prompt stringlengths 15 655k | completion stringlengths 3 32.4k | api stringlengths 8 52 |
|---|---|---|
import sqlite3
import numpy as np
import Helpers
conn = sqlite3.connect('../data/SandP500.sqlite3')
all_tickers = Helpers.get_all_tickers(conn)
cursor = conn.cursor()
prices_at_start = np.array([])
prices_at_end = np.array([])
for ticker in all_tickers:
cursor.execute("SELECT closing_price "
... | np.append(prices_at_start, price_at_start) | numpy.append |
import numpy as np
from ..visualization import Viewer
from ..utils import Subject, Observer, deprecated, matrices, NList
import copy
from numba import njit, int64, float64
from numba.types import ListType as LT
@njit(int64[:](LT(LT(int64))), cache=True)
def _valence(adj_x2y):
valences = np.zeros(len(adj_x2y), dtyp... | np.logical_xor(flip_z,((centroids[:,2] >= min_z) & (centroids[:,2] <= max_z))) | numpy.logical_xor |
import os, math
import _pickle as pickle
from datetime import datetime, timedelta
import numpy as np
import pandas as pd
from sklearn import preprocessing
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--data-folder', default='data', help='Parent dir of the dataset')
parser.add_argument('--f... | np.zeros(shape=(test_n, input_len)) | numpy.zeros |
import numpy as np
import sys, os
if __name__== "__main__":
# read samples mesh gids
smgids = np.loadtxt("sample_mesh_gids.dat", dtype=int)
print(smgids)
# read full velo
fv = np.loadtxt("./full/velo.txt")
# read full velo
fullJ = np.loadtxt("./full/jacobian.txt")
# read sample mesh velo
sv = np... | np.allclose(maskedJacob.shape, sjac.shape) | numpy.allclose |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Module for tools used in vaspy
"""
import bz2
from itertools import zip_longest
import os
import re
import numpy as np
from typing import List, Iterable, Sequence, Tuple, Union, IO, Any, Optional
def open_by_suffix(filename: str) -> IO[str]:
"""Open file."""
... | np.array(crystal_axes[2]) | numpy.array |
__author__ = 'Mario'
import numpy as np
from scipy.stats import norm
class EuropeanLookback():
def __init__(self, strike, expiry, spot, sigma, rate, dividend, M, flag, N=100, Vbar=.12, alpha=.69):
# Instantiate variables
self.strike = float(strike)
self.expiry = float(expiry)
self... | np.sqrt(Vtn) | numpy.sqrt |
import unittest
from scipy.stats import gaussian_kde
from scipy.linalg import cholesky
import numpy as np
from pyapprox.bayesian_inference.laplace import *
from pyapprox.density import NormalDensity, ObsDataDensity
from pyapprox.utilities import get_low_rank_matrix
from pyapprox.randomized_svd import randomized_svd, Ma... | np.dot(gradient,directions) | numpy.dot |
"""Class for playing and annotating video sources in Python using Tkinter."""
import json
import logging
import pathlib
import datetime
import tkinter
import tkinter.filedialog
import numpy as np
import cv2
import PIL.Image
import PIL.ImageTk
logger = logging.getLogger("VideoPyer")
logging.basicConfig(level=logging.I... | np.array([x1, y1]) | numpy.array |
from DNN.hans_on_feedforward_neural_network import Feedforward_neural_network
import numpy as np
Net = Feedforward_neural_network()
#--------------------------多元回归实验-----------------------------
# ---------------------------准备数据-------------------------------
#--------------------------------------------------------... | np.random.normal(0, 10, size=Y_data.shape) | numpy.random.normal |
#Contains MeldCohort and MeldSubject classes
from contextlib import contextmanager
from meld_classifier.paths import (
DEMOGRAPHIC_FEATURES_FILE,
CORTEX_LABEL_FILE,
SURFACE_FILE,
DEFAULT_HDF5_FILE_ROOT,
BOUNDARY_ZONE_FILE,
NVERT,
BASE_PATH,
)
import pandas as pd
import numpy as np
import ni... | np.sum(self.cohort.surf_area[lesion]) | numpy.sum |
import numpy as np
import math
import os
def load_obj(dire):
fin = open(dire,'r')
lines = fin.readlines()
fin.close()
vertices = []
triangles = []
for i in range(len(lines)):
line = lines[i].split()
if len(line)==0:
continue
if line[0] == 'v':
... | np.array(triangles, np.int32) | numpy.array |
# Licensed under an MIT open source license - see LICENSE
"""
SCOUSE - Semi-automated multi-COmponent Universal Spectral-line fitting Engine
Copyright (c) 2016-2018 <NAME>
CONTACT: <EMAIL>
"""
import numpy as np
import sys
import warnings
import pyspeckit
import matplotlib.pyplot as plt
import itertools
import time... | np.abs(velolist[i] - adjacent_velocity) | numpy.abs |
import math
import numpy as np
from scipy import signal
def gaussian_pdf_1d(mu, sigma, length):
'''Generate one dimension Gaussian distribution
- input mu: the mean of pdf
- input sigma: the standard derivation of pdf
- input length: the size of pdf
- output: a row vector represent... | np.arctan(ly/lx) | numpy.arctan |
"""
desisim.spec_qa.redshifts
=========================
Module to run high_level QA on a given DESI run
Written by JXP on 3 Sep 2015
"""
from __future__ import print_function, absolute_import, division
import matplotlib
# matplotlib.use('Agg')
import numpy as np
import sys, os, pdb, glob
from matplotlib import pyp... | np.max(xval) | numpy.max |
"""
fastspecfit.continuum
=====================
Methods and tools for continuum-fitting.
