repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
EnSpec/specdal.github.io | [
"4a89f5de6d8feb9472813da9767eafb78c0fe19a"
] | [
"specdal/containers/spectrum.py"
] | [
"# spectrum.py provides class for representing a single\n# spectrum. Spectrum class is essentially a wrapper around\n# pandas.Series.\nimport pandas as pd\nimport numpy as np\nimport specdal.operators as op\nfrom collections import OrderedDict\nfrom specdal.readers import read\nimport logging\nimport os\n\nlogging.... | [
[
"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": []
}
] |
MoritzSchueler96/stable-baselines | [
"834f88c636d80475b0c07aa1d26e2e2529280221"
] | [
"stable_baselines/common/input.py"
] | [
"import numpy as np\nimport tensorflow as tf\nfrom gym.spaces import Discrete, Box, MultiBinary, MultiDiscrete\n\n\ndef observation_input(ob_space, batch_size=None, name=\"Ob\", scale=False):\n \"\"\"\n Build observation input with encoding depending on the observation space type\n\n When using Box ob_spac... | [
[
"tensorflow.cast",
"tensorflow.placeholder",
"tensorflow.compat.v1.placeholder",
"tensorflow.one_hot",
"numpy.any",
"numpy.isinf"
]
] | [
{
"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... |
christinoleo/fastapi_crud_orm_connector | [
"e0698c09ae936535b2fe5961de17ae846e9768e0"
] | [
"fastapi_crud_orm_connector/orm/pandas_crud.py"
] | [
"from typing import Dict, List, Union, Type, Optional\n\nimport pandas as pd\nfrom fastapi import HTTPException\nfrom pydantic import BaseModel\nfrom starlette import status\n\nfrom fastapi_crud_orm_connector.orm.crud import Crud, GetAllResponse, DataSort, DataSortType, DataGroupBy, MathOperation\nfrom fastapi_crud... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
mrunalimanj/EVcouplings | [
"41e88ce057bc361eb27a938f069476a25aef94c6"
] | [
"evcouplings/utils/pipeline.py"
] | [
"\"\"\"\nPipelining of different stages of method\n\nAuthors:\n Thomas A. Hopf\n\"\"\"\n\n# chose backend for command-line usage\nimport matplotlib\nmatplotlib.use(\"Agg\")\n\nfrom copy import deepcopy\nimport signal\nimport os\nfrom os import path\nimport sys\nimport traceback\nimport tarfile\nimport zipfile\n\ni... | [
[
"matplotlib.use"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mattiavarile/SVRnet | [
"ff9d3ab87be915631f5ab3690f9b7902cb711fe6"
] | [
"generate_data/generate_fetal.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport glob\nimport imageio\nimport numpy as np\nimport SimpleITK as sitk\nimport transformations as T\nimport tensorflow as tf\n\nimport sys\nsys.path.append('/vol/medic01/users/bh1511/_bui... | [
[
"numpy.linalg.inv",
"numpy.random.randint",
"numpy.random.rand",
"tensorflow.train.FloatList",
"tensorflow.train.BytesList",
"numpy.array",
"tensorflow.train.Int64List"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lorenzwalthert/GPBoost | [
"81357054727b146bb3a5970451dbf6bf5e191e79"
] | [
"examples/python-guide/boosting.py"
] | [
"# coding: utf-8\n# pylint: disable = invalid-name, C0111\nimport gpboost as gpb\nimport numpy as np\nfrom sklearn.metrics import mean_squared_error\nimport matplotlib.pyplot as plt\nplt.style.use('ggplot')\nprint(\"It is recommended that the examples are run in interactive mode\")\n\nprint('Simulating data...')\n#... | [
[
"matplotlib.pyplot.legend",
"numpy.linspace",
"matplotlib.pyplot.title",
"sklearn.metrics.mean_squared_error",
"matplotlib.pyplot.plot",
"numpy.random.normal",
"numpy.mean",
"numpy.random.rand",
"numpy.exp",
"matplotlib.pyplot.show",
"numpy.zeros",
"matplotlib.pyplo... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
microsoft/mi-module-zoo | [
"eccb7c3f1852155922810423abacb6786f588e12",
"eccb7c3f1852155922810423abacb6786f588e12"
] | [
"mi_module_zoo/relationalmultiheadattention.py",
"tests/test_relationalmultiheadattention.py"
] | [
"import math\nimport torch\nfrom torch import nn\nfrom typing import Optional\n\n\nclass RelationalMultiheadAttention(nn.Module):\n \"\"\"\n A relational multihead implementation supporting two variations of using additional\n relationship (sparse) information between input elements:\n\n * Sparse relati... | [
[
"torch.nn.Dropout",
"torch.softmax",
"torch.einsum",
"torch.nn.Embedding",
"torch.nn.Linear"
],
[
"torch.stack",
"torch.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Clej/ncvx-sparse | [
"457a1a1e7c4422e1e7cd761d46c08055108d5c14"
] | [
"setup.py"
] | [
"#! /usr/bin/env python\n\nimport codecs\nimport os\n\nfrom setuptools.command.build_ext import build_ext\nfrom setuptools import Extension\nfrom setuptools import find_packages, setup\nfrom setuptools import dist\ndist.Distribution().fetch_build_eggs(['numpy>=1.12'])\nimport numpy as np\n\n# get __version__ from _... | [
[
"numpy.get_include"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
harshjp93/AI_Feynman_edit | [
"f98cae516bff8d78e59f7b9673eec6ddce29e6f0"
] | [
"feynman/S_run_bf_polyfit.py"
] | [
"# add a function to compte complexity\r\n\r\nfrom get_pareto import Point, ParetoSet\r\nfrom RPN_to_pytorch import RPN_to_pytorch\r\nfrom RPN_to_eq import RPN_to_eq\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom S_brute_force import brute_force\r\nfrom S_get_number_DL_snapped import get_number_DL... | [
[
"numpy.isnan",
"numpy.log2",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jamesscottbrown/parametric-sbolv-1 | [
"5638c2a30fc9bf14a5616cb18ba7dc3473b7e30f"
] | [
"gallery/00_glyph_sampler/00_glyph_sampler.py"
] | [
"\"\"\"\r\nDisplays all parametric SBOL Visual glyphs within the glyphs directory.\r\n\"\"\"\r\nimport parasbolv as psv\r\nimport matplotlib.pyplot as plt\r\n\r\nrenderer = psv.GlyphRenderer()\r\n\r\nparts = list(renderer.glyphs_library.keys())\r\n\r\n# Identify glyphs that aren't drawn on a backbone\r\nno_baseline... | [
[
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplots"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cbushomitre/coco | [
"905928b04e0b4f17cd41b58209c51c95000d5240"
] | [
"pycocotools/coco.py"
] | [
"__author__ = 'tylin'\n__version__ = '2.0'\n# Interface for accessing the Microsoft COCO dataset.\n\n# Microsoft COCO is a large image dataset designed for object detection,\n# segmentation, and caption generation. pycocotools is a Python API that\n# assists in loading, parsing and visualizing the annotations in CO... | [
[
"matplotlib.pyplot.gca",
"matplotlib.collections.PatchCollection",
"numpy.random.random",
"numpy.min",
"numpy.dstack",
"numpy.ones",
"matplotlib.pyplot.plot",
"numpy.all",
"numpy.max",
"numpy.array",
"matplotlib.patches.Polygon"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
e-dorigatti/torchinfo | [
"9fa0e677fb7002e89afd5b1bb372fe8c1dd813d6"
] | [
"fixtures/models.py"
] | [
"\"\"\" fixtures/models.py \"\"\"\nimport math\nfrom collections import namedtuple\nfrom typing import Any, Dict, Tuple, cast\n\nimport torch\nimport torch.nn as nn\nfrom torch.nn import functional as F\nfrom torch.nn.utils.rnn import pack_padded_sequence\n\n\nclass Identity(nn.Module):\n \"\"\" Model with a ver... | [
[
"torch.abs",
"torch.nn.Dropout",
"torch.nn.Dropout2d",
"torch.ones",
"torch.nn.functional.log_softmax",
"torch.cat",
"torch.nn.LSTM",
"torch.zeros",
"torch.nn.ModuleList",
"torch.nn.Conv2d",
"torch.nn.Embedding",
"torch.nn.utils.rnn.pack_padded_sequence",
