repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
evgeni-nikolaev/DL-DB | [
"32e75ed9235aaf5a6183d7f20cfd2dbd59ccbe18"
] | [
"tests/test.py"
] | [
"from featuretools.tests.testing_utils import make_ecommerce_entityset\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import roc_auc_score, f1_score, mean_absolute_error\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.svm import SVC\nfrom sklearn.linear_model import L... | [
[
"sklearn.metrics.roc_auc_score",
"numpy.random.random",
"sklearn.linear_model.LogisticRegression",
"sklearn.model_selection.train_test_split",
"pandas.DataFrame",
"sklearn.preprocessing.Imputer",
"sklearn.preprocessing.StandardScaler",
"sklearn.svm.SVC",
"sklearn.metrics.f1_sco... | [
{
"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": []
}
] |
Shilpaj1994/3D-Perception | [
"af5777c3ce5f86093e5eaa5b27c665db026c8c1e"
] | [
"pr2_robot/scripts/project.py"
] | [
"#!/usr/bin/env python\n\nimport pickle\nimport yaml\n\nimport numpy as np\nimport rospy\nimport sklearn\nfrom sklearn.preprocessing import LabelEncoder\nimport tf\n\nfrom geometry_msgs.msg import Pose\nfrom std_msgs.msg import Float64\nfrom std_msgs.msg import Int32\nfrom std_msgs.msg import String\nfrom visualiza... | [
[
"numpy.concatenate",
"numpy.asscalar",
"sklearn.preprocessing.LabelEncoder",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zshufan/Tattle-Tale | [
"f9d93051efb523f1bda0cead023c2f001e18cc85"
] | [
"testscript/imputation_algorithms.py"
] | [
"# some codes refer to Holoclean evaluation function\n# https://github.com/HoloClean/holoclean\n\nimport pandas as pd\nimport numpy as np\nimport logging\nimport random\nimport argparse\n\nparser = argparse.ArgumentParser(description='Predict on many examples')\nparser.add_argument(\"--dataset\", type=str, help=\"d... | [
[
"pandas.read_csv",
"numpy.random.choice"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
vasukalariya/Natural-Scene-Text-Detection | [
"fda5357797fa6a320f5238954d875f67c1b4271d"
] | [
"final_text.py"
] | [
"# USAGE : python text_detection.py --image images/lebron_james.jpg --east frozen_east_text_detection.pb\r\n\r\n\r\n# IMPORTING NECESSARY PACKAGES\r\n\r\n\r\nimport imutils\r\nfrom imutils import contours\r\nfrom imutils.object_detection import non_max_suppression\r\nfrom skimage.transform import resize\r\nfrom ski... | [
[
"numpy.lib.pad",
"numpy.hstack",
"matplotlib.pyplot.imshow",
"numpy.cos",
"numpy.ones",
"numpy.sin",
"scipy.ndimage.measurements.center_of_mass",
"numpy.delete",
"numpy.round",
"numpy.float32",
"numpy.array",
"numpy.sum",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"0.15",
"1.4",
"0.16",
"1.0",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"0.10",
"0.17",
"1.3"
],
"tensorflow": [... |
VUB-RMM/FABRIK_chain_3D | [
"ad1615ffabe6f35af6d2a8534d59a821b00a501c"
] | [
"fabrik_chain_3d/Chain.py"
] | [
"from mpl_toolkits.mplot3d import Axes3D\n\nfrom fabrik_chain_3d import Bone as Bone, Joint as Joint, Mat as Mat, Utils as Util\nfrom fabrik_chain_3d.output_writer import *\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport math\n\n\n\nclass Chain3d:\n def __init__(self, is_base_bone_fixed, base_addres... | [
[
"numpy.dot",
"numpy.abs",
"numpy.mean",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sahabi/OpenUxAS | [
"7982e32432b36e7877fe27bd4024e61fe316a5ea"
] | [
"resources/AutomationDiagramDataService/ProcessUniqueAutomationResponse.py"
] | [
"#! /usr/bin/env python\n\nimport xml.dom.minidom\nimport pandas as pd\nimport glob\n\ndef get_a_document(filename):\n return xml.dom.minidom.parse(filename)\n\ndef ProcessMissionCommand(missionCommand):\n\tisGoodMessage = True\n\ttry:\n\t\tfirstWaypoint = 0\n\t\telements = missionCommand.getElementsByTagName('F... | [
[
"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": []
}
] |
djbsmith/dmu_products | [
"4a6e1496a759782057c87ab5a65763282f61c497"
] | [
"dmu26/dmu26_XID+MIPS_ELAIS-N1/make_combined_map.py"
] | [
"import xidplus\nimport pickle\nimport numpy as np\nfrom xidplus import catalogue\nfrom xidplus import moc_routines\nfrom astropy import wcs\nfrom astropy.io import fits\nfrom xidplus import posterior_maps as postmaps\nfrom astropy import wcs\n\nimport os\noutput_folder='./output/'\n\nwith open(output_folder+'faile... | [
[
"numpy.arange"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ChenShuwei1001/mmediting | [
"285e629fe9da8a13c7538a6bb3347e8870cd7201",
"285e629fe9da8a13c7538a6bb3347e8870cd7201",
"285e629fe9da8a13c7538a6bb3347e8870cd7201",
"285e629fe9da8a13c7538a6bb3347e8870cd7201"
] | [
"mmedit/core/misc.py",
"mmedit/datasets/sr_facial_landmark_dataset.py",
"tests/test_models/test_extractors/test_feedback_hour_glass.py",
"mmedit/datasets/sr_vid4_dataset.py"
] | [
"import math\n\nimport numpy as np\nimport torch\nfrom torchvision.utils import make_grid\n\n\ndef tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):\n \"\"\"Convert torch Tensors into image numpy arrays.\n\n After clamping to (min, max), image values will be normalized to [0, 1].\n\n For differnet ten... | [
[
"torch.is_tensor",
"numpy.transpose"
],
[
"numpy.load"
],
[
"torch.rand"
],
[
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
figroc/pykg2vec | [
"b4f1507016a7a15e71e37c1440be9c20924d5f00"
] | [
"pykg2vec/core/SME.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport tensorflow as tf\n\nfrom pykg2vec.core.KGMeta import ModelMeta\n\n\nclass SME(ModelMeta):\n \"\"\" `A Semantic Matching Energy Function for Lea... | [
[
"tensorflow.nn.l2_normalize",
"tensorflow.negative",
"tensorflow.transpose",
"tensorflow.shape",
"tensorflow.maximum",
"tensorflow.placeholder",
"tensorflow.squeeze",
"tensorflow.expand_dims",
"tensorflow.name_scope",
"tensorflow.contrib.layers.xavier_initializer",
"ten... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
tygonorg/chinese-handwriting | [
"184913a0e10f14fba898b57502a416e929ae9d87"
] | [
"generate data/gendata.py"
] | [
"import time\nimport os\nimport cv2\nimport numpy as np\nfrom PIL import Image, ImageDraw, ImageFont\nfrom fontTools.ttLib import TTFont\n\n#check blank char\ndef char_in_font(unicode_char, font):\n for cmap in font['cmap'].tables:\n if cmap.isUnicode():\n if ord(unicode_char) in cmap.cmap:\n ... | [
[
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AdamuKaapan/natrium | [
"b94527b287ec2c626be2fe37208bb043aab68b48"
] | [
"application/visualization/words_breakdown.py"
] | [
"#!/usr/bin/env python3\n\"\"\"Tool for visualizing the part-of-speech distributions of a data series.\nSee -h for help\"\"\"\n\nimport argparse\nimport pandas as pd\nimport pandas.core.frame\nimport matplotlib as mpl\nmpl.use('Agg') # Necessary so we can work without Tkinter\nimport matplotlib.pyplot as plt\nimpo... | [
[
"matplotlib.use"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aburrell/ocbpy | [
"453c8ef5d2c5e3484d5a9cf40f490367bf71d4b0"
] | [
"ocbpy/ocb_time.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# Copyright (C) 2017, AGB & GC\n# Full license can be found in License.md\n# ----------------------------------------------------------------------------\n\"\"\"Routines to convert from different file timekeeping methods to datetime\n\nFunctions\n---------\nget_datet... | [
[
"numpy.asarray",
"numpy.isnan",
"numpy.modf",
