repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
alexaushev/LFIwithDGPs | [
"a2eed95554317a56451d6a2a15ae33eb11ad6037"
] | [
"elfidev/elfi/methods/bo/gpy_regression.py"
] | [
"\"\"\"This module contains an interface for using the GPy library in ELFI.\"\"\"\n\n# TODO: make own general GPRegression and kernel classes\n\nimport copy\nimport logging\n\nimport GPy\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nlogger = logging.getLogger(__name__)\nlogging.getLogger(\"GP\").setLevel(... | [
[
"matplotlib.pyplot.legend",
"numpy.linalg.solve",
"numpy.min",
"numpy.ones",
"matplotlib.pyplot.plot",
"numpy.max",
"numpy.asanyarray",
"matplotlib.pyplot.grid",
"numpy.moveaxis",
"numpy.exp",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
andmatt/gspread-pandas | [
"a862f995f50217be4e45c1db858e3b901f2b68df"
] | [
"gspread_pandas/client.py"
] | [
"from __future__ import print_function\n\nfrom builtins import range, str, super\nfrom functools import partial\nfrom re import match\n\nimport numpy as np\nimport pandas as pd\nfrom decorator import decorator\nfrom gspread.client import Client as ClientV4\nfrom gspread.exceptions import (\n APIError,\n NoVal... | [
[
"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": []
}
] |
YuShen0118/SAAP_Auto-driving_Platform | [
"785f899fb3b3ad92075318f9fcb69b8e09597202"
] | [
"End2EndLearning/library/statistics.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Apr 27 23:58:29 2020\n\n@author: Laura Zheng\n\"\"\"\nimport os\nimport csv\nimport numpy as np\n#import pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\ndef plot_steering_angle_dist(dataFilePath, label):\n ''' Plots the distribution for a s... | [
[
"matplotlib.pyplot.legend",
"numpy.min",
"numpy.linalg.norm",
"matplotlib.pyplot.savefig",
"numpy.max",
"matplotlib.pyplot.xlim",
"matplotlib.pyplot.clf",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kylezeeuwen/ds-stackoverflow2020-analysis | [
"5e60dbbcba78a6dde103bd48fbef14b9aabd8385"
] | [
"notebook/country_classifier/convert_choose_all_that_apply_responses.py"
] | [
"import pandas as pd\n\n\ndef convert_choose_all_that_apply_responses(df):\n '''\n INPUT:\n input_df - dataframe - survey responses\n\n OUTPUT:\n dataframe - survey responses with conversion described below\n\n convert all the \"a;b;d\" style survey responses to dummied columns\n '''\n\n mul... | [
[
"pandas.Series",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
sfeister/flsuite | [
"348c207f72f3bea3877afef46ab11cc472722f57"
] | [
"examples/laserexamples.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nlaserexamples.py: Show two different ways to create laser strings for a flash.par file\n\nCreated by Scott Feister on Fri Jul 27 15:37:49 2018\n\"\"\"\n \nfrom flsuite.parLaser import parLaser, parLasers\nimport numpy as np\n\n# Example 1: Three lasers, eac... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
adwwsd/HAWQ | [
"a8e40be0edd336b2554d88691b18ed51e7d32bf0"
] | [
"tvm_benchmark/test_resnet_inference_time.py"
] | [
"import tvm\n\nfrom tvm import relay\nfrom tvm import autotvm\nfrom tvm.relay.testing import run_infer_type\nfrom tvm.contrib import graph_runtime\nfrom tvm.contrib.debugger import debug_runtime\n\nimport os\nimport sys\nsys.path.append('..')\nimport mixed_precision_models.quantized_resnet_v1 as quantized_resnet_v1... | [
[
"torch.cuda.profiler.stop",
"torch.autograd.profiler.emit_nvtx",
"numpy.std",
"numpy.random.uniform",
"torch.cuda.profiler.start",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
chromi82/molpy | [
"299e1f462668a00b01eb7bd182da698310d024e7"
] | [
"util.py"
] | [
"import numpy as np\n\ndef test_distance(point1, point2):\n\n\tpoint1 = np.array(point1)\n\tpoint2 = np.array(point2)\n\treturn np.linalg.norm(point1-point2)\n\n"
] | [
[
"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": [],
... |
Krande/ipygany | [
"471355d043e3952ac68052613135fd5a5ee3a41b"
] | [
"ipygany/ipygany.py"
] | [
"\"\"\"Scientific Visualization in Jupyter.\"\"\"\n\nfrom array import array\n\nimport numpy as np\n\nfrom traitlets import (\n Bool, Dict, Enum, Unicode, List, Instance, CFloat, Tuple, TraitError, Union, default, validate, Any,\n)\nfrom traittypes import Array\nfrom ipywidgets import (\n widget_serialization... | [
[
"numpy.unique",
"numpy.asarray",
"numpy.arange",
"numpy.min",
"numpy.sort",
"numpy.concatenate",
"numpy.max"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
KORguy/PIFu_Part | [
"bd199d439a94f8bc8b4036898b0f1ec01e56ab9e"
] | [
"lib/model/MLP.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F \n\nclass MLP(nn.Module):\n def __init__(self, \n filter_channels, \n merge_layer=0,\n res_layers=[],\n ... | [
[
"torch.nn.BatchNorm1d",
"torch.cat",
"torch.nn.ModuleList",
"torch.nn.functional.leaky_relu",
"torch.nn.Conv1d",
"torch.nn.GroupNorm"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
stefanv/matplotlib | [
"7272f102482941ada6e5fd874bdf7f185f420526",
"2c6c3695ef989ec86b7c43740462ef992685022f"
] | [
"examples/pylab_examples/spine_placement_demo.py",
"lib/matplotlib/gridspec.py"
] | [
"import matplotlib.pyplot as plt\nimport numpy as np\nfrom matplotlib.pyplot import show\n\nfig = plt.figure()\nx = np.linspace(0,2*np.pi,100)\ny = 2*np.sin(x)\nax = fig.add_subplot(1,2,1)\nax.set_title('dropped spines')\nax.plot(x,y)\nfor loc, spine in ax.spines.iteritems():\n if loc in ['left','bottom']:\n ... | [
[
"numpy.linspace",
"numpy.sin",
"numpy.random.normal",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
],
[
"matplotlib._pylab_helpers.Gcf.figs.values",
"matplotlib.transforms.Bbox.from_extents",
"matplotlib.figure.SubplotParams"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
oleg-panichev/WiDS-Datathon-2020-Second-place-solution | [
"70af5466256152f3132e020e7d31b010e80c374b"
] | [
"src/__kinoa__/2020-02-18_17-08-18_Exp0/model0.py"
] | [
"import datetime\nimport gc\nimport glob\nimport numpy as np\nimport os\nimport pandas as pd\n\nos.environ['KMP_DUPLICATE_LIB_OK']='True' # MacOS fix for libomp issues (https://github.com/dmlc/xgboost/issues/1715)\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.linear_model import LogisticRegression\... | [
[
"numpy.nanmax",
"sklearn.metrics.roc_auc_score",
"pandas.concat",
"sklearn.model_selection.RepeatedKFold",
"pandas.read_csv",
"sklearn.linear_model.LogisticRegression",
"numpy.nanmin",
"pandas.DataFrame",
"numpy.round",
"numpy.std",
"numpy.mean",
"numpy.exp",
"s... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
timjim333/KADAL | [
"8d190e7a28c83d5d1edffb3f85f1a629a481e47f",
"8d190e7a28c83d5d1edffb3f85f1a629a481e47f"
] | [
"kadal/surrogate_models/kkpca_model.py",
"kadal/sensitivity_analysis/sobol_ind.py"
] | [
"import numpy as np\nfrom kadal.misc.sampling.samplingplan import sampling, standardize\nfrom sklearn.decomposition import KernelPCA as drm\nfrom scipy.optimize import minimize, fmin_cobyla\nfrom kadal.surrogate_models.supports.trendfunction import polytruncation, compute_regression_mat\nfrom kadal.surrogate_models... | [
[
"numpy.dot",
"numpy.min",
"scipy.spatial.distance.cdist",
"numpy.round",
"numpy.max",
"numpy.size",
"scipy.optimize.minimize",
"sklearn.decomposition.KernelPCA",
"numpy.array",
"numpy.sum",
"numpy.vstack"
],
[
"numpy.ceil",
"numpy.size",
"numpy.array",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"1.3",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"0.16",
"1.8"
... |
JacobGreen770/Pancham | [
"f1aaf62f181e19d716bc0ade018a8081c1c02d83"
] | [
"pandas/core/series.py"
] | [
