repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
Jason33Wang/PathPlanning | [
"ec6b5f95d73a5416bd70cd3bc63de676758223a4"
] | [
"Sampling_based_Planning/rrt_2D/rrt_connect.py"
] | [
"\"\"\"\nRRT_CONNECT_2D\n@author: huiming zhou\n\"\"\"\n\nimport os\nimport sys\nimport math\nimport copy\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nsys.path.append(os.path.dirname(os.path.abspath(__file__)) +\n \"/../../Sampling_based_Planning/\")\n\nfrom Sampling_based_Planning.rrt_2D ... | [
[
"numpy.random.uniform",
"numpy.random.random"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
whaonahee/Metalens | [
"2c0599f69c5a855107b7f71b1f9a976eb275b018"
] | [
"lens_center.py"
] | [
"# coding=UTF-8\n\"\"\"\n(C) 2015 Steven Byrnes\n\nThis script defines a HexGridSet object which is used to store a set of\nhexagonal grid cylinder patterns. Each unit cell in the center part of\nthe lens is chosen from this set to create the correct phase.\n\n\"\"\"\n\n\nimport math, cmath\nimport numpy as np\nimp... | [
[
"numpy.array2string",
"matplotlib.pyplot.title",
"numpy.linspace",
"scipy.interpolate.RegularGridInterpolator",
"matplotlib.pyplot.subplots",
"numpy.argmax",
"numpy.exp",
"matplotlib.pyplot.xlabel",
"numpy.angle",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.14",
"1.6",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"1.0",
"0.17",
"1.3",
"1.8"
],
"tensorflow": []
... |
mseryy01/DS-Unit-3-Sprint-2-SQL-and-Databases | [
"477491419f3b408e8eb807eff54bdf5b26407cda"
] | [
"module2-sql-for-analysis/insert_titanic.py"
] | [
"import pandas as pd\r\nimport sqlite3\r\nimport psycopg2\r\n\r\n\"\"\"Remove (') from names\"\"\"\r\ndf = pd.read_csv('titanic.csv')\r\ndf['Name'] = df['Name'].str.replace(\"'\", \" \")\r\n\r\n\"\"\"Make sqlite3 file and connect to cursor\"\"\"\r\nconn = sqlite3.connect('titanic.sqlite3')\r\ncurs = conn.cursor()\r... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
eddyfortier/task_stimuli | [
"b3e0c477775d42b0efa4389531042a80a848fe86"
] | [
"src/tasks/language.py"
] | [
"import os, sys, time\nfrom psychopy import visual, core, data, logging, event\nfrom pandas import read_csv\nfrom .task_base import Task\n\nfrom ..shared import config, utils\n\nSTIMULI_DURATION = 4\nBASELINE_BEGIN = 5\nBASELINE_END = 5\nTRIPLET_RIGHT_KEY = \"l\"\nTRIPLET_LEFT_KEY = \"d\"\nISI = 2\n\n\nclass Triple... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
nibalizer/tvm | [
"6981839bb30635c70ee25ed5b37bf85f4f9485d9"
] | [
"python/tvm/relay/frontend/onnx.py"
] | [
"# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements. See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership. The ASF licenses this file\n# to you under the Apache License, Version 2.0 (the\n# \"License\"); y... | [
[
"numpy.asarray",
"numpy.iinfo",
"numpy.prod",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jeffchiou/causalnex | [
"b063d379ff026189f5a98be0cf6bcd172b0c89c9"
] | [
"tests/structure/data_generators/test_core.py"
] | [
"# Copyright 2019-2020 QuantumBlack Visual Analytics 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# http://www.apache.org/licenses/LICENSE-2.0\n#\n# THE SOFTWARE IS P... | [
[
"numpy.random.seed",
"sklearn.gaussian_process.kernels.RBF",
"numpy.random.shuffle",
"numpy.ones",
"numpy.all",
"numpy.exp",
"numpy.isclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Tgordon523/linkedin-learning | [
"e4cd04820fe1eee36c1988d5f1e1b3691a4f5398"
] | [
"Week19/week_19.py"
] | [
"import pandas as pd\nimport numpy as np\n\n\n### Create dataset\ndf = pd.DataFrame(\n np.random.normal(7.5, 2, size=(100, 6)),\n columns=[\"Math_1\", \"Math_2\", \"Math_3\", \"EngLit_1\", \"EngLit_2\", \"EngLit_3\"],\n)\n\n\n### Column starts with filter\nmath_mask = df.columns[df.columns.str.startswith(\"Ma... | [
[
"numpy.random.normal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
guojm14/HRL | [
"b011fa65a82a861a89979257ed63ed3341b01b24"
] | [
"hrl/env/goal_env/goal_plane_env.py"
] | [
"import gym\nimport copy\nimport numpy as np\nimport cv2\nfrom collections import OrderedDict\n\n\nclass GoalPlane(gym.Env):\n def __init__(self, env_name, type='random', maze_size=16., action_size=1., distance=0.1, start=None, goals=None):\n super(GoalPlane, self).__init__()\n self.env = gym.make(... | [
[
"numpy.array",
"numpy.linalg.norm"
]
] | [
{
"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": [],
... |
amirpaia/election-campaign-dynamics | [
"b2b32c627cb79c7eb60e458511210308b7ff4035"
] | [
"Website/FlaskWebsite/env/Lib/site-packages/matplotlib/figure.py"
] | [
"\"\"\"\r\n`matplotlib.figure` implements the following classes:\r\n\r\n`Figure`\r\n Top level `~matplotlib.artist.Artist`, which holds all plot elements.\r\n Many methods are implemented in `FigureBase`.\r\n\r\n`SubFigure`\r\n A logical figure inside a figure, usually added to a figure (or parent\r\n `... | [
[
"matplotlib._api.check_in_list",
"matplotlib.transforms.Bbox.union",
"matplotlib.transforms.Affine2D",
"matplotlib._constrained_layout.do_constrained_layout",
"matplotlib.cbook.CallbackRegistry",
"matplotlib.cbook.is_scalar_or_string",
"numpy.max",
"matplotlib.legend._parse_legend_... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lijian10086/FOTS.PyTorch | [
"3bbebbf08f077bc998b5f0bbc5311e920a5f675d"
] | [
"model/loss.py"
] | [
"### 此处默认真实值和预测值的格式均为 bs * W * H * channels\nimport torch\nimport torch.nn as nn\n\n\nclass DetectionLoss(nn.Module):\n def __init__(self):\n super(DetectionLoss, self).__init__()\n return\n\n def forward(self, y_true_cls, y_pred_cls,\n y_true_geo, y_pred_geo,\n tra... | [
[
"torch.mean",
"torch.min",
"torch.sum",
"torch.log",
"torch.split",
"torch.cos"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
luckystarufo/PyNumDiff | [
"99ffeb0c118c6de715414af042020bb268941c99"
] | [
"pynumdiff/smooth_finite_difference/_smooth_finite_difference.py"
] | [
"\"\"\"\nApply smoothing method before finite difference\n\"\"\"\nimport numpy as np\nimport scipy.signal\n\n# included code\nfrom pynumdiff.finite_difference import first_order as finite_difference\nfrom pynumdiff.utils import utility\n__friedrichs_kernel__ = utility.__friedrichs_kernel__\n__gaussian_kernel__ = ut... | [
[
"numpy.convolve",
"numpy.hstack",
"numpy.max",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
KKKSQJ/robot_learning | [
"b4b56a4f183235b96a56d1447d2881cc73613d60"
] | [
"singshotpose/ObjectDatasetTools/aruco.py"
] | [
"\n\"\"\"\naruco.py\n---------------\n\nCode to examine if aruco markers can be detected on the recorded sequence\nA rectangle will be drawn on markers that are detected\n\n\"\"\"\n\nimport numpy as np\nimport cv2\nimport cv2.aruco as aruco\nimport glob\nimport png\nimport sys\n\ndef print_usage():\n \n print... | [
[
"numpy.all",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ThomasWWebb/mmdetection | [
"873858ef2eeb33a59b1c611e507091dfdf396afd"
] | [
"mmdet/models/backbones/resnet.py"
] | [
"import torch.nn as nn\nimport torch.utils.checkpoint as cp\nfrom mmcv.cnn import (build_conv_layer, build_norm_layer, build_plugin_layer,\n constant_init, kaiming_init, xavier_init, normal_init)\nfrom mmcv.runner import load_checkpoint\nfrom torch.nn.modules.batchnorm import _BatchNorm\n\nfrom... | [
[
"torch.nn.MaxPool2d",
"torch.nn.ReLU",
"torch.utils.checkpoint.checkpoint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tensorflow-pool/facenet | [
"27e49a56e861ac8061d02d18c8ab8455c22ea37a"
] | [