"numpy.ceil",
"numpy.any",
"numpy.floor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gfidanli/crypto-algo-trading | [
"e302935c62420774b50d4471a5227622003dbbec"
] | [
"trading_screeners/mean_reversion_screener.py"
] | [
"import pandas as pd\nfrom datetime import datetime\nimport sqlite3\n\ndef check_date_validity(date_input):\n '''\n Check user input to determine whether date is valid.\n '''\n\n if date_input == 'q':\n print(\"Quitting script...\")\n valid_date = False\n \n else:\n try:\n ... | [
[
"pandas.read_sql",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
urw7rs/spiral_gym | [
"db1aaade8ddc98b8471c1997fb8dbcab5a7ed805"
] | [
"spiral/envs/fluid.py"
] | [
"# Copyright 2019 DeepMind Technologies Limited.\n# 2 May 2020 - Modified by urw7rs\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# https://www.apache.org/licenses/LICENSE-2.0... | [
[
"numpy.abs",
"numpy.single",
"numpy.linspace",
"numpy.arange",
"numpy.float32",
"numpy.array",
"numpy.unravel_index"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AlexReyesR/EmotionAndGazeDetection | [
"10c638fcc07c8c8edb801052305802332fd0b1e8"
] | [
"emotions_RS.py"
] | [
"import cv2\nimport numpy as np\nimport dlib\nfrom imutils import face_utils\nimport face_recognition\nfrom keras.models import load_model\nfrom statistics import mode\nfrom utils.datasets import get_labels\nfrom utils.inference import detect_faces\nfrom utils.inference import draw_text\nfrom utils.inference import... | [
[
"numpy.asarray",
"numpy.max",
"numpy.expand_dims",
"numpy.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
holykikyou/Colorful-NLG | [
"97acd3988c35065238c5682afa3d441e14cd4daf"
] | [
"charrnn/data.py"
] | [
"import numpy as np\nfrom typing import Dict,Text\nimport torch as t\nimport torch.functional as f\nfrom torch import nn\nfrom torch.autograd import Variable\n\nrnn=nn.LSTM(10,20,2)\nh0=t.randn(2,3,20)\nc0=t.randn(2,3,20)\ninput=t.randn(5,3,10)\n#output,(cn,hn)=rnn(input,(h0,c0))\n#print(output.size())\n\nprint(inp... | [
[
"torch.randn",
"numpy.load",
"torch.nn.LSTM"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hajungong007/CrowdDet | [
"fd20aa60a68c6350ade808bf400ee82e803861f7"
] | [
"lib/det_tools/img_utils.py"
] | [
"import torch\n\ndef pad_tensor_to_multiple_number(tensor, multiple_number, pad_value=0):\n t_height, t_width = tensor.shape[-2], tensor.shape[-1]\n padded_height = (t_height + multiple_number - 1) // \\\n multiple_number * multiple_number\n padded_width = (t_width + multiple_number - 1)... | [
[
"torch.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
RealVincentBerthet/HDR | [
"c908f1e044b6f893ad37872e360529549225e33e"
] | [
"scripts/tests.py"
] | [
"import cv2 as cv\nimport numpy as np\n\n\n\ndef gaussian_kernel(size=5, sigma=0.4):\n return cv.getGaussianKernel(ksize=size, sigma=sigma)\n\n\ndef image_reduce(image):\n kernel = gaussian_kernel()\n out_image = cv.filter2D(image, cv.CV_8UC3, kernel)\n out_image = cv.resize(out_image, None, fx=0.5, fy=... | [
[
"numpy.expand_dims",
"numpy.tile",
"numpy.exp",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
richardangell/pitci | [
"28152233da13026df01cfac6a43abac34d25f80c"
] | [
"pitci/helpers.py"
] | [
"\"\"\"Module containing functions used for evaluating interval regions.\"\"\"\n\nimport pandas as pd\nimport numpy as np\n\nfrom typing import Union, List, Tuple, Optional\n\nfrom .checks import check_type\n\n\ndef gather_intervals(\n lower_interval: Optional[Union[np.ndarray, pd.Series]] = None,\n upper_int... | [
[
"pandas.qcut",
"pandas.Series",
"pandas.DataFrame",
"pandas.cut"
]
] | [
{
"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": []
}
] |
Genometric/ToolVisibilityQuantifier | [
"82572a678c27820ec1a8dbbc54dcee18ee601096"
] | [
"analytics/lib/cluster.py"
] | [
"\"\"\"\nTODO: add doc string \n\"\"\"\n\nimport numpy as np\nimport os\nimport sys\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport scipy.cluster.hierarchy as shc\nfrom sklearn.cluster import AgglomerativeClustering\nimport seaborn as sns\nimport sklearn\nfrom matplotlib.lines import Line2D\nfrom t_te... | [
[
"sklearn.metrics.silhouette_score",
"scipy.cluster.hierarchy.dendrogram",
"matplotlib.lines.Line2D",
"matplotlib.pyplot.subplots",
"pandas.DataFrame",
"matplotlib.pyplot.savefig",
"numpy.diff",
"matplotlib.pyplot.close",
"matplotlib.pyplot.subplots_adjust",
"scipy.cluster.h... | [
{
"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": [
"0.13",
"1.6",
"0.14",
"1.10",
"0... |
lin-zju/descriptor-space | [
"7b6aaa6ed710d7e3b097b0097c01f4562df75c59"
] | [
"lib/data/evaluators/pck.py"
] | [
"import torch\nfrom .base import Evaluator\nfrom lib.utils.misc import sample_descriptor, compute_scale\nimport numpy as np\nfrom lib.utils.nn_set2set_match.nn_set2set_match_layer import nn_set2set_match_cuda\nimport cv2\n\nclass DescPckEvaluator(Evaluator):\n def __init__(self, threshold):\n \"\"\"\n ... | [
[
"numpy.asarray",
"numpy.linalg.inv",
"numpy.linalg.norm",
"numpy.concatenate",
"numpy.round",
"numpy.mean",
"torch.device",
"matplotlib.pyplot.show",
"numpy.sum",
"torch.stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hetta-14/dashboard-scraping-coronavirus | [
"8cb534e534d0de8afdc368d87ff3ef316760f0d8"
] | [
"dashboard.py"
] | [
"import pandas as pd\nimport numpy as np\nimport plotly.io as pio\nimport plotly.graph_objs as go\nimport plotly.express as px\nimport plotly\nimport json\nfrom flask import Flask, render_template\npio.renderers.default = \"browser\"\n#Créer l'instance Flask\n\napp = Flask(__name__,template_folder='templates')\n\n#... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
manish1822510059/Hackerrank | [
"7c6e4553f033f067e04dc6c756ef90cb43f3c4a8"
] | [
"Hackerrank_python/15.numpy/10.Min and Max.py"
] | [
"import numpy as arr\nn,m=map(int,input().split())\nar=([list(map(int,input().split()))for _ in range(n)])\narr1=arr.min(ar,axis=1)\nprint(max(arr1))\n"
] | [
[
"numpy.min"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jcwright77/pleiades | [
"e3e208e94feee299589a094f361b301131d1bd15"
] | [
"pleiades/analysis/diagnostics.py"
] | [
"import numpy as np\n\ndef fit_to_diagnostic(d_mat):\n (u,s,vt) = np.linalg.svd(d_mat)\n Sinv = np.zeros_like(s.T)\n s[ s<1.0e-10 ] = 0.0\n s[ s>=1.0e-10 ] = 1.0/s[ s>=1.0e-10]\n Sinv[:n,:n] = np.diag(s)\n c = vt.T.dot(Sinv.dot(u.T))\n return c\n\ndef fit_BR(grid,locs,plas_currents,linear=True)... | [
[
"numpy.diag",
"numpy.ones_like",
"numpy.linalg.svd",
"numpy.zeros_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dabana/mytinyos | [
"4e984b134477dadfb671591c35a79180cf00d7b3"
] | [
"apps/OscilloscopeShort/java/parselog_adc.py"
] | [
"#this script parses the log file into a list and calibrates the ADC values\n#David Banville 14-07-2017\n\n#Import libraries\nimport numpy as np\n\n#Open up the file and strip it (remove all the /n 's)\nfilename = \"17-07-17-15-55_MgSSf1.txt\"\nf = open(filename)\ncontent = f.readlines()\ncontent = [x.strip() for x... | [
[
"numpy.asarray"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
adamnapieralski/ravvent-basecaller | [
"7d600ea8d8dba33538cc844baad225b9cc5d2885"
] | [
"utils.py"
] | [
