repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list |
|---|---|---|---|---|
llv22/jax-macOS-cuda | [
"fd1f8e6d612ae3eee24cfa5ee19e8d16ed89aecb"
] | [
"jax/_src/numpy/lax_numpy.py"
] | [
"# Copyright 2018 Google LLC\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# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed ... | [
[
"numpy.asarray",
"numpy.issubdtype",
"numpy.dtype",
"numpy.concatenate",
"numpy.all",
"numpy.any",
"numpy.where",
"numpy.arange",
"numpy.version.version.split",
"numpy.ceil",
"numpy.size",
"numpy.diff",
"numpy.insert",
"numpy.ravel",
"numpy.zeros",
"... |
PyWiFeS/tools | [
"f6f414134d1ce5c5a4c35add3400859900f3bca6"
] | [
"process_stellar.py"
] | [
"\"\"\"After analysis with WiFeS, this suite of routines can extract a star optimally\nand calculate its radial velocity.\n\nWARNING - this code is still not properly documented or complete. Any contributions\nwelcome!\n\nexample lines of code...\n\nExecuting from the code directory, e.g. with Margaret's output dir... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.imshow",
"numpy.sqrt",
"matplotlib.pyplot.plot",
"numpy.max",
"numpy.mean",
"numpy.exp",
"numpy.where",
"numpy.roll",
"scipy.interpolate.InterpolatedUnivariateSpline",
"numpy.arange",
"numpy.std",
"numpy.argmax",
... |
masmansouri/pyscf | [
"15957003aa26b165a823e7e2d0846a8564b2a10c"
] | [
"setup.py"
] | [
"#!/usr/bin/env python\n# Copyright 2014-2020 The PySCF Developers. 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/LIC... | [
[
"numpy.__config__.get_info"
]
] |
LedioTerolli/DrawToMars | [
"e3e476a218ced9dd9839b49f193846651d4e4c97"
] | [
"image_process.py"
] | [
"from win32api import GetSystemMetrics\nfrom math import sqrt\nimport numpy as np\nimport imutils\nimport cv2\n\n\nclass Obj:\n\n def __init__(self, tup_coor, coor, area, peri, center):\n self.tup_coor = tup_coor\n self.coor = coor\n self.area = area\n self.peri = peri\n self.c... | [
[
"numpy.delete",
"numpy.zeros",
"numpy.vstack"
]
] |
ilham-bintang/ScanSSD | [
"a7298bd3fb105b7d4fb2ea7054dcc87182be3667",
"a7298bd3fb105b7d4fb2ea7054dcc87182be3667"
] | [
"layers/functions/prior_box.py",
"gtdb/resize_gt.py"
] | [
"from __future__ import division\nfrom math import sqrt as sqrt\nfrom itertools import product as product\nimport torch\n\n\nclass PriorBox(object):\n \"\"\"Compute priorbox coordinates in center-offset form for each source\n feature map.\n \"\"\"\n def __init__(self, args, cfg):\n super(PriorBox... | [
[
"torch.Tensor"
],
[
"numpy.round"
]
] |
mdaquin/fca.js | [
"b565a593da314c3780e169c36cd4a5f0eaceb0f5"
] | [
"example_data/extract_attributes.py"
] | [
"import json\nimport requests\nimport numpy\n\nproperties = {}\n\nwith open(\"country_data.json\") as f:\n data = json.load(f)\n for c in data:\n for k in c[\"claims\"].keys():\n if k not in properties:\n properties[k] = {\"count\": 0, \"datatypes\": [], \"values\": [], \"all\... | [
[
"numpy.percentile"
]
] |
SuryaThiru/mljar-supervised | [
"e6b63d4fe6bd3ee0d9183728af8729dc85533c54"
] | [
"tests/tests_algorithms/test_decision_tree.py"
] | [
"import unittest\nimport tempfile\nimport json\nimport numpy as np\nimport pandas as pd\nimport os\nfrom numpy.testing import assert_almost_equal\nfrom sklearn import datasets\n\nfrom supervised.algorithms.decision_tree import (\n DecisionTreeAlgorithm,\n DecisionTreeRegressorAlgorithm,\n)\nfrom supervised.ut... | [
[
"sklearn.datasets.make_regression",
"numpy.testing.assert_almost_equal"
]
] |
os-climate/sostrades-core | [
"bcaa9b5e393ffbd0963e75a9315b27caf8b0abd9",
"bcaa9b5e393ffbd0963e75a9315b27caf8b0abd9",
"bcaa9b5e393ffbd0963e75a9315b27caf8b0abd9",
"bcaa9b5e393ffbd0963e75a9315b27caf8b0abd9"
] | [
"sos_trades_core/sos_processes/test/test_disc_hessian_doe_eval_from_proc/usecase.py",
"sos_trades_core/execution_engine/sos_scenario.py",
"sos_trades_core/tests/l0_test_42_newton_raphson_problem.py",
"sos_trades_core/tools/grad_solvers/validgrad/FDValidGrad.py"
] | [
"'''\nCopyright 2022 Airbus SA\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writin... | [
[
"pandas.DataFrame"
],
[
"numpy.delete",
"numpy.array"
],
[
"numpy.array",
"numpy.zeros"
],
[
"numpy.linalg.norm"
]
] |
RamiroIsaJ/TR_KF_Interface | [
"306896920cbf563d2d96e64d3ca6c3c9a20e6610"
] | [
"Trk_def.py"
] | [
"import numpy as np\nimport cv2\nimport glob\nimport math\n\n\ndef f_sorted(files_, id_sys):\n symbol = '\\\\' if id_sys == 0 else '/'\n ids = []\n for f in files_:\n parts = f.split(symbol)\n name_i = parts[len(parts) - 1]\n ids.append(name_i.split('.')[0].split('_')[-1])\n ids_1 =... | [
[
"numpy.append",
"numpy.array",
"numpy.where",
"numpy.delete"
]
] |
WISDEM/wake-exchange | [
"1e9bb1266799517afeca0358c3237f9250bacfa4"
] | [
"src/wakeexchange/gauss.py"
] | [
"\"\"\"\ngauss.py\nCreated by Jared J. Thomas, Jul. 2016.\nBrigham Young University\n\"\"\"\n\nfrom openmdao.api import IndepVarComp, Group\nfrom gaussianwake.gaussianwake import GaussianWake\nimport numpy as np\n\n\ndef add_gauss_params_IndepVarComps(openmdao_object, nRotorPoints=1):\n\n # openmdao_object.add('... | [
[
"numpy.zeros"
]
] |
hemantghuge/TensorFlow | [
"09f5609f0fd282943defd4608ee90bb6883a394b"
] | [
"tensorflow/python/ops/numpy_ops/np_interop_test.py"
] | [
"# Copyright 2020 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.python.ops.numpy_ops.random.randn",
"tensorflow.compat.v2.cond",
"tensorflow.python.ops.numpy_ops.sum",
"tensorflow.python.ops.numpy_ops.sqrt",
"tensorflow.compat.v2.linalg.diag",
"tensorflow.compat.v2.autodiff.ForwardAccumulator",
"tensorflow.python.ops.numpy_ops.square",
... |
iliasprc/IDPMetagenome | [
"519cec77bb55eb91dbb7b243a2d80999742c033d",
"519cec77bb55eb91dbb7b243a2d80999742c033d"
] | [
"idp_comparison/test_idp_predictors.py",
"tests/ssltrain.py"
] | [
"import argparse\nimport os\nimport sys\nsys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))\nimport torch\nimport torch.nn as nn\nfrom models.rnn import IDP_test_rnn\nfrom models.transformer import IDP_compare_Transformer\n\nfrom idp_methods.utils import *\n\nparser = argparse.ArgumentPars... | [
[
"torch.nn.CrossEntropyLoss",
"torch.softmax",
"torch.max",
"torch.cuda.manual_seed",
"torch.cat",
"torch.manual_seed",
"torch.from_numpy",
"torch.tensor",
"torch.cuda.is_available",
"torch.device"
],
[
"torch.backends.cudnn.version",
"torch.nn.Embedding",
"t... |
vm6502q/ProjectQ | [
"1eac4b1f529551dfc1668443eba0c68dee54120b"
] | [
"projectq/setups/decompositions/rz2rx_test.py"
] | [
"# Copyright 2017 ProjectQ-Framework (www.projectq.ch)\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.dot",
"numpy.absolute",
"numpy.zeros"
]
] |
SemihAkkoc/arz_cazibesi | [
"0a72da0d5c9e0f104f51cb375d858a7b9ac96eb5"
] | [
"arz_mk1_TR.py"
] | [
"\"\"\"\r\nTODO:\r\nmake choosable graphics NOT gravity[0], time[0]\r\n\"\"\"\r\n\r\nimport matplotlib.pyplot as plt\r\nimport pandas as pd\r\nimport array, math\r\nfrom tkinter import *\r\n\r\nsensitivity = 200\r\nheight = 2.0 # meters\r\ntime = [0.61, 0.622, 0.6423, 0.6323]\r\npressure = [1, 0.9, 0.81, 0.3]\r\ne... | [
[
"pandas.Series",
"pandas.DataFrame",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] |
eladj/ScrabbleOCR | [
"4470eca7dc476914ffaed71a6688c411fb958bdc"
] | [
"Python/findboard.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Sep 26 15:20:14 2015\n\n@author: elad\n\"\"\"\nfrom scipy.misc import imread\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport cv2\nfrom PIL import Image\nimport pytesseract\n\ndef four_point_transform(image, pts, dst=None):\n # obtain a consistent orde... | [
