repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
changchengxue/SpamFilter | [
"0f6c10bd12018da6e975c7821767551c5f523015"
] | [
"SimpleNavieBayes/test.py"
] | [
"#!/usr/bin/python3.6\n# -*- coding: utf-8 -*-\n\"\"\"\n@Author: changcheng\n\"\"\"\n\nimport random\nimport numpy as np\nimport SimpleNavieBayes.NaiveBayes as naiveBayes\n\n\ndef simple_test():\n \"\"\"\n 测试函数\n :return:\n \"\"\"\n vocabulary_list, prob_words_spam, prob_words_health, prob_spam = \\\... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yuezuegu/darts | [
"21af791837060b9e3372301c23cb94f74f56dbf1"
] | [
"cnn/architect.py"
] | [
"import torch\nimport numpy as np\nimport torch.nn as nn\nfrom torch.autograd import Variable\n\n\ndef _concat(xs):\n return torch.cat([x.view(-1) for x in xs])\n\n\nclass Architect(object):\n\n def __init__(self, model, args, use_cuda=False):\n self.network_momentum = args.momentum\n self.network_weight_de... | [
[
"torch.zeros_like",
"torch.autograd.Variable"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bddonovan/PyXFocus | [
"2d6722f0db28c045df35075487f9d4fdfed8b284"
] | [
"examples/axro/singlePassAlignment.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nimport pdb\nimport scipy.optimize as opt\n\nimport traces.surfaces as surf\nimport traces.transformations as tran\nimport traces.analyses as anal\nimport traces.sources as sources\nimport traces.conicsolve as conic\nimport traces.axro.slf as slf\n\nimport utilit... | [
[
"matplotlib.pyplot.imshow",
"numpy.sqrt",
"numpy.linspace",
"numpy.gradient",
"numpy.isnan",
"numpy.genfromtxt",
"numpy.concatenate",
"numpy.arctan2",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.colorbar",
"scipy.optimize.minimize",
"numpy.mean",
"numpy.shape",... | [
{
"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"
... |
ADGEfficiency/energy-py-3-dev | [
"9c5eb32718dec3f8195402d82c1d03d90fd1f5f9"
] | [
"energypy/envs/battery.py"
] | [
"from collections import namedtuple\nimport numpy as np\n\nfrom energypy import registry\nfrom energypy.envs.base import AbstractEnv\n\n\ndef battery_energy_balance(initial_charge, final_charge, import_energy, export_energy, losses):\n delta_charge = final_charge - initial_charge\n balance = import_energy - (... | [
[
"numpy.abs",
"numpy.clip",
"numpy.isnan",
"numpy.full",
"numpy.concatenate",
"numpy.testing.assert_almost_equal",
"numpy.zeros_like",
"numpy.random.uniform",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sleighsoft/fti | [
"f50db8320af613a7e91380a9e1988332abf38fe1"
] | [
"models/frozen/frozen_util.py"
] | [
"# Copyright 2019 Julian Niedermeier & Goncalo Mordido\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 ... | [
[
"tensorflow.python.framework.ops.device",
"tensorflow.python.framework.importer.import_graph_def",
"tensorflow.python.ops.array_ops.unstack",
"tensorflow.python.ops.map_fn.map_fn",
"tensorflow.python.ops.array_ops.squeeze",
"tensorflow.core.framework.graph_pb2.GraphDef.FromString",
"te... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.8",
"1.10",
"1.12",
"2.7",
"2.6",
"1.4",
"1.13",
"2.3",
"2.4",
"2.9",
"1.5",
"1.7",
"2.5",
"0.12",
"1.0",
"2.2",
"1... |
deecamp-chuangxin/DeepInvest | [
"58f6235fa2f55b0a0ceacb13a8b3e30ab7e291f4"
] | [
"misc/RL/plot.py"
] | [
"\nimport pandas as pd\nimport numpy as np\nimport pickle\nimport matplotlib.pyplot as plt\nimport os\nimport torch\ndef GetFileList(data_dir):\n train_dir = os.path.join(data_dir,'train')\n test_dir = os.path.join(data_dir,'test')\n \n train_files = os.listdir(train_dir)\n train_files=[i for i in tr... | [
[
"matplotlib.pyplot.scatter",
"torch.load",
"matplotlib.pyplot.savefig",
"numpy.array",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vutatthanh1702/ecs-demo-php-simple-app | [
"8d3982615fd0ab84b249fe1f900fd6434eb1be3d"
] | [
"tensor/tsne.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Sep 12 16:53:45 2018\n\n@author: co-well-752410\n\"\"\"\n\nprint(__doc__)\n\n\n# Code source: Gaël Varoquaux\n# Modified for documentation by Jaques Grobler\n# License: BSD 3 clause\n\nfrom sklearn import datasets\n\nimport matplotlib.pyplot a... | [
[
"matplotlib.pyplot.colorbar",
"sklearn.manifold.TSNE",
"sklearn.datasets.load_digits",
"matplotlib.pyplot.scatter"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
miguelzaqui/ElectricPy | [
"763feeca12da5e47315cf8de181b94a3eb724fef"
] | [
"electricpy/sim.py"
] | [
"################################################################################\n\"\"\"\n`electricpy.sim` - Simulation Module.\n\n>>> from electricpy import sim\n\"\"\"\n################################################################################\n\n# Import Required External Dependencies\nimport numpy as _... | [
[
"numpy.matrix",
"matplotlib.pyplot.legend",
"numpy.asarray",
"matplotlib.pyplot.plot",
"scipy.signal.lsim",
"numpy.roll",
"scipy.optimize.newton",
"numpy.matrix.reshape",
"matplotlib.pyplot.tight_layout",
"numpy.arange",
"numpy.eye",
"numpy.copy",
"matplotlib.py... | [
{
"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"... |
j-hase/reda | [
"8f0399031121f5a937171231a25f9ab03a3c8873"
] | [
"lib/reda/importers/eit_version_2010.py"
] | [
"\"\"\"Research Center Jülich - EIT40 system importer (2010 version)\n\"\"\"\nimport datetime\n\nimport numpy as np\nimport pandas as pd\n\n\ndef _average_swapped_current_injections(df):\n AB = df[['a', 'b']].values\n\n # get unique injections\n abu = np.unique(\n AB.flatten().view(AB.dtype.descr * ... | [
[
"numpy.hstack",
"pandas.concat",
"pandas.to_datetime",
"numpy.abs",
"pandas.DataFrame",
"numpy.ones",
"numpy.all",
"numpy.real",
"numpy.mean",
"numpy.where"
]
] | [
{
"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": []
}
] |
yjzhang96/DiffInDiff | [
"610b2f596fdfad531032042bb0e435a01edbaaae"
] | [
"train_multi_restore.py"
] | [
"import torch\nimport data as Data\nimport model as Model\nimport argparse\nimport logging\nimport core.logger as Logger\nimport core.metrics as Metrics\nfrom core.wandb_logger import WandbLogger\nfrom tensorboardX import SummaryWriter\nimport os\nimport numpy as np\nimport wandb\nimport random \n\nif __name__ == \... | [
[
"numpy.concatenate"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dplarson/solarforecastarbiter-core | [
"d4d85a344ef38024eb47c7daaa557d4fe17730ff",
"d4d85a344ef38024eb47c7daaa557d4fe17730ff"
] | [
"solarforecastarbiter/io/fetch/tests/test_fetch_nwp.py",
"solarforecastarbiter/io/reference_observations/arm.py"
] | [
"from pathlib import Path\nimport shutil\n\nimport pytest\n\npytest.importorskip(\"aoihttp\", reason=\"requires [fetch] packages\") # noqa:E402\n\nimport aiohttp\nfrom asynctest import CoroutineMock, MagicMock\nimport pandas as pd\nfrom pkg_resources import resource_filename, Requirement\n\nfrom solarforecastarbit... | [
[
"pandas.Timestamp",
"pandas.Timestamp.utcnow"
],
[
"pandas.concat",
"pandas.Timestamp",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"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",
"... |
jlobatop/GA-CFD-MO | [
"db03301a2ba3be48e89802a4c36b4834677493cd"
] | [
"cases/NSGA_cylinder/evolution.py"
] | [