"""
import pdb # for debugging
import os, time
import numpy as np
import astropy.units as u
from fastspecfit.util import C_LIGHT
from desiutil.log import get_logger
log = get_logger()
def _fnnls_continuum(myargs):
"""Mult... | np.nanmin(continuum_phot_abmag[indx]) | numpy.nanmin |
"""
Module for calculating metrics from CO2, usually as a baseline to compare other gases.
Author: <NAME> (UK)
Adapted by <NAME>
"""
import numpy as np
from fair.constants import molwt
from fair.constants.general import M_ATMOS
from fair.forcing.ghg import meinshausen
from fair.defaults.thermal import q, d
def ch4_... | np.array([co2, ch4, n2o]) | numpy.array |
"""Run chemical evolution model."""
from __future__ import print_function, division, absolute_import
import os
from os.path import join
import copy
import traceback
import time
import numpy as np
import pandas as pd
import utils
def integrate_power_law(exponent, bins=None):
"""Integrate a power law distributio... | np.array(tmp['ab']) | numpy.array |
import os
import sys
import h5py
import torch
import numpy as np
import importlib
import random
import shutil
from PIL import Image
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append(os.path.join(BASE_DIR, '../utils'))
from colors import colors
colors = np.array(colors, dtype=np.float32)
... | np.sin(theta) | numpy.sin |
import numpy as np
def gradient_descent(x, y):
m_curr = b_curr = 0
iterations = 1250
n = len(x)
learning_rate = 0.08
for i in range(iterations):
y_predicted = m_curr * x + b_curr
cost = (1 / n) * sum([val ** 2 for val in (y - y_predicted)])
md = -(2 / n) * sum(x * (y - y_p... | np.array([1, 2, 3, 4, 5]) | numpy.array |
import os
import tqdm
import shutil
import argparse
import setproctitle
import pandas as pd
import numpy as np
from skimage import measure
from skimage.io import imsave
import matplotlib.pyplot as plt
plt.switch_backend('agg')
from ast import literal_eval
import SimpleITK as sitk
import torch
import torch.nn as nn... | np.array(dsc_list) | numpy.array |
"""
Author: <NAME> <<EMAIL>>.
References:
-
-
-
"""
import os
import networkx as nx
import numpy as np
from hyperopt import fmin, tpe, hp, STATUS_OK, Trials
from sklearn.metrics import f1_score, accuracy_score
# For the plot functions.
_Z_ORDER_V = 10
_Z_ORDER_SE = _Z_ORDER_V - 1
_Z_ORDER_SSV = _Z_ORDE... | np.zeros(shape=nsamples, dtype=np.int8) | numpy.zeros |
"""
File: examples/distribution/binomial_distribution.py
Author: <NAME>
Date: Oct 15 2019
Description: Example of using the BinomialDistribution class.
"""
import os, time
import numpy as np
import matplotlib.pyplot as pl
from distpy import BinomialDistribution
sample_size = int(1e5)
distribution = BinomialDistribut... | np.std(sample) | numpy.std |
import numpy as np
def Fourier_shear(image,shear_factor,axis=[-1,-2],fftshifted=False):
"""Accomplishes the following affine transformation to an image:
[x'] = [ 1 shear_factor] [x]
[y'] [ 0 1 ] [y]
via Fourier transform based methods.
