code stringlengths 31 1.05M | apis list | extract_api stringlengths 97 1.91M |
|---|---|---|
import numpy as np
import tensorflow as tf
from Autoencoders import add_gaussian_noise
mnist = tf.keras.datasets.mnist
def load_auto_encoder_mnist_data(noise_sigma=None, with_test=False):
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
if wit... | [
"Autoencoders.add_gaussian_noise",
"numpy.concatenate"
] | [((691, 732), 'numpy.concatenate', 'np.concatenate', (['(x_train, x_test)'], {'axis': '(0)'}), '((x_train, x_test), axis=0)\n', (705, 732), True, 'import numpy as np\n'), ((1132, 1173), 'numpy.concatenate', 'np.concatenate', (['(x_train, x_test)'], {'axis': '(0)'}), '((x_train, x_test), axis=0)\n', (1146, 1173), True, ... |
"""
Possible bug in fb prophet for multiplicative seasonalities.
"""
# %%
import numpy as np
import pandas as pd
import prophet
# %%
df = pd.DataFrame()
df['ds'] = pd.date_range(start='2019-01-01',end='2021-01-01',freq='4H')
df['daily_effect'] = np.cos(df['ds'].dt.hour/24 * 2*np.pi)*-0.5+0.5
df['yearly_effect'] = np.... | [
"pandas.DataFrame",
"pandas.date_range",
"numpy.log",
"prophet.Prophet",
"numpy.exp",
"numpy.cos"
] | [((140, 154), 'pandas.DataFrame', 'pd.DataFrame', ([], {}), '()\n', (152, 154), True, 'import pandas as pd\n'), ((166, 228), 'pandas.date_range', 'pd.date_range', ([], {'start': '"""2019-01-01"""', 'end': '"""2021-01-01"""', 'freq': '"""4H"""'}), "(start='2019-01-01', end='2021-01-01', freq='4H')\n", (179, 228), True, ... |
import numpy as np
from ravel.schema import fields
class Array(fields.List):
def process(self, obj):
processed_obj, error = super().process(obj)
if error:
return (None, error)
arr = np.array(processed_obj, dtype=self.nested.np_dtype)
return (arr, None) | [
"numpy.array"
] | [((225, 276), 'numpy.array', 'np.array', (['processed_obj'], {'dtype': 'self.nested.np_dtype'}), '(processed_obj, dtype=self.nested.np_dtype)\n', (233, 276), True, 'import numpy as np\n')] |
# Authors: <NAME> <<EMAIL>>
# <NAME> <<EMAIL>>
# Supported by <NAME> <<EMAIL>>
# License: BSD 3 clause
import numbers
from warnings import warn
import numpy as np
from sklearn.base import BaseEstimator, OutlierMixin
from sklearn.metrics import euclidean_distances
from sklearn.utils.validation import... | [
"numpy.nanargmin",
"numpy.fill_diagonal",
"numpy.ones_like",
"numpy.amin",
"numpy.empty",
"numpy.iinfo",
"numpy.ones",
"sklearn.utils.validation.check_is_fitted",
"numpy.argmin",
"numpy.finfo",
"sklearn.utils.validation.check_random_state",
"numpy.mean",
"sklearn.metrics.euclidean_distances"... | [((368, 386), 'numpy.iinfo', 'np.iinfo', (['np.int32'], {}), '(np.int32)\n', (376, 386), True, 'import numpy as np\n'), ((403, 418), 'numpy.finfo', 'np.finfo', (['float'], {}), '(float)\n', (411, 418), True, 'import numpy as np\n'), ((5547, 5607), 'numpy.empty', 'np.empty', (['[self.n_estimators, self.max_samples_, n_f... |
"""Processes a CRD file.
Note: Interfacing with external files is done in the `interfacer.py` library.
"""
from pathlib import Path
from typing import List, Tuple
import warnings
from iniabu import ini
import numpy as np
from . import processor_utils
from .data_io.crd_reader import CRDReader
from .utilities import ... | [
"numpy.zeros_like",
"numpy.sum",
"numpy.abs",
"numpy.logical_and",
"numpy.where",
"numpy.array",
"numpy.arange",
"warnings.warn",
"numpy.delete"
] | [((8603, 8632), 'numpy.sum', 'np.sum', (['self.data_pkg'], {'axis': '(1)'}), '(self.data_pkg, axis=1)\n', (8609, 8632), True, 'import numpy as np\n'), ((8745, 8811), 'numpy.delete', 'np.delete', (['self.data_pkg', 'self._filter_max_ion_per_pkg_ind'], {'axis': '(0)'}), '(self.data_pkg, self._filter_max_ion_per_pkg_ind, ... |
# AUTOGENERATED! DO NOT EDIT! File to edit: utils.ipynb (unless otherwise specified).
__all__ = ['test', 'test_eq', 'simplify_qd']
# Cell
import open3d as o3d
import operator
import numpy as np
# Cell
def test(a,b,cmp,cname=None):
if cname is None: cname=cmp.__name__
assert cmp(a,b),f"{cname}:\n{a}\n{b}"
de... | [
"open3d.utility.Vector3iVector",
"numpy.asarray",
"open3d.geometry.TriangleMesh",
"open3d.utility.Vector3dVector"
] | [((441, 468), 'open3d.geometry.TriangleMesh', 'o3d.geometry.TriangleMesh', ([], {}), '()\n', (466, 468), True, 'import open3d as o3d\n'), ((490, 523), 'open3d.utility.Vector3iVector', 'o3d.utility.Vector3iVector', (['faces'], {}), '(faces)\n', (516, 523), True, 'import open3d as o3d\n'), ((544, 577), 'open3d.utility.Ve... |
"""
A script to calculate the p-values and averages for Section 6.3 of the paper.
"""
import json
import pickle
import numpy as np
from scipy.stats import mannwhitneyu, ttest_1samp
from bld.project_paths import project_paths_join as ppj
def calc_p_value_by_super_game(
data_experiment,
super_star_data... | [
"json.dump",
"bld.project_paths.project_paths_join",
"scipy.stats.mannwhitneyu",
"scipy.stats.ttest_1samp",
"pickle.load",
"numpy.array"
] | [((5296, 5344), 'numpy.array', 'np.array', (["all_output_grids_2_agents['avg_price']"], {}), "(all_output_grids_2_agents['avg_price'])\n", (5304, 5344), True, 'import numpy as np\n'), ((5670, 5718), 'numpy.array', 'np.array', (["all_output_grids_3_agents['avg_price']"], {}), "(all_output_grids_3_agents['avg_price'])\n"... |
"""
Main experimentation pipeline for measuring robustness of explainers.
Unlike the other pipelines, we just want to compare the original LIME with its robustified version,
so we do not require a list of configs to run through.
We mainly run three experiments:
* Robustness of original LIME against Fooling LIME attac... | [
"sklearn.externals.joblib.dump",
"sklearn.preprocessing.StandardScaler",
"numpy.random.seed",
"argparse.ArgumentParser",
"sklearn.model_selection.train_test_split",
"logging.getLogger",
"experiments.utils.explainers.get_explainer",
"experiments.utils.datasets.get_dataset",
"numpy.mean",
"pandas.Da... | [((4675, 4703), 'experiments.utils.datasets.get_dataset', 'get_dataset', (['dataset', 'params'], {}), '(dataset, params)\n', (4686, 4703), False, 'from experiments.utils.datasets import get_dataset\n'), ((7319, 7335), 'sklearn.preprocessing.StandardScaler', 'StandardScaler', ([], {}), '()\n', (7333, 7335), False, 'from... |
#!/usr/bin/env python3
"""
Copyright (c) Facebook, Inc. and its affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
import gtn
import numpy as np
def gen_transitions(num_classes, calc_grad=False):
"""Make a bigram transition gra... | [
"gtn.viterbi_path",
"gtn.backward",
"gtn.forward_score",
"gtn.subtract",
"numpy.random.randint",
"gtn.intersect",
"gtn.Graph",
"gtn.project_output",
"numpy.mean",
"gtn.compose"
] | [((335, 355), 'gtn.Graph', 'gtn.Graph', (['calc_grad'], {}), '(calc_grad)\n', (344, 355), False, 'import gtn\n'), ((738, 758), 'gtn.Graph', 'gtn.Graph', (['calc_grad'], {}), '(calc_grad)\n', (747, 758), False, 'import gtn\n'), ((1514, 1534), 'gtn.Graph', 'gtn.Graph', (['calc_grad'], {}), '(calc_grad)\n', (1523, 1534), ... |
import numpy as np
from scipy.stats import norm
def ci_test_fisher_z(data_matrix, x, y, s, **kwargs):
assert 'corr_matrix' in kwargs
cm = kwargs['corr_matrix']
n = data_matrix.shape[0]
z = zstat(x, y, list(s), cm, n)
p_val = 2.0 * norm.sf(np.absolute(z))
return p_val
def zstat(x, y, s, cm, n):... | [
"numpy.absolute",
"numpy.isnan",
"numpy.linalg.pinv",
"numpy.log1p",
"numpy.sqrt"
] | [((413, 425), 'numpy.isnan', 'np.isnan', (['zv'], {}), '(zv)\n', (421, 425), True, 'import numpy as np\n'), ((538, 563), 'numpy.log1p', 'np.log1p', (['(2 * r / (1 - r))'], {}), '(2 * r / (1 - r))\n', (546, 563), True, 'import numpy as np\n'), ((662, 710), 'numpy.linalg.pinv', 'np.linalg.pinv', (['cm[[x, y] + s, :][:, [... |
from math import sqrt
from random import choice
from re import escape
from socket import socket, AF_INET, SOCK_STREAM
from time import sleep
import cv2 as cv
import numpy as np
# ------------------------------- CONSTANTS -----------------------------------
IP = "172.16.17.32"
PORT = 3456
BUFFER_SIZE = 2**16
SOCKET = ... | [
"math.sqrt",
"cv2.waitKey",
"numpy.std",
"socket.socket",
"cv2.imread",
"numpy.fliplr",
"numpy.mean",
"cv2.destroyAllWindows",
"numpy.delete",
"cv2.resize",
"cv2.matchTemplate"
] | [((1804, 1817), 'cv2.waitKey', 'cv.waitKey', (['(0)'], {}), '(0)\n', (1814, 1817), True, 'import cv2 as cv\n'), ((1819, 1841), 'cv2.destroyAllWindows', 'cv.destroyAllWindows', ([], {}), '()\n', (1839, 1841), True, 'import cv2 as cv\n'), ((1984, 2013), 'numpy.delete', 'np.delete', (['img', 'white_cols', '(1)'], {}), '(i... |
# https://www.hackerrank.com/challenges/np-linear-algebra/problem
import numpy
# Inputs
standard_input = """2
1.1 1.1
1.1 1.1"""
n = int(input())
# 2
a = numpy.array([input().split() for _ in range(n)], float)
# 1.1 1.1
# 1.1 1.1
numpy.set_printoptions(legacy="1.13")
print(numpy.linalg.det(a))
# 0.0
""" Refer... | [
"numpy.linalg.det",
"numpy.set_printoptions"
] | [((238, 275), 'numpy.set_printoptions', 'numpy.set_printoptions', ([], {'legacy': '"""1.13"""'}), "(legacy='1.13')\n", (260, 275), False, 'import numpy\n'), ((282, 301), 'numpy.linalg.det', 'numpy.linalg.det', (['a'], {}), '(a)\n', (298, 301), False, 'import numpy\n')] |
#!/usr/bin/env python
import click
import matplotlib
import matplotlib.pyplot as plt
#matplotlib.style.use('ggplot')
import numpy as np
import pandas as pd
from projections.pd_utils import load_pandas
import projections.modelr as modelr
LU2 = {'annual': 'c3ann + c4ann',
'nitrogen': 'c3nfx',
'pasture': '... | [
"numpy.full",
"projections.modelr.load",
"matplotlib.pyplot.show",
"click.option",
"click.command",
"numpy.linspace",
"click.Path",
"matplotlib.pyplot.savefig"
] | [((731, 746), 'click.command', 'click.command', ([], {}), '()\n', (744, 746), False, 'import click\n'), ((811, 865), 'click.option', 'click.option', (['"""-m"""', '"""--max-x"""'], {'type': 'float', 'default': '(1.2)'}), "('-m', '--max-x', type=float, default=1.2)\n", (823, 865), False, 'import click\n'), ((867, 918), ... |
"""
@author: <NAME>
@description : Rest pose of a skeleton
File Format:
numpy file (npy)
Content:
1 numpy array
- float_array(num_bones, size(BONE_ATTRIBUTES))
"""
import deep_deformation.utils.common as common
import os
import numpy as np
class RestPoseData:
def __init__(self):
self.bone_data = No... | [
"numpy.load",
"numpy.save",
"numpy.zeros",
"os.path.exists",
"deep_deformation.utils.common.get_rest_pose_path"
] | [((451, 489), 'numpy.zeros', 'np.zeros', (['bone_data_shape'], {'dtype': 'float'}), '(bone_data_shape, dtype=float)\n', (459, 489), True, 'import numpy as np\n'), ((550, 577), 'deep_deformation.utils.common.get_rest_pose_path', 'common.get_rest_pose_path', ([], {}), '()\n', (575, 577), True, 'import deep_deformation.ut... |
# -*- coding: utf-8 -*-
"""
Environment
Functions:
initialize(unit, fovea_center, fovea_size, objects) - initialize unit size
environment containing objects and fovea with size fovea_size and center in
fovea_center.
redraw(environment, unit, objects) - redraw the unit size environment
containing objects.
