repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
fmaguire/ncov-parser | [
"bf3ba6e609f04e0a33c3779973bc4c9066e498b0"
] | [
"ncov/parser/primers.py"
] | [
"'''\nFunctions to handle and process primers.\n'''\n\nimport os\nimport sys\nimport re\nimport pandas as pd\nimport csv\n\ndef import_bed_file(bed):\n '''\n Import a BED file containing regions and return a matrix containing those\n regions.\n\n Arguments:\n * bed: full path to a BED file to pro... | [
[
"pandas.concat",
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
matsumotokoki/probrobotics2021 | [
"1ea724ffd11e95553af67da9330444d33d80051b"
] | [
"map.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\n\nclass Map(object):\n def __init__(self):\n self.map = np.array([[0,0,0,0,0,0,0,0,0,0,0,0,],\n [0,1,1,1,1,1,1,1,1,1,3,0,],\n [0,1,0,0,1,0,0,0,1,1,1,0,],\n [0,1,0,0,1,1... | [
[
"numpy.arange",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.hlines",
"matplotlib.pyplot.cla",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlim",
"numpy.argmax",
"matplotlib.pyplot.vlines",
"numpy.array",
"matplotlib.pyplot.pause",
"matplotlib.pyplot.axvspan",
"mat... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
2sin18/tensorflow | [
"0f2db8dbc97daaa36c5c47940c3a71ce974672d1"
] | [
"tensorflow/python/kernel_tests/segment_reduction_ops_test.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.gradient_checker.compute_gradient",
"numpy.sqrt",
"numpy.asarray",
"tensorflow.python.framework.ops.device",
"numpy.place",
"numpy.random.randint",
"numpy.arange",
"tensorflow.python.platform.test.main",
"numpy.zeros",
"tensorflow.python.ops.math_ops.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.8",
"1.10",
"1.12",
"2.7",
"2.6",
"1.4",
"1.13",
"2.3",
"2.4",
"2.9",
"1.5",
"1.7",
"2.5",
"0.12",
"1.0",
"2.2",
"1... |
VincentLuczkow/cerna | [
"66797c716fc8990e80ecf18d7347b68b0a150baf"
] | [
"ceRNA/NetworkTypes.py"
] | [
"import random\nfrom copy import deepcopy\nfrom pdb import set_trace\nfrom typing import Dict, List, Callable, Tuple\nimport scipy.stats as stats\nimport numpy as np\nfrom itertools import product\n\nimport ceRNA.TestTypes as TestTypes\nimport ceRNA.Estimators as Estimators\nimport ceRNA.NetworkFiles as NetworkFile... | [
[
"numpy.nonzero",
"numpy.ones",
"numpy.concatenate",
"numpy.random.normal",
"numpy.array",
"numpy.zeros",
"scipy.stats.truncnorm.rvs",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
openfur/Fursuit-Detector | [
"f18276e9da1731f439bfbbfe6809f52c0169dec7"
] | [
"tool/utils.py"
] | [
"import sys\nimport os\nimport time\nimport math\nimport numpy as np\n\nimport itertools\nimport struct # get_image_size\nimport imghdr # get_image_size\n\n\ndef sigmoid(x):\n return 1.0 / (np.exp(-x) + 1.)\n\n\ndef softmax(x):\n x = np.exp(x - np.expand_dims(np.max(x, axis=1), axis=1))\n x = x / np.expa... | [
[
"numpy.maximum",
"numpy.minimum",
"numpy.max",
"numpy.copy",
"numpy.argmax",
"numpy.exp",
"numpy.array",
"numpy.where",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tanxicccc/rsopt | [
"8705e937f95a4bbe6ed3fb1a04b78f724a5f3931"
] | [
"examples/run_libe_simple_efficiency_optimization.py"
] | [
"import sys, subprocess, os\nimport numpy as np\n\n# Import libEnsemble items for this test\nfrom libensemble.libE import libE\nfrom rsopt.codes.warp.libe_sim import simulate_tec_efficiency as sim_f\n\n# Only import the optimizer package we need for APOSMM\nimport libensemble.gen_funcs\nlibensemble.gen_funcs.rc.apo... | [
[
"numpy.load",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
EuphoriaYan/ctpn-crnn | [
"320ca79c5a86e95bb1278f9f71d16269cfe5490f"
] | [
"crnn/utils.py"
] | [
"#!/usr/bin/python\n# encoding: utf-8\n\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\nimport collections\n\n\nclass strLabelConverter(object):\n \"\"\"Convert between str and label.\n\n NOTE:\n Insert `blank` to the alphabet for CTC.\n\n Args:\n alphabet (str): se... | [
[
"torch.IntTensor",
"torch.FloatTensor",
"torch.nn.UpsamplingBilinear2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Dirguis/ipfn | [
"fa57b905291e4125c6e4f7ddacc8770218ad024e"
] | [
"tests/tests.py"
] | [
"from __future__ import print_function\nfrom context import ipfn\nimport numpy as np\nimport pandas as pd\nimport pytest\n\n\nclass TestIpfn:\n\n def test_bad_verbose(self):\n m = np.array([[8., 4., 6., 7.], [3., 6., 5., 2.], [9., 11., 3., 1.]], )\n xip = np.array([20., 18., 22.])\n xpj = np... | [
[
"pandas.DataFrame",
"numpy.random.rand",
"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": []
}
] |
glasgowneuro/fcl_doom | [
"70f76cd3ab57106003da0adaf67b118f8f66753a"
] | [
"intelact/IntelAct_track2/run_agent.py"
] | [
"from __future__ import print_function\nimport numpy as np\nimport sys\nsys.path.append('./bin/python')\nimport vizdoom \nfrom agent.doom_simulator import DoomSimulator\nfrom agent.agent import Agent\nimport tensorflow as tf\n\ndef main():\n \n ## Simulator\n simulator_args = {}\n simulator_args['config... | [
[
"numpy.arange",
"numpy.ones",
"tensorflow.ConfigProto",
"tensorflow.GPUOptions",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CagtayFabry/weldx-widgets | [
"e9c7e085628d08a1e2667d537e898b215debde15"
] | [
"weldx_widgets/tests/test_groove_sel.py"
] | [
"\"\"\"Tests for groove selection widget.\"\"\"\nimport pytest\n\nimport weldx\nfrom weldx.welding.groove.iso_9692_1 import _create_test_grooves\nfrom weldx_widgets import WidgetGrooveSelection, WidgetGrooveSelectionTCPMovement\n\ntest_grooves = _create_test_grooves()\n\n\ndef setup_module(module):\n \"\"\"Set A... | [
[
"matplotlib.use"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TJGreen0211/pandas-profiling | [
"ad765be82ba4c9338f3659480abbfd68e44918dd"
] | [
"src/pandas_profiling/report/presentation/frequency_table_utils.py"
] | [
"from typing import Any, Dict, List\n\nimport numpy as np\n\n\ndef freq_table(freqtable, n: int, max_number_to_print: int) -> List[Dict]:\n \"\"\"Render the rows for a frequency table (value, count).\n\n Args:\n freqtable: The frequency table.\n n: The total number of values.\n max_number_to_pr... | [
[
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wqwangchn/novice | [
"d52190a9cd5045726e49aff8610b718636c304c7",
"d52190a9cd5045726e49aff8610b718636c304c7"
] | [
"pynovice/geo_map/google_map.py",
"pynovice/score_card/model_logistic.py"
] | [
"# -*- coding: utf-8 -*-\n# /usr/bin/env python\n\n'''\nAuthor: wenqiangw\nEmail: wqwangchn@163.com\nDate: 2020/9/16 17:34\nDesc:\n'''\nimport gmplot\nimport webbrowser\nimport os\n\n# plot the locations on google map 真实经纬度信息\ndef plot_google_map(data, to_html_name,web_open=False):\n html_name = os.path.abspath... | [
[
"pandas.DataFrame"
],
[
"pandas.concat",
"sklearn.linear_model.LogisticRegression",
"sklearn.model_selection.train_test_split",
"pandas.DataFrame",
"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": []
},
{
"matplotlib": [],
"nump... |
DiogoRibeiro7/horovod | [
"770c7d8c2bbf2c8962529c24d18720514f440300"
] | [
"test/integration/data/elastic_torch_main.py"
] | [
"# Copyright 2020 Uber Technologies, Inc. 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... | [
[
"torch.nn.Linear",
"torch.randn",
"torch.LongTensor",
