repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
chemicstry/Ventilator | [
"4804d9d260c31325cfa72eece18d60f4c3624c78"
] | [
"software/utils/debug/debug_cli.py"
] | [
"#!/usr/bin/env python3\n\n# Ventilator debug self.interface: simple command line self.interface\n# For a list of available commands, enter 'help'\n\n__copyright__ = \"Copyright 2021 RespiraWorks\"\n\n__license__ = \"\"\"\n\n Copyright 2021 RespiraWorks\n\n Licensed under the Apache License, Version 2.0 (the ... | [
[
"matplotlib.pyplot.ioff",
"matplotlib.pyplot.ion"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
EiffL/bayesfast | [
"52e9f405e6c80232ab523165e54406449ac4d0e1"
] | [
"bayesfast/core/density.py"
] | [
"import numpy as np\nfrom collections import namedtuple, OrderedDict\nfrom ..utils.collections import VariableDict, PropertyList\nfrom copy import deepcopy\nimport warnings\nfrom .module import Module, Surrogate\nfrom ..transforms._constraint import *\n\n__all__ = ['Pipeline', 'Density', 'DensityLite']\n\n# TODO: a... | [
[
"numpy.dot",
"numpy.einsum",
"numpy.asarray",
"numpy.all",
"numpy.concatenate",
"numpy.max",
"numpy.mean",
"numpy.zeros_like",
"numpy.searchsorted",
"numpy.ones_like",
"numpy.clip",
"numpy.empty_like",
"numpy.eye",
"numpy.atleast_1d",
"numpy.copy",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ScalableEKNN2021/ColossalAI | [
"b9f8521f8c881c5c781e46afa0be7aedd83bdb9c"
] | [
"tests/test_layers/test_3d/checks_3d/check_layer_3d.py"
] | [
"#!/usr/bin/env python\n# -*- encoding: utf-8 -*-\n\nimport time\n\nimport torch\nfrom colossalai.constants import INPUT_GROUP_3D, OUTPUT_GROUP_3D, WEIGHT_GROUP_3D\nfrom colossalai.core import global_context\nfrom colossalai.logging import get_dist_logger\nfrom colossalai.nn import (Classifier3D, CrossEntropyLoss3D... | [
[
"torch.distributed.broadcast",
"torch.cuda.synchronize",
"torch.randint",
"torch.nn.CrossEntropyLoss",
"torch.randn",
"torch.nn.Embedding",
"torch.nn.LayerNorm",
"torch.nn.Linear",
"torch.nn.init.ones_",
"torch.chunk",
"torch.distributed.get_rank"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
raymondngiam/neural-translation-model-eng-to-ch | [
"1dfb76d011526e43fbc0200c98c1082ffae866d6"
] | [
"src/model.py"
] | [
"import tensorflow as tf\nimport numpy as np\n\nfrom tensorflow.keras.preprocessing.sequence import pad_sequences\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.layers import Layer, Input, Masking, LSTM, Embedding, Dense\n\nclass EndTokenEmbedLayer(Layer):\n def __init__(self):\n super(... | [
[
"tensorflow.concat",
"tensorflow.keras.layers.Masking",
"tensorflow.keras.models.Model",
"tensorflow.keras.layers.Embedding",
"tensorflow.keras.layers.Dense",
"tensorflow.shape",
"tensorflow.reshape",
"tensorflow.GradientTape",
"tensorflow.keras.layers.LSTM",
"tensorflow.ke... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
amodas/PRIME-augmentations | [
"89880bfe2800d8e59fa04232ffd36aa7fc8e8064"
] | [
"utils/diffeomorphism.py"
] | [
"import functools\nimport math\nimport torch\nfrom einops import rearrange\nfrom opt_einsum import contract\n\n\nclass Diffeo(torch.nn.Module):\n \"\"\"Randomly apply a diffeomorphism to the image(s).\n The image should be a Tensor and it is expected to have [..., n, n] shape,\n where ... means an arbitrar... | [
[
"torch.linspace",
"torch.randint",
"torch.sin",
"torch.randn",
"torch.distributions.beta.Beta",
"torch.arange",
"torch.get_default_dtype",
"torch.meshgrid"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DirkZomerdijk/status | [
"299aca6986c0b274500c40613151d55aa98d5f52"
] | [
"debugger.py"
] | [
"#%%\nimport pickle\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom copy import deepcopy\nfrom global_variables import *\nfrom model_functions import get_vulnerability, calculate_chronic_state\nimport glob\nimport os\nfrom scipy import stats\nfrom mpl_toolkits.mplot3d import Axes3D\n... | [
[
"matplotlib.pyplot.scatter",
"numpy.unique",
"numpy.arange",
"numpy.mean",
"numpy.argsort",
"numpy.array",
"matplotlib.pyplot.show",
"matplotlib.pyplot.hist"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
argearriojas/sparse | [
"aaf97b3933859dd6ff5a4230ecfffc4523cb02ce"
] | [
"sparse/slicing.py"
] | [
"# Most of this file is taken from https://github.com/dask/dask/blob/master/dask/array/slicing.py\n# See license at https://github.com/dask/dask/blob/master/LICENSE.txt\n\nimport math\nfrom numbers import Integral, Number\nfrom collections import Iterable\n\nimport numpy as np\n\n\ndef normalize_index(idx, shape):\... | [
[
"numpy.issubdtype",
"numpy.asanyarray",
"numpy.where",
"numpy.nonzero"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cecabert/onnx-tensorflow | [
"c60a32caef3271b93e843bac7a44eda388f67165"
] | [
"onnx_tf/handlers/backend/gru.py"
] | [
"from functools import partial\n\nimport tensorflow as tf\n\nfrom onnx_tf.common import get_unique_suffix\nfrom onnx_tf.common import exception\nfrom onnx_tf.handlers.backend_handler import BackendHandler\nfrom onnx_tf.handlers.handler import onnx_op\nfrom onnx_tf.handlers.handler import partial_support\nfrom onnx_... | [
[
"tensorflow.concat",
"tensorflow.transpose",
"tensorflow.expand_dims",
"tensorflow.squeeze",
"tensorflow.add",
"tensorflow.split"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
xealml/text_classification | [
"2be2e94b539bb1058ca1807f0002c7942ad60617"
] | [
"a02_TextCNN/other_experiement/data_util_zhihu.py"
] | [
"# -*- coding: utf-8 -*-\nimport codecs\nimport numpy as np\n# load data of zhihu\nimport word2vec\nimport os\nimport pickle\nPAD_ID = 0\nfrom tflearn.data_utils import pad_sequences\n_GO = \"_GO\"\n_END = \"_END\"\n_PAD = \"_PAD\"\n\n\n# use pretrained word embedding to get word vocabulary and labels, and its rela... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Kirkados/Field_Robotics_2021 | [
"26823b75d303386a17c06b643a471a771e342779",
"26823b75d303386a17c06b643a471a771e342779"
] | [
"learner.py",
"replay_buffer.py"
] | [
"\"\"\"\nThis Class builds the Learner which consititutes the Critic, the Agent, and their\ntarget networks. Additionally, it samples data from the replay_buffer and trains\nboth the Critic and Agent neural networks.\n\nWhen a Learner instance is created, all the appropriate networks and training\noperations are bu... | [
[
"numpy.expand_dims",
"tensorflow.multiply",
"numpy.abs",
"numpy.linspace",
"numpy.reshape",
"tensorflow.placeholder",
"numpy.ones",
"numpy.mean",
"tensorflow.variable_scope",
"tensorflow.summary.scalar",
"tensorflow.summary.merge"
],
[
"numpy.reshape",
"nump... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
arp95/til_biomarker_ovarian_cancer | [
"b4e9f8126a6468d547fe1935fc4a224b36703ebe",
"b4e9f8126a6468d547fe1935fc4a224b36703ebe"
] | [
"code/epithelium_stroma_segmentation.py",
"misc/epi_stroma_model/seg_GAN.py"
] | [
"\"\"\"\nOriginal Author: Cheng Lu\nModified By: Arpit Aggarwal\nDescription of the file: Epi/Stroma segmentation. Updated script for my use case.\n\"\"\"\n\n\n# header files needed\nfrom unet import *\nfrom glob import glob\nfrom PIL import Image\nimport numpy as np\nimport cv2\nimport torch\nimport torch.nn as nn... | [
[
"torch.sigmoid",
"torch.load",
"torch.from_numpy",
"numpy.array",
"numpy.zeros"
],
[
"torch.nn.Sequential",
"torch.nn.ReflectionPad2d",
"torch.nn.Conv2d",