"torch... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
grizli-project/grizli-aws | [
"71b80702c82f18b900c2a211fcf13893f6813476"
] | [
"scripts/grism_run_single.py"
] | [
"#!/usr/bin/env python\n\ndef redo_query(root='j023507-040202', instruments=['WFC3/IR','WFC3/UVIS','ACS/WFC'], filters=[], proposal_id=[]):\n \"\"\"\n Redo the MAST query based on the parent table to look for new data\n \"\"\"\n from astropy.io.misc import yaml \n from mastquery import query, overlap... | [
[
"matplotlib.pyplot.ioff",
"numpy.unique"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
alfonsoeromero/S2F | [
"fccb741b15acfdeb02ca0de411eb4b00ae73be85"
] | [
"GOTool/GOA_selection.py"
] | [
"import os\n\nimport pandas as pd\n\nfrom GeneOntology import GeneOntology\n\n__author__ = 'Mateo Torres'\n__email__ = 'Mateo.Torres.2015@live.rhul.ac.uk'\n__copyright__ = 'Copyright (c) 2020, Mateo Torres'\n__license__ = 'MIT'\n__version__ = '0.1'\n\ngo = GeneOntology('example data/go.obo')\ngo.build_structure()\n... | [
[
"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": []
}
] |
ChrisRBXiong/MatchZoo-py | [
"8883d0933a62610d71fec0215dce643630e03b1c"
] | [
"matchzoo/models/arci.py"
] | [
"\"\"\"An implementation of ArcI Model.\"\"\"\nimport typing\n\nimport torch\nimport torch.nn as nn\n\nfrom matchzoo.engine.param_table import ParamTable\nfrom matchzoo.engine.param import Param\nfrom matchzoo.engine.base_model import BaseModel\nfrom matchzoo.engine import hyper_spaces\nfrom matchzoo.dataloader imp... | [
[
"torch.nn.Sequential",
"torch.nn.Dropout",
"torch.nn.ConstantPad1d",
"torch.cat",
"torch.nn.MaxPool1d",
"torch.nn.Conv1d",
"torch.flatten"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sanghack81/SDCIT | [
"74af42d84b4013004049b3715fe5432bd35269f7"
] | [
"sdcit/tests/test_reproducibility.py"
] | [
"import numpy as np\n\nfrom sdcit.hsic import c_HSIC, HSIC\nfrom sdcit.kcit import python_kcit, python_kcit_K\nfrom sdcit.sdcit_mod import SDCIT, c_SDCIT, shuffling\nfrom sdcit.synthetic_data import henon\nfrom sdcit.utils import rbf_kernel_median\n\n\ndef test_hsics():\n np.random.seed(0)\n\n X = np.random.r... | [
[
"numpy.arange",
"numpy.random.randn",
"numpy.allclose",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
PaParaZz1/PointNet | [
"82695f209605411bcb5559201d710254dab33f1b"
] | [
"modules.py"
] | [
"import torch.nn as nn\nfrom torch.nn.init import xavier_normal, kaiming_normal\n\ndef WeightInitialize(init_type, weight, activation = None):\n\tif init_type is None:\n\t\treturn\n\tdef XavierInit(weight, activation):\n\t\txavier_normal(weight)\n\tdef KaimingInit(weight, activation):\n\t\tassert not activation is ... | [
[
"torch.nn.init.kaiming_normal",
"torch.nn.Sequential",
"torch.nn.BatchNorm1d",
"torch.nn.ConvTranspose2d",
"torch.nn.BatchNorm2d",
"torch.nn.Conv1d",
"torch.nn.ReLU",
"torch.nn.init.xavier_normal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Ericccccc1022/yolo3-pytorch | [
"1995e80464a5a7b51734a51154f910ea200882cb"
] | [
"Inference_realsense.py"
] | [
"# Author: Eric Tan\n# Creation Date: 2021/08/26\n# ---------------------------\n# Author: Eric Tan\n# Creation Date: 2021/08/26\n# ---------------------------\n# 对视频中的predict.py进行了修改，\n# 将单张图片预测、摄像头检测和FPS测试功能\n# 整合到了一个py文件中，通过指定mode进行模式的修改。\n\nimport time\nimport cv2\nimport numpy as np\nfrom PIL import Imag... | [
[
"numpy.uint8"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LIANGKE23/Siamese-FC-KF-CF | [
"bef9f21a09f2a4f30a9f4c3faeb52e0019c32cdf"
] | [
"test_and_evaluation/Evaluation_test/drawfigures_GOT-10k.py"
] | [
"from __future__ import absolute_import, division, print_function\r\n\r\nimport os\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport matplotlib\r\nimport json\r\nimport sys\r\n\r\ndef plot_curves(report_dir,report_files1,report_files2, extension='.png'):\r\n\r\n with open(report_file1) as f:\r\n... | [
[
"numpy.argsort",
"matplotlib.rcParams.update",
"matplotlib.pyplot.subplots",
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
admariner/ibis | [
"085722b281cad750012a17eabc8ad6e435080fd5"
] | [
"ibis/tests/sql/test_compiler.py"
] | [
"import datetime\nimport textwrap\nimport unittest\n\nimport pytest\n\nimport ibis\nimport ibis.expr.api as api\nimport ibis.expr.operations as ops\nfrom ibis.backends.base.sql.compiler import Compiler, QueryContext\nfrom ibis.tests.expr.mocks import MockBackend\n\npytest.importorskip('sqlalchemy')\n\n\nclass TestA... | [
[
"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": []
}
] |
cgkanchi/pandas | [
"0a2ea0ac39a74771d54b91522728f6092b0ef897"
] | [
"pandas/io/data.py"
] | [
"\"\"\"\nModule contains tools for collecting data from various remote sources\n\n\n\"\"\"\nimport warnings\nimport tempfile\nimport datetime as dt\nimport time\n\nfrom collections import defaultdict\n\nimport numpy as np\n\nfrom pandas.compat import(\n StringIO, bytes_to_str, range, lmap, zip\n)\nimport pandas.... | [
[
"pandas.core.common.is_list_like",
"pandas.Series",
"pandas.DataFrame",
"pandas.io.html.read_html",
"numpy.where",
"pandas.core.datetools.to_datetime",
"pandas.io.common.urlopen",
"pandas.read_csv",
"pandas.Panel",
"pandas.DatetimeIndex",
"pandas.compat.lmap",
"pand... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TatsuyaShirakawa/torch2ort | [
"6e9e4708bb5be2e11f4db5fd503df7a0efafcc17"
] | [
"ortmodel.py"
] | [
"import numpy as np\nimport torch\nimport onnxruntime as ort\n\n\ndef _tuple(x):\n if isinstance(x, (list, tuple)):\n return tuple(x)\n return (x, )\n\n\ndef to_numpy(input):\n if isinstance(input, torch.Tensor):\n return input.to('cpu').numpy()\n return np.asarray(input)\n\n\nclass ORTMod... | [
[
"numpy.asarray"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ideo/ideo-academy-survey | [
"82042e0783d991a68c0ad264ab4985c09651ba05"
] | [
"preprocess_crunchbase.py"
] | [
"import ipdb\nimport pathlib\nimport pandas as pd \n\nfrom settings import (CRUNCHBASE_2015_PATH, CRUNCHBASE_2020_PATH)\nfrom collections import Counter\n\n# maybe belongs in settings.py\nCSV_NAMES_2015 = [\"acquisitions\",\n \"additions\",\n \"companies\",\n \"investments\",\n \"rounds\"\n]\nTSV_LABELS... | [
[
"pandas.read_table",
"pandas.concat",
"pandas.isnull"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
yancz1989/tunas | [
"ea3a4a56295a3e8d0d3fbb4d7ec3c0a78500897d"
] | [
"tunas/arch/theano_arch/tensor.py"
] | [
"# -*- coding: utf-8 -*-\n# @Author: yancz1989\n# @Date: 2016-06-19 10:52:49\n# @Last Modified by: yancz1989\n# @Last Modified time: 2016-12-04 17:03:06\nfrom __future__ import absolute_import, print_function, division\n\nimport numpy as np\nimport tunas.arch.env as env\nimport tunas.core.interfaces\n\nimport t... | [
[
"numpy.array",
"numpy.zeros",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
emtpb/pyfds | [
"c6a7f2157dc8ae20fced6bd861eab8ab4a27a1c7"
] | [
"pyfds/electrostatics.py"
] | [
"import logging as lo\nimport numpy as np\nimport scipy.sparse as sp\nimport scipy.sparse.linalg as sl\nimport warnings as wn\nfrom . import fields as fld\n\nlogger = lo.getLogger('pyfds')\n\n\nclass Electrostatic1D(fld.Field1D):\n \"\"\"Class for simulation of one-dimensional electrostatic fields.\"\"\"\n\n ... | [
[
"scipy.sparse.linalg.inv"
]
] | [
{
"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"
... |
herjy/StackClub | [