"torch.nn.functional.cross_entropy"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
eichinflo/lit | [
"b46c0cac34baa571242637b53b78cfd69de536d0"
] | [
"lit_nlp/examples/models/glue_models.py"
] | [
"# Lint as: python3\n\"\"\"Wrapper for fine-tuned HuggingFace models in LIT.\"\"\"\n\nimport os\nimport re\nimport threading\nfrom typing import Optional, Dict, List, Iterable\n\nimport attr\nfrom lit_nlp.api import model as lit_model\nfrom lit_nlp.api import types as lit_types\nfrom lit_nlp.examples.models import ... | [
[
"tensorflow.convert_to_tensor",
"tensorflow.math.argmax",
"tensorflow.nn.softmax",
"tensorflow.constant",
"tensorflow.gather_nd",
"tensorflow.keras.losses.SparseCategoricalCrossentropy",
"tensorflow.keras.losses.MeanSquaredError",
"tensorflow.reduce_sum",
"tensorflow.cast",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
SirSharpest/NarrowEscapeSimulator | [
"e96cf96348a0d32184e9e9dc0d592a63c4618760"
] | [
"PyEscape/__main__.py"
] | [
"import argparse\nimport numpy as np\nfrom .escape_plan import escape\nfrom .escape_points import fibonacci_spheres, points_on_cube_surface\nimport multiprocessing\nfrom tqdm import tqdm\n\n\ndef esc(args):\n return escape(*args)\n\n\ndef run_simulations(D, v, a, s, p, N, dt, cpu):\n\n pores = fibonacci_spher... | [
[
"numpy.array",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rcorona/alfred | [
"dff465321d32ffdc13ec76addfc227a766fb9518"
] | [
"models/eval/eval_seq2seq.py"
] | [
"import os\nimport sys\nsys.path.append(os.path.join(os.environ['ALFRED_ROOT']))\nsys.path.append(os.path.join(os.environ['ALFRED_ROOT'], 'gen'))\nsys.path.append(os.path.join(os.environ['ALFRED_ROOT'], 'models'))\n\nimport argparse\nimport torch.multiprocessing as mp\nfrom eval_task import EvalTask\nfrom eval_subg... | [
[
"torch.multiprocessing.set_start_method",
"torch.multiprocessing.Manager"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DrawZeroPoint/RoRL | [
"90a3eebdca119d00fbb2911d37ca4447cd0450b3"
] | [
"rorlkit/envs/goal_generation/pickup_goal_dataset.py"
] | [
"from multiworld.envs.mujoco.sawyer_xyz.sawyer_pick_and_place import (\n get_image_presampled_goals\n)\nimport numpy as np\nimport cv2\nimport os.path as osp\nimport random\n\nfrom rlkit.util.io import local_path_from_s3_or_local_path\n\n\ndef setup_pickup_image_env(image_env, num_presampled_goals):\n \"\"\"\... | [
[
"numpy.load",
"numpy.save",
"numpy.zeros",
"numpy.random.shuffle"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hrishikeshrt/python-wordle | [
"574f4476a0a3b35ebb0030babc8bd49d8107f34c"
] | [
"wordle/evaluate.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nEvaluate Solver\n\n@author: Hrishikesh Terdalkar\n\"\"\"\n\n###############################################################################\n\nimport json\n\nfrom tqdm import tqdm\nfrom numpy import average\n\nfrom .vocab import Vocabulary\nfrom .wordle impo... | [
[
"numpy.average"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nschloe/krylov | [
"58813233ff732111aa56f7b1d71908fda78080be"
] | [
"src/krylov/bicg.py"
] | [
"\"\"\"\nH.A. Van der Vorst,\nBi-CGSTAB: A Fast and Smoothly Converging Variant of Bi-CG for the Solution of\nNonsymmetric Linear Systems,\nSIAM J. Sci. Stat. Comput. 13 (2): 631–644, 1992,\n<https://doi.org/10.1137%2F0913035>\n\nOther implementations:\n\nhttps://docs.scipy.org/doc/scipy/reference/generated/scipy.s... | [
[
"numpy.maximum",
"numpy.asarray",
"numpy.all",
"numpy.copy",
"numpy.zeros_like",
"numpy.array",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Gouthamyeluri/malaria-delection-using-images | [
"47424303dfa1612c79b889e774dea7129dd8d0e4"
] | [
"app.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\n\"\"\"\r\n\r\nfrom __future__ import division, print_function\r\n# coding=utf-8\r\nimport sys\r\nimport os\r\nimport glob\r\nimport re\r\nimport numpy as np\r\n\r\n# Keras\r\nfrom tensorflow.keras.applications.imagenet_utils import preprocess_input, decode_predictions\r\nfrom t... | [
[
"tensorflow.keras.models.load_model",
"numpy.expand_dims",
"tensorflow.keras.preprocessing.image.img_to_array",
"tensorflow.keras.preprocessing.image.load_img",
"numpy.argmax",
"tensorflow.keras.applications.imagenet_utils.preprocess_input"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.4",
"2.3",
"2.5",
"2.6"
]
}
] |
julencosme/python-crash-course | [
"6b37d7346e235273c266110932207cd67ce4eb0e"
] | [
"cubic_colorMap.py"
] | [
"# Adding colormap to 'cubes.py' file and using a scatter plot graph rather than\n# line plot graph.\n\nimport matplotlib.pyplot as plt\nx_values = [1, 2, 3, 4, 5]\ny_values = [1, 8, 27, 64, 125]\nplt.scatter(x_values, y_values, c=y_values,\n cmap=plt.cm.Reds, edgecolor='none', s=40)\n\nplt.title(\"Cubic... | [
[
"matplotlib.pyplot.title",
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.tick_params",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
2wind/eval_gui_example | [
"2f248472dd039b59b5410c29e82ad311130e0b67"
] | [
"utility.py"
] | [
"from matplotlib import patches, pyplot as plt\nimport numpy as np\nimport skimage\n\nfrom skimage.measure import find_contours\nfrom matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2Tk\n\nfrom config import *\n\n\"\"\"\nUtility functions for image processing and display.\nSome of them ... | [
[
"numpy.array",
"matplotlib.backends.backend_tkagg.FigureCanvasTkAgg"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dhlanm/gpt-2-simple | [
"556f59d51d4eea45939dca66b0b20fe3140c4069"
] | [
"gpt_2_simple/gpt_2.py"
] | [
"import tarfile\nimport os\nimport json\nimport requests\nimport sys\nimport shutil\nimport re\nfrom tqdm import tqdm, trange\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow.core.protobuf import rewriter_config_pb2\nfrom tensorflow.python.client import device_lib\nimport time\nfrom datetime import dat... | [
[
"tensorflow.python.client.device_lib.list_local_devices",
"numpy.concatenate",
"tensorflow.compat.v1.train.Saver",
"tensorflow.compat.v1.train.AdamOptimizer",
"tensorflow.gradients",
"tensorflow.compat.v1.trainable_variables",
"tensorflow.compat.v1.set_random_seed",
"tensorflow.com... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ThomasPak/cell-competition | [
"bb058d67e297d95c4c8ff2a0aea5b1fe5a82be09"
] | [
"sims/lib/g2_death_signal_simulation.py"
] | [
"import numpy as np\nimport pandas as pd\nfrom scipy.stats import expon, uniform\n\nimport sys\nsys.path.append('../../well_mixed')\nfrom well_mixed_death_clock import (WellMixedSimulator,\n WellMixedSimulationData, exponential_ccm_heterotypic,\n uniform_ccm_heterotypic, normalised_g2_death_signal_heterotypic... | [
[
"scipy.stats.expon.rvs",
"matplotlib.pyplot.rc",
"matplotlib.pyplot.subplots",
"numpy.concatenate",
"numpy.logical_or",
"scipy.stats.uniform.rvs",
"numpy.array",
"numpy.zeros",
"numpy.random.RandomState"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
T-TROUCHKINE/plotter | [
"214158d91378068e394fbaebdec2a890449d27bf"
] | [
"examples/vertical_lines.py"
] | [
"import numpy as np\nimport sys\n\nfrom plotter import Plotter, PlotterType\n\nx = np.linspace(-np.pi, np.pi, 201)\n\nto_plot = [\n {\n \"title\": \"Plot\",\n \"type\": PlotterType.PLOT,\n \"data\": [x, np.sin(x)],\n \"vlines\": [-0.3,1.2],\n \"vline_color\": [\"darkgrey\"]\n ... | [
