code stringlengths 31 1.05M | apis list | extract_api stringlengths 97 1.91M |
|---|---|---|
'''
@FileName : data_parser.py
@EditTime : 2021-11-29 13:59:47
@Author : <NAME>
@Email : <EMAIL>
@Description :
'''
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import sys
import os
import os.path as osp
import platform
import json... | [
"os.path.exists",
"collections.namedtuple",
"os.listdir",
"numpy.ones",
"os.path.join",
"numpy.max",
"numpy.array",
"numpy.zeros",
"torch.tensor",
"platform.system",
"numpy.concatenate",
"json.load"
] | [((452, 516), 'collections.namedtuple', 'namedtuple', (['"""Keypoints"""', "['keypoints', 'gender_gt', 'gender_pd']"], {}), "('Keypoints', ['keypoints', 'gender_gt', 'gender_pd'])\n", (462, 516), False, 'from collections import namedtuple\n'), ((676, 703), 'os.path.exists', 'os.path.exists', (['keypoint_fn'], {}), '(ke... |
import numpy as np
import matplotlib.pyplot as plt
import math
def log(list_name):
for i in range(len(list_name)):
list_name[i] = math.log10(list_name[i])
print(list_name[i])
return list_name
size = 4
x = np.arange(size)
video_file = [11132, 21164, 34452, 45208] # 每帧视频文件大小（byte）
video_file ... | [
"matplotlib.pyplot.xticks",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.legend",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.bar",
"matplotlib.pyplot.yticks",
"math.log10",
"numpy.arange",
"matplotlib.pyplot.show"
] | [((232, 247), 'numpy.arange', 'np.arange', (['size'], {}), '(size)\n', (241, 247), True, 'import numpy as np\n'), ((533, 580), 'matplotlib.pyplot.xlabel', 'plt.xlabel', (['"""Total Camera Numbers"""'], {'fontsize': '(20)'}), "('Total Camera Numbers', fontsize=20)\n", (543, 580), True, 'import matplotlib.pyplot as plt\n... |
import numpy
from chainer import cuda
import chainer.serializers as S
import chainer.links as L
from nltk.corpus import stopwords
from context_models import CbowContext, BiLstmContext
from defs import IN_TO_OUT_UNITS_RATIO, NEGATIVE_SAMPLING_NUM
class ModelReader(object):
'''
Reads a pre-trained model using ... | [
"nltk.corpus.stopwords.words",
"context_models.CbowContext",
"numpy.empty",
"context_models.BiLstmContext",
"chainer.links.NegativeSampling",
"chainer.serializers.load_npz"
] | [((2480, 2544), 'chainer.links.NegativeSampling', 'L.NegativeSampling', (['target_word_units', 'cs', 'NEGATIVE_SAMPLING_NUM'], {}), '(target_word_units, cs, NEGATIVE_SAMPLING_NUM)\n', (2498, 2544), True, 'import chainer.links as L\n'), ((2608, 2741), 'context_models.BiLstmContext', 'BiLstmContext', (['deep', 'self.gpu'... |
# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2012-2016 GEM Foundation
#
# OpenQuake is free software: you can redistribute it and/or modify it
# under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the Licen... | [
"numpy.sqrt",
"numpy.log",
"numpy.exp",
"openquake.hazardlib.gsim.base.CoeffsTable",
"numpy.zeros",
"copy.deepcopy"
] | [((23450, 23869), 'openquake.hazardlib.gsim.base.CoeffsTable', 'CoeffsTable', ([], {'sa_damping': '(5)', 'table': '""" IMT MF\n pga 0.50\n pgv 1.00\n 0.05 0.44\n 0.10 0.44\n 0.15 0.53\n 0.20 0.60\n 0.25 0.72\n 0.30 0.81\n 0.40 1.00\n 0.50 1.01\n 0.60 1.02\n... |
# PRISM CONVERSION FROM ASCII GRIDS -- TASMIN / TASMAX
# header info
# ncols 2015
# nrows 1320
# xllcorner -2301787.7731349
# yllcorner 108069.7858797
# cellsize 2000
# NODATA_value -9999
import rasterio, glob, os
from rasterio import Affine
import numpy as np
from pathos import multipro... | [
"os.path.exists",
"os.makedirs",
"pathos.multiprocessing.Pool",
"numpy.flipud",
"rasterio.open",
"scipy.interpolate.griddata",
"os.path.join",
"os.path.dirname",
"affine.Affine.translation",
"os.path.basename",
"pyproj.Proj",
"os.system",
"numpy.vectorize",
"numpy.arange"
] | [((7696, 7722), 'rasterio.open', 'rasterio.open', (['template_fn'], {}), '(template_fn)\n', (7709, 7722), False, 'import rasterio\n'), ((7764, 7883), 'os.path.join', 'os.path.join', (['"""/workspace/Shared/Tech_Projects/EPSCoR_Southcentral/project_data/prism_v2"""', 'variable', '"""merged"""'], {}), "(\n '/workspace... |
#!/usr/bin/py2
import cv2
import imutils
import numpy as np
from solver import Solver
from Recognizer import OCR
from skimage.segmentation import clear_border
from imutils.perspective import four_point_transform
class Sudoku(object):
def __init__(self, image):
self.image = image
self.gray = None
def initia... | [
"Recognizer.OCR",
"cv2.drawContours",
"cv2.countNonZero",
"cv2.threshold",
"solver.Solver",
"cv2.arcLength",
"cv2.bitwise_and",
"skimage.segmentation.clear_border",
"imutils.resize",
"cv2.adaptiveThreshold",
"imutils.grab_contours",
"numpy.zeros",
"cv2.approxPolyDP",
"cv2.cvtColor",
"cv2... | [((354, 376), 'cv2.imread', 'cv2.imread', (['self.image'], {}), '(self.image)\n', (364, 376), False, 'import cv2\n'), ((392, 429), 'imutils.resize', 'imutils.resize', (['self.image'], {'width': '(600)'}), '(self.image, width=600)\n', (406, 429), False, 'import imutils\n'), ((486, 530), 'cv2.cvtColor', 'cv2.cvtColor', (... |
import numpy as np
from public_tool.form_index import form_index
from XGB_HMM.form_B_matrix_by_XGB import form_B_matrix_by_XGB
from XGB_HMM.predict import self_pred
def pred_proba_XGB(A, model, pi, O, allow_flag, lengths):
# 对dataset形成pred_proba，注意这里的dataset是solve_on_raw_data后的结果，即附带allow_flag的数据
# ou... | [
"XGB_HMM.form_B_matrix_by_XGB.form_B_matrix_by_XGB",
"numpy.zeros",
"XGB_HMM.predict.self_pred",
"public_tool.form_index.form_index"
] | [((397, 429), 'numpy.zeros', 'np.zeros', (['(O.shape[0], n_states)'], {}), '((O.shape[0], n_states))\n', (405, 429), True, 'import numpy as np\n'), ((501, 523), 'public_tool.form_index.form_index', 'form_index', (['lengths', 'i'], {}), '(lengths, i)\n', (511, 523), False, 'from public_tool.form_index import form_index\... |
"""
Software that detects each of the yellow shapes on the video frames and
classifies the shapes into classes: circle, rectangle, triangle.
USAGE: python3 shape_detection.py <video path> <output video path>
"""
import sys
import cv2
import imutils
import numpy as np
from tqdm import tqdm
BOX_COLORS = {
"trian... | [
"numpy.array",
"cv2.approxPolyDP",
"sys.exit",
"cv2.threshold",
"cv2.arcLength",
"numpy.zeros_like",
"cv2.VideoWriter",
"cv2.contourArea",
"imutils.grab_contours",
"cv2.drawContours",
"cv2.putText",
"cv2.morphologyEx",
"cv2.cvtColor",
"cv2.moments",
"cv2.Canny",
"cv2.GaussianBlur",
"... | [((821, 846), 'cv2.Canny', 'cv2.Canny', (['image', '(10)', '(255)'], {}), '(image, 10, 255)\n', (830, 846), False, 'import cv2\n'), ((860, 912), 'cv2.getStructuringElement', 'cv2.getStructuringElement', (['cv2.MORPH_ELLIPSE', '(3, 3)'], {}), '(cv2.MORPH_ELLIPSE, (3, 3))\n', (885, 912), False, 'import cv2\n'), ((932, 98... |
# -*- coding: utf-8 -*-
# Copyright 2017, IBM.
#
# This source code is licensed under the Apache License, Version 2.0 found in
# the LICENSE.txt file in the root directory of this source tree.
# pylint: disable=invalid-name,missing-docstring
from test.python.common import QiskitTestCase
import json
import unittest
... | [
"numpy.trace",
"numpy.sqrt",
"numpy.array",
"qiskit.qasm.Qasm",
"numpy.linalg.norm",
"unittest.main",
"qiskit.QuantumJob",
"qiskit.backends.local.qasm_simulator_cpp.x90_error_matrix",
"qiskit.QuantumCircuit",
"qiskit.backends.local.qasm_simulator_cpp.cx_error_matrix",
"numpy.eye",
"qiskit._com... | [((22521, 22547), 'unittest.main', 'unittest.main', ([], {'verbosity': '(2)'}), '(verbosity=2)\n', (22534, 22547), False, 'import unittest\n'), ((1345, 1368), 'qiskit.QuantumRegister', 'QuantumRegister', (['(2)', '"""q"""'], {}), "(2, 'q')\n", (1360, 1368), False, 'from qiskit import QuantumRegister\n'), ((1382, 1407),... |
import json
import random
from typing import NamedTuple, Any
import numpy
from numpy.testing import assert_array_almost_equal, assert_almost_equal
import torch
import pytest
from flaky import flaky
from allennlp.common.checks import ConfigurationError
from allennlp.common.testing import AllenNlpTestCase
from allennlp... | [
"allennlp.nn.util.bucket_values",
"allennlp.nn.util.masked_topk",
"numpy.random.rand",
"torch.LongTensor",
"allennlp.nn.util.get_text_field_mask",
"torch.max",
"allennlp.nn.util.get_combined_dim",
"torch.from_numpy",
"allennlp.nn.util.add_sentence_boundary_token_ids",
"numpy.array",
"allennlp.nn... | [((40211, 40242), 'flaky.flaky', 'flaky', ([], {'max_runs': '(3)', 'min_passes': '(1)'}), '(max_runs=3, min_passes=1)\n', (40216, 40242), False, 'from flaky import flaky\n'), ((537, 725), 'torch.tensor', 'torch.tensor', (['[[True, True, True, False, False, False], [True, True, False, False, False,\n False], [True, T... |
"""PyMC3-ArviZ conversion code."""