[
"numpy.sqrt",
"numpy.uint8",
"numpy.ones",
"numpy.logical_or",
"numpy.std",
"numpy.argmin",
"numpy.array",
"numpy.zeros",
"numpy.vstack",
"matplotlib.pyplot.figure"
]
] |
luisggc/FConcrete | [
"0f8d003da965b359900acb084c0f8762aef1799e"
] | [
"fconcrete/Structural/Load.py"
] | [
"import numpy as np\r\nfrom fconcrete.helpers import to_unit\r\n\r\nclass Load:\r\n \"\"\"\r\n Class that defines a load.\r\n \"\"\"\r\n def __init__(self, force, momentum, x_begin, x_end, q=0, order=0, displacement=0):\r\n force = to_unit(force, \"kN\")\r\n momentum = to_unit(momentum... | [
[
"numpy.concatenate",
"numpy.argsort",
"numpy.array"
]
] |
CompRhys/botorch | [
"6965426853b7c2d61244f6874eff3317b3588554"
] | [
"botorch/models/multitask.py"
] | [
"#!/usr/bin/env python3\n# Copyright (c) Meta Platforms, Inc. and affiliates.\n#\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nr\"\"\"\nMulti-Task GP models.\n\nReferences\n\n.. [Doucet2010sampl]\n A. Doucet. A Note on Efficient C... | [
[
"torch.Size",
"torch.zeros",
"torch.cat",
"torch.no_grad",
"torch.arange"
]
] |
Siviuze/CL4P-TP | [
"8e0afced80cac7699764654719043ecb2d948bc0"
] | [
"simulation/src/claptrap_simu/core/claptrap.py"
] | [
"# Claptrap class\n# This class runs a simulation of Claptrap using pinocchio, given a controller as input.\n\n# A controller is defined by the following signature:\n# def controller(t, q, v):\n# return tau\nimport pinocchio as pnc\nimport numpy as np\nimport scipy.integrate\nimport tqdm\nimport pkg_resources\nim... | [
[
"numpy.linalg.solve",
"numpy.cos",
"numpy.sin",
"numpy.concatenate",
"numpy.array",
"numpy.zeros"
]
] |
shapelets/shapelets-compute | [
"1dffe62d4eab9b1115b95bda5aaa7a3392024d72"
] | [
"modules/test/test_array_manipulation.py"
] | [
"# Copyright (c) 2021 Grumpy Cat Software S.L.\n#\n# This Source Code is licensed under the MIT 2.0 license.\n# the terms can be found in LICENSE.md at the root of\n# this project, or at http://mozilla.org/MPL/2.0/.\n\nimport numpy as np\nimport shapelets.compute as sc\n\n\ndef test_explicit_list_creation():\n ... | [
[
"numpy.allclose",
"numpy.linspace",
"numpy.logspace",
"numpy.ones",
"numpy.geomspace",
"numpy.random.randn",
"numpy.array"
]
] |
krmurtha/aslprep | [
"5c00c2c9ad1daf93b056907596b7798ae8059efb",
"5c00c2c9ad1daf93b056907596b7798ae8059efb"
] | [
"aslprep/niworkflows/conftest.py",
"aslprep/pybids/analysis/transformations/base.py"
] | [
"\"\"\"py.test configuration\"\"\"\nimport os\nfrom sys import version_info\nfrom pathlib import Path\nimport numpy as np\nimport nibabel as nb\nimport pandas as pd\nimport pytest\nimport tempfile\nimport pkg_resources\n\nfrom .utils.bids import collect_data\n\ntest_data_env = os.getenv(\n \"TEST_DATA_HOME\", st... | [
[
"numpy.diag",
"numpy.eye",
"numpy.random.random"
],
[
"numpy.allclose",
"pandas.DataFrame"
]
] |
epintilii/deep-learning-coursera | [
"974794c446386e524446a10b8949db5cc547f936"
] | [
"Neural Networks and Deep Learning/planar_utils.py"
] | [
"import matplotlib.pyplot as plt\nimport numpy as np\nimport sklearn\nimport sklearn.linear_model\n\ndef plot_decision_boundary(model, X, y):\n # Set min and max values and give it some padding\n x_min, x_max = X[0, :].min() - 1, X[0, :].max() + 1\n y_min, y_max = X[1, :].min() - 1, X[1, :].max() + 1\n ... | [
[
"matplotlib.pyplot.contourf",
"sklearn.datasets.make_gaussian_quantiles",
"numpy.random.seed",
"numpy.linspace",
"sklearn.datasets.make_moons",
"numpy.arange",
"numpy.cos",
"numpy.sin",
"sklearn.datasets.make_circles",
"numpy.random.rand",
"numpy.random.randn",
"mat... |
3lectrologos/comet | [
"97c945a5f3a355028de302667cc2cb785b13bc8e"
] | [
"comet/setup.py"
] | [
"#!/usr/bin/python\n\n\"\"\"Compiles the C and Fortran modules used by CoMEt.\"\"\"\n\n############################################################################\n# First compile the C code\n\n# Load required modules\nfrom distutils.core import setup, Extension\nimport subprocess, numpy, os\n\nthisDir = os.path.d... | [
[
"numpy.get_include",
"numpy.distutils.core.Extension",
"numpy.distutils.core.setup"
]
] |
alexxfernandez13/bienes_inmuebles | [
"6ee2761f4e1e8092d5ec814228e7d5bd393067c4"
] | [
"tests/test_csv.py"
] | [
"import os\nimport copy\nimport pandas as pd\nimport numpy as np\nfrom bienes_inmuebles.dataset.csv_preprocesamiento import CSV, PATH4\n\n\ndef test_cvs_to_dataframe():\n objeto_csv = CSV(os.path.join(PATH4, \"data/csv_barcelona.csv\"))\n assert objeto_csv.df.columns[1] == \"listing_url\" # funcion de compro... | [
[
"pandas.DataFrame"
]
] |
guanzhchen/PETuning | [
"eb36327713e237ea95a8982ceabb71de5ca4b09d",
"eb36327713e237ea95a8982ceabb71de5ca4b09d"
] | [
"model/roberta/modeling_roberta.py",
"tasks/glue/glue.py"
] | [
"# coding=utf-8\n# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.\n# Copyright (c) 2018, NVIDIA 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... | [
[
"torch.nn.Softmax",
"torch.nn.Dropout",
"torch.nn.CrossEntropyLoss",
"torch.ones",
"torch.zeros",
"torch.cat",
"torch.einsum",
"torch.nn.Embedding",
"torch.nn.LayerNorm",
"torch.nn.Tanh",
"torch.nn.Linear",
"torch.matmul",
"torch.tanh",
"torch.nn.BCEWithLogi... |
HaaLeo/ant-colony-optimization | [
"044b10be5694359900495403cc9f0e84d38a9e88"
] | [
"swarmlib/woa/woa_problem.py"
] | [
"# ------------------------------------------------------------------------------------------------------\n# Copyright (c) Leo Hanisch. All rights reserved.\n# Licensed under the BSD 3-Clause License. See LICENSE.txt in the project root for license information.\n# -------------------------------------------------... | [
[
"numpy.amin"
]
] |
erisyon/plaster | [
"20af32aed2365c6351fe3c26293308960099152b",
"20af32aed2365c6351fe3c26293308960099152b"
] | [
"plaster/run/sigproc_v3/c/sigproc_v3.py",
"plaster/main.py"
] | [
"import ctypes as c\nimport pathlib\nfrom contextlib import contextmanager, redirect_stdout\nfrom io import StringIO\n\nimport numpy as np\nfrom plaster.run.sigproc_v3.c.build import build_dev\nfrom plaster.tools.c_common import c_common_tools\nfrom plaster.tools.c_common.c_common_tools import (\n CException,\n ... | [
[
"numpy.all",
"numpy.zeros",
"numpy.zeros_like",
"numpy.ascontiguousarray"
],
[
"numpy.linalg.inv",
"numpy.stack",
"numpy.save",
"pandas.DataFrame.from_dict",
"numpy.random.uniform"
]
] |
michel8195/CardDetection | [
"3f538e0ecb32f1bdafdb324f57d5c6fa17f19163"
] | [
"samples/gen_dataset/config.py"
] | [
"import numpy as np\nimport itertools\nimport os\n\n# imgW,imgH: dimensions of the generated dataset images\nimgW = 720\nimgH = 720\n\n\ncardW = 60\ncardH = 114\ncornerXmin = 3 * 4\ncornerXmax = 9 * 4\ncornerYmin = 3 * 4\ncornerYmax = 19 * 4\n\n# We convert the measures from mm to pixels: multiply by an arbitrary f... | [
[
"numpy.array"
]
] |
rajkaramchedu/DALI | [
"3ee7b3476cfaebb4d5299ca147def01086b39373"
] | [
"docs/examples/video/video_example.py"
] | [
"#!/bin/env python\n\nimport numpy as np\nfrom nvidia.dali.pipeline import Pipeline\nimport nvidia.dali.ops as ops\nimport nvidia.dali.types as types\n\ntry:\n from matplotlib import pyplot as plt\n has_matplotlib = True\nexcept ImportError:\n has_matplotlib = False\n\n\nBATCH_SIZE=4\nCOUNT=5\n\nVIDEO_FILE... | [
[
"matplotlib.pyplot.show",
"matplotlib.pyplot.savefig"
]
] |
zutn/incremental_breakdown | [
"eefa5442502ebcf74f13ff3d802938e2a43188d3",
"eefa5442502ebcf74f13ff3d802938e2a43188d3"
] | [
"model14_supercomputer.py",
"model1_supercomputer.py"
