repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
delta-mpc/delta-node | [
"674fc61f951e41ed353597f93ca6ea6bc74a102b"
] | [
"tests/chain/utils_test.py"
] | [
"from typing import List, Sequence\nfrom delta_node import utils\nfrom delta_node.crypto import ecdhe\nimport numpy as np\nimport os\n\n\ndef test_precision():\n precision = 8\n\n arr = np.random.random(10)\n arr_ = utils.unfix_precision(utils.fix_precision(arr, precision), precision)\n assert np.allclo... | [
[
"numpy.random.random",
"numpy.allclose",
"numpy.array_equal",
"numpy.mean",
"numpy.zeros_like",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rcmurray/WorkflowComponents | [
"368f8132ce5bef3ac3fb9d8e1a13a08f91764cfa",
"368f8132ce5bef3ac3fb9d8e1a13a08f91764cfa"
] | [
"AnalysisPyAfm/program/util.py",
"AnalysisDAFM/program/src/load_data.py"
] | [
"from __future__ import print_function\nfrom __future__ import unicode_literals\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom math import log\nfrom math import exp\n\nimport numpy as np\n\ndef log_one_plus_exp(z):\n \"\"\"\n This function returns log(1 + exp(z)) where it rewri... | [
[
"numpy.vectorize"
],
[
"numpy.concatenate",
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
tomasjakab/keypointgan | [
"541b769d536dc113fcf6da271ed72ae9d963dbb4"
] | [
"test_pose.py"
] | [
"import os\nfrom options.test_options import TestOptions\nfrom data import CreateDataLoader\nfrom models import create_model\nfrom util.visualizer import save_images\nfrom util import html\nfrom util import util\nimport torch\nfrom models import utils as mutils\nfrom data import human36m_skeleton\nimport math\nimpo... | [
[
"numpy.random.choice",
"torch.min",
"torch.tensor",
"numpy.mean",
"torch.stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pengwei-iie/Bert-TextClassification | [
"7050deaf225922dc25d4a4a8598f8c01d0ed15e2"
] | [
"run_rnn.py"
] | [
"# coding: utf-8\n\nfrom __future__ import print_function\n\nimport os\nimport sys\nimport time\nfrom datetime import timedelta\n\nimport numpy as np\nimport tensorflow as tf\n# from sklearn import metrics\n\nfrom rnn_model import TRNNConfig, TextRNN\nfrom lstm import *\nfrom data.cnews_loader import *\n# from serv... | [
[
"tensorflow.summary.FileWriter",
"tensorflow.global_variables_initializer",
"tensorflow.summary.merge_all",
"tensorflow.Session",
"tensorflow.train.Saver",
"numpy.load",
"tensorflow.summary.scalar",
"numpy.savetxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
dataiku-research/drift_detectors_benchmark | [
"9b1f34daecba3b18d26838d302456acff75dbe30"
] | [
"failing_loudly/shift_tester.py"
] | [
"# -------------------------------------------------\n# IMPORTS\n# -------------------------------------------------\n\nimport numpy as np\nimport torch\nimport random\nfrom torch import *\nfrom torch_two_sample import *\nfrom scipy.stats import ks_2samp, binom_test, chi2_contingency, anderson_ksamp\nfrom scipy.spa... | [
[
"scipy.stats.ks_2samp",
"scipy.stats.chi2_contingency",
"numpy.unique",
"numpy.min",
"scipy.stats.binom_test",
"numpy.median",
"numpy.amin",
"scipy.spatial.distance.cdist",
"torch.tensor",
"numpy.array",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"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"
... |
Alxaline/MONAI | [
"8d61ae2095c4453998a93e2cbc967e37286f072a"
] | [
"monai/metrics/utils.py"
] | [
"# Copyright 2020 MONAI Consortium\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n# http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by applicable law or agreed to i... | [
[
"numpy.expand_dims",
"numpy.ones_like",
"torch.isnan",
"torch.zeros",
"torch.sum",
"torch.is_tensor",
"numpy.zeros_like",
"numpy.any",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kosuke1701/optuna-metric-learning | [
"42a54dadf14a3ea7380b36189de014edd8078bce"
] | [
"examples/image_folder_examples_classifier.py"
] | [
"from glob import glob\nimport os\nimport re\n\nimport numpy as np\nfrom pytorch_metric_learning import samplers\nfrom radam import RAdam\nfrom RandAugment import RandAugment\nfrom sklearn.model_selection import KFold\nimport torch\nimport torch.nn as nn\nfrom torchvision import transforms, models\nfrom torchvision... | [
[
"torch.nn.Dropout",
"torch.nn.CrossEntropyLoss",
"sklearn.model_selection.KFold",
"torch.nn.Linear",
"torch.nn.Identity"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rish07/slicesim | [
"533bd7927e1e9c371b56a2b6ac9017f4ee7514af"
] | [
"slicesim/utils.py"
] | [
"import math\n\nfrom sklearn.neighbors import KDTree as kdt\n\ndef distance(a, b):\n return math.sqrt(sum((i-j)**2 for i,j in zip(a, b)))\n\n# Initial connections using k-d tree\ndef kdtree(clients, base_stations):\n\n c_coor = [(c.x,c.y) for c in clients]\n bs_coor = [p.coverage.center for p in base_stati... | [
[
"sklearn.neighbors.KDTree"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
huobanlqs/mask_rcnn_v2.1-win-tf2 | [
"51de876c4af56462e50aa0763721edd0349784dc"
] | [
"samples/balloon/utils.py"
] | [
"\"\"\"\nMask R-CNN\nCommon utility functions and classes.\n\nCopyright (c) 2017 Matterport, Inc.\nLicensed under the MIT License (see LICENSE for details)\nWritten by Waleed Abdulla\n\"\"\"\n\nimport sys\nimport os\nimport math\nimport random\nimport numpy as np\n#import tensorflow as tf\nimport tensorflow.compat.... | [
[
"numpy.dot",
"numpy.minimum",
"numpy.sqrt",
"numpy.cumsum",
"numpy.concatenate",
"numpy.max",
"numpy.all",
"numpy.any",
"numpy.exp",
"numpy.where",
"numpy.pad",
"numpy.reshape",
"numpy.arange",
"numpy.stack",
"numpy.argmax",
"numpy.zeros",
"numpy... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yy/clusim | [
"5d3081d74e08d3e42e99f27834e1f9408af222a1"
] | [
"clusim/clusimelement.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\n.. module:: clusimelement\n :synopsis: Element-centric Clustering Similarity\n\n.. moduleauthor:: Alex Gates <ajgates42@gmail.com>\n \"\"\"\n\nimport numpy as np\n\ntry:\n import igraph\nexcept ImportError:\n pass\n\nimport scipy.sparse as spsparse\n\nimport collections\ni... | [
[
"scipy.sparse.coo_matrix",
"numpy.abs",
"numpy.eye",
"numpy.ones",
"numpy.mean",
"numpy.array",
"numpy.exp",
"numpy.zeros"
]
] | [
{
"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"... |
jmaggio14/physops | [
"0d0f8f4758bc8616fa662a19b734a3977c2be759"
] | [
"display.py"
] | [
"import matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nimport numpy as np\nimport physops\n\n\ndef display(src,title=None):\n if isinstance(src,physops.Wavefront):\n fig = plt.figure(edgecolor=\"white\")\n\n plt.title(src.title if isinstance(title,type(None)) else str(title))\n\n ... | [
[
"numpy.asarray",
"matplotlib.pyplot.colorbar",
"numpy.max",
"matplotlib.pyplot.ion",
"numpy.flip",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
merijn/amuse | [
"2dd6a78b0edf46c03673b747328585ccd5e4b95b"
] | [
"src/amuse/__init__.py"
] | [
"\"\"\"\nThe Astrophysical Multipurpose Software Environment\n\nThe aim of AMUSE is to provide a software framework, in which existing codes for \ndynamics, stellar evolution, hydrodynamics and radiative transfer can easily be \ncoupled, in order to perform state-of-the-art simulations of a wide range of \ndifferen... | [
[
"numpy.set_printoptions"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
diegonat/smsantispam | [
"b43b6d91adda17eaf9f9969a56e9b247780424a1"
] | [
"SMS_keras_script.py"
] | [
"import numpy as np\nimport os\nimport tensorflow as tf\nimport pandas\nimport json\nfrom tensorflow.python.estimator.export.export import build_raw_serving_input_receiver_fn\nfrom tensorflow.python.estimator.export.export_output import PredictOutput\n\nINPUT_TENSOR_NAME = 'inputs'\nSIGNATURE_NAME = \"serving_defau... | [
