.*?)(?<=\\\\n)(?P=fence)[ ]*$', re.MULTILINE | re.DOTALL)\nCODE_WRAP = '%s
'\nLANG_TAG = ' class=\"%s\"'\n\nclass FencedCodeExtension(markdown.Extension):\n\n def extendMarkdown(self, md, md_globals):\n md.registerExtension(self)\n md.preprocessors.add('fenced_code_block', FencedBlockPreprocessor(md), '_begin')\n\n\nclass FencedBlockPreprocessor(markdown.preprocessors.Preprocessor):\n\n def __init__(self, md):\n markdown.preprocessors.Preprocessor.__init__(self, md)\n self.checked_for_codehilite = False\n self.codehilite_conf = {}\n\n def run(self, lines):\n if not self.checked_for_codehilite:\n for ext in self.markdown.registeredExtensions:\n if isinstance(ext, CodeHiliteExtension):\n self.codehilite_conf = ext.config\n break\n\n self.checked_for_codehilite = True\n text = '\\n'.join(lines)\n while 1:\n m = FENCED_BLOCK_RE.search(text)\n if m:\n lang = ''\n if m.group('lang'):\n lang = LANG_TAG % m.group('lang')\n if self.codehilite_conf:\n highliter = CodeHilite(m.group('code'), linenos=self.codehilite_conf['force_linenos'][0], guess_lang=self.codehilite_conf['guess_lang'][0], css_class=self.codehilite_conf['css_class'][0], style=self.codehilite_conf['pygments_style'][0], lang=m.group('lang') or None, noclasses=self.codehilite_conf['noclasses'][0])\n code = highliter.hilite()\n else:\n code = CODE_WRAP % (lang, self._escape(m.group('code')))\n placeholder = self.markdown.htmlStash.store(code, safe=True)\n text = '%s\\n%s\\n%s' % (text[:m.start()], placeholder, text[m.end():])\n else:\n break\n\n return text.split('\\n')\n\n def _escape(self, txt):\n txt = txt.replace('&', '&')\n txt = txt.replace('<', '<')\n txt = txt.replace('>', '>')\n txt = txt.replace('\"', '"')\n return txt\n\n\ndef makeExtension(configs=None):\n return FencedCodeExtension(configs=configs)\n\n\nif __name__ == '__main__':\n import doctest\n doctest.testmod()","sub_path":"client/markdown/extensions/fenced_code.py","file_name":"fenced_code.py","file_ext":"py","file_size_in_byte":2649,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"611421655","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri May 5 11:43:41 2017\n\n@author: neha\nchanged augumentataion for next learning\n\"\"\"\n\nfrom keras.models import Model\nfrom keras.layers import Dense, GlobalAveragePooling2D, Dropout, Flatten, AveragePooling2D, Conv2D\nfrom keras.layers import Input\nfrom keras.optimizers import SGD\nfrom keras.callbacks import ModelCheckpoint, EarlyStopping, TensorBoard, LearningRateScheduler, Callback, ReduceLROnPlateau\nfrom keras.preprocessing.image import ImageDataGenerator\nimport matplotlib.pyplot as plt\nfrom keras.models import Sequential\nfrom keras.optimizers import Adam, Adadelta\nfrom keras.layers.core import Flatten, Dense, Dropout, Activation\nfrom keras.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D\nfrom keras.layers.normalization import BatchNormalization\nfrom keras import regularizers\nfrom keras.initializers import RandomNormal\nfrom keras import backend as K\nfrom keras import layers\n\n#for large image size\nfrom PIL import ImageFile\nImageFile.LOAD_TRUNCATED_IMAGES = True\nimport numpy as np\n\n#np.random.seed(2017)\n\n# param setup\nimg_width = 200 #299 - Xception InceptionV3\nimg_height = 200 #224 - VGG19 VGG16 ResNet50\nimg_channel = 3\nnbr_train_samples = 1184\nnbr_validation_samples = 297\nnbr_epochs = 200\nbatch_size = 32\ntrain_step = 37\nval_step = 10\nn_classes = 3\n\n# traing the model for 80% 20% split\ntrain_data_dir = '../../data/train_split_clahe_256px_crop_auto_40_lcolor'\nval_data_dir = '../../data/val_split_clahe_256px_crop_auto_40_lcolor'\nbest_model_file = \"./ex1_model_clahe_lcolor_40wh_5.h5\"\n\nCtypes = ['Type_1', 'Type_2', 'Type_3']\n\ndef identity_block(input_tensor, kernel_size, filters, stage, block):\n \"\"\"The identity block is the block that has no conv layer at shortcut.\n # Arguments\n input_tensor: input tensor\n kernel_size: default 3, the kernel size of middle conv layer at main path\n filters: list of integers, the filterss of 3 conv layer at main path\n stage: integer, current stage label, used for generating layer names\n block: 'a','b'..., current block label, used for generating layer names\n # Returns\n Output tensor for the block.\n \"\"\"\n filters1, filters2, filters3 = filters\n if K.image_data_format() == 'channels_last':\n bn_axis = 3\n else:\n bn_axis = 1\n conv_name_base = 'res' + str(stage) + block + '_branch'\n bn_name_base = 'bn' + str(stage) + block + '_branch'\n\n x = Conv2D(filters1, (1, 1), name=conv_name_base + '2a')(input_tensor)\n x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)\n x = Activation('relu')(x)\n\n x = Conv2D(filters2, kernel_size,\n padding='same', name=conv_name_base + '2b')(x)\n x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)\n x = Activation('relu')(x)\n\n x = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x)\n x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)\n\n x = layers.add([x, input_tensor])\n x = Activation('relu')(x)\n return x\n\ndef conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2)):\n \"\"\"conv_block is the block that has a conv layer at shortcut\n # Arguments\n input_tensor: input tensor\n kernel_size: default 3, the kernel size of middle conv layer at main path\n filters: list of integers, the filterss of 3 conv layer at main path\n stage: integer, current stage label, used for generating layer names\n block: 'a','b'..., current block label, used for generating layer names\n # Returns\n Output tensor for the block.\n Note that from stage 3, the first conv layer at main path is with strides=(2,2)\n And the shortcut should have strides=(2,2) as well\n \"\"\"\n filters1, filters2, filters3 = filters\n if K.image_data_format() == 'channels_last':\n bn_axis = 3\n else:\n bn_axis = 1\n conv_name_base = 'res' + str(stage) + block + '_branch'\n bn_name_base = 'bn' + str(stage) + block + '_branch'\n\n x = Conv2D(filters1, (1, 1), strides=strides,\n name=conv_name_base + '2a')(input_tensor)\n x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)\n x = Activation('relu')(x)\n\n x = Conv2D(filters2, kernel_size, padding='same',\n name=conv_name_base + '2b')(x)\n x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)\n x = Activation('relu')(x)\n\n x = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x)\n x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)\n\n shortcut = Conv2D(filters3, (1, 1), strides=strides,\n name=conv_name_base + '1')(input_tensor)\n shortcut = BatchNormalization(axis=bn_axis, name=bn_name_base + '1')(shortcut)\n\n x = layers.add([x, shortcut])\n x = Activation('relu')(x)\n return x\n\n#create the model\nimg_input = Input(shape=(img_width,img_height,3))\n\nif K.image_data_format() == 'channels_last':\n bn_axis = 3\nelse:\n bn_axis = 1\n\nx = ZeroPadding2D((3, 3))(img_input)\nx = Conv2D(8, (7, 7), strides=(2, 2), name='conv1')(x)\nx = BatchNormalization(axis=bn_axis, name='bn_conv1')(x)\nx = Activation('relu')(x)\nx = MaxPooling2D((3, 3), strides=(2, 2))(x)\n\nx = conv_block(x, 3, [8, 8, 8], stage=2, block='a', strides=(1, 1))\nx = identity_block(x, 3, [8, 8, 8], stage=2, block='b')\nx = identity_block(x, 3, [8, 8, 8], stage=2, block='c')\n\nx = conv_block(x, 3, [8, 8, 8], stage=3, block='a')\nx = identity_block(x, 3, [8, 8, 8], stage=3, block='b')\nx = identity_block(x, 3, [8, 8, 8], stage=3, block='c')\nx = identity_block(x, 3, [8, 8, 8], stage=3, block='d')\n\nx = conv_block(x, 3, [8, 8, 8], stage=4, block='a')\nx = identity_block(x, 3, [8, 8, 8], stage=4, block='b')\nx = identity_block(x, 3, [8, 8, 8], stage=4, block='c')\nx = identity_block(x, 3, [8, 8, 8], stage=4, block='d')\nx = identity_block(x, 3, [8, 8, 8], stage=4, block='e')\nx = identity_block(x, 3, [8, 8, 8], stage=4, block='f')\n\nx = conv_block(x, 3, [12, 12, 12], stage=5, block='a')\nx = identity_block(x, 3, [12, 12, 12], stage=5, block='b')\nx = identity_block(x, 3, [12, 12, 12], stage=5, block='c')\n\nx = AveragePooling2D((7, 7), name='avg_pool')(x)\n#stage 6\n# dense layer\nx = Flatten()(x)\n#x = Dropout(0.1)(x)\n#, kernel_regularizer=regularizers.l2(0.0003)\nx = Dense(n_classes, activation='softmax')(x)\n\ninputs = img_input\n# Create model.\nmodel = Model(inputs, x, name='Cervix_pts_resnet_50')\n \n#get the model \n\n#optimizer = Adam(lr=0.00008, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)\noptimizer = Adam(lr=0.0005, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)\nmodel.compile(optimizer='adam', loss='categorical_crossentropy', metrics = ['accuracy', 'categorical_crossentropy'])\nmodel.summary()\n\n# autosave best Model\nbest_model = ModelCheckpoint(best_model_file, monitor='val_loss', verbose = 1, save_best_only = True)\nstop = EarlyStopping(monitor='val_loss', min_delta=0.0001, patience=4, verbose=0, mode='auto')\nlrreduce = ReduceLROnPlateau(monitor='val_loss', factor=0.3, patience=35, verbose=1, mode='auto', epsilon=0.0001, cooldown=0, min_lr=0)\n\n#preprocess image \ndef preprocess_input(x):\n x /= 255.\n x -= 0.5\n x *= 2.\n return x\n\n# train the model on the new data for a few epochs\n# this is the augmentation configuration we will use for training\ntrain_datagen = ImageDataGenerator(\n shear_range=0.02,\n rotation_range=360.,\n zoom_range=0.05,\n width_shift_range=0.08,\n height_shift_range=0.08,\n horizontal_flip=True,\n vertical_flip=True,\n preprocessing_function=preprocess_input)\n \n# this is the augmentation configuration we will use for validation: only rescaling\nval_datagen = ImageDataGenerator(\n shear_range=0.02,\n rotation_range=360.,\n zoom_range=0.05,\n width_shift_range=0.08,\n height_shift_range=0.08,\n horizontal_flip=True,\n vertical_flip=True,\n preprocessing_function=preprocess_input)\n\ntrain_generator = train_datagen.flow_from_directory(\n train_data_dir,\n color_mode = 'rgb',\n target_size = (img_width, img_height),\n batch_size = batch_size,\n shuffle = True,\n classes = Ctypes,\n class_mode = 'categorical'\n )\n\nvalidation_generator = val_datagen.flow_from_directory(\n val_data_dir,\n color_mode = 'rgb',\n target_size=(img_width, img_height),\n batch_size=batch_size,\n shuffle = True,\n classes = Ctypes,\n class_mode = 'categorical')\n\n#class_weight = {0 : 2., 1: 1., 2: 1.5}\n#class_weight = {0 : 3., 1: 1., 2: 1.9}\nclass_weight = {0 : 2.8, 1: 1., 2: 1.6} # applied smoothing .1\n\nhistory = model.fit_generator(\n train_generator,\n steps_per_epoch = train_step, \n epochs = nbr_epochs,\n validation_data = validation_generator,\n validation_steps = val_step,\n callbacks = [best_model, lrreduce],\n class_weight = class_weight,\n verbose = 1)\n \n# save the training history\nnp.savetxt(\"grey_200.csv\", history, delimiter=\",\")\n\n# list all data in history\nprint(history.history.keys())\n## summarize history for accuracy\nplt.plot(history.history['acc'])\nplt.plot(history.history['val_acc'])\nplt.title('model accuracy')\nplt.ylabel('accuracy')\nplt.xlabel('epoch')\nplt.legend(['train', 'test'], loc='upper left')\nplt.show()\n# summarize history for loss\nplt.plot(history.history['loss'][0:])\nplt.plot(history.history['val_loss'][0:])\nplt.plot(history.history['categorical_crossentropy'][0:])\nplt.title('model loss')\nplt.ylabel('loss')\nplt.xlabel('epoch')\nplt.legend(['train', 'test'], loc='upper left')\nplt.show()\n\n\nval_loss = history.history['val_loss']\ntrain_loss = history.history['categorical_crossentropy']\n\nval_loss.extend(history.history['val_loss'])\ntrain_loss.extend(history.history['categorical_crossentropy'])\n\nplt.plot(train_loss[0:])\nplt.plot(val_loss[0:])\nplt.title('model loss')\nplt.ylabel('loss')\nplt.xlabel('epoch')\nplt.legend(['train', 'test'], loc='upper left')\nplt.show()","sub_path":"cevicalC/Models/10_final_version/ctype_res50_5.py","file_name":"ctype_res50_5.py","file_ext":"py","file_size_in_byte":10071,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"391840032","text":"#!/usr/bin/env python2.6\n\"\"\"\ntsvcat [files]\n\nConcatenates TSV-with-header files, aligning columns with same name. \nCan rename columns and match columns across files with different names.\n\nModified by Lee Lichtenstein on June 20, 2012 to enable additional functionality and enable integration into Firehose\n\"\"\"\n\nimport sys, itertools\nimport tsvutil\ntsvutil.fix_stdio()\nimport csv\nfrom OrderedSet import OrderedSet\n#import codecs; sys.stdout = codecs.open('/dev/stdout','w',encoding='utf8',buffering=0)\nimport codecs\n\ndef flatten(iter):\n return list(itertools.chain(*iter))\n \ndef stable_uniq(x):\n s = set(); y = []\n for i in x:\n if i in s: continue\n s.add(i)\n y.append(i)\n return y\n \ndef tsv_reader(f):\n return csv.DictReader(f, dialect=None, delimiter=\"\\t\", quoting=csv.QUOTE_NONE)\n\n\ndef tsvcat(items, outFilePointer):\n alias_specs = [s for s in items if '=' in s]\n drop_specs = [s for s in items if s.startswith('-')]\n filenames = [s for s in items if s not in alias_specs and s not in drop_specs]\n #files = [open(f, 'r') for f in filenames]\n # codecs.open(filename, mode[, encoding\n\n files = [codecs.open(f, 'r', encoding=\"iso8859-1\") for f in filenames]\n if not filenames:\n files = [sys.stdin]\n # LTL: Did not handle prepended comment lines.\n # file_cols = [f.readline()[:-1].split(\"\\t\") for f in files]\n # Unique comment lines are prepended onto the final result.\n file_cols = []\n commentLines = OrderedSet()\n for f in files:\n isHeaderFound = False\n line = f.readline()\n while not isHeaderFound:\n if not line.startswith('#'):\n isHeaderFound = True\n file_cols.append(line[:-1].split(\"\\t\"))\n else:\n commentLines.add(line.rstrip())\n line = f.readline()\n all_cols = stable_uniq(flatten(file_cols))\n \n aliases = {}\n for alias_spec in alias_specs:\n left, right = alias_spec.split('=')\n assert left != right\n assert left and right\n assert left in all_cols\n aliases[right] = left\n if right not in all_cols:\n all_cols[ all_cols.index(left) ] = right\n else:\n all_cols.remove(left)\n \n for drop_spec in drop_specs:\n col = drop_spec[1:]\n #print col\n #print all_cols\n assert col in all_cols\n all_cols.remove(col)\n \n if len(commentLines) > 0:\n outFilePointer.write(\"\\n\".join(commentLines)+\"\\n\")\n\n outFilePointer.write(\"\\t\".join(all_cols)+\"\\n\")\n \n for i, f in enumerate(files):\n cols = file_cols[i]\n for line in f:\n #line = unicode(line,'utf8')\n parts = line[:-1].split(\"\\t\")\n hash = {}\n for j in range(len(cols)):\n hash[cols[j]] = parts[j]\n out = []\n for col in all_cols:\n if col in hash: \n out.append(hash[col])\n elif col in aliases:\n out.append(hash[aliases[col]])\n else:\n out.append('')\n outFilePointer.write(u\"\\t\".join(out) + '\\n')\n\n\nif __name__ == '__main__':\n items = sys.argv[1:]\n tsvcat(items,sys.stdout)\n\n","sub_path":"tasks/mutation_validator/src/fh_tsvCatFiles/tsvcat.py","file_name":"tsvcat.py","file_ext":"py","file_size_in_byte":3265,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"295929408","text":"from anytree import NodeMixin, RenderTree\r\n\r\nclass MyClass(NodeMixin):\r\n def __init__(self, name, start, end, parent = None):\r\n super(MyClass, self).__init__()\r\n self.name = name\r\n self.start = start\r\n self.end = end\r\n self.parent = parent\r\n\r\n def __repr__(self):\r\n return f\"({self.end-self.start:.2f}) - {self.name}\"\r\ndef main():\r\n a = MyClass(start=1, end=2, name='one')\r\n b = MyClass(start = 3, end = 4, name='two', parent=a)\r\n print(RenderTree(a))\r\n\r\nif __name__ == '__main__':\r\n main()","sub_path":"tests/test_anytree.py","file_name":"test_anytree.py","file_ext":"py","file_size_in_byte":550,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"236368365","text":"import pygame\nfrom pygame.locals import *\n\nimport levels\n\npygame.init()\nWIDTH = 1000\nHEIGHT = 1000\n\nclass GameDisplay:\n\n def __init__(self):\n self.surface = pygame.display.set_mode((WIDTH, HEIGHT))\n\n @property\n def size(self):\n return self.surface.get_width()\n\n def absolute(self, *cos):\n if len(cos) == 1:\n return int(cos[0]*self.size)\n return tuple(int(c*self.size) for c in cos)\n\n def draw_object(self, obj):\n color = pygame.Color(255, 255, 0)\n rect = self.absolute(*obj.top_left, *obj.box)\n pygame.draw.rect(self.surface, color, rect)\n\n def draw_game(self, game):\n self.surface.fill(pygame.Color(0, 0, 0))\n for player in game.players:\n self.draw_object(player)\n\n\nimport math, time\ndef game_loop():\n i = 0\n game = levels.level_1()\n display = GameDisplay()\n while True:\n pygame.display.update()\n i += 0.01\n time.sleep(0.01)\n game.tick()\n display.draw_game(game)\n for event in pygame.event.get():\n if event.type == pygame.KEYDOWN:\n if event.key == pygame.K_a:\n game.turn_cw()\n if event.key == pygame.K_s:\n game.turn_ccw()\n if event.type == QUIT:\n pygame.quit()\n break\n\ngame_loop()\n","sub_path":"interface.py","file_name":"interface.py","file_ext":"py","file_size_in_byte":1359,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"557794776","text":"# coding: utf-8\n# 例外メッセージを指定しよう\nimport sys\nenemies = [\"スライム\", \"ドラゴン\", \"魔王\"]\n\ntry:\n number = 0\n print(\"勇者は敵に遭遇した\")\n print(\"勇者は\" + enemies[2 / number] + \"と戦った\")\nexcept ZeroDivisionError as e:\n sys.stderr.write(\"その敵は表示できません\")\nfinally:\n print(\"勇者は勝利した\")\n","sub_path":"paiza/paiza_10/paiza_10_003_001.py","file_name":"paiza_10_003_001.py","file_ext":"py","file_size_in_byte":379,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"373922649","text":"import os\n\nimport pytest\nfrom django.conf import settings\nfrom django.core.management import call_command\nfrom django.utils import timezone\nfrom minio import Minio\nfrom simple_history.models import registered_models\n\nfrom caluma.core.management.commands import cleanup_history\n\nfrom ..models import Form\n\n\ndef test_create_bucket_command(mocker):\n mocker.patch.object(Minio, \"make_bucket\")\n call_command(\"create_bucket\", stdout=open(os.devnull, \"w\"))\n Minio.make_bucket.assert_called_once_with(settings.MINIO_STORAGE_MEDIA_BUCKET_NAME)\n\n\n@pytest.mark.parametrize(\"force\", [True, False])\n@pytest.mark.parametrize(\"keep,kept\", [(\"1 year\", 2), (\"1 day\", 1), (None, 2)])\ndef test_cleanup_history_command(db, force, keep, kept):\n # we need to override the registered models dict in order to get rid of the\n # fake models created in core tests\n cleanup_history.registered_models = {\n k: v for k, v in registered_models.items() if not k.startswith(\"core_\")\n }\n kwargs = {}\n if force:\n kwargs[\"force\"] = force\n else:\n kept = 3\n if keep:\n kwargs[\"keep\"] = keep\n\n Form.objects.create(slug=\"form 1\")\n\n f2 = Form.objects.create(slug=\"form 2\")\n f2_hist = f2.history.first()\n f2_hist.history_date = f2_hist.history_date - timezone.timedelta(days=2)\n f2_hist.save()\n\n f3 = Form.objects.create(slug=\"form 3\")\n f3_hist = f3.history.first()\n f3_hist.history_date = f3_hist.history_date - timezone.timedelta(days=730)\n f3_hist.save()\n\n call_command(\"cleanup_history\", **kwargs, stdout=open(os.devnull, \"w\"))\n\n assert Form.history.count() == kept\n","sub_path":"caluma/form/tests/test_command.py","file_name":"test_command.py","file_ext":"py","file_size_in_byte":1620,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"356686397","text":"'''\nCreated on Jul 10, 2018\n\n@author: daniel\n'''\n\n#from multiprocessing import Process, Manager\n#from keras.utils import np_utils\nimport sys\nimport os\nsys.path.append(os.path.join(os.path.dirname(__file__), '..'))\n\nfrom Utils.TimerModule import TimerModule\nfrom Exploratory_Stuff.BlockDataHandler import BlockDataHandler\n#from keras.callbacks import CSVLogger,ReduceLROnPlateau\nfrom keras.layers import Dense, BatchNormalization, Conv1D, Dropout\nfrom keras.models import Sequential\nfrom keras.callbacks import CSVLogger\n#from keras.optimizers import SGD\n#import os\nfrom Utils.EmailHandler import EmailHandler\nfrom Utils.HardwareHandler import HardwareHandler\nfrom datetime import datetime\n#from keras.utils.training_utils import multi_gpu_model\n#import keras.backend as K\n#import tensorflow as tf\nimport nibabel as nib\nimport numpy as np\nfrom NMFComputer.BasicNMFComputer import BasicNMFComputer\nimport matplotlib.pyplot as plt\nfrom keras.layers.advanced_activations import PReLU\nfrom Exploratory_Stuff.ExtendedBlockDataHandler import ExtendedBlockDataHandler\n \nfrom NMFComputer.SKNMFComputer import SKNMFComputer\nimport sys\nimport os\nimport cv2\nDATA_DIR = os.path.abspath(\"../\")\nsys.path.append(DATA_DIR)\nfrom numpy import genfromtxt\n\ndef main():\n hardwareHandler = HardwareHandler()\n emailHandler = EmailHandler()\n timer = TimerModule()\n now = datetime.now()\n date_string = now.strftime('%Y-%m-%d_%H_%M')\n \n print('Loading the data! This could take some time...')\n num_training_patients = 12;\n num_validation_patients = 1;\n nmfComp = BasicNMFComputer(block_dim=8, num_components=8)\n dataHandler = ExtendedBlockDataHandler(\"Data/BRATS_2018/HGG\", nmfComp, num_patients = num_training_patients, modes = [\"flair\", \"t1ce_bf_corrected\", \"t1_bf_corrected\", \"t2_bf_corrected\"])\n dataHandler.loadData()\n dataHandler.preprocessForNetwork()\n x_train = dataHandler.X\n labels = dataHandler.labels\n dataHandler.clear()\n \n dataHandler.setLoadingMode(\"validation\")\n\n dataHandler.setDataDirectory(\"Data/BRATS_2018/HGG_Validation\")\n dataHandler.setNumPatients(num_validation_patients)\n dataHandler.loadData()\n dataHandler.preprocessForNetwork()\n x_val = dataHandler.X\n val_labels = dataHandler.labels\n dataHandler.clear()\n \n print('Building the model now!')