repo_name stringlengths 8 130 | hexsha list | file_path list | code list | apis list |
|---|---|---|---|---|
robertjankowski/reproducing-dl-papers | [
"01ad85eac333b87358b3d2e2276292333cacf0e0"
] | [
"homophily_structural_balance/plotting/plot_positive_edge_density.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nimport argparse\n\n\ndef extract_name(word: str):\n return word.split('=')[-1]\n\n\ndef extract_info(filename: str):\n filename_splitted = filename.split('_')\n assert len(filename_splitted) == 7\n p = float(extract_name(filename_splitted[1]))\n i... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.rc",
"matplotlib.pyplot.xticks",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.yticks",
... |
elephantscale/facies | [
"ea78a4917ebb5dbbe478b9fc27200c67b6e5576f"
] | [
"code/faciesplot.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.colors as colors\nfrom mpl_toolkits.axes_grid1 import make_axes_locatable\n\n#Key:\n# 1=sandstone 2=c_siltstone 3=f_siltstone \n# 4=marine_silt_shale 5=mudstone 6=wackestone 7=dolomite\n# 8=packstone 9=bafflestone\n\n\nfacies_labels = ['SS',... | [
[
"matplotlib.pyplot.colorbar",
"numpy.expand_dims",
"matplotlib.pyplot.subplots"
]
] |
siyemuxu888/imagepy | [
"a933526483a15da282bacac54608d44d2173beb4",
"a933526483a15da282bacac54608d44d2173beb4"
] | [
"imagepy/tools/Transform/scale_tol.py",
"imagepy/menus/Plugins/Surf/surf_plg.py"
] | [
"import wx\nimport numpy as np\nfrom imagepy.core.engine import Tool, Filter\nimport scipy.ndimage as nimg\n\nclass ScaleTool(Tool):\n def __init__(self, plg):\n self.plg = plg\n self.para = plg.para\n self.moving = False\n \n def snap(self, x, y, lim):\n plg = self.plg\n ... | [
[
"numpy.array",
"scipy.ndimage.affine_transform"
],
[
"numpy.sqrt",
"numpy.float32"
]
] |
KedoKudo/jupyter-ht-hedm | [
"b447202fb9800e7b2916b38470db1b9a83357130"
] | [
"seisidd/tomo_plans.py"
] | [
"#!/usr/bin/env python\n\n\"\"\"\nPredefined bluesky scan plans\n\"\"\"\n\nimport numpy as np\nimport bluesky.plans as bp\nimport bluesky.preprocessors as bpp\nimport bluesky.plan_stubs as bps\n\nfrom .utility import load_config\n\n#@bpp.run_decorator()\ndef collect_white_field... | [
[
"numpy.cos",
"numpy.arange",
"numpy.array",
"numpy.sin",
"numpy.radians"
]
] |
petuum/tuun | [
"8eec472dbf0e5e695449b0fa2d98985469fd5b30"
] | [
"tuun/probo/models/gp_stan_transfer.py"
] | [
"\"\"\"\nClasses for GP models with Stan that perform transfer optimization.\n\"\"\"\n\nfrom argparse import Namespace\nimport numpy as np\nimport copy\n\nfrom .gp_stan import StanGp\nfrom .regression.transfer_regression import TransferRegression\nfrom ..util.misc_util import dict_to_namespace\n\n\nclass StanTransf... | [
[
"numpy.array",
"numpy.linalg.norm",
"numpy.min",
"numpy.mean"
]
] |
K4S4B4/learnable-triangulation-pytorch | [
"94f5121919785bf7c89dd973521a21c01104dbd5"
] | [
"mvn/utils/op.py"
] | [
"import numpy as np\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom mvn.utils.img import to_numpy, to_torch\nfrom mvn.utils import multiview\n\n\ndef integrate_tensor_2d(heatmaps, softmax=True):\n \"\"\"Applies softmax to heatmaps and integrates them to get their's \"center of mas... | [
[
"torch.stack",
"torch.nn.functional.softmax",
"torch.zeros_like",
"torch.exp",
"torch.nn.functional.relu",
"torch.arange",
"torch.nn.functional.grid_sample",
"torch.zeros",
"torch.einsum",
"torch.cat"
]
] |
ctiger34/BASIC-EMOTION-DETECTION | [
"1c2be519c70408159ea6e1093d5f139c99ea6e27"
] | [
"load_and_process.py"
] | [
"import pandas as pd\nimport cv2\nimport numpy as np\n\n\ndataset_path = 'fer2013/fer2013/fer2013.csv'\nimage_size=(48,48)\n\ndef load_fer2013():\n data = pd.read_csv(dataset_path)\n pixels = data['pixels'].tolist()\n width, height = 48, 48\n faces = []\n for pixel_sequence in pix... | [
[
"pandas.read_csv",
"numpy.expand_dims",
"numpy.asarray",
"pandas.get_dummies"
]
] |
ray-ruisun/FedML | [
"24ff30d636bb70f64e94e9ca205375033597d3dd",
"24ff30d636bb70f64e94e9ca205375033597d3dd"
] | [
"app/fedcv/medical_chest_xray_image_clf/data/chexpert/data_loader.py",
"app/fedgraphnn/ego_networks_link_pred/data/utils.py"
] | [
"import logging\n\nimport os\nimport numpy as np\nimport torch\nfrom torch.utils.data import Dataset, DataLoader\nimport torchvision.transforms as transforms\nfrom torch.utils.data.distributed import DistributedSampler\n\nfrom .dataset import CheXpert\n\n\ndef _get_mean_and_std(dataset: Dataset):\n \"\"\"Compute... | [
[
"torch.utils.data.DataLoader",
"numpy.ones",
"numpy.random.shuffle",
"torch.utils.data.distributed.DistributedSampler",
"numpy.random.choice",
"numpy.clip",
"torch.from_numpy",
"torch.zeros",
"numpy.random.randint"
],
[
"numpy.random.uniform",
"torch.as_tensor",
... |
openmcworkshop/paramak | [
"c41dc4c2e68183869556544ee7a72deb1d16a8dc"
] | [
"paramak/reactor.py"
] | [
"\nimport json\nfrom collections import Iterable\nfrom pathlib import Path\n\nimport cadquery as cq\nimport matplotlib.pyplot as plt\nimport plotly.graph_objects as go\nfrom cadquery import exporters\n\nimport paramak\nfrom paramak.neutronics_utils import (add_stl_to_moab_core,\n ... | [
[
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.close",
"matplotlib.pyplot.subplots"
]
] |
jaycosaur/spynet | [
"535841bcea761463d27f7f3eb745ffe186d9f763"
] | [
"streaming_helpers.py"
] | [
"import queue\nimport time\nimport numpy as np\n\n\nclass CameraInformation:\n def __init__(self, cam_id: str):\n self._frame_queue: queue.Queue = queue.Queue(maxsize=1)\n self._frame_shape = None\n self._last_frame_time = None\n self.is_online = True\n self.node_id = cam_id\n\... | [
[
"numpy.zeros"
]
] |
liiliiliil/ride-hailing-platform-with-simulator | [
"c9eae7f718c9e10c7ba4955e5093d4fb21d16d25"
] | [
"data_processing/draw_value_map.py"
] | [
"import os\nimport time\nimport pickle\n\nimport math\nimport numpy as np\nimport linecache\nimport matplotlib.pyplot as plt\n# from matplotlib.pyplot import MultipleLocator\nimport grid\n\ndata_path = 'E:/dataset/didi/processed'\nsave_path = 'E:/dataset/didi/processed/order_20161101_sampled_value_map_fig'\ndata_fi... | [
[
"matplotlib.pyplot.figure",
"numpy.arange",
"matplotlib.pyplot.title",
"numpy.max",
"numpy.min",
"numpy.array",
"matplotlib.pyplot.colorbar",
"matplotlib.pyplot.scatter"
]
] |
Amir-Mehrpanah/hgraph2graph | [
"6d37153afe09f7684381ce56e8366675e22833e9"
] | [
"hgraph/decoder.py"
] | [
"import torch\nimport torch.nn as nn\nimport rdkit.Chem as Chem\nimport torch.nn.functional as F\nfrom hgraph.nnutils import *\nfrom hgraph.encoder import IncHierMPNEncoder\nfrom hgraph.mol_graph import MolGraph\nfrom hgraph.inc_graph import IncTree, IncGraph\n\nclass HTuple():\n def __init__(self, node=None, me... | [
[
"torch.nn.Linear",
"torch.nn.Dropout",
"torch.nn.functional.softmax",
"torch.nn.functional.pad",
"torch.multinomial",
"torch.nn.CrossEntropyLoss",
"torch.exp",
"torch.eye",
"torch.nn.BCEWithLogitsLoss",
"torch.sigmoid",
"torch.LongTensor",
"torch.nn.ReLU",
"torc... |
johncliu/Horizon | [