"\"\"\"\nData structure for 1-dimensional cross-sectional and time series data\n\"\"\"\nfrom io import StringIO\nfrom shutil import get_terminal_size\nfrom textwrap import dedent\nfrom typing import (\n IO,\n TYPE_CHECKING,\n Any,\n Callable,\n Hashable,\n Iterable,\n List,\n Optional,\n ... | [
[
"pandas.core.nanops.nancov",
"pandas.util._validators.validate_bool_kwarg",
"pandas.core.dtypes.inference.is_hashable",
"pandas.core.dtypes.common.is_datetime64_dtype",
"pandas.core.common.standardize_mapping",
"pandas.core.construction.is_empty_data",
"pandas._libs.lib.map_infer",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"1.1",
"1.0",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
LvpengfeiNJ/My_Tracking | [
"aa382f134acef59ce02b6054a018688cf2f0c53b"
] | [
"visualization_bubble_chart.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.axes._axes as axes\nimport matplotlib.figure as figure\nfrom matplotlib.backends.backend_pdf import PdfPages\npdf = PdfPages('speed-eao2018.pdf')\nplt.rc('font',family='Times New Roman')\n\nfig, ax = plt.subplots() # type:figure.Figure, axes.A... | [
[
"matplotlib.backends.backend_pdf.PdfPages",
"matplotlib.pyplot.rc",
"matplotlib.pyplot.subplots",
"numpy.array",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kyleniemeyer/hardy | [
"35e2141466a972a291082cb031b8393b4b395d84"
] | [
"hardy/data_reporting/reporting.py"
] | [
"import numpy as np\nimport os\nimport yaml\n\nimport pandas as pd\nimport plotly.graph_objects as go\n\nfrom keras.preprocessing.image import NumpyArrayIterator\nfrom plotly.subplots import make_subplots\n\n\ndef report_dataframes(report_path):\n '''\n '''\n categories = [f for f in os.listdir(report_path... | [
[
"numpy.unique",
"pandas.DataFrame",
"numpy.round",
"numpy.argmax",
"pandas.DataFrame.from_dict",
"numpy.array"
]
] | [
{
"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": []
}
] |
mgrubisic/PySeismoSoil | [
"f8778be18a0d141918ff3f05c2e5279f5d02cdda",
"f8778be18a0d141918ff3f05c2e5279f5d02cdda"
] | [
"PySeismoSoil/tests/test_helper_hh_model.py",
"PySeismoSoil/class_site_factors.py"
] | [
"# Author: Jian Shi\n\nimport unittest\nimport numpy as np\n\nimport PySeismoSoil.helper_hh_model as hh\nimport PySeismoSoil.helper_site_response as sr\n\nclass Test_Helper_HH_Model(unittest.TestCase):\n '''\n Unit tests for helper functions in helper_hh_model.py\n '''\n def __init__(self, methodName='r... | [
[
"numpy.logspace",
"numpy.arange",
"numpy.array",
"numpy.allclose"
],
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.axes",
"matplotlib.pyplot.subplot",
"numpy.searchsorted",
"numpy.column_stack",
"numpy.array",
"scipy.interpolate.griddata",
"matplotlib.pyplot.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.14",
"0.19",
"1.5",
"0.12",
"0.17",
"0.13",
... |
YoungjuNa-KR/encoder4editing | [
"f3d29ab60614344945f0c157ed96e8d4a077f624"
] | [
"scripts/inference.py"
] | [
"import argparse\nfrom tqdm import tqdm\nimport torch\nimport numpy as np\nimport sys\nimport os\nimport dlib\n\nsys.path.append(\".\")\nsys.path.append(\"..\")\n\nfrom configs import data_configs, paths_config\nfrom datasets.inference_dataset import InferenceDataset\nfrom torch.utils.data import DataLoader\nfrom u... | [
[
"torch.load",
"torch.cat",
"torch.utils.data.DataLoader",
"torch.no_grad",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mhavgar/TSIClient | [
"88c232b54a3d3f440eecdc9678faf78392022576"
] | [
"TSIClient/query/query_api.py"
] | [
"from ..authorization.authorization_api import AuthorizationApi\nfrom ..common.common_funcs import CommonFuncs\nfrom ..types.types_api import TypesApi\nfrom ..exceptions import TSIQueryError, TSIStoreError\nimport requests\nimport json\nimport logging\nimport pandas as pd\n\n\n\nclass QueryApi():\n def __init__(... | [
[
"pandas.Timedelta",
"pandas.to_datetime",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
yanseim/Vision-Based-Control | [
"4a92103d99703ac2a45d4ad8d01a663e29c0aa7d"
] | [
"ws_icra2022/src/my_pkg/ur5_eye2hand/convert_bcT_cbT.py"
] | [
"import numpy as np \nfrom scipy.spatial.transform import Rotation as Rot\n\n# bcT = np.matrix([[-7.34639719e-01, -6.27919076e-04, 6.78457138e-01, -1.08283672e+00],\n# [-6.78432812e-01, 9.19848654e-03, -7.34604865e-01, 1.16241772e+00],\n# [-5.77950645e-03, -9.99957496e-01, -7.18357448e-03, 4.39619904e-01],\n#... | [
[
"numpy.matrix",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
torms3/DataTools | [
"8144a9485ca69dc2208bbcc20f59132def977b7a"
] | [
"datatools/cremi/border_mask.py"
] | [
"import numpy as np\nimport scipy\nimport scipy.ndimage\n\n\ndef create_border_mask(segmentation, max_dist, background_label=0, axis=0):\n \"\"\"\n Overlay a border mask with background_label onto input data.\n A pixel is part of a border if one of its 4-neighbors has different label.\n\n Parameters\n ... | [
[
"scipy.ndimage.distance_transform_edt",
"numpy.logical_and",
"numpy.zeros_like",
"numpy.pad"
]
] | [
{
"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"... |
lpuglia/torchvision_voc | [
"e73c448e574d1c3b48dde8ab138bb658177c0aa4"
] | [
"references/detection/voc_utils.py"
] | [
"import torch\nimport torchvision\n\nimport transforms as T\n\nclass ConvertVOCtoCOCO(object):\n CLASSES = (\n \"__background__\", \"aeroplane\", \"bicycle\",\n \"bird\", \"boat\", \"bottle\", \"bus\", \"car\",\n \"cat\", \"chair\", \"cow\", \"diningtable\", \"dog\",\n \"horse\", \"mo... | [
[
"torch.as_tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
husseina-nvidia/NeMo | [
"be693ad26d5c836c2fb7ea579ed3905ba86b581d"
] | [
"collections/nemo_asr/nemo_asr/parts/features.py"
] | [
"# Taken straight from Patter https://github.com/ryanleary/patter\n# TODO: review, and copyright and fix/add comments\nimport math\nimport librosa\nimport torch\nimport torch.nn as nn\nfrom .perturb import AudioAugmentor\nfrom .segment import AudioSegment\nfrom torch_stft import STFT\n\nCONSTANT = 1e-5\n\n\ndef nor... | [
[
"torch.randn_like",
"torch.zeros",
"torch.cat",
"torch.tensor",
"torch.no_grad",
"torch.log",
"torch.arange",
"torch.nn.functional.pad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Javk5pakfa/sailfish | [
"81e8ccacd7ff52854c76a3e132568541075f7ee4"
] | [
"scripts/plot.py"
] | [
"#!/usr/bin/env python3\n\nimport argparse\nimport pickle\nimport sys\n\nsys.path.insert(1, \".\")\n\n\ndef main(args):\n import matplotlib.pyplot as plt\n import sailfish\n\n fig, ax1 = plt.subplots()\n\n for checkpoint in args.checkpoints:\n with open(checkpoint, \"rb\") as f:\n chkp... | [
[
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplots"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
protossw512/PRNet | [
"a3832c16f9af2cfa9b4cc44c825c726b227f9309"
] | [
"demo.py"
] | [
"import numpy as np\nimport os\nfrom glob import glob\nimport scipy.io as sio\nfrom skimage.io import imread, imsave\nfrom skimage.transform import rescale, resize\nfrom time import time\nimport argparse\nimport ast\n\nfrom api import PRN\n\nfrom utils.estimate_pose import estimate_pose\nfrom utils.rotate_vertices ... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
searobbersduck/MedCommon_Self_Supervised_Learning | [
"e626fa25986bc3294b5789aa4b06fb5f52bf20d4"
] | [
"trainer/main_moco.py"
] | [
"#!/usr/bin/env python\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\nimport argparse\nimport builtins\nimport math\nimport os\nimport random\nimport shutil\nimport time\nimport warnings\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.parallel\nimport torch.backends.cudnn as cudnn\... | [
[
"torch.nn.CrossEntropyLoss",
"torch.distributed.init_process_group",
"torch.multiprocessing.spawn",
"torch.utils.data.distributed.DistributedSampler",
"torch.cuda.set_device",
"torch.manual_seed",
"torch.load",
