    # UNDERSTAND\n    # [ 6,   7, 8, 0, 1, 2, 3, 4, 5]\n    #           min max\n    #           mid\n    # [ 7, 0, 1,  2, 3, 4, 5, 6]\n    #  min\n    # max\n    # mid\n    # Plan:\n    # we should use some kind of a binary search\n    # max and min indices\n    min = 0\n    max = len(surnames) - 1\n    # find the middle index\n    mid = (min + max) // 2\n    # if the middle surname is the rotation point:  (base case)\n    # middle surname is rotation if prev element is > than current\n    # return the index\n    # otherwise:\n    # have we rotated yet?\n    # we can tell that by comparing to the first element in the array\n    first_element = surnames[0]\n    while not max <= min:\n        current_element = surnames[mid]\n        # if current element >= first element: we haven't rotated yet, rotation point is \"still ahead of us\"\n        if current_element >= first_element:\n            # search right\n            # move the min index to middle + 1\n            min = mid + 1\n        # else if current element < first element: we have rotated\n        else:\n            # current_element < first_element\n            # search left:\n            # move the max index to middle index\n            max = mid\n    # keep going until max and min cross\n    return min\n\n\n\"\"\"\nYou are a new author that is working on your first book. You are working on a\nseries of drafts. Each draft is based on the previous draft. The latest draft\nof your book has a serious typo. Since each newer draft is based on the\nprevious draft, all the drafts after the draft containing the typo also include\nthe typo.\nSuppose you have `n` drafts `[1, 2, 3, ..., n]` and you need to find out the\nfirst one containing the typo (which causes all the following drafts to have\nthe typo as well).\nYou are given access to an API tool `containsTypo(draft)` that will return\n`True` if the draft contains a typo and `False` if it does not.\nYou need to implement a function that will find the *first draft that contains\na typo*. Also, you have to pay a fee for every call to `containsTypo()`, so\nmake sure that your solution minimizes the number of API calls.\nExample:\nGiven `n = 5`, and `draft = 4` is the first draft containing a typo.\ncontainsTypo(3) -> False\ncontainsTypo(5) -> True\ncontainsTypo(4) -> True\n\"\"\"\n\nn = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n\n\ndef firstDraftWithTypo(n):\n    # Your code here\n    pass\n    contains_typo = 4\n    min = 0\n    max = len(n) - 1\n    while not max < min:\n        guess = (min + max) // 2\n        # if containsTypo were real this line would be:\n        # if containsTypo(n[guess]) and not containsTypo(n[guess -1]):\n        if n[guess] == contains_typo and n[guess - 1] != contains_typo:\n            # resulting in the first occurrence where the typo exists\n            return n[guess]\n        # if containsTypo were real this line would be:\n        # if not containsTypo(n[guess]):\n        elif n[guess] < contains_typo:\n            min = guess + 1\n        else:\n            max = guess - 1\n    return -1\n\n\n# print(firstDraftWithTypo(n))\n\n\"\"\"\nCookie Monster can eat either 1, 2, or 3 cookies at a time. If he were given a\njar of cookies with `n` cookies inside of it, how many ways could he eat all\n`n` cookies in the cookie jar? Implement a function `eating_cookies` that\ncounts the number of possible ways Cookie Monster can eat all of the cookies in\nthe jar.\nFor example, for a jar of cookies with n = 3 (the jar has 3 cookies inside it),\nthere are 4 possible ways for Cookie Monster to eat all the cookies inside it:\nHe can eat 1 cookie at a time 3 times\nHe can eat 1 cookie, then 2 cookies\nHe can eat 2 cookies, then 1 cookie\nHe can eat 3 cookies all at once.\nThus, eating_cookies(3) should return an answer of 4.\nCan you implement a solution that has a O(n) time complexity and a O(n) space\ncomplexity?\n*Note: Since this question is asking you to generate a bunch of possible\npermutations, you'll probably want to use recursion for this. Think about base\ncases that we would want our recursive function to stop recursing on (when do\nyou know you've found a \"way\" to eat the cookies versus when you have not?).*\n\"\"\"\n\n\"\"\"\n1\n1 once\n= 1\n********\n2\n1 twice\n2 once\n= 2\n*********\n3\n1 1 1 \n1 and 2\n2 and 1\n3 once\n********\n\"\"\"\n\nn = 3\n\n\ndef eating_cookies(n, cache=None):\n    # if n < 0:\n    #     return 0\n    # if n == 0:\n    #     return 1\n    # return eating_cookies(n-1) + eating_cookies(n - 2) + eating_cookies(n -3)\n\n    # let the cache be 2 longer than n\n    cache = [0] * (n + 2)\n    print(cache)\n    cache[0] = 1\n    cache[1] = 1\n    cache[2] = 2\n    for i in range(3, n + 1):\n        print('*******i:', i)\n        print(\n            f'cache[i]: {cache[i]} = cache[i - 1]: {cache[i - 1]} + cache[i - 2]: {cache[i - 2]} + cache[i - 3]: {cache[i - 3]}')\n        cache[i] = cache[i - 1] + cache[i - 2] + cache[i - 3]\n    return cache[n]\n\n\n\"\"\"\nwhen he has n cookies, he has three options: eat 1 cookie, eat 2 cookie, or eat 3 cookies. So if you go down each of those options, he then has n - x more cookies to eat, and y ways to eat that amount of cookies. So you can basically sum up the number of ways to eat (n-1) cookies + ways to eat (n-2) cookies + ways to eat (n-3) cookies\n\"\"\"\n\n# print(eating_cookies(n))\n\n\"\"\"\nSprint 1\n\"\"\"\n\n\"\"\"\nGiven a string, remove adjacent duplicate characters.\n\nExample\n\nFor s = \"aaaaa\", the output should be\nremoveAdjacent(s) = \"a\";\nFor s = \"abccaaab\", the output should be\nremoveAdjacent(s) = \"abcab\".\n\"\"\"\n\n\n# s = \"aaaaa\"\n# s = \"abccaaab\"\n\ndef removeAdjacent(s):\n    # add first letter to new string\n    if s == '':\n        return s\n    new_str = s[0]\n    # iterate the string\n    for letter in s:\n        # if the next letter is the same as previous continue\n        if letter == new_str[len(new_str) - 1]:\n            continue\n        else:\n            new_str += letter\n    return new_str\n\n\n# print(removeAdjacent(s))\n\n\"\"\"\nWrite a function to reverse the given string (the input string is given as an array of characters) and return the result.\n\nNote: your solution should be \"in-place\" with O(1) space complexity. Although many in-place functions do not return the modified input, in this case you should.\n\nHint: you should try using a \"two-pointers approach\".\n\n[execution time limit] 4 seconds (py3)\n\n[input] array.char input\n\n[output] array.char\n\"\"\"\n\nstr = 'reverse'\n\n\ndef reverse_String(str):\n    i, j = 0, len(str) - 1\n    print(i, j)\n    while i < j:\n        str[i], str[j] = str[j], str[i]\n        i += 1\n        j -= 1\n    return str\n\n\n# print(reverse_String(str))\n\n\"\"\"\nGiven the string, check if it is a palindrome.\n\nExample\n\nFor inputString = \"aabaa\", the output should be\ncheckPalindrome(inputString) = true;\nFor inputString = \"abac\", the output should be\ncheckPalindrome(inputString) = false;\nFor inputString = \"a\", the output should be\ncheckPalindrome(inputString) = true.\n\"\"\"\n\ninputString = \"aabaa\"\n# inputString = \"abac\"\n# inputString = \"a\"\ninputString = \"hlbeeykoqqqqokyeeblh\"\n\n\ndef checkPalindrome(inputString):\n    return inputString == inputString[::-1]\n\n\n# print(checkPalindrome(inputString))\n\n\"\"\"\n*** Data Structures and Algorithms Sprint ***\n---------------------------------------------\n\"\"\"\n\n\"\"\"\n*** Reverse Linked List ***\n---------------------------\n\nNote: Your solution should have O(l.length) time complexity and O(1) space complexity, since this is what you will be asked to accomplish in an interview.\n\nGiven a singly linked list, reverse and return it.\n\nExample\n\nFor l = [1, 2, 3, 4, 5], the output should be\nreverseLinkedList(l) = [5, 4, 3, 2, 1].\n\"\"\"\n\n\n# Singly-linked lists are already defined with this interface:\n# class ListNode(object):\n#   def __init__(self, x):\n#     self.value = x\n#     self.next = None\n#\ndef reverseLinkedList(l):\n    cur = l\n    prev = None\n    next = None\n\n    while cur:\n        next = cur.next\n\n        cur.next = prev\n\n        prev = cur\n        cur = next\n\n    return prev\n\n\n\"\"\"\n*** check Blanagrams ***\n------------------------\nTwo words are blanagrams if they are anagrams but exactly one letter has been substituted for another.\n\nGiven two words, check if they are blanagrams of each other.\n\nExample\n\nFor word1 = \"tangram\" and word2 = \"anagram\", the output should be\ncheckBlanagrams(word1, word2) = true;\n\nFor word1 = \"tangram\" and word2 = \"pangram\", the output should be\ncheckBlanagrams(word1, word2) = true.\n\nSince a word is an anagram of itself (a so-called trivial anagram), we are not obliged to rearrange letters. Only the substitution of a single letter is required for a word to be a blanagram, and here 't' is changed to 'p'.\n\nFor word1 = \"silent\" and word2 = \"listen\", the output should be\ncheckBlanagrams(word1, word2) = false.\n\nThese two words are anagrams of each other, but no letter substitution was made (the trivial substitution of a letter with itself shouldn't be considered), so they are not blanagrams.\n\"\"\"\n\nword1 = \"tangpam\"\nword2 = \"anagram\"\n\n\ndef checkBlanagrams(word1, word2):\n    if word1 == '' or word2 == '':\n        return False\n    work_string = ''\n    diff = 0\n    sort1 = sorted(word1)  # O(n) space O(nlogn) time\n    sort2 = sorted(word2)  # O(n) space O(nlogn) time\n    for i in range(len(word1)):  # O(n)\n        # check for substitutions\n        if sort1[i] != sort2[i]:\n            diff += 1\n        work_string += word1[i] # O(n) space\n    print(work_string)\n    print(word1)\n    count = 0\n    # check if there was more than 1 substitution made\n    for i in range(len(work_string)):  # O(n)\n        if work_string[i] not in word2:\n            count += 1\n    print('count:', count)\n    # if more than 1 substitution return False\n    if count > 1:\n        return False\n    # if no substitutions return false\n    if diff == 0:\n        return False\n    if sorted(work_string) == sorted(word1):\n        return True\n    return False\n\n\n# print(checkBlanagrams(word1, word2))\n\n\"\"\"\n*** Find value sorted shifted array ***\n---------------------------------------\nYou are given a sorted array in ascending order that is rotated at some unknown pivot (i.e., [0,1,2,4,5,6,7] might become [4,5,6,7,0,1,2]) and a target value.\n\nWrite a function that returns the target value's index. If the target value is not present in the array, return -1.\n\nYou may assume no duplicate exists in the array.\n\nYour algorithm's runtime complexity must be in the order of O(log n).\n\nExample 1:\n\nInput: nums = [4,5,6,7,0,1,2], target = 0\nOutput: 4\n\nExample 2:\n\nInput: nums = [4,5,6,7,0,1,2], target = 3\nOutput: -1\n\"\"\"\n\nnums = [4, 5, 6, 7, 0, 1, 2]\ntarget = 0\n\n\ndef findValueSortedShiftedArray(nums, target):\n    min = 0\n    max = len(nums) - 1\n\n    while not max < min:\n        guess = (max + min) // 2\n\n        if nums[guess] == target:\n            return guess\n        elif nums[min] <= nums[guess]:\n            if nums[min] <= target < nums[guess]:\n                max = guess\n            else:\n                min = guess + 1\n        else:\n            if nums[max - 1] >= target > nums[guess]:\n                min = guess + 1\n            else:\n                max = guess\n\n    return -1\n\n\n","sub_path":"Web-Dev-Hub-Docs/.gitbook/assets/cs-weeks3-4-projects (1).py","file_name":"cs-weeks3-4-projects (1).py","file_ext":"py","file_size_in_byte":50251,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"156373255","text":"from flask import Flask, render_template\napp = Flask(__name__)\n\n@app.route('/user/<name>')\ndef user(name):\n    users = {\n\t\"quy\":   {\n\t\t\t\"name\": \"Dinh Cong Quy\",\n\t\t\t\"age\": 20\n                    },\n    \"love\" : {\n\t\t\t\"name\" : \"love love\",\n\t\t\t\"age\" : 20\n             }\n            }\n\n    if name in users:\n        inf = users[name]\n        return render_template(\"ex2.html\",inf = inf)\n    else:\n        return \"User not found\"\n\nif __name__ == '__main__':\n    app.run(debug=True)\n","sub_path":"Web01/HomeWork/Exercise2.py","file_name":"Exercise2.py","file_ext":"py","file_size_in_byte":476,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"66198560","text":"\n# perfectNumbers.py\n# author: Raul Arriola\n\n\n# Function that checks if n is a perfect, abundant or defective number\ndef perfect_number(n):\n    total = 0\n    # print(str(n) \" + divisors\")\n    for i in range(1, n):\n        if n % i == 0:  # own divisors\n            # print(i)\n            total = total + i\n\n    if total == n:\n        print(str(n) + \" ==> Numero perfecto\")\n    elif total < n:\n        print(str(n) + \" ==> Numero defectivo\")\n    else:\n        print(str(n) + \" ==> Numero abundante\")\n\n\n# Call to perfect_number as an example\nperfect_number(12)\n","sub_path":"Ejercicio1/perfectNumbers.py","file_name":"perfectNumbers.py","file_ext":"py","file_size_in_byte":559,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"421162818","text":"from django.shortcuts import render\nfrom django.http import HttpResponse\nimport pandas as pd\nfrom faker import Faker\nfake = Faker()\nimport calendar\nfrom datetime import datetime\nfrom django.conf import settings\nimport os\n\ndef zestaw2(request):\n    path = os.path.join(settings.STATIC_DIR, 'logi.txt')\n    global data\n    data = pd.read_csv(path, sep=';', names = [\"Data\", \"Godzina\", \"Minuty\", \"Pracownik\",\"Zdarzenie\", \"Rejestracja\"])\n    slownik_mies, slownik3 = {}, {}\n    lista_map_mies = ['Styczeń','Luty','Marzec','Kwiecień','Maj','Czerwiec','Lipiec','Sierpień','Wrzesień','Październik','Listopad','Grudzień']\n    lista_rok = sorted(pd.DatetimeIndex(data['Data']).year.unique().tolist())\n    lista_mies = sorted(pd.DatetimeIndex(data['Data']).month.unique().tolist())\n    lista2 = data['Pracownik'].unique().tolist()\n\n    for key in lista2:\n        slownik3[key] = fake.seed_instance(key).name()\n\n    zestawienie1_wybory = [slownik3, slownik_mies, lista_rok, slownik3[lista2[0]], min(lista_mies), min(lista_rok)]\n    global dni_miesiaca\n    dni_miesiaca = calendar.monthcalendar(int(zestawienie1_wybory[5]), int(zestawienie1_wybory[4]))\n    przepracowany_czas = 0\n    data['Data'] = pd.to_datetime(data['Data'])\n\n    for key in lista2:\n        slownik3[key] = fake.seed_instance(key).name()\n\n\n    def glowne_obliczenie_godzin():\n        if request.method == 'POST':\n            global dni_miesiaca\n            zestawienie1_wybory[3] = request.POST.get('wybor_prac')\n            zestawienie1_wybory[4] = request.POST.get('wybor_mies')\n            zestawienie1_wybory[5] = request.POST.get('wybor_rok')\n            dni_miesiaca = calendar.monthcalendar(int(zestawienie1_wybory[5]), int(zestawienie1_wybory[4]))\n\n        global data\n        data = data[(data.Pracownik == zestawienie1_wybory[3]) & (data['Data'].dt.year == datetime(int(zestawienie1_wybory[5]), 1, 1).year) & (data['Data'].dt.month == datetime(2000, int(zestawienie1_wybory[4]), 1).month)]\n        data_wejscia = 0\n        data_wyjscia = 0\n        przepracowany_czas = 0\n        przepracowane_godziny = {}\n        dzien = 0\n\n        for tygodnie in dni_miesiaca:\n            for dni in tygodnie:\n                przepracowane_godziny[dni] = 0\n\n\n        for index, row in data.iterrows():\n            if row['Zdarzenie'] % 2 == 0:\n\n                if row[0].day != dzien:\n                    data_wejscia = 0\n\n                if data_wejscia == 0:\n                    data_wejscia = row[0] + pd.Timedelta(hours = row[1], minutes = row[2])\n                    dzien = row[0].day\n            else:\n                if data_wejscia != 0:\n                    data_wyjscia = row[0] + pd.Timedelta(hours = row[1], minutes = row[2])\n                    przepracowany_czas += pd.Timedelta(data_wyjscia - data_wejscia).total_seconds()\n                    przepracowany_dzien = pd.Timedelta(data_wyjscia - data_wejscia).total_seconds()\n                    przepracowane_godziny[row[0].day] += przepracowany_dzien\n                    data_wejscia = 0\n\n\n        for key, value in przepracowane_godziny.items():\n            przepracowane_godziny[key] = \"{0[0]:.0f}:{0[1]:02.0f}\".format(convert_seconds(value))\n\n\n        przepracowany_czas = \"{0[0]:.0f}:{0[1]:02.0f}\".format(convert_seconds(przepracowany_czas))\n        return render(request, 'first_app/zestaw2.html', {'data':data,'zestaw1':zestawienie1_wybory, 'dni_miesiaca':dni_miesiaca, 'czaspracy':przepracowany_czas, 'godzinydzien':przepracowane_godziny})\n\n\n    def convert_seconds(seconds):\n        hours, seconds = divmod(seconds, 60 * 60)\n        minutes, seconds = divmod(seconds, 60)\n        return hours, minutes\n\n    def mapowanie3(id):\n        return slownik3[id]\n\n    for i in range(len(lista_mies)):\n        slownik_mies[i+1] = lista_map_mies[i]\n\n\n    data.Pracownik = data.Pracownik.apply(mapowanie3)\n    return glowne_obliczenie_godzin()\n","sub_path":"first_app/zestaw2.py","file_name":"zestaw2.py","file_ext":"py","file_size_in_byte":3864,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"306463875","text":"\n\nfrom distutils.core import setup, Extension\nimport os\nimport sysconfig\nimport numpy\n\nextra_compile_args = sysconfig.get_config_var('CFLAGS').split()\nextra_compile_args += [\"-std=c++11\", \"-Wall\", \"-Wextra\", '-L/usr/local/lib/','-lgslcblas', '-lgsl' ,'-lm']\n\nmodule1 = Extension('cfar',\n                    include_dirs = [\n                        '/home/py_cfar', '/usr/include', '/usr/local/include', numpy.get_include()\n                    ],\n                    library_dirs = ['/usr/local/lib', '/usr/lib'],\n                    libraries = ['opencv_core', 'opencv_imgproc', 'opencv_imgcodecs', 'opencv_highgui','opencv_video','opencv_videoio', 'gsl', 'gslcblas'],\n                    extra_compile_args=extra_compile_args,\n                    sources = [ 'main.cpp']\n                     )\n\n\nsetup (name = 'cfar',\n       version = '1.0',\n       description = 'This is a demo package',\n       ext_modules = [module1])\n\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":923,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"270738188","text":"urunler = [\"Laptop\" , \"Mouse\" , \"Keyboard\"]\nprint(urunler)\nprint(type(urunler))\n\nurunler.append(\"Telefon\")\nprint(urunler)\n\nogrenciler1 = [\"Salih\" , \"Ayşe\" , \"Ahmet\"]\nogrenciler2 = [\"Ali\" , \"Zehra\" , \"Musa\"]\n\nogrenciler1 = ogrenciler2\nogrenciler2[0] = \"Engin\"\nprint(ogrenciler1)\nprint(ogrenciler2)\n#burada değer ve referans tip dediğimiz durum söz konusudur.\n\nsayi1 = 10\nsayi2 = 48\nsayi1 = sayi2\nsayi2 = 60\nprint(sayi1)\n\n#referans tip -> list\n#değer tip -> int        sayısal veri tipleri değer tiptir.\n#string, referans tiptir. ama birçok programlama dilinde değer tip gibi sonuç döndürür.\n\nisim = \"Mukaddes\"  #aslında burada karakterlerden oluşan bir liste var.\nprint(isim[0])\n\n#if'in az kullanımı programın kalitesini gösterir.\n#bir projede ki if sayısı o projenin ne kadar nesnel yazıldığıyla ters orantılıdır.\n\nbosListe = []\n\n#devops = yazılım geliştirme süreç yönetimi için kullanılan kısa isimlendirmedir. developer operations","sub_path":"list.py","file_name":"list.py","file_ext":"py","file_size_in_byte":969,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"174158320","text":"import argparse\nimport sys\n\nimport rasterio\n\nfrom plyflatten import plyflatten_from_plyfiles_list\nfrom plyflatten.__about__ import __description__, __title__\n\n\ndef main():\n    parser = argparse.ArgumentParser(description=(f\"{__title__}: {__description__}\"))\n    parser.add_argument(\"list_plys\", nargs=\"+\", help=(\"Space-separated list of .ply files\"))\n    parser.add_argument(\"dsm_path\", help=(\"Path to output DSM file\"))\n    parser.add_argument(\n        \"--resolution\",\n        default=1,\n        type=float,\n        help=(\"Resolution of the DSM in meters (defaults to 1m)\"),\n    )\n    args = parser.parse_args()\n    raster, profile = plyflatten_from_plyfiles_list(args.list_plys, args.resolution)\n    raster = raster[:, :, 0]\n    profile[\"dtype\"] = raster.dtype\n    profile[\"height\"] = raster.shape[0]\n    profile[\"width\"] = raster.shape[1]\n    profile[\"count\"] = 1\n    profile[\"driver\"] = \"GTiff\"\n\n    with rasterio.open(args.dsm_path, \"w\", **profile) as f:\n        f.write(raster, 1)\n\n\nif __name__ == \"__main__\":\n    sys.exit(main())  # pragma: no cover\n","sub_path":"plyflatten/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":1057,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"492165432","text":"from rest_framework import generics, permissions\nfrom rest_framework.views import APIView\nfrom rest_framework.response import Response\nfrom rest_framework import status\nfrom django.http import HttpResponseBadRequest\nfrom django.shortcuts import get_object_or_404\n\nfrom archive.models import Archive\nfrom user.models import CustomUser\nfrom review.models import Review\nfrom restaurant.models import Restaurant\nfrom restaurant.serializers import RestaurantSerializer\nfrom review.serializers import ReviewSerializer\nfrom user.serializers import UserSerializer\n\nfrom .serializers import RecommendSerializer\n\nfrom math import sqrt\n\ndef sim_pearson(data, name1, name2):\n    sum_x=0 # X의 합\n    sum_y=0 # Y의 합\n    sum_pow_x=0 # X 제곱의 합\n    sum_pow_y=0 # Y 제곱의 합\n    sum_xy=0 # X*Y의 합\n    count=0 # 식당 개수\n    for i in data[name1]: # i = key\n        if i in data[name2]: # 같은 식당을 평가했을때만\n            sum_x+=data[name1][i]\n            sum_y+=data[name2][i]\n            sum_pow_x+=pow(data[name1][i],2)\n            sum_pow_y+=pow(data[name2][i],2)\n            sum_xy+=data[name1][i]*data[name2][i]\n            count+=1\n\n    # 서로 겹치는 식당이 최소 4개 이상 있을 때만 상관계수를 측정함\n    if count < 4:\n        print(\"count is small\")\n        return -2\n    divider = sqrt((sum_pow_x-(pow(sum_x,2)/count))*(sum_pow_y-(pow(sum_y,2)/count)))\n    if divider == 0:\n        print(\"divider is zero\")\n        return -3\n    return (sum_xy - ((sum_x*sum_y)/count)) / divider\n\n# 딕셔너리 돌면서 상관계수순으로 정렬\ndef top_match(data, name, index=3):\n    li=[]\n    for i in data: #딕셔너리를 돌고\n        if name!=i: #자기 자신이 아닐때만\n            li.append((sim_pearson(data,name,i),i)) #sim_pearson()을 통해 상관계수를 구하고 li[]에 추가\n    li.sort() #오름차순\n    li.reverse() #내림차순\n    return li[:index]\n\n\ndef getRecommendation (data, person):\n    result = top_match(data, person ,len(data))\n\n    count=0\n    score=0 # 평점 합을 위한 변수\n    li=[] # 리턴을 위한 리스트\n    score_dic={} # 유사도 총합을 위한 dic\n    sim_dic={} # 평점 총합을 위한 dic\n    user_dic={} #\n\n    for sim,user_id in result: # 튜플이므로 한번에\n        if sim < 0 : continue #유사도가 양수인 사람만\n        count += 1\n\n        for restaurant in data[user_id]:\n            if restaurant not in data[person]: # person이 평가를 내리지 않은 식당\n                score += sim*data[user_id][restaurant] # 특정 유저와 person의 유사도 * 특정 유저가 매긴 식당평점\n                score_dic.setdefault(restaurant,0) # 기본값 설정\n                score_dic[restaurant] += score # 합계 구함\n                if restaurant in user_dic:\n                    if user_dic[restaurant][\"similarity\"] < sim:\n                        user_dic[restaurant][\"user\"] = user_id\n                        user_dic[restaurant][\"similarity\"] = sim\n                else:\n                    user_dic[restaurant] = {\n                        \"similarity\" : sim,\n                        \"user\" : user_id\n                    }\n\n                # 조건에 맞는 사람의 유사도의 누적합을 구한다\n                sim_dic.setdefault(restaurant,0)\n                sim_dic[restaurant] += sim\n\n            score=0  #식당이 바뀌었으니 초기화한다\n\n    # 상관 계수가 0보다 큰 유저가 2명 이상일 때만 적용함\n    if count < 2:\n        print(\"# of similar user is too small\")\n        return []\n    for key in score_dic:\n        score_dic[key] = score_dic[key]/sim_dic[key] # 평점 총합/ 유사도 총합\n        if score_dic[key] > 7.0:\n            li.append((score_dic[key],(key, user_dic[key][\"user\"]))) # (식당 id, 유저 id) 튜플 리스트를 리턴함\n    li.sort()\n    li.reverse()\n\n    return li[:10]\n\n\nclass RecommendationView(APIView):\n    def get(self, request, username):\n        user_queryset = CustomUser.objects.filter(username=username)\n        user = user_queryset[0]\n        user_set = user.follows.all()\n        user_set = user_set | user_queryset\n        new_user = CustomUser.objects.filter(followers__in=user_set).distinct()\n        user_number = new_user.count()\n        data = {}\n        id_list = []\n\n        # follow하는 유저와 follow한 유저가 follow하는 유저의 합이 3 이상일때 추천 시스템을 적용함\n        if user_number >= 3:\n            review_set = Review.objects.select_related('archive').select_related('archive__user').exclude(archive__user__username=username).filter(archive__user__in=new_user, public_status=True).order_by('-hits')\n            users_review = Review.objects.select_related('archive').select_related('archive__user').filter(archive__user__username=username)\n            users_review_value = users_review.values('restaurant_id', 'score')\n            data[user.id] = {}\n            temp_count = {}\n\n            # making data for user\n            for val in users_review_value:\n                key = val['restaurant_id']\n                if key in data[user.id]:\n                    if key in temp_count:\n                        temp_count[key] += 1\n                    else:\n                        temp_count[key] = 2\n                    data[user.id][key] += val['score']\n                else:\n                    data[user.id][key] = val['score']\n            for key in temp_count.keys():\n                data[user.id][key] /= temp_count[key]\n\n            # making data for other user\n            for i in range(user_number):\n                id = new_user.values('id')[i]['id']\n                review_list = review_set.filter(archive__user__id=id)\n                review_list_value = review_list.values('restaurant_id', 'score')\n                if review_list.exists():\n                    id_list.append(id)\n                    data[id] = {}\n                    temp_count = {}\n                    for val in review_list_value:\n                        key = val['restaurant_id']\n                        if key in data[id]:\n                            if key in temp_count:\n                                temp_count[key] += 1\n                            else:\n                                temp_count[key] = 2\n                            data[id][key] += val['score']\n                        else:\n                            data[id][key] = val['score']\n                    for key in temp_count.keys():\n                        data[id][key] /= temp_count[key]\n\n\n            list_recommend = getRecommendation(data, user.id)\n\n            result_list = []\n            # get recommend review list\n            for score, recommend in list_recommend:\n                 recommend_rest, recommend_user = recommend\n                 result_list.append(Review.objects.select_related('archive').select_related('archive__user').filter(restaurant_id=recommend_rest, archive__user__id=recommend_user).order_by('-score').first().id)\n            result_queryset = Review.objects.filter(id__in=result_list)\n\n            # 추천할 리뷰가 3개 이상일때만 추천 시스템을 적용함\n            if result_queryset.count() >= 3:\n                serializer = ReviewSerializer(result_queryset[:5], many = True)\n                return Response(serializer.data)\n\n            if review_set.count() >= 3:\n                serializer = ReviewSerializer(review_set[:5], many = True)\n                return Response(serializer.data)\n\n        review_set = Review.objects.select_related('archive').select_related('archive__user').exclude(archive__user__username=username).filter(public_status=True).order_by('-hits')\n        serializer = ReviewSerializer(review_set[:5], many = True)\n        return Response(serializer.data)\n","sub_path":"FD_backend/recommendation/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":7780,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"59541267","text":"#Laboratory 3, team: RED\n\nimport random\nimport time\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom functions import *\n\ndef attack2(n,lc,lk):\n\n\tsetup(lk)\n\n\t# cycle n\n\tphase1('0b1')\n\tc1 = phase2(lc, n)\n\tr1 = phase3(c1, n)\n\ts1 = int(r1, 2)\n\tsc1 = sumDigits(str(int(c1, 2)))\n\n\tif sc1 == 0: return 0\n\tst1 = int(s1 / sc1)\n\n\t#from cycle n, C knows (n,c1) and s1\n\n\t# cycle n + 25\n\tn = n + 25\n\tphase1('0b1')\n\tc2 = phase2(lc, n)\n\tsc2 = sumDigits(str(int(c2, 2)))\n\n\ts2_fake = (st1 - 2) * sc2\n\tr2_hat = phase4(c2, n)\n\ts2_hat = int(r2_hat, 2)\n\n\t# print('st: ',st1, int(s2_hat/sc2))\n\t#print('s: ', s2_fake, s2_hat)\n\tif s2_fake == s2_hat:\n\t\treturn 1\n\telse: return 0\n\n\ndef main():\n\n\tn = 0  # starting counter\n\tit_max = 1000\n\n\tfor lc in range(4,20,4):\n\t\ttimes = []\n\t\tfor lk in range(8,512,4):\n\t\t\tt = 0\n\t\t\tfor it in range(it_max):\n\t\t\t\tstart_time = time.time()\n\n\t\t\t\tsetup(lk)\n\n\t\t\t\tphase1('0b1')\n\t\t\t\tn = n + it\n\t\t\t\tc = phase2(lc, n)\n\t\t\t\tr = phase3(c, n)\n\t\t\t\ts = int(r, 2)\n\t\t\t\tsc = sumDigits(str(int(c, 2)))\n\t\t\t\tr_hat = phase4(c, n)\n\t\t\t\ts_hat = int(r_hat, 2)\n\n\t\t\t\tend_time = time.time()\n\n\t\t\t\tt += (end_time - start_time)\n\n\t\t\ttimes.append(t/it_max)\n\t\t\tprint(\"elapsed time:\", t/it_max)\n\n\t\tplt.plot(range(8, 512, 4), times, label=lc)\n\n\t#plt.ylim(0, 0.00003)\n\tplt.legend(title='lc value')\n\tplt.xlabel('lk')\n\tplt.ylabel('elapsed time')\n\tplt.show()\n\n\nif __name__ == '__main__':\n\tmain()","sub_path":"task1.py","file_name":"task1.py","file_ext":"py","file_size_in_byte":1367,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"2299115","text":"from setuptools import setup\nfrom Cython.Build import cythonize\nimport numpy as np\nimport cython_gsl\nimport os, sys, glob\n\n__version__ = '0.1'\n\nsetup(name = 'shapelets',\n      version = __version__,\n      description = 'Shapelet fitting and plotting',\n      long_description = 'Shapelet fitting and plotting',\n      author='Griffin Foster',\n      author_email='griffin.foster@gmail.com',\n      url='',\n      platforms=['*nix'],\n      license='GPL',\n      requires = ['pyrap','distutils','pylab','numpy','pyfits','PIL','cPickle','scipy'],\n      provides = ['shapelets'],\n      package_dir = {'shapelets':'src'},\n      packages = ['shapelets'],\n      ext_modules = cythonize('shapelets/cshapelet.pyx', annotate=True),\n      include_dirs=[np.get_include(), cython_gsl.get_include()],\n      libraries=[('gsl',{'sources':['shapelets/cshapelet.pyx']}),\n                 ('gslcblas', {'sources': ['shapelets/cshapelet.pyx']})],#cython_gsl.get_libraries(),\n      scripts=glob.glob('scripts/*.py'),\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":992,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"458547845","text":"#!/usr/bin/python3\n# -*- coding: utf8 -*-\n\n\"\"\"Convolutional Neural Networks for MNIST.\"\"\"\n\n\nimport pandas as pd\nimport numpy as np\n\n###### import pour affichage des résultats\nfrom report_result import visu_img_non_predict, report_conf_mat\n###################################################################\n\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras.layers import Dropout \nfrom keras.layers import Flatten\nfrom keras.layers.convolutional import Conv2D \nfrom keras.layers.convolutional import MaxPooling2D\nfrom keras.utils import np_utils \n\nfrom sklearn.metrics import classification_report, accuracy_score\n\n\n\n###################################################################\n###### chargement des data frame train et test à partir des csv\n\nX_test = pd.read_csv('./data/X_test.csv', header=None)\nX_train = pd.read_csv('./data/X_train.csv', header=None)\ny_test = pd.read_csv('./data/y_test.csv', header=None)\ny_train = pd.read_csv('./data/y_train.csv', header=None)\n\nX_train = np.asarray(X_train)\nX_test = np.asarray(X_test)\n\n##### Transformer les données X_train et X_test en matrices à 4 dimensions\n##### (nb_images, profondeur, largeur, hauteur). Chacune des images sera ainsi\n##### redimensionnée au format (1,28,28).\nX_train = X_train.reshape((X_train.shape[0], 1, 28, 28))\nX_test = X_test.reshape((X_test.shape[0], 1, 28, 28))\n\n\n##### Normalisation normaliser les pixels (de 0 à 255)\n##### des données X_train et X_test afin qu'ils soient compris entre 0 et 1\nX_train = X_train/255\nX_test = X_test/255\n\n##### Transformer les labels de y_train et y_test en vecteurs\n##### catégorielles binaires (one hot)\ny_train = np_utils.to_categorical(y_train)\ny_test = np_utils.to_categorical(y_test)\n\n###################################################################\n\n\n\n###### DNN méthode\n\nnum_pixels = X_train.shape[1]\nnum_classes = y_test.shape[1]\n\n##### Créer un nouveau modèle séquentiel de Neural Network, appelé \"model\nmodel = Sequential()\n\n##### Ajouter un premier layer de convolution, appelé Conv2D, avec les paramètres suivants:\n#####     nb_filter= 32 (nombre de filtres de convolution)\n#####     nb_row= 5 , nb_col= 5, (chancun des filtres à une taille de 5x5)\n#####     border_mode = 'valid\" (pour que les filtres ne s'appliquent qu'au\n#####                            partie entières de l'image, ne dépassent pas les bords)\n#####     input_shape= (1,28,28) (taille de chaque image)\n#####     activation = 'relu'\n#####     data_format = channel_first pour indiquer que la premiere coordonnée correspond\n#####                              aux channels de l'image (par défaut Conv2D considère\n#####                              qu'il s'agit de la derniere coordonnée)\nmodel.add(Conv2D(nb_filter= 32 , nb_row= 5 , nb_col= 5, border_mode = 'valid', input_shape= (1,28,28), activation='relu', data_format = 'channels_first'))\n\n##### Puis ajouter un layer de pooling qui renvoie le maximum appelé MaxPooling2D,\n##### que l'on configure avec un pool_size de taille (2,2).\nmodel.add(MaxPooling2D(pool_size = (2,2)))\n\n\n##### Dropout est un layer de régularisation pour réduire l'overfitting, qui exclue\n##### aléatoirement une fraction donnée des neurones à chaque étape de l'entraînement\n##### du modèle. Ainsi il réduit la co-adaptation, empêche certains neurones de trop \n##### en influencer d'autres et favorise la généralisation de l'apprentissage.\n#####     Ajouter un layer de régularisation Dropout. Ici, on choisit d'exclure 20% des inputs.\n#####     Ajouter un layer appelé \"Flatten\", qui convertit des données matricielles en un \n#####            vecteur, pour pouvoir passer dans un layer standard, fully-connected.\n#####            Cette layer ne contient pas de paramètres.\n#####     Ajouter par la suite un fully-connected layer , avec 128 neurones et la\n#####            fonction d'activation 'ReLu'\n#####     Puis le dernier, avec 10 neurones (pour 10 classes d'output) et une fonction\n#####            d'activation softmax pour renvoyer des prédictions de probabilité pour \n#####            chaque classe.\nmodel.add(Dropout(0.2))\nmodel.add(Flatten())\nmodel.add(Dense(128, activation ='relu'))\nmodel.add(Dense(10, activation ='softmax'))\n\n##### configurer le processus d'entraînement, avec la méthode compile,\nmodel.compile(optimizer = 'adam' , loss = 'categorical_crossentropy' , metrics = ['accuracy'])\n\n##### Entraîner le modèle avec les données X_train et y_train, grâce à la méthode\n##### modele.fit, avec 10 epochs, des batchs de taille 200 et rajouter \"verbose = 2\"\n##### pour avoir un aperçu détaillé de la progression de l'entraînement, epoch par epoch.\nmodel.fit(x = X_train , y = y_train, batch_size = 200 , epochs = 10 , verbose = 2)\n\ntest_pred = model.predict(X_test)\nscore = model.evaluate(X_test, y_test)\nprint(\" \\nPerte: %.3f. Erreur: %.2f%%\" % (score[0], 100-score[1]*100))\n\n\n\n##### La fonction d'activation softmax utilisée à la fin de notre modèle, fait que\n##### celui-ci retourne pour chaque image un vecteur de probabilités de longueur 10.\n\n#####     Dans un vecteur appelé \"pred\" remplacer ces vecteurs de probabilités par le\n#####     label avec la plus grande probailité, grâce à la méthode argmax.\n\n#####     De la même façon, dans un vecteur appelé \"y\", retransformer les vecteurs\n#####     one hot de y_test en labels.\n\n#####     Avec ces deux vecteurs, afficher un compte-rendu plus détaillé de l'évaluation\n#####     de la classification, grâce à la fonction classification_report.\npred = test_pred.argmax(axis=1)\ny = y_test.argmax(axis=1)\n\n\n###### affichage des résultats\nprint(\"Précision de la prédiction: %.2f%  % \" %(accuracy_score(y, pred)*100) )\nprint(classification_report(y,pred))\n\nvisu_img_non_predict(X_test,y,pred)\n \nreport_conf_mat(y, pred, limite = 10)\n\n","sub_path":"cnn.py","file_name":"cnn.py","file_ext":"py","file_size_in_byte":5824,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"653359356","text":"#Urisemine\n#Koostab UT sõnade statistika\nf=open('urisemine2.txt').read() # Tekstifail soovitatavalt UT\nf=f.replace('\\n\\n',' ').replace('\\n',' ').replace('.',' ').replace('\\'','')\nf=f.replace(',',' ').replace('\"','').replace('(','').replace(')','')\nf=f.replace('#','').replace('/',' ').replace(';',' ').replace('-',' ')\nfor i in range(10):\n    f=f.replace('  ',' ')\nf=f.lower().split()\nf.sort()\ns=set(f)\nprint(len(f))\nprint(len(s))\nprint('Erinevaid sõnu, mille pikkus>i')\nfor i in range(15):\n    print(i,len(list(filter(lambda x:len(x)>i,s))))\nprint('Väga aeglane. Oota')\nson=dict()\nkont=set()\nd=0\nfor unis in s:\n    d+=1\n    pre=0\n    for i in range(len(unis),3,-1):\n        stri=unis[:i]\n        if stri not in kont:\n            kont.add(stri)\n            c=0\n            for e in f:\n                if e[:i]==stri:\n                    c+=1\n                elif c!=0:\n                    if c!=pre:\n                        son[stri]=c\n                        pre=c\n                    break\n    if d%200==0:\n        print(d,unis)\ns=list(reversed(sorted(list(son.values()))))\nprint('Kokku sõnu',sum(son.values()))\nprint('Erinevaid sõnu',len(s))\nprint('Erinevaid sõnadehulki',len(set(s)))\nvas=s[:1500]\nfor i in sorted(son, key=lambda x:(son[x],len(x),x), reverse=False):\n    if son[i] in vas and (son[i]>29 or (len(i)>10 and son[i]!=1)):\n        print(i,son[i])\ncc=0\nss=list(sorted(son))\nif False:\n    for i in ss:\n        l=len(i)\n        if l<19:\n            print(i,end=' '*(20-l))\n        else:print(i,end=' ')\n        cc+=1\n        if cc%4==0:print()\ninput('väljub')","sub_path":"urisemistöö.py","file_name":"urisemistöö.py","file_ext":"py","file_size_in_byte":1577,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"504149942","text":"from __future__ import absolute_import\n\nfrom __future__ import unicode_literals\nfrom datetime import datetime\n\nfrom django.test import TestCase, override_settings\n\nfrom casexml.apps.case.mock import CaseStructure\nfrom casexml.apps.case.tests.util import delete_all_cases\nfrom corehq.motech.repeaters.dbaccessors import (\n    delete_all_repeat_records,\n    delete_all_repeaters,\n)\nfrom corehq.motech.repeaters.models import RepeatRecord\nfrom custom.enikshay.const import (\n    PRIMARY_PHONE_NUMBER,\n    TREATMENT_OUTCOME,\n    TREATMENT_SUPPORTER_FIRST_NAME,\n    TREATMENT_SUPPORTER_PHONE,\n)\nfrom custom.enikshay.integrations.ninetyninedots.repeaters import (\n    NinetyNineDotsAdherenceRepeater,\n    NinetyNineDotsRegisterPatientRepeater,\n    NinetyNineDotsTreatmentOutcomeRepeater,\n    NinetyNineDotsUnenrollPatientRepeater,\n    NinetyNineDotsUpdatePatientRepeater,\n)\nfrom custom.enikshay.tests.utils import (\n    ENikshayCaseStructureMixin,\n    ENikshayLocationStructureMixin,\n)\n\n\nclass ENikshayRepeaterTestBase(ENikshayCaseStructureMixin, TestCase):\n    maxDiff = None\n\n    def setUp(self):\n        super(ENikshayRepeaterTestBase, self).setUp()\n\n        delete_all_repeat_records()\n        delete_all_repeaters()\n        delete_all_cases()\n\n    def tearDown(self):\n        super(ENikshayRepeaterTestBase, self).tearDown()\n\n        delete_all_repeat_records()\n        delete_all_repeaters()\n        delete_all_cases()\n\n    def repeat_records(self):\n        return RepeatRecord.all(domain=self.domain, due_before=datetime.utcnow())\n\n    def _create_99dots_enabled_case(self):\n        dots_enabled_case = CaseStructure(\n            case_id=self.episode_id,\n            attrs={\n                'case_type': 'episode',\n                \"update\": dict(\n                    dots_99_enabled='true',\n                )\n            }\n        )\n        self.create_case(dots_enabled_case)\n\n    def _create_99dots_registered_case(self):\n        dots_registered_case = CaseStructure(\n            case_id=self.episode_id,\n            attrs={\n                'create': True,\n                'case_type': 'episode',\n                \"update\": dict(\n                    dots_99_registered='true',\n                )\n            }\n        )\n        self.create_case(dots_registered_case)\n\n    def _update_case(self, case_id, case_properties, close=False):\n        return self.create_case(\n            CaseStructure(\n                case_id=case_id,\n                attrs={\n                    \"close\": close,\n                    \"update\": case_properties,\n                }\n            )\n        )\n\n\n@override_settings(TESTS_SHOULD_USE_SQL_BACKEND=True, SERVER_ENVIRONMENT='enikshay')\nclass TestRegisterPatientRepeater(ENikshayLocationStructureMixin, ENikshayRepeaterTestBase):\n\n    def setUp(self):\n        super(TestRegisterPatientRepeater, self).setUp()\n\n        self.repeater = NinetyNineDotsRegisterPatientRepeater(\n            domain=self.domain,\n            url='case-repeater-url',\n        )\n        self.repeater.white_listed_case_types = ['episode']\n        self.repeater.save()\n\n    def test_trigger(self):\n        # 99dots not enabled\n        self.create_case(self.episode)\n        self.assign_person_to_location(self.phi.location_id)\n        self.assertEqual(0, len(self.repeat_records().all()))\n\n        # enable 99dots, should register a repeat record\n        self._create_99dots_enabled_case()\n        self.assertEqual(1, len(self.repeat_records().all()))\n\n        # updating some other random properties shouldn't create a new repeat record\n        self._update_case(self.episode_id, {'some_property': \"changed\"})\n        self.assertEqual(1, len(self.repeat_records().all()))\n\n        # set as registered, shouldn't register a new repeat record\n        self._create_99dots_registered_case()\n        self.assertEqual(1, len(self.repeat_records().all()))\n\n\n@override_settings(TESTS_SHOULD_USE_SQL_BACKEND=True, SERVER_ENVIRONMENT='enikshay')\nclass TestUpdatePatientRepeater(ENikshayLocationStructureMixin, ENikshayRepeaterTestBase):\n\n    def setUp(self):\n        super(TestUpdatePatientRepeater, self).setUp()\n        self.repeater = NinetyNineDotsUpdatePatientRepeater(\n            domain=self.domain,\n            url='case-repeater-url',\n        )\n        self.repeater.white_listed_case_types = ['person', 'episode']\n        self.repeater.save()\n\n    def test_trigger(self):\n        self.create_case_structure()\n        self._update_case(self.person_id, {PRIMARY_PHONE_NUMBER: '999999999', })\n        self.assign_person_to_location(self.phi.location_id)\n        self.assertEqual(0, len(self.repeat_records().all()))\n\n        self._create_99dots_registered_case()\n        self.assertEqual(0, len(self.repeat_records().all()))\n\n        self._update_case(self.person_id, {'name': 'Elrond', })\n        self.assertEqual(0, len(self.repeat_records().all()))\n\n        self._update_case(self.person_id, {PRIMARY_PHONE_NUMBER: '999999999', })\n        self.assertEqual(1, len(self.repeat_records().all()))\n\n        # update a pertinent case with something that shouldn't trigger,\n        # and a non-pertinent case with a property that is in the list of triggers\n        self.factory.create_or_update_cases([\n            CaseStructure(\n                case_id=self.person_id,\n                attrs={\n                    \"update\": {'name': 'Elrond', },\n                }\n            ),\n            CaseStructure(\n                case_id=self.occurrence_id,\n                attrs={\n                    \"update\": {PRIMARY_PHONE_NUMBER: '999999999', },\n                }\n            ),\n\n        ])\n        self.assertEqual(1, len(self.repeat_records().all()))\n\n        self._update_case(self.episode_id, {TREATMENT_SUPPORTER_PHONE: '999999999', })\n        self.assertEqual(2, len(self.repeat_records().all()))\n\n    def test_update_owner_id(self):\n        self.create_case_structure()\n        self.assign_person_to_location(self.phi.location_id)\n        self._create_99dots_registered_case()\n        self._update_case(self.person_id, {'owner_id': self.dto.location_id})\n        self.assertEqual(1, len(self.repeat_records().all()))\n\n    def test_trigger_multiple_cases(self):\n        \"\"\"Submitting a form with noop case blocks was throwing an exception\n        \"\"\"\n        self.create_case_structure()\n        self.assign_person_to_location(self.phi.location_id)\n        self._create_99dots_registered_case()\n\n        empty_case = CaseStructure(\n            case_id=self.episode_id,\n        )\n        person_case = CaseStructure(\n            case_id=self.person_id,\n            attrs={\n                'case_type': 'person',\n                'update': {PRIMARY_PHONE_NUMBER: '9999999999'}\n            }\n        )\n\n        self.factory.create_or_update_cases([empty_case, person_case])\n        self.assertEqual(1, len(self.repeat_records().all()))\n\n    def test_create_person_no_episode(self):\n        \"\"\"On registration this was failing hard if a phone number was added but no episode was created\n        http://manage.dimagi.com/default.asp?241290#1245284\n        \"\"\"\n        self.create_case(self.person)\n        self.assertEqual(0, len(self.repeat_records().all()))\n\n\n@override_settings(TESTS_SHOULD_USE_SQL_BACKEND=True, SERVER_ENVIRONMENT='enikshay')\nclass TestAdherenceRepeater(ENikshayLocationStructureMixin, ENikshayRepeaterTestBase):\n\n    def setUp(self):\n        super(TestAdherenceRepeater, self).setUp()\n        self.repeater = NinetyNineDotsAdherenceRepeater(\n            domain=self.domain,\n            url='case-repeater-url',\n        )\n        self.repeater.white_listed_case_types = ['adherence']\n        self.repeater.save()\n\n    def test_trigger(self):\n        self.create_case_structure()\n        self._create_99dots_registered_case()\n        self._create_99dots_enabled_case()\n        self.assign_person_to_location(self.phi.location_id)\n        self.assertEqual(0, len(self.repeat_records().all()))\n\n        self.create_adherence_cases([datetime(2017, 2, 17)])\n        self.assertEqual(0, len(self.repeat_records().all()))\n\n        self.create_adherence_cases([datetime(2017, 2, 18)], adherence_source='enikshay')\n        self.assertEqual(1, len(self.repeat_records().all()))\n\n        case = self.create_adherence_cases([datetime(2017, 2, 20)], adherence_source='enikshay')\n        self.assertEqual(2, len(self.repeat_records().all()))\n\n        # Updating the case doesn't make a new repeat record\n        self._update_case(case[0].case_id, {'dots_99_error': \"hello\"})\n        self.assertEqual(2, len(self.repeat_records().all()))\n\n\n@override_settings(TESTS_SHOULD_USE_SQL_BACKEND=True, SERVER_ENVIRONMENT='enikshay')\nclass TestTreatmentOutcomeRepeater(ENikshayLocationStructureMixin, ENikshayRepeaterTestBase):\n\n    def setUp(self):\n        super(TestTreatmentOutcomeRepeater, self).setUp()\n        self.repeater = NinetyNineDotsTreatmentOutcomeRepeater(\n            domain=self.domain,\n            url='case-repeater-url',\n        )\n        self.repeater.white_listed_case_types = ['episode']\n        self.repeater.save()\n\n    def test_trigger(self):\n        self.create_case_structure()\n        self._create_99dots_registered_case()\n        self._create_99dots_enabled_case()\n        self.assign_person_to_location(self.phi.location_id)\n        self.assertEqual(0, len(self.repeat_records().all()))\n\n        self._update_case(self.episode_id, {TREATMENT_OUTCOME: 'the_end_of_days'})\n        self.assertEqual(1, len(self.repeat_records().all()))\n\n        self._update_case(self.episode_id, {TREATMENT_SUPPORTER_FIRST_NAME: 'boo'})\n        self.assertEqual(1, len(self.repeat_records().all()))\n\n\n@override_settings(TESTS_SHOULD_USE_SQL_BACKEND=True, SERVER_ENVIRONMENT='enikshay')\nclass TestUnenrollPatientRepeater(ENikshayLocationStructureMixin, ENikshayRepeaterTestBase):\n\n    def setUp(self):\n        super(TestUnenrollPatientRepeater, self).setUp()\n        self.repeater = NinetyNineDotsUnenrollPatientRepeater(\n            domain=self.domain,\n            url='case-repeater-url',\n        )\n        self.repeater.white_listed_case_types = ['episode']\n        self.repeater.save()\n\n    def test_trigger_99dots_disabled(self):\n        self.create_case_structure()\n        self._create_99dots_registered_case()\n        self._create_99dots_enabled_case()\n        self.assign_person_to_location(self.phi.location_id)\n        self.assertEqual(0, len(self.repeat_records().all()))\n\n        self._update_case(self.episode_id, {'dots_99_enabled': 'false'})\n        self.assertEqual(1, len(self.repeat_records().all()))\n\n        self._update_case(self.episode_id, {'dots_99_enabled': 'true'})\n        self.assertEqual(1, len(self.repeat_records().all()))\n\n    def test_trigger_case_closed(self):\n        self.create_case_structure()\n        self._create_99dots_registered_case()\n        self._create_99dots_enabled_case()\n        self.assign_person_to_location(self.phi.location_id)\n        self.assertEqual(0, len(self.repeat_records().all()))\n\n        self._update_case(self.episode_id, {'close_reason': 'boo'}, close=True)\n        self.assertEqual(0, len(self.repeat_records().all()))\n\n        self._update_case(self.episode_id, {'close_reason': 'duplicate'}, close=True)\n        self.assertEqual(1, len(self.repeat_records().all()))\n","sub_path":"custom/enikshay/integrations/ninetyninedots/tests/test_repeaters.py","file_name":"test_repeaters.py","file_ext":"py","file_size_in_byte":11236,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"473427540","text":"import re,logging\nimport smtplib\ntry:\n    a = int(input('Enter the number of cup of TEA (0 if you don\\'t want)  -  '))\n    b = int(input('Enter the number of cup of COFFEE (0 if you don\\'t want)  -  '))\n    c = int(input('Enter the number of MASALA DOSA (0 if you don\\'t want)  -  '))\n    d = int(input('you have completed the order now press 4 to get your bill - '))\n    email = input('Enter your email id:')\n    emailid=re.match('^[a-z0-9]+[\\._]?[a-z0-9]+[@]\\w+[.]\\w{2,3}$',email)\n    if emailid:\n        Email=email\n    connection=smtplib.SMTP(\"smtp.gmail.com\",587)\n    connection.starttls()\n    connection.login(\"practicedigital12@gmail.com\",\"sandhya9@9\")\n    if(d==4):\n        message =a*7+b*10+c*50\n    message=str(message)\n    connection.sendmail(\"sandhyakopparla24@gmail.com\",Email, message)\n    connection.quit()\nexcept Exception:\n    logging.error(\"Your order is not in our menu\")\nelse:\n    print(\"your order amount is\",message)\n    print(\"Successfuly order placed\") \nfinally:\n    print(\"Thank you for visiting our restaurant!\")","sub_path":"practice/8thaug/menufood.py","file_name":"menufood.py","file_ext":"py","file_size_in_byte":1038,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"575396633","text":"#!/usr/bin/env python3\nfrom flask import Flask, request\nfrom flask_restful import Resource, Api\nfrom json import dumps\nfrom flask_jsonpify import jsonify\nimport subprocess\nimport re\n\napp = Flask(__name__)\napi = Api(app)\n\nclass upsapi(Resource):\n    def get(self):\n        status = subprocess.Popen(['pwrstat', '-status'], stdout=subprocess.PIPE).communicate()[0].decode('utf-8')\n        ups = {}\n\n        model_match = \"Model Name\"\n        firmware_match = \"Firmware Number\"\n        rating_volt_match = \"Rating Voltage\"\n        rating_watt_match = \"Rating Power\"\n        state_match = \"State\"\n        utility_volt_match = \"Utility Voltage\"\n        output_volt_match = \"Output Voltage\"\n        battery_capacity_match = \"Battery Capacity\"\n        load_watt_match = \"Load\"\n\n        period_to_end = r\"(?<=\\.\\.\\s)(.*)\"\n        load_percent_re = r\"(?<=\\()(.*?)(?=\\s*%\\))\"\n        load_watt_re = r\"(?<=\\.\\.\\s)(.*?)(?= Watt)\"\n        only_digits_re = r\"\\D\"\n\n        ups = {}\n        for line in status.splitlines():\n            if model_match in line:\n                ups['model_name'] = re.search(period_to_end, line).group().strip()\n            if firmware_match in line:\n                ups['firmware'] = re.search(period_to_end, line).group().strip()\n            if rating_volt_match in line:\n                ups['rating_volt'] = int(re.sub(only_digits_re, '', line))\n            if rating_watt_match in line:\n                ups['rating_watt'] = int(re.sub(only_digits_re, '', line))\n            if state_match in line:\n                ups['state'] = re.search(period_to_end, line).group().strip()\n            if utility_volt_match in line:\n                ups['utility_volt'] = int(re.sub(only_digits_re, '', line))\n            if output_volt_match in line:\n                ups['output_volt'] = int(re.sub(only_digits_re, '', line))\n            if battery_capacity_match in line:\n                ups['battery_capacity'] = int(re.sub(only_digits_re, '', line))\n            if load_watt_match in line:\n                ups['load_watt'] = int(re.search(load_watt_re, line).group().strip())\n                ups['load_percent'] = int(re.search(load_percent_re, line).group().strip())\n\n        return jsonify(ups)\n\napi.add_resource(upsapi, '/ups-api') # return all parameters\n\nif __name__ == '__main__':\n     app.run(port=5002, host='0.0.0.0')","sub_path":"ups-api.py","file_name":"ups-api.py","file_ext":"py","file_size_in_byte":2334,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"468532619","text":"#!/usr/bin/python\n# This code was developed using https://medium.com/@the1ju/simple-logistic-regression-using-keras-249e0cc9a970\n# Build the model of a logistic classifier\nimport os\nimport gzip\nimport pdb\nimport sys\nimport six.moves.cPickle as pickle\nimport numpy as np\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Activation\nfrom keras.datasets import mnist\nfrom keras.utils import np_utils\nfrom keras.layers import Conv2D\nfrom keras.layers import MaxPooling2D\nfrom keras.layers import Flatten\n\ndef build_logistic_model(input_shape, output_dim):\n    model = Sequential()\n    model.add(Conv2D(128, (3, 3), input_shape=input_shape, activation='relu'))\n    model.add(MaxPooling2D(pool_size=(2, 2)))\n    model.add(Conv2D(88, (2, 2), activation='relu'))\n    model.add(MaxPooling2D(pool_size=(2, 2)))\n    model.add(Flatten())\n    model.add(Dense(512, activation='relu'))\n    model.add(Dense(10, activation='softmax'))\n    return model\n\nbatch_size = 128\nnb_classes = 10\nnb_epoch = 20\ninput_dim = 784\nimg_x, img_y = 28, 28\ninput_shape = (img_x, img_y, 1)\n\n# the data, shuffled and split between train and test sets\n(X_train, y_train), (X_test, y_test) = mnist.load_data()\nTrain_sample_X = np.vsplit(X_train, [10000,60000])\nTrain_sample_Y = np.split(y_train, [10000,60000])\nX_train = Train_sample_X[1]\nX_train = X_train.reshape(50000, img_x, img_y, 1)\nX_train = X_train.astype('float32')\nX_train /= 255\n\nX_test = X_test.reshape(10000, img_x, img_y, 1)\nX_test = X_test.astype('float32')\nX_test /= 255\n\nX_val = Train_sample_X[0]\nX_val = X_val.reshape(10000, img_x, img_y, 1)\nX_val = X_val.astype('float32')\nX_val /= 255\nY_train = Train_sample_Y[1]\nY_val = Train_sample_Y[0]\n\nY_train = np_utils.to_categorical(Y_train, nb_classes)\nY_val = np_utils.to_categorical(Y_val, nb_classes)\nY_test = np_utils.to_categorical(y_test, nb_classes)\nmodel = build_logistic_model(input_shape, nb_classes)\n\nmodel.summary()\n# compile the model\nsgd = optimizers.SGD(lr=0.05, decay=0.0, momentum=0.1, nesterov=True)\nmodel.compile(optimizer=sgd, loss='categorical_crossentropy', metrics=['accuracy'])\nhistory = model.fit(X_train, Y_train,\n                    batch_size=batch_size, nb_epoch=nb_epoch,\n                    verbose=1, validation_data=(X_val, Y_val))\n\t\t\t\t\t\nscore = model.evaluate(X_test, Y_test, verbose=0)\n\nprint('Test score:', score[0])\nprint('Test accuracy:', score[1])\n","sub_path":"DL/class programs/CNN_2Layer-Assignment-2.py","file_name":"CNN_2Layer-Assignment-2.py","file_ext":"py","file_size_in_byte":2377,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"553745812","text":"import gym\nimport numpy as np\nimport random as pr\n\nenv = gym.make('CartPole-v0')\n\ngame_count = 100000\n\ngoal_step = 1001\nbaseline_step = 60\n\ntraining_data = []\n\nfor _ in range(game_count):\n    game_data = []\n    score = 0\n    observation = env.reset()\n    done = False\n    for _ in range(goal_step):\n        action = pr.randrange(0, 2)\n        game_data.append([observation, action])\n        observation, reward, done, _ = env.step(action)\n        score += reward\n        if done: break\n    if score >= baseline_step:\n        for data in game_data:\n            if data[1] == 0:\n                output = [1, 0]\n            else:\n                output = [0, 1]\n            training_data.append([data[0], output])\ntraining_data_save = np.array(training_data)\nnp.save('DATA.npy', training_data_save)\n","sub_path":"Cart_Pole/DQN/getData.py","file_name":"getData.py","file_ext":"py","file_size_in_byte":796,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"211005805","text":"'''\ngbts: helper functions for Grand Bay timeseries data\n'''\n\nfrom __future__ import division\nfrom __future__ import unicode_literals\nimport numpy as np\nfrom matlabtools import loadmat, matlab2datetime\nimport calendar\nimport socket\nimport os\nimport xarray as xr\nimport pandas as pd\nimport scipy\nimport djn\nimport datetime as dt\n\ndef tidalfilt(inmat, fs, cutoff=48.):\n    \"\"\"\n    Low-pass filter data using a 5th order Butterworth filter\n    \"\"\"\n\n    # fs in samples per hour\n    b, a = scipy.signal.butter(5, (1./cutoff)/(fs/2.))\n    return scipy.signal.filtfilt(b, a, inmat)\n\n\ndef sd2uv(s, d):\n    \"\"\"\n    Convert speed and direction to u, v components\n    \"\"\"\n\n    u = s * np.sin(d * np.pi / 180)\n    v = s * np.cos(d * np.pi / 180)\n    return u, v\n\n\ndef tots(d):\n    return calendar.timegm(d.timetuple())\n\n\ndef rot_earth(u, v, degrees):\n    \"\"\"\n    Rotate vectors u, v by given number of degrees using earthwise coordinates\n    (0=N, 90=E)\n    - Positive degrees results in a counterclockwise (CCW) rotation\n    - Negative degrees rotates values clockwise\n    \"\"\"\n\n    up = np.cos(np.deg2rad(degrees)) * u - np.sin(np.deg2rad(degrees)) * v\n    vp = np.sin(np.deg2rad(degrees)) * u + np.cos(np.deg2rad(degrees)) * v\n    return up, vp\n\n\ndef load_noaa_ndbc(filnam):\n    \"\"\"\n    Load NOAA NDBC buoy data from netCDF\n    \"\"\"\n\n    ds = xr.open_dataset(filnam, autoclose=True)\n\n    # Assign time coordinates to the row dimension\n    ds.swap_dims({'row': 'time'}, inplace=True)\n\n    return ds\n\n\ndef load_noaa_sal(filnam):\n    \"\"\"\n    Load NOAA salinity data from netCDF\n    \"\"\"\n\n    ds = xr.open_dataset(filnam)\n\n    # Assign time coordinates to the row dimension\n    ds.swap_dims({'row': 'time'}, inplace=True)\n\n    # Can't get _FillValue to work, so just keep positive salinity values\n    ds['value'] = ds['value'].where(ds.value >= 0)\n\n    return ds\n\n\ndef load_noaa_wind(fildir, degrees=False):\n    \"\"\"\n    Load NOAA wind data from netCDF. Hard-coded to load from a list of files\n    because of 30-day NOAA download limit.\n    \"\"\"\n\n    ds = xr.open_mfdataset([fildir + 'gb/noaa/nosCoopsMW_5c7f_7023_3e73.nc',\n        fildir + 'gb/noaa/nosCoopsMW_8728_cabb_217d.nc',\n        fildir + 'gb/noaa/nosCoopsMW_1ff7_b485_cac5.nc',\n        fildir + 'gb/noaa/nosCoopsMW_a324_c824_e355.nc',\n        fildir + 'gb/noaa/nosCoopsMW_6ef4_21e7_b576.nc',\n        fildir + 'gb/noaa/nosCoopsMW_c310_ebf7_1b35.nc'])\n\n    # Assign time coordinates to the row dimension\n    ds.swap_dims({'row': 'time'}, inplace=True)\n\n    ds['u'], ds['v'] = sd2uv(ds['WS'], ds['WD'])\n    ds['lpWS'] = xr.DataArray(tidalfilt(ds['WS'], 10), dims='time')\n    ds['lpu'] = xr.DataArray(tidalfilt(ds['u'], 10), dims='time')\n    ds['lpv'] = xr.DataArray(tidalfilt(ds['v'], 10), dims='time')\n\n    if degrees:\n        ds['ac'], ds['al'] = rot_earth(ds['u'], ds['v'], degrees)\n        ds['lpal'] = xr.DataArray(tidalfilt(ds['al'], 10), dims='time')\n        ds['lpac'] = xr.DataArray(tidalfilt(ds['ac'], 10), dims='time')\n\n    return ds\n\n\ndef atmos_correct(presdn, presval, metdn, metval, offset=0):\n    \"\"\"\n    Atmospherically correct pressure data using GNDCRMET\n    \"\"\"\n\n    ptime = np.array([tots(x) for x in presdn])\n    mtime = np.array([tots(x) for x in metdn])\n\n    metinterp = np.interp(ptime, mtime, metval)\n    # since the aquadopps already have atmos removed...\n    pres_ac = presval - (metinterp - offset)\n\n    return pres_ac\n\n\ndef mgl(ntu, err=False):\n    \"\"\"\n    Convert turbidity NTU to mg/L from calibration\n    mgl = 1.573 + 1.2\n    \"\"\"\n\n    if err == False:\n        mgl = 1.573 * ntu + 1.2\n    elif err == True:\n        err = np.max((1.729-1.573, 1.573-1.195)) # from fit, 95 confidence intervals\n        # this should give the plus/minus of worst case 95% confidence interval\n        mgl = np.random.normal(1.573, scale=err/2, size=len(ntu)) * ntu + 1.2\n\n    return mgl\n\n\ndef load_gb_aqd_nc(filnam):\n    \"\"\"\n    Load Aquadopp data from netCDF\n    \"\"\"\n\n    aqd = xr.open_dataset(filnam, autoclose=True, decode_times=False)\n\n    aqd['time'] = aqd['time_cf']\n    aqd = aqd.drop('time2')\n    aqd = xr.decode_cf(aqd)\n\n    # squeeze 1d dimensions (lat, lon)\n    aqd = aqd.squeeze(dim=('lat', 'lon'))\n\n    return aqd\n\n\ndef load_gb_exo(filnam, llim=False, ulim=False):\n    \"\"\"\n    Load EXO data from Matlab file to xarray Dataset. This function will change\n    once we have EXO data in netCDF.\n    \"\"\"\n\n    exo = loadmat(filnam)\n    exo = exo['exo']\n    if llim and ulim:\n        for k in exo:\n            # print k\n            exo[k] = exo[k][llim:ulim]\n\n    # FIXME: this rounds to the nearest second. Should not be necessary after netcdf files are loaded, hopefully\n    matdn = pd.to_datetime(np.array([matlab2datetime(x) for x in exo['dn']]))\n    matdn = np.round(matdn.astype(np.int64), -9).astype('datetime64[ns]')\n\n    exda = {} # EXO dictionary of DataArrays\n    exda['time'] = xr.DataArray(matdn, dims='time')\n\n    for k in exo:\n        exda[k] = xr.DataArray(exo[k], dims='time', coords={'time': exda['time']})\n\n    xrexo = xr.Dataset(exda) # create Dataset of dictionary of DataArrays\n    xrexo = xrexo.drop('dn')\n\n    return xrexo\n\n\ndef proc_gb_exo(exo, name=False, ac=False, offset=0, medianfilt=False):\n    \"\"\"\n    Process EXO data and create additional derrived variables\n    \"\"\"\n\n    if ac:\n        # atmospherically correct the pressure data\n        # FIXME: this might be wrong.\n        exo['depth'] = atmos_correct(exo['dn'], exo['depth'], ac['dn'], ac['BP']/100., offset=offset)\n\n    # NOTE: need to fill in a couple of bad points that are not caught by the median filter\n    if (name == 'ex1077') and (pd.to_datetime(exo['time'].to_series()[0]) >= dt.datetime(2016,10,23,12)):\n        fill = np.mean([exo['turb'].sel(time='2016-12-04 14:00'), exo['turb'].sel(time='2016-12-04 15:15')])\n        exo['turb'].loc[dict(time=slice('2016-12-04 14:15', '2016-12-04 15:00'))] = fill\n\n    if medianfilt == True:\n        turborig = exo['turb'].copy()\n        bads = np.squeeze(np.array([1 + np.where(np.abs(np.diff(turborig)) > 100)[0]])) # spikes greater than 100 NTU in a single sample\n        filtered = scipy.signal.medfilt(turborig)\n        turbnew = exo['turb'].copy()\n        turbnew[bads] = filtered[bads]\n        exo['turb'] = turbnew\n\n    #     exo['turb'] = medfilt(exo['turb'])\n    #     exo['sal'] = medfilt(exo['sal'])\n    #     exo['fdom'] = medfilt(exo['fdom'])\n    exo['mgl'] = mgl(exo['turb'], err=True)\n    exo['lpturb'] = xr.DataArray(tidalfilt(exo['turb'], 4), dims='time')\n    exo['lpmgl'] = xr.DataArray(tidalfilt(exo['mgl'], 4), dims='time')\n    exo['lpdepth'] = xr.DataArray(tidalfilt(exo['depth'], 4), dims='time')\n    exo['lpsal'] = xr.DataArray(tidalfilt(exo['sal'], 4), dims='time')\n\n    if name:\n        exo.attrs['name'] = name\n\n    return exo\n\n\ndef clip_gb_aqd(aqd, tmin, tmax):\n    \"\"\"\n    Clip Aquadopp data based on minimum and maximum time range\n    # FIXME: the astype(str) is a dirty hack but it works...\n    \"\"\"\n\n    return aqd.sel(time=slice(tmin.astype(str), tmax.astype(str)))\n\n\ndef get_lims(times):\n    \"\"\"\n    Find the earliest and latest times for which a grouping of Datasets (e.g.,\n    separate Aquadopps) all have valid data\n    \"\"\"\n\n    themin = max([time.min().values for time in times])\n    themax = min([time.max().values for time in times])\n\n    return themin, themax\n\n\ndef decompose(aq):\n    \"\"\"\n    Decompose tidally averaged data into advective, dispersive, Stokes and total\n    components\n    \"\"\"\n\n    goods = aq['lpmgl'].notnull()\n    aq['adv'] = xr.full_like(aq['lpmgl'], np.nan)\n    aq['adv'][goods] = xr.DataArray(tidalfilt(aq['lpdaal'][goods] * aq['lppress'][goods] * aq['lpmgl'][goods], 4), dims='time')\n    aq['disp'] = xr.full_like(aq['adv'], np.nan)\n    aq['disp'][goods] = xr.DataArray(tidalfilt((aq['daal'][goods] - aq['lpdaal'][goods]) * aq['lppress'][goods] * (aq['mgl'][goods] - aq['lpmgl'][goods]), 4), dims='time')\n    aq['stokes'] = xr.full_like(aq['adv'], np.nan)\n    aq['stokes'][goods] = xr.DataArray(tidalfilt((aq['daal'][goods] - aq['lpdaal'][goods]) * (aq['P_1ac'][goods] - aq['lppress'][goods]) * aq['lpmgl'][goods], 4), dims='time')\n    aq['total'] = xr.full_like(aq['adv'], np.nan)\n    aq['total'][goods] = xr.DataArray(tidalfilt(aq['daal'][goods] * aq['P_1ac'][goods] * aq['mgl'][goods], 4), dims='time')\n\n    return aq\n\n\ndef make_aq1077a(aqin):\n    \"\"\"\n    Make xarray Dataset for aq1077a, which has no usable data but needs dummy\n    variables for plotting etc.\n    \"\"\"\n\n    aqout = xr.full_like(aqin, np.nan)\n\n    aqout.attrs = {} # don't want misleading attributes\n    aqout.attrs.update({'name': 'aq1077'})\n\n    return aqout\n\n\ndef fill_aq1076b_gap(aqd):\n    \"\"\"\n    Fill in tiny gap in aq1076b\n    \"\"\"\n    if aqd.attrs['filename'] == '10761Baqd': # make sure we are working on the correct dataset\n        for var in ['u_1205', 'v_1206', 'w_1204']:\n            if np.all(np.isnan(aqd[var][8814])):\n                aqd[var][8814] = np.mean(np.vstack([aqd[var][8813].values, aqd[var][8815].values]), axis=0)\n\n        return aqd\n    else:\n        raise Error('Trying to fill gap on wrong Dataset')\n\n\ndef proc_gb_aqd(aqd, exo=False, name=False, wind=False, degrees=False,\n                allbad=False, ac=False, offset=0, bathy=False):\n    \"\"\"\n    Process Aquadopp velocity data and generate derived variables\n    \"\"\"\n\n    # # nan-out velocity data in air\n    # air = np.tile(aqd['z'], (len(aqd['press']), 1)) > 0.9 * np.tile(aqd['press'], (len(aqd['z']),1)).T #  % 90% pressure to minimize surface reflection\n\n    # deal with 1077a:\n    if (name == 'aq1077') and (aqd.Deployment_date == '2016-08-04 17:26'):\n        aq1077a = True\n    else:\n        aq1077a = False\n\n    if aq1077a:\n        aqd['u_1205'] = xr.full_like(aqd['u_1205'], np.nan)\n\n    # put velocity back into m/s\n    aqd['u_1205'] = aqd['u_1205']/100\n    aqd['v_1206'] = aqd['v_1206']/100\n    aqd['w_1204'] = aqd['w_1204']/100\n\n    aqd['dae'] = aqd['u_1205'].mean(dim='depth')\n    aqd['dan'] = aqd['v_1206'].mean(dim='depth')\n    aqd['spd'] = (aqd['u_1205'] ** 2 + aqd['v_1206'] ** 2) ** 0.5\n    aqd['das'] = aqd['spd'].mean(dim='depth')\n    aqd['lpdae'] = xr.DataArray(tidalfilt(aqd['dae'], 4), dims='time')\n    aqd['lpdan'] = xr.DataArray(tidalfilt(aqd['dan'], 4), dims='time')\n    aqd['lppress'] = xr.DataArray(tidalfilt(aqd['P_1ac'], 4), dims='time')\n\n    if degrees:\n        daac, daal = rot_earth(aqd['dae'].values, aqd['dan'].values, degrees)\n        aqd['daac'] = xr.DataArray(daac, dims='time')\n        aqd['daal'] = xr.DataArray(daal, dims='time')\n        aqd['ac'], aqd['al'] = rot_earth(aqd['u_1205'], aqd['v_1206'], degrees)\n        # aqd['daac'] = aqd['ac'].mean(dim='depth')\n        # aqd['daal'] = aqd['al'].mean(dim='depth')\n    else:\n        theta, majo, mini, wr, degrees = princax(aqd['dae'] + 1j * aqd['dan'])\n        aqd['daal'] = np.real(wr)\n        aqd['daac'] = np.imag(wr)\n        # need to rotate so flood is positive\n        aqd['daac'], aqd['daal'] = rot_earth(aqd['daac'], aqd['daal'], 180)\n    aqd['lpdaac'] = xr.DataArray(tidalfilt(aqd['daac'], 4), dims='time')\n    aqd['lpdaal'] = xr.DataArray(tidalfilt(aqd['daal'], 4), dims='time')\n    aqd['degrees'] = degrees\n\n    if exo.variables:\n        # deal with instrument records that are different lengths\n        # FIXME: this might not do interpolation correctly...\n        aqd['turb'] = exo['turb'].reindex_like(aqd, method='nearest', tolerance='5 min')\n        aqd['mgl'] = exo['mgl'].reindex_like(aqd, method='nearest', tolerance='5 min')\n        aqd['lpmgl'] = xr.full_like(aqd['mgl'], np.nan)\n        aqd['lpmgl'][aqd['mgl'].notnull()] = tidalfilt(aqd['mgl'][aqd['mgl'].notnull()], 4)\n        aqd['sedfluxe'] = aqd['dae'] * aqd['mgl'] * aqd['P_1ac']\n        aqd['sedfluxn'] = aqd['dan'] * aqd['mgl'] * aqd['P_1ac']\n        aqd['lpsedfluxe'] = xr.full_like(aqd['sedfluxe'], np.nan)\n        aqd['lpsedfluxn'] = xr.full_like(aqd['sedfluxn'], np.nan)\n        if not aq1077a:\n            aqd['lpsedfluxe'][aqd['sedfluxe'].notnull()] = tidalfilt(aqd['sedfluxe'][aqd['sedfluxe'].notnull()], 4)\n            aqd['lpsedfluxn'][aqd['sedfluxn'].notnull()] = tidalfilt(aqd['sedfluxn'][aqd['sedfluxn'].notnull()], 4)\n\n        if degrees:\n            aqd['sedfluxal'] = aqd['daal'] * aqd['mgl'] * aqd['P_1ac']\n            aqd['sedfluxac'] = aqd['daac'] * aqd['mgl'] * aqd['P_1ac']\n            aqd['lpsedfluxal'] = xr.full_like(aqd['sedfluxal'], np.nan)\n            aqd['lpsedfluxac'] = xr.full_like(aqd['sedfluxac'], np.nan)\n            if not aq1077a:\n                aqd['lpsedfluxal'][aqd['sedfluxal'].notnull()] =  tidalfilt(aqd['sedfluxal'][aqd['sedfluxal'].notnull()], 4)\n                aqd['lpsedfluxac'][aqd['sedfluxac'].notnull()] =  tidalfilt(aqd['sedfluxac'][aqd['sedfluxac'].notnull()], 4)\n\n        aqd = decompose(aqd)\n\n    if name:\n        aqd.attrs.update({'name': name})\n\n    if wind.variables:\n        aqd['metspd'] = xr.DataArray(np.interp(aqd['time'], wind['time'], wind['WS']), dims='time')\n        aqd['metu'] = xr.DataArray(np.interp(aqd['time'], wind['time'], wind['u']), dims='time')\n        aqd['metv'] = xr.DataArray(np.interp(aqd['time'], wind['time'], wind['v']), dims='time')\n        aqd['lpmetu'] = xr.DataArray(tidalfilt(aqd['metu'], 4), dims='time')\n        aqd['lpmetv'] = xr.DataArray(tidalfilt(aqd['metv'], 4), dims='time')\n        aqd['metac'], aqd['metal'] = rot_earth(aqd['metu'], aqd['metv'], degrees)\n        aqd['lpmetac'] = xr.DataArray(tidalfilt(aqd['metac'], 4), dims='time')\n        aqd['lpmetal'] = xr.DataArray(tidalfilt(aqd['metal'], 4), dims='time')\n\n    if bathy:\n        for k in bathy:\n            aqd[k] = xr.DataArray(bathy[k])\n\n    # compute index-velocity water flux for 1077\n    s, d = djn.uv2sd(aqd['daac'], aqd['daal']) # FIXME: change if necessary\n    s[(d > 90) & (d < 270)] = -s[(d > 90) & (d < 270)] # FIXME: change if necessary\n    s = xr.DataArray(s, dims='time')\n\n    if name == 'aq1076':\n        aqd['vca'] = 0.7 * s # FIXME: just using 0.7 because it's approximately what 1077 and 1079 are.\n        aqd['acalc'] = 620.287 * (aqd['P_1ac'] - 0.7) + 547.88  # 0.7 is the mean offset to MLLW...\n    elif name == 'aq1077':\n        aqd['vca'] = 0.6623 * s - 0.0029 # FIXME: change if necessary\n        aqd['acalc'] = 155.982 * aqd['P_1ac'] - 259.696 # FIXME: change if necessary\n    elif name == 'aq1078':\n        aqd['vca'] = 0.7 * s # FIXME: just using 0.7 because it's approximately what 1077 and 1079 are.\n        aqd['acalc'] = 774.605 * (aqd['P_1ac'] - 1.5) + 1156.623 # 1.5 is the mean offset to MLLW...\n    elif name == 'aq1079':\n        aqd['vca'] = 0.8003 * s - 0.0036 # FIXME: change if necessary\n        aqd['acalc'] = 44.608 * aqd['P_1ac'] - 44.292 # FIXME: change if necessary\n\n    aqd['q'] = aqd['vca'] * aqd['acalc']\n    aqd['lpq'] = xr.DataArray(tidalfilt(aqd['q'], 4), dims='time')\n\n    if bathy and degrees:\n        aqd['wisedfluxal'] = aqd['q'] * aqd['mgl']\n        aqd['wisedfluxalold'] = aqd['daal'] * aqd['mgl'] * aqd['area'] * aqd['P_1ac'] / aqd['depth'].max()\n\n        aqd['wisedfluxac'] = aqd['daac'] * aqd['mgl'] * aqd['area'] * aqd['P_1ac'] / aqd['depth'].max()\n\n        aqd['wilpsedfluxal'] = xr.full_like(aqd['wisedfluxal'], np.nan)\n        aqd['wilpsedfluxac'] = xr.full_like(aqd['wisedfluxac'], np.nan)\n        if not aq1077a:\n            aqd['wilpsedfluxal'][aqd['wisedfluxal'].notnull()] = xr.DataArray(tidalfilt(aqd['wisedfluxal'][aqd['wisedfluxal'].notnull()], 4), dims='time')\n            aqd['wilpsedfluxac'][aqd['wisedfluxac'].notnull()] = xr.DataArray(tidalfilt(aqd['wisedfluxac'][aqd['wisedfluxac'].notnull()], 4), dims='time')\n\n    return aqd\n\n\ndef load_gb_dwave_nc(filnam, name=False, exo=False, llim=False, ulim=False,\n                     wind=False):\n    \"\"\"\n    Load d|wave data from netCDF and create derived variables.\n    \"\"\"\n\n    dw = xr.open_dataset(filnam, autoclose=True, decode_times=False)\n\n    dw['time'] = dw['time_cf']\n    dw = dw.drop('time2')\n    dw = xr.decode_cf(dw)\n\n    # squeeze 1d dimensions (lat, lon)\n    dw = dw.squeeze(dim=('lat', 'lon'))\n\n    if name:\n        dw.attrs.update({'name': name})\n\n    if exo:\n        dw['turb'] = exo['turb'].reindex_like(dw, method='nearest', tolerance='5 min') # FIXME: might not be interpolating properly\n        dw['mgl'] = exo['mgl'].reindex_like(dw, method='nearest', tolerance='5 min')\n\n    if wind:\n        dw['metspd'] = wind['WS'].reindex_like(dw, method='nearest', tolerance='5 min') # FIXME: might not be interpolating properly\n        dw['metu'] = wind['u'].reindex_like(dw, method='nearest', tolerance='5 min')\n        dw['metv'] = wind['v'].reindex_like(dw, method='nearest', tolerance='5 min')\n\n    return dw\n\n\ndef read_nerrs(filnam):\n    \"\"\"\n    Read meteorological and water-quality data from NERRS CDMO CSV files\n    \"\"\"\n\n    a = pd.read_csv(filnam, header=2, parse_dates=[2],\n                    infer_datetime_format=True, index_col=2)\n\n    # force to CST, since that is what they do at GNDNERR according to Kim Cressman\n    # Because Pandas seemingly can't deal with things that stay CST throughout\n    # the year, just add 6 hours to get to UTC (but remian naive)\n    a.index = a.index + pd.Timedelta('6 hours')\n    a.index.rename('time', inplace=True)\n\n    a = xr.Dataset(a)\n\n    if 'WSpd' in a and 'Wdir' in a:\n        a['u'], a['v'] = sd2uv(a['WSpd'], a['Wdir'])\n        a['lpu'] = xr.DataArray(tidalfilt(a['u'], 4), dims='time')\n        a['lpv'] = xr.DataArray(tidalfilt(a['v'], 4), dims='time')\n\n    return a\n\n\ndef princax(w):\n    \"\"\"\n     PRINCAX Principal axis, rotation angle, principal ellipse\n\n       [theta,maj,min,wr]=princax(w)\n\n       Input:  w   = complex vector time series (u+i*v)\n\n       Output: theta = angle of maximum variance, math notation (east == 0, north=90)\n               maj   = major axis of principal ellipse\n               min   = minor axis of principal ellipse\n               wr    = rotated time series, where real(wr) is aligned with\n                       the major axis.\n\n     For derivation, see Emery and Thompson, \"Data Analysis Methods\n       in Oceanography\", 1998, Pergamon, pages 325-327.  ISBN 0 08 0314341\n    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n     Version 1.0 (12/4/1996) Rich Signell (rsignell@usgs.gov)\n     Version 1.1 (4/21/1999) Rich Signell (rsignell@usgs.gov)\n         fixed bug that sometimes caused the imaginary part\n         of the rotated time series to be aligned with major axis.\n         Also simplified the code.\n     Version 1.2 (3/1/2000) Rich Signell (rsignell@usgs.gov)\n         Simplified maj and min axis computations and added reference\n         to Emery and Thompson book\n    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n    \"\"\"\n\n    ind = np.nonzero(np.isfinite(w))\n    wr = w.copy()\n    w = w[ind].copy()\n\n    # find covariance matrix\n    cv = np.cov([np.real(w), np.imag(w)])\n\n    # find direction of maximum variance\n    theta = 0.5 * np.arctan2(2. * cv[1, 0], cv[0, 0] - cv[1, 1])\n\n    # find major and minor axis amplitudes\n    term1 = cv[0, 0] + cv[1, 1]\n    term2 = np.sqrt((cv[0, 0] - cv[1, 1])**2 + 4. * cv[1, 0]**2)\n    majo = np.sqrt(0.5 * (term1 + term2))\n    mini = np.sqrt(0.5 * (term1 - term2))\n\n    # rotate into principal ellipse orientation\n    wr[ind] = w * np.exp(-1j * theta)\n    theta = theta * 180 / np.pi\n\n    # convert from math notation to geographic coordinates\n    degrees = (450 - theta) % 360\n\n    return theta, majo, mini, wr, degrees\n\n\ndef gen_bathy(fname):\n    \"\"\"\n    Generate bathymetry from cross-section files generated in QGIS\n    \"\"\"\n\n    b = np.genfromtxt(fname)\n    ba = {}\n    ba['dist'] = b[:,0]-np.min(b[:,0])\n    ba['depth'] = -b[:,1]\n    ba['area'] = np.trapz(ba['depth'], ba['dist'])\n    ba['width'] = np.max(ba['dist']) - np.min(ba['dist'])\n\n    return ba\n\n\ndef get_fildir():\n    \"\"\"\n    Determine directory containing files based on computer hostname\n    \"\"\"\n\n    if 'igsagiegltdnom1' in socket.gethostname().lower():\n        # On MacBook Air\n        # see if external drive is attached\n        if os.path.isdir('/Volumes/Backstaff/field/'):\n            fildir = '/Volumes/Backstaff/field/'\n            figdir = fildir + 'gb/figs/'\n        else:\n            fildir = '/Users/dnowacki/Documents/Grand Bay/'\n            figdir = fildir + 'figs/'\n    elif 'igsagiegwsdnom0' in socket.gethostname().lower():\n        # On iMac\n        fildir = '/Volumes/Backstaff/field/'\n        figdir = fildir + 'gb/figs/'\n\n    return fildir, figdir\n","sub_path":"gbts.py","file_name":"gbts.py","file_ext":"py","file_size_in_byte":20389,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"194838524","text":"import argparse\nimport os\nimport re\nimport sys\n\nimport numpy as np\nimport tensorflow as tf\nfrom facenet_sandberg.models.keras_inception_resnet_v1 import *\n\nre_repeat = re.compile(r'Repeat_[0-9_]*b')\nre_block8 = re.compile(r'Block8_[A-Za-z]')\n\n\ndef main(tf_ckpt_path, output_base_path, output_model_name):\n    weights_filename = output_model_name + '_weights.h5'\n    model_filename = output_model_name + '.h5'\n\n    npy_weights_dir, weights_dir, model_dir = create_output_directories(\n        output_base_path)\n\n    extract_tensors_from_checkpoint_file(tf_ckpt_path, npy_weights_dir)\n    model = InceptionResNetV1()\n\n    print('Loading numpy weights from', npy_weights_dir)\n    for layer in model.layers:\n        if layer.weights:\n            weights = []\n            for w in layer.weights:\n                weight_name = os.path.basename(w.name).replace(':0', '')\n                weight_file = layer.name + '_' + weight_name + '.npy'\n                weight_arr = np.load(\n                    os.path.join(\n                        npy_weights_dir,\n                        weight_file))\n                weights.append(weight_arr)\n            layer.set_weights(weights)\n\n    print('Saving weights...')\n    model.save_weights(os.path.join(weights_dir, weights_filename))\n    print('Saving model...')\n    model.save(os.path.join(model_dir, model_filename))\n\n\ndef create_output_directories(output_base_path):\n    npy_weights_dir = os.path.join(output_base_path, 'npy_weights')\n    weights_dir = os.path.join(output_base_path, 'weights')\n    model_dir = os.path.join(output_base_path, 'model')\n    os.makedirs(npy_weights_dir, exist_ok=True)\n    os.makedirs(weights_dir, exist_ok=True)\n    os.makedirs(model_dir, exist_ok=True)\n    return npy_weights_dir, weights_dir, model_dir\n\n\ndef get_filename(key):\n    filename = str(key)\n    filename = filename.replace('/', '_')\n    filename = filename.replace('InceptionResnetV1_', '')\n\n    # remove \"Repeat\" scope from filename\n    filename = re_repeat.sub('B', filename)\n\n    if re_block8.match(filename):\n        # the last block8 has different name with the previous 5 occurrences\n        filename = filename.replace('Block8', 'Block8_6')\n\n    # from TF to Keras naming\n    filename = filename.replace('_weights', '_kernel')\n    filename = filename.replace('_biases', '_bias')\n\n    return filename + '.npy'\n\n\ndef extract_tensors_from_checkpoint_file(filename, output_folder):\n    reader = tf.train.NewCheckpointReader(filename)\n\n    for key in reader.get_variable_to_shape_map():\n        # not saving the following tensors\n        if key == 'global_step':\n            continue\n        if 'AuxLogit' in key:\n            continue\n\n        # convert tensor name into the corresponding Keras layer weight name\n        # and save\n        path = os.path.join(output_folder, get_filename(key))\n        arr = reader.get_tensor(key)\n        np.save(path, arr)\n\n\ndef parse_arguments(argv):\n    \"\"\"Argument parser\n    \"\"\"\n\n    parser = argparse.ArgumentParser()\n\n    parser.add_argument(\n        'tf_ckpt_path',\n        type=str,\n        help='Path to the directory containing pretrained tensorflow checkpoints.')\n\n    parser.add_argument(\n        'output_base_path',\n        type=str,\n        help='Base path for the desired output directory.')\n\n    parser.add_argument(\n        'output_model_name',\n        type=str,\n        help='Name for the new model (do not include .h5)')\n\n    return parser.parse_args(argv)\n\n\nif __name__ == '__main__':\n    args = parse_arguments(sys.argv[1:])\n    if args:\n        main(\n            args.tf_ckpt_path,\n            args.output_base_path,\n            args.output_model_name)\n","sub_path":"facenet_sandberg/models/convert_to_keras.py","file_name":"convert_to_keras.py","file_ext":"py","file_size_in_byte":3641,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"192690650","text":"from flask import Flask\nfrom flask import request\nfrom RecommandationFactory import *\nfrom SFrameFormatter import *\nfrom Utils import *\n\napp = Flask( __name__ );\n\n@app.route( \"/<algorithm>/<int:user_id>/<int:num_of_recommendations>\" )\ndef recommand( algorithm , user_id , num_of_recommendations ):\n\n    algo_instance = RecommendationFactory.create( algorithm );\n\n    algo_instance.recommand();\n\n    return;\n\n    if( algo_instance is False ):\n        response = create_notfound_json_response( message=\"algorithm not found\" )\n    else:\n        items_id = [user_id]\n        formater = SFrameFormatter( algo_instance.recommand( items_id , num_of_recommendations ) );\n        response = Response( response=formater.to_json(),\n                            status=200,\n                            mimetype=\"application/json\" )\n\n    return response;\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":841,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"440831703","text":"import os, subprocess, shutil, sys\nfrom multiprocessing import cpu_count\n# from argparse import ArgumentParser\n# parser = ArgumentParser()\n\nhere = os.path.abspath(os.path.dirname(__file__))\noutput_dir = os.path.join(here, '_build')\nif not os.path.exists(output_dir):\n    os.makedirs(output_dir)\n\nplatform = sys.platform.lower()\nif platform.startswith('linux'):\n    platform = 'Linux'\nelif platform.startswith('darwin'):\n    platform = 'Darwin'\nelif platform.startswith('win'):\n    platform = 'Windows'\nelse:\n    raise EnvironmentError(\"Unknown platform {}\".format(platform))\n\ngenerator = 'Unix Makefiles' if (platform == 'Linux' or platform == 'Darwin') else 'Visual Studio 15 2017 Win64'\n\nsubprocess.check_call(['cmake', '-G', generator, '-DCMAKE_BUILD_TYPE=Debug', here], cwd=output_dir)\n\nso_file_path = os.path.join(output_dir, 'I2CDummySlave.so')\nif generator == 'Unix Makefiles':\n    subprocess.check_call(['make', '-j', str(cpu_count())], cwd=output_dir)\n    if platform == 'Darwin':\n        shutil.copy(os.path.join(output_dir, 'libI2CDummySlave.dylib'), so_file_path)\n    else:\n        shutil.copy(os.path.join(output_dir, 'libI2CDummySlave.so'), so_file_path)\nelse:\n    subprocess.check_call(\n        ['msbuild.exe', 'I2CDummySlave.sln', '/m:{}'.format(cpu_count()), '/t:I2CDummySlave', '/p:Platform=x64'], cwd=output_dir\n    )\n    shutil.copy(os.path.join(output_dir, 'Debug', 'I2CDummySlave.dll'), so_file_path)\n","sub_path":"I2CDummySlave/build/build.py","file_name":"build.py","file_ext":"py","file_size_in_byte":1423,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"346548872","text":"from pylab import *\n\n# 指定默认字体\nplt.rcParams['font.sans-serif'] = ['SimHei']\nplt.rcParams['font.family'] = 'sans-serif'\n# 用来正常显示负号\nplt.rcParams['axes.unicode_minus'] = False\nimport matplotlib.pyplot as plt\n\nimport readAndWriteDataSet\nimport quantification\nimport kmeans\nimport tools\nimport numpy as np\nimport multiprocessing\nimport addNoise\nimport pca\nimport wt\n\n\ndef cluster(a, schedule, channelDataAll1, channelDataAll2, allCentroidsC, allCentroidUList, allCentroidsU,\n            allCentroidUList2):\n    newPca1 = []\n    newPca2 = []\n    newC1 = []\n    newC2 = []\n    newU1 = []\n    newU2 = []\n    newWt1 = []\n    newWt2 = []\n    for g in range(1, (1 << a) + 1):\n        schedule[1] += 1\n        tmpSchedule = schedule[1]\n        print(u'共' + str(schedule[0]) + u'部分，' + u'第' + str(tmpSchedule) + u'部分开始！')\n\n        channelData1 = []\n        channelData2 = []\n        for i in range(np.shape(channelDataAll1)[0]):\n            channelData1.append(channelDataAll1[i][:, (g - 1) * sub:g * sub])\n            channelData2.append(channelDataAll2[i][:, (g - 1) * sub:g * sub])\n\n        # 计算信道协方差矩阵\n        covMatrixList1 = tools.getCovMatrixList(channelData1)\n\n        # 无交互PCA\n        tmpNewPca1, tmpNewPca2 = clusterCore(channelData1, covMatrixList1, channelData2, allCentroidsC[g - 1],\n                                             allCentroidUList[g - 1], \"general\")\n        newPca1.append(tmpNewPca1)\n        newPca2.append(tmpNewPca2)\n\n        # 聚类协方差矩阵\n        tmpNewC1, tmpNewC2 = clusterCore(channelData1, covMatrixList1, channelData2, allCentroidsC[g - 1],\n                                         allCentroidUList[g - 1], \"C\")\n        newC1.append(tmpNewC1)\n        newC2.append(tmpNewC2)\n\n        # 聚类变换矩阵\n        tmpNewU1, tmpNewU2 = clusterCore(channelData1, covMatrixList1, channelData2, allCentroidsU[g - 1],\n                                         allCentroidUList2[g - 1], \"U\")\n        newU1.append(tmpNewU1)\n        newU2.append(tmpNewU2)\n\n        # DCT变换\n        tmpNewWt1, tmpNewWt2 = clusterCore(channelData1, covMatrixList1, channelData2, allCentroidsU[g - 1],\n                                           allCentroidUList2[g - 1], \"wt\")\n        newWt1.append(tmpNewWt1)\n        newWt2.append(tmpNewWt2)\n\n        # 显示进度\n        print(u'共' + str(schedule[0]) + u'部分，' + u'第' + str(tmpSchedule) + u'部分完成，' + u'已完成' + str(schedule[1]) + u'部分，' + u'完成度：' + '%.2f%%' % (schedule[1] / schedule[0] * 100) + u'！')\n\n    return newPca1, newPca2, newC1, newC2, newU1, newU2, newWt1, newWt2\n\n\ndef clusterCore(channelData1, covMatrixList1, channelData2, centroids, centroidUList, type):\n    newChannelData1 = []\n    newChannelData2 = []\n    newDimension = np.shape(centroidUList[0])[1]\n\n    if type == \"C\":\n        # 计算信道相关系数矩阵并输出，然后放到一个矩阵中\n        allCovMatrix1 = tools.matrixListToMatrix(covMatrixList1)\n\n        # 确定每个数据分别属于哪个簇\n        clusterAssment = kmeans.getClusterAssment(allCovMatrix1, centroids)\n\n        # 变换域\n        for i in range(np.shape(channelDataAll1)[0]):\n            newChannelData1.append(np.dot(channelData1[i], centroidUList[(int)(clusterAssment[i, 0].real)]))\n            newChannelData2.append(np.dot(channelData2[i], centroidUList[(int)(clusterAssment[i, 0].real)]))\n\n    if type == \"U\":\n        informations, SigmaList, UList = tools.getInformations(covMatrixList1)\n        allU = tools.matrixListToMatrix_U(UList)\n        weights = tools.matrixListToMatrix_U(SigmaList)\n\n        # 确定每个数据分别属于哪个簇\n        clusterAssment = kmeans.getClusterAssment_U(allU, weights, centroids, newDimension)\n\n        # 变换域\n        for i in range(np.shape(channelData1)[0]):\n            newChannelData1.append(np.dot(channelData1[i], centroidUList[(int)(clusterAssment[i, 0].real)]))\n            newChannelData2.append(np.dot(channelData2[i], centroidUList[(int)(clusterAssment[i, 0].real)]))\n\n    if type == \"general\":\n        newChannelData1 = pca.pca_general(channelData1, newDimension)\n        newChannelData2 = pca.pca_general(channelData2, newDimension)\n\n    if type == \"none\":\n        newChannelData1 = channelData1\n        newChannelData2 = channelData2\n\n    if type == \"wt\":\n        # 变换域\n        for i in range(np.shape(channelData1)[0]):\n            newChannelData1.append(wt.wt(channelData1[i], newDimension))\n            newChannelData2.append(wt.wt(channelData2[i], newDimension))\n\n    return newChannelData1, newChannelData2\n\n\nif __name__ == '__main__':\n    # 路径配置\n    path = u'/Users/jinruimeng/Downloads/keyan/'\n    # path = u'E:\\\\workspace\\\\keyan\\\\'\n\n    a = 0  # 分割次数\n    iRate = 5  # 预处理后维度\n\n    # 信噪比的上下限\n    low = -10\n    high = 30\n    step = 5\n    suffix = str(low) + u'to' + str(high) + u'dB'\n\n    # 显示进度\n    manager = multiprocessing.Manager()\n    schedule = manager.Array('i', [1, 0])\n    time1 = ((int)((high - low) / step + 1))\n    time2 = 1 << a\n    schedule[0] = time1 * time2\n\n    # 读入信道数据\n    channelDataPath = path + u'channelDataP.xlsx'\n    channelDataAll = readAndWriteDataSet.excelToMatrixList(channelDataPath)\n    n = np.shape(channelDataAll[0])[1]  # 列数\n    p = np.shape(channelDataAll)[0]  # 页数\n    sub = n >> a\n\n    if iRate > sub:\n        print(u'降维后维度不能大于样本原有的维度！')\n        sys.exit()\n\n    if iRate <= 0:\n        print(u'降维后维度不能小于1！')\n        sys.exit()\n\n    # 读入协方差聚类中心，计算变换矩阵\n    allCentroidsC, allCentroidUList = readAndWriteDataSet.readCentroids(path, iRate, u'C', a)\n    # 读入变换矩阵聚类中心，计算变换矩阵\n    allCentroidsU, allCentroidUList2 = readAndWriteDataSet.readCentroids(path, iRate, u'U', a)\n\n    newSNRsList = []\n    lengths = np.zeros((4, time1))\n    errorNums = np.zeros((4, time1))\n    ents = np.zeros((4, time1))\n    for h in range(time1):\n        SNR = low + h * step\n\n        # 添加噪声\n        channelDataAll1 = []\n        channelDataAll2 = []\n        npowers = []\n        for i in range(p):\n            noise1, noise2, npower = addNoise.wgn_abs(channelDataAll[i], SNR)\n            channelDataAll1.append(channelDataAll[i] + noise1)\n            channelDataAll2.append(channelDataAll[i] + noise2)\n            npowers.append(npower)\n\n        # nowTime = time.strftime(\"%Y-%m-%d.%H.%M.