repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list |
|---|---|---|---|---|
MridulS/REMARK | [
"65544dfca901d0ac2dad928cf08e3a8530422e41"
] | [
"REMARKs/EndogenousRetirement/Endogenous-Retirement.py"
] | [
"# -*- coding: utf-8 -*-\n# ---\n# jupyter:\n# jupytext:\n# cell_metadata_filter: collapsed\n# formats: ipynb,py:light\n# rst2md: false\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.4'\n# jupytext_version: 1.2.1\n# kernelspec:\n# ... | [
[
"numpy.divide",
"numpy.dot",
"matplotlib.pyplot.xlim",
"numpy.zeros",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.plot",
"numpy.copy",
"numpy.seterr",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.legend",
"matplotlib.pyplot.title",
... |
esennesh/categorical_bpl | [
"fa5a9d7a1e68ad4301905ab4798c020b562d03e9"
] | [
"base/base_trainer.py"
] | [
"import torch\nfrom abc import abstractmethod\nfrom numpy import inf\nfrom logger import TensorboardWriter\n\n\nclass BaseTrainer:\n \"\"\"\n Base class for all trainers\n \"\"\"\n def __init__(self, model, metric_ftns, optimizer, config):\n self.config = config\n self.logger = config.get_... | [
[
"torch.device",
"torch.save",
"torch.cuda.device_count",
"torch.load",
"torch.nn.DataParallel"
]
] |
RebekkalPangras/IDV_MaskRCNN_Image_Segmentation | [
"0c1e5b290fc0c7dd6937ac80ad5e5aca236016b0"
] | [
"mrcnn/visualize.py"
] | [
"\"\"\"\nMask R-CNN\nDisplay and Visualization Functions.\n\nCopyright (c) 2017 Matterport, Inc.\nLicensed under the MIT License (see LICENSE for details)\nWritten by Waleed Abdulla\n\"\"\"\n\nimport os\nimport sys\nimport logging\nimport random\nimport itertools\nimport colorsys\n\nimport numpy as np\nfrom skimage... | [
[
"numpy.random.choice",
"numpy.random.rand",
"numpy.where",
"matplotlib.patches.Rectangle",
"numpy.concatenate",
"matplotlib.pyplot.subplots",
"numpy.arange",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.subplot",
"matplotlib.lines.Line2... |
tylercroberts/pyvalet | [
"e6f63701829e5d27e2300275c591a7e32d7333d1"
] | [
"pyvalet/interpreter.py"
] | [
"\"\"\"\nInterpreter module for `pyvalet`\n\nThis module defines the base class, and concrete implementation for our API wrapper.\n\"\"\"\nimport re\nimport requests\nimport pandas as pd\nfrom io import StringIO\nfrom pathlib import Path\nfrom typing import Tuple, Any, Dict, List\nfrom requests.exceptions import Ti... | [
[
"pandas.DataFrame",
"pandas.read_csv"
]
] |
gcambara/cape | [
"c18c3c5e33f24a85506f30c399bf88b45f8a1787"
] | [
"test/test_cape2d.py"
] | [
"import torch\nfrom cape import CAPE2d\n\ndef test_cape2d():\n pos_emb = CAPE2d(d_model=512, max_global_shift=0.0, max_local_shift=0.0,\n max_global_scaling=1.0, batch_first=False)\n\n print(\"Checking correct dimensionality input/output (16x16) for batch_size = False...\")\n exp_shape ... | [
[
"torch.zeros",
"torch.linspace",
"torch.randn"
]
] |
Ren-Research/darts | [
"5cd5fd4dd8c13528ea9b76a1dbd63654ff41b10f"
] | [
"cnn/test.py"
] | [
"import os\nimport sys\nimport glob\nimport numpy as np\nimport torch\nimport utils\nimport logging\nimport argparse\nimport torch.nn as nn\nimport genotypes\nimport torch.utils\nimport torchvision.datasets as dset\nimport torch.backends.cudnn as cudnn\n\nfrom torch.autograd import Variable\nfrom model import Netwo... | [
[
"torch.cuda.manual_seed",
"numpy.random.seed",
"torch.autograd.Variable",
"torch.save",
"torch.manual_seed",
"torch.cuda.set_device",
"torch.cuda.is_available",
"torch.utils.data.DataLoader",
"torch.nn.CrossEntropyLoss"
]
] |
leqiao-1/OLive | [
"62e395034681c43cf1144ae209cfcd5b2b6bd178"
] | [
"docker-images/onnx-converter/src/check_model.py"
] | [
"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT License.\nimport onnxruntime\nimport onnx\nimport json\nimport numpy as np\nfrom onnx import helper, numpy_helper\nimport os\nimport tensorflow as tf\nfrom pathlib import Path\nimport torch\n\n\ndef caffeRunner(model_path, inputs_path):\n # TODO: ... | [
[
"numpy.testing.assert_almost_equal",
"tensorflow.Graph",
"tensorflow.Session",
"tensorflow.GraphDef",
"tensorflow.import_graph_def",
"tensorflow.train.import_meta_graph",
"tensorflow.gfile.GFile",
"sklearn.externals.joblib.load",
"torch.load",
"tensorflow.saved_model.loader... |
raviv/torchcv | [
"f4eabeb27dbba5c9bdcf83d03776bffa34995666"
] | [
"models/det/layers/rpn_target_assigner.py"
] | [
"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n# Author: Donny You(youansheng@gmail.com)\n\n\nimport numpy as np\nimport torch\n\nfrom utils.helpers.det_helper import DetHelper\nfrom models.det.layers.fr_priorbox_layer import FRPriorBoxLayer\n\n\nclass RPNTargetAssigner(object):\n \"\"\"Compute prior boxes coor... | [
[
"torch.cat",
"torch.stack",
"numpy.random.choice",
"torch.zeros_like",
"torch.log",
"torch.sum"
]
] |
cversteeg/osim-rl | [
"0702c2b8d99e40e4a27a4b5ce8891fc8f1788944"
] | [
"osim/env/osim.py"
] | [
"import math\nimport numpy as np\nimport os\nfrom .utils.mygym import convert_to_gym\nimport gym\nimport opensim\nimport random\nimport akro\n\n## OpenSim interface\n# The amin purpose of this class is to provide wrap all \n# the necessery elements of OpenSim in one place\n# The actual RL environment then only need... | [
[
"numpy.concatenate",
"numpy.square",
"numpy.array",
"numpy.isnan",
"numpy.sin",
"numpy.linalg.norm",
"numpy.cos",
"numpy.clip",
"numpy.ndarray.flatten"
]
] |
intLyc/Undergraduate-Courses | [
"8eba20b6b8c43d4eee0af4b0ebca68d708f27aa9"
] | [
"0DeepLearning/test_mnist.py"
] | [
"import numpy as np\nimport tensorflow as tf\nfrom PIL import Image\nfrom keras import backend as K\n\ndef load_data(path='./mnist.npz'):\n f = np.load(path)\n x_train = f['x_train']\n f.close()\n return (x_train)\n\nx_train = load_data()\n\nindex=15 #选择第n个数据\n\nim = Image.fromarray(x_train[index])\nim... | [
[
"tensorflow.keras.models.load_model",
"numpy.load"
]
] |
PinsaraKWeerasinghe/pygsom | [
"fdae01ae8239d240107ec99c360bfa77a1121d84",
"fdae01ae8239d240107ec99c360bfa77a1121d84"
] | [
"gsom/GSOM.py",
"gsom/visualize.py"
] | [
"import numpy as np\nimport pandas as pd\nfrom scipy.spatial import distance\nimport scipy\nfrom tqdm import tqdm\nimport math\n\ndata_filename = \"example/data/zoo.txt\".replace('\\\\', '/')\n\n\nclass GSOM:\n\n def __init__(self, spred_factor, dimensions, distance='euclidean', initialize='random', learning_rat... | [
[
"numpy.random.rand",
"numpy.zeros",
"numpy.random.seed",
"pandas.DataFrame",
"numpy.exp",
"pandas.read_csv",
"scipy.spatial.distance.cdist"
],
[
"matplotlib.cm.get_cmap",
"numpy.random.seed",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.subplots",
"matplotlib... |
arodiss/keras-contrib | [
"8ec9c77382120ac0cbd5ea55c6efda48d27af33a"
] | [
"examples/cifar10_densenet.py"
] | [
"'''\nTrains a DenseNet-40-12 model on the CIFAR-10 Dataset.\n\nGets a 94.84% accuracy score after 100 epochs.\n'''\nfrom __future__ import absolute_import\nfrom __future__ import print_function\nfrom __future__ import division\n\nimport numpy as np\n\nfrom keras import backend as K\nfrom keras.callbacks import Mod... | [