import logging
import warnings
from typing import ( # pylint: disable=unused-import
TYPE_CHECKING,
Any,
Dict,
Iterable,
List,
Mapping,
Optional,
Tuple,
Union,
)
import numpy as np
import xarray as xr
from aesara.graph.basic import Constant
from ... | [
"logging.getLogger",
"xarray.IndexVariable",
"arviz.data.base.dict_to_dataset",
"numpy.array",
"pymc3.aesaraf.extract_obs_data",
"numpy.where",
"numpy.ndim",
"numpy.stack",
"warnings.warn",
"pymc3.util.get_default_varnames",
"numpy.any",
"numpy.shape",
"arviz.data.base.generate_dims_coords",... | [((1054, 1080), 'logging.getLogger', 'logging.getLogger', (['"""pymc3"""'], {}), "('pymc3')\n", (1071, 1080), False, 'import logging\n'), ((11415, 11432), 'arviz.data.base.requires', 'requires', (['"""trace"""'], {}), "('trace')\n", (11423, 11432), False, 'from arviz.data.base import generate_dims_coords, make_attrs, r... |
from __future__ import print_function
import json
import os
import requests
from datetime import datetime
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
DEMO_UID = 0
PREDICTION_RESPONSE_KEY_QUERY_ID = "query_id"
PREDICTION_RESPONSE_KEY_OUTPUT = "output"
PREDICTION_RESPONSE_KEY_USED_DEFAULT = "... | [
"numpy.mean",
"json.loads",
"requests.post",
"numpy.sqrt",
"pandas.read_csv",
"os.path.join",
"numpy.array",
"datetime.datetime.now",
"matplotlib.pyplot.subplots",
"numpy.var"
] | [((883, 981), 'matplotlib.pyplot.subplots', 'plt.subplots', ([], {'nrows': 'num_rows', 'ncols': 'imgs_per_row', 'figsize': '(1.5 * imgs_per_row, 1.5 * num_rows)'}), '(nrows=num_rows, ncols=imgs_per_row, figsize=(1.5 *\n imgs_per_row, 1.5 * num_rows))\n', (895, 981), True, 'import matplotlib.pyplot as plt\n'), ((1542... |
"""
Module contains tools for processing files into DataFrames or other objects
"""
from __future__ import annotations
from collections import abc
import csv
import sys
from textwrap import fill
from typing import Any
import warnings
import numpy as np
import pandas._libs.lib as lib
from pandas._libs.parsers import ... | [
"csv.get_dialect",
"pandas.errors.AbstractMethodError",
"pandas.io.common.validate_header_arg",
"pandas.core.dtypes.common.is_file_like",
"pandas.core.dtypes.common.is_list_like",
"sys.getfilesystemencoding",
"pandas.io.parsers.base_parser.is_index_col",
"pandas.core.dtypes.common.is_float",
"csv.li... | [((20583, 20683), 'pandas.util._decorators.deprecate_nonkeyword_arguments', 'deprecate_nonkeyword_arguments', ([], {'version': 'None', 'allowed_args': "['filepath_or_buffer']", 'stacklevel': '(3)'}), "(version=None, allowed_args=[\n 'filepath_or_buffer'], stacklevel=3)\n", (20613, 20683), False, 'from pandas.util._d... |
# This file is part of the pyMOR project (http://www.pymor.org).
# Copyright 2013-2019 pyMOR developers and contributors. All rights reserved.
# License: BSD 2-Clause License (http://opensource.org/licenses/BSD-2-Clause)
import numpy as np
import scipy.linalg as spla
from pymor.algorithms.arnoldi import arnoldi
from ... | [
"pymor.algorithms.gram_schmidt.gram_schmidt_biorth",
"pymor.algorithms.gram_schmidt.gram_schmidt",
"numpy.ones",
"numpy.exp",
"numpy.zeros",
"numpy.empty",
"scipy.linalg.norm",
"pymor.algorithms.arnoldi.arnoldi",
"pymor.models.iosys.LTIModel.from_matrices"
] | [((7315, 7350), 'pymor.algorithms.arnoldi.arnoldi', 'arnoldi', (['fom.A', 'fom.E', 'fom.B', 'sigma'], {}), '(fom.A, fom.E, fom.B, sigma)\n', (7322, 7350), False, 'from pymor.algorithms.arnoldi import arnoldi\n'), ((7368, 7415), 'pymor.algorithms.arnoldi.arnoldi', 'arnoldi', (['fom.A', 'fom.E', 'fom.C', 'sigma'], {'tran... |
#!/usr/bin/env python
import random
import numpy as np
import tensorflow as tf
import cv2
import matplotlib.pyplot as plt
seed = 0
random.seed(seed)
np.random.seed(seed)
tf.random.set_seed(seed)
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
x_train = x_train[..., None]
x_test = x_test[... | [
"matplotlib.pyplot.imshow",
"tensorflow.random.set_seed",
"matplotlib.pyplot.xticks",
"tensorflow.keras.datasets.mnist.load_data",
"tensorflow.keras.preprocessing.image.ImageDataGenerator",
"random.seed",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.yticks",
"numpy.random.seed",
"matplotlib.pypl... | [((135, 152), 'random.seed', 'random.seed', (['seed'], {}), '(seed)\n', (146, 152), False, 'import random\n'), ((153, 173), 'numpy.random.seed', 'np.random.seed', (['seed'], {}), '(seed)\n', (167, 173), True, 'import numpy as np\n'), ((174, 198), 'tensorflow.random.set_seed', 'tf.random.set_seed', (['seed'], {}), '(see... |
import os, sys
import numpy as np
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
percentages = [0.01, 0.1, 0.2, 0.4, 0.5, 0.6]
for percentage in percentages:
data = []
save_path = '../logs/SOM_weights_MNIST_noise_{}.npy'.format(percentage)
wts = np.load(save_path).reshape(-1, 784)
print ("===... | [
"sklearn.cluster.KMeans",
"numpy.load",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.show"
] | [((647, 657), 'matplotlib.pyplot.show', 'plt.show', ([], {}), '()\n', (655, 657), True, 'import matplotlib.pyplot as plt\n'), ((272, 290), 'numpy.load', 'np.load', (['save_path'], {}), '(save_path)\n', (279, 290), True, 'import numpy as np\n'), ((374, 395), 'sklearn.cluster.KMeans', 'KMeans', ([], {'n_clusters': '(10)'... |
import matplotlib
matplotlib.use('Agg')
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
from matplotlib.pyplot import gcf
from flask import Flask, render_template, request, flash, redirect
import pandas as pd
import librosa
import ffmpeg
import librosa.... | [
"keras.preprocessing.image.img_to_array",
"numpy.argsort",
"numpy.array",
"tensorflow.keras.layers.Dense",
"pandas.read_excel",
"librosa.load",
"tensorflow.keras.models.Model",
"tensorflow.keras.applications.MobileNetV2",
"matplotlib.use",
"matplotlib.pyplot.gcf",
"tensorflow.keras.layers.Dropou... | [((18, 39), 'matplotlib.use', 'matplotlib.use', (['"""Agg"""'], {}), "('Agg')\n", (32, 39), False, 'import matplotlib\n'), ((767, 807), 'os.path.join', 'os.path.join', (['THIS_DIR', '"""bird_data.xlsx"""'], {}), "(THIS_DIR, 'bird_data.xlsx')\n", (779, 807), False, 'import os\n'), ((727, 753), 'os.path.realpath', 'os.pa... |
import os
from pathlib import Path
import cv2
import numpy as np
import pandas as pd
from pandas import DataFrame
from sklearn.model_selection import train_test_split
def create_info_csv(mvtec_dir: Path) -> DataFrame:
df = pd.DataFrame({})
for data_type in ["train", "test"]:
for p in mvtec_dir.glob... | [
"os.path.exists",
"cv2.imwrite",
"os.makedirs",
"pathlib.Path",
"os.rename",
"numpy.zeros",
"pandas.DataFrame",
"cv2.imread"
] | [((231, 247), 'pandas.DataFrame', 'pd.DataFrame', (['{}'], {}), '({})\n', (243, 247), True, 'import pandas as pd\n'), ((1542, 1584), 'os.makedirs', 'os.makedirs', (['"""/data/images"""'], {'exist_ok': '(True)'}), "('/data/images', exist_ok=True)\n", (1553, 1584), False, 'import os\n'), ((1589, 1630), 'os.makedirs', 'os... |
import tensorflow as tf
# numpy 是个科学计算的工具包，这里通过Numpy生成模拟数据
from numpy.random import RandomState
# 训练数据batch的大小
batch_size = 8
# 定义神经网络的参数，这里还是沿用3.4.2 小结中给出的神经网络结构
w1 = tf.Variable(tf.random_normal([2, 3], stddev=1, seed=1))
w2 = tf.Variable(tf.random_normal([3, 1], stddev=1, seed=1))
# 在shape的维度上使用None可以方便使用不打的batch... | [
"tensorflow.random_normal",
"tensorflow.Session",
"tensorflow.placeholder",
"tensorflow.global_variables_initializer",
"tensorflow.matmul",
"tensorflow.clip_by_value",
"tensorflow.train.AdamOptimizer",
"numpy.random.RandomState"
] | [((416, 475), 'tensorflow.placeholder', 'tf.placeholder', (['tf.float32'], {'shape': '(None, 2)', 'name': '"""x-input"""'}), "(tf.float32, shape=(None, 2), name='x-input')\n", (430, 475), True, 'import tensorflow as tf\n'), ((481, 540), 'tensorflow.placeholder', 'tf.placeholder', (['tf.float32'], {'shape': '(None, 1)',... |
"""Retokenization helpers
This module provides helpers for projecting span annotations from one tokenization to another.
Notes:
* Code is ported from https://github.com/nyu-mll/jiant/blob/master/jiant/utils/retokenize.py
* Please keep this code as a standalone utility; don't make this module depend on jiant m... | [
"numpy.identity",
"numpy.zeros",
"Levenshtein.StringMatcher.StringMatcher",
"nltk.tokenize.util.string_span_tokenize"
] | [((597, 647), 'numpy.zeros', 'np.zeros', (['(n_chars_src, n_chars_tgt)'], {'dtype': '_DTYPE'}), '((n_chars_src, n_chars_tgt), dtype=_DTYPE)\n', (605, 647), True, 'import numpy as np\n'), ((2133, 2168), 'nltk.tokenize.util.string_span_tokenize', 'string_span_tokenize', (['text'], {'sep': 'sep'}), '(text, sep=sep)\n', (2... |
# -*- coding: utf-8 -*-
"""
:math:`IC_TC_P` Colour Encoding
===============================
Defines the :math:`IC_TC_P` colour encoding related transformations:
- :func:`colour.RGB_to_ICtCp`
- :func:`colour.ICtCp_to_RGB`
- :func:`colour.XYZ_to_ICtCp`
- :func:`colour.ICtCp_to_XYZ`
References
----------
- :c... | [
"colour.algebra.vector_dot",
"colour.utilities.to_domain_1",
"colour.utilities.from_range_1",
"colour.models.rgb.RGB_to_XYZ",
"colour.models.rgb.transfer_functions.oetf_HLG_BT2100",
"colour.models.rgb.XYZ_to_RGB",
"colour.utilities.domain_range_scale",
"colour.models.rgb.transfer_functions.eotf_invers... | [((2348, 2386), 'numpy.linalg.inv', 'np.linalg.inv', (['MATRIX_ICTCP_RGB_TO_LMS'], {}), '(MATRIX_ICTCP_RGB_TO_LMS)\n', (2361, 2386), True, 'import numpy as np\n'), ((2861, 2903), 'numpy.linalg.inv', 'np.linalg.inv', (['MATRIX_ICTCP_LMS_P_TO_ICTCP'], {}), '(MATRIX_ICTCP_LMS_P_TO_ICTCP)\n', (2874, 2903), True, 'import nu... |
#!/usr/bin/env python3
import csv
import numpy
thr_sig=5.0
def sigmoid(x):
return 1.0/(1.0+numpy.exp(-(x-thr_sig)))
if __name__=="__main__":
#parameters
time_pitch=1.0 #ms
save_pitch=10
save_pitch_weight=1000
simlen_sec=900.0
simlen=int(simlen_sec*1000.0/time_pitch)
tauL=10.0 #ms
... | [
"numpy.sqrt",
"numpy.random.rand",
"numpy.hstack",
"csv.writer",
"numpy.exp",
"numpy.zeros",
"numpy.random.randn"
] | [((917, 943), 'numpy.zeros', 'numpy.zeros', (['input_src_num'], {}), '(input_src_num)\n', (928, 943), False, 'import numpy\n'), ((966, 992), 'numpy.zeros', 'numpy.zeros', (['som_input_num'], {}), '(som_input_num)\n', (977, 992), False, 'import numpy\n'), ((1015, 1041), 'numpy.zeros', 'numpy.zeros', (['dnd_input_num'], ... |
# Copyright 2020 Makani Technologies LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to... | [
"logging.Error",
"re.compile",
"makani.lib.python.debug_util.FormatTraceback",
"makani.gs.monitor2.apps.layout.autogen.GenerateScenario",
"django.core.urlresolvers.reverse",