%S\", time.localtime(time.time()))\n        # pathSuffix = str(SNR) + u'_' + nowTime\n\n        # 输出添加噪声后的信道数据\n        # outChannelAll1ListPath = path + \"clusterAddNoise_outChannelAll1List_\" + pathSuffix\n        # outChannelAll2ListPath = path + \"clusterAddNoise_outChannelAll2List_\" + pathSuffix\n        # readAndWriteDataSet.write(channelDataAll1, outChannelAll1ListPath, \".xlsx\")\n        # readAndWriteDataSet.write(channelDataAll2, outChannelAll2ListPath, \".xlsx\")\n\n        # meanAllOldCorr, meanAllNewCCorr, meanAllNewUCorr = ps.apply_async(cluster, args=(a, schedule, channelDataAll1, channelDataAll2, allCentroidsC, allCentroidUList, allCentroidsU, allCentroidUList2)).get()\n        newPca1, newPca2, newC1, newC2, newU1, newU2, newWt1, newWt2 = cluster(a, schedule, channelDataAll1, channelDataAll2, allCentroidsC, allCentroidUList, allCentroidsU, allCentroidUList2)\n\n        key_newPca1 = []\n        key_newPca2 = []\n        key_newC1 = []\n        key_newC2 = []\n        key_newU1 = []\n        key_newU2 = []\n        key_newWt1 = []\n        key_newWt2 = []\n        newSNRs = []\n\n        for g in range(1 << a):\n            for i in range(p):\n                tmpKey1, tmpKey2, SNRList = quantification.quantificate(newPca1[g][i], newPca2[g][i], npowers[i])\n                # print(np.shape(newPca1[g][i]))\n                # print(u'pca1_' + str(SNR) + u'dB_' + str(g) + u':')\n                # print(tmpKey[0])\n                # print(u'pca2_' + str(SNR) + u'dB_' + str(g) + u':')\n                # print(tmpKey[1])\n                key_newPca1.append(tmpKey1)\n                key_newPca2.append(tmpKey2)\n                keyLength, errorNum = quantification.getInconsistencyRate(tmpKey1, tmpKey2)\n                lengths[0, h] += keyLength\n                errorNums[0, h] += errorNum\n                if g == 0 and i == 0:\n                    newSNRs.append(SNRList)\n                else:\n                    newSNRs[0] = np.array(newSNRs[0]) + np.array(SNRList)\n\n                tmpKey1, tmpKey2, SNRList = quantification.quantificate(newC1[g][i], newC2[g][i], npowers[i])\n                # print(np.shape(newC1[g][i]))\n                # print(u'C1_' + str(SNR) + u'dB_' + str(g) + u':')\n                # print(tmpKey[0])\n                # print(u'C2_' + str(SNR) + u'dB_' + str(g) + u':')\n                # print(tmpKey[1])\n                key_newC1.append(tmpKey1)\n                key_newC2.append(tmpKey2)\n                keyLength, errorNum = quantification.getInconsistencyRate(tmpKey1, tmpKey2)\n                lengths[1, h] += keyLength\n                errorNums[1, h] += errorNum\n                if g == 0 and i == 0:\n                    newSNRs.append(SNRList)\n                else:\n                    newSNRs[1] = np.array(newSNRs[1]) + np.array(SNRList)\n\n                tmpKey1, tmpKey2, SNRList = quantification.quantificate(newU1[g][i], newU2[g][i], npowers[i])\n                # print(np.shape(newU1[g][i]))\n                # print(u'U1_' + str(SNR) + u'dB_' + str(g) + u':')\n                # print(tmpKey[0])\n                # print(u'U2_' + str(SNR) + u'dB_' + str(g) + u':')\n                # print(tmpKey[1])\n                key_newU1.append(tmpKey1)\n                key_newU2.append(tmpKey2)\n                keyLength, errorNum = quantification.getInconsistencyRate(tmpKey1, tmpKey2)\n                lengths[2, h] += keyLength\n                errorNums[2, h] += errorNum\n                if g == 0 and i == 0:\n                    newSNRs.append(SNRList)\n                else:\n                    newSNRs[2] = np.array(newSNRs[2]) + np.array(SNRList)\n\n                tmpKey1, tmpKey2, SNRList = quantification.quantificate(newWt1[g][i], newWt2[g][i], npowers[i])\n                # print(np.shape(newWt1[g][i]))\n                # print(u'dct1_' + str(SNR) + u'dB_' + str(g) + u':')\n                # print(tmpKey[0])\n                # print(u'dct2_' + str(SNR) + u'dB_' + str(g) + u':')\n                # print(tmpKey[1])\n                key_newWt1.append(tmpKey1)\n                key_newWt2.append(tmpKey2)\n                keyLength, errorNum = quantification.getInconsistencyRate(tmpKey1, tmpKey2)\n                lengths[3, h] += keyLength\n                errorNums[3, h] += errorNum\n                if g == 0 and i == 0:\n                    newSNRs.append(SNRList)\n                else:\n                    newSNRs[3] = np.array(newSNRs[3]) + np.array(SNRList)\n        newSNRsArray = tools.listToArray(newSNRs)\n        for i in range(np.shape(newSNRsArray)[0]):\n            for j in range(np.shape(newSNRsArray)[1]):\n                newSNRsArray[i, j] = newSNRsArray[i, j] / ((1 << a) * p)\n                newSNRsArray[i, j] = 10 * np.log10(newSNRsArray[i, j])\n        newSNRsList.append(newSNRsArray)\n\n        readAndWriteDataSet.writeKey(path, key_newPca1, key_newPca2, SNR, u'pca')\n        readAndWriteDataSet.writeKey(path, key_newC1, key_newC2, SNR, u'C')\n        readAndWriteDataSet.writeKey(path, key_newU1, key_newU2, SNR, u'U')\n        readAndWriteDataSet.writeKey(path, key_newWt1, key_newWt2, SNR, u'wt')\n\n        # 计算信息熵\n        total_key_pca = u''\n        total_key_C = u''\n        total_key_U = u''\n        total_key_wt = u''\n        for i in range(np.shape(key_newPca1)[0]):\n            total_key_pca = total_key_pca + key_newPca1[i] + key_newPca2[i]\n            total_key_C = total_key_C + key_newC1[i] + key_newC2[i]\n            total_key_U = total_key_U + key_newU1[i] + key_newU2[i]\n            total_key_wt = total_key_wt + key_newWt1[i] + key_newWt2[i]\n\n        ents[0, h] = quantification.calc_shannon_ent(total_key_pca)\n        ents[1, h] = quantification.calc_shannon_ent(total_key_C)\n        ents[2, h] = quantification.calc_shannon_ent(total_key_U)\n        ents[3, h] = quantification.calc_shannon_ent(total_key_wt)\n\n    readAndWriteDataSet.write(newSNRsList, path + u'newSNR_' + suffix)\n    readAndWriteDataSet.write(lengths, path + u'length_' + suffix)\n    readAndWriteDataSet.write(errorNums, path + u'errorNum_' + suffix)\n    readAndWriteDataSet.write(ents, path + u'ent_' + suffix)\n    print(\"主进程结束！\")\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":12609,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"246779528","text":"#    minmax.py\n#    minmax 알고리즘을 구현한 Ai1 클래스가 정의된 파일\n\nfrom evaluation_constant import *\nimport copy\nimport random\n\n\nclass Ai1:\n    def __init__(self, board):\n        self.goBoard = board\n        self.searchSpaceState, self.searchSpace = self.getInitialSearchSpace()\n        self.searchCandidate = copy.deepcopy(self.searchSpace)\n        self.evaluationSpaceState, self.evaluationSpace = self.getInitialEvaluationSpace()\n        self.stoneCnt = 1\n        self.searchSize = 20\n        pass\n\n    # 초기 바둑판 한가운데를 기준으로 사방의 2칸을 탐색공간으로 설정하는 메소드\n    def getInitialSearchSpace(self):\n        board = []\n        searchSpace = []\n        for i in range(GO_BOARD_X_COUNT):\n            board.append([EMPTY] * GO_BOARD_Y_COUNT)\n        for i in range(int(GO_BOARD_X_COUNT / 2) - 2, int(GO_BOARD_X_COUNT / 2) + 3):\n            for j in range(int(GO_BOARD_Y_COUNT / 2) - 2, int(GO_BOARD_Y_COUNT / 2) + 3):\n                board[i][j] = True\n                if i == j:\n                    continue\n                searchSpace.append((i, j))\n        return board, searchSpace\n\n    # 착수된 좌표를 기준으로 탐색 공간을 갱신하는 메소드\n    def resetSearchSpace(self, x, y):\n        self.searchSpaceState[x][y] = True\n        length = len(self.searchCandidate)\n        for i in range(length):\n            if self.searchCandidate[i] == (x, y):\n                del self.searchCandidate[i]\n                break\n\n        count = len(self.searchCandidate)\n        if self.searchSize <= count:\n            count = self.searchSize\n        self.searchSpace = random.sample(self.searchCandidate, count)\n\n        left = x - 2 if x - 2 >= 0 else 0\n        right = x + 2 if x + 2 < GO_BOARD_X_COUNT else GO_BOARD_X_COUNT\n        top = y - 2 if y - 2 >= 0 else 0\n        bottom = y + 2 if y + 2 < GO_BOARD_Y_COUNT else GO_BOARD_Y_COUNT\n        for i in range(left, right):\n            for j in range(top, bottom):\n                if self.searchSpaceState[i][j]:\n                    continue\n                self.searchSpaceState[i][j] = True\n                self.searchCandidate.append((i, j))\n                self.searchSpace.append((i, j))\n        pass\n\n    # 최초 바둑판 한가운데를 기준 사방으로 1칸 떨어진 공간을 평가함수가 적용될 공간으로 설정하는 메소드\n    def getInitialEvaluationSpace(self):\n        board = []\n        evaluationSpace = []\n        for i in range(GO_BOARD_X_COUNT):\n            board.append([EMPTY] * GO_BOARD_Y_COUNT)\n        for i in range(int(GO_BOARD_X_COUNT / 2) - 1, int(GO_BOARD_X_COUNT / 2) + 2):\n            for j in range(int(GO_BOARD_Y_COUNT / 2) - 1, int(GO_BOARD_Y_COUNT / 2) + 2):\n                board[i][j] = True\n                evaluationSpace.append((i, j))\n        return board, evaluationSpace\n\n    # 착수된 좌표를 기준으로 평가함수가 적용될 공간을 갱신하는 메소드\n    def resetEvaluationSpace(self, x, y):\n        left = x - 1 if x - 1 >= 0 else 0\n        right = x + 2 if x + 2 < GO_BOARD_X_COUNT else GO_BOARD_X_COUNT\n        top = y - 1 if y - 1 >= 0 else 0\n        bottom = y + 2 if y + 2 < GO_BOARD_Y_COUNT else GO_BOARD_Y_COUNT\n        for i in range(left, right):\n            for j in range(top, bottom):\n                if self.evaluationSpaceState[i][j]:\n                    continue\n                self.evaluationSpaceState[i][j] = True\n                self.evaluationSpace.append((i, j))\n        pass\n\n    # x, y 좌표에서 direction 방향으로 pattern 인자로 주어진 패턴이 존재하는지 확인하는 메소드\n    def patternCheck(self, x, y, pattern, direction):\n        if direction == HORIZONTAL and x >= GO_BOARD_X_COUNT - len(pattern) + 1:\n            return False\n        elif direction == VERTICAL and y >= GO_BOARD_Y_COUNT - len(pattern) + 1:\n            return False\n        elif direction == MAIN_DIAGONAL and\\\n                (x >= GO_BOARD_X_COUNT - len(pattern) + 1 or y >= GO_BOARD_Y_COUNT - len(pattern) + 1):\n            return False\n        elif direction == SUB_DIAGONAL and \\\n                (x < len(pattern) - 1 or y >= GO_BOARD_Y_COUNT - len(pattern) + 1):\n            return False\n\n        check = True\n        for i in range(len(pattern)):\n            nx = x + dx[direction] * i\n            ny = y + dy[direction] * i\n            if self.goBoard[nx][ny] != pattern[i]:\n                check = False\n                break\n        return check\n\n    # 필수로 방어를 해야하는 수가 있는지 확인하는 메소드\n    def defenceCheck(self):\n        retx, rety = (None, None)\n\n        for t in self.evaluationSpace:\n            x, y = t\n            for i in range(4):  # 방향\n                for p in AI_DEFENCE_PATTERN1:\n                    if self.patternCheck(x, y, p, i):\n                        loc = p.index(EMPTY)\n                        retx = x + dx[i] * loc\n                        rety = y + dy[i] * loc\n                        return retx, rety\n\n                for p in AI_DEFENCE_PATTERN2:\n                    if self.patternCheck(x, y, p, i):\n                        loc = p.index(EMPTY)\n                        loc += p[loc + 1:].index(EMPTY)\n                        loc += 1\n                        retx = x + dx[i] * loc\n                        rety = y + dy[i] * loc\n                        return retx, rety\n\n        return retx, rety\n\n    # 끝내기 수가 존재하는지 확인하는 메소드\n    def endGameCheck(self):\n        retx, rety = (None, None)\n\n        for t in self.evaluationSpace:\n            x, y = t\n            for i in range(4):  # 방향\n                for p in AI_ENDGAME_PATTERN1:\n                    if self.patternCheck(x, y, p, i):\n                        loc = p.index(EMPTY)\n                        retx = x + dx[i] * loc\n                        rety = y + dy[i] * loc\n                        return retx, rety\n\n                for p in AI_ENDGAME_PATTERN2:\n                    if self.patternCheck(x, y, p, i):\n                        loc = p.index(EMPTY)\n                        loc += p[loc + 1:].index(EMPTY)\n                        loc += 1\n                        retx = x + dx[i] * loc\n                        rety = y + dy[i] * loc\n                        return retx, rety\n\n        return retx, rety\n\n    # 오목판의 점수를 평가하는 평가함수\n    def evaluate(self):\n        ai_score = 0\n        player_score = 0\n\n        for k in self.evaluationSpace:\n            x, y = k\n            # AI 스코어 체크\n            for i in range(4):  # 방향\n                for p in range(len(AI_7PATTERNS)):\n                    if self.patternCheck(x, y, AI_7PATTERNS[p], i):\n                        ai_score += AI_7PATTERNS_SCORE[p]\n\n                for p in range(len(AI_6PATTERNS)):\n                    if self.patternCheck(x, y, AI_6PATTERNS[p], i):\n                        ai_score += AI_6PATTERNS_SCORE[p]\n\n                for p in range(len(AI_5PATTERNS)):\n                    if self.patternCheck(x, y, AI_5PATTERNS[p], i):\n                        ai_score += AI_5PATTERNS_SCORE[p]\n\n                for p in range(len(AI_4PATTERNS)):\n                    if self.patternCheck(x, y, AI_4PATTERNS[p], i):\n                        ai_score += AI_4PATTERNS_SCORE[p]\n\n\n            # 플레이어 스코어 체크\n            for i in range(4):  # 방향\n                for p in range(len(PLAYER1_7PATTERNS)):\n                    if self.patternCheck(x, y, PLAYER1_7PATTERNS[p], i):\n                        player_score += PLAYER1_7PATTERNS_SCORE[p]\n\n                for p in range(len(PLAYER1_6PATTERNS)):\n                    if self.patternCheck(x, y, PLAYER1_6PATTERNS[p], i):\n                        player_score += PLAYER1_6PATTERNS_SCORE[p]\n\n                for p in range(len(PLAYER1_5PATTERNS)):\n                    if self.patternCheck(x, y, PLAYER1_5PATTERNS[p], i):\n                        player_score += PLAYER1_5PATTERNS_SCORE[p]\n\n                for p in range(len(PLAYER1_4PATTERNS)):\n                    if self.patternCheck(x, y, PLAYER1_4PATTERNS[p], i):\n                        player_score += PLAYER1_4PATTERNS_SCORE[p]\n\n        return ai_score - player_score\n\n    def minmax(self, depth, player):\n        if depth == 0 or self.stoneCnt == GO_BOARD_X_COUNT * GO_BOARD_Y_COUNT:\n            return (self.evaluate(), None, None)\n        if player == AI:\n            maxValue = -INF\n            x = 0\n            y = 0\n            for k in self.searchSpace:\n                i, j = k\n                if self.goBoard[i][j] == EMPTY:\n                    self.goBoard[i][j] = AI\n                    self.stoneCnt += 1\n                    ret = self.minmax(depth - 1, PLAYER1)\n                    if ret[0] > maxValue:\n                        maxValue = ret[0]\n                        x = i\n                        y = j\n                    self.stoneCnt -= 1\n                    self.goBoard[i][j] = EMPTY\n            if depth == 1:\n                return (maxValue, x, y)\n            else:\n                return (maxValue, None, None)\n            pass\n        else: # player == PLAYER1\n            minValue = INF\n            x = 0\n            y = 0\n            for k in self.searchSpace:\n                i, j = k\n                if self.goBoard[i][j] == EMPTY:\n                    self.goBoard[i][j] = PLAYER1\n                    self.stoneCnt += 1\n                    ret = self.minmax(depth - 1, AI)\n                    if ret[0] < minValue:\n                        minValue = ret[0]\n                        x = i\n                        y = j\n                    self.stoneCnt -= 1\n                    self.goBoard[i][j] = EMPTY\n            if depth == 1:\n                return (minValue, x, y)\n            else:\n                return (minValue, None, None)\n            pass\n\n    # AI가 바둑판에 착수하는 메소드\n    def placement(self):\n        print(\"Search space size : \\n\", len(self.searchSpace))\n        print(\"Evaluation space size : \\n\", len(self.evaluationSpace))\n\n        x, y = self.endGameCheck()\n        if x is None and y is None:\n            x, y = self.defenceCheck()\n        if x is None and y is None:\n            place = self.minmax(1, AI)\n            x = place[1]\n            y = place[2]\n\n        self.goBoard[x][y] = AI\n        self.stoneCnt += 1\n        self.resetEvaluationSpace(x, y)\n        self.resetSearchSpace(x, y)\n        return x, y\n\n    pass\n","sub_path":"minmax.py","file_name":"minmax.py","file_ext":"py","file_size_in_byte":10437,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"102935079","text":"# -*- coding: utf-8 -*-\n\n#########################################################################\n## This scaffolding model makes your app work on Google App Engine too\n## File is released under public domain and you can use without limitations\n#########################################################################\n\n## if SSL/HTTPS is properly configured and you want all HTTP requests to\n## be redirected to HTTPS, uncomment the line below:\n# request.requires_https() \nimport locale\nlocale.setlocale(locale.LC_ALL,'')\nif not request.env.web2py_runtime_gae:\n    ## if NOT running on Google App Engine use SQLite or other DB\n    db = DAL('sqlite://storage.sqlite',pool_size=1,check_reserved=['all'])\nelse:\n    ## connect to Google BigTable (optional 'google:datastore://namespace')\n    db = DAL('google:datastore')\n    ## store sessions and tickets there\n    session.connect(request, response, db=db)\n    ## or store session in Memcache, Redis, etc.\n    ## from gluon.contrib.memdb import MEMDB\n    ## from google.appengine.api.memcache import Client\n    ## session.connect(request, response, db = MEMDB(Client()))\n\n## by default give a view/generic.extension to all actions from localhost\n## none otherwise. a pattern can be 'controller/function.extension'\nresponse.generic_patterns = ['*'] if request.is_local else []\n## (optional) optimize handling of static files\n# response.optimize_css = 'concat,minify,inline'\n# response.optimize_js = 'concat,minify,inline' \n\n#########################################################################\n## Here is sample code if you need for\n## - email capabilities\n## - authentication (registration, login, logout, ... )\n## - authorization (role based authorization)\n## - services (xml, csv, json, xmlrpc, jsonrpc, amf, rss)\n## - old style crud actions\n## (more options discussed in gluon/tools.py)\n#########################################################################\n\nfrom gluon.tools import Auth, Crud, Service, PluginManager, prettydate\nauth = Auth(db)\ncrud, service, plugins = Crud(db), Service(), PluginManager()\n\n## create all tables needed by auth if not custom tables\nauth.define_tables(username=True, signature=False)\n\nresponse.generic_patterns = ['*'] if request.is_local else []\nresponse.generic_patterns = ['load']\n## configure email\nmail = auth.settings.mailer\nmail.settings.server = 'logging' or 'mail.darwish-group.com:143'\nmail.settings.sender = 'hilario@darwish-group.com'\nmail.settings.login = 'hilario:password'\n\n## configure auth policy\nauth.settings.registration_requires_verification = False\nauth.settings.registration_requires_approval = False\nauth.settings.reset_password_requires_verification = True\n\n## if you need to use OpenID, Facebook, MySpace, Twitter, Linkedin, etc.\n## register with janrain.com, write your domain:api_key in private/janrain.key\nfrom gluon.contrib.login_methods.rpx_account import use_janrain\nuse_janrain(auth, filename='private/janrain.key')\n\n#########################################################################\n## Define your tables below (or better in another model file) for example\n##\n## >>> db.define_table('mytable',Field('myfield','string'))\n##\n## Fields can be 'string','text','password','integer','double','boolean'\n##       'date','time','datetime','blob','upload', 'reference TABLENAME'\n## There is an implicit 'id integer autoincrement' field\n## Consult manual for more options, validators, etc.\n##\n## More API examples for controllers:\n##\n## >>> db.mytable.insert(myfield='value')\n## >>> rows=db(db.mytable.myfield=='value').select(db.mytable.ALL)\n## >>> for row in rows: print row.id, row.myfield\n#########################################################################\n\n## after defining tables, uncomment below to enable auditing\nauth.enable_record_versioning(db)\n\n\n    \ndb = DAL('postgres://root:admin@localhost:5432/kds','postgres://root:admin@localhost:5432/kds_backup')\nauth = Auth(globals(),db)\n\n#auth.settings.actions_disabled.append('register')\nauth.settings.actions_disabled=['register', 'request_reset_password','retrieve_username']\nif request.controller != 'appadmin': auth.settings.actions_disabled +=['register']\n\ndb.define_table('company', \n    Field('company', requires = IS_UPPER()), \n    Field('cr', 'upload', label = 'Company ID'), format = '%(company)s')\n\ndb.define_table('division',\n    Field('company_id', db.company, readable = False, writable = False), \n    Field('division', requires = IS_UPPER(), label = 'Division'),format = '%(division)s')\n\ndb.define_table('department', \n    Field('division_id', db.division, readable = False, writable = False),\n    Field('name', requires = IS_UPPER(),label = 'Department'), format = '%(name)s')\n\n#auth.settings.extra_fields['auth_user'] = [Field('department_id', writable = False, readable = False, label = 'Department',\n#                                                 requires = IS_IN_DB(db, db.department.id,'%(name)s',zero= '-- Choose department --'))]\n###################################\ndb.define_table(\n    auth.settings.table_user_name,\n    Field('first_name', length=128, default=''),\n    Field('last_name', length=128, default=''),\n    Field('username', unique = True, readable = False, writable = False),\n    Field('email', length=128, default='', unique=True), # required\n    Field('division_id', db.division, label = 'Division', writable = False, readable = False),\n    #Field('department_id', db.department, label = 'Department', writable = False, readable = False),\n    Field('password', 'password', length=512,            # required\n          readable=False, label='Password', writable = False),\n    Field('po_box', label = 'PO Box'),\n    Field('mobile_no'),\n    Field('office_no'),\n    Field('fax_no'),\n    Field('registration_key', length=512,                # required\n          writable=False, readable=False, default=''),\n    Field('reset_password_key', length=512,              # required\n          writable=False, readable=False, default=''),\n    Field('registration_id', length=512,                 # required\n          writable=False, readable=False, default=''))\n\n## do not forget validators\ncustom_auth_table = db[auth.settings.table_user_name] # get the custom_auth_table\ncustom_auth_table.first_name.requires =   IS_NOT_EMPTY(error_message=auth.messages.is_empty)\ncustom_auth_table.last_name.requires =   IS_NOT_EMPTY(error_message=auth.messages.is_empty)\ncustom_auth_table.password.requires = [CRYPT()]\ncustom_auth_table.email.requires = [\n  IS_EMAIL(error_message=auth.messages.invalid_email),\n  IS_NOT_IN_DB(db, custom_auth_table.email)]\n\nauth.settings.table_user = custom_auth_table # tell auth to use custom_auth_table\n\n####################################\nauth.define_tables(username = True)\ncrud = Crud(globals(),db)\n\ndb.define_table('vehicle_type', Field('vehicle_name'), format = '%(vehicle_name)s')\n\ndb.define_table('vehicle_category', Field('category'),\n    Field('description', 'text',represent=lambda text, row: PRE(text)), format = '%(category)s' )\n\ndb.define_table('tyre_brand', Field('name'), format = '%(name)s')\n\ndb.define_table('vehicle_status', Field('status'), format = '%(status)s')\n\ndb.define_table('vehicle_insurance', Field('name'), format = '%(name)s')\n\ndb.define_table('vehicle_purpose', Field('purpose'), format = '%(purpose)s')\n\ndb.define_table('owner', Field('name'), format = '%(name)s')\ntoday = request.now\n\ndb.define_table('vehicle',\n    Field('company_id', 'reference company', requires = IS_IN_DB(db, db.company, '%(company)s', zero = 'Choose company'), \n        represent = lambda id, r: db.company(id).company, label = 'Company'),\n    Field('division_id', 'reference division', requires = IS_IN_DB(db, db.division, '%(division)s', zero = 'Choose division'),\n        represent = lambda id, r: db.division(id).division, label = 'Division'),\n    Field('department', 'reference department', requires = IS_IN_DB(db, db.department, '%(name)s', zero = 'Choose department'), \n        represent = lambda id, r: db.department(id).name, label = 'Department'),\n    Field('owner', 'reference owner', requires = IS_IN_DB(db, db.owner, '%(name)s', zero = 'Choose owner'),\n        represent = lambda id, r: db.owner(id).name, label = 'Owner'),\n    Field('vehicle_code', requires = IS_NOT_IN_DB(db, 'vehicle.vehicle_code', error_message = 'Vehicle code already exist or empty!') ,label = 'VEH.CD.'),\n    Field('reg_no', requires = (IS_NOT_EMPTY(), IS_NOT_IN_DB(db, 'vehicle.reg_no', error_message = 'Registration No already exist or empty!')),\n        ondelete = 'CASCADE'),\n    Field('vehicle_name_id', 'reference vehicle_type', requires = IS_IN_DB(db, db.vehicle_type, '%(vehicle_name)s', zero = 'Choose brand'), \n        represent = lambda id, r: db.vehicle_type(id).vehicle_name, label = 'Brand'),\n    Field('invoice', label = 'PO/Invoice'),\n    Field('model'),\n    Field('chassis_no', requires = (IS_UPPER(), IS_NOT_IN_DB(db, 'vehicle.chassis_no', error_message = 'Chassis No. already exist or empty!'))),\n    Field('exp_date', 'date', requires = IS_NOT_EMPTY(), label = 'RPEx Date'),\n    Field('datetoalert', 'date'),                \n    Field('reg_date', 'date', label = '1st Reg.Date'),\n    Field('value_purchase', 'decimal(10,2)'),\n    Field('date_of_sale', 'date'),\n    Field('depreciation_value', 'decimal(10,2)'),\n    Field('mileage', 'integer', default = 0, label = 'Mil.'),\n    Field('date_mileage', 'date', default = request.now, label = 'D8 Mil.'),\n    Field('car_type', label = 'Vehicle description'),\n    Field('plate_type'),\n    Field('cylinder_count'),\n    Field('transmission', requires = IS_IN_SET(['Automatic', 'Manual'], zero = 'Transmission Type')),\n    Field('ext_color_code'),\n    Field('accessories'),\n    Field('category_id', 'reference vehicle_category', requires = IS_IN_DB(db, db.vehicle_category, '%(category)s', zero = 'Choose category'), \n        represent = lambda id, r: db.vehicle_category(id).category ,label = 'Category'),\n    Field('tyre_size',label = 'Tyre Size'),\n    Field('purpose', 'reference vehicle_purpose', requires = IS_IN_DB(db, db.vehicle_purpose, '%(purpose)s', zero = 'Choose purpose'),\n        represent = lambda id, r: db.vehicle_purpose(id).purpose, label = 'Purpose'),\n    Field('status_id', 'reference vehicle_status', requires = IS_IN_DB(db, db.vehicle_status, '%(status)s', zero = 'Choose status'),\n        represent = lambda id, r: db.vehicle_status(id).status, label = 'Status'),\n    Field('remarks'),\n    Field('log_date', 'datetime', default = request.now, writable = False, readable = False),\n    Field('focal_person', db.auth_user, default=auth.user_id, readable=False, writable=False), format = '%(reg_no)s')\n    #Field('is_active','boolean', writable=False,readable=False,default=True), format = '%(reg_no)s')\n\n'''\ndb.vehicle.division_id.widget = options_widget(\n    'vehicle_company_id__selected', 'vehicle.company_id__unselected',\n    lambda company_id: (db.division.division == company_id),\n    request.vars.company_id,\n    orderby = db.division.division,\n    user_signature = True,\n    field = db.vehicle.division_id\n    )\n'''\ndef company_widget(field, value):\n    widget = SQLFORM.widgets.options.widget(field, value)\n    widget.attributes[\"_onchange\"] = 'updateMyDivision(jQuery(\"[name = company_id]\").val());'\n    return widget\n\ndef division_widget(field, value):\n    _id=\"%s_%s\" % (field._tablename, field.name)\n    if value:\n        company_id = db.division[value].company_id\n        # filter the ranks when there's an actual value recorded\n        field.requires = IS_IN_DB(db(db.division.company_id == company_id), db.division, \"%(division)s\", zero = 'Choose division')\n        \n\n    elif not request.function == \"division\":\n        # create an empty select if no rank was selected\n        # unless there's an options request\n        field.requires = IS_IN_SET([])\n\n    script = SCRIPT(\"\"\"\n    function updateMyDivision(companyID){\n        // remove the ye olde options\n        jQuery(\"[name=%(fieldname)s]\").html(\"\");\n        // fish the new ones\n        web2py_component(\"%(url)s/division/\" + companyID, \"%(_id)s\");\n    }\n    \"\"\" % dict(fieldname=field.name, _id=_id, url=URL(c=\"default\", f=\"division\", extension=\"load\")))\n    return DIV(script, SQLFORM.widgets.options.widget(field, value))\n    \n#db.vehicle.company_id.widget = company_widget\n#db.vehicle.division_id.widget = division_widget\n\ndb.vehicle.days_to_expire = Field.Method(lambda row: today - row.vehicle.exp_date)\n                                       \ndb.define_table('v_photos',\n    Field('reg_no_id', 'reference vehicle', requires = IS_IN_DB(db, db.vehicle, '%(reg_no)s', zero = 'Choose reg.no.'), \n        represent = lambda id, r: db.vehicle(id).reg_no, label = 'Reg. No.'),\n    Field('road_permit', 'upload', label = 'Road Permit'),\n    Field('photo', 'upload', label = 'Front Photo'),\n    Field('photo2', 'upload', label = 'Rear Photo'),\n    Field('photo3', 'upload', label = 'Left Photo'),\n    Field('photo4', 'upload', label = 'Right Photo'),\n    Field('focal_person', db.auth_user, default=auth.user_id, readable=False, writable=False),\n    Field('log_date', 'datetime', label = 'Date & Time Created', default = request.now,\n        writable = False, readable = True))\n\ndb.v_photos.reg_no_id.requires = IS_IN_DB(db, db.vehicle, '%(reg_no)s', zero = 'Choose Reg.No.')\n    \n#db.v_photos.reg_no_id.requires = IS_NOT_IN_DB(db, 'v_photos.reg_no_id',  error_message = 'Reg.No. is already exist or empty!')\n\ndb.define_table('ins_pol_status', Field('status'), format = '%(status)s')\n\ndb.define_table('insurance_policy',\n    Field('company_id', 'reference company', requires = IS_IN_DB(db, db.company, '%(company)s', zero = 'Choose company'), \n        represent = lambda id, r: db.company(id).company, label = 'Company'),\n    Field('insurance_company_id', 'reference vehicle_insurance', requires = IS_IN_DB(db, db.vehicle_insurance, '%(name)s', \n        zero = 'Choose insurance company'), represent = lambda id, r: db.vehicle_insurance(id).name, label = 'Insurance Company'),\n    Field('policy_no'),\n    Field('from_period_covered', 'date', default = request.now, label = 'Fr.Pe.Covered', comment = 'Starting period.'),\n    Field('period_covered', 'date', default = request.now, label = 'To.Pe.Covered', comment = 'Ending period.'),\n    Field('datetoalert', 'date', readable = False, writable = False),\n    Field('amount', 'decimal(10,2)', default = 0.0),\n    Field('status_id', 'reference ins_pol_status', requires = IS_IN_DB(db, db.ins_pol_status, '%(status)s', zero = 'Choose status'), \n        represent = lambda id, r: db.ins_pol_status(id).status,label = 'Status'),\n    Field('focal_person', db.auth_user, default=auth.user_id, readable=False, writable=False),\n    Field('log_date', 'datetime', default = request.now, writable = False, readable = False), format = '%(policy_no)s' )\n\ndb.insurance_policy.amount.represent = lambda value, row: DIV(locale.format('%.2F', value, grouping = True), _style = 'text-align: right')\n\ndb.define_table('insured_vehicles',\n    Field('policy_no_id', 'reference insurance_policy', requires = IS_IN_DB(db(db.insurance_policy.status_id == 1), db.insurance_policy, '%(policy_no)s', zero = 'Choose Policy No.'), \n        represent = lambda id, r: db.insurance_policy(id).policy_no, label = 'Policy No.'),\n    Field('reg_no_id', 'reference vehicle', requires = IS_IN_DB(db, db.vehicle, '%(reg_no)s', zero = 'Choose Reg.No.'), \n        represent = lambda id, r: db.vehicle(id).reg_no, label = 'Reg.No.'), \n    Field('passenger_covered'),\n    Field('amount_insured', 'decimal(10,2)', default = 0.0, \n        represent = lambda value, row: locale.format('%.2F' or 0, value, grouping = True)),\n    Field('excess', 'decimal(10,2)', default = 0.0, \n        represent = lambda value, row: locale.format('%.2F' or 0, value, grouping = True)),\n    Field('amount', 'decimal(10,2)', default = 0.0, \n        represent = lambda value, row: locale.format('%.2F' or 0, value, grouping = True)),\n    Field('focal_person', 'reference auth_user', default=auth.user_id, readable=False, writable=False),\n    Field('log_date', 'datetime', default = request.now, writable = False, readable = False))\n\n#db.repair_history.running_expenses.represent = lambda value, row: DIV(locale.format('%.2F', value, grouping = True), _style = 'text-align: right')\n#db.insured_vehicles.reg_no_id.widget = SQLFORM.widgets.autocomplete(request, db.vehicle.reg_no, id_field = db.vehicle.id, limitby = (0,10), min_length=2)\n#db.vehicle.chassis_no.requires = IS_NOT_IN_DB(db, 'vehicle.chassis_no', error_message = 'Chassis number is already existing or empty!')\n#db.insured_vehicles.reg_no_id.requires = IS_NOT_IN_DB(db, 'insured_vehicles.reg_no_id', error_message = 'Reg.No. is already exist or empty!')\n\n#db.insured_vehicles.insurance_id.requires = IS_IN_DB(db, db.insurance_policy, '%(insurance_company_id)s', zero = 'Choose insurance')\n\ndb.define_table('category_expenses', Field('name'), format = '%(name)s' )\n\ndb.define_table('workshop_done', Field('workshop'), format = '%(workshop)s' )\n\ndb.define_table('ads_status', Field('status'), format = '%(status)s')\n\ndb.define_table('advertisement',\n    Field('ads', requires = IS_IN_SET(['Company','Commercial'], zero = 'Choose advertisement')),\n    Field('logo'),\n    Field('license_no'),\n    Field('from_expiry_date', 'date', default = request.now, label = 'Fr.Exp.Date', comment = 'From expiry date.'),\n    Field('expiry_date', 'date', default = request.now, label = 'To.Exp.Date', comment = 'To expiry date.'),\n    Field('amount', 'decimal(10,2)', default = 0.0),\n    Field('status_id', 'reference ads_status', requires = IS_IN_DB(db, db.ads_status, '%(status)s', zero = 'Choose status'),\n        represent = lambda id, r: db.ads_status(id).status, label = 'Status'),\n    Field('focal_person', db.auth_user, default=auth.user_id, readable=False, writable=False),\n    Field('log_date', 'datetime', default = request.now, writable = False,\n                      readable = False), format = '%(license_no)s')\n\ndb.advertisement.license_no.requires = IS_NOT_IN_DB(db, 'advertisement.license_no', error_message = 'License No. is already exist or empty!')\ndb.advertisement.amount.represent = lambda value, row: DIV(locale.format('%.2F', value or 0, grouping = True), _style = 'text-align: right')\n\ndb.define_table('ads_vehicle',\n    Field('license_no_id', db.advertisement, requires = IS_IN_DB(db(db.advertisement.status_id != 2), db.advertisement, '%(license_no)s', \n        zero = 'Choose license no.'), label = 'License No', represent = lambda id, r: db.advertisement(id).license_no),\n    Field('reg_no_id', db.vehicle, requires = IS_IN_DB(db, db.vehicle, '%(reg_no)s', zero = 'Choose reg.no.'), \n        label = 'Reg.No.', represent = lambda id, r: db.vehicle(id).reg_no),\n    Field('amount', 'decimal(10,2)'),\n    Field('log_date', 'datetime', default = request.now, writable = False, readable = False))\n\ndb.ads_vehicle.amount.represent = lambda value, row: DIV(locale.format('%.2F', value or 0, grouping = True), _style = 'text-align: right')\n\ndb.define_table('repair_history',\n    Field('reg_no_id', 'reference vehicle', requires = IS_IN_DB(db, db.vehicle, '%(reg_no)s', zero = 'Choose Reg.No.'), \n        represent = lambda id, r: db.vehicle(id).reg_no, label = 'Reg.No.'),\n    Field('invoice_number',label = 'Invoice #', requires = (IS_NOT_EMPTY(), IS_NOT_IN_DB(db, 'repair_history.invoice_number', \n        error_message = 'invoice no. already exist!'))),\n    Field('invoice_date', 'date', default = request.now, label = 'Date Invoice'),\n    Field('workshop_done', 'reference workshop_done', requires = IS_IN_DB(db, db.workshop_done, '%(workshop)s', zero = 'Choose workshop'), \n        represent = lambda id, r: db.workshop_done(id).workshop, label = 'Workshop'),\n    Field('date_time_in', 'date', default = request.now, label = 'Date Begin'),\n    Field('date_time_out', 'date', default = request.now, label = 'Date End'),\n    Field('no_days_time', compute = lambda dt: dt['date_time_out'] - dt['date_time_in'], label = 'Duration'), \n    Field('mileage', 'integer', default = 0, represent = lambda value, row: locale.format('%d', value or 0, grouping = True)), \n    Field('regular_maintenance','decimal(10,2)',default = 00.00, label = 'LAB', comment = 'Service, Oil/Filter Change',\n        represent = lambda value, row: locale.format('%.2F', value, grouping = True)),\n    Field('spare_parts', 'decimal(10,2)', default = 00.00, label = 'SP', \n        represent = lambda value, row: locale.format('%.2F', value, grouping = True)),                \n    Field('statutory_expenses','decimal(10,2)',default = 00.00,label = 'SE', comment = 'Insurance, Road Permit Renewal, Advertisement License',\n        represent = lambda value, row: locale.format('%.2F', value, grouping = True)),\n    Field('accident_repair','decimal(10,2)',default = 00.00, label = 'AR', \n        represent = lambda value, row: locale.format('%.2F', value, grouping = True)),\n    Field('total_amount','decimal(10,2)', represent = lambda value, row: locale.format('%.2F', value or 0, grouping = True),\n        compute = lambda p: p['regular_maintenance'] + p['accident_repair'] + p['statutory_expenses'] + p['spare_parts']),\n    Field('details','text', requires = IS_UPPER()),\n    Field('focal_person', db.auth_user, default=auth.user_id, readable=False, writable=False),\n    Field('log_date', 'datetime', default = request.now, writable = False, readable = False))\n\n\ndef multiselect_widget(f,v):\n    import uuid\n    d_id = \"multiselect-\" + str(uuid.uuid4())[:8]\n    wrapper = DIV(_id=d_id)\n    inp = SQLFORM.widgets.options.widget(f,v)\n    inp['_multiple'] = 'multiple'\n    inp['_style'] = 'min-width: %spx;' % (len(f.name) * 20 + 50)\n    if v:\n        if not isinstance(v,list): v = str(v).split('|')\n        opts = inp.elements('option')\n        for op in opts:\n            if op['_value'] in v:\n                op['_selected'] = 'selected'            \n    scr = SCRIPT('jQuery(\"#%s select\").multiSelect({'\\\n                 'noneSelected:\"Select accessories\"});' % (d_id))\n    wrapper.append(inp)\n    wrapper.append(scr)\n    if request.vars.get(inp['_id']+'[]',None):\n        var = request.vars[inp['_id']+'[]']\n        if not isinstance(var,list): var = [var]\n        request.vars[f.name] = var\n        del request.vars[inp['_id']+'[]']\n    return wrapper\n\ndb.define_table('authorized_vehicle_category', Field('name'), format = '%(name)s' )\n\ndb.define_table('driver_position', Field('name'), format = '%(name)s')\n\ndb.define_table('driver',\n    Field('company_id', 'reference company', requires = IS_IN_DB(db, db.company, '%(company)s', zero = 'Choose company'), \n        represent = lambda id, r: db.company(id).company, label = 'Company'),\n    Field('division_id', 'reference division', requires = IS_IN_DB(db, db.division, '%(division)s', zero = 'Choose division'), \n        represent = lambda id, r: db.division(id).division, label = 'Division'),\n    Field('department_id', 'reference department', requires = IS_IN_DB(db, db.department, '%(name)s', zero = 'Choose department'), \n        represent = lambda id, r: db.department(id).name, label = 'Department'),\n    Field('employee_number', requires = IS_UPPER(), label = 'Emp.#'),\n    Field('driver_name', requires = IS_UPPER()),\n    Field('position_id', 'reference driver_position', requires = IS_IN_DB(db, db.driver_position, '%(name)s', zero = 'Choose position'),  \n        represent = lambda id, r: db.driver_position(id).name, label = 'Position'), \n    Field('driver_id', label = 'License No.'),\n    Field('expiry_date','date'),\n    Field('alertdate', 'date', writable = False, readable = False),\n    Field('license_category_id', 'list:reference authorized_vehicle_category', \n        label = 'Category Name'),\n    Field('contact_no'),\n    Field('driver_license', 'upload', label = 'Driver License', required=False),\n    Field('focal_person', db.auth_user, default=auth.user_id, readable=False, writable=False),\n    Field('log_date', 'datetime', default = request.now, writable = False, readable = False),\n        format = '%(driver_name)s')\n#db.item.discounted_total = Field.Method(lambda row, discount=0.0:        row.item.unit_price*row.item.quantity*(1.0-discount/100))\n#db.driver.alert = Field.Method(compute = lambda row: (row['driver.expiry_date'] + row['driver.expiry_date']))\n\ndb.driver.license_category_id.requires = IS_IN_DB(db, 'authorized_vehicle_category.id', '%(name)s', multiple = True)\n#db.driver.license_category_id.represent = lambda value, row: ', '.join(row.name for row in value or [])\ndb.driver.license_category_id.represent = lambda value, row: ', '.join(db.authorized_vehicle_category(i).name for i in value or [])\n\nvehicles_accessories = IS_IN_SET(['Original Keys', 'Original Road Permit', 'Pertol Card',\n    'Spare Tyre', 'Tools (jack)'], multiple = True)\n\ndb.define_table('vehicles_hand_over',\n    Field('reg_no_id', 'reference vehicle', requires = IS_IN_DB(db, db.vehicle, '%(reg_no)s', zero = 'Choose Reg.No.'),\n        represent = lambda id, r: db.vehicle(id).reg_no, label = 'Reg.No.'),\n    Field('date_and_time', 'datetime', default = request.now), \n    Field('from_department_id', 'reference department', requires = IS_IN_DB(db, db.department, '%(name)s', zero = 'Choose from department'), \n        represent = lambda id, r: db.department(id).name if id else '',label = 'From Department'),\n    Field('to_department_id', 'reference department', requires = IS_IN_DB(db, db.department, '%(name)s', zero = 'Choose to department'), \n        represent = lambda id, r: db.department(id).name if id else '', label = 'To Department'),\n    Field('from_driver_id', 'reference driver', requires = IS_IN_DB(db, db.driver, '%(driver_name)s', zero = 'Choose from driver'), \n        represent = lambda id, r: db.driver(id).driver_name, label = 'From Driver'),\n    Field('to_driver_id', 'reference driver', requires = IS_IN_DB(db, db.driver, '%(driver_name)s', zero = 'Choose to driver'), \n        represent = lambda id, r: db.driver(id).driver_name, label = 'To Driver'),\n    Field('mileage'),\n    Field('vehicles_acc', 'list:string', requires = vehicles_accessories, widget = SQLFORM.widgets.checkboxes.widget),\n    Field('remarks', 'text'),\n    Field('focal_person', db.auth_user, default=auth.user_id, readable=False, writable=False),\n    Field('log_date', 'datetime', writable = False, readable = False))\n\n\ndb.define_table('fuel_expenses',\n    Field('reg_no_id', 'reference vehicle', requires = IS_IN_DB(db, db.vehicle, '%(reg_no)s', zero = 'Choose Reg.No.'), \n        represent = lambda id, r: db.vehicle(id).reg_no, label = 'Reg.No.'),\n    Field('date_expense', 'date', default = request.now, label = 'Date'),\n    Field('amount', 'decimal(10,2)', default = 00.00, label = 'Amount Filled',\n        represent = lambda value, row: locale.format('%.2F', value, grouping = True)),\n    Field('paid_by', requires = IS_IN_SET(['Cash','Credit','Fuel Card'], zero = 'Choose paid by')),\n    Field('station'),\n    Field('remarks'),\n    Field('focal_person', db.auth_user, default=auth.user_id, readable=False, writable=False),\n    Field('log_date', 'datetime', default = request.now, writable = False, readable = False))\n\ndb.define_table('km_used',\n    Field('reg_no_id', 'reference vehicle', requires = IS_IN_DB(db, db.vehicle, '%(reg_no)s', zero = 'Choose Reg.No.'),\n        represent = lambda id, r: db.vehicle(id).reg_no, label = 'Reg.No.'),                \n    Field('log_date', 'datetime', default = request.now, writable = False, readable = False),\n    Field('given_month', 'date', label = 'Given Month', default = request.now.date, requires  = IS_NOT_EMPTY()), \n    Field('current_mil','integer', default = 0, label = 'Given Odometer',\n        represent = lambda value, row: locale.format('%d', value or 0, grouping = True),\n        requires = IS_INT_IN_RANGE(1, 1000000) ),\n    Field('consumed_mil','integer', default = 0, label = 'Diff. Odometer',writable = False,\n        represent = lambda value, row: locale.format('%d', value or 0, grouping = True)),\n    Field('focal_person', db.auth_user, default=auth.user_id, readable=False, writable=False),\n    Field('log_date', 'datetime', writable = False, readable = False))\n\n#db.km_used.current_mil.represent = lambda value, row: DIV(locale.format('%d', value or 0, grouping = True), _style = 'text-align: right')\n#db.km_used.consumed_mil.represent = lambda value, row: DIV(locale.format('%d', value or 0, grouping = True), _style = 'text-align: right')  \ndb.km_used.given_month.requires = IS_NOT_EMPTY()\n\ndb.define_table('activities',\n    Field('log_date', 'datetime'),\n    Field('person', label = 'Focal Person'),\n    Field('action', label = 'Action Done'),\n    Field('a_code', 'integer', label = 'Details'))\n\n\ndb.vehicle.mileage.represent = lambda value, row: DIV(locale.format('%d', value or 0, grouping = True), _style = 'text-align: right')\n#db.repair_history.mileage.represent = lambda value, row: DIV(locale.format('%d', value or 0, grouping = True), _style = 'text-align: right')\n#db.vehicles_hand_over.date_and_time.represent = lambda x, row: x.strftime(\"%d/%m/%Y %I:%M:%S %p\")\n\ndb.driver.expiry_date.requires = IS_NOT_EMPTY()\ndb.driver.license_category_id.widget = SQLFORM.widgets.checkboxes.widget\ndb.driver.license_category_id.requires = IS_IN_DB(db, db.authorized_vehicle_category, '%(name)s', multiple = True),\ndb.driver.employee_number.requires = (IS_UPPER(),IS_NOT_IN_DB(db, 'driver.employee_number', error_message = 'Employee number is already existing or empty!'))\ndb.driver.driver_id.requires = (IS_UPPER(),IS_NOT_IN_DB(db, 'driver.driver_id', error_message = 'License number is already existing or empty!'))\n\nrows = db(db.km_used.reg_no_id == db.vehicle(request.args(0))).select(orderby = db.km_used.given_month)\nr = 1\nrow = len(rows)\nwhile (r < row):\n    rows[r].update_record(consumed_mil = rows[r].current_mil - rows[r-1].current_mil)\n    r +=1\n ","sub_path":"controllers/db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":29794,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"399132438","text":"import json\n\nfrom django.contrib.auth.models import User\nfrom django.contrib.sessions.models import Session\n\nfrom instances.views import get_container_status, get_instances_of_user\n\ndef get_status(message, instance_id):\n    status = get_container_status(instance_id)\n    message.reply_channel.send({\n        'text': str(status)\n    })\n\ndef get_user_table(message, requested_user_id, session_key):\n    # Get the user id assigned to the session key\n    try:\n        s = Session.objects.get(pk=session_key)\n        auth_user_id = s.get_decoded()['_auth_user_id']\n    except:\n        return\n\n    # Check If the user request it's own instances\n    if auth_user_id == requested_user_id:\n        instances = get_instances_of_user(requested_user_id)\n\n        message.reply_channel.send({\n            \"text\": json.dumps(instances)\n        })\n\n    else:\n        # Otherwise check if the user is SuperUser\n        auth_user = User.objects.get(id=auth_user_id)\n        if auth_user.is_superuser:\n            instances = get_instances_of_user(requested_user_id)\n\n            message.reply_channel.send({\n                \"text\": json.dumps(instances)\n            })\n        else:\n            return\n","sub_path":"instances/consumers.py","file_name":"consumers.py","file_ext":"py","file_size_in_byte":1185,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"493592792","text":"import logging\nfrom django.core.exceptions import ObjectDoesNotExist\nfrom rest_framework import status\nfrom rest_framework.views import APIView\nfrom rest_framework.response import Response\nfrom rest_framework.permissions import IsAuthenticated\nfrom ecommerce_projec.ecommerce.serializers import PurchaseSerializer, MakePurchaseSerializer\nfrom ecommerce_projec.ecommerce.services import PurchaseService, ProductService, MakePurchaseService\n\n\nclass PurchaseView(APIView):\n    permission_classes = (IsAuthenticated,)\n\n    def get(self, request, purchase_id: int):\n        try:\n            purchase = PurchaseService.get_purchase(purchase_id)\n            serializer = PurchaseSerializer(purchase)\n            return Response(serializer.data, status.HTTP_200_OK)\n        except (ObjectDoesNotExist, Exception) as err:\n            logging.info(f\"PurchaseView::get Error: {err}\")\n            return Response(\n                dict(success=False, message=f\"{err}\"),\n                status.HTTP_400_BAD_REQUEST\n            )\n\n    def put(self, request, purchase_id: int):\n        try:\n            serializer = MakePurchaseSerializer(data=request.data)\n            if not serializer.is_valid():\n                raise Exception(serializer.errors)\n            product_id = serializer.data.get('product_id')\n            quantity = serializer.data.get('quantity')\n            customer = request.user.person.customer\n            product = ProductService.get_product(product_id)\n            products = [product for i in range(quantity)]\n            MakePurchaseService(customer).shop(products)\n            return Response(serializer.data, status.HTTP_201_CREATED)\n        except (ObjectDoesNotExist, Exception) as err:\n            logging.info(f\"PurchaseView::delete Error: {err}\")\n            return Response(\n                dict(success=False, message=f\"{err}\"),\n                status.HTTP_400_BAD_REQUEST\n            )\n\n    def delete(self, request, purchase_id: int):\n        try:\n            purchase = PurchaseService.get_purchase(purchase_id)\n            PurchaseService.delete(purchase)\n            return Response(None, status.HTTP_200_OK)\n        except (ObjectDoesNotExist, Exception) as err:\n            logging.info(f\"PurchaseView::delete Error: {err}\")\n            return Response(\n                dict(success=False, message=f\"{err}\"),\n                status.HTTP_400_BAD_REQUEST\n            )\n","sub_path":"ecommerce_projec/ecommerce/views/api/v1/purchase_view.py","file_name":"purchase_view.py","file_ext":"py","file_size_in_byte":2394,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"464644300","text":"import os\nfrom shutil import copytree\nfrom datetime import datetime\nfrom shlex import quote\nfrom igf_data.utils.singularity_run_wrapper import singularity_run\nfrom igf_data.utils.fileutils import get_temp_dir,remove_dir,check_file_path,copy_local_file\nfrom jinja2 import Template,Environment,FileSystemLoader,select_autoescape\n\nclass Notebook_runner:\n  '''\n  A class for setup and run notebooks in containers\n\n  :param template_ipynb_path: A template notebook file\n  :param output_dir: Output path\n  :param input_param_map: A dictionary containing input params and files\n  :param use_ephemeral_space: A toggle for temp dir settings, default False\n  :param singularity_image_path: Path to singularity image, default None\n  :param output_format: Notebook output format, default html\n  :param timeout: Timeout settings for jupyter nbconvert run, default 600\n  :param kernel: Kernel name for the jupyter nbconvert run, default python3\n  :param allow_errors: Allow notebook execution with errors, default False\n  :param dry_run: A toggle for test, default False\n  :param jupyter_exe: Jupyter exe path, default jupyter\n  :param singularity_options: A list of singularity options, default None\n  :param notebook_tag: A tag for dataflow to identify notebook output, default notebook\n  '''\n  def __init__(self,template_ipynb_path,output_dir,input_param_map,container_paths=None,\n               singularity_image_path=None,use_ephemeral_space=False,output_format='html',\n               singularity_options=None,timeout=600,kernel='python3',allow_errors=False,\n               jupyter_exe='jupyter',dry_run=False):\n    self.template_ipynb_path = template_ipynb_path\n    self.output_dir = output_dir\n    self.input_param_map = input_param_map\n    self.use_ephemeral_space = use_ephemeral_space\n    self.container_paths = container_paths\n    self.singularity_image_path = singularity_image_path\n    self.output_format = output_format\n    self.timeout = timeout\n    self.kernel = kernel\n    self.allow_errors = allow_errors\n    self.dry_run = dry_run\n    self.jupyter_exe = jupyter_exe\n    self.singularity_options = singularity_options\n    self.temp_dir = \\\n      get_temp_dir(use_ephemeral_space=self.use_ephemeral_space)\n\n  def _generate_formatted_notebook(self):\n    try:\n      check_file_path(self.template_ipynb_path)\n      check_file_path(self.temp_dir)\n      if not isinstance(self.input_param_map,dict):\n        raise TypeError(\n                \"Expecting a dictionary for notebook param substitution, got {0}\".\\\n                  format(type(self.input_param_map)))\n      notebook_output = \\\n        os.path.join(\n          self.temp_dir,\n          os.path.basename(self.template_ipynb_path))\n      template_env = \\\n        Environment(\n          loader=\\\n            FileSystemLoader(\n              searchpath=os.path.dirname(self.template_ipynb_path)),\n          autoescape=select_autoescape(['html', 'xml']))\n      notebook = \\\n        template_env.\\\n          get_template(\n            os.path.basename(self.template_ipynb_path))\n      notebook.\\\n        stream(**self.input_param_map).\\\n        dump(notebook_output)\n      return notebook_output\n    except:\n      raise\n\n\n  def execute_notebook_in_singularity(self):\n    '''\n    A method for executing formatted notebooks within singularity container\n\n    :returns: Output notebook path\n    '''\n    try:\n      if self.singularity_image_path is None:\n        raise ValueError('No valid image path found')\n      check_file_path(self.singularity_image_path)\n      formatted_notebook = \\\n        self._generate_formatted_notebook()\n      args_list = [\n        self.jupyter_exe,\n        'nbconvert',\n        '{0}'.format(quote(formatted_notebook)),\n        '--to={0}'.format(quote(self.output_format)),\n        '--execute',\n        '--ExecutePreprocessor.enabled=True',\n        '--ExecutePreprocessor.timeout={0}'.format(quote(str(self.timeout))),\n        '--ExecutePreprocessor.kernel_name={0}'.format(quote(self.kernel))]\n      if self.allow_errors:\n        args_list.append('--allow-errors')\n      container_paths = self.container_paths\n      container_tmp_dir = \\\n        get_temp_dir(use_ephemeral_space=self.use_ephemeral_space)\n      if isinstance(container_paths,list):\n        container_paths.\\\n          extend(['{0}:/tmp'.format(container_tmp_dir),self.temp_dir])\n      if container_paths is None:\n        container_paths = \\\n          ['{0}:/tmp'.format(container_tmp_dir),self.temp_dir]\n      cmd = singularity_run(\n            image_path=self.singularity_image_path,\n            args_list=args_list,\n            options=self.singularity_options,\n            bind_dir_list=container_paths,\n            dry_run=self.dry_run)\n      temp_notebook_path = \\\n        formatted_notebook\n      if self.output_format=='html':\n        temp_notebook_path = \\\n          temp_notebook_path.replace('.ipynb','.html')\n      elif self.output_format=='markdown':\n        temp_notebook_path = \\\n          temp_notebook_path.replace('.ipynb','.md')\n      elif self.output_format=='notebook':\n        temp_notebook_path = temp_notebook_path\n      elif self.output_format=='pdf':\n        temp_notebook_path = \\\n          temp_notebook_path.replace('.ipynb','.pdf')\n      elif self.output_format=='python':\n        temp_notebook_path = \\\n          temp_notebook_path.replace('.ipynb','.py')\n      elif self.output_format=='slide':\n        temp_notebook_path = \\\n          temp_notebook_path.replace('.ipynb','.html')\n      if self.dry_run:\n        return formatted_notebook,cmd\n      else:\n        check_file_path(temp_notebook_path)\n        output_notebook_path = \\\n          os.path.join(\n            self.output_dir,\n            os.path.basename(temp_notebook_path))\n        copy_local_file(\n          temp_notebook_path,\n          output_notebook_path)\n        return output_notebook_path,cmd\n    except Exception as e:\n      raise ValueError(\n              'Failed to execute notebook, error: {0}'.format(e))","sub_path":"igf_data/utils/jupyter_nbconvert_wrapper.py","file_name":"jupyter_nbconvert_wrapper.py","file_ext":"py","file_size_in_byte":5949,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"309915381","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat Apr 13 14:56:19 2019\r\n\r\n@author: YeNz\r\n\"\"\"\r\n\r\nfrom __future__ import print_function, division\r\nfrom builtins import range\r\n\r\n\r\nimport numpy as np\r\nimport string\r\nfrom scipy.special import expit as sigmoid\r\nfrom sklearn.utils import shuffle\r\n# from util import find_analogies\r\n\r\nfrom scipy.spatial.distance import cosine as cos_dist\r\nfrom sklearn.metrics.pairwise import pairwise_distances\r\n\r\n\r\ndef get_negative_sampling_distribution(sentences, vocab_size):\r\n  # Pn(w) = prob of word occuring\r\n  # we would like to sample the negative samples\r\n  # such that words that occur more often\r\n  # should be sampled more often\r\n\r\n  word_freq = np.zeros(vocab_size)\r\n  word_count = sum(len(sentence) for sentence in sentences)\r\n  for sentence in sentences:\r\n      for word in sentence:\r\n          word_freq[word] += 1\r\n\r\n  # smooth it\r\n  p_neg = word_freq**0.75\r\n\r\n  # normalize it\r\n  p_neg = p_neg / p_neg.sum()\r\n\r\n  assert(np.all(p_neg > 0))\r\n  return p_neg\r\n\r\n\r\ndef get_context(pos, sentence, window_size):\r\n  # input:\r\n  # a sentence of the form: x x x x c c c pos c c c x x x x\r\n  # output:\r\n  # the context word indices: c c c c c c\r\n\r\n  start = max(0, pos - window_size)\r\n  end_  = min(len(sentence), pos + window_size)\r\n\r\n  context = []\r\n  for ctx_pos, ctx_word_idx in enumerate(sentence[start:end_], start=start):\r\n    if ctx_pos != pos:\r\n      # don't include the input word itself as a target\r\n      context.append(ctx_word_idx)\r\n  return context\r\n\r\n\r\ndef sgd(input_, targets, label, learning_rate, W, V):\r\n  # W[input_] shape: D\r\n  # V[:,targets] shape: D x N\r\n  # activation shape: N\r\n  # print(\"input_:\", input_, \"targets:\", targets)\r\n  activation = W[input_].dot(V[:,targets])\r\n  prob = sigmoid(activation)\r\n\r\n  # gradients\r\n  gV = np.outer(W[input_], prob - label) # D x N\r\n  gW = np.sum((prob - label)*V[:,targets], axis=1) # D\r\n\r\n  V[:,targets] -= learning_rate*gV # D x N\r\n  W[input_] -= learning_rate*gW # D\r\n\r\n  # return cost (binary cross entropy)\r\n  cost = label * np.log(prob + 1e-10) + (1 - label) * np.log(1 - prob + 1e-10)\r\n  return cost.sum()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"word2vec_functions.py","file_name":"word2vec_functions.py","file_ext":"py","file_size_in_byte":2144,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"369806064","text":"import heapq\nimport math\n\nclass running_medium:\n\n    def __init__(self):\n        # min heap to store higher half elements( right side of the median)\n        self.min_heap_right = []\n        # max heap to store lower half elements( left side of the median)\n        self.max_heap_left = []\n\n\n    def heappeak(self,heap):\n        top = heapq.heappop(heap)\n        heapq.heappush(heap,top)\n        return top\n\n    def negate(self, num):\n        return -num\n\n    def calc_running_median(self, num, median = 0):\n        \"\"\"\n        :param num: Incoming new streaming number\n        :return: new medium number at this point of time\n         At any time we try to make heaps balanced and\n            their sizes differ by atmost 1. If heaps are\n            balanced,then we declare median as average of\n            min_heap_right.top() and max_heap_left.top()\n            If heaps are unbalanced,then median is defined\n            as the top element of heap of larger size  */\n\n        \"\"\"\n\n        #  left side heap has more elements\n        if len(self.max_heap_left) > len(self.min_heap_right):\n            if num < median:\n                heapq.heappush(self.min_heap_right, self.negate(heapq.heappop(self.max_heap_left)))\n                heapq.heappush(self.max_heap_left, self.negate(num))\n            else:\n                heapq.heappush(self.min_heap_right, num)\n            newmedian = int(math.ceil( (self.negate(self.heappeak(self.max_heap_left)) + self.heappeak(self.min_heap_right))/2.0))\n\n        # both heaps are balanced\n        elif len(self.max_heap_left) == len(self.min_heap_right):\n            if num < median:\n                heapq.heappush(self.max_heap_left,self.negate(num))\n                newmedian = self.negate(self.heappeak(self.max_heap_left))\n            else:\n                heapq.heappush(self.min_heap_right, num)\n                newmedian = self.heappeak(self.min_heap_right)\n\n        # right side heap has more elements\n        else:\n            if num > median:\n                heapq.heappush(self.max_heap_left, self.negate(heapq.heappop(self.min_heap_right)))\n                heapq.heappush(self.min_heap_right, num)\n            else:\n                heapq.heappush(self.max_heap_left, self.negate(num))\n            newmedian = int(math.ceil((self.heappeak(self.min_heap_right) + self.negate(self.heappeak(self.max_heap_left)))/2.0))\n\n        return newmedian\n\n\"\"\"\n# driver program to test\na = [5, 15, 10, 20, 3, 100, 200, 300, 50, 15]\nb = [250,333]\n\nC00177436|30004|384|1|384\nC00384818|02895|250|1|250\nC00177436|30750|230|1|230\nC00177436|04105|384|1|384\nC00384818|02895|292|2|583\nC00177436|04105|384|2|768\n\nr = running_medium()\nmedian = 0\nfor each in b:\n    print \"new median = \"\n    newmedian =   r.calc_running_median(each, median)\n    print newmedian\n    median = newmedian\n    print r.max_heap_left\n    print r.min_heap_right\n\"\"\"\n\n","sub_path":"src/find_median_for_streaming_data.py","file_name":"find_median_for_streaming_data.py","file_ext":"py","file_size_in_byte":2869,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"361192161","text":"\"\"\"Functions for working with MAC addresses.\"\"\"\n\nimport re\nfrom .constants import MAC_CREATE, MAC_REGEX\n\n\ndef _valid_mac(func):\n    \"\"\"Decorator to validate a MAC address is valid.\"\"\"\n\n    def decorated(*args, **kwargs):\n        if kwargs.get(\"mac\"):\n            mac = kwargs.get(\"mac\")\n        else:\n            mac = args[0]\n        if not is_valid_mac(mac):\n            raise ValueError(f\"There was not a valid mac address in: `{mac}`\")\n        return func(*args, **kwargs)\n\n    return decorated\n\n\ndef is_valid_mac(mac):\n    \"\"\"Verifies whether or not a string is a valid MAC address.\n\n    Args:\n        mac (str): A MAC address in string format that matches one of the defined regex patterns.\n\n    Returns:\n        bool: The result as to whether or not the string is a valid MAC address.\n\n    Example:\n        >>> from netutils.mac import is_valid_mac\n        >>> is_valid_mac(\"aa.bb.cc.dd.ee.ff\")\n        True\n        >>> is_valid_mac(\"aa.bb.cc.dd.ee.gg\")\n        False\n        >>>\n    \"\"\"\n    for pattern in list(MAC_REGEX.values()):\n        if re.fullmatch(pattern, mac):\n            return True\n    return False\n\n\n@_valid_mac\ndef mac_to_format(mac, frmt=\"MAC_NO_SPECIAL\"):\n    \"\"\"Converts the MAC address to a specific format.\n\n    Args:\n        mac (str): A MAC address in string format that matches one of the defined regex patterns.\n        frmt (str): A format in which the MAC address should be returned in.\n\n    Returns:\n        str: A MAC address in the specified format.\n\n    Example:\n        >>> from netutils.mac import mac_to_format\n        >>> mac_to_format(\"aa.bb.cc.dd.ee.ff\", \"MAC_DASH_FOUR\")\n        'aabb-ccdd-eeff'\n        >>>\n    \"\"\"\n    if not MAC_CREATE.get(frmt):\n        raise ValueError(f\"An invalid mac format was provided in: `{frmt}`\")\n    mac = mac_normalize(mac)\n    count = MAC_CREATE[frmt][\"count\"]\n    char = MAC_CREATE[frmt][\"char\"]\n    return char.join([mac[i : i + count] for i in range(0, len(mac), count)])  # noqa: E203\n\n\n@_valid_mac\ndef mac_to_int(mac):\n    \"\"\"Converts the MAC address to an integer.\n\n    Args:\n        mac (str): A MAC address in string format that matches one of the defined regex patterns.\n\n    Returns:\n        int: The valid MAC address converted to an integer.\n\n    Example:\n        >>> from netutils.mac import mac_to_int\n        >>> mac_to_int(\"aa.bb.cc.dd.ee.ff\")\n        187723572702975\n        >>>\n    \"\"\"\n    return int(mac_normalize(mac), 16)\n\n\n@_valid_mac\ndef mac_type(mac):\n    \"\"\"Retuns the \"type\" of MAC address, as defined by the regex pattern names.\n\n    Args:\n        mac (str): A MAC address in string format that matches one of the defined regex patterns.\n\n    Returns:\n        str: The regex pattern type of the MAC address.\n\n    Example:\n        >>> from netutils.mac import mac_type\n        >>> mac_type(\"aa.bb.cc.dd.ee.ff\")\n        'MAC_DOT_TWO'\n        >>> mac_type(\"aabb.ccdd.eeff\")\n        'MAC_DOT_FOUR'\n        >>>\n    \"\"\"\n    for name, pattern in MAC_REGEX.items():\n        if re.fullmatch(pattern, mac):\n            return name\n    raise ValueError(\"MAC pattern not found.\")\n\n\n@_valid_mac\ndef mac_normalize(mac):\n    \"\"\"Retuns the MAC address with only the address, and no special characters.\n\n    Args:\n        mac (str): A MAC address in string format that matches one of the defined regex patterns.\n\n    Returns:\n        str: The MAC address with no special characters.\n\n    Example:\n        >>> from netutils.mac import mac_normalize\n        >>> mac_normalize(\"aa.bb.cc.dd.ee.ff\")\n        'aabbccddeeff'\n        >>>\n    \"\"\"\n    chars = \":-.\"\n    for char in chars:\n        if char in mac:\n            mac = mac.replace(char, \"\")\n    return mac\n","sub_path":"netutils/mac.py","file_name":"mac.py","file_ext":"py","file_size_in_byte":3645,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"612401847","text":"import json\nimport unittest\n\nfrom tests.base import BaseTestCase\nfrom utils.cache_utils import cache_set\n\n\nclass TestLoewiScore(BaseTestCase):\n\n    def test_loewi_score(self):\n        data_login = self.login_user()\n        with self.client:\n            response = self.client.get(\n                '/api/v1/loewi_score_summary',\n                headers=dict(\n                    Authorization='JWT ' + data_login['token']\n                )\n            )\n            data = json.loads(response.data.decode())\n            self.assertEqual(data, [])\n            self.assertEqual(response.status_code, 200)\n\n    def test_loewi_score_cached(self):\n        data_login = self.login_user()\n        cache_set(str(self.user.id) + \"_loewiScore\", 10)\n        with self.client:\n            response = self.client.get(\n                '/api/v1/loewi_score_summary',\n                headers=dict(\n                    Authorization='JWT ' + data_login['token']\n                )\n            )\n            data = json.loads(response.data.decode())\n            self.assertEqual(data, 10)\n            self.assertEqual(response.status_code, 200)\n\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"tests/test_loewiscore.py","file_name":"test_loewiscore.py","file_ext":"py","file_size_in_byte":1174,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"307426605","text":"from __future__ import absolute_import, unicode_literals\n\nfrom collections import namedtuple\nimport logging\n\nfrom django.http import Http404, HttpResponse\nfrom django.shortcuts import get_object_or_404\nfrom django.utils.six.moves import http_client\nfrom django.views import generic\n\nfrom .base import APIException, api_reverse\nfrom .parsers import RequestParser\nfrom .serializers import Serializer\nfrom .utils import querystring\n\n\n#: The ``page`` object from ``Resource.objects``\nPage = namedtuple('Page', 'queryset offset limit full_queryset')\n\nlogger = logging.getLogger('towel.api')\n\n\nclass Resource(generic.View):\n    \"\"\"\n    Resource for exposing Django models somewhat RESTy\n    \"\"\"\n\n    #: The API to which this resource is bound to.\n    api = None\n\n    #: The model exposed by this resource.\n    model = None\n\n    #: Prefiltered queryset for this resource. Defaults to\n    #: ``model._default_manager.all()`` if unset.\n    queryset = None\n\n    #: Default instance count for list views\n    limit_per_page = 20\n    #: Higher values than this will not be accepted for ``limit``\n    max_limit_per_page = 1000\n\n    #: Almost the same as ``django.views.generic.View.http_method_names`` but\n    #: not quite, we allow ``patch`` as well.\n    http_method_names = [\n        'get', 'post', 'put', 'delete', 'head', 'patch', 'options', 'trace']\n\n    #: A list of URL patterns which will be used by ``API.register`` to build\n    #: the URLconf entries. The format is a list of tuples containing\n    #: (regular expression, URL name suffix).\n    urls = [\n        (r'^$', 'list', {\n            'request_type': 'list',\n        }),\n        (r'^(?P<pk>\\d+)/$', 'detail', {\n            'request_type': 'detail',\n        }),\n        (r'^(?P<pks>(?:\\d+;)*\\d+);?/$', 'set', {\n            'request_type': 'set',\n        }),\n    ]\n\n    def dispatch(self, request, *args, **kwargs):\n        \"\"\"\n        This method is almost the same as Django's own\n        ``generic.View.dispatch()``, but there are a few subtle differences:\n\n        - It uses ``self.request``, ``self.args`` and ``self.kwargs`` in all\n          places\n        - It calls ``self.unserialize_request()`` after assigning the\n          aforementioned variables on ``self`` which may modify all aspects and\n          all variables (f.e.  deserialize a JSON request and serialize it\n          again to look like a standard POST request) and only then determines\n          whether the request should be handled by this view at all.\n        - The return value of the ``get()``, ``post()`` etc. methods is passed\n          to ``self.serialize_response()`` and only then returned to the\n          client. The processing methods should return data (a ``dict``\n          instance most of the time) which is then serialized into the\n          requested format or some different supported format.\n        \"\"\"\n\n        # The following three lines can be removed when we move to\n        # Django 1.5 only\n        self.request = request\n        self.args = args\n        self.kwargs = kwargs\n\n        response = self.unserialize_request()\n        if response:\n            return response\n\n        # Try to dispatch to the right method; if a method doesn't exist,\n        # defer to the error handler. Also defer to the error handler if the\n        # request method isn't on the approved list.\n        method = self.request.method.lower()\n        if method == 'head':\n            method = 'get'\n        handler = self.http_method_not_allowed\n\n        if method in self.http_method_names:\n            self.request_type = kwargs.get('request_type')\n            if (self.request_type\n                    and hasattr(self, '%s_%s' % (method, self.request_type))):\n                handler = getattr(self, '%s_%s' % (method, self.request_type))\n            elif hasattr(self, method):\n                handler = getattr(self, method)\n\n        try:\n            return self.serialize_response(\n                handler(self.request, *self.args, **self.kwargs))\n        except Http404 as exc:\n            return self.serialize_response(\n                {'error': exc.args[0]},\n                status=http_client.NOT_FOUND)\n        except APIException as exc:\n            logger.warning(\n                'APIException: %s, %r' % (exc, exc.__dict__),\n                extra={\n                    'request': self.request,\n                })\n\n            data = {\n                'error': exc.error_message,\n            }\n            data.update(exc.data)\n            return self.serialize_response(data, status=exc.status)\n\n    def unserialize_request(self):\n        \"\"\"\n        This method standardizes various aspects of the incoming request, f.e.\n        decoding of JSON requests etc.\n\n        The \"real\" processing methods should not have to distinguish between\n        varying request types anymore.\n\n        If this method returns anything, it is treated as a response and\n        short-circuits the resource processing.\n        \"\"\"\n        return RequestParser().parse(self.request)\n\n    def serialize_response(self, response, status=http_client.OK,\n                           headers=None):\n        \"\"\"\n        Serializes the response into an appropriate format for the wire such\n        as JSON. ``HttpResponse`` instances are returned directly.\n        \"\"\"\n        if isinstance(response, HttpResponse):\n            return response\n\n        return Serializer().serialize(\n            response,\n            request=self.request,\n            status=status,\n            output_format=self.request.GET.get('format'),\n            headers=headers)\n\n    def get_query_set(self):\n        \"\"\"\n        Returns the queryset used by this resource. If you need access or\n        visibility control, add it here.\n        \"\"\"\n        if self.queryset:\n            return self.queryset._clone()\n        elif self.model:\n            return self.model._default_manager.all()\n\n    def apply_filters(self, queryset):\n        \"\"\"\n        Applies filters and search queries to the queryset. This method will\n        only be called for list views, not when the user requested sets or\n        single instances.\n        \"\"\"\n        return queryset\n\n    def detail_object_or_404(self):\n        \"\"\"\n        Returns the current object for detail resources such as\n        ``/api/product/1/``.\n        \"\"\"\n        return get_object_or_404(self.get_query_set(), pk=self.kwargs['pk'])\n\n    def set_objects_or_404(self):\n        \"\"\"\n        Returns the current set of objects for set resources such as\n        ``/api/product/1;3/``.\n        \"\"\"\n        pks = set(pk for pk in self.kwargs['pks'].split(';') if pk)\n        set_ = self.get_query_set().in_bulk(pks).values()\n\n        if len(pks) != len(set_):\n            raise Http404('Some objects do not exist.')\n\n        return set_\n\n    def page_objects_or_404(self):\n        \"\"\"\n        Returns the current page for list resources such as\n        ``/api/product/?limit=20&offset=40``. Applies filtering using\n        ``apply_filters`` as well.\n        \"\"\"\n        queryset = self.apply_filters(self.get_query_set())\n\n        try:\n            offset = int(self.request.GET.get('offset'))\n        except (TypeError, ValueError):\n            offset = 0\n\n        try:\n            limit = int(self.request.GET.get('limit'))\n        except (TypeError, ValueError):\n            limit = self.limit_per_page\n\n        # Do not allow more than max_limit_per_page entries in one request,\n        # ever\n        limit = min(limit, self.max_limit_per_page)\n\n        # Sanitize range\n        offset = max(offset, 0)\n        limit = max(limit, 0)\n\n        return Page(queryset[offset:offset + limit], offset, limit, queryset)\n\n    def get_detail(self, request, *args, **kwargs):\n        kw = {}\n        if request.GET.get('full'):\n            kw['inline_depth'] = 1\n        return self.api.serialize_instance(\n            self.detail_object_or_404(),\n            build_absolute_uri=request.build_absolute_uri,\n            **kw)\n\n    def get_set(self, request, *args, **kwargs):\n        return {\n            'objects': [\n                self.api.serialize_instance(\n                    instance,\n                    build_absolute_uri=request.build_absolute_uri,\n                ) for instance in self.set_objects_or_404()\n            ],\n        }\n\n    def get_list(self, request, *args, **kwargs):\n        page = self.page_objects_or_404()\n\n        list_url = api_reverse(\n            page.full_queryset.model, 'list', api_name=self.api.name)\n\n        meta = {\n            'offset': page.offset,\n            'limit': page.limit,\n            'total': page.full_queryset.count(),\n            'previous': None,\n            'next': None,\n        }\n\n        if page.offset > 0:\n            meta['previous'] = request.build_absolute_uri('%s?%s' % (\n                list_url,\n                querystring(\n                    self.request.GET,\n                    exclude=('offset', 'limit'),\n                    offset=max(0, page.offset - page.limit),\n                    limit=page.limit,\n                ),\n            ))\n\n        if page.offset + page.limit < meta['total']:\n            meta['next'] = request.build_absolute_uri('%s?%s' % (\n                list_url,\n                querystring(\n                    self.request.GET,\n                    exclude=('offset', 'limit'),\n                    offset=page.offset + page.limit,\n                    limit=page.limit,\n                ),\n            ))\n\n        return {\n            'objects': [\n                self.api.serialize_instance(\n                    instance,\n                    build_absolute_uri=request.build_absolute_uri,\n                ) for instance in page.queryset],\n            'meta': meta,\n        }\n\n    def options(self, request, *args, **kwargs):\n        # XXX This will be removed as soon as we switch to Django 1.5 only\n        response = HttpResponse()\n        response['Allow'] = ', '.join(self._allowed_methods())\n        response['Content-Length'] = 0\n        return response\n\n    def _allowed_methods(self):\n        methods = set(m.upper() for m in self.http_method_names if (\n            hasattr(self, m)\n            or hasattr(self, '%s_%s' % (m, self.request_type))\n        ))\n        if 'GET' in methods:\n            methods.add('HEAD')\n        return sorted(methods)\n","sub_path":"towel/api/resources.py","file_name":"resources.py","file_ext":"py","file_size_in_byte":10292,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"40800644","text":"#!/usr/bin/env python\n#\n# Secstruc.py \n#\n# Module managing dot-bracket RNA secondary structures.\n#\n__author__  = \"Kristian Rother\"\n__contributors__ = \"Tomasz Puton\"\n__credits__ = [\"Lukasz Kozlowski, Natalia Szostak, Joanna Kasprzak, Sandra Smit\"]\n__maintainer__ = \"Kristian Rother\"\n__email__ = \"krother@rubor.de\"\n__status__ = \"Prototype\"\n\nimport re\n\nclass SecstrucError(Exception): pass\nclass UnknownElementError(Exception): pass\n\nclass Secstruc:\n    \"\"\"\n    Secondary structure string with indices for each position.\n    Can handle pieces of secondary structure which are interrupted.\n    \"\"\"\n    def __init__(self,secstr,indices=None):\n        self.Secstruc = secstr\n        if indices == None:\n            indices = range(len(self))\n        if len(self.Secstruc) != len(indices):\n            raise SecstrucError(\"Cannot create Secstruc object (%s %s)\"%(secstr,str(indices)))\n        self.indices = indices\n\n    def find_overhang5(self):\n        \"\"\"Returns overhang on the 5' end as Secstruc objects.\"\"\"\n        n = len(self)\n        for i in range(2,n):\n            overhang = self[:i]\n            if overhang.get_type() == \"5'-overhang\":\n                return overhang\n\n    def find_overhang3(self):\n        \"\"\"Returns overhang on the 3' end as Secstruc objects.\"\"\"\n        n = len(self)\n        for i in range(n-1,0,-1):\n            overhang = self[i:]\n            if overhang.get_type() == \"3'-overhang\":\n                return overhang\n\n    def find_helices(self):\n        # return self.extract_elements('helix')\n        helices = []\n        begin = 0\n        end = 0\n        length = 0\n\n        for i,j in self.get_base_pair_indices():\n            # check if old helix continues\n            if i == begin+length and j==end-length:\n                length += 1\n            else:\n                # save old helix\n                if length >0:\n                    helices.append(self[begin:begin+length] + self[end-length+1:end+1])\n                # new helix starts\n                begin = i\n                end = j\n                length = 1\n        if length > 0:\n            helices.append(self[begin:begin+length] + self[end-length+1:end+1])\n        return helices\n\n    def check_bulge(self, i,j):\n        if i>len(self)-3 or j==0: return None, None\n        ele = self[i] + self[i+1] + self[i+2] + self[j-1] + self[j]\n        if ele.get_type() == 'bulge':\n            return ele, i+1\n        ele = self[i] + self[i+1] + self[j-2] + self[j-1] + self[j]\n        if ele.get_type() == 'bulge':\n            return ele, j-1\n        return None,None\n\n    def get_base_pair_indices(self):\n        \"\"\"generates (i,j) tuples iterating through base pairs.\"\"\"\n        n = len(self)\n        i = 0\n        while i<n-1:\n            # find next open pair\n            while i<n-1 and self.Secstruc[i]!='(': i += 1\n            # find corresponding pair\n            j = i+1\n            bps = 1\n            while j<n and bps>0:\n                if self.Secstruc[j]=='(': bps += 1\n                if self.Secstruc[j]==')': bps -= 1\n                if bps == 0: yield i,j\n                j += 1\n            i += 1\n \n    def find_bulges(self):        \n        bulges = []\n        for i,j in self.get_base_pair_indices():           \n            bulge, ind = self.check_bulge(i,j)\n            if bulge:\n                bulges.append(bulge)\n        return bulges\n\n    def find_loops(self):        \n        return self.find_elements('loop')\n    \n    def find_junctions(self):\n        \"\"\"Returns a list of all junctions.\n        Any region between two paired bases with more than 1 nested base pair\n        Inside is a junction.\n        \"\"\"\n        junctions = []\n        for i,j in self.get_base_pair_indices():\n            # check if i,j enclose a junction.\n            nested_bps = 0\n            skip_helix = 0\n            junction = self[i]\n            k = i+1\n            while k < j:\n                if self.Secstruc[k] == ')':\n                    skip_helix -= 1\n                    if skip_helix == 0:\n                        nested_bps += 1\n                if skip_helix == 0:\n                    junction += self[k]\n                if self.Secstruc[k] == '(':\n                    skip_helix += 1\n                k += 1\n            junction += self[j]\n            if nested_bps >= 2:\n                junctions.append(junction)\n        return junctions\n    \n    def find_elements(self, element_type):\n        \"\"\"Finds elements of a given type.\"\"\"\n        result = []\n        n = len(self)\n        i = 0\n        while i < n-1:\n            j = n\n            while j > i:\n                ele = self[i:j]\n                if ele.get_type() == element_type:\n                    result.append(ele)\n                j -= 1\n            i += 1\n        return result\n                            \n\n    def find_Secstruc_elements(self):\n        \"\"\"\n        Takes a secondary structure, and returns a list of\n        component secondary structure elements.\n        \"\"\"\n        result = []\n        overhang = self.find_overhang5()\n        if overhang: result.append(overhang)\n        overhang = self.find_overhang3()\n        if overhang: result.append(overhang)\n        loops = self.find_loops()\n        helices = self.find_helices()\n        bulges = self.find_bulges()\n        junctions = self.find_junctions()\n        result += loops + bulges + helices + junctions\n        return result\n\n    def get_type(self):\n        \"\"\"Returns a type classification of this secondary structure as a string,\n        if it is some basic type.\"\"\"\n        if re.search('^\\(\\(+\\)\\)+$',self.Secstruc):\n            # check both stems have equal length\n            half = len(self.Secstruc)/2\n            if len(self.Secstruc)%2 == 0 \\\n               and re.search('^\\(+$',self.Secstruc[:half]) \\\n               and re.search('^\\)+$',self.Secstruc[half:]):\n                return 'helix'\n            else: return None\n        if re.search('^\\.+\\($',self.Secstruc): return \"5'-overhang\"\n        if re.search('^\\)\\.+$',self.Secstruc): return \"3'-overhang\"\n        if re.search('^\\(\\.\\.+\\)$',self.Secstruc): return \"loop\"\n        if re.search('^\\(\\(\\)\\.+\\)|\\(\\.+\\(\\)\\)$',self.Secstruc): return \"bulge\"\n        if re.search('^\\((.*\\(\\)\\.*)+\\)$',self.Secstruc): return 'junction'\n        return None\n\n\n    def __eq__(self, other):\n        if not isinstance(other,Secstruc):\n            other = Secstruc(other)\n        if self.Secstruc == other.Secstruc \\\n           and self.indices == other.indices:\n            return True\n        \n    def __add__(self, other):\n        newsec = self.Secstruc + other.Secstruc\n        newind = self.indices + other.indices\n        return Secstruc(newsec, newind)\n\n    def __getitem__(self,index):\n        if type(index) == slice:\n            newsec = self.Secstruc[index.start:index.stop]\n            newind = self.indices[index.start:index.stop]\n            return Secstruc(newsec, newind)\n        else:\n            return Secstruc(self.Secstruc[index],[self.indices[index]])\n\n    def __repr__(self):\n        return self.Secstruc + ';' + str(self.indices)\n\n    def __len__(self):\n        return len(self.Secstruc)\n\n    def __str__(self):\n        return self.Secstruc\n\ndef contains_pseudoknot(bplist):\n    \"\"\"Checks if a list of base pairs contains a pseudoknot. Returns boolean\"\"\"\n    for a1, a2 in bplist:\n        for b1, b2 in bplist:\n            if (a1 < b1 < a2 < b2) or (a2 < b1 < a1 < b2) or (a2 < b2 < a1 < b1):\n                return True\n    return False\n    \n    \ndef get_pseudoknot_pairs(bplist):\n    \"\"\"Returns a list of base pairs that participate in pseudoknots.\"\"\"\n    result = []\n    for a1, a2 in bplist:\n        for b1, b2 in bplist:\n            if (a1 < b1 < a2 < b2) or (a2 < b1 < a1 < b2) or (a2 < b2 < a1 < b1) \\\n            or (b1 < a1 < b2 < a2) :\n                if (a1, a2) not in result:\n                    result.append((a1, a2))\n    return result\n                \ndef make_dotbracket_from_bplist(length, bplist):    \n    \"\"\"\n    Takes a structure as a list of basepairs\n     [(2,7),(4,5),(10,15),(17,20)]\n     and converts it to vienna format: \n     \"((...).(((.((....))).)))\"\n    \"\"\"\n    if contains_pseudoknot(bplist):\n        raise PseudoknotError(\"Base pairs contain a pseudoknot: %s\"%str(bplist))\n    Secstruc = ['.']*length\n    for bp in bplist:\n        Secstruc[bp[0]-1] = '(' \n        Secstruc[bp[1]-1] = ')'    \n    return ''.join(Secstruc)\n    \ndef make_bplist_from_dotbracket(Secstruc):\n    \"\"\"Reads a dot-bracket secondary structure and returns a list of base pair indices\"\"\"\n    result = []\n    for bp in PseudoknotSecstruc(Secstruc).get_base_pair_indices():\n        result.append((bp[0]+1, bp[1]+1))\n    return result\n\ndef has_overlapping_basepairs(bplist):\n    indices = {}\n    for bp in bplist:\n        if indices.has_key(bp[0]): return bp[0]\n        if indices.has_key(bp[1]): return bp[1]\n        indices[bp[0]]=True\n        indices[bp[1]]=True\n    return False\n    \n\"\"\"\nThis modules contains parses for output formats of secondary structure prediction programs\nfor single sequences or groups of similar sequences (comparative prediction).\nThey all can be parsed into lists of basepairs, \nand lists of base pairs can be converted to dot-bracket sequences\n\nExample of structure without pseudoknots, takes one line:\n    ((..(..)..).((...).(((...)))).)\nExample of structure with pseudoknots, takes more than one line:\n    ((..(..)..)..((...).(((...)))).)\n    .......((........).)............\n    ...........((..........)).......\n\"\"\"\n \nclass PseudoknotError(Exception): pass\n\nclass PseudoknotSecstruc(Secstruc):\n    symbols = {\n        '(':')', '[':']', '{':'}', \n        'a':'A', 'b':'B', 'c':'C', 'd':'D', 'e':'E', 'f':'F', \n        'g':'G', 'h':'H', 'i':'I', 'j':'J', 'k':'K', 'l':'L', \n        'm':'M', 'n':'N', 'o':'O', 'p':'P', 'q':'Q', 'r':'R', \n        's':'S', 't':'T', 'u':'U', 'v':'V', 'w':'W', 'x':'X', 'y':'Y', 'z':'Z', \n        }\n    open_symbols = symbols.keys()\n    \n    def get_base_pair_indices(self):\n        \"\"\"generates (i,j) tuples iterating through base pairs.\"\"\"\n        n = len(self)\n        i = 0\n        while i<n-1:\n            # find next open pair\n            while i<n-1 and self.Secstruc[i] not in self.open_symbols: i += 1\n            # find corresponding pair\n            open_symbol = self.Secstruc[i]\n            close_symbol = self.symbols.get(open_symbol)\n            j = i+1\n            bps = 1\n            while j<n and bps>0:\n                if self.Secstruc[j]==open_symbol: bps += 1\n                if self.Secstruc[j]==close_symbol: bps -= 1\n                if bps == 0: yield i,j\n                j += 1\n            i += 1\n            \n    def get_pseudoknot_indices(self):\n        \"\"\"Returns a list of base pair index tuples participating in pseudoknots.\"\"\"\n        bplist = [bp for bp in self.get_base_pair_indices()]\n        pknot = get_pseudoknot_pairs(bplist)\n        return pknot\n\n","sub_path":"Bio/RNA/Secstruc.py","file_name":"Secstruc.py","file_ext":"py","file_size_in_byte":10860,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"260752330","text":"import random\n\nimport logbook\n\n\nclass Creature:\n    def __init__(self, name, level):\n        if not isinstance(name, str):\n            raise TypeError(\"Creature name must be a string\")\n        self.log = logbook.Logger(name)\n        self.log.trace(f\"Setting up creature...\")\n        if not isinstance(level, int):\n            raise TypeError(\"level must be an int\")\n        self.name = name\n        self.level = level\n        self.log.trace(\n            f\"{self.name}, a level {self.level} {type(self).__name__}, setup successfully\"\n        )\n\n    def defensive_roll(self):\n        roll = random.randint(1, 12)\n        self.log.trace(\n            f\"{self.name} rolls:{roll}, level:{self.level}. Overall:{roll * self.level}\"\n        )\n        return roll * self.level\n\n\nclass Dragon(Creature):\n    def __init__(self, name, level, scaliness, breaths_fire):\n        super().