] | [
"#!/usr/bin/env python\r\n# coding: utf-8 \r\n\r\n\"\"\"\r\n\r\n\"\"\"\r\nfrom __future__ import division\r\nimport datetime\r\n\r\nimport cmf\r\nimport spotpy\r\nfrom spotpy.parameter import Uniform as param\r\nimport sys\r\nimport os\r\nimport numpy as np\r\nfrom datawriter_multi_objective import DataWriter\r\n\r... | [
[
"numpy.array",
"numpy.sum"
],
[
"numpy.array",
"numpy.sum"
]
] |
cclauss/mantra | [
"19e2f72960da8314f11768d9acfe7836629b817c"
] | [
"mantraml/models/pytorch/callbacks.py"
] | [
"import matplotlib.pyplot as plt\nimport os\nimport torch\n\n\nclass EvaluateTask:\n\n def __init__(self, mantra_model):\n\n if mantra_model.task:\n mantra_model.task.latest_loss = mantra_model.task.evaluate(mantra_model)\n\n print('%s: %s' % (mantra_model.task.evaluation_name, mantr... | [
[
"matplotlib.pyplot.savefig"
]
] |
dyeDeny/MachineLearninginAction | [
"4a6c0e1b719d797483a7360ad11fb218672a1025"
] | [
"ch03/treePlotter.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Jan 15 22:07:53 2018\n\n@author: dye\n\"\"\"\n\nimport matplotlib.pyplot as plt\n\ndecisionNode = dict(boxstyle=\"sawtooth\", fc=\"0.8\")\nleafNode = dict(boxstyle=\"round4\", fc=\"0.8\")\narrow_args = dict(arrowstyle=\"<-\")\n\ndef plotNode(nodeTxt, centerPt, parent... | [
[
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] |
Parskatt/caps | [
"030aea48a0b7c0480607fdf3a55fbdc3ffb47f9c"
] | [
"CAPS/utils.py"
] | [
"import numpy as np\nimport scipy\nimport cv2\n\n\ndef cycle(iterable):\n while True:\n for x in iterable:\n yield x\n\n\ndef evaluate_pose(E, P):\n R_gt = P[:3, :3]\n t_gt = P[:3, 3]\n R1, R2, t = cv2.decomposeEssentialMat(E)\n t = t.squeeze()\n theta_1 = np.linalg.norm(scipy.li... | [
[
"numpy.ones_like",
"numpy.sqrt",
"numpy.inner",
"matplotlib.pyplot.get_cmap",
"numpy.linalg.norm",
"numpy.arccos",
"numpy.concatenate",
"numpy.zeros_like",
"sklearn.metrics.average_precision_score",
"numpy.array",
"numpy.random.RandomState",
"numpy.sum",
"numpy.... |
dhirajpatnaik16297/text-gan-tensorflow | [
"fb9897ee55e8d674a16c6041a2c1fb67abad131b"
] | [
"layers.py"
] | [
"\"\"\" TensorFlow Layers\n\nConvenience functions but Input and Output should be tensors.\n\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport tensorflow as tf\nfrom tensorflow.contrib import seq2seq\n\n\n_phase = tf.Variable(False, name=... | [
[
"tensorflow.convert_to_tensor",
"tensorflow.cond",
"tensorflow.nn.dynamic_rnn",
"tensorflow.zeros",
"tensorflow.reduce_sum",
"tensorflow.cast",
"tensorflow.orthogonal_initializer",
"tensorflow.nn.sigmoid_cross_entropy_with_logits",
"tensorflow.contrib.rnn.BasicRNNCell",
"te... |
QuChemPedIA/scanlog | [
"55c9b31259725bcee60d3aff43ffaf2835286aac"
] | [
"src/scanlog/scanlog.py"
] | [
"import os\nimport sys\nimport json\nimport pickle\nimport hashlib\nimport traceback\n\nimport cclib\nimport openbabel.pybel as pybel\nimport numpy as np\nimport openbabel as ob\nimport scipy.sparse as sp\nimport sklearn.preprocessing\n\n# constants\nCstBohr2Ang = 0.52917721092\nCstHartree2eV = 27.21138505\nCstHart... | [
[
"numpy.allclose",
"numpy.abs",
"numpy.unique",
"numpy.isnan",
"numpy.linalg.norm",
"scipy.sparse.csr_matrix",
"numpy.array",
"numpy.where"
]
] |
MaximeJumelle/pymc3 | [
"4e695f635b2ead24e2e647651eadd2505ab1fa63"
] | [
"pymc3/tests/test_shared.py"
] | [
"import pymc3 as pm\nfrom .helpers import SeededTest\nimport numpy as np\nimport theano\n\n\nclass TestShared(SeededTest):\n def test_deterministic(self):\n with pm.Model() as model:\n data_values = np.array([.5, .4, 5, 2])\n X = theano.shared(np.asarray(data_values, dtype=theano.con... | [
[
"numpy.sqrt",
"numpy.linspace",
"numpy.asarray",
"numpy.random.normal",
"numpy.array"
]
] |
espectre/gcn_clustering | [
"46b6a82d92c95ef1ed9482c5a997b3138ade4143"
] | [
"feeder/feeder_visualization.py"
] | [
"###################################################################\n# File Name: feeder.py\n# Author: Zhongdao Wang\n# mail: wcd17@mails.tsinghua.edu.cn\n# Created Time: Thu 06 Sep 2018 01:06:16 PM CST\n###################################################################\n\nfrom __future__ import print_function\nf... | [
[
"torch.Tensor",
"numpy.random.seed",
"torch.zeros",
"numpy.asarray",
"torch.from_numpy",
"numpy.mean",
"numpy.load"
]
] |
dashiellfryer/Axelrod | [
"82918011c54f51624d78ae53a7d13de6460def86",
"0d684b3273d15e3e0ecf70be8e893fffc5277c84"
] | [
"axelrod/eigen.py",
"axelrod/moran.py"
] | [
"\"\"\"\nCompute the principal eigenvector of a matrix using power iteration.\n\nSee also numpy.linalg.eig which calculates all the eigenvalues and\neigenvectors.\n\"\"\"\n\nfrom typing import Tuple\n\nimport numpy\n\n\ndef _normalise(nvec: numpy.ndarray) -> numpy.ndarray:\n \"\"\"Normalises the given numpy arra... | [
[
"numpy.dot",
"numpy.sqrt",
"numpy.ones",
"numpy.errstate",
"numpy.array"
],
[
"matplotlib.pyplot.subplots",
"numpy.cumsum"
]
] |
BlueBrain/morphology-workflows | [
"629be1d2e68de33820f687a5e9018e4fb420e13f"
] | [
"tests/examples_test/get_dataset.py"
] | [
"\"\"\"Create the dataset.csv from some test morphologies.\"\"\"\nfrom pathlib import Path\n\nimport pandas as pd\n\nif __name__ == \"__main__\":\n dataset = pd.DataFrame(columns=[\"morph_path\", \"mtype\"])\n dataset.index.name = \"morph_name\"\n morph_path = Path(\"morphologies\")\n for morph in morph... | [
[
"pandas.DataFrame"
]
] |
AliBaheri/Cornell-MOE | [
"5c36a1c60eecfeea6e45c485179b671e12f07ad9"
] | [
"moe/optimal_learning/python/cpp_wrappers/knowledge_gradient.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"Tools to compute KnowledgeGradient and optimize the next best point(s) to sample using KG through C++ calls.\n\nThis file contains a class to compute Knowledge Gradient + derivatives and a functions to solve the q,p-KG optimization problem.\nThe :class:`moe.optimal_learning.python.cp... | [
[
"numpy.amin",
"numpy.concatenate",
"numpy.atleast_2d",
"numpy.copy",
"numpy.array",
"numpy.zeros"
]
] |
samiriff/scikit-dataaccess-ode | [
"935bfd54149abd9542fe38e77b7eabab48b1c3a1",
"dc08fd67c772d3cd83d0d34183196661b6b53778"
] | [
"skdaccess/engineering/la/traffic_counts/stream.py",
"skdaccess/planetary/ode/cache/data_fetcher_mini.py"
] | [
"# The MIT License (MIT)\n# Copyright (c) 2018 Massachusetts Institute of Technology\n#\n# Author: Cody Rude\n# This software has been created in projects supported by the US National\n# Science Foundation and NASA (PI: Pankratius)\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n... | [
[
"pandas.to_datetime"
],
[
"matplotlib.image.imread"
]
] |
cassianobecker/dnn | [
"bb2ea04f77733de9df10f795bb049ac3b9d30478",
"bb2ea04f77733de9df10f795bb049ac3b9d30478",
"bb2ea04f77733de9df10f795bb049ac3b9d30478",
"bb2ea04f77733de9df10f795bb049ac3b9d30478"
] | [
"dataset/synth/test/regression_test.py",
"dataset/hcp/dti/dti.py",
"dataset/mnist/dwi/sticks.py",
"experiments/synth/experiment.py"
] | [
"import os\nfrom os.path import join\nimport shutil\nimport subprocess\nimport numpy.random as npr\nimport numpy as np\nimport scipy.stats\n\nfrom dipy.io.image import load_nifti, save_nifti\nfrom dipy.io import read_bvals_bvecs\nfrom dipy.segment.mask import median_otsu\nfrom dipy.reconst.csdeconv import Constrain... | [
[
"numpy.diag",
"numpy.expand_dims",
"numpy.eye",
"numpy.full",
"numpy.random.rand",
"numpy.any",
"numpy.array"
],
[
"numpy.einsum"
],
[
"numpy.savez",
"numpy.meshgrid",
"numpy.arange",
"numpy.eye",
"numpy.max",
"numpy.exp",
"numpy.zeros",
"sci... |
5laps2go/xbrr | [