[
"tensorflow.metrics.accuracy",
"tensorflow.python.estimator.export.export.build_raw_serving_input_receiver_fn",
"tensorflow.keras.layers.Dense",
"tensorflow.contrib.framework.get_global_step",
"tensorflow.placeholder",
"tensorflow.estimator.EstimatorSpec",
"tensorflow.python.estimator.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lfwin/tflearn | [
"06015c73a4d9ad9edafeb9b61ee9004fd715f69e"
] | [
"tflearn/helpers/evaluator.py"
] | [
"from __future__ import division, print_function, absolute_import\n\nimport tensorflow as tf\n\nimport tflearn\nfrom ..utils import to_list\nfrom .. import data_flow\nfrom .. import metrics\nfrom .trainer import evaluate_flow\n\n\nclass Evaluator(object):\n\n \"\"\" Evaluator.\n\n A class used for performing ... | [
[
"tensorflow.get_collection",
"tensorflow.train.Saver",
"tensorflow.train.Coordinator",
"tensorflow.Session"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
LUMC/KeyGenes-dataprocessor | [
"bcbcf557592ed25c48367d7636fda59f2cd497f2"
] | [
"scripts/format_expression_data.py"
] | [
"import pandas as pd\nimport sys\n\ndata_groups = {\n 'adult': ['adult'],\n '9-weeks': ['9'],\n '16:18-weeks': ['16', '18'],\n '22-weeks': ['22'],\n}\n\n\ndef get_phase(tissue_ref):\n for index, item in data_groups.items():\n if tissue_ref in item:\n return index\n raise SystemEr... | [
[
"pandas.read_table"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
MateoLostanlen/Holocron | [
"65e570fc0b072226ae3299574b4926e2fa4e76b0"
] | [
"holocron/models/utils.py"
] | [
"# Copyright (C) 2019-2021, François-Guillaume Fernandez.\n\n# This program is licensed under the Apache License version 2.\n# See LICENSE or go to <https://www.apache.org/licenses/LICENSE-2.0.txt> for full license details.\n\nimport logging\nimport torch\nimport torch.nn as nn\nfrom holocron.nn import BlurPool2d\n... | [
[
"torch.sqrt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
RedCrow9564/NumericalMethodsForIVP-FinalProject | [
"2baab04b8e1d6906dcb2a906f87b249a2d3af492"
] | [
"Infrastructure/Matrices/sparse_matrices.py"
] | [
"import numpy as np\nfrom .circulant_sparse_product import compute, solve_almost_tridiagonal_system\nfrom Infrastructure.utils import List, Scalar, Matrix\n\n\nclass CirculantSparseMatrix(object):\n\n def __init__(self, n: int, nonzero_terms: List[Scalar], nonzero_indices: List[int]) -> None:\n self._n = ... | [
[
"numpy.empty_like",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cskokgibbs/srrTomat0 | [
"9255d5a52bc4425346f578841004955c72b4ba76"
] | [
"inferelator_prior/tests/test_motif_scan.py"
] | [
"import unittest\nimport os\nimport io\nimport pandas as pd\nimport numpy as np\nimport numpy.testing as npt\n\nfrom inferelator_prior.motifs._motif import MotifScanner as MotifScanner\nfrom inferelator_prior.motifs import Motif, fimo, homer, SCAN_SCORE_COL\n\nartifact_path = os.path.join(os.path.abspath(os.path.ex... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nathan-yan/groundNet | [
"7e15fb30c31212030b3fd5fa4371cfc48591d54d"
] | [
"ground-net.py"
] | [
"\"\"\"\n GroundNet\n For use in SLI 2018 to classify ground features\n Nathan Yan, 2017\n\"\"\"\n\nimport tensorflow as tf\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nclass GroundNet:\n def __init__(self, input_shape, layer_info, activation = tf.nn.relu, classes = 3):\n # output_sh... | [
[
"tensorflow.get_variable",
"tensorflow.nn.softmax",
"tensorflow.train.RMSPropOptimizer",
"tensorflow.placeholder",
"tensorflow.global_variables_initializer",
"numpy.random.randn",
"tensorflow.log",
"tensorflow.Session",
"tensorflow.nn.conv2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
ZhenshengLee/ros1_pendulum | [
"1dfb747b62311ee370ed392a0ad4a5cd2d11d3be"
] | [
"pendulum_analysis/trace_analysis/tracetools_analysis/data_model/profile.py"
] | [
"# Copyright 2019 Robert Bosch GmbH\n# Copyright 2021 Christophe Bedard\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# Un... | [
[
"pandas.DataFrame.from_dict"
]
] | [
{
"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": []
}
] |
gchrupala/phoneme-repr | [
"df59b57a72c1194c7447ea61aba5f29a6ce0791b"
] | [
"prepare.py"
] | [
"SEED=666\nimport torch\ntorch.manual_seed(SEED)\nimport random\nrandom.seed(SEED)\nimport numpy as np\nnp.random.seed(SEED)\ntorch.backends.cudnn.deterministic = True\ntorch.backends.cudnn.benchmark = False\n\nimport pickle\nimport logging\nimport platalea.asr as asr\nimport platalea.basic as basic\nimport platale... | [
[
"numpy.random.seed",
"torch.load",
"torch.manual_seed",
"torch.utils.data.DataLoader",
"numpy.stack",
"numpy.concatenate",
"torch.no_grad",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Causal-Inference-ZeroToAll/causalmlUber | [
"4951f02d5c841a8c2bb6e9f92b0eb339227400b8"
] | [
"causalml/inference/meta/tlearner.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom future.builtins import super\nfrom copy import deepcopy\nimport logging\nimport pandas as pd\nimport numpy as np\nfrom scipy.stats import norm\nfrom sklearn.exceptions import ConvergenceWarning\nfro... | [
[
"scipy.stats.norm.ppf",
"numpy.unique",
"numpy.arange",
"numpy.percentile",
"pandas.datetime.today",
"numpy.zeros_like",
"sklearn.utils.testing.ignore_warnings",
"numpy.zeros",
"sklearn.neural_network.MLPRegressor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
meowpunch/ConcreteDataset | [
"b8fe23a870881573d493421b57835b7742d7e5ee"
] | [
"model/gradient_boost.py"
] | [
"from sklearn.ensemble import GradientBoostingRegressor\r\nfrom sklearn.metrics import mean_absolute_error\r\n\r\nfrom model.parent import BaseSearcher\r\n\r\n\r\nclass GradientBoostSearcher(BaseSearcher):\r\n def __init__(self, x_train, y_train, columns, grid_params=None, score=mean_absolute_error):\r\n ... | [
[
"sklearn.ensemble.GradientBoostingRegressor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
luk9400/aisd | [
"4dd7e3c60b8a611b347a17d2c973d758bfca2dec"
] | [
"list5/flowTime.py"
] | [
"import matplotlib.pyplot as plt\nimport pandas as pd\n\ndata = pd.read_csv('statsFlow.csv', delimiter=';')\nk = data['k']\n\nflow = data['time']\n\nplt.plot(k, flow, label='time')\nplt.title('Times')\nplt.legend()\nplt.ylabel('Time [s]')\nplt.xlabel('K')\n#plt.yscale('log')\n\nplt.show()"
] | [
[
"matplotlib.pyplot.legend",
"pandas.read_csv",
"matplotlib.pyplot.title",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
benmoo23/DictionaryLearning | [
"f5ec6e8775a87796b4618c443d2e65862b10bb29"
] | [
"tests.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nTests for unofficial CI. Try to run these before commits.\n\n@author: Benjamin Cowen, Feb 24 2018\n@contact: bc1947@nyu.edu, bencowen.com\n\"\"\"\n# scientific computing\nimport numpy as np\nimport torch\nfrom torch.autograd import Variable\nimport random\n#... | [
[
"torch.norm",
"torch.ones",
"numpy.sqrt",
"numpy.abs",
"torch.zeros",
"torch.manual_seed",
"torch.eye",
"numpy.diff",
"torch.rand"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mcaldana/torchdiffeq | [
"6bf78dc7b07f127d280dd266ace24d5543295d35"
] | [
"bug39784test.py"
] | [
"import torch\n\nclass Print(torch.autograd.Function):\n @staticmethod\n def forward(ctx, x, op, final, unused):\n ctx.op = op\n ctx.final = final\n ctx.unused = unused\n return x\n\n @staticmethod\n def backward(ctx, x):\n print('op={}, final={}, unused={} has a bug'.... | [
[
"torch.autograd.grad",
"torch.rand"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cuongvomanh/facenet_study | [
"4fb6e7cf627fa83fc30a46bf6300490c1086eb0b"
] | [
"src/validate_on_lfw_my.py"
] | [