[
"numpy.sqrt"
]
] |
koson/uTensor | [
"d486d0be89ee98762d4e84b7614a30902413fdb5"
] | [
"python/test_scripts/gen_avgpool.py"
] | [
"import tensorflow as tf\nimport jinja2\n\noutput_file = \"test_avgpool.cpp\"\nconst_file = \"constants_avgpool.hpp\"\n\nPADDING = \"VALID\"\n\nconst_str = \"\"\"\nstatic const float s_in_{{ test_name }}[{{ input_size }}] = { {% for x in ref_in %} {{ x }}{{ \",\" if not loop.last }} {% endfor %} };\nstatic const fl... | [
[
"tensorflow.random.normal",
"tensorflow.nn.avg_pool2d"
]
] |
aljabr0/degraph | [
"ac44a1c979da7605ed424853e7fef8ffa8790b27"
] | [
"degraph/model.py"
] | [
"from .math import reduce_any_nan\nfrom .utils import export\nfrom .types import TensorRef\n\nimport tensorflow as tf\nimport pandas as pd\nfrom tqdm import tqdm\n\nfrom typing import Tuple, Sequence, List, Optional, Union, Callable, Dict, Any\nimport abc\nfrom datetime import datetime\nfrom itertools import chain\... | [
[
"pandas.DataFrame.from_records",
"tensorflow.summary.create_noop_writer",
"tensorflow.GradientTape",
"tensorflow.summary.histogram",
"tensorflow.summary.experimental.set_step",
"tensorflow.name_scope"
]
] |
JKamlah/ocromore | [
"f9d302eff234478c98e03740adf6bbeeafe7db8d"
] | [
"test_code/test_pandas_normalization_experiment.py"
] | [
"\"\"\"\nThis part of experiment which checks and normalizes\nconfidences of outputs in different engines.\nThis shows how histograms can look like to determine\nscalings. (main is test_pandas_normalization_experiment_database.py)\n\n\nThis could be used in future implementations to improve\nresults, at the moment ... | [
[
"numpy.random.lognormal",
"numpy.random.binomial",
"numpy.random.seed",
"matplotlib.pyplot.title",
"matplotlib.pyplot.suptitle",
"numpy.random.vonmises",
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplots_adjust",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.subplot"
]... |
songheony/rethinking-network-pruning | [
"f3cf85cbab20b4387f692ffae8901a9229161144"
] | [
"imagenet/regression-pruning/main_B.py"
] | [
"import argparse\nimport numpy as np\nimport os\nimport shutil\nimport time\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.parallel\nimport torch.backends.cudnn as cudnn\nimport torch.distributed as dist\nimport torch.optim\nimport torch.utils.data\nimport torch.utils.data.distributed\nimport torchvision.t... | [
[
"torch.distributed.init_process_group",
"torch.no_grad",
"torch.nn.parallel.DistributedDataParallel",
"torch.utils.data.DataLoader",
"torch.utils.data.distributed.DistributedSampler",
"torch.load",
"torch.nn.CrossEntropyLoss",
"torch.nn.DataParallel"
]
] |
pyobs/pyobs-core | [
"e3401e63eb31587c2bc535f7346b7e4ef69d64ab"
] | [
"pyobs/utils/focusseries/photometry.py"
] | [
"from typing import Tuple, Dict, List\n\nimport numpy as np\nimport logging\nfrom pyobs.object import get_object\n\nfrom pyobs.images.processors.detection import SourceDetection\nfrom .base import FocusSeries\nfrom pyobs.utils.curvefit import fit_hyperbola\nfrom pyobs.images import Image\n\n\nlog = logging.getLogge... | [
[
"numpy.median",
"numpy.std",
"numpy.max",
"numpy.min"
]
] |
Crisreyda/AudioVisualCrowdCounting | [
"b958d7c79edf92097f44722b3f4fddc3ed43238a"
] | [
"datasets/SHHA/loading_data.py"
] | [
"import torchvision.transforms as standard_transforms\nfrom torch.utils.data import DataLoader\nimport misc.transforms as own_transforms\nfrom .SHHA import SHHA\nfrom .setting import cfg_data \nimport torch\nimport random\n\n\ndef get_min_size(batch):\n\n min_ht = cfg_data.TRAIN_SIZE[0]\n min_wd = cfg_data.TR... | [
[
"torch.utils.data.DataLoader"
]
] |
rmvanhees/pys5p | [
"3b74f5e13ed39e00bc965c3d858a4daeecff5bac"
] | [
"src/pys5p/tol_colors.py"
] | [
"\"\"\"\nThis file is part of pyS5p\n\nhttps://github.com/rmvanhees/pys5p.git\n\nDefinition of colour schemes for lines and maps that also work for colour-blind\npeople. See https://personal.sron.nl/~pault/ for background information and\nbest usage of the schemes.\n\nReference\n---------\n https://personal.sron.... | [
[
"matplotlib.colors.to_rgba_array",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.show",
"matplotlib.colors.LinearSegmentedColormap",
"numpy.linspace",
"numpy.vstack",
"matplotlib.colors.LinearSegmentedColormap.from_list"
]
] |
merang/Deep-Learning-Experiments | [
"c53b7ded52631996e560b33cdf30ce915b18d079"
] | [
"Experiments/Tensorflow/RNN/rnn_words.py"
] | [
"'''\nA Recurrent Neural Network (LSTM) implementation example using TensorFlow..\nNext word prediction after n_input words learned from text file.\nA story is automatically generated if the predicted word is fed back as input.\n\nAuthor: Rowel Atienza\nProject: https://github.com/roatienza/Deep-Learning-Experiment... | [
[
"numpy.array",
"tensorflow.nn.softmax_cross_entropy_with_logits",
"numpy.reshape",
"numpy.zeros",
"tensorflow.argmax",
"tensorflow.train.RMSPropOptimizer",
"tensorflow.Session",
"tensorflow.matmul",
"tensorflow.contrib.rnn.BasicLSTMCell",
"tensorflow.reshape",
"tensorfl... |
zlab-foss/custom-pose-classifier | [
"b790f0c592f177c02d5268bc6e1001de083fd585"
] | [
"src/webcam_test_model.py"
] | [
"import cv2\nimport numpy as np\nimport tensorflow.lite as tflite\n\n\n\nfont = cv2.FONT_HERSHEY_SIMPLEX\nbottomLeftCornerOfText = (20,160)\nfontScale = .9\nfontColor = (255 ,0,100)\nthickness = 2\nlineType = 1\n\n\n\nCLASSIFIER_LABELS = (\"A\",... | [
[
"numpy.array",
"numpy.copy",
"tensorflow.lite.Interpreter",
"numpy.argmax",
"numpy.squeeze"
]
] |
PhiladelphiaController/gun-violence | [
"15749ccbf980d4035b3ed2b4d4217f8f045cbba6"
] | [
"gun_violence/charts/prices_near_homicides.py"
] | [
"\"\"\"\nA line chart showing the sale price per sq. ft. relative to the \ncitywide median, as a function of the distance from \n\"\"\"\nfrom .. import datasets as gv_data\nfrom ..modeling import get_sale_price_psf_from_homicide\nfrom . import default_style, palette, digital_standards\nimport numpy as np\nfrom matp... | [
[
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.style.context",
"numpy.arange",
"matplotlib.transforms.blended_transform_factory"
]
] |
asmeurer/sparse | [
"2f72c5c691e5709a0d23e9e99fc3ff5c7dd2f7f3"
] | [
"sparse/tests/test_coo.py"
] | [
"import operator\nimport pickle\nimport sys\n\nimport numpy as np\nimport pytest\nimport scipy.sparse\nimport scipy.stats\n\nimport sparse\nfrom sparse import COO\nfrom sparse.utils import assert_eq, random_value_array\n\n\n@pytest.fixture(scope='module', params=['f8', 'f4', 'i8', 'i4'])\ndef random_sparse(request)... | [
[
"numpy.int8",
"numpy.isclose",
"numpy.random.rand",
"numpy.dot",
"numpy.random.choice",
"numpy.copy",
"numpy.where",
"numpy.tensordot",
"numpy.random.random",
"numpy.issubdtype",
"numpy.broadcast_to",
"numpy.dtype",
"numpy.full_like",
"numpy.concatenate",
... |
meteoDaniel/wetterdienst | [
"106a2fa9f887983281a6886c15bb3a845850dfb7"
] | [
"tests/provider/dwd/observation/test_api_stations_geo.py"
] | [
