diff --git "a/4553.jsonl" "b/4553.jsonl"
new file mode 100644--- /dev/null
+++ "b/4553.jsonl"
@@ -0,0 +1,768 @@
+{"seq_id":"39067630551","text":"# Dictionary\nx, y, z = {'car': 45, 1: 'new', 'carbon': 32}\n\n# homer = 'never'\n# x = {'never': 'ever'}\nprint(type(y))\nnew_type = str(y)\nprint(new_type)\nprint(type(new_type))\n\ndictionary_items = {\n 'a': [1, 5, 6],\n 'b': 2,\n 'c': 3\n}\nprint(dictionary_items['a'][1])\n\nnew_list = [\n {\n 'a': [1, 5, 6],\n 'b': 2,\n 'c': 3\n },\n {\n 'f': [6, 2, 0],\n 'k': 2,\n 'l': 3\n }\n]\nprint(new_list[1])\nprint(new_list[1]['f'])\nprint(new_list[1]['f'][0])\n\n# Use the get method in order to find values from keys in a dictionary so it doesn't break the rest of the code execution\nprint(new_list[0].get('a', 'The value doesnt exist'))\n\nuser = {\n 'basket': [1, 3, 4],\n 'greet': 'jello',\n 'age': 30\n}\n\nprint('basket' in user)\nprint('size' in user)\nprint(user.update({'age': 55}))\nprint(user)","repo_name":"HamzahHal/Advanced_Python","sub_path":"Dictionary.py","file_name":"Dictionary.py","file_ext":"py","file_size_in_byte":833,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"2384993745","text":"from keras.models import Sequential, Model\nfrom keras.layers import Input, Dense, Dropout, Activation, \\\n Flatten, LeakyReLU, BatchNormalization, Conv2DTranspose, Conv2D,MaxPooling2D, Reshape\nfrom keras.layers.advanced_activations import LeakyReLU\nfrom keras.layers.convolutional import UpSampling2D\nfrom keras.optimizers import Adam, RMSprop,SGD\nfrom keras.initializers import RandomNormal\nfrom keras.models import load_model\nimport numpy as np\nimport Divers\nimport matplotlib.pyplot as plt\nimport copy\nimport time\n\n\ndef generator_8x8(input_gen=20, leaky_alpha=0.2,dropRate=0.3,output_img=(8,8,3)):\n model = Sequential()\n\n model.add(Dense(32*2*2,input_dim=input_gen, name=\"Dens1_generator_8x8\"))\n model.add(LeakyReLU(alpha=0.2))\n model.add(Dense(32 * 2 * 2, name=\"Dens2_generator_8x8\"))\n model.add(Dropout(dropRate))\n model.add(BatchNormalization(momentum=0.8))\n model.add(LeakyReLU(alpha=0.2))\n model.add(Reshape((2, 2, 32), input_shape=(32 * 2 * 2,)))\n\n model.add(Conv2DTranspose(64, kernel_size=(3,3), strides=(2,2),padding='same', name=\"ConvTransp1_generator_8x8\"))\n model.add(LeakyReLU(alpha=0.2))\n model.add(BatchNormalization(momentum=0.8))\n model.add(Conv2D(64, kernel_size=(3,3), padding='same', strides=(1,1), name=\"Conv1_generator_8x8\"))\n model.add(LeakyReLU(alpha=0.2))\n model.add(BatchNormalization(momentum=0.8))\n\n model.add(Conv2DTranspose(128, kernel_size=(3,3), strides=(2,2),padding='same', name=\"ConvTransp2_generator_8x8\"))\n model.add(LeakyReLU(alpha=0.2))\n model.add(BatchNormalization(momentum=0.8))\n model.add(Conv2D(128, kernel_size=(3,3), padding='same', strides=(1,1), name=\"Conv2_generator_8x8\"))\n model.add(LeakyReLU(alpha=0.2))\n model.add(BatchNormalization(momentum=0.8))\n \n model.add(Conv2D(3, kernel_size=(3,3), padding='same', activation='tanh', strides=(1,1), name=\"Conv3_generator_8x8\"))\n \n model.summary()\n model.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0001, beta_1=0.5), metrics=['accuracy'])\n return model\n\ndef generator_16x16(input_gen=20, leaky_alpha=0.2,dropRate=0.3,output_img=(16,16,3)):\n model = Sequential()\n\n model.add(Conv2DTranspose(256, kernel_size=(3,3), strides=(2,2),padding='same', name=\"convTransp1_generator_16x16\"))\n model.add(LeakyReLU(alpha=0.2))\n model.add(BatchNormalization(momentum=0.8))\n model.add(Conv2D(256, kernel_size=(3,3), padding='same', strides=(1,1), name=\"conv1_generator_16x16\"))\n model.add(LeakyReLU(alpha=0.2))\n model.add(BatchNormalization(momentum=0.8))\n model.add(Conv2D(256, kernel_size=(3,3), padding='same', strides=(1,1), name=\"conv2_generator_16x16\"))\n model.add(LeakyReLU(alpha=0.2))\n model.add(BatchNormalization(momentum=0.8))\n \n model.add(Conv2D(3, kernel_size=(3,3), padding='same', activation='tanh', strides=(1,1), name=\"conv3_generator_16x16\"))\n \n #model.summary()\n model.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0001, beta_1=0.5), metrics=['accuracy'])\n return model\n\ndef discriminator_16x16(leaky_alpha=0.2, dropRate=0.3, image_shape=(16,16,3), output_dim=(8,8,3)):\n model = Sequential()\n \n model.add(Conv2D(512, (3, 3),padding='same',input_shape=image_shape, strides=(1,1), name=\"conv1_discri_16x16\"))\n model.add(LeakyReLU(alpha=0.2))\n #model.add(BatchNormalization(momentum=0.8))\n model.add(Conv2D(512, (3, 3), strides=(1,1),padding='same', name=\"conv2_discri_16x16\"))\n model.add(LeakyReLU(alpha=0.2))\n #model.add(BatchNormalization(momentum=0.8))\n\n model.add(MaxPooling2D(pool_size=(2, 2)))\n\n model.add(Conv2D(256, (3, 3), strides=(1,1),padding='same', name=\"conv3_discri_16x16\"))\n model.add(LeakyReLU(alpha=0.2))\n model.add(Conv2D(256, (3, 3), strides=(1,1),padding='same', name=\"conv4_discri_16x16\"))\n model.add(LeakyReLU(alpha=0.2))\n\n #Pour coordonner les 2 models\n model.add(Conv2D(3, (3, 3), strides=(1,1),padding='same', name=\"conv5_discri_16x16\"))\n model.add(LeakyReLU(alpha=0.2))\n\n model.summary()\n sgd=SGD(lr=0.0002)\n model.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0001, beta_1=0.5), metrics=['accuracy'])\n return model\n\ndef discriminator_8x8(leaky_alpha=0.2, dropRate=0.3, image_shape=(8,8,3)):\n model = Sequential()\n \n model.add(Conv2D(64, (3, 3),padding='same',input_shape=image_shape, strides=(1,1), name=\"conv1_discri_8x8\"))\n model.add(LeakyReLU(alpha=0.2))\n #model.add(BatchNormalization(momentum=0.8))\n model.add(Conv2D(64, (3, 3), strides=(1,1),padding='same', name=\"conv2_discri_8x8\"))\n model.add(LeakyReLU(alpha=0.2))\n #model.add(BatchNormalization(momentum=0.8))\n model.add(MaxPooling2D(pool_size=(2, 2)))\n \n\n model.add(Conv2D(128, (3, 3), strides=(1,1),padding='same', name=\"conv3_discri_8x8\"))\n model.add(LeakyReLU(alpha=0.2))\n #model.add(BatchNormalization(momentum=0.8))\n model.add(Conv2D(128, (3, 3), strides=(1,1),padding='same', name=\"conv4_discri_8x8\"))\n model.add(LeakyReLU(alpha=0.2))\n #model.add(BatchNormalization(momentum=0.8))\n model.add(MaxPooling2D(pool_size=(2, 2)))\n\n model.add(Flatten())\n model.add(Dense(128, name=\"Dens1_discri_8x8\"))\n model.add(Dropout(dropRate))\n model.add(LeakyReLU(alpha=0.2))\n #model.add(BatchNormalization(momentum=0.8))\n\n model.add(Dense(1, name=\"Dens2_discri_8x8\"))\n model.add(Activation('sigmoid'))\n model.summary()\n sgd=SGD(lr=0.0002)\n model.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0001, beta_1=0.5), metrics=['accuracy'])\n return model\n\ndef DCGAN(input_gen,dataset):\n # generator\n #g_1 = generator_model(sample_size, 0.2)\n #On load le generator_1\n #g_1.load_weights('g.h5')\n #g_2 = generator_model_part1(sample_size, 0.2)\n #On ajoute la seconde partie\n #g = Sequential([g_1,g_2])\n\n #g.summary()\n g_8x8 = generator_8x8(input_gen,0.2)#generatorV1_model(input_gen, 0.2)\n g_8x8.load_weights('g.h5')\n g_16x16 = generator_16x16()\n g = Sequential([g_8x8, g_16x16])\n sgd=SGD()\n g.compile(optimizer=Adam(lr=0.0001, beta_1=0.5), loss='binary_crossentropy',metrics=['accuracy'])\n # discriminator\n d_8x8 = discriminator_8x8()\n d_8x8.load_weights('d.h5')\n d_16x16 = discriminator_16x16()\n #d.load_weights('d.h5')\n d_8x8.trainable = False\n d_16x16.trainable = False\n d = Sequential([d_16x16,d_8x8])\n sgd=SGD()\n d.compile(optimizer=Adam(lr=0.0001, beta_1=0.5), loss='binary_crossentropy',metrics=['accuracy'])\n # GAN\n gan = Sequential([g, d])\n gan.summary()\n sgd=SGD()\n gan.compile(optimizer=Adam(lr=0.0001, beta_1=0.5), loss='binary_crossentropy',metrics=['accuracy'])\n return gan, g, d\n\n\ndef generatorV1_model(input_gen=20, leaky_alpha=0.2,dropRate=0.3):\n model = Sequential()\n\n model.add(Dense(input_dim=input_gen, output_dim=2048))\n model.add(LeakyReLU(alpha=0.2))\n model.add(Dense(32 * 8 * 8))\n model.add(Dropout(dropRate))\n model.add(BatchNormalization(momentum=0.8))\n model.add(LeakyReLU(alpha=0.2))\n model.add(Reshape((8, 8, 32), input_shape=(32 * 8 * 8,)))\n #model.add(UpSampling2D(size=(2, 2)))\n model.add(Conv2DTranspose(64, kernel_size=(3,3), strides=(2,2),padding='same'))\n #model.add(BatchNormalization(momentum=0.8))\n #model.add(Dropout(dropRate))\n model.add(LeakyReLU(alpha=0.2))\n model.add(Conv2D(64, kernel_size=(3,3), padding='same', strides=(1,1)))\n model.add(LeakyReLU(alpha=0.2))\n #model.add(Dropout(dropRate))\n #model.add(BatchNormalization(momentum=0.8))\n #model.add(UpSampling2D(size=(2, 2)))\n model.add(Conv2DTranspose(128, kernel_size=(3,3), strides=(2,2),padding='same'))\n #model.add(BatchNormalization(momentum=0.8))\n #model.add(Dropout(dropRate))\n model.add(LeakyReLU(alpha=0.2))\n model.add(Conv2D(128, kernel_size=(3,3), padding='same', strides=(1,1)))\n model.add(LeakyReLU(alpha=0.2))\n #model.add(Dropout(dropRate))\n #model.add(BatchNormalization(momentum=0.8))\n #model.add(UpSampling2D(size=(2, 2)))\n \n model.add(Conv2DTranspose(256, kernel_size=(4,4), strides=(2,2),padding='same'))\n #model.add(BatchNormalization(momentum=0.8))\n #model.add(Dropout(dropRate))\n model.add(LeakyReLU(alpha=0.2))\n model.add(Conv2D(256, kernel_size=(3,3), padding='same', strides=(1,1)))\n model.add(LeakyReLU(alpha=0.2))\n #model.add(Dropout(dropRate))\n #model.add(BatchNormalization(momentum=0.8))\n model.add(Conv2D(3, kernel_size=(4,4), padding='same', activation='tanh', strides=(1,1)))\n #model.add(LeakyReLU(alpha=0.2)) \n\n model.summary()\n model.compile(loss='mean_squared_error', optimizer=Adam(lr=0.0001, beta_1=0.5), metrics=['accuracy'])\n return model\n\n#def discriminatorV1_model(leaky_alpha=0.2, dropRate=0.3, image_shape=(64,64,3)):\n# model = Sequential()\n \n# # layer1 (None,64,64,3)>>(None,32,32,32)\n# model.add(Conv2D(16, kernel_size=3, strides=2, input_shape=image_shape, padding=\"same\"))\n# model.add(LeakyReLU(alpha=leaky_alpha))\n\ndef generator_model_part1(dropRate=0.3, leaky_alpha=0.2):\n model = Sequential()\n\n\n # (None,16,16,128)>>(None,32,32,256)\n model.add(Conv2D(64, kernel_size=(2,2), padding=\"same\",input_shape=(64,64,3)))\n model.add(BatchNormalization(momentum=0.8))\n model.add(LeakyReLU(alpha=leaky_alpha))\n model.add(Dropout(dropRate))\n\n model.add(Conv2D(64, kernel_size=(2,2), padding=\"same\"))\n model.add(BatchNormalization(momentum=0.8))\n model.add(LeakyReLU(alpha=leaky_alpha))\n model.add(Dropout(dropRate))\n\n #(None,32,32,256)>>(None,32,32,256)\n model.add(Conv2D(3, kernel_size=(2,2), padding=\"same\"))\n model.add(BatchNormalization(momentum=0.8))\n model.add(Activation(\"sigmoid\")) \n \n model.summary()\n \n \n model.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0001, beta_1=0.5), metrics=['accuracy'])\n return model\n\n\ndef generator_model(nbrParamEntree=10, dropRate=0.3, leaky_alpha=0.2):\n model = Sequential()\n \n model.add(Dense(64*64*3, input_shape=(nbrParamEntree,)))\n model.add(BatchNormalization(momentum=0.8))\n model.add(LeakyReLU(alpha=leaky_alpha))\n\n # (None,16*16*128)>>(None,16,16,128)\n model.add(Reshape((64, 64, 3)))\n\n \n # (None,16,16,128)>>(None,32,32,256)\n model.add(Conv2D(128, kernel_size=(2,2), padding=\"same\"))\n model.add(BatchNormalization(momentum=0.8))\n model.add(LeakyReLU(alpha=leaky_alpha))\n model.add(Dropout(dropRate))\n\n model.add(Conv2D(128, kernel_size=(2,2), padding=\"same\"))\n model.add(BatchNormalization(momentum=0.8))\n model.add(LeakyReLU(alpha=leaky_alpha))\n model.add(Dropout(dropRate))\n\n #(None,32,32,256)>>(None,32,32,256)\n model.add(Conv2D(3, kernel_size=(2,2), padding=\"same\"))\n model.add(BatchNormalization(momentum=0.8))\n model.add(Activation(\"sigmoid\")) \n \n model.summary()\n \n \n model.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0001, beta_1=0.5), metrics=['accuracy'])\n return model\n\ndef discriminator_model(leaky_alpha=0.2, dropRate=0.3, image_shape=(32,32,3)):\n model = Sequential()\n \n # layer1 (None,64,64,3)>>(None,32,32,32)\n model.add(Conv2D(64, (3, 3),\n padding='same',\n input_shape=(64, 64, 3), strides=(1,1)))\n model.add(LeakyReLU(alpha=0.2))\n #model.add(Dropout(dropRate))\n\n model.add(Conv2D(64, (3, 3), strides=(1,1),padding='same'))\n model.add(LeakyReLU(alpha=0.2))\n #model.add(Dropout(dropRate))\n model.add(MaxPooling2D(pool_size=(2, 2)))\n \n\n model.add(Conv2D(128, (3, 3), strides=(1,1),padding='same'))\n model.add(LeakyReLU(alpha=0.2))\n #model.add(Dropout(dropRate))\n model.add(Conv2D(128, (3, 3), strides=(1,1),padding='same'))\n model.add(LeakyReLU(alpha=0.2))\n #model.add(Dropout(dropRate))\n model.add(MaxPooling2D(pool_size=(2, 2)))\n\n model.add(Conv2D(256, (3, 3), strides=(1,1),padding='same'))\n model.add(LeakyReLU(alpha=0.2))\n #model.add(Dropout(dropRate))\n model.add(Conv2D(256, (3, 3), strides=(1,1),padding='same'))\n model.add(LeakyReLU(alpha=0.2))\n #model.add(Dropout(dropRate))\n model.add(MaxPooling2D(pool_size=(2, 2)))\n #model.add(BatchNormalization(momentum=0.8))\n\n #model.add(Dropout(dropRate))\n model.add(Conv2D(512, (3, 3), strides=(1,1),padding='same'))\n model.add(LeakyReLU(alpha=0.2))\n #model.add(Dropout(dropRate))\n model.add(MaxPooling2D(pool_size=(2, 2)))\n #model.add(BatchNormalization(momentum=0.8))\n\n model.add(Flatten())\n model.add(Dense(256))\n model.add(Dropout(dropRate))\n model.add(LeakyReLU(alpha=0.2))\n \n #model.add(BatchNormalization(momentum=0.8))\n #model.add(Dropout(dropRate))\n\n model.add(Dense(1))\n model.add(Activation('sigmoid'))\n model.summary()\n sgd=SGD(lr=0.0002)\n model.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0001, beta_1=0.5), metrics=['binary_accuracy'])\n return model\n\ndef entrainement(epochs, nbrImageEntrainement, datasetImg, ChargeSauvegarde, epoch_start,pasEntrainement,input_gen,nbrColImgGen,nbrLigneColImgGen,epoch_report):\n #On crée le GAN\n (gan, g, d) = DCGAN(input_gen,datasetImg)\n\n if(ChargeSauvegarde == True):\n gan.load_weights('GAN.h5')\n g.load_weights('g.h5')\n d.load_weights('d.h5')\n\n #On doit entraîner un peu le discriminateur\n if(epoch_start == 0):\n d.trainable = True\n d.fit(datasetImg[0:nbrImageEntrainement],np.ones(nbrImageEntrainement),epochs=5)\n d.trainable = False\n\n\n #On se prépare un vecteur de bruit fixe pour pouvoir voir l'évolution de ce vecteur\n bruitFixe = np.random.rand(nbrColImgGen*nbrLigneColImgGen,input_gen)\n\n moyaccDiscriTrueImageArray = []\n moyAccGANArray = []\n moyaccDiscriFalseImageArray = []\n\n moyLossDiscriTrueImageArray = []\n moyLossGANArray = []\n moyLossDiscriFalseImageArray = []\n\n #On init la variable de stockage d'image\n imgGenereNonCalib = np.ndarray(shape=(nbrColImgGen*nbrLigneColImgGen, datasetImg.shape[1],datasetImg.shape[2],3),\n dtype=np.float32)\n\n genImage = []\n\n for e in range(epoch_start,epochs):\n\n moyaccDiscriTrueImage = 0.0\n moyaccDiscriFalseImage = 0.0\n moyAccGAN = 0.0\n\n moyLossDiscriTrueImage = 0.0\n moyLossDiscriFalseImage = 0.0\n moyLossGAN = 0.0\n\n #On init le start time\n start_time = time.clock()\n\n for a in range(0,nbrImageEntrainement,pasEntrainement):\n\n temps_restant = (float(nbrImageEntrainement - a) * (time.clock() - start_time) )\n temps_restant_formate = time.strftime('%H:%M:%S', time.gmtime(temps_restant))\n print(\"Epochs \" + str(e) + \"/\" + str(epochs) + \" image : \" + str(a) + \"/\" + str(nbrImageEntrainement) + \n \" temps restant = \" + temps_restant_formate, end=\"\\r\")\n\n #On démarre le chrono\n start_time = time.clock()\n\n bruit = np.random.rand(pasEntrainement,input_gen)\n #On génère l'image à partir du bruit\n genImage = g.predict(bruit)\n\n #On entraîne le discriminateur\n d.trainable = True\n discriTrueImage = d.fit(datasetImg[a:a+pasEntrainement], np.ones(pasEntrainement),verbose=0)\n discriFalseImage = d.fit(genImage, np.zeros(pasEntrainement),verbose=0)\n d.trainable = False\n #On entraîne le generateur\n ganHistoryImg = gan.fit(bruit, np.ones(pasEntrainement),verbose=0)\n \n moyaccDiscriTrueImage = moyaccDiscriTrueImage + (discriTrueImage.history['acc'][0])\n moyaccDiscriFalseImage = moyaccDiscriFalseImage + (discriFalseImage.history['acc'][0])\n moyAccGAN = moyAccGAN + (ganHistoryImg.history['acc'][0])\n \n \n #Pareil pour le loss\n moyLossDiscriTrueImage = moyLossDiscriTrueImage + (discriTrueImage.history['loss'][0])\n moyLossDiscriFalseImage = moyLossDiscriFalseImage + (discriFalseImage.history['loss'][0])\n moyLossGAN = moyLossGAN + (ganHistoryImg.history['loss'][0])\n \n \n if ( (a % epoch_report == 0) & (a != 0) ):\n\n epoch_temp = (a / nbrImageEntrainement) + e\n\n moyaccDiscriTrueImage = moyaccDiscriTrueImage / epoch_report\n moyAccGAN = moyAccGAN / epoch_report\n moyaccDiscriFalseImage = moyaccDiscriFalseImage / epoch_report\n moyLossDiscriTrueImage = moyLossDiscriTrueImage / epoch_report\n moyLossGAN = moyLossGAN / epoch_report\n moyLossDiscriFalseImage = moyLossDiscriFalseImage / epoch_report\n\n #On fait la moyenne de l'acc\n moyaccDiscriTrueImageArray.append([epoch_temp, moyaccDiscriTrueImage])\n moyAccGANArray.append([epoch_temp,moyAccGAN])\n moyaccDiscriFalseImageArray.append([epoch_temp, moyaccDiscriFalseImage])\n #Pareil pour le loss\n moyLossDiscriTrueImageArray.append([epoch_temp, moyLossDiscriTrueImage])\n moyLossGANArray.append([epoch_temp,moyLossGAN])\n moyLossDiscriFalseImageArray.append([epoch_temp, moyLossDiscriFalseImage])\n \n if( e >= 1):\n #Sauvegarde\n gan.save_weights('GAN.h5')\n g.save_weights('g.h5')\n d.save_weights('d.h5')\n\n #On enregistre les perfs du GAN acc\n Divers.SauvegardePerfGAN(epoch_temp,np.array(moyaccDiscriTrueImageArray),np.array(moyAccGANArray),np.array(moyaccDiscriFalseImageArray),\"AccGAN\")\n\n #On enregistre les perfs du GAN loss\n Divers.SauvegardePerfGAN(epoch_temp,np.array(moyLossDiscriTrueImageArray),np.array(moyLossGANArray),np.array(moyLossDiscriFalseImageArray),\"LossGAN\") \n\n\n bruit = np.random.rand((nbrColImgGen*nbrLigneColImgGen),input_gen)\n #On génère l'image à partir du bruit\n genImage = g.predict(bruit)\n imgGenereNonCalib = copy.copy(genImage)\n imgGenerePrAffichage = Divers.UndoCalibrationValeurPixelDataset(imgGenereNonCalib)\n\n #On enregistre l'image générée\n Divers.SauvegardeImageMatplot(nbrColImgGen,nbrLigneColImgGen,imgGenerePrAffichage,\"Resultat/ImageGenerees/epochs_\" + str(epoch_temp) + \".png\")\n #On enregistre les images générées avec le bruit fixe\n \n #On génère l'image à partir du bruit\n genImage = g.predict(bruitFixe)\n imgGenereNonCalib = copy.copy(genImage)\n imgGenerePrAffichage = Divers.UndoCalibrationValeurPixelDataset(imgGenereNonCalib)\n Divers.SauvegardeImageMatplot(nbrColImgGen,nbrLigneColImgGen,imgGenerePrAffichage,\"Resultat/ImageGenerees/bruitFixe/epochs_\" + str(epoch_temp) + \".png\")\n\n #Si le discriminateur se trompe plus de la moitié du temps on arrete\n if(moyLossDiscriTrueImage >= 0.5):\n print(\"FIN entrainement !\")\n return\n\ndef afficheMeilleurImageGAN(nombreImg,nbrColonne,nbrLigne,nomFichier,input_gen,pourcentage_reussite):\n\n bruit = np.random.rand(nombreImg+1,input_gen)\n\n #On init la variable de stockage d'image\n dataset = np.ndarray(shape=(nbrColonne*nbrLigne, 64,64,3),\n dtype=np.float32)\n\n #On crée le GAN\n (gan, g, d) = DCGAN(input_gen,dataset)\n\n gan.load_weights('GAN.h5')\n g.load_weights('g.h5')\n d.load_weights('d.h5')\n\n imgListe = g.predict(bruit)\n pourcentageReussite = gan.predict(bruit)\n\n i=0\n index = 0\n while(i < ((nbrColonne*nbrLigne)-2) | index < nombreImg):\n print(\"Images trouvés : \" + str(i) + \" / \" + str(nbrColonne*nbrLigne) + \" Image parcourue : \" + str(index) + \" / \" + str(nombreImg), end=\"\\r\")\n \n if pourcentageReussite[index] > pourcentage_reussite:\n if i < (nbrColonne*nbrLigne) :\n dataset[i] = imgListe[index]\n i+=1\n index+=1\n dataset = Divers.UndoCalibrationValeurPixelDataset(dataset)\n\n Divers.SauvegardeImageMatplot(nbrColonne,nbrLigne,dataset,\"Resultat/\"+nomFichier)\n\n\n\n","repo_name":"Dapawan/Test_GAN","sub_path":"GAN.py","file_name":"GAN.py","file_ext":"py","file_size_in_byte":20302,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"15594771365","text":"import sys\nfrom . import handcalcs as hand\nfrom . import sympy_kit as s_kit\n\ntry:\n from IPython.core.magic import (\n Magics,\n magics_class,\n cell_magic,\n register_cell_magic,\n register_line_magic,\n )\n from IPython import get_ipython\n from IPython.display import Latex, Markdown, display\n from IPython.utils.capture import capture_output\nexcept ImportError:\n pass\n\n\ntry:\n ip = get_ipython()\n cell_capture = capture_output(stdout=True, stderr=True, display=True)\nexcept AttributeError:\n raise ImportError(\n \"handcalcs.render is intended for a Jupyter environment.\"\n \" Use 'from handcalcs import handcalc' for the decorator interface.\"\n )\n\n\ndef parse_line_args(line: str) -> dict:\n \"\"\"\n Returns a dict that represents the validated arguments\n passed in as a line on the %%render or %%tex cell magics.\n \"\"\"\n valid_args = [\"params\", \"long\", \"short\", \"sympy\", \"symbolic\", \"_testing\"]\n # valid_args = [\"params\", \"long\", \"short\", \"sympy\", \"symbolic\", \"_testing\"]\n sympy_arg = [\"sympy\"]\n line_parts = line.split()\n parsed_args = {\"override\": \"\", \"precision\": None, \"sympy\": False, \"sci_not\": None}\n # parsed_args = {\n # \"override\": \"\",\n # \"precision\": \"\",\n # }\n precision = \"\"\n for arg in line_parts:\n if arg.lower() in sympy_arg:\n parsed_args[\"sympy\"] = True\n continue\n if arg.lower() == \"sci_not\":\n parsed_args[\"sci_not\"] = True\n for valid_arg in valid_args:\n if arg.lower() in valid_arg:\n parsed_args.update({\"override\": valid_arg})\n break\n try:\n precision = int(arg)\n except ValueError:\n pass\n if precision or precision == 0:\n parsed_args.update({\"precision\": precision})\n return parsed_args\n\n\n@register_line_magic\ndef decimal_separator(line):\n if len(line) == 1:\n hand.LatexRenderer.dec_sep = line\n\n\n@register_cell_magic\ndef render(line, cell):\n # Retrieve var dict from user namespace\n user_ns_prerun = ip.user_ns\n line_args = parse_line_args(line)\n\n if line_args[\"sympy\"]:\n cell = s_kit.convert_sympy_cell_to_py_cell(cell, user_ns_prerun)\n\n # Run the cell\n with cell_capture:\n exec_result = ip.run_cell(cell)\n\n if not exec_result.success:\n return None\n\n # Retrieve updated variables (after .run_cell(cell))\n user_ns_postrun = ip.user_ns\n\n # Do the handcalc conversion\n renderer = hand.LatexRenderer(cell, user_ns_postrun, line_args)\n latex_code = renderer.render()\n\n # Display, but not as an \"output\"\n display(Latex(latex_code))\n\n if line_args[\"override\"] == \"_testing\":\n return latex_code\n\n\n@register_cell_magic\ndef tex(line, cell):\n # Retrieve var dict from user namespace\n user_ns_prerun = ip.user_ns\n line_args = parse_line_args(line)\n\n if line_args[\"sympy\"]:\n cell = s_kit.convert_sympy_cell_to_py_cell(cell, user_ns_prerun)\n\n # Run the cell\n with cell_capture:\n exec_result = ip.run_cell(cell)\n\n if not exec_result.success:\n return None\n\n # Retrieve updated variables (after .run_cell(cell))\n user_ns_postrun = ip.user_ns\n\n # Do the handcalc conversion\n renderer = hand.LatexRenderer(cell, user_ns_postrun, line_args)\n latex_code = renderer.render()\n\n # Display, but not as an \"output\"\n print(latex_code)\n\n if line_args[\"override\"] == \"_testing\":\n return latex_code\n\n\ndef load_ipython_extension(ipython):\n \"\"\"This function is called when the extension is\n loaded. It accepts an IPython InteractiveShell\n instance. We can register the magic with the\n `register_magic_function` method of the shell\n instance.\"\"\"\n ipython.register_magic_function(render, \"cell\")\n\n\n# def unload_ipython_extension(ipython):\n# \"\"\"This function is called when the extension is\n# loaded. It accepts an IPython InteractiveShell\n# instance. We can register the magic with the\n# `register_magic_function` method of the shell\n# instance.\"\"\"\n# print(dir(ipython.magics_manager))\n# ipython.magics_manager.remove(render)\n","repo_name":"connorferster/handcalcs","sub_path":"handcalcs/render.py","file_name":"render.py","file_ext":"py","file_size_in_byte":4179,"program_lang":"python","lang":"en","doc_type":"code","stars":5272,"dataset":"github-code","pt":"77"}
+{"seq_id":"6056241189","text":"import shutil\nimport requests\nfrom googleapiclient.discovery import build\nfrom pytube import YouTube\nimport os\nimport base64\nimport webbrowser\n\ndef INITIALIZE_VARIABLES():\n #Spotify\n global client_id\n client_id = os.environ.get(\"CLIENT_ID\")\n\n global client_secret \n client_secret = os.environ.get(\"CLIENT_SECRET\")\n\n global base_64\n base_64 = Base64Encode(client_id, client_secret)\n\n #Youtube\n global api_key\n api_key = os.environ.get(\"API_KEY_YT\")\n global service_yt\n service_yt = build(\"youtube\", \"v3\", developerKey=api_key)\n\ndef Base64Encode(CLIENT_ID, CLIENT_SECRET):\n key = CLIENT_ID + \":\" + CLIENT_SECRET\n key_encoded_byte = base64.b64encode(key.encode(\"ascii\"))\n key_encoded = key_encoded_byte.decode(\"ascii\")\n return key_encoded\n\ndef Authorization():\n print(\"Connecting to Spotify...\")\n auth_query = \"https://accounts.spotify.com/api/token\"\n try:\n auth_res = requests.post(auth_query, data={\"grant_type\":\"client_credentials\"}, headers={\"Authorization\":\"Basic \" + base_64})\n access_token = auth_res.json()[\"access_token\"]\n print(\"Successfully Connected to Spotify\")\n return access_token\n except:\n print(\"Error Connecting to Spotify, Please Try Again Later\")\n print(\"----------The End----------\")\n quit()\n\ndef Get_Playlist_Songs(access_token):\n #playlist_link = \"https://open.spotify.com/playlist/63qp5ewWfM4aGrXWQ8rlrC?si=ab856c6055eb457a\"\n #playlist_link = \"https://open.spotify.com/playlist/1CFs9S4xEqd1zBY75rWNTN?si=19fd75c994174fb4\"\n #playlist_link = \"https://open.spotify.com/playlist/0yXlKEvlgpWJ5eNRth61El?si=a24bd19e75884bdf\"\n playlist_link = input()\n\n playlist_id = playlist_link[34:56]\n \n print(\"Getting Data from Playlist...\")\n\n offset = 0\n songs = {\"name\":\"\", \"num_of_songs\":0, \"song\":[], \"artists\":[]}\n try:\n while True:\n playlist_query = \"https://api.spotify.com/v1/playlists/{}?fields=name\".format(playlist_id)\n playlist_items_query = \"https://api.spotify.com/v1/playlists/{}/tracks?limit=100&offset={}\".format(playlist_id, offset * 100)\n\n playlist_res = requests.get(playlist_query, headers={\"Authorization\":\"Bearer \" + access_token})\n playlist_items_res = requests.get(playlist_items_query, headers={\"Authorization\":\"Bearer \"+ access_token})\n\n playlist_name = playlist_res.json()[\"name\"]\n\n res_json = playlist_items_res.json()\n num_of_songs = len(playlist_items_res.json()[\"items\"]) \n\n songs[\"name\"] = playlist_name\n songs[\"num_of_songs\"] = num_of_songs \n for i in range(num_of_songs):\n x = 0\n artists = []\n while True:\n try:\n artists.append(res_json[\"items\"][i][\"track\"][\"artists\"][x][\"name\"])\n x += 1\n except:\n break\n songs[\"artists\"].append(artists)\n songs[\"song\"].append(playlist_items_res.json()[\"items\"][i][\"track\"][\"name\"])\n if num_of_songs < 100:\n break\n else:\n offset += 1\n except:\n print(\"Error Connecting to Playlist\")\n print(\"----------The End----------\")\n quit()\n\n print(\"Playlist Data Successfully Obtained\")\n return songs\n\ndef Display_Playlist(playlist):\n for i in range(playlist[\"num_of_songs\"]):\n print(\"{:>4}.\".format(i+1), playlist[\"song\"][i], \"-\", playlist[\"artists\"][i])\n\ndef Youtube(playlist_data):\n playlist_name = playlist_data[\"name\"]\n path = os.path.expanduser(\"~\\\\Music\\\\\" + playlist_name) \n\n print(\"Creating Playlist Folder for '\" + playlist_name + \"'\")\n\n if (os.path.isdir(path)):\n shutil.rmtree(path)\n \"\"\" print(\"Playlist already exists\")\n print(\"----------The End----------\")\n quit() \"\"\"\n else:\n os.mkdir(path)\n \n num_of_songs = playlist_data[\"num_of_songs\"]\n \n for i in range(num_of_songs):\n song_name = playlist_data[\"song\"][i]\n song_artists = playlist_data[\"artists\"][i]\n yt_req = service_yt.search().list(part=\"snippet\", q=\"{} {} audio\".format(song_name, song_artists[0]), type=\"video\", maxResults=1)\n res = yt_req.execute()\n vid_id = res[\"items\"][0][\"id\"][\"videoId\"]\n yt = YouTube(\"http://youtube.com/watch?v=\" + vid_id)\n video = yt.streams.filter(only_audio=True).first()\n try: \n dl_file = video.download(output_path=path)\n print(dl_file)\n os.rename(dl_file, path + \"\\\\\" + song_name + \".mp3\")\n print(\"Downloaded \" + song_name)\n except:\n print(\"Could not download \" + song_name)\n os.remove(dl_file)\n print(\"\")\n webbrowser.open(path)\n \ndef main():\n INITIALIZE_VARIABLES()\n\n ACCESS_TOKEN = Authorization()\n Playlist_Data = Get_Playlist_Songs(ACCESS_TOKEN)\n Youtube(Playlist_Data)\n #Display_Playlist(Playlist_Data)\n\nif __name__ == \"__main__\":\n main()\n print(\"----------The End----------\")","repo_name":"rajmoham/Song-Downloader","sub_path":"program.py","file_name":"program.py","file_ext":"py","file_size_in_byte":5128,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"5014726937","text":"import wandb\nwandb.init(project='gan')\nimport torch, torch.optim as optim\nfrom torchvision import datasets, transforms\nimport matplotlib.pyplot as plt\nfrom tqdm import tqdm\nimport numpy as np\n\nfrom net import Generator, Discriminator, real_loss, fake_loss\n\ndef datamaker(batch_size=1):\n train_dataset = datasets.MNIST(root='dataset', \n train=True, \n transform=transforms.ToTensor(),\n download=True)\n\n train_loader = torch.utils.data.DataLoader(train_dataset, \n batch_size=batch_size,\n num_workers=2)\n\n return train_loader\n\ndef train(batch_size=1, latent_size=100, learning_rate=2e-3, num_epochs=100):\n cuda = torch.cuda.is_available()\n device = 'cuda:0' if cuda else 'cpu'\n dataloader = datamaker(batch_size=batch_size)\n fixed_img = np.random.uniform(-1, 1, size=(batch_size, latent_size))\n fixed_img = torch.from_numpy(fixed_img).float()\n gen_imgs = []\n\n G = Generator(input_size=latent_size)\n D = Discriminator()\n if cuda:\n print('Using CUDA')\n fixed_img = fixed_img.cuda()\n G.cuda()\n D.cuda()\n \n\n\n g_optimizer = optim.Adam(G.parameters(), lr=learning_rate)\n d_optimizer = optim.Adam(D.parameters(), lr=learning_rate)\n\n wandb.watch(G)\n wandb.watch(D)\n for epoch in range(num_epochs):\n D.train()\n G.train()\n for idx, ( real_images, _ ) in enumerate(tqdm(dataloader)):\n if cuda:\n real_images = real_images.cuda()\n\n batch_size = real_images.size(0)\n real_images = real_images * 2 - 1\n\n g_loss_value = 0.0\n d_loss_value = 0.0\n for phase in ['discriminator', 'generator']:\n # TRAIN DISCRIMINATOR\n if phase == 'discriminator':\n # generate fake images from latent vector\n latent_vector = np.random.uniform(-1, 1, size=(batch_size, latent_size))\n latent_vector = torch.from_numpy(latent_vector).float()\n if cuda:\n latent_vector = latent_vector.cuda()\n fake_images = G(latent_vector)\n\n # compute discriminator loss on real images\n d_optimizer.zero_grad()\n d_real = D(real_images)\n d_real_loss = real_loss(d_real, smooth=True)\n\n # compute discriminator loss in fake images\n d_fake = D(fake_images)\n d_fake_loss = fake_loss(d_fake)\n\n # total loss, backprop, optimize and update weights\n d_loss = d_real_loss + d_fake_loss\n d_loss_value = d_loss.item()\n\n d_loss.backward()\n d_optimizer.step()\n\n # TRAIN GENERATOR\n if phase == 'generator':\n latent_vector = np.random.uniform(-1, 1, size=(batch_size, latent_size))\n latent_vector = torch.from_numpy(latent_vector).float()\n if cuda:\n latent_vector = latent_vector.cuda()\n fake_images = G(latent_vector)\n \n g_optimizer.zero_grad()\n d_fake = D(fake_images)\n g_loss = real_loss(d_fake)\n g_loss_value = g_loss.item()\n\n g_loss.backward()\n g_optimizer.step()\n\n if idx % 100 == 0: \n pass\n wandb.log({ 'G Loss': g_loss_value, 'D Loss': d_loss_value })\n wandb.log({ 'G Epoch Loss': g_loss_value, 'D Epoch Loss': d_loss_value }, step=epoch)\n \n # test performance\n G.eval()\n gen_img = G(fixed_img)\n gen_imgs.append(gen_img)\n \n # dump generated images\n with open('gen_imgs.pkl', 'wb') as f:\n pkl.dump(gen_imgs, f)\n\n\nif __name__ == '__main__':\n # data_iter = iter(train_loader)\n # images, labels = next(data_iter)\n # images = torch.squeeze(images, 0).permute(1,2,0)\n # images = torch.cat((images, images, images), dim=2)\n # print(images.size())\n # plt.imshow(images)\n # plt.show()\n\n train(batch_size=128)","repo_name":"blueyellowpink/gan","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":4354,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"14311708568","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\ncalculate repeats similaritites for WDSP output file\nusage: python repeat_similarity.py wdsp_f\n\"\"\"\nimport sys\nfrom collections import OrderedDict\nfrom wdsp import Wdsp\n\n\ndef align(seq1, seq2):\n from Bio import pairwise2\n from Bio.SubsMat import MatrixInfo as matlist\n matrix = matlist.blosum62\n gap_open = -10 # usual value\n gap_extend = -0.5 # usual value\n\n alns = pairwise2.align.globalds(seq1, seq2, matrix, gap_open, gap_extend)\n\n seq1 = alns[0][0]\n seq2 = alns[0][1]\n identity = [1 for i, s in enumerate(seq1) if s == seq2[i]]\n identity = int(100 * len(identity) / len(seq1))\n\n return identity\n\n\ndef repeat_similarity(repeats):\n lens = len(repeats)\n sims = []\n for i in xrange(lens):\n sim_i = []\n for j in xrange(lens):\n if j < i:\n sim_i.append(sims[j][i])\n elif j >= i:\n sim_i.append(align(repeats[i], repeats[j]))\n sims.append(sim_i)\n average = (sum([sum(i) for i in sims]) - lens * 100) / (lens * (lens - 1))\n return average, sims\n\n\ndef main():\n with open(sys.argv[-1]) as o_f:\n w = Wdsp(o_f)\n sims = OrderedDict()\n for pro, repeats in w.repeats.iteritems():\n sims[pro] = repeat_similarity(repeats)\n\n with open('sims.txt', 'w') as w_f:\n for k, v in sims.iteritems():\n print >> w_f, '{0:<20}{1:<}'.format(k, v[0])\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"lituan/Topface","sub_path":"repeat_similarity.py","file_name":"repeat_similarity.py","file_ext":"py","file_size_in_byte":1501,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"42272391708","text":"from CSMA_agent import CsmaAgent\nimport gym\nfrom custom_env import threshold_env\nfrom DQN import KerasDQN\nimport numpy as np\nimport csv\n\n# --------------------> Parameters <--------------------\nsave = True # Save data to CSV\nsave_data_path = \"./data/\"\nn_iterations = 3 # How many full simulations to run\nfeature_histories = 1\n# ------------------------------------------------------------\n\n# --------------------- Create Env ---------------------\nn_agents = 4 \nthreshold = 1 \nn_steps = 1e4\ntransmit_and_sense = False\n# With buffer intervals\nbuffer_intervals = [2, 5, 8, 10] * 4\nenv = threshold_env(n_agents, threshold, n_steps, \n transmit_and_sense=transmit_and_sense,\n buffer_intervals=buffer_intervals)\n\"\"\"\nenv = threshold_env(n_agents, threshold, n_steps, \n transmit_and_sense=transmit_and_sense)\n\"\"\"\n# -----------------------------------------------------\n\n\ndef state_to_observations(state):\n \"\"\"\n Input:\n - obs [list or np.array]: Concatenated list of all observations\n\n Returns:\n - list of lists of observations for each agent\n \"\"\"\n n_obs_per_agent = len(state) // n_agents\n #print(\"n_obs_per_agent\", n_obs_per_agent)\n agent_obs = [np.array(state[i * n_obs_per_agent: (i + 1) * n_obs_per_agent]).reshape(1, -1) for i in range(n_agents)]\n\n return agent_obs\n\n# ---------------------- Training Loop --------------------\ncurrIt = 0\nwhile True:\n # --------------------- Create Agents ---------------------\n n_inputs = 4 * feature_histories \n n_actions = 5 \n # DQN\n \"\"\"\n agents = [KerasDQN(n_inputs, n_actions,\n hidden_layer_one_dims=128,\n hidden_layer_two_dims=256,\n batch_size=64,\n epsilon_min=0.05) for _ in range(n_agents)]\n \"\"\"\n # CSMA Agents\n #agents = [CsmaAgent(wait_for_idle=True) for _ in range(n_agents)]\n #agents = [CsmaAgent(wait_for_idle=True, back_off_strategy=\"fixed\", p=n_actions) for _ in range(n_agents)]\n #agents = [CsmaAgent(wait_for_idle=False) for _ in range(n_agents)] # not used in paper\n agents = [CsmaAgent(wait_for_idle=False, back_off_strategy=\"fixed\", p=n_actions) for _ in range(n_agents)]\n # ------------------------------------------------------\n\n stepIdx = 0\n rewards = []\n action_list = []\n states = []\n scores = [[] for _ in range(n_agents)] # is this the same as rewards?