[
"numpy.linspace",
"numpy.sin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
annisanurdiana/python_programming | [
"33ad8285b14de3527ed9524793153352c2f3e6cf"
] | [
"ImageProcessing/midterm3.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Oct 29 15:26:36 2021\r\n\r\n@author: Annisa Nurdiana\r\n\"\"\"\r\nimport cv2 as cv\r\nimport numpy as np\r\n\r\nimage = cv.imread('Lena.bmp')\r\nimage = cv.cvtColor(image, cv.COLOR_BGR2HSV)\r\n\r\nincrease = 100\r\n\r\nv = image[:, :, 2]\r\nv = np.where(v <= 255 ... | [
[
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ppfliu/emotion-recognition | [
"45addf96609145a5f36e6dacda8499bf03cc57ab"
] | [
"main.py"
] | [
"# Copyright (C) 2020 Pengfei Liu. 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 required by... | [
[
"tensorflow.keras.backend.set_session",
"numpy.random.seed",
"numpy.unique",
"sklearn.model_selection.train_test_split",
"sklearn.metrics.confusion_matrix",
"sklearn.utils.class_weight.compute_class_weight",
"tensorflow.ConfigProto",
"tensorflow.set_random_seed",
"tensorflow.ge... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
w4kpm/CartPoleEx | [
"7a141d1c64217ccf93e15f445922775112b49837"
] | [
"cartpole2.py"
] | [
"\"\"\"\nClassic cart-pole system implemented by Rich Sutton et al.\nCopied from http://incompleteideas.net/sutton/book/code/pole.c\npermalink: https://perma.cc/C9ZM-652R\nAltered from OpenAI Gym CartPole-v0\n2018 Kelly McDonald\n\"\"\"\n\nimport math\nimport gym\nfrom gym import spaces, logger\nfrom gym.utils impo... | [
[
"numpy.array",
"numpy.finfo"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
XuChanghhu/thgsp | [
"f2e8c8d04432d0204a999aad3e555171ffd0d432"
] | [
"tests/test_graphs/test_spectrum.py"
] | [
"from thgsp.graphs.spectrum import dynamic_spectrum_estimate_dense, spl, tripb\nimport numpy as np\nimport scipy.sparse as ssp\nimport scipy.sparse.linalg as lin\nfrom ..utils4t import float_np_dts\nimport pytest\n\nnp.set_printoptions(linewidth=2000000, precision=5)\n\n\n@pytest.mark.parametrize(\"dtype\", float_n... | [
[
"numpy.set_printoptions",
"scipy.sparse.rand",
"numpy.stack",
"numpy.copy",
"numpy.fill_diagonal",
"numpy.random.rand",
"numpy.array",
"scipy.sparse.linalg.eigsh"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"1.3",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"0.16",
"1.8"
... |
sarathykousik/NeuroKit.py | [
"79c163a9453fa4416d3bfe0b8ddb4ce9bbc3c17b"
] | [
"examples/UnderDev/ECG_PeakDetection/test.py"
] | [
"# -*- coding: utf-8 -*-\nimport numpy as np\nimport pandas as pd\nimport neurokit as nk\nimport biosppy\nimport matplotlib.pyplot as plt\nimport scipy\n\n\n\ndf = pd.read_csv('data.csv')\ndf = df.loc[10000:20000]\n\n\nsampling_rate=1000\ndf = nk.ecg_preprocess(ecg=df[\"ECG\"], sampling_rate=sampling_rate)[\"df\"]\... | [
[
"numpy.convolve",
"pandas.read_csv",
"scipy.signal.filtfilt",
"numpy.median",
"scipy.ndimage.gaussian_filter1d",
"numpy.flatnonzero",
"numpy.max",
"scipy.signal.butter",
"numpy.std",
"numpy.diff",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"1.3",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2"... |
dong1015323606/LKVOLearner | [
"6ac9fb5d3c22d6a81529063f8c52d6aa34166b2a"
] | [
"src/train_main_ddvo.py"
] | [
"import torch\nfrom torch import optim\nimport matplotlib.pyplot as plt\nimport torchvision\nimport numpy as np\nfrom torch.autograd import Variable\nfrom torch.utils.data import DataLoader\nimport scipy.io as sio\nimport os\n\nfrom LKVOLearner import LKVOLearner\nfrom KITTIdataset import KITTIdataset\n\nfrom colle... | [
[
"numpy.mod",
"torch.utils.data.DataLoader",
"torch.autograd.Variable"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kirienko/EXtra-data | [
"05de22b55fdc424473127335d2ca6b3a7026ab6d"
] | [
"extra_data/tests/test_reader_mockdata.py"
] | [
"from itertools import islice\n\nimport h5py\nimport numpy as np\nimport os\nimport pandas as pd\nimport pytest\nimport stat\nfrom tempfile import mkdtemp\nfrom testpath import assert_isfile\nfrom unittest import mock\nfrom xarray import DataArray\n\nfrom extra_data import (\n H5File, RunDirectory, by_index, by_... | [
[
"numpy.arange",
"numpy.testing.assert_array_less",
"numpy.int64",
"numpy.testing.assert_array_equal",
"numpy.uint16",
"numpy.repeat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rcmckee/BPT | [
"123a14d1864f7ab8c39b88200260fdfc38727bf1"
] | [
"utils.py"
] | [
"import torch as th\n\ndef unpack_params(params):\n embed_params, other_params, wd_params = [], [], []\n for k, v in params:\n if 'embed' in k:\n embed_params.append(v)\n elif 'norm' in k or 'bias' in k:\n other_params.append(v)\n else: # applies weight decay\n ... | [
[
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nate4bs/DataRepo | [
"916f75ff2996967c62684bc75be23dd23aa59a67"
] | [
"Bayesian Machine Learning/Bayesian AB Testing/Bayesian_Bandit.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n\n# In[35]:\n\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy.stats import beta\nimport seaborn as sns\nsns.set()\n\n\n# In[36]:\n\n\nNUM_T = 2000\nBANDIT_P = [0.2,0.5,0.75]\n\n\n# In[37]:\n\n\nclass Bandit:\n def __init__(self,p):\n self.p = p\n... | [
[
"matplotlib.pyplot.legend",
"numpy.random.beta",
"numpy.random.random",
"matplotlib.pyplot.title",
"numpy.linspace",
"scipy.stats.beta.pdf",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Julymycin/Self-Correction-Human-Parsing_with-libtorch-deployment | [
"0f7265fcf927e959e3746d4f32d6771dbb91392d"
] | [
"evaluate.py"
] | [
"#!/usr/bin/env python\n# -*- encoding: utf-8 -*-\n\n\"\"\"\n@Author : Peike Li\n@Contact : peike.li@yahoo.com\n@File : evaluate.py\n@Time : 8/4/19 3:36 PM\n@Desc :\n@License : This source code is licensed under the license found in the\n LICENSE file in the root directory of this so... | [
[
"torch.load",
"numpy.asarray",
"torch.utils.data.DataLoader",
"torch.nn.Upsample",
"torch.no_grad",
"torch.stack",
"numpy.zeros",
"torch.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
manesioz/koalas | [
"53905386a340df458781c8f4bf7002c0e3e11f48"
] | [
"databricks/koalas/frame.py"
] | [
"#\n# Copyright (C) 2019 Databricks, Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable la... | [
[
"pandas.concat",
"pandas.core.dtypes.common._get_dtype_from_object",
"pandas.api.types.is_scalar",
"pandas.MultiIndex.from_tuples",
"pandas.Index",
"pandas.api.types.is_dict_like",
"pandas.core.accessor.CachedAccessor",
"pandas.DataFrame",
"pandas.api.types.is_list_like",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.24"
],
"scipy": [],
"tensorflow": []
}
] |
shink00000/dl-study | [
"26c69ef984b7fbb0474b1eb01a281e4da2bb2998"
] | [
"tests/components/data_pipeline/transforms/test_color.py"
] | [
"import cv2\nimport numpy as np\nimport torch\nfrom PIL import Image, ImageDraw\nfrom pathlib import Path\n\nfrom cleardl.components.data_pipeline.transforms.color import PhotoMetricDistortion\n\n\ndef test_photometric_distortion():\n image = np.zeros((256, 256, 3), dtype='uint8')\n image = cv2.circle(image, ... | [
[
"torch.LongTensor",
"torch.Tensor",
"torch.from_numpy",
"numpy.stack",
"numpy.full_like",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wa-dev/PyFR | [
