text string |
|---|
import math
import os
import time
import numpy as np
import torch
from torch._C import dtype
import torch.nn as nn
from scipy.optimize import curve_fit
from scipy import asarray as ar,exp
import matplotlib.pyplot as plt
import tqdm
import PIL.Image as Image
from Network.model import get_model
from Network.dataloader... |
<filename>4/figs/figX8/classical.py
import matplotlib.pyplot as plt
import numpy as np
from scipy.integrate import quad
from scipy.stats import linregress
plt.style.use('paper')
def integrand(r, _beta, _k, _a):
return r**2 * np.exp(- _beta * _k * np.exp(_a * r))
def z_classical():
z_c = (np.exp(beta * k)
... |
import itertools
import math
import numpy as np
import pandas as pd
import scipy.integrate as integrate
from sklearn.preprocessing import LabelEncoder
from spn.structure.leaves.parametric.Parametric import Categorical
from spn.structure.leaves.piecewise.PiecewiseLinear import PiecewiseLinear
from spn.structure.Base im... |
print('--------------os--------------')
import os
print(f'current dir:{os.curdir}')
print(f'os name:{os.name}')
print(f'os path:{os.environ}')
print(f'os linesep:{os.linesep}')
print('--------------shutil--------------')
import shutil
hosts_file = r'C:\Windows\System32\drivers\etc\hosts'
dest_file = r'D:\Desktop\ho... |
import pyaudio
from matplotlib import pyplot as plt
from Source.IO.Sync import *
import numpy as np
import wave
from scipy import signal
CHUNKSIZE = int(44100//0.25) # fixed chunk size
sync = createSyncSignal()
success = createSuccessSignal()
error = createErrorSignal()
# initialize portaudio
p = pyaudio.PyAudio()... |
from typing import Union, List, Dict
import numpy as np
from causaldag.utils.ci_tests import kernels
from scipy.stats import gamma
from causaldag.utils.ci_tests._utils import residuals
from causaldag.utils.core_utils import to_list
from scipy.special import gdtr
import ipdb
import numexpr as ne
def hsic_test_vector(
... |
<gh_stars>1-10
import dace
import numpy as np
import sympy as sp
def _construct_graph(tbsize_1=None, tbsize_2=None) -> dace.SDFG:
"""
Construct a graph for the tests.
:param tbsize_1: The dimensions of the thread-block map in the first
map, or None for no thread-block map.
:par... |
"""
Module to solve sqrt-LASSO convex program using regreg.
"""
import numpy as np
from scipy import sparse
from scipy.stats import norm as ndist, chi as chidist
from scipy.interpolate import interp1d
# regreg http://github.com/regreg
import regreg.api as rr
import regreg.affine as ra
from regreg.smooth.glm import ... |
"""
Provides common methods for remapping a complex image to an 8-bit image.
"""