\n rewards = []\n\n state = env.reset() # If I refactor state, make this work\n state = [np.zeros(n_inputs).reshape(1, -1) for _ in range(n_agents)]\n next_state = [np.zeros(n_inputs).reshape(1, -1) for _ in range(n_agents)]\n\n # For multi-step actions\n state_at_action = [np.zeros(n_inputs).reshape(1, -1) for _ in range(n_agents)]\n future_actions = [[] for _ in range(n_agents)]\n action_duration = [0 for _ in range(n_agents)]\n reward_over_actions = [[] for _ in range(n_agents)]\n actions = [0 for _ in range(n_agents)] # Action selected by the agent (could be multi-step)\n actions_to_take = [0 for _ in range(n_agents)] # do/don't transmit on this step. In {0, 1}\n \n while True:\n # Get Actions ------------------------------\n for i in range(n_agents):\n # if buffer is 0 don't use RL, also don't save if no RL was used\n if state[i][0][-1] == 0:\n #actions.append(-1) # Original\n actions[i] = -1\n future_actions[i] = [-1]\n\n # If the action_duration is 0, get a new action,\n elif action_duration[i] == 0: # make sure this can't be negative\n # Get action, save state, set future actions, and action_duration\n agent_action = agents[i].choose_action(state[i])\n state_at_action[i] = state[i]\n\n if agent_action == 0:\n future_actions[i] = [0]\n elif agent_action == 1:\n future_actions[i] = [1]\n elif agent_action == 2:\n future_actions[i] = [0, 1]\n elif agent_action == 3:\n future_actions[i] = [0, 0, 1]\n elif agent_action == 4:\n future_actions[i] = [0, 0, 0, 1]\n elif agent_action == 5:\n future_actions[i] = [0, 0, 0, 0, 1]\n elif agent_action == 6:\n future_actions[i] = [0, 0, 0, 0, 0, 1]\n elif agent_action == 7:\n future_actions[i] = [0, 0, 0, 0, 0, 0, 1]\n elif agent_action == 8:\n future_actions[i] = [0, 0, 0, 0, 0, 0, 0, 1]\n elif agent_action == 9:\n future_actions[i] = [0, 0, 0, 0, 0, 0, 0, 0, 1]\n elif agent_action == 10:\n future_actions[i] = [0, 0, 0, 0, 0, 0, 0, 0, 0, 1]\n else:\n raise ValueError\n\n \"\"\"\n # Idea to try for 10 agents\n if agent_action == 0:\n future_actions[i] = [0]\n elif agent_action == 1:\n future_actions[i] = [1]\n elif agent_action == 2:\n future_actions[i] = [0, 0, 0, 0, 0, 0, 0, 0, 0, 1]\n \"\"\"\n\n # Update actions if a new decision is made\n actions[i] = agent_action\n\n action_duration[i] = len(future_actions[i])\n\n # Set action to take by popping future action\n actions_to_take[i] = future_actions[i].pop(0)\n # -------------------------------------------------------------------\n\n # Take an environment step\n new_state_info, reward, done, info = env.step(actions_to_take)\n next_state = state_to_observations(new_state_info)\n\n # Decrement all action durations\n action_duration = [duration - 1 for duration in action_duration]\n\n # Remember reward and transitions\n for i in range(n_agents):\n agent_action = actions[i]\n\n # Check if RL was not used\n if agent_action == -1: # RL agent not invoked. Do not save transition to memory\n continue\n\n # Add reward to reward_over_actions\n agent_reward = reward[i] # For now, reward is the same for all agents\n reward_over_actions[i].append(agent_reward)\n\n # Save transitions only when action_duration == 0\n if action_duration[i] == 0:\n agent_state = state_at_action[i]\n agent_next_state = next_state[i]\n\n # Average reward\n agent_average_reward_over_action = float(np.mean(reward_over_actions[i]))\n # Save transition with the state at the time of the action decision and\n # the average reward over the course of the action\n agents[i].remember(agent_state, agent_action, agent_average_reward_over_action,\n agent_next_state, done)\n\n agents[i].learn() # Could be moved outside of the \"if\" block\n\n # Clear reward_over_actions\n reward_over_actions = [[] for _ in range(n_agents)]\n\n #print(\"actions\", actions)\n\n for i in range(n_agents):\n scores[i].append(reward[i])\n\n rewards.append(reward.copy())\n action_list.append(actions.copy())\n states.append(state)\n\n state = next_state\n\n stepIdx += 1\n if stepIdx % 100 == 0:\n print(\"Step: \", stepIdx)\n for i in range(n_agents):\n print(\"mean (last 50)\", np.mean(scores[i][-50:]))\n if i == (n_agents - 1):\n print()\n\n if done:\n # Record data in CSV\n if save == True:\n data = [list(reward) + list(action) + list(np.array(state).flatten()) for reward, action, state in zip(rewards, action_list, states)]\n with open(save_data_path + \"data\" + str(currIt) + \".csv\", \"w\", newline=\"\") as f:\n writer = csv.writer(f)\n writer.writerows(data)\n break\n\n currIt += 1\n if currIt == n_iterations:\n break\n","repo_name":"Farquhar13/RL_Transmission_Control","sub_path":"agent.py","file_name":"agent.py","file_ext":"py","file_size_in_byte":7418,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"73967604729","text":"import pickle\nnumlst=list(range(1,51))\nprint(numlst)\n'''\nfor i in range(1,51):\n f=open('{0}주차.txt'.format(i),'w',encoding='utf8')\n f.write('- {0} 주차 주간보고 -\\n'.format(i))\n f.write('부서 :\\n이름 :\\n업무 요악 :')\nf.close()\n\nfor i in range(1,51):\n f=open('{0}주차.txt'.format(i),'r',encoding='utf8')\n print(f.read())\nf.close()\n\nfor i in range(1,51):\n f=open(\"{0}주차.pickle\".format(i),\"wb\")\n content='- {0} 주차 주간보고 -\\n부서 :\\n업무 :\\n업무 요약 :'.format(i)\n pickle.dump(content,f)\n f.close()\n\nfor i in range(1,51):\n f=open(\"{0}주차.pickle\".format(i),\"rb\")\n content = pickle.load(f)\n print(content)\n f.close()\n\nfor i in range(1,51):\n with open(\"{0}주차.txt\".format(i),'r',encoding='utf8') as f:\n print(f.read())\n'''\n\nwith open(\"{n}주차.txt\".format(n=numlst[:50]),'r',encoding='utf8') as f:\n print(f.read())\n","repo_name":"beans3142/study_language","sub_path":"나도코딩 파이썬/6H코딩/퀴즈/퀴즈6/퀴즈6.py","file_name":"퀴즈6.py","file_ext":"py","file_size_in_byte":912,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"14990791045","text":"# 16. 3Sum Closest\n# 🟠Medium\n#\n# https://leetcode.com/problems/3sum-closest/\n#\n# Tags: Array - Two Pointers - Sorting\n\nimport timeit\nfrom typing import List\n\n\n# Sort the input, fix one element starting from the left and use\n# two pointers encircling the remaining window to calculate possible\n# sums. When the current sum is bigger than target, shrink the window\n# from the right, when smaller, shrink it from the left, this allows us\n# to find the best 3sum with each element in O(n).\n#\n# Time complexity: O(n^2) - For each element, we find the best 3sum that\n# contains it in linear time.\n# Space complexity: O(1) - We keep one sum and 3 pointers in memory.\n#\n# Runtime: 7216 ms, faster than 40.63%\n# Memory Usage: 14 MB, less than 88.54%\nclass LoopAndTwoPointers:\n def threeSumClosest(self, nums: List[int], target: int) -> int:\n # If we only have 3 elements, return the input.\n if len(nums) < 4:\n return sum(nums)\n # Sort the input to be able to use a two pointer approach.\n nums.sort()\n # We are guaranteed to have at least 3 elements.\n closest = sum(nums[:3])\n # The outer loop iterates over all the values except the last\n # two, these are covered by the left and right pointer.\n for idx in range(len(nums) - 2):\n # Use a left and right pointer to calculate possible 3 sums.\n # Initialize the pointers to the biggest possible window.\n left, right = idx + 1, len(nums) - 1\n # Check sums while we haven't checked them all.\n while left < right:\n current = nums[idx] + nums[right] + nums[left]\n # If the current sum is greater than the target, find a\n # smaller sum by moving left the right pointer.\n if current > target:\n right -= 1\n # If the current sum is less than the target, find a\n # larger sum by moving right the left pointer.\n elif current < target:\n left += 1\n # If the target equals the sum, return the values, this\n # is the only match.\n else:\n return current\n # Check all possible sums against the best.\n if abs(current - target) < abs(closest - target):\n closest = current\n\n return closest\n\n\n# We can extend the previous solution to come up with a generic\n# solution that finds the sum of k elements that comes closer to target.\n#\n# Time complexity: O(n^2) - For each element, we find the best 3sum that\n# contains it in linear time.\n# Space complexity: O(n) - We keep the reversed input and use list\n# comprehension to calculate the result, linear space.\nclass KSum:\n def threeSumClosest(self, nums: List[int], target: int) -> int:\n nums.sort()\n return self.kSumClosest(nums, target, 3)\n\n def kSumClosest(self, nums: List[int], target: int, k: int) -> int:\n n = len(nums)\n if k == n:\n return sum(nums[:k])\n\n current = sum(nums[:k])\n if current >= target:\n return current\n\n current = sum(nums[-k:])\n if current <= target:\n return current\n\n if k == 1:\n return min(\n [(x, abs(target - x)) for x in nums],\n key=lambda tuple: tuple[1],\n )[0]\n\n closest = sum(nums[:k])\n for i in range(n - k + 1):\n if i > 0 and nums[i - 1] == nums[i]:\n continue\n\n current = (\n self.kSumClosest(nums[i + 1 :], target - nums[i], k - 1)\n + nums[i]\n )\n if abs(target - current) < abs(target - closest):\n if current != target:\n closest = current\n else:\n return target\n\n return closest\n\n\ndef test():\n executors = [\n LoopAndTwoPointers,\n KSum,\n ]\n tests = [\n [[-1, 2, 1, -4], 1, 2],\n [[0, 0, 0], 1, 0],\n [[1, 1, 1, 1], 0, 3],\n ]\n for executor in executors:\n start = timeit.default_timer()\n for _ in range(1):\n for n, t in enumerate(tests):\n sol = executor()\n result = sol.threeSumClosest(t[0], t[1])\n exp = t[2]\n assert result == exp, (\n f\"\\033[93m» {result} <> {exp}\\033[91m for \"\n + f\"test {n} using \\033[1m{executor.__name__}\"\n )\n stop = timeit.default_timer()\n used = str(round(stop - start, 5))\n cols = \"{0:20}{1:10}{2:10}\"\n res = cols.format(executor.__name__, used, \"seconds\")\n print(f\"\\033[92m» {res}\\033[0m\")\n\n\ntest()\n","repo_name":"raul-sauco/coding-challenges","sub_path":"leetcode/3sum-closest.py","file_name":"3sum-closest.py","file_ext":"py","file_size_in_byte":4772,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"45794013324","text":"\"\"\"\nDesenvolva um programa que leia seis números inteiros e mostre a soma apenas\ndaqueles que forem pares. Se o valor digitado for ímpar, descondidere-o\n\"\"\"\nsoma = 0\ncont = 0\nfor n in range(1, 7):\n num = int(input(f'Digite o {n}º número inteiro: '))\n if num % 2 == 0:\n soma += num\n cont += 1\nprint(f'Você informou {cont} números PARES e a soma destes é igual a {soma}.')\n","repo_name":"judigunkel/Exercicios-Python","sub_path":"Mundo 2/ex050.py","file_name":"ex050.py","file_ext":"py","file_size_in_byte":398,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"16134966407","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Nov 26 15:17:47 2019\n\n@author: Jonathan Schilling (jonathan.schilling@ipp.mpg.de)\n\"\"\"\n\n#%% prepare for code generation\n\ndef indented(tabs, lines, indentationChar=\"\\t\"):\n indentation = \"\"\n for i in range(tabs):\n indentation += indentationChar\n indented = ''\n if '\\n' in lines.strip():\n for line in lines.split('\\n'):\n if line != '':\n indented += indentation+line+'\\n'\n else:\n indented = indentation+lines#.strip()\n return indented\n\ndef indent(tabs, lines, indentationChar=\"\\t\"):\n return tabs+1, indented(tabs, lines, indentationChar)\n\ndef unindent(tabs, lines, indentationChar=\"\\t\"):\n return tabs-1, indented(tabs, lines, indentationChar)\n\n\n#%% document who created the reading routines when on which machine\n\nfrom datetime import datetime\nimport getpass\nimport platform\n\n# dd/mm/YY H:M:S in UTC\nnow_string = datetime.utcnow().strftime('%d/%m/%Y %H:%M:%S UTC')\nusername = getpass.getuser()\nhostname = platform.node()\n\ncreation_tag = 'auto-created by a user called \\''+username+'\\' on a machine called \\''+hostname+'\\' at '+now_string\n\n#%% generate Fortran type declarations\nfrom Hdf5File import Group, Dataset, Datatype\n\n# datatype in Fortran from specification file\ndef fortran_dtype(dtype):\n if dtype=='int':\n return 'INTEGER'\n elif dtype=='double':\n return 'DOUBLE PRECISION'\n elif dtype=='boolean':\n return 'LOGICAL'\n else:\n return 'TYPE('+dtype.upper()+')'\n\n# generate custom compound datatype declaration in Fortran\ndef fortran_genType(name, members):\n ret = 'TYPE '+name+'\\n'\n for member in members:\n if type(member) == Group or type(member) == Datatype:\n ret += ' TYPE('+member.name+')'\n else:\n ret += ' '+fortran_dtype(member.dtype)\n if member.rank>0:\n ret += ', ALLOCATABLE'\n ret += ' :: '+member.name\n if type(member) != Group and member.rank>0:\n ret += '('\n for i in range(member.rank):\n if i>0:\n ret += ',:'\n else:\n ret += ':'\n ret += ')'\n ret += '\\n'\n ret += 'END TYPE '+name\n return ret\n\n# initial code of loading routine\ndef fortran_startLoader(f):\n f.write(\"\"\"subroutine loadSpec(s, filename, ierr)\n use hdf5\n implicit none\n type(SpecOutput), intent(inout) :: s ! target datastructure\n character(len=*), intent(in) :: filename ! filename to load\n integer, intent(out), optional :: ierr ! error flag; .eq.0 if ok\n integer :: hdfier ! error flag for HDF5 API calls\n integer(hid_t) :: file_id ! identifier for current file\n integer(hid_t) :: dset_id ! temporary dataset id\n integer(hid_t) :: dataspace ! dataspace used to query dataset size\n integer(hsize_t) :: dims_1(1) ! current dimensions of rank-1 dataset\n integer(hsize_t) :: dims_2(2) ! current dimensions of rank-2 dataset\n integer(hsize_t) :: dims_3(3) ! current dimensions of rank-3 dataset\n integer(hsize_t) :: max_dims_1(1) ! maximum dimensions of rank-1 dataset\n integer(hsize_t) :: max_dims_2(2) ! maximum dimensions of rank-2 dataset\n integer(hsize_t) :: max_dims_3(3) ! maximum dimensions of rank-3 dataset\n integer :: logical_tmp ! temporary integer used to read logicals\n \n call h5open_f(hdfier)\n if (hdfier.ne.0) then ; write(*,*) \"error opening HDF5 library\" ; goto 9999 ; endif\n\n call h5fopen_f(filename, H5F_ACC_RDONLY_F, file_id, hdfier)\n if (hdfier.ne.0) then ; write(*,*) \"error opening HDF5 file '\",filename,\"'\" ; goto 9998 ; endif\n\"\"\")\n\n# finalizing code of loading routine\ndef fortran_endLoader(f):\n f.write(\"\"\"\n9998 continue\n \n call h5fclose_f(file_id, hdfier)\n if (hdfier.ne.0) then ; write(*,*) \"error closing HDF5 file '\",filename,\"'\" ; ierr = hdfier ; endif\n\n9999 continue\n\n call h5close_f(hdfier)\n if (hdfier.ne.0) then ; write(*,*) \"error closing HDF5 library\" ; ierr = hdfier ; endif \n \nend subroutine loadSpec\n\"\"\")\n\n# write demo code\ndef fortran_demoLoader(f):\n f.write(\"\"\"\nprogram test_read_spec\n use read_spec\n implicit none\n type(SpecOutput) :: s\n character(*), parameter :: filename = \"/home/jonathan/Uni/04_PhD/00_programs/SPEC/SPEC/InputFiles/TestCases/G3V02L1Fi.001.h5\"\n \n write(*,*) \"reading '\",filename,\"'...\"\n call loadSpec(s, filename)\n write(*,*) \"done\"\n \n write(*,\"(A,F4.2)\") \"SPEC version: \", s%version\n write(*,\"(A,99I2)\") \"Lrad:\", s%input%physics%Lrad\n \n call freeSpec(s)\nend program test_read_spec\n\"\"\")\n\n# read a scalar (int or double) from HDF5 variable srcPath into the source code variable targetPath\ndef fortran_loadItem(f, item):\n \n srcName = item.getFullName()\n \n targetName = \"s\"+srcName.replace(\"/\",\"%\")\n if item.rank>0:\n targetName += \"(\"\n if item.indexMapping is not None:\n for dim,idxRange in enumerate(item.indexMapping):\n if dim==0:\n targetName += idxRange\n else:\n targetName += \", \"+idxRange\n else:\n for dim in range(item.rank):\n if dim==0:\n targetName += \"1:dims_\"+str(item.rank)+\"(1)\"\n else:\n targetName += \", 1:dims_\"+str(item.rank)+\"(\"+str(dim+1)+\")\"\n targetName += \")\"\n \n #print(\"read {} into {}\".format(srcName, targetName))\n \n # translate dtype into HDF5 type\n h5type='ERROR'\n if item.dtype=='double':\n h5type='H5T_NATIVE_DOUBLE'\n elif item.dtype=='int' or item.dtype=='boolean':\n h5type='H5T_NATIVE_INTEGER'\n else:\n h5type='TYPE('+item.dtype.upper()+')'\n \n \n \n \n if item.rank==0:\n if (item.dtype=='boolean'):\n fmt=\"\"\"\n! {srcName} --> {targetName}; rank={rank}; h5type={h5type}\n call h5dopen_f(file_id, \"{srcName}\", dset_id, hdfier)\n if (hdfier.ne.0) then ; write(*,*) \"error opening dataset '{srcName}'\" ; goto 9998 ; endif\n call h5dread_f(dset_id, {h5type}, logical_tmp, int((/1/), HSIZE_T), hdfier)\n {targetName} = merge(.TRUE., .FALSE., logical_tmp.ne.0)\n if (hdfier.ne.0) then ; write(*,*) \"error reading dataset '{srcName}'\" ; goto 9998 ; endif\n call h5dclose_f(dset_id, hdfier)\n if (hdfier.ne.0) then ; write(*,*) \"error closing dataset '{srcName}'\" ; goto 9998 ; endif\n\"\"\"\n else:\n fmt=\"\"\"\n! {srcName} --> {targetName}; rank={rank}; h5type={h5type}\n call h5dopen_f(file_id, \"{srcName}\", dset_id, hdfier)\n if (hdfier.ne.0) then ; write(*,*) \"error opening dataset '{srcName}'\" ; goto 9998 ; endif\n call h5dread_f(dset_id, {h5type}, {targetName}, int((/1/), HSIZE_T), hdfier)\n if (hdfier.ne.0) then ; write(*,*) \"error reading dataset '{srcName}'\" ; goto 9998 ; endif\n call h5dclose_f(dset_id, hdfier)\n if (hdfier.ne.0) then ; write(*,*) \"error closing dataset '{srcName}'\" ; goto 9998 ; endif\n\"\"\"\n else:\n if (item.dtype=='boolean'):\n print(\"ERROR: cannot generate reader for logical array '\"+srcName+\"' yet!\")\n fmt=\"\"\"\n! {srcName} --> {targetName}; rank={rank}\n call h5dopen_f(file_id, \"{srcName}\", dset_id, hdfier)\n if (hdfier.ne.0) then ; write(*,*) \"error opening dataset '{srcName}'\" ; goto 9998 ; endif\n \n ! open dataspace to get current state of dataset\n call h5dget_space_f(dset_id, dataspace, hdfier)\n if (hdfier.ne.0) then ; write(*,*) \"error getting dataspace for dataset '{srcName}'\" ; goto 9998 ; endif\n \n ! get current size of dataset\n call h5sget_simple_extent_dims_f(dataspace, dims_{rank}, max_dims_{rank}, hdfier)\n if (hdfier.ne.{rank}) then ; write(*,*) \"unexpected rank of dataset '{srcName}': \",hdfier,\" .ne. {rank}\" ; goto 9998 ; endif\n\n ! close dataspace after it has been used to query the size of the variable\n call h5sclose_f(dataspace, hdfier)\n if (hdfier.ne.0) then ; write(*,*) \"error closing dataspace for dataset '{srcName}'\" ; goto 9998 ; endif\n \n allocate({targetName})\n \n call h5dread_f(dset_id, {h5type}, {targetName}, dims_{rank}, hdfier)\n if (hdfier.ne.0) then ; write(*,*) \"error reading dataset '{srcName}'\" ; goto 9998 ; endif\n \n call h5dclose_f(dset_id, hdfier)\n if (hdfier.ne.0) then ; write(*,*) \"error closing dataset '{srcName}'\" ; goto 9998 ; endif\n\"\"\"\n f.write(fmt.format(srcName=srcName, targetName=targetName, h5type=h5type, rank=item.rank))\n \n# initial code of loading routine\ndef fortran_startFree(f):\n f.write(\"\"\"subroutine freeSpec(s)\n implicit none\n type(SpecOutput), intent(inout) :: s ! datastructure to free\n\"\"\")\n\n# finalizing code of loading routine\ndef fortran_endFree(f):\n f.write(\"\"\"end subroutine freeSpec\n\"\"\")\n\n# free an allocated item of rank .ge. 1\ndef fortran_freeItem(f, item):\n \n srcName = item.getFullName()\n targetName = \"s\"+srcName.replace(\"/\",\"%\")\n \n if (item.rank > 0):\n print(\"free {}\".format(targetName))\n f.write(\" deallocate(\"+targetName+\")\\n\")\n \n\n#%% actually generate Fortran module for reading SPEC output files\ndef genFortranReader(outdir, moduleName, s):\n \n # we need to reverse the definition order so that types which are used inside other types\n # are already defined when used\n reverse_rootStack = []\n \n rootStack = []\n rootStack.append(s.rootGroup)\n while len(rootStack)>0:\n currentItem = rootStack[-1]\n rootStack = rootStack[:-1]\n \n if currentItem is not s.rootGroup:\n reverse_rootStack.append(currentItem)\n if type(currentItem)==Group:\n for item in currentItem.items:\n rootStack.append(item)\n \n \n fortranFilename = outdir+moduleName+\".f90\"\n print(\"creating Fortran reading module into '\"+fortranFilename+\"'\")\n \n # begin code for root group (== enclosing class)\n f=open(fortranFilename, \"w\")\n \n f.write(\"\"\"! AUTO-GENERATED; DO NOT COMMIT CHANGES TO THIS FILE !\n! \"\"\"+creation_tag+\"\"\"\nmodule \"\"\"+moduleName+\"\\n\")\n \n # custom datatypes come first\n for dtype in s.getDatatypes():\n f.write(fortran_genType(dtype.name, dtype.items)+'\\n')\n \n # we need to reverse the definition order so that types which are used inside other types\n # are already defined when used\n reverse_groupStack = []\n \n groupStack = []\n groupStack.append(s.rootGroup)\n while len(groupStack)>0:\n currentGroup = groupStack[-1]\n groupStack = groupStack[:-1]\n \n if type(currentGroup)==Group:\n reverse_groupStack.append(currentGroup)\n \n for item in currentGroup.items:\n if type(item)==Group:\n groupStack.append(item)\n \n # iterate in reverse order over the discovered variables to generate type definitions in correct order\n for currentGroup in reverse_groupStack[::-1]:\n f.write(fortran_genType(currentGroup.name, currentGroup.items)+'\\n')\n \n f.write(\"contains\\n\")\n \n # initial code of loading routine\n fortran_startLoader(f)\n \n # loop over all variables again and put the loader code for each of them one after another\n for currentGroup in reverse_groupStack[::-1]:\n for item in currentGroup.items:\n if type(item)==Dataset:\n fortran_loadItem(f, item)\n \n # finalizing code of loading routine\n fortran_endLoader(f)\n \n # write the freeSpec subroutine to free the memory it occupied\n fortran_startFree(f)\n \n for currentGroup in reverse_groupStack[::-1]:\n for item in currentGroup.items:\n if type(item)==Dataset:\n fortran_freeItem(f, item)\n \n # finalizing code of freeing routine\n fortran_endFree(f)\n \n f.write(\"end module read_spec\\n\")\n\n # write demo code\n #fortran_demoLoader(f)\n\n f.close()","repo_name":"PrincetonUniversity/SPEC","sub_path":"Utilities/pythontools/misc/genFortran.py","file_name":"genFortran.py","file_ext":"py","file_size_in_byte":12045,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"77"}
+{"seq_id":"74259599289","text":"import random\nfrom typing import Any\n\nimport folium\n\nfrom models import LocationInDb\nfrom config import app\n\n\nCOLORS = [\n \"red\",\n \"blue\",\n \"green\",\n \"purple\",\n \"orange\",\n \"darkred\",\n \"lightred\",\n \"beige\",\n \"darkblue\",\n \"darkgreen\",\n \"cadetblue\",\n \"darkpurple\",\n \"pink\",\n \"lightblue\",\n \"lightgreen\",\n]\n\n\ndef create_map(locations: list[LocationInDb]) -> folium.Map:\n if locations:\n loc = locations[0]\n m = folium.Map(\n zoom_start=8,\n location=[loc.latitude, loc.longitude],\n # tiles=\"http://127.0.0.1:8000/static/map.json\",\n # attr='© OpenStreetMap contributors',\n max_zoom=24,\n )\n else:\n m = folium.Map(\n max_zoom=24,\n )\n for loc in locations:\n folium.Marker(\n location=[loc.latitude, loc.longitude],\n tooltip=loc.name,\n popup=loc.name,\n icon=folium.Icon(icon=\"globe\", color=random.choice(COLORS)),\n ).add_to(m)\n\n return m\n\n\ndef get_map_html(locations: list[LocationInDb]) -> str:\n m = create_map(locations)\n html: str = m.get_root().render()\n return html\n\n\ndef get_map_bytes(locations: list[LocationInDb]) -> bytes:\n html: str = get_map_html(locations)\n html_bytes = html.encode(\"utf-8\")\n return html_bytes\n","repo_name":"crawlic-stud/map-bot","sub_path":"src/services/map_display.py","file_name":"map_display.py","file_ext":"py","file_size_in_byte":1399,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"5472761241","text":"def solution(clothes):\n answer = 1\n cloth_dict = {}\n for c in clothes:\n cloth_type = c[1]\n if cloth_type not in cloth_dict:\n cloth_dict[cloth_type] = 1\n else:\n cloth_dict[cloth_type] += 1\n\n for k in cloth_dict.keys():\n answer *= (cloth_dict[k] + 1)\n\n answer -= 1\n return answer\n\n\nprint(solution([[\"yellowhat\", \"headgear\"], [\"bluesunglasses\", \"eyewear\"], [\"green_turban\", \"headgear\"]])) # 5\nprint(solution([[\"crowmask\", \"face\"], [\"bluesunglasses\", \"face\"], [\"smoky_makeup\", \"face\"]])) # 3","repo_name":"UJHa/Codeit-Study","sub_path":"프로그래머스/00_코딩테스트_고득점_Kit/01_해시/3_위장/jinhwan.py","file_name":"jinhwan.py","file_ext":"py","file_size_in_byte":557,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"35126027349","text":"import hashlib\n\nclass MerkleTree:\n def __init__(self, transactions):\n self.transactions = transactions\n self.past_transaction = [hashlib.sha256(transaction.encode()).hexdigest() for transaction in transactions]\n self.current_transaction = []\n\n while len(self.past_transaction) > 1:\n if len(self.past_transaction) % 2 != 0:\n self.past_transaction.append(self.past_transaction[-1])\n for i in range(0, len(self.past_transaction), 2):\n transaction = self.past_transaction[i] + self.past_transaction[i+1]\n current = hashlib.sha256(transaction.encode()).hexdigest()\n self.current_transaction.append(current)\n self.past_transaction = self.current_transaction\n self.current_transaction = []\n\n self.root = self.past_transaction[0]\n\n\nclass Ledger:\n def __init__(self):\n self.transactions = []\n self.tree = None\n\n def add_transaction(self, transaction):\n self.transactions.append(transaction)\n self.tree = MerkleTree(self.transactions)\n\n def verify_transaction(self, transaction):\n return transaction in self.transactions\n\n def verify_ledger(self):\n return self.tree.root == hashlib.sha256(''.join(self.transactions).encode()).hexdigest()\n\n\nif __name__ == '__main__':\n\n # Create a new ledger\n ledger = Ledger()\n\n # Add some transactions\n ledger.add_transaction(\"Alice sends 1 BTC to Bob\")\n ledger.add_transaction(\"Bob sends 2 BTC to Charlie\")\n ledger.add_transaction(\"Charlie sends 0.5 BTC to David\")\n\n # Verify that the transactions are present in the ledger\n print(ledger.verify_transaction(\"Alice sends 1 BTC to Bob\")) # True\n print(ledger.verify_transaction(\"Bob sends 2 BTC to David\")) # False\n\n # Verify the integrity of the ledger\n print(ledger.verify_ledger()) # True\n","repo_name":"laiduy98/blockchain_project","sub_path":"merkel_test.py","file_name":"merkel_test.py","file_ext":"py","file_size_in_byte":1894,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"27965589053","text":"responses = {}\npolling_active = True\nwhile polling_active:\n\tname = raw_input(\"What is your name? \")\n\tresponse = raw_input(\"Which VA stream did you take measurments? \")\n\tresponses[name] = response\n\trepeat = raw_input(\"Is there another person on your team to respond? (yes/no \")\n\tif repeat == 'no':\n\t\tpolling_active = False\n\tprint (\"--- Stream Visitation Results ---\")\n\tfor name, response in responses.items():\n\t\tprint(name + \" took measurments at the \" + response + \" site.\")\n","repo_name":"APtech321/code_em","sub_path":"Alex Peskin/fill_a_dictionary.py","file_name":"fill_a_dictionary.py","file_ext":"py","file_size_in_byte":476,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29438132759","text":"def suma_divisores(a):\n divisores = [0]\n for i in range (1,a):\n if a % i == 0:\n divisores.append(i)\n if sum(divisores) == 1:\n x = True\n\n else:\n x = False\n\n return sum(divisores),x\nif __name__ == \"__main__\":\n x = eval(input(\"Ingrese el numero\"))\n print(suma_divisores(x))\n\n\n","repo_name":"pabloschwarzenberg/grader","sub_path":"tema3_ej1/tema3_ej1_43cf0697a40d89ce2202353334858aeb.py","file_name":"tema3_ej1_43cf0697a40d89ce2202353334858aeb.py","file_ext":"py","file_size_in_byte":330,"program_lang":"python","lang":"it","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"11118842589","text":"import pandas as pd\nimport os\nimport warnings\nwarnings.filterwarnings(action='ignore')\n\n\nall_data=pd.DataFrame(columns=['rate', 'review'])\n\nos.chdir('./infos')\nli=os.listdir()\nfor i in li:#모은 리뷰들을 하나의 데이터프레임으로 변경\n data= pd.read_table(i)\n all_data = pd.concat([all_data, data], ignore_index=True, axis=0)\n\n\nprint('초반 데이터 확인 :',len(all_data))#\nall_data.drop_duplicates(subset=['review'], inplace=True)#중복된 리뷰들을 제거한다\nprint('중복 제거 확인 :',len(all_data))\n\nall_data = all_data.dropna(how = 'any') # Null 값이 존재하는 행 제거\nprint('null값이 존재하는가?=',all_data.isnull().values.any()) # Null 값이 존재하는지 확인\n\nprint('\\n===================\\n')\n\n#이벤트/홍보 관련 리뷰 제거\nremove=['이벤트','핫티스트','선.+','십오야','홀세일','통장','작가님','썸딜','마크',\n '나인NINE9','무료','알람','감상후','수정','리뷰뿅','연재','리디','포인트백',\n '포백','이벤','이벵','1+1','나중에','후리뷰','.+님','리다무']\nfor i in remove:\n all_data = all_data[all_data[\"review\"].str.contains(i) == False]\n\nprint(all_data.groupby('rate').size().reset_index(name = 'count'))#원래 데이터양 확인\nall_data.to_csv('origin_all_data.txt',mode='w',index=False)\n\nprint('\\n===================\\n')\n\nn_data=all_data[all_data['rate']==-1]#부정 리뷰 추출\nn_data['rate']=n_data['rate'].apply(lambda x:x+1)\np_data=all_data[all_data['rate']==1].sample(n=len(n_data))#부정 리뷰 길이에 맞춰 긍정 리뷰 랜덤 추출\n\n#긍정/부정 리뷰 합쳐서 새로운 파일로 저장\nresult=pd.concat([n_data,p_data], ignore_index=True, axis=0).sample(frac=1).reset_index(drop=True)\nprint(result.groupby('rate').size().reset_index(name = 'count'))\nresult.to_csv('result_all_data.txt',mode='w', index=False)\n\n\n","repo_name":"world970511/RIDIBOOKS_romance_webnovel_review_Sentiment_Analysis","sub_path":"crawler_ code/clean_info.py","file_name":"clean_info.py","file_ext":"py","file_size_in_byte":1887,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"32783863177","text":"import tkinter as tk\r\n\r\nroot = tk.Tk()\r\n\r\ndef clear_entry(self): # function is passed event from bind\r\n print(\"clearin\")\r\n print(self.widget) # event.widget tells us which btn was clicked\r\n print(btn._name) # this correlates with btn._name\r\n \r\nbtn = tk.Button(root, text='Clear')\r\nbtn.grid()\r\nbtn.bind('', clear_entry) # no parentheses for function as it is\r\n # a reference not a call\r\nroot.mainloop()\r\n","repo_name":"ccnelson/Python","sub_path":"tkinter/button_bind.py","file_name":"button_bind.py","file_ext":"py","file_size_in_byte":460,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"23276276072","text":"import torch\nimport torch.nn as nn\n\nclass ContrastiveLoss(nn.Module):\n def __init__(self, alpha, beta, margin):\n super(ContrastiveLoss, self).