"src/train_softmax.py"
] | [
"# coding=utf-8\n\"\"\"Training a face recognizer with TensorFlow using softmax cross entropy loss\n\"\"\"\n# MIT License\n# \n# Copyright (c) 2016 David Sandberg\n# \n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Softwar... | [
[
"tensorflow.python.ops.array_ops.shape",
"tensorflow.global_variables",
"tensorflow.contrib.slim.l2_regularizer",
"numpy.cumsum",
"tensorflow.cast",
"numpy.mean",
"numpy.zeros_like",
"tensorflow.GPUOptions",
"numpy.histogram",
"tensorflow.summary.scalar",
"tensorflow.ad... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"1.12",
"2.6",
"2.2",
"1.13",
"2.3",
"2.4",
"1.4",
"2.9",
"1.5",
"1.7",
"2.5",
"0.12",
"1.0",
"2.8",
"1... |
conan7882/triplet-loss | [
"b79224572d793f950a430c08320e2f49a9fd7595"
] | [
"src/dataflow/base.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# File: base.py\n# Author: Qian Ge <geqian1001@gmail.com>\n\nimport os\nimport numpy as np \nimport src.utils.dataflow as dfutil\n\n\nclass DataflowBase(object):\n def __init__(self,\n data_dir,\n batch_dict_name=None,\n ... | [
[
"numpy.arange",
"numpy.array",
"numpy.minimum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pruthvistony/tensorflow-upstream | [
"93830ceb25f5f8202c86b8a491d8815ea70996fe"
] | [
"tensorflow/python/ops/nn_grad.py"
] | [
"# Copyright 2015 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.python.ops.array_ops.shape",
"tensorflow.python.ops.math_ops.rsqrt",
"tensorflow.python.ops.math_ops.exp",
"tensorflow.python.ops.array_ops.squeeze",
"tensorflow.python.ops.array_ops.zeros",
"tensorflow.python.eager.backprop.GradientTape",
"tensorflow.python.ops.array_ops.s... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.6",
"2.2",
"1.13",
"2.3",
"2.4",
"2.9",
"2.5",
"2.8",
"2.10"
]
}
] |
lsst/faro | [
"c9d0b822c4e3533531fa3849ae4bb92545373db5"
] | [
"python/lsst/faro/utils/tex.py"
] | [
"# This file is part of faro.\n#\n# Developed for the LSST Data Management System.\n# This product includes software developed by the LSST Project\n# (https://www.lsst.org).\n# See the COPYRIGHT file at the top-level directory of this distribution\n# for details of code ownership.\n#\n# This program is free softwar... | [
[
"numpy.sqrt",
"numpy.isfinite",
"numpy.rad2deg",
"numpy.array",
"numpy.exp",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Velumanir/Spark-Example | [
"4816c2ef5e04eb2dd70bed8b99882aa0b7fe7fd7"
] | [
"python/pyspark/ml/tuning.py"
] | [
"#\n# Licensed to the Apache Software Foundation (ASF) under one or more\n# contributor license agreements. See the NOTICE file distributed with\n# this work for additional information regarding copyright ownership.\n# The ASF licenses this file to You under the Apache License, Version 2.0\n# (the \"License\"); yo... | [
[
"numpy.argmax",
"numpy.argmin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gigisr/data_curation | [
"ddd2bf742615d659f96bfd6543a657ab195b67c7"
] | [
"data_etl/data_files.py"
] | [
"# Here we are defining a class that will deal with all the data storage and\n# manipulations\nimport logging\n\nimport pandas as pd\nimport numpy as np\n\nfrom data_etl.general_functions import import_attr\n\nmodule_logger = logging.getLogger(__name__)\n\n\nclass DataCuration:\n __step_no = 0\n df_issues = N... | [
[
"pandas.isnull",
"pandas.concat",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
natolambert/deepRL-homework | [
"223d2aabed2273283b6a6c7e5539dded901bc9a6"
] | [
"hw1/DAgger.py"
] | [
"import os\nimport pickle\nimport tensorflow as tf\nimport numpy as np\nimport tf_util\nimport gym\nimport load_policy\nimport datetime\nimport csv\nimport seaborn as sns\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport matplotlib\n\n\"\"\"\nCode to load an expert policy and generate a behavioral cloni... | [
[
"matplotlib.pyplot.legend",
"numpy.asarray",
"numpy.squeeze",
"pandas.DataFrame",
"numpy.concatenate",
"numpy.mean",
"tensorflow.train.AdamOptimizer",
"numpy.random.randint",
"tensorflow.layers.dense",
"tensorflow.ConfigProto",
"numpy.std",
"tensorflow.train.Saver",... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
ksboy/tensor2tensor | [
"22210925e4a7ddd3823d029e77724491293f4e21",
"22210925e4a7ddd3823d029e77724491293f4e21"
] | [
"tensor2tensor/trax/inputs.py",
"tensor2tensor/trax/stax/attention.py"
] | [
"# coding=utf-8\n# Copyright 2019 The Tensor2Tensor Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requir... | [
[
"tensorflow.shape",
"tensorflow.data.experimental.bucket_by_sequence_length",
"numpy.squeeze",
"tensorflow.cast",
"tensorflow.logging.info",
"tensorflow.Session",
"tensorflow.logging.fatal"
],
[
"numpy.log",
"numpy.arange",
"numpy.cos",
"numpy.sin",
"numpy.ones"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
igorss77/Disaster-Response | [
"f725fa6a6a13de0dffb56bab888bd73db963b768"
] | [
"data/process_data.py"
] | [
"import sys\nimport pandas as pd\nimport numpy as np\nfrom sqlalchemy import create_engine\n\ndef load_data(messages_filepath, categories_filepath):\n '''\n This function load Messages and Categories dataset and merge them\n Parameters\n ----------\n messages_filepath\n Message dataframe.\n ... | [
[
"pandas.merge",
"pandas.read_csv",
"pandas.concat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
antoinecarme/sklearn2sql_heroku | [
"d680db10683daa419324461eeea851dd8b103ad5"
] | [
"tests/databases/test_client_db2.py"
] | [
"\n\n\n\nimport pickle, json, requests, base64\n\nfrom sklearn import datasets\n\niris = datasets.load_iris()\nX = iris.data \nY = iris.target\n# print(iris.DESCR)\n\nfrom sklearn.neural_network import MLPClassifier\nclf = MLPClassifier()\nclf.fit(X, Y)\n\n\ndef test_ws_sql_gen(pickle_data):\n WS_URL=\"https://... | [
[
"sklearn.neural_network.MLPClassifier",
"sklearn.datasets.load_iris"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jurg96/PyEMD | [
"612474f616b5d68510a4a467b7fce7f2c7c12b48"
] | [
"example/hht_example.py"
] | [
"import numpy as np\nimport pylab as plt\nfrom scipy.signal import hilbert\n\nfrom PyEMD import EMD\n\n\ndef instant_phase(imfs):\n \"\"\"Extract analytical signal through Hilbert Transform.\"\"\"\n analytic_signal = hilbert(imfs) # Apply Hilbert transform to each row\n phase = np.unwrap(np.angle(analytic... | [
[
"numpy.linspace",
"numpy.sin",
"numpy.diff",
"numpy.angle",
"scipy.signal.hilbert"
]
] | [
{
"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"... |
hwreeves-USGS/pyemu | [
"6b443601fbb9bcb9e97a8c200a78480c11c51f22"
] | [
"verification/Freyberg/run.py"
] | [
"import os\nimport platform\nfrom datetime import datetime\n\nendpoint_file = os.path.join('extra_crispy', 'freyberg.mpenpt')\nlist_file = os.path.join(\"extra_crispy\", \"freyberg.list\")\nout_file = os.path.join(\"misc\", \"other.obs\")\nins_file = out_file + \".ins\"\nhds_file = os.path.join(\"extra_crispy\",\"f... | [
[
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
veaba/deeplearnjs | [
"fd6ac7fa6e33f15153f2a881f2f996d5b67fb38a"
] | [
"0_zero_learn/mnist.py"
] | [
"# 循环手写图片\n\nfrom tensorflow.examples.tutorials.mnist import input_data\nimport tensorflow as tf \nmnist= input_data.read_data_sets(\"MNIST_data/\",one_hot=True)\n\n\"\"\"\nmnist=\n Datasets(\n train=<tensorflow.contrib.learn.python.learn.datasets.mnist.DataSet object at 0x0000000002933630>, \n validation=<... | [
[
"tensorflow.matmul",
"tensorflow.zeros",
"tensorflow.cast",
"tensorflow.compat.v1.global_variables_initializer",