"\"\"\"Validate a face recognizer on the \"Labeled Faces in the Wild\" dataset (http://vis-www.cs.umass.edu/lfw/).\nEmbeddings are calculated using the pairs from http://vis-www.cs.umass.edu/lfw/pairs.txt and the ROC curve\nis calculated and plotted. Both the model metagraph and the model parameters need to exist\n... | [
[
"tensorflow.Graph",
"tensorflow.get_default_graph",
"numpy.ones_like",
"numpy.arange",
"tensorflow.train.start_queue_runners",
"tensorflow.train.Coordinator",
"tensorflow.placeholder",
"numpy.std",
"numpy.zeros_like",
"numpy.mean",
"tensorflow.Session",
"tensorflow.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
Jtaramon/Social-IAA | [
"588ed77b1963f8b5738430d32643e947bfa6d34c"
] | [
"main.py"
] | [
"# imports\nimport cv2\nimport numpy as np\nimport time\nimport argparse\n\n# own modules\nimport utills, plot\n\nconfid = 0.5\nthresh = 0.5\nmouse_pts = []\n\n# Función para obtener puntos por Región de interés (ROI) y escala de distancia. Se necesitarán 8 puntos en el primer fotograma con un clic del mouse\n# eve... | [
[
"numpy.sqrt",
"numpy.random.seed",
"numpy.copy",
"numpy.argmax",
"numpy.float32",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AICPS/hydrafusion | [
"368d8bfe577f535229bb7ad7154424200f6a659e"
] | [
"model/gate.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch import tensor\n\n\n\n'''Represents the base class Gating Module used for selecting branches of the hydranet for execution'''\nclass GateModule(nn.Module):\n def __init__(self):\n super(GateModule, self).__init__()\n\n\n\n'''... | [
[
"torch.nn.functional.dropout",
"torch.nn.Conv2d",
"torch.tensor",
"torch.flatten",
"torch.nn.Linear",
"torch.nn.MaxPool2d",
"torch.nn.Conv1d",
"torch.FloatTensor",
"torch.bmm",
"torch.nn.BatchNorm2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TheInterventionCentre/NorMIT-Plan-App | [
"765ed9a5dccc1cc134b65ccabe93fc132baeb2ea"
] | [
"Modules/Scripted/EditorLib/PaintEffect.py"
] | [
"import os\nimport vtk\nimport ctk\nimport qt\nimport slicer\nfrom EditOptions import HelpButton\nfrom EditUtil import EditUtil\nimport LabelEffect\nimport numpy\nfrom math import sqrt\n\n__all__ = [\n 'PaintEffectOptions',\n 'PaintEffectTool',\n 'PaintEffectLogic',\n 'PaintEffect'\n ]\n\n#####################... | [
[
"numpy.arange"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vlgiitr/ntm-pytorch | [
"9bf8e4533850a66bbef26390244f0d0ad30c067b"
] | [
"ntm/datasets/prioritysort.py"
] | [
"import torch\nfrom torch.utils.data import Dataset\nfrom torch.distributions.uniform import Uniform\nfrom torch.distributions.binomial import Binomial\n\n\nclass PrioritySort(Dataset):\n \"\"\"A Dataset class to generate random examples for priority sort task.\n\n In the input sequence, each vector is genera... | [
[
"torch.ones",
"torch.zeros",
"torch.tensor",
"torch.distributions.binomial.Binomial",
"torch.sort"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
chuangzhu/aXeleRate | [
"af3d45b6d44ac5815ae9e022fc49b28db855de4b",
"af3d45b6d44ac5815ae9e022fc49b28db855de4b"
] | [
"axelerate/networks/classifier/utils.py",
"axelerate/networks/common_utils/feature.py"
] | [
"\"\"\"Utilities for real-time data augmentation on image data.\n\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport warnings\n\nimport numpy as np\nimport cv2\ntry:\n from PIL import ImageEnhance\n from PIL import Image a... | [
[
"numpy.asarray",
"numpy.max",
"numpy.min"
],
[
"tensorflow.space_to_depth"
]
] | [
{
"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... |
t0nychn/options-2-trees | [
"3f93c5903973382ba0c44477c5b311279e10d6bc"
] | [
"tree_plotter/__init__.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat Jun 12 17:12:03 2021\r\n\r\n@author: ztche\r\n\"\"\"\r\nimport matplotlib.pyplot as plt\r\nimport pandas as pd\r\n\r\ndef plot_stock_lattice(tree, style='ko-'):\r\n \"\"\"\r\n plots stock lattice on matplotlib from chronological binary tree\r\n\r\n \"\"\... | [
[
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.annotate",
"pandas.Series",
"matplotlib.pyplot.ylabel"
]
] | [
{
"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": []
}
] |
SsnL/rlkit | [
"5274672e9ff6481def0ffed61cd1b1c52210a840"
] | [
"rlkit/torch/distributions.py"
] | [
"\"\"\"\nAdd custom distributions in addition to th existing ones\n\"\"\"\nimport torch\nfrom torch.distributions import Categorical, OneHotCategorical, kl_divergence\nfrom torch.distributions import Normal as TorchNormal\nfrom torch.distributions import Beta as TorchBeta\nfrom torch.distributions import Distributi... | [
[
"torch.distributions.OneHotCategorical",
"torch.Size",
"numpy.log",
"torch.distributions.kl.register_kl",
"torch.argmax",
"torch.gather",
"torch.tanh",
"torch.matmul",
"torch.exp",
"torch.distributions.Categorical",
"torch.log",
"torch.distributions.Normal",
"to... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lukenew2/ml-from-scratch | [
"9e2b7beaa22b581744d989a725d46f030933b6df"
] | [
"mlscratch/supervised/regression.py"
] | [
"\"\"\"Module containing classes for supervised linear regression models.\"\"\"\nimport numpy as np\nfrom scipy.linalg import lstsq\n\nfrom mlscratch.utils.metrics import mean_squared_error\nfrom mlscratch.utils.metrics import r2_score\nfrom mlscratch.utils.regularization import l1_regularization\nfrom mlscratch.ut... | [
[
"numpy.sqrt",
"scipy.linalg.lstsq",
"numpy.shape",
"numpy.insert",
"numpy.random.uniform"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.12",
"0.14",
"0.15"
],
"tensorflow": []
}
] |
wj1tr0y/RefineDet-Pytorch | [
"603f5584a8c67c8e1cc0ac9d5912357a0114c11f"
] | [
"data/coco.py"
] | [
"#!/usr/bin/env python\n# coding=UTF-8\n'''\n@Author: Jilong Wang\n@LastEditors: Jilong Wang\n@Email: jilong.wang@watrix.ai\n@Description: file content\n@Date: 2019-03-22 17:55:02\n@LastEditTime: 2019-03-23 16:42:18\n'''\n\"\"\"VOC Dataset Classes\n\nOriginal author: Francisco Massa\nhttps://github.com/fmassa/visio... | [
[
"numpy.minimum",
"numpy.max",
"numpy.mean",
"numpy.zeros",
"numpy.where",
"numpy.isclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
arfon/PyAutoLens | [
"e1a886fa0cbd9620efc1f88457d3f2c5afdae622"
] | [
"autolens/lens/ray_tracing.py"
] | [
"from abc import ABC\r\nimport pickle\r\nimport numpy as np\r\nfrom os import path\r\nfrom astropy import cosmology as cosmo\r\nfrom autoarray.inversion import pixelizations as pix\r\nfrom autoarray.inversion import inversions as inv\r\nfrom autoarray.structures.arrays import values\r\nfrom autoarray.structures.gri... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
valeeraZ/scikit-network | [
"ae4b8f148dc503c84553dbd806253c962d869c10"
] | [
"sknetwork/embedding/force_atlas.py"
] | [
"#!/usr/bin/env python3\n# coding: utf-8\n\"\"\"\nCreated on Jun 2020\n@author: Victor Manach <victor.manach@telecom-paris.fr>\n@author: Rémi Jaylet <remi.jaylet@telecom-paris.fr>\n\"\"\"\nfrom typing import Optional, Union\n\nimport numpy as np\nfrom scipy import sparse\nfrom scipy.spatial import cKDTree\n\nfrom s... | [
[
"numpy.abs",
"numpy.sqrt",
"numpy.arange",
"numpy.linalg.norm",
"numpy.ones",
"numpy.sign",
"numpy.random.randn",
"numpy.where",
"numpy.zeros",
"scipy.spatial.cKDTree"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mattWheeler1/spin-1 | [
"923cb8ec6f84f676845318428b0aa34d7a3b8b8f"
] | [
"kibble-zurek/cpu/1d_swislocki_cpu.py"
] | [
"import numpy as np\nimport h5py\nimport include.symplectic_cpu as sm\n\n# --------------------------------------------------------------------------------------------------------------------\n# Spatial and Potential parameters:\n# ------------------------------------------------------------------------------------... | [
[
