prompt stringlengths 19 879k | completion stringlengths 3 53.8k | api stringlengths 8 59 |
|---|---|---|
"""
Parsers for several prediction tool outputs.
"""
import numpy as np
max_solvent_acc = {'A': 106.0, 'C': 135.0, 'D': 163.0,
'E': 194.0, 'F': 197.0, 'G': 84.0,
'H': 184.0, 'I': 169.0, 'K': 205.0,
'L': 164.0, 'M': 188.0, 'N': 157.0,
'P': 136.... | np.array([result]) | numpy.array |
import numpy as np
from gym.spaces import Box
import pyflex
from softgym.envs.fluid_env import FluidEnv
import copy
from softgym.utils.misc import rotate_rigid_object, quatFromAxisAngle
from shapely.geometry import Polygon
import random, math
class PourWaterPosControlEnv(FluidEnv):
def __init__(self,... | np.array([0, 0, -1]) | numpy.array |
# pre/test_shift_scale.py
"""Tests for rom_operator_inference.pre._shift_scale.py."""
import os
import h5py
import pytest
import itertools
import numpy as np
import rom_operator_inference as opinf
# Data preprocessing: shifting and MinMax scaling / unscaling =================
def test_shift(set_up_basis_data):
... | np.random.randint(0, 100, (120,32)) | numpy.random.randint |
import numpy as np
import matplotlib.pyplot as plt
### Command Sequence for Main Odometry Scenario ###
main_sequence_commands = np.array([[0.5, 0, 0], [1.0, 0, 0], [1, 0, 0.785], [1, 0, 1.57], [0, 1, -0.785], [1, 0, 0], [1, 0 , -0.785], [0, -3, 1.57],
[0.5, 0, 0], [1.0, 0, 0], [1, ... | np.sin(pose[2] + rotation[0]) | numpy.sin |
"""
Linear dynamical system model for the AP text dataset.
Each document is modeled as a draw from an LDS with
categorical observations.
"""
import os
import gzip
import time
import pickle
import collections
import numpy as np
from scipy.misc import logsumexp
from sklearn.feature_extraction.text import CountVectorizer... | np.cumsum(times) | numpy.cumsum |
# -*- coding: utf-8 -*-
"""
Created on Fri Oct 8 14:36:04 2021
@author: sgboakes
"""
import numpy as np
import matplotlib.pyplot as plt
from pysatellite import Transformations, Functions, Filters
import pysatellite.config as cfg
import pandas as pd
if __name__ == "__main__":
plt.close('all')
... | np.identity(3) | numpy.identity |
"""
Recent upgrade of keras versions in TF 2.5+, keras has been moved to tf.keras
This has resulted in certain exceptions when keras models are attacked in parallel
This script fixes this behavior by adding an official hotfix for this situation detailed here:
https://github.com/tensorflow/tensorflow/issues/34697
All mo... | np.zeros((NUM_WORDS,)) | numpy.zeros |
import pandas as pd
import numpy as np
from pylab import rcParams
import glob
from natsort import natsorted
import re
from numpy import linalg as LA
import matplotlib.pyplot as plt
import datetime
import os
import matplotlib.gridspec as gridspec
import seaborn as sns
def dir_check(now_time):
if not os.path.exists(... | np.array(path) | numpy.array |
#!/usr/bin/env python
from mpi4py import MPI
import sys
sys.path.append( '../stochastic')
from st_utils.coords import *
import vtk
import numpy as np
class Args(object):
pass
def transform_back(pt,pd):
#The reconstructed surface is transformed back to where the
#original points are. (Hopefully) it is only a simil... | np.array(self.values) | numpy.array |
from time import sleep
import numpy as np
from scipy.fft import fft
from scipy.integrate import simps
NUM_SAMPLES = 1024
SAMPLING_RATE = 44100.