"tensorflow.compat.v1.Session",
"tensorflow.math.log",
"tensorflow.compat.v1.placeholder",
"tensorflow.argmax",
"tensorflow.examples.tutorials.mnist.input_data.read_... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
fengmaniu/SchoolFaceVersion_02 | [
"036d17b9080a817043fcb12f72ed4d47205f5383"
] | [
"src/train_tripletloss.py"
] | [
"\"\"\"Training a face recognizer with TensorFlow based on the FaceNet paper\nFaceNet: A Unified Embedding for Face Recognition and Clustering: http://arxiv.org/abs/1503.03832\n\"\"\"\n# MIT License\n# \n# Copyright (c) 2016 David Sandberg\n# \n# Permission is hereby granted, free of charge, to any person obtaining... | [
[
"tensorflow.global_variables",
"numpy.all",
"tensorflow.GPUOptions",
"numpy.mean",
"tensorflow.image.decode_image",
"tensorflow.summary.scalar",
"tensorflow.add_n",
"numpy.where",
"numpy.random.randint",
"numpy.square",
"tensorflow.Graph",
"tensorflow.image.random_f... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
cnheider/lhotse | [
"a45a0c2c17671d0e39eadd8956d883a7cf07d8d4"
] | [
"test/augmentation/test_torchaudio.py"
] | [
"import math\n\nimport pytest\nimport torch\n\ntorchaudio = pytest.importorskip('torchaudio', minversion='0.6')\n\nfrom lhotse.augmentation import SoxEffectTransform, pitch, reverb, speed\n\nSAMPLING_RATE = 16000\n\n\n@pytest.fixture\ndef audio():\n return torch.sin(2 * math.pi * torch.linspace(0, 1, 16000)).uns... | [
[
"torch.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sAviOr287/imagenet_ICLR | [
"1ac83d799f5335355161156aa9bba63e0d82a063"
] | [
"main_finetune_imagenet.py"
] | [
"import argparse\nimport os\nimport random\nimport shutil\nimport time\nimport warnings\nimport sys\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.multiprocessing as mp\nimport torch.utils.dat... | [
[
"torch.nn.CrossEntropyLoss",
"torch.nn.Parameter",
"torch.distributed.init_process_group",
"torch.multiprocessing.spawn",
"torch.utils.data.distributed.DistributedSampler",
"torch.load",
"torch.manual_seed",
"torch.cuda.set_device",
"torch.utils.data.DataLoader",
"torch.nn.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
theRealSuperMario/kornia | [
"2b32cd9ee79a76e2a6e308b27e4eba3c242d5f55"
] | [
"kornia/geometry/transform/projwarp.py"
] | [
"\"\"\"Module to perform projective transformations to tensors.\"\"\"\nfrom typing import Tuple, List\n\nimport torch\nimport kornia as K\n\n__all__ = [\n \"warp_projective\",\n \"get_projective_transform\",\n \"normal_transform3d_pixel\",\n \"matrix_to_homogeneous\",\n \"projection_from_Rt\",\n]\n\n... | [
[
"torch.nn.functional.affine_grid",
"torch.cat",
"torch.zeros_like",
"torch.eye",
"torch.inverse",
"torch.tensor",
"torch.nn.functional.grid_sample",
"torch.nn.functional.pad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
popellab/SPQSP_IO | [
"eca3ea55ec2f75b0db5d58da09500ddffabc001d"
] | [
"sbml_cvode/expBatchGen.py"
] | [
"#!/usr/bin/env python\n'''\nThis script generate batch exp parameter files\n\n### Parameter sweep\n\n1. For parameter sweep, the script can handle grid search, lhs or random samples\n1.a. For grid search, add attribute sample=\"grid\" to root tag (<Param>)\n Then specify n = \"<nr_sample>\" for each parameter t... | [
[
"scipy.stats.norm.ppf",
"numpy.log",
"numpy.log2",
"numpy.meshgrid",
"numpy.power",
"numpy.arange",
"numpy.interp",
"numpy.random.rand",
"numpy.prod",
"numpy.exp",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rbdavid/MolecDynamics | [
"8e7e878dad37458d912ca716d0e02a4584187d81"
] | [
"Analysis/MSD/plotting.py"
] | [
"#!/Library/Frameworks/Python.framework/Versions/2.7/bin/python\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport sys\n\ndata_file = sys.argv[1]\n\n# SUBROUTINES\n\n# MAIN PROGRAM:\n\ndata = np.loadtxt(data_file)\n\nplt.plot(data[:,0], data[:,1], label='MSD')\n\nplt.title('Mean-squared Displacement ver... | [
[
"matplotlib.pyplot.title",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.xlabel",
"numpy.loadtxt",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
abhisheklalwani/mmaction2 | [
"b716603d33f189387a4c60fa3e8fc2dc0c2e0fda"
] | [
"inferenceScript/quickInferenceFineGym.py"
] | [
"import torch\n\nfrom mmaction.apis import init_recognizer, inference_recognizer\n\nconfig_file = '/home/alalwani_umass_edu/scratch/mmaction2/configs/recognition/slowonly/slowonly_imagenet_pretrained_r50_4x16x1_120e_gym99_rgb.py'\n# download the checkpoint from model zoo and put it in `checkpoints/`\ncheckpoint_fil... | [
[
"torch.device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
omgwenxx/franzosa_scripts | [
"b83670955dbcfb8ccd30071226903913cc80ed01"
] | [
"postprocessing/unify_files.py"
] | [
"import pandas as pd\nimport argparse\nimport os\n\n\"\"\"\nThis script is unify the output files from mothur. Each summary file from mothur only shows the abundance\nfor the data sample that was processed. Therefore different visits have different number of features (hierachy steps).\nThe script takes the differen... | [
[
"pandas.merge",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
prise-3d/Thesis-NoiseDetection-SVD-Entropy | [
"bf8a2336a0c6e573c9c71717e5ab98ec338829f2"
] | [
"data_processing/estimate_sobel_svd_entropy.py"
] | [
"# main imports\nimport numpy as np\nimport pandas as pd\nimport sys, os, argparse\n\n# image processing\nfrom PIL import Image\nfrom ipfml import utils\nfrom ipfml.processing import transform, segmentation, compression\nimport cv2\n\n# modules and config imports\nsys.path.insert(0, '') # trick to enable import of ... | [
[
"numpy.array",
"numpy.hypot"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Haidaansko/GAN-coursework | [
"f2ed816950f16139877048ddc021460f69b96bbe"
] | [
"gaussian_quantile.py"
] | [
"from itertools import product\nimport numpy as np\nimport pandas as pd\n\n\nclass Model:\n def fit(self, X, Y, n_bins=5):\n self.means = {}\n self.stds = {}\n for col in Y.columns:\n self.means[col] = np.zeros((n_bins, n_bins, n_bins))\n self.stds[col] = np.zeros((n_bi... | [
[
"numpy.linspace",
"numpy.logical_and",
"pandas.DataFrame",
"numpy.std",
"numpy.random.normal",
"numpy.mean",
"numpy.count_nonzero",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
lucapascarella/JSS-2018-JIT-File-Level | [
"3337b85a6f3dcc70ac1ebe300f9d8fb605e01a6c"
] | [
"classifier-fully/classifier-full-5.py"
] | [
"import csv\n\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\nfrom sklearn import metrics\nfrom sklearn.metrics import classification_report, confusion_matrix, ac... | [
[
"sklearn.metrics.roc_auc_score",
"pandas.read_csv",
"sklearn.ensemble.RandomForestClassifier",
"sklearn.model_selection.train_test_split",
"sklearn.preprocessing.StandardScaler"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
developmentseed/cogeo-mosaic | [
"1dc3c873472c8cf7634ad893b9cdc40105ca3874"
] | [
"tests/test_backends.py"
] | [
"\"\"\"Test backends.\"\"\"\n\nimport json\nimport os\nimport time\nfrom decimal import Decimal\nfrom io import BytesIO\nfrom typing import Dict, List\nfrom unittest.mock import patch\n\nimport boto3\nimport numpy\nimport pytest\nfrom click.testing import CliRunner\nfrom httpx import HTTPStatusError, RequestError\n... | [
[
"numpy.array_equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Eveneko/AirGesture | [
"c5890a2ec1547c4184d6e52c00778880ca68813d"
] | [
"tmp/utils.py"
] | [
