repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
Belvarm/roguelike-tutorial | [
"ea989c080b0f7dd61c38b5719ab8e502a45a0489"
] | [
"actions/ai.py"
] | [
"from __future__ import annotations\n\nfrom typing import List, Tuple, TYPE_CHECKING, Optional\n\nimport numpy as np # type: ignore\nimport tcod.path\n\nfrom actions import Impossible, Action\nimport actions.common\nfrom states import ingame\n\nif TYPE_CHECKING:\n from actor import Actor\n\n\nclass PathTo(Actio... | [
[
"numpy.copy",
"numpy.transpose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rileyp2000/MLPytorchWork | [
"9b99e61b853e3d8ccfed847984bd0047885b8bdf"
] | [
"Human-in-the-Loop/DQN-TAMER/model.py"
] | [
"import torch\nimport torch.nn as nn\n\n\nclass Q(nn.Module):\n def __init__(self,env):\n super(Q, self).__init__()\n\n self.main = nn.Sequential(\n nn.Linear(env.observation_space.shape[0], 64),\n nn.ELU(),\n nn.Linear(64, 64),\n nn.ELU(),\n n... | [
[
"torch.nn.Linear",
"torch.nn.ELU",
"torch.FloatTensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Nightrider48/openpilot | [
"d4f9e524d0546171985e33877b5168020844922e"
] | [
"selfdrive/car/honda/interface.py"
] | [
"#!/usr/bin/env python3\nimport numpy as np\nfrom cereal import car\nfrom common.numpy_fast import clip, interp\nfrom common.realtime import DT_CTRL\nfrom selfdrive.swaglog import cloudlog\nfrom selfdrive.config import Conversions as CV\nfrom selfdrive.controls.lib.events import ET\nfrom selfdrive.car.honda.values ... | [
[
"numpy.dot",
"numpy.array",
"numpy.maximum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
PingjunChen/pytorch-study | [
"2bc05f3a310d4bf4f618b0a5adfc684a81f75efa"
] | [
"imagenet-pytorch/utils.py"
] | [
"# -*- coding: utf-8 -*-\n\nimport os, sys, pdb\nimport torch\nimport torch.nn as nn\n\n\ndef adjust_learning_rate(optimizer, epoch, args):\n \"\"\"Sets the learning rate to the initial LR decayed by 10 every 30 epochs\"\"\"\n lr = args.lr * (0.1 ** (epoch // 30))\n for param_group in optimizer.param_group... | [
[
"torch.no_grad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
chuusan/kt | [
"a04b41380f43ce9d7f928b6a7eaeef9ce2bbd907"
] | [
"kt/kt.py"
] | [
"import pandas as pd\n\n\ndef catbind(a, b):\n \"\"\"\n Concatenates two pandas categoricals.\n\n Parameters\n ----------\n a : pandas.core.arrays.categorical.Categorical\n A pandas categorical.\n b : pandas.core.arrays.categorical.Categorical\n A pandas categorical that you wish to conc... | [
[
"pandas.Categorical"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Anbyew/GPy__GPLRF-GPLMRD | [
"e249498aba0c661f2d6a919bb0e1fc282c9adfab",
"e249498aba0c661f2d6a919bb0e1fc282c9adfab"
] | [
"GPy/core/parameterization/priors.py",
"GPy/models/mrd.py"
] | [
"# Copyright (c) 2012 - 2014, GPy authors (see AUTHORS.txt).\n# Licensed under the BSD 3-clause license (see LICENSE.txt)\n\n\nimport numpy as np\nfrom scipy.special import gammaln, digamma\nfrom ...util.linalg import pdinv\nfrom paramz.domains import _REAL, _POSITIVE, _NEGATIVE\nimport warnings\nimport weakref\n\n... | [
[
"numpy.diag",
"numpy.dot",
"numpy.sqrt",
"numpy.random.multivariate_normal",
"numpy.concatenate",
"numpy.mean",
"numpy.zeros_like",
"numpy.random.randn",
"numpy.where",
"numpy.square",
"numpy.eye",
"scipy.special.digamma",
"scipy.stats.t.rvs",
"numpy.outer",... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yenchiah/deep-smoke-machine | [
"5f779f723a3c891145db43663c8825f9ab55dc74"
] | [
"back-end/www/model/timeception/nets/i3d_torch_charades.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\n\nimport numpy as np\n\nimport os\nimport sys\nfrom collections import OrderedDict\n\nclass MaxPool3dSamePadding(nn.MaxPool3d):\n\n def compute_pad(self, dim, s):\n if s % self.stride[dim] == 0:\n ... | [
[
"torch.nn.AvgPool3d",
"torch.nn.Dropout",
"torch.cat",
"torch.nn.Conv3d",
"torch.nn.BatchNorm3d",
"torch.nn.functional.pad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sdnjly/WSDL-AD | [
"7a166f8ae37e5d5fba5ca58fc88554da6b5c44dd"
] | [
"QRS_detection.py"
] | [
"import argparse\nimport os\nimport numpy as np\nimport scipy.io as sio\nfrom tensorflow.keras.models import model_from_json\n\nimport prepare_CINC_data\nimport QRSDetectorDNN\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--cinc_path', default=None, type=str)\nargs = parser.parse_args()\n\ncinc_main_p... | [
[
"tensorflow.keras.models.model_from_json",
"scipy.io.savemat",
"numpy.transpose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.14",
"0.19",
"1.5",
"0.12",
"0.17",
"0.13",
"1.6",
"1.4",
"1.9",
"1.3",
"1.10",
"0.15",
"0.18",
"0.16"... |
engvitormartins/Minicurso-Python-Hashtag | [
"b4d6a97d92af2ca4f03c1ac600786cea2963ef05"
] | [
"Minicurso-Hashtag/Aula06-Ajuste-Visualizacao-de-Dados.py"
] | [
"# importar bibliotecas\n\nimport pandas as pd\n\n# importar a base de dados\n\ntabela_vendas = pd.read_excel('Vendas.xlsx')\n\n# visualizar a base de dados\npd.set_option('display.max_columns', None)\n\nprint(tabela_vendas)\n\n# faturamento por loja\n\n# quantidade de produtos vendidos por loja\n\n# ticket médio p... | [
[
"pandas.set_option",
"pandas.read_excel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
maximocrv/ml_protein_interactions | [
"86b2158937a044316f48fc7a5856733454b328d8"
] | [
"scripts/preprocessing.py"
] | [
"#!/bin/python\n\"\"\"\nThis script preprocesses the generated features to create a CSV file to be used by the MLP and XGBoost models.\n\"\"\"\nimport os\nimport re\nimport math\nfrom pathlib import Path\nimport multiprocessing as mp\n\nimport numpy as np\nimport pandas as pd\n\nfrom constants import skempi_csv, wt... | [
[
"numpy.log",
"pandas.read_csv",
"pandas.DataFrame",
"numpy.std",
"numpy.mean",
"numpy.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
XiaohanZhangCMU/spinningup | [
"b5715f31ceb41f6711901179a1bc0ba9e5979cbd"
] | [
"spinup/utils/plot.py"
] | [
"import seaborn as sns\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport json\nimport os\nimport os.path as osp\nimport numpy as np\n\nDIV_LINE_WIDTH = 50\n\n# Global vars for tracking and labeling data at load time.\nexp_idx = 0\nunits = dict()\n\ndef plot_data(data, xaxis='Epoch', value=\"AverageEpRet... | [
[
"numpy.convolve",
"matplotlib.pyplot.legend",
"pandas.concat",
"matplotlib.pyplot.tight_layout",
"numpy.asarray",
"numpy.ones",
"matplotlib.pyplot.ticklabel_format",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
n-log-n/udacity-lyft-challenge | [
"eba0f349560e702abd68be1a9ce5f6d6437556da"
] | [
"utils/freeze_graph.py"
] | [
"\"\"\"\nhttps://stackoverflow.com/questions/45466020/how-to-export-keras-h5-to-tensorflow-pb\n\"\"\"\n\nimport keras\nfrom keras.models import load_model\nimport keras.backend as K\nimport tensorflow as tf\n\nfrom tensorflow.python.framework import graph_io\nfrom tensorflow.python.framework import graph_util\nfrom... | [
[
"tensorflow.python.platform.gfile.FastGFile",
"tensorflow.python.tools.optimize_for_inference_lib.optimize_for_inference",
"tensorflow.core.framework.graph_pb2.GraphDef",
"tensorflow.python.platform.gfile.Open"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"1.12",
"2.6",
"2.2",
"1.13",
"2.3",
"2.4",
"1.4",
"2.9",
"1.5",
"1.7",
"2.5",
"0.12",
"1.0",
"2.8",
"1... |
benhunter/py-stuff | [
"a04f94851370e08a65792a53a6207f3146eb130b"
] | [
"misc/csgo-stats.py"
] | [
"# https://old.reddit.com/r/GlobalOffensive/comments/8mjqgc/i_made_a_python_script_that_generates_stats_using/\n# https://pastebin.com/LLpym05c\n\nimport datetime\n\nimport matplotlib.pyplot as plt\n\n\ndef min_to_sec(line): # converts minutes in string format 'XXX:XX' to seconds\n seconds = 0\n seconds += (... | [
