text string |
|---|
""" Compute the features (r2,mse,granger) for a given set of trials
INPUT:
time_window_size: time points considered in the regression;
time_lag: time lag from which the selection of time_windows_size time points starts, if None it takes points from (-1)*time_window_size to -1;
N: time points considered... |
import time
from typing import Tuple
from statistics import mean, stdev
def timeit(num=1, eps=0.5, max_trial=5, warmup=0.01):
"""Create measurement function for excetution time
Args:
num (int): will execute the function as per the number is given here. (The default value is 1)
"""
if num <= 0... |
<reponame>Mailaender/spectrochempy<gh_stars>0
# -*- coding: utf-8 -*-
# ======================================================================================================================
# Copyright (©) 2015-2021 LCS - Laboratoire Catalyse et Spectrochimie, Caen, France. =
# CeCI... |
<reponame>skirpichev/Mathics
# -*- coding: utf-8 -*-
# cython: language_level=3
"""
Mathematical Functions
Basic arithmetic functions, including complex number arithmetic.
"""
from mathics.version import __version__ # noqa used in loading to check consistency.
import sympy
import sys
import mpmath
from functools i... |
<reponame>SherbyRobotics/PyRobotics
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Sep 10 11:12:11 2021
@author: alexandregirard
"""
from scipy.optimize import minimize
from cyipopt import minimize_ipopt
def cost (x):
j = x[0]+x[1]
return -j
def constraint_circle(x):
res... |
import os
import pathlib
import torch
import numpy as np
from imageio import imread
from scipy import linalg
from torch.nn.functional import adaptive_avg_pool2d
from skimage.measure import compare_ssim
from skimage.measure import compare_psnr
import glob
import argparse
import matplotlib.pyplot as plt
from tools.incept... |
from torchsummary import summary
import os
import glob
import torch
import librosa
import pickle
import copy
import random
import numpy as np
import pandas as pd
import scipy.signal as signal
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
from model import Net_ResNet50
from torch.... |
from sympy import bernoulli, Symbol, Sum, harmonic, Rational, oo, zoo, pi, I, bell, \
fibonacci, lucas, euler, catalan, binomial, gamma, sqrt, hyper, log, polygamma, diff
x = Symbol('x')
def test_bernoulli():
assert bernoulli(0) == 1
assert bernoulli(1) == Rational(-1,2)
assert bernoulli(2) == Rat... |
import matplotlib.pyplot as plt
from scipy.ndimage.filters import gaussian_filter1d
from matplotlib.pyplot import figure
def make_line_plot(save_to_folder: str, list_of_data_points, xlabel, ylabel, title, add_avg_line=False, sigma=0, all_xticks=False, custom_width=False, width=0):
if custom_width:
figure(... |
from cmath import phase
print((lambda x: '%.3f\n%.3f' % (abs(x), phase(x)))(complex(input()))) |
import numpy as np
import statistics
from Superposition_VariationalInference import *
import torch
import time
import pandas as pd
import timeit
import torch
name1 = '2cpd' #2nl7:139 #2do0=114 #2cpd=99
name2 ='2cpd'
from random import randint
def random_with_N_digits(n):
range_start = 10**(n-1)
range_end = (1... |
<gh_stars>0
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
import scipy.stats as ss
model = Pipeline(steps=[
('scl', StandardScaler()),
('lin', LogisticRegression(
penalty = 'l2',
solver = 'lbfgs',
... |
# Run using:
# casapy --nologger --nogui --log2term -c ms_to_mat <MS name>
import os
import sys
import scipy.io
import collections
# Get MS name from command line.
ms_path = os.path.abspath(sys.argv[-1])
if not os.path.isdir(ms_path):
raise ValueError('Specified measurement set not found!.')
print '=' * 80
prin... |
<reponame>davidstutz/daml-shape-completion<filename>data/shapenet/fast_post_process_cars.py
#!/usr/bin/env python
"""
Post-process computed volumes, i.e. compute the distance transforms.