"torch.utils.data.DataLoader",
"torch.no_grad",
"torch.cuda.dev... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MartinEthier/end-to-end-driving | [
"22ebee951283985cdb762882534dcc475d85f011"
] | [
"lib/orientation.py"
] | [
"import numpy as np\nfrom numpy import dot, inner, array, linalg\nfrom lib.coordinates import LocalCoord\n\n\n'''\nVectorized functions that transform between\nrotation matrices, euler angles and quaternions.\nAll support lists, array or array of arrays as inputs.\nSupports both x2y and y_from_x format (y_from_x pr... | [
[
"numpy.dot",
"numpy.inner",
"numpy.arcsin",
"numpy.eye",
"numpy.cos",
"numpy.tile",
"numpy.sin",
"numpy.arctan2",
"numpy.atleast_2d",
"numpy.linalg.eigh",
"numpy.array",
"numpy.zeros",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
FrancoisPgm/nilearn | [
"da47cd2adaabcc4e5cf6751dd2d2543a3c32da1b"
] | [
"nilearn/decoding/decoder.py"
] | [
"\"\"\"High-level decoding object that exposes standard classification and\nregression strategies such as SVM, LogisticRegression and Ridge, with optional\nfeature selection, integrated hyper-parameter selection and aggregation\nstrategy in which the best models within a cross validation loop are averaged.\n\nAlso ... | [
[
"sklearn.utils.validation.check_is_fitted",
"sklearn.utils.extmath.safe_sparse_dot",
"numpy.all",
"sklearn.clone",
"numpy.mean",
"sklearn.svm.LinearSVC",
"sklearn.model_selection.ShuffleSplit",
"numpy.reshape",
"numpy.std",
"sklearn.preprocessing.LabelBinarizer",
"sklea... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
knielbo/cvca-x | [
"895fab84bfa349404c5c4d06ba7324a4b17f576a"
] | [
"codebase/shallownet_cifar10.py"
] | [
"#!/home/knielbo/virtenvs/cv/bin/python\n\"\"\"\nShallowNet application to cifar10 dataset\n\"\"\"\nfrom sklearn.preprocessing import LabelBinarizer\nfrom sklearn.metrics import classification_report\nfrom kartina.nn.conv import ShallowNet\nfrom tensorflow.keras.optimizers import SGD\nfrom tensorflow.keras.datasets... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.title",
"numpy.arange",
"tensorflow.keras.optimizers.SGD",
"tensorflow.keras.datasets.cifar10.load_data",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.ylabel",
"sklearn.preprocessing.LabelBinari... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
slilichenko/dataflow-sample-applications | [
"3e3ab22c8b18cfb3923dabc63891cfab903c8412"
] | [
"timeseries-streaming/timeseries-python-applications/ml_pipeline/timeseries/encoder_decoder/transforms/process_encdec_inf_rtn.py"
] | [
"#\n# Licensed to the Apache Software Foundation (ASF) under one or more\n# contributor license agreements. See the NOTICE file distributed with\n# this work for additional information regarding copyright ownership.\n# The ASF licenses this file to You under the Apache License, Version 2.0\n# (the \"License\"); yo... | [
[
"tensorflow.sparse.to_dense"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Zeroto521/my-data-toolkit | [
"bde37f625aa81e65b97648798535f6d931864888"
] | [
"test/transformer/test_pipeline.py"
] | [
"from test.transformer import df_iris\nfrom test.transformer import df_label\nfrom test.transformer import df_mixed\nfrom test.transformer import feature_names\nfrom test.transformer import s\n\nimport joblib\nimport pandas as pd\nimport pytest\nfrom scipy import sparse\nfrom sklearn.pipeline import make_pipeline\n... | [
[
"scipy.sparse.isspmatrix",
"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": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.1... |
iamarchisha/multistep-io-timeseries | [
"003320b1413d18281c55b2387dc7c36e44c3b9ae"
] | [
"tslstm.py"
] | [
"import os\nimport numpy\nimport pandas\nimport logging\nimport seaborn\nimport warnings\n\nimport matplotlib.pyplot as plt\n\nfrom keras.models import Sequential\nfrom keras.callbacks import EarlyStopping, ReduceLROnPlateau\nfrom keras.layers import LSTM, Dense, Flatten, Dropout\nfrom sklearn.preprocessing import ... | [
[
"numpy.hstack",
"pandas.concat",
"pandas.read_csv",
"pandas.to_datetime",
"matplotlib.pyplot.legend",
"pandas.DataFrame",
"matplotlib.pyplot.savefig",
"sklearn.model_selection.TimeSeriesSplit",
"matplotlib.pyplot.fill_between",
"sklearn.preprocessing.StandardScaler",
"n... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
PacktPublishing/Deep-Learning-By-Example | [
"2755f92fad19c54ee15ad858ca20e8e64f58cc00"
] | [
"Chapter13/CANs.py"
] | [
"#importing the required packages\nimport numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\n\nfrom tensorflow.examples.tutorials.mnist import input_data\nmnist_dataset = input_data.read_data_sets('MNIST_data', validation_size=0)\n\n# Plotting one image from the training set.\nimage = mnist_data... | [
[
"tensorflow.layers.conv2d",
"tensorflow.nn.sigmoid",
"tensorflow.reduce_mean",
"tensorflow.image.resize_images",
"tensorflow.layers.max_pooling2d",
"tensorflow.placeholder",
"matplotlib.pyplot.subplots",
"tensorflow.nn.sigmoid_cross_entropy_with_logits",
"tensorflow.global_vari... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
donnaaboise/geoclaw-neck-test | [
"a55b6564d152b8fcba54f9b1033f61d171a11bf9"
] | [
"plotflow_cmd.py"
] | [
"#! /usr/bin/env python\n# -*- coding: utf-8 -*-\n# vim:fenc=utf-8\n#\n# Copyright © 2019 Pi-Yueh Chuang <pychuang@gwu.edu>\n#\n# Distributed under terms of the MIT license.\n\n\"\"\"\nA executable-like plotting utility for flow depths.\n\"\"\"\nimport os\nimport sys\nimport argparse\nimport logging\nimport numpy\n... | [
[
"numpy.ma.masked_less",
"matplotlib.pyplot.get_cmap",
"matplotlib.pyplot.close",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.13",
"1.16",
"1.9",
"1.18",
"1.20",
"1.7",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sandialabs/bcnn | [
"a64dd8e4dc439d77a700c8e35048ac7ebfc49ef3"
] | [
"dataset.py"
] | [
"import os\nimport numpy as np\nimport pickle\n\nfrom utils import absolute_file_paths, ex, round_down, standardize\n\n\n@ex.capture\ndef chunks(arr, batch_size, num_gpus, step, window, trim=True):\n \"\"\"Chunks a 4D numpy array into smaller 4D arrays.\"\"\"\n\n new = []\n coords = []\n shape = arr.sha... | [
[
"numpy.expand_dims",
"numpy.asarray",
"numpy.save",
"numpy.concatenate",
"numpy.load",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
owahlen/facerecognition | [
"ee5825bb6b0df3340b12f4988b788c2949f7a42e"
] | [
"src/align/align_server_mtcnn.py"
] | [
"#!/usr/bin/env python3\n\nimport argparse\nimport json\nimport sys\n\nimport cv2\nimport numpy as np\nimport zmq\n\nimport face_detector as detector\n\n\ndef main(args):\n context = zmq.Context()\n socket = context.socket(zmq.REP)\n socket.bind('tcp://*:%d' % (args.port))\n\n face_detector = detector.F... | [
[
"numpy.fromstring"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
seawander/DebisDiskFM | [
"717c09c914c53a5b5a7d18afe78307db79cd1c3e",
"717c09c914c53a5b5a7d18afe78307db79cd1c3e"
] | [
"debrisdiskfm/anadisk_sum_mask_MMB.py",
"debrisdiskfm/fm_klip.py"
] | [
"##### May 6, 2018 #####\n# The goal of this effort is to re-generalize things. In particular, I want only one function, that will generate x disks, based on x scattering phase functions. \n\n# Note on 2021.06.30: Download by Bin Ren from https://github.com/maxwellmb/anadisk_model/blob/master/anadisk_model/anadisk_... | [
[
"matplotlib.pyplot.imshow",
"numpy.radians",
"numpy.sqrt",
"numpy.linspace",
"scipy.ndimage.filters.gaussian_filter",
"numpy.exp",
"numpy.square",
"numpy.sin",
"scipy.interpolate.interp1d",
"numpy.zeros",
"matplotlib.pyplot.figure",
"numpy.arccos",
"numpy.floor"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"0.15",
"1.4",