"# -*- coding: utf-8 -*-\n# !/usr/bin/python3\n# utils for gesture recognition\n\nimport cv2\nimport math\nimport json\nimport base64\nimport numpy as np\n\n# 使用requests包发送post请求，需求事先在HiLens Kit上用 python3 -m pip install requests 安装\nimport requests\n\n# 手势识别模型输入尺寸\nnet_h = 288\nnet_w = 512\n\n# 可识别的手势类别，前3种为有效手势\nc... | [
[
"numpy.maximum",
"numpy.minimum",
"numpy.argmax",
"numpy.exp",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wangzpeng/tf-faster-rcnn | [
"0a3cf268a504452e802c1c9e487c734ab048ab62"
] | [
"lib/setup.py"
] | [
"# --------------------------------------------------------\n# Fast R-CNN\n# Copyright (c) 2015 Microsoft\n# Licensed under The MIT License [see LICENSE for details]\n# Written by Ross Girshick\n# --------------------------------------------------------\n\nimport os\nfrom os.path import join as pjoin\nimport numpy ... | [
[
"numpy.get_numpy_include",
"numpy.get_include"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hansonl02/frc-robot-code | [
"4b120c917a7709df9f010c9089a87c320bab3a16"
] | [
"c2018/subsystems/score_subsystem/elevator/elevator.py"
] | [
"import numpy as np\nimport sys\n\nfrom muan.control.state_space_gains import StateSpaceGains\nfrom muan.control.state_space_plant import StateSpacePlant\nfrom muan.control.state_space_controller import StateSpaceController\nfrom muan.control.state_space_observer import StateSpaceObserver\nfrom muan.control.trapezo... | [
[
"numpy.asmatrix",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
herjy/scarlet | [
"b34b32bf1a700276f5a6c178b11867f7af317f05"
] | [
"scarlet/display.py"
] | [
"from collections import Iterable\n\nimport numpy as np\nfrom matplotlib.colors import Normalize\n\nclass Asinh(Normalize):\n \"\"\"Use arcsinh to map image intensities in matplotlib\n \"\"\"\n def __init__(self, vmin=None, vmax=None, img=None, Q=10, clip=False):\n \"\"\"Initialize\n\n Parame... | [
[
"numpy.polyfit",
"matplotlib.colors.Normalize.__init__",
"numpy.min",
"numpy.arange",
"numpy.ma.min",
"numpy.sinh",
"numpy.max",
"numpy.ceil",
"numpy.zeros_like",
"numpy.floor",
"numpy.transpose",
"numpy.ma.array",
"numpy.ma.max",
"numpy.arcsinh"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SidaZh/EET | [
"6414faa734abfdb666556304ca3df5b7f5e54c38"
] | [
"python/eet/pipelines/fill_mask.py"
] | [
"#\n# Created by djz on 2022/04/01.\n#\nimport torch\nimport numpy as np\nfrom typing import Dict\nfrom transformers.utils import logging\nfrom .base import GenericTensor,Pipeline,PipelineException\nlogger = logging.get_logger(__name__)\n\nclass FillMaskPipeline(Pipeline):\n\n def get_masked_index(self, input_id... | [
[
"numpy.prod",
"numpy.array",
"numpy.where",
"torch.nonzero"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
UlricWu/machine_learning_library | [
"e405161cfa7255505965f024be5b5cd416f0a3a5"
] | [
"machine_learning_library/model_logistic_regression.py"
] | [
"from machine_learning_library.model_discriminant_analysis import Classification\nimport numpy as np\n\n\nclass LogsitcRegression(Classification):\n def __init__(self, learning_rate=0.01, threshold=1e-5, epochs=10000, method='MLE'):\n super(LogsitcRegression, self).__init__()\n self.learning_rate =... | [
[
"numpy.abs",
"numpy.diagflat",
"numpy.linalg.inv",
"numpy.ones",
"numpy.exp",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
brandjon/frexp | [
"246ca0d9c4f4e7a849fcd0709748a7f7ac0961bc"
] | [
"frexp/runner.py"
] | [
"\"\"\"Run the driver program multiple times and aggregate the results.\"\"\"\r\n\r\n\r\n__all__ = [\r\n 'Runner',\r\n]\r\n\r\n\r\nimport pickle\r\nimport os\r\nfrom multiprocessing import Process\r\n\r\nimport numpy as np\r\n\r\nfrom frexp.util import on_battery_power\r\nfrom frexp.workflow import Task\r\n\r\n\... | [
[
"numpy.std",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
raoulbq/WaveBlocksND | [
"225b5dd9b1af1998bd40b5f6467ee959292b6a83"
] | [
"WaveBlocksND/IOM_plugin_grid.py"
] | [
"\"\"\"The WaveBlocks Project\n\nIOM plugin providing functions for handling grid data.\n\n@author: R. Bourquin\n@copyright: Copyright (C) 2010, 2011, 2012 R. Bourquin\n@license: Modified BSD License\n\"\"\"\n\nimport numpy as np\n\n\ndef add_grid(self, parameters, blockid=0):\n r\"\"\"Add storage for a tensor p... | [
[
"numpy.real",
"numpy.prod",
"numpy.squeeze"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AnaSula/NYU-Python-Programming-Class | [
"53b88a2e018f70b00b273dbc2244b32711ab6402"
] | [
"course-2/project/get_data.py"
] | [
"import pysolr\nimport requests\nimport codecs\nimport pandas as pd\n\n\nclass GetData:\n\n\tconn = pysolr.Solr('https://cabidx1:Jkxkk3-POIO08723_eWWXeei1078TRw,6QI@aws-ec2-va-solr-4-10-cabinet.opensolr.com/solr/cab1')\n\n\tdef __init__(self):\n\t\tself.tids=['1772']\n\n\tdef get_train_data(self):\n\t\tfor tid in s... | [
[
"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": []
}
] |
MathPhysSim/FERMI_RL_Paper | [
"4529fcbfa0cc35e5fa59463df2c423a497736eba",
"4529fcbfa0cc35e5fa59463df2c423a497736eba"
] | [
"run_naf2_for_tests.py",
"naf2_new.py"
] | [
"import os\nimport pickle\nimport random\nimport sys\n\nimport gym\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport tensorflow as tf\n\nfrom inverted_pendulum import PendulumEnv\n\nfrom naf2_new import NAF\n\n# set random seed\nrandom_seed = 111\nnp.random.seed(random_seed)\nrandom.seed(random_seed)\n\n... | [
[
"matplotlib.pyplot.twinx",
"numpy.random.seed",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig",
"numpy.cumsum",
"numpy.random.uniform",
"numpy.mean",
"numpy.random.randn",
"tensorflow.random_normal_initializer",
"matplotlib.pyplot.show",
"numpy.sum",
"ten... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
vaughnparker/2020_Challenge_IOT_Analytics | [
"e4fe8bcd8df18ba375c4758af0cfb361706a65c8"
] | [
"example_prediction.py"
] | [
"import h5py\r\nimport os\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\n\r\nfrom scipy.stats import pearsonr\r\n# seed random number generator\r\n\r\ncwd = os.getcwd()\r\n\r\n#Open the data file\r\nfilepath = cwd + '\\\\demo.hdf'\r\nf = h5py.File(filepath, 'r')\r\n\r\n\r\n#Show all channels available ... | [
[
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.title",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LukeDemons/autoframes | [
"2a73c27489879d507677411a3a8bc4f3ccd42a25"
] | [
"animation_np.py"
] | [
"from PIL import Image\nimport numpy as np\n\n\n# from left to right\ndef roll_horizontal(image1, image2, num):\n img_list = []\n width, height = max(image1.size, image2.size)\n for p in range(num + 1):\n width_left = round(p / num * width)\n # arr2 = np.asarray(image2, dtype=\"int32\")\n ... | [
[
"numpy.asarray",
"numpy.hstack",
"numpy.ones_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
PolyakPavlo/DevOps | [
"ae19af6a100f1ffe7871716d593d1bfabfb3a759"
] | [
"lesson7/panda.py"
] | [
"#https://datatofish.com/sort-pandas-dataframe/\r\na=[]\r\nc=0\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\nimport re\r\n##l=[]\r\n\r\n##log = pd.read_table('logfile.log',header=None)\r\n##for index, row in log.iterrows():\r\n## for z in row:\r\n## l=re.findall(r'\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}... | [
[
"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": []
}
] |
heindelj/pyMD | [
"30fa9a9bf14b347f9dc5f246a100d832a93e07c8"
] | [
"py_MD/Gradient_Descent.py"
] | [