"a08df1992e5100fdc01d88f3f5a979e5b96841c3"
] | [
"Basics/dm_butler_skymap.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n\n# # Plotting the DC2 Run1.1p skyMap\n# <br>Owner: **Jim Chiang** ([@jchiang87](https://github.com/LSSTDESC/DC2-analysis/issues/new?body=@jchiang87))\n# <br>Last Verified to Run: **2018-10-26** (by @yymao)\n# \n# In this notebook, we show how to use the data butler to obtai... | [
[
"numpy.degrees",
"matplotlib.path.Path",
"numpy.array",
"matplotlib.patches.PathPatch",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cannsudemir/amanzi | [
"c6cd3287bdc2c6cf26c6f8b79e34799751385f9e"
] | [
"test_suites/benchmarking/chemistry/tritium_1d/tritium_1d_ms.py"
] | [
"# plots calcium concentration along x at last time step \n# benchmark: compares to pflotran simulation results\n# author: S.Molins - Sept. 2013\n\nimport os\nimport sys\nimport h5py\nimport numpy as np\nimport matplotlib\n#matplotlib.use('Agg')\nfrom matplotlib import pyplot as plt\n\ntry:\n sys.path.append('..... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.title",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.axes",
"matplotlib.pyplot.subplots_adjust",
"matplotlib.pyplot.tick_params"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
thangvubk/Cascade-RPN | [
"c832973b8d849acbe4c0ebf75f353c7f48ec457b"
] | [
"mmdet/models/anchor_heads/anchor_head.py"
] | [
"from __future__ import division\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom mmcv.cnn import normal_init\n\nfrom mmdet.core import (AnchorGenerator, anchor_target, delta2bbox, force_fp32,\n multi_apply, multiclass_nms)\nfrom ..builder import build_loss\nfrom ..registry im... | [
[
"numpy.ceil",
"torch.nn.Conv2d",
"torch.cat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Holy-Shine/carLogoRec | [
"f63b5db514788a97fc2ecd762496e2414814d7f5"
] | [
"CNN_Pytorch/cnn_lenet5.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.data import DataLoader,Dataset\nimport torch.optim as optim\nimport numpy as np\nimport pickle as pkl\nimport h5py\n\n\ndataset = h5py.File('./carDatasets.h5', 'r')\n\nclass LeNet5(nn.Module):\n def __init__(self,n_classes=5)... | [
[
"torch.nn.NLLLoss",
"torch.load",
"numpy.asarray",
"torch.nn.Conv2d",
"torch.utils.data.DataLoader",
"torch.sum",
"torch.nn.Linear",
"torch.nn.MaxPool2d",
"torch.nn.functional.relu",
"torch.no_grad",
"torch.FloatTensor",
"numpy.array",
"torch.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mcuntz/jams_python | [
"41b4504d2f55a77a7876fc6d146e4eb91dd8b2b9"
] | [
"jams/cuntz_gleixner.py"
] | [
"#!/usr/bin/env python\nfrom __future__ import division, absolute_import, print_function\nimport numpy as np\nimport jams.const as const\n\n\n__all__ = ['cuntz_gleixner']\n\n\ndef cuntz_gleixner(idecdate, iGPP, iRd, iCa, iRa, igtot, sunrise, Vcyt=None,\n date0=False,\n V0starch=c... | [
[
"numpy.fmod",
"numpy.amax",
"numpy.abs",
"numpy.amin",
"numpy.rint",
"numpy.ones",
"numpy.ndim",
"numpy.size",
"numpy.any",
"numpy.floor",
"numpy.exp",
"numpy.array",
"numpy.where",
"numpy.roll",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kans-ky/openpilot | [
"f65aca0e4ed42380d8f9618406beedce1861f0ff"
] | [
"selfdrive/controls/controlsd.py"
] | [
"#!/usr/bin/env python3\nimport os\nimport math\n\nimport numpy as np\nfrom numbers import Number\n\nfrom cereal import car, log\nfrom common.numpy_fast import clip, interp\nfrom common.realtime import sec_since_boot, config_realtime_process, Priority, Ratekeeper, DT_CTRL\nfrom common.profiler import Profiler\nfrom... | [
[
"numpy.isnan",
"numpy.mean",
"numpy.abs",
"numpy.gradient"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ToFeWe/q-learning-replication-code | [
"6fcfef0fe85529c4fdec83e5fd0e1797a99b052f"
] | [
"src/final/plot_deviation.py"
] | [
"\"\"\"\n\nA module to plot the deviation simulation\nresults.\n\"\"\"\nimport json\nimport pickle\nimport sys\n\nimport matplotlib\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom bld.project_paths import project_paths_join as ppj\n\n\n\n\ndef make_plot(devation_array, paramet... | [
[
"matplotlib.pyplot.gca",
"numpy.arange",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.rcParams.update",
"numpy.array",
"matplotlib.pyplot.style.use",
"matplotlib.rc"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
freedombenLiu/adanet | [
"97538cef6ae7968e2a6e5f9bbb78b63da4833268"
] | [
"adanet/core/candidate.py"
] | [
"\"\"\"The AdaNet candidate implementation in Tensorflow using a single graph.\n\nCopyright 2018 The AdaNet Authors. All Rights Reserved.\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\... | [
[
"tensorflow.get_variable",
"tensorflow.constant",
"tensorflow.python.training.moving_averages.assign_moving_average",
"tensorflow.control_dependencies",
"tensorflow.less",
"tensorflow.variable_scope"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
MarekUlip/TextAnalysis | [
"ec31e239c459c22d29918346a2e46a73cc74ac63"
] | [
"text_generators/text_generator.py"
] | [
"from neural_networks.aliaser import Sequence, Tokenizer, to_categorical\nimport numpy as np\nfrom dataset_loader.dataset_helper import Dataset_Helper\n\nclass TextGenerator(Sequence):\n def __init__(self, filename, batch_size, num_of_texts, num_of_words, tokenizer: Tokenizer, delimeter,\n datase... | [
[
"numpy.ceil",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sukritiverma1996/diverse-image-captioning | [
"70d659359b41fa2cd3ed2eaf8b5b214f1ad19330"
] | [
"models/controllable_captioning.py"
] | [
"from __future__ import division\nfrom __future__ import absolute_import\nimport torch\nfrom torch import nn\nfrom torch import distributions\nimport torch.nn.functional as F\nfrom models import _CaptioningModel\n\n\nclass ControllableCaptioningModel(_CaptioningModel):\n def __init__(self, seq_len, vocab_size, b... | [
[
"torch.nn.functional.softmax",
"torch.nn.functional.log_softmax",
"torch.cat",
"torch.nn.init.constant_",
"torch.zeros",
"torch.nn.init.xavier_normal_",
"torch.sum",
"torch.nn.Embedding",
"torch.nn.LSTMCell",
"torch.nn.Linear",
"torch.tanh",
"torch.nn.init.orthogona... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Dragonblf/artificial_manga_panel_dataset | [
"c2cf568caf95e7f3d657684b2c2e636938ae8d70",
"c2cf568caf95e7f3d657684b2c2e636938ae8d70"
] | [
"preprocesing/layout_engine/pages_segmenter.py",
"preprocesing/layout_engine/pages_annotator.py"
] | [
"\nimport cv2\nimport paths\nimport numpy as np\nfrom scipy import ndimage\n\nfrom tqdm import tqdm\nfrom .. import config_file as cfg\nfrom ..convert_images import find_contours\nfrom . import pages_annotator as annotator\nfrom ..multiprocessing import open_pool\n\n\ndef move_contours(contours, xy: list):\n new... | [
[
"numpy.array",
"numpy.zeros",
"scipy.ndimage.rotate"
],
[
"numpy.squeeze",
"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"... |
chardch/models | [
"c40b46ff63d1af2d32e6457dcb4a70d157648db2"
] | [
"official/resnet/resnet_run_loop.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.cond",
"tensorflow.cast",
"tensorflow.map_fn",
"tensorflow.estimator.RunConfig",
"tensorflow.group",
"tensorflow.estimator.export.PredictOutput",
"tensorflow.compat.v1.summary.image",
"tensorflow.compat.v1.trainable_variables",
"tensorflow.compat.v1.train.piecewise_... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nkennek/pytorch-cnn-visualizations | [