"makani.analysis.checks.log_util.GetOrderedDedupDataAndTimeByField",
"makani.lib.python.c_helpers.EnumHelper",
"os.listdir",
"... | [((1826, 1879), 'makani.lib.python.c_helpers.EnumHelper', 'c_helpers.EnumHelper', (['"""MessageType"""', 'aio_message_type'], {}), "('MessageType', aio_message_type)\n", (1846, 1879), False, 'from makani.lib.python import c_helpers\n'), ((1915, 1976), 'os.path.join', 'os.path.join', (['settings.MONITOR_PATH', '"""confi... |
# coding=utf-8
# Copyright 2018 The TF-Agents Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law... | [
"tensorflow.train.Checkpoint",
"tensorflow.shape",
"tensorflow.compat.v1.train.AdamOptimizer",
"tf_agents.specs.tensor_spec.BoundedTensorSpec",
"numpy.array",
"tf_agents.trajectories.time_step.time_step_spec",
"tensorflow.cast",
"tensorflow.compat.v1.global_variables_initializer",
"tensorflow.eye",
... | [((2541, 2735), 'absl.testing.parameterized.named_parameters', 'parameterized.named_parameters', (["{'testcase_name': '_batch1_contextdim10', 'batch_size': 1, 'context_dim': 10}", "{'testcase_name': '_batch4_contextdim5', 'batch_size': 4, 'context_dim': 5}"], {}), "({'testcase_name': '_batch1_contextdim10',\n 'batch... |
from __future__ import print_function, absolute_import
from distutils import sysconfig
from distutils import version
from distutils.core import Extension
import glob
import io
import multiprocessing
import os
import re
import subprocess
import sys
import warnings
from textwrap import fill
PY3 = (sys.version_info[0] ... | [
"io.BytesIO",
"sys.platform.startswith",
"gi.repository.Gtk.get_micro_version",
"re.search",
"os.path.exists",
"os.listdir",
"sys.getfilesystemencoding",
"pyparsing.Forward",
"CXX",
"subprocess.Popen",
"subprocess.CalledProcessError",
"os.path.split",
"os.path.isdir",
"os.popen",
"numpy.... | [((1564, 1606), 'os.environ.get', 'os.environ.get', (['"""MPLSETUPCFG"""', '"""setup.cfg"""'], {}), "('MPLSETUPCFG', 'setup.cfg')\n", (1578, 1606), False, 'import os\n'), ((1610, 1635), 'os.path.exists', 'os.path.exists', (['setup_cfg'], {}), '(setup_cfg)\n', (1624, 1635), False, 'import os\n'), ((1650, 1681), 'ConfigP... |
# Copyright 2021 Sony Group Corporation.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to ... | [
"numpy.flip",
"random.uniform",
"random.choice",
"numpy.arange",
"random.seed",
"numpy.stack",
"musdb.DB",
"numpy.sum",
"random.random",
"numpy.random.RandomState"
] | [((1174, 1199), 'random.uniform', 'random.uniform', (['low', 'high'], {}), '(low, high)\n', (1188, 1199), False, 'import random\n'), ((1395, 1412), 'numpy.flip', 'np.flip', (['audio', '(0)'], {}), '(audio, 0)\n', (1402, 1412), True, 'import numpy as np\n'), ((3811, 3828), 'random.seed', 'random.seed', (['seed'], {}), '... |
import unittest
import numpy as np
from revpy import fare_transformation
class FareTransformationTest(unittest.TestCase):
def setUp(self):
# example data from page 13 of research paper
# "Optimization of Mixed Fare Structures: Theory and Applications"
# by <NAME> al. (2010)
self... | [
"revpy.fare_transformation.calc_fare_transformation",
"revpy.fare_transformation.efficient_strategies",
"numpy.testing.assert_equal",
"numpy.array",
"numpy.zeros",
"numpy.testing.assert_almost_equal"
] | [((329, 371), 'numpy.array', 'np.array', (['[1200, 1000, 800, 600, 400, 200]'], {}), '([1200, 1000, 800, 600, 400, 200])\n', (337, 371), True, 'import numpy as np\n'), ((395, 441), 'numpy.array', 'np.array', (['[31.2, 10.9, 14.8, 19.9, 26.9, 36.3]'], {}), '([31.2, 10.9, 14.8, 19.9, 26.9, 36.3])\n', (403, 441), True, 'i... |
from __future__ import (absolute_import, division, print_function,
unicode_literals)
from matplotlib.externals import six
from matplotlib.tri import Triangulation
import _tri as _tri
import numpy as np
class TriFinder(object):
"""
Abstract base class for classes used to find the tria... | [
"numpy.asarray"
] | [((2063, 2094), 'numpy.asarray', 'np.asarray', (['x'], {'dtype': 'np.float64'}), '(x, dtype=np.float64)\n', (2073, 2094), True, 'import numpy as np\n'), ((2107, 2138), 'numpy.asarray', 'np.asarray', (['y'], {'dtype': 'np.float64'}), '(y, dtype=np.float64)\n', (2117, 2138), True, 'import numpy as np\n')] |
""" Generate BpForms for all of the proteins in PRO, verify
them, and calculate their properties
:Author: <NAME> <<EMAIL>>
:Date: 2019-06-24
:Copyright: 2019, Karr Lab
:License: MIT
"""
from Bio import SeqIO
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
from matplotlib import pyplot
from xml.etree impor... | [
"csv.DictReader",
"Bio.Seq.Seq",
"numpy.array",
"bpforms.protein_alphabet.monomers.values",
"copy.deepcopy",
"requests_cache.core.CachedSession",
"matplotlib.pyplot.style.use",
"bpforms.protein_alphabet.monomers.items",
"matplotlib.pyplot.close",
"os.path.isdir",
"os.mkdir",
"Bio.SeqIO.write",... | [((568, 615), 'os.path.join', 'os.path.join', (['"""examples"""', '"""pro_nonreasoned.obo"""'], {}), "('examples', 'pro_nonreasoned.obo')\n", (580, 615), False, 'import os\n'), ((634, 681), 'os.path.join', 'os.path.join', (['"""examples"""', '"""pro_nonreasoned.pkl"""'], {}), "('examples', 'pro_nonreasoned.pkl')\n", (6... |
import argparse
import contextlib
import csv
import logging
import os
import random
import subprocess
import tempfile
from typing import Callable, Dict, Iterable, List
import numpy as np
import ray
from ray.experimental.raysort import constants
from ray.experimental.raysort import logging_utils
from ray.experimental.... | [
"numpy.fromfile",
"numpy.random.bytes",
"ray.cluster_resources",
"ray.experimental.raysort.types.PartInfo",
"os.cpu_count",
"ray.init",
"logging.info",
"os.path.exists",
"os.listdir",
"argparse.ArgumentParser",
"ray.experimental.raysort.logging_utils.init",
"subprocess.run",
"ray.util.remove... | [((6260, 6287), 'ray.experimental.raysort.tracing_utils.timeit', 'tracing_utils.timeit', (['"""map"""'], {}), "('map')\n", (6280, 6287), False, 'from ray.experimental.raysort import tracing_utils\n'), ((7656, 7703), 'ray.remote', 'ray.remote', ([], {'num_cpus': '(0)', 'resources': "{'worker': 1}"}), "(num_cpus=0, resou... |
__author__ = 'sibirrer'
from lenstronomy.LensModel.Profiles.flexion import Flexion
from lenstronomy.LensModel.lens_model import LensModel
import numpy as np
import numpy.testing as npt
import pytest
class TestExternalShear(object):
"""
tests the Gaussian methods
"""
def setup(self):
self.fl... | [
"lenstronomy.LensModel.lens_model.LensModel",
"pytest.main",
"numpy.testing.assert_almost_equal",
"numpy.array",
"lenstronomy.LensModel.Profiles.flexion.Flexion"
] | [((3229, 3242), 'pytest.main', 'pytest.main', ([], {}), '()\n', (3240, 3242), False, 'import pytest\n'), ((325, 334), 'lenstronomy.LensModel.Profiles.flexion.Flexion', 'Flexion', ([], {}), '()\n', (332, 334), False, 'from lenstronomy.LensModel.Profiles.flexion import Flexion\n'), ((494, 507), 'numpy.array', 'np.array',... |
from unittest.mock import Mock, PropertyMock, MagicMock, patch
import numpy as np
import gym_connect4
from test_fixtures import Connect4Task
import regym
from regym.environments import EnvType
from regym.rl_algorithms import build_MCTS_Agent
from regym.rl_algorithms.agents import Agent, build_Deterministic_Agent, De... | [
"unittest.mock.Mock",
"regym.rl_algorithms.build_Deterministic_Agent",
"regym.rl_loops.Trajectory",
"test_fixtures.Connect4Task.run_episodes",
"regym.rl_loops.multiagent_loops.sequential_action_rl_loop.propagate_last_experience",
"regym.rl_loops.multiagent_loops.sequential_action_rl_loop.propagate_experie... | [((771, 857), 'regym.rl_algorithms.build_Deterministic_Agent', 'build_Deterministic_Agent', (['Connect4Task', "{'action': 0}", '"""Col-0-DeterministicAgent"""'], {}), "(Connect4Task, {'action': 0},\n 'Col-0-DeterministicAgent')\n", (796, 857), False, 'from regym.rl_algorithms import build_Deterministic_Agent, build_... |
import numpy as np
# import os
# current_directory = os.path.dirname(os.path.abspath(__file__)).replace('\\','/')
# from ctypes import *
# bro = cdll.LoadLibrary(current_directory+"/broken.so")
# bro.broken_frame.argtypes = [np.ctypeslib.ndpointer(dtype=np.int16, ndim=1, flags="C_CONTIGUOUS"),
# c_int,
# ... | [
"numpy.abs",
"numpy.log10"
] | [((1532, 1564), 'numpy.abs', 'np.abs', (['wdata[i * w:(i + 1) * w]'], {}), '(wdata[i * w:(i + 1) * w])\n', (1538, 1564), True, 'import numpy as np\n'), ((1584, 1597), 'numpy.log10', 'np.log10', (['tem'], {}), '(tem)\n', (1592, 1597), True, 'import numpy as np\n')] |
from os import listdir
from os.path import isfile
from PIL import Image
from tqdm import tqdm
import numpy as np
import imgaug.augmenters as iaa
import os
import random
from os.path import join
import matplotlib.pyplot as plt
DATA_DIR = 'DATA DIR'
os.chdir(DATA_DIR)
IMAGE_DIR = join(DATA_DIR, 'dat... | [
"matplotlib.pyplot.imshow",
"PIL.Image.fromarray",
"os.listdir",
"tqdm.tqdm",
"os.path.join",
"numpy.asarray",
"numpy.squeeze",
"os.chdir",
"numpy.expand_dims",
"imgaug.augmenters.JpegCompression",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.show"
] | [((265, 283), 'os.chdir', 'os.chdir', (['DATA_DIR'], {}), '(DATA_DIR)\n', (273, 283), False, 'import os\n'), ((301, 360), 'os.path.join', 'join', (['DATA_DIR', '"""dataset\\\\PascalVOC-OG-flipped\\\\JPEGImages"""'], {}), "(DATA_DIR, 'dataset\\\\PascalVOC-OG-flipped\\\\JPEGImages')\n", (305, 360), False, 'from os.path i... |
from typing import Dict
import numpy as np
def buffer_from_example(example: Dict[str, np.ndarray],
leading_dims) -> Dict[str, np.ndarray]:
buf = {}
for key, value in example.items():
buf[key] = np.zeros(leading_dims + value.shape, dtype=value.dtype)
return buf
def get_l... | [
"numpy.zeros"
] | [((237, 292), 'numpy.zeros', 'np.zeros', (['(leading_dims + value.shape)'], {'dtype': 'value.dtype'}), '(leading_dims + value.shape, dtype=value.dtype)\n', (245, 292), True, 'import numpy as np\n')] |
import numpy as np
import cv2
image = cv2.imread('images/unsharp_bird.jpg')
kernel = np.array([
[0, -1, 0],
[-1, 5, -1],
[0, -1, 0]
])
sharpen_iamge = cv2.filter2D(image, -1, kernel)
cv2.imshow("original image", image)
cv2.imshow("sharpen image", sharpen_iamge)
cv2.wait... | [
"cv2.filter2D",
"cv2.imshow",
"numpy.array",
"cv2.destroyAllWindows",
"cv2.waitKey",
"cv2.imread"
] | [((39, 76), 'cv2.imread', 'cv2.imread', (['"""images/unsharp_bird.jpg"""'], {}), "('images/unsharp_bird.jpg')\n", (49, 76), False, 'import cv2\n'), ((87, 134), 'numpy.array', 'np.array', (['[[0, -1, 0], [-1, 5, -1], [0, -1, 0]]'], {}), '([[0, -1, 0], [-1, 5, -1], [0, -1, 0]])\n', (95, 134), True, 'import numpy as np\n'... |