"# -*- coding: utf-8 -*-\n# Copyright (c) 2018-2021, earthobservations developers.\n# Distributed under the MIT License. See LICENSE for more info.\nfrom datetime import datetime\n\nimport numpy as np\nimport pandas as pd\nimport pytest\nimport pytz\nfrom pandas._libs.tslibs.timestamps import Timestamp\nfrom pandas... | [
[
"numpy.array",
"pandas._libs.tslibs.timestamps.Timestamp",
"pandas.DataFrame",
"pandas._testing.assert_frame_equal",
"pandas.Series"
]
] |
ihgazni2/sledgehammer4nut | [
"397ace55fc0113bcb2e8375ede055b469b5ef029"
] | [
"sldghmmr4nut/ndarr/convert.py"
] | [
"import elist.elist as elel\nimport estring.estring as eses\nimport numpy as np\nfrom sldghmmr4nut.lines import fmt\n\ndef txt2ndarr(s,**kwargs):\n if('line_sp' in kwargs):\n line_sp = kwargs['line_sp']\n else:\n line_sp = fmt.LINE_SP\n if('null' in kwargs):\n null = kwargs['line_sp']... | [
[
"numpy.array"
]
] |
tiwarias/django-ex | [
"5cea6c4f426fa1a5980b0bb2001aecddc26daef7"
] | [
"translate/views - Copy.py"
] | [
"from django.shortcuts import render\nfrom django.http import HttpResponse, JsonResponse\nfrom django.contrib.auth.decorators import login_required\nfrom django.utils.decorators import method_decorator\nimport os, requests, uuid, json\nimport pandas as pd\nimport numpy as np\nfrom io import BytesIO,StringIO\nimport... | [
[
"pandas.DataFrame",
"pandas.read_csv",
"pandas.read_excel",
"pandas.ExcelWriter"
]
] |
saic-vul/style-people | [
"a48418ace25b99a50801a54a9e282cd986c305ba"
] | [
"utils/common.py"
] | [
"import numpy as np\nimport random\nimport os\nimport time\nimport importlib\n\nimport cv2\nfrom PIL import Image\n\nimport math\nimport pickle\n\nimport torch\nfrom torch import distributed as dist\nfrom torch.utils.data.sampler import Sampler\n\n\ndef load_module(module_type, module_name):\n m = importlib.impo... | [
[
"torch.distributed.get_world_size",
"torch.cat",
"torch.utils.data.RandomSampler",
"torch.stack",
"numpy.mean",
"numpy.cos",
"torch.distributed.reduce",
"torch.sum",
"numpy.concatenate",
"numpy.sin",
"torch.is_tensor",
"torch.FloatTensor",
"torch.manual_seed",
... |
phamtrongthang123/AlphaPose_infer_folder_video | [
"5bb9560a2982c3f6ba4ec6ae5b6d000f9a7b3c64"
] | [
"alphapose/datasets/coco_wholebody.py"
] | [
"# -----------------------------------------------------\r\n# Copyright (c) Shanghai Jiao Tong University. All rights reserved.\r\n# Written by Haoyi Zhu and Hao-Shu Fang\r\n# -----------------------------------------------------\r\n\r\n\"\"\"Coco WholeBody (133 points) Human keypoint dataset.\"\"\"\r\nimport os\r\... | [
[
"numpy.square",
"numpy.sum",
"numpy.array",
"numpy.zeros"
]
] |
INT-NIT/BEP032tools | [
"3eee35af4c881e4d03bb22f0161ef349c5b564c0"
] | [
"bep032tools/generator/BEP032Templater.py"
] | [
"import shutil\nimport argparse\nimport re\n\nimport bep032tools.validator.BEP032Validator\n\ntry:\n import pandas as pd\n\n HAVE_PANDAS = True\nexcept ImportError:\n HAVE_PANDAS = False\nfrom bep032tools.validator.BEP032Validator import build_rule_regexp\nfrom bep032tools.rulesStructured import RULES_SET\... | [
[
"pandas.DataFrame",
"pandas.read_csv"
]
] |
gamerDecathlete/NormalizingFlowsNMT | [
"5bff15058cad26c52002c026daa906b5aa8b894b"
] | [
"models/VariationalEncoderDecoder.py"
] | [
"import torch\nimport torch.nn as nn\nfrom models.NormalizingFlowsEncoderDecoder import NormalizingFlowsEncoderDecoder\nfrom models.layers.GaussianLayer import GaussLayer\nimport pyro\nfrom pyro.distributions import Normal, Categorical, Bernoulli\nfrom pyro.nn import AutoRegressiveNN\nfrom pyro import poutine\n\n\n... | [
[
"torch.zeros_like",
"torch.cat",
"torch.sum"
]
] |
alfred100p/VC-R-CNN | [
"5b01e44618c406592184275b734d3fbd3f11234c"
] | [
"vc_rcnn/modeling/roi_heads/box_head/roi_box_predictors.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\nfrom vc_rcnn.modeling import registry\nfrom torch import nn\nimport torch.nn.functional as F\nimport torch\nimport numpy as np\n\n\n@registry.ROI_BOX_PREDICTOR.register(\"FastRCNNPredictor\")\nclass FastRCNNPredictor(nn.Module):\n def __in... | [
[
"torch.nn.Linear",
"torch.isnan",
"torch.nn.init.constant_",
"numpy.load",
"torch.nn.init.normal_",
"torch.nn.functional.softmax",
"torch.nn.AdaptiveAvgPool2d"
]
] |
wasit7/book_pae | [
"c53cca3342593a2769f398db9bf969515d3de117"
] | [
"pae/forcast/src/create_dfmore20_dropNanResult.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun May 29 20:49:37 2016\n\n@author: Methinee\n\"\"\"\nimport pandas as pd\n\ndf_file = pd.read_csv('../data/df_dropSub_less20.csv',delimiter=\",\", skip_blank_lines = True, \n error_bad_lines=False)\n \ndrop_naResult = df_file[df_file['4R... | [
[
"pandas.read_csv"
]
] |
fatihyildiz-cs/detectron2 | [
"700b1e6685ca95a60e27cb961f363a2ca7f30d3c"
] | [
"projects/PanopticFCN_cityscapes/panopticfcn/gt_generate.py"
] | [
"import numpy as np\nimport torch\nimport torch.nn.functional as F\n\nfrom .utils import multi_apply\n\n\nclass GenerateGT(object):\n \"\"\"\n Generate ground truth for Panoptic FCN.\n \"\"\"\n def __init__(self, cfg):\n self.device = torch.device(cfg.MODEL.DEVICE)\n self.ignore_val = cfg.... | [
[
"torch.zeros",
"torch.device",
"torch.nn.functional.one_hot",
"torch.sqrt",
"torch.stack",
"torch.min",
"torch.max",
"torch.no_grad",
"numpy.exp",
"torch.ones",
"numpy.finfo",
"torch.zeros_like",
"torch.Tensor",
"torch.clamp_min"
]
] |
avivajpeyi/advent_of_code | [
"7824e15d7825fdb7dee87074a6f036e1eff1142c"
] | [
"2020/day3/day3.py"
] | [
"import subprocess\nimport sys\nimport time\n\nimport numpy as np\n\nANIMATION_TIME = 0.1\n\n\nclass Forest:\n def __init__(self, file):\n self.file = file\n self.grid = []\n self.repeat_idx = 0\n self.num_rows = 0\n self.r = dict(x=0, y=0)\n self.reset()\n\n def rese... | [
[
"numpy.product"
]
] |
jkschluesener/ExactHistogramSpecification | [
"ee95ee7e8a672e510aac0e67b6780722503e4b40"
] | [
"doc/validate.py"
] | [
"from ExactHistogramSpecification import ExactHistogramMatcher # This package\nfrom PIL import Image # or use your preferred way of reading image data\nimport numpy as np # numerical stack\nimport matplotlib.pylab as plt # plotting\nimport seaborn as sns # fancier plotting\n\n# Load images\nreference_image = np.arr... | [
[
"matplotlib.pylab.subplots",
"matplotlib.pylab.tight_layout"
]
] |
zmm18/featurizer | [
"bb30ab8656af4bdd85b20bb85b481aeaef60c528"
] | [
"featurizer/functors/journalhub.py"
] | [
"# -*- coding: utf-8 -*-\n# Copyright StateOfTheArt.quant. \n#\n# * Commercial Usage: please contact allen.across@gmail.com\n# * Non-Commercial Usage:\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 obtai... | [
[
"torch.randn"
]
] |
AgnezIO/agnez | [
"b923b09b3c124ad41e233b45c63f2749b5f31617"
] | [
"agnez/sandbox/gaborfitting.py"
] | [
"import numpy as np\nimport theano\nimport theano.tensor as tensor\n\nfrom keras.layers.core import Layer\nfrom keras.models import Sequential\nfrom keras import initializations\nfrom keras.optimizers import RMSprop\n\nfloatX = theano.config.floatX\n\n\nclass GaborFit(Layer):\n def __init__(self, input_dim, outp... | [
[
"numpy.linspace",
"numpy.sqrt"
]
] |
agdenadel/CSCI2470-Final-Project | [
"5334ead1b6aadfb026345de61140dff6ca49a9c4"