MAX_FREQ = SAMPLING_RATE / 2
FREQ_SAMPLES = NUM_SAMPLES / 8
TIMESLICE = 100 # ms
NUM_BINS = 16
data = {'values': None}
try:
import pyaudio
def update_audio_data()... | np.random.randn() | numpy.random.randn |
# -*- coding: utf-8 -*-
"""
Copyright (c) 2016 <NAME>, <NAME>, and <NAME>
Permission is hereby granted, free of charge, to any person obtaining a copy of this software
and associated documentation files (the "Software"), to deal in the Software without restriction,
including without limitation the rights to use, copy... | np.argsort(dist_vect) | numpy.argsort |
import numpy as np
import cv2
from scipy.ndimage import label
from .vistools import norm_atten_map
import torch.nn.functional as F
def get_topk_boxes(logits, cam_map, im_file, input_size, crop_size, topk=(1, ), threshold=0.2, mode='union', gt=None):
maxk = max(topk)
maxk_cls = | np.argsort(logits) | numpy.argsort |
import tensorflow as tf
from keras.layers import Dense, Flatten, Lambda, Activation, MaxPooling2D
from keras.layers.convolutional import Convolution2D
from keras.models import Sequential
from keras.optimizers import Adam
import os, sys
import errno
import json
import cv2
import matplotlib.pyplot as plt
import numpy a... | np.max(data) | numpy.max |
import OpenEXR
import Imath
import numpy as np
import time
import data.util_exr as exr_utils
import os
def _crop(img, pos, size):
ow, oh = img.shape[0], img.shape[1]
x1, y1 = pos
tw = th = size
if (ow > tw or oh > th):
# return img.crop((x1, y1, x1 + tw, y1 + th)) #CHANGED
ret... | np.zeros((128,128,3)) | numpy.zeros |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
PyDDSBB @ GT - DDPSE
@author: JianyuanZhai
"""
import numpy as np
from PyDDSBB._utilis import LHS
import PyDDSBB._problem as _problem
import PyDDSBB._underestimators
import time
from PyDDSBB._node import Node
from PyDDSBB._splitter import Splitter
from PyDDSBB._mach... | np.concatenate((self.X, Xnew), axis=0) | numpy.concatenate |
import os
import numpy as np
from numpy.core.fromnumeric import ptp
import raisimpy as raisim
import time
import sys
import datetime
import matplotlib
import matplotlib.pyplot as plt
from xbox360controller import Xbox360Controller
xbox = Xbox360Controller(0, axis_threshold=0.02)
# v_ref = xbox.trigger_r.value * (-4) -... | np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0]) | numpy.array |
"""Tools for Loop-detection analysis."""
from multiprocessing import Pool
from typing import Tuple, Sequence, Iterator
from dataclasses import dataclass
import numpy as np
import pandas as pd
from scipy import ndimage, stats, sparse
from sklearn.cluster import DBSCAN
from statsmodels.stats import multitest
from .util... | np.where(dbscan.labels_ == cluster_id) | numpy.where |
import scipy
import scipy.misc
import numpy as np
def load(path):
img = scipy.misc.imread(path)
## TODO check what is the possible returned shapes
if img.shape[-1] == 1: # grey image
img = np.array([img, img, img])
elif img.shape[-1] == 4: # alpha component
img = img[:,:,:3]
return im... | np.clip(img, 0, 255) | numpy.clip |
# pylint: disable=E1101
"""
Generic classes and utility functions
"""
from datetime import timedelta
import numpy as np
class FlopyBinaryData:
"""
The FlopyBinaryData class is a class to that defines the data types for
integer, floating point, and character data in MODFLOW binary
files. The FlopyBin... | np.fromfile(self.file, dtype, count) | numpy.fromfile |
import datetime
from dateutil.relativedelta import *
from fuzzywuzzy import fuzz
import argparse
import glob
import numpy as np
import pandas as pd
from scipy.stats import ttest_1samp
import sys
import xarray as xr
from paths_bra import *
sys.path.append('./..')
from refuelplot import *
setup()
from utils import *
... | np.array(scores) | numpy.array |
import itertools
from typing import Union, Sequence, Optional
import numpy as np
_RealArraylike = Union[np.ndarray, float]
def _single_qubit_unitary(
theta: _RealArraylike, phi_d: _RealArraylike, phi_o: _RealArraylike
) -> np.ndarray:
"""Single qubit unitary matrix.
Args:
theta: cos(theta) is m... | np.einsum('...a,abc->...bc', vector, _kak_gens) | numpy.einsum |
"""
Created on June 6th, 2019. This script compares surface ozone data with hourly resolution from Summit (SUM) station
in Greenland with Summit GC ethane and acetylene data. 'ozone.py' compares the ozone with the residual values
The ozone data used here is courtesy of NOAA ESRL GMD. See the citation below.
<NAME>., ... | np.nanmean(ozoneData['resid'][indices].values) | numpy.nanmean |
# Client --> ./templates/index.html
# -*- coding: utf-8 -*-
# 导入常用的库
from flask import Flask, jsonify, render_template, request
from utils import Config, Logger, CharsetMapper
import torchvision.transforms as transforms
from PIL import Image
import torch.nn.functional as F
import numpy as np
import cv2
import PIL
impor... | np.array(img) | numpy.array |
import os
from pathlib import Path
import numpy as np
from sklearn.ensemble import RandomForestClassifier
print('hi')
os.getpid()
Path('/')
| np.array([1, 2, 3]) | numpy.array |
import numpy as np
import torch
from L96sim.L96_base import f1, f2, pf2
def init_torch_device():
if torch.cuda.is_available():
print('using CUDA !')
device = torch.device("cuda")
torch.set_default_tensor_type("torch.cuda.FloatTensor")
else:
print("CUDA not available")
de... | np.random.randn(K*(J+1),N_trials) | numpy.random.randn |
# coding: utf-8
'''
from: examples/tutorial/fifth.cc
to: fifth.py
time: 20101110.1948.