"import numpy as np\nimport tensorflow as tf\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.utils import shuffle as sklearn_shuffle\nimport h5py\nimport subprocess\nimport shlex\nimport shutil\nimport os\n\nfrom pathlib import Path\n\nfrom shape_checker import ShapeChecker\n\ndef masked_accurac... | [
[
"tensorflow.keras.losses.SparseCategoricalCrossentropy",
"tensorflow.reduce_sum",
"sklearn.utils.shuffle",
"tensorflow.cast",
"tensorflow.ones_like",
"sklearn.model_selection.train_test_split",
"tensorflow.reduce_all",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"1.12",
"2.6",
"2.2",
"1.4",
"2.3",
"2.4",
"2.9",
"1.5",
"1.7",
"2.5",
"0.12",
"1.0",
"2.8",
"1.2",
"2.... |
latencytime9527/Crowd-Counting-MFANet | [
"a6f70b50edea273f63dee73719dd284c34cf2aea"
] | [
"losses/nel.py"
] | [
"from torch.nn.modules import Module\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass NEL_Loss(Module):\n def __init__(self):\n super(NEL_Loss, self).__init__()\n\n \n def forward(self, target, prediction):\n prediction = prediction.requires_grad_()\n ... | [
[
"torch.randn",
"torch.abs",
"torch.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
masc-it/CVLAB | [
"9c6e25a800b532a440c660e10ea001c699da68f1"
] | [
"yolov5/models/experimental.py"
] | [
"# YOLOv5 🚀 by Ultralytics, GPL-3.0 license\n\"\"\"\nExperimental modules\n\"\"\"\nimport math\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\n\nfrom .common import Conv\nfrom utils.downloads import attempt_download\n\n\nclass CrossConv(nn.Module):\n # Cross Convolution Downsample\n def __init__(... | [
[
"torch.sigmoid",
"torch.linspace",
"torch.cat",
"torch.zeros",
"numpy.eye",
"torch.arange",
"numpy.linalg.lstsq",
"torch.nn.BatchNorm2d",
"torch.nn.SiLU",
"numpy.array",
"numpy.roll"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
syncle/3DFeatNet | [
"cf8ee0db066889df16774511a73c48c7b0bb5398"
] | [
"data/datagenerator.py"
] | [
"import logging\nimport numpy as np\nimport random\nimport os\nfrom collections import deque\n\n\nclass DataGenerator(object):\n\n def __init__(self, filename, num_cols=6):\n \"\"\" Constructor to data generator\n\n Args:\n num_cols (int): Number of columns in binary file\n \"\"\"... | [
[
"numpy.square",
"numpy.fromfile",
"numpy.random.choice",
"numpy.reshape",
"numpy.stack",
"numpy.concatenate",
"numpy.load",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
deep-spin/spec-blackboxnlp | [
"23db7a559e09ff7f63ede06b04cad226432b90db"
] | [
"spec/explainers/explainer.py"
] | [
"import logging\n\nimport torch\nfrom torch import nn\n\nfrom spec import constants\nfrom spec.explainers.utils import make_bow_matrix\n\nlogger = logging.getLogger(__name__)\n\n\nclass Explainer(nn.Module):\n\n def __init__(self, fields_tuples):\n super().__init__()\n self.fields_dict = dict(field... | [
[
"torch.tensor",
"torch.full",
"torch.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
thomasdubdub/traffic | [
"d96fd9ccbc98939250afcacbf6c45dd0b5d13caa"
] | [
"traffic/core/mixins.py"
] | [
"import warnings\nfrom functools import lru_cache\nfrom pathlib import Path\nfrom typing import (\n TYPE_CHECKING,\n Callable,\n Dict,\n List,\n Optional,\n Tuple,\n Type,\n TypeVar,\n Union,\n)\n\nimport pandas as pd\nimport pyproj\nfrom shapely.geometry import Point, base, mapping\nfrom... | [
[
"pandas.read_hdf",
"pandas.read_csv",
"pandas.read_parquet",
"matplotlib.transforms.offset_copy",
"pandas.read_json",
"pandas.read_pickle"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
syKevinPeng/TransDepth | [
"2282039da7bc0812e19a27b2d73a25bdef97d739"
] | [
"pytorch/TransUNet/networks/vit_seg_modeling.py"
] | [
"# coding=utf-8\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport copy\nimport logging\nimport math\n\nfrom os.path import join as pjoin\n\nimport torch\nimport torch.nn as nn\nimport numpy as np\n\nfrom torch.nn import CrossEntropyLoss, Dropou... | [
[
"torch.nn.Softmax",
"torch.nn.Dropout",
"torch.sigmoid",
"torch.nn.modules.utils._pair",
"torch.cat",
"torch.nn.ModuleList",
"torch.nn.Conv2d",
"torch.from_numpy",
"torch.nn.LayerNorm",
"torch.nn.UpsamplingBilinear2d",
"torch.nn.Linear",
"torch.matmul",
"torch.n... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
HieuNT1998/BraTS-DMFNet | [
"c12806a7ae4cf886f379d712fc85cc6fc37bd9a7"
] | [
"models/sync_batchnorm/batchnorm_reimpl.py"
] | [
"#! /usr/bin/env python3\r\n# -*- coding: utf-8 -*-\r\n# File : batchnorm_reimpl.py\r\n# Author : acgtyrant\r\n# Date : 11/01/2018\r\n#\r\n# This file is part of Synchronized-BatchNorm-PyTorch.\r\n# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch\r\n# Distributed under MIT License.\r\n\r\nimport torch... | [
[
"torch.nn.init.uniform_",
"torch.ones",
"torch.empty",
"torch.zeros",
"torch.nn.init.zeros_"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
RainOfPhone/ilf | [
"83c34c2147f05a26fde81f94acb58fe5719b05a2"
] | [
"ilf/symbolic/symbolic/svm_utils.py"
] | [
"from collections import defaultdict\nimport ethereum\nimport itertools\nfrom ilf.symbolic.solidity.soliditycontract import SolidityContract\nfrom ilf.symbolic.solidity import solidity_utils\nfrom ilf.symbolic.symbolic import constraints\nfrom ilf.symbolic.symbolic import svm_utils\nfrom ilf.symbolic import utils\n... | [
[
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AmulPatil/maskrcnn_test | [
"3a13425d0a3a40bd3589ffbd09f805aaff1e8aa8"
] | [
"mrcnn/config.py"
] | [
"\"\"\"\nMask R-CNN\nBase Configurations class.\n\nCopyright (c) 2017 Matterport, Inc.\nLicensed under the MIT License (see LICENSE for details)\nWritten by Waleed Abdulla\n\"\"\"\n\nimport math\nimport numpy as np\n\n\n# Base Configuration Class\n# Don't use this class directly. Instead, sub-class it and override\... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
OleMartinChristensen/MATS-L1-processsing | [
"29a4a8550294b11ac6e9118a9c6e6e7ae49160e3"
] | [
"src/mats_l1_processing/read_in_functions.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Apr 20 08:57:37 2020\n\n@author: lindamegner\n\nFunctions used to read in MATS images and data from rac files. \nThe other ways to read in (From KTH, from Immage viewer) is being moved to read_imgview_functions.py in database_generation. \nThe... | [
[
"matplotlib.pyplot.legend",
"pandas.read_csv",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlabel",
"numpy.array",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
hoangcuong2011/LDNMT | [
"b0154d4ee7aa776adf02ef6bba03c9312345038a"
] | [
"neuralmonkey/nn/noisy_gru_cell.py"
] | [
"import math\nfrom typing import Tuple\n\nimport tensorflow as tf\n\nfrom neuralmonkey.nn.projection import linear\n\n\nclass NoisyGRUCell(tf.contrib.rnn.RNNCell):\n \"\"\"\n Gated Recurrent Unit cell (cf. http://arxiv.org/abs/1406.1078) with noisy\n activation functions (http://arxiv.org/abs/1603.00391). ... | [
[
"tensorflow.sign",
"tensorflow.maximum",
"tensorflow.sigmoid",
"tensorflow.ones_initializer",
"tensorflow.variable_scope",
"tensorflow.random_normal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.4",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
ramonus/pyneuro | [
"0b89bfac3e41fd8b3d58df85c64fb19cf86cfa0b"
] | [
"nn.py"
] | [
"import numpy as np\nimport time, json, pickle\n\n# activation functions\ndef sigmoid(x,deriv=False):\n if deriv:\n return sigmoid(x)*(1-sigmoid(x))\n return 1/(1+np.exp(-x))\ndef relu(x,deriv=False):\n\n if deriv:\n return (x>0).astype(int)\n else:\n return np.maximum(x,0)\ndef so... | [