\n model = Sequential()\n model.add(Dense(2048, input_dim=len(dataHandler.modes)*dataHandler.nmfComp.num_components))\n model.add(BatchNormalization())\n model.add(PReLU())\n model.add(Dropout(0.5))\n \n model.add(Dense(1024))\n model.add(BatchNormalization())\n model.add(PReLU())\n model.add(Dropout(0.5))\n \n model.add(Dense(512))\n model.add(BatchNormalization())\n model.add(PReLU())\n model.add(Dropout(0.5))\n\n model.add(Dense(256))\n model.add(BatchNormalization())\n model.add(PReLU())\n model.add(Dropout(0.5))\n\n model.add(Dense(128))\n model.add(BatchNormalization())\n model.add(PReLU())\n model.add(Dropout(0.5))\n\n model.add(Dense(64))\n model.add(BatchNormalization())\n model.add(PReLU())\n model.add(Dropout(0.5))\n \n model.add(Dense(32))\n model.add(BatchNormalization())\n model.add(PReLU())\n model.add(Dropout(0.5))\n\n model.add(Dense(16))\n model.add(BatchNormalization())\n model.add(PReLU())\n model.add(Dense(labels.shape[1], activation='softmax'))\n \n \n# Compile model\n model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n \n \n \n model_directory = \"/home/daniel/eclipse-workspace/MRIMath/Models/extended_blocknet_\" + date_string\n if not os.path.exists(model_directory):\n os.makedirs(model_directory)\n log_info_filename = 'model_loss_log.csv'\n csv_logger = CSVLogger(model_directory + '/' + log_info_filename, append=True, separator=',')\n \n model_info_filename = 'model_info.txt'\n model_info_file = open(model_directory + '/' + model_info_filename, \"w\") \n model_info_file.write('Number of Patients (training): ' + str(num_training_patients) + '\\n')\n model_info_file.write('Number of Patients (validation): ' + str(num_validation_patients) + '\\n')\n\n model_info_file.write('Block Dimensions: ' + str(dataHandler.nmfComp.block_dim) + '\\n')\n model_info_file.write('Number of Components (k): ' + str(dataHandler.nmfComp.num_components) + '\\n')\n model_info_file.write('\\n\\n')\n model.summary(print_fn=lambda x: model_info_file.write(x + '\\n'))\n model_info_file.close();\n\n print('Training network!')\n model.fit(x_train,\n labels,\n epochs=500,\n validation_data=(x_val, val_labels),\n callbacks = [csv_logger],\n batch_size=x_train.shape[0])\n \n \n model.save(model_directory + '/model.h5')\n test_data_dir = \"Data/BRATS_2018/HGG\"\n image = None\n seg_image = None\n m = nmfComp.block_dim\n \n for subdir in os.listdir(test_data_dir):\n seg_est = np.zeros(shape=(dataHandler.W, dataHandler.H))\n data_dirs = os.listdir(test_data_dir + \"/\" + subdir)\n seg_image = nib.load(test_data_dir + \"/\" + subdir + \"/\" + [s for s in data_dirs if \"seg\" in s][0]).get_data()\n inds = [i for i in list(range(155)) if np.count_nonzero(seg_image[:,:,i]) > 0]\n for k in inds:\n X_test = []\n\n foo = []\n for path in data_dirs:\n for mode in dataHandler.modes:\n if mode in path:\n image = nib.load(test_data_dir + \"/\" + subdir + \"/\" + path).get_data()\n foo.extend(dataHandler.processData2(image[:,:,k]))\n \n chunks = [foo[x:x+int(len(foo)/len(dataHandler.modes))] for x in range(0, len(foo), int(len(foo)/len(dataHandler.modes)))]\n for i in range((int(len(foo)/len(dataHandler.modes)))):\n X_test.append(np.concatenate((chunks[0][i], chunks[1][i], chunks[2][i], chunks[3][i]), axis=None))\n\n print(len(X_test))\n est_labels = [model.predict(x.reshape((1, -1))) for x in X_test]\n #labels = model.predict(H.T)\n ind = 0\n for i in range(0, dataHandler.W, m):\n for j in range(0, dataHandler.H, m):\n seg_est[i:i+m, j:j+m] = np.full((m, m), np.argmax(est_labels[ind]))\n ind = ind+1\n fig = plt.figure()\n plt.gray();\n \n a=fig.add_subplot(1,2,1)\n plt.imshow(seg_image[:,:,k])\n plt.axis('off')\n plt.title('GT Segment')\n \n a=fig.add_subplot(1,2,2)\n plt.imshow(seg_est)\n plt.axis('off')\n plt.title('Estimate Segment')\n plt.show()\n\n \n# evaluate the model\n\nif __name__ == \"__main__\":\n main() \n","sub_path":"TestingExtendedBlockArchitecture.py","file_name":"TestingExtendedBlockArchitecture.py","file_ext":"py","file_size_in_byte":6815,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"468662641","text":"# coding=utf-8\n# Copyright 2019 The Tensor2Tensor Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"Tests for Gaussian process layers.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport numpy as np\n\nfrom tensor2tensor.layers import gaussian_process\nfrom tensor2tensor.utils import test_utils\n\nimport tensorflow as tf\ntf.compat.v1.enable_eager_execution()\n\n\nclass GaussianProcessTest(tf.test.TestCase):\n\n @test_utils.run_in_graph_and_eager_modes()\n def testGaussianProcessPosterior(self):\n train_batch_size = 3\n test_batch_size = 2\n input_dim = 4\n output_dim = 5\n features = tf.to_float(np.random.rand(train_batch_size, input_dim))\n labels = tf.to_float(np.random.rand(train_batch_size, output_dim))\n layer = gaussian_process.GaussianProcess(output_dim,\n conditional_inputs=features,\n conditional_outputs=labels)\n test_features = tf.to_float(np.random.rand(test_batch_size, input_dim))\n test_labels = tf.to_float(np.random.rand(test_batch_size, output_dim))\n test_outputs = layer(test_features)\n test_nats = -test_outputs.distribution.log_prob(test_labels)\n self.evaluate(tf.global_variables_initializer())\n test_nats_val, outputs_val = self.evaluate([test_nats, test_outputs])\n self.assertEqual(test_nats_val.shape, ())\n self.assertGreaterEqual(test_nats_val, 0.)\n self.assertEqual(outputs_val.shape, (test_batch_size, output_dim))\n\n @test_utils.run_in_graph_and_eager_modes()\n def testGaussianProcessPrior(self):\n batch_size = 3\n input_dim = 4\n output_dim = 5\n features = tf.to_float(np.random.rand(batch_size, input_dim))\n labels = tf.to_float(np.random.rand(batch_size, output_dim))\n model = tf.keras.Sequential([\n tf.keras.layers.Dense(2, activation=None),\n gaussian_process.GaussianProcess(output_dim),\n ])\n outputs = model(features)\n log_prob = outputs.distribution.log_prob(labels)\n self.evaluate(tf.global_variables_initializer())\n log_prob_val, outputs_val = self.evaluate([log_prob, outputs])\n self.assertEqual(log_prob_val.shape, ())\n self.assertLessEqual(log_prob_val, 0.)\n self.assertEqual(outputs_val.shape, (batch_size, output_dim))\n\n @test_utils.run_in_graph_and_eager_modes()\n def testSparseGaussianProcess(self):\n dataset_size = 10\n batch_size = 3\n input_dim = 4\n output_dim = 5\n features = tf.to_float(np.random.rand(batch_size, input_dim))\n labels = tf.to_float(np.random.rand(batch_size, output_dim))\n model = gaussian_process.SparseGaussianProcess(output_dim, num_inducing=2)\n with tf.GradientTape() as tape:\n predictions = model(features)\n nll = -tf.reduce_mean(predictions.distribution.log_prob(labels))\n kl = sum(model.losses) / dataset_size\n loss = nll + kl\n\n self.evaluate(tf.global_variables_initializer())\n grads = tape.gradient(nll, model.variables)\n for grad in grads:\n self.assertIsNotNone(grad)\n\n loss_val, predictions_val = self.evaluate([loss, predictions])\n self.assertEqual(loss_val.shape, ())\n self.assertGreaterEqual(loss_val, 0.)\n self.assertEqual(predictions_val.shape, (batch_size, output_dim))\n\n @test_utils.run_in_graph_and_eager_modes()\n def testBayesianLinearModel(self):\n \"\"\"Tests that model makes reasonable predictions.\"\"\"\n np.random.seed(42)\n train_batch_size = 5\n test_batch_size = 2\n num_features = 3\n noise_variance = 0.01\n coeffs = tf.range(num_features, dtype=tf.float32)\n features = tf.to_float(np.random.randn(train_batch_size, num_features))\n labels = (tf.tensordot(features, coeffs, [[-1], [0]])\n + noise_variance * tf.to_float(np.random.randn(train_batch_size)))\n\n model = gaussian_process.BayesianLinearModel(noise_variance=noise_variance)\n model.fit(features, labels)\n\n test_features = tf.to_float(np.random.randn(test_batch_size, num_features))\n test_labels = tf.tensordot(test_features, coeffs, [[-1], [0]])\n outputs = model(test_features)\n test_predictions = outputs.distribution.mean()\n test_predictions_variance = outputs.distribution.variance()\n\n [\n test_labels_val, test_predictions_val, test_predictions_variance_val,\n ] = self.evaluate(\n [test_labels, test_predictions, test_predictions_variance])\n self.assertEqual(test_predictions_val.shape, (test_batch_size,))\n self.assertEqual(test_predictions_variance_val.shape, (test_batch_size,))\n self.assertAllClose(test_predictions_val, test_labels_val, atol=0.1)\n self.assertAllLessEqual(test_predictions_variance_val, noise_variance)\n\n\nif __name__ == \"__main__\":\n tf.test.main()\n","sub_path":"tensor2tensor/layers/gaussian_process_test.py","file_name":"gaussian_process_test.py","file_ext":"py","file_size_in_byte":5240,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"37094500","text":"import numpy as np\nimport pandas as pd\n\nfrom os.path import join\nfrom time import time\nfrom sklearn.model_selection import train_test_split, KFold\nfrom sklearn.metrics import roc_auc_score\n\nfrom keras.models import Model\nfrom keras.layers import Input, Dense, Dropout, Conv1D, Embedding, SpatialDropout1D, concatenate\nfrom keras.layers import CuDNNGRU, CuDNNLSTM, Bidirectional, GlobalAveragePooling1D, GlobalMaxPooling1D\nfrom keras.callbacks import Callback, EarlyStopping, ModelCheckpoint\nfrom keras import optimizers\nfrom keras.layers import Lambda\n\nclass RocAucEvaluation( Callback ):\n def __init__( self, validation_data = (), interval = 1 ):\n super( Callback, self ).__init__()\n\n self.interval = interval\n self.X_val, self.y_val = validation_data\n self.max_score = 0\n self.not_better_count = 0\n\n def on_epoch_end( self, epoch, logs = {} ):\n if epoch % self.interval == 0:\n y_pred = self.model.predict( self.X_val, verbose = 1 )\n score = roc_auc_score( self.y_val, y_pred )\n print( \"\\n ROC-AUC - epoch: %d - score: %.6f \\n\" % ( epoch + 1, score ) )\n if ( score > self.max_score ):\n print( \"*** New High Score (previous: %.6f) \\n\" % self.max_score )\n self.model.save_weights(\"best_weights.h5\")\n self.max_score = score\n self.not_better_count = 0\n else:\n self.not_better_count += 1\n if self.not_better_count > 3: # patience\n print( \"Epoch %05d: early stopping, high score = %.6f\" % ( epoch, self.max_score ) )\n self.model.stop_training = True\n\ndef get_model( feature_size, maxlen, embedding_matrix ):\n\n features_input = Input( shape = ( feature_size, ) )\n tokens_input = Input( shape = ( maxlen, ) )\n\n x = Embedding( embedding_matrix.shape[0], embedding_matrix.shape[1],\n weights = [embedding_matrix], trainable = False )( tokens_input )\n x = SpatialDropout1D( 0.5 )( x )\n x = Bidirectional( CuDNNLSTM( 40, return_sequences = True ) )( x )\n x, x_h, x_c = Bidirectional( CuDNNGRU( 40, return_sequences = True, return_state = True ) )( x )\n avg_pool = GlobalAveragePooling1D()( x )\n max_pool = GlobalMaxPooling1D()( x )\n x = concatenate( [avg_pool, x_h, max_pool, features_input] )\n \n output = Dense( 6, activation = \"sigmoid\" )( x )\n\n model = Model( inputs = [tokens_input, features_input], outputs = output )\n adam = optimizers.adam( clipvalue = 1.0 )\n model.compile( loss='binary_crossentropy',\n optimizer = adam,\n metrics=['accuracy'] )\n return model\n\ndef get_score( pred_file, true_file ):\n pred = pd.read_csv( pred_file ).values[:,1:].astype( np.float64 )\n true = pd.read_csv( true_file ).values[:,1:].astype( np.int8 )\n scored_index = true[:,0] != -1\n pred_scored = pred[scored_index,:]\n true_scored = true[scored_index,:]\n score = roc_auc_score( true_scored, pred_scored )\n return score\n\nif __name__ == \"__main__\":\n exp_dir = \"/home/lizx/nlp_data/toxic/exp\"\n \n begin = time()\n X_train = np.load( join( exp_dir, \"X_train_300.npy\" ) )\n y_train = np.load( join( exp_dir, \"y_train.npy\" ) )\n X_test = np.load( join( exp_dir, \"X_test_300.npy\" ) )\n features = np.load( join( exp_dir, \"features.npy\" ) )\n test_features = np.load( join( exp_dir, \"test_features.npy\" ) )\n embedding_matrix = np.load( join( exp_dir, \"embedding_matrix.npy\" ) )\n consume = ( time() - begin ) / 60\n print( \"Loading data consumes %.2f min\" % consume )\n \n feature_size = features.shape[1]\n maxlen = 300\n\n predict = np.zeros( [X_test.shape[0], 6] )\n num_folds = 10\n kf = KFold( n_splits = num_folds, shuffle = True, random_state = 239 )\n for i, ( train_index, valid_index ) in enumerate( kf.split( X_train ) ):\n begin = time()\n kf_y_train, kf_y_valid = y_train[train_index], y_train[valid_index]\n kf_X_train, kf_X_valid = X_train[train_index], X_train[valid_index]\n kf_X_train_features, kf_X_valid_features = features[train_index], features[valid_index]\n\n model = get_model( feature_size, maxlen, embedding_matrix )\n ra_val = RocAucEvaluation( validation_data = ( [kf_X_valid, kf_X_valid_features], kf_y_valid ), interval = 1 )\n model.fit( [kf_X_train, kf_X_train_features], kf_y_train, \n batch_size = 32, epochs = 10, verbose = 1, callbacks = [ra_val] )\n \n model.load_weights( \"best_weights.h5\" )\n predict += model.predict( [X_test, test_features], batch_size = 32, verbose = 1 )\n consume = ( time() - begin ) / 60\n print( \"One fold consumes %.2f min\" % consume )\n\n sample_submission = pd.read_csv( join( exp_dir, \"sample_submission.csv\" ) )\n class_names = ['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']\n sample_submission[class_names] = predict / num_folds\n pred_file = join( exp_dir, \"submission.csv\" )\n sample_submission.to_csv( pred_file, index = False )\n\n true_file = join( exp_dir, \"test_labels.csv\" )\n score = get_score( pred_file, true_file )\n print( \"AUC: %f\" % score )\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"train_and_predict.py","file_name":"train_and_predict.py","file_ext":"py","file_size_in_byte":5208,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"13116020","text":"import pandas as pd\nimport numpy as np\nimport csv\nimport multiprocessing as mp\n\nclass_mean = pd.read_csv(\"class_mean.csv\").values\nclass_mean[:, 1] = list(map(lambda x: np.fromstring(x[1:-1], dtype='float', sep=' '), class_mean[:, 1]))\n\nwith open(\"test_set_features.csv\", \"r\") as f:\n test_features = f.readlines()\n\ncores = 6\np = int(len(test_features)/cores)\n\nclf = [[\"id\",\"landmarks\"]]\n\ndef find(fr):\n\n counter = fr\n\n for line in test_features[fr:fr+p]:\n\n if counter % 1000 == 0:\n\n print(\"Processed elements: \", counter)\n\n try:\n test_id, test_feature = line[:16], np.fromstring(line[17:-2], dtype='float', sep=\", \")\n\n class_dist = list(map(lambda x: np.linalg.norm(x - test_feature), class_mean[:,1]))\n\n clf_class = class_mean[np.argmin(class_dist), 0]\n\n clf.append([test_id, str(clf_class)])\n\n counter += 1\n\n except:\n continue\n\n\npool = mp.Pool(processes=cores)\nresults = pool.map(find, range(0, len(test_features), p))\npool.close()\npool.join()\n\nwith open(\"test_predictions.csv\", \"w\") as f:\n writer = csv.writer(f)\n writer.writerows(clf)\n","sub_path":"mp_megaclassifier.py","file_name":"mp_megaclassifier.py","file_ext":"py","file_size_in_byte":1151,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"542069304","text":"# Time Complexity : O(n)\n# Space Complexity :O(1)\n\nclass Solution:\n def rob(self, nums: List[int]) -> int:\n if len(nums) == 0:\n return 0\n\n # maintain teo pointers\n # one is if the house is chosen and the other is if the house is not chosen\n not_chosen = 0\n chosen = nums[0]\n\n # then iterate over all the houses and find the amount when that house is chose and when its not chosen\n for n in range(1, len(nums)):\n temp = not_chosen\n # if house is not chose then the amount will be max from not chosen previous home and chosen previous home\n not_chosen = max(chosen, not_chosen)\n # if house is chose then the amount will be the value of previous to previous house + current amount\n chosen = temp + nums[n]\n return max(chosen, not_chosen)","sub_path":"Problem 2.py","file_name":"Problem 2.py","file_ext":"py","file_size_in_byte":859,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"343388172","text":"# change raw data to arrays\n\ndef inOut(data,name):\n\tarrayData = []\n\tarrayDataMem = {}\n\tarrayDataMemList = [] \n\tarrayDataDic = {}\n\tfor i , val in enumerate(data[str(name)+'name']):\n\t\tarrayDataDic = {}\n\t\tarrayDataMem = {}\n\t\tarrayDataMemList = []\n\t\tarrayDataDic['name'] = val\n\t\tarrayDataDic['range'] = data[str(name)+'range'][i]\n\t\tmemnumber = data[str(name)+'mems'][i]\n\t\tfor x in range(1,int(memnumber)+1):\n\t\t\tarrayDataMem['name'] = data['mem'+str(name)+'name[' + str(val) + '][' + str(x) + ']'][0] \n\t\t\tarrayDataMem['type'] = data['mem'+str(name)+'type[' + str(val) + '][' + str(x) + ']'][0]\n\t\t\tarrayDataMem['amount'] = data['mem'+str(name)+'amount[' + str(val) + '][' + str(x) + ']'][0]\n\t\t\tarrayDataMemList.append(arrayDataMem)\n\t\t\tarrayDataMem = {} \n\t\tarrayDataDic['memship'] = arrayDataMemList\n\t\tarrayData.append(arrayDataDic)\n\treturn arrayData\n\ndef rule(data):\n\trulenumsArray = []\n\trulenums = int(data['rulenums'][0])\n\tfor i in range(1,rulenums+1):\n\t\trulenumsArray.append(data['rule'+str(i)][0])\n\treturn rulenumsArray\n","sub_path":"web/fuzzylib/fuzzyXml/arrayior.py","file_name":"arrayior.py","file_ext":"py","file_size_in_byte":1018,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"566331361","text":"from vizdoom import *\nfrom gym.core import Env\nimport math\nimport time\nfrom gym.spaces.discrete import Discrete\nfrom gym.spaces.box import Box\nfrom util import *\nimport sys\nimport cv2\nimport tensorflow as tf\nfrom baselines.common import set_global_seeds\n\ndef get_observation(s, real_frame = False, get_rid_of_value=[], resolution = 84):\n #returns: 84 x 84 x 1\n if not real_frame:\n if s is None:\n return np.zeros([resolution, resolution, 3], dtype=np.uint8)\n else:\n texture = depth2texture(s, get_rid_of_value)\n return np.expand_dims(cv2.resize(texture, (resolution, resolution)) * norm_factor, -1)\n else:\n if s is None:\n return np.zeros([resolution, resolution, 3], dtype=np.uint8)\n else:\n screen = s.screen_buffer\n screen = screen.transpose((1,2,0))\n return cv2.resize(screen, (resolution, resolution))\n\nclass NavigationEnv(Env):\n def __init__(self):\n self.game = DoomGame()\n self.game.add_available_game_variable(GameVariable.POSITION_X)\n self.game.add_available_game_variable(GameVariable.POSITION_Y)\n self.game.add_available_game_variable(GameVariable.POSITION_Z)\n self.game.set_depth_buffer_enabled(True)\n self.observation_space = Box(low=0, high=255, shape=(84, 84, 3), dtype=np.uint8)\n self.action_space = Discrete(3)\n self.available_actions = np.eye(4).tolist()\n\n #train configuration\n\n self.seed_min = 0\n self.seed_max = 12\n self.wait_time = 0\n self.num_envs = 16\n\n #test configuration\n '''\n self.seed_min = 10009\n self.seed_max = 10010\n self.wait_time = 0.15\n '''\n\n def seed(self, seed=None):\n return [seed]\n\n def reset(self):\n self.game.close()\n self.seed = np.random.randint(self.seed_min, self.seed_max)\n self.game.load_config('/home/robot/Desktop/PVZ/finddoor.cfg')\n self.game.set_doom_scenario_path('/home/robot/Desktop/PVZ/navigation/navigation/navigation_{}.wad'.format(self.seed))\n # print(\"initing\")\n # print(self.seed)\n # set_global_seeds(self.seed)\n self.game.init()\n # print(\"inited\")\n # sys.stdout.flush()\n\n if self.seed_min < 100:\n #avoid door\n for _ in range(4):\n self.game.make_action([0,0,1,0], 4)\n self.game.make_action([0,0,0,1])\n for _ in range(8):\n self.game.make_action([0,0,1,0])\n\n self.start_x = self.game.get_game_variable(GameVariable.POSITION_X)\n self.start_y = self.game.get_game_variable(GameVariable.POSITION_Y)\n # print(\"reset path history\")\n self._reset_path_history()\n # print(\"reseted path history\")\n return get_observation(self.game.get_state(), real_frame=True)\n\n def step(self, action):\n if self.wait_time > 0:\n time.sleep(self.wait_time)\n\n old_x = self.game.get_game_variable(GameVariable.POSITION_X)\n old_y = self.game.get_game_variable(GameVariable.POSITION_Y)\n old_z = self.game.get_game_variable(GameVariable.POSITION_Z)\n self.game.make_action(self.available_actions[action], 4)\n new_x = self.game.get_game_variable(GameVariable.POSITION_X)\n new_y = self.game.get_game_variable(GameVariable.POSITION_Y)\n new_z = self.game.get_game_variable(GameVariable.POSITION_Z)\n\n if self.game.is_player_dead() or self.game.is_episode_finished():\n return get_observation(None), 0, True, None\n else:\n\n depth_buffer = self.game.get_state().depth_buffer.astype(np.int32)\n print(np.array(depth_buffer).shape)\n if np.sum(np.max(depth_buffer, axis=0)) < 1000:\n depth_rew = -1\n else:\n depth_rew = 0\n\n explore_rew = self._register_visit()\n height_rew = new_z - old_z\n speed_rew = math.sqrt((new_x - old_x)**2 + (new_y - old_y)**2) / 33\n #original:0.05,0,1,0.05\n #print(explore_rew, speed_rew, depth_rew)\n rew = 0.25 * explore_rew + 1.0 * speed_rew + 0.05 * height_rew + 0.05*depth_rew\n rew = rew * 1000\n return get_observation(self.game.get_state(), real_frame=True), rew, False, None\n\n def _reset_path_history(self):\n self.path_history = np.ones([200,200], dtype=np.float32)\n\n def _register_visit(self):\n self.path_history = np.maximum(self.path_history * 0.98,1)\n pos_x = self.game.get_game_variable(GameVariable.POSITION_X)\n pos_y = self.game.get_game_variable(GameVariable.POSITION_Y)\n\n x_block = min(int(math.floor(pos_x)) // 50, 199)\n y_block = min(int(math.floor(pos_y)) // 50, 199)\n if self.path_history[x_block, y_block] < 10:\n self.path_history[x_block, y_block] += 1\n return 1 / float(self.path_history[x_block, y_block])\n\n","sub_path":"navigation_env.py","file_name":"navigation_env.py","file_ext":"py","file_size_in_byte":4950,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"369632785","text":"\"\"\"\n 给定一个 N 叉树，返回其节点值的层序遍历。 (即从左到右，逐层遍历)。\n\n 返回其层序遍历:\n [\n [1],\n [3,2,4],\n [5,6]\n ]\n \n 说明:\n 树的深度不会超过 1000。\n 树的节点总数不会超过 5000。\n\"\"\"\n\nfrom typing import List\n\n\nclass Node:\n def __init__(self, val=None, children=None):\n self.val = val\n self.children = children\n\n\nclass Solution:\n def levelOrder(self, root: Node) -> List[List[int]]:\n return self.use_queue(root)\n\n @classmethod\n def recursive(cls, root: Node) -> List[List[int]]:\n \"\"\"\n 递归\n 时间复杂度 O(n)\n 空间复杂度 O(n)\n \"\"\"\n\n def hand(node: Node, level) -> None:\n if len(res) == level:\n res.append([])\n res[level].append(node.val)\n\n for child in node.children:\n hand(child, level + 1)\n\n res = []\n if root:\n hand(root, 0)\n\n return res\n\n @classmethod\n def use_queue(cls, root: Node) -> List[List[int]]:\n \"\"\"\n 使用queue保存当前层的节点。