repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
ruinianxu/sentence-transformers | [
"b4ab6f0378ea1cb0c33a60163f0aabba6ac0a477"
] | [
"examples/training/sts/training_gt_rt_exonly_simple.py"
] | [
"\"\"\"\nThis examples trains BERT (or any other transformer model like RoBERTa, DistilBERT etc.) for the STSbenchmark from scratch. It generates sentence embeddings\nthat can be compared using cosine-similarity to measure the similarity.\n\nUsage:\npython training_nli.py\n\nOR\npython training_nli.py pretrained_tr... | [
[
"torch.utils.data.DataLoader"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xavialex/Instance-Segmentation | [
"7ad73ed8b352d8ffefe4265b2469949daae5e209"
] | [
"instance_segmentation_webcam.py"
] | [
"from pathlib import Path\nimport cv2\nimport time\nimport numpy as np\nimport tensorflow as tf\n\nfrom object_detection.utils import label_map_util\nfrom object_detection.utils import visualization_utils as vis_util\nfrom object_detection.utils import ops as utils_ops\n\nCWD_PATH = Path('.')\nTF_MODELS_PATH = Path... | [
[
"tensorflow.Graph",
"tensorflow.import_graph_def",
"numpy.expand_dims",
"tensorflow.greater",
"tensorflow.slice",
"tensorflow.cast",
"tensorflow.squeeze",
"tensorflow.expand_dims",
"tensorflow.Session",
"tensorflow.get_default_graph",
"tensorflow.GraphDef"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
omidsakhi/non_progressive_stylegan | [
"4a9c41a279fd10065764cf297f5925a74c164e5c"
] | [
"models.py"
] | [
"import tensorflow as tf\nimport ops\n\ndef discriminator(x, is_training=True, scope='Discriminator'):\n with tf.variable_scope(scope, reuse=tf.AUTO_REUSE):\n\n x = tf.concat(tf.split(x, 2, 0), axis=3)\n\n fn = 64 \n\n x = ops.conv2d_down('256-128', x, fn, 5, 'NHWC')\n x = ops.lrelu(x)\n\n x = op... | [
[
"tensorflow.get_variable",
"tensorflow.nn.tanh",
"tensorflow.variable_scope",
"tensorflow.split",
"tensorflow.tile"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
AndrewKinsman/rasa | [
"810d1c2f52261b3704b0b77b7fcb8c63eafaeb5e"
] | [
"rasa/utils/common.py"
] | [
"import logging\nimport os\nimport shutil\nfrom types import TracebackType\nfrom typing import Any, Callable, Dict, List, Text, Optional, Type\n\nimport rasa.core.utils\nimport rasa.utils.io\nfrom rasa.constants import (\n GLOBAL_USER_CONFIG_PATH,\n DEFAULT_LOG_LEVEL,\n ENV_LOG_LEVEL,\n DEFAULT_LOG_LEVE... | [
[
"tensorflow.compat.v1.logging.set_verbosity"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
seguri/machine-learning-for-financial-analysis | [
"3747461ec093629d3dae9bc88195d74672b00c6b"
] | [
"CAPM.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n\n# # Capital Asset Pricing Model\n\n# ## Milestone 1\n\n# In[65]:\n\n\nget_ipython().run_line_magic('reload_ext', 'dotenv')\nget_ipython().run_line_magic('dotenv', '')\nget_ipython().run_line_magic('matplotlib', 'inline')\n\nimport os\n\nimport matplotlib.pyplot as plt\nimp... | [
[
"matplotlib.pyplot.subplots"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
royukira/estimator | [
"64a11cddd0b17308cd60040d8487074ce9a9264f"
] | [
"tensorflow_estimator/python/estimator/canned/linear.py"
] | [
"# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.python.keras.optimizer_v2.ftrl.Ftrl",
"tensorflow.python.ops.math_ops.greater_equal",
"tensorflow.python.ops.array_ops.shape",
"tensorflow.python.ops.math_ops.reduce_max",
"tensorflow.python.summary.summary.scalar",
"tensorflow.python.summary.summary.histogram",
"tensorflow... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.2"
]
}
] |
SAP-samples/dwc-fedml | [
"39ea8cefc31741c4ec37eb6c0d70dd48e7c034ba"
] | [
"Azure/sample-notebooks/LinearRegression/Scikit-Learn-Linear-Regression/train_script.py"
] | [
"import os\nimport time\nimport pandas as pd\nimport argparse\nfrom sklearn import __version__ as sklearnver\nimport joblib\n\nfrom sklearn.linear_model import LinearRegression\nimport numpy as np\nfrom fedml_azure import DbConnection\nfrom sklearn.model_selection import train_test_split\nimport json\n\n\n# https:/... | [
[
"sklearn.linear_model.LinearRegression",
"numpy.shape",
"sklearn.model_selection.train_test_split",
"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": []
}
] |
dfm/mapping_stellar_surfaces | [
"52d4ba1a726c65868e4a1290a801fe046fb2155f"
] | [
"paper2/figures/latitude_pdf.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nfrom starry_process import StarryProcess, gauss2beta, beta2gauss\nimport theano\nimport theano.tensor as tt\nfrom scipy.stats import norm\nimport os\n\nphi = np.linspace(-90, 90, 1000)\na = tt.dscalar()\nb = tt.dscalar()\npdf = theano.function([a, b], StarryProc... | [
[
"scipy.stats.norm.pdf",
"numpy.cos",
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
quic-sendilk/aimet | [
"c6ffd3c31c290fe0913b50831d58534f6df61d76"
] | [
"TrainingExtensions/torch/test/python/test_utils.py"
] | [
"# /usr/bin/env python3.5\n# -*- mode: python -*-\n# =============================================================================\n# @@-COPYRIGHT-START-@@\n#\n# Copyright (c) 2017-2018, Qualcomm Innovation Center, Inc. All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or without... | [
[
"torch.device",
"torch.no_grad",
"torch.rand"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
haigh1510/TensorFlow2.0-Examples | [
"f99fcef22caa2758b5eefce10ee789384345506d"
] | [
"4-Object_Detection/MTCNN/utils.py"
] | [
"#! /usr/bin/env python\n# coding=utf-8\n#================================================================\n# Copyright (C) 2019 * Ltd. All rights reserved.\n#\n# Editor : VIM\n# File name : utils.py\n# Author : YunYang1994\n# Created date: 2019-10-27 01:18:15\n# Description :\n#\n#=========... | [
[
"numpy.expand_dims",
"numpy.minimum",
"numpy.flipud",
"numpy.zeros_like",
"numpy.fix",
"numpy.where",
"numpy.reshape",
"numpy.ceil",
"numpy.zeros",
"numpy.power",
"numpy.amin",
"numpy.append",
"numpy.transpose",
"numpy.argsort",
"numpy.maximum",
"num... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Nicolas-Reyland/OCR-Sudoku-Solver | [
"d5950cf4935c7ca9ae0ff2ce54178ce0eb08c75d"
] | [
"trainnn-converter.py"
] | [
"#!/usr/bin/env python3\nfrom __future__ import annotations\nfrom PIL import Image\nimport numpy as np\nimport os, sys\n\nbase_img_fn = lambda fp: int(os.path.basename(fp)[0])\n\ndef convert_img(img_path: str) -> str:\n img = Image.open(img_path)\n if img.size != (28, 28):\n print(f'resizing: {img_path... | [
[
"numpy.asarray",
"numpy.apply_along_axis",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xinrongl/mmdetection | [
"c6aaf04b36b7d30da039a490192b30794617fdcc"
] | [
"tools/slice_image.py"
] | [
"import numpy as np\nfrom PIL import Image\nimport matplotlib.pyplot as plt\n\n\ndef crop(image, return_array=False, shape=(3, 3), plot=True, figsize=(10, 10)):\n \"\"\"\n Slice image into multiple images.\n Params: \n image: str/pathlib object, image directory\n return_array: bool, if tru... | [
[
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplots",
"numpy.column_stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Purg/SMQTK | [
"705a2b2979935ed129aac7db578571c4ae1343e7"
] | [
"tests/algorithms/nn_index/test_NNI_FLANN.py"
] | [
"from __future__ import division, print_function\nimport mock\nimport random\nimport unittest\n\nimport numpy\nimport pytest\n\nfrom smqtk.algorithms import NearestNeighborsIndex\nfrom smqtk.algorithms.nn_index.flann import FlannNearestNeighborsIndex\nfrom smqtk.representation.descriptor_element.local_elements impo... | [