Paramet... | np.meshgrid(qxout,qyout) | numpy.meshgrid |
import numpy as np
import scipy.sparse
from numpy import sin, cos, tan
import sys
import slepc4py
slepc4py.init(sys.argv)
from petsc4py import PETSc
from slepc4py import SLEPc
opts = PETSc.Options()
import pickle as pkl
class Model():
def __init__(self, model_variables, model_parameters, physical_constants):
... | sin(th) | numpy.sin |
#!/usr/bin/env python3
# encoding: utf-8
# Copyright 2017 Johns Hopkins University (<NAME>)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Training/decoding definition for the speech recognition task."""
import copy
import json
import logging
import math
import os
import sys
from chainer import repo... | np.array([x.shape[0] for x in xs_list[i]]) | numpy.array |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
SeedEditor for organ segmentation
Example:
$ seed_editor_qp.py -f head.mat
"""
from loguru import logger
# try:
# QString = unicode
# except NameError:
# Python 3
# QString = str
QString = str
# import unittest
from optparse import OptionParser
from scipy.io import... | np.max(self.seeds) | numpy.max |
import cv2
import numpy as np
import os
from objects.bbox import BBox
from objects.image import Image
class BBoxList:
def __init__(self):
self.data = []
def __len__(self):
return len(self.data)
def reduce_to_classes(self, class_list):
new_data = []
for d in self.data:
... | np.mean(counts) | numpy.mean |
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 20 15:12:49 2016
@author: uzivatel
"""
import numpy as np
import timeit
from multiprocessing import Pool, cpu_count
from functools import partial
from sys import platform
import scipy
from copy import deepcopy
from ..qch_functions import overlap_STO, dipole... | np.power(Z_grid_loc,2) | numpy.power |
# -*- coding: utf-8 -*-
from datetime import datetime
from io import StringIO
import re
import numpy as np
import pytest
from pandas.compat import lrange
import pandas as pd
from pandas import DataFrame, Index, MultiIndex, option_context
from pandas.util import testing as tm
import pandas.io.formats.format as fmt
... | np.zeros((2, 2), dtype=int) | numpy.zeros |
import pandas as pd
import numpy as np
from ..dataModel.dataProcessing import DataContainer, myDataset
import matplotlib.pyplot as plt
from torch import nn
import torch as tc
from sklearn.metrics import *
from tqdm import tqdm
from torch.nn import functional as F
from ..utils import one_hot_embedding, window_padding
im... | np.mean(mini_loss) | numpy.mean |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on 11 August, 2018
Testing suite for Network class
@author: <NAME>
@email: <EMAIL>
@date: 11 August, 2018
@modified: 16 february, 2021
"""
import unittest
import numpy as np
from topopy import Flow, Network
import os
infolder = "data/in"
outfolder = "data/out... | np.array([]) | numpy.array |
'''
Helper functions for the puzzle.py
'''
import random
from simpleimage import SimpleImage
import math
import numpy as np
def create_solution(num_pieces, seed = 2000):
'''
Takes a number of pieces as input
Returns the original order and a random order of pieces as a dictionary with
{orignal_positio... | np.array([0, height - 1, 0]) | numpy.array |
import math
import numpy as np
import random
class Tiling():
def __init__(self, x_range=[-1.2,0.6], v_range= [-0.07,0.07], n_tiles=4, n_tilings=5, displacement_vector=[1,3]):
self.x_range = np.array(x_range)
self.v_range = np.array(v_range)
self.x = 0
self.v = 0
s... | np.shape(state) | numpy.shape |
# -*- coding: utf-8 -*-
import os
import json
from datetime import datetime
import numpy as np
from matplotlib import pyplot as plt
def visualize_result(
experiment_name,
X_test, Y_test, Y_hat, parameters,
losses=None, save_dir="results"
):
"""
结果可视化
"""
# 没有保存目录时创建... | np.arange(0.0, 1.0, 0.01) | numpy.arange |
import numpy as np
import pandas as pd
from typing import List
from brightwind.transform import transform as tf
from brightwind.analyse.plot import plot_scatter, plot_scatter_by_sector, plot_scatter_wdir
from scipy.odr import ODR, RealData, Model
from scipy.linalg import lstsq
from brightwind.analyse.analyse import mom... | np.isnan(sec_veers[i - 1]) | numpy.isnan |
from __future__ import print_function
from __future__ import division
from builtins import str
from flarestack.utils.prepare_catalogue import ps_catalogue_name
from flarestack.data.icecube.ps_tracks.ps_v002_p01 import IC86_1_dict, IC86_234_dict
from flarestack.core.results import ResultsHandler
from flarestack.cluster ... | np.array(sens) | numpy.array |
"""
1HN In-phase/Anti-phase Proton CEST
===================================
Analyzes chemical exchange during the CEST block. Magnetization evolution is
calculated using the (6n)×(6n), two-spin matrix, where n is the number of
states::
{ Ix(a), Iy(a), Iz(a), IxSz(a), IySz(a), IzSz(a),
Ix(b), Iy(b), Iz(b), I... | np.array([intst[offset] for offset in offsets]) | numpy.array |
from __future__ import division, absolute_import, print_function
import platform
import numpy as np
from numpy import uint16, float16, float32, float64
from numpy.testing import run_module_suite, assert_, assert_equal, dec
def assert_raises_fpe(strmatch, callable, *args, **kwargs):
try:
callable(*args, ... | np.array((1e4,), dtype=float16) | numpy.array |
from abc import ABCMeta
import numpy as np
from typing import List
import TransportMaps.Distributions as dist
import TransportMaps.Likelihoods as like
from utils.LinAlg import is_spd
class Distribution(metaclass=ABCMeta):
@property
def dim(self) -> int:
raise NotImplementedError
def ... | np.linalg.inv(self._precision) | numpy.linalg.inv |
"""Module defining backend agnostic containers for visualisation elements."""