\n 时间复杂度是 O(nk) k为节点最多一层的数目，因为extend的时间复杂度是O(k)\n 空间复杂度是 O(n)\n \"\"\"\n res = []\n if root:\n queue = [root]\n while queue:\n tmp_queue = []\n tmp_res = []\n\n for node in queue: # 每一次的循环都是一层\n if node:\n tmp_res.append(node.val)\n tmp_queue.extend(node.children)\n queue = tmp_queue\n res.append(tmp_res)\n return res\n","sub_path":"Week_02/G20200343030545/LeetCode_429_545.py","file_name":"LeetCode_429_545.py","file_ext":"py","file_size_in_byte":1802,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"193318759","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n# 视频信息封装\n\nimport json\nfrom NetworkAttributes import ENCRYPT_ONE, ENCRYPT_TWO\nfrom User import User\nimport os\nimport requests\nfrom Common import print_info\n\n\nclass Video(User):\n\n def __init__(self, jsonValue, type):\n super().__init__(jsonValue, type)\n self.gold = 0\n self.retime = 3\n if type == ENCRYPT_ONE:\n self.mvId = str(jsonValue.get(\"mv_id\"))\n self.title = str(jsonValue.get(\"mv_title\")).replace(\" \", \"\").replace('\\n', '')\n self.imgURL = str(jsonValue.get(\"mv_img_url\"))\n self.playURL = str(jsonValue.get(\"mv_play_url\"))\n self.downloadURL = str(jsonValue.get(\"mv_play_url\"))\n self.playWidth = str(jsonValue.get(\"mv_play_width\"))\n self.playHeight = str(jsonValue.get(\"mv_play_height\"))\n self.like = str(jsonValue.get(\"mv_like\"))\n self.isCatAds = bool(jsonValue.get(\"is_cat_ads\"))\n self.isCollect = str(jsonValue.get(\"is_collect\"))\n elif type == ENCRYPT_TWO:\n self.mvId = str(jsonValue.get(\"id\"))\n self.title = str(jsonValue.get(\"title\")).replace(\" \", \"\").replace('\\n', '')\n self.imgURL = str(jsonValue.get(\"cover\"))\n self.playURL = str(jsonValue.get(\"normal_url\"))\n self.downloadURL = str(jsonValue.get(\"normal_url\"))\n self.playWidth = str(jsonValue.get(\"width\"))\n self.playHeight = str(jsonValue.get(\"height\"))\n self.like = str(jsonValue.get(\"praise_num\"))\n self.isCatAds = False\n self.isCollect = str(jsonValue.get(\"is_collect\"))\n if 'type' in jsonValue.keys():\n videoType = int(jsonValue.get('type'))\n self.isCatAds = videoType == 2\n else:\n self.isCatAds = False\n\n if \"gold\" in jsonValue.keys():\n self.gold = int(jsonValue.get(\"gold\"))\n else:\n pass\n self.createSavePath()\n\n def createSavePath(self):\n \"\"\" 创建下载目录 \"\"\"\n if self.type == ENCRYPT_ONE:\n child = \"One\"\n elif self.type == ENCRYPT_TWO:\n child = \"Two\"\n else:\n child = \"Other\"\n fold = os.path.abspath('.')\n self.savePath = os.path.join(fold, \"FastVideo\", child)\n if not os.path.exists(self.savePath):\n os.makedirs(self.savePath)\n\n def download(self):\n \"\"\" 下载视频,返回文件名 \"\"\"\n url = self.downloadURL\n if url is None:\n print_info('{}_{} 下载链接为空'.format(self.type, self.mvId))\n return None\n name = '{}-{}_u:{}v:{}{}'.format(self.type, self.title, self.uId, self.mvId, os.path.splitext(url)[-1])\n\n path = os.path.join(self.savePath, name)\n if os.path.exists(path):\n print_info('{}_{} 文件存在，不用下载'.format(self.type, self.mvId))\n return name\n\n res = requests.get(url)\n # 获取文件大小\n # file_size_str = res.headers['Content-Length']\n # file_size = float(file_size_str)/1024/1024\n # print_info('开始下载：{}_{}'.format(self.type, self.mvId))\n with open(path, 'wb') as f:\n try:\n f.write(res.content)\n # 文件小于1M不上传\n if self.get_FileSize(path) < 1:\n return None\n # print_info('{}_{} 下载成功'.format(self.type, self.mvId))\n return name\n finally:\n f.close\n\n def get_FileSize(self, filePath):\n fsize = os.path.getsize(filePath)\n fsize = fsize/float(1024 * 1024)\n return round(fsize, 2)\n","sub_path":"Video.py","file_name":"Video.py","file_ext":"py","file_size_in_byte":3730,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"350156382","text":"import numpy as np\nimport cv2\n\n\ndef fluorescence(mask_in, gray_img, segmented_img):\n \"\"\"\n Indica los valores CTCF (Corrected Total Cell Fluorescence) y mean gray value (promedio del valor de gris) para una mascara dada, los cuales indican dos medidas de fluorescencia para las particulas detectadas\n Entradas:\n mask: imagen de tamaño NxM la cual muestra el area perteneciente a una particula con valor 1 y el resto 0 (atributo de la clase particle)\n gray_img: imagen original de tamaño NxM en valores de grises\n segmented_img: imagen segmentada de tamaño NxM (salida de la función segmentation)\n Salida:\n CTCF y mean_gray_value - medidas de fluorescencia para la mascara dada\n \"\"\"\n # mask = cv2.erode(mask_in, kernel=(3, 3), iterations=10)\n mask = mask_in\n # contruccion imagen \"fluorescent mask\"\n fluorescent_mask = gray_img * mask\n integrated_density = np.sum(fluorescent_mask)\n area_in_pixels = np.sum(mask)\n mean_gray_value = integrated_density / area_in_pixels\n\n # construccion imagen \"background\"\n segmented_img_inv = (segmented_img == 0).astype(np.uint8)\n background_img = gray_img * segmented_img_inv\n background_mean = np.sum(background_img) / (gray_img.shape[0] * gray_img.shape[1])\n CTCF = integrated_density - (area_in_pixels * background_mean)\n return CTCF, mean_gray_value\n","sub_path":"tool_containerized/docker_project/tool/src/fluorescence/fluorescence.py","file_name":"fluorescence.py","file_ext":"py","file_size_in_byte":1373,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"373352554","text":"from django.conf.urls import url\n\nfrom . import views\nfrom app.attendance.views import attendance\n\napp_name = 'student'\nurlpatterns = [\n url(r'^$', views.index, name='index'), # 学生登录进系统后的“首页”，还没确定放什么内容\n url(r'^member_evaluation$', views.member_evaluation, name='member_evaluation'), # 团队负责人的团队成员评价页面，可设置贡献度等\n url(r'^resources$', views.view_resources, name='resources'), # 资源列表页面\n url(r'^submits$', views.view_submitted_work, name='submitted_work'), # 查看作业提交情况\n url(r'^unsubmits$', views.view_unsubmitted_work, name='unsubmitted_work'), # 查看未提交情况\n url(r'^submit_team$', views.submit_team), # 提交组队请求\n url(r'^student_team_build$', views.student_team_build ,name='student_team_build'),\n url(r'^s$', views.download, name='download'), # 资源下载链接\n url(r'^workpage$', views.workRoot, name='workpage'), # 查看作业详情\n url(r'^work', views.workView, name='work'), # 查看作业详情\n url(r'^teampage$',views.teamRoot, name='teampage') ,# 团队主页，其下有多个功能\n url(r'^mycourse$',views.my_course,name='my_course'),\n url(r'^attendance', attendance), # 签到\n url(r'^apply_for_team', views.apply_for_team), # 申请加入团队\n url(r'^process_apply', views.process_apply), # 处理申请请求\n url(r'^finish_team_bulid', views.finish_team_bulid), # 结束组队\n url(r'^dismiss_team', views.dismiss_team), # 解散团队\n url(r'^view_score$',views.view_score,name='view_score'),\n url(r'^finish_team_bulid', views.finish_team_bulid), # 处理申请请求\n url(r'^preview_source_online/', views.preview_source_online, name='preview_source_online'),\n url(r'^view_notice$',views.viewNotice,name='view_notice'),\n\n]","sub_path":"app/student/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1852,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"141337986","text":"'''Jeu de Sudoku en simulation humaine. Joue au Sudoku de la même manière\nqu'un joueur humain en utilisant des capacités cognitives simulées avec leurs\nimperfections et leurs limites. Tente également d'apprendre à mieux jouer.\n'''\n\nimport sudoio\nfrom sudoio import sudoPause, display, displayError\nimport sudorules as rules\n\nimport sudogrid\nfrom sudogrid import SudoGrid\nfrom sudogame import SudoGame\nfrom sudoplayer import SudoPlayer\nfrom sudomemprofile import SudoMemProfile\nfrom sudothinking import SudoThinkProfile\n\nimport sudotest\nfrom sudotest import sudoTest as TEST #objet global\n\n\nclass Sudoku():\n '''Encapsule le jeu dans son ensemble. Une instance de Sudoku gère des\n grilles, des joueurs créés avec des niveaux de compétences, et fait se\n jouer des parties par les joueurs avec les grilles.\n '''\n\n def __init__(self):\n '''Instanciation'''\n self._initOk = False\n self.init()\n\n def init(self):\n '''initialisatino ou réinitialisation du jeu.'''\n display(\"Jeu de Sudoku - Simulation de joueur humain\")\n TEST.display(\"sudoku\", 3, \"Sudoku - dans init()\")\n self._player = None\n self._playerOk = False\n self._playerReady = False\n self._playerMemProfile = None\n self._playerThinkProfile = None\n self._playerProfilesOk = False\n self._gridOk = False\n self._gameOk = False\n\n self._initOk = True\n TEST.display(\"sudoku\", 1, \"Jeu de Sudoku initialisé.\")\n return\n\n def createPlayer(self, name=None):\n '''Crée un joueur en lui attribuant un nom. Le joueur n'a pas\n initialement de mémoire ni de pensée, cela est créé ensuite.\n '''\n assert self._initOk\n try:\n self._player = SudoPlayer(name)\n self._player.init()\n except:\n displayError(\"Erreur\",\"Impossible de créer le joueur\")\n self._player = None\n self._playerOk = False\n raise Sudoku_Error(\"Sudoku.createPlayer(): erreur de création\")\n self._playerOk = True\n TEST.display(\"sudoku\", 2, \"Sudoku - Joueur {0} créé\" \\\n .format(self._player.name()))\n return\n\n def playerProfile(self, memProfile=None, thinkProfile=None):\n '''Donne au joueur des capacités mémoire et pensée selon des profiles'''\n TEST.display(\"sudoku\", 3, \"Sudoku - dans playerProfile()\")\n assert self._initOk\n try:\n if memProfile is not None:\n assert isinstance(memProfile, SudoMemProfile)\n self._player.memProfile(memProfile)\n self._playerMemProfile = memProfile\n if thinkProfile is not None:\n assert isinstance(thinkProfile, SudoThinkProfile)\n self._player.thinkProfile(thinkProfile)\n self._playerThinkProfile = thinkProfile\n self._playerProfilesOk = True\n except:\n DisplayError(\"Erreur\", \"Impossible de créer le profil du joueur\")\n self._PlayerProfilesOk = False\n raise Sudoku_Error(\"Impossible de définir le profile du joueur\")\n return\n\n def playerPlay(self, player, grid):\n assert self._initOk\n assert player is not None and isinstance(player, SudoPlayer)\n assert grid is not None and isinstance(grid, SudoGrid)\n gameResult = player.play(grid)\n display(gameResult)\n return gameResult\n\n## def createGame(self, player, grid):\n## '''Crée une partie avec un joueur et une grille de Sudoku'''\n## assert self._initOk\n## assert isinstance(player, SudoPlayer) and self._playerOk\n## assert isinstance(grid, SudoGrid)\n## try:\n## self._game = SudoGame()\n## self._game.init(player, grid)\n## except:\n## DisplayError(\"Erreur\", \"Impossible de créer la partie\")\n## self._gameOk = False\n## raise Sudoku_Error(\"Impossible de créer la partie\")\n## self._grid = grid\n## self._gridOk = True\n## self._gameOk = True\n## TEST.display(\"sudoku\", 1, \"La partie est prête à être jouée\")\n## return self._game\n##\n## def play():\n## '''Lance le jeu.'''\n## result = self._game.play()\n## #resultDetails = self._game.details()\n## return\n \n \n\n#TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST\nif __name__ == \"__main__\":\n\n import sudotestall\n testAlllevel = 1\n TEST.levelAll(testAlllevel)\n display(\"Tous les niveaux de test sont à {0}\".format(testAlllevel))\n\n s = Sudoku()\n s.createPlayer(\"David\")\n \n","sub_path":"sudosimu/sudoku.py","file_name":"sudoku.py","file_ext":"py","file_size_in_byte":4669,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"505257126","text":"import numpy as np\nimport datetime as dt\nfrom copy import deepcopy\nimport xarray\nfrom sklearn.cluster import DBSCAN\nimport h5py\nimport scipy\nimport os\n\narray_list = ['lon', 'lat', 'ws', 'rcg', 'ws_yslf_nbrcs',\n 'ws_yslf_les', 'datetimes', 'sod']\nscalar_list = ['antenna', 'prn', 'sat']\n\n\ndef read_cygnss_l2(fname):\n return xarray.open_dataset(fname)\n\n\ndef read_imerg(fname):\n return Imerg(fname)\n\n\ndef split_tracks_in_time(track, gap=60):\n \"\"\"\n This function will split a CygnssTrack object into two separate tracks\n if there is a significant gap in time. Currently can split a track up to\n three times.\n\n Parameters\n ----------\n track : CygnssTrack object\n CYGNSS track that needs to be checked for breaks in time\n\n Other Parameters\n ----------------\n gap : int\n Number of seconds in a gap before a split is forced\n\n Returns\n -------\n track_list : list\n List of CygnssTrack objects broken up from original track\n \"\"\"\n indices = np.where(np.diff(track.sod) > gap)[0]\n if len(indices) > 0:\n if len(indices) == 1:\n track1 = subset_track(deepcopy(track), 0, indices[0]+1)\n track2 = subset_track(deepcopy(track), indices[0]+1,\n len(track.sod))\n return [track1, track2]\n if len(indices) == 2:\n track1 = subset_track(deepcopy(track), 0, indices[0]+1)\n track2 = subset_track(deepcopy(track), indices[0]+1,\n indices[1]+1)\n track3 = subset_track(deepcopy(track), indices[1]+1,\n len(track.sod))\n return [track1, track2, track3]\n if len(indices) == 3:\n track1 = subset_track(deepcopy(track), 0, indices[0]+1)\n track2 = subset_track(deepcopy(track), indices[0]+1, indices[1]+1)\n track3 = subset_track(deepcopy(track), indices[1]+1, indices[2]+1)\n track4 = subset_track(deepcopy(track), indices[2]+1,\n len(track.sod))\n return [track1, track2, track3, track4]\n else:\n print('Found more than four tracks!')\n return 0\n\n\ndef subset_track(track, index1, index2):\n \"\"\"\n This function subsets a CYGNSS track to only include data from a range\n defined by two indexes.\n\n Parameters\n ----------\n track : CygnssTrack object\n CygnssTrack to be subsetted\n index1 : int\n Starting index\n index2 : int\n Ending index\n\n Returns\n -------\n track : CygnssTrack object\n Subsetted CygnssTrack object\n \"\"\"\n for arr in array_list:\n setattr(track, arr, getattr(track, arr)[index1:index2])\n for scalar in scalar_list:\n setattr(track, scalar, getattr(track, scalar))\n return track\n\n\ndef get_tracks(data, sat, min_samples=10, verbose=False,\n filter=False, window=5, eps=1, gap=60):\n \"\"\"\n Returns a list of isolated CygnssTrack objects from a CYGNSS data object.\n\n Parameters\n ----------\n data : xarray.core.dataset.Dataset object\n CYGNSS data object as read by xarray.open_dataset\n sat : int\n CYGNSS satellite to be analyzed.\n\n Other Parameters\n ----------------\n min_samples : int\n Minimum allowable track size (number of specular points)\n verbose : bool\n True - Provide text updates while running\n\n False - Don't do this\n\n filter : bool\n True - Each track will receive a filter\n\n False - Don't do this\n\n window : int\n Window length of filter, in number of specular points. Must be odd.\n eps : scalar\n This is the eps keyword to be passed to DBSCAN. It is the max distance\n (in degrees lat/lon) between two tracks for them to be considered as\n part of the same track.\n gap : int\n Number of seconds in a track gap before a split is forced\n\n Returns\n -------\n trl : list\n List of isolated CygnssTrack objects\n \"\"\"\n trl = []\n dts = get_datetime(data)\n # Currently only works for one satellite at a time due to resource issues\n if type(sat) is not int or sat < 1 or sat > 8:\n raise ValueError('sat must be integer between 1 and 8')\n else:\n csat = sat\n if verbose:\n print('CYGNSS satellite', csat)\n print('GPS code (max =', str(int(np.max(data.prn_code.data)))+'):',\n end=' ')\n for gsat in range(np.int16(np.max(data.prn_code.data)+1)):\n if verbose:\n print(gsat, end=' ')\n # This will isolate most tracks, improving later cluster analysis\n for ant in range(np.int16(np.max(data.antenna.data)+1)):\n ds = CygnssTrack(data, datetimes=dts, gpsid=gsat,\n sat=csat, antenna=ant)\n if np.size(ds.lon) > 0:\n # Cluster analysis separates out additional grouped tracks\n # Only simplistic analysis of lat/lon gaps in degrees needed\n X = list(zip(ds.lon, ds.lat))\n db = DBSCAN(min_samples=min_samples, eps=eps).fit(X)\n labels = db.labels_\n uniq = np.unique(labels)\n for element in uniq[uniq >= 0]:\n # A bit clunky, but make a copy of the CygnssTrack object\n # to help separate out remaining tracks in the scene\n dsc = deepcopy(ds)\n for key in array_list:\n setattr(dsc, key, getattr(ds, key)[labels == element])\n dsc.lon[dsc.lon > 180] -= 360.0\n for key in scalar_list:\n setattr(dsc, key, np.array(getattr(ds, key))[0])\n # Final separation by splitting about major time gaps\n test = split_tracks_in_time(dsc, gap=gap)\n if test is None: # No time gap, append the original track\n trl.append(dsc)\n # Failsafe - Ignore difficult-to-split combined tracks\n elif test == 0:\n pass\n else: # Loop thru split-up tracks and append separately\n for t in test:\n trl.append(t)\n del dsc\n del db, labels, uniq, X\n del ds # This function is a resource hog, forcing some cleanup\n if filter:\n for tr in trl:\n tr.filter_track(window=window)\n return trl\n\n\ndef get_datetime(cyg):\n epoch_start = np.datetime64('1970-01-01T00:00:00Z')\n tdelta = np.timedelta64(1, 's')\n return np.array([dt.datetime.utcfromtimestamp((st - epoch_start) / tdelta)\n for st in cyg.sample_time.data])\n\n\nclass CygnssSubsection(object):\n\n \"\"\"\n Class to handle subsectioning CYGNSS data. Subsectioning by\n satellite (via CygnssSingleSat input), time indices, GPS satellite ID,\n range-corrected gain, etc. is supported.\n\n Main Attributes\n ---------------\n ws = Wind speed array\n lon = Longitude array\n lat = Latitude array\n rcg = RangeCorrectedGain array\n gps = GpsID array\n \"\"\"\n\n def __init__(self, data, gpsid=None, gain=None, sat=None, antenna=None):\n \"\"\"\n data = CygnssSingleSat, CygnssMultiSat, or CygnssL2WindDisplay object\n gpsid = Integer ID number for GPS satellite to examine\n gain = Threshold by range-corrected gain, values below will be masked\n bad = Value to compare against lat/lon to mask out missing data\n sat = CYGNSS satellite number (1-8)\n \"\"\"\n # Set basic attributes based on input data object\n self.ws = data.wind_speed.data\n self.ws_yslf_nbrcs = data.yslf_nbrcs_wind_speed.data\n self.ws_yslf_les = data.yslf_les_wind_speed.data\n self.lon = data.lon.data\n self.lat = data.lat.data\n self.gps = np.int16(data.prn_code.data)\n self.antenna = np.int16(data.antenna.data)\n self.rcg = data.range_corr_gain.data\n self.cygnum = np.int16(data.spacecraft_num.data)\n\n # Set keyword-based attributes\n self.gpsid = gpsid\n self.gain = gain\n self.sat = sat\n self.ant_num = antenna\n\n # Now subsection the data\n self.get_good_data_mask()\n\n def get_good_data_mask(self):\n \"\"\"\n Sets a mask used to limit the data plotted. Filtered out are data\n masked out by the GoodData mask (based on RangeCorrectedGain), missing\n lat/lon values, and bad data (ws < 0)\n \"\"\"\n good1 = self.ws >= 0\n good2 = np.logical_and(np.isfinite(self.lon), np.isfinite(self.lat))\n if self.gpsid is not None and type(self.gpsid) is int:\n good2 = np.logical_and(good2, self.gps == self.gpsid)\n if self.gain is not None:\n if np.size(self.gain) == 2:\n cond = np.logical_and(self.rcg >= self.gain[0],\n self.rcg < self.gain[1])\n good2 = np.logical_and(good2, cond)\n else:\n good2 = np.logical_and(good2, self.rcg >= self.gain)\n if self.sat is not None and type(self.sat) is int:\n good2 = np.logical_and(good2, self.cygnum == self.sat)\n if self.ant_num is not None and type(self.sat) is int:\n good2 = np.logical_and(good2, self.antenna == self.ant_num)\n self.good = np.logical_and(good1, good2)\n\n\nclass CygnssTrack(object):\n\n \"\"\"\n Class to facilitate extraction of a single track of specular points\n from a CygnssSingleSat, CygnssMultiSat, or CygnssL2WindDisplay object.\n\n Attributes\n ----------\n input = CygnssSubsection object\n ws = CYGNSS wind speeds\n lon = Longitudes of specular points\n lat = Latitudes of specular points\n rcg = Range-corrected gains of specular points\n datetimes = Datetime objects for specular points\n\n The following attributes are created by filter_track method:\n fws = Filtered wind speeds\n flon = Filtered longitudes\n flat = Filtered latitudes\n These attributes are shorter than the main attributes by the window length\n \"\"\"\n\n def __init__(self, data, datetimes=None, **kwargs):\n \"\"\"\n data = CygnssSingleSat, CygnssMultiSat, or CygnssL2WindDisplay object\n datetimes = List of datetime objects from get_datetime function.\n If None, this function is called.\n \"\"\"\n self.input = CygnssSubsection(data, **kwargs)\n self.ws = self.input.ws[self.input.good]\n self.ws_yslf_nbrcs = self.input.ws_yslf_nbrcs[self.input.good]\n self.ws_yslf_les = self.input.ws_yslf_les[self.input.good]\n self.lon = self.input.lon[self.input.good]\n self.lat = self.input.lat[self.input.good]\n self.rcg = self.input.rcg[self.input.good]\n self.antenna = self.input.antenna[self.input.good]\n self.prn = self.input.gps[self.input.good]\n self.sat = self.input.cygnum[self.input.good]\n if datetimes is None:\n dts = get_datetime(data)\n else:\n dts = datetimes\n self.datetimes = dts[self.input.good]\n sod = []\n for dt1 in self.datetimes:\n sod.append((dt1 - dt.datetime(\n self.datetimes[0].year, self.datetimes[0].month,\n self.datetimes[0].day)).total_seconds())\n self.sod = np.array(sod)\n\n def filter_track(self, window=5):\n \"\"\"\n Applies a running-mean filter to the track.\n\n window = Number of specular points in the running mean window.\n Must be odd.\n \"\"\"\n if window % 2 == 0:\n raise ValueError('Window must be odd length, not even.')