repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
elizavetaMakarova/AI_mincraft_village_building | [
"655bee6dd9757fd08c2c5327d9520abd1e34f864"
] | [
"mapUtils.py"
] | [
"\"\"\"\nUtilities for (height)maps\n\"\"\"\nimport cv2\nimport matplotlib.pyplot as plt\nimport numpy as np\n\ndef normalize(array):\n \"\"\"Normalizes the array so that the min value is 0 and the max value is 1\n \"\"\"\n return (array - array.min()) / (array.max() - array.min())\n\ndef visualize(*arrays... | [
[
"matplotlib.pyplot.imshow",
"numpy.minimum",
"matplotlib.pyplot.title",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
frederiknolte/tonic | [
"9c4e03ca44c4094f3e232f8dbe8c14ab9217fc17"
] | [
"tonic/torch/agents/td3.py"
] | [
"import torch\n\nfrom tonic import logger # noqa\nfrom tonic.torch import agents, models, normalizers, updaters\n\n\ndef default_model(device):\n return models.ActorTwinCriticWithTargets(\n actor=models.Actor(\n device=device,\n encoder=models.ObservationEncoder(),\n tors... | [
[
"torch.as_tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
chelsilarious/AutoDiff | [
"b4ff703f85288bafd85148edb093d7cd47cbed50"
] | [
"AutoDiff/ad.py"
] | [
"import numpy as np\nimport inspect\nimport re\nfrom AutoDiff.forwardNode import ForwardNode\nfrom AutoDiff.reverseNode import ReverseNode\nfrom AutoDiff.utils import *\n\n\ndef init_trace(var, variables):\n '''\n Initialize the trace for ForwardNode objects given all variables in function\n\n Input:\n ... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ocefpaf/argopy | [
"80a2bc04768b810a0116660df9298d07924fa5f2"
] | [
"argopy/stores/argo_index.py"
] | [
"import numpy as np\nimport pandas as pd\nfrom abc import ABC, abstractmethod\nimport hashlib\n\nfrom argopy.errors import DataNotFound\nfrom argopy.options import OPTIONS\nfrom .filesystems import filestore, memorystore\n\n\nclass indexfilter_proto(ABC):\n \"\"\" Class prototype for an Argo index filter\n\n ... | [
[
"pandas.to_datetime",
"numpy.abs",
"pandas.DataFrame",
"numpy.all",
"numpy.any",
"numpy.array"
]
] | [
{
"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": []
}
] |
maximekli/solopython | [
"529106724120f656a0f2a02b9c5d1a1cbe8de75f"
] | [
"utils/logger.py"
] | [
"'''This class will log 1d array in Nd matrix from device and qualisys object'''\nimport numpy as np\nclass Logger():\n def __init__(self, device, qualisys=None, logSize=60e3, ringBuffer=False): \n self.ringBuffer = ringBuffer\n self.logSize=logSize\n self.i=0\n nb_motors = device.... | [
[
"numpy.savez",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
olivaresluque/scrapATP | [
"1985d4c73fabd5f08f54b922e73a9306e09c77a5"
] | [
"ven/Lib/site-packages/pandas/core/indexes/api.py"
] | [
"from pandas.core.indexes.base import (Index,\n _new_Index,\n _ensure_index,\n _ensure_index_from_sequences,\n _get_na_value,\n Inv... | [
[
"pandas.core.common._any_not_none",
"pandas.core.common._try_sort",
"pandas.core.indexes.base.Index",
"pandas.core.indexes.base._ensure_index",
"pandas.core.common._get_distinct_objs"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
shigeokitamura/netkeiba_scraper | [
"a280f5e0a372e23ce52075ac55277f1e4d401598"
] | [
"scraping_csv.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n\nimport argparse\nimport time\nimport os\nimport pandas as pd\nfrom tqdm import tqdm\nimport scraper\n\ndef get_args():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--start_year\", type=int, default=1986)\n parser.add_argument(\"--end_year\", type=i... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Diego-Ibarra/ship-mapper | [
"f0959e9ae629c3933bfe4f794f96385a716218dc"
] | [
"ship_mapper/mapper.py"
] | [
"import matplotlib\r\nmatplotlib.use('Agg')\r\nimport matplotlib.pyplot as plt\r\nfrom matplotlib.patches import FancyBboxPatch\r\nfrom matplotlib.colors import LinearSegmentedColormap\r\nfrom mpl_toolkits.basemap import Basemap\r\n\r\nimport numpy as np\r\n# Suppress matplotlib warnings\r\nnp.warnings.filterwarnin... | [
[
"numpy.flipud",
"matplotlib.pyplot.get_cmap",
"numpy.round",
"numpy.max",
"numpy.nanmean",
"numpy.ma.masked_where",
"matplotlib.pyplot.subplot2grid",
"matplotlib.pyplot.gca",
"numpy.arange",
"matplotlib.colors.LinearSegmentedColormap",
"matplotlib.pyplot.gcf",
"matp... | [
{
"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": []
}
] |
arvkevi/awesome-streamlit | [
"be454e29c35a9a1a760b1737a5176a47f4f9717b"
] | [
"gallery/self_driving_cars/self_driving_cars.py"
] | [
"# -*- coding: utf-8 -*-\r\n# Copyright 2018-2019 Streamlit Inc.\r\n#\r\n# Licensed under the Apache License, Version 2.0 (the \"License\");\r\n# you may not use this file except in compliance with the License.\r\n# You may obtain a copy of the License at\r\n#\r\n# http://www.apache.org/licenses/LICENSE-2.0\r\n#... | [
[
"pandas.read_csv",
"pandas.DataFrame",
"numpy.argmax",
"numpy.array",
"numpy.logical_and",
"pandas.get_dummies"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
junsikh/pytorch3d | [
"a7948adc404d88a8f1247d7b37d3091e791b124a"
] | [
"pytorch3d/io/experimental_gltf_io.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n#\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\n\n\"\"\"\nThis module implements loading meshes from glTF 2 assets stored in a\nGLB container file or ... | [
[
"numpy.expand_dims",
"torch.from_numpy",
"numpy.dtype",
"numpy.frombuffer",
"torch.FloatTensor",
"numpy.prod",
"numpy.transpose",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AIDynamicAction/rcognita | [
"bac48bd4a53539e4f22094705b180de1d711ac8c"
] | [
"rcognita/systems.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nThis module contains a generic interface for systems (environments) as well as concrete systems as realizations of the former\n\nRemarks: \n\n- All vectors are treated as of type [n,]\n- All buffers are treated as of type [L, n] where each row is a vector\n-... | [
[
"numpy.clip",
"numpy.cos",
"numpy.sin",
"numpy.random.randn",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jzstark/ossiriand | [
"232d12fa7375ce9090f79c2fe3107f0cb8e6a8eb"
] | [
"image/model_update/FST_CG/tf_to_hdf5.py"
] | [
"import tensorflow as tf\nimport os\nimport h5py\nimport numpy as np\n\n# The name of each layer\n\nconv2d_n = 'conv2d'\nconv_trans2d_n = 'transpose_conv2d'\nnorm_n = 'normalisation'\nrelu_n = 'activation'\nadd_n = 'add'\n\nlayer_names = [\n conv2d_n, norm_n, relu_n,\n conv2d_n, norm_n, relu_n,\n conv2d_n,... | [
[
"tensorflow.train.NewCheckpointReader"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
junjungoal/RLbank | [
"e545fa854c203d7669d1d20d5fbe13365ba053ed"
] | [
"rl/trainers/on_policy_trainer.py"
] | [
"import os\nfrom time import time\nfrom collections import defaultdict, OrderedDict\nimport gzip\nimport pickle\n\nimport h5py\nimport torch\nimport wandb\nimport numpy as np\nimport moviepy.editor as mpy\nfrom tqdm import tqdm, trange\n\nfrom rl.policies import get_actor_critic_by_name\nfrom rl.trainers.base_train... | [
[
"numpy.isscalar"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ddaskan/trendy | [
"f438a98032a96a87144a46ecb8d52c5e84f7169b"
] | [
"trendypy/utils.py"
] | [