"numpy.sqrt",
"numpy.fft.fft",
"numpy.arange",
"numpy.fft.fftshift",
"numpy.fft.ifft",
"numpy.random.normal",
"numpy.mod",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SvenKlaassen/doubleml-for-py | [
"b3cbdb572fce435c18ec67ca323645900fc901b5"
] | [
"doubleml/double_ml_irm.py"
] | [
"import numpy as np\nfrom sklearn.utils import check_X_y\nfrom sklearn.utils.multiclass import type_of_target\n\nfrom .double_ml import DoubleML\nfrom ._utils import _dml_cv_predict, _get_cond_smpls, _dml_tune, _check_finite_predictions\n\n\nclass DoubleMLIRM(DoubleML):\n \"\"\"Double machine learning for intera... | [
[
"sklearn.utils.check_X_y",
"numpy.multiply",
"numpy.power",
"numpy.full_like",
"numpy.mean",
"sklearn.utils.multiclass.type_of_target",
"numpy.divide"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sniemi/SamPy | [
"e048756feca67197cf5f995afd7d75d8286e017b",
"e048756feca67197cf5f995afd7d75d8286e017b",
"e048756feca67197cf5f995afd7d75d8286e017b",
"e048756feca67197cf5f995afd7d75d8286e017b",
"e048756feca67197cf5f995afd7d75d8286e017b"
] | [
"sandbox/src1/tutorial/spec1.py",
"sandbox/src1/examples/custom_projection_example.py",
"bolshoi/plotMassRatios.py",
"sandbox/src1/examples/polar_legend.py",
"sandbox/src1/examples/xcorr_demo.py"
] | [
"import numpy as n\n\nclass SourceFunction: \n '''Abstract Class representing a source as a function of wavelength\n\n \nUnits are assumed to be Flambda\n\n'''\n def __add__(self, other):\n return CompositeSourceFunction(self, other, 'add') \n # __radd__ not needed\n\nclass CompositeSou... | [
[
"numpy.array",
"numpy.exp"
],
[
"matplotlib.projections.register_projection",
"matplotlib.axes.Axes.set_yscale",
"matplotlib.transforms.Affine2D",
"matplotlib.axes.Axes.set_ylim",
"matplotlib.patches.Circle",
"matplotlib.axes.Axes.__init__",
"matplotlib.transforms.BboxTrans... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
theochap/car_accidents_visualization | [
"6e294dd31bf5853cf0ba4c1fd5659d17f6dbe22c"
] | [
"datavisualization/extraction.py"
] | [
"\"\"\"\nModule qui gère l'extraction des données à partir des csv téléchargés.\n\"\"\"\n\nimport os\nimport pandas as pd\nimport pickle\n\nfrom data.telechargement import fetch_data\n\ndef get_csv_list():\n \"\"\"renvoie la liste des fichiers csv dans ./data/data\"\"\"\n\n file_list = [file[2] for file in os... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
bhaddow/pyAudioAnalysis | [
"510852380035d4324b2e13fddedeabb76ce9326e"
] | [
"pyAudioAnalysis/ShortTermFeatures.py"
] | [
"from __future__ import print_function\nimport math\nimport numpy as np\nfrom scipy.fftpack import fft\nimport matplotlib.pyplot as plt\nfrom scipy.signal import lfilter\nfrom scipy.fftpack.realtransforms import dct\n\neps = 0.00000001\n\n\ndef zero_crossing_rate(frame):\n \"\"\"Computes zero crossing rate of fr... | [
[
"numpy.dot",
"matplotlib.pyplot.imshow",
"numpy.imag",
"numpy.sqrt",
"numpy.cumsum",
"numpy.concatenate",
"numpy.round",
"numpy.max",
"scipy.fftpack.fft",
"numpy.hamming",
"scipy.fftpack.realtransforms.dct",
"numpy.double",
"numpy.unique",
"numpy.arange",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"0.15",
"1.4",
"0.16",
"1.0",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"0.10",
"0.17",
"1.3"
],
"tensorflow": [... |
geekjr/intelPythonBenchmarks | [
"3614d2f22c0d8b2c8e929da0a9025ef6e719ecfc"
] | [
"pandas_bench_n.py"
] | [
"import pandas as pd\n\npd.read_csv('match_data_version1.csv')\n"
] | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
hafixo/cudf | [
"7f91a17a967b0e0b502d56d37818aa0461d38c67"
] | [
"python/cudf/cudf/core/column/numerical.py"
] | [
"# Copyright (c) 2018-2020, NVIDIA CORPORATION.\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow as pa\nfrom pandas.api.types import is_integer_dtype\n\nimport cudf\nimport cudf._lib as libcudf\nfrom cudf._lib.nvtx import annotate\nfrom cudf._lib.scalar import Scalar\nfrom cudf.core.buffer import Buffer\n... | [
[
"numpy.can_cast",
"pandas.Series",
"numpy.isnan",
"numpy.issubdtype",
"numpy.promote_types",
"numpy.dtype",
"numpy.finfo",
"numpy.result_type",
"numpy.iinfo",
"numpy.isscalar",
"pandas.api.types.is_integer_dtype",
"numpy.min_scalar_type"
]
] | [
{
"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": []
}
] |
viniciusbarbosapaiva/bot-mini-indice | [
"70e38d73c90ac8c8a98ea7281f49bbf8eed9ac9b"
] | [
"BOT_Mini_Indice/bot_mini_indice.py"
] | [
"import numpy as np\nimport pandas as pd\nimport warnings\nwarnings.filterwarnings('ignore')\nimport seaborn as sns\nimport MetaTrader5 as mt\nfrom datetime import datetime\nimport plotly.graph_objects as go\nfrom plotly.offline import plot\nfrom plotly.subplots import make_subplots\nimport pytz\nimport calendar\nf... | [
[
"sklearn.ensemble.RandomForestRegressor",
"pandas.to_datetime",
"sklearn.metrics.r2_score",
"numpy.sqrt",
"sklearn.metrics.mean_absolute_error",
"sklearn.preprocessing.PolynomialFeatures",
"pandas.DataFrame",
"sklearn.metrics.mean_squared_error",
"numpy.round",
"matplotlib.... | [
{
"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": []
}
] |
ashish1sasmal/Hand-Gestures-OpenCV | [
"5664d67f700252239ab818ba30e4145579682d48"
] | [
"background_subtract.py"
] | [
"# @Author: ASHISH SASMAL <ashish>\n# @Date: 16-10-2020\n# @Last modified by: ashish\n# @Last modified time: 20-10-2020\n\nimport cv2\nimport numpy as np\nimport imutils\nfrom live_cam import live\nfrom sklearn.metrics.pairwise import euclidean_distances as eqd\nimport math\n\nfgbg = cv2.createBackgroundSubtrac... | [
[
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
umautobots/pytorch_wavelets | [
"e05b291db687231302175e15c19024ff2d1ad4ab"
] | [
"pytorch_wavelets/utils.py"
] | [
"\"\"\" Useful utilities for testing the 2-D DTCWT with synthetic images\"\"\"\n\nfrom __future__ import absolute_import\n\nimport functools\nimport numpy as np\n\n\ndef unpack(pyramid, backend='numpy'):\n \"\"\" Unpacks a pyramid give back the constituent parts.\n\n :param pyramid: The Pyramid of DTCWT trans... | [
[
"numpy.fmod",
"numpy.maximum",
"numpy.einsum",
"numpy.arange",
"numpy.cos",
"numpy.ones",
"numpy.asfarray",
"numpy.atleast_2d",
"numpy.issubsctype",
"numpy.asanyarray",
"numpy.sin",
"numpy.exp",
"numpy.array",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Irlirion/ocp | [
"6fb3e794eef31559db990300198eca20f41d8f37",
"6fb3e794eef31559db990300198eca20f41d8f37"
] | [
"ocpmodels/preprocessing/atoms_to_graphs.py",
"tests/models/test_cgcnn.py"
] | [
"\"\"\"\nCopyright (c) Facebook, Inc. and its affiliates.\n\nThis source code is licensed under the MIT license found in the\nLICENSE file in the root directory of this source tree.\n\"\"\"\n\nimport ase.db.sqlite\nimport ase.io.trajectory\nimport numpy as np\nimport torch\nfrom torch_geometric.data import Data\n\n... | [
[
"torch.LongTensor",
"torch.zeros",
"numpy.concatenate",
"torch.FloatTensor",
"torch.where",
"numpy.argsort",
"numpy.vstack",
"torch.save"
],
[
"numpy.testing.assert_equal",
"numpy.array_equal",
"torch.manual_seed",
"numpy.testing.assert_almost_equal",
"torch... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Vignesh-Desmond/attractors | [
"33b7315ec57de578ae15cc6ca9866b798145790d"
] | [
"tests/test_ffmpeg.py"
] | [
"import os\nimport shlex\nimport shutil\nimport subprocess\n\nimport matplotlib.pyplot as plt\nimport mpl_toolkits.mplot3d.axes3d as p3\nimport pytest\n\nfrom attractors import __version__\nfrom attractors.attractor import Attractor\n\nSIMTIME = 2\nSIMPOINTS = 10\n\n\ndef check_err(videopath):\n cmd = shlex.spli... | [
[
"matplotlib.pyplot.close"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LEFTazs/weighted-levenshtein | [
