repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
cpuimage/albumentations | [
"300ee99386ad27f482387047dac4f6dddff11ac2"
] | [
"tests/test_transforms.py"
] | [
"from functools import partial\n\nimport cv2\nimport numpy as np\nimport pytest\nimport random\n\nimport albumentations as A\nimport albumentations.augmentations.functional as F\nimport albumentations.augmentations.geometric.functional as FGeometric\n\nfrom .utils import get_transforms, get_image_only_transforms, g... | [
[
"numpy.random.random",
"numpy.allclose",
"numpy.array_equal",
"numpy.random.seed",
"numpy.abs",
"numpy.unique",
"numpy.eye",
"numpy.arange",
"numpy.ones",
"numpy.all",
"numpy.testing.assert_almost_equal",
"numpy.testing.assert_array_equal",
"numpy.random.rand",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cguccione/NeutralEvolutionModeling | [
"42b0498f51d35aaf6558f1ab08844965e06f087d"
] | [
"nevo/neutral_fit_utils.py"
] | [
"import os\nimport numpy as np\nfrom scipy.stats import beta \n\ndef beta_cdf(p, N, m):\n '''Expected long term distribution under the \n neutral model (truncated cumulative beta-distribution)\n \n Copyright\n ---------\n Github: https://github.com/misieber/neufit\n Theroy as described in [... | [
[
"numpy.arange",
"numpy.bincount",
"scipy.stats.beta.cdf",
"numpy.random.choice"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
m1so/pandera | [
"f22f9101f3a1671a64df7e3ac6c60eaca5810b36"
] | [
"pandera/schemas.py"
] | [
"\"\"\"Core pandera schema class definitions.\"\"\"\n# pylint: disable=too-many-lines\n\nimport copy\nimport json\nimport warnings\nfrom functools import wraps\nfrom pathlib import Path\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport pandas as pd\nfrom packaging import version\n\nfrom . imp... | [
[
"pandas.core.dtypes.dtypes.registry.find",
"pandas.concat",
"pandas.Index",
"pandas.api.types.is_extension_array_dtype"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"0.24",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
DaDaMrX/ReaLiSe | [
"25843e0c2c32b3a364cee857b2e4f5ba8b2764e9"
] | [
"src/models.py"
] | [
"from __future__ import absolute_import, division, print_function, unicode_literals\nimport logging\nimport math\nimport os\nimport sys\n\nimport opencc\nimport torch\nfrom torch import nn\nfrom torch.nn import CrossEntropyLoss, MSELoss\n\nfrom transformers.modeling_bert import *\nfrom utils import pho_convertor, p... | [
[
"torch.nn.Dropout",
"torch.nn.functional.softmax",
"torch.nn.CrossEntropyLoss",
"torch.cat",
"numpy.asarray",
"torch.nn.GRU",
"torch.from_numpy",
"torch.nn.utils.rnn.pack_padded_sequence",
"torch.tensor",
"torch.nn.Embedding",
"torch.nn.Linear",
"torch.nn.LayerNorm"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ali493/pyro | [
"1245340077a733e2ab35765eae783b358d2f3af9"
] | [
"old/examples/twolinkmanipulator_with_ComputedTorque.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Mar 5 20:24:50 2016\n\n@author: alex\n\"\"\"\n\nfrom AlexRobotics.dynamic import Manipulator as M\nfrom AlexRobotics.control import ComputedTorque as CTC\n\nimport matplotlib.pyplot as plt\n\n\n\"\"\" Define system \"\"\"\n\n# Define dynamic system\nR = M.TwoLi... | [
[
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
snlakshm/tensorflow-deeplab-v3 | [
"357a050c7c697f8d6e1bb7269ec3e03c7e032035"
] | [
"inference.py"
] | [
"\"\"\"Run inference a DeepLab v3 model using tf.estimator API.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport argparse\nimport os\nimport sys\n\nimport tensorflow as tf\n\nimport deeplab_model\nfrom utils import preprocessing\nfrom ... | [
[
"matplotlib.pyplot.imshow",
"tensorflow.estimator.Estimator",
"matplotlib.pyplot.savefig",
"tensorflow.logging.set_verbosity",
"matplotlib.pyplot.axis",
"tensorflow.python.debug.LocalCLIDebugHook",
"tensorflow.app.run"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
youngsm/chroma | [
"1e183c26aaff46fb9b0425ad8eef9995ebe0be2c"
] | [
"chroma/color/colormap.py"
] | [
"import numpy as np\nimport matplotlib.cm as cm\n\ndef map_to_color(a, range=None, map=cm.jet_r, weights=None):\n a = np.asarray(a,dtype=np.float32)\n \n if range is None:\n range = (a.min(), a.max())\n\n ax = (a - float(range[0]))/(float(range[1])-float(range[0]))\n\n frgba = map(ax)\n\n i... | [
[
"numpy.asarray"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
eng-tools/pysra | [
"aa4317eed85d7829fd862f85ec0f23fcc7723408"
] | [
"pysra/variation.py"
] | [
"# The MIT License (MIT)\n#\n# Copyright (c) 2016-2018 Albert Kottke\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 without limitation the rights\n... | [
[
"numpy.diag",
"numpy.log",
"scipy.stats.norm.cdf",
"numpy.abs",
"numpy.clip",
"numpy.power",
"numpy.random.multivariate_normal",
"numpy.random.exponential",
"numpy.asarray",
"scipy.sparse.diags",
"scipy.stats.truncnorm.stats",
"numpy.sqrt",
"numpy.ceil",
"nu... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zweien/pytorch-lightning | [
"ddc49c472fa1b0a320494508d7473640553cfeeb"
] | [
"pytorch_lightning/trainer/distrib_parts.py"
] | [
"\"\"\"\nLightning makes multi-gpu training and 16 bit training trivial.\n\n.. note:: None of the flags below require changing anything about your lightningModel definition.\n\nChoosing a backend\n==================\n\nLightning supports two backends. DataParallel and DistributedDataParallel.\n Both can be used for... | [
[
"torch.device",
"torch.cuda.device_count",
"torch.cuda.set_device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
elcorto/pwtools | [
"cee068d1c7984d85e94ace243f86de350d3a1dba"
] | [
"pwtools/test/test_pw_more_forces.py"
] | [
"# Parse verbose PWscf force printing, i.e. more then one force block per time\n# step, e.g. one blok for ions, one for vdw forces, ...\n\nimport numpy as np\nfrom pwtools.parse import PwMDOutputFile, PwSCFOutputFile\nfrom pwtools import common\nfrom pwtools.constants import Bohr,Ang,Ry,eV\n\n\ndef test_pw_more_for... | [
[
"numpy.allclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
e6-1/dl6-1 | [
"1eeeb5c2a81914da329a749e65b87973ae28dfff"
] | [
"helpers.py"
] | [
"import numpy as np\nimport pandas as pd\n\n\ndef get_sub_seq(seq, start, end):\n \"\"\"Get the sub sequence starting at the start index and ending at the end index.\"\"\"\n arr = seq[max([0, start]):end]\n if start < 0:\n arr = np.append(np.zeros((abs(start),2)), arr, axis=0)\n for i in range(le... | [
[
"numpy.random.permutation",
"numpy.load",
"numpy.array",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
katjaschwarz/layout2im | [
"f931a09e349941a760ef89c97ee5515058c510a1"
] | [
"utils/model_saver.py"
] | [
"import os\nimport re\nimport torch\nfrom pathlib import Path\n\n\ndef prepare_dir(name):\n log_save_dir = 'checkpoints/{}/logs'.format(name)\n model_save_dir = 'checkpoints/{}/models'.format(name)\n sample_save_dir = 'checkpoints/{}/samples'.format(name)\n result_save_dir = 'checkpoints/{}/results'.for... | [
[
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mao-wfs/nro45-merge | [
"ca15745c740b3f573e843de2c8c9954c45ba4b23"
] | [
"nro45_merge/correlator.py"
] | [
"__all__ = [\"convert\", \"to_zarr\"]\n\n\n# standard library\nfrom logging import getLogger\nfrom pathlib import Path\nfrom struct import Struct\nfrom typing import BinaryIO, Callable, Optional, Tuple\n\n\n# third-party packages\nimport zarr\nimport dask.array as da\nimport numpy as np\nimport xarray as xr\nfrom t... | [
[
"numpy.arange",
"numpy.dtype",
"numpy.datetime64",
"numpy.timedelta64",
"numpy.complex64"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
h1-the-swan/autoreview | [
"984834fbacd0ff81cf24dc6bf372bb2924818f7f"
] | [