"from .Fragments import Fragments\r\nfrom .Potential import *\r\nfrom .MBE_Potential import MBE_Potential\r\nfrom .Compute_Hessian import Hessian\r\nimport numpy as np\r\nimport sys, time\r\n\r\nclass Optimize:\r\n \"\"\"Simple gradient descent implementation which works by taking a potential_function\r\n and... | [
[
"numpy.abs",
"numpy.einsum",
"numpy.reshape",
"numpy.linalg.inv",
"numpy.copy",
"numpy.mean",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
EveCharbie/rectangle-labelling | [
"8dd9921059d10bf0dd378cb7f81333f7d4f7ec3b"
] | [
"autodetection/find_lines.py"
] | [
"\nimport cv2\nimport numpy as np\nfrom tqdm.notebook import tqdm\nimport pickle\n\n\n\ndef circle_positioning(event, x, y, flags, param):\n global points_labels, current_click, frame_counter\n\n if event == cv2.EVENT_LBUTTONDOWN:\n points_labels[label_keys[current_click]][:, frame_counter] = np.array(... | [
[
"numpy.sin",
"numpy.array",
"numpy.cos",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Tony607/Industrial-Defect-Inspection-segmentation | [
"71b90a32654ab4e26bda98ed0da143abe3284f6d"
] | [
"coslib/Transform.py"
] | [
"import matplotlib.image as mpimg\n\nimport glob\nimport os\nimport xmltodict\nimport numpy as np\nfrom .Utils import get_coordinates, load_coordinates\nimport cv2\nimport json\n\n\ndef xml_to_kitti(path_to_xml):\n \"\"\"transform xml format to xml format\n\n Arguments:\n path_to_xml (string):\n\n N... | [
[
"matplotlib.image.imread",
"numpy.array",
"numpy.zeros_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
VAW-SwissTerra/SwissTerra | [
"c681ade9064fea6b035bb184280f33df5baebfad"
] | [
"terra/base_dem.py"
] | [
"\"\"\"Helper functions for base DEM (swissALTI3D) operations.\"\"\"\nfrom __future__ import annotations\n\nimport os\nimport subprocess\nimport tempfile\n\nimport geopandas as gpd\nimport numpy as np\nimport rasterio as rio\nimport shapely\n\nfrom terra import files\nfrom terra.constants import CONSTANTS\n\nTEMP_D... | [
[
"numpy.isnan"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
greenrhyno/NeuralNetwork-Viterbi | [
"73f2a82b184a3b9f7016094f32ec46f9807eca7f"
] | [
"eval.py"
] | [
"#!/usr/bin/python\n\n#set default GPU to gpu 0\nimport os\nos.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n\nimport argparse\nimport glob\nimport re\nimport numpy as np\n\ndef recog_file(filename, ground_truth_path):\n\n # read ground truth\n gt_file = ground_truth_path + re.sub('.*/','/',filename) + '.txt'\n ... | [
[
"numpy.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ramdootp/cisco-kubeflow-starter-pack | [
"13f02cbad2c0f1278401f3bc6c4b89e9f87884a2"
] | [
"apps/networking/ble-localization/onprem/pipelines/components/v2/tf-webui-app/blerssi-webapp-client.py"
] | [
"import os\r\nimport random\r\nimport numpy\r\nimport pandas as pd\r\nimport tensorflow as tf\r\nimport grpc\r\nfrom tensorflow_serving.apis import predict_pb2\r\nfrom tensorflow_serving.apis import prediction_service_pb2\r\nfrom tensorflow_serving.apis import classification_pb2\r\nfrom tensorflow_serving.apis impo... | [
[
"pandas.read_csv",
"pandas.DataFrame",
"tensorflow.train.Example"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
ldirichlet/LAS-NL-Explanations | [
"9c6c18abd8bd95c675271df7a4a703c2769ea55d"
] | [
"sim_experiments/utils.py"
] | [
"import os\r\nfrom sacrebleu import corpus_bleu\r\nimport csv\r\nimport argparse\r\nimport logging\r\nimport json\r\nimport time\r\nimport torch\r\nimport torch.nn.functional as F\r\nimport numpy as np\r\nimport pandas as pd\r\n\r\n### METRICS ###\r\n\r\ndef computeBLEU(outputs, targets):\r\n # see https://githu... | [
[
"torch.nn.Softmax",
"torch.nn.functional.softmax",
"torch.mm",
"torch.nn.functional.gumbel_softmax",
"torch.ones",
"torch.cat",
"torch.zeros",
"torch.sum",
"torch.zeros_like",
"torch.tensor",
"torch.log",
"torch.no_grad",
"torch.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jeremiq/data-science-types | [
"ed32d873d54eec70d0e85dcc413b984923e282f2"
] | [
"tests/numpy_test.py"
] | [
"\"\"\"Tests for numpy\"\"\"\nfrom pathlib import Path\nfrom typing import List, Sequence, TypeVar, Type\n\nimport numpy as np\nimport pytest\n\nDType = TypeVar(\n \"DType\",\n np.bool_,\n np.float32,\n np.float64,\n np.int8,\n np.int16,\n np.int32,\n np.int64,\n np.str_,\n np.uint8,\n... | [
[
"numpy.asarray",
"numpy.dtype",
"numpy.concatenate",
"numpy.max",
"numpy.mean",
"numpy.where",
"numpy.divide",
"numpy.save",
"numpy.finfo",
"numpy.sin",
"numpy.std",
"numpy.interp",
"numpy.float32",
"numpy.load",
"numpy.repeat",
"numpy.random.choice"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Treinamento-Phyton/plotProject | [
"9073b731f419e6f4df4d04b6998495c92c1799e8"
] | [
"plot/main.py"
] | [
"import matplotlib.pyplot as plt\nimport numpy as np\n\nValor_medio = 1\nfrequencia = 2\ntempo_inicial = 0.0\ntempo_final = 2.0\ndelta_t = 0.01\n\n# Data for plotting\nt = np.arange(tempo_inicial, tempo_final, delta_t)\ns = Valor_medio + np.sin(frequencia * np.pi * t)\n\n#s = t*t - 3*t + 1\n\nfig, ax = plt.subplots... | [
[
"numpy.arange",
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplots",
"numpy.sin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
faku24/rqalpha | [
"22ac415a7304568e6286f0f1b3a2ae712671e091"
] | [
"rqalpha/model/order.py"
] | [
"# -*- coding: utf-8 -*-\n# 版权所有 2019 深圳米筐科技有限公司（下称“米筐科技”）\n#\n# 除非遵守当前许可，否则不得使用本软件。\n#\n# * 非商业用途（非商业用途指个人出于非商业目的使用本软件，或者高校、研究所等非营利机构出于教育、科研等目的使用本软件）：\n# 遵守 Apache License 2.0（下称“Apache 2.0 许可”），\n# 您可以在以下位置获得 Apache 2.0 许可的副本：http://www.apache.org/licenses/LICENSE-2.0。\n# 除非法律有要求或以书面形式... | [
[
"numpy.isnan"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
monjurulkarim/crack-localization | [
"58adf8036085b09cdda64fdf0fb1d809df733d35"
] | [
"inference.py"
] | [
"import torch\nfrom torchvision import datasets, models, transforms\nimport torch.nn as nn\nfrom torch.nn import functional as F\nimport torch.optim as optim\nimport torchvision\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom PIL import Image\nimport os\nimport random\nfrom src.model import CrackClassifi... | [
[
"matplotlib.pyplot.imshow",
"torch.nn.functional.softmax",
"torch.load",
"torch.cuda.is_available",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mrazomej/bebi103 | [
"acfe44feeb40a3b778979837fe9c98e7719cd4fc"
] | [
"bebi103/utils.py"
] | [
"import multiprocessing\nimport warnings\n\nimport tqdm\n\nimport numpy as np\nimport numba\n\n\n@numba.njit\ndef ecdf(x, data):\n \"\"\"\n Compute the values of the formal ECDF generated from `data` at\n points `x`. I.e., if F is the ECDF, return F(x).\n\n Parameters\n ----------\n x : array_like... | [
[
"numpy.sqrt",
"numpy.random.seed",
"numpy.random.choice",
"numpy.isnan",
"numpy.arange",
"numpy.abs",
"numpy.random.shuffle",
"numpy.sort",
"numpy.concatenate",
"numpy.std",
"numpy.mean",
"numpy.isscalar",
"numpy.var",
"numpy.array",
"numpy.isinf",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
anudeep23/CS7634-FinalProject-COMET2020 | [
"ef86531719a9016f2597516d84dbcf010fb8699c"
] | [
"split/split_transomcs.py"
] | [
"import argparse\nimport random\n\nfrom utils import read_csv, write_array2tsv, head_based_split\n\n\ndef load_transomcs(args):\n import matplotlib.pyplot as plt\n\n random.seed(args.random_seed)\n\n data_file = args.data_folder + args.data_file\n data = read_csv(data_file, delimiter=\"\\t\")\n\n sel... | [
[
"matplotlib.pyplot.yscale",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.hist",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zilabs/hummingbot | [
"f2212c86c57a04e4ebd5137cd0becf9774a7db28"