__init__()\n self.alpha = alpha\n self.beta = beta\n self.margin = margin\n\n def forward(self, x1, x2, y):\n distance = torch.pairwise_distance(x1, x2, p=2)\n loss = self.alpha * (1-y) * distance**2 + \\\n self.beta * y * (torch.max(torch.zeros_like(distance), self.margin - distance)**2)\n return torch.mean(loss, dtype=torch.float)","repo_name":"serkancancaglayan/Signature-Verification-SiameseNet-SVM","sub_path":"ContrastiveLoss.py","file_name":"ContrastiveLoss.py","file_ext":"py","file_size_in_byte":535,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"11668269782","text":"from ray import tune\n\nfrom toolbox.dece.dece import DECETrainer\nfrom toolbox.dece.utils import *\nfrom toolbox.env import FourWayGridWorld\nfrom toolbox.marl.test_extra_loss import _base\n\n\ndef test_dece(config={}, local_mode=False, t=2000, **kwargs):\n _base(\n trainer=DECETrainer,\n local_mode=local_mode,\n extra_config=config,\n env_name=\"Pendulum-v0\",\n t=t,\n **kwargs\n )\n\n\ndef test_dece_batch0(local_mode=False):\n test_dece(\n {\n DIVERSITY_ENCOURAGING: True,\n USE_BISECTOR: tune.grid_search([True, False]),\n USE_DIVERSITY_VALUE_NETWORK: tune.grid_search([True, False]),\n CLIP_DIVERSITY_GRADIENT: True,\n DELAY_UPDATE: tune.grid_search([True, False]),\n REPLAY_VALUES: tune.grid_search([True, False]),\n TWO_SIDE_CLIP_LOSS: tune.grid_search([True, False])\n }, local_mode\n )\n\n\ndef test_two_side_loss(local_mode=False):\n test_dece(\n {TWO_SIDE_CLIP_LOSS: tune.grid_search([True, False])}, local_mode\n )\n\n\ndef test_delay_update(local_mode=False):\n test_dece({DELAY_UPDATE: tune.grid_search([True, False])}, local_mode)\n\n\ndef test_three_tuning(local_mode=False):\n test_dece(\n {\n DELAY_UPDATE: tune.grid_search([True, False]),\n USE_DIVERSITY_VALUE_NETWORK: tune.grid_search([True, False]),\n REPLAY_VALUES: tune.grid_search([True, False])\n }, local_mode\n )\n\n\ndef test_vtrace(local_mode=False, hard=False):\n _base(\n trainer=DECETrainer,\n local_mode=local_mode,\n extra_config={\n REPLAY_VALUES: True,\n 'sample_batch_size': 50 if hard else 8,\n 'train_batch_size': 450 if hard else 96,\n 'num_sgd_iter': 10 if hard else 2,\n \"sgd_minibatch_size\": 150 if hard else 3 * 8,\n 'model': {\n 'fcnet_hiddens': [16, 16]\n },\n 'seed': 0\n # 'lr': 5e-3,\n },\n env_name=FourWayGridWorld,\n t=100000\n )\n\n\ndef test_vtrace_single_agent(local_mode=False):\n _base(\n trainer=DECETrainer,\n local_mode=local_mode,\n extra_config={\n REPLAY_VALUES: tune.grid_search([True, False]),\n 'sample_batch_size': 50,\n 'train_batch_size': 200,\n 'num_sgd_iter': 10,\n 'sgd_minibatch_size': 50\n },\n env_name=FourWayGridWorld,\n t=20000,\n num_agents=1\n )\n\n\ndef regression_test(local_mode=False):\n _base(\n trainer=DECETrainer,\n local_mode=local_mode,\n extra_config={\n REPLAY_VALUES: tune.grid_search([True, False]),\n # \"normalize_advantage\": tune.grid_search([True, False]),\n # 'use_vtrace': tune.grid_search([True]),\n 'sample_batch_size': 128,\n 'train_batch_size': 512,\n 'sgd_minibatch_size': 128,\n 'num_sgd_iter': 10,\n USE_BISECTOR: False,\n 'seed': tune.grid_search([432, 1920]),\n # 'lr': 5e-3,\n },\n # env_name=\"Pendulum-v0\",\n # env_name=\"CartPole-v0\",\n env_name=FourWayGridWorld,\n t={'time_total_s': 300},\n # t={'timesteps_total': 300000},\n num_agents=1\n )\n\n\ndef only_tnb(local_mode=False):\n test_dece(\n {\n DELAY_UPDATE: tune.grid_search([True, False]),\n ONLY_TNB: True,\n REPLAY_VALUES: False\n }, local_mode\n )\n\n\ndef single_agent_dece(lm=False):\n test_dece(\n {\n DELAY_UPDATE: tune.grid_search([True]),\n REPLAY_VALUES: tune.grid_search([False]),\n USE_DIVERSITY_VALUE_NETWORK: tune.grid_search([False]),\n NORMALIZE_ADVANTAGE: tune.grid_search([False]),\n 'sample_batch_size': 50,\n 'sgd_minibatch_size': 64,\n 'train_batch_size': 2048,\n \"num_cpus_per_worker\": 1,\n \"num_cpus_for_driver\": 1,\n \"num_envs_per_worker\": 5,\n 'num_workers': 1,\n },\n lm,\n num_agents=tune.grid_search([1]),\n t=10000\n )\n\n\ndef replay_values_or_not_test(lm=False):\n test_dece(\n {\n REPLAY_VALUES: tune.grid_search([True, False]),\n 'num_envs_per_worker': 3,\n 'sample_batch_size': 20,\n 'sgd_minibatch_size': 120,\n 'train_batch_size': 480\n },\n lm,\n num_agents=tune.grid_search([1, 3])\n )\n\n\ndef mock_experiment(lm=False):\n _base(\n trainer=DECETrainer,\n local_mode=lm,\n extra_config={\n DELAY_UPDATE: tune.grid_search([True, False]),\n REPLAY_VALUES: tune.grid_search([True, False]),\n 'sample_batch_size': 20,\n 'sgd_minibatch_size': 100,\n 'train_batch_size': 500,\n },\n env_name=FourWayGridWorld,\n t={'timesteps_total': 5000},\n num_agents=tune.grid_search([1, 5])\n )\n\n\ndef no_replay_values_batch_size_bug(lm=False):\n _base(\n trainer=DECETrainer,\n local_mode=lm,\n extra_config={\n REPLAY_VALUES: tune.grid_search([True, False]),\n CONSTRAIN_NOVELTY: tune.grid_search(['soft', 'hard', None]),\n 'num_envs_per_worker': 4,\n 'sample_batch_size': 20,\n 'sgd_minibatch_size': 100,\n 'train_batch_size': 1000,\n \"num_cpus_per_worker\": 1,\n \"num_cpus_for_driver\": 1,\n 'num_workers': 2,\n },\n env_name=FourWayGridWorld,\n t=1000000,\n num_agents=tune.grid_search([5])\n )\n\n\ndef test_constrain_novelty(lm=False):\n test_dece(\n {\n CONSTRAIN_NOVELTY: tune.grid_search(['soft', 'hard', None]),\n \"novelty_stat_length\": 2,\n }, lm\n )\n\n\ndef test_marginal_cases(lm=False):\n test_dece({ONLY_TNB: True}, local_mode=lm)\n # test_dece({USE_BISECTOR: False})\n # test_dece({USE_DIVERSITY_VALUE_NETWORK: False})\n # test_dece({PURE_OFF_POLICY: True}, local_mode=lm)\n\n\nif __name__ == '__main__':\n # test_dece(local_mode=False)\n # test_dece_batch0(local_mode=False)\n # test_two_side_loss(local_mode=True)\n # test_delay_update(local_mode=False)\n # test_three_tuning(local_mode=False)\n single_agent_dece()\n # only_tnb()\n # regression_test(local_mode=False)\n # test_vtrace(local_mode=True)\n # test_vtrace_single_agent(local_mode=False)\n # replay_values_or_not_test(False)\n # test_vtrace(local_mode=True, hard=True)\n # mock_experiment(False)\n # no_replay_values_batch_size_bug(True)\n # test_constrain_novelty(False)\n # test_marginal_cases(False)\n","repo_name":"pengzhenghao/rl-interpretation","sub_path":"toolbox/dece/test_dece.py","file_name":"test_dece.py","file_ext":"py","file_size_in_byte":6636,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"71690798649","text":"import pytest\r\nfrom myStack import MyStack\r\n\r\ndef test_push():\r\n s = MyStack()\r\n assert 1 == s.push(1)\r\n assert 2 == s.push(2)\r\n assert 2 == s.size()\r\n\r\ndef test_pop():\r\n s = MyStack()\r\n s.push(1)\r\n s.push(2)\r\n assert 2 == s.pop()\r\n assert 1 == s.pop()\r\n\r\ndef test_size():\r\n s = MyStack()\r\n s.push(1)\r\n s.push(2)\r\n s.push(3)\r\n assert 3 == s.size()\r\n s.pop()\r\n assert 2 == s.size()\r\n s.push(4)\r\n assert 3 == s.size()\r\n\r\ndef test_empty():\r\n s = MyStack()\r\n assert 1 == s.isEmpty()\r\n s.push(1)\r\n assert 0 == s.isEmpty()\r\n s.pop()\r\n assert 1 == s.isEmpty()\r\n\r\ndef test_peek():\r\n s = MyStack()\r\n s.push(1)\r\n s.push(2)\r\n assert 2 == s.peek()\r\n s.push(3)\r\n assert 3 == s.peek()\r\n s.pop()\r\n assert 2 == s.peek()\r\n\r\ndef test_sort():\r\n s = MyStack()\r\n s.push(2)\r\n s.push(3)\r\n s.push(1)\r\n s.push(10)\r\n s.push(5)\r\n s.sort()\r\n assert 10 == s.pop()\r\n assert 5 == s.pop()\r\n assert 3 == s.pop()\r\n assert 2 == s.pop()\r\n assert 1 == s.pop()","repo_name":"rjafar/cracking-coding-interview","sub_path":"data_structures/Stack/test_myStack.py","file_name":"test_myStack.py","file_ext":"py","file_size_in_byte":1054,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"70414815929","text":"import requests\n\nurl = \"http://127.0.0.1:8000/process-image\"\npayload = {\n \"image_src\": \"https://image-auto-enhance-brightness-ml.sgp1.digitaloceanspaces.com/SAMPLE%20(4).jpg\",\n \"output_name\": \"xyab123rc\"\n}\nheaders = {\n \"Content-Type\": \"application/json\"\n}\nresponse = requests.put(url, json=payload, headers=headers)\n\nprint(response.status_code)\nprint(response.json())","repo_name":"arnav-dev-git/fast-api-ml","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":376,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"10337481098","text":"from celery import shared_task\nfrom celery.utils.log import get_task_logger\n\nfrom bookmaker.rapid_api import (\n get_next_matches_and_save_to_db,\n get_odds_and_update_matches_on_db,\n get_results_and_update_matches_on_db,\n)\n\nlogger = get_task_logger(__name__)\n\n\n@shared_task\n# beat task test\ndef task_test():\n logger.info(\"Task test log\")\n return \"return test\"\n\n\n@shared_task\ndef next_matches():\n logger.info(\"Getting next matches\")\n try:\n get_next_matches_and_save_to_db()\n except Exception as e:\n logger.info(\"Next matches exception,\", e)\n\n\n@shared_task\ndef odds():\n logger.info(\"Getting odds\")\n try:\n get_odds_and_update_matches_on_db()\n except Exception as e:\n logger.info(\"Odds exception,\", e)\n\n\n@shared_task\ndef results():\n logger.info(\"Getting results\")\n try:\n get_results_and_update_matches_on_db()\n except Exception as e:\n logger.info(\"Results exception,\", e)\n","repo_name":"joshrobbinsuk/brokelads_django_react","sub_path":"backend/bookmaker/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":951,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"72977423290","text":"# -*- coding: utf-8 -*-\n\n\"\"\"\n@date: 2022/4/28 下午9:17\n@file: create_cccf.py\n@author: zj\n@description: \n\"\"\"\nfrom typing import List\nimport os\n\nimport numpy as np\nfrom tqdm import tqdm\nfrom zcls2.config.key_word import KEY_SEP\n\n\ndef load_data(data_root):\n assert os.path.isdir(data_root), data_root\n\n class_path = os.path.join(data_root, 'classes.txt')\n classes = np.loadtxt(class_path, dtype=str, delimiter=' ')\n\n train_list = list()\n train_path = os.path.join(data_root, 'train.txt')\n with open(train_path, 'r') as f:\n for line in f:\n tmp_list = line.strip().split(KEY_SEP)\n train_list.append(tmp_list)\n\n test_list = list()\n test_path = os.path.join(data_root, 'test.txt')\n with open(test_path, 'r') as f:\n for line in f:\n tmp_list = line.strip().split(KEY_SEP)\n test_list.append(tmp_list)\n\n return classes, train_list, test_list\n\n\ndef process(data_root, dst_classes: List, dst_train_list: List, dst_test_list: List):\n classes, train_list, test_list = load_data(data_root)\n\n current_class_num = len(dst_classes)\n dst_classes.extend(classes)\n\n for item in tqdm(train_list):\n img_path, target = item\n dst_train_list.append([os.path.join(data_root, img_path), int(target) + current_class_num])\n\n for item in tqdm(test_list):\n img_path, target = item\n dst_test_list.append([os.path.join(data_root, img_path), int(target) + current_class_num])\n\n\ndef save_classes(classes, class_path):\n assert not os.path.exists(class_path), class_path\n np.savetxt(class_path, classes, fmt='%s', delimiter=' ', newline='\\n', header='', )\n\n\ndef save_img_paths(img_path_list, data_path):\n assert not os.path.exists(data_path), data_path\n\n length = len(img_path_list)\n with open(data_path, 'w') as f:\n for idx, (img_path, target) in enumerate(img_path_list):\n if idx < (length - 1):\n f.write(f\"{img_path}{KEY_SEP}{target}\\n\")\n else:\n f.write(f\"{img_path}{KEY_SEP}{target}\")\n\n\ndef main():\n cifar100_dir = 'cifar100'\n caltech101_dir = 'caltech-101'\n cub200_dir = 'CUB_200_2011'\n food101_dir = 'food-101'\n\n print('process ...')\n classes = list()\n train_list = list()\n test_list = list()\n process(cifar100_dir, classes, train_list, test_list)\n process(caltech101_dir, classes, train_list, test_list)\n process(cub200_dir, classes, train_list, test_list)\n process(food101_dir, classes, train_list, test_list)\n\n print('save ...')\n dst_classes_path = './classes.txt'\n save_classes(classes, dst_classes_path)\n dst_train_path = './train.txt'\n save_img_paths(train_list, dst_train_path)\n dst_test_path = './test.txt'\n save_img_paths(test_list, dst_test_path)\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"zjykzj/cccf","sub_path":"scripts/create_cccf.py","file_name":"create_cccf.py","file_ext":"py","file_size_in_byte":2828,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"74227367287","text":"class Money:\n rate = 1\n\n def __init__(self, integer=0, fraction=0):\n self._integer = integer\n self._fraction = fraction\n\n @staticmethod\n def make_from_str(money_str):\n m = Money()\n money_list = money_str.split('.')\n if len(money_list) > 0:\n m._integer = int(money_list[0])\n if len(money_list) > 1:\n m._fraction = int(money_list[1])\n return m\n\n def to_float(self):\n return float('{}.{}'.format(self._integer, self._fraction))\n\n def __repr__(self):\n return '{},{}'.format(self._integer, self._fraction)\n\n def __add__(self, other):\n sum_num = self.to_float() + other.to_float()\n return Money.make_from_str(str(sum_num))\n\n def __sub__(self, other):\n sum_num = self.to_float() - other.to_float()\n return Money.make_from_str(str(sum_num))\n\n def __truediv__(self, other):\n if isinstance(other, Money):\n sum_num = self.to_float()/other.to_float()\n else:\n sum_num = self.to_float()/other\n this_num = self._integer * 100 + self._fraction\n return Money.make_from_str(str(sum_num))\n\n def __lt__(self, other):\n res = False\n if self._integer < other._integer:\n res = True\n elif self._integer == other._integer:\n res = self._fraction < other._fraction\n return res\n\n def __gt__(self, other):\n res = False\n if self._integer > other._integer:\n res = True\n elif self._integer == other._integer:\n res = self._fraction > other._fraction\n return res\n\n def __le__(self, other):\n res = False\n if self._integer < other._integer:\n res = True\n elif self._integer == other._integer:\n res = self._fraction <= other._fraction\n return res\n\n def __ge__(self, other):\n res = False\n if self._integer > other._integer:\n res = True\n elif self._integer == other._integer:\n res = self._fraction >= other._fraction\n return res\n\n def __eq__(self, other):\n if self._integer == other._integer and \\\n self._fraction == other._fraction:\n res = True\n else:\n res = False\n\n def __ne__(self, other):\n return not self.__eq__(other)\n\n def to_dollars(self):\n dol = self.to_float() / Money.rate\n return str(Money.make_from_str(str(dol)))\n\n\nif __name__ == '__main__':\n m1 = Money(10, 50)\n m2 = Money(100, 125)\n print('m1 = ', m1)\n print('m2 = ', m2)\n print('{} + {} = {}'.format(m1, m2, m1+m2))\n print('{} - {} = {}'.format(m1, m2, m1-m2))\n print('{} / {} = {}'.format(m1, m2, m1/m2))\n print('{} < {} = {}'.format(m1, m2, m1 {} = {}'.format(m1, m2, m1>m2))\n print('{} <= {} = {}'.format(m1, m2, m1<=m2))\n print('{} >= {} = {}'.format(m1, m2, m1>=m2))\n Money.rate = 60\n print('{} to dollars = {}'.format(m1, m1.to_dollars()))\n print('{} to dollars = {}'.format(m2, m2.to_dollars()))\n","repo_name":"ZloiGaMeR/PythonCourseATIS","sub_path":"Useful/for_lec14/task_10.py","file_name":"task_10.py","file_ext":"py","file_size_in_byte":3088,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"2923098607","text":"from django.urls import path\nfrom rest_framework import routers\nfrom .views import (UserViewSet , FriendList , Follow , UnFollow )\n\napp_name = 'accountapi'\n\nrouter = routers.SimpleRouter()\n\nrouter.register('user',UserViewSet,basename='user')\n\nurlpatterns = router.urls\n\nurlpatterns = [\n path('friend/',FriendList.as_view(),name='friend_list'),\n path('follow/',Follow.as_view(),name='follow'),\n path('unfollow/',UnFollow.as_view(),name='un_follow'),\n\n]","repo_name":"hanieh-mav/SocialNetwork-with-drf","sub_path":"accountapi/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":476,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"73070280250","text":"from flask import Flask, render_template, request\r\nfrom pymysql import connections\r\nimport boto3\r\nfrom config import *\r\n\r\napp = Flask(__name__)\r\n\r\nbucket = custombucket\r\nregion = customregion\r\n\r\ndb_conn = connections.Connection(\r\n host=customhost,\r\n port=3306,\r\n user=customuser,\r\n password=custompass,\r\n db=customdb\r\n)\r\n\r\n#routes\r\n\r\n@app.route(\"/\")\r\ndef index():\r\n return render_template('index.html')\r\n\r\n@app.route(\"/contact\")\r\ndef contact():\r\n return render_template('contact.html')\r\n\r\n@app.route(\"/portfolioSammi\")\r\ndef portfolioSammi():\r\n return render_template('portfolio-sammi.html')\r\n\r\n@app.route(\"/portfolioWl\")\r\ndef portfolioWl():\r\n return render_template('portfolio-wl.html')\r\n\r\n@app.route(\"/portfolioYz\")\r\ndef portfolioYz():\r\n return render_template('portfolio-yz.html')\r\n\r\n# four different features\r\n\r\n@app.route(\"/empMgr\", methods=['GET'])\r\ndef empMgr():\r\n db_conn.ping(reconnect=True)\r\n cursor = db_conn.cursor()\r\n\r\n cursor.execute('SELECT * FROM employee')\r\n rows = cursor.fetchall()\r\n cursor.close()\r\n\r\n return render_template('emp-mgr.html', rows=rows)\r\n\r\n@app.route(\"/payroll\", methods=['GET'])\r\ndef payroll():\r\n db_conn.ping(reconnect=True)\r\n cursor = db_conn.cursor()\r\n\r\n cursor.execute('SELECT * FROM payroll')\r\n rows = cursor.fetchall()\r\n cursor.close()\r\n\r\n return render_template('payroll.html', rows=rows)\r\n\r\n@app.route(\"/attendance\", methods=['GET'])\r\ndef attendance():\r\n db_conn.ping(reconnect=True)\r\n cursor = db_conn.cursor()\r\n\r\n cursor.execute('SELECT * FROM attendance')\r\n rows = cursor.fetchall()\r\n cursor.close()\r\n\r\n return render_template('attendance.html', rows=rows)\r\n\r\n@app.route(\"/leave\", methods=['GET'])\r\ndef leave():\r\n db_conn.ping(reconnect=True)\r\n cursor = db_conn.cursor()\r\n\r\n cursor.execute('SELECT * FROM leave_application')\r\n rows = cursor.fetchall()\r\n cursor.close()\r\n\r\n return render_template('leave.html', rows=rows)\r\n\r\n#database routes\r\n\r\n#EMP Manager\r\n@app.route(\"/addEmp\", methods=['GET', 'POST'])\r\ndef addEmp():\r\n return render_template('addEmp.html')\r\n\r\n@app.route(\"/addEmpProcess\", methods=['GET', 'POST'])\r\ndef addEmpProcess():\r\n emp_id = request.form['employee_id']\r\n emp_name = request.form['name']\r\n gender = request.form['gender']\r\n dob = request.form['dob']\r\n address = request.form['address']\r\n email = request.form['email']\r\n phone_num = request.form['phone']\r\n job_title = request.form['job_title']\r\n pay_scale = request.form['pay_scale']\r\n hire_date = request.form['hire_date']\r\n\r\n insert_sql = \"INSERT INTO employee VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s)\"\r\n db_conn.ping(reconnect=True)\r\n cursor = db_conn.cursor()\r\n\r\n cursor.execute(insert_sql, (emp_id, emp_name, gender, dob, address, email, phone_num, job_title, pay_scale, hire_date))\r\n db_conn.commit()\r\n cursor.close()\r\n\r\n cursor = db_conn.cursor()\r\n cursor.execute('SELECT * FROM employee')\r\n rows = cursor.fetchall()\r\n cursor.close()\r\n\r\n return render_template('emp-mgr.html', rows=rows)\r\n\r\n@app.route(\"/searchEmp\", methods=['GET', 'POST'])\r\ndef searchEmp():\r\n return render_template('searchEmp.html')\r\n\r\n@app.route(\"/searchEmpProcess\", methods=['GET', 'POST'])\r\ndef searchEmpProcess():\r\n emp_id = request.form['employee_id']\r\n\r\n search_sql = \"SELECT * FROM employee WHERE Employee_ID=%s\"\r\n db_conn.ping(reconnect=True)\r\n cursor = db_conn.cursor()\r\n\r\n cursor.execute(search_sql, (emp_id))\r\n rows = cursor.fetchall()\r\n cursor.close() \r\n\r\n return render_template('emp-mgr.html', rows=rows)\r\n\r\n@app.route(\"/removeEmp\", methods=['GET', 'POST'])\r\ndef removeEmp():\r\n return render_template('removeEmp.html')\r\n\r\n@app.route(\"/removeEmpProcess\", methods=['GET', 'POST'])\r\ndef removeEmpProcess():\r\n emp_id = request.form['employee_id']\r\n\r\n remove_sql = \"DELETE FROM employee WHERE Employee_ID = %s\"\r\n db_conn.ping(reconnect=True)\r\n cursor = db_conn.cursor()\r\n\r\n cursor.execute(remove_sql, emp_id)\r\n db_conn.commit()\r\n cursor.close()\r\n\r\n cursor = db_conn.cursor()\r\n cursor.execute('SELECT * FROM employee')\r\n rows = cursor.fetchall()\r\n cursor.close()\r\n\r\n return render_template('emp-mgr.html', rows=rows)\r\n \r\n\r\n#Payroll Manager\r\n@app.route(\"/payslip\")\r\ndef payslip():\r\n\r\n return render_template('payEmp.html')\r\n\r\n@app.route(\"/payslipProcess\", methods=['GET', 'POST'])\r\ndef payslipProcess():\r\n emp_id = request.form['employee_id']\r\n salary = request.form['salary']\r\n date = request.form['date']\r\n\r\n \r\n cursor = db_conn.cursor()\r\n insert_sql = \"INSERT INTO payroll (Employee_ID, Salary, Date) VALUES (%s, %s, %s)\"\r\n\r\n cursor.execute(insert_sql, (emp_id, salary, date))\r\n db_conn.commit()\r\n cursor.close()\r\n db_conn.ping(reconnect=True)\r\n cursor = db_conn.cursor()\r\n\r\n cursor.execute('SELECT * FROM payroll')\r\n rows = cursor.fetchall()\r\n cursor.close()\r\n\r\n return render_template('payroll.html', rows=rows)\r\n\r\n#Attendance Checker\r\n@app.route(\"/markAtt\")\r\ndef markAtt():\r\n\r\n return render_template('markAtt.html')\r\n\r\n@app.route(\"/markAttProcess\", methods=['GET', 'POST'])\r\ndef markAttProcess():\r\n emp_id = request.form['employee_id']\r\n status = request.form['status']\r\n \r\n db_conn.ping(reconnect=True)\r\n cursor = db_conn.cursor()\r\n #update_sql = \"UPDATE attendance SET Status=%s, Time_Stamp=SYSDATE() WHERE Employee_ID=%s\"\r\n insert_sql = \"INSERT INTO attendance VALUES (%s,SYSDATE(), %s)\"\r\n\r\n cursor.execute(insert_sql, (emp_id, status))\r\n db_conn.commit()\r\n cursor.close()\r\n\r\n cursor = db_conn.cursor()\r\n\r\n cursor.execute('SELECT * FROM attendance')\r\n rows = cursor.fetchall()\r\n cursor.close()\r\n\r\n return render_template('attendance.html', rows=rows)\r\n\r\n#Leave Application\r\n@app.route(\"/leaveApp\")\r\ndef leaveApp():\r\n\r\n return render_template('leaveApp.html')\r\n\r\n@app.route(\"/leaveAppProcess\", methods=['GET', 'POST'])\r\ndef leaveAppProcess():\r\n emp_id = request.form['employee_id']\r\n date = request.form['leave_date']\r\n reason = request.form['reason']\r\n days = request.form['days']\r\n\r\n mc = request.files['mc_evidence']\r\n db_conn.ping(reconnect=True)\r\n cursor = db_conn.cursor()\r\n insert_sql = \"INSERT INTO leave_application (Employee_ID, Submission_Date, Reason_of_Leave, Total_Day) VALUES (%s, %s, %s, %s)\"\r\n\r\n \r\n if mc.filename == \"\":\r\n return \"Please select a file\"\r\n\r\n try:\r\n \r\n cursor.execute(insert_sql, (emp_id, date, reason, days))\r\n db_conn.commit()\r\n # Uplaod image file in S3 #\r\n mc_file_name_in_s3 = \"emp-id-\" + str(emp_id) + \"_image_file\"\r\n s3 = boto3.resource('s3')\r\n\r\n try:\r\n print(\"Data inserted in MySQL RDS... uploading image to S3...\")\r\n s3.Bucket(custombucket).put_object(Key=mc_file_name_in_s3, Body=mc)\r\n bucket_location = boto3.client('s3').get_bucket_location(Bucket=custombucket)\r\n s3_location = (bucket_location['LocationConstraint'])\r\n\r\n if s3_location is None:\r\n s3_location = ''\r\n else:\r\n s3_location = '-' + s3_location\r\n\r\n object_url = \"https://s3{0}.amazonaws.com/{1}/{2}\".format(\r\n s3_location,\r\n custombucket,\r\n mc_file_name_in_s3)\r\n\r\n except Exception as e:\r\n return str(e)\r\n\r\n finally:\r\n cursor.close()\r\n\r\n cursor = db_conn.cursor()\r\n\r\n cursor.execute('SELECT * FROM leave_application')\r\n rows = cursor.fetchall()\r\n cursor.close()\r\n\r\n return render_template('leave.html', rows=rows)\r\n\r\nif __name__ == '__main__':\r\n app.run(host='0.0.0.0', port=80, debug=True)","repo_name":"elissammi/aws-empMgr","sub_path":"mlunaApp.py","file_name":"mlunaApp.py","file_ext":"py","file_size_in_byte":7739,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"15565202838","text":"import tkinter as tk\nfrom tkinter import ttk\nimport ui_subject as uisub\nimport ui_staff as uistaff\nimport ui_invigilator as uiinv\nimport ui_examhall as uiexamhall\nimport ui_exam as uiexam\nimport ui_class as uiclass\nimport ui_examinstance as uiexaminstance\nimport uiabstract\n\n\nclass HomeUI(uiabstract.ParentUI):\n def __init__(self, *args, **kwargs):\n uiabstract.ParentUI.__init__(self, *args, **kwargs)\n # child UI definitions\n self.update_UI = None\n\n\n self.mainLabel = ttk.Label(self.container, text=\"Please select a collection\")\n\n # button constants\n self.BTNSUBKEY = 0\n self.BTNSTAFFKEY = 1\n self.BTNINVIGILATORKEY = 2\n self.BTNEXAMHALLKEY = 3\n self.BTNEXAMKEY = 4\n self.BTNCLASSKEY = 5\n self.BTNEXAMINSTANCEKEY = 6\n\n # buttons\n self.btn_subject = ttk.Button(self.container, text=\"Subjects\", command=lambda: self.btn_handler(self.BTNSUBKEY))\n self.btn_staff = ttk.Button(self.container, text=\"Staffs\", command=lambda: self.btn_handler(self.BTNSTAFFKEY))\n self.btn_invigilator = ttk.Button(self.container, text=\"Invigilators\",\n command=lambda: self.btn_handler(self.BTNINVIGILATORKEY))\n self.btn_examhall = ttk.Button(self.container, text=\"Examhalls\",\n command=lambda: self.btn_handler(self.BTNEXAMHALLKEY))\n self.btn_exam = ttk.Button(self.container, text=\"Exams\", command=lambda: self.btn_handler(self.BTNEXAMKEY))\n self.btn_class = ttk.Button(self.container, text=\"Classes\", command=lambda: self.btn_handler(self.BTNCLASSKEY))\n self.btn_examinstance = ttk.Button(self.container, text=\"Exam instances\",\n command=lambda: self.btn_handler(self.BTNEXAMINSTANCEKEY))\n\n # layout\n self.mainLabel.grid(row=0, columnspan=3)\n self.btn_subject.grid(row=1)\n self.btn_staff.grid(row=1, column=1)\n self.btn_invigilator.grid(row=1, column=2)\n self.btn_examhall.grid(row=2)\n self.btn_exam.grid(row=2, column=1)\n self.btn_class.grid(row=2, column=2)\n self.btn_examinstance.grid(row=3)\n\n # padding configuration\n for child in self.container.winfo_children():\n child.grid_configure(padx=10, pady=20)\n\n # click event handlers\n\n def btn_handler(self, key):\n if key == self.BTNSUBKEY:\n self.update_UI = uisub.DisplayUpdateSubjectUI()\n elif key == self.BTNEXAMINSTANCEKEY:\n self.update_UI = uiexaminstance.DisplayUpdateExaminstanceUI()\n elif key == self.BTNCLASSKEY:\n self.update_UI = uiclass.DisplayUpdateClassUI()\n elif key == self.BTNEXAMKEY:\n self.update_UI = uiexam.DisplayUpdateExamUI()\n elif key == self.BTNEXAMHALLKEY:\n self.update_UI = uiexamhall.DisplayUpdateExamhallUI()\n elif key == self.BTNINVIGILATORKEY:\n self.update_UI = uiinv.DisplayUpdateInvigilatorUI()\n elif key == self.BTNSTAFFKEY:\n self.update_UI = uistaff.DisplayUpdateStaffUI()\n\n\n\n\n","repo_name":"peaceofmind123/invigilators","sub_path":"code/homeui.py","file_name":"homeui.py","file_ext":"py","file_size_in_byte":3125,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"9219898963","text":"from copy import deepcopy\n\nxlim, ylim = 3, 2 # board dimensions\n\nclass GameState:\n \"\"\"\n Attributes\n ----------\n _board: list(list)\n Represent the board with a 2d array _board[x][y]\n where open spaces are 0 and closed spaces are 1\n \n _parity: bool\n Keep track of active player initiative (which\n player has control to move) where 0 indicates that\n player one has initiative and 1 indicates player 2\n \n _player_locations: list(tuple)\n Keep track of the current location of each player\n on the board where position is encoded by the\n board indices of their last move, e.g., [(0, 0), (1, 0)]\n means player 1 is at (0, 0) and player 2 is at (1, 0)\n \n \"\"\"\n\n def __init__(self):\n self._board = [[0] * ylim for _ in range(xlim)]\n self._board[-1][-1] = 1 # block lower-right corner\n self._parity = 0\n self._player_locations = [None, None]\n\n def forecast_move(self, move):\n \"\"\" Return a new board object with the specified move\n applied to the current game state.\n \n Parameters\n ----------\n move: tuple\n The target position for the active player's next move\n \"\"\"\n if move not in self.get_legal_moves():\n raise RuntimeError(\"Attempted forecast of illegal move\")\n newBoard = deepcopy(self)\n newBoard._board[move[0]][move[1]] = 1\n newBoard._player_locations[self._parity] = move\n newBoard._parity ^= 1\n return newBoard\n\n def get_legal_moves(self):\n \"\"\" Return a list of all legal moves available to the\n active player. Each player should get a list of all\n empty spaces on the board on their first move, and\n otherwise they should get a list of all open spaces\n in a straight line along any row, column or diagonal\n from their current position. (Players CANNOT move\n through obstacles or blocked squares.) Moves should\n be a pair of integers in (column, row) order specifying\n the zero-indexed coordinates on the board.\n \"\"\"\n loc = self._player_locations[self._parity]\n if not loc:\n return self._get_blank_spaces()\n moves = []\n rays = [(1, 0), (1, -1), (0, -1), (-1, -1),\n (-1, 0), (-1, 1), (0, 1), (1, 1)]\n for dx, dy in rays:\n _x, _y = loc\n while 0 <= _x + dx < xlim and 0 <= _y + dy < ylim:\n _x, _y = _x + dx, _y + dy\n if self._board[_x][_y]:\n break\n moves.append((_x, _y))\n return moves\n\n def _get_blank_spaces(self):\n \"\"\" Return a list of blank spaces on the board.\"\"\"\n return [(x, y) for y in range(ylim) for x in range(xlim)\n if self._board[x][y] == 0]\n","repo_name":"bhupendpatil/Practice","sub_path":"Artificial Intelligence/Search and Optimization/Introduction to Game Playing/gamestate.py","file_name":"gamestate.py","file_ext":"py","file_size_in_byte":2849,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"77"}
+{"seq_id":"9868137856","text":"# Подсчитать, сколько было выделено памяти под переменные в ранее разработанных программах в рамках первых трех уроков.\n# Проанализировать результат и определить программы с наиболее эффективным использованием памяти.\n\n# Задача: Вводятся три разных числа. Найти, какое из них является средним (больше одного, но меньше другого).\n\nimport sys\n\n\ndef show_size(x, level=0):\n print('\\t' * level, f'type = {x.__class__}, size = {sys.getsizeof(x)}, object = {x}')\n\n\np = 'Введите три числа:'\nprint(p)\nshow_size(p)\n# type = , size = 74, object = Введите три числа:\n\na = int(input(\"a = \"))\nshow_size(a)\n# type = , size = 14, object = 5\n\nb = int(input(\"b = \"))\nshow_size(b)\n# type = , size = 14, object = 6\n\nc = int(input(\"c = \"))\nshow_size(c)\n# type = , size = 14, object = 7\n\nm = 'Среднее:'\nshow_size(m)\n# type = , size = 54, object = Среднее:\n\nif (b < a < c) or (c < a < b):\n print(m, a)\nelif (a < b < c) or (c < b < a):\n print(m, b)\nelse:\n print(m, c)\n\nshow_size((sys.getsizeof(p) + sys.getsizeof(a) + sys.getsizeof(b)+ sys.getsizeof(c) + sys.getsizeof(m)))\n# type = , size = 14, object = 170\n","repo_name":"NikolaySibekin/algorithms_lesson6","sub_path":"task_1_1.py","file_name":"task_1_1.py","file_ext":"py","file_size_in_byte":1490,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"70366415930","text":"from threading import Thread\nfrom socket import *\n\nlist_escolas=[\"Imperatriz Leopoldinense\",\"Estação Primeira de Mangueira\",\"Acadêmicos do Salgueiro\",\"São Clemente\",\"Unidos do Viradouro\",\"Beija-Flor de Nilópolis\",\n\"Paraíso do Tuiuti\",\"Portela\",\"Mocidade Independente de Padre Miguel\",\"Unidos da Tijuca\",\"Acadêmicos do Grande Rio\",\"Unidos de Vila Isabel\"]\n\n\n \n\ndef atende (conn, cliente):\n while True:\n data = conn.recv (8192)\n print(data)\n if not data or len(data) == 0:\n break\n\n print (str(cliente)+\"recebeu mensagem \"+data.decode(\"utf-8\") )\n \n \n \n # conn.send (str.encode (\"Eu sei que voce me mandou \"+data.decode(\"utf-8\") , \"UTF-8\"))\n\n print (\"Fim da conexao com \"+str(cliente))\n\n conn.close\n \n\ns = socket ()\n\nhost = \"0.0.0.0\"\nporta = 8192\ns.bind ((host, porta))\ns.listen (10)\nnthr = 0\n\nwhile True:\n print(\"Aguarde conexão de um cliente\")\n (conn, cliente) = s.accept ()\n","repo_name":"jiaxingc/client_server","sub_path":"servidor.py","file_name":"servidor.py","file_ext":"py","file_size_in_byte":1062,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"41621226101","text":"import pytiled_parser\n\nimport serialize\nfrom gametree import Component, Vector2f, Vector2\nimport environ\nimport utils\nimport numpy as np\nimport math\nfrom typing import Optional\nimport sys\n\nfrom shapely import speedups # type: ignore\nfrom shapely.geometry import Polygon, Point # type: ignore\nfrom shapely.ops import nearest_points\n\n_PRECISION = 2\nCOLLIDER_DRAW_COLOR = (76, 40, 130, 127)\n\nspeedups.enable()\n\n\nclass Collider(Component):\n colliders = serialize.Unserialized()\n _transformed_colliders = serialize.Unserialized()\n _transformed_collider_polygons = serialize.Unserialized()\n _transformed_updated = serialize.Unserialized()\n\n def __init__(self):\n super().__init__()\n self.colliders: Optional[list] = None\n\n self._transformed_colliders: Optional[list] = None\n self._transformed_collider_polygons: Optional[list[Polygon]] = None\n self._transformed_updated: int = -1\n self.solid: bool = True\n\n def init_unserialized(self, deserializer):\n super().init_unserialized(deserializer)\n self._init_unserialized_called = True\n self._transformed_colliders = None\n self._transformed_collider_polygons = None\n self._transformed_updated = -1\n\n def duplicate(self, new_entity):\n new_component = self.__class__()\n new_component.colliders = self.colliders\n new_component.solid = self.solid\n return new_component\n\n def test_intersection(self, other) -> bool:\n if self.colliders is None:\n return False\n try:\n for left_c, left_p in zip(self.transformed_colliders, self.transformed_collider_polygons):\n for right in other.get_pcolliders(left_c):\n if are_polygons_intersecting(left_p, right):\n return True\n except TypeError as e:\n sys.stderr.write(str(e) + \"\\n\")\n return False\n\n def _update_transformed_colliders(self):\n if self._transformed_updated < self.entity.transform_modified:\n self._transformed_colliders = None\n self._transformed_collider_polygons = None\n\n if self._transformed_colliders is None:\n self._transformed_colliders = [self.entity.transform.transform_points(c) for c in self.colliders]\n self._transformed_collider_polygons = [\n Polygon(c) for c in self._transformed_colliders]\n self._transformed_updated = environ.game.get_modification_stamp()\n\n @property\n def transformed_colliders(self) -> Optional[list]:\n self._update_transformed_colliders()\n return self._transformed_colliders\n\n @property\n def transformed_collider_polygons(self) -> Optional[list]:\n self._update_transformed_colliders()\n return self._transformed_collider_polygons\n\n def minimum_translation_vector(self, other) -> Optional[Vector2]:\n ret = Vector2f()\n for left in self.colliders:\n left_t = self.entity.transform.transform_points(left)\n for right in other.get_colliders(left_t):\n right_t = other.entity.transform.transform_points(right)\n mtv = minimum_translation_vector(\n left_t,\n right_t)\n if mtv is not None:\n ret += mtv\n if ret[0] == 0.0 and ret[1] == 0.0:\n return None\n return ret\n\n def get_colliders(self, other_collider) -> list:\n if self.colliders is None:\n raise RuntimeError(f\"Colliders are not set!\")\n return self.colliders\n\n def get_pcolliders(self, other_collider) -> list:\n return self.transformed_collider_polygons\n\n @environ.client_only\n def draw_colliders(self):\n for collider in self.colliders:\n points = self.entity.transform.transform_points_to_pyarcade_space(collider)\n points = [(point.x, point.y) for point in points]\n environ.arcade.draw_polygon_filled(points, COLLIDER_DRAW_COLOR)\n\n\nclass ManualCollider(Collider):\n def __init__(self, collision_data: list, solid: bool = True):\n super().__init__()\n self._colliders: list = collision_data\n self.colliders: list = collision_data\n self.solid: bool = solid\n\n def init_unserialized(self, deserializer):\n super().init_unserialized(deserializer)\n self.colliders = self._colliders\n\n def duplicate(self, new_entity):\n new_component = self.__class__(self.colliders, self.solid)\n return new_component\n\n\nclass SingleCollider(Collider):\n def __init__(self, tmx_data=None, tileset_name: str =None, relative_gid: int =None, solid: bool =None):\n \"\"\"Must provide either tmx_data or (tileset_name and relative_gid and solid).\"\"\"\n super().__init__()\n if tmx_data is not None:\n self.tileset_name, self.relative_gid = environ.game.gid_to_tile_reference(tmx_data.gid)\n self.solid: bool = tmx_data.properties.get('solid', True)\n if tileset_name is not None:\n self.tileset_name = tileset_name\n if relative_gid is not None:\n self.relative_gid = relative_gid\n if solid is not None:\n self.solid = solid\n\n self.colliders: list = make_colliders_from_tile(environ.game.get_tile_by_tile_reference(self.tileset_name, self.relative_gid))\n\n def init_unserialized(self, deserializer):\n self.colliders = make_colliders_from_tile(deserializer.context.get_tile_by_tile_reference(self.tileset_name, self.relative_gid))\n super().init_unserialized(deserializer)\n\n def duplicate(self, new_entity):\n new_component = self.__class__(tileset_name=self.tileset_name, relative_gid=self.relative_gid, solid=self.solid)\n return new_component\n\n\nclass MultiCollider(Collider):\n collider_map = serialize.Unserialized()\n\n def __init__(self):\n super().__init__()\n self.collider_map: Optional[dict] = None\n\n def duplicate(self, new_entity):\n # Collider_map is set up when the component is attached to its entity\n new_component = self.__class__()\n return new_component\n\n def on_framesets_changed(self, framesets: dict, game):\n self.collider_map = {}\n for name, frameset in framesets.items():\n t: pytiled_parser.Tile = game.find_frameset(\n frameset.frameset)\n\n if t is None:\n raise RuntimeError(f\"Frameset {frameset.frameset} does not exist in Tiled map.\")\n\n flipped_horizontally = utils.is_flipped_horizontally(self.entity.gid)\n flipped_vertically = utils.is_flipped_vertically(self.entity.gid)\n t.flipped_horizontally = flipped_horizontally != frameset.flipped_horizontally\n t.flipped_vertically = flipped_vertically != frameset.flipped_vertically\n\n addcol = make_colliders_from_tile(t)\n self.collider_map[name] = addcol\n\n t.flipped_horizontally = False\n t.flipped_vertically = False\n\n def on_frameset_changed(self, new_frameset: dict):\n self.colliders = self.collider_map.get(new_frameset, \"\")\n\n # Force a recalculation next time collision might occur\n self._transformed_colliders = None\n self._transformed_collider_polygons = None\n\n def init_unserialized(self, deserializer):\n self.colliders = None\n super().init_unserialized(deserializer)\n\n\ndef make_colliders_from_tile(tile) -> list[Optional[list[Vector2]]]:\n ret: list[Optional[list[Vector2]]] = []\n if tile.objects is None:\n return ret\n for hitbox in tile.objects.tiled_objects:\n ret.append(make_collider_from_hitbox(hitbox))\n return ret\n\n\ndef make_collider_from_hitbox(hitbox) -> Optional[list[Vector2]]:\n points = []\n if isinstance(hitbox, pytiled_parser.tiled_object.Rectangle):\n if hitbox.size is None:\n sys.stderr.write(\n \"Warning: Rectangle hitbox created for without a \"\n \"height or width Ignoring.\\n\"\n )\n return None\n\n points.append(Vector2f(hitbox.coordinates.x, hitbox.coordinates.y))\n points.append(Vector2f(hitbox.coordinates.x + hitbox.size.width, hitbox.coordinates.y))\n points.append(Vector2f(hitbox.coordinates.x + hitbox.size.width, hitbox.coordinates.y + hitbox.size.height))\n points.append(Vector2f(hitbox.coordinates.x, hitbox.coordinates.y + hitbox.size.height))\n\n elif isinstance(\n hitbox, pytiled_parser.tiled_object.Polygon\n ) or isinstance(hitbox, pytiled_parser.tiled_object.Polyline):\n for coord in hitbox.points:\n points.append(Vector2f(coord.x + hitbox.coordinates.x, coord.y + hitbox.coordinates.y))\n\n if points[0][0] == points[-1][0] and points[0][1] == points[-1][1]:\n points.pop()\n\n clockwise_counter = 0.0\n for i in range(0, len(points)):\n clockwise_counter += (points[i].x - points[i-1].x) * (points[i].y + points[i-1].y)\n if clockwise_counter > 0:\n points.reverse()\n\n elif isinstance(hitbox, pytiled_parser.tiled_object.Ellipse):\n if not hitbox.size:\n sys.stderr.write(\n f\"Warning: Ellipse hitbox created without a height \"\n f\" or width. Ignoring.\\n\"\n )\n return None\n\n hw = hitbox.size.width / 2\n hh = hitbox.size.height / 2\n cx = hitbox.coordinates.x\n cy = hitbox.coordinates.y\n\n total_steps = 8\n angles = [\n step / total_steps * 2 * math.pi for step in range(total_steps)\n ]\n for angle in angles:\n x = hw * math.cos(angle) + cx\n y = -(hh * math.sin(angle) + cy)\n points.append(Vector2f(x, y))\n elif isinstance(hitbox, pytiled_parser.tiled_object.Point):\n return None\n else:\n sys.stderr.write(f\"Warning: Hitbox type {type(hitbox)} not supported.\\n\")\n return None\n\n return points\n\n\ndef make_shapely_polygons(poly_a, poly_b) -> tuple:\n return Polygon(poly_a), Polygon(poly_b)\n\n\ndef are_polygons_intersecting(poly_a, poly_b) -> bool:\n r2 = False\n r1 = poly_a.intersects(poly_b)\n if r1:\n r2 = poly_a.touches(poly_b)\n return r1 and not r2\n\n\ndef minimum_translation_vector(poly_a, poly_b) -> Optional[Vector2]:\n \"\"\"\n If the polygons do not intersect, return None.\n If the polygons do intersect, return the minimum translation\n vector for poly_a. (That is, the translation vector to apply to\n poly_a such that it no longer intersects with poly_b.\n \"\"\"\n\n diff = minkowski_difference(poly_a, poly_b)\n minkowski_intersecting = is_point_in_polygon(0, 0, diff)\n\n if minkowski_intersecting:\n sdiff = Polygon(diff)\n npts = nearest_points(sdiff.exterior, Point(0, 0))\n return Vector2f(-npts[0].x, -npts[0].y)\n return None\n\n\ndef is_point_in_polygon(x: float, y: float, polygon_point_list: list) -> bool:\n\n shapely_point = Point(x, y)\n shapely_polygon = Polygon(polygon_point_list)\n\n return shapely_polygon.contains(shapely_point)\n\n\ndef _rotate_polygon_points(polygon) -> list:\n pos = 0\n for i in range(1, len(polygon)):\n if polygon[i].y < polygon[pos].y or polygon[i].y == polygon[pos].y and polygon[i].x < polygon[pos].x:\n pos = i\n\n # Rotate points:\n if pos != 0:\n return polygon[pos:] + polygon[:pos]\n return polygon[:]\n\n\ndef minkowski_sum(P: list, Q: list) -> list:\n # the first vertex must be the lowest\n P = _rotate_polygon_points(P)\n Q = _rotate_polygon_points(Q)\n\n # Ensure cyclic indexing\n P.append(P[0])\n P.append(P[1])\n Q.append(Q[0])\n Q.append(Q[1])\n\n # Run minkowski\n result = []\n i = 0\n j = 0\n while i < len(P) - 2 or j < len(Q) - 2:\n result.append(P[i] + Q[j])\n cross = np.cross(P[i + 1] - P[i], Q[j + 1] - Q[j])\n if cross >= 0: i += 1\n if cross <= 0: j += 1\n\n return result\n\n\ndef minkowski_difference(P: list, Q: list) -> list:\n return minkowski_sum(P, [q * -1 for q in Q])\n","repo_name":"google/google-ctf","sub_path":"2022/hackceler8/game/collision.py","file_name":"collision.py","file_ext":"py","file_size_in_byte":12060,"program_lang":"python","lang":"en","doc_type":"code","stars":4153,"dataset":"github-code","pt":"77"}