"... | [
"geometricshapes.Square",
"geometricshapes.Circle",
"numpy.zeros",
"geometricshapes.Fovea",
"numpy.array",
"geometricshapes.Rectangle"
] | [((972, 997), 'numpy.zeros', 'np.zeros', (['[unit, unit, 3]'], {}), '([unit, unit, 3])\n', (980, 997), True, 'import numpy as np\n'), ((1010, 1058), 'geometricshapes.Fovea', 'Fovea', (['fovea_center', 'fovea_size', '[0, 0, 0]', 'unit'], {}), '(fovea_center, fovea_size, [0, 0, 0], unit)\n', (1015, 1058), False, 'from ge... |
""" Implementation of filters for images and texts"""
import numpy as np
from jina import Executor, DocumentArray, requests
class ImageReader(Executor):
@requests(on='/index')
def index_read(self, docs: 'DocumentArray', **kwargs):
array = DocumentArray(list(filter(lambda doc: doc.modality=='image', d... | [
"jina.DocumentArray",
"numpy.array",
"jina.requests"
] | [((161, 182), 'jina.requests', 'requests', ([], {'on': '"""/index"""'}), "(on='/index')\n", (169, 182), False, 'from jina import Executor, DocumentArray, requests\n'), ((486, 508), 'jina.requests', 'requests', ([], {'on': '"""/search"""'}), "(on='/search')\n", (494, 508), False, 'from jina import Executor, DocumentArra... |
#!/usr/bin/env python
import nibabel as nib
import numpy as np
import glob
import nipype
from nipype.interfaces import niftyreg
import os
import tempfile
from tqdm import tqdm
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import argparse
def get_args():
parser = argparse.ArgumentParser(... | [
"os.mkdir",
"numpy.load",
"argparse.ArgumentParser",
"numpy.ones",
"numpy.clip",
"numpy.mean",
"os.path.join",
"tempfile.TemporaryDirectory",
"numpy.std",
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.close",
"os.path.exists",
"nipype.interfaces.niftyreg.RegAladin",
"numpy.save",
"os.pa... | [((195, 216), 'matplotlib.use', 'matplotlib.use', (['"""Agg"""'], {}), "('Agg')\n", (209, 216), False, 'import matplotlib\n'), ((296, 409), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': '"""performs 12-DOF affine registration of a T1 image to ICBM152 template"""'}), "(description=\n 'per... |
import copy
from fv3fit._shared.config import SliceConfig
from fv3fit._shared.packer import (
pack_tfdataset,
clip_sample,
)
from fv3fit.tfdataset import tfdataset_from_batches
import tensorflow as tf
from typing import Mapping, Sequence
import numpy as np
import pytest
import xarray as xr
import fv3fit.tfdatas... | [
"fv3fit._shared.packer.pack_tfdataset",
"fv3fit.tfdataset.tfdataset_from_batches",
"copy.deepcopy",
"numpy.random.uniform",
"tensorflow.abs",
"numpy.testing.assert_array_equal",
"numpy.asarray",
"fv3fit._shared.config.SliceConfig",
"pytest.param",
"pytest.raises",
"fv3fit._shared.packer.clip_sam... | [((4379, 4423), 'pytest.mark.parametrize', 'pytest.mark.parametrize', (['"""n_dims"""', '[2, 3, 5]'], {}), "('n_dims', [2, 3, 5])\n", (4402, 4423), False, 'import pytest\n'), ((5488, 5532), 'pytest.mark.parametrize', 'pytest.mark.parametrize', (['"""n_dims"""', '[2, 3, 5]'], {}), "('n_dims', [2, 3, 5])\n", (5511, 5532)... |
from math import ceil, floor, log2
import numpy as np
from CQT_Toolbox.winfuns import winfuns
def nsgcqwin(*args):
fmin, fmax, bins, sr, Ls = args[:5]
bwfac = 1
min_win = 4
fractional = 0
winfun = "hann"
gamma = 0
nargin = len(args)
if nargin < 5:
raise ValueError("Not enou... | [
"numpy.ceil",
"math.ceil",
"numpy.floor",
"numpy.zeros",
"numpy.empty_like",
"math.floor",
"numpy.insert",
"numpy.diff",
"numpy.where",
"numpy.arange",
"CQT_Toolbox.winfuns.winfuns",
"math.log2",
"numpy.round",
"numpy.all",
"numpy.sqrt"
] | [((1861, 1882), 'numpy.insert', 'np.insert', (['fbas', '(0)', '(0)'], {}), '(fbas, 0, 0)\n', (1870, 1882), True, 'import numpy as np\n'), ((2017, 2064), 'numpy.insert', 'np.insert', (['bw', '(0)', '(fbas[Lfbas + 2] - fbas[Lfbas])'], {}), '(bw, 0, fbas[Lfbas + 2] - fbas[Lfbas])\n', (2026, 2064), True, 'import numpy as n... |
import numpy as np
import os, sys
import pickle, functools, operator
import keras
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from sklearn.externals import joblib
from keras.utils import to_categorical
from keras.models import Model, load_model
from keras.layers... | [
"sklearn.externals.joblib.dump",
"argparse.ArgumentParser",
"keras.preprocessing.sequence.pad_sequences",
"keras.models.Model",
"argument.add_arguments",
"keras.layers.Input",
"os.path.join",
"keras.optimizers.adam",
"os.path.exists",
"keras.preprocessing.text.Tokenizer",
"keras.utils.to_categor... | [((494, 552), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': '"""Video to Text Model"""'}), "(description='Video to Text Model')\n", (517, 552), False, 'import argparse, json\n'), ((1285, 1306), 'argument.add_arguments', 'add_arguments', (['parser'], {}), '(parser)\n', (1298, 1306), False, '... |
"""Various one off plots.
Usage:
./plots.py
Author:
<NAME> - 2021-08-30
"""
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from typing import List, Optional
from scipy.stats import norm
from warzone.base import normalize, running_mean, cumulative_mean
from warzone.document_filter import Docu... | [
"matplotlib.pyplot.title",
"pandas.DataFrame",
"matplotlib.pyplot.show",
"pandas.DataFrame.from_dict",
"matplotlib.pyplot.get_cmap",
"matplotlib.pyplot.figure",
"numpy.mean",
"numpy.array",
"matplotlib.pyplot.subplots"
] | [((758, 806), 'matplotlib.pyplot.subplots', 'plt.subplots', ([], {'nrows': '(3)', 'ncols': '(2)', 'figsize': '(30, 30)'}), '(nrows=3, ncols=2, figsize=(30, 30))\n', (770, 806), True, 'import matplotlib.pyplot as plt\n'), ((811, 886), 'matplotlib.pyplot.title', 'plt.title', (["('Personal Data for: ' + doc_filter.usernam... |
# coding: utf-8
import tensorflow as tf
import numpy as np
class TCNNConfig(object):
"""CNN配置参数"""
embedding_dim = 64 # 词向量维度
seq_length = 30 # 序列长度
num_classes = 8 # 类别数
num_filters = 128 # 卷积核数目
vocab_size = 5000 # 词汇表达小
l2_reg_lambda = 0.0
filter_sizes = [2, 3, 4, 5]
kern... | [
"tensorflow.contrib.layers.xavier_initializer",
"tensorflow.reshape",
"tensorflow.train.AdamOptimizer",
"numpy.shape",
"tensorflow.multiply",
"tensorflow.sigmoid",
"tensorflow.Variable",
"tensorflow.nn.conv2d",
"tensorflow.reduce_max",
"tensorflow.sqrt",
"tensorflow.layers.batch_normalization",
... | [((817, 889), 'tensorflow.placeholder', 'tf.placeholder', (['tf.int32', '[None, self.config.seq_length]'], {'name': '"""input_x"""'}), "(tf.int32, [None, self.config.seq_length], name='input_x')\n", (831, 889), True, 'import tensorflow as tf\n'), ((913, 988), 'tensorflow.placeholder', 'tf.placeholder', (['tf.float32', ... |
import torch
import torch.nn as nn
import numpy as np
import math
from torch.nn import functional as F
from maskrcnn_benchmark.modeling.box_coder import BoxCoder
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.structures.boxlist_ops import boxlist_nms
from maskrcnn_benchmark.struc... | [
"torch.nn.functional.binary_cross_entropy",
"maskrcnn_benchmark.structures.bounding_box.BoxList",
"torch.bmm",
"numpy.argmax",
"maskrcnn_benchmark.structures.boxlist_ops.boxlist_iou",
"torch.cat",
"torch.full",
"torch.cos",
"numpy.arange",
"torch.arange",
"numpy.tile",
"torch.no_grad",
"torc... | [((30157, 30175), 'torch.sin', 'torch.sin', (['mul_mat'], {}), '(mul_mat)\n', (30166, 30175), False, 'import torch\n'), ((30190, 30208), 'torch.cos', 'torch.cos', (['mul_mat'], {}), '(mul_mat)\n', (30199, 30208), False, 'import torch\n'), ((30225, 30258), 'torch.cat', 'torch.cat', (['(sin_mat, cos_mat)', '(-1)'], {}), ... |
import json
import os
import numpy as np
from absl import flags, app
from sklearn.metrics import (
confusion_matrix,
f1_score,
precision_recall_fscore_support,
accuracy_score,
)
import seaborn as sns
import matplotlib.pyplot as plt
from dataloader.audio import EmotionDataset
# This scoring assumes only... | [
"seaborn.heatmap",
"matplotlib.pyplot.clf",
"numpy.argmax",
"sklearn.metrics.accuracy_score",
"json.dumps",
"sklearn.metrics.f1_score",
"absl.flags.DEFINE_boolean",
"absl.flags.DEFINE_list",
"sklearn.metrics.precision_recall_fscore_support",
"numpy.savetxt",
"absl.flags.mark_flag_as_required",
... | [((359, 421), 'absl.flags.DEFINE_string', 'flags.DEFINE_string', (['"""ref"""', 'None', '"""Path to reference emotions"""'], {}), "('ref', None, 'Path to reference emotions')\n", (378, 421), False, 'from absl import flags, app\n'), ((422, 485), 'absl.flags.DEFINE_string', 'flags.DEFINE_string', (['"""pred"""', 'None', ... |
import glob
import pandas as pd
from datetime import datetime
import xml.etree.ElementTree as ET
import numpy as np
# Define a a function to extract .csv files
def extract_from_csv(file_to_process):
dataframe = pd.read_csv(file_to_process, index_col=0)
return dataframe
# Define a a function to extract .json f... | [
"pandas.DataFrame",
"xml.etree.ElementTree.parse",
"pandas.read_csv",
"pandas.read_json",
"glob.glob",
"numpy.float64",
"datetime.datetime.now"
] | [((216, 257), 'pandas.read_csv', 'pd.read_csv', (['file_to_process'], {'index_col': '(0)'}), '(file_to_process, index_col=0)\n', (227, 257), True, 'import pandas as pd\n'), ((381, 422), 'pandas.read_json', 'pd.read_json', (['file_to_process'], {'lines': '(True)'}), '(file_to_process, lines=True)\n', (393, 422), True, '... |
import numpy as np
from scipy import sparse
def _preprare_data_in_groups(X, y=None, sample_weights=None):
"""
Takes the first column of the feature Matrix X given and
transforms the data into groups accordingly.
