repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
sangttruong/eforecast | [
"7929020fa58a04c0ec6ef5ecc474b113797899b6"
] | [
"hybcast/models/DeployedESTransformer.py"
] | [
"import os, time, torch, sys, inspect\ncurrentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))\nparentdir = os.path.dirname(currentdir)\nsys.path.insert(0, parentdir)\n\nimport numpy as np\nimport pandas as pd\n\nimport torch.optim as optim\nfrom torch.optim.lr_scheduler import StepLR... | [
[
"torch.mean",
"pandas.Series",
"numpy.random.seed",
"numpy.unique",
"pandas.date_range",
"torch.manual_seed",
"torch.zeros",
"pandas.DataFrame",
"numpy.round",
"numpy.mean",
"torch.no_grad",
"pandas.DataFrame.from_dict",
"torch.device",
"numpy.zeros",
"t... | [
{
"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": []
}
] |
Anirudh171202/PlanetHunter | [
"9561cc95678a10b10c194e0e4432b4251a006cf3"
] | [
"reduce_memory.py"
] | [
"import numpy as np\n\n#minimising data consumed for each value hmm\n\ndef reducememory(df):\n\tinitial_mem = df.memory_usage().sum/1024**2\n\n\tfor col in df.columns:\n\t\tmin_col = df[col].min()\n\t\tmax_col = df[col].max()\n\t\tif df[col].dtype != object:\n\n\t\t\tif str(df[col].dtype) == \"int\":\n\t\t\t\t\n\n\... | [
[
"numpy.iinfo",
"numpy.finfo"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LSY15756003050/yolov4-tf2 | [
"0f6ee7d851ba1fea9528c2d6064a016be4664171"
] | [
"test.py"
] | [
"from nets.yolo4 import yolo_body\r\nfrom tensorflow.keras.layers import Input\r\ninputs = Input([416,416,3])\r\nmodel = yolo_body(inputs,3,80)\r\nmodel.summary()\r\n\r\nfor i,layer in enumerate(model.layers):\r\n print(i,layer.name) "
] | [
[
"tensorflow.keras.layers.Input"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
jerke123/mridc | [
"7e22ac50f8df73f2305d61979da2a5d59874546e"
] | [
"tests/collections/reconstruction/models/test_kikinet.py"
] | [
"# coding=utf-8\n__author__ = \"Dimitrios Karkalousos\"\n\nimport pytest\nimport torch\nfrom omegaconf import OmegaConf\n\nfrom mridc.collections.reconstruction.data.subsample import RandomMaskFunc\nfrom mridc.collections.reconstruction.models.kikinet import KIKINet\nfrom mridc.collections.reconstruction.parts impo... | [
[
"torch.view_as_complex",
"torch.no_grad",
"torch.rand",
"torch.cat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
WanlongCai/tespy | [
"fd00cfaa1872c61a1806a270222de311b3ce0f86"
] | [
"src/tespy/components/turbomachinery.py"
] | [
"# -*- coding: utf-8\n\n\"\"\"Module for components of type turbomachinery.\n\nComponents in this module:\n\n- :func:`tespy.components.turbomachinery.compressor`\n- :func:`tespy.components.turbomachinery.pump`\n- :func:`tespy.components.turbomachinery.turbine`\n- :func:`tespy.components.turbomachinery.turbomachine`... | [
[
"numpy.absolute",
"numpy.sqrt",
"numpy.isnan",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kangnuli/pytorch2caffe | [
"726cf3b1020a86a9a7370849a55a8edafd9f8854"
] | [
"model/pelee_cnet.py"
] | [
"import torch\nimport torchvision\nimport torchvision.transforms as transforms\nfrom torchvision.utils import save_image\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nimport numpy as np\nimport os\nimport glob\nimport PIL\nfrom PIL import Image\nfrom torch.utils import data a... | [
[
"torch.nn.Sequential",
"torch.cat",
"torch.nn.functional.dropout",
"torch.nn.Conv2d",
"torch.nn.MaxPool2d",
"torch.nn.Linear",
"torch.nn.AvgPool2d",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jackpegler/sheet-sql-report | [
"3b75a6e1c31438f16a80382c00d27119596cedc0"
] | [
"sheet_sql_report/sheet2sql.py"
] | [
"### Connect to Google API\nimport gspread\nfrom oauth2client.service_account import ServiceAccountCredentials\n\n### Connect to mzee\nfrom sqlalchemy import create_engine\n\n### General Libraries\nimport pandas as pd\n\nclass SheetToSQL():\n ### SHOULD THE WHOLE THING JUST FULLY INHERIT FROM GSPREAD?\n def _... | [
[
"pandas.read_sql",
"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": []
}
] |
cjekel/similarity_measures | [
"d5aeb970d13eef9f2aa0dbbefd5c3ca64fff2a10"
] | [
"tests/tests.py"
] | [
"import numpy as np\nimport unittest\nimport similaritymeasures\nfrom scipy.spatial.distance import cdist\n\n# let's just define some data\nx1 = np.linspace(0.0, 1.0, 100)\ny1 = np.ones(100)*2\nx2 = np.linspace(0.0, 1.0, 50)\ny2 = np.ones(50)\n\nnp.random.seed(1212121)\ncurve_a_rand = np.random.random((100, 2))\ncu... | [
[
"numpy.random.random",
"numpy.random.seed",
"numpy.linspace",
"numpy.cos",
"scipy.spatial.distance.cdist",
"numpy.ones",
"numpy.sin",
"numpy.array",
"numpy.zeros",
"numpy.sum",
"numpy.isclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"1.3",
"1.8"
... |
HWChang/emmer | [
"9d1ca071bd9f8d0e1ed49910de33a865d82df4c2",
"9d1ca071bd9f8d0e1ed49910de33a865d82df4c2"
] | [
"piemmer/troubleshoot/inquire/input.py",
"piemmer/test/test_iteration.py"
] | [
"#!/usr/bin/env python3\n\nfrom ..warn.warning import *\nfrom ..err.error import *\n\nimport matplotlib.colors as mcolors\nfrom matplotlib import markers\nimport sys\nimport re\n\n\n\n\"\"\"\nHandle all the input messages in piemmer\n\"\"\"\n\n## TODO: no second change after failing input evaluation\n# pytho... | [
[
"matplotlib.markers.MarkerStyle.markers.keys",
"matplotlib.colors.cnames.keys"
],
[
"numpy.round",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dsconnelly/isofit | [
"e05e4b898021ea4d422bdc3f3437424d942d1b2f"
] | [
"isofit/geometry.py"
] | [
"#! /usr/bin/env python3\n#\n# Copyright 2018 California Institute of Technology\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/LICENS... | [
[
"scipy.cos"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Rufaim/EvolveGCN | [
"c8766b6e7c7eab760bcb39f91615b4f3140647c3"
] | [
"data_loader.py"
] | [
"import numpy as np\nimport pandas as pd\nimport os\nimport networkx as nx\n\nfrom utils import normalize_adjencency_mat, convert_scipy_CRS_space_to_tensor\n\n\n\nclass EllipticDatasetLoader(object):\n def __init__(self, datadir_path, test_portion=0.3, filter_unknown=False, local_features_only=True):\n se... | [
[
"pandas.concat",
"pandas.read_csv",
"numpy.unique",
"numpy.eye",
"numpy.random.shuffle",
"numpy.array",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
1012598167/Factor_Analasis_by_Python | [
"c5d4ff0b4ba2bbb3a253a9006552f86a5adf3aaf"
] | [
"py/ditu.py"
] | [
"from pyecharts import Geo\nimport pandas as pd\ndf = pd.read_csv('ditu.csv',encoding=\"gb2312\")\ndata = [(df.iloc[i][0], df.iloc[i][1]) for i in range(df.shape[0])]\ngeo = Geo(\"幸福指数评分\", title_color=\"#fff\",\n title_pos=\"center\", width=1000,\n height=600, background_color='#404a59')\nattr, v... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
volkamerlab/TeachOpenCADD | [
"bccfe61e03b14278e19baa30fc60c9e4a26b3047"
] | [
"teachopencadd/talktorials/T018_automated_cadd_pipeline/utils/interaction_analysis.py"
] | [
"\"\"\"\nContains interaction analysis class.\n\"\"\"\n\nfrom pathlib import Path\n\nimport pandas as pd # for creating dataframes and handling data\nimport matplotlib.pyplot as plt # for plotting of data\nimport matplotlib as mpl # for changing the display settings of plots (see Settings below)\n\nfrom .consts ... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.show",
"pandas.to_numeric",
"matplotlib.pyplot.subplots"
]
] | [
{
"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": []
}
] |
Arka161/transformers | [
"cc04fb13c0c8ce2a72b13bf58227b2360eccc9e5"
] | [
"src/transformers/models/must/modeling_must.py"
] | [