"54699710b1beae1edae4bc12e9403080191c40ed"
] | [
"cnn_visualization/gradcam/guided_gradcam.py"
] | [
"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\n\nfrom cnn_visualization.backprop.guidedbp import GuidedBackProp\nfrom .gradcam import CamExtractor, GradCam\n\n\nclass GuidedGradCam(GradCam):\n \"\"\"\n Guided Gradcam is just pointwise multiplic... | [
[
"numpy.multiply"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MuellerDominik/AIonFPGA | [
"f2379782660d4053a5bb60b9f6c6dea17363f96d"
] | [
"doc/presentation/graphics/scripts/weighting_function.py"
] | [
"#!/usr/bin/env python3\n\n'''aionfpga ~ weighting function\nCopyright (C) 2020 Dominik Müller and Nico Canzani\n'''\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nend = 44\n\n# adapted from https://stackoverflow.com/questions/33737736/matplotlib-axis-arrow-tip\ndef arrowed_spines(fig, ax):\n\n xmin, ... | [
[
"numpy.linspace",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig",
"numpy.sin",
"matplotlib.pyplot.rc_context",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
peterk87/nf-iav-illumina | [
"b21b3df5ad45ea1f3f140baa93caa137ef7ffc93"
] | [
"bin/check_sample_sheet.py"
] | [
"#!/usr/bin/env python\n\nfrom pathlib import Path\n\nimport pandas as pd\nimport typer\nfrom rich.console import Console\nfrom rich.logging import RichHandler\nimport logging\n\n\ndef check_sample_names(df: pd.DataFrame) -> None:\n have_whitespace = df['sample'].str.contains(r'\\s', regex=True)\n n_samples_w... | [
[
"pandas.read_excel",
"pandas.read_table",
"pandas.read_csv",
"pandas.isnull"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
ronan-keane/hav-sim | [
"0aaf9674e987822ff2dc90c74613d5e68e8ef0ce"
] | [
"scripts/2018-2019/2018 AY papers/useful misc/dothecalibration.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Aug 10 01:44:13 2018\nTesting and debugging functions.\nI also use this script to benchmark the speed and accuracy of the adjoint method.\nThere are also examples of how to setup the optimization problem and call different algorithms. However these examples are prett... | [
[
"scipy.optimize.differential_evolution",
"numpy.linalg.norm",
"numpy.tile",
"matplotlib.pyplot.plot",
"numpy.divide"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"1.0",
"0.17",
"1.3",
"1.8"
],
"tensorflow": []
}
] |
MaksimEkin/pyDNMFk | [
"3fbe327c32cb6fc0b81697bd5c37d50ae39d48aa"
] | [
"tests/test_dist_utils.py"
] | [
"import sys\nimport os\n\nos.environ[\"OMP_NUM_THREADS\"] = \"1\"\nfrom pyDNMFk.utils import *\nfrom mpi4py import MPI\nfrom scipy.io import loadmat\nimport pytest\nfrom pyDNMFk.dist_comm import *\n\n\n@pytest.mark.mpi\ndef test_dist_prune_unprune_1d():\n comm = MPI.COMM_WORLD\n\n\n for grid in ([1,2],[2,1]):... | [
[
"scipy.io.loadmat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.14",
"0.19",
"1.5",
"0.12",
"0.17",
"0.13",
"1.6",
"1.4",
"1.9",
"1.3",
"1.10",
"0.15",
"0.18",
"0.16"... |
mjyoo2/stable-baselines3 | [
"ef7a580219df6d977b56fb99e503890bd5211195"
] | [
"stable_baselines3/iqn/iqn.py"
] | [
"from typing import Any, Dict, List, Optional, Tuple, Type, Union\n\nimport gym\nimport numpy as np\nimport torch as th\nfrom torch.nn import functional as F\n\nfrom stable_baselines3.common.buffers import ReplayBuffer\nfrom stable_baselines3.common.off_policy_algorithm import OffPolicyAlgorithm\nfrom stable_baseli... | [
[
"torch.no_grad",
"numpy.random.rand",
"numpy.mean",
"torch.arange",
"torch.sort"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nasim-aust/faceRecognition-using-faceNet-and-Flask-Framework | [
"6a9e42ad507ef47c6f8c30962266524bc2d5b21d"
] | [
"app/process.py"
] | [
"import sys\nimport dlib\nimport numpy as np\nimport scipy.misc\nimport imageio \nimport os\nimport cv2\nimport csv\nfrom PIL import Image\nimport simplejson as json\nimport numpy as np\nimport ast\n\nfrom os.path import join, dirname, realpath\n\n#test_img = sys.argv[1]\ntrain_image_path = ''\ntrain_image_name = '... | [
[
"numpy.square",
"numpy.array",
"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": [],
... |
ngglasgow/biophys_glm_show | [
"6554fa395974d95ba78fd5cbe2cf0272e7d69df4"
] | [
"archive/plots_old_summed_slopes.py"
] | [
"import os\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.cm as cm\nfrom matplotlib.colors import Normalize\nimport set_paths\nimport conversions\nimport seaborn as sns\nfrom matplotlib.ticker import MultipleLocator\n\n# set paths\nhome_dir = os.path.expanduser(\"~\")\n... | [
[
"matplotlib.ticker.MultipleLocator",
"numpy.ones_like",
"numpy.arange",
"matplotlib.pyplot.subplots",
"matplotlib.colors.Normalize",
"numpy.zeros_like",
"matplotlib.cm.ScalarMappable",
"numpy.meshgrid"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dbrcina/NENR-FER-2020-21 | [
"80146440487f508c90c916903ca9f86b62f2095b"
] | [
"dz6/generate.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\ndef zadatak3():\n\tx = np.outer(np.arange(-4, 5, 1), np.ones(9))\n\tprint(x)\n\ty = x.copy().T\n\tz = ((x-1)**2 + (y+2)**2 - 5*x*y + 3) * np.power(np.cos(x/5), 2)\n\tax = plt.axes(projection='3d', xlabel='x', ylabel='y', title='Generirajuć... | [
[
"matplotlib.pyplot.legend",
"pandas.read_csv",
"matplotlib.pyplot.title",
"numpy.arange",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.yscale",
"numpy.cos",
"matplotlib.pyplot.get_cmap",
"numpy.ones",
"matplotlib.pyplot.axes",
"numpy.exp",
"matplotlib.pyplot.xlabel"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
cassiePython/HashResnet1D | [
"0b562fc1bd3178621ac4e07b8b0f904b920fe9bc"
] | [
"hashresnet1d.py"
] | [
"from resnet1d import ResNet1D\r\nimport torch\r\nimport torch.nn as nn\r\nimport math\r\n\r\nclass HashResNet1D(ResNet1D):\r\n \"\"\"\r\n \r\n Input:\r\n X: (n_samples, n_channel, n_length)\r\n Y: (n_samples)\r\n \r\n Output:\r\n out: (n_samples)\r\n \r\n Pararmete... | [
[
"torch.nn.Linear",
"torch.nn.Tanh"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
allenai/cordial-sync | [
"4005fdc4816c86f6489e5f4b9252fa66b79602be",
"4005fdc4816c86f6489e5f4b9252fa66b79602be"
] | [
"rl_multi_agent/analysis/summarize_furnmove_eval_results.py",
"rl_multi_agent/analysis/visualize_visionbased_furnmove_trajectories.py"
] | [
"\"\"\"\nA script to summarize data saved in the `data/furnmove_evaluations__test/` directory,\nas a result of downloading data or running evaluation using the script:\n`rl_multi_agent/scripts/run_furnmove_or_furnlift_evaluations.py`\n\nSet the metrics, methods, dataset split and generate a csv-styled table of metr... | [
[
"pandas.set_option",
"numpy.array",
"pandas.DataFrame"
],
[
"numpy.dot",
"numpy.linalg.norm",
"numpy.stack",
"numpy.full",
"numpy.concatenate",
"numpy.sign",
"numpy.copy",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"nump... |
Leo-Ryu/gtfspy | [
"732abdf6bfb6427454ac4c0a676dc3f8fc838cf4",
"732abdf6bfb6427454ac4c0a676dc3f8fc838cf4"
] | [
"gtfspy/plots.py",
"gtfspy/gtfs.py"
] | [
"import pandas\nfrom matplotlib import pyplot as plt\n\n\"\"\"\nA collection of various useful plots.\n\"\"\"\n\n\ndef plot_trip_counts_per_day(G, ax=None, highlight_dates=None, highlight_date_labels=None, show=False):\n \"\"\"\n Parameters\n ----------\n G: gtfspy.GTFS\n ax: maptlotlib.Axes, optiona... | [
[
"pandas.to_datetime",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.show"