# -*- coding: utf-8 -*-
import csv
import logging
import math
import multiprocessing
import os
import shutil
import time
from concurrent.futures import ThreadPoolExecutor
from pathlib import Path
from typing import Dict, List, Tuple
from django.utils import timezone
import numpy as np
import pandas as pd
import psut... | [
"csv.DictWriter",
"rpy2.robjects.pandas2ri.activate",
"pandas.read_csv",
"data_refinery_common.logging.get_and_configure_logger",
"multiprocessing.cpu_count",
"numpy.array",
"rpy2.robjects.r",
"os.path.exists",
"pathlib.Path",
"rpy2.robjects.packages.importr",
"django.utils.timezone.now",
"os.... | [((811, 881), 'data_refinery_common.utils.get_env_variable', 'get_env_variable', (['"""S3_RESULTS_BUCKET_NAME"""', '"""refinebio-results-bucket"""'], {}), "('S3_RESULTS_BUCKET_NAME', 'refinebio-results-bucket')\n", (827, 881), False, 'from data_refinery_common.utils import get_env_variable\n'), ((899, 950), 'data_refin... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
# from smac.env.multiagentenv import MultiAgentEnv
# from smac.env.starcraft2.maps import get_map_params
from ..multiagentenv import MultiAgentEnv
from ..starcraft2.maps import get_map_params
import atexit
fro... | [
"operator.attrgetter",
"numpy.eye",
"s2clientprotocol.raw_pb2.ActionRawUnitCommand",
"copy.deepcopy",
"s2clientprotocol.sc2api_pb2.InterfaceOptions",
"s2clientprotocol.sc2api_pb2.RequestJoinGame",
"absl.logging.info",
"time.sleep",
"s2clientprotocol.raw_pb2.ActionRaw",
"numpy.append",
"numpy.arr... | [((7454, 7469), 'time.sleep', 'time.sleep', (['(100)'], {}), '(100)\n', (7464, 7469), False, 'import math, time\n'), ((10200, 10223), 'numpy.zeros', 'np.zeros', (['self.n_agents'], {}), '(self.n_agents)\n', (10208, 10223), True, 'import numpy as np\n'), ((10259, 10283), 'numpy.zeros', 'np.zeros', (['self.n_enemies'], {... |
import numbers
from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
import torch
from PIL import Image, ImageOps, ImageEnhance
from typing_extensions import Literal
try:
import accimage
except ImportError:
accimage = None
@torch.jit.unused
def _is_pil_image(img: Any) -> bo... | [
"numpy.uint8",
"PIL.ImageEnhance.Contrast",
"numpy.array",
"PIL.ImageOps.posterize",
"PIL.ImageOps.autocontrast",
"PIL.ImageOps.expand",
"numpy.asarray",
"PIL.ImageEnhance.Sharpness",
"PIL.ImageEnhance.Color",
"PIL.ImageOps.invert",
"numpy.maximum",
"PIL.ImageOps.equalize",
"PIL.ImageOps.sol... | [((1472, 1500), 'PIL.ImageEnhance.Brightness', 'ImageEnhance.Brightness', (['img'], {}), '(img)\n', (1495, 1500), False, 'from PIL import Image, ImageOps, ImageEnhance\n'), ((1776, 1802), 'PIL.ImageEnhance.Contrast', 'ImageEnhance.Contrast', (['img'], {}), '(img)\n', (1797, 1802), False, 'from PIL import Image, ImageOp... |
import numpy as np
from pyray.shapes.twod.paraboloid import *
from pyray.shapes.twod.functional import *
from pyray.rotation import *
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import matplotlib as mpl... | [
"numpy.eye",
"numpy.sqrt",
"numpy.array",
"matplotlib.pyplot.figure",
"numpy.linspace",
"numpy.dot",
"numpy.cos",
"numpy.sin",
"numpy.meshgrid",
"numpy.arange",
"matplotlib.pyplot.show"
] | [((1463, 1475), 'matplotlib.pyplot.figure', 'plt.figure', ([], {}), '()\n', (1473, 1475), True, 'import matplotlib.pyplot as plt\n'), ((1514, 1544), 'numpy.linspace', 'np.linspace', (['(0)', '(2 * np.pi)', '(100)'], {}), '(0, 2 * np.pi, 100)\n', (1525, 1544), True, 'import numpy as np\n'), ((1555, 1579), 'numpy.arange'... |
import os
import numpy as np
from vmaf import plt
from vmaf.core.cross_validation import ModelCrossValidation
from vmaf.core.feature_assembler import FeatureAssembler
from vmaf.core.quality_runner import VmafQualityRunner
from vmaf.core.result_store import FileSystemResultStore
from vmaf.tools.misc import indices, get... | [
"vmaf.core.train_test_model.TrainTestModel.find_subclass",
"vmaf.core.quality_runner.VmafQualityRunner.predict_with_model",
"numpy.array",
"vmaf.core.local_explainer.LocalExplainer",
"vmaf.config.VmafConfig.file_result_store_path",
"vmaf.config.VmafConfig.workspace_path",
"vmaf.plt.subplots",
"vmaf.co... | [((19415, 19682), 'vmaf.core.feature_assembler.FeatureAssembler', 'FeatureAssembler', ([], {'feature_dict': 'feature_param.feature_dict', 'feature_option_dict': 'None', 'assets': 'train_assets', 'logger': 'logger', 'fifo_mode': 'fifo_mode', 'delete_workdir': '(True)', 'result_store': 'result_store', 'optional_dict': 'N... |
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | [
"paddle.fluid.dygraph.guard",
"paddle.fluid.dygraph.Embedding",
"paddle.fluid.layers.split",
"paddle.fluid.layers.piecewise_decay",
"unittest.main",
"paddle.fluid.layers.transpose",
"paddle.fluid.layers.matmul",
"numpy.arange",
"paddle.fluid.layers.reshape",
"paddle.fluid.save_dygraph",
"paddle.... | [((34298, 34313), 'unittest.main', 'unittest.main', ([], {}), '()\n', (34311, 34313), False, 'import unittest\n'), ((5049, 5076), 'paddle.fluid.layers.concat', 'fluid.layers.concat', (['res', '(0)'], {}), '(res, 0)\n', (5068, 5076), True, 'import paddle.fluid as fluid\n'), ((5096, 5146), 'paddle.fluid.layers.transpose'... |
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applic... | [
"image_util.expand_image",
"PIL.Image.fromarray",
"PIL.Image.open",
"random.uniform",
"random.shuffle",
"image_util.sampler",
"image_util.generate_batch_samples",
"image_util.distort_image",
"os.path.join",
"numpy.swapaxes",
"numpy.array",
"image_util.crop_image"
] | [((996, 1030), 'os.path.join', 'os.path.join', (['data_dir', 'label_file'], {}), '(data_dir, label_file)\n', (1008, 1030), False, 'import os\n'), ((2454, 2475), 'random.shuffle', 'random.shuffle', (['lines'], {}), '(lines)\n', (2468, 2475), False, 'import random\n'), ((2897, 2917), 'PIL.Image.open', 'Image.open', (['im... |
import torch
import unittest
import numpy as np
from torch.autograd import Variable
from losses.svm import SmoothTop1SVM, SmoothTopkSVM, MaxTop1SVM, MaxTopkSVM
from losses.functional import Topk_Smooth_SVM
from tests.utils import assert_all_close, V
from tests.py_ref import svm_topk_smooth_py_1, svm_topk_smooth_py_2,\... | [
"torch.manual_seed",
"numpy.arange",
"losses.svm.MaxTopkSVM",
"tests.utils.assert_all_close",
"losses.functional.Topk_Smooth_SVM",
"numpy.random.randint",
"tests.utils.V",
"numpy.random.seed",
"losses.svm.MaxTop1SVM",
"torch.autograd.Variable",
"torch.randn",
"losses.svm.SmoothTop1SVM",
"los... | [((486, 509), 'torch.manual_seed', 'torch.manual_seed', (['(1234)'], {}), '(1234)\n', (503, 509), False, 'import torch\n'), ((518, 538), 'numpy.random.seed', 'np.random.seed', (['(1234)'], {}), '(1234)\n', (532, 538), True, 'import numpy as np\n'), ((636, 679), 'torch.randn', 'torch.randn', (['self.n_samples', 'self.n_... |
# pylint: disable=g-bad-file-header
# Copyright 2019 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/... | [
"bsuite.experiments.mnist.mnist.MNISTBandit",
"absl.testing.absltest.main",
"numpy.random.RandomState"
] | [((1312, 1327), 'absl.testing.absltest.main', 'absltest.main', ([], {}), '()\n', (1325, 1327), False, 'from absl.testing import absltest\n'), ((1060, 1087), 'bsuite.experiments.mnist.mnist.MNISTBandit', 'mnist.MNISTBandit', ([], {'seed': '(101)'}), '(seed=101)\n', (1077, 1087), False, 'from bsuite.experiments.mnist imp... |
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | [
"tensorflow_estimator.python.estimator.run_config.RunConfig",
"tensorflow.python.keras.metrics.Mean",
"numpy.ones",
"tensorflow.compat.v1.data.make_one_shot_iterator",
"tensorflow.test.main",
"tensorflow.feature_column.numeric_column",
"tensorflow.compat.v1.data.Dataset.from_tensor_slices",
"tensorflo... | [((5382, 5396), 'tensorflow.test.main', 'tf.test.main', ([], {}), '()\n', (5394, 5396), True, 'import tensorflow as tf\n'), ((1391, 1453), 'tensorflow.compat.v1.data.Dataset.from_tensor_slices', 'tf.compat.v1.data.Dataset.from_tensor_slices', (["{'x': x, 'y': y}"], {}), "({'x': x, 'y': y})\n", (1435, 1453), True, 'impo... |
import numpy as np
from scipy.sparse import csc_matrix, save_npz, hstack
import time
import argparse
import gzip
from pysam import VariantFile, TabixFile
import json
import os
import itertools
parser = argparse.ArgumentParser(description='Pull genotypes.')
parser.add_argument('vcf_file', type=str, help='VCF file to p... | [
"pysam.VariantFile",
"argparse.ArgumentParser",
"gzip.open",
"numpy.asarray",
"os.path.isfile",
"numpy.zeros",
"pysam.TabixFile",
"json.load",
"scipy.sparse.save_npz",
"time.time",
"json.dump"
] | [((204, 258), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': '"""Pull genotypes."""'}), "(description='Pull genotypes.')\n", (227, 258), False, 'import argparse\n'), ((1546, 1557), 'time.time', 'time.time', ([], {}), '()\n', (1555, 1557), False, 'import time\n'), ((4632, 4658), 'pysam.Varian... |
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... | [
"parakeet.data.datacargo.DataCargo",
"parakeet.data.dataset.TransformDataset",
"parakeet.audio.AudioProcessor",
"pandas.read_csv",
"pathlib.Path",
"paddle.fluid.io.DataLoader.from_generator",
"parakeet.g2p.en.text_to_sequence",
"paddle.fluid.CPUPlace",
"numpy.array",
"parakeet.data.batch.TextIDBat... | [((1375, 1395), 'pathlib.Path', 'Path', (['args.data_path'], {}), '(args.data_path)\n', (1379, 1395), False, 'from pathlib import Path\n'), ((1504, 1543), 'parakeet.data.dataset.TransformDataset', 'TransformDataset', (['metadata', 'transformer'], {}), '(metadata, transformer)\n', (1520, 1543), False, 'from parakeet.dat... |
import numpy as np
import geocoder
def process_df(df):
"""
df: pd.DataFrame
"""
df.dropna(subset=['lat', 'lon'], axis=0, inplace=True)
df.reset_index(drop=True, inplace=True)
# Add new column to hold the years
df["year"] = [int(x.split("-")[0]) for x in df['date']]
# Convert coo... | [
"geocoder.google",
"numpy.logical_and"
] | [((409, 461), 'numpy.logical_and', 'np.logical_and', (["(df['lat-dir'] == 'S')", "(df['lat'] >= 0)"], {}), "(df['lat-dir'] == 'S', df['lat'] >= 0)\n", (423, 461), True, 'import numpy as np\n'), ((511, 563), 'numpy.logical_and', 'np.logical_and', (["(df['lon-dir'] == 'W')", "(df['lon'] >= 0)"], {}), "(df['lon-dir'] == '... |
"""
Offset Mirror Classes.
This module contains all the classes relating to the offset mirrors used in the
FEE and XRT. Each offset mirror contains a stepper motor and piezo motor to
control the pitch, and two pairs of motors to control the horizontal and
vertical gantries.