[
"matplotlib.pyplot.gca",
"matplotlib.pyplot.title",
"matplotlib.pyplot.rc",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.clf",
"matplotlib.pyplot.bar",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.xticks",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lwaw/simple_stock_market_simulator | [
"82459625a01b34ae840b37e7284dd02785946822"
] | [
"simple_stock_market.py"
] | [
"import seaborn as sns\nimport matplotlib.pyplot as plt\nimport math \nimport random\nimport datetime\nimport sys\nimport json\n\nbegin_time = datetime.datetime.now()\nsimulation_time = int\nglobal_sentiment = random.choice([\"bullish\", \"bearish\"])\nglobal_market = False\nglobal_market_strength = 100 #smaller gl... | [
[
"matplotlib.pyplot.close"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
suvampattnaik/ML-OpenCV-Projects | [
"a6984ffe8a4cd2bd1fcaa731abe0aad60ae1d147"
] | [
"Existing Projects/Cancer-Prediction/main.py"
] | [
" \nimport pandas as pd\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn import metrics\nimport matplotlib.pyplot as plt\n\ndata=pd.read_csv(\"datasets_180... | [
[
"pandas.read_csv",
"sklearn.impute.SimpleImputer",
"sklearn.model_selection.train_test_split",
"sklearn.tree.DecisionTreeClassifier",
"sklearn.preprocessing.StandardScaler",
"sklearn.metrics.accuracy_score"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
seankmartin/lfp_atn | [
"647889eddfa9ba3910c74df7e61f10fd98c61854"
] | [
"tests/lfp_rate_test.py"
] | [
"import os\nimport sys\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom lfp_atn_simuran.analysis.lfp_rate_map import lfp_rate, lfp_rate_plot\nfrom lfp_atn_simuran.analysis.lfp_clean import LFPClean\nimport astropy.units as u\n\nsys.path.insert(0, \"..\")\nfrom lib.plots import plot_pos_over_time\n\nfrom... | [
[
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.close"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
parthrjpt/CSE573_Projects | [
"b06e11964e99509ff3a93feea954d57714e848b7"
] | [
"Project3/FaceDetector.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Apr 20 14:56:33 2021\n\n@author: parth\n\"\"\"\n\nimport cv2\nimport numpy as np\nimport glob\nimport matplotlib.pyplot as plt\nimport json\nimport pandas as pd\nimport os\nimport sys\n\ndef read_images(imgpath):\n \n imgpath = glob.glob(imgpath)\n imgs = []... | [
[
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Nyanng/Cham3 | [
"6721442ecf06ee8f985b82e0bf7817994f510bf7"
] | [
"search.py"
] | [
"import numpy as np\nimport cv2\n\nvideo_capture = cv2.VideoCapture(0)\nwidth = int(video_capture.get(cv2.CAP_PROP_FRAME_WIDTH))\nheight = int(video_capture.get(cv2.CAP_PROP_FRAME_HEIGHT))\ndeviation = width * 0.15\nright = int(width/2 - deviation)\nleft = int(width/2 + deviation)\n\n\ndef update_result():\n res... | [
[
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
caffeine110/Sentiment_Analyzer | [
"5eb243a8b8e78f3ced4d2e13640b9ad6c96fa9b5"
] | [
"phase_4_model_lstm/model/model_result_presentation_test.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Mar 24 02:30:41 2019\n\n@author: gaurav\n\"\"\"\n\n\n\n##########################################################################################\nfrom model_result_presentation_pre import X, Y, X_train, X_test, Y_train, Y_test\nX_test = X_tra... | [
[
"sklearn.metrics.accuracy_score"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
homeoffice-ys/EliteQuant_Python | [
"28ed64d742d9f010836d4070cd26bab78d9623d0"
] | [
"source/data/tick_event.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nfrom pandas import Timestamp\nfrom enum import Enum\nfrom ..event.event import *\n\n\nclass TickType(Enum):\n TRADE = 0\n BID = 1\n ASK = 2\n FULL = 3\n\nclass TickEvent(Event):\n \"\"\"\n Tick event\n \"\"\"\n\n def __init__(self):\n \... | [
[
"pandas.Timestamp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DemyCode/ml-agents | [
"238043d68b4195cd33c2b75d408dee39708ffb32"
] | [
"ml-agents/mlagents/trainers/tests/test_simple_rl.py"
] | [
"import math\nimport tempfile\nimport pytest\nimport yaml\nimport numpy as np\nfrom typing import Dict, Any\n\nfrom mlagents.trainers.tests.simple_test_envs import (\n SimpleEnvironment,\n MemoryEnvironment,\n RecordEnvironment,\n)\nfrom mlagents.trainers.trainer_controller import TrainerController\nfrom m... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mucharafal/optics_generator_python | [
"c14d4e5f19f921f4dc0a98129bca9d31754b72ad"
] | [
"src/utils/differential_quotient.py"
] | [
"import numpy as np\n\n\ndef compute_optical_function(universal_transporter, particles, delta_parameter_name, transported_parameter_name,\n delta_alternative_value):\n \"\"\"\n (x2 - x1) / delta_x\n :param universal_transporter: has to take and return numpy array with columns x,... | [
[
"numpy.max",
"numpy.min"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
respecteverything/analytics-zoo | [
"a8843c73c24b5026d93d46fa9268eb41a958cf6d"
] | [
"pyzoo/zoo/examples/tensorflow/tfpark/tf_optimizer/train_mnist_keras.py"
] | [
"#\n# Copyright 2018 Analytics Zoo 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 ... | [
[
"tensorflow.keras.models.Model"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
mwaskom/pandas | [
"d1010643fea058ba43c2c7124af75cc462ccf242",
"d1010643fea058ba43c2c7124af75cc462ccf242"
] | [
"pandas/tests/io/formats/test_to_html.py",
"pandas/tests/io/json/test_pandas.py"
] | [
"# -*- coding: utf-8 -*-\n\nimport re\nfrom textwrap import dedent\nfrom datetime import datetime\nfrom distutils.version import LooseVersion\n\nimport pytest\nimport numpy as np\nimport pandas as pd\nfrom pandas import compat, DataFrame, MultiIndex, option_context, Index\nfrom pandas.compat import u, lrange, Strin... | [
[
"pandas.to_datetime",
"pandas.util.testing.ensure_clean",
"pandas.util.testing.assert_produces_warning",
"pandas.MultiIndex.from_tuples",
"pandas.DataFrame",
"numpy.random.randn",
"numpy.arange",
"pandas.compat.StringIO",
"pandas.Index",
"pandas.DatetimeIndex",
"pandas.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vikas-kundu/phonedict | [
"6795cab0024e792340c43d95552162a985b891f6"
] | [
"lib/country/india_generator.py"
] | [
"#!/usr/bin/env python3\r\n# -*- coding:utf-8 -*-\r\n# coded by Vikas Kundu https://github.com/vikas-kundu\r\n# -------------------------------------------\r\n\r\nimport sys\r\nimport urllib\r\nfrom pkg_resources import resource_filename\r\n\r\nimport pandas as pd\r\npd.set_option('precision', 0)\r\npd.options.disp... | [
[
"pandas.set_option",
"pandas.DataFrame",
"pandas.read_html"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
Svolcano/python_exercise | [
"a50e05891cc7f1fbb40ebcae324b09b6a14473d2"
] | [
"dianhua/worker/crawler/china_telecom/tianjin/main.py"
] | [
"# -*- coding: utf-8 -*-\r\nimport json\r\nimport pandas as pd\r\nimport StringIO\r\nfrom lxml import etree\r\n\r\nfrom datetime import date\r\nimport traceback\r\nimport calendar\r\nimport time\r\nimport sys\r\nimport base64\r\nimport re\r\nimport random\r\n# 这段代码是用于解决中文报错的问题\r\n\r\nreload(sys)\r\nsys.setdefaulten... | [
[
"pandas.read_excel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
christopheroates/probnum | [
"4ae63da307bd7279c3ce477ef68cbd0b8e30c73a",
"4ae63da307bd7279c3ce477ef68cbd0b8e30c73a"
] | [
"tests/test_statespace/test_preconditioner.py",
"tests/test_linalg/test_linearsolvers/test_linearsolvers.py"
] | [
"import numpy as np\nimport pytest\n\nimport probnum.statespace as pnss\n\n\n@pytest.fixture\ndef precon():\n some_order = 3\n some_dim = 1\n return pnss.NordsieckLikeCoordinates.from_order(some_order, some_dim)\n\n\ndef test_call(precon):\n P = precon(0.5)\n assert isinstance(P, np.ndarray)\n\n\ndef... | [