__classification__ = "UNCLASSIFIED"
__author__ = ("<NAME>", "<NAME>")
import logging
from collections import OrderedDict
import numpy
from scipy.stats import scoreatpercentile as prctile
from sarpy.compliance import string_types
log... |
<filename>shabanipy/jj/plotting_general.py
# -*- coding: utf-8 -*-
# -----------------------------------------------------------------------------
# Copyright 2020 by ShabaniPy Authors, see AUTHORS for more details.
#
# Distributed under the terms of the MIT license.
#
# The full license is in the file LICENCE, distrib... |
<filename>modeling_example_2D.py
import sys, os
sys.path.insert(0,'/home/pwitte/devito4batch/pysource/')
#sys.path.insert(0,'/usr/local/devito4batch/pysource/')
import numpy as np
import matplotlib.pyplot as plt
from models import Model
from sources import RickerSource, TimeAxis, Receiver
from propagators import born, ... |
'''
Updated May 2020
@fkdd @omarcillo
'''
from .base import Base
from ...utils.cart2pol import cart2pol
from ...utils.short_time import short_time
from .. import schema
from ...detection import beamforming_new
import sys, pdb
import sqlalchemy as sa
from sqlalchemy.orm import Session
from sqlalchemy.ext.declarative i... |
########### Importar Librerías Externas Útiles al Programa ###############
import numpy as np #librería que maneja las matrices
from scipy.optimize import linear_sum_assignment as hungarianAlgorithm #librería donde se define el algoritmo húngaro para resolver el TSP como problema de asignación
#Aclaración: como las l... |
import os
import re
import sys
import logging
logger = logging.getLogger(__name__)
import getpass
import platform
import distro
import subprocess
import numpy
import scipy
import astropy
import matplotlib
if sys.version_info[0] < 3:
import Tkinter as tk
import ttk
else:
import tkinter as tk
import tki... |
import torch
from torch.utils.data import DataLoader
import numpy as np
import scipy.io as sio
from .utils import TedataLoader_supervised, TrdataLoader_supervised, get_PSNR, get_SSIM
from .loss_functions import mse_bias, mse_linear, mse_polynomial
from .logger import Logger
from .models import FC_AIDE
class Train_su... |
<gh_stars>10-100
import datetime
from sklearn.feature_selection import chi2, SelectPercentile
from sklearn.preprocessing import OneHotEncoder, LabelEncoder
from sklearn.model_selection import StratifiedKFold
from sklearn.feature_extraction.text import CountVectorizer
from scipy import sparse
import lightgbm as lgb
impo... |
<filename>mplop/mplop.py
import logging
import numpy
import typing
import matplotlib
import matplotlib.pyplot as pyplot
from mpl_toolkits.axes_grid1 import make_axes_locatable
from statistics import mean
logger = logging.getLogger(__file__)
class Figure(matplotlib.figure.Figure):
"""
Specialized class inheri... |
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import argparse
import os
from matplotlib.ticker import MaxNLocator
from scipy.stats import pearsonr, spearmanr
matplotlib.rcParams.update({'font.size': 14})
parser = argparse.ArgumentParser()
parser.add_argument('--gen_scores'... |
#!/usr/bin/env python
import matplotlib.pyplot as plt
import numpy as np
from scipy.optimize import curve_fit
def inverse(x, a):
return a / x
def squared(x, a):
return a / x**2
x = np.array([1, 2, 3, 4, 5, 7, 10, 15])
x_fine = np.linspace(x[0], x[-1], 1000)
y = np.array([1.77, 0.999, .642, 0.465, .372, .264, .18... |
import numpy as np
import networkx as nx
import networkx.algorithms.isomorphism as iso
import itertools
import os.path
import subprocess
import torch.nn.functional as F
import pickle
import os
import scipy.sparse as sp
import torch
import json
import scipy.io as sio
mapping = {"Case_Based": 0, "Genetic_Algorithms": 1,... |
<filename>bin/paired_t_test_shape.py
#! /usr/local/epd/bin/python
"""Perform a two-sample paired t-test between shapes"""
__author__ = "<NAME>"
__copyright__ = "Copyright 2013, <NAME> Ahmanson-Lovelace Brain Mapping Center, \
University of California Los Angeles"
__email__ = "<EMAIL>"
__credits__ = '... |
<filename>src/utils.py
from sklearn.utils import as_float_array
from scipy.sparse import csr_matrix
from sklearn.metrics import mean_squared_error
import numpy as np
import pandas as pd
def sparse_matrix_input(X):
X = as_float_array(X)[:,:3]
return X
def sparse_matrix(X,n,p,w=1,names=['row','col','val']):
... |
import os
import time
import numpy as np
import matplotlib.pyplot as plt
from ultrasound_data import TRAIN_DATA_FILE, AUG_TRAIN_DATA_FILE, IMG_SHAPE, VAL_SET
from scipy import ndimage
from scipy import interpolate
from scipy.ndimage.interpolation import map_coordinates
GRID_SIZE_X = 116
GRID_SIZE_Y = 105
MAX_DEFORM ... |
<filename>ctsimu/image_analysis/mtf.py
# -*- coding: UTF-8 -*-
"""Calculate the MTF from a given ESF or LSF."""