[
"numpy.array",
"numpy.zeros",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kingmbc/iris-python | [
"824a3dd9cf9fd9685ece05298423a4b8e78dccb7"
] | [
"Lasagne/iris_lasagne.py"
] | [
"from __future__ import print_function\nfrom builtins import range\n\n\"\"\"\nSECTION 1 : Load and setup data for training\n\nthe datasets has separated to two file from originai datasets:\niris_train.csv = datasets for training purpose, 80% from the original data\niris_test.csv = datasets for testing purpose, 20%... | [
[
"numpy.asarray",
"pandas.read_csv",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
edfong/conformal_bayes | [
"0c6d1c1e8e2d41480b57125c52665fc8be182908"
] | [
"run_scripts/load_data.py"
] | [
"from sklearn.datasets import load_boston,load_diabetes,load_breast_cancer\nfrom sklearn.model_selection import train_test_split\nimport time\nimport numpy as np\nfrom tqdm import tqdm\nimport pandas as pd\nimport scipy as sp\n\n## Sparse Regression ##\n#Well specified?\ndef load_traintest_sparsereg(train_frac, dat... | [
[
"numpy.dot",
"numpy.linspace",
"sklearn.datasets.load_diabetes",
"numpy.max",
"numpy.random.randn",
"numpy.mean",
"sklearn.datasets.load_boston",
"pandas.read_csv",
"numpy.arange",
"numpy.std",
"numpy.zeros",
"numpy.log",
"numpy.min",
"numpy.array",
"skl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
dsa110/dsa110-meridian-fs | [
"2c68ae5091f5a4984208c18666c744ec2febde2d"
] | [
"tests/travis/test_fringestopping.py"
] | [
"import os\nimport pytest\nfrom antpos import utils\nfrom dsamfs import fringestopping\nfrom dsamfs.utils import parse_params\nimport dsacalib.utils as du\nimport dsacalib.constants as ct\nfrom dsacalib.fringestopping import calc_uvw\nimport numpy as np\nimport astropy.units as u\n\ndef test_gentable(tmpdir):\n ... | [
[
"numpy.abs",
"numpy.arange",
"numpy.tile",
"numpy.ones",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JonathanAMichaels/NeuropixelsRegistration | [
"b2623f0777b12c72ab304f7f2c1477e9954ef54a"
] | [
"python/estimate_displacement.py"
] | [
"#################################\n# register raw data\n# input: raw data\n# output: registered raw data: float 32\n#################################\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom tqdm import tqdm, notebook\nimport torch\nimport pickle\nimport os\n\nfrom scipy.signal import butter, f... | [
[
"numpy.sqrt",
"numpy.linspace",
"torch.zeros",
"numpy.asarray",
"scipy.stats.zscore",
"matplotlib.pyplot.get_cmap",
"numpy.fft.fftshift",
"numpy.round",
"numpy.zeros_like",
"numpy.mean",
"scipy.interpolate.griddata",
"torch.allclose",
"numpy.where",
"torch.n... | [
{
"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"... |
pressler-vsc/sarpy | [
"fa6c951c42b9a7d9df2edfa53c771494cb0246fb"
] | [
"docs/examples/polygon_example.py"
] | [
"import numpy\nfrom matplotlib import pyplot\nimport time\n\nfrom sarpy.geometry.geometry_elements import Polygon\n\n\ndef generate_random_polygon(segment_count=12):\n \"\"\"\n Generate the coordinates for a random polygon going from (-1, 0) to (1, 0) and back.\n It will be contained in square [-1, 1] x [-... | [
[
"numpy.linspace",
"matplotlib.pyplot.subplots",
"numpy.cumsum",
"numpy.random.rand",
"numpy.array",
"numpy.meshgrid",
"numpy.zeros",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lyj911111/OpenCV_Project | [
"9acbfbf666188b6ebb7f2ec4500bb3ab3d2994b9"
] | [
"AceVision/new/Air3239/Air3239_v2.py"
] | [
"# -*- coding: utf-8 -*-\nimport numpy as np # pip install numpy==1.15.4\nimport cv2 # pip install opencv-python==3.4.4.19\nfrom PIL import Image as Img # pip install image==1.5.27\nimport pyzbar.pyzbar as pyzbar # pip install pyzbar==0.1.7\nfrom more_itertools import unique_everseen # pip install more_itertoo... | [
[
"numpy.around"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nd1511/pytorch2keras | [
"db9c50ebea98dc59c85f9b1066fa06bcc78ff316"
] | [
"tests/models/resnet18_channels_last.py"
] | [
"import numpy as np\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\nfrom pytorch2keras.converter import pytorch_to_keras\nimport torchvision\n\n\ndef check_error(output, k_model, input_np, epsilon=1e-5):\n pytorch_output = output.data.numpy()\n keras_output = k_model.predict(input_n... | [
[
"numpy.max",
"torch.FloatTensor",
"numpy.random.uniform"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
akesandgren/numpy | [
"cc51ba88af13175d4bdf9e7bf9675a88e3cb10ea"
] | [
"numpy/core/tests/test_api.py"
] | [
"from __future__ import division, absolute_import, print_function\n\nimport sys\n\nimport numpy as np\nfrom numpy.compat import sixu\nfrom numpy.testing import (\n run_module_suite, assert_, assert_equal, assert_array_equal,\n assert_raises\n)\n\n# Switch between new behaviour when NPY_RELAXED_STRIDES_CHECK... | [
[
"numpy.matrix",
"numpy.ndarray",
"numpy.dtype",
"numpy.copyto",
"numpy.testing.assert_equal",
"numpy.arange",
"numpy.full",
"numpy.copy",
"numpy.zeros",
"numpy.testing.assert_raises",
"numpy.testing.assert_",
"numpy.broadcast_arrays",
"numpy.compat.sixu",
"n... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
almaan/sepal | [
"9befea4b8960cb3cb6f09634886f3b57e8c56bcb"
] | [
"compare/run-SpatialDE.py"
] | [
"#!/usr/bin/env python3\n\n##-------------------------------------------------------------\n## SpatialDE Data Analysis \n##-------------------------------------------------------------\n\n## From : https://github.com/Teichlab/SpatialDE/README.md\n## Downloaded : 2020-08-07\n\n## Modified to time the analysis\n\ni... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
jostmey/RWA | [
"41d73d0bd9ffba71ea5ead5b3de2704dacbe531d"
] | [
"copy_problem_1000/dataset/input_data.py"
] | [
"#!/usr/bin/env python3\n##########################################################################################\n# Author: Jared L. Ostmeyer\n# Date Started: 2017-01-01\n# Purpose: Load dataset or generate it if it does not exist yet.\n# License: For legal information see LICENSE in the home directory.\n#######... | [
[
"numpy.save",
"numpy.ones",
"numpy.load",
"numpy.zeros",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
c60evaporator/param_tuning_utility | [
"8518b76369dcc918172a87ab4c975ee3a12f7045"
] | [
"examples/muscle_brain_tuning/muscle_multiclass.py"
] | [
"# %% MuscleTuning, multiclass, no argument\nimport parent_import\nfrom muscle_tuning import MuscleTuning\nimport seaborn as sns\n\niris = sns.load_dataset(\"iris\")\nOBJECTIVE_VARIALBLE = 'species' # 目的変数\nUSE_EXPLANATORY = ['petal_width', 'petal_length', 'sepal_width', 'sepal_length'] # 説明変数\ny = iris[OBJECTIVE... | [
[
"sklearn.svm.SVC"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
0b10010010/KalmanMPI | [
"e82f900ac41173643fffec2b08250cddcb56dca8"
] | [
"KalmanGUI.py"
] | [
"import sys\nimport platform\nimport numpy as np # this has to be imported before the ones in line 11 and 12\nfrom math import floor\nimport control\nimport control.matlab as matlab\n#import matplotlib.animation as animation\n\nfrom PyQt5.QtWidgets import (QMainWindow, QApplication, QDialog, QLineEdit, \n ... | [
[
"numpy.sqrt",
"matplotlib.figure.Figure",
"numpy.linalg.inv",
"numpy.arange",
"matplotlib.backends.backend_qt5agg.NavigationToolbar2QT",