"cfa7a873ada5de3bb01e78e2f237d9849b8270b2"
] | [
"ml/rl/test/test_normalization.py"
] | [
"#!/usr/bin/env python3\n# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.\n\nimport unittest\n\nimport numpy as np\nimport numpy.testing as npt\nimport six\nfrom caffe2.python import core, workspace\nfrom ml.rl.caffe_utils import C2\nfrom ml.rl.preprocessing import identify_types, normalizati... | [
[
"numpy.logical_or",
"numpy.squeeze",
"numpy.zeros",
"numpy.less",
"numpy.mean",
"numpy.isclose",
"numpy.greater",
"numpy.expand_dims",
"scipy.special.expit",
"numpy.all",
"numpy.array",
"numpy.std",
"numpy.where",
"numpy.isfinite"
]
] |
boycehbz/DMMR | [
"18fcee7ce584fdccfa08bcda883d9b4fcb962c04"
] | [
"core/smplx/lbs_.py"
] | [
"# -*- coding: utf-8 -*-\n\n# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is\n# holder of all proprietary rights on this computer program.\n# You can only use this computer program if you have closed\n# a license agreement with MPG or you get the right to use the computer\n# program from som... | [
[
"torch.unsqueeze",
"torch.ones",
"torch.stack",
"torch.split",
"torch.cos",
"torch.bmm",
"torch.nn.functional.pad",
"torch.norm",
"torch.sin",
"torch.arange",
"torch.index_select",
"torch.zeros",
"torch.einsum",
"torch.eye",
"torch.cat",
"torch.matmu... |
ardhanii/covid19-sir | [
"59d95156b375c41259c46ce4e656b86903f92ec2"
] | [
"covsirphy/loading/db_owid.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport country_converter as coco\nimport pandas as pd\nfrom covsirphy.util.term import Term\nfrom covsirphy.loading.db_base import _RemoteDatabase\n\n\nclass _OWID(_RemoteDatabase):\n \"\"\"\n Access \"Our World In Data\".\n https://github.com/owid/covid-1... | [
[
"pandas.read_csv"
]
] |
egonrian/google-research | [
"9049acf9246c1b75170f0c6757e62a8f619a9db6",
"2c0043ecd507e75e2df9973a3015daf9253e1467",
"2c0043ecd507e75e2df9973a3015daf9253e1467"
] | [
"task_set/tasks/fixed/fixed_text_rnn_classification_test.py",
"depth_and_motion_learning/consistency_losses.py",
"protein_lm/embed_test.py"
] | [
"# coding=utf-8\n# Copyright 2020 The Google Research 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 requ... | [
[
"tensorflow.compat.v1.test.main"
],
[
"tensorflow.compat.v1.stop_gradient",
"tensorflow.compat.v1.math.multiply_no_nan",
"tensorflow.compat.v1.less",
"tensorflow.compat.v1.reduce_mean",
"tensorflow.compat.v1.expand_dims",
"tensorflow.compat.v1.squeeze",
"tensorflow.compat.v1.nn... |
shanky1947/Air-Draw | [
"ab370f96384414ba5c4e369f5465cd8e28b4f3f0"
] | [
"Air Canvas (only)/Different Canvas Codes/detect1.py"
] | [
"# get hsv values using trackbar\nimport cv2\nimport numpy as np\nimport time\n\n# A required callback method that goes into the trackbar function.\ndef nothing(x):\n pass\n\n# Initializing the webcam feed.\ncap = cv2.VideoCapture(0)\ncap.set(3,1280)\ncap.set(4,720)\n\n# Create a window named trackbars.\ncv2.nam... | [
[
"numpy.array",
"numpy.save",
"numpy.hstack"
]
] |
123972/PCA-nutricion | [
"aff3c51a71c887c3fa367dbf9d599be5915c80cc",
"aff3c51a71c887c3fa367dbf9d599be5915c80cc"
] | [
"src/pca/todoJunto.py",
"environment/lib/python3.8/site-packages/sklearn/decomposition/tests/test_dict_learning.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n\nimport codecs\nimport sys\n\nimport sklearn as sk\nimport pandas as pd\nimport numpy as np \nimport math\n\nfrom sklearn import preprocessing\nfrom sklearn.decomposition import PCA\n\nfrom src.pca.algoritmo_QR import eigenvectores_eigenvalores_QR_vf\nfrom src.pca.metodo_po... | [
[
"numpy.sum",
"numpy.transpose",
"numpy.zeros",
"sklearn.decomposition.PCA",
"pandas.DataFrame",
"numpy.linalg.svd",
"numpy.array",
"numpy.dot",
"numpy.mean"
],
[
"numpy.sum",
"sklearn.utils._testing.TempMemmap",
"numpy.random.RandomState",
"sklearn.utils._te... |
mnabavi84/dcamp-intro-python | [
"218b67106061d45cfa18a1b1d46487900f9aa539"
] | [
"11-Numpy Basic Statistics.py"
] | [
"# np_baseball is available\r\n\r\n# Import numpy\r\nimport numpy as np\r\n\r\n# Create np_height_in from np_baseball\r\nnp_height_in = np_baseball[:,0]\r\n\r\n# Print out the mean of np_height_in\r\nprint(np.mean(np_height_in))\r\n\r\n# Print out the median of np_height_in\r\nprint(np.median(np_height_in))\r\n\r\n... | [
[
"numpy.median",
"numpy.array",
"numpy.std",
"numpy.mean",
"numpy.corrcoef"
]
] |
kili-technology/active-learning | [
"72dce7d91b988264dd7fa1a972d9af45e9648c4c"
] | [
"experiments/mnist_simple/class_imbalance.py"
] | [
"import os\nimport logging\nimport pickle\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nimport al\nfrom al.dataset import mnist\nfrom al.model.model_zoo.simple_cnn import ConvModel\nfrom al.model.mnist import MnistLearner\nfrom al.dataset.mnist import MnistDat... | [
[
"numpy.array",
"numpy.arange",
"numpy.random.permutation",
"numpy.random.rand"
]
] |
yuancaimaiyi/gtsfm | [
"cc5781c35af23498d45cd96a1818e4786c5cca80"
] | [
"gtsfm/common/gtsfm_data.py"
] | [
"\"\"\"Class to hold the tracks and cameras of a 3D scene.\nThis can be the output of either data association or of bundle adjustment.\n\nAuthors: Ayush Baid, John Lambert, Xiaolong Wu\n\"\"\"\nimport itertools\nfrom typing import Any, Dict, List, Optional, Tuple\n\nimport numpy as np\nfrom gtsam import PinholeCame... | [
[
"numpy.allclose",
"numpy.median",
"numpy.max",
"numpy.all",
"numpy.min",
"numpy.isnan",
"numpy.array",
"numpy.round",
"numpy.mean"
]
] |
EmergentSystemLabStudent/Prosodic-DAA | [
"068af5db337ed977c059e788353414d3aa9a8ac8"
] | [
"prosodic_daa/sample/pyhlm_sample_murakami.py"
] | [
"import os\nimport numpy as np\nfrom pyhlm.model import WeakLimitHDPHLM, WeakLimitHDPHLMPython\nfrom pyhlm.internals.hlm_states import WeakLimitHDPHLMStates\nfrom pyhlm.word_model import LetterHSMM, LetterHSMMPython\nimport pyhsmm\nimport warnings\nfrom tqdm import trange\nwarnings.filterwarnings('ignore')\nimport ... | [
[
"numpy.cumsum",
"numpy.zeros",
"numpy.savetxt",
"numpy.hstack",
"numpy.identity",
"numpy.loadtxt"
]
] |
trojanjay/sfa-numpy | [
"bff5737ef429f31228d20a9e1d0ce7d46d3080d3"
] | [
"examples/modal_beamforming_open_circular_array.py"
] | [
"\"\"\"\n Compute the plane wave decomposition for an incident broadband plane wave\n on an open circular array using a modal beamformer of finite order.\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport micarray\nfrom micarray.util import db\n\nNsf = 50 # order of the incident sound fie... | [
[
"numpy.matmul",
"numpy.squeeze",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.savefig",
"numpy.conj",
"numpy.fft.irfft",
"matplotlib.pyplot.title",
"numpy.expand_dims",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.colorbar",
"numpy.linspace",
"matplotlib.pyplot.... |
uclyyu/over9000 | [
"9e2e0aa4be9da941372a21ea627c38a3eb7be617"
] | [
"ralamb.py"
] | [
"import torch, math\nfrom torch.optim.optimizer import Optimizer\n\n# RAdam + LARS\nclass Ralamb(Optimizer):\n\n def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0):\n defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)\n self.buffer = [[None, None,... | [