//
// node 0 node 1
// +----------------+ +----------------+
// | ns-3 TCP | | ns-3 TCP |
// +----------------+ +----------------+
// | 10.1.1.1 | | 10.1.1.2 |... | np.cumsum(y_counts) | numpy.cumsum |
from .mcmcposteriorsamplernorm import fit
from scipy.stats import norm
import pandas as pd
import numpy as np
import pickle as pk
from sklearn.cluster import KMeans
from ..shared_functions import *
class mcmcsamplernorm:
"""
Class for the mcmc sampler of the deconvolution gaussian model
"""
def __init... | np.sum(ids==i) | numpy.sum |
import numpy
import sys
import math
import logic
from scipy.integrate import odeint
import scipy.optimize as optim
import NNEX_DEEP_NETWORK as NNEX
import NNEX_DEEP_NETWORKY as NNEXY
#import NNEX
def DISCON(avrSWAP_py, from_SC_py, to_SC_py):
if logic.counter == 0:
import globalDISCON
import OB... | numpy.delete(yawerrmeas.bl3_old, [inddel[0][:-2]], 0) | numpy.delete |
import numpy as np
import numpy.linalg as npl
from dipy.core.triangle_subdivide import create_half_unit_sphere
from dipy.reconst.dti import design_matrix, lower_triangular
from nose.tools import assert_equal, assert_raises, assert_true, assert_false
from numpy.testing import assert_array_equal, assert_array_almost_eq... | np.arange(10) | numpy.arange |
#!/usr/bin/python
# coding:utf-8
import numpy as np
import random
import string
from requests import Request, Session
from MyDecision import Decision
from MyWord2Vec import Word2Vec
PROXY = {'http': '127.0.0.1:8083'}
# CredentialsTBLのカラム情報
str_col_credentialstbl = "site_id, " \
"... | np.argsort(nd_values) | numpy.argsort |
import numpy as np
import scipy
from scipy import optimize as opt
from sklearn.decomposition import PCA
from utils import *
from functools import partial
class PNS(object):
"""
Fit nested_spheres to data. This is a python code to PNS matlab code
See Sungkyu Jung et al, 2012 for the original PNS.
For Kur... | np.cos(geodmean + res[0, :]) | numpy.cos |
# ******************************************************************************
# Copyright 2017-2021 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apa... | np.int16(-12345) | numpy.int16 |
#!/usr/bin/env python
#
# Author: <NAME> <<EMAIL>>
#
'''
XC functional, the interface to xcfun (https://github.com/dftlibs/xcfun)
U. Ekstrom et al, J. Chem. Theory Comput., 6, 1971
'''
import copy
import ctypes
import math
import numpy
from pyscf import lib
_itrf = lib.load_library('libxcfun_itrf')
XC = XC_CODES = ... | numpy.empty((ngrids,outlen)) | numpy.empty |
import skimage.feature
import skimage.transform
import skimage.filters
import scipy.interpolate
import scipy.ndimage
import scipy.spatial
import scipy.optimize
import numpy as np
import pandas
import plot
class ParticleFinder:
def __init__(self, image):
"""
Class for finding circular particles
... | np.sin(t) | numpy.sin |
from __future__ import division
import torch
import torch.nn.functional as F
from utils import setup_logger
from model import agentNET
from torch.autograd import Variable
from env import *
import numpy as np
import time
import random
S_INFO = 6 # bit_rate, buffer_size, next_chunk_size, bandwidth_measurement(throughpu... | np.max(VIDEO_BIT_RATE) | numpy.max |
#Core Imports for experiments
import shap
import numpy as np
import pandas as pd
import math
import matplotlib.pyplot as plt
import seaborn as sns
import random
import itertools
from statistics import mean
from sklearn.datasets import make_blobs
from sklearn.ensemble import IsolationForest
from sklearn.preprocessing im... | np.array(orig_aws_l) | numpy.array |
#########################
#########################
# Need to account for limit in input period
#########################
#########################
# Baseline M67 long script -- NO crowding
# New script copied from quest - want to take p and ecc from each population (all, obs, rec) and put them into separate file
# Do... | np.log10(Phs) | numpy.log10 |
import time
import sys
import json
import argparse
from tqdm import trange
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
import numpy as np
from scipy.spatial.distance import jensenshannon
import gym
import matplotlib.pyplot as plt
from matplotlib.axes import Axes
fr... | np.sum(episode_true_rewards2) | numpy.sum |
from ..meshio import form_mesh
import numpy as np
import logging
def merge_meshes(input_meshes):
""" Merge multiple meshes into a single mesh.