"numpy.identity",
"numpy.random.randn",
"matplotlib.backends.backend_qt5agg.FigureCanvas.__init__",
"matplotlib.backends.backend_qt5agg.Figur... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
arthurelmes/geo_tools | [
"6621d134aff0d05f185a1fcfbf9431a5f1f9764e",
"6621d134aff0d05f185a1fcfbf9431a5f1f9764e"
] | [
"validation/plot_two_tifs.py",
"time_series/extract_samples_modviirs43.py"
] | [
"import pandas as pd\nimport rasterio as rio\nimport matplotlib.pyplot as plt\nfrom matplotlib.colors import LogNorm\nimport numpy as np\nimport os, sys\n\n\nf1 = sys.argv[1]\nf2 = sys.argv[2]\nf1_qa = sys.argv[3]\nf2_qa = sys.argv[4]\n\nf1_name = os.path.basename(f1)\nf2_name = os.path.basename(f2)\nout_file_name ... | [
[
"matplotlib.colors.LogNorm",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.subplots",
"pandas.DataFrame",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlim"
],
[
"pandas.concat",
"matplotlib.pyplot.title",
"numpy.multiply",
"pandas.DataFrame",
"matplotlib.pyplot.clf"... | [
{
"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": []
},
{
"matplotlib": [],
"nump... |
behjava/automatic_data_reduction | [
"dd1eb78b22db7c27ccd26d496babc8ffe12d41c0"
] | [
"modules/master_maker.py"
] | [
"import numpy as np\n\n####### Master making function ###########################\ndef MASTER(cube, method):\n\tif method=='sum':\n\t\tout=np.sum(cube, axis=2)\n\tif method=='mean':\n\t\tout=np.mean(cube, axis=2)\n\tif method=='median':\n\t\tout=np.median(cube, axis=2)\n\treturn out\n###############################... | [
[
"numpy.median",
"numpy.mean",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
chuckie82/ami | [
"7adb72c709afe4c1af53ef7f0d2b0e3639c63bf3"
] | [
"ami/graph_nodes.py"
] | [
"import abc\nimport operator\nimport numpy as np\nfrom networkfox import operation\n\n\nclass Transformation(abc.ABC):\n\n def __init__(self, **kwargs):\n \"\"\"\n Keyword Arguments:\n name (str): Name of node\n inputs (list): List of inputs\n outputs (list): List o... | [
[
"numpy.zeros",
"numpy.roll"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
chohner/sound_field_analysis-py | [
"9d3686ae77e96654450fd454de24fe845c363ac8"
] | [
"test/time_sph_harm.py"
] | [
"\"\"\"This test the equality and execution speed of different implementations to\ngenerate spherical harmonics.\n\nExemplary execution:\n======================\n_TIMEIT_REPEAT = 5\n_TIMEIT_NUMBER = 5000\n_N_MAX = 8\n_KIND = real\n======================\nnode \"C18TTLT\"\n======================\n\nsph_harm_1\ntime:... | [
[
"numpy.imag",
"numpy.sqrt",
"numpy.float_power",
"numpy.asarray",
"scipy.special.sph_harm",
"numpy.ones",
"numpy.real",
"numpy.meshgrid",
"numpy.zeros",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
guoshzhao/antares | [
"30a6338dd6ce4100922cf26ec515e615b449f76a",
"30a6338dd6ce4100922cf26ec515e615b449f76a"
] | [
"frameworks/tensorflow/perf_tests/transpose.py",
"frameworks/pytorch/examples/3_multi_outputs.py"
] | [
"#!/usr/bin/env python3\n\n# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nimport tensorflow as tf\nfrom tensorflow.contrib import antares\n\nif tf.version.VERSION.startswith('2.'):\n tf = tf.compat.v1\n tf.disable_eager_execution()\n\nfrom _common import *\n\nx = create_variable([64,... | [
[
"tensorflow.version.VERSION.startswith",
"tensorflow.disable_eager_execution",
"tensorflow.transpose",
"tensorflow.contrib.antares.make_op"
],
[
"torch.device",
"torch.contrib.antares.custom_op.CustomOp",
"torch.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MBravoS/splotch | [
"6b7f5d368f1d1457e6fa3ea6b83596e325fb4383"
] | [
"src/splotch/plots_2d.py"
] | [
"########################################################################\n############## Definition of all wrappers for 2D plotting ##############\n########################################################################\n\n####################################\n# Level contours\n###################################... | [
[
"matplotlib.pyplot.legend",
"numpy.radians",
"numpy.linspace",
"matplotlib.transforms.Affine2D",
"numpy.dtype",
"matplotlib.pyplot.plot",
"numpy.round",
"scipy.stats.gaussian_kde",
"scipy.ndimage.filters.gaussian_filter",
"scipy.stats.binned_statistic",
"numpy.where",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"0.15",
"1.4",
"0.16",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"1.3"
],
"tensorflow": []
}
] |
osiriszjq/RND-pytorch | [
"17582a90cfcb84b6e0dc8c5cfba8b9c5472c7fb3"
] | [
"train_sil.py"
] | [
"import numpy as np\nimport os\n\nimport torch.nn.functional as F\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torch.distributions.categorical import Categorical\nfrom torch.multiprocessing import Pipe\n\nfrom model import CnnActorCriticNetwork, RNDModel\n... | [
[
"numpy.sqrt",
"torch.load",
"numpy.zeros_like",
"torch.no_grad",
"torch.FloatTensor",
"numpy.mean",
"torch.device",
"numpy.random.randint",
"numpy.hstack",
"numpy.stack",
"numpy.std",
"numpy.float32",
"numpy.zeros",
"torch.LongTensor",
"torch.min",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Vaibhavs10/jax | [
"c9d8acd2e921efdbc01b2208fa9f790a9c312933"
] | [
"tests/infeed_test.py"
] | [
"# Copyright 2019 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed ... | [
[
"numpy.arange",
"numpy.random.randn",
"numpy.float32"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AntixK/Ezhil | [
"0ed84901852e37cd3cc5ac72cf828642caa35b8d"
] | [
"kivy/c4.py"
] | [
"try:\n import numpy as np\nexcept ImportError:\n print(\"Sorry, this example requires Numpy installed !\")\n raise\n\nfrom easyAI import TwoPlayersGame\n\n\nclass ConnectFour(TwoPlayersGame):\n \"\"\"\n The game of Connect Four, as described here:\n http://en.wikipedia.org/wiki/Connect_Four\n ... | [
[
"numpy.argmin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Max-Coy/Physics5CLSpring2022 | [
"ff2b4a431027569f3d6ec604819fdbb88b03bafc"
] | [
"Lab 6/Experiment 1 and 4 Analysis.py"
] | [
"import matplotlib.pyplot as plt\nimport numpy as np\nimport scipy.optimize as opt\nimport math\nimport csv\n\n\ndef sample_stdev(data):\n return np.std(data, ddof = 1, axis = -1)\n\n\ndef standard_error(data):\n return sample_stdev(data) / np.sqrt(len(data) if data.ndim == 1 else len(data[0]))\n\n\ndef data_... | [
[
"matplotlib.pyplot.legend",
"numpy.sqrt",
"matplotlib.pyplot.title",
"numpy.multiply",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.ylabel",
"numpy.std",
"numpy.mean",
"numpy.exp",
"matplotlib.pyplot.xlabel",
"numpy.array",
"scipy.optimize.curve_fit",
"matplotli... | [
{
"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"... |
joselusl/ars_sim_mapper | [
"6d216c0e5e9916203a1dbba167130b25f0dc464d"
] | [
"source/ars_sim_mapper_ros.py"
] | [
"#!/usr/bin/env python\n\nimport numpy as np\nfrom numpy import *\n\nimport os\n\nimport copy\n\n\n\n\n# ROS\n\nimport rospy\n\nimport rospkg\n\nimport std_msgs.msg\nfrom std_msgs.msg import Header\n\n\nimport geometry_msgs.msg\nfrom geometry_msgs.msg import PoseStamped\n\nimport visualization_msgs.msg\nfrom visual... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Daya-Jin/- | [
"28787974c40f5c3c151b1c4a567a7eb6c51604ba"
] | [
"6. catboost.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Nov 6 16:45:27 2018\n@author: wzy\n\"\"\"\nimport pandas as pd\nimport copy\nimport numpy as np\nfrom math import isnan\nimport catboost as cb\nfrom catboost import Pool\n\ntrain = pd.read_csv('data/train.csv')\ntest = pd.read_csv('data/test.csv')\n\n# 房屋朝向的汉字编码处理\n... | [