__init__(name, level)\n        if not isinstance(scaliness, int):\n            raise TypeError(\"Scaliness must be an int\")\n        self.scaliness = scaliness\n        self.breaths_fire = bool(breaths_fire)\n        self.log.trace(\"Special dragon features set-up successfully\")\n\n    def defensive_roll(self):\n        roll = super().defensive_roll()\n        value = roll * self.scaliness\n        if self.breaths_fire:\n            value = value * 2\n            self.log.trace(f\"Dragon breaths fire! overall roll: {value}\")\n        else:\n            self.log.trace(\n                f\"Dragon doesn't breath fire. Scales protect! overall roll: {value}\"\n            )\n        return value\n\n\nclass Wizard(Creature):\n    def attack(self, creature):\n        my_roll = self.defensive_roll()\n        their_roll = creature.defensive_roll()\n        return my_roll >= their_roll\n","sub_path":"days/31-33-logging/dnd_game/actors.py","file_name":"actors.py","file_ext":"py","file_size_in_byte":1731,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"64805304","text":"\nnumber = int(input())\nif number<=1:\n    print(number)\ndef lastdigitfibo(num):\n    num_a, num_b = 0, 1\n    for _ in range(num - 1):\n        num_c = num_a + num_b\n        num_c = num_c%10\n        num_b, num_a = num_c, num_b\n    print(num_c)\n\nlastdigitfibo(number)","sub_path":"Data Structures and Algorithms Specialization/Algorithmic Toolbox/Coursera-Algorithmic-Toolbox-master/assignment solutions/2.2 lastfibodigit.py","file_name":"2.2 lastfibodigit.py","file_ext":"py","file_size_in_byte":262,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"154592651","text":"import time\n\n\ndef generator(n):\n    result = []\n    m = 2**13 - 1\n    a = 5**5\n    c = 3\n    x = int(round(time.time()))\n\n    for i in range(n):\n        xn = (a * x + c) % m\n        result.append(xn)\n        x = xn\n    return result\n\n","sub_path":"RC5/lab1.py","file_name":"lab1.py","file_ext":"py","file_size_in_byte":234,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"273691417","text":"import numpy as np\nimport cv2\nimport math\nimport socket\nimport time\n\nfrom hand_detector_utils import *\n\nUDP_IP = \"127.0.0.1\"\nUDP_PORT = 5065\n\nsock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n\nlast = []\n\ngood_condition = False\ndrawing_box = True\nfull_frame = False\nstabilize_highest_point = True\n\nold_highest_point = (-1, -1)\n\nx1_crop = 0\ny1_crop = 60\nx2_crop = 320\ny2_crop = 420\n\n# Open Camera\ntry:\n    default = 1 # Try Changing it to 1 if webcam not found\n    capture = cv2.VideoCapture(default)\nexcept:\n    print(\"No Camera Source Found!\")\n\nwhile capture.isOpened():\n    \n    # Capture frames from the camera\n    ret, frame = capture.read()\n    \n    width = frame.shape[1]\n                \n    img_left = frame[y1_crop:y2_crop, 0:int(width/3)]\n    img_right = frame[y1_crop:y2_crop, int(width/3 * 2): int(width)]\n        \n    try:\n\n        contour_left = detectHand(img_left)\n        contour_right = detectHand(img_right)\n        \n        defects_left, drawing_left = findDefects(img_left, contour_left)\n        defects_right, drawing_right = findDefects(img_right, contour_right)\n        \n        # Count defects (in the right image)\n        count_defects = countDefects(defects_right, contour_right, img_right)\n        \n        # Track highest point (in the left image)\n        highest_point = trackHighestPoint(defects_left, contour_left)\n        \n        if(stabilize_highest_point):\n            if( old_highest_point == (-1, -1)): old_highest_point = highest_point\n            else:\n                # Evaluate the magnitude of the difference\n                diag_difference = np.linalg.norm(np.asarray(old_highest_point) - np.asarray(highest_point))\n                \n                # If the difference is bigger than a threshold then I actually moved my finger\n                if(diag_difference >= 9.5): \n                    # print(\"diag_difference = \", diag_difference)\n                    old_highest_point = highest_point\n                else: highest_point = old_highest_point;\n            \n        if(full_frame):\n            highest_point = (highest_point[0], highest_point[1])\n            cv2.circle(frame, highest_point, 10, [255,0,255], -1)\n        else:\n            cv2.circle(img_left, highest_point, 10, [255,0,255], -1)\n            highest_point = (highest_point[0] + x1_crop, highest_point[1] + y1_crop)\n            cv2.circle(frame, highest_point, 10, [255,0,255], -1)\n    \n        # Print number of fingers\n        textDefects(frame, count_defects,debug_var = False)\n    \n        # Show required images\n        if(drawing_box):\n            cv2.rectangle(frame, (x1_crop, y1_crop), (int(width/3), y2_crop),(0,0,255), 1)\n            cv2.rectangle(frame, (int(width/3 * 2), y1_crop), (int(width), y2_crop),(0,0,255), 1)\n        cv2.imshow(\"Full Frame\", frame)\n        \n        all_image_left = np.hstack((drawing_left, img_left))\n        cv2.imshow('Recognition Left', all_image_left)\n        \n        all_image_right = np.hstack((drawing_right, img_right))\n        cv2.imshow('Recognition Right', all_image_right)\n    \n        last.append(count_defects)\n        if(len(last) > 5):\n            last = last[-5:]\n            # last = []\n        \n    \n        # Check if previously hand was wide open (3/4 fingers in previous frames), and is now a fist (0 fingers)\n        if(good_condition):\n            if(count_defects == 0 and 4 in last):\n                last = []\n                sendCommand(sock, UDP_IP, UDP_PORT, \"ACTION\")\n                \n            elif(count_defects == 0 and 2 in last):\n                last = []\n                sendCommand(sock, UDP_IP, UDP_PORT, \"BACK\")\n                \n        else:\n            if(count_defects == 0 and 4 in last):\n                last = []\n                sendCommand(sock, UDP_IP, UDP_PORT, \"ACTION\")\n        \n        command = \"l \" + str(highest_point[0]) + \" \" + str(highest_point[1])\n        \n        \n        sendCommand(sock, UDP_IP, UDP_PORT, command, debug_var = False)\n        \n    except:\n        pass\n    \n    # Close the camera if 'q' is pressed\n    if cv2.waitKey(1) == ord('q'):\n        break\n\ncapture.release()\ncv2.destroyAllWindows()","sub_path":"Tracking Python Algorithm/hand_detector.py","file_name":"hand_detector.py","file_ext":"py","file_size_in_byte":4130,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"76251442","text":"from sklearn.preprocessing import Imputer\n\nfrom pandas_csv import df\n\n# 2019.09.08 add : print original data\nprint(df)\n\n# impute missing values via the column mean\n\n\nimr = Imputer(missing_values='NaN', strategy='mean', axis=0)\nimr = imr.fit(df.values)\nimputed_data = imr.transform(df.values)\n# 2019.09.08 change\n#imputed_data\nprint(imputed_data)\n","sub_path":"ch04/impute_mean.py","file_name":"impute_mean.py","file_ext":"py","file_size_in_byte":346,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"169047211","text":"import json\r\nimport turtle\r\nimport urllib.request\r\n\r\nurl = 'http://api.open-notify.org/astros.json'\r\n\r\nresponse = urllib.request.urlopen(url)\r\n\r\ntext = response.read()\r\n\r\nprint(text)\r\n\r\nresult = json.loads(text)\r\n\r\n\r\nprint(result)\r\n\r\n\r\nscreen = turtle.Screen()\r\nscreen.setup(1280,640)\r\nscreen.setworldcoordinates(-180, -90, 180, 90)\r\nscreen.bgpic(\"map.gif\")\r\n\r\nlocation = turtle.Turtle()\r\nlocation.penup()\r\nlocation.color('yellow')\r\nlocation.goto(10.0, 60.0)\r\nlocation.dot(5)\r\nlocation.hideturtle()\r\n","sub_path":"astro.py","file_name":"astro.py","file_ext":"py","file_size_in_byte":500,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"92016926","text":"from telethon.sync import TelegramClient, events\nfrom telethon.tl.functions.channels import JoinChannelRequest\nfrom telethon.errors import FloodWaitError\nfrom telethon import functions, types\nfrom config import api_hash, api_id\nimport time\n\nwith TelegramClient('anon', api_id, api_hash) as Client:\n    while True:\n        try:\n            joinedgroups = []\n            alreadyjoined = []\n            with open(\"grouptojoin.txt\",\"r\") as f:\n                groupstojoin = f.read().splitlines()\n                if groupstojoin == []:\n                    continue\n                for group in groupstojoin:\n                    if len(joinedgroups) == 5:\n                        break\n                    elif len(joinedgroups) != 5:\n                        n = Client(JoinChannelRequest(group))\n                        if n.updates:\n                            print(\"Joined\")\n                            joinedgroups.append(group)\n                            groupstojoin.remove(group)\n                        elif n.updates == []:\n                            print(\"already joined\")\n                            alreadyjoined.append(group)\n                            groupstojoin.remove(group)\n            with open(\"grouptojoin.txt\",\"w\") as g:\n                for group in groupstojoin:\n                    g.write(group + \"\\n\")\n            print(\"sleeping for 360 seconds\")\n            time.sleep(360)\n        except Exception as error:\n            if \"A wait of \" in str(error):\n                s = str(error).split()[3]\n                print(\"Sleeping for \" + str(s) + \" seconds to start again cause by telegram server\")\n                time.sleep(int(s))\n            else:\n                print(error)\n                continue\n    Client.run_until_disconnected()\n","sub_path":"joinerwithoutcommand.py","file_name":"joinerwithoutcommand.py","file_ext":"py","file_size_in_byte":1766,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"333685768","text":"from flask import session,redirect,url_for\nfrom functools import wraps\nfrom flask_login import current_user\n#func是个方法\n\n# @login_required\n#def index()\n#如果登录了  会执行index方法\n# 把index 当作参数传给 login_required\n#\ndef login_requireds(func):\n    @wraps(func)\n    def inner(*args,**kwargs):\n         user = session.get('user_id')\n         if user:\n            return func(*args,**kwargs)\n         else:\n             return redirect(url_for('users.login'))\n    return inner","sub_path":"apps/views/decorators.py","file_name":"decorators.py","file_ext":"py","file_size_in_byte":500,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"342590754","text":"#!/usr/bin/python\ntry:\n    import Tkinter as tk\nexcept ImportError:\n    import tkinter as tk\n\nimport numpy as np\n\nlabstyle={'bg':'black', 'fg':'#00fa32'}\n\nfr = {'bg':None}\nfrpk = {'padx':5, 'pady':5}\nclass RangeSlider(tk.Frame):\n    '''\n    range slider \n    Ideas: https://stackoverflow.com/a/6789351/2077270\n    '''\n    def __init__(self, master, color='#00fa32', \n            range_slider_len=400, \n            minval=None, maxval=None, *args, **kwargs):\n       \n        tk.Frame.__init__(self, master,  *args, **kwargs)\n        \n        self.color = color\n      \n        self.master = master\n\n#       set max and min vals...\n        self._create_min_max_entry()\n        self.minval = minval\n        self.maxval = maxval\n        self.dmin = minval\n        self.dmax = maxval\n        self._set_min_max_vals()\n\n#       slide bounds\n\n        self.range_slider_len = range_slider_len\n        self.range_slider_height= 30\n        self.range_slider_token_height=20\n        self.range_slider_token_halfheight=10\n        self.token_text_vertical_position=7.5\n        self.range_slider_token_vertical_offset = 12.5\n        self.range_slider_token_width = 5\n        self.range_slider_token_halfwidth = 2.5\n        self.range_slider_token_LHS_offset = 15\n        self.range_slider_token_RHS_offset = 15\n        self.text_offset_LHS = 2.5\n        self.text_offset_RHS = -2.5\n        self.range_slider_value_range = self.range_slider_len \\\n            -2*self.range_slider_token_width - self.range_slider_token_LHS_offset\\\n            -self.range_slider_token_RHS_offset\n        \n        self.range_slider_maxRHS = self.range_slider_len - self.range_slider_token_halfwidth - self.range_slider_token_RHS_offset\n\n        self.range_slider_minLHS = self.range_slider_token_halfwidth + self.range_slider_token_LHS_offset\n        \n#       create a canvas for drawing the range-slider...\n        self.canvas_frame = tk.Frame( self.master, bg=\"black\", ) \n        self.canvas = tk.Canvas(self.canvas_frame, width=self.range_slider_len, \n            height=self.range_slider_height, bg='black', highlightbackground=\"black\")\n       \n#       reate horizontal line sepcifying the slider token track\n        self.canvas.create_line( ( self.range_slider_token_LHS_offset, \n            self.range_slider_token_vertical_offset+self.range_slider_token_halfheight, \n            self.range_slider_len-self.range_slider_token_RHS_offset, \n            self.range_slider_token_vertical_offset+self.range_slider_token_halfheight), fill=self.color) \n        \n#       this data is used to keep track the slider components as they are dragged\n        self._drag_data = {\"x\": 0, \"y\": 0, \"item\": None}\n\n#       create a couple of movable sliders\n#       SLIDER TOKENS \n        i1 = self._create_token((self.range_slider_minLHS, \n            self.range_slider_token_vertical_offset+self.range_slider_token_halfheight), \n            self.color)\n        i2 = self._create_token((self.range_slider_maxRHS,\n            self.range_slider_token_vertical_offset+self.range_slider_token_halfheight), \n            self.color)\n        self.items = [i1, i2] # store the IDs\n\n        self._set_text_label()\n\n        # add bindings for clicking, dragging and releasing over\n        # any object with the \"token\" tag\n        self.canvas.tag_bind(\"token\", \"<ButtonPress-1>\", self.on_token_press)\n        self.canvas.tag_bind(\"token\", \"<ButtonRelease-1>\", self.on_token_release)\n        self.canvas.tag_bind(\"token\", \"<B1-Motion>\", self.on_token_motion)\n        \n        self._pack_widgets()\n\n    def _pack_widgets(self):\n        self.entry_minval.pack(side=tk.LEFT,)# expand=tk.YES)\n        self.canvas_frame.pack(side=tk.LEFT,expand=tk.NO, padx=10)\n        self.canvas.pack(expand=tk.NO, fill=tk.BOTH,side=tk.LEFT,)\n        self.entry_maxval.pack(side=tk.LEFT, expand=tk.NO)\n\n    def _create_min_max_entry(self):\n        self.entry_minval = tk.Entry(self.master, bd=2, \n            relief=tk.GROOVE, \n            width=5)\n        self.entry_maxval = tk.Entry(self.master, bd=2, relief=tk.GROOVE, width=5)\n        self.entry_minval.bind('<Return>', self._get_min_max_vals )\n        self.entry_maxval.bind('<Return>', self._get_min_max_vals )\n\n        self.entry_maxval.config( bg='black', fg=self.color, insertbackground=self.color)\n        self.entry_minval.config( bg='black', fg=self.color, insertbackground=self.color)\n\n\n    def _get_min_max_vals(self ,event):\n        dmin = float( self.entry_minval.get() )\n        dmax = float( self.entry_maxval.get() )\n        \n        assert( dmin < dmax)\n        \n        # move the left item\n        if self.dmin != dmin:\n            self.minval = dmin\n            self.dmin = dmin\n            \n            left_item = self.items[0]\n            (left_x,left_y) = (self.range_slider_minLHS, \n                self.range_slider_token_vertical_offset+self.range_slider_token_halfheight)\n            x1,y1,x2,y2 = (left_x-self.range_slider_token_halfwidth, \n                        left_y-self.range_slider_token_halfheight, \n                        left_x+self.range_slider_token_halfwidth, \n                        left_y+self.range_slider_token_halfheight)\n            self.canvas.coords( left_item, (x1,y1,x2,y2))\n            #   move the label\n            self.canvas.itemconfig( self.minvaltext, text=\"%.2f\"%self.minval)\n            self.canvas.coords( self.minvaltext, \n                        (self.range_slider_minLHS-self.text_offset_LHS, \n                        self.token_text_vertical_position))\n           \n        # move the right item\n        if self.dmax != dmax:\n            self.dmax = dmax\n            self.maxval = dmax\n            right_item = self.items[1]\n            (right_x, right_y) = (self.range_slider_maxRHS,\n                self.range_slider_token_vertical_offset+self.range_slider_token_halfheight)\n            x1,y1,x2,y2 = (right_x-self.range_slider_token_halfwidth, \n                        right_y-self.range_slider_token_halfheight, \n                        right_x+self.range_slider_token_halfwidth, \n                        right_y+self.range_slider_token_halfheight)\n            self.canvas.coords( right_item, (x1,y1,x2,y2))\n        \n       \n            # move the label\n            self.canvas.itemconfig( self.maxvaltext, text=\"%.2f\"%self.maxval)\n            \n            self.canvas.coords( self.maxvaltext, (self.range_slider_maxRHS+self.text_offset_RHS, \n                    self.token_text_vertical_position))\n\n    def _set_min_max_vals(self): \n        self.entry_minval.delete(0,tk.END)\n        self.entry_minval.insert(0,\"%.1f\"%self.minval)\n        self.entry_maxval.delete(0,tk.END)\n        self.entry_maxval.insert(0,\"%.1f\"%self.maxval)\n        \n    def _set_text_label(self):\n#       TEXT LABEL\n        self.minvaltext = self.canvas.create_text( \n            self.range_slider_minLHS-self.text_offset_LHS, \n            self.token_text_vertical_position, text=\"%.2f\"%self.dmin, font=\"Helvetica 11 bold\", fill=\"white\")\n        self.maxvaltext = self.canvas.create_text( \n            self.range_slider_maxRHS+self.text_offset_RHS, \n            self.token_text_vertical_position, text=\"%.2f\"%self.dmax,font=\"Helvetica 11 bold\", fill=\"white\")\n        \n    def _create_token(self, coord, color):\n        '''Create a token at the given coordinate in the given color'''\n        (x,y) = coord\n        return self.canvas.create_rectangle(x-self.range_slider_token_halfwidth, \n            y-self.range_slider_token_halfheight, \n            x+self.range_slider_token_halfwidth, \n            y+self.range_slider_token_halfheight, \n            outline=color, fill=color, tags=\"token\")\n\n    def on_token_press(self, event):\n        '''Begining drag of an object'''\n        # record the item and its location\n        self._drag_data[\"item\"] = self.canvas.find_closest(event.x, event.y)[0]\n        self._drag_data[\"x\"] = event.x\n\n    def on_token_release(self, event):\n        '''End drag of an object'''\n        # reset the drag information\n        self._drag_data[\"item\"] = None\n        self._drag_data[\"x\"] = 0\n\n    def on_token_motion(self, event):\n        '''Handle dragging of an object'''\n        clicked_item = self._drag_data[\"item\"]\n        coors = self.canvas.coords(clicked_item)\n        xleft, ybottom, xright, ytop = coors\n        clicked_x = .5* (xleft + xright)\n        clicked_y = .5* (ybottom + ytop)\n        \n        other_item = [i for i in self.items if i != clicked_item][0]\n        other_coors = self.canvas.coords(other_item)\n        other_x = .5*(other_coors[0] + other_coors[2])\n        other_y = .5*(other_coors[1]+ other_coors[3])\n        if clicked_x > other_x:\n            #   RIGHT HAND SIDE ITEM\n            delta_x = event.x - self._drag_data[\"x\"]\n            \n            x_new = min( clicked_x + delta_x, self.range_slider_maxRHS) \n            x_new = max( x_new, other_x+self.range_slider_token_width) \n\n            self.canvas.coords( clicked_item, (x_new-self.range_slider_token_halfwidth, \n                clicked_y-self.range_slider_token_halfheight, \n                    x_new+self.range_slider_token_halfwidth, \n                    clicked_y+self.range_slider_token_halfheight))\n            self.canvas.coords( self.maxvaltext, (x_new+self.text_offset_LHS,\n                self.token_text_vertical_position))\n            self.canvas.itemconfig( self.maxvaltext, text=\"%.2f\"%self.maxval)\n            \n            new_drag = min( self.range_slider_maxRHS,  event.x)\n            new_drag = max(  other_x , new_drag)\n            self._drag_data[\"x\"] = new_drag \n            \n            self.maxval = ( (x_new) / self.range_slider_value_range ) \\\n                * ( self.dmax - self.dmin ) + self.dmin\n            \n        else:\n            #   LEFT HAND SIDE ITEM\n            delta_x = event.x - self._drag_data[\"x\"]\n            \n            x_new = max( clicked_x + delta_x, self.range_slider_minLHS) \n            x_new = min( x_new, other_x-self.range_slider_token_width) \n            \n            self.canvas.coords( clicked_item, \n                (x_new-self.range_slider_token_halfwidth, \n                    clicked_y-self.range_slider_token_halfheight, \n                    x_new+self.range_slider_token_halfwidth, \n                    clicked_y+self.range_slider_token_halfheight))\n            self.canvas.coords( self.minvaltext, (x_new-self.text_offset_RHS, \n                self.token_text_vertical_position))\n            self.canvas.itemconfig( self.minvaltext, text=\"%.2f\"%self.minval)\n            new_drag = max( self.range_slider_minLHS,  event.x)\n            new_drag = min(  other_x , new_drag)\n            self._drag_data[\"x\"] = new_drag \n            \n            self.minval = ((x_new) / self.range_slider_value_range ) \\\n                * ( self.dmax - self.dmin ) + self.dmin\n\ndef main():\n    root = tk.Tk()\n    RS = RangeSlider(root,color='blue', minval=0, maxval=1000, range_slider_len=800,) \n    RS.pack(side=tk.TOP, expand=tk.YES, fill=tk.BOTH) \n    root.mainloop()\n    \nif __name__ == \"__main__\":\n    main()\n","sub_path":"range_slider.py","file_name":"range_slider.py","file_ext":"py","file_size_in_byte":10997,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"100742321","text":"# https://leetcode.com/problems/shortest-palindrome/\n\nclass Solution:\n    def shortestPalindrome(self, s):\n        '''\n        Given a string s, you are allowed to convert it to a palindrome by adding characters in front of it. Find and return the shortest palindrome you can find by performing this transformation.\n\n        Example 1:\n\n        Input: \"aacecaaa\"\n        Output: \"aaacecaaa\"\n        \n        Example 2:\n\n        Input: \"abcd\"\n        Output: \"dcbabcd\"\n\n        Algorithm: While it is NOT a palindrome, add letters from the end to the front.\n        '''\n        i = -1 # backwards indexer\n        s_orig = s # making a copy of original since I will be modifying s\n        while s != s[::-1]:\n            section = s[i:][::-1]\n            s = section + s_orig\n            print(s)\n            i -= 1\n        return s\n\n\n    \n# Test Cases:\ns = Solution()\nmystr = 'abcd'\nprint(s.shortestPalindrome(mystr))","sub_path":"Solved/shortestPalindrome.py","file_name":"shortestPalindrome.py","file_ext":"py","file_size_in_byte":916,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"209711056","text":"class Solution(object):\n    def trap(self, height):\n        stack = []\n        start = 0\n        i = 0\n        sumV = 0\n        while i < len(height):\n            print(\"index is %d\" %(i))\n            print(\"stack is %s\" % (stack)),\n            print(\"sumV is %d\" % sumV),\n            print(\"\\n\")\n            cur = height[i]\n            if len(stack) == 0 or cur < height[stack[-1]]:\n                stack.append(i)\n                i += 1\n            else: \n                print(\"start popping\"),\n                while cur < height[stack[-1]] and len(Stack) > 0:\n                    top = stack.pop()\n                    area = heihgt[cur] - heihgt[top] * (i - top)\n                    sumV += area\n        return sumV\narr = [0,1,0,2,1,0,1,3,2,1,2,1]\nprint(arr)\nprint(Solution().trap(arr))","sub_path":"traprainwater.py","file_name":"traprainwater.py","file_ext":"py","file_size_in_byte":790,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"63260","text":"from behave import fixture\nfrom selenium import webdriver\nfrom selenium.common.exceptions import WebDriverException\nfrom selenium.webdriver.chrome.options import Options\nfrom selenium.webdriver.firefox.firefox_binary import FirefoxBinary\nfrom selenium.webdriver.common.desired_capabilities import DesiredCapabilities\nfrom selenium.webdriver.support.wait import WebDriverWait\n\n\n@fixture\ndef browser_chrome(context):\n    try:\n        options = Options()\n        options.add_argument(\"--window-size=1200,1080\")\n        context.driver = webdriver.Chrome(options=options)\n        context.driver.set_window_position(0, 0)\n        context.delay = 10\n        context.driver.implicitly_wait(10)\n        context.wait = WebDriverWait(context.driver, context.delay)\n        yield context.driver\n    except WebDriverException(\"Cannot start Chrome driver\"):\n        raise\n    finally:\n        context.driver.quit()\n\n\n# Firefox: When use move_to() if the element is not in the viewport, you would have to manually scroll the page,\n# executing javascript ( context.execute_script( window.scrollTo(...) ) ),\n# otherwise you get MoveTargetOutOfBoundsException\n# Chrome: It just works\n@fixture\ndef browser_firefox(context):\n    try:\n        binary = FirefoxBinary()\n        caps = DesiredCapabilities().FIREFOX\n        caps[\"marionette\"] = True\n        context.driver = webdriver.Firefox(capabilities=caps, firefox_binary=binary)\n        context.driver.set_window_position(0, 0)\n        context.delay = 10\n        context.driver.implicitly_wait(10)\n        context.wait = WebDriverWait(context.driver, context.delay)\n        yield context.driver\n    except WebDriverException(f\"Cannot start Firefox driver\"):\n        raise\n    finally:\n        context.driver.quit()\n\n\n# Safari driver version is not supported with 2.46.\n# Try downgrading Selenium Version:\n# pip install -Iv selenium==2.45 or pip install selenium==2.45\n@fixture\ndef browser_safari(context):\n    try:\n        context.driver = webdriver.Safari()\n        context.driver.set_window_position(0, 0)\n        context.delay = 10\n        context.driver.implicitly_wait(10)\n        context.wait = WebDriverWait(context.driver, context.delay)\n        yield context.driver\n    except WebDriverException(f\"Cannot start Safari driver\"):\n        raise\n    finally:\n        context.driver.quit()\n","sub_path":"behave_ebay/fixtures.py","file_name":"fixtures.py","file_ext":"py","file_size_in_byte":2326,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"337862502","text":"from PyQt5 import QtCore, QtGui, QtWidgets\nfrom PyQt5.QtWidgets import *\nfrom PyQt5.QtGui import *\nfrom PyQt5.QtCore import *\nfrom PyQt5.QtGui import *\nimport sys\nimport video_demo as vd\nimport popupWindow as detectionWindow\nimport cv2\n\n\nclass MyWindow(QMainWindow):\n    def __init__(self):\n        super(MyWindow,self).__init__()\n        self.dialogs = list()\n        self.initUI()\n    \n    # Start yolov3 detection\n    def start(self):\n        if self.runButton.text() == \"Run\":\n            self.runButton.setText(\"Stop\")\n            self.runButton.setStyleSheet(\"\"\"color: #000000;\n                                        background-color: #d4d4d4;\n                                        border-width: 1px;\n                                        border-color: #1e1e1e;\n                                        border-style: solid;\n                                        border-radius: 6;\n                                        padding: 3px;\n                                        font-size: 12px;\n                                        padding-left: 5px;\n                                        padding-right: 5px;\n                                        min-width: 40px;\"\"\")\n            message = vd.startDetection(self, self.spinBox.value(), self.filePath.text())\n        else:\n            # System exit on stop pressing because I dont know how to reinitialize all the variables\n            sys.exit()\n           \n\n\n    def initUI(self):\n        # Main window\n        self.resize(1471, 877)\n        \n        # Main window title\n        \n        font = QtGui.QFont()\n        font.setFamily(\"Calibri\")\n        font.setPointSize(15)\n        font.setBold(True)\n        font.setWeight(75)\n        font.setKerning(False)\n        self.title = QtWidgets.QLabel(self)\n        self.title.setFont(font)\n        self.title.setGeometry(QtCore.QRect(450, 10, 600, 100))\n        self.title.setTabletTracking(False)\n        self.title.setWordWrap(True)\n        self.title.setObjectName(\"label\")\n        self.title.setAlignment(QtCore.Qt.AlignCenter)\n        # Start detection button\n        self.runButton = QtWidgets.QPushButton(self)\n        self.runButton.setGeometry(QtCore.QRect(1310, 820, 121, 41))\n        self.runButton.setIconSize(QtCore.QSize(20, 20))\n        self.runButton.setFlat(False)\n        self.runButton.setObjectName(\"runButton\")\n        self.runButton.setStyleSheet(\"\"\"color: #ffffff;\n                                        background-color: #006e0f;\n                                        border-width: 1px;\n                                        border-color: #1e1e1e;\n                                        border-style: solid;\n                                        border-radius: 6;\n                                        padding: 3px;\n                                        font-size: 12px;\n                                        padding-left: 5px;\n                                        padding-right: 5px;\n                                        min-width: 40px;\"\"\")\n        self.runButton.clicked.connect(self.start)\n        \n        # Separator line\n        self.line = QtWidgets.QFrame(self)\n        self.line.setGeometry(QtCore.QRect(1240, 20, 21, 841))\n        self.line.setFrameShape(QtWidgets.QFrame.VLine)\n        self.line.setFrameShadow(QtWidgets.QFrame.Sunken)\n        self.line.setObjectName(\"line\")\n        \n        # Spinner box title\n        self.label_2 = QtWidgets.QLabel(self)\n        self.label_2.setGeometry(QtCore.QRect(1260, 240, 90, 16))\n        self.label_2.setObjectName(\"label_2\")\n        # Confidence spinbox\n        self.spinBox = QtWidgets.QSpinBox(self)\n        self.spinBox.setGeometry(QtCore.QRect(1260, 260, 42, 22))\n        self.spinBox.setMinimum(1)\n        self.spinBox.setObjectName(\"spinBox\")\n        # Sets default value \n        self.spinBox.setValue(80)\n        # self.spinBox.valueChanged.connect(self.valuechange)\n        \n        # Big main label to display an image\n        self.imageDisplay = QtWidgets.QLabel(self)\n        self.imageDisplay.setGeometry(QtCore.QRect(10, 60, 1221, 801))\n        self.imageDisplay.setObjectName(\"label_4\")\n\n        #Video file input\n        self.label_5 = QtWidgets.QLabel(self)\n        self.label_5.setGeometry(QtCore.QRect(1260, 130, 81, 16))\n        self.label_5.setObjectName(\"label_5\")\n        self.filePath = QtWidgets.QLineEdit(self)\n        self.filePath.setGeometry(QtCore.QRect(1260, 150, 131, 20))\n        self.filePath.setObjectName(\"lineEdit\")\n        self.examineButton = QtWidgets.QPushButton(self)\n        self.examineButton.setGeometry(QtCore.QRect(1400, 150, 61, 20))\n        self.examineButton.setObjectName(\"pushButton\")\n        self.examineButton.setText(\"Examine\")\n        self.examineButton.clicked.connect(self.getfiles)\n\n        # Values initialization\n        self.setWindowTitle(\"Neural Network surveillance\")\n        self.title.setText(\"PFC - 2020 - V1.1.1\")\n        self.runButton.setText(\"Run\")\n        self.label_2.setText(\"Confidence  (1-99)\")\n        self.label_5.setText(\"Video file\")\n        self.imageDisplay.setText(\"\")\n\n    # Get files from a path\n    def getfiles(self):\n      dlg = QFileDialog(self)\n      # Set available file types \n      dlg.setNameFilters([\"Videos (*.mp4 *.mkv)\"])\n\n\t\t\n      if dlg.exec_():\n         filenames = dlg.selectedFiles()\n         f = open(filenames[0], 'r')\n\t\t\t\n         with f:\n            # Update input text field and label \n            self.filePath.setText(f.name)\n\n    def button_clicked(self):\n        dialog = detectionWindow.DetectionWindow(self)\n        self.dialogs.append(dialog)\n        dialog.show()\n\ndef window():\n    app = QApplication(sys.argv)\n    app.setStyle(\"Fusion\")\n    win = MyWindow()\n    win.show()\n    sys.exit(app.exec_())\n\nwindow()","sub_path":"MainWIndowAlpha.py","file_name":"MainWIndowAlpha.py","file_ext":"py","file_size_in_byte":5740,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"409090573","text":"\r\n\r\n\r\n# Given a singly linked list L: L0→L1→…→Ln-1→Ln,\r\n# reorder it to: L0→Ln→L1→Ln-1→L2→Ln-2→…\r\n\r\n# You may not modify the values in the list's nodes, only nodes itself may be changed.\r\n\r\n# 找第一个中点，翻转后半段，再穿插连起来\r\n\r\n# Definition for singly-linked list.\r\nclass ListNode(object):\r\n    def __init__(self, x):\r\n        self.val = x\r\n        self.next = None\r\n    def __str__(self):\r\n        if self:\r\n            return \"{}->{}\".format(self.val,str(self.next))\r\n            \r\n#class Solution(object):\r\n#    def reorderList(self, head):\r\n#        \"\"\"\r\n#        :type head: ListNode\r\n#        :rtype: void Do not return anything, modify head in-place instead.\r\n#        \"\"\"\r\n#        if head is None or head.next is None:\r\n#            return head\r\n#            \r\n#        # break linked list into two equal length\r\n#        slow,fast=head,head\r\n#        while fast and fast.next:\r\n#            slow,fast=slow.next,fast.next.next\r\n#        head1=head\r\n#        head2=slow.next\r\n#        if head2 is None:\r\n#            return head1\r\n#        slow.next=None\r\n#        \r\n#        # reverse linked list head2 \r\n#        dummy=ListNode(-1)\r\n#        dummy.next=head2\r\n#        curr=head2.next\r\n#        head2.next=None\r\n#        while curr:\r\n#            tmp=curr\r\n#            curr=curr.next\r\n#            tmp.next=dummy.next\r\n#            dummy.next=tmp\r\n#        head2=dummy.next\r\n#        # merge two linked list head1 and head2\r\n#        p1,p2=head1,head2\r\n#        while p2: # head1 maybe longer than head2 by one node\r\n#            tmp1,tmp2=p1.next,p2.next\r\n#            p1.next,p2.next=p2,tmp1\r\n#            p1,p2=tmp1,tmp2\r\n#        return head1\r\n\r\n# http://bookshadow.com/weblog/2015/01/29/leetcode-reorder-list/\r\n#class Solution(object):\r\n#    def reorderList(self, head):\r\n#        \"\"\"\r\n#        :type head: ListNode\r\n#        :rtype: void Do not return anything, modify head in-place instead.\r\n#        \"\"\"\r\n#        if head is None or head.next is None:\r\n#            return head\r\n#\r\n#        # break linked list into two equal length\r\n#        slow,fast=head,head\r\n#        while fast and fast.next:\r\n#            slow,fast=slow.next,fast.next.next\r\n#        head1=head\r\n#        head2=slow.next\r\n#        if head2 is None:\r\n#            return head1\r\n#        slow.next=None\r\n#\r\n#        curr=head2.next\r\n#        head2.next=None\r\n#        while curr:\r\n#            next=curr.next\r\n#            curr.next=head2\r\n#            head2=curr\r\n#            curr=next\r\n#            \r\n#        #merge left and right\r\n#        dummy=ListNode(-1)\r\n#        while head1 or head2:\r\n#            if head1:\r\n#                dummy.next=head1\r\n#                head1=head1.next\r\n#                dummy=dummy.next\r\n#            if head2:\r\n#                dummy.next=head2\r\n#                head2=head2.next\r\n#                dummy=dummy.next\r\n#        return head\r\n\r\n# http://blog.csdn.net/linhuanmars/article/details/21503215\r\nclass Solution:\r\n    def reorderList(self, head):\r\n        \"\"\"\r\n        :type head: ListNode\r\n        :rtype: void Do not return anything, modify head in-place instead.\r\n        \"\"\"\r\n        if not head or not head.next: # at least have two nodes\r\n            return\r\n        \r\n        slow, fast = head, head\r\n        while fast.next and fast.next.next: # return first middle\r\n            slow = slow.next\r\n            fast = fast.next.next\r\n            \r\n        curr = slow.next # the head of latter part\r\n        slow.next = None # Notice!! must have 必须有！\r\n        prev = None \r\n        while curr:  # reverse it\r\n            nxt = curr.next\r\n            curr.next = prev\r\n            prev = curr\r\n            curr = nxt\r\n        \r\n        right = prev  # head of right part\r\n        left = head   # head of left part\r\n        while left and right:\r\n            right_nxt = right.next\r\n            right.next = left.next\r\n            left.next = right\r\n            left = right.next\r\n            right = right_nxt\r\n\r\n        \r\nif __name__ == \"__main__\":\r\n    head = ListNode(1)\r\n    head.next = ListNode(2)\r\n    head.next.next = ListNode(3)\r\n    head.next.next.next = ListNode(4)\r\n    head.next.next.next.next = ListNode(5)\r\n    print(Solution().reorderList(head))\r\n    \r\n","sub_path":"143. Reorder List.py","file_name":"143. Reorder List.py","file_ext":"py","file_size_in_byte":4264,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"116781222","text":"from map.tile import Tile\nfrom variables.constants import *\nimport random\n\nmap = [[Tile(True)\n            for y in range(MAP_HEIGHT)]\n                for x in range(MAP_WIDTH)]\n\nx = 30\ny = 30\nmap[x][y].blocked = False\n\nfor i in range(0,3000):\n    move = random.randint(0,3)\n\n    if move == 0:\n        if x + 1 < MAP_WIDTH - 1:\n            x += 1\n\n    elif move == 1:\n        if x - 1 > 0:\n            x -= 1\n\n    elif move == 2:\n        if y+1 < MAP_HEIGHT - 1:\n            y += 1\n\n    elif move == 3:\n        if y - 1 > 0:\n            y -= 1\n        \n    map[x][y].blocked = False\n","sub_path":"map/cave.py","file_name":"cave.py","file_ext":"py","file_size_in_byte":582,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"44056137","text":"import sublime\nimport sublime_plugin\nfrom ..utils import *\n\n\nclass JavatarBuildCommand(sublime_plugin.WindowCommand):\n\tdef buildFile(self, file):\n\t\tgetAction().addAction(\"javatar.command.build.build_file\", \"Build file\")\n\t\tif isJava(file):\n\t\t\twindow = sublime.active_window()\n\t\t\tbuildScript = getSettings(\"build_system\")\n\t\t\tbuildScript = buildScript.replace(\"$file_path\", getPath(\"parent\", file))\n\t\t\tbuildScript = buildScript.replace(\"$file_name\", getPath(\"name\", file))\n\t\t\tbuildScript = buildScript.replace(\"$file\", file)\n\t\t\tbuildScript = buildScript.replace(\"$packages\", sublime.packages_path())\n\t\t\tif window.project_file_name() is not None:\n\t\t\t\tbuildScript = buildScript.replace(\"$project_path\", getPath(\"parent\", window.project_file_name()))\n\t\t\t\tbuildScript = buildScript.replace(\"$project_name\", getPath(\"name\", window.project_file_name()))\n\t\t\t\tbuildScript = buildScript.replace(\"$project\", window.project_file_name())\n\t\t\tsublime.active_window().run_command(\"exec\", {getSettings(\"build_command\"): buildScript})\n\t\treturn isJava(file)\n\n\tdef buildAll(self, dir):\n\t\tgetAction().addAction(\"javatar.command.build.build_all\", \"Build all\")\n\t\tbuild = False\n\t\tfor path, subdirs, files in os.walk(dir):\n\t\t\tfor filename in files:\n\t\t\t\tif isJava(getPath(\"join\", path, filename)):\n\t\t\t\t\tbuild = True\n\t\t\t\t\tself.buildFile(getPath(\"join\", path, filename))\n\t\treturn build\n\n\tdef run(self, type=\"\"):\n\t\tgetAction().addAction(\"javatar.command.build.run\", \"Build [type=\" + type + \"]\")\n\t\tview = sublime.active_window().active_view()\n\t\tif type == \"project\":\n\t\t\tif isProject() or isFile():\n\t\t\t\tif not self.buildAll(getPackageRootDir()):\n\t\t\t\t\tsublime.error_message(\"No class to build\")\n\t\t\telse:\n\t\t\t\tsublime.error_message(\"Unknown package location\")\n\t\telif type == \"package\":\n\t\t\tif isProject() or isFile():\n\t\t\t\tif not self.buildAll(getPath(\"current_dir\")):\n\t\t\t\t\tsublime.error_message(\"No class to build\")\n\t\t\telse:\n\t\t\t\tsublime.error_message(\"Unknown package location\")\n\t\telif type == \"class\":\n\t\t\tif isFile():\n\t\t\t\tif not self.buildFile(view.file_name()):\n\t\t\t\t\tsublime.error_message(\"Current file is not Java\")\n\t\t\telse:\n\t\t\t\tsublime.error_message(\"Unknown class location\")\n\t\telif type == \"test\":\n\t\t\twindow = sublime.active_window()\n\t\t\tpanel = window.create_output_panel(\"Javatar\")\n\t\t\twindow.run_command(\"show_panel\", {\"panel\": \"output.Javatar\"})\n\t\t\tpanel.run_command(\"insert_snippet\", {\"contents\": \"Hello, World?\"})\n\t\t\tpanel.set_read_only(True)\n","sub_path":"commands/javatar_build.py","file_name":"javatar_build.py","file_ext":"py","file_size_in_byte":2415,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"120960482","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport numpy\nimport sys\n\n## First of all the functions needed are imported.\n\n# 'read_fasta' takes a string which is the name of the FASTA file which contains the reads obtained from the sequencing.\n# The function returns a dictionary: the keys are the head and the values the sequence of each read. \ndef read_fasta (fasta_file):\n    multifasta={}\n    for lines in open(fasta_file, \"r\"):\n        lines = lines.rstrip()\n        if lines.startswith('>'):\n            head = lines[1:]\n            multifasta[head] = ''\n        else:\n            multifasta[head] += lines\n    return multifasta\n\ndef read_fasta_gen (fasta_file):\n    fasta = open(fasta_file, \"r\")\n    line = fasta.readline()\n    head = \"\"\n    sequence = \"\"\n    while line:\n        line = line.rstrip(\"\\n\")\n        if \">\" in line:\n            head = line\n        else:\n            sequence = sequence+line\n        line = fasta.readline()\n    return head, sequence\n\n\n# 'read_bed' takes a string which is the name of the BED file which contains the chromosome, init and end position of each read in the chromosome, \n    #the ID of the read, the quality and the strand (+: forward, -:reverse) of the read.\n# The function returns a list by row of the BED file.\ndef read_bed (bed_file):\n    content = []\n    with open(bed_file)as f:\n        for line in f:\n            content.append(line.strip().split())\n    return content\n\n\n# 'pos_reads' takes 5 arguments:\n    # 1) A string (the sequence) of the gene studied.\n    # 2) The lists with the rows of the bed file.\n    # 3) The dictionary of the fasta file with the information of the reads\n    # 4) The gene's first position in the choromosome (as a number).\n    # 5) The gene's end position in the choromosome (as a number).\n# The function returns a dictionary: the keys are the position of the gene and the values the bases and the number of each base in\n    #that position of the gene. \ndef pos_reads(gene, bed_file, fasta_file, pos_init, pos_end):\n    dicc = {}\n    value=[]\n    for j in range(len(gene)):\n        diferents = [] \n        for i in bed_file:\n            if i[5] == '+':\n                init = int(i[1]) - pos_init\n                end = int(i[2]) - pos_init - 1    \n                long = list(range(init, end))       \n                ID = i[3]        \n                if j in long and end-init == len(fasta_file[ID])-1:\n                    pos_read = j - init -1 \n                    base_read = fasta_file[ID][pos_read]\n                    value = [ID, pos_read, base_read, i[5]]\n                    diferents.append(value) \n            \n            elif i[5] == '-':\n                init = pos_end - int(i[1]) -1\n                end = pos_end - int(i[2])     \n                long = list(range(end, init))  \n                ID = i[3]        \n                if j in long and init-end == len(fasta_file[ID])-1:\n                    pos_read = j - end-1\n                    base_read = fasta_file[ID][pos_read]\n                    value = [ID, pos_read, base_read, i[5]]\n                    diferents.append(value)  \n        dicc[j] = diferents\n    \n    cuant={}\n\n    for i in dicc:\n        alelos = []\n        count_A = 0\n        count_C = 0\n        count_G = 0\n        count_T = 0\n        count_r = 0\n        count_f = 0\n        for j in range(len(dicc[i])):\n            if len(dicc[i][j]) > 0:\n                base = dicc[i][j][2]\n                strand = dicc[i][j][3]\n                if base == \"A\":\n                    count_A += 1\n                elif base == \"T\":\n                    count_T += 1\n                elif base == \"C\":\n                    count_C += 1\n                elif base == \"G\":\n                    count_G += 1\n                if strand == \"+\":\n                    count_f += 1\n                elif strand == \"-\":\n                    count_r += 1\n            alelos = [\"A:\", count_A, \"T:\", count_T, \"C:\", count_C, \"G:\", count_G, \"-\", count_r, \"+\", count_f]\n            \n            total = alelos[1] + alelos[3] + alelos[5] + alelos[7]\n            \n            if count_r > n_lec or count_f > n_lec:\n                if (alelos[1]*100)/total > 79 or (alelos[3]*100)/total > 79 or (alelos[5]*100)/total > 79 or (alelos[7]*100)/total > 79:\n                        continue\n                else:                             \n                    cuant[i] = alelos\n            \n    return cuant\n\n# if the first argument is '-h', the help is shown\nif sys.argv[1] == \"-h\":       \n    print(\"Usage: SCRIPT_GAMETOGENESIS_ESTRUCTURAL.py [reads_file_patient.fa] [reads_file_patient.bed] [gene_sequence.fa] [gene start position in chromosome] [gene end position in chromosome] [minimun number of reads with the pathogenic variation] [reads_file_progenitor.fa] [reads_file_progenitor.bed] [name of the CSV output file]\")\n    exit ()\n\n# First argument: reads file name in fasta format\nfasta_file = read_fasta(sys.argv[1])\n# Second argument: reads file name in bed format\nbed_file = read_bed(sys.argv[2])\nlec_pat = len(bed_file)\n# Third argument: gene file name in fasta format\ngene_file = read_fasta_gen(sys.argv[3])\ngene=gene_file[1]\n# Fourth argument: number of the chromosome position where the gene starts\npos_init = int(sys.argv[4])\n# Fifth: number of the chromosome position where the gene ends\npos_end = int(sys.argv[5])\n# Sixth: cut-off of lectures\nn_lec = int(sys.argv[6])\n# Seventh argument: parental reads file name in fasta format\nfasta_progenitor = read_fasta(sys.argv[7])\n# Eigth argument: parental reads file name in bed format\nbed_progenitor = read_bed(sys.argv[8])\nlec_prog = len(bed_progenitor)\n# Ninth: name of the output file \noutput = sys.argv[9]\nextension = output+\".csv\" #the output file will be a csv file\n\n\n\n#Forward strand of the gene\ngen_f=''\nreverse = gene[::-1]\nfor i in reverse:\n    if i == 'A':\n        gen_f = gen_f+'T'\n    elif i == 'T':\n        gen_f = gen_f+'A'\n    elif i == 'C':\n        gen_f = gen_f+'G'\n    elif i == 'G':\n        gen_f = gen_f+'C'\n\n        \nnewbed = bed_file\nnewbed_progenitor = bed_progenitor\n\n\n#Extract variants from the patient and the progenitor\nprint(\"Extracting variants positions in the patient...\")\npatient = pos_reads(gene, newbed, fasta_file, pos_init, pos_end)\nprint(\"Variants in reads with mutation in patient:\", patient)\n\nprint(\"Extracting variants positions in the progenitor...\")\nprogenitor = pos_reads(gene, newbed_progenitor, fasta_progenitor, pos_init, pos_end)\nprint(\"Variants in reads with mutation in progenitor:\", progenitor)\n# \n\n\n#csv with variants\nwith open(output+\"_DIC\"+\".csv\", 'w') as a:\n    \n    #Column names\n    a.write(\"%s,%s\\n\"%(\"variant position in gene\", \"alleles\"))\n    for i in patient:\n        #column data\n        a.write(\"%s,%s\\n\"%(i, patient[i]))\n    a.write(\"%s,%s\\n\"%(\"progenitor variants\", \"\"))\n    for j in progenitor:\n        #column data\n        a.write(\"%s,%s\\n\"%(j, progenitor[j]))\n\n# shared variants\nvariants_r_dic = {}\nvariants_f_dic = {}\n\nfor i in patient:\n    if i in progenitor.keys():\n        if patient[i][9] > n_lec:\n            variants_r_dic[i] = patient[i][9]\n        elif patient[i][11] > n_lec:\n            variants_f_dic[i] = patient[i][11]\n            \nvariants_r = list(variants_r_dic.keys())\nvariants_f = list(variants_f_dic.keys())\nprint(\"Shared variants in reverse strand:\", variants_r, \"\\nShared variants in forward strand:\", variants_f)\n\n\n\n#csv with shared variants\nwith open(output+\"_variants_shared\"+\".csv\", 'w') as a:\n    \n    #Column names\n    a.write(\"%s,%s,%s\\n\"%(\"variant position in gene\", \"reverse\", \"forward\"))\n    for i in variants_r:\n        #column data\n        a.write(\"%s,%s,%s\\n\"%(i, \"rev\", \"-\"))\n    for j in variants_f:\n        #column data\n        a.write(\"%s,%s,%s\\n\"%(j, \"-\", \"forward\"))\n\n\n\n\n## Find variants in normal allele\nresult_nor = []\nresult_nor_f = []\ndic_nor = {}\ndic_nor_f = {}\nfor i in newbed:\n    if i[5] == \"+\":\n        ID = i[3]\n        posin = int(i[1])\n        posend = int(i[2])\n        seq = fasta_file[ID]\n        for j in variants_f_dic:\n            pos_variante_global = pos_init + j\n            pos_variante_read = pos_variante_global-posin-1\n            if pos_variante_read < len(seq) and pos_variante_global > posin-1  and pos_variante_global < posend+1:\n                variante = seq[pos_variante_read]\n                pos_var_gen = gen_f[j]\n                if pos_var_gen != variante:\n                    result_nor_f.append(j+pos_init)\n                    print (\"NORMAL ALLELE: Shared variant found (ref-variant in + strand):, {}-{} {} ({} chromosome position)\\n\".format(pos_var_gen,variante,j,pos_variante_global))\n                    dic_nor_f[pos_variante_global] = [pos_var_gen, variante, len(newbed), len(newbed_progenitor)]\n    \n    else:\n        ID = i[3]\n        posin = int(i[1])\n        posend = int(i[2])\n        seq = fasta_file[ID]\n        for j in variants_r_dic:\n            pos_variante_global = pos_end - (j-1)\n            pos_variante_read = posend-pos_variante_global\n            if pos_variante_read < len(seq) and pos_variante_global > posin-1 and pos_variante_global < posend+1: \n                variante = seq[pos_variante_read]\n                pos_var_gen = gene[j-1]\n                if pos_var_gen != variante:\n                    result_nor.append(pos_end-(j-1))\n                    print (\"NORMAL ALLELE: Shared variant found (ref-variant in - strand):, {}-{} {} ({} chromosome position)\\n\".format(pos_var_gen,variante,j-1,pos_variante_global))\n                    dic_nor[pos_variante_global] = [pos_var_gen, variante, len(newbed), len(newbed_progenitor)]\n\nfinal_n_f = numpy.unique(result_nor_f)      \nprint(\"There are {} shared variants (+) in normal allele between patient and progenitor, in positions {}\".format(len(final_n_f), final_n_f))\n\nfinal_n = numpy.unique(result_nor)      \nprint(\"There are {} shared variants (-) in normal allele between patient and progenitor, in positions {}\".format(len(final_n), final_n))\n\n \n#The CSV output file is written\nwith open(extension, 'w') as f:\n\n#Column names\n    f.write(\"%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s\\n\"%(\"Strand\",\"Position_mutation\", \"Position_shared_variant_mutated_allele\", \"Position_shared_variant_normal_allele\", \"Allele _ref\", \"Allele_variant\", \"Reads_with_variant_in_mutated_allele\", \"Reads_with_variant_in_normal_allele\", \"Reads_with_pathogenic_patient\", \"Reads_with_pathogenic_progenitor\", \"Total_patient_reads\", \"Total_progenitor_reads\")) \n#column data\n    for b in dic_nor_f:\n        contador = result_nor_f.count(b)\n        f.write(\"%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s\\n\"%(\"forward\",\"-\", \"-\", b, dic_nor_f[b][0], dic_nor_f[b][1], \"-\", contador, dic_nor_f[b][2], dic_nor_f[b][3], lec_pat, lec_prog)) \n\n    for j in dic_nor:\n        contador = result_nor.count(j)\n        f.write(\"%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s\\n\"%(\"reverse\",\"-\", \"-\", j, dic_nor[j][0], dic_nor[j][1], \"-\", contador, dic_nor[j][2], dic_nor[j][3], lec_pat, lec_prog)) \n","sub_path":"SCRIPT_GAMETOGENESIS_ESTRUCTURAL.py","file_name":"SCRIPT_GAMETOGENESIS_ESTRUCTURAL.py","file_ext":"py","file_size_in_byte":10888,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"294569891","text":"import paho.mqtt.client as mqtt\nfrom binascii import hexlify\nfrom past.builtins import xrange\nimport time\nimport socket\n\n\nclass MQTT(object):\n    def __init__(self, client_name=\"RoboController\", host_name=\"Robo_IoT\", broker_address = \"192.168.3.254\"):\n        self.broker = host_name\n        self.broker_address = broker_address\n        self.client = mqtt.Client(client_name)\n        self.client.on_message = self.on_message  # attach function to callback\n        self.MQTT_Connected = False\n        self.message = \"None\"\n        self.topic = None\n        self.roboName = None\n        self.build_cmd = '01'\n        self.event_cmd = 'c0'\n        self.QOS = 0 # 0-2\n        self.robo_dict = {}\n        self.client.loop_stop() # make sure we start fresh\n        self.connect()\n\n    def get_broker_address(self): # doesn't seem to always work\n        address = None\n        try:\n            address = socket.gethostbyname(self.broker)\n            print(address)\n        except socket.gaierror:\n            print(\"Could not find broker on this network: \", self.broker)\n            return\n        return address\n\n    def get_mqtt_cmd(self, cmd):\n        mqtt_command = \"\"\n        for item in cmd: \n            if isinstance(item, str):\n                mqtt_command += item\n                continue\n            byte = str(hex(item))[2:]\n            if len(byte) == 1:              # if the number is just a single digit\n                byte = '0' + byte\n            mqtt_command += byte\n        return mqtt_command\n\n    def add_robo(self, robo_name, robo_inst):\n        self.robo_dict[robo_name] = robo_inst\n        print(\"Added Robo - \" + robo_name)\n\n    def remove_robo(self, robo_name, robo_inst):\n        del self.robo_dict[robo_inst]\n        print(\"Removed Robo - \" + robo_name)\n\n    def get_robo_name(self):\n        name = None \n        indeces = [i for i, ltr in enumerate(self.topic) if ltr == '/']\n        if len(indeces) == 2:\n            name = self.topic[indeces[0]+1:indeces[1]]\n            return name\n        return None\n\n    def on_message(self, client, userdata, message):\n        self.message = str(message.payload)\n        self.topic = str(message.topic)\n        #print(\"message received \" + self.message)\n        #print(\"message topic = \" + self.topic)\n        #print(\"message qos = \" + str(message.qos))\n        #print(\"message retain flag = \" + str(message.retain))\n        if self.topic is not None:\n            self.roboName = self.get_robo_name()\n            if self.roboName in self.robo_dict:\n                msg = [self.message[i:i + 2] for i in xrange(0, len(self.message), 2)]\n                cmd = msg[0]\n                robo = self.robo_dict[self.roboName]\n                if cmd == self.build_cmd:\n                    build_data = msg\n                    print(\"Updating Build of: \" + self.roboName)\n                    robo.update_build(build_data)\n                    return\n                if cmd == self.event_cmd:\n                    event_id = str(int(msg[2], 16))\n                    result = int(msg[3], 16)\n                    if event_id in robo.triggers:\n                        robo.triggers[event_id].triggered(int(event_id), result)\n                        return\n                    elif event_id in robo.actions:\n                        robo.actions[event_id].action_complete(int(event_id), result)\n                        return\n                    return\n\n    def connect(self):\n        if self.broker_address is None:\n            print(\"Broker Not Found, Cannot Connect\")\n            return\n        self.MQTT_Connected = True\n        print(\"Connecting to MQTT\")\n        try:\n            self.client.connect(self.broker_address, 1883, 30)  # connect to broker\n        except BaseException:\n            self.MQTT_Connected = False\n            print(\"Failed to connect\")\n            return False\n        self.client.loop_stop()  # stop the loop\n        self.client.loop_start()  # start the loop\n        #self.client.loop_forever()\n        print(\"MQTT Connection Status - \" + str(self.MQTT_Connected))\n        return True\n\n    def disconnect(self):\n        if self.MQTT_Connected:\n            self.client.loop_stop()\n            self.client.disconnect()\n\n    def publish(self, topic, data, retain = False):\n        if self.MQTT_Connected:\n            self.client.publish(topic, data, self.QOS, retain)\n\n    def subscribe(self, topic):\n        if self.MQTT_Connected:\n            self.client.subscribe((topic, self.QOS))\n\n    def unsubscribe(self, topic):\n        if self.MQTT_Connected:\n            self.client.unsubscribe(topic)\n","sub_path":"build/lib/robopython/mqtt_robo.py","file_name":"mqtt_robo.py","file_ext":"py","file_size_in_byte":4572,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"16844078","text":"import heapq\nfrom enum import Enum\nimport math\nimport itertools\n\ndiameter = 2\ninitial_pos = None\n\nCELLROWS=7\nCELLCOLS=14\n\nclass Direction(Enum):\n    UP = 90\n    RIGHT = 0\n    DOWN = -90\n    LEFT = 180\n\n\nrev_dict = {\n        Direction.RIGHT: (1, 0, Direction.LEFT), \n        Direction.DOWN: (0, -1, Direction.UP), \n        Direction.LEFT: (-1, 0, Direction.RIGHT), \n        Direction.UP: (0, 1, Direction.DOWN)\n    }\nclass Node:\n    def __init__(self, cell, prev, dir, heuristic, cost):\n        self.cell = cell\n        self.prev = prev\n        self.dir = dir\n        self.heuristic = heuristic\n        self.cost = cost\n        self.evalfunc = heuristic + cost\n\n    def __lt__(self, other):\n        return self.evalfunc < other.evalfunc\n\nclass Cell:\n    def __init__(self, pos, up, right, down, left, visited):\n        self.pos = pos\n        self.up = up\n        self.right = right\n        self.down = down\n        self.left = left\n        self.visited = visited\n        self.measurements = [[], [], [], []]\n        self.beacon = -1\n\n    def __str__(self):\n        return \"<CELL \" + str(self.pos) + \" \" + (\"?\" if self.up is None else (\"T\" if self.up else \"F\"))  + (\"?\" if self.right is None else (\"T\" if self.right else \"F\")) + (\"?\" if self.down is None else (\"T\" if self.down else \"F\")) + (\"?\" if self.left is None else (\"T\" if self.left else \"F\")) + (\"V\" if self.visited else \"~V\") + \">\"\n\n    def __repr__(self):\n        return str(self)\n\n\n# Manhattan distance between two points\ndef distance(p1, p2):\n    return abs(p1[0] - p2[0]) + abs(p1[1] - p2[1])\n\n\n# Get a path between two points using the A* algorithm\ndef get_path(map, start, target):\n    dict = {}\n    q = []\n    root = Node(start, None, None, distance(start.pos, target), 0)\n    heapq.heappush(q, (root.evalfunc, root))\n    \n    while len(q) > 0:\n\n        evalfunc, current = heapq.heappop(q)\n\n        for (dir, cell) in get_possible_next_nodes(map, current.cell):\n\n            cost = current.cost + 1\n\n            already_seen = cell in dict\n            if not already_seen or (already_seen and cost < dict[cell].cost):\n                node = Node(cell, current, dir, distance(cell.pos, target), cost)\n                if node.cell.pos == target:\n                    return reconstruct_path(node)\n                dict[cell] = node\n                if (node.evalfunc, node) not in q:\n                    heapq.heappush(q, (node.evalfunc, node))\n\n    return None\n\n# Reconstruct path to a given node (using Node class prev field)\ndef reconstruct_path(target):\n    path = []\n    current = target\n    while current.prev is not None:\n        path.insert(0, (current.dir, current.cell))\n        current = current.prev\n    return path\n\n\n# Get list of free nodes that neighbor a given node\ndef get_possible_next_nodes(map, cell):\n    list = []\n    for (d, x, y) in [(Direction.RIGHT, 1, 0), (Direction.DOWN, 0, -1), (Direction.LEFT, -1, 0), (Direction.UP, 0, 1)]:\n        try:\n            candidate_cell = cell_at_pos(map, (cell.pos[0] + x, cell.pos[1] + y))\n            wall = getattr(cell, d.name.lower())\n            if candidate_cell is not None and wall is not None and not wall:\n                list.append((d, candidate_cell))\n        except IndexError:\n            pass\n    return list\n\n# Ensure that the irSensor measurement list is always [UP, RIGHT, DOWN, LEFT] regardless of current robot orientation\ndef get_compass_adjusted_measurements(irSensor, compass):\n    measurements = [None, None, None, None]\n    adjustment = round(compass/90)\n    measurements[(0 - adjustment) % 4] = irSensor[1] # Left Sensor \n    measurements[(1 - adjustment) % 4] = irSensor[0] # Center Sensor \n    measurements[(2 - adjustment) % 4] = irSensor[2] # Right Sensor \n    measurements[(3 - adjustment) % 4] = irSensor[3] # Back Sensor \n    return measurements\n\ndef gps_to_relative(x, y):\n    return ((x - initial_pos[0]) / diameter, (y - initial_pos[1]) / diameter)\n\ndef relative_to_grid(rel):\n    return (rel[0] + 13, rel[1] + 6)\n\ndef grid_to_relative(grd):\n    return (grd[0] - 13, grd[1] - 6)\n\ndef cell_at_pos(map, pos):\n    try:\n        pos = relative_to_grid(pos)\n        return map[pos[1]][pos[0]]\n    except IndexError:\n        return None\n\ndef is_improper(cell):\n    return cell is None or not any([f == False for f in [cell.up, cell.right, cell.down, cell.left]])\n\ndef build_map(map):\n    corner = (0,0)\n    for row in map:\n        for cell in row:\n            if not is_improper(cell):\n                corner = cell.pos if (cell.pos[0] < corner[0]) or (cell.pos[1] > corner[1]) else corner\n\n    #for y in range(corner[1], 7 + corner[1]):\n    #    for x in range(corner[0], 14 + corner[0]):\n\n    print(corner, end=\"\\n\\n\")\n\n    top_offset = ((-1 * corner[1]) + 6) * 2\n    right_offset = abs((corner[0]) * 2)\n    bottom_offset = abs((corner[1]) * 2)\n    left_offset = (corner[0] + 13) * 2\n\n    string = ((\" \" * (29 + left_offset + right_offset)) + \"\\n\") * top_offset\n\n    for y in range(0,-15,-1):\n        for x in range(0, 14):\n            pos = (x + corner[0], math.ceil(y/2) + corner[1])\n\n            if y % 2 == 0:\n                top = cell_at_pos(map, (pos[0], pos[1] + 1))\n                bot = cell_at_pos(map, (pos[0], pos[1]))\n\n                #print(bot)\n\n                if x == 0:\n                    string += (\" \" * (left_offset + 1))\n                string += \"- \" if (not is_improper(top) and top.down == True) or (not is_improper(bot) and bot.up == True) else (\"X \" if not is_improper(top) and not is_improper(bot) else \"  \")\n            else:\n                left = cell_at_pos(map, (pos[0], pos[1]))\n                right = cell_at_pos(map, (pos[0] + 1, pos[1])) \n\n                if x == 0:\n                    string += (\" \" * left_offset) + (\"|\" if not is_improper(left) else \" \")\n                \n                string += (\"I\" if left.pos == (0,0) else \"X\") if not is_improper(left) else \" \"\n                string += \"|\" if (not is_improper(left) and left.right == True) or (not is_improper(right) and right.left == True) else (\"X\" if not is_improper(left) and not is_improper(right) else \" \")\n        string += (\" \" * right_offset) + \"\\n\"\n\n    string += ((\" \" * (29 + left_offset + right_offset)) + \"\\n\") * bottom_offset\n\n    return string\n\ndef get_adjacent_cells(cell,angle):\n    dirs = [(cell.right, (1, 0), \"left\"), (cell.up, (0, 1), \"down\"), (cell.left, (-1, 0), \"right\"), (cell.down, (0, -1), \"up\")]\n    phase = round(angle/90)\n    if phase < 0:\n        phase += 4\n    # right: [left, down, up, right]\n    # up: [down, right, left, up]\n    # left: [right, up, down, left]\n    # down: [up, left, right, down]\n    return [dirs[(phase+2)%4], dirs[(phase+3)%4], dirs[(phase+1)%4], dirs[phase%4]]\n\ndef eval_beacon_path(map, beacons, max_beacons):\n    if len(beacons) < max_beacons:\n        return (False, None)\n\n    actual = get_beacon_path(map, beacons, max_beacons)\n\n    speculative_map = [[None for col in range(CELLCOLS*2-1)] for row in range(CELLROWS*2-1)]\n    for row in range(len(map)):\n        for cell in range(len(map[row])):\n            if map[row][cell] is None:\n                c = Cell(grid_to_relative((cell, row)), False, False, False, False, False)\n            else:\n                ac = map[row][cell]\n                c = Cell(grid_to_relative((cell, row)), False if ac.up is None else ac.up, False if ac.right is None else ac.right, False if ac.down is None else ac.down, False if ac.left is None else ac.left, False)\n            speculative_map[row][cell] = c\n\n    best_case = get_beacon_path(speculative_map, beacons, max_beacons)\n\n    if best_case[1] < actual[1]:\n        return (False, actual)\n    else:\n        return (True, actual)\n\n    \n\ndef get_beacon_path(map, beacons, max_beacons):\n    paths = {}\n\n    for i in range(0, len(beacons)):\n        for j in range(i+1, len(beacons)):\n            paths[(i,j)] = get_path(map, beacons[i], beacons[j].pos)\n\n    perms = list(beacons.keys())\n    perms.remove(0)\n    perms = itertools.permutations(perms)\n    best = (None, math.inf, [])\n    for p in perms:\n        p = list(p)\n        p.insert(0,0)\n        p.append(0)\n        path = []\n        for b in range(1, len(p)):\n            tpath = paths[tuple(sorted([p[b-1], p[b]]))]\n            #print(tpath)\n            if p[b-1] > p[b]:\n                tpath.pop()\n                tpath.reverse()\n                tpath.append((None, beacons[p[b]]))\n            #print(tpath)\n            path += tpath\n\n        if len(path) < best[1]:\n            best = (p,len(path),path)\n\n    res = pos_to_output(beacons[0].pos) + \"\\n\"\n    for d, c in best[2]:\n        res += pos_to_output(c.pos) + ((\" #\" + str(c.beacon)) if c.beacon not in [-1,0] else \"\") + \"\\n\"\n\n    best = (best[0], best[1], res)\n    return best\n\n    \ndef pos_to_output(pos):\n    return str(pos[0]*2) + \" \" + str(pos[1]*2)\n","sub_path":"5th Year/[RMI] Assignment 1 - Robotic Agent to solve Location, Mapping, and Planning problems on a maze/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":8769,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"471743614","text":"# -*- coding: utf-8 -*-\nfrom django.db import models\nfrom django.conf import settings\nfrom django.apps import apps\n\nclass AlertManager(models.Manager):\n\n\tdef get_employee_items(self, *args, **kwargs):\n\n\t\temployee = kwargs[\"employee\"]\n\t\ttimestamp = kwargs[\"timestamp\"]\n\t\told_items = kwargs.get(\"old_items\", False)\n\t\tfargs = {\"user_employee\":employee}\n\t\tif old_items:\n\t\t\tfargs[\"timestamp__lte\"] = timestamp\n\t\telse:\n\t\t\tfargs[\"timestamp__gt\"] = timestamp\n\t\treturn self.filter(**fargs)\n","sub_path":"alerts/models/managers.py","file_name":"managers.py","file_ext":"py","file_size_in_byte":481,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"109950199","text":"noms=input(\"Entrer les noms des deux enfants (séparés d'un espace)\")\nnoms=noms.split(\" \")\nsomme=0\nfor i in range(2) :\n\tsomme=0\n\tfor k in range(len(noms[i])):\n\t\tsomme+=ord(noms[i][k])-65\n\tif somme>10:\n\t\tsomme=somme%9\n\tprint(somme,\" \",end=\"\")\nprint()\n","sub_path":"Lvl-3/01-Gestion de caractères/02-Nombre d'amour.py","file_name":"02-Nombre d'amour.py","file_ext":"py","file_size_in_byte":251,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"437924852","text":"from db_operations import *\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom datetime import date\nfrom datetime import timedelta\n\ndef green_score_stats(UserId):\n    \"\"\"\n    GREEN SCORE requires access to show_user_orders() connection function to DB\n    \"\"\"\n    user_order_history = show_user_orders(UserId)[0]\n    user_num_orders = len(show_user_orders(UserId)[0])\n    user_name = show_user_orders(UserId)[0][0][1],show_user_orders(UserId)[0][0][2]\n\n    # hard code these parametes for now...\n    # the data is taken from the SBB keynote slides\n    amount_trees_saved_per_cup = 0.0005517\n    amount_liters_water_saved_per_cup = 1.724\n    amount_tons_CO2_emission_saved_per_cup = 0.0001103\n\n    # use parameters of the train-ride ie. of place1 and place2 to deduce saved CO2 emission\n    # from riding the train instead of going by car!\n    CO2_saved_train = 0\n\n    green_score = user_num_orders*(amount_trees_saved_per_cup + amount_liters_water_saved_per_cup + amount_tons_CO2_emission_saved_per_cup) + CO2_saved_train\n\n    stats_list = [green_score, user_num_orders*amount_trees_saved_per_cup, user_num_orders*amount_liters_water_saved_per_cup, user_num_orders*amount_tons_CO2_emission_saved_per_cup]\n\n    stats = {}\n    stats[\"stats\"] = [{\n        \"green_score\": str(user_num_orders*(amount_trees_saved_per_cup + amount_liters_water_saved_per_cup + amount_tons_CO2_emission_saved_per_cup) + CO2_saved_train),\n        \"amt_tree_saved\": str(user_num_orders*amount_trees_saved_per_cup),\n        \"ltr_water_saved\": str(user_num_orders*amount_liters_water_saved_per_cup),\n        \"tons_coo_saved\": str(user_num_orders*amount_tons_CO2_emission_saved_per_cup)\n\n    }]\n\n    return stats, stats_list\n\ndef plot_stats_last7days(UserId):\n    \"\"\"\n    Takes as input the UserId and outputs a plot of green score and the partitions of what make up the individual green scores.\n    \"\"\"\n\n    # find the usage data from last 7 days\n    today = date.today()\n\n    last_week = []\n    for i in range(0,7):\n        last_week.append(str(today - timedelta(days=i)))\n    last_week.reverse()\n\n    dates = []\n    for i in range(len(show_user_orders_ordered_date(UserId)[0])):\n        dates.append(show_user_orders_ordered_date(UserId)[0][i][3].split(' '))\n\n    weeks = list(np.asarray(dates).T[0])\n\n    indices = []\n    for i in range(len(weeks)):\n        for j in range(len(last_week)):\n            if last_week[j] == weeks[i]:\n                indices.append((i,weeks[i]))\n\n    # assert indices != 0\n    if len(indices) == 0:\n        print('no coffee-orders in last 7 days! save the polar bears bro!')\n        return\n\n    mpl_fig = plt.figure(figsize=(18,10))\n    ax = mpl_fig.add_subplot(111)\n    N = 7\n\n    # generate individual daily data\n    TREES_stats_array = np.zeros((7,))\n    H2O_stats_array = np.zeros((7,))\n    COO_stats_array = np.zeros((7,))\n\n    # here we could also include the effects of the distance between buy_cup and return_cup\n    # points, ie. the effects of carbon emission reduction due to taking the train!\n    for index,weekday in enumerate(last_week):\n        for j in range(len(indices)):\n            if weekday == indices[j][1]:\n                TREES_stats_array[index] += 0.0005517\n                H2O_stats_array[index] += 1.724\n                COO_stats_array[index] += 0.0001103\n\n    # make the graphs a little more readable, we should correct for units\n    TREES_stats_array *= 1000\n    COO_stats_array *= 1000\n\n    width = 0.35\n    ind = np.arange(N)\n    p1 = plt.bar(ind, H2O_stats_array, width)\n    p2 = plt.bar(ind, TREES_stats_array, width)\n    p3 = plt.bar(ind, COO_stats_array, width)\n\n    plt.ylabel('GREEN SCORE')\n    #plt.xticks(ind, ('01.03','02.03','03.03','04.03','05.03','06.03','07.03'))\n    plt.xticks(ind, last_week)\n    #plt.yticks(np.arange(0, 10, 10))\n    plt.legend((p1[0], p2[0], p3[0]), ('H2O','TREES','COO'))\n    plt.savefig('7dayGS-userId-'+ str(UserId) + '.png')\n\n    plt.show()\n","sub_path":"green.py","file_name":"green.py","file_ext":"py","file_size_in_byte":3921,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"627791483","text":"# -*- coding:utf-8 -*-\nimport os\nimport argparse\nimport dataclasses\nimport json\nimport re\nimport shutil\nimport time\nimport zipfile\nfrom typing import List, Optional\n\nWIN32_PATH_SEP = '\\\\'\nPATH_SEP = '/'\nPACKAGE_PATTERN = re.compile(r'^(package( +)(.+);)\\n$', re.M)\n\nDEFAULT_SPEC_FILE = 'package.json'\nSPEC_FILE_ENCODING = 'utf-8'\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--output', '-o', help=\"Output file name.\", default='')\nparser.add_argument('--spec', '-c', help=\"Path to package.json\", default=DEFAULT_SPEC_FILE)\nparser.add_argument('dirname', help=\"Directory name containing files to pack.\")\n\ntemp_dir = f'temp_dir_{int(time.time())}'  # Use different dirname each time.\n\n\n@dataclasses.dataclass\nclass PackageSpec:\n    required_files: List[str]\n    remove_package_statement: bool = True\n\n\ndef package(dirname: str,\n            spec_file: str,\n            output_file: Optional[str] = None):\n    if not os.path.isdir(dirname):\n        raise ValueError(f\"Target directory {dirname} does not exist or not a directory.\")\n\n    if DEFAULT_SPEC_FILE == spec_file:\n        spec_file = os.path.join(dirname, spec_file)\n\n    with open(spec_file, 'r', encoding=SPEC_FILE_ENCODING) as f:\n        kw = json.load(f)\n        spec = PackageSpec(**kw)\n\n    os.makedirs(temp_dir, exist_ok=True)\n    for name in spec.required_files:\n        src = os.path.join(dirname, name)\n        rfp = open(src, 'r', encoding=SPEC_FILE_ENCODING)\n        content = rfp.read()\n        rfp.close()\n\n        if spec.remove_package_statement:\n            # Remove leading 'package {package_name};' from each source code file.\n            content = PACKAGE_PATTERN.sub('', content, 1)\n\n        dest = os.path.join(temp_dir, name)\n        wfp = open(dest, 'w', encoding=SPEC_FILE_ENCODING)\n        wfp.write(content)\n        wfp.close()\n\n        print(f'Added {src} -> {dest}')\n\n    # Create zip file.\n    if not output_file:\n        _, name = os.path.split(dirname)\n        output_file = f'{name}.zip'\n\n    zfp = zipfile.ZipFile(output_file, 'w')\n    for name in spec.required_files:\n        path = os.path.join(temp_dir, name)\n        zfp.write(path, name)\n\n    zfp.close()\n    print(f'Package {output_file} created.')\n\n\ndef cleanup():\n    if not os.path.exists(temp_dir):\n        return\n\n    shutil.rmtree(temp_dir)\n    print(f\"Temporary directory {temp_dir} cleaned.\")\n\n\nif __name__ == '__main__':\n    args = parser.parse_args()\n    dirname_ = args.dirname.replace(WIN32_PATH_SEP, PATH_SEP)\n    dirname_ = dirname_.rstrip('/')\n\n    spec_file_ = args.spec.replace(WIN32_PATH_SEP, PATH_SEP)\n    output_file_ = args.output.replace(WIN32_PATH_SEP, PATH_SEP)\n\n    try:\n        package(dirname_, spec_file_, output_file_)\n    finally:\n        cleanup()\n","sub_path":"package.py","file_name":"package.py","file_ext":"py","file_size_in_byte":2734,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"50003536","text":"# Definition for singly-linked list.\nclass ListNode:\n    def __init__(self, x):\n        self.val = x\n        self.next = None\n\nclass Solution:\n    # @param head, a ListNode\n    # @return a boolean\n    def hasCycle(self, head):\n        if head is None:\n            return False\n        slow = head\n        fast = head\n        while True:\n            fast = self.fastMove(fast)\n            if fast == False:\n                return False\n            slow = self.slowMove(slow)\n            if fast == slow:\n                return True\n        return False\n\n    def fastMove(self,fast):\n        if fast.next is not None:\n            fast = fast.next\n        else:\n            return False\n        if fast.next is not None:\n            fast = fast.next\n        else:\n            return False\n        return fast\n\n    def slowMove(self,slow):\n        if slow.next is not None:\n            slow = slow.next\n            return slow\n        else:\n            return False\n","sub_path":"11.py","file_name":"11.py","file_ext":"py","file_size_in_byte":962,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"212079447","text":"\nimport re,vavava.util\nfrom vavava.httpclient import HttpClient\nfrom vavava.util import LogAdapter\nfrom time import localtime,time\nimport codecs,os\n\nclass Handler:\n    def __init__(self,log,conn=None):\n        self.log = log\n        self.conn = conn\n\n    def process(self):\n        pass\n\nclass Fetcher:\n    def __init__(self,item,debug_level=0,soc_timeout=10,log=None,name=\"<unknown>\"):\n        self._name = name\n        self.log= LogAdapter(log=log)\n        self._client = HttpClient(log,debug_level=debug_level,req_timeout=soc_timeout)\n        self._client.timeout=11\n        self._html = \"\"\n        self.result_handlers = []\n        self.exception_handler = []\n        self.result = []\n\n    def Fetch(self):\n        try:\n            self._fetch()\n        except Exception as e:\n            self.log.exception(e)\n            self.handle_exception()\n        self.handle_exception()\n\n    def add_handler(self,handler=None,exception_handler=None):\n        if exception_handler:\n            self.exception_handlers.append(exception_handler)\n        if handler:\n            self.result_handlers.append(handler)\n\n    def handle_result(self):\n        for handler in self.result_handlers:\n            handler.process()\n\n    def handle_exception(self):\n        for handler in self.exception_handler:\n            handler.process()","sub_path":"vavava/fetcher.py","file_name":"fetcher.py","file_ext":"py","file_size_in_byte":1322,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"202415848","text":"# -*- coding: utf-8 -*-\n\"\"\"\nThis script holds functions that I use in the PD analysis\n\"\"\"\n\nfrom __future__ import division\nimport numpy as np\nimport math\nfrom scipy.signal import medfilt\nfrom foof.util import rflatten\nfrom sklearn import linear_model\nimport matplotlib.pyplot as plt\n\ndef fftmed(x, Fs=1000, Hzmed=10, zeropad=False, usehanning = False, usemedfilt = True):\n    '''\n    Calculate the power spectrum of a signal by first taking its FFT and then\n    applying a median filter\n    \n    Parameters\n    ----------\n    x : array\n        temporal signal\n    Fs : integer\n        sampling rate\n    Hzmed : float\n        Frequency width of the median filter\n    zeropad : Boolean\n        False to calculate the FFT using the number of points as the length of\n        the signal. True to increase the number of points to the next power of\n        2 by zero padding the signal\n        \n    Returns\n    -------\n    f : array\n        frequencies corresponding to the PSD output\n    psd : array\n        power spectrum\n    '''\n    if zeropad:\n        N = 2**(int(math.log(len(x), 2))+1)\n        x = np.hstack([x,np.zeros(N-len(x))])\n    else:\n        N = len(x)\n        \n    \n    # Calculate PSD\n    f = np.arange(0,Fs/2,Fs/N)\n    if usehanning:\n        win = np.hanning(N)\n        rawfft = np.fft.fft(x*win)\n    else:\n        rawfft = np.fft.fft(x)\n    psd = np.abs(rawfft[:len(f)]**2)\n    \n    # Median filter\n    if usemedfilt:\n        sampmed = np.argmin(np.abs(f-Hzmed/2.0))\n        psd = medfilt(psd,sampmed*2+1)\n    \n    return f, psd\n    \ndef slope(f, psd, fslopelim = (80,200), flatten_thresh = 0):\n    '''\n    Calculate the slope of the power spectrum\n    \n    Parameters\n    ----------\n    f : array\n        frequencies corresponding to power spectrum\n    psd : array\n        power spectrum\n    fslopelim : 2-element list\n        frequency range to fit slope\n    flatten_thresh : float\n        See foof.utils\n        \n    Returns\n    -------\n    slope : float\n        slope of psd\n    slopebyf : array\n        linear fit of the PSD in log-log space (includes information of offset)\n    fslope : array\n        frequency array corresponding to slopebyf\n    \n    '''\n    fslopeidx = np.logical_and(f>=fslopelim[0],f<=fslopelim[1])\n    fslope = f[fslopeidx]\n    _, _, slope = rflatten(fslope, psd[fslopeidx], flatten_thresh)\n    slope = slope[0][0]\n    \n    x = np.log10(fslope)\n    y = np.log10(psd[fslopeidx])\n    lm = linear_model.RANSACRegressor(random_state=42)\n    lm.fit(x[:, np.newaxis], y)\n    slopebyf = lm.predict(x[:, np.newaxis])\n    return slope, slopebyf, fslope\n    \n        \ndef centerfreq(x, frange= [13,30], Fs = 1000, Hzmed = 10, plot_psd = False,\n               importpsd = False, f = None, psd = None):\n    \n    # Calculate PSD\n    if importpsd == False:\n        f, psd = fftmed(x, Hzmed = Hzmed)\n    \n    # Calculate center frequencies\n    frangeidx = np.logical_and(f>frange[0], f<frange[1])\n    psdbeta = psd[frangeidx]\n    cfs_idx = psdbeta.argmax() + np.where(frangeidx)[0][0]\n    cf = f[cfs_idx]\n    \n    # Plot PSDs for subjects ECoG during task\n    if plot_psd:\n        plt.figure()\n        plt.plot(f, np.log10(psd), 'k')\n        plt.plot([cf, cf], [min(np.log10(psd)), max(np.log10(psd))], 'k--')\n        plt.xlim(0,40)\n        plt.xlabel('f [Hz]')\n        plt.ylabel('log power')\n    \n    return cf\n\ndef calcpow(f,psd,frange):\n    frangeidx = np.logical_and(f>frange[0], f<frange[1])\n    return np.sum(psd[frangeidx])  ","sub_path":"misc/spec_tools.py","file_name":"spec_tools.py","file_ext":"py","file_size_in_byte":3457,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"51606652","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Oct 23 10:03:38 2020\n\n@author: pierc\n\"\"\"\n\nfrom kivy.lang import Builder\nfrom kivymd.app import MDApp\nfrom kivymd.uix.button import MDRectangleFlatButton, MDFlatButton\nfrom kivymd.uix.dialog import MDDialog\nfrom kivy.properties import ObjectProperty\nfrom kivy.properties import StringProperty\nimport os\nimport qrcode\nimport kivymd\n\nKV = '''\n\nBoxLayout:\n    shoal: shoal_id\n    fish_num: fish_number\n    dobs: dob\n    strains: strain\n    image: image\n    \n    orientation:'vertical'\n    Image:\n        id: image\n        #source: 'mylogo.png'\n        #size: self.texture_size\n    \n    MDTextField:\n        id: shoal_id\n        hint_text: \"Whats the shoal ID?\"\n        #helper_text: \"This will disappear when you click off\"\n        helper_text_mode: \"on_focus\"\n        pos_hint: {\"center_x\": .5, \"center_y\": .1}\n        #text: root.shoal\n        padding_x:\n            [self.center[0] - self._get_text_width(max(self._lines, key=len), self.tab_width, self._label_cached) / 2.0,\n            0] if self.text else [self.center[0], 0]\n        \n    MDTextField:\n        id: fish_number\n        hint_text: \"Number of fish\"\n        #helper_text: \"This will disappear when you click off\"\n        helper_text_mode: \"on_focus\"\n        pos_hint: {\"center_x\": .5, \"center_y\": .25}\n        #text: root.fish_num\n        padding_x:\n            [self.center[0] - self._get_text_width(max(self._lines, key=len), self.tab_width, self._label_cached) / 2.0,\n            0] if self.text else [self.center[0], 0]\n        \n    MDTextField:\n        id: dob\n        hint_text: \"Date of hatching?\"\n        #helper_text: \"This will disappear when you click off\"\n        helper_text_mode: \"on_focus\"\n        pos_hint: {\"center_x\": .5, \"center_y\": .4}\n        padding_x:\n            [self.center[0] - self._get_text_width(max(self._lines, key=len), self.tab_width, self._label_cached) / 2.0,\n            0] if self.text else [self.center[0], 0]\n    \n        \n    MDTextField:\n        id: strain\n        hint_text: \"What strain?\"\n        #helper_text: \"This will disappear when you click off\"\n        helper_text_mode: \"on_focus\"\n        pos_hint: {\"center_x\": .5, \"center_y\": .55}\n        padding_x:\n            [self.center[0] - self._get_text_width(max(self._lines, key=len), self.tab_width, self._label_cached) / 2.0,\n            0] if self.text else [self.center[0], 0]\n    \n    MDRaisedButton:\n        text: \"Create QR Code\"\n        #text_color: 0, 0, 1, 1\n        pos_hint: {\"center_x\": .5, \"center_y\": .8}\n        on_press: app.create_qr()\n            \n            \n            \n    \n    '''\n\nclass MainApp(MDApp):\n    shoal = ObjectProperty()\n    fish_num = ObjectProperty()\n    dobs = ObjectProperty()\n    strains = ObjectProperty()\n    image = ObjectProperty()\n    \n    dialog =None\n    \n    def build(self):\n        #self.theme_cls.theme_style = \"Dark\"\n        try:\n            self.create_img_folder()\n        except:\n            print(\"failed\")\n        return Builder.load_string(KV)\n    \n    def create_img_folder(self):\n        \"\"\"Create image folder\"\"\"\n        image_folder = \"./QRCodes\"\n        if not os.path.exists(image_folder):\n            os.makedirs(image_folder)\n            \n    def create_qr(self):\n        \"\"\"Make qr code\"\"\"\n        \n        \n        shoal_text = (self.root.shoal.text).strip()\n        fish_number_text = (self.root.fish_num.text).strip()\n        dob_text = (self.root.dobs.text).strip()\n        strain_text = (self.root.strains.text).strip()\n        \n        img = qrcode.make({\"id\":shoal_text, \n                           \"total_fish\":fish_number_text, \n                           \"DOB\":dob_text, \n                           \"strain\":strain_text})\n        \n        \"\"\"Save qr code in folder called the ID\"\"\"\n        path = \"./QRCodes//\"+shoal_text\n        if not os.path.exists(path):\n            os.makedirs(path)\n        img_path = os.path.join(path, shoal_text+\".png\")\n        try:\n            img.save(img_path)\n            self.root.image.source = img_path\n            self.show_alert_dialog(True)\n        except:\n            self.show_alert_dialog(False)\n            \n            \n    def reset_fields(self, instance):\n        self.dialog.dismiss()\n        self.root.shoal.text = \"\"\n        self.root.fish_num.text = \"\"\n        self.root.dobs.text = \"\"\n        self.root.strains.text = \"\"\n        self.root.image.source = \"\"\n        \n    \n    def show_alert_dialog(self, success):\n        if success == True:\n            btn1_txt = \"Exit\"\n            btn2_txt = \"Create Another\"\n            dialog_txt = \"QRCode successful :)\"\n        \n        if success == False:\n            btn1_txt = \"Exit\"\n            btn2_txt = \"Try again\"\n            dialog_txt = \"QRCode unsuccessful\"\n            \n        btn1 = MDFlatButton(\n                        text=\"Exit\", text_color=self.theme_cls.primary_color\n                    )\n        btn2 = MDFlatButton(\n                        text=\"Create Another\", text_color=self.theme_cls.primary_color\n                    )\n        \n        if not self.dialog:\n            self.dialog = MDDialog(\n                text=\"QRCode successful :)\",\n                buttons=[btn1,\n                    btn2])\n        self.dialog.open()\n        btn2.bind(on_press = self.reset_fields)\n        btn1.bind(on_press = self.close_app)\n    \n    \n\n    def close_app(self, instance):\n        self.dialog.dismiss()\n        MDApp.get_running_app().stop()\n        \nMainApp().run()\n\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5455,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"534403849","text":"import telebot\nfrom __Xiaoya__ import xiaoya\n\nTOKEN = '121899714:AAF3xShKMc52iV5yN93fiIjOH98ZXP1zcOc'\n\nx = xiaoya('xiaoya', 17, 'telegram', '__all__')\nbot = telebot.TeleBot(TOKEN)\n\n@bot.message_handler(commands=['chat_id'])\ndef get_chat_id(msg):\n    if msg.chat.type == 'supergroup':\n        reply = 'Chat_id of this group:\\n\\n{}'.format(str(msg.chat.id))\n        reply += '\\n\\n' + '-'*20 + '\\n\\n' + 'Who sent this message:\\n\\n{}'.format(str(msg.from_user.id))\n        bot.reply_to(msg, reply)\n        \n@bot.message_handler(commands=['go'])\ndef send_a_piece_of_knowledge(msg):\n    result = x.reply(msg.text)\n    if result != '':\n        bot.reply_to(msg, result)\n\n@bot.message_handler(content_types=['text'])\ndef handle(msg):\n    if not x.is_English(msg.text) and msg.chat.title in ['Python for English']:\n        admin_list = bot.get_chat_administrators(msg.chat.id)\n        admin_ids = [member.user.id for member in admin_list]\n        \n        if msg.from_user.id in admin_ids:\n            bot.reply_to(msg, 'Please speak English!')\n        else:\n            bot.kick_chat_member(msg.chat.id, msg.from_user.id)\n            bot.unban_chat_member(msg.chat.id, msg.from_user.id)\n\nbot.polling()\n","sub_path":"Telegram.py","file_name":"Telegram.py","file_ext":"py","file_size_in_byte":1194,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"544340438","text":"# File name: samsungcalendar.py\n\"\"\"\nMIT License\nCopyright (c) 2019 Samsung. n.herriot@samsung.com\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n\"\"\"\n\n\nfrom kivy.app import App\nfrom kivy.lang import Builder\nfrom kivy.uix.anchorlayout import AnchorLayout\nfrom kivy.uix.boxlayout import BoxLayout\nfrom kivy.config import Config\nfrom kivy.uix.screenmanager import ScreenManager\n\n# Set Initial Window Size\nConfig.set('graphics', 'width', '550')\nConfig.set('graphics', 'height', '750')\n\n# Load kivy files that describe the GUI\nBuilder.load_file('newmeetingstatus.kv')\nBuilder.load_file('schedulemeeting.kv')\nBuilder.load_file('calendarwidget.kv')\nBuilder.load_file('samsungcalendar.kv')\nBuilder.load_file('days.kv')\n#Builder.load_file('months.kv')\nBuilder.load_file('events.kv')\nBuilder.load_file('calendarnav.kv')\nBuilder.load_file('calendarstatus.kv')\nBuilder.load_file('calendar.kv')\n\n\n\nclass SamsungScreenManager(ScreenManager):\n    pass\n\n\nclass SamsungScreenManagerApp(App):\n\n    def on_start(self):\n        print(\"*** Application Started ***\")\n\n    def build(self):\n        return SamsungScreenManager()\n\n\nif __name__ == \"__main__\":\n    SamsungScreenManagerApp().run()\n","sub_path":"calendar/samsungcalendar.py","file_name":"samsungcalendar.py","file_ext":"py","file_size_in_byte":2143,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"280408755","text":"\n\n#calss header\nclass _TURNAROUND():\n\tdef __init__(self,): \n\t\tself.name = \"TURNAROUND\"\n\t\tself.definitions = [u'the amount of time taken for something to happen after a vehicle, an instruction, or an order for goods arrives at a place: ', u'an occasion when a business, plan, or system suddenly becomes successful: ', u'any change from one thing to its opposite: ']\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'nouns'\n\n\n\tdef run(self, obj1 = [], obj2 = []):\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/nouns/_turnaround.py","file_name":"_turnaround.py","file_ext":"py","file_size_in_byte":539,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"24578588","text":"#! /usr/bin/python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Oct 14 16:35:29 2018\n@author: vicio75\n\"\"\"\nimport math\nimport tkinter as tk\nfrom tkinter import filedialog\nfrom tkinter import  messagebox\nimport datetime as tt\nfrom tkinter import PhotoImage\n\n##To solve import PIL\ntry:\n    from PIL import Image, ImageTk\nexcept ImportError:\n    print(\"NO module found\")\n    import Image  #noshow\n    print(\"Image has been imported\")\n\n# pylint: disable=locally-disabled, invalid-name\n#Time\ntime_ = tt.datetime.now().strftime(\"%d/%m/%Y - %H:%M:%S\")\n#Tkinter window\nroot = tk.Tk() #new window # pylint: disable=locally-disabled, invalid-name\nwidth = 1210\nheight = 500\nscreen_width = (root.winfo_screenwidth())\nscreen_height = (root.winfo_screenheight())\nxs = int(screen_width*0.5 - width*0.5)\nys = int(screen_height*0.5 - height*0.5)\ngeom = \"{}x{}+{:d}+{:d}\".format(width,height,xs,ys)\nroot.geometry(geom)\nroot.title(\"Pitot-static probe\")\nroot.resizable(width=False, height=False)\nroot.configure(bg=\"grey77\")\n#root.iconbitmap(r'\\fan.ico')\nicon = tk.Image(\"photo\", file=\"imgs/icon.png\")\nroot.tk.call('wm','iconphoto',root._w,icon)\n\n### Decorators\nframe00 = tk.Frame(width=320, height=280, colormap=\"new\", relief=\"sunken\", bd=1)\nframe00.place(x=25, y=5)\nframe01 = tk.Frame(width=845, height=280, colormap=\"new\", relief=\"sunken\", bd=1)\nframe01.place(x=350, y=5)\nframe02 = tk.Frame(width=875, height=200, colormap=\"new\", relief=\"sunken\", bd=1)\nframe02.place(x=25, y=288)\n\n#Fonts\nf_ver = (\"arial\", 7)\nf_8 = (\"arial\", 8)\nf_9 = (\"arial\", 9)\nf_10 = (\"arial\", 10)\nf_12 = (\"arial\", 12)\nf_IT6 = (\"arial\", 6, \"italic\")\nf_IT8 = (\"arial\", 8, \"italic\")\nf_IT9 = (\"arial\", 9, \"italic\")\nf_IT11 = (\"arial\", 11, \"italic\")\nf_BO7 = (\"arial\", 7, \"bold\")\nf_BO9 = (\"arial\", 9, \"bold\")\nf_BO10 = (\"arial\", 10, \"bold\")\nf_BO12 = (\"arial\", 12, \"bold\")\n\n##rows and columns configuration\nrc = 40\ncc = 40\nfor i in range(40):\n    root.rowconfigure(i, minsize=rc)\nfor i in range(40):\n    root.columnconfigure(i, minsize=cc)\nroot.columnconfigure(0, minsize=160)#col0\nroot.columnconfigure(2, minsize=120)#col0\nroot.columnconfigure(3, minsize=80)#col0\nroot.columnconfigure(6, minsize=130)#col0\nroot.columnconfigure(9, minsize=160)#col0\nwid = 10\nwidout = 80\n\n#Constants\nRf = 287.058 # Universal Constant of Gases [J/(Kg K)]\ng = 9.806   # [m/s2] gravitational accelaration\n###\n\ndata = []\n\n##Functions\ndef fluid(pAtm):\n    \"\"\"Return the Fluid characteristics\"\"\"\n    T = float(T_.get())\n    t = T + 273.15 # Kelvin\n    rot = pAtm/(Rf*t) #Density as function of temperature in Kelvin [Kg/mc]\n    gamma_t = rot * g  #specific weight at t°C\n    #Sutherland Equation\n    ba = 1.458*10**(-6)\n    sa = 110.4 #Kelvin\n    mi_ = ba * (t**1.5)/(t+sa) #Dinamic Viscosity  Pa s = kg m^-1 s^-1\n    ni = mi_ / rot         #Cinematic Viscosity  m2·s-1\n    Fp = [rot, gamma_t, mi_, ni, T, t]\n    return Fp\n\ndef calculon():\n    \"\"\"Execute the velocity estimation\"\"\"\n    global Par, staticP_, staticFan_, staticBody_, P_first, P_second, AVGH,\\\n    Vduct, Qduct, devPow, conPow, effFan, effHop, remark\n\n    #Parameters reading\n    Par = [atm_.get(), SpeedEn_.get(), SpeedFan_.get(), D_.get(), Tor_.get()]\n    Par = [float(p) for p in Par]\n    #Fluid properties\n    propfluid = fluid(Par[0]*100) #Patm 1 mbar = 100 Pa\n    #propfluid = fluid(101325) #Patm 1 mbar = 100 Pa\n\n    t = float(propfluid[5]) #temperature in Kelvin\n\n    staticP_ = float(staticP.get())\n    staticFan_ = float(staticFan.get())\n    staticBody_ = float(staticBody.get())\n  \n    #getting the total pressure values\n    P_first = [P_1_1.get(), P_2_1.get(), P_3_1.get(), P_4_1.get(), P_5_1.get(), P_6_1.get()]\n    P_first = [float(p) for p in P_first]\n    P_second = [P_1_2.get(), P_2_2.get(), P_3_2.get(), P_4_2.get(), P_5_2.get(), P_6_2.get()]\n    P_second = [float(p) for p in P_second]\n\n    P_first2 = [(p)**2 for p in P_first]\n    P_second2 = [(p)**2 for p in P_second]\n    \n    ####Error Messages\n    if Par[1] == 0:\n        messagebox.showwarning(\"Warning\", \"The Engine speed is 0!