"1.3",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"0.16"
],
"tensorflow": []
},
{
... |
talonchandler/pmst | [
"c7d4d00a9a377726f8996cb416970037af92c40a"
] | [
"pmst/examples/lens_array_point_source/lens_array_point_source.py"
] | [
"import sys\nsys.path.append(\"../../../\")\n\nfrom pmst.source import DirectedPointSource\nfrom pmst.microscope import Microscope\nfrom pmst.detector import Detector\nfrom pmst.geometry import Point\nfrom pmst.component import Lens\nimport numpy as np\nimport time; start = time.time(); print('Running...')\n\nt = t... | [
[
"numpy.arange",
"numpy.floor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
eshanmherath/linear-regression | [
"5b473586679a4b4594706faeb2bb7e4922c7ab38"
] | [
"tensorflow_simple_linear_regression.py"
] | [
"import tensorflow as tf\nimport numpy as np\n\nnp.random.seed(111)\n\n'''\nThe data is generated adding noise to the values from y = 0.8x + 2 equation\nTherefore the expectation of the auto encoder is to get the values w and b closer to 0.8 and 2 respectively\n'''\n\n'''generate random x values'''\nX_train = np.r... | [
[
"tensorflow.multiply",
"numpy.random.random",
"numpy.sqrt",
"numpy.random.seed",
"tensorflow.pow",
"tensorflow.placeholder",
"tensorflow.global_variables_initializer",
"tensorflow.train.GradientDescentOptimizer",
"numpy.random.randn",
"tensorflow.Session"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
Brodong/vizdoommaze | [
"1b7531f59f6eb5e94d22193d95febaf52ff6a874"
] | [
"vizdoommaze/envs/vizdoomenv.py"
] | [
"import gym\nfrom gym import spaces\nfrom vizdoom import *\nimport numpy as np\nimport os\nfrom gym.envs.classic_control import rendering\nimport cv2\n\nCONFIGS = [['basic.cfg', 3], # 0\n ['deadly_corridor.cfg', 7], # 1\n ['defend_the_center.cfg', 3], # 2\n ['def... | [
[
"numpy.uint8",
"numpy.zeros",
"numpy.transpose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hussaintamboli/pandas | [
"520f87b95639e4fc0344a6c6b9851b5cc5a1376b",
"520f87b95639e4fc0344a6c6b9851b5cc5a1376b"
] | [
"pandas/core/reshape/merge.py",
"pandas/tests/indexing/test_indexing_slow.py"
] | [
"\"\"\"\nSQL-style merge routines\n\"\"\"\n\nimport copy\nimport warnings\nimport string\n\nimport numpy as np\nfrom pandas.compat import range, lzip, zip, map, filter\nimport pandas.compat as compat\n\nfrom pandas import (Categorical, Series, DataFrame,\n Index, MultiIndex, Timedelta)\nfrom pand... | [
[
"pandas.core.dtypes.common.is_dtype_equal",
"pandas.core.dtypes.common.is_datetime64tz_dtype",
"numpy.concatenate",
"pandas.compat.map",
"pandas.core.dtypes.common.is_datetime64_dtype",
"pandas.core.dtypes.common._ensure_int64",
"pandas.core.sorting.is_int64_overflow_possible",
"pa... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
... |
nimadehmamy/L-conv-code | [
"5a8abfbff3f6564771234df3e177d1d4aafe371d"
] | [
"paper-code/D-image-experiments/lconv.py"
] | [
"from tensorflow import reduce_sum, concat, reduce_max\nfrom tensorflow.keras import Model\nfrom tensorflow.keras.layers import Layer\nfrom tensorflow.keras.activations import deserialize\n\nfrom numpy import newaxis,prod\n\n\nclass L_module(Layer):\n def __init__(self, n_L, out_dim = None, hidden_units = [], ac... | [
[
"tensorflow.reduce_max",
"tensorflow.concat",
"tensorflow.keras.activations.deserialize",
"tensorflow.reduce_sum",
"numpy.prod"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
JadeCong/jade_robosuite | [
"0d4b4efd9ca95742ed375ce0b0d4e16d499cec06"
] | [
"robosuite/environments/manipulation/stack.py"
] | [
"from collections import OrderedDict\nimport numpy as np\n\nfrom robosuite.utils.transform_utils import convert_quat\nfrom robosuite.utils.mjcf_utils import CustomMaterial\n\nfrom robosuite.environments.manipulation.single_arm_env import SingleArmEnv\n\nfrom robosuite.models.arenas import TableArena\nfrom robosuite... | [
[
"numpy.tanh",
"numpy.array",
"numpy.zeros",
"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": [],
... |
headupinclouds/LightNet | [
"04af22501d644b30a93b30b28b32163d60ae2266"
] | [
"modules/residual.py"
] | [
"import torch.nn as nn\nfrom .bn import ABN\nfrom .misc import SEBlock\nfrom collections import OrderedDict\n\n\nclass IdentityResidualBlock(nn.Module):\n def __init__(self, in_channels, channels, stride=1, dilation=1, groups=1, norm_act=ABN, is_se=False, dropout=None):\n \"\"\"Configurable identity-mappi... | [
[
"torch.nn.Conv2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
linksdl/meta-project-artificial_intelligence_projects | [
"3abe0dc59aa891717a661b3ad1e987c14536bd62",
"3abe0dc59aa891717a661b3ad1e987c14536bd62"
] | [
"ai-engineer/course0_20190911/a6_visualize/6_hotpot.py",
"ai-engineer/course0_20190911/back_feature_engineer/2_feature_extend/5_OneHotEncoder.py"
] | [
"# 13 - image\n\"\"\"\nPlease note, this script is for python3+.\nIf you are using python2+, please modify it accordingly.\n\"\"\"\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n# image data\na = np.array([0.313660827978, 0.365348418405, 0.423733120134,\n 0.365348418405, 0.439599930621, 0.52... | [
[
"numpy.array",
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.colorbar",
"matplotlib.pyplot.yticks",
"matplotlib.pyplot.show",
"matplotlib.pyplot.xticks"
],
[
"sklearn.preprocessing.OneHotEncoder"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
fergald/gpl-covid | [
"d577b6e76f30f843542ef2cf19e30a0d20fa93d0"
] | [
"code/src/models/epi.py"
] | [
"\"\"\"\nFunctions to help in infectious disease simulation.\n\"\"\"\n\nimport warnings\nfrom collections import OrderedDict\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\nimport xarray as xr\nfrom statsmodels.api import OLS, add_constant\n\n\ndef init_reg_ds(n_samples, LHS_vars, policies, ... | [
[
"numpy.log",
"numpy.sqrt",
"numpy.random.seed",
"numpy.linspace",
"numpy.unique",
"numpy.arange",
"numpy.random.exponential",
"numpy.ones",
"numpy.round",
"numpy.random.normal",
"numpy.prod",
"numpy.random.uniform",
"numpy.exp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
felixVil/LDASegment | [
"25f59c9f43c76e64c0a1e4131fa3c12bab60b716"
] | [
"KerasTracker/LDA/utils.py"
] | [
"import numpy as np\nimport UtilFunctions as uf\nfrom math import pi\nimport cv2\nfrom Tracker_Params import Tracker_params\n\nSTABILITY_EPSILON = Tracker_params['regularization_epsilon']\nDEBUG_MODE = Tracker_params['is_debug_mode']\n\n\ndef split_background_inds_2_parts(foreground_mask_2d_inds, background_mask_in... | [
[
"numpy.sqrt",
"numpy.nonzero",
"numpy.arange",
"numpy.eye",
"numpy.ones",
"numpy.arctan2",
"numpy.cov",
"numpy.mean",
"numpy.digitize",
"numpy.zeros",
"numpy.trace",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xbresson/CE7454_2018 | [
"32647cae8122ddb50775329f76f7a0c4a26f3276"
] | [
"projects/project05/Notebook/imageUtils.py"
] | [
"import cv2\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\nclass Struct(object):\r\n def __init__(self, **kwargs):\r\n self.__dict__.update(kwargs)\r\n\r\ndef point_to_stroke(point):\r\n\r\n point = normalise_point(point)\r\n num_point = len(point)\r\n #stroke =[dx,dy,dt]\r\n\r\n... | [
[
"numpy.arange",
"numpy.zeros",
"numpy.floor",
"numpy.full"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
namdo281/modern-cnn-models | [
"d8e40a2f96f32debc3171bc2e610489438fcaa6d"
] | [
"model/models/ImageNetModels/modules/densenet.py"
] | [
"import torch\nfrom torch import nn\nimport torch.nn.functional as F\n\nclass ConvLayer(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size = 3, stride = 1, padding = 1):\n super().__init__()\n self.bn = nn.BatchNorm2d(in_channels)\n self.relu = nn.ReLU()\n self.co... | [
[
"torch.concat",
"torch.nn.Sequential",