"'''\nUtility functions for the package.\n'''\nimport numpy as np\n\ndef scale_01(x):\n '''Scales array to 0-1.\n\n Args:\n x (iter): 1d array of float\n\n Returns:\n np.array: scaled 1d array\n\n Example:\n >>> scale_01([1, 2, 3, 5]).tolist()\n [0.0, 0.25, 0.5, 1.0]\n\n '... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MrPluto/ml | [
"dbd5ae0ae59a11cc04fb4c38c01ba73b5fe2c895",
"dbd5ae0ae59a11cc04fb4c38c01ba73b5fe2c895"
] | [
"practices/softmaxRegression.py",
"deep_exercises/part4/convModel/cnn_utils.py"
] | [
"# -*- coding: utf-8 -*-\n\n\"\"\"A very simple MNIST classifier.\nSee extensive documentation at\nhttps://www.tensorflow.org/get_started/mnist/beginners\n\"\"\"\n# from __future__ import absolute_import\n# from __future__ import division\n# from __future__ import print_function\n#\n# import argparse\n# import sys\... | [
[
"tensorflow.matmul",
"tensorflow.nn.softmax_cross_entropy_with_logits",
"tensorflow.InteractiveSession",
"tensorflow.zeros",
"tensorflow.cast",
"tensorflow.placeholder",
"tensorflow.global_variables_initializer",
"tensorflow.train.GradientDescentOptimizer",
"tensorflow.argmax",... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
oasys-lnls-kit/OASYS1-LNLS-ShadowOui | [
"91d4baeb403289fdbd6e1ec555980415ce87eff3"
] | [
"orangecontrib/shadow/lnls/widgets/utility/ow_flux_widget.py"
] | [
"# -*- coding: utf-8 -*-\n\nimport os\nimport sys\nimport time\nimport numpy\n\n\nfrom orangewidget import gui, widget\nfrom orangewidget.settings import Setting\nfrom oasys.widgets import gui as oasysgui\nfrom oasys.util.oasys_util import EmittingStream, TTYGrabber\nfrom oasys.widgets import congruence\nfrom silx.... | [
[
"numpy.sqrt",
"numpy.linspace",
"matplotlib.pyplot.minorticks_on",
"matplotlib.pyplot.plot",
"numpy.max",
"numpy.exp",
"numpy.histogram",
"matplotlib.pyplot.figure",
"numpy.min",
"matplotlib.pyplot.ylim",
"scipy.integrate.simps",
"numpy.array",
"matplotlib.pyplo... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
uridabomb/AzureChestXRay | [
"2e1a6dde1b701f259a6299166a7dd6fecda81164"
] | [
"AzureChestXRay_AMLWB/Code/src/azure_chestxray_cam.py"
] | [
"### Copyright (C) Microsoft Corporation. \n\nimport keras.backend as K\nimport sys, os, io\nimport numpy as np\nimport cv2\n\nimport matplotlib\nmatplotlib.use('agg')\n\npaths_to_append = [os.path.join(os.getcwd(), os.path.join(*(['Code', 'src'])))]\ndef add_path_to_sys_path(path_to_append):\n if not (any(pat... | [
[
"numpy.power",
"matplotlib.use",
"matplotlib.pyplot.get_cmap",
"matplotlib.pyplot.colorbar",
"numpy.argmax",
"matplotlib.pyplot.axis",
"numpy.zeros",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jecker7/BioPlate | [
"2339cca2187af522cba9b1bbc8791bdbf8e0a6bd"
] | [
"tests/test_inserts.py"
] | [
"import unittest\nimport contextlib\nimport numpy as np\nimport BioPlate.utilitis as bpu\n\nfrom pathlib import Path, PurePath\nfrom BioPlate import BioPlate\nfrom BioPlate.database.plate_db import PlateDB\nfrom BioPlate.database.plate_historic_db import PlateHist\nfrom string import ascii_uppercase\nfrom tabulate ... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mrek235/robotLearning | [
"4da7282f169f5ed2735c6a70084ff1e4decd2cd2"
] | [
"973lossFor50000ForFirstJoint.py"
] | [
"from keras.models import Sequential\nfrom keras.layers import Dense,Flatten,LSTM,Conv1D,Conv2D, MaxPool1D\nfrom keras.optimizers import Adam,SGD\nimport numpy as np\nfrom numpy.random import seed\nfrom sklearn.preprocessing import MinMaxScaler\nimport glob\n\nseed(42)\n\nmodel = Sequential()\n\ntimesteps = 30\ndat... | [
[
"numpy.load",
"numpy.array",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
matt-graham/boltzmann-machine-tools | [
"528e61c42fc3de05e00176ebf724aacddbef6824"
] | [
"bmtools/relaxations/gm_relaxations.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"Gaussian mixture Boltzman machine relaxations.\n\nHelper classes for performing inference with Gaussian mixture Boltzmann machine\nrelaxations [1].\n\nThe original discrete-space Boltzmann machine is assumed to have weight\nparameters `W` and bias parameters `b` and have a signed-bin... | [
[
"numpy.log",
"numpy.cosh",
"numpy.nonzero",
"numpy.random.RandomState",
"numpy.eye",
"numpy.all",
"scipy.linalg.eigh",
"numpy.outer",
"numpy.tanh",
"numpy.array",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"0.12",
"0.10"
],
"tensorflow": []
}
] |
einbandi/kerneloose | [
"2becc7f8df2ae4d79cb5ffef58b8cce440ff6a1e"
] | [
"kerneloose/kerneloose.py"
] | [
"import numpy as np\nimport scipy.spatial.distance as dist\nimport pickle\n\n\ndef determine_sigma(x_dist, k_nb, local=True):\n \"\"\" Choose the width of the Gaussian kernels.\n\n Automatically choose the width of the Gaussian kernels based\n on the distance to the k-th nearest neighbor.\n\n ... | [
[
"numpy.dot",
"numpy.arange",
"scipy.spatial.distance.cdist",
"numpy.sort",
"numpy.linalg.pinv",
"scipy.spatial.distance.pdist",
"numpy.mean",
"numpy.exp",
"numpy.unravel_index",
"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"
... |
vycezhong/segment_mm | [
"9b3a183932363668ddb33fc23acca0afc2978ad4"
] | [
"benchmark.py"
] | [
"import argparse\nimport time\nimport torch\nimport numpy as np\nfrom tqdm import tqdm \n\nparser = argparse.ArgumentParser()\nparser.add_argument('example', choices=['naive', 'bmm', 'cuda'])\nparser.add_argument('-n', type=int, default=512)\nparser.add_argument('-m', type=int, default=512)\nparser.add_argument('-d... | [
[
"torch.randn",
"torch.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dhas/apex | [
"ebcd7f084bba96bdb0c3fdf396c3c6b02e745042"
] | [
"apex/contrib/test/layer_norm/test_fast_layer_norm.py"
] | [
"import torch\nimport unittest\nimport numpy as np\n\nimport torch.nn.functional as F\n\nfrom apex.contrib.layer_norm import FastLayerNorm\n\nimport fast_layer_norm as fln\n\n\nclass GPUTimer:\n def __init__(self, stream):\n self.start_ = torch.cuda.Event(enable_timing=True)\n self.stop_ = torch.cu... | [
[
"torch.randn_like",
"torch.nn.functional.layer_norm",
"torch.manual_seed",
"torch.randn",
"torch.cuda.Event",
"torch.numel",
"torch.cuda.manual_seed_all",
"torch.cuda.stream",
"torch.device",
"torch.allclose",
"torch.cuda.Stream"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rctcwyvrn/rust-ncc | [
"1dc03bd6fa1d7314e5aaf6b1509ef7f299f2b4ab"
] | [
"py_model/environment.py"
] | [
"import copy\nimport time\n\nimport numba as nb\nimport numpy as np\n\nimport cell\nimport geometry\nimport parameters\n\nimport hardio\nfrom hardio import Writer\nimport chemistry\n\n\"\"\"\nEnvironment of cells.s\n\"\"\"\n\nMODE_EXECUTE = 0\nMODE_OBSERVE = 1\n\n\n@nb.jit(nopython=True)\ndef custom_floor(fp_number... | [
[
"numpy.linspace",
"numpy.arange",
"numpy.cos",
"numpy.sin",
"numpy.round",
"numpy.ceil",
"numpy.copy",
"numpy.sign",
"numpy.average",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
garcia-science/pt_fluids | [
"7153d6b0f733e94b07e36299936b93635e392dbe"
] | [
"visualizacion.py"
] | [
"from matplotlib import pyplot as plt\nfrom matplotlib.animation import FuncAnimation\nfrom matplotlib.colors import TwoSlopeNorm\nimport numpy as np\nfrom directorios import *\n\n\ndef visualizacion(*args, tipo, guardar, path, file, nombre, **kwargs):\n if 'xlabel' not in kwargs:\n xlabel = ' '\n else... | [
[
"matplotlib.pyplot.gca",
"numpy.amax",
"matplotlib.pyplot.title",
"numpy.amin",