from collections import OrderedDict
from collections.abc import Mapping
from copy import deepcopy
from typing import List
import numpy as np
class Element(object):
"""Representation of a single element.
Implemented as a frozen dic... | np.array(self._positions) | numpy.array |
import os
import numpy as np
import matplotlib.pyplot as plt
class YOLO_Kmeans:
def __init__(self, cluster_number, filename, save_path):
self.cluster_number = cluster_number
self.filename = filename
self.save_path = save_path
def iou(self, boxes, clusters): # 1 box -> k clusters
... | np.shape(data) | numpy.shape |
"""Module handling the creation and use of migration matrices."""
from copy import deepcopy
from warnings import warn
import numpy as np
from .binning import Binning, CartesianProductBinning
class ResponseMatrix:
"""Matrix that describes the detector response to true events.
Parameters
----------
... | np.append(resp2, truth2[np.newaxis, :], axis=0) | numpy.append |
import numpy as np
from numpy import random
from scipy.interpolate import interp1d
import pandas as pd
msun = 1.9891e30
rsun = 695500000.0
G = 6.67384e-11
AU = 149597870700.0
def component_noise(tessmag, readmod=1, zodimod=1):
sys = 59.785
star_mag_level, star_noise_level = np.array(
[
[4... | np.zeros(nselect) | numpy.zeros |
from sklearn import metrics
import numpy as np
import pandas as pd
import seaborn as sns
from .stats import *
from .scn_train import *
import matplotlib
import matplotlib.pyplot as plt
def divide_sampTab(sampTab, prop, dLevel="cell_ontology_class"):
cts = set(sampTab[dLevel])
trainingids = np.empty(0)
for... | np.sum(cm) | numpy.sum |
from __future__ import division, print_function
import cmath
import time
from copy import copy
import os
import argparse
import inspect
from collections import OrderedDict
from timeit import default_timer as timer
try:
from inspect import getfullargspec
except ImportError:
from inspect import getargspec as ge... | cos(x) | numpy.cos |
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse
from scipy.stats import multivariate_normal
from tqdm import tqdm
class GMM:
def __init__(self, x):
self.x = x
self.pts = x.shape[0]
self.k, self.w, self.pi, self.mu, self.sigma = None, None, None, Non... | np.zeros((k, self.x.shape[-1], self.x.shape[-1])) | numpy.zeros |
""""
Library of different error metrics like mean squared error, KL-divergence, etc.
Used to compute the reconstruction error of the autoencoder
"""
import os
import numpy as np
from src.preprocessing import heartbeat_split
import random
import matplotlib.pyplot as plt
from scipy import signal
from scipy.stats import... | np.mean(kld, axis=1) | numpy.mean |
#! /usr/bin/env python
"""Phase contraint overlap tool. This tool calculates the minimum and maximum phase of
the primary or secondary transit (by default, primary) based on parameters provided by the user.
Authors:
<NAME>, 2018
<NAME>, 2018
<NAME>, 2020
Usage:
calculate_constraint <target_name> [--t0=<... | np.cos(x) | numpy.cos |
import pandas as pd
import numpy as np
import codecs
import time
from org.mk.training.dl.rnn import bidirectional_dynamic_rnn
from org.mk.training.dl.rnn import dynamic_rnn
from org.mk.training.dl.rnn import MultiRNNCell
from org.mk.training.dl.nn import embedding_lookup
from org.mk.training.dl.nn import TrainableVaria... | np.amax(en_text_len) | numpy.amax |
# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2014-2018 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... | np.radians(dists.azimuth) | numpy.radians |
import string
import torch
from net import RINet, RINet_attention
from database import evalDataset_kitti360, SigmoidDataset_kitti360, SigmoidDataset_train, SigmoidDataset_eval
import numpy as np
from torch.utils.data import DataLoader
from tqdm import tqdm
from sklearn import metrics
import os
import argparse
# from te... | np.nan_to_num(pred) | numpy.nan_to_num |
""" A distributed version of the paraboloid model with an extra input that can be used to shift
each index.