\")\n    if Par[2] == 0:\n        messagebox.showwarning(\"Warning\", \"The Fan speed is 0!\")\n    if Par[3] == 0:\n        messagebox.showwarning(\"Warning\", \"The intake tube diameter is 0!\")\n    if Par[4] == 0:\n        messagebox.showwarning(\"Warning\", \"The torque is 0!\")\n\n    #pp = []\n    tt = \"\"\n    for j, p in enumerate(P_first):\n        if p == 0: tt += \" value #\"+(str(j+1)+\" = 0\\n\")\n    if 0 in P_first:\n        tx0 = \"In first pass:\\n{}\".format(tt)\n        messagebox.showwarning(\"Warning\", tx0)\n\n    #pp = []\n    tt = \"\"\n    for ii, p in enumerate(P_second):\n        if p == 0: tt += \" value #\"+(str(ii+1)+\" = 0\\n\")\n    if 0 in P_second:\n        tx1 = \"In second pass:\\n{}\".format(tt)\n        messagebox.showwarning(\"Warning\", tx1)\n\n    if staticP_ == 0:\n        messagebox.showwarning(\"Warning\", \"Intake static pressure is 0!\")\n\n    if staticFan_ == 0:\n        messagebox.showwarning(\"Warning\", \"Fan static pressure is 0!\")\n        \n    if staticBody_ == 0:\n        messagebox.showwarning(\"Warning\", \"Body static pressure is 0!\")\n       \n    ##################\n\n    ########>>>> Estimations <<<<##############\n    AVGH = (((sum(P_first2)+sum(P_second2))/12)**0.5)*10 \\\n    * (t/293) * (1013/Par[0]) * (10363/(10363-(abs(staticP_)*10))) #averaged value of the dynamic pressure\n    Aduct = (0.25*math.pi*(Par[3]/1000)**2)\n    Vduct = 4.032*(AVGH)**0.5 #averaged value of the velocity\n    Qduct = Aduct*Vduct #volume flow rate\n    #static pressure correction\n    staticP_   = abs(staticP_) * (t/293) * (1013/Par[0])\n    staticFan_ = abs(staticFan_) * (t/293) * (1013/Par[0])\n    staticBody_ = abs(staticBody_) * (t/293) * (1013/Par[0])\n    #Developed Power\n    devPow = Qduct * (staticFan_*58.84*1000/600)/1000\n    #Consumed Power\n    conPow = Par[4]*(Par[2]*2*math.pi/60)/1000\n    #Efficiencies\n    if conPow == 0:\n        effFan = 0\n    else: effFan = 100*devPow/conPow\n    if (staticFan_ - staticP_) == 0:\n        effHop = 0\n    else: effHop = 100*(staticBody_ - staticP_)/(staticFan_ - staticP_)\n    ###########################################\n\n    #Estimation labels\n    avg = tk.Label(root, text=\"{:2.2f}\".format(AVGH), padx=10, bg=\"white\", font=f_BO10)\n    avg.grid(row=8, column=1)\n\n    vlab = tk.Label(root, text=\"{:2.3f}\".format(Vduct), padx=10, bg=\"white\", font=f_BO10)\n    vlab.grid(row=9, column=1)\n\n    qlab = tk.Label(root, text=\"{:2.3f}\".format(Qduct), padx=10, bg=\"white\", font=f_BO10)\n    qlab.grid(row=10, column=1)\n\n    den = tk.Label(root, text=\"{:2.5f}\".format(propfluid[0]), padx=10, bg=\"white\", font=f_BO10)\n    den.grid(row=11, column=1)\n\n    fan = tk.Label(root, text=\"{:2.2f}\".format(staticFan_), padx=10, bg=\"white\", font=f_BO10)\n    fan.grid(row=8, column=4)\n\n    body = tk.Label(root, text=\"{:2.2f}\".format(staticBody_), padx=10, bg=\"white\", font=f_BO10)\n    body.grid(row=9, column=4)\n\n    devl = tk.Label(root, text=\"{:2.2f}\".format(devPow), padx=10, bg=\"white\", font=f_BO10)\n    devl.grid(row=10, column=4)\n\n    conl = tk.Label(root, text=\"{:2.2f}\".format(conPow), padx=10, bg=\"white\", font=f_BO10)\n    conl.grid(row=11, column=4)\n\n    effF_l = tk.Label(root, text=\"{:2.2f}\".format(effFan), padx=10, bg=\"white\", font=f_BO10)\n    effF_l.grid(row=8, column=7)\n\n    effH_l = tk.Label(root, text=\"{:2.2f}\".format(effHop), padx=10, bg=\"white\", font=f_BO10)\n    effH_l.grid(row=9, column=7)\n\n    remark = text.get(\"1.0\", \"end-1c\")\n    if remark == \"  Insert here some remarks\":\n        messagebox.showwarning(\"Warning\", \"No comments have been added!\")\n\n#Cleaning values\ndef ClEaN_1():\n    \"\"\" Clean values first pass \"\"\"\n    messagebox.showwarning(\"Warning\", \"You are deleting the First Pass data!\")\n    for ll in [P_1_1, P_2_1, P_3_1, P_4_1, P_5_1, P_6_1]:\n        ll.set(0)\n\ndef ClEaN_2():\n    \"\"\" Clean values second pass \"\"\"\n    messagebox.showwarning(\"Warning\", \"You are deleting the Second Pass data!\")\n    for m in [P_1_2, P_2_2, P_3_2, P_4_2, P_5_2, P_6_2]:\n        m.set(0)\n\ndef saveEx(Par, staticP_, staticFan_, staticBody_, P_first, P_second, AVGH, \\\n            Vduct, Qduct, devPow, conPow, effFan, effHop, remark):\n    \n    \"\"\"\n    \n    - Creating the data list and Saving data\n    \n    \"\"\"\n    header = [\"Atm pressure(mbars)\", \"Engine Speed(rpm)\", \"Fan Speed(rpm)\",\n              \"Inlet Diameter(mm)\", \"Torque(Nm)\", \"Static Pressure(cmH2O)\",\n              \"Static Fan Pressure(cmH2O)\", \"Static Body Pressure(cmH2O)\",\n              \"P1st(cmH2O)\", \"P2st(cmH2O)\", \"P3st(cmH2O)\",\n              \"P4st(cmH2O)\", \"P5st(cmH2O)\", \"P6st(cmH2O)\",\n              \"P1nd(cmH2O)\", \"P2nd(cmH2O)\", \"P3nd(cmH2O)\",\n              \"P4nd(cmH2O)\", \"P5nd(cmH2O)\", \"P6nd(cmH2O)\",\n              \"Avg Tot pressure (mmH2O)\", \"Velocity(m/s)\",\n              \"Flow Rate(m^3/s)\", \"Fan Pressure(cmH2O)\",\n              \"Body Pressure(cmH2O)\", \"Developed Power(kW)\",\n              \"Consumed Power(kW)\", \"Fan Efficiency(%)\",\n              \"Hopper Efficiency(%)\", \"Remarks\"]\n    \n    ##### data list creation\n    for I in Par:\n        data.append(I)\n    data.append(staticP_)\n    data.append(staticFan_)\n    data.append(staticBody_)\n    for J in P_first:\n        data.append(J)\n    for II in P_second:\n        data.append(II)\n    for JJ in [AVGH, Vduct, Qduct, staticFan_, staticBody_, devPow, conPow, effFan, effHop]:\n        data.append(JJ)\n    data.append(remark)\n    data.append(\"\\n\")\n\n    f = filedialog.asksaveasfile(mode=\"w\", defaultextension=\".csv\")\n    f.write(time_+\"\\n\")\n    if f is None:\n        return\n    for head in header:\n        ss0 = head + \",\"\n        f.write(ss0)\n    f.write(\"\\n\")\n\n    for item in data:\n        if item == \"\\n\":\n            ss1 = \"\\n\"\n        elif isinstance(item, str):\n            ss1 = item+\",\"\n        else:\n            ss1 = str(\"{:2.5f}\".format(item))+\",\"\n        f.write(ss1)\n    f.close()\n    \ndef close():\n    \"\"\"\n    Close the UI\n    \"\"\"\n    root.destroy()\n\n###############\n### inputs section\n#Labels\nl00 = tk.Label(root, text=\"Parameters\", font=f_BO12)\nl00.place(x=156, y=18)\nl01 = tk.Label(root, text=\"Dynamic Pressure - Pass #1\", font=f_BO12)\nl01.place(x=365, y=18)\nl02 = tk.Label(root, text=\"Dynamic Pressure - Pass #2\", font=f_BO12)\nl02.place(x=640, y=18)\nl03 = tk.Label(root, text=\"Static Pressure values\", font=f_BO12, padx=10)\nl03.place(x=940, y=18)\n\n#Temperature selection\nl0 = tk.Label(root, text=\"Temperature\", font=f_BO10, padx=0)\nl0.grid(row=1, column=0, sticky=\"e\", ipadx=5)\nT_ = tk.StringVar()\nt1 = tk.Entry(root, textvariable=T_, width=wid, justify=\"center\", font=f_10)\nt1.grid(row=1, column=1, sticky=\"we\")\nt1.insert(\"end\", 20)\nl0_1 = tk.Label(root, text=\"[°C]\", padx=5, font=f_BO10)\nl0_1.grid(row=1, column=2, sticky=\"w\")\n\n#Atmospheric pressure selection\natm0 = tk.Label(root, text=\"ATM pressure\", font=f_BO10)\natm0.grid(row=2, column=0, sticky=\"e\", ipadx=5)\natm_ = tk.StringVar()\natm1 = tk.Entry(root, textvariable=atm_, width=wid, justify=\"center\", font=f_10)\natm1.grid(row=2, column=1, sticky=\"we\")\natm1.insert(\"end\", 1016)\natm0_1 = tk.Label(root, text=\"[mbars]\", padx=5, font=f_BO10)\natm0_1.grid(row=2, column=2, sticky=\"w\")\n\n#Engine Speed selection\nSpeedEn0 = tk.Label(root, text=\"Engine speed\", font=f_BO10)\nSpeedEn0.grid(row=3, column=0, sticky=\"e\", ipadx=5)\nSpeedEn_ = tk.StringVar()\nSpeedEn1 = tk.Entry(root, textvariable=SpeedEn_, width=wid, justify=\"center\", font=f_10)\nSpeedEn1.grid(row=3, column=1, sticky=\"we\")\nSpeedEn1.insert(\"end\", 0)\nSpeedEn0_1 = tk.Label(root, text=\"[rpm]\", padx=5, font=f_BO10)\nSpeedEn0_1.grid(row=3, column=2, sticky=\"w\")\n\n#Fan Speed selection\nSpeedFan0 = tk.Label(root, text=\"Fan speed\", font=f_BO10)\nSpeedFan0.grid(row=4, column=0, sticky=\"e\", ipadx=5)\nSpeedFan_ = tk.StringVar()\nSpeedFan1 = tk.Entry(root, textvariable=SpeedFan_, width=wid, justify=\"center\", font=f_10)\nSpeedFan1.grid(row=4, column=1, sticky=\"we\")\nSpeedFan1.insert(\"end\", 0)\nSpeedFan0_1 = tk.Label(root, text=\"[rpm]\", padx=5, font=f_BO10)\nSpeedFan0_1.grid(row=4, column=2, sticky=\"w\")\n\n#Inlet tube Diameter\nD0 = tk.Label(root, text=\"Inlet Diameter\", font=f_BO10)\nD0.grid(row=5, column=0, sticky=\"e\", ipadx=5)\nD_ = tk.StringVar()\nD1 = tk.Entry(root, textvariable=D_, width=wid, justify=\"center\", font=f_10)\nD1.grid(row=5, column=1, sticky=\"we\")\nD1.insert(\"end\", 0)\nD0_1 = tk.Label(root, text=\"[mm]\", padx=5, font=f_BO10)\nD0_1.grid(row=5, column=2, sticky=\"w\")\n\n#Impeller torque\nTor = tk.Label(root, text=\"Torque\", font=f_BO10)\nTor.grid(row=6, column=0, sticky=\"e\", ipadx=5)\nTor_ = tk.StringVar()\nT1 = tk.Entry(root, textvariable=Tor_, width=wid, justify=\"center\", font=f_10)\nT1.grid(row=6, column=1, sticky=\"we\")\nT1.insert(\"end\", 0)\nT1_1 = tk.Label(root, text=\"[Nm]\", padx=5, font=f_BO10)\nT1_1.grid(row=6, column=2, sticky=\"w\")\n\nlastRow = 6\nRow0 = lastRow + 1 #last row used in the this previous part + 1\nRowF = Row0 + 6\nRows = [i for i in range(Row0, RowF)] #list of rows\n\ns = 1\n##Dynamic Pressure first pass\nfor i in Rows:\n    ttt = \"Value #{}\".format(str(s))\n    l = tk.Label(root, text=ttt, padx=0, font=f_BO10)\n    l.grid(row=s, column=3, ipadx=0)\n    l_1 = tk.Label(root, text=\"[cmH20]\", padx=5, font=f_BO10)\n    l_1.grid(row=s, column=5, sticky=\"w\", ipadx=0)\n    s += 1\ns = 1\n##Dynamic Pressure second pass\nfor i in Rows:\n    ttt = \"Value #{}\".format(str(s))\n    ll = tk.Label(root, text=ttt, padx=0, font=f_BO10)\n    ll.grid(row=s, column=6, sticky=\"e\", ipadx=10)\n    l_2 = tk.Label(root, text=\"[cmH20]\", padx=5, font=f_BO10)\n    l_2.grid(row=s, column=8, sticky=\"w\", ipadx=0)\n    s += 1\n\n#Values First Pass\nP_1_1 = tk.StringVar()\np1_1 = tk.Entry(root, textvariable=P_1_1, width=wid, justify=\"center\", font=f_10)\np1_1.grid(row=1, column=4)\np1_1.insert(\"end\", 0)\n\nP_2_1 = tk.StringVar()\np2_1 = tk.Entry(root, textvariable=P_2_1, width=wid, justify=\"center\", font=f_10)\np2_1.grid(row=2, column=4)\np2_1.insert(\"end\", 0)\n\nP_3_1 = tk.StringVar()\np3_1 = tk.Entry(root, textvariable=P_3_1, width=wid, justify=\"center\", font=f_10)\np3_1.grid(row=3, column=4)\np3_1.insert(\"end\", 0)\n\nP_4_1 = tk.StringVar()\np4_1 = tk.Entry(root, textvariable=P_4_1, width=wid, justify=\"center\", font=f_10)\np4_1.grid(row=4, column=4)\np4_1.insert(\"end\", 0)\n\nP_5_1 = tk.StringVar()\np5_1 = tk.Entry(root, textvariable=P_5_1, width=wid, justify=\"center\", font=f_10)\np5_1.grid(row=5, column=4)\np5_1.insert(\"end\", 0)\n\nP_6_1 = tk.StringVar()\np6_1 = tk.Entry(root, textvariable=P_6_1, width=wid, justify=\"center\", font=f_10)\np6_1.grid(row=6, column=4) \np6_1.insert(\"end\", 0)\n\n#Values Second Pass\nP_1_2 = tk.StringVar()\np1_2 = tk.Entry(root, textvariable=P_1_2, width=wid, justify=\"center\", font=f_10)\np1_2.grid(row=1, column=7)\np1_2.insert(\"end\", 0)\n\nP_2_2 = tk.StringVar()\np2_2 = tk.Entry(root, textvariable=P_2_2, width=wid, justify=\"center\", font=f_10)\np2_2.grid(row=2, column=7)\np2_2.insert(\"end\", 0)\n\nP_3_2 = tk.StringVar()\np3_2 = tk.Entry(root, textvariable=P_3_2, width=wid, justify=\"center\", font=f_10)\np3_2.grid(row=3, column=7)\np3_2.insert(\"end\", 0)\n\nP_4_2 = tk.StringVar()\np4_2 = tk.Entry(root, textvariable=P_4_2, width=wid, justify=\"center\", font=f_10)\np4_2.grid(row=4, column=7)\np4_2.insert(\"end\", 0)\n\nP_5_2 = tk.StringVar()\np5_2 = tk.Entry(root, textvariable=P_5_2, width=wid, justify=\"center\", font=f_10)\np5_2.grid(row=5, column=7)\np5_2.insert(\"end\", 0)\n\nP_6_2 = tk.StringVar()\np6_2 = tk.Entry(root, textvariable=P_6_2, width=wid, justify=\"center\", font=f_10)\np6_2.grid(row=6, column=7)\np6_2.insert(\"end\", 0)\n\n#Duct Pressure\ns1 = \"Duct Pressure\"\ns1 = tk.Label(root, text=s1, padx=0, font=f_BO10)\ns1.grid(row=1, column=9, sticky=\"e\", ipadx=10)\ns1_1 = tk.Label(root, text=\"[cmH20]\", padx=5, font=f_BO10)\ns1_1.grid(row=1, column=11, sticky=\"w\", ipadx=0)\n\nstaticP = tk.StringVar()\nstp = tk.Entry(root, textvariable=staticP, width=wid, justify=\"center\", font=f_10)\nstp.grid(row=1, column=10)\nstp.insert(\"end\", 0)\n\n#Fan pressure\ns2 = \"Fan Pressure\"\ns2 = tk.Label(root, text=s2, padx=0, font=f_BO10)\ns2.grid(row=2, column=9, sticky=\"e\", ipadx=10)\ns2_1 = tk.Label(root, text=\"[cmH20]\", padx=5, font=f_BO10)\ns2_1.grid(row=2, column=11, sticky=\"w\", ipadx=0)\n\nstaticFan = tk.StringVar()\nstf = tk.Entry(root, textvariable=staticFan, width=wid, justify=\"center\", font=f_10)\nstf.grid(row=2, column=10)\nstf.insert(\"end\", 0)\n\n#Body pressure\ns3 = \"Body Pressure\"\ns3 = tk.Label(root, text=s3, padx=0, font=f_BO10)\ns3.grid(row=3, column=9, sticky=\"e\", ipadx=10)\ns3_1 = tk.Label(root, text=\"[cmH20]\", padx=5, font=f_BO10)\ns3_1.grid(row=3, column=11, sticky=\"w\", ipadx=0)\n\nstaticBody = tk.StringVar()\nstb = tk.Entry(root, textvariable=staticBody, width=wid, justify=\"center\", font=f_10)\nstb.grid(row=3, column=10)\nstb.insert(\"end\", 0)\n\n#################Text Remark\ntext = tk.Text(root, state='normal', width=32, height=6, wrap='none')\ntext.insert(\"end\", '  Insert here some remarks')\ntext.place(x=910, y=172)\n\n########################################################### END input section ###\n\n######################\n###### Outputs Section\nl03 = tk.Label(root, text=\"Tube air-flow\", font=f_BO12)\nl03.place(x=150, y=295)\nl04 = tk.Label(root, text=\"Flow specific energy\", font=f_BO12)\nl04.place(x=405, y=295)\nl05 = tk.Label(root, text=\"System performance\", font=f_BO12)\nl05.place(x=670, y=295)\n\nr1 = \"Dynamic pressure\"\nr1 = tk.Label(root, text=r1, padx=0, font=f_BO10)\nr1.grid(row=8, column=0, sticky=\"e\", ipadx=5)\nr1_1 = tk.Label(root, text=\"[mmH20]\", padx=5, font=f_BO10)\nr1_1.grid(row=8, column=2, sticky=\"w\")\nframe1 = tk.Frame(width=widout, height=25, bg=\"white\", colormap=\"new\", relief=tk.SUNKEN, bd=1)\nframe1.grid(row=8, column=1, sticky=\"we\")\n\nr2 = \"Duct Velocity\"\nr2 = tk.Label(root, text=r2, padx=0, font=f_BO10)\nr2.grid(row=9, column=0, sticky=\"e\", ipadx=5)\nr2_1 = tk.Label(root, text=\"[m/s]\", padx=5, font=f_BO10)\nr2_1.grid(row=9, column=2, sticky=\"w\")\nframe2 = tk.Frame(width=widout, height=25, bg=\"white\", colormap=\"new\", relief=tk.SUNKEN, bd=1)\nframe2.grid(row=9, column=1, sticky=\"we\")\n\nr3 = \"Flow rate\"\nr3 = tk.Label(root, text=r3, padx=0, font=f_BO10)\nr3.grid(row=10, column=0, sticky=\"e\", ipadx=5)\nr3_1 = tk.Label(root, text=\"[m\\u00b3/s]\", padx=5, font=f_BO10)\nr3_1.grid(row=10, column=2, sticky=\"w\")\nframe3 = tk.Frame(width=widout, height=25, bg=\"white\", colormap=\"new\", relief=tk.SUNKEN, bd=1)\nframe3.grid(row=10, column=1, sticky=\"we\")\n\nr4 = \"Density\"\nr4 = tk.Label(root, text=r4, padx=0, font=f_BO10)\nr4.grid(row=11, column=0, sticky=\"e\", ipadx=5)\nr4_1 = tk.Label(root, text=\"[kg/m\\u00b3]\", padx=5, font=f_BO10)\nr4_1.grid(row=11, column=2, sticky=\"w\")\nframe4 = tk.Frame(width=widout, height=25, bg=\"white\", colormap=\"new\", relief=tk.SUNKEN, bd=1)\nframe4.grid(row=11, column=1, sticky=\"we\")\n\nr5 = \"Fan Pressure\"\nr5 = tk.Label(root, text=r5, padx=0, font=f_BO10)\nr5.place(x=350, y=332)\nr5_1 = tk.Label(root, text=\"[cmH20]\", padx=5, font=f_BO10)\nr5_1.grid(row=8, column=5, sticky=\"w\")\nframe5 = tk.Frame(width=widout, height=25, bg=\"white\", colormap=\"new\", relief=tk.SUNKEN, bd=1)\nframe5.grid(row=8, column=4, sticky=\"we\")\n\nr6 = \"Body Pressure\"\nr6 = tk.Label(root, text=r6, padx=0, font=f_BO10)\nr6.place(x=340, y=370)\nr6_1 = tk.Label(root, text=\"[cmH20]\", padx=5, font=f_BO10)\nr6_1.grid(row=9, column=5, sticky=\"w\")\nframe6 = tk.Frame(width=widout, height=25, bg=\"white\", colormap=\"new\", relief=tk.SUNKEN, bd=1)\nframe6.grid(row=9, column=4, sticky=\"we\")\n\nr7 = \"Developed Power\"\nr7 = tk.Label(root, text=r7, padx=0, font=f_BO10)\nr7.place(x=320, y=412)\nr7_1 = tk.Label(root, text=\"[kW]\", padx=5, font=f_BO10)\nr7_1.grid(row=10, column=5, sticky=\"w\")\nframe7 = tk.Frame(width=widout, height=25, bg=\"white\", colormap=\"new\", relief=tk.SUNKEN, bd=1)\nframe7.grid(row=10, column=4, sticky=\"we\")\n\nr8 = \"Consumed Power\"\nr8 = tk.Label(root, text=r8, padx=0, font=f_BO10)\nr8.place(x=320, y=451)\nr8_1 = tk.Label(root, text=\"[kW]\", padx=5, font=f_BO10)\nr8_1.grid(row=11, column=5, sticky=\"w\")\nframe8 = tk.Frame(width=widout, height=25, bg=\"white\", colormap=\"new\", relief=tk.SUNKEN, bd=1)\nframe8.grid(row=11, column=4, sticky=\"we\")\n\nr9 = \"Fan\\nEfficiency\"\nr9 = tk.Label(root, text=r9, padx=0, font=f_BO10)\nr9.place(x=639, y=323)\nr9_1 = tk.Label(root, text=\"[%]\", padx=5, font=f_BO10)\nr9_1.grid(row=8, column=8, sticky=\"w\")\nframe9 = tk.Frame(width=widout, height=25, bg=\"white\", colormap=\"new\", relief=tk.SUNKEN, bd=1)\nframe9.grid(row=8, column=7, sticky=\"we\")\nframe9.grid_propagate(0)\n\nr10 = \"Hopper\\nEfficiency\"\nr10 = tk.Label(root, text=r10, padx=0, font=f_BO10)\nr10.place(x=639, y=364)\nr10_1 = tk.Label(root, text=\"[%]\", padx=5, font=f_BO10)\nr10_1.grid(row=9, column=8, sticky=\"w\")\nframe10 = tk.Frame(width=widout, height=25, bg=\"white\", colormap=\"new\", relief=tk.SUNKEN, bd=1)\nframe10.grid(row=9, column=7, sticky=\"we\")\nframe10.grid_propagate(0)\n\n###################\n#####   Buttons\nxb1=950\nyb1=292\nphoto_cal = ImageTk.PhotoImage(file=\"imgs/calculate.png\")\nb0 = tk.Button(root,image=photo_cal, text=\"Calculate\", command=calculon, font=f_BO10)\nb0.config(height=88, width=90)\nb0[\"bg\"] = \"grey77\"\nb0[\"border\"] = \"2\"\nb0[\"activebackground\"]= \"grey77\"\nb0[\"highlightthickness\"] = \"0\"\nb0.place(x=xb1, y=yb1)\n\nphoto_canc1 = ImageTk.PhotoImage(file=\"imgs/clean1.png\")\n#cltx1 = \"Clean\"+\"\\nPass#1\"\ncl1 = tk.Button(root, image=photo_canc1, command=ClEaN_1, font=f_BO10)\ncl1.config(height=88, width=90)\ncl1[\"bg\"] = \"grey77\"\ncl1[\"border\"] = \"2\"\ncl1[\"highlightthickness\"] = \"0\"\ncl1[\"activebackground\"]= \"grey77\"\ncl1.place(x=xb1, y=yb1+100)\n\nphoto_canc2 = ImageTk.PhotoImage(file=\"imgs/clean2.png\")\n#cltx2 = \"Clean\"+\"\\nPass#2\"\ncl2 = tk.Button(root, image=photo_canc2, command=ClEaN_2, font=f_BO10)\ncl2.config(height=88, width=90)\ncl2[\"bg\"] = \"grey77\"\ncl2[\"border\"] = \"2\"\ncl2[\"highlightthickness\"] = \"0\"\ncl2[\"activebackground\"]= \"grey77\"\ncl2.place(x=xb1+110, y=yb1+100)\n\nphoto_save = ImageTk.PhotoImage(file=\"imgs/save.png\")\nb1 = tk.Button(root, image=photo_save, command=lambda: saveEx(Par, staticP_,\\\nstaticFan_, staticBody_, P_first, P_second, AVGH, Vduct, Qduct, devPow, conPow,\\\neffFan, effHop, remark),font=f_BO10)\nb1.config(height=88, width=90)\nb1[\"bg\"] = \"grey77\"\nb1[\"border\"] = \"2\"\nb1[\"highlightthickness\"] = \"0\"\nb1[\"activebackground\"]= \"grey77\"\nb1.place(x=xb1+110, y=yb1)\n\n#####################\nvv = \"2.0\"\nvers = tk.Label(root, text=\"Ver.{}\".format(vv), font=f_ver)\nvers.place(x=1160, y=480)\nvers.configure(bg=\"grey77\")\nroot.mainloop() #looping the frame\n\n#######################################################################\n##http://www.science.smith.edu/dftwiki/index.php/Color_Charts_for_TKinter\n\n","sub_path":"Experimental00.pyw","file_name":"Experimental00.pyw","file_ext":"pyw","file_size_in_byte":22228,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"244483123","text":"import couchdb\nimport shapefile\nimport json\n\n\ndef inPolygonCheck(polys, test):\n    retVal = False\n    i=0\n    j=len(polys)-1\n\n    while i<len(polys):\n        if ( ((polys[i][1]>=test.y) != (polys[j][1]>=test.y)) and (test.x<=(polys[j][0]-polys[i][0])*(test.y-polys[i][1])/(polys[j][1]-polys[i][1])+polys[i][0]) ):\n            retVal = not retVal\n        j = i\n        i += 1\n\n    return retVal\n\n# a = [[1, 20], [10, 20], [10, 3], [1, 3]]\n# b = type('Point', (object,), {\"x\": 10, \"y\": 10})\n# print(inPolygonCheck(a, b))\n\n# sf = shapefile.Reader(\"blackspots/98a94386-6975-42b4-97db-bc09f3a9805c\")\n# sf = shapefile.Reader(\"data/Victorian_Features_of_Interest__Polygon__-_Vicmap_FOI/shp/242af68e-b541-4548-ad12-deb4f2102f57\")\nf = open('data/Victorian_Road_Traffic_Volumes.json/data2412558373899220745.json', 'r')\n\nwhile 1:\n    line = f.readline()\n    if not line:\n        break\n\n    d = json.loads(line)\n\n    # print (d['features'][0]['geometry'].keys())\n    # print (d['features'][0]['properties'].keys())\n    # print()\n\n# (sport facility:*), (reserve:park, city square), (recreational resource:*)\n    polys = []\n    for geom in d['features']:\n        temp = type('Polygon', (object,), { \"prop\": [], \"coor\":[] })\n        temp.prop = geom['properties']\n        temp.coor = geom['geometry']['coordinates'][0]\n        polys.append(temp)\n\ncouch = couchdb.Server('http://115.146.93.17:5984')\ndbsource = couch['aus_dev2']\n\nitems = []\n# for row in L:\nfor id in dbsource:\n    row = dbsource[id]\n    # print(row['sentiment'])\n    item = type('Point', (object,), {\"x\": 0, \"y\": 0, \"sentiment\": 0, \"properties\": \"unknown\"})\n    # item.x = row['value'][0]['coordinates'][1]\n    # item.y = row['value'][0]['coordinates'][0]\n    if ('geo' in row and row['geo'] is not None):\n        item.x = row['geo']['coordinates'][1]\n        item.y = row['geo']['coordinates'][0]\n    if ('sentiment' in row):\n        item.sentiment = row['sentiment']\n    else:\n        print(row)\n        continue\n    for poly in polys:\n        retVal = inPolygonCheck(poly.coor, item)\n        if retVal:\n            item.properties = str(poly.prop)\n            break\n    items.append(item)\n\nitemgrouppos = dict()\nitemgroupneg = dict()\nitemgroupneut = dict()\ncountneut = 0\ncountpos = 0\ncountneg = 0\nfor item in items:\n    if item.properties not in itemgrouppos:\n        itemgrouppos[item.properties] = 0\n        itemgroupneg[item.properties] = 0\n        itemgroupneut[item.properties] = 0\n    if item.sentiment == 'negative':\n        itemgroupneg[item.properties] += 1\n        countneg += 1\n    elif item.sentiment == 'neutral':\n        itemgroupneut[item.properties] += 1\n        countneut += 1\n    else:\n        itemgrouppos[item.properties] += 1\n        countpos += 1\n\nfor key in itemgrouppos.keys():\n    print (key, 'pos', itemgrouppos[key], 'neg', itemgroupneg[key], 'neut', itemgroupneut[key])\nprint('pos', countpos, 'neg', countneg, 'neut', countneut)\n\n\n\n\n","sub_path":"存档/欣然/past example/vicroadtagger.py","file_name":"vicroadtagger.py","file_ext":"py","file_size_in_byte":2915,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"430065088","text":"# -*- coding: utf-8 -*-\n\n\"\"\"Functions for Condition CRUD.\"\"\"\nfrom typing import Optional\n\nfrom django.contrib.auth.decorators import user_passes_test\nfrom django.http import HttpRequest, JsonResponse\nfrom django.template.loader import render_to_string\nfrom django.utils.decorators import method_decorator\nfrom django.utils.html import escape\nfrom django.views import generic\n\nfrom ontask.action.forms import ConditionForm, FilterForm\nfrom ontask.core.decorators import ajax_required, get_action, get_condition\nfrom ontask.core.permissions import UserIsInstructor, is_instructor\nfrom ontask.dataops.formula import EVAL_TXT, evaluate_formula, get_variables\nfrom ontask.models import Action, Condition, Log, Workflow\n\n\ndef save_condition_form(\n    request: HttpRequest,\n    form,\n    template_name: str,\n    action: Action,\n    is_filter: Optional[bool] = False,\n) -> JsonResponse:\n    \"\"\"\n    Process the AJAX form to create and update conditions and filters.\n\n    :param request: HTTP request\n\n    :param form: Form being used to ask for the fields\n\n    :param template_name: Template being used to render the form\n\n    :param action: The action to which the condition is attached to\n\n    :param is_filter: The condition is a filter\n\n    :return: JSON response\n    \"\"\"\n    if request.method == 'POST' and form.is_valid():\n\n        if not form.has_changed():\n            return JsonResponse({'html_redirect': None})\n\n        if is_filter and form.instance.id is None and action.get_filter():\n            # Should not happen. Go back to editing the action\n            return JsonResponse({'html_redirect': ''})\n\n        is_new = form.instance.id is None\n\n        # Update fields and save the condition\n        condition = form.save(commit=False)\n        condition.formula_text = None\n        condition.action = action\n        condition.is_filter = is_filter\n        condition.save()\n        condition.columns.set(action.workflow.columns.filter(\n            name__in=get_variables(condition.formula),\n        ))\n\n        # If the request has the 'action_content' field, update the action\n        action_content = request.POST.get('action_content')\n        if action_content:\n            action.set_text_content(action_content)\n\n        propagate_changes(condition, form.changed_data, form.old_name, is_new)\n\n        # Store the type of event to log\n        if is_new:\n            if is_filter:\n                log_type = Log.FILTER_CREATE\n            else:\n                log_type = Log.CONDITION_CREATE\n        else:\n            if is_filter:\n                log_type = Log.FILTER_UPDATE\n            else:\n                log_type = Log.CONDITION_UPDATE\n\n        # Log the event\n        Log.objects.register(\n            request.user,\n            log_type,\n            action.workflow,\n            {\n                'id': condition.id,\n                'name': condition.name,\n                'selected_rows': condition.n_rows_selected,\n                'formula': condition.get_formula_text(),\n            })\n\n        return JsonResponse({'html_redirect': ''})\n\n    # GET request or invalid form\n    return JsonResponse({\n        'html_form': render_to_string(\n            template_name,\n            {\n                'form': form,\n                'action_id': action.id,\n                'condition': form.instance},\n            request=request),\n    })\n\n\nclass ConditionFilterCreateView(UserIsInstructor, generic.TemplateView):\n    \"\"\"Class to create a filter.\"\"\"\n\n    form_class = None\n\n    template_name = None\n\n    @method_decorator(user_passes_test(is_instructor))\n    @method_decorator(ajax_required)\n    @method_decorator(get_action(pf_related=['actions', 'columns']))\n    def get(self, request: HttpRequest, *args, **kwargs) -> JsonResponse:\n        \"\"\"Process GET request to create a filter.\"\"\"\n        return save_condition_form(\n            request,\n            self.form_class(action=kwargs['action']),\n            self.template_name,\n            kwargs.get('action'),\n            is_filter=isinstance(self, FilterCreateView))\n\n    @method_decorator(user_passes_test(is_instructor))\n    @method_decorator(ajax_required)\n    @method_decorator(get_action(pf_related=['actions', 'columns']))\n    def post(self, request: HttpRequest, *args, **kwargs) -> JsonResponse:\n        \"\"\"Process POST request to  create a filter.\"\"\"\n        return save_condition_form(\n            request,\n            self.form_class(request.POST, action=kwargs['action']),\n            self.template_name,\n            kwargs['action'],\n            is_filter=isinstance(self, FilterCreateView))\n\n\nclass FilterCreateView(ConditionFilterCreateView):\n    \"\"\"Process AJAX request to create a filter through AJAX calls.\n\n    It receives the action IDwhere the condition needs to be connected.\n    \"\"\"\n\n    form_class = FilterForm\n\n    template_name = 'action/includes/partial_filter_addedit.html'\n\n\n@user_passes_test(is_instructor)\n@ajax_required\n@get_condition(pf_related='columns', is_filter=True)\ndef edit_filter(\n    request: HttpRequest,\n    pk: int,\n    workflow: Optional[Workflow] = None,\n    condition: Optional[Condition] = None,\n) -> JsonResponse:\n    \"\"\"Edit the filter of an action through AJAX.\n\n    :param request: HTTP request\n\n    :param pk: condition ID\n\n    :return: AJAX response\n    \"\"\"\n    # Render the form with the Condition information\n    return save_condition_form(\n        request,\n        FilterForm(\n            request.POST or None,\n            instance=condition,\n            action=condition.action),\n        'action/includes/partial_filter_addedit.html',\n        condition.action,\n        is_filter=True)\n\n\n@user_passes_test(is_instructor)\n@ajax_required\n@get_condition(pf_related='columns', is_filter=True)\ndef delete_filter(\n    request: HttpRequest,\n    pk: int,\n    workflow: Optional[Workflow] = None,\n    condition: Optional[Condition] = None,\n) -> JsonResponse:\n    \"\"\"Handle the AJAX request to delete a filter.\n\n    :param request: AJAX request\n\n    :param pk: Filter ID\n\n    :return: AJAX response\n    \"\"\"\n    if request.method == 'GET':\n        return JsonResponse({\n            'html_form': render_to_string(\n                'action/includes/partial_filter_delete.html',\n                {'id': condition.id},\n                request=request),\n        })\n\n    # If the request has 'action_content', update the action\n    action_content = request.POST.get('action_content')\n    if action_content:\n        condition.action.set_text_content(action_content)\n        condition.action.save()\n\n    # Log the event\n    formula = condition.get_formula_text()\n\n    Log.objects.register(\n        request.user,\n        Log.FILTER_DELETE,\n        condition.action.workflow,\n        {\n            'id': condition.id,\n            'name': condition.name,\n            'selected_rows': condition.n_rows_selected,\n            'formula': formula,\n        },\n    )\n\n    # Get the action object for further processing\n    action = condition.action\n\n    # Perform the delete operation\n    condition.delete()\n\n    # Number of selected rows now needs to be updated in all remaining\n    # conditions\n    action.update_n_rows_selected()\n    action.rows_all_false = None\n    action.save()\n\n    return JsonResponse({'html_redirect': ''})\n\n\nclass ConditionCreateView(ConditionFilterCreateView):\n    \"\"\"Handle AJAX requests to create a non-filter condition.\"\"\"\n\n    form_class = ConditionForm\n\n    template_name = 'action/includes/partial_condition_addedit.html'\n\n\n@user_passes_test(is_instructor)\n@ajax_required\n@get_condition(pf_related=['columns'])\ndef edit_condition(\n    request: HttpRequest,\n    pk: int,\n    workflow: Optional[Workflow] = None,\n    condition: Optional[Condition] = None,\n) -> JsonResponse:\n    \"\"\"Handle the AJAX request to edit a condition.\n\n    :param request: AJAX request\n\n    :param pk: Condition ID\n\n    :return: AJAX reponse\n    \"\"\"\n    # Render the form with the Condition information\n    return save_condition_form(\n        request,\n        ConditionForm(\n            request.POST or None,\n            instance=condition,\n            action=condition.action),\n        'action/includes/partial_condition_addedit.html',\n        condition.action)\n\n\n@user_passes_test(is_instructor)\n@ajax_required\n@get_condition(pf_related='columns')\ndef delete_condition(\n    request: HttpRequest,\n    pk: int,\n    workflow: Optional[Workflow] = None,\n    condition: Optional[Condition] = None,\n) -> JsonResponse:\n    \"\"\"Handle the AJAX request to delete a condition.\n\n    :param request: HTTP request\n\n    :param pk: condition or filter id\n\n    :return: AJAX response to render\n    \"\"\"\n    # Treat the two types of requests\n    if request.method == 'POST':\n        # If the request has the 'action_content', update the action\n        action_content = request.POST.get('action_content')\n        if action_content:\n            condition.action.set_text_content(action_content)\n            condition.action.save()\n\n        formula = condition.get_formula_text()\n\n        Log.objects.register(\n            request.user,\n            Log.CONDITION_DELETE,\n            condition.action.workflow,\n            {'id': condition.id,\n             'name': condition.name,\n             'formula': formula})\n\n        # Perform the delete operation\n        condition.delete()\n\n        # Reset the count of number of rows with all conditions false\n        condition.action.rows_all_false = None\n        condition.action.save()\n\n        return JsonResponse({'html_redirect': ''})\n\n    return JsonResponse({\n        'html_form': render_to_string(\n            'action/includes/partial_condition_delete.html',\n            {'condition_id': condition.id},\n            request=request),\n    })\n\n\ndef propagate_changes(condition, changed_data, old_name, is_new):\n    \"\"\"Propagate changes in the condition to the rest of the action.\n\n    If the formula has been modified, the action rows_all_false is flushed and\n    the relevant conditions are updated.\n\n    If the name has changed, the text_content in the action is updated.\n\n    :param condition: Object being manipulated\n\n    :param changed_data: Non-empty list of fields that have changed\n\n    :param old_name: Previous name of the condition\n\n    :param is_new: if the condition has just been created\n\n    :return: Nothing\n    \"\"\"\n    if is_new or 'formula' in changed_data:\n        # Reset the counter of rows with all conditions false\n        condition.action.rows_all_false = None\n        condition.action.save()\n\n        if condition.is_filter:\n            # This update must propagate to the rest of conditions\n            condition.action.update_n_rows_selected()\n            condition.refresh_from_db(fields=['n_rows_selected'])\n        else:\n            # Update the number of rows selected in the condition\n            condition.update_n_rows_selected()\n\n    # If condition name has changed, rename appearances in the content\n    # field of the action.\n    if is_new or 'name' in changed_data:\n        # Performing string substitution in the content and saving\n        replacing = '{{% if {0} %}}'\n        condition.action.text_content = condition.action.text_content.replace(\n            escape(replacing.format(old_name)),\n            escape(replacing.format(condition.name)))\n        condition.action.save()\n","sub_path":"ontask/action/views/condition.py","file_name":"condition.py","file_ext":"py","file_size_in_byte":11252,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"112767297","text":"import os\nimport sys\nsys.path.append(os.path.join(os.path.dirname(__file__), '..'))\nfrom argparse import *\nimport PyFrensie.Geometry.DagMC as DagMC\nimport PyFrensie.Utility.Distribution as Distribution\nimport PyFrensie.MonteCarlo as MonteCarlo\nimport PyFrensie.MonteCarlo.Collision as Collision\nimport PyFrensie.MonteCarlo.ActiveRegion as ActiveRegion\nimport PyFrensie.MonteCarlo.Event as Event\nimport PyFrensie.MonteCarlo.Manager as Manager\nimport PyFrensie.Data as Data\nimport PyFrensie.Utility.Mesh as Mesh\nimport PyFrensie.Utility as Utility\nimport PyFrensie.Utility.MPI as MPI\n\nclass forward_simulation_handler:\n\n  def __init__(self, simulation_name):\n\n    self.simulation_name = simulation_name\n\n  def runSimulation(self,\n                    history_start,\n                    num_histories,\n                    db_path,\n                    geometry_filename,\n                    threads,\n                    adjoint_estimator,\n                    mesh,\n                    energy_discretization,\n                    mpi_session,\n                    iteration):\n    \n    threshold_weight = 1e-7\n    survival_weight = 1\n\n\n    ## Set the simulation properties for forward\n    simulation_properties = MonteCarlo.SimulationProperties()\n    simulation_properties.setParticleMode( MonteCarlo.PHOTON_MODE )\n    simulation_properties.setNumberOfHistories( num_histories )\n    if iteration > 0:\n      simulation_properties.setPhotonRouletteThresholdWeight( threshold_weight )\n      simulation_properties.setPhotonRouletteSurvivalWeight( survival_weight )\n      simulation_properties.setImplicitCaptureModeOn()\n    simulation_properties.setMaxRendezvousBatchSize( int(num_histories) )\n\n    model_properties = DagMC.DagMCModelProperties(geometry_filename)\n    model_properties.setMaterialPropertyName( \"material\" )\n    model_properties.setDensityPropertyName( \"density\" )\n    model_properties.setTerminationCellPropertyName( \"termination.cell\" )\n    model_properties.useFastIdLookup()\n    model = DagMC.DagMCModel( model_properties )\n\n    data_file_type = Data.PhotoatomicDataProperties.Native_EPR_FILE\n\n    ## Set up the materials\n    database = Data.ScatteringCenterPropertiesDatabase( db_path )\n      \n    # Extract the properties for Mn from the database\n    Mn_properties = database.getAtomProperties( Data.ZAID(25000) )\n    \n    # Extract the properties for Ge from the database\n    Ge_properties = database.getAtomProperties( Data.ZAID(32000) )\n\n    # Extract the properties for H from the database\n    H_properties = database.getAtomProperties( Data.ZAID(1000) )\n    \n    # Extract the properties for Pb from the database\n    Pb_properties = database.getAtomProperties( Data.ZAID(82000) )\n\n    # Set the definition for H, Pb, Mn, Ge for this simulation\n    scattering_center_definitions = Collision.ScatteringCenterDefinitionDatabase()\n    Mn_definition = scattering_center_definitions.createDefinition( \"Mn\", Data.ZAID(25000) )\n    Ge_definition = scattering_center_definitions.createDefinition( \"Ge\", Data.ZAID(32000) )\n    H_definition = scattering_center_definitions.createDefinition( \"H\", Data.ZAID(1000) )\n    Pb_definition = scattering_center_definitions.createDefinition( \"Pb\", Data.ZAID(82000) )\n\n    file_version = 0\n    \n    Mn_definition.setPhotoatomicDataProperties( Mn_properties.getSharedPhotoatomicDataProperties( data_file_type, file_version) )\n    Ge_definition.setPhotoatomicDataProperties( Ge_properties.getSharedPhotoatomicDataProperties( data_file_type, file_version) )\n    H_definition.setPhotoatomicDataProperties( H_properties.getSharedPhotoatomicDataProperties( data_file_type, file_version) )\n    Pb_definition.setPhotoatomicDataProperties( Pb_properties.getSharedPhotoatomicDataProperties( data_file_type, file_version) )\n\n    # Set the definition for materials\n    material_definitions = Collision.MaterialDefinitionDatabase()\n\n    material_definitions.addDefinition( \"Mn\", 1, [\"Mn\"], [1.0] )\n    material_definitions.addDefinition( \"Ge\", 2, [\"Ge\"], [1.0] )\n    material_definitions.addDefinition( \"H\", 3, [\"H\"], [1.0] )\n    material_definitions.addDefinition( \"Pb\", 4, [\"Pb\"], [1.0] )\n\n    filled_model = Collision.FilledGeometryModel( db_path, scattering_center_definitions, material_definitions, simulation_properties, model, True )\n\n    event_handler = Event.EventHandler( model, simulation_properties )\n\n    cell_integral_estimator = Event.WeightMultipliedCellCollisionFluxEstimator(1, 1.0, [5], model)\n    cell_integral_estimator.setParticleTypes([MonteCarlo.PHOTON])\n    cell_integral_estimator.setEnergyDiscretization([1e-3, 0.835])\n    event_handler.addEstimator(cell_integral_estimator)\n\n    cell_integral_tl_estimator = Event.WeightMultipliedCellTrackLengthFluxEstimator(2, 1.0, [5], model)\n    cell_integral_tl_estimator.setParticleTypes([MonteCarlo.PHOTON])\n    cell_integral_tl_estimator.setEnergyDiscretization([1e-3, 0.835])\n    event_handler.addEstimator(cell_integral_tl_estimator)\n\n    mesh_estimator = Event.WeightMultipliedMeshTrackLengthFluxEstimator(3, 1.0, mesh)\n    mesh_estimator.setParticleTypes([MonteCarlo.PHOTON])\n    mesh_estimator.setDirectionDiscretization(Event.PQLA, 2, False)\n    mesh_estimator.setEnergyDiscretization(energy_discretization)\n    event_handler.addEstimator(mesh_estimator)\n\n    source, importance_weight_mesh = adjoint_estimator.getForwardSourceAndImportanceWeightMesh(model)\n\n    factory = Manager.ParticleSimulationManagerFactory( filled_model,\n                                                    source,\n                                                    event_handler,\n                                                    simulation_properties,\n                                                    self.simulation_name,\n                                                    \"bin\",\n                                                    threads,\n                                                    history_start )\n\n    factory.setPopulationControl(importance_weight_mesh)\n\n    manager = factory.getManager()\n    manager.useSingleRendezvousFile()\n    \n    ## Run the simulation\n    mpi_session.restoreOutputStreams()\n    manager.runSimulation()\n    mpi_session.initializeOutputStream(0, True)","sub_path":"variance_reduction/weight_importances_tests/shielding_problem_1/iterative/forward_simulation_handler.py","file_name":"forward_simulation_handler.py","file_ext":"py","file_size_in_byte":6172,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"52933117","text":"'''\nThis is the unittest for modified_dqn module\n'''\nimport unittest\n\nimport gym\nimport os\nimport sys\nmodule_path = os.path.abspath(os.path.join('..'))\nif module_path not in sys.path:\n    sys.path.append(module_path)\n\nimport numpy as np\n    \nfrom agents.modified_dqn import DQN, _time_reward\n\nenv = gym.make('CartPole-v0')\nagent = DQN(env)\n\nclass UnitTests(unittest.TestCase):\n    '''\n    Unittest suite\n    '''\n\n    def test_x(self):\n        x_threshold = env.x_threshold\n        low_reward_state = np.array([x_threshold*0.9, 0.1, 0, 0.1])\n        high_reward_state = np.array([0, 0.1, 0, 0.1])\n        low_reward = _time_reward(low_reward_state,env)\n        high_reward = _time_reward(high_reward_state,env)\n        assert low_reward < high_reward, \"The reward structure does not follow desired physics\"\n        \n    def test_x_dot(self):\n        low_reward_state = np.array([1, 0.2, 0, 0.1])\n        high_reward_state = np.array([1, 0.1, 0, 0.1])\n        low_reward = _time_reward(low_reward_state,env)\n        high_reward = _time_reward(high_reward_state,env)\n        assert low_reward < high_reward, \"The reward structure does not follow desired physics\"\n        \n    def test_theta(self):\n        theta_threshold = env.theta_threshold_radians\n        low_reward_state = np.array([0, 0.1, theta_threshold*0.9, 0.1])\n        high_reward_state = np.array([0, 0.1, 0, 0.1])\n        low_reward = _time_reward(low_reward_state,env)\n        high_reward = _time_reward(high_reward_state,env)\n        assert low_reward < high_reward, \"The reward structure does not follow desired physics\"\n        \n    def test_theta_dot(self):\n        low_reward_state = np.array([1, 0.1, 0, 0.2])\n        high_reward_state = np.array([1, 0.1, 0, 0.1])\n        low_reward = _time_reward(low_reward_state,env)\n        high_reward = _time_reward(high_reward_state,env)\n        assert low_reward < high_reward, \"The reward structure does not follow desired physics\"\n        \n        \nsuite = unittest.TestLoader().loadTestsFromTestCase(UnitTests)\n_ = unittest.TextTestRunner().run(suite)\n\nagent.train_file.close()\nos.remove(agent.train_file.name)","sub_path":"cartpole_RL/tests/test_modified_dqn.py","file_name":"test_modified_dqn.py","file_ext":"py","file_size_in_byte":2124,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"450419690","text":"import asyncio\nfrom typing import AsyncGenerator, Any, Type, List\n\nfrom datetime import timedelta\nfrom deepdiff import DeepDiff\nfrom pytest import fixture, mark\n\nfrom core.message_bus import MessageBus, Message, Event, Action, ActionDone, ActionError\nfrom core.model.typed_model import to_js, from_js\nfrom core.util import AnyT, utc, first\n\n\n@fixture\ndef message_bus() -> MessageBus:\n    return MessageBus()\n\n\n@fixture\nasync def all_events(message_bus: MessageBus) -> AsyncGenerator[List[Message], None]:\n    events: List[Message] = []\n\n    async def gather_events() -> None:\n        async with message_bus.subscribe(\"test\") as event_queue:\n            while True:\n                events.append(await event_queue.get())\n\n    run_gather = asyncio.create_task(gather_events())\n    try:\n        yield events\n    finally:\n        run_gather.cancel()\n\n\nasync def wait_for_message(\n    all_events: List[Message], message_type: str, t: Type[AnyT], timeout: timedelta = timedelta(seconds=1)\n) -> AnyT:\n    stop_at = utc() + timeout\n\n    async def find() -> AnyT:\n        result = first(lambda m: isinstance(m, t) and m.message_type == message_type, all_events)  # type: ignore\n        if result:\n            return result  # type: ignore\n        elif utc() > stop_at:\n            raise TimeoutError()\n        else:\n            await asyncio.sleep(0.1)\n            return await find()\n\n    return await find()\n\n\n@mark.asyncio\nasync def test_handler(message_bus: MessageBus) -> None:\n    foos: List[Message] = []\n    blas: List[Message] = []\n\n    async def emit() -> None:\n        await message_bus.emit(Event(\"foo\"))\n        await message_bus.emit(Event(\"foo\"))\n        await message_bus.emit(Event(\"bla\"))\n        await message_bus.emit(Event(\"bar\"))\n\n    async def wait_for(name: str, list: List[Message]) -> None:\n        async with message_bus.subscribe(\"test\", [name]) as events:\n            while True:\n                list.append(await events.get())\n\n    foo_t = asyncio.create_task(wait_for(\"foo\", foos))\n    bla_t = asyncio.create_task(wait_for(\"bla\", blas))\n\n    await asyncio.sleep(0.1)\n    await emit()\n    await asyncio.sleep(0.1)\n    assert len(foos) == 2\n    assert len(blas) == 1\n    foo_t.cancel()\n    await emit()\n    await asyncio.sleep(0.1)\n    assert len(foos) == 2\n    assert len(blas) == 2\n    bla_t.cancel()\n\n\ndef test_message_serialization() -> None:\n    roundtrip(Event(\"test\", {\"a\": \"b\", \"c\": 1, \"d\": \"bla\"}))\n    roundtrip(Action(\"test\", \"123\", \"step_name\"))\n    roundtrip(Action(\"test\", \"123\", \"step_name\", {\"test\": 1}))\n    roundtrip(ActionDone(\"test\", \"123\", \"step_name\", \"sub\"))\n    roundtrip(ActionDone(\"test\", \"123\", \"step_name\", \"sub\", {\"test\": 1}))\n    roundtrip(ActionError(\"test\", \"123\", \"step_name\", \"sub\", \"oops\"))\n    roundtrip(ActionError(\"test\", \"123\", \"step_name\", \"sub\", \"oops\", {\"test\": 23}))\n\n\ndef roundtrip(obj: Any) -> None:\n    js = to_js(obj)\n    again = from_js(js, type(obj))\n    assert DeepDiff(obj, again) == {}, f\"Json: {js} serialized as {again}\"\n","sub_path":"ckcore/tests/core/message_bus_test.py","file_name":"message_bus_test.py","file_ext":"py","file_size_in_byte":2995,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"400267766","text":"# -*- coding: utf-8 -*-\n# Copyright (c) 2019, Dirk van der Laarse and contributors\n# For license information, please see license.txt\n\nfrom __future__ import unicode_literals\nimport frappe\nfrom frappe.model.document import Document\nfrom frappe import _\nfrom frappe.desk.form import assign_to\n\n\nclass EngineeringChangeRequest(Document):\n    def validate(self):\n        # Set title on save\n        self.title = self.name  + \" - \" + self.part_name\n\n        # Change status if new ECR is submitted\n        if self.get(\"__islocal\"):\n            self.status = \"Needs Review\"\n            self.ecr_requested_date = self.creation\n\n\n    def create_tasks(self):\n        # Check that at least one item is \"Applicable\" in checklist table\n        applicable_items = 0\n        all_required_dates_entered = True\n        for item in self.ecr_checklist:\n            if item.is_applicable == 1:\n                applicable_items += 1\n                if not item.required_date:\n                    all_required_dates_entered = False\n\n        if applicable_items == 0:\n            frappe.throw(_(\"Please mark at least one checklist item as applicable\"))\n        elif not all_required_dates_entered:\n            frappe.throw(_(\"Please add a Required Date for all checklist items\"))\n        else:\n            tasks_created = 0\n            for checklist_item in self.ecr_checklist:\n                if not checklist_item.task and checklist_item.is_applicable == 1:\n                    # Create task\n                    task = frappe.new_doc(\"Task\")\n                    task.flags.ignore_permissions = True\n                    task.subject = self.name + \": \" + checklist_item.description\n                    task.project = \"ECR Open Item List\"\n                    task.origin_document_type = \"Engineering Change Request\"\n                    task.origin_document = self.name\n                    task.exp_end_date = checklist_item.required_date\n                    task.save()\n                    task.origin_child_field_reference = str(self.idx) + \": \" + checklist_item.description\n                    # Assign Task to user (as ToDo)\n                    args = {\n                        'doctype' : 'Task',\n                        'assign_to' : checklist_item.responsible,\n                        'name' : task.name,\n                        'description' : task.subject,\n                        'date' : checklist_item.required_date,\n                        'notify' : 1\n                    }\n                    assign_to.add(args)\n\n                    checklist_item.task = task.name\n                    tasks_created += 1\n            self.status = \"In Progress\"\n            frappe.msgprint(str(tasks_created) + \" New Tasks Created\")\n            self.save()\n\n    def remove_non_applicable(self):\n        # Remove all non-applicable items from checklist\n        items_to_remove = []\n        for checklist_item in self.ecr_checklist:\n            if checklist_item.is_applicable == 0:\n              items_to_remove.append(checklist_item)\n        for i in items_to_remove:\n              self.remove(i)\n\n        # Recalculate idx for child records\n        x = 1\n        for checklist_item in self.ecr_checklist:\n            checklist_item.idx = x\n            x = x + 1\n\n        self.save()\n\n    def get_checklist(self):\n        checks = frappe.db.sql(\"\"\"\n            SELECT responsible, description\n            FROM `tabEngineering Change Request Requirement Master`\"\"\", as_dict=True)\n\n        rows = self.get(\"ecr_checklist\")\n        for row in rows:\n            self.get(\"ecr_checklist\").remove(row)#delete all rows in the table\n        for i in checks:\n            row = self.append('ecr_checklist', {})#Append row to checklist with description & responsible person set by default\n            row.description = i.description\n            row.is_applicable = 0\n            row.responsible = i.responsible\n            row.required_date = \"\"\n            row.completed = \"\"\n            row.completed_date = \"\"\n        self.status = \"Review in Progress\"\n\n    def close(self):\n        self.add_comment('Comment', text=_(\"ECR Approved and Closed\"), link_doctype=\"Engineering Change Request\", link_name=self.name)\n        self.status = \"Closed\"\n\n@frappe.whitelist()\ndef update_checklist(doc, method):\n    \"\"\"\n    Check if item checklist needs to be updated\n    \"\"\"\n    if (doc.origin_document_type == \"Engineering Change Request\"):\n        # If task is closed, mark it as such on the ECR and find all tasks linked to this task's ECR to check if all of them are closed out\n        ecr = frappe.get_doc(\"Engineering Change Request\", doc.origin_document)\n        ecr.flags.ignore_permissions = True\n        if (doc.status == \"Closed\"):\n            for r in ecr.ecr_checklist:\n                if r.task == doc.name:\n                    r.completed = 1\n                    r.completed_date = doc.modified\n                    break\n            frappe.msgprint(ecr.name + \" updated\" )\n            ecr.save()\n\n        # If task is open, revert checklist and audit status\n        if (doc.status == \"Open\"):\n            update = False\n            if ecr.status != 'In Progress' and ecr.status != 'Review in Progress':\n                update = True\n            for r in ecr.ecr_checklist:\n                if r.task == doc.name and r.completed == 1:\n                    r.completed = 0\n                    update = True\n                    break\n            if update:\n                ecr.save()\n","sub_path":"quality_management_system/development/doctype/engineering_change_request/engineering_change_request.py","file_name":"engineering_change_request.py","file_ext":"py","file_size_in_byte":5446,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"41799637","text":"import time\nimport torch\nfrom torch import nn\nfrom torch.utils.tensorboard import SummaryWriter\nfrom torchsummary import summary\nimport os\nimport os.path as osp\nimport sys\nimport torch\nfrom torch.optim.lr_scheduler import _LRScheduler\n\n\nclass WarmUpLR(_LRScheduler):\n\n    def __init__(self, optimizer, total_epoch, init_lr=1e-6, mode='linear', last_epoch=-1, verbose=False):\n\n        self.total_epoch = total_epoch\n        self.init_lr = init_lr\n        self.mode = mode\n\n        super(WarmUpLR, self).__init__(optimizer, last_epoch, verbose)\n        for base_lr in self.base_lrs:\n            assert self.init_lr < base_lr\n\n    def get_lr(self):\n        if self.mode == 'constant':\n            if self._step_count > self.total_epoch:\n                return self.base_lrs\n            return [self.init_lr] * len(self.base_lrs)\n        if self.mode == 'linear':\n            if self._step_count == 1:\n                self.linear_coef = [(base_lr - self.init_lr) / (self.total_epoch + 1e-8) for base_lr in self.base_lrs]\n            return [self.init_lr + coef * self.last_epoch for coef in self.linear_coef]\n        if self.mode == 'exponential':\n            if self._step_count == 1:\n                self.exp_coef = [(base_lr / self.init_lr) ** (1 / self.total_epoch) for base_lr in self.base_lrs]\n            return [self.init_lr * (coef ** self.last_epoch) for coef in self.exp_coef]\n\n\nclass Logger(object):\n    \"\"\"将stdout重定位到log文件和控制台\n        即print时会将信息打印在控制台的\n        同时也将信息保存在log文件中\n    \"\"\"\n\n    def __init__(self, filename):\n        self.console = sys.stdout\n        self.log = open(filename, \"a\")\n\n    def write(self, message):\n        self.console.write(message)\n        self.log.write(message)\n\n    # 清空log文件中的内容\n    def clean(self):\n        self.log.truncate(0)\n\n    def flush(self):\n        pass\n\n\nclass TemplateModel:\n\n    def __init__(self):\n        # 必须设定\n        # 模型架构\n        # 将模型和优化器以list保存，方便对整个模型的多个部分设定对应的优化器\n        self.model_list = None  # 模型的list\n        self.optimizer_list = None  # 优化器的list\n        self.criterion = None\n        # 数据集\n        self.train_loader = None\n        self.eval_loader = None\n        # check_point 目录\n        self.ckpt_dir = \"./check_point/\" + time.strftime(\"%Y-%m-%d::%H:%M:%S\")\n        # 运行设备\n        self.device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\n        # 下面的可以不设定\n        # 推荐设置\n        self.lr_scheduler_list = None\n        self.lr_scheduler_type = None  # None \"metric\" \"loss\" \"annealing\" \"step\"\n        self.warm_up_epoch = 0\n        self.warm_up_mode = 'linear'\n        self.warm_up_scheduler_list = None\n        # tensorboard\n        self.writer = SummaryWriter(log_dir=os.path.join(self.ckpt_dir, 'runs'))\n\n        # 按需求设定\n        # 训练时print的间隔\n        self.log_per_step = 5  # 推荐按数据集大小设定\n\n        # 训练状态\n        self.global_step = 0\n        self.global_step_eval = 0\n        self.epoch = 1\n        self.best_metric = {}\n        self.key_metric = None  # 记得在self.metric中设定\n\n    def check_init(self, log_name=\"log.txt\", clean_log=False, arg=None, use_tb=True):\n        # 检测摸板的初始状态，可以在这加上很多在训练之前的操作\n        assert isinstance(self.model_list, list)\n        assert isinstance(self.optimizer_list, list)\n        assert self.criterion\n        assert self.train_loader\n        assert self.eval_loader\n        assert self.device\n        assert self.ckpt_dir\n        assert self.log_per_step\n        assert self.lr_scheduler_type in [None, \"metric\", \"loss\", \"annealing\", \"step\"]\n        # 建立check point 目录\n        if not osp.exists(self.ckpt_dir):\n            os.makedirs(self.ckpt_dir)\n        # 默认的warm up，也可以自定义self.warm_up_scheduler_list\n        if self.warm_up_epoch > 0 and self.warm_up_scheduler_list is None:\n            self.warm_up_scheduler_list = [WarmUpLR(optimizer,\n                                                    total_epoch=self.warm_up_epoch,\n                                                    last_epoch=-1,\n                                                    mode=self.warm_up_mode,\n                                                    verbose=True\n                                                    )\n                                           for optimizer in self.optimizer_list]\n\n        # 默认的lr_scheduler，也可以自定义self.lr_scheduler_list\n        if self.lr_scheduler_list is None and self.lr_scheduler_type is not None:\n            # 如果以测试集的metric为学习率改变的依据，选择mode=\"max\";以loss为依据，选择mode=\"min\"\n            if self.lr_scheduler_type in ['metric', 'loss']:\n                mode = None\n                if self.lr_scheduler_type == \"metric\":\n                    mode = \"max\"\n                elif self.lr_scheduler_type == \"loss\":\n                    mode = \"min\"\n                self.lr_scheduler_list = [torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,\n                                                                                     mode=mode,\n                                                                                     factor=0.1,\n                                                                                     patience=5,\n                                                                                     cooldown=5,\n                                                                                     min_lr=1e-9,\n                                                                                     verbose=True\n                                                                                     )\n                                          for optimizer in self.optimizer_list]\n            elif self.lr_scheduler_type == 'step':\n                self.lr_scheduler_list = [torch.optim.lr_scheduler.StepLR(optimizer,\n                                                                          step_size=15,\n                                                                          gamma=0.1,\n                                                                          last_epoch=-1,\n                                                                          verbose=True\n                                                                          )\n                                          for optimizer in self.optimizer_list]\n            elif self.lr_scheduler_type == 'annealing':\n                self.lr_scheduler_list = [torch.optim.lr_scheduler.CosineAnnealingLR(optimizer,\n                                                                                     T_max=15,\n                                                                                     eta_min=0,\n                                                                                     last_epoch=-1,\n                                                                                     verbose=True\n                                                                                     )\n                                          for optimizer in self.optimizer_list]\n\n        # 设置log,log保存目录为\"self.ckpt_dir/log_name\"\n        logger = Logger(os.path.join(self.ckpt_dir, log_name))\n        if clean_log:\n            logger.clean()\n        sys.stdout = logger\n        print(time.strftime(\"%Y-%m-%d::%H-%M-%S\"))\n        # 如果有，打印arg\n        if arg is not None:\n            print(15 * \"=\", \"args\", 15 * \"=\")\n            arg_dict = arg.__dict__\n            for key in arg_dict.keys():\n                print(f\"{key}:{arg_dict[key]}\")\n        # 不使用TensorBoard，比如debug时\n        if not use_tb:\n            self.writer = None\n        # 清空cuda中的cache\n        torch.cuda.empty_cache()\n        # 模型默认放置于GPU上\n        for model in self.model_list:\n            model.to(self.device)\n\n    def load_state(self, fname, optim=True, lr_list=None):\n        # 读取保存的模型到模板之中。如果要继续训练的模型optim=True；使用最佳模型做推断optim=False\n        state = torch.load(fname)\n        for idx, model in enumerate(self.model_list):\n            if isinstance(model, torch.nn.DataParallel):  # 多卡训练\n                model.module.load_state_dict(state[f'model{idx}'])\n            else:  # 非多卡训练\n                model.load_state_dict(state[f'model{idx}'])\n            # 恢复一些状态参数\n            if optim and f'optimizer_list{idx}' in state:\n                self.optimizer_list[idx].load_state_dict(state[f'optimizer_list{idx}'])\n                # 改变先前模型的优化器中保存的学习率\n                if lr_list is not None:\n                    self.change_lr(lr_list)\n\n        self.global_step = state['global_step']\n        self.global_step_eval = state[\"global_step_eval\"]\n        self.epoch = state['epoch']\n        self.best_metric = state['best_metric']\n        self.key_metric = state['key_metric']\n        print('load model state from {}'.format(fname))\n\n    def save_state(self, fname, optim=True):\n        # 保存模型，其中最佳模型不用保存优化器中的参数。\n        # 而训练过程中保存的其他模型需要保存优化器中的梯度以便继续训练\n        state = {}\n        for idx, model in enumerate(self.model_list):\n            if isinstance(model, torch.nn.DataParallel):\n                state[f'model{idx}'] = model.module.state_dict()\n            else:\n                state[f'model{idx}'] = model.state_dict()\n            # 训练过程中的模型除了保存模型的参数外，还要保存当前训练的状态：optim中的参数\n            if optim:\n                state[f'optimizer_list{idx}'] = self.optimizer_list[idx].state_dict()\n        state['global_step'] = self.global_step\n        state['global_step_eval'] = self.global_step_eval\n        state['epoch'] = self.epoch\n        state['best_metric'] = self.best_metric\n        state['key_metric'] = self.key_metric\n        torch.save(state, fname)\n        print('save model state at {}'.format(fname))\n\n    def save_model(self, fname):\n        \"\"\"\n        直接保存整个模型，不是模板！模型复杂时，生成文件会很大\n        \"\"\"\n        all_models = {}\n        for idx, model in enumerate(self.model_list):\n            if isinstance(model, torch.nn.DataParallel):\n                all_models[f'model{idx}'] = model.module\n            else:\n                all_models[f'model{idx}'] = model\n        torch.save(all_models, fname)\n        print('save model at {}'.format(fname))\n\n    def train_loop(self, write_params=False, clip_grad=None):\n        \"\"\"训练一个epoch，一般来说不用改\"\"\"\n        print(\"*\" * 15, f\"epoch:{self.epoch}\", \"*\" * 15)\n        for model in self.model_list:\n            model.train()\n\n        running_loss_dict = None  # other loss only for tensorboard\n        all_avg_loss_dict = None\n        cnt_loss = 0\n        for step, batch in enumerate(self.train_loader):\n            self.global_step += 1\n            batch_loss_dict = self.loss_per_batch(batch)\n            # total loss used for backward\n            batch_tol_loss = batch_loss_dict['tol_loss']\n\n            # 多个优化器需要按逆序更新每一个优化器\n            for optimizer in reversed(self.optimizer_list):\n                optimizer.zero_grad()\n\n            batch_tol_loss.backward()\n\n            # 截断太大的梯度\n            if clip_grad is not None:\n                for model in self.model_list:\n                    nn.utils.clip_grad_norm_(model.parameters(), max_norm=clip_grad, norm_type=2)\n\n            for optimizer in reversed(self.optimizer_list):\n                optimizer.step()\n\n            # 累计running loss\n            if running_loss_dict is None:\n                running_loss_dict = batch_loss_dict\n            else:\n                for loss, value in batch_loss_dict.items():\n                    running_loss_dict[loss] += value\n\n            # 记录损失除了训练刚开始时是用此时的loss外，其他都是用一批loss的平均loss\n            # 但是为了tensorboard的曲线好看，不记录第一个样本的loss\n            if self.global_step == 1:\n                # if self.writer is not None:\n                #     self.writer.add_scalar('train_loss', batch_loss.item(), self.global_step):\n                print(f\"loss:{batch_tol_loss.item() : .5f}\\t\"\n                      f\"cur:[{step * self.train_loader.batch_size}]\\[{len(self.train_loader.dataset)}]\"\n                      )\n\n            # 每处理self.log_per_step批数据就打印和记录这批数据的的平均loss\n            elif (step + 1) % self.log_per_step == 0:\n                avg_loss_dict = {}\n                for loss, value in running_loss_dict.items():\n                    avg_loss_dict[loss] = value / (self.log_per_step * len(batch))\n                for avg_loss, value in avg_loss_dict.items():\n                    print(f\"{avg_loss}:{value : .5f}\\t\"\n                          f\"cur:[{(step + 1) * self.train_loader.batch_size}]\\[{len(self.train_loader.dataset)}]\"\n                          )\n                # 累计avg_loss\n                if all_avg_loss_dict is None:\n                    all_avg_loss_dict = avg_loss_dict\n                else:\n                    for loss, value in avg_loss_dict.items():\n                        all_avg_loss_dict[loss] += value\n\n                cnt_loss += 1\n                for loss, value in running_loss_dict.items():\n                    running_loss_dict[loss] = 0.0  # running loss 归零.用于累计下N批次数据的loss\n\n                # Tensorboard记录self.log_per_step批数据的的平均loss\n                if self.writer is not None:\n                    # 平均loss\n                    if len(avg_loss_dict) == 1:\n                        self.writer.add_scalar('train_loss', avg_loss_dict['tol_loss'], self.global_step)\n                    else:\n                        self.writer.add_scalars('train_loss', avg_loss_dict, self.global_step)\n\n                    # write_params=True在Tensorboard中记录模型的参数和梯度的分布情况，但是也费时间。默认关闭\n                    if write_params:\n                        for model in self.model_list:\n                            for tag, value in model.named_parameters():\n                                tag = tag.replace('.', '/')\n                                self.writer.add_histogram('weights/' + tag, value.data.cpu().numpy())\n                                if value.grad is not None:  # 在FineTurn时有些参数被冻结了，没有梯度。也就不用记录了\n                                    self.writer.add_histogram('grads/' + tag, value.grad.data.cpu().numpy())\n\n        # 训练一个epoch后，记录训练集全部样本的平均loss\n        avg_batch_loss_dict = {}\n        for loss, value in all_avg_loss_dict.items():\n            avg_batch_loss_dict[loss] = value / cnt_loss\n        for loss, value in avg_batch_loss_dict.items():\n            print(f\"epoch:{self.epoch}\\tavg_epoch|{loss}:{value:.5f}\")\n\n        # Tensorboard记录一个epoch的平均loss\n        if self.writer is not None:\n            if len(avg_batch_loss_dict) == 1:\n                self.writer.add_scalar(\"avg_epoch_train_loss\",\n                                       avg_batch_loss_dict['tol_loss'],\n                                       self.epoch\n                                       )\n            else:\n                self.writer.add_scalars(\"avg_epoch_train_loss\", avg_batch_loss_dict, self.epoch)\n            # 用于观测模型是否过拟合/欠拟合\n            self.writer.add_scalars(\"avg_epoch_train&eval|tol_loss\",\n                                    {\"train\": avg_batch_loss_dict[\"tol_loss\"]},\n                                    self.epoch\n                                    )\n\n        # step模式更新lr,在每个train_loop后更新\n        # annealing模式 更新lr. 根据迭代step更新\n        if self.lr_scheduler_type == 'annealing':\n            self.update_lr_scheduler()\n        if self.lr_scheduler_type == 'step':\n            self.update_lr_scheduler()\n\n        # self.epoch += 1\n\n    def eval_loop(self, save_per_epochs=1):\n        \"\"\"一个epoch的评估。一般不用改\"\"\"\n        print(\"-\" * 15, \"Evaluation\", \"-\" * 15)\n        # 在整个测试集上做评估，使用分批次的metric的平均值表示训练集整体的metric\n        with torch.no_grad():\n            # 先计算，当前模型在验证集上时的loss\n            # 累计running loss\n            running_loss_dict = None\n            all_avg_loss_dict = None\n            # 保存各种metric的得分\n            scores = {}\n            cnt = 0\n            cnt_loss = 0\n            for step, batch in enumerate(self.eval_loader):\n                cnt += 1  # 记录迭代了多少batch\n                self.global_step_eval += 1\n                # 计算评估时多个批次的平均loss\n                batch_loss_dict = self.loss_per_batch(batch)\n                # 累计running loss\n                if running_loss_dict is None:\n                    running_loss_dict = batch_loss_dict\n                else:\n                    for loss, value in batch_loss_dict.items():\n                        running_loss_dict[loss] += value\n\n                # 每self.log_per_step个step打印信息\n                if (step + 1) % self.log_per_step == 0:\n                    avg_loss_dict = {}\n                    for loss, value in running_loss_dict.items():\n                        avg_loss_dict[loss] = value / (self.log_per_step * len(batch))\n                    for avg_loss, value in avg_loss_dict.items():\n                        print(f\"{avg_loss}:{value : .5f}\\t\"\n                              f\"cur:[{(step + 1) * self.eval_loader.batch_size}]\\[{len(self.eval_loader.dataset)}]\"\n                              )\n\n                    if all_avg_loss_dict is None:\n                        all_avg_loss_dict = avg_loss_dict\n                    else:\n                        for loss, value in avg_loss_dict.items():\n                            all_avg_loss_dict[loss] += value\n\n                    cnt_loss += 1\n                    for loss, value in running_loss_dict.items():\n                        running_loss_dict[loss] = 0.0  # running loss 归零.用于累计下N批次数据的loss\n\n                    if self.writer is not None:\n                        # 平均loss\n                        if len(avg_loss_dict) == 1:\n                            self.writer.add_scalar('eval_loss', avg_loss_dict['tol_loss'], self.global_step_eval)\n                        else:\n                            self.writer.add_scalars('eval_loss', avg_loss_dict, self.global_step_eval)\n\n                # 分批计算metric，并累计\n                if step == 0:\n                    scores = self.eval_scores_per_batch(batch)  # 每个epoch初始化scores\n                # 累加所有批次的metric。这里有个问题：\n                # 准确率可以使用分批的准确率之和除以分批数量得到，并且与用全部数据集计算准确率是等价的\n                # 但是有的metric使用一部分批次的计算出来的结果可能与使用全部数据集计算出来的结果不同\n                else:\n                    batch_scores = self.eval_scores_per_batch(batch)\n                    for key in scores.keys():\n                        scores[key] += batch_scores[key]\n                if self.global_step_eval == 1:\n                    self.best_metric = scores  # 第一次eval时，初始化self.best_metric\n\n            # 整个验证集上的平均running_loss\n            avg_batch_loss_dict = {}\n            for loss, value in all_avg_loss_dict.items():\n                avg_batch_loss_dict[loss] = value / cnt_loss\n            # for loss, value in avg_batch_loss_dict.items():\n            #     print(f\"epoch:{self.epoch}\\tavg_epoch_{loss}:{value:.5f}\")\n\n            # 整个验证集上的平均metric\n            for key in scores.keys():\n                scores[key] /= cnt\n            # 打印信息:一个epoch上的平均loss和关键指标\n            for avg_loss, value in avg_batch_loss_dict.items():\n                print(f\"epoch:{self.epoch}\\tavg_epoch_eval|{avg_loss}:{value:.5f}\")\n            for avg_metric, value in scores.items():\n                print(f'epoch:{self.epoch}\\tavg_metric|{avg_metric}:{value:.5f}')\n\n            # 根据scores[self.key_metric]来判定是否保存最佳模型.\n            # self.key_metric需要在metric函数中初始化，分类任务常用self.key_metric = \"acc\"\n            for key in scores.keys():\n                # 更新所有metric的最佳结果到self.best_metric字典中\n                if scores[key] >= self.best_metric[key]:\n                    self.best_metric[key] = scores[key]\n\n                    # 保存最佳模型\n                    if key == self.key_metric:\n                        self.save_state(osp.join(self.ckpt_dir, f'best.pth'), False)\n\n            # 每save_per_epochs次评估就保存当前模型，这种模型一般用于继续训练\n            if self.epoch % save_per_epochs == 0:\n                self.save_state(osp.join(self.ckpt_dir, f'epoch{self.epoch}.pth'), True)\n\n            # Tensorboard\n            if self.writer is not None:\n                # 记录每个epoch的metric\n                # for key in scores.keys():\n                self.writer.add_scalars(f\"avg_epoch_eval_metric\", scores, self.epoch)\n\n                # 记录一个epoch中验证集中全部样本的平均loss\n                if len(avg_batch_loss_dict) == 1:\n                    self.writer.add_scalar(\"avg_epoch_eval_loss\",\n                                           avg_batch_loss_dict['tol_loss'],\n                                           self.epoch\n                                           )\n                else:\n                    self.writer.add_scalars(\"avg_epoch_eval_loss\", avg_batch_loss_dict, self.epoch)\n                self.writer.add_scalars(\"avg_epoch_train&eval|tol_loss\",\n                                        {\"eval\": avg_batch_loss_dict['tol_loss']},\n                                        self.epoch\n                                        )\n\n            # 根据验证集的情况更新lr.在每个eval_loop后更新\n            if self.lr_scheduler_type == 'metric':\n                self.update_lr_scheduler(key_metric=scores[self.key_metric])\n            elif self.lr_scheduler_type == 'loss':\n                self.update_lr_scheduler(avg_batch_loss=avg_batch_loss_dict['tol_loss'])\n            # self.epoch += 1\n        return scores[self.key_metric]\n\n    def update_lr_scheduler(self, **kwargs):\n        \"\"\"\n        更新lr_scheduler.使用lr_scheduler.step()的时机主要分3种\n        1. 在train_loop后使用，此时lr_scheduler根据训练的epoch来更新lr.适用于\"annealing\", \"step\"type\n        2. 在eval_loop后使用，此时lr_scheduler根据评估的指标来更新lr.适用于\"metric\", \"loss\"type\n        3. 在train_loop中的loos_per_batch后使用，此时lr_scheduler根据训练的iter来更新lr.\n        warm up机制使用epoch作为lr更新的milestone\n        \"\"\"\n        assert self.lr_scheduler_list is not None\n        if self.warm_up_epoch > 0 and self.epoch <= self.warm_up_epoch:\n            print(f'Warm UP:\\tcur epoch[{self.epoch}]/[{self.warm_up_epoch}]')\n            for warm_up_scheduler in self.warm_up_scheduler_list:\n                warm_up_scheduler.step()\n\n        else:\n            if self.lr_scheduler_type in [\"annealing\", \"step\"]:\n                if self.lr_scheduler_type == 'annealing':\n                    for lr_scheduler in self.lr_scheduler_list:\n                        lr_scheduler.step()\n                if self.lr_scheduler_type == 'step':\n                    for lr_scheduler in self.lr_scheduler_list:\n                        lr_scheduler.step()\n            if self.lr_scheduler_type in [\"metric\", \"loss\"]:\n                for i, lr_scheduler in enumerate(self.lr_scheduler_list):\n                    if self.lr_scheduler_type == \"metric\":\n                        lr_scheduler.step(kwargs['key_metric'])\n                    elif self.lr_scheduler_type == \"loss\":\n                        lr_scheduler.step(kwargs['avg_batch_loss'])\n        # tensorboard记录学习率\n        if self.writer is not None:\n            for i, lr_scheduler in enumerate(self.lr_scheduler_list):\n                self.writer.add_scalar(f\"lr_scheduler{i}\",\n                                       self.optimizer_list[i].param_groups[0][\"lr\"],\n                                       self.epoch\n                                       )\n\n    def loss_per_batch(self, **kwargs):\n        \"\"\"\n        每个batch，计算损失的逻辑。按需求修改。\n        保证输出为loss字典，其中必须包含loss['tol_loss']\n        \"\"\"\n        raise NotImplementedError\n\n    def eval_scores_per_batch(self, **kwargs):\n        \"\"\"\n        每个batch，计算评估分数的逻辑，配合metric使用。按需求修改\n        保证输出为scores字典，其中必须包含scores[self.key_metric]\n        \"\"\"\n        raise NotImplementedError\n\n    def metric(self, **kwargs):\n        \"\"\"\n        计算性能指标，配合eval_scores_per_batch使用。\n        在这里面需要设置self.key_metric\n        \"\"\"\n        raise NotImplementedError\n\n    def inference(self, **kwargs):\n        \"\"\"不需要使用就pass\"\"\"\n        raise NotImplementedError\n\n    def get_model_info(self, fake_inp):\n        # 输出模型信息和在Tensorboard中绘制模型计算图\n        print(15 * \"=\", \"model info\", 15 * \"=\")\n        if self.writer is not None:\n            for model in self.model_list:\n                self.writer.add_graph(model, fake_inp.to(self.device))\n                # summary对transformer有BUG\n                print(summary(model, batch_size=32, input_size=fake_inp.shape[1:], device=self.device))\n\n    def num_parameters(self):\n        num = 0\n        for model in self.model_list:\n            num += sum([p.data.nelement() for p in model.parameters()])\n        return num\n\n    def print_best_metrics(self):\n        print(f\"Bast Metric\")\n        for key in self.best_metric.keys():\n            print(f\"{key}:\\t{self.best_metric[key]}\")\n\n    def print_final_lr(self):\n        print(\"Final Learning Rate:\")\n        for i, optimizer in enumerate(self.optimizer_list):\n            print(f\"final_lr_{i}:{optimizer.param_groups[0]['lr']}\")\n\n    def print_all_member(self, print_model=False):\n        print(15 * \"=\", \"template config\", 15 * \"=\")\n        # 不重要，不需要打印的信息\n        except_member = [\"best_metric\", \"key_metric\", \"train_loader\", \"test_loader\", \"writer\"]\n        # 模型信息太长了，选择打印\n        if not print_model:\n            except_member.append(\"model_list\")\n        for name, value in vars(self).items():\n            if name not in except_member:\n                print(f\"{name}:{value}\")\n\n    def change_lr(self, lr_list):\n        \"\"\"\n        改变优化器中记录的学习率，主要用于使用已有的模型继续训练时。\n        指定新的学习率，而不是已有模型的优化器中保存的学习率。\n        \"\"\"\n        for i, optimizer in enumerate(self.optimizer_list):\n            optimizer.param_groups[0]['lr'] = lr_list[i]\n","sub_path":"template.py","file_name":"template.py","file_ext":"py","file_size_in_byte":27533,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"448122973","text":"from KratosMultiphysics import *\nimport KratosMultiphysics.ConstitutiveModelsApplication as KratosMaterialModels\n\n######################### general parameters\nnnodes = 3\ndim = 3\n\n#define a model part and create new nodes\nmodel = Model()\nmodel_part = model.CreateModelPart(\"test\")\nnode1 = model_part.CreateNewNode(1,0.0,0.0,0.0)\nnode2 = model_part.CreateNewNode(2,1.0,0.0,0.0)\nnode3 = model_part.CreateNewNode(3,0.0,1.0,0.0)\n\n#material properties\nprop_id = 0\nproperties = model_part.Properties[prop_id]\nproperties.SetValue(YOUNG_MODULUS, 100.0)\nproperties.SetValue(POISSON_RATIO, 0.3)\n\nC10 = 200e9/(4*(1+0.3))\nproperties.SetValue(KratosMaterialModels.C10, C10)\n\n#allocate a geometry\n#a = PointerVector()\n#a.append(node1)\n#geom = Geometry(a)\n#geom = Geometry()\ngeom = Triangle2D3(node1,node2,node3)\nprint(geom)\n\nN = Vector(3)\nDN_DX = Matrix(3,2)\n\n######################################## here we choose the constitutive law #########################\n#construct a constitutive law\nelasticity_model = KratosMaterialModels.SaintVenantKirchhoffModel()\ncl  = KratosMaterialModels.LargeStrain3DLaw(elasticity_model)\n\n#plasticity_model = KratosMaterialModels.VonMisesNeoHookeanPlasticityModel()\n#cl  = KratosMaterialModels.LargeStrain3DLaw(plasticity_model)\n\ncl.Check( properties, geom, model_part.ProcessInfo )\n\nif(cl.WorkingSpaceDimension() != dim):\n    raise Exception( \"mismatch between the WorkingSpaceDimension of the Constitutive Law and the dimension of the space in which the test is performed\")\n\n##set the parameters to be employed\n#note that here i am adding them all to check that this does not fail\ncl_options = Flags()\ncl_options.Set(ConstitutiveLaw.USE_ELEMENT_PROVIDED_STRAIN, True)\ncl_options.Set(ConstitutiveLaw.COMPUTE_STRESS, True)\ncl_options.Set(ConstitutiveLaw.COMPUTE_CONSTITUTIVE_TENSOR, True)\n#cl_options.Set(ConstitutiveLaw.COMPUTE_STRAIN_ENERGY, False)\n#cl_options.Set(ConstitutiveLaw.ISOCHORIC_TENSOR_ONLY, False)\n#cl_options.Set(ConstitutiveLaw.VOLUMETRIC_TENSOR_ONLY, False)\n#cl_options.Set(ConstitutiveLaw.FINALIZE_MATERIAL_RESPONSE, False)\n\n##from here below it should be an output not an input\n#cl_options.Set(ConstitutiveLaw.FINITE_STRAINS, False)\n#cl_options.Set(ConstitutiveLaw.INFINITESIMAL_STRAINS, False)\n#cl_options.Set(ConstitutiveLaw.PLANE_STRAIN_LAW, False)\n#cl_options.Set(ConstitutiveLaw.PLANE_STRESS_LAW, False)\n#cl_options.Set(ConstitutiveLaw.AXISYMMETRIC_LAW, False)\n#cl_options.Set(ConstitutiveLaw.U_P_LAW, False)\n#cl_options.Set(ConstitutiveLaw.ISOTROPIC, False)\n#cl_options.Set(ConstitutiveLaw.ANISOTROPIC, False)\n\nfrom math import sqrt\n\nF = Matrix(3,3)\nF[0,0] = 1.0; F[0,1] = 0.0; F[0,2] = 2.0;\nF[1,0] = 0.0; F[1,1] = 0.9; F[1,2] = 0.0;\nF[2,0] = 0.0; F[2,1] = 0.0; F[2,2] = 0.1;\ndetF = 0.09\n\nstress_vector = Vector(cl.GetStrainSize())\nstrain_vector = Vector(cl.GetStrainSize())\n\n\nconstitutive_matrix = Matrix(cl.GetStrainSize(),cl.GetStrainSize())\n\n#setting the parameters - note that a constitutive law may not need them all!\ncl_params = ConstitutiveLawParameters()\ncl_params.SetOptions( cl_options )\ncl_params.SetDeformationGradientF( F )\ncl_params.SetDeterminantF( detF )\ncl_params.SetStrainVector( strain_vector )\ncl_params.SetStressVector( stress_vector )\ncl_params.SetConstitutiveMatrix( constitutive_matrix )\ncl_params.SetShapeFunctionsValues( N )\ncl_params.SetShapeFunctionsDerivatives( DN_DX )\ncl_params.SetProcessInfo( model_part.ProcessInfo )\ncl_params.SetMaterialProperties( properties )\ncl_params.SetElementGeometry(geom)\n\n##do all sort of checks\ncl_params.CheckAllParameters() #can not use this until the geometry is correctly exported to python\ncl_params.CheckMechanicalVariables()\ncl_params.CheckShapeFunctions()\n\nprint(\"The Material Response PK2\")\ncl.CalculateMaterialResponsePK2( cl_params )\nprint( \"stress = \", cl_params.GetStressVector() )\nprint( \"strain = \", cl_params.GetStrainVector() )\nprint( \"C      = \", cl_params.GetConstitutiveMatrix() )\n\n#cl.FinalizeMaterialResponsePK2( cl_params )\n\nprint(\"\\n The Material Response Kirchhoff\")\ncl.CalculateMaterialResponseKirchhoff( cl_params )\nprint( \"stress = \", cl_params.GetStressVector() )\nprint( \"strain = \", cl_params.GetStrainVector() )\nprint( \"C      = \", cl_params.GetConstitutiveMatrix() )\n\ncl.FinalizeMaterialResponseKirchhoff( cl_params )\n\nprint(\"\\n The Material Response Cauchy\")\ncl.CalculateMaterialResponseCauchy( cl_params )\nprint( \"stress = \", cl_params.GetStressVector() )\nprint( \"strain = \", cl_params.GetStrainVector() )\nprint( \"C      = \", cl_params.GetConstitutiveMatrix() )\n\ncl.FinalizeMaterialResponseCauchy( cl_params )\n","sub_path":"applications/ConstitutiveModelsApplication/tests/examples/begin_J2_SS_example/example_constitutive_model_call.py","file_name":"example_constitutive_model_call.py","file_ext":"py","file_size_in_byte":4552,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"211036448","text":"import trafaret as t\n\nfrom datarobot.models.api_object import APIObject\nfrom datarobot.utils import encode_utf8_if_py2\n\nResidualsTrafaret = {\n    t.Key(\"residual_mean\"): t.Float,\n    t.Key(\"coefficient_of_determination\"): t.Float,\n    t.Key(\"standard_deviation\", optional=True): t.Float,\n}\n\n\nclass ResidualsChart(APIObject):\n    \"\"\" Residual analysis chart data for model.\n\n    .. versionadded:: v2.18\n\n    This data is calculated over a randomly downsampled subset of the source data\n    (capped at 1000 rows).\n\n    Notes\n    -----\n\n    ``ResidualsChartRow`` is a list of floats and ints containing the following:\n        * Element 0 (float) is the actual target value for the source data row.\n        * Element 1 (float) is the predicted target value for that row.\n        * Element 2 (float) is the error rate of predicted - actual and is optional.\n        * Element 3 (int) is the row number in the source dataset from which the values\n          were selected and is optional.\n\n    Attributes\n    ----------\n    source : str\n        Lift chart data source. Can be 'validation', 'crossValidation' or 'holdout'.\n    data : list\n        List of lists with schema described as ``ResidualsChartRow`` above.\n    coefficient_of_determination : float\n        The r-squared value for the downsampled dataset\n    residual_mean : float\n        The arithmetic mean of the residual (predicted value minus actual value)\n    source_model_id : str\n        ID of the model this chart represents; in some cases,\n        insights from the parent of a frozen model may be used\n    standard_deviation : float\n        standard_deviation of residual values\n    \"\"\"\n\n    _converter = (\n        t.Dict(\n            {\n                t.Key(\"source\"): t.String,\n                t.Key(\"data\"): t.List(t.List(t.Float)),\n                t.Key(\"source_model_id\"): t.String,\n            }\n        )\n        .merge(ResidualsTrafaret)\n        .ignore_extra(\"*\")\n    )\n\n    def __init__(\n        self,\n        source,\n        data,\n        residual_mean,\n        coefficient_of_determination,\n        source_model_id,\n        standard_deviation=None,\n    ):\n        self.source = source\n        self.data = data\n        self.source_model_id = source_model_id\n        self.coefficient_of_determination = coefficient_of_determination\n        self.residual_mean = residual_mean\n        self.standard_deviation = standard_deviation\n\n    def __repr__(self):\n        return encode_utf8_if_py2(u\"ResidualChart({})\".format(self.source))\n","sub_path":"datarobot/models/residuals.py","file_name":"residuals.py","file_ext":"py","file_size_in_byte":2498,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"507426525","text":"from ipywidgets import Checkbox, VBox\n\nfrom ...link import link\nfrom ...widgets import LinkedDropdown\n\n\n__all__ = ['Viewer3DStateWidget']\n\n\nclass Viewer3DStateWidget(VBox):\n\n    def __init__(self, viewer_state):\n\n        self.state = viewer_state\n\n        self.widget_show_axes = Checkbox(value=False, description=\"Show axes\")\n        link((self.state, 'visible_axes'), (self.widget_show_axes, 'value'))\n\n        self.widgets_axis = []\n        for i, axis_name in enumerate('xyz'):\n            widget_axis = LinkedDropdown(self.state, axis_name + '_att', label=axis_name + ' axis')\n            self.widgets_axis.append(widget_axis)\n\n        super().