"matplotlib.pyplot.xscale",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.yscale",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.colorbar",
"matplotlib.colors.Tw... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Intelligent-Systems-Laboratory/cvat | [
"e2c5d70348001ffd3b0fdce6b4ea06bb95260d68"
] | [
"cvat/apps/engine/efficientcut.py"
] | [
"import cv2\nimport os\nimport numpy as np\nimport time\nimport torch\nfrom torch.backends import cudnn\nfrom matplotlib import colors\nfrom cvat.apps.engine.backbone import EfficientDetBackbone\nfrom cvat.apps.engine.efficientdet.utils import BBoxTransform, ClipBoxes\nfrom cvat.apps.engine.efficientdet_utils.utils... | [
[
"numpy.array",
"torch.no_grad",
"torch.from_numpy",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xhosaBoy/HypER-normalised-relations | [
"8809e5d3781e2c8bc3e7e724725b5c47ea9694da"
] | [
"HypER/models.py"
] | [
"import torch\nfrom torch.nn import functional as F, Parameter\nfrom torch.nn.init import xavier_normal_, xavier_uniform_\n\n\nclass ConvE(torch.nn.Module):\n def __init__(self, d, d1, d2, **kwargs):\n super(ConvE, self).__init__()\n self.in_channels = kwargs[\"in_channels\"]\n self.out_chan... | [
[
"torch.nn.BatchNorm1d",
"torch.nn.Dropout",
"torch.nn.Dropout2d",
"torch.cat",
"torch.nn.Conv2d",
"torch.nn.init.xavier_normal_",
"torch.sum",
"torch.nn.BCELoss",
"torch.nn.Linear",
"torch.nn.functional.sigmoid",
"torch.nn.functional.relu",
"torch.nn.BatchNorm2d"
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rainbean/Container_TensorFlow | [
"af3fc40dd281a0971ace3c15fb9e7a636ce19273"
] | [
"jupyter/startup.py"
] | [
"import numpy as np\nimport pandas as pd\nimport cv2\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nmpl.rcParams['figure.figsize'] = [20, 10]\nnp.set_printoptions(linewidth=200)\n"
] | [
[
"numpy.set_printoptions"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TauferLab/llnl-hatchet | [
"c7d12888d71d2b23058facd3025e7dcfa12cbb39"
] | [
"hatchet/readers/pyinstrument_reader.py"
] | [
"# Copyright 2017-2022 Lawrence Livermore National Security, LLC and other\n# Hatchet Project Developers. See the top-level LICENSE file for details.\n#\n# SPDX-License-Identifier: MIT\n\nimport json\n\nimport pandas as pd\n\nimport hatchet.graphframe\nfrom hatchet.node import Node\nfrom hatchet.graph import Graph\... | [
[
"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": []
}
] |
tranmanhdat/tts_vn | [
"3a72beb66099f1b5ed6c24c625085b46628997a0"
] | [
"encoder/preprocess.py"
] | [
"import os\n\nfrom multiprocess.pool import ThreadPool\nfrom encoder.params_data import *\nfrom encoder.config import librispeech_datasets, anglophone_nationalites\nfrom datetime import datetime\nfrom encoder import audio\nfrom pathlib import Path\nfrom tqdm import tqdm\nimport numpy as np\n\n\nclass DatasetLog:\n ... | [
[
"numpy.min",
"numpy.median",
"numpy.save",
"numpy.max",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Patte1808/moda | [
"312c9594754ae0f6d17cbfafaa2c4c790c58efe5"
] | [
"moda/models/twitter/twitter_trendiness_detector.py"
] | [
"import numpy as np\nimport pandas as pd\n\nfrom moda.models.trend_detector import AbstractTrendDetector, MIN_SAMPLES_PER_CATEGORY\nfrom moda.models.twitter.anomaly_detect_ts import anomaly_detect_ts\n\n\nclass TwitterAnomalyTrendinessDetector(AbstractTrendDetector):\n GENERAL_CATEGORY = 'general'\n\n __name_... | [
[
"matplotlib.pyplot.legend",
"pandas.concat",
"pandas.merge",
"numpy.isnan",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.xticks",
"numpy.where"
]
] | [
{
"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": []
}
] |
FrappucinoGithub/school_meal_forecast_xgboost | [
"ebb10a8395b9b8158685953b030e664337cf20e0"
] | [
"tests/calculators/test_strikes.py"
] | [
"#!/usr/bin/python3\nimport unittest\n\nimport pandas as pd\n\nimport app.calculators as calc\n\n\nclass TestStrikes(unittest.TestCase):\n # pylint: disable=too-many-statements\n def test_add_feature_strikes(self):\n dtf = pd.DataFrame({\n 'index_date': [\"2011-12-15\", \"2017-09-04\"],\n ... | [
[
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
brunoklaus/PS-001-ML5G-GNNetworkingChallenge2021-PARANA | [
"c37abbab19f05388b1ac61a3cc287ff36bdd0e19"
] | [
"datanetAPI.py"
] | [
"'''\n *\n * Copyright (C) 2020 Universitat Politècnica de Catalunya.\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 *... | [
[
"numpy.asmatrix",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lyn1874/anomaly_detection_under_varying_visibility | [
"3d3cbd8344fa8cd6af7f54f7c3f9a3e9ef0994f7"
] | [
"memae-anomaly-detection/Testing.py"
] | [
"import torch \nfrom torch import nn\nimport summary\nimport os\nimport sys\nimport numpy as np\nimport time\nfrom torchvision import transforms\nfrom torch.utils.data import DataLoader\nimport utils\nfrom models import AutoEncoderCov3D, AutoEncoderCov3DMem\nimport data.utils as data_utils\nimport argparse\nfrom tq... | [
[
"torch.load",
"torch.utils.data.DataLoader",
"numpy.save",
"numpy.mean",
"numpy.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mihirkestur/neowise | [
"2bce24812fb276f07bdbced605c28e0f865dd6eb"
] | [
"neowise/neowise/optimizers.py"
] | [
"import numpy as np\n\n\nclass OptimizerHelpers:\n \"\"\"\n Optimizer Helpers\n Used to initiate the instance variables of nw.optimizers\n\n Arguments:\n alpha: Learning rate of the network (float)\n layers_arr: List containing the objects of nw.layers (List)\n V_dict: Dictionary co... | [
[
"numpy.square",
"numpy.power"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
engineerscodes/PyVisionHUB | [
"512ff1fde5dd9849e555bc7848a11cee65ee9068"
] | [
"Matplotlib/Histogram.py"
] | [
"import matplotlib.pyplot as plt\r\n\r\ndata = [18,55,21,25,21,22,34,42,42,4,99,102,110,120,121,122,130,111,115,112,80,75,65,54,44,43,42,48]\r\n\r\n# ids = [x for x in range(len(ages))]\r\n\r\nbins = [0,10,20,30,40,50,60,70,80,90,100,110,120,130]\r\n\r\nplt.hist(data, bins, histtype='bar' , rwidth=0.8 )\r\n\r\nplt.... | [
[
"matplotlib.pyplot.title",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.hist",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tatHi/cwr | [
"1a2b15066ee46a5597f2702b55f7ae482ad754c2"
] | [
"wordReplacer.py"
] | [
"from scipy.stats import poisson\nimport numpy as np\nimport random\n\nclass WordReplacer:\n def __init__(self, word2id, usePoisson=False, useCache=False, cacheCoef=5.0, ignoreFirst=0):\n '''\n word2id: a dictionary of word to idx like {a:1, b:2, ...}\n usePoisson: use poisson weighting cons... | [
[
"numpy.array",
"scipy.stats.poisson.pmf"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xpmemeda/mgeconvert | [
"d30e28dffaa9c42cbbefd7a8c41c688f9d2a8acd"
] | [
"mgeconvert/cambricon_converter/cambricon_converter.py"
] | [
"# -*- coding: utf-8 -*-\n# MegEngine is Licensed under the Apache License, Version 2.0 (the \"License\")\n#\n# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.\n#\n# Unless required by applicable law or agreed to in writing,\n# software distributed under the License is distributed on an\n# \"AS IS\" BASIS,... | [
[
"numpy.broadcast_to"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Joseph-tsai415/Msc-All-Terrain-Robot | [
"40973b8d1b088d37efc372f6313ee58d18792c02"
] | [
"src/Guidance_system/guidance_sys/gen_marker/gen_marker.py"