"""
import numpy as np
from scipy import ndimage
import os
import sys
sys.path.insert(1, os.path.realpath(__file__ + '../lib/'))
im... |
import os
import ntpath
import time
from . import util
from . import html
import scipy.misc
# try:
# from StringIO import StringIO # Python 2.7
# except ImportError:
# from io import BytesIO # Python 3.x
from io import BytesIO
# from .SpectralUtils import savePNG, projectToRGB, toPNG
from .SpectralUtil... |
<reponame>davmre/sigvisa
import numpy as np
import scipy.optimize
import itertools
import pandas as pd
from sigvisa import Sigvisa
from sigvisa.signals.io import load_event_station_chan
grid = dict()
grid['exp'] = np.array([x for x in itertools.product( (26, 30, 32 ), (0.0, 2.0), (-1.0, 1.0), (-5, -3, -1))])
grid['p... |
<reponame>king821221/tf-mmoe-mgda<filename>census_income_demo.py<gh_stars>1-10
"""
Multi-gate Mixture-of-Experts demo with census income data.
Copyright (c) 2018 Drawbridge, Inc
Licensed under the MIT License (see LICENSE for details)
Written by <NAME>
"""
import argparse
import random
import traceback
import json
im... |
<filename>SimpleFilterDesign/Wavelets/MultiStepHaar/IterativeHaar.py
from cmath import log, pi
from math import sin
import matplotlib.pyplot as mplt
def iterativeHaar(data):
nSamples = len(data)
nSweeps = int(log(nSamples).real / log(2.0).real)
print(nSweeps)
i = 1; gap = 2
s = 0; d = 0
for... |
<reponame>dan-git/outdoor_bot
#!/usr/bin/python
# Software License Agreement (BSD License)
#
# Copyright (c) 2009, <NAME>, Inc.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# * Redistribut... |
<reponame>FrictionTribologyEnigma/SlipPY<filename>slippy/contact/adhesion_models.py
import numpy as np
from scipy.misc import derivative
from functools import partial
from typing import Callable, Optional
from slippy.core import _AdhesionModelABC
__all__ = ['MDAdhesionPotential', 'AdhesionModelFromFunction']
class M... |
<filename>modeling/position_average.py
#!/usr/bin/env python
'''
Given a prediction of the effect of each single mutation and the sequence of the reference in amino acids and nucleotides, average the effect of the given measurement for each position
'''
import argparse
import copy
from Bio import AlignIO
import re
im... |
<reponame>CliveUnger/BigGAN-Videos<gh_stars>0
#Imports
import cv2
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import truncnorm
from numpy import linalg as LA
import random
from scipy.signal import savgol_filter
from image_net_labels import labels_to_idx
import tensorflow.compat.v1 as tf
tf.dis... |
"""Test CZT package.
To run:
pytest test_czt.py -v
To run (with coverage):
pytest --cov . --cov-report html test_czt.py
"""
import numpy as np
import matplotlib.pyplot as plt
import pytest
import scipy
import czt
def test_compare_different_czt_methods(debug=False):
print("Compare different CZT calc... |
"""Tests for arviz.utils."""
# pylint: disable=redefined-outer-name, no-member
from unittest.mock import Mock
import numpy as np
import pytest
import scipy.stats as st
from ...data import dict_to_dataset, from_dict, load_arviz_data
from ...stats.density_utils import _circular_mean, _normalize_angle, _find_hdi_contour... |
<gh_stars>0
#
# Copyright (c) 2022 salesforce.com, inc.
# All rights reserved.
# SPDX-License-Identifier: BSD-3-Clause
# For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
#
"""
ETS (Error, Trend, Seasonal) forecasting model.