Args:
input_meshes (``list``): a list of input :class:`Mesh` objects.
Returns:
A :py:class:`Mesh` consists of all vertices, faces and... | np.vstack(voxels) | numpy.vstack |
#!/usr/bin/python3
# -*- coding: utf-8 -*-
"""
======================================
Clustering by rotation of eigenvectors
======================================
cluster by rotating eigenvectors to align with the canonical coordinate system
usage:
nc = cluster_rotate(evecs, evals, group_num, method, verbose)
Inpu... | np.argsort(mag) | numpy.argsort |
import networkx as nx
import numpy as np
import pandas as pd
import itertools
from functools import reduce
import operator as op
import string
# Returns product of all the element in a list/tuple
def Prod(v):
return reduce(op.mul, v, 1)
# Transposes a list of lists
def TransposeLists(l):
return [list(x) for... | np.prod(dom_per_proc[:, :-1], axis=1) | numpy.prod |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Feb 9 20:17:38 2021
@author: lucasmurtinho
"""
import numpy as np
from ExKMC.Tree import Node
import time
import random
from find_cut import get_distances
def get_best_cut_makarychev(data, data_count, valid_data, centers, valid_centers,
... | np.argmax(valid_centers) | numpy.argmax |
#
# works with polynomial (linear) fit
#
"""
functions:
goFromTo: calculates the phase shift matrix
"""
__author__ = "<NAME>"
__contact__ = "<EMAIL>"
__copyright = "ESRF, 2012"
import numpy, math
#from scipy import stats
import Shadow as sh
import Shadow.ShadowTools as st
def goFromTo(source,i... | numpy.abs(fieldComplexAmplitude) | numpy.abs |
#!/usr/bin/env python
import argparse, sys
from argparse import RawTextHelpFormatter
import numpy as np
import scipy.optimize
import scipy.sparse as sp
from scipy.stats import multinomial
from sklearn.preprocessing import quantile_transform
from sklearn.model_selection import train_test_split
from sklearn.model_selecti... | np.finfo(np.float32) | numpy.finfo |
# -*- coding: utf-8 -*-
"""
The below functions can be used to import delimited data files into Numpy or
Matlab database format.
"""
import argparse
import copy
import glob
import math
import os
import re
from enum import Enum
import numpy as np
import pkg_resources
# pylint: disable=no-member
import scipy.io
cl... | np.append(current_step_end[0], [step_time.shape[0] - 1]) | numpy.append |
import numpy as np
from scipy import ndimage as nd
from .pyudwt import Denoise2D1DHardMRS
b3spline = | np.array([1.,4.,6.,4.,1.]) | numpy.array |
# -*- coding: utf-8 -*-
#
#
# lssa.py
#
# purpose: Tutorial on lssa
# author: <NAME>
# e-mail: <EMAIL>
# web: http://ocefpaf.tiddlyspot.com/
# created: 16-Jul-2012
# modified: Fri 27 Jul 2012 05:32:29 PM BRT
#
# obs: Least-squares spectral analysis
# http://en.wikipedia.org/wiki/Least-squares_spectral_analy... | np.cos(2 * np.pi * f1 * ager) | numpy.cos |
import numpy as np
# we build some distributions and load them into a dict
mu, sigma = 0, 0.5
normal = np.random.normal(mu, sigma, 1000)
lognormal = | np.random.lognormal(mu, sigma, 1000) | numpy.random.lognormal |
import random
import numpy as np
import torch
import torch.utils.data
from io import BytesIO
from google.cloud import storage
client = storage.Client()
bucket = client.bucket('your-bucket-name')
class VocalRemoverCloudDataset(torch.utils.data.Dataset):
def __init__(self, dataset, vocal_dataset, num_training_item... | np.abs(X) | numpy.abs |
#!/usr/bin/env python
#
# __init__.py -
#
# Author: <NAME> <<EMAIL>>
#
import os
import os.path as op
import gc
import re
import sys
import time
import shlex
import shutil
import logging
import tempfile
import ... | np.loadtxt(infile) | numpy.loadtxt |
import torch
import torch.nn as nn
import numpy as np
from lib.config import cfg
import lib.utils.kitti_utils as kitti_utils
import lib.utils.roipool3d.roipool3d_utils as roipool3d_utils
import lib.utils.iou3d.iou3d_utils as iou3d_utils
class ProposalTargetLayer(nn.Module):
def __init__(self):
super().__i... | np.random.permutation(fg_num_rois) | numpy.random.permutation |
import pytest
import numpy as np
from PythonLinearNonlinearControl.models.two_wheeled import TwoWheeledModel
from PythonLinearNonlinearControl.configs.two_wheeled \
import TwoWheeledConfigModule
class TestTwoWheeledModel():
"""
"""
def test_step(self):
config = TwoWheeledConfigModule()