] | [
"import cv2 as cv\nimport numpy as np\n\n# Load the predefined dictionary\n\ndictionary = cv.aruco.Dictionary_get(cv.aruco.DICT_4X4_50)\n# Generate the marker\n\nmarkerImage = np.zeros((400, 400), dtype=np.uint8)\nfor i in range(50):\n markerImage = cv.aruco.drawMarker(dictionary, i, 400, markerImage, 1);\n c... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bayesquant/VDSH | [
"19dc616c8b91fee5d47854163bb1dc71bbefd553"
] | [
"datasets.py"
] | [
"import os\nfrom os.path import join\nimport numpy as np\nimport torch\n#from scipy.sparse import csr_matrix\nimport pandas as pd\nimport pickle\nfrom torch.utils.data import Dataset\n\n##########################################################################################################################\n\nclas... | [
[
"pandas.read_pickle"
]
] | [
{
"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": []
}
] |
bonskotti/desdeo-mcdm | [
"33a94e9ac701712ef9669641da4a36d99084d50f"
] | [
"desdeo_mcdm/interactive/NautilusNavigator.py"
] | [
"from typing import Callable, Dict, List, Optional, Tuple\n\nimport numpy as np\nimport pandas as pd\n\nfrom desdeo_mcdm.interactive.InteractiveMethod import InteractiveMethod\nfrom desdeo_tools.interaction.request import BaseRequest, SimplePlotRequest\nfrom desdeo_tools.scalarization.ASF import PointMethodASF\nfro... | [
[
"numpy.allclose",
"numpy.linspace",
"numpy.min",
"numpy.linalg.norm",
"numpy.stack",
"numpy.argwhere",
"numpy.all",
"numpy.max",
"numpy.copy",
"numpy.atleast_2d",
"numpy.any",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Calvin-Ruiz/entitylib2 | [
"8a8d6142628a91c24dd7c9d3e7c3c52eda20ca30"
] | [
"entitylib2.py"
] | [
"from random import randint\r\nfrom time import time, sleep\r\ntry:\r\n import pygame\r\nexcept:\r\n import os\r\n print(\"ImportError : Pygame wasn't installed !\")\r\n print(\"Should I install pygame right now ?\")\r\n if input().upper() in [\"OUI\", \"O\", \"Y\", \"YES\", \"INSTALL\", \"INSTALLER\... | [
[
"numpy.load",
"numpy.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
schmidtand/water-anomaly-detection | [
"65a8d09f7fa4d1899c89c1a69ba3a468de4d7cd5"
] | [
"src/classifier.py"
] | [
"\"\"\"\ncontains classes of the supported models\n\"\"\"\nimport os\nimport numpy as np\nimport pandas as pd\nimport pickle\nimport datetime\nimport logging\nimport sklearn.ensemble\nimport sklearn.svm\n\n\n\n\nclass AdvancedThresholdClassifier:\n def __init__(self, y_name, *args, lower_threshold=0, upper_thres... | [
[
"numpy.convolve",
"numpy.hstack",
"pandas.Series",
"numpy.isnan",
"numpy.errstate",
"numpy.where"
]
] | [
{
"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": []
}
] |
epochml/paintWebcam | [
"93ea24603869f3cae2a0ab4f591ba4cbc8f9c533"
] | [
"webcamDraw.py"
] | [
"import numpy as np\nimport cv2\nfrom collections import deque\n\n\nDblueLower = np.array([100, 60, 60])\nDblueUpper = np.array([140, 255, 255])\n\n\nkernel = np.ones((5, 5), np.uint8)\n\n\nbpoints = [deque(maxlen=512)]\ngpoints = [deque(maxlen=512)]\nrpoints = [deque(maxlen=512)]\nypoints = [deque(maxlen=512)]\n\n... | [
[
"numpy.array",
"numpy.zeros",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pinakinathc/task_image_enhancement | [
"a8b628f10c02dcf047382ef0774817cb6c47780a"
] | [
"unet.py"
] | [
"import numpy as np \nimport os\nimport tensorflow as tf\nimport numpy as np\n# from keras.models import *\nfrom tensorflow.keras.layers import *\n\n\ndef unet(inputs, input_size=(256, 256)):\n # inputs = Input(input_size)\n input_rs = tf.image.resize_nearest_neighbor(inputs, input_size)\n conv1 = Conv2D(6... | [
[
"tensorflow.image.resize_nearest_neighbor",
"tensorflow.shape"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
suhangpro/deeprl | [
"9dcd1905b016a823fe92bf92dc00d3e56d732925"
] | [
"main.py"
] | [
"#######################################################################\n# Copyright (C) 2017 Shangtong Zhang(zhangshangtong.cpp@gmail.com) #\n# Permission given to modify the code as long as you keep this #\n# declaration at the top #\n#######################... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
urikz/language | [
"503aca178c98fed4c606cf83e58ae0f84012a4d9",
"503aca178c98fed4c606cf83e58ae0f84012a4d9"
] | [
"language/canine/tydiqa/tf_io.py",
"language/mentionmemory/modules/mention_losses_test.py"
] | [
"# coding=utf-8\n# Copyright 2018 The Google AI Language Team 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# Unl... | [
[
"tensorflow.compat.v1.data.TFRecordDataset",
"tensorflow.compat.v1.to_int32",
"tensorflow.compat.v1.gfile.Open",
"tensorflow.compat.v1.FixedLenFeature",
"tensorflow.compat.v1.gfile.Glob",
"tensorflow.compat.v1.logging.info",
"tensorflow.compat.v1.parse_single_example",
"tensorflow.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
boryana-ilieva/soepy_dev | [
"98655bb1d7012c5554201a8dcfb728eeeaadf5bf"
] | [
"development/tests/property/property_auxiliary.py"
] | [
"\"\"\"This module contains some auxiliary functions for the property testing.\"\"\"\nfrom datetime import datetime\nimport importlib\nimport argparse\nimport shutil\nimport string\nimport glob\nimport os\n\nimport numpy as np\n\nfrom soepy.python.soepy_config import PACKAGE_DIR\n\n\ndef collect_tests():\n \"\"\... | [
[
"numpy.sum",
"numpy.random.choice"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
liangdaojun/MSIN | [
"36acc8e3b746a7245a2d13e9c0cada1ccda06a91"
] | [
"utils.py"
] | [
"'''Some helper functions for PyTorch, including:\n - get_mean_and_std: calculate the mean and std value of dataset.\n - msr_init: net parameter initialization.\n - progress_bar: progress bar mimic xlua.progress.\n'''\nimport os\nimport sys\nimport time\n\nimport torch.nn as nn\nimport torch.nn.init as ini... | [
[
"torch.nn.init.kaiming_normal",
"torch.Tensor",
"torch.zeros",
"torch.cat",
"torch.utils.data.DataLoader",
"numpy.concatenate",
"torch.nn.init.constant",
"torch.nn.init.normal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dolaver/calsagos | [
"9c4f77c9952ede6ffc4cb9dd915bb1cb2ca80b1c"
] | [
"calsagos/redshift_boundaries.py"
] | [
"# Filename: redshift_boundaries.py\r\n# Here we found a serie of scripts develop to estimate the\r\n# central redshift and velocity dispersion of the cluster\r\n# and to establish the limits in the redshift distribution\r\nimport numpy as np\r\n\r\n# Section dedicated to importing the modules from SubFind\r\nfrom ... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
FabulousFatTiger/ml-mipt | [
"770f84acee786a2d4c1bd7f13f8f3f7b1124464e"
] | [
"homeworks_basic/assignment0_01_kNN/k_nearest_neighbor.py"
] | [
"import numpy as np\n\"\"\"\nCredits: the original code belongs to Stanford CS231n course assignment1. Source link: http://cs231n.github.io/assignments2019/assignment1/\n\"\"\"\n\nclass KNearestNeighbor:\n \"\"\" a kNN classifier with L2 distance \"\"\"\n\n def __init__(self):\n pass\n\n def fit(sel... | [
[
"numpy.dot",
"numpy.bincount",
"numpy.argsort",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yfe3/Intelligent-Systems | [
"14c22e144e1a7f8625b2ff3f63e9c0545f946f69"
] | [
"Random walk experiment with Funtion Approximation/state_aggregation_agent.py"
] | [