"old/wos_review_paper_collect_data_run_experiments.py"
] | [
"import sys, os, time, json\nimport subprocess\nimport shlex\nfrom multiprocessing.pool import ThreadPool\nfrom datetime import datetime\nfrom timeit import default_timer as timer\ntry:\n from humanfriendly import format_timespan\nexcept ImportError:\n def format_timespan(seconds):\n return \"{:.2f} se... | [
[
"pandas.read_table",
"pandas.to_datetime"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
Payal4/ga-learner-dsmp-repo | [
"74f4ccf4f99739429b854aec3e072ae6ba7ad19f"
] | [
"Amazon-Alexa-Reviews/code.py"
] | [
"# --------------\n# import packages\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\n# Load the dataset\ndf = pd.read_csv(path, sep=\"\\t\")\n\n# Converting date attribute from string to datetime.date datatyp... | [
[
"pandas.read_csv",
"pandas.to_datetime",
"matplotlib.pyplot.title",
"sklearn.ensemble.RandomForestClassifier",
"sklearn.metrics.accuracy_score",
"sklearn.metrics.precision_score",
"sklearn.model_selection.train_test_split",
"sklearn.feature_extraction.text.CountVectorizer",
"ma... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
alexrudy/aopy | [
"0242bdc81a10ac1a025e6e4cc447cfe90f16dd33"
] | [
"tests/postmortem/reconstructor.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# \n# reconstructor.py\n# aopy\n# \n# Created by Alexander Rudy on 2013-08-14.\n# Copyright 2013 Alexander Rudy. All rights reserved.\n# \n\n\nimport os, os.path, glob\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.gridspec import GridSpec... | [
[
"numpy.load",
"matplotlib.pyplot.show",
"matplotlib.gridspec.GridSpec",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
goncaloperes/scikit-learn-mooc | [
"b46dc840111b7bc6ca643e0f1cc79499c246ca8b"
] | [
"python_scripts/metrics_sol_01.py"
] | [
"# %% [markdown]\n# # 📃 Solution for Exercise M7.02\n#\n# We presented different classification metrics in the previous notebook.\n# However, we did not use it with a cross-validation. This exercise aims at\n# practicing and implementing cross-validation.\n#\n# We will reuse the blood transfusion dataset.\n\n# %%\... | [
[
"pandas.read_csv",
"sklearn.model_selection.cross_val_score",
"matplotlib.pyplot.title",
"sklearn.model_selection.StratifiedKFold",
"pandas.DataFrame",
"sklearn.tree.DecisionTreeClassifier",
"sklearn.metrics.make_scorer",
"sklearn.model_selection.cross_validate"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
cnarte/meta_occ | [
"6f595e8283ba33615787cc4491b3aecfdd236177"
] | [
"meta_occ/utils.py"
] | [
"import numpy as np\nimport torch\nfrom torch.utils.data.dataloader import default_collate\nfrom sklearn.metrics import roc_auc_score\n\nfrom torchmeta.datasets.helpers import (\n omniglot,\n miniimagenet,\n tieredimagenet,\n cifar_fs,\n)\nfrom torchmeta.utils.data import BatchMetaDataLoader, Combinatio... | [
[
"sklearn.metrics.roc_auc_score",
"numpy.std",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SwastiKh/geomstats | [
"685f843e12188057f226e67769e2d500c8f57d2f"
] | [
"geomstats/_backend/tensorflow/__init__.py"
] | [
"\"\"\"Tensorflow based computation backend.\"\"\"\n\nimport math\nfrom collections import Counter\nfrom itertools import product\n\nimport numpy as _np\nimport tensorflow as tf\nfrom tensorflow import ( # NOQA\n abs,\n acos as arccos,\n acosh as arccosh,\n argmax,\n argmin,\n asin as arcsin,\n ... | [
[
"tensorflow.convert_to_tensor",
"tensorflow.linalg.diag_part",
"tensorflow.concat",
"tensorflow.reduce_sum",
"tensorflow.equal",
"tensorflow.cast",
"tensorflow.linalg.matmul",
"tensorflow.abs",
"tensorflow.map_fn",
"tensorflow.where",
"tensorflow.rank",
"tensorflow.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
INM-6/pcp_use_cases | [
"6a8b7dae88e7cb2b11ac831c3e72b978b925d8c0"
] | [
"ASSET/asset.py"
] | [
"\"\"\"\nASSET is a statistical method [1] for the detection of repeating sequences\nof synchronous spiking events in parallel spike trains.\nGiven a list `sts` of spike trains, the analysis comprises the following\nsteps:\n\n1) Build the intersection matrix `imat` (optional) and the associated\n probability matr... | [
[
"numpy.minimum",
"numpy.sqrt",
"numpy.arctan",
"numpy.linspace",
"numpy.all",
"numpy.exp",
"numpy.where",
"numpy.tril",
"sklearn.cluster.dbscan",
"numpy.fliplr",
"numpy.arange",
"numpy.finfo",
"numpy.sin",
"numpy.diff",
"numpy.outer",
"numpy.triu",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
HazyResearch/mongoose | [
"890043b39b59a93b8e91a30bc79f4b8125febb78"
] | [
"mongoose_reformer/reformer_lib/generative_tools.py"
] | [
"from functools import partial\nimport torch\nfrom torch import nn\nimport torch.nn.functional as F\nfrom torch.nn.utils.rnn import pad_sequence\nfrom reformer_lib.reformer_pytorch import ReformerLM,ReformerLM_tune\nfrom reformer_lib.autopadder import Autopadder\n\ndef top_p(logits, thres = 0.9):\n sorted_logits... | [
[
"torch.nn.functional.softmax",
"torch.cat",
"torch.sum",
"torch.multinomial",
"torch.full_like",
"torch.no_grad",
"torch.sort",
"torch.topk",
"torch.nn.functional.pad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vbrichzin/udacity-ND-SDCE-BehavioralCloning-P4 | [
"8b0d961cd2cdc0ce500a27fd5542f52c08a93f23"
] | [
"model_fit_generator.py"
] | [
"import csv\nimport cv2\nimport numpy as np\nimport sklearn\nfrom sklearn.utils import shuffle\nfrom scipy import ndimage\n\nlines = []\nwith open('../data/driving_log.csv') as csvfile:\n reader = csv.reader(csvfile)\n for line in reader:\n lines.append(line)\n\nfrom sklearn.model_selection import trai... | [
[
"scipy.ndimage.imread",
"numpy.fliplr",
"sklearn.utils.shuffle",
"sklearn.model_selection.train_test_split",
"numpy.array"
]
] | [
{
"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": []
}
] |
RelevanceAI/RelevanceAI | [
"a0542f35153d9c842f3d2cd0955d6b07f6dfc07b"
] | [
"tests/unit/test_operations/test_operation.py"
] | [
"from relevanceai.dataset import Dataset\n\nfrom sklearn.cluster import KMeans\n\n\nclass TestOperation:\n def test_subcluster(self, test_dataset: Dataset):\n model = KMeans(n_clusters=4)\n\n vector_field = \"sample_1_vector_\"\n alias = \"cluster_test_1\"\n test_dataset.cluster(\n ... | [
[
"sklearn.cluster.KMeans"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SamsadSajid/mpl-probscale | [
"c65feeddbd8eb6ccca897b27dcd6738d72bd9fb2"
] | [
"probscale/probscale.py"
] | [
"import numpy\nfrom matplotlib.scale import ScaleBase\nfrom matplotlib.ticker import (\n FixedLocator,\n NullLocator,\n NullFormatter,\n FuncFormatter,\n)\n\nfrom .transforms import ProbTransform\nfrom .formatters import PctFormatter, ProbFormatter\n\n\nclass _minimal_norm(object):\n \"\"\"\n A ba... | [
[
"numpy.hstack",
"numpy.log",
"numpy.sqrt",
"numpy.sign",
"matplotlib.ticker.NullFormatter",
"numpy.log10",
"numpy.exp",
"numpy.array",
"matplotlib.ticker.NullLocator"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xiawenwen49/IMGUCB | [
"18350ca24bbaf23834794dc188885517ded607ea"
] | [
"BanditAlg/CUCB.py"
] | [
"from random import choice, random, sample\nimport numpy as np\nimport networkx as nx\n\nclass ArmBaseStruct(object):\n def __init__(self, armID):\n self.armID = armID\n self.totalReward = 0.0\n self.numPlayed = 0\n self.averageReward = 0.0\n self.p_max = 1\n \n def u... | [
[
"numpy.asarray",
"numpy.log",
"numpy.abs"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
george0st/mlrun | [
"6467d3a5ceadf6cd35512b84b3ddc3da611cf39a",
"6467d3a5ceadf6cd35512b84b3ddc3da611cf39a"
] | [
"mlrun/frameworks/tf_keras/callbacks/logging_callback.py",
"mlrun/frameworks/_ml_common/plans/calibration_curve_plan.py"
] | [