] | [
"project/visualization/count_labels.py"
] | [
"import csv \nimport matplotlib.pyplot as plt\nimport numpy as np\n\nf = open('train.csv')\ncsv = csv.reader(f, delimiter=' ')\nlines = [row for row in csv]\nfor line in lines:\n line[0] = (line[0].split(','))[1]\nlines = lines[1:]\n\nlabels = ['0. Nucleoplasm',\n '1. Nuclear membrane',\n '2.... | [
[
"numpy.array",
"numpy.zeros",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.pie",
"matplotlib.pyplot.title",
"matplotlib.pyplot.legend",
"numpy.arange",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.axis"
]
] |
yilingjia/FairExp | [
"f0aaff805f61ad8066dd5e69e62d9f55bd7869d0"
] | [
"algorithms/DBGD/tddbgd.py"
] | [
"# -*- coding: utf-8 -*-\n\nimport sys\nimport os\n\nsys.path.append(os.path.join(os.path.dirname(__file__), '../..'))\nimport numpy as np\nimport utils.rankings as rnk\nfrom models.linearmodel import LinearModel\nfrom algorithms.basiconlineranker import BasicOnlineRanker\nfrom multileaving.TeamDraftMultileave impo... | [
[
"numpy.dot"
]
] |
emilbaekdahl/RelationPrediction | [
"9bde31d7535613cecf85aec883a9d7b3e151e612"
] | [
"code/optimization/optimize.py"
] | [
"import numpy as np\nimport tensorflow as tf\nimport theano\nfrom theano import tensor as T\n\nimport shared.algorithms as shared_algorithms\nimport tensorflow_backend.algorithms as tensorflow_algorithms\nimport theano_backend.algorithms as theano_algorithms\nfrom abstract import BaseOptimizer\n\n\"\"\"\nPartial cl... | [
[
"tensorflow.initialize_all_variables",
"tensorflow.sigmoid",
"tensorflow.matmul",
"numpy.random.randn",
"tensorflow.nn.sigmoid_cross_entropy_with_logits",
"tensorflow.placeholder"
]
] |
mvcisback/magnumSTL | [
"e48d641118bc9c1fb28be2a38a55654441a78701"
] | [
"magnum/examples/rock_paper_scissors_spock.py"
] | [
"import stl\n\nfrom magnum import game as G\n\nimport numpy as np\n\nH = 1\n\nmodel = G.Model(\n dt=1,\n vars=G.Vars(state=(\"x\", \"y\"), input=(\"u\", ), env=(\"w\", )),\n dyn=G.Dynamics(\n A=np.array([[0, 0], [0, 0]]),\n B=np.array([60, 0]).reshape((2, 1)),\n C=np.array([0, 60]).res... | [
[
"numpy.array"
]
] |
17barbieri/pytorch-cnn-visualizations | [
"2ee8e7102277f21b5b89b0fe3cabfdfcb44dd684"
] | [
"src/gradcam.py"
] | [
"\"\"\"\nCreated on Thu Oct 26 11:06:51 2017\n\n@author: Utku Ozbulak - github.com/utkuozbulak\n\"\"\"\nimport os\nimport pandas as pd\nimport argparse\nfrom PIL import Image\nimport numpy as np\nimport torch\n\nfrom misc_functions import get_example_params, save_class_activation_images, model_selection\n\n\nclass ... | [
[
"numpy.max",
"numpy.uint8",
"pandas.DataFrame",
"numpy.ones",
"numpy.min",
"numpy.mean",
"pandas.concat",
"numpy.maximum"
]
] |
malook665/model-optimization | [
"f78f9740bb317d8caf847a1d3ccc981064f0d23f"
] | [
"tensorflow_model_optimization/python/core/quantization/keras/quantize_wrapper.py"
] | [
"# Copyright 2019 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.keras.utils.generic_utils.serialize_keras_object",
"tensorflow.python.keras.initializers.Constant",
"tensorflow.python.keras.backend.learning_phase"
]
] |
wfanzju/FBGEMM | [
"a58ab65e27da27949ca0125683a6f83e1e464064"
] | [
"fbgemm_gpu/test/permute_pooled_embedding_modules_test.py"
] | [
"# Copyright (c) Meta Platforms, Inc. and 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 inspect\nimport sys\nimport unittest\nfrom typing import Tuple\n\nimport fbgemm_gpu\nimport torc... | [
[
"torch.nn.Linear",
"torch.cat",
"torch.nn.EmbeddingBag",
"torch.tensor",
"torch.testing.assert_close",
"torch.nn.init.uniform_",
"torch.randn"
]
] |
Hira63S/PseudoLidar-Argo | [
"668c12e596c40b221adb2ded70c846d29b2b1745"
] | [
"assignment1_colab/assignment1/cs231n/classifiers/k_nearest_neighbor.py"
] | [
"from builtins import range\nfrom builtins import object\nimport numpy as np\nfrom past.builtins import xrange\n\n\nclass KNearestNeighbor(object):\n \"\"\" a kNN classifier with L2 distance \"\"\"\n\n def __init__(self):\n pass\n\n def train(self, X, y):\n \"\"\"\n Train the classifie... | [
[
"numpy.argsort",
"numpy.zeros"
]
] |
noyahoch/Marabou | [
"03eb551498287e5372d462e3c2ad4fcc3210a5fa"
] | [
"NNet/python/nnet.py"
] | [
"from math import inf\n\nimport numpy as np\nimport copy\nclass NNet():\n \"\"\"\n Class that represents a fully connected ReLU network from a .nnet file\n \n Args:\n filename (str): A .nnet file to load\n \n Attributes:\n numLayers (int): Number of weight matrices or bias vectors in... | [
[
"numpy.array",
"numpy.dot",
"numpy.zeros"
]
] |
wusi05/keras-2.2.2-learning | [
"c7b7328cc99fd5d7c298e57c6020043451d89a61"
] | [
"tests/keras/engine/test_topology.py"
] | [
"import pytest\nimport json\nimport numpy as np\n\nfrom keras.layers import Dense, Dropout, Conv2D, InputLayer\nfrom keras import layers\nfrom keras.engine import Input, Layer, saving, get_source_inputs\nfrom keras.models import Model, Sequential\nfrom keras import backend as K\nfrom keras.models import model_from_... | [
[
"numpy.testing.assert_allclose",
"numpy.array",
"numpy.zeros",
"numpy.ones",
"numpy.split",
"numpy.allclose",
"numpy.transpose",
"numpy.random.random"
]
] |
vwesselkamp/deepfake-fingerprint-atacks | [
"0befc913b081913255399d4264f09bce0d39cbcb"
] | [
"lib/GANDCTAnalysis/src/dataset.py"
] | [
"from pathlib import Path\n\nimport numpy as np\n\n\ndef _find_images(data_path):\n paths = list(Path(data_path).glob(\"*.jpeg\"))\n\n if len(paths) == 0:\n paths = list(Path(data_path).glob(\"*.jpg\"))\n if len(paths) == 0:\n paths = list(Path(data_path).glob(\"*.png\"))\n\n return paths\... | [
[
"tensorflow.train.Features",
"tensorflow.train.Int64List",
"tensorflow.io.FixedLenFeature",
"tensorflow.reshape",
"tensorflow.io.parse_single_example"
]
] |
bdevans/PyRhO | [
"3bb12210b68d84e3432911d8095f47fa40a1cbf4"
] | [
"pyrho/protocols.py"
] | [
"\"\"\"\nStimulation protocols to run on the opsin models\n * Neuro-engineering stimuli: ``step``, ``sinusoid``, ``chirp``, ``ramp``, ``delta``\n * Opsin-specific protocols: ``rectifier``, ``shortPulse``, ``recovery``.\n * The ``custom`` protocol can be used with arbitrary interpolation fuctions\n\"\"\"\n\... | [
[
"matplotlib.pyplot.annotate",
"numpy.isclose",
"matplotlib.pyplot.xlim",
"matplotlib.pyplot.GridSpec",
"numpy.cos",
"numpy.zeros_like",
"scipy.interpolate.InterpolatedUnivariateSpline",
"matplotlib.ticker.AutoMinorLocator",
"numpy.argmax",
"matplotlib.pyplot.tight_layout",
... |
saeta/flax | [
"fb4f2a455d4cae383f11d77707bf0d0f18a45e70",
"fb4f2a455d4cae383f11d77707bf0d0f18a45e70"
] | [
"examples/wmt/tokenizer.py",
"examples/nlp_seq/models.py"
] | [
"# Copyright 2021 The Flax Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or a... | [
[
"tensorflow.io.gfile.exists",
"tensorflow.io.gfile.copy",
"tensorflow.io.gfile.GFile",
"tensorflow.io.gfile.rename"
],
[
"numpy.sin",
"numpy.log",
"numpy.zeros",
"numpy.arange",
"numpy.cos"
]
] |
mwregan2/MiriTE | [
"6b65939454db60bf10619d50fcb5769d23598b76",
"6b65939454db60bf10619d50fcb5769d23598b76"
] | [
"datamodels/tests/test_exposure_model.py",