+{"seq_id":"71018797050","text":"#! /usr/bin/env python\r\nimport os\r\nimport sys\r\nimport time\r\nimport readfil\r\nimport astropy.io.fits as pyfits\r\nimport numpy as np\r\nimport torch\r\n\r\ndef printcuda(cuda):\r\n print(\"GPU Memory Using: \",\r\n torch.cuda.memory_allocated(cuda)//(1024*1024), torch.cuda.max_memory_allocated(cuda)//(1024*1024), \r\n torch.cuda.memory_cached(cuda)//(1024*1024), torch.cuda.max_memory_cached(cuda)//(1024*1024))\r\n\r\ndef readplotini(inifile):\r\n FILENAME = []\r\n FITSFILE = []\r\n PlotReady = 0\r\n PLOTFILE = []\r\n with open(inifile,'r') as fd:\r\n all_lines = fd.readlines()\r\n for i in range(len(all_lines)):\r\n #### Skip Empty Line ####\r\n if len(all_lines[i].split()) == 0:\r\n continue\r\n #### Skip # Line ####\r\n elif \"#\" in all_lines[i].split()[0]:\r\n continue\r\n elif 'PlotReady' in all_lines[i]:\r\n PlotReady = int(all_lines[i].split()[2]) \r\n elif 'SearchPath' in all_lines[i]:\r\n SearchPath = all_lines[i].split()[2]\r\n elif 'PLOTFILE' in all_lines[i]:\r\n PLOTFILE = all_lines[i].split()[2]\r\n for root, _, files in os.walk(SearchPath):\r\n for fil in files:\r\n if fil.endswith(\".fil\"):\r\n FILENAME.append(os.path.join(root, fil))\r\n elif fil.endswith(\".fits\"):\r\n FILENAME.append(os.path.join(root, fil))\r\n return PlotReady, FILENAME, PLOTFILE\r\n\r\ndef readini(inifile):\r\n PlotTime = []\r\n Plotrange = 0\r\n PlotDM = 0.0\r\n WINDOWSIZE = 250\r\n RFITHR = 4.0\r\n IGNORE = []\r\n CHOFF_LOW = 0\r\n CHOFF_HIGH = 0\r\n THRESH = 1.0\r\n NSMAX = 1\r\n LODM = 0.0\r\n HIDM = 1.0\r\n DDM = 0.1\r\n PlotPersent = 1.0\r\n useGPU = True #False #\r\n BlockSize = 1000\r\n with open(inifile,'r') as fd:\r\n all_lines = fd.readlines()\r\n for i in range(len(all_lines)):\r\n #### Skip Empty Line ####\r\n if len(all_lines[i].split()) == 0:\r\n continue\r\n #### Skip # Line ####\r\n elif \"#\" in all_lines[i].split()[0]:\r\n continue\r\n elif 'THRESH' in all_lines[i]:\r\n THRESH = float(all_lines[i].split()[2])\r\n elif 'NSMAX' in all_lines[i]:\r\n NSMAX = int(all_lines[i].split()[2])\r\n elif 'LODM' in all_lines[i]:\r\n LODM = float(all_lines[i].split()[2])\r\n elif 'HIDM' in all_lines[i]:\r\n HIDM = float(all_lines[i].split()[2])\r\n elif 'DDM' in all_lines[i]:\r\n DDM = float(all_lines[i].split()[2])\r\n elif 'RFITHR' in all_lines[i]:\r\n RFITHR = float(all_lines[i].split()[2])\r\n elif 'IGNORE' in all_lines[i]:\r\n # IGNORE = int(all_lines[i].split()[2])\r\n for s in range(len(all_lines[i].split()) - 2):\r\n IGNORE.append(all_lines[i].split()[2+s]) \r\n elif 'WINDOWSIZE' in all_lines[i]:\r\n WINDOWSIZE = int(all_lines[i].split()[2])\r\n elif 'CHOFF_LOW' in all_lines[i]:\r\n CHOFF_LOW = int(all_lines[i].split()[2])\r\n elif 'CHOFF_HIGH' in all_lines[i]:\r\n CHOFF_HIGH = int(all_lines[i].split()[2])\r\n elif 'AVERAGE' in all_lines[i]:\r\n AVERAGE = int(all_lines[i].split()[2])\r\n elif 'FREQAVG' in all_lines[i]:\r\n FREQAVG = int(all_lines[i].split()[2])\r\n elif 'PlotTime' in all_lines[i]:\r\n for s in range(len(all_lines[i].split()) - 2):\r\n PlotTime.append(all_lines[i].split()[2+s]) \r\n elif 'Plotrange' in all_lines[i]:\r\n Plotrange = float(all_lines[i].split()[2]) \r\n elif 'PlotDM' in all_lines[i]:\r\n PlotDM = float(all_lines[i].split()[2]) \r\n elif 'PlotPersent' in all_lines[i]:\r\n PlotPersent = float(all_lines[i].split()[2]) \r\n if PlotPersent <= 0:\r\n print(\"PlotPersent can't <= 0\")\r\n exit()\r\n elif PlotPersent > 1:\r\n print(\"PlotPersent can't > 1\")\r\n exit()\r\n elif 'PlotBoxcar' in all_lines[i]:\r\n PlotBoxcar = float(all_lines[i].split()[2]) \r\n elif 'BlockSize' in all_lines[i]:\r\n BlockSize = int(all_lines[i].split()[2])\r\n elif 'useGPU' in all_lines[i]:\r\n if int(all_lines[i].split()[2]) == 0:\r\n print(\"Using CPU\")\r\n useGPU = False\r\n else:\r\n print(\"Using GPU\")\r\n useGPU = True\r\n sys.stdout.flush()\r\n \r\n if (FREQAVG == 0 or AVERAGE == 0) :\r\n print(\"AVERAGE or FREQAVG can't be Zero !!!\")\r\n exit()\r\n return (THRESH, NSMAX, LODM, HIDM, DDM, RFITHR, IGNORE, WINDOWSIZE, CHOFF_LOW, \r\n CHOFF_HIGH, PlotPersent, PlotBoxcar, PlotTime, Plotrange, PlotDM, AVERAGE, \r\n FREQAVG, useGPU, BlockSize,)\r\n\r\ndef convolve(dn, boxcar):\r\n conv = dn.copy()\r\n for i in range(1, boxcar):\r\n # conv[i:] += dn[:-i]\r\n # conv[:i] += dn[-i:]\r\n conv += np.roll(dn, i, axis = 0)\r\n return conv\r\n\r\ndef convolve_gpu(dn, boxcar):\r\n conv = dn.detach().clone()\r\n for i in range(1, boxcar):\r\n # conv[i:] += dn[:-i]\r\n # conv[:i] += dn[-i:]\r\n conv += torch.roll(dn, i, dims = 0)\r\n return conv\r\n\r\ndef mad(din, nbl, wsize):\r\n # tmp_des = np.sort(din.copy().mean(axis= 1).reshape(nbl, wsize), axis=1)\r\n # med = tmp_des[:, wsize//2].reshape(nbl, 1)\r\n # rms = np.sort(np.abs(tmp_des - med))[:, wsize//2] #\r\n # din = din.mean(axis= 1).reshape(nbl, wsize)\r\n med = np.median(din, axis=1).reshape(nbl, 1)\r\n rms = np.median(np.abs(din-med), axis=1)\r\n return med, 1.4826*rms\r\n\r\ndef mad_gpu(din, nbl, wsize):\r\n # tmp_des, _ = torch.sort(din.mean(dim= 1).view(nbl, wsize), dim=1)\r\n # med = tmp_des[:, wsize//2].view(nbl, 1)\r\n # tmp_des, _ = torch.sort(torch.abs(tmp_des - med))\r\n # rms = tmp_des[:, wsize//2] #\r\n # din = din.mean(dim= 1).view(nbl, wsize)\r\n med, _ = torch.median(din, 1)\r\n med = med.view(nbl, 1)\r\n rms, _ = torch.median(torch.abs(din-med), 1)\r\n return med, 1.4826*rms\r\n\r\ndef cleanning(din, tthresh, totalch, choff_low, choff_high, nbl, wsize, sample, ignore, plotbc):\r\n #### Remove offset channel ####\r\n nch = totalch-choff_low-choff_high\r\n data_conv = din.copy()[:, choff_high: totalch-choff_low]\r\n #### Convolve ####\r\n data_rfi = convolve(data_conv, int(plotbc))\r\n #### Ignore channels ####\r\n channel_med = np.median(data_rfi, axis=1)\r\n for i in range(len(ignore)):\r\n for s in range(5):\r\n data_rfi.transpose()[int(ignore[i])-2+s] = (\r\n np.random.normal(channel_med.mean(), np.std(channel_med), data_rfi.shape[0]))\r\n #(med_rfi.reshape(1, -1)).repeat(5, axis=0)\r\n #### Remove RFI in time ####\r\n # med_rfi = np.median(data_rfi.copy(), axis=1)\r\n med_tim = np.median(data_rfi.copy(), axis=0)\r\n # med, rms = mad(data_rfi, nbl, wsize)\r\n # sigma = ((data_rfi.copy().mean(axis = 1).reshape(nbl, wsize) - med\r\n # )/rms.reshape(nbl, 1)).reshape(-1)\r\n # # data_rfi[np.where(sigma > tthresh)] = np.random.chisquare(wsize, \r\n # # nch)/wsize*np.sqrt((med**2).mean())\r\n # data_rfi[np.where(sigma > tthresh)] = data_rfi.copy().mean(axis=0)\r\n data_time = data_rfi.copy().mean(axis= 1).reshape(nbl, wsize)\r\n med_time, rms_time = mad(data_time, nbl, wsize)\r\n sigma_time = ((data_time - med_time)/rms_time.reshape(nbl, 1)).reshape(-1)\r\n data_rfi[np.where(sigma_time > tthresh)] = med_tim\r\n\r\n #### Remove RFI in frequency ####\r\n # data_frq = data_rfi.copy().mean(axis= 0)\r\n # med_frq = np.median(data_frq)\r\n # rms_frq = np.median(np.abs(data_frq - med_frq))\r\n # sigma_frq = ((data_frq - med_frq)/rms_frq).reshape(-1)\r\n # data_rfi.transpose()[np.where(sigma_frq > tthresh)] = med_rfi\r\n # print(med_rfi.shape, data_rfi.transpose().shape, ignore)\r\n # data_rfi.transpose()[ignore] = med_rfi\r\n return data_rfi\r\n\r\ndef cleanning_gpu(din, tthresh, totalch, choff_low, choff_high, nbl, wsize, sample):\r\n #### Remove RFI in time ####\r\n nch = totalch-choff_low-choff_high\r\n data_rfi = din[:, choff_high: totalch-choff_low]\r\n # data_rfi = data_rfi - data_rfi.mean(dim = 0)\r\n # med, rms = mad_gpu(data_rfi.detach().clone(), nbl, wsize)\r\n # sigma = ((data_rfi.mean(dim = 1).view(nbl, wsize) - med\r\n # )/rms.view(nbl, 1)).view(-1)\r\n # data_rfi[torch.where(sigma > tthresh)] = data_rfi.mean(dim=0)\r\n\r\n # #### Remove RFI in frequency ####\r\n # tmp_frq, _ = torch.sort(data_rfi.mean(dim= 0), dim=0) \r\n # med_frq = tmp_frq[nch//2]\r\n # tmp_frq, _ = torch.sort(torch.abs(tmp_frq - med_frq))\r\n # rms_frq = tmp_frq[nch//2]\r\n # sigma_frq = ((data_rfi.mean(dim = 0) - med_frq)/rms_frq).view(-1)\r\n # data_rfi.transpose()[torch.where(sigma_frq > tthresh)] = data_rfi.mean(dim=1)\r\n return data_rfi\r\n\r\n# def disbar(max, dn):\r\n# jd = '\\r %2d%% [%s%s]'\r\n# a = '*'* np.ceil(dn*100/max)\r\n# b = ' '* ((max-dn)*100//max)\r\n# c = (dn/max)*100+1\r\n# print(jd % (c,a,b), end=\"\", flush=True)\r\n\r\ndef read_psrfits_head(psrfits_file, ststart):\r\n \"\"\"\r\n Modified from presto prsfit.py\r\n \"\"\"\r\n global nsampsub, nsubints, numpolns, polnorder\r\n header = {'ibeam':0, 'nbeams':1,}\r\n print(\"Reading...\", psrfits_file, time.time() - ststart)\r\n sys.stdout.flush()\r\n with open (psrfits_file,'rb') as fn:\r\n psr01 = pyfits.open(fn, mode='readonly', memmap=True)\r\n fits_header = psr01['PRIMARY'].header\r\n sub_header = psr01['SUBINT'].header\r\n header['telescope_id'] = fits_header['TELESCOP']\r\n header['machine_id'] = fits_header['BACKEND']\r\n header['source_name'] = fits_header['SRC_NAME']\r\n header['src_raj'] = float(fits_header['RA'].replace(':',''))\r\n header['src_dej'] = float(fits_header['DEC'].replace(':',''))\r\n header['tstart'] = (fits_header['STT_IMJD'] + fits_header['STT_SMJD']/86400.0 + \r\n fits_header['STT_OFFS']/86400.0)\r\n header['fch1'] = (fits_header['OBSFREQ'] + np.abs(fits_header['OBSBW'])/2.0 - \r\n np.abs(sub_header['CHAN_BW'])/2.0)\r\n header['foff'] = -1.0*np.abs(sub_header['CHAN_BW'])\r\n header['nchans'] = sub_header['NCHAN']\r\n header['nbits'] = sub_header['NBITS']\r\n header['tsamp'] = sub_header['TBIN']\r\n header['nifs'] = sub_header['NPOL']\r\n header['totalsm'] = sub_header['NSBLK']*sub_header['NAXIS2']\r\n nsampsub = sub_header['NSBLK']\r\n nsubints = sub_header['NAXIS2'] \r\n numpolns = sub_header['NPOL']\r\n polnorder = sub_header['POL_TYPE']\r\n return header\r\n\r\ndef read_psrfits(psrfits_file, ststart):\r\n \"\"\"\r\n Modified from presto prsfit.py\r\n \"\"\"\r\n header = {'ibeam':0, 'nbeams':1,}\r\n print(\"Reading...\", psrfits_file, time.time() - ststart)\r\n sys.stdout.flush()\r\n with open (psrfits_file,'rb') as fn:\r\n psr01 = pyfits.open(fn, mode='readonly', memmap=True)\r\n fits_header = psr01['PRIMARY'].header\r\n sub_header = psr01['SUBINT'].header\r\n header['telescope_id'] = fits_header['TELESCOP']\r\n header['machine_id'] = fits_header['BACKEND']\r\n header['source_name'] = fits_header['SRC_NAME']\r\n header['src_raj'] = float(fits_header['RA'].replace(':',''))\r\n header['src_dej'] = float(fits_header['DEC'].replace(':',''))\r\n header['tstart'] = (fits_header['STT_IMJD'] + fits_header['STT_SMJD']/86400.0 + \r\n fits_header['STT_OFFS']/86400.0)\r\n header['fch1'] = (fits_header['OBSFREQ'] + np.abs(fits_header['OBSBW'])/2.0 - \r\n np.abs(sub_header['CHAN_BW'])/2.0)\r\n header['foff'] = -1.0*np.abs(sub_header['CHAN_BW'])\r\n header['nchans'] = sub_header['NCHAN']\r\n header['nbits'] = sub_header['NBITS']\r\n header['tsamp'] = sub_header['TBIN']\r\n header['nifs'] = sub_header['NPOL']\r\n header['totalsm'] = sub_header['NSBLK']*sub_header['NAXIS2']\r\n nsampsub = sub_header['NSBLK']\r\n nsubints = sub_header['NAXIS2'] \r\n numpolns = sub_header['NPOL']\r\n polnorder = sub_header['POL_TYPE']\r\n data = np.zeros((header['totalsm'], header['nchans']), dtype=np.float32)\r\n for i in range(nsubints):\r\n psrdata = psr01['SUBINT'].data[i]['DATA']\r\n shp = psrdata.squeeze().shape\r\n if (len(shp)==3 and shp[1]==numpolns and polnorder == 'IQUV'):\r\n # print(\"Polarization is IQUV, just using Stokes I\")\r\n data[i*nsampsub: (i+1)*nsampsub]= psrdata[:,0,:].squeeze()\r\n else:\r\n data[i*nsampsub: (i+1)*nsampsub] = np.asarray(psrdata.squeeze())\r\n return header, data[:, ::-1]\r\n\r\ndef read_file(filen, data_raw, numbits, headsize, countsize, smaple, average, \r\n nchan, freqavg, tstart):\r\n if numbits >= 8: # BITS NUMBER 8/16/32\r\n # with open(str(filen),'rb') as fn:\r\n fn = open(str(filen),'rb')\r\n fn.seek(headsize)\r\n if numbits == 32:\r\n data_raw = np.fromfile(fn, dtype=np.float32, count=countsize)\r\n elif numbits == 16:\r\n data_raw = np.fromfile(fn, dtype=np.uint16, count=countsize)\r\n elif numbits == 8:\r\n data_raw = np.fromfile(fn, dtype=np.uint8, count=countsize)\r\n fn.close()\r\n\r\n if data_raw.size != countsize:\r\n print(\"FILE SIZE ERROR %d / %d %s Time:%.2f sec\"%(data_raw.size, \r\n countsize, filen, (time.time() - tstart)))\r\n sys.stdout.flush()\r\n exit()\r\n data_raw = data_raw.reshape(smaple, average, nchan, freqavg).mean(axis=(1,3))\r\n else: # BITS NUMBER 1/2/4\r\n numbtch = 8//numbits\r\n # with open(str(filen),'rb') as fn:\r\n fn = open(str(filen),'rb')\r\n fn.seek(headsize)\r\n data_raw = np.fromfile(fn, dtype=np.uint8, count=countsize//numbtch)\r\n fn.close()\r\n\r\n if data_raw.size != countsize//numbtch :\r\n print(\"FILE SIZE ERROR %s Time:%.2f sec\"%(filen, \r\n (time.time() - tstart)))\r\n sys.stdout.flush()\r\n exit()\r\n data_raw = data_raw.reshape(smaple*average, (nchan*freqavg)//numbtch, 1).repeat(numbtch, axis=2) \r\n if numbtch == 2 :\r\n for i in range(numbtch):\r\n data_raw[:, :, i] >> i*numbits & 0x0f\r\n elif numbtch == 4 :\r\n for i in range(numbtch):\r\n data_raw[:, :, i] >> i*numbits & 0x03\r\n elif numbtch == 8 :\r\n for i in range(numbtch):\r\n data_raw[:, :, i] >> i*numbits & 0x01 \r\n data_raw = data_raw.reshape(smaple, average, nchan, freqavg).mean(axis=(1,3))\r\n return data_raw ","repo_name":"Xu-Zhijun/STEP","sub_path":"step_lib_comm.py","file_name":"step_lib_comm.py","file_ext":"py","file_size_in_byte":14769,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"2129755743","text":"import numpy as np\nfrom scipy.signal import argrelextrema\n\n\ndef compress_eq_neighbor(idxes):\n new_idxes = np.append(idxes, 0)\n return np.asarray([new_idxes[i] for i in range(new_idxes.size - 1)\n if not new_idxes[i] + 1 == new_idxes[i + 1]])\n\n\ndef extr_points(points, indexes=None, extr_type=np.greater_equal):\n if indexes is None:\n indexes = np.asarray(range(points.size))\n\n extr_idx = argrelextrema(points, extr_type)[0]\n extr_idx = compress_eq_neighbor(extr_idx)\n extr_points = np.take(points, extr_idx)\n\n extr_idx = np.take(indexes, extr_idx)\n\n return extr_points, extr_idx","repo_name":"ErkinVasiliy/binance_bot","sub_path":"bot/math.py","file_name":"math.py","file_ext":"py","file_size_in_byte":630,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"71339025208","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport math\nfrom hessQuik.layers import hessQuikLayer\nimport hessQuik.activations as act\nfrom typing import Union, Tuple\n\n\nclass ICNNLayer(hessQuikLayer):\n r\"\"\"\n Evaluate and compute derivatives of a single layer.\n\n Examples::\n\n >>> import torch, hessQuik.layers as lay\n >>> f = lay.ICNNLayer(4, None, 7)\n >>> x = torch.randn(10, 4)\n >>> fx, dfdx, d2fd2x = f(x, do_gradient=True, do_Hessian=True)\n >>> print(fx.shape, dfdx.shape, d2fd2x.shape)\n torch.Size([10, 11]) torch.Size([10, 4, 11]) torch.Size([10, 4, 4, 11])\n\n \"\"\"\n\n def __init__(self, input_dim: int, in_features: Union[int, None], out_features: int,\n act: act.hessQuikActivationFunction = act.softplusActivation(),\n bias: bool = True,\n device=None, dtype=None) -> None:\n r\"\"\"\n\n :param input_dim: dimension of network inputs\n :type input_dim: int\n :param in_features: number of input features. For first ICNN layer, set ``in_features = None``\n :type in_features: int or``None``\n :param out_features: number of output features\n :type out_features: int\n :param act: activation function\n :type act: hessQuikActivationFunction\n :var K: weight matrix for the network inputs of size :math:`(d, n_{out})`\n :var b: bias vector of size :math:`(n_{out},)`\n :var L: weight matrix for the input features of size :math:`(n_{in}, n_{out})`\n :var nonneg: pointwise function to force :math:`l` to have nonnegative weights. Default: ``torch.nn.functional.softplus``\n \"\"\"\n factory_kwargs = {'device': device, 'dtype': dtype}\n super(ICNNLayer, self).__init__()\n\n self.input_dim = input_dim\n self.in_features = in_features\n self.out_features = out_features\n self.act = act\n\n # extract nonnegative weights\n self.nonneg = F.softplus\n\n self.K = nn.Parameter(torch.empty(input_dim, out_features, **factory_kwargs))\n\n if in_features is not None:\n self.L = nn.Parameter(torch.empty(in_features, out_features, **factory_kwargs))\n else:\n self.register_parameter('L', None)\n\n if bias:\n self.b = nn.Parameter(torch.empty(out_features, **factory_kwargs))\n else:\n self.register_parameter('b', None)\n\n self.reset_parameters()\n\n def reset_parameters(self):\n nn.init.kaiming_uniform_(self.K, a=math.sqrt(self.input_dim))\n\n if self.L is not None:\n nn.init.kaiming_uniform_(self.L, a=math.sqrt(self.in_features))\n fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.L)\n else:\n fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.K)\n\n bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0\n\n if self.b is not None:\n nn.init.uniform_(self.b, -bound, bound)\n\n def dim_input(self) -> int:\n r\"\"\"\n number of input features + dimension of network inputs\n \"\"\"\n n = self.input_dim\n if self.in_features is not None:\n n += self.in_features\n return n\n\n def dim_output(self) -> int:\n r\"\"\"\n number of output features + dimension of network inputs\n \"\"\"\n return self.out_features + self.input_dim\n\n def forward(self, ux, do_gradient=False, do_Hessian=False, do_Laplacian=False, forward_mode=True,\n dudx=None, d2ud2x=None, v=None):\n r\"\"\"\n Forward propagation through ICNN layer of the form\n\n .. math::\n\n f(x) =\n \\left[\\begin{array}{c} \\sigma\\left(\\left[\\begin{array}{c}u(x) & x\\end{array}\\right]\n \\left[\\begin{array}{c}L^+ \\\\ K\\end{array}\\right] + b\\right) & x \\end{array}\\right]\n\n Here, :math:`u(x)` is the input into the layer of size :math:`(n_s, n_{in})` which is\n a function of the input of the network, :math:`x` of size :math:`(n_s, d)`.\n The output features, :math:`f(x)`, are of size :math:`(n_s, n_{out} + d)`.\n The notation :math:`(\\cdot)^+` is a function that makes the weights of a matrix nonnegative.\n\n As an example, for one sample, :math:`n_s = 1`, the gradient with respect to\n :math:`\\begin{bmatrix} u & x \\end{bmatrix}` is of the form\n\n .. math::\n\n \\nabla_x f = \\text{diag}\\left(\\sigma'\\left(\\left[\\begin{array}{c}u(x) & x\\end{array}\\right]\n \\left[\\begin{array}{c}L^+ \\\\ K\\end{array}\\right] + b\\right)\\right)\n \\left[\\begin{array}{c}(L^+)^\\top & K^\\top\\end{array}\\right]\n \\left[\\begin{array}{c}\\nabla_x u \\\\ I\\end{array}\\right]\n\n where :math:`\\text{diag}` transforms a vector into the entries of a diagonal matrix and :math:`I` is\n the :math:`d \\times d` identity matrix.\n\n \"\"\"\n\n (dfdx, d2fd2x) = (None, None)\n\n M = self.K\n if self.L is not None:\n M = torch.cat((self.nonneg(self.L), M), dim=0)\n\n # affine transformation\n z = ux @ M\n\n if self.b is not None:\n z += self.b\n\n # forward pass\n f, dsig, d2sig = self.act.forward(z, do_gradient=do_gradient, do_Hessian=do_Hessian,\n forward_mode=True if forward_mode is True else None)\n f = torch.cat((f, ux[:, -self.input_dim:]), dim=1)\n\n if (do_gradient or do_Hessian) and forward_mode is True:\n dfdx = dsig.unsqueeze(1) * M\n\n # -------------------------------------------------------------------------------------------------------- #\n if do_Hessian:\n d2fd2x = (d2sig.unsqueeze(1) * M).unsqueeze(2) * M.unsqueeze(0).unsqueeze(0)\n\n # Gauss-Newton approximation\n if dudx is not None:\n d2fd2x = dudx.unsqueeze(1) @ (d2fd2x.permute(0, 3, 1, 2) @ dudx.unsqueeze(1).permute(0, 1, 3, 2))\n d2fd2x = d2fd2x.permute(0, 2, 3, 1)\n\n if d2ud2x is not None:\n # extra term to compute full Hessian\n d2fd2x += d2ud2x @ dfdx.unsqueeze(1)\n\n # concatenate zeros\n Z = torch.zeros(d2fd2x.shape[0], d2fd2x.shape[1], d2fd2x.shape[2], self.input_dim,\n dtype=d2fd2x.dtype, device=d2fd2x.device)\n d2fd2x = torch.cat((d2fd2x, Z), dim=-1)\n # -------------------------------------------------------------------------------------------------------- #\n\n # finish computing gradient\n if dudx is not None:\n dfdx = dudx @ dfdx\n\n I = torch.ones(dfdx.shape[0], 1, 1, dtype=dfdx.dtype, device=dfdx.device) \\\n * torch.eye(self.input_dim, dtype=dfdx.dtype, device=dfdx.device).unsqueeze(0)\n dfdx = torch.cat((dfdx, I), dim=-1)\n\n if (do_gradient or do_Hessian) and forward_mode is False:\n dfdx, d2fd2x = self.backward(do_Hessian=do_Hessian)\n\n return f, dfdx, d2fd2x\n\n def backward(self, do_Hessian=False, dgdf=None, d2gd2f=None, v=None):\n r\"\"\"\n Backward propagation through ICNN layer of the form\n\n .. math::\n\n f(u) =\n \\left[\\begin{array}{c} \\sigma\\left(\\left[\\begin{array}{c}u & x\\end{array}\\right]\n \\left[\\begin{array}{c}L^+ \\\\ K\\end{array}\\right] + b\\right) & x \\end{array}\\right]\n\n Here, the network is :math:`g` is a function of :math:`f(u)`.\n\n As an example, for one sample, :math:`n_s = 1`, the gradient of the network with respect to :math:`u` is of the form\n\n .. math::\n\n \\nabla_{[u,x]} g = \\left(\\sigma'\\left(\\left[\\begin{array}{c}u & x\\end{array}\\right]\n \\left[\\begin{array}{c}L^+ \\\\ K\\end{array}\\right] + b\\right) \\odot \\nabla_{[f, x]} g\\right)\n \\left[\\begin{array}{c}(L^+)^\\top & K^\\top\\end{array}\\right]\n\n where :math:`\\odot` denotes the pointwise product.\n\n \"\"\"\n M = self.K\n if self.L is not None:\n M = torch.cat((self.nonneg(self.L), M), dim=0)\n\n # obtain stored information from backward pass\n d2gd2ux = None\n dsig, d2sig = self.act.backward(do_Hessian=do_Hessian)\n\n # compute gradient\n dgdux = dsig.unsqueeze(1) * M\n\n # augment gradient\n M2 = torch.ones(dgdux.shape[0], 1, 1, dtype=dgdux.dtype, device=dgdux.device) \\\n * torch.eye(self.input_dim, dtype=dgdux.dtype, device=dgdux.device).unsqueeze(0)\n\n if self.in_features is not None:\n Z = torch.zeros(dgdux.shape[0], self.input_dim, self.in_features)\n M2 = torch.cat((Z, M2), dim=-1).permute(0, 2, 1)\n\n dgdux = torch.cat((dgdux, M2), dim=-1)\n\n if do_Hessian:\n # TODO: change order of operations, multiply K's first; check if logic with better naming\n d2gd2ux = (d2sig.unsqueeze(1) * M.unsqueeze(0)).unsqueeze(2) * M.unsqueeze(0).unsqueeze(0)\n\n # concatenate zeros\n Z = torch.zeros(d2gd2ux.shape[0], d2gd2ux.shape[1], d2gd2ux.shape[2], self.input_dim,\n dtype=d2gd2ux.dtype, device=d2gd2ux.device)\n d2gd2ux = torch.cat((d2gd2ux, Z), dim=-1)\n\n if d2gd2f is not None:\n # Gauss-Newton approximation\n h1 = (dgdux.unsqueeze(1) @ d2gd2f.permute(0, 3, 1, 2) @ dgdux.permute(0, 2, 1).unsqueeze(1))\n h1 = h1.permute(0, 2, 3, 1)\n\n # extra term to compute full Hessian\n N, _, _, m = d2gd2ux.shape\n h2 = d2gd2ux.view(N, -1, m) @ dgdf.view(N, m, -1)\n h2 = h2.view(h1.shape)\n\n # combine\n d2gd2ux = h1 + h2\n\n # finish computing gradient\n if dgdf is not None:\n dgdux = dgdux @ dgdf\n\n return dgdux, d2gd2ux\n\n\nif __name__ == '__main__':\n from hessQuik.utils import input_derivative_check\n torch.set_default_dtype(torch.float64)\n\n nex = 11 # no. of examples\n d = 3 # no. of input features\n m = 5 # no. of output features\n x = torch.randn(nex, d)\n f = ICNNLayer(d, None, m, act=act.softplusActivation())\n\n print('======= FORWARD =======')\n input_derivative_check(f, x, do_Hessian=True, verbose=True, forward_mode=True)\n\n print('======= BACKWARD =======')\n input_derivative_check(f, x, do_Hessian=True, verbose=True, forward_mode=False)\n","repo_name":"elizabethnewman/hessQuik","sub_path":"hessQuik/layers/icnn_layer.py","file_name":"icnn_layer.py","file_ext":"py","file_size_in_byte":10415,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"77"}
+{"seq_id":"18998724149","text":"import os\nimport jwt\n\nfrom eve import Eve\nfrom eve.auth import TokenAuth\nfrom flask import abort, current_app as api\n\nclass MyAuth(TokenAuth):\n def check_auth(self, token, allowed_roles, resource, method):\n \"\"\" Checks for the validity of the provided JWT and grants access.\n\n First we check to see if any user with a given token exists. If it does,\n we decode the token and check if the payload is verifiable. If so, we're\n good to go. If not, we abort with 401 unauthorized.\n \"\"\"\n\n accounts = api.data.driver.db['accounts']\n lookup = { 'access_token': token }\n if allowed_roles:\n # only retrieve a user if his roles match ``allowed_roles``\n lookup['roles'] = {'$in': allowed_roles}\n account = accounts.find_one(lookup)\n\n if not account:\n abort(401, description=\"The provided access token is invalid or role no allowed\")\n\n try:\n access_payload = jwt.decode(token, os.environ.get('APP_SECRET', 'sekkret'), algorithms=['HS256'])\n\n return account and account['username'] == access_payload['username']\n except jwt.ExpiredSignatureError:\n abort(401, description=\"Your access token is expired.\")\n","repo_name":"caiohsramos/usp-smped-api","sub_path":"app/api/auth/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1243,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"38526526403","text":"#====================================#\n# Date: 2023.8.19\n#====================================#\nimport os\nimport cv2\nfrom base_camera import BaseCamera\nimport time\n\n#====================================#\n# Camera\n#====================================#\nclass Camera(BaseCamera):\n video_source = 0 #select camera, 0 for default camera\n\n def __init__(self):\n if os.environ.get('OPENCV_CAMERA_SOURCE'):\n Camera.set_video_source(int(os.environ['OPENCV_CAMERA_SOURCE']))\n super(Camera, self).__init__()\n\n @staticmethod\n def set_video_source(source):\n Camera.video_source = source\n\n @staticmethod\n def frames():\n camera = cv2.VideoCapture(Camera.video_source)\n if not camera.isOpened():\n raise RuntimeError('Could not start camera.')\n\n while True:\n # read current frame\n _, img = camera.read()\n\n # get local time\n timeArray = time.localtime()\n otherStyleTime = time.strftime(\"%Y-%m-%d %H:%M:%S\", timeArray)\n cv2.putText(img, str(otherStyleTime), (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)\n\n # encode as a jpeg image and return it\n yield cv2.imencode('.jpg', img)[1].tobytes()","repo_name":"GengJie-Jay/Wildlife-monitor-ENG5105","sub_path":"camera_opencv.py","file_name":"camera_opencv.py","file_ext":"py","file_size_in_byte":1250,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"32919576443","text":"\"\"\"\nLintCode problem 116: https://www.lintcode.com/problem/116/\n\"\"\"\n\nfrom typing import (\n List,\n)\n\nclass Solution:\n \"\"\"\n @param a: A list of integers\n @return: A boolean\n \"\"\"\n \"\"\"\n Logic: keep track of the previous number, i.e. where are you coming from \n\n Edge case:\n 1. if a list is empty, return False \n \n Initialize a list, tracker, with length of a, populated with False. \n Since it's made clear that the first element of a list >= 1, set \n tracker[0] to True.\n\n For range of (1, length of a), with i:\n For range of i, with j:\n if tracker[j] and j + a[j] >= i:\n tracker[i] = True \n break \n \n return tracker[-1]\n \"\"\"\n def can_jump(self, a: List[int]) -> bool:\n if not a:\n return False \n \n tracker = [False] * len(a) \n tracker[0] = True \n\n for i in range(1, len(a)):\n for j in range(i):\n # 1. make sure where we are coming from is a valid point\n # 2. if the jump length of the j point could reach current i\n if tracker[j] and j + a[j] >= i:\n tracker[i] = True\n break \n \n return tracker[-1]\n","repo_name":"sherry-debug715/Algorithms-notes","sub_path":"Dynamic Programming/coordinates/JumpGame.py","file_name":"JumpGame.py","file_ext":"py","file_size_in_byte":1251,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"14069740378","text":"\nimport gc\nimport numpy as np\nimport pandas as pd\nimport sys\n\nfrom scipy.optimize import minimize\nfrom tqdm import tqdm\n\nfrom utils import amex_metric_mod, line_notify\n\n#==============================================================================\n# Ensemble by seed\n#==============================================================================\n\nsub_path = '../output/submission_ensemble_seed_avg.csv'\noof_path = '../output/oof_ensemble_seed_avg.csv'\n\nsub_path_lgbm_seed_avg = '../output/submission_lgbm_agg_seed_avg.csv'\nsub_path_cb_seed_avg = '../output/submission_cb_agg_seed_avg.csv'\nsub_path_xgb_seed_avg = '../output/submission_xgb_agg_seed_avg.csv'\n\noof_path_lgbm_seed_avg = '../output/oof_lgbm_agg_seed_avg.csv'\noof_path_cb_seed_avg = '../output/oof_cb_agg_seed_avg.csv'\noof_path_xgb_seed_avg = '../output/oof_xgb_agg_seed_avg.csv'\n\ndef main():\n # load csv\n sub = pd.read_csv('../input/sample_submission.csv')\n oof = pd.read_csv('../input/train_labels.csv')\n\n sub_lgbm = pd.DataFrame()\n sub_cb = pd.DataFrame()\n sub_xgb = pd.DataFrame()\n\n oof_lgbm = pd.DataFrame()\n oof_cb = pd.DataFrame()\n oof_xgb = pd.DataFrame()\n\n sub_lgbm['customer_ID'] = sub['customer_ID']\n sub_cb['customer_ID'] = sub['customer_ID']\n sub_xgb['customer_ID'] = sub['customer_ID']\n\n oof_lgbm['customer_ID'] = oof['customer_ID']\n oof_cb['customer_ID'] = oof['customer_ID']\n oof_xgb['customer_ID'] = oof['customer_ID']\n\n oof_lgbm['target'] = oof['target']\n oof_cb['target'] = oof['target']\n oof_xgb['target'] = oof['target']\n\n sub_lgbm['prediction'] = np.zeros(sub.shape[0])\n sub_cb['prediction'] = np.zeros(sub.shape[0])\n sub_xgb['prediction'] = np.zeros(sub.shape[0])\n\n oof_lgbm['prediction'] = np.zeros(oof.shape[0])\n oof_cb['prediction'] = np.zeros(oof.shape[0])\n oof_xgb['prediction'] = np.zeros(oof.shape[0])\n\n print('seed averaging...')