"""
import logging
from typing impo... |
<gh_stars>1-10
import sys,os
sys.path.append(os.path.join(os.path.dirname(__file__), os.path.pardir))
from PyEFVLib import MSHReader, Grid, ProblemData, CsvSaver, CgnsSaver, VtuSaver
from PyEFVLib import MSHReader, Grid, ProblemData, CsvSaver, CgnsSaver
from PyEFVLib.simulation import LinearSystem as ls
import numpy a... |
<reponame>ngunnar/tracking_reg
from scipy import ndimage
import numpy as np
def inverse_distance(segmentation_masks, num_of_points, l=1, alpha = 1):
img_height = segmentation_masks.shape[0]
dt = ndimage.distance_transform_edt(segmentation_masks == False) + alpha
dt = (1 + dt/l)**(-alpha)
probability =... |
# -*- coding: iso-8859-15 -*-
"""
Traveling salesman problem solved using Simulated Annealing.
<NAME>
email: <EMAIL>
The steps consis of::
• Pick initial configuration X and an initial temperature T0. T0 should be
much higher than the changes in function f to be minimized when typical
Monte Carlo step is take... |
# -*- coding: utf-8
"""Module for fluid property integration.
TESPy uses the CoolProp python interface for all fluid property functions. The
TESPyFluid class allows the creation of lookup table for custom fluid
mixtures.
This file is part of project TESPy (github.com/oemof/tespy). It's copyrighted
by the contributo... |
<filename>Python code/EX02_Lorenz.py
#import os # clear screen
import numpy as np # matrix calc
from scipy.integrate import odeint # scientific computation (ode solver)
from utils.methods import poolData, sparsifyDynamics, sparseGalerkin3D , lorenz
import matplotlib.pyplot as py
#os.system('clear')
# Generate data
... |
<reponame>rubende/mupeg<filename>experiments/test/PredictModel_two_persons_all.py
import tensorflow as tf
import os
import numpy as np
import glob
from scipy import stats
import math
import importlib
createDatasetRecord = importlib.import_module('common.createDatasetRecord')
Model = importlib.import_module('common.Mode... |
from contextlib import contextmanager
from phi.math import batch, spatial
def assert_minimal_config(): # raises AssertionError
import sys
assert sys.version_info.major == 3 and sys.version_info.minor >= 6, "phiflow requires Python 3.6 or newer to run"
try:
import numpy
except ImportError:
... |
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import rv_discrete
import scipy
import datetime
import dataset
# import pickle
plt.rcdefaults()
# Debug mode
debug = True
def printdebug(debugmode, string=None, vartuple=None):
"""
prints string, varname and var for debug purposes
:par... |
#!/usr/bin/env python3
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import scipy.stats
from math import sqrt
import operator
BASE_PATH = "data/"
PLOTS_PATH = "plots/"
DATA_INDEX = 2
DEFAULT_INDEX = 3
KNN_INDEX = 4
LINEAR_INDEX = 5
LOGISTIC_INDEX = 6
TREE_INDEX = 7
FOREST_INDEX = 8
XMEA... |
from dataclasses import dataclass, field
from typing import Any, Dict
from scipy import stats
import numpy as np
from .estimator import QuantileEstimator
from .utils import compute_pr_x_ge_y
from .tables import MANN_WHITNEY_UP010
@dataclass(frozen=True)
class TwoSampleHypothesisTest(object):
options: Dict[str, ... |
<gh_stars>0
import os
import shutil
import tqdm
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import pickle
import findiff
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import TruncatedSVD
from scipy.integrate import ode
from scipy import in... |
#!/usr/bin/env python3
# Plots loss functions of form |x|**q
import matplotlib.pyplot as pl
import numpy as np
from scipy.stats import t, laplace, norm
x = np.linspace(-4, 4, 100)
pl.title('|x|^0.2')
pl.plot(x, np.absolute(x)**.2)
pl.savefig('lossFunctionFig_01.png')
pl.figure()
pl.title('|x|')
pl.plot(x, np.absolu... |
<reponame>jam-world/hongyuan_code<gh_stars>0
# -*- coding: utf-8 -*-
"""
run file for neural walker
@author: hongyuan
"""
import pickle
import time
import numpy
import theano
from theano import sandbox
import theano.tensor as tensor
import os
import scipy.io
from collections import defaultdict
from theano.tensor.shar... |