[
"numpy.eye"
],
[
"numpy.diag",
"numpy.dot",
"numpy.linalg.solve",
"numpy.linalg.eigvals",
"numpy.random.seed",
"numpy.eye",
"numpy.linalg.norm",
"numpy.random.uniform",
"numpy.random.normal",
"numpy.log10",
"numpy.zeros_like",
"numpy.random.rand",
"numpy... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
foxan/sagemaker-deployment | [
"bb63532fb981349b041a9502f9ccf9c7e02ac0d6"
] | [
"Project/SageMaker Project.py"
] | [
"# ---\n# jupyter:\n# jupytext:\n# formats: ipynb,py:percent\n# text_representation:\n# extension: .py\n# format_name: percent\n# format_version: '1.2'\n# jupytext_version: 1.1.1\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n#... | [
[
"matplotlib.pyplot.title",
"sklearn.utils.shuffle",
"torch.utils.data.TensorDataset",
"torch.utils.data.DataLoader",
"torch.from_numpy",
"pandas.DataFrame",
"torch.nn.BCELoss",
"numpy.apply_along_axis",
"torch.cuda.is_available",
"numpy.array",
"matplotlib.pyplot.show",... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
stoewer/nixpy | [
"ce61d594e633baabe405772bb1284bca2ef8bdc9"
] | [
"nixio/test/test_tag.py"
] | [
"# Copyright (c) 2014, German Neuroinformatics Node (G-Node)\n#\n# All rights reserved.\n#\n# Redistribution and use in section and binary forms, with or without\n# modification, are permitted under the terms of the BSD License. See\n# LICENSE file in the root of the Project.\n\nfrom __future__ import (absolute_imp... | [
[
"numpy.asarray",
"numpy.random.random"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hubert-pietron/pandas | [
"afc06e780e267b7eaac8651c7feb53f682d5b4a9"
] | [
"pandas/core/frame.py"
] | [
"\"\"\"\nDataFrame\n---------\nAn efficient 2D container for potentially mixed-type time series or other\nlabeled data series.\n\nSimilar to its R counterpart, data.frame, except providing automatic data\nalignment and a host of useful data manipulation methods having to do with the\nlabeling information\n\"\"\"\nf... | [
[
"pandas.util._validators.validate_bool_kwarg",
"numpy.all",
"pandas.core.dtypes.common.infer_dtype_from_object",
"pandas.util._decorators.deprecate_nonkeyword_arguments",
"pandas.core.internals.construction.mgr_to_mgr",
"numpy.where",
"pandas._libs.algos.nancorr",
"pandas.core.comm... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.0",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
allenai/rainbow | [
"045d726355364b7495fa2e72bb05316545a5f2b0"
] | [
"src/rainbow/preparation/cyc.py"
] | [
"\"\"\"Dataset preparation for CycIC.\"\"\"\n\nimport csv\nimport hashlib\nimport json\nimport logging\nimport os\nimport zipfile\n\nimport tensorflow as tf\n\nfrom .. import settings\nfrom . import preparer, utils\n\n\nlogger = logging.getLogger(__name__)\n\n\n# main class\n\n\nclass CycICPreparer(preparer.Prepare... | [
[
"tensorflow.io.gfile.exists",
"tensorflow.io.gfile.GFile"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pierocor/gpytorch | [
"f06004e888542289d19c155e4e2ad35aad0573b7"
] | [
"gpytorch/models/pyro/_pyro_mixin.py"
] | [
"#!/usr/bin/env python3\n\nimport torch\n\nimport pyro\n\n\nclass _PyroMixin(object):\n def pyro_guide(self, input, beta=1.0, name_prefix=\"\"):\n # Inducing values q(u)\n with pyro.poutine.scale(scale=beta):\n variational_distribution = self.variational_strategy.variational_distribution... | [
[
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MarcMoesser/Protein-Ligand-Interaction-Graphs | [
"b1fc5e3016c193f56c2495aa57b1070c04722f4c"
] | [
"models_main/models/gat_PLIG_with_p.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.nn import Sequential, Linear, ReLU\nfrom torch_geometric.nn import GATConv\nfrom torch_geometric.nn import global_max_pool as gmp\nfrom models import activation_function_dict\n\n# GAT model\nclass GATNet_PLIG_with_p(torch.nn.Module):... | [
[
"torch.nn.Dropout",
"torch.cat",
"torch.nn.Embedding",
"torch.nn.Linear",
"torch.nn.Conv1d",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
leekh7411/Text-Binary-Classification | [
"73b9656a60c6dc1a692e2bff459dd404e2af3745"
] | [
"train.py"
] | [
"from model import TextBinaryClf\nfrom config import get_config\nfrom data_loader import KinQueryDataset,_batch_loader\nimport tensorflow as tf\nimport numpy as np\nif __name__ == '__main__':\n\n config = get_config()\n\n if config.mode == 'train':\n\n model = TextBinaryClf(config)\n dataset = K... | [
[
"tensorflow.global_variables_initializer",
"numpy.random.permutation",
"tensorflow.InteractiveSession"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
JIC-Image-Analysis/stomata-characterisation | [
"322576361b81c26262f3dcc9e2fb92a5c98d81c1"
] | [
"scripts/util/line_profile.py"
] | [
"\"\"\"Line profile helper funcitons.\"\"\"\n\nimport numpy as np\nimport scipy.ndimage\nimport skimage.measure\n\nfrom jicimagelib.geometry import Point2D\nfrom jicimagelib.util.array import normalise\n\nfrom util.geometry import angle2vector\n\nclass LineProfile(object):\n \"\"\"Class for working with line pro... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
elissyah/econml | [
"73a21bfe3470e7f0d1702a6db71efd0892cfee9d"
] | [
"econml/utilities.py"
] | [
"# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License.\n\n\"\"\"Utility methods.\"\"\"\n\nimport numpy as np\nimport scipy.sparse\nimport sparse as sp\nimport itertools\nfrom operator import getitem\nfrom collections import defaultdict, Counter\nfrom sklearn.base import Tran... | [
[
"numpy.diag",
"sklearn.utils.check_X_y",
"numpy.sqrt",
"numpy.random.choice",
"numpy.min",
"numpy.nonzero",
"numpy.matmul",
"numpy.stack",
"numpy.ndim",
"numpy.concatenate",
"numpy.size",
"numpy.shape",
"numpy.iinfo",
"numpy.tensordot",
"numpy.transpose"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rahul1704/pennylane | [
"8076c18b301dbc5aa2bd40fd858d7ed403afa831"
] | [
"tests/test_templates_embeddings.py"
] | [
"# Copyright 2018-2020 Xanadu Quantum Technologies Inc.\n\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n\n# http://www.apache.org/licenses/LICENSE-2.0\n\n# Unless required by ap... | [
[
"numpy.sqrt",
"numpy.array",
"numpy.zeros",
"numpy.allclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MatthewWolff/DESMAN | [
"3f683e75830c4862b5f2c7577ef31b4cc86bdd61"
] | [
"scripts/ExtractCountFreqGenes.py"
] | [
"#!/usr/bin/env python3\n\nimport sys, getopt\nimport os\nimport pandas as p\nimport numpy as np\nimport scipy.stats as ss\nfrom scipy.stats import norm\nimport argparse\nimport math\nimport glob\nimport re\nimport gzip\n\nfrom Bio import SeqIO\n\nfrom collections import defaultdict\nfrom collections import Counter... | [
[
"numpy.reshape",
"pandas.concat",
"numpy.zeros",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
twitterdev/jupyter-holder | [
"0fa9d848d392660efbf4ed9fae744a9b91101a0c"
] | [
"examples/timeseries/timeseries_utils.py"
] | [
"import re\nimport itertools as it\nimport numpy as np\nimport pandas as pd\nfrom string import punctuation\nimport unicodedata\n\nfrom sklearn.feature_extraction.text import CountVectorizer\nimport nltk\nfrom nltk.tokenize import TweetTokenizer\n\n# import tweepy\nimport matplotlib.pyplot as plt\n\nfrom matplotlib... | [
[
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.axhline",
"matplotlib.pyplot.title",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.annotate",
"pandas.DataFrame",
"matplotlib.pyplot.plot",
"sklearn.feature_extraction.text.CountVectorizer",
"matplotlib.pyplot.ylabel",
"nu... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
nmiz1987/analyse-respiratory-diseases | [