"""
import logging
import numpy as np
from ... | [
"logging.getLogger",
"ophyd.Component",
"numpy.isnan",
"numpy.isinf",
"ophyd.FormattedComponent"
] | [((810, 837), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (827, 837), False, 'import logging\n'), ((1023, 1083), 'ophyd.Component', 'Cpt', (['EpicsSignalRO', '""":RBV"""'], {'auto_monitor': '(True)', 'kind': '"""hinted"""'}), "(EpicsSignalRO, ':RBV', auto_monitor=True, kind='hinted')\n... |
# Copyright (c) 2020 <NAME>
from baselines.common import Dataset, explained_variance, fmt_row, zipsame
from baselines import logger
import baselines.common.tf_util as U
import tensorflow as tf, numpy as np
import time
from baselines.common.mpi_adam import MpiAdam
from baselines.common.mpi_moments import mpi_moments
fr... | [
"baselines.common.tf_util.get_session",
"baselines.common.mpi_adam.MpiAdam",
"tensorflow.transpose",
"tensorflow.reduce_sum",
"mpi4py.MPI.COMM_WORLD.allgather",
"numpy.array",
"baselines.logger.log",
"tensorflow.set_random_seed",
"baselines.common.tf_util.get_placeholder_cached",
"baselines.common... | [((895, 907), 'numpy.shape', 'np.shape', (['ob'], {}), '(ob)\n', (903, 907), True, 'import tensorflow as tf, numpy as np\n'), ((927, 939), 'numpy.shape', 'np.shape', (['ac'], {}), '(ac)\n', (935, 939), True, 'import tensorflow as tf, numpy as np\n'), ((987, 1011), 'numpy.concatenate', 'np.concatenate', (['(ob, ac)'], {... |
import time
from unittest.case import SkipTest
from ddtrace.context import Context
from ddtrace.constants import ANALYTICS_SAMPLE_RATE_KEY
from ddtrace.span import Span
from ddtrace.ext import errors
def test_ids():
s = Span(tracer=None, name='span.test')
assert s.trace_id
assert s.span_id
assert no... | [
"ddtrace.span.Span",
"numpy.int64",
"unittest.case.SkipTest",
"time.sleep",
"ddtrace.context.Context"
] | [((228, 263), 'ddtrace.span.Span', 'Span', ([], {'tracer': 'None', 'name': '"""span.test"""'}), "(tracer=None, name='span.test')\n", (232, 263), False, 'from ddtrace.span import Span\n'), ((344, 407), 'ddtrace.span.Span', 'Span', ([], {'tracer': 'None', 'name': '"""t"""', 'trace_id': '(1)', 'span_id': '(2)', 'parent_id... |
from gzip import (
compress,
GzipFile
)
import numpy as np
from .record import Record
UNK = '<unk>'
PAD = '<pad>'
class Vocab(Record):
__attributes__ = ['words', 'counts']
def __init__(self, words, counts):
self.words = words
self.counts = counts
self.word_ids = {
... | [
"gzip.GzipFile",
"numpy.array",
"numpy.frombuffer",
"gzip.compress"
] | [((2259, 2290), 'gzip.compress', 'compress', (['(meta + counts + words)'], {}), '(meta + counts + words)\n', (2267, 2290), False, 'from gzip import compress, GzipFile\n'), ((2354, 2387), 'gzip.GzipFile', 'GzipFile', ([], {'mode': '"""rb"""', 'fileobj': 'file'}), "(mode='rb', fileobj=file)\n", (2362, 2387), False, 'from... |
# -*- coding: utf-8 -*-
from EXOSIMS.Prototypes.OpticalSystem import OpticalSystem
import astropy.units as u
import numpy as np
import scipy.stats as st
import scipy.optimize as opt
class Nemati(OpticalSystem):
"""Nemati Optical System class
This class contains all variables and methods necessar... | [
"numpy.sqrt",
"numpy.log",
"EXOSIMS.Prototypes.OpticalSystem.OpticalSystem.__init__",
"numpy.array",
"numpy.errstate",
"numpy.isnan",
"numpy.true_divide",
"numpy.isinf"
] | [((590, 627), 'EXOSIMS.Prototypes.OpticalSystem.OpticalSystem.__init__', 'OpticalSystem.__init__', (['self'], {}), '(self, **specs)\n', (612, 627), False, 'from EXOSIMS.Prototypes.OpticalSystem import OpticalSystem\n'), ((3905, 3941), 'numpy.array', 'np.array', (['sInds'], {'ndmin': '(1)', 'copy': '(False)'}), '(sInds,... |
#!/usr/bin/env python
import numpy as np
from olympus.surfaces import AbstractSurface
class AckleyPath(AbstractSurface):
def __init__(self, param_dim=2, noise=None):
"""Ackley path function.
Args:
param_dim (int): Number of input dimensions. Default is 2.
noise (Noise): ... | [
"numpy.exp",
"numpy.array",
"numpy.sum",
"numpy.cos"
] | [((761, 777), 'numpy.array', 'np.array', (['params'], {}), '(params)\n', (769, 777), True, 'import numpy as np\n'), ((950, 966), 'numpy.array', 'np.array', (['params'], {}), '(params)\n', (958, 966), True, 'import numpy as np\n'), ((1084, 1095), 'numpy.exp', 'np.exp', (['(1.0)'], {}), '(1.0)\n', (1090, 1095), True, 'im... |
import random
import numpy as np
import cv2
from utils.transforms.transforms import CustomTransform
class RandomFlip(CustomTransform):
def __init__(self, prob_x=0, prob_y=0):
"""
Arguments:
----------
prob_x: range [0, 1], probability to use horizontal flip, setting to 0 means dis... | [
"numpy.random.choice",
"numpy.flip",
"numpy.random.uniform"
] | [((675, 740), 'numpy.random.choice', 'np.random.choice', (['[False, True]'], {'p': '(1 - self.prob_x, self.prob_x)'}), '([False, True], p=(1 - self.prob_x, self.prob_x))\n', (691, 740), True, 'import numpy as np\n'), ((758, 823), 'numpy.random.choice', 'np.random.choice', (['[False, True]'], {'p': '(1 - self.prob_y, se... |
'''Utility functions and classes for handling image datasets.'''
import cPickle
import cv2
import os.path as osp
import numpy as np
import tensorflow as tf
from config_tfvgg import cfg
FLAGS = tf.app.flags.FLAGS
def get_facebox_dims(img_shape,face_bbox,target_size,crop_size,spec,crop_ind):
face_bbox = np.zeros_li... | [
"tensorflow.image.resize_images",
"tensorflow.shape",
"tensorflow.read_file",
"numpy.array",
"tensorflow.FIFOQueue",
"numpy.min",
"numpy.ceil",
"tensorflow.reverse",
"os.path.splitext",
"tensorflow.to_int32",
"tensorflow.range",
"cv2.imread",
"tensorflow.minimum",
"tensorflow.image.decode_... | [((309, 333), 'numpy.zeros_like', 'np.zeros_like', (['face_bbox'], {}), '(face_bbox)\n', (322, 333), True, 'import numpy as np\n'), ((925, 1001), 'numpy.array', 'np.array', (['(face_bbox[3] - face_bbox[1] + 1, face_bbox[2] - face_bbox[0] + 1)'], {}), '((face_bbox[3] - face_bbox[1] + 1, face_bbox[2] - face_bbox[0] + 1))... |
import spartan
from spartan import core, expr, util, blob_ctx
import numpy as np
from .qr import qr
def svd(A, k=None):
"""
Stochastic SVD.
Parameters
----------
A : spartan matrix
Array to compute the SVD on, of shape (M, N)
k : int, optional
Number of singular values and vectors to compute.... | [
"spartan.expr.randn",
"spartan.expr.dot",
"numpy.sqrt",
"numpy.linalg.eig",
"numpy.ones",
"spartan.expr.transpose",
"numpy.argsort"
] | [((594, 619), 'spartan.expr.randn', 'expr.randn', (['A.shape[1]', 'k'], {}), '(A.shape[1], k)\n', (604, 619), False, 'from spartan import core, expr, util, blob_ctx\n'), ((627, 645), 'spartan.expr.dot', 'expr.dot', (['A', 'Omega'], {}), '(A, Omega)\n', (635, 645), False, 'from spartan import core, expr, util, blob_ctx\... |
from collections import namedtuple
import tensorflow as tf
import numpy as np
from rl.agents.a2c.agent import A2CAgent
TestArgType = namedtuple('ArgType', ['name'])
arg_type = TestArgType('arg')
A = np.array
class A2CAgentTest(tf.test.TestCase):
def test_compute_policy_log_probs(self):
from rl.agents.a2c.a... | [
"collections.namedtuple",
"rl.agents.a2c.agent.compute_policy_entropy",
"numpy.log",
"tensorflow.test.main",
"rl.agents.a2c.agent.compute_policy_log_probs"
] | [((137, 168), 'collections.namedtuple', 'namedtuple', (['"""ArgType"""', "['name']"], {}), "('ArgType', ['name'])\n", (147, 168), False, 'from collections import namedtuple\n'), ((2115, 2129), 'tensorflow.test.main', 'tf.test.main', ([], {}), '()\n', (2127, 2129), True, 'import tensorflow as tf\n'), ((862, 941), 'rl.ag... |
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
def rescale(data, to=[0, 1]):
"""Rescale data.
Rescale a numeric variable to a new range.
Parameters
----------
data : list, array or Series
Raw data.
to : list
New range of values of the data after rescaling.