"9359c5229050b12fb12f6c7b2452c1d7f561e942"
] | [
"pyfr/solvers/base/system.py"
] | [
"# -*- coding: utf-8 -*-\n\nfrom collections import defaultdict\nimport itertools as it\nimport re\n\nimport numpy as np\n\nfrom pyfr.inifile import Inifile\nfrom pyfr.shapes import BaseShape\nfrom pyfr.util import proxylist, subclasses\n\n\nclass BaseSystem(object):\n elementscls = None\n intinterscls = None... | [
[
"numpy.nonzero"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mahdinobar/V2V-PoseNet_RELEASE | [
"e7021cfb16db30b7a967c66d073f04bb909ec216"
] | [
"vis/itop/world2pixel.py"
] | [
"import numpy as np\n\ndb = 'ITOP'\n\nif db == 'ICVL':\n\n def world2pixel(coord,fx,fy,imgWidth,imgHeight):\n\n pixelX = imgWidth/2 + np.divide(coord[:,:,0],coord[:,:,2]) * fx\n pixelY = imgHeight/2 + np.divide(coord[:,:,1],coord[:,:,2]) * fy\n \n return pixelX, pixelY\nelse:\n\n d... | [
[
"numpy.reshape",
"numpy.divide",
"numpy.loadtxt",
"numpy.savetxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
fdschmidt93/procrustes | [
"663224f38ed4b34371378b325f6adb78a6943d8f"
] | [
"src/utils.py"
] | [
"from collections import defaultdict\nfrom dataclasses import dataclass, field\nimport os\nimport pickle\nfrom typing import Dict, Optional\n\nimport numpy as np\n\ndef list_duplicates(tokens):\n tally = defaultdict(list)\n for i,item in enumerate(tokens):\n tally[item].append(i)\n return [(key,locs... | [
[
"numpy.asarray",
"numpy.load",
"numpy.linalg.norm",
"numpy.stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aangelopoulos/event_based_gaze_tracking | [
"94d71b3dfc38e723b626348aae2a82020eb63a9d"
] | [
"visualize.py"
] | [
"#!/usr/bin/env python3\n\"\"\"\nAmit Kohli, Julien Martel, and Anastasios Angelopoulos\nAugust 10, 2020\nThis script provides a visualization of the ebv-eye data.\n\"\"\"\n\nimport argparse\nimport struct\nimport glob\nimport os\nimport matplotlib.pyplot as plt\nfrom PIL import Image\nfrom collections import named... | [
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.draw",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.show",
"matplotlib.pyplot.pause"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
palerdot/BlingFire | [
"7e0fbc719042a4857c824b42d9cb5fbefe48aa3e"
] | [
"scripts/blingfiretok.py"
] | [
"import os\nfrom ctypes import *\nimport inspect\nimport os.path\nimport numpy as np\nimport platform\n\nfilename = inspect.getframeinfo(inspect.currentframe()).filename\npath = os.path.dirname(os.path.abspath(filename))\n\n# load the DLL\nblingfire = None\n\n# detect windows\nif platform.system() == \"Windows\":\n... | [
[
"numpy.frombuffer"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
martihmy/Compliant_control | [
"485f627fa83d59f414f41bd57c5d37528ef5f1ec",
"485f627fa83d59f414f41bd57c5d37528ef5f1ec"
] | [
"panda_simulator_examples/scripts/Impedance_FC_scripts/force_control_impedance(different_control_space).py",
"Compliant_control/tests/r_d-test.py"
] | [
"#! /usr/bin/env python\nimport copy\nfrom copy import deepcopy\nimport rospy\nimport threading\nimport quaternion\nimport numpy as np\nfrom geometry_msgs.msg import Point\nfrom visualization_msgs.msg import *\n#from interactive_markers.interactive_marker_server import *\nfrom franka_interface import ArmInterface\n... | [
[
"matplotlib.pyplot.legend",
"numpy.dot",
"matplotlib.pyplot.title",
"numpy.linalg.inv",
"numpy.set_printoptions",
"numpy.linalg.multi_dot",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.subplot",
"numpy.identity",
"matplotlib.pyplot.xlabel",
"numpy.array",
"matplotli... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.5",
"1.2",
"1.3",
"1.4"
],
"tensorflow": []
}
] |
ZhongHouyu/CVCode | [
"e9576dfbdd1ae2ff986dadde3183eb6bc0380f76"
] | [
"Deep-Learning/Fast-RCNN/Fast-RCNN(version: li)/dataloader.py"
] | [
"# --------------------------------------------------------\n# Fast R-CNN\n# Copyright (c) 2018 Fudan-VTS\n# Licensed under The MIT License [see LICENSE for details]\n# Written by liwenxi\n# --------------------------------------------------------\n\nimport os\nimport torch\nimport xml.etree.ElementTree as ET\nfrom... | [
[
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wpumacay/tysocTerrain | [
"78b6d9804ade89a483fb60952ed6e1bf50fbf3da"
] | [
"tests/python/test_scenario.py"
] | [
"#!/usr/bin/env python\n\nimport loco\nimport tinymath as tm\nimport numpy as np\nimport gc\n\ndef test_scenario_constructor() :\n scenario = loco.sim.Scenario()\n scenario.Initialize()\n scenario.PreStep()\n scenario.PostStep()\n scenario.Reset()\n assert ( scenario.HasSingleBodyNamed( 'foo' ) ==... | [
[
"numpy.identity"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LAL/trackml-library | [
"53a165e15a2c885f54c2bef1bd1ed53db6ed9648"
] | [
"trackml/weights.py"
] | [
"\"\"\"TrackML metric weight calculation\"\"\"\n\nfrom __future__ import print_function\n\n__authors__ = ['Moritz Kiehn']\n\nimport math\n\nimport numpy\nimport pandas\n\nfrom .utils import decode_particle_id\n\ndef _compute_order_weight_matrix(proposal, min_hits, max_hits):\n \"\"\"Compute the hit order weight ... | [
[
"pandas.merge",
"numpy.absolute",
"numpy.arange",
"numpy.interp",
"numpy.zeros",
"numpy.hypot"
]
] | [
{
"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": []
}
] |
PawelMlyniec/Dail-a-ride | [
"7d9b3cd904d3194dccad31fec2533e2cf58cad0c"
] | [
"dialRL/environments/pixel_instance.py"
] | [
"\"\"\" File defining the data instances \"\"\"\n\nimport numpy as np\nimport logging\nimport random\nimport time\nimport matplotlib.pyplot as plt\nimport os\n\nimport pickle\n\nfrom dialRL.utils import image_coordonates2indices, indice2image_coordonates\n\nclass PixelInstance():\n \"\"\" 2 Dimentional insance ... | [
[
"matplotlib.pyplot.imshow",
"numpy.random.seed",
"numpy.linalg.norm",
"numpy.stack",
"numpy.append",
"matplotlib.pyplot.show",
"numpy.zeros",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
snaka0213/graph_neural_network | [
"07d66d6a7832720d9fb1207e7eb437a12a42af86"
] | [
"src/functions.py"
] | [
"#!/usr/bin/env python3\n\nimport numpy as np\nfrom myClass import Theta\nfrom readout import readout\nfrom hyperParameters import hyperParameters\n\n# hyperParameters\nT, epsilon = hyperParameters.T, hyperParameters.epsilon\n\n# Let adj be the adjacency matrix of given graph G,\n# y be the label of G, W be a lear... | [
[
"numpy.exp",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
BotsByNikki/ME-599-Solid-Modeling-Homeworks | [
"b8babe53f1992e4fe92b4514406b884e9f10e926"
] | [
"HW4/599_HW4_Q2.py"
] | [
"#!/usr/bin/env python\r\n\r\n#Nicole Guymer and Niklas Delboi\r\n#ME 599: Solid Modeling\r\n#Homework 4\r\n#2/27/2019\r\n\r\n\"\"\"Question 2: Draw an egg shape comprised of four function calls to your function from question 1.\"\"\"\r\n\r\n\"\"\"Question 2.1: The polygonal area of the egg is lower than the actual... | [
[
"matplotlib.pyplot.title",
"numpy.arange",
"matplotlib.pyplot.plot",
"numpy.concatenate",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.xlabel",
"numpy.array",
"numpy.zeros",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
uva-hydroinformatics/Norfolk_Groundwater_Model | [
"1d0d8be5b87fac1c9febea5877d44645e9deae9d"
] | [
