repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
hikmatkhan/Continuum | [
"83302753648e0d521d8b5f57edae5df4307c0a5c"
] | [
"ContinuumBenchmarks/CIFAR10/Continual-Learning-Benchmark/iBatchLearn.py"
] | [
"import os\nimport random\nimport sys\nimport argparse\nimport torch\nimport numpy as np\nfrom random import shuffle\nfrom collections import OrderedDict\nimport dataloaders.base\nfrom dataloaders.datasetGen import SplitGen, PermutedGen\nimport agents\nimport os\n# This is not needed. We have already set the GPUID ... | [
[
"torch.cuda.manual_seed",
"numpy.random.seed",
"torch.manual_seed",
"torch.utils.data.DataLoader",
"torch.cuda.device_count",
"numpy.zeros",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mahroo12/pythonWorkShop | [
"70f22586f56db3b814da9043a93e09f27122fc73"
] | [
"calculator.py"
] | [
"import numpy as np\nA = np.array([[1,1], [0,1]])\nmystring = 'McGill'\nprint(mystring)\nprint(mystring.lower())\nprint(type(mystring))\na = ''\nb = 5\nc = 6\nprint(c + b)\n\nmynumber = 1\nmynumber2 = 1.\n\nmylist = [1, 1., 'str']\nprint(mylist)\nprint(type(mylist))\n\nmydict = {}\nprint(type(mydict))\n\nmydict['my... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hellolzc/SBN_gumbel | [
"2fad872beffe7265c5363d32984eb27c86cbffaa"
] | [
"demo_gumbel_sigmoid.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n\n# In[ ]:\n\n\nfrom gumbel_sigmoid_softmax import gumbel_sigmoid\nimport torch\nimport numpy as np\n\n\n# ### Simple demo\n# * Sample from gumbel-softmax\n# * Average over samples\n\n# In[ ]:\n\n\ntemperature = 0.1\nlogits = np.linspace(-5, 5, 10).reshape([1,-1])\nlogits = ... | [
[
"matplotlib.pyplot.legend",
"torch.mean",
"numpy.linspace",
"matplotlib.pyplot.plot",
"torch.no_grad",
"torch.cuda.is_available",
"numpy.clip",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.figure",
"torch.sigmoid",
"matplotlib.pyplot.title",
"numpy.logspace",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
piotrmirowski/DALLE-pytorch | [
"cbc7e8f8bf60c4558e6dd9d6c6f0de293b579e4b"
] | [
"dalle_pytorch/dalle_pytorch.py"
] | [
"from math import log2, sqrt\nimport torch\nfrom torch import nn, einsum\nimport torch.nn.functional as F\n\nfrom axial_positional_embedding import AxialPositionalEmbedding\nfrom einops import rearrange\n\nfrom dalle_pytorch.vae import OpenAIDiscreteVAE\nfrom dalle_pytorch.transformer import Transformer\n\n# helper... | [
[
"torch.nn.functional.kl_div",
"torch.nn.functional.softmax",
"torch.cat",
"torch.nn.Embedding",
"torch.multinomial",
"torch.no_grad",
"torch.topk",
"torch.finfo",
"torch.einsum",
"torch.tensor",
"torch.arange",
"torch.nn.functional.pad",
"torch.nn.Sequential",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kleinschmidt/formulaic | [
"bd2ad61e1f06b5ef083a9d91804a47b69cf06ae1"
] | [
"tests/test_model_spec.py"
] | [
"from collections import OrderedDict\n\nimport pytest\n\nimport pandas\nfrom formulaic import Formula\n\n\nclass TestModelSpec:\n\n @pytest.fixture\n def data(self):\n return pandas.DataFrame({\n 'A': ['a', 'b', 'c'],\n 'a': [0, 0, 1],\n })\n\n @pytest.fixture\n def d... | [
[
"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": []
}
] |
HolgerPeters/matplotlib | [
"42ee643a554fffc44594f95ef07e4a16e9695d8e"
] | [
"lib/matplotlib/tests/test_triangulation.py"
] | [
"from __future__ import (absolute_import, division, print_function,\n unicode_literals)\n\nimport six\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.tri as mtri\nfrom nose.tools import assert_equal, assert_raises, assert_true, assert_false\nfrom numpy.testing import... | [
[
"numpy.dot",
"matplotlib.tri.UniformTriRefiner",
"numpy.sqrt",
"numpy.linspace",
"numpy.asarray",
"numpy.around",
"numpy.concatenate",
"numpy.max",
"numpy.arctan2",
"matplotlib.pyplot.quiver",
"numpy.ma.array",
"numpy.exp",
"matplotlib.tri.Triangulation",
"n... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sawyre/labelImg-YOLO | [
"47562318c5605db508d3a859e9615669ba293f69"
] | [
"DataUtils/compute_metrics.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport numpy as np\nfrom utils import intersection\nimport os\n\ndef IoU(box1, box2):\n # Найдем площади данных боксов\n s1 = (box1[3] - box1[1]) * (box1[2] - box1[0])\n s2 = (box2[3] - box2[1]) * (box2[2] - box2[0])\n # Найдем точки бокса-пересечения\n... | [
[
"numpy.round"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Mikhail-Klochkov/Deep3dFaceRecognitionMod | [
"6ba3d22f84bf508f0dde002da8fff277196fef21"
] | [
"util/nvdiffrast.py"
] | [
"\"\"\"This script is the differentiable renderer for Deep3DFaceRecon_pytorch\n Attention, antialiasing step is missing in current version.\n\"\"\"\n\nimport torch\nimport torch.nn.functional as F\nimport kornia\nfrom kornia.geometry.camera import pixel2cam\nimport numpy as np\nfrom typing import List\nimport nv... | [
[
"torch.cumsum",
"torch.ones",
"torch.cat",
"torch.tensor",
"numpy.deg2rad",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MLerik/binary_wall_clock | [
"10933c2130fb06819d8bae9d688a6d559ae584c1"
] | [
"display.py"
] | [
"import RPi.GPIO as IO\r\nimport time # calling for time to provide delays in program\r\nimport numpy as np\r\nfrom PIL import Image\r\n\r\n\r\nclass LED_Display():\r\n\r\n def __init__(self):\r\n self.pos_pins = [12, 22, 27, 25, 17, 24, 23, 18]\r\n self.neg_pins = [21, 20, 26, 16, 19, 13, 6, 5]\r... | [
[
"numpy.concatenate",
"numpy.array",
"numpy.shape"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
haoxusci/cellpose | [
"ea3cdf687cb026608f2e6a97d3d1e4fac61257d3"
] | [
"cellpose/models.py"
] | [
"import numpy as np\nimport os, sys, time, shutil, tempfile\nfrom tqdm import trange, tqdm\nfrom urllib.request import urlopen\nfrom urllib.parse import urlparse\nimport tempfile\n\nfrom scipy.ndimage import median_filter\nimport cv2\n\nfrom mxnet import gluon, nd\nfrom mxnet.gluon import nn\nimport mxnet as mx\n\n... | [
[
"numpy.log",
"numpy.expand_dims",
"numpy.maximum",
"numpy.isnan",
"numpy.arange",
"numpy.stack",
"numpy.ones",
"numpy.concatenate",
"numpy.ceil",
"numpy.transpose",
"numpy.load",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
millcityrunner/carball | [
"c94cb8caf2f00e616409d2c22b73475e6268fffa"
] | [
"hiddenfigures/analysis/stats/boost/boost.py"
] | [
"import logging\nfrom typing import Dict\n\nimport numpy as np\nimport pandas as pd\nfrom hiddenfigures.analysis.constants.field_constants import FieldConstants\n\nfrom hiddenfigures.analysis.stats.utils.pandas_utils import sum_deltas_by_truthy_data\nfrom ....analysis.stats.stats import BaseStat\nfrom ....generated... | [
[
"numpy.abs",
"pandas.concat",
"numpy.append",
"numpy.empty"
]
] | [
{
"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": []
}
] |
nspotrepka/NeuronInference | [
"080fc7762e755875b4241f7f892037a5858edec8"
] | [
"network.py"
] | [
"import math\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom neuron import Neuron\n\ndef gabor1D(phi, m):\n sigmaX = 1\n scalar = 1.0/(2*math.pi*sigmaX)\n k = 2\n width = 10\n g = np.zeros(m)\n for i in range(m):\n x = (i - m/2.0) / m * width\n g[i] = scalar * math.exp(-... | [
[
"numpy.dot",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TinaZetongJia/jax | [
"5303ea676145f595a34ddc2bb232369f215cae71"
] | [
"jax/experimental/pjit.py"
] | [
"# Copyright 2021 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.prod"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ehpc/ta | [
"185538e138b2901a7677cb8d20b016a40f861ce3"
] | [
"test/unit/volatility.py"
] | [