\n for seed in tqdm([42, 52, 62]):\n # load csv\n sub_path_lgbm = f'../output/submission_lgbm_agg_{seed}.csv'\n sub_path_cb = f'../output/submission_cb_agg_{seed}.csv'\n sub_path_xgb = f'../output/submission_xgb_agg_{seed}.csv'\n\n oof_path_lgbm = f'../output/oof_lgbm_agg_{seed}.csv'\n oof_path_cb = f'../output/oof_cb_agg_{seed}.csv'\n oof_path_xgb = f'../output/oof_xgb_agg_{seed}.csv'\n\n tmp_sub_lgbm = pd.read_csv(sub_path_lgbm)\n tmp_sub_cb = pd.read_csv(sub_path_cb)\n tmp_sub_xgb = pd.read_csv(sub_path_xgb)\n\n tmp_oof_lgbm = pd.read_csv(oof_path_lgbm)\n tmp_oof_cb = pd.read_csv(oof_path_cb)\n tmp_oof_xgb = pd.read_csv(oof_path_xgb)\n\n # to rank\n tmp_sub_lgbm['prediction'] = tmp_sub_lgbm['prediction'].rank() / len(tmp_sub_lgbm)\n tmp_sub_cb['prediction'] = tmp_sub_cb['prediction'].rank() / len(tmp_sub_cb)\n tmp_sub_xgb['prediction'] = tmp_sub_xgb['prediction'].rank() / len(tmp_sub_xgb)\n\n tmp_oof_lgbm['prediction'] = tmp_oof_lgbm['prediction'].rank() / len(tmp_oof_lgbm)\n tmp_oof_cb['prediction'] = tmp_oof_cb['prediction'].rank() / len(tmp_oof_cb)\n tmp_oof_xgb['prediction'] = tmp_oof_xgb['prediction'].rank() / len(tmp_oof_xgb)\n \n # average predictions\n sub_lgbm['prediction'] += tmp_sub_lgbm['prediction'] / 3\n sub_cb['prediction'] += tmp_sub_cb['prediction'] / 3\n sub_xgb['prediction'] += tmp_sub_xgb['prediction'] / 3\n\n oof_lgbm['prediction'] += tmp_oof_lgbm['prediction'] / 3\n oof_cb['prediction'] += tmp_oof_cb['prediction'] / 3\n oof_xgb['prediction'] += tmp_oof_xgb['prediction'] / 3\n\n # to rank\n sub_lgbm['prediction'] = sub_lgbm['prediction'].rank() / len(sub_lgbm)\n sub_cb['prediction'] = sub_cb['prediction'].rank() / len(sub_cb)\n sub_xgb['prediction'] = sub_xgb['prediction'].rank() / len(sub_xgb)\n\n oof_lgbm['prediction'] = oof_lgbm['prediction'].rank() / len(oof_lgbm)\n oof_cb['prediction'] = oof_cb['prediction'].rank() / len(oof_cb)\n oof_xgb['prediction'] = oof_xgb['prediction'].rank() / len(oof_xgb)\n\n # calc full score\n full_score_lgbm = round(amex_metric_mod(oof_lgbm['target'], oof_lgbm['prediction']),6)\n full_score_cb = round(amex_metric_mod(oof_cb['target'], oof_cb['prediction']),6)\n full_score_xgb = round(amex_metric_mod(oof_xgb['target'], oof_xgb['prediction']),6)\n\n # LINE notify\n line_notify(f'Full kaggle metric lgbm: {full_score_lgbm}')\n line_notify(f'Full kaggle metric cb: {full_score_cb}')\n line_notify(f'Full kaggle metric xgb: {full_score_xgb}')\n\n # save csv\n sub_lgbm[['customer_ID','prediction']].to_csv(sub_path_lgbm_seed_avg, index=False)\n sub_cb[['customer_ID','prediction']].to_csv(sub_path_cb_seed_avg, index=False)\n sub_xgb[['customer_ID','prediction']].to_csv(sub_path_xgb_seed_avg, index=False)\n\n oof_lgbm[['customer_ID','prediction']].to_csv(oof_path_lgbm_seed_avg, index=False)\n oof_cb[['customer_ID','prediction']].to_csv(oof_path_cb_seed_avg, index=False)\n oof_xgb[['customer_ID','prediction']].to_csv(oof_path_xgb_seed_avg, index=False)\n\n # rename columns\n oof_lgbm.rename(columns={'prediction': 'prediction_lgbm'},inplace=True)\n oof_cb.rename(columns={'prediction': 'prediction_cb'},inplace=True)\n oof_xgb.rename(columns={'prediction': 'prediction_xgb'},inplace=True)\n\n # merge oof\n oof = oof.merge(oof_lgbm,on=['customer_ID','target'],how='left')\n oof = oof.merge(oof_cb,on=['customer_ID','target'],how='left')\n oof = oof.merge(oof_xgb,on=['customer_ID','target'],how='left')\n\n del oof_lgbm, oof_cb, oof_xgb\n gc.collect()\n\n # cols to use\n cols_pred = ['prediction_lgbm','prediction_cb','prediction_xgb']\n\n # objective function for scipy optimize\n def obj_func(weights):\n ''' scipy minimize will pass the weights as a numpy array '''\n final_prediction = 0\n for weight, c in zip(weights, cols_pred):\n final_prediction += weight*oof[c]\n\n return -amex_metric_mod(oof['target'], final_prediction) \n\n # Optimization runs 100 times.\n lls = []\n wghts = []\n print('Optimization runs 100 times...')\n for i in tqdm(range(100)):\n starting_values = np.random.uniform(size=len(cols_pred))\n # cons are given as constraints.\n cons = ({'type':'eq','fun':lambda w: 1-sum(w)})\n bounds = [(0,1)]*len(cols_pred)\n \n res = minimize(obj_func, \n starting_values, \n constraints=cons,\n bounds = bounds, \n method='SLSQP')\n\n lls.append(res['fun'])\n wghts.append(res['x'])\n\n # get weights\n bestSC = np.min(lls)\n w = wghts[np.argmin(lls)] # [0.62779581 0.00637736 0.36582683]\n print('\\n Ensemble Score: {best_score:.7f}'.format(best_score=bestSC))\n print('weights: {}'.format(w))\n\n # calc prediction\n preds = [sub_lgbm, sub_cb, sub_xgb]\n\n oof['prediction'] = 0.0\n for i, (p, c) in enumerate(zip(preds,cols_pred)):\n sub['prediction'] += w[i]*p['prediction']\n oof['prediction'] += w[i]*oof[c]\n\n # save csv\n oof[['customer_ID','target','prediction']].to_csv(oof_path, index=False)\n sub[['customer_ID','prediction']].to_csv(sub_path, index=False)\n\n # Full score and LINE Notify\n full_score = round(amex_metric_mod(oof['target'], oof['prediction']),6)\n\n # LINE notify\n line_notify(f'{sys.argv[0]} done. Full kaggle metric: {full_score}')\n\nif __name__ == '__main__':\n main()","repo_name":"MitsuruFujiwara/Amex-Default-Prediction","sub_path":"src/201_ensemble_seed_avg.py","file_name":"201_ensemble_seed_avg.py","file_ext":"py","file_size_in_byte":7464,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"33403020599","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\n# Dependency imports\nimport numpy as np\nimport tensorflow as tf\n\nfrom tensorflow_probability.python.internal import distribution_util as util\nfrom tensorflow.python.ops import control_flow_ops\n\n__all__ = [\n 'percentile',\n 'quantiles',\n]\n\n\ndef percentile(x,\n q,\n axis=None,\n interpolation=None,\n keep_dims=False,\n validate_args=False,\n preserve_gradients=True,\n name=None):\n \"\"\"Compute the `q`-th percentile(s) of `x`.\n\n Given a vector `x`, the `q`-th percentile of `x` is the value `q / 100` of the\n way from the minimum to the maximum in a sorted copy of `x`.\n\n The values and distances of the two nearest neighbors as well as the\n `interpolation` parameter will determine the percentile if the normalized\n ranking does not match the location of `q` exactly.\n\n This function is the same as the median if `q = 50`, the same as the minimum\n if `q = 0` and the same as the maximum if `q = 100`.\n\n Multiple percentiles can be computed at once by using `1-D` vector `q`.\n Dimension zero of the returned `Tensor` will index the different percentiles.\n\n\n ```python\n # Get 30th percentile with default ('nearest') interpolation.\n x = [1., 2., 3., 4.]\n tfp.stats.percentile(x, q=30.)\n ==> 2.0\n\n # Get 30th percentile with 'linear' interpolation.\n x = [1., 2., 3., 4.]\n tfp.stats.percentile(x, q=30., interpolation='linear')\n ==> 1.9\n\n # Get 30th and 70th percentiles with 'lower' interpolation\n x = [1., 2., 3., 4.]\n tfp.stats.percentile(x, q=[30., 70.], interpolation='lower')\n ==> [1., 3.]\n\n # Get 100th percentile (maximum). By default, this is computed over every dim\n x = [[1., 2.]\n [3., 4.]]\n tfp.stats.percentile(x, q=100.)\n ==> 4.\n\n # Treat the leading dim as indexing samples, and find the 100th quantile (max)\n # over all such samples.\n x = [[1., 2.]\n [3., 4.]]\n tfp.stats.percentile(x, q=100., axis=[0])\n ==> [3., 4.]\n ```\n\n Compare to `numpy.percentile`.\n\n Args:\n x: Floating point `N-D` `Tensor` with `N > 0`. If `axis` is not `None`,\n `x` must have statically known number of dimensions.\n q: Scalar or vector `Tensor` with values in `[0, 100]`. The percentile(s).\n axis: Optional `0-D` or `1-D` integer `Tensor` with constant values. The\n axis that hold independent samples over which to return the desired\n percentile. If `None` (the default), treat every dimension as a sample\n dimension, returning a scalar.\n interpolation : {'nearest', 'linear', 'lower', 'higher', 'midpoint'}.\n Default value: 'nearest'. This specifies the interpolation method to\n use when the desired quantile lies between two data points `i < j`:\n * linear: i + (j - i) * fraction, where fraction is the fractional part\n of the index surrounded by i and j.\n * lower: `i`.\n * higher: `j`.\n * nearest: `i` or `j`, whichever is nearest.\n * midpoint: (i + j) / 2.\n `linear` and `midpoint` interpolation do not work with integer dtypes.\n keep_dims: Python `bool`. If `True`, the last dimension is kept with size 1\n If `False`, the last dimension is removed from the output shape.\n validate_args: Whether to add runtime checks of argument validity. If\n False, and arguments are incorrect, correct behavior is not guaranteed.\n preserve_gradients: Python `bool`. If `True`, ensure that gradient w.r.t\n the percentile `q` is preserved in the case of linear interpolation.\n If `False`, the gradient will be (incorrectly) zero when `q` corresponds\n to a point in `x`.\n name: A Python string name to give this `Op`. Default is 'percentile'\n\n Returns:\n A `(rank(q) + N - len(axis))` dimensional `Tensor` of same dtype as `x`, or,\n if `axis` is `None`, a `rank(q)` `Tensor`. The first `rank(q)` dimensions\n index quantiles for different values of `q`.\n\n Raises:\n ValueError: If argument 'interpolation' is not an allowed type.\n ValueError: If interpolation type not compatible with `dtype`.\n \"\"\"\n name = name or 'percentile'\n allowed_interpolations = {'linear', 'lower', 'higher', 'nearest', 'midpoint'}\n\n if interpolation is None:\n interpolation = 'nearest'\n else:\n if interpolation not in allowed_interpolations:\n raise ValueError('Argument `interpolation` must be in %s. Found %s' %\n (allowed_interpolations, interpolation))\n\n with tf.name_scope(name, values=[x, q]):\n x = tf.convert_to_tensor(x, name='x')\n\n if interpolation in {'linear', 'midpoint'} and x.dtype.is_integer:\n raise TypeError('{} interpolation not allowed with dtype {}'.format(\n interpolation, x.dtype))\n\n # Double is needed here and below, else we get the wrong index if the array\n # is huge along axis.\n q = tf.cast(q, tf.float64)\n _get_static_ndims(q, expect_ndims_no_more_than=1)\n\n if validate_args:\n q = control_flow_ops.with_dependencies([\n tf.assert_rank_in(q, [0, 1]),\n tf.assert_greater_equal(q, tf.cast(0., tf.float64)),\n tf.assert_less_equal(q, tf.cast(100., tf.float64))\n ], q)\n\n if axis is None:\n y = tf.reshape(x, [-1])\n else:\n axis = tf.convert_to_tensor(axis, name='axis', dtype=tf.int32)\n tf.assert_integer(axis)\n axis_ndims = _get_static_ndims(\n axis, expect_static=True, expect_ndims_no_more_than=1)\n axis_const = tf.contrib.util.constant_value(axis)\n if axis_const is None:\n raise ValueError(\n 'Expected argument `axis` to be statically available. Found: %s' %\n axis)\n axis = axis_const\n if axis_ndims == 0:\n axis = [axis]\n axis = [int(a) for a in axis]\n x_ndims = _get_static_ndims(\n x, expect_static=True, expect_ndims_at_least=1)\n axis = _make_static_axis_non_negative(axis, x_ndims)\n # Move dims in axis to the end, since _sort_tensor, which calls top_k,\n # only sorts the last dim.\n y = _move_dims_to_flat_end(x, axis, x_ndims)\n\n frac_at_q_or_above = 1. - q / 100.\n\n # Sort everything, not just the top 'k' entries, which allows multiple calls\n # to sort only once (under the hood) and use CSE.\n sorted_y = _sort_tensor(y)\n\n d = tf.cast(tf.shape(y)[-1], tf.float64)\n\n def _get_indices(interp_type):\n \"\"\"Get values of y at the indices implied by interp_type.\"\"\"\n # Note `lower` <--> ceiling. Confusing, huh? Due to the fact that\n # _sort_tensor sorts highest to lowest, tf.ceil corresponds to the higher\n # index, but the lower value of y!\n if interp_type == 'lower':\n indices = tf.ceil((d - 1) * frac_at_q_or_above)\n elif interp_type == 'higher':\n indices = tf.floor((d - 1) * frac_at_q_or_above)\n elif interp_type == 'nearest':\n indices = tf.round((d - 1) * frac_at_q_or_above)\n # d - 1 will be distinct from d in int32, but not necessarily double.\n # So clip to avoid out of bounds errors.\n return tf.clip_by_value(\n tf.cast(indices, tf.int32), 0, tf.shape(y)[-1] - 1)\n\n if interpolation in ['nearest', 'lower', 'higher']:\n gathered_y = tf.gather(sorted_y, _get_indices(interpolation), axis=-1)\n elif interpolation == 'midpoint':\n gathered_y = 0.5 * (\n tf.gather(sorted_y, _get_indices('lower'), axis=-1) +\n tf.gather(sorted_y, _get_indices('higher'), axis=-1))\n elif interpolation == 'linear':\n # Copy-paste of docstring on interpolation:\n # linear: i + (j - i) * fraction, where fraction is the fractional part\n # of the index surrounded by i and j.\n larger_y_idx = _get_indices('lower')\n exact_idx = (d - 1) * frac_at_q_or_above\n if preserve_gradients:\n # If q cooresponds to a point in x, we will initially have\n # larger_y_idx == smaller_y_idx.\n # This results in the gradient w.r.t. fraction being zero (recall `q`\n # enters only through `fraction`...and see that things cancel).\n # The fix is to ensure that smaller_y_idx and larger_y_idx are always\n # separated by exactly 1.\n smaller_y_idx = tf.maximum(larger_y_idx - 1, 0)\n larger_y_idx = tf.minimum(smaller_y_idx + 1, tf.shape(y)[-1] - 1)\n fraction = tf.cast(larger_y_idx, tf.float64) - exact_idx\n else:\n smaller_y_idx = _get_indices('higher')\n fraction = tf.ceil((d - 1) * frac_at_q_or_above) - exact_idx\n\n fraction = tf.cast(fraction, y.dtype)\n gathered_y = (\n tf.gather(sorted_y, larger_y_idx, axis=-1) * (1 - fraction) +\n tf.gather(sorted_y, smaller_y_idx, axis=-1) * fraction)\n\n if keep_dims:\n if axis is None:\n ones_vec = tf.ones(\n shape=[_get_best_effort_ndims(x) + _get_best_effort_ndims(q)],\n dtype=tf.int32)\n gathered_y *= tf.ones(ones_vec, dtype=x.dtype)\n else:\n gathered_y = _insert_back_keep_dims(gathered_y, axis)\n\n # If q is a scalar, then result has the right shape.\n # If q is a vector, then result has trailing dim of shape q.shape, which\n # needs to be rotated to dim 0.\n return util.rotate_transpose(gathered_y, tf.rank(q))\n\n\ndef quantiles(x,\n num_quantiles,\n axis=None,\n interpolation=None,\n keep_dims=False,\n validate_args=False,\n name=None):\n \"\"\"Compute quantiles of `x` along `axis`.\n\n The quantiles of a distribution are cut points dividing the range into\n intervals with equal probabilities.\n\n Given a vector `x` of samples, this function estimates the cut points by\n returning `num_quantiles + 1` cut points, `(c0, ..., cn)`, such that, roughly\n speaking, equal number of sample points lie in the `num_quantiles` intervals\n `[c0, c1), [c1, c2), ..., [c_{n-1}, cn]`. That is,\n\n * About `1 / n` fraction of the data lies in `[c_{k-1}, c_k)`, `k = 1, ..., n`\n * About `k / n` fraction of the data lies below `c_k`.\n * `c0` is the sample minimum and `cn` is the maximum.\n\n The exact number of data points in each interval depends on the size of\n `x` (e.g. whether the size is divisible by `n`) and the `interpolation` kwarg.\n\n\n ```python\n # Get quartiles of x with various interpolation choices.\n x = [0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10.]\n\n tfp.stats.quantiles(x, num_quantiles=4, interpolation='nearest')\n ==> [ 0., 2., 5., 8., 10.]\n\n tfp.stats.quantiles(x, num_quantiles=4, interpolation='linear')\n ==> [ 0. , 2.5, 5. , 7.5, 10. ]\n\n tfp.stats.quantiles(x, num_quantiles=4, interpolation='lower')\n ==> [ 0., 2., 5., 7., 10.]\n\n # Get deciles of columns of an R x C data set.\n data = load_my_columnar_data(...)\n tfp.stats.quantiles(data, num_quantiles=10)\n ==> Shape [11, C] Tensor\n ```\n\n Args:\n x: Floating point `N-D` `Tensor` with `N > 0`. If `axis` is not `None`,\n `x` must have statically known number of dimensions.\n num_quantiles: Scalar `integer` `Tensor`. The number of intervals the\n returned `num_quantiles + 1` cut points divide the range into.\n axis: Optional `0-D` or `1-D` integer `Tensor` with constant values. The\n axis that hold independent samples over which to return the desired\n percentile. If `None` (the default), treat every dimension as a sample\n dimension, returning a scalar.\n interpolation : {'nearest', 'linear', 'lower', 'higher', 'midpoint'}.\n Default value: 'nearest'. This specifies the interpolation method to\n use when the fractions `k / n` lie between two data points `i < j`:\n * linear: i + (j - i) * fraction, where fraction is the fractional part\n of the index surrounded by i and j.\n * lower: `i`.\n * higher: `j`.\n * nearest: `i` or `j`, whichever is nearest.\n * midpoint: (i + j) / 2. `linear` and `midpoint` interpolation do not\n work with integer dtypes.\n keep_dims: Python `bool`. If `True`, the last dimension is kept with size 1\n If `False`, the last dimension is removed from the output shape.\n validate_args: Whether to add runtime checks of argument validity. If\n False, and arguments are incorrect, correct behavior is not guaranteed.\n name: A Python string name to give this `Op`. Default is 'percentile'\n\n Returns:\n cut_points: A `rank(x) + 1 - len(axis)` dimensional `Tensor` with same\n `dtype` as `x` and shape `[num_quantiles + 1, ...]` where the trailing shape\n is that of `x` without the dimensions in `axis` (unless `keep_dims is True`)\n\n Raises:\n ValueError: If argument 'interpolation' is not an allowed type.\n ValueError: If interpolation type not compatible with `dtype`.\n \"\"\"\n with tf.name_scope(name, 'quantiles', values=[x, num_quantiles, axis]):\n x = tf.convert_to_tensor(x, name='x')\n return percentile(\n x,\n q=tf.linspace(\n tf.convert_to_tensor(0, dtype=x.dtype),\n tf.convert_to_tensor(100, dtype=x.dtype),\n num=num_quantiles + 1),\n axis=axis,\n interpolation=interpolation,\n keep_dims=keep_dims,\n validate_args=validate_args,\n preserve_gradients=False)\n\n\ndef _get_static_ndims(x,\n expect_static=False,\n expect_ndims=None,\n expect_ndims_no_more_than=None,\n expect_ndims_at_least=None):\n \"\"\"Get static number of dimensions and assert that some expectations are met.\n\n This function returns the number of dimensions 'ndims' of x, as a Python int.\n\n The optional expect arguments are used to check the ndims of x, but this is\n only done if the static ndims of x is not None.\n\n Args:\n x: A Tensor.\n expect_static: Expect `x` to have statically defined `ndims`.\n expect_ndims: Optional Python integer. If provided, assert that x has\n number of dimensions equal to this.\n expect_ndims_no_more_than: Optional Python integer. If provided, assert\n that x has no more than this many dimensions.\n expect_ndims_at_least: Optional Python integer. If provided, assert that x\n has at least this many dimensions.\n\n Returns:\n ndims: A Python integer.\n\n Raises:\n ValueError: If any of the expectations above are violated.\n \"\"\"\n ndims = x.shape.ndims\n if ndims is None:\n shape_const = tf.contrib.util.constant_value(tf.shape(x))\n if shape_const is not None:\n ndims = shape_const.ndim\n\n if ndims is None:\n if expect_static:\n raise ValueError(\n 'Expected argument `x` to have statically defined `ndims`. Found: ' %\n x)\n return\n\n if expect_ndims is not None:\n ndims_message = ('Expected argument `x` to have ndims %s. Found tensor %s'\n % (expect_ndims, x))\n if ndims != expect_ndims:\n raise ValueError(ndims_message)\n\n if expect_ndims_at_least is not None:\n ndims_at_least_message = (\n 'Expected argument `x` to have ndims >= %d. Found tensor %s' %\n (expect_ndims_at_least, x))\n if ndims < expect_ndims_at_least:\n raise ValueError(ndims_at_least_message)\n\n if expect_ndims_no_more_than is not None:\n ndims_no_more_than_message = (\n 'Expected argument `x` to have ndims <= %d. Found tensor %s' %\n (expect_ndims_no_more_than, x))\n if ndims > expect_ndims_no_more_than:\n raise ValueError(ndims_no_more_than_message)\n\n return ndims\n\n\ndef _get_best_effort_ndims(x,\n expect_ndims=None,\n expect_ndims_at_least=None,\n expect_ndims_no_more_than=None):\n \"\"\"Get static ndims if possible. Fallback on `tf.rank(x)`.\"\"\"\n ndims_static = _get_static_ndims(\n x,\n expect_ndims=expect_ndims,\n expect_ndims_at_least=expect_ndims_at_least,\n expect_ndims_no_more_than=expect_ndims_no_more_than)\n if ndims_static is not None:\n return ndims_static\n return tf.rank(x)\n\n\ndef _insert_back_keep_dims(x, axis):\n \"\"\"Insert the dims in `axis` back as singletons after being removed.\n\n Args:\n x: `Tensor`.\n axis: Python list of integers.\n\n Returns:\n `Tensor` with same values as `x`, but additional singleton dimensions.\n \"\"\"\n for i in sorted(axis):\n x = tf.expand_dims(x, axis=i)\n return x\n\n\ndef _make_static_axis_non_negative(axis, ndims):\n \"\"\"Convert possibly negatively indexed axis to non-negative.\n\n Args:\n axis: Iterable over Python integers.\n ndims: Number of dimensions into which axis indexes.\n\n Returns:\n A list of non-negative Python integers.\n\n Raises:\n ValueError: If values in `axis` are too big/small to index into `ndims`.\n \"\"\"\n non_negative_axis = []\n for d in axis:\n if d >= 0:\n if d >= ndims:\n raise ValueError('dim %d not in the interval [0, %d].' % (d, ndims - 1))\n non_negative_axis.append(d)\n else:\n if d < -1 * ndims:\n raise ValueError(\n 'Negatively indexed dim %d not in the interval [-%d, -1]' % (d,\n ndims))\n non_negative_axis.append(ndims + d)\n return non_negative_axis\n\n\ndef _move_dims_to_flat_end(x, axis, x_ndims):\n \"\"\"Move dims corresponding to `axis` in `x` to the end, then flatten.\n\n Args:\n x: `Tensor` with shape `[B0,B1,...,Bb]`.\n axis: Python list of indices into dimensions of `x`.\n x_ndims: Python integer holding number of dimensions in `x`.\n\n Returns:\n `Tensor` with value from `x` and dims in `axis` moved to end into one single\n dimension.\n \"\"\"\n # Suppose x.shape = [a, b, c, d]\n # Suppose axis = [1, 3]\n\n # front_dims = [0, 2] in example above.\n front_dims = sorted(set(range(x_ndims)).difference(axis))\n # x_permed.shape = [a, c, b, d]\n x_permed = tf.transpose(x, perm=front_dims + list(axis))\n\n if x.shape.is_fully_defined():\n x_shape = x.shape.as_list()\n # front_shape = [a, c], end_shape = [b * d]\n front_shape = [x_shape[i] for i in front_dims]\n end_shape = [np.prod([x_shape[i] for i in axis])]\n full_shape = front_shape + end_shape\n else:\n front_shape = tf.shape(x_permed)[:x_ndims - len(axis)]\n end_shape = [-1]\n full_shape = tf.concat([front_shape, end_shape], axis=0)\n return tf.reshape(x_permed, shape=full_shape)\n\n\ndef _sort_tensor(tensor):\n \"\"\"Use `top_k` to sort a `Tensor` along the last dimension.\"\"\"\n sorted_, _ = tf.nn.top_k(tensor, k=tf.shape(tensor)[-1])\n sorted_.set_shape(tensor.shape)\n return sorted_\n","repo_name":"xlandscape/CmfContinuous-Component","sub_path":"module/bin/python/Lib/site-packages/tensorflow_probability/python/stats/quantiles.py","file_name":"quantiles.py","file_ext":"py","file_size_in_byte":18542,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"12734593893","text":"import os.path\nimport shutil\n\nfrom .FilesetInfoAccumulator import FilesetInfoAccumulator\nfrom .FilespecMerger import FilespecMerger\nfrom .PooledFile import listdir\nfrom .util import debug_log, verbose_stderr, warning\n\nclass FilesetCache(object):\n \"\"\"FilesetCache is a base class.\"\"\"\n\n def __init__(self, parent, path, deltadir, ctx, attrs, sel, next):\n self._parent = parent\n self._path = path\n self._deltadir = deltadir\n self._ctx = ctx\n self._attrs = attrs\n self._sel = sel\n self._next = next\n self._fileinfo = None\n self._deletedInfo = FilesetInfoAccumulator(self._attrs)\n\n def __hash__(self):\n \"\"\"For storage in sets.\"\"\"\n return id(self)\n\n def __eq__(self, other):\n \"\"\"For storage in sets.\"\"\"\n return self is other\n\n def _subpath(self, x):\n return os.path.join(self._path, '_' + str(x))\n\n def _subdeltadir(self, x):\n return os.path.join(self._deltadir, '_' + str(x))\n\n def children(self):\n \"\"\"Child filesets are stored with a leading underscore, to leave room for metadata.\"\"\"\n for x in listdir(self._path):\n if x.startswith('_'):\n yield x[1:]\n\n def infopath(self, deleted=False):\n if deleted:\n return os.path.join(self._deltadir, \"deleted.info\")\n else:\n return os.path.join(self._path, \"info\")\n\n def purge(self):\n \"\"\"Purge existing cache on disk, and create empty.\"\"\"\n #debug_log(\"FilesetCache purging %s\\n\" % self._path)\n if os.path.exists(self._path):\n # Purge existing cache.\n # For safety in case of misconfiguration, we only delete directories with a leading underscore\n for x in listdir(self._path):\n px = os.path.join(self._path, x)\n if x.startswith('_'):\n shutil.rmtree(px)\n elif x == 'info':\n os.remove(px)\n else:\n verbose_stderr(\"WARNING: cache purge ignoring %s\\n\" % px)\n else:\n os.makedirs(self._path)\n\n def select(self, filter=None):\n merger = FilespecMerger()\n for f, f1 in self.filtered(filter):\n merger.add(f.select(f1))\n # no yield from in python 2, so:\n for filespec in merger.merge():\n yield filespec\n\n def merge_info(self, acc, filter=None):\n \"\"\"Return whether merged from cache; otherwise caller will have to scan over filespecs.\"\"\"\n #debug_log(\"FilesetCache(%s) merge_info\\n\" % self._path)\n if filter is None:\n #debug_log(\"FilesetCache(%s)::merge_info(None)\\n\" % self._path)\n if self._fileinfo is None:\n #debug_log(\"FilesetCache(%s)::merge_info(None) reading info file\\n\" % self._path)\n infofile = self.infopath()\n deletedInfofile = self.infopath(deleted=True)\n try:\n if os.path.exists(deletedInfofile):\n # if deleted filelist is older than cache, remove it\n if os.stat(deletedInfofile).st_mtime < os.stat(infofile).st_mtime:\n #debug_log(\"removing obsolete deleted infofile %s\\n\" % deletedInfofile)\n os.remove(deletedInfofile)\n else:\n #debug_log(\"reading deleted infofile %s\\n\" % deletedInfofile)\n with open(deletedInfofile, 'r') as f:\n self._deletedInfo = FilesetInfoAccumulator.fromFile(f, self._attrs)\n except IOError:\n warning(\"can't read deleted info %s, ignoring\" % deletedInfofile)\n self._deletedInfo = FilesetInfoAccumulator(self._attrs)\n try:\n with open(infofile, 'r') as f:\n self._fileinfo = FilesetInfoAccumulator.fromFile(f, self._attrs)\n except IOError:\n warning(\"can't read info %s, ignoring\" % infofile)\n\n if self._fileinfo is not None:\n acc.accumulate(self._fileinfo)\n acc.decumulate(self._deletedInfo)\n #debug_log(\"FilesetCache(%s)::merge_info() done\\n\" % self._path)\n return True\n #debug_log(\"FilesetCache(%s)::merge_info() not merged yet\\n\" % self._path)\n\n #debug_log(\"FilesetCache(%s)::merge_info() still here\\n\" % self._path)\n # didn't manage to read infofile, or we need a filtered scan\n if self._next is not None:\n #debug_log(\"FilesetCache(%s)::merge_info() asking children\\n\" % self._path)\n for f, f1 in self.filtered(filter):\n f.merge_info(acc, f1)\n return True\n else:\n #debug_log(\"FilesetCache(%s)::merge_info() baling\\n\" % self._path)\n return False\n\n def add(self, filespec):\n if self._next is not None:\n self.filesetFor(filespec).add(filespec)\n if self._fileinfo is None:\n self._fileinfo = FilesetInfoAccumulator(self._attrs)\n self._fileinfo.add(filespec)\n\n def finalize(self):\n #debug_log(\"FilesetCache::finalize(%s)\\n\" % self._path)\n finalized = False\n if self._next is not None:\n for f, f1 in self.filtered(None):\n f.finalize()\n if not finalized:\n finalized = True\n\n # write info file, only if a child did something\n if self._next is None or finalized:\n with open(self.infopath(), 'w') as infofile:\n if self._fileinfo is not None:\n self._fileinfo.write(infofile)\n\n def delete(self, filespec):\n #debug_log(\"FilesetCache(%s)::delete %s\\n\" % (self._path, filespec.path))\n self._deletedInfo.add(filespec)\n self._ctx.pendingCaches.add(self)\n if self._parent is not None:\n self._parent.delete(filespec)\n\n def saveDeletions(self):\n #debug_log(\"FilesetCache(%s)::saveDeletions\\n\" % self._path)\n try:\n if not os.path.exists(self._deltadir):\n os.makedirs(self._deltadir)\n except IOError:\n warning(\"can't create deltadir %s, ignoring\" % self._deltadir)\n return\n if self._deletedInfo.nFiles > 0:\n deletedInfofile = self.infopath(deleted=True)\n #debug_log(\"FilesetCache(%s)::saveDeletions deletedInfo\\n\" % self._path)\n try:\n with open(deletedInfofile, 'w') as f:\n self._deletedInfo.write(f)\n except IOError:\n warning(\"can't write deleted info %s, ignoring\" % deletedInfofile)\n self._deletedInfo = FilesetInfoAccumulator(self._attrs)\n","repo_name":"tesujimath/filebutler","sub_path":"filebutler/FilesetCache.py","file_name":"FilesetCache.py","file_ext":"py","file_size_in_byte":6803,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"38269483342","text":"import os\nimport random\nimport string\nimport subprocess\n\ndef randstr(l):\n return ''.join([random.choice(string.ascii_letters) for i in range(l)])\n\ndef check(code):\n if len(code) > 0x1000:\n print(\"[-] Too large\")\n return False\n if 'incbin' in code:\n print(\"[-] You can't guess the filename of the flag\")\n return False\n if '%' in code:\n print(\"[-] Macro is disabled just in case\")\n return False\n return True\n\nif __name__ == '__main__':\n print(\"* Paste your assembly code to emulate ('EOF' to end)\")\n\n # read code\n code = 'BITS 64\\n'\n code += 'ORG 0\\n'\n while True:\n line = input()\n if line == 'EOF':\n break\n code += line + '\\n'\n\n # check code\n if not check(code):\n exit(1)\n\n # save to file\n name = \"/tmp/\" + randstr(32)\n with open(f\"{name}.S\", \"w\") as f:\n f.write(code)\n\n # assemble\n p = subprocess.Popen([\"/usr/bin/nasm\",\n \"-fbin\", f\"{name}.S\",\n \"-o\", f\"{name}.bin\"])\n if p.wait(timeout=1) != 0:\n print(\"[-] Assemble failed\")\n exit(1)\n\n os.remove(f\"{name}.S\")\n\n # emulate\n try:\n pid = os.fork()\n if pid == 0:\n os.execl(\"./x64-emulator\", \"./x64-emulator\", f\"{name}.bin\")\n os._exit(0)\n else:\n os.waitpid(pid, 0)\n except Exception as e:\n print(e)\n finally:\n os.remove(f\"{name}.bin\")\n","repo_name":"sajjadium/ctf-archives","sub_path":"ctfs/zer0pts/2021/pwn/nasm_kit/bin/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1469,"program_lang":"python","lang":"en","doc_type":"code","stars":490,"dataset":"github-code","pt":"77"}
+{"seq_id":"5415052671","text":"from ikea.fields import BaseColumn\nfrom collections import OrderedDict\n\n\nclass Metabase(type):\n\n def __new__(cls, name, bases, clsdict):\n fields = OrderedDict()\n clsobj = super().__new__(cls, name, bases, clsdict)\n\n if not hasattr(clsobj, '__tablename__'):\n raise AttributeError(\"Models must have a __tablename__!\")\n\n if not hasattr(clsobj, '_registry'):\n clsobj._registry = set()\n else:\n clsobj._registry.add(clsobj)\n\n for attr, value in clsdict.items():\n if isinstance(value, BaseColumn):\n fields[attr] = value\n\n for attr, _ in fields.items():\n clsdict.pop(attr)\n\n setattr(clsobj, 'fields', fields)\n\n return clsobj\n\n\ndef main():\n pass\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"Nimor111/101-v5","sub_path":"week13/HomemadeORM/ikea/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":816,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"71740815290","text":"import random\nrandlist = \"1234567890qwertyuiopasdfghjklzxcvbnm-*/+.,?[]}{_\\=''\"\ndef my_fun(n):\n my_list = []\n for i in range (1, n):\n x = random.choice(randlist)\n my_list.append (x)\n print (my_list)\nn = int (input())\nmy_fun(n)","repo_name":"Malika939/Funkzii2","sub_path":"Problem11.py","file_name":"Problem11.py","file_ext":"py","file_size_in_byte":249,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"31863391028","text":"import copy\nimport unittest\nimport warnings\n\ntry:\n import numpy\n from numpy import linalg # missing in PyPy's micronumpy\nexcept ImportError:\n from Bio import MissingExternalDependencyError\n\n raise MissingExternalDependencyError(\n \"Install NumPy if you want to use Bio.LogisticRegression.\"\n ) from None\n\nfrom Bio import BiopythonDeprecationWarning\n\nwith warnings.catch_warnings():\n warnings.simplefilter(\"ignore\", category=BiopythonDeprecationWarning)\n from Bio import LogisticRegression\n\n\nxs = [\n [-53, -200.78],\n [117, -267.14],\n [57, -163.47],\n [16, -190.30],\n [11, -220.94],\n [85, -193.94],\n [16, -182.71],\n [15, -180.41],\n [-26, -181.73],\n [58, -259.87],\n [126, -414.53],\n [191, -249.57],\n [113, -265.28],\n [145, -312.99],\n [154, -213.83],\n [147, -380.85],\n [93, -291.13],\n]\n\nys = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0]\n\n\ndef show_progress(iteration, loglikelihood):\n \"\"\"No action callback function, used when training the model.\"\"\"\n pass\n\n\nclass TestLogisticRegression(unittest.TestCase):\n def test_xs_and_ys_input_parameter_lengths(self):\n modified_xs = copy.copy(xs)\n modified_xs.pop()\n self.assertRaises(ValueError, LogisticRegression.train, modified_xs, ys)\n\n def test_ys_input_class_assignments(self):\n modified_ys = copy.copy(ys)\n modified_ys.pop()\n modified_ys.append(2)\n self.assertRaises(ValueError, LogisticRegression.train, xs, modified_ys)\n\n def test_dimensionality_of_input_xs(self):\n modified_xs = copy.copy(xs)\n modified_xs[0] = []\n self.assertRaises(ValueError, LogisticRegression.train, modified_xs, ys)\n\n def test_calculate_model(self):\n model = LogisticRegression.train(xs, ys)\n beta = model.beta\n self.assertAlmostEqual(beta[0], 8.9830, places=4)\n self.assertAlmostEqual(beta[1], -0.0360, places=4)\n self.assertAlmostEqual(beta[2], 0.0218, places=4)\n\n def test_calculate_model_with_update_callback(self):\n model = LogisticRegression.train(xs, ys, update_fn=show_progress)\n beta = model.beta\n self.assertAlmostEqual(beta[0], 8.9830, places=4)\n\n def test_classify(self):\n model = LogisticRegression.train(xs, ys)\n result = LogisticRegression.classify(model, [6, -173.143442352])\n self.assertEqual(result, 1)\n result = LogisticRegression.classify(model, [309, -271.005880394])\n self.assertEqual(result, 0)\n\n def test_calculate_probability(self):\n model = LogisticRegression.train(xs, ys)\n q, p = LogisticRegression.calculate(model, [6, -173.143442352])\n self.assertAlmostEqual(p, 0.993242, places=6)\n self.assertAlmostEqual(q, 0.006758, places=6)\n q, p = LogisticRegression.calculate(model, [309, -271.005880394])\n self.assertAlmostEqual(p, 0.000321, places=6)\n self.assertAlmostEqual(q, 0.999679, places=6)\n\n def test_model_accuracy(self):\n correct = 0\n model = LogisticRegression.train(xs, ys)\n predictions = [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0]\n for i in range(len(predictions)):\n prediction = LogisticRegression.classify(model, xs[i])\n self.assertEqual(prediction, predictions[i])\n if prediction == ys[i]:\n correct += 1\n self.assertEqual(correct, 16)\n\n def test_leave_one_out(self):\n correct = 0\n predictions = [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0]\n for i in range(len(predictions)):\n model = LogisticRegression.train(xs[:i] + xs[i + 1 :], ys[:i] + ys[i + 1 :])\n prediction = LogisticRegression.classify(model, xs[i])\n self.assertEqual(prediction, predictions[i])\n if prediction == ys[i]:\n correct += 1\n self.assertEqual(correct, 15)\n\n\nif __name__ == \"__main__\":\n runner = unittest.TextTestRunner(verbosity=2)\n unittest.main(testRunner=runner)\n","repo_name":"biopython/biopython","sub_path":"Tests/test_LogisticRegression.py","file_name":"test_LogisticRegression.py","file_ext":"py","file_size_in_byte":4011,"program_lang":"python","lang":"en","doc_type":"code","stars":3852,"dataset":"github-code","pt":"77"}