"torch.nn.Tanh",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DataResponsibly/fairDAGs | [
"ee6cfb447044af35b457f606ebcc0b70a7e7de77"
] | [
"fairness_instru.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n\n# # Fairness-Aware Instrumentation of ML-Pipelines\n\n# ## Preparations\n\nimport os\nfrom collections import defaultdict\nimport inspect\nimport pandas as pd\nimport numpy as np\nfrom scipy import stats\nimport re\nfrom graphviz import Digraph\nimport pickle\nimport rando... | [
[
"pandas.set_option",
"numpy.set_printoptions",
"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": []
}
] |
baofff/stability_ho | [
"1fa378209acde9c223855659c43f5ae842d37eb4"
] | [
"core/datasets.py"
] | [
"import torch\nimport torch.nn.functional as F\nfrom torchvision import datasets\nimport torchvision.transforms as transforms\nimport random\nfrom collections import defaultdict\nfrom torch.utils.data import Dataset\n\n\nclass PMLabel(object):\n def __init__(self, num_classes):\n self.num_classes = num_cl... | [
[
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
QROST/SHMS_2015 | [
"975c1c5837513260c6741c8f53d693f458c544b1"
] | [
"bridge.py"
] | [
"# coding: utf-8\r\n\r\nimport numpy as np\r\n\r\nm_span = 50.0 # m 主跨\r\ns_span = 50.0 # m 边跨\r\nE = 36500000000 # Pa # C65混凝土\r\nI = 1.24 # m^4 # T形，上翼缘宽度3.5m，梁高3.3m，翼缘厚0.2m，腹板厚0.25m\r\nm = 148600.0 / 25 # kg/m # (3.5*0.2+2.1*0.2)*50*26.0/9.8 # 预应力混凝土重力密度 26kN/m^3\r\ndamp_c = 50000\r\n\r\n\r\nclass Bridge:\r... | [
[
"numpy.sqrt",
"numpy.linspace",
"numpy.sin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jjerphan/pils | [
"a7b3f4bd8204f56b24c793c9f8a32df80d7d4e9c"
] | [
"pils/problems/tsp/optimizers.py"
] | [
"import os\nimport csv\nimport numpy as np\n\nimport optunity\nfrom hyperopt import hp, tpe, fmin\n\nfrom pils.optimizers import Optimizer\nfrom pils.settings import BIN_FOLDER, clean_lines\nfrom pils.problems.tsp.settings import TSP_INSTANCES_FOLDER, TSP_INSTANCES, NAIVE_COST_CSV, OPT_COST_CSV, \\\n TSP_OPTS_FO... | [
[
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cdigap/Python_Project_2018 | [
"136e70fb781ebd7aede0f2f11e57fb8f64ee0e22"
] | [
"Iris_versicolor.py"
] | [
"# Ashok Gangadharan 2018-04-09\n# Python Project...\n# \n# Plotting Graph for the different Iris Setosa flower , Average Sepal & Petal data\n#\n\nimport matplotlib.pyplot as plt\nimport csv\n\nx = []\ny = []\na = []\nb = []\ncount = 0\nsl = 0\nsw = 0\npl = 0\npw = 0\nasl = 0\nasw = 0\napl = 0\napw = 0\n\n\ndef avg... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.title",
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
XuCheney/Face_Recognition | [
"9112439e3ba37f0ba1bd7665da2c28d8543bf364"
] | [
"face_recognition.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: UTF-8 -*-\n# author:cheney<XZCheney@gmail.com>\n# 人脸识别\n\nimport os\nimport sys\nimport cv2\nimport dlib\nimport queue\nimport logging\nimport logging.config\nimport threading\nimport numpy as np\nimport pandas as pd\nfrom datetime import datetime\n\nfrom PyQt5.QtCore import ... | [
[
"numpy.square",
"numpy.array",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Tobias-Fischer/ros_people_object_detection_tensorflow | [
"2a0af311b4eef55c053bd2349e1dff10abe1f32a",
"2a0af311b4eef55c053bd2349e1dff10abe1f32a"
] | [
"src/object_detection/models/ssd_inception_v3_feature_extractor.py",
"src/object_detection/model_test_util.py"
] | [
"# Copyright 2017 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.compat.v1.variable_scope"
],
[
"tensorflow.compat.v1.contrib.learn.RunConfig"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ElegantLin/CVWC-2019 | [
"41c3d35c8a5eb21d109da137b75a872def301765"
] | [
"reid/main.py"
] | [
"# Creator: Tennant\n# Email: Tennant_1999@outlook.com\n\nimport os\nimport os.path as osp\n\n# PyTorch as the main lib for neural network\nimport torch\ntorch.backends.cudnn.benchmark = True\ntorch.multiprocessing.set_sharing_strategy('file_system')\nimport torch.nn as nn\nimport torchvision as tv\nimport numpy as... | [
[
"torch.cat",
"torch.load",
"torch.nn.DataParallel",
"torch.no_grad",
"numpy.argsort",
"torch.cuda.device_count",
"numpy.array",
"torch.multiprocessing.set_sharing_strategy"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
goodok/sgnn | [
"a1ea5023c5b7e4f1a66afd1daed10a60786e6ac1"
] | [
"old_versions/sgnn_original_python2.7/torch/train.py"
] | [
"from __future__ import division\nfrom __future__ import print_function\n\nimport argparse\nimport os, sys, time\nimport shutil\nimport random\nimport torch\nimport numpy as np\nimport gc\n\nimport data_util\nimport scene_dataloader\nimport model\nimport loss as loss_util\n\n\n# python train.py --gpu 0 --data_path ... | [
[
"torch.load",
"torch.utils.data.DataLoader",
"torch.nn.Sigmoid",
"numpy.all",
"numpy.mean",
"torch.no_grad",
"numpy.array",
"numpy.zeros",
"torch.optim.lr_scheduler.StepLR"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
quarkfin/QF-Lib | [
"1504c65c9ed8bbbd19948088fe7b924a7b6be709"
] | [
"qf_lib_tests/unit_tests/data_providers/test_general_price_provider_mock.py"
] | [
"# Copyright 2016-present CERN – European Organization for Nuclear Research\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/licen... | [
[
"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": []
}
] |
QuantTraderEd/vnpy_crypto | [
"844381797a475a01c05a4e162592a5a6e3a48032"
] | [
"venv/lib/python3.6/site-packages/pykalman/datasets/base.py"
] | [
"\"\"\"\nDataset\n\"\"\"\n\nfrom os.path import dirname, join\n\nimport numpy as np\nfrom numpy import ma\nfrom scipy import io\n\nfrom ..utils import Bunch, check_random_state\n\n\ndef load_robot():\n \"\"\"Load and return synthetic robot state data (state estimation)\n\n =================================\n ... | [
[
"numpy.ma.array",
"numpy.eye",
"numpy.zeros",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
afshinamini/Geoscience-BC-project-2019-014 | [
"3f91a021ad99ef02950e2ae919c132e8409d35d0"
] | [
"Data/Geological_Features/Formation Tops/Montney Grid/rbfInterp.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Nov 24 18:41:36 2020\r\n\r\n@author: aamini\r\n\"\"\"\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom scipy.interpolate import Rbf\r\nfrom sklearn.neighbors import NearestNeighbors\r\n\r\nd1 = pd.read_csv('Montney.csv')\r\nd2 = pd.read_csv('Montney2.5kmg... | [
[
"scipy.interpolate.Rbf",
"pandas.read_csv",
"sklearn.neighbors.NearestNeighbors"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"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"... |
MalcolmGomes/CPS040-Thesis | [
"1d7a750169f56923ffbd14d96c7c8e4c5d377bf9"
] | [
"Main/MemNet/MemNet_PyTorch-master/eval.py"
] | [
"import argparse, os\nimport torch\nfrom torch.autograd import Variable\nimport numpy as np\nimport time, math, glob\nimport scipy.io as sio\nfrom torch.backends import cudnn\nfrom memnet1 import MemNet\nfrom utils import convert_state_dict\n\ntorch.backends.cudnn.benchmark = True\ncudnn.benchmark = True\n\nparser... | [
[
"torch.load",
"scipy.io.loadmat",
"torch.from_numpy",
"numpy.mean",
"torch.cuda.is_available"
]
] | [
{
"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"... |
991166chun/TeaDisease | [
"3cf6499617c01b3a22babcbf65e8241c9cac3c06"
] | [
"mmdet/datasets/multistage.py"
] | [