\n hl = int((window - 1) / 2)\n self.fws = np.convolve(\n self.ws, np.ones((window,))/window, mode='valid')\n self.flon = self.lon[hl:-1*hl]\n self.flat = self.lat[hl:-1*hl]\n\n\nclass Imerg(object):\n\n def __init__(self, filen):\n self.read_imerg(filen)\n\n def read_imerg(self, filen):\n imerg = h5py.File(filen, 'r')\n self.datetime = dt.datetime.strptime(os.path.basename(filen)[23:39],\n '%Y%m%d-S%H%M%S')\n self.precip = np.ma.masked_where(\n np.transpose(imerg['Grid']['precipitationCal']) <= 0,\n np.transpose(imerg['Grid']['precipitationCal']))\n self.lon = np.array(imerg['Grid']['lon'])\n self.lat = np.array(imerg['Grid']['lat'])\n self.filename = os.path.basename(filen)\n imerg.close()\n\n def downsample(self):\n filled_precip = self.precip.filled(fill_value=0.0)\n dummy = scipy.ndimage.interpolation.zoom(filled_precip, 0.5)\n self.coarse_precip = np.ma.masked_where(dummy <= 0, dummy)\n self.coarse_lon = self.lon[::2]\n self.coarse_lat = self.lat[::2]\n\n\ndef add_imerg(trl, ifiles, dt_imerg):\n for ii in range(len(trl)):\n check_dt = trl[ii].datetimes[len(trl[ii].sod)//2]\n # diff = np.abs(check_dt - dt_imerg)\n index = np.where(dt_imerg <= check_dt)[0][-1] # np.argmin(diff)\n imerg = Imerg(ifiles[index])\n if ii % 50 == 0:\n print(ii, end=' ')\n precip = []\n for j in range(len(trl[ii].lon)):\n ilon = int(np.round((trl[ii].lon[j] - imerg.lon[0]) / 0.10))\n ilat = int(np.round((trl[ii].lat[j] - imerg.lat[0]) / 0.10))\n precip.append(imerg.precip[ilat, ilon])\n precip = np.array(precip)\n precip[~np.isfinite(precip)] = 0.0\n setattr(trl[ii], 'precip', precip)\n setattr(trl[ii], 'imerg', os.path.basename(ifiles[index]))\n print()\n return trl\n\n\ndef write_netcdfs(trl, path):\n for i, track in enumerate(trl):\n fname = 'track_' + str(track.sat).zfill(2) + '_' + \\\n str(track.prn).zfill(2) + \\\n '_' + str(track.antenna).zfill(2) + '_' + str(i).zfill(4) + \\\n track.datetimes[0].strftime('_%Y%m%d_s%H%M%S_') + \\\n track.datetimes[-1].strftime('e%H%M%S.nc')\n ds = xarray.Dataset(\n {'ws': (['nt'], track.ws),\n 'ws_yslf_nbrcs': (['nt'], track.ws_yslf_nbrcs),\n 'ws_yslf_les': (['nt'], track.ws_yslf_les),\n 'lat': (['nt'], track.lat),\n 'lon': (['nt'], track.lon),\n 'datetimes': (['nt'], track.datetimes),\n 'rcg': (['nt'], track.rcg),\n 'precip': (['nt'], track.precip),\n 'sod': (['nt'], track.sod)},\n coords={'nt': (['nt'], np.arange(len(track.ws)))},\n attrs={'imerg': track.imerg})\n ds.to_netcdf(path + fname, format='NETCDF3_CLASSIC')\n ds.close()\n del(ds)\n","sub_path":"pygnss/orbit.py","file_name":"orbit.py","file_ext":"py","file_size_in_byte":14691,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"393065436","text":"import sopel.module\n\n@sopel.module.commands('sucker','suckers')\ndef rules(bot, trigger):\n if not trigger.group(2):\n myline='suckers'\n else:\n myline = trigger.group(2).strip()\n \n if myline.endswith('s'):\n bot.say(myline + ' are for suckers!!')\n else:\n bot.say(myline + ' is for suckers!!')\n \n","sub_path":"suckers.py","file_name":"suckers.py","file_ext":"py","file_size_in_byte":409,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"205727072","text":"class _movie_database:\n\n\tdef __init__(self):\n\t\tself.movies_titles = {} #uses movie ID as the key\n\t\tself.movies_genres = {} #uses movie ID as the key\n\t\tself.movies_images = {} #uses movie ID as the key\n\t\tself.users_gender = {} #uses user ID as the key\n\t\tself.users_age = {} #uses user ID as the key\n\t\tself.users_occupation = {} #uses user ID as the key\n\t\tself.users_zipcode = {} #uses user ID as the key\n\t\tself.ratings = {} #uses movie ID as the key\n\t\tself.mid = 1\n\n\tdef load_movies(self, movie_file):\n\t\t# If movies were already loaded, clear records\n\t\tif self.movies_titles:\n\t\t self.movies_titles.clear()\n\t\tif self.movies_genres:\n\t\t self.movies_genres.clear()\n\n\t# If/once records are clear, populate records\n\t\tfor line in open(movie_file, 'r', encoding = 'ISO-8859-1'):\n\t\t parts = line.split(\"::\")\n\t\t self.movies_titles[int(parts[0])] = parts[1]\n\t\t self.movies_genres[int(parts[0])] = parts[2].rstrip()\n\n\tdef modified_load_movies(self, mid):\n\t\tfor line in open(movie_file, 'r', encoding = 'ISO-8859-1'):\n\t\t parts = line.split(\"::\")\n\t\t if int(line) is int(mid):\n\t\t\t self.movies_titles[int(parts[0])] = parts[1]\n\t\t\t self.movies_genres[int(parts[0])] = parts[2].rstrip()\n\n\n\tdef load_images(self, image_file):\n\t\t# If movies were already loaded, clear records\n\t\tif self.movies_images:\n\t\t self.movies_images.clear()\n\t\t# If/once records are clear, populate records\n\t\tfor line in open(image_file, 'r', encoding = 'ISO-8859-1'):\n\t\t parts = line.split(\"::\")\n\t\t self.movies_images[int(parts[0])] = parts[2]\n\n\tdef get_image(self, mid):\n\t\tif int(mid) in self.movies_images:\n\t\t\treturn self.movies_images[int(mid)]\n\t\telse:\n\t\t\treturn None\n\n\tdef print_sorted_movies(self):\n\t\tfor key, value in sorted(self.movies_titles.items()):\n\t\t print (key, value)\n\n\tdef print_movie(self, mid):\n\t\tprint (self.movies_titles[int(mid)])\n\n\tdef get_movie(self, mid):\n\t\tif int(mid) in self.movies_titles:\n\t\t return list([self.movies_titles[int(mid)], self.movies_genres[int(mid)]])\n\t\telse:\n\t\t return None\n\n\tdef get_movies(self):\n\t\tmovieIDlist = []\n\t\tfor key in sorted(self.movies_titles.items()):\n\t\t movieIDlist.append(int(key))\n\t\treturn movieIDlist\n\n\tdef set_movie(self, mid, titleandgenre):\n\t\tself.movies_titles[int(mid)]=titleandgenre[0]\n\t\tself.movies_genres[int(mid)]=titleandgenre[1]\n\n\tdef add_movie(self, genres, title):\n\t\tthinglist = sorted(self.movies_titles.keys())\n\t\tself.mid = thinglist[-1] + 1 \n\t\tself.movies_titles[int(self.mid)]=title\n\t\tself.movies_genres[int(self.mid)]=genres\n\n\tdef delete_movie(self, mid):\n\t\tif int(mid) in self.movies_titles:\n\t\t\tdel self.movies_titles[int(mid)]\n\t\t\tdel self.movies_genres[int(mid)]\n\n\tdef load_users(self, users_file):\n\t\t# If users were already loaded, clear records\n\t\tif self.users_gender:\n\t\t\tself.users_gender.clear()\n\t\tif self.users_age:\n\t\t\tself.users_age.clear()\n\t\tif self.users_occupation:\n\t\t\tself.users_occupation.clear()\n\t\tif self.users_zipcode:\n\t\t\tself.users_zipcode.clear()\n\n\t# If/once records are clear, populate records\n\t\tfor line in open(users_file, 'r', encoding = 'ISO-8859-1'):\n\t\t\tparts = line.split(\"::\")\n\t\t\tself.users_gender[int(parts[0])] = parts[1]\n\t\t\tself.users_age[int(parts[0])] = parts[2]\n\t\t\tself.users_occupation[int(parts[0])] = parts[3]\n\t\t\tself.users_zipcode[int(parts[0])] = parts[4].rstrip()\n\n\tdef get_user(self, uid):\n\t\tif int(uid) in self.users_gender:\n\t\t\treturn list([self.users_gender[int(uid)], int(self.users_age[int(uid)]), int(self.users_occupation[int(uid)]), self.users_zipcode[int(uid)]])\n\t\telse:\n\t\t\treturn None\n\n\tdef get_users(self):\n\t\tuserIDlist = []\n\t\tfor key in sorted(self.users_gender.items()):\n\t\t\tuserIDlist.append(int(key))\n\t\treturn userIDlist\n\n\tdef add_user(self, userstuff):\n\t\tthinglist = sorted(self.users_gender.keys())\n\t\tself.uid = thinglist[-1] + 1 \n\t\tself.users_gender[int(self.uid)]=userstuff[0]\n\t\tself.users_age[int(self.uid)]=int(userstuff[1])\n\t\tself.users_occupation[int(self.uid)]=int(userstuff[2])\n\t\tself.users_zipcode[int(self.uid)]=userstuff[3]\n\n\tdef set_user(self, uid, userstuff):\n\t\tself.users_gender[int(uid)]=userstuff[0]\n\t\tself.users_age[int(uid)]=int(userstuff[1])\n\t\tself.users_occupation[int(uid)]=int(userstuff[2])\n\t\tself.users_zipcode[int(uid)]=userstuff[3]\n\n\tdef delete_user(self, uid):\n\t\tif int(uid) in self.users_gender:\n\t\t\tdel self.users_gender[int(uid)]\n\t\t\tdel self.users_age[int(uid)]\n\t\t\tdel self.users_occupation[int(uid)]\n\t\t\tdel self.users_zipcode[int(uid)]\n\n\tdef load_ratings(self, ratings_file):\n\t\t# If ratings were already loaded, clear records\n\t\tif self.ratings:\n\t\t\tself.ratings.clear()\n\n\t\t# If/once records are clear, populate records\n\t\tfor line in open(ratings_file, 'r', encoding = 'ISO-8859-1'):\n\t\t\tparts = line.split(\"::\")\n\t\t\ttry:\n\t\t\t\tself.ratings[int(parts[1])][int(parts[0])] = int(parts[2])\n\t\t\texcept KeyError:\n\t\t\t\tself.ratings[int(parts[1])] = {int(parts[0]) : int(parts[2])}\n\n\tdef get_rating(self, mid):\n\t\tsum = float(0)\n\t\tcount = float(0)\n\t\taverage = float(0)\n\t\tif mid in self.ratings:\n\t\t\tfor key, value in self.ratings[mid].items():\n\t\t\t\tcount += float(1)\n\t\t\t\tsum += value\n\t\t\taverage = float(sum)/float(count)\n\t\t\treturn float(average)\n\t\telse:\n\t\t\treturn float(0)\n\n\tdef get_highest_rated_movie(self):\n\t\tmaximum = float(0)\n\t\tthisdict = {}\n\t\tif len(self.ratings) != 0:\n\t\t\tfor key,value in self.ratings.items():\n\t\t\t\tthisdict[key] = self.get_rating(int(key))\n\t\t\tfor key, value in thisdict.items():\n\t\t\t\tif value > maximum:\n\t\t\t\t\tmaxmid = key\n\t\t\t\t\tmaximum = value\n\t\t\treturn maxmid\n\t\telse:\n\t\t\treturn None\n\n\tdef set_user_movie_rating(self, uid, mid, rating):\n\t\tif int(mid) in self.ratings:\n\t\t\tself.ratings[int(mid)][int(uid)] = int(rating)\n\t\telse:\n\t\t\tself.ratings[int(mid)] = {int(uid) : int(rating)}\n\n\tdef get_user_movie_rating(self, uid, mid):\n\t\tif int(mid) in self.ratings:\n\t\t\tif int(uid) in self.ratings[int(mid)]:\n\t\t\t\treturn self.ratings[int(mid)][int(uid)]\n\t\telse:\n\t\t\treturn None\n\n\tdef make_recommendation(self, uid):\n\t\tmaximum = float(0)\n\t\tthisdict = {}\n\t\tif len(self.ratings) != 0:\n\t\t\tfor key,value in self.ratings.items():\n\t\t\t\tthisdict[key] = self.get_rating(int(key))\n\t\t\tfor key, value in thisdict.items():\n\t\t\t\tif value > maximum:\n\t\t\t\t\tif self.get_user_movie_rating(uid, key) is None:\n\t\t\t\t\t\tmaxmid = key\n\t\t\t\t\t\tmaximum = value\n\t\t\treturn maxmid\n\t\telse:\n\t\t\treturn None\t\t\n\n\tdef delete_all_ratings(self):\n\t\tif self.ratings:\n\t\t\tself.ratings.clear()\n\nif __name__ == \"__main__\":\n mdb = _movie_database()\n\n #### MOVIES ########\n mdb.load_movies('ml-1m/movies.dat')\n mdb.load_ratings('ml-1m/ratings.dat')\n mdb.load_users('ml-1m/users.dat')\n mdb.load_images('ml-1m/images.dat')\n# print (mdb.get_rating(557))\n#\n","sub_path":"CherryPy/_movie_database.py","file_name":"_movie_database.py","file_ext":"py","file_size_in_byte":6594,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"532284888","text":"from tkinter import *\nfrom screens.showMarks import *\n\nclass Stud_marks():\n def __init__(self):\n self.root = Toplevel()\n self.root.geometry(\"2000x1024\")\n self.root.title(\"Marks\")\n self.c = Canvas(self.root,bg = \"gray\",height=2000,width=2024)\n \n photo = PhotoImage(file = \"images/v1.png\")\n \n # Setting the background\n self.c.create_image((0,0), image=photo, anchor=\"nw\")\n\n self.c.create_text((700, 400), text=\"Enter the USN: \", fill=\"white\", anchor=\"nw\"\n ,font=('Times',50,'italic bold'))\n \n self.button1 = Entry(self.c,font=('Times',20,'bold'))\n self.button1.configure(width = 40,relief = FLAT) \n button1_window = self.c.create_window(700, 550, anchor=NW, window=self.button1)\n\n\n self.back = Button(self.c,text='Back',bg='red',fg='white',activebackground='black',activeforeground='white',width=10,height=2, font=(\"Times\",20,'bold'),command=lambda:back())\n self.back.place(x=1400,y=900,width=100,height=40)\n\n\n self.back = Button(self.c,text='Submit',bg='red',fg='white',activebackground='black',activeforeground='white',width=10,height=2, font=(\"Times\",20,'bold'),command=lambda:submit())\n self.back.place(x=1250,y=900,width=100,height=40)\n\n\n def back():\n self.root.destroy()\n\n\n def submit():\n usn = self.button1.get()\n \n usn = usn.upper()\n \n s = ShowMarks(usn)\n\n\n self.c.pack()\n\n self.root.mainloop()","sub_path":"screens/stud_marks.py","file_name":"stud_marks.py","file_ext":"py","file_size_in_byte":1548,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"487911152","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\ndata = ([], [], [])\n\nf = open('bc-n-iter.txt')\nlines = f.readlines()\nlines.sort(key=lambda x: int(x.strip().split(' ')[0]))\nprint(lines)\nfor line in lines:\n if line.find(' ') != -1:\n n, a, b = line.strip().split(' ')\n data[0].append(n)\n data[1].append(1.0 - float(a))\n data[2].append(1.0 - float(b))\n\nfig, ax = plt.subplots()\nax.plot(data[0], data[1], color='b', label='Train')\nax.plot(data[0], data[2], color='r', label='Test')\nax.set_ylim(0, ax.get_ylim()[1] * 1.1)\nax.set_title('Neural Network (Breast Cancer)')\nax.set_ylabel('% Error')\nax.set_xlabel('# of Epochs')\nax.legend()\nplt.show()\n","sub_path":"src/python/plot/old/plot_bc_n_iter.py","file_name":"plot_bc_n_iter.py","file_ext":"py","file_size_in_byte":657,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"98891250","text":"import requests\n\nurl = \"https://my-json-server.typicode.com/quyentx/my-typicode-json-server/employee\"\n\n\ndef get_request():\n # get request without parameter\n x = requests.get(url + \"/1\")\n print(f\"Content type is {x.headers['content-type']}\")\n print(f\"Body text is: {x.text}\")\n print(f\"Body json is: {x.json()}\")\n print(f\"Encoding method is: {x.encoding}\")\n print(f\"Status code is: {x.status_code}\")\n\n\ndef get_request_with_params():\n # get request with parameters in URL\n pl = {\"id\": 2, \"name\": [\"John Delaware\", \"Jack Sparrow\"]}\n y = requests.get(url, params=pl)\n print(y.url)\n print(y.json())\n\n\ndef get_with_custom_headers():\n # get request with custom header\n headers = {\"content-type\": \"plain/text\"}\n z = requests.get(url, headers=headers)\n print(z.request.headers[\"content-type\"])\n\n\ndef post_request():\n post_json = {\n \"id\": 3,\n \"name\": \"John Human\",\n \"role\": \"CEO\",\n \"age\": 34,\n \"species\": \"human\"\n }\n r = requests.post(url, data=post_json)\n print(\"==========================================\")\n print(f\"Content type is {r.headers['content-type']}\")\n print(f\"Body text is: {r.text}\")\n print(f\"Body json is: {r.json()}\")\n print(f\"Encoding method is: {r.encoding}\")\n print(f\"Status code is: {r.status_code} {r.reason}\")\n print(f\"Request is: {r.request}\")\n print(f\"Response URL is: {r.url}\")\n print(f\"Time elapsed is: {r.elapsed}\")\n assert r.json()[\"id\"] == \"3\"\n assert r.json()[\"name\"] == \"John Human\"\n assert r.json()[\"role\"] == \"CEO\"\n assert r.json()[\"age\"] == \"34\"\n assert r.json()[\"species\"] == \"human\"\n assert r.status_code == 201\n r.close()\n\n\ndef post_with_encoded_form():\n pl = {\"name\": \"Mike Powder\", \"age\": 30}\n r = requests.post(\"https://httpbin.org/post\", data=pl)\n print(r.json())\n print(r.status_code)\n\n\ndef post_with_file_content():\n files = {'file': open('rps.py', 'rb')}\n # files = {'file': open('report.xls', 'rb')}\n r = requests.post(\"https://httpbin.org/post\", files=files)\n print(r.json())\n\n\ndef put_request():\n put_json = {\n \"id\": 2,\n \"name\": \"John Button\",\n \"role\": \"Director\",\n \"age\": 33,\n \"species\": \"human\"\n }\n r = requests.put(\"https://httpbin.org/put\", json=put_json)\n print(f\"Content type is {r.headers['content-type']}\")\n print(f\"Body text is: {r.text}\")\n print(f\"Body json is: {r.json()}\")\n print(f\"Encoding method is: {r.encoding}\")\n print(f\"Status code is: {r.status_code} {r.reason}\")\n print(f\"Response URL is: {r.url}\")\n print(f\"Time elapsed is: {r.elapsed}\")\n assert r.status_code == 200\n r.close()\n\n\ndef delete_request():\n # get request without parameter\n r = requests.delete(url + \"/1\")\n print(f\"Content type is {r.headers['content-type']}\")\n print(f\"Body text is: {r.text}\")\n print(f\"Body json is: {r.json()}\")\n print(f\"Encoding method is: {r.encoding}\")\n print(f\"Status code is: {r.status_code}\")\n assert r.status_code == 200\n assert r.json() == {}\n\n\nget_request()\nget_request_with_params()\nget_with_custom_headers()\n\npost_request()\npost_with_encoded_form()\npost_with_file_content()\n\nput_request()\n\ndelete_request()\n","sub_path":"python_requests.py","file_name":"python_requests.py","file_ext":"py","file_size_in_byte":3218,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"88515101","text":"# coding: utf-8\n\nimport RPi.GPIO as GPIO\nimport time\n\nL_VREF = 16 # 左車輪\nL_IN1 = 20\nL_IN2 = 21\n\nR_VREF = 13 # 右車輪\nR_IN1 = 19\nR_IN2 = 26\n\nmotor_ports = [[L_IN1, L_IN2, L_VREF], [R_IN1, R_IN2, R_VREF]]\n\nGPIO.setmode(GPIO.BCM)\nfor ports in motor_ports: # 出力先が多いので，リストにまとめた後，一斉に設定\n GPIO.setup(ports, GPIO.OUT)\n\n\n# モーターを制御する関数の定義\ndef set_motor(pno,job):\n ta_switch = [ # モータドライバに対応する仕事の指示リストのリスト\n [0, 0], # 停止\n [1, 0], # 時計回り\n [0, 1]] # 反時計回り\n # GPIO.HIGH(LOW)の代わりに，1(0)が使える\n \n ports = motor_ports[pno] # motor_portsのpno番目のリスト．車輪の指定．\n sw = ta_switch[job] # ta_switchのjob番目のリスト.仕事の指示． \n GPIO.output(ports[0], sw[0]) # 左右車輪ともにIN1は，指示リストの0番目と対応．ex:[1, 0]の1.\n GPIO.output(ports[1], sw[1]) # 左右車輪ともにIN2は，指示リストの1番目と対応．ex:[1, 0]の0.\n\n\n# 両方のモーターを同時に制御する．左右同じ指示を受ける\ndef set_motor2(job):\n set_motor(0, job) # 左車輪\n set_motor(1, job) # 右車輪\n\n\npwm_l = GPIO.PWM(L_VREF, 50)\npwm_r = GPIO.PWM(R_VREF, 50)\npwm_l.start(100)\npwm_r.start(100)\n\nif __name__ == \"__main__\":\n try:\n while True:\n set_motor2(1) # 前へ（時計回り）\n time.sleep(1.5)\n set_motor2(2) # 後ろへ（反時計回り）\n time.sleep(1.5)\n set_motor2(0) # 停止\n time.sleep(1.5)\n\n except KeyboardInterrupt:\n pass\n GPIO.cleanup()\n","sub_path":"y_motor.py","file_name":"y_motor.py","file_ext":"py","file_size_in_byte":1732,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"599070509","text":"# Importo la librería random para utilizarlo en el cálculo del tiempo hecho por el piloto\nfrom math import pi\nimport random\n\n# Variable global\nundefined = \"Unknown\"\n\n\n\nbono = [\"chasis\",\"motor\",\"aerodinamica\"]\npilotos = []\npistas = []\ndirectores = [\"J. Montoya\", \"T, Aprilla\"]\nmecanicos = []\n# mecanicos = [\"Chavez\", \"Villanueva\", \"Ramirez\", \"Duque\", \"Henao\", \"Quintero\", \"Avellaneda\", \"Ortiz\"]\nsueldos = []\nnombre_Equipos = []\n\n\nclass Pista:\n # Definición de atributos\n nombre = undefined\n tipo_exigencia_1 = undefined\n tipo_exigencia_2 = undefined\n\n # Definición del método constructor\n def __init__(self, nombre, tipo_exigencia_1, tipo_exigencia_2):\n self.nombre = nombre\n self.tipo_exigencia_1 = tipo_exigencia_1\n self.tipo_exigencia_2 = tipo_exigencia_2\n\nclass Equipo:\n # Definición de atributos\n nombre_Equipo = undefined\n director = undefined\n mecanico1 = undefined\n mecanico2 = undefined\n mecanico3 = undefined\n mecanico4 = undefined\n piloto1 = undefined\n piloto2 = undefined\n sueldo_Director = undefined\n sueldo_Mecanico = undefined\n sueldo_Piloto = undefined\n\n # Definición del método constructor\n def __init__(self, nombre_Equipo, director, mecanico1, mecanico2, mecanico3, mecanico4, piloto1, piloto2,sueldo_Director,sueldo_Mecanico,sueldo_Piloto):\n self.nombre_Equipo = nombre_Equipo\n self.director = director\n self.mecanico1 = mecanico1\n self.mecanico2 = mecanico2\n self.mecanico3 = mecanico3\n self.mecanico4 = mecanico4\n self.piloto1 = piloto1\n self.piloto2 = piloto2\n self.sueldo_Director = sueldo_Director\n self.sueldo_Mecanico = sueldo_Mecanico\n self.sueldo_Piloto = sueldo_Piloto\n\nclass Piloto:\n nombre = undefined\n tiempo_piloto = undefined\n bono_piloto = undefined\n\n # Definición del método constructor\n def __init__(self, nombre, bono_piloto):\n self.nombre = nombre\n self.bono_piloto = bono_piloto\n\nclass Mecanico:\n nombre = undefined\n tiempo_mecanico = undefined\n\n # Definición del método constructor\n def __init__(self, nombre):\n self.nombre = nombre\n\n\nclass Ejecutora:\n pista = []\n pilotos = []\n mecanicos = []\n sueldos = []\n nombre_Equipos = []\n \n \n # Crear pilotos y agregarlos al arreglo vacío\n piloto1 = Piloto(\"C. Muñoz\", bono[0])\n piloto2 = Piloto(\"Kobayashi\", bono[1])\n piloto3 = Piloto(\"G. Chavez\", bono[2])\n piloto4 = Piloto(\"P. Wherlein\", bono[1])\n \n pilotos.append(piloto1)\n pilotos.append(piloto2)\n pilotos.append(piloto3)\n pilotos.append(piloto4)\n \n\n\n # Crear mecánicos y agregarlos al arreglo vacío\n mecanico1 = Mecanico(\"Chavez\")\n mecanico2 = Mecanico(\"Villanueva\")\n mecanico3 = Mecanico(\"Ramirez\")\n mecanico4 = Mecanico(\"Duque\")\n mecanico5 = Mecanico(\"Henao\")\n mecanico6 = Mecanico(\"Quintero\")\n mecanico7 = Mecanico(\"Avellaneda\")\n mecanico8 = Mecanico(\"Ortiz\")\n\n mecanicos.append(mecanico1)\n mecanicos.append(mecanico2)\n mecanicos.append(mecanico3)\n mecanicos.append(mecanico4)\n mecanicos.append(mecanico5)\n mecanicos.append(mecanico6)\n mecanicos.append(mecanico7)\n mecanicos.append(mecanico8)\n\n\n #Crear pistas con sus respectivas exigencias \n pista1 = Pista(\"Long Beach\",bono[0],bono[2])\n pista2 = Pista(\"Interlagos\",bono[1],bono[0])\n pista3 = Pista(\"Suzuka\",bono[2],bono[0])\n pista4 = Pista(\"Silverstone\",bono[1],bono[2])\n\n \n \n #Crear equipos\n equipo1 = Equipo(\"Equipo 1\",directores[0],mecanicos[0],mecanicos[1],mecanicos[2],mecanicos[3],pilotos[0],pilotos[1],4000,3000,2000)\n equipo2 = Equipo(\"Equipo 2\",directores[1],mecanicos[4],mecanicos[5],mecanicos[6],mecanicos[7],pilotos[2],pilotos[3],4000,3000,2000)\n \n print(\"Seleccione el número de la pista que desea correr\")\n print(\"1 = Long Beach\")\n print(\"2 = Interlagos\")\n print(\"3 = Suzuka\")\n print(\"4 = Silverstone\")\n ingreso_pista = int(input(\"¿Que pista correrán los pilotos?: \"))\n print(ingreso_pista)\n\n pista_seleccionada = undefined\n\n if ingreso_pista == 1:\n #print(\"ENTRE\")\n pista_seleccionada = pista1\n elif ingreso_pista == 2:\n pista_seleccionada = pista2\n elif ingreso_pista == 3:\n pista_seleccionada = pista3\n elif ingreso_pista == 4:\n pista_seleccionada = pista4\n \n\n\n #Retorna el tiempo del piloto\n def calcular_pole_position(self):\n # Recorrer el listado de pilotos\n for i in self.pilotos:\n # Calcular el tiempo por medio de este random\n tiempo_piloto = random.randint(1,70)\n # A cada piloto le guardo el tiempo que obtuvo\n i.tiempo_piloto = tiempo_piloto\n # print(\"El tiempo del piloto \", i.nombre, \" es: \", i.tiempo_piloto)\n \n #for j in self.pilotos:\n # print(\"Actualizacion \", j.nombre, \" es: \", j.tiempo_piloto)\n \n # Ordenar el tiempo de los pilotos de menor a mayor\n n = len(self.pilotos)\n l = len(self.pilotos)\n\n for i in range(1,len(self.pilotos)):\n for j in range(0,len(self.pilotos)-i):\n if(self.pilotos[j+1].tiempo_piloto < self.pilotos[j].tiempo_piloto):\n temp = self.pilotos[j]\n self.pilotos[j] = self.pilotos[j+1]\n self.pilotos[j+1] = temp \n \n return self.pilotos\n\n # Calcular tiempo en pits de los mecánicos\n def calcular_tiempo_pits(self):\n tiempo_mecanico = random.randint(1,3)\n return tiempo_mecanico\n \n\n # calcular nomina piloto primer