],
[
"pandas.read_sql_query",
"pandas.merge",
"pandas.read_csv",
"pandas.Series",
"numpy.nonzero",
"pandas.DataFrame",
"pandas.DataFrame.from_records",
"pandas.read_sql"
]
] | [
{
"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... |
jiongjiongli/mytensorflow | [
"1712002ad02f044f7569224bf465e0ea00e6a6c4"
] | [
"tensorflow/python/keras/_impl/keras/engine/training.py"
] | [
"# Copyright 2015 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.python.keras._impl.keras.engine.training_utils.standardize_input_data",
"tensorflow.python.keras._impl.keras.engine.training_utils.check_array_lengths",
"numpy.expand_dims",
"tensorflow.python.keras._impl.keras.engine.training_eager.test_loop",
"tensorflow.python.keras._impl.keras.... | [
{
"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... |
JetBrains-Research/washu | [
"e39d547c3876044db7ab2b8ac1d7f2ac8e4fc9b6"
] | [
"pipeline_tf.py"
] | [
"#!/usr/bin/env python\n\n# TODO: Encode pipeline suggest using bowtie1 + SPP aligner for TF data\n# TODO: table 'folder' value not used\n\nimport sys\nfrom typing import Tuple, List\n\nimport pandas as pd\n\nfrom pipeline_utils import *\nfrom scripts.util import run_macs2\n\n\ndef cli():\n parser = argparse.Arg... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
jeasinema/dm_control | [
"d5efbddf9b41c03cd33f4829f4d12e8a38f38960"
] | [
"dm_control/utils/inverse_kinematics_test.py"
] | [
"# Copyright 2017-2018 The dm_control Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicabl... | [
[
"numpy.empty_like",
"numpy.array",
"numpy.random.RandomState",
"numpy.testing.assert_array_almost_equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jingslaw/ab-crystal-library | [
"aa60bbc79b3f1fb023dbf1cf9e6dac4517a5feaf"
] | [
"method/write.py"
] | [
"###############################\n# This file is part of PyLaDa.\n#\n# Copyright (C) 2013 National Renewable Energy Lab\n#\n# PyLaDa is a high throughput computational platform for Physics. It aims to make it easier to submit\n# large numbers of jobs on supercomputers. It provides a python interface to physical... | [
[
"numpy.matrix",
"numpy.dot",
"numpy.identity"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
10088/numba | [
"2f89d45807b55c676646338bb4d730b69658d2c5"
] | [
"numba/np/npdatetime_helpers.py"
] | [
"\"\"\"\nHelper functions for np.timedelta64 and np.datetime64.\nFor now, multiples-of-units (for example timedeltas expressed in tens\nof seconds) are not supported.\n\"\"\"\n\n\nimport numpy as np\n\n\nDATETIME_UNITS = {\n 'Y': 0, # Years\n 'M': 1, # Months\n 'W': 2, # Weeks\n # Yes, there's a g... | [
[
"numpy.timedelta64"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
seeker1943/GPF | [
"478e3c121f8ca774b9c6fefcfe1180ab4b7aa918",
"478e3c121f8ca774b9c6fefcfe1180ab4b7aa918"
] | [
"GPF/example/main_single.py",
"GPF/src/classifier.py"
] | [
"#!/usr/bin/env python\n# encoding: utf-8\n\nimport sys\n#指定路径\nsys.path.append(\"../src/\") \n\nimport graph\nimport embeddings\nimport sample\nimport classifier\nfrom classifier import loop_dataset\nimport subgraphs\nimport argparse\nimport torch\nimport torch.optim as optim\nimport networkx as nx\nimport until\n... | [
[
"torch.manual_seed",
"torch.cuda.manual_seed",
"torch.cuda.is_available"
],
[
"torch.cat",
"sklearn.metrics.roc_curve",
"numpy.concatenate",
"sklearn.metrics.auc",
"numpy.array",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
steverab/cleverhans | [
"5187c0f3b26faf50ff7ddb746d67d755c72bf181"
] | [
"cleverhans/torch/attacks/fast_gradient_method.py"
] | [
"\"\"\"The Fast Gradient Method attack.\"\"\"\nimport numpy as np\nimport torch\n\nfrom cleverhans.torch.utils import optimize_linear\n\n\ndef fast_gradient_method(model_fn, x, eps, norm,\n clip_min=None, clip_max=None, y=None, targeted=False, sanity_checks=False):\n \"\"\"\n PyTorch imple... | [
[
"torch.clamp",
"numpy.all",
"torch.nn.CrossEntropyLoss",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aruberts/blogs | [
"17ed1a155692e91ae876bb76385aafcc1c64e272"
] | [
"vime/vime/vime.py"
] | [
"import numpy as np\nimport tensorflow as tf\nfrom tensorflow.keras import Sequential\nfrom tensorflow.keras.layers import Dense, Dropout\n\n\nclass VIME_Self(tf.keras.models.Model):\n def __init__(self, num_features):\n super().__init__()\n self.num_features = num_features\n\n self.encoder ... | [
[
"tensorflow.keras.layers.Dense"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
Justprogramer/JBLACN | [
"68c92afa1bc1da26ab182f377ffc3b22b621fe36"
] | [
"joint_model/functions.py"
] | [
"# -*- coding: utf-8 -*-\n# @Author: Jie\n# @Date: 2017-06-15 14:23:06\n# @Last Modified by: Jie Yang, Contact: jieynlp@gmail.com\n# @Last Modified time: 2018-06-10 17:49:50\nfrom __future__ import print_function\nfrom __future__ import absolute_import\nimport sys\nimport numpy as np\n\nfrom utils.log impor... | [
[
"numpy.square",
"numpy.random.uniform",
"numpy.sqrt",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
faezezps/SiMQC | [
"cb1f4dc02469189dfb4713b194bdb691f99aa930"
] | [
"hotpot/nn/recurrent_layers.py"
] | [
"from typing import Optional\n\nimport tensorflow as tf\nfrom hotpot.nn.layers import get_keras_activation, get_keras_initialization, SequenceMapper, SequenceEncoder, \\\n MergeLayer, Encoder\nfrom tensorflow.contrib.cudnn_rnn.python.ops import cudnn_rnn_ops\nfrom tensorflow.contrib.cudnn_rnn.python.ops.cudnn_rn... | [
[
"tensorflow.get_variable",
"tensorflow.concat",
"tensorflow.zeros",
"tensorflow.contrib.cudnn_rnn.python.ops.cudnn_rnn_ops.CudnnGRU",
"tensorflow.contrib.cudnn_rnn.python.ops.cudnn_rnn_ops.CudnnLSTM",
"tensorflow.python.ops.rnn.bidirectional_dynamic_rnn",
"tensorflow.python.keras.initi... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.5",
"1.7",
"1.4"
]
}
] |
jespercodes/pandera | [
"a0813b7d85c5564fa98554196d459477ca2d272f"
] | [
"pandera/error_formatters.py"
] | [
"\"\"\"Make schema error messages human-friendly.\"\"\"\n\nfrom typing import Union\n\nimport pandas as pd\n\nfrom .checks import _CheckBase\n\n\ndef format_generic_error_message(\n parent_schema,\n check: _CheckBase,\n check_index: int,\n) -> str:\n \"\"\"Construct an error message when a check validat... | [
[
"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": []
}
] |
igorvlnascimento/DeepREF | [
"0fed8120571e44e12ee3d1861289bc101c0a275f"
] | [
"opennre/dataset/preprocess.py"
] | [
"'''\nAuthor: Geeticka Chauhan\nPerforms pre-processing on a csv file independent of the dataset (once converters have been applied). \nRefer to notebooks/Data-Preprocessing for more details. The methods are specifically used in the non\n_original notebooks for all datasets.\n'''\n\nimport os\nimport pandas as pd\n... | [
[
"pandas.read_csv",
"pandas.Series"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
openml/openml-python-contrib | [
"a3480b05483bd66e0fe42347b1f93261d7373ec9"
] | [
"examples/heatmap_surrogate.py"
] | [
"import argparse\nimport ConfigSpace\nimport logging\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport openml\nimport openmlcontrib.testing\nimport os\nimport sklearn.ensemble\n\n\ndef parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument('--cache_directory', type=str, default=os... | [
[
"numpy.abs",
"numpy.linspace",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig",