"from typing import Callable, Dict, List, Union\n\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow import Tensor, Variable\nfrom tensorflow.keras.callbacks import Callback\n\nimport mlrun\n\nfrom ..._common import TrackableType\nfrom ..._dl_common.loggers import Logger, LoggerMode\n\n\nclass LoggingCal... | [
[
"tensorflow.size"
],
[
"sklearn.calibration.calibration_curve"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensor... |
MasterXiYu/stargan_mul | [
"98fa1fe618592c3cad57195a6a21de59aa97da3b"
] | [
"solver.py"
] | [
"from Stargan_net import Generator\nfrom Stargan_net import Discriminator\nfrom torch.autograd import Variable\nfrom torchvision.utils import save_image\nimport torch\nimport torch.nn.functional as F\nimport numpy as np\nimport os\nimport time\nimport datetime\n\n\nclass Solver(object):\n \"\"\"Solver for traini... | [
[
"torch.mean",
"torch.abs",
"torch.load",
"torch.zeros",
"torch.cat",
"torch.nn.functional.binary_cross_entropy_with_logits",
"torch.nn.functional.cross_entropy",
"torch.sum",
"numpy.arange",
"torch.no_grad",
"torch.cuda.is_available",
"torch.autograd.grad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aws-samples/amazon-sagemaker-autopilot-demo | [
"38f6358ce7cc58220f54077dff3c2f880d81f0ed"
] | [
"Autopilot-HAC-DEMO-artifacts/generated_module/candidate_data_processors/dpp9.py"
] | [
"from numpy import nan\nfrom sagemaker_sklearn_extension.externals import Header\nfrom sagemaker_sklearn_extension.impute import RobustImputer\nfrom sagemaker_sklearn_extension.preprocessing import RobustLabelEncoder\nfrom sagemaker_sklearn_extension.preprocessing import RobustStandardScaler\nfrom sklearn.compose i... | [
[
"sklearn.compose.ColumnTransformer"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Pedroknoll/Chicago-Bikeshare-Analysis | [
"1ad7a5e0b7057f0ef87a45e3f691cc502aac745a"
] | [
"chicago_bikeshare.py"
] | [
"\n# coding: utf-8\n\n# Here goes the imports\nimport csv\nimport matplotlib.pyplot as plt\n\n# Let's read the data as a list\nprint(\"Reading the document...\")\nwith open(\"chicago.csv\", \"r\") as file_read:\n reader = csv.reader(file_read)\n data_list = list(reader)\nprint(\"Ok!\")\n\n# Let's check how ma... | [
[
"matplotlib.pyplot.title",
"matplotlib.pyplot.bar",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.xticks",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AndrewPaulChester/sage-code | [
"9fe676bfbcbc6f642eca29b30a1027fba2a426a0",
"9fe676bfbcbc6f642eca29b30a1027fba2a426a0"
] | [
"domains/gym_craft/tests/matrix_construction.py",
"forks/baselines/baselines/common/vec_env/test_vec_env.py"
] | [
"import numpy as np\n\nfrom matplotlib import pyplot as plt\n\n\nterrain = np.array([[0,0,0],[0,1,1],[0,1,2]])\n\n\ncolours = np.array([[0,0,255],[255,0,0],[0,255,0]])\n\n\nimage = colours[terrain]\n\n\nplt.ion()\nfig, ax = plt.subplots()\nim = ax.imshow(image)\nplt.draw()\nplt.pause(10)\nplt.draw()\n",
"\"\"\"\n... | [
[
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.draw",
"numpy.array",
"matplotlib.pyplot.pause",
"matplotlib.pyplot.ion"
],
[
"numpy.array",
"numpy.allclose",
"numpy.random.seed",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
KAIST-vilab/OC-CSE | [
"35703390e13621a865aef4d9b75202c8c9e5822b"
] | [
"voc12/data.py"
] | [
"import os.path\nimport random\n\nimport numpy as np\nimport torch\nfrom torch.utils.data import Dataset\nfrom torchvision import transforms\n\nimport PIL.Image\nfrom matplotlib import pyplot as plt\nimport tools.imutils as imutils\n\nIMG_FOLDER_NAME = \"JPEGImages\"\nANNOT_FOLDER_NAME = \"Annotations\"\n\nCAT_LIST... | [
[
"matplotlib.pyplot.gca",
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.tight_layout",
"numpy.asarray",
"matplotlib.pyplot.margins",
"torch.from_numpy",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.close",
"matplotlib.pyplot.subplots_adjust",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
spacegal-spiff/AST341 | [
"cce49d730b3e6a5048549f6d777e3528b3090c13"
] | [
"Spring2020/PythonPhotometry/Sarah_DiffPhotometry.py"
] | [
"# python 2/3 compatibility\nfrom __future__ import print_function\n# numerical python\nimport numpy as np\n# file management tools\nimport glob\nimport os\n# good module for timing tests\nimport time\n# plotting stuff\nimport matplotlib.pyplot as plt\nimport matplotlib.cm as cm\n# ability to read/write fits files\... | [
[
"numpy.nanmax",
"numpy.nanmedian",
"numpy.savez",
"numpy.linspace",
"numpy.nanmin",
"matplotlib.pyplot.plot",
"numpy.zeros_like",
"numpy.nanstd",
"numpy.where",
"numpy.arange",
"numpy.std",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.title",
"numpy.isnan"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kcyu2014/eval-nas | [
"385376a3ef96336b54ee7e696af1d02b97aa5c32",
"385376a3ef96336b54ee7e696af1d02b97aa5c32",
"385376a3ef96336b54ee7e696af1d02b97aa5c32"
] | [
"visualization/summarize_weight_sharing.py",
"search_policies/cnn/search_space/nas_bench/model.py",
"search_policies/cnn/nao_policy/model_search.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib import cm\n\n\ndef summarize_weight_sharing_plot(gt_data, ws_data, gt_ylim=None, ws_ylim=None, filename='ws_vs_gt.pdf'):\n \"\"\"\n\n :param gt_data: sorted.\n :param ws_data: index sort based on GT data.\n :param gt_ylim:\n :param... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig",
"matplotlib.cm.get_cmap"
],
[
"torch.cat",
"torch.nn.ModuleList",
"torch.nn.ModuleDict",
"torch.nn.functional.avg_pool2d",
"torch.nn.Linear",
"torch.nn.MaxPool2d",
"torch.nn.ZeroP... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JamesBrofos/Evaluating-the-Implicit-Midpoint-Integrator | [
"333dd79dfaeac2f7df0dcc7c41506efe8562a619"
] | [
"examples/banana/main.py"
] | [
"import argparse\nimport os\nimport time\nfrom typing import Callable, Tuple\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport scipy.linalg as spla\nimport scipy.stats as spst\nimport tqdm\n\nimport hmc\n\nparser = argparse.ArgumentParser(description='Comparison of implicit midpoint and generalized lea... | [
[
"numpy.min",
"numpy.isnan",
"numpy.median",
"numpy.percentile",
"numpy.max",
"numpy.zeros",
"numpy.isinf"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Kwongrf/B-ALL | [
"f190b3d0d3c47ffb05be7c6b33e5d8e71a6cfdf1"
] | [
"t_cnn.py"
] | [
"import torch.nn as nn\nimport torch.utils.model_zoo as model_zoo\n\n\nmodel_urls = {\n 'alexnet': 'https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth',\n}\nsettings = { 'input_space': 'RGB',\n 'input_size': [3, 450, 450],\n 'input_range': [0, 1],\n 'mean': [0.485, 0... | [
[
"torch.nn.Conv2d",
"torch.nn.Linear",
"torch.nn.MaxPool2d",
"torch.nn.AvgPool2d",
"torch.nn.ReLU",
"torch.utils.model_zoo.load_url"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Maosef/options_pricing | [
"669fed3eeeac62495624f8b113c16aa4cdb7407f"
] | [
"functions/Solvers.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Jul 27 11:13:45 2019\n\n@author: cantaro86\n\"\"\"\n\nimport numpy as np\nfrom scipy import sparse\nfrom scipy.linalg import norm, solve_triangular\nfrom scipy.linalg.lapack import get_lapack_funcs\nfrom scipy.linalg.misc import LinAlgError\n\... | [
[
"numpy.diag",
"numpy.ones_like",
"scipy.sparse.issparse",
"numpy.eye",
"scipy.sparse.tril",
"scipy.sparse.triu",
"scipy.linalg.norm",
"scipy.linalg.lapack.get_lapack_funcs",
"scipy.linalg.misc.LinAlgError",
"numpy.triu",
"scipy.linalg.solve_triangular",
"numpy.tril"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.12",
"0.14",
"0.15"
],
"tensorflow": []
}
] |
liangz1/petastorm | [
"d2fbdaefc24a9f07e846bb157b4e7c785a45ca04"
] | [
"petastorm/tests/test_parquet_reader.py"
] | [
"# Copyright (c) 2017-2018 Uber Technologies, Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by app... | [