"4c0824b53bfe971111d60e6c1ff4e36f4f4845a3"
] | [
"xbrr/edinet/reader/aspects/finance.py"
] | [
"import warnings\nimport re\nimport collections\nimport importlib\nif importlib.util.find_spec(\"pandas\") is not None:\n import pandas as pd\nfrom xbrr.base.reader.base_parser import BaseParser\nfrom xbrr.edinet.reader.element_value import ElementValue\n\n\nclass Finance(BaseParser):\n\n def __init__(self, r... | [
[
"pandas.DataFrame"
]
] |
marcobb8/tr_bn | [
"f6600d046a34266ec815384790659b4e33db325c"
] | [
"example_mbcs.py"
] | [
"# Import packages\nimport pandas\nfrom mbc import learn_mbc_cll, learn_mbc_generative, l_bfgs\nfrom export import get_adjacency_matrix_from_et\nfrom var_elim import PyFactorTree\nfrom data_type import data as datat\n\n# Load ASIA dataframe\nfile_name = \"data/asia.csv\"\ndata = pandas.read_csv(file_name)\nvar_clas... | [
[
"pandas.read_csv"
]
] |
anas-awadalla/mmf | [
"306f8f758831b2abf2c7ef5a8f010670a2cb33ed",
"306f8f758831b2abf2c7ef5a8f010670a2cb33ed",
"306f8f758831b2abf2c7ef5a8f010670a2cb33ed",
"306f8f758831b2abf2c7ef5a8f010670a2cb33ed",
"306f8f758831b2abf2c7ef5a8f010670a2cb33ed",
"306f8f758831b2abf2c7ef5a8f010670a2cb33ed",
"306f8f758831b2abf2c7ef5a8f010670a2cb33e... | [
"mmf/models/unimodal.py",
"mmf/utils/phoc/build_phoc.py",
"tests/datasets/test_processors.py",
"mmf/utils/checkpoint.py",
"mmf/modules/decoders.py",
"tests/modules/test_metrics.py",
"projects/m4c/scripts/extract_ocr_frcn_feature.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates.\n\nfrom copy import deepcopy\n\nimport torch\nfrom mmf.common.registry import registry\nfrom mmf.models.base_model import BaseModel\nfrom mmf.modules.encoders import MultiModalEncoderBase\nfrom mmf.utils.build import build_classifier_layer\n\n\nclass UnimodalBase(... | [
[
"torch.mean",
"torch.is_tensor",
"torch.flatten"
],
[
"numpy.array"
],
[
"torch.tensor",
"numpy.array",
"torch.zeros"
],
[
"torch.save",
"torch.load"
],
[
"torch.nn.Dropout",
"torch.cat",
"torch.sum",
"torch.nn.LSTMCell",
"torch.nn.Linear"
... |
rlleshi/mmaction2 | [
"6993693f178b1a59e5eb07f1a3db484d5e5de61a",
"6993693f178b1a59e5eb07f1a3db484d5e5de61a"
] | [
"mmaction/models/common/transformer.py",
"tests/test_data/test_pipelines/test_loadings/test_sampling.py"
] | [
"# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\nimport torch.nn as nn\nfrom einops import rearrange\nfrom mmcv.cnn import build_norm_layer, constant_init\nfrom mmcv.cnn.bricks.registry import ATTENTION, FEEDFORWARD_NETWORK\nfrom mmcv.cnn.bricks.transformer import FFN, build_dropout\nfrom mmcv.runner... | [
[
"torch.mean",
"torch.nn.Dropout",
"torch.nn.MultiheadAttention",
"torch.cat",
"torch.nn.Linear",
"torch.nn.Identity"
],
[
"numpy.min",
"numpy.arange",
"numpy.testing.assert_array_equal",
"numpy.max",
"numpy.array"
]
] |
Mingy2018/SwitchVAE | [
"cf9c06ce3af50a559d79b9cba14851472e43a70b",
"cf9c06ce3af50a559d79b9cba14851472e43a70b"
] | [
"utils/metrics.py",
"analyse/generate_latent.py"
] | [
"import numpy as np\nimport tensorflow as tf\n\n\ndef evaluate_voxel_prediction(predictions, gt, threshold=1):\n \"\"\"\n Calculate metrics based on the output of model\n Args:\n predictions: the ouput of voxel decoder\n gt: the ground truth of object\n Returns:\n \"\"\"\n predtions_... | [
[
"numpy.logical_not",
"tensorflow.cast",
"tensorflow.ones_like",
"numpy.logical_or",
"tensorflow.zeros_like",
"tensorflow.math.logical_and",
"tensorflow.where",
"tensorflow.math.logical_or",
"numpy.logical_and"
],
[
"tensorflow.keras.models.Model",
"sklearn.utils.shu... |
Hari-07/manim | [
"bbe113e7d33636c8901d6c7cee81cb2f4b69cc8b"
] | [
"manim/mobject/matrix.py"
] | [
"r\"\"\"Mobjects representing matrices.\n\nExamples\n--------\n\n.. manim:: MatrixExamples\n :save_last_frame:\n\n class MatrixExamples(Scene):\n def construct(self):\n m0 = Matrix([[2, 0], [-1, 1]])\n m1 = Matrix([[1, 0], [0, 1]],\n left_bracket=\"\\\\big(\... | [
[
"numpy.array",
"numpy.vectorize"
]
] |
xchen034/dyson_sphere_kg | [
"d9c6f28f5ae01f9087ecc4462e0d247640576f3e"
] | [
"dyson_search/read_tuples.py"
] | [
"import numpy as np\nimport pandas as pd\nimport re\nimport math\nfrom typing import Union\n\ndef get_all_entity(tuples: list) -> list:\n entities = []\n for _tuple in tuples:\n if _tuple[1] in [\"类型\", \"产地\", \"生产时间(s)\"]:\n if not \"公式\" in _tuple[0]:\n entities.append(_tup... | [
[
"pandas.DataFrame",
"pandas.pivot_table"
]
] |
biocore/microsetta-public-api | [
"1ec4c31e11127a8f480e4921b71ad36aa7d39c76"
] | [
"microsetta_public_api/resources.py"
] | [
"import os\nimport pandas as pd\nimport biom\nfrom copy import deepcopy\nfrom microsetta_public_api.exceptions import ConfigurationError\nfrom skbio.stats.ordination import OrdinationResults\nfrom qiime2.core.type.grammar import TypeExp\nfrom qiime2 import Artifact, Metadata\nfrom qiime2.metadata.io import Metadata... | [
[
"pandas.read_csv"
]
] |
Maurynho/tcc_ciencia_dados | [
"f92c773da118f530d48159d38ea606307a65ba9c"
] | [
"codigo_fonte_python/dados_idh.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\n#Estabelecendo conexão e requisitando a página que onde os dados estão\nimport urllib3\nurl = 'https://www.br.undp.org/content/brazil/pt/home/idh0/rankings/idhm-uf-2010.html'\nconexao = urllib3.PoolManager()\nretorno = conexao.request('GET',url)\n\n#Iniciando... | [
[
"numpy.reshape",
"numpy.array",
"pandas.DataFrame"
]
] |
prasoongoyal/pixl2r | [
"b0691be6b27e705a62534b58f97ff7b8b6655c7d",
"b0691be6b27e705a62534b58f97ff7b8b6655c7d"
] | [
"metaworld/metaworld/envs/mujoco/mujoco_env.py",
"metaworld/metaworld/core/wrapper_env.py"
] | [
"import os\n\nfrom gym import error, spaces\nfrom gym.utils import seeding\nimport numpy as np\nfrom os import path\nimport gym\n\ntry:\n\timport mujoco_py\nexcept ImportError as e:\n\traise error.DependencyNotInstalled(\"{}. (HINT: you need to install mujoco_py, and also perform the setup instructions here: https:... | [
[
"numpy.concatenate",
"numpy.round",
"numpy.zeros",
"numpy.ones"
],
[
"numpy.array",
"numpy.zeros_like",
"numpy.clip",
"numpy.ones"
]
] |
xchange11/bundesterminator | [
"d7e92bfa8ffda54821364c74ac33a48ffa5f51b9",
"d7e92bfa8ffda54821364c74ac33a48ffa5f51b9"
] | [
"bundestag/bundestrainer.py",
"bundestag/trainer.py"
] | [
"import os\nimport pickle\n\nfrom google.cloud import storage\nfrom bundestag import data, utils\nfrom bundestag.bundes_w2v import BundesW2V\n\nimport pandas as pd\nimport numpy as np\n\nfrom tensorflow import keras\nfrom tensorflow.keras import Sequential, layers\nfrom tensorflow.keras.callbacks import EarlyStoppi... | [
[
"tensorflow.keras.models.load_model",
"pandas.Series",
"tensorflow.keras.layers.Masking",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.Sequential",
"sklearn.model_selection.train_test_split",
"pandas.DataFrame",
"sklearn.preprocessing.LabelEncoder",
"tensorflow.keras.laye... |
wangyunjeff/yolov3 | [
"9d8fc6c6a028d92a9e1f761c086a28ba1c392b40"
] | [
"utils/dataloader.py"
] | [
"from random import shuffle\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport math\nimport torch.nn.functional as F\nfrom PIL import Image\nfrom torch.autograd import Variable\nfrom torch.utils.data import DataLoader\nfrom torch.utils.data.dataset import Dataset\nfrom matplotlib.colors import rgb_to_... | [
[
"numpy.minimum",
"numpy.logical_and",
"matplotlib.colors.hsv_to_rgb",
"numpy.random.shuffle",
"numpy.concatenate",
"numpy.random.rand",
"numpy.transpose",
"numpy.array",
"numpy.random.randint"
]
] |
luissen/SSDT-A-single-shot-detector-for-PCB--defects | [