[
"numpy.maximum",
"numpy.round",
"numpy.max",
"numpy.random.rand",
"numpy.array",
"numpy.exp",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yufeiwang63/Pointnet_Pointnet2_pytorch | [
"f9078a71b973c13ae7ffa897e142dc7b1e8e88be"
] | [
"data_utils/HapticDataLoader.py"
] | [
"import os\nimport numpy as np\nimport os.path as osp\n\nfrom tqdm import tqdm\nfrom torch.utils.data import Dataset\n\n\nclass HapticDataset(Dataset):\n def __init__(self, split='train', data_root='data/hapticdata/', num_point=1000,\n transform=None, use_random_center=False, traj_num=100):\n\n ... | [
[
"numpy.random.choice",
"numpy.amin",
"numpy.mean",
"numpy.load",
"numpy.zeros",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
fhalbritter/pypiper | [
"67908f2ee5f51fa5fdddb67eb6d7891aefeeda6a"
] | [
"pypiper/ngstk.py"
] | [
"\"\"\" Broadly applicable NGS processing/analysis functionality \"\"\"\n\nimport os\nimport re\nimport subprocess\nimport errno\nfrom attmap import AttMapEcho\nfrom yacman import load_yaml\nfrom .exceptions import UnsupportedFiletypeException\nfrom .utils import is_fastq, is_gzipped_fastq, is_sam_or_bam\n\n\nclass... | [
[
"matplotlib.pyplot.legend",
"pandas.Series",
"numpy.linspace",
"matplotlib.use",
"scipy.integrate.simps",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.plot",
"scipy.optimize.curve_fit",
"matplotlib.pyplot.ylabel",
"numpy.exp",
"matplotlib.pyplot.xlabel",
"numpy.a... | [
{
"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": [
"0.13",
"1.6",
"0.14",
"1.10",
"0... |
hectorcarrion/sleap | [
"1eb06f81eb8f0bc1beedd1c3dd10902f8ff9e724",
"1eb06f81eb8f0bc1beedd1c3dd10902f8ff9e724"
] | [
"sleap/nn/data/instance_cropping.py",
"sleap/nn/data/utils.py"
] | [
"\"\"\"Transformers for cropping instances for topdown processing.\"\"\"\n\nimport tensorflow as tf\nimport numpy as np\nimport attr\nfrom typing import Optional, List, Text\nimport sleap\nfrom sleap.nn.config import InstanceCroppingConfig\n\n\ndef find_instance_crop_size(\n labels: sleap.Labels,\n padding: i... | [
[
"tensorflow.convert_to_tensor",
"tensorflow.ensure_shape",
"numpy.nanmax",
"tensorflow.gather_nd",
"tensorflow.range",
"tensorflow.shape",
"tensorflow.cast",
"numpy.nanmin",
"tensorflow.expand_dims",
"tensorflow.reshape",
"tensorflow.ones",
"tensorflow.gather",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.3",
"2.2",
"2.4"
]
}
] |
CooperDrones/VIP_Crazyswarm | [
"331c8018efa8972d6f115798ea1dfda0dcb095b5",
"331c8018efa8972d6f115798ea1dfda0dcb095b5"
] | [
"crazyflie_demo/scripts/waypoints.py",
"crazyflie_demo/scripts/rc_car_tracking_3.py"
] | [
"#!/usr/bin/env python\nimport rospy\nfrom geometry_msgs.msg import Twist,Vector3,TransformStamped # twist used in cmd_vel\nfrom crazyflie_driver.msg import Hover # used in cmd_hover commands vel, yaw rate, and hover height\nfrom crazyflie_driver.srv import Takeoff\nfrom std_msgs.msg import Duration\nfrom vicon_bri... | [
[
"numpy.dot",
"scipy.interpolate.splrep",
"scipy.spatial.transform.Rotation.from_quat",
"numpy.cos",
"scipy.interpolate.splev",
"numpy.sin",
"numpy.cross",
"numpy.array"
],
[
"numpy.isnan",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.5",
"1.2",
"1.3",
"1.4"
],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
noavilk/IML.HUJI | [
"35aa4e6fbe489239e4fe72bf38c0dba3e6c81f37"
] | [
"IMLearn/learners/classifiers/linear_discriminant_analysis.py"
] | [
"from typing import NoReturn\nfrom ...base import BaseEstimator\nimport numpy as np\nfrom numpy.linalg import det, inv\n\n\nclass LDA(BaseEstimator):\n \"\"\"\n Linear Discriminant Analysis (LDA) classifier\n\n Attributes\n ----------\n self.classes_ : np.ndarray of shape (n_classes,)\n The di... | [
[
"numpy.add.reduceat",
"numpy.log",
"numpy.unique",
"numpy.linalg.inv",
"numpy.cumsum",
"numpy.argmax",
"numpy.insert",
"numpy.argsort",
"numpy.repeat",
"numpy.diagonal",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rodriguesfas/Vision-Computer | [
"9621f1020ea5f2f4dcabbb48e7fe2a4ef69c0ba5"
] | [
"histograms/histogram_equalization.py"
] | [
"#coding: utf-8\n#!/usr/bin/python\n# Python 2/3 compatibility\n\n'''\nHistograms - 2: Histogram Equalization\n<https://docs.opencv.org/3.1.0/d5/daf/tutorial_py_histogram_equalization.html>\n'''\n\nimport cv2\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\nimg = cv2.imread('../src/histeq_numpy1.jpg', 0... | [
[
"matplotlib.pyplot.plot",
"matplotlib.pyplot.legend",
"matplotlib.pyplot.xlim",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
riven314/capstone_dash_interface | [
"5eab25f4c15ad09aa889554820231175b0a3ed28"
] | [
"mobilenet_segment/test/test_processpredict.py"
] | [
"\"\"\"\r\nagenda:\r\n1. speedup visualize_result\r\n2. grouping labels\r\n\r\nspeed bottlenecks:\r\n1. colorEncoding\r\n\r\nresults:\r\n1. with visualize_result optimize: 0.045s --> 0.002s\r\n2. with grouping labels: 0.002s --> 0.002-0.003s\r\n\"\"\"\r\nimport os\r\nimport sys\r\nimport time\r\nPATH = os.path.join... | [
[
"matplotlib.pyplot.imshow",
"torch.cuda.synchronize",
"torch.max",
"numpy.unique",
"numpy.int32",
"scipy.io.loadmat",
"numpy.argsort",
"matplotlib.pyplot.show"
]
] | [
{
"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"... |
S0mbre/pydistrocomp | [
"4f3509ae345587e31e0139f572742fd25860875f"
] | [
"pydistro.py"
] | [
"# -*- coding: utf-8 -*-\nfrom typing import KeysView\nimport requests, sys, os, json\nimport subprocess as sp\nimport concurrent.futures\nimport pandas as pd\nfrom openpyxl import load_workbook, worksheet, styles\nimport packaging.version as pkvers\nfrom tabulate import tabulate\nfrom utils import Utils\n\n## ----... | [
[
"pandas.DataFrame",
"pandas.DataFrame.from_dict"
]
] | [
{
"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": []
}
] |
othadem/my_deep_semantic_code_search | [
"ef6ce6e9e3c1f43e119a89c423b93b14da4d33ec"
] | [
"models.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import print_function\n\nimport logging\nimport os\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.nn.init as weight_init\n\nlogger = logging.getLogger(__name__)\n\n\nclass SkipAttention(nn.Module):\n def __init__(self... | [
[
"torch.nn.functional.softmax",
"torch.nn.Dropout",
"torch.nn.functional.dropout",
"torch.cat",
"torch.nn.LSTM",
"torch.nn.Conv2d",
"torch.nn.Embedding",
"torch.nn.Linear",
"torch.nn.functional.cosine_similarity",
"torch.nn.init.orthogonal_",
"torch.nn.functional.tanh"
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
KarlNaumann/DynamicSolowModel | [
"fc6c8c41d1be04797f8ed0930124839ed75e9edb"
] | [
"graphs.py"
] | [
"\"\"\"Graphing File\n--------------------------\nFile applying matplotlib to generate all the figures present in the paper by\nNaumann-Woleske et al.\n\"\"\"\n\n__author__ = \"Karl Naumann-Woleske\"\n__version__ = \"0.0.1\"\n__license__ = \"MIT\"\n\nimport copy\nimport os\nimport pickle\n\nimport numpy as np\nimpo... | [
[
"pandas.Series",
"numpy.linspace",
"numpy.exp",
"numpy.histogram",
"pandas.read_csv",
"numpy.arange",
"numpy.argmax",
"matplotlib.pyplot.close",
"matplotlib.pyplot.figure",
"numpy.log",
"pandas.concat",
"matplotlib.pyplot.locator_params",
"matplotlib.pyplot.marg... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2... |
anukaal/learnergy | [