puesto\n def calcular_nomina_primer_lugar(self):\n # Lista ordenada\n pole_position = ejecutar.calcular_pole_position()\n piloto_ganador = pole_position[0]\n\n if self.equipo1.piloto1.nombre == piloto_ganador.nombre or self.equipo1.piloto2.nombre == piloto_ganador.nombre:\n print(\"GANADOR!: \", piloto_ganador.nombre, \" : \", piloto_ganador.tiempo_piloto, \" sg y pertenece al equipo equipo 1 \")\n beneficio_primer_puesto = (2000 + 0.10)\n print(\"El sueldo + beneficio para el piloto \", piloto_ganador.nombre, \" del equipo 1 es de: \", beneficio_primer_puesto)\n elif self.equipo2.piloto1.nombre == piloto_ganador.nombre or self.equipo2.piloto2.nombre == piloto_ganador.nombre:\n print(\"GANADOR!: \", piloto_ganador.nombre, \" : \", piloto_ganador.tiempo_piloto, \" sg pertenece al equipo equipo 2 \")\n beneficio_primer_puesto = (2000 + 0.10)\n print(\"El sueldo + beneficio para el director \", piloto_ganador.nombre, \" del equipo 2 es de: \", beneficio_primer_puesto)\n\n \n def calcular_nomina_segundo_lugar(self):\n # Lista ordenada\n pole_position = ejecutar.calcular_pole_position()\n piloto_segunda_posicion = pole_position[1]\n\n # Piloto: 10% extra si clasifica 2do o mejor\n # 20% si lo hace en una pista donde no tiene bono\n if self.equipo1.piloto1.nombre == piloto_segunda_posicion.nombre or self.equipo1.piloto2.nombre == piloto_segunda_posicion.nombre:\n if self.pista_seleccionada.tipo_exigencia_1 != piloto_segunda_posicion.bono_piloto and self.pista_seleccionada.tipo_exigencia_2 != piloto_segunda_posicion.bono_piloto:\n print(\"Segunda posición: \", piloto_segunda_posicion.nombre, \" : \", piloto_segunda_posicion.tiempo_piloto, \" sg y pertenece al equipo 1\")\n beneficio_segunda_posición = (2000 + 0.10)\n print(\"El sueldo + beneficio por segunda posición para el piloto \", piloto_segunda_posicion.nombre, \" del equipo 1 es de: \", beneficio_segunda_posición )\n print(\"El sueldo por segunda posición para el piloto \", piloto_segunda_posicion.nombre, \" del equipo 1 es de: \", self.equipo1.sueldo_Piloto)\n elif self.equipo2.piloto1.nombre == piloto_segunda_posicion.nombre or self.equipo2.piloto2.nombre == piloto_segunda_posicion.nombre:\n if self.pista_seleccionada.tipo_exigencia_1 != piloto_segunda_posicion.bono_piloto and self.pista_seleccionada.tipo_exigencia_2 != piloto_segunda_posicion.bono_piloto:\n print(\"Segunda posición: \", piloto_segunda_posicion.nombre, \" : \", piloto_segunda_posicion.tiempo_piloto, \" sg y pertenece al equipo 2\")\n beneficio_segunda_posición = (2000 + 0.10)\n print(\"El sueldo + beneficio por segunda posición para el piloto \", piloto_segunda_posicion.nombre, \" del equipo 2 es de: \", beneficio_segunda_posición )\n print(\"El sueldo por segunda posición para el piloto \", piloto_segunda_posicion.nombre, \" del equipo 2 es de: \", self.equipo2.sueldo_Piloto)\n \n \n \n \n # Calcular la nómina para los directores\n def calcular_nomina_directores(self):\n # Director equipo: 10% bono por pole position\n\n # Lista ordenada\n pole_position = ejecutar.calcular_pole_position()\n piloto_ganador = pole_position[0]\n \n\n if self.equipo1.piloto1.nombre == piloto_ganador.nombre or self.equipo1.piloto2.nombre == piloto_ganador.nombre:\n #print(\"GANADOR!: \", piloto_ganador.nombre, \" : \", piloto_ganador.tiempo_piloto, \" sg y pertenece al equipo equipo 1 \")\n beneficio_director = (4000 + 0.10)\n print(\"El sueldo + beneficio para el director \", directores[0], \" del equipo 1 es de: \", beneficio_director)\n elif self.equipo2.piloto1.nombre == piloto_ganador.nombre or self.equipo2.piloto2.nombre == piloto_ganador.nombre:\n #print(\"GANADOR!: \", piloto_ganador.nombre, \" : \", piloto_ganador.tiempo_piloto, \" sg pertenece al equipo equipo 2 \")\n beneficio_director = (4000 + 0.10)\n print(\"El sueldo + beneficio para el director \", directores[1], \" del equipo 2 es de: \", beneficio_director)\n \n\n # Calcular la nómina para los mecánicos\n # Mecánico: 5% extra si logra una parada en pits menor 2.2 segundos\n def calcular_nomina_mecanicos(self):\n print(\"metodo mecanicos\")\n \n for i in (self.mecanicos):\n tiempo_total_mecanico = ejecutar.calcular_tiempo_pits()\n i.tiempo_mecanico = tiempo_total_mecanico\n \n if i.tiempo_mecanico <= 2.2:\n beneficio_mecanico = (3000 + 0.5)\n print(\"El mecánico \", i.nombre, \" hizo un tiempo de: \", i.tiempo_mecanico, \"El salario + beneficio es de: \", beneficio_mecanico)\n else:\n print(\"El mecánico \", i.nombre, \" hizo un tiempo de: \", i.tiempo_mecanico, \"El salario es de: \", 3000)\n \n\nejecutar = Ejecutora()\n# Lista ordenada\n#pole_position = ejecutar.calcular_pole_position()\n# Ver la pole position \n#for j in pole_position:\n # print(\"Pole position: \", j.nombre, \" es: \", j.tiempo_piloto)\n\nnomina_primer_lugar = ejecutar.calcular_nomina_primer_lugar()\nprint(\"#########################################################################\")\nnomina_segundo_lugar = ejecutar.calcular_nomina_segundo_lugar()\nprint(\"#########################################################################\")\nnomina_director = ejecutar.calcular_nomina_directores()\nprint(\"#########################################################################\")\nnomina_mecanico = ejecutar.calcular_nomina_mecanicos()\n\n","sub_path":"Taller_Final_2/TallerFinal/prueba5.py","file_name":"prueba5.py","file_ext":"py","file_size_in_byte":11356,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"383158322","text":"dict = {\"P\" : \"Pikachu\", \n \"M\" : \"Mickey Mouse\", \n \"H\" : \"Hello kitty\"}\n\nwhile True :\n x = str(input())\n if x == \"-1\" :\n break\n elif x in dict :\n print(dict[x])\n else :\n print(x, \"does not exist. Enter a new one:\")\n dict[x] = str(input())","sub_path":"3068-dict字典練��II (C++ STL map).py","file_name":"3068-dict字典練習II (C++ STL map).py","file_ext":"py","file_size_in_byte":292,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"194527964","text":"t = int(input())\nwhile(t):\n t-=1\n n = int(input())\n arr = list(map(int, input().split()))\n if(n == 1):\n print(\"YES\")\n break\n\n preArr = [arr[0]]\n for i in range(1, n):\n preArr.append(preArr[i-1]+arr[i])\n \n flag = \"NO\"\n for i in range(1, n):\n if(preArr[i-1] == preArr[-1]-preArr[i]):\n # print(i)\n flag = \"YES\"\n break\n \n print(flag)\n\n # print(preArr)","sub_path":"Assignment/128_sherlockAndArray.py","file_name":"128_sherlockAndArray.py","file_ext":"py","file_size_in_byte":445,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"494890934","text":"with open('file',encoding='utf-8') as f:\n\tl = f.readlines()\npage_num = int(input('请输入页码：'))\n#1 1-5\n#2 6-10\n#3 11-15\n#4 16-20\n#(n-1)*5+1\npages,mod = divmod(len(l),5)\n#print(mod)\nif mod:\n\tpages += 1 #一个多少页\nif page_num >pages or page_num <= 0:\n\tprint('输入有误！')\nelif page_num == pages and mod !=0:\n\tfor i in range(mod):\n\t\tprint(l[(page_num-1)*5 +i].strip())\nelse:\n\tfor i in range(5):\n\t\tprint(l[(page_num-1)*5 +i].strip())\n","sub_path":"fenye.py","file_name":"fenye.py","file_ext":"py","file_size_in_byte":449,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"58297390","text":"import os\nimport glob\nimport exifread\n\nNAME_LENGTH = 10\n\njpg_files = glob.glob('*.jpg')\n\nfor a_file in jpg_files:\n try:\n tags = exifread.process_file(open(a_file, 'rb'))\n except Exception as e:\n print(e)\n print(\"Couldn't open the file, skipping: {}\".format(a_file))\n continue\n uid = str(tags.get('EXIF ImageUniqueID', ''))\n if not len(uid):\n print(\"{} --> UniqueID not present in EXIF\".format(a_file))\n elif len(uid) < 30:\n print(\"{} --> Possibly corrupted EXIF; {}\".format(a_file, uid))\n else:\n new_name = uid[:NAME_LENGTH] + '.jpg'\n print(\"{} --> new name: {}\".format(a_file, new_name))","sub_path":"dockerized-gists/1e3263c4b26c4c1674b8b29274d528d8/snippet.py","file_name":"snippet.py","file_ext":"py","file_size_in_byte":662,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"312647230","text":"\"\"\"Add additional indexing\n\nRevision ID: 06ce82a384b0\nRevises: 3a298d774d3f\nCreate Date: 2017-06-13 14:24:17.794833\n\n\"\"\"\n\n# revision identifiers, used by Alembic.\nrevision = '06ce82a384b0'\ndown_revision = '3a298d774d3f'\n\nfrom alembic import op\nimport sqlalchemy as sa\n\n\ndef upgrade():\n op.create_index('ix_item_item', 'item', [sa.text('item jsonb_path_ops')], postgresql_using='gin')\n op.create_index('ix_item_item_end-date', 'item', [sa.text(\"(item ->> 'end-date')\")], postgresql_using='btree')\n\n\ndef downgrade():\n op.drop_index('ix_item_item')\n op.drop_index('ix_item_item_end-date')\n","sub_path":"migrations/versions/06ce82a384b0_add_additional_indexing.py","file_name":"06ce82a384b0_add_additional_indexing.py","file_ext":"py","file_size_in_byte":598,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"458965389","text":"# produce allRuns.txt with: RecoLuminosity/LumiDB/scripts/lumiCalc2.py overview -i RecoLuminosity/LumiDB/Cert_190456-198485_8TeV_PromptReco_Collisions12_JSON.txt -o MyAnalysis/ZLumiStudy/test/macros/allRuns.txt\n\nnumbers = []\n\nread_file = open(\"allRuns.txt\", \"r\")\nfor line in read_file:\n\tlineparts = line.split(\",\")\n\trunnumber, fill = lineparts[0].split(\":\")\n\tif runnumber != \"Run\":\n\t\tnumbers.append(runnumber)\n\t\n\nread_file.close()\n\nwrite_file = open(\"runnumberSorted_Number.txt\", \"w\")\nfor n in numbers:\n\twrite_file.write(n + '\\n')\n\nwrite_file.close()\n\n","sub_path":"MyAnalysis/ZLumiStudy/test/macros/save_allRuns.py","file_name":"save_allRuns.py","file_ext":"py","file_size_in_byte":552,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"372064131","text":"import logging\n\n# GAE Patch main():\nfrom common.appenginepatch.main import *\n\n# Our own patches follow:\n\n# Local imports\nimport db_log\nimport db_cache\n\n# Verify that we're running django 1.0 or later\nlogging.info('django.__file__ = %r, django.VERSION = %r',\n django.__file__, django.VERSION)\nassert django.VERSION[0] >= 1, \"This Django version is too old\"\n\ndef log_exception(*args, **kwds):\n \"\"\"Django signal handler to log an exception.\"\"\"\n cls, err = sys.exc_info()[:2]\n logging.exception('Exception in request: %s: %s', cls.__name__, err)\n\n# Log all exceptions detected by Django.\ndjango.core.signals.got_request_exception.connect(log_exception)\n\ndef our_profile_main():\n db_log.clear_callmap()\n db_log.clear_requestmap()\n\n # From appenginepatch\n profile_main()\n\n db_log.log_callmap()\n\n# Turn on logging of profiling results\n#main = our_profile_main\n# No profiling. real_main is from appenginepatch.\nmain = real_main\n\n# Turn on per-request cache of datastore calls\ndb_cache.patch_db_get()\n# assert there was no across-request caching\nassert 0 == db_cache.get_cache_size()\n\n# Turn on logging of datastore calls\n#db_log.patch_appengine()\n\n# We have to be logging debug level to see messages from db_cache or db_log\n#logging.getLogger().setLevel(logging.DEBUG)\n#logging.debug('Set logging level to DEBUG')\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"main_old.py","file_name":"main_old.py","file_ext":"py","file_size_in_byte":1365,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"222631430","text":"import argparse\n\nimport matplotlib\nimport numpy as np\n\nfrom posenet.core.image_reader import ImageReader\nfrom posenet.core.localiser import Localiser\nfrom posenet.utils import progress_bar\nfrom posenet.utils import to_spherical\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--agg', action='store_true')\nparser.add_argument('--model', action='store', required=True)\nparser.add_argument('--dataset', action='store', required=True)\nparser.add_argument('--limits', action='store', nargs=2, type=int, \n required=False, default=[-90,90])\nparser.add_argument('--spacing', action='store', type=int, required=True)\nparser.add_argument('--verbose', action='store_true')\nparser.add_argument('--save', action='store', required=False)\nargs = parser.parse_args()\n\nif args.agg:\n matplotlib.use('Agg')\nimport matplotlib.pyplot as plt\n\ninput_size = 224\ntest_reader = ImageReader(args.dataset, batch_size=1, \n image_size=[input_size, input_size],\n randomise=False)\nn_images = test_reader.total_images()\n\nazimuthal = []\nwith Localiser(args.model) as localiser:\n for i in range(n_images):\n images_feed, labels_feed = test_reader.next_batch()\n gt = {'x': labels_feed[0][0:3], 'q': labels_feed[0][3:7]}\n\n # Make prediction\n predicted = localiser.localise(images_feed)\n x, y, z = predicted['x']\n azimuthal.append(to_spherical(x, y, z)[1]*180/np.pi)\n\n if args.verbose:\n print('-------------{}-------------'.format(i))\n print(predicted)\n else:\n progress_bar(1.0*(i+1)/n_images, 30, text='Localising')\n print('')\n\n\nfig = plt.figure()\nfig.patch.set_facecolor('white')\nx = np.linspace(args.limits[0], args.limits[1], n_images)\nplt.plot(x, azimuthal, color='black')\nplt.plot(x[0::args.spacing], azimuthal[0::args.spacing], 'ro', ms=4)\nplt.xlim([args.limits[0], args.limits[1]])\nplt.ylabel(\"Predicted azimuthal angle\")\nplt.xlabel(\"True azimuthal angle\")\n\nif args.save:\n plt.savefig(args.save, bbox_inches='tight')\nelse:\n plt.show()","sub_path":"plot_interpolation.py","file_name":"plot_interpolation.py","file_ext":"py","file_size_in_byte":2081,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"352597054","text":"from pyDatalog import pyDatalog\n# pyDatalog.create_terms(\"\"\"lin_alg,math_logic,depends,\n# logic_prog,comp,prog, S, D, X, Y,\n# depends,requires,need_to_study\n# \"\"\")\n#\n# +(depends(lin_alg,math_logic))\n# +(depends(logic_prog,math_logic))\n# +(depends(comp,lin_alg))\n#\n# +(requires(prog,comp))\n#\n# need_to_study(S,D) <= requires(S,D)\n# need_to_study(S,D) <= need_to_study(S,X) & depends(X,D)\n#\n# print(need_to_study(X, math_logic))\n\npyDatalog.create_terms('X,Y,Z,pow')\nN = 4\nprint(X.in_(range(0,10)) &\n Y.in_(range(0,10)) &\n Z.in_(range(0,20)) &\n (X < Y) &\n (N*Z==pow(X,N)+Y))\n\npyDatalog.create_terms('X,Y,Z,gt')\n\ngt(X, Y) <= (X == Y + 1)\ngt(X, Y) <= gt(X,Z) & (Z == Y + 1)\n\nprint(gt(5,8))\n","sub_path":"python/logic_programming/tasks/log1.py","file_name":"log1.py","file_ext":"py","file_size_in_byte":774,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"408492496","text":"import os\nimport re\nfrom typing import Any, Dict\n\n# Override much of pygments' CMakeLexer.\n# We need to parse CMake syntax definitions, not CMake code.\n\n# For hard test cases that use much of the syntax below, see\n# - module/FindPkgConfig.html (with \"glib-2.0>=2.10 gtk+-2.0\" and similar)\n# - module/ExternalProject.html (with http:// https:// git@; also has command options -E --build)\n# - manual/cmake-buildsystem.7.html (with nested $<..>; relative and absolute paths, \"::\")\n\nfrom pygments.lexers import CMakeLexer\nfrom pygments.token import Name, Operator, Punctuation, String, Text, Comment,\\\n Generic, Whitespace, Number, Keyword, Literal\nfrom pygments.lexer import bygroups, include, RegexLexer\nfrom sphinx.highlighting import lexers\n\n# Notes on regular expressions below:\n# - [\\.\\+-] are needed for string constants like gtk+-2.0\n# - Unix paths are recognized by '/'; support for Windows paths may be added if needed\n# - (\\\\.) allows for \\-escapes (used in manual/cmake-language.7)\n# - $<..$<..$>..> nested occurrence in cmake-buildsystem\n# - Nested variable evaluations are only supported in a limited capacity. Only\n# one level of nesting is supported and at most one nested variable can be present.\n\nCMakeLexer.tokens[\"root\"] = [\n (r'\\b(\\w+)([ \\t]*)(\\()', bygroups(Name.Function, Text, Name.Function), '#push'), # fctn(\n (r'\\(', Name.Function, '#push'),\n (r'\\)', Name.Function, '#pop'),\n (r'\\[', Punctuation, '#push'),\n (r'\\]', Punctuation, '#pop'),\n (r'[|;,.=*\\-]', Punctuation),\n (r'\\\\\\\\', Punctuation), # used in commands/source_group\n (r'[:]', Operator),\n (r'[<>]=', Punctuation), # used in FindPkgConfig.cmake\n (r'\\$<', Operator, '#push'), # $<...>\n (r'<[^<|]+?>(\\w*\\.\\.\\.)?', Name.Variable), # {escape(headers)}\n {escape(params)}\n {escape(request_context)}\n {escape(full_event)}\n {escape(environ)}\n \"\"\"\n )\n","sub_path":"example/app/testapp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":824,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"23903866","text":"#!/usr/bin/env python\r\n\r\n\r\n\"\"\"\r\nPrint.py: How a Character works.\r\n\"\"\"\r\n\r\n\r\n__author__ = \"Bo Claes\"\r\n__email__ = \"bo.claes@student.kdg.be\"\r\n__status__ = \"Development\"\r\n\r\n\r\ndef main():\r\n\r\n word = input(\"Give me a word user \") # asking the user for a word\r\n\r\n print(\"This is how many characters are in your word \"+str(len(word))) # printing out the characters of that word\r\n\r\n\r\nif __name__ == '__main__': # code to execute if called from command-line\r\n main()","sub_path":"Print1.py","file_name":"Print1.py","file_ext":"py","file_size_in_byte":503,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"295901175","text":"import win32com.client\nimport win32con\nimport win32api\nimport pywintypes\nimport re\n\ndef get_software_updates(update_seeker, installed):\n # Search installed/not installed Software Windows Updates\n search_string = \"IsInstalled=%d and Type='Software'\" % installed\n search_update = update_seeker.Search(search_string)\n _ = win32com.client.Dispatch(\"Microsoft.Update.UpdateColl\")\n updates = []\n categories = []\n update_dict = {}\n # compiles the regex pattern for finding Windows Update codes\n updates_pattern = re.compile(r'KB+\\d+')\n for update in search_update.Updates:\n update_str = str(update)\n # extracts Windows Update code using regex\n update_code = updates_pattern.findall(update_str)\n for category in update.Categories:\n category_name = category.Name\n print(\"[*] Name: \" + update_str + \" - \" +\n \"url: \" + \"https://support.microsoft.com/en-us/kb/{}\".format(\n \"\".join(update_code).strip(\"KB\")) + \" - \" +\n \"Category: \" + category_name)\n updates.append(update_str)\n categories.append(category_name)\n # converts lists to tuples in order to be used as a dictionary key\n hashable = tuple(updates)\n hashable_category = tuple(categories)\n # creates category:update dictionary\n for update in hashable:\n for category_update in hashable_category:\n update_dict[category_update] = str(update)\n return update_dict\n\ndef enum_winupdates():\n wua = win32com.client.Dispatch(\"Microsoft.Update.Session\")\n update_seeker = wua.CreateUpdateSearcher()\n print(\"\\n[+] Enumerating installed Windows or Drivers' Updates...(if any)\\n\")\n installed = get_software_updates(update_seeker, installed=True)\n print(\"\\n[+] Enumerating available Windows or Drivers' Updates not installed...(if any)\\n\")\n available = get_software_updates(update_seeker, installed=False)\n return installed, available\n\nenum_winupdates()\n","sub_path":"updates.py","file_name":"updates.py","file_ext":"py","file_size_in_byte":1989,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"450796508","text":"#!/usr/bin/env python\n\"\"\"Functions to build count structures from car-dog hashtag coocurrence data\"\"\"\n\nimport os\nimport json\nfrom collections import defaultdict\nimport csv\nfrom carroperro.util import name_from_hashtag\n\n\ndef build_counts_from_coocs(cooc_path, cars, dogs):\n \"\"\"Walk the tree of json cooc files and build a dict of dicts of the\n form:\n\n {car1: {dog1: count, dog2: count, ...}, car2: {dog1: count,...}...}\n\n The keys of the outer dict are all the car names, and the values are\n dictionaries from dog names to counts. The counts correspond to the\n number of times that dog name occurred as a hashtag in the same user\n timeline as the hashtag for that car name (or vice versa).\n\n Args:\n cooc_path: location of the directory that contains the coocurrence\n data for cars and dogs, with each file being named after a\n hashtag and contain a JSON dict from hashtag: count\n cars: an instance of Cars\n dogs: an instance of Dogs\n\n Returns:\n defaultdict: of (defaultdict(int)), cooc structure described above\n\n \"\"\"\n\n counts = defaultdict(lambda: defaultdict(int))\n\n for root, dirs, files in os.walk(cooc_path):\n for file in files:\n print(file)\n if file[-5:] == '.json':\n print(cooc_path + '/' + file)\n type_a, name_a = name_from_hashtag(file[:-5], cars, dogs)\n print('name_a', name_a)\n with open(cooc_path + '/' + file, 'r') as f:\n coocs = json.loads(f.read())\n for hashtag, count in coocs.items():\n type_b, name_b = name_from_hashtag(hashtag, cars, dogs)\n if type_a != type_b:\n if type_a == 'car':\n counts[name_a][name_b] += count\n else:\n counts[name_b][name_a] += count\n\n # json roundtrip turns nested defaultdict structure into normal dicts\n return json.loads(json.dumps(counts))\n\n\ndef reverse_counts(counts):\n \"\"\"Create a dog-oriented view of the counts:\n\n {dog1: {car1: count, car2: count, ...}, dog2: {car1: count,...}...}\n\n Args:\n counts (dict): the car-oriented counts as created by\n build_counts_from_coocs\n\n Returns:\n dict: the counts re-cast to have dogs as the first-level key\n\n \"\"\"\n\n new = defaultdict(lambda: defaultdict(int))\n\n for car, dog_counts in counts.items():\n for dog, count in dog_counts.items():\n new[dog][car] += count\n\n return json.loads(json.dumps(new))\n\n\ndef write_counts_csv(counts, csv_path):\n \"\"\"Takes a dictionary of dictionaries of counts and writes a CSV file\n from them. Could use csvwriter for more safety, but we know we have\n no problematic values in our names and no escaping needed.