"d15c355e89fdde1f11c72fb5a5a68eb59fea6818"
] | [
"models/MOD_vgg_1125_2.py"
] | [
"'''\nMicro Object Detector Net\nthe author:Luis\ndate : 11.25\n'''\nimport os\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom layers import *\nfrom .base_models import vgg, vgg_base\n\n\nclass BasicConv(nn.Module):\n\n def __init__(self, in_planes, out_planes, kernel_size, stride=1... | [
[
"torch.nn.Softmax",
"torch.nn.ModuleList",
"torch.nn.Conv2d",
"torch.nn.Sigmoid",
"torch.nn.Linear",
"torch.nn.AdaptiveAvgPool2d",
"torch.nn.Upsample",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU"
]
] |
janvdvegt/scikit-lego | [
"774e557c4d19f67ef54f3f0d1622c64ef9903b63"
] | [
"tests/conftest.py"
] | [
"import itertools as it\n\nimport numpy as np\nimport pandas as pd\nimport pytest\nfrom sklearn.utils import estimator_checks\n\nn_vals = (10, 100, 5000)\nk_vals = (1, 2, 5)\nnp_types = (np.int32, np.float32, np.float64)\n\ntransformer_checks = (\n estimator_checks.check_transformer_data_not_an_array,\n estim... | [
[
"numpy.random.normal",
"numpy.random.seed",
"pandas.DataFrame"
]
] |
cevans216/yt | [
"c19c3c615b996c8a6e418362ffea9041a616d673",
"c19c3c615b996c8a6e418362ffea9041a616d673",
"c19c3c615b996c8a6e418362ffea9041a616d673",
"c19c3c615b996c8a6e418362ffea9041a616d673"
] | [
"yt/visualization/profile_plotter.py",
"yt/frontends/enzo_p/data_structures.py",
"yt/frontends/enzo/tests/test_outputs.py",
"yt/visualization/volume_rendering/tests/test_composite.py"
] | [
"import base64\nimport builtins\nimport os\nfrom collections import OrderedDict\nfrom distutils.version import LooseVersion\nfrom functools import wraps\n\nimport matplotlib\nimport numpy as np\n\nfrom yt.data_objects.data_containers import YTSelectionContainer\nfrom yt.data_objects.profiles import create_profile, ... | [
[
"numpy.nanmax",
"matplotlib.colors.LogNorm",
"numpy.sqrt",
"numpy.nanmin",
"matplotlib.font_manager.FontProperties",
"matplotlib.colors.Normalize",
"matplotlib.colors.ListedColormap",
"numpy.float64",
"numpy.array"
],
[
"numpy.sqrt",
"numpy.empty",
"numpy.ones"
... |
stargazerSwc/SRGAN | [
"eef41597a95660cf3dc08937edeeaf905ee563d4"
] | [
"kaggle_train2.py"
] | [
"import argparse\nimport os\nfrom math import log10\n\nimport pandas as pd\nimport torch.optim as optim\nimport torch.utils.data\nimport torchvision.utils as utils\nfrom torch.autograd import Variable\nfrom torch.utils.data import DataLoader\nfrom tqdm import tqdm\n\nimport pytorch_ssim\nfrom data_utils import Trai... | [
[
"torch.utils.data.DataLoader",
"torch.autograd.Variable"
]
] |
mutterer/pycro-manager | [
"3404e4c968fcef5bf9cb57927f4921f203c7bbe0"
] | [
"pycromanager/data.py"
] | [
"\"\"\"\nLibrary for reading multiresolution micro-magellan\n\"\"\"\nimport os\nimport mmap\nimport numpy as np\nimport sys\nimport json\nimport platform\nimport dask.array as da\nimport dask\nimport warnings\nfrom pycromanager.core import Bridge\nimport struct\nfrom pycromanager.legacy_data import Legacy_NDTiff_Da... | [
[
"numpy.memmap",
"numpy.array",
"numpy.zeros"
]
] |
Fakor/HojDoj | [
"5363b38e5d145785208ef81b9033132c7ac3b2f2"
] | [
"hojdoj/DTools/tools.py"
] | [
"import numpy as np\nimport PIL\nfrom PIL import ImageTk\n\n\ndef value_to_string(value):\n if isinstance(value, str):\n return '\"{}\"'.format(value)\n return str(value)\n\n\ndef value_from_string(text):\n txt_tmp = text.strip(\" \\\"\\'\")\n if txt_tmp[0] == '(' or txt_tmp[0] == '[':\n t... | [
[
"numpy.abs",
"numpy.min",
"numpy.sign",
"numpy.array",
"numpy.zeros",
"numpy.hypot"
]
] |
felixhao28/PaddleNLP | [
"e60dec5c6b2da2991d26b7c2c66e58c3c382532b"
] | [
"examples/experimental/faster_ernie/seq_cls/train.py"
] | [
"# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless re... | [
[
"numpy.mean",
"numpy.random.seed"
]
] |
taoyilee/ml_final_project | [
"0ac5ee3938d70e9ffcae8e186e0ef1a621391980"
] | [
"models/problem_2.py"
] | [
"import numpy as np\nfrom sklearn.linear_model import SGDClassifier\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.preprocessing import StandardScaler\nfrom multiprocessing import Pool\nimport matplotlib.pyplot as plt\nimport pickle\n\n\ndef sample_and_split(raw_data, train_percentage=10, dev_percentage=1... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.title",
"numpy.min",
"numpy.logspace",
"matplotlib.pyplot.semilogx",
"numpy.random.shuffle",
"matplotlib.pyplot.savefig",
"numpy.int",
"matplotlib.pyplot.ylabel",
"numpy.max",
"numpy.mean",
"matplotlib.pyplot.grid",
... |
carolineechen/vision | [
"07fb8ba7fad7b5b458ff862919825df4e6f60b52"
] | [
"torchvision/transforms/transforms.py"
] | [
"import math\nimport numbers\nimport random\nimport warnings\nfrom collections.abc import Sequence\nfrom typing import Tuple, List, Optional\n\nimport torch\nfrom torch import Tensor\n\ntry:\n import accimage\nexcept ImportError:\n accimage = None\n\nfrom . import functional as F\nfrom .functional import Inte... | [
[
"torch.mm",
"torch.randint",
"torch.empty",
"torch.randperm",
"torch.tensor",
"torch.rand"
]
] |
sourcery-ai-bot/streamlit | [
"cbfa69c8ec310a839148cfa4bac5697e6f392a79",
"cbfa69c8ec310a839148cfa4bac5697e6f392a79"
] | [
"lib/streamlit/caching/hashing.py",
"lib/streamlit/elements/image.py"
] | [
"# Copyright 2018-2022 Streamlit 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 applicable law or ... | [
[
"pandas.util.hash_pandas_object",
"numpy.random.RandomState"
],
[
"numpy.amin",
"numpy.amax",
"numpy.clip"
]
] |
xxdreck/google-research | [
"dac724bc2b9362d65c26747a8754504fe4c615f8",
"dac724bc2b9362d65c26747a8754504fe4c615f8",
"7cee4b22b925581d912e8d993625c180da2a5a4f",
"dac724bc2b9362d65c26747a8754504fe4c615f8",
"7cee4b22b925581d912e8d993625c180da2a5a4f",
"7cee4b22b925581d912e8d993625c180da2a5a4f",
"7cee4b22b925581d912e8d993625c180da2a5a4... | [
"graph_embedding/simulations/sbm_simulator.py",
"non_semantic_speech_benchmark/data_prep/augmentation.py",
"basisnet/personalization/centralized_so_nwp/trainer_so.py",
"dedal/models/encoders.py",
"proxy_rewards/train.py",
"pde_preconditioner/unet.py",
"linear_eval/linear_eval_test.py",
"aqt/jax/comput... | [
"# coding=utf-8\n# Copyright 2021 The Google Research 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 requ... | [
[
"numpy.random.multivariate_normal",
"numpy.matmul",
"numpy.ones",
"numpy.sign",
"numpy.identity",
"numpy.flip",
"numpy.argsort",
"numpy.array",
"numpy.zeros",
"numpy.sum",
"numpy.outer"
],
[
"tensorflow.squeeze",
"tensorflow.shape"
],
[
"numpy.random... |
clementjumel/master_thesis | [
"5a39657a212f794690e7c426f60e10ba70d50da9"
] | [
"tesa/modeling/modeling_task.py"
] | [
"from modeling.ranking_task import RankingTask\nfrom modeling.utils import format_context, format_targets\n\nfrom collections import defaultdict\nfrom numpy import asarray, split, concatenate\nfrom numpy.random import seed, shuffle\nfrom pickle import dump, load\nfrom re import findall\nfrom csv import writer\nfrom... | [
[
"numpy.asarray",
"numpy.random.shuffle",
"numpy.random.seed",
"numpy.split"
]
] |
Jerryxiaoyu/maml_rl_v2 | [
"fda134dcbd87ef3e91f339ea2f836f28ec5f7784",
"6091f996ff1be8e80d80331e510087868461b8e6"
] | [
"sandbox/rocky/tf/policies/maml_minimal_categorical_mlp_policy.py",
"maml_examples/test_maml_cellrobot.py"
] | [
"from contextlib import contextmanager\nimport itertools\nimport numpy as np\nimport sandbox.rocky.tf.core.layers as L\nfrom rllab.core.serializable import Serializable\nfrom sandbox.rocky.tf.distributions.categorical import Categorical\nfrom sandbox.rocky.tf.policies.base import StochasticPolicy\nfrom rllab.misc i... | [