"812c347dd7f36f6e37b3b7e7619f74be99d7d52c"
] | [
"Perceptron.py"
] | [
"'''\nThe code contains noted parts that are intended for analysis by child.\nIn order to analyze by child, eliminate the comments and define the parts of the adults as comments.\n'''\n\nimport csv\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split # train_test_split fun... | [
[
"sklearn.metrics.recall_score",
"matplotlib.pyplot.figure",
"sklearn.metrics.precision_score",
"sklearn.metrics.confusion_matrix",
"sklearn.model_selection.train_test_split",
"pandas.DataFrame",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
nomisto/OpenBioLink | [
"f7ef423975ce36a435f0cfb52ba8b1b38d138a47"
] | [
"src/openbiolink/obl2021/obl2021.py"
] | [
"import os\nimport pickle\nimport urllib\nimport zipfile\nfrom os import path\nfrom typing import cast, Iterable, Tuple\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom openbiolink.utils import split_list_in_batches_iter\nfrom tqdm import tqdm\n\nfrom openbiolink.graph_creation.file_downloader import ... | [
[
"pandas.read_csv",
"torch.cat",
"torch.from_numpy",
"torch.tensor",
"numpy.savez_compressed",
"torch.arange"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
DPBayes/DP-MCMC-NeurIPS2019 | [
"bf5349835b2044135749ea6dbedea2e310d7d2f2",
"bf5349835b2044135749ea6dbedea2e310d7d2f2"
] | [
"gmm_code/plot_supplement_cdfs.py",
"gmm_code/plot_var_vs_clip.py"
] | [
"'''\nScipt for plotting approximate V_log cdfs for supplement \n'''\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport pickle\nfrom scipy.stats import logistic\nfrom scipy import stats\n\nimport X_corr_seita\nfrom X_corr import *\nfrom plot_path import path\n\nnp.random.seed(1606)\... | [
[
"matplotlib.pyplot.legend",
"numpy.sqrt",
"numpy.random.seed",
"numpy.linspace",
"matplotlib.pyplot.title",
"scipy.stats.logistic.cdf",
"pandas.DataFrame",
"numpy.sort",
"matplotlib.pyplot.plot",
"numpy.round",
"numpy.random.normal",
"matplotlib.pyplot.xlim",
"m... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"nump... |
hkirvesl/auto3dgm | [
"e2132a992616c430f60af4871a62c0167e0917da"
] | [
"auto3dgm/mesh/meshfactory.py"
] | [
"from os.path import isfile, splitext\nfrom auto3dgm.mesh.mesh import Mesh\nfrom numpy import array, ndarray, concatenate, empty, full\nfrom vtk import vtkPLYReader,vtkOBJReader,vtkSTLReader,vtkPolyData, vtkPoints, vtkCellArray\nfrom vtk.util.numpy_support import vtk_to_numpy, numpy_to_vtk, numpy_to_vtkIdTypeArray\... | [
[
"numpy.array",
"numpy.empty",
"numpy.full"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jejdc/dc_html_styler | [
"6e406f5a271be72ef729fd9f7e7e32189104cd27"
] | [
"dc_html_styler/dc_html_styler.py"
] | [
"import pandas as pd\nimport pandas.io.formats.style\nimport numpy as np\nimport jinja2\nimport numpy as np\n\nclass dcstyler(pd.io.formats.style.Styler):\n '''\n Expansion of the pandas.io.formats.style.Styler class.\n\n Attributes\n\n '''\n def __init__(self, data):\n self.titles = []\n ... | [
[
"matplotlib.colors.LinearSegmentedColormap.from_list"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
anhthyngo/vietnamese-chatbot | [
"a3c81bfa93b42daa3ca463d5b3a4b9da040d9b7f",
"a3c81bfa93b42daa3ca463d5b3a4b9da040d9b7f"
] | [
"scripts/archive/train_attention.py",
"scripts/archive/train_attention_test.py"
] | [
"\"\"\"\nMain run script to execute NMT evaluation\n\"\"\"\n# =============== Import Modules ==============\nimport os\nimport time\nimport torch\nfrom torch.utils.data import DataLoader\nfrom functools import partial\nimport sys\nimport logging as log\nfrom datetime import datetime as dt\nimport time\nimport rando... | [
[
"torch.manual_seed",
"torch.cuda.is_available",
"numpy.random.seed",
"torch.cuda.manual_seed_all"
],
[
"torch.manual_seed",
"torch.cuda.is_available",
"numpy.random.seed",
"torch.cuda.manual_seed_all"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CircuitLaunch/reachy-tictactoe | [
"83b07224212b61af4eb13dfd11fc775ae3fbea9c"
] | [
"tools/listMoves.py"
] | [
"import os\nimport numpy as np\n\nfrom glob import glob\n\ndir_path = os.path.join(os.path.dirname(os.path.dirname(os.path.realpath(__file__))), \"reachy_tictactoe\", \"moves\")\n\n\nnames = [\n os.path.splitext(os.path.basename(f))[0]\n for f in glob(os.path.join(dir_path, '*.npz'))\n]\n\nmoves = {\n name... | [
[
"numpy.ndim"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
acadTags/Explainable-Automated-Medical-Coding | [
"a4b0b400dcbc2957af72137dd13f84d62de6961b",
"a4b0b400dcbc2957af72137dd13f84d62de6961b"
] | [
"HLAN/model_predict_util.py",
"HLAN/multi_level_eval.py"
] | [
"#!/usr/bin/env python\r\n# coding: utf-8\r\n\r\n# -*- coding: utf-8 -*-\r\n#training the model.\r\n#process--->1.load data(X:list of lint,y:int). 2.create session. 3.feed data. 4.training (5.validation) ,(6.prediction)\r\nimport tensorflow as tf\r\nimport numpy as np\r\nimport pandas as pd\r\nimport time\r\nimport... | [
[
"numpy.split",
"numpy.expand_dims",
"pandas.read_csv",
"tensorflow.train.latest_checkpoint",
"numpy.reshape",
"sklearn.metrics.pairwise.cosine_similarity",
"pandas.DataFrame",
"tensorflow.ConfigProto",
"numpy.copy",
"tensorflow.reset_default_graph",
"tensorflow.Session"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": [
"1.10"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Sungyeop/IPRL | [
"6ee17f415998ac5cc058c63cea06a5cad40b267c"
] | [
"EMNIST/Deep_AE.py"
] | [
"import numpy as np\nimport copy\nimport torch\nimport torchvision\nfrom torch import nn, optim\nfrom torchvision import transforms, datasets\nfrom scipy.spatial.distance import pdist, squareform\nfrom scipy.special import expit\nimport matplotlib.pyplot as plt\n\n\n# Training Options\n#============================... | [
[
"matplotlib.pyplot.legend",
"numpy.linspace",
"numpy.einsum",
"torch.utils.data.DataLoader",
"matplotlib.pyplot.plot",
"numpy.int",
"numpy.exp",
"numpy.where",
"numpy.trace",
"numpy.linalg.svd",
"matplotlib.pyplot.tight_layout",
"numpy.size",
"matplotlib.pyplot.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"1.3",
"1.8"
... |
alonme/pandas | [
"b6434fa31ac90c23c0b2f89d653a59c0f0d6d217"
] | [
"pandas/tests/tseries/offsets/test_offsets_properties.py"
] | [
"\"\"\"\nBehavioral based tests for offsets and date_range.\n\nThis file is adapted from https://github.com/pandas-dev/pandas/pull/18761 -\nwhich was more ambitious but less idiomatic in its use of Hypothesis.\n\nYou may wish to consult the previous version for inspiration on further\ntests, or when trying to pin d... | [
[
"pandas.Timestamp",
"pandas.Series",
"pandas.date_range"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
ishaanchandratreya/phyre-fwd | [
"1b651f9584175fb4aca76f2de95bf9ceb9fe18ef"
] | [
"agents/im_fwd_agent.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates.\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 app... | [
[
"torch.mean",
"torch.optim.lr_scheduler.CosineAnnealingLR",
"torch.zeros",
"torch.load",
"torch.cat",
"torch.sum",
"numpy.stack",
"torch.nn.Sigmoid",
"numpy.concatenate",
"torch.cuda.max_memory_allocated",
"torch.no_grad",
"numpy.zeros_like",
"numpy.mean",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jessicazhang236/SfmLearner-Pytorch | [
"f820020e872848af6a56a96b82f3c33214404879"
] | [
"inverse_warp.py"
] | [
"from __future__ import division\nimport torch\nimport torch.nn.functional as F\n\npixel_coords = None\n\n\ndef set_id_grid(depth): \n # creates the pixel coordinate tensor\n global pixel_coords\n b, h, w = depth.size()\n i_range = torch.arange(0, h).view(1, h, 1).expand(1,h,w).type_as(depth) # [1, H, ... | [
[
"torch.ones",
"torch.cat",