"# coding=utf-8\n# Copyright 2018 Mesh TensorFlow authors, Must Authors and HuggingFace Inc. 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/li... | [
[
"torch.all",
"torch.nn.Softmax",
"torch.abs",
"numpy.expand_dims",
"torch.zeros",
"torch.nn.functional.dropout",
"torch.cat",
"torch.nn.Embedding",
"torch.rsqrt",
"torch.where",
"torch.full_like",
"torch.device",
"torch.finfo",
"torch.nn.Dropout",
"torch... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
abhi-glitchhg/vformer | [
"f046d8b5cfcaaff265fd4ef83a95d013c4499457",
"f046d8b5cfcaaff265fd4ef83a95d013c4499457"
] | [
"vformer/models/classification/swin.py",
"vformer/encoder/vivit.py"
] | [
"import torch\nimport torch.nn as nn\n\nfrom ...common import BaseClassificationModel\nfrom ...decoder import MLPDecoder\nfrom ...encoder import PatchEmbedding, PosEmbedding, SwinEncoder\nfrom ...functional import PatchMerging\nfrom ...utils import MODEL_REGISTRY\n\n\n@MODEL_REGISTRY.register()\nclass SwinTransform... | [
[
"torch.nn.Dropout",
"torch.nn.AdaptiveAvgPool1d",
"torch.nn.ModuleList",
"torch.nn.Identity"
],
[
"torch.nn.ModuleList",
"torch.flatten"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Daulbaev/IRDM | [
"4bb60191ac0072e4349ca47092675d06b39a979a"
] | [
"experiments/classification/models/sqnxt.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nimport math\n\nfrom copy import deepcopy\n\n\nclass BasicBlock(nn.Module):\n def __init__(self, in_channels, out_channels, stride):\n super(BasicBlock, self).__init__()\n reduction = 0.5\n if 2 == stri... | [
[
"torch.nn.Sequential",
"torch.nn.functional.avg_pool2d",
"torch.nn.Conv2d",
"torch.nn.functional.relu",
"numpy.mean",
"torch.nn.BatchNorm2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Shuai-Xie/RSRailway | [
"f710b6720abd1a8356004bd0b1b4db4dab2592ab",
"f710b6720abd1a8356004bd0b1b4db4dab2592ab"
] | [
"datasets/rs_detect/helper.py",
"demo/demo_dec.py"
] | [
"import os\r\nfrom tqdm import tqdm\r\nimport cv2\r\nimport random\r\nfrom pprint import pprint\r\nfrom utils.misc import *\r\nimport matplotlib.pyplot as plt\r\n\r\n\r\ndef save_data_path():\r\n \"\"\"save image paths to *.txt\"\"\"\r\n data_dir = f'/datasets/rs_detect/DOTA/train_split'\r\n img_ids = [p[:... | [
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.show"
],
[
"torch.no_grad",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
McHughes288/emotion_detection_cpc | [
"92549a093721d7e7b89ef004c7a178bc759b4827"
] | [
"emotion_id/model.py"
] | [
"from torch import nn\nfrom math import ceil\nfrom emotion_id.wavenet import Conv1dMasked, Conv1dSamePadding, ResidualStack\nfrom util import GlobalNormalization, BatchNorm, Permute\n\n\nclass BaseModel:\n @property\n def num_params(self) -> int:\n \"\"\"\n :return: number of parameters in the m... | [
[
"torch.nn.Sequential",
"torch.nn.Dropout",
"torch.nn.GRU",
"torch.nn.Linear",
"torch.nn.MaxPool1d",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dkkim93/cavia | [
"d83f44b8443f4d202396b827e13eced2cfb40b11"
] | [
"rl/metalearner.py"
] | [
"import torch\nimport numpy as np\nfrom torch.distributions.kl import kl_divergence\nfrom torch.nn.utils.convert_parameters import vector_to_parameters, parameters_to_vector\nfrom misc.optimization import conjugate_gradient\nfrom misc.torch_utils import weighted_mean, detach_distribution, weighted_normalize\n\n\ncl... | [
[
"torch.nn.utils.convert_parameters.parameters_to_vector",
"torch.sqrt",
"torch.distributions.kl.kl_divergence",
"torch.sum",
"numpy.save",
"torch.exp",
"torch.set_grad_enabled",
"torch.stack",
"torch.dot"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Gaskell-1206/fastMRI | [
"1b6d1f9020bc9209afa65ef9b9f2f3fa3348901c"
] | [
"fastmri/data/subsample.py"
] | [
"\"\"\"\nCopyright (c) Facebook, Inc. and its affiliates.\n\nThis source code is licensed under the MIT license found in the\nLICENSE file in the root directory of this source tree.\n\"\"\"\n\nimport contextlib\nfrom typing import Optional, Sequence, Tuple, Union\n\nimport numpy as np\nimport torch\n\n\n@contextlib... | [
[
"torch.max",
"numpy.arange",
"numpy.around",
"numpy.fft.fftshift",
"numpy.concatenate",
"numpy.flip",
"numpy.random.RandomState",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pvzandbergen/MolSSI_project_FSHXF | [
"0ec8ede01ffe5f47c7a1ed4e0dd034b8d4eb3e30"
] | [
"src/misc.py"
] | [
"from functools import wraps\nimport sys, time, os\nimport numpy as np\n\n# Atomic weight\ndata = { \"xx\" : 1.00794, \"H\" : 1.00794, \"He\" : 4.00260, \"Li\" : 6.941, \"Be\" : 9.012187, \"B\" : 10.811,\n \"C\" : 12.0107, \"N\" : 14.00674, \"O\" : 15.9994, \"F\" : 18.99840, \"Ne\" : 20.1797, \"Na\" : 22.989... | [
[
"numpy.exp",
"numpy.sqrt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MSLNZ/Mass-Circular-Weighing | [
"f144158b9e2337d7e9446326d6927e1dd606ed38"
] | [
"other/try_included_rows.py"
] | [
"\"\"\"\nA helper script for testing collation of data.\nThis script can be used to collate weighings from selected scheme entries only.\n\"\"\"\nimport numpy as np\nimport sys\n\nfrom msl.qt import application, excepthook\n\nfrom mass_circular_weighing.configuration import Configuration\nfrom mass_circular_weighin... | [
[
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LSSTDESC/CLMM | [
"7922ad984a297393d57b3f89ed708fec18e04e3a"
] | [
"clmm/support/mock_data.py"
] | [
"\"\"\"Functions to generate mock source galaxy distributions to demo lensing code\"\"\"\nimport warnings\nimport numpy as np\nfrom scipy import integrate\nfrom scipy.interpolate import interp1d\n\nfrom ..gcdata import GCData\nfrom ..theory import compute_tangential_shear, compute_convergence\nfrom ..utils import c... | [
[
"numpy.iterable",
"numpy.sqrt",
"numpy.arange",
"numpy.random.standard_normal",
"numpy.cos",
"numpy.rad2deg",
"numpy.sin",
"numpy.arctan2",
"numpy.ones",
"numpy.random.normal",
"scipy.interpolate.interp1d",
"scipy.integrate.quad",
"numpy.random.uniform",
"nu... | [
{
"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"
... |
brunorigal/autograd_minimize | [
"c405cd150d2d47c48a4b71f90ec312874d8788da",
"c405cd150d2d47c48a4b71f90ec312874d8788da"
] | [
"autograd_minimize/tf_wrapper.py",
"autograd_minimize/base_wrapper.py"
] | [
"import tensorflow as tf\nimport numpy as np\nfrom numpy.random import random\nfrom .base_wrapper import BaseWrapper\nfrom tensorflow.python.eager import forwardprop\n\n\nclass TfWrapper(BaseWrapper):\n def __init__(self, func, precision='float32', hvp_type='back_over_back_hvp'):\n self.func = func\n ... | [
[
"tensorflow.constant",
"tensorflow.concat",
"tensorflow.range",
"tensorflow.reduce_mean",
"numpy.reshape",
"tensorflow.reshape",
"tensorflow.gradients",
"tensorflow.hessians",
"numpy.concatenate",
"tensorflow.python.eager.forwardprop.ForwardAccumulator",
"tensorflow.nes... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"1.12",
"2.6",
"2.2",
"1.13",
"2.3",
"2.4",
"2.9",
"2.5",
"2.8",
"2.10"
]
},
{
"matplotlib": [],
"numpy": [],
"pa... |
gina-alaska/alaska_affordable_energy_model | [
"96fed0137152985ce280ea37e0affec131e3087f"
] | [
"aaem/components/non_residential/preprocessing.py"
] | [
"\"\"\"\nNon-Residential Efficiency preprocessing\n----------------------------------------\n\npreprocessing functions for Non-residential Efficiency component\n\n\"\"\"\n#~ import os.path\n#~ from pandas import read_csv\n\n# preprocessing in preprocessor\nimport os\nfrom pandas import read_csv, concat, DataFrame\n... | [
[
"pandas.concat",
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