"torch.nn.Conv2d",
"torch.nn.AvgPool2d",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mulhod/reviewer_experience_prediction | [
"7fea577d69e86940b7b121fa901c6cf243962828"
] | [
"tests/test_src.py"
] | [
"\"\"\"\nTest utility functions in `src`, i.e., for parsing command-line\narguments, etc.\n\"\"\"\nfrom os.path import join\nfrom shutil import rmtree\nfrom tempfile import mkdtemp\n\nimport numpy as np\nfrom itertools import chain\nfrom typing import (List,\n Tuple,\n Optional... | [
[
"numpy.random.shuffle"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
miaecle/DeepLearningLifeSciences | [
"dce775a045e867abcadb57a3408ad719046205aa"
] | [
"Chapter07/cell_counting.py"
] | [
"import deepchem as dc\nimport deepchem.models.tensorgraph.layers as layers\nimport numpy as np\nimport os\nimport re\n\nRETRAIN = False\n\n# Load the datasets.\nimage_dir = 'BBBC005_v1_images'\nfiles = []\nlabels = []\nfor f in os.listdir(image_dir):\n if f.endswith('.TIF'):\n files.append(os.path.join(image_d... | [
[
"numpy.array",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Hosseinabady/caffe2_fpga | [
"d7a31107332d9288a672e8dadda7774cffb78561"
] | [
"caffe2/python/operator_test/elementwise_op_broadcast_test.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\nimport unittest\n\nfrom hypothesis import given\nimport numpy as np\n\nfrom caffe2.proto import caffe2_pb2\nfrom caffe2.python import core, workspace\nimport ca... | [
[
"numpy.random.rand",
"numpy.sum",
"numpy.testing.assert_array_almost_equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
matteobachetti/MaLTPyNT | [
"6c93d2e23041b6c932810b5a8d727ee1b6dabfed"
] | [
"maltpynt/tests/test_unit.py"
] | [
"# Licensed under a 3-clause BSD style license - see LICENSE.rst\n\"\"\"First set of tests.\"\"\"\n\nfrom __future__ import (absolute_import, unicode_literals, division,\n print_function)\n\nimport maltpynt as mp\nimport numpy as np\nimport logging\nimport os\nimport unittest\nimport pytest\n... | [
[
"numpy.diag",
"numpy.abs",
"numpy.random.seed",
"numpy.sqrt",
"numpy.allclose",
"numpy.arange",
"numpy.all",
"numpy.random.poisson",
"numpy.testing.assert_almost_equal",
"numpy.longdouble",
"numpy.mean",
"numpy.var",
"numpy.random.uniform",
"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"... |
nkami/rl_grammar_constrained | [
"fb88253e308ff0b1a59406c63d93e42ff500ddc0"
] | [
"stable_baselines_master/stable_baselines/deepq/dqn.py"
] | [
"from functools import partial\n\nimport tensorflow as tf\nimport numpy as np\nimport gym\n\nfrom stable_baselines import logger\nfrom stable_baselines.common import tf_util, OffPolicyRLModel, SetVerbosity, TensorboardWriter\nfrom stable_baselines.common.vec_env import VecEnv\nfrom stable_baselines.common.schedules... | [
[
"tensorflow.Graph",
"numpy.log",
"numpy.ones_like",
"numpy.abs",
"numpy.arange",
"tensorflow.RunOptions",
"tensorflow.RunMetadata",
"tensorflow.placeholder",
"tensorflow.summary.merge_all",
"numpy.mean",
"tensorflow.train.AdamOptimizer",
"numpy.array",
"numpy.ze... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
GO-Eratosthenes/dhdt | [
"332b4cea01a7a7afbfa271714718e81c0424f64e"
] | [
"dhdt/processing/geometric_image_describtion.py"
] | [
"import numpy as np\n\nfrom tqdm import tqdm\n\n# image libraries\nfrom skimage.transform import radon\nfrom scipy.signal import find_peaks\n\n# local functions\nfrom ..generic.mapping_tools import map2pix\nfrom .matching_tools import pad_radius\nfrom .matching_tools_frequency_filters import low_pass_circle\nfrom .... | [
[
"numpy.polyfit",
"scipy.signal.find_peaks",
"numpy.linspace",
"numpy.unique",
"numpy.ptp",
"numpy.round",
"numpy.std",
"numpy.zeros_like",
"numpy.unravel_index",
"numpy.roll"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.10",
"1.4",
"1.9",
"1.5",
"1.2",
"1.7",
"1.3",
"1.8"
],
"tensorflow": []
}
] |
airistoteles/rcnn1d | [
"115f6f25ac34919bb9bda2cf24e1a22fbc07164e"
] | [
"rcnn.py"
] | [
"\"\"\"\nThis module contains one-dimensional (Recurrent) Convolutional Neural Networks - Classifiers\n\nOne-dimensional as in Keras' definition of Conv1D. Kernelsize is always (num_channels, kernel_width)\n -> Convolution only in one direction\n -> channel order doesn't matter\n -> but because of this cha... | [
[
"numpy.random.seed",
"sklearn.metrics.roc_curve",
"tensorflow.set_random_seed",
"sklearn.metrics.auc",
"numpy.extract"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
yluo54301/dwarf_photo-z | [
"3a564d97c43a27e101a402a2f6e8ae788a43af23"
] | [
"dwarfz/DNN/preprocessing.py"
] | [
"import os\nimport glob\nfrom astropy.io import fits\nfrom matplotlib import pyplot as plt\nimport matplotlib as mpl\nimport numpy as np\n\nimport geometry # local module\n\nimages_dir = \"../data/galaxy_images_training/quarry_files/\"\n\ndef get_image(HSC_id, band, images_dir=images_dir):\n \"\"\"Reads a single... | [
[
"matplotlib.pyplot.gca",
"matplotlib.rc_context",
"matplotlib.pyplot.get_cmap",
"numpy.max",
"numpy.exp",
"numpy.arcsinh",
"matplotlib.pyplot.NullLocator"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vcerqueira/vest-python | [
"146e1ee50463637c89e32112283cf857e2eb190a"
] | [
"vest/aggregations/poincare_variability.py"
] | [
"import numpy as np\n\n\ndef st_var(x: np.ndarray) -> float:\n \"\"\" Short-term Poincare variability\n \"\"\"\n return np.std(np.diff(x)) / np.sqrt(2)\n\n\ndef lt_var(x: np.ndarray) -> float:\n \"\"\" Long-term Poincare variability\n \"\"\"\n st_var = np.std(np.diff(x)) / np.sqrt(2)\n lt_var =... | [
[
"numpy.var",
"numpy.diff",
"numpy.sqrt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mknw/mask-rcnn | [
"0e7d14abeecb208e63dc5a9f7c05dbd0419afbe7"
] | [
"imagenet.py"
] | [
"import tensorflow_datasets as tfds\nimport tensorflow as tf\nimport numpy as np\nfrom model import ResNet\nfrom utils import onetwentyseven, test_model, LearningRateReducer\nfrom config import Config\nfrom viz import save_plot\n\n############### We might have a problem with the optimizer, calling \n\nif __name__ =... | [
[
"tensorflow.nn.compute_average_loss",
"tensorflow.keras.losses.SparseCategoricalCrossentropy",
"tensorflow.config.experimental.list_physical_devices",
"tensorflow.keras.optimizers.SGD",
"tensorflow.GradientTape",
"tensorflow.keras.metrics.SparseCategoricalAccuracy",
"tensorflow.keras.m... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
alecadub/englympics2020 | [
"190c041d5dc015e31b24a55952ae1e4edad7e3c8"
] | [
"search.py"
] | [
"from pandas import read_csv\nfrom matchstrings import MatchString\n\n\ndef binary_search_on_suppliers(supplier_lists_path, target, search_type):\n # raw_data = []\n #\n # for x in range(len(supplier_lists_path)):\n # raw_data.append(read_csv(supplier_lists_path[x]))\n\n dataframe_csv1 = read_csv... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
mschmidt87/python-es | [
"10ba043a8b54335597f73aa39e3f5026f0e065db"
] | [
"es/plain_es.py"
] | [
"import numpy as np\n\nfrom . import lib\n\n\ndef optimize(func, mu, sigma,\n learning_rate_mu=None, learning_rate_sigma=None, population_size=None,\n sigma_lower_bound=0.1, max_iter=2000,\n fitness_shaping=True, mirrored_sampling=True, record_history=False):\n \"\"\"\n Evo... | [
[
"numpy.dot",
"numpy.random.normal",
"numpy.any",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
luphord/nelson_siegel_svensson | [
"b5c652f5f6d134457571467055fa12cd7df57213"
] | [
"tests/test_nelson_siegel_curve_implementation.py"
] | [
"# -*- coding: utf-8 -*-\n\nimport unittest\n\nimport numpy as np\n\nfrom nelson_siegel_svensson import NelsonSiegelCurve\n\n\nclass TestNelsonSiegelCurveImplementation(unittest.TestCase):\n '''Tests for Nelson-Siegel curve implementation.'''\n\n def setUp(self):\n self.y = NelsonSiegelCurve(0.017, -0.... | [