"torch.sin",
"torch.nn.functional.grid_sample",
"torch.arange",
"torch.stack",
"torch.cos"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
GiggleLiu/pymps | [
"c8314581010d68d3fa34af6e87b6af2969fc261d",
"c8314581010d68d3fa34af6e87b6af2969fc261d"
] | [
"pymps/blockmarker/blockmarker.py",
"pymps/toolbox/tests/test_utils.py"
] | [
"'''\nTurn a symmetric matrix into block diagonal form.\n'''\n\nfrom numpy import *\nfrom abc import ABCMeta, abstractmethod\nimport scipy.sparse as sps\n\nfrom . import fblock as flib\nfrom ..spaceconfig import SuperSpaceConfig, SpinSpaceConfig\n\n__all__ = ['BlockMarker', 'BlockMarkerGenerator', 'SimpleBMG', 'tru... | [
[
"scipy.sparse.issparse"
],
[
"numpy.testing.assert_"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.14",
"0.19",
"1.5",
"0.12",
"0.17",
"0.13",
"1.6",
"1.4",
"1.9",
"1.3",
"1.10",
"0.15",
"0.18",
"0.16"... |
tgmtejas/Plant-Seedling-Classification-using-Deep-Convolution-Neural-Network | [
"154158b36d5b2d288e8bb61c0032d5f60ab0d9e7",
"154158b36d5b2d288e8bb61c0032d5f60ab0d9e7"
] | [
"Tejas_Plant_classification CODE files/base_code_salar_et_al.py",
"Tejas_Plant_classification CODE files/data_processing.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"Copy of Base_Model.ipynb\n\nAutomatically generated by Colaboratory.\n\nOriginal file is located at\n https://colab.research.google.com/drive/1m1KvteauOHiq_hGWx032ZODRzcoI7W7c\n\"\"\"\n\n# Base Model\n\nimport os\nimport numpy as np\nimport pandas as pd\nimport cv2\nimport matplot... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.yticks",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.title",
"numpy.set_printoptions",
"scipy.io.loadmat",
"sklearn.metrics.confusion_matrix",
"sklearn.model_selection.train_test_split",
"ma... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.14",
"0.19",
"1.5",
"0.12",
"0.17",
"0.13",
"1.6",
"1.4",
"1.9",
"1.3",
"1.10",
"0.15",
"0.18",
"0.16"... |
Samuel-Buteau/EISFitting | [
"f90e5356f3965106615396d7f6c5ccda47f20dc8"
] | [
"EIS/management/commands/eis_main.py"
] | [
"import contextlib\nimport csv\nimport math\nimport os\nimport pickle\nimport random\n\n\n\n\nimport copy\n\n\nimport matplotlib\n\n\nimport matplotlib.pyplot as plt\nimport numpy\nimport tensorflow as tf\nfrom django.core.management.base import BaseCommand\nimport re\n\nfrom import_eis_files import import_eis_file... | [
[
"numpy.abs",
"numpy.stack",
"matplotlib.gridspec.GridSpec",
"matplotlib.pyplot.close",
"numpy.exp",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dandamanwiddaplan/DOOM_AI | [
"e52dc64db5c4ebb48665ce38123e43772df62b42"
] | [
"doom-rl1-duel-20210120T201129Z-001/doom-rl1-duel/dqn/basic_scenario.py"
] | [
"import random\n\nimport skimage as skimage\nfrom skimage import transform, color, exposure\nfrom skimage.viewer import ImageViewer\nimport random\nfrom random import choice\nimport numpy as np\nfrom collections import deque\nimport time\n\nimport json\nfrom keras.models import model_from_json\nfrom keras.models im... | [
[
"numpy.rollaxis",
"tensorflow.keras.models.load_model",
"numpy.expand_dims",
"tensorflow.keras.initializers.GlorotNormal",
"numpy.reshape",
"tensorflow.keras.layers.Dense",
"numpy.arange",
"tensorflow.keras.Sequential",
"numpy.stack",
"numpy.max",
"numpy.append",
"n... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
iamjavaexpert/Kamodo | [
"26e7de66e67b9196ab19f13e73136db75832813c"
] | [
"kamodo/readers/tiegcm.py"
] | [
"import numpy as np\n\nfrom kamodo import Kamodo, kamodofy, gridify\n\nfrom scipy.interpolate import RegularGridInterpolator, interp1d\n\n# pip install pytiegcm\nfrom tiegcm.tiegcm import TIEGCM\n\ndef parse_units(varname, variable):\n \"\"\"Parses units based on variable input\"\"\"\n try:\n units = v... | [
[
"numpy.array",
"scipy.interpolate.RegularGridInterpolator",
"scipy.interpolate.interp1d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.14",
"1.6",
"1.10",
"0.15",
"1.4",
"1.3",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"1.0",
"0.17",
"0.16",
"1.8"
],
"tensorflow": []
... |
Prukutu/nyc_collisions | [
"09f9906cf150f18744891e773fb71d04cdcee6be"
] | [
"explore.py"
] | [
"import pickle\n\nimport pandas as pd\nimport numpy as np\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n\ndf = pickle.load(open('collisions.p', 'rb'))\n\ndf_yearly = df.groupby('YEAR')\ndf_monthly = df.groupby('MONTH')\ndf_hourly = df.groupby('TIME')\ndf_vehicle = df.groupby('VEHICLE TYPE CODE 1')\nd... | [
[
"matplotlib.pyplot.subplots",
"pandas.concat",
"matplotlib.pyplot.style.use",
"pandas.get_dummies"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
csmwteam/wtools | [
"fb6fd667a3005560955b1dd72154b8bccc798453",
"fb6fd667a3005560955b1dd72154b8bccc798453"
] | [
"wtools/geostats/raster.py",
"wtools/geostats/grids.py"
] | [
"\"\"\"This module provides useful methods for operating on 1D and 2D rasters such\nas making variogram or covariograms.\n\"\"\"\n\n__all__ = [\n 'raster_to_struct_grid',\n 'suprts2modelcovFFT',\n]\n\n__displayname__ = 'Rasters'\n\nimport numpy as np\n\n\ndef raster_to_struct_grid(datain, imeas='covar', rtol=... | [
[
"numpy.logical_not",
"numpy.abs",
"numpy.conj",
"numpy.isnan",
"numpy.arange",
"numpy.fft.fftn",
"numpy.fft.fftshift",
"numpy.max",
"numpy.meshgrid"
],
[
"numpy.reshape",
"numpy.swapaxes"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zmoon92/personal-website-nbs | [
"4bc9ea2bacb9aa3d90ccea095c1f51fddadb0a1f"
] | [
"nb-src/sample_jnb_1.py"
] | [
"# ---\n# jupyter:\n# jupytext:\n# formats: ipynb,py:percent\n# text_representation:\n# extension: .py\n# format_name: percent\n# format_version: '1.3'\n# jupytext_version: 1.4.2\n# kernelspec:\n# display_name: Python [conda env:.conda-cf1]\n# language: python\n# name... | [
[
"numpy.linspace",
"numpy.sin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
olsson-group/RL-GraphINVENT | [
"d4629a3c411c793e1ed1682592d5bf67937564a1",
"d4629a3c411c793e1ed1682592d5bf67937564a1"
] | [
"fine-tuning/gnn/mpnn.py",
"fine-tuning/Workflow.py"
] | [
"# load general packages and functions\nimport math\nimport torch\n\n# load program-specific functions\nimport gnn.summation_mpnn\nimport gnn.modules\n\n# defines specific MPNN implementations\n\n\n# some constants\nBIG_NEGATIVE = -1e6\nBIG_POSITIVE = 1e6\n\n\nclass GGNN(gnn.summation_mpnn.SummationMPNN):\n \"\"... | [
[
"torch.nn.GRUCell",
"torch.nn.ModuleList"
],
[
"torch.optim.lr_scheduler.OneCycleLR",
"torch.mean",
"torch.load",
"torch.set_default_dtype",
"torch.cat",
"torch.clone",
"torch.tensor",
"torch.no_grad",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sansbacon/nflnames | [
"6e76b061b2c3b4b4455c5afc79bb050f4c540a1d"
] | [
"nflnames/players.py"
] | [
"# nflnames/nflnames/players.py\n# -*- coding: utf-8 -*-\n# Copyright (C) 2020 Eric Truett\n# Licensed under the MIT License\n\nfrom functools import lru_cache\nfrom pathlib import Path\nimport re\nfrom typing import Iterable, Set, Tuple, Union\n\nimport pandas as pd\nfrom rapidfuzz import process, fuzz\n\n\nDATADI... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
NLashkarashvili/Toxicity_Detection_in_Georgian_Discussions | [
"d5a639c866d33258e91bc401b087b9fba969384e"
] | [
"data_preparation/data_preparation_without.py"
] | [
"import pandas as pd\nimport numpy as np\nimport math\nimport string\nimport copy\nfrom collections import Counter\nfrom tqdm import tqdm\nimport warnings\nimport os\nwarnings.filterwarnings(\"ignore\")\n\ndata = pd.read_csv('../comments.csv')\ndata = data[['comment', 'label']]\ndata.drop_duplicates(inplace=True)\n... | [
[
"pandas.concat",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
