repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
jhuapl-boss/cloud-volume | [
"bde393b48031dd14339a841ac15ba5acc9981ef3"
] | [
"cloudvolume/frontends/graphene.py"
] | [
"from collections import defaultdict\nfrom datetime import datetime\nimport math\nimport orjson\nimport os\nimport pickle\nimport posixpath\nimport re\nimport requests\nimport sys\n\nimport dateutil.parser\nimport fastremap\nimport numpy as np\n\nfrom .. import compression\nfrom .. import exceptions\nfrom ..cachese... | [
[
"numpy.issubdtype",
"numpy.all",
"numpy.frombuffer",
"numpy.iinfo",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yuchen-x/CoRL2019 | [
"d482a90441bc8eb0461f1f22fbd65d96584f6914"
] | [
"test/test_osd_s_policy.py"
] | [
"import argparse\nimport numpy as np\nimport torch\nimport os\nimport sys\nimport IPython\nimport logging\n\nsys.path.append(\"..\")\nimport time\nimport IPython\n\nfrom rlmamr.my_env.osd_ma_single_room import ObjSearchDelivery_v4 as OSD_S_4\nfrom rlmamr.MA_cen_condi_ddrqn.utils.utils import Linear_Decay, get_condi... | [
[
"torch.load",
"torch.cat",
"torch.from_numpy",
"torch.tensor",
"torch.no_grad",
"numpy.unravel_index",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dengdan/FastMaskRCNN | [
"ebd54a5b61da06c3b2b65acd74383a5b4b1e6730"
] | [
"test/layer_test.py"
] | [
"#!/usr/bin/env python \n# coding=utf-8\n\nimport numpy as np\nimport sys\nimport os\nimport tensorflow as tf \n\nsys.path.append(os.path.join(os.path.dirname(__file__), '..'))\nfrom libs.boxes.roi import roi_cropping\nfrom libs.layers import anchor_encoder\nfrom libs.layers import anchor_decoder\nfrom libs.layers ... | [
[
"numpy.hstack",
"tensorflow.constant",
"numpy.asarray",
"numpy.set_printoptions",
"tensorflow.reshape",
"numpy.random.rand",
"tensorflow.Session",
"numpy.zeros",
"numpy.vstack",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
yarray/hpdbscan | [
"c01f7a2657e91365fb846bbb492d5b66434160e8"
] | [
"benchmarks/benchmark.py"
] | [
"#!/usr/bin/env python\n\nimport numpy as np\nimport subprocess\nimport sys\nimport time\n\nDATASET_PARAMETERS = {# eps, min_points\n #'bremen_small.h5': (100, 312),\n 'iris.h5': (0.32, 3),\n #'twitter_small.h5': (0.01, 40),\n}\nTRIALS = 10\n\ndef run_benchmark(command, log_path):\n sys_std... | [
[
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jlrzarcor/ITAM-dpa2021 | [
"17dc9cd53716d8dbd704e8907ac6422bc1ad2b49",
"17dc9cd53716d8dbd704e8907ac6422bc1ad2b49",
"17dc9cd53716d8dbd704e8907ac6422bc1ad2b49"
] | [
"src/test/test_almacenamiento.py",
"src/pipeline/task_monitoreo_modelo.py",
"src/test/test_limpieza.py"
] | [
"# ================================= LIBRARIES ================================= #\n\n# importing packages\nimport pandas as pd\nimport marbles.core\nimport pickle\nimport json\nimport boto3\nfrom datetime import datetime\nimport os\n\n# importing especific functions\nfrom luigi.contrib.s3 import S3Target\n\n# imp... | [
[
"pandas.read_csv",
"pandas.DataFrame"
],
[
"pandas.io.sql.execute",
"pandas.concat",
"pandas.DataFrame",
"pandas.io.sql.read_sql_query"
],
[
"pandas.read_csv",
"pandas.io.sql.read_sql_query"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
... |
JennyLynnFletcher/RL_Environment_Design | [
"dc42e668581c3a3901732230eb3561efd72a64a6"
] | [
"Data_Analysis/gen_gradient_key.py"
] | [
"from colour import Color\nimport matplotlib.pyplot as plt\nfrom matplotlib import rc\n\nrc('font', **{'family': 'serif', 'serif': ['Computer Modern'], 'serif': 'cm'})\nrc('text', usetex=True)\n\ngradient_0 = list(Color(\"green\").range_to(Color(\"blue\"),500))\n\nfig, ax = plt.subplots()\nax.spines['top'].set_visi... | [
[
"matplotlib.pyplot.gca",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.bar",
"matplotlib.pyplot.xlabel",
"matplotlib.rc"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bubbliiiing/unet-tf2 | [
"059d4723472b451e5633727492bf6712f3107ac3"
] | [
"nets/resnet50.py"
] | [
"from tensorflow.keras import layers\r\nfrom tensorflow.keras.layers import (Activation, BatchNormalization, Conv2D,\r\n MaxPooling2D, ZeroPadding2D)\r\n\r\n\r\ndef identity_block(input_tensor, kernel_size, filters, stage, block):\r\n\r\n filters1, filters2, filters3 = filters... | [
[
"tensorflow.keras.layers.Activation",
"tensorflow.keras.layers.Conv2D",
"tensorflow.keras.layers.ZeroPadding2D",
"tensorflow.keras.layers.BatchNormalization",
"tensorflow.keras.layers.add",
"tensorflow.keras.layers.MaxPooling2D"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
svyhlidka/Python-Projects | [
"7c297bf7248a1e61699a69d62eb83f15cf90d352"
] | [
"3D/Scripts/tests.py"
] | [
"from django.test import TestCase\n\nfrom calcFence import *\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as patches #polygon draw\n\nframesList = [[2.93,.99]]\n# [2.80,1.71],[2.81,0.90],[2.81,0.90],\n# [2.81,0.90],[2.60,1.71],[2.81,1.71]\n# ]\nframe_length = ... | [
[
"matplotlib.pyplot.title",
"numpy.arange",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.show",
"matplotlib.patches.Polygon",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ClimateMind/climatemind-nlp | [
"66c0b7819d3fd488a6e08489c29f372cd3ad61d6"
] | [
"src/utils/diversity_analysis.py"
] | [
"#convert data to longform. only take 'change_direction', 'type_of', 'base', 'aspect_changing', 'text'\n#for each entity that has multiples, expand the concept into multiple rows with each possible permutation (uses Cartesian product)\n\nimport pandas as pd\nfrom itertools import product\nimport spacy\nimport srsly... | [
[
"pandas.read_csv",
"pandas.Series",
"pandas.DataFrame",
"pandas.melt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
nims-dpfc/M-DaC_XPS | [
"49057887c48874f6ec9bcd86175e78ebbd6292c7"
] | [
"PHI_XPS_survey_narrow_tools/csv2graph.py"
] | [
"#-------------------------------------------------\n# csv2graph.py\n#\n# Copyright (c) 2018, Data PlatForm Center, NIMS\n#\n# This software is released under the MIT License.\n#-------------------------------------------------\n# coding: utf-8\n\n__package__ = \"M-DaC_XPS/PHI_XPS_survey_narrow_tools\"\n__version__... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.gca",
"pandas.read_csv",
"matplotlib.pyplot.title",
"matplotlib.font_manager.FontProperties",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.ticklabel_format",
"matplotli... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
KrissLin/tensorflow-triplet-loss | [
"cf39a6ec70ae7a297feef62b6cd89f86e72f5453"
] | [
"train.py"
] | [
"\"\"\"Train the model\"\"\"\n\nimport argparse\nimport os\n\nimport tensorflow as tf\n\nfrom model.input_fn import train_input_fn\nfrom model.input_fn import test_input_fn\nfrom model.model_fn import model_fn\nfrom model.utils import Params\nfrom tensorflow.python import debug as tf_debug\n\nparser = argparse.Argu... | [
[
"tensorflow.estimator.Estimator",
"tensorflow.reset_default_graph",
"tensorflow.logging.info",
"tensorflow.logging.set_verbosity",
"tensorflow.estimator.RunConfig",
"tensorflow.python.debug.LocalCLIDebugHook"
]
] | [
{
"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... |
eduardofv/ComplexSystems.jl | [
"9d18df59c9b0862a03cf6644ace691fd8f43d94b"
] | [
"covid_analysis.py"
] | [
"import os\nimport datetime as dt\nimport numpy as np\nimport pandas as pd\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n#from scipy.interpolate import make_interp_spline, BSpline\n\n#get_ipython().run_line_magic('matplotlib', 'inline')\n#sns.set(style=\"whitegrid\")\nFIGSIZE = [8, 5... | [
[
"matplotlib.pyplot.legend",
"pandas.to_datetime",
"pandas.read_csv",
"pandas.Series",
"matplotlib.pyplot.subplots",
"pandas.DataFrame",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.xticks",