This version is used for testing, so it will have different options.
"""
import numpy as np
import openmdao.api as om
from openmdao.utils.mpi import MPI
from openmdao.utils.array_utils import evenly_distrib_idx... | np.arange(io_size) | numpy.arange |
import pytest
import numpy as np
import pandas as pd
from ..utils import _check_random_state
from ..utils import _check_min_supp
from ..utils import _check_growth_rate
from ..utils import filter_maximal
from ..utils import filter_minimal
from ..utils import intersect2d
def test_check_random_state():
random_state ... | np.array([1]) | numpy.array |
#
# Created by: <NAME>, September 2002
#
import sys
import subprocess
import time
from functools import reduce
from numpy.testing import (assert_equal, assert_array_almost_equal, assert_,
assert_allclose, assert_almost_equal,
assert_array_equal)
import pytest
from... | assert_equal(info, 0) | numpy.testing.assert_equal |
from __future__ import absolute_import, division, print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
import cv2
import os
import sys
from torch.optim.lr_scheduler import ExponentialLR
from collections import namedtuple
from got10k.trackers ... | np.hanning(self.response_sz) | numpy.hanning |
from abc import ABC, abstractmethod
from typing import Optional
import numpy as np
from gym.spaces import Discrete, MultiDiscrete
from torch.distributions import Normal, kl_divergence
from pearll.common.type_aliases import (
CrossoverFunc,
MutationFunc,
SelectionFunc,
UpdaterLog,
)
from pearll.common.... | np.min(new_population, axis=0) | numpy.min |
import matplotlib.pyplot as plt
#import matplotlib.axes as axes
import numpy as np
#axes.Axis.set_axisbelow(True)
x = np.array([1,2,3,4,5,6,7])
my_xticks = ['1','2','3','4','5','6','7']
plt.xticks(x, my_xticks)
# for L=1,w=1,d=1
# for L=1,w=2,d=1
# for L=1,w=3,d=1
# for L=1,w=4,d=1
y = np.array([0.207044,np.nan,np.nan... | np.array([np.nan,np.nan,0.375075,0.325434,np.nan,np.nan,np.nan]) | numpy.array |
from collections import namedtuple
from rlpyt.utils.collections import namedarraytuple, AttrDict
import numpy as np
Samples = namedarraytuple("Samples", ["agent", "env"])
AgentSamples = namedarraytuple("AgentSamples",
["action", "prev_action", "agent_info"])
AgentSamplesBsv = namedarraytuple("AgentSamplesBsv",
... | np.sum(obs_act) | numpy.sum |
#!/usr/bin/env python3
def get_input():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument(
'Output-File',
help='Output-File from a RASSI calculation'
)
parser.add_argument(
'-s',
'--sigma',
required=False,
type=float,
default=150,
help='Plotting option for gaussian broadening... | np.array([]) | numpy.array |
# # Short Assignment 2: Image Restoration
# ## SCC0251.2020.1 - Image Processing
# ### Prof. Dr. <NAME>
# ### 10284952 - <NAME>
# https://github.com/vitorgt/SCC0251
# Imports
import numpy as np
import imageio
# import matplotlib.pyplot as plt
r = imageio.imread(str(input()).rstrip()).astype(np.uint8)
k = int(input(... | np.std(r_denoi_deblur) | numpy.std |
# -*- coding:utf8 -*-
# File : rng.py
# Author : <NAME>
# Email : <EMAIL>
# Date : 2/23/17
#
# This file is part of TensorArtist.
# This file is part of NeuArtist2
import os
import numpy as np
import numpy.random as npr
__all__ = ['rng', 'reset_rng', 'gen_seed', 'gen_rng', 'shuffle_multiarray']
rng = None
de... | npr.RandomState(seed) | numpy.random.RandomState |
r"""
Python module to compute the Mann-Kendall test for trend in time series data.
This module contains a single function 'test' which implements the Mann-Kendall
test for a linear trend in a given time series.
Introduction to the Mann-Kendall test
-------------------------------------
The Mann-Kendall test is used... | np.fabs(S) | numpy.fabs |
"""Linear operator tests.