CausalML/LatentConfounderBalancing | [
"4c39c1af71beab6a751b17faf1803497545eaa9a"
] | [
"direct_method/simple_direct.py"
] | [
"import copy\nimport random\nimport torch\nimport torch.optim as optim\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\n\nfrom data_model.simple_continuous_model import SimpleContinuousModel\nfrom direct_method.abstract_direct_model import AbstractDirectModel\n\n\nclass SimpleDirectModel... | [
[
"torch.nn.Linear",
"torch.from_numpy",
"torch.nn.Module.__init__"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tjhunter/phd-thesis-tjhunter | [
"8238e156b5dba9940bdda2a46cfffb62699f364d"
] | [
"python/kdd/plot_subnetworks.py"
] | [
"__author__ = 'tjhunter'\n\nimport build\nimport json\nimport pylab as pl\nimport numpy as np\n# Draws the network as a pdf and SVG file.\n\nfname = build.data_name('kdd/hmm_graph_export.json')\nfig = pl.figure(\"fig1\",figsize=(10,10))\nax = fig.gca()\nax.set_axis_off()\nnode_style={'c':'g', 's':80}\nlink_style=di... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ML-KULeuven/soccer_xg | [
"a2589ed8686f2ccc24f64d3dbca6f4076da3c9a7"
] | [
"soccer_xg/calibration.py"
] | [
"from __future__ import division\n\nimport warnings\nfrom inspect import signature\n\nimport numpy as np\nfrom betacal import BetaCalibration\nfrom sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin, clone\nfrom sklearn.isotonic import IsotonicRegression\nfrom sklearn.linear_model import LogisticReg... | [
[
"sklearn.isotonic.IsotonicRegression",
"sklearn.utils.validation.check_is_fitted",
"sklearn.linear_model.LogisticRegression",
"numpy.linspace",
"numpy.unique",
"numpy.isnan",
"numpy.arange",
"sklearn.preprocessing.label_binarize",
"sklearn.model_selection.check_cv",
"sklear... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
RolnickLab/ocp | [
"e120c3b90203a46f5fc7626f0b5c8979e4944765",
"e120c3b90203a46f5fc7626f0b5c8979e4944765"
] | [
"ocpmodels/datasets/lmdb_dataset.py",
"gflownet/electrocatalysts.py"
] | [
"\"\"\"\nCopyright (c) Facebook, Inc. and its affiliates.\n\nThis source code is licensed under the MIT license found in the\nLICENSE file in the root directory of this source tree.\n\"\"\"\n\nimport bisect\nimport logging\nimport math\nimport pickle\nimport random\nimport warnings\nfrom pathlib import Path\n\nimpo... | [
[
"numpy.cumsum",
"torch.tensor"
],
[
"numpy.log",
"numpy.reshape",
"numpy.arange",
"numpy.ones",
"numpy.array",
"numpy.zeros",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
feipenghe/EGG | [
"b29abaff7c08b7f8adcae3031583e35a1ae69214"
] | [
"egg/core/callbacks.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates.\n\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport json\nfrom typing import Dict, Any, Union, NamedTuple\nimport pathlib\n\nimport torch\n\nfrom egg.core.util import get_summar... | [
[
"torch.is_tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JRCSTU/wltp | [
"18650f372044371ed994a161a93473a75e76d2a9"
] | [
"Notebooks/CarsDB-compare.py"
] | [
"# ---\n# jupyter:\n# jupytext:\n# formats: ipynb,py:percent\n# text_representation:\n# extension: .py\n# format_name: percent\n# format_version: '1.3'\n# jupytext_version: 1.6.0\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n#... | [
[
"pandas.concat",
"numpy.arange",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.get_fignums",
"matplotlib.pyplot.close",
"pandas.set_option",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TransitionProjects/RWServices | [
"76b15f9c55221216d1065efd6373292e4a77c92d"
] | [
"rentwell.py"
] | [
"\"\"\"\nA report for showing all Rent Well Services during a time period\n\"\"\"\n\n__version__ = \"1.0\"\n__author__ = \"David Marienburg\"\n__maintainer__ = \"David Marienburg\"\n\nimport pandas as pd\nfrom tkinter.filedialog import askopenfilename\nfrom tkinter.filedialog import asksaveasfilename\n\nfrom dateti... | [
[
"pandas.read_excel"
]
] | [
{
"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": []
}
] |
3dlook-me/incubator-superset | [
"26cf160d20d429ead58d78535842f319a4e797ae"
] | [
"superset/connectors/druid/models.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.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": []
}
] |
NVnavkumar/cudf | [
"ef6a3907d0fc9223fe3f0e61b73a0ea9f5c7281f",
"ef6a3907d0fc9223fe3f0e61b73a0ea9f5c7281f"
] | [
"python/cudf/cudf/core/column/column.py",
"python/dask_cudf/dask_cudf/sorting.py"
] | [
"# Copyright (c) 2018-2022, NVIDIA CORPORATION.\n\nfrom __future__ import annotations\n\nimport pickle\nimport warnings\nfrom functools import cached_property\nfrom itertools import chain\nfrom types import SimpleNamespace\nfrom typing import (\n Any,\n Dict,\n List,\n MutableSequence,\n Optional,\n ... | [
[
"pandas.Series",
"numpy.isnan",
"numpy.asarray",
"numpy.issubdtype",
"numpy.int32",
"numpy.ascontiguousarray",
"pandas.StringDtype",
"numpy.dtype",
"numpy.datetime64",
"numpy.timedelta64",
"numpy.isscalar",
"numpy.find_common_type",
"numpy.array"
],
[
"n... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.0",
"1.2"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cloudmesh/cloudmesh-mpi | [
"58479926da029967fc3b19892cbaff140ff77e9e"
] | [
"examples/futures/julia-futures.py"
] | [
"from mpi4py.futures import MPIPoolExecutor\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom cloudmesh.common.StopWatch import StopWatch\nfrom cloudmesh.common.variables import Variables\nimport multiprocessing\n\nStopWatch.start(\"Overall time\")\n\nv = Variables()\n\nif (v[\"multiplier\"]):\n multipl... | [
[
"matplotlib.pyplot.imsave"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
whj0401/RLOBF | [
"2755eb5e21e4f2445a7791a1159962e80a5739ca"
] | [
"obfs_analysis/stealth/fig7.py"
] | [
"# -*- coding: utf-8 -*-\nimport sys\nimport os\nimport copy\nimport json\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as mpatches\nimport matplotlib.lines as mlines\nmatplotlib.use('Agg')\nfont = {'size': 10}\nmatplotlib.rc('font', **font)\n\ndef load_distribution_json(path):\n ... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.boxplot",
"matplotlib.pyplot.title",
"matplotlib.use",
"matplotlib.lines.Line2D",
"matplotlib.pyplot.yscale",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.clf",
"matplotlib.pyplot.xticks",
"matp... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dushyantkhosla/classification_models | [
"e70c2aa61c7388e47d9e879699903ec8ce15e7d2"
] | [
"ensemble.py"
] | [
"import os\nimport pandas as pd\n\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.tree import DecisionTree... | [
[
"sklearn.model_selection.GridSearchCV",
"sklearn.linear_model.LogisticRegression",
"sklearn.ensemble.RandomForestClassifier",
"sklearn.neighbors.KNeighborsClassifier",
"sklearn.tree.DecisionTreeClassifier",
"sklearn.ensemble.AdaBoostClassifier",
"sklearn.svm.SVC",
"sklearn.preproce... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xlchan/ee240b_project | [
"a682ded66ee479359779715bac3e330724b27b31"
] | [
"scripts_char/hw3_input.py"
] | [
"import pprint\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport scipy.signal as sig\nimport scipy.interpolate as interp\nimport scipy.optimize as opt\n\nfrom bag.util.search import minimize_cost_golden_float\nfrom bag.data.lti import LTICircuit\nfrom verification_ec.mos.query import MOSDBDiscrete\n\n\... | [
[
"numpy.ceil",
"scipy.optimize.brentq",
"numpy.linspace"
]
] | [
{
"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"... |
ubercomrade/MultiDeNA | [
"128f2963cf0a49f94c85744c5eaaf5c41f0e161c"