[
"numpy.array",
"numpy.allclose",
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lematt1991/pytorch-lightning | [
"38f8029874e1a01ace5cef29ef7ea2c5b85dcead"
] | [
"pytorch_lightning/trainer/supporters.py"
] | [
"# Copyright The PyTorch Lightning team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law... | [
[
"torch.no_grad",
"torch.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
choderalab/bellin | [
"b6c03b900d34f8a5570c51af22ef2d589da2a050",
"b6c03b900d34f8a5570c51af22ef2d589da2a050"
] | [
"bellini/tests/test_quantity.py",
"bellini/api/utils.py"
] | [
"import pytest\n\ndef test_new_quantity():\n import bellini\n import pint\n ureg = pint.UnitRegistry()\n\n volume = bellini.quantity.Quantity(\n 1.0,\n ureg.liter\n )\n\ndef test_change_mutable():\n import bellini\n import pint\n ureg = pint.UnitRegistry()\n\n volume = belli... | [
[
"torch.ones"
],
[
"numpy.broadcast",
"numpy.ones_like",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
krutik2966/Facial-Expression-Recognition-in-Video | [
"313ddead4a4f64d0a12c6e3e81078391f7521c78"
] | [
"Training Code/basic_code/util.py"
] | [
"# # Emotion-FAN.pytorch\n# ICIP 2019: Frame Attention Networks for Facial Expression Recognition in Videos [pdf](https://arxiv.org/pdf/1907.00193.pdf)\n \n# [Debin Meng](michaeldbmeng19@outlook.com), [Xiaojiang Peng](https://pengxj.github.io/), [Yu Qiao](http://mmlab.siat.ac.cn/yuqiao/), etc.\n\n# ## Citation\n... | [
[
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
adamb314/ServoProject | [
"e3570cfea726a41b1101f1d73e622ac4903a2240"
] | [
"ArduinoSketch/configurationWizard.py"
] | [
"#!/bin/python3\nimport numpy as np\nimport scipy.signal\nimport math\nimport random\nimport matplotlib.pyplot as plt\nfrom matplotlib.backends.backend_gtk3agg import (\n FigureCanvasGTK3Agg as FigureCanvas)\nfrom matplotlib.figure import Figure\n\nimport numba\nimport serial\nimport serial.tools.list_ports\nimp... | [
[
"matplotlib.backends.backend_gtk3agg.FigureCanvasGTK3Agg",
"numpy.matrix",
"numpy.polyfit",
"matplotlib.pyplot.plot",
"numpy.mean",
"numpy.hstack",
"numpy.zeros",
"matplotlib.pyplot.figure",
"numpy.linalg.inv",
"numpy.delete",
"numpy.append",
"numpy.transpose",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TomQuinnell/covid19-sounds-neurips | [
"b01bcaba139a0843ad0c2aa1ad4dffa927a5cfaa"
] | [
"YAMNet/export.py"
] | [
"\"\"\"Exports YAMNet as: TF2 SavedModel, TF-Lite model, TF-JS model.\n\nThe exported models all accept as input:\n- 1-d float32 Tensor of arbitrary shape containing an audio waveform\n (assumed to be mono 16 kHz samples in the [-1, +1] range)\nand return as output:\n- a 2-d float32 Tensor of shape [num_frames, nu... | [
[
"tensorflow.saved_model.load",
"numpy.random.seed",
"tensorflow.lite.Interpreter",
"numpy.arange",
"tensorflow.saved_model.Asset",
"tensorflow.compat.v1.global_variables_initializer",
"tensorflow.saved_model.save",
"tensorflow.compat.v1.Session",
"tensorflow.compat.v1.Graph",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.6",
"2.2",
"1.13",
"2.3",
"2.4",
"2.9",
"2.5",
"2.8",
"2.10"
]
}
] |
Egolas/TC_LAAU | [
"6b9f700f642ca3187f1556434b5fb2308f065564"
] | [
"pretrain_linear_eval.py"
] | [
"import argparse\nimport gc\nimport os\nimport sys\nimport timeit\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom torch import optim\nfrom tqdm import tqdm\n\n# import project module\nimport feeders\nfro... | [
[
"torch.nn.BatchNorm1d",
"torch.nn.CrossEntropyLoss",
"torch.nn.Dropout",
"torch.max",
"numpy.asarray",
"torch.nn.Flatten",
"sklearn.neighbors.KNeighborsClassifier",
"numpy.concatenate",
"torch.nn.Linear",
"numpy.argmax",
"torch.no_grad",
"torch.utils.tensorboard.Sum... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Semanti1/smarts_baseline | [
"77dbc350f37ae7735c74a2b8f1585c2818ac3421",
"77dbc350f37ae7735c74a2b8f1585c2818ac3421"
] | [
"ultra/ultra/scenarios/common/begin_time_init_funcs.py",
"smarts/core/tests/test_vehicle.py"
] | [
"# MIT License\n#\n# Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without ... | [
[
"numpy.random.exponential",
"numpy.cumsum",
"numpy.random.uniform",
"numpy.random.randint"
],
[
"numpy.array_equal",
"numpy.isclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
WillRobotics/p3dpy | [
"a607d0840b0871ee3122df7756336316435d0f18"
] | [
"examples/save_h5.py"
] | [
"import sys\nimport pyrealsense2 as rs\nimport numpy as np\nfrom enum import IntEnum\nimport datetime\nimport h5py\n\nimport p3dpy as pp\n\nimport argparse\nparser = argparse.ArgumentParser(description='Visualization client example.')\nparser.add_argument('--host', type=str, default='localhost', help=\"Host address... | [
[
"numpy.array",
"numpy.identity"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
markub3327/rl-agent | [
"69365b27a99113f15b53bcebf81637793fd2f72e"
] | [
"rl_toolkit/networks/models/actor_critic.py"
] | [
"import tensorflow as tf\nfrom tensorflow.keras import Model\n\nfrom .actor import Actor\nfrom .critic import MultiCritic\n\n\nclass ActorCritic(Model):\n \"\"\"\n Actor-Critic\n ===========\n\n Attributes:\n actor_units (list): list of the numbers of units in each Actor's layer\n critic_u... | [
[
"tensorflow.constant",
"tensorflow.range",
"tensorflow.reduce_mean",
"tensorflow.reshape",
"tensorflow.cast",
"tensorflow.exp",
"tensorflow.math.log",
"tensorflow.stop_gradient",
"tensorflow.math.softplus",
"tensorflow.GradientTape"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
mgorkove/trading-bot | [
"3dc5450a6c6816b433cc9f7077e09fa07f9218df"
] | [
"trading_bot/agent.py"
] | [
"import random\n\nfrom collections import deque\n\nimport numpy as np\nimport tensorflow as tf\nimport keras.backend as K\n\nfrom keras.models import Sequential\nfrom keras.models import load_model, clone_model\nfrom keras.layers import Dense\nfrom tensorflow.keras.optimizers import Adam\n\n\ndef huber_loss(y_true,... | [
[
"numpy.array",
"tensorflow.keras.optimizers.Adam",
"numpy.argmax",
"tensorflow.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
SaurabhChakravorty/Data-Science-Projects | [
"dfd55b74622761a60f924b1f24ada3c58ec9e94b"
] | [
"Interview_Case_Studies/Forecasty.ai/regression.py"
] | [
"\nimport numpy as np\nimport pandas as pd\nfrom sklearn.preprocessing import StandardScaler as std\nfrom sklearn.preprocessing import OneHotEncoder, LabelEncoder\nfrom sklearn.model_selection import train_test_split\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom mpl_toolkits.mplot3d import Axes3D\ni... | [
[
"sklearn.ensemble.RandomForestRegressor",
"sklearn.model_selection.GridSearchCV",
"sklearn.metrics.r2_score",
"sklearn.metrics.mean_absolute_error",
"sklearn.model_selection.train_test_split",
"pandas.DataFrame",
"sklearn.metrics.mean_squared_error",
"sklearn.svm.SVR",
"sklearn... | [
{
"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": []
}
] |
kondys/mlf_assignment | [
"5f206feae1c6d6f8d05471699a1bd76d4a61c934"
] | [
"COD_app/assign.py"
] | [
"from os import system\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nimport time\nimport concurrent\nfrom pebble import ProcessPool\n\nnp.warnings.filterwarnings('ignore', 'overflow') #disabled warnings for the sigmoid function\n\nCPU_PROCESSES = 12 #CPU processes, ... | [
[
"numpy.dot",
"pandas.read_csv",
"sklearn.model_selection.train_test_split",
"numpy.warnings.filterwarnings",
"numpy.exp",
"numpy.array",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
chuliuT/PaddleViT | [
"282e5013f0460fa9f9b010775ff4d2607e7370ef",
"282e5013f0460fa9f9b010775ff4d2607e7370ef",