"#!/usr/bin/env python\n\n\"\"\"\n Author: Adam White, Matthew Schlegel, Mohammad M. Ajallooeian, Sina Ghiassian\n Purpose: Skeleton code for Monte Carlo Exploring Starts Control Agent\n for use on A3 of Reinforcement learning course University of Alberta Fall 2017\n\n\"\"\"\n\nfrom utils import rand_in... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dgriffiths3/finding-your-center | [
"6eea1ed5d1bbd9064828133111e96f3395a3afcc"
] | [
"scene_network/inference.py"
] | [
"import os\nimport sys\nimport glob\nimport colorsys\n\nsys.path.insert(0, './')\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n\nimport numpy as np\nimport pyvista as pv\nimport tensorflow as tf\n\nfrom scene_network.model import ObjDetectorModel\nfrom utils import helpers, tf_utils, losses\n\n\ndef plot_scene(scene, ... | [
[
"numpy.hstack",
"numpy.linspace",
"numpy.min",
"tensorflow.zeros",
"tensorflow.expand_dims",
"numpy.max",
"numpy.ceil",
"tensorflow.gather",
"numpy.mean",
"numpy.load",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
passerby233/keratitis_imb | [
"a7fb12f39b2084b82e19da6dfe0097a6003a9ea2"
] | [
"supervise_cross_ddp.py"
] | [
"import os\n#os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"4\"\n\nimport numpy as np\nimport random, torch, json\nfrom sklearn import metrics\nfrom functools import reduce\n\nfrom torch.nn import functional as F\nfrom torch.utils.data import DataLoader\nfrom torch.optim.lr_scheduler import MultiStepLR\nfrom torch.cuda.a... | [
[
"torch.optim.lr_scheduler.MultiStepLR",
"sklearn.metrics.roc_auc_score",
"torch.nn.functional.softmax",
"torch.distributed.init_process_group",
"torch.cuda.set_device",
"torch.utils.data.distributed.DistributedSampler",
"torch.load",
"torch.nn.functional.cross_entropy",
"torch.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
WestCityInstitute/InvDN | [
"3846cf3548ccf6690e58be3aafe1f6d98c56b90d"
] | [
"Crop_DIV2K.py"
] | [
"import os\nimport cv2\nimport glob\nimport numpy as np\n\ncropSize = 256\nnum = 100\n\n# Crop SIDD\n# GT = glob.glob(os.path.join('/home/yang/Documents/Yang_Liu/Research_Projects/Data/SIDD/SIDD_Medium_Srgb/GT', '*.PNG'))\n# Noisy = glob.glob(os.path.join('/home/yang/Documents/Yang_Liu/Research_Projects/Data/SIDD/S... | [
[
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
spejamchr/unties | [
"5fd4a6ddf95310f008c89eeaa17598513022aba1"
] | [
"unties/tests/test_unties.py"
] | [
"from unittest import TestCase\nimport math\nimport numpy as np\nfrom scipy.optimize import fsolve\n\nimport unties as _\nimport unties.utilities.errors as ue\n\n\ndef _deep_map(func, *args):\n \"\"\"Like map, but recursively enters iterables\n\n Ex:\n\n >>> _deep_map(lambda a, b: a + b,\n ... | [
[
"numpy.log",
"scipy.optimize.fsolve",
"numpy.linspace",
"numpy.cos",
"numpy.sin",
"numpy.all",
"numpy.log10",
"numpy.array",
"numpy.exp"
]
] | [
{
"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"... |
a7b23/CS236G-project | [
"8bde7d54ca3d22e13703fe72dfb998b38d9cd8b4"
] | [
"BigBiGAN-TensorFlow2.0/data_all.py"
] | [
"import tensorflow as tf\n\nimport tensorflow_datasets as tfds\nfrom data_util import preprocess_image\n\nNUM_CALLS = tf.data.experimental.AUTOTUNE\nNUM_PREFETCH = tf.data.experimental.AUTOTUNE\n\nmnist = tf.keras.datasets.mnist\n(mnist_images, mnist_labels), _ = mnist.load_data()\n\n\ndef map_fn(image, label):\n ... | [
[
"tensorflow.concat",
"tensorflow.slice",
"tensorflow.stack",
"tensorflow.cast",
"tensorflow.expand_dims",
"tensorflow.image.resize",
"tensorflow.image.convert_image_dtype",
"tensorflow.pad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
mriedem/qiskit-ibm | [
"be7056e9f59098cb3097d9bdcf6e9f2bbc006455"
] | [
"test/ibm/experiment/test_experiment_server_integration.py"
] | [
"# This code is part of Qiskit.\n#\n# (C) Copyright IBM 2021.\n#\n# This code is licensed under the Apache License, Version 2.0. You may\n# obtain a copy of this license in the LICENSE.txt file in the root directory\n# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.\n#\n# Any modifications or ... | [
[
"numpy.zeros",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zanellia/ctree | [
"d53d29d972ac06b60d8fc4fb81f5553d2ffbfda0"
] | [
"examples/OclDoubler.py"
] | [
"\"\"\"\nParses the python AST below, transforms it to C, JITs it, and runs it.\n\"\"\"\n\nimport logging\n\nlogging.basicConfig(level=20)\n\nimport numpy as np\nimport ctypes as ct\nimport pycl as cl\n\nimport ctree.np\nfrom ctree.c.nodes import *\nfrom ctree.cpp.nodes import *\nfrom ctree.ocl.nodes import *\nfrom... | [
[
"numpy.arange",
"numpy.testing.assert_array_equal",
"numpy.vectorize",
"numpy.prod",
"numpy.ctypeslib.ndpointer"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.24",
"1.13",
"1.16",
"1.9",
"1.18",
"1.23",
"1.21",
"1.22",
"1.20",
"1.7",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [],
... |
alexandroskoliousis/benchmarks | [
"6a6b95b97e53244ac8c01577801cfbf0926b718c"
] | [
"scripts/tf_cnn_benchmarks/preprocessing.py"
] | [
"# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.convert_to_tensor",
"tensorflow.concat",
"tensorflow.FixedLenFeature",
"tensorflow.image.random_contrast",
"tensorflow.zeros",
"tensorflow.stack",
"tensorflow.cast",
"tensorflow.image.random_saturation",
"tensorflow.image.decode_and_crop_jpeg",
"tensorflow.data.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ChrisDemahy/word-rnn-tensorflow | [
"7dbd7f4c9e84c1d374391d422dfa11f6ffe5ae7e"
] | [
"utils.py"
] | [
"# -*- coding: utf-8 -*-\r\nimport os\r\nimport codecs\r\nimport collections\r\nfrom six.moves import cPickle\r\nimport numpy as np\r\nimport re\r\nimport itertools\r\n\r\nclass TextLoader():\r\n def __init__(self, data_dir, batch_size, seq_length, encoding=None):\r\n self.data_dir = data_dir\r\n s... | [
[
"numpy.load",
"numpy.copy",
"numpy.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
icyda17/MNT | [
"f88a0a199e79d6d4201ec724478c710380e71ea3"
] | [
"model/model.py"
] | [
"import math\nimport torch\nimport random\nfrom torch import nn\nfrom torch import optim\nfrom torch.autograd import Variable\nimport torch.nn.functional as F\nfrom torch.nn.utils import clip_grad_norm_\nimport numpy as np\nimport time\n# from reporter import Reporter\nimport os\nfrom tqdm import tqdm\nfrom queue i... | [
[
"torch.nn.Dropout",
"torch.nn.functional.softmax",
"torch.LongTensor",
"torch.nn.functional.log_softmax",
"torch.cat",
"torch.zeros",
"torch.load",
"torch.nn.GRU",
"torch.nn.Embedding",
"torch.nn.Linear",
"torch.no_grad",
"torch.bmm",
"torch.rand",
"torch.to... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jasonkaye/libdlr | [
"7dcccc9713615e1ec17d4d0d069b19a5eecbc60d"
] | [
"pydlr/test/test_semi_circ.py"
] | [
"\"\"\" Evaluation of integral between a semi-circular \nspectral function and the imaginary time kernel, giving \nthe imaginary time Green's function analytically.\n\nThe analytical result is compared with the DLR representation\non a dense tau mesh.\n\nThe result is also compared to the iterative solution of\nthe... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.tight_layout",
"numpy.allclose",
"numpy.linspace",
"matplotlib.pyplot.title",
"numpy.abs",
"numpy.squeeze",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.ylabel",
"numpy.vectorize",
"matplotl... | [
{
"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"... |