"numpy.log10",
"matplotlib.pyplot.close",
"matplotlib.pyplot.show",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
abhik-99/qiskit-terra | [
"ad1680f54fecb415fa2131200365e47c6d00bbb1"
] | [
"qiskit/transpiler/passes/layout/sabre_layout.py"
] | [
"# This code is part of Qiskit.\n#\n# (C) Copyright IBM 2017, 2020.\n#\n# This code is licensed under the Apache License, Version 2.0. You may\n# obtain a copy of this license in the LICENSE.txt file in the root directory\n# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.\n#\n# Any modificatio... | [
[
"numpy.iinfo",
"numpy.random.default_rng"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
thtang/ML2017FALL | [
"838924edf3933fa9a67bae7198d3daebf21716f3"
] | [
"final/src/train.py"
] | [
"import numpy as np\nimport keras.backend as K\nimport keras\nfrom sklearn.model_selection import train_test_split\nfrom keras.models import Model, load_model\nfrom keras.layers import Input, LSTM, Dense, GRU, Embedding, Bidirectional, BatchNormalization, TimeDistributed\nfrom keras.preprocessing.text import Tokeni... | [
[
"numpy.random.choice",
"sklearn.model_selection.train_test_split",
"numpy.concatenate",
"numpy.load",
"numpy.array",
"numpy.roll"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
drex44/kaggle-Titanic | [
"74377a9f7962bfe919c93c1efb9e3fdd5a3300f3"
] | [
"RandomForest.py"
] | [
"import pandas\r\nimport math\r\nimport numpy as np\r\nimport re\r\nfrom sklearn.ensemble import RandomForestClassifier as Rforest\r\nfrom sklearn import cross_validation as validate\r\nfrom sklearn.feature_selection import SelectKBest, f_classif\r\nimport operator\r\n\r\ndef _readData(filepath):\r\n data = pand... | [
[
"sklearn.cross_validation.cross_val_score",
"pandas.read_csv",
"sklearn.ensemble.RandomForestClassifier",
"pandas.DataFrame",
"sklearn.feature_selection.SelectKBest",
"numpy.log10",
"pandas.to_numeric"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Sh-Zh-7/zombie-enterprise-recognition | [
"f0adbb360b21509aa4860efd3d4115edd672b291"
] | [
"TraditionML/src/evaluate.py"
] | [
"from utils import *\nfrom main import model_path, models\nfrom sklearn.metrics import auc, roc_curve, \\\n accuracy_score, precision_score, recall_score\n\ndef GetAuc(model, X, Y):\n fpr, tpr, thresholds = roc_curve(Y, model.predict_proba(X)[:, 1])\n auc_score = auc(fpr, tpr)\n return auc_score\n\ndef ... | [
[
"sklearn.metrics.auc",
"sklearn.metrics.precision_score",
"sklearn.metrics.recall_score",
"sklearn.metrics.accuracy_score"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
fensta/bracid2019 | [
"ad9bf0b4e44c19f66c1597e857ef6cf70f56a646",
"ad9bf0b4e44c19f66c1597e857ef6cf70f56a646",
"ad9bf0b4e44c19f66c1597e857ef6cf70f56a646"
] | [
"scripts/utils.py",
"unit_tests/test_evaluate_f1_temporarily.py",
"unit_tests/test_find_nearest_rule.py"
] | [
"from collections import Counter, deque, namedtuple\nimport os\nimport itertools\nimport warnings\nimport copy\nfrom operator import itemgetter\nimport math\n\nimport pandas as pd\nfrom pandas.api.types import is_numeric_dtype, is_string_dtype\nimport sklearn.datasets\nfrom sklearn.metrics import f1_score\nimport n... | [
[
"pandas.read_csv",
"pandas.Series",
"pandas.isnull",
"pandas.DataFrame",
"pandas.api.types.is_numeric_dtype",
"sklearn.metrics.f1_score",
"numpy.array",
"pandas.api.types.is_string_dtype"
],
[
"pandas.DataFrame"
],
[
"pandas.Series",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
... |
bdice/seaborn | [
"29f5807efdf0544b16c088beac7c1c5eda174084"
] | [
"seaborn/tests/test_relational.py"
] | [
"from itertools import product\nimport warnings\nimport numpy as np\nimport pandas as pd\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nfrom matplotlib.colors import same_color\n\nimport pytest\nfrom numpy.testing import assert_array_equal\n\nfrom ..palettes import color_palette\n\nfrom ..relational im... | [
[
"numpy.product",
"pandas.Series",
"numpy.asarray",
"pandas.DataFrame",
"numpy.concatenate",
"numpy.random.randn",
"numpy.ma.getmask",
"matplotlib.colors.same_color",
"numpy.arange",
"numpy.std",
"numpy.repeat",
"pandas.Categorical",
"numpy.array",
"matplotli... | [
{
"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": []
}
] |
frommwonderland/pytorch_connectomics | [
"95859ec5257d6991ca64782523637f947a8485e2"
] | [
"connectomics/data/utils/data_io.py"
] | [
"from __future__ import print_function, division\nfrom typing import Optional, List\n\nimport os\nimport h5py\nimport glob\nimport numpy as np\nimport imageio\nfrom scipy.ndimage import zoom\n\ndef readh5(filename, dataset = None):\n fid = h5py.File(filename,'r')\n if dataset is None:\n dataset = list(... | [
[
"numpy.pad",
"numpy.squeeze",
"numpy.ones",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dlongnecke-cray/arkouda | [
"cd1032c9afe3ca6ae5837d6d510d72844b5d515f"
] | [
"arkouda/pdarrayclass.py"
] | [
"import json, struct\nimport numpy as np\n\nfrom arkouda.client import generic_msg, verbose, maxTransferBytes, pdarrayIterThresh\nfrom arkouda.dtypes import *\nfrom arkouda.dtypes import structDtypeCodes, NUMBER_FORMAT_STRINGS\n\n__all__ = [\"pdarray\", \"info\", \"any\", \"all\", \"is_sorted\", \"sum\", \"prod\", ... | [
[
"numpy.isscalar"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
brackham/PypeIt | [
"8769f06ae8e8f18d3a55d12b01dd3dde50b98040"
] | [
"pypeit/spectrographs/lbt_mods.py"
] | [
"\"\"\"\nModule for LBT/MODS specific methods.\n\n.. include:: ../include/links.rst\n\"\"\"\nimport glob\nimport numpy as np\nfrom astropy.io import fits\n\nfrom pypeit import msgs\nfrom pypeit import telescopes\nfrom pypeit import io\nfrom pypeit.core import framematch\nfrom pypeit.par import pypeitpar\nfrom pypei... | [
[
"numpy.atleast_1d",
"numpy.array",
"numpy.flipud",
"numpy.zeros_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
alshedivat/fedpa | [
"695c400c17672f70971599513f0e2388cd302078"
] | [
"federated/utils/plotting.py"
] | [
"# coding=utf-8\n# Copyright 2020 Maruan Al-Shedivat.\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 a... | [
[
"numpy.linspace",
"numpy.logspace",
"numpy.arange",
"numpy.stack",
"numpy.meshgrid"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hrgi2269/O-1-memory-Glow | [
"70f03f2f1a3e8c232a77a3a2a5e2aa88a11345fe"
] | [
"flows/flip.py"
] | [
"import torch\nimport torch.nn as nn\n\nclass Flip(nn.Module):\n def __init__(self):\n super(Flip, self).__init__()\n\n def forward(self, inputs, inverse=False):\n batch_size = inputs.size(0)\n return inputs.flip([1]), torch.zeros(batch_size, device=inputs.device)\n"
] | [
[
"torch.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
reejit/ImageRecognitionAPI- | [
"f229c1ef609dab3308cf4bd40c28c3dfb3256492"
] | [
"web/classify_image.py"
] | [
"# Copyright 2015 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.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"
]
}
] |
lichuminglcm/DI-engine | [
"e9052f195d231a9875afb053ba815c6341857571"
] | [
"dizoo/d4rl/envs/d4rl_env.py"
] | [
"from typing import Any, Union, List\nimport copy\nimport torch\nimport numpy as np\n\nfrom ding.envs import BaseEnv, BaseEnvTimestep, BaseEnvInfo, update_shape\nfrom ding.envs.common.env_element import EnvElement, EnvElementInfo\nfrom ding.envs.common.common_function import affine_transform\nfrom ding.torch_utils ... | [