"matplotlib.pyplot.ylab... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
AhabbscienceStudioPak/moviepy | [
"ef55d435eaafca04d76526d7ac6ce385b4ff6710"
] | [
"moviepy/video/io/ffmpeg_reader.py"
] | [
"\"\"\"\nThis module implements all the functions to read a video or a picture\nusing ffmpeg. It is quite ugly, as there are many pitfalls to avoid\n\"\"\"\n\nfrom __future__ import division\n\nimport logging\nimport os\nimport re\nimport subprocess as sp\nimport warnings\n\nimport numpy as np\n\nfrom moviepy.confi... | [
[
"numpy.frombuffer",
"numpy.fromstring"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
salmedina/hmr | [
"ad4a272712078edb0abe4e19dde1b6b4ced7d7f1"
] | [
"src/tf_smpl/batch_lbs.py"
] | [
"\"\"\" Util functions for SMPL\n@@batch_skew\n@@batch_rodrigues\n@@batch_lrotmin\n@@batch_global_rigid_transformation\n\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport tensorflow as tf\n\n\ndef batch_skew(vec, batch_size=None):\n ... | [
[
"tensorflow.matmul",
"tensorflow.sin",
"tensorflow.constant",
"tensorflow.cos",
"tensorflow.norm",
"tensorflow.concat",
"tensorflow.zeros",
"tensorflow.stack",
"tensorflow.range",
"tensorflow.reshape",
"tensorflow.scatter_nd",
"tensorflow.expand_dims",
"tensorfl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
decabyte/saetta_energy | [
"b425ff88ba14cea0b70d57e732c2e90bead6d89f"
] | [
"src/node_battery.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nfrom __future__ import division\n\nimport numpy as np\nnp.set_printoptions(precision=3, suppress=True)\n\nimport control\n\n# ros imports\nimport rospy\nimport roslib\nroslib.load_manifest('saetta_energy')\n\nfrom std_srvs.srv import Empty\nfrom saetta_energy.msg imp... | [
[
"numpy.dot",
"numpy.clip",
"numpy.set_printoptions",
"numpy.copy",
"numpy.array",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
neurodata/graphbook-code | [
"acd56347f568c3f0ac53e925fa886e8890ca1d83"
] | [
"graphbook_code/sbm.py"
] | [
"# Copyright (c) Microsoft Corporation and contributors.\n# Licensed under the MIT License.\n\nfrom typing import Any, Collection, Optional\n\nimport numpy as np\nfrom sklearn.utils import check_X_y\n\nfrom typing import Dict, List, Set, Tuple\n\nfrom graspologic.cluster import GaussianCluster\nfrom graspologic.emb... | [
[
"scipy.stats.f_oneway",
"sklearn.utils.check_X_y",
"scipy.stats.chi2_contingency",
"numpy.unique",
"scipy.stats.kruskal",
"numpy.concatenate",
"numpy.outer",
"scipy.stats.chi2.sf"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
chunduri11/ar1-pytorch | [
"813f9778fe9a68cd38fca0c352e43f45e450a7ef"
] | [
"utils.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n################################################################################\n# Copyright (c) 2020. Vincenzo Lomonaco, Gabriele Graffieti, Lorenzo #\n# Pellegrini, Davide Maltoni. All rights reserved. #\n# See the accompany... | [
[
"torch.max",
"numpy.sqrt",
"torch.zeros",
"numpy.asarray",
"numpy.concatenate",
"numpy.max",
"torch.no_grad",
"torch.FloatTensor",
"torch.cuda.is_available",
"numpy.square",
"torch.from_numpy",
"torch.tensor",
"numpy.random.set_state",
"torch.dot",
"torc... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rafaol/bore | [
"b598c9711b00ae9bd0176a1048bc0c472986f692"
] | [
"bore/base.py"
] | [
"import tensorflow as tf\n\nfrom scipy.stats import truncnorm\nfrom .decorators import unbatch, value_and_gradient, numpy_io, squeeze\n\n\ndef convert(model, transform=tf.identity):\n \"\"\"\n Given a Keras model, builds a callable that takes a single array as input\n (rather than a batch of Tensors) and r... | [
[
"scipy.stats.truncnorm"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
deklanw/transformers | [
"38580455dea435acd4a261e788d237d3421d65b2",
"8e908c8c74f556a82534f4cf1e7a1b4f7b55d24c"
] | [
"src/transformers/models/bart/modeling_bart.py",
"examples/pytorch/summarization/run_summarization_no_trainer.py"
] | [
"# coding=utf-8\n# Copyright 2021 The Fairseq Authors and The HuggingFace Inc. team. 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... | [
[
"torch.nn.functional.softmax",
"torch.nn.Dropout",
"torch.nn.CrossEntropyLoss",
"torch.isinf",
"torch.isnan",
"torch.nn.functional.dropout",
"torch.zeros",
"torch.cat",
"torch.nn.Embedding",
"torch.nn.LayerNorm",
"torch.tanh",
"torch.nn.Linear",
"torch.tensor",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DimianZhan/flight_review | [
"c6c40531a9dac1ed94bf597f68b340c7fa0962e7"
] | [
"plot_app/helper.py"
] | [
"\"\"\" some helper methods that don't fit in elsewhere \"\"\"\nimport json\nfrom timeit import default_timer as timer\nimport time\nimport re\nimport os\nimport traceback\nimport sys\nfrom functools import lru_cache\nfrom urllib.request import urlretrieve\nimport xml.etree.ElementTree # airframe parsing\nimport sh... | [
[
"numpy.abs",
"numpy.set_printoptions",
"numpy.arccos",
"numpy.cos",
"numpy.sin",
"numpy.finfo",
"numpy.copy"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
caifederated/mlhead-release | [
"703fe2294f210b7259cd1404632d7757766f5a7d",
"703fe2294f210b7259cd1404632d7757766f5a7d"
] | [
"all_baselines/fed-dane/flearn/models/nist/mclr.py",
"all_baselines/fed-dane/flearn/optimizer/pggd.py"
] | [
"import numpy as np\nimport tensorflow as tf\nfrom tqdm import trange\n\nfrom flearn.utils.model_utils import batch_data, batch_data_multiple_iters\nfrom flearn.utils.tf_utils import graph_size\nfrom flearn.utils.tf_utils import process_grad\n\nfrom flearn.utils.stat_utils import get_f1\n\n\nclass Model(object):\n ... | [
[
"tensorflow.Graph",
"tensorflow.nn.softmax",
"tensorflow.losses.sparse_softmax_cross_entropy",
"tensorflow.RunMetadata",
"tensorflow.equal",
"tensorflow.argmax",
"tensorflow.placeholder",
"tensorflow.train.get_global_step",
"tensorflow.trainable_variables",
"tensorflow.glob... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
... |
rossumai/tensorpack | [
"5854c7de8c2c9192446be6cceeec51ed6439c23c"
] | [
"examples/FasterRCNN/coco.py"
] | [
"# -*- coding: utf-8 -*-\n# File: coco.py\n\nimport numpy as np\nimport os\nfrom termcolor import colored\nfrom tabulate import tabulate\n\nfrom tensorpack.utils import logger\nfrom tensorpack.utils.rect import FloatBox\nfrom tensorpack.utils.timer import timed_operation\nfrom tensorpack.utils.argtools import log_o... | [
[
"numpy.asarray",
"numpy.arange",
"numpy.histogram",
"numpy.where",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mikeanthony321/PacMan_Learner | [
"7eda0150e581870e66223f9c078087e3b0efd2c0"
] | [
"game/agent.py"
] | [
"import threading\nimport settings as s\nfrom collections import namedtuple, deque\n\nfrom api.actions import Actions\nfrom analytics_frame import Analytics\nfrom api.agent_analytics_frame import AgentAnalyticsFrameAPI\nimport random\nimport math\nimport copy\n\nfrom sklearn.preprocessing import MinMaxScaler\nimpor... | [
[
"torch.Tensor",
"torch.autograd.set_detect_anomaly",
"torch.cat",
"torch.zeros",
"torch.tensor",
"torch.nn.Tanh",
"torch.nn.Linear",
"torch.no_grad",
"torch.device",
"numpy.array",
"sklearn.preprocessing.MinMaxScaler"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
softwaresaved/UK-carpentry-workshops-extractor | [
"63629f821e7c62a1425618c66273c73b3c2efc2f"
] | [
"lib/helper.py"
] | [
"import pandas as pd\nimport numpy as np\nimport os\nimport argparse\nimport json\nimport datetime\nimport re\nimport folium\nfrom folium.plugins import MarkerCluster\nfrom folium.plugins import HeatMap\nfrom shapely.geometry import shape, Point\nimport traceback\nimport getpass\nimport tldextract\n\nCURRENT_DIR = ... | [
[
"pandas.notna",
"pandas.concat",
"pandas.read_csv",
"pandas.to_datetime",
"pandas.DataFrame",