[
"torch.zeros_like"
]
] |
micbia/tools21cm | [
"72081e94e4d83511380baacce427d79d13da2fa5"
] | [
"t2c/segmentation.py"
] | [
"\"\"\"\nCreated by Michele Bianco, 9 July 2021\n\"\"\"\n\nimport numpy as np, pkg_resources\nfrom tqdm import tqdm\n\nimport tensorflow as tf\nfrom tensorflow.keras.models import load_model\nfrom tensorflow.keras import backend as K\nfrom tensorflow.python.ops import nn_ops\nfrom tensorflow.python.framework import... | [
[
"tensorflow.keras.backend.sum",
"tensorflow.keras.backend.log",
"tensorflow.keras.backend.epsilon",
"tensorflow.python.ops.nn_ops._ensure_xent_args",
"numpy.ascontiguousarray",
"numpy.fliplr",
"numpy.mean",
"tensorflow.keras.backend.abs",
"numpy.std",
"tensorflow.python.fra... |
jrobertojunior/face-parsing.PyTorch | [
"d34f39c9ae9726ac8eaf39ecff824a14ec4e15b9"
] | [
"preprocessing/main.py"
] | [
"import cv2 as cv\nimport numpy as np\nimport os\n\ndef preprocess(labels_path, sep_labels_path):\n # list all files on labels_path\n labels_filenames = os.listdir(labels_path)\n\n count = 0\n for label_filename in labels_filenames:\n label_path = os.path.join(labels_path, label_filename)\n\n ... | [
[
"numpy.zeros"
]
] |
Principe92/contextualbandits | [
"43cf5be10b3d39d74f9da5c5fe1cfae5bc2dd6f5"
] | [
"example/loc3/rewards.py"
] | [
"import pandas\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport scipy.stats as st\nfrom pylab import rcParams\n\n\ndf = pandas.read_csv('rewards_loc3.csv')\n\nucb,ts,ovr,egr,egr2,agr,agr2,efr,ac,aac,sft = df['ucb'],df['ts'],df['ovr'],\\\ndf['egr'],df['egr2'],df['agr'],df['agr2'],df['efr'],df['ac'],df['a... | [
[
"scipy.stats.t.ppf",
"pandas.read_csv",
"matplotlib.pyplot.savefig",
"scipy.stats.sem",
"matplotlib.pyplot.title",
"matplotlib.pyplot.errorbar",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.xlabel"
]
] |
Jesmine0902/TSP_CPLEX_2 | [
"8853d6837bd5408b8925eb5f45e21c79945a5904"
] | [
"Add/add_heuristic_engine.py"
] | [
"import pandas as pd\n\n__author__ = 'slei'\n\n\nclass AddHeuristicTSP:\n \"\"\" Finds the shortest path using a heuristic method \"\"\"\n\n def __init__(self, cities_df):\n self.df = cities_df\n self.edges = list((t.origin, t.destination) for t in df.itertuples())\n self.distance = dict(... | [
[
"pandas.read_csv",
"pandas.DataFrame"
]
] |
YellowOfTheEgg/ots-eval | [
"8ec08e60330d41f8f7ffd571dd6301cdedaefd99"
] | [
"ots_eval/stability_evaluation/close.py"
] | [
"import numpy as np\nfrom scipy.spatial.distance import euclidean\nfrom typing import Union\nimport pandas\n\n\nclass CLOSE(object):\n\n def __init__(self, data: pandas.DataFrame, measure: Union[str, callable] = 'mse', minPts: int = None, output: bool = False,\n jaccard: bool = False, weighting: ... | [
[
"numpy.sum",
"numpy.abs",
"numpy.power",
"numpy.array",
"numpy.where",
"numpy.average"
]
] |
dajes/labelfficient | [
"5dd0566224fb04285e690bf8576eacc04a7c87cd"
] | [
"commons/siam_mask/experiments/siammask_sharp/resnet.py"
] | [
"import torch.nn as nn\nimport torch\nfrom torch.autograd import Variable\nimport math\nimport torch.utils.model_zoo as model_zoo\nfrom commons.siam_mask.models.features import Features\n\n__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',\n 'resnet152']\n\n\nmodel_urls = {\n 'resnet... | [
[
"torch.nn.MaxPool2d",
"torch.nn.BatchNorm2d",
"torch.FloatTensor",
"torch.autograd.Variable",
"torch.nn.Conv2d",
"torch.nn.Sequential",
"torch.utils.model_zoo.load_url",
"torch.nn.ReLU"
]
] |
AlexBlack2202/EigenGAN-Tensorflow | [
"86b21a47a824a2bb04a088c3e78b03d03a53735c"
] | [
"tflib/distribute/distribute.py"
] | [
"import tensorflow as tf\n\nfrom tensorflow.python.client import device_lib\n\n\ndef get_available_gpus():\n local_device_protos = device_lib.list_local_devices()\n return [x.name for x in local_device_protos if x.device_type == 'GPU']\n\ngpus = get_available_gpus\n\n\ndef split_nest(nest, num_or_size_splits,... | [
[
"tensorflow.python.client.device_lib.list_local_devices",
"tensorflow.nest.pack_sequence_as",
"tensorflow.device",
"tensorflow.reduce_mean",
"tensorflow.expand_dims",
"tensorflow.concat",
"tensorflow.nest.flatten",
"tensorflow.split"
]
] |
vdutor/VFF | [
"459be5b480bba49e8c15dc7daeca5fd1ddd762df"
] | [
"experiments/increasing_dim/Exp_1/kron.py"
] | [
"import numpy as np\nimport sys\nimport gpflow\nimport VFF\n\nfrom time import time\n\nfrom config import *\n\ndim = sys.argv[1]\nrep = sys.argv[2]\n\nprint('vff: dimension {}, replicate {}'.format(dim, r))\n\n# data\ndata = np.load('data/data_dim{}_rep{}.npz'.format(dim, 0))\n\n# full_gp\ndef prodkern(dim):\n r... | [
[
"numpy.arange",
"numpy.ones"
]
] |
Akshat-unt/jina | [
"b0b058f99f3ee4dcbcbbf2acbf04c5d7e7e9c717"
] | [
"tests/integration/issues/hanging_termination/test_hanging_termination.py"
] | [
"import os\nfrom pathlib import Path\n\nimport numpy as np\nimport pytest\n\nfrom jina import Flow, Document\nfrom jina.clients import Client\nfrom jina.logging.profile import TimeContext\nfrom jina.parsers import set_client_cli_parser\nfrom typing import Dict\nfrom jina import DocumentArray, Executor, requests\n\n... | [
[
"numpy.random.random"
]
] |
claireguichon/pynet | [
"92706375e61fb5cb523548303b7d04769c9de134"
] | [
"pynet/cam.py"
] | [
"# -*- coding: utf-8 -*-\n##########################################################################\n# NSAp - Copyright (C) CEA, 2019\n# Distributed under the terms of the CeCILL-B license, as published by\n# the CEA-CNRS-INRIA. Refer to the LICENSE file or to\n# http://www.cecill.info/licences/Licence_CeCILL-B_V1... | [
[
"torch.sum",
"torch.nn.functional.softmax",
"numpy.max",
"torch.from_numpy",
"numpy.min",
"numpy.maximum",
"numpy.mean"
]
] |
GatherLab/OLED-evaluation | [
"419dfd5d2c3773f5f90d76aef634f8b1cc0b6378"
] | [
"src/UI_assign_group_window.py"
] | [
"# -*- coding: utf-8 -*-\nfrom PySide2 import QtCore, QtGui, QtWidgets\n\nimport json\nimport core_functions as cf\nimport numpy as np\n\nfrom UI_labeled_slider import LabeledSlider\n\n\nclass Ui_AssignGroup(object):\n def setupUi(self, AssignGroups):\n # Note: this is not how it should be done but curren... | [
[
"numpy.unique",
"numpy.empty"
]
] |
czw1296924847/ResGraphNet | [
"1638236e4138719c324afc3137f31cfec8a9de64"
] | [
"run/run_ResGraphNet.py"
] | [
"\"\"\"\nTesting ResGraphNet\n\"\"\"\nimport datetime\nimport numpy as np\nimport pandas as pd\nimport torch\nimport os\nimport os.path as osp\nimport matplotlib.pyplot as plt\n\nimport sys\nsys.path.append(\"..\")\nimport func.cal as cal\n\n\ndevice = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n# device ... | [
[
"numpy.load",
"torch.nn.MSELoss",
"pandas.DataFrame",
"matplotlib.pyplot.show",
"torch.arange",
"torch.cuda.is_available",
"torch.from_numpy",
"numpy.square",
"torch.cat",
"numpy.loadtxt"
]
] |
JaworWr/Dynamic-inverse-kinematics | [
"b9da50b88152682060075a44da940e6f98690a9a"
] | [
"idea.py"
] | [