Parameters
----------
X : (2d-array like) Feature matrix with the first column th... | [
"scipy.sparse.issparse",
"numpy.unique"
] | [((699, 717), 'scipy.sparse.issparse', 'sparse.issparse', (['X'], {}), '(X)\n', (714, 717), False, 'from scipy import sparse\n'), ((916, 959), 'numpy.unique', 'np.unique', (['group_labels'], {'return_counts': '(True)'}), '(group_labels, return_counts=True)\n', (925, 959), True, 'import numpy as np\n')] |
from rdkit import Chem
from rdkit.Chem import MACCSkeys
from random import shuffle
import numpy as np
import pandas as pd
#import torch
import scipy
from scipy import sparse
from rdkit.Chem.AllChem import GetMorganFingerprintAsBitVect as Morgan
from functools import partial
import os
#print(os.listdir())
_mcf = pd.read... | [
"rdkit.Chem.AllChem.GetMorganFingerprintAsBitVect",
"scipy.sparse.vstack",
"pandas.read_csv",
"scipy.sparse.issparse",
"rdkit.Chem.MolFromSmarts",
"numpy.asarray",
"numpy.zeros",
"rdkit.Chem.SanitizeMol",
"numpy.vstack",
"numpy.array",
"rdkit.Chem.MolToSmiles",
"rdkit.Chem.AddHs",
"rdkit.Che... | [((313, 352), 'pandas.read_csv', 'pd.read_csv', (['"""pt_metrics_utils/mcf.csv"""'], {}), "('pt_metrics_utils/mcf.csv')\n", (324, 352), True, 'import pandas as pd\n'), ((362, 435), 'pandas.read_csv', 'pd.read_csv', (['"""pt_metrics_utils/wehi_pains.csv"""'], {'names': "['smarts', 'names']"}), "('pt_metrics_utils/wehi_p... |
#Import necessarry tools from torch
import torch
import torch.nn as nn
#Import necessarry tools from tpstorch
from tpstorch.ml.data import EXPReweightSimulation
from tpstorch.ml.optim import ParallelAdam, ParallelSGD
from tpstorch.ml.nn import BKELossEXP, BKELossFTS
#Import model-specific classes
from brownian_ml im... | [
"brownian_ml.CommittorNet",
"numpy.random.seed",
"torch.manual_seed",
"brownian_ml.BrownianParticle",
"torch.load",
"time.time",
"tpstorch.ml.nn.BKELossEXP",
"tpstorch.ml.data.EXPReweightSimulation",
"torch.no_grad",
"torch.tensor"
] | [((528, 548), 'torch.manual_seed', 'torch.manual_seed', (['(0)'], {}), '(0)\n', (545, 548), False, 'import torch\n'), ((549, 566), 'numpy.random.seed', 'np.random.seed', (['(0)'], {}), '(0)\n', (563, 566), True, 'import numpy as np\n'), ((640, 662), 'torch.tensor', 'torch.tensor', (['[[-1.0]]'], {}), '([[-1.0]])\n', (6... |
import numpy as np
from PIL import Image
from tqdm import tqdm
import paddle
from paddle.vision.datasets import Cifar10
from paddle.vision import transforms
def config_dataset(config):
if "cifar" in config["dataset"]:
config["topK"] = -1
config["n_class"] = 10
elif config["dataset"] in ["nusw... | [
"numpy.sum",
"paddle.concat",
"numpy.argsort",
"numpy.mean",
"paddle.no_grad",
"paddle.vision.transforms.CenterCrop",
"numpy.reshape",
"numpy.linspace",
"paddle.vision.transforms.RandomCrop",
"tqdm.tqdm",
"paddle.vision.transforms.ToTensor",
"numpy.random.permutation",
"paddle.sign",
"nump... | [((7717, 7733), 'paddle.no_grad', 'paddle.no_grad', ([], {}), '()\n', (7731, 7733), False, 'import paddle\n'), ((3369, 3383), 'numpy.array', 'np.array', (['rslt'], {}), '(rslt)\n', (3377, 3383), True, 'import numpy as np\n'), ((5903, 6003), 'paddle.io.DataLoader', 'paddle.io.DataLoader', ([], {'dataset': 'train_dataset... |
#! /usr/bin/env python
from __future__ import print_function
import numpy as np
from scipy.special import gamma, gammaincc
from scipy.interpolate import RegularGridInterpolator
from k_correction import GAMA_KCorrection
class LuminosityFunction(object):
def __init__(self):
pass
def __initialize_interp... | [
"matplotlib.pyplot.title",
"matplotlib.pyplot.xlim",
"matplotlib.pyplot.yscale",
"matplotlib.pyplot.show",
"numpy.log",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.ylim",
"scipy.special.gamma",
"numpy.searchsorted",
"scipy.interpolate.RegularGridInterpolator",
"numpy.arange",
"numpy.exp",
"... | [((5543, 5566), 'numpy.arange', 'np.arange', (['(0)', '(-25)', '(-0.1)'], {}), '(0, -25, -0.1)\n', (5552, 5566), True, 'import numpy as np\n'), ((5843, 5863), 'matplotlib.pyplot.plot', 'plt.plot', (['mags', 'logn'], {}), '(mags, logn)\n', (5851, 5863), True, 'import matplotlib.pyplot as plt\n'), ((5868, 5896), 'matplot... |
#! /usr/bin/env python3
import argparse
import logging
import os
import pickle
import sys
import numpy as np
import pandas as pd
import misc.logging_utils as logging_utils
import misc.parallel as parallel
import misc.utils as utils
import misc.pandas_utils as pandas_utils
from misc.suppress_stdout_stderr import sup... | [
"pandas.DataFrame",
"misc.pandas_utils.write_df",
"numpy.sum",
"argparse.ArgumentParser",
"numpy.argmax",
"pandas.read_csv",
"logging.warning",
"misc.logging_utils.update_logging",
"misc.utils.remove_nones",
"misc.logging_utils.add_logging_options",
"misc.suppress_stdout_stderr.suppress_stdout_s... | [((350, 377), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (367, 377), False, 'import logging\n'), ((4920, 4937), 'pandas.DataFrame', 'pd.DataFrame', (['ret'], {}), '(ret)\n', (4932, 4937), True, 'import pandas as pd\n'), ((5007, 5416), 'argparse.ArgumentParser', 'argparse.ArgumentParse... |
"""
A module that contains a metaclass mixin that provides Galois field class properties.
"""
import math
import numpy as np
from .._poly_conversion import integer_to_poly, poly_to_str
from ._dtypes import DTYPES
class PropertiesMeta(type):
"""
A mixin metaclass that contains Galois field properties.
"... | [
"numpy.sort",
"numpy.array",
"numpy.iinfo",
"math.gcd"
] | [((7639, 7658), 'numpy.array', 'np.array', (['totatives'], {}), '(totatives)\n', (7647, 7658), True, 'import numpy as np\n'), ((7674, 7714), 'numpy.sort', 'np.sort', (['(cls.primitive_element ** powers)'], {}), '(cls.primitive_element ** powers)\n', (7681, 7714), True, 'import numpy as np\n'), ((7601, 7615), 'math.gcd'... |
from logging import getLogger
import numpy as np
from eit_ai.pytorch.dataset import PYTORCH_DATASET_HANDLERS
from eit_ai.pytorch.models import (PYTORCH_MODEL_HANDLERS, PYTORCH_MODELS,
StdPytorchModelHandler)
from eit_ai.raw_data.raw_samples import RawSamples
from eit_ai.train_utils.... | [
"eit_ai.train_utils.workspace.WrongSingleXError",
"glob_utils.log.log.change_level_logging",
"eit_ai.train_utils.lists.get_from_dict",
"eit_ai.train_utils.workspace.WrongDatasetError",
"numpy.reshape",
"glob_utils.log.log.main_log",
"logging.getLogger"
] | [((817, 836), 'logging.getLogger', 'getLogger', (['__name__'], {}), '(__name__)\n', (826, 836), False, 'from logging import getLogger\n'), ((4960, 4970), 'glob_utils.log.log.main_log', 'main_log', ([], {}), '()\n', (4968, 4970), False, 'from glob_utils.log.log import change_level_logging, main_log\n'), ((4975, 5010), '... |
import numpy as np
import matplotlib.pyplot as plt
import padding
def filtering(src, mask, pad_type='zero', return_uint8=True):
print('filtering start...')
h, w = src.shape[:2]
mh, mw = mask.shape[:2]
pad_img = padding.padding(src=src, pad_size=(mh//2, mw//2), pad_type=pad_type)
dst = np.zeros((... | [
"matplotlib.pyplot.title",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.show",
"numpy.sum",
"matplotlib.pyplot.imshow",
"numpy.zeros",
"padding.padding",
"numpy.clip",
"numpy.ones",
"matplotlib.pyplot.figure",
"numpy.arange",
"numpy.exp",
"numpy.round",
"numpy.sqrt"
] | [((230, 302), 'padding.padding', 'padding.padding', ([], {'src': 'src', 'pad_size': '(mh // 2, mw // 2)', 'pad_type': 'pad_type'}), '(src=src, pad_size=(mh // 2, mw // 2), pad_type=pad_type)\n', (245, 302), False, 'import padding\n'), ((310, 326), 'numpy.zeros', 'np.zeros', (['(h, w)'], {}), '((h, w))\n', (318, 326), T... |
import os
import subprocess
import sys
import importlib
import inspect
import functools
import tensorflow as tf
import numpy as np
from baselines.common import tf_util as U
import re
def store_args(method):
"""Stores provided method args as instance attributes.
"""
argspec = inspect.getfullargspec(method)
... | [
"tensorflow.contrib.layers.xavier_initializer",
"numpy.argmax",
"os.environ.copy",
"tensorflow.reshape",
"sys.stdout.flush",
"subprocess.check_call",
"tensorflow.concat",
"tensorflow.cast",
"sys.stderr.flush",
"importlib.import_module",
"subprocess.check_output",
"baselines.common.tf_util.nume... | [((289, 319), 'inspect.getfullargspec', 'inspect.getfullargspec', (['method'], {}), '(method)\n', (311, 319), False, 'import inspect\n'), ((604, 627), 'functools.wraps', 'functools.wraps', (['method'], {}), '(method)\n', (619, 627), False, 'import functools\n'), ((2849, 2882), 'importlib.import_module', 'importlib.impo... |
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Fri Nov 16 11:19:34 2018
@author: jennywong
"""
###############################################################################
# GETPARAMETERS.PY #
##############################################... | [
"slurpy.lookup.liquidus",
"numpy.zeros",
"slurpy.lookup.ohtaki",
"slurpy.lookup.premgravity",
"numpy.linspace",
"scipy.interpolate.interp1d",
"slurpy.lookup.premvp",
"slurpy.lookup.premdensity",
"scipy.integrate.simps"
] | [((1203, 1223), 'slurpy.lookup.liquidus', 'liquidus', (['csb_radius'], {}), '(csb_radius)\n', (1211, 1223), False, 'from slurpy.lookup import liquidus, premgravity, premdensity, premvp, ohtaki\n'), ((4057, 4076), 'slurpy.lookup.premgravity', 'premgravity', (['radius'], {}), '(radius)\n', (4068, 4076), False, 'from slur... |
from moviepy.editor import *
from PIL import Image, ImageOps
import numpy as np
from glob import glob
def make_youtube_video(introfile, audiofile, cover_img, outputfn, firstchapter=False):
"""
Generate a video of an audiobook file given following parameters.