... | np.ones((1, config.STATE_SIZE)) | numpy.ones |
# Part of the psychopy.iohub library.
# Copyright (C) 2012-2016 iSolver Software Solutions
# Distributed under the terms of the GNU General Public License (GPL).
from __future__ import division
"""
ioHub Eye Tracker Online Sample Event Parser
WORK IN PROGRESS - VERY EXPERIMENTAL
Copyright (C) 2012-2014 iSolver Softw... | np.arctan(yDiff, xDiff) | numpy.arctan |
import numpy as np
import joblib
from .rbm import RBM
from .utils import sigmoid
# TODO(anna): add sparsity constraint
# TODO(anna): add entroty loss term
# TODO(anna): add monitoring kl divergence (and reverse kl divergence)
# TODO(anna): run on the paper examples again
# TODO(anna): try unit test case? say in a 3x3... | np.sign(self.W) | numpy.sign |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
project: https://github.com/charnley/rmsd
license: https://github.com/charnley/rmsd/blob/master/LICENSE
"""
import os
import sys
import unittest
import numpy as np
from contextlib import contextmanager
try:
from StringIO import StringIO
except ImportError:
fr... | np.array([-22.018, 17.551, 26.0], dtype=float) | numpy.array |
from __future__ import division, print_function, absolute_import
__usage__ = """
To run tests locally:
python tests/test_arpack.py [-l<int>] [-v<int>]
"""
import warnings
import numpy as np
from numpy.testing import assert_allclose, \
assert_array_almost_equal_nulp, TestCase, run_module_suite, dec, \
... | np.dot(b, evec) | numpy.dot |
import numpy as np
from ..tools import n_ball_volume, n_sphere_area, delay_coordinates, lstsqr
from ..tools.nd_utils import nd_function
from .math_utils import _lstsqr_design_matrix
from scipy.special import gamma
from nolds.measures import poly_fit
from tqdm import tqdm
from typing import Union
import plotly.graph_ob... | np.log(log_base) | numpy.log |
# -*- coding: utf-8 -*-
#
# Copyright (C) 2019 Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG),
# acting on behalf of its Max Planck Institute for Intelligent Systems and the
# Max Planck Institute for Biological Cybernetics. All rights reserved.
#
# Max-Planck-Gesellschaft zur Förderung der Wissens... | np.zeros((batch_size, 1 * 3)) | numpy.zeros |
import numpy as np
import segyio as so
from scipy.signal import butter, sosfilt
import time
# Simple timer with message
def timer(start, message):
end = time.time()
hours, rem = divmod(end-start, 3600)
minutes, seconds = divmod(rem, 60)
info('{}: {:d}:{:02d}:{:02d}'.format(message, int(hours), int(min... | np.zeros(1, dtype='int') | numpy.zeros |
import numpy as np
import attr_dict
import cfg
import yaml
from contextlib import contextmanager
import tactics_utils
class Player:
def __init__(self, pos=None, angle=0, label='', role=''):
self.pos = np.array(pos)
self.angle = int(angle)
self.label = label
self.role = role
@p... | np.array([self.pos[0], self.pos[1], 1]) | numpy.array |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Plots
plotrange, Btau, Ctau, ellipse, SUE
plotool:
set_clib, set_fig, set_ax,
reset_handles, append_handles, get_handles, set_legend,
plot, eplot, save, show, close
pplot(plotool):
add_plot, add_legend
"""
import warnin... | np.isscalar(sigmay) | numpy.isscalar |
"""Filter design.