"import mmcv\nimport numpy as np\n\nfrom mmdet.core import eval_map\nfrom .builder import DATASETS\nfrom .custom import CustomDataset\n\n\n@DATASETS.register_module()\nclass MultiStageDataset(CustomDataset):\n\n CLASSES_1 = ('disease','back')\n\n CLASSES_2 = ('brownblight', 'blister', 'algal', 'fungi_early',... | [
[
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ciubecca/3dunet-cavity | [
"cfcc827773b18a95d221ab86c1afc5e2f7c30ecb"
] | [
"tests/random_tests.py"
] | [
"import unittest\n\nfrom pytorch3dunet.datasets.featurizer import get_features, ComposedFeatures, LabelClass\nfrom pytorch3dunet.datasets.features import PotentialGrid\nfrom pytorch3dunet.augment.transforms import Phase\nimport numpy as np\nfrom typing import Mapping, Iterable, Callable\nfrom pytorch3dunet.augment.... | [
[
"numpy.random.normal",
"numpy.expand_dims",
"numpy.array_equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
soyoung97/MixText | [
"22993cd028a4223a54e138a89b53cd7978a5e38b"
] | [
"code/mixtext.py"
] | [
"import torch\nimport torch.nn as nn\nfrom transformers import *\nfrom transformers.modeling_bert import BertEmbeddings, BertPooler, BertLayer\nfrom normal_bert import ClassificationBert, MixupBert\n\nclass BertModel4Mix(BertPreTrainedModel):\n def __init__(self, config):\n super(BertModel4Mix, self).__in... | [
[
"torch.mean",
"torch.zeros_like",
"torch.nn.Tanh",
"torch.nn.Linear",
"torch.ones_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pculliton/PySyft | [
"23a0d1442d3d901b1139aeabe079ccf4177ebc0d"
] | [
"packages/syft/src/syft/core/node/common/client.py"
] | [
"# stdlib\nimport sys\nfrom typing import Any\nfrom typing import Dict\nfrom typing import Iterator\nfrom typing import List\nfrom typing import Optional\nfrom typing import Tuple\nfrom typing import Union\n\n# third party\nfrom google.protobuf.reflection import GeneratedProtocolMessageType\nfrom nacl.signing impor... | [
[
"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": []
}
] |
AyufhSri/GANAccImprover | [
"eff3a944bd6e5d9761ec815f28c0d32c87096308"
] | [
"utils.py"
] | [
"import torch\nimport torch.nn as nn\nimport os\nimport numpy as np\nimport shutil\nimport torchvision.transforms as transforms\nfrom torch.autograd import Variable\n\n\n\n\ndef label_level_loss(model,data, target,criterion,args):\n model.eval()\n n=10\n if args.is_cifar100:\n n=100\n l=[0]*n\n ... | [
[
"numpy.clip",
"torch.load",
"torch.from_numpy",
"numpy.ones",
"torch.no_grad",
"torch.save",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gallantlab/Eyetracking | [
"8cbf9251897672ca6d8ce6028bca4f23d7973a80"
] | [
"EyetrackingUtilities.py"
] | [
"import numpy\nfrom enum import IntEnum\ntry:\n\timport cPickle\nexcept:\n\timport _pickle as cPickle\nimport re\nimport io\n\nimport multiprocessing\n\ndef parallelize(function, iterable, nThreads = multiprocessing.cpu_count()):\n\t\"\"\"\n\tParallelizes a function. Copied from pycortex so as to not have that impo... | [
[
"numpy.round",
"numpy.load",
"numpy.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
scivision/robust-flow | [
"d9b52a70e62995cf06743275509b9ac726df2b51"
] | [
"BlackRobustFlow.py"
] | [
"#!/usr/bin/env python3\nimport logging\nimport imageio\nfrom pathlib import Path\nimport numpy as np\nfrom robustflow import runblack, loadflow\n\ntry:\n from matplotlib.pyplot import figure, show\nexcept ImportError:\n figure = show = None\n\n\ndef main(stem: Path, frames, outpath: Path):\n stem = Path(s... | [
[
"numpy.arange",
"numpy.meshgrid",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
csdongxian/PaddleSleeve | [
"4322d70ec21460e657a57f2fa9b09e5efc420efb",
"4322d70ec21460e657a57f2fa9b09e5efc420efb"
] | [
"AdvBox/attacks/gradient_method.py",
"AdvBox/examples/image_adversarial_training/cifar10_tutorial_fgsm_advtraining.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 ... | [
[
"numpy.expand_dims",
"numpy.linspace",
"numpy.squeeze",
"numpy.linalg.norm",
"numpy.sign"
],
[
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
shiaki/sforzando | [
"24aa5c49693fe783336cf41847b1b361e709d086"
] | [
"scripts/search-vizier.py"
] | [
"#!/usr/bin/python\n\n'''\n Find possible host galaxies of these candidates.\n'''\n\nimport os\nimport sys\nimport json\nfrom collections import OrderedDict\n\nimport numpy as np\n\nfrom tqdm import tqdm\nfrom astropy.coordinates import SkyCoord\nimport astropy.units as u\nfrom astroquery.vizier import Vizier\n\... | [
[
"numpy.ma.is_masked"
]
] | [
{
"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": []
}
] |
alephdata/followthemoney-predict | [
"77626c81908b071296c9fd3496ca309d97e128a8"
] | [
"followthemoney_predict/pipelines/xref/models/util.py"
] | [
"import numpy as np\nimport pandas as pd\n\nfrom .. import settings\n\n\ndef value_or_first_list_item(value):\n if isinstance(value, (list, tuple)):\n return value[0]\n return value\n\n\ndef aux_fields(sample, prefix):\n for feature in settings.FEATURE_IDXS:\n key = f\"{prefix}_{feature}\"\n ... | [
[
"numpy.asarray",
"pandas.notna"
]
] | [
{
"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": []
}
] |
cyclone923/op3 | [
"81050a38d81da2de27f463d6d5823154ec46cd0e",
"81050a38d81da2de27f463d6d5823154ec46cd0e"
] | [
"op3/core/logging.py",
"op3/launchers/launcher_util.py"
] | [
"\"\"\"\nBased on rllab's logger.\n\nhttps://github.com/rll/rllab\n\"\"\"\nfrom enum import Enum\nfrom contextlib import contextmanager\nimport numpy as np\nimport os\nimport os.path as osp\nimport sys\nimport datetime\nimport dateutil.tz\nimport csv\nimport json\nimport pickle\nimport errno\nfrom collections impor... | [
[
"numpy.min",
"numpy.median",
"numpy.max",
"numpy.std",
"numpy.average"
],
[
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
adalisan/keras-visual-semantic-embedding | [
"0c50d12799a2be0f51692d176ad7803245fcf053"
] | [
"keras_vse/eval.py"
] | [
"#!/usr/bin/env python3\n#encoding: utf-8\nimport os ,sys\nimport argparse\nimport datetime\nfrom os.path import join as osp\nfrom shutil import copytree, rmtree\nfrom math import ceil\nimport json\nimport numpy as np\nfrom models import encode_sentences\nfrom models import build_pretrained_models\nimport pandas as... | [
[
"pandas.read_csv",
"pandas.isnull",
"tensorflow.ConfigProto",
"pandas.lib.infer_dtype",
"numpy.argmax",
"pandas.unique",
"tensorflow.Session",
"numpy.array",
"numpy.zeros"
]
] | [
{
"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": [
"1.10",
"1.12",
"1.4",
... |
ignaciodsimon/optimised_biquad_filter | [
"0d85dc42033e767eeb55107e72dba98417377686"
] | [
"test_filters.py"
] | [
"\"\"\"\n This script is used to test both implementations of \n the biquad filter and measure their time performance.\n\n Joe Simon 2018.\n\"\"\"\n\nimport biquad_filter_optimised\nimport biquad_filter_original\nimport numpy\nimport matplotlib.pyplot as plot\nimport time\n\n\nif __name__ == '__main__':\n\... | [
[
"matplotlib.pyplot.semilogx",
"numpy.fft.rfft",
"matplotlib.pyplot.ylim",
"numpy.mean",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Quentin18/Matplotlib-fractals | [
"cbf8c39bd4da04446638408eff72fca21a6d8580"
] | [
"fractals/kochSnowflake.py"
] | [
"\"\"\"\nKoch snowflake\nhttps://en.wikipedia.org/wiki/Koch_snowflake\n\"\"\"\nimport sys\nfrom math import sqrt\nimport matplotlib.pyplot as plt\n\n\ndef kochCurve(n, xA, yA, xB, yB):\n if n != 0:\n xC = xA + (xB - xA) / 3\n yC = yA + (yB - yA) / 3\n xD = xA + 2 * (xB - xA) / 3\n yD ... | [
[