[
"pandas.concat",
"pandas.read_csv",
"numpy.arange",
"pandas.DataFrame",
"numpy.sort",
"numpy.mean",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
cmgreivel/spaCy | [
"a31506e06060c559abfeda043503935691af2e98"
] | [
"examples/keras_parikh_entailment/__main__.py"
] | [
"from __future__ import division, unicode_literals, print_function\nimport spacy\n\nimport plac\nfrom pathlib import Path\nimport ujson as json\nimport numpy\nfrom keras.utils.np_utils import to_categorical\n\nfrom spacy_hook import get_embeddings, get_word_ids\nfrom spacy_hook import create_similarity_pipeline\n\n... | [
[
"numpy.asarray"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AnveshAeturi/deep-learning-workshop | [
"20be7d92f310ca27a176e96e3a0b557f3fee2ec2"
] | [
"notebooks/work-in-progress/2018-10_ZeroShotRelationships/train_on_onestep.py"
] | [
"import os, sys\n\nimport argparse\nimport random\n\nimport time, pytz\nfrom datetime import datetime, timezone\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nimport h5py\nfrom torch.utils.data import Dataset\nfrom torch.utils.data import DataLoader\n\n#from text_util... | [
[
"torch.nn.Dropout",
"torch.nn.CrossEntropyLoss",
"torch.ones",
"numpy.sqrt",
"numpy.random.seed",
"numpy.clip",
"torch.load",
"torch.manual_seed",
"numpy.arange",
"numpy.nonzero",
"torch.utils.data.DataLoader",
"torch.matmul",
"torch.nn.DataParallel",
"torch... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
shhong/pycabnn | [
"e346b5436d3bf265b527669708eb611a8cf12abc"
] | [
"notebooks/fig_POPGEN/plot_cell_position_figures.py"
] | [
"# %%\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport pds_plots as ppl\n\n# %%\nfname = \"../../test_data/generated_positions/coords_20190626_1_6.npz\"\nf = np.load(fname)\n\nbbox = [[0, 700], [0-20, 700], [0, 200]]\n\n\ndef limit_to_box(x, box):\n mf = x.copy()\n for i, t in enumerate(box):\n ... | [
[
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.savefig",
"numpy.ones",
"numpy.random.randn",
"numpy.load",
"matplotlib.pyplot.style.use"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
davidmpinho/public-vs-priv-schools | [
"ad02a289410bdceecf5be749f102b6136857b773"
] | [
"src/school_rank/pipelines.py"
] | [
"from itemadapter import ItemAdapter\nimport pandas as pd\nfrom sqlalchemy import create_engine\n\n\nclass CsvPipeline:\n def process_item(self, item, spider):\n print('Processing item ' + item['name_file'])\n d_item = ItemAdapter(item).asdict()\n\n # TODO: add the html file names to the fil... | [
[
"pandas.DataFrame.from_dict"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
yalechang/adversarial-robustness-toolbox | [
"df4efc86c3ed963c82687b3ab8cd5cdbd5fe5c57"
] | [
"art/attacks/fast_gradient_unittest.py"
] | [
"# MIT License\n#\n# Copyright (C) IBM Corporation 2018\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated\n# documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the\n# rights to use, copy, ... | [
[
"tensorflow.Session"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
FyodorLikhachev/Keyworder | [
"70c3f1057d382a84bbb0079980b5b7b96fbdbb7d"
] | [
"metrics.py"
] | [
"from sklearn import metrics\nfrom torch import device\nfrom constants import DEVICE, TRAIN_KEYWORD_MARKER\n\ndevice = device(DEVICE)\n\ndef showMetrics(model ,dataset, title):\n y_pred = []\n y_true = []\n\n for x, y, c in dataset:\n x = x.to(device)\n y = y.to(device)\n c = c\n ... | [
[
"sklearn.metrics.precision_score",
"sklearn.metrics.recall_score",
"sklearn.metrics.accuracy_score"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yop0/pykeos | [
"02c141cfb3a56a221a3a47ab90b8da047bee2daf"
] | [
"pykeos/systems/Rossler.py"
] | [
"from ..systems import ContinuousSys\nfrom ..tools import nd_rand_init\n\nimport numpy as np\n\n\nclass Rossler(ContinuousSys):\n def __init__(self, a=0.1, b=0.1, c=14, n_points=2000, t_min=0, t_max=100):\n def ode(X, t):\n x,y,z = X\n return np.asarray([\n - y - z,\n ... | [
[
"numpy.asarray"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jmftrindade/miniplaces_challenge | [
"9325e814571000633e233433006b4e913194a34e"
] | [
"model/alexnet_2/preprocessing/no_preprocessing.py"
] | [
"# Copyright 2016 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.clip_by_value",
"tensorflow.image.resize_bilinear",
"tensorflow.multiply",
"tensorflow.image.random_brightness",
"tensorflow.image.random_hue",
"tensorflow.image.random_contrast",
"tensorflow.shape",
"tensorflow.slice",
"tensorflow.equal",
"tensorflow.expand_dim... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
kowalskaw/Perspective_transformation | [
"dcce673469355898399621b92247a40d94c3ff36"
] | [
"Prescp_transf/venv_lapt/main.py"
] | [
"import cv2\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\nl: list = []\nimg = None\nimg_cp = None\n\n\ndef draw_circle(event, x, y, flags, param):\n global l\n global img\n global img_cp\n if event == cv2.EVENT_LBUTTONDOWN:\n cv2.circle(img_cp, (x, y), 5, (255, 0, 0), -1)\n ... | [
[
"numpy.float32"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
codeupada1/san-antonio-datathon-2019 | [
"766e6530903be2f9fa5c9d4e93bed243d0e23516",
"766e6530903be2f9fa5c9d4e93bed243d0e23516"
] | [
"prepare_sso.py",
"prepare_sara.py"
] | [
"#!/usr/bin/env python\n\n\"\"\"\nThis script contains code used by the following jupytr notebooks:\n\n1. master-sso.ipynb\n2.\n3.\n\n\"\"\"\n\n\n\n# ===========\n# ENVIRONMENT\n# ===========\n\n\nimport os\nimport sys\n\nimport pandas as pd\nimport numpy as np\n\n\n\n# ===========\n# PREPARATION\n# ===========\n\n... | [
[
"pandas.to_datetime",
"pandas.DataFrame"
],
[
"pandas.to_datetime",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"nump... |
prcaaf/kornia | [
"87a0ead264ce5fc97997071acb9fe2286d6c425c"
] | [
"kornia/geometry/transform/crop/crop2d.py"
] | [
"from typing import Tuple, Union\n\nimport torch\n\nfrom kornia.geometry.transform.imgwarp import (\n warp_perspective, get_perspective_transform, warp_affine\n)\n\n__all__ = [\n \"crop_and_resize\",\n \"crop_by_boxes\",\n \"center_crop\",\n \"bbox_to_mask\",\n \"infer_box_shape\",\n \"validate... | [
[
"torch.stack",
"torch.allclose",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Drajan/DDxNet | [
"c9f9cb09a5c83ae7582255cb2a5aa9c738f7f628"
] | [
"train.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n#\n# Copyright 2019 IBM Corporation and others\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/lic... | [
[
"torch.nn.Softmax",
"torch.nn.CrossEntropyLoss",
"torch.optim.lr_scheduler.ReduceLROnPlateau",
"torch.max",
"sklearn.metrics.confusion_matrix",
"torch.nn.DataParallel",
"torch.no_grad",
"torch.cuda.is_available",
"sklearn.metrics.f1_score",
"torch.cuda.device_count",
"n... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
princeton-vl/selfstudy | [
"d18ab26c11843557ee75aaccf6ccb63de85b397c"
] | [
"tasks/cmc.py"
] | [
"\"\"\"CMC backbone definition.\n\nModified from: https://github.com/HobbitLong/CMC\n\"\"\"\n\nimport gin\n\nimport numpy as np\nimport torch\nfrom torch import nn\n\nfrom .classify import ClassifyTask, ce_loss\n\neps = 1e-7\n\n\n# (not used anymore)\nclass NCECriterion(nn.Module):\n \"\"\"Eq. (12): L_{NCE} \"\"\"... | [
[
"numpy.log",
"torch.log",