"import unittest\n\nimport pandas as pd\nfrom ta.volatility import (\n AverageTrueRange,\n BollingerBands,\n DonchianChannel,\n KeltnerChannel,\n UlcerIndex,\n average_true_range,\n bollinger_hband,\n bollinger_hband_indicator,\n bollinger_lband,\n bollinger_lband_indicator,\n bolli... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
keepangry/ai_algorithm | [
"21d8024296a2f2d2797448ed34eb383359259684"
] | [
"deep_learning/DeepLearningWithPython/chapter3-classifying-regression/3.7-predicting-house-prices.py"
] | [
"import keras\nkeras.__version__\n\nfrom keras.datasets import boston_housing\n\n(train_data, train_targets), (test_data, test_targets) = boston_housing.load_data()\n\nmean = train_data.mean(axis=0)\ntrain_data -= mean\nstd = train_data.std(axis=0)\ntrain_data /= std\n\ntest_data -= mean\ntest_data /= std\n\n\nfro... | [
[
"numpy.concatenate"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Captmoonshot/TwitOff | [
"f3c88e8e67349673fef9c0141c52b01d44e4469b"
] | [
"twitoff/app.py"
] | [
"\"\"\"Main application and routing logic for TwitOff.\"\"\"\nfrom flask import Flask, render_template, request\nfrom decouple import config\nfrom .models import DB, User, Tweet\nfrom .twitter import add_or_update_user\nfrom .predict import predict_user\n\n# Imports for make_predict()\nfrom flask import abort, json... | [
[
"numpy.round",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
harry-uglow/pytorch-a2c-ppo-acktr | [
"712ec435611e82232c6625c6cff4956c28f1bd91"
] | [
"vrep_example.py"
] | [
"####################################################################################################################\n# Example code for controlling V-Rep using the remote API\n# This is based on the old API (Legacy API) rather than the new API (BlueZero API)\n# Read here for the Legacy API: http://www.coppeliarob... | [
[
"numpy.random.uniform",
"numpy.set_printoptions",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
astraw/pinpoint | [
"5d6d038c88c8464df4878caa9d964aa24c67fda5"
] | [
"pinpoint/util.py"
] | [
"import numpy as np\n\ndef open_file(name_or_obj,mode='rb'):\n \"\"\"return an open file object from a filename or open file object\n\n If a new file object was opened, it the return variable close_file\n will be True.\n \"\"\"\n fd = name_or_obj\n close_file = False\n if isinstance(fd,basestri... | [
[
"numpy.asarray",
"numpy.reshape"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
GPCRmd/GPCRmd | [
"7dc75359ace4a00c1597bdb7a86ebee17d51f09c"
] | [
"view/views.py"
] | [
"\nfrom django.shortcuts import render\nfrom django.views.decorators.csrf import ensure_csrf_cookie\nfrom django.contrib.sites.shortcuts import get_current_site\nfrom django.http import HttpResponse\nfrom django.conf import settings\nfrom dynadb.models import DyndbFiles, DyndbFilesDynamics, DyndbModelComponents, Dy... | [
[
"pandas.read_csv",
"pandas.io.json.loads",
"numpy.save",
"numpy.append",
"numpy.mean",
"numpy.load",
"numpy.array",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wheelerMT/spin-1_BEC | [
"e8ea34699b4001847c6b4c7451c11be241ce598f",
"e8ea34699b4001847c6b4c7451c11be241ce598f"
] | [
"diagnostics/plots/kibble-zurek/size_vs_domains.py",
"scalar/diagnostics/curlVel.py"
] | [
"import h5py\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib\nmatplotlib.use('TkAgg')\n\n# Load in data:\ngrid_sizes = [128, 256, 512, 1024, 2048, 4096]\ndomain_count_list = []\nfor size in grid_sizes:\n domain_count_plus = 0\n domain_count_minus = 0\n\n with h5py.File('../../../dat... | [
[
"matplotlib.pyplot.title",
"matplotlib.use",
"matplotlib.pyplot.loglog",
"numpy.size",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
],
[
"numpy.fft.fft2",
"numpy.conj",
"numpy.linspace",
"matplotlib.use",
"numpy.arange",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sdhnshu/Fusion-Vision | [
"8374569cec36a2cb2cb642357e49ce245e5e309d",
"8374569cec36a2cb2cb642357e49ce245e5e309d"
] | [
"ganspace/models/stylegan2.py",
"stylegan2/lpips/pretrained_networks.py"
] | [
"#################################################################################################\n############################# IMPORTANT ###############################\n#################### This file and ganspace/models/op folder are replicas #######################\n##################... | [
[
"torch.nn.functional.conv_transpose2d",
"torch.nn.Sequential",
"torch.mean",
"torch.cat",
"torch.zeros",
"torch.randn",
"torch.nn.functional.conv2d",
"torch.nn.ModuleList",
"torch.tensor",
"torch.nn.Module",
"torch.nn.functional.leaky_relu",
"torch.stack",
"torc... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
notlixiang/robotics_setup | [
"610e2d0ce3b6f3f02dacc05d7078e0b9355f8c3f"
] | [
"datasets/google_brain_robot_data/depth_image_encoding.py"
] | [
"\"\"\"Creates an image from a numpy array of floating point depth data.\n\nExamples:\n\n depth_array is a 2D numpy array of floating point depth data in meters.\n\n depth_rgb = FloatArrayToRgbImage(depth_array)\n depth_rgb is a PIL Image object containing the same data as 24-bit\n integers encoded in the RGB b... | [
[
"numpy.clip",
"numpy.iinfo",
"numpy.floor",
"numpy.mod",
"numpy.array",
"numpy.zeros",
"numpy.sum",
"numpy.divide"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
neurodata/bgm | [
"b04162f84820f81cf719e8a5ddd4dae34d8f5f41"
] | [
"sandbox/rdpg_sweep.py"
] | [
"#%%\nimport datetime\nimport time\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nfrom graspologic.embed import AdjacencySpectralEmbed\nfrom pkg.data import load_split_connectome\nfrom pkg.io import OUT_PATH\nfrom pkg.io import glue as default_glue\nfrom pkg.io i... | [
[
"numpy.log",
"numpy.tril_indices_from",
"matplotlib.pyplot.autoscale",
"numpy.arange",
"numpy.triu_indices_from",
"matplotlib.pyplot.subplots",
"pandas.DataFrame",
"numpy.concatenate",
"matplotlib.pyplot.close",
"numpy.random.default_rng"
]
] | [
{
"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": []
}
] |
onlyrico/autogluon | [
"1a11d29aab8c7a1fc35d5f362c6413776794569a"
] | [
"tabular/src/autogluon/tabular/models/fastainn/tabular_nn_fastai.py"
] | [
"import copy\nimport logging\nimport time\nfrom builtins import classmethod\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport sklearn\n\nfrom autogluon.common.features.types import R_OBJECT, R_INT, R_FLOAT, R_DATETIME, R_CATEGORY, R_BOOL, S_TEXT_SPECIAL\nfrom autogluon.common.utils.multip... | [
[
"sklearn.preprocessing.StandardScaler",
"pandas.concat",
"numpy.array",
"sklearn.preprocessing.MinMaxScaler"
]
] | [
{
"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": []
}
] |
GoldenholzLab/rct-SNR | [
"d5d2268585add08f80cef883e6cd11443f0f7349"
] | [
"main_python_scripts/plot_NV_model_predictions.py"
] | [
"import json\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker as ticker\nfrom matplotlib.patches import Rectangle as rectangle\nimport seaborn as sns\nimport string\nfrom PIL import Image\nimport io\nimport sys\nimport matplotlib.font_manager as fm\n\n\ndef retrieve_expected_responses_... | [
[
"matplotlib.pyplot.gca",
"matplotlib.pyplot.legend",
"matplotlib.ticker.PercentFormatter",
"matplotlib.pyplot.title",
"numpy.arange",
"matplotlib.patches.Rectangle",
"matplotlib.font_manager.FontProperties",
"numpy.round",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.subp... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Queen-s-Cerebral-Language-Innovation/BCI-Subject-Identification | [
"71558fcef60d242b4695cc82f4da13b1b1bbb49c"
] | [
"ML Subject Train and Identify.py"
] | [