[
"tensorflow.get_default_session",
"tensorflow.all_variables",
"tensorflow.cast",
"tensorflow.gradients",
"tensorflow.placeholder",
"tensorflow.trainable_variables",
"tensorflow.assign",
"tensorflow.variable_scope",
"tensorflow.split",
"numpy.array"
],
[
"numpy.rando... |
aqui-tna/darts-UNIQ | [
"293a27b104bc0f53c6093829d1184686b788fba9",
"293a27b104bc0f53c6093829d1184686b788fba9",
"293a27b104bc0f53c6093829d1184686b788fba9"
] | [
"cnn/models/BaseNet.py",
"cnn/MixedLayer.py",
"cnn/gradEstimators/random_path.py"
] | [
"from abc import abstractmethod\nfrom pandas import DataFrame\nfrom os.path import exists\nfrom numpy import argmax\n\nfrom torch.nn import Module, Conv2d\nfrom torch.nn import functional as F\nfrom torch import load as loadModel\n\nfrom cnn.MixedFilter import MixedConvBNWithReLU as MixedConvWithReLU\nfrom cnn.uniq... | [
[
"torch.nn.functional.softmax",
"numpy.argmax",
"pandas.DataFrame"
],
[
"torch.nn.functional.softmax",
"torch.cat",
"torch.nn.ModuleList",
"torch.tensor",
"torch.nn.BatchNorm2d"
],
[
"torch.nn.functional.softmax",
"torch.no_grad",
"torch.tensor"
]
] |
Knowledge-Precipitation-Tribe/Recurrent-neural-network | [
"44faf239784d6318c986ae39a0a1982786e951fe"
] | [
"code/BinaryNumberMinus.py"
] | [
"# -*- coding: utf-8 -*-#\n'''\n# Name: BinaryNumberMinus\n# Description: \n# Author: super\n# Date: 2020/6/18\n'''\n\nfrom matplotlib import pyplot as plt\nimport numpy as np\nimport math\n\nfrom MiniFramework.EnumDef_6_0 import *\nfrom MiniFramework.DataReader_2_0 import *\nfrom MiniFramewo... | [
[
"numpy.dot",
"numpy.allclose",
"numpy.multiply",
"numpy.concatenate",
"numpy.round",
"numpy.random.normal",
"numpy.random.randint"
]
] |
omron-sinicx/ctrm | [
"83e7fe4abb8ad8559bfb6e64170878575a03fd20"
] | [
"src/ctrm/planner/prioritized_planning.py"
] | [
"\"\"\"implementation of a standard prioritized planning\nAuthor: Keisuke Okumura\nAffiliation: TokyoTech & OSX\n\nRef:\n- Silver, D. (2005).\n Cooperative Pathfinding.\n Aiide, 1, 117-122.\n\n- Erdmann, M., & Lozano-Perez, T. (1987).\n On multiple moving objects.\n Algorithmica, 2(1), 477-521.\n\"\"\"\n\nfrom ... | [
[
"numpy.random.rand",
"numpy.linalg.norm"
]
] |
The-SocialLion/Cervical-Cancer-Detection-using-CNN | [
"ffbeb8b6985b67226d2eb5464e742775595689d1"
] | [
"new/test.py"
] | [
"import numpy as np\r\nimport os\r\nfrom tensorflow.keras.models import load_model\r\nfrom PIL import ImageOps\r\nfrom tensorflow.keras.preprocessing import image# used for preproccesing \r\nmodel = load_model('cc.h5')\r\nprint(\"Loaded model from disk\")\r\n\r\nclasss = { 1:\"Type-1\",\r\n 2:\"Type-2\",\... | [
[
"tensorflow.keras.models.load_model",
"numpy.array",
"numpy.expand_dims",
"tensorflow.keras.preprocessing.image.load_img"
]
] |
bmeyers/VirtualMicrogridSegmentation | [
"cd9e7ef1a2ccc438a855765e4c07904740ec12ee",
"cd9e7ef1a2ccc438a855765e4c07904740ec12ee"
] | [
"virtual_microgrids/agents/actor_network.py",
"virtual_microgrids/powerflow/network_generation.py"
] | [
"# Actor and Critic DNNs\n# Based on code published by Patrick Emami on his blog \"Deep\n# Deterministic Policy Gradients in TensorFlow\":\n# https://pemami4911.github.io/blog/2016/08/21/ddpg-rl.html\n\nimport tensorflow as tf\n\nclass ActorNetwork(object):\n \"\"\"\n Input to the network is the state... | [
[
"tensorflow.layers.flatten",
"tensorflow.initializers.random_uniform",
"tensorflow.multiply",
"tensorflow.keras.activations.tanh",
"tensorflow.control_dependencies",
"tensorflow.get_collection",
"tensorflow.keras.layers.Dense",
"tensorflow.gradients",
"tensorflow.placeholder",
... |
xenomarz/deep-signature | [
"f831f05971727c5d00cf3b5c556b6a8b658048df",
"f831f05971727c5d00cf3b5c556b6a8b658048df"
] | [
"notebooks/utils/utils.py",
"deep_signature/linalg/affine_transform.py"
] | [
"# python peripherals\nimport random\n\n# scipy\nimport scipy.io\nimport scipy.stats as ss\n\n# numpy\nimport numpy\n\n# matplotlib\nimport matplotlib.pyplot as plt\nimport matplotlib.collections as mcoll\nimport matplotlib.ticker as ticker\nimport matplotlib.lines\n\n# pytorch\nimport torch\n\n# pandas\nimport pan... | [
[
"numpy.abs",
"numpy.linspace",
"numpy.asarray",
"matplotlib.collections.LineCollection",
"numpy.arange",
"matplotlib.pyplot.subplots",
"pandas.DataFrame",
"matplotlib.pyplot.Normalize",
"numpy.concatenate",
"matplotlib.pyplot.Circle",
"torch.from_numpy",
"numpy.mean... |
lippman1125/pytorch-ssd | [
"d2c31b0d69e82ab2c46f2eecbbf9c12f3dd73309"
] | [
"eval_ssd.py"
] | [
"import torch\nfrom vision.ssd.vgg_ssd import create_vgg_ssd, create_vgg_ssd_predictor\nfrom vision.ssd.mobilenetv1_ssd import create_mobilenetv1_ssd, create_mobilenetv1_ssd_predictor\nfrom vision.ssd.mobilenetv1_ssd_lite import create_mobilenetv1_ssd_lite, create_mobilenetv1_ssd_lite_predictor\nfrom vision.ssd.mob... | [
[
"torch.max",
"torch.cat",
"torch.from_numpy",
"torch.tensor",
"torch.cuda.is_available",
"torch.stack",
"numpy.argsort",
"numpy.array",
"torch.argmax"
]
] |
aglitoiu/pennylane | [
"fd99be754d55bbb919aadbbbdff70e40fbe3bcbf",
"fd99be754d55bbb919aadbbbdff70e40fbe3bcbf",
"fd99be754d55bbb919aadbbbdff70e40fbe3bcbf"
] | [
"pennylane/qnn/keras.py",
"tests/devices/test_default_qubit_tf.py",
"tests/math/test_functions.py"
] | [
"# Copyright 2018-2021 Xanadu Quantum Technologies Inc.\n\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n\n# http://www.apache.org/licenses/LICENSE-2.0\n\n# Unless required by ap... | [
[
"tensorflow.keras.backend.floatx",
"tensorflow.__version__.split",
"tensorflow.TensorShape",
"tensorflow.unstack",
"tensorflow.shape",
"tensorflow.stack"
],
[
"numpy.random.random",
"numpy.sqrt",
"numpy.random.seed",
"numpy.linspace",
"numpy.allclose",
"numpy.co... |
vishrawji/Task-DyVA | [
"6cf68210a85e1afb4fd0dc82e912e577d0ce1ec6",
"6cf68210a85e1afb4fd0dc82e912e577d0ce1ec6",
"6cf68210a85e1afb4fd0dc82e912e577d0ce1ec6"
] | [
"task_dyva/visualization.py",
"manuscript/preprocessing.py",
"manuscript/figureS4.py"
] | [
"import matplotlib.pyplot as plt\nimport seaborn as sns\nimport pandas as pd\nimport numpy as np\n\nfrom .taskdataset import EbbFlowStats\n\n\nclass PlotRTs(EbbFlowStats):\n \"\"\"Plot RT distributions.\n\n Args\n ----\n stats_obj (EbbFlowStats instance): Data from the model/participant.\n palette (s... | [
[
"pandas.concat",
"pandas.DataFrame",
"numpy.round",
"numpy.std",
"numpy.mean",
"numpy.array"
],
[
"numpy.concatenate",
"numpy.arange",
"numpy.array"
],
[
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.subplots"
]
] |
WolfsSky/poliastro | [
"fc5e0825b110a0d6095b4b174e47624147ae1a29"
] | [
"src/poliastro/plotting/static.py"
] | [
"from typing import List\n\nimport matplotlib as mpl\nimport numpy as np\nfrom astropy import units as u\nfrom astropy.coordinates import CartesianRepresentation\nfrom matplotlib import pyplot as plt\n\nfrom poliastro.plotting.util import BODY_COLORS, generate_label\nfrom poliastro.util import norm\n\nfrom ._base i... | [
[
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.style.context",
"matplotlib.patches.Circle"
]
] |
Yui010206/mmf | [
"01e7ccd664a4492f65ba10aeb3eeeafef62c3b87"
] | [
"mmf/trainers/mmf_trainer.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates.\nimport logging\nimport warnings\n\nimport omegaconf\nimport torch\nfrom mmf.common.dataset_loader import DatasetLoader\nfrom mmf.common.registry import registry\nfrom mmf.modules.metrics import Metrics\nfrom mmf.trainers.base_trainer import BaseTrainer\nfrom mmf.... | [