import math
import numpy
from scipy import optimize, fft
def MTF(positions, ESF=None, LSF=None, ESFsmoothingWidth=8):
""" Calculate the MTF for given edge spread function (ESF) or line spread function (... |
<filename>recommendation-service/model_builder.py
from scipy.sparse import save_npz
from kafka_connect import connect_kafka_consumer
import threading
import scipy.sparse as sparse
import pandas as pd
import implicit
import pickle
import queue
import argparse
# Create and store an ALS recommendations model
def create_... |
import os
import glob
import numpy as np
import sympy
from sympy import pi, sin, cos, var, Matrix
var('i1, j1, k1, l1', integer=True)
var('x, t, xa, xb, tmin, tmax, L, r, r1, sina, cosa')
var('A11, A12, A16, A22, A26, A66')
var('B11, B12, B16, B22, B26, B66')
var('D11, D12, D16, D22, D26, D66')
var('wx, wt, w0, w0x, ... |
<filename>python/uw/stacklike/limits.py
#!/usr/bin/env python
# $Header: /nfs/slac/g/glast/ground/cvs/users/mdwood/python/DMLimits.py,v 1.6 2011/03/31 17:02:25 kadrlica Exp $
"""
@author <NAME> <<EMAIL>>
"""
__author__ = "<NAME>"
__date__ = "12/20/2010"
import sys
import scipy as sp
import numpy as np
import m... |
import os
import pickle
import sys
from datetime import datetime
from scipy import sparse as sp
from sklearn.linear_model import ElasticNet
from sklearn.model_selection import ParameterGrid
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
sys.path.append(os.path.abspath('../../'))
from algorith... |
<filename>src/cascade_at/dismod/api/fill_extract_helpers/posterior_to_prior.py
import pandas as pd
from typing import Dict, List
import numpy as np
from scipy import stats
from cascade_at.dismod.api.fill_extract_helpers.utils import vec_to_midpoint
from cascade_at.model.utilities.grid_helpers import expand_grid
from c... |
<reponame>hugoleeney/python_benchmarks
from time import time
from scipy.special import comb as scipy_choose
import math
import numpy as np
class timer:
def __init__(self, name="timer"):
self.name = name
def __enter__(self):
print("timing: %s"%self.name)
self.starttime = time()
def... |
<reponame>cjauvin/RavenPy
"""
Library to perform graphs for the streamflow time series analysis.
The following graphs can be plotted:
- hydrograph
- mean_annual_hydrograph
- spaghetti_annual_hydrograph
"""
import numpy as np
import pandas as pd
import xarray as xr
from matplotlib import pyplot as plt
from... |
#This files extracts knowledge from the document collection.
# Users makes some queries regarding some properties of the virus and the functions searches the collection for answers
# It returns the sentences with the exact properties
# Approach:
# The user makes two queries.
# A general and a specific one. The genera... |
from __future__ import division
import numpy as np
from numpy.core.numeric import NaN
from scipy.signal import spectrogram
from . import timbral_util
import tensorflow as tf
import tensorflow.keras.backend as K
def timbral_depth(fname, fs=0, dev_output=False, phase_correction=False, clip_output=False, threshold_db=-6... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 24 10:45:47 2020
@author: ben05
"""
import numpy as np
from scipy import stats
from netCDF4 import Dataset
import shutil
import uuid
import io, re, os, glob
import h5py
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.featu... |
<reponame>evertonbotan/VVV-RRL-Search<gh_stars>0
# -*- coding: utf-8 -*-
"""
Created on Jul 3 2020
@author: <NAME>
@supervisor: <NAME>, <NAME>
imputs:
x: time.
y: signal.
yerr: signal error.
minP: minimal period for frequency grid.
maxP: ... |
<filename>linlearn/estimator/ch.py
# Authors: <NAME> <<EMAIL>>
# <NAME> <<EMAIL>>
# License: BSD 3 clause
"""
This module implement the ``CH`` class for the Catoni-Holland robust estimator.