__init__([self.widget_show_axes] + self.widgets_axis)\n","sub_path":"glue_jupyter/ipyvolume/common/viewer_options_widget.py","file_name":"viewer_options_widget.py","file_ext":"py","file_size_in_byte":703,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"505834178","text":"#Imports\nimport serial\nimport serial.tools.list_ports as list_ports\nimport threading\nimport queue\nimport numpy as np\nimport struct\n\n################################################################################\nclass Chunked_Arduino_ADC:\n\t\"\"\"\n\tReads values from the arduino and writes them to a queue\n\t\"\"\"\n\tdef __init__(self, ts_us, chunk_size, record_q, ser_port=None):\n\t\t\"\"\"\n\t\tPURPOSE: creates a new Chunked_Arduino_ADC\n\t\tARGS:\n\t\t\tts_us (int): sampling period of arduino (microseconds)\n\t\t\tchunk_size (int): number of samples to expect in one chunk\n\t\t\trecord_q (Queue): queue to push chunks to\n\t\t\tser_port (str): serial port to listen on, will try to find arduino \n\t\t\t\tif left as None\n\t\tRETURNS: new instance of an Chunked_Arduino_ADC\n\t\tNOTES:\n\t\t\"\"\"\n\t\t#Save arguments\n\t\tself.ts_us = int(ts_us)\n\t\tself.chunk_size = int(chunk_size)\n\t\tself.record_q = record_q\n\t\tself.ser_timeout = self.chunk_size * self.ts_us / 1e6 * 2.5\n\t\tself.ser_port = ser_port\n\n\t\t#Setup record thread variables\n\t\tself.record_thread = None\n\t\tself.record_keep_going = threading.Event()\n\t\tself.record_keep_going.clear()\n\n\t\t#Status variables\n\t\tself.connected = False\n\t\tself.receiving_data = False\n\n\t############################################################################\n\tdef __del__(self):\n\t\t\"\"\"\n\t\tPURPOSE: performs any necessary cleanup\n\t\tARGS: none\n\t\tRETURNS: none\n\t\tNOTES:\n\t\t\"\"\"\n\t\tself.stop()\n\n\t############################################################################\n\tdef start(self):\n\t\t\"\"\"\n\t\tPURPOSE: starts the recording thread\n\t\tARGS: none\n\t\tRETURNS: none\n\t\tNOTES:\n\t\t\"\"\"\n\t\tif self.record_thread == None or not self.is_running():\n\t\t\tself.record_thread = threading.Thread(target = self.run)\n\t\t\tself.record_thread.start()\n\n\t############################################################################\n\tdef stop(self):\n\t\t\"\"\"\n\t\tPURPOSE: stops the recording thread\n\t\tARGS: none\n\t\tRETURNS: none\n\t\tNOTES: blocks until thread stops\n\t\t\"\"\"\n\t\tif self.record_thread:\n\t\t\tself.record_keep_going.clear()\n\t\t\tself.record_thread.join()\n\t\t\tself.record_thread = None\n\n\t############################################################################\n\tdef is_running(self):\n\t\t\"\"\"\n\t\tPURPOSE: checks if the recording thread is running\n\t\tARGS: none\n\t\tRETURNS: True if running, False if stopped\n\t\tNOTES:\n\t\t\"\"\"\n\t\treturn self.record_keep_going.is_set()\n\n\t############################################################################\n\tdef get_status(self):\n\t\t\"\"\"\n\t\tPURPOSE: gets the status of this thread\n\t\tARGS: none\n\t\tRETURNS: dictionary of statuses\n\t\tNOTES:\n\t\t\"\"\"\n\t\tstatus = {\n\t\t\t\"running\" : self.is_running(),\n\t\t\t\"connected\" : self.connected,\n\t\t\t\"receiving_data\" : self.receiving_data\n\t\t}\n\t\treturn status\n\n\t############################################################################\n\tdef run(self):\n\t\t\"\"\"\n\t\tPURPOSE: performs the recording\n\t\tARGS: none\n\t\tRETURNS: none\n\t\tNOTES: calling 'start' runs this in a separate thread\n\t\t\"\"\"\n\t\t#Indicate thread is running\n\t\tself.record_keep_going.set()\n\n\t\tsh = None\n\t\ttry:\n\t\t\t#Run until told to stop\n\t\t\twhile self.is_running():\n\t\t\t\t#Connect to arduino\n\t\t\t\twhile self.is_running() and not self.connected:\n\t\t\t\t\ttry:\n\t\t\t\t\t\tif self.ser_port:\n\t\t\t\t\t\t\tser_port = self.ser_port\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tser_port = None\n\t\t\t\t\t\t\tports = list_ports.comports()\n\t\t\t\t\t\t\tfor port in ports:\n\t\t\t\t\t\t\t\tif port[1].find(\"Arduino Mega 2560\") >= 0:\n\t\t\t\t\t\t\t\t\tser_port = port[0]\n\t\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\tsh = serial.Serial(ser_port, 115200, timeout=self.ser_timeout)\n\t\t\t\t\t\tif sh and not sh.isOpen():\n\t\t\t\t\t\t\tsh.close()\n\t\t\t\t\t\t\tsh = None\n\t\t\t\t\t\t\tself.connected = False\n\t\t\t\t\t\t\tself.receiving_data = False\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tself.connected = True\n\t\t\t\t\texcept serial.serialutil.SerialException as e:\n\t\t\t\t\t\tif sh:\n\t\t\t\t\t\t\tsh.close()\n\t\t\t\t\t\tsh = None\n\t\t\t\t\t\tself.connected = False\n\t\t\t\t\t\tself.receiving_data = False\n\t\t\t\t#We are now connected to the arduino so reset cur idx\n\t\t\t\tcur_idx = 0\n\t\t\t\t#Record from arduino\n\t\t\t\twhile self.is_running() and self.connected:\n\t\t\t\t\ttry:\n\t\t\t\t\t\tsync_count = 0\n\t\t\t\t\t\twhile sync_count < 2:\n\t\t\t\t\t\t\tdata = sh.read(1)\n\t\t\t\t\t\t\tif len(data):\n\t\t\t\t\t\t\t\tif data[0] == 255:\n\t\t\t\t\t\t\t\t\tsync_count += 1\n\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\tsync_count = 0\n\t\t\t\t\t\tdata = sh.read(self.chunk_size * 2)\n\t\t\t\t\t\tsample_chunk = np.array(struct.unpack('<%dH' % self.chunk_size, data))\n\t\t\t\t\t\tself.record_q.put(sample_chunk / 1023 * 5)\n\t\t\t\t\t\tself.receiving_data = True\n\t\t\t\t\texcept (serial.serialutil.SerialException, struct.error) as e:\n\t\t\t\t\t\tself.receiving_data = False\n\t\t\t\t\t\tif not sh.isOpen():\n\t\t\t\t\t\t\tsh.close()\n\t\t\t\t\t\t\tsh = None\n\t\t\t\t\t\tself.connected = False\n\t\texcept Exception as e:\n\t\t\tprint(\"ERROR: 'recorder thread' got exception %s\" % type(e))\n\t\t\tprint(e)\n\t\t\tself.record_keep_going.clear()\n\n\t\t#Cleanup\n\t\tif sh:\n\t\t\tsh.close()\n\t\t\tsh = None\n\t\t\tself.connected = False\n\t\t\tself.receiving_data = False\n\n\t############################################################################\n\n################################################################################\nif __name__ == \"__main__\":\n\timport time\n\timport matplotlib.pyplot as plt\n\n\trecord_q = queue.Queue()\n\trecorder = Chunked_Arduino_ADC(200, 2500, record_q)\n\n\trecorder.start()\n\n\ttry:\n\t\twhile recorder.is_running():\n\t\t\ttime.sleep(1)\n\t\t\tprint(recorder.get_status())\n\texcept KeyboardInterrupt as e:\n\t\tpass\n\n\trecorder.stop()","sub_path":"src/python/Chunked_Arduino_ADC.py","file_name":"Chunked_Arduino_ADC.py","file_ext":"py","file_size_in_byte":5212,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"611320196","text":"#!/usr/bin/env python\n#\n# Copyright 2014 Shea G. Craig\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\nimport glob\nimport os\nimport re\n\nfrom autopkglib import Processor, ProcessorError\n\n\n__all__ = [\"CowsaySourceFinder\"]\n\n\n\nclass CowsaySourceFinder(Processor):\n    \"\"\"Finds the root schacon-cowsay-foo folder from the expanded\n    zip archive, and determines the version, setting the env variable. Also,\n    sets the %PREFIX% variable where needed.\n\n    \"\"\"\n    input_variables = {\n        \"input_path\": {\n            \"required\": True,\n            \"description\": \"Path the zip archive was expanded to.\",\n        },\n    }\n    output_variables = {\n        \"cowsay_path\": {\n            \"description\": \"Root path of expanded cowsay archive.\",\n        },\n        \"version\": {\n            \"description\": \"The version of the software.\",\n        },\n    }\n    description = __doc__\n\n    def find_match(self, root_dir, match_string):\n        \"\"\"Finds a file or directory using shell globbing\"\"\"\n        matches = glob.glob(os.path.join(root_dir, match_string))\n        if matches:\n            return matches[0][len(root_dir) + 1:]\n        else:\n            return \"\"\n\n    def find_version(self, filename):\n        \"\"\"Grabs the version from the cowsay perl file.\"\"\"\n\n        with open(filename, 'r') as f:\n            match = re.search('\\$version = \"([0-9.]+)', f.read())\n        self.env[\"version\"] = match.group(1)\n\n    def replace_text(self, filename, replacement_code, replacement):\n        with open(filename, 'r') as f:\n            cowsay_text = f.read()\n        cowsay_replaced_text = cowsay_text.replace(replacement_code,\n                                                   replacement)\n        with open(filename, 'w') as f:\n            f.write(cowsay_replaced_text)\n\n    def main(self):\n        # Get root dir\n        root_dir = self.env[\"input_path\"]\n        try:\n            autopkg_dir = self.find_match(\n                root_dir, 'schacon-cowsay-*')\n            self.env[\"cowsay_path\"] = os.path.join(\n                root_dir, autopkg_dir)\n            self.output(self.env[\"cowsay_path\"])\n            self.output(\"autopkg_dir %s\" % autopkg_dir)\n            cowsay_file = os.path.join(self.env['cowsay_path'], 'cowsay')\n\n            self.find_version(cowsay_file)\n            self.replace_text(cowsay_file, '%BANGPERL%', '!/usr/bin/perl')\n            self.output(\"Set perl shebang.\")\n            self.replace_text(cowsay_file, '%PREFIX%', '/usr/local')\n            self.output(\"Set cows folder default.\")\n            self.output(\"Found %s\" % self.env[\"cowsay_path\"])\n            self.output(\"Found version is %s\" % self.env[\"version\"])\n        except BaseException as err:\n            raise ProcessorError(err)\n\n\nif __name__ == \"__main__\":\n    processor = CowsaySourceFinder()\n    processor.execute_shell()\n","sub_path":"Cowsay/CowsaySourceFinder.py","file_name":"CowsaySourceFinder.py","file_ext":"py","file_size_in_byte":3308,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"574327165","text":"from __future__ import division\nfrom __future__ import print_function\n\nimport random\nfrom copy import copy\n\nimport graph_tool.all as gt\n\n\ndef random_directed_weights(g):\n    #     ...\n    #     Every edge has random weight.\n    #     After Weights assigned, they have to be nomalized that the summation\n    #     is equal to or less than 1\n    #     ...\n\n    assert type(g) == gt.graph_tool.Graph, 'Graph G should be an instance of graph-tool.Graph'\n    \n    eprop_weight = g.new_edge_property(\"double\")\n\n    for e in g.edges():\n        eprop_weight[e] = random.randint(1, 99)\n\n    for v in g.vertices():\n        _sum = 0\n        for u in v.in_neighbors():\n            _sum += eprop_weight[g.edge(g.vertex_index[u], g.vertex_index[v])]\n        for u in v.in_neighbors():\n            eprop_weight[g.edge(g.vertex_index[u], g.vertex_index[v])] /= _sum\n\n    g.ep.weight = eprop_weight\n\n\ndef initialize_threshold(g, value=0, random=True):\n    if not random:\n        vprop_threshold = g.new_vertex_property(\"double\", val=value)\n    else:\n        print(\"Come here\")\n        vprop_threshold = g.new_vertex_property(\"double\")\n        for v in g.vertices():\n            vprop_threshold[v] = random.random()\n\n    g.vp.threshold = vprop_threshold\n\n\ndef initialize_properties(g):\n    vprop_color = g.new_vertex_property(\"short\", val= -1)\n    g.vp.color = vprop_color\n\n    vprop_weight = g.new_vertex_property(\"object\", val=dict())\n    g.vp.weight = vprop_weight\n\n\ndef single_round_diffusion(g, players):\n    #     ...\n    #     Input: g -- graph\n    #            players -- player class list\n    #     Note that the initial seeds have not colored yet\n    #     ...\n\n    assert type(g) == gt.graph_tool.Graph, 'Graph G should be an instance of graph-tool.Graph'\n    assert type(players) == list, 'Players seed should be an instance of dict'\n\n    vprop_color = g.vp.color\n    vprop_threshold = g.vp.threshold\n    vprop_weight = g.vp.weight\n    eprop_weight = g.ep.weight\n\n    I = -1  # inactivated node with white color\n    temp_activated = dict()\n\n    for p in players:\n        if not p.newly_activated_nodes:\n            continue\n        for u_index in p.newly_activated_nodes:\n            u = g.vertex(u_index)\n            for v in u.out_neighbors():\n                if vprop_color[v] == I:\n                    if p in vprop_weight[v]:\n                        vprop_weight[v][p] += eprop_weight[g.edge(g.vertex_index[u], g.vertex_index[v])]\n                    else:\n                        vprop_weight[v][p] = eprop_weight[g.edge(g.vertex_index[u], g.vertex_index[v])]\n                    \n                    if v not in temp_activated:\n                        temp_activated[v] = p\n                    elif temp_activated[v].id != p.id:\n                        if vprop_weight[v][p] > vprop_weight[v][temp_activated[v]]:\n                            temp_activated[v] = p\n                        elif vprop_weight[v][p] == vprop_weight[v][temp_activated[v]] and random.random() > 0.5:\n                            temp_activated[v] = p\n\n\n\n    for p in players:\n        p.newly_activated_nodes = []\n\n    for v, p in temp_activated.items():\n        vprop_color[v] = p.color\n        p.newly_activated_nodes.append(g.vertex_index[v])\n","sub_path":"scripts/LT.py","file_name":"LT.py","file_ext":"py","file_size_in_byte":3221,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"291891495","text":"import model\r\n\r\nwhile True:\r\n    print(\"\"\"\r\n    1. Add Item\r\n    2. Read Item\r\n    3. Update Item\r\n    4. Delete Item\r\n    5. Search Item\r\n    6. Sort Item\r\n    7. Save Item\r\n    8. Load Item\r\n    9. Exit\r\n    \"\"\")\r\n\r\n    todo = {\r\n        \"1\" : model.add,\r\n        \"2\" : model.read,\r\n        \"3\" : model.update,\r\n        \"4\" : model.delete,\r\n        \"5\" : model.search,\r\n        \"6\" : model.sorting,\r\n        \"7\" : model.save,\r\n        \"8\" : model.load,\r\n        \"9\" : quit\r\n    }\r\n\r\n    user_choice = input(\"Enter your choice : \")\r\n\r\n    todo.get(user_choice,model.errHandler)()\r\n","sub_path":"Applications/Item_CRUD_2/view.py","file_name":"view.py","file_ext":"py","file_size_in_byte":582,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"491698209","text":"'''\nThis version is used when you want to access a remote postgresql database\nand synch it with toodledo\nDon't start with -s\nand when you want to create the database need --db_create\n'''\nimport sip  \nfrom PyQt5 import QtCore, QtGui, QtWidgets\n\nQt = QtCore.Qt\nQAction = QtWidgets.QAction\n\nimport notetextedit\nimport lmdialogs\n\nimport os\nfrom time import asctime, sleep\nimport sys\nimport datetime\nimport platform\nimport configparser as configparser\nimport json\nimport urllib.request, urllib.parse, urllib.error\nimport re\nimport textwrap\nimport base64\nimport io\nimport importlib #for plugins\nimport argparse\nimport tempfile\nfrom subprocess import Popen\nimport resources\nimport requests\nimport base64\nfrom optparse import OptionParser\nfrom functools import partial\n\nimport markdown2 as markdown\nimport config as c\nimport lmglobals_p as g\n\nimport lminterpreter\n\nfrom whoosh.index import create_in\nfrom whoosh.fields import Schema, TEXT, KEYWORD, NUMERIC\nimport whoosh.index as index\nfrom whoosh.query import Term, Or, Prefix\nfrom whoosh.filedb.filestore import FileStorage\nfrom whoosh import analysis\nfrom lmdb_p import *\n\nparser = argparse.ArgumentParser(description='Command line options mainly for debugging purposes.')\n\n# for all of the following: if the command line option is not present then the value is the opposite of the action\nparser.add_argument('-q', '--qsettings', action='store_false', help=\"Don't use QSettings during startup (will *not* save to QSettings on closing\")\nparser.add_argument('-c', '--console', action='store_false', help=\"Disable the use of the console so it doesn't swallow errors during __init__\")\nparser.add_argument('-i', '--ini', action='store_false', help=\"Don't load the tabs on startup that are stored in the ini file (will save to ini file on closing)\")\n\nargs = parser.parse_args()\n\n# age is cython-created function more to check cython out than that it was absolutely necessary\n#try:\n#    from age_mod3 import age # c library using agelib.c that I created in C - note that there are age_mod.pyd, age_mod2.pyd and age_mod3.pyd (the latter tries to free the memory)\n#except ImportError:\n#    print(\"Unable to import age c funtion\")\n#    def age(z):\n#        if z > 1:\n#            return \"{} days\".format(z)\n#        elif z == 1:\n#            return \"yesterday\"\n#        else:\n#            return \"today\"\n\ndef age(z):\n    if z > 1:\n        return \"{} days\".format(z)\n    elif z == 1:\n        return \"yesterday\"\n    else:\n        return \"today\"\n\nsession = remote_session\nengine = remote_engine\n\nVERSION = '0.9'\n\nIMAGES_DIR = g.IMAGES_DIR\n\nTODAY = datetime.date.today()\n\n#decorators\ndef check_modified(f):\n    ''' A decorator that checks if there have been any field changes'''\n    \n    def fn(lm, *args, **kwargs):\n        if lm.modified:\n            print(\"self.modified dict is not empty and so savenote was called\")\n            print(\"self.modified={0}\".format(repr(lm.modified)))\n            print(\"The method that triggered savenote was {0}\".format(f.__name__))\n\n            lm.savenote()\n        return f(lm, *args, **kwargs)\n    return fn\n    \ndef check_task_selected(f):\n    '''A decorator that checks to see if a task was selected before an action'''\n    \n    def fn(lm, *args, **kwargs):\n        if not lm.task or lm.index==-1:\n            QtWidgets.QMessageBox.information(lm,  'Note', \"No row is selected\")\n            return\n        else:\n            return f(lm, *args, **kwargs)\n    return fn\n\ndef update_whooshdb(f):\n    ''' A decorator that updates the whoosh db because of a task change'''\n    \n    def fn(lm, *args, **kwargs):\n        z = f(lm, *args, **kwargs)\n        lm.updatewhooshentry(False, task=lm.task)\n        return z\n    return fn\n   \ndef update_row(f):\n    ''' A decorator that updates the changed row in the table'''\n\n    def fn(lm, *args, **kwargs):\n        z = f(lm, *args, **kwargs)\n        \n        #qtablewidgetitem = QtGui.QTableWidgetItem\n        qcolor = QtGui.QColor\n        #qicon = QtGui.QIcon\n        display = lm.display #dict that just holds display lambda's\n            \n        type_ = 'folder' if lm.Properties['tab']['type'] == 'context' else 'context'\n\n        table = lm.table\n        col_order = lm.col_order\n\n        #deleteditemfont = lm.deleteditemfont\n        #itemfont = lm.itemfont\n\n        \n        task = lm.task\n        n= lm.index\n     \n        if task.completed:\n            table.item(n,0).setIcon(lm.idx0)\n            for c,col in enumerate(col_order[1:],start=1):\n                item = QtWidgets.QTableWidgetItem(*display[col](task))\n                item.setForeground(qcolor('gray'))\n                item.setFont(lm.itemfont[task.priority])\n                table.setItem(n, c, item)\n\n        elif task.deleted:\n            for c,col in enumerate(col_order[1:],start=1):\n                item = QtWidgets.QTableWidgetItem(*display[col](task))\n                item.setForeground(qcolor('gray'))\n                item.setFont(lm.deleteditemfont[task.priority])\n                table.setItem(n, c, item)\n                \n        elif getattr(task, type_).textcolor:\n            for c,col in enumerate(col_order[1:],start=1):\n                item = QtWidgets.QTableWidgetItem(*display[col](task))\n                item.setForeground(qcolor(getattr(task, type_).textcolor))\n                item.setFont(lm.itemfont[task.priority])\n                table.setItem(n, c, item)\n        else:\n            for c,col in enumerate(col_order[1:],start=1):\n                item = QtWidgets.QTableWidgetItem(*display[col](task))\n                item.setFont(lm.itemfont[task.priority])\n                table.setItem(n, c, item)\n                    \n                \n        return z\n    return fn\n\n#check_modified = g.check_modified\n#check_task_selected = g.check_task_selected\n#update_whooshdb = g.update_whooshdb\n#update_row = g.update_row\n\n######################################################################################################################################################\n\ndef create_action(parent, text, slot=None, shortcut=None, icon=None, icon_res=None, image=None, tip=None, checkable=False):\n\n    action = QtWidgets.QAction(parent)\n    action.setText(text)\n    \n    if icon:\n        action.setIcon(QtGui.QIcon(os.path.join(IMAGES_DIR, icon))) #works without extension but finds bmp before png\n    elif icon_res:\n        action.setIcon(QtGui.QIcon(icon_res)) # uses resource.py\n    elif image:\n        pxmap = QtGui.QPixmap()\n        pxmap.loadFromData(image, 'PNG')\n        action.setIcon(QtGui.QIcon(pxmap))\n        \n    if shortcut:\n        action.setShortcut(shortcut)\n\n    tip = tip if tip else text.lstrip('&')\n    action.setToolTip(tip)\n    action.setStatusTip(tip)\n\n    #note: default overload is triggered(bool checked=False) meaning that slots would have to have two arguments, self and checked\n    #the below gets you the non-default triggered() overload\n    if slot:\n        #action.triggered[()].connect(slot)\n        action.triggered.connect(slot) #12-17-2014\n        #action.connect(slot)\n        \n    if checkable:\n        action.setCheckable(True)\n\n    return action\n\ndef add_actions(target, actions):\n    for action in actions:\n        if action is None:\n            target.addSeparator()\n        else:\n            target.addAction(action)\n\n######################################################################################################################################################\n\nclass ListManager(QtWidgets.QMainWindow):\n    def __init__(self, parent=None):\n        super(ListManager, self).__init__(parent)\n        \n        \n        self.setWindowTitle(\"Listmanager \" + engine.name)\n\n        status = self.statusBar()\n        status.setSizeGripEnabled(False)\n        status.showMessage(\"Ready\", 5000)\n\n        g.pb = self.pb = QtWidgets.QProgressBar(status) # putting pb 'into' lmglobals\n        status.addPermanentWidget(self.pb)\n        self.pb.hide()\n\n        self.status = status\n\n        if args.qsettings:\n            settings = QtCore.QSettings()\n\n            if settings.contains(\"MainWindow/Geometry\"):\n                self.restoreGeometry(settings.value('MainWindow/Geometry'))\n                self.restoreState(settings.value('MainWindow/State')) \n\n        # self.PageProperties is the dictionary of dictionaries that carry the various properties for each notebook page\n        # the key is the splitter for that page which is the widget managed by the tabmanager\n        # self.PageProperties[splitter] = ...\n        self.PageProperties = {} \n\n        self.index = -1 # this is row number of the currently highlighted row\n        self.modified = {} # tracks whether note or db_note have been modified\n\n        self.priorities = [-1,0,1,2,3] # the possible priorities - matches Toodledo\n\n        self.active_search = None\n        self.search_contexts = None # for when we're searching specific context(s)\n\n        self.sync_log = ''\n        self.vim_files = {} #dictionary to hold files being edited in VIM\n\n        #action = partial(g.create_action, self)\n        action = partial(create_action, self)\n        #add_actions = g.add_actions\n        #add_actions = add_actions\n        #IMAGES_DIR = g.IMAGES_DIR\n        im_file = partial(os.path.join, IMAGES_DIR)\n\n        self.arrows = ('bitmaps/down_arrow.png','bitmaps/up_arrow.png')\n\n        self.idx1 = QtGui.QIcon(':/bitmaps/box.png')\n        self.idx0 = QtGui.QIcon(im_file('filledwhitebox.bmp'))\n\n        star = QtGui.QIcon(':/bitmaps/star.png')\n        starno = QtGui.QIcon(im_file('starno.gif'))\n\n        note_icon = QtGui.QIcon(':/bitmaps/note.png')\n\n        alarm_clock = QtGui.QIcon(':/bitmaps/alarm-clock.png')\n\n        alarm_clock_disable = QtGui.QIcon(':/bitmaps/alarm-clock-disable.png')\n\n        #Check for Whoosh directory but doubt things work if not there\n        if os.path.exists(g.WHOOSH_DIR): \n            self.ix = index.open_dir(g.WHOOSH_DIR)\n            self.searcher = self.ix.searcher()\n        else:\n            self.searcher = None\n\n        self.logger = g.logger = Logger(self, logfile=g.LOG_FILE)\n\n        a_transfer = action(\"Transfer to Editor\", self.logger.transfer)\n        a_save = action(\"Save to Log File\", self.logger.save)\n        a_clear_text = action(\"Clear Window\", self.logger.clear_text)\n        a_save_and_clear = action(\"Save to Log File and Clear Window\", self.logger.save_and_clear)\n\n        self.logger.setContextMenuPolicy(Qt.ActionsContextMenu)\n        add_actions(self.logger, (a_transfer, a_save, a_clear_text, a_save_and_clear))\n\n        self.db_note = QtWidgets.QTextEdit()\n        self.db_note.setAcceptRichText(False)\n        self.db_note.setObjectName('plain_note')\n        self.db_note.setEnabled(False)\n        self.db_note.textChanged.connect(self.note_modified)\n\n        self.note_manager = NoteManager(main_window=self)\n        self.note_manager.menuBar().setVisible(False)\n        self.note_manager.format_toolbar.setEnabled(False)\n        self.note = self.note_manager.note\n        self.highlighter = self.note.highlighter\n        self.highlighter.setDocument(None) #the default state is that there is no document highlighting\n        self.note.setEnabled(False)\n        self.note.textChanged.connect(self.note_modified)\n\n        self.tab_manager = QtWidgets.QTabWidget()\n        self.tab_manager.setMovable(True)\n        self.tab_manager.setTabsClosable (True)\n        self.tab_manager.setObjectName(\"Main Tab\")\n\n        self.setCentralWidget(self.tab_manager)\n\n        # tab manager context menu set later when action have been created\n\n        self.tab_manager.tabCloseRequested.connect(self.closetab) #SIGNAL(\"tabCloseRequested(int)\")\n        self.tab_manager.currentChanged.connect(self.onpagechange) # new style signal/slot\n\n        # the menus created below will become contextmenu menus for each table widget that is created\n        # they are also used as the menus in the toolbar\n\n        #createprioritymenu()\n        self.p_menu = QtWidgets.QMenu(self)\n        priorityGroup = QtWidgets.QActionGroup(self)\n        for p in reversed(self.priorities):\n            a = action(str(p), partial(self.setpriority, priority=p), checkable=True)\n            priorityGroup.addAction(a)\n            self.p_menu.addAction(a)\n\n        #self.c_menu\n        self.createcontextmenu()\n\n        #self.f_menu\n        self.createfoldermenu()\n\n        loc_dict = {}\n        loc_dict = dict(globals())\n        loc_dict['self'] = locals()['self']\n\n        if args.console:\n            self.console = lminterpreter.MyConsole(self, loc_dict)\n        else:\n            self.console = QtWidgets.QWidget()\n\n        self.setCorner(Qt.BottomRightCorner, Qt.RightDockWidgetArea)\n\n        DockWidget = QtWidgets.QDockWidget(\"Log\", self)\n        DockWidget.setObjectName(\"log\")\n        DockWidget.setAllowedAreas(Qt.BottomDockWidgetArea|Qt.RightDockWidgetArea)\n        DockWidget.setWidget(self.logger)\n        #self.addDockWidget(QtCore.Qt.RightDockWidgetArea, DockWidget) #QtCore.Qt.BottomDockWidgetArea\n\n        DockWidget2 = QtWidgets.QDockWidget(\"Note\", self)\n        DockWidget2.setObjectName(\"note\")\n        DockWidget2.setAllowedAreas(Qt.RightDockWidgetArea)\n        DockWidget2.setFeatures (QtWidgets.QDockWidget.DockWidgetMovable|QtWidgets.QDockWidget.DockWidgetFloatable) ##### but not closable\n        DockWidget2.setWidget(self.note_manager)\n        self.addDockWidget(Qt.RightDockWidgetArea, DockWidget2)\n\n        DockWidget2.topLevelChanged.connect(self.shownotemenus)\n\n        DockWidget3 = QtWidgets.QDockWidget(\"DB Note\", self)\n        DockWidget3.setObjectName(\"db_note\")\n        DockWidget3.setAllowedAreas(Qt.RightDockWidgetArea)\n        DockWidget3.setWidget(self.db_note)\n        self.addDockWidget(Qt.RightDockWidgetArea, DockWidget3)\n\n        DockWidget4 = QtWidgets.QDockWidget(\"Console\", self)\n        DockWidget4.setObjectName(\"console\")\n        DockWidget4.setAllowedAreas(Qt.RightDockWidgetArea)\n        DockWidget4.setWidget(self.console) #to get all the locals going to set in __main__\n        self.addDockWidget(Qt.RightDockWidgetArea, DockWidget4)\n\n        self.DockWidget4 = DockWidget4 #need to get at this DockWidget from __main__\n\n        self.addDockWidget(Qt.RightDockWidgetArea, DockWidget) #Qt.BottomDockWidgetArea\n\n        # Klugy but makes sure that the note and db_note dockwidgets are\n        # the right size when they are undocked - couldn't find any other way\n        rect = self.geometry()\n        z = (rect.x() + 75, rect.y() + 100, 800, 500)\n\n        DockWidget.setFloating(True)\n        DockWidget.setGeometry(*z)\n        DockWidget.setFloating(False)\n\n        DockWidget2.setFloating(True)\n        DockWidget2.setGeometry(*z)\n        DockWidget2.setFloating(False)\n\n        DockWidget3.setFloating(True)\n        DockWidget3.setGeometry(*z)\n        DockWidget3.setFloating(False)\n\n        DockWidget4.setFloating(True)\n        DockWidget4.setGeometry(*z)\n        DockWidget4.setFloating(False)\n\n        # makes sure that Note is first (on the left) and the selected tab\n        self.tabifyDockWidget(DockWidget2, DockWidget3)\n        self.tabifyDockWidget(DockWidget2, DockWidget4)\n        DockWidget2.raise_()\n\n        log_dock_action = DockWidget.toggleViewAction()\n        note_db_dock_action = DockWidget3.toggleViewAction() \n\n        self.note_manager.menuBar().setVisible(False)\n        self.note_manager.format_toolbar.setEnabled(False)\n        self.note.setEnabled(False)\n        self.db_note.setEnabled(False)\n\n        filemenu = self.menuBar().addMenu(\"&File\")\n\n        a_opencontext = action(\"&Context(s)...\", partial(self.opentabs2, type_='context'), icon_res=':/bitmaps/open_context') # doesn't need png\n        a_openfolder = action(\"&Folders(s)...\", partial(self.opentabs2, type_='folder'), icon_res=':/bitmaps/open_folder')\n        a_opentag = action(\"&Tag(s)...\", partial(self.opentabs, type_='tag'), icon_res=':/bitmaps/open_tag')\n\n        m_openmenu = filemenu.addMenu(\"&Open\")\n        add_actions(m_openmenu, (a_opencontext, a_openfolder, a_opentag))\n\n        a_recent_items_all = action(\"All\", partial(self.showrecentitems, tab_value='ALL'))\n        a_recent_items_created = action(\"Created (on this client)\", partial(self.showrecentitems, tab_value='Created'))\n        a_recent_items_modified = action(\"Modified\", partial(self.showrecentitems, tab_value='Modified'))\n        a_recent_items_completed = action(\"Completed\", partial(self.showrecentitems, tab_value='Completed'))\n\n        m_recentfiles = filemenu.addMenu(\"&Recent Tasks\")\n        add_actions(m_recentfiles, (a_recent_items_all, a_recent_items_created, a_recent_items_modified, a_recent_items_completed))\n\n        a_starstab = action(\"Create Stars Tab\",\n                                     partial(self.createnewtab,\n                                     title = '*Stars*',\n                                     tab = {'type':'app','value':'star'}, \n                                     filter_by = {'column':'context', 'value':'*ALL'},\n                                     sort ={'column':'priority','direction':0},\n                                     col_order = ['box', 'alarm', 'priority', 'star', 'title', 'note', 'context', 'startdate'],\n                                     collapsible = False,\n                                     show_completed=False),\n                                                         \n                                     checkable=False, icon='display_starred')\n                                                                         \n        a_alarmtab = action(\"Create Alarm Tab\", \n                                     partial(self.createnewtab,\n                                     title = 'Alarms',\n                                     tab ={'type':'app','value':'alarm'},\n                                     filter_by = {'column':'context', 'value':'*ALL'},\n                                     sort ={'column':'duedate','direction':0},\n                                     collapsible = False,\n                                     show_completed=False,\n                                     col_order = ['box','priority','alarm','duedate','duetime','star','title','note','context']),\n\n                                     checkable=False, icon='display_alarmed')\n\n        add_actions(filemenu, (None, a_starstab, a_alarmtab))\n\n        a_multitab_folders = action(\"Folders\", partial(self.create_multi_tab, type_='folder'))\n        a_multitab_contexts = action(\"Contexts\", partial(self.create_multi_tab, type_='context'))\n\n        m_multimenu = filemenu.addMenu(QtGui.QIcon(os.path.join(IMAGES_DIR, 'display_all_todo_related.png')), \"Create Multi-value Tab\") #os.path.join(IMAGES_DIR, '{0}.png'.format(icon)))\n        add_actions(m_multimenu, (a_multitab_folders, a_multitab_contexts))\n\n        a_newcontext = action(\"&New Context\", self.newcontext)\n        a_savenote = action(\"Save Note\", self.savenote, 'Ctrl+S')\n        a_closetab = action(\"Close Current Tab\", self.closetab)\n        a_close_all = action(\"Close All\", self.close_all)\n        a_saveconfiguration = action(\"Save Configuration\", self.saveconfiguration)\n        a_loadconfiguration = action(\"Load Configuration\", self.loadconfiguration)\n        a_savetab = action(\"Save Tab\", self.savetab)\n\n        a_quit = action(\"&Quit\", self.close, \"Ctrl+Q\", \"filequit\", \"Close the application\")\n\n\n        add_actions(filemenu, (None, a_newcontext, None, a_savenote, None, a_closetab, a_close_all, None, a_saveconfiguration, a_loadconfiguration, \n                                        None,  a_savetab))\n\n        if args.qsettings:\n            savedtabs = settings.value('savedtabs')\n            self.savedtabs = [] if savedtabs is None else savedtabs # shouldn't be necessary but seems to be 10.27.2012\n        else:\n            self.savedtabs = []\n            \n        self.m_savedtabsmenu = filemenu.addMenu(\"Saved Tabs\")\n\n        for i, properties in enumerate(self.savedtabs):\n            aa = self.createsavedtab(properties, i)\n            self.m_savedtabsmenu.addAction(aa)\n\n        filemenu.addSeparator() \n        filemenu.addAction(a_quit)\n\n        taskmenu = self.menuBar().addMenu(\"&Task\")\n\n        a_newtask = action(\"&New Task\", self.newtask, 'Alt+I', icon='list_add')\n        a_togglecompleted = action(\"Toggle Completed\", self.togglecompleted, icon_res=':/bitmaps/filledbox.png') \n        a_togglestar = action(\"Toggle Star\", self.togglestar, icon_res=':/bitmaps/star.png', checkable=True)\n         \n        p_action = QtWidgets.QAction(QtGui.QIcon(':/bitmaps/priority'), 'Set Priority', self)\n\n        self.a_select_tags = action(\"Select &Tag\", self.select_tags, 'Ctrl+T', icon='tag-new')\n        a_selectfolder = action(\"Select Folder\", self.selectcontextfolder, 'Ctrl+F', icon='folder_new')\n        a_selectcontext = action(\"Select Context\", self.selectcontextfolder, icon='context')\n        a_duedate = action(\"Set Duedate/Time\", self.setduedate, icon='office-calendar')\n        a_deletetask = action(\"Delete/Undelete\", self.deletetask, icon_res=':/bitmaps/delete.png')\n\n        add_actions(taskmenu, (a_newtask, None, a_togglecompleted, a_togglestar, p_action, a_selectcontext, a_selectfolder, self.a_select_tags, \n                                              a_duedate, None, a_deletetask)) \n        displaymenu = self.menuBar().addMenu(\"&Display\")\n\n        a_refresh = action(\"&Refresh Display\", self.refresh, 'Ctrl+R', icon='view-refresh')\n        a_refreshlistonly = action(\"Refresh &List Only\", self.refreshlistonly)\n        a_note = action(\"Dock/Undock Note\", partial(self.ondockwindow, dw=DockWidget2), 'Ctrl+N')\n        a_dbnote = action(\"Dock/Undock DB Note\", partial(self.ondockwindow, dw=DockWidget3), 'Ctrl+D')\n        a_logger = action(\"Dock/Undock Log\", partial(self.ondockwindow, dw=DockWidget, cur=False, check_task_selected=False), 'Ctrl+L')\n        a_console = action(\"Dock/Undock Console\", partial(self.ondockwindow, dw=DockWidget4, check_task_selected=False), 'Alt+C')\n        a_modifycolumns =  action(\"Modify Columns...\", self.modifycolumns, icon='modifycolumns')\n        a_showcompleted = action(\"Show Completed\", self.showcompleted, icon='showcompleted', checkable=True)\n        a_showhidefilterby = action(\"Show\\Hide Filter-by List\", self.showhidefilterby, checkable=True)\n        a_changefilterbycolumn = action(\"Change Column to Filter By\", self.changefilterbycolumn)\n        a_toggle_collapsible = action(\"Collapsible by Priority\", self.toggle_collapsible, checkable=True, icon='collapsible')\n        a_change_tab_name = action(\"Rename or save search as ...\", self.change_tab_name)\n        a_removesort = action(\"Remove sorting\", self.removesort, icon='sort')\n        add_actions(displaymenu, (a_refresh, a_refreshlistonly, None, log_dock_action, note_db_dock_action, None, a_note, a_dbnote, a_console, a_logger, None, a_change_tab_name, a_showcompleted, a_toggle_collapsible, a_removesort, None, a_showhidefilterby, a_changefilterbycolumn, a_modifycolumns))\n\n        self.a_showcompleted = a_showcompleted\n        self.a_showhidefilterby = a_showhidefilterby\n        self.a_toggle_collapsible = a_toggle_collapsible\n\n        a_folder_icon_color = action(\"Folder\", partial(self.iconcolordialog, type_='folder'))\n        a_context_icon_color = action(\"Context\", partial(self.iconcolordialog, type_='context'))\n        m_icon_color = displaymenu.addMenu(\"Chose Icon/Text Color\")\n        add_actions(m_icon_color, (a_folder_icon_color, a_context_icon_color))\n        toolmenu = self.menuBar().addMenu(\"&Tools\")\n\n        a_synchronize = action(\"&Synchronize (with toodledo)\", self.synchronize, 'Alt+S', icon='arrow_ns')\n        #a_synchronize_remote = action(\"Synchronize (remote)\", partial(self.synchronize, local=False))\n        a_showsync_log = action(\"Show Synchronize Log\", self.showsync_log)\n        a_showdeleted = action(\"Show Deleted\", self.showdeleted)\n        a_print_note_to_log = action(\"Print Note to Log\", self.print_note_to_log)\n        a_close_event = action(\"Write ini file\", self.closeEvent)\n        a_on_simple_html2log = action(\"Print simple html to log\", self.on_simple_html2log)\n        a_ontaskinfo = action(\"Show Task Info\", partial(self.ontaskinfo, retrieve_server=True))\n        a_deletecontexts = action(\"Delete Context(s)...\", partial(self.deletecontexts, type_='context'))\n        a_updatewhooshentry = action(\"Update Whoosh Entry Manually\", self.updatewhooshentry) \n        a_get_tabinfo = action(\"Show Tab Info\", self.get_tabinfo)\n        a_whooshtaskinfo = action(\"Check Whoosh Task Position\", self.whooshtaskinfo)\n        a_removedeadkeywords = action(\"Remove Unused Keywords ...\", self.removedeadkeywords)\n        a_renew_alarms = action(\"Renew Expired Alarms\", self.renew_alarms)\n        a_startdate = action(\"Set Startdate\", partial(self.setdate, which='startdate'), icon='office-calendar')\n        a_resetinterp = action(\"Reset Console\", self.resetinterp)\n        a_clearsavedtabs = action(\"Clear Saved Tabs\", self.clearsavedtabs)\n        a_create_whooshdb = action(\"Create Whoosh Database\", self.create_whooshdb2)\n        a_edit_note_in_vim = action(\"Edit note in vim\", self.edit_note_in_vim, 'Alt+N')\n\n        add_actions(toolmenu, (a_synchronize, a_showsync_log, None, a_updatewhooshentry,\n                                         a_whooshtaskinfo, None, a_print_note_to_log, a_close_event, a_on_simple_html2log, None,\n                                         a_ontaskinfo, a_get_tabinfo, None, a_showdeleted, None, a_removedeadkeywords, \n                                         a_deletecontexts, None, a_renew_alarms, a_startdate, a_resetinterp, a_clearsavedtabs, None, a_create_whooshdb, a_edit_note_in_vim))\n                                         \n        a_create_image_string = action(\"Create image string\", self.create_image_string)\n\n        toolmenu.addAction(a_create_image_string)\n\n        # plugins need to define a function that takes ListManager() => self as an argument - essentially makes it a method\n        # They need to include title, icon, function\n\n        if g.plugins_enabled:\n            plugin_files = os.listdir(g.PLUGIN_DIR)\n            \n            if plugin_files:\n                pluginmenu = self.menuBar().addMenu(\"Plugin\")\n            \n            for name in plugin_files:\n                if name.endswith(\".py\") and not name.startswith(\"_\"):\n                    modulename = 'plugins.{}'.format(name.rsplit('.',1)[0])\n                    module = importlib.import_module(modulename)\n            \n                    a_plugin = action(module.title, partial(module.function, self), image=module.image)\n                    pluginmenu.addAction(a_plugin)\n                \n        helpmenu = self.menuBar().addMenu(\"&Help\")\n        a_showversions = action(\"Versions\", self.showversions)\n        add_actions(helpmenu, (a_showversions,))\n        #Toolbar\n        fileToolbar = self.addToolBar(\"File\")\n        fileToolbar.setObjectName(\"FileToolBar\")\n        add_actions(fileToolbar, (a_opencontext, a_openfolder, a_opentag, a_starstab, a_alarmtab, None))\n\n        if g.plugins_enabled:\n\n            plugin_actions = pluginmenu.actions()\n            \n            if plugin_actions:\n                pluginToolbar = self.addToolBar(\"Plugins\")\n                pluginToolbar.setObjectName(\"Plugins ToolBar\")\n            \n            for a_plugin in plugin_actions:\n                pluginToolbar.addAction(a_plugin)\n            \n        p_action.setMenu(self.p_menu)\n        self.p_action = p_action\n\n        a_incrementpriority = action(\"Increment Priority\", self.incrementpriority, icon='priority')\n        a_incrementpriority.setMenu(self.p_menu) \n\n        a_selectfolder = action(\"Select Folder\", partial(self.selectcontextfolder, type_='folder'), 'Ctrl+F', icon='folder_new')\n        a_selectfolder.setMenu(self.f_menu)\n        self.a_selectfolder = a_selectfolder\n\n        a_selectcontext = action(\"Select Context\", partial(self.selectcontextfolder, type_='context'), icon='context')\n        a_selectcontext.setMenu (self.c_menu)\n        self.a_selectcontext = a_selectcontext \n\n        tasktb = self.addToolBar(\"Task\")\n        tasktb.setObjectName(\"TaskToolBar\")\n\n        add_actions(tasktb, (a_newtask, None, a_togglecompleted, a_togglestar, a_deletetask, a_incrementpriority, a_selectcontext, a_selectfolder, self.a_select_tags, a_duedate))\n\n        displayToolbar = self.addToolBar(\"Display\")\n        displayToolbar.setObjectName(\"DisplayToolBar\")\n        add_actions(displayToolbar, (a_refresh, None, a_showcompleted, a_toggle_collapsible, a_removesort, None, a_modifycolumns))\n        toolsToolbar = self.addToolBar(\"Tools\")\n        toolsToolbar.setObjectName(\"Tools ToolBar\")\n        toolsToolbar.addAction(a_synchronize)\n        search_tb = QtWidgets.QToolButton()\n        search_tb.setIcon(QtGui.QIcon('bitmaps/magnifier-left.png'))\n        search_tb.setPopupMode(QtWidgets.QToolButton.InstantPopup)\n\n        search_menu = QtWidgets.QMenu(search_tb)\n        a_search_all = action(\"Search All Contexts\", partial(self.searchcontext, search='all'), checkable=True)\n        a_search_all.setChecked(True)\n        self.a_search_all = a_search_all\n\n        search_context = action(\"Search Context...\", partial(self.searchcontext, search='context'), checkable=True)\n\n        search_what = QtWidgets.QActionGroup(self)\n        search_what.addAction(a_search_all)\n        search_what.addAction(search_context)\n\n        search_menu.addAction(a_search_all)\n        search_menu.addAction(search_context)\n\n        search_tb.setMenu(search_menu)\n\n        lineEdit = QtWidgets.QLineEdit()\n        lineEdit.setWindowIcon(QtGui.QIcon(\"bitmaps/refresh.png\"))    \n        lineEdit.setFixedSize(140,25)\n        searchToolbar=self.addToolBar(\"Search\")\n        searchToolbar.setObjectName(\"SearchToolBar\")\n        searchToolbar.addWidget(search_tb)\n        searchToolbar.addWidget(lineEdit)\n\n        self.search = lineEdit\n\n        #lineEdit.setDisabled(False)\n        #self.query_parse_flag = 10   \n        self.search.textEdited.connect(self.do_search)\n\n        # needs to be here because the actions need to have been defined (in Menus section)\n        self.tab_manager.setContextMenuPolicy(Qt.ActionsContextMenu)\n        add_actions(self.tab_manager, (a_change_tab_name, a_showcompleted, a_showhidefilterby, a_toggle_collapsible, a_savetab))#####\n        format1 = '%m/%d/%y'\n        format2 = '%m/%d/%y %H:%M:%S'\n        format3 =  '%H:%M'\n\n        center = Qt.AlignHCenter\n        left = Qt.AlignLeft\n\n        qicon = QtGui.QIcon\n                \n        now = datetime.datetime.now\n\n        self.col_info = {\n            'box':        {'label':'',         'width':20, 'align': center, 'sortable':False,'fixed':True, 'action':self.togglecompleted,'display': None},\n            'priority':   {'label':'p',        'width':20, 'align': center, 'sortable':True, 'fixed':True, 'action':self.on_pmenu,   'display': lambda x: (str(x.priority),)},\n            'star':       {'label':'s',        'width':22, 'align': center, 'sortable':True, 'fixed':True, 'action':self.togglestar,     'display': lambda x: (star if x.star else starno,\"\")},\n            'folder':     {'label':'folder',   'width':100,'align': center, 'sortable':True, 'fixed':False,'action':partial(self.selectcontextfolder, type_='folder'),   'display': lambda x:(x.folder.title if x.folder.tid else '',)},\n            'f_icon':       {'label':'f-i',        'width':25, 'align': center, 'sortable':False,'fixed':True, 'action':partial(self.selectcontextfolder, type_='folder'),   'display': lambda x: self.folder_icons[x.folder.title]},\n            'c_icon':       {'label':'c-i',        'width':25, 'align': center, 'sortable':False,'fixed':True, 'action':partial(self.selectcontextfolder, type_='context'),   'display': lambda x: self.context_icons[x.context.title]},\n            'title':      {'label':'title',    'width':550,'align': center, 'sortable':True, 'fixed':False,'action':lambda x: None,     'display': lambda x:(x.title,)},\n            'tid':        {'label':'tid',      'width':75, 'align': center, 'sortable':True, 'fixed':True, 'action':lambda x: None,     'display': lambda x: (str(x.tid) if x.tid else '',)},\n            'folder_tid': {'label':'f_tid',    'width':75, 'align': center, 'sortable':True, 'fixed':True, 'action':lambda x: None,     'display': lambda x: (str(x.folder_tid) if x.folder_tid else '',)},\n            #'modified':   {'label':'modified', 'width':85, 'align': center, 'sortable':True, 'fixed':True, 'action':lambda x: None,     'display': lambda x: (x.modified.strftime(format1) if x.added else '',)},\n            'modified':   {'label':'modified', 'width':85, 'align': center, 'sortable':True, 'fixed':True, 'action':lambda x: None,     'display': lambda x: (age((TODAY - x.modified.date()).days),)},\n            'completed':  {'label':'completed','width':75, 'align': center, 'sortable':True, 'fixed':True, 'action':lambda x: None,     'display': lambda x: (x.completed.strftime(format1) if x.completed else '',)},\n            'added':      {'label':'added',    'width':75, 'align': center, 'sortable':True, 'fixed':True, 'action':lambda x: None,     'display': lambda x: (x.added.strftime(format1) if x.added else '',)},\n            'created':    {'label':'created',  'width':115, 'align': center, 'sortable':True, 'fixed':True, 'action':lambda x: None,     'display': lambda x: (x.created.strftime(format2) if x.created else '',)},\n            'duedate':    {'label':'duedate',     'width':66, 'align': center, 'sortable':True, 'fixed':True, 'action':self.setduedate,  'display': lambda x: (x.duedate.strftime(format1) if x.duedate else '',)},\n            'duetime':    {'label':'duetime',     'width':55, 'align': center, 'sortable':True, 'fixed':True, 'action':self.setduedate,  'display': lambda x: (x.duetime.strftime(format3) if x.duetime else '',)},\n            'startdate':    {'label':'date',     'width':75, 'align': center, 'sortable':True, 'fixed':False, 'action':partial(self.setdate, which='startdate'),  'display': lambda x: (age((TODAY - x.startdate).days) if x.startdate else '',)},\n             'alarm':      {'label':'a',        'width':20,'align':center, 'sortable':False, 'fixed':True, 'action':self.setduedate,       'display': lambda x: (alarm_clock if x.duetime>now() else alarm_clock_disable,'') if x.remind else ('',)},\n            'tag':        {'label':'tag',      'width':60, 'align': center, 'sortable':True, 'fixed':False,'action':self.select_tags,      'display': lambda x: (x.tag if x.tag else '',)},\n            'note':       {'label':'notes',    'width':30, 'align': center, 'sortable':False,'fixed':True,'action':partial(self.ondockwindow, dw=DockWidget2),     'display': lambda x: (note_icon, '') if x.note else ('',)},   \n            'context':    {'label':'context',  'width':80, 'align': center, 'sortable':True, 'fixed':False,'action':partial(self.selectcontextfolder, type_='context'),  'display': lambda x: (x.context.title,)}, \n            }\n\n        self.display = {x:self.col_info[x]['display'] for x in self.col_info}\n        self.action = {x:self.col_info[x]['action'] for x in self.col_info}\n\n        #self.display['icon'] = lambda x: self.folder_icons[x.folder.title]\n\n        # the initial set of properties for each page\n        #tab['type']  are strings like 'context', 'folder', 'active_search', 'saved_search','all', 'recent' and \n        #tab['value'] are strings like 'work', 'todo' and for searches the query terms and \n        #both type and value need to be filled in when creating tab\n        self.InitialProperties = {\n                                'tab':{'type':None,'value':None},\n                                'title':None, #set when creating tab\n                                'tab_icon':None,\n                                'filter_by':{'column':'folder','value':'*ALL'},\n                                'sort':{'column':None,'direction':0}, #don't sort to start with\n                                'show_completed':False,\n                                'split_window':True,\n                                'priority_display':{'-1':False,'0':False,'1':False,'2':True,'3':True}, #just have 2 and 3 expanded for starters, probably should be [True, False, True ...] and not a dic\n                                'col_widths':None, #will be defined when table created\n                                'col_order':['box','startdate','priority','star','title','note'], # important initial columns and their order; completed needs to be first\n                                'collapsible':True\n                                }     \n                                \n        normal = QtGui.QFont()\n        bold = QtGui.QFont()\n        bold.setBold(True)\n        strikeout = QtGui.QFont()\n        strikeout.setStrikeOut(True)\n        boldstrikeout = QtGui.QFont(bold)\n        boldstrikeout.setStrikeOut(True)\n                 \n        self.itemfont = {-1:normal, 0:normal, 1:normal, 2:normal, 3:bold}         \n        self.deleteditemfont = {-1:strikeout, 0:strikeout, 1:strikeout, 2:strikeout, 3:boldstrikeout}\n        \n        self.folder_icons = {c.title:(QtGui.QIcon(\"bitmaps/folder_icons/{}.png\".format(c.title)), '') for c in session.query(Folder)}\n\n        #self.folder_icons = {}\n        #for f in session.query(Folder):\n        #    if f.image:\n        #        pxmap = QtGui.QPixmap()\n        #        pxmap.loadFromData(f.image, 'PNG')\n        #        icon = (QtGui.QIcon(pxmap), '') \n        #    else:\n        #        icon = ('',)\n        #    self.folder_icons[f.title] = icon\n\n        self.context_icons = {c.title:(QtGui.QIcon(\"bitmaps/folder_icons/{}.png\".format(c.title)), '') for c in session.query(Context)}\n        #self.context_icons = {}\n        #for c in session.query(Context):\n        #    if c.image:\n        #        pxmap = QtGui.QPixmap()\n        #        pxmap.loadFromData(c.image, 'PNG')\n        #        icon = (QtGui.QIcon(pxmap), '') \n        #    else:\n        #        icon = ('',)\n        #    self.context_icons[c.title] = icon\n            \n        #{'context': self.context_icons, 'folder' self.folder_icons, 'app':{'star':fjdsl,'alarm':jfkdjfdk, 'search':lkfdldskf}, 'recent', {}, 'tag': {}}\n\n        self.icons = {'context': self.context_icons, 'folder':self.folder_icons, 'app':{'star':(QtGui.QIcon(':/bitmaps/star.png'),''),'alarm':(QtGui.QIcon(':/bitmaps/alarm-clock.png'),'')}}\n         \n        # the below work\n        #self.context_icons = {c.title:(QtGui.QIcon(\"bitmaps/context_icons/{}\".format(c.icon)), '') if c.icon else ('',) for c in session.query(Context)}\n        #self.folder_icons = {f.title:(QtGui.QIcon(\"bitmaps/folder_icons/{}\".format(f.icon)), '') if f.icon else ('',) for f in session.query(Folder)}\n\n        # new user location\n        # self.folder_icons = {f.title:(QtGui.QIcon(\"bitmaps/user/{}\".format(f.icon)), '') if f.icon else ('',) for f in session.query(Folder)}\n        # self.context_icons = {c.title:(QtGui.QIcon(\"bitmaps/user/{}\".format(c.icon)), '') if c.icon else ('',) for c in session.query(Context)}\n\n        #user_icon = partial(os.path.join,'bitmaps','user')\n        #user_icon(f.icon)\n\n        self.myevent = MyEvent()\n\n        try:\n            import myevents\n        except ImportError as e:\n            print(e)\n            self.myevent.signal.connect(lambda x,y:None)\n        else:\n            self.myevent.signal.connect(myevents.responses) # since only one signal don't need ...signal[str, dict].connect....\n\n        # watch files that vim is editing\n        self.fs_watcher = QtCore.QFileSystemWatcher()\n        self.fs_watcher.fileChanged.connect(self.file_changed_in_vim)\n        \n        if args.ini:\n            QtCore.QTimer.singleShot(0, self.loadtabs)\n\n    def loadtabs(self):\n        # open the tabs that were last open when the application was closed\n        \n        if g.config.has_option('Properties', 'tab_properties'):\n            properties = json.loads(g.config.get('Properties', 'tab_properties'))\n            \n            self.setUpdatesEnabled(False)\n\n            # note the one problem with this is that it prevents the fullsize method from stretching\n            # columns to the full width of the table widget\n            # I think the main reason to block signals was not to trigger onpagechange\n            # although not sure that's a big deal so should probably try to remove these (May 24, 2012)\n            self.tab_manager.blockSignals(True)\n                \n            for tab in properties:\n                self.createnewtab(**tab)\n        \n            self.tab_manager.blockSignals(False)\n        \n            if g.config.has_option('Properties', 'current' ):\n                current = g.config.getint('Properties', 'current')\n                if current != -1:\n                    self.tab_manager.setCurrentIndex(current)\n                    tab_count = self.tab_manager.count()\n                    if current == tab_count -1:  # need to trigger if the current page is the last tab since there wouldn't be a pagechage\n                        self.onpagechange(current) \n        \n            self.setUpdatesEnabled(True)\n\n    def downloadtasksfromserver(self):\n        '''\n        sends all tasks on server down to client\n        '''\n        print_(\"Got here in downloadtasksfromserver\")\n        if not toodledo2.keycheck():\n            print_(\"Unable to get toodledo key\")\n            return\n        \n        synchronize_p.downloadtasksfromtoodledo(local=False)\n        \n        if self.confirm(\"Would you like to enable full-text search (uses Whoosh)?\"):\n            self.create_whooshdb2()\n            \n            #self.ix = index.open_dir(g.WHOOSH_DIR)\n            #self.searcher = self.ix.searcher()\n\n    def note_modified(self):\n        which = self.sender().objectName()\n        self.modified[which] = True\n\n    def createfoldermenu(self):\n        self.f_menu = QtWidgets.QMenu(self)\n        \n        folders = session.query(Folder).filter(Folder.id!=1).all()\n        folders.sort(key=lambda f:str.lower(f.title))\n        no_folder = session.query(Folder).filter_by(id=1).one()\n        folders = [no_folder] + folders\n        \n        iconGroup = QtWidgets.QActionGroup(self)\n        \n        for f in folders:\n            #icon = 'folder_icons/'+f.icon if f.icon else ''\n            #a = g.create_action(self, f.title, partial(self.updatefolder, title=f.title), icon=icon , checkable=True)\n            a = create_action(self, f.title, partial(self.updatefolder, title=f.title), image=f.image , checkable=True)\n            iconGroup.addAction(a)\n            self.f_menu.addAction(a)\n\n    def createcontextmenu(self):\n        self.c_menu = QtWidgets.QMenu(self)\n        \n        contexts = session.query(Context).filter(Context.id!=1).all()\n        contexts.sort(key=lambda c:str.lower(c.title))\n        no_context = session.query(Context).filter_by(id=1).one()\n        contexts = [no_context] + contexts\n        \n        self.contextGroup = QtWidgets.QActionGroup(self)\n        \n        for c in contexts:\n            a = create_action(self, c.title, partial(self.updatecontext, title=c.title), image=c.image, checkable=True)\n            self.contextGroup.addAction(a)\n            self.c_menu.addAction(a)\n            \n    def createimagewithoverlay(self, base_image='bitmaps/unordered-list-tag.png', overlay_image=None):\n\n        base_image=QtGui.QImage(base_image)\n        imagewithoverlay = QtGui.QImage(base_image.size(), QtGui.QImage.Format_ARGB32_Premultiplied)\n        painter = QtGui.QPainter(imagewithoverlay)\n\n        painter.setCompositionMode(QtGui.QPainter.CompositionMode_Source)\n        painter.fillRect(imagewithoverlay.rect(), Qt.transparent)\n\n        painter.setCompositionMode(QtGui.QPainter.CompositionMode_SourceOver)\n        painter.drawImage(0, 0, base_image)\n\n        painter.setCompositionMode(QtGui.QPainter.CompositionMode_SourceOver);\n        painter.drawImage(10, 10, overlay_image);\n\n        painter.end()\n\n        return imagewithoverlay\n\n    def createsavedtab(self, properties, n): \n\n        #for i, properties in enumerate(self.savedtabs): # probably wouldn't need self\n     \n        aa = QtGui.QAction(properties['title'], self)\n        \n        qimage = QtGui.QImage()\n        if properties['tab']['type'] == 'folder':\n            z = session.query(Folder).filter_by(title=properties['tab']['value']).one()\n            qimage.loadFromData(z.image, 'PNG')\n        elif properties['tab']['type'] == 'context':\n            z = session.query(Context).filter_by(title=properties['tab']['value']).one()\n            qimage.loadFromData(z.image, 'PNG')\n        elif properties['tab']['type'] == 'star':\n            qimage.load(':/bitmaps/star.png', 'PNG')\n        else:\n            qimage.loadFromData('', 'PNG')\n        \n        image = self.createimagewithoverlay(overlay_image=qimage)\n        pxmap = QtGui.QPixmap()\n        pxmap.convertFromImage(image)\n        aa.setIcon(QtGui.QIcon(pxmap))\n            \n        self.connect(aa, QtCore.SIGNAL(\"triggered()\"), partial(self.loadtab, n))\n        \n        return aa\n\n        #self.m_savedtabsmenu.addAction(aa) # would need self\n        #self.savedtabsactions.append(aa) # probably wouldn't need self\n\n    def shownotemenus(self, floating):\n        self.note_manager.menuBar().setVisible(floating)\n\n    def createnewtab(self, **kw):\n        '''\n        must have title=xxxx and tab={'type':xxxx,'value':yyyy}\n        self.InitialProperties = {\n                            'tab':{'type':None,'value':None},\n                            'title':None, #set when creating tab\n                            'tab_icon':None,\n                            'filter_by':{'column':'folder','value':'*ALL'},\n                            'sort':{'column':None,'direction':0}, #don't sort to start with\n                            'show_completed':False,\n                            'split_window':True,\n                            'priority_display':{'-1':False,'0':False,'1':False,'2':True,'3':True}, #just have 2 and 3 expanded for starters, probably should be [True, False, True ...] and not a dic\n                            'col_widths':None, #will be defined when table created\n                            'col_order':['box','priority','star','title','note','tag','folder'], # important initial columns and their order; completed needs to be first\n                            'collapsible':True\n                            }     \n        '''\n\n        Properties = dict(self.InitialProperties)\n        Properties.update(kw)\n        self.Properties = Properties\n        self.col_order = Properties['col_order'] #not sure if I should just use Properties['col_order']\n\n        self.LBox = LBox = QtWidgets.QListWidget()\n\n        splitter = QtWidgets.QSplitter(Qt.Horizontal)\n        splitter.addWidget(LBox)\n\n        self.table = table = self.createtable() \n\n        splitter.addWidget(table)\n\n        if 'window_sizes' not in Properties:\n            Properties['window_sizes'] = [0,500] #[75,500] -> [0,500] should hide filterby list\n\n        splitter.setSizes(Properties['window_sizes'])\n\n        self.PageProperties[splitter] = Properties # addTab triggers onpagechange so this needs to preceed\n        \n        # tab icon - it is no longer part of Properties but is determined by the nature of the tab \n        # (ie folder -specifically which one, context - specifically which one, star and alarm)#####\n        # However the below if elif is really ugly and maybe it's a self.tab_icons dict that's \n        #{'context': self.context_icons, 'folder' self.folder_icons, 'app':{'star':fjdsl,'alarm':jfkdjfdk, 'search':lkfdldskf}, 'recent', {}, 'tag': {}}\n        # tab_icon = self.tab_icons[tab_type].get(tab_value)\n        \n        if Properties['tab']['type'] == 'tag':\n            tab_icon = QtGui.QIcon(':/bitmaps/tag.png')\n        else:\n            tab_icon = self.icons.get(Properties['tab']['type'], {}).get(Properties['tab']['value'], (None,))[0]\n        \n        tab_num = self.tab_manager.addTab(splitter, tab_icon, Properties['title']) if tab_icon else self.tab_manager.addTab(splitter, Properties['title']) \n\n        sort = Properties['sort']\n        if sort['column']:\n            col = Properties['col_order'].index(sort['column']) #prob in some places just do col_order Properties['col_order']\n            img = QtGui.QIcon(self.arrows[sort['direction']])\n            col_item = table.horizontalHeaderItem(col)\n            col_item.setIcon(img)\n\n        self.tab_manager.setCurrentIndex(tab_num) # moved up for tabtooltip on 1/15/2012 - might not work\n        \n        self.refresh()\n\n        self.status.showMessage(\"Successfully loaded %s\"%Properties['title'])\n\n        LBox.currentItemChanged.connect(self.filterlist) #12-19-2014\n\n    def createtable(self):\n\n        Properties = self.Properties\n        col_order = Properties['col_order']\n\n        table = Table(self, col_info=self.col_info, col_order=col_order, col_widths=Properties['col_widths']) #self.col_order\n        table.setItemDelegateForColumn(col_order.index('title'), TitleDelegate(self)) # Custom Delegate for title editor\n\n        header = table.horizontalHeader()\n        #header.setStretchLastSection(True) ##### screws up table.columnCount among other things\n        \n        header.sectionResized.connect(self.updatecolumnwidths) #SIGNAL(\"sectionResized(int,int,int)\")\n        header.sectionMoved.connect(self.updatecolumnorder) #SIGNAL(\"sectionMoved(int,int,int)\")\n        header.sectionClicked.connect(self.columnclick) #SIGNAL('sectionClicked(int)')\n\n        table.cellClicked.connect(self.cellclick) #SIGNAL(\"cellClicked(int,int)\")\n        table.itemSelectionChanged.connect(self.itemselected) #SIGNAL(\"itemSelectionChanged()\")\n\n        # this is the signal sent by the table when a cell editor is closed - other choice is to send it \"manually\" and not do this\n        table.itemDelegateForColumn(col_order.index('title')).commitData.connect(self.save_title) # SIGNAL(\"commitData(QWidget*)\")\n        table.return_sc = QtWidgets.QShortcut(QtGui.QKeySequence(\"Return\"), table, self.edittableitem) #for some reason context=QtCore.Qt.WidgetShortcut doesn't work but set below\n        table.return_sc.setContext(Qt.WidgetShortcut)\n\n        if Properties['col_widths'] is None: \n            Properties['col_widths'] = [table.columnWidth(n) for n in range(table.columnCount())]  # might want len(col_order)\n\n        table.setContextMenuPolicy(Qt.ActionsContextMenu)\n        add_actions(table, (self.p_action, self.a_selectcontext, self.a_selectfolder, self.a_select_tags))\n\n        return table\n\n    def replacetable(self, context_title=None):\n        '''Called by modifycolumns and updatecolumnorder'''\n\n        splitter = self.tab_manager.currentWidget()\n        \n        self.table.deleteLater()\n\n        self.table = table = self.createtable()\n        sizes = splitter.sizes()\n        splitter.addWidget(table)\n        splitter.setSizes([sizes[0], 0, sizes[1]]) \n        self.displayitems()\n\n    @check_modified\n    def onpagechange(self, page_index):\n        '''This is the slot for self.tab_manager - SIGNAL(\"currentChanged(int)\") - returns the index'''\n        print_(\"At the beginning of onpagechange\")\n\n        if page_index == -1:\n            return\n\n        splitter = self.tab_manager.currentWidget()\n\n        self.Properties = Properties = self.PageProperties[splitter]\n        \n        # ? following is necessary, not sure it's a speed up but it does also simplify reading the code\n        #maybe should just set in the Table instances ie self.table.col_order\n        self.col_order = Properties['col_order'] # ? necessary, it's a maybe speed that also simplifies reading the code\n\n        self.LBox = splitter.widget(0)\n        self.table = splitter.widget(1)\n\n        self.setWindowTitle(\"Listmanager \" + engine.url.drivername + \" \" + engine.url.host + \" \" + engine.url.database + \" - type: %s; value: %s filtered by: %s\"%(Properties['tab']['type'], Properties['tab']['value'],Properties['filter_by']['column']))\n\n        self.itemselected()\n\n        self.a_showcompleted.setChecked(self.Properties['show_completed'])\n        self.a_showhidefilterby.setChecked(bool(self.Properties['window_sizes'][0]))\n        self.a_toggle_collapsible.setChecked(self.Properties['collapsible'])\n\n        self.table.return_sc.setEnabled(True) ##### not sure I need this but may be belt & suspenders b/o escape key\n        \n        print_(\"onpagechange -- table width {}\".format(self.table.size().width()))\n        print_(\"title width = {}\".format(Properties['col_widths'][self.col_order.index('title')]))\n        \n        print_(\"onpagechange -- page_index {0}\".format(page_index))\n\n    def cellclick(self, r, c):\n\n        '''\n        The cellClicked(int,int) SIGNAL comes after the \n        \"itemSelectionChanged() SIGNAL. \n        '''\n        \n        # note that self-index can still be -1 but act of clicking on a cell should set it to something other than -1\n        #if self.index == -1: # now that the number of rows is correct shouldn't need this but we'll see\n         #   return\n        \n        # set in table's mousePressEvent method\n        if not self.table.leftpressed:\n            return\n        \n        id_ = self.table.item(self.index,0).data(Qt.UserRole)\n\n        if id_ < 4:\n            self.Properties['priority_display'][str(id_)] = not self.Properties['priority_display'][str(id_)]\n            self.refreshlistonly() \n            return\n\n        # the first click into an unselected row should selelct the row but otherwise not do anything\n        if not self.new_row: \n            self.action[self.col_order[c]](False) # to deal with qActions sending checked = False; it's reason in column stuff it's now lambda x\n        else:\n            self.new_row = False\n            \n    def edittableitem(self): # Native table text editor\n        '''uses delegate'''\n\n        print(\"In edittableitem\")\n        print(\"return pressed\")\n\n        if self.index != -1: \n\n            item = self.table.item(self.index, self.Properties['col_order'].index('title')) #self.Properties['col_order'] = self.col_order.index\n            self.table.editItem(item)\n            \n            # I believe that the table detects the keying of a RETURN in QLineEdit and then issues a CommitData and we don't want that RETURN to be intercepted\n            # Will enable RETURN again in save_title\n            self.table.return_sc.setEnabled(False) \n\n    def updatecolumnwidths(self, col, old, new):  \n        '''slot for sectionResized(int,int,int)'''\n        print_(\"in updatecolumnwidths\")\n        #print_(\"col = {0}; old = {1}, new = {2}\".format(col, old, new))\n        #print_(\"{}\".format(repr(self.tab_manager.currentWidget())))\n        #print_(\"{}\".format(repr(self.table)))\n\n        self.Properties['col_widths'][col] = new\n\n    def updatecolumnorder(self, logical, old, new):\n        '''slot for sectionMoved(int,int,int)'''\n        \n        moved_col = self.Properties['col_order'][old]\n        moved_width = self.Properties['col_widths'][old]\n        \n        del self.Properties['col_order'][old]\n        del self.Properties['col_widths'][old]\n        \n        self.Properties['col_order'].insert(new,moved_col)\n        self.Properties['col_widths'].insert(new,moved_width)\n\n        self.replacetable()\n\n    def removecolumn(self, c):\n\n        del self.col_order[c]\n        del self.Properties['col_widths'][c]\n        \n        self.replacetable()\n        \n    @update_row\n    @check_task_selected\n    @check_modified\n    def togglecompleted(self, checked):\n\n        task = self.task \n\n        if not task.completed:\n            task.completed = datetime.datetime.now().date()\n        else:\n            task.completed = None\n            # below not done in updaterow\n            self.table.item(self.index,0).setIcon(self.idx1)\n\n        session.commit()\n        \n    @update_row\n    @check_task_selected\n    @check_modified\n    def togglestar(self, checked):    \n\n        self.task.star = not self.task.star\n        session.commit()\n\n        #self.a_togglestar.setChecked(self.task.star) # decided cute but unnecessary\n\n    def on_pmenu(self, checked):\n        self.p_menu.exec_(QtGui.QCursor.pos())\n\n    # order is bottom to top\n    @update_row\n    @check_task_selected\n    @check_modified\n    def incrementpriority(self, checked):\n        task = self.task\n        priority = task.priority\n        priority += 1\n        if priority ==4:\n            priority = -1\n\n        task.priority = priority\n        session.commit()\n\n        p_list = self.priorities[:]\n        p_list.reverse()\n        i = p_list.index(priority)\n        self.p_menu.actions()[i].setChecked(True)\n        \n        self.myevent.signal.emit('incrementpriority', {'task':task, 'priority':priority})\n\n    # order is bottom to top\n    @update_row\n    @check_task_selected\n    def setpriority(self, checked, priority=0):\n\n        self.task.priority = priority\n        session.commit()\n        \n    @update_row\n    @check_modified\n    @check_task_selected\n    def setduedate(self, checked):\n\n        task = self.task\n        idx = self.index\n\n        table = self.table\n        Properties = self.Properties\n\n        if task.duedate:\n            duedate = task.duedate\n            d = QtCore.QDate(duedate.year, duedate.month,duedate.day)\n        else:\n            d = QtCore.QDate.currentDate()\n            \n        if task.duetime:\n            duetime = task.duetime\n            t = QtCore.QTime(duetime.hour, duetime.minute, duetime.second)\n        else:\n            t = QtCore.QTime.currentTime()\n            \n        qdt = QtCore.QDateTime(d,t)\n        \n        state = bool(task.remind)\n\n        dlg = lmdialogs.TaskDueDateTime(title=\"Select a date\", qdatetime=qdt, state=state, parent=self)\n\n        if dlg.exec_(): # if cancel - dlg.exec_ is False\n            qdatetime = dlg.qdatetime # QDateTime\n            \n            #################### this shouldn't be necessary when they all go back to naive\n            task.duetime = None #sqlite tries to copmare old datetime to new and if it's naive compared to aware get TypeError\n            session.commit()\n            \n            task.duedate = task.duetime = datetime.datetime.fromtimestamp(qdatetime.toTime_t())\n            \n            if dlg.setalarm:\n                task.remind = 1\n            else:\n                task.remind = None # or maybe should be zero since that's what server will send back\n            \n            session.commit()\n\n    @update_row\n    @check_modified\n    @check_task_selected\n    def setdate(self, checked, which=None): #not a qAction method but need this because treating them the same\n\n        task = self.task\n        idx = self.index\n\n        table = self.table\n        Properties = self.Properties\n\n        taskdate = getattr(task, which)\n        if taskdate:\n            date = QtCore.QDate()\n            date.setDate(taskdate.year, taskdate.month,taskdate.day)\n        else:\n            date = None\n\n        dlg = lmdialogs.TaskDueDate(title=\"Select a date\", date=date)\n\n        if dlg.exec_(): # if cancel - dlg.exec_ is False\n            date = dlg.date # QDate\n\n            print_(repr(date.getDate()))\n            z = date.getDate()\n\n            dt = datetime.datetime(*z)\n            setattr(task, which, dt.date())\n            session.commit()\n\n    @check_task_selected\n    @check_modified\n    def selectcontextfolder(self, checked, type_='context'): \n        '''\n        Called by Select Context of Task Menu and toolbar context push button\n        Displays a dialog with both existing contexts and place to type new context\n        '''\n        task = self.task\n        \n        if type_ == 'context':\n            things = self.context_icons\n            sel = task.context.title if task.context else None # is this necessary or is no context still a context?\n        else:\n            things = self.folder_icons\n            sel = task.folder.title if task.context else None # is this necessary or is no context still a context?\n        \n        dlg = lmdialogs.SelectContextFolder(\"Select\", things, parent=self, initial_selection=sel)\n\n        if dlg.exec_():\n            list_choice = dlg.listWidget.selectedItems()[0].text()\n            new_name = dlg.new_name\n\n            print_(\"list_choice={0}\\nnew_name={1}\".format(list_choice, new_name))\n\n            # new entry trumps a choice from the list\n            title = new_name if new_name else list_choice\n            if type_=='context':\n                self.updatecontext(title=title)\n            else:\n                self.updatefolder(title=title)\n\n    @update_row\n    @update_whooshdb\n    @check_task_selected\n    def updatefolder(self, checked=None, title=None):\n\n        task = self.task\n\n        folder = session.query(Folder).filter_by(title=title).first()\n\n        if folder:\n            task.folder = folder\n        else:\n            reply = QtWidgets.QMessageBox.question(self,\n                                              \"Confirmation\",\n                                              \"Do you want to create a new folder?\",\n                                              QtWidgets.QMessageBox.Yes|QtWidgets.QMessageBox.No)\n\n            if reply == QtWidgets.QMessageBox.No:\n                print(\"You said no to creating a new folder\")\n                return\n            \n            # Need to set the folder.tid to the server value next synch but doesn't affect foreign key\n            new_folder = Folder(title=title) #new_folder.tid==None\n            task.folder = new_folder \n            session.add(new_folder)\n            \n            a_newfolder = create_action(self, new_folder.title, partial(self.updatefolder, new_folder.title), checkable=True)\n            self.iconGroup.addAction(a_newcontext)\n            self.f_menu.addAction(a_newfolder)   \n            \n        session.commit()\n\n    @update_row\n    @update_whooshdb\n    @check_task_selected\n    def updatecontext(self, checked=None, title=None):\n        '''\n        Called by each of the table widget context submenu actions and the toolbar menu widget\n        and by method selectcontext (and appears in synchronize\n         if there is a new context, because menus need to be updated)\n        '''\n        task = self.task\n\n        context = session.query(Context).filter_by(title=title).first()\n        if context:\n            task.context = context\n        else:\n\n            reply = QtGui.QMessageBox.question(self, \"Confirmation\", \"Do you want to create a new context?\",\n                                                                QtGui.QMessageBox.Yes|QtGui.QMessageBox.No)\n\n            if reply == QtGui.QMessageBox.No:\n                print_(\"You said no to creating a new context\")\n                return\n\n            # Need to set the context.tid to the server value next synch but doesn't affect foreign key\n            new_context = Context(title=title) #new_context.tid==None\n            task.context = new_context \n            session.add(new_context)\n            \n            a_newcontext = create_action(self, new_context.title, partial(self.updatecontext, new_context.title), checkable=True)\n            self.contextGroup.addAction(a_newcontext)\n            self.c_menu.addAction(a_newcontext)   \n\n        session.commit()\n\n    @check_task_selected\n    @check_modified\n    def select_tags(self, checked):\n        \n        context = self.task.context.title\n        titles = sorted(self.get_keywords('context', context), key=str.lower)\n\n        sel = [kw.title for kw in self.task.keywords]\n        \n        print(\"sel={0}\".format(sel))\n\n        dlg = lmdialogs.MultiChoiceOrNew(\"Select\", titles, parent=self, initial_selection=sel)\n\n        if dlg.exec_():\n            selections = dlg.choices\n            newentries = dlg.newentry\n            \n            newentries = [z.strip() for z in newentries.split(',') if z] #if gets rid of '' new entries\n\n            print(\"selections={0}\\nnewentries={1}\".format(selections, newentries))\n            \n            selections_lower = [z.lower() for z in selections]\n            \n            # remove any new entries that match selections in the listwidget\n            newentries = [z for z in newentries if z.lower() not in selections_lower]\n            \n            # make sure that any new entries are really new and also not just different because of letter case, if not new, use existing\n            for n,text in enumerate(newentries):\n                result = dlg.listWidget.findItems(text, Qt.MatchFixedString) #case insensitive match\n                if result:\n                    newentries[n] = result[0].text()\n                \n            # this worked too to eliminate letter case issues\n            #title_dict = dict(zip([t.lower() for t in titles],titles))\n            #newentries = [title_dict.get(z.lower(), z) for z in newentries]\n            \n            print(\"newentries={0}\".format(newentries))\n            \n            tag = selections + newentries\n            \n            print(\"new tag list = {0}\".format(tag))\n            \n            self.updatetag(tag)\n\n    @update_row\n    @update_whooshdb\n    @check_task_selected\n    def updatetag(self, tag):\n        '''tag is a list''' \n\n        task = self.task\n        \n        # select_tags should be sending clean list with stripped white space\n        # would be better creating tag after doing keywords see below\n        #task.tag = \",\".join(tag) if tag else None\n        #session.commit()\n        \n        print_(\"In updatetag: tag={0}\".format(tag))\n\n        for tk in task.taskkeywords:\n            session.delete(tk)\n        session.commit()\n        \n        for kwn in tag: # tag will be '' when task.tag is None\n            keyword = session.query(Keyword).filter_by(name=kwn).first()\n            if keyword is None:\n                keyword = Keyword(kwn)\n                session.add(keyword)\n            tk = TaskKeyword(task,keyword)\n            session.add(tk)\n\n        session.commit()\n        \n        #task.tag is a string of keywords separated by commas\n        task.tag = ','.join(kwn.name for kwn in task.keywords)\n        session.commit()\n\n    #@update_row #doesn't work for a new task but abbreviated version in body of method\n    @check_modified\n    def newtask(self, checked):\n\n        table = self.table\n        Properties = self.Properties\n\n        #Should probably check that 'tab' is in Properties\n        tab_type = Properties['tab']['type']\n        tab_value = Properties['tab']['value']\n\n        task = Task(priority=3, title='<New Item>')\n        \n        ############################## this is now in a plugin myevent - could be an option that all new items remind/alarm\n        #if 1:\n        #    task.remind = 1\n        #    task.duedate=task.duetime = datetime.datetime.now() + datetime.timedelta(days=1)\n        #############################################################\n        \n        session.add(task)\n        session.commit()\n\n        self.task = task\n\n        if tab_type in ('context', 'folder'):\n            mapper_class = Context if tab_type == 'context' else Folder\n            tab_value_object = session.query(mapper_class).filter_by(title=tab_value).one()\n            setattr(task, tab_type, tab_value_object) \n            session.commit()\n        elif tab_type == 'app' and tab_value=='star':\n            task.star = True\n            session.commit()\n            \n        task.startdate = datetime.datetime.today().date() ############### may 2, 2012\n        session.commit()\n\n        self.note.clear() # these trigger note_edit\n        self.db_note.clear()\n        self.modified = {} \n\n        table.insertRow(0)\n\n        item0 = QtWidgets.QTableWidgetItem(self.idx1,'')\n        item0.setData(Qt.UserRole, int(task.id))\n        table.setItem(0, 0, item0)            \n\n        self.index = 0\n        \n        # an abbreviated version of @update_row - note that can't be at the end of this method but should be here\n        type_ = 'folder' if Properties['tab']['type'] == 'context' else 'context'\n        color = QtGui.QColor(getattr(task, type_).textcolor)\n        for c,col in enumerate(self.col_order[1:],start=1):\n            item = QtWidgets.QTableWidgetItem(*self.display[col](task))\n            item.setFont(self.itemfont[task.priority])\n            item.setForeground(color)\n            table.setItem(0, c, item)\n        \n        item = table.item(0, self.col_order.index('title'))\n        table.setCurrentItem(item)\n\n        table.editItem(item) #initiates the editing of the table item and in this case triggers delegate code; could call edittableitem but the latter would need to be changed\n        self.table.return_sc.setEnabled(False) ## this means return will not be intercepted\n\n        self.new_row = False \n        \n        self.myevent.signal.emit('set_reminder', {'task':task, 'session':session})\n\n    @update_row\n    @check_modified\n    @check_task_selected\n    def deletetask(self, checked):\n\n        self.task.deleted = not self.task.deleted\n        session.commit()\n            \n    @update_row\n    @update_whooshdb\n    def save_title(self, editor=None):  #editor=None was added on April 22, 2012 but surprising it worked without it.\n        '''\n        ... QtCore.SIGNAL(\"commitData(QWidget*)\"), self.save_title) --> widget is the editor\n        '''\n        print_(\"In save_title\")\n\n        task = self.task\n        \n        task.title = title = editor.text()\n        session.commit()\n\n        print_(\"title={0} was saved to the database\".format(title))\n\n        # if this doesn't get enabled then won't be able to edit items\n        self.table.return_sc.setEnabled(True) \n\n    @check_modified\n    def newcontext(self, evt=None):\n        '''\n        Creates a new context on the client that will be synced to the server\n        Also creates a new tab with the context\n        '''\n\n        result = QtGui.QInputDialog.getText(self,\"New Context\",\"Enter new context:\", QtGui.QLineEdit.Normal)\n\n        new_context_title, b = result\n\n        if not b:\n            return\n\n        context_titles = [c.title.lower() for c in session.query(Context)]\n\n        if new_context_title.lower() in context_titles:\n\n            reply = QtGui.QMessageBox.warning(self,\n                                 \"Duplicate Context\",\n                                 \"Context '{0}' already exists\".format(new_context_title))\n\n            return\n\n        reply = QtGui.QMessageBox.question(self,\n                                \"Create New Context?\",\n                                \"Are you sure you want to create Context '{0}'?\".format(new_context_title),\n                                QtGui.QMessageBox.Yes|QtGui.QMessageBox.No)\n\n        if reply == QtGui.QMessageBox.Yes:\n            new_context = Context(title=new_context_title)#new_context.tid==None \n            session.add(new_context)\n            session.commit()\n\n            a = create_action(self, new_context_title, partial(self.updatecontext, new_context_title), checkable=True)\n            self.contextGroup.addAction(a)\n            self.c_menu.addAction(a)\n\n            self.createnewtab(title=new_context_title, tab={'type':'context', 'value':new_context_title})\n\n    @check_modified\n    def opentabs(self, check, type_=None):\n\n        class_ = {'folder':Folder, 'context':Context, 'tag':Keyword}\n\n        open_titles = [d['tab']['value'] for d in list(self.PageProperties.values()) if d['tab']['type']==type_]\n        all = session.query(class_[type_]).all()\n        unopened_titles = sorted([z.title for z in all if not z.title in open_titles], key=str.lower)\n\n        dlg = lmdialogs.ChoiceDlg(\"Select\", unopened_titles, parent=self)\n        \n        if dlg.exec_():\n            for title in dlg.choices:\n                tab_title =\"{} ({})\".format(title, type_[0])\n                filter_by = {'column':'folder','value':'*ALL'} if type_=='context' else {'column':'context','value':'*ALL'}\n                self.createnewtab(title=tab_title, tab={'type':type_, 'value':title}, filter_by=filter_by)\n\n\n    @check_modified\n    def opentabs2(self, checked, type_='context'): # check is there because qaction.trigger.connect(method) returns false\n\n        '''\n        Called by File OpenTabs\n        '''\n\n        things = self.context_icons if type_=='context' else self.folder_icons\n        \n        dlg = lmdialogs.SelectContextFolder(\"Select\", things, parent=self, initial_selection=None, multi=True, new_entry=False)\n\n        if dlg.exec_():\n            \n            for item in dlg.listWidget.selectedItems():\n                title = item.text()\n                tab_title =\"{} ({})\".format(title, type_[0])\n                filter_by = {'column':'folder','value':'*ALL'} if type_=='context' else {'column':'context','value':'*ALL'}\n                tab_icon = things[title]\n                \n                self.createnewtab(title=tab_title, tab={'type':type_, 'value':title}, tab_icon=tab_icon, filter_by=filter_by)\n\n    def showrecentitems(self, tab_value=None):\n\n        show_completed = True if tab_value in ('ALL', 'Completed') else False\n\n        self.createnewtab(\n                            title = '*Recently* '+tab_value,\n                            tab = {'type':'recent', 'value':tab_value},\n                            filter_by={'column':'context','value':'*ALL'}, \n                            show_completed=show_completed)\n\n\n    @check_modified\n    def create_multi_tab(self, type_=None):\n        \n        class_ = {'folder':Folder, 'context':Context, 'tag':Keyword}\n\n        filter_by = {'column':'folder','value':'*ALL'} if type_=='context' else {'column':'context','value':'*ALL'}\n\n        all = session.query(class_[type_]).all()\n        names = sorted([z.title for z in all], key=str.lower)\n        if len(names) < 2:\n            #should issue a dialog\n            return\n\n        dlg = lmdialogs.ChoiceDlg(\"Select\", names, parent=self)\n        \n        tab_icon = os.path.join('folder_icons', 'scream')\n        \n        if dlg.exec_():\n            tab_title = '-'.join(dlg.choices)\n            tab_value = ','.join(dlg.choices)\n            self.createnewtab( title=tab_title,\n                                         tab={'type':type_+'s', 'value':tab_value},\n                                         tab_icon=tab_icon,\n                                         filter_by=filter_by,\n                                         sort ={'column':'priority','direction':0},\n                                         collapsible = False,\n                                         col_order = ['box','icon','added','priority','star','title','context','tag','folder'],\n                                         col_widths = None,\n                                         show_completed=False)\n                            \n\n    def showallitems(self):\n        self.createnewtab('*ALL', tab_type='all', filter_by={'column':'context','value':'*ALL'})\n\n    def deletecontexts(self, type_=None):\n        '''\n        Note that you can delete contexts and folders on the server and\n        have those changes propogate properly to the client.  \n        ''' \n        \n        reply = QtGui.QMessageBox.question(self,\n                                    \"Delete Context\",\n                                    \"Would you like to delete one or more contexts - this is serious?\",\n                                    QtGui.QMessageBox.Yes|QtGui.QMessageBox.No)\n\n        if reply == QtGui.QMessageBox.No:\n            return\n        \n        class_ = {'folder':Folder, 'context':Context, 'tag':Keyword}\n\n        list_ = session.query(class_[type_]).all()\n        \n        titles = sorted([z.title for z in list_ if z.tid != 0], key=str.lower)\n        if not len(titles):\n            print_(\"Nothing to delete\")\n            #should show a dialog\n            return\n\n        dlg = lmdialogs.ChoiceDlg(\"Select\", titles, parent=self)\n        \n        if dlg.exec_():\n            list_ = []\n            for title in dlg.choices:\n                x = session.query(class_[type_]).filter_by(title=title).first()\n                list_.append(x)\n            \n            print(list_)\n            return       \n            \n            log = ''\n            for c in list_:\n                c.deleted = True #!!!!!! can't do this until create this field\n                tasks = c.tasks\n                for t in tasks:\n                    t.context_tid = 0\n                    log+= \"{title} now has 'No Context'\\n\".format(title=t.title)\n        \n            session.commit()\n\n    @check_modified\n    def close_all(self):\n\n        pc = self.tab_manager.count()\n        self.tab_manager.setCurrentIndex(pc-1)\n\n        # the following may be necessary or you  mayget errors when deleting pages - need to check but no reason for signals anyway\n        self.tab_manager.blockSignals(True)\n\n        while self.tab_manager.count():\n            self.closetab()\n\n        self.note.clear()\n        #note that Clearing does set self.modified (eg {'name':1})\n        self.modified = {}\n\n        self.tab_manager.blockSignals(False)\n\n    @check_modified\n    def closeEvent(self, event=None): #event is necessary\n\n        cg = g.config\n        \n        #if args.qsettings: #removed \"if\" on 10.27.2012\n        settings = QtCore.QSettings()\n        settings.setValue(\"MainWindow/Geometry\",self.saveGeometry())\n        settings.setValue(\"MainWindow/State\",self.saveState())\n        settings.setValue(\"savedtabs\", self.savedtabs) # appears to write None if self.savedtabs is []\n\n        # save tabs and their properties\n        x=[]\n        for p in range(self.tab_manager.count()):\n            sw = self.tab_manager.widget(p)\n            Properties = self.PageProperties[sw] \n            tab = Properties['tab']\n      \n            if  tab['value'] != 'active_search':\n                x.append(Properties)\n\n        if not cg.has_section('Startup'):\n            cg.add_section('Startup')\n\n        tab = self.Properties['tab']\n\n        if  tab['value'] != 'active_search':\n            if 'tab_icon' in self.Properties:\n                del self.Properties['tab_icon']\n            cg.set('Startup', 'current_tab', json.dumps(self.Properties))\n\n        if not cg.has_section('Properties'):\n            cg.add_section('Properties')\n            \n        cg.set('Properties', 'tab_properties', json.dumps(x))\n        current = self.tab_manager.currentIndex()\n        cg.set('Properties', 'current', current)\n\n        with open(g.CONFIG_FILE, 'w') as configfile:\n            cg.write(configfile)\n\n    @check_modified\n    def saveconfiguration(self):\n        \n        cg = g.config\n\n        # save tabs and their properties\n        cg = configparser.RawConfigParser()\n        \n        z=[]\n        for p in range(self.tab_manager.count()):\n            sw = self.tab_manager.widget(p)\n            Properties = self.PageProperties[sw] \n            tab = Properties['tab']\n      \n            if  tab['value'] != 'active_search':\n                #z.append((tab.get('type'),tab.get('value'))) # this would be better but involves other changes\n                z.append((Properties.get('title'), tab.get('type'),tab.get('value'))) #don't need title\n\n        cg.add_section('Startup')\n        cg.set('Startup', 'tabs', json.dumps(z))\n        \n        tab = self.Properties['tab']\n        if  tab['value'] != 'active_search':\n            cg.set('Startup', 'current_tab', json.dumps(self.Properties['tab']))\n\n        tab_properties = []\n        for Properties in list(self.PageProperties.values()):\n            tab = Properties['tab']\n\n            if  tab['value'] != 'active_search':\n                #t = (tab['type'], tab['value']) 05132012\n                tab_properties.append(Properties)  \n\n        cg.add_section('Properties')\n\n        cg.set('Properties', 'tab_properties', json.dumps(tab_properties))\n        \n        cwd = os.getcwd()  #cwd => /home/slzatz/mylistmanager\n        file_ = os.path.join(cwd,'untitled.lmc')\n        file_ = QtGui.QFileDialog().getSaveFileName(self, \"Save File As\",file_, \"LMC (*.lmc)\")\n        print(file_)\n\n        with open(file_, 'w') as configfile:\n            cg.write(configfile)\n\n    @check_modified\n    def loadconfiguration(self):\n        reply = QtGui.QMessageBox.warning(self,\n                                                   \"Confirmation\",\n                                                    \"This will close all the current tabs; Do you want to proceed?