repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
RoliKhanna/Anchor-Free | [
"e3d599b7cbdc988ad7720c1e8324cabe87917d59"
] | [
"baseline/spa.py"
] | [
"\nfrom nltk.corpus import reuters\nimport sys\nimport numpy as np\nfrom scipy import optimize\n\n# Loading data here\ntrain_documents, train_categories = zip(*[(reuters.raw(i), reuters.categories(i)) for i in reuters.fileids() if i.startswith('training/')])\ntest_documents, test_categories = zip(*[(reuters.raw(i),... | [
[
"numpy.diag",
"numpy.dot",
"numpy.linalg.svd",
"numpy.abs",
"numpy.reshape",
"numpy.eye",
"scipy.optimize.nnls",
"numpy.linalg.norm",
"numpy.copy",
"numpy.argmax",
"numpy.array"
]
] | [
{
"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"... |
AllenNeuralDynamics/ephys-framework-tests | [
"ee940afeab54e5e25765a903a6b65f2e95be4c48",
"ee940afeab54e5e25765a903a6b65f2e95be4c48"
] | [
"pgaf/ingest_stimulus.py",
"django/helpers.py"
] | [
"from allensdk.brain_observatory.ecephys.ecephys_project_cache import EcephysProjectCache\nimport os\nfrom sqlalchemy import delete\nfrom sqlalchemy.orm import sessionmaker\n\nimport json\nimport numpy as np\nimport pandas as pd\nfrom datetime import date,datetime,timedelta\n\nimport sqla_schema as sch\n\nimport in... | [
[
"numpy.fromstring",
"pandas.read_sql"
],
[
"numpy.logical_and"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"nump... |
mamingjie-China/Paddle | [
"68c6160e639be38c57a7dd831f7b841b33e92676"
] | [
"python/paddle/fluid/tests/unittests/test_sort_op.py"
] | [
"# Copyright (c) 2020 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 re... | [
[
"numpy.array",
"numpy.random.rand",
"numpy.sort"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pg1647/self_supervised_rotnet | [
"5edfa31dd7b2dde8e1d093037c6c8c9745ba8f71"
] | [
"rotnet_nonlinear_finetuned.py"
] | [
"from __future__ import absolute_import\r\nfrom __future__ import division\r\nfrom __future__ import print_function\r\n\r\nimport torch\r\nimport torchvision\r\nfrom torchvision import datasets, transforms\r\nimport torch.nn.functional as F\r\nimport torch.optim as optim\r\n\r\nimport models as mdl\r\n\r\nimport nu... | [
[
"torch.optim.lr_scheduler.MultiStepLR",
"torch.load",
"torch.nn.functional.cross_entropy",
"torch.utils.data.DataLoader",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kayfuku/fsdl-text-recognizer-2021-labs | [
"91fbf12d0fbee248c7bc97bcb47b71710fb62655"
] | [
"lab1/text_recognizer/lit_models/base.py"
] | [
"import argparse\nimport pytorch_lightning as pl\nimport torch\n\n\nOPTIMIZER = \"Adam\"\nLR = 1e-3\nLOSS = \"cross_entropy\"\nONE_CYCLE_TOTAL_STEPS = 100\n\n\nclass Accuracy(pl.metrics.Accuracy):\n \"\"\"Accuracy Metric with a hack.\"\"\"\n\n def update(self, preds: torch.Tensor, target: torch.Tensor) -> Non... | [
[
"torch.optim.lr_scheduler.OneCycleLR",
"torch.nn.functional.softmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
igormq/sbrt2017 | [
"a66c33a55970fb56d91d31a9509cac7ae7ccf4c5"
] | [
"preprocessing.py"
] | [
"# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import, division, print_function\n\nimport os\nimport math\nimport decimal\nimport string\nimport numpy as np\nfrom unidecode import unidecode\nimport logging\n\nfrom scipy import signal\nfrom scipy.fftpack import dct\nimport librosa\n\n\ndef round_half_up(n... | [
[
"numpy.dot",
"numpy.linspace",
"numpy.concatenate",
"numpy.max",
"numpy.mean",
"scipy.fftpack.dct",
"numpy.hstack",
"numpy.arange",
"numpy.finfo",
"numpy.sin",
"numpy.std",
"numpy.zeros",
"numpy.log",
"numpy.append",
"numpy.log10",
"numpy.array",
... | [
{
"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"... |
icedcoffeeee/manim | [
"880e8999a81f1e85bc1d20921b1efb9c4ed1565a"
] | [
"manim/mobject/opengl_mobject.py"
] | [
"import copy\nimport itertools as it\nimport random\nimport sys\nfrom functools import wraps\nfrom math import ceil\nfrom typing import Iterable, Optional, Tuple, Union\n\nimport moderngl\nimport numpy as np\nfrom colour import Color\n\nfrom .. import config\nfrom ..constants import *\nfrom ..utils.bezier import in... | [
[
"numpy.cross",
"numpy.dot",
"numpy.abs",
"numpy.linspace",
"numpy.arange",
"numpy.eye",
"numpy.linalg.norm",
"numpy.all",
"numpy.sign",
"numpy.identity",
"numpy.transpose",
"numpy.repeat",
"numpy.array",
"numpy.zeros",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Sanofi-Public/IDD-papers-avoiding_failure_modes | [
"3463a277c3be20ce08602b912e3fa73388097e9b"
] | [
"guacamol_baselines/smiles_lstm_ppo/goal_directed_generation.py"
] | [
"from __future__ import print_function\n\nimport argparse\nimport json\nimport logging\nimport os\n\nimport numpy as np\nfrom guacamol.assess_goal_directed_generation import assess_goal_directed_generation\nfrom guacamol.utils.helpers import setup_default_logger\n\nfrom smiles_lstm_ppo.ppo_directed_generator import... | [
[
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dangom/niworkflows | [
"e1eabe54e963b2829c1f7dc71abe80ca5767d687"
] | [
"niworkflows/interfaces/tests/test_nibabel.py"
] | [
"\"\"\"test nibabel interfaces.\"\"\"\nimport os\nimport numpy as np\nimport nibabel as nb\n\nfrom ..nibabel import Binarize\n\n\ndef test_Binarize(tmp_path):\n \"\"\"Test binarization interface.\"\"\"\n os.chdir(str(tmp_path))\n\n mask = np.zeros((20, 20, 20), dtype=bool)\n mask[5:15, 5:15, 5:15] = boo... | [
[
"numpy.all",
"numpy.eye",
"numpy.zeros",
"numpy.zeros_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
maodoc33/ReinforcementLearning | [
"11cea9bd4b3c8482811a825c839cdb2cabe73206"
] | [
"footballTests/GfootballEnv.py"
] | [
"# from __future__ import absolute_import\n# from __future__ import division\n# from __future__ import print_function\nimport gym\nimport tensorflow as tf\nimport numpy as np\nfrom tf_agents.trajectories import time_step as ts\n\nimport abc\nimport tensorflow as tf\nimport numpy as np\nfrom tf_agents.environments i... | [
[
"numpy.squeeze",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JoaoLages/relational-transformers | [
"7bf401afe8e2b12531dc75f5402c2f9ffd99aaf7"
] | [
"src/ratransformers/t5.py"
] | [
"from transformers.models.t5.modeling_t5 import T5Attention\nimport torch.nn as nn\nimport torch\n\n\nclass T5RelationalAttention(T5Attention):\n def __init__(self, *args, num_relation_kinds: int, use_same_relation_kv_emb: bool = True, **kwargs):\n super().__init__(*args, **kwargs)\n self.num_relat... | [
[
"torch.zeros",
"torch.cat",
"torch.nn.functional.dropout",
"torch.nn.Embedding",
"torch.matmul"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Ellon-M/visualizations | [
"5a42c213ea8fd0597e2035778d9ae6460eb9e821",
"5a42c213ea8fd0597e2035778d9ae6460eb9e821",
"5a42c213ea8fd0597e2035778d9ae6460eb9e821"
] | [
"src/hextiles/hextiles.py",
"src/area-charts/stackedarea.py",
"src/dendrograms/dendrogram.py"
] | [
"# hextiles\n\n# holoviews\nimport numpy as np\nimport holoviews as hv\nfrom holoviews import opts\nfrom holoviews import dim\nhv.extension('bokeh')\n\nnp.random.seed(44)\nhex_tiles = hv.HexTiles(np.random.randn(100000, 2))\nhex_tiles.opts(opts.HexTiles(width=500, height=400, tools=['hover'], colorbar=True))\n",
... | [
[
"numpy.random.randn",
"numpy.random.seed"
],
[
"matplotlib.pyplot.legend",
"matplotlib.patches.Patch",
"matplotlib.pyplot.title",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
],
[
"scipy.cluster.hiera... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"... |