``StateCH`` is a place-holder for the CH estimator containing:
gradient: numpy.ndarray
A numpy array of s... |
<gh_stars>1-10
# NOTE: You don't need to edit this code.
import time
import tensorflow as tf
import numpy as np
from scipy.misc import imread
from caffe_classes import class_names
from alexnet import AlexNet
# placeholders
x = tf.placeholder(tf.float32, (None, 227, 227, 3))
# By keeping `feature_extract` set to `Fal... |
<filename>autosklearn/metalearning/metalearning/clustering/cluster_instances.py
import argparse
from collections import deque
import logging
import time
import numpy as np
import scipy.stats
import sklearn.cluster
import sklearn.manifold
import sklearn.preprocessing
import sklearn.utils
from pyMetaLearn.metalearning.... |
"""Transducer speech recognition model (pytorch)."""
from distutils.util import strtobool
import logging
import math
import chainer
from chainer import reporter
import torch
import numpy
from espnet.nets.pytorch_backend.nets_utils import pad_list
from espnet.nets.ctc_prefix_score import CTCPrefixScore
from espnet.ne... |
<reponame>pjs902/GCfit
import warnings
import scipy.stats
import numpy as np
import astropy.units as u
from astropy.units.equivalencies import Equivalency
__all__ = ['angular_width', 'QuantitySpline']
def angular_width(D):
'''AstroPy units conversion equivalency for angular to linear distances
See: https:... |
'''
Functions dealing with (n,d) points
'''
import numpy as np
from .constants import log, tol
from .geometry import plane_transform
from . import transformations
from . import util
def point_plane_distance(points, plane_normal, plane_origin=[0, 0, 0]):
w = np.array(points) - plane_origin
distances = np.dot... |
<gh_stars>1-10
import pickle
import pandas as pd
import quandl
import matplotlib.pyplot as plt
from matplotlib import style
import numpy as np
from statistics import mean
from sklearn import svm
from sklearn.preprocessing import scale, MinMaxScaler, MaxAbsScaler
from sklearn.linear_model import LogisticRegression
... |
#!/bin/python
import sys
import numpy as np
class TITAN:
'''Titan orbit functions and parametes'''
def __init__(self):
'''Init default parameters'''
self.Tday = 15.945 # Earth days / 1 Saturn orbit
# Default orbit parameters calculated with NAIF Space Kernels
self.date = [np... |
<reponame>goodtune/vitriolic<filename>tournamentcontrol/competition/rank.py
# -*- coding: utf-8 -*-
import logging
from datetime import datetime
from decimal import Decimal
from statistics import StatisticsError, mean
from dateutil.parser import parse as date_parse
from dateutil.relativedelta import relativedelta
fro... |
<filename>misc/draw-wireframe.py
#!/usr/bin/env python3
import os
import glob
import os.path as osp
import cv2
import numpy as np
import scipy.io
import matplotlib as mpl
import matplotlib.pyplot as plt
from lcnn.utils import parmap
GT = "data/wireframe/valid/*.npz"
WF = "/data/lcnn/wirebase/result/wir... |
"""
This is the implementation of the AYS Environment in the form
that it can used within the Agent-Environment interface
in combination with the DRL-agent.
@author: <NAME>
"""
import sys
import numpy as np
from scipy.integrate import odeint
import AYS.ays_model as ays
import AYS.ays_general as ays_general
from D... |
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
from fatiando.vis import mpl
from fatiando.gravmag import polyprism
import scipy.stats as sp
def plot_prisms(prisms, scale=1.):
'''
Returns a l... |
import sys
from itertools import permutations
import numpy as np
from scipy.sparse import csr_matrix
from scipy.sparse.csgraph import floyd_warshall
I = np.array(sys.stdin.read().split(), dtype=np.int64)
n, m, R = I[:3]
r = I[3 : 3 + R] - 1
a, b, c = I[3 + R :].reshape(-1, 3).T
graph = csr_matrix((c, (a - ... |
import os
from pathlib import Path
import nibabel as nb
import numpy as np
import utils.preprocessor as preprocessor
import random
from scipy.ndimage.interpolation import map_coordinates
from scipy import interpolate as ipol
from sklearn.utils import shuffle
import utils.kits_data_utils as kutils
from skimage.trans... |
<gh_stars>0
import argparse
import csv
import json
import os
import pandas as pd
import scipy.stats as stats
'''
Usage:
python3 main.py \
-b all
-d domains.json \
-f 0.01 \
-g genemap.json \
-i refseqp \
-s saint.txt \
-t 0
output: domain-enrichment-saint.xlsx
'''
def enrich():
options = parse_args()
domains = ... |
# -*- coding: utf-8 -*-
"""
Created on Sat May 9 16:41:38 2020
@author: <NAME>
contact: <EMAIL>
Visualization tool for simulating pixel intensity in XMLD-PEEM experiments.