"704fc2b3fcb80df41ed28d750dc4e6475df23315"
] | [
"learnergy/visual/convergence.py"
] | [
"\"\"\"Convergence-related visualization.\n\"\"\"\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nimport learnergy.utils.exception as e\n\n\ndef plot(*args, labels=None, title='', subtitle='', xlabel='epoch', ylabel='value', grid=True, legend=True):\n \"\"\"Plots the convergence graph of desired variab... | [
[
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplots"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rcrisanti/fantasy-baseball-draft-strategy | [
"c292f04e799b148b26f23f1d260aef574f1b4fdf"
] | [
"pitching_rankings.py"
] | [
"import pandas as pd\nfrom sklearn.preprocessing import MinMaxScaler\n\n\ndef main():\n YEAR = 2019\n stats = pd.read_csv(f\"data/pitching-stats-{YEAR}.csv\", index_col=\"player_id\")\n stats.drop(index=stats[stats.ip == 0].index, inplace=True)\n stats[\"era\"] = stats.er / stats.ip * 9\n stats[\"whi... | [
[
"pandas.merge",
"pandas.read_csv",
"sklearn.preprocessing.MinMaxScaler"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
neurospin/pylearn-parsimony | [
"4c7583902c60ad6a92970c9d0b292ef0137c42ff"
] | [
"parsimony/algorithms/proximal.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nThe :mod:`parsimony.algorithms.proximal` module contains several algorithms\nthat involve proximal operators.\n\nAlgorithms may not store states. I.e., if they are classes, do not keep\nreferences to objects with state in the algorithm objects. It should be\npossible to copy and sh... | [
[
"numpy.sqrt",
"numpy.isfinite",
"numpy.copy",
"scipy.interpolate.interp1d",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"1.3",
"1.8"
... |
twilightdema/ALBERT_Thai | [
"2c5612237a6843c4949dd941dbcd01ca91f82f2b"
] | [
"create_pretraining_data.py"
] | [
"# coding=utf-8\r\n# Copyright 2018 The Google AI Team Authors.\r\n#\r\n# Licensed under the Apache License, Version 2.0 (the \"License\");\r\n# you may not use this file except in compliance with the License.\r\n# You may obtain a copy of the License at\r\n#\r\n# http://www.apache.org/licenses/LICENSE-2.0\r\n#... | [
[
"numpy.arange",
"tensorflow.compat.v1.logging.set_verbosity",
"tensorflow.compat.v1.gfile.Glob",
"tensorflow.compat.v1.logging.info",
"tensorflow.compat.v1.gfile.GFile",
"tensorflow.compat.v1.python_io.TFRecordWriter",
"tensorflow.compat.v1.train.Features",
"tensorflow.compat.v1.ap... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lasseufpa/emulation-setup-networking | [
"3263774e09bea69704a97916ce9fad3b316c3e59"
] | [
"src/topologies/nsfnet.py"
] | [
"import os\nimport sys\nimport heapq\n\nimport networkx as nx\n\nfrom numpy.random import randint, seed\n\nfrom mininet.net import Mininet\nfrom mininet.topo import Topo\nfrom mininet.cli import CLI\nfrom mininet.link import TCLink\n\ndef int_to_mac(macint):\n if type(macint) != int:\n raise ValueError('i... | [
[
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
liusf15/blackbox_selectinf | [
"874c073ca56c042cbaaed606bf52d6b36ccebd59"
] | [
"examples/DTL_incre.py"
] | [
"# from importlib import reload\n# import blackbox_selectinf.usecase.Lasso\n# reload(blackbox_selectinf.usecase.Lasso)\n# import blackbox_selectinf.learning.learning\n# reload(blackbox_selectinf.learning.learning)\nfrom blackbox_selectinf.usecase.DTL import DropTheLoser\nfrom blackbox_selectinf.learning.learning im... | [
[
"matplotlib.pyplot.legend",
"scipy.stats.norm.ppf",
"numpy.sqrt",
"numpy.linspace",
"scipy.stats.norm.cdf",
"numpy.squeeze",
"numpy.concatenate",
"matplotlib.pyplot.plot",
"numpy.mean",
"numpy.random.randn",
"torch.tensor",
"numpy.std",
"numpy.argmax",
"matp... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zdemat/Savu | [
"145366f60d7020650c406661d2efadc076fdf360"
] | [
"savu/plugins/visualisation/ortho_slice.py"
] | [
"# Copyright 2014 Diamond Light Source Ltd.\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 ... | [
[
"numpy.array",
"matplotlib.pyplot.get_cmap"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Frank1126lin/AIServer | [
"1b77bdb3cc92cab3cd2e10cc6f3754182397abc5"
] | [
"yolov5_tcp_deploy/models/common.py"
] | [
"# This file contains modules common to various models\r\n\r\nimport math\r\n\r\nimport numpy as np\r\nimport torch\r\nimport torch.nn as nn\r\nfrom PIL import Image, ImageDraw\r\n\r\nfrom utils.datasets import letterbox\r\nfrom utils.general import non_max_suppression, make_divisible, scale_coords, xyxy2xywh\r\nfr... | [
[
"torch.cat",
"torch.nn.Hardswish",
"torch.nn.Conv2d",
"numpy.tile",
"numpy.stack",
"torch.tensor",
"torch.from_numpy",
"torch.nn.MaxPool2d",
"torch.nn.Identity",
"torch.nn.AdaptiveAvgPool2d",
"torch.nn.LeakyReLU",
"torch.no_grad",
"torch.nn.BatchNorm2d",
"nu... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JSKenyon/QuartiCal | [
"2113855b080cfecc4a1c77cc9dad346ef3619716"
] | [
"testing/tests/gains/test_crosshand_phase.py"
] | [
"from copy import deepcopy\nimport pytest\nimport numpy as np\nimport dask.array as da\nfrom quartical.calibration.calibrate import add_calibration_graph\nfrom testing.utils.gains import apply_gains, reference_gains\n\n\n@pytest.fixture(scope=\"module\")\ndef opts(base_opts):\n\n # Don't overwrite base config - ... | [
[
"numpy.abs",
"numpy.unique",
"numpy.testing.assert_array_equal",
"numpy.any",
"numpy.array",
"numpy.where",
"numpy.testing.assert_array_almost_equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
O-Gu/astr-19 | [
"d8e7156b057be36cc327fb77672129fe1bedaa3a"
] | [
"prompt5.py"
] | [
"import numpy as np \r\nimport matplotlib.pyplot as plt\r\n\r\nimport sys\r\nimport os\r\n\r\nx= np.linspace(0,2*np.pi, num=1000)\r\ny= np.sin(x)\r\n\r\narray= np.array([[x],[y]])\r\nprint(np.array2string(array).replace('[[',' [').replace(']]',']'))"
] | [
[
"numpy.array2string",
"numpy.array",
"numpy.linspace",
"numpy.sin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ahriley/advent-of-code | [
"2484ff4e520215b5dfb855761465d683aa2b91eb"
] | [
"2018/src/python/day4.py"
] | [
"import numpy as np\n\n# read and sort the data by time\nwith open('data/day4.txt') as f:\n lines = f.readlines()\n datetimes = [np.datetime64(line[1:11]+'T'+line[12:17]) for line in lines]\n log = [line for _,line in sorted(zip(datetimes,lines))]\ndatetimes = np.sort(datetimes)\ndates = datetimes.astype('... | [
[
"numpy.timedelta64",
"numpy.sort",
"numpy.datetime64"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kuntzer/SALSA-public | [
"79fd601d3999ac977bbc97be010b2c4ef81e4c35"
] | [
"resources/coordinates.py"
] | [
"import numpy as np\n\ndef equatorial2ecliptic(a,b,obliquity=np.deg2rad(23.4333)):\n\t# http://en.wikipedia.org/wiki/Celestial_coordinate_system\n\te=obliquity\n\n\tup=np.sin(a)*np.cos(e)+np.tan(b)*np.sin(e)\n\tdw=np.cos(a)\n\tra=np.arctan2(up,dw)\n\n\tdec = np.sin(b)*np.cos(e)-np.cos(b)*np.sin(e)*np.sin(a)\n\tdec ... | [
[
"numpy.arcsin",
"numpy.asarray",
"numpy.cos",
"numpy.sin",
"numpy.arctan2",
"numpy.tan",
"numpy.deg2rad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Mehooz/VGSNet | [
"18ddae20fb3ccc440a38bd8b23cba8fcaa753518",
"18ddae20fb3ccc440a38bd8b23cba8fcaa753518",
"18ddae20fb3ccc440a38bd8b23cba8fcaa753518"
] | [
"code/utils.py",
"code/train_pc_img.py",
"utils/datasetSamplerImg.py"
] | [