+{"seq_id":"71627019770","text":"import json\nimport boto3\nimport datetime\nfrom requests_aws4auth import AWS4Auth\nimport requests\nfrom boto3.dynamodb.conditions import Key, Attr\nfrom pprint import pprint\n\n\ndef lambda_handler(event, context):\n ingredients = event[\"ingredients\"]\n search_time = event[\"search_time\"]\n picture_url = event[\"picture_url\"]\n user_id = event[\"user_id\"]\n\n dynamodb = boto3.resource('dynamodb', region_name='us-east-1',\n aws_secret_access_key=\"*\",\n aws_access_key_id=\"*\")\n recipe_table = dynamodb.Table(\"recipe\")\n visitors_table = dynamodb.Table(\"user\")\n\n host = '*'\n region = 'us-east-1'\n service = 'es'\n access_access_key_id = \"*\"\n access_secrect_access_key = \"*\"\n awsauth = AWS4Auth(access_access_key_id, access_secrect_access_key, region, service)\n\n # fuzzy search setting:\n # fuzziness: allow up to 2 characters to change\n # fuzzy_max_expansions: expand up to 50 characters for fuzzy matches\n # fuzzy_prefix_length: 1 character at the beginning of terms should not be changed for fuzzy matches\n json_q = json.dumps({\n \"query\": {\n \"query_string\": {\n \"query\": \" \".join(ingredients),\n \"fuzziness\": 2,\n \"default_operator\": \"OR\",\n \"fuzzy_max_expansions\": 50,\n \"fuzzy_prefix_length\": 1,\n },\n },\n \"size\": 20\n })\n\n headers = {'Content-Type': 'application/json'}\n r = requests.get(host + \"/\" + \"recipes\" + \"/_search\", auth=awsauth, data=json_q, headers=headers)\n res = json.loads(r.text)\n\n reply = \"\"\n for i in range(len(res[\"hits\"][\"hits\"])):\n try:\n recipe_id = res[\"hits\"][\"hits\"][i][\"_id\"]\n response = recipe_table.query(KeyConditionExpression=Key(\"recipe_id\").eq(recipe_id))\n reply = json.dumps({\n \"recipe_id\": recipe_id,\n \"minutes\": str(response[\"Items\"][0][\"minutes\"]),\n \"name\": response[\"Items\"][0][\"name\"],\n \"tags\": response[\"Items\"][0][\"tags\"],\n })\n except Exception as e:\n print(e)\n\n new_history = {\n 'picture_url': picture_url,\n 'search_time': search_time,\n 'query': ' '.join(ingredients)\n }\n response = visitors_table.get_item(Key={\"user_id\": user_id})\n user = (response[\"Item\"] if \"Item\" in response else None)\n if user == None:\n item = {\"user_id\": user_id, }\n response = visitors_table.put_item(Item=item)\n response = visitors_table.get_item(Key={\"user_id\": user_id})\n user = (response[\"Item\"] if \"Item\" in response else None)\n\n query_history = user[\"query_history\"] if \"query_history\" in user else []\n query_history.append(new_history)\n query_history_len = len(query_history)\n if query_history_len > 10:\n query_history = query_history[query_history_len - 10:]\n\n response = visitors_table.update_item(Key={\"user_id\": user_id},\n UpdateExpression=\"set query_history=:h\",\n ExpressionAttributeValues={\":h\": query_history})\n return {\n 'statusCode': 200,\n 'body': json.dumps(reply)\n }\n","repo_name":"tonywyb/AIChef","sub_path":"backend/searchRecipe.py","file_name":"searchRecipe.py","file_ext":"py","file_size_in_byte":3251,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"41047040218","text":"#!/usr/bin/env python\n\nimport sys\nimport re\nimport json\nfrom operator import add\n\nfrom pyspark import SparkContext, SparkConf\nfrom pyspark.sql import SQLContext,Row\nconf=SparkConf().setAppName(\"TransactionEvaluation\")\nsc=SparkContext(conf=conf)\nsqlContext=SQLContext(sc)\nif __name__ == \"__main__\":\n if len(sys.argv) != 3:\n print(\"Usage: need two files as parameters 1. Input_StartOfDay_Positions.txt 2. Input_Transactions.txt\", file=sys.stderr)\n sys.exit(-1)\n\n # read Input_StartOfDay_Positions.txt ignoring column header\n lines = sc.textFile(sys.argv[1])\n dataLines = lines.filter(lambda h: \"Instrument\" not in h)\n inpStartDayPosition = dataLines.map(lambda l: l.split(\",\"))\n dfInpStartDayPosition = inpStartDayPosition.toDF([\"Instrument\", \"Account\", \"AccountType\", \"Quantity\"])\n\n # read Input_Transactions.txt\n inpTransactionjsonRDD = sc.wholeTextFiles(sys.argv[2]).map(lambda x : x[1])\n js = inpTransactionjsonRDD.map(lambda x: re.sub(r\"\\s+\", \"\", x, re.UNICODE))\n transactions = sqlContext.jsonRDD(js)\n # Join both datasets and get aggregated transaction value (buy and sell) for complete day.\n transPositionJoin = dfInpStartDayPosition.join(transactions, dfInpStartDayPosition.Instrument == transactions.Instrument, 'leftouter').drop(transactions.Instrument).drop(transactions.TransactionId)\n transPositionJoinAgg = transPositionJoin.groupBy(\"Instrument\",\"Account\",\"AccountType\",\"Quantity\",\"TransactionType\").agg({\"TransactionQuantity\": \"sum\"})\n\n def calculateTransactions(line):\n instrument = line[0]\n account = int(line[1])\n accountType = line[2]\n quantity = int(line[3])\n transactionType = line[4]\n sumTransactionQuantity = line[5]\n if accountType == \"E\" and transactionType == \"B\":\n sumTransactionQuantity = sumTransactionQuantity * -1\n if accountType == \"I\" and transactionType == \"S\":\n sumTransactionQuantity = sumTransactionQuantity * -1\n return (instrument, account, accountType, quantity, sumTransactionQuantity)\n\n transPositionJoinDelta = transPositionJoinAgg.map(calculateTransactions)\n dfTransPositionJoinDelta = transPositionJoinDelta.toDF([\"instrument\", \"account\", \"accountType\", \"quantity\", \"delta\"])\n dfTransPositionJoinDeltaAgg = dfTransPositionJoinDelta.groupBy(\"instrument\", \"account\", \"accountType\", \"quantity\").agg({\"delta\": \"sum\"}).withColumnRenamed(\"sum(delta)\",\"delta\").na.fill(0)\n dfEndofDayPosition = dfTransPositionJoinDeltaAgg.select(dfTransPositionJoinDeltaAgg['instrument'],dfTransPositionJoinDeltaAgg['account'],dfTransPositionJoinDeltaAgg['accountType'],dfTransPositionJoinDeltaAgg['quantity'] - dfTransPositionJoinDeltaAgg['delta'], dfTransPositionJoinDeltaAgg['delta'])\n rddEndofDayPositionAbs = dfEndofDayPosition.map(lambda r : Row(instrument=r[0], account=r[1], accountType=r[2], quantity=r[3], delta=r[4], absDelta=abs(r[4])))\n dfEndofDayPositionAbs = sqlContext.createDataFrame(rddEndofDayPositionAbs)\n dfEndofDayPositionSorted = dfEndofDayPositionAbs.sort(\"absDelta\", ascending=False)\n dfEndofDayPositionSortedOutput = dfEndofDayPositionSorted.select(dfEndofDayPositionSorted['instrument'],dfEndofDayPositionSorted['account'],dfEndofDayPositionSorted['accountType'],dfEndofDayPositionSorted['quantity'],dfEndofDayPositionSorted['delta'])\n dfEndofDayPositionSortedOutput.rdd.map(lambda l: (l[0].encode('ascii', 'ignore'),l[1],l[2].encode('ascii', 'ignore'),l[3],l[4])).saveAsTextFile(\"/user/training/EndofDayPosition_Output.txt\")\n\n #to display largest and lowest net transaction volumes for the day\n print(\"largest & lowest transactions:\", dfEndofDayPositionSortedOutput.head(2))\n\n #sqlContext.stop()\n sys.exit(0)\n","repo_name":"sagarGitcode/SagarKamble_Python","sub_path":"spark_transactions_evaluation.py","file_name":"spark_transactions_evaluation.py","file_ext":"py","file_size_in_byte":3735,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"5542373178","text":"import requests\nimport json\nimport time\nimport random\nimport pandas as pd\nimport pymysql\n\ndef connection():\n conn = pymysql.connect(\n host='localhost',\n user='root',\n password='admin',\n db='circuit',\n charset='utf8mb4',\n cursorclass=pymysql.cursors.DictCursor\n )\n return conn\n\n\ndef get_job():\n conn = connection()\n cursor = conn.cursor()\n sql_str = \"SELECT DISTINCT com_name FROM job_shanghai_clean_final_3 WHERE job_address = '上海'\"\n cursor.execute(sql_str)\n results = cursor.fetchall()\n cursor.close()\n conn.close()\n return results\n\ndef get_part_name(org_name):\n url = \"https://restapi.amap.com/v3/place/text\"\n params = {\n 'key': '1f950b2dd2067775c0a1de7be920cda0',\n 'keywords': org_name,\n 'types': '科教文化服务',\n 'city': '上海',\n # 'offset': 10,\n # 'output':'JSON',\n }\n headers = {\n 'Cookie': 'BAIDUID=FBAA261874C5A1FCE6DBA9B6FA4F06B5:FG=1'\n }\n try:\n response = requests.request(\"GET\", url, headers=headers, params=params,timeout=10)\n # print(response.text)\n rs_json = json.loads(response.text)\n # print(rs_json)\n\n part_name = str(rs_json['pois'][0]['adname'])\n # print(part_name)\n # print('所在行政区{}'.format(part_name))\n return part_name\n except Exception as e:\n print(e)\n return ''\n\ndef update_data(com_name, job_address):\n conn = connection()\n cursor = conn.cursor()\n sql = \"update `job_shanghai_clean_final_3` set job_address='{}' where com_name = '{}'\".format(\n job_address, com_name)\n # print(sql)\n cursor.execute(sql)\n conn.commit()\n cursor.close()\n conn.close()\n\ndef main():\n results = get_job()\n print('数据输入完成{}'.format(len(results)))\n i=0\n for rs in results:\n # org_id = row['org_id']\n org_name = rs['com_name']\n if type(org_name) is float:\n print('无中文')\n part_name=''\n else:\n org_name = org_name.strip().replace('。','')\n # lng,lat = get_lng_lat(org_name)\n part_name = get_part_name(org_name)\n if '[' in part_name or part_name =='':\n print('{}找不到地区'.format(org_name))\n part_name = ''\n continue\n part_name = '上海-'+part_name\n print(\"{}是：{}\".format(org_name, part_name))\n # update_date_lng_lat(shop_id,lng,lat)\n # print(lng,lat)\n update_data(org_name, part_name)\n # break\n time.sleep(0.4)\n\nmain()","repo_name":"SherryLee725/circuit_talent_needs","sub_path":"job_place.py","file_name":"job_place.py","file_ext":"py","file_size_in_byte":2611,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"14448207170","text":"import requests\nfrom bs4 import BeautifulSoup\nimport csv\nfrom time import sleep\nimport sys\nsys.path.append('../../classes')\nimport notify\n\n# import ssl\n# ssl._create_default_https_context = ssl._create_unverified_context\n\n\ndef scrape_for_page(url):\n flg = True\n\n print(\"page\\t\" + url)\n\n sleep(1)\n\n r = requests.get(url,verify=False)\n\n # htmlをBeautifulSoupで扱う\n soup = BeautifulSoup(r.text, \"lxml\")\n\n if soup.find(class_=\"view-content\") == None:\n return False\n\n arr_a = soup.find(class_=\"view-content\").find_all(\"a\")\n\n if len(arr_a) > 0:\n for element_a in arr_a:\n\n href = element_a.get(\"href\")\n\n sleep(1)\n\n html = requests.get(\"https://www.iiif.ku-orcas.kansai-u.ac.jp/\"+href,verify=False)\n\n # htmlをBeautifulSoupで扱う\n soup = BeautifulSoup(html.text, \"lxml\")\n\n aas = soup.find_all(\"a\")\n\n manifest = \"\"\n\n for a in aas:\n href = a.get(\"href\")\n if href and \"manifest.json\" in href:\n manifest = href\n break\n\n if manifest != \"\":\n\n print(manifest)\n manifest_arr.append(manifest)\n\n if len(manifest_arr) % 100 == 1:\n notify.Notify.send(\"kansai\\t\"+str(len(manifest_arr)))\n\n else:\n flg = False\n\n return flg\n\n\nif __name__ == '__main__':\n\n manifest_arr = []\n\n output_path = \"data/manifest_list.csv\"\n\n url_array = [\n \"https://www.iiif.ku-orcas.kansai-u.ac.jp/books?page=\", \n \"https://www.iiif.ku-orcas.kansai-u.ac.jp/osaka_gadan?page=\", \n \"https://www.iiif.ku-orcas.kansai-u.ac.jp/hakuen_bunko?page=\", \n \"https://www.iiif.ku-orcas.kansai-u.ac.jp/hakuen_yinpu?page=\"\n ]\n\n for base_url in url_array:\n\n loop_flg = True\n page = 1\n\n while loop_flg:\n url = base_url + str(page)\n\n loop_flg = scrape_for_page(url)\n\n page += 1\n\n f = open(output_path, 'w')\n\n writer = csv.writer(f, lineterminator='\\n')\n writer.writerow([\"Manifest\"])\n\n for manifest in manifest_arr:\n writer.writerow([manifest])\n\n f.close()\n","repo_name":"nakamura196/iiif","sub_path":"src/collections/kansai/createManifestList.py","file_name":"createManifestList.py","file_ext":"py","file_size_in_byte":2199,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"77"}
+{"seq_id":"34719936881","text":"from PySide6.QtWidgets import QWidget, QLabel, QVBoxLayout, QHBoxLayout, QPushButton, QListWidget, \\\n QInputDialog, QLineEdit\n\nfrom GlobalSettings import global_settings\n\n\nclass SecondaryFolderChooseWidget(QWidget):\n def __init__(self):\n super(SecondaryFolderChooseWidget, self).__init__()\n self.setup_ui()\n\n def setup_ui(self):\n layout_main = QVBoxLayout()\n layout_main.setContentsMargins(0, 0, 0, 0)\n layout_main.setSpacing(0)\n layout_title = QHBoxLayout()\n layout_title.setContentsMargins(0, 0, 0, 0)\n layout_title.setSpacing(0)\n title_label = QLabel(\"需要处理的二级文件夹名称\")\n title_label.setStyleSheet(\"font-size: 15px; color: #000000;\")\n layout_title.addWidget(title_label)\n layout_title.addStretch(1)\n self.add_folder_button = QPushButton(\"添加\")\n self.add_folder_button.setStyleSheet(f\"\"\"\n QPushButton {{\n font-size: 14px;\n color: #000000;\n background-color: #FFFFFF;\n border: 1px solid #000000;\n border-radius: 5px;\n }}\"\"\")\n self.add_folder_button.setFixedSize(60, 30)\n self.add_folder_button.clicked.connect(self.add_folder_button_clicked)\n layout_title.addWidget(self.add_folder_button)\n layout_title.addSpacing(5)\n self.delete_folder_button = QPushButton(\"删除\")\n self.delete_folder_button.setStyleSheet(f\"\"\"\n QPushButton {{\n font-size: 14px;\n color: #000000;\n background-color: #FFFFFF;\n border: 1px solid #000000;\n border-radius: 5px;\n }}\"\"\")\n self.delete_folder_button.setFixedSize(60, 30)\n self.delete_folder_button.clicked.connect(self.delete_folder_button_clicked)\n layout_title.addWidget(self.delete_folder_button)\n layout_main.addLayout(layout_title)\n layout_main.addSpacing(5)\n layout_hint = QHBoxLayout()\n layout_hint.setContentsMargins(0, 0, 0, 0)\n layout_hint.setSpacing(0)\n hint_label = QLabel(\"如输入”同人志“，则所有名称中含有”同人志“的二级文件夹都会被处理。\\n如果此处不输入任何内容，则全部适用是否复制未选二级文件夹设置项。\")\n hint_label.setStyleSheet(\"font-size: 12px; color: #000000;\")\n layout_hint.addWidget(hint_label)\n layout_main.addLayout(layout_hint)\n layout_main.addSpacing(5)\n self.show_list = QListWidget()\n self.show_list.setStyleSheet(\"\"\"\n QListWidget {\n font-size: 12px;\n color: #000000;\n background-color: #7f7f7f;\n border: 2px solid #000000;\n border-radius: 5px;\n outline: none;\n }\n QListWidget::item {\n height: 30px;\n padding: 5px;\n background-color: #aaaaaa;\n }\n QListWidget::item:selected {\n background-color: #dddddd;\n color: #000000;\n }\"\"\")\n for name in global_settings.choose_folder_names:\n self.show_list.addItem(name)\n layout_main.addWidget(self.show_list)\n self.setLayout(layout_main)\n\n def add_folder_button_clicked(self):\n new_path = QInputDialog.getText(self, \"二级文件夹设置\", \"请输入二级文件夹筛选词\", QLineEdit.Normal)\n if new_path[0] and new_path[1]:\n for name in global_settings.choose_folder_names:\n if new_path[0] == name:\n return\n global_settings.choose_folder_names.append(new_path[0])\n self.show_list.clear()\n for name in global_settings.choose_folder_names:\n self.show_list.addItem(name)\n\n def delete_folder_button_clicked(self):\n if self.show_list.currentItem() is None:\n return\n global_settings.choose_folder_names.remove(self.show_list.currentItem().text())\n self.show_list.clear()\n for name in global_settings.choose_folder_names:\n self.show_list.addItem(name)\n","repo_name":"Overseer-Council/handleComicCollection","sub_path":"Widgets/SecondaryFolderChoose.py","file_name":"SecondaryFolderChoose.py","file_ext":"py","file_size_in_byte":4107,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"6670018486","text":"# 덧칠하기\ndef solution(n, m, section):\n answer = 0\n l = 0\n\n for i in section:\n if i <= l:\n continue\n l = i + m - 1\n answer += 1\n\n return answer\n\nprint(solution(8, 4, [2, 3, 6]))","repo_name":"surpmh/algorithms","sub_path":"Programmers/level1/161989.py","file_name":"161989.py","file_ext":"py","file_size_in_byte":226,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"12260193456","text":"#!/usr/bin/env python3\r\nimport rospy\r\nfrom std_msgs.msg import String\r\nfrom geometry_msgs.msg import Twist\r\nfrom Motors import MotorLib\r\n\r\n\r\nmotors = MotorLib.Motors()\r\n\r\ndef callback(data):\r\n rospy.loginfo(\"Received Command: {0}\\n{1}\".format(data.linear, data.angular))\r\n x = data.linear.x\r\n w = data.angular.z\r\n if (w == 0):\r\n if (x>0):\r\n motors.forward(x*10)\r\n else:\r\n motors.backward(-x*10)\r\n else:\r\n if (w>0):\r\n motors.left(w*10)\r\n else:\r\n motors.right(-w*10)\r\n\r\ndef listener():\r\n rospy.init_node('firefighter_velReceiver', anonymous=True)\r\n\r\n rospy.Subscriber(\"firefighter/cmd_vel\", Twist, callback)\r\n rospy.loginfo(\"Initialised Node\")\r\n # spin() simply keeps python from exiting until this node is stopped\r\n rospy.spin()\r\n\r\n\r\n\r\nif __name__ == '__main__':\r\n try:\r\n listener()\r\n except rospy.ROSInterruptException:\r\n pass\r\n finally:\r\n motors.exit()\r\n print(\"Finished Operation\")\r\n\r\n \r\n\r\n \r\n","repo_name":"csmithcripps/controller_firefighter","sub_path":"scripts/velReceiver.py","file_name":"velReceiver.py","file_ext":"py","file_size_in_byte":1038,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"45489534916","text":"import numpy as np\nfrom tqdm import tqdm\nfrom gensim.models import Word2Vec\nfrom nltk.tokenize import sent_tokenize\n\nfrom data_preprocessor import DataPreprocessor\n\n\nclass Word2VecWrapper:\n\n def __init__(self):\n self.data_preprocessor = DataPreprocessor()\n self.model = None\n\n def fit(self, data):\n sentences = self.data_preparing(data)\n self.model = Word2Vec(sentences, vector_size=500, window=5, min_count=5, epochs=10)\n\n def transform(self, data):\n sentences = self.data_preparing(data)\n embedded_sentences = np.array([])\n for sentence in tqdm(sentences):\n vector = [self.model.wv[word] for word in sentence\n if word in list(self.model.wv.key_to_index.keys())]\n if vector:\n vector = np.mean(vector, axis=0)\n embedded_sentences = vector if embedded_sentences.size == 0 else np.vstack((embedded_sentences, vector))\n return embedded_sentences\n\n def data_preparing(self, data):\n return [self.data_preprocessor.run_pipeline(sent) for sent in sent_tokenize(' '.join(data), 'russian')]\n","repo_name":"answerIII/Chatbot","sub_path":"src/word2vec.py","file_name":"word2vec.py","file_ext":"py","file_size_in_byte":1130,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"9963719044","text":"with open(\"inputs/09.txt\", \"r\") as f:\n data=f.read().strip()\nlines = data.split(\"\\n\")\n\ndef move_tail(r1,r2):\n (x,y),(xt,yt) = r1, r2\n dx_abs, dy_abs = abs(x-xt), abs(y-yt)\n if dx_abs>1:\n xt = xt-1 if x0:\n yt = yt-1 if y1:\n yt = yt-1 if y0:\n xt = xt-1 if x.\n'''\n\nimport os\nimport re\nimport sys\nimport time\nfrom pathlib import Path\n#from struct import pack, unpack\n\n# TODO: relative import. fix this latter.\nsys.path.append(\".\")\nfrom py import lsh\n\n\n\ndef parse_tc(path):\n parsed_tc = ''\n with open(path, 'r') as fin:\n line = fin.readline()\n while line:\n if line != \"\\n\":\n newline = line.strip()\n parsed_tc += newline + \" \"\n line = fin.readline()\n parsed_tc = re.sub(' +', ' ', parsed_tc.strip())\n return parsed_tc\n\n\n# TODO: store a single pickle file for each test\ndef preprocess_test_cases(inputdir, outputdir, singlefile=True):\n if singlefile:\n with open(JTeC_preproc_single, 'w') as fout, open(JTeC_preproc_map, 'w') as mapfile:\n for path in Path(JTeC_dir).rglob('*.java'): # java files\n tc = parse_tc(path) # parses the test files to remove line breaks and empty lines\n fout.write(tc + '\\n')\n mapfile.write(str(path.relative_to(JTeC_dir)) + '\\n')\n else:\n tcID = 1\n with open(JTeC_preproc_map, 'w') as mapfile:\n for path in Path(JTeC_dir).rglob('*.java'): # java files\n outfile = os.path.join(JTeC_preproc_dir, '{}.txt'.format(tcID))\n with open(outfile, 'w') as fout:\n tc = parse_tc(path) # parses the test files to remove line breaks and empty lines\n fout.write(tc)\n mapfile.write(str(path.relative_to(JTeC_dir)) + '\\n')\n tcID += 1\n \n\n# TODO: skip preprocessing if already done\ndef storeSignatures():\n mh_t = time.perf_counter() \n with open(mapfileloc, 'w') as mapfile, open(sigfileloc, 'w') as sigfile:\n for path in Path(JTeC_dir).rglob('*.java'): # java files\n tc = parse_tc(path) # parses the test files to remove line breaks and empty lines\n tc_shingles = set()\n for i in range(len(tc) - k + 1):\n tc_shingles.add(hash(tc[i:i + k])) \n sig = lsh.tcMinhashing((None, set(tc_shingles)), hashes)\n\n #print(sig)\n\n for hash_ in sig:\n #sigfile.write(repr(unpack('>d', hash_)[0]))\n sigfile.write(hash_)\n sigfile.write(\" \")\n sigfile.write(\"\\n\")\n\n mapfile.write(str(path.relative_to(JTeC_dir)) + '\\n')\n mh_time = time.perf_counter() - mh_t\n with open(sigtimefileloc, \"w\") as fout:\n fout.write(repr(mh_time)) \n\n\n\nif __name__ == '__main__':\n #JTeC_dir = '/home/breno/research/JTEC/JTeC-Bundle/JTeC/'\n JTeC_dir = 'scalability/input/JTeC'\n mapfileloc = 'scalability/input/JTeC_map.txt'\n sigfileloc = 'scalability/input/JTeC.sig'\n sigtimefileloc = 'scalability/input/JTeC_sigtime.txt'\n #-----\n JTeC_preproc_dir = 'scalability/input/JTeC_preproc'\n JTeC_preproc_single = 'scalability/input/JTeC_preproc_all.txt'\n JTeC_preproc_map = 'scalability/input/JTeC_preproc_all_map.txt'\n\n if not os.path.exists(JTeC_preproc_dir):\n os.makedirs(JTeC_preproc_dir)\n\n # TODO: add all FAST parameters in a config file\n # FAST parameters\n k, n, r, b = 5, 10, 1, 10\n\n hashes = [lsh.hashFamily(i) for i in range(n)]\n\n #storeSignatures()\n \n preprocess_test_cases(JTeC_dir, JTeC_preproc_dir, singlefile=True)\n #preprocess_test_cases(JTeC_dir, JTeC_preproc_dir, singlefile=False)","repo_name":"Liviocsouza/FAST","sub_path":"tools/prepare-scalability-input.py","file_name":"prepare-scalability-input.py","file_ext":"py","file_size_in_byte":4183,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"77"}
+{"seq_id":"6332872713","text":"\"\"\"project URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/2.0/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.conf import settings\nfrom django.contrib import admin\nfrom django.views.generic import TemplateView\nfrom django.urls import path, re_path, include\nfrom time import sleep\n\n\ndef sample_api_view(request):\n from django.http import JsonResponse\n\n sleep(2)\n return JsonResponse(\n {\n \"message\": \"\"\"This message is coming from the backend.\n The django view is inside `project/urls.py` and the redux code is in `react-app/src/js/welcome/(actions|reducers).js`.\n Please remove them when starting your project :]\"\"\"\n }\n )\n\n\nfrontend_urls = [\n re_path(r\"^.*$\", TemplateView.as_view(template_name=\"frontend/index.html\")),\n]\n\n\nif not settings.DEBUG:\n frontend_urls.insert(0, path(\"\", include(\"pwa.urls\")))\n\n# if you wish to test the PWA on dev, uncomment the following lines,\n# so that django serves static files.\n# remember to built the frontend manually and run collectstatic as well.\n# from django.views.static import serve\n# frontend_urls += [\n# re_path(r'^static/(?P.*)$', serve, {'document_root': settings.STATIC_ROOT})\n# ]\n\nurlpatterns = [\n path(\"admin/\", admin.site.urls),\n path(\"api/sample-api-view/\", sample_api_view),\n] + frontend_urls\n","repo_name":"labcodes/django-react-boilerplate","sub_path":"project/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1877,"program_lang":"python","lang":"en","doc_type":"code","stars":38,"dataset":"github-code","pt":"77"}
+{"seq_id":"28236063166","text":"# Escape sequence\ntabby_cat = \"\\tI'm tabbed in.\"\npersian_cat = \"I'm split\\non a line.\"\n# \\n stands for ASCII linefeed.\n\nbackslash_cat = \"I'm \\\\ a \\\\ cat.\"\n# \\\\ symbolieses backslash.\n\nfat_cat = \"\"\"\nI'll do a list:\n\\t* Cat food\n\\t* Fishies\n\\t* catnip\\n\\t* Grass\n\"\"\"\n# \\t symbolises horizontal tab (TAB)\n\nprint(tabby_cat)\nprint(persian_cat)\nprint(backslash_cat)\nprint(fat_cat)\n\n# test a few escape sequence below:\nbackslash = \"\\\\ backslash\"\nsingle_quote = \"\\' single-quote\"\ndouble_quote = \"\\\" double-quote\"\nASCII_bell = \"\\a ASCII bell\"\nASCII_backspace = \"\\b ASCII backspace\"\nASCII_formfeed = \"\\f ASCII Formfeed\"\nASCII_linefeed = \"\\n ASCII Linefeed\"\nCarriage_return = \"\\r Carriage return\"\nHorizontal_return = \"\\t Horizontal return\"\nASCII_vertical_tab = \"\\v ASCII vertical tab\"\n\nprint(backslash)\nprint(single_quote)\nprint(double_quote)\nprint(ASCII_bell)\nprint(ASCII_backspace)\nprint(ASCII_formfeed)\nprint(ASCII_linefeed)\nprint(Carriage_return)\nprint(Horizontal_return)\nprint(ASCII_vertical_tab)\nprint('''\nHow will it appear\nIf I change double-quotes\ninto single-quotes?\n''')\n# as far as it appears,\n# three single-quotes serve as the same thing as three double-quotes.\n# Am I right?\n\nanswer = \"\"\"\n\\b\\\"I do not have any apple, but I have other stuff.\\\"\n\\t* Pineaplle\\n\\t* Banana\\n\\t* Pear\\n\\t* Orange\\n\\t* Tomato\\r\n\\\"So do you want any of these?\\\"\n\"\"\"\n\nprint('Mc\\'Donalds asked: \"How many apples do you have?\"')\nprint(f\"Irvin answerd: {answer}\")\nprint(\"What a sad story!\")\n","repo_name":"espererwyd/PythonFile","sub_path":"ex10.py","file_name":"ex10.py","file_ext":"py","file_size_in_byte":1468,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"30198477392","text":"import streamlit as slt\n\n# import requests\n# from bs4 import BeautifulSoup\n\n# baseUrl = \"http://gepia.cancer-pku.cn/detail.php\"\n# query = {\"gene\": \"ERBB2\", \"tag\": \"expdiy\"}\n\n# res = requests.post(url=baseUrl, data=query)\n# # response.text\n# # slt.markdown(response.text[17:], unsafe_allow_html=True)\n# # response.text[17:]\n# soup = BeautifulSoup(res.text, \"html.parser\")\n# soup.current_data\n# with open(\"temp.html\", \"wb\") as fout:\n# fout.write(res.content)\n\nimport gepia\n\nbp = gepia.boxplot()\nslt.text(str(bp.showParams()))\n\nslt.write(gepia.CANCERType)\nbp.setParam(\"dataset\", gepia.CANCERType)\nresult = bp.query()\n\n# IFrame(result, width=500, height=500)\n","repo_name":"TeddyHuang-00/Bioinformatics-Lab","sub_path":"hw7/cancer.py","file_name":"cancer.py","file_ext":"py","file_size_in_byte":659,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"9991355992","text":"from django.shortcuts import render, HttpResponse, redirect\nfrom .models import *\nfrom django.template.defaulttags import register\n# Create your views here.\nfrom quiz.dto import *\nfrom quiz.services import *\nimport re\nimport copy\n@register.filter\ndef get_answer_with_uuid(value):\n uuid = 1\n result = []\n for a in value:\n result.append({\"answer\":a,\"uuid\":uuid})\n uuid+=1\n return result\n\n\n@register.filter\ndef check_old_answer(uuid,cookies):\n for a in cookies:\n print(a)\n if a == \"uuid\"+str(uuid):\n b = cookies.get(a).split(\" \")\n buffer = []\n for a in b:\n try:\n buffer.append(int(a))\n except Exception:\n pass\n return buffer\n\n\ndef start(request):\n\n ren = render(request, \"start.html\")\n for a in request.COOKIES:\n ren.delete_cookie(a)\n return ren\n\n\ndef questions(request, number):\n def save_result():\n '''\n Записывает переданные пользователем значения в cookie\n '''\n def get_form_request_get():\n '''\n функция возврощает результат ответа пользователя\n '''\n result = \"\"\n if request.GET.get(\"previous\") != None:\n return result\n result = \" \"\n for a in request.GET:\n if a != \"previous\":\n result += str(a)+\" \"\n return result\n if get_form_request_get() != \"\":\n questions_render.set_cookie(\"uuid\"+str(int(number)-1), get_form_request_get())\n new_question_url = {\"have\": False, \"url\": \"\"}\n old_question_url = {\"have\": False, \"url\": \"\"}\n def check_new_question_url(new_question_url):\n '''\n функция возвращает url на новый вопрос если такое возможно\n '''\n try:\n Question.objects.get(uuid=int(number)+1)\n return {\"have\": True, \"url\":\"/questions_\"+str(int(number)+1), }\n except Exception:\n pass\n\n def check_old_question_url(old_question_url):\n '''\n функция возвращает url на старый вопрос если такое возможно\n '''\n try:\n Question.objects.get(uuid=int(number)-1)\n return {\"have\": True, \"url\":\"/questions_\"+str(int(number)-1),}\n except Exception:\n pass\n\n try:\n question = Question.objects.get(uuid = int(number))\n answer = Choice.objects.filter(Question = question)\n new_question_url = check_new_question_url(new_question_url)\n old_question_url = check_old_question_url(old_question_url)\n except Exception as e:\n def check_last_number():\n '''\n функция возвращает в виде числа последний\n записанный uuid ответа пользователя\n '''\n last_count = \"\"\n max = 0\n for b in request.COOKIES:\n last_count = re.findall(r\"[0-9]{1,3}\", b)\n if int(last_count[0])>int(max):\n max = copy.copy(last_count[0])\n return int(max)\n result_user = \"\"\n for a in request.GET:\n result_user += a + \" \"\n count = check_last_number() + 1\n request.COOKIES['uuid' + str(count)] = result_user\n return result(request)\n\n\n context = {\n \"question\":question,\n \"answer\":answer,\n \"new_question_url\":new_question_url,\n \"old_question_url\":old_question_url,\n 'cookies':request.COOKIES\n }\n questions_render = render(request, 'questions.html', context)\n save_result()\n return questions_render\n\ndef result(request):\n def answer():\n '''\n функция возвращает словарь типа AnswersDTO\n с ответами пользователя считанного из куки\n '''\n list_AnswersDTO = []\n #записываем последний ответ на вопрос\n for a in request.COOKIES:\n try:\n number = re.findall(r\"[0-9]{1,3}\", a)\n one_answer = request.COOKIES.get(\"uuid\"+number[0])\n list_AnswersDTO.append(AnswerDTO(question_uuid=number,\n choices = re.findall(r\"[0-9]{1,3}\", one_answer)))\n except Exception:\n pass\n return list_AnswersDTO\n\n def question():\n '''\n функция возвращает словарь типа QuestionDTO\n с всеми вопросами\n '''\n # заполянме ChoiceDTO и Question\n list_QuestionDTO = []\n for a in Question.objects.all():\n list_ChoiceDTO = []\n count_list_ChoiceDTO = 1\n for b in Choice.objects.filter(Question = a):\n list_ChoiceDTO.append(ChoiceDTO(uuid = str(count_list_ChoiceDTO),\n text = b.text,\n is_correct=b.is_correct))\n count_list_ChoiceDTO += 1\n list_QuestionDTO.append(QuestionDTO(uuid = str(a.uuid),\n text = a.text,\n choices = list_ChoiceDTO))\n return list_QuestionDTO\n quiz = QuizResultService(QuizDTO(\n uuid = \"1\",\n title = \"Тест по теме язык python\",\n questions = question()),\n AnswersDTO(\n quiz_uuid = \"1\",\n answers = answer(),\n ))\n\n return render(request, \"finish.html\", {\n \"result\": str(quiz.get_result()),\n })","repo_name":"severmen/simbirsoft_test-task","sub_path":"quiz_app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5863,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"22969137369","text":"from big_vision.pp import utils\nfrom big_vision.pp.registry import Registry\nimport numpy as np\nimport tensorflow as tf\n\n\n@Registry.register(\"preprocess_ops.rgb_to_grayscale_to_rgb\")\n@utils.InKeyOutKey(indefault=\"image\", outdefault=\"image\")\ndef get_rgb_to_grayscale_to_rgb():\n def _rgb_to_grayscale_to_rgb(image):\n return tf.image.grayscale_to_rgb(tf.image.rgb_to_grayscale(image))\n return _rgb_to_grayscale_to_rgb\n\n\n@Registry.register(\"preprocess_ops.nyu_eval_crop\")\ndef get_nyu_eval_crop():\n \"\"\"Crops labels and image to valid eval area.\"\"\"\n # crop_h = slice(45, 471)\n # crop_w = slice(41, 601)\n crop_h_start = 54\n crop_h_size = 426\n crop_w_start = 41\n crop_w_size = 560\n\n def _pp(data):\n tf.debugging.assert_equal(tf.shape(data[\"labels\"]), (480, 640, 1))\n tf.debugging.assert_equal(tf.shape(data[\"image\"]), (480, 640, 3))\n data[\"labels\"] = tf.slice(data[\"labels\"],\n [crop_h_start, crop_w_start, 0],\n [crop_h_size, crop_w_size, -1])\n data[\"image\"] = tf.slice(data[\"image\"],\n [crop_h_start, crop_w_start, 0],\n [crop_h_size, crop_w_size, -1])\n return data\n return _pp\n\n\n@Registry.register(\"preprocess_ops.nyu_depth\")\n@utils.InKeyOutKey(indefault=\"depth\", outdefault=\"labels\")\ndef get_nyu_depth():\n \"\"\"Preprocesses NYU depth data.\"\"\"\n def _pp(depth):\n return tf.expand_dims(tf.cast(depth, tf.float32), -1)\n return _pp\n\n\n@Registry.register(\"preprocess_ops.coco_panoptic\")\ndef get_coco_panoptic_pp():\n \"\"\"COCO-panoptic: produces a mask with labels and a mask with instance ids.\n\n Instance channel will have values between 1 and N, and -1 for non-annotated\n pixels.\n\n Returns:\n COCO panoptic preprocessign op.\n \"\"\"\n def _coco_panoptic(data):\n instance_ids = tf.cast(data[\"panoptic_objects\"][\"id\"], tf.int32)\n instance_labels = tf.cast(data[\"panoptic_objects\"][\"label\"], tf.int32)\n\n # Convert image with ids split in 3 channels into a an integer id.\n id_mask = tf.einsum(\n \"hwc,c->hw\",\n tf.cast(data[\"panoptic_image\"], tf.int32),\n tf.constant([1, 256, 256**2], tf.int32))\n\n # Broadcast into N boolean masks one per instance_id.\n n_masks = tf.cast(\n id_mask[:, :, None] == instance_ids[None, None, :], tf.int32)\n\n # Merge into a semantic and an instance id mask.\n # Note: pixels which do not belong to any mask, will have value=-1\n # which creates an empty one_hot masks.\n # Number instances starting at 1 (0 is treated specially by make_canonical).\n instance_idx = tf.range(tf.shape(instance_ids)[-1])\n instances = tf.einsum(\"hwc,c->hw\", n_masks, instance_idx + 1)\n semantics = tf.einsum(\"hwc,c->hw\", n_masks, instance_labels + 1)\n\n data[\"instances\"] = instances[:, :, None]\n data[\"semantics\"] = semantics[:, :, None]\n return data\n\n return _coco_panoptic\n\n\n@Registry.register(\"preprocess_ops.make_canonical\")\n@utils.InKeyOutKey(indefault=\"labels\", outdefault=\"labels\")\ndef get_make_canonical(random=False, main_sort_axis=\"y\"):\n \"\"\"Makes id mask ordered from left to right based on the center of mass.\"\"\"\n # By convention, instances are in the last channel.\n def _make_canonical(image):\n \"\"\"Op.\"\"\"\n instimg = image[..., -1]\n\n # Compute binary instance masks. Note, we do not touch 0 and neg. ids.\n ids = tf.unique(tf.reshape(instimg, [-1])).y\n ids = ids[ids > 0]\n n_masks = tf.cast(\n instimg[None, :, :] == ids[:, None, None], tf.int32)\n\n if not random:\n f = lambda x: tf.reduce_mean(tf.cast(tf.where(x), tf.float32), axis=0)\n centers = tf.map_fn(f, tf.cast(n_masks, tf.int64), dtype=tf.float32)\n centers = tf.reshape(centers, (tf.shape(centers)[0], 2))\n major = {\"y\": 0, \"x\": 1}[main_sort_axis]\n perm = tf.argsort(\n centers[:, 1 - major] +\n tf.cast(tf.shape(instimg)[major], tf.float32) * centers[:, major])\n n_masks = tf.gather(n_masks, perm)\n else:\n n_masks = tf.random.shuffle(n_masks)\n\n idx = tf.range(tf.shape(ids)[0])\n can_mask = tf.einsum(\"chw,c->hw\", n_masks, idx + 2) - 1\n # Now, all 0 and neg. ids have collapsed to -1. Thus, we recover 0 id from\n # the original mask.\n can_mask = tf.where(instimg == 0, 0, can_mask)\n return tf.concat([image[..., :-1], can_mask[..., None]], axis=-1)\n\n return _make_canonical\n\n\n@Registry.register(\"preprocess_ops.inception_box\")\ndef get_inception_box(\n *, area=(0.05, 1.0), aspect=(0.75, 1.33), min_obj_cover=0.0,\n outkey=\"box\", inkey=\"image\"):\n \"\"\"Creates an inception style bounding box which can be used to crop.\"\"\"\n def _inception_box(data):\n _, _, box = tf.image.sample_distorted_bounding_box(\n tf.shape(data[inkey]),\n area_range=area,\n aspect_ratio_range=aspect,\n min_object_covered=min_obj_cover,\n bounding_boxes=(data[\"objects\"][\"bbox\"][None, :, :]\n if min_obj_cover else tf.zeros([0, 0, 4])),\n use_image_if_no_bounding_boxes=True)\n # bbox is [[[y0,x0,y1,x1]]]\n data[outkey] = (box[0, 0, :2], box[0, 0, 2:] - box[0, 0, :2])\n return data\n return _inception_box\n\n\n@Registry.register(\"preprocess_ops.crop_box\")\n@utils.InKeyOutKey(with_data=True)\ndef get_crop_box(*, boxkey=\"box\"):\n \"\"\"Crops an image according to bounding box in `boxkey`.\"\"\"\n def _crop_box(image, data):\n shape = tf.shape(image)[:-1]\n begin, size = data[boxkey]\n begin = tf.cast(begin * tf.cast(shape, tf.float32), tf.int32)\n size = tf.cast(size * tf.cast(shape, tf.float32), tf.int32)\n begin = tf.concat([begin, tf.constant((0,))], axis=0)\n size = tf.concat([size, tf.constant((-1,))], axis=0)\n crop = tf.slice(image, begin, size)\n # Unfortunately, the above operation loses the depth-dimension. So we need\n # to restore it the manual way.\n crop.set_shape([None, None, image.shape[-1]])\n return crop\n return _crop_box\n\n\n@Registry.register(\"preprocess_ops.randu\")\ndef get_randu(key):\n \"\"\"Creates a random uniform float [0, 1) in `key`.\"\"\"\n def _randu(data):\n data[key] = tf.random.uniform([])\n return data\n return _randu\n\n\n@Registry.register(\"preprocess_ops.det_fliplr\")\n@utils.InKeyOutKey(with_data=True)\ndef get_det_fliplr(*, randkey=\"fliplr\"):\n \"\"\"Flips an image horizontally based on `randkey`.\"\"\"\n # NOTE: we could unify this with regular flip when randkey=None.\n def _det_fliplr(orig_image, data):\n flip_image = tf.image.flip_left_right(orig_image)\n flip = tf.cast(data[randkey] > 0.5, orig_image.dtype)\n return flip_image * flip + orig_image * (1 - flip)\n return _det_fliplr\n\n\n@Registry.register(\"preprocess_ops.strong_hash\")\n@utils.InKeyOutKey(indefault=\"tfds_id\", outdefault=\"tfds_id\")\ndef get_strong_hash():\n \"\"\"Preprocessing that hashes a string.\"\"\"\n def _strong_hash(string):\n return tf.strings.to_hash_bucket_strong(\n string,\n np.iinfo(int).max, [3714561454027272724, 8800639020734831960])\n return _strong_hash\n","repo_name":"google-research/big_vision","sub_path":"big_vision/pp/proj/uvim/pp_ops.py","file_name":"pp_ops.py","file_ext":"py","file_size_in_byte":6938,"program_lang":"python","lang":"en","doc_type":"code","stars":1152,"dataset":"github-code","pt":"77"}