"datamodels/miri_badpixel_model.py"
] | [
"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n\n\"\"\"\n\nModule test_exposure_model - Contains the unit tests for the classes\nin the datamodels.miri_exposure_model module.\n\n:History:\n\n07 Mar 2013: Created.\n29 Jul 2013: stats() method added.\n14 Aug 2013: Updated test to include groupdq and pixeldq\n20 Jun ... | [
[
"numpy.array",
"numpy.zeros",
"numpy.ones",
"numpy.allclose",
"numpy.all"
],
[
"numpy.array"
]
] |
enrique-lelodelarrea/entropy-graph-simulation | [
"d174890392da8347b393f3bffd51f01ca0d98d3c"
] | [
"graphsim/bipartite_graphs.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Apr 14 17:32:18 2021\n\n@author: Enrique\n\"\"\"\n\n''' Functions to sample bipartite graphs with prescribed\ndegree sequences, using the sequential algorithm. '''\n\nimport numpy as np\nfrom .sequential_algo_binary import seq_algo_binary\n\nd... | [
[
"numpy.concatenate",
"numpy.reshape",
"numpy.zeros",
"numpy.ones",
"numpy.eye"
]
] |
NunoEdgarGFlowHub/blaze | [
"eb146966791fdef931abe4ba02e30bb4c0cb6794"
] | [
"blaze/tests/test_interactive.py"
] | [
"import datetime\nimport pickle\nimport sys\nfrom types import MethodType\n\nimport dask.array as da\nfrom datashape import dshape\nfrom datashape.util.testing import assert_dshape_equal\nimport pandas as pd\nimport pandas.util.testing as tm\nimport pytest\nimport numpy as np\nfrom odo import into, append\nfrom odo... | [
[
"numpy.array",
"pandas.DataFrame",
"numpy.ones",
"pandas.util.testing.makeMixedDataFrame",
"pandas.util.testing.assert_series_equal",
"pandas.Timestamp.now",
"numpy.ndarray",
"pandas.Series"
]
] |
and-kul/catalyst | [
"51428d7756e62b9b8ee5379f38e9fd576eeb36e5"
] | [
"catalyst/contrib/datasets/movielens.py"
] | [
"import itertools\nimport os\n\nimport numpy as np\nimport scipy.sparse as sp\n\nimport torch\nfrom torch.utils.data import Dataset\n\nfrom catalyst.contrib.datasets.functional import download_and_extract_archive\n\n\nclass MovieLens(Dataset):\n \"\"\"\n MovieLens data sets were collected by the GroupLens Res... | [
[
"torch.Size",
"torch.FloatTensor",
"torch.save",
"torch.LongTensor",
"scipy.sparse.lil_matrix",
"numpy.vstack"
]
] |
GeoAI4EO/GeoData4EO | [
"8da7df7fd8375efc3c584c6603622060384fe906"
] | [
"Dataset4EO/transforms/functional/_misc.py"
] | [
"from typing import Optional, List\n\nimport PIL.Image\nimport torch\nfrom torch import Tensor\nfrom torchvision.transforms import functional_tensor as _FT\nfrom torchvision.transforms.functional import pil_to_tensor, to_pil_image\n\n\ndef normalize(tensor: Tensor, mean: List[float], std: List[float], inplace: bool... | [
[
"torch.as_tensor"
]
] |
qiancao/BoneBox | [
"0d10dac7c93f16f0643bebc62c63be2f4bd099f6"
] | [
"bonebox/metrics/FeaturesRadiomics.py"
] | [
"# -*- coding: utf-8 -*-\n\n\"\"\"\n\nCompute radiomic features from images\n\nTODO: https://github.com/AIM-Harvard/pyradiomics/blob/master/examples/batchprocessing_parallel.py\n\n\"\"\"\n\nfrom __future__ import print_function\n\nimport numpy as np\nimport SimpleITK as sitk\nfrom radiomics import featureextractor\... | [
[
"numpy.concatenate",
"numpy.random.rand",
"numpy.zeros",
"numpy.ones",
"numpy.take",
"numpy.vstack"
]
] |
cristicmf/tensorflow | [
"7da613d8b8b30c6f7e951af5611bc0ae7508fd44"
] | [
"tensorflow/python/util/compat.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.util.tf_export.tf_export"
]
] |
tomsep/robot-lego-assembler | [
"600adad1a8d3f80bb248be845b822b4103952d4b"
] | [
"live_predict.py"
] | [
"import cv2 as cv\nimport tensorflow as tf\nimport yaml\nimport time\nimport numpy as np\n\nfrom legoassembler.communication import Client\nfrom legoassembler.vision import remote_capture\nfrom legoassembler.utils import load_config\n\n\ntf.enable_eager_execution()\n\n\ndef load_colors(fname):\n with open(fname,... | [
[
"tensorflow.control_dependencies",
"tensorflow.convert_to_tensor",
"tensorflow.shape",
"tensorflow.concat",
"tensorflow.expand_dims",
"tensorflow.image.convert_image_dtype",
"tensorflow.reshape",
"numpy.shape",
"tensorflow.reverse",
"tensorflow.keras.models.load_model",
... |
Superar/SemOpinionS | [
"9b0b0f9050bb1f7d66b91d5804f89b2eea9fbc20"
] | [
"src/openie.py"
] | [
"import pandas as pd\n\n\nclass OpenIE(object):\n \"\"\"\n Class that represents the data triples from an Information Extraction tool.\n\n Attributes:\n triples (pandas.DataFrame): DataFrame contanining all triples with the following columns:\n sentence id, triple ... | [
[
"pandas.read_csv"
]
] |
jochenruland/stock_price_indicator | [
"5efed86c7094aadff9b2ebc2bb276365d1b93d51"
] | [
"app/data_wrangling/train_classifier.py"
] | [
"import sys\nfrom data_wrangling.process_data import StockDataAnalysis\nimport datetime as dt\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\nfrom sklearn import preprocessing\nfrom sklearn import linear_model\nfrom sklearn.ensemble import RandomForestRegressor\n\nfrom sklearn.metrics i... | [
[
"pandas.to_datetime",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.legend",
"sklearn.metrics.mean_absolute_percentage_error",
"matplotlib.pyplot.figure",
"sklearn.preprocessing.scale",
"sklearn.metrics.mean_absolute_error",
"matplotlib.pyplot.show",
... |
judithliatsf/reef | [
"6272786bd2a558dfa67bb391f1f3754b426e7915"
] | [
"data/loader.py"
] | [
"import numpy as np\nimport scipy\nimport json\nimport sklearn.cross_validation\n\nfrom scipy import sparse\nfrom sklearn.feature_extraction.text import CountVectorizer\n\ndef parse_file(filename):\n\n def parse(filename):\n movies = []\n with open(filename) as f:\n for line in f:\n ... | [
[
"numpy.array",
"numpy.random.seed",
"numpy.sum",
"numpy.shape",
"sklearn.feature_extraction.text.CountVectorizer",
"numpy.abs"
]
] |
sciamlab/osmnx | [
"b4b7835a766a0ea581a5b66f479f3cfa90ad1d1e"
] | [
"osmnx/plot.py"
] | [
"\"\"\"Plot spatial geometries, street networks, and routes.\"\"\"\n\nfrom pathlib import Path\n\nimport matplotlib.cm as cm\nimport matplotlib.colors as colors\nimport matplotlib.pyplot as plt\nimport networkx as nx\nimport numpy as np\nimport pandas as pd\n\nfrom . import graph\nfrom . import projection\nfrom . i... | [
[
"pandas.isnull",
"pandas.notnull",
"matplotlib.colors.to_hex",
"matplotlib.cm.get_cmap",
"matplotlib.pyplot.close",
"matplotlib.pyplot.subplots",
"matplotlib.colors.Normalize",
"numpy.cos",
"pandas.Series",
"matplotlib.pyplot.show",
"numpy.linspace"
]
] |
mkingopng/NBME_score_clinical_patient_notes | [
"4ca9816be2665d7585ab0d168376a340aa800088"
] | [
"dberta_large_config.py"
] | [
"\"\"\"\nlibraries\n\"\"\"\nimport os\nimport gc\nimport re\nimport ast\nimport sys\nimport copy\nimport json\nimport time\nimport math\nimport string\nimport pickle\nimport random\nimport joblib\nimport itertools\nimport warnings\nfrom IPython.core.display_functions import display\nimport scipy as sp\nimport numpy... | [
[
"torch.cuda.is_available",
"pandas.set_option"
]
] |
sunho/ray | [
"0ac8138b26cc66978df150c89ef291263f23c9a1"
] | [
"python/ray/tests/test_advanced_3.py"
] | [