[
"torch.device",
"torch.cuda.amp.GradScaler"
]
] |
michaelfedell/instacart | [
"dc7a49d1247e0a894cc1b1efa5fa876df6bd5683"
] | [
"src/db.py"
] | [
"import argparse\nimport logging\nimport os\nimport sys\n\nimport pandas as pd\nimport sqlalchemy as sql\nfrom sqlalchemy import Column, Integer, Float, String\nfrom sqlalchemy.ext.declarative import declarative_base\nsys.path.append(os.path.dirname(sys.path[0])) # so that config can be imported from project root\... | [
[
"pandas.read_csv"
]
] |
darpa-sail-on/sail-on-client | [
"1fd7c0ec359469040fd7af0c8e56fe53277d4a27",
"1fd7c0ec359469040fd7af0c8e56fe53277d4a27",
"1fd7c0ec359469040fd7af0c8e56fe53277d4a27"
] | [
"sail_on_client/feedback/document_transcription_feedback.py",
"sail_on_client/agent/mock_condda_agents.py",
"sail_on_client/feedback/feedback.py"
] | [
"\"\"\"Document Transcription Feedback.\"\"\"\n\nimport pandas as pd\nfrom sail_on_client.harness.local_harness import LocalHarness\nfrom sail_on_client.harness.par_harness import ParHarness\nfrom sail_on_client.feedback.feedback import Feedback\n\nfrom typing import Union, Dict\n\nSUPPORTED_FEEDBACK = [\"classific... | [
[
"pandas.read_csv"
],
[
"torch.eq"
],
[
"pandas.read_csv"
]
] |
kvlsky/interactive-bitcoin-price-prediction | [
"39413fc595473f24594cd7cff85b84baed1a90a5"
] | [
"api/server.py"
] | [
"from flask import Flask, render_template, request, jsonify\nfrom flask_bootstrap import Bootstrap\nimport matplotlib\n\nfrom datetime import datetime\n\nfrom api.plots import Plots\nfrom api.data_loader import load_currency\nfrom models.predictor import Predictor\n\nmatplotlib.use('Agg')\n\napp = Flask(__name__)\n... | [
[
"matplotlib.use"
]
] |
busyboxs/faster_rcnn_voc | [
"58a158b6568a9b4545f4a8e97d731ac7322da84f"
] | [
"lib/rpn/generate.py"
] | [
"# --------------------------------------------------------\n# Faster R-CNN\n# Copyright (c) 2015 Microsoft\n# Licensed under The MIT License [see LICENSE for details]\n# Written by Ross Girshick\n# --------------------------------------------------------\n\nfrom fast_rcnn.config import cfg\nfrom utils.blob import ... | [
[
"numpy.hstack",
"numpy.min",
"numpy.round",
"numpy.max",
"numpy.where"
]
] |
VictorAlulema/Analysis-of-Mission-Planner-log-files | [
"1b55768db6545c5c989c180975deb3b5622edaab"
] | [
"FileProcessing.py"
] | [
"from LogFile import Parameters\r\nimport matplotlib.pyplot as plt\r\nfrom matplotlib import rcParams\r\nfrom matplotlib import cm\r\nimport numpy as np\r\n\r\n\r\nclass LogFileProcessing:\r\n \"\"\"\r\n Provide the filename of the log to be analyzed\r\n example: '000039.log'\r\n To instantiate the clas... | [
[
"matplotlib.pyplot.rc",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.show",
"numpy.where",
"matplotlib.pyplot.figure"
]
] |
coolteemf/coolteemf-deformetrica | [
"f965d6ecc0d04f243e487468a9dafe9fe864eed2",
"dbcb69962dd02f14dde5d63a9abc1de69112f273",
"dbcb69962dd02f14dde5d63a9abc1de69112f273",
"dbcb69962dd02f14dde5d63a9abc1de69112f273"
] | [
"deformetrica/support/kernels/keops_kernel.py",
"tests/functional_tests/data/atlas/brain_structures/run.py",
"deformetrica/core/model_tools/manifolds/logistic_exponential.py",
"deformetrica/core/models/abstract_statistical_model.py"
] | [
"import torch\n\nfrom ...support.kernels import AbstractKernel\nfrom ...core import default, GpuMode\nfrom pykeops.torch import Genred\n\n\nimport logging\nlogger = logging.getLogger(__name__)\n\n\nclass KeopsKernel(AbstractKernel):\n def __init__(self, gpu_mode=default.gpu_mode, kernel_width=None, cuda_type=Non... | [
[
"torch.ones"
],
[
"torch.cuda.is_available"
],
[
"torch.exp",
"torch.diag"
],
[
"torch.multiprocessing.current_process",
"torch.multiprocessing.Value",
"torch.multiprocessing.active_children",
"torch.multiprocessing.Pool",
"torch.cuda.is_available",
"torch.cuda.... |
1044197988/TF.Keras-Commonly-used-models | [
"b37276bcee454b2c39b8fcc60e87b72ec8a6a5d4",
"b37276bcee454b2c39b8fcc60e87b72ec8a6a5d4",
"b37276bcee454b2c39b8fcc60e87b72ec8a6a5d4"
] | [
"常用分割损失函数和指标/C_Focal_loss.py",
"常用分割模型/ICNet.py",
"常用分割损失函数和指标/WCCE.py"
] | [
"# focal loss with multi label\r\ndef focal_loss(classes_num, gamma=2., alpha=.25, e=0.1):\r\n # classes_num contains sample number of each classes\r\n def focal_loss_fixed(target_tensor, prediction_tensor):\r\n '''\r\n prediction_tensor is the output tensor with shape [None, 100], where 100 is ... | [
[
"tensorflow.convert_to_tensor",
"tensorflow.clip_by_value",
"tensorflow.greater",
"tensorflow.reduce_mean",
"tensorflow.python.ops.array_ops.zeros_like"
],
[
"tensorflow.keras.layers.AveragePooling2D",
"tensorflow.keras.layers.Activation",
"tensorflow.keras.layers.Lambda",
... |
Laknath1996/kdg | [
"11d6286c1eafefc09a86c5bb0a1a6b0d2e4b4a83"
] | [
"benchmarks/openml_cc18_wmle.py"
] | [
"#%%\nfrom kdg import kdf\nfrom kdg.utils import get_ece\nimport openml\nfrom joblib import Parallel, delayed\nimport numpy as np\nimport pandas as pd\nfrom sklearn.ensemble import RandomForestClassifier as rf\nfrom sklearn.metrics import cohen_kappa_score\nfrom kdg.utils import get_ece\nimport os\nfrom os import l... | [
[
"numpy.unique",
"sklearn.metrics.cohen_kappa_score",
"pandas.DataFrame",
"numpy.random.shuffle",
"numpy.log10",
"numpy.argmax",
"numpy.mean",
"numpy.where",
"numpy.sum"
]
] |
spott/matplotlib2tikz | [
"0a362a2078b067c527c568b0c564cf384e8cc213"
] | [
"matplotlib2tikz/text.py"
] | [
"# -*- coding: utf-8 -*-\n#\nfrom . import color\n\nimport matplotlib as mpl\n\n\ndef draw_text(data, obj):\n '''Paints text on the graph.\n '''\n content = []\n properties = []\n style = []\n if isinstance(obj, mpl.text.Annotation):\n ann_xy = obj.xy\n ann_xycoords = obj.xycoords\n ... | [
[
"matplotlib.colors.ColorConverter"
]
] |
shengfuintel/tensorflow | [
"e67f3af48c94c9456c3ff376dc30c82a4bf982cd",
"e67f3af48c94c9456c3ff376dc30c82a4bf982cd",
"5828e285209ff8c3d1bef2e4bd7c55ca611080d5",
"e67f3af48c94c9456c3ff376dc30c82a4bf982cd"
] | [
"tensorflow/python/ops/gradients_impl.py",
"tensorflow/python/kernel_tests/depthtospace_op_test.py",
"tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_ops_benchmark.py",
"tensorflow/python/estimator/canned/head.py"
] | [
"# Copyright 2015 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.python.ops.array_ops.constant",
"tensorflow.python.ops.control_flow_ops.IsLoopExit",
"tensorflow.python.ops.array_ops.shape",
"tensorflow.python.eager.context.in_eager_mode",
"tensorflow.python.framework.ops.convert_n_to_tensor_or_indexed_slices",
"tensorflow.python.framework.o... |
SecantZhang/adversarial-robustness-toolbox | [
"065ac24e7f5ec124f6cfe39ce21f085f4c87a401",
"065ac24e7f5ec124f6cfe39ce21f085f4c87a401",
"80f1e7e0348a5e438c2712f8087003c713a4c6e3",
"80f1e7e0348a5e438c2712f8087003c713a4c6e3",
"80f1e7e0348a5e438c2712f8087003c713a4c6e3"
] | [
"tests/attacks/test_projected_gradient_descent.py",
"tests/attacks/test_threshold_attack.py",
"art/defences/detector/poison/ground_truth_evaluator.py",
"tests/metrics/test_verification_decision_trees.py",
"art/metrics/gradient_check.py"
] | [
"# MIT License\n#\n# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated\n# documentation files (the \"Software\"), to deal in the Software without restriction, including without limita... | [
[
"numpy.swapaxes",
"numpy.ones_like",
"sklearn.linear_model.LogisticRegression",
"numpy.abs",
"numpy.ones",
"numpy.argmax",
"numpy.mean",
"numpy.zeros_like",
"sklearn.svm.LinearSVC",
"sklearn.svm.SVC",
"numpy.prod"
],
[