\",\n                                                     QtGui.QMessageBox.Yes|QtGui.QMessageBox.No)\n\n        if reply == QtGui.QMessageBox.Yes:\n\n            self.close_all()\n            \n            file_ = QtGui.QFileDialog().getOpenFileName(self, \"Open File\", os.getcwd(), \"LMC (*.lmc)\")\n            print(file_)\n            g.config = configparser.RawConfigParser()\n            g.config.read(file_) # if file doesn't exist returns [] else returns list of files\n            \n            QtCore.QTimer.singleShot(0, self.loadtabs)\n        \n    @check_modified\n    def savetab(self):\n        \n        if  self.Properties['tab']['value'] == 'active_search':\n            return\n\n        ln = len(self.savedtabs)\n        if ln > 3:\n            del self.savedtabs[-1]\n            \n        self.savedtabs.insert(0, dict(self.Properties))\n        \n        self.m_savedtabsmenu.clear()\n        #self.savedtabsactions = []\n        \n        for i, properties in enumerate(self.savedtabs):\n            aa = self.createsavedtab(properties, i)\n            self.m_savedtabsmenu.addAction(aa)\n            #self.savedtabsactions.append(aa)\n         \n         #seems like you would need the following if you were going to update the toolbar and probably don't need toolbar actions at all\n         #for a in self.savedtabsactions:\n             #fileToolbar.addAction(a)\n            \n    @check_modified\n    def loadtab(self, n):\n        \n        properties = self.savedtabs[n]\n        \n        self.createnewtab(**properties)\n        \n    @check_modified\n    def closetab(self, L=-1):\n        \n        '''\n        Important:  the current Index is where the tab is at the time so it represents a position and is not permanently associated\n        with a specific tab and its content -- the current index of the far left tab is always 0, the next tab is 1, etc.\n        \n        L is returned correctly  even if you click the close box on a tab that doesn't have focus\n        However, the action of clicking on the tab close box doesn't change the current Index\n        That needs to be done explicitly -- see below\n        '''\n        \n        print(\"tab requested to close is {0}: {1}\".format(L, self.tab_manager.tabText(L)))\n\n        if L==-1: # called by menu item\n            L = self.tab_manager.currentIndex() #a tab changes its Index to whatever position it is moved to (makes things easier)\n        else:\n            self.tab_manager.setCurrentIndex(L) #because clicking on an inactive tab's close doesn't set current index\n            \n        # self.Properties will be correct because setting current index generates a pagechange\n            \n        tab = self.Properties['tab']\n\n        # two situations - tab being closed is inc_search or inc_search is higher num than tab being closed\n        # this will need to change because the incremental search can move (qt)\n        if tab['value'] =='active_search':\n            self.search.clear() # this may generate a text updated event that we need to catch\n            self.active_search = None\n\n        #current widget should be correct because of setting current index\n        splitter = self.tab_manager.currentWidget() # this is not working and is removing another tab\n        self.tab_manager.removeTab(L)\n        splitter.deleteLater() # not sure if you need this or not\n\n        del self.PageProperties[splitter]\n\n\n\n\n\n    @update_row\n    @update_whooshdb\n    @check_task_selected\n    def savenote(self, check=False):\n\n        if 'plain_note' in self.modified:  #self.modified.get('plain_note', False)\n            text = self.db_note.toPlainText()\n        elif 'note' in self.modified:\n            text = self.note.toMarkdown()\n        else:\n            return\n\n        text= re.sub('\\n\\n\\n*\\n','\\n\\n', text)\n\n        self.task.note = text\n        session.commit()\n\n        simple_html = markdown.markdown(text)\n\n        # kluge to get rid of extra line after a pre block\n        simple_html = simple_html.replace('\\n</code></pre>', '</code></pre>')\n\n        self.note.setHtml(simple_html)\n        self.db_note.setPlainText(text)\n\n        self.modified = {}\n\n        print_(\"Note Saved\")\n\n        self.myevent.signal.emit('set_reminder', {'task':self.task, 'session':session})\n\n    def displayitems(self):\n\n        qtablewidgetitem = QtWidgets.QTableWidgetItem\n        qcolor = QtGui.QColor\n        #qicon = QtGui.QIcon\n\n        display = self.display #dict that just holds display lambda's\n            \n        type_ = 'folder' if self.Properties['tab']['type'] == 'context' else 'context'\n\n        table = self.table\n        col_order = self.col_order\n\n        itemfont = self.itemfont\n        deleteditemfont = self.deleteditemfont\n\n        idx1 = self.idx1\n        idx0 = self.idx0\n        \n        query = self.get_current_tasks()\n        rows = query.count() ########\n        \n        table.clearContents()\n\n        if not self.Properties['collapsible']:\n            \n            table.setRowCount(rows)\n\n            for n,task in enumerate(query):\n                item = qtablewidgetitem(idx1 if not task.completed else idx0, '')\n                item.setData(Qt.UserRole, int(task.id))\n                table.setItem(n, 0, item)\n                color = qcolor(getattr(task, type_).textcolor) if not (task.deleted or task.completed) else qcolor('gray')\n                font = itemfont if not task.deleted else deleteditemfont\n                \n                for c,col in enumerate(col_order[1:],start=1):\n                    item = qtablewidgetitem(*display[col](task))\n                    item.setFont(font[task.priority])\n                    item.setForeground(color)\n                    table.setItem(n, c, item)\n\n        else:\n            collapsed = QtGui.QIcon('bitmaps/collapsed.png')\n            expanded = QtGui.QIcon('bitmaps/expanded.png')\n            table.setRowCount(rows+len(self.priorities)) ############# max needed; will reduce, see below\n            n=0 # the row count\n            for p,b in reversed(sorted(self.Properties['priority_display'].items())):\n\n                item = qtablewidgetitem(expanded if b else collapsed, '')\n                item.setData(Qt.UserRole, int(p))\n                item.setBackground(qcolor(\"GREY\"))\n                table.setItem(n, 0, item)\n\n                for c in range(1, len(col_order)):\n                    item = qtablewidgetitem()\n                    item.setBackground(qcolor(\"GREY\"))\n                    table.setItem(n, c, item)            \n\n                p_query = query.filter(Task.priority==p)\n                s = \"Priority %d: %d items\"%(int(p), p_query.count())\n                item = qtablewidgetitem(s)\n                item.setBackground(qcolor(\"GREY\"))\n                #item.setTextColor(qcolor(\"WHITE\"))\n                item.setForeground(qcolor(\"WHITE\")) #12-19-2014\n                table.setItem(n, col_order.index('title'), item)   \n                n+=1\n\n                if b:\n                    for task in p_query:\n                        item = qtablewidgetitem(idx1 if not task.completed else idx0, '')\n                        item.setData(Qt.UserRole, int(task.id))\n                        table.setItem(n, 0, item)\n                        color = qcolor(getattr(task, type_).textcolor) if not (task.deleted or task.completed) else qcolor('gray')\n                        font = itemfont if not task.deleted else deleteditemfont\n                        \n                        for c,col in enumerate(col_order[1:],start=1):\n                            item = qtablewidgetitem(*display[col](task))\n                            item.setFont(font[task.priority])\n                            item.setForeground(color)\n                            table.setItem(n, c, item)\n\n                        n+=1\n            \n            table.setRowCount(n) # correct the row count\n            \n        return rows            \n\n    @check_modified\n    def itemselected(self):\n\n        '''the \"itemSelectionChanged() SIGNAL is only emitted if the row wasn't previously selected - it is SIGNALLED \n        *before* the cellClicked(int,int) is issued.  Note that onpagechange calls itemselected and nothing may be selected'''\n\n        table = self.table\n\n        ranges = table.selectedRanges()\n        self.index = ranges[0].topRow() if ranges else -1  \n\n        table.return_sc.setEnabled(True) ####### not sure I need this but may be belt & suspenders - escape key\n\n        if self.index == -1:\n            self.task = None\n            self.note.clear() #triggers a note modified\n            self.db_note.clear()\n            print_(\"No selection during itemselected\")\n            self.modified = {}\n\n            self.note.setEnabled(False)\n            self.db_note.setEnabled(False)\n            self.note_manager.format_toolbar.setEnabled(False)\n\n            return\n\n        #if table.item(self.index, 0): # once number of rows is same as number of tasks - don't need to check this #### May 19, 2012\n        id_ = table.item(self.index,0).data(Qt.UserRole)\n\n        if id_ < 4:\n            self.task = None\n            self.note.clear() #triggers a note modified \n            self.db_note.clear() #triggers a note modified even if the note doesn't change\n            #self.modified = {} ### May 19, 2012\n\n            self.note.setEnabled(False)\n            self.db_note.setEnabled(False)\n            self.note_manager.format_toolbar.setEnabled(False)\n\n        else:\n\n            self.task = task = session.query(Task).get(id_)\n            note = task.note if task.note else ''\n\n            # kluge -- needs to be fixed in markdown2\n            simple_html = markdown.markdown(note)\n            simple_html = simple_html.replace('\\n</code></pre>', '</code></pre>')\n            self.note.setHtml(simple_html)\n\n            self.db_note.setPlainText(note)\n\n            p_list = self.priorities[:]\n            p_list.reverse()\n            i = p_list.index(self.task.priority)\n            self.p_menu.actions()[i].setChecked(True)\n\n            folders = session.query(Folder).filter(Folder.id!=1).all()\n            folders.sort(key=lambda f:str.lower(f.title))            \n            f_list = ['No Folder'] + [f.title for f in folders]\n            i = f_list.index(self.task.folder.title)\n            self.f_menu.actions()[i].setChecked(True)\n\n            contexts = session.query(Context).filter(Context.id!=1).all()\n            contexts.sort(key=lambda c:str.lower(c.title))\n            c_list = ['No Context'] + [c.title for c in contexts]\n            i = c_list.index(self.task.context.title)\n            self.c_menu.actions()[i].setChecked(True)\n\n            self.new_row = True #because method is called because a new row was selected\n\n            self.note.setEnabled(True)\n            self.db_note.setEnabled(True)\n            self.note_manager.format_toolbar.setEnabled(True)\n            \n            # highlight search terms in note if on search tab\n            if self.Properties['tab']['type'] == 'search':\n                self.highlightsearchterms()\n            else:\n                self.highlighter.setDocument(None)\n\n            delegate = table.itemDelegateForColumn(self.col_order.index('title'))\n            title_col = self.col_order.index('title')\n\n            #note that tooltip gets created only and each time the row is selected\n            #once the row is selected once tooltip will appear on hover whether row is selected or not\n\n            if delegate.sizeHint(table.viewOptions(), table.indexFromItem(table.item(self.index, title_col))).width() > table.columnWidth(title_col):\n                table.item(self.index, title_col).setToolTip(\"{0} ({1})\".format(task.title, task.context.title)) # task.title+' ('+task.context.title+')'\n            else:\n                table.item(self.index, title_col).setToolTip(task.context.title)\n            \n            #self.a_togglestar.setChecked(task.star) # decided cute but unnecessary\n     \n        QtCore.QTimer.singleShot(0, lambda: self.modified.clear())\n\n    def showcompleted(self, evt=None):\n\n        self.Properties['show_completed'] = not self.Properties['show_completed']\n        self.refresh()\n\n    def removesort(self):\n        '''\n        Once you sort a table, without this method there is no way to remove the sorting criteria - \n        this is particularly important in being able to sort and remove sort from xapian searches\n        ? if this still applies 12-28-2014\n        '''\n        sort_col = self.Properties['sort']['column']\n        \n        if sort_col is None:\n            return\n        \n        col_item = self.table.horizontalHeaderItem(self.col_order.index(sort_col))\n        col_item.setIcon(QtGui.QIcon())\n        \n        self.Properties['sort'] = {'column':None,'direction':0}\n        \n        self.refresh()\n\n    @check_modified\n    #@check_task_selected\n    def ondockwindow(self, checked, dw=None, cur=True, check_task_selected=True): \n\n        if check_task_selected and (not self.task or self.index==-1):\n            QtWidgets.QMessageBox.information(self,  'Note', \"There was no row selected.  Please select one.\") \n            return\n\n        if not dw.isVisible():\n            dw.show()\n\n        dw.setFloating(not dw.isFloating())\n\n        # note needed when both floating and docked to raise and activiate\n        dw.activateWindow() \n        dw.raise_()  \n        \n        txt = dw.widget()\n\n        if cur:\n            cursor = txt.textCursor()\n            cursor.movePosition(QtGui.QTextCursor.End)\n            txt.setTextCursor(cursor)\n\n    @check_modified\n    def refresh(self, check=False): \n\n        if not self.PageProperties: # no tabs need this in other places\n            return\n\n        #self.table.blockSignals(True)\n        num_tasks = self.displayitems()\n        #self.table.blockSignals(False)\n\n        #self.note.blockSignals(True)\n        self.note.clear() # this triggers a textChanged() event\n        #self.note.blockSignals(False)\n\n        #self.db_note.blockSignals(True)\n        self.db_note.clear() # this triggers a textChanged() event\n        #self.db_note.blockSignals(False)\n\n        Properties = self.Properties\n        tab_value = Properties['tab']['value']\n        tab_type = Properties['tab']['type']\n\n        self.index = -1\n        self.task = None\n\n        self.modified = {} #not necessary if you block the signal\n\n        LBox = self.LBox\n\n        if Properties['filter_by']['column'] == 'context':\n            if tab_type == 'folder':\n                contexts = session.query(Context).join(Task,Folder).filter(Folder.title==tab_value)\n                LB_items = ['*ALL','No Context'] + sorted([c.title for c in contexts if c.id!=1], key=str.lower)\n            else:    \n                LB_items = ['*ALL','No Context'] + sorted([c.title for c in session.query(Context).filter(Context.id!=1)], key=str.lower)\n\n        elif Properties['filter_by']['column'] == 'folder':\n            if tab_type == 'context':\n                #context = session.query(Context).filter_by(title=tab_value).one()\n                folders = session.query(Folder).join(Task,Context).filter(Context.title==tab_value)\n                #LB_items = ['*ALL','No Folder'] + sorted([f.title for f in context.folders if f.tid!=0], key=unicode.lower)\n                LB_items = ['*ALL','No Folder'] + sorted([f.title for f in folders if f.id!=1], key=str.lower)\n            else:\n                LB_items = ['*ALL','No Folder'] + sorted([f.title for f in session.query(Folder).filter(Folder.id!=1)], key=str.lower)\n\n        elif Properties['filter_by']['column'] == 'priority':\n            LB_items = ['*ALL'] + ['3','2','1','0','-1']\n\n        elif Properties['filter_by']['column'] == 'tag':\n                LB_items = ['*ALL'] + self.get_keywords(tab_type, tab_value)\n\n        LBox.blockSignals(True)\n\n        LBox.clear()\n        LBox.addItems(LB_items)\n\n        LBox.setCurrentRow(0)\n\n        items = LBox.findItems(Properties['filter_by']['value'], Qt.MatchExactly)\n        if items:\n            LBox.setCurrentItem(items[0])\n\n        LBox.blockSignals(False)\n        \n        self.tab_manager.setTabToolTip(self.tab_manager.currentIndex(), str(num_tasks)+\" tasks\")\n\n    @check_modified\n    def refreshlistonly(self): \n\n        if not self.PageProperties: # no tabs need this in other places\n            return\n\n        #self.table.blockSignals(True)\n        num_tasks = self.displayitems()\n        #self.table.blockSignals(False)\n\n        self.index = -1\n        self.task = None\n\n        #self.note.blockSignals(True)\n        self.note.clear() # this triggers a textChanged() event\n        self.db_note.clear()\n        #self.note.blockSignals(False)\n\n        self.modified = {} #not necessary if you block the signal\n        \n        self.tab_manager.setTabToolTip(self.tab_manager.currentIndex(), str(num_tasks)+\" tasks\")\n\n    @check_modified\n    def filterlist(self, new_item, prev_item):\n\n        self.Properties['filter_by']['value'] = new_item.text()\n        self.refreshlistonly()\n\n    def columnclick(self, col):\n        \n        print(\"section clicked\")\n\n        if not self.col_info[self.col_order[col]]['sortable']: \n            return\n\n        table = self.table\n        sort = self.Properties['sort']\n\n        prev_sort_col = sort['column'] #.get('column') #if this is the first sort Properties['sort'] is {}\n\n        sort['column'] = self.col_order[col]\n\n        if prev_sort_col == sort['column']:\n            sort['direction'] = not sort['direction']\n        else:\n            sort['direction'] = 0\n\n        self.refresh()\n\n        if prev_sort_col:\n            col_item = table.horizontalHeaderItem(self.col_order.index(prev_sort_col))\n            col_item.setIcon(QtGui.QIcon())\n\n        col_item = table.horizontalHeaderItem(col)\n        img = QtGui.QIcon(self.arrows[sort['direction']])\n        col_item.setIcon(img)\n\n    def changefilterbycolumn(self):\n\n        filter_by = ['folder','context','priority','tag'] #modified\n\n        dlg = lmdialogs.ChoiceDlg(\"Select\", filter_by, multi=False, parent=self)\n        filter_column = self.Properties['filter_by']['column']\n        dlg.listWidget.setCurrentRow(filter_by.index(filter_column))\n\n        if dlg.exec_():\n            filter_column = dlg.choices[0]\n\n            Properties = self.Properties\n            Properties['filter_by']['column'] = filter_column\n            Properties['filter_by']['value'] = '*ALL'\n            if filter_column == 'priority':\n                Properties['collapsible'] = False\n\n            if Properties['window_sizes'][0]==0:\n                Properties['window_sizes'][0]=75\n                splitter = self.tab_manager.currentWidget()\n                splitter.setSizes(Properties['window_sizes']) \n\n            self.refresh()\n\n            self.setWindowTitle(\"List Manager AWS - type: %s; value: %s filtered by: %s\"%(Properties['tab']['type'], Properties['tab']['value'],Properties['filter_by']['column']))\n\n    def toggle_collapsible(self):\n        self.Properties['collapsible'] = not self.Properties['collapsible']\n        self.refreshlistonly()\n\n    def modifycolumns(self):\n\n        full_list = list(self.col_info.keys())\n        full_list.remove('box') # completion box is not optional\n\n        in_use_list  = self.col_order[:]\n        in_use_list.remove('box')\n\n        full_list_set = set(full_list)\n        in_use_set = set(in_use_list)\n\n        available_set = full_list_set - in_use_set\n\n        prev_col_widths = dict(list(zip(self.col_order, self.Properties['col_widths'])))\n        \n        print(\"launched modify columns ...\")\n\n        dlg = lmdialogs.ModifyColumns(self,list(available_set),in_use_list)\n\n        if dlg.exec_():\n            print(repr(dlg.lst))\n            self.col_order = ['box']+dlg.lst #adding back completed column in 0th position\n\n            # title is the one column along with completed (in zero position) that needs to be there\n            if 'title' not in self.col_order:\n                self.col_order.append('title')\n\n            # since we may have removed the column we were sorting by\n            self.Properties['sort'] = {'column':None,'direction':0} \n\n            # set the col_order property; probably in wrong place but need to set self.col_order\n            self.Properties['col_order'] = self.col_order       \n\n            self.Properties['col_widths'] = [prev_col_widths.get(name, self.col_info[name]['width']) for name in self.col_order]\n\n            self.replacetable()\n\n    def display_danger_items(self):\n        self.display_danger_in_all_contexts = not self.display_danger_in_all_contexts\n        self.display_danger_action.setChecked(self.display_danger_in_all_contexts)\n        self.refreshlistonly()\n        \n    def showhidefilterby(self):\n\n        splitter = self.tab_manager.currentWidget()\n        Properties = self.Properties\n        \n        Properties['window_sizes'][0] = 75 if Properties['window_sizes'][0]==0 else 0\n        \n        splitter.setSizes(Properties['window_sizes'])\n\n    def change_tab_name(self):\n        '''\n        Saves tab name for regular tabs and saves searches so they will be reloaded\n        '''\n        \n        #also used to save a search\n        \n        tab_idx = self.tab_manager.currentIndex()\n        \n        tab_title = self.tab_manager.tabText(tab_idx)\n        result = QtGui.QInputDialog.getText(self,\"Tag\",\"Enter Tab Name\", QtGui.QLineEdit.Normal, tab_title)\n\n        tab_title, b = result\n\n        if not b:\n            return\n\n        print(\"New tab name={0}\".format(tab_title))\n        \n        self.tab_manager.setTabText(tab_idx, tab_title)\n        \n        self.Properties['title'] = tab_title\n        \n        if self.active_search == self.tab_manager.currentWidget():\n            self.Properties['tab']['value'] = 'saved_search'  \n            self.active_search = None\n            self.search.clear()\n        \n    @check_modified\n    @check_task_selected\n    def ontouch(self):\n\n        self.task.modified = datetime.datetime.now()\n        session.commit()\n\n        self.refreshlistonly()\n\n    def highlightsearchterms(self):\n        terms = self.Properties['query_string']\n        self.highlighter.setDocument(self.note.document())\n        term_list = [x for x in terms.split() if x.lower() not in ('and','or')] #don't want to highlight and, or\n        self.highlighter.setkeywords(term_list)\n\n    def iconcolordialog(self, type_): #'folder';'context'\n        \n        class_ = {'folder':Folder, 'context':Context}\n        things = session.query(class_[type_]).all()\n        \n        dlg = lmdialogs.TextColor(\"Select\", things, parent=self, type_=type_)\n\n        if dlg.exec_(): #True if user clicks on a row and false if they click cancel/done or window close box\n        \n            title = dlg.title\n            column = dlg.column\n            \n            thing = session.query(class_[type_]).filter_by(title=title).first()\n            \n            qcolor = QtGui.QColor(thing.textcolor)\n            \n            if column=='icon':\n\n                #pngs = os.listdir(os.path.join(g.IMAGES_DIR,'{}_icons'.format(type_)))\n                pngs = os.listdir(os.path.join(g.IMAGES_DIR,'folder_icons'))\n                \n                \n                dlg = lmdialogs.SelectIcon(\"Select an icon for {} {}\".format(type_, title), things, pngs, parent=self, initial_selection=None, type_=type_)\n                if dlg.exec_(): # if cancel - dlg.exec_ is False\n                    print_(\"icon choice={}\".format(dlg.icon_choice))\n                    \n                    if dlg.icon_choice == 'No Icon':\n                        thing.icon = None\n                        thing.image = None\n                    else:\n                        thing.icon = dlg.icon_choice\n                        \n                        image = QtGui.QImage(\"bitmaps/folder_icons/{}\".format(dlg.icon_choice))\n                        if image.isNull():\n                            print_(\"Image is Null\")\n                        else:\n                            ba = QtCore.QByteArray()\n                            buf = QtCore.QBuffer(ba)\n                            buf.open(QtCore.QIODevice.ReadWrite)\n                            zz = image.save(buf, 'PNG')\n                            if zz:\n                                thing.image = ba.data()\n                            else:\n                                print_(\"Could not save image to buffer\")\n                    \n                    session.commit()\n\n                    #update the menus that include icons and the display dictionaries (self.folder_icons and self.context_icons)\n                    #### Note that you don't need else because thing.image==None creates a Null icon but just seems better to have no icon at all\n                    if thing.image:\n                        pxmap = QtGui.QPixmap()\n                        pxmap.loadFromData(thing.image, 'PNG')\n                        icon = (QtGui.QIcon(pxmap), '')\n                    else:\n                        icon = ('',)  \n                    \n                    if type_=='folder':\n                        self.folder_icons[thing.title] = icon\n                        self.createfoldermenu() #need creaticon(folder)menu and context menu\n                        self.a_selectfolder.setMenu(self.f_menu)\n                    else:\n                        self.context_icons[thing.title] = icon\n                        self.createcontextmenu() #need creaticon(folder)menu and context menu\n                        self.a_selectcontext.setMenu(self.c_menu)\n\n            else:\n            \n                print_(\"column={}\".format(column))\n                print_(\"rgb_color={}\".format(qcolor.getRgb()))\n                \n                newqcolor = QtGui.QColorDialog.getColor(qcolor, self, \"Choose a color for {} text\".format(title))\n                if newqcolor.isValid():\n                    print_(\"new color = {}\".format(repr(newqcolor)))\n                    \n                    c = newqcolor.rgb() \n                    print_(\"Rgb int = {}\".format(c))\n                    c &= 0xffffffff #needs to be an unsigned long (may not be necessary but Web postings suggested it might be)\n                    print_(\"Rgb int made long = {}\".format(c))\n                    thing.textcolor = c  \n                    session.commit() \n                                \n            self.iconcolordialog(type_)\n            \n    def fullsize(self, tablewidth):\n\n        #splitter = self.tab_manager.currentWidget() # will be the new splitter on tab change\n        table = self.table # for some reason is the 'old\" table (the one prior to the click on the tab changing the tab)\n        \n        # Appears that when you click on a tab the old table is still active and the old table's title may be changed incorrectly\n        # However, the new splitter is present so there should be a way to detect splitter/table mismatch\n        # Instead went with QTimer in resizeEvent and that worked\n        #if splitter.widget(1) != table: return\n      \n        z = sum(table.columnWidth(n) for n in range(table.columnCount()))\n        c = self.col_order.index('title')\n        w = self.Properties['col_widths'][c]\n\n        w += tablewidth - z \n        table.setColumnWidth(c, w)\n        \n        # don't need to do following because resizing title column triggers method updatecolumnwidths\n        #self.Properties['col_widths'][c] = w \n        \n    @check_modified\n    def synchronize(self, checked):\n\n        if not g.internet_accessible():\n            QtWidgets.QMessageBox.warning(self,  'Alert', \"Internet not accessible right now.\")\n            return\n            \n        if not toodledo2.keycheck():\n            print_(\"Unable to get toodledo key\")\n            return\n        \n        self.sync_log, changes, tasklist, deletelist = synchronize_p.synchronizetotoodledo(parent=self, \n                                                                                showlogdialog=True, \n                                                                                OkCancel=True,\n                                                                                local=False)\n        print(\"changes={0}\".format(changes))\n        print(\"tasklist={0}\".format([t.title for t in tasklist])) #this is a goofy print\n        print(\"deletelist={0}\".format(deletelist))\n        \n        if 'contexts' in changes:\n            self.createcontextmenu()\n            \n        if 'folders' in changes:\n            self.createfoldermenu()\n            \n        for task in tasklist:\n            self.updatewhooshentry(False, task=task) #False -> checked is because of QAction\n            \n        for id_ in deletelist: #these ids are local client task.ids and that is what whoosh uses\n            self.deletefromwhooshdb(id_)\n\n        self.refreshlistonly()\n\n    def showsync_log(self):\n        dlg = lmdialogs.SynchResults(\"Synchronization Results\", self.sync_log, parent=self)\n        dlg.exec_()\n        \n    def create_whooshdb(self):\n        tasks = session.query(Task)\n        r = tasks.count()\n        self.pb.setRange(0, r)\n        self.pb.setValue(0)\n        self.pb.show()\n        \n        #If unique=True on a field then value of this field may be used to replace documents with the same value when the user calls document_update() on an IndexWriter. \n        #the below works - I used that schema and then search on Prefix\n        #schema = Schema(title=TEXT, tag=KEYWORD, note=TEXT, task_id=NUMERIC(numtype=int, bits=64, unique=True, stored=True)) #probably better to do signed=False \n\n        #Below is my take on how you use a custom Tokenizer including Ngram that only looks at the start of words\n        #phrase = False means we're not indexing across words which doesn't make sense when doing real-time incremental searching\n        my_analyzer =analysis.RegexTokenizer() | analysis.LowercaseFilter() | analysis.StopFilter() | analysis.NgramFilter(3,7,at='start')\n        #schema = Schema(title=TEXT(my_analyzer, phrase=False), note=TEXT(my_analyzer, phrase=False), task_id=NUMERIC(numtype=int, bits=64, unique=True, stored=True))\n        schema = Schema(title=TEXT(my_analyzer, phrase=False), tag=KEYWORD(commas=True, lowercase=True, scorable=True), note=TEXT(my_analyzer, phrase=False), task_id=NUMERIC(numtype=int, bits=64, unique=True, stored=True))\n        if not os.path.exists(g.WHOOSH_DIR):\n            os.mkdir(g.WHOOSH_DIR)\n        \n        # Calling index.create_in on a directory with an existing index will clear the current contents of the index.\n        ix = create_in(g.WHOOSH_DIR, schema)\n        writer = ix.writer()\n\n        for n,task in enumerate(tasks):\n\n            writer.add_document(title = task.title,\n                                tag = task.tag, \n                                note = task.note, \n                                task_id = task.id) #str(task.id) if using ID(unique=True, stored=True)) \n                                           \n            self.pb.setValue(n)\n             \n        writer.commit()\n        \n        print_(\"Whoosh database indexing complete\")\n        \n        self.pb.hide()\n\n    def create_whooshdb2(self):\n        tasks = session.query(Task)\n        r = tasks.count()\n        self.pb.setRange(0, r)\n        self.pb.setValue(0)\n        self.pb.show()\n        \n        my_analyzer =analysis.RegexTokenizer() | analysis.LowercaseFilter() | analysis.StopFilter() | analysis.NgramFilter(3,7,at='start')\n        schema = Schema(content=TEXT(my_analyzer, phrase=False), task_id=NUMERIC(numtype=int, bits=64, unique=True, stored=True))\n        if not os.path.exists(g.WHOOSH_DIR):\n            os.mkdir(g.WHOOSH_DIR)\n        \n        # Calling index.create_in on a directory with an existing index will clear the current contents of the index.\n        ix = create_in(g.WHOOSH_DIR, schema)\n        writer = ix.writer()\n\n        for n,task in enumerate(tasks):\n\n            note = task.note if task.note else '' \n            text = ' '.join(k.name for k in task.keywords) + ' ' + task.title + ' ' + note\n\n            writer.add_document(content = text, \n                                task_id = task.id) #str(task.id) if using ID(unique=True, stored=True)) \n                                           \n            self.pb.setValue(n)\n             \n        writer.commit()\n        \n        print_(\"Whoosh database indexing complete\")\n        \n        self.pb.hide()\n\n        self.ix = index.open_dir(g.WHOOSH_DIR)\n        self.searcher = self.ix.searcher()\n\n    def file_changed_in_vim(self, path):\n        print('File Changed: %s' % path)\n        task_id = self.vim_files[path]\n        f = open(path, mode='r')\n        text = f.read()\n        self.task.note = text\n        session.commit()\n       # kluge to get rid of extra line after a pre block\n        simple_html = markdown.markdown(text)\n        simple_html = simple_html.replace('\\n</code></pre>', '</code></pre>')\n\n        self.note.setHtml(simple_html)\n        self.db_note.setPlainText(text)\n\n        print(self.modified) #the writes to self.note and self.db_note produce self.modified={'plain_note':True,'note':True}\n        self.modified = {}\n        task = session.query(Task).get(task_id)\n        \n        print_(\"Note Saved from Vim was note id: {} title: {}\".format(task_id,task.title))\n\n    def edit_note_in_vim(self):\n        note = self.task.note if self.task.note else ''\n        temp = tempfile.NamedTemporaryFile(mode='w', prefix='lm', suffix='.tmp', delete=False)\n        temp.write(note)\n        temp.flush()\n        temp.close()\n        self.vim_files[temp.name] = self.task.id\n        print(repr(self.vim_files))\n        Popen([g.VIM, \"+set nonu\", \"+set lines=40\", \"+set columns=100\", os.path.abspath(temp.name)])\n        self.fs_watcher.addPath(os.path.abspath(temp.name))\n        \n    def print_note_to_log(self):\n        print_(self.note.toHtml())\n\n    def get_tabinfo(self): #check\n        \n        #print_(\"check={}\".format(check))\n        \n        class_ = {'folder':Folder, 'context':Context, 'tag':Keyword}\n\n        Properties = self.Properties\n        \n        type_ = Properties['tab']['type']\n        \n        print(\"type_={0}\".format(type_))\n        \n        if not (type_ == 'context' or type_ == 'folder' ):\n            return\n\n        title =  Properties['tab']['value']\n\n        query = session.query(Task).join(class_[type_]).filter(class_[type_].title==title)\n\n        count = query.count()\n        msg = \"Total number of tasks in %s: %d\\n\"%(title,count)\n\n        active = query.filter(and_(Task.completed==None, Task.deleted==False)).count()\n        msg = msg + \"Total number of active tasks in %s: %d\\n\"%(title,active)\n\n        complete = query.filter(~(or_(Task.completed==None, Task.deleted==True))).count() #not_ also works\n        msg = msg + \"Total number of completed tasks in %s: %d\\n\"%(title,complete) \n\n        deleted = query.filter(Task.deleted==True).count() #not_ also works\n        msg = msg + \"Total number of deleted tasks in %s: %d\\n\"%(title,deleted) \n\n        msg = msg + \"\\nOf active tasks...\\n\"\n        count = query.filter(and_(Task.completed==None, Task.star==True, Task.deleted==False)).count()\n        msg = msg + \"Starred tasks: %d\\n\"%count\n        for p in reversed(self.priorities):\n            count = query.filter(and_(Task.completed==None, Task.priority==p, Task.deleted==False)).count()\n            msg = msg + \"Priority %d tasks: %d\\n\"%(p, count)\n\n        task = query.filter(and_(Task.completed==None, Task.deleted==False)).order_by(Task.created).first()\n        if task:\n            msg = msg + \"\\nOldest active record:\\n%s\\ncreated on: %s\\n\"%(task.title, task.created.strftime(\"%m/%d/%y\")) \n        if task:\n            task = query.filter(and_(Task.completed==None, Task.deleted==False)).order_by(Task.modified).first()\n            msg = msg + \"\\nActive record with oldest modification:\\n%s\\nlast modified: %s\\n\"%(task.title, task.modified.strftime(\"%m/%d/%y %H:%M:%S\"))\n\n        dlg = lmdialogs.SynchResults(\"Synchronization Results\", msg, parent=self, OkCancel=True)\n        \n        dlg.exec_()\n\n    @check_task_selected\n    def whooshtaskinfo(self, check=False):\n        id_ = self.task.id\n        docnum = self.searcher.document_number(task_id=id_)\n        stored_fields = self.searcher.stored_fields(docnum)\n        print(\"stored fields (task.id)={} and whoosh docnum={}\".format(stored_fields, docnum))\n        #keywords_and_scores = self.searcher.key_terms([docnum], 'title') #need to be vectored, which I don't really get\n        #print(\"key *title* terms=\", keywords_and_scores)\n        text, ok = QtWidgets.QInputDialog.getText(self,\"Whoosh\",\"Enter search terms for task.id {}; Whoosh docnum {}\".format(id_, docnum)) \n        \n        if ok and text:\n            print_(text)\n            results = self.get_query_ids(text)\n            \n            try:\n                pos = results.index(id_)\n            except ValueError:\n                print_(\"Not in list\")\n            else:\n                print_(\"position: {}\".format(pos))\n            \n    def showdeleted(self):\n\n            self.createnewtab(title='*Deleted*', tab={'type':'deleted_items', 'value':None}, filter_by={'column':'context','value':'*ALL'}, collapsible=False)\n\n    def recreatekeywordsfromtag(self):\n        '''May be possible to have unused keywords and unused taskkeywords (e.g., deleted task)\n           and this wipes the slate clean and lets you recreate kewyord_table and taskkeyword_table'''\n           \n        if self.confirm(\"Do you really want to recreate keywords from tags?\"):\n            \n            keyword_table.drop()   \n            taskkeyword_table.drop()\n        \n            keyword_table.create()   \n            taskkeyword_table.create()\n        \n            tasks = session.query(Task).filter(Task.tag != None).all()\n            \n            pb = self.pb\n            \n            pb.setRange(0, len(tasks))\n            pb.setValue(0)\n            pb.show()\n        \n            for n,task in enumerate(tasks):\n                for kwn in task.tag.split(','):\n                    # We could do kwn.strip and then write them back to tab\n                    # but new logic should do this when the tag is entered\n                    keyword = session.query(Keyword).filter_by(name=kwn).first()\n                    if keyword is None:\n                        keyword = Keyword(kwn)\n                        session.add(keyword)\n                        session.commit()\n                    taskkeyword = TaskKeyword(task, keyword)\n                    session.add(taskkeyword)\n                    \n                pb.setValue(n)\n                    \n            session.commit()\n            \n            pb.hide()\n                \n    def removedeadkeywords(self):\n        '''May be possible to have unused keywords and unused taskkeywords (e.g., deleted task)\n           and this wipes the slate clean and lets you recreate kewyord_table and taskkeyword_table'''\n           \n        keywords = session.query(Keyword).all()\n                \n        keyword_list = sorted([kw.name for kw in keywords if not kw.taskkeywords], key=str.lower)\n        \n        text = \"Do you want to eliminate these orphan keywords?\"\n        \n        dlg = lmdialogs.ChoiceDlg(\"Orphan Keywords\", keyword_list, label_text=text, parent=self)\n        if dlg.exec_():\n            for kw in keywords:\n                if not kw.taskkeywords:\n                    print(kw.name)\n                    session.delete(kw)\n            session.commit()\n\n    @check_task_selected\n    @check_modified\n    def ontaskinfo(self, check, retrieve_server=False): #appear to have to add check to all of them because triggered is returning check 12-21-2014\n\n        print_(\"check={}\".format(check))\n\n        task = self.task\n\n        f = lambda x: x if x is not None else ''\n\n        c_map = {'sqlite_id':'id', 'context_id':'context_tid', 'folder_id':'folder_tid'} #dialog label:actual label\n        s_map = {'tid':'id', 'folder_id':'folder', 'context_id':'context'}\n\n        labels = ('sqlite_id', 'tid', 'star', 'priority', 'title', 'context','context_id', 'folder','folder_id', 'tag', 'added', 'modified', 'duedate', 'duetime','startdate','completed','note')\n\n        c_task = dict(task.__dict__)\n        c_task = {lab:f(c_task[c_map.get(lab,lab)]) for lab in labels} \n        c_task.update(context=c_task['context'].title, folder=c_task['folder'].title)\n\n        if task.tid and retrieve_server: # if it's a new task that hasn't gotten a tid from server yet, there won't be a server task to retrieve\n            \n            if not toodledo2.keycheck():\n                print_(\"Could not retrieve server info because could not get key\")\n                return\n\n            toodledo_call = toodledo2.toodledo_call\n              \n            s_folders = toodledo_call('folders/get') #[{\"id\":\"123\",\"name\":\"Shopping\",\"private\":\"0\",\"archived\":\"0\",\"ord\":\"1\"},...\n            s_folders_map =  {z['id']:z['name'] for z in s_folders}   \n\n            s_contexts = toodledo_call('contexts/get') # [{\"id\":\"123\",\"name\":\"Work\"},{\"id\":\"456\",\"name\":\"Home\"},{\"id\":\"789\",\"name\":\"Car\"}]\n            s_contexts_map =  {z['id']:z['name'] for z in s_contexts}   \n\n            stats, s_task = toodledo_call('tasks/get', id_=task.tid, fields='folder,star,priority,duedate,duetime,startdate,context,tag,added,note') #{\"num\":\"2\",\"total\":\"2\"}, [{\"id\":\"1234\",\"title\":\"Buy Milk\",\"modified\":1281990824,\"completed\":0,\"folder\":\"5409195\",\"star\":\"1\",\"priority\":\"-1\"},{\"id\":\"1235\",\"title\":\"Fix flat tire\",\"modified\":1280877483,\"completed\":1280808000,\"folder\":\"0\",\"star\":\"0\",\"priority\":\"0\"}]\n\n            if s_task:\n                s_task = s_task[0]\n                s_task['sqlite_id'] = ''\n                s_task = {lab:f(s_task[s_map.get(lab,lab)]) for lab in labels}\n                s_task['folder'] = s_folders_map.get(s_task['folder_id']  , 'No Folder')\n                s_task['context'] = s_contexts_map.get(s_task['context_id']  , 'No Context')\n        else:\n            s_task = {}\n\n        dlg = lmdialogs.TaskInfo(\"Compare\", data = [c_task, s_task], labels=labels, parent=self)\n\n        dlg.exec_()\n\n    def on_simple_html2log(self):\n        print(markdown.markdown(self.task.note))\n\n    def resetinterp(self):\n        palette = QtGui.QPalette()\n        palette.setColor(QtGui.QPalette.Base, Qt.black)\n        palette.setColor(QtGui.QPalette.Text, QtGui.QColor(0, 255, 0)) # lime green\n        self.console.setPalette(palette)\n\n    def renew_alarms(self):\n        \n        now = datetime.datetime.now()\n        tasks = session.query(Task).filter(and_(Task.remind != None, Task.duetime<now))\n        \n        interval = 30\n        \n        for n,t in enumerate(tasks[:10]):\n            t.duetime = t.duedate = now + datetime.timedelta(minutes=n*interval)\n            print_('\"{0}\" now has an alarm set for {1}'.format(t.title[:40],t.duetime))\n            \n        session.commit()\n\n        \n    def create_image_string(self):\n        \n        file_ = QtGui.QFileDialog().getOpenFileName(self, \"Select PNG File\", g.IMAGES_DIR, \"PNG (*.png)\")\n        print(file_)\n        \n        image = QtGui.QImage(file_)\n        if image.isNull():\n            print_(\"Image is Null\")\n        else:\n            ba = QtCore.QByteArray()\n            buf = QtCore.QBuffer(ba)\n            buf.open(QtCore.QIODevice.ReadWrite)\n            zz = image.save(buf, 'PNG')\n            if zz:\n                self.zz = ba.data()\n                print_(repr(ba.data()))\n                f = file('test12345','w')\n                f.write(repr(ba.data())) #works\n                f.close()\n            else:\n                print_(\"Could not save image to buffer\")\n                    \n        return\n        \n        #######################################\n                    \n        pngs = os.listdir(os.path.join(g.IMAGES_DIR,'folder_icons'))\n        dlg = lmdialogs.SelectIcon(\"Select an icon to create a string image\", parent=self, pngs=pngs)\n        if dlg.exec_(): # if cancel - dlg.exec_ is False\n            print_(\"icon choice={}\".format(dlg.icon_choice))\n            \n            if dlg.icon_choice == 'No Icon':\n                print_(\"Chose no icon\")\n                return\n                \n            image = QtGui.QImage(\"bitmaps/folder_icons/{}\".format(dlg.icon_choice))\n            if image.isNull():\n                print_(\"Image is Null\")\n            else:\n                ba = QtCore.QByteArray()\n                buf = QtCore.QBuffer(ba)\n                buf.open(QtCore.QIODevice.ReadWrite)\n                zz = image.save(buf, 'PNG')\n                if zz:\n                    self.zz = ba.data()\n                    print_(repr(ba.data()))\n                    f = file('test12345','w')\n                    f.write(repr(ba.data())) #works\n                    f.close()\n                else:\n                    print_(\"Could not save image to buffer\")\n                    \n    def clearsavedtabs(self):\n        self.savedtabs = []\n        self.m_savedtabsmenu.clear()\n        QtGui.QMessageBox.information(self,  'Information', \"Saved tabs were cleared\")\n        \n    def searchcontext(self, search=None):\n\n        if search == 'context':\n            print('search context')\n            titles = sorted([c.title for c in session.query(Context).filter(Context.id!=1)], key=str.lower)\n            titles = ['No Context'] + titles\n            dlg = lmdialogs.ChoiceDlg(\"Select\", titles, parent=self)\n            if dlg.exec_():\n                print(\"dlg.choices={0}\".format(dlg.choices))\n                if dlg.choices:\n                    self.search_contexts = dlg.choices\n                else:\n                    self.a_search_all.setChecked(True)\n                    self.search_contexts = None\n            else:\n                self.a_search_all.setChecked(True)\n                self.search_contexts = None\n            \n                    \n                # note if choices is [] need to check all - too lazy to address now\n            \n        else:\n            self.search_contexts = None\n            print('all')\n\n    def do_search(self, text=None):\n        \n        print(\"do search\")\n\n        query_string = self.search.text().lower()\n\n        if not query_string and self.active_search is None: \n            return\n\n        # You can backspace to no characters on incremental or return with no characters in standard\n        if not query_string:\n            self.table.clearContents() \n            return\n        \n        if len(query_string) < 3:\n            return\n        \n        if len(query_string) > 7:\n            query_string = query_string[:7]\n\n        if self.active_search:\n            self.tab_manager.setCurrentWidget(self.active_search)\n            self.Properties['query_string'] = query_string\n            self.Properties['search_contexts'] = self.search_contexts\n            self.refreshlistonly()\n        else:\n            tab_title = '*SEARCH'\n            \n            if self.search_contexts:\n                tab_title = tab_title + ' ' + ','.join(self.search_contexts)\n                \n            self.createnewtab(\n                                       title=tab_title,\n                                       tab={'type':'search', 'value':'active_search'},\n                                       filter_by={'column':'context','value':'*ALL'},\n                                       collapsible=False,\n                                       col_order = ['box','startdate','star','title','context','tag'],\n                                       search_contexts=self.search_contexts,\n                                       query_string=query_string)\n\n            self.active_search = self.tab_manager.currentWidget()\n\n    def get_query_ids(self, query_string):\n        '''\n        Now based on Whoosh \n        '''\n        #Not sure whether this should be an n-gram search or a Prefix search - below uses n-gram except for tag\n        #query = Or([Term('title', query_string), Term('note', query_string), Prefix('tag', query_string)])\n        \n        #the below works and uses Prefix to search on beginning of work v. n-gram\n        #query = Or([Prefix('title', query_string), Prefix('note', query_string)]) #needs tags\n       \n        #print(repr(query))\n        #results = self.searcher.search(query, limit=50)\n        results = self.searcher.search(Term('content', query_string), limit=50)\n        \n        return [r['task_id'] for r in results] \n\n    def get_current_tasks(self, priority=None): \n        '''returns a query unless its a Find - need to think about that'''\n\n        Properties = self.Properties\n        sort = Properties['sort']     # the sort property is:{'column':c_name or None,'direction':0}\n        tab_type = Properties['tab']['type']\n        tab_value = Properties['tab']['value']\n        show_completed = Properties['show_completed']\n        filter_by = Properties['filter_by']\n\n        session.expunge_all() # not sure this is necessary but trying to create a clean slate on which to retrieve tasks\n\n        tasks = session.query(Task) # there is a tab_type = '*ALL' that this covers\n\n        #tab_type\n        if tab_type == 'context':\n            tasks = tasks.join(Context).filter(Context.title==tab_value)\n\n        elif tab_type == 'search':\n            \n            query_ids = self.get_query_ids(Properties['query_string']) # tab_value contains the search terms\n            #print_(\"query_ids = {0}\".format(query_ids))\n            \n            if not query_ids:\n                # using query.count later so do query below as opposed to returning []\n                return tasks.filter_by(id=0) #producing an empty result set query\n            else:\n                if Properties['search_contexts']:\n                    conditions = [Context.title == term for term in Properties['search_contexts']]\n                    tasks = tasks.join(Context).filter(or_(*conditions))\n                \n                if sort['column']:\n                    # no need to sort because they will be sorted in the sort section and ? you can't sort twice anyway\n                    tasks = tasks.filter(Task.id.in_(query_ids))\n                else:\n                     tasks = tasks.filter(Task.id.in_(query_ids)).order_by(case([(Task.id == value, literal(index)) for index, value in enumerate(query_ids)]))    \n        \n        elif tab_type == 'folder':\n            tasks = tasks.join(Folder).filter(Folder.title==tab_value)\n\n        elif tab_type == 'tag': \n            tasks = tasks.join(TaskKeyword, Keyword).filter(and_(Task.id==TaskKeyword.task_id,TaskKeyword.keyword_id==Keyword.id, Keyword.name==tab_value))\n\n        elif tab_type == 'recent':\n\n            if tab_value == 'ALL':\n                tasks = tasks.filter(Task.modified > (datetime.datetime.now()-datetime.timedelta(days=2)))\n            elif tab_value == 'Created':\n                tasks = tasks.filter(Task.created > (datetime.datetime.now()-datetime.timedelta(days=2)).date())\n            elif tab_value == 'Completed':\n                tasks = tasks.filter(Task.completed > (datetime.datetime.now()-datetime.timedelta(days=2)).date())\n            elif tab_value == 'Modified':\n                tasks = tasks.filter(and_(Task.modified > (datetime.datetime.now()-datetime.timedelta(days=2)), ~(Task.created > (datetime.datetime.now()-datetime.timedelta(days=2)).date())))\n\n        elif tab_type == 'deleted_items':\n            tasks = tasks.filter(Task.deleted==True)\n             \n        elif tab_type == 'app':\n            if tab_value == 'star':\n                tasks = session.query(Task).filter(Task.star==True)\n            \n            elif tab_value == 'alarm':\n                #tasks = session.query(Task).filter(~or_(Task.remind == None, Task.remind ==0))  # note server thinks it's zero but I have lots of None should decide\n                tasks = session.query(Task).filter(and_(or_(Task.remind != None, Task.remind != 0), Task.duedate > datetime.datetime.now()))\n                \n            else:\n               print(\"We have a problem because that is a tab value of tab type app  that I don't recognize; tab_value =\",tab_value)\n            \n        elif tab_type == 'folders' or tab_type == 'contexts':\n            class_ = {'folders':Folder, 'contexts':Context}\n            conditions = []\n            for term in tab_value.split(','):\n                 #conditions.append(Folder.title == term) ###############\n                 conditions.append(getattr(class_[tab_type], 'title') == term)\n                 \n            condition = or_(*conditions)\n            #print(condition)\n            tasks = tasks.join(class_[tab_type]).filter(condition)\n\n        else:\n            print(\"We have a problem because that is a tab type that I don't recognize; tab_type =\",tab_type)\n\n        #don't show deleted tasks\n        #tasks = tasks.filter(Task.deleted==False)\n\n        #filter_by\n        if filter_by['column'] == 'folder':\n             if filter_by['value']  != '*ALL':\n                tasks = tasks.outerjoin(Folder).filter(Folder.title==filter_by['value']) #need outer join for sorting\n                folder_join = True\n        elif filter_by['column'] == 'context':\n            if filter_by['value']  != '*ALL':\n                tasks = tasks.join(Context).filter(and_(Context.title==filter_by['value'], Task.deleted==False))\n        elif filter_by['column'] == 'tag':\n            if filter_by['value'] != '*ALL':\n                keyword = session.query(Keyword).filter_by(name=filter_by['value']).one()\n                tasks = tasks.join(TaskKeyword).filter(and_(Task.id==TaskKeyword.task_id,TaskKeyword.keyword_id==keyword.id))\n\n        #priorities\n        if priority is not None: #priority can be zero\n            tasks = tasks.filter(Task.priority==priority)\n\n        if Properties['filter_by']['column'] == 'priority' and not Properties['filter_by']['value']=='*ALL':\n            tasks = tasks.filter(Task.priority==int(Properties['filter_by']['value']))\n\n        #show_completed \n        if not show_completed:\n            tasks = tasks.filter(Task.completed==None)\n\n        #sort\n        if sort['column']:\n            direction = asc if sort['direction'] else desc #asc and desc must be sqlalchemy constants\n            if sort['column'] == 'folder':\n                tasks = tasks.outerjoin(Folder).order_by(direction(Folder.title)) \n            elif sort['column'] == 'context':\n                tasks = tasks.join(Context).order_by(direction(Context.title)) \n            else:\n                sort_column = getattr(Task, sort['column'])\n                tasks = tasks.order_by(direction(sort_column))\n        else:\n            tasks = tasks.order_by(desc(Task.modified))\n\n        return tasks\n\n    def get_keywords(self, tab_type, tab_value):\n\n       # I believe the context and folder queries only pick up keywords where there is an associated task\n       # orphan keywords are not picked up by the queries.\n        \n        if tab_type == 'context':\n            keywords = session.query(Keyword).join(TaskKeyword,Task,Context).filter(Context.title==tab_value).all()\n        elif tab_type == 'folder':\n            keywords = session.query(Keyword).join(TaskKeyword,Task,Folder).filter(Folder.title==tab_value).all()\n        else:\n            keywords = session.query(Keyword).all()\n\n        keywords.sort(key=lambda x:str.lower(x.name))\n        keyword_names = [keyword.name for keyword in keywords]\n\n        return keyword_names\n\n    def updatewhooshentry(self, checked, task=None):\n        \n        if task is None:\n            task = self.task\n        \n        writer = self.ix.writer()\n#        writer.update_document(task_id=task.id,\n#                               title=task.title,\n#                               tag=task.tag,\n#                               note=task.note)\n#\n        note = task.note if task.note else '' \n        text = ' '.join(k.name for k in task.keywords) + ' ' + task.title + ' ' + note\n\n        writer.update_document(content = text, \n                            task_id = task.id) #str(task.id) if using ID(unique=True, stored=True)) \n        writer.commit()\n        \n        #Note While the writer is open and during the commit, the index is still available for reading. \n        #Existing readers are unaffected and new readers can open the current index normally. \n        #Once the commit is finished, existing readers continue to see the previous version of the index \n        #(that is, they do not automatically see the newly committed changes). New readers will see the updated index.\n        \n        self.ix = index.open_dir(g.WHOOSH_DIR) #(\"indexdir\")\n        self.searcher = self.ix.searcher()\n        \n     \n        print_(\"Updated in Whoosh DB: task id: {0}, {1} ...\".format(task.id, task.title[:40]))\n        \n    def deletefromwhooshdb(self, id_):\n\n        writer = self.ix.writer()\n        try:\n            #note documentation (in at least one place: How to index documents) has a mistake that says you can delete\n            #by doing ix.delete_by_term;ix.commit() but the right way is below: \n            writer.delete_by_term('task_id', id_) \n            writer.commit()\n        except Exception as e:\n            print(\"Problem deleting term from whoosh--\",e)\n            print(\"Task {0}:  -- was not in Whoosh DB\".format(id_))\n\n    def confirm(self, text):\n        reply = QtWidgets.QMessageBox.question(self, \"Confirmation\", text, QtWidgets.QMessageBox.Yes|QtWidgets.QMessageBox.No)\n        return reply==QtWidgets.QMessageBox.Yes\n\n    def showversions(self):\n\n        import sqlalchemy\n\n        python_version = sys.version.split()[0]\n        sqla_version = sqlalchemy.__version__ \n\n        whoosh_version = index.version(FileStorage(g.WHOOSH_DIR))[0]\n        whoosh_version = '.'.join(str(x) for x in list(whoosh_version))\n        \n        values = (python_version, QtCore.PYQT_VERSION_STR, QtCore.QT_VERSION_STR, sip.SIP_VERSION_STR, sqla_version, whoosh_version)\n        text = \"python:\\t{}\\npyqt:\\t{}\\nqt:\\t{}\\nsip:\\t{}\\nsqla:\\t{}\\nwhoosh:\\t{}\".format(*values)\n        \n        dlg = lmdialogs.VersionsDlg('Versions', text, self)\n        dlg.exec_()\n\nclass Table(QtWidgets.QTableWidget):\n    def __init__(self, parent, col_info=None, col_order=None, col_widths=None): #added parent\n        #QTableWidget.__init__(self) \n        super(Table, self).__init__(parent)\n        \n        self.parent = parent\n        \n        self.setHorizontalHeader(Header(self))\n        \n        self.hheader = self.horizontalHeader()\n\n        self.column_info = col_info\n        self.column_order = col_order\n        self.column_widths = col_widths\n        self.setMinimumSize(600,300)\n        \n        #self.setStyleSheet(\"QTableWidget { } QTableWidget::item { padding-bottom 5px; padding-bottom 5px; border-bottom: 5px solid #000; }\") # does something but not what you want\n\n        self.setup()\n\n    def setup(self):\n\n        col_order = self.column_order\n        col_info = self.column_info\n\n        self.setColumnCount(len(col_order))\n\n        if self.column_widths:\n            for c,w in enumerate(self.column_widths):\n                self.setColumnWidth(c, w)\n                if col_info[col_order[c]]['fixed']:\n                    self.hheader.setSectionResizeMode(c, QtWidgets.QHeaderView.Fixed) # testing\n\n        else:\n            for c,c_name in enumerate(col_order):\n                self.setColumnWidth(c, col_info[c_name]['width'])\n                if col_info[c_name]['fixed']:\n                    self.hheader.setSectionResizeMode(c, QtWidgets.QHeaderView.Fixed) # testing\n\n        self.setHorizontalHeaderLabels([col_info[c_name]['label'] for c_name in col_order])\n\n        self.verticalHeader().setDefaultSectionSize(18) #somewhat cryptic way to set row sizes\n        self.verticalHeader().setVisible(False)\n\n        #In Header Class\n        #self.horizontalHeader().setDefaultAlignment(QtCore.Qt.AlignLeft) # set column header text alignment - doesn't affect cells\n        #self.horizontalHeader().setMovable(True)\n\n        self.setShowGrid(False)\n\n        self.setAlternatingRowColors(True)\n        self.setEditTriggers(QtWidgets.QTableWidget.NoEditTriggers)\n        self.setSelectionBehavior(QtWidgets.QTableWidget.SelectRows)\n        self.setSelectionMode(QtWidgets.QTableWidget.SingleSelection)\n        \n        action = QtWidgets.QAction(\"Remove Column\", self)\n        action.triggered.connect(self.removecolumn)\n        self.hheader.addAction(action)\n        self.hheader.setContextMenuPolicy(Qt.ActionsContextMenu)\n        \n    def contextMenuEvent_(self, event):\n        # this worked but there was a simpler way\n        self.obj.tableContextMenuEvent(event)\n\n    def mousePressEvent(self, e):\n              \n        self.leftpressed = e.button() == Qt.LeftButton\n        \n        super(Table, self).mousePressEvent(e)\n\n    def edit__(self, index, trigger, event):\n        '''this isn't doing anything but for testing'''\n\n        if QtGui.QTableWidget.edit(self, index, trigger, event):\n            print('editing:', index.row(), index.column())\n            #self.editor_open = True\n            print(\"shortcut inactive\")\n            return True\n        else:\n            self.editor_open = False\n            print(\"shortcut active\")\n            return False\n            \n    def removecolumn(self):\n        print(\"Remove column\")\n        print(\"column={0}\".format(self.hheader.selectedcolumn))\n        self.parent.removecolumn(self.hheader.selectedcolumn)\n\n    def resizeEvent(self, event):\n        w = event.size().width() # note this width takes into account presence or absence of scrollbar\n        \n        #print_(\"In table's resize event: {}\".format(w))\n        \n        # Appears that at least on Windows when you click on a tab to change tabs the 'old' table from the previoius tab is still active \n        # and therefore the old table's title width may be changed (incorrectly)\n        # However, the new splitter is present so it also worked to check for new splitter old table but QTimer worked\n        \n        QtCore.QTimer.singleShot(0, lambda: self.parent.fullsize(w))\n        \nclass Header(QtWidgets.QHeaderView):\n    def __init__(self, parent):    \n        super(Header, self).__init__(Qt.Horizontal, parent)\n        \n        self.setSectionsClickable(True)\n        self.setSectionsMovable(True)\n        self.setDefaultAlignment(Qt.AlignLeft) # set column header text alignment - doesn't affect cells\n        \n        #self.leftpressed = False # not sure this is necessary but might as well define it\n        #self.rightpressed = False # not sure this is necessary but might as well define it\n        \n    def mousePressEvent(self, e):\n        \n        if e.button() == Qt.RightButton:\n            self.selectedcolumn = self.logicalIndexAt(e.pos())\n        \n        super(Header, self).mousePressEvent(e)\n        \nclass TitleDelegate(QtWidgets.QItemDelegate):\n    '''\n    self.view.setItemDelegate(ViewDelegate(self))\n    where self.view is the QTableView and self is the parent (a QMainWindow class).\n    Note that the delegate has been implemented both to learn about delegates\n    and to allow the return key to be used to trigger editing and also to\n    close the editor.  As simple as that sounds, it seemed necessary to implement\n    the delegate to do that.\n    '''\n    def __init__(self, parent=None):\n        super(TitleDelegate, self).__init__(parent)\n        self.parent = parent\n\n    def createEditor(self, parent, option, index):\n\n        print(\"in createEditor\")\n        \n        editor = QtWidgets.QLineEdit(parent)\n        #editor = QItemDelegate.createEditor(self, parent, option, index) # this creates the default editor for the cell and should work (untested)\n        \n        #somehow the editingFinished signal seems to trigger a bunch of things without explicitly connecting it to anything\n        #self.connect(editor, QtCore.SIGNAL(\"editingFinished()\"), self.commitAndCloseEditor) \n        \n        return editor\n\n    def commitAndCloseEditor(self):\n        '''not in use'''\n        \n        editor = self.sender()\n        print(\"In commitAndCloseEditor\")\n\n        #self.editor_active = False\n\n        # right now this is method is not being called\n\n\n        # this signal is emitted for saving the data into the model; seems to be emitted automatically by the editor\n        # not sure why it isn't necessary to emit this signal\n        # I wonder if this will save automatically but don't think so\n        self.emit(QtCore.SIGNAL(\"commitData(QWidget*)\"), editor) #editor emits this signal without need to do it explicitly\n\n        # this signal is emitted to close the editor widget\n        self.emit(QtCore.SIGNAL(\"closeEditor(QWidget*)\"), editor) \n\n\n    def setEditorData(self, editor, index):\n        '''Note that also using this method to deselect the text in the title since I think that is what I prefer'''\n\n        print(\"In setEditorData\")\n\n        text = index.model().data(index, Qt.DisplayRole)\n        editor.setText(text)\n        \n        if text != '<New Item>':\n            # I don't want the text all selected (the default) because then it's easy to wipe it out\n            QtCore.QTimer.singleShot(0, editor.deselect) # needed to delay this call and this works\n\n    def setModelData(self, editor, model, index):\n\n        # note that setEditorData is called after this method - not sure why\n        # although presumably calling model.setData creates some signal\n\n        print(\"In setModelData\")\n\n        # this is necessary to get the table to update and then save_title can access it\n        model.setData(index, editor.text())\n\nclass NoteManager(QtWidgets.QMainWindow):\n    '''\n      Needs to be a QMainWindow so it can get a toolbar\n      used in one of the dockwidgets\n      '''\n    def __init__(self, main_window=None, parent=None):\n        super(NoteManager, self).__init__(parent)\n        self.main_window = main_window\n        self.note = notetextedit.NoteTextEdit()\n        self.note.setObjectName('note')\n\n        self.setCentralWidget(self.note)\n\n        #self.highlighter = notetextedit.MyHighlighter(self.note) #put it in NoteTextEdit\n\n        action = partial(create_action, self)\n\n        self.setStyleSheet(\"QTextEdit { background-color: #FFFFCC;}\") # I think you can only set the background of a QAbstractScrollArea-derived object\n\n        stylesheet = '''\n        h1 {color:red;}\n        h2 {color:green;}\n        li {margin-bottom:10px;}\n        code {color:blue;}\n        '''\n\n        self.note.document().setDefaultStyleSheet(stylesheet)\n\n        fileMenu = self.menuBar().addMenu(\"&File\")\n        fileSaveNote = action(\"Save Note \\tCtrl+S\", self.savenote, icon='document-save') #, 'Ctrl+S') ####### 3/29/2012\n        filePrintNote = action(\"Print Note\", self.OnPrintNote, icon='document-print')\n        filePageSetup = action(\"Page Setup\", self.OnPageSetup, icon='document-print-preview')\n\n        add_actions(fileMenu, (fileSaveNote, filePageSetup, filePrintNote))\n\n        editMenu = self.menuBar().addMenu(\"&Edit\")\n\n        editCopyAction = action(\"&Copy\", self.note.copy, QtGui.QKeySequence.Copy, \"copy\",\n                    \"Copy text to the clipboard\")\n        editCutAction = action(\"Cu&t\", self.note.cut, QtGui.QKeySequence.Cut, \"editcut\",\n                    \"Cut text to the clipboard\")\n        editPasteAction = action(\"&Paste\", self.note.paste, QtGui.QKeySequence.Paste, \"paste\",\n                    \"Paste in the clipboard's text\")\n\n        add_actions(editMenu, (editCopyAction, editCutAction, editPasteAction))\n\n        formatMenu = self.menuBar().addMenu(\"&Format\")\n        bold = action(\"&Bold\", self.note.toggleBold, 'Ctrl+B', icon='format-text-bold')\n        italic = action(\"&Italic\", self.note.toggleItalic, 'Ctrl+I', icon='format-text-italic')\n        mono = action(\"&Mono\", self.note.toggleCode, icon='format-text-mono')\n        preformat = action(\"&Code Block\", self.note.make_pre_block, icon='pre-tag')\n        num_list = action(\"&Numbered List\", self.note.create_numbered_list, icon='ordered-list-tag')\n        bullet_list = action(\"&Bulleted List\", self.note.create_bulleted_list, icon='unordered-list-tag')\n        remove_formatting = action(\"&Clear Formatting\", self.note.make_plain_text, icon='edit-clear')\n        anchor = action(\"&Anchor\", self.note.create_anchor, 'Ctrl+A', icon='internet-web-browser')\n\n        ## header submenu ################################################\n        headerMenu = QtWidgets.QMenu(self)\n        headerMenuAction = action(\"&Header\", self.note.increment_heading, 'Ctrl+H', icon='heading-tag')\n        h1 = action(\"H&1\", partial(self.note.make_heading,1))\n        h2 = action(\"H&2\", partial(self.note.make_heading,2))\n        h3 = action(\"H&3\", partial(self.note.make_heading,3))\n        add_actions(headerMenu, (h1, h2, h3))\n        headerMenuAction.setMenu(headerMenu)\n        #################################################################\n\n        add_actions(formatMenu, (bold, italic, mono, None, anchor, preformat, None, num_list, bullet_list, None, headerMenuAction, None, remove_formatting))\n\n        formatToolbar = self.addToolBar(\"Format\")\n        formatToolbar.setObjectName(\"NoteToolBar\")\n        add_actions(formatToolbar, (filePrintNote, None, bold,italic, preformat, mono, None, preformat, anchor, None, headerMenuAction, num_list, bullet_list, remove_formatting))\n\n        self.format_toolbar = formatToolbar\n        #noteToolbar.setIconSize(QSize(24,16))\n        #formatToolbar.setFixedHeight(24)\n\n    def OnPrintNote(self):\n        printer = QtGui.QPrinter()\n        printDialog = QtGui.QPrintDialog(printer, self)\n        if printDialog.exec_()== QtGui.QDialog.Accepted:\n            self.note.print_(printer)\n\n    def OnPageSetup(self):\n        printer = QtGui.QPrinter()\n        z = QtGui.QPageSetupDialog(printer)\n        if z.exec_() == QtGui.QDialog.Accepted:\n            print(printer.orientation())\n\n    def savenote(self):\n        self.main_window.savenote()\n\n    def textCursor(self):\n        return self.note.textCursor()\n        \n    def setTextCursor(self, cursor):\n        self.note.setTextCursor(cursor)\n        \nclass Logger (QtWidgets.QPlainTextEdit):\n    def __init__(self, parent=None, logfile=None):\n        #QtGui.QPlainTextEdit.__init__(self, parent)\n        super(Logger, self).__init__(parent)\n\n        self.appendPlainText(\"%s\\n\"%asctime())\n\n        self.path = logfile\n\n        self.setWordWrapMode(QtGui.QTextOption.NoWrap)\n\n        self.document().setDefaultFont(QtGui.QFont('Helvetica', 8))\n        \n    def write(self, msg):\n        # for some reason each print seems to create two writes, one of which is just a line feed\n        msg = textwrap.fill(msg.strip(), 175)\n        if msg: \n            self.appendPlainText(msg)\n            \n    def transfer(self):    \n        path = os.path.join(os.environ['TMP'],'logger.%s'%NOTE_EXT)\n\n        f = file(path,'w')\n        f.write(self.toPlainText())\n        f.close()\n\n        os.startfile(path)\n        \n    def save(self):\n        f = file(self.path,'a')\n        f.write(self.toPlainText())\n        f.close()\n\n        QtGui.QMessageBox.information(self,  'Information', \"Appended test to logfile.txt\")\n        \n    def clear_text(self):\n        self.clear()\n        self.appendPlainText(\"%s\\n\"%asctime()) \n\n    def save_and_clear(self):\n        f = file(self.path,'a')\n        f.write(self.toPlainText())\n        f.close()\n\n        self.clear()\n        self.appendPlainText(\"%s\\n\"%asctime())\n\n        QtGui.QMessageBox.information(self,  'Information', \"Appended test to logfile.txt\")\n\nclass MyEvent(QtCore.QObject):\n    signal = QtCore.pyqtSignal(str, dict)\n\nif __name__ == '__main__':  \n\n    # only needed if running two apps on some machine\n    if g.config.has_option('Application', 'name' ):\n        appname = g.config.get('Application', 'name' )\n    else:\n        appname = 'mylistmanager'\n\n    app = QtWidgets.QApplication(sys.argv)\n    app.setOrganizationName(\"SLZ Inc.\")\n    app.setOrganizationDomain(\"kayakroll.blogspot.com\")\n    app.setApplicationName(appname)\n    app.setWindowIcon(QtGui.QIcon('bitmaps/mlm.png')) \n    mainwin = ListManager()\n    \n    #print = print_ = mainwin.logger.write # or print or print_ = g.logger.write (eventually leave print alone)\n    print_ = mainwin.logger.write #made this change on 12-24-2014 - assume now print actually prints to the console but we'll see \n    \n    # import is here so synchronize and toodledo are imported after ListManager instance is created since synchronize accesses pb and logger\n    #and toodledo2 prints to logger\n \n    import synchronize_p\n    import toodledo2\n    \n    mainwin.show()\n    app.exec_() \n\n\n\n","sub_path":"listmanager_p.py","file_name":"listmanager_p.py","file_ext":"py","file_size_in_byte":152573,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"162314676","text":"# Chapter 6\n# A dictionary in Python is a collection of key-value pairs. Each key is connected to a value, and you can use a key to access the value associated with that key. A key’s value can be a number, a string, a list, or even another dictionary. In fact, you can use any object that you can create in Python as a value in a dictionary.\nalien_0 = {'color': 'green', 'points': 5}  # a dictionary is wrapped in braces, {}\n\n# A key-value pair is a set of values associated with each other. Every key is connected to its value by a colon, and individual key-value pairs are separated by commas.\n\n# Accessing Values in a Dictionary\n# When you provide a key, Python returns the value associated with that key.\nprint(alien_0['color'])\nprint(alien_0['points'])\n\nnew_points = alien_0['points']\nprint(\"You just earned \" + str(new_points) + \" points!\")\n\n# Adding New Key-Value Pairs\n# Dictionaries are dynamic structures, and you can add new key-value pairs to a dictionary at any time.\nprint(alien_0)  # {'color': 'green', 'points': 5}\nalien_0['x_position'] = 0\nalien_0['y_position'] = 25\nprint(alien_0)  # {'color': 'green', 'points': 5, 'x_position': 0, 'y_position': 25}\n# Python doesn’t care about the order in which you store each key-value pair; it cares only about the connection between each key and its value.\n\n# Starting with an Empty Dictionary\n# Typically, you’ll use empty dictionaries when storing user-supplied data in a dictionary or when you write code that generates a large number of key-value pairs automatically.\nalien_0 = {}\nalien_0['color'] = 'green'\nalien_0['points'] = 5\nprint(alien_0)\n\n# Modifying Values in a Dictionary\nalien_0 = {'color': 'green'}\nprint(\"The alien is \" + alien_0['color'] + \".\")\nalien_0['color'] = 'yellow'\nprint(\"The alien is now \" + alien_0['color'] + \".\")\n\nalien_0 = {'x_position': 0, 'y_position': 25, 'speed': 'medium'}\nprint(\"Original x-position: \" + str(alien_0['x_position']))\nif alien_0['speed'] == 'slow':\n    x_increment = 1\nelif alien_0['speed'] == 'medium':\n    x_increment = 2\nelse:\n    x_increment = 3\nalien_0['x_position'] = alien_0['x_position'] + x_increment\nprint(\"New x-position: \" + str(alien_0['x_position']))\n\n# Removing Key-Value Pairs\n# When you no longer need a piece of information that’s stored in a diction- ary, you can use the del statement to completely remove a key-value pair.\nalien_0 = {'color': 'green', 'points': 5}\nprint(alien_0)\ndel alien_0['points']\nprint(alien_0)\n# The deleted key-value pair is removed permanently.\n\n# A Dictionary of Similar Objects\n# You can also use a dictionary to store one kind of information about many objects.\nfavorite_languages = {  # a way to format long dictionaries\n    'jen': 'python',\n    'sarah': 'c',\n    'edward': 'ruby',\n    'phil': 'python',\n}\nprint(f\"Sarah's favorite language is {favorite_languages['sarah'].title()}.\")\n\n# Exercise 6-1\nprint(\"Exercise 6-1---------------------------\")\namigo = {'first_name': 'Vitaly', 'last_name': 'Didkivsky', 'age': 19, 'city': 'Florence'}\nprint(f\"my friend's first name is : {amigo['first_name']}\")\nprint(f\"my friend's last name is : {amigo['last_name']}\")\nprint(f\"my friend is : {amigo['age']} years old\")\nprint(f\"my friend lives in : {amigo['city']}\")\n\n# Exercise 6-2\nfnumbers = {'jane': 5, 'tom': 11, 'john': 25, 'sarah': 5, 'jack': 44}\nprint(fnumbers['jane'])\nprint(fnumbers['john'])\nprint(fnumbers['sarah'])\n\n# Exercise 6-3\nglossary = {\n    'list': 'a collection of items in a particular order',\n    'loop': 'a process of iterating, going through the elements',\n    'tuple': 'a data structure similar to a list but which cannot be modified',\n    'immutable': 'unchangeable, unmodifiable',\n    'slice': 'a specific group of items in a list'\n}\nprint(f\"List \\n\\t{glossary['list']}\\n\")\nprint(f\"Loop \\n\\t{glossary['loop']}\\n\")\nprint(f\"Tuple \\n\\t{glossary['tuple']}\\n\")\nprint(f\"Immutable \\n\\t{glossary['immutable']}\\n\")\nprint(f\"Slice \\n\\t{glossary['slice']}\\n\")\n\n# Looping through a dictionary\n# Looping Through All Key-Value Pairs\nprint(\"----------Looping Through All Key-Value Pairs----------\")\nuser_0 = {'username': 'efermi', 'first': 'enrico', 'last': 'fermi', }\nfor key, value in user_0.items():\n    print(\"\\nKey: \" + key)\n    print(\"Value: \" + value)\n# To write a for loop for a dictionary, you create names for the two variables that will hold the key and value in each key-value pair. You can choose any names you want for these two variables.\n# items() method returns a list of key-value pairs\n# Python doesn’t care about the order in which key-value pairs are stored; it tracks only the connections between individual keys and their values.\nfavorite_languages = {'jen': 'python', 'sarah': 'c', 'edward': 'ruby', 'phil': 'python', }\nfor name, language in favorite_languages.items():\n    print(f\"{name.title()}'s favorite language is {language.title()}.\")\n\n# Looping Through All the Keys in a Dictionary\nprint(\"------Looping Through All the Keys in a Dictionary------\")\n# keys() method is useful when you don’t need to work with all of the values in a dictionary. The keys() method isn’t just for looping: It actually returns a list of all the keys. You can omit key() bcz it is default behavior of looping through the dictionary\nfor name in favorite_languages.keys():\n    print(name.title())\n\nfor name in favorite_languages:\n    print(\n        name.title())  # Looping through the keys is actually the default behavior when looping through a dictionary, so this code would have exactly the same output\nfavorite_languages = {'jen': 'python', 'sarah': 'c', 'edward': 'ruby', 'phil': 'python', }\nfriends = ['phil', 'sarah']\nfor name in favorite_languages.keys():\n    print(name.title())\n    if name in friends:\n        print(f\"Hi {name.title()}, I see your favorite language is {favorite_languages[name].title()}!\")\n\nif 'erin' not in favorite_languages.keys():\n    print(\"Erin, please take our poll!\")\n\n# Looping Through a Dictionary’s Keys in Order\nfor name in sorted(favorite_languages.keys()):\n    print(f\"{name.title()}, thank you for taking the poll.\")\n# sorted(favorite_languages.keys()) tells Python to list all keys in the dictionary and sort that list before looping through it.\n\n# Looping Through All Values in a Dictionary\n# values() method returns a list of values without any keys.\nprint(\"------Looping Through All Values in a Dictionary------\")\nfor language in favorite_languages.values():\n    print(language.title())\n\n# When you wrap set() around a list that contains duplicate items, Python identifies the unique items in the list and builds a set from those items. We use set() to pull out the unique, nonrepetitive values.\nfor language in set(favorite_languages.values()):\n    print(language.title())\n\nprint(\"-------------------Exercises------------------\")\n# Exercise 6-4\nglossary = {\n    'list': 'a collection of items in a particular order',\n    'loop': 'a process of iterating, going through the elements',\n    'tuple': 'a data structure similar to a list but which cannot be modified',\n    'immutable': 'unchangeable, unmodifiable',\n    'slice': 'a specific group of items in a list'\n}\n\nglossary['dictionary'] = 'a collection of key-value pairs'\nglossary['key-value pair'] = 'a set of values associated with each other where each key is connected to a certain value'\nglossary['boolean value'] = 'is either True or False'\nglossary['list comprehension'] = 'allows to generate the same list in just one line of code'\nglossary['indentation error'] = 'an error that occurs when there extra or lack of indents on blocks of code'\nprint(glossary)\n\nfor key, value in sorted(glossary.items()):\n    print(key.title())\n    print(\"\\t\\t\" + value + \"\\n\")\n\n# Exercise 6-5\nrivers = {'nile': 'egypt', 'amazon': 'brazil', 'yangtze': 'china'}\nfor river, country in rivers.items():\n    print(f\"The {river.title()} runs through {country.title()}\")\n\nfor river in rivers:\n    print(river.title())\nfor country in rivers.values():\n    print(country.title())\n\n# Exercise 6-6\nfavorite_languages = {\n    'jen': 'python',\n    'sarah': 'c',\n    'edward': 'ruby',\n    'phil': 'python',\n}\npoll = ['sarah', 'jack', 'jen', 'tom']\nfor name in sorted(poll):\n    if name in favorite_languages:\n        print(f\"{name.title()}, thank you for responding.\")\n    else:\n        print(f\"{name.title()}, you should definitely take the poll!\")\n\nprint(\"--------------------nesting------------------------\")\n# Nesting\n# is storing set of dictionaries in a list or a list of items as a value in a dictionary, or even a dictionary inside another dictionary.\n\n# A List of Dictionaries\nalien_0 = {'color': 'green', 'points': 5}\nalien_1 = {'color': 'yellow', 'points': 10}\nalien_2 = {'color': 'red', 'points': 15}\naliens = [alien_0, alien_1, alien_2]\n\nfor alien in aliens:\n    print(alien)\n\n# In the following example we use range() to create a fleet of 30 aliens:\naliens = []  # Make an empty list for storing aliens\nfor alien_number in range(30):\n    new_alien = {'color': 'green', 'points': 5, 'speed': 'slow'}\n    aliens.append(new_alien)\n\n# to change the first three aliens to yellow, medium-speed aliens worth 10 points each:\nfor alien in aliens[:3]:\n    if alien['color'] == 'green':\n        alien['color'] = 'yellow'\n        alien['speed'] = 'medium'\n        alien['points'] = 10\n    elif alien[\n        'color'] == 'yellow':  # You could expand this loop by adding an elif block that turns yellow aliens into red, fast-moving ones worth 15 points each.\n        alien['color'] = 'red'\n        alien['speed'] = 'fast'\n        alien['points'] = 15\n\nfor alien in aliens[:5]:  # Show the first 5 aliens\n    print(alien)\nprint(\"Total number of aliens: \" + str(len(aliens)))  # Show how many aliens have been created\n\nprint(\"-------------------------------------\")\n# A List in a Dictionary\n# You can nest a list inside a dictionary any time you want more than one value to be associated with a single key in a dictionary.