:param introfile: introduction video clip (ie. ... | [
"PIL.ImageOps.pad",
"numpy.asarray",
"PIL.Image.open"
] | [((842, 863), 'PIL.Image.open', 'Image.open', (['cover_img'], {}), '(cover_img)\n', (852, 863), False, 'from PIL import Image, ImageOps\n'), ((1022, 1048), 'PIL.ImageOps.pad', 'ImageOps.pad', (['im_rsz', 'd_sz'], {}), '(im_rsz, d_sz)\n', (1034, 1048), False, 'from PIL import Image, ImageOps\n'), ((1067, 1089), 'numpy.a... |
#!/usr/bin/env python3
#-*- coding:utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
np.random.seed(42) #设置随机种子
x = np.random.randn(100) #产生100符合均值为0，方差为1的正太分布数据
#手动计算直方图
bins = np.linspace(-5, 5, 20) #直方图的区间
counts = np.zeros_like(bins) #产生一个形状（维度）和bins相同，值全为0的矩阵
#为x中的每个元素在bins中找出其所在的... | [
"numpy.zeros_like",
"numpy.random.seed",
"matplotlib.pyplot.show",
"matplotlib.pyplot.plot",
"numpy.random.randn",
"numpy.searchsorted",
"numpy.linspace",
"numpy.add.at"
] | [((117, 135), 'numpy.random.seed', 'np.random.seed', (['(42)'], {}), '(42)\n', (131, 135), True, 'import numpy as np\n'), ((148, 168), 'numpy.random.randn', 'np.random.randn', (['(100)'], {}), '(100)\n', (163, 168), True, 'import numpy as np\n'), ((211, 233), 'numpy.linspace', 'np.linspace', (['(-5)', '(5)', '(20)'], {... |
#!/usr/bin/env python
# Copyright 2019 <NAME>
# author: <NAME> <<EMAIL>>
#
# 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... | [
"dask.dataframe.core.new_dd_object",
"dask.dot.to_graphviz",
"dask.delayed",
"dask.highlevelgraph.HighLevelGraph.from_collections",
"dask.optimization.fuse",
"dask.base.tokenize",
"itertools.zip_longest",
"dask.optimization.cull",
"numpy.cumsum",
"numpy.array",
"xarray.DataArray",
"dask.highle... | [((10737, 10809), 'typing.TypeVar', 'T.TypeVar', (['"""ArrayVar"""', 'xarray.DataArray', 'dask.array.Array', 'numpy.ndarray'], {}), "('ArrayVar', xarray.DataArray, dask.array.Array, numpy.ndarray)\n", (10746, 10809), True, 'import typing as T\n'), ((2601, 2629), 'itertools.product', 'itertools.product', (['*block_id'],... |
import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
path = os.path.join('output', 'test_1.csv')
df = pd.read_csv(path)
df = df.groupby(['Index', 'Asked for articles'])['Time in MS'].mean().reset_index()
data_np = df.values # converting to numpy
unique_classes = np.unique(data_np[:, 0])
... | [
"matplotlib.pyplot.title",
"matplotlib.pyplot.yscale",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.show",
"matplotlib.pyplot.plot",
"pandas.read_csv",
"matplotlib.pyplot.legend",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.xlabel",
"os.path.join",
"numpy.unique"
] | [((91, 127), 'os.path.join', 'os.path.join', (['"""output"""', '"""test_1.csv"""'], {}), "('output', 'test_1.csv')\n", (103, 127), False, 'import os\n'), ((133, 150), 'pandas.read_csv', 'pd.read_csv', (['path'], {}), '(path)\n', (144, 150), True, 'import pandas as pd\n'), ((295, 319), 'numpy.unique', 'np.unique', (['da... |
import copy
import numpy as np
import time
from .freezable import Freezable
class Timing(Freezable):
def __init__(self, node, method_name=None):
self.__name = type(node).__name__
self.__method_name = method_name
self.__start = 0
self.__first_start = 0
self.__last_stop = 0
... | [
"copy.deepcopy",
"numpy.median",
"time.time",
"numpy.min",
"numpy.mean",
"numpy.max"
] | [((411, 422), 'time.time', 'time.time', ([], {}), '()\n', (420, 422), False, 'import time\n'), ((587, 598), 'time.time', 'time.time', ([], {}), '()\n', (596, 598), False, 'import time\n'), ((1795, 1813), 'numpy.min', 'np.min', (['self.times'], {}), '(self.times)\n', (1801, 1813), True, 'import numpy as np\n'), ((1849, ... |
import unittest
import numpy as np
from mil.models import AttentionDeepPoolingMil
from mil.utils.padding import Padding
class TestAttentionDeepPoolingMil(unittest.TestCase):
def setUp(self):
self.training_bag = np.random.normal(0, 1, (30, 3, 28, 28, 1))
self.training_label = np.ze... | [
"unittest.main",
"mil.models.AttentionDeepPoolingMil",
"numpy.zeros",
"numpy.random.normal",
"mil.utils.padding.Padding"
] | [((2370, 2385), 'unittest.main', 'unittest.main', ([], {}), '()\n', (2383, 2385), False, 'import unittest\n'), ((241, 283), 'numpy.random.normal', 'np.random.normal', (['(0)', '(1)', '(30, 3, 28, 28, 1)'], {}), '(0, 1, (30, 3, 28, 28, 1))\n', (257, 283), True, 'import numpy as np\n'), ((315, 327), 'numpy.zeros', 'np.ze... |
from tensorflow.keras.models import load_model
from tensorflow.python.keras.backend import set_session
import tensorflow as tf
from flask import Flask, request, render_template, jsonify, send_file, url_for
import os
from PIL import Image, ImageOps
import numpy as np
import math
import time
import base64
app = Flask(__... | [
"base64.b64decode",
"flask.jsonify",
"numpy.ndarray",
"flask.request.get_json",
"tensorflow.python.keras.backend.set_session",
"numpy.set_printoptions",
"flask.request.headers.get",
"flask.request.files.keys",
"tensorflow.keras.models.load_model",
"PIL.ImageOps.fit",
"tensorflow.compat.v1.get_de... | [((312, 327), 'flask.Flask', 'Flask', (['__name__'], {}), '(__name__)\n', (317, 327), False, 'from flask import Flask, request, render_template, jsonify, send_file, url_for\n'), ((531, 543), 'tensorflow.Session', 'tf.Session', ([], {}), '()\n', (541, 543), True, 'import tensorflow as tf\n'), ((553, 585), 'tensorflow.co... |
import numpy as np
class Control:
def __init__(self, model):
# Bind model
self.model = model
# Desired x_pos
self.xd = 0.0
# Control parameters
self.N = 100 # Prediction and control horizon
# Control parameters
if se... | [
"numpy.zeros",
"numpy.identity",
"numpy.linalg.matrix_power",
"numpy.arange",
"numpy.kron",
"numpy.eye",
"numpy.mat"
] | [((803, 828), 'numpy.mat', 'np.mat', (['self.model.A_disc'], {}), '(self.model.A_disc)\n', (809, 828), True, 'import numpy as np\n'), ((841, 866), 'numpy.mat', 'np.mat', (['self.model.B_disc'], {}), '(self.model.B_disc)\n', (847, 866), True, 'import numpy as np\n'), ((1838, 1852), 'numpy.eye', 'np.eye', (['self.N'], {}... |
from matplotlib import pyplot as plt
import numpy as np
forceControlData = np.load('force_control_response.npz')
F = forceControlData['force']
U = forceControlData['displacement']
plt.plot(U, F, marker='o')
plt.xlabel('Displacement')
plt.ylabel('Force')
plotComparison = True
if plotComparison:
dispControlData = ... | [
"numpy.load",
"matplotlib.pyplot.show",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.xlabel"
] | [((76, 113), 'numpy.load', 'np.load', (['"""force_control_response.npz"""'], {}), "('force_control_response.npz')\n", (83, 113), True, 'import numpy as np\n'), ((182, 208), 'matplotlib.pyplot.plot', 'plt.plot', (['U', 'F'], {'marker': '"""o"""'}), "(U, F, marker='o')\n", (190, 208), True, 'from matplotlib import pyplot... |
import numpy as np
import random
import matplotlib.pyplot as plt
import matplotlib
from io_utilities import load_data
from visualizations import show_clusters_centroids
def distance(a,b):
"""
Compute Euclidean Distance Between Two Points
Input:
a (list): an n-dimensional list or array
b (l... | [
"matplotlib.pyplot.show",
"numpy.argmin",
"io_utilities.load_data",
"visualizations.show_clusters_centroids",
"numpy.mean",
"numpy.array"
] | [((2602, 2631), 'io_utilities.load_data', 'load_data', (['"""./data/iris.data"""'], {}), "('./data/iris.data')\n", (2611, 2631), False, 'from io_utilities import load_data\n'), ((2651, 2683), 'numpy.array', 'np.array', (['[f[:-1] for f in data]'], {}), '([f[:-1] for f in data])\n', (2659, 2683), True, 'import numpy as ... |
import numpy as np
def simpson(f, a, b, nstrips):
'''
Compute the quadrature of f on [a, b].
Parameters
----------
f : function
The integrand
a : float
The start of the domain
b : float
The end of the domain
nstrips : int
The number of strips
Retur... | [
"numpy.linspace"
] | [((405, 472), 'numpy.linspace', 'np.linspace', (['a', 'b'], {'num': '(2 * nstrips + 1)', 'endpoint': '(True)', 'retstep': '(True)'}), '(a, b, num=2 * nstrips + 1, endpoint=True, retstep=True)\n', (416, 472), True, 'import numpy as np\n')] |
import numpy as np
from sklearn import svm
import matplotlib.pyplot as plt
X = np.array([[0,0],[1,1]])
Y = [0,1]
clf.fit(X,Y)
clf =svm.SVC()
svc(kernel='precomputed')
clf.predict(gram)
plt.scatter(X[:,0],X[:,1],c=Y,s=50,cmap='spring')
plt.show()
| [
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.show",
"numpy.array",
"sklearn.svm.SVC"
] | [((82, 108), 'numpy.array', 'np.array', (['[[0, 0], [1, 1]]'], {}), '([[0, 0], [1, 1]])\n', (90, 108), True, 'import numpy as np\n'), ((137, 146), 'sklearn.svm.SVC', 'svm.SVC', ([], {}), '()\n', (144, 146), False, 'from sklearn import svm\n'), ((194, 249), 'matplotlib.pyplot.scatter', 'plt.scatter', (['X[:, 0]', 'X[:, ... |
#!/usr/bin/env python3
import math
from decimal import *
import numpy as np
# CHANGEME:
probtab = np.loadtxt(open("data/probability_table.csv", "rb"), delimiter=",").astype(Decimal)
print("The marginal probabilities of all the values of 𝑋")
print(probtab)
print("- " * 10)
px = np.sum(probtab, axis=0)
py = np.sum(... | [
"math.log2",
"numpy.sum"
] | [((283, 306), 'numpy.sum', 'np.sum', (['probtab'], {'axis': '(0)'}), '(probtab, axis=0)\n', (289, 306), True, 'import numpy as np\n'), ((312, 335), 'numpy.sum', 'np.sum', (['probtab'], {'axis': '(1)'}), '(probtab, axis=1)\n', (318, 335), True, 'import numpy as np\n'), ((864, 880), 'math.log2', 'math.log2', (['(j / k)']... |
"""
Copyright (c) College of Mechatronics and Control Engineering, Shenzhen University.
All rights reserved.