[
"numpy.testing.assert_equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
educauchy/kaggle-tab-playground-aug21 | [
"e740b251b8ab95c309255a42d32c5f105dccb35e"
] | [
"app/ml_tools/models/MetaRegressor.py"
] | [
"from sklearn.base import RegressorMixin\nfrom sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor, \\\n StackingRegressor, BaggingRegressor\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.tree import DecisionTreeRegressor\nfr... | [
[
"sklearn.linear_model.LogisticRegression"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ChairOfStructuralMechanicsTUM/Mechanics_Apps | [
"b064a42d4df3fa9bde62a5cff9cb27ca61b0127c"
] | [
"Normal_Force_Rod/NFR_equations.py"
] | [
"\"\"\"\nNormal Force Rod - force and deformation equations\n\n\"\"\"\n# general imports\nfrom __future__ import division # float division only, like in python 3\nimport numpy as np # needs at least version 1.17\n\n# bokeh imports\n\n# internal imports\nfrom NFR_constants import(\n F, L, E, A, sigma, p0, T, ... | [
[
"numpy.linspace",
"numpy.ones",
"numpy.concatenate",
"numpy.errstate",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
quantum-devices-physics-lab/qutip-parallel-simulator | [
"a4ba50a2b7e5f7744406bdfd2323859d228202ab"
] | [
"Example.py"
] | [
"from QutipSimulator import *\nimport numpy as np\nfrom qutip import *\n\n# This is script is an example of how to use QutipSimulator\n\n# Create a class that inherit class QuantumSystem\n# One have to implement the method run, which is the one that will be runned in parallel\nclass MyQuantumExperiment(QuantumSyste... | [
[
"numpy.array",
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dovletov/fantastic-robot | [
"3e95e06511da6295f02513ea0a871a139cc0cf02"
] | [
"utils/models.py"
] | [
"import tensorflow as tf\nimport tensorflow.contrib.slim as slim\nfrom tensorflow import initializers as tfinit\n\ndef cnn2d_example(inputs, pkeep_conv, pkeep_hidden):\n \"\"\"\n \"\"\"\n print(inputs.shape)\n net = slim.conv2d(inputs = inputs,\n num_outputs = 64,\n ... | [
[
"tensorflow.contrib.slim.flatten",
"tensorflow.contrib.slim.dropout",
"tensorflow.initializers.zeros",
"tensorflow.initializers.truncated_normal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MrMorning/solutionOfCS131 | [
"36dc9f197335d70601973b024e3ad5cb816bd26e"
] | [
"hw5_release/segmentation.py"
] | [
"\"\"\"\nCS131 - Computer Vision: Foundations and Applications\nAssignment 5\nAuthor: Donsuk Lee (donlee90@stanford.edu)\nDate created: 09/2017\nLast modified: 09/25/2018\nPython Version: 3.5+\n\"\"\"\n\nimport numpy as np\nimport scipy\nimport random\nfrom scipy.spatial.distance import squareform, pdist\nfrom skim... | [
[
"numpy.allclose",
"numpy.random.choice",
"numpy.arange",
"numpy.linalg.norm",
"numpy.dstack",
"numpy.logical_or",
"numpy.max",
"numpy.copy",
"numpy.std",
"numpy.mean",
"numpy.argmin",
"numpy.fill_diagonal",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
karshUniBremen/ActivityTracker | [
"26357008c66ddc116e3c3174abfa3cd3a1a3e846"
] | [
"MLP/activityApp.py"
] | [
"from glob import glob\n\nimport pandas as pd\n\nfrom activityAnalysis import MeasurementExtractor\nfrom activityClassifier import ActivityFeatureExtraction, ActivityClassifier\n\n\ndef app_main():\n files = sorted(glob(\"../FinaldataSet/*\"))\n measurements = list()\n for measurement_file in files:\n ... | [
[
"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": []
}
] |
joselynzhao/Pytorch_advance | [
"c08c8362bc7812176fc1e366d2bcd5adc91bec72"
] | [
"Define/01.py"
] | [
"# from __future__ import print_function\n\nimport torch\n\nx = torch.empty(5,3) # 不进行初始化\nprint(x) # 生成了5*3的张亮\n\nx = torch.rand(5,3) # 随机初始化\nprint(x)\n\nx = torch.zeros(5,3,dtype=torch.long) # all are zero,\nprint(x)\n\nx = torch.tensor([5.5,3]) # 构造一个张量\nprint(x) #如果是浮点数,小数点是4位\n\nx = x.new_ones(5,3,dtype=tor... | [
[
"torch.randn_like",
"torch.empty",
"torch.add",
"torch.zeros",
"torch.randn",
"torch.tensor",
"torch.rand"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
grantgasser/vehicle-detection | [
"812932d545eba088f530fa77dc9ef0399d30b713"
] | [
"src/train.py"
] | [
"import glob \nimport os\nimport time\n\nimport cv2\nimport numpy as np\nimport pickle\nimport matplotlib.image as mpimg\nimport matplotlib.pyplot as plt\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.svm import SVC\nfrom sklearn.decomposition i... | [
[
"sklearn.model_selection.train_test_split",
"numpy.concatenate",
"sklearn.svm.SVC",
"sklearn.preprocessing.StandardScaler",
"sklearn.decomposition.PCA",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Gibbsdavidl/GeneSignalProc | [
"9710b495b969b1b16b43228fb6d2ff7004d819be"
] | [
"src/simulateData_DisjointSets.py"
] | [
"\n# create a set of genes, set membership, a weighted network, and\n# create two groups of samples with simulated expression data.\n# ... right now the sets are disjoint ...\n\n#simsets.py\n\n# need a set of sets, to find an ordering over all.\n# related to contigs, but with repeated sets.\n\nimport sys\nimport n... | [
[
"numpy.random.choice",
"numpy.random.multivariate_normal",
"numpy.random.exponential",
"numpy.transpose",
"numpy.savetxt",
"numpy.array",
"numpy.zeros",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
madwilliam/skccm | [
"82f0fd44f98b3efe67fdaa3ce5bd78cd0b49a9ee"
] | [
"skccm/paper.py"
] | [
"# \n# Data for analyzing causality.\n# By Nick Cortale\n#\n# Classes:\n# \tccm\n# \tembed\n#\n# Paper:\n# Detecting Causality in Complex Ecosystems\n# George Sugihara et al. 2012\n#\n# Thanks to Kenneth Ells and Dylan McNamara\n#\n# Notes:\n# Originally I thought this can be made way faster by only calculting the\... | [
[
"numpy.sqrt",
"numpy.linspace",
"numpy.min",
"numpy.arange",
"sklearn.metrics.mutual_info_score",
"pandas.DataFrame",
"sklearn.neighbors.KNeighborsRegressor",
"numpy.max",
"numpy.zeros_like",
"numpy.mean",
"numpy.logical_and",
"numpy.zeros",
"numpy.sum",
"nu... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
mayank-sfdc/dpu-utils | [
"72cac67490ccf19fede6b86269c57843549b526c"
] | [
"python/tests/ptutils/testmodel.py"
] | [
"from typing import NamedTuple, List, Optional, Dict, Any\r\n\r\nimport numpy as np\r\nimport torch\r\nfrom torch import nn as nn\r\n\r\nfrom dpu_utils.ptutils import BaseComponent\r\n\r\n\r\nclass SampleDatapoint(NamedTuple):\r\n input_features: List[float]\r\n target_class: bool\r\n\r\n\r\nclass TensorizedD... | [
[
"numpy.stack",
"torch.tensor",
"torch.nn.Linear",
"torch.nn.BCEWithLogitsLoss",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
scrasmussen/icar | [
"88c59fed7595b176a81127993785fdeb514f28a3"
] | [
"helpers/ideal_linear.py"
] | [
"#!/usr/bin/env python\nimport sys\n\nimport matplotlib.pyplot as plt #graphics library\nimport numpy as np #numerical library (FFT etc)\n\nimport Nio # NCAR python io library\n\n# set up the physical domain of the experiment to be run\n# zs = topography (Fzs = FFT(zs))\n# dx ... | [
[
"matplotlib.pyplot.legend",
"numpy.sqrt",
"numpy.linspace",
"matplotlib.pyplot.plot",
"numpy.argmin",
"numpy.exp",
"numpy.where",
"numpy.arange",
"matplotlib.pyplot.gcf",
"numpy.fft.ifftshift",
"numpy.zeros",
"matplotlib.pyplot.title",
"matplotlib.pyplot.ylim",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
IDEMSInternational/opencdms-components-server-demo | [
"6fe056448b4f03d77d76fe29b9caef73c2781fc7"
] | [
"app/utils/product_data.py"
] | [
"import os\n\nfrom pandas import DataFrame, read_csv, read_sql\nfrom datetime import datetime\nfrom opencdms.models.climsoft import v4_1_1_core as climsoft\nfrom sqlalchemy.orm.session import Session\nfrom sqlalchemy.orm.query import Query as SqlQuery\n\nfrom app.api.products.schema import ProductDataParams\nfrom a... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