"""
# Importing libraries
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.widgets imp... |
<reponame>DragonMyth/MyDartEnv
import os
from gym import error, spaces
from gym.utils import seeding
import numpy as np
from gym.envs.flex import flex_env
import pygame as pg
import itertools
from pygame.locals import *
from OpenGL.GL import *
from OpenGL.GLU import *
from scipy.spatial.distance import cdist
try:
... |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import json
import os
import statistics
from os.path import isfile, join
import cv2
import numpy as np
import tensorflow as tf
from sklearn.metrics import roc_auc_score as roc_auc
from sklearn.model_selection import train_test_split
from tensorfl... |
<reponame>gucky92/seaborn
import numpy as np
from scipy import integrate
import pytest
from numpy.testing import assert_array_equal
from .._statistics import (
KDE,
)
class TestKDE:
def test_gridsize(self, rng):
x = rng.normal(0, 3, 1000)
n = 200
kde = KDE(gridsize=n)
dens... |
<reponame>mirca/yacoub
import numpy as np
import scipy.stats as stats
def marcumq(x, df, nc):
"""Computes the Marcum-Q function.
Parameters
----------
x : input array
The points where to evaluate the Marcum-Q function.
df : int
The degrees of freedom.
nc : float
The non... |
<gh_stars>0
import numpy as _np
import pandas as _pd
import scipy as _sp
def crossCorrelation(y1,y2,mode='same'):
""" cross correlation wrapper for numpy function """
CC=_np.correlate(y1,y2,mode=mode)
return CC
def correlationCoefficient(data,fit):
"""
Correlation coefficient.
Compares a fit to data. Note ... |
<reponame>Christovis/wys-ars
import os, sys, glob
import argparse
from pathlib import Path
from typing import Dict, List, Optional, Union
import pandas as pd
import numpy as np
from scipy.interpolate import RectBivariateSpline
from scipy.ndimage.filters import convolve
import astropy
from astropy.io import fits
from ... |
import numpy as np
from numpy.random import exponential, multinomial
from scipy import integrate
import matplotlib.pyplot as plt
''' Function definitions '''
def update_simulation(old_concentrations):
reaction_rates = list(rates)
for i in range(0,54):
species_used = reactants[i]
for reactant in species_used:
... |
"""
This file is used to pre-process voxlization and aggregation weights, in order to save training time.
Re-project simplified point clouds to multi-plane, 32 planes are used.