"""
from __future__ import division, absolute_import
import unittest
import numpy as np
from bcn.linear_operators import LinearOperatorEntry, LinearOperatorDense, LinearOperatorKsparse, LinearOperatorCustom, integer_to_matrix, sample_n_choose_k, choose_random_matrix_elements
from bcn.data i... | np.array(self.signal) | numpy.array |
__author__ = '<NAME>'
import numpy as np
def myKMeans(k, Data):
dataL = np.zeros((Data.shape[0], Data.shape[1]+1), dtype=np.float64)
dataL[:,1:] = Data
randInd = np.random.randint(0, Data.shape[0], k, np.int64)
centroids = Data[randInd, :]
labelsC = np.asarray(range(k))
flag = ... | np.sum((dataL[:, 1:] - meanCent) ** 2, axis=1) | numpy.sum |
import numpy as np
import matplotlib.pyplot as plt
from quat import Quat
from sys import exit
def ori_matrix(phi1,Phi,phi2,passive=True):
'''
Returns (passive) orientation matrix, as a np.matrix from
3 euler angles (in degrees).
'''
phi1=np.radians(phi1)
Phi=np.radians(Phi)
phi2=np.... | np.isclose(alpha,0.0) | numpy.isclose |
import numpy as np
import matplotlib.pyplot as plt
from sklearn import metrics
from scipy.stats import spearmanr, combine_pvalues, friedmanchisquare
from scikit_posthocs import posthoc_nemenyi_friedman
from tabulate import tabulate
from Orange.evaluation import compute_CD, graph_ranks
from hmeasure import h_scor... | np.array(all_aps) | numpy.array |
# coding: utf-8
# # Creating a dataset of Ohio injection wells
import matplotlib.pyplot as plt
import random
import numpy as np
import pandas as pd
import os
# set datadir to the directory that holds the zipfile
datadir = 'c:\MyDocs/sandbox/data/datasets/FracFocus/'
outdir = datadir+'output/'
indir = datadir+'OH_... | np.where(four.NoAPIstr,'No API string recorded',four.APIstr) | numpy.where |
"""
Testing module for Domain.py, Shape.py, BC.py
Work in progress
TO DO:
test inertia
test rigid body calculations
"""
from __future__ import division
from builtins import range
from past.utils import old_div
import unittest
import numpy.testing as npt
import numpy as np
from nose.tools import eq_
from proteus import ... | np.max(flags_v2DRANS) | numpy.max |
#!/usr/bin/env python3
#
# Tests the cone distribution.
#
# This file is part of PINTS (https://github.com/pints-team/pints/) which is
# released under the BSD 3-clause license. See accompanying LICENSE.md for
# copyright notice and full license details.
#
import pints
import pints.toy
import unittest
import numpy as n... | np.ones((100, 6)) | numpy.ones |
"""
Calculate the wavelet and its significance.
"""
from __future__ import division, absolute_import
import numpy as np
from scipy.special._ufuncs import gamma, gammainc
from scipy.optimize import fminbound as fmin
from scipy.fftpack import fft, ifft
__author__ = "<NAME>"
__email__ = "<EMAIL>"
__all__ = ['Wavelet', ... | np.concatenate(([0.],k1,k2)) | numpy.concatenate |
'''
Contains the following neural pooling functions:
1. min
2. max
3. avg
Which are from
`Tang et al <https://aclanthology.coli.uni-saarland.de/papers/P14-1146/p14-1146>`_.
and the following pooling functions:
4. prod
5. std
Which are from
`Vo and Zhang <https://www.ijcai.org/Proceedings/15/Papers/194.pdf>`_.