"# Package importation\nimport numpy as np\nimport math\nimport random\nimport scipy.stats as stats\nimport optunity\nfrom problemSetup import *\nimport sys\n\n################################################################################\n# FUNCTION DEFINITION ... | [
[
"numpy.linspace",
"numpy.concatenate",
"numpy.max",
"numpy.zeros_like",
"numpy.hstack",
"numpy.unique",
"numpy.lexsort",
"numpy.zeros",
"numpy.random.choice",
"numpy.genfromtxt",
"numpy.logical_or",
"numpy.append",
"numpy.random.rand",
"numpy.savetxt",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mirca/graphgraph.py | [
"68b29a2dfeae1654db85a3b03c51ce4798c6608d"
] | [
"graphgraph/tests/test_operators.py"
] | [
"import pytest\nimport numpy as np\nimport inspect\nimport random\nfrom graphgraph import operators as op\n\n\nclass LaplacianConstraintError(RuntimeError):\n pass\n\n\nclass LaplacianConstraints:\n def __init__(self, in_matrix):\n self.in_matrix = in_matrix\n self.validate()\n\n def is_symme... | [
[
"numpy.diag",
"numpy.triu_indices",
"numpy.all",
"numpy.testing.assert_allclose",
"numpy.random.uniform",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dlohmeier/pandapipes | [
"899bb44fdf5a4ff6aa3cac0a7bf88bc30611c73f"
] | [
"pandapipes/component_models/abstract_models/branch_models.py"
] | [
"# Copyright (c) 2020-2021 by Fraunhofer Institute for Energy Economics\n# and Energy System Technology (IEE), Kassel, and University of Kassel. All rights reserved.\n# Use of this source code is governed by a BSD-style license that can be found in the LICENSE file.\n\nimport numpy as np\n\nfrom pandapipes.componen... | [
[
"numpy.empty_like",
"numpy.array",
"numpy.ones_like",
"numpy.abs"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
PrasadKolase/ga-learner-dsb-repo | [
"bba56432b476cfb165c03c0a80fe132092e76618"
] | [
"Loan-Approval-Analysis/code.py"
] | [
"# --------------\n# Import packages\nimport numpy as np\nimport pandas as pd\nfrom scipy.stats import mode \n \n\n\n\n# code starts here\nbank = pd.read_csv(path)\nprint(bank.head())\n\ncategorical_var = bank.select_dtypes(include='object')\nprint(categorical_var.head())\n\nnumerical_var = bank.select_dtypes(inclu... | [
[
"pandas.read_csv",
"pandas.pivot_table"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
chakkritte/SSD | [
"64b84dfa88a1686593addaa9941cc14579e129ee"
] | [
"ssd/modeling/box_head/inference.py"
] | [
"import torch\n\nfrom ssd.structures.container import Container\nfrom ssd.utils.nms import batched_nms\n\n\nclass PostProcessor:\n def __init__(self, cfg):\n super().__init__()\n self.cfg = cfg\n self.width = cfg.INPUT.IMAGE_SIZE\n self.height = cfg.INPUT.IMAGE_SIZE\n\n def __call_... | [
[
"torch.nonzero",
"torch.arange"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pacslab/serverless-performance-modeling | [
"4b889700542848bf2fb6a737c6cdf6285f18a9ca"
] | [
"deployments/01-fixed-rate-experiment.py"
] | [
"# %% imports\nimport boto3\nimport os\nimport time\n\nfrom pacsltk import deployer\nfrom pacsltk import client\n\n# Load secrets\nfrom dotenv import load_dotenv\nfrom pathlib import Path # python3 only\nenv_path = Path('.') / '.secret-env'\nload_dotenv(dotenv_path=env_path)\n\n# Processing Imports\nimport pandas ... | [
[
"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": []
}
] |
shpsi/Emoji-predictor-using-Deep-Learning | [
"09411c19462af8a65bd38cc2d93915c5f8be287c"
] | [
"src/predictor.py"
] | [
"import os\nimport sys\nimport cv2\nimport time\nimport logging\nimport tensorflow as tf\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport glob\nimport tqdm\n\nsys.path.append(os.path.join(os.path.dirname(__file__), os.pardir))\nimport src\nfrom src.model import model\n\nconfig = tf.ConfigProto()\nconfig... | [
[
"tensorflow.placeholder",
"tensorflow.ConfigProto",
"numpy.max",
"numpy.argmax",
"tensorflow.reset_default_graph",
"tensorflow.Session",
"tensorflow.train.Saver"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
54chen/deep | [
"230012b7267ffc64eb703224c53a2e96292056b6"
] | [
"test1.py"
] | [
"import numpy\nfrom sklearn.model_selection import GridSearchCV\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras.wrappers.scikit_learn import KerasClassifier\nfrom keras.wrappers.scikit_learn import BaseWrapper\nimport copy\ndef custom_get_params(self, **params):\n res = copy.deep... | [
[
"numpy.loadtxt",
"sklearn.model_selection.GridSearchCV",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
personads/ud-genre | [
"5ab79a34a5ea7035b785622ea4fa551779bae0cc"
] | [
"classify/zero.py"
] | [
"#!/usr/bin/python3\n\nimport argparse, json, logging, os, sys\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\n\n# local imports\nsys.path.append(os.path.join(os.path.dirname(__file__), '..'))\nfrom data.ud import *\nfrom data.utils import *\n\n\ndef parse_arguments():\n\targ_parser = argparse... | [
[
"torch.mean",
"torch.nn.functional.softmax",
"numpy.random.seed",
"torch.zeros",
"torch.random.manual_seed",
"torch.no_grad",
"torch.cuda.is_available",
"torch.device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Khanatoshs/MET_BAP | [
"51dd28e7ef909fe259164415c2d3fa8427a9329d"
] | [
"main/main_functions.py"
] | [
"import pandas as pd\nfrom bokeh.plotting import figure\nfrom bokeh.tile_providers import CARTODBPOSITRON, get_provider\nfrom bokeh.models import HoverTool,IndexFilter,CDSView,Circle,Range1d,LinearAxis,LegendItem,Legend\n\nfrom extras import dataframe_minutes_to_degrees,dataframe_wgs84_to_mercator\n\ndef get_dataf... | [
[
"pandas.read_csv",
"pandas.to_datetime",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
icouckuy/trieste | [
"92e6e51bb218e2215f93cf3994b4ce9d8a4743dc"
] | [
"trieste/utils/objectives.py"
] | [
"# Copyright 2020 The Trieste Contributors\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 l... | [
[
"tensorflow.sin",
"tensorflow.constant",
"tensorflow.cos",
"tensorflow.math.log",
"tensorflow.debugging.assert_shapes",
"tensorflow.split"
]
] | [
{
"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... |
drewleonard42/sunpy | [
"79ca90a032213d82d42a3657a693b20b99b22464"
] | [
"sunpy/instr/tests/test_rhessi.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nUnit tests for `sunpy.instr.rhessi`\n\"\"\"\nimport sys\nimport textwrap\nfrom distutils.version import LooseVersion\n\nfrom unittest import mock\nimport numpy as np\nimport pytest\n\nimport sunpy.io\nimport sunpy.map\nfrom sunpy.data.test import get_test_filepath\nimport sunpy.ins... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ojss/c3lr | [
"a018c5a793a2c9eedc3f0fefcca0970f0be35ffc"
] | [
"unsupervised_meta_learning/common/utils.py"
] | [
"import plotly.express as px\nimport matplotlib.pyplot as plt\nimport pytorch_lightning as pl\nimport seaborn as sns\nimport wandb\n\n__all__ = [\"log_plotly_graph\", \"log_sns_plot\"]\n\ndef log_plotly_graph(z, y, title, global_step, pl_module: pl.LightningModule, dims=2):\n if dims == 3:\n fig = px.scat... | [
[
"matplotlib.pyplot.clf"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
joshloyal/forest-hypothesis-tests | [
"ce75a11a3ad80667329118359cd6e4a4d5d93296"
] | [