] | [
"tools/creat_optimized_strum_model.py"
] | [
"import random\nimport shutil\nimport os\nimport subprocess\nimport bisect\nimport pickle\nfrom shutil import copyfile\nfrom operator import itemgetter\nimport numpy as np\nfrom strum import strum\nfrom lib.common import read_peaks, creat_background, \\\nwrite_fasta, complement, \\\ncalculate_particial_auc, \\\nwri... | [
[
"numpy.array",
"numpy.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
arunpersaud/becquerel | [
"5f2aa2e00a62e022c061e4343117e3b0365b2a45"
] | [
"tests/isotope_test.py"
] | [
"\"\"\"Test isotope.py classes.\"\"\"\n\nimport numpy as np\nfrom becquerel.tools import element\nfrom becquerel.tools import isotope\nimport pytest\n\nTEST_ISOTOPES = [\n (\"H-3\", \"H\", 3, \"\"),\n (\"He-4\", \"He\", 4, \"\"),\n (\"K-40\", \"K\", 40, \"\"),\n (\"Co-60\", \"Co\", 60, \"\"),\n (\"U-... | [
[
"numpy.isinf",
"numpy.isclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Bahaduraly/superes | [
"2a4ad48790788c29720472c5bf1fade1f2e4b59d"
] | [
"main_test_swinir.py"
] | [
"from tqdm import tqdm\nimport argparse\nimport cv2\nimport glob\nimport numpy as np\nfrom collections import OrderedDict\nimport os\nimport torch\nimport requests\n\nfrom models.network_swinir import SwinIR as net\nfrom utils import util_calculate_psnr_ssim as util\n\n\ndef main():\n parser = argparse.ArgumentP... | [
[
"torch.load",
"torch.zeros",
"numpy.squeeze",
"torch.zeros_like",
"torch.from_numpy",
"torch.no_grad",
"torch.cuda.is_available",
"numpy.transpose",
"torch.flip",
"torch.ones_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
arunj088/barc | [
"859b385480e16de16cc4c4adb57f49e98bfd3ade"
] | [
"workspace/src/tensorflow_object_detector/scripts/ros_plot.py"
] | [
"#!/usr/bin/env python\nimport rospy\nfrom math import sin, cos, radians\nimport numpy as np\nfrom sensor_msgs.msg import LaserScan\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as patches\nfrom barc.msg import Lanepoints\nimport monocular as mono\n\n\n\nxy_loc = np.zeros((10,2))\nlane_loc = np.zeros(... | [
[
"matplotlib.patches.Rectangle",
"numpy.array",
"matplotlib.pyplot.ion",
"numpy.zeros",
"matplotlib.pyplot.style.use",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
KarlTDebiec/myplotspec_sim | [
"f63ebf446ff6365857c544508931a21eb75e57e7"
] | [
"moldynplot/dataset/ChemicalShiftSequenceDataset.py"
] | [
"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n# moldynplot.dataset.ChemicalShiftDataset.py\n#\n# Copyright (C) 2015-2017 Karl T Debiec\n# All rights reserved.\n#\n# This software may be modified and distributed under the terms of the\n# BSD license. See the LICENSE file for details.\n\"\"\"\nRepresents an ... | [
[
"pandas.Series",
"numpy.in1d",
"numpy.random.normal",
"numpy.std",
"numpy.exp",
"scipy.optimize.curve_fit",
"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": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.1... |
gate42qc/gnet | [
"bb84d4c489b8d3a01368638a3602de19e50547ae"
] | [
"GNet/protocol/random_generator.py"
] | [
"import numpy as np\nimport random\nimport secrets\n\nclass NumpyRandomGenerator:\n @classmethod\n def get_randomly_choosen_from(cls, choices, length):\n return np.random.choice(choices, length)\n \n @classmethod\n def get_random_sample(cls, array, sample_size):\n return random.sample(a... | [
[
"numpy.random.randint",
"numpy.random.choice"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
IndexFziQ/IIE-NLP-Eyas-SemEval2021 | [
"f403ac463177bbec3120142cded6751029588121"
] | [
"src/transformers/tokenization_transfo_xl.py"
] | [
"# coding=utf-8\n# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.\n# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance w... | [
[
"torch.LongTensor",
"numpy.random.random",
"torch.load",
"torch.cat",
"numpy.random.shuffle",
"torch.tensor",
"numpy.random.normal",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SeregaOsipov/ClimPy | [
"0d13642bf19b70c92381a88386465d7764e3e7cb"
] | [
"climpy/utils/plotting_utils.py"
] | [
"import os as os\nfrom matplotlib import pyplot as plt\nimport netCDF4\nimport numpy as np\nfrom climpy.utils import wrf_utils as wrf_utils\nimport pandas as pd\n\n__author__ = 'Sergey Osipov <Serega.Osipov@gmail.com>'\n\n\nMY_DPI = 96.0\n\n\ndef JGR_page_width_inches():\n return 190 / 25.4\n\n\ndef screen_width... | [
[
"numpy.logical_not",
"pandas.Series",
"numpy.isnan",
"matplotlib.pyplot.savefig",
"numpy.logical_or",
"numpy.intersect1d",
"numpy.array",
"matplotlib.pyplot.figure"
]
] | [
{
"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": []
}
] |
TKForgeron/INFOMDSS | [
"3a0424adb469a6f463a6b2847a099297726e93d1"
] | [
"cases.py"
] | [
"import pandas as pd\nfrom datetime import datetime\nimport requests\nimport json\nimport populations\n\n\ndef get_cases_df_il(start_date: datetime = None) -> pd.DataFrame:\n\n \"\"\"\n Function that cleans the cases dataframe for Israel, and calculates daily cases from accumulated cases\n\n Parameters:\n ... | [
[
"pandas.to_datetime",
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
npshub/last_layer_laplace | [
"31ec9b70a292e9783e9005425793199a8830bf01",
"31ec9b70a292e9783e9005425793199a8830bf01"
] | [
"paper/laplace/kfla.py",
"paper/notebooks/util/kfac.py"
] | [
"##########################################################################\n#\n# Taken with modifications from\n# https://github.com/wjmaddox/swa_gaussian/\n#\n##########################################################################\n\n\nimport torch\nfrom torch import nn, optim, autograd\nfrom torch.nn import... | [
[
"torch.nn.CrossEntropyLoss",
"torch.zeros",
"torch.sqrt",
"torch.randn",
"torch.eye",
"torch.inverse",
"torch.nn.BCEWithLogitsLoss",
"torch.distributions.Categorical",
"torch.no_grad",
"numpy.argmin",
"numpy.prod",
"torch.log",
"torch.distributions.Binomial",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
luozm/frog | [
"ce29cdaddba5ecf78cc66bce1bfc64b301b706ca"
] | [
"code/net/lib/box/nms/torch_nms/__init__.py"
] | [
"from net.lib.box.nms.torch_nms.extension import *\n\nimport torch\nimport numpy as np\n\ndef torch_nms(dets, thresh):\n \"\"\"\n dets has to be a tensor\n \"\"\"\n if not dets.is_cuda:\n x1 = dets[:, 0]\n y1 = dets[:, 1]\n x2 = dets[:, 2]\n y2 = dets[:, 3]\n scores = dets[:, 4]\n\n areas = (x... | [
[
"torch.LongTensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
baab0x00/ARC | [
"21250aa36e197ee6af521a923dbd23a88ca57530"
] | [
"src/manual_solve.py"
] | [
"#!/usr/bin/python\n\nimport os, sys\nimport json\nimport numpy as np\nimport re\n\n### YOUR CODE HERE: write at least three functions which solve\n### specific tasks by transforming the input x and returning the\n### result. Name them according to the task ID as in the three\n### examples below. Delete the three e... | [
[
"numpy.all",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
guipsamora/pandas | [
"6985079f3c083e860e7cc938c07d00001a7e3fae"
] | [
"pandas/io/tests/parser/test_unsupported.py"
] | [
"# -*- coding: utf-8 -*-\n\n\"\"\"\nTests that features that are currently unsupported in\neither the Python or C parser are actually enforced\nand are clearly communicated to the user.\n\nUltimately, the goal is to remove test cases from this\ntest suite as new feature support is added to the parsers.\n\"\"\"\n\ni... | [
[
"pandas.compat.StringIO",
"pandas.util.testing.assert_produces_warning",
"pandas.util.testing.assertRaisesRegexp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.19"
],