... | [
"numpy.nanmin",
"numpy.array",
"numpy.nanmax"
] | [((623, 637), 'numpy.array', 'np.array', (['data'], {}), '(data)\n', (631, 637), True, 'import numpy as np\n'), ((821, 836), 'numpy.nanmin', 'np.nanmin', (['data'], {}), '(data)\n', (830, 836), True, 'import numpy as np\n'), ((776, 791), 'numpy.nanmax', 'np.nanmax', (['data'], {}), '(data)\n', (785, 791), True, 'import... |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
#Libraries
import cv2
import numpy as np
import pyautogui
import keyboard
# In[2]:
#Color to detect BGR
l = [17, 15, 100] #lower
u = [80, 76, 220] #upper
# In[3]:
#region coordinates
k_left, k_top, k_right, k_bottom = 640, 30, 440, 130
h_left, h_top, h_right, h... | [
"cv2.rectangle",
"cv2.imshow",
"numpy.array",
"cv2.destroyAllWindows",
"cv2.threshold",
"cv2.erode",
"cv2.waitKey",
"pyautogui.keyDown",
"cv2.putText",
"cv2.cvtColor",
"cv2.resize",
"cv2.GaussianBlur",
"cv2.namedWindow",
"cv2.flip",
"cv2.inRange",
"cv2.bitwise_and",
"cv2.VideoCapture... | [((566, 589), 'pyautogui.keyDown', 'pyautogui.keyDown', (['"""up"""'], {}), "('up')\n", (583, 589), False, 'import pyautogui\n'), ((685, 711), 'pyautogui.keyDown', 'pyautogui.keyDown', (['"""right"""'], {}), "('right')\n", (702, 711), False, 'import pyautogui\n'), ((805, 830), 'pyautogui.keyDown', 'pyautogui.keyDown', ... |
# Copyright 2018 The TensorFlow Probability Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law o... | [
"tensorflow.compat.v2.nest.map_structure",
"numpy.log",
"tensorflow.compat.v2.einsum",
"tensorflow_probability.python.internal.test_util.jax_disable_test_missing_functionality",
"tensorflow.compat.v2.cast",
"tensorflow.compat.v2.nest.pack_sequence_as",
"tensorflow.compat.v2.nest.assert_same_structure",
... | [((1233, 1535), 'absl.testing.parameterized.named_parameters', 'parameterized.named_parameters', (["{'testcase_name': 'coroutine', 'jd_class': tfd.\n JointDistributionCoroutineAutoBatched}", "{'testcase_name': 'sequential', 'jd_class': tfd.\n JointDistributionSequentialAutoBatched}", "{'testcase_name': 'named', '... |
import numpy as np
from sklearn.decomposition import PCA
import pandas as pd
import matplotlib.pyplot as plt
import random
import seaborn as sns
from sklearn.cluster import KMeans
from sklearn.metrics import confusion_matrix
from sklearn.metrics.cluster import adjusted_rand_score
from sklearn.datasets import fetch_open... | [
"numpy.trace",
"pandas.read_csv",
"matplotlib.pyplot.ylabel",
"sklearn.neighbors.kneighbors_graph",
"random.choices",
"numpy.linalg.norm",
"sklearn.decomposition.sparse_encode",
"numpy.arange",
"sklearn.cluster.k_means",
"seaborn.distplot",
"sklearn.decomposition.PCA",
"matplotlib.pyplot.xlabe... | [((15196, 15201), 'sklearn.decomposition.PCA', 'PCA', ([], {}), '()\n', (15199, 15201), False, 'from sklearn.decomposition import PCA\n'), ((16649, 16657), 'sklearn.decomposition.PCA', 'PCA', (['(0.9)'], {}), '(0.9)\n', (16652, 16657), False, 'from sklearn.decomposition import PCA\n'), ((17838, 17857), 'sklearn.decompo... |
#!/usr/bin/env python3
# class file uppergeodesic.py
# started as a script to visualize what happens to hyperbolic plane
# if different isometries act on it
import geodesic as gd
import numpy as np
import numpy.linalg as lina
import matplotlib.pyplot as plt
# upper half space as the basic model
class UpperGeodesic... | [
"numpy.sin",
"numpy.matrix",
"numpy.linspace",
"numpy.cos"
] | [((4613, 4650), 'numpy.matrix', 'np.matrix', (['[[diag, off], [off, diag]]'], {}), '([[diag, off], [off, diag]])\n', (4622, 4650), True, 'import numpy as np\n'), ((2161, 2192), 'numpy.linspace', 'np.linspace', (['(0)', 'np.pi', 'self.res'], {}), '(0, np.pi, self.res)\n', (2172, 2192), True, 'import numpy as np\n'), ((2... |
"""
Created on Thu Sept 24 2020-
@author: <NAME>
GitHub username: esgomezm
"""
import tensorflow as tf
from tensorflow.keras import backend as K
from tensorflow.keras.losses import binary_crossentropy
import numpy as np
from tensorflow.keras import losses
# --------------------------------
# ## Unet with tf 2.0.0
# h... | [
"tensorflow.keras.backend.log",
"tensorflow.shape",
"tensorflow.keras.backend.floatx",
"tensorflow.keras.backend.greater",
"tensorflow.keras.backend.ones_like",
"tensorflow.keras.losses.binary_crossentropy",
"tensorflow.cast",
"tensorflow.keras.backend.conv2d",
"tensorflow.keras.backend.conv3d",
"... | [((1129, 1167), 'tensorflow.keras.backend.clip', 'K.clip', (['y_pred', 'epsilon', '(1.0 - epsilon)'], {}), '(y_pred, epsilon, 1.0 - epsilon)\n', (1135, 1167), True, 'from tensorflow.keras import backend as K\n'), ((1186, 1216), 'tensorflow.keras.backend.log', 'K.log', (['(y_pred / (1.0 - y_pred))'], {}), '(y_pred / (1.... |
import json
import bz2
import gzip
import _pickle as cPickle
import gym
import numpy as np
import quaternion
import skimage.morphology
import habitat
from envs.utils.fmm_planner import FMMPlanner
from constants import coco_categories
import envs.utils.pose as pu
class ObjectGoal_Env(habitat.RLEnv):
"""The Object... | [
"quaternion.as_rotation_vector",
"quaternion.as_float_array",
"envs.utils.fmm_planner.FMMPlanner",
"numpy.random.rand",
"gzip.open",
"envs.utils.pose.get_l2_distance",
"numpy.arange",
"_pickle.load",
"bz2.BZ2File",
"numpy.concatenate",
"constants.coco_categories.items",
"numpy.rad2deg",
"qua... | [((1554, 1576), 'gym.spaces.Discrete', 'gym.spaces.Discrete', (['(3)'], {}), '(3)\n', (1573, 1576), False, 'import gym\n'), ((1611, 1690), 'gym.spaces.Box', 'gym.spaces.Box', (['(0)', '(255)', '(3, args.frame_height, args.frame_width)'], {'dtype': '"""uint8"""'}), "(0, 255, (3, args.frame_height, args.frame_width), dty... |
import math
import re
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from go_utils.cleanup import ( # isort: skip
rename_latlon_cols,
replace_column_prefix,
round_cols,
standardize_null_vals,
)
from go_utils.plot import ( # isort: skip
completeness_histogram,
plot_fr... | [
"go_utils.plot.completeness_histogram",
"go_utils.plot.plot_int_distribution",
"matplotlib.pyplot.ylabel",
"go_utils.cleanup.round_cols",
"matplotlib.pyplot.xlabel",
"go_utils.cleanup.rename_latlon_cols",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.bar",
"re.sub",
"go_utils.cleanup.standardize_... | [((2550, 2624), 'go_utils.cleanup.replace_column_prefix', 'replace_column_prefix', (['df', '"""mosquitohabitatmapper"""', '"""mhm"""'], {'inplace': 'inplace'}), "(df, 'mosquitohabitatmapper', 'mhm', inplace=inplace)\n", (2571, 2624), False, 'from go_utils.cleanup import rename_latlon_cols, replace_column_prefix, round_... |
# -*- coding: utf-8 -*-
# This code is part of Qiskit.
#
# (C) Copyright IBM 2020.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modif... | [
"itertools.permutations",
"itertools.product",
"numpy.isclose"
] | [((3021, 3053), 'itertools.product', 'itertools.product', (['mos'], {'repeat': '(2)'}), '(mos, repeat=2)\n', (3038, 3053), False, 'import itertools\n'), ((3677, 3709), 'itertools.product', 'itertools.product', (['mos'], {'repeat': '(4)'}), '(mos, repeat=4)\n', (3694, 3709), False, 'import itertools\n'), ((3722, 3762), ... |
from flask import Flask, flash, request, redirect, url_for, render_template
from werkzeug.utils import secure_filename
import os
from keras.models import load_model
from keras.applications.inception_resnet_v2 import InceptionResNetV2
import tensorflow as tf
from skimage.io import imsave
from skimage.transform import re... | [
"flask.render_template",
"flask.Flask",
"PIL.Image.new",
"logging.exception",
"numpy.array",
"werkzeug.utils.secure_filename",
"os.listdir",
"skimage.color.rgb2lab",
"flask.flash",
"keras.applications.inception_resnet_v2.preprocess_input",
"skimage.color.lab2rgb",
"keras.applications.inception... | [((642, 664), 'tensorflow.get_default_graph', 'tf.get_default_graph', ([], {}), '()\n', (662, 664), True, 'import tensorflow as tf\n'), ((671, 686), 'flask.Flask', 'Flask', (['__name__'], {}), '(__name__)\n', (676, 686), False, 'from flask import Flask, flash, request, redirect, url_for, render_template\n'), ((769, 799... |
from collections import ChainMap
from collections.abc import Mapping, Iterable
from itertools import groupby
from operator import itemgetter
import numpy as np
from probability import RowKey
from probability import TableColumns
# from probability.core_1 import RowKey
# from probability.core_1 import TableColumns
def... | [
"probability.RowKey",
"numpy.max",
"operator.itemgetter",
"numpy.all",
"probability.TableColumns"
] | [((14467, 14529), 'numpy.all', 'np.all', (['(arr_counter[:, columns_info.indices] == values)'], {'axis': '(1)'}), '(arr_counter[:, columns_info.indices] == values, axis=1)\n', (14473, 14529), True, 'import numpy as np\n'), ((16044, 16075), 'numpy.max', 'np.max', (['arr_len[:, :-1]'], {'axis': '(0)'}), '(arr_len[:, :-1]... |
# Copyright (C) 2020-2021 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
from copy import deepcopy
from functools import partial
import numpy as np
import scipy
from addict import Dict
from ....algorithms.quantization import utils as eu
from ....engines.ac_engine import ACEngine
from ....graph.model_utils i... | [
"addict.Dict",
"numpy.mean",
"numpy.flip",
"numpy.asarray",
"numpy.argsort",
"numpy.nanmean",
"functools.partial",
"copy.deepcopy",
"scipy.special.softmax"
] | [((5558, 5564), 'addict.Dict', 'Dict', ([], {}), '()\n', (5562, 5564), False, 'from addict import Dict\n'), ((7929, 7965), 'numpy.argsort', 'np.argsort', (['distance_between_samples'], {}), '(distance_between_samples)\n', (7939, 7965), True, 'import numpy as np\n'), ((8299, 8312), 'numpy.asarray', 'np.asarray', (['u'],... |
from collections import defaultdict
import time
from joblib import Parallel, delayed
from multiprocessing import cpu_count
from math import ceil
import torch
from torch import nn
import torch.multiprocessing as mp
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.u... | [
"torch.nn.CrossEntropyLoss",
"multiprocessing.cpu_count",
"numpy.array",
"torch.utils.data.distributed.DistributedSampler",
"torch.sum",
"sys.exit",
"joblib.delayed",
"torch.arange",
"os.path.exists",
"numpy.mean",
"os.listdir",
"nltk.corpus.stopwords.words",
"numpy.delete",
"numpy.max",
... | [((970, 1003), 'warnings.filterwarnings', 'warnings.filterwarnings', (['"""ignore"""'], {}), "('ignore')\n", (993, 1003), False, 'import warnings\n'), ((1868, 1937), 'transformers.BertTokenizer.from_pretrained', 'BertTokenizer.from_pretrained', (['self.pretrained_lm'], {'do_lower_case': '(True)'}), '(self.pretrained_lm... |
#!/usr/bin/env python
import os
os.environ["TF_CPP_MIN_LOG_LEVEL"] = '2'
import sys
import csv
import numpy as np
import pandas as pd
import random
from time import time, strftime, gmtime, sleep
from optparse import OptionParser
from pylsl import StreamInlet, resolve_byprop
from sklearn.linear_model import LinearRegre... | [
"subprocess.check_output",
"numpy.abs",
"os.listdir",
"random.choice",
"pylsl.StreamInlet",
"subprocess.Popen",
"os.path.join",
"optparse.OptionParser",
"pylsl.resolve_byprop",
"time.sleep",
"os.path.realpath",
"numpy.array",
"time.gmtime",
"numpy.concatenate",
"sys.exit",
"pandas.Data... | [((531, 545), 'optparse.OptionParser', 'OptionParser', ([], {}), '()\n', (543, 545), False, 'from optparse import OptionParser\n'), ((2457, 2497), 'pylsl.resolve_byprop', 'resolve_byprop', (['"""type"""', '"""EEG"""'], {'timeout': '(2)'}), "('type', 'EEG', timeout=2)\n", (2471, 2497), False, 'from pylsl import StreamIn... |