"examples/slr.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.ensemble import RandomForestRegressor\n\nfrom hypoforest import random_forest_error\n\nn_samples = 200\n\nrng = np.random.RandomState(123)\n\nX = rng.uniform(0, 20, n_samples).reshape(-1, 1)\n\ny = (2 * X + np.sqrt(10) * rng.randn(n_samples, 1)).r... | [
[
"sklearn.ensemble.RandomForestRegressor",
"numpy.sqrt",
"matplotlib.pyplot.plot",
"numpy.random.RandomState",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Test00DezWebSite/eo-learn | [
"1390c52c0bfc71308fc7e0d7fa3fe71a2d591a37"
] | [
"core/eolearn/core/eodata.py"
] | [
"\"\"\"\nThe eodata module provides core objects for handling remote sensing multi-temporal data (such as satellite imagery).\n\nCredits:\nCopyright (c) 2017-2019 Matej Aleksandrov, Matej Batič, Andrej Burja, Eva Erzin (Sinergise)\nCopyright (c) 2017-2019 Grega Milčinski, Matic Lubej, Devis Peresutti, Jernej Puc, T... | [
[
"numpy.concatenate"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Donokami/vaccintracker | [
"92698ca5cc3f9f756961d4fdbc8be78c11b703f3"
] | [
"scripts/sg-epci-opendata.py"
] | [
"import json\nimport pandas as pd\nimport requests\nimport numpy as np\n\n## FRANCE\ndef download_fra_data():\n url = \"https://www.data.gouv.fr/fr/datasets/r/34dcc90c-aec9-48ee-9fd3-a972b44202c0\"\n data = requests.get(url)\n\n with open('data/input/sg-epci-opendata.csv', 'wb') as f:\n f.write(data.con... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
jjerphan/dask-ml | [
"cfa57e5789fc29bdcee5d846b5116c07f5fbe292"
] | [
"dask_ml/impute.py"
] | [
"import dask\nimport dask.array as da\nimport dask.dataframe as dd\nimport numpy as np\nimport pandas as pd\nimport sklearn.impute\n\nfrom .utils import check_array\n\n\nclass SimpleImputer(sklearn.impute.SimpleImputer):\n _types = (pd.Series, pd.DataFrame, dd.Series, dd.DataFrame, da.Array)\n\n def _check_ar... | [
[
"pandas.isna",
"numpy.full"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"0.24",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
GuQiangJS/QUANTAXIS | [
"80b2c1e5c094ed7c362607b74b3d1df5525043ac"
] | [
"QUANTAXIS/QAFetch/QATradeFile.py"
] | [
"# coding=utf-8\n#\n# The MIT License (MIT)\n#\n# Copyright (c) 2016-2018 yutiansut/QUANTAXIS\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including withou... | [
[
"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": []
}
] |
emdata-ailab/LPCC-Net | [
"d39ce63699998fa4403f20f02d19ab5f53843614"
] | [
"data/dual_detection_box.py"
] | [
"import os\nimport fire\nimport torch\nimport random\nimport pickle\nimport numpy as np\nimport torchvision.transforms as transforms\n\nfrom tqdm import tqdm\nfrom PIL import Image\nfrom torch.utils.data import Dataset\nfrom data.voxel_generator import kitti_voxel_generator\nfrom ops.common import read_pkl, box3d_t... | [
[
"torch.tensor",
"numpy.array",
"numpy.clip"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TeddyFirman/Algorithm_Python | [
"edbd50a97a62c2beb2a187e4c411c677aa43115e",
"edbd50a97a62c2beb2a187e4c411c677aa43115e"
] | [
"maths/gaussian.py",
"other/dijkstra_bankers_algorithm.py"
] | [
"\"\"\"\r\nReference: https://en.wikipedia.org/wiki/Gaussian_function\r\n\"\"\"\r\nfrom numpy import exp, pi, sqrt\r\n\r\n\r\ndef gaussian(x, mu: float = 0.0, sigma: float = 1.0) -> int:\r\n \"\"\"\r\n >>> gaussian(1)\r\n 0.24197072451914337\r\n\r\n >>> gaussian(24)\r\n 3.342714441794458e-126\r\n\r\n... | [
[
"numpy.exp",
"numpy.sqrt"
],
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
anonymouscode114/iclr2021_rlreg | [
"142566f4316bfed9e3858b92f87a792b7c7b8b1b"
] | [
"baselines_release/baselines/trpo_mpi/trpo_mpi.py"
] | [
"from baselines.common import explained_variance, zipsame, dataset\nfrom baselines import logger\nimport baselines.common.tf_util as U\nimport tensorflow as tf, numpy as np\nfrom gym import spaces\nimport time\nfrom baselines.common import colorize\nfrom collections import deque\nfrom baselines.common import set_gl... | [
[
"numpy.sqrt",
"tensorflow.contrib.layers.apply_regularization",
"tensorflow.contrib.layers.l1_l2_regularizer",
"tensorflow.reduce_sum",
"numpy.concatenate",
"numpy.mean",
"tensorflow.group",
"numpy.allclose",
"tensorflow.get_collection",
"numpy.empty_like",
"tensorflow.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
lupalab/flowscan | [
"a0fc8fd25cb62c9eeb6583c3d7505d54b969f88c"
] | [
"experiments/experiment.py"
] | [
"import tensorflow as tf\nfrom . import trainer\nfrom ..model import model as mod\n\n\nclass Experiment:\n\n # TODO: make all arguements optional, load config from\n # save_location+'config.p' when not given, resolve dimension, and add\n # option to restore\n def __init__(self, config, summary_location,... | [
[
"tensorflow.Graph",
"tensorflow.placeholder",
"tensorflow.reset_default_graph",
"tensorflow.Session",
"tensorflow.variable_scope"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
ABDELHAMED2017/hybrid_beamforming_notebook | [
"645652cbbe44eb383cf8797914de3837a9a35d7b"
] | [
"classification.py"
] | [
"# To support both python 2 and python 3\r\nfrom __future__ import division, print_function, unicode_literals\r\nimport math\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\nN = 100 # number of points per class\r\nd0 = 2 # dimensionality\r\nC = 3 # number of classes\r\nX = np.zeros((d0, N*C)) # data ... | [
[
"numpy.linspace",
"numpy.cos",
"numpy.sin",
"matplotlib.pyplot.plot",
"numpy.random.randn",
"matplotlib.pyplot.show",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xuyinhai22/Client-selection-of-Federated-Learning | [
"bb8b911b5676dcc6e2c2c39edaaaaeac3af01f09"
] | [
"client.py"
] | [
"import logging\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\n\n\nclass Client(object):\n \"\"\"Simulated federated learning client.\"\"\"\n\n def __init__(self, client_id):\n self.client_id = client_id\n\n def __repr__(self):\n return 'Client #{}: {} samples in labels: {... | [
[
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dddaga/word-tree | [
"ed6c59c16feee04d5c6003b3f5f4df68e6808e04"
] | [
"src/services/train.py"
] | [
"import torch\nimport time\nfrom numba import jit\nfrom src.utilities.graph_operations.in_memory import get_ngram\nfrom config import CONTEXT_DIMENSION, DB,collection\nfrom src.services.get_corpus import load_corpus, train_step_genrator\nfrom src.utilities.policies.v_1 import train_graph\nimport uuid \n\n\nif DB =... | [
[
"torch.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mpuighol/k2rotation | [
"48cce999825cdd75394d31f73a69fa1d2c556354"
] | [
"explore.py"
] | [
"from astropy.io import fits\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom gatspy import periodic\nimport glob\n\nstash = glob.glob('./FITSfiles/*.fits')\n\n#file = 'hlsp_everest_k2_llc_220133060-c08_kepler_v2.0_lc.fits'\n#hdu = fits.open(file)\n#data = hdu[1].data\n\nok = np.where((data['QUALITY'] == ... | [
[
"numpy.where",
"matplotlib.pyplot.subplots",
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sergeychuvakin/Scientific_Search | [
"ab8dba70c01c770e82ef50f348d96c4311dfc973"
] | [
"main/KnowledgeGraphs[OLD].py"
] | [
"import networkx as nx\nfrom bs4 import BeautifulSoup\nimport os\nimport tempfile\nimport numpy as np\nimport pylab as plt\n\ntext = []\narray = []\nj, i = 0, 0\nsudopass = 'grakachak94' #Your sudo password to create temporary files\ntemp_out = tempfile.NamedTemporaryFile()\ntempfile.tempdir = \"/home/paul/Biocad... | [