"282e5013f0460fa9f9b010775ff4d2607e7370ef"
] | [
"image_classification/ViT/port_weights/load_pytorch_weights.py",
"image_classification/DeiT/main_single_gpu.py",
"image_classification/CrossViT/main_multi_gpu.py"
] | [
"# Copyright (c) 2021 PPViT 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 require... | [
[
"torch.device",
"numpy.random.randn",
"numpy.allclose",
"torch.Tensor"
],
[
"numpy.random.seed"
],
[
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dhuppenkothen/astroABC | [
"a4d04a3f2735883918b880b0de536119ad976585"
] | [
"examples/bin_data.py"
] | [
"import numpy as np\n\ndef read_fitres(fname):\n dtype=[('z',np.float32),('mu',np.float32),('mu_err',np.float32)]\n data=np.loadtxt(fname,skiprows = 15,dtype =dtype, usecols=(34,48,50))\n return data\n\ndef bin_data(z,mu,err,Nbins=10):\n binw = (z.max()-z.min())/Nbins... | [
[
"numpy.append",
"numpy.mean",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
anjukan/CityLearn | [
"b60ff1702db7558934467e8e348caba90097a099"
] | [
"train_SAC.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n\n\"\"\"\nImplementation of Soft Actor Critic (SAC) network\nusing PyTorch.\nSee https://arxiv.org/pdf/1801.01290.pdf for algorithm details.\n\n@author: Anjukan Kathirgamanathan 2020 (k.anjukan@gmail.com) and Kacper\nTwardowski (kanexer@gmail.com) \n\nProject for CityLearn C... | [
[
"numpy.random.seed",
"torch.manual_seed",
"numpy.save",
"numpy.append",
"torch.utils.tensorboard.SummaryWriter",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
phinate/cabinetry | [
"10571e83155b0fd47796b93b85919975c3b364ed"
] | [
"src/cabinetry/template_postprocessor.py"
] | [
"import copy\nimport logging\nimport pathlib\nfrom typing import Any, Dict, Optional\n\nimport numpy as np\n\nfrom . import configuration\nfrom . import histo\nfrom . import route\nfrom . import smooth\n\n\nlog = logging.getLogger(__name__)\n\n\ndef _fix_stat_unc(histogram: histo.Histogram, name: str) -> None:\n ... | [
[
"numpy.isnan",
"numpy.nan_to_num"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
UT-ADL/lidar-as-camera | [
"daccb2ae21b4899ecfd8611b7a27f91681617383"
] | [
"trajectory.py"
] | [
"import math\n\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom scipy import optimize\n\n\ndef get_points(dataset, frame_idx, num_waypoints=10):\n wp_x = [f\"wp_x_offset_{i}\" for i in range(1, num_waypoints + 1)]\n wp_y = [f\"wp_y_offset_{i}\" for i in range(1, num_waypoints + 1)]\n\n row =... | [
[
"matplotlib.pyplot.gca",
"numpy.sqrt",
"numpy.linspace",
"matplotlib.pyplot.scatter",
"numpy.arctan",
"numpy.cos",
"numpy.sin",
"matplotlib.pyplot.plot",
"scipy.optimize.leastsq",
"numpy.mean",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"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"... |
artzet-s/pointnet | [
"5b49b61d630e791406d8132cebb0e616aaa0ec07"
] | [
"models/pointnet_cls.py"
] | [
"import tensorflow as tf\nimport numpy as np\nimport math\nimport sys\nimport os\nBASE_DIR = os.path.dirname(os.path.abspath(__file__))\nsys.path.append(BASE_DIR)\nsys.path.append(os.path.join(BASE_DIR, '../utils'))\nimport tf_util\nfrom transform_nets import input_transform_net, feature_transform_net\n\n\ndef plac... | [
[
"tensorflow.matmul",
"tensorflow.Graph",
"tensorflow.transpose",
"tensorflow.constant",
"tensorflow.reduce_mean",
"tensorflow.zeros",
"numpy.eye",
"tensorflow.reshape",
"tensorflow.expand_dims",
"tensorflow.placeholder",
"tensorflow.squeeze",
"tensorflow.nn.l2_loss"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
Abhin02/federated | [
"6a709f5598450232b918c046cfeba849f479d5cb"
] | [
"periodic_distribution_shift/models/dual_branch_resnet_models.py"
] | [
"# Copyright 2021, Google LLC.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed... | [
[
"tensorflow.keras.layers.Activation",
"tensorflow.keras.layers.GlobalAveragePooling2D",
"tensorflow.keras.backend.image_data_format",
"tensorflow.keras.models.Model",
"tensorflow.keras.backend.int_shape",
"tensorflow.keras.regularizers.l2",
"tensorflow.keras.initializers.RandomNormal",... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.2"
]
}
] |
michaelaerni/ip6-molanet | [
"cb226d3866f86b030fa3951eefdba0da85f0dd92"
] | [
"molanet/data/load_isic.py"
] | [
"import argparse\nimport io\nimport uuid\nfrom typing import Iterable, Union\n\nimport numpy as np\nimport requests\nfrom PIL import Image\n\nfrom molanet.data.database import DatabaseConnection\nfrom molanet.data.entities import MoleSample, Diagnosis, Segmentation\n\n\ndef parse_diagnosis(raw_diagnosis: str) -> Un... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rclough/tfx | [
"ff6917997340401570d05a4d3ebd6e8ab5760495"
] | [
"tfx/orchestration/kubeflow/test_utils.py"
] | [
"# Lint as: python2, python3\n# Copyright 2019 Google LLC. 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\... | [
[
"tensorflow.io.gfile.exists"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vishwas1234567/models | [
"fa32bb5f6627a386b352d18a2495ab2bbf7f6129"
] | [
"official/resnet/cifar10_test.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.test.main",
"tensorflow.logging.set_verbosity",
"tensorflow.one_hot",
"numpy.array",
"tensorflow.random_uniform"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
stephan-hesselmann-by/pandas | [
"74e50ec515d668842f6ce55ef4d96a0f6001ccd8"
] | [
"pandas/tests/io/formats/style/test_style.py"
] | [
"import copy\nimport re\n\nimport numpy as np\nimport pytest\n\nimport pandas as pd\nfrom pandas import (\n DataFrame,\n MultiIndex,\n)\nimport pandas._testing as tm\n\njinja2 = pytest.importorskip(\"jinja2\")\nfrom pandas.io.formats.style import ( # isort:skip\n Styler,\n)\nfrom pandas.io.formats.style_r... | [
[
"pandas.Series",
"pandas._testing.assert_dict_equal",
"pandas.DataFrame",
"pandas.io.formats.style_render._get_trimming_maximums",
"numpy.random.randn",
"pandas.io.formats.style_render.non_reducing_slice",
"pandas._testing.assert_frame_equal",
"numpy.unique",
"numpy.arange",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
kyoty/spark | [
"4a4f207f4215d56f126c2474fd7a94f427937a2f"
] | [
"python/pyspark/pandas/tests/test_csv.py"
] | [
"#\n# Licensed to the Apache Software Foundation (ASF) under one or more\n# contributor license agreements. See the NOTICE file distributed with\n# this work for additional information regarding copyright ownership.\n# The ASF licenses this file to You under the Apache License, Version 2.0\n# (the \"License\"); yo... | [
[
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
obilaniu/efficientdet | [
"98c79c3707ecf1c4e66484b4e0757618f522a62e"
] | [
"main.py"
] | [
"import argparse\n\nimport torch\nfrom torch.utils.tensorboard import SummaryWriter\nimport torch.utils.mobile_optimizer\n\nimport config as cfg\nfrom dataloader import get_loader\nfrom log.logger import logger\nfrom model import EfficientDet\nfrom train import train\nfrom utils import (CosineLRScheduler, Detection... | [
[
"torch.device",
"torch.no_grad",
"torch.utils.tensorboard.SummaryWriter",
"torch.jit.script"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tilacyn/CT-GAN | [
"50c47bb03b99345c9257654aa2af8d644751cc86"
] | [
"GUI.py"
] | [
"import sys\r\nif len(sys.argv) == 2:\r\n if sys.argv[1] == '-h':\r\n print(\"python \"+sys.argv[0]+\" <path to dir of scans>\")\r\n print(\"python \"+sys.argv[0]+\" <path to dir of scans> <path to save dir>\")\r\n exit(1)\r\n\r\nimport matplotlib.pyplot as plt\r\nfrom matplotlib.widgets imp... | [
[
"matplotlib.widgets.Button",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.axes",
"matplotlib.pyplot.get_current_fig_manager",