"scipy": [],
"tensorflow": []
}
] |
manhhabui/CenterDepth | [
"c40f85641d8e530b11077d2dcea9debca7b6bffd",
"c40f85641d8e530b11077d2dcea9debca7b6bffd"
] | [
"algorithms/cd_regression_v1/src/models/networks/mobilenet.py",
"algorithms/cd_regression_v1/src/trainer.py"
] | [
"import os\nimport math\nimport logging\nimport numpy as np\n\nimport torch\nfrom torch import nn\nimport torch.nn.functional as F\nimport torch.utils.model_zoo as model_zoo\nfrom torchvision.models.utils import load_state_dict_from_url\nfrom .msraup import MSRAUp\n\nBN_MOMENTUM = 0.1\n\nmodel_urls = {\n 'mobil... | [
[
"torch.nn.Sequential",
"torch.nn.ReLU6",
"torch.nn.init.constant_",
"torch.nn.ModuleList",
"torch.nn.Conv2d",
"torch.nn.ReLU",
"torch.nn.init.ones_",
"torch.nn.init.normal_",
"torch.nn.BatchNorm2d",
"torch.nn.init.zeros_",
"torch.nn.init.kaiming_normal_"
],
[
"t... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JosephLalli/tensorqtl | [
"ec4851f8a479ec284921a59a1b0b644d3cab4fa1"
] | [
"tensorqtl/post.py"
] | [
"import numpy as np\nimport pandas as pd\nimport torch\nimport scipy.stats as stats\nimport subprocess\nimport sys\nimport os\nimport glob\nfrom datetime import datetime\n\nsys.path.insert(1, os.path.dirname(__file__))\nfrom core import SimpleLogger, Residualizer, center_normalize, impute_mean, get_allele_stats\nim... | [
[
"pandas.concat",
"numpy.log2",
"torch.transpose",
"torch.sqrt",
"scipy.stats.pearsonr",
"torch.eye",
"torch.from_numpy",
"pandas.DataFrame",
"torch.tensor",
"numpy.all",
"pandas.read_parquet",
"scipy.stats.beta.ppf",
"torch.cuda.is_available",
"torch.stack"
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"0.24",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
zacker150/sklearn-extensions | [
"329f3efdb8c3c3a367b264f7c76c76411a784530",
"329f3efdb8c3c3a367b264f7c76c76411a784530"
] | [
"sklearn_extensions/fuzzy_kmeans/kmeans.py",
"sklearn_extensions/non_negative_garotte/non_negative_garotte.py"
] | [
"# CODE FROM: https://gist.github.com/mblondel/1451300\n# Copyright Mathieu Blondel December 2011\n# License: BSD 3 clause\n\nimport numpy as np\nfrom sklearn.base import BaseEstimator\nfrom sklearn.utils import check_random_state\nfrom sklearn.metrics.pairwise import euclidean_distances, manhattan_distances\nfrom ... | [
[
"sklearn.utils.check_random_state",
"numpy.dot",
"numpy.median",
"sklearn.metrics.pairwise.manhattan_distances",
"sklearn.metrics.pairwise.euclidean_distances",
"numpy.any",
"numpy.var",
"numpy.sum"
],
[
"numpy.dot",
"sklearn.linear_model.LinearRegression",
"sklearn... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Andrey-Nikitin/LightAutoML | [
"fe58d98d1ab05e177f0b9dea918fef8b922ae922"
] | [
"lightautoml/addons/interpretation/l2x_model.py"
] | [
"from typing import Optional\n\nimport torch\nfrom torch import nn\nfrom torch.distributions.utils import clamp_probs\nimport torch.nn.functional as F\n\nfrom .data_process import create_emb_layer\n\n\nclass TIModel(nn.Module):\n def __init__(self,\n voc_size: int,\n embed_dim: in... | [
[
"torch.nn.Softmax",
"torch.nn.Dropout",
"torch.nn.functional.softmax",
"torch.max",
"torch.rand_like",
"torch.cat",
"torch.zeros_like",
"torch.tensor",
"torch.nn.Sigmoid",
"torch.nn.Linear",
"torch.log",
"torch.rand",
"torch.nn.Conv1d",
"torch.topk",
"to... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dennisgrobe/btc_data | [
"dfecd49eb3118afa5195cd6220761dc2cb9a2f6a"
] | [
"PCA_dev_DG.py"
] | [
"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.decomposition import PCA\nfrom sklearn.preprocessing import Normalizer, StandardScaler, MinMaxScaler, RobustScaler\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metric... | [
[
"pandas.read_csv",
"matplotlib.pyplot.title",
"numpy.cumsum",
"sklearn.pipeline.Pipeline",
"pandas.DataFrame",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"sklearn.decomposition.PCA",
"sklearn.preprocessing.MinMaxScaler",
"matplotli... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
fidsusj/HateSpeechDetection | [
"1306a8a901aed856e51ee8fe16158ff267fb5405"
] | [
"src/classifiers/hyperparameters.py"
] | [
"\"\"\" Module containing hyperparameter search spaces for each classifier \"\"\"\n\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.svm import SVC\nfrom sklearn.tree import DecisionTreeClassifier\n\nhyperparameter_search_space = {\n \"random... | [
[
"sklearn.svm.SVC",
"sklearn.tree.DecisionTreeClassifier",
"sklearn.linear_model.LogisticRegression",
"sklearn.ensemble.RandomForestClassifier"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ReaAntKour/NTL8TemperatureGrowth | [
"8b574a3939f576b747eb15c6fc99d16e06ec5248"
] | [
"image_process_fluorescence.py"
] | [
"import czifile\nimport numpy as np\nimport scipy.ndimage as ndi\nimport csv\nimport sys\nimport random\nfrom skimage import io\nfrom skimage import filters\nfrom skimage.morphology import reconstruction\nimport os \nimport matplotlib.pyplot as plt\nimport copy\n\ndef main(argv):\n\tthreshold=2600# set threshold of... | [
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.savefig",
"numpy.dstack",
"matplotlib.pyplot.gcf",
"scipy.ndimage.label",
"numpy.argsort"
]
] | [
{
"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"... |
leesavide/pythonista-docs-deprecated | [
"9ec3363f07e328bde0a58738a16907f11dfd06e1",
"9ec3363f07e328bde0a58738a16907f11dfd06e1"
] | [
"Documentation/matplotlib/users/recipes-2.py",
"Documentation/numpy/reference/generated/numpy-random-vonmises-1.py"
] | [
"import matplotlib.cbook as cbook\ndatafile = cbook.get_sample_data('goog.npy')\nr = np.load(datafile).view(np.recarray)\nplt.figure()\nplt.plot(r.date, r.close)\nplt.title('Default date handling can cause overlapping labels')",
"# Draw samples from the distribution:\n\nmu, kappa = 0.0, 4.0 # mean and dispersion\... | [
[
"matplotlib.cbook.get_sample_data"
],
[
"scipy.special.jn",
"matplotlib.pyplot.show",
"matplotlib.pyplot.hist"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
guen-a-park/stablebl_safedagger | [
"a5b1b9357c2f50f32dbfdba1444499aae52f3de5"
] | [
"mujoco_py/tests/test_cymj.py"
] | [
"import pytest\nfrom numbers import Number\nfrom io import BytesIO, StringIO\nimport numpy as np\nfrom numpy.testing import assert_array_equal, assert_array_almost_equal\nfrom mujoco_py import (MjSim, MjSimPool, load_model_from_xml,\n load_model_from_path, MjSimState,\n i... | [
[
"numpy.allclose",
"numpy.array_equal",
"numpy.min",
"numpy.asarray",
"numpy.eye",
"numpy.linalg.norm",
"numpy.ones",
"numpy.max",
"numpy.testing.assert_allclose",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
daurer/phasing-tutorial | [
"a73f7196f8444ed9f54aefdaeb853f5682b417c4"
] | [
"scripts/simulate_protein.py"
] | [
"#!/usr/bin/env python\nimport h5py\nimport condor\nimport copy\nimport numpy as np\nimport condor.utils.linalg as linalg\nimport matplotlib.pyplot as plt\nimport matplotlib.colors as colors\nimport matplotlib.cm as cm\n\n# Detector (pnCCD)\npixelsize = 4*75e-6\nnx,ny = (1024//4,1024//4)\ndetector_distance = 300e-3... | [
[
"numpy.isnan",
"numpy.fft.fftshift",
"numpy.fft.ifftn",
"numpy.angle",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
abbottLane/morph-completion-visualization | [
"3149ae712ceb72199bf05f30c1dab530e603f644"
] | [
"visualize_morph_data.py"
] | [
"import seaborn as sns; sns.set()\nimport matplotlib.pyplot as plt\nimport os\nimport pandas as pd\n\nDATA_DIR = 'data/tsv/cree-prefix-list.tsv'\n\ndef main():\n build_uniqueness_plot(\"WordSample\",load_tsv(DATA_DIR))\n\ndef load_tsv(filepath):\n return pd.read_csv(filepath,index_col=None,sep='\\t')\n\ndef bu... | [