"# coding: utf-8\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport glob\nimport logging\nimport os\nimport setproctitle\nimport shutil\nimport json\nimport sys\nimport socket\nimport subprocess\nimport tempfile\nimport time\n\nimport numpy as n... | [
[
"pandas.DataFrame",
"numpy.zeros"
]
] |
erikliland/TTK4550-Python | [
"58b2cd13a3fb38563a302c0f8380de3a5b0fccb1"
] | [
"pymht/models/polar.py"
] | [
"import numpy as np\nfrom .constants import *\nfrom .ais import R as R_AIS\nfrom .ais import Phi as A_AIS\nfrom .ais import C as C_AIS\n\nC_RADAR = np.array([[1.0, 0, 0, 0],\n [0, 1.0, 0, 0]], dtype=defaultType)\nH_radar = C_RADAR\n\np = 2.5**2 # Initial system state variance\nP0 = np.array(np.d... | [
[
"numpy.array",
"numpy.diag",
"numpy.power",
"numpy.eye"
]
] |
mkeshita/strawberryfields | [
"670ee5fc08cda9d641af9227472bd8b3e622d906"
] | [
"tests/backend/test_states_polyquad.py"
] | [
"# Copyright 2019 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 applica... | [
[
"scipy.stats.multivariate_normal.pdf",
"numpy.array",
"scipy.integrate.simps",
"numpy.angle",
"numpy.trace",
"numpy.zeros",
"numpy.exp",
"numpy.linalg.det",
"numpy.identity",
"numpy.allclose",
"numpy.prod",
"numpy.sinh",
"numpy.arange",
"numpy.sqrt",
"nu... |
danzelenak-usgs/TAP_Tool | [
"4ae3f4105df47efaa57283b25019e398b32de7b9"
] | [
"lcmap_tap/PlotFrame/plotwindow.py"
] | [
"\"\"\"Generate a matplotlib canvas and add it to a QWidget contained in a QMainWindow. This will provide the\ndisplay and interactions for the PyCCD plots.\"\"\"\n\nfrom lcmap_tap.logger import log, exc_handler\nfrom lcmap_tap.Plotting import POINTS, LINES\n\nimport sys\nimport datetime as dt\nimport numpy as np\... | [
[
"matplotlib.use",
"matplotlib.backends.backend_qt5agg.FigureCanvasQTAgg.__init__",
"matplotlib.backends.backend_qt5agg.NavigationToolbar2QT",
"numpy.take",
"matplotlib.backends.backend_qt5agg.FigureCanvasQTAgg.updateGeometry",
"matplotlib.backends.backend_qt5agg.FigureCanvasQTAgg.setSizePo... |
pesekon2/Mask_RCNN | [
"a40d1cf1ce1e4e501bc82399caf569e95ef67bd9"
] | [
"mrcnn/model.py"
] | [
"\"\"\"\nMask R-CNN\nThe main Mask R-CNN model implementation.\n\nCopyright (c) 2017 Matterport, Inc.\nLicensed under the MIT License (see LICENSE for details)\nWritten by Waleed Abdulla\n\"\"\"\n\nimport os\nimport random\nimport datetime\nimport re\nimport math\nimport logging\nfrom collections import OrderedDict... | [
[
"tensorflow.exp",
"numpy.random.choice",
"tensorflow.image.non_max_suppression",
"numpy.copy",
"tensorflow.unique",
"tensorflow.reshape",
"numpy.where",
"tensorflow.sqrt",
"numpy.sort",
"tensorflow.stack",
"tensorflow.control_dependencies",
"numpy.broadcast_to",
... |
sv641/km | [
"fc7c70bf691692a06f219c2c6a8f658a91e81ae6"
] | [
"demo/test-app/app.py"
] | [
"# USAGE\n# Start the server:\n# python app.py\n# Submit a request via cURL:\n# curl -X POST -F image=@dog.jpg 'http://localhost:5000/predict'\n\n# import the necessary packages\nfrom tensorflow.keras.applications import ResNet50\nfrom tensorflow.keras.preprocessing.image import img_to_array\nfrom tensorflo... | [
[
"tensorflow.keras.applications.ResNet50",
"tensorflow.keras.applications.imagenet_utils.preprocess_input",
"tensorflow.compat.v1.get_default_graph",
"tensorflow.compat.v1.Session",
"tensorflow.keras.applications.imagenet_utils.decode_predictions",
"tensorflow.keras.preprocessing.image.img_... |
yueyechen/cvpr20 | [
"69e6f9fd2393048bf48d7542a19ff0087b97d033",
"69e6f9fd2393048bf48d7542a19ff0087b97d033"
] | [
"PAD2/configs/config_20200301_4@2_ir_02.py",
"PAD1/resnet_new.py"
] | [
"import torch.nn as nn\n\nfrom easydict import EasyDict as edict\n\nfrom torchvision import transforms as trans\n# from common.transform import MotionBlur\nfrom nn.center_loss import CenterLoss\n\n\ncolor_channel_index = {\n 'L': 1,\n 'RGB': 3,\n 'YCbCr': 3,\n}\n\n\ndef get_config():\n conf = edict()\n\... | [
[
"torch.nn.SmoothL1Loss",
"torch.nn.CrossEntropyLoss"
],
[
"torch.nn.Linear",
"torch.nn.functional.softplus",
"torch.nn.MaxPool2d",
"torch.nn.Sequential",
"torch.nn.AvgPool2d",
"torch.nn.BatchNorm2d",
"torch.nn.Sigmoid",
"torch.nn.init.kaiming_normal_",
"torch.nn.ini... |
n-hutton/colearn | [
"4e1257dae1316a4366a745fa965ea5e28d0ead14"
] | [
"colearn_keras/keras_cifar10.py"
] | [
"# ------------------------------------------------------------------------------\n#\n# Copyright 2021 Fetch.AI Limited\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#... | [
[
"tensorflow.data.Dataset.from_tensor_slices",
"numpy.random.seed",
"tensorflow.keras.layers.Flatten",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.layers.Conv2D",
"tensorflow.keras.metrics.SparseCategoricalAccuracy",
"tensorflow.keras.Model",
"tensorflow.keras.layers.MaxPooli... |
tam17aki/dcase2021_task2_baseline_ae | [
"ecf9f2a503faf648d47a78624964157e6f971e9a"
] | [
"baseline_keras/common.py"
] | [
"########################################################################\n# import python-library\n########################################################################\n# default\nimport glob\nimport argparse\nimport sys\nimport os\nimport itertools\nimport re\n\n# additional\nimport numpy as np\nimport libros... | [
[
"numpy.concatenate",
"numpy.empty",
"numpy.zeros",
"numpy.maximum"
]
] |
TommyX12/kaolin | [
"c91c40601958ad73356e854e88240fa7dd89d144"
] | [
"kaolin/graphics/SoftRenderer.py"
] | [
"# Copyright (c) 2019, 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 obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless req... | [
[
"torch.zeros",
"torch.nn.functional.normalize",
"torch.cat",
"torch.nn.functional.avg_pool2d",
"torch.stack",
"torch.tan",
"torch.arange",
"torch.ones",
"torch.tensor",
"torch.eye",
"torch.cross",
"torch.flip"
]
] |
ykwon0407/VIPS | [
"91d940304b34d702c1a8b12363b5fff38455ef88"
] | [
"python/experiments/SVGD/iono.py"
] | [
"from time import time\n\nimport numpy as np\nfrom scipy.stats import multivariate_normal\n\nfrom experiments.lnpdfs.create_target_lnpfs import build_GPR_iono_with_grad_lnpdf\nfrom sampler.SVGD.python.svgd import SVGD as SVGD\n\nnum_dimensions = 34\n\ntmp_lnpdf = build_GPR_iono_with_grad_lnpdf(True)\ndef dlnpdf(the... | [
[
"numpy.eye",
"numpy.savez",
"numpy.zeros",
"numpy.atleast_2d"
]
] |
fnrizzi/pressio-demoapps | [
"6ff10bbcf4d526610580940753c9620725bff1ba"
] | [
"tests_cpp/eigen_1d_linear_advection_convergence/weno5/conv.py"
] | [
"#!/usr/bin/env python3\n\nimport matplotlib.pyplot as plt\nfrom matplotlib import cm\nimport numpy as np\nfrom numpy import linalg as LA\nimport re\n\ndef sol(xin, t):\n return np.sin(np.pi*(xin-t))\n\ndef extractN(ns):\n reg = re.compile(r''+ns+'.+')\n file1 = open('info.dat', 'r')\n strings = re.search(reg, ... | [
[
"numpy.sin",
"numpy.linalg.norm",
"numpy.reshape",
"numpy.allclose",
"numpy.size",
"numpy.log2"
]
] |
girlrilaz/blood-donation-prediction-3 | [
"065fb6936c6e930328d685b8bed1c4017bea4b60"
] | [
"src/models/predict_model.py"
] | [