[
"numpy.float64",
"numpy.random.seed",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SylvainCorlay/pythran | [
"908ec070d837baf77d828d01c3e35e2f4bfa2bfa"
] | [
"pythran/tests/cases/ldpc_decoder.py"
] | [
"# coding: utf-8\nimport numpy as np\nfrom math import log, tanh\n\n#pythran export phi0(float)\ndef phi0(x):\n x = abs(x)\n if (x < 9.08e-5 ):\n return( 10 );\n else:\n return -log (tanh (x/2))\n\n\n#pythran export G(float[:])\ndef G(Lq):\n X = sum (phi0(e) for e in Lq)\n s = np.prod(n... | [
[
"numpy.clip",
"numpy.sign",
"numpy.array",
"numpy.zeros",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
raguvarankr/deep_learning | [
"45571c00d22f339024d96b734728b4e40b6915f0"
] | [
"Plotting/1.General_plot.py"
] | [
"import matplotlib.pyplot as plt\nimport numpy as np\nimport seaborn as sns\n\n\nx = np.random.random((10, 1))\n\nprint(x)\n\nplt.plot(x, '*-')\nplt.show()\n\nx = np.linspace(0, 100, 50)\ny = np.power(x, .5)\nplt.plot(x, y, '+-')\nplt.show()\n\nsns.set()\nsns.lineplot(x, y)\nplt.show()\n\nuniform_data = np.random.r... | [
[
"numpy.random.random",
"numpy.linspace",
"numpy.power",
"matplotlib.pyplot.plot",
"numpy.random.rand",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dnelson86/sublink | [
"b5ef5938868f7f1095b564f1e529675cfab44f85"
] | [
"Python/create_columns.py"
] | [
"import numpy as np\nimport h5py\nimport sys\nimport os\n\n\"\"\"\nRead a tree in \"minimal\" format and add all Subhalo fields\nfrom the Subfind catalogs, as well as a few FoF group quantities.\nStore each new field in a separate file. Deal with one field at a time.\n\"\"\"\n\ndef read_block(basedir, snapnum, fiel... | [
[
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kaist-amsg/LocalTransform | [
"57502ba92da9495fce22a3875352507026501da1"
] | [
"scripts/utils.py"
] | [
"import torch\nimport sklearn\nimport dgl\nimport errno\nimport json\nimport os\nimport time\nimport numpy as np\nimport pandas as pd\nfrom functools import partial\n\nimport torch.nn as nn\nfrom torch.utils.data import DataLoader\nfrom torch.optim import Adam, lr_scheduler\n\nfrom rdkit import Chem\nfrom dgl.data.... | [
[
"torch.LongTensor",
"pandas.read_csv",
"torch.ones",
"torch.cat",
"torch.load",
"torch.utils.data.DataLoader",
"numpy.ones",
"torch.tensor",
"torch.optim.lr_scheduler.StepLR"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
encresearch/mqtt-subscriber-server | [
"2e068e204749ee43eea39e6100cab84f5e91f953"
] | [
"tests/test_generator.py"
] | [
"\"\"\"CI Tests.\"\"\"\nimport pandas as pd\nfrom connector.connector import (\n connect_to_db,\n connect_to_mqtt_broker,\n write_to_db,\n MV_PER_BIT,\n DB_CONNECTOR_TABLE\n)\n\n\ndef test_connect_to_db():\n \"\"\"Test the system is able to connect to an infuxdb instance.\"\"\"\n db_client = co... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
lzhangbv/PaGraph | [
"2686a4e2ffcba7de3af1e6bb015b8a4d211160b8"
] | [
"examples/profile/pa_gs.py"
] | [
"import os\nimport sys\nimport argparse, time\nimport torch\nimport torch.nn as nn\nimport torch.distributed as dist\nimport torch.multiprocessing as mp\nimport torch.nn.functional as F\nimport numpy as np\nimport dgl\nfrom dgl import DGLGraph\n\nfrom PaGraph.model.graphsage_nssc import GraphSageSampling\nimport Pa... | [
[
"torch.autograd.profiler.record_function",
"torch.nn.CrossEntropyLoss",
"torch.LongTensor",
"torch.distributed.init_process_group",
"torch.cuda.set_device",
"torch.multiprocessing.spawn",
"torch.manual_seed",
"numpy.max",
"torch.device",
"numpy.array",
"torch.autograd.p... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Hello-World-Test12/pymatting | [
"424ebd4b8b4709793651f373c7543e137b1496a9",
"424ebd4b8b4709793651f373c7543e137b1496a9"
] | [
"tests/test_preconditioners.py",
"pymatting/laplacian/uniform_laplacian.py"
] | [
"import numpy as np\nfrom pymatting import (\n load_image,\n cf_laplacian,\n make_linear_system,\n cg,\n jacobi,\n ichol,\n vcycle,\n CounterCallback,\n)\n\n\ndef test_preconditioners():\n atol = 1e-6\n index = 1\n scale = 0.2\n\n name = f\"GT{index:02d}\"\n # print(name)\n\n ... | [
[
"numpy.linalg.norm"
],
[
"numpy.ones"
]
] | [
{
"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": [],
... |
priooods/Angeline | [
"68608d16a74d689cb2e7e95351951fab0cd9ac24"
] | [
"artificial/voice_biometric/prepocessing.py"
] | [
"import numpy as np\nfrom scipy.stats import gmean\nfrom pyAudioAnalysis import audioFeatureExtraction\nimport librosa\n\nclass Preposessing():\n \n def __init__(self, _audio_file):\n '''\n Argument : \n - _audio_file : single file audio yang ingin kita extraksi, file audio ... | [
[
"scipy.stats.gmean",
"numpy.fft.rfft",
"numpy.cumsum",
"numpy.argmax",
"numpy.mean",
"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"
... |
NeuralFlux/rl-analysis | [
"bb45e1f8bb9da4683cce4bd0a5e687770a4005e2"
] | [
"pong/checkpoints_vpg/log2graph.py"
] | [
"import sys\nimport pandas as pd\nfrom matplotlib import pyplot as plt\nimport seaborn as sns\nsns.set()\n\n\ndef parse_line(line):\n args = line.split()\n epoch = int(args[0][1:-2])\n avg = float(args[3][:-1])\n return (epoch + 1), avg\n\nif __name__ == \"__main__\":\n with open(sys.argv[1], 'r') as... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.savefig"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kim-lab/kmbio | [
"ad2afc429d8990487c1094bdd69112cab91c03c9"
] | [
"kmbio/PDB/tools/superimposer.py"
] | [
"# Copyright (C) 2002, Thomas Hamelryck (thamelry@binf.ku.dk)\n# This code is part of the Biopython distribution and governed by its\n# license. Please see the LICENSE file that should have been included\n# as part of this package.\n\"\"\"Superimpose two structures.\"\"\"\nimport numpy as np\n\nfrom kmbio.PDB.exce... | [
[
"numpy.array",
"numpy.identity"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
srjit/ares | [
"b9fb66ab18747aadf8a46d67454d34937c45eb3b"
] | [
"word2vec.py"
] | [
"from gensim.models.keyedvectors import KeyedVectors\nimport config\nimport numpy as np\nimport pandas as pd\n\n__author__ = \"Sreejith Sreekumar\"\n__email__ = \"sreekumar.s@husky.neu.edu\"\n__version__ = \"0.0.1\"\n\n\ncfg = config.read()\nword2vec_model_loc = cfg.get(\"vector\",\"word2vec_model_loc\")\n\n#model ... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
karthik/berkeley | [
"966563f24320cb002a6e48318ece0e8fe9cf6983"
] | [
"extendingPython_CAPI-cython/c_code/setup.py"
] | [
"from distutils.core import setup, Extension\nimport numpy as np\n\next_modules = [Extension('c_version', ['src/my_function.c', 'src/pywrapper.c'],\n include_dirs=[np.get_include(), 'include'],\n extra_compile_args=[\"-O3\", \"-std=c99\"],\n ex... | [
[
"numpy.get_include"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
BTETON/finance_ml | [
"c348556fa3e13417e8fcf02999f42d5e72f0501b"
] | [
"finance_ml/model_selection/hyper.py"
] | [
"from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\nfrom sklearn.ensemble import BaggingClassifier\n\nfrom .kfold import PurgedKFold\nfrom .pipeline import Pipeline\n\n\ndef clf_hyper_fit(feat, label, t1, pipe_clf, search_params, scoring=None,\n n_splits=3, bagging=[0, None, 1.],... | [
[
"sklearn.model_selection.GridSearchCV",
"sklearn.model_selection.RandomizedSearchCV"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zongzefang/deeprl_signal_control | [