pnovoa/metaheuristics-environmental-impact | [
"491a288eaad56815f6c99795e627c732d284fa3f"
] | [
"edo_experiment.py"
] | [
"import argparse\nimport sys\nfrom compute_tracker import ImpactTracker\nfrom data_interface import DataInterface\n\nimport numpy\nimport operator\n\nfrom deap import base\nfrom deap import benchmarks\nfrom deap.benchmarks import movingpeaks\nfrom deap import creator\nfrom deap import tools\nimport pandas as pd\nim... | [
[
"numpy.isnan",
"pandas.read_csv",
"numpy.random.seed",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
clbarnes/catmaid-catnap | [
"7785512a89c386cdd2258cf22fd0e24b0054ea41"
] | [
"catnap/assess.py"
] | [
"from __future__ import annotations\nfrom typing import Iterable, Iterator, Tuple, DefaultDict\nfrom itertools import combinations\nimport logging\nfrom collections import defaultdict\nimport warnings\n\nimport pandas as pd\nimport numpy as np\nfrom scipy.spatial.distance import cdist\nfrom skimage import measure\n... | [
[
"pandas.merge",
"numpy.unique",
"numpy.asarray",
"scipy.spatial.distance.cdist",
"numpy.setdiff1d",
"pandas.DataFrame",
"numpy.argmin",
"pandas.isna"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"0.24",
"1.0",
"0.25",
"1.2"
],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.... |
ClementPinard/unsupervised-depthnet | [
"71bc54afd8a22d5c99e1db88618119c33956b8c4"
] | [
"datasets/shifted_sequence_folders.py"
] | [
"import numpy as np\nimport torch.utils.data as data\nfrom imageio import imread\nimport random\nimport json\nfrom path import Path\n\n\ndef load_as_float(path):\n return imread(path).astype(np.float32)\n\n\ndef quat2mat(quat):\n w, x, y, z = quat[:,0], quat[:,1], quat[:,2], quat[:,3]\n w2, x2, y2, z2 = w*... | [
[
"numpy.random.seed",
"numpy.clip",
"numpy.linalg.inv",
"numpy.arange",
"numpy.stack",
"numpy.tan",
"numpy.float32",
"numpy.load",
"numpy.array",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Vikas-kum/horovod | [
"1d8abeb7cd94eca0d75972f62fe910233ba9ed11"
] | [
"test/test_timeline.py"
] | [
"# Copyright 2019 Uber Technologies, Inc. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
klauscc/dl-hammer | [
"5bd8d2e75f6a2b6051e99ad9b0e1384c8c43de26"
] | [
"dlhammer/utils/checkpointer.py"
] | [
"__all__ = ['setup_checkpointer']\n\nimport os\nimport os.path as osp\n\nimport torch\n\n\nclass Checkpointer(object):\n\n def __init__(self, cfg, phase):\n\n # Load pretrained checkpoint\n if not hasattr(cfg.MODEL, 'CHECKPOINT'):\n cfg.MODEL.CHECKPOINT = ''\n self.checkpoint = se... | [
[
"torch.device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wmyw96/domain_align | [
"27c09e0a1f4c43f1ba7112a2a46f32cbc7609c4a"
] | [
"mnist_model.py"
] | [
"import tensorflow as tf\n\n\ntf.set_random_seed(123456)\n\n\ndef show_params(name, params):\n print(\"Training Parameters for {}\".format(name))\n for para in params:\n print(para.name + ': ' + str(para.get_shape()))\n\n\ndef embed_norm(x, axes):\n norm2 = tf.reduce_sum(tf.square(x), axes, keep_dim... | [
[
"tensorflow.get_variable",
"tensorflow.concat",
"tensorflow.control_dependencies",
"tensorflow.reduce_sum",
"tensorflow.layers.conv2d_transpose",
"tensorflow.train.AdamOptimizer",
"tensorflow.get_collection",
"tensorflow.gradients",
"tensorflow.layers.dense",
"tensorflow.sq... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
Tompes/text_data_enhancement_with_LaserTagger | [
"9eb30cb870d8319cd59a2a4a8463dee7a99a87ad"
] | [
"official_transformer/model_utils.py"
] | [
"# coding=utf-8\n# Copyright 2018 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n... | [
[
"tensorflow.sin",
"tensorflow.cos",
"tensorflow.range",
"tensorflow.reshape",
"tensorflow.cast",
"tensorflow.expand_dims",
"tensorflow.equal",
"numpy.finfo",
"tensorflow.ones",
"tensorflow.name_scope"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
nickspell/imgaug-doc | [
"82be93b189a43e47448e1514efff8e296ba4a865"
] | [
"imgaug/augmenters/meta.py"
] | [
"\"\"\"\nAugmenters that don't apply augmentations themselves, but are needed\nfor meta usage.\n\nDo not import directly from this file, as the categorization is not final.\nUse instead ::\n\n from imgaug import augmenters as iaa\n\nand then e.g. ::\n\n seq = iaa.Sequential([...])\n\nList of augmenters:\n\n ... | [
[
"numpy.clip",
"numpy.arange",
"numpy.squeeze",
"numpy.copy",
"numpy.array",
"numpy.random.RandomState",
"numpy.zeros",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
abhishekkrthakur/Theano | [
"c5d3942b7e16fa8b425a3e09079b8056603b0c99",
"c5d3942b7e16fa8b425a3e09079b8056603b0c99"
] | [
"theano/gpuarray/tests/test_basic_ops.py",
"theano/compile/debugmode.py"
] | [
"from __future__ import absolute_import, print_function, division\nimport unittest\nfrom theano.compat import izip\n\nfrom six import iteritems\n\nimport numpy\nimport theano\nimport theano.tensor as T\nfrom theano.tensor import TensorType\nfrom theano.tensor.basic import alloc\n\n# Don't import test classes otherw... | [
[
"numpy.asarray",
"numpy.eye",
"numpy.int32",
"numpy.dtype",
"numpy.all",
"numpy.random.rand"
],
[
"numpy.absolute",
"numpy.isfinite",
"numpy.nonzero",
"numpy.asarray",
"numpy.median",
"numpy.max",
"numpy.std",
"numpy.argmax",
"numpy.mean",
"numpy... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ncos/hometasks | [
"9504ef7ed8fe30b5bc78ca1e423a2b85e46734a1"
] | [
"Teorphys/graph.py"
] | [
"#!/usr/bin/python\n\nimport matplotlib.pyplot as plt\nfrom math import *\nfrom scipy import special\nfrom optparse import OptionParser\n\ndef psi(n, x):\n a = sqrt(2.0 * n + 1.0)\n A = sqrt(2.0 / pi)\n g = x/a\n p = sqrt(fabs(2.0 * n + 1.0 - x**2))\n\n if (x == a) or (x == -a):\n return 0\n\n... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.axvline",
"matplotlib.pyplot.axhline",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.axis",
"scipy.special.hermite",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"1.4",
"0.16",
"1.0",
"0.19",
"0.18",
"1.2",
"0.12",
"0.10",
"0.17",
"1.3"
],
"tensorflow": []
}
] |
dmyersturnbull/pocketutils | [
"57139e65cb4a7901c546c0623caf06cd384177d1"
] | [
"tests/pocketutils/tools/test_base_tools.py"
] | [
"import numpy as np\nimport pytest\n\nfrom pocketutils.core.exceptions import LengthMismatchError, MultipleMatchesError\nfrom pocketutils.core.mocks import *\nfrom pocketutils.tools.base_tools import *\n\nraises = pytest.raises\n\n\nclass TestBaseTools:\n def test_is_lambda(self):\n f = BaseTools.is_lambd... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xSakix/chaos_evaluator | [
"689f812f94e5e62b32c9572ff955d0192718cd13"
] | [
"test_chaos.py"
] | [
"import chaos_sim as chs\nimport sys\nimport os\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport tabulate\n\nsys.path.insert(0, '../etf_data')\nfrom etf_data_loader import load_all_data_from_file\n\nsys.path.insert(0, '../buy_hold_simulation')\nfrom result_loa... | [
[
"matplotlib.pyplot.legend",
"pandas.read_csv",
"numpy.abs",
"matplotlib.pyplot.title",
"numpy.min",
"numpy.isnan",
"matplotlib.pyplot.savefig",
"pandas.DataFrame",
"matplotlib.pyplot.plot",
"numpy.max",
"numpy.std",
"matplotlib.pyplot.clf",
"numpy.mean",
"nu... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ifrit98/music-transformer | [
"37c5eb8f6895c48b9251b19681f44c74c4d0f636"
] | [
"box-plotIR.py"
] | [
"import matplotlib.pyplot as plt\nimport glob\n\n\ndef norm(IR):\n normed = list()\n lo, hi = 99999., 0.\n\n for ir in IR:\n if ir < lo:\n lo = ir\n if ir > hi:\n hi = ir\n\n alpha = 1./(hi-lo)\n for ir in IR:\n normed.append(alpha*ir)\n \n return norm... | [
[
"matplotlib.pyplot.boxplot",
"matplotlib.pyplot.show",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hrsdawson/parcels | [