ElnuraMusaoglu/SingleObjectTracking | [
"282a6312be23f6c4bce3b38c19045a1d1a3bce3b"
] | [
"hierarchical_conv_features_tracking/hcf_old.py"
] | [
"import cv2\nimport numpy as np\nfrom numpy.fft import fft2, ifft2, fftshift\nfrom numpy import conj, real\nimport argparse\nimport cv2\nimport os\nfrom os.path import join, realpath, dirname\nfrom keras.applications.vgg19 import VGG19\nfrom keras.models import Model\nfrom matplotlib import pyplot\nfrom numpy impor... | [
[
"numpy.expand_dims",
"numpy.minimum",
"numpy.sqrt",
"numpy.max",
"numpy.mean",
"numpy.exp",
"scipy.io.loadmat",
"numpy.subtract",
"numpy.full",
"numpy.size",
"numpy.argmax",
"numpy.std",
"numpy.zeros",
"numpy.fft.fft2",
"numpy.log",
"numpy.multiply",... | [
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.13",
"1.16",
"1.9",
"1.18",
"1.21",
"1.20",
"1.7",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [],
"scipy": [
"1.7",
"1.0",
... |
daniilpastukhov/serendipity_experiments | [
"5ff544937d1796dbb20e11137f364972935be967"
] | [
"models/mf.py"
] | [
"# ref: Stanislav Kuznetsov\nimport pandas as pd\n\nimport numpy as np\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.neighbors import NearestNeighbors\nfrom scipy.sparse import csr_matrix\nfrom tqdm import tqdm\n\nfrom utils.helpers import get_movies_by_ids\nfrom utils.metrics import unexpectedness,... | [
[
"sklearn.decomposition.TruncatedSVD",
"numpy.unique",
"scipy.sparse.csr_matrix",
"numpy.mean",
"sklearn.neighbors.NearestNeighbors",
"numpy.argsort",
"numpy.array",
"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"... |
francesconazzaro/pangeo-forge | [
"5c80733a1002c3b5f281e373746c9bc580453204"
] | [
"pangeo_forge/recipes/xarray_zarr.py"
] | [
"\"\"\"\nA Pangeo Forge Recipe\n\"\"\"\n\nimport json\nimport logging\nimport os\nimport tempfile\nimport warnings\nfrom contextlib import ExitStack, contextmanager\nfrom dataclasses import dataclass, field\nfrom itertools import product\nfrom typing import Callable, Dict, List, Optional, Sequence, Tuple\n\nimport ... | [
[
"numpy.asarray"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
fakotakis/Natural-Language_Processing | [
"6efd454c50b58fa62dbeae90128f43902bf882d0"
] | [
"plotting_results.py"
] | [
"import matplotlib.pyplot as plt\n\n\nacc = history.history['acc']\nval_acc = history.history['val_acc']\nloss = history.history['loss']\nval_loss = history.history['val_loss']\n\nepochs = range(1, len(acc) + 1)\n\nplt.plot(epochs, acc, 'bo', label='Training acc')\nplt.plot(epochs, val_acc, 'b', label='Validation a... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.title",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
thantuongaotrang/AlphaToe | [
"1220f4f883dbbd7ac1d84092bdaf04ca18a4dbc2"
] | [
"value_network.py"
] | [
"\"\"\"\nAfter using reinforcement learning to train a network, e.g. policy_gradient.py, to play a game well. We then want to\nlearn to estimate weather that network would win, lose or draw from a given position.\n\nAlpha Go used a database of real positions to get it's predictions from, we don't have that for tic-... | [
[
"tensorflow.train.RMSPropOptimizer",
"tensorflow.placeholder",
"numpy.random.shuffle",
"tensorflow.global_variables_initializer",
"numpy.argmax",
"tensorflow.Session",
"tensorflow.square",
"numpy.ravel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
mofanv/privacy | [
"452031ecb1761712188155e4b36d6d01abf162aa"
] | [
"tensorflow_privacy/privacy/membership_inference_attack/utils.py"
] | [
"# Copyright 2020, The TensorFlow Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable ... | [
[
"numpy.logical_not",
"sklearn.metrics.recall_score",
"numpy.abs",
"numpy.random.seed",
"sklearn.metrics.precision_score",
"sklearn.metrics.roc_curve",
"numpy.sort",
"numpy.atleast_1d",
"numpy.concatenate",
"sklearn.metrics.auc",
"sklearn.metrics.f1_score",
"numpy.ar... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
choderalab/DDR1_and_kinase_promiscuity_materials | [
"510184518fdba59930bbed08331668d1267ecbf8"
] | [
"2.WT_SIMULATIONS/MSM_analysis/MSM_analysis.py"
] | [
"import matplotlib\nmatplotlib.use('Agg')\n\n# import general libraries\nimport mdtraj as md\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport pyemma.coordinates as coor\nimport pyemma.msm as msm\nimport pyemma.plots as mplt\n\nprint('loading trajectories')\n\n# load trajectories\npath_to_trajs = './WT... | [
[
"numpy.cumsum",
"matplotlib.pyplot.plot",
"numpy.hstack",
"matplotlib.pyplot.tight_layout",
"numpy.save",
"matplotlib.pyplot.errorbar",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.title",
"matplotlib.pyplot.savefig",
"numpy.append",
"numpy.array",
"numpy.sum",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LiYuRio/Paddle | [
"dbd6e2df9d074973b7ee177e2d6b96ed2318008e"
] | [
"python/paddle/fluid/tests/unittests/test_switch_autotune.py"
] | [
"# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.\n# \n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n# \n# http://www.apache.org/licenses/LICENSE-2.0\n# \n# Unless... | [
[
"numpy.random.random"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
WangFeng18/dino | [
"1a4e49bd0e99d7e205338b14994a1d57c3084cfe"
] | [
"FastLinear/vision_transformer.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates.\n# \n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n# \n# http://www.apache.org/licenses/LICENSE-2.0\n# \n# Unless required by ... | [
[
"torch.nn.Dropout",
"torch.linspace",
"torch.cat",
"torch.zeros",
"torch.nn.init.constant_",
"torch.nn.Conv2d",
"torch.nn.Linear",
"torch.nn.Identity",
"torch.rand"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jarad/dep | [
"fe73982f4c70039e1a31b9e8e2d9aac31502f803"
] | [
"scripts/util/dump_ofe_results.py"
] | [
"\"\"\"Summarize the OFE files\"\"\"\nfrom __future__ import print_function\nimport os\nimport datetime\n\nimport pandas as pd\nfrom tqdm import tqdm\nfrom pyiem.dep import read_ofe, read_man, read_slp\n\n\"\"\"\"\n No-till (1) (2-5)\n B - Soy B1 B25 IniCropDef.Defau... | [
[
"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": []
}
] |
dfyz/DeepSpeed | [
"163b392661c9f1dda09246e2309c6caf53d70992"
] | [
"deepspeed/inference/engine.py"
] | [
"'''\nCopyright 2021 The Microsoft DeepSpeed Team\n'''\nimport torch\nimport os\nfrom datetime import timedelta\nfrom torch.nn.modules import Module\nimport torch.distributed as dist\nfrom ..runtime.state_dict_factory import SDLoaderFactory\nfrom ..runtime.weight_quantizer import WeightQuantization\nfrom ..module_i... | [
[
"torch.distributed.broadcast",
"torch.cuda.set_device",
"torch.cuda.current_device",
"torch.distributed.is_initialized",
"torch.is_tensor",
"torch.distributed.new_group",
"torch.distributed.get_rank"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
qihongl/ntm_keras | [
"f33d3c444ea4dd62bceb5011e46c452c51c9cf4a"
] | [
"view_weights.py"
] | [
"from keras.models import Sequential\nfrom keras.layers import Dense\nimport numpy as np\nfrom skimage.viewer import ImageViewer, CollectionViewer\n\nm = Sequential()\nm.add(Dense(100, input_dim=30))\n\n\ndef weights_viewer(path):\n w = []\n for i in range(1000):\n filename = \"model.ckpt.{:04d}.hdf5\"... | [
[
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
chengxinjiang/NoisePy | [