"pandas.ExcelWriter",
"pandas.isna"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
RoryDH/ML1819--task-107--team-47 | [
"9a111586f2217186a8a3972be8973fa78dad3757",
"9a111586f2217186a8a3972be8973fa78dad3757"
] | [
"phoneme_experiment/classifier_phoneme.py",
"Part2/FinalClassifier.py"
] | [
"import tensorflow as tf\r\nimport numpy as np\r\nimport json, unidecode\r\nfrom tensorflow import keras\r\n\r\nTESTING_GENDS = 4\r\nGENDER_MAP = {\r\n \"male\": 0,\r\n \"female\": 1,\r\n \"brand\": 2,\r\n \"unknown\": 3\r\n}\r\n\r\n\r\ndef generate_feature(user):\r\n feats = []\r\n\r\n # insert r... | [
[
"numpy.split",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.Sequential",
"tensorflow.keras.layers.InputLayer",
"tensorflow.train.AdamOptimizer",
"tensorflow.keras.layers.Dropout",
"numpy.array"
],
[
"numpy.split",
"tensorflow.keras.layers.Dense",
"tensorflow.keras... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
... |
Rajasvi/Adverse-Food-Events-Analysis | [
"8fb87cfaa4c55eaae56325e516623ad8661d7fb8"
] | [
"src/visualization/visualize.py"
] | [
"import plotly.express as px\nfrom collections import defaultdict\nfrom plotly.subplots import make_subplots\nimport plotly.graph_objects as go\nimport plotly.figure_factory as ff\nimport pandas as pd\nimport numpy as np\n\n\ndef brands_vs_outcomes_plot(\n baseDf,\n category,\n title,\n relv_outcomes=[\... | [
[
"pandas.isnull",
"numpy.full"
]
] | [
{
"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": []
}
] |
yangyxt/QBiC-Pred | [
"fc4a7ca042fc89ee9b726dc99c981be11030e29c"
] | [
"website/config.py"
] | [
"import configparser\nimport importlib.util\nimport pandas as pd\n\ndef import_from_file(filepath):\n # https://stackoverflow.com/questions/67631/how-to-import-a-module-given-the-full-path\n spec = importlib.util.spec_from_file_location(\"\", filepath)\n foo = importlib.util.module_from_spec(spec)\n spe... | [
[
"pandas.read_csv",
"pandas.Series"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
Herrccc/DR-TANet | [
"37cc3929833d61451b2fa4a92ccd4286cfc4fd34",
"4edd6401b59e842f23a040535969192fdd943ac0"
] | [
"eval.py",
"util.py"
] | [
"import datasets\nfrom TANet import TANet\nimport os\nimport csv\nimport cv2\nimport torch\nimport torch.nn as nn\nimport numpy as np\nfrom os.path import join as pjoin\nfrom tqdm import tqdm\nimport torch.nn.functional as F\nimport argparse\n\nclass Evaluate:\n\n def __init__(self):\n self.args = None\n ... | [
[
"torch.nn.functional.softmax",
"torch.load",
"numpy.dstack",
"numpy.all",
"numpy.concatenate",
"torch.nn.DataParallel",
"numpy.array",
"numpy.zeros",
"numpy.where"
],
[
"torch.nn.ReLU",
"torch.nn.Conv2d",
"torch.nn.functional.interpolate",
"torch.nn.BatchNor... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cribbslab/mclumi | [
"3c38f1dd4de11f0f20121a4027f7e104c4059bbe"
] | [
"mclumi/deduplicate/monomer/DedupGene.py"
] | [
"__version__ = \"v1.0\"\n__copyright__ = \"Copyright 2021\"\n__license__ = \"MIT\"\n__lab__ = \"Adam Cribbs lab\"\n\nimport os\nimport sys\nimport time\nimport argparse\nimport numpy as np\nimport pandas as pd\nfrom mclumi.align.Read import read as aliread\nfrom mclumi.align.Write import write as aliwrite\nfrom mcl... | [
[
"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": []
}
] |
mstim/ms_deisotope | [
"29f4f466e92e66b65a2d21eca714aa627caa21db"
] | [
"ms_deisotope/deconvolution/utils.py"
] | [
"# -*- coding: utf-8 -*-\n\nimport logging\n\nimport numpy as np\n\nfrom ms_peak_picker import (\n FittedPeak, PeakSet, PeakIndex, simple_peak, is_peak)\n\nfrom ms_deisotope.averagine import (isotopic_shift, neutral_mass)\nfrom ms_deisotope.peak_set import DeconvolutedPeak, Envelope\nfrom ms_deisotope.constants ... | [
[
"numpy.where",
"numpy.array",
"numpy.zeros",
"numpy.any"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
chuckcho/youtube-8m-axon | [
"cfaf364824ac91478f9da879d991958cf0f15288"
] | [
"eval.py"
] | [
"# Copyright 2016 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 app... | [
[
"tensorflow.cast",
"tensorflow.global_variables",
"tensorflow.get_default_graph",
"tensorflow.Graph",
"tensorflow.Variable",
"tensorflow.get_collection",
"tensorflow.logging.set_verbosity",
"tensorflow.name_scope",
"tensorflow.Session",
"tensorflow.app.run",
"tensorflow... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
eonu/tempora | [
"b857c72737532983ebe7403d627fc632cbc06dde"
] | [
"lib/sequentia/classifiers/hmm/hmm_classifier.py"
] | [
"import tqdm, tqdm.auto, numpy as np, pickle\nfrom joblib import Parallel, delayed\nfrom multiprocessing import cpu_count\nfrom .gmmhmm import GMMHMM\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.preprocessing import LabelEncoder\nfrom ...internals import _Validator\n\nclass HMMClassifier:\n \"\"\"... | [
[
"numpy.diag",
"numpy.log",
"numpy.concatenate",
"sklearn.preprocessing.LabelEncoder",
"numpy.array",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xynazog/mltk-algo-contrib | [
"c6c99ce686aa549b58aae8e5c5da9cd78e10b97a"
] | [
"src/bin/algos_contrib/TFBinary.py"
] | [
"#!/usr/bin/env python\n'''\nCopy of existing TFIDF algo but with 2 boolean options added and 3 options set\nso that binary output is achieved.\n'''\n\nfrom sklearn.feature_extraction.text import TfidfVectorizer as _TfidfVectorizer\n\nfrom base import BaseAlgo\nfrom codec import codecs_manager\nfrom util import df_... | [
[
"sklearn.feature_extraction.text.TfidfVectorizer"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MartinSandeCosta/qutip | [
"308624c12a9c9c629a80e6233b864e3efa3eb784",
"308624c12a9c9c629a80e6233b864e3efa3eb784"
] | [
"qutip/wigner.py",
"qutip/qip/device/cavityqed.py"
] | [
"# This file is part of QuTiP: Quantum Toolbox in Python.\n#\n# Copyright (c) 2011 and later, Paul D. Nation and Robert J. Johansson.\n# All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions are... | [
[
"numpy.diag",
"numpy.dot",
"numpy.sqrt",
"numpy.linspace",
"numpy.asarray",
"scipy.special.genlaguerre",
"numpy.kron",
"numpy.concatenate",
"scipy.fftpack.fft",
"numpy.zeros_like",
"numpy.exp",
"numpy.where",
"numpy.conjugate",
"numpy.ones_like",
"numpy.... | [
{
"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"... |
dereklarson/ARC_study | [
"ba299e82d162ee317caa14739faa96989ea0cf31"
] | [
"tests/test_grid_methods.py"
] | [
"import numpy as np\nimport pytest\nfrom arc.definitions import Constants as cst\nfrom arc.grid_methods import (\n connect,\n get_boundary,\n gridify,\n mirror_order,\n norm_points,\n rotational_order,\n translational_order,\n)\nfrom arc.types import BoardData, Grid\n\n\n@pytest.fixture(scope=\... | [
[
"numpy.concatenate",
"numpy.rot90"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
denfed/ACDriver | [
"b70a0d3677df2a797a1dd377af4aca6b1837a114"
] | [
"v1/Utilities/kerascnn.py"
] | [
"import keras\nimport numpy as np\nfrom keras.models import Sequential\nfrom keras.layers import Conv2D, MaxPooling2D\nfrom keras.layers import Activation, Dropout, Flatten, Dense\nfrom keras import backend as K\nfrom keras.models import model_from_json\n\nbatch_size = 25\nepochs = 5\nn = 1000\nen = n - 50\n\nK.set... | [
[
"numpy.load",
"numpy.append",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Tijuk/AnaliseDeAlgoritmos | [
"1dc7b6c2b1006b21ffa5c4e3fa5d26b31b14360c"
] | [
"T2/factory.py"
] | [
"import numpy as np\nfrom nodes import *\nfrom debug import log\nimport math\n\n__emptyNode__ = -1\n\ndef resolveIndex(i,j):\n return j + 3*i;\n\nclass GraphFactory:\n def __init__(self):\n pass\n\n def create3x3_matrix(self, size = 3):\n ret = np.empty((size,size))\n\n counter = 0\n ... | [
[
"numpy.array",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
duebukua/safekit | [