"454af44c1e14ffcb44a2f84514ade82e02ad9b3d"
] | [
"test/test.py"
] | [
"from __future__ import absolute_import, print_function\n\nimport unittest\n\nimport numpy as np\n\nfrom weighted_levenshtein import dam_lev, lev, osa\n\n\nclass TestClev(unittest.TestCase):\n\n def setUp(self):\n self.iw = np.ones(128, dtype=np.float64)\n self.dw = np.ones(128, dtype=np.float64)\n... | [
[
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
coobas/modin | [
"6433bb80f87d79f4f69d27697a50f206e7a736d0",
"6433bb80f87d79f4f69d27697a50f206e7a736d0"
] | [
"modin/data_management/partitioning/axis_partition/pandas_on_ray.py",
"modin/data_management/query_compiler/pandas_query_compiler.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport pandas\nimport ray\n\nfrom .axis_partition import BaseAxisPartition\nfrom ...partitioning.remote_partition import PandasOnRayRemotePartition\nfrom .utils import split_result_of_axis_func_pandas\... | [
[
"pandas.concat",
"pandas.Series"
],
[
"pandas.core.dtypes.common.is_list_like",
"pandas.to_datetime",
"pandas.core.dtypes.common.is_numeric_dtype",
"pandas.Series",
"pandas.core.dtypes.common.is_bool_dtype",
"pandas.RangeIndex",
"pandas.Float64Index",
"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": []
},
{
"matplotlib": [],
"nump... |
rudyrim/SchieldRPI | [
"154b2003611b0ad2e558d512c984d6e1ae5072cb"
] | [
"Software/Software/ADC.py"
] | [
"# Importing modules\nimport spidev # To communicate with SPI devices\nfrom numpy import interp # To scale values\nfrom time import sleep # To add delay\nimport RPi.GPIO as GPIO # To use GPIO pins\nimport time\n# Start SPI connection\nspi = spidev.SpiDev() # Created an object\nspi.open(0,0) \n\n# Initializing... | [
[
"numpy.interp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Richert/BrainNetworks | [
"52119446191dabf0fcef2d3dda203c9fb5730c7d"
] | [
"BasalGanglia/stn_gpe_simple_cfit_worker.py"
] | [
"# my_cgs_worker.py\nfrom pyrates.utility.grid_search import ClusterWorkerTemplate\nimport os\nfrom pandas import DataFrame\nfrom pyrates.utility import grid_search, welch\nimport numpy as np\nfrom copy import deepcopy\n\n\nclass MinimalWorker(ClusterWorkerTemplate):\n def worker_postprocessing(self, **kwargs):\... | [
[
"numpy.sqrt",
"numpy.asarray",
"numpy.isnan",
"pandas.DataFrame",
"numpy.mean",
"numpy.var"
]
] | [
{
"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": []
}
] |
VTrappler/SWE1D | [
"ed19be4c0d37441f0f312baa403f368bb2c3afba"
] | [
"swe/numerics/direct_MLT_ADJ_LF.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport numpy as np\n\n# from LaxFriedrichs import LF_flux\nfrom .compute_flux_1d import compute_flux_1d, compute_flux_1d_bis\nfrom .variables import ConservedVars, PrimitiveVars\nfrom .adjoint_function import ALFcons, BLFcons, CLFcons, DLFcons\n\ng = 9.81\n\n\n# --... | [
[
"numpy.diag",
"numpy.sqrt",
"numpy.linspace",
"numpy.isnan",
"numpy.fabs",
"numpy.ones",
"numpy.sign",
"numpy.append",
"numpy.fmax",
"numpy.insert",
"numpy.diff",
"numpy.zeros",
"numpy.sum",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
huwan/torch-quiver | [
"f5b554ca98e88df92c68398b783f6cf713b9e6a0"
] | [
"examples/multi_gpu/pyg/reddit/dist_sampling_ogb_reddit_quiver.py"
] | [
"import os\n\nimport torch\nfrom tqdm import tqdm\nimport torch.nn.functional as F\nimport torch.multiprocessing as mp\nimport torch.distributed as dist\nfrom torch.nn.parallel import DistributedDataParallel\n\nfrom torch_geometric.nn import SAGEConv\nfrom torch_geometric.datasets import Reddit\nfrom torch_geometri... | [
[
"torch.torch.cuda.set_device",
"torch.distributed.init_process_group",
"torch.multiprocessing.spawn",
"torch.cat",
"torch.manual_seed",
"torch.nn.ModuleList",
"torch.nn.functional.nll_loss",
"torch.utils.data.DataLoader",
"torch.nn.functional.dropout",
"torch.distributed.ba... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lingomaniac88/ktaneVideoPoker | [
"933eb7c2635f6455db4fab8091aa7d7d3d5a8e3b"
] | [
"Python/doubleDoubleBonus/ace_or_tj.py"
] | [
"import numpy as np\nimport re\nimport sklearn.svm\n\npattern = '^.*\\\\[(..),(..),(..),(..),(..)\\\\].*\\\\[(\\\\d+)\\\\],.*$'\n\nf = open('aceOrTJ.txt')\nlines = map(lambda line: re.match(pattern, line), f.readlines())\nf.close()\n\nhands = [(tuple(line[i] for i in [1, 2, 3, 4, 5]), int(line[6]), line[0]) for lin... | [
[
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
HubFire/Reinforcement-learning-with-tensorflow | [
"59ac4d7c446d0c903047560458de388757ac82bd"
] | [
"contents/10_A3C/A3C_RNN.py"
] | [
"\"\"\"\nAsynchronous Advantage Actor Critic (A3C) + RNN with continuous action space, Reinforcement Learning.\n\nThe Pendulum example.\n\nView more on my tutorial page: https://morvanzhou.github.io/tutorials/\n\nUsing:\ntensorflow 1.0\ngym 0.8.0\n\"\"\"\n\nimport multiprocessing\nimport threading\nimport tensorflo... | [
[
"tensorflow.device",
"tensorflow.nn.dynamic_rnn",
"tensorflow.contrib.rnn.BasicRNNCell",
"tensorflow.get_collection",
"tensorflow.gradients",
"tensorflow.layers.dense",
"tensorflow.stop_gradient",
"tensorflow.subtract",
"tensorflow.contrib.distributions.Normal",
"tensorflow... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
nikhil-sarin/redback | [
"b0023b770a3c0a25a18c4f6ff1a07339be7f83fe"
] | [
"redback/likelihoods.py"
] | [
"import numpy as np\nfrom typing import Any, Union\n\nimport bilby\nfrom scipy.special import gammaln\n\n\nclass _RedbackLikelihood(bilby.Likelihood):\n\n def __init__(self, x: np.ndarray, y: np.ndarray, function: callable, kwargs: dict = None) -> None:\n \"\"\"\n\n :param x: The x values.\n ... | [
[
"numpy.log",
"numpy.sqrt",
"numpy.nan_to_num",
"numpy.ones",
"scipy.special.gammaln",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.18",
"0.19"
],
"tensorflow": []
}
] |
euxhenh/cellar | [
"679387216043f3d287ea29a15f78868f412d2948"
] | [
"controller/cellar/core/_cluster.py"
] | [
"import igraph as ig\nimport leidenalg as la\nimport numpy as np\nfrom app import logger\nfrom faiss.swigfaiss_avx2 import compute_PQ_dis_tables_dsub2\nfrom scipy.sparse import csr_matrix, issparse\nfrom sklearn.cluster import AgglomerativeClustering, KMeans, SpectralClustering\nfrom sklearn_extra.cluster import KM... | [
[
"scipy.sparse.issparse",
"numpy.unique",
"scipy.sparse.csr_matrix",
"numpy.logical_or",
"numpy.max",
"numpy.argmin",
"numpy.array"
]
] | [
{
"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"... |
weiserhei/music_led_strip_control | [
"33d6e9ca3c5cc699f1f700e6bf00727fa2f65f3c"
] | [
"server/libs/effects/effect_twinkle.py"
] | [
"from libs.effects.effect import Effect # pylint: disable=E0611, E0401\n\nfrom scipy.ndimage.filters import gaussian_filter1d\nimport numpy as np\nimport random\n\n\nclass EffectTwinkle(Effect):\n\n def __init__(self, device):\n\n # Call the constructor of the base class.\n super(EffectTwinkle, se... | [
[
"scipy.ndimage.filters.gaussian_filter1d",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"0.15",
"1.4",
"0.16",
"1.0",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"0.10",
"0.17",
"1.3"
],
"tensorflow": [... |
jawj/ipython-sql | [
"296e4c4c20e14e1966490e43f16b1a2364e51ed0"
] | [
"src/sql/run.py"
] | [
"import codecs\nimport csv\nimport operator\nimport os.path\nimport re\nfrom functools import reduce\n\nimport prettytable\nimport six\nimport sqlalchemy\nimport sqlparse\n\nfrom .column_guesser import ColumnGuesserMixin\n\ntry:\n from pgspecial.main import PGSpecial\nexcept ImportError:\n PGSpecial = None\n\... | [
[
"matplotlib.pylab.title",
"pandas.DataFrame",
"matplotlib.pylab.pie",
"matplotlib.pylab.ylabel",
"matplotlib.pylab.plot",
"matplotlib.pylab.xlabel"
]
] | [
{