"85931c7705b81f718322d8150a505e7174ee51f1"
] | [
"parcels/examples/example_peninsula.py"
] | [
"import gc\nimport math # NOQA\nfrom argparse import ArgumentParser\nfrom datetime import timedelta as delta\n\nimport numpy as np\nimport pytest\n\nfrom parcels import AdvectionEE\nfrom parcels import AdvectionRK4\nfrom parcels import AdvectionRK45\nfrom parcels import AdvectionAnalytical\nfrom parcels import Fie... | [
[
"numpy.meshgrid",
"numpy.zeros",
"numpy.abs",
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
alexquach/gansformer | [
"9b1e0d6633ca4d763c2e2e578d47960350797c57",
"424fa33a33bbcb37af45ffa87b26f25369978d7a"
] | [
"pytorch_version/generate.py",
"training/training_loop.py"
] | [
"# Generate images using pretrained network pickle.\nimport os\nimport numpy as np\nimport PIL.Image\nfrom tqdm import trange \nimport argparse\n\nimport dnnlib\nimport torch\nimport loader\nfrom tqdm import tqdm\n\nimport warnings\nfrom training import misc\nfrom training.misc import crop_max_rectangle as crop\n\n... | [
[
"torch.load",
"numpy.stack",
"numpy.random.randn",
"torch.device",
"torch.hub.load"
],
[
"tensorflow.contrib.memory_stats.MaxBytesInUse",
"tensorflow.device",
"tensorflow.reverse",
"tensorflow.constant",
"tensorflow.concat",
"tensorflow.reduce_mean",
"tensorflow... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.5",
"1.7",
"1.2"
]
}
] |
theetails/Overwatch-Team-Companion | [
"03c6784d4f7642983dd6a00cf3929c802b6a5907"
] | [
"PILTest.py"
] | [
"from PIL import Image, ImageFilter, ImageOps\r\nfrom scipy.misc import imresize\r\nfrom scipy.ndimage.filters import gaussian_filter\r\n\r\nimport numpy as np\r\n\r\nfrom GameObject import GameObject\r\n\r\n\r\nclass PILTest:\r\n\r\n @staticmethod\r\n def main():\r\n go = GameObject()\r\n map_i... | [
[
"numpy.asarray",
"scipy.misc.imresize",
"scipy.ndimage.filters.gaussian_filter"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"0.10",
"0.16",
"0.19",
"0.18",
"0.12",
"1.0",
"0.17",
"1.2"
],
"tensorflow": []
}
] |
MorganeAyle/SNIP-it | [
"df2bf44d6d3f7e4ea7733242a79c916735a7b49e",
"df2bf44d6d3f7e4ea7733242a79c916735a7b49e"
] | [
"models/criterions/Synflow.py",
"models/networks/ResNet50.py"
] | [
"import os\n\nimport torch\nimport torch.nn.functional as F\n\nfrom models.criterions.General import General\nfrom utils.constants import OUTPUT_DIR\nfrom copy import deepcopy\n\n\nclass Synflow(General):\n \"\"\"\n Our interpretation/implementation of SNIP from the paper:\n SNIP: Single-shot Network Pruni... | [
[
"torch.abs",
"torch.ones",
"torch.sum",
"torch.flatten",
"torch.topk"
],
[
"torch.randn"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
PANDASANG1231/tools | [
"e741b75bd2be1c92638d67f1fef03c09a3926ab0"
] | [
"src/risk_model_tool/analysis/calculate_psi.py"
] | [
"import sys\nimport numpy as np\n\ndef scale_range (input_, min_, max_):\n input_ += -(np.min(input_))\n scaler = np.max(input_) / np.array(max_ - min_)\n input_ = input_ / scaler\n input_ += min_\n return input_\n\ndef sub_psi(e_perc, a_perc):\n \"\"\"Calculate the actual PSI value from comparing... | [
[
"numpy.log",
"numpy.min",
"numpy.arange",
"numpy.percentile",
"numpy.max",
"numpy.array",
"numpy.histogram",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ekohn13/Python-Challenge | [
"32230ae94d443c83c03cc479db86c29c51328b50"
] | [
"PyPoll/main.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n\n# In[33]:\n\n\nimport pandas as pd\n\n#open csv with pandas\ndf = pd.read_csv('/Users/edwardkohn/Desktop/Homeworks/Python/election_data.csv')\ndf.head()\n\n\n# In[34]:\n\n\n# Total Votes Casted\ntotal_votes_casted = df.County.count()\n\n\n\n# In[42]:\n\n\n#List of candidat... | [
[
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
wrensuess/prospector | [
"08173f84ddfc2b031c78822344fc821778d35bae",
"08173f84ddfc2b031c78822344fc821778d35bae"
] | [
"prospect/likelihood/noise_model.py",
"demo/demo_params.py"
] | [
"import numpy as np\nfrom scipy.linalg import cho_factor, cho_solve\n\n__all__ = [\"NoiseModel\"]\n\n\nclass NoiseModel(object):\n\n def __init__(self, metric_name='', mask_name='mask', kernels=[],\n weight_by=[]):\n assert len(kernels) == len(weight_by)\n self.kernels = kernels\n ... | [
[
"numpy.diag",
"numpy.dot",
"numpy.log",
"numpy.isfinite",
"scipy.linalg.cho_factor",
"scipy.linalg.cho_solve",
"numpy.array",
"numpy.zeros"
],
[
"numpy.log10",
"numpy.squeeze",
"numpy.array",
"numpy.dtype"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.12",
"0.14",
"0.15"
],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
danielstaps/prototorch_models | [
"1498c4bde5954d1cd3f5b1c0382e40cf763770e2"
] | [
"prototorch/models/abstract.py"
] | [
"import pytorch_lightning as pl\nimport torch\nfrom torch.optim.lr_scheduler import ExponentialLR\n\n\nclass AbstractPrototypeModel(pl.LightningModule):\n @property\n def prototypes(self):\n return self.proto_layer.components.detach().cpu()\n\n def configure_optimizers(self):\n optimizer = se... | [
[
"torch.optim.lr_scheduler.ExponentialLR"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
skidzo/mubosym | [
"308f7c1c16fe9aa4b92bf791f170e8830295eb7d"
] | [
"exa_10_bending_stiffness.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Jun 9 23:07:13 2015\n\n@author: oliver\n\"\"\"\nimport numpy as np\nfrom sympy import symbols, sin\nimport mubosym as mbs\n\n###############################################################\n# general system setup example\nmyMBS = mbs.MBSworld('bending_stiffness', co... | [
[
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yrtf/QuantLib-SWIG | [
"d63049a018795cc7f66d57944a6564af2fb9ae9f"
] | [
"Python/examples/american-option.py"
] | [
"# ---\n# jupyter:\n# jupytext:\n# formats: py:percent\n# text_representation:\n# extension: .py\n# format_name: percent\n# format_version: '1.3'\n# jupytext_version: 1.4.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# %% [m... | [
[
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
TBuyukakin/CarND-Capstone | [
"965bb98f74f58549e7ecbcd345e6869cab1e31ae",
"965bb98f74f58549e7ecbcd345e6869cab1e31ae"
] | [
"ros/src/tl_detector/light_classification/tl_classifier.py",
"ros/src/tl_detector/light_classification/utils/label_map_util.py"
] | [
"from styx_msgs.msg import TrafficLight\n\nimport tensorflow as tf\nimport os\nimport cv2\nimport numpy as np\nimport time\n\nimport rospy\nimport sys\n\nfrom cv_bridge import CvBridge, CvBridgeError\nfrom std_msgs.msg import Bool\nfrom sensor_msgs.msg import Image\n\nfrom utils import label_map_util\nfrom utils im... | [
[
"tensorflow.Graph",
"numpy.expand_dims",
"tensorflow.import_graph_def",
"tensorflow.gfile.GFile",
"numpy.squeeze",
"tensorflow.ConfigProto",
"tensorflow.Session",
"tensorflow.GraphDef"
],
[
"tensorflow.gfile.GFile"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
sThalham/SyDPose | [
"a5221fce1cf600b382999903db769024ded53256"
] | [
"SyDPose/preprocessing/ycbv.py"
] | [
"\nfrom ..preprocessing.generator import Generator\nfrom ..utils.image import read_image_bgr\nfrom collections import defaultdict\n\nimport os\nimport json\nimport numpy as np\nimport itertools\nimport cv2\n\n\ndef _isArrayLike(obj):\n return hasattr(obj, '__iter__') and hasattr(obj, '__len__')\n\n\nclass YCBVGe... | [
[
"numpy.concatenate",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hydrogen602/C_rayTracing | [
"7e75ee5c502de5a4de4918d69ab4da38ec6328b6"
] | [
"setup.py"
] | [
"from distutils.core import setup\nfrom distutils.extension import Extension\nfrom Cython.Build import cythonize\nimport numpy as np\n\next_modules = [\n Extension(\"main\",\n sources=[\"interface.pyx\", \"array.c\", \"grid.c\", \"pythonInterface.c\", \"dataTypes/dColor.c\", \"dataTypes/ray.c\", \"d... | [