\n\n Args:\n counts: dict of dicts as generated by build_counts_from_coocs\n csv_path: location to write csv file\n \"\"\"\n\n with open(csv_path, 'w', newline='') as f:\n writer = csv.DictWriter(f, fieldnames=['car', 'dog', 'count'])\n writer.writeheader()\n for car, dog_counts in counts.items():\n for dog, count in dog_counts.items():\n writer.writerow({'car': car, 'dog': dog, 'count': str(count)})\n\n\ndef read_count_csv(csv_path):\n \"\"\"Reads the csv file written by write_csv_from_countsand returns the count\n structure built by build_counts_from_coocs\n \"\"\"\n\n counts = defaultdict(lambda: defaultdict(int))\n\n with open(csv_path, 'r', newline='') as f:\n reader = csv.DictReader(f)\n for row in reader:\n counts[row['car']][row['dog']] += int(row['count'])\n\n return json.loads(json.dumps(counts))\n","sub_path":"carroperro/counting.py","file_name":"counting.py","file_ext":"py","file_size_in_byte":3789,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"616918427","text":"#coding:utf-8\n\"\"\"\n@file: SearchPageInfoGenerator\n@author: lyn\n@contact: tonylu716@gmail.com\n@python: 3.5\n@editor: PyCharm Mac\n@create: 2016/11/2 02:13\n@description:\n 本模块用于调用淘宝后端的ajax接口，拼接二者的json做转发\n\"\"\"\n\nimport requests,json\n\nclass StoreInfoGenerator:\n '''\n sample url: http://pub.alimama.com/promo/search/index.htm\n 本页的加载流程分为两个ajax，一是基本信息，二是详情\n 初始化此类后直接调用to_json()方法即可得到处理后的完整json\n '''\n def __init__(self,store_url):\n self.url = store_url\n self.user_view_url = None\n self.basic_info = None\n self.detail_info = None\n\n def generate_basic_info(self):\n #得到店铺基本信息\n json_url = 'http://pub.alimama.com/items/search.json?q={}'\\\n .format(self.url)\n self.user_view_url = 'http://pub.alimama.com/promo/search/index.htm?q={}'\\\n .format(self.url)\n #print(json_url)\n full_json = requests.get(json_url).text\n #print(full_json)\n jd = json.loads(full_json)\n #先将拿到的完整json转置为python字典，直接取第一个结果\n self.basic_info = jd['data']['pageList'][0]\n if len(jd['data']['pageList'])!=1:\n raise Exception('[Error] in get basic info: Multi Results')\n\n def generate_detail_info(self,seller_id):\n # 得到店铺细节信息\n if not seller_id:\n raise Exception('[Error] in get promote info: seller_id cannot be empty.')\n json_url = 'http://pub.alimama.com/pubauc/searchPromotionInfo.json?oriMemberId={}'\\\n .format(seller_id)\n self.temp_url = json_url\n full_json = requests.get(json_url).text\n #print(full_json)\n jd = json.loads(full_json)['data']\n #由��淘宝该接口是允许多商铺并行请求的，会返回列表，但我们只需要第一个\n #故对该字典做如下处理\n for key in jd.keys():\n jd[key] = jd[key][0]\n self.detail_info = jd\n\n def run(self):\n try:\n self.generate_basic_info()\n except:\n #此情况是店铺url链接错误，淘宝后端无法匹配得唯一条目（可能给予关键词重定向产生多项条目）\n print('Error in generate_basic_info():\\n\\tMaybe no located result and redirect worked in this page:{}'\\\n .format(self.user_view_url))\n return -1\n try:\n self.generate_detail_info(\n seller_id=self.basic_info['sellerId'])\n except Exception as e:\n print('Error in generate_detail_info(): page in :{}\\n\\t{}'\\\n .format(self.temp_url,str(e)))\n return -2\n #print('basic',self.basic_info)\n #print('detail',self.detail_info)\n for key in self.detail_info.keys():\n self.basic_info[key] = self.detail_info[key]\n return 1\n\n def to_json(self):\n res = self.run()\n if res==1:\n print('Result OK')\n return self.basic_info\n else:\n return res\n\n\nif __name__==\"__main__\":\n url = 'https://detail.tmall.com/item.htm?id=44895723989'\n json = StoreInfoGenerator(url).to_json()\n print(json)","sub_path":"scholar/SearchPageInfoGenerator.py","file_name":"SearchPageInfoGenerator.py","file_ext":"py","file_size_in_byte":3372,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"71054689","text":"#!/usr/bin/env python\n\nimport numpy as np\nimport cv2\n\nclass RGBHistogram:\n def __init__(self, bins):\n ''' Store the number of bins ths histogram will use'''\n self.bins = bins\n def describe(self, image):\n ''' Computes a 3D histogram in RGB colourspace then\n Normalize the histogram so that images iwth the same\n content, but either scaled larger or smaller will \n have (roughly) the same histgram'''\n hist = cv2.calcHist([image], [0, 1, 2], None, \n self.bins, [0, 256, 0, 256, 0, 256])\n hist = cv2.normalize(hist)\n \n # return 3D histogram as a flattened array\n return hist.flatten()\n","sub_path":"rgbhistogram.py","file_name":"rgbhistogram.py","file_ext":"py","file_size_in_byte":676,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"4505821","text":"import nltk\nimport random \nfrom nltk.corpus import movie_reviews\nimport pickle\n\ndocuments = [(list(movie_reviews.words(fileid)), category)\n\t\t\tfor category in movie_reviews.categories()\n\t\t\tfor fileid in movie_reviews.fileids(category)]\n\nrandom.shuffle(documents)\n\nall_words = []\n\nfor w in movie_reviews.words():\n\tall_words.append(w.lower())\n\n\nall_words = nltk.FreqDist(all_words)\n\nword_features = list(all_words.keys())[:3000]\n\ndef find_features(document):\n\twords = set (document)\n\tfeatures ={}\n\tfor w in word_features:\n\t\tfeatures[w] =(w in words)\n\n\treturn features\n\n#print((find_features(movie_reviews.words(\"neg/cv000_29416.txt\"))))\n\nfeaturesets = [(find_features(rev),category) for (rev,category) in documents]\n\n#starts from here \ntraining_set = featuresets[:1900]\ntesting_set = featuresets[1900:]\n\n#classirier = nltk.NaiveBayesClassifier.train(training_set)\nclassifier_f = open(\"naivebayes.pickle\",\"rb\")\nclassirier = pickle.load(classifier_f)\nclassifier_f.close()\n\nprint(\"Naive Bayes Algo accuracy percentage:\", (nltk.classify.accuracy(classirier, testing_set))*100)\nclassirier.show_most_informative_features(15)\n\n# save_slassifier = open(\"naivebayes.pickle\",\"wb\")\n# pickle.dump(classirier, save_slassifier)\n# save_slassifier.close()","sub_path":"NLTK tutorial/picklejhk.py","file_name":"picklejhk.py","file_ext":"py","file_size_in_byte":1236,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"611449669","text":"\"\"\"\n\tWritten By @searpheon - Arka\n\tUpdated By @nishantcoder97 - Nishant Nahata\n\tBINARY SEARCH\n\"\"\"\n\n\"\"\"\n\tCondition: List should be sorted in ascending order\n\"\"\"\ndef binary_search(item_list, item):\n\t\"\"\"\n\tparam: list item_list: List to be searched\n\tparam: int item: Item to be searched for\n\treturns: int index: Index of the first occurrence of item, or len(tem_list) if not found\n\t\"\"\"\n\tfirst = 0\n\tlast = len(item_list)-1 \n\tindex = len(item_list)\n\twhile first < last:\n\t\tmid = int((first + last) / 2)\n\t\tif item_list[mid] >= item:\n\t\t\tlast = mid\n\t\telse:\n\t\t\tfirst = mid + 1\n\tif item_list[first] == item:\n\t\tindex = first\n\treturn index\n\n\nif __name__ == '__main__':\n\t ### Tests ###\n\tprint(binary_search([1,2,3,5,8], 6)) # returns len(item_list)\n\tprint(binary_search([1,2,3,5,8], 5)) # returns 3\n\tprint(binary_search([1, 2, 3, 3, 3, 4, 4, 5, 10], 4)) # returns 5\n\n\n","sub_path":"Arrays-searching/src/binary_search/binary_search.py","file_name":"binary_search.py","file_ext":"py","file_size_in_byte":855,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"466477023","text":"\ndef solve(k,n):\n max_hour=0\n for _ in range(n):\n k_i,s_i=map(int,(input()).split())\n hour=(k-k_i)/s_i\n if hour>max_hour:\n max_hour=hour\n return k/max_hour\n \n\n\n\nif __name__=='__main__':\n T=int(input())\n for i in range(T):\n k,n=map(int,(input()).split())\n print('Case #%i: %.6f'%(i+1,solve(k,n)))\n","sub_path":"2018/Practice/C.py","file_name":"C.py","file_ext":"py","file_size_in_byte":360,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"99222422","text":"\"\"\"\nParadrop command line utility.\n\nEnvironment Variables:\n PDSERVER_URL Paradrop controller URL [default: https://paradrop.org].\n\"\"\"\nimport os\n\nimport click\n\nfrom . import chute\nfrom . import device\nfrom . import groups\nfrom . import routers\nfrom . import store\n\n\nPDSERVER_URL = os.environ.get(\"PDSERVER_URL\", \"https://paradrop.org\")\n\nCONTEXT_SETTINGS = dict(\n # Options can be parsed from PDTOOLS_* environment variables.\n auto_envvar_prefix = 'PDTOOLS',\n\n # Respond to both -h and --help for all commands.\n help_option_names = ['-h', '--help'],\n\n obj = {\n 'pdserver_url': PDSERVER_URL\n }\n)\n\n\n@click.group(context_settings=CONTEXT_SETTINGS)\ndef root():\n \"\"\"\n Paradrop command line utility.\n\n Environment Variables\n PDSERVER_URL ParaDrop controller URL [default: https://paradrop.org]\n \"\"\"\n pass\n\n\nroot.add_command(chute.chute)\nroot.add_command(device.device)\nroot.add_command(routers.routers)\nroot.add_command(store.store)\ngroups.register_commands(root)\n\n\ndef main():\n \"\"\"\n Entry point for the pdtools Python package.\n \"\"\"\n root()\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"tools/pdtools/pdtools/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":1141,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"180376348","text":"#!/usr/bin/python\n\n# nnet forward pass and error backpropagation\n\nimport csv\nimport numpy as np\nfrom random import seed, random, choice\n\nX = []\nY = []\n\n# read in feature data\nwith open('wheat-seeds.csv') as csvfile:\n readCSV = csv.reader(csvfile, delimiter=',')\n for row in readCSV:\n X.append([float(f) if f else 0.0 for f in row[:-1]])\n y = [0, 0, 0]\n y[int(row[-1])-1] = 1\n Y.append(y)\n\nX = np.array(X)\nY = np.array(Y)\n\n# partition data in train ({X,Y}tr) and test ({X,Y}te)\nnte = 10\nXtr = X[:-nte]\nYtr = Y[:-nte]\nXte = X[-nte:]\nYte = Y[-nte:]\n\n\n# nonlinearity: Sigmoid, and its derivative\ndef nonlin(x, deriv=False):\n\tif deriv:\n\t\treturn x * (1 - x)\n\telse:\n\t\treturn 1. / (1. + np.exp(-x))\n\n\n# inner layer is a matrix (outputs, inputs+1)\n# to add the bias term\ndef mkinner(inputs, outputs):\n\treturn np.random.rand(outputs, inputs+1)\n\n\n#def softmax(x):\n# e_x = np.exp(x)\n# return e_x / e_x.sum()\n\n# compute a forward pass, store the activations\n# forward pass per node is \n# act = nonlin(b + sum_i w_i x_i)\n# = nonlin(dot([w_0, ..., w_d, bias], [x_0, ..., x_d, 1]))\ndef forward(x, layers):\n\tacts = []\n\tfor l in layers:\n\t\tact = np.matmul(l, np.append(x, 1))\n\t\tx = nonlin(a)\n\treturn acts\n\n\n","sub_path":"06-nnets/nnet.py","file_name":"nnet.py","file_ext":"py","file_size_in_byte":1236,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"532377325","text":"from __future__ import unicode_literals\n\nimport datetime\n\nfrom django.test import TestCase\n\nfrom holidays.models import NthXDayHoliday, Holiday\n\nclass NthXDayHolidayTest(TestCase):\n def setUp(self):\n self.mlkday = NthXDayHoliday.objects.create(\n name=\"MLK Day\",\n month=1, # January\n nth=3,\n day_of_week=0, # Monday\n paid_holiday=False,\n )\n self.laborday = NthXDayHoliday.objects.create(\n name=\"Labor Day\",\n month=9, # September\n nth=1,\n day_of_week=0, # Monday\n paid_holiday=True,\n )\n self.thanksgiving = NthXDayHoliday.objects.create(\n name=\"Thanksgiving\",\n month=11, # November\n nth=4,\n day_of_week=3, # Thursday\n paid_holiday=True,\n )\n\n def test_get_holidays(self):\n self.assertListEqual(\n list(Holiday.get_available_holidays()),\n ['MLK Day', 'Labor Day', 'Thanksgiving'])\n\n def test_holiday_json(self):\n self.assertDictEqual(\n self.mlkday.to_json(2016),\n {\n 'id': self.mlkday.pk,\n 'name': 'MLK Day',\n 'date': datetime.date(year=2016, month=1, day=18),\n 'paid': False,\n 'type': 'Nth X Day Holiday',\n })\n self.assertDictEqual(\n self.laborday.to_json(2016),\n {\n 'id': self.laborday.pk,\n 'name': 'Labor Day',\n 'date': datetime.date(year=2016, month=9, day=5),\n 'paid': True,\n 'type': 'Nth X Day Holiday',\n })\n self.assertDictEqual(\n self.thanksgiving.to_json(2016),\n {\n 'id': self.thanksgiving.pk,\n 'name': 'Thanksgiving',\n 'date': datetime.date(year=2016, month=11, day=24),\n 'paid': True,\n 'type': 'Nth X Day Holiday',\n })\n\n def test_get_holidays_year(self):\n holidays = Holiday.get_holidays_for_year(2017)\n self.assertEqual(len(holidays), 3)\n self.assertListEqual(holidays, [\n self.mlkday.to_json(2017),\n self.laborday.to_json(2017),\n self.thanksgiving.to_json(2017),\n ])\n\n holidays = Holiday.get_holidays_for_year(2018)\n self.assertEqual(len(holidays), 3)\n self.assertListEqual(holidays, [\n self.mlkday.to_json(2018),\n self.laborday.to_json(2018),\n self.thanksgiving.to_json(2018),\n ])\n\n holidays = Holiday.get_holidays_for_year(2019)\n self.assertEqual(len(holidays), 3)\n self.assertListEqual(holidays, [\n self.mlkday.to_json(2019),\n self.laborday.to_json(2019),\n self.thanksgiving.to_json(2019),\n ])\n\n def test_holidays_between_dates(self):\n start_date = datetime.date(year=2017, month=1, day=1)\n end_date = datetime.date(year=2017, month=12, day=31)\n holidays = Holiday.holidays_between_dates(start_date, end_date)\n self.assertEqual(len(holidays), 3)\n self.assertListEqual(holidays, [\n self.mlkday.to_json(2017),\n self.laborday.to_json(2017),\n self.thanksgiving.to_json(2017),\n ])\n\n end_date = datetime.date(year=2018, month=9, day=4)\n holidays = Holiday.holidays_between_dates(start_date, end_date)\n self.assertEqual(len(holidays), 5)\n self.assertListEqual(holidays, [\n self.mlkday.to_json(2017),\n self.laborday.to_json(2017),\n self.thanksgiving.to_json(2017),\n self.mlkday.to_json(2018),\n self.laborday.to_json(2018),\n ])\n\n start_date = datetime.date(year=2016, month=1, day=19)\n end_date = datetime.date(year=2016, month=9, day=4)\n holidays = Holiday.holidays_between_dates(start_date, end_date)\n self.assertEqual(len(holidays), 0)\n\n def test_is_holiday(self):\n self.assertEqual(\n Holiday.is_holiday(datetime.date(year=2016, month=11, day=24)),\n [self.thanksgiving.to_json(2016)])\n self.assertEqual(\n Holiday.is_holiday(datetime.date(year=2017, month=11, day=23)),\n [self.thanksgiving.to_json(2017)])\n self.assertEqual(\n Holiday.is_holiday(datetime.date(year=2018, month=11, day=22)),\n [self.thanksgiving.to_json(2018)])\n\n self.assertFalse(Holiday.is_holiday(datetime.date(year=2016, month=11, day=23)))\n self.assertFalse(Holiday.is_holiday(datetime.date(year=2017, month=11, day=24)))\n self.assertFalse(Holiday.is_holiday(datetime.date(year=2018, month=11, day=23)))\n\n def test_is_paid_holiday(self):\n self.assertFalse(Holiday.is_paid_holiday(datetime.date(year=2016, month=1, day=18)))\n self.assertFalse(Holiday.is_paid_holiday(datetime.date(year=2017, month=1, day=16)))\n\n self.assertEqual(\n Holiday.is_paid_holiday(datetime.date(year=2017, month=9, day=4)),\n [self.laborday.to_json(2017)])\n\n self.assertEqual(\n Holiday.is_paid_holiday(datetime.date(year=2017, month=11, day=23)),\n [self.thanksgiving.to_json(2017)])\n","sub_path":"holidays/tests/test_nthxday.py","file_name":"test_nthxday.py","file_ext":"py","file_size_in_byte":5287,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"140299039","text":"\"\"\"\n回溯法+排序剪枝：\n(1) 每一层往下搜索的时候，只能从这个数的后面开始进行搜索（而不是从这个数的位置开始进行搜索）。\n(2) 还有一点可能引起重复的情况，就是同一层中，如果后面的数和前面的数相同，就会引发重复，这个时候直接continue。\n\"\"\"\n\n\nclass Solution:\n def combinationSum2(self, candidates: List[int], target: int) -> List[List[int]]:\n res = []\n # 排序便于剪枝去重\n candidates.sort()\n self.helper(candidates, 0, target, [], res)\n return res\n\n def helper(self, array, start, tar, path, res):\n # 剪枝\n if tar < 0:\n return\n if tar == 0:\n res.append(path[:])\n return\n for i in range(start, len(array)):\n # \"剪枝\" 检测到重复分支的条件：\n # （1）不是这一层的第一个分支\n # （2）当前选出来的数和前一个分支相等\n if i > start and array[i] == array[i - 1]:\n continue\n path.append(array[i])\n self.helper(array, i + 1, tar - array[i], path, res)\n path.pop()","sub_path":"Backtracking/40. Combination Sum II.py","file_name":"40. Combination Sum II.py","file_ext":"py","file_size_in_byte":1192,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"525477494","text":"from webium.driver import get_driver\r\nfrom webium.driver import close_driver\r\nfrom Login import loginpage\r\nfrom selenium.webdriver.support.ui import Select\r\nfrom activity_hub_page import ActivityHubPage\r\nfrom activity_page import AddEditActivityPage, switcher_OFF\r\nimport time\r\nfrom creds import admin_login, admin_password, server, database, username, password\r\nfrom random import choice\r\nfrom string import digits\r\nimport pyodbc\r\n\r\nclass BaseTest(object):\r\n def teardown_class(self):\r\n close_driver()\r\n\r\nclass Test_GODO600(BaseTest):\r\n\r\n def test_600(self):\r\n get_driver().maximize_window()\r\n page = loginpage()\r\n page.open()\r\n page.login_field.send_keys(admin_login)\r\n page.password_field.send_keys(admin_password)\r\n page.button.click()\r\n page=ActivityHubPage() #STEP1\r\n page.open()\r\n page.add_activity_button.click() #STEP2\r\n page=AddEditActivityPage()\r\n time.sleep(15)\r\n assert page.switchers1[0].get_attribute(\"outerHTML\") == switcher_OFF\r\n page.stop_no_sales.send_keys('-1')#STEP3\r\n page.minimum_not_met_alert.click()\r\n assert page.stop_no_sales.get_attribute('value')=='0'\r\n page.stop_no_sales.send_keys('10000001')#STEP4\r\n page.minimum_not_met_alert.click()\r\n assert page.stop_no_sales.get_attribute('value')=='100'\r\n page.stop_no_sales.clear()#STEP5\r\n page.stop_no_sales.send_keys('1')\r\n page.minimum_not_met_alert.click()\r\n assert page.stop_no_sales.get_attribute('value')=='1'\r\n NewActivityName = (\"AutoTest600_\" + ''.join(choice(digits) for i in range(4)))\r\n page.activity_name.send_keys(NewActivityName)\r\n select = Select(page.activity_status)\r\n NewActivityStatus = \"Inactive\"\r\n select.select_by_visible_text(NewActivityStatus)\r\n select = Select(page.branch)\r\n NewActivityBranch = \"AlexeyBranch\"\r\n select.select_by_visible_text(NewActivityBranch)\r\n select = Select(page.starting_location)\r\n NewActivityLocation = \"Hotel California\"\r\n select.select_by_visible_text(NewActivityLocation)\r\n select = Select(page.time_zone)\r\n NewActivityTimezone = \"Pacific\"\r\n select.select_by_visible_text(NewActivityTimezone)\r\n NewActivityCancellationPolicy = 'We can cancel an event any time we want.'\r\n page.cancellation_policy.send_keys(NewActivityCancellationPolicy)\r\n NewActivityDurationMinutes = '15'\r\n page.activity_duration_minutes.send_keys(NewActivityDurationMinutes)\r\n page.ticket_maximum.clear()\r\n NewActivityMaxTickets = '100'\r\n page.ticket_maximum.send_keys(NewActivityMaxTickets)\r\n NewActivityFirstTicketType = \"Adult\"\r\n page.first_ticket_type.send_keys(NewActivityFirstTicketType)\r\n NewActivityFirstTicketPrice = '9.99'\r\n page.first_ticket_price.send_keys(NewActivityFirstTicketPrice)\r\n page.stop_booking_sold.click()\r\n select = Select(page.stop_booking_sold)\r\n NewActivityStopbookingSold = \"15 m\"\r\n select.select_by_visible_text(NewActivityStopbookingSold)\r\n page.save_button.click()\r\n time.sleep(5)\r\n page = ActivityHubPage()\r\n time.sleep(5)\r\n page.show_inactive.click()\r\n page.search_activity_field.send_keys(NewActivityName) # STEP6\r\n time.sleep(5)\r\n page.activity_actions.click()\r\n page.edit_activity.click()\r\n page = AddEditActivityPage()\r\n time.sleep(15)\r\n assert page.switchers1[0].get_attribute(\"outerHTML\") == switcher_OFF\r\n assert page.stop_no_sales.get_attribute('value') == '1'\r\n cnxn = pyodbc.connect(\r\n 'DRIVER={ODBC Driver 17 for SQL Server};SERVER=' + server + ';DATABASE=' + database + ';UID=' + username + ';PWD=' + password)# STEP7\r\n cursor = cnxn.cursor()\r\n cursor.execute(\"SELECT TOP 1 activity_stopbooking_hoursbefore, activity_stopbooking_midnightbefore, activity_name FROM activity ORDER BY activity_id DESC\")\r\n row = cursor.fetchone()\r\n assert row[0] == 1\r\n assert row[1] == 0\r\n assert row[2] == NewActivityName\r\n page.switchers1[0].click()#STEP8\r\n assert page.switchers1[0].get_attribute(\"outerHTML\") != switcher_OFF\r\n assert page.stop_no_sales.get_attribute('value') == ''\r\n assert page.stop_no_sales.is_enabled()==False\r\n page.save_button.click()#STEP9\r\n time.sleep(5)\r\n page = ActivityHubPage()\r\n time.sleep(5)\r\n page.show_inactive.click()\r\n page.search_activity_field.send_keys(NewActivityName)\r\n time.sleep(5)\r\n page.activity_actions.click()\r\n page.edit_activity.click()\r\n page = AddEditActivityPage()\r\n time.sleep(15)\r\n assert page.switchers1[0].get_attribute(\"outerHTML\") != switcher_OFF\r\n assert page.stop_no_sales.is_enabled() == False\r\n cursor.execute(\"SELECT TOP 1 activity_stopbooking_hoursbefore, activity_stopbooking_midnightbefore, activity_name FROM activity ORDER BY activity_id DESC\")#STEP10\r\n row = cursor.fetchone()\r\n assert row[0] == 0\r\n assert row[1] == 1\r\n assert row[2] == NewActivityName\r\n","sub_path":"Tests_Activity Hub- Activities/test_GODO-600 Checking Stop booking if no sales field.py","file_name":"test_GODO-600 Checking Stop booking if no sales field.py","file_ext":"py","file_size_in_byte":5227,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"417126433","text":"n=int(input())\r\nk=[]\r\nA=[]\r\nfor i in range(n):\r\n expo=0\r\n x=input()\r\n N,M=x.split( )\r\n N=int(N)\r\n M=int(M)\r\n a=[int(y) for y in input().split()]\r\n b=[int(z) for z in input().split()]\r\n ans=0\r\n for b1 in b:\r\n #b1=int(b1)\r\n for a1 in range(len(a)):\r\n if a1+b1<=len(a):\r\n k.append(sum(a[a1:a1+b1]))\r\n m=max(k)\r\n #print(m)\r\n pos=k.index(max(k))\r\n ans=ans+((-1)**(expo))*m\r\n expo=expo+1\r\n k=[]\r\n a=a[pos+1:pos+b1-1]\r\n #print(a)\r\n A.append(ans)\r\nfor q in A:\r\n print(q,end=\"\\n\")\r\n \r\n","sub_path":"ninterval.py","file_name":"ninterval.py","file_ext":"py","file_size_in_byte":612,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"564500393","text":"import numpy as np\nimport pandas_ml\nimport sklearn\nfrom keras.layers import Dense, Dropout\nfrom keras.layers import LSTM, Embedding\nfrom keras.models import Sequential\nfrom keras.preprocessing.sequence import pad_sequences\nfrom keras.preprocessing.text import Tokenizer\nfrom keras.utils import to_categorical\nfrom sklearn.model_selection import train_test_split\n\nfrom archived import data_helper\n\nMAX_SEQUENCE_LENGTH = 100\nEMBEDDING_DIM = 300\nVALIDATION_SPLIT = 0.1\nTEST_SPLIT = 0.1\n\n# Number of Neurons default: 200\nUNITS = 200\n# Drop out default: 0.2\nDROPOUT = 0.2\n# Activation default: 'softmax' or 'sigmoid'\nACTIVATION = 'softmax'\n# optimizer default: 'rmsprop' or 'Adam'\nOPTIMIZER = 'rmsprop'\n# losses default: 'categorical_crossentropy' or 'binary_crossentropy'\nLOSS = 'categorical_crossentropy'\n# default 2\nEPOCH = 2\nBATCH_SIZE = 128\n\ntrain_texts, train_labels, test_texts, test_labels = data_helper.makeDataForSentences(\n filename='mydata/data_722.xlsx')\nall_texts = train_texts + test_texts\nall_labels = train_labels + test_labels\n\nprint('(2) doc to var...')