Description :
Author：<NAME>
"""
import os, glob
from xml.dom.minidom import parse
import xml.dom.minidom
import config
import numpy as np
from sklearn.cluster import KMeans
datase_name = 'inria_person'
in... | [
"numpy.array",
"sklearn.cluster.KMeans",
"os.path.join"
] | [((2015, 2035), 'sklearn.cluster.KMeans', 'KMeans', ([], {'n_clusters': '(2)'}), '(n_clusters=2)\n', (2021, 2035), False, 'from sklearn.cluster import KMeans\n'), ((790, 858), 'os.path.join', 'os.path.join', (["dataset_info_map[datase_name]['anotation_dir']", '"""*xml"""'], {}), "(dataset_info_map[datase_name]['anotati... |
# Copyright 2021 TileDB Inc.
# Licensed under the MIT License.
import numpy as np
import pytest
from tiledb.cf.creator import DataspaceRegistry
from tiledb.cf.netcdf_engine import (
NetCDF4CoordToDimConverter,
NetCDF4DimToDimConverter,
NetCDF4ScalarToDimConverter,
)
netCDF4 = pytest.importorskip("netCDF4"... | [
"pytest.importorskip",
"tiledb.cf.netcdf_engine.NetCDF4CoordToDimConverter.from_netcdf",
"numpy.dtype",
"tiledb.cf.netcdf_engine.NetCDF4DimToDimConverter.from_netcdf",
"pytest.raises",
"numpy.arange",
"numpy.testing.assert_equal",
"numpy.random.rand",
"pytest.mark.parametrize",
"tiledb.cf.netcdf_e... | [((291, 321), 'pytest.importorskip', 'pytest.importorskip', (['"""netCDF4"""'], {}), "('netCDF4')\n", (310, 321), False, 'import pytest\n'), ((10119, 10167), 'pytest.mark.parametrize', 'pytest.mark.parametrize', (['"""sparse"""', '[True, False]'], {}), "('sparse', [True, False])\n", (10142, 10167), False, 'import pytes... |
from __future__ import print_function, division
from keras.layers import Input, Dense, Reshape, Flatten, Dropout, concatenate
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from k... | [
"utils.dataset_utils.load_dataset",
"numpy.ones",
"keras.models.Model",
"numpy.random.randint",
"numpy.random.normal",
"keras.layers.ZeroPadding2D",
"keras.layers.Input",
"keras.layers.Reshape",
"keras.layers.concatenate",
"tensorflow.GPUOptions",
"pandas.DataFrame",
"keras.layers.convolutiona... | [((521, 535), 'matplotlib.use', 'mpl.use', (['"""Agg"""'], {}), "('Agg')\n", (528, 535), True, 'import matplotlib as mpl\n'), ((990, 1015), 'tensorflow.Session', 'tf.Session', ([], {'config': 'config'}), '(config=config)\n', (1000, 1015), True, 'import tensorflow as tf\n'), ((12406, 12498), 'numpy.zeros', 'np.zeros', (... |
import models
import numpy as np
import torch
import torch.optim as optim
import torch.nn.functional as F
import torch.nn as nn
from functools import reduce
import torch.optim as optim
import utils
from datetime import datetime
from PIL import Image
from scipy.spatial.distance import pdist, squareform
import torc... | [
"torch.nn.Dropout",
"models.VGG16",
"argparse.ArgumentParser",
"torch.cat",
"numpy.argsort",
"numpy.mean",
"scipy.spatial.distance.pdist",
"torch.device",
"glob.glob",
"torch.no_grad",
"torch.utils.data.DataLoader",
"utils.get_stats",
"torchvision.transforms.Compose",
"numpy.random.choice"... | [((402, 458), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': '"""VAE MNIST Example"""'}), "(description='VAE MNIST Example')\n", (425, 458), False, 'import argparse\n'), ((1571, 1615), 'torch.device', 'torch.device', (["('cuda' if args.cuda else 'cpu')"], {}), "('cuda' if args.cuda else 'cpu... |
import torch
import logging
import sys
import sacred
import scipy.ndimage
import numpy as np
def place_tensor(tensor):
"""
Places a tensor on GPU, if PyTorch sees CUDA; otherwise, the returned tensor
remains on CPU.
"""
if torch.cuda.is_available():
return tensor.cuda()
return tensor
... | [
"torch.load",
"numpy.zeros",
"torch.save",
"torch.squeeze",
"torch.cuda.is_available",
"torch.nn.functional.conv1d",
"torch.unsqueeze",
"torch.tensor"
] | [((244, 269), 'torch.cuda.is_available', 'torch.cuda.is_available', ([], {}), '()\n', (267, 269), False, 'import torch\n'), ((1567, 1599), 'torch.save', 'torch.save', (['save_dict', 'save_path'], {}), '(save_dict, save_path)\n', (1577, 1599), False, 'import torch\n'), ((1942, 1963), 'torch.load', 'torch.load', (['load_... |
#!/usr/local/anaconda/bin/python
# -*- coding: utf-8 -*-
"""
Created on Thu Oct 08 14:16:09 2015
@author: Derrick
"""
import glob
import os
import shutil
import sys
import numpy as np
import obspy
import pandas as pd
# get start and stop times
# stations = glob.glob(os.path.join(conDir,'*'))... | [
"pandas.DataFrame",
"numpy.sum",
"os.makedirs",
"pandas.read_csv",
"os.walk",
"os.path.exists",
"sys.stdout.flush",
"obspy.UTCDateTime",
"pandas.Series",
"glob.glob",
"os.path.join",
"sys.exit",
"obspy.read"
] | [((625, 648), 'obspy.UTCDateTime', 'obspy.UTCDateTime', (['utc1'], {}), '(utc1)\n', (642, 648), False, 'import obspy\n'), ((661, 684), 'obspy.UTCDateTime', 'obspy.UTCDateTime', (['utc2'], {}), '(utc2)\n', (678, 684), False, 'import obspy\n'), ((1260, 1361), 'os.path.join', 'os.path.join', (['conDir', 'stanet', 'year', ... |
import requests
import torch
import numpy as np
from . import payloadutils
class Helper():
def __init__(self, datasource, task='binary_classifier'):
self.dp_settings = None
self.agg_settings = None
self.task = task
self.datasource = datasource
def set_diff_privacy(self, mechan... | [
"numpy.random.uniform",
"torch.repeat_interleave",
"numpy.asarray",
"numpy.array",
"requests.post",
"torch.is_tensor"
] | [((1582, 1601), 'numpy.random.uniform', 'np.random.uniform', ([], {}), '()\n', (1599, 1601), True, 'import numpy as np\n'), ((1797, 1825), 'torch.is_tensor', 'torch.is_tensor', (['input_point'], {}), '(input_point)\n', (1812, 1825), False, 'import torch\n'), ((1983, 2007), 'torch.is_tensor', 'torch.is_tensor', (['targe... |
import numpy as np
import heapq
from dataclasses import dataclass, field
from typing import Tuple, DefaultDict, Dict, List, Set
from collections import defaultdict
def read_file(filename):
with open(filename) as f:
return [line.strip() for line in f.readlines()]
def parse_input(data: List[str]) -> np.ndar... | [
"heapq.heappush",
"numpy.mod",
"heapq.heappop",
"dataclasses.field",
"collections.defaultdict",
"numpy.array"
] | [((336, 393), 'numpy.array', 'np.array', (['[[c for c in line] for line in data]'], {'dtype': 'int'}), '([[c for c in line] for line in data], dtype=int)\n', (344, 393), True, 'import numpy as np\n'), ((681, 720), 'dataclasses.field', 'field', ([], {'default_factory': 'list', 'repr': '(False)'}), '(default_factory=list... |
"""
Base class for Flat File vocabulary importers
"""
import numpy as np
from collections import OrderedDict
from os import path
from vocabulary_importers.vocabulary_importer import VocabularyImporter
class FlatFileVocabularyImporter(VocabularyImporter):
"""Base class for Flat File vocabulary importers
"""
... | [
"collections.OrderedDict",
"os.path.join",
"numpy.array"
] | [((1387, 1449), 'os.path.join', 'path.join', (['vocabulary_dir', 'self.tokens_and_embeddings_filename'], {}), '(vocabulary_dir, self.tokens_and_embeddings_filename)\n', (1396, 1449), False, 'from os import path\n'), ((1483, 1496), 'collections.OrderedDict', 'OrderedDict', ([], {}), '()\n', (1494, 1496), False, 'from co... |
# ===============================================================================================================
# Copyright (c) 2019, Cornell University. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification, are permitted provided that
# the following conditions... | [
"scipy.linalg.rq",
"numpy.zeros",
"numpy.all",
"numpy.ones",
"numpy.hstack",
"numpy.vstack",
"scipy.linalg.svd",
"numpy.array",
"scipy.linalg.det",
"scipy.linalg.lstsq",
"numpy.real",
"numpy.dot",
"numpy.diag"
] | [((2285, 2309), 'scipy.linalg.rq', 'linalg.rq', (['matrix[:, :3]'], {}), '(matrix[:, :3])\n', (2294, 2309), False, 'from scipy import linalg\n'), ((2959, 2977), 'numpy.diag', 'np.diag', (['(1, 1, 1)'], {}), '((1, 1, 1))\n', (2966, 2977), True, 'import numpy as np\n'), ((3203, 3217), 'numpy.dot', 'np.dot', (['r', 'fix']... |
from collections import OrderedDict, namedtuple
from functools import partial
from six import string_types
from artemis.config import get_artemis_config_value
from artemis.general.checkpoint_counter import Checkpoints
from artemis.plotting.matplotlib_backend import BarPlot, BoundingBoxPlot, ResamplingLineHistory
from... | [
"artemis.general.checkpoint_counter.Checkpoints",
"matplotlib.pyplot.clf",
"artemis.config.get_artemis_config_value",
"matplotlib.pyplot.figure",
"artemis.remote.plotting.plotting_client.dbplot_remotely",
"matplotlib.pyplot.close",
"artemis.plotting.drawing_plots.redraw_figure",
"artemis.plotting.expa... | [((878, 901), 'artemis.plotting.matplotlib_backend.is_server_plotting_on', 'is_server_plotting_on', ([], {}), '()\n', (899, 901), False, 'from artemis.plotting.matplotlib_backend import LinePlot, ImagePlot, is_server_plotting_on\n'), ((8117, 8173), 'collections.namedtuple', 'namedtuple', (['"""PlotWindow"""', "['figure... |
import numpy as np
import torch
import shadow.losses
def test_softmax_mse_loss():
"""Simple test for softmax mse loss."""
input_logits = torch.tensor([[0.5, 1.]])
target_logits = torch.tensor([[1., 1.]])