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.title",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
arshjot/knowledge-graphs | [
"14e2f6c141a361a9b973cefcfbfdd9209eff64c7"
] | [
"run.py"
] | [
"#!/usr/bin/python3\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport argparse\nimport json\nimport logging\nimport os\nimport random\n\nimport numpy as np\nimport torch\n\nfrom torch.utils.data import DataLoader\n\nfrom model import KGEModel... | [
[
"torch.device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
davidfpc/AoC2021 | [
"b526e606dbf1cc59de4951a321aa9b98d04fde4c"
] | [
"day1.py"
] | [
"import numpy as np\n\n\ndef read_input(file_name):\n with open(\"inputFiles/\" + file_name, \"r\") as file:\n lines = file.read().splitlines()\n return [int(i, base=16) for i in lines]\n\n\ndef part1(input_value):\n prev_value = input_value[0]\n counter = 0\n for i in input_value[1:]:\n ... | [
[
"numpy.lib.stride_tricks.sliding_window_view"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.24",
"1.21",
"1.23",
"1.22",
"1.20"
],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jacke121/Deep-Feature-Flow | [
"8034c0d4169e57db9a6d9add68275722dd20a8ba"
] | [
"fgfa_rfcn/config/config.py"
] | [
"# --------------------------------------------------------\n# Flow-Guided Feature Aggregation\n# Copyright (c) 2016 by Contributors\n# Copyright (c) 2017 Microsoft\n# Licensed under The Apache-2.0 License [see LICENSE for details]\n# Modified by Yuqing Zhu, Shuhao Fu, Xizhou Zhu, Yuwen Xiong, Bin Xiao\n# ---------... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
EqThinker/deep-track | [
"c72dc7b182c66c13fb6f5df38b6ed6e78f625a41"
] | [
"pred_learn/models/rnn.py"
] | [
"import torch\nfrom torch import nn\n\n\nclass PredictorRNN(nn.Module):\n def __init__(self, obs_shape, action_shape, hidden_size=16):\n super(PredictorRNN, self).__init__()\n self.rnn = nn.GRU(obs_shape + action_shape, hidden_size, num_layers=1, batch_first=True)\n\n self.mlp_us = nn.Sequen... | [
[
"torch.nn.Linear",
"torch.nn.ReLU",
"torch.nn.GRU",
"torch.cat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bda2017-shallowermind/MustGAN | [
"b06cbcf573461f88444d39ca6371d9912213d6f2",
"b06cbcf573461f88444d39ca6371d9912213d6f2"
] | [
"magenta/magenta/models/nsynth/ours/train.py",
"magenta/magenta/models/nsynth/gan/model.py"
] | [
"# Copyright 2017 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 appli... | [
[
"tensorflow.Graph",
"tensorflow.device",
"tensorflow.constant",
"tensorflow.reduce_mean",
"tensorflow.less",
"tensorflow.app.flags.DEFINE_integer",
"tensorflow.trainable_variables",
"tensorflow.ConfigProto",
"tensorflow.constant_initializer",
"tensorflow.train.ExponentialMo... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
InvisibleNemo/NIH_ChestXRay | [
"649f2aa7b9edc0426066fcd51beaeab33f1e4d1d"
] | [
"codes/one_hot_labels.py"
] | [
"\"\"\"\nproject: NIH Chest XRay dataset\ndate: 04/06/2018\ndeveloped by: Debanjan Paul\nfilename: one_hot_labels.py\nversion: 0.1\ndescription: Converts csv into one hot encoded labels\ndependencies: Pandas\n\t\t\n\"\"\"\n\n# Imports\nimport pandas as pd\n\n# Read csv file into pandas datafr... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
bradfordlynch/space_time_pde | [
"5e355b0434baf1757d071ce993b84073c8426223"
] | [
"experiments/rb2d/dataloader_spacetime.py"
] | [
"\"\"\"RB2 Experiment Dataloader\"\"\"\nimport os\nimport torch\nfrom torch.utils.data import Dataset, Sampler\nimport numpy as np\nfrom scipy.interpolate import RegularGridInterpolator\nfrom scipy import ndimage\nimport warnings\n# pylint: disable=too-manz-arguments, too-manz-instance-attributes, too-manz-locals\n... | [
[
"scipy.ndimage.gaussian_filter",
"numpy.meshgrid",
"numpy.linspace",
"numpy.arange",
"torch.utils.data.DataLoader",
"scipy.ndimage.median_filter",
"numpy.stack",
"torch.tensor",
"scipy.ndimage.uniform_filter",
"scipy.ndimage.maximum_filter",
"numpy.std",
"numpy.mean... | [
{
"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"... |
Gauthams1/smalltrain | [
"ac833d58ff2b577277079633da1b20eb50b8d332"
] | [
"src/smalltrain/utils/tf_log_to_csv.py"
] | [
"import os\nimport numpy as np\nimport pandas as pd\n\nfrom collections import defaultdict\nfrom tensorboard.backend.event_processing.event_accumulator import EventAccumulator\n\n\ndef tabulate_events(dir_path):\n summary_iterators = [EventAccumulator(os.path.join(dir_path, dname)).Reload() for dname in os.listd... | [
[
"numpy.array",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
marctimjen/Artefact-Rejection | [
"4e850d172fa8c08ba1776c46e760484673d7e7ad"
] | [
"LoaderPACK/trainer.py"
] | [
"import neptune.new as neptune\nimport os\nimport torch.nn as nn\nimport torch\nimport torch.nn.functional as F\nimport numpy as np\nimport re\n\nimport sys\nsys.path.append(\"..\") # adds higher directory to python modules path\n\nfrom LoaderPACK.Accuarcy_finder import Accuarcy_find\nfrom LoaderPACK.Accuarcy_uploa... | [
[
"torch.tensor",
"torch.mean",
"numpy.array",
"torch.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
qiuxin2012/BigDL | [
"e3cd7499c0f850eb003163df8f090e7e92841ad0"
] | [
"pyspark/bigdl/keras/backend.py"
] | [
"#\n# Copyright 2016 The BigDL Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law ... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cancan101/matplotlib | [
"9c60c583f63da64bfcb9bcadcf6cf4df6a165714"
] | [
"lib/matplotlib/legend.py"
] | [
"\"\"\"\nThe legend module defines the Legend class, which is responsible for\ndrawing legends associated with axes and/or figures.\n\nThe Legend class can be considered as a container of legend handles\nand legend texts. Creation of corresponding legend handles from the\nplot elements in the axes or figures (e.g.,... | [
[
"numpy.asarray",
"matplotlib.cbook.iterable",
"matplotlib.offsetbox.DraggableOffsetBox.__init__",
"matplotlib.cbook.is_string_like",
"matplotlib.transforms.Bbox.from_bounds",
"matplotlib.offsetbox.HPacker",
"matplotlib.artist.Artist.__init__",
"matplotlib.offsetbox.DrawingArea",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
joseppinilla/dwave-system | [
"86a1698f15ccd8b0ece0ed868ee49292d3f67f5b"
] | [
"tests/test_dwave_sampler.py"
] | [
"# Copyright 2018 D-Wave Systems Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by ap... | [
[
"numpy.all",
"numpy.any"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
IlyaGusev/rudetox | [
"e1c6334744bf9d28639efbb61c3605be51642ce9"
] | [
"rudetox/marker/train.py"
] | [
"import argparse\nimport json\nimport random\n\nimport numpy as np\nimport torch\nfrom torch.utils.data import Dataset\nfrom transformers import AutoTokenizer, AutoModelForTokenClassification\nfrom transformers import Trainer, TrainingArguments, pipeline, AdamW, get_cosine_schedule_with_warmup\nfrom tqdm import tqd... | [
[
"torch.cuda.is_available",
"torch.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ash-aldujaili/blackbox-adv-examples-signhunter | [
"9279730522d6127ecb332133a090256e90904f2a"
] | [
"src/lib/challenges/cifar10_challenge/train.py"
] | [
"\"\"\"Trains a model, saving checkpoints and tensorboard summaries along\n the way.\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport json\nimport os\nimport shutil\nfrom datetime import datetime\nfrom timeit import default_timer as ti... | [
[
"tensorflow.summary.FileWriter",