and... | np.std(matrix, axis=0) | numpy.std |
import numpy as np
from surpyval import nonparametric as nonp
from scipy.stats import t, norm
from .kaplan_meier import KaplanMeier
from .nelson_aalen import NelsonAalen
from .fleming_harrington import FlemingHarrington_
from scipy.interpolate import interp1d
from autograd import jacobian
import matplotlib.pyplot as ... | np.log(self.R) | numpy.log |
import os, sys, pdb, pickle
from profilehooks import profile
import time, math, random
import numpy as np
import scipy as sp
from scipy.spatial.distance import cosine
from lr.sks import SKS
from lr.utils import *
def im2col(X, kernel, strides=(1,1), padding=(0,0)):
'''
Views X as the matrix-version of a str... | np.ones((1,channels), dtype=dt) | numpy.ones |
from pickle import load
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from keras.utils import to_categorical
from numpy import array, argmax
# load doc into memory
def load_doc(filename):
# open the file as read only
file = open(filename, 'r')
# rea... | array(x1) | numpy.array |
import numpy as np
import pandas as pd
import scipy.integrate as intg
import matplotlib.pyplot as plt
from matplotlib import cm
from mpl_toolkits.axes_grid1 import make_axes_locatable
from matplotlib.colors import LogNorm
import scipy.ndimage.interpolation as interpol
import scipy.spatial as sp
import decimal
import te... | np.mean(tau) | numpy.mean |
from __future__ import absolute_import, division, print_function
from java import constructor, method, static_proxy, jint, jarray, jdouble, jboolean, jclass
from java.lang import String
from scipy.signal import butter, lfilter
from sklearn.decomposition import FastICA
import numpy as np
import scipy
class NpScip... | np.fft.fftfreq(a, b) | numpy.fft.fftfreq |
"""
Main script for semantic experiments
Author: <NAME> (github/VSainteuf)
License: MIT
"""
import argparse
import json
import os
import pickle as pkl
import pprint
import time
import numpy as np
import torch
import torch.nn as nn
import torch.utils.data as data
import torchnet as tnt
from src import utils, model_uti... | np.delete(cm, config.ignore_index, axis=0) | numpy.delete |
import cv2
import numpy as np
import matplotlib.pyplot as plt
class Lane:
def __init__(self, windows_count = 9, margin = 100, minpix = 50,
color=(255, 0, 0), show_image = False):
self.show_image = show_image
self.color = color
# HYPERPARAMETERS
# Choose ... | np.concatenate(lane_inds) | numpy.concatenate |
from typing import List, Union
import numpy as np
def get_test_function_method_min(n: int, a: List[List[float]], c: List[List[float]],
p: List[List[float]], b: List[float]):
"""
Функция-замыкание, генерирует и возвращает тестовую функцию, применяя метод Фельдбаума,
т. е.... | np.array(l) | numpy.array |
"""classify.py"""
import sys
import os
import acl
path = os.path.dirname(os.path.abspath(__file__))
sys.path.append(os.path.join(path, ".."))
sys.path.append(os.path.join(path, "../../../../common/"))
sys.path.append(os.path.join(path, "../../../../common/atlas_utils"))
from constants import ACL_MEM_MALLOC_HUGE_FIRS... | np.exp(vals[i] - max) | numpy.exp |
# -*- coding: utf-8 -*-
#######################################
# StabilityMap_2d.py
#######################################
# analysis of two coupled tipping elements
# for manuscript:
# "Emergence of cascading dynamics in interacting tipping elements of ecology and climate"
# two coupled tipping elemen... | np.real(x1_stab) | numpy.real |
# coding=utf-8
# Copyright 2018 The Google Research 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 applicab... | np.array([[2]]) | numpy.array |
import csv
import os
import numpy as np
import cv2
import matplotlib.image as mpimg
from keras.models import Sequential
from keras.models import Model
import matplotlib.pyplot as plt
from keras.layers.core import Dense, Activation, Flatten, Dropout, Lambda
from keras.layers.normalization import BatchNormalization
from ... | np.copy(angle) | numpy.copy |
"""
This script produces the Figures 13 from Amaral+2021, the
pearson correlation between stellar and planetary mass and
surface water loss percentage.
@autor: <NAME>, Universidad Nacional Autónoma de México, 2021
@email: <EMAIL>
"""
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats
import pandas ... | np.genfromtxt(f2, usecols=0 ,unpack=True) | numpy.genfromtxt |
import numpy as np
from pandas import Series, DataFrame
from scipy.signal import savgol_filter, boxcar
from scipy import interpolate
from matplotlib import pyplot as plt
from numpy import abs
from numpy import array, poly1d, polyfit
def peak_detector(tic, max_tic):
dy = derivate(tic)
indexes = np.where((... | array(nodes) | numpy.array |
import tkinter as tk
import tkinter.filedialog as fd
import tkinter.messagebox as mb
import numpy as np
import pyknotid
import pyknotid.spacecurves as pkidsc
from pyknotid.spacecurves import Knot
import sympy
import csv
import os
# set initial values
gc_str = ""