] | [
"hummingbot/connector/exchange/kucoin/kucoin_api_order_book_data_source.py"
] | [
"#!/usr/bin/env python\nfrom itertools import islice\n\nimport aiohttp\nimport asyncio\nfrom async_timeout import timeout\nfrom collections import defaultdict\nfrom enum import Enum\nimport json\nimport logging\nimport pandas as pd\nimport time\nfrom typing import (\n Any,\n AsyncIterable,\n Dict,\n Lis... | [
[
"pandas.Timedelta",
"pandas.Timestamp.utcnow"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kiva/kiva-pricing-tool | [
"a21d06b8ded95e434f5effbae976e13dc87d4201"
] | [
"backend/tests/input_form_offline.py"
] | [
"import numpy as np\nimport itertools as it\nimport sys\nimport datetime\nfrom calendar import monthrange\nimport calendar\nsys.path.insert(0, '../api')\n\nfrom utils import get_num_days_to_incre, calc_origin_days, on_days_change\ndef round_float(f, n):\n return np.floor(f * 10 ** n + 0.5) / 10**n\n\n \na = n... | [
[
"numpy.floor",
"numpy.array",
"numpy.zeros",
"numpy.sum",
"numpy.irr"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
WeavingWong/DigiX_HuaWei_Population_Age_Attribution_Predict | [
"2af7ce6490864e05b6ac0afed6ec549ddd625750"
] | [
"code/nn_model/act_use_mlp.py"
] | [
"import os\r\nos.environ['CUDA_DEVICE_ORDER']='PCI_BUS_ID'\r\nos.environ['CUDA_VISIBLE_DEVICES']='0'\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom models import mlp_v2 \r\nimport gc\r\nfrom keras.models import Model\r\nfrom sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\r\nfrom keras... | [
[
"pandas.merge",
"pandas.read_csv",
"numpy.arange",
"sklearn.model_selection.StratifiedKFold",
"pandas.DataFrame",
"sklearn.feature_extraction.text.CountVectorizer",
"numpy.argmax",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
amilacsw/fastai-course-amila | [
"c11ac1ce47f2498b830817268a6be62ea039d5e8"
] | [
"dllib/logger.py"
] | [
"# A simple torch style logger\r\n# (C) Wei YANG 2017\r\nfrom __future__ import absolute_import\r\nimport matplotlib.pyplot as plt\r\nimport os\r\nimport sys\r\nimport numpy as np\r\n\r\nclass Logger(object):\r\n '''Save training process to log file with simple plot function.'''\r\n def __init__(self, fpath, ... | [
[
"numpy.asarray",
"matplotlib.pyplot.legend",
"matplotlib.pyplot.grid"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ronkow/scraping-wikipedia | [
"d15450ba39bc688d7b82f3fcc74f994cf2fbb6b1"
] | [
"wikipedia_single.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport wikipedia\n\n# Store the text in the Summary section as a string\ntext_summary = wikipedia.WikipediaPage(title = 'Yesterday (2019 film)').summary\nprint(text_summary)\n\n\n# In[2]:\n\n\n# Store the text in four other sections as strings\ntext_plot = wi... | [
[
"numpy.array",
"pandas.read_html"
]
] | [
{
"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": []
}
] |
motrom/kittitracking-pdfmht | [
"77b9b7e823107924741d488051b74780687cdecc"
] | [
"kittiGT.py"
] | [
"# -*- coding: utf-8 -*-\nimport numpy as np\nfrom grid import gridstart, gridstep, gridlen\n\n# kitti scoring rules\ntruncated_cutoffs = np.array((.15, .3, .5))\noccluded_cutoffs = np.array((0, 1, 2))\nheight_cutoffs = np.array((40, 25, 25))\nscored_classes = ('Car', 'Pedestrian', 'Cyclist')\n\n\n\"\"\"\nConverts ... | [
[
"numpy.min",
"numpy.cos",
"numpy.sin",
"numpy.arctan2",
"numpy.max",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
NeuroLang/dask-sql | [
"e5fac1ab0f0b873b8b14e5cec021f5edd00ddff4"
] | [
"tests/integration/test_sort.py"
] | [
"from dask_sql.context import Context\nimport pytest\n\nfrom pandas.testing import assert_frame_equal, assert_series_equal\n\nimport pandas as pd\nimport dask.dataframe as dd\n\n\ndef test_sort(c, user_table_1, df):\n df_result = c.sql(\n \"\"\"\n SELECT\n *\n FROM user_table_1\n ORDER BY ... | [
[
"pandas.testing.assert_frame_equal",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
fiatlux316/tacotron1-kss | [
"9c647b88e62bbe91ce5fe68b1c71e4ec7e4eab39"
] | [
"synthesizer.py"
] | [
"import io\nimport os\nimport re\nimport librosa\nimport argparse\nimport numpy as np\nfrom glob import glob\nfrom tqdm import tqdm\nimport tensorflow as tf\nfrom functools import partial\n\nfrom hparams import hparams\nfrom models import create_model, get_most_recent_checkpoint\nfrom audio import save_audio, inv_s... | [
[
"tensorflow.zeros",
"numpy.zeros_like",
"numpy.var",
"numpy.save",
"tensorflow.ConfigProto",
"tensorflow.reset_default_graph",
"tensorflow.Session",
"tensorflow.train.Saver",
"numpy.load",
"numpy.zeros",
"numpy.lib.pad",
"tensorflow.shape",
"tensorflow.placehold... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
sisl/rllab | [
"932d6cc0911071443d1d43277ee011f4d832631e"
] | [
"FirestormProject/OldFireExtinguishing/simpy_fire_smdp.py"
] | [
"import copy\nimport math\nimport sys\nimport itertools\nimport os.path as osp\n\nimport numpy as np\n#from gym import spaces\nfrom rllab.spaces import Box, Discrete\n# from sandbox.rocky.tf.spaces import Box, Discrete\nimport simpy\n\n\nfrom gym.utils import colorize, seeding\n\nfrom eventdriven.madrl_environments... | [
[
"numpy.random.exponential",
"numpy.array",
"numpy.linalg.norm",
"numpy.random.random_sample"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wh200720041/MMS_SLAM | [
"c7fd9a87d3cef0036ca50bea6ef7d039522a1b53"
] | [
"dependency/mmdet/mmdet/core/evaluation/coco_utils.py"
] | [
"import itertools\n\nimport mmcv\nimport numpy as np\nfrom pycocotools.coco import COCO\nfrom pycocotools.cocoeval import COCOeval\nfrom terminaltables import AsciiTable\n\nfrom .recall import eval_recalls\nfrom alfred.utils.log import logger as logging\n\ndef coco_eval(result_files,\n result_types,\n ... | [
[
"numpy.arange",
"numpy.array",
"numpy.zeros",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
KayaDevSolutions/GazeML | [
"a0cc072bad7d77b8c5b5698082b77cfb1011f45b"
] | [
"src/databasequery.py"
] | [
"import psycopg2\r\nimport pandas as pd\r\nimport sqlalchemy as db \r\nfrom sqlalchemy import select\r\nfrom sqlalchemy.orm import sessionmaker\r\nfrom sqlalchemy.ext.automap import automap_base\r\nimport numpy as np\r\nimport base64\r\nimport numpy as np\r\nfrom PIL import Image\r\nimport PIL\r\nimport cv2\r\nclas... | [
[
"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": []
}
] |
antoinech2/simulation_virus_covid-19 | [
"de710055765ea0858fb449de700256a72e8262b8"
] | [
"src/population.py"
] | [
"#Fichier pour générer la population\n#Objectif : recréer une population représentative de la France par rapport à différents critères.\n\n#Modules internes\nfrom constants import *\n\n#Modules externes\nimport sqlite3\nimport numpy as np\nfrom sklearn.datasets import make_blobs\nfrom scipy.spatial import distance\... | [
[
"sklearn.datasets.make_blobs",
"scipy.spatial.distance.euclidean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"1.3",
"1.8"
... |
gavin-parker/Thesis | [
"09c6a579206b1d05072e59af72e134ecfed6378c"
] | [
"lighting_model/trainer/preprocessing_ops.py"
] | [
"import tensorflow as tf\nimport cv2\nimport numpy as np\nimport math\nfrom StringIO import StringIO\nfrom tensorflow.python.lib.io import file_io\n\nHDR_MIN = 0.0\nHDR_MAX = 1.0\nEPS = 1e-12\n\nthetas = np.arange(0,128)\nrhos = np.arange(0,128)\nconvmap = np.ones([128,128,2])\n\n#for i in thetas:\n# for j in rh... | [
[
"tensorflow.is_nan",
"tensorflow.convert_to_tensor",
"numpy.expand_dims",
"tensorflow.concat",