"import numpy as np\n\n\ndef FNS(scores):\n domination = np.all(scores[:, None, :] <= scores[None, :, :], axis=2) # domination[i, j] = \"i dominuje j\"\n domination &= np.any(scores[:, None, :] < scores[None, :, :], axis=2)\n Nx = domination.sum(0)\n\n Pf = []\n ranks = np.zeros(scores.shape[0])\n ... | [
[
"numpy.sum",
"numpy.any",
"numpy.argsort",
"numpy.stack",
"numpy.vstack",
"numpy.random.choice",
"numpy.take_along_axis",
"numpy.where",
"numpy.nonzero",
"numpy.unique",
"numpy.random.uniform",
"numpy.zeros",
"numpy.repeat",
"numpy.all",
"numpy.power",
... |
Keck-FOBOS/producer | [
"6f2b0d3f29f62187bf593567081061e53ddb5a4e"
] | [
"producer/util.py"
] | [
"\"\"\"\nMiscellaneous package utilities.\n\n.. include:: ../include/links.rst\n\"\"\"\n\nfrom itertools import chain, combinations\n\nfrom IPython import embed \n\nimport numpy\n\n\ndef all_subclasses(cls):\n \"\"\"\n Collect all the subclasses of the provided class.\n\n The search follows the inheritance... | [
[
"numpy.sum",
"numpy.roll",
"numpy.atleast_2d",
"numpy.zeros",
"numpy.logical_not"
]
] |
chundiliu/slim_for_Cdiscount | [
"ea7f9d56072072c031094c12c803c63591066c6c"
] | [
"generate_cdiscount_predictions.py"
] | [
"import math\nimport tensorflow as tf\nimport os\nimport struct\nimport pdb\nimport numpy as np\nfrom datasets import dataset_factory\nfrom nets import nets_factory\nimport nets.resnet_v2 as resnet_v2\nfrom preprocessing import preprocessing_factory\nslim = tf.contrib.slim\n\ndef merge_predictions(predictions_fn):\... | [
[
"tensorflow.global_variables_initializer",
"tensorflow.train.latest_checkpoint",
"tensorflow.train.start_queue_runners",
"tensorflow.Session",
"tensorflow.train.Saver",
"tensorflow.train.batch",
"tensorflow.train.Coordinator"
]
] |
jeantardelli/math-with-python | [
"119bbbc62329c0d834d965232239bd3b39116cc1"
] | [
"data-and-statistics/understanding-a-population-using-sampling.py"
] | [
"\"\"\"\nOne of the central problems in statistics is to make estimations — and quantify\nhow good these estimations are — of the distribution of an entire population\ngiven only a small (random) sample. A classic example is to estimate the average\nheight of all the people in a country when measuring the height of... | [
[
"pandas.Series",
"scipy.stats.t.ppf"
]
] |
Bhavay192/keras | [
"f1e9c76675981ee6683f54a3ce569212d551d12d"
] | [
"keras/optimizer_v2/rmsprop_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... | [
[
"numpy.sqrt",
"tensorflow.compat.v2.compat.v1.nn.embedding_lookup",
"tensorflow.compat.v2.compat.v1.get_default_graph",
"tensorflow.compat.v2.compat.v1.global_variables_initializer",
"tensorflow.compat.v2.Graph",
"tensorflow.compat.v2.test.main",
"tensorflow.compat.v2.device",
"num... |
shivampotdar/Artificial-Intelligence-with-Python | [
"00221c3b1a6d8003765d1ca48b5c95f86da375d9"
] | [
"Chapter 10/code/category_predictor.py"
] | [
"from sklearn.datasets import fetch_20newsgroups\nfrom sklearn.naive_bayes import MultinomialNB\nfrom sklearn.feature_extraction.text import TfidfTransformer\nfrom sklearn.feature_extraction.text import CountVectorizer\n\n# Define the category map\ncategory_map = {'talk.politics.misc': 'Politics', 'rec.autos': 'Aut... | [
[
"sklearn.naive_bayes.MultinomialNB",
"sklearn.feature_extraction.text.TfidfTransformer",
"sklearn.feature_extraction.text.CountVectorizer"
]
] |
andrzejmalota/StockPricePrediction | [
"a6d7da353b706fb2d970f2883841db14d896268f"
] | [
"src/trading_simulation/simulation.py"
] | [
"import sys\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\n\nclass Simulation:\n def __init__(self, init_investment, stock_returns, strategy, predicted_movements=None):\n self.init_investment = init_investment\n self.predicted_movements = predicted_movements\n s... | [
[
"pandas.DataFrame",
"numpy.insert",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.show",
"numpy.array",
"matplotlib.pyplot.xlabel"
]
] |
pranavrajpal/scipy | [
"7dcdeffed53483a60b3e054618520e0f28adeba4",
"859c1061b3d5aa30c4466824049d69edde5499a2"
] | [
"scipy/optimize/tests/test_linprog.py",
"scipy/integrate/_ivp/rk.py"
] | [
"\"\"\"\nUnit test for Linear Programming\n\"\"\"\nimport sys\n\nimport numpy as np\nfrom numpy.testing import (assert_, assert_allclose, assert_equal,\n assert_array_less, assert_warns, suppress_warnings)\nfrom pytest import raises as assert_raises\nfrom scipy.optimize import linprog, Opt... | [
[
"numpy.ones",
"numpy.sum",
"numpy.testing.assert_equal",
"numpy.random.seed",
"numpy.testing.assert_warns",
"numpy.vstack",
"numpy.cos",
"numpy.random.rand",
"numpy.identity",
"numpy.eye",
"numpy.zeros",
"numpy.testing.assert_array_less",
"scipy.optimize.linprog... |
RodrigoATorres/hermione | [
"c51f5e54a41609099eef48990c7ad7018dcdf41a"
] | [
"hermione/module_templates/__IMPLEMENTED_BASE__/src/predict.py"
] | [
"import pandas as pd\nimport io\nfrom joblib import load\nimport logging\n\nlogging.getLogger().setLevel(logging.INFO)\n\ndef generate_data():\n new_data = pd.DataFrame({\n 'Pclass':[3,2,1],\n 'Sex': ['male', 'female', 'male'],\n 'Age':[4, 22, 28]\n })\n return new_data\n\n\ndef load_m... | [
[
"pandas.DataFrame"
]
] |
bee-hive/nested-policy-rl | [
"56b0be37ed814265cb3ef26ea0a1a62b5cd7f05c"
] | [
"tests/test_networks.py"
] | [
"import torch\nimport torch.optim as optim\nimport torch.nn.functional as F\nimport torch.nn as nn\n\n# import sys\n# sys.path.append(\"../simulated_fqi/\")\nfrom simulated_fqi import NFQNetwork, ContrastiveNFQNetwork\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\ndef train(x, y, groups, network, optimiz... | [
[
"torch.nn.functional.mse_loss"
]
] |
SixHeo/IVOS-ATNet | [
"1cf574953a96bd680c518c6362b510fd103ff271"
] | [
"libs/utils_torch.py"
] | [
"import torch\n\ndef combine_masks_with_batch(masks, n_obj, th=0.5, return_as_onehot = False):\n \"\"\" Combine mask for different objects.\n\n Different methods are the following:\n\n * `max_per_pixel`: Computes the final mask taking the pixel with the highest\n probability for every... | [
[
"torch.zeros_like",
"torch.argmax"
]
] |
muchemwal/models | [
"49fd0a8a61b0e5dab196014bf47de7f62d97c884"
] | [
"tensorflow/super_resolution/syndicai.py"
] | [
"import os\nimport io\nimport time\nimport base64\nimport functools\n\nfrom PIL import Image\nimport numpy as np\nimport tensorflow as tf\nimport tensorflow_hub as hub\n\nfrom helpers import *\nos.environ[\"TFHUB_DOWNLOAD_PROGRESS\"] = \"True\"\n\n\nclass PythonPredictor:\n\n def __init__(self, config):\n ... | [
[
"tensorflow.squeeze"
]
] |
Ricechrispi/sc2_academy | [
"9ffed467fe019262035ac61d10c5cc3ee64a7bb2"
] | [
"sc2_academy/ppo/my_epsilon_greedy_policy.py"
] | [
"# coding=utf-8\n# Copyright 2018 The TF-Agents 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 b... | [
[
"tensorflow.shape",
"tensorflow.logical_not",
"tensorflow.ones_like",
"tensorflow.nest.map_structure",
"tensorflow.compat.v1.where",
"tensorflow.zeros_like"
]
] |
ronnith24/NeuralNetworksFromScratch | [
"5c831de8954a4b84fef7b70b16f9d9e6c1cb24b9"
] | [
"NeuralNetwork.py"
] | [