"fe9c48c0cf26e3e71da9a679e333bcb23e40b5f7"
] | [
"safekit/models/lstm_agg.py"
] | [
"\"\"\"\n\n\"\"\"\nimport json\nimport os\nimport sys\n# TODO: Comment for usage.\n# TODO: Test this on DS data\n# TODO: skipheader error message that is informative\n# TODO: Make consistent output printing and writing to file for aggregate and baseline models.\n# TODO: Comment crazy transform functions\n\n# So we... | [
[
"tensorflow.concat",
"tensorflow.unstack",
"numpy.random.seed",
"tensorflow.reduce_mean",
"tensorflow.reduce_sum",
"tensorflow.placeholder",
"tensorflow.variable_scope",
"tensorflow.set_random_seed",
"numpy.array",
"tensorflow.nn.embedding_lookup",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
JosefinaMedina/Deep-Learning-2021-P2 | [
"b26927c470b21481eab70b8710dbade46c63070e"
] | [
"Punto4.py"
] | [
"import os \r\nimport pandas as pd\r\nfrom sklearn import preprocessing\r\nimport RN_Perceptron as rn\r\nimport chardet\r\nimport numpy as np\r\n\r\nos.chdir('../02_Perceptron/')\r\nnomArch='zoo.csv'\r\n\r\n#-- detectando la codificacion de caracteres usada----\r\nwith open(nomArch, 'rb') as f:\r\n result = char... | [
[
"pandas.read_csv",
"numpy.mean",
"numpy.argsort",
"numpy.array",
"numpy.zeros",
"numpy.sum",
"sklearn.preprocessing.MinMaxScaler"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
codecheckers/causality-review | [
"7db86588b6f77b26298f81074f0be02ec136e7f7"
] | [
"causality-review-code/causality_indices.py"
] | [
"##############################################################\n##\nimport numpy as np\nfrom scipy.special import digamma\nfrom sklearn.neighbors import KDTree\nimport pandas as pd\nimport statsmodels.api as sm\nfrom scipy.spatial.distance import cdist\nfrom scipy.linalg import norm\nfrom scipy.optimize import cur... | [
[
"numpy.amax",
"numpy.expand_dims",
"sklearn.cluster.KMeans",
"sklearn.neighbors.KDTree",
"numpy.max",
"numpy.mean",
"numpy.nanmean",
"numpy.exp",
"numpy.where",
"scipy.optimize.curve_fit",
"numpy.square",
"numpy.hstack",
"numpy.unique",
"numpy.arange",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.12",
"0.14",
"0.15"
],
"tensorflow": []
}
] |
sngjuk/Meme-Glossary | [
"b44081ed165c2beedc11be4ff658e2966f6039b7"
] | [
"server/server.py"
] | [
"\"\"\"\nServe 'dank', 'random' request from MgClient with ZMQ.\n\"\"\"\nimport os\nimport faiss\nimport numpy as np\nimport random\nimport re\nimport base64\nimport json\nimport threading\nimport zmq\nimport pickle\nfrom collections import OrderedDict\nfrom lxml import objectify\nfrom datetime import datetime\nfro... | [
[
"numpy.any"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
malikcaukiel/Ad_Clicks | [
"5af1cab16f71dcc3577e1fdbd633c2ea92ad70c5"
] | [
"04.2b) ad_clicks (Comparison between two ads) (uses ad_clicks.csv).py"
] | [
"import pandas as pd\r\n\r\n# A company wants to perform an A/B test.\r\n# They have two different versions of an ad, which they have placed in emails, as well as in banner ads on Facebook, Twitter, and Google.\r\n# They want to know how the two ads are performing on each of the different platforms on each day of t... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
MattiasDC/mercs | [
"466962e254c4f56f4a16a31b1a3d7bd893c8e23e"
] | [
"src/mercs/algo/imputation.py"
] | [
"from sklearn.impute import SimpleImputer\nimport numpy as np\n\n\ndef nan_imputation(X, nominal_attributes):\n # Init\n n_rows, n_cols = X.shape\n i_list = []\n\n # Make imputers\n for c in range(n_cols):\n i_config = dict(missing_values=np.nan, strategy=\"constant\", fill_value=np.nan)\n\n ... | [
[
"sklearn.impute.SimpleImputer"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
IEWbgfnYDwHRoRRSKtkdyMDUzgdwuBYgDKtDJWd/plotbitrate | [
"1c1249792c40a79348356b08e1fc81b64050755d"
] | [
"plotbitrate.py"
] | [
"#!/usr/bin/env python3\n#\n# FFProbe Bitrate Graph\n#\n# Original work Copyright (c) 2013-2020, Eric Work\n# Modified work Copyright (c) 2019-2020, Steve Schmidt\n# All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the followi... | [
[
"matplotlib.pyplot.gca",
"matplotlib.pyplot.axhline",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.title",
"matplotlib.figure.Figure",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.gcf",
"matplotlib.pyplot.plot... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DeuroIO/Deuro-horovod | [
"e75b93ef532b237ad96b29dce9042557cd6919cf"
] | [
"examples/pytorch_mnist.py"
] | [
"from __future__ import print_function\nimport argparse\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torchvision import datasets, transforms\nimport torch.utils.data.distributed\nimport horovod.torch as hvd\n\n# Training settings\nparser = argparse.ArgumentParser(descri... | [
[
"torch.nn.Dropout2d",
"torch.nn.functional.log_softmax",
"torch.nn.functional.dropout",
"torch.nn.functional.nll_loss",
"torch.nn.Conv2d",
"torch.nn.Linear"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gdsa-upc/2017-EZficy | [
"4b44dc23ccfca44f66726664cf15b189ea9fcd52"
] | [
"sessio3/resize_nocut.py"
] | [
"# Reads an image from disk and scales and crops to match a target resolution and aspect ratio.\nimport os\nfrom scipy import misc\n\n# Specifies which is the largest size on any side of the picture. (Caters for portrait and landscape)\nFIXED_MAX_DIMENSION = 500.0\n\n# For each allocated class, save pictures in the... | [
[
"scipy.misc.imsave",
"scipy.misc.imread"
]
] | [
{
"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": []
}
] |
KelvinYang0320/deepbots-panda | [
"072ed5609431d6aeeeb8d200f6ad909a78bf6083"
] | [
"Panda_RL/controllers/robot_supervisor_manager/robot_supervisor_ddpg.py"
] | [
"from deepbots.supervisor.controllers.robot_supervisor import RobotSupervisor\nfrom gym.spaces import Box, Discrete\nimport numpy as np\nfrom ArmUtil import Func, ToArmCoord\n\nfrom robot_supervisor_manager import STEPS_PER_EPISODE, MOTOR_VELOCITY\n\nclass PandaRobotSupervisor(RobotSupervisor):\n \"\"\"\n Obs... | [
[
"numpy.clip",
"numpy.linalg.norm",
"numpy.all",
"numpy.mean",
"numpy.array",
"numpy.zeros",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lee-jinhee/self-diagnosing-gan | [
"da87dd1ef10f2d630d6904ced63ae8805b5db356"
] | [
"diagan-pkg/diagan/models/inception.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torchvision\n\ntry:\n from torchvision.models.utils import load_state_dict_from_url\nexcept ImportError:\n from torch.utils.model_zoo import load_url as load_state_dict_from_url\n\n# Inception weights ported to Pytorch from\n# http:... | [
[
"torch.nn.Sequential",
"torch.cat",
"torch.utils.model_zoo.load_url",
"torch.nn.ModuleList",
"torch.nn.functional.avg_pool2d",
"torch.nn.MaxPool2d",
"torch.nn.AdaptiveAvgPool2d",
"torch.nn.functional.interpolate",
"torch.nn.functional.max_pool2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
r03922123/yolo3-pytorch | [
"e0c54c1eac8cd55de618fca73bfaefca49b47601"
] | [
"yolo.py"
] | [
"#-------------------------------------#\r\n# 创建YOLO类\r\n#-------------------------------------#\r\nimport colorsys\r\nimport os\r\nimport time\r\n\r\nimport numpy as np\r\nimport torch\r\nimport torch.nn as nn\r\nfrom PIL import Image, ImageDraw, ImageFont\r\n\r\nfrom nets.yolo3 import YoloBody\r\nfrom utils... | [
[
"numpy.expand_dims",
"torch.load",
"torch.cat",
"numpy.asarray",
"numpy.concatenate",
"torch.no_grad",
"numpy.shape",
"torch.cuda.is_available",
"numpy.transpose",
"numpy.floor",
"torch.nn.DataParallel",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sahilshah/sparse-app | [
"a483cb7093c1f52f69a0a8f73df60ca50a986eb2"
] | [
"server/OpenSfM/opensfm/matching.py"
] | [