"# may 7th 2021\n# 15 subs 6 ica_comp. each\n# combs = task 1 + task 3, task 2 + task 4\n# total = 3runs*2combs = 6 data points for ea. sub\n# the reason why only 6: a person never has the exact same brain data,\n# so we can't reuse a run. 12 runs total => max 6 pts per sub if we want to have\n# a sequence of movem... | [
[
"numpy.asarray",
"tensorflow.keras.layers.Dense",
"sklearn.model_selection.train_test_split",
"numpy.concatenate",
"tensorflow.keras.layers.Dropout",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
ModernElectron/WarpX | [
"563813bc125a01a1a54267a3d4bb3ba77bcc68a3"
] | [
"mewarpx/mewarpx/mwxrun.py"
] | [
"\"\"\"\nThis facilitates a central place to grab lots of simulation information for our\nuse.\n\nSETUP order:\n - Before importing other mewarpx modules::\n\n from mewarpx import util as mwxutil\n mwxutil.init_libwarpx(ndim=ndim, rz=rz)\n\n - Import other mewarpx modules\n - Use ``from mewar... | [
[
"numpy.sqrt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
endrebak/ldetect | [
"620f60a6758b448c6f16bb529b8ac1afd6b88a6a"
] | [
"scripts/find_minima.py"
] | [
"import pandas as pd\n\nfrom sys import argv\nimport scipy.ndimage.filters as filters\nimport scipy.signal as sig\n\nfrom sklearn.utils.fast_dict import IntFloatDict as fdict\n\ndef create_fastdict(keys=None, vals=None):\n if keys is None:\n d = fdict(np.array([], dtype=int), np.array([]))\n return... | [
[
"pandas.concat",
"pandas.Series",
"pandas.read_table",
"pandas.read_parquet",
"numpy.searchsorted",
"sklearn.utils.fast_dict.IntFloatDict",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
o74589055/tf_asr | [
"1801a035d15253fd25df4f9541457dd635f6d10d"
] | [
"examples/demonstration/tensorflow_asr/models/transducer.py"
] | [
"# Copyright 2020 Huy Le Nguyen (@usimarit)\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 ... | [
[
"tensorflow.keras.layers.LayerNormalization",
"tensorflow.zeros",
"tensorflow.stack",
"tensorflow.cast",
"tensorflow.equal",
"tensorflow.where",
"tensorflow.keras.layers.Dropout",
"tensorflow.keras.Input",
"tensorflow.while_loop",
"tensorflow.squeeze",
"tensorflow.name_... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.6",
"2.2",
"1.13",
"2.3",
"2.4",
"2.9",
"2.5",
"2.8",
"2.10"
]
}
] |
gpp-rnd/rs3 | [
"00b414b020a200d8cc5c7cdace0474dad9a4752c"
] | [
"rs3/targetfeat.py"
] | [
"# AUTOGENERATED! DO NOT EDIT! File to edit: 02_targetfeat.ipynb (unless otherwise specified).\n\n__all__ = ['add_target_columns', 'get_position_features', 'get_one_aa_frac', 'get_aa_aromaticity',\n 'get_aa_hydrophobicity', 'get_aa_ip', 'get_aa_secondary_structure', 'featurize_aa_seqs',\n 'extra... | [
[
"pandas.merge",
"pandas.concat",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
shinexia/machine-learning | [
"3af42f49eb785ee40b378160325b4e1fca64db15"
] | [
"mnist/mnist-pytorch.py"
] | [
"from __future__ import print_function\nimport argparse\nimport time\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torchvision import datasets, transforms\nfrom torch.optim.lr_scheduler import StepLR\n\n\nclass Net(nn.Module):\n def __init__(self):\n ... | [
[
"torch.nn.Dropout",
"torch.nn.functional.log_softmax",
"torch.nn.functional.nll_loss",
"torch.manual_seed",
"torch.nn.Conv2d",
"torch.utils.data.DataLoader",
"torch.nn.Linear",
"torch.nn.functional.relu",
"torch.no_grad",
"torch.cuda.is_available",
"torch.flatten",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Ayonveig/Video-Object-Removal | [
"c05f7036f5e51a03b9a0f5d1e0b669545f00bd06"
] | [
"inpainting/utils/region_fill.py"
] | [
"import cv2\nimport numpy as np\nfrom scipy import sparse\nfrom scipy.sparse.linalg import spsolve\n\ndef regionfill(I, mask, factor=1.0):\n if np.count_nonzero(mask) == 0:\n return I.copy()\n resize_mask = cv2.resize(\n mask.astype(float), (0, 0), fx=factor, fy=factor) > 0\n resize_I = cv2.r... | [
[
"numpy.linspace",
"scipy.sparse.linalg.spsolve",
"numpy.arange",
"numpy.ones",
"numpy.concatenate",
"numpy.count_nonzero",
"numpy.transpose",
"numpy.meshgrid",
"numpy.where",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.14",
"0.19",
"1.5",
"0.12",
"0.17",
"0.13",
"1.6",
"1.4",
"1.9",
"1.3",
"1.10",
"0.15",
"0.18",
"0.16"... |
sopherrmann/meshio | [
"f7e756b4eae3fec1d36791e0d16044b5d39589ad"
] | [
"meshio/vtu/_vtu.py"
] | [
"\"\"\"\nI/O for VTU.\n<https://vtk.org/Wiki/VTK_XML_Formats>\n<https://vtk.org/wp-content/uploads/2015/04/file-formats.pdf>\n\"\"\"\nimport base64\nimport logging\nimport lzma\nimport re\nimport sys\nimport zlib\n\nimport numpy\n\nfrom ..__about__ import __version__\nfrom .._common import (\n _meshio_to_vtk_ord... | [
[
"numpy.hstack",
"numpy.unique",
"numpy.arange",
"numpy.cumsum",
"numpy.dtype",
"numpy.concatenate",
"numpy.frombuffer",
"numpy.fromstring",
"numpy.diff",
"numpy.zeros",
"numpy.where",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Roseck16/Interpolation | [
"20513e02241824e37c9eab6642fc2f3139dd8e00"
] | [
"src/Vandermonde/Vandermonde.py"
] | [
"import numpy as np\n\ndef different(x):\n n = len(x)\n unique = np.unique(x)\n if len(unique) == n:\n return True\n return False\n\ndef vandermonde_matrix(x):\n if not different(x):\n print(\"The values of x must not repeat!\")\n return\n n = len(x)\n V = np.ones([n,n])\n ... | [
[
"numpy.linalg.solve",
"numpy.ones",
"numpy.unique"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dylanli073/superset | [
"286ba5d37d4e240d01bbefd2307a816829cf512d"
] | [
"superset/examples/bart_lines.py"
] | [
"# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements. See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership. The ASF licenses this file\n# to you under the Apache License, Version 2.0 (the\n# \"License\"); y... | [
[
"pandas.read_json"
]
] | [
{
"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": []
}
] |
speciallan/mmdetection | [
"786a6b4eb9f0b9bb3fd5b3ab81ede7d1b23573d6"
] | [
"work_dirs_bottle/cascade_rcnn_r50_fpn/swa.py"
] | [
"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n# Author:Speciallan\n\nimport torch\nimport os\nimport mmcv\nfrom mmdet.models import build_detector\n\n\ndef get_model(config, model_dir):\n model = build_detector(config.model, test_cfg=config.test_cfg)\n checkpoint = torch.load(model_dir)\n state_dict = ch... | [
[
"torch.save",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
violetaria/saveourfaves-server | [
"f8777b137c2fb8a715afa3408a0a081cec3b93b9"
] | [
"backend/scripts/load_neighborhoods.py"
] | [
"import json\nimport django\nimport sys\nimport os\n# os.environ['DJANGO_SETTINGS_MODULE'] = 'carebackend.settings.base'\nsys.path.append(os.path.dirname(__file__) + '/..')\ndjango.setup()\nfrom places.models import Neighborhood, NeighborhoodEntry, Place, Area\nfrom django.contrib.gis.geos import Polygon\nimport pa... | [
[
"pandas.isna",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
siddk/annotated-butd | [
"71043a885208aad4258b11375c6d1aa586956371",
"71043a885208aad4258b11375c6d1aa586956371"
] | [
"src/preprocessing/nlvr2/questions.py",
"src/preprocessing/vqa2/questions.py"
] | [
"\"\"\"\ndictionary.py\n\nRead and preprocess all questions/statements in NLVR2 Dataset, and create dictionary (for initializing embeddings).\nAdditionally load and process GloVe embeddings.\n\nReference: https://github.com/hengyuan-hu/bottom-up-attention-vqa/blob/master/tools/create_dictionary.py\n\"\"\"\nimport j... | [
[
"numpy.load",
"numpy.save"
],
[
"numpy.load",
"numpy.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
QuLogic/zarr | [
"f582737671ec0f9d6c8dd08cecf433c05da7936c"
] | [
"zarr/tests/test_core.py"
] | [
"# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import, print_function, division\nimport unittest\nfrom tempfile import mkdtemp, mktemp\nimport atexit\nimport json\nimport shutil\nimport pickle\nimport os\nimport warnings\n\n\nimport numpy as np\nfrom numpy.testing import assert_array_equal, assert_array_... | [
[
"numpy.product",
"numpy.take",
"numpy.linspace",
"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.assert_array_almost_equal"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wantonsolutions/warm | [
"7ab78d0cf6e34dd60fa8a10a92543d3b9b7ab09d"
] | [
"presentation/017_latency_reduction-Nov_28_2021/Experiment_2.py"
] | [
"import matplotlib\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n#fig, (ax1, ax2, ax3) = plt.subplots(1,3, figsize=(20,5))\nfig, ax1 = plt.subplots(1,1, figsize=(6,5))\n#labels = ['2', '4', '8', '16', '32', '48', '64']\n\nlabels=[2,4,8,16,32,48,64]\n\ncns_label='cns -> write'\nqp_mapping_label='qp mappin... | [
[
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
saurav111/xgboost | [
"a13a3d1552610df734566df9a4e283560eef8981"
] | [
"python-package/xgboost/core.py"
] | [
"# coding: utf-8\n# pylint: disable=too-many-arguments\n\"\"\"Core XGBoost Library.\"\"\"\nfrom __future__ import absolute_import\n\nimport os\nimport sys\nimport ctypes\nimport platform\nimport collections\n\nimport numpy as np\nimport scipy.sparse\n\n\nclass XGBoostLibraryNotFound(Exception):\n \"\"\"Error thr... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
luna-component/decodense | [
"2579b7a3b7500b32ab231ebacb35c91e87e96735"
] | [
"tests/test_c5h5n_hf_ndo.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*\n\nimport unittest\nimport numpy as np\nfrom pyscf import gto, scf, dft\n\nimport decodense\n\n# decimal tolerance\nTOL = 9\n\n# settings\nLOC = ('', 'fb', 'pm', 'ibo-2', 'ibo-4')\nPOP = ('mulliken', 'iao')\nPART = ('orbitals', 'eda', 'atoms')\n\n# 1a2 state\nOCC_IDX, ... | [
[
"numpy.asarray",
"numpy.where",
"numpy.sum",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sophiehuiberts/polyhedra-viewer | [
"3965635f95a3645649536ff66453d9b5b26de677"
] | [
"src/data/polyhedra/polytope.py"
] | [
"#!/bin/env python3\n# coding: utf-8\n\n# script to generate polytopes in json\n\nimport json\n\n## space for your own config options\nvertexcount = 20\n\n## pick a name\nname = \"random gaussian \" + str(vertexcount)\nfilename = \"random-gaussian-\" + str(vertexcount) + \".json\"\n\n## this is where you define you... | [
[
"numpy.linalg.svd",
"numpy.array",
"scipy.spatial.ConvexHull"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
markliou/LR_PSO_Tensorflow | [
"0f2526640625fd904e81a5b26f239aabdae2f7ce"
] | [
"logistic_regression_simplepso_batch.py"
] | [
"import tensorflow as tf\nimport tools \nimport random\n\nimport SimplePSO\nimport numpy as np\nfrom functools import partial\n\n\n# Parameters\nlearning_rate = 0.01\ntraining_epochs = 5\nbatch_size = 30\ndisplay_step = 1\nFeatureNo = 4\nLabelNo = 3\nSampleNo = 120\n\n# tf Graph Input\nx = tf.placeholder(tf.float32... | [
[
"tensorflow.matmul",
"numpy.reshape",
"tensorflow.cast",
"tensorflow.placeholder",
"tensorflow.global_variables_initializer",
"tensorflow.log",
"tensorflow.Session",
"tensorflow.argmax",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
ece324-2020/Monumentum | [
"cb52b9d8e19dd922f044a761d6523400d274709e"
] | [
"models/LeNet.py"
] | [
"'''\nImplementation of Baseline LeNet-5 on RGB Input and modified input size and modified output vector size (50 instead of 10)\nAssume input height and width of 56x56\n'''\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass LeNet(nn.Module):\n def __init__(self,input_size):\n super(LeNet, s... | [
[
"torch.nn.Linear",
"torch.nn.ReLU",
"torch.nn.Conv2d",
"torch.nn.AvgPool2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sankhaMukherjee/RLalgos | [
"80d19a39af29947db2fc73b0443b9c3bb66d6fc0"
] | [
"src/modules/playAgent/playAgent.py"
] | [
"from logs import logDecorator as lD \nimport json, pprint\nimport numpy as np\n\nfrom tqdm import tqdm\n\nfrom lib.envs import envUnity\nfrom lib.utils import ReplayBuffer as RB\n\nconfig = json.load(open('../config/config.json'))\nlogBase = config['logging']['logBase'] + '.modules.playAgent.playAgent'\n\nimp... | [
[
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dpmaloney/Pokemon-RL | [
"f1e01f014e38d766c88443a9ad4929fe06cdd066"
] | [
"pokemonlearner.py"
] | [
"# -*- coding: utf-8 -*-\nimport numpy as np\nimport tensorflow as tf\n\nfrom poke_env.player.env_player import Gen8EnvSinglePlayer\nfrom poke_env.player.random_player import RandomPlayer\n\nfrom rl.agents.dqn import DQNAgent\nfrom rl.policy import LinearAnnealedPolicy, EpsGreedyQPolicy\nfrom rl.policy import Boltz... | [
[
"numpy.random.seed",
"tensorflow.keras.layers.Dense",
"numpy.concatenate",
"tensorflow.keras.optimizers.Adam",
"tensorflow.keras.layers.Flatten",
"tensorflow.keras.models.Sequential",
"tensorflow.random.set_seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
siddharthvaria/AML-Project | [
"9125c595d61fc261ec94d84e2800f6568a4d51dc"
] | [
"src/Hypothesis1.py"
] | [
"import csv\n#import nltk\nimport numpy as np\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom collections import Counter, defaultdict\nimport sys\nimport re\n\nfrom nltk import word_tokenize\n\nreload(sys)\nsys.setdefaultencoding('utf8')\n\n'''\nfrom nltk.parse.stanford import StanfordDependency... | [
[
"sklearn.feature_extraction.text.CountVectorizer"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
YWaller/DataProfiler | [
"31bc81767f65155ddb837551b6d8a9f1493d037a"
] | [
"dataprofiler/tests/profilers/profiler_options/test_profiler_options.py"
] | [
"import os\nimport unittest\nfrom unittest import mock\n\nimport numpy as np\nimport pandas as pd\n\nfrom dataprofiler import Data, ProfilerOptions, Profiler\nfrom dataprofiler.profilers.profiler_options import IntOptions, \\\n FloatOptions\nfrom dataprofiler.labelers.base_data_labeler import BaseDataLabeler\n\n... | [
[
"numpy.isnan",
"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": []
}
] |
shinmai/stylegan2-ada | [
"f81dbfc9499e105d3f9b776debbf181e8963e4fa"
] | [
"train.py"
] | [
"# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.\n#\n# NVIDIA CORPORATION and its licensors retain all intellectual property\n# and proprietary rights in and to this software, related documentation\n# and any modifications thereto. Any use, reproduction, disclosure or\n# distribution of this softwa... | [
[
"tensorflow.Graph"
]
] | [
{
"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... |
nwillems94/dgen | [
"0397248a5ce986a110e4c867a3655c10069e7274"
] | [
"dgen_os/python/dgen_model.py"
] | [
"\"\"\"\nDistributed Generation Market Demand Model (dGen) - Final Release\nNational Renewable Energy Lab\n\"\"\"\n\nimport time\nimport os\nimport pandas as pd\nimport psycopg2.extras as pgx\nimport numpy as np\nimport data_functions as datfunc\nimport utility_functions as utilfunc\nimport settings\nimport agent_m... | [
[
"pandas.set_option",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
chawins/entangle-rep | [
"3e9e0d6e7536b0de0e35d7f8717f2ccc8e887759",
"3e9e0d6e7536b0de0e35d7f8717f2ccc8e887759",
"3e9e0d6e7536b0de0e35d7f8717f2ccc8e887759"
] | [
"lib/cifar10_model.py",
"_deprecated/nin.py",
"_deprecated/rotate_loader.py"
] | [
"'''\nDefine CIFAR-10 CNN models excluding ResNet models which are in cifar_resnet.py\n'''\n\nimport copy\nimport random\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.distributions.normal import Normal\n\nfrom lib.cifar_resnet import *\n\n\nclass CIFAR10_VAE... | [
[
"torch.randn_like",
"torch.cat",
"torch.sum",
"torch.set_grad_enabled",