# softmax of input_logits is 0.3775, 0.6225, and target_logits is 0.5, 0.5
# therefore would... | [
"numpy.array",
"torch.tensor"
] | [((148, 174), 'torch.tensor', 'torch.tensor', (['[[0.5, 1.0]]'], {}), '([[0.5, 1.0]])\n', (160, 174), False, 'import torch\n'), ((194, 220), 'torch.tensor', 'torch.tensor', (['[[1.0, 1.0]]'], {}), '([[1.0, 1.0]])\n', (206, 220), False, 'import torch\n'), ((652, 678), 'torch.tensor', 'torch.tensor', (['[[0.5, 1.0]]'], {... |
# -*- coding: utf-8 -*-
# extract Balvan data from tifs
import pandas as pd
import skimage.io as skio
import skimage.transform as skt
import skimage.util as sku
from tqdm import tqdm
from glob import glob
import os, random, math, cv2, re
import numpy as np
import matplotlib.pyplot as plt
# %%
src_dir = '.... | [
"os.makedirs",
"os.path.basename",
"skimage.util.img_as_ubyte",
"numpy.asarray",
"os.path.exists",
"glob.glob",
"skimage.io.imsave",
"skimage.io.imread"
] | [((1242, 1260), 'numpy.asarray', 'np.asarray', (['shapes'], {}), '(shapes)\n', (1252, 1260), True, 'import numpy as np\n'), ((459, 481), 'os.path.exists', 'os.path.exists', (['dir_IR'], {}), '(dir_IR)\n', (473, 481), False, 'import os, random, math, cv2, re\n'), ((488, 507), 'os.makedirs', 'os.makedirs', (['dir_IR'], {... |
from __future__ import division
from builtins import next
from builtins import str
from builtins import range
from past.utils import old_div
import re
import numpy as np
def parse_file(self):
if self.prog == "GAUSSIAN":
parse_file_gaussian(self)
elif self.prog == "CQ":
parse_file_cq(self)
def parse_file_... | [
"past.utils.old_div",
"numpy.asarray",
"numpy.zeros",
"numpy.genfromtxt",
"builtins.next",
"re.findall",
"numpy.array",
"builtins.str",
"builtins.range",
"numpy.delete"
] | [((400, 444), 'numpy.genfromtxt', 'np.genfromtxt', (['self.fieldFile'], {'delimiter': '""","""'}), "(self.fieldFile, delimiter=',')\n", (413, 444), True, 'import numpy as np\n'), ((460, 486), 'numpy.delete', 'np.delete', (['FieldData', '(0)', '(0)'], {}), '(FieldData, 0, 0)\n', (469, 486), True, 'import numpy as np\n')... |
# Authors: <NAME> <<EMAIL>>
#
# License: BSD-3 (3-clause)
import pytest
import numpy as np
import torch
from braindecode.training.losses import mixup_criterion
def test_mixup_criterion():
n_classes = 2
n_samples = 5
y_a = torch.zeros(n_samples, dtype=torch.int64)
y_b = torch.ones(n_samples, dtype=tor... | [
"torch.ones",
"braindecode.training.losses.mixup_criterion",
"numpy.random.RandomState",
"torch.arange",
"torch.zeros",
"pytest.approx"
] | [((237, 278), 'torch.zeros', 'torch.zeros', (['n_samples'], {'dtype': 'torch.int64'}), '(n_samples, dtype=torch.int64)\n', (248, 278), False, 'import torch\n'), ((289, 329), 'torch.ones', 'torch.ones', (['n_samples'], {'dtype': 'torch.int64'}), '(n_samples, dtype=torch.int64)\n', (299, 329), False, 'import torch\n'), (... |
# 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... | [
"sys.path.append",
"unittest.main",
"functools.partial",
"program_config.OpConfig",
"auto_scan_test.AutoScanTest.__init__",
"hypothesis.strategies.sampled_from",
"numpy.random.random",
"numpy.array",
"hypothesis.strategies.integers"
] | [((622, 644), 'sys.path.append', 'sys.path.append', (['"""../"""'], {}), "('../')\n", (637, 644), False, 'import sys\n'), ((4218, 4242), 'unittest.main', 'unittest.main', ([], {'argv': "['']"}), "(argv=[''])\n", (4231, 4242), False, 'import unittest\n'), ((1111, 1155), 'auto_scan_test.AutoScanTest.__init__', 'AutoScanT... |
import scipy.io
import torch
import numpy as np
#import time
import os
#######################################################################
# Evaluate
'''
验证，传参：query_feature[i],query_label[i],query_cam[i],gallery_feature,gallery_label,gallery_cam
:param qf torch.Tensor，待识别图片的特征
:param ql numpy.int32，待识... | [
"torch.mean",
"torch.FloatTensor",
"torch.mm",
"numpy.setdiff1d",
"numpy.argsort",
"numpy.append",
"os.path.isfile",
"numpy.argwhere",
"numpy.intersect1d",
"numpy.in1d"
] | [((3248, 3284), 'torch.FloatTensor', 'torch.FloatTensor', (["result['query_f']"], {}), "(result['query_f'])\n", (3265, 3284), False, 'import torch\n'), ((3454, 3492), 'torch.FloatTensor', 'torch.FloatTensor', (["result['gallery_f']"], {}), "(result['gallery_f'])\n", (3471, 3492), False, 'import torch\n'), ((3651, 3684)... |
import sys
import os
# sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from src.insightface.deploy import face_model
from imutils import paths
import numpy as np
import pickle
import cv2
import os
def genFaceEmbedings():
print('---->>> Creating data embeddings <<<---')
embedding_mo... | [
"imutils.paths.list_images",
"pickle.dump",
"cv2.cvtColor",
"numpy.transpose",
"cv2.imread",
"src.insightface.deploy.face_model.FaceModel"
] | [((326, 419), 'src.insightface.deploy.face_model.FaceModel', 'face_model.FaceModel', (['"""112,112"""', '"""src/insightface/models/model-y1-test2/model,0"""', '(1.24)', '(0)'], {}), "('112,112',\n 'src/insightface/models/model-y1-test2/model,0', 1.24, 0)\n", (346, 419), False, 'from src.insightface.deploy import fac... |
import unittest
import numpy as np
import tensorflow.keras.backend as K
import lib.losses as losses
import lib.spatial_geometry as spatial_geometry
from lib.cameras import PinholeCamera
# Losses assume the input:
# y = [ quaternion, translation, shape3D ]
# y = [ q0,q1,q2,q3, t0,t1,t2, sx0,sy0,sz0,...,sxN,syN,s... | [
"numpy.abs",
"lib.losses.reprojection",
"lib.losses.multiview_reprojection",
"lib.losses.xqt",
"numpy.mean",
"unittest.main",
"numpy.eye",
"lib.losses.geometric_alignment",
"numpy.reshape",
"tensorflow.keras.backend.variable",
"lib.spatial_geometry.quaternion_translation_to_pose",
"numpy.squar... | [((766, 971), 'numpy.array', 'np.array', (['[[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, -30.0, -0.98854052, 2.12746976, -3.8310884,\n -0.41849216, 2.69751813, -3.26104, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, -40.0,\n 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, -20.0]]'], {}), '([[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, -30.0, -0.98854052, 2.12746976, -\n ... |
import numpy as np
from ...core.likelihood import Likelihood
class BasicGravitationalWaveTransient(Likelihood):
def __init__(self, interferometers, waveform_generator):
"""
A likelihood object, able to compute the likelihood of the data given
some model parameters
The simplest ... | [
"numpy.vdot",
"numpy.nan_to_num"
] | [((2103, 2125), 'numpy.nan_to_num', 'np.nan_to_num', (['(-np.inf)'], {}), '(-np.inf)\n', (2116, 2125), True, 'import numpy as np\n'), ((3052, 3223), 'numpy.vdot', 'np.vdot', (['(interferometer.frequency_domain_strain - signal_ifo)', '((interferometer.frequency_domain_strain - signal_ifo) / interferometer.\n power_sp... |
import numpy as np
def l21shrink(epsilon, x):
"""
Args:
epsilon: the shrinkage parameter
x: matrix to shrink on
Ref:
wiki Regularization: {https://en.wikipedia.org/wiki/Regularization_(mathematics)}
Returns:
The shrunk matrix
"""
output = x.copy()
norm = ... | [
"numpy.linalg.norm"
] | [((320, 352), 'numpy.linalg.norm', 'np.linalg.norm', (['x'], {'ord': '(2)', 'axis': '(0)'}), '(x, ord=2, axis=0)\n', (334, 352), True, 'import numpy as np\n')] |
from styx_msgs.msg import TrafficLight
import rospy
import numpy as np
import os
import tensorflow as tf
from utilities import label_map_util
from utilities import visualization_utils as vis_util
import cv2
class TLClassifier(object):
def __init__(self, is_site):
# set default value for no detection
... | [
"utilities.label_map_util.load_labelmap",
"utilities.label_map_util.convert_label_map_to_categories",
"utilities.visualization_utils.visualize_boxes_and_labels_on_image_array",
"os.path.realpath",
"tensorflow.Session",
"numpy.expand_dims",
"tensorflow.ConfigProto",
"tensorflow.gfile.GFile",
"tensorf... | [((790, 831), 'utilities.label_map_util.load_labelmap', 'label_map_util.load_labelmap', (['labels_file'], {}), '(labels_file)\n', (818, 831), False, 'from utilities import label_map_util\n'), ((853, 967), 'utilities.label_map_util.convert_label_map_to_categories', 'label_map_util.convert_label_map_to_categories', (['la... |
import matplotlib.pyplot as plt
from scipy import integrate
import numpy as np
plt.style.use('paper')
reds = plt.get_cmap('Reds')
blues = plt.get_cmap('Blues')
class Constants:
hbar_au = 1.0
h_au = hbar_au * 2.0 * np.pi
kb_au = 3.1668114E-6 # hartrees K-1
h_SI = 6.62607004E-34 # J s
na = 6.... | [
"matplotlib.pyplot.xlim",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.get_cmap",
"numpy.log",
"matplotlib.pyplot.ylim",
"numpy.std",
"matplotlib.pyplot.style.use",
"numpy.mean",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig"
] | [((79, 101), 'matplotlib.pyplot.style.use', 'plt.style.use', (['"""paper"""'], {}), "('paper')\n", (92, 101), True, 'import matplotlib.pyplot as plt\n'), ((109, 129), 'matplotlib.pyplot.get_cmap', 'plt.get_cmap', (['"""Reds"""'], {}), "('Reds')\n", (121, 129), True, 'import matplotlib.pyplot as plt\n'), ((138, 159), 'm... |
import numpy as np
import random
import matplotlib.pyplot as plt
import time
from matplotlib.animation import FuncAnimation
import matplotlib.animation as animation
# from p5 import Vector, stroke, circle
import warnings
warnings.simplefilter('error')
class Boid(object):
def __init__(self, n_boids, width, delta_T,... | [
"numpy.fill_diagonal",
"numpy.abs",
"warnings.simplefilter",
"numpy.random.randn",
"numpy.zeros",
"numpy.cross",
"numpy.ones",
"numpy.arccos",
"numpy.mean",
"numpy.array",
"pdb.set_trace",
"numpy.linalg.norm",
"numpy.sign",
"numpy.random.rand",
"numpy.dot",
"numpy.cos",
"numpy.sin",
... | [((221, 251), 'warnings.simplefilter', 'warnings.simplefilter', (['"""error"""'], {}), "('error')\n", (242, 251), False, 'import warnings\n'), ((1557, 1585), 'numpy.all', 'np.all', (['(loc < self.width * 3)'], {}), '(loc < self.width * 3)\n', (1563, 1585), True, 'import numpy as np\n'), ((1601, 1630), 'numpy.all', 'np.... |
import matplotlib.pyplot as plt
import numpy as np
results = []
for line in open("results_target.txt","r"):
eval(line)
results_target = results
x_targets = []
y_targets = []
for line in results_target:
x_targets.append(line["target"])
y_targets.append(line["perfs_ft"][-1])
indices = np.argsort(x_targets)
... | [
"matplotlib.pyplot.xscale",
"matplotlib.pyplot.show",
"matplotlib.pyplot.plot",
"numpy.asarray",
"matplotlib.pyplot.legend",
"numpy.argsort",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.xlabel"
] | [((298, 319), 'numpy.argsort', 'np.argsort', (['x_targets'], {}), '(x_targets)\n', (308, 319), True, 'import numpy as np\n'), ((643, 696), 'matplotlib.pyplot.plot', 'plt.plot', (['x_targets', 'y_targets'], {'label': '"""Dynamic width"""'}), "(x_targets, y_targets, label='Dynamic width')\n", (651, 696), True, 'import ma... |
import os
import torch
import numpy as np
import cv2
from torch.utils.data import Dataset
from torch.nn.functional import interpolate
import matplotlib.pyplot as plt
class LOLDataset(Dataset):
"""LOL Sony dataset."""