"tensorflow.reduce_sum",
"tensorflow.stack",
"tensorflow.cast",
"tensorflow.image.decode_png",
"tensorflow.map_fn",
"tensorflow.py_func",
"tensorflow.nn.conv2d",
"numpy.sq... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
mrmelchi/ML_for_trading | [
"7713267ee5c92e488086588ac41490c44b4f7350",
"7713267ee5c92e488086588ac41490c44b4f7350"
] | [
"45_crypto_optimized.py",
"37_minimizer.py"
] | [
"import numpy as np\nimport pandas as pd\nfrom matplotlib import pyplot as plt\nfrom datetime import datetime\nimport json\nfrom bs4 import BeautifulSoup\nimport requests\nfrom tqdm import tqdm\n\n\ndef timestamp2date(timestamp):\n # function converts a Uniloc timestamp into Gregorian date\n return datetime.f... | [
[
"matplotlib.pyplot.legend",
"pandas.concat",
"pandas.to_datetime",
"numpy.random.random",
"matplotlib.pyplot.scatter",
"pandas.DataFrame",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.colorbar",
"numpy.array",
"matplotlib.pyplot.show",
"numpy.zeros",
"numpy.sum",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"nump... |
Javed-Akhtar/ga-learner-dsb-repo | [
"e12974370cbce021efd66053ea677ce7c2f11857"
] | [
"Superhero-Statistics/code.py"
] | [
"# --------------\n#Header files\r\nimport pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\n#path of the data file- path\r\n#loading data\r\ndata = pd.read_csv(path)\r\n\r\n#replacing data in Gender Column\r\ndata.Gender.replace('-','Agender',inplace=True)\r\n\r\n#counting different type... | [
[
"pandas.read_csv",
"matplotlib.pyplot.subplots",
"numpy.cov",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.pie"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
wdurno/rl-hypothesis-2 | [
"f45fc2d98ec38783b71b308529af95a605fa6c37"
] | [
"notes/python/manual_linear_regression.py"
] | [
"## Just learning how to pytorch \n\nimport numpy as np\nimport torch\n\nx = torch.tensor(3.)\nw = torch.tensor(4., requires_grad=True)\nb = torch.tensor(5., requires_grad=True)\n\ny = w * x + b\nprint(y)\n\n# Compute gradients\ny.backward() \n\n# Display gradients\nprint('dy/dw:', w.grad)\nprint('dy/db:', b.grad)\... | [
[
"torch.randn",
"torch.sum",
"torch.from_numpy",
"torch.tensor",
"torch.no_grad",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jvolden/major_leagues | [
"1245baab2c4af92285fe3a026391e429cec5af57"
] | [
"notebooks/major_leagues.py"
] | [
"\"\"\"Main module.\"\"\"\n\nimport pandas as pd\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.pipeline import Pipeline\n\nclass MultiColumnLabelEncoder:\n def __init__(self,columns = None):\n self.columns = columns # array of column names to encode\n\n def fit(self,X,y=None):\n ... | [
[
"sklearn.preprocessing.LabelEncoder"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wchen459/MO-PaDGAN-Optimization | [
"101d389049e16574dac0b3dbae3a2578d600560c"
] | [
"airfoil/surrogate/surrogate_model.py"
] | [
"\"\"\"\nBezierGAN for capturing the airfoil manifold\n\nAuthor(s): Wei Chen (wchen459@gmail.com)\n\"\"\"\n\nimport numpy as np\nimport tensorflow as tf\n\n\ndef preprocess(X):\n X = np.expand_dims(X, axis=-1)\n return X.astype(np.float32)\n\ndef postprocess(X):\n X = np.squeeze(X)\n return X\n\nEPSILON... | [
[
"numpy.expand_dims",
"tensorflow.control_dependencies",
"numpy.squeeze",
"tensorflow.train.AdamOptimizer",
"tensorflow.get_default_graph",
"tensorflow.summary.scalar",
"tensorflow.layers.batch_normalization",
"tensorflow.contrib.layers.variance_scaling_initializer",
"tensorflow... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
gilberthon/pandas | [
"4d2f8339c1fd6c054582d87f271006448cd07d38"
] | [
"ple.py"
] | [
"import pandas as pd\n\n\ndf = pd.read_csv('examples/ex1.csv')\nprint(df)\n"
] | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
kevinzakka/walle | [
"b8e937f6a15c7523690a6d926478471678c0df9c"
] | [
"walle/core/matrix.py"
] | [
"\"\"\"Rotation matrices.\n\"\"\"\n\nimport numpy as np\n\n\nclass RotationMatrix:\n \"\"\"A convenience class for dealing with 3-D rotation matrices.\n\n References:\n .. [3] James Arvo, \"Fast Random Rotation Matrices\", The Graphics Gems Series, III.4\n \"\"\"\n\n def to_quaternion(self):\n ... | [
[
"numpy.dot",
"numpy.sqrt",
"numpy.eye",
"numpy.cos",
"numpy.sin",
"numpy.random.uniform"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
helenyu18/blue-marlin | [
"668985fad1993a682808e271610c1cf2cec6a6f5"
] | [
"Processes/optimizer/tests/tests_fully_overlapped/test_hwm_allocations_2.py"
] | [
"# Copyright 2019, Futurewei Technologies\n#\n# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements. See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership. The ASF licenses this file\n# to you under the Apache... | [
[
"pandas.testing.assert_frame_equal",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
PetchMa/turbo_seti | [
"c9dd39e370a4628c400faa7dfd5be9f50f35e299"
] | [
"turbo_seti/findoppler/data_handler.py"
] | [
"#!/usr/bin/env python\n\nimport os\nimport numpy as np\nimport math\nfrom pkg_resources import resource_filename\nfrom blimpy import Waterfall\nfrom blimpy.io import sigproc\nimport h5py\n\nimport logging\nlogger = logging.getLogger(__name__)\n\n#For debugging\n#import cProfile\n#import pdb;# pdb.set_trace()\n\nSI... | [
[
"numpy.log2",
"numpy.abs",
"numpy.squeeze",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pjuckem/GRTD | [
"b01d11bc036bd0ec9abda8c908d5791900a5db87"
] | [
"Scripts/07a+Calculate+flux-weighted+whole+aquifer--Process+Wells.py"
] | [
"\n# coding: utf-8\n\n# # Create a general MODFLOW model from the NHDPlus dataset--calculate age distributions and tracer concentrations at pumping wells \n\n# In[ ]:\n\n__author__ = 'Jeff Starn' \n# get_ipython().magic('matplotlib notebook')\n# from IPython.display import set_matplotlib_formats\n# set_matplotlib... | [
[
"numpy.minimum",
"numpy.maximum",
"numpy.isfinite",
"numpy.unique",
"numpy.linalg.inv",
"numpy.arange",
"matplotlib.patches.Rectangle",
"numpy.int32",
"numpy.indices",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig",
"pandas.read_table",
"matplotlib.py... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
giussepi/semantic-segmentation-codebase | [
"163b0edfa30a8e1147b532a737d0784ea09f4fc2",
"163b0edfa30a8e1147b532a737d0784ea09f4fc2"
] | [
"lib/net/backbone/resnet38d.py",
"lib/net/deeplabv3plus.py"
] | [
"import torch\nfrom torch import nn\nimport numpy as np\nimport torch.nn.functional as F\nfrom lib.utils.registry import BACKBONES\n\nmodel_url = '/home1/wangyude/project/SEAM/models/ilsvrc-cls_rna-a1_cls1000_ep-0001.params'\nbn_mom = 0.0003\n\n\nclass ResBlock(nn.Module):\n def __init__(self, in_channels, mid_c... | [
[
"torch.nn.Dropout2d",
"numpy.asarray",
"numpy.empty_like",
"torch.nn.Conv2d",
"torch.nn.functional.relu"
],
[
"torch.norm",
"torch.cat",
"torch.nn.init.constant_",
"torch.nn.Conv2d",
"torch.eye",
"torch.nn.Sigmoid",
"torch.nn.InstanceNorm2d",
"torch.nn.funct... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
thestephencasper/BPref | [
"f3ece2ecf04b5d11b276d9bbb19b8004c29429d1"
] | [
"stable_baselines3/common/on_policy_algorithm.py"
] | [
"import time\nfrom typing import Any, Dict, List, Optional, Tuple, Type, Union\n\nimport gym\nimport numpy as np\nimport torch as th\n\nfrom stable_baselines3.common import logger\nfrom stable_baselines3.common.base_class import BaseAlgorithm\nfrom stable_baselines3.common.buffers import RolloutBuffer\nfrom stable_... | [
[
"torch.as_tensor",
"torch.no_grad",