\n\ntokenizer = Tokenizer()\ntokenizer.fit_on_texts(all_texts)\nsequences = tokenizer.texts_to_sequences(all_texts)\nword_index = tokenizer.word_index\n\ndata = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH)\nlabels = to_categorical(np.asarray(all_labels))\n\nx_train, x_test, y_train, y_test = train_test_split(data, labels, test_size=TEST_SPLIT, random_state=42)\nx_train, x_val, y_train, y_val = train_test_split(x_train, y_train, test_size=VALIDATION_SPLIT, random_state=42)\n\nmodel = Sequential()\nmodel.add(Embedding(len(word_index) + 1, EMBEDDING_DIM, input_length=MAX_SEQUENCE_LENGTH)) # input, output, max_length\nmodel.add(LSTM(UNITS, dropout=DROPOUT, recurrent_dropout=DROPOUT))\nmodel.add(Dropout(DROPOUT))\nmodel.add(Dense(labels.shape[1], activation=ACTIVATION))\nmodel.summary()\n\nmodel.compile(loss=LOSS,\n optimizer=OPTIMIZER,\n metrics=['acc'])\nprint(model.metrics_names)\nmodel.fit(x_train, y_train, validation_data=(x_val, y_val), epochs=EPOCH, batch_size=BATCH_SIZE)\n\nprint('(6) testing model...')\nprint(model.evaluate(x_test, y_test))\n\n# Confusion Matrix\nprediction = model.predict_classes(x_test, verbose=0)\nreal = []\nfor item in y_test:\n real.append(data_helper.matrix2value(item))\nmatrix = pandas_ml.ConfusionMatrix(real, prediction)\nprint(\"Confusion Matrix:\\n%s\\nReport:\\n%s\\n\" % (\n matrix, sklearn.metrics.classification_report(real, prediction, digits=4)))\n","sub_path":"archived/one_LSTM.py","file_name":"one_LSTM.py","file_ext":"py","file_size_in_byte":2474,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"242054980","text":"from django.shortcuts import render,redirect\nfrom django.contrib.auth import logout,login,authenticate\nfrom .models import *\nfrom .forms import *\n\n# Create your views here.\n\ndef index(request):\n return render(request,'index.html')\n\ndef Details(request):\n profiles=Profile.objects.all()\n return render(request,'details.html',{'profiles':profiles})\n\ndef CreateUser(request):\n context={}\n if request.POST:\n form1=UserForm(request.POST)\n form2=ProfileForm(request.POST)\n if form1.is_valid() and form2.is_valid():\n user=form1.save(commit=False)\n user.save()\n f2=form2.save(commit=False)\n f2.user=user\n f2.save()\n return redirect('index')\n else:\n context['form1']=form1\n context['form2']=form2\n else:\n form1=UserForm()\n form2=ProfileForm()\n context['form1']=form1\n context['form2']=form2\n return render(request,'register.html',context)\n\ndef Edit(request,pk):\n if not request.user.is_authenticated:\n return redirect('login')\n context={}\n user=MyUser.objects.get(pk=pk)\n profile=Profile.objects.get(user=user)\n form1=UserForm(request.POST or None, request.FILES or None,instance=user)\n form2=ProfileForm(request.POST or None, request.FILES or None,instance=profile)\n if request.POST:\n if form1.is_valid() and form2.is_valid():\n form1.save()\n form2.save()\n return redirect('details')\n context={'form1':form1,'form2':form2}\n return render(request,'edit.html',context)\n\ndef Delete(request,pk):\n user=MyUser.objects.get(pk=pk)\n # profile=Profile.objects.get(user=user)\n user.delete()\n return redirect('details')\n\ndef logout_view(request):\n logout(request)\n return redirect('index')\n","sub_path":"core/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1827,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"52491884","text":"def multiply(num1,num2):\n if num1<10 and num2<10:\n return num1*num2\n else:\n num_length=len(str(num1)) if len(str(num1)) >len(str(num2)) else len(str(num2))\n num1_length=len(str(num1))\n num2_length=len(str(num2))\n num1_partition=num_length//2\n num2_partition=num_length//2\n \n a=num1//(10**num1_partition)\n b=num1%(10**num1_partition)\n c=num2//(10**num2_partition)\n d=num2%(10**num2_partition)\n #print(a,b,c,d)\n # we have to find -> ac* 10^(Na+Nb)/2 + bd + \n # ad\n firstcoeff=multiply(a,c)\n third_coef=multiply(b,d)\n second_coef=multiply(a+b,c+d)-firstcoeff-third_coef\n \n return 10**num1_length*firstcoeff+10**num1_partition*(second_coef)+third_coef\n\ndef karat(x,y):\n if len(str(x))== 1 or len(str(y))==1:\n return x*y\n\n n = max(len(str(x)),len(str(y))) // 2\n\n a = x // 10**(n)\n b = x % 10**(n)\n c = y // 10**(n)\n d = y % 10**(n)\n\n z0 = karat(b,d)\n z1 = karat((a+b), (c+d))\n z2 = karat(a,c)\n\n return ((10**(2*n))*z2)+((10**n)*(z1-z2-z0))+z0\nif __name__==\"__main__\":\n print(karat(3141592653589793238462643383279502884197169399375105820974944592\n\n,2718281828459045235360287471352662497757247093699959574966967627))\n # we have to find ac bd and bd+dc","sub_path":"karatsuba.py","file_name":"karatsuba.py","file_ext":"py","file_size_in_byte":1329,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"268185664","text":"import structlog\n\nfrom . import signals\n\n\nlogger = structlog.getLogger(__name__)\n\n\ndef receiver_before_task_publish(sender=None, headers=None, body=None, **kwargs):\n context = structlog.contextvars.get_merged_contextvars(logger)\n if \"task_id\" in context:\n context[\"parent_task_id\"] = context.pop(\"task_id\")\n\n signals.modify_context_before_task_publish.send(\n sender=receiver_before_task_publish, context=context\n )\n\n import celery\n\n if celery.VERSION > (4,):\n headers[\"__django_structlog__\"] = context\n else:\n body[\"__django_structlog__\"] = context\n\n\ndef receiver_after_task_publish(sender=None, headers=None, body=None, **kwargs):\n logger.info(\n \"task_enqueued\",\n child_task_id=headers.get(\"id\") if headers else body.get(\"id\"),\n child_task_name=headers.get(\"task\") if headers else body.get(\"task\"),\n )\n\n\ndef receiver_task_pre_run(task_id, task, *args, **kwargs):\n structlog.contextvars.clear_contextvars()\n structlog.contextvars.bind_contextvars(task_id=task_id)\n metadata = getattr(task.request, \"__django_structlog__\", {})\n structlog.contextvars.bind_contextvars(**metadata)\n signals.bind_extra_task_metadata.send(\n sender=receiver_task_pre_run, task=task, logger=logger\n )\n logger.info(\"task_started\", task=task.name)\n\n\ndef receiver_task_retry(request=None, reason=None, einfo=None, **kwargs):\n logger.warning(\"task_retrying\", reason=reason)\n\n\ndef receiver_task_success(result=None, **kwargs):\n signals.pre_task_succeeded.send(\n sender=receiver_task_success, logger=logger, result=result\n )\n logger.info(\"task_succeeded\")\n\n\ndef receiver_task_failure(\n task_id=None,\n exception=None,\n traceback=None,\n einfo=None,\n sender=None,\n *args,\n **kwargs,\n):\n throws = getattr(sender, \"throws\", ())\n if isinstance(exception, throws):\n logger.info(\n \"task_failed\",\n error=str(exception),\n )\n else:\n logger.exception(\n \"task_failed\",\n error=str(exception),\n exception=exception,\n )\n\n\ndef receiver_task_revoked(\n request=None, terminated=None, signum=None, expired=None, **kwargs\n):\n logger.warning(\n \"task_revoked\", terminated=terminated, signum=signum, expired=expired\n )\n\n\ndef receiver_task_unknown(message=None, exc=None, name=None, id=None, **kwargs):\n logger.error(\"task_not_found\", message=message)\n\n\ndef receiver_task_rejected(message=None, exc=None, **kwargs):\n logger.error(\"task_rejected\", message=message)\n\n\ndef connect_celery_signals():\n from celery.signals import before_task_publish, after_task_publish\n\n before_task_publish.connect(receiver_before_task_publish)\n after_task_publish.connect(receiver_after_task_publish)\n","sub_path":"django_structlog/celery/receivers.py","file_name":"receivers.py","file_ext":"py","file_size_in_byte":2793,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"428274677","text":"########Bootstrap config constants\n\nfrom enum import Enum\n\nLEAF_P4_INFO_FILE_PATH = \"./p4src/Build/leaf_p4info.txt\"\nLEAF_BMV2_JSON_FILE_PATH = \"./p4src/Build/leaf.json\"\n\nSPINE_P4_INFO_FILE_PATH = \"./p4src/Build/spine_p4info.txt\"\nSPINE_BMV2_JSON_FILE_PATH = \"./p4src/Build/spine.json\"\n\nSUPER_SPINE_P4_INFO_FILE_PATH = \"./p4src/Build/spine_p4info.txt\"\nSUPER_SPINE_BMV2_JSON_FILE_PATH = \"./p4src/Build/spine.json\"\n\n#This is the file, that contains the topology details. This file is generated from mininet simuator. After starting mininet simulator, this file is used by the\n#controller and result processor\nTOPOLOGY_CONFIG_FILE = \"./MininetSimulator/Build/Internalnetcfg.json\"\nHOST_COMMAND_FOLDER = \"./MininetSimulator/PER_HOST_COMMANDS/\"\n\n# This is the file where all controller logs wil be written\nCONTROLLER_LOG_FILE_PATH = \"./log/CONTROLLER.log\"\nSTATISTICS_LOG_FILE_PATH = \"./log/STATISTICS.log\"\nMAX_LOG_FILE_SIZE = 52428800 #50 MB\nMAX_LOG_FILE_BACKUP_COUNT = 250 # MAximum 25 files will be kept\nIPERF_MAX_FLOW_RATE_FOR_SERVER = \"4K\" #Iperf flow rate is made maximum to 64K. if we keep ubnlimited it swamps the buffer and experiemnts doesn't work really good\nIPERF_DEFAULT_WINDOW_SIZE_FOR_SERVER = \"1.4K\"\nIPERF_PACING_TIMER = 32\n#This is the path where all the counter values from devices will be written. Or we can directly show some data in live view of gnuplot\nLAMBDA = 25\nCONTROLLER_STATISTICS_RESULT_FILE_PATH = \"./result/\"\n#This is the path where all logs while processing the results willl be written\nRESULT_PROCESSOR_LOG_FILE_PATH = \"./log/RESULT_PROCESSOR_LOG.log\"\n\n\n\n\n\n#------------Usually buffer size should be Delay * bandwidth . for bmv2 based testing this have to be represented and configured through Queue depth.\n# ------ So we will multiply port bandwidth by a factor to estimate the Delay * BW . So by this factor we are actually estimating the Delay factor.\nQUEUE_RATE_TO_QUEUE_DEPTH_FACTOR = 2 # this means if for a port queu rate is x it's queue deth will be 5x\nMAX_PORT_NUMBER = 256 # This field means each switch will have maximum 1024 ports. Corresponding value (MAX_PORTS_IN_SWITCH=1024) also needed to be set in P4 constant.p4 file\nMAX_PORT_NUMBER_PLUS_ONE = MAX_PORT_NUMBER+1 # This special number is used for creating multicast sessions\n\n#=======this parameter is required for meters of each port. We have, setup queue rate for each ports. So the CIR will be queue_rate * CIR threshold factor and PIR will be queue rate\n#===== This parameter is not used at this moment\nINGRESS_STATS_METER_CIR_THRESHOLD_FACTOR = 0.6 # This means each port will color packet yellow when it reaches 70% of the queu rate and red when. These are initial rate. In runtime we will set them dynamically\nINGRESS_STATS_METER_CBURST_FACTOR = 0.1\nINGRESS_STATS_METER_PIR_FACTOR = 0.8\nINGRESS_STATS_METER_PBURST_FACTOR = 0.2 #--- This 4 parameters are not used at this moment\n\n#==== This is one of our major parameter and used\nEGRESS_STATS_METER_CIR_THRESHOLD_FACTOR = 0.70 # This means each port will color packet yellow when it reaches 70% of the queu rate and red when\nEGRESS_STATS_METER_CBURST_FACTOR = 0.1\nEGRESS_STATS_METER_PIR_FACTOR = 0.9\nEGRESS_STATS_METER_PBURST_FACTOR = 0.1\n\n# === These 2 arrays defines, what portion of total upward traffic processing capcicyt is reserved for which class of trffic\n#========= First array lists the accepted traffic classes, 2 nd array defines corresponding percentage to be configred in meter for ingress rate monitoring\n# ==== at this moment we are setting equal percentage for all 3 types of switches. But may need to make 3 different types of percentage for different types of switches\n# IPTOS_LOWDELAY minimize delay 0x10\n# IPTOS_THROUGHPUT maximize throughput 0x08\n# IPTOS_RELIABILITY maximize reliability 0x04\n# IPTOS_LOWCOST minimize cost 0x02\nTRAFFIC_CLASS_LOW_DELAY = 0x04\nTRAFFIC_CLASS_MAXIMIZE_THROUGHPUT = 0x02\nTRAFFIC_CLASS_MAXIMIZE_PROFIT = 0x08 # LOW cost .. if we want to present our low cost that means profit is maximized. but that will require dvisiion. so we are taking direct maximize profit\nTRAFFIC_CLASS_AS_LIST = [TRAFFIC_CLASS_LOW_DELAY, TRAFFIC_CLASS_MAXIMIZE_THROUGHPUT , TRAFFIC_CLASS_MAXIMIZE_PROFIT]\n#-- for only one category of flow if we prioritize rate for that we can get better perofrmance.\n#here for large flow giving 70%- gives better peprformance compare to ECMP\n#PERCENTAGE_OF_TOTAL_UPWARD_TRAFFIC_FOR_TRAFFIC_CLASS = [40, 70, 10]\nPERCENTAGE_OF_TOTAL_UPWARD_TRAFFIC_FOR_TRAFFIC_CLASS = [10,40, 5] # How much of the link capacity should a traffic class get.\n#======================thread control and timer related\nSTATISTICS_PULLING_INTERVAL = 1 # This meand after each 1 second controller will wake up the StatisticsPuller thread and collect stats from the switches\nPORT_STATISTICS_HISTORY_LENGTH = 1000 # this means the history will be\n#======================= Different Test Scenarios\nclass DataplnaeAlgorithm(Enum):\n DP_ALGO_BASIC_ECMP = \"ecmp\"\n DP_ALGO_BASIC_HULA = \"hula\"\n DP_ALGO_BASIC_CLB = \"clb\"\n\nALGORITHM_IN_USE = DataplnaeAlgorithm.DP_ALGO_BASIC_ECMP #For CLB it will be always ECMP\n\n\nqueueRateForHostFacingPortsOfLeafSwitch = 64\nqueueRateForSpineFacingPortsOfLeafSwitch = 32\nqueueRateForLeafSwitchFacingPortsOfSpineSwitch= 32\nqueueRateForSuperSpineSwitchFacingPortsOfSpineSwitch=512\nqueueRateForSpineSwitchFacingPortsOfSuperSpineSwitch=512\nqueueRateForExternalInternetFacingPortsOfSuperSpineSwitch=2048\n\n\n\n# #============================= Security access==========================\n\n\n#========================= Metrics Level Related configuration-- these are not uused at this moment=======================\n\n#=============================Port to Port delay levels: each tuple are of format (low, hi, level,weight)================\n\n\nPORT_TO_PORT_DELAY_LEVELS_LINEAR = [(0, 1000, 0, 0),(1001,5000,1,0), (5001, 75000,2,00)]\nEGRESS_QUEUE_DEPTH_DELAY_LEVELS_LINEAR = [(0, 2, 0, 0),(3,5,1,0), (6, 10,2,00)]\n\n\n\n\n#######################################################################################################################################################################################\n#######################################################################################################################################################################################\n#######################################################################################################################################################################################\n############################################################ All CONFIGURATIONS RELATED TO RESULT PROCESSING ########################################################################\n############################################################# Starts from Here ##########################################################################\n#######################################################################################################################################################################################\n#######################################################################################################################################################################################\n#######################################################################################################################################################################################\n\nFLOW_TYPE_IDENTIFIER_BY_FLOW_VOLUME_IN_KB = [ 50, 128, 256,1024] # These means in our experiments we will consider 2 types of traffic . one with 50 KB size another 1 MB or 1024 KB\nFLOW_TYPE_LOAD_RATIO = [ 10,5, 5, 80] # This means 80% flows are short and 20# are large\nFLOW_VOLUME_IDENTIFIER_VARIATION_LIMIT_IN_PERCENTAGE = 80 # this means any flow size within range of 15% defined in previous array will be categorized as flow of same type. 80 percent is configured to acoomdate both 10kb and 50 kb flow\nPACKET_SIZE = 1024 # Each packet will be 1200 Byte size\n\n\n\n\n\n\n\n\n\n\n\n#######################################################################################################################################################################################\n#######################################################################################################################################################################################\n#######################################################################################################################################################################################\n############################################################ All CONFIGURATIONS RELATED TO TEST EXECUTION ########################################################################\n############################################################# Starts from Here ##########################################################################\n#######################################################################################################################################################################################\n#######################################################################################################################################################################################\n#######################################################################################################################################################################################\n\n\n\n\n\nIPERF3_SERVER_PORT_START = 42000\nIPERF3_CLIENT_PORT_START = 32000\n#We are forced to pass the absolute path because the Iperf tests are ctually run from mininet hosts. which do not understand\n#the relative path. so please use the absolute path where you want to store your result\nTEST_RESULT_FOLDER = \"/home/deba/Desktop/CLB/testAndMeasurement/TEST_RESULTS\"\nTEST_RESULT_FOLDER_SERVER = \"/server-logs\"\nTEST_RESULT_FOLDER_CLIENT = \"/client-logs\"\nTEST_START_TIME_FILE_NAME =\"/test_start_timer.txt\"\n\nMAX_PORT_COUNT = 4\n\n\nSSH_USER_NAME = \"YOUR user name\"\nSSH_PASSWORD = \"Your pass word\" # These access are not required. we are not using them\nSSH_PORT = 22\n\nLINUX_CC_ALGORITHM_DCTCP = \"dctcp\"\nLINUX_CC_ALGORITHM_CUBIC = \"cubic\"\n\n\n\n#=======================configurations for CLB\nCPU_PORT = 255\nCLB_TESTER_DEVICE_NAME = \"p0l0\" # As out target is only testing algorithm we will only run the CLB from one switch.\n#This parameter defines that name. The algorithm will be only run with that device\nLOAD_DISTRIBUTION_1 = [(5,2),(6,10),(7,1),(8,3)]\nLOAD_DISTRIBUTION_2 = [(5,7),(6,1),(7,6),(8,2)]\n\nDISTRO1_INSTALL_DELAY = 0 # Weight distribution 1 will be installed after 50 second of the controller thread starts\nDISTRO2_INSTALL_DELAY = 110 # Weight distribution 2 will be installed after 50 second of the controller thread starts\n\n\n\n\n\n#======================= Must match with the P4 program for CLB\nMAX_PORTS_IN_SWITCH = 8; #Maximum Supported ports in a switch to reflect the dataplane configuration\nMAX_TOR_SUBNET = 4; #Maximum ToR supported by our simulation\nBITMASK_LENGTH = 16\nPRECISION_OF_LOAD_BALANCING = 8","sub_path":"ConfigConst.py","file_name":"ConfigConst.py","file_ext":"py","file_size_in_byte":11084,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"645667157","text":"'''\n\nWrite a script that reads in the words from the words.txt file and finds and prints:\n\n1. The shortest word (if there is a tie, print all)\n2. The longest word (if there is a tie, print all)\n3. The total number of words in the file.\n\n\n'''\n\n\n# empty list\nshortest_words = []\nlongest_words = []\nword_list = []\nnumber = 0\n\n# open with 'with'//'as' and read the lines\nwith open(\"words.txt\", \"r\") as file:\n content = file.read()\n word_list = content.split()\n word_list.sort(key=len)\n\n\n# --- Task 1 ---\n# finding the shortest word overall in the file\n# word_list is sorted, that's why the shortest word is at the front\nshort = word_list[0]\n\n# iterate trough the rest of the file and append every word that is as short as the shortest\nfor word in word_list:\n if len(word) <= len(short):\n shortest_words.append(word)\n\nprint(shortest_words)\n\n# --- Task 2 ---\n# finding the longest word overall in the file\n# word_list is sorted, that's why the longest word is in the back\nlong = word_list[-1]\n\n# iterate trough the rest of the file and append every word that is as long as the shortest\nfor word in word_list:\n if len(word) >= len(long):\n longest_words.append(word)\n\nprint(longest_words)\n\n# --- Task 3 ---\nfor word in word_list:\n number += 1\n\nprint(f\"The total number of words in 'words.txt' is: {number}\")\n\n","sub_path":"07_file_io/07_01_words_analysis.py","file_name":"07_01_words_analysis.py","file_ext":"py","file_size_in_byte":1333,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"110720620","text":"\"\"\"The Intel MPI Benchmarks\n https://software.intel.com/en-us/articles/intel-mpi-benchmarks\n\"\"\"\nfrom abc import abstractmethod, abstractproperty\nimport re\n\nfrom cached_property import cached_property\n\nfrom hpcbench.api import (\n Benchmark,\n Metrics,\n MetricsExtractor,\n)\nfrom hpcbench.toolbox.process import find_executable\n\n\nclass IMBExtractor(MetricsExtractor):\n \"\"\"Abstract class for IMB benchmark metrics extractor\n \"\"\"\n @abstractproperty\n def metrics(self):\n \"\"\" The metrics to be extracted.\n This property can not be replaced, but can be mutated as required\n \"\"\"\n\n @abstractproperty\n def stdout_ignore_prior(self):\n \"\"\"Ignore stdout until this line\"\"\"\n\n @cached_property\n def metrics_names(self):\n \"\"\"get metrics names\"\"\"\n return set(self.metrics)\n\n def extract_metrics(self, outdir, metas):\n # parse stdout and extract desired metrics\n with open(self.stdout(outdir)) as istr:\n for line in istr:\n if line.strip() == self.stdout_ignore_prior:\n break\n for line in istr:\n self.process_line(line.strip())\n return self.epilog()\n\n @abstractmethod\n def process_line(self, line):\n \"\"\"Process a line\n \"\"\"\n\n @abstractmethod\n def epilog(self):\n \"\"\":return: extracted metrics as a dictionary\n \"\"\"\n\n\nclass IMBPingPongExtractor(IMBExtractor):\n \"\"\"Metrics extractor for PingPong IMB benchmark\"\"\"\n\n LATENCY_BANDWIDTH_RE = re.compile(\n r'^\\s*(\\d+)\\s+\\d+\\s+(\\d*\\.?