<filename>sknetwork/utils/knn.py
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on October 2019
@author: <NAME> <<EMAIL>>
"""
from abc import ABC
import numpy as np
from scipy import sparse
from scipy.spatial import cKDTree
from sknetwork.utils.knn1d import knn1d
from sknetwork.utils.format import directe... |
<filename>dataset/spotify_million_playlist_dataset/subset/preprocessing.py<gh_stars>1-10
import matplotlib.pyplot as plt
from dataset.spotify_million_playlist_dataset.src import *
from dataset.spotify_million_playlist_dataset.src import stats
from scipy.interpolate import make_interp_spline, BSpline
path = "D:/Pycharm... |
import torch
import torch.nn.functional as F
import numpy as np
from scipy import interpolate
class InputPadder:
""" Pads images such that dimensions are divisible by 8 """
def __init__(self, dims, mode='sintel'):
self.ht, self.wd = dims[-2:]
pad_ht = (((self.ht // 8) + 1) * 8 - self.ht) % 8
... |
<reponame>asjohnston-asf/isce2
#
# Author: <NAME>
# Copyright 2016
#
import numpy as np
import os
import isceobj
import logging
import scipy.signal as SS
from isceobj.Util.ImageUtil import ImageLib as IML
import datetime
import pprint
from .runFineResamp import getRelativeShifts
def multilook(intname, alks=5, rlks=... |
<gh_stars>10-100
import numpy as np
import pandas as pd
import numba as nb
#import numba_stats as nbs
import plotly.express as px
import plotly.graph_objects as go
from sklearn.linear_model import LinearRegression
from scipy import stats
#import statsmodels.api as sm
from collections import Counter
import multiprocessi... |
'''
Deep Learning for Data Science: Assignment 3
Submitted by: <NAME> (<EMAIL>)
Description - Generalize to k-layer Neural Network
- Implement Batch Normalization
- Implement moving average
- Training using mini batch Gradient Descent
'''
import numpy as np
from nump... |
# SimpleOfficeEnv_py
import numpy as np
import matplotlib.pyplot as plt
from owcsimpy.geoobjects.models.humanwithactivity_py import HumanWithActivity_py as Human
from owcsimpy.geoutils.draw import draw
from owcsimpy.geoobjects.bases.vector_py import Vector_py as Vector
from owcsimpy.geoobjects.bases.cube_py import Cu... |
<gh_stars>0
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.backends.backend_agg as plt_backend_agg
import seaborn as sns
from sklearn import metrics
from scipy import interp
def get_confusion_matrix_figure(y_true, y_pred, n_classes, labels=None):
"""Plot visualization.
Param... |
import numpy as np
from scipy import ndimage
from scipy import stats
from skimage.color import rgb2grey
from skimage.util import img_as_float
from skimage.feature import peak_local_max
def _compute_derivatives(image):
"""Compute derivatives in x and y direction using the Sobel operator.
Parameters
------... |
"""
Tools for representing and manipulating rooted trees.
The main component of this module is the RootedTree object,
whose documentation contains further details.
To facilitate the display of trees, and since it is not
convenient to use graph representations in most contexts,
the module also defines canonical names f... |
"""A few numpy and scipy helper functions for the processing pipelines.
Author: <NAME>, March 2021
"""
import numpy as np
from scipy.signal import butter, filtfilt
def butter_lowpass(cutoff, fs, order=2):
"Create a lowpass filter."
nyq = 0.5 * fs
normal_cutoff = cutoff / nyq
b, a = butter(order, norm... |
<gh_stars>10-100
from __future__ import absolute_import
import scipy.stats
import autograd.numpy as np
from autograd.numpy.numpy_vjps import unbroadcast_f
from autograd.extend import primitive, defvjp
pdf = primitive(scipy.stats.multivariate_normal.pdf)
logpdf = primitive(scipy.stats.multivariate_normal.logpdf)... |
"""
Successive Halving Iterations
=============================
This example illustrates how a successive halving search
(:class:`~sklearn.model_selection.HalvingGridSearchCV` and
:class:`~sklearn.model_selection.HalvingRandomSearchCV`)
iteratively chooses the best parameter combination out of
multiple candidates.