"torch.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ashikrafi/stellargraph | [
"0f6f24d21ec9c016bb370c2d705af4e876495cb4"
] | [
"stellargraph/mapper/sampled_node_generators.py"
] | [
"# -*- coding: utf-8 -*-\n#\n# Copyright 2018-2020 Data61, CSIRO\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"numpy.size",
"numpy.cumprod"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ina-foss/inaFaceGender | [
"4c2e2588684d5813732648201f70b61fa5b85142"
] | [
"inaFaceAnalyzer/face_classifier.py"
] | [
"#!/usr/bin/env python\n# encoding: utf-8\n\n# The MIT License\n\n# Copyright (c) 2021 Ina (David Doukhan & Zohra Rezgui- http://www.ina.fr/)\n\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the... | [
[
"pandas.concat",
"tensorflow.keras.preprocessing.image.img_to_array",
"tensorflow.keras.applications.resnet50.preprocess_input",
"tensorflow.keras.Model",
"numpy.all",
"numpy.round",
"numpy.concatenate",
"numpy.array",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"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",
... |
FatimaAfilala/ExcelProject | [
"43602ad569c0ed0649de3777900f444c8a3a6f7c"
] | [
"cara/tests/test_monte_carlo_full_models.py"
] | [
"import numpy as np\nimport numpy.testing as npt\nimport pytest\n\nimport cara.monte_carlo as mc\nfrom cara import models,data\nfrom cara.monte_carlo.data import activity_distributions, virus_distributions\n\n# TODO: seed better the random number generators\nnp.random.seed(2000)\nSAMPLE_SIZE = 50000\nTOLERANCE = 0.... | [
[
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
IJSComplexMatter/dtmm | [
"08c66ddf55f9276bc713e921eaadbadbb4a28fe2",
"08c66ddf55f9276bc713e921eaadbadbb4a28fe2"
] | [
"dtmm/jones.py",
"dtmm/denoise.py"
] | [
"\"\"\"\nJones calculus functions. You can use this module for simple normal incidence\njones calculus.\n\n2x2 matrices\n------------\n\n* :func:`.jonesvec` : jones vector\n* :func:`.mirror` : mirror matrix\n* :func:`.polarizer` : polarizer matrix\n* :func:`.retarder` : retarder matrix\n* :func:`.quarter_waveplate`... | [
[
"numpy.swapaxes",
"numpy.conj",
"numpy.asarray",
"numpy.cos",
"numpy.sin",
"numpy.array",
"numpy.zeros",
"numpy.empty"
],
[
"numpy.abs",
"numpy.multiply",
"numpy.asarray",
"numpy.cos",
"numpy.ones",
"numpy.exp",
"numpy.logical_and"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
NkosenhleDuma/pandas-ta | [
"7ae7b1695c1fcc26c5a012cacca7aec8220599e0"
] | [
"pandas_ta/utils/_math.py"
] | [
"# -*- coding: utf-8 -*-\nfrom functools import reduce\nfrom math import fabs, floor\nfrom operator import mul\nfrom sys import float_info as sflt\n\nfrom numpy import dot, ones, triu\nfrom numpy import append as npAppend\nfrom numpy import array as npArray\nfrom numpy import corrcoef as npCorrcoef\nfrom numpy impo... | [
[
"numpy.dot",
"numpy.sqrt",
"pandas.DataFrame",
"numpy.ones",
"numpy.seterr",
"numpy.append",
"sklearn.linear_model.LinearRegression",
"numpy.corrcoef",
"numpy.array",
"numpy.sum"
]
] | [
{
"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": []
}
] |
mxvscorrupcion/pemex_contrataciones | [
"6c5e44aa29bf7f7d1bc17f376fd11eb3d146d55d"
] | [
"src/pemex_contratos/load_data_proveedores.py"
] | [
"import pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom .utils import homologar_razon_social\n\n\ndef cargar_no_localizados(path: str):\n df = pd.read_csv(path, encoding='latin-1', usecols=['RFC', 'RAZÓN SOCIAL'])\n df = df.rename(columns={'RAZÓN SOCIAL': 'razon_social'})\n rs = df.razon_s... | [
[
"pandas.concat",
"pandas.read_csv",
"pandas.read_json"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
vatervonacht/dagster | [
"595d78c883ef20618052ac1575fe46cde51fd541"
] | [
"examples/dagster_examples_tests/bay_bikes_tests/test_solids.py"
] | [
"# pylint: disable=redefined-outer-name, W0613\nimport os\n\nimport pytest\nfrom dagster_examples.bay_bikes.resources import credentials_vault\nfrom dagster_examples.bay_bikes.solids import (\n MultivariateTimeseries,\n Timeseries,\n download_weather_report_from_weather_api,\n)\nfrom numpy import array\nfr... | [
[
"numpy.array",
"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": []
}
] |
yas-sim/openvino-wrapper | [
"97493b6ac98e3d02cc0cde5b259be4a5a0fc3cf4"
] | [
"iewrap_object_detection.py"
] | [
"import iewrap\r\n\r\nimport cv2\r\nimport numpy as np\r\n\r\nlabel = open('voc_labels.txt').readlines()\r\nimg = cv2.imread('car_1.bmp')\r\n\r\nie = iewrap.ieWrapper('public/mobilenet-ssd/FP16/mobilenet-ssd.xml', 'CPU')\r\n\r\noutput = ie.blockInfer(img)[0] # Inference\r\n\r\n# Draw bounding boxes and labels o... | [
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jon--lee/Mask_RCNN | [
"10bd4484b5163e160325005d1d3621dd888b3c3e"
] | [
"samples/shapes/eval_rgbd.py"
] | [
"import matplotlib\nmatplotlib.use('Agg')\nimport os\nimport sys\nimport random\nimport math\nimport re\nimport time\nimport numpy as np\nimport cv2\nimport skimage\nimport json\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport IPython\nfrom eval_rgb import run_eval\n\n# Root directory of the project\nROO... | [
[
"matplotlib.use"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
asl-epfl/asl-eusipco2020 | [
"60fa42f598c8b39c9d3379df9a085607f8d873c5"
] | [
"codefig1.py"
] | [
"\"\"\"\nThis code can be used to generate simulations similar to Fig. 1 in the following paper:\nVirginia Bordignon, Vincenzo Matta, and Ali H. Sayed, \"Adaptation in online social learning,'' Proc. EUSIPCO, pp. 1-5, Amsterdam, The Netherlands, August 2020.\n\nPlease note that the code is not generally perfected... | [
[
"matplotlib.pyplot.rcParams.update",
"matplotlib.pyplot.subplots",
"matplotlib.style.use"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sehovaclj/fruit_classification | [
"8a235e3f40c5e8d3033e8f4a4f1aedeb26e799af"
] | [
"creating_datasets.py"
] | [
"# Run this code first!\n\n# Author: Ljubisa Sehovac\n# github: sehovaclj\n\n# email: lsehovac@uwo.ca\n# feel free to email me with questions, concerns, fixes, etc.\n\n# the datasets were obtained from Kaggle.com. \n# Make sure to download these datasets and store them in your working directory. They will be downlo... | [
[
"numpy.array",
"torch.from_numpy"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kim-younghan/CompoundScalingSeg | [
"8fad8f03ac02f3613466572a542ae5b0f50ed067"
] | [
"CompoundScalingSeg/trainer.py"
] | [
"import abc\nimport os\nimport sys\nimport tqdm\nimport torch\nimport datetime\n\nfrom torch.utils.data import DataLoader\nfrom typing import Callable, Any\nfrom typing import NamedTuple, List\nfrom torchvision.utils import make_grid\n\ndef to_np(x):\n return x.data.cpu().numpy()\n\nclass BatchResult(NamedTuple)... | [
[
"torch.no_grad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
KrishnaswamyLab/scpreprocess | [
"926d7be1282d7218a9b61ad555f74489cf3927be"
] | [
"scprep/run/slingshot.py"
] | [
"from .. import utils\nfrom . import r_function\n\nimport numpy as np\nimport pandas as pd\nimport warnings\n\n\ndef install(site_repository=None, update=False, version=None, verbose=True):\n \"\"\"Install the required R packages to run Slingshot.\n\n Parameters\n ----------\n site_repository : string, ... | [
[
"pandas.Series",
"numpy.random.seed",
"numpy.unique",
"numpy.isnan",
"numpy.arange",
"pandas.DataFrame",
"numpy.nanmean",
"numpy.argsort",
"numpy.random.randint"
]
] | [
{
"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": []