"matplotlib.animation.FuncAnimation",
"matplotlib.pyplot.subplots_adjust",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jordanparker6/TransformerSeries | [
"02756aca83d83bfd5829f031b687939a5902205e"
] | [
"transformerseries/models/base.py"
] | [
"import math\nimport torch\nfrom torch import nn\nimport config\nfrom pytorch_lightning import LightningModule\n\nfrom dataset import TimeSeriesDataset\n\n#########################\n## Utility Functions ####\n#########################\n\ndef coin_flip(p):\n return True if torch.rand(1) < p else False\n\ndef sigm... | [
[
"torch.exp",
"torch.rand",
"torch.cat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Tao-wecorp/drone_cadrl | [
"748a948dc05d78fb91b9ff62a390dd8286342872"
] | [
"src/drone_openai/scripts/yaw_vel_pid_baseline.py"
] | [
"#! /usr/bin/env python\n\nimport rospy\nfrom gazebo_msgs.msg import ModelState, ModelStates\nfrom sensor_msgs.msg import Image\nfrom std_msgs.msg import Float64\nfrom std_srvs.srv import Empty\n\nfrom tf.transformations import euler_from_quaternion, quaternion_from_euler\nfrom geometry_msgs.msg import Point, Pose,... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
RobinYaoWenbin/python- | [
"9607219b8d057ab896ecae5326daadd7dcfb6112"
] | [
"python GTWR/mgtwr-1.0.4/new_mgtwr/diagnosis.py"
] | [
"\r\nimport numpy as np\r\n\r\ndef get_AICc(gtwr):\r\n \"\"\"\r\n Get AICc value\r\n \r\n Gaussian: p61, (2.33), Fotheringham, Brunsdon and Charlton (2002)\r\n \r\n GWGLM: AICc=AIC+2k(k+1)/(n-k-1), Nakaya et al. (2005): p2704, (36)\r\n\r\n \"\"\"\r\n n = gtwr.n\r\n k = gtwr.tr_S\r\n \r... | [
[
"numpy.diag",
"numpy.dot",
"numpy.log"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gf712/AbPyTools | [
"9ff0d4346ad80487d43875bc77d99fbe76170db4"
] | [
"abpytools/core/chain.py"
] | [
"import re\nimport numpy as np\nfrom ..utils import DataLoader, Download, NumberingException\nimport pandas as pd\nfrom .helper_functions import numbering_table_sequences, numbering_table_region, numbering_table_multiindex\nfrom . import Cache\nfrom .flags import *\n\n\nclass Chain:\n \"\"\"The Chain object repr... | [
[
"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": []
}
] |
MoniFarsang/vowpal_wabbit | [
"e37d4505a4830c73306180adadb5b3abbb5c0fc1"
] | [
"utl/vw-hyperopt.py"
] | [
"#!/usr/bin/env python3\n# coding: utf-8\n\n\"\"\"\nGithub version of hyperparameter optimization for Vowpal Wabbit via hyperopt\n\"\"\"\n\n__author__ = \"kurtosis\"\n\nfrom hyperopt import hp, fmin, tpe, rand, Trials, STATUS_OK\nfrom hyperopt.pyll import scope\nfrom sklearn.metrics import (\n roc_curve,\n au... | [
[
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.title",
"sklearn.metrics.auc",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.yscale",
"matplotlib.pyplot.savefig",
"sklearn.metrics.mean_squared_error",
"sklearn.metrics.roc_curve",
"sklearn.metrics.log_loss",
"matplotli... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
reiase/towhee | [
"55c55fd961229575b75eae269b55090c839f8dcd"
] | [
"towhee/hub/builtin/operators/computer_vision.py"
] | [
"# Copyright 2021 Zilliz. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicab... | [
[
"numpy.frombuffer"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Tommer-R/youtube_scraper | [
"b35e91538c5d950e455583a7ecdf50e7aba21f5f"
] | [
"video_class.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Feb 4 10:09:24 2021\r\n\r\n@author: Tommer\r\n\"\"\"\r\n\r\nfrom datetime import date\r\n\r\nimport pandas as pd\r\n\r\n\r\n# <codecell>\r\n\r\n\r\nclass Video:\r\n # creates the object and assigns default values to attributes\r\n def __init__(self, channe... | [
[
"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": []
}
] |
alprnbg/DeepDTA-MAML | [
"78a1df71517cdec6aaaf207683115d77cd426b29"
] | [
"meta_neural_network_architectures.py"
] | [
"import numbers,math\nfrom copy import copy\n\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch\nimport numpy as np\n\nfrom collections import OrderedDict\n\n\n\ndef extract_top_level_dict(current_dict):\n \"\"\"\n Builds a graph dictionary from the passed depth_keys, value pair. Useful fo... | [
[
"torch.nn.functional.embedding",
"torch.nn.functional.batch_norm",
"torch.nn.init.uniform_",
"torch.ones",
"torch.max",
"torch.Tensor",
"torch.zeros",
"torch.cat",
"torch.load",
"torch.empty",
"torch.nn.functional.conv1d",
"torch.sum",
"torch.nn.init.normal_",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
PatWie/tensorpack-recipes | [
"33962bb45e81f3619bfa6a8aeae5556cc7534caf"
] | [
"OpticalFlow/user_ops/test_correlation.py"
] | [
"#! /usr/bin/env python\n# -*- coding: utf-8 -*-\n# Author: Patrick Wieschollek <mail@patwie.com>\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport numpy as np\n\nfrom __init__ import correlation_cost\nimport tensorflow as tf\nfrom tensorflow... | [
[
"tensorflow.python.ops.array_ops.transpose",
"tensorflow.concat",
"tensorflow.reduce_mean",
"numpy.ceil",
"tensorflow.python.platform.test.main",
"numpy.random.randn",
"tensorflow.Session",
"tensorflow.pad",
"tensorflow.python.framework.ops.convert_to_tensor",
"tensorflow.p... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
yifuwang/elastic | [
"250667dc77fd95a0dd60d144fb452f896d95682b"
] | [
"torchelastic/agent/server/test/local_elastic_agent_test.py"
] | [
"#!/usr/bin/env python3\n\n# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the BSD-style license found in the\n# LICENSE file in the root directory of this source tree.\nimport multiprocessing\nimport os\nimport signal\nimport time\nimport unittest... | [
[
"torch.distributed.init_process_group",
"torch.tensor",
"torch.distributed.rpc.rpc_sync",
"torch.distributed.rpc.shutdown",
"torch.distributed.rpc.init_rpc",
"torch.distributed.all_reduce"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ored95/data-analysis-course | [
"f61a953769b8e7c502f2bec28158ec1bd344f72a"
] | [
"course-page/example_figure.py"
] | [
"#!/usr/bin/env python3\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport sys\n\n#import seaborn as sns; sns.set()\n\nplt.rcParams[\"figure.dpi\"]=158 # on laptop this was incorrectly set\n\nfig, ax = plt.subplots(1,2, figsize=(4,2))\na=np.arange(8)\nax[0].plot(a)\nax[0].set_title(\"First degree\")\nax... | [
[
"numpy.arange",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.subplots_adjust",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LouisdeBruijn/Medium | [
"afc66ee061c10b7107ba1661d2b9dfed0559dfc3"
] | [
"MovieLens recommender/alsrecommender.py"
] | [
"#!/usr/bin/env python3\n# File name: analyse.py\n# Description: Analyses MovieLens Dataset and builds model from it using Implicit library\n# Author: Louis de Bruijn\n# Date: 07-08-2019\n\nimport os\nimport sys\nimport pandas as pd\nimport numpy as np\nfrom scipy.sparse import csr_matrix, save_npz, load_npz, vstac... | [
[
"numpy.hstack",
"pandas.merge",
"pandas.read_csv",
"scipy.sparse.load_npz",
"pandas.DataFrame",
"scipy.sparse.save_npz"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [
"1.6",
"1.10",
"1.4",
"1.3",
"1.9",
"0.19",
"1.5",
"1.7",
"1.0",
"1.2",
"1.8"
],
"tens... |
Salah856/ReAgent | [
"67434f458cde1f2c946237e866a73392279a7ede"
] | [
"reagent/test/workflow/test_oss_workflows.py"
] | [
"#!/usr/bin/env python3\n# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.\n\nimport json\nimport os\nimport unittest\nimport zipfile\nfrom typing import Dict\nfrom unittest.mock import patch\n\nimport reagent\n\n# pyre-fixme[21]: Could not find module `reagent.workflow.cli`.\nimport reagent.w... | [
[
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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