[
"numpy.arange",
"numpy.save",
"numpy.ones",
"numpy.append",
"numpy.delete",
"numpy.insert",
"numpy.ndarray.tolist",
"numpy.load",
"numpy.array",
"numpy.zeros",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
trougnouf/distiller | [
"92f9bd9be6b8948b4e7d94e57f8d915afff3e361"
] | [
"distiller/utils.py"
] | [
"#\n# Copyright (c) 2018 Intel Corporation\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 ... | [
[
"numpy.random.seed",
"torch.manual_seed",
"torch.randn",
"torch.numel",
"numpy.prod",
"torch.nonzero"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
HaoRiziyou/light-field-networks | [
"438c198dc024b1a90f5d98d5c1e6cde078de2ec0"
] | [
"loss_functions.py"
] | [
"import torch.nn as nn\n\n\ndef image_loss(model_out, gt, mask=None):\n gt_rgb = gt['rgb']\n return nn.MSELoss()(gt_rgb, model_out['rgb']) * 200\n\n\nclass LFLoss():\n def __init__(self, l2_weight=1, reg_weight=1e2):\n self.l2_weight = l2_weight\n self.reg_weight = reg_weight\n\n def __cal... | [
[
"torch.nn.MSELoss"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dwhan89/pitas | [
"5da2d5fe84714e082e76a8b50c31f6faebeb8579"
] | [
"test/scripts/legacy/fullsky_spectra.py"
] | [
"import cmblens, cusps\nfrom cusps import power\nfrom actsims import simTools as act_sim\nfrom enlib import enmap, curvedsky\nfrom orphics import stats, io, maps\nimport numpy as np\nimport os, flipper\nfrom cmblens.util import cl2dl\n\nlog = cmblens.logger.getLogger()\n\ncmblens.config.argparser.add_argument('-s',... | [
[
"numpy.linspace",
"numpy.isnan",
"numpy.arange",
"numpy.mean",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hshany/alphafold | [
"997d8031cdb67cb1ef82860b71416349a08b0646"
] | [
"run_alphafold.py"
] | [
"# Copyright 2021 DeepMind Technologies Limited\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 applic... | [
[
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Orlogskapten/tsNostradamus | [
"707cbc23fac3e0f92875d89550046e5c3b7b17d2"
] | [
"src/nostradamus/models/mother.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.ticker import MaxNLocator\nimport seaborn as sns\n\nsns.set()\nimport warnings\n\nwarnings.filterwarnings(\"ignore\")\n\nimport scipy\nfrom scipy.stats import skewnorm\nfrom scipy import stats\n\nfrom typing import Any, Iterable, List, Dict, Tupl... | [
[
"matplotlib.pyplot.legend",
"numpy.linspace",
"numpy.cumsum",
"numpy.nan_to_num",
"matplotlib.pyplot.plot",
"numpy.mean",
"numpy.where",
"numpy.random.randint",
"numpy.square",
"scipy.stats.skewnorm.fit",
"numpy.ones_like",
"scipy.stats.norm.fit",
"numpy.arange"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zhaoyuchen100/DDPG_CCL | [
"26f93aaf733a4a67754f753afd2bda270c547e67"
] | [
"threelinkarm.py"
] | [
"# Copyright (c) Microsoft. All rights reserved.\n\n# Licensed under the MIT license. See LICENSE.md file in the project root\n# for full license information.\n# ==============================================================================\n\nimport gym\nimport numpy as np\nfrom gym import spaces\nfrom gym.utils i... | [
[
"tensorflow.InteractiveSession",
"numpy.copy",
"numpy.random.rand",
"numpy.random.uniform",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
dmitry-kabanov/fickettmodel | [
"255b1e9cae1cfb7a6b914ad61a17288d52215cc4"
] | [
"saf/nonlinear/weno5js.py"
] | [
"import numpy as np\n\nfrom . import weno5js_impl_c\n\n\nclass WENO5JS:\n\n \"\"\"Docstring for WenoInterpolation. \"\"\"\n\n def __init__(self, eps, size):\n \"\"\"@todo: to be defined1. \"\"\"\n self._eps = eps\n self._size = size - 2 - 3 # Strip off boundary and ghost points.\n ... | [
[
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
euetova/quantulum | [
"00941347dabb6d7dc5002d44818571257d9834e8"
] | [
"quantulum/classifier.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n'''\n:mod:`Quantulum` classifier functions.\n'''\n\n# Standard library\nimport os\nimport json\nimport pickle\nimport logging\nimport re\nimport string\n\n# Dependences\nimport wikipedia\nfrom stemming.porter2 import stem\ntry:\n from sklearn.linear_model import... | [
[
"sklearn.feature_extraction.text.TfidfVectorizer",
"sklearn.linear_model.SGDClassifier"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tangochuynam/human_recognition | [
"4d2422c8664cad936c455b78e25e7160cace7dba"
] | [
"Tracking/deep_sort/preprocessing.py"
] | [
"import numpy as np\r\n\r\n\r\ndef non_max_suppression(boxes, max_bbox_overlap, scores=None):\r\n \"\"\"Suppress overlapping detections.\r\n\r\n Original code from [1]_ has been adapted to include confidence score.\r\n\r\n .. [1] http://www.pyimagesearch.com/2015/02/16/\r\n faster-non-maximum-sup... | [
[
"numpy.argsort",
"numpy.where",
"numpy.maximum",
"numpy.minimum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LowPass-DataScience/avito-demand-prediction-chanllege | [
"966be906f30470a97b88cb05bec5cce7e4f3aa7b"
] | [
"avito-demand-prediction-challenge/python_codes/prepare_active_HDF5.py"
] | [
"import pandas as pd\nimport numpy as np\nimport string\nimport os\nimport json\nimport gc\nfrom sklearn.preprocessing import LabelEncoder\ngc.enable()\n\ndataPath='../../../data/avito-demand-prediction'\n\n## Load csv.zip data\ntrain_active = pd.read_csv(os.path.join(dataPath, 'train_active.csv.zip'), compression=... | [
[
"sklearn.preprocessing.LabelEncoder",
"pandas.concat",
"pandas.to_datetime",
"pandas.HDFStore"
]
] | [
{
"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": []
}
] |
ktho22/vctts | [
"84e8bc6c4b5586aa319c7c21c4325f879f2cd3ba"
] | [
"dataset.py"
] | [
"import torch\nimport torch.utils.data as data\nfrom glob import glob\nfrom os.path import join, basename, exists\nimport numpy as np\nimport pickle as pkl\nfrom random import random\nnp.random.seed(123)\n\nclass TTSDataset(data.Dataset):\n def __init__(self, which_set='train', datapath='./samples'):\n # ... | [
[
"numpy.asarray",
"numpy.random.permutation",
"numpy.random.seed",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
OatmealLiu/DGLN-model-for-Person-Re-ID-with-frozen-learning | [
"50837c2074ad82df4c149036ab4544758a1274b2"
] | [
"master/train_personID.py"
] | [
"# -*- coding: utf-8 -*-\r\nfrom __future__ import print_function, division\r\n\r\nimport god\r\nimport argparse\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.optim as optim\r\nfrom torch.autograd import Variable\r\nfrom torchvision import transforms\r\nimport matplotlib\r\nmatplotlib.use('agg')\r\nimpor... | [
[
"torch.optim.Adam",
"torch.nn.CrossEntropyLoss",
"pandas.read_csv",
"torch.max",
"matplotlib.use",
"torch.utils.data.DataLoader",
"torch.sum",
"torch.autograd.Variable",
"torch.no_grad",
"torch.cuda.is_available",
"torch.optim.SGD",
"torch.squeeze",
"matplotlib.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
jcrvz/unfolded-metaheuristics-preliminary | [
"ab9d4070ffb3c3150534422913f8cbb5b77d6243"
] | [
"paper_unfolding_popvar.py"
] | [
"# Load data\nimport tools as tl\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom scipy import stats\n# from scipy.stats import rankdata\n# from mpl_toolkits.mplot3d import Axes3D\nimport os\nimport seaborn as sns\nimport benchmark_func as bf\nsns.set(context=\"paper\", font_scale=1, ... | [