"Model/Rivanna_HPC/keras_mmps155_fullBS_fcst_rnn_rivanna.py"
] | [
"\"\"\"\r\nWritten by Benjamin Bowes, 10-23-2018\r\n\r\nModel: RNN\r\nData: full data set, bootstrapped and forecast data\r\n\r\nThis network uses the last 28 observations of gwl, tide, and rain to predict the next 18\r\nvalues of gwl for well MMPS-155\r\n\"\"\"\r\n\r\nimport tensorflow as tf\r\nimport keras\r\nfro... | [
[
"tensorflow.ConfigProto",
"tensorflow.get_default_graph",
"tensorflow.set_random_seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
YunchuZhang/Sawyer_arm | [
"efb1b9e1bf42a2369f7f0a2434e78730e2dc60b9"
] | [
"multiworld/core/image_env.py"
] | [
"import random\n\nimport cv2\nimport numpy as np\nimport warnings\nfrom PIL import Image\nfrom gym.spaces import Box, Dict\n\nfrom multiworld.core.multitask_env import MultitaskEnv\nfrom multiworld.core.wrapper_env import ProxyEnv\nfrom multiworld.envs.env_util import concatenate_box_spaces\nfrom multiworld.envs.en... | [
[
"numpy.uint8",
"numpy.linalg.norm",
"numpy.concatenate",
"numpy.array",
"numpy.zeros",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ZhaoqingLiu/FuzzyTrees | [
"6350987606588197b44847414a3d01fa460a1ed1"
] | [
"run.py"
] | [
"\"\"\"\n@author: Zhaoqing Liu\n@email : Zhaoqing.Liu-1@student.uts.edu.au\n@desc : Examples using the algorithms provided by the framework.\n\"\"\"\nimport numpy as np\nfrom sklearn import datasets\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.model_selection import train_test_split\n\nfrom fuzzytrees... | [
[
"sklearn.datasets.load_wine",
"sklearn.model_selection.train_test_split",
"sklearn.datasets.load_diabetes",
"numpy.concatenate",
"sklearn.metrics.accuracy_score"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sunnyh2024/ImageEditor | [
"9f9627eb7f09041352cd6ae63b25160479d0d861"
] | [
"src/test.py"
] | [
"from PIL import Image\nimport model\nimport seamCarver as sc\nimport numpy as np\nimport random as rand\n\n# img = Image.open(r\"C:\\users\\sunny\\OneDrive\\Desktop\\ImageEditor\\test.png\")\n# seam = sc.findLowestSeam(np.array(img), 0)\n# print(seam)\n# img = sc.carveSeam(img, 2, 3)\n# seam = sc.findLowestSeam(im... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dizhouwu/QuantBeat | [
"6bafa30d8b2416ef9cf4cab2e442d009c44b7705"
] | [
"external/analytic_shrinkage/high_p.py"
] | [
"\"\"\"This code replicates the high dimensional results for the analytic\nshrinkage estimator from the working paper called\n'Analytical Nonlinear Shrinkage of Large-Dimensional Covariance Matrices'\nLedoit and Wolf 2018\nFigure 4\n\"\"\"\n\nimport numpy as np\nimport nonlinshrink.nonlinshrink as nls\n\nCONST = 1... | [
[
"numpy.dot",
"numpy.minimum",
"numpy.sqrt",
"numpy.arange",
"numpy.eye",
"numpy.ones",
"numpy.linalg.eigh",
"numpy.mean",
"numpy.random.randn",
"numpy.cov",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zuoanqh/trfl | [
"eee6c84bc565517c56e74828e26f7e7e401b33a0"
] | [
"trfl/sequence_ops.py"
] | [
"# Copyright 2018 The trfl 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 required by... | [
[
"tensorflow.convert_to_tensor",
"tensorflow.reverse",
"tensorflow.transpose",
"tensorflow.reshape",
"tensorflow.assert_equal",
"tensorflow.reverse_sequence",
"tensorflow.name_scope",
"tensorflow.sequence_mask"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
shane-breeze/zinv-analysis | [
"496abf9cb0e77831d580be417bcad7845c347704"
] | [
"zinv/modules/readers/WeightPdfScale.py"
] | [
"import numpy as np\n\ndef evaluate_pdf_variations():\n def ret_func(ev, pos):\n pdf = np.ones(ev.size, dtype=np.float32)\n if ev.hasbranch(\"LHEPdfWeight\"):\n mask = ev.nLHEPdfWeight>pos\n pdf[mask] = ev.LHEPdfWeight[mask,pos]\n return pdf\n\n return ret_func\n\nde... | [
[
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
9xEzreaL/HEDUnet | [
"0ef1c746df7a6e67bd1cc69a75a5a76c91a0702e"
] | [
"DiceCofficient.py"
] | [
"import torch\nimport torch.nn as nn\nimport numpy as np\n\n\ndef accuracy(target, y_hat):\n seg_pred = torch.argmax(y_hat[:, 1:], dim=1)\n seg_acc = (seg_pred == target[:, 1]).float().mean()\n\n edge_pred = (y_hat[:, 0] > 0).float()\n edge_acc = (edge_pred == target[:, 0]).float().mean()\n\n return ... | [
[
"torch.nn.Softmax",
"torch.max",
"torch.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lukasmweber/spn-compiler | [
"acab827e8b8df69d1a4e83a209e14f62bd8d967e"
] | [
"python-interface/test/vector/test_vector_slp_escaping_users.py"
] | [
"import numpy as np\nimport pytest\nimport time\nfrom spn.algorithms.Inference import log_likelihood\nfrom spn.structure.Base import Product, Sum\nfrom spn.structure.leaves.histogram.Histograms import Histogram\nfrom spn.structure.leaves.parametric.Parametric import Categorical\nfrom spn.structure.leaves.parametric... | [
[
"numpy.random.normal",
"numpy.array",
"numpy.random.randint",
"numpy.isclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rdenadai/WordEmbeddingPortugues | [
"b3af4f01133ba258d1f750f2394ed1a8272f13f8",
"b3af4f01133ba258d1f750f2394ed1a8272f13f8"
] | [
"src/data/scraping/embedding/ms_extractor.py",
"src/data/scraping/embedding/frases_extractor.py"
] | [
"import os\nimport codecs\nimport asyncio\nfrom itertools import chain\n\nimport numpy as np\nimport httpx\nfrom bs4 import BeautifulSoup\nfrom aiomultiprocess import Pool\n\n\nurls = []\nurl = \"https://www.saude.gov.br/saude-de-a-z/\"\n\n\nasync def get_links(url):\n links = []\n async with httpx.AsyncClien... | [
[
"numpy.savetxt"
],
[
"numpy.savetxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
RLInstitut/feedinlib | [
"b4d0481e74100a33a397c23e6bae531f0f4cbada"
] | [
"src/feedinlib/era5.py"
] | [
"import geopandas as gpd\nimport numpy as np\nimport pandas as pd\nimport xarray as xr\nfrom shapely.geometry import Point\n\nfrom feedinlib.cds_request_tools import get_cds_data_from_datespan_and_position\n\n\ndef get_era5_data_from_datespan_and_position(\n start_date,\n end_date,\n target_file,\n vari... | [
[
"numpy.sqrt",
"pandas.MultiIndex",
"pandas.Timedelta",
"pandas.DataFrame",
"numpy.logical_or",
"numpy.size",
"numpy.logical_and"
]
] | [
{
"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": []
}
] |
Stella925/Programming-Projects | [
"5d6357390e7494a10b675f02bd47cee75424c95f"
] | [
"train_main.py"
] | [
"###############################################################################\n# 重要: 请务必把任务(jobs)中需要保存的文件存放在 results 文件夹内\n# Important : Please make sure your files are saved to the 'results' folder\n# in your jobs\n# 本代码来源于 Notebook cell 里面的模型，大家进行离线任务时尽量只训练模型，不要进行模型评估等操作\n######################################... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kevinyu/multiple_mics_test | [
"241836ac7631fcf8290c1e91486d754ac27e46c3"
] | [
"code/listeners.py"
] | [
"import collections\nimport datetime\nimport logging\n\nimport os\nimport scipy.io.wavfile\n\nimport numpy as np\nfrom PyQt5.QtCore import pyqtSignal, pyqtSlot, QThread, QObject, QTimer\n\nfrom settings import Settings\nfrom utils import datetime2str\nfrom ringbuffer import RingBuffer\n\n\nlogger = logging.getLogge... | [
[
"numpy.array",
"numpy.abs"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lpsinger/regions | [
"55cb07f3ae54759637ba26d35bfcdf6043b825fb"
] | [