[
"numpy.get_include"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ais97/bayesvp | [
"dedf38dd7236f040301a84e5578f7202a2da07aa"
] | [
"bayesvp/utilities.py"
] | [
"################################################################################\n#\n# utilities.py \t\t(c) Cameron Liang \n#\t\t\t\t\t\tUniversity of Chicago\n# \t\t\t\t jwliang@oddjob.uchicago.edu\n#\n# Utility functions used throughout BayesVP \n######################################################... | [
[
"numpy.linspace",
"numpy.flipud",
"matplotlib.pyplot.rc",
"numpy.concatenate",
"numpy.seterr",
"numpy.max",
"numpy.mean",
"numpy.argmin",
"numpy.var",
"numpy.exp",
"numpy.where",
"numpy.ma.masked_where",
"numpy.hstack",
"numpy.arange",
"numpy.atleast_1d"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.14",
"0.19",
"1.5",
"0.12",
"0.17",
"0.13",
"1.6",
"1.4",
"1.9",
"1.3",
"1.10",
"0.15",
"0.18",
"0.16"... |
Muennighoff/py-bottom-up-attention | [
"992137749f98e76f12583e5ab740de1180255fda"
] | [
"detectron2_mscoco_proposal_maxnms.py"
] | [
"# coding=utf-8\n# Copyleft 2019 Project LXRT\n\nimport argparse\nimport base64\nimport csv\nimport json\nimport math\nimport os\nimport random\nimport sys\nimport time\ncsv.field_size_limit(sys.maxsize)\n\n# import some common libraries\nimport cv2\nimport numpy as np\nimport torch\nimport tqdm\n\nimport detectron... | [
[
"numpy.arange",
"torch.no_grad",
"numpy.zeros",
"torch.arange"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
itsb/cmt | [
"dd37a2bcc7d477d0ee82994711acd13ee02f47a9"
] | [
"code/cmt/python/tests/mlr_test.py"
] | [
"import unittest\n\nfrom tempfile import mkstemp\nfrom numpy import *\nfrom numpy import max, abs\nfrom numpy.random import randn, randint\nfrom cmt.models import MLR\nfrom pickle import load, dump\n\nclass Tests(unittest.TestCase):\n\tdef test_mlr_train(self):\n\t\tmlr = MLR(3, 3)\n\t\t\n\t\tN = 1000\n\t\tinputs =... | [
[
"numpy.random.randn",
"numpy.abs",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ken2403/schnetpack | [
"6617dbf4edd1fc4d4aae0c984bc7a747a4fe9c0c"
] | [
"src/schnetpack/data/atoms.py"
] | [
"\"\"\"\nThis module contains all functionalities required to load atomistic data,\ngenerate batches and compute statistics. It makes use of the ASE database\nfor atoms [#ase2]_.\n\nReferences\n----------\n.. [#ase2] Larsen, Mortensen, Blomqvist, Castelli, Christensen, Dułak, Friis,\n Groves, Hammer, Hargus:\n ... | [
[
"numpy.dot",
"torch.LongTensor",
"numpy.array_equal",
"torch.device",
"numpy.array",
"torch.ones_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ma0511/quant-econ | [
"a7a0c609a2ab3267df835783b58b46cba15819f2",
"a7a0c609a2ab3267df835783b58b46cba15819f2"
] | [
"quantecon/robustlq.py",
"quantecon/arma.py"
] | [
"\"\"\"\nFilename: robustlq.py\n\nAuthors: Chase Coleman, Spencer Lyon, Thomas Sargent, John Stachurski\n\nSolves robust LQ control problems.\n\n\"\"\"\n\nfrom __future__ import division # Remove for Python 3.sx\nimport numpy as np\nfrom .lqcontrol import LQ\nfrom .quadsums import var_quadratic_sum\nfrom numpy imp... | [
[
"numpy.dot",
"numpy.hstack",
"numpy.sqrt",
"scipy.linalg.det",
"numpy.trace",
"numpy.identity",
"scipy.linalg.inv",
"scipy.linalg.solve",
"numpy.sum",
"numpy.zeros"
],
[
"numpy.hstack",
"numpy.conj",
"scipy.signal.freqz",
"numpy.asarray",
"matplotlib... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.12",
"0.14",
"0.15"
],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.... |
jmduarte/coffea | [
"3368702a7bfd7860c6a8cb3c3288119a632d3932"
] | [
"tests/test_spark.py"
] | [
"from coffea import (hist,processor)\n\nimport warnings\n\nimport numpy as np\n\nimport pytest\nimport sys\n\nif sys.version.startswith(\"3.8\"):\n pytest.skip(\"pyspark not yet functional in python 3.8\", allow_module_level=True)\n\ndef test_spark_imports():\n pyspark = pytest.importorskip(\"pyspark\", minve... | [
[
"pandas.Series",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
patricio-astudillo/MONAI | [
"ff9bbfa82763de46cbac75553e340633e3d84ecb"
] | [
"monai/transforms/utility/dictionary.py"
] | [
"# Copyright 2020 - 2021 MONAI Consortium\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# http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by applicable law or agre... | [
[
"numpy.concatenate",
"numpy.argsort",
"numpy.array",
"torch.cat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sarubi/fairseq | [
"ce45df179134be5a9962d695e3de827cf61172fe"
] | [
"fairseq/data/data_utils.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates.\n#\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\ntry:\n from collections.abc import Iterable\nexcept ImportError:\n from collections import Iterable\nimport contextlib\nimport... | [
[
"torch.max",
"numpy.linspace",
"numpy.asarray",
"numpy.random.randint",
"numpy.unique",
"numpy.full",
"numpy.random.poisson",
"numpy.copy",
"numpy.random.set_state",
"torch.arange",
"numpy.min",
"numpy.random.choice",
"numpy.random.rand",
"numpy.fromiter",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jpbonson/SBBReinforcementLearning | [
"78ccc5f1d8b84b6f5668940e6cf5391265f043e3"
] | [
"SBB/config.py"
] | [
"import sys\r\nimport re\r\nimport json\r\nimport numpy\r\nfrom collections import deque\r\n\r\nclass Config():\r\n \"\"\"\r\n This class contain all the configurations and restrictions that will be used by SBB.\r\n \"\"\"\r\n\r\n # user configurable options, choose a .json file when initializing main.p... | [
[
"numpy.iinfo"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
karsti11/caffe_bar_sales_analysis | [
"f7001bbf2d09c1ceeb8aef35322652a8495949ed"
] | [
"src/models/train_model.py"
] | [
"import os\nimport numpy as np\nimport pandas as pd\nfrom tqdm import tqdm\nfrom sklearn.metrics import make_scorer\nfrom sklearn.linear_model import Ridge\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.model_selection import GridSearchCV\n#from src.features.build_features import add_calendar_features, add_ho... | [
[
"sklearn.model_selection.GridSearchCV",
"pandas.to_datetime",
"pandas.Series",
"pandas.DataFrame",
"sklearn.metrics.make_scorer"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
hakib/posthog | [
"1260a01facceafb8c75ee014ed11f0bac07b5c10"
] | [
"posthog/api/action.py"
] | [
"from posthog.models import Event, Team, Action, ActionStep, Element, User, Person\nfrom posthog.utils import relative_date_parse, properties_to_Q\nfrom posthog.constants import TREND_FILTER_TYPE_ACTIONS, TREND_FILTER_TYPE_EVENTS\nfrom rest_framework import request, serializers, viewsets, authentication # type: ign... | [
[
"pandas.offsets.Week",
"pandas.DataFrame",
"pandas.date_range",
"pandas.offsets.MonthEnd",
"pandas.Timestamp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DirkEilander/glofrim_v2.0-experiments | [
"303d77533d772f75f3df5ee403e5d2020581037a"
] | [
"analysis/0_preprocess_and_sample_CMF_output.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Nov 27 13:47:00 2018\r\n\r\nThis is an auxiliary script to pre-process and sample CaMa-FLood (CMF) output from binary files.\r\nRunning this script may be required if CMF is not compiled with netCDF. Output from PCR-GLOBWB and \r\nLisdflood-FP is either already s... | [
[
"numpy.fromfile",
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.ylim",
"numpy.ma.masked_greater",
"matplotlib.pyplot.plot",
"numpy.append",
"numpy.savetxt",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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