"\"\"\"\n This file contains all helper utility functions.\n\"\"\"\n\nimport os\nimport sys\nimport math\nimport importlib\nfrom scipy.optimize import linear_sum_assignment\nimport torch\nimport numpy as np\nimport trimesh\n\ndef save_checkpoint(models, model_names, dirname, epoch=None, prepend_epoch=False, opti... | [
[
"torch.cat",
"torch.load",
"numpy.matmul",
"torch.is_tensor",
"numpy.linalg.norm",
"torch.arange",
"numpy.cross",
"numpy.array",
"numpy.vstack",
"torch.cross"
],
[
"torch.chunk",
"numpy.random.seed",
"torch.zeros",
"torch.manual_seed",
"torch.utils.d... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
uw-bionlp/ards | [
"e9fc27f7034cc6b54f0ccdba4a58377948cf0258"
] | [
"src/corpus/corpus_brat.py"
] | [
"\n\nimport numpy as np\nimport pandas as pd\nfrom tqdm import tqdm\nimport os\nimport re\nfrom collections import OrderedDict, Counter\nimport hashlib\nimport logging\n\nimport matplotlib as mpl\nmpl.use('Agg')\nimport matplotlib.pyplot as plt\n\n\n\n\nfrom config.constants import ENCODING\nfrom corpus.corpus impo... | [
[
"matplotlib.use",
"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": []
}
] |
PyCN/brainstorm | [
"8f1fc886faf268b25085fa5c95bf106b1805d766"
] | [
"brainstorm/handlers/pycuda_handler.py"
] | [
"#!/usr/bin/env python\n# coding=utf-8\nfrom __future__ import division, print_function\n\nimport numpy as np\nimport pycuda\nimport skcuda.linalg as culinalg\nimport skcuda.misc as cumisc\nfrom pycuda import cumath, gpuarray\nfrom pycuda.compiler import SourceModule\nfrom pycuda.curandom import XORWOWRandomNumberG... | [
[
"numpy.int32",
"numpy.prod",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
neka-nat/Klampt-examples | [
"66eee40d18cd352704978873ddaaf612205b09c1"
] | [
"Python3/demos/sensor_test.py"
] | [
"import klampt\nimport math\nfrom klampt import vis, Appearance\nfrom klampt.math import so3,se3,vectorops\nfrom klampt.vis.glinterface import GLPluginInterface\nfrom klampt.model import sensing\nimport math\nimport time\nimport random\nimport math\n\ntry:\n\timport matplotlib.pyplot as plt\n\tHAVE_PYPLOT = True\ne... | [
[
"numpy.savetxt",
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AntjeUhde/RanForCorine | [
"aef08485842a0169d78ee4a644f14df97d669e49"
] | [
"RanForCorine/geodata_handling.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\ngeodata_handling.py: Operations on the geodata\n\n@autor: Theresa Möller, Antje Uhde\n\"\"\"\nfrom osgeo import gdal, osr, ogr\nimport os\nimport rasterio as rio\nimport pandas as pd\nimport numpy as np\nimport osr\nimport sys\n\ndef read_file_gdal(fp,hdrp=None):\n \"\"\"\n O... | [
[
"numpy.array",
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
LTS4/TIGraNet | [
"22ba11b665e8445f1f759c0d13414429d9a03265"
] | [
"comparison_debug.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"\n This modules contains some testing of intermediary results between the PyTorch and the Theano framework.\n\"\"\"\n\nimport numpy as np\n\nfrom paths import DEBUG_DIR_MNIST_012, DEBUG_DIR_MNIST_rot, DEBUG_DIR_ETH80\nfrom debug import *\n\n###############... | [
[
"numpy.load",
"numpy.transpose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
veerabhadraks/Personalized-image-styler | [
"a20e69c795f4a62816d9192bad21df8f375385c9"
] | [
"stylize.py"
] | [
"import vgg\n\nimport tensorflow as tf\nimport numpy as np\n\nfrom sys import stderr\n\nfrom PIL import Image\n\nCONTENT_LAYERS = ('relu4_2', 'relu5_2')\nSTYLE_LAYERS = ('relu1_1', 'relu2_1', 'relu3_1', 'relu4_1', 'relu5_1')\n\ntry:\n reduce\nexcept NameError:\n from functools import reduce\n\n\ndef stylize(n... | [
[
"numpy.dot",
"tensorflow.Graph",
"tensorflow.transpose",
"tensorflow.Variable",
"numpy.clip",
"numpy.reshape",
"tensorflow.reshape",
"numpy.matmul",
"tensorflow.placeholder",
"tensorflow.global_variables_initializer",
"tensorflow.nn.l2_loss",
"numpy.std",
"tenso... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
530824679/FCOS | [
"c95546f483d43d792dc587fa4bc8dd4b2aa8498e"
] | [
"train.py"
] | [
"\nimport os\nimport torch\nimport argparse\n\ndef train(cfg):\n device = torch.device()\n\n model = build_model(cfg).to(device)\n\n optimizer = make_optimizer(cfg, model)\n\n scheduler = make_lr_scheduler(cfg, optimizer)\n\n dataset = make_dataloader(cfg, is_train=True)\n\n for iter, (images, tar... | [
[
"torch.device",
"torch.cuda.max_memory_allocated"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Edderic/unlikely | [
"75d748a169cbeb56425a7914c61fe33e0551033c"
] | [
"unlikely/misc.py"
] | [
"\"\"\"\nMiscellaneous functions\n\"\"\"\nimport logging\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef create_images_from_data(\n data,\n xlim,\n ylim=None,\n save_path=None,\n alpha=0.5,\n bins=None,\n figsize_mult=None\n):\n \"\"\"\n Parameters:\n data: dict\n\n... | [
[
"matplotlib.pyplot.subplots"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
calebho/gameanalysis | [
"280726a8aa125850b827a1e10b1203e91f515461",
"280726a8aa125850b827a1e10b1203e91f515461",
"280726a8aa125850b827a1e10b1203e91f515461"
] | [
"gameanalysis/rsgame.py",
"test/test_learning.py",
"test/test_bootstrap.py"
] | [
"\"\"\"Module for base role symmetric game structures\n\nRole symmetric games have a number of common attributes and functions that are\ndefined in the RsGame class that actual RsGame interfaces should support. In\naddition, an implementation of an EmptyGame is provided for convenience\npurposes. Note, that the con... | [
[
"numpy.split",
"numpy.asarray",
"numpy.maximum.reduceat",
"numpy.dtype",
"numpy.all",
"numpy.concatenate",
"numpy.fill_diagonal",
"numpy.any",
"numpy.iinfo",
"numpy.where",
"numpy.allclose",
"numpy.arange",
"numpy.eye",
"numpy.stack",
"numpy.full",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
erdemunal35/WaterSegNets | [
"ec0e57169b06b03ef59fd7fdbbf6954c933e1a09"
] | [
"convert_onnx.py"
] | [
"import torch\nfrom models.model_builder import build_model\n\n# Create the model and load the weights\nmodel = torch.load('trained_models/model_Linknet_resnet18_DiceLoss_best_model40.pth')\n# Create dummy input \ndummy_input = torch.rand(1, 3, 256, 256).cuda()\n\n# Define input / output names\ninput_names = [\"my_... | [
[
"torch.onnx.export",
"torch.rand",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Duncan-Haywood/finance_ml_analysis | [
"ce42917fe69e81cf19f3f4893d3cc0f60cfd961c"
] | [
"forecasting/preprocessing/timeseries_train_test_split.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\nimport pandas as pd\nimport numpy as np\nfrom datetime import date\nclass TimeseriesTestTrainSplit:\n def __init__(self):\n pass\n @classmethod\n def timeseries_test_train_split(cls, stock_name='GSIT'):\n stocks_df = cls.load_df()\n data = cls.g... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
danijoo/flamel | [
"b050e3ef0b70acfa21f4d9d706400349ffca3225"
] | [
"uncorrelate/statistical_inefficiency_dhdl_all.py"
] | [
"import alchemlyb.preprocessing\nimport pandas\nimport numpy as np\n\n\n# Todo: Use interface here\nclass StatisticalInefficiencyDhdlAll:\n name = 'dhdl_all'\n\n needs_dhdls = True\n needs_u_nks = False\n\n dhdl = None\n\n def set_dhdls(self, dhdls):\n \"\"\"\n :param dhdls: Series\n ... | [
[
"pandas.concat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