andrewfowlie/accumulating_evidence | [
"bcb11634d259543a32d6b98cb45d952d0dcd8c4c"
] | [
"data.py"
] | [
"\"\"\"\nGenerate pseudo-data\n====================\n\"\"\"\n\nimport numpy as np\nfrom scipy.stats import poisson\n\n# number of pseudo-data sets and channels\n\nn_pseudo = 100000\nn_channels = 6\n\n# detector resolution per channel\n\nsigma_gg = 1.5\nsigma_bb = 14.\nresolutions = [sigma_gg] * 5 + [sigma_bb]\n\n# ... | [
[
"numpy.save",
"numpy.zeros_like",
"numpy.random.seed",
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
junhyeokahn/PyPnC | [
"1a038ac282e0e8cf19a7af04928271ed00b61407"
] | [
"simulator/pybullet/draco_manipulation_main.py"
] | [
"import os\nimport sys\n\ncwd = os.getcwd()\nsys.path.append(cwd)\nimport time, math\nfrom collections import OrderedDict\nimport copy\nimport signal\nimport shutil\n\nimport cv2\nimport pybullet as p\nimport numpy as np\n\nnp.set_printoptions(precision=2)\n\nfrom config.draco_manipulation_config import SimConfig\n... | [
[
"numpy.set_printoptions",
"numpy.radians"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
maliha93/Fairness-Analysis-Code | [
"acf13c6e7993704fc627249fe4ada44d8b616264",
"acf13c6e7993704fc627249fe4ada44d8b616264",
"acf13c6e7993704fc627249fe4ada44d8b616264",
"acf13c6e7993704fc627249fe4ada44d8b616264"
] | [
"Inprocessing/Thomas/Python/core/optimizers/bfgs.py",
"Postprocessing/Hardt/Hardt.py",
"Inprocessing/Thomas/Python/experiments/classification/credit_predictions.py",
"Inprocessing/Thomas/Python/baselines/fairlearn/classred.py"
] | [
"import cma\nimport numpy as np\n\nfrom core.optimizers import SMLAOptimizer\nfrom time import time\n\nfrom scipy.optimize import minimize\n\nclass BFGSOptimizer(SMLAOptimizer):\n\n\tdef __init__(self, n_features, sigma0=1.0, restarts=5, *args, **kwargs):\n\t\tself.n_features = n_features\n\t\tself.sigma0 = sig... | [
[
"numpy.random.random",
"scipy.optimize.minimize"
],
[
"numpy.log",
"pandas.read_csv",
"numpy.random.shuffle",
"numpy.mean",
"numpy.savetxt",
"numpy.array"
],
[
"numpy.savez"
],
[
"pandas.Series",
"numpy.sqrt",
"pandas.DataFrame",
"numpy.ones",
"n... | [
{
"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"
... |
hexieshenghuo/Paddle | [
"2497f4392fe60f4c72e9b7ff5de9b8b6117aacac"
] | [
"python/paddle/fluid/tests/custom_op/test_jit_load.py"
] | [
"# Copyright (c) 2021 PaddlePaddle 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 re... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
barblin/lodestar | [
"b36c9b76374c111b24abad87fa4f573bfd129d0e"
] | [
"lodestar-backend/code/LineGeometry/line_projection.py"
] | [
"import numpy as np\nfrom code.LineGeometry.utils import distance\nfrom code.miscellaneous.utils import pairwise_loop\n\n\ndef distance2projectedVector(a, b, vec):\n \"\"\"Project point p onto line spanned by a & b\n Returns distance a-p on line a-b\n \"\"\"\n ap = vec-a\n ab = b-a\n # distance be... | [
[
"numpy.dot",
"numpy.array",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ubc-vision/RefTR | [
"f31b28ba98d5716f8c5ba994af6cd284d016ebba"
] | [
"util/transforms.py"
] | [
"# -*- coding: utf-8 -*-\n\n\"\"\"\nGeneric Image Transform utillities.\n\"\"\"\n\nimport cv2\nimport random, math\nimport numpy as np\nfrom collections import Iterable\n\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\n\n\ndef letterbox(img, mask, height, color=(123.7, 116.3, 103.5)): # resi... | [
[
"numpy.maximum",
"numpy.clip",
"numpy.eye",
"numpy.ones",
"numpy.concatenate"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jofrony/Neuromodulation | [
"b5e1502701399c02cecff20b85d4ff8af7772ec9"
] | [
"neuromodcell/analysis.py"
] | [
"import pathlib\nimport numpy as np\nimport json\nimport neuromodcell.modulation_functions as mf\nimport matplotlib.pyplot as plt\n\n\n'''\nClass for loading the result of the optimisation\n\nfunctions for analysing the data\n\n'''\n\n\nclass OptimisationResult:\n \n def __init__(self,dir_path):\n \n ... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.title",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.show",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.xlabel",
"numpy.array",
"numpy.loadtxt",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
weegreenblobbie/sd_audio_hackers | [
"25a9cfa5049d5a1155c80fb1242c061c096a0d14"
] | [
"20160821_wavetable_chorus/code/sdaudio/wavio.py"
] | [
"\"\"\"\nWavefile IO\n\nReads and writes RIFF WAVE files and headers.\n\n\nReference: http://soundfile.sapp.org/doc/WaveFormat/\n\n-------------------------------------------------------------------------------\nRIFF WAVE file format\n-------------------------------------------------------------------------------\n... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
taylormordan/openpifpaf | [
"c516dded6554de51d1126fabb748cdd782993cb1"
] | [
"openpifpaf/show/painters.py"
] | [
"import logging\n\nimport numpy as np\n\nfrom ..configurable import Configurable\n\ntry:\n import matplotlib\n import matplotlib.animation\n import matplotlib.collections\n import matplotlib.patches\nexcept ImportError:\n matplotlib = None\n\n\nLOG = logging.getLogger(__name__)\n\n\nclass DetectionPa... | [
[
"matplotlib.collections.PatchCollection",
"numpy.asarray",
"matplotlib.patches.Rectangle",
"numpy.zeros_like",
"numpy.any",
"matplotlib.cm.get_cmap",
"numpy.argsort",
"numpy.array",
"numpy.logical_and",
"matplotlib.patches.Polygon",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wotmd5731/pytorch_dqn | [
"fb3062c3aff1e5e249551807e53e974363f7595c"
] | [
"test.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Jan 21 17:36:23 2018\n\n@author: JAE\n\"\"\"\nimport numpy as np\ntt=[]\n\nkk = [[5,5,5,5],[1],[2],[6,6,6,6]]\n\ntt.append([[5,5,5,5],[1],[2],[6,6,6,6]]) \ntt.append([[5,5,5,5],[1],[2],[6,6,6,6]]) \ntt.append([[5,5,5,5],[1],[2],[6,6,6,6]]) \ntt.append([[5,5,5,5],[1],... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
BILLXZY1215/museflow | [
"241a98ef7b3f435f29bd5d2861ac7b17d4c091d8"
] | [
"museflow/nn/rnn.py"
] | [
"import tensorflow.compat.v1 as tf\ntf.disable_v2_behavior()\n\n\nclass DropoutWrapper(tf.nn.rnn_cell.DropoutWrapper): # pylint: disable=abstract-method\n \"\"\"A version of `tf.nn.rnn_cell.DropoutWrapper` that disables dropout during inference.\"\"\"\n\n def __init__(self, cell, training, **kwargs):\n ... | [
[
"tensorflow.compat.v1.shape",
"tensorflow.compat.v1.convert_to_tensor",
"tensorflow.compat.v1.constant",
"tensorflow.compat.v1.disable_v2_behavior"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
consbio/MPilot | [
"e0c48b6640982c8234404b2f2ca34859b0c96dc0"
] | [
"mpilot/libraries/eems/csv/io.py"
] | [
"from __future__ import absolute_import\n\nimport csv\n\nimport numpy\n\nfrom mpilot import params\nfrom mpilot.commands import Command\nfrom mpilot.libraries.eems.exceptions import EmptyDataFile, InvalidDataFile\nfrom mpilot.libraries.eems.mixins import SameArrayShapeMixin\n\n\nclass EEMSRead(Command):\n \"\"\"... | [
[
"numpy.ma.array"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.13",
"1.16",
"1.9",
"1.18",
"1.20",
"1.7",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Janie1996/PredNet_pytorch | [
"5581eed55a0a3506d175a124d7a6f9e55023c4ed"
] | [
"debug/reproduce_bug.py"
] | [
"# -*- coding: utf-8 -*-\n\nimport numpy as np\nimport torch\nfrom torch.autograd import Variable\n\n# a = Variable(torch.from_numpy(np.arange(12).reshape(3, 4)).float(), requires_grad=True)\nL = [Variable(torch.from_numpy(np.arange(12).reshape(3, 4)).float(), requires_grad=True),\n\t Variable(torch.from_numpy(np.a... | [