""... |
<filename>Codes/Compute_AC_PWS.py<gh_stars>1-10
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jul 6 11:02:13 2020
@author: <NAME>
This code reads 1-h data files (20 Hz sampling frequency) from MeSO-net stations, computes the autocorrelation function (ACF), and stack the ACFs using the Phase Weigh... |
# ---
# jupyter:
# jupytext:
# formats: ipynb,py:light
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.3'
# jupytext_version: 1.0.0
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# %matplotlib inline
# %a... |
<reponame>DSIP-UPatras/sEMG-hilbert-curve
import numpy as np
import scipy.io
import os.path
import glob
def load_dataset(input_directory, subject, classes, repetitions, rest_reps=None):
'''Loads data and applies preprocess_function_1'''
X, y, r = [], [], []
if rest_reps is None:
rest_reps = len(re... |
<filename>hyperparameter_search_dd2.py
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import time
import os
import numpy as np
import scipy
import scipy.misc
from skopt import gp_minimize
from skopt.space import Categorical
def expansion_number_to_string(e... |
#!/usr/bin/env python2
# Author: <NAME> (<EMAIL>)
# Time-stamp: <2013-12-14 14:15:26 (jonah)>
# This is a companion program to my FLRW simulator. It takes a data
# file and generates a plot of the scale factor, its derivative, the
# density, and the pressure of the matter.
# Call the program with
# python2 plot_all_... |
# From the course: Bayesin Machine Learning in Python: A/B Testing
# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing
# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing
from __future__ import print_function, division
from builtins import range
# Note: you may need to ... |
<filename>tests/test_pickle.py
import pytest
import numpy as np
from sympy import Symbol
import pickle
from conftest import skipif
from devito import (Constant, Eq, Function, TimeFunction, SparseFunction, Grid,
Dimension, SubDimension, ConditionalDimension, IncrDimension,
TimeDi... |
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from scipy.interpolate import griddata
npzfile = np.load("/home/henry/dev/roomba/logger/2018-08-05_123107_wifi.npz")
points = npzfile["points"] * 11.8 # convert to mm
values = npzfile["values"]
minx=np.amin(points, axis=0)[0]
maxx=np.amax(points,... |
import sys
import numpy as np
import scipy
import scipy.io
import scipy.ndimage
import mahotas
import math
import h5py
import time
import pymaxflow
import timer
import os
import glob
try:
input_paths = ['D:\dev\datasets\conn\ecs\ecs20_crop1kds2\diced_xy=512_z=32_xyOv=128_zOv=12_dwnSmp=1\cubes\cubeId=000001_Z=1... |
<filename>hypertrees/ensemble/_forest.py
"""
Forest of trees-based ensemble methods.
Those methods include random forests and extremely randomized trees.
The module structure is the following:
- The ``BaseForest`` base class implements a common ``fit`` method for all
the estimators in the module. The ``fit`` metho... |
<filename>advanced/scipy_sparse/examples/direct_solve.py
"""
Construct a 1000x1000 lil_matrix and add some values to it, convert it
to CSR format and solve A x = b for x:and solve a linear system with a
direct solver.