"torch.no_grad",
"torch.eye",
"torch.nn.Sigmoid",
"torch.tensor",
"torch.autograd.grad",
"torch.nn.Sequential",
"numpy.log",
"torch.enable_grad",
"torch.nn.ConvTranspose2d",
"torch.nn.in... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cpgoncalves/qneural | [
"9ed2d527e8726dca46d8e58c5fbbe436c83cd052"
] | [
"qneural.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\n@author: Carlos Pedro Gonçalves, University of Lisbon\r\n\"\"\"\r\n\r\nimport svect\r\nimport numpy as np\r\nfrom numpy import linalg as la\r\nfrom scipy import stats\r\nfrom matplotlib import pyplot as plt\r\nfrom numpy.matlib import repmat\r\nimport pandas as pd\r\nfrom math ... | [
[
"numpy.matrix",
"numpy.dot",
"matplotlib.pyplot.imshow",
"numpy.sqrt",
"pandas.DataFrame",
"matplotlib.pyplot.plot",
"numpy.trace",
"pandas.crosstab",
"numpy.linalg.eig",
"numpy.size",
"numpy.matlib.repmat",
"numpy.zeros",
"numpy.log",
"scipy.stats.linregres... | [
{
"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": [
"0.13",
"1.6",
"0.14",
"1.10",
"0... |
DanielBok/scify | [
"9d4d31deb4379b9782e09f56fa39249a70f9e495"
] | [
"setup.py"
] | [
"import os\nimport platform\nimport re\nimport sys\n\nimport numpy as np\nfrom setuptools import setup, find_packages, Extension\n\nENV = 'DEV'\nargv = sys.argv\nfor e in argv:\n if e.startswith('--env'):\n _, ENV = e.upper().split('=')\n argv.remove(e)\n\nIS_DEV_MODE = ENV == 'DEV'\n\ntry:\n fr... | [
[
"numpy.get_include"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
optima2005/incubator-tvm | [
"545f6ea3fede7a99f0a1b2c6933875550214a46d",
"545f6ea3fede7a99f0a1b2c6933875550214a46d",
"545f6ea3fede7a99f0a1b2c6933875550214a46d"
] | [
"topi/tests/python/test_topi_pooling.py",
"topi/tests/python/test_topi_conv2d_hwcn.py",
"topi/tests/python/test_topi_group_conv2d_NCHWc_int8.py"
] | [
"# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements. See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership. The ASF licenses this file\n# to you under the Apache License, Version 2.0 (the\n# \"License\"); y... | [
[
"numpy.ix_",
"numpy.maximum",
"numpy.max",
"numpy.ceil",
"numpy.mean",
"numpy.floor",
"numpy.random.uniform",
"numpy.zeros",
"numpy.sum"
],
[
"numpy.random.uniform",
"numpy.maximum"
],
[
"numpy.reshape",
"numpy.random.uniform",
"numpy.transpose"
]
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
HDApowersystems/pandapower | [
"d5925a05280ce7cfcf5e56a6755fb0a8780f6c77"
] | [
"pandapower/test/loadflow/test_runpp.py"
] | [
"# -*- coding: utf-8 -*-\n\n# Copyright (c) 2016-2022 by University of Kassel and Fraunhofer Institute for Energy Economics\n# and Energy System Technology (IEE), Kassel. All rights reserved.\n\n\nimport copy\nimport os\n\nimport numpy as np\nimport pandas as pd\nimport pytest\n\nimport pandapower as pp\nimport pan... | [
[
"numpy.allclose",
"pandas.isnull",
"numpy.sqrt",
"numpy.isnan",
"numpy.arange",
"numpy.all",
"numpy.exp",
"numpy.array",
"numpy.isclose"
]
] | [
{
"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": []
}
] |
bobcjxin/FALdetector | [
"e9a26b9058a6f68b35155326b52407c381d75580"
] | [
"networks/drn_seg.py"
] | [
"import math\nimport torch\nimport torch.nn as nn\nfrom networks.drn import drn_c_26\n\n\ndef fill_up_weights(up):\n w = up.weight.data\n f = math.ceil(w.size(2) / 2)\n c = (2 * f - 1 - f % 2) / (2. * f)\n for i in range(w.size(2)):\n for j in range(w.size(3)):\n w[0, 0, i, j] = \\\n ... | [
[
"torch.nn.ConvTranspose2d",
"torch.load",
"torch.nn.Conv2d",
"torch.nn.UpsamplingBilinear2d",
"torch.nn.Linear",
"torch.nn.AdaptiveAvgPool2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tomsaunders98/BiggestRebellion | [
"1df58a846b24ee1a0a0b53010ebaba0cf8603475"
] | [
"initcode.py"
] | [
"import pandas as pd, requests\n\nmps = pd.read_csv(\"mps.csv\")\n\nfor i in range(0, len(mps)):\n FirstName = mps.loc[i, \"First name\"]\n Lastname = mps.loc[i, \"Last name\"]\n Constituency = mps.loc[i, \"Constituency\"]\n URI = mps.loc[i, \"Uri\"]\n Name = f\"{FirstName} {Lastname}\"\n url = f\... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
jaredchandler/nemesys | [
"9f8d87ac545748766e58495cd7c2f447ae2bd225"
] | [
"src/nemere/visualization/multiPlotter.py"
] | [
"from typing import Dict, Tuple, List, Any, Union\n\nimport numpy\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker as ticker\n\nfrom netzob.Model.Vocabulary.Messages.AbstractMessage import AbstractMessage\n\nfrom nemere.visualization.plotter import MessagePlotter\nfrom nemere.inference.segments import Mes... | [
[
"numpy.isnan",
"matplotlib.ticker.MultipleLocator",
"numpy.array",
"matplotlib.pyplot.subplots"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
IMOKURI/Hungry-Geese | [
"5e770b3278452c2ba4006c18a43a16d572c636ac",
"5e770b3278452c2ba4006c18a43a16d572c636ac"
] | [
"handyrl/envs/kaggle/hungry_geese.py",
"handyrl/envs/kaggle/geese/005_v3_epoch_9321_4aug.py"
] | [
"# Copyright (c) 2020 DeNA Co., Ltd.\n# Licensed under The MIT License [see LICENSE for details]\n\n# kaggle_environments licensed under Copyright 2020 Kaggle Inc. and the Apache License, Version 2.0\n# (see https://github.com/Kaggle/kaggle-environments/blob/master/LICENSE for details)\n\n# wrapper of Hungry Geese ... | [
[
"torch.cat",
"torch.nn.Conv2d",
"torch.clip",
"torch.nn.Linear",
"numpy.concatenate",
"torch.nn.BatchNorm2d",
"torch.stack",
"torch.device",
"numpy.zeros"
],
[
"torch.cat",
"numpy.arange",
"torch.nn.Conv2d",
"torch.from_numpy",
"numpy.concatenate",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
itsfrank98/MIGUS_guillottine_solver | [
"3e07d99958117ab156e9d98ded95e638f7e8a952"
] | [
"src/main.py"
] | [
"import os.path\nimport numpy as np\nimport math\nfrom utils import load_from_pickle, take_only_dict_relevant_keys\nfrom xml.dom import minidom\nimport argparse\nimport time\nmissing_clues = []\n\n\ndef mutual_information(co_oc, oi, oj, n):\n \"\"\"\n :param co_oc: Number of co occurrences of the terms oi and... | [
[
"numpy.multiply",
"numpy.asarray",
"numpy.count_nonzero",
"numpy.argsort",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Sadiqush/CaffeCNN | [
"54a01368e14317d705bfa068c8d3f26fc540d676"
] | [
"code/make_predictions_2.py"
] | [
"'''\nTitle :make_predictions_2.py\nDescription :This script makes predictions using the 2nd trained model and generates a submission file.\nAuthor :Adil Moujahid\nDate Created :20160623\nDate Modified :20160625\nversion :0.2\nusage :python make_predictions_2.py\npython... | [
[
"numpy.asarray"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ricomnl/iScore | [
"2889c902b43af35aceb031e5fad7b4d8edaed76d"
] | [
"iScore/graphrank/graph.py"
] | [
"import os\nimport pickle\nimport warnings\n\nimport h5py\nimport numpy as np\nimport scipy.io as spio\nfrom Bio import pairwise2\nfrom pdb2sql import interface, pdb2sql\n\n\nclass Graph(object):\n def __init__(\n self,\n fname=None,\n file_type=None,\n chain_label=[\"A\", \"B\"],\n ... | [
[
"numpy.all",
"numpy.array",
"scipy.io.loadmat"
]
] | [
{
"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"... |
xy1999729/expRNN | [
"e2bde3a7fb61329b512701a94d4ad6cd879a7b77"
] | [
"orthogonal.py"
] | [
"import torch\nimport torch.nn as nn\n\nfrom parametrization import Parametrization\n\n\nclass Orthogonal(Parametrization):\n \"\"\" Class that implements optimization restricted to the Stiefel manifold \"\"\"\n def __init__(self, input_size, output_size, initializer_skew, mode, param):\n \"\"\"\n ... | [
[
"torch.abs",
"torch.svd",
"torch.empty",
"torch.Tensor",
"torch.sign",
"torch.qr",
"torch.nn.Linear",
"torch.nn.functional.relu",