[
"numpy.arange",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SBU-BMI/quip_prad_cancer_detection | [
"23b9c23c82bde63f2f2b681388e20b8951a9e2a4"
] | [
"training_codes/utils.py"
] | [
"import torch\nfrom torch.utils.data import DataLoader, Dataset\nimport torch.nn.parallel\nimport torch.backends.cudnn as cudnn\nfrom torch.autograd import Variable\nimport sys\nimport argparse\nfrom PIL import Image\n\n\ndef get_label_from_filename(fn):\n # *_{lb}.png --> extract the label\n lb = int(fn[-5])... | [
[
"torch.zeros",
"torch.utils.data.DataLoader",
"torch.cuda.is_available",
"torch.nn.DataParallel",
"torch.autograd.Variable"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Telofy/swungdash | [
"b17f3983f3aa462912f914521ce1534c6591a82a"
] | [
"squigglypy/resolvers.py"
] | [
"from typing import Callable, Tuple, Union\n\nfrom scipy.integrate import quad # type: ignore\n\nfrom .context import Context\nfrom .tree import BaseValue, Resolveable\nfrom .utils import aslist\n\nquad: Callable[..., Tuple[float, float]]\n\n\nclass Integral(Resolveable):\n def __init__(\n self,\n ... | [
[
"scipy.integrate.quad"
]
] | [
{
"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"... |
jestra52/twitter-sentiment-analysis | [
"24e8d639dfec784b6ead0451e921323c761db315"
] | [
"ml_source/format_dataset.py"
] | [
"import numpy as np\nimport os\nimport pandas as pd\nimport re\n\nHTML_TAGS = re.compile(r'<.*?>')\nSPECIAL_CHARS_NO_SPACE = re.compile(r'[.;:!\\'?,\\\"()\\[\\]]')\nSPECIAL_CHARS_WITH_SPACE = re.compile(r'(<br\\s*/><br\\s*/>)|(\\-)|(\\/)')\n\nclass FormatDataset:\n def load_train_test_imdb_data(self, data_dir):\... | [
[
"numpy.random.shuffle",
"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": []
}
] |
etiennecollin/glycine-max-mitosis-ml | [
"cacd68f2a56df078cb1193bcc7ed4a0f86a659d4"
] | [
"src/yolov5/utils/activations.py"
] | [
"# YOLOv5 🚀 by Ultralytics, GPL-3.0 license\n\"\"\"\nActivation functions\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass SiLU(nn.Module):\n # SiLU activation https://arxiv.org/pdf/1606.08415.pdf\n @staticmethod\n def forward(x):\n return x * torch.sigmoid... | [
[
"torch.sigmoid",
"torch.ones",
"torch.randn",
"torch.nn.Conv2d",
"torch.nn.functional.hardtanh",
"torch.nn.BatchNorm2d",
"torch.nn.functional.softplus"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kelvin34501/pytorch3d | [
"36b451a49bdc481fb32707323c5bca53c34ac369"
] | [
"projects/nerf/nerf/nerf_renderer.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.\nfrom typing import List, Optional, Tuple\n\nimport torch\nfrom pytorch3d.renderer import ImplicitRenderer, ray_bundle_to_ray_points\nfrom pytorch3d.renderer.cameras import CamerasBase\nfrom pytorch3d.structures import Pointclouds\nfrom pytorc... | [
[
"torch.stack",
"torch.nn.ModuleDict",
"torch.cat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ShNadi/study_motivation | [
"cff7c3995e69ce2d91d0a2753b57d8089df3cad2"
] | [
"src/lda_document_topic.py"
] | [
"from sklearn.utils import shuffle\nimport nltk\nnltk.download('stopwords')\nfrom nltk.corpus import stopwords\nimport re\nimport numpy as np\nimport pandas as pd\nfrom contextlib import redirect_stdout\nimport pickle\nimport os\n\n# Gensim\nimport gensim\nimport gensim.corpora as corpora\nfrom gensim.utils import ... | [
[
"sklearn.utils.shuffle",
"numpy.array",
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
kseetharam/zwPolaron | [
"b2599622cb1a1444cc0df9d6addb48170938a67d"
] | [
"zw_scratch.py"
] | [
"import numpy as np\nimport Grid\nimport pf_static_sph as pfs\nimport os\nfrom timeit import default_timer as timer\n\n\nif __name__ == \"__main__\":\n\n start = timer()\n\n # ---- INITIALIZE GRIDS ----\n\n (Lx, Ly, Lz) = (20, 20, 20)\n (dx, dy, dz) = (0.2, 0.2, 0.2)\n\n # (Lx, Ly, Lz) = (21, 21, 21)... | [
[
"numpy.ceil",
"numpy.array",
"numpy.load",
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
huachuan/Tiny_EKF | [
"003b0a955cb7891889400ad7c9567447fe7e8547"
] | [
"extras/python/altitude_fuser.py"
] | [
"#!/usr/bin/env python3\n'''\naltitude_fuser.py - Sonar / Barometer fusion example using TinyEKF. \n\nWe model a single state variable, altitude above sea level (ASL) in centimeters.\nThis is obtained by fusing the barometer pressure in Pascals and sonar above-ground level\n(ASL) in centimters.\n\nAlso requires Re... | [
[
"numpy.copy",
"numpy.array",
"numpy.eye",
"numpy.random.rand"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gwaygenomics/OpticalPooledScreens | [
"ac67b4e55562217055ed9283d0bdca4629582bce"
] | [
"ops/pool_design.py"
] | [
"from collections import defaultdict, Counter\nimport scipy.sparse\nimport numpy as np\nimport pandas as pd\nimport os\nfrom Levenshtein import distance\n\nimport ops.utils\nfrom ops.constants import *\n\n\n# LOAD TABLES\n\ndef validate_design(df_design):\n for group, df in df_design.groupby('group'):\n x... | [
[
"pandas.read_excel",
"numpy.array",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
AhmadQasim/MedAL | [
"0ad6064d0d07f23722034b866ba86d93b62517f4",
"0ad6064d0d07f23722034b866ba86d93b62517f4"
] | [
"code/active_learning/learning_loss.py",
"code/semi_supervised/auto_encoder_cl.py"
] | [
"import time\nimport torch\nfrom torch.utils.data import DataLoader\n\nfrom active_learning.others import UncertaintySamplingOthers\nfrom data.isic_dataset import ISICDataset\nfrom data.matek_dataset import MatekDataset\nfrom data.cifar10_dataset import Cifar10Dataset\nfrom data.jurkat_dataset import JurkatDataset\... | [
[
"pandas.concat",
"torch.nn.functional.softmax",
"torch.max",
"torch.cat",
"torch.utils.data.DataLoader",
"torch.sum",
"pandas.DataFrame",
"torch.no_grad",
"pandas.DataFrame.from_dict",
"numpy.array"
],
[
"pandas.concat",
"torch.autograd.set_detect_anomaly",
... | [
{
"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": []
},
{
"matplotlib": [],
"nump... |
emilydolson/genomic_stability_model | [
"07e8c7fe5ac633be5aecbbe8ebc8fe2c6b247886"
] | [
"scripts/munge_data.py"
] | [
"import sys\nimport pandas as pd\nimport glob\nimport os\n\n# This script expects two command line arguments:\n# - a glob-style pattern indicating which directories to analyze\n# (remember to put quotation marks around it)\n# - the prefix filename to store data in (main data fill get stored in filename.... | [
[
"pandas.concat",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
parallelpro/Yaguang_stripped_rg_repo | [
"993f7459827b8e8f820a26768b5d2a281fe81709"
] | [
"modelling/coarse_v1/template/driver_template.py"
] | [
"from __future__ import print_function\nimport re\nimport numpy as np\nimport os\nimport sys\nfrom astropy.io import ascii\nimport glob\nfrom time import sleep\n\ndef set_gyre_inlist(inputFileName, summaryFileName, nu_max, delta_nu):\n # reads in the template inlist and writes out a new inlist with the \n # p... | [
[
"numpy.arange",
"numpy.log",
"numpy.where",
"numpy.genfromtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ericyi/articulated-part-induction | [
"d02dc890400e6e996cac3adde739652aef7837a9"
] | [
"model.py"
] | [
"import os\nimport sys\nBASE_DIR = os.path.dirname(os.path.abspath(__file__))\nPN_DIR = os.path.join(BASE_DIR,'pointnet2')\nsys.path.append(os.path.join(PN_DIR,'utils'))\nimport tensorflow as tf\nimport numpy as np\nimport tf_util\nfrom pointnet_util import pointnet_sa_module, pointnet_fp_module, pointnet_sa_module... | [
[
"tensorflow.nn.dynamic_rnn",
"tensorflow.concat",
"tensorflow.zeros",
"tensorflow.reduce_sum",
"tensorflow.cast",
"tensorflow.nn.sigmoid_cross_entropy_with_logits",
"scipy.optimize.linear_sum_assignment",