"3a7c23cf3a069db10a620e1519f67434db0dd892"
] | [
"src/noise_module.py"
] | [
"import os\nimport glob\nimport copy\nimport obspy\nimport scipy\nimport time\nimport pycwt\nimport pyasdf\nimport datetime\nimport numpy as np\nimport pandas as pd\nfrom numba import jit\nfrom scipy.signal import hilbert\nfrom obspy.signal.util import _npts2nfft\nfrom obspy.signal.invsim import cosine_taper\nfrom ... | [
[
"numpy.dot",
"numpy.imag",
"numpy.sqrt",
"numpy.linspace",
"numpy.asarray",
"pandas.DataFrame",
"numpy.concatenate",
"numpy.max",
"scipy.fftpack.fft",
"numpy.int",
"numpy.mean",
"numpy.argmin",
"scipy.fftpack.fftfreq",
"numpy.round",
"numpy.linalg.qr",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [
"0.18",
"1.0",
"0.19"
],
"tensorflow": []
}
] |
mforbes/mmfutils-fork | [
"77deabc243e27cadedff3c2088dd3ff440da2da6"
] | [
"mmfutils/tests/test_context.py"
] | [
"import os\nimport signal\nimport time\n\nimport numpy as np\n\nimport pytest\n\nfrom mmfutils import contexts\n\n@pytest.yield_fixture\ndef NoInterrupt():\n yield contexts.NoInterrupt\n # Restore original handlers\n contexts.NoInterrupt.unregister()\n\n \nclass TestNoInterrupt(object):\n @staticmeth... | [
[
"numpy.allclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ellisdg/BRATS | [
"668728b9683316f57b999d4bbccc68dfe6f9769d"
] | [
"unet3d/scripts/train.py"
] | [
"import os\nimport argparse\nimport pandas as pd\nimport numpy as np\nfrom unet3d.train import run_training\nfrom unet3d.utils.filenames import wrapped_partial, generate_filenames, load_bias, load_sequence\nfrom unet3d.utils.sequences import (WholeVolumeToSurfaceSequence, HCPRegressionSequence, ParcelBasedSequence,... | [
[
"numpy.mod",
"numpy.round",
"pandas.read_csv",
"numpy.asarray"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
elixiruhig/DRDetetction-Final | [
"a00e957424a95d41b232b9d3cdf7d11ceca83174"
] | [
"DiabeticRetinopathy/inference.py"
] | [
"# Ignore the warnings\nimport warnings\n\nwarnings.filterwarnings('always')\nwarnings.filterwarnings('ignore')\n\n# data visualisation and manipulation\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom matplotlib import style\nimport seaborn as sns\n\n# configure\n# sets matplotlib t... | [
[
"pandas.DataFrame",
"numpy.unique"
]
] | [
{
"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": []
}
] |
andrewwarrington/vesicle-cnn-2 | [
"cff34d1451fb196616624efaeba00b6e6816ed13"
] | [
"vesicle-cnn-2/vesicle-cnn-2.py"
] | [
"# MIT License\n#\n# Copyright (c) 2017, Andrew Warrington\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, ... | [
[
"numpy.nanmax",
"numpy.squeeze",
"tensorflow.cast",
"tensorflow.train.AdamOptimizer",
"tensorflow.get_default_graph",
"tensorflow.summary.scalar",
"numpy.where",
"numpy.reshape",
"tensorflow.ConfigProto",
"tensorflow.name_scope",
"tensorflow.Session",
"tensorflow.tr... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
sedgewickmm18/mmfunctions | [
"1969eb198058ec4d4339f3a953f33d4d09675de8"
] | [
"mmfunctions/anomaly.py"
] | [
"# *****************************************************************************\n# © Copyright IBM Corp. 2018-2021. All Rights Reserved.\n#\n# This program and the accompanying materials\n# are made available under the terms of the Apache V2.0 license\n# which accompanies this distribution, and is available at\n#... | [
[
"torch.randn_like",
"numpy.dot",
"scipy.stats.norm.ppf",
"numpy.minimum",
"numpy.sqrt",
"numpy.linspace",
"torch.cat",
"numpy.asarray",
"scipy.stats.zscore",
"numpy.cumsum",
"pandas.api.types.is_numeric_dtype",
"sklearn.neighbors.LocalOutlierFactor",
"scipy.fftp... | [
{
"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"... |
AllenDowney/pandas | [
"4875a3dcc23fac851627c0c6b93ded9d6b1aca5a"
] | [
"pandas/tests/indexes/timedeltas/test_timedelta.py"
] | [
"import pytest\n\nimport numpy as np\nfrom datetime import timedelta\n\nimport pandas as pd\nimport pandas.util.testing as tm\nfrom pandas import (timedelta_range, date_range, Series, Timedelta,\n DatetimeIndex, TimedeltaIndex, Index, DataFrame,\n Int64Index, _np_version_under1... | [
[
"pandas.tseries.offsets.Day",
"pandas.Series",
"pandas.DataFrame",
"numpy.dtype",
"pandas.util.testing.assert_index_equal",
"pandas.util.testing.round_trip_pickle",
"numpy.negative",
"numpy.divide",
"pandas.util.testing.assert_numpy_array_equal",
"numpy.arange",
"pandas... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ZhengPeng7/GLCNet | [
"5ec7d4eb0ddece3f789df0b25f414bc4b5ac1d9e"
] | [
"models/seqnet.py"
] | [
"from copy import deepcopy\nfrom collections import OrderedDict\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.nn import init\nfrom torchvision.models.detection.faster_rcnn import FastRCNNPredictor\nfrom torchvision.models.detection.roi_heads import RoIHeads\nfrom torchvision.mo... | [
[
"torch.mean",
"torch.cat",
"torch.zeros",
"torch.sum",
"torch.nn.functional.smooth_l1_loss",
"torch.nn.AdaptiveMaxPool2d",
"torch.ones",
"torch.nn.ModuleDict",
"torch.tensor",
"torch.nonzero",
"torch.nn.Sequential",
"torch.sigmoid",
"torch.nn.BatchNorm1d",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LafeLabs/codeserver | [
"ca3f7bbe3acbcd6755e04627c97fe4fcd5186711"
] | [
"matplotlib/code/php.py"
] | [
"############### GET INPUT FILE ###############\ninfile = open(\"data.txt\",\"r\")\ntextin = infile.read()\ninfile.close()\n##############################################\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport json\njsondata = json.loads(textin)\nt = []\nv = []\nfor i in range(len(jsondata)):\... | [
[
"matplotlib.pyplot.subplots"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yangxin0/waymo-open-dataset | [
"1297cdcbcd103d4befb4b498e50a0c030053e6c0"
] | [
"waymo_open_dataset/utils/keypoint_draw_test.py"
] | [
"# Copyright 2022 The Waymo Open Dataset 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# Unl... | [
[
"matplotlib.pyplot.subplots",
"tensorflow.test.main",
"numpy.testing.assert_allclose",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Xlinford/TDNet | [
"e7cb59c40b8751b6dab9691d26ad224fd61c24d1"
] | [
"Training/ptsemseg/optimizers/adaoptimizer.py"
] | [
"#!/usr/bin/python\r\n# -*- encoding: utf-8 -*-\r\n\r\n\r\nimport torch\r\nimport logging\r\n\r\nlogger = logging.getLogger(\"ptsemseg\")\r\n\r\nclass AdaOptimizer(object):\r\n def __init__(self,\r\n model,\r\n lr0,\r\n momentum,\r\n wd,\r\n ... | [
[
"torch.optim.SGD"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
A03ki/PFN_internship2019_ML | [
"2353005e16217ec7b55d98da9c125d44005f3960"
] | [
"1/src/gnn.py"
] | [
"import numpy as np\n\n\nclass GraphNeuralNetwork:\n def __init__(self, vertex_size, vector_size):\n np.random.seed(0)\n self.W = np.random.randn(vector_size, vector_size)\n self.x = np.zeros((vertex_size, vector_size))\n self.x[:, 0] = 1 # one-hot of row\n\n def forward(self, gra... | [
[
"numpy.dot",
"numpy.maximum",
"numpy.random.seed",
"numpy.random.randn",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
2AUK/SFED | [
"6c50c727ea6c63b31af04a648b3d9cb481e1d6ae"
] | [
"test/test_GF_KH.py"
] | [
"from gridData import Grid\nimport numpy as np\nimport sys\n\nsys.path.append(\"/home/abdullah/Code/Python/SFED/\")\nfrom gridcollector import GridCollector\nfrom SFED_routines import sfed_gf_3drism, integrate_sfed\nfrom pathlib import Path\n\nbase_path = Path(__file__).parent\ndata_path = file_path = (base_path / ... | [