"""
from __future__ import division, print_function, absolute_import
import warnings
import numpy
from numpy import (atleast_1d, poly, polyval, roots, real, asarray, allclose,
resize, pi, absolute, logspace, r_, sqrt, tan, log10,
arctan, arcsinh, sin, e... | np.delete(z, z1_idx) | numpy.delete |
import numpy as np
import HyperUtils as hu
check_eps = 0.3
check_sig = 2.0
check_alp = np.array([0.2, 0.18, 0.16, 0.14])
check_chi = np.array([0.9, 1.0, 1.1, 1.2])
file = "h1epmk_nest"
name = "1D Linear Elastic-Plastic with Multisurface Kinematic Hardening - Nested"
mode = 0
ndim = 1
const = [100.0, 4, 0.... | np.zeros(n_int) | numpy.zeros |
'''
Implementation of long-time intensity autocorrelation analysis according to
Houel et al. ACS Nano 2015, 9, 1, 886–893
Fitting Eq. 3 therein to long-time-scale (> milliseconds) autocorrelation
which for simple two-level dots gives a measure related to the power law exponent of switching
Autocorrelations are obta... | np.concatenate((timestamps_chA_bin, timestamps_chB_bin)) | numpy.concatenate |
import matplotlib.pyplot as plt
import h5py, argparse
import numpy as np
from matplotlib import cm
from matplotlib.colors import ListedColormap, LinearSegmentedColormap
import matplotlib.colors as colors
from matplotlib.backends.backend_pdf import PdfPages
from matplotlib.ticker import MultipleLocator, FormatStrFormatt... | np.arange(limits_mu-3*limits_sigma, limits_mu+3*limits_sigma, 0.1) | numpy.arange |
import sys
import tensorflow as tf
import numpy as np
import librosa
from python_speech_features import fbank,delta
import scipy.io.wavfile as wave
from tensorflow.python.client import device_lib
def _parse_function(example_proto):
''' Function to parse tfrecords file '''
feature = {'data': tf.VarLenFeature(tf... | np.hanning(x) | numpy.hanning |
import numpy as np
from datayoink.coordconverter import get_axis_info, get_step, get_x_scale, pixel_to_coords, closest,\
unify_x, get_pixels_2d, create_pixel_dict, create_coordinate_dict, get_start_end
def test_get_axis_info():
"""
Tests the get_axis_info function
"""
... | np.diff(unified_x) | numpy.diff |
"""
This module contains the implementation of block norms, i.e.
l1/l*, linf/l* norms. These are used in multiresponse LASSOs.
"""
from __future__ import print_function, division, absolute_import
import warnings
from copy import copy
import numpy as np
from . import seminorms
from ..identity_quadratic import identi... | np.maximum(norms - l2_weight, 0) | numpy.maximum |
# <NAME>
import argparse, sys, os
import numpy as np
import pylab as plt
from glob import glob
from spectral.io import envi
from scipy.stats import norm
from scipy.linalg import solve, inv
from astropy import modeling
from sklearn.linear_model import RANSACRegressor
from scipy.optimize import minimize
from scipy.interp... | np.isnan(evec) | numpy.isnan |
# Python 3.5
# Script written by <NAME> (<EMAIL>), <NAME> (<EMAIL>), and <NAME> (<EMAIL>)
# VERSION 0.1 - JUNE 2020
#--------TURN OFF MAGMASAT WARNING--------#
import warnings
warnings.filterwarnings("ignore", message="rubicon.objc.ctypes_patch has only been tested ")
warnings.filterwarnings("ignore", message="The han... | np.shape(Px_new) | numpy.shape |
import pytest
import numpy as np
from functools import reduce
from myml.dl import Tensor
def test_add():
a = Tensor([[1, 2], [3, 4]])
b = Tensor([[0, -1], [-1, 0]])
assert ((a + b).array == a.array + b.array).all()
assert ((b + 2).array == b.array + 2).all()
assert ((2 + b).array == b.array + 2)... | np.array([0, 1]) | numpy.array |
import math as mt
import numpy as np
import byxtal.find_csl_dsc as fcd
import byxtal.integer_manipulations as iman
import byxtal.bp_basis as bpb
import byxtal.pick_fz_bpl as pfb
import numpy.linalg as nla
import ovito.data as ovd
from ovito.pipeline import StaticSource, Pipeline
import ovito.modifiers as ovm
from ovit... | np.shape(Y) | numpy.shape |
import numpy as np
from torch.utils.data import Dataset
class GridSampler(Dataset):
"""
Adapted from NiftyNet
"""
def __init__(self, data, window_size, border):
self.array = data
self.locations = self.grid_spatial_coordinates(
self.array,
window_size,
... | np.any(location < 0) | numpy.any |
import os
import numpy as np
import pandas as pd
import yaml
from . import model as model_lib
from . import training, tensorize, io_local
def main():
#Turn off warnings:
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
###Load training data - Put the path to your own data here
training_data_path = "/root/trai... | np.sqrt(iRT_raw_var) | numpy.sqrt |
# -*- coding: utf-8 -*-
"""
This module is used for calculations of the orthonormalization matrix for
the boundary wavelets.