abhinav-2912/Gesture-Detection | [
"2419e8008697e4f1bcdd54dada6e70a4253d338c"
] | [
"train_on_the_fly/train_resnet_main.py"
] | [
"import argparse\nimport os\nimport pickle\nimport random\nimport sys\nimport time\n\nimport numpy as np\nimport pandas as pd\nimport resnet_10 as resnet1\nimport resnet_10_copy as resnet2\nimport resnet_10_copy2 as resnet3\nimport tensorflow as tf\nimport resnet_utils as utils\nfrom tensorflow.python.client import... | [
[
"tensorflow.device",
"tensorflow.nn.softmax_cross_entropy_with_logits",
"numpy.random.seed",
"tensorflow.control_dependencies",
"tensorflow.get_collection",
"tensorflow.python.client.device_lib.list_local_devices",
"tensorflow.cast",
"tensorflow.placeholder",
"tensorflow.Config... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
kilianFatras/unbisad_minibatch_sinkhorn_GAN | [
"cd21033e5bbf974283fcb1b88e586270e5a6ba7e",
"cd21033e5bbf974283fcb1b88e586270e5a6ba7e"
] | [
"positivity/mbot_with_replacement.py",
"Gromov_Wasserstein/spiral/mini_batch_gw.py"
] | [
"import numpy as np\nimport ot\nfrom utils import update_gamma\n\n\ndef get_mbot_with_r(a, b, all_arr_s, all_arr_t, bs_s, bs_t, M, p):\n '''Compute the expectation of MBOT with replacement\n\n Parameters:\n -------------------------------------------\n a : ndarray, shape (ns,)\n Sou... | [
[
"numpy.prod"
],
[
"numpy.shape",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
akaroui/fractal-tree | [
"1a7054fd950ec0fa558303af9e0b853ddc28cbff"
] | [
"FractalTree/FractalTree.py"
] | [
"import logging\nimport sys\nfrom random import shuffle\n\nimport numpy as np\nimport treefiles as tf\nfrom MeshObject import Object, TObjectLoadable\nfrom tqdm import tqdm\n\nfrom FractalTree.Branch3D import set_log_level, Nodes, Branch\nfrom FractalTree.Mesh import Mesh\n\n\nclass Curvature:\n def __init__(sel... | [
[
"numpy.abs",
"numpy.quantile",
"numpy.linalg.norm",
"numpy.random.normal",
"numpy.mean",
"numpy.savetxt",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ctargon/TensorRT-Examples | [
"0fc7e6362c764caa87887c22924ae2864a07a7a5"
] | [
"utils/helpers.py"
] | [
"#/usr/bin/python\n\nimport tensorflow as tf\nimport re\n\nTOWER_NAME = 'tower'\n\n\"\"\"\n\tfunction: _activation_summary\n\n\tHelper to create summaries for activations.\n\tCreates a summary that provides a histogram of activations.\n\tCreates a summary that measures the sparsity of activations.\n\t\n\tArgs:\n\t\... | [
[
"tensorflow.device",
"tensorflow.get_variable",
"tensorflow.truncated_normal_initializer",
"tensorflow.nn.zero_fraction",
"tensorflow.nn.l2_loss",
"tensorflow.add_to_collection",
"tensorflow.summary.histogram"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
michaelnny/rlax | [
"d7598a241d33d706bb405e6a229536bd75f8104a"
] | [
"rlax/_src/value_learning_test.py"
] | [
"# Copyright 2019 DeepMind Technologies Limited. 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# Unle... | [
[
"numpy.square",
"numpy.random.random",
"numpy.arange",
"numpy.sort",
"numpy.stack",
"numpy.concatenate",
"numpy.ones",
"numpy.random.normal",
"numpy.testing.assert_allclose",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gigasquid/gluon-nlp | [
"58237c038cb1e80edd2dbd237cb7670ec98ee92b"
] | [
"scripts/bert/dataset.py"
] | [
"# coding=utf-8\n# Copyright 2018 The Google AI Language Team Authors and DMLC.\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\... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
goran-mahovlic/onebitbt | [
"fe509bd8a300810439f267fe84b6899b2612b96b"
] | [
"alldigitalradio/oscillator.py"
] | [
"import numpy as np\nfrom nmigen import *\nfrom nmigen.sim import Simulator\nfrom alldigitalradio.util import (\n binarize,\n make_carrier,\n pack_mem\n)\nimport json\n\nclass OneBitFixedOscillator(Elaboratable):\n def __init__(self, sample_rate: float, frequency: float, max_error: float, width: int, ph... | [
[
"numpy.round",
"numpy.zeros",
"numpy.abs"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
filipe-research/PropMix | [
"994685567a9cf9c8e99d61f381d1a9c528569d47"
] | [
"simclr.py"
] | [
"import argparse\nimport os\nimport torch\nimport numpy as np\n\nfrom utils.config import create_config\nfrom utils.common_config import get_criterion, get_model, get_train_dataset,\\\n get_val_dataset, get_train_dataloader,\\\n get_val_dataloader, get_t... | [
[
"numpy.save",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
StefanBrand/rasterio | [
"fde60624682b306f799c38247930f1efdba99178"
] | [
"tests/test_overviews.py"
] | [
"\"\"\"Tests of overview counting and creation.\"\"\"\nimport math\n\nimport numpy as np\nimport pytest\n\nfrom .conftest import requires_gdal2, requires_gdal33\n\nimport rasterio\nfrom rasterio.enums import Resampling\nfrom rasterio.env import GDALVersion\nfrom rasterio.errors import OverviewCreationError\n\n\ndef... | [
[
"numpy.array_equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zanven42/dreamerv2 | [
"9c3fb5454d8ff5be73c4ea2c8c90d35e71162285"
] | [
"dreamerv2/train.py"
] | [
"import collections\nimport functools\nimport logging\nimport os\nimport pathlib\nimport sys\nimport warnings\n\ntry:\n import rich.traceback\n rich.traceback.install()\nexcept ImportError:\n pass\n\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\nlogging.getLogger().setLevel('ERROR')\nwarnings.filterwarnings('ignore'... | [
[
"tensorflow.config.experimental.set_memory_growth",
"tensorflow.keras.mixed_precision.experimental.Policy",
"tensorflow.config.experimental.list_physical_devices",
"tensorflow.config.experimental_run_functions_eagerly",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mmatena/wise-ft | [
"2630c366d252ad32db82ea886f7ab6a752142792"
] | [
"clip/clip.py"
] | [
"# Code ported from https://github.com/openai/CLIP\n\nimport hashlib\nimport os\nimport urllib\nimport warnings\nfrom typing import Union, List\n\nimport torch\nfrom PIL import Image\nfrom torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize, RandomResizedCrop\nfrom tqdm import tqdm\n\nfro... | [
[
"torch.jit.load",
"torch.ones",
"torch.load",
"torch.tensor",
"torch.cuda.is_available",
"torch.device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
alexander-pv/inference-gstreamer | [
"9b9bdc0bc266735b151282fdab633acc19421cce"
] | [
"src/python/gst_read_multiple_rtsp.py"
] | [
"import logging\nimport sys\n\nimport gi\nimport numpy as np\n\ngi.require_version('Gst', '1.0')\ngi.require_version('GstRtsp', '1.0')\n\nfrom gi.repository import GObject, Gst\nfrom common import nvutils\nfrom common import gstreamer_wrappers as gsw\nfrom common import utils\n\n\ndef main():\n args = utils.pars... | [
[
"numpy.math.ceil"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ReemDAlsh/camera-python-opencv | [
"6adb12b682907554645211217e970480685347b0"
] | [
"camera-opencv/04-knn_ocr/knn_hand_written.py"
] | [
"#!/usr/bin/python\n#\n# knn_hand_written.py\n# Test the hand written classification with KNN\n#\n# Author : Ashing Tsai\n# Date : 2016/11/17\n# Origin : http://arbu00.blogspot.tw/2016/11/1-opencv-knn.html\n# Usage : DO \"python knn_ocr_sample.py\" BEFORE \"python knn_hand_written.py\"\n\nimport numpy as np\nimp... | [
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.yticks",
"matplotlib.pyplot.title",
"matplotlib.pyplot.subplot",
"numpy.load",
"matplotlib.pyplot.xticks",