"numpy.random.seed",
"tensorflow.summary.image",
"tensorflow.cast",
"tensorflow.global_variables_initializer",
"tensorflow.train.MomentumOptimizer",
"tensorflow.summary.merge_all",
"tensorflow.Session",
"tensorflow.set_random_seed",
"tensorf... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
RoyalTS/dstoolbox | [
"2b79dd3f70882c90b03c5c898d82f795a0ae7a78"
] | [
"src/dstoolbox/sklearn/feature_importance.py"
] | [
"import numpy as np\nimport pandas as pd\nimport shap\n\n\n# FIXME?: This doesn't seem to return quite the same things as shap.summary_plot()\ndef shap_importances(model, X: pd.DataFrame) -> pd.DataFrame:\n \"\"\"Return a dataframe containing the features sorted by Shap importance\n\n Parameters\n --------... | [
[
"numpy.mean",
"numpy.abs"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
markgrobman/hailo_model_zoo | [
"2ea72272ed2debd7f6bee7c4a65bd41de57ec9cf"
] | [
"hailo_model_zoo/datasets/create_d2s_tfrecord.py"
] | [
"#!/usr/bin/env python\n\nimport os\nimport argparse\nimport tensorflow as tf\nimport numpy as np\nimport json\nimport collections\n\n\ndef _int64_feature(values):\n if not isinstance(values, (tuple, list)):\n values = [values]\n return tf.train.Feature(int64_list=tf.train.Int64List(value=values))\n\n\... | [
[
"tensorflow.io.TFRecordWriter",
"tensorflow.io.gfile.GFile",
"tensorflow.train.FloatList",
"tensorflow.train.BytesList",
"numpy.array",
"tensorflow.train.Int64List"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sarangi777/DeepStack | [
"cba38629ea86d004b0e1ddcf0c9d7997ff78c43b"
] | [
"deepstack/ensemble.py"
] | [
"\"\"\"\nModule representing the Meta-Learners, containing an Ensemble of Base-Learners\n\"\"\"\nimport numpy as np\nfrom sklearn import metrics\nimport warnings\nfrom abc import abstractmethod\nfrom sklearn.ensemble import RandomForestRegressor\nimport os\nimport joblib\nimport glob\nfrom deepstack.base import Mem... | [
[
"sklearn.ensemble.RandomForestRegressor",
"sklearn.metrics.roc_auc_score",
"numpy.array_equal",
"numpy.ones",
"numpy.concatenate",
"numpy.argmax",
"numpy.prod",
"numpy.array",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
minssoj/Learning_OpenCV-Python | [
"63f175985a1d9645191c49e16ab6bb91a4f6b7fb"
] | [
"Code/26.2DImageHistogram.py"
] | [
"# =================================================\n# minso.jeong@daum.net\n# 26. 2D 이미지 히스토그램 \n# Reference : samsjang@naver.com\n# =================================================\nimport numpy as np\nimport cv2 as cv\nimport matplotlib.pyplot as plt\n\nhscale = 10\n\ndef hist2D_cv():\n\timg = cv.imread('../Im... | [
[
"matplotlib.pyplot.imshow",
"numpy.clip",
"numpy.indices",
"matplotlib.pyplot.show",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aponom84/FARZ | [
"db292dfe6555aa7e117f445b4962b4b1df2f4bbf"
] | [
"src/network_models.py"
] | [
"import igraph as ig\nimport networkx as nx\nimport numpy as np\n\n\nclass Graph:\n def __init__(self, n, edge_list, directed=False):\n self.edge_list = edge_list \n self.n = n\n self.directed=directed\n # self.C = None\n # self.Cid = None\n self.rewiring... | [
[
"numpy.random.choice"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
chathumal93/Flood-Detection-ALOS2 | [
"aa8cc1e9c9cff5c2522287ebae065278964f4dc9"
] | [
"ALOS/process.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n\n\nimport os\nfrom zipfile import ZipFile\nimport gdal\nimport glob\nimport numpy as np\nimport pathlib\nimport rasterio\nfrom rasterio.warp import calculate_default_transform,reproject, Resampling\nfrom rasterio.merge import merge\nfrom rasterio import Affine\nfrom rasteri... | [
[
"numpy.subtract",
"numpy.append",
"numpy.log10",
"numpy.float32",
"numpy.warnings.filterwarnings",
"numpy.array",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MauricioSalazare/conditonal-copula | [
"68a9be3e0af7e541bca1b5bca28b45848420a583"
] | [
"models/elliptical_distributions_study.py"
] | [
"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom scipy.stats import multivariate_normal, chi2, norm, t\nfrom scipy.special import gamma, stdtr, stdtridf, stdtrit # x = stdtrit(2, 0.1) == t(df=2).ppf(0.1) // x = t.inv(u)\nfrom scipy import opti... | [
[
"numpy.nanmax",
"scipy.stats.norm.ppf",
"scipy.stats.norm.cdf",
"numpy.linspace",
"numpy.sqrt",
"numpy.vstack",
"numpy.nanmin",
"matplotlib.pyplot.get_cmap",
"pandas.DataFrame",
"matplotlib.pyplot.plot",
"scipy.stats.gaussian_kde",
"numpy.argmin",
"scipy.interpo... | [
{
"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": []
}
] |
Xiang-cd/realsafe | [
"39f632e950562fa00ac26d34d13b2691c9c5f013",
"39f632e950562fa00ac26d34d13b2691c9c5f013"
] | [
"realsafe/defense/bit_depth_reduction.py",
"realsafe/benchmark/attack_cli.py"
] | [
"''' The bit depth reduction defense method. '''\n\nimport tensorflow as tf\nimport numpy as np\n\nfrom realsafe.defense.input_transformation import input_transformation\n\n\ndef bit_depth_reduce(xs, x_min, x_max, step_num, alpha=1e6):\n ''' Run bit depth reduce on xs.\n\n :param xs: A batch of images to appl... | [
[
"numpy.arange",
"tensorflow.sigmoid",
"tensorflow.constant",
"tensorflow.expand_dims"
],
[
"tensorflow.ConfigProto",
"tensorflow.Session",
"numpy.mean",
"tensorflow.get_logger"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
... |
GMadorell/programming-challenges | [
"b4fd6cf9bc4a61a6f3efc2c5ab2be43743044df8"
] | [
"tuenti/tuenti_challenge_4/qualification/8_tuenti_restructuration/tuenti_restructuration.py"
] | [
"#!/usr/bin/env python\n\"\"\"\nProblem description.\n\"\"\"\n\nfrom __future__ import division\nfrom Queue import PriorityQueue\nimport sys\nimport math\nimport numpy\nfrom pprintpp import pprint\nfrom scipy.spatial.distance import cityblock\n\n\nclass TuentiRestructurationSolver(object):\n def __init__(self, o... | [
[
"numpy.nonzero",
"numpy.zeros",
"numpy.array_equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
timgates42/hyperopt | [
"63b5b9bf379fc55f6a158e17c400c1d8bb780fff"
] | [
"hyperopt/tests/test_fmin.py"
] | [
"import unittest\nimport numpy as np\nimport nose.tools\nfrom timeit import default_timer as timer\nimport time\nfrom hyperopt.early_stop import no_progress_loss\nfrom hyperopt.fmin import generate_trials_to_calculate\n\nfrom hyperopt import (\n fmin,\n rand,\n tpe,\n hp,\n Trials,\n exceptions,\n... | [
[
"numpy.random.RandomState"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
photoszzt/cupy | [
"05b7a50815b7f43ccfb504cf8c8b104a7093f9eb"
] | [
"cupy/__init__.py"
] | [
"import functools as _functools\nimport sys as _sys\nimport warnings as _warnings\n\nimport numpy as _numpy\n\nfrom cupy import _environment\nfrom cupy import _version\n\n\n_environment._detect_duplicate_installation() # NOQA\n_environment._setup_win32_dll_directory() # NOQA\n_environment._preload_libraries() # ... | [
[
"numpy.can_cast",
"numpy.asarray",
"numpy.dtype",
"numpy.binary_repr",
"numpy.result_type",
"numpy.base_repr",
"numpy.ndim",
"numpy.isscalar"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Goliath-Research/Computational-Statistics | [
"0eee6231da2f203c5cd393f8429177cc9c1e27cf"
] | [
"GoliathResearch/goliath_research/bootstrap.py"
] | [
"import numpy as np\nimport pandas as pd\nfrom scipy import stats as st\nfrom statsmodels.distributions.empirical_distribution import ECDF\nimport matplotlib.pyplot as plt\nimport seaborn as sns;\nfrom typing import Callable\n\nsns.set_style(\"whitegrid\") \n\nclass Bootstrap(object):\n '''\n\n '''\n\n de... | [