[
"numpy.testing.assert_almost_equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kant/rl-baselines3-zoo | [
"75f9c4c8395d622184177795696973ae37c58ae7"
] | [
"enjoy.py"
] | [
"import argparse\nimport glob\nimport importlib\nimport os\nimport sys\n\nimport numpy as np\nimport torch as th\nimport yaml\nfrom stable_baselines3.common.utils import set_random_seed\n\nimport utils.import_envs # noqa: F401 pylint: disable=unused-import\nfrom utils import ALGOS, create_test_env, get_latest_run_... | [
[
"numpy.std",
"torch.set_num_threads",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ikestar99/endopy | [
"f8d330d93ad12fa1654f717fef8a0ce28540a1c9"
] | [
"endopy/data/endodataset.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jun 24 21:28:00 2020\n\n\n@author: ike\n\"\"\"\n\nimport numpy as np\n\nimport torch\n\nfrom ..utils.base import BaseDataset\nfrom ..utils.pathutils import firstGlob, recursiveGlob\nfrom ..utils.multipagetiff import MultipageTiff\n\n\nclass Fl... | [
[
"numpy.reshape",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CSCfi/machine-learning-scripts | [
"005f9343fb703ca2b6b11b5c2369e19efcaa5f62"
] | [
"examples/tf2-dvc-vit.py"
] | [
"# coding: utf-8\n\n# # Dogs-vs-cats classification with ViT\n# \n# In this notebook, we'll finetune a [Vision Transformer]\n# (https://arxiv.org/abs/2010.11929) (ViT) to classify images of dogs\n# from images of cats using TensorFlow 2 / Keras and HuggingFace's\n# [Transformers](https://github.com/huggingface/tran... | [
[
"tensorflow.keras.callbacks.TensorBoard",
"tensorflow.data.Dataset.from_tensor_slices",
"tensorflow.keras.optimizers.Adam",
"tensorflow.keras.losses.BinaryCrossentropy"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
avalenzu/manipulation | [
"13cacd5731557c3f2d844e7d72d8c40af2762821"
] | [
"manipulation/test/test_open3d_utils.py"
] | [
"\"\"\"\nAn example of converting from the Drake RGBD image representation to the\nOpen3D RGBD image representation.\n\"\"\"\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport open3d as o3d\nimport meshcat\n\nfrom manipulation.mustard_depth_camera_example import MustardExampleSystem\nfrom manipulation.m... | [
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.title"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mynameisjanus/irisNN | [
"b4b15ba0b952c75bc7034049c822edb7217f7cd6"
] | [
"irisneuralnet.py"
] | [
"# Neural Network\nimport numpy as np\n\ndef relu(x):\n return np.maximum(0, x)\n\ndef drelu(x):\n return 1 * (x > 0)\n\ndef output_layer_activation(x):\n return x\n\ndef output_layer_activation_derivative(x):\n return 1\n \nclass NeuralNet():\n \n def __init__(self,... | [
[
"numpy.maximum",
"numpy.ones",
"numpy.vectorize",
"numpy.mean",
"numpy.outer",
"numpy.zeros",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
albertoa/keras_zmq_scaling_data_generators | [
"bfd68e1a28de72c8dbb6669d20f97fe9cdfdeade"
] | [
"testsink_zmq.py"
] | [
"\n# Copyright (c) 2018 Alberto Alonso\n# MIT license. See LICENSE file for details\n\n# Import standard python modules\nimport numpy as np\nimport argparse\nimport json\nimport os\nimport sys\nimport zmq\nimport cv2\nimport time\n\n# Get the base configuration for the project\nimport cfg.base as cfg\n\n# Default p... | [
[
"numpy.hstack",
"numpy.zeros",
"numpy.vstack",
"numpy.frombuffer"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Fudan-Autonomous-Driving-Perception/HRNet-Semantic-Segmentation | [
"37a403e68047b29b03d564ee5c6df6c27b332c64"
] | [
"lib/datasets/a2d2-split-mask.py"
] | [
"import os\n\nimport cv2\nimport numpy as np\nfrom PIL import Image\n\nimport torch\nfrom torch.nn import functional as F\n\nfrom .base_dataset import BaseDataset\n\nclass A2D2(BaseDataset):\n def __init__(self, \n root, \n list_path, \n num_samples=None, \n ... | [
[
"numpy.expand_dims",
"torch.zeros",
"torch.from_numpy",
"numpy.int",
"numpy.ceil",
"numpy.zeros_like",
"torch.FloatTensor",
"torch.nn.functional.interpolate",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kuredatan/suboptimal-buqo | [
"1bb892bfac6f895153cd34679f4d493a2920ce50"
] | [
"main.py"
] | [
"#coding:utf-8\n\nfrom parameters import selection_phi, noise, P, print_probas, print_image, get_image, g1, g2, l2, iota, N, alpha, compute_epsilon, compute_tau, characteristic\nfrom tools import estimation_MAP, projection_C, projection_S, in_S\nimport numpy as np\n\n#-------------- BUQO ALGORITHM\n\ndef main(x, si... | [
[
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SweepTest/HUXt | [
"0f5ad68aa6a88eff7921a5325aefa71be92af781"
] | [
"code/huxt_inputs.py"
] | [
"import httplib2\nimport urllib\nimport huxt as H\nimport os\nfrom pyhdf.SD import SD, SDC \nimport numpy as np\nimport astropy.units as u\nfrom scipy.io import netcdf\n\n\ndef get_MAS_boundary_conditions(cr=np.NaN, observatory='', runtype='', runnumber=''):\n \"\"\"\n A function to grab the Vr and Br bound... | [
[
"numpy.rot90",
"numpy.arcsin",
"numpy.fliplr",
"numpy.isnan",
"numpy.flipud",
"scipy.io.netcdf.netcdf_file",
"numpy.interp",
"numpy.exp"
]
] | [
{
"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": [... |
TomF98/torchphysics | [
"775d9aca71752a568f1fca972c958b99107f3b7c",
"775d9aca71752a568f1fca972c958b99107f3b7c"
] | [
"src/torchphysics/models/qres.py",
"tests/test_points.py"
] | [
"import torch\nimport torch.nn as nn\n\nfrom .model import Model\nfrom ..problem.spaces import Points\n\n\nclass Quadratic(nn.Module):\n \"\"\"Implements a quadratic layer of the form: W_1*x (*) W_2*x + W_1*x + b.\n Here (*) means the hadamard product of two vectors (elementwise multiplication).\n W_1, W_... | [
[
"torch.nn.Sequential",
"torch.nn.Parameter",
"torch.zeros",
"torch.nn.init.xavier_normal_",
"torch.nn.Tanh",
"torch.nn.Linear"
],
[
"torch.ones",
"torch.zeros",
"torch.neg",
"torch.equal",
"torch.tensor",
"torch.repeat_interleave",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hammerlab/stanity | [
"6c36abc207c4ce94f78968501dab839a56f35a41"
] | [
"stanity/psis.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"Pareto smoothed importance sampling (PSIS)\n\nThis module implements Pareto smoothed importance sampling (PSIS) and PSIS\nleave-one-out (LOO) cross-validation for Python (Numpy).\n\nIncluded functions\n------------------\npsisloo\n Pareto smoothed importance sampling leave-one-out... | [
[
"numpy.sqrt",
"numpy.squeeze",
"numpy.all",
"numpy.max",
"numpy.mean",
"numpy.negative",
"numpy.exp",
"numpy.where",
"numpy.divide",
"numpy.arange",
"numpy.subtract",
"numpy.finfo",
"numpy.copy",
"numpy.log1p",
"numpy.log",
"numpy.argsort",
"nump... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hash-ir/Facial-Keypoints-Detection | [
"7d1bf3559ec4d6283b7f07dec16327862e084a8d"
] | [
"data_loader.py"
] | [
"from torch.utils.data import Dataset, DataLoader\nimport matplotlib.image as mpimg\nimport pandas as pd\nimport os\nimport numpy as np\n\nclass FacialKeypointsDataset(Dataset):\n \"\"\"Face Landmarks dataset.\"\"\"\n\n def __init__(self, csv_file, transform=None):\n self.key_pts_frame = pd.read_csv(cs... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
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