Pang1987/Python-code-PIGP-PINN | [
"e22f54a3ec4ce1644386a7a667fb56e6f99ab51c"
] | [
"github_code/Discrete_GP_inv_2D_NS_equ_backward_Euler1.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Sep 17 12:28:19 2019\r\n\r\n@author: gpang\r\n\"\"\"\r\n\r\n\r\n\r\n\r\n\r\nimport tensorflow as tf\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport time\r\nfrom SALib.sample import sobol_sequence\r\nimport scipy as sci\r\nimport scipy.io as sio... | [
[
"tensorflow.concat",
"numpy.linspace",
"tensorflow.zeros",
"tensorflow.reduce_sum",
"tensorflow.diag",
"matplotlib.pyplot.plot",
"numpy.concatenate",
"numpy.random.randn",
"tensorflow.train.AdamOptimizer",
"tensorflow.cholesky",
"tensorflow.cholesky_solve",
"tensorf... | [
{
"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"... |
lidiaToropova/openvino | [
"298cced3b30f056428d3f5977cd194837dd89062"
] | [
"tools/mo/openvino/tools/mo/graph/graph.py"
] | [
"# Copyright (C) 2018-2022 Intel Corporation\n# SPDX-License-Identifier: Apache-2.0\n\nimport collections\nimport logging as log\nfrom copy import deepcopy\nfrom typing import List\n\nimport networkx as nx\nimport numpy as np\n\nfrom openvino.tools.mo.graph.port import Port\nfrom openvino.tools.mo.middle.passes.eli... | [
[
"numpy.any"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
victorkich/RiVAE | [
"a153f88a296a9fa36805e21eb33dca07ecf91706"
] | [
"rigan.py"
] | [
"from model import Generator, Discriminator\nfrom dataset_creator import RiVAEDataset\nfrom torchvision.utils import save_image\nfrom torch.utils.data import DataLoader\nfrom torch.backends import cudnn\nfrom torch.optim import Adam\nfrom tqdm import tqdm\nfrom torch import nn\nfrom os import path\nimport torch\n\n... | [
[
"torch.full",
"torch.randn",
"torch.utils.data.DataLoader",
"torch.nn.BCELoss",
"torch.nn.init.normal_",
"torch.cuda.is_available",
"torch.nn.init.zeros_",
"torch.device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
anhhuyalex/ax | [
"f802121b4fcc19b9ed36bf2392ebb3821ff819a9"
] | [
"ax/service/ax_client.py"
] | [
"#!/usr/bin/env python3\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\n\nimport json\nimport logging\nimport warnings\nfrom typing import Any, Dict, List, Optional, Tuple, Union\n\nimport ax.service.utils.best_point as best_point_utils\nimport numpy as np\nimport pandas as pd\nfrom ax.cor... | [
[
"numpy.minimum.accumulate",
"numpy.maximum.accumulate"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
msra-nlc/MSParS-V2.0- | [
"3e215b5f6ef47040275b3612fd2e1d5591909039"
] | [
"code/generation/OpenNMT-py-master-our-model/onmt/decoders/transformer.py"
] | [
"\"\"\"\nImplementation of \"Attention is All You Need\"\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nimport numpy as np\n\nimport onmt\nfrom onmt.modules.position_ffn import PositionwiseFeedForward\n\nMAX_SIZE = 5000\n\n\nclass TransformerDecoderLayer(nn.Module):\n \"\"\"\n Args:\n d_model (int): the... | [
[
"torch.nn.Dropout",
"torch.cat",
"torch.zeros",
"torch.from_numpy",
"torch.nn.LayerNorm",
"numpy.ones",
"torch.stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ravising-h/tf-pose-estimation | [
"95f131ce8f082f7f788c023278465cb6a42b9420"
] | [
"tf_pose/pose_dataset.py"
] | [
"import logging\nimport math\nimport multiprocessing\nimport struct\nimport sys\nimport threading\n\ntry:\n from StringIO import StringIO\nexcept ImportError:\n from io import StringIO\n\nfrom contextlib import contextmanager\n\nimport os\nimport random\nimport requests\nimport cv2\nimport numpy as np\nimport... | [
[
"tensorflow.get_default_session",
"numpy.amax",
"matplotlib.pyplot.imshow",
"numpy.absolute",
"numpy.nonzero",
"matplotlib.pyplot.colorbar",
"numpy.fromstring",
"tensorflow.Session",
"matplotlib.pyplot.close",
"numpy.array",
"numpy.zeros",
"matplotlib.pyplot.show",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
daniel-zullo-sociomantic/mxnet | [
"f3e89b1324986238425121a32abb4d4b39f52cab"
] | [
"python/mxnet/test_utils.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.sqrt",
"numpy.asarray",
"numpy.dtype",
"numpy.max",
"numpy.random.randn",
"numpy.searchsorted",
"numpy.random.randint",
"numpy.copy",
"numpy.argmax",
"numpy.unravel_index",
"numpy.zeros",
"numpy.isnan",
"numpy.ascontiguousarray",
"numpy.random.rand",
... | [
{
"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"
... |
dabito-san/Diabetic_Retinopathy_Detection | [
"ec1ea85773cf401d439f47fd9c30a56f0882b6bb"
] | [
"utils/util.py"
] | [
"# internal imports\nfrom configs import config\n\n# external imports\nfrom numpy.random import RandomState\nimport torch\nfrom torch.utils.data import DataLoader, WeightedRandomSampler\nimport numpy as np\n\n\ndef get_images_mean_std(dataloader):\n channels_sum, channels_squared_sum, num_batches = 0, 0, 0\n\n ... | [
[
"torch.utils.data.DataLoader",
"numpy.concatenate",
"torch.no_grad",
"numpy.array",
"numpy.random.RandomState",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pingxue-hfut/SD-BNN | [
"c22be1799b199cba5233fedbe9e38aee6da53782"
] | [
"imagenet/birealnet.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom utils import se_module\n\n'''\nReference:\n[20] Zechun Liu, Baoyuan Wu, Wenhan Luo, Xin Yang, Wei Liu,\nand Kwang-Ting Cheng. Bi-real net: Enhancing the performance of 1-bit cnns with improved representational capability and advanced traini... | [
[
"torch.nn.Sequential",
"torch.sign",
"torch.zeros",
"torch.nn.PReLU",
"torch.nn.functional.conv2d",
"torch.nn.Conv2d",
"torch.nn.MaxPool2d",
"torch.nn.Linear",
"torch.nn.AvgPool2d",
"torch.nn.AdaptiveAvgPool2d",
"torch.rand",
"torch.nn.BatchNorm2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mintusf/land_cover_segmentation | [
"6e136c076d50aa6a85b9628e5cdcbedcf0dd9836",
"6e136c076d50aa6a85b9628e5cdcbedcf0dd9836"
] | [
"tests/models/test_deeplab_forward.py",
"models/hrnet/hrnet.py"
] | [
"import torch\nfrom torchvision.transforms import Compose\n\nfrom dataset import PatchDataset, get_transform\nfrom models import get_model\nfrom utils.io_utils import load_yaml\n\n\ndef test_deeplab_forward(test_config):\n channels_in = len(test_config.DATASET.INPUT.USED_CHANNELS)\n labels_config = load_yaml(... | [
[
"torch.stack"
],
[
"torch.nn.Sequential",
"torch.cat",
"torch.nn.init.constant_",
"torch.nn.ModuleList",
"torch.nn.Conv2d",
"torch.nn.init.normal_",
"torch.nn.functional.interpolate",
"torch.nn.ReLU",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
fulankun1412/Table-and-Image-Extractor-from-PDF-Using-Tabula-and-PyPDF2 | [
"373b7f32075bad875beeae0ff431c45a279d7230"
] | [
"main.py"
] | [
"import PyPDF2\nimport os\nfrom PIL import Image\nimport pandas as pd\nimport tabula as tb\nimport os\nimport pandas as pd\nimport PIL\nimport mysql.connector as sql\n\n#### Extracting Data Table From PDF file\n### Declare Variables\ndirr = \"C:/Users/afkaa/Google Drive/Multimatics/Data/UINSA/\"\ntable_dir = \"tabl... | [
[
"pandas.concat",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
G-Doxastakis/SparseLSH | [
"156a3772a1447b87b1cb9860218a24246f9e61fd"
] | [
"sparselsh/lsh.py"
] | [
"from __future__ import print_function\n\nimport os\nimport json\nimport numpy as np\nfrom scipy import sparse\n\nfrom .storage import storage, serialize, deserialize\n\nclass LSH(object):\n \"\"\" LSH implments locality sensitive hashing using random projection for\n input vectors of dimension `input_dim`.\n... | [
[
"scipy.sparse.issparse",