[
"matplotlib.pyplot.axvline",
"numpy.random.seed",
"matplotlib.pyplot.title",
"numpy.random.choice",
"numpy.percentile",
"pandas.DataFrame",
"numpy.round",
"numpy.mean",
"matplotlib.pyplot.hist",
"numpy.random.randint"
]
] | [
{
"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": []
}
] |
shreyas-bk/TPN | [
"f761af1e61086733a882cc37e0556cb47116f574"
] | [
"mmaction/models/tenons/necks/tpn.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmcv.cnn import xavier_init\nfrom mmcv import Config\nimport numpy as np\n\nfrom ...registry import NECKS\n\n\nclass Identity(nn.Module):\n\n def __init__(self):\n super(Identity, self).__init__()\n\n def forward(self, x):\n ... | [
[
"torch.nn.Dropout",
"numpy.log2",
"torch.cat",
"torch.nn.init.constant_",
"torch.nn.ModuleList",
"torch.nn.functional.cross_entropy",
"torch.nn.functional.adaptive_avg_pool3d",
"torch.nn.Linear",
"torch.nn.Conv3d",
"torch.nn.MaxPool3d",
"torch.nn.init.normal_",
"tor... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
allenai/scruples | [
"9a43459c507e57d89ab8442a4f3985cedecb8710",
"9a43459c507e57d89ab8442a4f3985cedecb8710"
] | [
"src/scruples/baselines/train.py",
"src/scruples/baselines/utils.py"
] | [
"\"\"\"Fine-tune pre-trained LMs on the scruples datasets.\"\"\"\n\nimport gc\nimport json\nimport logging\nimport math\nimport os\nimport shutil\nfrom typing import (\n Any,\n Dict,\n List,\n Optional)\n\nimport numpy as np\nfrom transformers import (\n AdamW,\n WarmupLinearSchedule)\nfrom scipy.... | [
[
"torch.nn.CrossEntropyLoss",
"torch.sum",
"torch.no_grad",
"torch.cuda.is_available",
"torch.device",
"torch.nn.DataParallel",
"numpy.array",
"scipy.special.softmax"
],
[
"pandas.concat",
"numpy.exp",
"sklearn.utils.validation.check_is_fitted",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.10",
"1.4",
"1.9",
"1.5",
"1.2",
"1.7",
"1.3",
"1.8"
],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
... |
fakegit/RestoreGAN | [
"eb64d65da1dba289349530960eafdbcebfa0e9a8"
] | [
"train.py"
] | [
"import logging\nfrom functools import partial\n\nimport cv2\nimport torch\nimport torch.optim as optim\nimport tqdm\nimport yaml\nfrom joblib import cpu_count\nfrom torch.utils.data import DataLoader\n\nfrom adversarial_trainer import GANFactory\nfrom dataset import PairedDataset\nfrom metric_counter import Metric... | [
[
"torch.optim.Adam",
"torch.optim.Adadelta",
"torch.optim.lr_scheduler.ReduceLROnPlateau",
"torch.optim.SGD"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Algogator/posthog | [
"764e10696b6ee9cba927b38e0789ed896f5d67dd"
] | [
"posthog/queries/sessions.py"
] | [
"import datetime\nfrom typing import Any, Dict, List, Optional, Tuple\n\nimport pandas as pd\nfrom dateutil.relativedelta import relativedelta\nfrom django.db import connection\nfrom django.db.models import F, Q, QuerySet\nfrom django.db.models.expressions import Window\nfrom django.db.models.functions import Lag\n... | [
[
"pandas.offsets.MonthEnd",
"pandas.offsets.Week",
"pandas.DataFrame",
"pandas.date_range"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
pcmoritz/flow | [
"bc97132e9e2d05262bb6bbad5bda173fd9f4ae92"
] | [
"flow/benchmarks/baselines/merge012.py"
] | [
"\"\"\"Evaluates the baseline performance of merge without RL control.\n\nBaseline is no AVs.\n\"\"\"\n\nfrom flow.core.params import SumoParams, EnvParams, InitialConfig, NetParams, \\\n InFlows\nfrom flow.scenarios.merge.scenario import ADDITIONAL_NET_PARAMS\nfrom flow.core.vehicles import Vehicles\nfrom flow.... | [
[
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
makailove123/tensor2tensor | [
"dde1661ab04149d02fb74ee62d0c82157f5e046a",
"f5d73746f7a46dc18fdd541b1f9265c7f3ad2918"
] | [
"tensor2tensor/data_generators/problem.py",
"tensor2tensor/layers/common_layers_test.py"
] | [
"# coding=utf-8\n# Copyright 2020 The Tensor2Tensor Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requir... | [
[
"tensorflow.compat.v1.data.TFRecordDataset",
"tensorflow.compat.v1.estimator.export.ServingInputReceiver",
"tensorflow.compat.v1.concat",
"tensorflow.compat.v1.data.Dataset.from_tensors",
"tensorflow.compat.v1.data.experimental.parallel_interleave",
"tensorflow.compat.v1.reshape",
"ten... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sudo-michael/optimized_dp | [
"da4bfdd15c0dc91e4f62e6036f6de77b4a99c40c"
] | [
"drafts/6d_graph.py"
] | [
"import heterocl as hcl\nimport numpy as np\nimport time\nimport plotly.graph_objects as go\nfrom gridProcessing import Grid\nfrom shape_functions import *\nfrom custom_graph_functions import *\nfrom Humannoid6D_sys1 import *\nfrom argparse import ArgumentParser\n\nimport scipy.io as sio\n\nimport math\n\n\"\"\" US... | [
[
"numpy.reshape",
"numpy.arange",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
harirakul/PlagiarismDetection | [
"f6ddff2392590fde85d1958068ebc3ff5acc8cee"
] | [
"similarity.py"
] | [
"import nltk\nimport websearch\nfrom difflib import SequenceMatcher\nimport pandas as pd\n\nnltk.download('stopwords')\nnltk.download('punkt')\nstop_words = set(nltk.corpus.stopwords.words('english')) \n\ndef purifyText(string):\n words = nltk.word_tokenize(string)\n return (\" \".join([word for word in words... | [
[
"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": []
}
] |
matsavage/models | [
"634309ac537bbfc5198197b92096a59b52b0bb45",
"42f98218d7b0ee54077d4e07658442bc7ae0e661"
] | [
"official/recommendation/data_async_generation.py",
"research/object_detection/predictors/convolutional_keras_box_predictor_test.py"
] | [
"# Copyright 2018 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.gfile.Exists",
"numpy.concatenate",
"tensorflow.gfile.MakeDirs",
"numpy.random.randint",
"numpy.arange",
"tensorflow.python_io.TFRecordWriter",
"numpy.ceil",
"tensorflow.gfile.Remove",
"numpy.zeros",
"tensorflow.gfile.ListDirectory",
"tensorflow.gfile.Open",... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1... |
DrowseyDevelopers/create-spectrograms | [
"889cd93fc6fd86c7e691b74083b8595d59632d60"
] | [
"__main__.py"
] | [
"#!/usr/bin/env python3\n\"\"\"\n Module to take in .mat MatLab files and generate spectrogram images via Short Time Fourier Transform\n ---------- ------------------------------ --------------------\n | Data.mat | -> | Short-Time Fourier Transform | -> | Spectrogram Imag... | [
[
"numpy.abs",
"matplotlib.pyplot.title",
"scipy.signal.stft",
"matplotlib.use",
"scipy.signal.tukey",
"matplotlib.pyplot.savefig",
"numpy.genfromtxt",
"matplotlib.pyplot.plot",
"numpy.seterr",
"matplotlib.pyplot.set_cmap",
"matplotlib.pyplot.clf",
"numpy.log10",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.0",
"0.19"
],
"tensorflow": []
}
] |
phil-hoang/general-object-detector | [
"a59fcfd4cf237dda7bde370b947d0d3096631d56"
] | [
"detr/detr.py"
] | [
"import torchvision.transforms as T\nimport torch\n\n\"\"\"\nFunctions for the detr object detection model\n\n\"\"\"\n\ndef detr_load():\n \"\"\"\n Loads the detr model using resnet50\n\n Returns: the detr model pretrained on COCO dataset\n \"\"\"\n\n model = torch.hub.load('facebookresearch/detr', '... | [
[
"torch.stack",
"torch.hub.load",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
P1R/cinves | [
"8251acfa00a9a26d9b0665e1897316b6664fb9bb",