"import numpy as np\n\nclass NeuralNetwork(object):\n def __init__(self, topology, epsilon, numLabels):\n self.theta = []\n self.topology = topology\n self.numLabels = numLabels\n self.gradientChecking = False\n for layer in range(len(self.topology)):\n if layer == 0... | [
[
"numpy.matmul",
"numpy.zeros",
"numpy.exp",
"numpy.random.rand",
"numpy.concatenate"
]
] |
swcho84/image-segmentation | [
"ef9b9b3d832e9efe6f43522cc5ca0e17279d6608"
] | [
"image-segmentation/data_generators/kitti/kitti_dataset.py"
] | [
"from collections import namedtuple\n\nimport os\nimport json\nimport numpy as np\n\nfrom tqdm import tqdm\nfrom data_generators.utils import load_image_rgb\n\n# Copied from: https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/helpers/labels.py\n#\n# Cityscapes labels\n#\n#-------------------... | [
[
"numpy.arange",
"numpy.zeros"
]
] |
pearcandy/pennylane | [
"dfa35989cd0798496e41999a197bcf0eb26185df"
] | [
"tests/devices/test_default_qubit_jax.py"
] | [
"import pytest\r\n\r\njax = pytest.importorskip(\"jax\", minversion=\"0.2\")\r\njnp = jax.numpy\r\nimport numpy as np\r\nimport pennylane as qml\r\nfrom pennylane.devices.default_qubit_jax import DefaultQubitJax\r\n\r\npytestmark = pytest.mark.usefixtures(\"tape_mode\")\r\n\r\n\r\nclass TestQNodeIntegration:\r\n ... | [
[
"numpy.array",
"numpy.all",
"numpy.testing.assert_array_equal"
]
] |
RotemBadash/IML.HUJI | [
"2b20d074c159123f61b321a7e84312ab82400949"
] | [
"IMLearn/learners/regressors/polynomial_fitting.py"
] | [
"from __future__ import annotations\nfrom typing import NoReturn\nfrom . import LinearRegression\nfrom ...base import BaseEstimator\nimport numpy as np\n\n\nclass PolynomialFitting(BaseEstimator):\n \"\"\"\n Polynomial Fitting using Least Squares estimation\n \"\"\"\n def __init__(self, k: int) -> Polyn... | [
[
"numpy.vander"
]
] |
LvJC/cpp-libtorch | [
"4a56dda616bde50423591e7a4d4d7be6a978f6bf"
] | [
"MyModule.py"
] | [
"import torch\nimport torchvision\n\n# An instance of your model.\nmodel = torchvision.models.resnet18()\n\n# An example input you would normally provide to your model's forward() method.\nexample = torch.rand(1, 3, 224, 224)\n\n# Use torch.jit.trace to generate a torch.jit.ScriptModule via tracing.\ntraced_script_... | [
[
"torch.rand",
"torch.jit.trace"
]
] |
demarley/leopard | [
"52c5eb2dd732798972d429887c273f8449039c8f"
] | [
"python/deepLearningTorch.py"
] | [
"\"\"\"\nCreated: 16 August 2018\nLast Updated: 16 August 2018\n\nDan Marley\ndaniel.edison.marley@cernSPAMNOT.ch\nTexas A&M University\n-----\n\nClass for performing deep learning in pytorch\n\nDesigned for running on desktop at TAMU\nwith specific set of software installed\n--> not guaranteed to work in ... | [
[
"torch.nn.Linear",
"torch.load",
"sklearn.model_selection.StratifiedKFold",
"torch.nn.functional.sigmoid",
"sklearn.metrics.roc_curve",
"torch.autograd.Variable",
"numpy.std",
"torch.nn.functional.relu",
"torch.from_numpy",
"torch.nn.ModuleList",
"torch.nn.BCELoss",
... |
THU-DA-6D-Pose-Group/self6dpp | [
"c267cfa55e440e212136a5e9940598720fa21d16"
] | [
"core/csrc/torch_nndistance/test.py"
] | [
"import torch\nimport os.path as osp\nimport sys\nfrom torch.autograd import Variable\n\ncur_dir = osp.dirname(osp.abspath(__file__))\nsys.path.insert(0, cur_dir)\nimport torch_nndistance as NND\n\n\np1 = torch.rand(10, 1000, 3)\np2 = torch.rand(10, 1500, 3)\npoints1 = Variable(p1, requires_grad=True)\npoints2 = p2... | [
[
"torch.sum",
"torch.rand",
"torch.autograd.Variable"
]
] |
gorff/Toric-Code-Correlated-Error-Decoder | [
"c43cf34c22f03334add078f5d02e6604e5c89cba"
] | [
"project/correctiondemos/pythag_test.py"
] | [
"\nimport matplotlib.pyplot as plt\nimport matplotlib.mlab as mlab\nimport numpy as np\nimport os,sys,inspect\nimport imageio\n\nsys.path.insert(1, os.path.join(sys.path[0], '..')) #go up a dir to import\nimport CodePy2.funmath as funmath\n#import imageio\nn = 1.0\nsizes = [i/n for i in range(33*int(n))]\n\nxvals ... | [
[
"matplotlib.pyplot.figure",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.title",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.close",
"numpy.sqrt",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlabel"
]
] |
SunYanCN/nlp-experiments-in-pytorch | [
"5d05a53146dffd707e4d037230656f980d7be05c"
] | [
"models/Transformer.py"
] | [
"import copy\nimport math\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\n\nfrom utils.utils import clones\n\n\nclass LayerNormGoogle(nn.Module):\n def __init__(self, features, epsilon=1e-6):\n ... | [
[
"torch.ones",
"matplotlib.pyplot.legend",
"torch.nn.Linear",
"torch.nn.init.xavier_uniform_",
"torch.cos",
"torch.mean",
"matplotlib.pyplot.figure",
"torch.nn.functional.softmax",
"torch.nn.Embedding",
"numpy.arange",
"torch.sin",
"matplotlib.pyplot.show",
"torc... |
donyori/2018ccf_bdci_inter_fund_correlation_prediction | [
"6e06a3e192e05ae1e9822111cf323eda3a61bf4e"
] | [
"program/model/version/ver1_2.py"
] | [
"from tensorflow import keras\n\nfrom constants import TRADING_DAYS_PER_WEEK, INDEX_RETURN_INDICATOR_NUMBER\nfrom ..constants import *\n\nMODEL_NAME = 'ifcp_model_ver1_2'\nROLLING_WINDOW_SIZE = TRADING_DAYS_PER_WEEK\n\n\ndef build_model():\n fund1_return = keras.Input(shape=(ROLLING_WINDOW_SIZE, 1), name=FUND1_R... | [
[
"tensorflow.keras.layers.concatenate",
"tensorflow.keras.regularizers.l2",
"tensorflow.keras.layers.subtract",
"tensorflow.keras.Model",
"tensorflow.keras.regularizers.l1",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.Input"
]
] |
simonfong6/micro-projects | [
"5be195ea72ce117df6da041446f11c18e102b5df"
] | [
"ml_tutorial/test.py"
] | [
"import svm as SVM\nimport numpy as np\n\ndata_dict = {\t-1:np.array(\t[[10,9,1],\n\t\t\t\t[2,8,1],\n\t\t\t\t[3,8,1],]),\n \n\t\t1:np.array(\t[[5,1,1],\n \t [6,-1,1],\n \t [7,3,1],])}\n\nsvm = SVM.Support_Vector_Machine()\nsvm.fit(data=data_dict)\n\npredict_... | [
[
"numpy.array"
]
] |
OnionIoT/tau-lidar-camera | [
"a70b24e18be8e4c5abfe525c6768fbc10a492fd8"
] | [
"examples/distancePlusAmplitude.py"
] | [
"import argparse\nimport numpy as np\nimport cv2\n\nfrom TauLidarCommon.frame import FrameType\nfrom TauLidarCamera.camera import Camera\n\ndef setup(serialPort=None):\n port = None\n camera = None\n\n # if no serial port is specified, scan for available Tau Camera devices\n if serialPort is None:\n ... | [
[
"numpy.frombuffer"
]
] |
R-aryan/Jigsaw-Toxic-Comment-Classification | [
"e5e4da7df379ac1b315f2bde655386180f39c517"
] | [
"backend/services/toxic_comment_jigsaw/application/ai/training/src/train.py"
] | [
"import pandas as pd\nimport numpy as np\nimport torch\n\nfrom sklearn.model_selection import train_test_split\nfrom backend.services.toxic_comment_jigsaw.application.ai.model import BERTClassifier\nfrom backend.services.toxic_comment_jigsaw.application.ai.training.src.dataset import BERTDataset\nfrom backend.servi... | [
[
"numpy.array",
"torch.utils.data.DataLoader",
"sklearn.model_selection.train_test_split",
"pandas.read_csv"
]
] |
chensnathan/CARAFE_CUDA | [