\npizza = {\n    'crust': 'thick',\n    'toppings': ['mushrooms', 'extra cheese'],\n}\nprint(f\"You ordered a {pizza['crust']}\"\"-crust pizza with the following toppings:\")\nfor topping in pizza['toppings']:\n    print(\"\\t\" + topping)\n\n# A Dictionary in a Dictionary\nusers = {\n    'aeinstein': {\n        'first': 'albert',\n        'last': 'einstein',\n        'location': 'princeton',\n    },\n    'mcurie': {\n        'first': 'marie',\n        'last': 'curie',\n        'location': 'paris',\n    },\n}\nfor username, user_info in users.items():\n    print(\"\\nUsername: \" + username)\n    full_name = user_info['first'] + \" \" + user_info['last']\n    location = user_info['location']\n    print(\"\\tFull name: \" + full_name.title())\n    print(\"\\tLocation: \" + location.title())\n\nprint(\"--------------------------------------\")\n# Exercise 6-7\namigo1 = {'first_name': 'Victor', 'last_name': 'Krum', 'age': 25, 'city': 'Chicago'}\namigo2 = {'first_name': 'Linda', 'last_name': 'Brian', 'age': 50, 'city': 'New York'}\namigo3 = {'first_name': 'Nicole', 'last_name': 'Johnson', 'age': 30, 'city': 'Pittsburgh'}\n\npeople = [amigo1, amigo2, amigo3]\nfor amigo in people:\n    print(amigo['first_name'], end=' || ')\n    print(amigo['last_name'], end=' || ')\n    print(amigo['age'], end=' || ')\n    print(amigo['city'])\n\npeople = [amigo1, amigo2, amigo3]\nfor amigo in people:\n    for key, value in amigo.items():\n        print(f\"{key}: {value}\")\n\n# Exercise 6-8\nbob = {'animal': 'dog', 'owner': 'linda'}\nstacy = {'animal': 'cat', 'owner': 'victor'}\nebony = {'animal': 'snake', 'owner': 'nicole'}\nfluffy = {'animal': 'hamster', 'owner': 'john'}\npets = [bob, stacy, ebony, fluffy]\n\nfor pet in pets:\n    for animal, owner in pet.items():\n        print(f\"{animal}: {owner}\")\n\npets = {\n    'bob': {'animal': 'dog', 'owner': 'linda'},\n    'stacy': {'animal': 'cat', 'owner': 'victor'},\n    'ebony': {'animal': 'snake', 'owner': 'nicole'},\n    'fluffy': {'animal': 'hamster', 'owner': 'john'}\n}\n\nfor pet, details in pets.items():\n    print(pet.title())\n    for key, value in details.items():\n        print(f\"{key}: {value}\")\n\n# Exercise 6-9\nfavorite_places = {\n    'victor': ['moscow', 'miami', 'paris'],\n    'linda': ['tokyo', 'new york', 'oranjestad'],\n    'nicole': ['sarasota', 'paris', 'barcelona']\n}\n\nfor name, places in favorite_places.items():\n    print(f\"\\n{name.title()}'s favorite places are:\")\n    for place in places:\n        print(place.title(), end=' | ')\n\n# Exercise 6-10\nfavorite_numbers = {\n    'jane': [5, 10, 99],\n    'tom': [11, 14, 54],\n    'john': [25, 10, 1],\n    'sarah': [5, 50, 100],\n    'jack': [44, 2, 18]\n}\n\nfor name, numbers in favorite_numbers.items():\n    print(f\"\\n{name.title()}'s favorite numbers:\")\n    for num in numbers:\n        print(num, end=\"\\t\\t\")\n\n# Exercise 6-11\ncities = {\n    'tashkent': {'country': 'uzbekistan', 'population': '2.5 mln', 'fact': 'largest city in Central Asia'},\n    'new york': {'country': 'usa', 'population': '8.5 mln', 'fact': 'more than 800 languages are spoken'},\n    'tokyo': {'country': 'japan', 'population': '37 mln', 'fact': 'the most populated city in the world'}\n}\n\nfor city, details in cities.items():\n    print(\"\\n\" + city.upper() + \":\")\n    for key, value in details.items():\n        print(f\"\\t{key.title()}:\", end='\\t')\n        print(\"\\t\" + value.title())\n\n# Exercise 6-12\ncities = {\n    'tashkent': {'country': 'uzbekistan', 'population': '2.5 mln', 'established': '2200 years ago',\n                 'fact': 'largest city in Central Asia'},\n    'new york': {'country': 'usa', 'population': '8.5 mln', 'established': '1624 year',\n                 'fact': 'more than 800 languages are spoken'},\n    'tokyo': {'country': 'japan', 'population': '37 mln', 'established': '1603 year',\n              'fact': 'the most populated city in the world'}\n}\nfor city, details in cities.items():\n    print(\"\\n\" + city.upper() + \":\")\n    for key, value in details.items():\n        print(f\"\\t{key.title()}:\", end='\\t')\n        print(value.title())\n","sub_path":"chapter6.py","file_name":"chapter6.py","file_ext":"py","file_size_in_byte":13839,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"474914380","text":"# -*- coding: utf-8 -*-\n\n#    File name: ytKeywordSearch.py\n#    Author: Kaustav Basu\n#    Date created: 01/07/2019\n#    Date last modified: 01/07/2019\n#    Python Version: 2.7.10\n\nimport sys\nimport os\nimport gender_guesser.detector as gender\nimport pandas as pd\nimport progressbar\nimport xlrd\nfrom time import sleep\nfrom googleapiclient.discovery import build\nfrom googleapiclient.errors import HttpError\nfrom textblob import TextBlob\nfrom agefromname import AgeFromName\n#from ethnicolr import census_ln, pred_census_ln\n\n\n# API key for YouTube\nAPI_KEY = ''\n# Array to hold all vidoes returned by the API\nVIDEOS = []\n\n# Create a list of video ids and get stats for each video\ndef get_video_data(response):\n  videoIds = ''\n  numResults = len(response['items'])\n  for x in range(numResults):\n      if 'id' in response['items'][x]:\n          videoIds = videoIds+response['items'][x]['id']['videoId']+','\n  videoIds = videoIds[0:len(videoIds)-1]\n  response = ''\n  if numResults > 0:\n      response = videos_list_multiple_ids(service,\n              part='snippet,contentDetails,statistics',\n              id=videoIds)\n  return response\n\n# Remove keyword arguments that are not set\ndef remove_empty_kwargs(**kwargs):\n  good_kwargs = {}\n  if kwargs is not None:\n    for key, value in kwargs.iteritems():\n      if value:\n        good_kwargs[key] = value\n  return good_kwargs\n\n# Take keywords and returns videos\ndef search_list_by_keyword(client, **kwargs):\n  kwargs = remove_empty_kwargs(**kwargs)\n  response = client.search().list(\n    **kwargs\n  ).execute()\n  return response\n\n# Take video ids and returns video info and stats\ndef videos_list_multiple_ids(client, **kwargs):\n  kwargs = remove_empty_kwargs(**kwargs)\n  response = client.videos().list(\n    **kwargs\n  ).execute()\n  return response\n\n# Take channel ids and returns channel info and stats\ndef channels_list_by_id(client, **kwargs):\n  d = gender.Detector(case_sensitive=False)\n  kwargs = remove_empty_kwargs(**kwargs)\n  response = client.channels().list(\n    **kwargs\n  ).execute()\n  channelTitle = 'No Title'\n  channelSubscribers = 0\n  if len(response['items']) > 0:\n      channelTitle = response['items'][0]['snippet']['title']\n      channelSubscribers = response['items'][0]['statistics']['subscriberCount']\n  firstName = channelTitle.split(' ', 1)[0]\n  if firstName == 'Dr.' or firstName == 'The':\n      firstName = channelTitle.split(' ')[1]\n  lastName = channelTitle.split(' ')[-1]\n  channelOwnerGender = d.get_gender(firstName)\n  channelOwner = [channelTitle, channelOwnerGender, channelSubscribers]\n  return channelOwner\n\ndef agg_results(keywords, response, client):\n     numResults = response['pageInfo']['totalResults']\n     for i in range(numResults):\n         videoTitle = response['items'][i]['snippet']['title']\n         videoDescription = response['items'][i]['snippet']['description']\n         videoId = response['items'][i]['id']\n         videoPublished = response['items'][i]['snippet']['publishedAt']\n         channelId = response['items'][i]['snippet']['channelId']\n         channelOwner = channels_list_by_id(client,\n               part='snippet,contentDetails,statistics',\n               id=channelId)\n         channelTitle = channelOwner[0]\n         channelOwnerGender = channelOwner[1]\n         channelSubscribers = channelOwner[2]\n         videoViews = 0\n         if 'viewCount' in response['items'][i]['statistics']:\n             videoViews = response['items'][i]['statistics']['viewCount']\n         videoLikes = 0\n         if 'likeCount' in response['items'][i]['statistics']:\n             videoLikes = response['items'][i]['statistics']['likeCount']\n         videoDislikes = 0\n         if 'dislikeCount' in response['items'][i]['statistics']:\n             videoDislikes = response['items'][i]['statistics']['dislikeCount']\n         videoFavorites = 0\n         if 'favoriteCount' in response['items'][i]['statistics']:\n             videoFavorites = response['items'][i]['statistics']['favoriteCount']\n         videoComments = 0\n         if 'commentCount' in response['items'][i]['statistics']:\n             videoComments = response['items'][i]['statistics']['commentCount']\n         text = TextBlob(videoTitle)\n         phrases = \",\".join(text.noun_phrases)\n         video = [keywords, videoTitle, videoId, videoPublished, channelId, channelTitle,\n                  channelOwnerGender, channelSubscribers, videoViews, videoLikes,\n                  videoDislikes, videoFavorites, videoComments, phrases, videoDescription]\n         VIDEOS.append(video)\n\ndef keywordSearch(keywords, orderBy, client):\n     # How many results to return in each page\n     resPerPage = 50\n     # Should more than one page of results be returned\n     paginate = False\n     # How many total pages to return\n     maxPages = 10\n     # Counter for pages returned\n     pageCounter = 1\n     # Instantiate progress bar and start it\n     bar = progressbar.ProgressBar(maxval=maxPages, \\\n     widgets=[progressbar.Bar('=', 'Progress [', ']'), ' ', progressbar.Percentage()], term_width=100)\n     bar.start()\n     # Call API and return first page of results\n     response = search_list_by_keyword(service,\n     \tpart='snippet',\n     \tmaxResults=resPerPage,\n     \tq=keywords,\n        order=orderBy,\n        relevanceLanguage='en',\n     \ttype='video')\n     # Get token for next page\n     nextPageToken = ''\n     if 'nextPageToken' in response:\n         nextPageToken = response['nextPageToken']\n     numResults = response['pageInfo']['resultsPerPage']\n     # Get performance data for each video returned\n     videoData = get_video_data(response)\n     # Aggregate results\n     agg_results(keywords, videoData, service)\n     # Update progress bar\n     bar.update(pageCounter)\n     # If returning more than one page of results and if maxPages has not been returned\n     while nextPageToken != None and pageCounter < maxPages and paginate and numResults >= resPerPage:\n         pageCounter = pageCounter + 1\n         response = search_list_by_keyword(service,\n         \tpart='snippet',\n         \tmaxResults=resPerPage,\n         \tq=keywords,\n            order=orderBy,\n            relevanceLanguage='en',\n            pageToken=nextPageToken,\n         \ttype='video')\n         nextPageToken = response['nextPageToken']\n         if len(response['items']) == 0:\n             numResults = 0\n         else:\n             numResults = response['pageInfo']['resultsPerPage']\n             videoData = get_video_data(response)\n             agg_results(keywords, videoData, service)\n         bar.update(pageCounter)\n     # End progress bar\n     bar.finish()\n     # Print results\n     print('Total results: '+str(response['pageInfo']['totalResults']))\n     printResults(keywords)\n\ndef fileSearch(keywords, orderBy, client):\n     # How many results to return in each page\n     resPerPage = 50\n     # Should more than one page of results be returned\n     paginate = False\n     # How many total pages to return\n     maxPages = 1\n     # Counter for pages returned\n     pageCounter = 1\n     # Location of input file\n     loc = keywords\n     wb = xlrd.open_workbook(loc)\n     sheet = wb.sheet_by_index(0)\n     # Instantiate progress bar and start it\n     bar = progressbar.ProgressBar(maxval=sheet.nrows, \\\n     widgets=[progressbar.Bar('=', 'Progress [', ']'), ' ', progressbar.Percentage()], term_width=100)\n     bar.start()\n     for x in range(sheet.nrows):\n         # Call API and return first page of results\n         response = search_list_by_keyword(service,\n         \tpart='snippet',\n         \tmaxResults=resPerPage,\n         \tq=sheet.cell_value(x, 0),\n            order=orderBy,\n            relevanceLanguage='en',\n         \ttype='video')\n         # Get token for next page\n         #nextPageToken = response['nextPageToken']\n         numResults = response['pageInfo']['resultsPerPage']\n         # Get performance data for each video returned\n         videoData = get_video_data(response)\n         # Aggregate results\n         agg_results(sheet.cell_value(x, 0), videoData, service)\n         # Update progress bar\n         bar.update(x)\n         # If returning more than one page of results and if maxPages has not been returned\n         #while nextPageToken != None and pageCounter < maxPages and paginate and numResults >= resPerPage:\n             #pageCounter = pageCounter + 1\n             #response = search_list_by_keyword(service,\n             \t#part='snippet',\n             \t#maxResults=resPerPage,\n             \t#q=keywords,\n                #order=orderBy,\n                #relevanceLanguage='en',\n                #pageToken=nextPageToken,\n             \t#type='video')\n             #nextPageToken = response['nextPageToken']\n             #if len(response['items']) == 0:\n                 #numResults = 0\n             #else:\n                 #numResults = response['pageInfo']['resultsPerPage']\n                 #videoData = get_video_data(response)\n                 #agg_results(sheet.cell_value(x, 0), videoData, service)\n             #bar.update(x)\n     # End progress bar\n     bar.finish()\n     # Print results\n     printResults('output')\n\ndef printResults(name):\n    pd.set_option('display.max_rows', 1000)\n    df = pd.DataFrame(VIDEOS, columns=['Keyword','Video','Video ID','Published Date','Channel ID','Channel Name',\n                                            'Channel Owner Gender','Subscribers','Views','Likes','Dislikes',\n                                            'Favorites','Comments','Keywords','Video Description'])\n    if name is 'output':\n        df.to_excel('crawl_outputs/output.xlsx', index=False)\n        print('Done! Check output.xlsx file for results')\n    else:\n        df.to_excel('crawl_outputs/'+name+'.xlsx', index=False)\n        print('Done! Check \"crawl_outputs/'+name+'.xlsx\\\" file for results')\n    print('\\n\\n')\n    script = 'python /Users/kaustavbasu/Desktop/API/crawls/extractKeywords.py \\''+name+'\\'.xlsx'\n    os.system(script)\n\n# Main function\nif __name__ == '__main__':\n    os.system('clear')\n    # Get command line args\n    keywords = sys.argv[1]\n    orderBy = 'viewCount'\n    if len(sys.argv) > 2:\n        orderBy = sys.argv[2]\n    # Build YouTube API service\n    service = build('youtube', 'v3', developerKey = API_KEY)\n    if \".xlsx\" not in keywords:\n        keywordSearch(keywords, orderBy, service)\n    else:\n        fileSearch(keywords, orderBy, service)\n","sub_path":"ytKeywordSearch.py","file_name":"ytKeywordSearch.py","file_ext":"py","file_size_in_byte":10343,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"173244907","text":"\n\n#calss header\nclass _OFFSIDE():\n\tdef __init__(self,): \n\t\tself.name = \"OFFSIDE\"\n\t\tself.definitions = [u'in sports such as football, an occasion when a player is offside: ', u'the side of a vehicle that is furthest from the edge of the road and closest to the centre of the road when you are driving, i.e. (in the UK) the right side: ']\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'nouns'\n\n\n\tdef run(self, obj1 = [], obj2 = []):\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/nouns/_offside.py","file_name":"_offside.py","file_ext":"py","file_size_in_byte":511,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"484026972","text":"import requests\nfrom lxml import html\n\ndef get_name(src):\n    arr = src.split('/')\n\n    while '' in arr:\n        arr.remove('')\n\n    arr = arr[:4]\n    arr.append('strip')\n    url = 'http://' + '/'.join(arr)\n\n    return [arr[2].split('?')[0] , url]\n\np_id = input('输入一个文章ID：')\nurl = 'http://www.jianshu.com/p/{}'.format(p_id)\n\nobject = requests.get(url).content\ndocument = html.fromstring(object)\n\nprint(requests.get(url).text)\n\ntitle = document.xpath('//h1[@class=\"title\"]/text()')[0].strip()\nimages = document.xpath('//div[@class=\"show-content\"]/div[@class=\"image-package\"]/img/@src')\nexit()\nfor item in images:\n    file_path = get_name(item)\n    img_obj = requests.get(file_path[1]).content\n    with open('image/'+file_path[0], 'wb') as p:\n        p.write(img_obj)\n\n    print(file_path[0])\n\n\n","sub_path":"image.py","file_name":"image.py","file_ext":"py","file_size_in_byte":807,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"427003599","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n# 0xCCCCCCCC\n\nimport argparse\nimport os\nimport shlex\nimport shutil\nimport subprocess\n\nfrom distutils.util import strtobool\nfrom os import path\n\nPROJECT_ROOT = path.dirname(path.abspath(__file__))\nCMAKE_ROOT = path.dirname(path.abspath(__file__))\n\n# Ordered in precedence.\n# And we assume make is always available on any *nix system.\nBUILD_SYSTEMS = (\n    ('ninja', 'Ninja'),\n    ('make', 'Unix Makefiles'),\n)\n\ndef run(cmd):\n    subprocess.call(shlex.split(cmd))\n\ndef find_program(exe):\n    return subprocess.call(['which', exe],\n                           stdout=subprocess.DEVNULL, stderr=subprocess.STDOUT) == 0\n\ndef search_preferred_build_system():\n    print('* Auto-search building system...')\n    for tool, gen_name in BUILD_SYSTEMS:\n        if find_program(tool):\n            print('* {} found and would be used'.format(tool))\n            return gen_name\n\ndef main():\n    parser = argparse.ArgumentParser()\n\n    parser.add_argument('--build-type', dest='build_type', type=str, default='Debug')\n    parser.add_argument('--out-dir', dest='out_dir', type=str)\n    parser.add_argument('--generator', dest='generator', type=str)\n    parser.add_argument('--rebuild', dest='rebuild', action='store_true')\n\n    parser.add_argument('--no-test', dest='build_no_test', action='store_true')\n\n    args = parser.parse_args()\n\n    build_type = args.build_type.capitalize()\n\n    our_dir_name = args.out_dir if args.out_dir else build_type\n    out_dir = path.join(PROJECT_ROOT, 'build', our_dir_name)\n\n    if args.rebuild:\n        print('* Rebuild mode, clean first...')\n        if path.exists(out_dir):\n            shutil.rmtree(out_dir, True)\n\n    if not path.exists(out_dir):\n        os.makedirs(out_dir)\n\n    os.chdir(out_dir)\n\n    generator = args.generator if args.generator else search_preferred_build_system()\n\n    run('cmake'\n        ' -DCMAKE_BUILD_TYPE={}'\n        ' -DBUILD_NO_UNITTEST={}'\n        ' -G \"{}\"'\n        ' {}'\n        .format(build_type,\n                args.build_no_test,\n                generator,\n                CMAKE_ROOT))\n\n    building_thread_count = os.cpu_count() + 2\n\n    run('cmake'\n        ' --build {}'\n        ' -- -j {}'\n        .format(out_dir, building_thread_count))\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"gen.py","file_name":"gen.py","file_ext":"py","file_size_in_byte":2283,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"435719423","text":"from django.conf.urls import url, include\nfrom . import views\n\nurlpatterns = [\n    url(r'^list/$', views.post_list, name=\"list\"),\n    url(r'^content/$', views.post_content, name=\"content\"),\n    url(r'^intro/$', views.post_introduce, name=\"introduce\"),\n    url(r'^about/$', views.post_about, name=\"about\"),\n\n]","sub_path":"start/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":308,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"534881394","text":"# coding=utf-8\n\n\"\"\"\nBefore any code in your main module, import this module to hack the system paths\n\"\"\"\nimport os\nimport sys\nfrom pprint import pformat\n\n__docformat__ = 'restructuredtext en'\n\n\n# noinspection PyUnusedLocal\ndef hack_sys_path(debug=False):\n    \"\"\"\n    When called the sys.path may contain this files directory when it really needs the\n    parent directory (example, from repo root running \"audrey2/audrey2.py\"), then this\n    directory.  So we need to remove this directory and this directory's parent where ever\n    they are in the sys.path, then insert this directory's parent followed by this directory\n    into the start of sys.path.\n\n    ::\n\n        Example file structure:\n\n        * repo_root\n        * repo_root/proj_package\n        * repo_root/proj_package/main.py\n\n        for the following to work:\n\n        cd repo_root\n        proj_package/main.py\n\n        then sys.path needs to be:\n\n            [repo_root, repo_root/proj_package, ...]\n\n    .. note::\n\n        Must be executed before importing any project specific packages\n\n    :param debug: assert for debug info\n    :type debug: bool\n    \"\"\"\n    # if debug:\n    #     print(\"original sys.path: %s\" % pformat(sys.path))\n    this_dir = os.path.abspath(os.path.dirname(__file__))\n    parent_dir = os.path.dirname(this_dir)\n    # if debug:\n    #     print(\"this_dir: %s\" % this_dir)\n    #     print(\"parent_dir: %s\" % parent_dir)\n    if this_dir in sys.path:\n        sys.path.remove(this_dir)\n    if parent_dir in sys.path:\n        sys.path.remove(parent_dir)\n    sys.path.insert(0, parent_dir)\n    sys.path.insert(1, this_dir)\n    if debug:\n        print(\"adjusted sys.path: %s\" % pformat(sys.path))\n\n\n# noinspection PyArgumentEqualDefault\nhack_sys_path(debug=False)\n","sub_path":"herring/hack_sys_path.py","file_name":"hack_sys_path.py","file_ext":"py","file_size_in_byte":1747,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"401198754","text":"\"\"\"\nMIT License\n\nZarena Discord Bot\nCopyright (c) 2021 itsbravestone\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n\"\"\"\n\nimport discord\nimport random\nimport asyncio\nimport json\nfrom discord.ext import commands\nimport aiohttp\n\nclass fun:\n    def __init__(self, bot):\n        self.bot = bot\n        \n\n    @commands.command()\n    async def hug(self, ctx, *,member : discord.Member = None):\n        if member is None:\n            await ctx.send(ctx.message.author.mention + \" has been hugged! 💘 \")\n        else:\n            if member.id == ctx.message.author.id:\n                await ctx.send (ctx.message.author.mention + \" hugged themselve because they are a loner 🤦 \")\n            else:\n                await ctx.send(member.mention + \" was hugged by \" + ctx.message.author.mention + \" 💝 \")\n\n\n    @commands.command(pass_context=True)\n    async def coinflip(self, ctx):\n        '''Select Random'''\n        choice = random.randint(1,2)\n        if choice == 1:\n            await ctx.send(\"Tails\")\n        else:\n            await ctx.send(\"Heads\")\n\n    @commands.command()\n    async def greet(self, ctx):\n         await ctx.send(\":smiley: :wave: Hello, there!\")\n    \n    @commands.command()\n    async def hi(self, ctx):\n        await ctx.send(\"Hello !\")\n        await ctx.message.delete()\n        \n    @commands.command()\n    async def cat(self, ctx):\n        await ctx.send(\"https://moderncat-wpengine.netdna-ssl.com/sites/default/files/images/uploads/ScienceKittens.gif\")\n        \n    @commands.command(description='Lets see, what bot prefers')\n    async def choose(self, ctx, *choices : str):\n        \"\"\"Chooses between multiple choices.\"\"\"\n        await ctx.send(random.choice(choices))\n\n    @commands.command()\n    async def kill(self, ctx, *, member: discord.Member = None):\n        if member is None:\n            embed=discord.Embed(title=\"No one to kill!\", description=\"You havent mentioned anyone to kill!\", color=0xff0000)\n            embed.set_thumbnail(url=\"http://i.imgur.com/6YToyEF.png\")\n            embed.set_footer(text = \"I thought you are not enough stupid\")\n            await ctx.send(embed=embed)\n        elif member.id == ctx.message.author.id:\n            embed=discord.Embed(title=\"Call this number\", description=\"1-800-784-2433\", color=0xff0000)\n            embed.set_thumbnail(url=\"https://upload.wikimedia.org/wikipedia/commons/thumb/f/fa/NHS-Logo.svg/1200px-NHS-Logo.svg.png\")\n            embed.set_image(url=\"http://4.bp.blogspot.com/-FL6mKTZOk94/UBb_9EuAYNI/AAAAAAAAOco/JWsTlyInMeQ/s400/Jean+Reno.gif\")\n            embed.set_footer(text=\"~~You are good~~\")\n            await ctx.send(embed=embed)\n        else:\n            embed=discord.Embed(title=\"Killed!\", description=\"{} Was killed by {} OOF \".format(member.mention, ctx.message.author.name),color=0x00ff00)\n            embed.set_image(url=\"https://media.giphy.com/media/kOA5F569qO4RG/giphy.gif\")\n            embed.set_footer(text= \"RIP\")\n            await ctx.send(embed=embed)\n\n    @commands.command()\n    async def slap(self, ctx, *, member: discord.Member = None):\n        if member is None:\n            embed=discord.Embed(title=\"No one to slap!\", description=\"You havent mentioned anyone to slap!\", color=0xff0000)\n            embed.set_thumbnail(url=\"http://i.imgur.com/6YToyEF.png\")\n            embed.set_footer(text = \"I thought you are not enough stupid\")\n            await ctx.send(embed=embed)\n        elif member.id == ctx.message.author.id:\n            embed=discord.Embed(title=\"Call this number\", description=\"1-800-784-2433\", color=0xff0000)\n            embed.set_thumbnail(url=\"https://upload.wikimedia.org/wikipedia/commons/thumb/f/fa/NHS-Logo.svg/1200px-NHS-Logo.svg.png\")\n            embed.set_image(url=\"https://media.giphy.com/media/pVi6sMBJhJ0E8/giphy.gif\")\n            embed.set_footer(text=\"~~You are good~~\")\n            await ctx.send(embed=embed)\n        else:\n            embed=discord.Embed(title=\"slapped!\", description=\"{} Was slapped by {} OOF \".format(member.mention, ctx.message.author.name),color=0x00ff00)\n            embed.set_image(url=\"https://media.giphy.com/media/jLeyZWgtwgr2U/giphy.gif\")\n            embed.set_footer(text= \"RIP\")\n            await ctx.send(embed=embed)\n\n    \n    \n    @commands.command()\n    async def face(self, ctx):\n        faces=[\"¯\\_(ツ)_/¯\", \"̿̿ ̿̿ ̿̿ ̿'̿'\\̵͇̿̿\\З= ( ▀ ͜͞ʖ▀) =Ε/̵͇̿̿/’̿’̿ ̿ ̿̿ ̿̿ ̿̿\", \"( ͡°( ͡° ͜ʖ( ͡° ͜ʖ ͡°)ʖ ͡°) ͡°)\", \"ʕ•ᴥ•ʔ\", \"(▀̿Ĺ̯▀̿ ̿)\", \"(ง ͠° ͟ل͜ ͡°)ง\", \"༼ つ ◕_◕ ༽つ\", \"ಠ_ಠ\", \"(づ｡◕‿‿◕｡)づ\", \"̿'̿'\\̵͇̿̿\\З=( ͠° ͟ʖ ͡°)=Ε/̵͇̿̿/'̿̿ ̿ ̿ ̿ ̿ ̿\", \"(ﾉ◕ヮ◕)ﾉ*:･ﾟ✧ ✧ﾟ･: *ヽ(◕ヮ◕ヽ)\", \"┬┴┬┴┤ ͜ʖ ͡°) ├┬┴┬┴\", \"( ͡°╭͜ʖ╮͡° )\", \"(͡ ͡° ͜ つ ͡͡°)\", \"(• ε •)\", \"(ง'̀-'́)ง\", \"(ಥ﹏ಥ)\", \"(ノಠ益ಠ)ノ彡┻━┻\", \"[̲̅$̲̅(̲̅ ͡° ͜ʖ ͡°̲̅)̲̅$̲̅]\", \"(ﾉ◕ヮ◕)ﾉ*:･ﾟ✧\", \"(☞ﾟ∀ﾟ)☞\", \"| (• ◡•)| (❍ᴥ❍ʋ)\", \"(◕‿◕✿)\", \"(ᵔᴥᵔ)\", \"(¬‿¬)\", \"(☞ﾟヮﾟ)☞ ☜(ﾟヮﾟ☜)\", \"(づ￣ ³￣)づ\", \"ლ(ಠ益ಠლ)\", \"ಠ╭╮ಠ\", \"̿ ̿ ̿'̿'\\̵͇̿̿\\з=(•_•)=ε/̵͇̿̿/'̿'̿ ̿\", \"(;´༎ຶД༎ຶ`)\", \"༼ つ  ͡° ͜ʖ ͡° ༽つ\", \"(╯°□°）╯︵ ┻━┻\"]\n        face=random.choice(faces)\n        await ctx.send(face)\n\n    @commands.command()\n    async def tableflip(self, ctx):\n        x = await ctx.send(content=\"┬─┬ノ( º _ ºノ)\")\n        await asyncio.sleep(1)\n        await x.edit(content='(°-°)\\\\ ┬─┬')\n        await asyncio.sleep(1)\n        await x.edit(content='(╯°□°)╯    ]')\n        await asyncio.sleep(0.2)\n        await x.edit(content='(╯°□°)╯  ︵  ┻━┻')\n      \n    \n    @commands.command()\n    async def meme(self, ctx):\n        \"\"\"Pulls a random meme from r/me_irl\"\"\"\n        async with aiohttp.ClientSession() as session:\n            async with session.get(\"https://api.reddit.com/r/me_irl/random\") as r:\n                data = await r.json()\n                await ctx.send(data[0][\"data\"][\"children\"][0][\"data\"][\"url\"])\n     \n\n    @commands.command()\n    async def quotes(self, ctx):\n        \"\"\"*quotes\n        A command that will return a random quotation.\n        \"\"\"\n        self.session = aiohttp.ClientSession()\n        params = {'method': 'getQuote', 'lang': 'en', 'format': 'json'}\n        async with self.session.get('https://api.forismatic.com/api/1.0/', params=params) as response:\n            data = await response.json()\n\n            embed = discord.Embed(colour=discord.Colour.purple())\n            embed.add_field(name=data['quoteText'], value=f\"- {data['quoteAuthor']}\")\n\n            await ctx.send(embed=embed)\n            \n    @commands.command()\n    async def repeat(self, ctx, amount: int, *, message):\n        if amount <= 5:\n            for i in range(amount):\n                await ctx.send(message)     \n        else:\n            await ctx.send('Please use a number less than or equal to five.')\n            \n    @commands.command()\n    async def reverse(self, ctx, *, message):\n        message = message.split()\n        await ctx.send(' '.join(reversed(message)))\n\n\ndef setup(bot):\n    bot.add_cog(fun(bot))\n","sub_path":"cogs/fun.py","file_name":"fun.py","file_ext":"py","file_size_in_byte":8245,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"512273910","text":"import re\nimport csv\n\nf = open('faculty.csv')\ncsv_f = csv.reader(f)\ncsv_f.next()\n\n# List of email addresses\nparsed_data = [re.findall('[a-z0-9]*@\\S*', row[3]) for row in csv_f]\n\nwith open(\"emails.csv\", \"wb\") as file:\n    writer = csv.writer(file)\n    writer.writerows(parsed_data)\n\n","sub_path":"python/advanced_python_csv.py","file_name":"advanced_python_csv.py","file_ext":"py","file_size_in_byte":282,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"309136408","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# vim: set fileencoding=utf-8\n#\n# GA: RWA with GOF\n# Genetic Algorithm\n# Routing and Wavelength Assignment\n# General Objective Function\n#\n# Copyright 2017\n# Programa de Pós-Graduação em Ciência da Computação (PPGCC)\n# Universidade Federal do Pará (UFPA)\n#\n# Author: April 2016\n# Cassio Trindade Batista - cassio.batista.13@gmail.com\n#\n# Last revised on June 2020\n\n\nimport random\nimport numpy as np\n\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as anim\nfrom matplotlib.ticker import EngFormatter\n\nimport info\n\n\nSOURCE_NODE  = 0      # source for all cals\nDEST_NODE    = 12     # destination node\nNUM_NODES    = 14     # number of nodes on NSF network\n\n\ndef generate():\n    def set_wave_availability():\n        nwaves = 2**info.NUM_CHANNELS\n        if info.CHANNEL_FREE:\n            return np.uint8(nwaves-1)\n        return np.uint8(random.randrange(1, nwaves))\n\n    # define links or edges as node index pairs \n    nsf_links = [\\\n        (0,1), (0,2), (0,5),  # 0\n        (1,2), (1,3),         # 1\n        (2,8),                # 2\n        (3,4), (3,6), (3,13), # 3\n        (4,9),                # 4\n        (5,6), (5,10),        # 5\n        (6,7),                # 6\n        (7,8),                # 7\n        (8,9),                # 8\n        (9,11), (9,12),       # 9\n        (10,11), (10,12),     # 10\n        (11,13)               # 11\n    ]\n\n    nsf_wave = np.zeros((NUM_NODES, NUM_NODES), dtype=np.uint8)\n    for link in nsf_links:\n        nsf_wave[link[0]][link[1]] = set_wave_availability() \n        nsf_wave[link[1]][link[0]] = nsf_wave[link[0]][link[1]]\n\n    nsf_adj = np.zeros((NUM_NODES, NUM_NODES), dtype=np.uint8)\n    for link in nsf_links:\n        nsf_adj[link[0]][link[1]] = 1\n        nsf_adj[link[1]][link[0]] = nsf_adj[link[0]][link[1]] \n\n    nsf_time = np.zeros((NUM_NODES, NUM_NODES, info.NUM_CHANNELS))\n    for link in nsf_links:\n        availability = format(nsf_wave[link[0]][link[1]], '0%db' % info.NUM_CHANNELS)\n        for w in range(info.NUM_CHANNELS):\n            nsf_time[link[0]][link[1]][w] = int(availability[w]) * np.random.rand()\n            nsf_time[link[1]][link[0]][w] = nsf_time[link[0]][link[1]][w]\n\n    return nsf_wave, nsf_adj, nsf_time, nsf_links\n\ndef plot_fits(fits):\n    plt.interactive(True) # don't interrupt program flow to plot\n    fig, ax = plt.subplots()\n    ax.set_xscale('linear')\n    formatter = EngFormatter(unit='', places=1)\n    ax.xaxis.set_major_formatter(formatter)\n    ax.set_xlabel(u'Generations', fontsize=20)\n    ax.set_ylabel(u'Number of attended calls', fontsize=20)\n\n    ax.grid()\n    ax.plot(fits, linewidth=2.0)\n\n    x = range(0, info.GA_MAX_GEN, 5)\n    y = np.arange(0, 8, 1)\n\n    fig.suptitle(u'Best fits of %d individuals per generation'\\\n                % info.GA_SIZE_POP, fontsize=20)\n    #plt.margins(0.02)\n    plt.subplots_adjust(bottom=0.12)\n\n    #plt.xticks(x)\n    plt.yticks(y)\n    plt.draw()\n    plt.show(block=True)\n\n\ndef plot_graph(bestroute=False):\n    fig = plt.figure()\n    ax = fig.add_subplot(111)\n\n    # define vertices or nodes as points in 2D cartesian plan\n    nsfnodes = [\\\n        (0.70, 2.70), # 0\n        (1.20, 1.70), # 1\n        (1.00, 4.00), # 2\n        (3.10, 1.00), # 3\n        (4.90, 0.70), # 4\n        (2.00, 2.74), # 5\n        (2.90, 2.66), # 6\n        (3.70, 2.80), # 7\n        (4.60, 2.80), # 8\n        (5.80, 3.10), # 9\n        (5.50, 3.90), # 10\n        (6.60, 4.60), # 11\n        (7.40, 3.30), # 12\n        (6.50, 2.40), # 13\n    ]\n\n    # define links or edges as node index ordered pairs in cartesian plan\n    nsf_links = [\\\n        (0,1), (0,2), (0,5),  # 0\n        (1,2), (1,3),         # 1\n        (2,8),                # 2\n        (3,4), (3,6), (3,13), # 3\n        (4,9),                # 4\n        (5,6), (5,10),        # 5\n        (6,7),                # 6\n        (7,8),                # 7\n        (8,9),                # 8\n        (9,11), (9,12),       # 9\n        (10,11), (10,12),     # 10\n        (11,13)               # 11\n    ]\n\n    # grid\n    ax.grid()\n\n    # draw edges before vertices\n    for link in nsf_links:\n        x = [ nsfnodes[link[0]][0], nsfnodes[link[1]][0] ]\n        y = [ nsfnodes[link[0]][1], nsfnodes[link[1]][1] ]\n        plt.plot(x, y, 'k', linewidth=2)\n\n    # highlight in red the shortest path with wavelength(s) available\n    if bestroute:\n        for i in range(len(bestroute)-1):\n            x = [ nsfnodes[bestroute[i]][0], nsfnodes[bestroute[i+1]][0] ]\n            y = [ nsfnodes[bestroute[i]][1], nsfnodes[bestroute[i+1]][1] ]\n            plt.plot(x, y, 'r', linewidth=2.5)\n\n    # draw vertices\n    i = 0\n    for node in nsfnodes:\n        # parameter to adjust text on the center of the vertice\n        if i < 10:\n            corr = 0.060\n        else:\n            corr = 0.085\n\n        plt.plot(node[0], node[1], 'wo', ms=25, mec='k')\n\n        # write node index on the center of the node\n        ax.annotate(str(i), xy=(node[0]-corr, node[1]-corr))\n        i += 1\n\n    plt.xticks(np.arange(0, 9, 1))\n    plt.yticks(np.arange(0, 7, 1))\n    plt.show(block=True)\n\nif __name__ == '__main__':\n    plot_graph()\n","sub_path":"python/net/nsf.py","file_name":"nsf.py","file_ext":"py","file_size_in_byte":5125,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"138048637","text":"import argparse\nfrom collections.__init__ import namedtuple\nfrom contextlib import contextmanager\nfrom itertools import filterfalse\nfrom pathlib import Path\nimport re\nimport tempfile\nfrom typing import List, Tuple\nfrom xml.etree import ElementTree as ET\n\nfrom gym import spaces\nfrom gym.spaces import Box\nimport numpy as np\n\n\ndef make_box(*tuples: Tuple[float, float]):\n    low, high = map(np.array, zip(*[(map(float, m)) for m in tuples]))\n    return spaces.Box(low=low, high=high, dtype=np.float32)\n\n\ndef parse_space(dim: int):\n    def _parse_space(arg: str):\n        regex = re.compile(\"\\((-?[\\.\\d]+),(-?[\\.\\d]+)\\)\")\n        matches = regex.findall(arg)\n        if len(matches) != dim:\n            raise argparse.ArgumentTypeError(\n                f\"Arg {arg} must have {dim} substrings \" f\"matching pattern {regex}.\"\n            )\n        return make_box(*matches)\n\n    return _parse_space\n\n\ndef parse_vector(length: int, delim: str):\n    def _parse_vector(arg: str):\n        vector = tuple(map(float, arg.split(delim)))\n        if len(vector) != length:\n            raise argparse.ArgumentError(\n                f\"Arg {arg} must include {length} float values\"\n                f'delimited by \"{delim}\".'\n            )\n        return vector\n\n    return _parse_vector\n\n\ndef cast_to_int(arg: str):\n    return int(float(arg))\n\n\ndef put_in_xml_setter(arg: str):\n    setters = [XMLSetter(*v.split(\",\")) for v in arg]\n    mirroring = [\n        XMLSetter(p.replace(\"_l_\", \"_r_\"), v) for p, v in setters if \"_l_\" in p\n    ] + [XMLSetter(p.replace(\"_r_\", \"_l_\"), v) for p, v in setters if \"_r_\" in p]\n    return [s._replace(path=s.path) for s in setters + mirroring]\n\n\nXMLSetter = namedtuple(\"XMLSetter\", \"path value\")\n\n\n@contextmanager\ndef mutate_xml(\n    changes: List[XMLSetter],\n    dofs: List[str],\n    goal_space: Box,\n    n_blocks: int,\n    xml_filepath: Path,\n):\n    def rel_to_abs(path: Path):\n        return Path(xml_filepath.parent, path)\n\n    def mutate_tree(tree: ET.ElementTree):\n\n        worldbody = tree.getroot().find(\"./worldbody\")\n        rgba = [\n            \"0 1 0 1\",\n            \"0 0 1 1\",\n            \"0 1 1 1\",\n            \"1 0 0 1\",\n            \"1 0 1 1\",\n            \"1 1 0 1\",\n            \"1 1 1 1\",\n        ]\n\n        if worldbody:\n            for i in range(n_blocks):\n                pos = \" \".join(map(str, goal_space.sample()))\n                name = f\"block{i}\"\n\n                body = ET.SubElement(worldbody, \"body\", attrib=dict(name=name, pos=pos))\n                ET.SubElement(\n                    body,\n                    \"geom\",\n                    attrib=dict(\n                        name=name,\n                        type=\"box\",\n                        mass=\"1\",\n                        size=\".05 .025 .017\",\n                        rgba=rgba[i],\n                        condim=\"6\",\n                        solimp=\"0.99 0.99 \" \"0.01\",\n                        solref=\"0.01 1\",\n                    ),\n                )\n                ET.SubElement(body, \"freejoint\", attrib=dict(name=f\"block{i}joint\"))\n\n        for change in changes:\n            parent = re.sub(\"/[^/]*$\", \"\", change.path)\n            element_to_change = tree.find(parent)\n            if isinstance(element_to_change, ET.Element):\n                print(\"setting\", change.path, \"to\", change.value)\n                name = re.search(\"[^/]*$\", change.path)[0]\n                element_to_change.set(name, change.value)\n\n        for actuators in tree.iter(\"actuator\"):\n            for actuator in list(actuators):\n                if actuator.get(\"joint\") not in dofs:\n                    print(\"removing\", actuator.get(\"name\"))\n                    actuators.remove(actuator)\n        for body in tree.iter(\"body\"):\n            for joint in body.findall(\"joint\"):\n                if not joint.get(\"name\") in dofs:\n                    print(\"removing\", joint.get(\"name\"))\n                    body.remove(joint)\n\n        parent = Path(temp[xml_filepath].name).parent\n\n        for include_elt in tree.findall(\"*/include\"):\n            original_abs_path = rel_to_abs(include_elt.get(\"file\"))\n            tmp_abs_path = Path(temp[original_abs_path].name)\n            include_elt.set(\"file\", str(tmp_abs_path.relative_to(parent)))\n\n        for compiler in tree.findall(\"compiler\"):\n            abs_path = rel_to_abs(compiler.get(\"meshdir\"))\n            compiler.set(\"meshdir\", str(abs_path))\n\n        return tree\n\n    included_files = [\n        rel_to_abs(e.get(\"file\")) for e in ET.parse(xml_filepath).findall(\"*/include\")\n    ]\n\n    temp = {\n        path: tempfile.NamedTemporaryFile()\n        for path in (included_files + [xml_filepath])\n    }\n    try:\n        for path, f in temp.items():\n            tree = ET.parse(path)\n            mutate_tree(tree)\n            tree.write(f)\n            f.flush()\n\n        yield Path(temp[xml_filepath].name)\n    finally:\n        for f in temp.values():\n            f.close()\n\n\ndef parse_groups(parser: argparse.ArgumentParser):\n    args = parser.parse_args()\n\n    def is_optional(group):\n        return group.title == \"optional arguments\"\n\n    def parse_group(group):\n        # noinspection PyProtectedMember\n        return {a.dest: getattr(args, a.dest, None) for a in group._group_actions}\n\n    # noinspection PyUnresolvedReferences,PyProtectedMember\n    groups = [g for g in parser._action_groups if g.title != \"positional arguments\"]\n    optional = filter(is_optional, groups)\n    not_optional = filterfalse(is_optional, groups)\n\n    kwarg_dicts = {group.title: parse_group(group) for group in not_optional}\n    kwargs = parse_group(next(optional))\n    del kwargs[\"help\"]\n    return {**kwarg_dicts, **kwargs}\n","sub_path":"utils/arguments.py","file_name":"arguments.py","file_ext":"py","file_size_in_byte":5649,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"481019387","text":"import quanzhan.urls\r\ndef func():\r\n    new_box = []\r\n    page_box = quanzhan.urls.fun()\r\n    #print(page_box)\r\n    for num in range(1,501):\r\n        page_boxs = ['\"'+start_url+\"?order_by=commented_at&page={0}\".format(num)+'\",' for start_url in page_box]\r\n        every_pages = \"\".join(page_boxs)\r\n        new_box.append(every_pages)\r\n    new_list=\"\".join(new_box)\r\n    #print(list(eval(new_list)))\r\n    return(eval(new_list))\r\nfunc()\r\n","sub_path":"quanzhan/quanzhan/liebiao.py","file_name":"liebiao.py","file_ext":"py","file_size_in_byte":435,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"293750141","text":"from __future__ import print_function\nimport __init__  # noqa\nimport os\nimport sys\nfrom utils import iotools\nimport subprocess\n\n\nDRIVING_TERMS_FILENAME = \"DrivingTerms\"\nDRIVING_TERMS_TEMPL = \"\"\"\n%(SDDS_PATH)s %(START_TURN)i %(END_TURN)i\n\"\"\"\n\nDRIVE_INP_FILENAME = \"Drive.inp\"\nDRIVE_INP_TEMPL = \"\"\"\nKICK=1\nCASE(1[H], 0[V])=1\nKPER(KICK PERCE.)=0.5\nTUNE X=%(TUNE_X)s\nTUNE Y=%(TUNE_Y)s\nPICKUP START=0\nPICKUP END=538\nISTUN=%(ISTUN)s\nLABEL RUN (1[yes])=0\nWINDOWa1=0.04\nWINDOWa2=0.1\nWINDOWb1=0.4\nWINDOWb2=0.45\n%(NAT_TUNE_TEXT)s\n\"\"\"\nNAT_TUNE_TMPL = \"\"\"\nNATURAL X=%(NAT_TUNE_X)s\nNATURAL Y=%(NAT_TUNE_Y)s\n\"\"\"\n\n\ndef run_drive(sdds_file_path, start_turn, end_turn, tune_x, tune_y,\n              nat_tune_x=None, nat_tune_y=None, clean_up=False,\n              stdout=None, stderr=None, tune_window=0.01):\n    output_path = os.path.abspath(os.path.dirname(sdds_file_path))\n    driving_terms_path = _generate_driving_terms(sdds_file_path, output_path,\n                                                 start_turn, end_turn)\n    drive_inp_path = _generate_drive_inp(output_path, tune_x, tune_y,\n                                         nat_tune_x, nat_tune_y, tune_window)\n    _run_drive_exe(output_path, stdout, stderr)\n    if clean_up:\n        iotools.delete_item(driving_terms_path)\n        iotools.delete_item(drive_inp_path)\n\n\ndef _generate_driving_terms(sdds_file_path, output_path, start_turn, end_turn):\n    driving_terms_path = os.path.join(output_path, DRIVING_TERMS_FILENAME)\n    driving_terms_text = DRIVING_TERMS_TEMPL % {\"SDDS_PATH\": sdds_file_path,\n                                                \"START_TURN\": start_turn,\n                                                \"END_TURN\": end_turn}\n    print(\"Writing DrivingTerms file into: \", driving_terms_path)\n    iotools.write_string_into_new_file(driving_terms_path, driving_terms_text)\n    return driving_terms_path\n\n\ndef _generate_drive_inp(output_path, tune_x, tune_y,\n                        nat_tune_x, nat_tune_y, tune_window):\n    drive_inp_path = os.path.join(output_path, DRIVE_INP_FILENAME)\n    if nat_tune_x is not None and nat_tune_y is not None:\n        nat_tune_text = NAT_TUNE_TMPL % {\"NAT_TUNE_X\": str(nat_tune_x),\n                                         \"NAT_TUNE_Y\": str(nat_tune_y)}\n    else:\n        nat_tune_text = \"\"\n    drive_inp_text = DRIVE_INP_TEMPL % {\"TUNE_X\": str(tune_x),\n                                        \"TUNE_Y\": str(tune_y),\n                                        \"ISTUN\": str(tune_window),\n                                        \"NAT_TUNE_TEXT\": nat_tune_text}\n    print(\"Writing Drive.inp file into: \", drive_inp_path)\n    iotools.write_string_into_new_file(drive_inp_path, drive_inp_text)\n    return drive_inp_path\n\n\ndef _run_drive_exe(output_path, stdout=None, stderr=None):\n    drive_exe_path = _get_so_independent_drive_exe_path()\n    process = subprocess.Popen([drive_exe_path, output_path],\n                               stdin=subprocess.PIPE,\n                               stdout=stdout, stderr=stderr)\n    process.communicate()\n    return_code = process.wait()\n    return return_code\n\n\ndef _get_so_independent_drive_exe_path():\n    this_dir_path = os.path.abspath(os.path.dirname(__file__))\n    if sys.platform.startswith(\"linux\"):\n        return os.path.join(this_dir_path, \"Drive_God_lin\")\n    elif sys.platform == \"win32\":  # Windows...\n        return os.path.join(this_dir_path, \"Drive_God_lin_win.exe\")\n    else:\n        raise OSError(\"No drive version for given os: \" + sys.platform)\n\n\nif __name__ == '__main__':\n    print >> sys.stderr, \"Standalone usage not implemented yet.\"\n    sys.exit(-1)\n","sub_path":"drive/drive_runner.py","file_name":"drive_runner.py","file_ext":"py","file_size_in_byte":3604,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"138959671","text":"# Importing required modules #\nimport random\nimport time\n\n# Defining required class #\n# =============================================================== #\n\nclass color:\n\tPURPLE = '\\033[95m'\n\tCYAN = '\\033[96m'\n\tDARKCYAN = '\\033[36m'\n\tBLUE = '\\033[94m'\n\tGREEN = '\\033[92m'\n\tYELLOW = '\\033[93m'\n\tRED = '\\033[91m'\n\tBOLD = '\\033[1m'\n\tUNDERLINE = '\\033[4m'\n\tEND = '\\033[0m'\n# =============================================================== #\n\n# Setting it up #\nsets = ['Heart', 'Diamond', 'Spade', 'Club']\nnumbers = ['A', '2', '3', '4', '5', '6', '7', '8', '9', '10', 'J', 'Q', 'K']\ncards = []\nfor set in sets:\n\tfor number in numbers:\n\t\tcards.append(f\"{set} {number}\")\n\nshuffledCards = list()\nshuffledCards.extend(cards)\nrandom.shuffle(shuffledCards)\nshuffledCards1 = shuffledCards[:21]\nfor card in shuffledCards1:\n\tprint(color.BLUE + card + color.END)\n\nprint(color.CYAN + \"\\nChoose a card from the listed cards and remember it or write it down somewhere !\")\nprint(\"I am going to find what card you chose :)\")\nprint(\"Hit enter when you're ready !\" + color.END)\ninput()\nprint(\"\\n\")\nrandom.shuffle(shuffledCards1)\n\n\n# Defining required functions #\n# =============================================================== #\n\ndef splitIntoThree(deck):\n\tnum = 0\n\tdeck1 = list()\n\tdeck2 = list()\n\tdeck3 = list()\n\tfor card in deck :\n\t\tnum += 1\n\t\tif num == 1:\n\t\t\tdeck1.append(card)\n\t\tif num == 2:\n\t\t\tdeck2.append(card)\n\t\tif num == 3:\n\t\t\tdeck3.append(card)\n\t\tif num > 3:\n\t\t\tnum = 1\n\t\t\tdeck1.append(card)\n\tprint(\"  {}Deck 1     {}|   {}Deck 2      {}|    {}Deck 3 \".format(color.RED, color.END, color.GREEN, color.END, color.BLUE))\n\tfor card1, card2, card3 in zip(deck1, deck2, deck3):\n\t\tprint(color.RED + card1, \" \"* (15-len(card1)), color.GREEN + card2, \" \" *(15-len(card2)), color.BLUE + card3 + color.END)\n\treturn deck1, deck2, deck3\n# =============================================================== #\n\ndef whereIs(splitNDeck1, splitNDeck2, splitNDeck3):\n\tn = 1\n\twhile True:\n\t\twhile True:\n\t\t\tif n>1:\n\t\t\t\tprint(color.RED + \"Invalid deck number ! (re-enter deck number)\")\n\t\t\telse:\n\t\t\t\tprint()\n\t\t\t\tprint(color.GREEN + \"Which deck the card you chose is ?\\n(Enter the deck number)\")\n\t\t\tdeckNo = input()\n\t\t\tif deckNo == \"1\" or deckNo == \"2\" or deckNo == \"3\":\n\t\t\t\tdeckNo = int(deckNo)\n\t\t\t\tbreak\n\t\t\telse:\n\t\t\t\tn += 1\n\t\t\t\tcontinue\n\t\tprint()\n\t\tprint(color.PURPLE + \"Are you sure that the card you chose is in this deck ?\")\n\n\t\tif deckNo == 1:\n\t\t\tfor card in splitNDeck1:\n\t\t\t\tprint(color.RED + card)\n\t\telif deckNo == 2:\n\t\t\tfor card in splitNDeck2:\n\t\t\t\tprint(color.GREEN + card)\n\t\telif deckNo == 3:\n\t\t\tfor card in splitNDeck3:\n\t\t\t\tprint(color.BLUE + card)\n\n\t\twhile True:\n\t\t\tprint()\n\t\t\tconfirm = input(color.END + \"({}y{}/{}n{})\\n\".format(color.GREEN,color.END,color.RED,color.END))\n\t\t\tprint()\n\t\t\tif confirm.lower() == \"y\" or confirm.lower() == \"n\":\n\t\t\t\tbreak\n\t\t\telse:\n\t\t\t\tcontinue\n\t\tif confirm.lower() == \"n\":\n\t\t\tcontinue\n\t\telse:\n\t\t\tbreak\n\treturn deckNo\n# =============================================================== #\n\ndef orderDecks(shuffledCardsN, splitNDeck1, splitNDeck2, splitNDeck3, deckNo):\n\tif deckNo == 1:\n\t\tshuffledCardsN.extend(splitNDeck2)\n\t\tshuffledCardsN.extend(splitNDeck1)\n\t\tshuffledCardsN.extend(splitNDeck3)\n\tif deckNo == 2:\n\t\tshuffledCardsN.extend(splitNDeck1)\n\t\tshuffledCardsN.extend(splitNDeck2)\n\t\tshuffledCardsN.extend(splitNDeck3)\n\tif deckNo == 3:\n\t\tshuffledCardsN.extend(splitNDeck2)\n\t\tshuffledCardsN.extend(splitNDeck3)\n\t\tshuffledCardsN.extend(splitNDeck1)\n\n\treturn shuffledCardsN\n\n# =============================================================== #\n\nprint(\"-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-  Round 1  -_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-\\n\")\nsplit1 = splitIntoThree(shuffledCards1)\nsplit1Deck1 = split1[0]\nsplit1Deck2 = split1[1]\nsplit1Deck3 = split1[2]\n\ndeckNo = whereIs(split1Deck1, split1Deck2, split1Deck3)\n\nshuffledCards2 = list()\nshuffledCards2 = orderDecks(shuffledCards2, split1Deck1, split1Deck2, split1Deck3, deckNo)\n\n# -_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_\n\nprint(\"-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-  Round 2  -_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-\\n\")\nsplit2 = splitIntoThree(shuffledCards2)\nsplit2Deck1 = split2[0]\nsplit2Deck2 = split2[1]\nsplit2Deck3 = split2[2]\n\ndeckNo = whereIs(split2Deck1, split2Deck2, split2Deck3)\n\nshuffledCards3 = list()\nshuffledCards3 = orderDecks(shuffledCards3, split2Deck1, split2Deck2, split2Deck3, deckNo)\n\n\n# -_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_\n\nprint(\"-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-  Round 3  -_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-\\n\")\nsplit3 = splitIntoThree(shuffledCards3)\nsplit3Deck1 = split3[0]\nsplit3Deck2 = split3[1]\nsplit3Deck3 = split3[2]\n\ndeckNo = whereIs(split3Deck1, split3Deck2, split3Deck3)\n\nshuffledCards4 = list()\nshuffledCards4 = orderDecks(shuffledCards4, split3Deck1, split3Deck2, split3Deck3, deckNo)\n\n# -_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_\n\nprint(color.YELLOW + \"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\")\nprint(color.BLUE + \"The card you chose is :\\n\")\n\nheart = [\n\tcolor.RED,\n\t'        00000000000           000000000000',\n\t'      00000000     00000   000000     0000000',\n\t'    0000000             000              00000',\n\t'   0000000               0                 0000',\n\t'  000000                                    0000',\n\t'  00000                                      0000',\n\t' 00000                                      00000',\n\tf' 00000{((37-len(shuffledCards4[10]))//2)*\" \"}{color.CYAN}{shuffledCards4[10]}{color.END}{color.RED}{((37-len(shuffledCards4[10]))//2)*\" \"}000000',\n\t'  000000                                 0000000',\n\t'   0000000                              0000000',\n\t'     000000                            000000',\n\t'       000000                        000000',\n\t'          00000                     0000',\n\t'             0000                 0000',\n\t'               0000             000',\n\t'                 000         000',\n\t'                    000     00',\n\t'                      00  00',\n\t'                        00',\n\tf'{color.END}\\n'\n\t]\n\nfor line in heart:\n\ttime.sleep(0.02)\n\tprint(line)\n","sub_path":"cards.py","file_name":"cards.py","file_ext":"py","file_size_in_byte":6188,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"434020083","text":"#!/usr/bin/env python2\nimport numpy as np\nimport rospy\nimport tf\nimport roslib\nimport sys\nimport math\nfrom collections import deque\nfrom nav_msgs.msg import Odometry\nfrom geometry_msgs.msg import PoseWithCovarianceStamped, PointStamped\nfrom tf.transformations import euler_from_quaternion, quaternion_from_euler\nfrom std_msgs.msg import Int16, UInt8\nfrom std_msgs.msg import Float32\nfrom std_msgs.msg import Float64\nfrom geometry_msgs.msg import Pose\nfrom geometry_msgs.msg import Point\nfrom visualization_msgs.msg import Marker\n\n#http://docs.ros.org/api/visualization_msgs/html/msg/ImageMarker.html\n\nMAX_ANGLE_RIGHT = 0\nANGLE_STRAIGHT = 90\nMAX_ANGLE_LEFT = 180\nINITIAL_ERROR = 0.0\nYAW_TO_STEERING_FACTOR = 180.0 / np.pi\nMAX_SPEED = 1000\nWHEELBASE = 0.28 # 28cm\n\nKP = 3.8\nKI = 1.9\nKD = 0.1\nSPEED = 500\n\n# WORKS VERY WELL\n#~ KP = 3.6\n#~ KI = 1.8\n#~ KD = 0.1\n#~ SPEED = 520\n\n# MORE INTEGRAL SETTINGS\nERROR_QUEUE_SIZE = 3\nKI_UPPER_LIMIT = 15\nKI_LOWER_LIMIT = -15\n\nPATH = \"/root/catkin_ws_user/src/assignment7_vector_field/src/\"\n\nclass ForceController:\n    def __init__(self):\n        self.previous_error = 0.0\n        self.integral = 0.0\n        self.derivative = 0.0        \n        self.error_queue = deque(np.zeros(ERROR_QUEUE_SIZE), maxlen = ERROR_QUEUE_SIZE)\n        self.error = INITIAL_ERROR\n        self.Kp = KP\n        self.Ki = KI\n        self.Kd = KD\n        self.shutdown_ = False\n        self.map_size_x = 600 #cm\n        self.map_size_y = 400 #cm\n        self.resolution = 10 # cm\n        self.time_new = rospy.Time.now()\n        self.time_old = rospy.Time.now()\n        self.init_time = 0\n        self.last_yaw = 0.0\n        self.matrix = np.load(PATH + \"matrix100cm_lane1.npy\")\n        self.lane = 1\n        #~ self.turning_circle = 6.0\n        #~ self.truning_circle_pub = rospy.Publisher(\"AljoschaTim/turning_circle\", Marker, queue_size=1, latch=True)\n        self.steering_pub = rospy.Publisher(\"AljoschaTim/steering\", UInt8, queue_size=1)\n        self.speed_pub = rospy.Publisher(\"AljoschaTim/speed\", Int16, queue_size = 10, latch=True)\n        self.heading_pub = rospy.Publisher(\"AljoschaTim/heading\", Float32, queue_size = 1, latch=True)\n        self.desired_yaw_pub = rospy.Publisher(\"AljoschaTim/desired_yaw\", Float32, queue_size = 1, latch=True)\n        self.desired_yaw_pub_2 = rospy.Publisher(\"AljoschaTim/desired_yaw_2\", Float32, queue_size = 1, latch=True)\n        self.desired_yaw_pub_3 = rospy.Publisher(\"AljoschaTim/desired_yaw_3\", Float32, queue_size = 1, latch=True)\n        self.error_pub = rospy.Publisher(\"AljoschaTim/error\", Float32, queue_size = 1, latch=True)\n        self.lane_sub = rospy.Subscriber(\"/lane_change\", Int16, self.lane_callback, queue_size = 1)\n        self.odometry_sub = rospy.Subscriber(\"/localization/odom/1\", Odometry, self.odometry_callback, queue_size = 1)\n        rospy.on_shutdown(self.shutdown) # on shutdown set speed to zero\n\n    def lane_callback(self, data):\n        if (self.lane == 1):\n            print(\"Switch to outer lane.\")\n            self.lane = 2\n            self.matrix = np.load(PATH + \"matrix100cm_lane1.npy\")\n        else:\n            print(\"Switch to inner lane.\")\n            self.lane = 1\n            self.matrix = np.load(PATH + \"matrix100cm_lane2.npy\")\n\n    def odometry_callback(self, data):\n        self.time_new = rospy.Time.now()\n        \n        x = data.pose.pose.position.x\n        y = data.pose.pose.position.y\n        orientation_q = data.pose.pose.orientation\n        orientation_list = [orientation_q.x, orientation_q.y, orientation_q.z, orientation_q.w]\n        (roll, pitch, yaw) = euler_from_quaternion(orientation_list)\n        self.heading_pub.publish(Float32(yaw))\n\n        x_index = np.int(x * self.resolution)\n        y_index = np.int(y * self.resolution)\n        \n        if (x_index < 0):\n            x_index = 0\n        if (x_index > ((self.map_size_x / self.resolution) - 1)):\n            x_index = (self.map_size_x / self.resolution) - 1\n\n        if (y_index < 0):\n            y_index = 0\n        if (y_index > ((self.map_size_y / self.resolution) - 1)):\n            y_index = (self.map_size_y / self.resolution) - 1\n\n        x3, y3 = self.matrix[x_index, y_index, :]\n        \n        self.desired_yaw_pub.publish(Float32(np.arctan2(y3, x3)))\n        \n        f_x = np.cos(yaw) * x3 + np.sin(yaw) * y3\n        f_y = -np.sin(yaw) * x3 + np.cos(yaw) * y3\n        \n        # calculate error\n        self.error = np.arctan(f_y / (2.5 * f_x))\n        if(self.error > np.pi):\n            self.error = self.error - 2 * np.pi\n        if(self.error < -np.pi):\n            self.error = self.error + 2 * np.pi\n            \n        self.error_pub.publish(Float32(self.error))\n        \n        time = self.time_new - self.time_old\n        dt = float(time.secs + (time.nsecs / 1000000000.0))\n        \n        if(self.init_time and dt > 0.01):\n            # limit error history, calculate integral control\n            self.error_queue.append(self.error * dt)\n            self.integral = 0\n            for e in self.error_queue:\n                self.integral += e\n            if(self.integral > KI_UPPER_LIMIT):\n                self.integral = KI_UPPER_LIMIT\n            elif(self.integral < KI_LOWER_LIMIT):\n                self.integral = KI_LOWER_LIMIT\n            \n            # correct sudden huge errors, calculate differential control\n            #~ self.derivative = (yaw - self.last_yaw)\n            self.derivative = (self.error - self.previous_error)\n            if(abs(self.derivative) > 0.001):\n                if(self.derivative > np.pi):\n                    self.derivative = self.derivative - 2 * np.pi\n                if(self.error < -np.pi):\n                    self.derivative = self.derivative + 2 * np.pi\n                self.derivative = self.derivative / dt\n                control = self.Kp * self.error + self.Ki * self.integral + self.Kd * self.derivative\n            else:\n                control = self.Kp * self.error + self.Ki * self.integral\n            \n            self.previous_error = self.error\n            \n        else:\n            control = self.Kp * self.error\n\n        # When the f_x_car_coordinate is negative, the car should move backwards\n        if (f_x > 0):\n            speed = SPEED\n            \n            if(abs(self.error) <= (np.pi / 16)):\n                speed = speed * 1.15\n                if(abs(self.integral) <= (np.pi / 16)):\n                    speed = speed * 1.15\n                    if(abs(self.derivative) <= (np.pi / 16)):\n                        speed = speed * 1.15\n            \n            if speed > MAX_SPEED:\n                speed = MAX_SPEED\n\n        else:\n            speed = int(-SPEED / 2)\n            if (f_y > 0):\n                control = -np.pi / 2\n            if (f_y < 0):\n                control = np.pi / 2\n        \n        steering = control * YAW_TO_STEERING_FACTOR + ANGLE_STRAIGHT\n        \n        if(steering >= MAX_ANGLE_LEFT):\n            steering = UInt8(int(MAX_ANGLE_LEFT))\n        elif(steering <= MAX_ANGLE_RIGHT):\n            steering = UInt8(int(MAX_ANGLE_RIGHT))\n        else:\n            steering = UInt8(int(steering))\n        \n        # turning circle data for visualization in rviz\n        #~ deg_steering = float(steering.data - 90)\n        #~ self.turning_circle = 2.0 * (WHEELBASE / math.sin(math.radians(deg_steering)))\n        #~ marker = Marker()\n        #~ marker.header.frame_id = \"map\"\n        #~ marker.type = Marker.CYLINDER\n        #~ marker.action = Marker.ADD\n        #~ marker.pose.position.x = x;\n        #~ marker.pose.position.y = y;\n        #~ marker.pose.position.z = 0;\n        #~ marker.pose.orientation.x = orientation_q.x;\n        #~ marker.pose.orientation.y = orientation_q.y;\n        #~ marker.pose.orientation.z = orientation_q.z;\n        #~ marker.pose.orientation.w = orientation_q.w;\n        #~ marker.scale.x = self.turning_circle;\n        #~ marker.scale.y = self.turning_circle;\n        #~ marker.scale.z = 0.1;\n        #~ marker.color.a = 0.5;\n        #~ marker.color.r = 1.0;\n        #~ marker.color.g = 0.1;\n        #~ marker.color.b = 0.1;\n        #~ self.truning_circle_pub.publish(marker)\n        \n        self.last_yaw = yaw\n        self.time_old = self.time_new\n        self.init_time = 1\n        \n        if not self.shutdown_:\n            self.steering_pub.publish(steering)\n            self.speed_pub.publish(Int16(speed))\n\n    #ein Listener wir bei der init() erstellt, dieser called die shutdown Funktion, wenn das script beendet wird. \n    #Setzt den Speed des autos auf 0\n    def shutdown(self):\n        print(\"shutdown!\")\n        self.shutdown_ = True\n        self.speed_pub.publish(Int16(0))\n        rospy.sleep(1)\n\ndef main():\n    print(\"Running\\n\")\n    rospy.init_node('AljoschaTimRacer')\n    ForceController()  # constructor creates publishers / subscribers\n    rospy.spin()\n\nif __name__ == '__main__':\n    main()\n","sub_path":"src/assignment7_vector_field/src/control_2.py","file_name":"control_2.py","file_ext":"py","file_size_in_byte":8864,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"18847310","text":"import threading\nimport time\nimport random\n\nmovimientos = [-10000, 50000]\n\noperaciones = []\n\nclass cuentaBancaria():\n    def __init__(self, saldo_inicial):\n        self.saldo = saldo_inicial\n        self.lock = threading.Lock()\n\n    def movimiento(self,monto):\n        self.lock.acquire()\n        try:\n            time.sleep(random.randint(1,5)/10)\n            copia_local = self.saldo\n            copia_local += monto\n            time.sleep(random.randint(1,5)/10)\n            self.saldo = copia_local\n        finally:\n            self.lock.release()\n\ncuenta = cuentaBancaria(20000)\n\nprint(\"Saldo Inicial:\", cuenta.saldo)\n\nfor i in range(2):\n    operacion = threading.Thread(target=cuenta.movimiento, args = (movimientos[i],))\n    operacion.start()\n    operaciones.append(operacion)\n\nfor i in range(2):\n    operaciones[i].join()\n\nprint(\"Saldo Final:\",cuenta.saldo)\n","sub_path":"raceConditionBankLock.py","file_name":"raceConditionBankLock.py","file_ext":"py","file_size_in_byte":866,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"315226275","text":"import copy\nimport operator\nimport sys, math, random\nimport numpy as np\n\nfrom sklearn.base import ClassifierMixin\nfrom sklearn.tree import DecisionTreeClassifier\n\nclass LogitBoostClassifier(ClassifierMixin):\n    \"\"\"A LogitBoost classifier.\n\n    http://nbviewer.ipython.org/github/anfibil/cse40647.sp14/blob/master/30%20-%20Boosting.ipynb\n\n    A LogitBoost [1] classifier is a meta-estimator that begins by fitting a\n    classifier on the original dataset and then fits additional copies of the\n    classifier on the same dataset but where the weights of incorrectly\n    classified instances are adjusted such that subsequent classifiers focus\n    more on difficult cases.\n\n    Parameters\n    ----------\n    base_estimator : object, optional (default=DecisionTreeClassifier)\n        The base estimator from which the boosted ensemble is built.\n        Support for sample weighting is required, as well as proper `classes_`\n        and `n_classes_` attributes.\n\n    n_estimators : integer, optional (default=50)\n        The maximum number of estimators at which boosting is terminated.\n        In case of perfect fit, the learning procedure is stopped early.\n\n    learning_rate : float, optional (default=1.)\n        Learning rate shrinks the contribution of each classifier by\n        ``learning_rate``. There is a trade-off between ``learning_rate`` and\n        ``n_estimators``.\n\n    algorithm : {'SAMME', 'SAMME.R'}, optional (default='SAMME.R')\n        If 'SAMME.R' then use the SAMME.R real boosting algorithm.\n        ``base_estimator`` must support calculation of class probabilities.\n        If 'SAMME' then use the SAMME discrete boosting algorithm.\n        The SAMME.R algorithm typically converges faster than SAMME,\n        achieving a lower test error with fewer boosting iterations.\n\n    random_state : int, RandomState instance or None, optional (default=None)\n        If int, random_state is the seed used by the random number generator;\n        If RandomState instance, random_state is the random number generator;\n        If None, the random number generator is the RandomState instance used\n        by `np.random`.\n\n    Attributes\n    ----------\n    `estimators_` : list of classifiers\n        The collection of fitted sub-estimators.\n\n    `classes_` : array of shape = [n_classes]\n        The classes labels.\n\n    `n_classes_` : int\n        The number of classes.\n\n    `estimator_weights_` : array of floats\n        Weights for each estimator in the boosted ensemble.\n\n    `estimator_errors_` : array of floats\n        Classification error for each estimator in the boosted\n        ensemble.\n\n    `feature_importances_` : array of shape = [n_features]\n        The feature importances if supported by the ``base_estimator``.\n\n    References\n    ----------\n    .. [1] J. Friedman, T. Hastie, R. Tibshirani, \"Additive Logistic Regression:\n           A Statistical View of Boosting\", 2000.\n\n    \"\"\"\n    def __init__(self,\n                 base_estimator=DecisionTreeClassifier(max_depth=1),\n                 n_estimators=50,\n                 estimator_params=tuple(),\n                 learning_rate=1.,\n                 algorithm='SAMME.R'):\n        self.base_estimator = base_estimator\n        self.n_estimators = n_estimators\n        self.estimator_params = estimator_params\n        self.learning_rate = learning_rate\n        self.algorithm = algorithm\n\n    def _make_estimator(self, append=True):\n        \"\"\"Make and configure a copy of the `base_estimator_` array.\n\n        Warning: This method should be used to properly instantiate new\n        sub-estimators.\n        \"\"\"\n        estimator = copy.deepcopy(self.base_estimator)\n        estimator.set_params(**dict((p, getattr(self, p))\n                                    for p in self.estimator_params))\n\n        if append:\n            self.estimators_.append(estimator)\n\n        return estimator\n\n    def fit(self, X, y, sample_weight=None):\n        # Check parameters.\n        if self.learning_rate <= 0:\n            raise ValueError(\"learning_rate must be greater than zero.\")\n\n        if sample_weight is None:\n            # Initialize weights to 1 / n_samples.\n            sample_weight = np.empty(X.shape[0], dtype=np.float)\n            sample_weight[:] = 1. / X.shape[0]\n        else:\n            # Normalize existing weights.\n            sample_weight = sample_weight / sample_weight.sum(dtype=np.float64)\n\n        # Check that the sample weights sum is positive.\n        if sample_weight.sum() <= 0:\n            raise ValueError(\n                \"Attempting to fit with a non-positive \"\n                \"weighted number of samples.\")\n\n        # Clear any previous fit results.\n        self.estimators_ = []\n        self.estimator_weights_ = np.zeros(self.n_estimators, dtype=np.float)\n        self.estimator_errors_ = np.ones(self.n_estimators, dtype=np.float)\n\n        for iboost in range(self.n_estimators):\n            #print 'Iteration [%s]' % (iboost)\n\n            # Fit the estimator.\n            estimator = self._make_estimator()\n            estimator.fit(X, y, sample_weight=sample_weight)\n\n            if iboost == 0:\n                self.classes_ = getattr(estimator, 'classes_', None)\n                self.n_classes_ = len(self.classes_)\n\n            # Generate estimator predictions.\n            y_pred = estimator.predict(X)\n\n            # Instances incorrectly classified.\n            incorrect = y_pred != y\n\n            # Error fraction.\n            estimator_error = np.mean(\n                np.average(incorrect, weights=sample_weight, axis=0))\n\n            # Boost weight using multi-class AdaBoost SAMME alg.\n            estimator_weight = self.learning_rate * (\n                np.log((1. - estimator_error) / estimator_error) +\n                np.log(self.n_classes_ - 1.))\n\n            # Only boost the weights if there is another iteration of fitting.\n            if not iboost == self.n_estimators - 1:\n                # Only boost positive weights (logistic loss).\n                sample_weight *= np.log(1 + np.exp(estimator_weight * incorrect *\n                                        ((sample_weight > 0) |\n                                         (estimator_weight < 0))))\n\n            self.estimator_weights_[iboost] = estimator_weight\n            self.estimator_errors_[iboost] = estimator_error\n\n    def _check_fitted(self):\n        if not hasattr(self, \"estimators_\"):\n            raise ValueError(\"Call 'fit' first.\")\n\n    def predict(self, X):\n        X = numpy.array(X)\n        N, d = X.shape\n        pred = numpy.zeros(N)\n        for estimator, w in zip(self.estimators_, self.estimator_weights_):\n            pred += estimator.predict(X) * w\n        pred /= self.estimator_weights_.sum()\n\n        return pred\n\n    def decision_function(self, X):\n        \"\"\"Compute the decision function of ``X``.\n\n        Parameters\n        ----------\n        X : array-like of shape = [n_samples, n_features]\n            The input samples.\n\n        Returns\n        -------\n        score : array, shape = [n_samples, k]\n            The decision function of the input samples. The order of\n            outputs is the same of that of the `classes_` attribute.\n            Binary classification is a special cases with ``k == 1``,\n            otherwise ``k==n_classes``. For binary classification,\n            values closer to -1 or 1 mean more like the first or second\n            class in ``classes_``, respectively.\n        \"\"\"\n        self._check_fitted()\n        X = np.asarray(X)\n\n        n_classes = self.n_classes_\n        classes = self.classes_[:, np.newaxis]\n        pred = None\n\n        if self.algorithm == 'SAMME.R':\n            # The weights are all 1. for SAMME.R\n            pred = sum(_samme_proba(estimator, n_classes, X)\n                       for estimator in self.estimators_)\n        else:   # self.algorithm == \"SAMME\"\n            pred = sum((estimator.predict(X) == classes).T * w\n                       for estimator, w in zip(self.estimators_,\n                                               self.estimator_weights_))\n\n        pred /= self.estimator_weights_.sum()\n        if n_classes == 2:\n            pred[:, 0] *= -1\n            return pred.sum(axis=1)\n        return pred\n\n    def predict(self, X):\n        \"\"\"Predict classes for X.\n\n        The predicted class of an input sample is computed as the weighted mean\n        prediction of the classifiers in the ensemble.\n\n        Parameters\n        ----------\n        X : array-like of shape = [n_samples, n_features]\n            The input samples.\n\n        Returns\n        -------\n        y : array of shape = [n_samples]\n            The predicted classes.\n        \"\"\"\n        pred = self.decision_function(X)\n\n        if self.n_classes_ == 2:\n            return self.classes_.take(pred > 0, axis=0)\n\n        return self.classes_.take(np.argmax(pred, axis=1), axis=0)\n\n    def predict_proba(self, X):\n        \"\"\"Predict class probabilities for X.\n\n        The predicted class probabilities of an input sample is computed as\n        the weighted mean predicted class probabilities of the classifiers\n        in the ensemble.\n\n        Parameters\n        ----------\n        X : array-like of shape = [n_samples, n_features]\n            The input samples.\n\n        Returns\n        -------\n        p : array of shape = [n_samples]\n            The class probabilities of the input samples. The order of\n            outputs is the same of that of the `classes_` attribute.\n        \"\"\"\n        X = np.asarray(X)\n        n_classes = self.n_classes_\n\n        if self.algorithm == 'SAMME.R':\n            # The weights are all 1. for SAMME.R\n            proba = sum(_samme_proba(estimator, n_classes, X)\n                        for estimator in self.estimators_)\n        else:   # self.algorithm == \"SAMME\"\n            proba = sum(estimator.predict_proba(X) * w\n                        for estimator, w in zip(self.estimators_,\n                                                self.estimator_weights_))\n\n        proba /= self.estimator_weights_.sum()\n        proba = np.log(1 + np.exp((1. / (n_classes - 1)) * proba))\n        normalizer = proba.sum(axis=1)[:, np.newaxis]\n        normalizer[normalizer == 0.0] = 1.0\n        proba /= normalizer\n\n        return proba\n\ndef _samme_proba(estimator, n_classes, X):\n    \"\"\"Calculate algorithm 4, step 2, equation c) of Zhu et al [1].\n\n    References\n    ----------\n    .. [1] J. Zhu, H. Zou, S. Rosset, T. Hastie, \"Multi-class AdaBoost\", 2009.\n\n    \"\"\"\n    proba = estimator.predict_proba(X)\n\n    # Displace zero probabilities so the log is defined.\n    # Also fix negative elements which may occur with\n    # negative sample weights.\n    proba[proba <= 0] = 1e-5\n    log_proba = np.log(proba)\n\n    return (n_classes - 1) * (log_proba - (1. / n_classes)\n                           * log_proba.sum(axis=1)[:, np.newaxis])\n","sub_path":"logitboost_sample.py","file_name":"logitboost_sample.py","file_ext":"py","file_size_in_byte":10833,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"26677912","text":"# SYSTEM IMPORTS\nimport os\nimport sys\n\ncurrentDir = os.path.dirname(os.path.realpath(__file__))\nbuildScriptsDir = os.path.join(currentDir, \"buildscripts\")\nsys.path.extend([currentDir, buildScriptsDir])\n\n# PYTHON PROJECT IMPORTS\nfrom buildtypes import buildFactory as buildFactory\nimport buildutils as Utils\n\n\ndef parseCommandLine(commandLine):\n    parsedCommandedValues = {}\n    parsedCommandedMethods = []\n    for command in commandLine:\n        if '-' in command:\n            commandVariable = command.replace('-', '', 1).split(\"=\")\n            if len(commandVariable) == 2:\n                # parse list if necessary using:\n                # listArgs = commandVariable[1].split(\",\")\n                # if len(listArgs) > 1:\n                    # we have a list\n                # else\n                parsedCommandedValues[commandVariable[0]] = commandVariable[1]\n            elif len(commandVariable) == 1:\n                parsedCommandedValues[commandVariable[0]] = True\n            else:\n                Utils.failExecution(\"Unknown command: [%s]\" % command)\n        else:\n            parsedCommandedMethods.append(command)\n    return (parsedCommandedMethods, parsedCommandedValues)\n\n\nif __name__ == \"__main__\":\n    commandMethods, commandValues = parseCommandLine(sys.argv[1:])\n    if \"project\" not in commandValues:\n        print(\"Usage:\")\n        print(\"\\t-project=server|client|all\")\n    else:\n        # currentDir = os.path.dirname(os.path.realpath(__file__))\n        if commandValues[\"project\"].lower() == \"all\":\n            for project in [\"client\", \"server\"]:\n                print(\"building project: %s\" % project)\n                commandValues[\"project\"] = project\n                buildObj = buildFactory()\n                buildObj.run(commandMethods, commandValues)\n        else:\n            buildObj = buildFactory()\n            buildObj.run(commandMethods, commandValues)\n","sub_path":"build.py","file_name":"build.py","file_ext":"py","file_size_in_byte":1888,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"292086257","text":"\n\"\"\"\nThis is the evaluation module of the Baseline class initialized in implementation.py.\n\n--------------------------------------------------------------------------------------\n--------------------------------------------------------------------------------------\n\nevaluation()\n\n# input 1: incorrect_file: list with with original lines to be corrected\n\nExample:\n[\"wtf sinds wanneer. En hoeveel hebt ge hiervoor moeten follow- unfollowen :p\",\n\"I haz plafon in da kitchen en da living #verfraaiBoek\"]\n\n# input 2: correct_file: list with corresponding gold standard corrected lines\n\nExample:\n[\"what the fuck sinds wanneer . En hoeveel hebt ge hiervoor moeten follow - unfollowen :p\",\n\"I have plafond in de kitchen en de living #verfraaiBoek\"]\n\n# input 3: detection_coordinates: list of tuples of two coordinates responding to the line index and the position within\nline index of each detected misspelling, as provided by the detection module\n\n# input 4: correction_list: list of selected corrections, as provided by the ranking module\n\n# output: 2 evaluation dictionaries:\n\n1) error detection: precision, recall, f-score\n2) error correction: accuracy (percentage correct forms returned for correctly detected misspellings)\n\n--------------------------------------------------------------------------------------\n--------------------------------------------------------------------------------------\n\nskip_detection()\n\n# used to skip the detection process, instantly yields the correct misspellings and coordinates\n\n# input 1: incorrect_file: list with with original lines to be corrected\n\n# input 2: correct_file: list with corresponding gold standard corrected lines\n\n# output 1: detection_list: list of misspelled words\n\n# output 2: detection_coordinates: list of tuples of two coordinates responding to the line index and the position within\nline index of each detected misspelling\n\n--------------------------------------------------------------------------------------\n--------------------------------------------------------------------------------------\n\ncorrected_list()\n\n# input 1: incorrect_file: list with with original lines to be corrected\n\n# input 2: correct_file: list with corresponding gold standard corrected lines\n\n# output: corrected_list: list of correct replacements\n\n--------------------------------------------------------------------------------------\n--------------------------------------------------------------------------------------\n\ncandidates_recall()\n\n# used to check how many times the correct word is among the candidates\n\n# input 1: corrected_list: list of correct replacements\n\n# input 2: candidates_list: list of lists of replacement candidates\n\n# output: recall_dict: dictionary with statistics\n\"\"\"\n\nfrom scipy.stats import hmean\n\nif __name__ == \"__main__\":\n    print(__doc__)\n\n\ndef evaluation(incorrect_file, correct_file, detection_coordinates, correction_list):\n\n    original_list = []\n    corrected_list = []\n    coordinates_canon = []\n    counter = 0\n\n    for line1, line2 in zip(incorrect_file, correct_file):\n        for k, token in enumerate(line1.split()):\n                original_list.append(token)\n                corrected_list.append(line2.split()[k])\n                coordinates_canon.append((counter, k))\n\n        counter += 1\n\n    det_true_positives = [i for i in detection_coordinates if i in coordinates_canon]\n\n    # error detection\n    detection_dict = {}\n    prec = len(det_true_positives) / len(detection_coordinates)\n    recall = len(det_true_positives) / len(coordinates_canon)\n    detection_dict[\"Task\"] = \"Detection\"\n    detection_dict[\"Precision\"] = prec\n    detection_dict[\"Recall\"] = recall\n    detection_dict[\"F-score\"] = hmean([prec, recall])\n\n    # error correction\n    correction_dict = {}\n    corr_true_positives = []\n    for element in det_true_positives:\n        word1 = correction_list[detection_coordinates.index(element)]\n        word2 = corrected_list[coordinates_canon.index(element)]\n        if word1.lower() == word2.lower(): # disregard case for now\n            corr_true_positives.append(word1)\n\n    print(\"Corr true positives\")\n    print(corr_true_positives)\n    detection_dict[\"Task\"] = \"Correction\"\n    correction_dict[\"Accuracy\"] = len(corr_true_positives) / len(det_true_positives)\n\n    return detection_dict, correction_dict\n\ndef ranking_accuracy(correction_list, corrected_list):\n\n    # error detection\n    nb_correct = 0\n    for correction, corrected in zip(correction_list, corrected_list):\n        if correction == corrected:\n            nb_correct += 1\n\n    return nb_correct / len(correction_list)\n\n\ndef skip_detection(incorrect_file, correct_file):\n\n    detection_list = []\n    detection_coordinates = []\n    corrected_list = []\n    counter = 0\n\n    for line1, line2 in zip(incorrect_file, correct_file):\n\n        for k, token in enumerate(line1.split()):\n            if token != line2.split()[k]:\n                detection_list.append(token)\n                corrected_list.append(line2.split()[k])\n                detection_coordinates.append((counter, k))\n\n        counter += 1\n\n    return detection_list, detection_coordinates, corrected_list\n\n\ndef corrected_list(incorrect_file, correct_file):\n\n    corrected_list = []\n\n    for line1, line2 in zip(incorrect_file, correct_file):\n        if len(line1.split()) == len(line2.split()): # no contractions\n            for k, token in enumerate(line1.split()):\n                if token != line2.split()[k]:\n                    corrected_list.append(line2.split()[k])\n        else: # contractions, no solution yet, disregard these lines for now :(\n            pass\n\n    return corrected_list\n\ndef candidates_recall(detection_list, corrected_list, candidates_list, vector_vocabulary):\n\n    absolute_presence = 0\n\n    for correct_spelling, candidates in zip(corrected_list, candidates_list):\n\n        if correct_spelling.lower() in candidates:\n            absolute_presence += 1\n\n    recall_dict = {}\n    recall_dict[\"# present\"] = absolute_presence\n    recall_dict[\"# misspellings\"] = len(corrected_list)\n    recall_dict[\"# candidates\"] = len(candidates_list[0])\n    recall_dict[\"Recall\"] = absolute_presence / len(corrected_list) * 100\n\n    invoc_items = []\n    count = 0\n    for item in set(corrected_list):\n        if item in vector_vocabulary:\n            count += 1\n            invoc_items.append(item)\n\n    det_invoc_absolute_presence = 0\n    det_outvoc_absolute_presence = 0\n    det_invoc_count = 0\n    det_outvoc_count = 0\n\n    for correct_spelling, detection, candidates in zip(corrected_list, detection_list, candidates_list):\n\n        if correct_spelling in invoc_items:\n\n            if detection in invoc_items:\n\n                det_invoc_count += 1\n                if correct_spelling.lower() in candidates:\n                    det_invoc_absolute_presence += 1\n\n            elif detection not in invoc_items:\n\n                det_outvoc_count += 1\n                if correct_spelling.lower() in candidates:\n                    det_outvoc_absolute_presence += 1\n\n    recall_dict[\"# test items in vector voc\"] = count\n    recall_dict[\"percentage items in vector voc\"] = count / len(set(corrected_list))\n    recall_dict[\"Recall invoc\"] = (det_invoc_absolute_presence + det_outvoc_absolute_presence) / (det_invoc_count + det_outvoc_count) * 100\n    recall_dict[\"Recall correction invoc - misspelling invoc\"] = det_invoc_absolute_presence / det_invoc_count * 100\n    recall_dict[\"Recall correction invoc - misspelling outvoc\"] = det_outvoc_absolute_presence / det_outvoc_count * 100\n\n    return recall_dict\n\n\n\n\n\n\n\n\n","sub_path":"evaluation.py","file_name":"evaluation.py","file_ext":"py","file_size_in_byte":7564,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"534397407","text":"\"\"\"\nSupport for interacting with Smappee Comport Plugs.\n\nFor more details about this component, please refer to the documentation at\nhttps://home-assistant.io/components/switch.smappee/\n\"\"\"\nimport logging\n\n#from homeassistant.components.smappee import DATA_SMAPPEE, DOMAIN\nfrom custom_components.smappee import DATA_SMAPPEE, DOMAIN\nfrom homeassistant.components.switch import (SwitchDevice, PLATFORM_SCHEMA)\n\n_LOGGER = logging.getLogger(__name__)\n\nDEFAULT_NAME = 'Comfort Plug'\nICON = 'mdi:power-plug'\n\n\ndef setup_platform(hass, config, add_devices, discovery_info=None):\n    \"\"\"Set up the Smappee Comfort Plugs.\"\"\"\n    smappee = hass.data[DATA_SMAPPEE]\n\n    dev = []\n    for location_id, location_name in smappee.locations.items():\n        for items in smappee.info[location_id].get('actuators'):\n            if items.get('name') is not '':\n                dev.append(SmappeeSwitch(smappee, location_id, items.get('id')))\n\n    add_devices(dev)\n\n\nclass SmappeeSwitch(SwitchDevice):\n    \"\"\"Representation of a Smappee Comport Plug.\"\"\"\n\n    def __init__(self, smappee, location_id, switch_id):\n        \"\"\"Initialize a new Smappee Comfort Plug.\"\"\"\n        self._name = DEFAULT_NAME\n        self._state = False\n        self._smappee = smappee\n        self._location_id = location_id\n        self._switch_id = switch_id\n        self.data = None\n\n        self.update()\n\n    @property\n    def name(self):\n        \"\"\"Return the name of the switch.\"\"\"\n        return self._name\n\n    @property\n    def is_on(self):\n        \"\"\"Return true if switch is on.\"\"\"\n        return self._state\n\n    @property\n    def icon(self):\n        \"\"\"Icon to use in the frontend.\"\"\"\n        return ICON\n\n    def turn_on(self, **kwargs):\n        \"\"\"Turn on Comport Plug.\"\"\"\n        self._smappee.actuator_on(self._location_id, self._switch_id)\n        self._state = True\n\n    def turn_off(self, **kwargs):\n        \"\"\"Turn off Comport Plug.\"\"\"\n        self._smappee.actuator_off(self._location_id, self._switch_id)\n        self._state = False\n\n    def update(self):\n        \"\"\"Get the latest data from the device and update the data.\"\"\"\n        info = self._smappee.info[self._location_id].get('actuators')\n\n        for item in info:\n            if item.get('id') == self._switch_id:\n                self.data = item\n                self._name = item.get('name')\n","sub_path":"custom_components/switch/smappee.py","file_name":"smappee.py","file_ext":"py","file_size_in_byte":2331,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"181013209","text":"# Run this file in your terminal like this: python -m src.run_query --output_file_name \"file_name.csv\" --query_string\n# \"[[Query::here]]\"\n\nimport argparse\nfrom src.query import *\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser(description='Run an Ask query against the FNA Beta API')\n    parser.add_argument('--output_file_name', type=str, help='output_file_name')\n    parser.add_argument('--query_string', type=str, help='query_string')\n    args = parser.parse_args()\n    ask_query(args.query_string, args.output_file_name)\n","sub_path":"python/src/run_query.py","file_name":"run_query.py","file_ext":"py","file_size_in_byte":542,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"207872238","text":"import datetime\nimport os\nimport time\nimport threading\nimport queue\nimport multiprocessing\n\nfrom gym.wrappers.monitoring.video_recorder import VideoRecorder\nimport torch\nimport gpytorch\nimport numpy as np\nimport matplotlib\nimport matplotlib.pyplot as plt\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.neighbors import KNeighborsRegressor\n\nfrom control_objects.gp_models import ExactGPModelMonoTask\n\nmatplotlib.rc('font', size='6')\nnp.warnings.filterwarnings('ignore', category=np.VisibleDeprecationWarning)\nALPHA_CONFIDENCE_BOUNDS = 0.3\nFONTSIZE = 6\n\n# Values used for graph visualizations only\nMEAN_PRED_COST_INIT = 1\nSTD_MEAN_PRED_COST_INIT = 10\n\n\ndef create_models(train_inputs, train_targets, params, constraints_gp, num_models=None, num_inputs=None):\n\t\"\"\"\n\tDefine gaussian process models used for predicting state transition,\n\tusing constraints and init values for (outputscale, noise, lengthscale).\n\n\tArgs:\n\t\ttrain_inputs (torch.Tensor or None): Input values in the memory of the gps\n\t\ttrain_targets (torch.Tensor or None): target values in the memory of the gps.\n\t\t\t\t\t\t\t\t\t\t\t\tRepresent the change in state values\n\t\tparams (dict or list of dict): Value of the hyper-parameters of the gaussian processes.\n\t\t\t\t\t\t\t\t\t\tDict type is used to create the models with init values from the json file.\n\t\t\t\t\t\t\t\t\t\tList of dict type is used to create the models\n\t\t\t\t\t\t\t\t\t\t\t\t\twith exported parameters, such as in the parallel training process.\n\t\tconstraints_gp (dict): See the ReadMe about parameters for information about keys\n\t\tnum_models (int or None): Must be provided when train_inputs or train_targets are None.\n\t\t\t\t\t\t\t\t\tThe number of models should be equal to the dimension of state,\n\t\t\t\t\t\t\t\t\tso that the transition for each state can be predicted with a different gp.\n\t\t\t\t\t\t\t\t\tDefault=None\n\t\tnum_inputs (int or None): Must be provided when train_inputs or train_targets are None.\n\t\t\t\t\t\t\t\t\tThe number of inputs should be equal to the sum of the dimension of state\n\t\t\t\t\t\t\t\t\tand dimension of action. Default=None\n\t\tinclude_time (bool): If True, gp will have one additional input corresponding to the time of the observation.\n\t\t\t\t\t\t\t\tThis is usefull if the env change with time,\n\t\t\t\t\t\t\t\tas more recent points will be trusted more than past points\n\t\t\t\t\t\t\t\t(time closer to the point to make inference at).\n\t\t\t\t\t\t\t\tIt is to be specified only if\n\n\tReturns:\n\t\tmodels (list of gpytorch.models.ExactGP): models containing the parameters, memory,\n\t\t\t\t\t\t\t\t\t\t\t\t\tconstraints of the gps and functions for exact predictions\n\t\"\"\"\n\tif train_inputs is not None and train_targets is not None:\n\t\tnum_models = len(train_targets[0])\n\t\tmodels = [ExactGPModelMonoTask(train_inputs, train_targets[:, idx_model], len(train_inputs[0]))\n\t\t\tfor idx_model in range(num_models)]\n\telse:\n\t\tif num_models is None or num_inputs is None:\n\t\t\traise(ValueError('If train_inputs or train_targets are None, num_models and num_inputs must be defined'))\n\t\telse:\n\t\t\tmodels = [ExactGPModelMonoTask(None, None, num_inputs) for _ in range(num_models)]\n\n\tfor idx_model in range(num_models):\n\t\tif constraints_gp is not None:\n\t\t\tif \"min_std_noise\" in constraints_gp.keys():\n\t\t\t\tif type(constraints_gp['min_std_noise']) != float and \\\n\t\t\t\t\t\ttype(constraints_gp['min_std_noise']) != int:\n\t\t\t\t\tmin_var_noise = np.power(constraints_gp['min_std_noise'][idx_model], 2)\n\t\t\t\telse:\n\t\t\t\t\tmin_var_noise = np.power(constraints_gp['min_std_noise'], 2)\n\t\t\t\tif type(constraints_gp['max_std_noise']) != float and \\\n\t\t\t\t\t\ttype(constraints_gp['max_std_noise']) != int:\n\t\t\t\t\tmax_var_noise = np.power(constraints_gp['max_std_noise'][idx_model], 2)\n\t\t\t\telse:\n\t\t\t\t\tmax_var_noise = np.power(constraints_gp['max_std_noise'], 2)\n\t\t\t\tmodels[idx_model].likelihood.noise_covar.register_constraint(\"raw_noise\",\n\t\t\t\t\tgpytorch.constraints.Interval(lower_bound=min_var_noise, upper_bound=max_var_noise))\n\n\t\t\tif \"min_outputscale\" in constraints_gp.keys():\n\t\t\t\tif type(constraints_gp['min_outputscale']) != float and \\\n\t\t\t\t\t\ttype(constraints_gp['min_outputscale']) != int:\n\t\t\t\t\tmin_outputscale = constraints_gp['min_outputscale'][idx_model]\n\t\t\t\telse:\n\t\t\t\t\tmin_outputscale = constraints_gp['min_outputscale']\n\n\t\t\t\tif type(constraints_gp['max_outputscale']) != float and \\\n\t\t\t\t\t\ttype(constraints_gp['max_outputscale']) != int:\n\t\t\t\t\tmax_outputscale = constraints_gp['max_outputscale'][idx_model]\n\t\t\t\telse:\n\t\t\t\t\tmax_outputscale = constraints_gp['max_outputscale']\n\t\t\t\tmodels[idx_model].covar_module.register_constraint(\"raw_outputscale\",\n\t\t\t\t\tgpytorch.constraints.Interval(lower_bound=min_outputscale, upper_bound=max_outputscale))\n\n\t\t\tif \"min_lengthscale\" in constraints_gp.keys():\n\t\t\t\tif type(constraints_gp['min_lengthscale']) == float or type(constraints_gp['min_lengthscale']) == int:\n\t\t\t\t\tmin_lengthscale = constraints_gp['min_lengthscale']\n\t\t\t\telse:\n\t\t\t\t\tmin_lengthscale = constraints_gp['min_lengthscale'][idx_model]\n\t\t\t\tif type(constraints_gp['min_lengthscale']) == float or type(constraints_gp['min_lengthscale']) == int:\n\t\t\t\t\tmax_lengthscale = constraints_gp['max_lengthscale']\n\t\t\t\telse:\n\t\t\t\t\tmax_lengthscale = constraints_gp['max_lengthscale'][idx_model]\n\t\t\t\tmodels[idx_model].covar_module.base_kernel.register_constraint(\"raw_lengthscale\",\n\t\t\t\t\tgpytorch.constraints.Interval(lower_bound=min_lengthscale, upper_bound=max_lengthscale))\n\t\t# load parameters\n\t\t# dict type is used when initializing the models from the json config file\n\t\t# list type is used when initializing the models in the parallel training process\n\t\t# using the exported parameters\n\t\tif type(params) == dict:\n\t\t\thypers = {'base_kernel.lengthscale': params['base_kernel.lengthscale'][idx_model],\n\t\t\t\t'outputscale': params['outputscale'][idx_model]}\n\t\t\thypers_likelihood = {'noise_covar.noise': params['noise_covar.noise'][idx_model]}\n\t\t\tmodels[idx_model].likelihood.initialize(**hypers_likelihood)\n\t\t\tmodels[idx_model].covar_module.initialize(**hypers)\n\t\telif type(params) == list:\n\t\t\tmodels[idx_model].load_state_dict(params[idx_model])\n\treturn models\n\n\ndef init_control(ctrl_obj, env, live_plot_obj, rec, params_general, random_actions_init,\n\t\t\t\t\tcosts_tests, idx_test, num_repeat_actions=1):\n\t\"\"\"\n\tInit the gym env and memory. Define the lists containing the points for visualisations.\n\tControl the env with random actions for a number of steps.\n\tArgs:\n\t\tctrl_obj (control_objects.gp_mpc_controller.GpMpcController):\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tObject containing the control functions.\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUsed here to compute cost and add_memory.\n\t\tenv (gym env): environment used to get the next observation given current observation and action\n\t\tlive_plot_obj (object): object used for visualisation in real time of the control in a 2d graph.\n\t\t\t\t\t\t\t\tMust contain a function called update, that adds new points on the graph\n\t\trec (object): object used to visualise the gym env in real-time.\n\t\t\t\t\t\tMust heve a function called capture_frame that updates the visualisation\n\t\tparams_general (dict): see parameters.md for more information\n\t\trandom_actions_init (int): number of random actions to apply for the initialization.\n\t\tcosts_tests (numpy.array): contains the costs of all the tests realized, which is used to compute\n\t\t\t\t\t\t\t\t\tthe mean cost over a large number of control. Parameter not relevant if num_tests=1.\n\t\t\t\t\t\t\t\t\tDim=(num_runs, num_timesteps)\n\t\tidx_test (int): index of the test realized\n\t\tnum_repeat_actions (int): number of consecutive actions that are constant.\n\t\t\t\t\t\t\t\t\tThe GPs only store points at which the control change.\n\t\"\"\"\n\t# Lists used for plotting values\n\tobs_lst = []\n\tactions_lst = []\n\trewards_lst = []\n\tenv.reset()\n\tobs, reward, done, info = env.step(env.action_space.sample())\n\tobs_prev_ctrl = None\n\taction = None\n\tcost = None\n\tfor idx_action in range(random_actions_init):\n\t\tif idx_action % num_repeat_actions == 0:\n\t\t\tif obs_prev_ctrl is not None and action is not None and cost is not None:\n\t\t\t\tctrl_obj.add_memory(obs=obs_prev_ctrl, action=action, obs_new=obs,\n\t\t\t\t\t\t\t\t\treward=-cost, check_storage=False)\n\t\t\tobs_prev_ctrl = obs\n\t\t\taction = env.action_space.sample()\n\t\tobs_new, reward, done, info = env.step(action)\n\t\tif params_general['render_env']:\n\t\t\ttry: env.render()\n\t\t\texcept: pass\n\t\tif params_general['save_render_env']:\n\t\t\ttry: rec.capture_frame()\n\t\t\texcept: pass\n\n\t\tcost, cost_var = ctrl_obj.compute_cost_unnormalized(obs_new, action)\n\t\tcosts_tests[idx_test, idx_action] = cost\n\t\tobs_lst.append(obs)\n\t\tactions_lst.append(action)\n\t\trewards_lst.append(-cost)\n\n\t\tif params_general['render_live_plots_2d']:\n\t\t\tlive_plot_obj.update(obs=obs, action=action, cost=cost, info_dict=None)\n\n\t\tobs = obs_new\n\t\t# Necessary to store the last action in case that the parameter limit_action_change is set to True\n\t\tctrl_obj.action_previous_iter = torch.Tensor(action)\n\n\treturn ctrl_obj, env, live_plot_obj, rec, obs, action, cost, obs_prev_ctrl, \\\n\t\t\tcosts_tests, obs_lst, actions_lst, rewards_lst\n\n\ndef init_visu_and_folders(env, num_steps, env_str, params_general, params_controller_dict):\n\t\"\"\"\n\tCreate and return the objects used for visualisation in real-time.\n\tAlso create the folder where to save the figures.\n\tArgs:\n\t\tenv (gym env): gym environment\n\t\tnum_steps (int): number of steps during which the action is maintained constant\n\t\tenv_str (str): string that describes the env name\n\t\tparams_general (dict): general parameters (see parameters.md for more info)\n\t\tparams_controller_dict (dict): controller parameters (see parameters.md for more info)\n\n\tReturns:\n\t\tlive_plot_obj (object): object used to visualize the control and observation evolution in a 2d graph\n\n\t\trec (object): object used to visualize the gym env\n\n\t\tfolder_save (str): name of the folder where the figures will be saved\n\t\"\"\"\n\tif params_general['render_live_plots_2d']:\n\t\tif params_general['run_live_graph_parallel_process']:\n\t\t\tlive_plot_obj = LivePlotParallel(num_steps,\n\t\t\t\tenv.observation_space, env.action_space,\n\t\t\t\tstep_pred=params_controller_dict['controller']['num_repeat_actions'],\n\t\t\t\tuse_constraints=bool(params_controller_dict['constraints_states']['use_constraints']),\n\t\t\t\tstate_min=params_controller_dict['constraints_states']['state_min'],\n\t\t\t\tstate_max=params_controller_dict['constraints_states']['state_max'])\n\t\telse:\n\t\t\tlive_plot_obj = LivePlotSequential(num_steps,\n\t\t\t\tenv.observation_space, env.action_space,\n\t\t\t\tstep_pred=params_controller_dict['controller']['num_repeat_actions'],\n\t\t\t\tuse_constraints=bool(params_controller_dict['constraints_states']['use_constraints']),\n\t\t\t\tstate_min=params_controller_dict['constraints_states']['state_min'],\n\t\t\t\tstate_max=params_controller_dict['constraints_states']['state_max'])\n\telse:\n\t\tlive_plot_obj = None\n\n\tdatetime_now = datetime.datetime.now()\n\tfolder_save = os.path.join('folder_save', env_str, 'y' + str(datetime_now.year) +\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t'_mon' + str(datetime_now.month) +\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t'_d' + str(datetime_now.day) +\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t'_h' + str(datetime_now.hour) +\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t'_min' + str(datetime_now.minute) +\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t'_s' + str(datetime_now.second))\n\tif not os.path.exists(folder_save):\n\t\tos.makedirs(folder_save)\n\n\tif params_general['save_render_env']:\n\t\ttry:\n\t\t\trec = VideoRecorder(env, path=os.path.join(folder_save, 'anim' + env_str + '.mp4'))\n\t\texcept:\n\t\t\tpass\n\telse:\n\t\trec = None\n\treturn live_plot_obj, rec, folder_save\n\n\ndef close_run(ctrl_obj, env, obs_lst, actions_lst, rewards_lst, random_actions_init,\n\t\t\t\t\tlive_plot_obj=None, rec=None, save_plots_3d=False, save_plots_2d=False):\n\t\"\"\"\n\tClose all visualisations and parallel processes that are still running.