[
"numpy.linspace",
"matplotlib.pyplot.rc",
"matplotlib.pyplot.get_cmap",
"numpy.concatenate",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.hlines",
"numpy.copy",
"matplotlib.pyplot.figure",
"pandas.concat",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.savefig",
... | [
{
"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": [
"0.13",
"1.6",
"0.14",
"1.10",
"0... |
dholstein/elephant | [
"acb1501102b81898ca98b960569b45dc6ee3e1d6",
"acb1501102b81898ca98b960569b45dc6ee3e1d6"
] | [
"elephant/test/test_asset.py",
"elephant/sta.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Oct 22 14:35:49 2014\n\n@author: torre\n\"\"\"\n\nimport unittest\nimport numpy as np\nimport scipy.spatial\nimport quantities as pq\nimport neo\n\ntry:\n import sklearn\nexcept ImportError:\n HAVE_SKLEARN = False\nelse:\n import elephant.asset as asset\n ... | [
[
"numpy.arange",
"numpy.testing.assert_array_equal",
"numpy.all",
"numpy.array",
"numpy.zeros",
"numpy.vstack",
"numpy.testing.assert_array_almost_equal"
],
[
"numpy.ceil",
"numpy.repeat",
"numpy.zeros",
"numpy.floor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AhmedHani/FCIS-Machine-Learning-2017 | [
"f241d989fdccfabfe351cd9c01f5de4da8df6ef3"
] | [
"Profiles/Ahmed Samir/Xor nn.py"
] | [
"import tensorflow as tf\nimport matplotlib.pyplot as plt #we will use it to draw the learning curve after training the network\n\ntrain_x = [[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]]\ntrain_y = [[0], [1], [1], [0]]\n\nINPUT_NEURONS = 2\nHIDDEN_NEURONS = 3\nOUTPUT_NEURONS = 1\n\nNUM_OF_EPOCHS = 100000\n\n\"\"... | [
[
"tensorflow.matmul",
"tensorflow.nn.sigmoid",
"tensorflow.Variable",
"matplotlib.pyplot.title",
"tensorflow.placeholder",
"matplotlib.pyplot.plot",
"tensorflow.initialize_all_variables",
"tensorflow.train.GradientDescentOptimizer",
"tensorflow.Session",
"tensorflow.square",... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
bthtsang/FrEIA | [
"b16c2e1981863ee7a43789d76b49c5e9e6070cc9"
] | [
"inn4iip.py"
] | [
"import configargparse\nimport torch\nimport torch.nn as nn\nimport os\nimport numpy as np\n\n# FrEIA imports\nimport FrEIA.framework as Ff\nimport FrEIA.modules as Fm\n\n# model summary\nfrom torchinfo import summary\n\n# input config file parser\ndef parse_model_args():\n parser = configargparse.ArgParser()\n ... | [
[
"numpy.savez",
"torch.manual_seed",
"numpy.arange",
"torch.utils.data.TensorDataset",
"torch.utils.data.DataLoader",
"torch.from_numpy",
"numpy.random.shuffle",
"torch.randn",
"torch.sum",
"torch.repeat_interleave",
"torch.nn.Linear",
"numpy.random.rand",
"torch... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
seanlane/datashader | [
"12bfe0ac573a26d3baaf35c5517c3be07f7988e8"
] | [
"datashader/tests/test_pipeline.py"
] | [
"import numpy as np\nimport pandas as pd\nimport datashader as ds\nimport datashader.transfer_functions as tf\n\n\ndf = pd.DataFrame({'x': np.array(([0.] * 10 + [1] * 10)),\n 'y': np.array(([0.] * 5 + [1] * 5 + [0] * 5 + [1] * 5)),\n 'f64': np.arange(20, dtype='f8')})\ndf.f64.ilo... | [
[
"numpy.arange",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
justusschock/delira-unet-baseline | [
"f572d29de61e68ea3e8f738eff8b1e935ebb03b5"
] | [
"delira_unet/utils/onehot.py"
] | [
"import torch\nimport numpy as np\n\n\ndef make_onehot_npy(labels, n_classes):\n \"\"\"\n Function to convert a batch of class indices to onehot encoding\n Parameters\n ----------\n labels : np.ndarray\n the batch of class indices\n n_classes : int\n the number of classes\n Return... | [
[
"numpy.eye",
"torch.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Sy-Zhang/MMC-PCFG | [
"86cf4b87505066bf455c43a6a7989ebc44d5a47e"
] | [
"lib/model/vpcfg/transformer.py"
] | [
"import copy\nfrom typing import Optional, List\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn, Tensor\n\n\nclass Transformer(nn.Module):\n\n def __init__(self, d_model=512, nhead=8, num_encoder_layers=6,\n num_decoder_layers=6, dim_feedforward=2048, dropout=0.1,\n ... | [
[
"torch.nn.Dropout",
"torch.nn.MultiheadAttention",
"torch.zeros_like",
"torch.nn.LayerNorm",
"torch.nn.Linear",
"torch.nn.init.xavier_uniform_",
"torch.stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
adswa/URIAL | [
"6f93ab4169707cc1424a6dfe0c9f5df383be4438"
] | [
"urial/utils/rdm_images.py"
] | [
"\nfrom os.path import join as opj\nfrom glob import glob\nimport numpy as np\nimport pandas as pd\nimport nibabel as nib\n\nfrom dipy.align.imwarp import SymmetricDiffeomorphicRegistration\nfrom dipy.align.imwarp import DiffeomorphicMap\nfrom dipy.align.metrics import CCMetric\nfrom dipy.align.imaffine import (tra... | [
[
"numpy.eye",
"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": []
}
] |
gbeckers/sound | [
"eb3c6a97974c6a9c1d86d37278048d7838a906c1"
] | [
"sound/testsnds.py"
] | [
"import numpy as np\nfrom .snd import Snd\n\n__all__ = ['noise_PCM32', 'sine_DOUBLE', 'sine_PCM32']\n\n# 24 bits\t−8388608 to 8388607\n\naudiofloat_to_PCM32_factor = 0x7FFFFFFF # 2147483647\nPCM32_to_audiofloat_factor = 1 / 0x80000000 # 1 / 2147483648\n\ndef sine_DOUBLE(f=441., nframes=441, fs=44100):\n t =... | [
[
"numpy.random.random",
"numpy.random.seed",
"numpy.arange",
"numpy.sin",
"numpy.round",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zxc1342802/tacticsgroup | [
"f65789747ad46f4cefac1aea8dcc5825e8c6364a"
] | [
"crawl_data.py"
] | [
"\"\"\"\n 我们使用币安原生的api进行数据爬取.\n 1. 增加代理配置\n\n\"\"\"\n\nimport pandas as pd\nimport time\nfrom datetime import datetime\nimport requests\nimport pytz\nfrom jiamtrader.trader.database import database_manager\n\npd.set_option('expand_frame_repr', False) #\nfrom jiamtrader.trader.object import BarData, Interval,... | [
[
"pandas.set_option"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MihoHatanaka/Stochastic-Threshold-Model-Trees | [
"165af34f4359fd0a4c7beba246b78e87f624ab2b",
"165af34f4359fd0a4c7beba246b78e87f624ab2b"
] | [
"stmt/regressor/mean_regressor.py",
"stmt/regressor/regression_tree.py"
] | [
"import numpy as np\r\n\r\n\r\nclass MeanRegressor():\r\n \"\"\"Class for outputting the mean value of each node as a prediction.\r\n The predicted results are comparable to those of Random Forest.\r\n \"\"\"\r\n def __init__(self):\r\n pass\r\n\r\n def fit(self, x, y):\r\n self.mean = ... | [
[
"numpy.mean",
"numpy.full"
],
[
"sklearn.utils.check_array",
"sklearn.utils.check_X_y"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
fanxlxmu/GMAN | [
"5ebd63776c26d88318d87420fa1b2e5ee0551112"
] | [
"METR/model.py"
] | [
"import tf_utils\nimport tensorflow as tf\n\ndef placeholder(P, Q, N):\n X = tf.compat.v1.placeholder(\n shape = (None, P, N), dtype = tf.float32, name = 'X')\n TE = tf.compat.v1.placeholder(\n shape = (None, P + Q, 2), dtype = tf.int32, name = 'TE')\n label = tf.compat.v1.placeholder(\n ... | [
[
"tensorflow.concat",
"tensorflow.cast",
"tensorflow.squeeze",
"tensorflow.compat.v2.where",
"tensorflow.subtract",
"tensorflow.add",
"tensorflow.tile",
"tensorflow.matmul",
"tensorflow.shape",
"tensorflow.one_hot",
"tensorflow.split",
"tensorflow.not_equal",
"te... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