"scipy.sparse.csr_matrix",
"numpy.savez_compressed",
"numpy.random.randn",
"numpy.load"
]
] | [
{
"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"... |
ptbrown1729/exact_diag | [
"16c284aea88dd1fb795e89b8e877d564045b8269"
] | [
"ed_nlce.py"
] | [
"import numpy as np\r\nimport scipy.special\r\nimport scipy.sparse as sp\r\nimport ed_geometry as geom\r\nimport ed_symmetry as symm\r\n\r\n# TODO: some ideas\r\n# 1. Identify clusters which are topologically the same, to save on work done for diagonalization\r\n# 2. Automatically identify symmetry group of cluster... | [
[
"scipy.sparse.csc_matrix",
"scipy.sparse.issparse",
"numpy.power",
"numpy.reshape",
"numpy.arange",
"scipy.sparse.diags",
"scipy.sparse.csr_matrix",
"numpy.tensordot",
"numpy.array",
"numpy.zeros",
"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"
... |
martijnbentum/immuno | [
"fc4d2c2e5b6e65de8bcea9e8ae43db7005eb3155"
] | [
"repo/utils/view_ocr.py"
] | [
"import matplotlib\nfrom matplotlib import pyplot as plt\nfrom matplotlib.patches import Rectangle\n\n\ndef page2xlim_ylim(page, extra = 100):\n\txlim = (0,page.page_width + extra)\n\tylim = (0,page.page_height + extra)\n\treturn xlim,ylim\n\t\ndef make_figure(xlim = (0,4250),ylim=(0,5500)):\n\tplt.ion()\n\tplt.fig... | [
[
"matplotlib.pyplot.gca",
"matplotlib.patches.Rectangle",
"matplotlib.pyplot.text",
"matplotlib.pyplot.ion",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
romanroibu/pyglui | [
"ab316d094e975491032ce2b395a03d922b03222c"
] | [
"setup.py"
] | [
"from __future__ import print_function\n\nimport io\nimport os\nimport platform\nimport re\nimport sys\nfrom stat import ST_MTIME\n\nimport numpy\nfrom Cython.Build import cythonize\nfrom setuptools import Extension, setup\n\ndir_containing_glew = os.path.dirname(__file__)\nsys.path.append(dir_containing_glew)\nfro... | [
[
"numpy.get_include"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
poelsner/nappy | [
"d3ede80138f0fc41deba9b7338932b8be2943e3f"
] | [
"nappy/nc_interface/na_to_nc.py"
] | [
"# Copyright (C) 2004 CCLRC & NERC( Natural Environment Research Council ).\r\n# This software may be distributed under the terms of the\r\n# Q Public License, version 1.0 or later. http://ndg.nerc.ac.uk/public_docs/QPublic_license.txt\r\n\r\n\"\"\"\r\nna_to_nc.py\r\n===========\r\n\r\nContains the NAToNC cla... | [
[
"numpy.int32"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
adriansmares/openml-deeplearning | [
"44856ae5f79b553acdb3001e52ca981922f3093f"
] | [
"tests/test_flows/test_flow_functions.py"
] | [
"from collections import OrderedDict\nimport copy\nimport unittest\n\nfrom distutils.version import LooseVersion\nimport sklearn\nimport pandas as pd\n\nimport openml\nfrom openml.testing import TestBase\nimport openml.extensions.sklearn\n\n\nclass TestFlowFunctions(TestBase):\n _multiprocess_can_split_ = True\n... | [
[
"sklearn.preprocessing.OrdinalEncoder"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DDMGNI/viIMHD2D | [
"d7c3546a6245fa0b12538a785146af3895f16e18"
] | [
"examples/current_sheet_continuous_steep.py"
] | [
"\nimport numpy as np\n\nu0 = 0.1\nB0 = 1.0\np0 = 0.1\n\nx1 = 0.5\nx2 = 1.5\nc = 20.\n\n\ndef magnetic_x(x, y, hx, hy):\n return 0.0\n\ndef magnetic_y(x, y, hx, hy):\n return np.tanh(c*(x-x1)) - np.tanh(c*(x-x2)) - 1.\n \ndef velocity_x(x, y, hx, hy):\n return u0 * np.sin(np.pi * y)\n\ndef velocity... | [
[
"numpy.tanh",
"numpy.sin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hanjinda/word2vec-explorer | [
"46c3f45e647dab82110bc8d21824b8d3063574c6"
] | [
"explorer_old.py"
] | [
"import math\nimport gensim\nimport cPickle\nimport numpy as np\nfrom tsne import bh_sne\nfrom sklearn.cluster import KMeans\nfrom gensim.models import KeyedVectors\n\nclass Exploration(dict):\n\n def __init__(self, query, labels=[], vectors=[]):\n self.query = query\n self.parsed_query = {}\n ... | [
[
"sklearn.cluster.KMeans",
"numpy.asarray",
"numpy.argmin",
"numpy.array",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JesseTG/Sock | [
"97b2f76dae324708a26bb46ce466680e6e4c769e"
] | [
"tests/test_pinning.py"
] | [
"import pytest\nimport torch\nfrom torch import Tensor\n\nfrom .marks import *\n\nSIZE = torch.Size([100, 100, 100])\n\n\n@pytest.fixture(scope=\"module\")\ndef data_cpu():\n return torch.rand(SIZE, dtype=torch.float64, device=\"cpu\")\n\n\n@pytest.fixture(scope=\"module\")\ndef data_cuda():\n return torch.ra... | [
[
"torch.Size",
"torch.empty_like",
"torch.rand"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
piinghel/JupyterWorkflow_TemplateJVD | [
"2d493edbfea9d79cd5d70b4d2241b44512657b6e"
] | [
"jupyterworkflow/data.py"
] | [
"import os\nfrom urllib.request import urlretrieve\nimport pandas as pd\n\nFREMONT_URL = 'https://data.seattle.gov/api/views/65db-xm6k/rows.csv?accessType=DOWNLOAD'\n\ndef get_fremont_data(filename='Fremont.csv', url=FREMONT_URL, \n force_download=False):\n \n \"\"\"Download and cache the ... | [
[
"pandas.read_csv",
"pandas.to_datetime"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
NOBLES5E/bagua | [
"c6f029f14c7c08383282f8efe22e7c0aee35fab1"
] | [
"bagua/contrib/load_balancing_data_loader/__init__.py"
] | [
"import torch\nimport math\nimport torch.distributed as dist\nfrom torch.utils.data.sampler import Sampler\nfrom torch.utils.data.dataset import Dataset\nfrom typing import TypeVar, Optional, Iterator\nfrom collections import OrderedDict\nimport logging\n\n\nclass LoadBalancingDistributedSampler(Sampler):\n r\"\... | [
[
"torch.distributed.get_rank",
"torch.distributed.get_world_size",
"torch.distributed.is_available",
"torch.Generator"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mef51/plawt | [
"7d5dbcd64d97499eaf7896d2f6e50826d54d2e6c"
] | [
"examples/subplotstest.py"
] | [
"#!/usr/bin/python3\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport plawt\nimport matplotlib as mpl\n\nx = np.linspace(0, 2 * np.pi, 400)\ny = np.sin(x ** 2)\n\n##### Vanilla Matplotlib #####\n\n# Three subplots sharing both x/y axes\n# f, (ax1, ax2, ax3) = plt.subplots(3, sharex=True, sharey=True)\n... | [
[
"numpy.linspace",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig",
"numpy.sin",
"matplotlib.pyplot.close",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.tick_params",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
metocean/tcrm | [
"ea782c8a6245419105a0a131ebe8ec61479feb25",
"ea782c8a6245419105a0a131ebe8ec61479feb25"
] | [
"Utilities/timeseries.py",
"Utilities/track.py"
] | [
"\"\"\"\n:mod:`timeseries` - Extract timeseries from each timestep of a simulation\n=========================================================================\n\nExtract station timeseries from each timestep of a simulation.\nThis samples the regional wind speed, not the site-specific wind speed.\nTo include site-sp... | [
[
"numpy.arctan2",
"numpy.argmax",
"numpy.argmin",
"numpy.any",
"numpy.savetxt",
"numpy.array",
"numpy.where"
],
[
"numpy.min",
"numpy.empty",
"numpy.dtype",
"numpy.max",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
boasvdp/sailor | [
"978e11b8d1624631203fce0ff65d13f6ebd5ba05"
] | [
"workflow/scripts/define_core.py"
] | [
"#!/usr/bin/env python3\n\nimport argparse\n\ndef filter_no_genomes(df, threshold):\n total_genomes = df.shape[1] - 20\n threshold_genomes = total_genomes * threshold\n df_filt = df[df['number_genomes'] > threshold_genomes]\n return df_filt\n\ndef filter_dosage(df, dosage):\n df_filt = df[df['max_dose'] <= dos... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