"import time,os,re,csv,sys,uuid,joblib, yaml\nimport pickle\nfrom datetime import date\nimport numpy as np\nimport pandas as pd\nfrom dotenv import find_dotenv, load_dotenv\nfrom sklearn import ensemble\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nfrom skl... | [
[
"pandas.DataFrame"
]
] |
uuid0000/DI-engine | [
"cc2713fa01e5288bae21cfeb595729d665e092d1",
"cc2713fa01e5288bae21cfeb595729d665e092d1"
] | [
"ding/entry/dist_entry.py",
"ding/rl_utils/upgo.py"
] | [
"import os\nimport sys\nimport subprocess\nimport signal\nimport pickle\nimport logging\nimport time\nfrom threading import Thread\nfrom easydict import EasyDict\nimport numpy as np\nfrom ding.worker import Coordinator, create_comm_collector, create_comm_learner, LearnerAggregator\nfrom ding.config import read_conf... | [
[
"numpy.isscalar"
],
[
"torch.no_grad",
"torch.nn.functional.cross_entropy",
"torch.ones_like"
]
] |
jerinka/traffic_sign_frcnn | [
"3a6cd77af965ea88de61d6718a4f539f58b46a13"
] | [
"utilities/data_utils/Dataset.py"
] | [
"import torch\nimport os\nfrom PIL import Image\nfrom xml.dom.minidom import parse\nimport numpy as np\nimport utilities.transforms as T\n\n\nclass FacialDataset(object):\n def __init__(self, root, transforms, train=True):\n self.root = root\n self.transforms = transforms\n # load all image ... | [
[
"torch.as_tensor",
"torch.tensor"
]
] |
ashishrana160796/fibonacci-initialized-neural-networks | [
"d19641d09c6dce4e822a863d837bdc2ee5fde4b9"
] | [
"plot_util.py"
] | [
"import matplotlib.pyplot as plt\nimport pickle\nimport numpy as np\n\n\nwith open('cost_fib.pkl','rb') as inp:\n cost_gld = pickle.load(inp)\n \nwith open('cost_rand.pkl','rb') as inp:\n cost_reg = pickle.load(inp)\n \n# print(cost_gld)\n# print(cost_reg)\n\nx_ax = np.arange(300)\n\nplt.plot(x_ax, cost... | [
[
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.legend",
"numpy.arange",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.show"
]
] |
sjurajpuchky/rust-lexical | [
"827dd8722967030bfe75cfbd786f0e160ffb9c0c"
] | [
"lexical-parse-float/etc/correctness/test-parse-unittests/to_toml.py"
] | [
"'''\n to_toml\n -------\n\n Convert float conformance test cases to TOML.\n'''\n\nimport argparse\nimport json\nimport sys\nimport unittest\n\nimport numpy as np\nimport tomlkit\n\n# ARGPARSE\n# --------\n\nparser = argparse.ArgumentParser(\n description='Convert floats to TOML.',\n allow_abbrev=Fal... | [
[
"numpy.uint32",
"numpy.float64",
"numpy.float32",
"numpy.uint64",
"numpy.int32"
]
] |
carlculator/timeseriesx2 | [
"2309f34c9848331c2c4eab0113ea7147933cd9e0"
] | [
"timeseriesx/validation/frequency.py"
] | [
"import pandas as pd\n\n\ndef infer_freq(series):\n \"\"\"\n infer the frequency from a pandas Series with `DatetimeIndex`\n\n :param pandas.Series series: a pandas Series with `DatetimeIndex`\n :return: the inferred frequency object\n :rtype: pandas.DateOffset\n \"\"\"\n if series.index.freq i... | [
[
"pandas.tseries.frequencies.to_offset"
]
] |
abillscmu/beep | [
"39c4cb952294d3e5ebcd2c85877d538f50d91979"
] | [
"beep/structure/arbin.py"
] | [
"\"\"\"Classes and functions for handling Arbin battery cycler data.\n\n\"\"\"\nimport os\nfrom datetime import datetime\n\nimport pytz\nimport pandas as pd\n\nfrom beep.conversion_schemas import ARBIN_CONFIG\nfrom beep import logger\nfrom beep.structure.base import BEEPDatapath\n\n\nclass ArbinDatapath(BEEPDatapat... | [
[
"pandas.read_csv"
]
] |
woollysocks/Betelgeuse_SPINN | [
"1b3fd7563622554d8a90c5d5cf2108f8a77059ea"
] | [
"python/spinn/models/supervised_classifier.py"
] | [
"import os\nimport json\nimport random\nimport sys\nimport time\nimport math\n\nimport gflags\nimport numpy as np\n\nfrom spinn.util import afs_safe_logger\nfrom spinn.util.data import SimpleProgressBar\nfrom spinn.util.blocks import to_gpu\nfrom spinn.util.misc import Accumulator, EvalReporter\nfrom spinn.util.log... | [
[
"torch.autograd.Variable",
"torch.nn.CrossEntropyLoss",
"numpy.concatenate",
"torch.from_numpy"
]
] |
JCfrW/alpha-zero-general | [
"a3c39931166a0e49b4b4a631363d62eb30290b2c",
"a3c39931166a0e49b4b4a631363d62eb30290b2c"
] | [
"othello/pytorch/NNet.py",
"othello/pytorch/OthelloNNet.py"
] | [
"\nimport os\nimport random\nimport sys\nimport time\n\nimport torch.cuda\n\nfrom NeuralNet import NeuralNet\nimport numpy as np\nfrom pytorch_classification.utils import Bar, AverageMeter\nimport torch.optim as optim\nfrom utils import *\n\nfrom .OthelloNNet import OthelloNNet as onnet\n\n\nsys.path.append('../../... | [
[
"numpy.array"
],
[
"torch.nn.Linear",
"torch.nn.BatchNorm2d",
"torch.nn.functional.log_softmax",
"torch.nn.Conv2d",
"torch.nn.BatchNorm1d",
"torch.nn.functional.tanh"
]
] |
AK391/detr | [
"112396eec6b70b6bd1bb180d91c6e3b0e391cdc6"
] | [
"demo.py"
] | [
"from PIL import Image\nimport requests\nimport matplotlib.pyplot as plt\nimport torch\nfrom torch import nn\nfrom torchvision.models import resnet50\nimport torchvision.transforms as T\ntorch.set_grad_enabled(False);\nimport gradio as gr\nimport io\n\nmodel = torch.hub.load('facebookresearch/detr', 'detr_resnet50'... | [
[
"torch.stack",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.Rectangle",
"torch.tensor",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.gca",
"torch.set_grad_enabled",
"torch.hub.load",
"matplotlib.pyplot.imshow"
]
] |
AbdullahAmer86/maskrCNN | [
"f00e21b646f44ce1590e8fd6d8b5ecba56bf68db"
] | [
"GPT2-HarryPotter-Training/examples/run_squad.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.distributed.get_world_size",
"torch.utils.data.RandomSampler",
"torch.cuda.is_available",
"torch.load",
"torch.nn.DataParallel",
"torch.distributed.init_process_group",
"torch.manual_seed",
"torch.utils.data.DataLoader",
"torch.distributed.get_rank",
"torch.device",
... |
BINAYKUMAR943/sktime | [
"294429e7f2ac5824171bb61ad075e0af0055cb02",
"f02f656de86da420b1c14e58dc60194261969eb3",
"294429e7f2ac5824171bb61ad075e0af0055cb02"
] | [
"sktime/classification/shapelet_based/tests/test_mrseql.py",
"sktime/contrib/classification_intervals/_continuous_interval_tree.py",
"sktime/forecasting/compose/tests/test_ensemble.py"
] | [
"# -*- coding: utf-8 -*-\n\nimport numpy as np\nfrom sktime.classification.shapelet_based import MrSEQLClassifier\nfrom sktime.datasets import load_gunpoint\n\n\ndef test_mrseql_on_gunpoint():\n # load training data\n X_train, y_train = load_gunpoint(split=\"train\", return_X_y=True)\n X_test, y_test = loa... | [
[
"numpy.testing.assert_equal"
],
[
"numpy.max",
"numpy.isnan",
"sklearn.utils.check_X_y",
"numpy.ndenumerate",
"numpy.zeros",
"numpy.nan_to_num",
"numpy.median",
"numpy.asarray",
"numpy.min",
"numpy.mean",
"numpy.std",
"sklearn.utils.check_random_state",
... |
NoemieJaquier/GaBOflow | [
"82c647fbaf7a511b38f64c8bf0429cbcf4e8de60"
] | [
"BoManifolds/BO_utils/manifold_bound_constrained_conjugate_gradient.py"
] | [
"from __future__ import print_function, division\n\nimport time\nfrom copy import deepcopy\n\nimport numpy as np\n\nfrom pymanopt.solvers.linesearch import LineSearchAdaptive\nfrom pymanopt.solvers.solver import Solver\nfrom pymanopt import tools\n\n'''\nAuthors: Noemie Jaquier and Leonel Rozo, 2019\nLicense: MIT\n... | [