"33d3d3af69b24fc679f6a3a071a19070dc46664b"
] | [
"carafe_layer/setup.py"
] | [
"from setuptools import setup\nfrom torch.utils.cpp_extension import BuildExtension, CUDAExtension\n\nsetup(\n name='carafe_layer_cuda',\n ext_modules=[\n CUDAExtension('carafe_layer_cuda', [\n 'src/carafe_layer_cuda.cpp',\n 'src/carafe_layer_kernel.cu',\n ])\n ],\n c... | [
[
"torch.utils.cpp_extension.CUDAExtension"
]
] |
AU-DATALAB/newsFluxus | [
"20522b2c8c830d2377a9620d149a515baaaa9cf4"
] | [
"src/saffine/detrending_coeff.py"
] | [
"from numpy import *\r\nimport numpy as np\r\n# from numba import jit\r\n\r\n# @jit\r\n\r\ndef detrending_coeff(win_len , order):\r\n\r\n#win_len = 51\r\n#order = 2\r\n\tn = (win_len-1)/2\r\n\tA = mat(ones((win_len,order+1)))\r\n\tx = np.arange(-n , n+1)\r\n\tfor j in range(0 , order + 1):\r\n\t\tA[:,j] = mat(x ** ... | [
[
"numpy.arange"
]
] |
RedTachyon/OpenTraj | [
"8277f526d714a4e77d0f9f354259ff5b74e59fd2"
] | [
"opentraj/toolkit/loaders/loader_pets.py"
] | [
"# Author: Javad Amirian\n# Email: amiryan.j@gmail.com\n\nimport xml.etree.ElementTree as et\n\nimport numpy as np\nimport pandas as pd\n\nfrom opentraj.toolkit.core.trajdataset import TrajDataset\nfrom opentraj.toolkit.utils.calibration.camera_calibration_tsai import *\n\n\ndef load_pets(path, **kwargs):\n \"\"... | [
[
"numpy.array"
]
] |
t-brink/pyiron | [
"c07552b54a39e3f036ba395325cd4b372af0f794"
] | [
"pyiron/vasp/potential.py"
] | [
"# coding: utf-8\n# Copyright (c) Max-Planck-Institut für Eisenforschung GmbH - Computational Materials Design (CM) Department\n# Distributed under the terms of \"New BSD License\", see the LICENSE file.\n\nimport os\nimport posixpath\n\nimport numpy as np\nimport pandas\nimport tables\nimport warnings\nfrom pyiron... | [
[
"pandas.Series",
"pandas.DataFrame",
"pandas.concat",
"numpy.core.defchararray.find",
"numpy.where"
]
] |
MECLabTUDA/ACS | [
"bb418c5479a3585138c48c63112352f5cc8f64b1"
] | [
"mp/models/continual/model_utils.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mp.models.segmentation.unet_fepegar import UNet2D\n\n### UNet Wrapper ###\nclass UNet2D_dis(UNet2D):\n r\"\"\"Wrapper for UNet2D to access encoder and decoder seperately.\n \"\"\"\n def __init__(self, *args, **kwargs):\n sup... | [
[
"torch.stack",
"torch.rand",
"torch.nn.Conv2d",
"torch.nn.InstanceNorm2d",
"torch.nn.Sigmoid",
"torch.nn.ConvTranspose2d",
"torch.nn.BatchNorm2d",
"torch.nn.Softmax",
"torch.var",
"torch.nn.AvgPool2d",
"torch.mean",
"torch.nn.AdaptiveMaxPool2d",
"torch.tensor",
... |
hashi0203/deep-video-mvs | [
"b3943a9249d522dca3e6cd603e427f611cc7bad5",
"fa14288f149c5af7b2a49092f729f5c4f44517ba"
] | [
"dataset/7scenes-export/7scenes-export-color.py",
"dvmvs/pairnet/run-testing.py"
] | [
"import os\nimport shutil\nfrom multiprocessing.pool import Pool\n\nimport cv2\nimport numpy as np\nfrom functools import partial\nfrom path import Path\n\n\ndef process_scene(input_directory, output_folder):\n K = np.array([[525.0, 0.0, 320.0],\n [0.0, 525.0, 240.0],\n [0.0, 0.... | [
[
"numpy.array",
"numpy.loadtxt"
],
[
"numpy.fromfile",
"torch.load",
"numpy.transpose",
"torch.no_grad",
"torch.from_numpy",
"torch.device",
"numpy.loadtxt"
]
] |
lukasc-ch/QuantLab | [
"7ddcc51ec1131a58269768cd898ce04e8b49beb6"
] | [
"quantlab/COCO/YOLOv3Tiny/postprocess.py"
] | [
"# Copyright (c) 2019 UniMoRe, Matteo Spallanzani\n\nimport torch\n\nfrom ..utils.utils import xywh2xyxy, bbox_iou\n\n\ndef clip_boxes(boxes):\n boxes[:, [0, 2]] = boxes[:, [0, 2]].clamp(min=0, max=1)\n boxes[:, [1, 3]] = boxes[:, [1, 3]].clamp(min=0, max=1)\n\n\ndef postprocess_pr(pr_outs, conf_thres=0.001, ... | [
[
"torch.cat",
"torch.isfinite"
]
] |
NicoSerranoP/PySyft | [
"87fcd566c46fce4c16d363c94396dd26bd82a016"
] | [
"syft/frameworks/torch/mpc/fss.py"
] | [
"\"\"\"\nThis is an implementation of Function Secret Sharing\n\nUseful papers are:\n- Function Secret Sharing- Improvements and Extensions, Boyle 2017\n Link: https://eprint.iacr.org/2018/707.pdf\n- Secure Computation with Preprocessing via Function Secret Sharing, Boyle 2019\n Link: https://eprint.iacr.org/2019... | [
[
"torch.empty",
"torch.ones",
"torch.randint",
"torch.split",
"torch.tensor",
"torch.gather",
"torch.arange",
"torch.IntTensor",
"torch.cat"
]
] |
neurodebian/scikits.image-1 | [
"33206f87c5e0208e7ff0d5910ac082b3353fe04e",
"33206f87c5e0208e7ff0d5910ac082b3353fe04e"
] | [
"skimage/exposure/exposure.py",
"skimage/filter/_gabor.py"
] | [
"import warnings\nimport numpy as np\n\nfrom skimage import img_as_float\nfrom skimage.util.dtype import dtype_range, dtype_limits\nfrom skimage._shared.utils import deprecated\n\n\n__all__ = ['histogram', 'cumulative_distribution', 'equalize',\n 'rescale_intensity', 'adjust_gamma',\n 'adjust_lo... | [
[
"numpy.log2",
"numpy.interp",
"numpy.histogram",
"numpy.any",
"numpy.issubdtype",
"numpy.exp",
"numpy.clip",
"numpy.max",
"numpy.min",
"numpy.nonzero"
],
[
"numpy.zeros",
"numpy.cos",
"numpy.exp",
"numpy.log",
"numpy.sin",
"numpy.real",
"nump... |
daoran/opendr | [
"bca25f6a43244fe9c219a24576181f94a0726923"
] | [
"tests/sources/tools/perception/object_tracking_2d/deep_sort/test_object_tracking_2d_deep_sort.py"
] | [
"# Copyright 2020-2022 OpenDR European Project\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 applicab... | [
[
"torch.equal"
]
] |
maxiaoba/rlk | [
"3e23473f6bbc59552b6b2bcd97245e024d7ca95d",
"3e23473f6bbc59552b6b2bcd97245e024d7ca95d",
"3e23473f6bbc59552b6b2bcd97245e024d7ca95d",
"3e23473f6bbc59552b6b2bcd97245e024d7ca95d"
] | [
"tests/DifferentialGame/masac_gnn_gaussian.py",
"tests/Simple/SupLstm/visualize_policy.py",
"tests/MultiDifferentialGame/r2g_gnn11_share_gaussian.py",
"tests/Particle/coma_gaussian.py"
] | [
"import copy\nimport torch.nn as nn\nfrom rlkit.launchers.launcher_util import setup_logger\nimport rlkit.torch.pytorch_util as ptu\nfrom rlkit.core.ma_eval_util import get_generic_ma_path_information\n\ndef experiment(variant):\n num_agent = variant['num_agent']\n from differential_game import DifferentialGa... | [
[
"torch.nn.Linear",
"torch.nn.ReLU",
"torch.manual_seed",
"numpy.random.seed"
],
[
"torch.load"
],
[
"torch.nn.Linear",
"torch.manual_seed",
"torch.save",
"numpy.random.seed",
"torch.nn.LeakyReLU"
],
[
"torch.nn.Linear",
"torch.manual_seed",
"numpy.ra... |
jrsassen/megaman | [
"faccaf267aad0a8b18ec8a705735fd9dd838ca1e"
] | [
"megaman/geometry/tests/test_adjacency.py"
] | [
"# LICENSE: Simplified BSD https://github.com/mmp2/megaman/blob/master/LICENSE\n\nfrom nose import SkipTest\n\nimport numpy as np\nfrom numpy.testing import assert_allclose, assert_raises, assert_equal\nfrom scipy.sparse import isspmatrix\nfrom scipy.spatial.distance import cdist, pdist, squareform\n\nfrom megaman.... | [
[
"numpy.testing.assert_raises",
"scipy.spatial.distance.pdist",
"scipy.spatial.distance.cdist",
"numpy.random.RandomState",
"numpy.arange",
"numpy.random.rand",
"scipy.sparse.isspmatrix"
]
] |
scopatz/PyTables | [
"05a74def785688abd802224a5ba44393a701ebc7",
"05a74def785688abd802224a5ba44393a701ebc7",