"""
from __future__ import division
import numpy as np
import os, cv2, time, math, scipy
import scipy.io as io
def loadfile(ply_path):
... |
<reponame>coinflip112/ml_101
#!/usr/bin/env python3
import argparse
import sys
import matplotlib.pyplot as plt
import numpy as np
import sklearn.datasets
import sklearn.metrics
import sklearn.model_selection
from scipy.special import softmax
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser... |
import multiprocessing
from operator import itemgetter
import os
import cPickle as pickle
from friendly_name_mixin import FriendlyNameFromClassMixin
from simple_plugins import AttrDict, PluginMount
from sympy import Abs, Float, nan, mpmath, sign
from . import PROJECT_SETTINGS_DIR, DEFAULT_MAX_MODE, DEFAULT_DECIM... |
<reponame>lvclark/h3agwas
#!/usr/bin/env python3
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from matplotlib.ticker import MaxNLocator
import pandas as pd
import numpy as np
import argparse
import sys
import math
import bisect
from scipy.stats import ga... |
<filename>data/Pascal3D/create_annotation.py
import os
import scipy.io as sio
import numpy as np
from PIL import Image
from tqdm import trange
import cv2
cats = ['aeroplane', 'bicycle', 'boat', 'bottle', 'bus', 'car', 'chair', 'diningtable', 'motorbike', 'sofa', 'train', 'tvmonitor']
databases = ['imagenet', 'pascal'... |
<gh_stars>1-10
import sys
import numpy as np
import scipy.stats as st
from gensim.models import Word2Vec
from fasttext import load_model as load_ft_model
from sklearn.preprocessing import normalize
from soynlp.hangle import compose, character_is_korean
from preprocess import get_tokenizer, jamo_sentence
sys.path.appe... |
<reponame>xiaozhah/wavenet_vocoder
# -*- coding: utf-8 -*-
import sys
sys.path.append('../../../tools')
from tools import *
import numpy as np
import os,shutil
from tqdm import tqdm
from scipy.io.wavfile import read
def processWav(ori_datadir, new_datadir, ref_file):
SaveMkdir(new_datadir)
for lin... |
<reponame>Dibakar1/Hidden-Period-detection-and-Signal-Reconstruction<filename>energy-proj.py
import numpy as np
import fractions as f
from scipy.linalg import circulant
import matplotlib.pyplot as plt
from scipy import signal
plt.close('all')
def phi(n):
if n == 0:
return 0
num = 0
for k in range(1, n+1):
if f.... |
import matplotlib.pyplot as plt
import scipy.signal
import numpy as np
import time
from signal_functions import *
def filter_signal():
match_filter = make_match_filter()
signal_in = import_wav("rec.wav")
# plot_waveform(match_filter, downsample=1, title="Match Filter", ax_labels=["Samples", "Magnitude"])... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
2D Convolutional Layer with multiple output channels
Reference:
http://machinelearninguru.com/computer_vision/basics/convolution/convolution_layer.html
<NAME>, 2018-12-13
"""
import matplotlib.pyplot as plt
from scipy import misc
import numpy as np
from skimage ... |
<filename>ecm/module_ecm.py<gh_stars>10-100
import numpy as np
from scipy.optimize import curve_fit
class ModuleEcm:
"""
Equivalent circuit model (ECM) developed from HPPC battery module data.
Parameters
----------
data : ModuleHppcData
Data from the HPPC battery module test.
params :... |
<reponame>jamesjcai/scGEAToolbox
# harmonypy - A data alignment algorithm.
# Copyright (C) 2018 <NAME>
# 2019 <NAME> <<EMAIL>>
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, e... |
'''Train CaPE with PyTorch.'''
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import numpy as np
import torchvision
import torchvision.transforms as transforms
import os
import argparse
import pickle
from sklearn.metrics import log_l... |
<filename>_site/public/post-assets/SVD/SVD_image.py
import scipy
from scipy import misc
from scipy import linalg
import numpy as np
import math
import matplotlib
from matplotlib import pyplot as plt
# load image test.png and store pixel values in matrix A
# Dimesional of image (1024x768)
A = scipy.misc.imread('test.pn... |
<gh_stars>1-10
__all__ = ["interactive_threshold"]
import matplotlib.pyplot as plt
import mpl_interactions.ipyplot as iplt
import numpy as np
import scipy.ndimage as ndi
import scipy
from scipy.optimize import linear_sum_assignment
from skimage.segmentation import watershed
from skimage.feature import peak_local_max
... |
"""Test for c file
"""
import matplotlib.pyplot as plt
import numpy as np
import math
import scipy.signal as signal
def test_filt():
result = [
0.000000,
0.000137,
0.000815,
0.002562,
0.005889,
0.011288,
0.019233,
0.030185,
0.044583,
... |
import numpy as np
import scipy.io
from scipy.sparse import csr_matrix, coo_matrix
from gensim.matutils import Sparse2Corpus, corpus2csc
from gensim.models import TfidfModel
from sklearn.metrics import pairwise_distances
from sklearn.preprocessing import normalize
from sklearn.model_selection import train_test_split
... |
"""
Alternative method for estimating the canopy inertial properties. Very ugly and
hacky, but still useful for checking the much improved mesh-based method.