"import numpy as np\nimport json\nimport cv2\nimport networkx as nx\nimport logging\n\nlogger = logging.getLogger(__name__)\n\n\n# pairwise matches\ndef match_lowe(index, f2, config):\n search_params = dict(checks=config.get('flann_checks', 200))\n results, dists = index.knnSearch(f2, 2, params=search_params)... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wilson1yan/virtex | [
"7ec20424bf78e751788c6ff17d0482fac7c27bd8"
] | [
"scripts/get_yfcc100m_photoids.py"
] | [
"import argparse\nfrom tqdm import tqdm\nimport json\nimport os\nfrom typing import Any, Dict, List\n\nfrom loguru import logger\nimport torch\nfrom torch.utils.data import DataLoader, DistributedSampler\n\nfrom virtex.config import Config\nfrom virtex.factories import TokenizerFactory, PretrainingModelFactory, Pre... | [
[
"torch.device",
"torch.utils.data.DataLoader",
"torch.cuda.current_device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
illidanlab/baselines | [
"b9ed3ba9855c525405c5456e12f5e094079ab432"
] | [
"baselines/a2c/a2c.py"
] | [
"import time\nimport functools\nimport tensorflow as tf\n\nfrom baselines import logger\n\nfrom baselines.common import set_global_seeds, explained_variance\nfrom baselines.common import tf_util\nfrom baselines.common.policies import build_policy\n\n\nfrom baselines.a2c.utils import Scheduler, find_trainable_variab... | [
[
"tensorflow.train.RMSPropOptimizer",
"tensorflow.reduce_mean",
"tensorflow.gradients",
"tensorflow.placeholder",
"tensorflow.squeeze",
"tensorflow.global_variables_initializer",
"tensorflow.clip_by_global_norm",
"tensorflow.variable_scope"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
elisabethzinck/Fairness-oriented-interpretability-of-predictive-algorithms | [
"e2b737109c75e4f1ddf3e423251417c35fa68083",
"e2b737109c75e4f1ddf3e423251417c35fa68083",
"e2b737109c75e4f1ddf3e423251417c35fa68083"
] | [
"src/data/create_anonymous_data.py",
"src/visualization_description/describe_taiwanese.py",
"src/visualization_description/evaluater_chexpert.py"
] | [
"# Create anonymous data used for presenting fairness toolkit\n\n#%% Initialization\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\ninput_path = 'data/predictions/german_credit_nn_pred.csv'\noutput_path = 'data/processed/anonymous_data.csv'\n\n# %% Load data\ngerman_data = pd.read_csv(inp... | [
[
"matplotlib.pyplot.xlabel",
"pandas.read_csv",
"pandas.cut",
"matplotlib.pyplot.axvline"
],
[
"pandas.read_csv",
"matplotlib.pyplot.savefig"
],
[
"sklearn.metrics.roc_auc_score",
"pandas.concat",
"pandas.read_csv",
"sklearn.metrics.roc_curve",
"pandas.DataFrame"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1... |
luozhengrong/BraTS-DMFNet | [
"3194707fdf5fe7cb5352101a9add99dec2f780b6"
] | [
"utils/criterions.py"
] | [
"import torch.nn.functional as F\r\nimport torch\r\nimport logging\r\nimport torch.nn as nn\r\n\r\n\r\n__all__ = ['sigmoid_dice_loss','softmax_dice_loss','GeneralizedDiceLoss','FocalLoss']\r\n\r\ncross_entropy = F.cross_entropy\r\n\r\n\r\ndef FocalLoss(output, target, alpha=0.25, gamma=2.0):\r\n target[target ==... | [
[
"torch.exp",
"torch.sqrt",
"torch.nn.functional.cross_entropy",
"torch.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jlucas-esri/Geospatial-Center-Code | [
"a8a1c7028d254690af788cbdd9cbdf859a422413"
] | [
"python/arcpy/dataFrameToTable/dataFrameToTable.py"
] | [
"import pandas as pd\nfrom typing import Union, List\nfrom pandas.errors import EmptyDataError\nimport re\nimport arcpy\nimport os\nimport logging\nimport pprint\nimport time\n\nclass DataFrameToTable:\n\n def __init__(self, Df:Union[pd.DataFrame, pd.io.parsers.TextFileReader], outTableName:str, outGDB:str=arcpy... | [
[
"pandas.errors.EmptyDataError"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
twylie/bviRNASeq | [
"4e9521d395d2c3fc764d22500319ec778df5f9da"
] | [
"bin/ensembl_to_dataframes.py"
] | [
"#!/usr/bin/python3.7\n\n# USAGE: ensembl_to_dataframes.py Homo_sapiens.GRCh38.cdna.all.fa\n\nimport sys\nimport re\nfrom Bio import SeqIO\nimport pandas as pd\n\n\ndef parse_header_fields(line, sequence):\n\n # Some of the header fields may be blank. We evaluate for existence and set\n # the field to null if... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
hi0001234d/Adversarial_Video_Generation | [
"288b45cd2c692e3dcd8ab423536497e3881c5612"
] | [
"Code/g_model.py"
] | [
"import tensorflow as tf\nimport numpy as np\nfrom scipy.misc import imsave\nfrom skimage.transform import resize\nfrom copy import deepcopy\nimport os\n\nimport constants as c\nfrom loss_functions import combined_loss\nfrom utils import psnr_error, sharp_diff_error\nfrom tfutils import w, b\n\n# noinspection PySha... | [
[
"tensorflow.nn.relu",
"tensorflow.concat",
"tensorflow.Variable",
"tensorflow.shape",
"scipy.misc.imsave",
"tensorflow.image.resize_images",
"tensorflow.scalar_summary",
"tensorflow.placeholder",
"tensorflow.nn.tanh",
"numpy.concatenate",
"tensorflow.name_scope",
"t... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"1.0",
"0.19",
"0.18",
"1.2",
"0.12",
"0.10",
"0.17",
"0.16"
],
"tensorflow": [
"1.10"
]
}
] |
jaesik817/nips17_adv_attack | [
"41741e98a4c26185316f6bafa4a41c5f9f1e503b"
] | [
"attack_gan/mark2_1_notavg.py"
] | [
"import tensorflow as tf\nimport numpy as np\nimport os,sys,glob\nimport shutil\nimport argparse\nfrom scipy.misc import imread\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nfrom gene import build_generator\n#from disc import InceptionModel\n\nfrom tensorflow.contrib.slim.nets impo... | [
[
"matplotlib.pyplot.imshow",
"tensorflow.nn.softmax_cross_entropy_with_logits",
"tensorflow.train.AdamOptimizer",
"tensorflow.contrib.slim.nets.inception.inception_v3",
"tensorflow.summary.scalar",
"tensorflow.Graph",
"numpy.copy",
"tensorflow.logging.set_verbosity",
"tensorflow... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"1.0",
"0.19",
"0.18",
"1.2",
"0.12",
"0.10",
"0.17",
"0.16"
],
"tensorflow": []
}
] |
cwharris/rmm | [
"eca14a9a6429158afc7ab74f536aec595d485e91"
] | [
"python/rmm/rmm.py"
] | [
"# Copyright (c) 2019, NVIDIA 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 l... | [
[
"numpy.asarray",
"numpy.dtype"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
fnauman/template_travis_sklearn | [
"41af5b79d410703491ce92687ed088a41b5edbd2"
] | [
"lasso_model/processing/data_management.py"
] | [
"import pandas as pd\nimport joblib\nfrom sklearn.pipeline import Pipeline\n\nfrom lasso_model.config import config\nfrom lasso_model import __version__ as _version\n\n#import logging\nfrom lasso_model.config.logging_config import get_logger\n\n_logger = get_logger(__name__)\n#_logger = logging.getLogger(__name__)\... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
abhilashabhilash3/Robust-vision-based-obstacle-detection | [
"e8f48a2dd157a02844cde893e4944ae920361d20"
] | [
"anomaly detector/scripts/07_rasterization_benchmark.py"
] | [
"#! /usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport consts\nimport argparse\n\nparser = argparse.ArgumentParser(description=\"Benchmark rasterization for spatial binning.\",\n formatter_class=argparse.RawTextHelpFormatter)\n\nparser.add_argument(\"--files\", metavar=\"F\", des... | [
[
"numpy.max",
"numpy.vectorize",
"numpy.min"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
v1docq/3_track_emergency | [
"06315413172ccf11998aafac83c52e0e469e327d"
] | [
"final_submit/run_model.py"
] | [
"import pandas as pd\nfrom catboost import CatBoostClassifier\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom datetime import datetime, timedelta\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import MinMaxScaler\nimport numpy as np\nfrom collections import Counter\n... | [
[
"matplotlib.pyplot.legend",
"pandas.merge",
"pandas.Series",
"numpy.linspace",