"torch.nn.init.kaiming_normal_"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dkeefe3773/pong-rl | [
"e0d548267d7e0ca1b8690224cfd827af8ff571ab"
] | [
"paddles/paddle_0402/whiteboard/from_scratch/nmist/ann_utilities.py"
] | [
"from abc import ABC, abstractmethod\nfrom typing import List, Dict, Tuple\n\nimport numpy\nfrom matplotlib import pylab\nfrom numpy import random\nfrom sklearn.datasets import load_digits\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing i... | [
[
"matplotlib.pylab.show",
"numpy.random.choice",
"numpy.arange",
"sklearn.model_selection.train_test_split",
"numpy.random.random_sample",
"numpy.transpose",
"numpy.argmax",
"sklearn.datasets.load_digits",
"matplotlib.pylab.ylabel",
"matplotlib.pylab.plot",
"numpy.exp",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
elphinkuo/tvm | [
"49c99edf9277d36315c353121a3f20c64bc820f3"
] | [
"tests/python/frontend/onnx/test_forward.py"
] | [
"# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements. See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership. The ASF licenses this file\n# to you under the Apache License, Version 2.0 (the\n# \"License\"); y... | [
[
"numpy.logical_xor",
"torch.randint",
"numpy.take",
"numpy.asarray",
"numpy.dtype",
"torch.set_grad_enabled",
"numpy.random.randn",
"numpy.exp",
"numpy.where",
"scipy.special.softmax",
"torch.nn.EmbeddingBag",
"numpy.random.randint",
"torch.onnx.export",
"to... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
seungjaeryanlee/baselines-tf2 | [
"299c2e6fb0b1dc8dd5f25c826eb004cf276a5bfe"
] | [
"baselines/a2c/a2c.py"
] | [
"import time\nimport functools\nimport tensorflow as tf\n\nfrom baselines import logger\n\nfrom baselines.common import set_global_seeds, explained_variance\nfrom baselines.common.models import get_network_builder\nfrom baselines.common.policies import PolicyWithValue\n\nfrom baselines.a2c.utils import InverseLinea... | [
[
"tensorflow.train.CheckpointManager",
"tensorflow.constant",
"tensorflow.reduce_mean",
"tensorflow.train.Checkpoint",
"tensorflow.keras.optimizers.RMSprop",
"tensorflow.clip_by_global_norm",
"tensorflow.square",
"tensorflow.GradientTape"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
yger/spikeforest2 | [
"a957812afc9787280e6e32f4b7521c53f56b4caa"
] | [
"working/runs/2020_01_01a/main_analysis.py"
] | [
"#!/usr/bin/env python\n\nimport argparse\nimport json\nfrom spikeforest2_utils import aggregate_sorting_results\nimport numpy as np\nfrom typing import List, Union, Dict\nimport kachery as ka\nimport hither\n\ndef main():\n from spikeforest2 import sorters\n from spikeforest2 import processing\n\n parser ... | [
[
"numpy.array",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
FinMacDov/PhD_codes | [
"44e781c270fa9822a8137ef271f35c6e945c5828"
] | [
"python/data_gather.py"
] | [
"import sys\nimport matplotlib\nmatplotlib.use('Agg')\n#matplotlib.use('TkAgg') # revert above\nimport matplotlib.pyplot as plt\nimport os\nimport numpy as np\n#from PIL import Image\n#import img2vid as i2v\nimport glob\nfrom pathlib import Path\nimport matplotlib.pyplot as plt\nimport pickle\nimport pandas as pd\n... | [
[
"numpy.linspace",
"numpy.asarray",
"matplotlib.pyplot.rc",
"pandas.DataFrame",
"numpy.where",
"matplotlib.pyplot.gca",
"numpy.argmax",
"matplotlib.pyplot.close",
"numpy.rot90",
"pandas.concat",
"numpy.nonzero",
"matplotlib.pyplot.ylim",
"numpy.floor",
"numpy... | [
{
"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": []
}
] |
nguyenthanhhoan/LoadPredictLSSVM | [
"cca8190e7ff02d7456e3dbbb62c8747f863fbaaf"
] | [
"aux_func.py"
] | [
"from sklearn.preprocessing import MinMaxScaler, StandardScaler\n# Função para mudar a escala dos dados\ndef scale_feat(X_train, X_test, scaleType='min-max'):\n if scaleType=='min-max' or scaleType=='std':\n X_tr_norm = np.copy(X_train) # fazendo cópia para deixar original disponível\n X_ts_norm = ... | [
[
"sklearn.preprocessing.StandardScaler",
"sklearn.preprocessing.MinMaxScaler"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Sowmik23/Data-Mining-Lab | [
"b52f50c45ca561d7d06a4505e2e2628a12419bb2"
] | [
"LabWork03(Clustering)/Rough2/k_means.py"
] | [
"import timeit\nimport numpy as np\nfrom tabulate import tabulate\nfrom sklearn.decomposition import PCA\nimport matplotlib.pyplot as plt\n\n\nclass Cluster_viz:\n def __init__(self, data: np.ndarray):\n pca = PCA(n_components=2).fit(data)\n self.pca_data = pca.transform(data)\n\n def visualize_... | [
[
"matplotlib.pyplot.scatter",
"numpy.isnan",
"numpy.genfromtxt",
"numpy.delete",
"numpy.mean",
"sklearn.decomposition.PCA",
"matplotlib.pyplot.show",
"numpy.zeros",
"numpy.sum",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
patilpushkarp/algo-trading-strategies | [
"9ae5faaa006788e7e8e6129bf9586c12502b0fc0"
] | [
"dash_app/app.py"
] | [
"# -*- coding: utf-8 -*-\n\n# Run this app with `python app.py` and\n# visit http://127.0.0.1:8050/ in your web browser.\n\nimport dash\nimport dash_core_components as dcc\nimport dash_html_components as html\nimport dash_bootstrap_components as dbc\nfrom dash.dependencies import Input, Output\n\nimport plotly.expr... | [
[
"pandas.read_csv",
"pandas.to_datetime",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
A-Hilaly/Theano | [
"2c069ad3e508bc1061d0727f4ec7248feaf7477a"
] | [
"theano/tensor/tests/test_basic.py"
] | [
"from __future__ import absolute_import, print_function, division\nimport itertools\nimport logging\nimport operator\nimport os\nimport sys\nfrom tempfile import mkstemp\nimport unittest\nimport warnings\nfrom copy import copy, deepcopy\n# Import builtin min to be able to use it after importing the tensor version.\... | [
[
"numpy.diag",
"numpy.complex128",
"numpy.true_divide",
"numpy.dot",
"numpy.minimum",
"numpy.sqrt",
"numpy.asarray",
"numpy.vstack",
"numpy.ndarray",
"numpy.dtype",
"numpy.all",
"numpy.max",
"numpy.mean",
"numpy.zeros_like",
"numpy.argmin",
"numpy.ran... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
insaneyilin/latte | [
"b30ea4ee95efdbf52a274f504cb9920c5695acf9"
] | [
"app/models.py"
] | [
"import numpy as np\n\n\nclass Frame():\n\tdef __init__(self, fname, bounding_boxes):\n\t\tself.fname = fname\n\t\tself.bounding_boxes = bounding_boxes\n\n\t@staticmethod\n\tdef parse_json(json_frame):\n\t\tjson_bounding_boxes = json_frame['frame']['bounding_boxes']\n\t\tbounding_boxes = BoundingBox.parse_json(json... | [
[
"numpy.abs",
"numpy.cos",
"numpy.sin",
"numpy.intersect1d",
"numpy.delete",
"numpy.array",
"numpy.zeros",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
do-not-be-hasty/seed_rl | [
"b738be03e4d3c49ca259fae88d26cb747b771a65"
] | [
"tests/normalizer_test.py"
] | [
"# coding=utf-8\n# Copyright 2019 The SEED Authors\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applica... | [
[
"tensorflow.norm",
"tensorflow.reduce_mean",
"tensorflow.zeros",
"tensorflow.tpu.experimental.initialize_tpu_system",
"tensorflow.random.uniform",
"tensorflow.test.main",
"tensorflow.ones",
"tensorflow.distribute.cluster_resolver.TPUClusterResolver",
"tensorflow.math.reduce_std... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ivanovmg/pandas | [
"274b886a66eda490f3403ca619d54fd05108b05d"
] | [
"pandas/core/generic.py"
] | [
"from __future__ import annotations\n\nimport collections\nfrom datetime import timedelta\nimport functools\nimport gc\nfrom io import StringIO\nimport json\nimport operator\nimport pickle\nimport re\nfrom typing import (\n TYPE_CHECKING,\n Any,\n Callable,\n Dict,\n FrozenSet,\n Hashable,\n Li... | [
[
"pandas.util._validators.validate_bool_kwarg",