The boundary_wavelets.py package is licensed under the MIT "Expat" license.
Copyright (c) 2019: <NAME> and <NAME>.
"""
# ========================================================================... | np.linalg.cholesky(ML) | numpy.linalg.cholesky |
import cvxpy as cp
import matplotlib.pyplot as matplt
from utils import *
from test_ddpg import *
from ddpg_alg_spinup import ddpg
import tensorflow as tf
from env_mra import ResourceEnv
import numpy as np
import time
import pickle
import scipy.io
from parameters import *
from functions import *
import multiprocessing
... | np.clip(z - u, Rmin, Rmax) | numpy.clip |
import joblib
from sklearn.gaussian_process.kernels import Matern, WhiteKernel
from utils.bayesian_optimization import Bayesian_Optimization, UtilityFunction
from utils.utils import plot_gp, posterior
from sklearn.preprocessing import normalize
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams['font.fami... | np.array([res["target"] for res in optimizer_ls[2].res[:4]]) | numpy.array |
#List of functions :
# colorsGraphs(df, feature, genderConfidence = 1, nbToRemove = 1)
# text_normalizer(s)
# compute_bag_of_words(text)
# print_most_frequent(bow, vocab, gender, n=20)
# model_test(model,X_train,y_train,X_test,y_test, full_voc, displayResults = True, displayColors = False)
# predictors(df,... | np.arange(20) | numpy.arange |
"""
Created on Mon Jun 24 10:52:25 2019
Reads a wav file with SDR IQ capture of FM stations located in :
https://mega.nz/#F!3UUUnSiD!WLhWZ3ff4f4Pi7Ko_zcodQ
Also: https://drive.google.com/open?id=1itb_ePcPeDRXrVBIVL-1Y3wrt8yvpW28
Also generates IQ stream sampled at 2.4Msps to simulate a ... | np.zeros(N) | numpy.zeros |
# pylint: disable=invalid-name,too-many-lines
"""Density estimation functions for ArviZ."""
import warnings
import numpy as np
from scipy.fftpack import fft
from scipy.optimize import brentq
from scipy.signal import convolve, convolve2d, gaussian # pylint: disable=no-name-in-module
from scipy.sparse import coo_matrix... | np.arange(x_min, x_max + width + 1, width) | numpy.arange |
# -*- coding: utf-8 -*-
"""
pmutt.empirical.nasa
Operations related to Nasa polynomials
"""
import inspect
from copy import copy
from warnings import warn
import numpy as np
from scipy.optimize import Bounds, LinearConstraint, minimize, minimize_scalar
from pmutt import (_apply_numpy_operation, _get_R_adj, _is_iter... | np.array(T) | numpy.array |
from recsys.preprocess import *
from sklearn import model_selection
import numpy as np
from recsys.utility import *
RANDOM_STATE = 42
np.random.seed(RANDOM_STATE)
train = get_train()
target_playlist = get_target_playlists()
target_tracks = get_target_tracks()
# Uncomment if you want to test
# train, test, target_pla... | np.array(pred) | numpy.array |
"""
Mask R-CNN
Train on the Paper dataset and implement warp and threshold.
------------------------------------------------------------
Usage: import the module (see Jupyter notebooks for examples), or run from
the command line as such:
# Train a new model starting from pre-trained COCO weights
pytho... | np.concatenate([[0], m, [0]]) | numpy.concatenate |
""" FuelMap file building tools
"""
import sys
import os
from math import pow, sqrt
from shutil import copy2
import numpy as np
import pkg_resources
import yaml
from netCDF4 import Dataset
import f90nml
from .fuels import (
_ROSMODEL_FUELCLASS_REGISTER,
_ROSMODEL_NB_PROPERTIES,
BalbiFuel,
)
from .patch i... | np.intc(4) | numpy.intc |
import copy
from logging import getLogger
from collections import deque
import os
import gym
import numpy as np
import cv2
from pfrl.wrappers import ContinuingTimeLimit, RandomizeAction, Monitor
from pfrl.wrappers.atari_wrappers import ScaledFloatFrame, LazyFrames
cv2.ocl.setUseOpenCL(False)
logger = getLogger(__nam... | np.concatenate([obs, inventory_channel], axis=-1) | numpy.concatenate |