}
] |
Tanishadel/Mastering-Machine-Learning-for-Penetration-Testing | [
"5aecc2603faaa97dafc1fa4c86f4e5f530bb4877"
] | [
"Chapter09/mnist_loader.py"
] | [
"\"\"\"\nmnist_loader\n~~~~~~~~~~~~\n\nA library to load the MNIST image data. For details of the data\nstructures that are returned, see the doc strings for ``load_data``\nand ``load_data_wrapper``. In practice, ``load_data_wrapper`` is the\nfunction usually called by our neural network code.\n\"\"\"\n\n#### Lib... | [
[
"numpy.reshape",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
glasslucas00/Meter-google-project | [
"862e90747b7762e8c3ad107ad8d8c657829390aa"
] | [
"nets/yolo4.py"
] | [
"from functools import wraps\r\n\r\nimport numpy as np\r\nimport tensorflow as tf\r\nfrom keras import backend as K\r\nfrom keras.layers import Conv2D, Add, ZeroPadding2D, UpSampling2D, Concatenate, MaxPooling2D\r\nfrom keras.layers.advanced_activations import LeakyReLU\r\nfrom keras.layers.normalization import Bat... | [
[
"tensorflow.boolean_mask",
"tensorflow.image.non_max_suppression"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
yanqiangmiffy/Text-Classification-Application | [
"c9608f5857991ae4701c7d82bcac537b7961691f"
] | [
"train_model_english_.py"
] | [
"from keras.layers import Input,Dense,Embedding,Conv2D,MaxPool2D\nfrom keras.layers import Reshape,Flatten,Dropout,Concatenate\nfrom keras.callbacks import ModelCheckpoint\nfrom keras.optimizers import Adam\nfrom keras.models import Model\nfrom sklearn.model_selection import train_test_split\nfrom data_helpers_engl... | [
[
"sklearn.model_selection.train_test_split"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jinga-lala/stupidNMT | [
"2a41c072c2bc622c7edd8556f552f38556d70dae"
] | [
"models/new_encoder.py"
] | [
"'''\nA module which implements the new Encoder\n'''\nimport uuid\nimport threading\nimport pdb\nimport torch\nfrom torch import nn\n\nfrom models.new_attention import NewAttention\nfrom models.attention import MultiHeadedAttention\nfrom models.embeddings import PositionEmbedding, TokenEmbedding\nfrom models.utils ... | [
[
"torch.nn.init.calculate_gain",
"torch.nn.Dropout",
"torch.nn.CrossEntropyLoss",
"torch.cat",
"torch.nn.init.constant_",
"torch.nn.LayerNorm",
"torch.nn.Linear",
"torch.nn.init.xavier_uniform_",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nsriniva/Spotifinder | [
"1522290231b73966070975a966f141d28e006583"
] | [
"app/data_model/find_songs.py"
] | [
"'''\nContains the implementation of the FindSongs class.\n'''\nfrom re import compile as rcompile\nfrom zipfile import ZipFile\nfrom os.path import dirname\nfrom importlib import import_module\nimport pandas as pd\nfrom joblib import load\n\nload_model = None\nNearestNeighbors = None\n\ndef import_from_sk_tf():\n ... | [
[
"pandas.read_csv",
"pandas.read_json"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
erickRochaIP/SuperNimLearner | [
"896acb00b1b30b36cb7aa924852e1a43020c6e48"
] | [
"play.py"
] | [
"import nim\nimport sys\nimport numpy as np\n\ndef main():\n\n\t# Check command-line arguments\n\tif len(sys.argv) != 2:\n\t\tsys.exit(\"Usage: python play.py filename.npy\")\n\n\tai = nim.NimAI()\n\tai.q = np.load(sys.argv[1], allow_pickle=\"True\").item()\n\n\tnim.play(ai)\n\nif __name__ == \"__main__\":\n\tmain(... | [
[
"numpy.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CG2016/barkovsky_3 | [
"784bb3e419736cd11b9bc84b3055b8a4b9ec57e6"
] | [
"lab3/color_stats.py"
] | [
"#!/usr/bin/env python\nimport argparse\nimport statistics\nimport tkinter as tk\n\nimport numpy\nimport matplotlib\nmatplotlib.use(\"TkAgg\")\nfrom matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2TkAgg\nfrom matplotlib.figure import Figure\nimport PIL.ImageTk\nimport PIL.Image\n\n\nIM... | [
[
"matplotlib.use",
"matplotlib.figure.Figure",
"matplotlib.backends.backend_tkagg.FigureCanvasTkAgg"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JasonSanchez/machine-learning-for-all | [
"e4a7d581ccdc5f2faa71ff14109095f425a55e0b"
] | [
"flask/app/viz.py"
] | [
"from __future__ import division\n\nfrom flask import render_template, request, Response, jsonify,redirect,url_for,flash\nfrom werkzeug.utils import secure_filename\n\nfrom app import app\n\nimport json\nimport psycopg2\nimport psycopg2.extras\nimport os\nimport pandas as pd\nimport hashlib\nimport datetime\nfrom d... | [
[
"pandas.util.hash_pandas_object"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
parthxtripathi/zarr-python | [
"c7494a9e21df777f7b6f962ea5c41b803f7f418c"
] | [
"zarr/tests/test_core.py"
] | [
"import atexit\nimport os\nimport sys\nimport pickle\nimport shutil\nimport unittest\nfrom itertools import zip_longest\nfrom tempfile import mkdtemp, mktemp\n\nimport numpy as np\nimport pytest\nfrom numcodecs import (BZ2, JSON, LZ4, Blosc, Categorize, Delta,\n FixedScaleOffset, GZip, MsgPack... | [
[
"numpy.product",
"numpy.take",
"numpy.linspace",
"numpy.asarray",
"numpy.dtype",
"numpy.all",
"numpy.zeros_like",
"numpy.mean",
"numpy.iinfo",
"numpy.random.randint",
"numpy.uint32",
"numpy.arange",
"numpy.empty_like",
"numpy.zeros",
"numpy.testing.asser... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sameertodkar/Car-Health-Monitor | [
"ff43dbe513e2fd35e37909b0021c3e08e95cc959"
] | [
"Source/GUI_2/MonitorGUI.py"
] | [
"import plotting_function\nimport os\nimport tkinter as tk\nfrom matplotlib import pyplot as plt\nfrom matplotlib import style\nimport matplotlib.animation as animation\nfrom matplotlib.backends.backend_tkagg import FigureCanvasTkAgg\nimport matplotlib\n\nmatplotlib.use(\"TkAgg\")\n\n# Defining some style and fonts... | [
[
"matplotlib.style.use",
"matplotlib.use",
"matplotlib.pyplot.figure",
"matplotlib.animation.FuncAnimation",
"matplotlib.pyplot.subplot2grid",
"matplotlib.backends.backend_tkagg.FigureCanvasTkAgg"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
shartoo/Tacotron-2 | [
"24d4f2891c9def484406ec53b811d0caff15a62b"
] | [
"datasets/preprocessor.py"
] | [
"import glob, os\nfrom concurrent.futures import ProcessPoolExecutor\nfrom functools import partial\n\nimport numpy as np\nfrom datasets import audio\nfrom wavenet_vocoder.util import is_mulaw, is_mulaw_quantize, mulaw, mulaw_quantize\n\n\ndef build_from_path(hparams, input_dirs, mel_dir, linear_dir, wav_dir, n_job... | [
[
"numpy.abs",
"numpy.pad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dcorre/ImSimpy | [
"9c9a23e7f0d757204d181c25acfe178d0a8aebbf"
] | [
"ImSimpy/utils/plot_3D.py"
] | [
"from mpl_toolkits.mplot3d import Axes3D\nimport matplotlib.pyplot as plt\nfrom matplotlib import cm\nfrom matplotlib.ticker import LinearLocator, FormatStrFormatter\nimport numpy as np\nfrom astropy.io import fits\n\n\n\ndata=fits.getdata('zemax/PSF_z_edge.fits')\n#data=fits.getdata('oversampled_psf/zemax_moffat_1... | [
[
"numpy.arange",
"numpy.meshgrid",
"numpy.sum",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DelSquared/cdf-transform-rng | [
"8515aeea030220b729eedf9229380136c4f393ea"
] | [
"RNGDistributionTransform.py"
] | [
"import numpy as np\r\nfrom matplotlib import pyplot as plt\r\n\r\nn=5000\r\n#number of generated points\r\nUnifR=np.random.uniform(size=(1,n))\r\n#uniform array\r\nDistR=np.array([1,n])\r\n#output array radial\r\nUnifT=2*np.pi*np.random.uniform(size=(1,n))\r\n# output array angular\r\nx=np.arange(0,10,0.1)\r\ny=np... | [