maichmueller/CausalPy | [
"d2dcd2c557e1ccdfa1fb77f3adbf78b0ea63c735"
] | [
"test/test_icp_linear.py"
] | [
"from functools import reduce\nfrom test.builld_scm_funcs import *\nfrom causalpy import (\n LinPredictor,\n)\nimport numpy as np\nimport pandas as pd\n\n\ndef test_linear_icp_simple():\n cn = build_scm_simple()\n # cn.plot(alpha=1)\n # plt.show()\n cn_vars = list(cn.get_variables())\n data_uninte... | [
[
"pandas.concat"
]
] | [
{
"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": []
}
] |
sitina/udacity-drl-navigation | [
"af64cfc5a3691627c28a1838cb0e4212074adfe6"
] | [
"testing.py"
] | [
"from unityagents import UnityEnvironment\nimport numpy as np\nimport random\nimport torch\nfrom collections import deque\nimport argparse\n\nfrom agent import Agent\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n\n parser.add_argument('--environment', type=str, help='Path to Unity enviro... | [
[
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zuoguoqing/gqfacenet_recognition | [
"fdb294f419b752f65fbfdc1a29ee2e73ff6fce99"
] | [
"test_insightface_register.py"
] | [
"import glob\nimport os\nimport uuid\n\nimport cv2\nimport insightface\nimport numpy as np\nimport torch\nimport yaml\nfrom sklearn import preprocessing\nfrom paz.pipelines import DetectMiniXceptionFER\nfrom PIL import ImageFont, Image\nimport detection_profile\nimport detection_eyeblink_mouthopen\nimport detection... | [
[
"numpy.square",
"numpy.fromfile",
"torch.load",
"numpy.subtract",
"sklearn.preprocessing.normalize",
"numpy.array",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bmhopkinson/Marsh_Ann | [
"7d1baaa444392622967dd1ed12f9c7a23c5fb018"
] | [
"Trainer.py"
] | [
"import numpy as np\nimport torch\nimport torch.nn as nn\nimport torchvision\nfrom torchvision import transforms, models\nfrom modeling.backbone.resnet import ResNet101\nfrom RN101_newtop import RN101_newtop\nfrom PerformanceMetrics import PerformanceMetrics\n\nclass Trainer(object):\n\tdef __init__(self, train_par... | [
[
"torch.utils.data.DataLoader",
"torch.nn.Sigmoid",
"torch.nn.Linear",
"torch.set_grad_enabled",
"torch.nn.parallel.DistributedDataParallel",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
EngineerMateo/digital-signal-processing- | [
"c58b826493a704724769d9f8d3605f5ca1d445c1"
] | [
"Signal_Display/model/open.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Aug 4 12:06:33 2020\r\n\r\n@author: Carlos Mateo Jurado Díaz\r\n\"\"\"\r\n\r\nfrom view.mainwindow.graph import GRAPH\r\n\r\nfrom numpy import loadtxt\r\nfrom PyQt5.QtWidgets import QFileDialog\r\nfrom scipy.signal import detrend\r\n\r\ndef OPEN2(time,t):\r\n ... | [
[
"numpy.loadtxt",
"scipy.signal.detrend"
]
] | [
{
"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"... |
tsherwen/AC_tools | [
"17bc2ca5c59f1dc1cb7727baf7cee68fde89d2b0"
] | [
"AC_tools/KPP.py"
] | [
"from __future__ import print_function\n#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\"\"\"\n\nFunctions for KPP input/output file Processing\n\nNotes\n-------\nThese functions are specifically for GEOS-Chem versions v11-1g and later. They have been tested on v11-2d-R1,v11-2d-R2, v11-2d-R3, v12.0, v12.1, v12.2., v12... | [
[
"pandas.Series",
"pandas.DataFrame",
"numpy.log10",
"numpy.ma.array",
"numpy.array"
]
] | [
{
"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": []
}
] |
dallascard/proportions | [
"f01502428333e45310654a36d26503612fe45234"
] | [
"util/file_handling.py"
] | [
"import os\nimport gzip\nimport json\nimport codecs\nimport pickle\n\nimport numpy as np\nfrom scipy import sparse\n\n\ndef makedirs(directory):\n if not os.path.exists(directory):\n os.makedirs(directory)\n\n\ndef write_to_json(data, output_filename, indent=2, sort_keys=True, do_gzip=False):\n if do_g... | [
[
"scipy.sparse.coo_matrix",
"numpy.savez",
"scipy.sparse.isspmatrix_coo",
"scipy.sparse.issparse",
"numpy.load"
]
] | [
{
"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"... |
hardikp/selu_snli | [
"ec63b08a523beb75260cab5ff96d578504c81678"
] | [
"snli_rnn.py"
] | [
"'''\n300D Model - Train / Test (epochs)\n=-=-=\nBatch size = 512\nFixed GloVe\n- 300D SumRNN + Translate + 3 MLP (1.2 million parameters) - 0.8315 / 0.8235 / 0.8249 (22 epochs)\n- 300D GRU + Translate + 3 MLP (1.7 million parameters) - 0.8431 / 0.8303 / 0.8233 (17 epochs)\n- 300D LSTM + Translate + 3 MLP (1.9 mill... | [
[
"numpy.random.seed",
"numpy.asarray",
"numpy.save",
"numpy.load",
"numpy.array",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lfloretta/tf-estimator-tutorials | [
"2fe38d352d8ab8e5c2c9c729f7aaac7b628c691d"
] | [
"00_Miscellaneous/model_optimisation/optimize_graph.py"
] | [
"# Copyright 2018 Google 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 required by appl... | [
[
"tensorflow.gfile.DeleteRecursively",
"tensorflow.gfile.Exists",
"tensorflow.tools.graph_transforms.TransformGraph",
"tensorflow.estimator.export.build_raw_serving_input_receiver_fn",
"tensorflow.gfile.GFile",
"tensorflow.train.AdamOptimizer",
"tensorflow.estimator.RunConfig",
"ten... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
zdai257/pyroomacoustics | [
"b2a36c5bd9a543113029f4a4208c76faee9cd356"
] | [
"pra_workspace/pra_creator.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.io import wavfile\nfrom scipy.signal import fftconvolve\nimport IPython\nimport pyroomacoustics as pra\nimport os\nfrom os.path import join\nimport librosa\nimport random\nimport pandas as pd\nimport argparse\nimport json\nfrom math import floor, e\ni... | [
[
"numpy.sum",
"numpy.arange",
"numpy.linalg.norm",
"numpy.ndenumerate",
"numpy.array",
"numpy.zeros",
"scipy.io.wavfile.read"
]
] | [
{
"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"... |
MonodetH/ImageSoundTransmitter | [
"117943f2b27e5af4984c4dfe3da5f6ccd7fcca7e"
] | [
"Sandbox/test_Proc.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nfrom Source.Modulation.Digital import FSK\nfrom Source.Modulation.Digital import ASK\nfrom Source.IO.ReadImage import readImageBN\nfrom Source.IO.WriteImage import writeImageBN\nfrom Source.IO.WriteAudio import writeAudio\nfrom Source.IO.ReadAudio import readAud... | [
[
"numpy.asarray",
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
shazaAhmed/DeepFake | [
"d24997f64435f259b969c0a543865dfa70882cc6"
] | [
"preprocessing/generate_folds.py"
] | [
"import argparse\nimport json\nimport os\nimport random\nfrom functools import partial\nfrom multiprocessing.pool import Pool\nfrom pathlib import Path\n\nos.environ[\"MKL_NUM_THREADS\"] = \"1\"\nos.environ[\"NUMEXPR_NUM_THREADS\"] = \"1\"\nos.environ[\"OMP_NUM_THREADS\"] = \"1\"\nimport pandas as pd\n\nfrom tqdm i... | [
[
"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": []
}
] |
open-space-collective/library-mathematics | [
"4b97f97f4aaa87bff848381a3519c6f764461378"
] | [
"bindings/python/test/geometry/d3/objects/test_segment.py"
] | [
"################################################################################################################################################################\n\n# @project Open Space Toolkit ▸ Mathematics\n# @file bindings/python/test/geometry/d3/objects/test_segment.py\n# @author Remy ... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Cryoris/qiskit-ibmq-provider | [