\n\tSave all visualisations one last time if save args set to True\n\tArgs:\n\t\tctrl_obj:\n\t\tenv (gym env): gym environment\n\t\tobs_lst (list of numpy array): Contains all the past observations\n\t\tactions_lst (list of numpy array): Contains all the past actions\n\t\trewards_lst(list): Contains all the past rewards (or -cost)\n\t\trandom_actions_init (int): number of initial actions where the controller was not used (random actions)\n\t\tlive_plot_obj (object or None): object used for visualisation in real time of the control in a 2d graph.\n\t\t\t\t\t\t\t\tIf None, not need to close it. Default=None\n\t\trec (object or None): object used to visualise the gym env in real-time.\n\t\t\t\t\t\t\t\tIf None, no need to close it. default=None\n\t\tsave_plots_3d (bool): If set to True, will save the 3d plots. Default=False\n\t\tsave_plots_2d (bool): If set to True, will save the 2d plots. Default=False\n\t\"\"\"\n\n\tenv.__exit__()\n\tif rec is not None:\n\t\trec.close()\n\tif live_plot_obj is not None:\n\t\tlive_plot_obj.graph_p.terminate()\n\t# save plots at the end\n\tctrl_obj.check_and_close_processes()\n\tif save_plots_3d:\n\t\tctrl_obj.save_plots_model_3d()\n\t\t# wait for the process to be finished\n\t\tctrl_obj.p_save_plots_model_3d.join()\n\t\tctrl_obj.p_save_plots_model_3d.close()\n\tif save_plots_2d:\n\t\tctrl_obj.save_plots_2d(states=obs_lst, actions=actions_lst, rewards=rewards_lst,\n\t\t\t\t\t\t\t\trandom_actions_init=random_actions_init)\n\t\t# wait for the process to be finished\n\t\tctrl_obj.p_save_plots_2d.join()\n\t\tctrl_obj.p_save_plots_2d.close()\n\tplt.close()\n\n\ndef plot_costs(costs, env_str):\n\t\"\"\"\n\tPlot the mean and standard deviation of the costs of all the runs.\n\tArgs:\n\t\tcosts (numpy.array): contains all the costs (or -rewards) of all the runs. Dim=(num_runs, num_timesteps)\n\t\tenv_str (str): name of the env, used to save the plot in the right folder\n\t\"\"\"\n\tmean_cost_runs = costs.mean(0)\n\tstd_cost_runs = costs.std(0)\n\tplt.figure()\n\tindexes = (np.arange(costs.shape[1]))\n\tplt.plot(indexes, mean_cost_runs, label='mean cost run')\n\tplt.fill_between(indexes, mean_cost_runs - 2 * std_cost_runs, mean_cost_runs + 2 * std_cost_runs,\n\t\tlabel='cost runs 2 std', alpha=0.6)\n\tplt.grid()\n\tplt.title('Cost of the different runs for env ' + env_str)\n\tplt.ylabel('Cost')\n\tplt.xlabel('Number of environment steps')\n\tplt.savefig(os.path.join('../folder_save', env_str, 'Cost_runs_' + env_str))\n\tplt.show()\n\n\ndef draw_plots_2d(states, actions, costs, pred_info, num_repeat_actions=1, random_actions_init=0,\n\t\tstate_min_constraint=None, state_max_constraint=None, iter_ahead_show=3, fig=None, axes=None,\n\t\tupdate_x_limit=True, mul_std_bounds=3):\n\tidxs_x = np.arange(0, len(states))\n\tpred_iters = np.array(pred_info['iteration'])\n\tidxs_ctrl = pred_iters + random_actions_init\n\ttry:\n\t\tstates_pred_show = np.zeros((len(idxs_ctrl) - iter_ahead_show, states.shape[1]))\n\t\tstates_pred_show[:] = [pred[iter_ahead_show - 1].numpy() for pred in pred_info['predicted states']][:-iter_ahead_show]\n\n\t\tstates_pred_std_show = np.ones((len(idxs_ctrl) - iter_ahead_show, states.shape[1]))\n\t\tstates_pred_std_show[:] = [pred[iter_ahead_show - 1].numpy() for pred in pred_info['predicted states std']][:-iter_ahead_show]\n\texcept ValueError:\n\t\tstates_pred_show = None\n\t\tstates_pred_std_show = None\n\n\tmean_cost_pred = np.array([element for element in pred_info['mean predicted cost']])\n\tmean_cost_std_pred = np.array([element for element in pred_info['mean predicted cost std']])\n\tnp.nan_to_num(states_pred_show, copy=False, nan=-1, posinf=-1, neginf=-1)\n\tnp.nan_to_num(states_pred_std_show, copy=False, nan=np.max(states_pred_std_show), posinf=99, neginf=0)\n\tnp.nan_to_num(mean_cost_pred, copy=False, nan=-1, posinf=-1, neginf=-1)\n\tnp.nan_to_num(states_pred_std_show, copy=False, nan=np.max(mean_cost_std_pred), posinf=99, neginf=0)\n\tfor state_idx in range(len(states[0])):\n\t\taxes[0].plot(idxs_x, np.array(states)[:, state_idx], label='state' + str(state_idx),\n\t\t\tcolor=plt.get_cmap('tab20').colors[2 * state_idx])\n\t\tif states_pred_show is not None:\n\t\t\taxes[0].plot(idxs_ctrl[iter_ahead_show:], states_pred_show[:, state_idx],\n\t\t\t\tlabel=str(iter_ahead_show * num_repeat_actions) + 'step_' + 'prediction' + str(state_idx),\n\t\t\t\tcolor=plt.get_cmap('tab20').colors[2 * state_idx], linestyle='--')\n\t\tif states_pred_std_show is not None:\n\t\t\taxes[0].fill_between(idxs_ctrl[iter_ahead_show:],\n\t\t\t\tstates_pred_show[:, state_idx] - mul_std_bounds * states_pred_std_show[:, state_idx],\n\t\t\t\tstates_pred_show[:, state_idx] + mul_std_bounds * states_pred_std_show[:, state_idx],\n\t\t\t\tcolor=plt.get_cmap('tab20').colors[2 * state_idx], alpha=ALPHA_CONFIDENCE_BOUNDS)\n\t\t# label=str(num_iter_prediction_ahead_show * num_repeat_actions) + 'step_' + 'bounds_prediction' + str(\n\t\t# \t\t\t\tstate_idx)\n\t\tif state_min_constraint is not None and state_max_constraint is not None:\n\t\t\taxes[0].axhline(y=state_min_constraint[state_idx],\n\t\t\t\tcolor=plt.get_cmap('tab20').colors[2 * state_idx], linestyle='-.')\n\t\t\taxes[0].axhline(y=state_max_constraint[state_idx],\n\t\t\t\tcolor=plt.get_cmap('tab20').colors[2 * state_idx], linestyle='-.')\n\t# costs = np.array([element for element in pred_info['cost']])\n\taxes[2].plot(idxs_x, costs, label='cost', color='k')\n\n\taxes[2].plot(list(idxs_ctrl), mean_cost_pred, label='mean predicted cost', color='orange')\n\taxes[2].fill_between(list(idxs_ctrl),\n\t\t\t\t\t\t\tmean_cost_pred - mul_std_bounds * mean_cost_std_pred,\n\t\t\t\t\t\t\tmean_cost_pred + mul_std_bounds * mean_cost_std_pred,\n\t\t\t\t\t\t\tcolor='orange', alpha=ALPHA_CONFIDENCE_BOUNDS)\n\n\tfor idx_action in range(len(actions[0])):\n\t\taxes[1].step(idxs_x, np.array(actions)[:, idx_action], label='a_' + str(idx_action),\n\t\t\tcolor=plt.get_cmap('tab20').colors[1 + 2 * idx_action])\n\n\tif update_x_limit is True:\n\t\taxes[2].set_xlim(0, np.max(idxs_x))\n\taxes[2].set_ylim(0, np.max([np.max(mean_cost_pred), np.max(costs)]) * 1.2)\n\tplt.xlabel('Env steps')\n\treturn fig, axes\n\n\ndef save_plot_2d(states, actions, costs, pred_info, folder_save,\n\t\tnum_repeat_actions=1, random_actions_init=0,\n\t\tstate_min_constraint=None, state_max_constraint=None,\n\t\titer_ahead_show=3, mul_std_bounds=3):\n\tfig, axes = plt.subplots(nrows=3, figsize=(6, 5), sharex=True)\n\taxes[0].set_title('Normed states and predictions')\n\taxes[1].set_title('Normed actions')\n\taxes[2].set_title('Cost and horizon cost')\n\tplt.xlabel(\"time\")\n\tmax_state_plot = np.max([states.max(), 1.03])\n\tmin_state_plot = np.min([states.min(), -0.03])\n\taxes[0].set_ylim(min_state_plot, max_state_plot)\n\taxes[1].set_ylim(0, 1.02)\n\tplt.tight_layout()\n\tfig, axes = draw_plots_2d(states=states, actions=actions, costs=costs,\n\t\t\t\t\t\t\tpred_info=pred_info, num_repeat_actions=num_repeat_actions,\n\t\t\t\t\t\t\trandom_actions_init=random_actions_init,\n\t\t\t\t\t\t\tstate_min_constraint=state_min_constraint,\n\t\t\t\t\t\t\tstate_max_constraint=state_max_constraint,\n\t\t\t\t\t\t\titer_ahead_show=iter_ahead_show, fig=fig, axes=axes,\n\t\t\t\t\t\t\tmul_std_bounds=mul_std_bounds)\n\taxes[0].legend()\n\taxes[0].grid()\n\taxes[1].legend()\n\taxes[1].grid()\n\taxes[2].legend()\n\taxes[2].grid()\n\thour = datetime.datetime.now().hour\n\tminute = datetime.datetime.now().minute\n\tsecond = datetime.datetime.now().second\n\tfig.savefig(os.path.join(folder_save, 'history' + '_h' +\n\t\t\t\t\t\t\t\t\t\t  str(hour) + '_m' +\n\t\t\t\t\t\t\t\t\t\t  str(minute) + '_s' +\n\t\t\t\t\t\t\t\t\t\t  str(second) + '.png'))\n\tplt.close()\n\n\ndef anim_plots_2d_p(queue, num_steps_total, step_pred, obs_space, action_space,\n\t\t\t\t\tuse_constraints=False, state_min=None, state_max=None, time_between_updates=0.5, mul_std_bounds=3):\n\tfig, axes = plt.subplots(nrows=3, figsize=(6, 5), sharex=True)\n\taxes[0].set_title('Normed states and predictions')\n\taxes[1].set_title('Normed actions')\n\taxes[2].set_title('Cost and horizon cost')\n\tplt.xlabel(\"Env steps\")\n\tmin_state = -0.03\n\tmax_state = 1.03\n\taxes[0].set_ylim(min_state, max_state)\n\taxes[0].grid()\n\taxes[1].set_ylim(-0.03, 1.03)\n\taxes[1].grid()\n\taxes[2].set_xlim(0, num_steps_total)\n\taxes[2].grid()\n\tplt.tight_layout()\n\n\tstates = np.empty((num_steps_total, obs_space.shape[0]))\n\tactions = np.empty((num_steps_total, action_space.shape[0]))\n\tcosts = np.empty(num_steps_total)\n\tmean_cost_pred = np.empty_like(costs)\n\tmean_cost_std_pred = np.empty_like(mean_cost_pred)\n\t\n\tmin_obs = obs_space.low\n\tmax_obs = obs_space.high\n\tmin_action = action_space.low\n\tmax_action = action_space.high\n\tif use_constraints:\n\t\tif state_min is not None:\n\t\t\tfor idx_state in range(obs_space.shape[0]):\n\t\t\t\taxes[0].axhline(y=state_min[idx_state],\n\t\t\t\t\tcolor=plt.get_cmap('tab20').colors[2 * idx_state],\n\t\t\t\t\tlinestyle='-.')\n\t\tif state_max is not None:\n\t\t\tfor idx_state in range(obs_space.shape[0]):\n\t\t\t\taxes[0].axhline(y=state_max[idx_state],\n\t\t\t\t\tcolor=plt.get_cmap('tab20').colors[2 * idx_state],\n\t\t\t\t\tlinestyle='-.')\n\tlines_states = [axes[0].plot([], [], label='state' + str(state_idx),\n\t\tcolor=plt.get_cmap('tab20').colors[2 * state_idx]) for state_idx in range(obs_space.shape[0])]\n\tlines_actions = [axes[1].step([], [], label='action' + str(action_idx),\n\t\tcolor=plt.get_cmap('tab20').colors[1 + 2 * action_idx]) for action_idx in range(action_space.shape[0])]\n\n\tline_cost = axes[2].plot([], [], label='cost', color='k')\n\tline_costs_mean_pred = axes[2].plot([], [], label='mean predicted cost', color='orange')\n\n\tlines_states_pred = [axes[0].plot([], [],\n\t\tlabel='predicted_states' + str(state_idx),\n\t\tcolor=plt.get_cmap('tab20').colors[2 * state_idx], linestyle='dashed')\n\t\tfor state_idx in range(obs_space.shape[0])]\n\tlines_actions_pred = [axes[1].step([], [], label='predicted_action' + str(action_idx),\n\t\tcolor=plt.get_cmap('tab20').colors[1 + 2 * action_idx], linestyle='dashed')\n\t\tfor action_idx in range(action_space.shape[0])]\n\tline_costs_pred = axes[2].plot([], [], label='predicted cost', color='k', linestyle='dashed')\n\n\taxes[0].legend(fontsize=FONTSIZE)\n\taxes[1].legend(fontsize=FONTSIZE)\n\taxes[2].legend(fontsize=FONTSIZE)\n\tfig.canvas.draw()  # draw and show it\n\tplt.show(block=False)\n\n\tnum_pts_show = 0\n\tlast_update_time = time.time()\n\twhile True:\n\t\tif (time.time() - last_update_time) > time_between_updates:\n\t\t\trecieved_data = False\n\t\t\tlast_update_time = time.time()\n\t\t\twhile not (queue.empty()):\n\t\t\t\tmsg = queue.get()  # Read from the queue\n\t\t\t\tobs_norm = (np.array(msg[0]) - min_obs) / (max_obs - min_obs)\n\t\t\t\taction_norm = (np.array(msg[1]) - min_action) / (max_action - min_action)\n\t\t\t\tstates[num_pts_show] = obs_norm\n\t\t\t\tactions[num_pts_show] = action_norm\n\t\t\t\tcosts[num_pts_show] = msg[2]\n\n\t\t\t\tupdate_limits = False\n\t\t\t\tmin_state_actual = np.min(states[num_pts_show])\n\t\t\t\tif min_state_actual < min_state:\n\t\t\t\t\tmin_state = min_state_actual\n\t\t\t\t\tupdate_limits = True\n\n\t\t\t\tmax_state_actual = np.max(states[num_pts_show])\n\t\t\t\tif max_state_actual > max_state:\n\t\t\t\t\tmax_state = max_state_actual\n\t\t\t\t\tupdate_limits = True\n\n\t\t\t\tif update_limits:\n\t\t\t\t\taxes[0].set_ylim(min_state, max_state)\n\n\t\t\t\tif len(msg) > 3:\n\t\t\t\t\tmean_cost_pred[num_pts_show] = np.nan_to_num(msg[3], nan=-1, posinf=-1, neginf=-1)\n\t\t\t\t\tmean_cost_std_pred[num_pts_show] = np.nan_to_num(msg[4], copy=True, nan=99, posinf=99, neginf=99)\n\t\t\t\telse:\n\t\t\t\t\tif num_pts_show == 0:\n\t\t\t\t\t\t# Init values for mean cost pred and mean cost std pred at the beginning when the control\n\t\t\t\t\t\t# has not started yet and only random actions has been applied.\n\t\t\t\t\t\t# Thus, the mean future cost and the std of the mean future cost has not been computed yet\n\t\t\t\t\t\t# and have not been provided to the graph object for the update\n\t\t\t\t\t\tmean_cost_pred[num_pts_show] = MEAN_PRED_COST_INIT\n\t\t\t\t\t\tmean_cost_std_pred[num_pts_show] = STD_MEAN_PRED_COST_INIT\n\t\t\t\t\telse:\n\t\t\t\t\t\t# For when the information about prediction have not been provided to the graph object\n\t\t\t\t\t\t# for the update, for example at the init period when random actions are applied\n\t\t\t\t\t\tmean_cost_pred[num_pts_show] = mean_cost_pred[num_pts_show - 1]\n\t\t\t\t\t\tmean_cost_std_pred[num_pts_show] = mean_cost_std_pred[num_pts_show - 1]\n\t\t\t\tnum_pts_show += 1\n\t\t\t\trecieved_data = True\n\t\t\t\tlast_update_time = time.time()\n\n\t\t\tif recieved_data:\n\t\t\t\tfor idx_axes in range(len(axes)):\n\t\t\t\t\taxes[idx_axes].collections.clear()\n\n\t\t\t\tidxs = np.arange(0, num_pts_show)\n\t\t\t\tfor idx_axes in range(len(axes)):\n\t\t\t\t\taxes[idx_axes].collections.clear()\n\n\t\t\t\tfor idx_state in range(len(obs_norm)):\n\t\t\t\t\tlines_states[idx_state][0].set_data(idxs, states[:num_pts_show, idx_state])\n\n\t\t\t\tfor idx_action in range(len(action_norm)):\n\t\t\t\t\tlines_actions[idx_action][0].set_data(idxs, actions[:num_pts_show, idx_action])\n\n\t\t\t\tline_cost[0].set_data(idxs, costs[:num_pts_show])\n\n\t\t\t\tline_costs_mean_pred[0].set_data(idxs, mean_cost_pred[:num_pts_show])\n\t\t\t\taxes[2].fill_between(idxs,\n\t\t\t\t\tmean_cost_pred[:num_pts_show] -\n\t\t\t\t\tmean_cost_std_pred[:num_pts_show] * mul_std_bounds,\n\t\t\t\t\tmean_cost_pred[:num_pts_show] +\n\t\t\t\t\tmean_cost_std_pred[:num_pts_show] * mul_std_bounds,\n\t\t\t\t\tfacecolor='orange', alpha=ALPHA_CONFIDENCE_BOUNDS,\n\t\t\t\t\tlabel='mean predicted ' + str(mul_std_bounds) + ' std cost bounds')\n\t\t\t\t\n\t\t\t\taxes[2].set_ylim(0, np.max([np.max(mean_cost_pred[:num_pts_show]),\n\t\t\t\t\tnp.max(costs[:num_pts_show])]) * 1.1)\n\t\t\t\taxes[2].set_ylim(0, np.max(line_cost[0].get_ydata()) * 1.1)\n\t\t\t\t\n\t\t\t\t# If predictions are not given in the last update, do not show them on the graph\n\t\t\t\tif len(msg) > 3:\n\t\t\t\t\tstates_pred = np.nan_to_num(msg[5], nan=-1, posinf=-1, neginf=-1)\n\t\t\t\t\tstates_std_pred = np.nan_to_num(msg[6], copy=False, nan=99, posinf=99, neginf=99)\n\t\t\t\t\tactions_pred = msg[7]\n\t\t\t\t\tcosts_pred = np.nan_to_num(msg[8], nan=-1, posinf=-1, neginf=-1)\n\t\t\t\t\tcosts_std_pred = np.nan_to_num(msg[9], nan=99, posinf=99, neginf=0)\n\t\t\t\t\t# if num_repeat_action is not 1, the control do not happen at each iteration,\n\t\t\t\t\t# we must select the last index where the control happened as the start of the prediction horizon\n\t\t\t\t\tidx_prev_control = (idxs[-1] // step_pred) * step_pred\n\t\t\t\t\tidxs_future = np.arange(idx_prev_control,\n\t\t\t\t\t\t\t\t\t\t\t\tidx_prev_control + step_pred + len(states_pred) * step_pred,\n\t\t\t\t\t\t\t\t\t\t\t\tstep_pred)\n\n\t\t\t\t\tfor idx_state in range(len(obs_norm)):\n\t\t\t\t\t\tfuture_states_show = np.concatenate(([states[idx_prev_control, idx_state]],\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tstates_pred[:, idx_state]))\n\t\t\t\t\t\tlines_states_pred[idx_state][0].set_data(idxs_future, future_states_show)\n\n\t\t\t\t\t\tfuture_states_std_show = np.concatenate(([0], states_std_pred[:, idx_state]))\n\t\t\t\t\t\taxes[0].fill_between(idxs_future,\n\t\t\t\t\t\t\tfuture_states_show - future_states_std_show * mul_std_bounds,\n\t\t\t\t\t\t\tfuture_states_show + future_states_std_show * mul_std_bounds,\n\t\t\t\t\t\t\tfacecolor=plt.get_cmap('tab20').colors[2 * idx_state], alpha=ALPHA_CONFIDENCE_BOUNDS,\n\t\t\t\t\t\t\tlabel='predicted ' + str(mul_std_bounds) + ' std bounds state ' + str(idx_state))\n\n\t\t\t\t\tfor idx_action in range(len(action_norm)):\n\t\t\t\t\t\tfuture_actions_show = \\\n\t\t\t\t\t\t\tnp.concatenate(([actions[idx_prev_control, idx_action]], actions_pred[:, idx_action]))\n\t\t\t\t\t\tlines_actions_pred[idx_action][0].set_data(idxs_future, future_actions_show)\n\t\n\t\t\t\t\tfuture_costs_show = np.concatenate(([costs[idx_prev_control]], costs_pred))\n\t\t\t\t\tline_costs_pred[0].set_data(idxs_future, future_costs_show)\n\t\n\t\t\t\t\tfuture_cost_std_show = np.concatenate(([0], costs_std_pred))\n\t\t\t\t\taxes[2].fill_between(idxs_future,\n\t\t\t\t\t\tfuture_costs_show - future_cost_std_show * mul_std_bounds,\n\t\t\t\t\t\tfuture_costs_show + future_cost_std_show * mul_std_bounds,\n\t\t\t\t\t\tfacecolor='black', alpha=ALPHA_CONFIDENCE_BOUNDS,\n\t\t\t\t\t\tlabel='predicted ' + str(mul_std_bounds) + ' std cost bounds')\n\n\t\t\t\tfor ax in axes:\n\t\t\t\t\tax.relim()\n\t\t\t\t\tax.autoscale_view(True, True, True)\n\n\t\t\t\tfig.canvas.draw()\n\t\t\t\tplt.pause(0.01)\n\n\nclass LivePlotParallel:\n\tdef __init__(self, num_steps_total, obs_space, action_space, step_pred, use_constraints=False,\n\t\t\tstate_min=None, state_max=None, time_between_updates=0.75, use_thread=False):\n\t\tif use_thread:\n\t\t\tself.pqueue = queue.Queue()\n\t\t\tself.graph_p = threading.Thread(target=anim_plots_2d_p,\n\t\t\t\t\t\t\t\t\targs=(self.pqueue, num_steps_total, step_pred, obs_space,\n\t\t\t\t\t\t\t\t\t\taction_space, use_constraints,\n\t\t\t\t\t\t\t\t\t\tstate_min, state_max, time_between_updates))\n\t\telse:\n\t\t\tself.pqueue = multiprocessing.Queue()\n\t\t\tself.graph_p = multiprocessing.Process(target=anim_plots_2d_p,\n\t\t\t\t\t\t\t\t\targs=(self.pqueue, num_steps_total, step_pred, obs_space,\n\t\t\t\t\t\t\t\t\t\t\taction_space, use_constraints,\n\t\t\t\t\t\t\t\t\t\t\tstate_min, state_max, time_between_updates))\n\t\t\t# graph_p.daemon = True\n\t\tself.graph_p.start()\n\n\tdef update(self, obs, action, cost, info_dict=None):\n\t\tif info_dict is None:\n\t\t\tself.pqueue.put([obs, action, cost])\n\t\telse:\n\t\t\tself.pqueue.put(\n\t\t\t[obs, action, cost, info_dict['mean predicted cost'],\n\t\t\t\tinfo_dict['mean predicted cost std'], info_dict['predicted states'],\n\t\t\t\tinfo_dict['predicted states std'], info_dict['predicted actions'],\n\t\t\t\tinfo_dict['predicted costs'], info_dict['predicted costs std']])\n\n\nclass LivePlotSequential:\n\tdef __init__(self, num_steps_total, obs_space, action_space, step_pred, use_constraints=False,\n\t\t\tstate_min=None, state_max=None, mul_std_bounds=3, fontsize=6):\n\t\tself.fig, self.axes = plt.subplots(nrows=3, figsize=(6, 5), sharex=True)\n\t\tself.axes[0].set_title('Normed states and predictions')\n\t\tself.axes[1].set_title('Normed actions')\n\t\tself.axes[2].set_title('Cost and horizon cost')\n\t\tplt.xlabel(\"Env steps\")\n\t\tself.min_state = -0.03\n\t\tself.max_state = 1.03\n\t\tself.axes[0].set_ylim(self.min_state, self.max_state)\n\t\tself.axes[1].set_ylim(-0.03, 1.03)\n\t\tself.axes[2].set_xlim(0, num_steps_total)\n\t\tplt.tight_layout()\n\n\t\tself.step_pred = step_pred\n\t\tself.mul_std_bounds = mul_std_bounds\n\n\t\tself.states = np.empty((num_steps_total, obs_space.shape[0]))\n\t\tself.actions = np.empty((num_steps_total, action_space.shape[0]))\n\t\tself.costs = np.empty(num_steps_total)\n\n\t\tself.mean_costs_pred = np.empty_like(self.costs)\n\t\tself.mean_costs_std_pred = np.empty_like(self.costs)\n\n\t\tself.min_obs = obs_space.low\n\t\tself.max_obs = obs_space.high\n\t\tself.min_action = action_space.low\n\t\tself.max_action = action_space.high\n\n\t\tself.num_points_show = 0\n\t\tif use_constraints:\n\t\t\tif state_min is not None:\n\t\t\t\tfor state_idx in range(obs_space.shape[0]):\n\t\t\t\t\tself.axes[0].axhline(y=state_min[state_idx],\n\t\t\t\t\t\tcolor=plt.get_cmap('tab20').colors[2 * state_idx],\n\t\t\t\t\t\tlinestyle='-.')\n\t\t\tif state_max is not None:\n\t\t\t\tfor state_idx in range(obs_space.shape[0]):\n\t\t\t\t\tself.axes[0].axhline(y=state_max[state_idx],\n\t\t\t\t\t\tcolor=plt.get_cmap('tab20').colors[2 * state_idx],\n\t\t\t\t\t\tlinestyle='-.')\n\t\tself.lines_states = [self.axes[0].plot([], [], label='state' + str(state_idx),\n\t\t\t\tcolor=plt.get_cmap('tab20').colors[2 * state_idx]) for state_idx in range(obs_space.shape[0])]\n\t\tself.line_cost = self.axes[2].plot([], [], label='cost', color='k')\n\n\t\tself.lines_actions = [self.axes[1].step([], [], label='action' + str(action_idx),\n\t\t\t\tcolor=plt.get_cmap('tab20').colors[1 + 2 * action_idx]) for action_idx in range(action_space.shape[0])]\n\n\t\tself.line_mean_costs_pred = self.axes[2].plot([], [], label='mean predicted cost', color='orange')\n\n\t\tself.lines_states_pred = [self.axes[0].plot([], [],\n\t\t\t\tlabel='predicted_states' + str(state_idx),\n\t\t\t\tcolor=plt.get_cmap('tab20').colors[2 * state_idx], linestyle='dashed')\n\t\t\tfor state_idx in range(obs_space.shape[0])]\n\t\tself.lines_actions_pred = [self.axes[1].step([], [], label='predicted_action' + str(action_idx),\n\t\t\t\tcolor=plt.get_cmap('tab20').colors[1 + 2 * action_idx], linestyle='dashed')\n\t\t\t\tfor action_idx in range(action_space.shape[0])]\n\t\tself.line_costs_pred = self.axes[2].plot([], [], label='predicted cost', color='k', linestyle='dashed')\n\n\t\tself.axes[0].legend(fontsize=fontsize)\n\t\tself.axes[0].grid()\n\t\tself.axes[1].legend(fontsize=fontsize)\n\t\tself.axes[1].grid()\n\t\tself.axes[2].legend(fontsize=fontsize)\n\t\tself.axes[2].grid()\n\t\tplt.show(block=False)\n\n\tdef update(self, obs, action, cost, info_dict=None):\n\t\tobs_norm = (obs - self.min_obs) / (self.max_obs - self.min_obs)\n\t\taction_norm = (action - self.min_action) / (self.max_action - self.min_action)\n\t\tself.states[self.num_points_show] = obs_norm\n\t\tself.costs[self.num_points_show] = cost\n\n\t\tupdate_limits = False\n\t\tmin_state_actual = np.min(obs_norm)\n\t\tif min_state_actual < self.min_state:\n\t\t\tself.min_state = min_state_actual\n\t\t\tupdate_limits = True\n\n\t\tmax_state_actual = np.max(obs_norm)\n\t\tif max_state_actual > self.max_state:\n\t\t\tself.max_state = max_state_actual\n\t\t\tupdate_limits = True\n\n\t\tif update_limits:\n\t\t\tself.axes[0].set_ylim(self.min_state, self.max_state)\n\n\t\tidxs = np.arange(0, (self.num_points_show + 1))\n\t\tfor idx_axes in range(len(self.axes)):\n\t\t\tself.axes[idx_axes].collections.clear()\n\n\t\tfor idx_state in range(len(obs_norm)):\n\t\t\tself.lines_states[idx_state][0].set_data(idxs, self.states[:(self.num_points_show + 1), idx_state])\n\n\t\tself.actions[self.num_points_show] = action_norm\n\t\tfor idx_action in range(len(action_norm)):\n\t\t\tself.lines_actions[idx_action][0].set_data(idxs, self.actions[:(self.num_points_show + 1), idx_action])\n\t\t\t\n\t\tself.line_cost[0].set_data(idxs, self.costs[:(self.num_points_show + 1)])\n\n\t\tif info_dict is not None:\n\t\t\tmean_costs_pred = info_dict['mean predicted cost']\n\t\t\tmean_costs_std_pred = info_dict['mean predicted cost std']\n\t\t\tstates_pred = info_dict['predicted states']\n\t\t\tstates_std_pred = info_dict['predicted states std']\n\t\t\tactions_pred = info_dict['predicted actions']\n\t\t\tcosts_pred = info_dict['predicted costs']\n\t\t\tcosts_std_pred = info_dict['predicted costs std']\n\t\t\tnp.nan_to_num(mean_costs_pred, copy=False, nan=-1, posinf=-1, neginf=-1)\n\t\t\tnp.nan_to_num(mean_costs_std_pred, copy=False, nan=99, posinf=99, neginf=99)\n\t\t\tnp.nan_to_num(states_pred, copy=False, nan=-1, posinf=-1, neginf=-1)\n\t\t\tnp.nan_to_num(states_std_pred, copy=False, nan=99, posinf=99, neginf=0)\n\t\t\tnp.nan_to_num(costs_pred, copy=False, nan=-1, posinf=-1, neginf=-1)\n\t\t\tnp.nan_to_num(costs_std_pred, copy=False, nan=99, posinf=99, neginf=0)\n\t\t\t# if num_repeat_action is not 1, the control do not happen at each iteration,\n\t\t\t# we must select the last index where the control happened as the start of the prediction horizon\n\t\t\tidx_prev_control = (idxs[-1] // self.step_pred) * self.step_pred\n\t\t\tidxs_future = np.arange(idx_prev_control,\n\t\t\t\t\t\t\t\t\t\tidx_prev_control + self.step_pred + len(states_pred) * self.step_pred,\n\t\t\t\t\t\t\t\t\t\tself.step_pred)\n\n\t\t\tself.mean_costs_pred[self.num_points_show] = mean_costs_pred\n\t\t\tself.mean_costs_std_pred[self.num_points_show] = mean_costs_std_pred\n\n\t\t\tfor idx_state in range(len(obs_norm)):\n\t\t\t\tfuture_states_show = np.concatenate(([self.states[idx_prev_control, idx_state]], states_pred[:, idx_state]))\n\t\t\t\tself.lines_states_pred[idx_state][0].set_data(idxs_future, future_states_show)\n\t\t\t\tfuture_states_std_show = np.concatenate(([0], states_std_pred[:, idx_state]))\n\t\t\t\tself.axes[0].fill_between(idxs_future,\n\t\t\t\t\tfuture_states_show - future_states_std_show * self.mul_std_bounds,\n\t\t\t\t\tfuture_states_show + future_states_std_show * self.mul_std_bounds,\n\t\t\t\t\tfacecolor=plt.get_cmap('tab20').colors[2 * idx_state], alpha=ALPHA_CONFIDENCE_BOUNDS,\n\t\t\t\t\tlabel='predicted ' + str(self.mul_std_bounds) + ' std bounds state ' + str(idx_state))\n\t\t\tfor idx_action in range(len(action_norm)):\n\t\t\t\tself.lines_actions_pred[idx_action][0].set_data(idxs_future,\n\t\t\t\t\tnp.concatenate(([self.actions[idx_prev_control, idx_action]], actions_pred[:, idx_action])))\n\n\t\t\tfuture_costs_show = np.concatenate(([self.costs[idx_prev_control]], costs_pred))\n\t\t\tself.line_costs_pred[0].set_data(idxs_future, future_costs_show)\n\n\t\t\tfuture_cost_std_show = np.concatenate(([0], costs_std_pred))\n\t\t\tself.axes[2].fill_between(idxs_future,\n\t\t\t\tfuture_costs_show - future_cost_std_show * self.mul_std_bounds,\n\t\t\t\tfuture_costs_show + future_cost_std_show * self.mul_std_bounds,\n\t\t\t\tfacecolor='black', alpha=ALPHA_CONFIDENCE_BOUNDS,\n\t\t\t\tlabel='predicted ' + str(self.mul_std_bounds) + ' std cost bounds')\n\t\telse:\n\t\t\tif self.num_points_show == 0:\n\t\t\t\tself.mean_costs_pred[self.num_points_show] = MEAN_PRED_COST_INIT\n\t\t\t\tself.mean_costs_std_pred[self.num_points_show] = STD_MEAN_PRED_COST_INIT\n\t\t\telse:\n\t\t\t\tself.mean_costs_pred[self.num_points_show] = self.mean_costs_pred[self.num_points_show - 1]\n\t\t\t\tself.mean_costs_std_pred[self.num_points_show] = self.mean_costs_std_pred[self.num_points_show - 1]\n\n\t\tself.line_mean_costs_pred[0].set_data(idxs, self.mean_costs_pred[:(self.num_points_show + 1)])\n\t\tself.axes[2].set_ylim(0, np.max([np.max(self.mean_costs_pred[:(self.num_points_show + 1)]),\n\t\t\t\t\t\t\t\t\t\t\t\tnp.max(self.costs[:(self.num_points_show + 1)])]) * 1.1)\n\t\tself.axes[2].fill_between(idxs,\n\t\t\tself.mean_costs_pred[:(self.num_points_show + 1)] -\n\t\t\tself.mean_costs_std_pred[:(self.num_points_show + 1)] * self.mul_std_bounds,\n\t\t\tself.mean_costs_pred[:(self.num_points_show + 1)] +\n\t\t\tself.mean_costs_std_pred[:(self.num_points_show + 1)] * self.mul_std_bounds,\n\t\t\tfacecolor='orange', alpha=ALPHA_CONFIDENCE_BOUNDS,\n\t\t\tlabel='mean predicted ' + str(self.mul_std_bounds) + ' std cost bounds')\n\n\t\tself.fig.canvas.draw()\n\t\tplt.pause(0.01)\n\t\tself.num_points_show += 1\n\n\ndef save_plot_model_3d_process(inputs, targets, parameters, constraints_gp, folder_save,\n\t\tidxs_in_gp_memory=None, prop_extend_domain=1, n_ticks=150, total_col_max=3, fontsize=6):\n\twith torch.no_grad(), gpytorch.settings.fast_pred_var():\n\t\ttorch.set_num_threads(1)\n\t\tnum_input_model = len(inputs[0])\n\t\tnum_models = len(targets[0])\n\t\tidxs_outside_gp_memory = [\n\t\t\tnp.delete(np.arange(len(inputs)), idxs_in_gp_memory[idx_model]) for idx_model in range(num_models)]\n\n\t\tmodels = create_models(inputs, targets, parameters, constraints_gp)\n\t\tfor idx_model in range(len(models)):\n\t\t\tmodels[idx_model].eval()\n\t\ttargets = targets.numpy()\n\n\t\tnum_figures = int(num_input_model / total_col_max + 0.5)\n\t\tfigs = []\n\t\taxes_s = []\n\t\tfor index_figure in range(num_figures):\n\t\t\tfig = plt.figure(figsize=(15, 6))\n\t\t\taxes = []\n\t\t\tcolumns_active = np.min([num_models - (total_col_max * index_figure), total_col_max])\n\t\t\tfor idx_ax in range(columns_active * 2):\n\t\t\t\taxes.append(fig.add_subplot(2, columns_active, idx_ax + 1, projection='3d'))\n\t\t\taxes_s.append(axes)\n\t\t\tfigs.append(fig)\n\t\t\tfor idx_subplot in range(columns_active):\n\t\t\t\tidx_obs_repr = index_figure * columns_active + idx_subplot\n\t\t\t\tif idx_obs_repr >= (num_models):\n\t\t\t\t\tbreak\n\t\t\t\tfeat_importance = (1 / models[idx_obs_repr].covar_module.base_kernel.lengthscale).numpy()\n\t\t\t\tfeat_importance = feat_importance / np.sum(feat_importance)\n\t\t\t\tbest_features = np.argsort(-feat_importance)[0, :2]\n\t\t\t\t'''estimator_other_columns = Pipeline(\n\t\t\t\t\tsteps=[('standardscaler', StandardScaler()),\n\t\t\t\t\t\t('features', PolynomialFeatures(2)),\n\t\t\t\t\t\t('model', LinearRegression())])'''\n\t\t\t\testimator_other_columns = Pipeline(\n\t\t\t\t\tsteps=[('standardscaler', StandardScaler()),\n\t\t\t\t\t\t('features', KNeighborsRegressor(n_neighbors=3, weights='distance'))])\n\n\t\t\t\testimator_other_columns.fit(inputs[:, best_features].numpy(),\n\t\t\t\t\tnp.delete(inputs.numpy(), best_features, axis=1))\n\n\t\t\t\tdomain_extension_x = (prop_extend_domain - 1) * (\n\t\t\t\t\t\tinputs[:, best_features[0]].max() - inputs[:, best_features[0]].min())\n\t\t\t\tdomain_extension_y = (prop_extend_domain - 1) * (\n\t\t\t\t\t\tinputs[:, best_features[1]].max() - inputs[:, best_features[1]].min())\n\t\t\t\tx_grid = np.linspace(inputs[:, best_features[0]].min() - domain_extension_x / 2,\n\t\t\t\t\tinputs[:, best_features[0]].max() + domain_extension_x / 2, n_ticks)\n\t\t\t\ty_grid = np.linspace(inputs[:, best_features[1]].min() - domain_extension_y / 2,\n\t\t\t\t\tinputs[:, best_features[1]].max() + domain_extension_y / 2, n_ticks)\n\n\t\t\t\tx_grid, y_grid = np.meshgrid(x_grid, y_grid)\n\n\t\t\t\tx_grid_1d = np.expand_dims(x_grid.flatten(), axis=-1)\n\t\t\t\ty_grid_1d = np.expand_dims(y_grid.flatten(), axis=-1)\n\t\t\t\txy_grid = np.concatenate((x_grid_1d, y_grid_1d), axis=1)\n\t\t\t\tpred_other_columns = estimator_other_columns.predict(xy_grid)\n\t\t\t\tall_col = np.zeros((xy_grid.shape[0], num_input_model))\n\t\t\t\tall_col[:, best_features] = xy_grid\n\t\t\t\tall_col[:, np.delete(np.arange(num_input_model), best_features)] = pred_other_columns\n\t\t\t\tgauss_pred = models[idx_obs_repr].likelihood(models[idx_obs_repr](torch.from_numpy(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tall_col.astype(np.float64))))\n\t\t\t\tz_grid_1d_mean = gauss_pred.mean.numpy()\n\t\t\t\tz_grid_1d_std = np.sqrt(gauss_pred.stddev.numpy())\n\n\t\t\t\tz_grid_mean = z_grid_1d_mean.reshape(x_grid.shape)\n\t\t\t\tz_grid_std = z_grid_1d_std.reshape(x_grid.shape)\n\t\t\t\tsurf1 = axes[idx_subplot].contour3D(x_grid, y_grid, z_grid_mean, n_ticks, cmap='cool')\n\t\t\t\taxes[idx_subplot].set_xlabel('Input ' + str(best_features[0]), fontsize=fontsize, rotation=150)\n\t\t\t\taxes[idx_subplot].set_ylabel('Input ' + str(best_features[1]), fontsize=fontsize, rotation=150)\n\t\t\t\taxes[idx_subplot].set_zlabel('Variation state ' + str(idx_obs_repr), fontsize=fontsize,\n\t\t\t\t\trotation=60)\n\t\t\t\tif targets is not None:\n\t\t\t\t\taxes[idx_subplot].scatter(\n\t\t\t\t\tinputs[idxs_in_gp_memory[idx_obs_repr], best_features[0]],\n\t\t\t\t\tinputs[idxs_in_gp_memory[idx_obs_repr], best_features[1]],\n\t\t\t\t\ttargets[idxs_in_gp_memory[idx_obs_repr], idx_obs_repr],\n\t\t\t\t\tmarker='x', c='g')\n\n\t\t\t\t\taxes[idx_subplot].scatter(\n\t\t\t\t\tinputs[idxs_outside_gp_memory[idx_obs_repr], best_features[0]],\n\t\t\t\t\tinputs[idxs_outside_gp_memory[idx_obs_repr], best_features[1]],\n\t\t\t\t\ttargets[idxs_outside_gp_memory[idx_obs_repr], idx_obs_repr],\n\t\t\t\t\tmarker='x', c='k')\n\n\t\t\t\t\taxes[idx_subplot].quiver(inputs[:-1, best_features[0]], inputs[:-1, best_features[1]],\n\t\t\t\t\t\ttargets[:-1, idx_obs_repr],\n\t\t\t\t\t\tinputs[1:, best_features[0]] - inputs[:-1, best_features[0]],\n\t\t\t\t\t\tinputs[1:, best_features[1]] - inputs[:-1, best_features[1]],\n\t\t\t\t\t\ttargets[1:, idx_obs_repr] - targets[:-1, idx_obs_repr],\n\t\t\t\t\t\tcolor='k', linestyle=\"solid\", alpha=0.3, arrow_length_ratio=0.001, length=0.9)\n\n\t\t\t\tsurf2 = axes[idx_subplot + columns_active].contour3D(x_grid, y_grid, z_grid_std, n_ticks, cmap='cool')\n\t\t\t\taxes[idx_subplot + columns_active].set_xlabel('Input ' + str(best_features[0]), fontsize=fontsize, rotation=150)\n\t\t\t\taxes[idx_subplot + columns_active].set_ylabel('Input ' + str(best_features[1]), fontsize=fontsize, rotation=150)\n\t\t\t\taxes[idx_subplot + columns_active].set_zlabel('Uncertainty: std state ' + str(idx_obs_repr),\n\t\t\t\t\tfontsize=fontsize, rotation=60)\n\n\t\t\t\tif targets is not None:\n\t\t\t\t\tpred_outside_memory = models[idx_obs_repr].likelihood(\n\t\t\t\t\t\tmodels[idx_obs_repr](inputs[idxs_outside_gp_memory[idx_obs_repr]]))\n\t\t\t\t\terrors_outside_memory = np.abs(pred_outside_memory.mean.numpy() -\n\t\t\t\t\t\t\t\t\t\t\t\t   targets[idxs_outside_gp_memory[idx_obs_repr], idx_obs_repr])\n\t\t\t\t\terrors = np.zeros_like(inputs[:, 0])\n\t\t\t\t\terrors[idxs_outside_gp_memory[idx_obs_repr]] = errors_outside_memory\n\t\t\t\t\taxes[idx_subplot + columns_active].scatter(\n\t\t\t\t\t\tinputs[idxs_in_gp_memory[idx_obs_repr], best_features[0]],\n\t\t\t\t\t\tinputs[idxs_in_gp_memory[idx_obs_repr], best_features[1]],\n\t\t\t\t\t\terrors[idxs_in_gp_memory[idx_obs_repr]], marker='x', c='g')\n\t\t\t\t\taxes[idx_subplot + columns_active].scatter(\n\t\t\t\t\t\tinputs[idxs_outside_gp_memory[idx_obs_repr], best_features[0]],\n\t\t\t\t\t\tinputs[idxs_outside_gp_memory[idx_obs_repr], best_features[1]],\n\t\t\t\t\t\terrors_outside_memory, marker='x', c='k')\n\t\t\t\t\taxes[idx_subplot + columns_active].quiver(\n\t\t\t\t\t\tinputs[:-1, best_features[0]], inputs[:-1, best_features[1]],\n\t\t\t\t\t\terrors[:-1],\n\t\t\t\t\t\tinputs[1:, best_features[0]] - inputs[:-1, best_features[0]],\n\t\t\t\t\t\tinputs[1:, best_features[1]] - inputs[:-1, best_features[1]],\n\t\t\t\t\t\terrors[1:] - errors[:-1],\n\t\t\t\t\t\tcolor='k', linestyle=\"solid\", alpha=0.3, arrow_length_ratio=0.001, length=0.9)\n\n\t\t\tplt.tight_layout()\n\t\t\thour = datetime.datetime.now().hour\n\t\t\tminute = datetime.datetime.now().minute\n\t\t\tsecond = datetime.datetime.now().second\n\t\t\tfig.savefig(os.path.join(folder_save, 'model_3d' + '_h' + str(hour) +\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t'_m' + str(minute) +\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t  \t'_s' + str(second) + '.png'))\n\t\t\tplt.close()","sub_path":"utils/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":42932,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"197238962","text":"import os\nimport sys\nfrom stat import *\nimport time\n\nif len(sys.argv) < 2:\n    print(\"brak argumentu\")\n    exit()\n\nfiles = os.listdir(sys.argv[1])\nprint(files)\n\nfor f in files:\n    st = os.stat(f)\n    atime = st[ST_ATIME] #access time\n    mtime = st[ST_MTIME] #modification time\n    \n    #print(mtime)\n    \n    new_mtime = time.time()\n    \n    #modify the file timestamp\n    os.utime(f,(atime,new_mtime))\n     \n","sub_path":"lab7/14.py","file_name":"14.py","file_ext":"py","file_size_in_byte":411,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"25084882","text":"import math\n\nclass Node():\n\n    def __init__(self, x, y):\n        self.x = x\n        self.y = y\n\n    def calc_angle(self, origin, aruco_center):\n        x_c, y_c = origin\n        x_2, y_2 = aruco_center\n        a = ((y_c-self.y)**2 + (x_c-self.x)**2)**0.5                  \n        b = ((y_c-y_2)**2 + (x_c-x_2)**2)**0.5\n        c = ((self.y-y_2)**2 + (self.x-x_2)**2)**0.5\n\n        num = a**2 + b**2 - c**2\n        den = 2*a*b\n        angle = math.acos(num/den)  # rads\n        self.angle = (angle*180)/math.pi\n        \n    def __lt__(self, other):\n        return self.angle < other.angle\n\n    def __eq__(self,other):\n        return self.angle == other.angle \n\n    def below_line(self, origin, aruco_center):\n        x_c, y_c = origin\n        x_2, y_2 = aruco_center\n        v1 = (x_2 - x_c, y_2 - y_c)   # Vector 1\n        v2 = (self.x - x_c,self.y - y_c)   # Vector 2\n        xp = v1[0]*v2[1] - v1[1]*v2[0]  # Cross product\n        if xp > 0:\n            return True\n        else:\n            return False\n\n# ----utils-------#\n    \ndef sort_nodes(nodes, origin, capital):\n\n    below_nodes, above_nodes = [], []\n    \n    for n in nodes:\n        if n.below_line(origin, capital):\n            below_nodes.append(n)\n        else:\n            above_nodes.append(n)\n\n    ######## check this once  ################\n    sort_bn = sorted(below_nodes, reverse=True)\n    sort_an = sorted(above_nodes,)\n    sorted_nodes = sort_an + sort_bn\n    return sorted_nodes\n\n","sub_path":"IP/node_new.py","file_name":"node_new.py","file_ext":"py","file_size_in_byte":1456,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"300771479","text":"def translate_dependencies(alr_rls, src_acc_grps, cln_acc_grps, src_rsc_lists, cln_rsc_lists, src_plcs, cln_plcs):\n    for ar in alr_rls:\n        #Account Groups\n        acc_grp_ids = ar.get('target', {}).get('accountGroups', [])\n        translated_acc_grp_ids = translate_acc_groups(acc_grp_ids, src_acc_grps, cln_acc_grps)\n        ar['target']['accountGroups'] = translated_acc_grp_ids\n\n        #Target Resource List\n        if 'targetResourceList' in ar['target']:\n            rsc_list_ids = ar.get('target', {}).get('targetResourceList', {}).get('ids', [])\n            translated_rsc_list_ids = translate_rsc_lists(rsc_list_ids, src_rsc_lists, cln_rsc_lists)\n            ar['target']['targetResourceList']['ids'] = translated_rsc_list_ids\n\n        if 'policies' in ar:\n            plc_ids = ar.get('policies', [])\n            translated_plc_ids = translate_plcs(plc_ids, src_plcs, cln_plcs)\n            ar['policies'] = translated_plc_ids\n\n    return alr_rls\n\n\ndef translate_acc_groups(acc_grp_ids, src_acc_grps, cln_acc_grps):\n    translated_ids = []\n    for acc_id in acc_grp_ids:\n        src_acc_name = [acc['name'] for acc in src_acc_grps if acc['id'] == acc_id]\n        if src_acc_name:\n            name = src_acc_name[0]\n            cln_acc_id = [acc['id'] for acc in cln_acc_grps if acc['name'] == name]\n            if cln_acc_id:\n                #only append the transalted ID if it is found\n                translated_ids.append(cln_acc_id[0])\n            else:\n                translated_ids.append(acc_id)\n        else:\n            translated_ids.append(acc_id)\n\n    return translated_ids\n\n\ndef translate_rsc_lists(rsc_list_ids, src_rsc_lists, cln_rsc_lists):\n    translated_ids = []\n    for rsc_id in rsc_list_ids:\n        src_rsc_name = [rsc['name'] for rsc in src_rsc_lists if rsc['id'] == rsc_id]\n        if src_rsc_name:\n            name = src_rsc_name[0]\n            cln_rsc_id = [rsc['id'] for rsc in cln_rsc_lists if rsc['name'] == name]\n            if cln_rsc_id:\n                translated_ids.append(cln_rsc_id[0])\n            else:\n                translated_ids.append(rsc_id)\n        else:\n            translated_ids.append(rsc_id)\n\n    return translated_ids\n\ndef translate_plcs(plc_ids, src_plcs, cln_plcs):\n    translated_ids = []\n    for plc_id in plc_ids:\n        src_plc_name = [plc['name'] for plc in src_plcs if plc['policyId'] == plc_id]\n        if src_plc_name:\n            name = src_plc_name[0]\n            cln_plc_id = [plc['policyId'] for plc in cln_plcs if plc['name'] == name]\n            if cln_plc_id:\n                translated_ids.append(cln_plc_id[0])\n            else:\n                translated_ids.append(plc_id)\n        else:\n            translated_ids.append(plc_id)\n\n    return translated_ids","sub_path":"alert_rules/alr_translate.py","file_name":"alr_translate.py","file_ext":"py","file_size_in_byte":2747,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"650858807","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Apr 28 08:18:55 2022\n\n@author: sonounoteam\n\nThis script open the file and apply specific transforms to it.\n\"\"\"\n\nimport math\nfrom . import lhc_plot\nfrom . import lhc_sonification\n\n#Global lists to sonification and plot\nsonified_cluster_list = []\nsonified_tracks_list = []\ncluster_tosonify = []\n\ndef openfile(path):\n    \"\"\"\n    This method open the file with the given path, read its lines and return \n    a list with its content.\n    \"\"\"\n    file = open(path,'r')\n    lines = file.readlines()\n    return lines\n\ndef read_content(file):\n    \"\"\"\n    This method require the lines readed from a file that contain the lhc events\n\n    Parameters\n    ----------\n    file : TYPE list\n    \n    Returns\n    -------\n    particles : TYPE list, contain diferent lists of each event, where inside \n        each event there are lists of each track and cluster found in it.\n    \"\"\"\n    # Search the separator '------------' on the file, which one indicate the\n    # beginning of a new event, and store the index\n    count = 0\n    element_list = [0]\n    for line in file:\n        count = count + 1\n        if '---------' in str(line) and count <= len(file):\n            element_list.append(count)\n    # Initialize the counter and the list where events will be saved\n    count = 0\n    particles = []\n    # Initialize a list of tracks and clusters where their will be stored before\n    # to save it in the final list\n    particles_1_tracks = []\n    particles_1_clusters = []\n    # Generate a loop with the number of events found\n    for i in element_list:\n        if i == 0:\n            iant = i\n            continue\n        # Iterates through the file as many times as there are events in the file\n        for line in file[iant:i]:\n            split = str(line).split()\n            if len(split) == 1 and not '------' in line:\n                particles.append(line[:-1])\n            # Store each track on a list and each cluster on other list\n            if 'track' in str(line):\n                particles_1_tracks.append(line)\n            if 'cluster' in str(line):\n                particles_1_clusters.append(line)\n        # Add the tracks and cluster lists of the specific event on the general\n        # list\n        particles.append(particles_1_tracks)\n        particles.append(particles_1_clusters)\n        # Restore the specific track and cluster lists for the next iteration\n        particles_1_tracks = []\n        particles_1_clusters = []\n        # Update the iant for stablish the next file part to be revised\n        iant = i\n    return particles\n\ndef particles_sonification(index, element, track_list, cluster_list, reset_status, play_sound_status=True):\n    \"\"\"\n    This method allows to iterate through a given event ploting and sonifying \n    the data provided.\n\n    Parameters\n    ----------\n    index : TYPE int, where we are located in the event reproduction\n    element : TYPE str, indicate Track or Cluster\n    track_list : TYPE list, contain the track elements\n    cluster_list : TYPE list, contain the cluster element\n    play_sound_status : TYPE bool, optional-default True, to not play sound\n    \"\"\"\n    # Enable the global variables to be used and modified\n    global cluster_tosonify, sonified_cluster_list, sonified_tracks_list\n    # If this is the first track element of the event, initialize colour counter\n    # and sonified elements lists\n    if reset_status:\n        lhc_plot.set_count_colors(0)\n        sonified_cluster_list = []\n        sonified_tracks_list = []\n    # Restore variables\n    cluster_tosonify = []\n    converted_photon = ' '\n    # Copy the track list to be used to search a close track\n    track_list_2 = track_list.copy()\n    # Select action depending on Track or Cluster reproduction\n    if element == 'Track':\n        # Obtain track elements to be reproduced\n        track = track_list[index]\n        track_elements = str(track).split()\n        nextindex = index + 1\n        # Check if the track has been sonified\n        if not track_elements[0] in sonified_tracks_list:\n            if int(track_elements[11])==1:\n                # If the track is a muon plot it\n                lhc_plot.plot_muontrack(track_elements)\n            else:\n                # If the track is not a muon plot a simple track\n                lhc_plot.plot_innertrack(track_elements)\n            # With each track calculate if it points out a cluster or not, if points a\n            # cluster we will sonify the track and the cluster; and check if there\n            # are a close track\n            for cluster in cluster_list:\n                cluster_elements = str(cluster).split()\n                # Search if the track points a cluster\n                value = math.sqrt(\n                    pow(\n                        (float(track_elements[4])-float(cluster_elements[4])),\n                        2) \n                    + pow(\n                        (float(track_elements[5])-float(cluster_elements[5])),\n                        2)\n                    )\n                if value < 0.07:\n                    # If the track points to the cluster, plot it and include it\n                    # in the list to sonify.\n                    if not cluster_elements[0] in sonified_cluster_list:\n                        sonified_cluster_list.append(cluster_elements[0])\n                    lhc_plot.plot_cluster(\n                        phi=float(track_elements[4]),\n                        theta=float(track_elements[5]),\n                        eta=float(track_elements[6]),\n                        amplitud=float(cluster_elements[3])/100)\n                    cluster_tosonify.append(cluster)\n                    # In addition, search if there are a close track\n                    if nextindex < len(track_list):\n                        for track2 in track_list_2[nextindex:]:\n                            track2_elements = str(track2).split()\n                            value_tracks = math.sqrt(\n                                pow(\n                                    (float(track_elements[4])-float(track2_elements[4])),\n                                    2) \n                                + pow(\n                                    (float(track_elements[5])-float(track2_elements[5])),\n                                    2)\n                                )\n                            if value_tracks < 0.04 and track_elements[1] != track2_elements[1]:\n                                # If exist a track very close, plot it and set the variable\n                                # to reproduce the converted photon sound\n                                if not track2_elements[0] in sonified_tracks_list:\n                                    sonified_tracks_list.append(track2_elements[0])\n                                lhc_plot.plot_innertrack(track2_elements)\n                                converted_photon = track2_elements[0]\n            \"\"\"\n            Sonification part\n            \"\"\"\n            if cluster_tosonify:\n                # The track point out a cluster\n                if len(cluster_tosonify) > 1:\n                    #Arreglar el mensaje aqui\n                    print('Could a track points out to more than one cluster?')\n                    print(cluster_tosonify)\n                    return\n                cluster_elements = str(cluster_tosonify[0]).split()\n                if converted_photon == ' ':\n                    print('Sonifying '+track_elements[0]+' and '+cluster_elements[0])\n                    if int(track_elements[11])==1:\n                        # The element is a muon with cluster\n                        \"\"\"\n                        1) bip: the beginning of the detector\n                        2) continuous sound during 2 seconds: the track in the inner detector\n                        3) a tone with different frequency: change from inner detector to red calorimeter\n                        4) sound corresponding to the cluster with the continuous sound of the track of the muon\n                        \"\"\"\n                        # For the amplitud of the sound we use the transverse energy\n                        # supposing a range of [0;100], we devide the value by 100\n                        # to normalize it.\n                        sound = lhc_sonification.muontrack_withcluster(float(cluster_elements[3])/100)\n                    else:\n                        # The element is an electron\n                        \"\"\"\n                        1) bip: the beginning of the detector\n                        2) continuous sound during 2 seconds: the track in the inner detector\n                        3) a tone with different frequency: change from inner detector to red calorimeter\n                        4) sound corresponding to the cluster\n                        \"\"\"\n                        sound = lhc_sonification.singletrack_withcluster(float(cluster_elements[3])/100)\n                else:\n                    # The element is a converted photon\n                    \"\"\"\n                    1) bip: the beginning of the detector\n                    2) two continuous sound during 2 seconds: the tracks in the inner detector\n                    3) a tone with different frequency: change from inner detector to red calorimeter\n                    4) sound corresponding to the cluster\n                    \"\"\"\n                    print('Sonifying '+track_elements[0]+', '+converted_photon+' and '+cluster_elements[0])\n                    sound = lhc_sonification.doubletrack_withcluster(float(cluster_elements[3])/100)\n            else:\n                # The track don't point out a cluster\n                \"\"\"\n                1) bip: the beginning of the detector\n                2) continuous sound during 2 seconds: the track in the inner detector\n                3) a tone with different frequency: change from inner detector to red calorimeter\n                \"\"\"\n                print('Sonifying '+track_elements[0])\n                sound = lhc_sonification.singletrack_only()\n                if int(track_elements[11])==1:\n                    # The element is a muon\n                    sound = lhc_sonification.muontrack_only()\n            # check the flag and sonify\n            if play_sound_status:\n                lhc_sonification.play_sound(sound)\n            # Store the element in the array to save sound\n            if nextindex == 1:\n                lhc_sonification.array_savesound(sound)\n            else:\n                lhc_sonification.add_array_savesound(sound)\n            # Add a silence between each element\n            lhc_sonification.add_array_savesound(lhc_sonification.get_silence(1))\n            # Restore variables\n            cluster_tosonify = []\n            converted_photon = ' '\n    elif element == 'Cluster':\n        # Obtain Cluster elements to be reproduced\n        cluster = cluster_list[index]\n        cluster_elements = str(cluster).split()\n        # Check if the cluster has been sonified\n        if not cluster_elements[0] in sonified_cluster_list:\n            # Plot the cluster\n            lhc_plot.plot_cluster(\n                phi=float(cluster_elements[4]),\n                theta=float(cluster_elements[5]),\n                eta=float(cluster_elements[6]),\n                amplitud=float(cluster_elements[3])/100)\n            # Sonify the cluster\n            \"\"\"\n            1) bip: the beginning of the detector\n            2) silence during 2 seconds: there are no track in the inner detector\n            3) a tone with different frequency: change from inner detector to red calorimeter\n            4) sound corresponding to the cluster\n            \"\"\"\n            print('Sonifying '+cluster_elements[0])\n            sound = lhc_sonification.cluster_only(float(cluster_elements[3])/100)\n            # Check the flag and reproduce the sound\n            if play_sound_status:\n                lhc_sonification.play_sound(sound)\n            # Store the sound to be saved later\n            if track_list == []:\n                lhc_sonification.array_savesound(sound)\n            else:\n                lhc_sonification.add_array_savesound(sound)\n            lhc_sonification.add_array_savesound(lhc_sonification.get_silence(1))\n    else:\n        print(\"The element provided is unknown\")\n","sub_path":"sonoUno/data_lhc/lhc_data.py","file_name":"lhc_data.py","file_ext":"py","file_size_in_byte":12280,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"126325985","text":"print(\"YO wat wil je laten stotteren? \\n\")\n\nrunning = True\nwhile running == True:\n    antwoord = input(\"typ hier: \")\n    userlist = antwoord.split()\n    for x in userlist:\n        if len(x) > 2:\n            print(x[0:2]+\"..\", x[0:2]+\"..\" ,x)\n        else:\n            print(x)\n    break\n","sub_path":"stuttersimulator.py","file_name":"stuttersimulator.py","file_ext":"py","file_size_in_byte":287,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"10358364","text":"import discord\nimport os\nimport random\nimport get_moe_news\n\nclient = discord.Client()\ntoken = os.environ['DISCORD_BOT_TOKEN']\n\n\n# with open('greeting.txt') as f:\n#     greeting_list = [s.strip() for s in f.readlines()]\n\n\n@client.event\nasync def on_ready():\n    print('ログインしました')\n    print(client.user.name)\n    print(client.user.id)\n    print(discord.__version__)\n    print('------')\n\n\n@client.event\nasync def on_message(ctx):\n    if ctx.author.bot:\n        return\n\n    if str(ctx.content).startswith('!moenews'):\n        cmd_str = str(ctx.content).split(\" \")\n\n        if str(cmd_str[1:2]).strip() == \"['all']\":\n            await ctx.channel.send('トップに表示されているニュースを全件表示します。')\n            news = get_moe_news.get_moe_news(get_all=True)\n        else:\n            await ctx.channel.send('固定ニュース以外を表示します。')\n            news = get_moe_news.get_moe_news(get_all=False)\n\n        news_text = \"\"\n        for d in news:\n            news_text = news_text + d.get('title') + \"\\n\"\n            news_text = news_text + d.get('url') + \"\\n\"\n        await ctx.channel.send(news_text)\n\n    if ctx.content == '!dice_test':\n        await ctx.channel.send(f\"{ctx.author.mention}はマジックダイスを振った！\")\n\n    if str(ctx.content).startswith('/dice'):\n        cmd = ['/dice', '0', '1000']\n\n        if ' ' in str(ctx.content).strip():\n            cmd = str(ctx.content).split(' ')\n\n        rand_min = 0\n        rand_max = 1000\n\n        if cmd[0:1]:\n            rand_min = int(cmd[1])\n\n        if cmd[1:2]:\n            rand_max = int(cmd[2])\n\n        dice = random.randint(rand_min, rand_max)\n        await ctx.channel.send(ctx.author.mention + ' はマジックダイス' + '(' + str(rand_min) + '-' + str(rand_max) + ')' + 'を振った！ ' +str(dice) + ' が出た！')\n\n    if ctx.content == '!chum':\n        with open('chum.txt') as d:\n            l_dic = [s.strip() for s in d.readlines()]\n        await ctx.channel.send(random.choice(l_dic))\n\n    if ctx.content == '!sou':\n        with open('sou.txt') as d:\n            l_dic = [s.strip() for s in d.readlines()]\n        await ctx.channel.send(random.choice(l_dic))\n\n    if ctx.content == '!ken':\n        with open('ken.txt') as d:\n            l_dic = [s.strip() for s in d.readlines()]\n        await ctx.channel.send(random.choice(l_dic))\n\n    # for row in greeting_list:\n    #     if str(ctx.content).startswith(row):\n    #         await ctx.channel.send(f\"へいよーぐっつすっす\\n{ctx.author.mention}さん、いらっしゃ～い\")\n    #         break\n\n\nclient.run(token)\n","sub_path":"discordbot.py","file_name":"discordbot.py","file_ext":"py","file_size_in_byte":2625,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"422670187","text":"from bs4 import BeautifulSoup\nimport requests\nimport time\nfrom crawler import listCrawler\nfrom runner import runner\n\n\nclass ListTask:\n    url = None\n\n    def __init__(self, url, *array, **dic):\n        self.url = url\n\n\n    def run(self):\n        html = requests.get(self.url, timeout=(10.0, 10.0)).text\n        soup = BeautifulSoup(html, \"html.parser\")\n        pageSize = listCrawler.getPageSize(soup)\n        for index in range(pageSize):\n            page = index+1\n            html2 = requests.get(self.url + str(page), timeout=(10.0, 10.0)).text\n            soup2 = BeautifulSoup(html2, \"html.parser\")\n            for bookurl in listCrawler.getBookList(soup2):\n                runner.booksQueue.put(bookurl)\n        time.sleep(1)\n\n\n","sub_path":"BooksCrawler/bookthread/listTask.py","file_name":"listTask.py","file_ext":"py","file_size_in_byte":735,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"17731961","text":"import chromedriver_autoinstaller\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.options import Options\nfrom tqdm import tqdm\nimport time\nimport re\n\nimport pandas as pd\n\nfrom multiprocessing import Pool\n\nCHROME_VER = chromedriver_autoinstaller.get_chrome_version().split('.')[0]\nBASE_DRIVER_PATH = f'./{CHROME_VER}/chromedriver.exe'\n\nfor _ in range(3):\n    options = Options()\n    options.add_argument('headless')\n    # options.add_argument(\"--proxy-server=socks5://127.0.0.1:9150\")\n    options.add_argument(\n        \"user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) \"\n        \"Chrome/91.0.4472.124 Safari/537.36\")\n    options.add_argument(\n        \"app-version=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) \"\n        \"Chrome/91.0.4472.124 Safari/537.36\")\n\nCHECK_POINT = 560\nCRAWLING_FLAG = True\n\nURL_LIST = list()\nTITLE_LIST = list()\nCONTENT_LIST = list()\nCOMMENT_LISt = list()\nFAIL_URL_LIST = list()\n\ntry:\n    driver = webdriver.Chrome(f\"{BASE_DRIVER_PATH}\", chrome_options=options)\nexcept:\n    chromedriver_autoinstaller.install(True)\n    driver = webdriver.Chrome(f\"{BASE_DRIVER_PATH}\", chrome_options=options)\n\n\ndef get_num(text):\n    result = re.sub('[^0-9]', '', text)\n\n    return result\n\n\ndef get_url(keyword):\n    global CHECK_POINT, CRAWLING_FLAG\n\n    url_list = []\n    max_page_no = 572\n\n    for page in range(1, max_page_no):\n        url = f\"https://section.blog.naver.com/Search/Post.naver?pageNo={page}\" \\\n              f\"&rangeType=ALL&orderBy=sim&keyword={keyword}\"\n        driver.get(url)\n        driver.implicitly_wait(2)\n\n        for blog_no in range(1, 8):\n            try:\n                titles = driver.find_element_by_xpath(f'/html/body/ui-view/div/main/div/div/section/div[2]/div[{blog_no}]'\n                                                      f'/div/div[1]/div[1]/a[1]')\n                blog_url = titles.get_attribute('href')\n                url_list.append(blog_url)\n            except:\n                print(\"url 수집 중 에러발생\")\n                time.sleep(2)\n\n        if not page % 10:\n            print(f'url{page}개 수집완료')\n\n    print(f\"총 {len(url_list)}개의 url 수집 완료\")\n\n    return url_list\n\n\ndef get_content(link):\n    driver.get(link)\n    driver.implicitly_wait(2)\n    error_count = 0\n    try:\n        driver.switch_to.frame('mainFrame')\n        overlays = \".se-fs-.se-ff-\"\n        title = driver.find_element_by_css_selector(overlays)\n        title_text = title.text\n\n        overlays = \".se-component.se-text.se-l-default\"\n        content = driver.find_element_by_css_selector(overlays)\n        content_text = content.text\n\n        # comment_info = driver.find_element_by_css_selector('em#commentCount')\n        # comment_count = comment_info.text\n        #\n        # comment_list = \"\"\n        # if not comment_count:\n        #     pass\n        # else:\n        #     comment_info.click()\n        #     comments = driver.find_elements_by_css_selector('span.u_cbox_contents')\n        #     for comment in comments:\n        #         comment_list += comment.text\n\n        print(f\"{link} 데이터 수집 완료\")\n\n        return [link, title_text, content_text]\n    except:\n        error_count += 1\n        FAIL_URL_LIST.append(link)\n        print(f\"에러 발생{link}\")\n\n\ndef to_csv(keyword):\n    blog_text_df = pd.DataFrame({\n        'url': URL_LIST,\n        'title': TITLE_LIST,\n        'content': CONTENT_LIST,\n    })\n\n    blog_fail_df = pd.DataFrame({\n        'fail_url': FAIL_URL_LIST\n    })\n\n    blog_text_df.to_csv(\n        f'naver_blog_data{CHECK_POINT - 1}page_{keyword}.csv',\n        header=True,\n        index=True,\n        encoding='utf-8-sig'\n    )\n\n    blog_fail_df.to_csv(\n        f\"naver_blog_fail_url{CHECK_POINT - 1}page_{keyword}.csv\",\n        header=True,\n        index=True,\n        encoding='utf-8-sig'\n    )\n\n\nif __name__ == '__main__':\n    pool = Pool(processes=6)\n\n    keyword_list = [\n        '리뷰',\n        '후기',\n        '맛집',\n        '연애',\n        '결혼',\n        '관광지',\n        '코로나',\n        '날씨',\n        '꽃',\n        '명소',\n        '다이어트',\n        '학교',\n        '자격증',\n        '라인',\n        '학교',\n        '주사',\n        '성형',\n        '교정',\n        '여드름',\n        '제주',\n        '키보드',\n        '마우스',\n        '카페',\n        '아이폰',\n        '일상',\n        '내돈내산',\n        '웹툰',\n        '리그오브레전드',\n        '배그',\n        '헤드셋',\n        '로아',\n        '서든어택',\n        'NLP',\n        '딥러닝',\n        '이미지처리',\n        '챗봇',\n        '페르소나',\n        '교감',\n        '고양이',\n        '멍멍이',\n        '진돗개',\n        '인공지능',\n        '로꼬',\n        '쇼미더머니',\n        '릴보이',\n        'aomg',\n        '산이',\n        '비와이',\n        '마우스패드',\n        '비트코인',\n        '그래픽카드'\n    ]\n    for keyword in keyword_list:\n        print(f'{keyword}키워드 크롤링 시작')\n        for blog_info in tqdm(pool.map(get_content, get_url(keyword)), desc=f'{keyword}_crawling'):\n            if not blog_info:\n                pass\n            else:\n                URL_LIST.append(blog_info[0])\n                TITLE_LIST.append(blog_info[1])\n                CONTENT_LIST.append(blog_info[2])\n                # COMMENT_LISt.append(blog_info[3])\n        to_csv(keyword)\n\n        if not CRAWLING_FLAG:\n            break\n\n        URL_LIST = []\n        TITLE_LIST = []\n        CONTENT_LIST = []\n        FAIL_URL_LIST = []\n            \n        print(f'{keyword}키워드 크롤링 끝')\n        CHECK_POINT = 1\n        # COMMENT_LISt = []\n\n","sub_path":"Web Crawling/naver-blog/naver_blog_crawler/naver_blog_crawler.py","file_name":"naver_blog_crawler.py","file_ext":"py","file_size_in_byte":5757,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"193067329","text":"# uncompyle6 version 3.7.4\n# Python bytecode 3.7 (3394)\n# Decompiled from: Python 3.7.9 (tags/v3.7.9:13c94747c7, Aug 17 2020, 18:58:18) [MSC v.1900 64 bit (AMD64)]\n# Embedded file name: T:\\InGame\\Gameplay\\Scripts\\Server\\objects\\components\\consumable_component.py\n# Compiled at: 2020-09-04 01:25:03\n# Size of source mod 2**32: 10900 bytes\nfrom event_testing.resolver import SingleObjectResolver\nfrom interactions import ParticipantType\nfrom interactions.utils.loot import LootActions\nfrom objects.components import Component, types\nfrom objects.components.state import TunableStateTypeReference\nfrom tunable_multiplier import TestedSum\nfrom sims4.tuning.tunable import Tunable, TunableTuple, TunableList, TunableReference, TunableRange, TunableEnumEntry, TunableVariant, HasTunableFactory, AutoFactoryInit\nfrom statistics.statistic_ops import StatisticChangeOp, StatisticAddRelationship, StatisticOperation, RelationshipOperation\nimport enum, services, sims4.log\nlogger = sims4.log.Logger('ConsumableComponent')\n\nclass ConsumptionEffects(enum.Int):\n    NO_EFFECT = 0\n    CALORIE_LOSS = 1\n    CALORIE_GAIN = 2\n\n\ndebug_consumables_are_infinite = False\n\nclass ConsumableComponent(Component, HasTunableFactory, AutoFactoryInit, component_name=types.CONSUMABLE_COMPONENT):\n\n    @staticmethod\n    def _create_calorie_constants_callback(instance_class, tunable_name, source, value, **kwargs):\n        commodity_range = ConsumableComponent.FAT_COMMODITY.max_value_tuning - ConsumableComponent.FAT_COMMODITY.min_value_tuning\n        ConsumableComponent.global_calorie_range_modifier = ConsumableComponent.CALORIES_PER_POUND * ConsumableComponent.SIM_WEIGHT_RANGE / commodity_range\n\n    manager = services.get_instance_manager(sims4.resources.Types.STATISTIC)\n    CALORIES_PER_POUND = Tunable(int, 3500, description='Number of calories in 1 pound of fat.', deferred=True, callback=_create_calorie_constants_callback)\n    SIM_WEIGHT_RANGE = Tunable(int, 100, description='The difference in pounds between Sims with empty and full fat commodities.')\n    FAT_COMMODITY = TunableReference(manager, description=\"A reference to the Sim's fat commodity.\")\n    FIT_COMMODITY = TunableReference(manager, description=\"A reference to the Sim's fit commodity.\")\n    CONSUMABLE_COMMODITY = TunableReference(manager, description=\"A reference to the Object's consumable commodity.\")\n    FAT_STATE = TunableStateTypeReference(description='The fatness state type.')\n    FIT_STATE = TunableStateTypeReference(description='The fit state type.')\n    global_calorie_range_modifier = None\n    FACTORY_TUNABLES = {'consumption_turns':TunableRange(description='\\n            An integer value specifying the number of turns it would take a Sim\\n            to completely consume this object.\\n            ',\n       tunable_type=int,\n       default=10,\n       minimum=1), \n     'consumption_statistics':TunableList(description=\"\\n            Statistic changes whose values represent the values that the\\n            complete consumption of this object would provide.\\n            \\n            e.g. A statistic change of 50 for the hunger commodity will fill a\\n            Sim's hunger commodity by 25 if they consume half of this object,\\n            and by 50 if they consume all of it.\\n            \\n            The following commodities will have statistic changes automatically\\n            generated based on other information and do not need to be added\\n            explicitly:\\n            \\n             * Fat commodity\\n             * Fit commodity\\n             * Consumable commodity\\n            \",\n       tunable=TunableVariant(description='\\n                The operation that defines the consumption statistic change.\\n                ',\n       statistic_change=(StatisticChangeOp.TunableFactory)(statistic_override=StatisticChangeOp.get_statistic_override(pack_safe=True), **StatisticOperation.DEFAULT_PARTICIPANT_ARGUMENTS))), \n     'fitness_info':TunableTuple(description='\\n            A list of tunables that affect Sim fitness.\\n            ',\n       calories=Tunable(description='\\n                The number of calories contained in this consumable.\\n                \\n                If this object is marked as having a consumption effect, this\\n                value will be used to generate appropriate fat gains or losses\\n                for the Sim consuming this object.\\n                ',\n       tunable_type=int,\n       default=500),\n       consumption_effect=TunableEnumEntry(description='\\n                The effect that consuming this object will have on the Sim.\\n                ',\n       tunable_type=ConsumptionEffects,\n       default=(ConsumptionEffects.NO_EFFECT)),\n       calorie_modifier=TestedSum.TunableFactory(description='\\n                An added modifier to the calorie count of this object\\n                when being consumed.  Tests will be done with the owning\\n                object.  If the consumption effect is set to NO EFFECT then\\n                these tests will not run or increase the calories.  If the\\n                consumption effect is set to CALORIE_LOSS then this modifier\\n                will end up decreasing the calorie loss if the added value\\n                is positive.\\n                ')), \n     'consume_affordances':TunableList(description='\\n            List of consume affordances that are forwarded to the consumable.\\n            ',\n       tunable=TunableReference(description=\"\\n                The affordance that interfaces with this component and consumes the\\n                owning object.  This affordance will be dynamically added to the\\n                owning object's super affordance list at runtime.\\n                \",\n       manager=(services.affordance_manager()),\n       class_restrictions=('SuperInteraction', ),\n       pack_safe=True)), \n     'allow_destruction_on_inventory_transfer':Tunable(description=\"\\n            If checked, this consumable is not going to survive attempts to\\n            automatically be placed in a Sim's inventory. \\n            \\n            For instance, it would not survive a transfer from a Sim's inventory\\n            to its household inventory upon death. Likewise, it would not\\n            survive an automatic transfer from the world to a Sim's inventory\\n            when its parent object is inventoried.\\n            \\n            Regular consumables, such as food, would probably want to leave this\\n            checked. However, more meaningful consumables, such as Potions,\\n            might want to prevent this behavior.\\n            \",\n       tunable_type=bool,\n       default=True)}\n\n    def __init__(self, *args, **kwargs):\n        (super().__init__)(*args, **kwargs)\n        self._loot_list = None\n\n    @property\n    def loot_list(self):\n        if self._loot_list is None:\n            self._derive_consumption_operations()\n        return self._loot_list\n\n    def component_super_affordances_gen(self, **kwargs):\n        if self.consume_affordances is not None:\n            yield from self.consume_affordances\n        if False:\n            yield None\n\n    def get_calorie_amount(self):\n        if self.fitness_info.consumption_effect == ConsumptionEffects.NO_EFFECT:\n            return 0\n        elif self.fitness_info.consumption_effect == ConsumptionEffects.CALORIE_GAIN:\n            amount = self.fitness_info.calories\n        else:\n            amount = -self.fitness_info.calories\n        amount += self.fitness_info.calorie_modifier.get_modified_value(SingleObjectResolver(self.owner))\n        return amount\n\n    def _derive_consumption_operations(self):\n        new_statistics = []\n        for stat in self.consumption_statistics:\n            amount = stat._amount / self.consumption_turns\n            stat_change = StatisticChangeOp(amount=amount, stat=(stat._stat), subject=(stat._subject), tests=(stat._tests))\n            new_statistics.append(stat_change)\n\n        amount = self.get_calorie_amount()\n        if amount != 0:\n            amount = amount / self.global_calorie_range_modifier\n            amount /= self.consumption_turns\n            stat_change = StatisticChangeOp(amount=amount, stat=(self.FAT_COMMODITY), subject=(ParticipantType.Actor))\n            new_statistics.append(stat_change)\n        if not debug_consumables_are_infinite:\n            commodity_range = self.CONSUMABLE_COMMODITY.max_value_tuning - self.CONSUMABLE_COMMODITY.min_value_tuning\n            amount = commodity_range / self.consumption_turns\n            stat_change = StatisticChangeOp(amount=(-amount), stat=(self.CONSUMABLE_COMMODITY), subject=(ParticipantType.Object))\n            new_statistics.append(stat_change)\n        loot_actions = LootActions(run_test_first=False, loot_actions=new_statistics)\n        self._loot_list = [loot_actions]\n\n    def bites_left(self):\n        commodity_range = self.CONSUMABLE_COMMODITY.max_value_tuning - self.CONSUMABLE_COMMODITY.min_value_tuning\n        amount_per_turn = commodity_range / self.consumption_turns\n        current_value = self.owner.commodity_tracker.get_value(self.CONSUMABLE_COMMODITY)\n        bites_left = current_value / amount_per_turn\n        return bites_left","sub_path":"Scripts/simulation/objects/components/consumable_component.py","file_name":"consumable_component.py","file_ext":"py","file_size_in_byte":9149,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"469164210","text":"from bs4 import BeautifulSoup\nimport requests\nprint(\"简易手机号码抓取工具 By WeepingDogel\")\nurl=\"http://www.hiphop8.com/mobile/xian_1319339.html\"\npage=requests.get(url)\npage_info=page.content\nsoup = BeautifulSoup(page_info, \"html.parser\")\nnumbers = soup.find_all('a')\nwith open(\"1319339.txt\",\"w\") as file:\n    for number in numbers:\n        print(number.string)\n        file.write(str(number.string)+\"\\n\")\n","sub_path":"source.py","file_name":"source.py","file_ext":"py","file_size_in_byte":416,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"276533363","text":"#\n# Thierry Parmentelat - INRIA\n#\n#from PLC.Nodes import Node\n#from PLC.Interfaces import Interface\n#from PLC.Slices import Slice\n#from PLC.Sites import Site\nfrom PLC.Persons import Person\n\nfrom PLC.Accessors.Factory import define_accessors, admin_roles, all_roles, tech_roles\n\nimport sys\ncurrent_module = sys.modules[__name__]\n\ndefine_accessors(current_module, Person, \"Columnconf\", \"columnconf\",\n                  \"person/myslice\", \"column configuration\",\n                  get_roles=all_roles, set_roles=all_roles, expose_in_api=True)\n\ndefine_accessors(current_module, Person, \"Showconf\", \"showconf\",\n                  \"person/myslice\", \"show configuration\",\n                  get_roles=all_roles, set_roles=all_roles, expose_in_api=True)\n","sub_path":"PLC/Accessors/Accessors_myslice.py","file_name":"Accessors_myslice.py","file_ext":"py","file_size_in_byte":742,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"382747306","text":"#!/usr/bin/python\n# coding: utf-8\n\nimport operator\nfrom random import shuffle\n\n\nclass Card:\n\n    values = {\n        \"Ace\": 1,\n        \"Jack\": 11,\n        \"Queen\": 12,\n        \"King\": 13\n    }\n\n    def __init__(self, color, value):\n        self.color = color #spades, hearts, diamonds, clubs\n        self.name = value\n        if not self.values.get(value):\n            self.value = value\n        else:\n            self.value = self.values.get(value)\n\n\n    def compare(self, other_card):\n        \"\"\" niech ta funkcja zwraca liczbę dodatnią(jeśli self > other_card),\n        ujemną w przeciwnym wypadku i zero gdy są równe \"\"\"\n        return self.value - other_card.value\n\n\n    def print_card(self):\n        # wypisuje w ładnej postaci kartę\n        print(\"Card:  %s %s\" % (self.name, self.color))\n\n\nclass Player:\n    def __init__(self, player_name):\n        self.name = player_name\n        self.cards = []\n\n\n    def print_player_cards(self):\n        # wypisuje w ładnej formie jakie karty gracz ma na ręce\n        if self.cards:\n            print(\"%s posiada: \" % self.name, end = \"\")\n            for card in self.cards:\n                print(\"%s %s, \" % (card.color, card.name), end = \"\")\n        else:\n            print(\"Gracz %s nie posiada żadnych kart\" % self.name)\n        print(\"\\n\")\n\n\n    def add_card(self, card):\n        # dodaje kartę\n        self.cards.append(card)\n\n\n    def get_card(self, color, name):\n        # zwraca dowolną kartę\n        for card in self.cards:\n            if card.color is color and card.name is name:\n                temp_card = card\n                self.cards.remove(card)\n                return temp_card\n\n\n    def get_cards(self):\n        # oddaje swoje karty\n        list_of_cards = self.cards\n        for card in self.cards:\n            self.cards.remove(card)\n        return list_of_cards\n\n\nclass Deck:\n    def __init__(self):\n        self.values_name = ['Ace','2','3','4','5','6','7','8','9','10','Jack','Queen','King']\n        self.colors_name = ['Spades','Hearts', 'Diamonds','Clubs']\n        self.cards_in_deck = self.create_deck()\n\n\n    def create_deck(self):\n        new_deck = []\n        for value in self.values_name: # od Asa od Króla\n            for color in self.colors_name:\n                new_deck.append(Card(color,value))\n        return new_deck\n\n\n    def sort(self):\n        # sortuje talię\n        self.cards_in_deck.sort(key = operator.attrgetter('color'))\n\n\n    def shuffle(self):\n        # tasuje talię\n        shuffle(self.cards_in_deck)\n\n\n    def get_card(self):\n        # zwraca pierwsza kartę z wierzchu\n        last_card = self.cards_in_deck.pop()\n        print(\"Pobrano kartę: %s %s\" % (last_card.color, last_card.name))\n        return last_card\n\n\n    def add_card(self, card):\n        # dodaje kartę do talii\n        self.cards_in_deck.append(card)\n\n    def print_deck(self):\n        # wypisuje w ładnej postaci talię\n        for card in self.cards_in_deck:\n            print(\"%s %s \" %(card.name, card.color))\n        print(\"\\n\")\n\n    def deal(self, players, card_amount):\n        # rozdaje każdemu z gracz card_amount kart\n        for player in players:\n            for num in range(card_amount):\n                player.add_card(self.get_card())\n\n\nclass Game:\n    \"\"\" należy do waszej fantazji :P Dobrze, żeby gra miała talię kart\n    i jakichś graczy oraz metody sterujące grą takie jak start, albo stop\n    \"\"\"\n    def __init__(self, name):\n        self.name = name\n        self.players = []\n        self.deck = Deck()\n        self.game_started = False # gra po utworzeniu nie jest rozpoczęta\n        self.game_stoped = False # i nie jest zakończona\n\n\n    def start(self):\n        print(\"Gra %s rozpoczęta\" % self.name)\n        self.game_started = True\n\n    def stop(self):\n        self.game_started = False\n        self.game_stoped = True\n        print(\"Gra %s zakończona\" % self.name)\n\n\n    def get_name(self):\n        # pobieranie i wyswietlanie nazwy gry\n        print(\"Gra o nazwie %s\" % self.name)\n        return self.name\n\n\n    def add_players(self, players):\n        # dodawanie graczy do gry, tylko przed rozpoczeciem gry\n        if not (self.game_started and self.game_stoped):\n            for player in players:\n                self.players.append(player)\n        else:\n            print(\"Nie można dodać graczy na tym etapie.\")\n\n\n\nprint(\"Tworzenie gry.\")\nwojna = Game(\"Wojna\")\nwojna.get_name()\nprint(\"\\nTworznie graczy i dodawanie do gry.\")\nadam = Player(\"Adam\")\nbartosz = Player(\"Bartosz\")\nczeslaw = Player(\"Czesław\")\ndaniel = Player(\"Daniel\")\nwojna.add_players([adam,bartosz,czeslaw])\nprint(\"\\nRozpoczynanie gry.\")\nwojna.start()\nprint(\"\\nPróba dodania nowych graczy.\")\nwojna.add_players([daniel])\nprint(\"\\nZatrzymywanie rozgrywki.\")\nwojna.stop()\n\n\nwojna.deck.print_deck()\n","sub_path":"Py3_Kurs02/Pawlus_Piotr_02.py","file_name":"Pawlus_Piotr_02.py","file_ext":"py","file_size_in_byte":4797,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"497233470","text":"\n\nfrom xai.brain.wordbase.nouns._ermine import _ERMINE\n\n#calss header\nclass _ERMINES(_ERMINE, ):\n\tdef __init__(self,): \n\t\t_ERMINE.__init__(self)\n\t\tself.name = \"ERMINES\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"ermine\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_ermines.py","file_name":"_ermines.py","file_ext":"py","file_size_in_byte":238,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"233055207","text":"import copy\n\ncols = [\"a\", \"b\", \"c\", \"d\", \"e\", \"f\", \"g\", \"h\"]\nrows = [\"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\"]\n\n\ndef new_board():\n    board = [0] * 8\n    for column in range(0,8):\n        board[column] = [0] * 8\n\n    board[3][3] = 1\n    board[3][4] = 2\n    board[4][3] = 2\n    board[4][4] = 1\n\n    return board\n\n\ndef print_board(board):\n    #end stops a new line from being made when print is looping (loop across)\n    print()\n    for column in range(0,9):\n        print(\"---+\", end=\"\")\n    print()\n\n    for row in range(0,8):\n        # Row number\n        print(\" \" + str(row+1) + \" |\", end=\"\")\n\n        for column in range(0,8):\n            print(\" \", end=\"\")\n            if board[row][column] == 0:\n                print(\" \", end=\"\")\n            elif board[row][column] == 1:\n                print(\"B\", end=\"\")\n            elif board[row][column] == 2:\n                print(\"W\", end=\"\")\n            print(\" |\", end=\"\")\n\n        print()\n\n        for column in range(0,9):\n            print(\"---+\", end=\"\")\n        print()\n\n    print(\"   |\", end=\"\")\n    for c in [\"a\",\"b\",\"c\",\"d\",\"e\",\"f\",\"g\",\"h\"]:\n        print(\" \" + c + \" |\", end=\"\")\n\n    print()\n    print()\n\n\ndef score(board):\n    scores = [0,0]\n\n    for row in range (0,8):\n\n        for column in range (0,8):\n            if board[row][column] == 1:\n                scores[0] +=1\n            elif board[row][column] == 2:\n                scores[1] +=1\n\n    return scores\n\n\ndef enclosing(board, player, pos, direction):\n    if player == 1:\n        opposition = 2\n    else:\n        opposition = 1\n\n    # Take a copy of the position to work with so that there is no interference\n    pos1 = copy.copy(pos)\n\n    # Advance in the direction first\n    pos1[0] += direction[0]\n    pos1[1] += direction[1]\n    # Boundary check\n    if pos1[0] < 0 or pos1[0] > 7 or pos1[1] < 0 or pos1[1] > 7:\n        return False\n\n    # Check that it is the opposition.  If not, return false\n    if board[pos1[0]][pos1[1]] == player:\n        return False\n    elif board[pos1[0]][pos1[1]] == 0:\n        return False\n    else:\n        # Keep moving through in the same direction until we find a match for player.  If not return false\n        while board[pos1[0]][pos1[1]] == opposition:\n            # Advance in the direction\n            pos1[0] += direction[0]\n            pos1[1] += direction[1]\n            # Boundary check\n            if pos1[0] < 0 or pos1[0] > 7 or pos1[1] < 0 or pos1[1] > 7:\n                return False\n\n        # No longer on the opposition.  If we're on a player token, then we're enclosing\n        if board[pos1[0]][pos1[1]] == player:\n            return True\n        else:\n            return False\n\n\ndef valid_moves(board, player):\n\n    valid_positions = []\n    for row in range(0, 8):\n        for column in range(0, 8): # Two if statements scan through each position on board\n            pos = [row, column]\n            # Check if we're on a blank\n            if board[pos[0]][pos[1]] == 0:\n                # Now check in all directions\n                for dr in range(-1, 2):\n                    for dc in range(-1, 2):\n                        direction = [dr, dc]\n                        if enclosing(board, player, pos, direction) == True:\n                            valid_positions.append(pos)\n\n    return valid_positions\n\n\ndef next_state(board, player, pos):\n\n    next_board = copy.deepcopy(board)\n\n    if player == 1:\n        next_player = 2\n    else:\n        next_player = 1\n\n    # Find elements to flip\n    for dr in range(-1, 2):\n        for dc in range(-1, 2):\n            direction = [dr, dc]\n            pos1 = copy.copy(pos)\n            while enclosing(next_board, player, pos1, direction) == True:\n                # Now keep flipping until we find ourself\n                next_board[pos1[0]][pos1[1]] = player\n                pos1[0] += direction[0]\n                pos1[1] += direction[1]\n\n            # Flip our current position\n            next_board[pos1[0]][pos1[1]] = player\n\n    if not valid_moves(next_board, next_player):\n        next_player = 0\n\n    next_move = [next_board, next_player]\n\n    return next_move\n\ndef position(string):\n\n    pos = [0,0]\n    # must be 2 characters\n    if len(string) == 2:\n        chars = list(string.lower())\n        try:\n            # First character is column\n            pos[1] = cols.index(chars[0])\n            pos[0] = rows.index(chars[1])\n\n            return pos\n        except:\n            return None\n    else:\n        return None\n\n\ndef run_two_players():\n    game = [0,1]\n    game[0] = new_board()\n    game[1] = 1\n\n\n    while game[1] != 0:\n        print_board(game[0])\n        if game[1] == 1:\n            player = \"Black\"\n        else:\n            player = \"White\"\n        print(\"It is player \" + player + \"'s turn\")\n        valid_move = False\n    #    pos = None\n        while not valid_move:\n            pos = None\n            while pos == None:\n                move = input(\"Where would you like to go? \")\n                pos = position(move)\n\n                if not pos:\n                    print(\"Invalid move - try again\")\n\n            moves = valid_moves(game[0], game[1])\n            for m in moves:\n                if pos == m:\n                    valid_move = True\n\n            if not valid_move:\n                print(\"Invalid move - try again\")\n\n        # We now have a valid move\n        game = next_state(game[0], game[1], pos)\n        scores = score(game[0])\n        print(\"Score: Black = \" + str(scores[0]) + \" White = \" + str(scores[1]))\n\n    print(\"Game over\")\n    if scores[0] > scores[1]:\n        print(\"The winner is Black!\")\n    elif scores[1] > scores[0]:\n        print(\"The winner is White!\")\n    else:\n        print(\"It's a Draw!\")\n\n\ndef run_single_player():\n    game = [0,1]\n    game[0] = new_board()\n    game[1] = 1\n\n\n    while game[1] != 0:\n        print_board(game[0])\n        if game[1] == 1:\n            player = \"Black\"\n            print(\"It is player \" + player + \"'s turn\")\n            print()\n            valid_move = False\n            #    pos = None\n            while not valid_move:\n                pos = None\n                while pos == None:\n                    move = input(\"Where would you like to go? \")\n                    pos = position(move)\n\n                    if not pos:\n                        print(\"Invalid move - try again\")\n\n                moves = valid_moves(game[0], game[1])\n                for m in moves:\n                    if pos == m:\n                        valid_move = True\n\n                if not valid_move:\n                    print(\"Invalid move - try again\")\n        else:\n            max_score = 0\n            player = \"White\"\n            print(\"It is player \" + player + \"'s turn\")\n            print()\n            moves = valid_moves(game[0], game[1])\n            for move in moves:\n                trial = copy.deepcopy(game[0])\n                nextgame = next_state(trial, game[1], move)\n                scores = score(nextgame[0])\n                if scores[1] > max_score:\n                    max_score = scores[1]\n                    pos = copy.copy(move)\n            print(\"White placed at: \" + cols[pos[1]]+rows[pos[0]])\n\n\n        # We now have a valid move\n        game = next_state(game[0], game[1], pos)\n        scores = score(game[0])\n        print(\"Score: Black = \" + str(scores[0]) + \" White = \" + str(scores[1]))\n\n    print(\"Game over\")\n    if scores[0] > scores[1]:\n        print(\"The winner is Black!\")\n    elif scores[1] > scores[0]:\n        print(\"The winner is White!\")\n    else:\n        print(\"It's a Draw!\")\n\ngame_option = 0\n\nwhile game_option != 1 and game_option != 2:\n    game_option = int(input(\"1 or 2 players? \"))\n\nif game_option == 1:\n    run_single_player()\nelse:\n    run_two_players()\n","sub_path":"Assessment 1/reversi.py","file_name":"reversi.py","file_ext":"py","file_size_in_byte":7711,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}
+{"seq_id":"231283487","text":"\"\"\"\nEstimate the error done when using less sensors\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as ppl\nimport tables as pt\nfrom itertools import combinations\nfrom math import factorial\nfrom scipy.signal import detrend\nfrom scipy.special import gammainc\nfrom scipy.optimize import fmin\n\nppl.close('all')\n\n# Get data only after this time (in days)\nrMin = 5\n\n# We open the file for output\nh5file = pt.open_file(\"/home/cimatori/Analyses/Drift/exp1/process_data/results/Drift.h5\", mode = \"r\")\n\n# Pointers to variables\nSBA = h5file.root.SB.SBA\nth  = h5file.root.th.th\n\n# How many nioz4 and how many nioz4c?\nth_id = th[:]['Id']\nnew   = np.sum(th_id>100)\nold   = np.sum(th_id<=100)\n\ncolors_new = ppl.cm.get_cmap('autumn',new)(range(new))\ncolors_old = ppl.cm.get_cmap('winter',old)(range(old))\n\n# Good data\nSBtime = SBA[0]['time']\nSBtemp = SBA[0]['temp'][np.where(SBtime>rMin)]\nSBtime=SBtime[np.where(SBtime>rMin)]\n\nTHtime = np.array([x['time'][np.where(x['time']>rMin)] \\\n                   if \"weak\" not in x[\"diag\"] else \\\n                   x['time'][np.where(x['time']>rMin)]*np.nan \\\n                   for x in th.iterrows()])\nTHtemp = np.array([x['temp'][np.where(x['time']>rMin)] \\\n                   if \"weak\" not in x[\"diag\"] else \\\n                   x['time'][np.where(x['time']>rMin)]*np.nan \\\n                   for x in th.iterrows()])\n\nTHave  = np.nanmean(THtemp, axis=0)\n\n# Part of the deviation common to all thermistors\nref_temp = np.interp(THtime[0], SBtime, SBtemp)\nComDev = np.nanmean(THtemp, axis=0) - ref_temp\n#ComDev = detrend(ComDev)\n\nnth = len(THtime)\nnP = factorial(nth) // (factorial(3) * factorial(nth - 3))\ncd = np.zeros((nP,len(THtime[0])))\ncombins = []\nnP = 0\nfor inds in combinations(range(nth), 3):\n    cd[nP, :] = np.nanmean(THtemp[inds, :], axis=0) - ref_temp \n    combins.append(inds)\n    nP += 1\n\ncd_std = cd.std(axis=0)\n\n# Plot deviation from reference shifted\nF = ppl.figure()\nAX = F.add_subplot(111)\n\ncnew = -1; cold = -1\nfor k in xrange(len(THtime)):\n    if th[k]['Id']<100:\n        colors = colors_old\n        cold  += 1\n        cind   = cold\n    else:\n        colors = colors_new\n        cnew  += 1\n        cind   = cnew\n    AX.plot(THtime[k], THtemp[k] - np.interp(THtime[k], SBtime,SBtemp), \\\n            lw=1.2, c=colors[cind])\n\nAX.plot(THtime[0], ComDev, 'k-', lw=2)\nAX.plot(THtime[0], ComDev + cd_std, c=\"gray\", lw=2)\nAX.plot(THtime[0], ComDev - cd_std, c=\"gray\", lw=2)\n\nAX.yaxis.get_major_formatter().set_powerlimits((-2,2))\n\nAX.set_ylabel(\"$T_{therm}-T_{Seabird}$ $[^\\circ C]$\")\nAX.set_xlabel(\"time $\\\\mathrm{[day]}$\")\nAX.set_xlim(SBtime.min(), SBtime.max())\n\ntoff = AX.yaxis.get_offset_text()\ntoff.set_size('xx-large')\n\nF.savefig(\"/home/cimatori/Analyses/Drift/exp1/process_data/figures/com_fluct_errors.png\")\n\nerrs = []\nerrmax = []\nfor kk in xrange(len(THtime)):\n    temp = []\n    for ii in xrange(len(combins)):\n        if kk in combins[ii]:\n            temp.append(cd[ii,:] - ComDev)\n    errs.append(np.array(temp).std(axis=0))\n    errmax.append(np.array(np.abs(temp)).max(axis=0))\n\nF = ppl.figure()\n# Plot deviation from ComDev\nAX = F.add_subplot(111)\n\ncnew = -1; cold = -1\nfor k in xrange(len(THtime)):\n    if th[k]['Id']<100:\n        colors = colors_old\n        cold  += 1\n        cind   = cold\n    else:\n        colors = colors_new\n        cnew  += 1\n        cind   = cnew\n    AX.plot(THtime[k], errmax[k], lw=1.2, c=colors[cind])\n\nAX.yaxis.get_major_formatter().set_powerlimits((-2,2))\n\nAX.set_xlabel(\"time $\\\\mathrm{[day]}$\")\nAX.set_ylabel(\"$\\\\max_k(\\\\varepsilon^{i,k})$ $[^\\circ C]$\")\nAX.set_xlim(SBtime.min(), SBtime.max())\n\ntoff = AX.yaxis.get_offset_text()\ntoff.set_size('xx-large')\n\nF.savefig(\"/home/cimatori/Analyses/Drift/exp1/process_data/figures/dev_fig_errors.png\")\n\nh5file.close()\n","sub_path":"drift_exp1/error_deviations.py","file_name":"error_deviations.py","file_ext":"py","file_size_in_byte":3751,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}