+{"seq_id":"15546991541","text":"finEntrada = False\nnombres = []\n\ndef licitacion(*nombres, equipo):\n print(\"equipo \" + equipo + \" :\")\n if(len(*nombres)==0):\n print(\"sin Jugadores\")\n else:\n for c in nombres:\n print(\"\\t\" + str(c))\n\nwhile(finEntrada == False):\n print(\"Escriba 0 si ya no desea incorporar mas jugadores\")\n if(input()=='0'):\n finEntrada =True\n break\n print(\"introduzca un miembro y pulse enter\")\n nombres.append(input())\n\n\n\n\nlicitacion(nombres, equipo=\"buff\")\n","repo_name":"paulatw20/PythonSh","sub_path":"Funciones/funciones2.py","file_name":"funciones2.py","file_ext":"py","file_size_in_byte":499,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"19066722038","text":"#!/usr/bin/env python\n\n\"\"\"\nCV2 video capture example from Pure Thermal 1\n\"\"\"\n\ntry:\n import cv2\nexcept ImportError:\n print(\"ERROR python-opencv must be installed\")\n exit(1)\n\nclass OpenCvCapture(object):\n \"\"\"\n Encapsulate state for capture from Pure Thermal 1 with OpenCV\n \"\"\"\n\n def __init__(self):\n cv2_cap = cv2.VideoCapture(0)\n if cv2_cap.isOpened():\n print(\"Thermal Camera Found\")\n if not cv2_cap.isOpened():\n print(\"Thermal Camera not found!\")\n exit(1)\n\n self.cv2_cap = cv2_cap\n\n def show_video(self):\n \"\"\"\n Run loop for cv2 capture from lepton\n \"\"\"\n cv2.namedWindow(\"lepton\", cv2.WINDOW_NORMAL)\n print(\"Running, ESC or Ctrl-c to exit...\")\n while True:\n ret, img = self.cv2_cap.read()\n img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n #_, binary = cv2.threshold(img, 88, 255, cv2.THRESH_BINARY)\n #im3 = cv2.bitwise_and(img, binary)\n #im3[binary == 0] = 0\n blur = cv2.GaussianBlur(img, (5,5),cv2.BORDER_DEFAULT)\n canny = cv2.Canny(blur, 10, 50)\n contours = cv2.findContours(canny, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)[0]\n cnt = contours[4]\n cv2.drawContours()\n\n if ret == False:\n print(\"Error reading image\")\n break\n\n cv2.imshow(\"lepton\", cv2.resize(img, (640, 480)))\n if cv2.waitKey(5) == 27:\n break\n\n cv2.destroyAllWindows()\n\nif __name__ == '__main__':\n OpenCvCapture().show_video()\n","repo_name":"maykef/DIY_Fluorometer","sub_path":"archive/opencv_capture_thermal.py","file_name":"opencv_capture_thermal.py","file_ext":"py","file_size_in_byte":1613,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"14804153419","text":"# -*- coding: utf-8 -*-\n'''\nCreated on Oct 22, 2017\n\n@author: Administrator\n'''\n\nimport telebot\nimport config\nimport csv\nimport time\nimport datetime\nimport logging\n\n# bot plugins\nimport start\n# import temperature\nimport audio\nimport sotd_song\n# import cotd_plugin\nimport announce\nimport roll\nimport webcomics\nimport slap_plugin\nimport likezor_plugin\nimport btc_plugin\nimport yankovic_plugin\nimport rip_plugin\nimport mini_project_number_guess\nimport mini_project_magic_ball\nimport mini_project_hangman\n\ncommands = { # command description used in the \"help\" command\n 'start': 'Get used to the bot',\n 'help': 'Gives you information about the available commands',\n # 'temperature': 'Shows current temperature in my kitchen',\n 'guess': 'Числовая угадайка',\n 'ball': 'Магический шар',\n 'hangman': 'Игра в Виселицу',\n 'song': 'A random song from the database',\n 'song [username] ': 'A list of songs submitted by [username], i.e. /song username 3',\n 'sotd': 'Links a song of the day',\n # 'cotd': 'Cartoon of the day',\n 'smbc': 'Recent Saturday Morning Breakfast Cereal (SMBC)',\n 'xkcd': 'Recent XKCD',\n 'phd': 'Recent PhD comic',\n 'dilbert': 'Recent Dilbert by Scott Adams',\n 'dino': 'Recent Dinosaur Comic',\n 'calvin': 'Random Calvin and Hobbes by Bill Watterson',\n 'announce': 'Check daily announcements',\n 'slap [target]': 'Slap somebody',\n 'roll [nDr]': 'Roll a dice in nDr format, i.e. /roll 3d8',\n 'btc [delta]': 'Average USD market price historical chart across major bitcoin exchanges over delta, which can be either day or week or month or year, i.e. /btc week or just /btc to get current price',\n 'weirdal': 'Random Weird Al Yankovic music video.',\n 'rip [youtube url] ': 'Rip an .mp3 file from youtube and optionally add it to database',\n 'likezor [download] ': 'Download likes of a Twitter user, i.e. /likezor download gaestlic'\n}\n\n\n# Keyboard = types.ReplyKeyboardMarkup(one_time_keyboard=True, resize_keyboard=True)\n# Keyboard.add('')\n# hideKeyboard = types.ReplyKeyboardRemove() # if sent as reply_markup, will hide the keyboard\n\n# logger = telebot.logger\n# telebot.logger.setLevel(logging.DEBUG) # Outputs debug messages to console\n\n\n# telebot.apihelper.proxy = {\n# 'https':'socks5://{}:{}'.format(config.ip,config.port)\n# }\n\ndef listener(messages):\n \"\"\"\n When new messages arrive TeleBot will call this function.\n \"\"\"\n for message in messages:\n if message.content_type == 'text':\n # print the sent message to the console\n if message.chat.type == 'private':\n if message.chat.username:\n print(message.chat.username + \" [\" + str(message.chat.id) + \"]: \" + message.text)\n else:\n print(message.chat.first_name + \" [\" + str(message.chat.id) + \"]: \" + message.text)\n else:\n print(message.chat.title + \" [\" + str(message.chat.id) + \"]: \" + message.text)\n\n\nbot = telebot.TeleBot(config.token)\nbot.remove_webhook()\nbot.set_update_listener(listener) # register listener\n\n\n# handle the \"/start\" command\n@bot.message_handler(commands=['start'])\ndef handle_start_help(message):\n start.handle_start_help(bot, message)\n\n\n# help page\n@bot.message_handler(commands=['help'])\ndef command_help(message):\n help_text = \"The following commands are available: \\n\"\n for key in commands: # generate help text out of the commands dictionary defined at the top\n help_text += \"/\" + key + \": \"\n help_text += commands[key] + \"\\n\"\n bot.send_message(message.chat.id, help_text) # send the generated help page\n\n\n# @bot.message_handler(commands=['temperature'])\n# @bot.message_handler(func=lambda message: message.text == 'Temperature')\n# def handle_temperature(message):\n# temperature.handle_temperature(bot, message)\n\nfrom slippy_bot import bot\n\n\n@bot.message_handler(content_types=['audio'])\ndef handle_drop_audio(message):\n audio.handle_drop_audio(bot, message)\n\n\n@bot.message_handler(func=lambda message: message.text == 'SOTD')\n@bot.message_handler(commands=['sotd'])\ndef sotd(message):\n sotd_song.sotd(bot, message)\n\n\n@bot.message_handler(func=lambda message: message.text == 'Song')\n@bot.message_handler(commands=['song'])\ndef song(message):\n sotd_song.song(bot, message)\n\n\n# @bot.message_handler(commands=['cotd'])\n# def cotd(message):\n# cotd_plugin.handle_cotd(bot, message)\n\n\n@bot.message_handler(commands=['announce'])\ndef announce_command(message):\n announce.announce_command(bot, message)\n\n\n@bot.message_handler(commands=['roll'])\ndef dice(message):\n roll.dice(bot, message)\n\n\n@bot.message_handler(commands=['smbc'])\ndef smbc(message):\n webcomics.smbc(bot, message)\n\n\n@bot.message_handler(commands=['calvin'])\ndef calvin(message):\n webcomics.calvin(bot, message)\n\n\n@bot.message_handler(commands=['xkcd'])\ndef xkcd(message):\n webcomics.xkcd(bot, message)\n\n\n@bot.message_handler(commands=['dilbert'])\ndef dilbert(message):\n webcomics.dilbert(bot, message)\n\n\n@bot.message_handler(commands=['phd'])\ndef phd(message):\n webcomics.phd(bot, message)\n\n\n@bot.message_handler(commands=['dino'])\ndef dino(message):\n webcomics.dinosaur(bot, message)\n\n\n@bot.message_handler(commands=['slap'])\ndef slap(message):\n slap_plugin.slap(bot, message)\n\n\n@bot.message_handler(commands=['likezor'])\ndef likezor(message):\n likezor_plugin.likezor(bot, message)\n\n\n@bot.message_handler(commands=['btc'])\ndef btc(message):\n btc_plugin.btc(bot, message)\n\n\n@bot.message_handler(commands=['weirdal'])\n# http://pantuts.com/2013/02/16/youparse-extract-urls-from-youtube/\ndef yankovic(message):\n yankovic_plugin.yankovic(bot, message)\n\n\n@bot.message_handler(commands=['rip'])\n# https://stackoverflow.com/questions/27473526/download-only-audio-from-youtube-video-using-youtube-dl-in-python-script \ndef rip(message):\n rip_plugin.rip(bot, message)\n\n\n@bot.message_handler(commands=['guess'])\ndef guess(message):\n mini_project_number_guess.guess(bot, message)\n\n\n@bot.message_handler(commands=['ball'])\ndef ball(message):\n mini_project_magic_ball.ball(bot, message)\n\n\n@bot.message_handler(commands=['hangman'])\ndef hangman(message):\n mini_project_hangman.play_hangman(bot, message)\n\n\n@bot.message_handler(content_types=['document'])\ndef handle_docs_audio(message):\n # bot.reply_to(message, \"Sorry, I don't work with documents.\")\n pass\n\n\n# default handler for every other text\n@bot.message_handler(func=lambda message: True, content_types=['text'])\ndef command_default(m):\n # this is the standard reply to a normal message\n # bot.send_message(m.chat.id, \"I don't understand \\\"\" + m.text + \"\\\"\\nMaybe try the help page at /help\")\n pass\n\n\n@bot.message_handler(func=lambda msg: msg.text == u'\\U0001F4A9')\ndef set_ro(message):\n bot.send_message(message.chat.id, \"Sorry, no shit posting.\", reply_to_message_id=message.message_id)\n bot.restrict_chat_member(message.chat.id, message.from_user.id, until_date=time.time() + 31)\n\n\ndef telegram_polling():\n \"\"\"\n https://github.com/eternnoir/pyTelegramBotAPI/issues/206\n https://github.com/eternnoir/pyTelegramBotAPI/issues/401\n \"\"\"\n try:\n bot.polling(none_stop=True, timeout=100) # constantly get messages from Telegram\n except Exception as err:\n logging.error(err)\n bot.stop_polling()\n print(\"Internet error!\")\n time.sleep(10)\n telegram_polling()\n\n\nif __name__ == '__main__':\n telegram_polling()\n","repo_name":"a-maksimov/Sleepy_bot","sub_path":"slippy_bot.py","file_name":"slippy_bot.py","file_ext":"py","file_size_in_byte":7562,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"25051547021","text":"import numpy as np\nfrom math import sqrt\n\n\ndef calc_dist(x1: float, y1: float, x2: float, y2: float):\n return sqrt((x1-x2)**2 + (y1-y2)**2)\n\n\n# def find_min(ind, data): # to zakomentowane to raczej zle xD\n# for index in range(len(data)):\n# if not np.array_equal(data[ind], data[index]):\n# tmp_min = calc_dist(data[ind][0], data[ind][1], data[index][0], data[index][1])\n# tmp_index = index\n# break\n# for index in range(len(data)):\n# next_min = calc_dist(data[ind][0], data[ind][1], data[index][0], data[index][1])\n# if next_min < tmp_min and next_min != 0:\n# tmp_min = next_min\n# tmp_index = index\n# return tmp_min, tmp_index\n#\n#\n# def main(data):\n# dist = 0\n# current_pos = 0\n# while len(data) > 1:\n# to_add, current_pos = find_min(current_pos, data)\n# dist += to_add\n# data = np.delete(data, current_pos, axis=0)\n# return dist\ndef find_min(curr_pnt, data, vis):\n for index in range(len(data)):\n if not np.array_equal(data[curr_pnt], data[index]) and index not in vis:\n tmp_min = calc_dist(data[curr_pnt][0], data[curr_pnt][1], data[index][0], data[index][1])\n tmp_index = index\n break\n for index in range(len(data)):\n next_min = calc_dist(data[curr_pnt][0], data[curr_pnt][1], data[index][0], data[index][1])\n if index not in vis and tmp_min > next_min > 0:\n tmp_min = next_min\n tmp_index = index\n return tmp_min, tmp_index\n\n\ndef main(data):\n pos = 0\n dist = 0\n vis = set()\n vis.add(pos)\n path = [pos+1]\n coordinates = [data[pos]]\n while len(vis) < len(data):\n to_add, pos = find_min(pos, data, vis)\n dist += to_add\n vis.add(pos)\n path.append(pos+1)\n coordinates.append(data[pos])\n dist += calc_dist(data[0][0], data[0][1], data[pos][0], data[pos][1])\n path.append(path[0])\n coordinates.append(data[0])\n coordinates = np.array(coordinates)\n coordinates = np.transpose(coordinates)\n return dist, path, coordinates\n\n\nif __name__ == \"__main__\":\n cities = np.array([[1, 1], [2, 2], [3, 3], [4, 4], [5, 5]], dtype=np.float32)\n print(main(cities))\n","repo_name":"justdodo27/TSP-python","sub_path":"greedy.py","file_name":"greedy.py","file_ext":"py","file_size_in_byte":2247,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"28034869930","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nODell_udf.py\n python script of functions written by me or by others passed on to me\nCreated on Wed Sep 8 09:09:22 2021\n@author: kodell\n\"\"\"\n#%% packages needed\nimport numpy as np\nimport cartopy.feature as cfeature\nimport cartopy.crs as ccrs\nimport cartopy.io.shapereader as shpreader\nimport matplotlib.pyplot as plt\nimport matplotlib as mplt\nfrom matplotlib import colors\nmplt.rcParams['font.size'] = '14'\nmplt.rcParams['font.family'] = 'sans-serif'\n#mplt.rcParams['font.sans-serif'] = 'Veranda'\n#%% make a basic map of the US using cartopy, written by Katelyn O'Dell\n# NOTE this assumes a PlateCaree projection\ndef plt_map(dlon,dlat,data,cmap,clabel,title,**kwargs):\n vlim = kwargs.get('clim', None)\n outpath = kwargs.get('outname',None)\n vpts = kwargs.get('cpts',None)\n multi = kwargs.get('multi',None)\n if multi:\n nd = len(data)\n fig, axarr = plt.subplots(nrows=multi[0],ncols=multi[1],subplot_kw={'projection': ccrs.PlateCarree()},\n figsize=(11,8.5))\n axarr = axarr.flatten()\n for di in range(nd):\n ax = axarr[di]\n ax.patch.set_visible(False)\n # plot shapfile with colors\n ax.add_feature(cfeature.LAND.with_scale('50m'),facecolor='gray',alpha=0.5)\n ax.add_feature(cfeature.OCEAN.with_scale('50m'))\n ax.add_feature(cfeature.STATES.with_scale('50m'),edgecolor='lightgray')\n ax.outline_patch.set_edgecolor('white')\n if vlim:\n cs = ax.scatter(dlon,dlat,c=data[di],s=1,#shading='nearest',\n transform=ccrs.PlateCarree(),cmap=cmap[di],vmin=vlim[di][0],vmax=vlim[di][1])\n elif vpts:\n divnorm=colors.TwoSlopeNorm(vmin=vpts[di][0], vcenter=vpts[di][1], vmax=vpts[di][2])\n cs = ax.scatter(dlon,dlat,c=data[di],s=1,#shading='nearest',\n transform=ccrs.PlateCarree(),cmap=cmap[di],norm=divnorm)\n else:\n cs = ax.scatter(dlon,dlat,c=data[di],s=1,#shading='nearest',\n transform=ccrs.PlateCarree(),cmap=cmap[di])\n cbar = fig.colorbar(cs,ax=ax,orientation='horizontal',pad=0,shrink=0.6)\n #cbar = fig.colorbar(cs,ax=ax,orientation='vertical',pad=0,shrink=0.5)\n cbar.set_label(label=clabel[di],size=16)\n ax.set_title(title[di],fontsize=18)\n plt.tight_layout()\n else: \n fig, ax = plt.subplots(nrows=1,ncols=1,\n subplot_kw={'projection': ccrs.PlateCarree()},\n figsize=(11,8.5))\n ax.patch.set_visible(False)\n # plot shapfile with colors\n ax.add_feature(cfeature.LAND.with_scale('50m'),facecolor='gray',alpha=0.5)\n ax.add_feature(cfeature.OCEAN.with_scale('50m'))\n ax.add_feature(cfeature.STATES.with_scale('50m'),edgecolor='lightgray')\n ax.outline_patch.set_edgecolor('white')\n if vlim:\n cs = ax.scatter(dlon,dlat,c=data,s=10,#shading='nearest',\n transform=ccrs.PlateCarree(),cmap=cmap,vmin=vlim[0],vmax=vlim[1])\n elif vpts:\n divnorm=colors.TwoSlopeNorm(vmin=vpts[0], vcenter=vpts[1], vmax=vpts[2])\n cs = ax.scatter(dlon,dlat,c=data,s=10,#shading='nearest',\n transform=ccrs.PlateCarree(),cmap=cmap,norm=divnorm)\n else:\n cs = ax.scatter(dlon,dlat,c=data,s=10,#shading='nearest',\n transform=ccrs.PlateCarree(),cmap=cmap)\n #cbar = fig.colorbar(cs,ax=ax,orientation='vertical',pad=0,shrink=0.7)\n cbar = fig.colorbar(cs,ax=ax,orientation='vertical',pad=0,shrink=0.5)\n cbar.set_label(label=clabel,size=16)\n ax.set_title(title,fontsize=18)\n plt.tight_layout()\n\n if outpath:\n plt.savefig(outpath)\n plt.show()\n","repo_name":"kaodell/Sandberg_CDC_smokePM","sub_path":"ODell_udf_CDCprj.py","file_name":"ODell_udf_CDCprj.py","file_ext":"py","file_size_in_byte":3923,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"44945272592","text":"# Подключаем необходимые библиотеки\nfrom PyQt5.QtWidgets import *\nfrom PyQt5.QtCore import pyqtSlot, Qt\nfrom PyQt5.QtGui import *\nfrom PyQt5.QtSql import *\nimport mainwindow_ui as ui\n\n# Класс главного окна\n# Наследуется от базового класса QMainWindow\nclass MainWindow(QMainWindow):\n\t# Конструктор\n\tdef __init__(self):\n\t\t# Вызываем конструктор базового класса\n\t\tsuper(MainWindow, self).__init__()\n\t\t# Создаем экземпляр класса окна из ui файла\n\t\tself.ui = ui.Ui_MainWindow()\n\t\t# Устанавливаем окно на текущую форму\n\t\tself.ui.setupUi(self)\n\t\t# Задаём переменную для работы с БД по умолчанию пустой\n\t\tself.db = None\n\t\t# Создаем лист для Id удаленных элементов\n\t\tself.remove = []\n\t\t# Создаем лист для Id пользователей\n\t\tself.userId = []\n\n\t\n\t# Инициализируем подключение БД в локальную переменную\n\t# dbName - Путь к БД\n\t# Если всё корректно - возвращает True\n\tdef prepareDatabase(self, dbName):\n\t\tself.db = QSqlDatabase.addDatabase('QSQLITE', 'db')\n\t\tself.db.setDatabaseName(dbName)\n\t\t\n\t\tif not self.db.open():\n\t\t\tQMessageBox.critical(self, 'Ошибка', 'Не удалось подключиться к БД:\\n' + self.db.lastError().text())\n\t\t\treturn False\n\t\treturn True\n\t\n\t# Задание списка таблиц из БД вручную\n\tdef showTables(self):\n\t\tfor i in range(3):\n\t\t\tself.ui.tableWidget.removeRow(i)\n\t\t\tself.ui.tableWidget.insertRow(i)\n\t\tusers = QTableWidgetItem('Пользователи')\n\t\tusers.setData(Qt.UserRole, ['users', True])\n\t\tself.ui.tableWidget.setItem(0,0,users)\n\t\tusers.setFlags(Qt.ItemIsSelectable|Qt.ItemIsEnabled)\n\t\t\n\t\tgroups = QTableWidgetItem('Группы')\n\t\tgroups.setData(Qt.UserRole, ['groups', True])\n\t\tself.ui.tableWidget.setItem(1,0,groups)\n\t\tgroups.setFlags(Qt.ItemIsSelectable|Qt.ItemIsEnabled)\n\t\t\n\t\tuserGroups = QTableWidgetItem('Пользователи-группы')\n\t\tuserGroups.setData(Qt.UserRole, ['user_groups', False])\n\t\tself.ui.tableWidget.setItem(2,0,userGroups)\n\t\tuserGroups.setFlags(Qt.ItemIsSelectable|Qt.ItemIsEnabled)\n\t\t\n\t# Обработчик кнопки создания БД\n\t@pyqtSlot()\n\tdef on_actionCreateDB_triggered(self):\n\t\tname = QFileDialog.getSaveFileName(self, 'Выберите файл БД для создания','', 'SQLite database (*.sqlite)')[0]\n\t\tif not name:\n\t\t\treturn\n\t\tif not self.prepareDatabase(name):\n\t\t\treturn\n\t\t\n\t\tquery = QSqlQuery(self.db)\n\t\t\n\t\tqStr = ['''CREATE TABLE users(\n\t\t\tid INTEGER PRIMARY KEY AUTOINCREMENT,\n\t\t\tname VARCHAR,\n\t\t\tpassword VARCHAR\n\t\t)''',\n\t\t\n\t\t'''CREATE TABLE groups (\n\t\t\tid INTEGER PRIMARY KEY AUTOINCREMENT,\n\t\t\tname VARCHAR\n\t\t);''',\n\t\t\n\t\t'''CREATE TABLE user_groups(\n\t\t\tuser_id INTEGER CONSTRAINT user_id_fk REFERENCES users(id) ON DELETE CASCADE,\n\t\t\tgroup_id INTEGER CONSTRAINT group_id_fk REFERENCES groups(id) ON DELETE CASCADE\n\t\t);\n\t\t''']\n\t\t\n\t\tfor str in qStr:\n\t\t\tif not query.exec(str):\n\t\t\t\tQMessageBox.critical(self, 'Ошибка', 'Не удалось создать БД:\\n' + query.lastError().text())\n\t\t\t\treturn\n\t\t\t\n\t\tself.showTables()\n\t\t\n\t# Обработчик кнопки открытия БД\n\t@pyqtSlot()\n\tdef on_actionOpenDB_triggered(self):\n\t\tname = QFileDialog.getOpenFileName(self, 'Выберите файл БД','', 'SQLite database (*.sqlite)')[0]\n\t\tif not name:\n\t\t\treturn\n\t\tif not self.prepareDatabase(name):\n\t\t\treturn\n\t\tself.showTables()\n\t\t\n\t# Обработчик кнопки добавления записи\n\t@pyqtSlot()\n\tdef on_actionAdd_triggered(self):\t\n\t\tlastStr = self.ui.twSimple.rowCount()\n\t\tself.ui.twSimple.insertRow(lastStr)\n\t\tself.ui.twSimple.setItem(lastStr, 0, QTableWidgetItem(''))\n\t\tself.ui.twSimple.item(lastStr, 0).setData(Qt.UserRole, 0)\n\n\t\t\n\t# Обработчик кнопки удаления записи\n\t@pyqtSlot()\n\tdef on_actionDelete_triggered(self):\n\t\tif self.ui.twSimple.currentRow() >= 0:\n\t\t\titem = self.ui.twSimple.currentRow()\n\t\t\tidC = self.ui.twSimple.item(item, 0).data(Qt.UserRole)\n\t\t\tif idC != 0:\n\t\t\t\tself.remove.append(idC)\n\t\t\tself.ui.twSimple.removeRow(item)\n\n\t# Обработчик кнопки сохранения\n\t@pyqtSlot()\n\tdef on_actionSave_triggered(self):\n\t\tif self.ui.tableWidget.currentRow() == 0 or self.ui.tableWidget.currentRow() == 1:\n\t\t\tif self.ui.tableWidget.currentRow() == 0:\n\t\t\t\tself.deleteFromTable(\"users\")\n\t\t\tif self.ui.tableWidget.currentRow() == 1:\n\t\t\t\tself.deleteFromTable(\"groups\")\n\t\t\tself.updateTable()\n\t\tif self.ui.tableWidget.currentRow() == 2:\n\t\t\tself.removeFromTable()\n\t\t\tself.insertToTableUserGroups()\n\t\tQMessageBox.information(self, 'Успешно', 'Сохранено')\n\t# Удаление элементов из таблицы пользователи-группы\n\tdef removeFromTable(self):\n\t\tquery = QSqlQuery(self.db)\n\t\tfor c in self.remove:\n\t\t\tif not query.exec(f'DELETE FROM user_groups WHERE group_id = {self.ui.twMain.item(self.ui.twMain.currentRow(), 0).data(Qt.UserRole)} AND user_id = {c}'):\n\t\t\t\tQMessageBox.critical(self, 'Ошибка', 'Не удалось удалить строку:\\n' + query.lastError().text())\n\t\t\t\treturn\n\n\t# Проверка элементов на добавление в таблицу пользователи-группы\n\tdef insertToTableUserGroups(self):\n\t\tfor i in range(self.ui.twListInMain.rowCount()):\n\t\t\tif self.ui.twListInMain.item(i, 0).data(Qt.UserRole) == 0:\n\t\t\t\tself.insertToTableUserGroupsProcess(i)\n\n\t# Добавление элементов в список пользователи-группы\n\tdef insertToTableUserGroupsProcess(self, i):\n\t\tquery = QSqlQuery(self.db)\n\t\tgroup = self.ui.twMain.item(self.ui.twMain.currentRow(),0).data(Qt.UserRole)\n\t\tuserName = self.ui.twListInMain.item(i,0).text()\n\t\tqStr = f'INSERT INTO user_groups(user_id, group_id) VALUES((SELECT id FROM users WHERE name=\"{userName}\"),{group})'\n\t\tif not query.exec(qStr):\n\t\t\tQMessageBox.critical(self, 'Ошибка', 'Не удалось добавить строку:\\n' + query.lastError().text())\n\t\t\treturn\n\t\tself.getLastIdUserGroups(i)\n\n\t# Удаление элементов из таблицы\n\tdef deleteFromTable (self, table):\n\t\tquery = QSqlQuery(self.db)\n\t\tfor c in self.remove:\n\t\t\tif not query.exec(f'DELETE FROM {table} WHERE id = {c}'):\n\t\t\t\tQMessageBox.critical(self, 'Ошибка', 'Не удалось удалить строку:\\n' + query.lastError().text())\n\t\t\t\treturn\n\t\n\t# Добавление в таблицу пользователи\n\tdef insertToTableUsers (self, i):\n\t\tquery = QSqlQuery(self.db)\n\t\tif self.ui.twSimple.item(i ,0).text() and self.ui.twSimple.item(i ,1).text():\n\t\t\tname = self.ui.twSimple.item(i ,0).text()\n\t\t\tpassword = self.ui.twSimple.item(i ,1).text()\n\t\t\tqStr = f\"INSERT INTO users(name, password) VALUES('{name}','{password}')\"\n\t\t\tif not query.exec(qStr):\n\t\t\t\tQMessageBox.critical(self, 'Ошибка', 'Не удалось добавить строку:\\n' + query.lastError().text())\n\t\t\t\treturn\n\t\t\tself.getLastId(i)\n\n\t# Добавление в таблицу группы\n\tdef insertToTableGroups (self, i):\n\t\tquery = QSqlQuery(self.db)\n\t\tif self.ui.twSimple.item(i ,0).text():\n\t\t\tname = self.ui.twSimple.item(i ,0).text()\n\t\t\tqStr = f\"INSERT INTO groups(name) VALUES('{name}')\"\n\t\t\tif not query.exec(qStr):\n\t\t\t\tQMessageBox.critical(self, 'Ошибка', 'Не удалос�� добавить строку:\\n' + query.lastError().text())\n\t\t\t\treturn\n\t\t\tself.getLastId(i)\n\n\t# Получение последнего добавленного id таблицы пользователи-группы\n\tdef getLastIdUserGroups(self, i):\n\t\tquery = QSqlQuery(self.db)\n\t\tqStr = f\"SELECT last_insert_rowid()\"\n\t\tif not query.exec(qStr) or not query.next():\n\t\t\tQMessageBox.critical(self, 'Ошибка', 'Не удалось выбрать последнюю строку:\\n' + query.lastError().text())\n\t\t\treturn\n\t\tself.ui.twListInMain.item(i ,0).setData(Qt.UserRole, query.value(0))\n\n\t# Получение последнего добавленного id\n\tdef getLastId (self, i):\n\t\tquery = QSqlQuery(self.db)\n\t\tqStr = f\"SELECT last_insert_rowid()\"\n\t\tif not query.exec(qStr) or not query.next():\n\t\t\tQMessageBox.critical(self, 'Ошибка', 'Не удалось выбрать последнюю строку:\\n' + query.lastError().text())\n\t\t\treturn\n\t\tself.ui.twSimple.item(i ,0).setData(Qt.UserRole, query.value(0))\n\n\t# Обновление таблицы пользователи\n\tdef updateTableUsers(self, i):\n\t\tquery = QSqlQuery(self.db)\n\t\tif self.ui.twSimple.item(i ,0).text() and self.ui.twSimple.item(i ,1).text():\n\t\t\tname = self.ui.twSimple.item(i ,0).text()\n\t\t\tpassword = self.ui.twSimple.item(i ,1).text()\n\t\t\tidC = self.ui.twSimple.item(i, 0).data(Qt.UserRole)\n\t\t\tqStr = f\"UPDATE users SET name = '{name}', password = '{password}' WHERE id = {idC}\"\n\t\t\tif not query.exec(qStr):\n\t\t\t\tQMessageBox.critical(self, 'Ошибка', 'Не удалось обновить строку:\\n' + query.lastError().text())\n\t\t\t\treturn\n\n\t# Обновление таблицы группы\n\tdef updateTableGroups(self, i):\n\t\tquery = QSqlQuery(self.db)\n\t\tif self.ui.twSimple.item(i ,0).text():\n\t\t\tname = self.ui.twSimple.item(i ,0).text()\n\t\t\tidC = self.ui.twSimple.item(i, 0).data(Qt.UserRole)\n\t\t\tqStr = f\"UPDATE groups SET name = '{name}' WHERE id = {idC}\"\n\t\t\tif not query.exec(qStr):\n\t\t\t\tQMessageBox.critical(self, 'Ошибка', 'Не удалось обновить строку:\\n' + query.lastError().text())\n\t\t\t\treturn\n\t\t\n\t# Проверка на действие с элементом (добавить/обновить)\t\n\tdef updateTable(self):\n\t\tfor i in range(self.ui.twSimple.rowCount()):\n\t\t\tif self.ui.twSimple.item(i, 0).data(Qt.UserRole) == 0:\n\t\t\t\tif self.ui.tableWidget.currentRow() == 0:\n\t\t\t\t\tself.insertToTableUsers(i)\n\t\t\t\tif self.ui.tableWidget.currentRow() == 1:\n\t\t\t\t\tself.insertToTableGroups(i)\n\t\t\telse:\n\t\t\t\tif self.ui.tableWidget.currentRow() == 0:\n\t\t\t\t\tself.updateTableUsers(i)\n\t\t\t\tif self.ui.tableWidget.currentRow() == 1:\n\t\t\t\t\tself.updateTableGroups(i)\n\t\t\t\t\t\n\t# Обработчик смены элемента в таблице с именами таблиц из БД\n\t@pyqtSlot()\n\tdef on_tableWidget_itemSelectionChanged(self):\n\t\tif not len(self.ui.tableWidget.selectedItems()):\n\t\t\treturn\n\t\titem = self.ui.tableWidget.selectedItems()[0]\n\t\tself.prepareTables(item.data(Qt.UserRole))\n\t\tself.remove.clear()\n\n\t# Подготовка таблиц редактирования\n\tdef prepareTables(self, dataList):\n\t\tif dataList[1]:\n\t\t\tself.prepareSimple(dataList[0])\n\t\telse:\n\t\t\tself.prepareConnection(dataList[0])\n\n\t# Обработчик смены элемента в таблице с ролями\n\t@pyqtSlot()\n\tdef on_twMain_itemSelectionChanged(self):\n\t\tif not len(self.ui.twMain.selectedItems()):\n\t\t\treturn\n\t\titem = self.ui.twMain.selectedItems()[0]\n\t\tself.prepareUserGroupsСompliance(item.data(Qt.UserRole))\n\t\tself.remove.clear()\n\t\tself.userId.clear()\n\n\t# Подготовка таблиц редактирования\n\tdef prepareTables(self, dataList):\n\t\tif dataList[1]:\n\t\t\tself.prepareSimple(dataList[0])\n\t\telse:\n\t\t\tself.prepareConnection(dataList[0])\n\n\t# Обработчик стрелочки добавления\n\t@pyqtSlot()\n\tdef on_tbtnAddTo_clicked(self):\n\t\tif self.ui.twAll.currentRow() >= 0:\n\t\t\tuser = self.ui.twAll.item(self.ui.twAll.currentRow(),0).text()\n\t\t\tlastStr = self.ui.twListInMain.rowCount()\n\t\t\tself.ui.twListInMain.insertRow(lastStr)\n\t\t\tself.ui.twListInMain.setItem(lastStr, 0, QTableWidgetItem(user))\n\t\t\tself.ui.twListInMain.item(lastStr, 0).setData(Qt.UserRole, 0)\n\n\t# Обработчик стрелочки удаления\n\t@pyqtSlot()\n\tdef on_tbtnRemoveFrom_clicked(self):\n\t\tif self.ui.twListInMain.currentRow() >= 0:\n\t\t\titem = self.ui.twListInMain.currentRow()\n\t\t\tidC = self.ui.twListInMain.item(item, 0).data(Qt.UserRole)\n\t\t\tif idC != 0:\n\t\t\t\tself.remove.append(idC)\n\t\t\tself.ui.twListInMain.removeRow(item)\n\n\t# Подготовка таблиц типа \"Связка\"\n\tdef prepareConnection(self, name):\n\t\tself.ui.stackedWidget.setCurrentIndex(1)\n\t\tif name == 'user_groups':\n\t\t\tres = self.prepareUserGroups()\n\t\telse:\n\t\t\tres = False\n\t\tif res:\n\t\t\tself.ui.actionAdd.setEnabled(False)\n\t\t\tself.ui.actionDelete.setEnabled(False)\n\t\t\tself.ui.actionSave.setEnabled(True)\n\t\telse:\n\t\t\tself.ui.actionAdd.setEnabled(False)\n\t\t\tself.ui.actionDelete.setEnabled(False)\n\t\t\tself.ui.actionSave.setEnabled(False)\n\t\t\n\t# Подготовка простых таблиц\n\tdef prepareSimple(self, name):\n\t\tself.ui.stackedWidget.setCurrentIndex(0)\n\t\tself.tableName = name\n\t\t\n\t\tif name == 'users':\n\t\t\tres = self.prepareUsers()\n\t\telif name == 'groups':\n\t\t\tres = self.prepareGroups()\n\t\telse:\n\t\t\tres = False\n\t\tif res:\n\t\t\tself.ui.actionAdd.setEnabled(True)\n\t\t\tself.ui.actionDelete.setEnabled(True)\n\t\t\tself.ui.actionSave.setEnabled(True)\n\t\telse:\n\t\t\tself.ui.actionAdd.setEnabled(False)\n\t\t\tself.ui.actionDelete.setEnabled(False)\n\t\t\tself.ui.actionSave.setEnabled(False)\n\t\t\n\t# Подготовка таблицы пользователей\n\tdef prepareUsers(self):\n\t\tself.ui.twSimple.setRowCount(0)\n\t\tself.ui.twSimple.setColumnCount(2)\n\t\tself.ui.twSimple.setHorizontalHeaderItem(0, QTableWidgetItem('Имя пользователя'))\n\t\tself.ui.twSimple.setHorizontalHeaderItem(1, QTableWidgetItem('Пароль'))\n\t\t\n\t\tquery = QSqlQuery(self.db)\n\t\tstr = 'SELECT id, name, password FROM users'\n\t\t\n\t\tif not query.exec(str):\n\t\t\tQMessageBox.critical(self, 'Ошибка', 'Не удалось получить список пользователей:\\n' + query.lastError().text())\n\t\t\treturn False\n\t\t\n\t\ti = 0\n\t\twhile query.next():\n\t\t\tself.ui.twSimple.insertRow(i)\n\t\t\tself.ui.twSimple.setItem(i, 0, QTableWidgetItem(query.value(1)))\n\t\t\tself.ui.twSimple.setItem(i, 1, QTableWidgetItem(query.value(2)))\n\t\t\tself.ui.twSimple.item(i, 0).setData(Qt.UserRole, query.value(0))\n\t\t\ti += 1\n\t\tself.ui.twSimple.resizeColumnsToContents()\n\t\treturn True\n\t\n\t# Подготовка таблицы групп\n\tdef prepareGroups(self):\n\t\tself.ui.twSimple.setRowCount(0)\n\t\tself.ui.twSimple.setColumnCount(1)\n\t\tself.ui.twSimple.setHorizontalHeaderItem(0, QTableWidgetItem('Наименование группы'))\n\t\t\n\t\tquery = QSqlQuery(self.db)\n\t\tstr = 'SELECT id, name FROM groups'\n\t\t\n\t\tif not query.exec(str):\n\t\t\tQMessageBox.critical(self, 'Ошибка', 'Не удалось получить список групп:\\n' + query.lastError().text())\n\t\t\treturn False\n\t\t\n\t\ti = 0\n\t\twhile query.next():\n\t\t\tself.ui.twSimple.insertRow(i)\n\t\t\tself.ui.twSimple.setItem(i, 0, QTableWidgetItem(query.value(1)))\n\t\t\tself.ui.twSimple.item(i, 0).setData(Qt.UserRole, query.value(0))\n\t\t\ti += 1\n\t\tself.ui.twSimple.resizeColumnsToContents()\n\t\treturn True\n\t\n\t# Подготовка таблицы пользователи-группы\n\tdef prepareUserGroups(self):\n\t\tself.ui.twMain.clear()\n\t\tself.ui.twMain.setColumnCount(1)\n\t\tself.ui.twMain.setRowCount(0)\n\t\tself.ui.twMain.setHorizontalHeaderItem(0, QTableWidgetItem('Роли'))\n\t\tself.ui.twMain.horizontalHeader().setStretchLastSection(True)\n\t\t\n\t\tquery = QSqlQuery(self.db)\n\t\tstr = 'SELECT id, name FROM groups'\n\t\t\n\t\tif not query.exec(str):\n\t\t\tQMessageBox.critical(self, 'Ошибка', 'Не удалось получить список групп:\\n' + query.lastError().text())\n\t\t\treturn False\n\t\t\n\t\ti = 0\n\t\twhile query.next():\n\t\t\tself.ui.twMain.insertRow(i)\n\t\t\tself.ui.twMain.setItem(i, 0, QTableWidgetItem(query.value(1)))\n\t\t\tself.ui.twMain.item(i, 0).setData(Qt.UserRole, query.value(0))\n\t\t\tself.ui.twMain.item(i, 0).setFlags(Qt.ItemIsSelectable|Qt.ItemIsEnabled)\n\t\t\ti += 1\n\t\tself.ui.twMain.resizeColumnsToContents()\n\t\tself.ui.twAll.clear()\n\t\tself.ui.twAll.setColumnCount(1)\n\t\tself.ui.twAll.setRowCount(0)\n\t\tself.ui.twAll.setHorizontalHeaderItem(0, QTableWidgetItem('Пользователи'))\n\t\tself.ui.twAll.horizontalHeader().setStretchLastSection(True)\n\t\t\n\t\t\n\t\tquery = QSqlQuery(self.db)\n\t\tstr = 'SELECT id, name FROM users'\n\t\t\n\t\tif not query.exec(str):\n\t\t\tQMessageBox.critical(self, 'Ошибка', 'Не удалось получить список пользователей:\\n' + query.lastError().text())\n\t\t\treturn False\n\t\t\n\t\ti = 0\n\t\twhile query.next():\n\t\t\tself.ui.twAll.insertRow(i)\n\t\t\tself.ui.twAll.setItem(i, 0, QTableWidgetItem(query.value(1)))\n\t\t\tself.ui.twAll.item(i, 0).setData(Qt.UserRole, query.value(0))\n\t\t\tself.ui.twAll.item(i, 0).setFlags(Qt.ItemIsSelectable|Qt.ItemIsEnabled)\n\t\t\ti += 1\n\t\tself.ui.twAll.resizeColumnsToContents()\n\t\treturn True\n\n\t# Подготовка таблицы пользователей в определенной группе\n\tdef prepareUserGroupsСompliance(self, data):\n\t\tself.ui.twListInMain.clear()\n\t\tself.ui.twListInMain.setRowCount(0)\n\t\tself.ui.twListInMain.setColumnCount(1)\n\t\tself.ui.twListInMain.setHorizontalHeaderItem(0, QTableWidgetItem('Назначенные на роль'))\n\t\tself.ui.twListInMain.horizontalHeader().setStretchLastSection(True)\n\t\t\n\t\tquery = QSqlQuery(self.db)\n\t\tstr = f'SELECT user_id FROM user_groups WHERE group_id = {data}'\n\t\t\n\t\tif not query.exec(str):\n\t\t\tQMessageBox.critical(self, 'Ошибка', 'Не удалось получить список пользователей в этой группе:\\n' + query.lastError().text())\n\t\t\treturn False\n\n\t\twhile query.next():\n\t\t\tself.userId.append(query.value(0))\n\n\t\tfor i in range(0, len(self.userId)):\n\t\t\tuserId = self.userId[i]\n\t\t\tstr = f'SELECT name FROM users WHERE id = {userId}'\n\t\t\tif not query.exec(str):\n\t\t\t\tQMessageBox.critical(self, 'Ошибка', 'Не удалось получить имя пользователя:\\n' + query.lastError().text())\n\t\t\t\treturn False\n\t\t\twhile query.next():\n\t\t\t\tself.ui.twListInMain.insertRow(i)\n\t\t\t\tself.ui.twListInMain.setItem(i, 0, QTableWidgetItem(query.value(0)))\n\t\t\t\tself.ui.twListInMain.item(i, 0).setData(Qt.UserRole, self.userId[i])\n\t\t\t\tself.ui.twListInMain.item(i, 0).setFlags(Qt.ItemIsSelectable|Qt.ItemIsEnabled)\n\t\t\tself.ui.twListInMain.resizeColumnsToContents()\n\t\treturn True","repo_name":"uGodNick/python-table","sub_path":"src/mainwindow.py","file_name":"mainwindow.py","file_ext":"py","file_size_in_byte":18167,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"41502898428","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Feb 7 19:09:32 2021\n\nMake method to loop through single round to determine which player fills what role\ncreate train set size vs accuracy graph\n\nThe General Making Choices Approach\n\n####Raymond To-Do####\nTRADE_KILL \n\n####Grant To-Do####\n\n####PreGame##### Making use of Clustering \n\nFinish clustering with new features, classify test set into clusters, make a new column for cluster\nDetermine success and faiulure statistics for team comp matchups (Team Gamma vs Team Omega)\n - 11111 vs 01342\n - Who is 11111's best and worst matchup\nDetermine which individual team comp is the most winningest -\n - 11111 win rate alone\nWin percentage of team comp before and after switch\n - How does 11111 do on T vs CT?