"pandas.DataFrame",
"matplotlib.pyplot.plot",
"sklearn.metrics.f1_score",
"sklearn.preprocessing.LabelEncoder",
"pandas.read_csv",
"sklearn.metrics.precision_recall_curve",
"matplotlib.pyplot... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
mwesleyj/geo-deep-learning | [
"9bb8942156fc1a19f8f5ac911e237daa30740ca9"
] | [
"inference.py"
] | [
"import gc\nimport logging\nimport warnings\nfrom math import sqrt\nfrom typing import List\n\nimport torch\nimport torch.nn.functional as F\n# import torch should be first. Unclear issue, mentionned here: https://github.com/pytorch/pytorch/issues/2083\nimport numpy as np\nimport os\nimport csv\nimport time\nimport... | [
[
"pandas.concat",
"torch.nn.functional.softmax",
"torch.max",
"numpy.unique",
"numpy.arange",
"torch.cuda.empty_cache",
"torch.stack",
"torch.mul",
"torch.no_grad",
"numpy.count_nonzero",
"numpy.moveaxis",
"torch.device",
"numpy.empty",
"numpy.savetxt"
]
] | [
{
"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": []
}
] |
dumpmemory/fastmoe | [
"b861e928000ddf1a94bb5f795e6286769e743bd9"
] | [
"tests/test_local_exchange.py"
] | [
"import sys\nfrom collections import OrderedDict\nfrom typing import List, Type, Union\n\nimport pytest\nimport torch\nimport torch.nn as nn\nimport numpy as np\n\nfrom copy import deepcopy\nfrom fmoe.functions import MOEGather, MOEScatter, count_by_gate\n\nfrom test_numerical import _assert_numerical\n\n@pytest.ma... | [
[
"torch.randint",
"torch.empty",
"torch.rand"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vkresch/neuron_poker | [
"ac8c976202b8b5e60a8e6b8ff7b7c4b86a2d4413"
] | [
"main.py"
] | [
"\"\"\"\nneuron poker\n\nUsage:\n main.py random [options]\n main.py keypress [options]\n main.py consider_equity [options]\n main.py equity_improvement --improvement_rounds=<> [options]\n main.py dqn_train [options]\n main.py dqn_play [options]\n\noptions:\n -h --help Show this screen.\n -r --rende... | [
[
"numpy.mean",
"pandas.Series",
"numpy.random.seed"
]
] | [
{
"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": []
}
] |
lesteve/fastmri-reproducible-benchmark | [
"8c9289c825c498ad0c4220d44d7270a98845ddb8"
] | [
"fastmri_recon/data/datasets/multicoil/fastmri_pyfunc.py"
] | [
"import tensorflow as tf\n\nfrom .preprocessing import generic_from_kspace_to_masked_kspace_and_mask\nfrom ...utils.h5 import from_multicoil_train_file_to_image_and_kspace_and_contrast, from_test_file_to_mask_and_kspace_and_contrast\nfrom ...utils.masking.acceleration_factor import tf_af\n\n# TODO: add unet and kik... | [
[
"tensorflow.convert_to_tensor",
"tensorflow.py_function",
"tensorflow.data.Dataset.list_files"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
raybellwaves/tropycal | [
"20b4305b4489ef4a40f8b857d4a7f5ddcd904c28"
] | [
"src/tropycal/realtime/realtime.py"
] | [
"r\"\"\"Functionality for managing real-time tropical cyclone data.\"\"\"\n\nimport calendar\nimport numpy as np\nimport pandas as pd\nimport re\nimport scipy.interpolate as interp\nimport urllib\nimport warnings\nfrom datetime import datetime as dt,timedelta\n\ntry:\n import cartopy.feature as cfeature\n fro... | [
[
"numpy.isnan",
"numpy.round"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ssameerr/pyro | [
"c04fc931631ec9e8694def207b5ca0e432d5e501"
] | [
"examples/bayesian_regression.py"
] | [
"import numpy as np\nimport argparse\nimport torch\nimport torch.nn as nn\nfrom torch.nn.functional import normalize # noqa: F401\n\nfrom torch.autograd import Variable\n\nimport pyro\nfrom pyro.distributions import Normal, Bernoulli # noqa: F401\nfrom pyro.infer import SVI\nfrom pyro.optim import Adam\n\n\"\"\"\... | [
[
"torch.nn.Softplus",
"torch.ones",
"numpy.linspace",
"torch.cat",
"torch.Tensor",
"numpy.arange",
"torch.zeros",
"torch.randn",
"torch.randperm",
"torch.nn.Linear",
"numpy.random.normal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xiaomaiAI/pyansys | [
"a62ec1c8f794ca90ed890acf65ba98276caf3c67"
] | [
"pyansys/geometry.py"
] | [
"\"\"\"Module for common class between gRPC, Archive, and Result geometry\"\"\"\nimport warnings\n\nimport pyvista as pv\nimport vtk\nimport numpy as np\n\nfrom pyansys import _relaxmidside, _reader\nfrom pyansys.elements import ETYPE_MAP\n\n\nVTK9 = vtk.vtkVersion().GetVTKMajorVersion() >= 9\nINVALID_ALLOWABLE_TYP... | [
[
"numpy.split",
"numpy.unique",
"numpy.ascontiguousarray",
"numpy.arange",
"numpy.in1d",
"numpy.asarray",
"numpy.dtype",
"numpy.zeros_like",
"numpy.any",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
BigHeartDB/pysaliency | [
"55b5f7b4808075ba9cab7d31a27ff215fbc28642"
] | [
"pysaliency/metric_optimization.py"
] | [
"from __future__ import print_function, division, absolute_import, unicode_literals\n\nfrom tqdm import tqdm\n\nimport numpy as np\nfrom scipy.ndimage import gaussian_filter\nfrom scipy.special import logsumexp\nimport tensorflow as tf\n\nfrom .models import sample_from_logdensity\nfrom .saliency_map_models import ... | [
[
"tensorflow.get_variable",
"numpy.asarray",
"tensorflow.stack",
"tensorflow.reduce_sum",
"tensorflow.cast",
"numpy.argmin",
"numpy.exp",
"tensorflow.Graph",
"tensorflow.Variable",
"numpy.arange",
"tensorflow.gradients",
"tensorflow.square",
"tensorflow.Session",... | [
{
"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": [
"1.10"
]
}
] |
u1234x1234/kaggle-dstl-satellite-imagery-feature-detection | [
"c0b143d584ccd9b36b8003724147fc85c9bf32f0"
] | [
"utils.py"
] | [
"# -*- coding: utf-8 -*-\nimport tifffile as tiff\nimport cv2\nimport numpy as np\nimport pandas as pd\nfrom shapely import wkt\nfrom shapely import affinity\nfrom rasterio.features import rasterize\nfrom rasterio import features\nfrom shapely import geometry\nfrom collections import defaultdict\nfrom shapely.geome... | [
[
"pandas.read_csv",
"numpy.clip",
"numpy.stack",
"numpy.percentile",
"numpy.concatenate",
"numpy.max",
"numpy.array",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Smytten/Tangible_NFT_Thesis | [
"50e6b43c85ec2836b3628015eac1f1389de4a261"
] | [
"mountdisplay/inky_fast/inky_fast.py"
] | [
"import sys\nimport time\nimport struct\n\nimport spidev\n\ntry:\n import RPi.GPIO as GPIO\nexcept ImportError:\n sys.exit('This library requires the RPi.GPIO module\\nInstall with: sudo apt install python-rpi.gpio')\n\ntry:\n import numpy\nexcept ImportError:\n sys.exit('This library requires the numpy... | [
[
"numpy.rot90",
"numpy.fliplr",
"numpy.flipud",
"numpy.array",
"numpy.zeros",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jolsten/float-interpreter | [
"ed090cee55012f2e72d451ca85ba0528ed203b4d"
] | [
"tests/test_onescomp.py"
] | [
"import pytest\r\nimport numpy as np\r\nfrom itertools import zip_longest\r\nfrom typeconvert.utils import bits_to_wordsize, mask\r\nfrom typeconvert.types.onescomp import func, jfunc, ufunc\r\n\r\nTEST_ARRAY_SIZE = 100\r\nTEST_CASES = {\r\n 3: [\r\n (0b000, 0),\r\n (0b001, 1),\r\n (0b010,... | [
[
"numpy.uint8",
"numpy.arange"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dhruv-vora/Computing-Mathemtical-Equations-using-Visual-Input | [
"509de89f4fd07d052b917f8ea64b922881ead6cc"
] | [
"quadra_eq.py"
] | [
"import math\n# import time\nfrom collections import deque\n\nimport cv2\nimport numpy as np\n\nimport digit_recognizer as dr\n\ndef ans_print(frame, x):\n cv2.putText(frame, x, (5, 460), cv2.FONT_HERSHEY_DUPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n\ndef quadr():\n dp = \"%.2f\"\n number = []\n coeff=[... | [
[
"numpy.max",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
msarahan/geopandas | [
"4945a185885b470026e284fa35310db40bff514f"
] | [
"geopandas/tests/test_array.py"
] | [