[
"numpy.log",
"numpy.arange",
"numpy.cos",
"numpy.ones",
"matplotlib.pyplot.plot",
"numpy.sin",
"numpy.exp",
"numpy.random.uniform",
"numpy.array",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
oscovida/oscovida | [
"6afb3fb5f8c94d19b60463c311812fb291b28a91"
] | [
"oscovida/oscovida.py"
] | [
"\"\"\"Code used for notebooks and data exploration on\nhttps://github.com/oscovida/oscovida\"\"\"\n\n\nimport datetime\nimport math\nimport os\nimport pytz\nimport time\nimport joblib\nimport numpy as np\nimport pandas as pd\nimport IPython.display\nfrom typing import Tuple, Union\n# choose font - can be deactivat... | [
[
"pandas.merge",
"pandas.to_datetime",
"pandas.Series",
"pandas.DataFrame",
"numpy.concatenate",
"matplotlib.ticker.FixedLocator",
"pandas.read_csv",
"matplotlib.pyplot.twinx",
"matplotlib.pyplot.gcf",
"numpy.full",
"pandas.plotting.register_matplotlib_converters",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kimbring2/pysc2_transformer | [
"a147d29342ac2b75b6e837bcd3c2d6b802595427"
] | [
"pseudocode/network.py"
] | [
"import tensorflow as tf\nimport numpy as np\n\n\ndef scaled_dot_product_attention(q, k, v, mask):\n \"\"\"Calculate the attention weights.\n q, k, v must have matching leading dimensions.\n k, v must have matching penultimate dimension, i.e.: seq_len_k = seq_len_v.\n The mask has different shapes depending on ... | [
[
"tensorflow.concat",
"tensorflow.zeros",
"tensorflow.keras.layers.Conv2DTranspose",
"numpy.exp",
"tensorflow.keras.layers.Conv2D",
"numpy.random.multinomial",
"tensorflow.keras.layers.Multiply",
"tensorflow.keras.layers.Flatten",
"tensorflow.keras.layers.experimental.preprocess... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
gmuraru/PySyft | [
"ae25783f0ab36aeb1497eaa03a6b1c531caeec3d"
] | [
"test/crypten/test_utils.py"
] | [
"import pytest\nimport torch as th\nimport crypten\nfrom syft.frameworks.crypten import utils\nfrom syft.frameworks import crypten as syft_crypten\n\n\n@pytest.mark.parametrize(\n \"tensors\",\n [\n # return tensor\n th.tensor([1, 2, 3, 4]),\n # return tensor1, tensor2, tensor3\n (... | [
[
"torch.all",
"torch.ones",
"torch.zeros_like",
"torch.tensor",
"torch.nn.Linear",
"torch.nn.functional.relu",
"torch.no_grad",
"torch.rand",
"torch.allclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ConduitDan/Python-Models | [
"fbd79ccce05201e00a64629ef5b28921a51640cd"
] | [
"IFSDrawer.py"
] | [
"\"\"\"\r\nThis class draws iterated function systems\r\n\"\"\"\r\nimport numpy as np\r\nfrom matplotlib import pyplot as plt\r\n\r\n\r\nclass IFSGenerator:\r\n def __init__(self, IFS=None, depth=10, numPoints = None, overwrite=False):\r\n if IFS is None:\r\n raise \"Please supply an IFS\"\r\n ... | [
[
"numpy.log",
"numpy.random.random",
"numpy.sqrt",
"numpy.matmul",
"numpy.cos",
"numpy.arccos",
"numpy.sin",
"numpy.array",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
guochengqian/ASSANet | [
"f7a63d787df0ade975085acf0504b9b06f62c722"
] | [
"models/segmentation/assanet_seg.py"
] | [
"import torch.nn as nn\nfrom ..encoder.assanet_encoder import ASSANetEncoder\nfrom ..decoder.assanet_decoder import ASSANetDecoder\nfrom .segmentation_head import SceneSegHeadPointNet\n\n\nclass ASSANetSeg(nn.Module):\n def __init__(self, cfg):\n \"\"\"ASSA-Net implementation for paper:\n Anisotrop... | [
[
"torch.nn.init.zeros_",
"torch.nn.init.kaiming_normal_"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
prashantraina/PlaneNet | [
"51fa261f7e958191fce6077cf923539acadb707b"
] | [
"evaluate.py"
] | [
"import tensorflow as tf\nimport numpy as np\nnp.set_printoptions(precision=2, linewidth=200)\nimport cv2\nimport os\nimport time\nimport sys\n#import tf_nndistance\nimport argparse\nimport glob\nimport PIL\nimport scipy.ndimage as ndimage\n\nsys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)... | [
[
"numpy.expand_dims",
"tensorflow.concat",
"numpy.squeeze",
"tensorflow.global_variables",
"numpy.concatenate",
"numpy.arange",
"numpy.save",
"tensorflow.ConfigProto",
"tensorflow.reset_default_graph",
"numpy.argmax",
"tensorflow.Session",
"tensorflow.train.Saver",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
rick-kline/cnaa-434-etf | [
"5e5c2de8c8851109075eed50e1f9a2846c5b4884"
] | [
"build_etf_model/main.py"
] | [
"\nfrom datetime import datetime, timedelta\nfrom dateutil.relativedelta import relativedelta\nfrom pytz import timezone\nfrom google.cloud import bigquery\nimport pandas as pd\nimport google.auth\nimport json\nimport base64\nimport logging\nfrom google.cloud import pubsub_v1\n\n# Triggered from a message on a Clou... | [
[
"pandas.to_datetime"
]
] | [
{
"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": []
}
] |
open-risk/Academy-Course-DAT31018 | [
"551e9128ef2c07302552ad3fa554ebee69aa8f14"
] | [
"Step_5b_outlier_report.py"
] | [
"# (c) 2019 Open Risk (https://www.openriskmanagement.com)\n#\n# This code is licensed under the Apache 2.0 license a copy of which is included\n# in the source distribution of the course. This is notwithstanding any licenses of\n# third-party software included in this distribution. You may not use this file except... | [
[
"pandas.read_hdf"
]
] | [
{
"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": []
}
] |
us4useu/arrus | [
"10487b09f556e327ddb1bec28fbaccf3b8b08064"
] | [
"api/python/arrus/devices/probe.py"
] | [
"import dataclasses\nimport numpy as np\nimport math\n\n\n@dataclasses.dataclass(frozen=True)\nclass ProbeModelId:\n manufacturer: str\n name: str\n\n\n@dataclasses.dataclass(frozen=True)\nclass ProbeModel:\n model_id: ProbeModelId\n n_elements: int\n pitch: float\n curvature_radius: float\n\n ... | [
[
"numpy.min",
"numpy.arange",
"numpy.cos",
"numpy.sin",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
guimarais/dapc | [
"5b99fd511325d91c0cb05d9e29aeaf238f128c13"
] | [
"my_utils/utils.py"
] | [
"import numpy as np\n\ndef testf(xmin=-5.0, xmax=5.0, dstep=0.01, noise=0.0):\n \"\"\"Returns a simple sin function to test several examples\n \n Parameters:\n -----------\n xmin: Lowest x axis value\n xmax: Highest x axis value\n dstep: Step for x-axis\n noise: Amplitude of the noise to add... | [
[
"numpy.arange",
"numpy.ones",
"numpy.sin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Napam/MayhemPacman | [
"cbcb3b4a2c83ed920e32748a8aaadb29b19ab5bf",
"cbcb3b4a2c83ed920e32748a8aaadb29b19ab5bf"
] | [
"states/game_state_online.py",
"classes/planet.py"
] | [
"'''\r\nModule containing the online game class called mayhem\r\n\r\nWritten by Naphat Amundsen\r\n'''\r\nimport pygame as pg\r\nimport numpy as np \r\nimport pickle\r\nimport time\r\nimport itertools\r\nfrom multiprocessing import Pipe, Process\r\nfrom importlib import reload\r\n\r\nimport user_settings as cng\r\n... | [
[
"numpy.array"
],
[
"numpy.linalg.norm"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"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",
... |
Jhsmit/PyHDX-paper | [
"50b756c47488365e5f43eadc373f2943c0cee5b3"
] | [
"anal_chem/fig_2fg_rainbowclouds.py"
] | [
"from functions.logging import write_log\nfrom functions.base import *\nfrom functions.rainbows import label_axes, kdeplot, boxplot, stripplot\nfrom functions.formatting import *\nimport proplot as pplt\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom pyhdx.fileIO import csv_to_protein\n\n# Rainclouds:\n#... | [
[