[
"matplotlib.pyplot.show",
"pandas.read_csv",
"matplotlib.pyplot.axhline",
"matplotlib.pyplot.annotate"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
123chengbo/caffe2 | [
"0a68778916f3280b5292fce0d74b73b70fb0f7e8"
] | [
"caffe2/python/gradient_checker.py"
] | [
"import numpy as np\nfrom caffe2.python import core, workspace\nfrom caffe2.proto import caffe2_pb2\n\n\nclass GradientChecker:\n \"\"\"A gradient checker in Python.\n\n This is not the most efficient way to check gradients, as the Python\n interface will involve a lot of copy back and forth operations. Us... | [
[
"numpy.flatnonzero",
"numpy.zeros_like",
"numpy.any",
"numpy.vstack",
"numpy.isclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
FangFeng-077/Explainable_Lending_Decision | [
"8ea5c2ed5950df913195f8ad05df9b07ef74f1e5"
] | [
"src/model/RandomForest.py"
] | [
"from sklearn.ensemble import RandomForestClassifier\n\n\nclass RandomForest:\n def __init__(self):\n self.rf = RandomForestClassifier(\n n_estimators=80, max_depth=50 # class_weight= {0:.1, 1:.9},\n )\n self.title = 'Random Forest'\n\n def fit(self, X_train, y_train):\n ... | [
[
"sklearn.ensemble.RandomForestClassifier"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DoctorKey/realistic-ssl-evaluation | [
"c6960862d7b8e2a988dcf0c4fbc3e1d52ae9c909"
] | [
"lib/data_provider.py"
] | [
"#!/usr/bin/python3\n#\n# Copyright 2018 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# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by ap... | [
[
"tensorflow.constant",
"tensorflow.summary.image",
"tensorflow.reshape",
"tensorflow.logging.info",
"tensorflow.add_to_collection",
"tensorflow.summary.histogram"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
ovanov/Kollokationsprofile-von-DFCP | [
"4d45410a4f0b1e692f18da965d51d9a8e5eb7a08"
] | [
"verteilung_daten/pre_plots/w_element/w_element_10words_pre.py"
] | [
"from typing import Tuple, List\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n\n\ndef calculate_collocators(filename: str) -> List[List[Tuple]]:\n df_pos = pd.read_csv(f'{filename}', sep='\\t')\n freq_collocators = []\n\n for n in range(10):\n # print(f\"SORTED AT COLUMN: {n}\")\n ... | [
[
"matplotlib.pyplot.legend",
"pandas.read_csv",
"matplotlib.pyplot.title",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.bar",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.xticks",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
GiaLacTRAN/convolutional_deep_gp_random_features | [
"93330f3171ab4e9539f6bae0d4a68ae1f6a1e104"
] | [
"code/exp.py"
] | [
"import tensorflow as tf \ndef create_mask(batch_size, D, D_init, keep_prob):\n nrf = tf.shape(D)[1]\n ones = tf.ones([batch_size, nrf])\n keep_prob_matrix = tf.multiply(keep_prob, ones)\n D_init_over_D = tf.divide(D_init, D)\n D_init_over_D_tile = tf.tile(D_init_over_D, [batch_size, 1])\n\n r_u =... | [
[
"tensorflow.multiply",
"tensorflow.shape",
"tensorflow.ones",
"tensorflow.divide",
"tensorflow.global_variables_initializer",
"tensorflow.where",
"tensorflow.Session",
"tensorflow.random_uniform",
"tensorflow.tile",
"tensorflow.random_normal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"1.0",
"1.2"
]
}
] |
ZanMax/gpt-2-web | [
"ea1a888813796f38c6e041103b60981ba614650c"
] | [
"model.py"
] | [
"import numpy as np\nimport tensorflow as tf\n\nfrom hparam import HParams\n\n\ndef default_hparams():\n return HParams(\n n_vocab=0,\n n_ctx=1024,\n n_embd=768,\n n_head=12,\n n_layer=12,\n )\n\n\ndef shape_list(x):\n \"\"\"Deal with dynamic shape in tensorflow cleanly.\... | [
[
"tensorflow.convert_to_tensor",
"tensorflow.concat",
"numpy.sqrt",
"tensorflow.reduce_sum",
"tensorflow.stack",
"tensorflow.cast",
"tensorflow.gather",
"tensorflow.square",
"tensorflow.random_normal_initializer",
"tensorflow.compat.v1.variable_scope",
"tensorflow.matmul... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
brsoyanvn/catboost | [
"949d18601bb68acacaab0f01dd4a0b328aa12681"
] | [
"catboost/python-package/ut/medium/test.py"
] | [
"import hashlib\nimport math\nimport numpy as np\nimport pprint\nimport pytest\nimport re\nimport subprocess\nimport sys\nimport tempfile\nimport time\n\nfrom catboost import (\n CatBoost,\n CatBoostClassifier,\n CatBoostRegressor,\n CatboostError,\n EFstrType,\n FeaturesData,\n Pool,\n cv,\... | [
[
"pandas.Series",
"pandas.DataFrame",
"numpy.all",
"numpy.zeros_like",
"numpy.random.randint",
"numpy.isclose",
"numpy.putmask",
"numpy.min",
"numpy.isnan",
"numpy.random.choice",
"pandas.read_table",
"numpy.int64",
"numpy.random.rand",
"numpy.transpose",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
weikang9009/esda | [
"06120c7b5faeaff32cbfaf1ef4859587056e6b98"
] | [
"esda/tests/test_geary.py"
] | [
"\"\"\"Geary Unittest.\"\"\"\nimport unittest\n\nfrom libpysal import open as popen\nfrom libpysal import examples\nfrom libpysal.common import pandas\n\nfrom .. import geary\nimport numpy as np\n\nPANDAS_EXTINCT = pandas is None\n\nclass Geary_Tester(unittest.TestCase):\n \"\"\"Geary class for unit tests.\"\"\"... | [
[
"numpy.array",
"numpy.unique",
"numpy.random.seed",
"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": []
}
] |
emadboctorx/stable-baselines | [
"9bce185538e8bf69836371286e23919fd85eec64"
] | [
"tests/test_identity.py"
] | [
"import numpy as np\nimport pytest\nfrom stable_baselines import (A2C, ACER, ACKTR, DDPG, DQN, PPO1, PPO2, SAC,\n TD3, TRPO)\nfrom stable_baselines.common.evaluation import evaluate_policy\nfrom stable_baselines.common.identity_env import IdentityEnv, IdentityEnvBox\nfrom stable_baselin... | [
[
"numpy.exp",
"numpy.zeros",
"numpy.prod",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
winnerineast/imagepy | [
"465fcf63875c5605bb907275e89c162b224db12b",
"43cd5c4dcb9d6fefdcf11b8b9e9c0d56e11fab1e"
] | [
"imagepy/core/wraper/imageplus.py",
"imagepy/ui/tablewindow.py"
] | [
"import numpy as np\nfrom time import time\nfrom ..manager import ImageManager, ColorManager\n\ndef get_img_type(imgs):\n if imgs[0].ndim==3 and imgs[0].dtype==np.uint8:return 'rgb'\n if imgs[0].dtype == np.uint8:return '8-bit'\n if imgs[0].dtype == np.uint16:return '16-bit'\n if imgs[0].dtype == np.int... | [
[
"numpy.linspace",
"numpy.clip",
"numpy.multiply",
"numpy.subtract",
"numpy.array",
"numpy.histogram",
"numpy.sum",
"numpy.zeros"
],
[
"numpy.issubdtype",
"numpy.random.randn",
"pandas.date_range"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"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",
"... |
zhongwei/math | [
"b73f155d319fdf59749832cd57e54b50dcc4c595"
] | [
"tools/anim1.py"
] | [
"import matplotlib.pyplot as plt\nimport matplotlib.animation as animation\nfrom matplotlib import style\n\nfrom IPython.display import HTML\n\nstyle.use('fivethirtyeight')\n\nfig = plt.figure()\nax1 = fig.add_subplot(1, 1, 1)\n\ndef animate(i):\n graph_data = open('example.txt', 'r').read()\n lines = graph_d... | [
[
"matplotlib.pyplot.figure",
"matplotlib.pyplot.show",
"matplotlib.animation.FuncAnimation",
"matplotlib.style.use"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
IcarusWizard/Paddle | [
"c07b632cd4c7faac3150e8454d5800643faff4c9"
] | [
"python/paddle/distributed/fleet/meta_optimizers/dygraph_optimizer/sharding_optimizer_stage2.py"
] | [
"# Copyright (c) 2021 PaddlePaddle 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 ... | [
[
"numpy.prod"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
apetsiuk/Raccoon | [