supercarryleoliao/IA-Flood-Warning-System-58 | [
"af980dddc72d45d50ee70d9d10f6077948396d92"
] | [
"floodsystem/plot.py"
] | [
"import matplotlib\nfrom .analysis import polyfit\nimport matplotlib.pyplot as plt\ndef plot_water_level_with_fit(station, dates, levels, p):\n poly,d0=polyfit(dates, levels, p)\n typical_range=station.typical_range\n x = matplotlib.dates.date2num(dates)\n high_value=typical_range[1]*len(x-d0)\n low_... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.plot",
"matplotlib.dates.date2num",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
glwagner/Exasim | [
"ee4540443435f958fa2ca78d59cbf9cff0fe69de"
] | [
"Version0.2/Python/Gencode/gencodedef.py"
] | [
"import pdeapp\nimport numpy as np\n\n#def gencode(app):\n\nparam = np.array([1.0]);\nxdg = np.array([0.0, 0.0]);\nudg = np.array([2.0, 2.0, 2.0]);\nodg = np.array([0.0]);\nwdg = np.array([0.0]);\nuinf = np.array([0.0]);\ntime = np.array([0.0]);\nprint(pdeapp.flux(xdg, udg, odg, wdg, uinf, param, time))\n #Flux ... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
statpng/KaggleTranscript | [
"b110482a2adcf0390fac0d54c890c95894f98dea"
] | [
"(1)Titanic2.py"
] | [
"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nplt.style.use(\"seaborn\")\nsns.set(font_scale=2.5)\n\nimport missingno as msno\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\ndf_train = pd.read_csv(\"./titanic/train.csv\")\ndf_test = pd.read_csv(\"./ti... | [
[
"pandas.crosstab",
"matplotlib.pyplot.legend",
"pandas.read_csv",
"numpy.log",
"matplotlib.pyplot.title",
"matplotlib.pyplot.subplots",
"pandas.DataFrame",
"matplotlib.pyplot.tile",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.subplots_adjust... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
strongio/strong-glm | [
"db05cb8a297858e46961e5d91105a515531dfdbb"
] | [
"strong_glm/preprocessing/features.py"
] | [
"from warnings import warn\n\nimport numpy as np\n\nfrom typing import Union, Sequence, Tuple\n\nfrom sklearn.base import TransformerMixin, BaseEstimator, clone\nfrom sklearn.preprocessing import FunctionTransformer, StandardScaler, MinMaxScaler, RobustScaler\nfrom sklearn.utils.metaestimators import _BaseCompositi... | [
[
"sklearn.base.clone",
"sklearn.preprocessing.FunctionTransformer"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
StanSStanman/frites | [
"53f4745979dc2e7b27145cd63eab6a82fe893ec7"
] | [
"frites/dataset/tests/test_ds_ephy_io.py"
] | [
"\"\"\"Test the different supported I/O types for DatasetEphy.\"\"\"\nimport numpy as np\n\nfrom mne import EpochsArray, create_info\nfrom xarray import DataArray\nimport pandas as pd\n\nfrom frites.dataset.ds_ephy_io import (ds_ephy_io, mne_to_arr, xr_to_arr)\n\n\nn_epochs = 5\nn_roi = 3\nn_times = 10\nn_suj = 2\n... | [
[
"numpy.arange",
"pandas.MultiIndex.from_arrays",
"numpy.testing.assert_array_equal",
"numpy.random.rand",
"numpy.random.randint"
]
] | [
{
"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": []
}
] |
klieret/pandas | [
"1739199759f6dd0580a079d6ee96bc4de98ade97"
] | [
"pandas/core/indexes/multi.py"
] | [
"from __future__ import annotations\n\nfrom functools import wraps\nfrom sys import getsizeof\nfrom typing import (\n TYPE_CHECKING,\n Any,\n Callable,\n Collection,\n Hashable,\n Iterable,\n List,\n Sequence,\n Tuple,\n cast,\n)\nimport warnings\n\nimport numpy as np\n\nfrom pandas._c... | [
[
"pandas.Series",
"numpy.asarray",
"pandas._libs.lib.tuples_to_object_array",
"numpy.cumsum",
"numpy.dtype",
"pandas.core.indexes.base.Index",
"pandas.core.indexes.frozen.FrozenList",
"numpy.concatenate",
"pandas._config.get_option",
"numpy.all",
"numpy.any",
"pandas... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wailamjonathanlee/predicting-alignment-algorithm-performance-using-machine-learning | [
"5255be1f5382390f87cc10b6cb377bffe84a46a3"
] | [
"src/run_create_clf_data.py"
] | [
"#!/usr/bin/env python3\n\n\nimport os, sys, json\nimport numpy as np\nimport pandas as pd\nimport functools as fct\nimport collections as cols\n\n\nfrom alignclf import create_clf_data\n\n\nif __name__ == '__main__':\n result_dnames = [\n 'clst-2018-12-generic_50-inc0-net1',\n 'clst-2018-12-generi... | [
[
"pandas.concat",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
code-burster/tensorflow | [
"203c4f750e33ff1927506f9ba585bc6e69d92725"
] | [
"tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py"
] | [
"# Copyright 2018 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.python.data.ops.dataset_ops.Dataset.from_tensors",
"tensorflow.python.ops.variable_scope.variable_creator_scope",
"tensorflow.python.distribute.values.ReplicaDeviceMap",
"tensorflow.python.training.optimizer.get_filtered_grad_fn",
"tensorflow.contrib.distribute.python.mirrored_stra... | [
{
"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... |
ZihaoChen0319/CMB-Segmentation | [
"99c5788baacc280ca5dbe02f3e18403e399fb238"
] | [
"DiscriTrainer.py"
] | [
"import torch.nn as nn\r\nimport os\r\nimport torch.optim as optim\r\nfrom tqdm import tqdm\r\nimport numpy as np\r\nimport torch\r\nimport torch.nn.functional as nnf\r\nimport json\r\nfrom PairwiseMeasures_modified import PairwiseMeasures\r\nimport medpy.io as mio\r\n\r\nfrom ScreenTrainer import ScreenTrainer\r\n... | [
[
"torch.nn.CrossEntropyLoss",
"torch.nn.functional.softmax",
"numpy.pad",
"torch.load",
"numpy.clip",
"numpy.random.choice",
"numpy.stack",
"torch.tensor",
"numpy.max",
"numpy.argmax",
"numpy.load",
"numpy.array",
"numpy.zeros",
"numpy.sum",
"torch.save"
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jmonrods/pyeis | [
"e354691cd4d3a9a80c4e42e99129d6ee704f0f46"
] | [
"replot2.py"
] | [
"#Algorithm to plot data as Nyquist plot, Bode plot, or Real and Imagine component plot\r\n# ------------------------------------------------------\r\n# Copyright (C) 2020 Gustavo Rodriguez Gutierrez\r\n# Licensed under the MIT license, see LICENSE.\r\n# ------------------------------------------------------\r\n\r\... | [
[
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.setp",
"matplotlib.pyplot.subplots_adjust",
"numpy.array",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ajbouh/tfi | [
"6e89e8c8f1ca3b285c788cc6b802fc44f9001290"
] | [
"src/tfi/serve/rest.py"
] | [
"import base64\nimport json\nimport inspect\nimport opentracing\n\nimport tfi.tensor.codec\n\nfrom flask import request, make_response\nfrom opentracing.ext import tags as opentracing_ext_tags\nfrom urllib.parse import urlparse\nfrom werkzeug.exceptions import BadRequest\n\nfrom tfi import data as tfi_data\nfrom tf... | [
[
"tensorflow.python.framework.tensor_util.MakeNdarray"
]
] | [
{
"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... |
ishakir/ml-tictactoe | [
"904eb24aa2c76e081138a1a90740173b403097d8"
] | [
"mlgames/config/connect4_learnanynonlosing_config.py"
] | [
"import tensorflow as tf\nfrom tensorflow import keras\nfrom collections import defaultdict\n\nfrom mlgames.config.config_abc import ConfigABC\n\nfrom mlgames.connect4.board import empty\n\nBOARD_HEIGHT = 6\nBOARD_WIDTH = 7\n\nclass Connect4LearnAnyNonLosingConfig(ConfigABC):\n\tdef name(self):\n\t\treturn \"connec... | [
[
"tensorflow.keras.layers.Dense"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
javier-ruiz-b/eToro-Anlage-KAP-calculator | [
"27ca81292c48de00b468a781405b3e996d212708"