"05a74def785688abd802224a5ba44393a701ebc7"
] | [
"bench/create-large-number-objects.py",
"tables/tests/common.py",
"examples/tutorial2.py"
] | [
"\"This creates an HDF5 file with a potentially large number of objects\"\n\nimport sys\nimport numpy\nimport tables\n\nfilename = sys.argv[1]\n\n# Open a new empty HDF5 file\nfileh = tables.open_file(filename, mode=\"w\")\n\n# nlevels -- Number of levels in hierarchy\n# ngroups -- Number of groups on each level\n#... | [
[
"numpy.array"
],
[
"numpy.all"
],
[
"numpy.arange",
"numpy.dtype"
]
] |
aljanabim/svea | [
"37d27089237af3777456d7664473ffb811dabf33"
] | [
"src/teleop_tools/mouse_teleop/scripts/mouse_teleop.py"
] | [
"#! /usr/bin/env python\n# -*- coding: utf-8 -*-\n#\n# Copyright (c) 2015 Enrique Fernandez\n# Released under the BSD License.\n#\n# Authors:\n# * Enrique Fernandez\n\nimport Tkinter\n\nimport rospy\nfrom geometry_msgs.msg import Twist, Vector3\n\nimport numpy\n\n\nclass MouseTeleop():\n def __init__(self):\n ... | [
[
"numpy.rad2deg"
]
] |
dnjst/squidpy | [
"ca765d04b9621debb8752d3d4693dd68f6909513"
] | [
"tests/image/test_segmentation.py"
] | [
"from typing import Tuple, Union, Callable, Optional, Sequence\nfrom pytest_mock import MockerFixture\nimport pytest\n\nimport numpy as np\nimport dask.array as da\n\nfrom squidpy.im import (\n segment,\n ImageContainer,\n SegmentationCustom,\n SegmentationWatershed,\n)\nfrom squidpy.im._segment import ... | [
[
"numpy.zeros_like",
"numpy.ones",
"numpy.zeros",
"numpy.testing.assert_array_equal",
"numpy.mean"
]
] |
schibsen/MLops_exercises_organized | [
"2c9b386fed7b1e400524905cb68f220caf9d015b"
] | [
"src/models/model.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass MyAwesomeModel(nn.Module):\n def __init__(self, n_classes):\n super(MyAwesomeModel, self).__init__()\n\n self.feature_extractor = nn.Sequential(\n nn.Conv2d(in_channels=1, out_channels=6, kernel_size=4, stri... | [
[
"torch.nn.functional.log_softmax",
"torch.nn.Linear",
"torch.flatten",
"torch.nn.Tanh",
"torch.nn.Conv2d",
"torch.nn.AvgPool2d"
]
] |
hugerepo-tianhang/low_dim_update_stable | [
"565f6cbf886d266d0633bc112ccae28f1d116ee1"
] | [
"stable_baselines/cmaes/cma_redo.py"
] | [
"from stable_baselines.ppo2.run_mujoco import eval_return\nimport cma\n\nimport numpy as np\nfrom stable_baselines.low_dim_analysis.eval_util import *\nfrom stable_baselines.low_dim_analysis.common import do_pca, plot_2d, \\\n dump_rows_write_csv, generate_run_dir, do_proj_on_first_n_IPCA, get_allinone_concat_df... | [
[
"numpy.vstack",
"numpy.sum",
"numpy.matmul",
"pandas.read_csv",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel",
"numpy.min",
"numpy.max",
"numpy.array",
"sklearn.decomposition.IncrementalPCA",
"numpy.linalg.norm",
"matplotlib.... |
larsbarring/icclim | [
"f3685c77a1a3aaff58b0d05609380c9387e9aa99"
] | [
"icclim/user_indices/stat.py"
] | [
"from typing import Sequence\n\nimport numpy as np\nimport xarray\nfrom xarray import DataArray\nfrom xclim.indices.run_length import rle_1d\n\n\ndef get_longest_run_start_index(\n arr: DataArray,\n window: int = 1,\n dim: str = \"time\",\n) -> DataArray:\n return xarray.apply_ufunc(\n get_index_... | [
[
"numpy.any",
"numpy.where",
"numpy.all"
]
] |
Ikerlz/dcd | [
"056e5c4060f9d655ce4f6234b86481ae4b3f7106"
] | [
"DC_method/util.py"
] | [
"import numpy as np\nimport pandas as pd\nfrom sklearn.cluster import KMeans\nimport itertools\nimport findspark\nimport pyspark\nfrom pyspark.sql.functions import pandas_udf, PandasUDFType\nfrom pyspark.sql.types import *\nimport time\n\n\ndef simulate_sbm_dc_data(sbm_matrix, sample_size=1000, partition_num=10, cl... | [
[
"numpy.random.binomial",
"numpy.sum",
"numpy.empty",
"numpy.zeros",
"numpy.append",
"pandas.DataFrame",
"numpy.argsort",
"numpy.linalg.svd",
"sklearn.cluster.KMeans",
"numpy.array",
"numpy.dot",
"numpy.random.randint",
"numpy.linalg.norm",
"numpy.linalg.eig"... |
denix56/fcdd | [
"d110aa8b141dc13f47156da913a6b4f9d64ddc74"
] | [
"python/fcdd/datasets/outlier_exposure/emnist.py"
] | [
"import os.path as pt\n\nimport numpy as np\nimport torchvision.transforms as transforms\nimport torch\nfrom torch.utils.data import DataLoader\nfrom torchvision.datasets import EMNIST\n\n\ndef ceil(x: float):\n return int(np.ceil(x))\n\n\nclass MyEMNIST(EMNIST):\n \"\"\" Reimplements get_item to transform te... | [
[
"torch.utils.data.DataLoader",
"numpy.ceil"
]
] |
ava6969/rgb_stacking_extend | [
"a36f1e35aa796e77201321161056e174966e7707"
] | [
"rgb_stacking/contrib/common.py"
] | [
"import numpy as np\nimport torch\nimport torch.nn as nn\nfrom rgb_stacking.utils.utils import init\n\n\nclass Flatten(nn.Module):\n def forward(self, x):\n return x.view(x.size(0), -1)\n\n\nclass Sum(nn.Module):\n def __init__(self, dim):\n super().__init__()\n self.dim = dim\n\n def ... | [
[
"torch.sum",
"torch.nn.init.constant_",
"numpy.sqrt",
"torch.nn.init.orthogonal_",
"torch.mean"
]
] |
SunsetWolf/qlib | [
"89972f6c6f9fa629b4f74093d4ba1e93c9f7a5e5"
] | [
"qlib/contrib/data/highfreq_processor.py"
] | [
"import os\n\nimport numpy as np\nimport pandas as pd\nfrom qlib.data.dataset.processor import Processor\nfrom qlib.data.dataset.utils import fetch_df_by_index\nfrom typing import Dict\n\n\nclass HighFreqTrans(Processor):\n def __init__(self, dtype: str = \"bool\"):\n self.dtype = dtype\n\n def fit(sel... | [
[
"numpy.save",
"numpy.log1p",
"numpy.load",
"numpy.nanmax",
"numpy.nanmean",
"pandas.DataFrame",
"numpy.nanmin",
"numpy.absolute"
]
] |
hgKwak/SeriesSleepNet- | [
"1e90c3a0ed6244c2b876979194d7cd94056f5c8a"
] | [
"network/cnn.py"
] | [
"import torch\nimport torch.nn as nn\n\nuse_cuda = torch.cuda.is_available()\nclass CNNClassifier(nn.Module):\n def __init__(self, channel, SHHS=False):\n super(CNNClassifier, self).__init__()\n conv1 = nn.Conv2d(1, 10, (1, 200))\n pool1 = nn.MaxPool2d((1, 2))\n if channel == 1:\n ... | [
[
"torch.nn.MaxPool2d",
"torch.nn.Linear",
"torch.cuda.is_available",
"torch.nn.Conv2d",
"torch.nn.ReLU"
]
] |
WildflowerSchools/wf-cv-utils | [
"647a2a46e3d6e6e14a1f813d17064cb33a3ced92"
] | [
"cv_utils/core.py"
] | [
"import cv_datetime_utils\nimport cv2 as cv\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport scipy.optimize\nimport json\nimport os\n\ndef compose_transformations(\n rotation_vector_1,\n translation_vector_1,\n rotation_vector_2,\n translation_vector_2):\n rotation_vector_... | [
[
"numpy.arctan2",
"numpy.matmul",
"numpy.sort",
"numpy.multiply",
"numpy.empty",
"numpy.squeeze",
"numpy.zeros",
"numpy.linalg.inv",
"numpy.full_like",
"numpy.arctan",
"numpy.subtract",
"numpy.asarray",
"numpy.arange",
"numpy.array",
"numpy.square",
"... |
teomores/kafka-twitter | [
"29d7c48fd1d225e33ec06be9bfed1826fa4d6b60"
] | [
"data_preprocessing/tweet_api.py"
] | [
"# Import the Twython class\nfrom twython import Twython\nimport json\nimport os\nimport pandas as pd\nfrom tqdm import tqdm\n\ntry:\n os.remove('twitter_dataset.csv')\nexcept OSError:\n pass\n\ndef main():\n old_df = pd.read_csv('data/twitter_dataset_2.csv', lineterminator='\\n')\n #first load the dict... | [