"pandas.core.dtypes.inference.is_hashable",
"numpy.unique",
"numpy.asanyarray",
"pandas.core.dtypes.common.is_re_compilable",
"pandas.concat",
"pandas.core.dtypes.common.is_list_like",
"pandas.compat.numpy.function.validate_cum_func_wi... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"0.24",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
sergeyprokudin/HDNet_TikTok | [
"aba881e85d802338eecd52e37d1e61dd98840528"
] | [
"training/training_code/training_HDNet.py"
] | [
"## ********************** import **********************\nfrom __future__ import absolute_import, division, print_function, unicode_literals\n\nimport tensorflow as tf\nimport os.path\nimport os\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' \nfrom os import path\nimport numpy as np\nimport skimage.data\nfrom PIL impor... | [
[
"tensorflow.Graph",
"tensorflow.placeholder",
"tensorflow.train.import_meta_graph",
"tensorflow.python.platform.gfile.MakeDirs",
"tensorflow.global_variables_initializer",
"tensorflow.python.platform.gfile.Exists",
"tensorflow.Session",
"tensorflow.train.AdamOptimizer",
"tensor... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
dbstein/pyfmmlib2d | [
"ce84f2c0a3bc5c45cf398ea291ff8651645906f1"
] | [
"pyfmmlib2d/periodized/old_versions/stokes_without_correct_fluxes.py"
] | [
"import numpy as np\nfrom pyfmmlib2d import SFMM\n\ndef stokes_kernel(sx, tx, w):\n ns = sx.shape[1]\n nt = tx.shape[1]\n dx = tx[0][:,None] - sx[0]\n dy = tx[1][:,None] - sx[1]\n d2 = dx**2 + dy**2\n d = np.sqrt(d2)\n logr = np.log(d)\n c = 0.25/np.pi\n G00 = (-logr + dx*dx/d2)\n G01 ... | [
[
"numpy.log",
"numpy.linalg.svd",
"numpy.sqrt",
"numpy.abs",
"numpy.arange",
"numpy.tile",
"numpy.concatenate",
"numpy.fft.ifft",
"numpy.logical_or.reduce",
"numpy.logical_or",
"numpy.row_stack",
"numpy.column_stack",
"numpy.exp",
"numpy.repeat",
"numpy.l... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
VahidZee/nads | [
"cba2fe7864109c1c1cde380ce375b72adb33ee7e"
] | [
"nads/nads.py"
] | [
"from dataclasses import dataclass\nfrom .visualization import visualize_spectrum, visualize_nads\nfrom .utils import make_subdirectories\nimport typing as th\nimport torch\n\n\n@dataclass\nclass NADs:\n \"\"\"\n Utility dataclass which manages NADs saving/loading, visualization and potentially other operatio... | [
[
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
geostacks/GeoStacks | [
"d0546048902aa281047d5589a5ff6974420fdaf4"
] | [
"geostacks/tests/test_contains.py"
] | [
"from geostacks.utils import representation, bearing\nimport pandas as pd\nimport numpy as np\n\n# Reference data for test\nls8 = pd.read_excel('./LS8_cornerPts.xlsx')\n\n\n# Calculate Bearing per Row as Average Note that the `asending` and\n# `dsending` varibles refer to direction of the moving window with\n# rega... | [
[
"pandas.read_excel",
"numpy.radians",
"numpy.degrees",
"numpy.mean",
"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": []
}
] |
Caius-Lu/Savior | [
"47c22e06c38cc9b5f7007d79f791015c8b2b76aa"
] | [
"Utils/misc.py"
] | [
"import datetime\nimport uuid\nimport numpy as np\nimport cv2\nfrom PIL import Image\n\nfrom Utils.Exceptions import ImageFormatNotSupportException\n\n\ndef get_date_string():\n return datetime.date.today().strftime('%Y%m%d')\n\n\ndef get_uuid_name():\n return str(uuid.uuid4()).replace('-', '')\n\n\ndef conve... | [
[
"numpy.asarray"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nathantspencer/SimpleElastix | [
"a9641c1197e58a4ff614145e9ba5ca43c2833ebf"
] | [
"Utilities/Statistics/download_stats.py"
] | [
"#!/usr/bin/env python\n\n\n# /files/some/path/stats/json\n\nfrom __future__ import print_function\nimport requests\nimport json\nimport datetime\n\nimport pandas as pd\nimport numpy as np\n\nimport cartopy.crs as ccrs\nimport cartopy.feature as cf\nimport cartopy.io.shapereader as shpreader\n\nimport matplotlib... | [
[
"matplotlib.pyplot.legend",
"pandas.to_datetime",
"matplotlib.colors.LogNorm",
"pandas.read_csv",
"matplotlib.pyplot.title",
"matplotlib.style.use",
"pandas.Series",
"matplotlib.pyplot.figure",
"pandas.DataFrame",
"matplotlib.pyplot.colorbar",
"matplotlib.pyplot.show",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
dtmcdona/DMAutomate | [
"72c7ed01a92de535c9e137869cdaf333e4e3d0bb"
] | [
"screenshare_client.py"
] | [
"import socket\nimport cv2\nimport pickle\nimport struct\nimport pyautogui\nimport numpy as np\nimport pygame\nimport time\nfrom pynput import keyboard\nfrom pynput import mouse\nfrom pynput.mouse import Button as mouseButton\n\n\nclass Client:\n def __init__(self):\n self.running = True\n self.act... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
larrybradley/astroimtools | [
"dca4f7bf5eff5a4f4e7682634d9d323ab848e410"
] | [
"astroimtools/filtering.py"
] | [
"# Licensed under a 3-clause BSD style license - see LICENSE.rst\n\"\"\"\nImage filtering utilities.\n\"\"\"\n\nimport numpy as np\nfrom astropy.modeling.models import Ellipse2D\n\n\n__all__ = ['circular_footprint', 'circular_annulus_footprint',\n 'elliptical_footprint', 'elliptical_annulus_footprint']\n\... | [
[
"numpy.asarray",
"numpy.arange",
"numpy.array",
"numpy.meshgrid"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tianxin1860/FSL-Mate | [
"b490c795f4aaf2c4a86b56721481d48f7c3aadfc"
] | [
"PaddleFSL/paddlefsl/model_zoo/anil.py"
] | [
"import paddle\nimport paddlefsl.utils as utils\nimport numpy as np\n\n\ndef inner_adapt(feature_model,\n head_layer,\n data,\n loss_fn,\n inner_lr,\n steps=1,\n approximate=True):\n \"\"\"\n Take several adaptation step... | [
[
"numpy.std",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ghchen18/leca | [
"325c3112d325e671cd5f3ba5f2c7a7bf19abd5e6"
] | [
"fairseq/modules/multihead_attention.py"
] | [
"# Copyright (c) 2017-present, Facebook, Inc.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the LICENSE file in\n# the root directory of this source tree. An additional grant of patent rights\n# can be found in the PATENTS file in the same directory.\n\nimport torch\nfrom tor... | [
[
"torch.Tensor",
"torch.nn.functional.dropout",
"torch.nn.init.constant_",
"torch.cat",
"torch.nn.init.xavier_normal_",
"torch.nn.Linear",
"torch.bmm",
"torch.nn.init.xavier_uniform_",
"torch.nn.functional.linear"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
savikhin-lab/fmo-calculations | [
"adf5a42c7414bfa88be9b0ec9cf608fb72fb64e3"
] | [
"exciton.py"
] | [
"# import numpy as np\r\n# import math\r\n# from openpyxl import Workbook\r\n# from openpyxl import load_workbook\r\n# from openpyxl.chart import (ScatterChart, Reference, Series)\r\nimport matplotlib\r\n\r\nmatplotlib.use(\"TkAgg\")\r\nimport matplotlib.pyplot as plt\r\nimport time\r\n\r\nfrom ClassExciton import ... | [
[
"matplotlib.use",
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplots"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tristan2077/tacotron | [
"c753a0a05abc91c661caf8cd977c2572a1fb1a02"
] | [
"cn_tacotron/cbgh.py"
] | [
"import torch\nfrom torch import nn\n\n\n\nclass BatchNormConv1d(nn.Module):\n r\"\"\"A wrapper for Conv1d with BatchNorm. It sets the activation\n function between Conv and BatchNorm layers. BatchNorm layer\n is initialized with the TF default values for momentum and eps.\n Args:\n in_channels: ... | [
[
"torch.nn.ConstantPad1d",
"torch.LongTensor",
"torch.nn.BatchNorm1d",
"torch.nn.init.calculate_gain",
"torch.cat",
"torch.nn.ModuleList",
"torch.nn.GRU",
"torch.nn.Sigmoid",
"torch.nn.Linear",
"torch.nn.Conv1d",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JiangXiaobai00/FCNonKitti-Cityscapes | [
"a067ef3bfd233eea248b68526f6c624caea5df1d"