"""
import numpy as np
from scipy.integrate import simps
import pfh.glidersim as gsim
# Yanked from from `AirfoilGeometry`
def airfoil_mass_properties(airfoil... |
import numpy as np
import scipy.special
#import click
import gym
batch_size=2000
discount=0.99
n_itrs=200
render=False
use_baseline=True
env_id='CartPole-v0'
natural_step_size=0.01
env = gym.make('CartPole-v0')
nprs=np.random.RandomState
rng = np.random.RandomState(42)
env = gym.make('CartPole-v0')
obs_d... |
import numpy as np
from scipy import stats
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.feature_selection import SelectKBest, chi2
class Singular_to_all_description(object):
'''
Show column relation statistics according to rest columns in data frame.
Base class for... |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import sacrebleu
from . instance import TextInstance, AudioInstance
import os
import sys
import logging
from statistics import mean
logger = ... |
#!/usr/bin/env python3
import sympy as sp
import numpy as np
import matplotlib.pyplot as plt
n = 1.6
n2 = 2
IS = 1.6e-6
IS_3 = 1.6e-5
k = 1.38e-23
T = 300
q = 1.609e-19
U = sp.Symbol('U')
#func1 = 1/2 * (2 + sp.cos(2*w1*x) + sp.cos(2*w2*x) + 2*sp.cos((w1+w2)*x) + 2*sp.cos((w1-w2)*x))
vi_pos = IS * (sp.exp(U/(... |
__author__="<NAME>"
import os
import pickle
import shutil
from typing import List
import numpy
from scipy import sparse
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.metrics.pairwise import linear_kernel
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from tqdm impor... |
<gh_stars>1-10
from typing import Optional, Tuple
import numpy as np
import pandas as pd
from anndata import AnnData
from scipy.spatial import cKDTree
from scipy.stats import norm
from spatialtis_core import getis_ord, points_bbox
from spatialtis.abc import AnalysisBase
from spatialtis.spatial.utils import QuadStats
... |
<filename>src/abg_python/fitting_utils.py
import numpy as np
from scipy.optimize import leastsq as opt
def fitAXb(xs,ys,yerrs,fixed_a=None,fixed_b=None):
"""Fits a linear trendline to some data"""
if yerrs is None:
yerrs=np.array([1]*len(xs))
weights=yerrs**-2.
X=np.matrix([[sum(ys*weights)],[s... |
<filename>symbolic/ode_jac.py
#!/usr/bin/env python
"""
Script that derives the ODE's Jacobian matrix
Used only in development. Running this script requires SymPy.