"""
import numpy as np
import scipy.sparse as sps
from matplotlib import pyplot as plt
from scipy.spar... |
from __future__ import print_function, division, absolute_import
import numpy as np
from scipy.misc import imsave, imread, imresize
from sklearn.feature_extraction.image import reconstruct_from_patches_2d, extract_patches_2d
from scipy.ndimage.filters import gaussian_filter
from keras import backend as K
import os
i... |
import numpy as np
import os
import pickle
import ra
import fft
import argparse
from gaussian2d import gaussian2d
from hashkey import hashkey
from math import floor
from matplotlib import pyplot as plt
from scipy import interpolate
# Define parameters
R = 2
patchsize = 11
gradientsize = 9
Qangle = 24
Qstrength = 3
Qc... |
<reponame>wcbeard/fission-dashboard<gh_stars>0
import datetime as dt
import itertools as it
import operator as op
import os
import re
import sys
import time
from collections import Counter, OrderedDict, defaultdict
from functools import lru_cache, partial, reduce, wraps
from glob import glob
from importlib import reloa... |
<gh_stars>10-100
from __future__ import absolute_import
import numpy as np
from matplotlib import pyplot as plt
from matplotlib import animation
from JSAnimation import IPython_display
from six.moves import zip
def basic_animation(frames=100, interval=30):
"""Plot a basic sine wave with oscillating amplitude"""
... |
<reponame>keener101/lkpy<gh_stars>100-1000
from lenskit import DataWarning
from lenskit.algorithms import Recommender
from lenskit.algorithms.basic import Fallback
from lenskit.algorithms.bias import Bias
from lenskit import batch
import lenskit.algorithms.item_knn as knn
from lenskit.sharing import persist
from lenski... |
<gh_stars>1-10
###############################################################################
## Library code for designing CRISPRi screens
## <NAME>
## November 10, 2019
## Based on Charlie's gRNA design
## Tested with: "use .python-3.5.1; source /seq/lincRNA/Ben/VENV_MIP/bin/activate"
## TODO: Factor out helper co... |
import numpy as np
import scipy.sparse as sp
import torch
import random
def encode_onehot(labels):
classes = set(labels)
classes_dict = {c: np.identity(len(classes))[i, :] for i, c in enumerate(classes)}
labels_onehot = np.array(list(map(classes_dict.get, labels)), dtype=np.int32)
return labels_onehot... |
import argparse
import scipy.stats
import numpy as np
import pandas as pd
import json
def summarize_pmf(pmf_dict):
keys = np.fromiter(pmf_dict.keys(), dtype=np.float64)
probas = np.fromiter(pmf_dict.values(), dtype=np.float64)
dist = scipy.stats.rv_discrete(name='custom', values=(keys, probas))
samps =... |
<reponame>CTJChen/ctc_astropylib
from scipy.optimize import nnls
import multiprocessing as mp
from ctc_filters import *
from ggplot import *
from astropy.coordinates import Distance
from astropy import units as u
import seaborn as sns
'''
Perform non negative least square SED fitting using
the Assef et al. 2010 templa... |
<gh_stars>1-10
import numpy as np
from scipy.spatial.distance import cdist
from stumpy import core
def z_norm(a, axis=0, threshold=1e-7):
std = np.std(a, axis, keepdims=True)
std[np.less(std, threshold, where=~np.isnan(std))] = 1.0
return (a - np.mean(a, axis, keepdims=True)) / std
def distance(a, b, a... |
<reponame>Evan1415/QMLA<filename>qmla/analysis/genetic_algorithm_analysis.py
import sys
import os
import numpy as np
import pandas as pd
import pickle
import itertools
import random
import copy
import scipy
import time
import sklearn
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
from matplotlib.g... |
<gh_stars>0
import argparse
import re
import warnings
from pathlib import Path
import numpy as np
import scipy.sparse as sp
import spacy
from sklearn.feature_extraction.text import CountVectorizer, ENGLISH_STOP_WORDS
from utils import createDirs, createLogger
INPUT_DIR = Path(r'../data/aclImdb/')
OUTPUT_DIR = Path(r... |
<reponame>vinbigdata-medical/abdomen-phases<filename>study_evaluation/draw_ci(study).py<gh_stars>0
from os import replace
import pandas as pd
import numpy as np
from scipy.sparse import data
from tqdm import tqdm
from multiprocessing import Pool
from sklearn.metrics import classification_report
import matplotlib.pypl... |
'''
Prediction Experiments
Usage: python3 03_excitation_03_prediction.py [effect_type] [model_type] [time_span]
Usage example: python3 03_excitation_03_prediction.py early reduced 3
'''
import json
import os
import sys
import numpy as np
from scipy import stats
import sklearn.metrics as metrics
from tick.inference imp... |
import numpy as np
from scipy.interpolate import interp1d
__all__ = ["extend_xmax_range", "extend_distance_range", "extend_template_coverage"]
def extend_xmax_range(xmax_bins, templates):
"""
Copy templates in empty xmax bins, helps to prevent problems from reaching the
edge of the interpolation space.