# Hungarian algorithm (Kuhn-Munkres) for solving the linear sum assignment
# problem. Taken from scikit-learn. Based on original code by <NAME>,
# adapted to NumPy by <NAME>.
# Further improvements by <NAME>, <NAME> and <NAME>.
#
# Copyright (c) 2008 <NAME> <<EMAIL>>, <NAME>
# Author: <NAME>, <NAME>
# License: 3... | [
"numpy.ones",
"numpy.where",
"numpy.asarray",
"numpy.argmax",
"numpy.any",
"numpy.zeros",
"numpy.min"
] | [((2927, 2950), 'numpy.asarray', 'np.asarray', (['cost_matrix'], {}), '(cost_matrix)\n', (2937, 2950), True, 'import numpy as np\n'), ((3713, 3734), 'numpy.where', 'np.where', (['(marked == 1)'], {}), '(marked == 1)\n', (3721, 3734), True, 'import numpy as np\n'), ((6247, 6289), 'numpy.asarray', 'np.asarray', (['state.... |
from abc import ABC, abstractmethod
import numpy as np
import pandas as pd
from pvrpm.core.enums import ConfigKeys as ck
from pvrpm.core.case import SamCase
from pvrpm.core.utils import sample, get_higher_components
from pvrpm.core.modules.monitor import IndepMonitor
class Failure(ABC):
"""
This abstract cl... | [
"numpy.random.random_sample",
"numpy.amin",
"pvrpm.core.utils.sample",
"numpy.array",
"numpy.zeros",
"numpy.finfo",
"numpy.argmin"
] | [((4175, 4216), 'numpy.argmin', 'np.argmin', (['possible_failure_times'], {'axis': '(1)'}), '(possible_failure_times, axis=1)\n', (4184, 4216), True, 'import numpy as np\n'), ((4249, 4288), 'numpy.amin', 'np.amin', (['possible_failure_times'], {'axis': '(1)'}), '(possible_failure_times, axis=1)\n', (4256, 4288), True, ... |
'''
load lottery tickets and evaluation
support datasets: cifar10, Fashionmnist, cifar100
'''
import os
import time
import random
import shutil
import argparse
import numpy as np
from copy import deepcopy
import matplotlib.pyplot as plt
import torch
import torch.optim
import torch.nn as nn
import torch.utils.data... | [
"torch.cuda.manual_seed_all",
"torch.manual_seed",
"torch.optim.lr_scheduler.MultiStepLR",
"os.makedirs",
"torch.nn.CrossEntropyLoss",
"argparse.ArgumentParser",
"torch.load",
"matplotlib.pyplot.plot",
"os.path.join",
"random.seed",
"matplotlib.pyplot.close",
"numpy.array",
"numpy.random.see... | [((719, 784), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': '"""PyTorch Evaluation Tickets"""'}), "(description='PyTorch Evaluation Tickets')\n", (742, 784), False, 'import argparse\n'), ((3787, 3828), 'os.makedirs', 'os.makedirs', (['args.save_dir'], {'exist_ok': '(True)'}), '(args.save_di... |
import time
from absl import app, flags, logging
from absl.flags import FLAGS
import cv2
import numpy as np
import tensorflow as tf
from yolov3_tf2.models import (
YoloV3, YoloV3Tiny
)
from yolov3_tf2.dataset import transform_images, load_tfrecord_dataset
from yolov3_tf2.utils import draw_outputs
flags.DEFINE_stri... | [
"cv2.imwrite",
"yolov3_tf2.dataset.transform_images",
"tensorflow.config.experimental.set_memory_growth",
"absl.flags.DEFINE_integer",
"absl.logging.info",
"absl.flags.DEFINE_boolean",
"absl.app.run",
"numpy.array",
"yolov3_tf2.dataset.load_tfrecord_dataset",
"yolov3_tf2.utils.draw_outputs",
"ti... | [((303, 381), 'absl.flags.DEFINE_string', 'flags.DEFINE_string', (['"""classes"""', '"""./data/vocmine.names"""', '"""path to classes file"""'], {}), "('classes', './data/vocmine.names', 'path to classes file')\n", (322, 381), False, 'from absl import app, flags, logging\n'), ((382, 475), 'absl.flags.DEFINE_string', 'f... |
import matplotlib.pyplot as plt
from time import time
import numpy as np
from .plotter_utils import figure_ratio, xarray_set_axes_labels, retrieve_or_create_fig_ax
# Change the bands (RGB) here if you want other false color combinations
def rgb(dataset, at_index=0, x_coord='longitude', y_coord='latitude',
ban... | [
"numpy.stack",
"numpy.array",
"numpy.nanmax",
"numpy.interp",
"numpy.nanmin"
] | [((2732, 2808), 'numpy.stack', 'np.stack', (['[dataset[bands[0]], dataset[bands[1]], dataset[bands[2]]]'], {'axis': '(-1)'}), '([dataset[bands[0]], dataset[bands[1]], dataset[bands[2]]], axis=-1)\n', (2740, 2808), True, 'import numpy as np\n'), ((3061, 3119), 'numpy.interp', 'np.interp', (['rgb', '(min_rgb, max_rgb)', ... |
import logging
import os
import numpy as np
import xml.etree.ElementTree as ET
from PIL import Image
from paths import DATASETS_ROOT
log = logging.getLogger()
VOC_CATS = ['__background__', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle',
'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'hors... | [
"logging.getLogger",
"PIL.Image.open",
"xml.etree.ElementTree.parse",
"os.path.join",
"numpy.array",
"numpy.zeros"
] | [((143, 162), 'logging.getLogger', 'logging.getLogger', ([], {}), '()\n', (160, 162), False, 'import logging\n'), ((634, 690), 'os.path.join', 'os.path.join', (['DATASETS_ROOT', "('VOCdevkit/VOC20%s/' % year)"], {}), "(DATASETS_ROOT, 'VOCdevkit/VOC20%s/' % year)\n", (646, 690), False, 'import os\n'), ((1978, 2030), 'xm... |
import numpy as np
from skmultiflow.drift_detection import ADWIN
def demo():
""" _test_adwin
In this demo, an ADWIN object evaluates a sequence of numbers corresponding to 2 distributions.
The ADWIN object indicates the indices where change is detected.
The first half of the data is a sequence ... | [
"skmultiflow.drift_detection.ADWIN",
"numpy.random.randint",
"numpy.random.seed"
] | [((463, 470), 'skmultiflow.drift_detection.ADWIN', 'ADWIN', ([], {}), '()\n', (468, 470), False, 'from skmultiflow.drift_detection import ADWIN\n'), ((514, 531), 'numpy.random.seed', 'np.random.seed', (['(1)'], {}), '(1)\n', (528, 531), True, 'import numpy as np\n'), ((550, 581), 'numpy.random.randint', 'np.random.rand... |
# ==========================================================================
#
# Copyright NumFOCUS
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/... | [
"itk.PyBuffer.keys",
"re.compile",
"itk.down_cast",
"itk.output",
"numpy.array",
"numpy.moveaxis",
"numpy.arange",
"itk.array_from_matrix",
"numpy.flip",
"itk.origin",
"numpy.asarray",
"itk.ImageIOFactory.CreateImageIO",
"itk.MeshIOFactory.CreateMeshIO",
"numpy.issubdtype",
"numpy.linspa... | [((3438, 3529), 'warnings.warn', 'warnings.warn', (['"""WrapITK warning: itk.image() is deprecated. Use itk.output() instead."""'], {}), "(\n 'WrapITK warning: itk.image() is deprecated. Use itk.output() instead.')\n", (3451, 3529), False, 'import warnings\n'), ((3940, 3967), 'itk.MultiThreaderBase.New', 'itk.MultiT... |
import pandas as pd
from scipy.stats import t
import numpy as np
import requests
def make_dataframe(r):
rows = []
for item in r['data']:
rows.append([item['lat'], item['lon'], item['aqi'], item['station']['name']])
df = pd.DataFrame(rows, columns=['lat', 'lon', 'aqi', 'name'])
df['aqi'] = pd.t... | [
"numpy.abs",
"requests.get",
"pandas.to_numeric",
"numpy.isnan",
"pandas.DataFrame"
] | [((242, 299), 'pandas.DataFrame', 'pd.DataFrame', (['rows'], {'columns': "['lat', 'lon', 'aqi', 'name']"}), "(rows, columns=['lat', 'lon', 'aqi', 'name'])\n", (254, 299), True, 'import pandas as pd\n'), ((316, 354), 'pandas.to_numeric', 'pd.to_numeric', (['df.aqi'], {'errors': '"""coerce"""'}), "(df.aqi, errors='coerce... |
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | [
"tensorflow.shape",
"tensorflow.split",
"tensorflow.multiply",
"numpy.array",
"object_detection.tensorflow_detect.core.preprocessor.resize_image",
"object_detection.tensorflow_detect.core.preprocessor.get_default_func_arg_map",
"object_detection.tensorflow_detect.core.preprocessor.scale_boxes_to_pixel_c... | [((125987, 126001), 'tensorflow.test.main', 'tf.test.main', ([], {}), '()\n', (125999, 126001), True, 'import tensorflow as tf\n'), ((1360, 1391), 'tensorflow.concat', 'tf.concat', (['[ch255, ch0, ch0]', '(3)'], {}), '([ch255, ch0, ch0], 3)\n', (1369, 1391), True, 'import tensorflow as tf\n'), ((1402, 1435), 'tensorflo... |
# -*- coding: utf-8 -*-
import logging
import utool as ut
import numpy as np
import vtool as vt
import pandas as pd
from wbia.algo.graph.nx_utils import ensure_multi_index
from wbia.algo.graph.state import POSTV, NEGTV, INCMP
print, rrr, profile = ut.inject2(__name__)
logger = logging.getLogger('wbia')
class Groundt... | [
"logging.getLogger",
"utool.inject2",
"utool.itake_column",
"wbia.algo.graph.nx_utils.ensure_multi_index",
"numpy.asarray",
"numpy.equal",
"numpy.array",
"utool.take_column",
"vtool.ensure_shape",
"utool.nx_node_dict",
"pandas.MultiIndex.from_tuples",
"utool.aslist",
"pandas.Series.from_arra... | [((249, 269), 'utool.inject2', 'ut.inject2', (['__name__'], {}), '(__name__)\n', (259, 269), True, 'import utool as ut\n'), ((279, 304), 'logging.getLogger', 'logging.getLogger', (['"""wbia"""'], {}), "('wbia')\n", (296, 304), False, 'import logging\n'), ((776, 793), 'numpy.array', 'np.array', (['is_comp'], {}), '(is_c... |
from collections import OrderedDict
import numpy as np
from pypospack.qoi import Qoi
class ThermalExpansion(Qoi):
"""
Args:
temperature_min (float,int): beginning of the temperature range in Kelvin
temperature_max (float,int): end of the temperature range in Kelvin
temperature_step (f... | [
"numpy.array",
"collections.OrderedDict",
"pypospack.qoi.Qoi.__init__",
"numpy.linalg.lstsq"
] | [((819, 832), 'collections.OrderedDict', 'OrderedDict', ([], {}), '()\n', (830, 832), False, 'from collections import OrderedDict\n'), ((893, 980), 'pypospack.qoi.Qoi.__init__', 'Qoi.__init__', (['self'], {'qoi_name': '_qoi_name', 'qoi_type': '_qoi_type', 'structures': '_structures'}), '(self, qoi_name=_qoi_name, qoi_t... |
import sys
import math
import random
from collections import namedtuple
import time
from pyrf.util import (compute_usable_bins, adjust_usable_fstart_fstop,
trim_to_usable_fstart_fstop, find_saturation)
import numpy as np
from twisted.internet import defer
from pyrf.numpy_util import compute_fft
import struct
MAXI... | [
"numpy.log10",
"numpy.arange",
"pyrf.util.compute_usable_bins",
"numpy.where",
"numpy.flipud",
"pyrf.util.trim_to_usable_fstart_fstop",
"pyrf.numpy_util.compute_fft",
"numpy.linspace",
"struct.unpack",
"numpy.zeros",
"numpy.interp",
"numpy.frombuffer",
"numpy.dtype",
"twisted.internet.defe... | [((2239, 2255), 'twisted.internet.defer.Deferred', 'defer.Deferred', ([], {}), '()\n', (2253, 2255), False, 'from twisted.internet import defer\n'), ((2682, 2700), 'numpy.dtype', 'np.dtype', (['np.int32'], {}), '(np.int32)\n', (2690, 2700), True, 'import numpy as np\n'), ((3283, 3320), 'numpy.arange', 'np.arange', (['(... |
# Import libraries and set random seeds for reproducibility
random_seed = 1237
import random
random.seed( random_seed )
import numpy as np
np.random.seed( random_seed )
import tensorflow as tf
tf.set_random_seed( random_seed )
# Import model and instance loader
import model
from instance_loader import InstanceLoader
im... | [
"instance_loader.InstanceLoader",
"util.sparse_to_dense",
"random.Random",
"model.separate_batch",
"tensorflow.Session",
"random.seed",
"util.save_weights",
"tensorflow.global_variables_initializer",
"model.model_builder",
"numpy.random.seed",
"scipy.stats.pearsonr",
"tensorflow.set_random_see... | [((93, 117), 'random.seed', 'random.seed', (['random_seed'], {}), '(random_seed)\n', (104, 117), False, 'import random\n'), ((139, 166), 'numpy.random.seed', 'np.random.seed', (['random_seed'], {}), '(random_seed)\n', (153, 166), True, 'import numpy as np\n'), ((193, 224), 'tensorflow.set_random_seed', 'tf.set_random_s... |
#!/usr/bin/env python
import rospy
import math
import numpy as np
from sensor_msgs.msg import LaserScan
#######################################
# Laser Scan:
# Header: Seq, Stamp, frame_id
# Angle_min, Angle_max, Angle_Increment, Time_Increment
# Scan time, range_min, range_max, ranges, intensities
###############... | [
"sensor_msgs.msg.LaserScan",
"rospy.Subscriber",
"rospy.init_node",
"rospy.get_param",
"numpy.array",
"rospy.spin",
"rospy.Publisher"
] | [((1698, 1739), 'rospy.init_node', 'rospy.init_node', (['"""noiser"""'], {'anonymous': '(True)'}), "('noiser', anonymous=True)\n", (1713, 1739), False, 'import rospy\n'), ((441, 503), 'rospy.Subscriber', 'rospy.Subscriber', (['"""/base_scan"""', 'LaserScan', 'self.laser_callback'], {}), "('/base_scan', LaserScan, self.... |
#!/usr/bin/env python3
import sys
import numpy as np
from example import AmiciExample
class ExampleCalvetti(AmiciExample):
def __init__(self):
AmiciExample.__init__( self )
self.numX = 6
self.numP = 0
self.numK = 6
self.modelOptions['theta'] = []
self.modelOption... | [
"numpy.linspace",
"example.AmiciExample.__init__",
"sys.exit"
] | [((158, 185), 'example.AmiciExample.__init__', 'AmiciExample.__init__', (['self'], {}), '(self)\n', (179, 185), False, 'from example import AmiciExample\n'), ((404, 427), 'numpy.linspace', 'np.linspace', (['(0)', '(20)', '(201)'], {}), '(0, 20, 201)\n', (415, 427), True, 'import numpy as np\n'), ((922, 933), 'sys.exit'... |
from PIL import Image
from tflite_runtime.interpreter import Interpreter
from tflite_runtime.interpreter import load_delegate
from video import create_capture
import numpy as np
import cv2 as cv
import io
import picamera
import simpleaudio as sa
# tf model upload
def load_label... | [
"simpleaudio.WaveObject.from_wave_file",
"getopt.getopt",
"PIL.Image.open",
"numpy.argpartition",
"cv2.samples.findFile",
"io.BytesIO",
"picamera.PiCamera",
"cv2.equalizeHist",
"tflite_runtime.interpreter.load_delegate",
"cv2.destroyAllWindows",
"cv2.cvtColor"
] | [((1193, 1224), 'numpy.argpartition', 'np.argpartition', (['(-output)', 'top_k'], {}), '(-output, top_k)\n', (1208, 1224), True, 'import numpy as np\n'), ((5829, 5851), 'cv2.destroyAllWindows', 'cv.destroyAllWindows', ([], {}), '()\n', (5849, 5851), True, 'import cv2 as cv\n'), ((1887, 1952), 'getopt.getopt', 'getopt.g... |
"""
tellotracker:
Allows manual operation of the drone and demo tracking mode.