def __init__(self, list_file ,root_dir, ps,transform=None):
self.ps = p... | [
"numpy.minimum",
"numpy.maximum",
"numpy.flip",
"cv2.cvtColor",
"numpy.float32",
"numpy.transpose",
"cv2.imread",
"numpy.random.randint",
"os.path.join",
"torch.from_numpy"
] | [((2369, 2402), 'numpy.random.randint', 'np.random.randint', (['(0)', '(W - self.ps)'], {}), '(0, W - self.ps)\n', (2386, 2402), True, 'import numpy as np\n'), ((2417, 2450), 'numpy.random.randint', 'np.random.randint', (['(0)', '(H - self.ps)'], {}), '(0, H - self.ps)\n', (2434, 2450), True, 'import numpy as np\n'), (... |
import os
import sys
from typing import Any
import numpy as np
import torch
from PIL import Image
from torchvision import transforms
from torchvision.datasets.utils import extract_archive
from torchvision.datasets.vision import VisionDataset
from src.datasets.specs import Input2dSpec
# From DATASET_ROOT/chexpert/Che... | [
"numpy.maximum",
"os.makedirs",
"os.path.exists",
"os.system",
"torchvision.transforms.ToTensor",
"numpy.array",
"numpy.loadtxt",
"src.datasets.specs.Input2dSpec",
"torch.tensor",
"torchvision.datasets.utils.extract_archive",
"os.path.join",
"os.startfile",
"torchvision.transforms.Resize"
] | [((1252, 1448), 'numpy.array', 'np.array', (["[CHEXPERT_LABELS['Atelectasis'], CHEXPERT_LABELS['Cardiomegaly'],\n CHEXPERT_LABELS['Consolidation'], CHEXPERT_LABELS['Edema'],\n CHEXPERT_LABELS['Pleural Effusion']]"], {'dtype': 'np.int32'}), "([CHEXPERT_LABELS['Atelectasis'], CHEXPERT_LABELS['Cardiomegaly'],\n C... |
import argparse
import sys
from tensorflow.python.framework import dtypes
import tensorflow as tf
import numpy as np
from collections import namedtuple
import json
from os import makedirs
from os import path
FLAGS = None
Datasets = namedtuple('Datasets', ['train', 'validation', 'test'])
def export_def_graph(outdir=... | [
"argparse.ArgumentParser",
"tensorflow.reshape",
"tensorflow.matmul",
"tensorflow.Variable",
"numpy.arange",
"tensorflow.nn.conv2d",
"tensorflow.InteractiveSession",
"tensorflow.get_default_graph",
"tensorflow.truncated_normal",
"numpy.multiply",
"tensorflow.nn.softmax_cross_entropy_with_logits"... | [((234, 289), 'collections.namedtuple', 'namedtuple', (['"""Datasets"""', "['train', 'validation', 'test']"], {}), "('Datasets', ['train', 'validation', 'test'])\n", (244, 289), False, 'from collections import namedtuple\n'), ((545, 583), 'tensorflow.truncated_normal', 'tf.truncated_normal', (['shape'], {'stddev': '(0.... |
from fypy.model.FourierModel import FourierModel
import numpy as np
from scipy.fft import ifft
from fypy.pricing.StrikesPricer import StrikesPricer
from scipy.interpolate import interp1d
from scipy.integrate import quad
class LewisEuropeanPricer(StrikesPricer):
def __init__(self,
model: FourierMo... | [
"numpy.log",
"scipy.integrate.quad",
"numpy.arange",
"numpy.exp",
"scipy.interpolate.interp1d",
"scipy.fft.ifft",
"numpy.sqrt"
] | [((1590, 1602), 'numpy.arange', 'np.arange', (['N'], {}), '(N)\n', (1599, 1602), True, 'import numpy as np\n'), ((3134, 3148), 'numpy.log', 'np.log', (['(S0 / K)'], {}), '(S0 / K)\n', (3140, 3148), True, 'import numpy as np\n'), ((1517, 1529), 'numpy.arange', 'np.arange', (['N'], {}), '(N)\n', (1526, 1529), True, 'impo... |
#!/usr/bin/env python
# Copyright 2017 Johns Hopkins University (<NAME>)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
import copy
import json
import logging
import math
import os
import re
# chainer related
import chainer
from chainer.datasets import TransformDataset
from chainer import reporter as ... | [
"espnet.asr.asr_utils.adadelta_eps_decay",
"espnet.nets.e2e_asr_th.E2E",
"torch.optim.Adadelta",
"espnet.asr.asr_utils.restore_snapshot",
"json.dumps",
"espnet.asr.asr_utils.CompareValueTrigger",
"numpy.exp",
"torch.device",
"torch.no_grad",
"chainer.training.extensions.LogReport",
"espnet.lm.ex... | [((1364, 1385), 'matplotlib.use', 'matplotlib.use', (['"""Agg"""'], {}), "('Agg')\n", (1378, 1385), False, 'import matplotlib\n'), ((1444, 1465), 're.search', 're.search', (['"""\\\\d+$"""', 's'], {}), "('\\\\d+$', s)\n", (1453, 1465), False, 'import re\n'), ((1513, 1536), 're.search', 're.search', (['"""^[a-z]+"""', '... |
# -*- coding: utf-8 -*-
"""
2019-10-07-adjustables.py: Experiment runner file for generating data for
many combinations of numbers of high- vs. low-fidelity samples, specifically
for the adjustable benchmark functions.
A specific adjustable parameter can be given as commandline argument.
"""
__author__ = '<NAME>'
__e... | [
"experiments.create_model_error_grid",
"numpy.linspace",
"itertools.product",
"experiments.Instance",
"pyprojroot.here"
] | [((512, 549), 'pyprojroot.here', 'here', (['"""files/2019-10-07-adjustables/"""'], {}), "('files/2019-10-07-adjustables/')\n", (516, 549), False, 'from pyprojroot import here\n'), ((1427, 1467), 'itertools.product', 'product', (['cases', 'kernels', 'scaling_options'], {}), '(cases, kernels, scaling_options)\n', (1434, ... |
import numpy as np
from cost_functions import trajectory_cost_fn
import time
import logging
def dd(s):
logging.getLogger("hw4").debug(s)
def di(s):
logging.getLogger("hw4").info(s)
class Controller():
def __init__(self):
pass
# Get the appropriate action(s) for this state(s)
def get_act... | [
"cost_functions.trajectory_cost_fn",
"logging.getLogger",
"numpy.argmin",
"numpy.tile",
"numpy.concatenate"
] | [((1504, 1549), 'numpy.tile', 'np.tile', (['state', '(self.num_simulated_paths, 1)'], {}), '(state, (self.num_simulated_paths, 1))\n', (1511, 1549), True, 'import numpy as np\n'), ((2957, 3019), 'cost_functions.trajectory_cost_fn', 'trajectory_cost_fn', (['self.cost_fn', 'states', 'actions', 'next_states'], {}), '(self... |
"""
Movie recommender.
Copyright (C) <NAME> 2019
See the LICENSE file for more information.
Example usage:
$ python rmovie.py "lord of the rings"
$ python rmovie.py "shawshank redemption"
"""
import argparse
import numpy as np
import pandas as pd
if __name__ != '__main__':
m = '"rmovie.py" can\'t be imported'
... | [
"pandas.read_csv",
"numpy.any",
"argparse.ArgumentParser",
"numpy.unique"
] | [((358, 383), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {}), '()\n', (381, 383), False, 'import argparse\n'), ((3288, 3317), 'numpy.unique', 'np.unique', (['recommended_movies'], {}), '(recommended_movies)\n', (3297, 3317), True, 'import numpy as np\n'), ((610, 641), 'pandas.read_csv', 'pd.read_csv', (... |
import numpy as np
import tensorflow as tf
tf.compat.v1.disable_eager_execution() # need to disable eager in TF2.x
input_size = 3 # the length of input feature vectors to simple RNN cells
output_size = 5 # the length of output feature vectors produced by simple RNN cells
"""
* When thinking in low level, the op... | [
"tensorflow.random.normal",
"tensorflow.compat.v1.placeholder",
"tensorflow.compat.v1.disable_eager_execution",
"tensorflow.compat.v1.Session",
"tensorflow.matmul",
"numpy.array",
"tensorflow.compat.v1.global_variables_initializer"
] | [((44, 82), 'tensorflow.compat.v1.disable_eager_execution', 'tf.compat.v1.disable_eager_execution', ([], {}), '()\n', (80, 82), True, 'import tensorflow as tf\n'), ((1188, 1244), 'tensorflow.compat.v1.placeholder', 'tf.compat.v1.placeholder', (['tf.float32', '[input_size, None]'], {}), '(tf.float32, [input_size, None])... |
import pickle
import fire
import matplotlib.pyplot as plt
import numpy as np
def create_graph(time_trace_path, out, roi_select="0 1 2 3 4 5 6 7 8 9 10 11"):
with open(time_trace_path, "rb") as f:
time_traces = pickle.load(f)
std_past = None
for num_1, key in enumerate(time_traces.keys()):
... | [
"fire.Fire",
"numpy.std",
"numpy.hstack",
"matplotlib.pyplot.figure",
"pickle.load",
"numpy.arange",
"matplotlib.pyplot.gcf",
"numpy.vstack"
] | [((1802, 1825), 'fire.Fire', 'fire.Fire', (['create_graph'], {}), '(create_graph)\n', (1811, 1825), False, 'import fire\n'), ((225, 239), 'pickle.load', 'pickle.load', (['f'], {}), '(f)\n', (236, 239), False, 'import pickle\n'), ((686, 710), 'numpy.vstack', 'np.vstack', (['mean_flor_den'], {}), '(mean_flor_den)\n', (69... |
# ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by <NAME> (<EMAIL>)
# ------------------------------------------------------------------------------
from __future__ import absolute_import
from __future__ impor... | [
"numpy.array",
"yaml.load",
"os.path.join",
"easydict.EasyDict"
] | [((472, 479), 'easydict.EasyDict', 'edict', ([], {}), '()\n', (477, 479), True, 'from easydict import EasyDict as edict\n'), ((654, 661), 'easydict.EasyDict', 'edict', ([], {}), '()\n', (659, 661), True, 'from easydict import EasyDict as edict\n'), ((805, 812), 'easydict.EasyDict', 'edict', ([], {}), '()\n', (810, 812)... |
import os
import time
import numpy as np
import cv2
def getPathList(path, suffix='png'):
if (path[-1] != '/') & (path[-1] != '\\'):
path = path + '/'
pathlist = list()
g = os.walk(path)
for p, d, filelist in g:
for filename in filelist:
if filename.endswith(suffix):
... | [
"os.mkdir",
"os.path.isdir",
"os.walk",
"numpy.ascontiguousarray",
"numpy.clip",
"time.time",
"numpy.array",
"os.path.split",
"os.path.join"
] | [((194, 207), 'os.walk', 'os.walk', (['path'], {}), '(path)\n', (201, 207), False, 'import os\n'), ((671, 703), 'numpy.array', 'np.array', (['mean'], {'dtype': 'np.float32'}), '(mean, dtype=np.float32)\n', (679, 703), True, 'import numpy as np\n'), ((714, 745), 'numpy.array', 'np.array', (['std'], {'dtype': 'np.float32... |
import os, sys
from pdb import set_trace as st
import numpy as np
from functools import partial
import copy
from model.config import LENET
import nninst_mode as mode
from dataset import mnist
from dataset.config import MNIST_TRAIN
from dataset.mnist_transforms import *
from trace.lenet_mnist_class_trace_v2 import (
... | [
"functools.partial",
"model.config.LENET.network_class.graph",
"numpy.set_printoptions",
"numpy.count_nonzero",
"copy.deepcopy",
"numpy.packbits",
"numpy.zeros",
"numpy.unravel_index",
"numpy.array",
"numpy.reshape",
"numpy.logical_or",
"numpy.ravel_multi_index",
"trace.common.class_trace",
... | [((1007, 1075), 'trace.common.class_trace', 'class_trace', (['trace_name'], {'model_config': 'LENET', 'data_config': 'data_config'}), '(trace_name, model_config=LENET, data_config=data_config)\n', (1018, 1075), False, 'from trace.common import class_trace\n'), ((1493, 1510), 'numpy.packbits', 'np.packbits', (['mask'], ... |
import numpy as np
from layers import (
FullyConnectedLayer, ReLULayer,
ConvolutionalLayer, MaxPoolingLayer, Flattener,