"8251acfa00a9a26d9b0665e1897316b6664fb9bb"
] | [
"TrabajoFinal/PortadoraVariableModuladaFija/TvsFreq-FM.py",
"TrabajoFinal/PortadoraVariableModuladaFija/AM/TvsFrqRate-AM-pawn50.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\n#la frecuencia de la modulada FM es de 50 hz en todas las variaciones de la portadora\nFreq=np.array([20,30,40,50,60,70,80,90,100,110,120,130,140,150,160]);\nDeltaTemp=np.array([0.5,1.2,3.2,4.1,2.3,2.0,1.8,0.8,0.2,1.2,2.3,4.1,8.5,3.4,0.1])\nTempT1=np.array([20.8... | [
[
"matplotlib.pyplot.title",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.xlabel",
"numpy.array",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
],
[
"matplotlib.pyplot.title",
"matplotlib.pyplot.plot",
"matplo... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nsnmsak/graphillion_tutorial | [
"d5446b15f8a59784b37ef1786d1150ee59fe4a3a"
] | [
"ja/tutorial_util.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n\nfrom graphillion import GraphSet\nfrom graphviz import Digraph\nimport networkx as nx\nimport json\nimport matplotlib.pyplot as plt\nfrom IPython.display import Image\n\ndef zdd_size(graph_set):\n zdd = dump2zdd(graph_set.dumps().split(\"\\n\"))\n return len(zdd)\n\n... | [
[
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
manmanCover/OCNet.pytorch | [
"8484daaac4fab5b513a45e56b1b04cdebc620116"
] | [
"utils/loss.py"
] | [
"import pdb\nimport torch\nimport torch.nn.functional as F\nimport torch.nn as nn\nfrom torch.autograd import Variable\nimport numpy as np\nimport cv2\n\nclass CrossEntropy2d(nn.Module):\n\n def __init__(self, size_average=True, ignore_label=255, use_weight=True):\n super(CrossEntropy2d, self).__init__()\... | [
[
"torch.nn.CrossEntropyLoss",
"numpy.sum",
"torch.zeros",
"torch.sum",
"torch.FloatTensor",
"numpy.zeros",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
K-ona/-------- | [
"1bae093758c61e4863ca0b150195286e189af591"
] | [
"mtl.py"
] | [
"import matplotlib.pyplot as plt\nplt.style.use('ggplot')\nimport pandas as pd\nimport numpy as np\n\n#随机生成两个dataframe\nd1 = pd.DataFrame(columns=['x', 'y'])\nd1['x'] = np.random.normal(0, 1, 100)\nd1['y'] = np.random.normal(0, 1, 100)\nd2 = pd.DataFrame(columns=['x', 'y'])\nd2['x'] = np.random.normal(2, 1, 100)\nd... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.scatter",
"pandas.DataFrame",
"numpy.random.normal",
"matplotlib.pyplot.show",
"matplotlib.pyplot.style.use"
]
] | [
{
"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": []
}
] |
rebeccadavidsson/covid19-sir | [
"ca7a408c5fcf87e4857edd14a9276cae0b6737cf"
] | [
"covsirphy/cleaning/pcr_data.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nfrom dask import dataframe as dd\nfrom covsirphy.util.plotting import line_plot\nfrom covsirphy.util.error import PCRIncorrectPreconditionError, SubsetNotFoundError\nfrom covsirphy.cleaning.cbase im... | [
[
"pandas.concat",
"pandas.to_datetime",
"pandas.to_numeric",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
TheCheeseToast/fooof | [
"f3f8422af7d87fa73772e083deaf8439ca59908d"
] | [
"fooof/synth.py"
] | [
"\"\"\"Synthesis functions for generating model components and synthetic power spectra.\"\"\"\n\nimport numpy as np\n\nfrom fooof.core.funcs import gaussian_function, get_bg_func, infer_bg_func\n\n###################################################################################################\n##################... | [
[
"numpy.arange",
"numpy.power"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
deniskamazur/hm-debug | [
"cf31951504c38a1ea5e868e607ea74691092561a"
] | [
"tests/test_p2p_daemon.py"
] | [
"import asyncio\nimport multiprocessing as mp\nimport subprocess\nfrom contextlib import closing\nfrom functools import partial\nfrom typing import List\n\nimport numpy as np\nimport pytest\nfrom multiaddr import Multiaddr\n\nfrom hivemind.p2p import P2P, P2PDaemonError, P2PHandlerError\nfrom hivemind.proto import ... | [
[
"numpy.ctypeslib.as_ctypes_type",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
OmnesRes/ATGC2 | [
"53ee01e60fc6f180b590f5acc5f083155581c96c",
"53ee01e60fc6f180b590f5acc5f083155581c96c"
] | [
"figures/tmb/tcga/nonsyn_table/VICC_01_R2/analysis.py",
"figures/controls/samples/sim_data/regression/experiment_3/sim_run_instance.py"
] | [
"import numpy as np\nimport tensorflow as tf\nimport pandas as pd\nfrom model.Sample_MIL import InstanceModels, RaggedModels\nfrom model.KerasLayers import Losses, Metrics\nfrom model import DatasetsUtils\nfrom sklearn.model_selection import StratifiedKFold\nfrom sklearn.metrics import r2_score\nimport pickle\nphys... | [
[
"numpy.log",
"numpy.ones_like",
"tensorflow.config.experimental.set_memory_growth",
"tensorflow.config.experimental.list_physical_devices",
"tensorflow.data.Dataset.from_tensor_slices",
"numpy.stack",
"sklearn.model_selection.StratifiedKFold",
"numpy.concatenate",
"numpy.apply_... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ReinardKuroi/worldgen | [
"4cd6cca4547579ca89e8e5ccdcb2d360796efc4c"
] | [
"worldgen/marching_cubes/cube.py"
] | [
"import numpy\n\n\ndef random_cube():\n return numpy.random.randint(low=0, high=2, size=(2, 2, 2))\n\n\ndef iterate_as_cube_data(data: numpy.ndarray) -> numpy.ndarray:\n for x in range(data.size):\n yield random_cube()\n\n\ndef check_hash(cube, cube_hash):\n check = int(''.join([str(s) for s in reve... | [
[
"numpy.zeros",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Zhe-Cai/pyCM | [
"15823c9812ce779d453b65c31be7b1a0ee13c9ee"
] | [
"pyCM/align_average.py"
] | [
"#!/usr/bin/env python\n'''\nUses VTK python to allow for editing point clouds associated with the contour \nmethod. Full interaction requires a 3-button mouse and keyboard.\n-------------------------------------------------------------------------------\nCurrent mapping is as follows:\nLMB - rotate about point clo... | [
[
"numpy.matrix",
"numpy.dot",
"numpy.amax",
"scipy.io.whosmat",
"numpy.mean",
"scipy.interpolate.griddata",
"numpy.where",
"scipy.io.loadmat",
"numpy.sin",
"numpy.ravel",
"numpy.amin",
"numpy.linalg.inv",
"matplotlib.path.Path",
"numpy.append",
"numpy.ide... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"1.3",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"0.16",
"1.8"
... |
chorng/eo-learn | [
"a1a3c6fa5568d398f5e43f5ad5aecdfeb05e8d3c"
] | [
"features/eolearn/tests/test_doubly_logistic_approximation.py"
] | [
"\"\"\"\nCredits:\nCopyright (c) 2020 Beno Šircelj (Josef Stefan Institute)\nCopyright (c) 2017-2022 Matej Aleksandrov, Žiga Lukšič (Sinergise)\n\nThis source code is licensed under the MIT license found in the LICENSE\nfile in the root directory of this source tree.\n\"\"\"\n\nfrom pytest import approx\nimport num... | [
[
"numpy.reshape",
"numpy.nonzero"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
chrisdonlan/pandas | [
"af4e2ce19c9c0b89db4bc06d7730b68068c6aeae"
] | [
"pandas/core/indexes/base.py"
] | [
"from datetime import datetime\nimport operator\nfrom textwrap import dedent\nfrom typing import Dict, FrozenSet, Hashable, Optional, Union\nimport warnings\n\nimport numpy as np\n\nfrom pandas._libs import algos as libalgos, index as libindex, lib\nimport pandas._libs.join as libjoin\nfrom pandas._libs.lib import ... | [
[
"pandas.PeriodIndex",
"pandas.core.indexes.range.RangeIndex",
"pandas.core.dtypes.common.ensure_object",
"numpy.where",
"pandas.core.dtypes.common.is_interval_dtype",