] | [
"kap_summary.py"
] | [
"import detailed_table\nimport pandas as pd\nfrom locale import atof\nimport json\n\n# KAP 19\nCOL_PROFIT_STOCKS = 'Aktien G/V'\nCOL_DIVIDENDS_STOCKS = 'Aktien Dividende'\nCOL_PROFIT_CFDS = 'CFD G/V'\nCOL_FEES_CFDS = 'CFD Gebühren'\n\n# KAP 20\nCOL_PROFIT_ON_SALE_STOCKS = 'Aktien - Enthaltene Gewinne aus Aktienverä... | [
[
"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": []
}
] |
ahmadki/SSD-ResNet50 | [
"754ee6a3e9a5ba563d8ec6284c2745850ad5dcda"
] | [
"ssd/coco_pipeline.py"
] | [
"# Copyright (c) 2018-2019, 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 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# Unles... | [
[
"torch.cuda.synchronize",
"torch.zeros",
"torch.cat",
"torch.distributed.is_initialized",
"numpy.dtype",
"torch.IntTensor",
"torch.device",
"torch.distributed.get_rank"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
XuBLin/590PresentGroupProject | [
"7126a43ed53f7ae528c350a289273884eaaf160e"
] | [
"genre_classification/evaluation.py"
] | [
"# Load the data from dataset and load the model we have built.\n# Make evaluation of the 1D CNN model\nimport os\nfrom sklearn.preprocessing import LabelBinarizer\nimport utils\nfrom sklearn.model_selection import train_test_split\nfrom keras.models import load_model\n\n# load data\nos.environ['AUDIO_DIR']='../dat... | [
[
"sklearn.model_selection.train_test_split",
"sklearn.preprocessing.LabelBinarizer"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bwalker1/GAT | [
"fb3c6ca528ad773bfcfc22eb7e6ec6a5197d6e66"
] | [
"execute_cora_sparse.py"
] | [
"import time\nimport scipy.sparse as sp\nimport numpy as np\nimport tensorflow as tf\nimport argparse\n\nfrom gat import GAT\nfrom gat import SpGAT\nfrom utils import process\n\ncheckpt_file = 'pre_trained/cora/mod_cora.ckpt'\n\ndataset = 'cora'\n\n# training params\nbatch_size = 1\nnb_epochs = 100000\npatience = 1... | [
[
"tensorflow.Graph",
"tensorflow.sparse_placeholder",
"tensorflow.local_variables_initializer",
"numpy.min",
"tensorflow.reshape",
"tensorflow.placeholder",
"tensorflow.global_variables_initializer",
"numpy.max",
"tensorflow.name_scope",
"tensorflow.Session",
"tensorflow... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
gmelodie/adventofcode2019 | [
"4f18945b8b671590d6998fa7df8c373fbc641dbf"
] | [
"day11/part2/painting.py"
] | [
"import sys\nimport numpy as np\nfrom PIL import Image\nimport intcode\n\nDEBUG = False\n\ndef turn_right(direction):\n if direction == \"up\":\n return \"right\"\n elif direction == \"right\":\n return \"down\"\n elif direction == \"down\":\n return \"left\"\n elif direction == \"l... | [
[
"numpy.uint8",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MeetGandhi/MeetGandhi-Post-hoc-Explainability-of-Deep-Learning-Models-using-Conditional-Adversarial-Networks | [
"089226dce6d318247111ea60c2cc15c247b430d2"
] | [
"braXAI/mean_plots.py"
] | [
"import matplotlib.pyplot as plt\nfrom mpl_toolkits.axes_grid1 import make_axes_locatable\nimport numpy as np\nimport os\n\nx_train = np.load(\"x_train.npy\")\nx_test = np.load(\"x_test.npy\")\nx = np.concatenate((x_train, x_test))\n\ny_train = np.load(\"y_train.npy\")\ny_test = np.load(\"y_test.npy\")\ny = np.conc... | [
[
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.cla",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig",
"numpy.concatenate",
"numpy.add",
"matplotlib.pyplot.clf",
"matplotlib.pyplot.close",
"numpy.load",
"numpy.zeros",
"numpy.where",
"matplotlib.pyplot... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cppxaxa/PyTFObjectDetection | [
"6818f672b7d20d0de93b920b6ec29ddbca3d1d0a"
] | [
"classify_image.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport argparse\nimport os.path, os\nimport re\nimport sys\nimport tarfile\n\nimport query\n\nimport numpy as np\nfrom six.moves import urllib\nimport tensorflow as tf\n\nFLAGS = None\nDATA_URL = 'http... | [
[
"tensorflow.import_graph_def",
"tensorflow.gfile.Exists",
"tensorflow.gfile.GFile",
"numpy.squeeze",
"tensorflow.Session",
"tensorflow.GraphDef",
"tensorflow.logging.fatal",
"tensorflow.gfile.FastGFile",
"tensorflow.app.run"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
venkatrajun/qlikds | [
"2833c0d611f22da79f1008f2333da97dcd5da5ad"
] | [
"core/_spacy.py"
] | [
"import os\nimport re\nimport sys\nimport time\nimport string\nimport pathlib\nimport random\nimport warnings\nimport numpy as np\nimport pandas as pd\n\n# Suppress warnings\nif not sys.warnoptions:\n warnings.simplefilter(\"ignore\")\n\nimport spacy\nfrom spacy.util import minibatch, compounding, decaying\nfrom... | [
[
"sklearn.model_selection.train_test_split",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
escorciav/kaust-cluster-status | [
"c67f372c5484424dabe58bcafe8413ef784cdca0"
] | [
"cluster.py"
] | [
"import subprocess\n\nimport pandas as pd\n\n\ndef parse_slurm_line(x, strip=True, add_ngpu=True):\n \"\"\"Split info from slurm and (optionally) add GPU\n\n TODO:\n PR to get rid of this in favor of other SLURM command is welcome.\n \"\"\"\n ind_slice = slice(1, None)\n if strip:\n ind... | [
[
"pandas.Series",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
jklewis99/magical-movie-poster-processing | [
"88aefe4c446fd3d8366b527f59e20c04ac584fb4"
] | [
"generate_train_test.py"
] | [
"import pandas as pd\r\nfrom sklearn.model_selection import train_test_split\r\n\r\ndef main():\r\n metadata = pd.read_csv(\"data/movies-metadata-cleaned.csv\").drop(\r\n columns=['Language', 'Poster', 'Country', 'Director', 'Released', 'Writer', 'Genre', 'Actors'])\r\n ratings = pd.get_dummies(metadat... | [
[
"pandas.read_csv",
"sklearn.model_selection.train_test_split",
"pandas.get_dummies"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
ldqcarbon/RTK | [
"88df8ed953805aca3c5a73c22cb940164e7cc296"
] | [
"utilities/SimpleRTK/Examples/RTKFirstCudaReconstruction.py"
] | [
"#!/usr/bin/env python\nfrom __future__ import print_function\nimport SimpleRTK as srtk\nimport sys\nimport os\nimport matplotlib.pyplot as plt\nimport matplotlib.cm as cm\nimport time\n\nif len ( sys.argv ) < 3:\n print( \"Usage: RTKFirstReconstruction <outputimage> <outputgeometry>\" )\n sys.exit ( 1 )\n\n#... | [
[
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Waste-Wood/HGM-GIF | [
"969b4c213360a5e47369c0072f9fe20ded0c1570"
] | [
"module/PositionEmbedding.py"
] | [
"#!/usr/bin/python\r\n# -*- coding: utf-8 -*-\r\n#\r\n# Licensed under the Apache License, Version 2.0 (the \"License\");\r\n# you may not use this file except in compliance with the License.\r\n# You may obtain a copy of the License at\r\n#\r\n# http://www.apache.org/licenses/LICENSE-2.0\r\n#\r\n# Unless r... | [
[
"torch.FloatTensor",
"numpy.cos",
"numpy.power",
"numpy.sin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CoderTousif/python | [
"e96c6eebf6be89dc9f81660c8e8826efefb1ef3a"
] | [
"img/row.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Dec 10 18:27:48 2019\n\n@author: sandipan\n\"\"\"\n\nimport cv2\nimport numpy as np\nfrom matplotlib import pyplot as plt\nimport os\nimport glob\nk = 0\n\nnew = input(\"ENTER YOUR INPUT IMAGE DIRECTORY: \")\ninpath = os.getcwd() + new\nentrie... | [
[
"numpy.mean",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hellohwang/tf2.0-semantic-segmentation | [
"fe407376efc310ef5a3afb5787a04e34f420cdb8"
] | [
"tools/train1.py"
] | [