softmax_with_cross_entropy, l2_regularization, softmax_with_cross_entropy, softmax
)
class ConvNet:
"""
Implements a very simple conv net
Input -> Conv[3x3] -> Relu -> ... | [
"layers.Flattener",
"numpy.zeros_like",
"layers.ConvolutionalLayer",
"numpy.argmax",
"layers.MaxPoolingLayer",
"layers.softmax_with_cross_entropy",
"layers.FullyConnectedLayer",
"layers.softmax",
"layers.ReLULayer"
] | [((1115, 1167), 'layers.ConvolutionalLayer', 'ConvolutionalLayer', (['n_channels', 'conv1_channels', '(3)', '(1)'], {}), '(n_channels, conv1_channels, 3, 1)\n', (1133, 1167), False, 'from layers import FullyConnectedLayer, ReLULayer, ConvolutionalLayer, MaxPoolingLayer, Flattener, softmax_with_cross_entropy, l2_regular... |
#!/usr/bin/python3
import matplotlib.pyplot as plt
import numpy as np
import copy
import re
# Helper functions
def getRowsAndCols(line):
index = 0
words = line.split()
for word in words:
if index == 0:
try:
rows = int(word)
except:
raise Exce... | [
"numpy.zeros"
] | [((1062, 1078), 'numpy.zeros', 'np.zeros', (['(0, 0)'], {}), '((0, 0))\n', (1070, 1078), True, 'import numpy as np\n')] |
import sys
import os
import cv2 as cv
import numpy as np
import piexif
import piexif.helper
class Photo:
''' Photo class to store individual photo data '''
def __init__(self, image):
self.image = image
def imageCV(self, image):
''' Return image as cv image array '''
im = cv.imrea... | [
"os.path.join",
"cv2.waitKey",
"cv2.destroyAllWindows",
"numpy.asarray",
"os.walk",
"cv2.imwrite",
"cv2.createAlignMTB",
"cv2.imread",
"piexif.load",
"cv2.createMergeDebevec",
"cv2.createCalibrateDebevec",
"cv2.imshow",
"cv2.createTonemapReinhard"
] | [((2352, 2380), 'cv2.imread', 'cv.imread', (['"""hdr_preview.jpg"""'], {}), "('hdr_preview.jpg')\n", (2361, 2380), True, 'import cv2 as cv\n'), ((2381, 2402), 'cv2.imshow', 'cv.imshow', (['"""HDR"""', 'hdr'], {}), "('HDR', hdr)\n", (2390, 2402), True, 'import cv2 as cv\n'), ((2403, 2416), 'cv2.waitKey', 'cv.waitKey', (... |
from matplotlib import pyplot as plt
import numpy as np
file_name='../result_split.txt'
def draw_best():
with open(file_name,'r') as f:
plt.figure()
old_freq=0
accuracy=[]
best=[]
for line in f.readlines():
freq=line.strip().split()[2][:-1]
accu=line.s... | [
"matplotlib.pyplot.plot",
"matplotlib.pyplot.legend",
"matplotlib.pyplot.figure",
"numpy.array",
"matplotlib.pyplot.savefig"
] | [((1537, 1549), 'matplotlib.pyplot.figure', 'plt.figure', ([], {}), '()\n', (1547, 1549), True, 'from matplotlib import pyplot as plt\n'), ((1694, 1706), 'matplotlib.pyplot.legend', 'plt.legend', ([], {}), '()\n', (1704, 1706), True, 'from matplotlib import pyplot as plt\n'), ((1711, 1740), 'matplotlib.pyplot.savefig',... |
import subprocess
import pandas
import io
import requests
import time
import json
from matplotlib import pyplot as plt
import numpy as np
from merlin.core import analysistask
class SlurmReport(analysistask.AnalysisTask):
"""
An analysis task that generates reports on previously completed analysis
tasks ... | [
"matplotlib.pyplot.title",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.yscale",
"io.StringIO",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.suptitle",
"matplotlib.pyplot.legend",
"matplotlib.pyplot.setp",
"json.dumps",
"time.time",
"matplotlib.pyplot.figure",
"n... | [((3037, 3064), 'matplotlib.pyplot.figure', 'plt.figure', ([], {'figsize': '(15, 4)'}), '(figsize=(15, 4))\n', (3047, 3064), True, 'from matplotlib import pyplot as plt\n'), ((3074, 3094), 'matplotlib.pyplot.subplot', 'plt.subplot', (['(1)', '(4)', '(1)'], {}), '(1, 4, 1)\n', (3085, 3094), True, 'from matplotlib import... |
import numpy as np
import cv2 as cv
from matplotlib import pyplot as plt
from tkinter import filedialog
from tkinter import *
import matplotlib.pyplot as plt
from skimage import data, util
from skimage.draw import ellipse
from skimage.measure import label, regionprops
from skimage.transform import rotate
impo... | [
"matplotlib.pyplot.title",
"numpy.sum",
"matplotlib.pyplot.figure",
"cv2.ellipse",
"numpy.mean",
"numpy.sin",
"cv2.erode",
"cv2.imshow",
"cv2.line",
"numpy.zeros_like",
"numpy.std",
"cv2.cvtColor",
"tkinter.filedialog.askopenfilename",
"cv2.setMouseCallback",
"cv2.fitEllipse",
"cv2.dra... | [((474, 598), 'tkinter.filedialog.askopenfilename', 'filedialog.askopenfilename', ([], {'initialdir': '"""/"""', 'title': '"""Select file"""', 'filetypes': "(('all files', '.*'), ('jpg files', '.jpg'))"}), "(initialdir='/', title='Select file', filetypes=(\n ('all files', '.*'), ('jpg files', '.jpg')))\n", (500, 598... |
import cv2 as cv
import numpy as np
IMAGE = cv.imread('D:\@Semester 06\Digital Image Processing\Lab\Manuals\Figures\lab7\_img1.png', 0)
cv.imshow('Original Image', IMAGE)
cv.waitKey()
cv.destroyAllWindows()
print(IMAGE.shape)
def globalAdaptiveThreshold(_img):
size = np.shape(_img) # Find img size
rows = s... | [
"cv2.waitKey",
"cv2.destroyAllWindows",
"numpy.zeros",
"numpy.shape",
"cv2.imread",
"numpy.mean",
"cv2.imshow"
] | [((45, 153), 'cv2.imread', 'cv.imread', (['"""D:\\\\@Semester 06\\\\Digital Image Processing\\\\Lab\\\\Manuals\\\\Figures\\\\lab7\\\\_img1.png"""', '(0)'], {}), "(\n 'D:\\\\@Semester 06\\\\Digital Image Processing\\\\Lab\\\\Manuals\\\\Figures\\\\lab7\\\\_img1.png'\n , 0)\n", (54, 153), True, 'import cv2 as cv\n')... |
'''
ModelNet dataset. Support ModelNet40, ModelNet10, XYZ and normal channels. Up to 10000 points.
'''
import os
import os.path
import numpy as np
import sys
from glob import glob
from collections import Counter
import tensorflow as tf
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT_DIR = BASE_DIR
sys.... | [
"provider.shuffle_points",
"numpy.load",
"numpy.sum",
"numpy.mean",
"numpy.arange",
"provider.rotate_perturbation_point_cloud_with_normal",
"glob.glob",
"os.path.join",
"provider.rotate_perturbation_point_cloud",
"os.path.abspath",
"numpy.random.choice",
"collections.Counter",
"provider.rota... | [((269, 294), 'os.path.abspath', 'os.path.abspath', (['__file__'], {}), '(__file__)\n', (284, 294), False, 'import os\n'), ((332, 363), 'os.path.join', 'os.path.join', (['ROOT_DIR', '"""utils"""'], {}), "(ROOT_DIR, 'utils')\n", (344, 363), False, 'import os\n'), ((439, 458), 'numpy.mean', 'np.mean', (['pc'], {'axis': '... |
import setting.constant as const
import numpy as np
import cv2
def overlay(image, layer):
if (len(layer.shape) == 2):
layer = cv2.cvtColor(layer, cv2.COLOR_GRAY2BGR)
image = cv2.cvtColor(image, cv2.COLOR_BGR2BGRA)
layer = cv2.cvtColor(layer, cv2.COLOR_BGR2BGRA)
layer[np.where((layer == [0... | [
"cv2.equalizeHist",
"cv2.Canny",
"numpy.uint8",
"cv2.bitwise_not",
"cv2.GaussianBlur",
"cv2.medianBlur",
"cv2.cvtColor",
"cv2.threshold",
"numpy.zeros",
"numpy.clip",
"cv2.addWeighted",
"cv2.split",
"numpy.array",
"numpy.mean",
"cv2.createCLAHE",
"cv2.merge"
] | [((196, 235), 'cv2.cvtColor', 'cv2.cvtColor', (['image', 'cv2.COLOR_BGR2BGRA'], {}), '(image, cv2.COLOR_BGR2BGRA)\n', (208, 235), False, 'import cv2\n'), ((248, 287), 'cv2.cvtColor', 'cv2.cvtColor', (['layer', 'cv2.COLOR_BGR2BGRA'], {}), '(layer, cv2.COLOR_BGR2BGRA)\n', (260, 287), False, 'import cv2\n'), ((487, 529), ... |
import sys
sys.path.append("..")
sys.path.append("../..")
import numpy as np
import os
import argparse
from sklearn.metrics import accuracy_score
import tensorflow as tf
from data.synthetic_dataset_wt_conf_za_link import SyntheticDataset, confounder_monotonicities_1,get_subportion_confounders
import pandas as pd
fro... | [
"tensorflow.random.set_seed",
"numpy.random.seed",
"argparse.ArgumentParser",
"numpy.sum",
"sklearn.metrics.accuracy_score",
"sklearn.preprocessing.MinMaxScaler",
"numpy.ones",
"data.synthetic_dataset_wt_conf_za_link.get_subportion_confounders",
"sklearn.metrics.f1_score",
"numpy.random.randint",
... | [((12, 33), 'sys.path.append', 'sys.path.append', (['""".."""'], {}), "('..')\n", (27, 33), False, 'import sys\n'), ((34, 58), 'sys.path.append', 'sys.path.append', (['"""../.."""'], {}), "('../..')\n", (49, 58), False, 'import sys\n'), ((1653, 1667), 'sklearn.preprocessing.MinMaxScaler', 'MinMaxScaler', ([], {}), '()\... |
from kuruve.envs.GymEnv import KuruveGymEnv
from kuruve.KurveGame import *
from gym import spaces
import pygame
import numpy as np
import math
class SimpleAiEnv(KuruveGymEnv):
"""
Environment with an AI opponent
"""
def __init__(self, headless=False, observation_size=(64, 64), fps_cap=0, frameskip=0,... | [
"numpy.dstack",
"pygame.Surface",
"math.ceil",
"pygame.draw.rect",
"gym.spaces.Discrete",
"gym.spaces.Box",
"pygame.surfarray.pixels3d",
"numpy.dot"
] | [((758, 790), 'pygame.Surface', 'pygame.Surface', (['self.screen_size'], {}), '(self.screen_size)\n', (772, 790), False, 'import pygame\n'), ((895, 913), 'gym.spaces.Discrete', 'spaces.Discrete', (['(3)'], {}), '(3)\n', (910, 913), False, 'from gym import spaces\n'), ((947, 1047), 'gym.spaces.Box', 'spaces.Box', ([], {... |
import os
import sys
import argparse
import cv2
import numpy as np
from tensorflow import keras
sys.path.insert(1, './src/file_management')
import file_manager
###############################################################################################################
def pre_process(image):
""... | [
"tensorflow.keras.models.load_model",
"argparse.ArgumentParser",
"file_manager.get_content_from_folder",
"numpy.expand_dims",
"sys.path.insert",
"numpy.mean",
"os.path.join"
] | [((106, 149), 'sys.path.insert', 'sys.path.insert', (['(1)', '"""./src/file_management"""'], {}), "(1, './src/file_management')\n", (121, 149), False, 'import sys\n'), ((846, 890), 'file_manager.get_content_from_folder', 'file_manager.get_content_from_folder', (['folder'], {}), '(folder)\n', (882, 890), False, 'import ... |
"""
This class will be removed before the first stable version of ZairaChem.
Computing time is simply unaffordable for the stacking methodology, unfortunately.
"""
import os
import numpy as np
import pandas as pd
import json
import h5py
import joblib
from .. import ZairaBase
from ..estimators.base import BaseEstimator... | [
"pandas.DataFrame",
"json.load",
"pandas.read_csv",
"numpy.hstack",
"numpy.array",
"os.path.join",
"pandas.concat"
] | [((2990, 3007), 'numpy.hstack', 'np.hstack', (['self.X'], {}), '(self.X)\n', (2999, 3007), True, 'import numpy as np\n'), ((3021, 3058), 'pandas.DataFrame', 'pd.DataFrame', (['X'], {'columns': 'self.columns'}), '(X, columns=self.columns)\n', (3033, 3058), True, 'import pandas as pd\n'), ((3610, 3628), 'numpy.array', 'n... |
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