"e718865c0a56a7defed0b983c69c8c2bf08a5be1"
] | [
"test/ibmq/runtime/test_runtime.py"
] | [
"# This code is part of Qiskit.\n#\n# (C) Copyright IBM 2021.\n#\n# This code is licensed under the Apache License, Version 2.0. You may\n# obtain a copy of this license in the LICENSE.txt file in the root directory\n# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.\n#\n# Any modifications or ... | [
[
"numpy.array",
"numpy.sqrt",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
PeterouZh/nni | [
"0a6c234a2ebb2c368d9bbfe2685e14ad12afc6ff"
] | [
"examples/model_compress/BNN_quantizer_cifar10.py"
] | [
"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torchvision import datasets, transforms\nfrom nni.compression.torch import BNNQuantizer\n\n\nclass VGG_Cifar10(nn.Module):\n def __init__(self, num_classes=1000... | [
[
"torch.nn.BatchNorm1d",
"torch.nn.functional.nll_loss",
"torch.manual_seed",
"torch.nn.Conv2d",
"torch.nn.functional.cross_entropy",
"torch.nn.MaxPool2d",
"torch.nn.Linear",
"torch.no_grad",
"torch.cuda.is_available",
"torch.nn.BatchNorm2d",
"torch.nn.Hardtanh"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Mighstye/uqac_IAProject | [
"4b3f84ede95c87f145b9fc091fefe97dd43f9ac7"
] | [
"AIProject/threads/environmentthread.py"
] | [
"import logging\nimport threading\nimport random\nimport AIProject.environment.env as env\nimport time\nfrom AIProject.robot import robot\nimport numpy as np\n\n# The thread for the environment gestion\n\n\n\nclass environmentthread(threading.Thread):\n stopsignal = False # This boolean is used to stop the thre... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
albertobagnacani/Deblur | [
"83c54a18e7ce3dd9165ebe125819c37c3645f4e3"
] | [
"src/dataset/TensorflowDatasetLoader.py"
] | [
"from os import listdir\nfrom os.path import isfile, join\n\nimport tensorflow as tf\nfrom numpy import float32\n\n\ndef select_patch(sharp, blur, patch_size_x, patch_size_y):\n \"\"\"\n Select a patch on both sharp and blur images at the same localization.\n\n :param:\n sharp (tf.Tensor): Tensor fo... | [
[
"tensorflow.stack",
"tensorflow.image.decode_png",
"tensorflow.data.Dataset.from_tensor_slices",
"tensorflow.image.random_crop",
"tensorflow.data.Dataset.zip",
"tensorflow.image.convert_image_dtype",
"tensorflow.io.read_file"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
wangsd01/taichi | [
"fcbe8c9c4a5a0c964be2810adec15166143eb57e"
] | [
"python/taichi/lang/impl.py"
] | [
"import numbers\nfrom types import FunctionType, MethodType\nfrom typing import Iterable\n\nimport numpy as np\nfrom taichi.core.util import ti_core as _ti_core\nfrom taichi.lang._ndarray import ScalarNdarray\nfrom taichi.lang.any_array import AnyArray, AnyArrayAccess\nfrom taichi.lang.exception import InvalidOpera... | [
[
"numpy.iinfo"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Jakob-Bach/DS-Lab-2021 | [
"01cb1f9392c811d195edafdc2f027d455da21787"
] | [
"Task_1_DMC_2021/recommend_cooccurring_favorites.py"
] | [
"\"\"\"Recommend co-occurring favorites\n\nSimple solution script which scans the training data for all transactions containing the evaluation\nitem at-hand and recommnends the most popular co-occurrring items. Defaults to the globally\nmost popular items if the item-at-hand does not occur in the training data. We ... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
jonasrothfuss/f-pacoh-torch | [
"746ef8155659c0060504874e4118a1a4fddf9f30"
] | [
"meta_bo/environment.py"
] | [
"import numpy as np\nimport os\nimport time\nimport json\nimport glob\n\nfrom meta_bo.domain import ContinuousDomain, DiscreteDomain\nfrom meta_bo.solver import EvolutionarySolver\nfrom config import BASE_DIR, DATA_DIR\nfrom typing import Optional, Dict, List, Tuple\n\nfrom functools import cached_property\n\n\ncla... | [
[
"numpy.sqrt",
"numpy.min",
"numpy.reshape",
"numpy.linalg.norm",
"numpy.cos",
"numpy.ones",
"numpy.max",
"numpy.std",
"numpy.mean",
"numpy.prod",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
smart-cowboy/modernTTS | [
"579e63a75aa46960267935168f0d8ccf12ac90f6"
] | [
"train_autoregressive.py"
] | [
"import tensorflow as tf\nimport numpy as np\nfrom tqdm import trange\n\nfrom utils.config_manager import Config\nfrom preprocessing.datasets import TextMelDataset, AutoregressivePreprocessor\nfrom utils.decorators import ignore_exception, time_it\nfrom utils.scheduling import piecewise_linear_schedule, reduction_s... | [
[
"tensorflow.Variable",
"tensorflow.shape",
"tensorflow.reduce_mean",
"numpy.random.seed",
"tensorflow.cast",
"tensorflow.random.set_seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
KrzysiekDD/parachute-simulation | [
"2a8195a41c2760b8c15994273c8690dbb6de2f25"
] | [
"main.py"
] | [
"import tkinter as tk\nimport matplotlib.pyplot as plt\nimport matplotlib\nfrom pandas import DataFrame\nfrom TaylorMethod import TaylorMethod\nfrom matplotlib.backends.backend_tkagg import (\n FigureCanvasTkAgg,\n NavigationToolbar2Tk\n)\n\n\nclass Paratrooper:\n def __init__(self, mass: float, B: float, ... | [
[
"matplotlib.pyplot.Figure",
"matplotlib.use",
"pandas.DataFrame",
"matplotlib.backends.backend_tkagg.NavigationToolbar2Tk",
"matplotlib.backends.backend_tkagg.FigureCanvasTkAgg"
]
] | [
{
"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": []
}
] |
lmcarboneau/PBjam | [
"3c57614b2b79e5113b10aa5bab2d23a9ffe0b279"
] | [
"pbjam/plotting.py"
] | [
"\"\"\" \n\nThis module contains a general set of plotting methods for that are inherited by\nthe different classes of PBjam, so that they can be used to show the status of \neach step that has been performed. \n\n\"\"\"\n\nimport matplotlib.pyplot as plt\nimport astropy.convolution as conv\nimport pbjam, os, corne... | [
[
"pandas.read_csv",
"numpy.arange",
"numpy.median",
"matplotlib.pyplot.subplots",
"numpy.percentile",
"numpy.diff",
"numpy.exp",
"numpy.array",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
psuto/BayesNet4Nephrology | [
"da606b1191b9445fc90308777daaed9a77a6cc99"
] | [
"test/test_jsonOutput2Csv.py"
] | [
"import pytest\nimport jsonOutput2Csv\nimport pandas as pd\n\n\ndef test_add_data():\n colNames = ['a']\n inputDir = r'..\\output\\paramsAndREsults_20-05-08_00-14-30.json'\n df = pd.DataFrame(columns=['bnVersion', 'numTimeSteps', 'orderAutoregression', 'targetNode', 'outcomeID', 'accuracy', 'tp', 'fp', 'fn... | [
[
"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": []
}
] |
Assassinsarms/Deep-Active-Learning-Network-for-Medical-Image-Segmentation | [
"a5d731ce7fec934b6e5a036c3b9709bb1b1707cf"
] | [
"train_val.py"
] | [
"from glob import glob\r\nimport os\r\nimport sys\r\nimport time\r\nfrom collections import OrderedDict\r\nimport logging\r\n\r\nimport numpy as np\r\nfrom sklearn.model_selection import train_test_split\r\n\r\nimport torch\r\nimport torchvision\r\nimport torch.nn as nn\r\nimport torch.optim as optim\r\nimport torc... | [
[
"pandas.Series",
"torch.utils.data.DataLoader",
"sklearn.model_selection.train_test_split",
"pandas.DataFrame",
"torch.cuda.empty_cache",
"torch.no_grad",
"torch.cuda.is_available"
]
] | [
{