"matplotlib.pyplot.show",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AaltoRSE/habitat-sim | [
"16b8dbd91951ee11c9a96e57693edb1e9086f684"
] | [
"tests/test_simulator.py"
] | [
"import random\nfrom copy import copy\nfrom os import path as osp\n\nimport magnum as mn\nimport numpy as np\nimport pytest\n\nimport examples.settings\nimport habitat_sim\n\n\ndef is_same_state(initial_state, new_state) -> bool:\n same_position = all(initial_state.position == new_state.position)\n same_rotat... | [
[
"numpy.array",
"numpy.isclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bleutooth65/SpanishAcquisition3 | [
"50d1445c57f7ecf3bbf03a2cb28befedba1bd57a",
"50d1445c57f7ecf3bbf03a2cb28befedba1bd57a"
] | [
"spacq/tool/box.py",
"spacq/gui/display/plot/live/scalar.py"
] | [
"from functools import wraps\nfrom itertools import chain\nfrom numpy import linspace, meshgrid, sort, unique, where, nan, zeros, ones, arange, fliplr\nfrom numpy import min as npmin\nfrom scipy.interpolate import griddata, interp1d\n\n\"\"\"\nGeneric tools.\n\"\"\"\n\n\ndef flatten(iterable):\n\t\"\"\"\n\tFlatten ... | [
[
"numpy.linspace",
"numpy.min",
"numpy.fliplr",
"numpy.unique",
"numpy.arange",
"numpy.ones",
"scipy.interpolate.interp1d",
"scipy.interpolate.griddata",
"numpy.meshgrid",
"numpy.where",
"numpy.zeros"
],
[
"numpy.append",
"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"
... |
slawler/SI_2019_Coastal | [
"4064d323bc62ce2f47a7af41b9a11ea5538ad181",
"4064d323bc62ce2f47a7af41b9a11ea5538ad181"
] | [
"results/discharges/plot_water_level.py",
"src/east_coast_images/plot_ratios.py"
] | [
"#!/usr/bin/env python\nimport pandas as pd\nfrom pathlib import Path\nfrom geopy import distance\nimport utils\n\n\ndata = pd.read_csv('Graph-1_WL_D0_4obspoints.csv', parse_dates=[0])\ndata.set_index('DateTime', drop=True, inplace=True)\nhourly = data.groupby(pd.Grouper(freq='2H')).mean()\n\nfig, gs, canvas = util... | [
[
"pandas.read_csv",
"pandas.Grouper"
],
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1... |
Sergiodiaz53/saint | [
"4d0294ddae5dc79035c252e88fd176af5e417a8e"
] | [
"pretraining.py"
] | [
"import torch\nfrom torch import nn\n\nfrom baselines.data_openml import data_prep_openml,task_dset_ids,DataSetCatCon\nfrom torch.utils.data import DataLoader\nimport torch.optim as optim\nfrom augmentations import embed_data_mask\nfrom augmentations import add_noise\n\nimport os\nimport numpy as np\n\ndef SAINT_pr... | [
[
"torch.nn.CrossEntropyLoss",
"torch.diagonal",
"torch.cat",
"torch.utils.data.DataLoader",
"torch.nn.MSELoss"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
holgerroth/tutorials | [
"929c8b7f6a67768fad03a6f11ccfc0934bef0ad1"
] | [
"performance_profiling/train_fast_nvtx.py"
] | [
"#!/usr/bin/env python\n\n# Copyright 2020 MONAI Consortium\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# http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by appl... | [
[
"torch.autograd.profiler.emit_nvtx",
"torch.cuda.amp.autocast",
"torch.cuda.amp.GradScaler",
"torch.no_grad",
"torch.utils.tensorboard.SummaryWriter",
"torch.device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
prakhar134/clean-or-messy | [
"b40028cb4c4c8bbefb91a4b016096953b445c146"
] | [
"streamlit/main.py"
] | [
"from fastai.vision.all import *\nfrom PIL import Image\nimport streamlit as st\nimport numpy as np\nfrom io import BytesIO\nfrom .config import imgWidth, imgHeight\n\nst.title(\"CleanvsMessy\")\nst.markdown('''\n## Upload the image''',True)\nst.set_option('deprecation.showfileUploaderEncoding', False)\nfile = st.f... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cleichner/flax | [
"f1a1be119942444d564ad112bf0ea38145271ae9"
] | [
"flax/optim/adafactor.py"
] | [
"# Copyright 2021 The Flax Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or a... | [
[
"numpy.argsort",
"numpy.delete"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DrFirestream/NLP | [
"fcd14ae0e2744b3f0a532e7f0b1bc4c16918d1a7"
] | [
"FARM/farm/modeling/tokenization.py"
] | [
"# coding=utf-8\n# Copyright 2018 deepset 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 applica... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gnawhleinad/gdspy | [
"0f733d8f2aa6d7595a5bbac1562f16ea292f6157"
] | [
"gdspy/gdsiiformat.py"
] | [
"######################################################################\n# #\n# Copyright 2009-2019 Lucas Heitzmann Gabrielli. #\n# This file is part of gdspy, distributed under the terms of the #\n# Boost Software License -... | [
[
"numpy.ceil",
"numpy.log2"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mawanda-jun/SteelDefectDetection | [
"2db19b5c93ef697741f21e35b5b1a0b7c553c510"
] | [
"Dataset/preprocess_dataset.py"
] | [
"import os\nfrom typing import List, Dict\nfrom config import conf\nfrom PIL import Image\nfrom tqdm import tqdm\nimport h5py\nimport numpy as np\n\n\ndef images_in_paths(folder_path: str) -> List[str]:\n \"\"\"\n Collects paths to all images from one folder and return them as a list\n :param folder_path:\... | [
[
"numpy.subtract",
"numpy.expand_dims",
"numpy.zeros",
"numpy.divide"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
webclinic017/PyAutoFinance | [
"532cb1c5418dd9eeb07f2f08646170cde1fe0303"
] | [
"pyautofinance/common/metrics/learn_metrics/precision.py"
] | [
"from sklearn.metrics import precision_score\n\nfrom pyautofinance.common.metrics.learn_metrics.learn_metric import LearnMetric\n\n\nclass Precision(LearnMetric):\n\n name = 'Precision'\n value = 0\n\n def _get_metric_value(self):\n return precision_score(self.y_true, self.y_pred)\n\n def __gt__(... | [
[
"sklearn.metrics.precision_score"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nuguziii/Mask_RCNN | [
"3ec7d2ab7839e15310c82970b9ad4fd7393e831b"
] | [
"mrcnn_/config.py"
] | [
"\"\"\"\nMask R-CNN\nBase Configurations class.\n\nCopyright (c) 2017 Matterport, Inc.\nLicensed under the MIT License (see LICENSE for details)\nWritten by Waleed Abdulla\n\"\"\"\n\nimport numpy as np\n\n\n# Base Configuration Class\n# Don't use this class directly. Instead, sub-class it and override\n# the config... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Hermann-web/taboo-algorithm-for-jobshop | [
"068116c5b6b85027b33c4e9f96c044a3c8f9dce2"
] | [
"main.py"
] | [
"import numpy as np\nimport copy\n\n\nGammes= [ [1,2,3],\n [2,1,3],\n [1,2,3]\n ]\n\nS = [ [1,2,3],\n [2,1,3],\n [1,2,3]\n ]#s est une solution. On a une machine sur chaque ligne\n\nN =3 #nb de pièces\nM = 3 #nb dem\n#numero_noeuds = 1 + (n -1)*m + n \nINF = float('inf')\n\n#co... | [
[
"numpy.log",
"numpy.array",
"numpy.zeros",
"numpy.full"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
linZHank/gazebo_rl | [
"827e8f6ad0b0d0a912b80c14e20edc5e1ae5be53"
] | [
"scripts/turtlebot_crib/crib_nav_pid.py"
] | [
"#! /usr/bin/env python\n\n\"\"\"\nPID control for crib nav task.\n\nAuthor: LinZHanK (linzhank@gmail.com)\n\n\"\"\"\nfrom __future__ import absolute_import, division, print_function\n\nimport numpy as np\nimport gym\nimport rospy\nimport random\nimport os\nimport time\nimport datetime\nimport matplotlib.pyplot as ... | [
[
"numpy.dot",
"numpy.linalg.norm",
"numpy.arctan2",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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