"pandas.core.common.cast_scalar_indexer",
"pandas.core.arrays.PeriodArray._from_sequence",
"pandas.core.dtypes.comm... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.0",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
akvelon/Bitcoin-Transaction-Optimization | [
"e3740fe37869a0b84a472b19dbc5d879ec857837"
] | [
"predictor-trainer/trainer/predictor_trainer.py"
] | [
"\"\"\"\r\nCopyright 2019 Akvelon Inc.\r\nLicensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at \r\n\r\n http://www.apache.org/licenses/LICENSE-2.0\r\n\r\nUnless required by applicable law or agre... | [
[
"tensorflow.keras.models.load_model",
"sklearn.externals.joblib.dump",
"pandas.read_csv",
"tensorflow.keras.layers.PReLU",
"sklearn.preprocessing.RobustScaler",
"tensorflow.keras.layers.Dense",
"numpy.median",
"pandas.DataFrame",
"numpy.mean",
"numpy.floor",
"tensorflow... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
kkosmo/orbitize | [
"5790100122f42224f9982e53d7338540a87c5fbc"
] | [
"tests/test_read_input.py"
] | [
"import pytest\nimport deprecation\nimport numpy as np\nimport os\nimport orbitize\nfrom orbitize.read_input import read_file, write_orbitize_input, read_formatted_file, read_orbitize_input\n\n\ndef _compare_table(input_table):\n \"\"\"\n Tests input table to expected values, which are:\n epoch object... | [
[
"numpy.isnan"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
afshimono/data_analyst_nanodegree | [
"8a047abe3770fbd2865c078ecaa121ce096189c2"
] | [
"Intro to Machine Learning/outliers/outlier_cleaner.py"
] | [
"#!/usr/bin/python\n\n\ndef outlierCleaner(predictions, ages, net_worths):\n \"\"\"\n Clean away the 10% of points that have the largest\n residual errors (difference between the prediction\n and the actual net worth).\n\n Return a list of tuples named cleaned_data where \n eac... | [
[
"numpy.asarray",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mscelnik/concurrency-demos | [
"aba31b5fba48b7e843aee016a8261d1494c0c65d"
] | [
"randdata.py"
] | [
"\"\"\" Make random dataframes.\n\"\"\"\n\nfrom string import ascii_uppercase\n\nLETTERS = list(ascii_uppercase)\nMAX_COLUMNS = len(LETTERS)\n\n\ndef make_df(row_count, columns):\n import numpy as np\n import pandas as pd\n values = np.random.rand(row_count, len(columns)) * 100.0\n return pd.DataFrame(v... | [
[
"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": []
}
] |
weizi-li/flow | [
"958b64ece8af6db715e6fb3b6042035b05b93bc2"
] | [
"flow/benchmarks/baselines/bottleneck1.py"
] | [
"\"\"\"Evaluates the baseline performance of bottleneck1 without RL control.\n\nBaseline is no AVs.\n\"\"\"\n\nimport numpy as np\nfrom flow.core.experiment import Experiment\nfrom flow.core.params import InitialConfig\nfrom flow.core.params import InFlows\nfrom flow.core.params import SumoLaneChangeParams\nfrom fl... | [
[
"numpy.std",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Averylamp/composer | [
"1afc56e9c207734aee75ff8c5b046fb55d928fb5"
] | [
"tests/trainer/test_ddp.py"
] | [
"# Copyright 2021 MosaicML. All Rights Reserved.\n\nimport collections.abc\nimport os\nfrom dataclasses import dataclass\nfrom typing import Dict, List, Optional, Sequence\nfrom unittest import mock\n\nimport pytest\nimport torch\nimport torch.distributed\nimport yahp as hp\nfrom _pytest.monkeypatch import MonkeyPa... | [
[
"torch.all",
"torch.distributed.get_rank",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
typhoonzero/elasticdl | [
"c4966a66d72b0b24f4174f2fe7ef308db21a8cac"
] | [
"elasticdl/python/master/servicer.py"
] | [
"import threading\n\nimport numpy as np\nimport tensorflow as tf\nfrom google.protobuf import empty_pb2\n\nfrom elasticdl.proto import elasticdl_pb2, elasticdl_pb2_grpc\nfrom elasticdl.python.common.file_utils import copy_if_not_exists\nfrom elasticdl.python.common.log_utils import default_logger as logger\nfrom el... | [
[
"tensorflow.math.reduce_max"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bgraedel/arcos-gui | [
"aaeeba3aae1bc9a23c635ebabf6309f878ad8a39"
] | [
"src/arcos_gui/temp_data_storage.py"
] | [
"import pandas as pd\nfrom arcos4py import ARCOS\nfrom napari.utils.colormaps import AVAILABLE_COLORMAPS\n\n\n# store and retrive a number of variables\nclass data_storage:\n def __init__(self):\n self.layer_names: list = []\n self.data_merged: pd.DataFrame = pd.DataFrame()\n self.arcos: ARC... | [
[
"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": []
}
] |
voidful/s3prl | [
"78cd91d717abf151e855a874070ef17679136e5f"
] | [
"s3prl/upstream/hubert_code_centroid/expert.py"
] | [
"from collections import defaultdict\nfrom typing import List\n\nimport torch.nn as nn\nfrom torch import Tensor\n# from transformers import Wav2Vec2FeatureExtractor, HubertModel\nimport joblib\nimport torch\n\nSAMPLE_RATE = 16000\n\n\nclass UpstreamExpert(nn.Module):\n def __init__(self, ckpt: str = None, model... | [
[
"torch.from_numpy",
"torch.matmul",
"torch.no_grad",
"torch.cuda.is_available",
"torch.hub.load",
"torch.index_select"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dollking/AL-test | [
"0e698156ed3ed48f736560e508554ea04b933b0b"
] | [
"query/graph/vae.py"
] | [
"import random\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass Residual(nn.Module):\n def __init__(self, in_channels, num_hiddens, num_residual_hiddens):\n super(Residual, self).__init__()\n self._block = nn.Sequential(\n nn.ReLU(True),\n nn.Co... | [
[
"torch.randn_like",
"torch.nn.ConvTranspose2d",
"torch.sign",
"torch.cat",
"torch.nn.Conv2d",
"torch.exp",
"torch.nn.functional.relu",
"torch.nn.AdaptiveAvgPool2d",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
RyanArnasonML/stock-analysis | [
"a5c79d9c438f095dc370f2db4e4780356cdc5d01"
] | [
"stock_analysis/stock_modeler.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nSimple time series modeling for stocks.\r\n\r\nCreated on Sat Oct 31 15:16:24 2020\r\n\r\n@author: ryanar\r\n\"\"\"\r\n\r\nimport matplotlib.pyplot as plt\r\nimport pandas as pd\r\n\r\nfrom statsmodels.tsa.seasonal import seasonal_decompose\r\n\r\nimport statsmodels.api as sm\r... | [
[
"matplotlib.pyplot.subplots",
"pandas.Series",
"pandas.date_range"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
mgrubisic/coronavirus-2020 | [
"0242b4f18416bcc055326d6ddcb300d8edd6baa9"
] | [
"tests/test_metadata.py"
] | [
"import datetime\nimport json\nimport math\nimport os\nimport time\nimport pytest\nimport numpy as np\nimport pandas as pd\n\n\nfrom oscovida import MetadataRegion\n\n\ndef test_MetadataRegion_basics():\n m = MetadataRegion(\"Germany\", \"w\")\n # assert os.path.exists(MetadataStorageLocation)\n\n m['html'... | [
[
"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": []
}
] |
IronSublimate/CenterNet-IS-old | [
"a3df08e17d47a63e40f020e6cf2a0c8ec347ac12"
] | [
"src/lib/datasets/sample/multi_pose.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nfrom datasets.base import BaseDataset\nimport numpy as np\nimport torch\nimport json\nimport cv2\nimport os\nfrom utils.image import flip, color_aug\nfrom utils.image import get_affine_transform, affin... | [
[
"numpy.random.random",
"numpy.clip",
"numpy.arange",
"numpy.concatenate",
"numpy.random.randn",
"numpy.array",
"numpy.zeros",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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