"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": []
}
] |
tinix84/pyplecs | [
"6aff204b144bc983325b7c710721334b9eaa487b"
] | [
"pyplecs/pyplecs.py"
] | [
"import time\nimport shutil\nimport os\nimport pywinauto\n\nfrom pathlib import Path\n\nimport xmlrpc.client\n\nimport psutil\nimport subprocess\nimport re\nimport copy\n\nimport scipy.io as sio\n\ncommand = r\"C:/Program Files/Plexim/PLECS 4.2 (64 bit)/plecs.exe\"\ncommand = r\"C:/Program Files/Plexim/PLECS 4.3 (6... | [
[
"scipy.io.loadmat",
"scipy.io.savemat"
]
] | [
{
"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"... |
calmisential/SkeNetch | [
"c014b9d2caa0a3a9809b437c957cea3b73af685d"
] | [
"core/YOLOv3/anchor.py"
] | [
"import torch\n\n\ndef get_anchor(cfg, i, device):\n anchors = cfg[\"Train\"][\"anchor\"]\n anchors = torch.tensor(anchors, dtype=torch.float32, device=device)\n anchors = torch.reshape(anchors, shape=(-1, 2))\n # 归一化\n anchors /= cfg[\"Train\"][\"input_size\"]\n return anchors[3 * i: 3 * (i + 1),... | [
[
"torch.reshape",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ooici/pyon | [
"122c629290d27f32f2f41dafd5c12469295e8acf"
] | [
"prototype/hdf/hdf_codec.py"
] | [
"#!/usr/bin/env python\n\n\n'''\n@package prototype.science_object_codec\n@file prototype/science_object_codec.py\n@author David Stuebe\n@author Swarbhanu Chatterjee\n@brief prototype codec for the science data object\n\nTo run the encoder and decoder, please ensure that numpy and h5py are installed. Also make sure... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wackymaster/UpStat | [
"60a0b3572045e53473f7409d27b305d496a02d94"
] | [
"Matplotlib Graphing XY Scatter.py"
] | [
"import csv\r\nimport matplotlib.pyplot as plt\r\nfrom tkinter import *\r\nimport tkinter.messagebox\r\nfrom tkinter.filedialog import askopenfilename\r\n\r\nsave_png = True\r\n\r\n\r\ndef plot(x, y):\r\n # Get Name of Headers and Remove them to keep Data Fresh\r\n x_header, y_header = x[0], y[0]\r\n x, y ... | [
[
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.show",
"matplotlib.pyplot.title"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zh794390558/lingvo | [
"fb929def2f27cf73a6ee1b1eaa8bee982bd92987",
"ecdf678179018ca07f4f52d065b9bf3fe2dc7c5a"
] | [
"lingvo/tools/wpm_encode_file.py",
"lingvo/core/layers.py"
] | [
"# Lint as: python2, python3\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/... | [
[
"numpy.ones_like",
"numpy.zeros_like"
],
[
"tensorflow.python.tpu.tpu_embedding.TableConfig",
"tensorflow.python.tpu.tpu_embedding.DeviceConfig",
"tensorflow.python.tpu.tpu_embedding.AdagradParameters",
"numpy.min",
"tensorflow.python.ops.inplace_ops.empty",
"tensorflow.python.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.8",
"1.10",
"1.12",
"2.7",
"2.6",
"1.13",
"2.3",
"2.4",
"2.... |
majumderb/pabst | [
"cbf4dbbc8f38c91023ebc21569077ed5a00ccfc4"
] | [
"pabst/comac_compatibility.py"
] | [
"from transformers import (GPT2LMHeadModel, GPT2Tokenizer, WEIGHTS_NAME, CONFIG_NAME)\nfrom models.reinforce_model.data import PADDED_INPUTS, ATTR_TO_SPECIAL_TOKEN\nfrom models.reinforce_model.model_with_inferencenw import LatentVariableInferenceModel\nimport torch\nimport os \n\ntraining_args_path = '/data3/bodhi/... | [
[
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ugrky/WhenWor-l-dCollide | [
"fcebfe08c6a9d5db72f43688cd9c00d9a8fc70e3"
] | [
"Emotion.py"
] | [
"import numpy as np\n\nclass Emotion:\n def __init__(self):\n self.angry = np.array([2, 3, 4, 5])\n self.fear = np.array([2, 3, 4, 5])\n self.happy = np.array([2, 3, 4, 5])\n self.sad = np.array([2, 3, 4, 5])\n self.surprise = np.array([2, 3, 4, 5])\n self.neutral = np.a... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AltmanD/deep-q-learning | [
"7416ef986377856dd542f0598529082ae7d444d9"
] | [
"learner.py"
] | [
"import zmq\nimport numpy as np\nimport tensorflow as tf\nimport horovod.tensorflow.keras as hvd\nfrom data_pb2 import Data\nfrom env_wrappers import make_env\nfrom DQNagent import Learner\nfrom tensorflow.keras.optimizers import RMSprop\nfrom tensorflow.keras import backend as K\n\n\nif __name__ == '__main__':\n ... | [
[
"tensorflow.ConfigProto",
"numpy.array",
"tensorflow.Session"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
fredshone/Mode-Share-Anomoly-Detection | [
"1a319624346d1754a6c425a61f13ee67dc80e0e0"
] | [
"python/hist/multiple_regression_python.py"
] | [
"# This program performs a multiple linear regression from data stored in a csv file.\n\nimport matplotlib.pyplot as plt\nimport statsmodels.formula.api as smf\nimport pandas as pd\n\ndata_filename = 'coursework_1_data_2017_v2_input.csv'\n\nall_data = pd.read_csv(data_filename, index_col = 'local_authority_area')\n... | [
[
"pandas.concat",
"pandas.read_csv",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
ixtiyoruz/ssd-tf2 | [
"01a4ad8ff504128fbae08ea66396172478bd1f79"
] | [
"anchor.py"
] | [
"import itertools\nimport math\nimport tensorflow as tf\nimport numpy as np\nfrom collections import namedtuple\ndef generate_default_boxes(config):\n \"\"\" Generate default boxes for all feature maps\n\n Args:\n config: information of feature maps\n scales: boxes' size relative to image's ... | [
[
"tensorflow.clip_by_value",
"numpy.expand_dims",
"tensorflow.constant",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.8",
"1.10",
"1.12",
"2.7",
"2.6",
"1.4",
"2.3",
"2.4",
"2.9",
"1.5",
"1.7",
"2.5",
"0.12",
"1.0",
"2.2",
"1.2",
"2.... |
AdoNunes/mne-python | [
"4ec3e165285aa5e64b40eaf219415fbc8cef8ee1"
] | [
"mne/preprocessing/flat.py"
] | [
"# Authors: Eric Larson <larson.eric.d@gmail.com>\n#\n# License: BSD (3-clause)\n\nimport numpy as np\n\nfrom ..annotations import _annotations_starts_stops\nfrom ..io import BaseRaw\nfrom ..io.pick import _picks_to_idx\nfrom ..utils import (_validate_type, verbose, logger, _pl,\n _mask_to_onset... | [
[
"numpy.arange",
"numpy.round",
"numpy.concatenate",
"numpy.diff",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zhangyewu/edward | [
"8ec452eb0a3801df8bda984796034a9e945faec7"
] | [
"examples/pp_persistent_randomness.py"
] | [
"\"\"\"Persistent randomness.\n\nOur language defines random variables. They enable memoization in the\nsense that the generative process of any values which depend on the\nsame random variable will be generated conditioned on the same samples.\nSimulating the world multiple times (i.e., fetching the value out of\n... | [
[
"tensorflow.ones",
"tensorflow.app.run"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
ShenDezhou/GCN | [
"6ec2c741a8d7a9c9bd18e64a99cb48c4c0ac5037"
] | [
"B100-E10000-K8-MSGN-GRU-MC.py"
] | [
"import codecs\nimport os\nimport string\n\nimport numpy\nfrom keras import regularizers\nfrom keras.layers import Dense, Embedding, LSTM, CuDNNLSTM, SpatialDropout1D, Input, Bidirectional, Dropout, \\\n BatchNormalization, Lambda, concatenate, Flatten, Conv1D, CuDNNGRU, MaxPooling1D\nfrom keras.models import Mo... | [
[
"numpy.logspace",
"numpy.array",
"scipy.sparse.load_npz",
"numpy.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.10",
"1.4",
"1.9",
"0.19",
"1.5",
"1.2",
"1.7",
"1.0",
"1.3",
"1.8"
],
"tensorflow": []
}
] |
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