"regions/core/tests/test_mask.py"
] | [
"# Licensed under a 3-clause BSD style license - see LICENSE.rst\nimport numpy as np\nfrom numpy.testing import assert_allclose\nimport pytest\n\nfrom ..bounding_box import BoundingBox\nfrom ..mask import RegionMask\nfrom ..pixcoord import PixCoord\nfrom ...shapes import CirclePixelRegion\n\ntry:\n import matplo... | [
[
"numpy.arange",
"numpy.ones",
"numpy.testing.assert_allclose",
"numpy.array",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hehewa/CAT | [
"448b5e9bc8b81706a325cfdcb2d7f44a38c9a6f1"
] | [
"core/samples/gmm_manager.py"
] | [
"import os\n\nimport cv2\nimport numpy as np\nimport torch\nimport torchvision\n\nfrom net.wae64 import WAE64\nfrom tracking.options import opts\n\nBIGNUM = 1e22 # e22\n\nclass postive_samples_manager():\n def __init__(self, pos_num):\n # postive samples\n self.pos_features = []\n # ... | [
[
"torch.ones",
"torch.load",
"torch.sqrt",
"numpy.power",
"torch.min",
"torch.zeros",
"torch.sum",
"torch.argmin",
"torch.equal",
"torch.stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CompRhys/ornstein-zernike | [
"ed17e8aa57a0a470752eb440137b7b527adcce27"
] | [
"process/core/block.py"
] | [
"import numpy as np\nimport warnings\n\n\ndef block_data(series, length):\n \"\"\"\n This function takes the data an combines it into blocks. Only works with powers of 2.\n \"\"\"\n return np.mean(series.reshape(-1, length, series.shape[-1]), 1)\n\n\ndef block_transformation(series):\n \"\"\"\n Do... | [
[
"numpy.sqrt",
"numpy.take",
"numpy.abs",
"numpy.power",
"numpy.arange",
"numpy.atleast_1d",
"numpy.append",
"numpy.any",
"numpy.var",
"numpy.zeros",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
RC-Dynamics/multiagent-particle-envs | [
"cd24175ab092b7970416f89a92dbcf695059115f"
] | [
"torch/maddpg.py"
] | [
"import os\n\nimport numpy as np\n\nimport torch\nfrom buffer import ReplayBuffer\nfrom ddpg import DDPGAgentTrainer\nfrom network import Actor, Critic, TargetActor, TargetCritic\nfrom torch.autograd import Variable\nfrom torch.nn import functional as F\nfrom torch.nn.utils import clip_grad_norm_\nfrom torch.optim ... | [
[
"torch.Tensor",
"torch.cat",
"torch.load",
"torch.tanh",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Zbot21/zcoinbase | [
"8e595e264b7ebef059bf2399cc1c7b4fc98152ce"
] | [
"zcoinbase/historical_data_downloader.py"
] | [
"import csv\nimport datetime\nimport math\nimport sys\nimport pandas as pd\n\nfrom dateutil import parser\nfrom functools import partial\nfrom typing import Text, Callable\nfrom zcoinbase import PublicClient\nfrom zcoinbase.internal import RateLimitedExecutionQueue\n\n\n# Magically use absl logging if available.\ni... | [
[
"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": []
}
] |
zju3dv/SMAP | [
"3e07b78c401bea73de5beb0c942fc0ba6a32ba77"
] | [
"lib/preprocess/create_annot.py"
] | [
"from pycocotools.coco import COCO\nimport numpy as np\nimport json\nimport os\n\nroot_dir = 'data/coco2017'\ndata_type = 'train2017'\nanno_name = 'person_keypoints_{}.json'.format(data_type)\nanno_file = os.path.join(root_dir, 'annotations', anno_name)\noutput_json_file = os.path.join(root_dir, 'coco_keypoints_{}.... | [
[
"numpy.asarray",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
snugfox/finfast | [
"ccfcc67deae24a29d3dfcd5ffa51988d13791b07"
] | [
"finfast/analyze/indicators/trend.py"
] | [
"import numba\nimport numpy as np\n\n\n@numba.njit(fastmath=True)\ndef sma_host(x: np.ndarray, period: int) -> np.ndarray:\n last_out = np.mean(x[:period])\n\n out = np.empty_like(x)\n out[: period - 1] = np.nan\n out[period - 1] = last_out\n for idx in range(period, out.shape[0]):\n last_out ... | [
[
"numpy.log",
"numpy.empty_like",
"numpy.full_like",
"numpy.cumprod",
"numpy.mean",
"numpy.prod",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
oesteban/pyregseg | [
"16639a048a6990465d460bfce65623556b104e51"
] | [
"regseg/interfaces/regseg.py"
] | [
"# -*- coding: utf-8 -*-\n# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-\n# vi: set ft=python sts=4 ts=4 sw=4 et:\n#\n# @Author: Oscar Esteban - code@oscaresteban.es\n# @Date: 2014-03-12 13:20:04\n# @Last Modified by: oesteban\n# @Last Modified time: 2017-10-13 14:55:55\nfrom __future... | [
[
"pandas.concat",
"numpy.array",
"pandas.Series",
"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": []
}
] |
JBoshoff/Replicate-night-light | [
"5bdfbb99fe38f98f61f733f4e847be2bb6f559ef"
] | [
"scripts/mx_fig_validate.py"
] | [
"import os\nimport numpy as np\nimport pandas as pd\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom scipy.stats import pearsonr\nfrom sklearn.decomposition import PCA\nfrom skmisc.loess import loess\n\nfrom maskrcnn.postprocess.analysis import winsorize, load_nightlight_from_point\n... | [
[
"pandas.merge",
"pandas.read_csv",
"scipy.stats.pearsonr",
"matplotlib.pyplot.rc",
"matplotlib.pyplot.subplots",
"numpy.cos",
"numpy.log10",
"pandas.NamedAgg",
"pandas.isna",
"sklearn.decomposition.PCA",
"matplotlib.rc"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"1.5",
"2.0",
"1.4"
],
"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.... |
Trixter9994/vplanet | [
"7af9838688888f4f05da7919d1478d91b5670a36"
] | [
"tests/TidalEarth/test_TidalEarth.py"
] | [
"from vplot import GetOutput\nimport subprocess\nimport numpy as np\nimport os\ncwd = os.path.dirname(os.path.realpath(__file__))\n\n\ndef test_TidalEarth():\n \"\"\"Test the coupling between radheat, thermint, and eqtide.\"\"\"\n # Remove old log file\n subprocess.run(['rm', 'tidalearth.log'], cwd=cwd)\n ... | [
[
"numpy.isclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
RouSage/YOLOV3 | [
"40d432a82d8d202c97087491f98e43addbacf614"
] | [
"model/yolov3_l.py"
] | [
"import sys\n\nimport config.yolov3_config_voc as cfg\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom utils.tools import *\n\nfrom model.backbones.darknet65 import Darknet65\nfrom model.head.yolo_head import Yolo_head\nfrom model.layers.conv_module import Convolutional\nfrom model.necks.yolo_fpn_l im... | [
[
"torch.nn.init.constant_",
"torch.nn.init.normal_",
"torch.FloatTensor",
"torch.cat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
datacrypto-analytics/binance-algorithms | [
"bd41016f04d816d4bd653f9b482e0fa5a8bf4b7c"
] | [
"treinamento-algoritmo.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\n\nUse esta classe para treinar um modelo básico de aprendizado de máquina. Ou modifique-o para incorporar seus próprios modelos de aprendizado de máquina ou pipelines\nusando outras bibliotecas como XGBoost, Keras ou estratégias como Spiking Neural Networks!\n\n\"\"\"\n\nimport num... | [
[
"sklearn.metrics.mean_squared_error",
"sklearn.metrics.accuracy_score",
"pandas.DataFrame",
"sklearn.ensemble.GradientBoostingClassifier"
]
] | [
{
"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": []
}
] |
yipliu/NLPseq2seq | [
"cf676387bb120cd974da71d848c95adb4aa46b19",
"cf676387bb120cd974da71d848c95adb4aa46b19"
] | [
"Class8/utils/model_func.py",
"Class8/utils/data_cl.py"
] | [
"import torch\nimport torch.nn as nn\n\nimport time\nimport math\nimport spacy\n\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker as ticker\n\n# initialize the model\ndef init_weights(m):\n for name, param in m.named_parameters():\n if 'weight' in name:\n nn.init.normal_(param.data, m... | [