[
"pandas.read_csv",
"pandas.DataFrame"
]
] |
RaulAstudillo/bocf | [
"cd84eab2d1b4ea5a4bdeeb452df92296afbafb87"
] | [
"GPy/kern/src/static.py"
] | [
"# Copyright (c) 2012, GPy authors (see AUTHORS.txt).\n# Licensed under the BSD 3-clause license (see LICENSE.txt)\n\n\nfrom .kern import Kern\nimport numpy as np\nfrom ...core.parameterization import Param\nfrom paramz.transformations import Logexp\nfrom paramz.caching import Cache_this\n\nclass Static(Kern):\n ... | [
[
"numpy.ones",
"numpy.eye",
"numpy.empty",
"numpy.zeros",
"numpy.trace",
"numpy.einsum",
"numpy.diagonal",
"numpy.full"
]
] |
jacenkow/inside | [
"6f860420644b50b78981158a59ceed8cdbd209bf"
] | [
"inside/pipelines/clevr.py"
] | [
"# -*- coding: utf-8 -*-\n#\n# Copyright (C) 2020 Grzegorz Jacenków.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may not\n# use this file except in compliance with the License. You may obtain a copy of\n# the License at http://www.apache.org/licenses/LICENSE-2.0.\n#\n# Unless require... | [
[
"tensorflow.GradientTape",
"tensorflow.keras.metrics.Mean"
]
] |
BaratiLab/GAMD | [
"7de91526f1c8c06ea005920e6a55c3cf031c26b2"
] | [
"dataset/generate_tip4p_data.py"
] | [
"from openmmtools import testsystems\nfrom simtk.openmm.app import *\nimport simtk.unit as unit\n\nimport logging\n\nimport numpy as np\n\nfrom openmmtools.constants import kB\nfrom openmmtools import respa, utils\n\nlogger = logging.getLogger(__name__)\n\n# Energy unit used by OpenMM unit system\nfrom openmmtools ... | [
[
"numpy.random.uniform",
"numpy.matmul",
"numpy.savez",
"numpy.random.randn",
"numpy.cos",
"numpy.sin",
"numpy.mean"
]
] |
Intelligent-Systems-Lab/ISL-BCFL | [
"42ceb86708a76e28b31c22b33c15ee9a6a745ec7"
] | [
"script/app/agg.py"
] | [
"import os\n# import torch\nimport argparse\nimport base64\nimport sys\nimport io\n\nimport torch\nimport torch.nn as nn\nfrom torchvision import transforms\nfrom torch.utils.data import DataLoader\nfrom torch.utils.data.sampler import SubsetRandomSampler\n\n\ndef fullmodel2base64(model):\n buffer = io.BytesIO()... | [
[
"torch.save"
]
] |
lanagarmire/granatumx | [
"3dee3a8fb2ba851c31a9f6338aef1817217769f9"
] | [
"g_packages/deepImpute/docker/deepimpute/deepimpute/multinet.py"
] | [
"import os\nimport numpy as np\nimport pandas as pd\nimport binascii\nimport warnings\nimport tempfile\nfrom math import ceil\nfrom multiprocessing import cpu_count, sharedctypes\nfrom multiprocessing.pool import Pool\nfrom sklearn.metrics import r2_score\n\nfrom deepimpute.net import Net\nfrom deepimpute.normalize... | [
[
"numpy.intersect1d",
"pandas.DataFrame",
"numpy.random.seed",
"numpy.reshape",
"numpy.random.choice",
"numpy.setdiff1d",
"pandas.concat",
"numpy.ctypeslib.as_array",
"numpy.corrcoef"
]
] |
XinChCh/singa | [
"93fd9da72694e68bfe3fb29d0183a65263d238a1"
] | [
"test/python/test_tensor.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.sum",
"numpy.matmul",
"numpy.transpose",
"numpy.ceil",
"numpy.einsum",
"numpy.reshape",
"numpy.random.seed",
"numpy.asarray",
"numpy.repeat",
"numpy.random.randn",
"numpy.random.random",
"numpy.tensordot",
"numpy.testing.assert_array_almost_equal",
"n... |
hyperfraise/action-detection | [
"a3ee263ed701ed251cd0a79830ef796889ff366e"
] | [
"ssn_dataset.py"
] | [
"import torch.utils.data as data\n\nimport os\nimport os.path\nfrom numpy.random import randint\nfrom ops.io import load_proposal_file\nfrom transforms import *\nfrom ops.utils import temporal_iou\n\n\nclass SSNInstance:\n def __init__(\n self,\n start_frame,\n end_frame,\n video_fram... | [
[
"numpy.random.randint"
]
] |
delldu/EDSR | [
"98752b57a3091e693c523e710380d369f9913041"
] | [
"src/model/vdsr.py"
] | [
"from model import common\n\nimport torch.nn as nn\nimport torch.nn.init as init\n\nurl = {\n 'r20f64': ''\n}\n\ndef make_model(args, parent=False):\n return VDSR(args)\n\nclass VDSR(nn.Module):\n def __init__(self, args, conv=common.default_conv):\n super(VDSR, self).__init__()\n\n n_resbloc... | [
[
"torch.nn.ReLU",
"torch.nn.Sequential"
]
] |
NunoEdgarGFlowHub/PyBaMM | [
"4e4e1ab8c488b0c0a6efdb9934c5ac59e947a190",
"4e4e1ab8c488b0c0a6efdb9934c5ac59e947a190"
] | [
"tests/unit/test_parameters/test_current_functions.py",
"pybamm/expression_tree/binary_operators.py"
] | [
"#\n# Tests for current input functions\n#\nimport pybamm\nimport numbers\nimport unittest\nimport numpy as np\n\n\nclass TestCurrentFunctions(unittest.TestCase):\n def test_constant_current(self):\n # test simplify\n current = pybamm.electrical_parameters.current_with_time\n parameter_value... | [
[
"numpy.sin",
"numpy.linspace",
"numpy.zeros"
],
[
"numpy.ones",
"numpy.zeros",
"scipy.sparse.issparse",
"scipy.sparse.csr_matrix",
"numpy.errstate",
"numpy.all",
"numpy.maximum",
"numpy.minimum"
]
] |
YongLiuLab/BrainRadiomicsTools | [
"19b440acd554ee920857c306442b6d2c411dca88"
] | [
"Core/hippoSeg/LiviaNet/startTraining.py"
] | [
"\"\"\" \nCopyright (c) 2016, Jose Dolz .All rights reserved.\n\nRedistribution and use in source and binary forms, with or without modification,\nare permitted provided that the following conditions are met:\n\n 1. Redistributions of source code must retain the above copyright notice,\n this list of condi... | [
[
"numpy.array",
"numpy.ones",
"numpy.zeros",
"sklearn.cross_validation.LeaveOneOut"
]
] |
T3p/policy-optimization | [
"77006545779823737c4ca3b19e9d80506015c132"
] | [
"potion/envs/minigolf.py"
] | [
"from numbers import Number\n\nimport gym\nfrom gym import spaces\nfrom gym.utils import seeding\nimport numpy as np\nimport math as m\nfrom scipy.stats import norm\n\n\"\"\"\nMinigolf task.\nReferences\n----------\n - Penner, A. R. \"The physics of putting.\" Canadian Journal of Physics 80.2 (2002): 83-96.\n\"\"\... | [
[
"numpy.sqrt",
"numpy.zeros",
"scipy.stats.norm.pdf",
"numpy.abs",
"numpy.ravel",
"numpy.clip",
"numpy.array"
]
] |
jenildesai25/WebScrapping | [
"41937094a7963d53ab09e3ceff055dca4a95f13f"
] | [
"WebScraping2.py"
] | [
"\n# Online References used :\n# https://github.com/imadmali/movie-scraper/blob/master/MojoLinkExtract.py\n# https://www.crummy.com/software/BeautifulSoup/bs4/doc/\n# https://nycdatascience.com/blog/student-works/scraping-box-office-mojo/\n# https://www.youtube.com/watch?v=XQgXKtPSzUI\n# https://www.youtube.com/wat... | [
[
"pandas.read_csv",
"pandas.concat",
"pandas.to_numeric"
]
] |
IKupriyanov-HORIS/lets-plot-docs | [
"30fd31cb03dc649a03518b0c9348639ebfe09d53"
] | [
"docs/_downloads/e0051c6e37b730111a06abd85529e288/plot__2d_distributions.py"
] | [
"\"\"\"\r\n2D Distributions\r\r\n================\r\r\n\r\r\nSome plots visualize a transformation of the original data set. Use a\r\r\nstat parameter to choose a common transformation to visualize.\r\r\n\r\r\nEach stat creates additional variables to map aesthetics to. These\r\r\nvariables use a common ..name.. sy... | [
[
"pandas.read_csv"
]
] |
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