... |
<filename>sox/transform.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
'''
Python wrapper around the SoX library.
This module requires that SoX is installed.
'''
from __future__ import print_function
from .log import logger
import random
import os
from io import BytesIO
import numpy as np
from scipy.io import wavf... |
<gh_stars>0
# -*- coding: utf-8 -*-
import sklearn as sk
import scipy as sc
import numpy as np
import pandas as pd
import stemming.paicehusk as st
from Tkinter import Tk
from tkFileDialog import askopenfilename
import nltk as nltk
import asciitable as asci
import re
import math
import random as rand
... |
<gh_stars>0
import numpy as np
import logging
logger = logging.getLogger('legacypipe.blobmask')
def info(*args):
from legacypipe.utils import log_info
log_info(logger, args)
def debug(*args):
from legacypipe.utils import log_debug
log_debug(logger, args)
def stage_blobmask(targetwcs=None,
... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Tests for `univariate.gaussian_kde` module."""
from unittest import TestCase
from unittest.mock import MagicMock, patch
import numpy as np
from scipy.stats import gaussian_kde
from copulas.univariate.gaussian_kde import GaussianKDE
class TestGaussianKDE(TestCase):
... |
#ipython --pylab
import scipy
from mpl_toolkits.basemap import Basemap, addcyclic, shiftgrid
from netCDF4 import Dataset
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
import pandas
import pylab
np.set_printoptions(threshold=np.nan)
Folder = 'results'
species1 = ['Thunnus_obesus', 'T... |
"""from numpy import *
from pylab import *
import math
x = np.arange(0, math.pi*2, 0.05)
y = np.sin(x)
fig = plt.figure()
ax = fig.add_axes([0,0,1,0.8])
ax.plot(x,y)
ax.set_title("sine wave")
ax.set_xlabel('angle')
ax.set_ylabel('sine')
plt.show()"""
#etiquetas y dibujos
"""import matplotlib.pyplot as plt
y = [1, 4, 9... |
import platform
import sys
import subprocess
armv6 = bool(platform.machine() == 'armv6l')
if sys.platform == 'darwin':
osx105 = b'10.5.' in subprocess.check_output('sw_vers')
else:
osx105 = False
print('sys.version:', sys.version)
print('sys.platform:', sys.platform)
print('sys.abiflags:', sys.abiflags)
print... |
# New ks_2samp version submitted to scipy (see
# https://github.com/scipy/scipy/pull/8).
import numpy as np
from numpy import asarray
from scipy.stats import ksprob
def ks_2samp(data1, data2, signed=False):
"""
Computes the Kolmogorov-Smirnov statistic on two samples, as well
as its two-sided p-value.