[
"numpy.isinf",
"numpy.linalg.norm",
"numpy.isnan"
]
] |
jviquerat/twin_autoencoder | [
"fd9385ec3f461bf123086809861791b818fb2faf"
] | [
"mistake_minimization.py"
] | [
"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker as mtick\n\n\ne_f_star = 5e-5# give a tolerance on the mean squared error of flow prediction\n\n\n### load MSE on the training/validation/test set\nmse_tr = pd.read_csv('Depth5/mse_train.csv')\nmse_tr.columns = ['ind... | [
[
"numpy.argmin",
"matplotlib.pyplot.gca",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.title",
"matplotlib.pyplot.legend",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.figure",
"matplotlib.ticker.PercentFormatter",
... |
YehCF/pytorch-lightning | [
"6666ca5af39aa2d3e5a483da3d7f6bb76514cc9f"
] | [
"pytorch_lightning/trainer/trainer.py"
] | [
"\"\"\"\nThe trainer handles all the logic for running a val loop, training loop, distributing, etc.. .\n\"\"\"\n\nimport os\nimport sys\nimport warnings\nimport logging\n\nimport torch\nimport torch.distributed as dist\nimport torch.multiprocessing as mp\nimport tqdm\nfrom torch.optim.optimizer import Optimizer\n\... | [
[
"torch.multiprocessing.spawn",
"torch.cuda.current_device",
"torch.cuda.device_count",
"torch.cuda.empty_cache",
"torch.cuda.is_available",
"torch.distributed.barrier"
]
] |
chogba6/ONE | [
"3d35259f89ee3109cfd35ab6f38c231904487f3b"
] | [
"tools/nnpackage_tool/gen_golden/gen_golden.py"
] | [
"#!/usr/bin/env python3\n\n# Copyright (c) 2019 Samsung Electronics Co., Ltd. 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/lice... | [
[
"tensorflow.io.gfile.GFile",
"tensorflow.compat.v1.get_default_graph",
"tensorflow.compat.v1.ConfigProto",
"tensorflow.lite.Interpreter",
"tensorflow.import_graph_def",
"tensorflow.compat.v1.GraphDef",
"tensorflow.compat.v1.Session",
"numpy.random.random_sample",
"numpy.random.... |
shalevy1/OpenLabeling | [
"e4c7532e4c8d2e74a1612e9bb64efd9f9df04ad6"
] | [
"main/main.py"
] | [
"#!/bin/python\nimport argparse\nimport glob\nimport json\nimport os\nimport re\n\nimport cv2\nimport numpy as np\nfrom tqdm import tqdm\n\nfrom lxml import etree\nimport xml.etree.cElementTree as ET\n\nfrom dasiamrpn import dasiamrpn\n\nDELAY = 20 # keyboard delay (in milliseconds)\nWITH_QT = False\ntry:\n cv2.... | [
[
"numpy.array",
"numpy.bitwise_or",
"numpy.random.randint",
"numpy.vstack"
]
] |
emsellem/pymusepipe | [
"8d06597ef21f2690921ea08148e223f914a79559",
"8d06597ef21f2690921ea08148e223f914a79559"
] | [
"src/pymusepipe/combine.py",
"build/lib/pymusepipe/mpdaf_pipe.py"
] | [
"# Licensed under a MIT license - see LICENSE\n\n\"\"\"MUSE-PHANGS combine module\n\"\"\"\n\n__authors__ = \"Eric Emsellem\"\n__copyright__ = \"(c) 2017-2019, ESO + CRAL\"\n__license__ = \"MIT License\"\n__contact__ = \" <eric.emsellem@eso.org>\"\n\n# Importing modules\nimport numpy as np\nimport os\nfrom os.path i... | [
[
"numpy.int",
"numpy.unique",
"numpy.argwhere"
],
[
"numpy.max",
"numpy.int",
"numpy.array",
"numpy.isnan",
"numpy.zeros",
"numpy.sum",
"numpy.ones",
"numpy.interp",
"numpy.min",
"numpy.where",
"numpy.loadtxt",
"numpy.arctan2",
"numpy.abs",
"n... |
salah608/OPENPILOT | [
"be214b44947d2a52571b1031c25dde5d54a5fe10"
] | [
"selfdrive/controls/lib/latcontrol_indi.py"
] | [
"import math\nimport numpy as np\n\nfrom cereal import log\nfrom common.filter_simple import FirstOrderFilter\nfrom common.numpy_fast import clip, interp\nfrom common.realtime import DT_CTRL\nfrom selfdrive.controls.lib.latcontrol import LatControl, MIN_STEER_SPEED\n\n\nclass LatControlINDI(LatControl):\n def __in... | [
[
"numpy.array",
"numpy.dot"
]
] |
ragesh88/crazyswarm | [
"7f9a69b1c45e4655777fb2daf1dde256675005cf"
] | [
"ros_ws/src/crazyswarm/scripts/csv_sequence.py"
] | [
"#!/usr/bin/env python\n\nfrom __future__ import print_function\n\nimport argparse\nimport os\nimport os.path\n\nimport numpy as np\nimport csv\n\nfrom pycrazyswarm import *\nimport uav_trajectory\n\nimport pycrazyswarm.cfsim.cffirmware as firm\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.ad... | [
[
"numpy.array",
"numpy.linalg.norm",
"numpy.zeros",
"numpy.stack",
"numpy.abs"
]
] |
MAXIORBOY/SudokuSolver | [
"9046feda159ff32613e9c02f5f6f21a57f09ef23"
] | [
"visualize_results.py"
] | [
"import matplotlib.pyplot as plt\nimport numpy as np\n\n\nclass Visualize:\n def __init__(self, solved_grid, old_grid):\n self.solved_grid = solved_grid\n self.old_grid = old_grid\n\n def show_solution(self, cell_dimension=0.1):\n cell_text = [[str(el) for el in row] for row in self.solve... | [
[
"matplotlib.pyplot.box",
"matplotlib.pyplot.show",
"matplotlib.pyplot.gca",
"matplotlib.pyplot.table"
]
] |
s4465413/dolo.py | [
"6a1bfbd65a8f69a7495c5ade51af8cc6c35eded3"
] | [
"dolo/tests/test_iid_processes.py"
] | [
"\nimport unittest\nimport scipy\nfrom scipy.integrate import quad\nimport numpy as np\nfrom dolo.numeric.distribution import *\n\n## Polynomial\ndef f(x):\n return x**2\n\n def f(x):\n return x**2\n\ndef test_UNormal():\n σ = 0.1\n μ = 0.2\n N = 10\n distNorm = UNormal(mu=μ, sigma=σ)\n ... | [
[
"numpy.random.normal",
"numpy.random.lognormal",
"numpy.exp",
"numpy.random.beta",
"numpy.random.uniform",
"numpy.sqrt"
]
] |
saraansh/adaptive-test-serverless | [
"9d70d2c4e64cbcc972000448c338884b7c7ee42a"
] | [
"layers/python/lib/python3.7/site-packages/catsim/irt.py"
] | [
"import math\nfrom math import pi\nfrom typing import List\n\nimport numexpr\nimport numpy\n\n\ndef icc(theta: float, a: float, b: float, c: float = 0, d: float = 1) -> float:\n \"\"\"Item Response Theory four-parameter logistic function [Ayala2009]_, [Magis13]_:\n\n .. math:: P(X_i = 1| \\\\theta) = c_i + \\... | [
[
"numpy.ones",
"numpy.zeros",
"numpy.expand_dims"
]
] |
pjmaker/python-ts-tools | [
"f16f389e9c4f08767588c096ea4d085073842f98"
] | [
"weather/heatmap-solar.py"
] | [
"import numpy as np\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\nnp.random.seed(0)\nsns.set()\n\nngui = 0\ntitree = 1\n\nseasons = ['Summer', 'Autumn', 'Winter', 'Spring']\nmonths = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n\n# by month\ndata = np.zeros((2, ... | [
[
"numpy.array",
"numpy.zeros",
"numpy.random.seed",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.show"
]
] |
kbuc/xraylarch | [
"3abb0d6bdc65cf2747a03dd114d98df317c0ac9f",
"3abb0d6bdc65cf2747a03dd114d98df317c0ac9f"
] | [
"larch/__init__.py",
"larch/wxmap/mapviewer.py"
] | [
"#!/usr/bin/env python\n\n\"\"\"\n Larch: a scientific data processing macro language based on python\n\"\"\"\nimport os\nimport sys\nimport logging\n\n# note: may need to set CONDA env *before* loading numpy!\nif os.name == 'nt':\n os.environ['CONDA_DLL_SEARCH_MODIFICATION_ENABLE'] = '1'\n\nimport numpy\nimpor... | [
[
"matplotlib.use"
],
[
"scipy.stats.mode",
"numpy.array",
"numpy.zeros",
"numpy.median",
"scipy.stats.hmean",
"numpy.where",
"scipy.stats.skew",
"numpy.swapaxes",
"numpy.einsum",
"numpy.arange",
"scipy.stats.gmean",
"scipy.stats.kurtosis"
]
] |
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