"5674152d2d043ec644ca9bedf49707a2c2716dc7"
] | [
"quad_pkg/src/scripts/person_following2.py"
] | [
"#!/usr/bin/env python\nimport rospy\nimport numpy as np\nimport cv2, cv_bridge\nfrom geometry_msgs.msg import Twist\nfrom sensor_msgs.msg import Image,LaserScan\n\nclass PersonFollow(object):\n def __init__(self):\n rospy.init_node(\"PersonFollow\")\n self.bridge = cv_bridge.CvBridge()\n cv... | [
[
"numpy.asarray",
"numpy.array",
"numpy.clip"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
toanquachp/srgan | [
"032353b9766f08e87beed846382b3d3afc47fe38"
] | [
"train.py"
] | [
"import numpy as np\nfrom utils import load_training_data_as_images, plot_generated_images\nfrom model import build_adversarial_model\n\n\ndef train_gan(models, data, parameters):\n \n '''\n Train adversarial model\n Steps:\n 1. Generate fake HR images and get real HR images \n 2. Train discrimina... | [
[
"numpy.concatenate",
"numpy.ones",
"numpy.zeros",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yehoshuadimarsky/pandas | [
"910576a6a381c3a6230ab771f29763d65b3d3c3d"
] | [
"pandas/core/arrays/sparse/array.py"
] | [
"\"\"\"\nSparseArray data structure\n\"\"\"\nfrom __future__ import annotations\n\nfrom collections import abc\nimport numbers\nimport operator\nfrom typing import (\n TYPE_CHECKING,\n Any,\n Callable,\n Literal,\n Sequence,\n TypeVar,\n cast,\n overload,\n)\nimport warnings\n\nimport numpy ... | [
[
"pandas._libs.sparse.make_mask_object_ndarray",
"pandas.core.algorithms.value_counts_arraylike",
"pandas.Series",
"numpy.asarray",
"pandas.core.dtypes.common.is_dtype_equal",
"pandas._libs.sparse.IntIndex",
"pandas.core.dtypes.cast.maybe_box_datetimelike",
"pandas.compat.numpy.func... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.0",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
ART-Group-it/KERMIT | [
"e3ed25bb0d67e77fc3afdef04007b34df1805ba1"
] | [
"kerMIT/kerMIT/explain_pytorch/LRP_linear_layer.py"
] | [
"import torch\n\ndef prepare_input_LRP(y_predict, dtk_sentence, model, index):\n hout = y_predict\n hout = hout.view(hout.shape[1],-1)\n hin = dtk_sentence.view(dtk_sentence.shape[1],-1)\n\n params = list(model.parameters())\n w = params[-2].view(params[-2].shape[1],-1)[index:].cpu() # faccio il resh... | [
[
"torch.argmax",
"torch.Tensor",
"torch.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ksteensig/channel-estimation | [
"30b1133ade2527b772a51dc5294cd4be71abd5b0",
"30b1133ade2527b772a51dc5294cd4be71abd5b0"
] | [
"nn_doa_analysis_v2.py",
"experiments/cbn_ae_datagen.py"
] | [
"import os\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' \n\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom sklearn.model_selection import train_test_split\nfrom numpy import sqrt,pi\nimport matplotlib.pyplot as plt\nfrom tensorflow.keras.models import load_model\nimport numpy as np\nimport pandas as pd\n\... | [
[
"tensorflow.convert_to_tensor",
"tensorflow.keras.models.load_model",
"tensorflow.norm",
"tensorflow.transpose",
"numpy.sqrt",
"tensorflow.concat",
"tensorflow.cast",
"tensorflow.math.imag",
"numpy.random.uniform",
"tensorflow.map_fn",
"numpy.mean",
"tensorflow.math... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Alagirl/keras-yolov3-tianchi | [
"015866c119a07f386b70a3087a8fc175cc4091a2"
] | [
"train.py"
] | [
"\"\"\"\nRetrain the YOLO model for your own dataset.\n\"\"\"\n\nimport numpy as np\nimport keras.backend as K\nfrom keras.layers import Input, Lambda\nfrom keras.models import Model\nfrom keras.optimizers import Adam\nfrom keras.callbacks import TensorBoard, ModelCheckpoint, ReduceLROnPlateau, EarlyStopping\n\nfro... | [
[
"numpy.random.shuffle",
"numpy.array",
"numpy.zeros",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yfletberliac/minigo | [
"69ea576351c95225543b281de6db94fb738f8958"
] | [
"rl_loop/example_buffer.py"
] | [
"# Copyright 2018 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# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed ... | [
[
"tensorflow.gfile.Exists",
"tensorflow.python_io.tf_record_iterator"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
tnarg999/CS238FinalProject | [
"0ecbb66678f60d6cc525936e737d310361f171ad"
] | [
"tests/test_flatland_envs_rail_env_shortest_paths.py"
] | [
"import sys\n\nimport numpy as np\n\nfrom flatland.core.grid.grid4 import Grid4TransitionsEnum\nfrom flatland.envs.observations import GlobalObsForRailEnv\nfrom flatland.envs.rail_env import RailEnv\nfrom flatland.envs.rail_env_shortest_paths import get_shortest_paths, get_k_shortest_paths\nfrom flatland.envs.rail_... | [
[
"numpy.array_equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
RyonSayer/augraphy | [
"be1e8dcf0f129ac3fc30ba1cad0d8de02443f67f"
] | [
"augraphy/augmentations/bleedthrough.py"
] | [
"import os\nimport random\nfrom glob import glob\n\nimport cv2\nimport numpy as np\n\nfrom augraphy.augmentations.lib import sobel\nfrom augraphy.base.augmentation import Augmentation\nfrom augraphy.utilities import *\n\n\nclass BleedThrough(Augmentation):\n \"\"\"Emulates bleed through effect from the combinati... | [
[
"numpy.vectorize"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tallamjr/arviz | [
"9d0746972c9bcb1a3063c0ca00ff08a0b0c093ee"
] | [
"arviz/plots/backends/bokeh/pairplot.py"
] | [
"\"\"\"Bokeh pairplot.\"\"\"\nimport warnings\nfrom copy import deepcopy\nfrom uuid import uuid4\n\nimport bokeh.plotting as bkp\nimport numpy as np\nfrom bokeh.models import CDSView, ColumnDataSource, GroupFilter, Span\n\nfrom ....rcparams import rcParams\nfrom ...distplot import plot_dist\nfrom ...kdeplot import ... | [
[
"numpy.arange",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mobeixiaoxin/deep-high-resolution-net.pytorch | [
"081dea8b60726cc0db45ef907f49b4748ac7bba2"
] | [
"lib/core/function.py"
] | [
"# ------------------------------------------------------------------------------\n# Copyright (c) Microsoft\n# Licensed under the MIT License.\n# Written by Bin Xiao (Bin.Xiao@microsoft.com)\n# ------------------------------------------------------------------------------\n\nfrom __future__ import absolute_import\... | [
[
"torch.no_grad",
"numpy.zeros",
"numpy.prod"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
star-baba/res50_sa_ssd | [
"c7c0f218307b50e7ac1fd8945868df01f1743467"
] | [
"ssd_data/datasets/coco.py"
] | [
"import cv2, os\nimport numpy as np\nfrom pycocotools.coco import COCO\n\nfrom .base import ObjectDetectionDatasetBase, Compose\nfrom .._utils import DATA_ROOT, _get_xml_et_value, _check_ins\n\nCOCO_class_labels = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',\n 'train', 'truck', '... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ruhrscholz/adventofcode-2021 | [
"9a752910ea81e272ae71ec0f2c028e15fec056a1"
] | [
"06a/main.py"
] | [
"import numpy as np\n\nMAX_DAYS_TO_FERTILITY = 8\nDAYS_TO_FERTILITY = 6\n\n\nif __name__ == '__main__':\n input = np.loadtxt('input', delimiter=',', dtype=int)\n unique, counts = np.unique(input, return_counts=True)\n hist = np.zeros(MAX_DAYS_TO_FERTILITY+2, dtype=int)\n hist[unique] = counts\n\n for... | [
[
"numpy.zeros",
"numpy.pad",
"numpy.loadtxt",
"numpy.unique"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
u7javed/AI-Chatbot | [
"d86916537e7b0b9a45f11d0fe0367fe9f66721e7"
] | [
"evaluate.py"
] | [
"import torch\r\nimport torchvision\r\nimport torchvision.transforms as transforms\r\nimport numpy as np \r\nimport time\r\nimport matplotlib.pyplot as plt\r\nimport argparse\r\nimport torch.optim as optim\r\nfrom random import shuffle\r\nimport pickle\r\n\r\nfrom models import *\r\nfrom utilities import *\r\n\r\nP... | [