Requires mplayer to record/save video.
Controls:
- tab to lift off
- WASD to move the drone
- space/shift to ascend/escent slowly
- Q/E to yaw slowly
- arrow keys to ascend, descend, or yaw quickly
- backspace to land, or P to palm-land
- en... | [
"time.sleep",
"cv2.imshow",
"sys.exc_info",
"av.open",
"cv2.destroyAllWindows",
"math.exp",
"traceback.print_exception",
"tracker.Tracker",
"cv2.waitKey",
"cv2.getTickFrequency",
"numpy.float32",
"cv2.putText",
"cv2.cvtColor",
"threading.Thread",
"cv2.resize",
"time.time",
"numpy.vec... | [((2062, 2073), 'os.getcwd', 'os.getcwd', ([], {}), '()\n', (2071, 2073), False, 'import os\n'), ((2173, 2219), 'os.path.join', 'os.path.join', (['CWD_PATH', 'MODEL_NAME', 'GRAPH_NAME'], {}), '(CWD_PATH, MODEL_NAME, GRAPH_NAME)\n', (2185, 2219), False, 'import os\n'), ((2261, 2310), 'os.path.join', 'os.path.join', (['C... |
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... | [
"numpy.prod",
"hypothesis.strategies.integers",
"program_config.TensorConfig",
"hypothesis.strategies.booleans",
"unittest.main",
"program_config.OpConfig"
] | [((7681, 7696), 'unittest.main', 'unittest.main', ([], {}), '()\n', (7694, 7696), False, 'import unittest\n'), ((6363, 6516), 'program_config.OpConfig', 'OpConfig', (['"""mul"""'], {'inputs': "{'X': ['mul_x'], 'Y': ['mul_y']}", 'outputs': "{'Out': ['mul_out']}", 'x_num_col_dims': 'x_num_col_dims', 'y_num_col_dims': 'y_... |
import cv2
from tkinter import Tk
from tkinter.filedialog import askopenfilename
import numpy as np
import imutils
import threading
def main():
cap = cv2.VideoCapture(vid_path)
status1, previous_frame = cap.read()
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
copy_frame ... | [
"cv2.createBackgroundSubtractorMOG2",
"imutils.is_cv2",
"cv2.imshow",
"cv2.destroyAllWindows",
"cv2.threshold",
"cv2.waitKey",
"tkinter.filedialog.askopenfilename",
"cv2.putText",
"cv2.circle",
"cv2.cvtColor",
"cv2.GaussianBlur",
"cv2.bitwise_and",
"numpy.zeros",
"cv2.connectedComponentsWi... | [((3251, 3404), 'tkinter.filedialog.askopenfilename', 'askopenfilename', ([], {'filetypes': "(('Video File', '*.mp4'), ('Video File', '*.avi'), ('Video File', '*.flv'),\n ('All Files', '*.*'))", 'title': '"""Choose a video."""'}), "(filetypes=(('Video File', '*.mp4'), ('Video File', '*.avi'),\n ('Video File', '*.... |
import numpy as np
import scipy.stats as stats
import scipy.special as spec
import util
class HMCParams:
def __init__(self, tau, tau_g, L, eta, mass, r_clip, grad_clip):
self.tau = tau
self.tau_g = tau_g
self.L = L
self.eta = eta
self.mass = mass
self.r_clip = r_... | [
"numpy.clip",
"numpy.abs",
"numpy.sqrt",
"numpy.random.rand",
"numpy.log",
"scipy.stats.norm.rvs",
"numpy.exp",
"numpy.sum",
"numpy.zeros"
] | [((2760, 2786), 'numpy.zeros', 'np.zeros', (['(iters + 1, dim)'], {}), '((iters + 1, dim))\n', (2768, 2786), True, 'import numpy as np\n'), ((2833, 2859), 'numpy.zeros', 'np.zeros', (['(iters * L, dim)'], {}), '((iters * L, dim))\n', (2841, 2859), True, 'import numpy as np\n'), ((2876, 2891), 'numpy.zeros', 'np.zeros',... |
# -*- coding: utf-8 -*-
import numpy
from simmate.toolkit import Structure
from pymatgen.analysis.diffusion.neb.pathfinder import (
DistinctPathFinder,
MigrationHop as PymatgenMigrationHop,
IDPPSolver,
)
from typing import List
class MigrationImages(list):
"""
This class is just a list of stru... | [
"simmate.toolkit.Structure",
"numpy.isclose",
"pymatgen.analysis.diffusion.neb.pathfinder.MigrationHop",
"pymatgen.analysis.diffusion.neb.pathfinder.IDPPSolver.from_endpoints",
"pymatgen.analysis.diffusion.neb.pathfinder.DistinctPathFinder"
] | [((3195, 3280), 'simmate.toolkit.Structure', 'Structure', ([], {'lattice': 'structure.lattice', 'species': 'final_species', 'coords': 'final_coords'}), '(lattice=structure.lattice, species=final_species, coords=final_coords\n )\n', (3204, 3280), False, 'from simmate.toolkit import Structure\n'), ((7774, 7864), 'pyma... |
# Copyright 2020 LMNT, Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | [
"wavegrad.model.WaveGrad",
"torch.nn.L1Loss",
"numpy.array",
"wavegrad.dataset.from_path",
"os.path.islink",
"torch.isnan",
"torch.utils.tensorboard.SummaryWriter",
"torch.randint",
"os.unlink",
"numpy.concatenate",
"torch.nn.parallel.DistributedDataParallel",
"torch.randn_like",
"torch.cuda... | [((6383, 6424), 'wavegrad.dataset.from_path', 'dataset_from_path', (['args.data_dirs', 'params'], {}), '(args.data_dirs, params)\n', (6400, 6424), True, 'from wavegrad.dataset import from_path as dataset_from_path\n'), ((6662, 6754), 'torch.distributed.init_process_group', 'torch.distributed.init_process_group', (['"""... |
# Import necessary packages here
from typing import List
import warnings
from datetime import datetime
import pandas as pd
import numpy as np
import matplotlib.dates as mdates
from matplotlib import rc, pyplot as plt
# ============================================================================
# ======================... | [
"matplotlib.pyplot.grid",
"matplotlib.pyplot.hist",
"matplotlib.pyplot.ylabel",
"matplotlib.rc",
"matplotlib.dates.DayLocator",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.style.use",
"matplotlib.pyplot.close",
"warnings.warn",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.MaxNLocator",
"... | [((5218, 5232), 'matplotlib.pyplot.subplots', 'plt.subplots', ([], {}), '()\n', (5230, 5232), True, 'from matplotlib import rc, pyplot as plt\n'), ((5237, 5285), 'matplotlib.pyplot.rcParams.update', 'plt.rcParams.update', (["{'figure.autolayout': True}"], {}), "({'figure.autolayout': True})\n", (5256, 5285), True, 'fro... |
import tempfile
from concurrent.futures import ProcessPoolExecutor, as_completed
import numpy as np
import pytest
import zarr
from dask.distributed import LocalCluster
from swyft import Dataset, DirectoryStore, Prior, Simulator
from swyft.store.simulator import SimulationStatus
PARAMS = ["z1", "z2"]
PRIOR = Prior.fr... | [
"tempfile.TemporaryDirectory",
"dask.distributed.LocalCluster",
"numpy.random.random",
"swyft.Simulator",
"numpy.any",
"swyft.Dataset",
"concurrent.futures.as_completed",
"numpy.array",
"zarr.open",
"swyft.DirectoryStore",
"zarr.open_group",
"pytest.fixture",
"concurrent.futures.ProcessPoolE... | [((700, 732), 'pytest.fixture', 'pytest.fixture', ([], {'scope': '"""function"""'}), "(scope='function')\n", (714, 732), False, 'import pytest\n'), ((969, 999), 'pytest.fixture', 'pytest.fixture', ([], {'scope': '"""module"""'}), "(scope='module')\n", (983, 999), False, 'import pytest\n'), ((762, 801), 'swyft.Simulator... |
# @Author: <NAME>
# @Date: 2021-03-22 09:43:07
# @Last Modified by: <NAME>
# @Last Modified time: 2021-11-08 15:09:29
#!/usr/bin/env python
## based on: detectron2.modeling.roi_heads.box_head
## based on: detectron2.modeling.roi_heads.fast_rcnn
import torch
from torch import nn
import numpy as np
import logging
... | [
"numpy.prod",
"torch.nn.ReLU",
"torch.nn.Dropout",
"detectron2.modeling.roi_heads.fast_rcnn._log_classification_stats",
"torch.nn.init.constant_",
"detectron2.layers.cross_entropy",
"fvcore.nn.smooth_l1_loss",
"torch.nn.functional.softmax",
"torch.arange",
"detectron2.layers.ShapeSpec",
"torch.n... | [((1340, 1372), 'detectron2.modeling.roi_heads.box_head.ROI_BOX_HEAD_REGISTRY.register', 'ROI_BOX_HEAD_REGISTRY.register', ([], {}), '()\n', (1370, 1372), False, 'from detectron2.modeling.roi_heads.box_head import ROI_BOX_HEAD_REGISTRY\n'), ((18533, 18562), 'detectron2.modeling.roi_heads.ROI_HEADS_REGISTRY.register', '... |
#thomas feiring model
import math
import numpy as np
import pandas as pd
#enter the year for which you need prediction starting 2019
year=2019
number_of_days=365
day=0
df = pd.read_csv('groundtruth.csv')
u=df['Mean']
X_t= u[0]
sd=df['St dev']
print("Month,Year,Inflow")
#lag -1 correlation
lag=df['co relation']
np.rand... | [
"numpy.random.normal",
"math.sqrt",
"numpy.random.seed",
"pandas.read_csv"
] | [((174, 204), 'pandas.read_csv', 'pd.read_csv', (['"""groundtruth.csv"""'], {}), "('groundtruth.csv')\n", (185, 204), True, 'import pandas as pd\n'), ((313, 333), 'numpy.random.seed', 'np.random.seed', (['(9001)'], {}), '(9001)\n', (327, 333), True, 'import numpy as np\n'), ((373, 398), 'numpy.random.normal', 'np.rando... |
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