"4fe55b5ec9fd8e98e3330bf0985696106ef1000a"
] | [
"envs/small_grid_env.py"
] | [
"\"\"\"\nParticular class of small traffic network\n@author: Tianshu Chu\n\"\"\"\n\nimport configparser\nimport logging\nimport numpy as np\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nimport os\nimport seaborn as sns\nimport time\nfrom envs.env import PhaseMap, PhaseSet, TrafficSimul... | [
[
"matplotlib.use",
"numpy.sort",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.ylabel",
"numpy.argmax",
"numpy.mean",
"numpy.array",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rh01/Deep-reinforcement-learning-with-pytorch | [
"fd1853495b885514927c82834f562d2a4df06b28"
] | [
"More/MARL/env/gridworld.py"
] | [
"import matplotlib.pyplot as plt\nimport numpy as np\nimport warnings\nwarnings.filterwarnings('ignore')\n\nclass CrossRoadGridWorld():\n def __init__(self, size=(14,14)):\n super(CrossRoadGridWorld, self).__init__()\n self.state = np.zeros(size)\n self.size = size\n self.init_state =... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
5hulse/bruker_utils | [
"f115081358fec89c8566c133d121c0b5c90da701"
] | [
"tests/test.py"
] | [
"import pathlib\nimport pytest\nfrom typing import Union\nimport numpy as np\nfrom context import bruker_utils\n\n\nDIR = pathlib.Path(__file__).resolve().parent\nFIDPATHS = [(DIR / f'../examples/data/{i}').resolve() for i in range(1, 3)]\nPDATAPATHS = [fidpath / 'pdata/1' for fidpath in FIDPATHS]\nALLPATHS = FIDPA... | [
[
"numpy.round"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AtharvBhat/EstimateDepth | [
"f440a9e8372ca2346cae8634f396bac06d004bf7"
] | [
"discriminator.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass Discriminator(nn.Module):\n def __init__(self):\n super(Discriminator, self).__init__()\n \n self.conv1 = nn.Conv2d(1, 64, 7, 1, 3, padding_mode='reflect')\n self.bn1 = nn.BatchNorm2d(64)\n self.relu... | [
[
"torch.nn.Conv2d",
"torch.nn.LeakyReLU",
"torch.nn.BatchNorm2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bobdavies2000/OpenCVB | [
"1d339a94643a97e2d34f82dc7776677a8566d71d"
] | [
"VB_Classes/Histogram_BackProject_Demo2.py"
] | [
"import cv2 as cv\nimport numpy as np\nimport argparse\ntitleWindow = 'Histogram_BackProject_Demo2.py'\nimport ctypes\ndef Mbox(title, text, style):\n return ctypes.windll.user32.MessageBoxW(0, text, title, style)\n\nlow = 20\nup = 20\n\ndef callback_low(val):\n global low\n low = val\n\ndef callback_up(va... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tacaswell/tiled | [
"4727c775dae1b40da01cba419b4cec7aee0de039"
] | [
"tiled/client/xarray.py"
] | [
"from collections.abc import Iterable\nimport builtins\nimport itertools\n\nimport pandas\nimport xarray\n\nfrom ..structures.dataframe import deserialize_arrow\nfrom ..structures.xarray import (\n APACHE_ARROW_FILE_MIME_TYPE,\n DataArrayStructure,\n DatasetStructure,\n VariableStructure,\n)\n\nfrom .ar... | [
[
"pandas.concat"
]
] | [
{
"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": []
}
] |
sallyrose0425/SmallMolEval | [
"1903ee56828db14ee271b0101de315395da463c6"
] | [
"remove_AVE_bias2.py"
] | [
"#\n# Copyright 2017 Atomwise Inc.\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and \n# associated documentation files (the \"Software\"), to deal in the Software without restriction, \n# including without limitation the rights to use, copy, modify, merge, publ... | [
[
"numpy.hstack",
"numpy.ix_",
"numpy.linspace",
"numpy.unique",
"scipy.spatial.distance.cdist",
"numpy.mean",
"numpy.random.rand",
"numpy.any",
"numpy.where",
"numpy.vstack"
]
] | [
{
"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"
... |
Nirngen/opt-mmd | [
"17b36fd744dedfacdbf1f445f49dc59716d59206"
] | [
"gan/model_mmd_fm.py"
] | [
"from __future__ import division, print_function\nfrom glob import glob\nimport os\nimport time\n\nimport numpy as np\nimport scipy.misc\nfrom six.moves import xrange\nimport tensorflow as tf\n\nfrom mmd import mix_rbf_mmd2\nfrom ops import batch_norm, conv2d, deconv2d, linear, lrelu\nfrom utils import save_images\... | [
[
"tensorflow.concat",
"numpy.asarray",
"numpy.concatenate",
"tensorflow.summary.scalar",
"tensorflow.Variable",
"tensorflow.train.MomentumOptimizer",
"tensorflow.batch_to_space",
"tensorflow.train.Saver",
"tensorflow.trainable_variables",
"numpy.zeros",
"tensorflow.nn.si... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
aharini94/assign2 | [
"bc9241d51bd4ad40d4863fde5ac84d13fe42b48e"
] | [
"venv/Lib/site-packages/pandas/core/reshape/reshape.py"
] | [
"# pylint: disable=E1101,E1103\n# pylint: disable=W0703,W0622,W0613,W0201\nfrom pandas.compat import range, text_type, zip\nfrom pandas import compat\nfrom functools import partial\nimport itertools\n\nimport numpy as np\n\nfrom pandas.core.dtypes.common import (\n _ensure_platform_int,\n is_list_like, is_boo... | [
[
"pandas._libs.sparse.IntIndex",
"numpy.dtype",
"pandas.core.dtypes.missing.notna",
"pandas.compat.range",
"pandas.core.sparse.api.SparseDataFrame",
"pandas.core.sorting.get_group_index",
"pandas.core.frame.DataFrame",
"pandas.core.series.Series",
"numpy.arange",
"numpy.eye"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
fangxu622/Depth_Point_Location_Attention | [
"17449320192e83519591f72547da53d01b82d4a2",
"17449320192e83519591f72547da53d01b82d4a2"
] | [
"config/conf_1.py",
"model/minkfpn.py"
] | [
"\nfrom typing import Optional\nimport torch\n\n#cuda = torch.cuda.is_available()\ndtype = torch.cuda.FloatTensor if torch.cuda.is_available() else torch.FloatTensor\n\n\n# datasetting\ndata_dir = \"/media/fangxu/Disk4T/LQ/data/\"\nscene = \"chess\" #optional \"chess\", \ntrain_seq_list = [1,2,3,4]#\nval_seq_list =... | [
[
"torch.cuda.is_available"
],
[
"torch.nn.ModuleList"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
thekingofkings/embedding | [
"ff1ab13efd27f0b487afd7c2c9e5e23dffff2193"
] | [
"python/caseStudy.py"
] | [
"#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\"\"\"\nCase study to support the flow idea.\n\nIdeal case:\n The hypothesis is true: in the morning, people go to office tract from residential area.\n In the afternoon, when office hour is over, they went to nightlife tracts for fun. In the night, they\n ... | [
[
"sklearn.cluster.KMeans",
"numpy.median",
"numpy.argwhere",
"numpy.intersect1d",
"matplotlib.pyplot.subplot",
"numpy.mean",
"matplotlib.pyplot.suptitle",
"matplotlib.pyplot.show",
"numpy.loadtxt",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
alexeykarnachev/pytorch-lightning | [
"9045b6c599df3871da6aaaa310f62d3f1364c632"
] | [
"pytorch_lightning/metrics/utils.py"
] | [
"import numbers\nfrom typing import Union, Any, Optional\n\nimport numpy as np\nimport torch\nfrom torch.utils.data._utils.collate import default_convert\n\nfrom pytorch_lightning.utilities.apply_func import apply_to_collection\n\n\ndef _apply_to_inputs(func_to_apply, *dec_args, **dec_kwargs):\n def decorator_fn... | [
[
"torch.distributed.is_initialized",
"torch.distributed.barrier",
"torch.utils.data._utils.collate.default_convert",
"torch.tensor",
"torch.distributed.is_available",
"numpy.array",
"torch.distributed.all_reduce"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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