fileopen = False
t = sympy.Symbol("t") # for use in dis... | np.abs(x0) | numpy.abs |
#- This simulation with gpu (with the below parameters) took 14h
#- In this experiment we also set lr from 0.01 to 0.0025
# but here with masking is like the no masking case (exp2a-d) with 0.03 to 0.0075
# thefactor of corecction is approx 3.
# So: probably we should set the next time for masking case: lr=0.005-0.00... | np.array(predict) | numpy.array |
from .interval import IntervalGoalEnv
from abc import ABC, abstractmethod
import numpy as np
import copy
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import math
#todo first run jsut the algorithm with the minimizer of collision along side to see what Q values it does create
#A space visualizer... | np.reshape(extension, (-1, 6)) | numpy.reshape |
import unittest
import numpy as np
import numpy.testing as npt
from scipy.sparse.csr import csr_matrix
from pylogit.scipy_utils import identity_matrix
def sparse_assert_equal(a1, a2):
"""Assert equality of two sparse matrices"""
assert type(a1) is type(a2)
npt.assert_array_equal(a1.data, a2.data)
| npt.assert_array_equal(a1.indices, a2.indices) | numpy.testing.assert_array_equal |
import math
import numpy as np
import torch
from torch import nn
from utils.geometric import pairwise_distance, calc_angle, calc_dihedral
from . import spline
from common import config
from common.config import EPS
from common import constants
class OmegaRestraint(nn.Module):
"""Omega angle is defined as dehidral... | np.concatenate([_y, _y[:, :, :1]], axis=-1) | numpy.concatenate |
#Entry point for the LEC agent
#Script has anomaly detectors, assurance monitors and risk computations
import os
import cv2
import torch
import torchvision
import carla
import csv
import math
import pathlib
import datetime
import gc
import time
from numba import cuda
from PIL import Image, ImageDraw,ImageFont
import th... | np.random.normal(0, 0.5, result['cloud'][0].shape) | numpy.random.normal |
""" Routines for building qutrit gates and models """
#***************************************************************************************************
# Copyright 2015, 2019 National Technology & Engineering Solutions of Sandia, LLC (NTESS).
# Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government... | _np.array(inputArr) | numpy.array |
# -*- coding: utf-8 -*-
"""
Test nematusLL for consistency with nematus
"""
import os
import unittest
import sys
import numpy as np
import logging
import Pyro4
nem_path = os.path.abspath(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../'))
sys.path.insert(1, nem_path)
from nematus.pyro_utils import setu... | np.tile(x0_state2, [2, 1]) | numpy.tile |
#!/usr/bin/env python
# Copyright 2014-2019 The PySCF Developers. 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
#
# U... | numpy.sqrt(coulG[p0:p1]) | numpy.sqrt |
"""Defines SinglePath MDP and utils."""
from __future__ import print_function
from __future__ import division
import numpy as np
def sample(mdp, pi):
"""Generate a trajectory from mdp with pi."""
done = False
obs = mdp.reset()
G = 0
path = {}
path['obs'] = []
path['acts'] = []
path['r... | np.zeros((L + 1, S + 1)) | numpy.zeros |
import unittest
import six
import tensorflow as tf
import numpy as np
import GPflow
from GPflow import settings
float_type = settings.dtypes.float_type
np_float_type = np.float32 if float_type is tf.float32 else np.float64
class TestSetup(object):
def __init__(self, likelihood, Y, tolerance):
self.likelih... | np.concatenate(self.Y_label) | numpy.concatenate |
"""Test the text data reader"""
import numpy as np
import pytest
import tensorflow as tf
from src.data.text_data_reader import Set, TextDataReader
def test_initialization():
dr = TextDataReader()
assert dr.name == "text"
assert dr.folder == "data/train/text"
for set_type in [Set.TRAIN, Set.VAL, Set.... | np.concatenate([dataset_labels, labels], axis=0) | numpy.concatenate |
"""Tests for chebyshev module.
"""
from functools import reduce
import numpy as np
import numpy.polynomial.chebyshev as cheb
from numpy.polynomial.polynomial import polyval
from numpy.testing import (
assert_almost_equal, assert_raises, assert_equal, assert_,
)
def trim(x):
return cheb.che... | assert_(v.shape == (3, 2, 4)) | numpy.testing.assert_ |
import time
import torch
import random
import numpy as np
from tqdm import tqdm, trange
# from torch_geometric.nn import GCNConv
from layers_batch import AttentionModule, TenorNetworkModule
from utils import *
from tensorboardX import SummaryWriter
# from warmup_scheduler import GradualWarmupScheduler
import os
import ... | np.array(batch_target) | numpy.array |
import pandas as pd
import numpy as np
import spacy
from tqdm import tqdm
from collections import defaultdict
nlp = spacy.load("en_core_sci_lg", disable=['ner', 'parser'])
path = "../data/"
def tokenize(string):
doc = nlp.make_doc(string)
words = [token.text.lower() for token in doc if token.is_alpha and not ... | np.dot(w_emb, new_word_emb) | numpy.dot |
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