"import os\nimport sys\nimport random\nimport shutil\nimport cv2\nimport time\nimport math\nimport pprint\nimport numpy as np\nimport pandas as pd\nfrom tensorboardX import SummaryWriter\nfrom dataset import Dataset, BatchGenerator\nimport tensorflow as tf\n\nfrom utils.experiments import LabelSmoother\nfrom utils.... | [
[
"tensorflow.keras.metrics.BinaryAccuracy",
"tensorflow.keras.callbacks.ModelCheckpoint",
"numpy.random.seed",
"tensorflow.random.set_seed",
"tensorflow.cast",
"tensorflow.math.rsqrt",
"tensorflow.GradientTape",
"tensorflow.keras.optimizers.Adam",
"tensorflow.keras.metrics.Mean"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
choltz95/DREAMPlace | [
"fd3fec54bdafddb9c80909d3768ddbae9256c118"
] | [
"dreamplace/ops/dct/dct.py"
] | [
"##\n# @file dct.py\n# @author Yibo Lin\n# @date Jun 2018\n#\n\nimport os \nimport sys \nimport numpy as np\nimport torch\nfrom torch.autograd import Function\nfrom torch import nn\n\nimport dreamplace.ops.dct.dct_cpp as dct_cpp\ntry: \n import dreamplace.ops.dct.dct_cuda as dct_cuda\nexcept:\n pass \n\ni... | [
[
"torch.get_num_threads"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
moorea1/DS595CS525-RL-Projects | [
"96931df2ca61dc3ce33393cdcbaa20d414081ea7"
] | [
"Project2/Project2-2/td.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\nimport numpy as np\nimport random\nfrom collections import defaultdict\n#-------------------------------------------------------------------------\n'''\n Temporal Difference\n In this problem, you will implememnt an AI player for cliffwalking.\n The main go... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wenyudu/SDLM | [
"7446445e4e932bf9c645b3c8be1bf7068ad8af5b"
] | [
"ON_LSTM.py"
] | [
"import torch.nn.functional as F\nimport torch.nn as nn\nimport torch\nimport numpy\n\nfrom locked_dropout import LockedDropout\n\n\nclass LayerNorm(nn.Module):\n\n def __init__(self, features, eps=1e-6):\n super(LayerNorm, self).__init__()\n self.gamma = nn.Parameter(torch.ones(features))\n ... | [
[
"torch.sigmoid",
"torch.nn.functional.softmax",
"torch.ones",
"torch.Tensor",
"torch.zeros",
"torch.tanh",
"torch.nn.Linear",
"torch.stack",
"torch.cumsum",
"torch.nn.functional.linear"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Mozartich/triqler | [
"b958958342f6c78a218aefa605955ec3fb520e28"
] | [
"triqler/triqler.py"
] | [
"from __future__ import print_function\n\n\"\"\"triqler.triqler: provides entry point main().\"\"\"\n\n__version__ = \"0.4.0\"\n__copyright__ = '''Copyright (c) 2018-2020 Matthew The. All rights reserved.\nWritten by Matthew The (matthew.the@scilifelab.se) in the\nSchool of Engineering Sciences in Chemistry, Biotec... | [
[
"numpy.log",
"numpy.nonzero",
"numpy.isnan",
"numpy.random.shuffle",
"numpy.log10",
"numpy.prod",
"numpy.argsort",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
KevinLiao159/TalkingData | [
"c4a4095c0d805ef54b4f56d7b0e66f2883bb394a",
"c4a4095c0d805ef54b4f56d7b0e66f2883bb394a"
] | [
"insample_iterations/scripts/train_model.py",
"insample_iterations/scripts/dump_insample_data.py"
] | [
"import os\nimport psutil\nimport time\nimport gc\nimport numpy as np\nimport pandas as pd\nimport lightgbm\n# sklearn imports\nfrom sklearn.metrics.scorer import roc_auc_score\n\n# memory status\nprocess = psutil.Process(os.getpid())\nmemused = process.memory_info().rss\nprint('Total memory in use before reading d... | [
[
"numpy.round",
"pandas.read_hdf",
"sklearn.metrics.scorer.roc_auc_score"
],
[
"numpy.round",
"pandas.concat",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"nump... |
YJulyXing/SECNVs-SimulateCNVs-2.0- | [
"e1a4a7fe6ca4370e9fe3d7b92ecdf3ec5c55cbd4"
] | [
"snp_rate.py"
] | [
"#!/usr/bin/python\n\nimport random\nimport os\nimport subprocess\nimport math\nimport sys\nimport time\nimport copy\nfrom numpy.random import choice as choices\n\ndef switch_nt(nt):\n\tswitcher = {\n\t\t\"A\": list(choices([\"C\",\"T\",\"G\"],1,p=[0.14, 0.04, 0.82])),\n\t\t\"T\": list(choices([\"C\",\"A\",\"G\"],1... | [
[
"numpy.random.choice"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
alexandru-dinu/cae | [
"f0f60f768c78cf1d182c6778df6cad6d69712634"
] | [
"src/train.py"
] | [
"import os\nimport yaml\nimport argparse\nfrom pathlib import Path\n\nimport numpy as np\nimport torch as T\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.utils.data import DataLoader\nfrom torch.utils.tensorboard import SummaryWriter\n\nfrom data_loader import ImageFolder720p\nfrom utils import sa... | [
[
"torch.zeros",
"numpy.reshape",
"torch.cat",
"torch.utils.tensorboard.SummaryWriter",
"torch.cuda.is_available",
"numpy.transpose",
"torch.nn.MSELoss"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
java-abhinav07/formreader | [
"63aa695eb4b8547bc56c6f070dd8d1fadbdf29b1",
"63aa695eb4b8547bc56c6f070dd8d1fadbdf29b1"
] | [
"text_renderer/text_renderer/utils/draw_utils.py",
"text_renderer/main.py"
] | [
"from typing import Tuple, Union\n\nfrom PIL import ImageDraw, Image\nfrom PIL.Image import Image as PILImage\nimport numpy as np\n\nfrom text_renderer.utils.font_text import FontText\n\n\ndef transparent_img(size: Tuple[int, int]) -> PILImage:\n \"\"\"\n\n Args:\n size: (width, height)\n\n Returns:... | [
[
"numpy.random.uniform",
"numpy.array",
"numpy.random.randint",
"numpy.random.choice"
],
[
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DerThorsten/kipoi | [
"7110ea9f157c6996197bc75d5c2516d33a9e2861"
] | [
"tests/test_20_cli_examples.py"
] | [
"\"\"\"Run the example scripts\n\"\"\"\nimport os\nimport subprocess\nimport sys\n\nimport pandas as pd\nimport pytest\nimport yaml\n\nimport config\n# import filecmp\nimport kipoi\nimport kipoi_conda\nimport kipoi_utils\nfrom kipoi.env_db import EnvDbEntry\nfrom kipoi.readers import HDF5Reader\nfrom utils import c... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
wahjtran/transform_encoders | [
"855f1879b174bf0b25443dd35ca850055af6d287"
] | [
"intencoder.py"
] | [
"\n\n# Author: Wah (Nick) Tran\n# Status: Production\n\n\nimport numpy as np\nimport pandas as pd\n\nfrom sklearn.preprocessing import LabelEncoder\n\n\n\nclass IntegerEncoder(object):\n '''\n Transformer to encode multiple categorical features as integers.\n\n Applies sklearn's LabelEncoder to dtype:objec... | [
[
"sklearn.preprocessing.LabelEncoder"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Dootmaan/Point-Unet | [
"f5f9732702f991d277c1b006ca8164f76d295b22"
] | [
"train_saliencyAttentionNet.py"
] | [
"from model.SaliencyAttentionNet import SaliencyAttentionNet\n# from model.SaliencyAttentionNet import UNet\nfrom dataset.BraTSDataset3D import BraTSDataset3D\nfrom config import config\nimport torch as pt\nimport numpy as np\n# from loss.FALoss3D import FALoss3D\nimport cv2\nfrom medpy.metric.binary import jc, dc,... | [
[
"torch.utils.data.DataLoader",
"torch.optim.lr_scheduler.ExponentialLR",
"torch.nn.BCELoss",
"torch.no_grad",
"numpy.array",
"numpy.zeros",
"numpy.sum",
"torch.autograd.Variable"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
uvuvwewe/celp | [
"1da84d005fd0f0705704c6e555b821ce0cfa8ae1"
] | [
".~c9_invoke_whzHnd.py"
] | [
"from data import CITIES, BUSINESSES, USERS, REVIEWS, TIPS, CHECKINS\n\nimport random\nimport json\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\n\n# HELPERS\ndef create_similarity_matrix_categories(matrix):\n \"\"\"Create a \"\"\"\n npu = matrix.values\n m1 = npu @ npu.T\n diag ... | [
[
"numpy.diag",
"numpy.minimum",