"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": []
}
] |
brunobeltran/multi_locus_analysis | [
"bef1e25ad5ca848cc5900d4b0207a93f48cf8c62",
"bef1e25ad5ca848cc5900d4b0207a93f48cf8c62"
] | [
"multi_locus_analysis/finite_window/simulation.py",
"multi_locus_analysis/examples/burgess/analysis.py"
] | [
"from multiprocessing import Pool, cpu_count\n\nimport lifelines\nimport numpy as np\nimport pandas as pd\nimport scipy.stats\nfrom scipy.stats import expon\n\nimport bruno_util\nimport bruno_util.random\nfrom .. import stats as _mla_stats\n\n\n@bruno_util.random.strong_default_seed\ndef ab_window_fast(rands, means... | [
[
"numpy.sqrt",
"numpy.linspace",
"numpy.random.random_sample",
"pandas.DataFrame",
"numpy.concatenate",
"numpy.max",
"numpy.zeros_like",
"numpy.ones_like",
"numpy.diff",
"numpy.interp",
"numpy.log",
"numpy.random.choice",
"numpy.logspace",
"numpy.log10",
... | [
{
"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... |
jessicaengel451/BiblioPixelAnimations | [
"37570003ff3460f23cd33bd30625611fdc6e9228",
"37570003ff3460f23cd33bd30625611fdc6e9228"
] | [
"BiblioPixelAnimations/matrix/ScreenGrab.py",
"BiblioPixelAnimations/matrix/kimotion.py"
] | [
"from bibliopixel.animation.matrix import Matrix\nfrom bibliopixel.util import log\nimport numpy as np\ntry:\n import cv2\nexcept ImportError:\n log.error('Could not import cv2 library')\nimport os\n\ngrab = None\n\nif os.name == 'nt':\n try:\n from desktopmagic.screengrab_win32 import getRectAsImag... | [
[
"numpy.array"
],
[
"numpy.fliplr",
"numpy.dtype",
"numpy.frombuffer",
"numpy.copy",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
limresgrp/free-energy-gnn | [
"22827686b488ec0f0ffa77367dba9e23122714ac"
] | [
"multigraph.py"
] | [
"# system imports\nimport pickle\nimport numpy as np\nfrom warnings import warn\nimport random\nimport json\nfrom datetime import datetime\nimport os\n\n# pytorch imports\nimport torch\nfrom torch.nn import L1Loss\nfrom torch_geometric.data import Data\n\n# Custom imports\nfrom helpers import mol2graph\nfrom helper... | [
[
"torch.mean",
"numpy.asarray",
"torch.manual_seed",
"torch.tensor",
"torch.std",
"torch.cuda.is_available",
"torch.nn.L1Loss",
"torch.as_tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
khatna/sent_debias | [
"b444c49475fedbbffa817717b1ff9484348a9fee"
] | [
"debias-BERT/experiments/extract_elmo.py"
] | [
"from __future__ import print_function, division\nfrom allennlp.modules.elmo import Elmo, batch_to_ids\nimport torch\nimport time\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\n\noptions_file = \"https://s3-us-west-2.amazonaws.com/allennlp/models/elmo/2x4096_512_2048cnn_2xhighway/elmo_2x4096_512_... | [
[
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
iCounterBOX/TensorFlow_CarOccupancyDetector_V2 | [
"95240a8e333d31823d14bf7224f98c7d0967c25d"
] | [
"a_carOccupancy_PreSelector_Cars_V2.py"
] | [
"'''\n OBJECT DETECTION - local V I D E O & local cocoInceptionModel \n CKOSS / 28.08.19 / 20:34\n \n \n \n From a MP4 Video we predict CARS and write those single CAR-Pictures to a Folder\n OFFICIAL TUTORIAL and source\n - https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/lat... | [
[
"tensorflow.Graph",
"tensorflow.import_graph_def",
"numpy.expand_dims",
"tensorflow.gfile.GFile",
"numpy.squeeze",
"tensorflow.Session",
"tensorflow.GraphDef"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SwastiKh/LayoutNet-2 | [
"820c0af059e1c51c1f7289be090b9efcb8a5e680"
] | [
".ipynb_checkpoints/test-checkpoint.py"
] | [
"import os\nimport sys\nimport scipy.misc\nimport numpy as np\nimport config\nimport trainer_step\n\n\ndef main():\n cfg = config.Config(filenamequeue=\"./dataset/layout_1205.tfrecords\",\n logdir=\"log\")\n t = trainer_step.Trainer(cfg)\n\n if not os.path.exists(cfg.sampledir):\n ... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wwlswj/spectral | [
"e886e4d9f8c34f512c8e81867f0de76e15550572"
] | [
"spectral/io/envi.py"
] | [
"'''\nENVI [#envi-trademark]_ is a popular commercial software package for processing\nand analyzing geospatial imagery. SPy supports reading imagery with associated\nENVI header files and reading & writing spectral libraries with ENVI headers.\nENVI files are opened automatically by the SPy :func:`~spectral.image... | [
[
"numpy.fromfile",
"numpy.memmap",
"numpy.dtype",
"numpy.max",
"numpy.equal",
"numpy.iterable"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sakshimishra12/greyatom-python-for-data-science | [
"a9ae2225d47172111214d156d6de7df3610934a7"
] | [
"Census-Project/code.py"
] | [
"# --------------\nimport numpy as np\r\nimport warnings\r\n\r\nwarnings.filterwarnings('ignore')\r\n\r\n#New record\r\nnew_record=[[50, 9, 4, 1, 0, 0, 40, 0]]\r\n\r\n#Reading file\r\ndata = np.genfromtxt(path, delimiter=\",\", skip_header=1)\r\n\r\n#Code starts here\r\ncensus = np.concatenate([data ,new_reco... | [
[
"numpy.min",
"numpy.genfromtxt",
"numpy.concatenate",
"numpy.max",
"numpy.std",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ShrohanMohapatra/softMatterAlgos | [
"03637ff85439306c4221299e69352d98e6503882"
] | [
"dataArrange5.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Jun 28 23:54:33 2021\n\n@author: shrohanmohapatra\n\"\"\"\nimport numpy as np\nimport matplotlib.pyplot as plt\nfileReader = open('TerminalDataForLaminar')\npointerGroups = {\n 'Re1':[[],[]],'Re2':[[],[]],\n 'Re3':[[],[]],'Re4':[[],[]]\n... | [
[
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kodavatimahendra/nnabla | [
"72009f670af075f17ffca9c809b07d48cca30bd9"
] | [
"python/src/nnabla/utils/data_source.py"
] | [
"# Copyright (c) 2017 Sony Corporation. 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 requir... | [
[
"numpy.array",
"numpy.random.RandomState"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
anubhav-jangra/CCA-images-text | [
"bdb912abeda326e066051aead4ce1b6c08ad58e3"
] | [
"main/image_to_tags.py"
] | [
"from gensim.models import word2vec\nfrom pycocotools.coco import COCO\nfrom scipy.spatial import distance\nimport logging\nimport numpy as np\nimport os\nimport pickle\nimport time\n\nimport features\n\nlogging.basicConfig(filename='cca.log', format='%(asctime)s %(message)s', level=logging.INFO)\n\nannFile = 'anno... | [
[
"numpy.dot",
"numpy.load",
"scipy.spatial.distance.euclidean",
"numpy.argsort",
"numpy.zeros"
]
] | [
{
"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"
... |
RomainSabathe/kaggle_airbnb2015 | [
"85af89a60ae0774ea7c7141a7b9054542c8a4076"
] | [
"Code/score.py"
] | [
"import numpy as np\nimport pandas as pd\n\ndef calc_score_slow(predictions, targets):\n max_submission = len(predictions.loc[predictions.index[0]])\n scores = []\n idcgs = {i: idcg(i) for i in range(1, max_submission+1)}\n for id in targets.index:\n target = targets.loc[id]['country']\n p... | [
[
"numpy.log2",
"numpy.arange",
"pandas.DataFrame",
"numpy.asfarray",
"numpy.mean"
]
] | [
{
"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": []
}
] |
kegashi/att-aware | [
"394b1d84a9b054c694a464ee954750ad719ea38a"
] | [
"src/scripts/experiment_maad_denoising.py"
] | [
"# Copyright 2020 Toyota Research Institute. All rights reserved.\nimport uuid\nimport copy\nimport functools\nimport numpy as np\n\nfrom collections import OrderedDict\n\nfrom maad.configs.args_file import parse_arguments\nfrom maad.experiments.maad_experiments import MAADExperiment\n\nfrom maad.utils.maad_consts... | [
[
"numpy.sqrt",
"numpy.median",
"numpy.std",
"numpy.random.normal",
"numpy.identity",
"numpy.mean",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Neuraxio/Neuraxle | [
"0615701b781c948e4ec38fa61c6b3a5d8d72c147",
"0615701b781c948e4ec38fa61c6b3a5d8d72c147"