"numpy.mean",
"numpy.clip"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DavideA/ai4automotive | [
"4f8223a81af88bbd701c8475db86cfe19efea6b2"
] | [
"datasets/dreyeve.py"
] | [
"from os.path import join\n\nimport numpy as np\nimport skimage.io as io\nimport torch\nfrom skimage.transform import resize\nfrom torch.utils.data import Dataset\n\nfrom utils import set_random_seed\n\n\nclass Preprocess:\n\n def __init__(self, raw_shape, small_shape, pre_crop_shape):\n self.raw_shape = ... | [
[
"numpy.fromfile",
"numpy.random.choice",
"numpy.reshape",
"matplotlib.use",
"numpy.squeeze",
"torch.utils.data.DataLoader",
"matplotlib.pyplot.subplots",
"torch.FloatTensor",
"numpy.transpose",
"matplotlib.pyplot.ion",
"numpy.zeros",
"matplotlib.pyplot.pause",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wolf71/Qsqlite | [
"8a16748b92dbe2512abb539d20e75d3565c84e6c"
] | [
"Qsqlite/Qsqlite.py"
] | [
"'''\n Qsqlite Quick sqlite Tools\n (c) 2020 - 2022\n By: Charles Lai\n'''\n\n__version__ = 0.972\n__author__ = 'Charles Lai'\n\nhelp_str = '''\n====== Qsqlite (Quick Sqlite Tools) Help (V0.972) ======\n# command\n @ q - quit\n @ ?/h - help or ? querystr etc: ? draw; cls / clear - cle... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.yscale",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.close",
"matplotlib.pyplot.subplots_adjust",
"matplotlib.pyplot.xscale",
"matplotlib.pyplot.xticks",
"matpl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nate-io/google-ml-crash-course | [
"c15d227076ec5ff31725fdc103fcae241ac0cf9c"
] | [
"functions.py"
] | [
"'''\n Google ML Crash Course Setup Functions\n'''\n\n#@title Import relevant modules\nimport pandas as pd\nimport tensorflow as tf\nfrom matplotlib import pyplot as plt\n\n# The following lines adjust the granularity of reporting. \npd.options.display.max_rows = 10\npd.options.display.float_format = \"{:.1f}\".fo... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.figure",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.optimizers.RMSprop",
"pandas.DataFrame",
"matplotlib.pyplot.plot",
"tensorflow.keras.metrics.RootMeanSquaredError",
"matplotlib.pyplot.xlabel"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": [
"2.7",
"2.6",
"2.4",
... |
GeminiDRSoftware/GHOSTDR | [
"79cd1ac81a7458e06668d6dac51fc6f9c9c61b31",
"79cd1ac81a7458e06668d6dac51fc6f9c9c61b31",
"79cd1ac81a7458e06668d6dac51fc6f9c9c61b31"
] | [
"utils/wavelength_fit.py",
"utils/show_slit.py",
"ghostdr/ghost/recipes/test/111_masterBias_test.py"
] | [
"#!/usr/bin/env python3\n\n\"\"\"A script to fit tramlines and arcs for Ghost data.\n\nThis script is used to make sure the wavelength scale is working properly. \nIt is an intermediate script and is not designed to be used for commissioning,\nbut instead for debugging and development purposes. \n\n\"\"\"\n\nfrom _... | [
[
"numpy.median",
"numpy.loadtxt"
],
[
"matplotlib.pylab.show",
"matplotlib.pylab.subplots"
],
[
"numpy.abs",
"numpy.ma.std",
"numpy.ma.median",
"numpy.all",
"numpy.mean",
"numpy.ma.mean",
"numpy.ma.max"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.13",... |
oschusler/PycQED_py3 | [
"a24c81684ff22f11a62dfe191817029749115e10",
"a24c81684ff22f11a62dfe191817029749115e10",
"a24c81684ff22f11a62dfe191817029749115e10"
] | [
"pycqed/simulations/cz_superoperator_simulation_new2.py",
"pycqed/measurement/optimization.py",
"pycqed/measurement/openql_experiments/multi_qubit_oql.py"
] | [
"from pycqed.measurement import measurement_control as mc\n\nimport adaptive\nfrom pycqed.instrument_drivers.meta_instrument.LutMans import flux_lutman as flm\nfrom pycqed.instrument_drivers.virtual_instruments import sim_control_CZ as scCZ\n\nfrom pycqed.simulations import cz_superoperator_simulation_new_functions... | [
[
"numpy.linspace",
"numpy.arange",
"numpy.set_printoptions",
"numpy.concatenate",
"numpy.append",
"scipy.interpolate.interp1d",
"numpy.size",
"numpy.deg2rad",
"numpy.log10",
"numpy.array",
"numpy.sum"
],
[
"numpy.diag",
"numpy.min",
"numpy.asarray",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"1.3",
"1.8"
... |
levi-oh/pic2animation | [
"cdd465e5deea23fd3e0c4bee80455bccf066e191"
] | [
"src/renderer/python/image_animation.py"
] | [
"import imageio\nimport torch\nimport time\nfrom tqdm import tqdm\nfrom animate import normalize_kp\nfrom demo import load_checkpoints\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as animation\nfrom skimage import img_as_ubyte\nfrom skimage.transform import resize\nimport cv2\ni... | [
[
"torch.no_grad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
davidbarkhuizen/plotcsv | [
"4ff44420f0f761278adac6aec81d661ad0327b34"
] | [
"lineplot.py"
] | [
"import sys\nif (sys.version_info < (3, 0)): raise Exception(\"!python3\")\n\nfrom time import perf_counter\n\n# TODO\n# plot series from 2 diff CSV files, with common x date axis\n# allow mapping of series name\n\n# ---------------------------------------------------------\n# command line optionss\n\nimport argpar... | [
[
"matplotlib.dates.DateFormatter",
"matplotlib.pyplot.title",
"matplotlib.pyplot.yticks",
"matplotlib.pyplot.xticks",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
anonymous-rev/review | [
"2cf8ef091ef4efc33752a184ab54ea95f1c921e5"
] | [
"gym/envs/mujoco/inverted_pendulum.py"
] | [
"import numpy as np\nfrom gym import utils\nfrom gym.envs.mujoco import mujoco_env\n\nclass InvertedPendulumEnv(mujoco_env.MujocoEnv, utils.EzPickle):\n def __init__(self, xml_file='inverted_pendulum.xml'):\n utils.EzPickle.__init__(self)\n mujoco_env.MujocoEnv.__init__(self, xml_file, 2)\n\n de... | [
[
"numpy.concatenate",
"numpy.abs",
"numpy.isfinite"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
erv4gen/Tools-WebScraping | [
"710e918ea0b5335fee000f6f62cf27930e9ad601"
] | [
"ContentDownloader/ContentDownloader.py"
] | [
"import re , time , ipdb , random\nimport pandas as pd\nimport multiprocessing as mp\nfrom tqdm import tqdm\nfrom pprint import pprint\n\nfrom requests import get\nfrom requests.exceptions import RequestException\nfrom contextlib import closing\nfrom user_agent import generate_user_agent\n\nfrom bs4 import Beauti... | [
[
"pandas.concat",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
ielab/asyncval | [
"18e03bd216d05be23be248b8e7f7934d10e17baf"
] | [
"src/asyncval/__main__.py"
] | [
"import os\nimport time\nimport logging\nfrom transformers import AutoTokenizer\nfrom transformers import (\n HfArgumentParser,\n TrainingArguments,\n)\nfrom asyncval.callbacks import get_reporting_integration_callbacks, CallbackHandler\nfrom torch.utils.data import DataLoader\nfrom asyncval.arguments import ... | [
[
"torch.cat",
"torch.cuda.empty_cache",
"torch.cuda.amp.autocast",
"torch.no_grad",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tek5030/lab-mosaic-py | [
"46fdff6c3d3c08b84a012f5bd097c7cbb7ecb670"
] | [
"lab_mosaic.py"
] | [
"import cv2\nimport numpy as np\nfrom numpy import linalg\nimport timeit\n\nfrom common_lab_utils import HomographyEstimate, homogeneous, hnormalized, \\\n retain_best, extract_matching_points, randomly_select_points, \\\n colours, draw_keypoint_detections, draw_keypoint_matches, draw_estimation_details\n\n\n... | [
[
"numpy.linalg.svd",
"numpy.log",
"numpy.sqrt",
"numpy.asarray",
"numpy.linalg.inv",
"numpy.linalg.norm",
"numpy.copy",
"numpy.mean",
"numpy.count_nonzero",
"numpy.array",
"numpy.flip"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.