[
"matplotlib.ticker.MultipleLocator",
"torch.LongTensor",
"torch.nn.init.constant_",
"torch.no_grad",
"torch.nn.init.normal_",
"matplotlib.pyplot.close",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
],
[
"numpy.array",
"sklearn.model_selection.KFold"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
KonstantinDob/gyomei_trainer | [
"028628d09205b0bb4c411d39d51a00add58aecc2"
] | [
"examples/build_trainer.py"
] | [
"import torch\nfrom torch.utils.data import DataLoader, Dataset\n\nimport segmentation_models_pytorch as smp\nfrom segmentation_models_pytorch import losses\n\nfrom gyomei_trainer.model.model import Model\nfrom gyomei_trainer.builder.builder import Builder\n\n\nclass DummyDataset(Dataset):\n \"\"\"Create empty D... | [
[
"torch.optim.lr_scheduler.LinearLR"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pedMatias/matias_hfo | [
"6d88e1043a1455f5c1f6cc11b9380869772f4176"
] | [
"agents/dqn_v1/features/continuous_features.py"
] | [
"import numpy as np\n\nfrom environement_features.discrete_features import DiscreteHighLevelFeatures\n\n\nclass ContFeatures1Teammate(DiscreteHighLevelFeatures):\n \"\"\"\n 0: x_pos (float) [-1, 1]\n 1: y_pos (float) [-1, 1]\n \"\"\"\n num_features = 2\n features = np.zeros(num_features)\n # nu... | [
[
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
algrs/chumpy | [
"80bbdab6b6b695f5bcdb82e0ddc40bb1eb847d49"
] | [
"chumpy/extras.py"
] | [
"__author__ = 'matt'\n\nimport ch\nimport numpy as np\nfrom utils import row, col\nimport scipy.sparse as sp\nimport scipy.special\n\nclass Interp3D(ch.Ch):\n dterms = 'locations'\n terms = 'image'\n\n def on_changed(self, which):\n if 'image' in which:\n self.gx, self.gy, self.gz = np.gr... | [
[
"numpy.clip",
"numpy.gradient",
"numpy.arange",
"numpy.float64",
"numpy.floor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
PerceptronV/denoising-text-autoencoders | [
"8ef59e2531a4fc7531002cffd3546f27eadb8ec9"
] | [
"seq2seq/seq_model.py"
] | [
"# Following code modified from https://pytorch.org/tutorials/beginner/torchtext_translation_tutorial.html\n# © Copyright 2017, PyTorch.\n\nimport random\nfrom typing import Tuple\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\nfrom torch import Tensor\n\n\nclas... | [
[
"torch.nn.Dropout",
"torch.nn.functional.softmax",
"torch.full",
"torch.cat",
"torch.nn.init.constant_",
"torch.zeros",
"torch.nn.GRU",
"torch.sum",
"torch.nn.Embedding",
"torch.nn.Linear",
"torch.nn.init.normal_",
"torch.bmm"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vlad17/mve | [
"19835bba87a2e5abc9bca653d011bf2eed68dd62"
] | [
"mve/env_info.py"
] | [
"\"\"\"\nContains metadata relating to an environment. These are useful constants\nthat need to be passed around everywhere but are instead maintained in\nthis single metadata class.\n\nAll continuous environments simply assume the actions are in [-1, 1]^n for\nsome n. This really simplifies a lot of logic.\n\"\"\"... | [
[
"numpy.frombuffer",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ehauckdo/mcedit-filters | [
"d5e52a24230b73ae39a76cbd5a008c288bf067f0"
] | [
"Generators/GenerateCarpet.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n\nimport numpy as np\nimport random\n\ncarpetID = 171\n\n# Sub generators\n\ndef border(matrix, color, h, x_min, x_max, z_min, z_max):\n for i in range(x_min, x_max):\n matrix[h][i][z_min] = (carpetID, color)\n matrix[h][i][z_max-1] = (carpetID, color)\n ... | [
[
"numpy.sort",
"numpy.random.choice"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dnth/tsai | [
"641d5bb75f3aa75889c00a4bb60d96510b4c5605"
] | [
"tsai/models/layers.py"
] | [
"# AUTOGENERATED! DO NOT EDIT! File to edit: nbs/100_models.layers.ipynb (unless otherwise specified).\n\n__all__ = ['noop', 'init_lin_zero', 'lin_zero_init', 'SwishBeta', 'Chomp1d', 'same_padding1d', 'Pad1d', 'SameConv1d',\n 'same_padding2d', 'Pad2d', 'Conv2dSame', 'Conv2d', 'CausalConv1d', 'Conv1d', 'Se... | [
[
"torch.nn.utils.weight_norm",
"torch.nn.utils.spectral_norm",
"torch.nn.init.normal_"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ForexRobotics/mask_camera | [
"79e61f40b50ea6d4840b8aebaafaaf7ef7bcb3ab"
] | [
"src/mask_camera.py"
] | [
"import cv2\nimport os\nimport sys\nimport time\nimport tensorflow.keras\nfrom PIL import Image, ImageOps\nimport numpy as np\n\nimport busio\nimport board\n\nimport adafruit_amg88xx\n\n#messages\nMASKED_MSG = 'Thank you for wearing a mask. :-)'\nUNMASKED_MSG = 'Please wear a mask.'\nNOBODY_MSG = 'Please set your f... | [
[
"numpy.asarray",
"numpy.set_printoptions",
"numpy.ndarray"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ZangaZanga/SimpleShipAI | [
"3fac51071269720dedec9c1560ee5ec3f50952fb"
] | [
"ship_env.py"
] | [
"#!/usr/bin/python\n#-*- coding: utf-8 -*-\n\nimport numpy as np\nfrom shapely.geometry import LineString, Point\nfrom viewer import Viewer\nfrom simulator_simple import Simulator\nfrom gym import Env, spaces\n\n\nclass ShipEnv(Env):\n def __init__(self, type='continuous', action_dim = 1):\n self.type = t... | [
[
"numpy.abs",
"numpy.cos",
"numpy.sin",
"numpy.sign",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nanomiter/stock-analysis | [
"74dc99a3acd871b558607078a1ee7787a73a9c85",
"74dc99a3acd871b558607078a1ee7787a73a9c85"
] | [
"xuangu.py",
"func_TDX.py"
] | [
"\"\"\"\n选股多线程版本文件。导入数据——执行策略——显示结果\n为保证和通达信选股一致，需使用前复权数据\n\"\"\"\nimport os\nimport sys\nimport time\nimport pandas as pd\nfrom multiprocessing import Pool, RLock, freeze_support\nfrom rich import print\nfrom tqdm import tqdm\nimport CeLue # 个人策略文件，不分享\nimport func\nimport user_config as ucfg\n\n# 配置部分\nstart_dat... | [
[
"pandas.read_csv",
"pandas.to_datetime",
"pandas.read_pickle"
],
[
"numpy.lib.stride_tricks.sliding_window_view",
"pandas.Series"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [
"1.24",
"1.21",
"1.23",
"1.22",
"1.20"
],
"pandas": [... |
bytesapart/numpy | [
"e80b1935d81b8ea36e43f7f5ac63629b6dd756f9"
] | [
"numpy/core/numeric.py"
] | [
"import functools\nimport itertools\nimport operator\nimport sys\nimport warnings\nimport numbers\n\nimport numpy as np\nfrom . import multiarray\nfrom .multiarray import (\n _fastCopyAndTranspose as fastCopyAndTranspose, ALLOW_THREADS,\n BUFSIZE, CLIP, MAXDIMS, MAY_SHARE_BOUNDS, MAY_SHARE_EXACT, RAISE,\n ... | [
[
"numpy.ndim",
"numpy.ravel",
"numpy.issubdtype",
"numpy.eye"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
clamtian/MCNN | [
"becd86af6a38d0c4747ff18cc824f301b478fcdc"
] | [
"test.py"
] | [
"import matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport os\n\ndef sigmoid(x):\n # 直接返回sigmoid函数\n return np.where(x < 0, 0.2 * x, x)\n\n\ndef plot_sigmoid():\n # param:起点，终点，间距\n x = np.arange(-8, 8, 0.2)\n y = sigmoid(x)\n plt.plot(x, y)\n plt.show()\n\n\nif __name__ ... | [
[
"pandas.read_csv",
"numpy.arange",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.show",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
watanka/CRAFTS_implementation | [
"29ae4c016ebf93bc1a0355220dd12cb65295a7ad",
"29ae4c016ebf93bc1a0355220dd12cb65295a7ad"
] | [
"detection/gaussian.py",
"recognition/model.py"