] | [
"testing/steps/test_sklearn_wrapper.py",
"neuraxle/data_container.py"
] | [
"import numpy as np\nimport pytest\nfrom sklearn.decomposition import PCA\nfrom sklearn.ensemble import BaggingRegressor, GradientBoostingRegressor\nfrom sklearn.linear_model import LinearRegression, SGDRegressor, SGDClassifier\nfrom sklearn.metrics import median_absolute_error\n\nfrom neuraxle.base import Identity... | [
[
"numpy.expand_dims",
"numpy.random.random",
"sklearn.ensemble.BaggingRegressor",
"numpy.array_equal",
"sklearn.linear_model.SGDRegressor",
"sklearn.ensemble.GradientBoostingRegressor",
"sklearn.linear_model.LinearRegression",
"numpy.array",
"sklearn.decomposition.PCA",
"skl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
priydarshiroopak/Python_DS_20CS30042 | [
"e45a539610bb3aa990e80bbc4dfb318b7ed7b4d8"
] | [
"my_package/data/dataset.py"
] | [
"#Imports\nfrom __future__ import annotations\nimport json\nfrom PIL import Image\nimport numpy as np\n\n\nclass Dataset(object):\n '''\n A class for the dataset that will return data items as per the given index\n '''\n\n def __init__(self, annotation_file, transforms = None):\n '''\n ... | [
[
"numpy.asarray"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
awesome-archive/scikit-plot | [
"ccb83bfde20f51102cd720de299d6c2fcc6ac0a7"
] | [
"examples/plot_roc_curve.py"
] | [
"\"\"\"An example showing the plot_roc_curve method used by a scikit-learn classifier\"\"\"\nfrom __future__ import absolute_import\nimport matplotlib.pyplot as plt\nfrom scikitplot import classifier_factory\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.datasets import load_digits as load_data\n\n\nX, y... | [
[
"matplotlib.pyplot.show",
"sklearn.naive_bayes.GaussianNB",
"sklearn.datasets.load_digits"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Spotify-picker-69/Front-end | [
"841386e368e0d57458b6de34888ada8ba55c1a39"
] | [
"Pages/model.py"
] | [
"\nfrom sklearn.cluster import KMeans\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.pipeline import Pipeline\nfrom scipy.spatial.distance import cdist\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom sklearn.manifold import TSNE\nimport plotly.express as px\nimport spotipy\nfrom spotipy.... | [
[
"pandas.read_csv",
"matplotlib.pyplot.subplots",
"scipy.spatial.distance.cdist",
"pandas.DataFrame",
"numpy.mean",
"numpy.argsort",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"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... |
sagnikghoshcr7/Volcanoes-in-the-United-States | [
"71ea21a8b3f1ae213478d735a10a240524b89702"
] | [
"geo7.py"
] | [
"#Import Library\nimport folium\nfrom folium.plugins import MarkerCluster\nimport pandas as pd\n\n#Load Data\ndata = pd.read_csv(\"Volcanoes_USA.txt\")\nlat = data['LAT']\nlon = data['LON']\nelevation = data['ELEV']\n\n#Function to change colors\ndef color_change(elev):\n if(elev < 1000):\n return('green'... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
valgur/QuaPy | [
"6b1ba4886a1d64b086829306cbba689cdcfd60e8"
] | [
"quapy/evaluation.py"
] | [
"from typing import Union, Callable, Iterable\nimport numpy as np\nfrom tqdm import tqdm\nimport inspect\n\nimport quapy as qp\nfrom quapy.data import LabelledCollection\nfrom quapy.method.base import BaseQuantifier\nfrom quapy.util import temp_seed\nimport quapy.functional as F\nimport pandas as pd\n\n\ndef artifi... | [
[
"numpy.asarray",
"numpy.mean",
"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": []
}
] |
enry12/FALKON | [
"4246362dcf60c259ec0718eb7e25aeba74724792"
] | [
"higgs.py"
] | [
"import argparse\nimport numpy as np\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.metrics import accuracy_score, roc_auc_score\n\nfrom time import time\n\nfrom falkon import Falkon\nfrom utility.kernel import *\n\n\ndef main(path, kernel_fun... | [
[
"sklearn.metrics.roc_auc_score",
"sklearn.model_selection.train_test_split",
"numpy.sign",
"numpy.load",
"sklearn.preprocessing.StandardScaler",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AllenXiangX/PMP-Net | [
"c6a65da629f0faafd3b1e2dd060e84ab53b9379f"
] | [
"utils/data_transforms.py"
] | [
"# -*- coding: utf-8 -*-\n# @Author: Haozhe Xie\n# @Date: 2019-08-02 14:38:36\n# @Last Modified by: Haozhe Xie\n# @Last Modified time: 2020-07-03 09:23:07\n# @Email: cshzxie@gmail.com\n\nimport cv2\nimport math\nimport numpy as np\nimport torch\nimport transforms3d\n\nclass Compose(object):\n def __init__(s... | [
[
"numpy.dot",
"numpy.expand_dims",
"numpy.fliplr",
"numpy.tile",
"numpy.cos",
"numpy.sin",
"numpy.concatenate",
"numpy.arctan2",
"numpy.random.permutation",
"numpy.random.randn",
"numpy.random.uniform",
"numpy.array",
"numpy.zeros",
"numpy.random.randint"
]... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
benpolletta/egly-driver-network | [
"cff36a857e22358d122f24fb0100be26483a3caf",
"cff36a857e22358d122f24fb0100be26483a3caf"
] | [
"LIP_full_alte.py",
"parallel_alternate3b.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Dec 2 14:28:44 2019\n\n@author: aaussel\n\"\"\"\n#import matplotlib\n#matplotlib.use('Agg')\n\nfrom brian2 import *\n\nstart_scope()\n\nfrom scipy import signal\nfrom cells.RS_LIP_alte import *\nfrom cells.FS_LIP_alte import *\nfrom cells.SI_LIP import *\nfrom cells... | [
[
"scipy.signal.periodogram"
],
[
"matplotlib.pyplot.switch_backend"
]
] | [
{
"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"
... |
dalupus/keras | [
"af5c5b6a55528a255500b733439a66d25b5647ec"
] | [
"keras/layers/core.py"
] | [
"# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import\nfrom __future__ import division\n\nimport numpy as np\n\nimport copy\nimport inspect\nimport types as python_types\nimport marshal\nimport sys\nimport warnings\n\nfrom .. import backend as K\nfrom .. import activations, initializations, regularizers,... | [
[
"numpy.prod",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
filliat/swarm-rescue | [
"ce323c90ccb4a25216c63abccd09d3f31eea1189",
"ce323c90ccb4a25216c63abccd09d3f31eea1189"
] | [
"src/swarm_rescue/tuto_spg_jupyter/tuto_01.py",
"src/swarm_rescue/tuto_spg_jupyter/tuto_02_03_rooms.py"
] | [
"from simple_playgrounds.playground import SingleRoom\nfrom simple_playgrounds.engine import Engine\nfrom simple_playgrounds.element.elements.basic import Physical\nfrom simple_playgrounds.agent.controllers import Keyboard\nfrom simple_playgrounds.agent.agents import BaseAgent\n\n# matplotlib inline\nimport matplot... | [
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.show",
"matplotlib.pyplot.axis"
],
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.show",
"matplotlib.pyplot.axis"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
davidyzeng/sigpy | [
"56f8eb9be57b5a80e53ae09f2ba0802586fe69bc"
] | [
"sigpy/plot/line.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"Line plot.\n\"\"\"\nimport os\nimport uuid\nimport subprocess\nimport datetime\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sigpy.util import prod\n\n\nclass Line(object):\n \"\"\"Plot array as lines.\n\n Keyword Args:\n x: select current dimension as x\n... | [
[
"numpy.imag",
"numpy.abs",
"numpy.real",
"numpy.angle",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
anoburn/mspypeline | [
"b815ea7ebe4c99696a5867f6fad7ae189fcca20d"
] | [
"mspypeline/plotting_backend/plotly_plots.py"
] | [
"import plotly.express as px\nfrom plotly.subplots import make_subplots\nimport plotly.graph_objects as go\n\nfrom mspypeline.helpers import get_number_rows_cols_for_fig\n\n\ndef create_plot():\n import plotly\n import plotly.graph_objs as go\n\n import pandas as pd\n import numpy as np\n import json... | [
[
"numpy.random.randn",