# DMD algorithms by <NAME>.
#
# TODO:
# - Should we create an ABC interface for DMD?
# - __init__.py and separate files
#
import numpy as np
from numpy.linalg import svd, pinv, eig
from scipy.linalg import expm
from .process import _threshold_svd, dag
class DMD:
def __init__(self, X2, X1, ts, **kwargs):
... | eig(self.Atilde) | numpy.linalg.eig |
# Copyright 2016 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to... | np.random.RandomState(self.rand_seed) | numpy.random.RandomState |
#!/usr/bin/python3
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import random
import numpy as np
import torch
from torch.utils.data import Dataset
from scipy.sparse import coo_matrix
class TrainTestDataset(Dataset):
def __init__(self, triples, nre... | np.array(edges_list) | numpy.array |
#! /usr/bin/Python
from gensim.models.keyedvectors import KeyedVectors
from scipy import spatial
from numpy import linalg
import argparse
import sys
vector_file = sys.argv[1]
if len(sys.argv) != 6:
print('arguments wrong!')
print(len(sys.argv))
exit()
else:
words = [sys.argv[2], sys.argv[3], sys.arg... | linalg.norm(w1) | numpy.linalg.norm |
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | np.array([image['id']]) | numpy.array |
"""
Copyright 2019 <NAME>
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to i... | np.unique(Y) | numpy.unique |
import numpy as np
def arrow(headHeight, headRadius, shaftRadius, ns=8):
profile = np.array([[0, 0, 0], [0, shaftRadius, 0], [1 - headHeight, shaftRadius, 0], [1 - headHeight, headRadius, 0], [1, 0, 0]], dtype=np.float32)
coneNormal = np.array([headRadius, headHeight, 0])
coneNormal /= np.linalg.norm(coneN... | np.zeros_like(angles) | numpy.zeros_like |
import numpy as np
from baselines.ecbp.agents.buffer.ps_learning_process import PSLearningProcess
# from baselines.ecbp.agents.graph.build_graph_mer_attention import *
from baselines.ecbp.agents.graph.build_graph_mer_bvae_attention import *
import logging
from multiprocessing import Pipe
import os
from baselines.ecbp.... | np.isnan(value_tar) | numpy.isnan |
'''
Climatological mean
'''
import sys
from glob import glob
import h5py
import numpy as np
import numba as nb
import pandas as pd
from datetime import datetime, timedelta
sys.path.insert(0, '/glade/u/home/ksha/WORKSPACE/utils/')
sys.path.insert(0, '/glade/u/home/ksha/WORKSPACE/QC_OBS/')
sys.path.insert(0, '/glade/... | np.empty((12, N_grids)) | numpy.empty |
"""
pyart.testing.sample_objects
============================
Functions for creating sample Radar and Grid objects.
.. autosummary::
:toctree: generated/
make_empty_ppi_radar
make_target_radar
make_velocity_aliased_radar
make_single_ray_radar
make_empty_grid
make_target_grid
"""
import ... | np.zeros((2, 400, 320), dtype='float32') | numpy.zeros |
import numpy as np
def scan(X,Y):
'''
Calculates the solution for the constrained regression called SCAN
given in the publication: Maag et al. "SCAN: Multi-Hop Calibration for Mobile Sensor Arrays".
In particuluar it solves: min_B trace( (Y-BX)(Y-BX)^T ) subject to BXX^TB^T = YY^T
Inputs:
... | np.transpose(Vx) | numpy.transpose |
import os, sys
import numpy as np
import csv,argparse
import collections
from pyAudioAnalysis import audioSegmentation as aS
def read_segmentation_gt(gt_file):
"""
This function reads a segmentation ground truth file,
following a simple CSV format with the following columns:
<segment start>,<segment ... | np.array([]) | numpy.array |
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from torch.utils.data import RandomSampler, BatchSampler
from .utils import calculate_accuracy
from .trainer import Trainer
from .utils import EarlyStopping
class CPCTrainer(Trainer):
# TODO: Make it work for all modes... | np.mean(step_accuracies[i]) | numpy.mean |
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
from pathlib import Path
from moviepy.editor import VideoFileClip
from line_class import Line
def test_functions(path,foo=None,cmap=None):
"""
Function to test a function (foo) with a folder of imgs (path)
All im... | np.zeros_like(img) | numpy.zeros_like |
import time as tm
import numpy as np
from pylab import *
def Jacobi(A, b, x, eps=1e-4, xs=None):
x = x.copy()
cnt = 0
while True:
cnt += 1
x_old = x.copy()
for i in range(b.shape[0]):
x[i] += (b[i] - A[i].dot(x_old)) / A[i, i]
if abs(x_old - x).max() < eps:
return x, cnt
def GS(A, b, x, eps=1e-4, xs... | np.arange(0 + h, 1 + h, h) | numpy.arange |
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