"tensorflow.Graph",
"tensorflow.squeeze",
"tensorflow.stop_gradient",... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.4",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"1.0",
"0.17",
"1.3",
"1.8"
],
"tensorflow": [
"1.10"
]
}
] |
miaecle/dynamorph | [
"9bc04ae771e66938273eee102d404947546a69c5"
] | [
"plot_scripts/plottings.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Sep 17 15:20:21 2019\n\n@author: michaelwu\n\"\"\"\nimport numpy as np\nimport cv2\nimport os\nimport pickle\nimport torch as t\nimport torch\nimport h5py\nimport pandas as pd\nfrom NNsegmentation.models import Segment\nfrom NNsegmentation.dat... | [
[
"matplotlib.pyplot.legend",
"numpy.expand_dims",
"numpy.linspace",
"torch.load",
"numpy.cumsum",
"pandas.DataFrame",
"numpy.concatenate",
"numpy.mean",
"scipy.stats.spearmanr",
"matplotlib.patches.Polygon",
"numpy.where",
"numpy.square",
"matplotlib.pyplot.gca",... | [
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.24",
"1.13",
"1.16",
"1.9",
"1.18",
"1.23",
"1.21",
"1.22",
"1.20",
"1.7",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [
... |
RudieLi/nni | [
"5a911b3061751a3441f18ff69a9279b16da9bda3"
] | [
"src/sdk/pynni/nni/compression/torch/speedup/compressor.py"
] | [
"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nimport logging\nimport torch\nfrom nni._graph_utils import build_module_graph\nfrom .compress_modules import replace_module\nfrom .infer_shape import ModuleMasks, infer_from_mask, infer_from_inshape, infer_from_outshape\n\n_logger = loggi... | [
[
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
chemicalfiend/Oceananigans.jl | [
"04ca8e2f143afd53fd60bdaeb885a4dc1ed5825c"
] | [
"validation/lid_driven_cavity/plot_lid_driven_cavity.py"
] | [
"import joblib\nimport numpy as np\nimport xarray as xr\nimport matplotlib.pyplot as plt\nimport matplotlib.colors as colors\nimport cmocean\nimport ffmpeg\n\nfrom numpy import nan\nfrom xarray.ufuncs import fabs\n\n#####\n##### Data from tables 1, 2, and 4 of Ghia et al. (1982).\n#####\n\nj_Ghia = [1, 8, ... | [
[
"matplotlib.colors.SymLogNorm",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.close",
"matplotlib.pyplot.subplots_adjust"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
melloddy/MELLODDY-TUNERv1 | [
"37a70402bee53fc4aa221257213e87ac4d6d750a"
] | [
"unit_test/test_descriptors.py"
] | [
"from melloddy_tuner.utils.helper import format_dataframe\nfrom melloddy_tuner.utils.df_transformer import DfTransformer\nfrom melloddy_tuner.utils.helper import read_csv\nfrom melloddy_tuner.utils.descriptor_calculator import DescriptorCalculator\nfrom melloddy_tuner.utils.chem_utils import run_fingerprint\nfrom m... | [
[
"numpy.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sakdag/experiments-on-classifiers | [
"294bd0850ca42c22311343c65626484e5853093c"
] | [
"src/supervised_learning/k_neighbors_regressor.py"
] | [
"import argparse\nimport os\nimport time\n\nimport numpy as np\nimport pandas as pd\n\nfrom sklearn.metrics import mean_squared_error, mean_absolute_percentage_error\nfrom sklearn.neighbors import KNeighborsRegressor\n\nimport src.config.config as conf\nimport src.preprocessing.preprocessing as prep\n\n\n# This cla... | [
[
"pandas.read_csv",
"pandas.DataFrame",
"sklearn.metrics.mean_squared_error",
"sklearn.neighbors.KNeighborsRegressor",
"sklearn.metrics.mean_absolute_percentage_error",
"numpy.array",
"numpy.array_split"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
johnsoncn/compression | [
"b470221bd5b8d28bf8e128bbe7aa4f45192b4711"
] | [
"tensorflow_compression/python/entropy_models/universal_test.py"
] | [
"# Copyright 2020 Google LLC. 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.convert_to_tensor",
"tensorflow.nn.softmax",
"tensorflow.random.stateless_normal",
"tensorflow.reduce_mean",
"tensorflow.range",
"tensorflow.reduce_sum",
"tensorflow.test.main",
"tensorflow.ones",
"tensorflow.math.log",
"tensorflow.exp",
"tensorflow.abs",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"1.12",
"2.6",
"2.2",
"1.13",
"2.3",
"2.4",
"2.9",
"2.5",
"2.8",
"2.10"
]
}
] |
fmmanicacci/ppo | [
"4b97ba4d0d334d84b77635503b4838fd145a24b4"
] | [
"networks.py"
] | [
"\"\"\"\"\"\"\n\n# +\n# | DEPENDENCIES\n# +\n\n# | STANDARD LIBRARIES\nfrom copy import deepcopy\nfrom typing import Optional, Tuple, Union\n# | THIRD-PARTY LIBRARIES\nimport torch as th\n\n# +\n# | HELPER FUNCTIONS\n# +\n\ndef build_MLP(\n inputs:int,\n hidden:Union[int, Tuple[int]],\n outputs:int,\n a... | [
[
"torch.nn.Linear",
"torch.nn.Sequential"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kngo107/testing | [
"81a225e36a9bd7160118171de19b98f6df5f009a"
] | [
"tests/scoring_tests.py"
] | [
"import os\nimport sys\nsys.path = [os.path.abspath(os.path.dirname(__file__))] + sys.path\n\nfrom auto_ml import Predictor\nimport numpy as np\n\nimport utils_testing as utils\n\ndef always_return_ten_thousand(estimator=None, actuals=None, probas=None):\n return 10000\n\ndef test_binary_classification():\n n... | [
[
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Ravi-0809/FlowDelta | [
"0321ea38401568f5d2330e72ce6bd8a8649c5563"
] | [
"FlowDeltaQA/general_utils.py"
] | [
"import re\nimport os\nimport sys\nimport random\nimport string\nimport logging\nimport argparse\nimport unicodedata\nfrom shutil import copyfile\nfrom datetime import datetime\nfrom collections import Counter\nimport torch\nimport msgpack\nimport json\nimport numpy as np\nimport pandas as pd\nfrom allennlp.modules... | [
[
"torch.ByteTensor",
"torch.LongTensor",
"torch.Tensor",
"torch.eq",
"numpy.random.permutation",
"numpy.random.uniform"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nevinadalal/Hyperactive | [
"3232ffeda70c5d4853b9e71aaf5d1e761c0db9c2"
] | [
"examples/optimization_techniques/tpe.py"
] | [
"from sklearn.datasets import load_iris\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.model_selection import cross_val_score\n\nfrom hyperactive import Hyperactive, TreeStructuredParzenEstimators\n\ndata = load_iris()\nX, y = data.data, data.target\n\n\ndef model(opt):\n knr = KNeighborsClass... | [
[
"sklearn.datasets.load_iris",
"sklearn.model_selection.cross_val_score",
"sklearn.neighbors.KNeighborsClassifier"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ROCmSoftwarePlatform/DeepSpeed | [
"bb4d5bf6f70aaf3ef7afbef817dbef6047b0a242"
] | [
"op_builder/builder.py"
] | [
"\"\"\"\nCopyright 2020 The Microsoft DeepSpeed Team\n\"\"\"\nimport os\nimport sys\nimport time\nimport importlib\nfrom pathlib import Path\nimport subprocess\nimport shlex\nimport shutil\nimport tempfile\nimport distutils.ccompiler\nimport distutils.log\nimport distutils.sysconfig\nfrom distutils.errors import Co... | [
[
"torch.version.hip.split",
"torch.__version__.split",
"torch.cuda.get_device_capability",
"torch.version.cuda.split",
"torch.cuda.device_count"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
c-hydro/dryes-idx | [
"74969687766ab0ede0bd4a69a61c390b64f2ad35"
] | [
"downloader/hsaf/dryes_downloader_hsaf_h141_h142.py"
] | [
"#!/usr/bin/python3\r\n\"\"\"\r\nDRYES Downloading Tool - SATELLITE H SAF h141 and h142\r\n\r\n__date__ = '20210920'\r\n__version__ = '1.0.0'\r\n__author__ =\r\n 'Francesco Avanzi (francesco.avanzi@cimafoundation.org',\r\n 'Fabio Delogu (fabio.delogu@cimafoundation.org',\r\n 'Andrea Libertino (... | [
[
"pandas.DatetimeIndex"
]
] | [
{
"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": []
}
] |
Erenhub/Graph2Gauss | [
"49d54c1950fb9dd50b0e68a9a377b35bd57e3967"
] | [
"g2g/model.py"
] | [