"numpy.linspace",
"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": []
}
] |
tmiv/Real-Time-Voice-Cloning | [
"76a22ca6f589e4835f9e663658961f8504f37d09"
] | [
"toolbox/__init__.py"
] | [
"from toolbox.ui import UI\nfrom encoder import inference as encoder\nfrom synthesizer.inference import Synthesizer\nfrom vocoder import inference as vocoder\nfrom pathlib import Path\nfrom time import perf_counter as timer\nfrom toolbox.utterance import Utterance\nimport numpy as np\nimport traceback\nimport sys\n... | [
[
"numpy.concatenate",
"numpy.array",
"numpy.abs",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JavierAntoran/Bayesain-Neural-Networks | [
"1f867a5bcbd1abfecede99807eb0b5f97ed8be7c"
] | [
"train_KFLaplace_MNIST.py"
] | [
"from __future__ import division, print_function\nimport time\nimport torch.utils.data\nfrom torchvision import transforms, datasets\nimport argparse\nimport matplotlib\nfrom src.KF_Laplace.model import *\nfrom src.KF_Laplace.hessian_operations import chol_scale_invert_kron_factor\n\nmatplotlib.use('Agg')\nimport m... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.gca",
"matplotlib.pyplot.title",
"matplotlib.use",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig",
"matplotlib.ticker.ScalarFormatter",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.figure"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zmxdream/FlexFlow | [
"7ea50d71a02e853af7ae573d88c911511b3e82e0",
"7ea50d71a02e853af7ae573d88c911511b3e82e0",
"7ea50d71a02e853af7ae573d88c911511b3e82e0"
] | [
"examples/python/keras_exp/func_cifar10_cnn.py",
"examples/python/pytorch/mnist_mlp_torch2.py",
"examples/python/keras_exp/func_cifar10_cnn_nested.py"
] | [
"# Copyright 2020 Stanford University, Los Alamos National Laboratory\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# Unle... | [
[
"tensorflow.keras.layers.Activation",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.layers.Conv2D",
"tensorflow.keras.optimizers.SGD",
"tensorflow.keras.backend.set_image_data_format",
"tensorflow.keras.layers.MaxPooling2D",
"tensorflow.keras.layers.Flatten",
"tensorflow.keras... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matpl... |
OsamaMazhar/Random-Shadows-Highlights | [
"8454a412ed0f1989a599917a1b926816d5c404f0"
] | [
"main.py"
] | [
"\"\"\"\nTraining Script for:\n \"Random Shadows and Highlights: A new data augmentation method for extreme lighting conditions\"\n \"https://arxiv.org/abs/2101.05361\"\nCopyright (c) 2021 Osama MAZHAR and Jens KOBER\nLicensed under the Apache License 2.0 (See LICENSE for details)\nOriginally Written by Osama... | [
[
"torch.nn.CrossEntropyLoss",
"numpy.arange",
"torch.utils.data.SequentialSampler",
"torch.utils.data.DataLoader",
"torch.utils.data.RandomSampler",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
discort/metrics | [
"5c2069eb2087ede7f008222ebab286aedad14dfb"
] | [
"torchmetrics/functional/classification/precision_recall_curve.py"
] | [
"# Copyright The PyTorch Lightning team.\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... | [
[
"torch.ones",
"torch.zeros",
"torch.tensor",
"torch.no_grad",
"torch.where",
"torch.argsort",
"torch.cumsum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
animeshh/nuclei-analysis | [
"b5256d1733a2b5b6be3bef295af1b18a53027834"
] | [
"hackrpi/kmean_cluster.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nimport os\nfrom sklearn.cluster import KMeans\nfrom scipy.cluster.vq import kmeans,vq\nfrom scipy.spatial.distance import cdist\n\n#fileNum = '01'\n#dataDir = 'data/path-image-1' + str(fileNum) + '.tif/'\n#ftPath = dataDir + 'path-image-1' + str(fileNum) + '.seg... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.title",
"sklearn.cluster.KMeans",
"scipy.cluster.vq.kmeans",
"numpy.min",
"scipy.spatial.distance.cdist",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.clf",
"numpy.ar... | [
{
"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"
... |
jairoadiazr/rtcolombia | [
"e48760a915076b4eb6747357f2831688864ea9c1"
] | [
"covid.py"
] | [
"import pandas as pd\nimport numpy as np\nfrom datetime import timedelta\n\nclass CovidData:\n def __init__(self, covid_data: pd.DataFrame):\n self.covid_data = covid_data\n self.d_hat = None\n self.w_hat = None\n\n \n def preprocessing_data(self):\n self.rename_columns()\n ... | [
[
"pandas.to_datetime",
"numpy.datetime64"
]
] | [
{
"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": []
}
] |
konsan1101/pycv1 | [
"247ec71c455e4b8e846eca63de21423120e2aa7e"
] | [
"testM2.py"
] | [
"#!/usr/bin/python\n\nimport os\nimport sys\nimport numpy as np\nimport cv2\nimport time\nimport threading\nfrom multiprocessing import Process, Queue, Pipe, Manager, Lock\n\nprint(os.path.dirname(__file__))\nprint(os.path.basename(__file__))\nprint(sys.version_info)\nprint(cv2.__version__)\n\nclass fpsWithTick(o... | [
[
"numpy.around",
"numpy.zeros",
"numpy.cos",
"numpy.sin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
WanQiyang/mmdetection | [
"aac62a693d1374ca605cede6d1f8ea5621ff4e46"
] | [
"mmdet/models/dense_heads/center_head.py"
] | [
"from logging import warning\nfrom math import ceil\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmcv.cnn import ConvModule, bias_init_with_prob\nfrom mmcv.ops import batched_nms\n\nfrom mmdet.core import multi_apply\nfrom ..builder import HEADS, build_loss\nfrom ..utils import gaus... | [
[
"torch.topk",
"torch.nn.ModuleList",
"torch.stack",
"torch.argsort",
"torch.nn.functional.max_pool2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
stx1998x/autoeq-pkg | [
"5c6b0110cf71e549dffcef3412458e66aeeb49dd"
] | [
"autoeq/frequency_response.py"
] | [
"# -*- coding: utf-8 -*_\n\nimport os\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker as ticker\nimport math\nimport pandas as pd\nfrom io import StringIO\nfrom scipy.interpolate import InterpolatedUnivariateSpline\nfrom scipy.signal import savgol_filter, find_peaks, minimum_phase, firwin2\nfrom scipy.sp... | [
[
"tensorflow.compat.v1.sin",
"numpy.expand_dims",
"scipy.signal.find_peaks",
"numpy.sqrt",
"tensorflow.compat.v1.concat",
"numpy.linspace",
"numpy.squeeze",
"numpy.vstack",
"tensorflow.compat.v1.sqrt",
"numpy.concatenate",
"numpy.max",
"numpy.all",
"numpy.round",... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.10",
"1.4",
"1.9",
"1.5",
"1.2",
"1.7",
"1.3",
"1.8"
],
"tensorflow": []
}
] |
SuhitK/DeepEvent-VO | [
"4277c64ecfe1823ce2cd910b8a4ddf9943f4bae8"
] | [
"frozen/lieFunctions.py"
] | [
"\"\"\"\nHelper functions for working with the Lie groups SO(3) and SE(3)\n\"\"\"\n\nimport functools\nimport numpy as np\n\n\n# # Log map for SO(3). Returns a 3-vector of so(3) exponential coordinates\n# # Actually, this does vee(SO3_log(R)), also denoted Log(R) in a few conventions\n# # Not to be confused with lo... | [
[
"numpy.sqrt",
"numpy.clip",
"numpy.reshape",
"numpy.asarray",
"numpy.eye",
"numpy.matmul",
"numpy.arccos",
"numpy.stack",
"numpy.sin",
"numpy.cos",
"numpy.arctan2",
"numpy.finfo",
"numpy.transpose",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
chainer/chainer-benchmark | [
"8d0c8f5052b5e2a85ad522ff48899ffc9a2bfafb"
] | [
"benchmarks/functions/connection/local_convolution_2d.py"
] | [
"import numpy\n\nimport chainer.functions as F\n\nfrom benchmarks.functions import FunctionBenchmark\nfrom benchmarks.utils import backends\nfrom benchmarks.utils import parameterize\n\n\n@backends('gpu', 'cpu')\n@parameterize([('batches', [1, 16])])\nclass LocalConvolution2D(FunctionBenchmark):\n def setup(self... | [
[
"numpy.prod"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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