... |
import numpy as np
import pandas as pd
import collections
import matplotlib.pyplot as plt
from sklearn.mixture import GaussianMixture
from sklearn.model_selection import KFold
from random import seed
from random import randrange
from scipy.stats import multivariate_normal
#Import Dataset and store features in X
datase... |
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
import biosppy
import scipy
# ==============================================================================
# ==============================================================================
# ===============================================================... |
import argparse
import numpy as np
import scipy.stats
import matplotlib as mpl
mpl.use('Agg')
from matplotlib import pyplot as plt
def makenormal(mu=1, sigma=1, n=1000, **kwargs):
data = np.random.normal(loc=mu, scale=sigma, size=n)
return data
def makebimodal(mu=1, mu2=2, sigma=1, sigma2=1, n=1000, n2=0, **... |
from sympy import symbols, Mul, sin, Integral, oo, Eq, Sum
from sympy.core.expr import unchanged
from sympy.stats import (
Normal,
Poisson,
variance,
Covariance,
Variance,
Probability,
Expectation,
)
from sympy.stats.rv import probability, expectation
def test_literal_probability():
X ... |
<filename>tests/python/test_dmatrix.py
# -*- coding: utf-8 -*-
import os
import tempfile
import numpy as np
import xgboost as xgb
import scipy.sparse
import pytest
from scipy.sparse import rand, csr_matrix
import testing as tm
rng = np.random.RandomState(1)
dpath = 'demo/data/'
rng = np.random.RandomState(1994)
cl... |
<reponame>albertoOA/Medical-Entities-Similarity-Measurements
# !/usr/bin/env python3
import scipy
import sklearn
def compute_similarity_between_pairs(drugids, sim_matrix, list_of_pairs):
n = len(list_of_pairs)
total_list = list()
for i in range(0, n):
aux_list = list()
pair = ... |
<filename>modelnet40_cls/evaluate_modelnet.py
import argparse
import time
import numpy as np
import tensorflow as tf
import socket
import importlib
import os
import sys
import scipy.io as sio
ROTATION = False
os.environ["CUDA_VISIBLE_DEVICES"]='0'
# baseDir = os.path.dirname(os.path.abspath(__file__))
# rootDir = os.... |
import os
import re
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import math
import random
import skimage.io
import skimage.transform
import skimage.filters
import skimage.feature
import skimage.morphology
import scipy.ndimage.morphology
import skimage.mea... |
<reponame>dcslagel/welly<filename>welly/curve.py
"""
Defines log curves.
:copyright: 2021 Agile Scientific
:license: Apache 2.0
"""
from __future__ import division
import numpy as np
from scipy.interpolate import interp1d
import matplotlib.pyplot as plt
import matplotlib as mpl
from matplotlib.patches import PathPat... |
"""This module handles experiments related to matrix connections."""
import os
import gc
import numpy as np
from collections import OrderedDict
from pprint import pprint
import pickle
from scipy import sparse
from mpmath import mpf
import pandas as pd
from .connectivity_patterns import MatrixConnectivity
from .simpl... |
<reponame>haoruilee/statslibrary<gh_stars>10-100
import CorreCoef as co
import numpy as np
import scipy.stats as sc
if __name__ == "__main__":
# 1. pearson相关系数
# pearson相关系数计算公式参见https://docs.scipy.org/doc/numpy/reference/generated/numpy.corrcoef.html?highlight=corrcoef#numpy.corrcoef
data=n... |
<filename>VAT_iVAT/Vat_exp_random_data.py
import time
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import cm
from scipy.stats import mode
from data_generate_non_cs import data_generate_non_cs
from distance2 import distance2
from iVAT import iVAT
from VAT import VAT
from scipy.io import savemat
... |
# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2012-2018 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... |
#!/usr/bin/env python
'''
Miscellaneous utilities that are extremely helpful but cannot be clubbed
into other modules.
'''
import torch
# Scientific computing
import numpy as np
import scipy.linalg as lin
from scipy import io
# Plotting
import cv2
import matplotlib.pyplot as plt
def nextpow2(x):
'''
... |
<gh_stars>10-100
import onnx
from onnx import helper, shape_inference
from onnx import TensorProto
import onnx.optimizer
import math
import numpy as np
import scipy.stats as stats
def truncated_normal(dims):
size = 1
for dim in dims:
size *= dim
mu, stddev = 0, 1/math.sqrt(size)
lower, upp... |
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