] | [
"import math\nfrom math import exp\nimport numpy as np\nimport cv2\nimport os\nimport detection.imgproc as imgproc\nfrom data_utils import sort_rectangle, sort_rectangle_custom, is_convex_polygon\n\ndef get_perpendicular_points(pt1, pt2, thickness) :\n '''get perpendicular points to line(pt1, pt2) with thickness... | [
[
"numpy.expand_dims",
"numpy.clip",
"numpy.reshape",
"numpy.int32",
"numpy.concatenate",
"numpy.max",
"numpy.mean",
"numpy.any",
"numpy.float32",
"numpy.exp",
"numpy.array",
"numpy.zeros",
"numpy.where"
],
[
"torch.nn.Linear",
"torch.nn.AdaptiveAvgPoo... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bwuzhang/TF_Deformable_Ops | [
"f8f8b954285bf7755e251605c630f7fe601a6961"
] | [
"lib/deform_conv_grad.py"
] | [
"import tensorflow as tf\nfrom tensorflow.python.framework import ops\nimport deform_conv_op\n\n@ops.RegisterGradient(\"DeformConvOp\")\ndef _deform_conv_grad(op, grad):\n \"\"\"The gradients for `deform_conv`.\n Args:\n op: The `deform_conv` `Operation` that we are differentiating, which we can use\n to ... | [
[
"tensorflow.python.framework.ops.RegisterGradient"
]
] | [
{
"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... |
omsrisagar/pytorch-fid | [
"9dfb184dceeca2d2e5d50155a1703ccbca7e32a3"
] | [
"src/pytorch_fid/inception.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torchvision\n\ntry:\n from torchvision.models.utils import load_state_dict_from_url\nexcept ImportError:\n from torch.utils.model_zoo import load_url as load_state_dict_from_url\n\n# Inception weights ported to Pytorch from\n# http:... | [
[
"torch.nn.Sequential",
"torch.cat",
"torch.load",
"torch.nn.ModuleList",
"torch.nn.functional.avg_pool2d",
"torch.nn.MaxPool2d",
"torch.nn.AdaptiveAvgPool2d",
"torch.nn.functional.interpolate",
"torch.nn.functional.max_pool2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
joey1993/DBRCNN | [
"395e07364befc133c958e677b3e6af7696d3dadb"
] | [
"train.py"
] | [
"#! /usr/bin/env python\n\nimport tensorflow as tf\nimport numpy as np\nimport os\nimport time\nimport datetime\nimport data_helpers\nfrom text_dbrcnn import TextDBRCNN\nfrom tensorflow.contrib import learn\nimport yaml\n\n# Parameters\n# ==================================================\n\n# Data loading params\n... | [
[
"tensorflow.train.global_step",
"tensorflow.global_variables",
"numpy.concatenate",
"tensorflow.train.AdamOptimizer",
"tensorflow.flags.DEFINE_float",
"tensorflow.summary.scalar",
"tensorflow.Graph",
"tensorflow.Variable",
"tensorflow.ConfigProto",
"tensorflow.Session",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lyffly/Python-3DPointCloud-and-RGBD | [
"b3b973ec96045a34d3540522a115c5a77f4de136"
] | [
"1_open3d/28_iteractive_visualization.py"
] | [
"# coding = utf-8\n# coding by liuyunfei\n# origin code from open3d samples(github)\n\nimport numpy as np\nimport open3d as op3\nimport matplotlib.pyplot as plt\nimport copy\nimport time\nfrom trajectory_io import *\nimport os\nimport sys\n\n\ndef demo_crop_geometry():\n print(\"Demo for manual geometry cropping... | [
[
"numpy.identity"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ElliotPenson/embark | [
"be549073e8d05e7220fdf42271e5474bed719322"
] | [
"embark/evolution.py"
] | [
"\"\"\"\nevolution.py\n\n@author Elliot Penson\n\"\"\"\n\nfrom random import random, choice\nfrom itertools import combinations\nimport csv\n\nimport numpy\nfrom progress.bar import ChargingBar\n\nfrom embark.machi_koro import Game, Player, ALL_CARDS\nfrom embark.parameters import (NUMBER_OF_ROUNDS, GENERATION_SIZE... | [
[
"numpy.random.normal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Valenzione/Elections2018Fraud_Graph_Analysis | [
"36ce44d84668165694dc13d0f20b94f5a31195ad"
] | [
"gis_parser.py"
] | [
"import contextlib\nimport multiprocessing as mp\nimport sys\n\nimport numpy as np\nimport pandas as pd\nfrom fuzzywuzzy import process\nfrom tqdm import tqdm\n\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.expected_conditions import staleness_of\nfrom... | [
[
"pandas.concat",
"pandas.read_csv",
"numpy.array_split",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
asrafulashiq/pytorch-lightning | [
"0b7f5a88a0f4691ec228c4708295a10d403fd592"
] | [
"pytorch_lightning/accelerators/tpu_accelerator.py"
] | [
"# Copyright The PyTorch Lightning team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law... | [
[
"torch.norm",
"torch.load",
"torch.tensor",
"torch.multiprocessing.get_context",
"torch.device",
"torch.ones_like",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Infinity8sailor/Plastic-Detection-Model | [
"8a8bf15d8c892f02d2da11f147f571acaf298d42"
] | [
"retrain.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.framework.tensor_shape.scalar",
"tensorflow.python.platform.gfile.Walk",
"numpy.squeeze",
"tensorflow.python.platform.gfile.Exists",
"tensorflow.compat.v1.truncated_normal",
"tensorflow.compat.v1.placeholder_with_default",
"tensorflow.compat.v1.constant",
"tensor... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kant/open-solution-toxic-comments | [
"ff942f38b6db76b62e4db35d721ed165591135e4"
] | [
"steps/keras/contrib.py"
] | [
"# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import, division\n\nimport sys\nfrom os.path import dirname\n\nsys.path.append(dirname(dirname(__file__)))\nfrom keras import initializers\nfrom keras.engine import InputSpec, Layer\nfrom keras import backend as K\nimport tensorflow as tf\n\n\nclass Attentio... | [
[
"tensorflow.cast",
"tensorflow.expand_dims",
"tensorflow.dynamic_partition"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
jrs584/toast | [
"c0e2a9296348a22075271236457ad5c23713af82"
] | [
"src/toast/todmap/pointing.py"
] | [
"# Copyright (c) 2015-2020 by the parties listed in the AUTHORS file.\n# All rights reserved. Use of this source code is governed by\n# a BSD-style license that can be found in the LICENSE file.\n\nimport numpy as np\n\nfrom ..utils import Environment\n\nfrom ..healpix import HealpixPixels\n\nfrom ..op import Oper... | [
[
"numpy.arange",
"numpy.zeros",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Juggernaut93/Detectron | [
"727b1f2291edf07cf9d20b6a869d5ad1307f0ffc"
] | [
"lib/utils/vis.py"
] | [
"# Copyright (c) 2017-present, Facebook, Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicabl... | [
[
"matplotlib.pyplot.Rectangle",
"numpy.nonzero",
"matplotlib.pyplot.autoscale",
"matplotlib.pyplot.Axes",
"matplotlib.pyplot.get_cmap",
"numpy.ones",
"numpy.concatenate",
"matplotlib.pyplot.plot",
"numpy.copy",
"matplotlib.pyplot.setp",
"matplotlib.pyplot.close",
"nu... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jpdunc23/sparkle | [
"623813ed18a58929f5885fa6954045e3f5d9915f"
] | [
"sparkle/tests/test_distributed.py"
] | [
"\"\"\"\nUnit tests for sparkle.distributed\n\"\"\"\nimport numpy as np\nimport unittest\n\nfrom sparkle.distributed import *\nfrom sparkle.tests.test import SparkTestCase\n\nclass ColBlockMatrixTests(SparkTestCase):\n\n def test_leftMultiply(self):\n local = [[1,2,3,4,5,6,7,8,9],\n [1,2,3... | [
[
"numpy.all",
"numpy.array",
"numpy.zeros",
"numpy.sqrt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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