\\d+)[\\s]+(\\d*\\.?\\d+)'\n )\n\n def __init__(self):\n super(IMBPingPongExtractor, self).__init__()\n self.s_latency = set()\n self.s_bandwidth = set()\n\n @cached_property\n def metrics(self):\n return dict(\n latency=Metrics.Second,\n bandwidth=Metrics.MegaBytesPerSecond,\n )\n\n @cached_property\n def stdout_ignore_prior(self):\n return \"# Benchmarking PingPong\"\n\n def process_line(self, line):\n search = self.LATENCY_BANDWIDTH_RE.search(line)\n if search:\n byte = int(search.group(1))\n if byte != 0:\n self.s_latency.add(float(search.group(2)))\n self.s_bandwidth.add(float(search.group(3)))\n\n def epilog(self):\n return dict(\n latency=min(self.s_latency),\n bandwidth=max(self.s_bandwidth),\n )\n\n\nclass IMBAllToAllExtractor(IMBExtractor):\n \"\"\"Metrics extractor for AllToAll IMB benchmark\"\"\"\n\n LATENCY_RE = re.compile(\n r'^\\s*(\\d+)\\s+\\d+\\s+\\d*\\.?\\d+[\\s]+\\d*\\.?\\d+[\\s]+(\\d*\\.?\\d+)'\n )\n\n def __init__(self):\n super(IMBAllToAllExtractor, self).__init__()\n self.s_res = set()\n\n @property\n def metrics(self):\n return dict(latency=Metrics.Second)\n\n @cached_property\n def stdout_ignore_prior(self):\n return \"# Benchmarking Alltoallv\"\n\n def process_line(self, line):\n search = self.LATENCY_RE.search(line)\n if search:\n byte = int(search.group(1))\n if byte != 0:\n self.s_res.add(float(search.group(2)))\n\n def epilog(self):\n return dict(latency=min(self.s_res))\n\n\nclass IMBAllGatherExtractor(IMBAllToAllExtractor):\n \"\"\"Metrics extractor for AllGather IMB benchmark\"\"\"\n\n def __init__(self):\n super(IMBAllGatherExtractor, self).__init__()\n\n @cached_property\n def stdout_ignore_prior(self):\n return \"# Benchmarking Allgather\"\n\n\nclass IMB(Benchmark):\n \"\"\"Benchmark wrapper for the IMBbench utility\n \"\"\"\n DEFAULT_EXECUTABLE = 'IMB-MPI1'\n PING_PONG = 'PingPong'\n ALL_TO_ALL = 'Alltoallv'\n ALL_GATHER = 'Allgather'\n DEFAULT_CATEGORIES = [\n PING_PONG,\n ALL_TO_ALL,\n ALL_GATHER,\n ]\n DEFAULT_ARGUMENTS = {\n ALL_GATHER: [\"-nmpmin\", \"{process_count}\"]\n }\n\n def __init__(self):\n super(IMB, self).__init__(\n attributes=dict(\n data=\"\",\n executable=IMB.DEFAULT_EXECUTABLE,\n categories=IMB.DEFAULT_CATEGORIES,\n arguments=IMB.DEFAULT_ARGUMENTS,\n )\n )\n name = 'imb'\n\n description = \"Provides latency/bandwidth of the network.\"\n\n @cached_property\n def executable(self):\n \"\"\"Get absolute path to executable\n \"\"\"\n return find_executable(self.attributes['executable'])\n\n def execution_matrix(self, context):\n for category in self.attributes['categories']:\n arguments = self.attributes['arguments'].get(category) or []\n if category == IMB.PING_PONG:\n for pair in IMB.host_pairs(context):\n yield dict(\n category=category,\n command=[self.executable, category] + arguments,\n srun_nodes=pair,\n )\n else:\n yield dict(\n category=category,\n command=[self.executable, category] + arguments,\n srun_nodes=2,\n )\n\n @staticmethod\n def host_pairs(context):\n try:\n pos = context.nodes.index(context.node)\n except ValueError:\n context.logger.error(\n 'Could not find current node %s in nodes %s',\n context.node,\n ', '.join(context.nodes)\n )\n return []\n else:\n return [\n [context.node, context.nodes[i]]\n for i in range(pos + 1, len(context.nodes))\n ]\n\n @cached_property\n def metrics_extractors(self):\n return {\n IMB.PING_PONG: IMBPingPongExtractor(),\n IMB.ALL_TO_ALL: IMBAllToAllExtractor(),\n IMB.ALL_GATHER: IMBAllGatherExtractor(),\n }\n","sub_path":"hpcbench/benchmark/imb.py","file_name":"imb.py","file_ext":"py","file_size_in_byte":5849,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"144901197","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time : 4/28/17 3:45 PM\n# @Author : xiaowa\n\nimport logging\nLOG_FORMAT = logging.Formatter(\"%(asctime)s %(name)s [%(levelname)s] %(filename)s:%(lineno)d - %(message)s\")\n\nCONSOLE_HANDLER = logging.StreamHandler()\nCONSOLE_HANDLER.setFormatter(LOG_FORMAT)\n\n\ndefault_logger = logging.getLogger(\"default_logger\")\ndefault_logger.addHandler(CONSOLE_HANDLER)\ndefault_logger.setLevel(logging.INFO)\n\n\n\n\n\n\n","sub_path":"lml/lib/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":447,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"248491615","text":"class Utente:\n def __init__(self, idUtente, morada, telefoneCasa, telefoneTrabalho, nome, sexo, estadoCivil, ssn, nacionalidade,\n cidadania):\n self.idUtente = idUtente\n self.morada = morada\n self.telefoneCasa = telefoneCasa\n self.telefoneTrabalho = telefoneTrabalho\n self.nome = nome\n self.sexo = sexo\n self.estadoCivil = estadoCivil\n self.ssn = ssn\n self.nacionalidade = nacionalidade\n self.cidadania = cidadania\n\n def toString(self):\n string = \"Identificador: \" + str(self.idUtente) + \"\\n\"\n string += \"Morada: \" + self.morada + \"\\n\"\n string += \"Telefone de Casa: \" + self.telefoneCasa + \"\\n\"\n string += \"Telefone do Trabalho: \" + self.telefoneTrabalho + \"\\n\"\n string += \"Nome: \" + self.nome + \"\\n\"\n string += \"Sexo: \" + self.sexo + \"\\n\"\n string += \"Estado Civil: \" + self.estadoCivil + \"\\n\"\n string += \"Número de Utente de Saúde: \" + self.ssn + \"\\n\"\n string += \"Nacionalidade: \" + self.nacionalidade + \"\\n\"\n string += \"Cidadania: \" + self.cidadania + \"\\n\\n\"\n return string\n","sub_path":"FE01/Maquina1/Business/Models/Utente.py","file_name":"Utente.py","file_ext":"py","file_size_in_byte":1146,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"626765935","text":"#Name - Jyostna Thanjavur \r\n#importing Python Libraries \r\n\r\nfrom reportlab.lib import colors #reportlab library for mapping colors and matplot library\r\nfrom reportlab.lib.units import cm \r\nfrom Bio.Graphics import GenomeDiagram #biopython library for genomic diagram\r\nfrom Bio import SeqIO #library to read file\r\n\r\n#Reading the genome file using read function \r\nStore_File = SeqIO. read(\"Genome.gb\", \"genbank\")\r\n\r\nGen_Diagram = GenomeDiagram.Diagram(\"Tomato curly stunt virus\", \"complete genome\") #Creating empty diagram\r\nGen_Track = Gen_Diagram.new_track(1, name = \"Annotated Features\") #Adding empty Diagram \r\nGen_newSet = Gen_Track.new_set() #Adding empty feature set \r\n\r\n#Initiating diagram features for the objects in the genome sequence file\r\n#Switching colors between pink and green \r\n\r\nfor feature in Store_File.features:\r\n if feature.type != \"gene\":\r\n continue\r\n if len(feature) % 9 == 0: #if length of genome multiple of 9 - the gene is represented by pink color\r\n color = colors.pink\r\n else:\r\n color = colors.green # else green\r\n\r\n Gen_newSet.add_feature(feature, color = color, label = True, label_size = 20) #Adding and labeling color and size features to the diagram\r\n\r\n #Creating the output file using write and draw functions\r\n Gen_Diagram.draw(format = \"circular\", circular = True, pagesize = (30 * cm, 30 * cm), start = 0, end = len(Store_File), circle_core = 0.8)\r\n Gen_Diagram.write(\"OutputFile.jpg\", \"JPG\")","sub_path":"GenomeCode.py","file_name":"GenomeCode.py","file_ext":"py","file_size_in_byte":1469,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"569662751","text":"mins = [1, 2, 3]\nsecs = [m*60 for m in mins]\n\"\"\"\nlist comprehensions always have the same format:\n`new_list = [ function(x) for x in old_list ]` where _x_ is the target identifier and the *function(x)* is the \ntransformation. \n\nThis is somewhat of a _functional programming_ concept. \n\nNOTE that list comprehensions can only deal with situations where each item in the list has to be transformed, and thus\nif the transformation has to occur on the basis of certain criteria, then it doesn't work. \n\"\"\"\nprint(secs)\n\n# Transforming meters into feet:\nmeters = [1, 10, 3]\nfeet = [m*3.281 for m in meters]\nprint(feet)\n\n# Lower and Mixed case to uppercase:\nlower = [\"I\", \"don't\", \"like\", \"spam\"]\nupper = [s.upper() for s in lower] # .upper() method of a string converts it to all-uppercase.\nprint(upper)\n\n# Converting string to float using float() BIF:\ntimeStr = ['2.12', '2.76', '2.35']\ntimeFloat = [float(t) for t in timeStr]\nprint(timeFloat)","sub_path":"HeadFirstPython/listComprehensions.py","file_name":"listComprehensions.py","file_ext":"py","file_size_in_byte":943,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"166150562","text":"'''\nполучить список пользователей с их полом\n\nпройтись по всему списку\nесли пол = 1, то\nполучить номер пользователя в списке\nзанести номер в список\n\nпройтись с этими номерами в read_from_file_to_list.py\nзанести все группы в список\n\nотправить этот список в edit_file.py\n'''\n\nimport urllib.request\nimport json\n\n#https://oauth.vk.com/authorize?client_id=5097800&display=page&redirect_uri=https://oauth.vk.com/blank.html&scope=friends&response_type=token&v=5.37\n\n\nACCESS_TOKEN = 'f7bc8dfed9eb001f003a979c5774d153a49ba80cb6e7744bef98d90f05b028571dc05a627fd5418e1653e'\n#GROUPS_ID = 24098940 #83000 m.kala\n#GROUPS_ID = 59142119 #1237 Peri\n#GROUPS_ID = 67824212 #139 zerkalny_mir\nGROUPS_ID = 43817239 #78 v efire\n#GROUPS_ID = 51094072 #25\n#GROUPS_ID = 91843912 #409\n#GROUPS_ID = 66066496 #kasp\n#GROUPS_ID = 68017962 #derbent\n#GROUPS_ID = 19514611 #antimesto\n#GROUPS_ID = 51392247 #friendzone\nurl_begin = 'https://api.vk.com/method/execute?code=return['\n\ndef get_data_from_response(loc_offset, loc_edge):\n url_tmp = 'API.groups.getMembers({\"group_id\":%d,\"offset\":' % (GROUPS_ID)\n url_end = \"\"\n for i in range(loc_offset, loc_edge, 1000):\n url_end += url_tmp + str(i) + \"}),\"\n\n response = urllib.request.urlopen(url_begin + url_end + '];&access_token=' + ACCESS_TOKEN)\n data = json.loads(response.read().decode('utf-8'))['response']\n\n return get_uids_from_data(data)\n\n\ndef get_uids_from_data(loc_data):\n users_list = []\n for i in range(0, 25):\n users_list.extend(loc_data[i]['users'])\n\n return users_list","sub_path":"get_data.py","file_name":"get_data.py","file_ext":"py","file_size_in_byte":1753,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"173634798","text":"# -*- coding: utf-8 -*-\nimport logging\nimport random\n\nimport numpy as np\n\n\nclass SBM(object):\n\n def __init__(self, num_vertices, communities, vertex_labels, p_matrix, p_matrix_2):\n logging.info('Initializing SBM Model ...')\n self.num_vertices = num_vertices\n self.communities = communities\n self.vertex_labels = vertex_labels\n self.p_matrix = p_matrix\n self.p_matrix_2 = p_matrix_2\n self.block_matrix, self.block_matrix_2 = self.generate()\n \n\n def generate(self):\n logging.info('Generating SBM ...')\n v_label_shape = (1, self.num_vertices)\n p_matrix_shape = (self.communities, self.communities)\n block_matrix_shape = (self.num_vertices, self.num_vertices)\n block_matrix = np.zeros(block_matrix_shape, dtype=int)\n block_matrix_2 = np.zeros(block_matrix_shape, dtype=int)\n\n for row, _row in enumerate(block_matrix):\n for col, _col in enumerate(block_matrix[row]):\n if row>col:\n community_a = self.vertex_labels[row]\n community_b = self.vertex_labels[col]\n\n p = random.random()\n \n val = self.p_matrix[community_a][community_b]\n\n if p < val:\n block_matrix[row][col] = 1\n block_matrix[col][row] = 1\n\n val = self.p_matrix_2[community_a][community_b]\n\n if p < val:\n block_matrix_2[row][col] = 1\n block_matrix_2[col][row] = 1\n\n return block_matrix, block_matrix_2\n\n\npass","sub_path":"nettack/sbm.py","file_name":"sbm.py","file_ext":"py","file_size_in_byte":1646,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"447196924","text":"#!/usr/bin/python\n# ^_^ coding: utf-8 ^_^\nsrc_path = \"/home/wwwroot/test.txt\"\ndst_path = \"/home/wwwroot/test2.txt\"\nsrc_file = open(src_path,\"r\")\ndst_file = open(dst_path,\"w\")\nsize = 1024\nwhile True:\n file = src_file.read(size)\n if file ==\"\":\n break\n dst_file.write(file)\n\nsrc_file.close()\ndst_file.close()\n","sub_path":"class4/copfile1.py","file_name":"copfile1.py","file_ext":"py","file_size_in_byte":322,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"523079281","text":"# Always set X is shorter list, Y is longer list\n# Perform binary search on X, and cut X and Y, to become two balanced pieces since we are calculating median.\n# We want both piece equal number or diff one\n# And all numbers in left side should smaller than any number in right side.\n# Left side piece number = X left side piece plus Y left side piece\n# Right side piece number = X right side piece plus Y right side piece\n# And since we know the total number, if we know X left side piece total number\n# Then we know Y left side size, then know Y cut point index.\n# Then verify if its legal. Special attention to four number besides cut line.\n# xLeft always < xRight, yLeft always < yRight. But xLeft yRight, xRight yLeft is uncertain.\n# If xLeft < yRight, yLeft < xRight, then its a legal cut. Depend on total number is odd or even, we get its median\n# If its illegal, we should move cut point in X either moving left or moving right.\n# If we move X cut point to left, then for the left piece, it contains less number from X and more number from Y.\n# At the same time, for the cutting line four number, cutting number from X is smaller, and cutting number from Y is bigger.\n# If we move X cut point to right, then for the left piece, it contains more number from X and less number from Y.\n# At the same time, for the cutting line four number, cutting number from X is bigger, and cutting number from Y is smaller.\nclass Solution:\n def findMedianSortedArrays(self, nums1: List[int], nums2: List[int]) -> float:\n left = 0\n right = len(nums1)\n if len(nums1) > len(nums2):\n return self.findMedianSortedArrays(nums2, nums1)\n while left <= right:\n nums1SplitIndex = left + int((right-left)/2)\n nums2SplitIndex = len(nums1) + len(nums2)\n nums2SplitIndex = int(nums2SplitIndex/2) - nums1SplitIndex\n if nums1SplitIndex > 0:\n xLeft = nums1[nums1SplitIndex -1]\n else:\n xLeft = float(\"-inf\")\n if nums1SplitIndex < len(nums1):\n xRight = nums1[nums1SplitIndex]\n else:\n xRight = float(\"inf\")\n if nums2SplitIndex >0:\n yLeft = nums2[nums2SplitIndex -1]\n else:\n yLeft = float(\"-inf\")\n if nums2SplitIndex < len(nums2):\n yRight = nums2[nums2SplitIndex]\n else:\n yRight = float(\"inf\")\n if xLeft <= yRight and yLeft <= xRight:\n if (len(nums1) + len(nums2))%2 ==0:\n return float(max(xLeft, yLeft) + min(xRight, yRight))/2\n else:\n return min(xRight,yRight)\n if yLeft > xRight:\n left = nums1SplitIndex + 1\n else:\n right = nums1SplitIndex -1\n\n","sub_path":"面试-LeetCode题/基础算法4-二分搜索/LeetCode4(Median of Two Sorted Arrays)/Solution.py","file_name":"Solution.py","file_ext":"py","file_size_in_byte":2830,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"548575268","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('participants', '0011_auto_20150726_2337'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='participant',\n name='rnd_str',\n field=models.CharField(help_text=b'Unique string per participant', unique=True, max_length=30, verbose_name=b'random string', blank=True),\n ),\n ]\n","sub_path":"participants/migrations/0012_auto_20150726_2348.py","file_name":"0012_auto_20150726_2348.py","file_ext":"py","file_size_in_byte":513,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"333787674","text":"from __future__ import print_function\n\n# *-* coding: utf-8 *-*\n\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom HodaDatasetReader import read_hoda_dataset\nfrom sklearn.metrics import classification_report\n\nimport keras\nfrom keras import backend as K\nfrom keras.datasets import mnist\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout\nfrom keras.optimizers import RMSprop\n\ndef recall_m(y_true, y_pred):\n true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n recall = true_positives / (possible_positives + K.epsilon())\n return recall\n\ndef precision_m(y_true, y_pred):\n true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n precision = true_positives / (predicted_positives + K.epsilon())\n return precision\n\ndef f1_m(y_true, y_pred):\n precision = precision_m(y_true, y_pred)\n recall = recall_m(y_true, y_pred)\n return 2*((precision*recall)/(precision+recall+K.epsilon()))\n\nbatch_size = 128\nnum_classes = 10\nepochs = 5\n\nprint('Reading train dataset (Train 60000.cdb)...')\nx_train, y_train = read_hoda_dataset(dataset_path='./DigitDB/Train 60000.cdb',\n images_height=32,\n images_width=32,\n one_hot=False,\n reshape=True)\n\nprint('Reading test dataset (Test 20000.cdb)...')\nx_test, y_test = read_hoda_dataset(dataset_path='./DigitDB/Test 20000.cdb',\n images_height=32,\n images_width=32,\n one_hot=False,\n reshape=True)\n\nprint()\n\n# convert class vectors to binary class matrices\ny_train = keras.utils.to_categorical(y_train, num_classes)\ny_test = keras.utils.to_categorical(y_test, num_classes)\n\nmodel = Sequential()\nmodel.add(Dense(512, activation='relu', input_shape=(1024,)))\n#model.add(Dropout(0.2))\n#model.add(Dense(512, activation='relu'))\n#model.add(Dropout(0.2))\nmodel.add(Dense(num_classes, activation='softmax'))\n\nmodel.summary()\n\nmodel.compile(loss='categorical_crossentropy',\n optimizer=RMSprop(),\n metrics=['accuracy', f1_m, precision_m, recall_m])\n\nhistory = model.fit(x_train, y_train,\n epochs=epochs,\n verbose=1,\n validation_split=0.1)\n\ny_pred = model.predict(x_test, batch_size=64, verbose=1)\ny_pred_max = np.argmax(y_pred, axis=1)\ny_test_max = np.argmax(y_test, axis=1)\n\nprint(classification_report(y_test_max, y_pred_max))\n\nscore = model.evaluate(x_test, y_test, verbose=0)\n\nprint('Test loss:', score[0])\nprint('Test accuracy:', score[1])\nprint('Test f1_score:', score[2])\nprint('Test precision:', score[3])\nprint('Test recall:', score[4])\n\nplt.plot(history.history['loss'])\nplt.plot(history.history['val_loss'])\nplt.title('Model loss')\nplt.ylabel('Loss')\nplt.xlabel('Epoch')\nplt.legend(['Train', 'Test'], loc='upper left')\nplt.savefig('loss.png')\nplt.show()\n","sub_path":"HomeWork2/MLP_b.py","file_name":"MLP_b.py","file_ext":"py","file_size_in_byte":3134,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"39511418","text":"import time\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.wait import WebDriverWait\n\n\nclass BaseAction:\n\n def __init__(self, driver):\n self.driver = driver\n\n def click(self, loc, time=10, poll=1):\n self.find_element(loc, time, poll).click()\n\n def input_text(self, loc, text, time=10, poll=1):\n self.find_element(loc, time, poll).send_keys(text)\n\n def clear_text(self, loc, time=10, poll=1):\n self.find_element(loc, time, poll).clear()\n\n def back(self):\n self.driver.keyevent(4)\n\n def find_element(self, loc, time=10, poll=1):\n loc_by, loc_value = loc\n if loc_by == By.XPATH:\n loc_value = self.make_xpath_with_feature(loc_value)\n return WebDriverWait(self.driver, time, poll).until(lambda x: x.find_element(loc_by, loc_value))\n\n def find_elements(self, loc, time=10, poll=1):\n loc_by, loc_value = loc\n if loc_by == By.XPATH:\n loc_value = self.make_xpath_with_feature(loc_value)\n return WebDriverWait(self.driver, time, poll).until(lambda x: x.find_elements(loc_by, loc_value))\n\n def scroll_page_one_time(self, direction=\"down\"):\n window_size = self.driver.get_window_size()\n window_height = window_size[\"height\"]\n window_width = window_size[\"width\"]\n end_y = window_height * 0.25\n start_y = end_y * 3\n center_x = window_width * 0.5\n\n if direction == \"down\":\n self.driver.swipe(center_x, start_y, center_x, end_y)\n elif direction == \"up\":\n self.driver.swipe(center_x, end_y, center_x, start_y)\n else:\n raise Exception(\"请输入正确的direction参数\")\n\n\n # 滑动当前页面到某个元素出现\n def scroll_page_until_loc(self, loc, direction=\"down\"):\n while True:\n try:\n self.find_element(loc)\n break\n except Exception:\n\n self.scroll_page_one_time(direction)\n time.sleep(1)\n\n def make_xpath_with_feature(self, feature):\n xpath_start = \"//*[\"\n xpath_end = \"]\"\n xpath = \"\"\n if isinstance(feature, str):\n xpath = self.make_xpath_with_unit_feature(feature)\n else:\n for i in feature:\n xpath = xpath + self.make_xpath_with_unit_feature(i)\n xpath = xpath.rstrip(\"and\")\n xpath = xpath_start + xpath + xpath_end\n return xpath\n\n def make_xpath_with_unit_feature(self, unit_feature):\n xpath = \"\"\n args = unit_feature.split(\",\")\n if len(args) == 2:\n xpath = xpath + \"@\" + args[0] + \"='\" + args[1] + \"'and\"\n elif len(args) == 3:\n if args[2] == \"1\":\n xpath = xpath + \"contains(@\" + args[0] + \",'\" + args[1] + \"')and\"\n elif args[2] == \"0\":\n xpath = xpath + \"@\" + args[0] + \"='\" + args[1] + \"'and\"\n return xpath\n\n","sub_path":"base/base_action.py","file_name":"base_action.py","file_ext":"py","file_size_in_byte":2942,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"552305927","text":"from selenium import webdriver\nfrom selenium.webdriver.support.select import By\nfrom features.pageobjects.page import Page\nfrom hamcrest import assert_that, contains_string, equal_to\n\ndriver = webdriver.Chrome(\"E:\\Soft\\chromedriver_win32\\chromedriver.exe\")\npage = Page (driver)\npage.maximize_window()\npage.navigate('http://localhost:8080/Shop/')\nassert_that (driver.title, contains_string ('opencart'))\n\nSEARCH_BOX = driver.find_element_by_name('search')\nSEARCH_RESULT = (By.CSS_SELECTOR,'#content > div:nth-child(8)')\n\npage.send_key_and_press_enter(SEARCH_BOX, 'phone')\nhas_result = page. is_element_visible(SEARCH_RESULT)\nassert_that(has_result,equal_to(True))\n\npage.quit()\n","sub_path":"exercises/linhhoang16/search_product.py","file_name":"search_product.py","file_ext":"py","file_size_in_byte":676,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"126086564","text":"from django.conf.urls import url\nfrom django.contrib import admin\n\nfrom .views import (\n\tCommentDetailAPIView, \n\tCommentListAPIView,\n\t) \n\nurlpatterns = [\n\turl(r'^$', CommentListAPIView.as_view(), name= \"list\"),\n url(r'^(?P