[
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gammamathematics/manim | [
"adf3e20ea81a9a45caf2a7c105bb65a775fc480a"
] | [
"manim/renderer/opengl_renderer.py"
] | [
"from __future__ import annotations\n\nimport itertools as it\nimport sys\nimport time\nfrom typing import Any\n\nif sys.version_info < (3, 8):\n from backports.cached_property import cached_property\nelse:\n from functools import cached_property\n\nimport moderngl\nimport numpy as np\nfrom PIL import Image\n... | [
[
"numpy.dot",
"numpy.linalg.inv",
"numpy.flipud",
"numpy.arccos",
"numpy.frombuffer",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SvenBollweg/eqnet | [
"76bc51fdff8bef1b898820329e1ddf212323d0b0"
] | [
"encoders/evaluation/encodingvis.py"
] | [
"import sys\n\nimport matplotlib\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom scipy.spatial.distance import squareform, pdist\nfrom sklearn.manifold import TSNE\n\nfrom data.dataimport import import_data\nfrom encoders.baseencoder import AbstractEncoder\n\nif __name__ == '__main__':\n if len(s... | [
[
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.get_cmap",
"matplotlib.pyplot.plot",
"sklearn.manifold.TSNE",
"scipy.spatial.distance.pdist",
"numpy.array",
"matplotlib.pyplot.show",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"1.3",
"1.8"
... |
lmb633/gnn | [
"98289d32f4be88a10714f1ac3bd0539e8cbc221f",
"98289d32f4be88a10714f1ac3bd0539e8cbc221f"
] | [
"yoochoose/data_gen.py",
"gcn_test/cora.py"
] | [
"import torch\nfrom sklearn.preprocessing import LabelEncoder\nfrom torch_geometric.data import Data\nfrom torch_geometric.data import InMemoryDataset\nfrom tqdm import tqdm\n\n# from config import c_file, c_index\n\ndf = torch.load('data/yoochoose_click_binary_1M_sess.dataset')\n\n\nclass YooChooseBinaryDataset(In... | [
[
"torch.LongTensor",
"torch.load",
"torch.tensor",
"torch.FloatTensor",
"sklearn.preprocessing.LabelEncoder",
"torch.save"
],
[
"torch.nn.NLLLoss"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
waverDeep/WaveBYOL | [
"ab062c26598e0fa6ab8426498f9920048988b5c1",
"ab062c26598e0fa6ab8426498f9920048988b5c1"
] | [
"src/optimizers/optimizer.py",
"src/models/model_feature_encoder.py"
] | [
"import torch.optim as optimizer\nfrom adamp import AdamP\n\n\ndef get_optimizer(model_parameter, config):\n optimizer_name = config['optimizer_name']\n if optimizer_name == 'Adam':\n return optimizer.Adam(params=model_parameter,\n lr=config['learning_rate'],\n ... | [
[
"torch.optim.Adam",
"torch.optim.SGD"
],
[
"torch.nn.Sequential",
"torch.nn.Conv2d",
"torch.nn.MaxPool2d",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
caofanCPU/Python | [
"57d290918fd3f6af1e5c0940aed95642f931d32e"
] | [
"graphs/multi_hueristic_astar.py"
] | [
"from __future__ import print_function\n\nimport heapq\n\nimport numpy as np\n\ntry:\n xrange # Python 2\nexcept NameError:\n xrange = range # Python 3\n\n\nclass PriorityQueue:\n def __init__(self):\n self.elements = []\n self.set = set()\n\n def minkey(self):\n if not self.empty... | [
[
"numpy.array",
"numpy.chararray",
"numpy.linalg.norm"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.24",
"1.13",
"1.16",
"1.9",
"1.18",
"1.23",
"1.21",
"1.22",
"1.20",
"1.7",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [],
... |
wsmorgan/LatticeVisualizationTools | [
"8e7fd2b0d19703522ed2d6b12c703da6f846bffc"
] | [
"src/surface_structs.py"
] | [
"import matplotlib.pyplot as plt\nimport matplotlib as mpl\nimport matplotlib.cm as cm\nimport math as mt\nfrom itertools import product\n\n\"\"\"\nThis program is meant to be able to take a set of lattice vectors\nfor the most commonlattices, sc, fcc, hcp, bcc, and retur a picture\nwith the desired surface, eg (10... | [
[
"matplotlib.cm.gist_ncar",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.xlim",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nschaffner/wradlib | [
"eaa74701464e4ca2e01c96be7d47667983b6cfd6"
] | [
"wradlib/vpr.py"
] | [
"#!/usr/bin/env python\r\n# Copyright (c) 2011-2018, wradlib developers.\r\n# Distributed under the MIT License. See LICENSE.txt for more info.\r\n\r\n\"\"\"\r\nVertical Profile of Reflectivity (VPR)\r\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n\r\nPrecipitation is 3-dimensional in space. The vertical distribution ... | [
[
"numpy.logical_not",
"numpy.meshgrid",
"numpy.min",
"numpy.arange",
"numpy.dtype",
"numpy.sin",
"numpy.max",
"numpy.mean",
"numpy.prod",
"numpy.array",
"numpy.exp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jopdorp/classical-polyphony-rnn | [
"8fcdce4c974b1c5a463cbd30ea85bdf98d4f8bde"
] | [
"magenta/scripts/convert_dir_to_note_sequences.py"
] | [
"# Copyright 2016 Google 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 required by appli... | [
[
"tensorflow.logging.warning",
"tensorflow.app.flags.DEFINE_bool",
"tensorflow.app.flags.DEFINE_string",
"tensorflow.logging.set_verbosity",
"tensorflow.logging.info",
"tensorflow.gfile.IsDirectory",
"tensorflow.logging.fatal",
"tensorflow.gfile.FastGFile",
"tensorflow.app.run"
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
Holldean/pytorch-models | [
"9509d0d462b1a98164b266d49ada199071a855ac"
] | [
"model/rs/nfm.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom base import BaseModel\nfrom model.layers import DNNLayer, PredictionLayer\n\n\nclass BiInteractionPooling(nn.Module):\n\n def __init__(self):\n super(BiInteractionPooling, self).__init__()\n\n def forward(self, inputs):\n ... | [
[
"torch.sigmoid",
"torch.cat",
"torch.manual_seed",
"torch.sum",
"torch.nn.Embedding",
"torch.nn.Linear",
"torch.nn.init.normal_",
"torch.stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
severinhaller/einf-machinelearning | [
"4dfc8f1da0d81c5aa800d1459f81b72d1bf6dd9b"
] | [
"Matrizen und Vektoren/Matrizen und Vektoren/diabetes.py"
] | [
"import numpy as np\n\ndiabetes_data_set = np.genfromtxt('diabetes_daten.csv', delimiter=',', encoding=\"utf_8_sig\")\nmatrix_A = diabetes_data_set[:, :10]\ny = diabetes_data_set[:, np.newaxis, 10]\n\nprint(matrix_A, y)\n\n\nw = np.matmul(np.matmul(np.linalg.inv(np.matmul(matrix_A.T, matrix_A)),matrix_A.T),y)\n\npr... | [
[
"numpy.matmul",
"numpy.genfromtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
iwritecodesallday/OSRS-OpenCV | [
"f9294609967275fe159f8cce8392ede6e4acb93d"
] | [
"Classes/WindowCapture.py"
] | [
"import numpy as np\nimport win32gui, win32ui, win32con\n\n\nclass WindowCapture:\n \"\"\"\n Properties\n \"\"\"\n w = 0\n h = 0\n hwnd = None\n cropped_x = 0\n cropped_y = 0\n offset_x = 0\n offset_y = 0\n\n \"\"\"\n Constructor\n \"\"\"\n def __init__(self, window_name=No... | [
[
"numpy.ascontiguousarray",
"numpy.fromstring"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
saghosh/HGCalML | [
"5d8a1f7efba79df191cefe1f221fd92c79aa49b4"
] | [
"Train/simplified_trainer_example_cheplike.py"
] | [
"'''\n\nCompatible with the dataset here:\n/eos/cms/store/cmst3/group/hgcal/CMG_studies/pepr/Oct2021_production/Gun20Part_CHEPDef_NoPropagate/NanoML\nand (soon)\n/eos/cms/store/cmst3/group/hgcal/CMG_studies/pepr/Oct2021_production/Gun20Part_CHEPDef_NoPropagate/NanoMLTracks\n\nOn flatiron:\n/mnt/ceph/users/jkieseler... | [
[
"tensorflow.keras.layers.Concatenate",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.optimizers.Adam"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
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