"numpy.array",
"numpy.mean",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sum-coderepo/seaborn | [
"069d53a3dd29a965987705c823883a93700bbe5e"
] | [
"seaborn/distributions.py"
] | [
"\"\"\"Plotting functions for visualizing distributions.\"\"\"\nfrom numbers import Number\nfrom functools import partial\nimport math\nimport warnings\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nimport matplotlib.transforms as tx\nfrom matplotlib.colors im... | [
[
"numpy.take",
"numpy.linspace",
"pandas.Series",
"numpy.asarray",
"matplotlib.colors.to_rgba",
"numpy.sqrt",
"numpy.cumsum",
"pandas.DataFrame",
"numpy.zeros_like",
"numpy.searchsorted",
"matplotlib.pyplot.gca",
"pandas.Index",
"numpy.diff",
"numpy.ravel",
... | [
{
"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": []
}
] |
jlpearso/coastlines-at-risk-of-hypoxia-in-the-northern-indian-ocean | [
"dc70df36c2139a685c5f2cd7dc901e7b11bb0cc7"
] | [
"data_processing/0_WOD/raw_data/get_data/concat_and_process.py"
] | [
"inpath = 'individual/'\noutfn = 'all_casts_beginning_to_aug_2020_indian_ocean.nc'\n \n\n###############################################################################################################\n# No need to change anything below this line\n\nimport xarray as xr\nimport numpy as np\nimport pandas as pd\nimp... | [
[
"numpy.min",
"numpy.isnan",
"numpy.arange",
"matplotlib.use",
"matplotlib.pyplot.savefig",
"numpy.max",
"numpy.array",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Achuan-2/doubantop250 | [
"a7da15a96277d02e9f5fd9d46134025ab1edae99"
] | [
"douban250.py"
] | [
"import requests\nimport pandas as pd\nimport bs4\nimport os\nfrom tqdm import tqdm\nfrom lxml import etree\nfrom bs4 import BeautifulSoup\n\"\"\"\n注意这个脚本是必须要有豆瓣登陆的cookie才能正常运行的，因为脚本会解析10+250=260个页面，会被豆瓣识别为异常访问，限制ip访问的\n\"\"\"\ndef find_page_num(res):\n soup = bs4.BeautifulSoup(res.text, 'html.parser')\n dept... | [
[
"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": []
}
] |
Fabus1184/stonks-bot | [
"a98e0be699b10caffca8e0e5e5544d1d3e4fa18f"
] | [
"stonks.py"
] | [
"from matplotlib import markers\r\nimport requests\r\nimport matplotlib.pyplot as plt\r\nimport matplotlib.ticker as mticker \r\nimport matplotlib.dates as mdates\r\nimport pandas as pd\r\nimport numpy as np\r\nimport time\r\nimport json\r\nfrom discord_webhook import DiscordWebhook\r\n\r\n(a,b) = (time.time()-604... | [
[
"numpy.polyfit",
"matplotlib.pyplot.tight_layout",
"matplotlib.dates.DateFormatter",
"pandas.to_datetime",
"matplotlib.pyplot.title",
"numpy.around",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig",
"numpy.sort",
"matplotlib.pyplot.clf",
"numpy.diff",
"mat... | [
{
"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": []
}
] |
srio/paper-transfocators-resources | [
"917d8b4114056f62c84b295579e55bf5f0b56b6b"
] | [
"monochromator/DCM_vibrational_study_v1.py"
] | [
"#\n# Python script to run shadow3. Created automatically with ShadowTools.make_python_script_from_list().\n#\nimport Shadow\nimport numpy\nfrom srxraylib.plot.gol import plot, plot_image\nimport pandas as pd\n\n\ndef run_beamline(beam, x_rot1=2e-10, x_rot2=2e-10, DCM_pos=60):\n \"\"\"This function runs the beam... | [
[
"numpy.ones_like",
"numpy.sqrt",
"numpy.linspace",
"numpy.abs",
"pandas.DataFrame",
"numpy.random.normal",
"numpy.zeros_like",
"numpy.average",
"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": []
}
] |
lhq1208/object_detection | [
"ab3265e0f09b78415299c1a0b38254176a0565ed"
] | [
"detection/YOLO_pytorch/train.py"
] | [
"import torch\nfrom YOLO import config as cfg\nfrom torch import optim\nfrom torch.autograd import Variable\nfrom YOLO_pytorch import yolo_net\nfrom YOLO.pascal_voc import *\n\n\nclass Solver():\n def __init__(self, net, data, use_gpu=True):\n self.net = net\n self.data = data\n self.max_ite... | [
[
"torch.FloatTensor",
"torch.from_numpy",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nmichlo/disent | [
"c4fb871cdcdbf833e95e88a4637700c5b0e75613"
] | [
"disent/dataset/util/stats.py"
] | [
"# ~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~\n# MIT License\n#\n# Copyright (c) 2021 Nathan Juraj Michlo\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# ... | [
[
"torch.mean",
"torch.cat",
"torch.utils.data.DataLoader",
"torch.std",
"torch.no_grad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jpsteinb/svgpathtools | [
"2123fa085e0c400bd42e00af3e1b151c70fa9344"
] | [
"test/test_parsing.py"
] | [
"# Note: This file was taken mostly as is from the svg.path module (v 2.0)\nfrom __future__ import division, absolute_import, print_function\nimport unittest\nfrom svgpathtools import *\nimport svgpathtools\nimport numpy as np\n\n\ndef construct_rotation_tf(a, x, y):\n a = a * np.pi / 180.0\n tf_offset = np.i... | [
[
"numpy.array_equal",
"numpy.cos",
"numpy.sin",
"numpy.tan",
"numpy.identity",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Aliang-CN/DeepMatch | [
"ea69d9cc9aec3742ae70f487b3ec9491fdfb14a8"
] | [
"tests/utils.py"
] | [
"from __future__ import absolute_import, division, print_function\n\nimport inspect\nimport os\nimport sys\n\nimport numpy as np\nimport tensorflow as tf\nfrom numpy.testing import assert_allclose\nfrom tensorflow.python.keras import backend as K\nfrom tensorflow.python.keras.layers import Input, Masking\nfrom tens... | [
[
"tensorflow.python.keras.models.save_model",
"numpy.random.random",
"tensorflow.python.keras.layers.Masking",
"tensorflow.python.keras.models.load_model",
"numpy.full",
"tensorflow.python.keras.backend.dtype",
"tensorflow.python.keras.models.Model",
"numpy.random.randn",
"tenso... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.5",
"1.4"
]
}
] |
BordensteinLaboratory/VMI-diet-challenge | [
"5e31a3fec3d481e2b62987d176e33ebf00279778"
] | [
"Metagenome_shortread_analysis/merge_metaphlan_tables_readcount.py"
] | [
"#!/usr/bin/env python3\n\n# This utility script is adapted from the Biobakery merge_metaphlan_tables.py utility script\n# Instead of merging the relative abundances for all matched tables, it merges the estimated read counts\n\nimport argparse\nimport os\nimport sys\nimport re\nimport pandas as pd\nfrom itertools ... | [
[
"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": []
}
] |
morgenst/PyAnalysisTools | [
"f3b1f89870e7bbae1549c228a56d2c36bbba7af3"
] | [
"tests/unit/TestMLHelpers.py"
] | [
"import os\nimport unittest\nfrom builtins import range\n\nimport matplotlib\nimport mock\nimport numpy as np\nimport pandas as pd\nimport root_numpy\nfrom mock import MagicMock, patch, mock_open\nimport six\nfrom numpy.testing import assert_array_equal\nfrom pandas.util.testing import assert_frame_equal\n\nimport ... | [
[
"pandas.DataFrame",
"numpy.testing.assert_array_equal",
"numpy.std",
"numpy.mean",
"numpy.array"
]
] | [
{
"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": []
}
] |
willi-z/msys-opt | [
"2054931737893b4ea77a4ba2dbfb6a3e2bce7779"
] | [
"src/msys_opt/types/vector.py"
] | [
"from ..core import OptimizableType\nimport numpy as np\nfrom ..core.generator import Limiter\n\n\nclass VectorType(OptimizableType):\n def __init__(self, default_value):\n super().__init__(np.array([0]), Limiter())\n self.set_value(default_value)\n\n def is_same(self, value) -> bool:\n r... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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