"pandas.Series",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
pioy/incubator-mxnet | [
"9d432079f489327ec6daab61326a3e4f3c7cb8b3"
] | [
"benchmark/opperf/utils/profiler_utils.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.mean",
"numpy.percentile"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
uberman4740/AlgoRepo | [
"a0ef89f9ec2bb7de7d9377147f06013a682470e6"
] | [
"pykalman/plot_online.py"
] | [
"'''\r\n==============================================\r\nOnline State Estimation with the Kalman Filter\r\n==============================================\r\n\r\nThis example shows how :class:`KalmanFilter` can be used to estimate hidden\r\nstates in an online setting.\r\n\r\nWhile the Kalman Smoother is able to es... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
peteykun/R-Net | [
"b5525b98e0f8a3f41be2b7bb102ca24bff1ef3b8"
] | [
"util.py"
] | [
"import tensorflow as tf\nimport numpy as np\nimport re\nfrom collections import Counter\nimport string\n\n\ndef get_record_parser(config, is_test=False):\n def parse(example):\n para_limit = config.test_para_limit if is_test else config.para_limit\n ques_limit = config.test_ques_limit if is_test e... | [
[
"tensorflow.constant",
"tensorflow.FixedLenFeature",
"tensorflow.less",
"tensorflow.data.TFRecordDataset",
"tensorflow.decode_raw",
"tensorflow.less_equal",
"tensorflow.cast",
"numpy.iinfo",
"tensorflow.where",
"tensorflow.contrib.data.group_by_window"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
Delebrith/leaf-recognition | [
"c58ccd84dfcbff8be98aa1ea5797fa489ae8f73b"
] | [
"src/visualize_svm.py"
] | [
"from src.DfPerceptron import DfPerceptron\nfrom src.LeNet5 import LeNet5\nfrom matplotlib import pyplot as plt\nfrom sklearn.metrics import auc\n\nfrom src.file_utils import get_images_in_classes\nfrom src.svm import get_train_data, SVC\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics im... | [
[
"matplotlib.pyplot.legend",
"pandas.read_csv",
"sklearn.model_selection.train_test_split",
"sklearn.metrics.roc_curve",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.xlabel",
"sklearn.metrics.auc",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figu... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
sandroci/dedupe | [
"20736bb693aeffa0ac6fb1990485fb39f60c0d7b"
] | [
"dedupe/clustering.py"
] | [
"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nimport itertools\nfrom collections import defaultdict\nimport array\nimport logging\n\nimport numpy\nimport fastcluster\nimport hcluster\n\nfrom typing import (Iterable,\n Dict,\n ValuesView,\n cast,\n ... | [
[
"numpy.split",
"numpy.log",
"numpy.sqrt",
"numpy.nditer",
"numpy.min",
"numpy.unique",
"numpy.exp",
"numpy.roll"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zge/tacotron2-vae | [
"f0f5dc69bdbeb5331ee039afdbfdc1dea65ab033"
] | [
"data_utils.py"
] | [
"import numpy as np\nimport torch\nimport torch.utils.data\nimport os\n\nimport layers\nfrom utils import load_wav_to_torch, load_filepaths_and_text\n# for individual & batch level permuting\nfrom utils import permute_filelist, permute_batch_from_filelist\n# for pre-batching\nfrom utils import batching, get_batch_s... | [
[
"numpy.log2",
"torch.tensor",
"numpy.ceil",
"numpy.load",
"numpy.zeros",
"torch.squeeze",
"torch.autograd.Variable"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nagyation/Audioscope | [
"dbe86005bcd0bd58cb2cc9b28624dccc7b5d5867"
] | [
"signalplotter.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\n\nclass SignalPlotter:\n\n\n def __init__(self,timeStep,timeMax):\n \"\"\"\n timeStep : the step for time Axis from second\n timeMax : the max time in the time Axis in same scale\n \n \"\"\"\n self.time = []\n ... | [
[
"matplotlib.pyplot.plot",
"matplotlib.pyplot.gca",
"matplotlib.pyplot.ion",
"matplotlib.pyplot.pause"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
amorehead/alphafold_multimer_non_docker | [
"32f3b81fc4bbc2d8750da27158b42c2412fdfd3f"
] | [
"alphafold/model/common_modules.py"
] | [
"# Copyright 2021 DeepMind Technologies Limited\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applic... | [
[
"numpy.asarray",
"numpy.sqrt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cpuimage/MADGRAD | [
"eba15646230944a53a237e94fa92b4568bb7bf3b"
] | [
"MadGrad.py"
] | [
"\"\"\"Madgrad optimizer implementation.\"\"\"\n# pylint: disable=g-classes-have-attributes\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nfrom tensorflow.python.framework import ops\nfrom tensorflow.python.keras import backend_config\nfrom tensorf... | [
[
"tensorflow.python.ops.control_flow_ops.group",
"tensorflow.python.ops.state_ops.assign_add",
"tensorflow.python.ops.math_ops.pow",
"tensorflow.python.framework.ops.convert_to_tensor_v2",
"tensorflow.python.ops.array_ops.gather",
"tensorflow.python.ops.math_ops.sqrt",
"tensorflow.pytho... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.6",
"2.2",
"2.3",
"2.4",
"2.9",
"2.5",
"2.8",
"2.10"
]
}
] |
ydhongHIT/DDRNet | [
"f2f91b4053831fd54b04e30f60c9f1d4b55cd5b9",
"f2f91b4053831fd54b04e30f60c9f1d4b55cd5b9"
] | [
"classification/DDRNet_39.py",
"segmentation/DDRNet_23_slim_eval_speed.py"
] | [
"import math\r\nimport torch \r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\n\r\nBatchNorm2d = nn.BatchNorm2d\r\nbn_mom = 0.1\r\n\r\n\r\ndef conv3x3(in_planes, out_planes, stride=1):\r\n \"\"\"3x3 convolution with padding\"\"\"\r\n return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=... | [
[
"torch.nn.Sequential",
"torch.nn.init.constant_",
"torch.nn.Conv2d",
"torch.nn.Linear",
"torch.nn.AdaptiveAvgPool2d",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU",
"torch.nn.init.kaiming_normal_"
],
[
"torch.nn.Sequential",
"torch.cuda.synchronize",
"torch.load",
"to... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
BSRushwanth/cakechat | [
"9d5a572cf3f452e48139184c7b78f6b589bebf27"
] | [
"cakechat/dialogue_model/inference/candidates/sampling.py"
] | [
"import keras.backend as K\nimport numpy as np\n\nfrom cakechat.config import INTX\nfrom cakechat.dialog_model.inference.candidates.abstract_generator import AbstractCandidatesGenerator\nfrom cakechat.dialog_model.inference.service_tokens import ServiceTokensIDs\nfrom cakechat.dialog_model.inference.utils import ge... | [
[
"numpy.swapaxes",
"numpy.log",
"numpy.random.choice",
"numpy.full",
"numpy.all",
"numpy.copy",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
caljrobe/numba | [
"97c954100cb8bef8a563cec71e1f3751af3ecb08"
] | [
"numba/tests/test_dyn_array.py"
] | [
"import contextlib\nimport sys\nimport numpy as np\nimport random\nimport re\nimport threading\nimport gc\n\nfrom numba.core.errors import TypingError\nfrom numba import njit\nfrom numba.core import types, utils, config\nfrom numba.tests.support import MemoryLeakMixin, TestCase, tag\nimport unittest\n\n\nnrtjit = n... | [
[
"numpy.diag",
"numpy.complex128",
"numpy.linspace",
"numpy.vstack",
"numpy.dtype",
"numpy.concatenate",
"numpy.random.randint",
"numpy.complex64",
"numpy.hstack",
"numpy.testing.assert_equal",
"numpy.arange",
"numpy.empty_like",
"numpy.eye",
"numpy.int8",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Santidb/CS224U-Natural-Language-Understanding | [
"5fafc491ae46f6cb10bc5b0caaefcd98126ed78b"
] | [
"utils.py"
] | [