\nIf matchup between team is bad, AI system could recommend different comp\n\nDo individual players occupy different clusters?\nCheck if individual players occur in different games? \n\n\n\n####MidGame/Post-Plant Applied Statistics#####\n\nWithin +-5 seconds of A site bomb plant determine where T players are playing (boxes) regardless of the round outcome\nLook at their team comp-> is it a succeessful team comp? Or do they lose bc of the wrong combination of players and match up. \nDo they play in the positions we think are supposed to succeed based off heat maps or statistics of successful box loactions for individual player types, their clusters? Or do they succeed from new postions? How is the team spread spatially?\n We can extract spatial features of player sot one another and bombsite and perform statistics on what wins\n\n# # # (Later) Have AI learn from each role (guess best move for each role)\n\n@author: Grant\n\"\"\"\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n#import seaborn as sns\nimport os\nimport pickle\nfrom src import Writer\nimport sim as sim\nwriter = Writer()\n\n#make empty df \ndata = pd.DataFrame()\n\niterations = 245\n\n#gets copy of original data\\\nwriter.main()\ndata = writer.get_data()\n\n#reading\nwith open('file_to_rounds.txt', 'rb') as handle:\n _input = handle.read()\n \n\n#dictionary\nfile_to_rounds = pickle.loads(_input)\n\n#main df\ncolumn_names = ['ID','Health','damage','kills','rifle','sniper','pistol','smg',\n 'grenade','preplant kill','postplant kill','fast_kill_rating (first_kill)',\n 'time of kills','total kills','total deaths', 'avg kill time', \n 'assists', 'team','positioning type', 'last x', 'last y', \n 'alone kills', 'distance to A bomb (on kill list)', 'Avg Distance to A bomb (on kill)',\n 'times in catwalk_box', 'times in topmid_box', 'times in chair_box', 'times in midlane_box', 'times in underpass_box', 'times in window_box',\\\n 'times in stairs_box', 'times in tetris_box', 'times in sandwhich_box', 'times in Asite_box', 'times in firebox_box', 'times in jungle_box', 'times in connector_box',\n 'times in opening_box', 'times in opening2_box', 'times in A_main_box', 'times in T_ramp_box', 'times in hell_box', 'times in palace_box', 'times in pillars_box', \n 'times in ticket_box', 'times in CT_ramp_box', 'alone_death', 'total_distance_traveled']\n\nmain_df = pd.DataFrame(columns = column_names)\n\n#get all player ids\ndef find_team_ids(file):\n list_of_ct_ids = []\n list_of_t_ids = []\n\n game_data = data[(data['file'] == file) & (data['round'] < 16) & (data['att_id'] != 0)]\n for index, row in game_data.iterrows():\n if row['att_side'] == \"CounterTerrorist\":\n if row['att_id'] not in list_of_ct_ids:\n list_of_ct_ids.append(row['att_id'])\n \n else:\n if row['att_id'] not in list_of_t_ids:\n list_of_t_ids.append(row['att_id'])\n \n return list_of_ct_ids, list_of_t_ids\n\ndef distance_between_points(P, Q):\n \n x1 = P[0]\n x2 = Q[0]\n y1 = P[1]\n y2 = Q[1]\n \n result = ((((x2 - x1 )**2) + ((y2-y1)**2) )**0.5)\n return result\n\ndef all_roles_in_round(df, file, rnds): \n \n############################### VARIABLES ###############################\n ct_list, t_list = find_team_ids(file)\n\n #print(\"ct\", ct_list)\n #print(\"t\", t_list)\n \n if len(ct_list) < 5:\n #print(\"CT LIST TOO SMALL:\", len(ct_list))\n #print(\"Skipping\")\n return\n \n if len(t_list) < 5:\n #print(\"T LIST TOO SMALL:\", len(t_list))\n #print(\"Skipping\")\n return\n \n \n \n ''' GAME VARIABLES '''\n #ID, Health, damage, kills, rifle, sniper, pistol, smg, grenade, preplant kill(9), \n # postplant kill, fast_kill_rating (first_kill), time of kills, total kills(13), \n #total deaths, avg kill time, assists, 'team', positioning type (att or vic)(18), \n # last x (vic/att), last y(vic/att), alone kills, \n # distance to A bomb (on kill list), Avg Distance to A bomb (on kills)\n #'times in catwalk_box', 'times in topmid_box', 'times in chair_box', 'times in midlane_box', 'times in underpass_box', 'times in window_box'(29), \n #'times in stairs_box', 'times in tetris_box', 'times in sandwhich_box', 'times in Asite_box', 'times in firebox_box', 'times in jungle_box', 'times in connector_box',\n # 'times in opening_box', 'times in opening2_box', 'times in A_main_box', 'times in T_ramp_box', 'times in hell_box', 'times in palace_box', 'times in pillars_box', \n # 'times in ticket_box', 'times in CT_ramp_box', alone_death, total_distance_traveled (47)\n \n ct_player_1 = [ct_list[0], 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, [], 0, 0, 0, 0, \"CounterTerrorist\", \"N/A\", 0, 0, 0, [], 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ct_player_2 = [ct_list[1], 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, [], 0, 0, 0, 0, \"CounterTerrorist\", \"N/A\", 0, 0, 0, [], 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ct_player_3 = [ct_list[2], 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, [], 0, 0, 0, 0, \"CounterTerrorist\", \"N/A\", 0, 0, 0, [], 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ct_player_4 = [ct_list[3], 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, [], 0, 0, 0, 0, \"CounterTerrorist\", \"N/A\", 0, 0, 0, [], 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ct_player_5 = [ct_list[4], 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, [], 0, 0, 0, 0, \"CounterTerrorist\", \"N/A\", 0, 0, 0, [], 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n \n t_player_1 = [t_list[0], 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, [], 0, 0, 0, 0, \"Terrorist\", \"N/A\", 0, 0, 0, [], 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n t_player_2 = [t_list[1], 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, [], 0, 0, 0, 0, \"Terrorist\", \"N/A\", 0, 0, 0, [], 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n t_player_3 = [t_list[2], 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, [], 0, 0, 0, 0, \"Terrorist\", \"N/A\", 0, 0, 0, [], 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n t_player_4 = [t_list[3], 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, [], 0, 0, 0, 0, \"Terrorist\", \"N/A\", 0, 0, 0, [], 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n t_player_5 = [t_list[4], 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, [], 0, 0, 0, 0, \"Terrorist\", \"N/A\", 0, 0, 0, [], 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n \n \n \n all_players = [ct_player_1, ct_player_2, ct_player_3, ct_player_4, ct_player_5, \n t_player_1, t_player_2, t_player_3, t_player_4, t_player_5]\n \n \n ''' ROUNDS LOOP '''\n \n # single_game = data[(data['file'] == file)]\n # highest_round = single_game.loc[single_game['round'].idxmax()]\n # print(\"Running file\", file,\"with max round\", highest_round['round'], \"...\")\n # for i in range(1, highest_round['round'] + 1):\n \n rounds = rnds\n print(\"Running file\", file, \"with\", len(rounds), \"rounds...\")\n for i in rounds:\n \n #reset health at beginning of round\n for player in all_players:\n player[1] = 100\n\n \n ''' ROUND VARIABLES '''\n first_dmg_turn_counter = 20\n first_dmg_award = [1,1,2,2,3,3,4,4,5,5]\n first_dmg_index = len(first_dmg_award) - 1\n assists = []\n \n ''' ROWS LOOP '''\n print(\"Running round\", i,\"...\")\n single_round = data[(data['round'] == i) & (data['file'] == file)]\n \n for index, row in single_round.iterrows():\n '''when a player dies in row do calculations then'''\n victim = []\n attacker = []\n kill_flag = False\n for curr_player in all_players:\n pos_data = np.array([[row['attacker_mapX'], row['attacker_mapY']], [row['victim_mapX'], row['victim_mapY']]])\n x, y = pos_data.T\n \n if curr_player[0] == row['vic_id']:\n victim = curr_player\n \n #Set postioning type == VIC\n victim[18] = \"VIC\"\n \n #total distance travel (victim check)\n if victim[19] and victim[20] != 0: \n \n victim[47] += abs(distance_between_points([victim[19], victim[20]],[x[1], y[1]]))\n # print(victim[1])\n # print(\"victim last known x/y (\", victim[19], victim[20], \")\")\n # print(\"victim current x/y (\", x[1], y[1], \")\" )\n # print(\"victim DBP: \", abs(distance_between_points([victim[19], victim[20]],[x[1], y[1]])))\n # print(\"victim total distance: \", victim[47])\n # print()\n \n #Set last x and last y\n victim[19] = row['victim_mapX']\n victim[20] = row['victim_mapY']\n \n victim[1] -= row['hp_dmg']\n \n if curr_player[0] == row['att_id']:\n attacker = curr_player\n \n #Set postioning type == ATT\n attacker[18] = \"ATT\"\n \n #total distance travel (attacker check)\n if attacker[19] and attacker[20] != 0: \n attacker[47] += abs(distance_between_points([attacker[19], attacker[20]],[x[0], y[0]]))\n # print(attacker[1])\n # print(\"attacker last known x/y (\", attacker[19], attacker[20], \")\")\n # print(\"attacker current x/y (\", x[0], y[0], \")\" )\n # print(\"attacker DBP: \", abs(distance_between_points([attacker[19], attacker[20]],[x[0], y[0]])))\n # print(\"attacker total distance: \", attacker[47])\n # print()\n \n \n #Set last x and last y\n attacker[19] = row['attacker_mapX']\n attacker[20] = row['attacker_mapY']\n \n attacker[2] += row['hp_dmg']\n \n if len(attacker) != 0 and len(victim) != 0:\n \n #if a attacker does damage give him an assist,\n if ([attacker[0], victim[0]]) not in assists:\n assists.append([attacker[0], victim[0]])\n \n \n if victim[1] <= 0 and kill_flag == False:\n kill_flag = True\n \n \n #alone kill and alone death\n alone_kill = True\n alone_death = True\n distance_to_nearest_teammate = 99999\n for player in all_players:\n if ((player[17] == attacker[17]) and (player[1] > 0) and player[0] != attacker[0]):\n result = distance_between_points([player[19], player[20]], [attacker[19], attacker[20]])\n if result < distance_to_nearest_teammate:\n distance_to_nearest_teammate = result\n if result < 400:\n alone_kill = False\n if ((player[17] == victim[17]) and (player[1] > 0) and player[0] != victim[0]):\n result2 = distance_between_points([player[19], player[20]], [victim[19], victim[20]])\n if result2 < distance_to_nearest_teammate:\n distance_to_nearest_teammate = result2\n if result2 < 400:\n alone_death = False\n \n if alone_kill == True:\n attacker[21] += 1\n if alone_death == True:\n victim[46] += 1\n\n #remove assist from list when they kill\n assists.remove([attacker[0], victim[0]])\n attacker[3] += 1\n \n if row['wp_type'] == 'Rifle':\n attacker[4] += 1\n if row['wp_type'] == 'Sniper':\n attacker[5] += 1\n if row['wp_type'] == 'Pistol':\n attacker[6] += 1\n if row['wp_type'] == 'SMG':\n attacker[7] += 1\n if row['wp_type'] == 'Grenade':\n attacker[8] += 1\n if row['is_bomb_planted'] != True:\n attacker[9] += 1\n if row['is_bomb_planted'] == True:\n attacker[10] += 1\n if first_dmg_turn_counter > 0 and attacker[11] == 0 and first_dmg_index >= 0:\n attacker[11] = first_dmg_award[first_dmg_index]\n first_dmg_index -= 1\n attacker[12].append(row['seconds'])\n attacker[13] += 1\n victim[14] += 1\n \n #Append distance to A bomb on kill\n attacker[22].append(abs(distance_between_points([attacker[19], attacker[20]], sim.CenterA)))\n \n \n #mid boxes and A_site boxes check (attacker only)\n if row['att_id'] == attacker[0]:\n \n index_counter = 0\n for box in sim.list_of_boxes:\n if (box[0] < x[0] < box[2]) and (box[3] < y[0] < box[1]):\n #print(\"Attacker\", row['att_id'], \"in\", box[4],\"box\")\n #print(index_counter)\n if index_counter == 0:\n attacker[24] += 1\n \n elif index_counter == 1:\n attacker[25] += 1\n \n elif index_counter == 2:\n attacker[26] += 1\n \n elif index_counter == 3:\n attacker[27] += 1\n \n elif index_counter == 4:\n attacker[28] += 1\n \n elif index_counter == 5:\n attacker[29] += 1 \n \n elif index_counter == 6:\n attacker[30] += 1\n \n elif index_counter == 7:\n attacker[31] += 1\n \n elif index_counter == 8:\n attacker[32] += 1\n \n elif index_counter == 9:\n attacker[33] += 1\n \n elif index_counter == 10:\n attacker[34] += 1\n \n elif index_counter == 11:\n attacker[35] += 1\n \n elif index_counter == 12:\n attacker[36] += 1\n \n elif index_counter == 13:\n attacker[37] += 1\n \n elif index_counter == 14:\n attacker[38] += 1\n \n elif index_counter == 15:\n attacker[39] += 1\n \n elif index_counter == 16:\n attacker[40] += 1\n \n elif index_counter == 17:\n attacker[41] += 1\n \n elif index_counter == 18:\n attacker[42] += 1\n \n elif index_counter == 19:\n attacker[43] += 1\n \n elif index_counter == 20:\n attacker[44] += 1\n \n else:\n attacker[45] += 1\n index_counter += 1\n \n \n\n ''' POST ROUND CHECKS '''\n for player in all_players:\n \n time_delta = 0\n #assists checks\n if len(assists) > 0:\n for assist_pair in assists:\n if assist_pair[0] == player[0]:\n player[16] += 1\n assists.remove(assist_pair)\n \n #average time of kills\n if player[12] and len(player[12]) > 1:\n for i in range(len(player[12])):\n if (i + 1) < (len(player[12])):\n time_delta += player[12][i+1] - player[12][i]\n else:\n #will get more accurate each round and harder to change\n player[15] = time_delta/(len(player[12]) - 1)\n \n #average of distance to A bomb kills \n if player[22] and len(player[22]) > 1:\n for i in range(len(player[22])):\n if (i + 1) < (len(player[22])):\n time_delta += player[22][i+1] - player[22][i]\n else:\n #will get more accurate each round and harder to change\n player[23] = abs(time_delta/(len(player[22]) - 1))\n\n ''' POST ROUND VARIABLE CHANGES '''\n first_dmg_turn_counter -= 1\n \n \n \n players_df = pd.DataFrame(all_players)\n players_df.columns = column_names\n return players_df\n \n \nall_files = data.file.unique()\n\n\n\nfor f,rnd in file_to_rounds.items():\n round_df = all_roles_in_round(data, f, rnd)\n main_df = main_df.append(round_df, ignore_index = True)\n \n \n# for f in all_files:\n# if index == iterations:\n# break\n# round_df = all_roles_in_round(data, f)\n# #print('--------------------------------------')\n# main_df = main_df.append(round_df, ignore_index = True)\n \n# index += 1\n\n#Remove outlier players with a K/D less than 0.2\n# main_df = main_df.loc[main_df['total deaths'] > 0]\n# main_df = main_df.loc[main_df['total kills']/main_df['total deaths'] > 0.2]\n\n#main_df.to_csv('doNOTdelete.csv', index = False, encoding='utf-8')\n \nmain_df = main_df.drop(['time of kills', 'Health', 'team', 'positioning type', 'last x', 'last y', 'distance to A bomb (on kill list)'], axis=1)\npd.set_option(\"display.max_rows\", None, \"display.max_columns\", None, 'expand_frame_repr', False)\n\n#save to csv\nmain_df.to_csv('with_file_to_rounds.csv', index = False, encoding='utf-8')\n \n\n\n\n \n \n \n\n\n\n\n\n","repo_name":"G-Armstrong/CSGO-Spatial-Analytics","sub_path":"roles.py","file_name":"roles.py","file_ext":"py","file_size_in_byte":21363,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"19642390040","text":"#!/usr/bin/python\n# coding:utf-8\n\nimport turtle \n\n# 画布即绘图区域相关\n'''\n方式一：screensize\n参数为：宽，高，颜色\n'''\n#turtle.screensize(canvwidth=400, canvheight=300, bg='blue')\n\n'''\n方式二： setup\nwidth, height 为整数时，表示像素；若为浮点数时，表示栈屏幕的比例，默认情况下width为0.5，height为0.75\nstartx,starty 表示左上角顶点的位置，以像素为单位，默认窗口居中\n 若starx为正，表示从左侧开始计算，若为负，则从右侧边缘开始计算\n 若starty为正，表示从顶部计算，若为负，表示从下边缘计算\n'''\nturtle.setup(width=0.5, height=0.75, startx=0, starty=0)\n\n# 设置箭头不可见,别名: ht\nturtle.hideturtle()\n# 设置箭头可见，别名: st\nturtle.showturtle()\n# 获取箭头是否可见\nisShow = turtle.isvisible()\nprint(isShow)\n# 设置画笔的位置为原点,即为turtle.goto(0,0) turtle.setheading(0)\nturtle.home()\n# 设置画笔的颜色和填充颜色\nturtle.color(color1, color2)\n\n\n'''\n# 画笔相关\n# 设置画笔宽度；别名： width\nturtle.pensize(width=10)\n# 设置画笔的颜色,可使用字符串如\"blue\"或者RGB值比如(255,2,55)\nturtle.pencolor('blue')\n# 画笔移动时，不绘制；别名： pu | up\nturtle.penup()\n# 画笔移动时，绘制；别名： pd | down\nturtle.pendown()\n# 画笔是否在绘制,若pendown则True,若penup则False\nturtle.isdown()\n# 画笔的绘制速度，取值范围[0,10],其取值范围阶段为：(1,3,6,10,0)分别对应(最慢，慢，正常，快，超快)\nturtle.speed(1)\n\n# 移动相关(画笔的起始位置，默认为(0,0))\n# 向当前画笔方向移动distance像素长度，别名：fd\nturtle.forward(distance=100)\n# 向当前画笔反方向移动distance像素长度,别名：back | bk\n#turtle.backward(distance=200)\n# 顺时针旋转角度,别名：rt\nturtle.right(90)\nturtle.backward(distance=200)\n# 逆时针旋转角度，别名：lt\nturtle.left(90)\nturtle.backward(distance=100)\n# 画笔移动到指定位置，别名:setpos | setposition | goto\nturtle.goto(x=0, y=0)\n# 绘制圆.参数依次为半径，角度,绘制的步数\n# 若半径为正，则圆心在画笔的左边，若为负则在画笔的右边；后两个参数可省略，默认为360度，步数为1\nturtle.circle(radius=-100, extent=180, steps=6)\n\n# 设置图形填充颜色，可使用字符串如\"blue\"或者RGB值比如(255,2,55)\nturtle.fillcolor('red')\n# 设置图形开始填充\nturtle.begin_fill()\n# 设置图形填充完成\nturtle.end_fill()\n\n# 编写文本：\nturtle.write(\"王 江 烟\", move=True, align='left', font=('Arial',30,'normal'))\n# 设置x坐标\nturtle.setx(x=100)\n# 设置y坐标\nturtle.sety(y=100)\n# 设置箭头的朝向,别名： seth\nturtle.setheading(to_angle=90)\n'''\n\n\n\n\nturtle.done()\n","repo_name":"wangxuhe/Python","sub_path":"_turtle/_turtlefunc.py","file_name":"_turtlefunc.py","file_ext":"py","file_size_in_byte":2789,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"21298736085","text":"from typing import Optional\n\n\n# Definition for singly-linked list.\nclass ListNode:\n def __init__(self, val=0, next=None):\n self.val = val\n self.next = next\n\n# case 1 : 끝까지 확인해 리스트에 value 넣고 체크하기\nclass Solution:\n def isPalindrome(self, head: Optional[ListNode]) -> bool:\n node2list = []\n while head:\n node2list.append(head.val)\n head = head.next\n if node2list == node2list[::-1]:\n return True\n else:\n return False\n\n# case 2 : Reversed first half == Second half?\n\n\"\"\"\nPhase 1: Reverse the first half while finding the middle.\nPhase 2: Compare the reversed first half with the second half.\n*link: https://leetcode.com/explore/interview/card/top-interview-questions-easy/93/linked-list/772/discuss/64500/11-lines-12-with-restore-O(n)-time-O(1)-space\n\"\"\"\nclass Solution:\n def isPalindrome(self, head: Optional[ListNode]) -> bool:\n rev = None\n slow = fast = head\n while fast and fast.next:\n fast = fast.next.next\n rev, rev.next, slow = slow, rev, slow.next\n if fast:\n slow = slow.next\n while rev and rev.val == slow.val:\n slow = slow.next\n rev = rev.next\n return not rev\n","repo_name":"jarammm/TIL","sub_path":"Algorithm/LinkedList/Palindrome Linked List.py","file_name":"Palindrome Linked List.py","file_ext":"py","file_size_in_byte":1289,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"40732372089","text":"import csv\nfrom typing import Dict\n\n\nclass CSVCustom:\n\n def __init__(self, file_path: str, fieldnames: list = None):\n self.file_path = file_path\n self.file = None\n self.fieldnames = fieldnames\n\n def __enter__(self):\n try:\n self.file = csv.DictReader(open(self.file_path))\n except FileNotFoundError:\n self.file = csv.DictWriter(open(self.file_path, 'w'), fieldnames=self.fieldnames)\n self.file.writeheader()\n finally:\n if not self.file:\n raise\n return self\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n pass\n\n def read_row(self) -> Dict[str, str]:\n if isinstance(self.file, csv.DictWriter):\n raise RuntimeError('Can read while file is open as write mode.')\n yield from self.file\n\n def write_row(self, row: Dict[str, str]):\n if isinstance(self.file, csv.DictReader):\n raise RuntimeError('Can write while file is open as read mode.')\n self.file.writerow(row)\n","repo_name":"senavs/GooApple","sub_path":"utils/context_csv.py","file_name":"context_csv.py","file_ext":"py","file_size_in_byte":1044,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"7391215442","text":"\"\"\"Does url processing.\"\"\"\n# pylint: disable=W0312\nimport os\nfrom flask import Flask\nfrom flask import request\nfrom identifyurl import Identify\n\n# check if logger is enabled\nLOG_ENABLE = os.environ[\"DEPLOYED\"] if \"DEPLOYED\" in os.environ else ''\n\nif LOG_ENABLE == \"1\":\n from logger import Logger\n LOG = Logger(os.getenv('LOGGER_ADDR'))\n\n\napp = Flask(__name__)\n@app.route('/extract_page', methods=['POST'])\ndef processurl():\n\t\"\"\"Processes the url as html or pdf.Arg: data => input json object with url.\"\"\"\n\tdata = request.get_json(force=True)\n\t# print(data)\n\turl = data['url']\n\t# pdf has the routes to pdf parser\n\tpdf = {'pdf_upload' : data['pdf_upload'], 'pdf_parser' : data['pdf_parser']}\n\t# print('hello')\n\tif LOG_ENABLE == \"1\":\n\t\tLOG.info('url_processing', 'POST', 'NULL', 'NULL', 'URL processed successfully')\n\tresult = Identify(url, pdf).classify_url()\n\treturn result\n\n@app.route('/')\ndef hello():\n\t\"\"\"Hello world.\"\"\"\n\treturn \"hello world from url_processing\"\n\nif __name__ == '__main__':\n\tapp.run('0.0.0.0', debug=True, port=80)\n","repo_name":"sravani-kaza/BTP_1","sub_path":"url_processing/app/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1040,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"20259255462","text":"from locale import LC_NUMERIC\nfrom src.models.bert import (\n BertConfig, BertModel, BertOnlyMLMHead, BertOnlyNSPHead, BertForMaskedLM)\nfrom src.models.video_encoder import SwinTransformer3D\nfrom src.models.text_encoder import TextEncoderForPretraining\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\nimport numpy as np\nimport random\nimport einops\nfrom src.utils.logger import LOGGER\nfrom src.utils.dist import SyncFunction\nfrom src.utils.misc import vector_gather\nfrom timm.models.vision_transformer import Block\n\n\nclass VideoTokenPos(nn.Module):\n def __init__(self,num_patches=6, num_frames=32, hidden_size=768):\n super().__init__()\n self.s_pos_embed = nn.Parameter(0.02*torch.randn(1, 1, num_patches, hidden_size), requires_grad=True)\n self.t_pos_embed = nn.Parameter(0.02*torch.randn(1, num_frames, 1, hidden_size), requires_grad=True)\n self.norm = nn.LayerNorm(hidden_size)\n\n def forward(self, video_embd):\n video_embd = video_embd + self.s_pos_embed + self.t_pos_embed\n video_embd = self.norm(video_embd)\n return video_embd\n\nclass SentEmbedding(nn.Module):\n def __init__(self, cfg):\n super().__init__()\n self.embed_dim = cfg.hidden_size\n self.position_embeddings = nn.Embedding(cfg.max_position_embeddings, cfg.hidden_size)\n self.segment_embeddings = nn.Embedding(cfg.type_vocab_size, cfg.hidden_size)\n self.norm = nn.LayerNorm(cfg.hidden_size, eps=cfg.layer_norm_eps)\n self.dropout = nn.Dropout(cfg.hidden_dropout_prob)\n self.register_buffer(\"position_ids\", torch.arange(cfg.max_position_embeddings).expand((1, -1)))\n\n def forward(self, inputs_embeds, token_type_ids):\n segment_embeddings = self.segment_embeddings(token_type_ids) # B, N, C\n seq_length = inputs_embeds.shape[1]\n position_ids = self.position_ids[:, :seq_length]\n position_embeddings = self.position_embeddings(position_ids)\n embeddings = inputs_embeds + position_embeddings + segment_embeddings\n embeddings = self.norm(embeddings)\n embeddings = self.dropout(embeddings)\n return embeddings\n\n\nclass LFVILA_Pretrain(nn.Module):\n def __init__(self, args, config):\n super().__init__()\n self.cfg = config\n self.args = args\n self.video_encoder = SwinTransformer3D(**config.VideoEncoder)\n bert_config = BertConfig.from_json_file(config.bert_config)\n setattr(bert_config,'stage',config.stage)\n setattr(bert_config,'num_local_layers',config.num_local_layers)\n setattr(bert_config,'stage1_layers',config.stage1_layers)\n setattr(bert_config,'bert_frozen_stage',config.bert_frozen_stage)\n self.text_encoder = TextEncoderForPretraining(args, config=bert_config)\n self.video_downsample = nn.MaxPool2d((2,3), stride=(1,1))\n\n self.video_local_proj = nn.Linear(bert_config.hidden_size, bert_config.hidden_size)\n self.text_local_proj = nn.Linear(bert_config.hidden_size, bert_config.hidden_size)\n\n self.video_global_proj = nn.Linear(bert_config.hidden_size, bert_config.hidden_size)\n self.text_global_proj = nn.Linear(bert_config.hidden_size, bert_config.hidden_size)\n\n if config.stage == 2:\n self._freeze_stage_one_params()\n self.video_token_pos = VideoTokenPos(num_patches=config.final_num_patches,\n num_frames=config.DATA.sample_frame,\n hidden_size=bert_config.hidden_size)\n\n setattr(bert_config,'type_vocab_size',config.type_vocab_size)\n self.sent_embedding = SentEmbedding(bert_config)\n\n def _init_sent_embedding(self):\n self.sent_embedding.position_embeddings.weight.data.copy_(self.text_encoder.bert.embeddings.position_embeddings.weight.data)\n\n def _freeze_stage_one_params(self):\n freeze_modules = [\"video_encoder\", \"video_local_proj\", \"text_local_proj\", \"video_global_proj\", \"text_global_proj\", \"sent_embedding\"]\n for i in freeze_modules:\n m = getattr(self, i)\n m.eval()\n for param in m.parameters():\n param.requires_grad = False\n\n for m in [self.text_encoder.bert.embeddings]:\n m.eval()\n for param in m.parameters():\n param.requires_grad = False\n\n for i in range(0, 12):\n m = self.text_encoder.bert.encoder.layer[i]\n m.eval()\n for param in m.parameters():\n param.requires_grad = False\n\n def ct_global_loss(self, video_feat, text_feat):\n temp = self.cfg.TRAINING.temp\n t2v = torch.matmul(video_feat, text_feat.permute(1, 0)) / temp # temperature\n v2t = t2v.permute(1, 0)\n t2v_label = torch.arange(t2v.shape[0], device=t2v.device)\n v2t_label = t2v_label\n loss = (F.cross_entropy(t2v, t2v_label) + F.cross_entropy(v2t, v2t_label)).mean()\n return loss\n\n def ct_time_loss(self, video_local_feat,text_local_feat):\n b,m,c = video_local_feat.shape\n temp = self.cfg.TRAINING.time_temp\n num_key = self.cfg.TRAINING.num_key\n num_value = self.cfg.TRAINING.num_value\n num_other_neg=self.cfg.TRAINING.num_other_neg\n \n key_indices = torch.cat([torch.randperm(m).unsqueeze(0) for x in range(b)],dim=0)[:,:num_key].to(text_local_feat.device)\n value_indices = torch.cat([torch.randperm(m).unsqueeze(0) for x in range(b)],dim=0)[:,:num_value].to(text_local_feat.device)\n text_key_feat = vector_gather(text_local_feat, key_indices) \n video_value_feat = vector_gather(video_local_feat, value_indices)\n\n if num_other_neg > 0:\n other_neg_indices = torch.cat([torch.randperm(m).unsqueeze(0) for x in range(b)],dim=0)[:,0].to(text_local_feat.device)\n video_other_neg = vector_gather(video_local_feat, other_neg_indices)\n video_other_neg = torch.cat([video_other_neg.roll(shifts=x, dims=0).unsqueeze(1) for x in range(num_other_neg)],dim=1)\n video_value_feat = torch.cat([video_value_feat, video_other_neg],dim=1)\n\n sim_t2v = torch.matmul(text_key_feat, video_value_feat.permute(0,2,1)).flatten(0,1) / temp\n\n t2v_label = ((value_indices.unsqueeze(1) - key_indices.unsqueeze(2))).abs().argmin(dim=-1).flatten(0,1)\n\n minus = ((value_indices.unsqueeze(1) - key_indices.unsqueeze(2))).abs()\n mask = ((minus[:,:,0] - minus[:,:,-1]) == 0 ).flatten(0,1)\n t2v_label = t2v_label.masked_fill_(mask, -100)\n \n video_key_feat = vector_gather(video_local_feat, key_indices) \n text_value_feat = vector_gather(text_local_feat, value_indices)\n\n if num_other_neg > 0:\n text_other_neg = vector_gather(text_local_feat, other_neg_indices)\n text_other_neg = torch.cat([text_other_neg.roll(shifts=x, dims=0).unsqueeze(1) for x in range(num_other_neg)],dim=1)\n text_value_feat = torch.cat([text_value_feat, text_other_neg],dim=1)\n\n sim_v2t = torch.matmul(video_key_feat, text_value_feat.permute(0,2,1)).flatten(0,1) / temp\n\n v2t_label = t2v_label\n\n loss = (F.cross_entropy(sim_t2v, t2v_label) + F.cross_entropy(sim_v2t, v2t_label)).mean()\n \n return loss\n\n\n def downsample_video_embd(self, video_embd):\n sample_clip = self.cfg.DATA.sample_clip\n B, N, H, W, C = video_embd.size() # B, N, H, W, C\n video_embd = video_embd.permute(0,1,4,2,3)\n video_embd = self.video_downsample(video_embd.view(B*N, C, H, W))\n video_embd = video_embd.permute(0,2,3,1) # B*N, H, W, C\n video_embd = video_embd.view(B, N, video_embd.size(-3), video_embd.size(-2),video_embd.size(-1))\n video_embd = video_embd.flatten(2,3) # B, N, X, C\n\n video_feat = video_embd.view(B, sample_clip, int(N/sample_clip), -1, C)\n video_feat = video_feat.mean(dim=[2,3])\n\n return video_feat, video_embd\n\n def shuffle_embd_for_vtm(self, video_embd):\n B, L, C = video_embd.shape\n video_embd_neg = torch.roll(video_embd[:(B//2)],1,0)\n video_embd = torch.cat([video_embd_neg, video_embd[(B//2):]], dim=0)\n vtm_label = torch.cat([torch.zeros((B//2),device=video_embd.device,dtype=torch.long), torch.ones((B-B//2),device=video_embd.device,dtype=torch.long)])\n return video_embd, vtm_label\n\n\n def forward(self, video_frames, text_ids, \n attention_mask, mlm_labels = None, \n stage=2,is_train=True,is_pretrain_val=False):\n\n # extract video feature\n B, C, N, H, W = video_frames.size()\n video_global_embd, video_local_embd = self.video_encoder(video_frames) # B, N, H, W, C\n\n video_local_feat1, _ = self.downsample_video_embd(video_local_embd)\n video_local_feat2, video_stage1_embd = self.downsample_video_embd(video_global_embd)\n\n # extract text feature\n B,M,L = text_ids.shape\n text_local_embd = self.text_encoder(text_ids.view(B*M, L), attention_mask=attention_mask.view(B*M, L), return_dict=True, stage=0).view(B, M, L, -1) # B, M, L, C\n\n if stage == 1:\n\n text_local_feat = text_local_embd[:,:,0,:] # B, M, C\n video_local_feat = F.normalize(self.video_local_proj(video_local_feat1),dim=-1)\n text_local_feat = F.normalize(self.text_local_proj(text_local_feat),dim=-1)\n else:\n video_local_feat, text_local_feat = None, None\n\n B,M,L,C = text_local_embd.shape\n\n text_segment_id = torch.arange(M, device=text_local_embd.device).repeat(B,1).repeat_interleave(L,dim=1)# B, N\n text_local_embd = self.sent_embedding(text_local_embd.view(B,M*L,-1), text_segment_id)\n\n text_local_cls = text_local_embd.view(B,M,L,-1)[:,:,0,:].mean(dim=1) # B,C\n text_global_embd = torch.cat([text_local_cls.unsqueeze(1),text_local_embd], dim=-2) # b, 1+M*L, c\n attention_mask = torch.cat([torch.tensor([1.],dtype=attention_mask.dtype, device=attention_mask.device).repeat(B,1),attention_mask.view(B,M*L)], dim=-1) # b, 1+M*L\n text_global_embd = self.text_encoder(text_global_embd, attention_mask=attention_mask, return_dict=True, stage=1) # B, 1+M*L, C\n \n if stage == 1:\n text_global_feat = text_global_embd[:,0,:] # B, C\n video_global_feat = video_local_feat2.mean(dim=1)\n\n video_global_feat = F.normalize(self.video_global_proj(video_global_feat),dim=-1)\n text_global_feat = F.normalize(self.text_global_proj(text_global_feat),dim=-1)\n\n else:\n text_global_feat, video_global_feat = None, None\n\n if stage == 1:\n if self.args.distributed:\n text_global_feat = SyncFunction.apply(text_global_feat)\n video_global_feat = SyncFunction.apply(video_global_feat)\n\n if self.cfg.TRAINING.use_time_match:\n text_local_feat = SyncFunction.apply(text_local_feat)\n video_local_feat = SyncFunction.apply(video_local_feat) \n\n ct_global_loss, ct_time_loss = 0, 0\n if is_train or is_pretrain_val:\n if stage == 1:\n ct_global_loss = self.ct_global_loss(video_global_feat, text_global_feat)\n weight=self.cfg.TRAINING.ct_global_loss_weight\n ct_global_loss = weight*ct_global_loss\n\n if self.cfg.TRAINING.use_time_match:\n ct_time_loss = self.ct_time_loss(text_local_feat,video_local_feat)\n weight=self.cfg.TRAINING.ct_time_loss_weight\n ct_time_loss = weight*ct_time_loss\n\n if stage == 1:\n \n return dict(text_global_feat = text_global_feat,\n video_global_feat = video_global_feat,\n ct_global_loss = ct_global_loss,\n ct_time_loss = ct_time_loss,\n mlm_loss=0,\n vtm_loss=0,\n mlm_prediction=0,\n mlm_acc = 0,\n vtm_acc = 0\n )\n\n video_stage1_embd = self.video_token_pos(video_stage1_embd)\n\n video_stage1_embd = video_stage1_embd.flatten(1,2)\n\n visual_attention_mask = attention_mask.new_ones(\n video_stage1_embd.shape[:2])\n attention_mask = torch.cat(\n [attention_mask, visual_attention_mask], dim=-1)\n\n\n video_stage1_embd, vtm_labels = self.shuffle_embd_for_vtm(video_stage1_embd)\n\n stage1_embedding_output = torch.cat([text_global_embd, video_stage1_embd], dim=1)\n\n mlm_labels = torch.cat([-100*mlm_labels.new_ones(mlm_labels.shape[:1]).unsqueeze(1), mlm_labels, -100*mlm_labels.new_ones(video_stage1_embd.shape[:2])], dim=1)\n\n fusion_output = self.text_encoder(stage1_embedding_output, attention_mask=attention_mask, mlm_labels = mlm_labels, vtm_labels=vtm_labels, return_dict=True, stage=2)\n\n\n mlm_loss = self.cfg.TRAINING.mlm_loss_weight * fusion_output['mlm_loss']\n mlm_acc = fusion_output['mlm_acc']\n mlm_prediction = fusion_output['mlm_logits']\n vtm_acc = fusion_output['vtm_acc']\n vtm_loss = self.cfg.TRAINING.vtm_loss_weight * fusion_output['vtm_loss']\n\n return dict(mlm_loss=mlm_loss,\n vtm_loss=vtm_loss,\n mlm_prediction=mlm_prediction,\n mlm_acc = mlm_acc,\n vtm_acc = vtm_acc,\n ct_global_loss = 0.,\n ct_time_loss = 0.,\n )\n \n\n","repo_name":"microsoft/XPretrain","sub_path":"LF-VILA/src/models/lfvila_pretrain.py","file_name":"lfvila_pretrain.py","file_ext":"py","file_size_in_byte":13580,"program_lang":"python","lang":"en","doc_type":"code","stars":369,"dataset":"github-code","pt":"77"}
+{"seq_id":"16611583177","text":"import os\nimport shutil\n\n\nif __name__ == \"__main__\":\n processed_path = \"./DL_test\"\n base_path = \"./DL_data\"\n val_txt_path = base_path + \"/val.txt\"\n\n with open(val_txt_path) as f:\n lines = f.readlines()\n for i, line in enumerate(lines):\n img_name, label = line.split(\" \")\n\n label = label.replace(\"\\n\",\"\")\n\n img_dir_path = processed_path + \"/\" + label\n img_src_path = base_path + \"/test_images/\" + img_name\n img_dst_path = img_dir_path + \"/\" + img_name\n if not os.path.isdir(img_dir_path):\n os.mkdir(img_dir_path)\n\n shutil.copyfile(img_src_path, img_dst_path)\n\n\n\n","repo_name":"divanoLetto/Explicability-of-decisions-and-uncertainty-in-Deep-Learning","sub_path":"dataset/process_data.py","file_name":"process_data.py","file_ext":"py","file_size_in_byte":679,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"43703568551","text":"# Detects cells in the specified frames.\n# Standard imports\nimport cv2\nimport numpy as np\nimport scipy\nimport scipy.ndimage.measurements as measurements\nimport sys\nimport os\nimport getopt\nfrom video_capture_function import *\nfrom detector_function import *\nfrom background_remover_function import *\nfrom track_cells import *\nimport pandas as pd\n\ndef usage():\n script = os.path.basename(__file__)\n print(\"\\n\\nUsage: \" + script + \" [options]