"import numpy as np\nimport tensorflow as tf\nfrom .utils import *\n\n\nclass Graph2Gauss:\n \"\"\"\n Implementation of the method proposed in the paper:\n 'Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking'\n by Aleksandar Bojchevski and Stephan Günnemann,\n published at... | [
[
"tensorflow.sparse_placeholder",
"tensorflow.stack",
"tensorflow.reduce_sum",
"tensorflow.sparse_tensor_dense_matmul",
"numpy.concatenate",
"tensorflow.GPUOptions",
"tensorflow.data.Dataset.from_generator",
"tensorflow.train.AdamOptimizer",
"numpy.arange",
"tensorflow.gathe... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
drewmee/opcsim | [
"c430d359196939e219a2bdd20cd277f3c3dcc548"
] | [
"opcsim/metrics.py"
] | [
"\"\"\"Contains the scoring algorithms used in the model.\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\nfrom .models import OPC\nfrom .utils import k_kohler, ri_eff\nfrom .mie import cscat\n\n\ndef compute_bin_assessment(opc, refr, kappa, rh_values=[0., 35., 95.]):\n \"\"\"Assess the ability of an OPC to a... | [
[
"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": []
}
] |
mrmattuschka/DeePiCt | [
"0bdf1cd845cc306e66e30face1010c12ca3a38d0",
"0bdf1cd845cc306e66e30face1010c12ca3a38d0",
"0bdf1cd845cc306e66e30face1010c12ca3a38d0",
"0bdf1cd845cc306e66e30face1010c12ca3a38d0"
] | [
"3d_cnn/src/tomogram_utils/coordinates_toolbox/clustering.py",
"3d_cnn/src/pytorch_cnn/classes/metrics.py",
"3d_cnn/pipelines/particle_picking/mask_motl.py",
"3d_cnn/src/networks/utils.py"
] | [
"import h5py\nimport numpy as np\nfrom os.path import join\nfrom scipy import ndimage\nfrom skimage import morphology as morph\nfrom skimage.measure import regionprops_table\nfrom tqdm import tqdm\nfrom scipy import ndimage\n\nfrom constants import h5_internal_paths\nfrom tomogram_utils.coordinates_toolbox.subtomos... | [
[
"numpy.pad",
"numpy.unique",
"numpy.min",
"numpy.rint",
"numpy.ones",
"numpy.max",
"numpy.intersect1d",
"numpy.array",
"scipy.ndimage.distance_transform_cdt",
"numpy.zeros",
"numpy.isin"
],
[
"numpy.sum",
"numpy.multiply"
],
[
"numpy.array",
"pan... | [
{
"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"... |
AntonPotapchuk/playground | [
"e29cbc974c4d08f27a2078d47d1a79080e712efe"
] | [
"rl_agent/tambet/mcts_agent5.py"
] | [
"import argparse\nimport multiprocessing\nfrom queue import Empty\nimport random\nimport time\nimport os\nimport re\nimport glob\nimport pickle\nimport numpy as np\nimport time\n\nimport pommerman\nfrom pommerman.agents import BaseAgent, SimpleAgent\nfrom pommerman import constants\n\nfrom keras.models import Model... | [
[
"numpy.log",
"numpy.random.choice",
"numpy.stack",
"numpy.full",
"tensorflow.ConfigProto",
"numpy.argmax",
"numpy.mean",
"numpy.any",
"tensorflow.Session",
"numpy.array",
"numpy.zeros",
"numpy.sum",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
fcggamou/simpletransformers | [
"fd6914a76d125b09acc1fb931f0ec2727ce22a57"
] | [
"simpletransformers/conv_ai/conv_ai_model.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n\n\nfrom __future__ import absolute_import, division, print_function\n\nimport json\nimport logging\nimport math\nimport os\nimport random\nimport statistics\nimport warnings\nfrom collections import defaultdict\nfrom itertools import chain\nfrom multiprocessing import cpu_c... | [
[
"torch.nn.functional.softmax",
"torch.utils.data.DataLoader",
"pandas.DataFrame",
"torch.multinomial",
"torch.no_grad",
"torch.cuda.is_available",
"torch.cuda.manual_seed_all",
"torch.topk",
"torch.device",
"torch.nn.CrossEntropyLoss",
"torch.utils.data.TensorDataset",
... | [
{
"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": []
}
] |
Enteee/DeepPoseKit | [
"7533569481be03fec6cfedd50a0fbfbcdcae4c54",
"7533569481be03fec6cfedd50a0fbfbcdcae4c54"
] | [
"deepposekit/models/saving.py",
"deepposekit/io/video.py"
] | [
"# -*- coding: utf-8 -*-\n# Copyright 2018-2019 Jacob M. Graving <jgraving@gmail.com>\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-... | [
[
"tensorflow.python.keras.engine.saving.save_model"
],
[
"numpy.stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.13",
"1.10",
"1.12"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kgrigaityte/BayesPowerlaw | [
"994f60d6274036e05f8481dc5f98113c0c5f3ab7"
] | [
"BayesPowerlaw/docs/BayesPowerlaw.py"
] | [
"from scipy.special import zeta\nfrom scipy.optimize import newton\nimport scipy as sp\nimport numpy as np\nfrom scipy.stats import uniform\nimport matplotlib.pyplot as plt\nimport warnings\n\n\nclass bayes(object):\n \"\"\"This function fits the data to powerlaw distribution and outputs the exponent\n using ... | [
[
"scipy.special.zeta",
"numpy.linspace",
"matplotlib.pyplot.plot",
"numpy.mean",
"scipy.optimize.newton",
"numpy.unique",
"numpy.arange",
"numpy.log",
"numpy.random.choice",
"matplotlib.pyplot.xscale",
"scipy.stats.uniform",
"numpy.array",
"numpy.sum",
"matpl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
facebookresearch/phyre-fwd | [
"18eb9d5849a22172c8e6b92b2a6453b3931b7781"
] | [
"agents/offline_agents.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by app... | [
[
"numpy.concatenate",
"numpy.mean",
"numpy.nanmean",
"numpy.argsort",
"numpy.array",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CheYulin/ScanOptimizing | [
"691b39309da1c6b5df46b264b5a300a35d644f70"
] | [
"paper/scalability_vary_threads/scalability_vary_threads.py"
] | [
"import math\nimport matplotlib.pyplot as plt\nimport os\n\nLABEL_SIZE = 24\nTICK_SIZE = 24\nLEGEND_SIZE = 24\nMARK_SIZE = 18\n\nserver_scalability_folder = '/home/yche/mnt/luocpu8/nfsshare/share/python_experiments/scalability_simd_paper2'\nu = 5\neps = 0.2\nthread_lst = [2 ** i for i in range(9)]\ntag_lst = ['prun... | [
[
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.rc",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.close",
"matplotlib.pyplot.subplots_adjust"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LinearZoetrope/abp | [
"2459c1b4d77606c1d70715ce8378d738ba102f37"
] | [
"abp/adaptives/hra/adaptive.py"
] | [
"import logging\nimport time\nimport random\nimport pickle\nimport os\nimport sys\n\nimport torch\nfrom baselines.common.schedules import LinearSchedule\n\n\nfrom abp.adaptives.common.prioritized_memory.memory import PrioritizedReplayBuffer\nfrom abp.utils import clear_summary_path\nfrom abp.models import HRAModel\... | [
[
"torch.abs",
"torch.sum",
"torch.cuda.is_available",
"torch.arange"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vbvg2008/RUA | [
"adeb719e479cbe4169b7847bab61ee670b9fb944"
] | [
"wrn2810_cifar10/rua/wrn2810_cifar10_rua.py"
] | [
"import os\nimport random\nimport tempfile\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom PIL import Image, ImageEnhance, ImageOps, ImageTransform\n\nimport fastestimator as fe\nfrom fastestimator.dataset.data import cifar10\nfrom fastestimator.op.numpyop import Nu... | [
[
"torch.nn.Sequential",
"torch.nn.functional.dropout",
"numpy.asarray",
"torch.nn.functional.avg_pool2d",
"torch.nn.Conv2d",
"torch.nn.Linear",
"torch.nn.BatchNorm2d",
"torch.optim.SGD",
"torch.nn.ReLU",
"numpy.where",
"torch.nn.init.kaiming_normal_"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
oushu1zhangxiangxuan1/learn-neat | [
"f185e12282bf4346351cc4d6cfa75238c38afa51"
] | [
"meat/utils/playground.py"
] | [
"\ndef Test_OnehotEncoder_Inverse_Transform():\n from sklearn.preprocessing import OneHotEncoder\n enc = OneHotEncoder(handle_unknown='ignore')\n X = [['Male', 1], ['Female', 3], ['Female', 2]]\n enc.fit(X)\n print(enc.categories_)\n enc.transform([['Female', 1], ['Male', 4]]).toarray()\n enc.i... | [
[
"sklearn.preprocessing.OneHotEncoder"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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