"import random\n\nimport numpy as np\nimport pandas as pd\n\nimport shapely\nimport shapely.geometry\nimport shapely.wkb\n\nimport geopandas\nfrom geopandas.array import (\n GeometryArray, points_from_xy, from_shapely, from_wkb, from_wkt, to_wkb,\n to_wkt)\n\nimport pytest\nimport six\n\n\ntriangles = [shapel... | [
[
"numpy.testing.assert_array_equal",
"numpy.arange",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
thodorisn/SentimentAnalysis | [
"d24f5eb3a18a8cf08f3b7abd681849e2914cbdab"
] | [
"main.py"
] | [
"from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\nfrom fuzzywuzzy import fuzz\nimport pandas as pd\nimport reviewsInitialize\n\nreviews = reviewsInitialize.getReviews()\nbubbles = reviewsInitialize.getBubbles()\n\n# Lists initialization\n# TODO: check if I don't need to use user_reviews/user_bu... | [
[
"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": []
}
] |
anthonyreis/TesteCienciaDados | [
"56730fc6ecd0bd478968076f763a063b5606122f"
] | [
"utils/plotByValues.py"
] | [
"import matplotlib.pyplot as plt\nimport os\n\n# Salva uma figura do gráfico de acordo com os parâmetros fornecidos\n\ndef plotGraph(values, ticks, xLabel, yLabel, title, color, bar, new):\n\n if(new):\n plt.figure(figsize=(15, 9))\n\n if(bar):\n plt.bar(ticks, values, color=color)\n else:\n ... | [
[
"matplotlib.pyplot.title",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.bar",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.xticks",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
BudhirajaChinmay/Orthogonal-Convolutional-Neural-Networks | [
"cc0e6fbccecdee1886037ce22d5b87a981991357"
] | [
"imagenet/main_orth50.py"
] | [
"import argparse\nimport os\nimport random\nimport shutil\nimport time\nimport warnings\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.parallel\nimport torch.backends.cudnn as cudnn\nimport torch.distributed as dist\nimport torch.optim\nimport torch.multiprocessing as mp\nimport torch.utils.data\nimport to... | [
[
"torch.nn.CrossEntropyLoss",
"torch.distributed.init_process_group",
"torch.multiprocessing.spawn",
"torch.utils.data.distributed.DistributedSampler",
"torch.cuda.set_device",
"torch.manual_seed",
"torch.load",
"torch.nn.DataParallel",
"torch.no_grad",
"torch.cuda.device_co... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
fmi-basel/kbest-intervals | [
"cc79ed7671c3453e46c5360ce4a7b5ce9747e1e2"
] | [
"tests/test_kbest.py"
] | [
"import itertools\nimport kbest_intervals\n\nimport pytest\nimport numpy as np\nfrom scipy.ndimage.measurements import label\n\nnp.random.seed(15)\n\n\n@pytest.mark.parametrize(\"size,k\",\n list(\n itertools.product([\n 5,\n ... | [
[
"numpy.random.seed",
"scipy.ndimage.measurements.label",
"numpy.ones",
"numpy.round",
"numpy.random.randn",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"0.15",
"1.4",
"0.16",
"1.0",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"0.10",
"0.17",
"1.3"
],
"tensorflow": [... |
wenliangzhao2018/d2go | [
"a9dce74e5caf4c2260371a1abb603e3d5f14d763",
"a9dce74e5caf4c2260371a1abb603e3d5f14d763"
] | [
"d2go/utils/testing/helper.py",
"d2go/utils/testing/rcnn_helper.py"
] | [
"#!/usr/bin/env python3\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\n\nimport importlib\nimport os\nimport socket\nimport uuid\nfrom functools import wraps\nfrom tempfile import TemporaryDirectory\nfrom typing import Optional\n\nimport torch\nimport torch.distributed as dist\n\n\ndef g... | [
[
"torch.cuda.device_count",
"torch.cuda.is_available",
"torch.distributed.destroy_process_group"
],
[
"torch.ones",
"torch.Tensor",
"torch.zeros",
"torch.unique",
"torch.no_grad",
"torch.device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
johnkittelman/pypow | [
"388932556b2c3acd893edc3f4b5943f36998afce"
] | [
"pypow/thermodynamics/simulation.py"
] | [
"#! /usr/bin/env python\n\nfrom __future__ import division\n# import modules needed\nimport math\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom timeit import default_timer as timer\n# Files imported\nfrom Cylinder_Geometry import Cylinder_Geometry\n# import SimFunctions #not used anymore\n\n\n\n########... | [
[
"matplotlib.pyplot.title",
"numpy.arange",
"matplotlib.pyplot.plot",
"numpy.mean",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
PatrickPrakash/tensorflow | [
"651b0bcbbc7aae34054f2d5357cc0ce37415dd18"
] | [
"tensorflow/python/ops/variables.py"
] | [
"# Copyright 2015 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#\n# Unless requ... | [
[
"tensorflow.python.ops.state_ops.batch_scatter_update",
"tensorflow.python.ops.array_ops.constant",
"tensorflow.python.ops.state_ops.assign_add",
"tensorflow.python.ops.array_ops.split",
"tensorflow.python.ops.gen_state_ops.scatter_nd_update",
"tensorflow.python.ops.state_ops.assign_sub",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
axju/blurring | [
"2f2e50b7f47d8556be2d74687f34ac6eabd8d235"
] | [
"src/blurring/blur.py"
] | [
"\"\"\"The main module\"\"\"\nimport os\nfrom logging import getLogger\nfrom shutil import copy, copyfile\nimport cv2\nimport numpy as np\nfrom blurring.utils import TempGen, WorkFolder, create_frames, save_frames, find_area\n\n\nclass BlurImage():\n \"\"\"docstring for BlurImage.\"\"\"\n\n def __init__(self,... | [
[
"numpy.zeros",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Buhua-Liu/Bag-of-Tricks-for-AT | [
"08a24f28b38831ef70e064907d4280f00a192bed"
] | [
"utils.py"
] | [
"import numpy as np\nfrom collections import namedtuple\nimport torch\nfrom torch import nn\nimport torchvision\nfrom torch.optim.optimizer import Optimizer, required\nimport collections\n\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n#################################################... | [
[
"numpy.log",
"numpy.pad",
"numpy.random.seed",
"numpy.random.choice",
"torch.clone",
"torch.utils.data.DataLoader",
"torch.cuda.is_available",
"numpy.exp",
"numpy.array",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
protsenkovi/diydetr | [
"a7c601e7841717e3c128c970ead87b063c48b4ee"
] | [
"models/components/cnn_encoder.py"
] | [
"import torch\nfrom torch import nn\nimport torchvision\nfrom utils.masked_tensor import MaskedTensor\nfrom models.components.resnet import ResNet\n\n\nclass CNNEncoder(nn.Module):\n def __init__(\n self,\n embed_dim,\n number_of_resnet_layers = 18,\n train_backbone = True,\n return_interm... | [
[
"torch.nn.Conv2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Unn20/achtung_die_kurve | [
"e2dbb1752c070cfc398e415d5a427384c0230f3c"
] | [
"evaluate_single_players.py"
] | [
"import argparse\nimport os\nimport torch\n\nfrom game.Player import RandomPlayer\nfrom ai.HeuristicPlayer import HeuristicPlayer1, HeuristicPlayer2\nfrom ai.RLPlayer import RLPlayer\nfrom ai.EAPlayer import EAPlayer\n\nfrom game.Game import Game\nimport numpy as np\nfrom tqdm import tqdm\n\n\ndef parse_args():\n ... | [
[
"numpy.savetxt",
"numpy.array",
"torch.cuda.is_available",
"numpy.genfromtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
elangovana/kegg-pathway-extractor | [
"08e9a28199bb4454e2e1a09c5d833f243f6f5768",
"08e9a28199bb4454e2e1a09c5d833f243f6f5768",
"08e9a28199bb4454e2e1a09c5d833f243f6f5768"
] | [
"source/algorithms/main_train_biocreative.py",
"tests/test_algorithms/test_relationExtractorBiLstmNetworkNoPos.py",
"source/algorithms/RelationExtractorCnnNetwork.py"
] | [
"import logging\n\nimport numpy as np\nimport pandas as pd\n\n\ndef prepare_data(self_relations_filter, data_df):\n logger = logging.getLogger(__name__)\n\n if self_relations_filter:\n logger.info(\"Removing self relations\")\n\n data_df = data_df.query('participant1 != participant2')\n label... | [
[
"pandas.concat"
],
[
"torch.LongTensor",
"torch.Size",
"numpy.array"