] | [
"train.py"
] | [
"# -*- coding:utf-8 -*-\nfrom __future__ import print_function\nimport os\nimport random\nimport torch\nimport torch.nn as nn\nimport torch.nn.parallel\nimport torch.backends.cudnn as cudnn\nimport torch.optim as optim\nimport torch.utils.data as data\nfrom torch.autograd import Variable\nimport torch.nn.functional... | [
[
"torch.cuda.manual_seed",
"torch.cuda.set_device",
"torch.load",
"torch.manual_seed",
"torch.utils.data.DataLoader",
"torch.cuda.empty_cache",
"torch.no_grad",
"torch.cuda.is_available",
"torch.autograd.Variable"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
padr31/dgl-lifesci | [
"932581468b330862836c0f050077fa33d0eb9405"
] | [
"examples/property_prediction/csv_data_configuration/classification_train.py"
] | [
"# -*- coding: utf-8 -*-\n#\n# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.\n# SPDX-License-Identifier: Apache-2.0\n\nimport json\nimport pandas as pd\nimport numpy as np\nimport torch\nimport torch.nn as nn\n\nfrom copy import deepcopy\nfrom dgllife.utils import Meter, CanonicalAtomFeaturizer... | [
[
"pandas.read_csv",
"torch.utils.data.DataLoader",
"torch.nn.BCEWithLogitsLoss",
"torch.no_grad",
"torch.cuda.is_available",
"torch.device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
dpsugasa/tia | [
"1187324469f70d6d72187393accdfa01d8246eeb"
] | [
"tia/analysis/model/ins.py"
] | [
"import pandas as pd\nimport numpy as np\n#from pandas.io.data import get_data_yahoo\nfrom pandas_datareader import get_data_yahoo\n\nfrom tia.analysis.perf import periods_in_year\nfrom tia.analysis.model.interface import CostCalculator, EodMarketData\n\n\n__all__ = ['InstrumentPrices', 'Instrument', 'Instruments',... | [
[
"pandas.io.common.urlopen",
"pandas.to_datetime",
"pandas.Series",
"pandas.ewmstd",
"numpy.sqrt",
"pandas.compat.bytes_to_str",
"pandas.Index",
"pandas.DataFrame",
"pandas.datetime.now",
"pandas.datetools.relativedelta",
"pandas.rolling_std"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mcx/autokeras | [
"b349e06f3b6e80ba527347b2b0463bcc403ae8c5"
] | [
"autokeras/utils/utils.py"
] | [
"# Copyright 2020 The AutoKeras Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law... | [
[
"tensorflow.python.util.nest.flatten"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"1.4",
"2.2",
"2.3",
"2.4",
"2.9",
"1.5",
"1.7",
"2.5",
"0.12",
"1.0",
"2.8",
"1.2",
"2.10"
]
}
... |
Forshining/Forshining.github.io | [
"d08412a220bbf8ca881b8d284181db843f09e099"
] | [
"markdown_generator/publications.py"
] | [
"\n# coding: utf-8\n\n# # s markdown generator for academicpages\n# \n# Takes a TSV of publications with metadata and converts them for use with [academicpages.github.io](academicpages.github.io). This is an interactive Jupyter notebook, with the core python code in publications.py. Run either from the `markdown_ge... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
chrisseiler96/bert-client-server-tests | [
"a5b8ead400e91a3b3dbb67295e17583d714869c4"
] | [
"run_classifier.py"
] | [
"# coding=utf-8\n# Copyright 2018 The Google AI Language Team Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unl... | [
[
"tensorflow.contrib.cluster_resolver.TPUClusterResolver",
"tensorflow.metrics.accuracy",
"tensorflow.FixedLenFeature",
"tensorflow.nn.log_softmax",
"tensorflow.reduce_sum",
"tensorflow.gfile.GFile",
"tensorflow.cast",
"tensorflow.train.init_from_checkpoint",
"tensorflow.gfile.M... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
mrtrkmn/yellowbrick | [
"efd942063455f1c148c3c691d8100d726b09ac90"
] | [
"tests/test_contrib/test_missing/test_dispersion.py"
] | [
"# tests.test_contrib.test_missing.test_dispersion\n# Tests for the alpha selection visualizations.\n#\n# Author: Nathan Danielsen <nathan.danielsen@gmail.com>\n# Created: Thu Mar 29 12:13:04 2018 -0500\n#\n# Copyright (C) 2018 The scikit-yb developers\n# For license information, see LICENSE.txt\n#\n# ID: test_di... | [
[
"sklearn.datasets.make_classification",
"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": []
}
] |
MaelG/tslearn | [
"32a669e1d2e51b1129eb8cfb7b12fe2ae46a2476"
] | [
"tslearn/docs/examples/plot_barycenter_interpolate.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nSoft-DTW weighted barycenters\n=============================\n\nThis example presents the weighted Soft-DTW time series barycenter method.\n\n:math:`X_0, X_1, X_2` and :math:`X_3` are time series from 4 different classes in the Trace dataset.\nOther time series are weighted barycen... | [
[
"numpy.array",
"numpy.dot",
"matplotlib.pyplot.yticks",
"matplotlib.pyplot.tight_layout",
"numpy.random.seed",
"matplotlib.pyplot.xticks",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.text",
"matplotlib.pyplot.show",
"numpy.zeros",
"numpy.empty",
"matplotlib.pypl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ITJEONG-NAN-JJANG/keras-yolo3 | [
"60b076fede0282085a20d91b21ae51a7dffc5221"
] | [
"yolo.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nClass definition of YOLO_v3 style detection model on image and video\n\"\"\"\n\nimport colorsys\nimport os\nfrom timeit import default_timer as timer\n\nimport numpy as np\nfrom keras import backend as K\nfrom keras.models import load_model\nfrom keras.layers import Input\nfrom PIL... | [
[
"numpy.expand_dims",
"numpy.random.seed",
"numpy.asarray",
"numpy.random.shuffle",
"numpy.floor",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
arvados/bcbio-nextgen | [
"2a5cfa8c3a1d540bb2f2e66f51835042195cbc87"
] | [
"bcbio/variation/validateplot.py"
] | [
"\"\"\"Plot validation results from variant calling comparisons.\n\nHandles data normalization and plotting, emphasizing comparisons on methodology\ndifferences.\n\"\"\"\nimport collections\nimport os\n\nimport numpy as np\nimport pandas as pd\n\ntry:\n import matplotlib as mpl\n mpl.use('Agg', force=True)\n ... | [
[
"pandas.read_csv",
"matplotlib.use",
"pandas.DataFrame",
"matplotlib.pyplot.ioff",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
OscarDing/Oscar-s-Machine-Learning-in-Python | [
"d9dd359a178f5435b405235821147e5ea8a73c80"
] | [
"Chapter10/ch10_oscar.py"
] | [
"import pandas as pd\n\n\ndf = pd.read_csv('./housing.data.txt',\n sep='\\s+')\n\ndf.columns = ['CRIM', 'ZN', 'INDUS', 'CHAS',\n 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\n 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']\ndf.head()\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\... | [
[
"sklearn.ensemble.RandomForestRegressor",
"matplotlib.pyplot.legend",
"numpy.dot",
"sklearn.metrics.r2_score",
"numpy.sqrt",
"sklearn.linear_model.ElasticNet",
"sklearn.preprocessing.PolynomialFeatures",
"sklearn.metrics.mean_squared_error",
"matplotlib.pyplot.plot",
"matpl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
nakaotatsuya/wavegan | [
"a2dfe070da6307d3b54d521b747a132340a6f4ff"
] | [
"loader.py"
] | [
"from scipy.io.wavfile import read as wavread\nimport numpy as np\n\nimport tensorflow as tf\n\nimport sys\n\n\ndef decode_audio(fp, fs=None, num_channels=1, normalize=False, fast_wav=False):\n \"\"\"Decodes audio file paths into 32-bit floating point vectors.\n\n Args:\n fp: Audio file path.\n fs: If speci... | [
[
"numpy.swapaxes",
"numpy.abs",
"tensorflow.shape",
"numpy.reshape",
"tensorflow.data.TFRecordDataset",
"tensorflow.data.Dataset.from_tensor_slices",
"tensorflow.maximum",
"tensorflow.contrib.signal.frame",
"numpy.concatenate",
"tensorflow.FixedLenSequenceFeature",
"nump... | [
{
"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"... |
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