"from collections import Counter\r\nimport csv\r\nimport logging\r\nimport numpy as np\r\nimport pandas as pd\r\nimport random\r\nfrom scipy import stats\r\nfrom sklearn.base import TransformerMixin\r\nfrom sklearn.metrics import f1_score\r\nfrom sklearn.model_selection import GridSearchCV, StratifiedShuffleSplit\r... | [
[
"numpy.array",
"numpy.log",
"sklearn.model_selection.GridSearchCV",
"numpy.maximum",
"numpy.sum",
"numpy.random.seed",
"numpy.abs",
"torch.manual_seed",
"numpy.max",
"sklearn.metrics.f1_score",
"sklearn.model_selection.StratifiedShuffleSplit",
"numpy.where",
"sc... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
seibs/ibis-bigquery | [
"86594aa938d1dbfe80c4f7598085c49f4b817e69"
] | [
"tests/system/udf/test_udf_execute.py"
] | [
"import os\n\nimport ibis\nimport ibis.expr.datatypes as dt\nimport pandas as pd\nimport pandas.testing as tm\nimport pytest\nfrom pytest import param\n\nimport ibis_bigquery\nfrom ibis_bigquery import udf # noqa: E402\n\nPROJECT_ID = os.environ.get(\"GOOGLE_BIGQUERY_PROJECT_ID\", \"ibis-gbq\")\nDATASET_ID = \"tes... | [
[
"pandas.testing.assert_series_equal",
"pandas.Series"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
ghliu/10703_HW1 | [
"61e1180a97e601128a6ea37e693b359f2af8929e"
] | [
"build/lib.linux-x86_64-2.7/deeprl_hw1/rl.py"
] | [
"# coding: utf-8\nfrom __future__ import division, absolute_import\nfrom __future__ import print_function, unicode_literals\n\nimport numpy as np\n\n\ndef evaluate_policy(env, gamma, policy, max_iterations=int(1e3), tol=1e-3):\n \"\"\"Evaluate the value of a policy.\n\n See page 87 (pg 105 pdf) of the Sutton ... | [
[
"numpy.zeros",
"numpy.place"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ispc-lab/ResRace | [
"2661b42de40a5fc3e782a0865116d18c0b20c7d2"
] | [
"utils/functionswo.py"
] | [
"import math\nimport random\nimport numpy as np\nimport keyboard\nimport sys\nimport tty\nimport termios\nimport time\n'''\ndef lidar2binary(lidar_obs, map_size=[250, 250]):\n p_augment = random.uniform(0, 1)\n\n binary_map = np.ones(map_size)\n center = [125, 125]\n binary_map[center[0]-5:center[0]+6, ... | [
[
"numpy.concatenate",
"numpy.array",
"numpy.clip"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ibrahimsoliman97/TF_TRACK | [
"29218702a697900ac98e448873b21e24f9c93db4"
] | [
"research/object_detection/data_decoders/tf_example_decoder.py"
] | [
"# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.compat.v1.concat",
"tensorflow.compat.v1.equal",
"tensorflow.compat.v1.boolean_mask",
"tensorflow.compat.v1.shape",
"tensorflow.compat.v1.constant",
"tensorflow.compat.v1.logical_not",
"tensorflow.compat.v1.sparse_tensor_to_dense",
"tensorflow.compat.v1.math.equal",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jzstark/icfp-demo | [
"c37ee25eec729c292e1717697e76e717abf20a5f"
] | [
"mnist_train/tfgraph_train.py"
] | [
"#!/usr/bin/env python\n\nimport numpy as np\nimport os\nimport tensorflow as tf\nfrom google.protobuf import text_format\nfrom tensorflow.python.framework import graph_io\n\nos.environ['KMP_DUPLICATE_LIB_OK']='True'\n\nfrom tensorflow.examples.tutorials.mnist import input_data\nmnist = input_data.read_data_sets(\"... | [
[
"tensorflow.compat.v1.get_default_graph",
"tensorflow.compat.v1.ConfigProto",
"tensorflow.compat.v1.train.import_meta_graph",
"tensorflow.compat.v1.MetaGraphDef",
"tensorflow.Graph",
"numpy.reshape",
"tensorflow.cast",
"tensorflow.compat.v1.Session",
"tensorflow.global_variable... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
nicolasmota/movies-recommender | [
"ab6ffeaeed2e341cc6298ceffeae037056ba9ed0"
] | [
"movies_recommender/item_similiarity/main.py"
] | [
"import argparse\nimport json\nimport numpy as np\nimport pandas as pd\n\nfrom sklearn.metrics import pairwise_distances\n\n\nclass MoviesRecommender:\n\n def __init__(self, data_path):\n self.data_file = self._get_data_file(data_path)\n\n def _get_data_file(self, data_path):\n \"\"\" Return a j... | [
[
"sklearn.metrics.pairwise_distances",
"numpy.isnan",
"pandas.DataFrame",
"numpy.all",
"numpy.argsort",
"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": []
}
] |
pdn4kd/reimagined-palm-tree | [
"55791b0b647788f56f7eff5e8d5da4fa7ea2e7cf"
] | [
"rvrms_batch.py"
] | [
"'''\nUsing functionality of rvrms.py to get a bunch of RV precisions from existing observation logs. Also throws in some Gaussian noise.\n'''\nimport numpy as np\nimport rvrms\nfrom astropy import units as u\n\n#load up full simulation bits, will drop later\nimport simulation\nsim = simulation.simulation('simulati... | [
[
"numpy.sqrt",
"numpy.isnan",
"numpy.cos",
"numpy.genfromtxt",
"numpy.random.normal",
"numpy.exp",
"numpy.float"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Rashmeet09/Hierarchical-Actor-Critic-Pytorch | [
"90df3f356c828e08b7a7514abb934ec1b11a8521"
] | [
"src/DDPG.py"
] | [
"'''\nAuthor: Rashmeet Kaur Nayyar\nDeep Deterministic Policy Gradient (DDPG)\n'''\n\nimport torch\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n\n# Policy Network that maps states to actions\nclass Actor(torch.nn.Module):\n def __init__(self, state_dim, action_dim, action_max_bou... | [
[
"torch.nn.Sequential",
"torch.cat",
"torch.nn.Tanh",
"torch.nn.Sigmoid",
"torch.nn.Linear",
"torch.cuda.is_available",
"torch.nn.ReLU",
"torch.nn.MSELoss"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
samils7/BLG561E-Class-Incremental-Learning | [
"cb0e8d39eb0c469da46c7c550c19229927a2bec5"
] | [
"trainer/incremental_icarl.py"
] | [
"##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n## Created by: Yaoyao Liu\n## Modified from: https://github.com/hshustc/CVPR19_Incremental_Learning\n## Max Planck Institute for Informatics\n## yaoyao.liu@mpi-inf.mpg.de\n## Copyright (c) 2019\n##\n## This source code is licensed under t... | [
[
"torch.nn.CrossEntropyLoss",
"torch.nn.KLDivLoss",
"torch.nn.functional.log_softmax",
"torch.no_grad",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JiayiFu/onnx-tensorflow | [
"578dc6a2b6f7fea2bbcd396d387841a4949e6ef9"
] | [
"onnx_tf/backend.py"
] | [
"\"\"\"Backend for running ONNX on Tensorflow\n\nTo run this, you will need to have Tensorflow installed as well.\n\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\ntry:\n from itertools import izip as ... | [
[
"tensorflow.Graph",
"tensorflow.device",
"tensorflow.constant",
"tensorflow.global_variables_initializer",
"tensorflow.name_scope",
"tensorflow.Session"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
iynaur/pytorch-grad-cam | [
"ffec997bff40c7dba2b29005528c6a5893a11849"
] | [
"pytorch_grad_cam/base_cam.py"
] | [
"import cv2\nimport numpy as np\nimport torch\nimport ttach as tta\nfrom pytorch_grad_cam.activations_and_gradients import ActivationsAndGradients\nfrom pytorch_grad_cam.utils.svd_on_activations import get_2d_projection\n\n\nclass BaseCAM:\n def __init__(self, \n model, \n target_... | [
[
"numpy.maximum",
"numpy.min",
"torch.from_numpy",
"numpy.max",
"numpy.float32"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Sid-Bisram/mcb_pipeline | [
"d129603b1236279d7b09307b7420edc72f2d7555"
] | [
"question_3.py"
] | [
"import db\r\n\r\nimport pandas as pd\r\ndef func_step_3(report_year,country_name,export_type):\r\n with db.create_db_connection() as connection:\r\n cursor=connection.cursor()\r\n cursor.callproc('Report_3',(report_year,country_name))\r\n answer=cursor.fetchall()\r\n\r\n ans_dataframe=pd... | [
[
"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": []
}
] |
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