],
[
"torch.cat",
"torch.manual_seed",
"torch.nn.Embedding",
"torch.tensor",
"torch.nn.Linear",
"torch.nn.MaxPool1d",
"torch.FloatTensor",
"torch.nn.Conv1d",
"torch.nn.ReLU"
]
] | [
{
"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... |
FelixBoelle/aiida-ase | [
"01cc27b016e850fdb47b6057d5de0125e557ff71"
] | [
"aiida_ase/parsers/ase.py"
] | [
"# -*- coding: utf-8 -*-\nfrom aiida_ase.calculations.ase import AseCalculation\nfrom aiida.orm.data.folder import FolderData\nfrom aiida.parsers.parser import Parser\nfrom aiida.common.datastructures import calc_states\nfrom aiida.parsers.exceptions import OutputParsingError\nfrom aiida.common.exceptions import Un... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
micstein89/cosmo-emission-processing | [
"7628b6354abb80c7c69b1f13ac3d1757f0455272"
] | [
"emiproc/hourly_emissions/speciation.py"
] | [
"\n\nfrom fnmatch import fnmatch\nimport os\n\nimport numpy as np\n\n# COSMOART speciation function starts\n# calculate the fraction of single tracer out of inventory species \n\n# input files\nDATA_PATH = os.path.join(os.path.dirname(__file__), '..', '..', 'files',\n 'speciation')\n\npm2... | [
[
"numpy.arange"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rogerbao/deep-text-recognition-benchmark | [
"05d9092b762603a7a676d32c10e1fe7b6be44b92"
] | [
"demo.py"
] | [
"import string\nimport argparse\n\nimport torch\nimport torch.backends.cudnn as cudnn\nimport torch.utils.data\nimport torch.nn.functional as F\n\nfrom utils import CTCLabelConverter, AttnLabelConverter\nfrom dataset import RawDataset, AlignCollate\nfrom model import Model\ndevice = torch.device('cuda' if torch.cud... | [
[
"torch.nn.functional.softmax",
"torch.LongTensor",
"torch.load",
"torch.nn.DataParallel",
"torch.no_grad",
"torch.cuda.is_available",
"torch.IntTensor",
"torch.cuda.device_count"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jpauwels/hrtfdata | [
"cadcf1d72395de15d1d114fb88a3b64e283df899"
] | [
"hrtfdata/util.py"
] | [
"import numpy as np\n\ndef wrap_closed_open_interval(values, lower, upper):\n return (np.asarray(values) - lower) % (upper - lower) + lower\n\n\ndef wrap_open_closed_interval(values, lower, upper):\n return -((lower - np.asarray(values)) % (upper - lower)) - lower\n\n\ndef spherical2interaural(azimuth, elevat... | [
[
"numpy.sqrt",
"numpy.asarray",
"numpy.cos",
"numpy.rad2deg",
"numpy.sin",
"numpy.arctan2",
"numpy.tan",
"numpy.deg2rad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yg13/Codes-for-Lane-Detection | [
"3d44e0f62122c1d7757d3e5335b54d66aaa0aa52"
] | [
"ERFNet-CULane-PyTorch/models/erfnet.py"
] | [
"# ERFNET full network definition for Pytorch\n# Sept 2017\n# Eduardo Romera\n#######################\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.init as init\nimport torch.nn.functional as F\n\n\nclass DownsamplerBlock (nn.Module):\n def __init__(self, ninput, noutput):\n super().__init__()\n... | [
[
"torch.nn.functional.softmax",
"torch.nn.Dropout2d",
"torch.nn.ConvTranspose2d",
"torch.nn.ModuleList",
"torch.nn.Conv2d",
"torch.nn.MaxPool2d",
"torch.nn.Linear",
"torch.nn.functional.relu",
"torch.nn.functional.sigmoid",
"torch.nn.BatchNorm2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
eskalnes/RL_memory | [
"bd1a5cc07a41be89ea9f8de9edc2a2b557dcedbb"
] | [
"rl_memory/models/archive/DQN copy/dqn.py"
] | [
"from q_network import Q\nimport numpy as np\nimport gym\nfrom tools import epsilon, run_target_update, plot_episode_rewards, ReplayBuffer\nimport torch\n\n# buffer hyperparameters\nbatchsize = 200 # batchsize for buffer sampling\nbuffer_maxlength = 1000 # max number of tuples held by buffer\nepisodes_til_buffer_... | [
[
"torch.tensor",
"torch.max",
"numpy.random.rand",
"torch.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
IMMM-SFA/im3components | [
"0c3c2d41377107200aef041b4e77d6ba15b2f9f8"
] | [
"im3components/wrf_tell/wrf_tell_counties.py"
] | [
"import argparse\nimport datetime\nfrom os.path import isfile, join\nfrom typing import List\n\nimport geopandas as gpd\nfrom joblib import Parallel, delayed\nimport pandas as pd\nimport salem\n\n\ndef compute_county_weighted_mean(\n df: pd.DataFrame,\n columns: List[str],\n precisions: List[in... | [
[
"pandas.read_parquet"
]
] | [
{
"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": []
}
] |
xdrshjr/CivilWospERT | [
"1cd6d2f90da354e3a29203b2cd9f3348149bc64d"
] | [
"sampling.py"
] | [
"import random\n\nimport torch\nfrom torch import nn\n\nfrom cival_wospert import util\n\n\ndef create_train_sample(doc, neg_entity_count: int, neg_rel_count: int, max_span_size: int, rel_type_count: int):\n # assert len(doc.encoding) < 512\n encodings = doc.encoding\n # 1.1特殊构建的lattice机制嵌入\n lattice_en... | [
[
"torch.ones",
"torch.zeros",
"torch.cat",
"torch.tensor",
"torch.stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Debosmit-Neogi/Deep-neural-approach-to-colorise-monochrome-images | [
"a3cf83abb9728fe2ce6d673b583bcd34f5723d7a"
] | [
"main.py"
] | [
"import numpy as np\nimport cv2\nimport pickle\n\nprint(\"[INFO] loading model...\")\n\nnet = cv2.dnn.readNetFromCaffe(\"./model/model/colorization_deploy_v2.prototxt\",\"./model/model/colorization_release_v2.caffeModel\")\n\npts= np.load(\"./model/model/pts_in_hull.npy\")\n\nclass8=net.getLayerId(\"class8_ab\")\nc... | [
[
"numpy.concatenate",
"numpy.load",
"numpy.full",
"numpy.clip"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Shubham-1304/dataops | [
"59854b1cdda79fc3b4d0911f4f996797deb99752"
] | [
"plot_data.py"
] | [
"\n'''\nconverts raw data into one dataframe,\nfor export to the public html repo\n'''\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport os, os.path\n\nOUTPUT_DATA = 'output_data'\n\ndef main():\n\n\tdf = pd.read_csv(OUTPUT_DATA+os.sep+'co2.csv')\n\t#print(df)\n\t#df.set_index('date')\n\n\tdf.plot(x='... | [
[
"matplotlib.pyplot.xlabel",
"pandas.read_csv",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
gvvynplaine/face-identification-tpe | [
"5b09c0e2629582465d71ac8ddd9bb7fcdeb47dac"
] | [
"utils/organize_data.py"
] | [
"import os\nimport os.path\nimport random\nimport math\nimport itertools\nimport shutil\n\nimport numpy as np\n\nfrom collections import namedtuple\n\n\nFORMATS = {'.jpg', '.jpeg', '.png'}\nDATA_DIR = './data/'\nTRAIN_DIR = DATA_DIR + 'train/'\nTEST_DIR = DATA_DIR + 'test/'\nDEV_DIR = DATA_DIR + 'dev/'\nPROTOCOLS_D... | [
[
"numpy.zeros",
"numpy.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bip-team/humoto-module-vertical-CoM | [
"665d7c192eaed69135e7b25f1b56bd531317f4bb"
] | [
"extra_modules/wpg05/include/tools/python/computeKinematicTask.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport matplotlib as mpl\nfrom mpl_toolkits.mplot3d import Axes3D\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport argparse\nimport collections\n\nnp.set_printoptions(threshold=100000000000000)\n\n#Default values\ndefault_height = 0.5\ndefault_nIter = ... | [
[
"numpy.dot",
"numpy.arange",
"numpy.set_printoptions",
"numpy.linalg.norm",
"numpy.append",
"numpy.random.rand",
"numpy.cross",
"numpy.array",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
501Good/SemSketches2021 | [
"a76e3f7479c46b206034447f977681b936216cf2"
] | [
"modules/pooling.py"
] | [
"from sentence_transformers.models import Pooling\nimport torch\nimport os\nimport json\n\nclass EntityPooling(Pooling):\n def __init__(self, \n entity_token_id: int, \n word_embedding_dimension: int,\n pooling_mode_cls_token: bool = False,\n poolin... | [
[
"torch.max",
"torch.cat",
"torch.sqrt",
"torch.sum",
"torch.where",
"torch.clamp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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