"""
from __future__ import division, absolute_import, print_function
import sympy
o = sympy.Symbol('o', real=True)
D_ = sympy.Symbol('D_', real=True, ... |
<reponame>sakshi87/pymc3
# Copyright 2020 The PyMC Developers
#
# 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 require... |
#!/usr/bin/python
"""
A set of useful functions to derive the gravitational potential via pytorque
"""
__version__ = '1.1.0 (25-06, 2019)'
## Changes --
## 25/06/19- EE - v1.1.0: Python 3
## 13/04/07- EE - v1.0.1: Addition of stop_program()
"""
Import of the required modules
"""
# Astropy
import astropy
from a... |
<gh_stars>1-10
from __future__ import print_function, absolute_import
import os
import numpy as np
import json
import random
import math
import torch
import torch.utils.data as data
import random
# from utils.imutils2 import *
# from utils.transforms import *
import torchvision.transforms as transforms
import scipy... |
<reponame>camiloramirezgo/NWSAS-paper-model
#Standard library imports
import os
import glob
import geopandas as gpd
import pandas as pd
import numpy as np
import fiona
import shapely
import rasterio
import rasterio.mask
from rasterio.merge import merge
from rasterio.warp import calculate_default_transform, reproject, R... |
"""
Author : <NAME>
File : Coupled_Channels.py
Language : Python 3.6
Created : 7/17/2018
Edited : 7/18/2018
San Digeo State University
Department of Physics and Astronomy
"""
import numpy as np
import argonneV14 as av14
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integr... |
<filename>src/python/data_combine.py
from pathlib import Path
import pandas as pd
import numpy as np
from scipy.sparse import load_npz, csc_matrix, hstack, save_npz
import os
class Combiner:
def __init__(self):
self.prefix = Path("data")
dtypes = {"sample_id": "int", "query_id": "int", "doc_id"... |
# -*- coding: utf-8 -*-
# <nbformat>3.0</nbformat>
# <headingcell level=1>
# Aging Analysis of Cytokine and SNP data
# <markdowncell>
# This analysis will look at how aging effects HIV-1 disease progression. This will include looking at things like clinical parameters, LTR SNPs, Cytokine Profiling, and NeuroCog Imp... |
'''
Multiple composite SIR models.
This is the simulation of two overlapping and time-unbounded SIR models, A and B. Model B starts with a random time
delay and one of its parameters (gamma) random. Additional gamma flu process is added as well to further demonstrate
how more complicated systems can be composed from... |
#! /usr/bin/env python
import louvain
import leidenalg
import networkx as nx
import igraph as ig
import argparse
import os, time
import pickle
import numpy as np
import pandas as pd
import scipy as sp
from hidef import weaver
from hidef.utils import *
from hidef import LOGGER
import multiprocessing as mp
class Clus... |
import numpy as np
import scipy.interpolate as interpolate
import h5py as h5
import os
from lxml import objectify, etree
import sharpy.utils.generator_interface as generator_interface
import sharpy.utils.settings as settings
import sharpy.utils.cout_utils as cout
@generator_interface.generator
class TurbVelocityFiel... |
<reponame>hanyas/mimo
import numpy as np
import numpy.random as npr
from scipy.special import digamma, gammaln, betaln
from mimo.abstraction import Distribution
import warnings
class Dirichlet(Distribution):
def __init__(self, K=None, alphas=None):
self.K = K
self.alphas = alphas
@propert... |
<gh_stars>1-10
import numpy as np
from scipy import integrate
f1 = lambda x, y: 1 / (np.sqrt(x+y) * (1+x+y))
f2 = lambda x, y: x**2 + y ** 2
def trapezoidal_rule_2D(f, xmin, xmax, xnum, ymin, ymax, ynum):
integral = 0
hx = (xmax - xmin) / xnum
for i in range(0, xnum + 1):
x = xmin + i * hx
... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jan 18 09:58:42 2021
Script that corrects for the change in the CNEMC measurement protocol
The change in measurement protocol occured on September 1st 2018
The change is described in 'Impact of China?s Recent Amendments to Air
Quality Monitoring Protoc... |
<filename>hoqunm/simulation/reader.py
"""Read all important model information from an excel file.
The logic therefore is implemented here.
"""
from pathlib import Path
from typing import Dict, List, Tuple, Union
import numpy as np
import pandas as pd
import scipy.stats
from hoqunm.simulation.hospital import Hospita... |
<filename>timedependent/algorithms.py
import numpy as np
import scipy.sparse as sparse
import scipy
from gridlod import coef, fem, interp, lod, pglod, util
from gridlod.world import Patch
def scaleCoefficient(aFine):
aMean = None
if aFine.ndim == 1:
aMean = np.mean(aFine, axis=0)
elif aFine.ndim... |
<filename>scripts/scil_perform_majority_vote.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import argparse
from dipy.io.stateful_tractogram import Space, StatefulTractogram
from dipy.io.streamline import save_tractogram
from dipy.io.utils import is_header_compatible, get_reference_info
import nibabel as nib
from n... |
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