"numpy.abs",
"numpy.unique",
"numpy... |
ashokei/FBGEMM | [
"bf9ed7dfce1fc2386349908950764bfda7089b4a"
] | [
"fbgemm_gpu/setup.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n# This source code is licensed under the BSD-style license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport glob\nimport os\nimport shutil\nimport sysconfig\n\nfrom codegen.embedding_backward_code_generator... | [
[
"torch.utils.cpp_extension.BuildExtension.with_options"
]
] |
EH94/ensemble-server | [
"852948ee90834266d9fa09562443e916316dede5"
] | [
"app.py"
] | [
"import io\nimport time\nimport keras\nimport numpy as np\nfrom os.path import join\nfrom PIL import Image\nfrom base64 import encodebytes\nfrom keras import backend as K\nfrom keras.models import Model, Input\nfrom keras.layers.merge import average\nfrom flask import Flask, request, jsonify, send_from_directory\n\... | [
[
"numpy.array"
]
] |
plant99/pygeoapi | [
"4dfbe42a7ddecc04e76cbcfe5e7c376318919d9e"
] | [
"pygeoapi/provider/xarray_.py"
] | [
"# =================================================================\n#\n# Authors: Gregory Petrochenkov <gpetrochenkov@usgs.gov>\n#\n# Copyright (c) 2020 Gregory Petrochenkov\n#\n# Permission is hereby granted, free of charge, to any person\n# obtaining a copy of this software and associated documentation\n# files... | [
[
"numpy.abs",
"numpy.datetime_as_string"
]
] |
mcuttler/pyTMD | [
"40ec2ec4c170c821a2558f52c1aeac6c5f9dfeff",
"40ec2ec4c170c821a2558f52c1aeac6c5f9dfeff"
] | [
"pyTMD/load_nodal_corrections.py",
"pyTMD/tidal_ellipse.py"
] | [
"#!/usr/bin/env python\nu\"\"\"\nload_nodal_corrections.py (12/2020)\nCalculates the nodal corrections for tidal constituents\nModification of ARGUMENTS fortran subroutine by Richard Ray 03/1999\n\nCALLING SEQUENCE:\n pu,pf,G = load_nodal_corrections(MJD,constituents)\n\nINPUTS:\n MJD: Modified Julian Day of ... | [
[
"numpy.sqrt",
"numpy.arctan",
"numpy.cos",
"numpy.sin",
"numpy.atleast_1d",
"numpy.arctan2",
"numpy.tan",
"numpy.zeros"
],
[
"numpy.arctan2",
"numpy.sqrt"
]
] |
vishalbelsare/textwiser | [
"2c5bdd73c26bd3fb7bd2f324f57d99233aa9c17f",
"2c5bdd73c26bd3fb7bd2f324f57d99233aa9c17f"
] | [
"textwiser/transformations/base.py",
"textwiser/factory.py"
] | [
"# Copyright 2019 FMR LLC <opensource@fidelity.com>\n# SPDX-License-Identifer: Apache-2.0\n\nimport numpy as np\nimport torch\n\nfrom textwiser.base import BaseFeaturizer\nfrom textwiser.utils import convert, OutputType\n\n\nclass _BaseTransformation(BaseFeaturizer):\n def __init__(self, wrap_list_input=True):\n... | [
[
"numpy.cumsum",
"torch.cat"
],
[
"torch.cat",
"torch.nn.ModuleList",
"numpy.concatenate",
"torch.nn.Sequential.forward",
"scipy.sparse.hstack"
]
] |
jonrbates/ignite | [
"15eeb8791a2e0c2f55265e1f6b91f91dc35286c5",
"15eeb8791a2e0c2f55265e1f6b91f91dc35286c5"
] | [
"tests/ignite/contrib/handlers/test_param_scheduler.py",
"ignite/contrib/metrics/regression/geometric_mean_absolute_error.py"
] | [
"import numpy as np\nimport pytest\nimport torch\n\nfrom ignite.contrib.handlers.param_scheduler import (\n ConcatScheduler,\n CosineAnnealingScheduler,\n LinearCyclicalScheduler,\n LRScheduler,\n ParamGroupScheduler,\n PiecewiseLinear,\n create_lr_scheduler_with_warmup,\n)\nfrom ignite.engine ... | [
[
"torch.ones",
"torch.zeros",
"matplotlib.use",
"torch.optim.lr_scheduler.ExponentialLR",
"numpy.int64",
"torch.optim.SGD",
"torch.optim.lr_scheduler.StepLR"
],
[
"torch.exp",
"torch.sum"
]
] |
nicolahunfeld/CLMM | [
"a431649713e56b907a7366bdf21693c30851dee7"
] | [
"clmm/theory/parent_class.py"
] | [
"\"\"\"@file parent_class.py\nCLMModeling abstract class\n\"\"\"\nimport numpy as np\n\n# functions for the 2h term\nfrom scipy.integrate import simps\nfrom scipy.special import jv\nfrom scipy.interpolate import interp1d\n\nfrom .generic import compute_reduced_shear_from_convergence\nimport warnings\nfrom .generic ... | [
[
"numpy.logspace",
"scipy.interpolate.interp1d",
"scipy.special.jv"
]
] |
PrzemyslawSwiderski/PLCOpythonTool | [
"513db830e2b6b396393896735dd81bd2dd845ba2",
"513db830e2b6b396393896735dd81bd2dd845ba2"
] | [
"src/classes/mysql_fetcher.py",
"src/utils/unpickle_training_results.py"
] | [
"import logging\n\nimport pandas\nimport pymysql\n\nfrom classes.file_query_loader import FileQueryLoader\n\n\nclass MySqlFetcher:\n def __init__(self, query_loader=FileQueryLoader()):\n self.__db_connection = self.open_connection()\n self.data_set = pandas.DataFrame()\n self.query_loader = ... | [
[
"pandas.read_sql_query",
"pandas.DataFrame"
],
[
"sklearn.externals.joblib.load"
]
] |
FeynmanDNA/singa-auto | [
"e96982adc689335a323a5a32d03b23942e01d09f",
"e96982adc689335a323a5a32d03b23942e01d09f",
"e96982adc689335a323a5a32d03b23942e01d09f",
"e96982adc689335a323a5a32d03b23942e01d09f"
] | [
"examples/models/tabular_classification/RandomForestClf.py",
"examples/models/image_classification/TfEnas.py",
"examples/models/tabular_classification/GaussianNBClf.py",
"examples/models/tabular_classification/XgbClf.py"
] | [
"from sklearn.ensemble import RandomForestClassifier\nimport pickle\nimport base64\nimport pandas as pd\nimport numpy as np\nimport json\n\nfrom singa_auto.model import BaseModel, IntegerKnob, CategoricalKnob, logger\nfrom singa_auto.model.dev import test_model_class\nfrom singa_auto.constants import ModelDependenc... | [
[
"pandas.read_csv",
"sklearn.ensemble.RandomForestClassifier",
"pandas.DataFrame"
],
[
"tensorflow.get_variable",
"tensorflow.concat",
"tensorflow.contrib.keras.initializers.he_normal",
"tensorflow.control_dependencies",
"numpy.asarray",
"tensorflow.stack",
"tensorflow.n... |
ElmerJeanpierreLopez/wradlib | [
"ae6aa24c68f431b735a742510cea3475fb55059d",
"ae6aa24c68f431b735a742510cea3475fb55059d"
] | [
"wradlib/georef/vector.py",
"wradlib/georef/rect.py"
] | [
"#!/usr/bin/env python\n# -*- coding: UTF-8 -*-\n# Copyright (c) 2011-2019, wradlib developers.\n# Distributed under the MIT License. See LICENSE.txt for more info.\n\n\"\"\"\nVector Functions (GDAL)\n^^^^^^^^^^^^^^^^^^^^^^^\n\n.. autosummary::\n :nosignatures:\n :toctree: generated/\n\n get_vector_coordinate... | [
[
"numpy.squeeze",
"numpy.array"
],
[
"numpy.radians",
"numpy.arctan",
"numpy.arcsin",
"numpy.arange",
"numpy.degrees",
"numpy.cos",
"numpy.dstack",
"numpy.sin",
"numpy.arctan2",
"numpy.meshgrid",
"numpy.sum"
]
] |
tadyvn/Orchid_combine | [
"adbb372c6fd2719d9c2377a5249c58bfeea5132e"
] | [
"data/config.py"
] | [
"from backbone import ResNetBackbone, VGGBackbone, ResNetBackboneGN, DarkNetBackbone, MobileNetV2Backbone\nfrom math import sqrt\nimport torch\n\n# for making bounding boxes pretty\nCOLORS = ((244, 67, 54),\n (233, 30, 99),\n (156, 39, 176),\n (103, 58, 183),\n ( 63, 81, 1... | [
[
"torch.nn.functional.softmax",
"torch.nn.functional.relu"
]
] |
cmlab-mira/Efficient-and-Phase-aware-Video-Super-resolution-for-Cardiac-MRI | [
"ec01b783f8acd41a7056431bad615896b8495f95",
"ec01b783f8acd41a7056431bad615896b8495f95"
] | [
"src/data/datasets/dsb15_vsr_dataset.py",
"src/runner/predictors/dsb15_misr_predictor.py"
] | [
"import numpy as np\nimport nibabel as nib\n\nfrom src.data.datasets.base_dataset import BaseDataset\nfrom src.data.transforms import compose\n\n\nclass Dsb15VSRDataset(BaseDataset):\n \"\"\"The dataset of the 2015 Data Science Bowl challenge for the Video Super-Resolution.\n \n Ref: https://www.kaggle.com... | [
[
"numpy.concatenate"
],
[
"scipy.misc.imsave",
"torch.no_grad",
"torch.stack"
]
] |
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