text string |
|---|
#!/usr/bin/env python
# Cluster_Ensembles/src/Cluster_Ensembles/Cluster_Ensembles.py;
# Author: <NAME> for the GC Yuan Lab
# Affiliation: Harvard University
# Contact: <EMAIL>, <EMAIL>
"""Cluster_Ensembles is a package for combining multiple partitions
into a consolidated clustering.
The combinatorial optimization... |
<reponame>NegriLuca/pigasus
# -*- coding: UTF-8 -*-
#! /usr/bin/python
import sys
import numpy as np
from scipy.sparse.linalg import spsolve
from pigasus.fem.constants import *
from pigasus.fem.field import *
from pigasus.fem.norm import *
from pigasus.fem.grids import *
from pigasus.fem.matrix import *
from pigasus.... |
# --------------
# Import packages
import numpy as np
import pandas as pd
from scipy.stats import mode
# code starts here
bank = pd.read_csv(path)
categorical_var = bank.select_dtypes(include = 'object')
print(categorical_var)
print('='*50)
numerical_var = bank.select_dtypes(include = 'number')
print(numerical_var... |
import warnings
import numpy as np
import scipy as sp
from scipy.stats import ttest_ind
from statsmodels.stats.multitest import multipletests
def welch_ttest(x1, x2):
"""
Welch t-test for multiple arrays.
Parameters
__________
x1: array, shape (n1, n_features)
x2: array, shape (n2, n_feature... |
#!/usr/bin/env python
from pylab import *
from scipy.optimize import *
from numpy import *
import time
import sys
import bisect
ion()
N = 64
upsample_factor = 16
fftsize = N * upsample_factor
F = matrix(exp(1j*2*pi*arange(fftsize)[:,newaxis]*arange(fftsize)[newaxis,:]/float(fftsize)) / fftsize)
M = hstack((F[:,0], F[... |
import torch
import numpy as np
import pandas as pd
from torch.nn import BCELoss
from pathlib import Path
from collections import namedtuple
from sklearn.metrics import classification_report
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier, VotingClassifier
from s... |
import cv2
import dlib
import numpy as np
from scipy.spatial import distance
from collections import deque
def filter(frame,landmarks):
crownImg = cv2.imread("wanda.png",-1)
crownMask = crownImg[:, :, 3] # binary image
crownMaskInv = cv2.bitwise_not(crownMask) # inverting the binary img
crown... |
# -*- coding: utf-8 -*-
#Created on Wed Sep 28 10:15:56 2016
#@author: ryanday
#MIT License
#Copyright (c) 2018 <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 rest... |
<reponame>Nachiket18/TreeNash
'''
Created on 28-Mar-2021
@author: <NAME>
'''
from distutils.core import setup
from distutils.extension import Extension
from Cython.Distutils import build_ext
import numpy
import scipy
ext_modules = [Extension("anomaly_detection", ["anomaly_detection.pyx"])]
setup(
package_dir={'s... |
<filename>SparseAMsWithInteractions/src/AMsWithInteractionsL0/optimizer.py
from copy import deepcopy
from IPython.display import Math
from ipywidgets import *
import numpy as np
import scipy.sparse as sp
from scipy.special import comb
from sklearn.metrics import mean_squared_error
from tqdm import notebook
import warni... |
<gh_stars>0
import enum
from numpy.core.fromnumeric import argmin
from skimage import filters
from skimage import segmentation as seg
import scipy.ndimage as ndi
from scipy.spatial.distance import cdist
import numpy as np
from warnings import warn
from typing import Union, List
from .. import *
def __diamond_kernel(r,... |
from __future__ import print_function
"Fingerprint Image denoising using M-net"
import os
import glob
import numpy as np
from PIL import Image
import datetime
import scipy as sp
import sys
import keras as keras
from keras.models import Model
from keras.layers import Input, Activation, Conv2D, MaxPooling2D, UpSampling... |
# -*- coding: utf-8 -*-
"""
Created on Sun Nov 14 17:16:16 2021
@author: jnguy
"""
import sys
import numpy as np
import math
from scipy import spatial
from queue import PriorityQueue
class Node():
"""
parent = parent of current node
posiition = position of node right now it will be x,y coordinates
g ... |
<gh_stars>10-100
"""
New modules with utils from the earlier util module which are needed for crispor.
"""
import numpy as np
import pandas
import scipy.stats as st
import scipy as sp
def spearmanr_nonan(x,y):
'''
same as scipy.stats.spearmanr, but if all values are unique, returns 0 instead of nan
(Outpu... |
<gh_stars>0
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon May 11 21:33:50 2020
@author: darrenhsu
"""
import matplotlib.pyplot as plt
import numpy as np
from scipy.optimize import curve_fit
from scipy.special import erf
#%%
def func(x, a, b, c, d, e, f, IRF):
return (a * np.exp(-x/b) + c * n... |
import networkx as nx
import matplotlib.pyplot
import numpy as np
import pickle
from relax import nonzeroColumnMethodRelaxation
from random_strategy import greedyInitialStrategy
from random_strategy import randomStrategy
from random_strategy import enumerateAll
from max_iteration import maxIterationComb
from compute_re... |
<gh_stars>0
'''
MIT License
Copyright (c) 2018 <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, modify, merge, pub... |
<reponame>MargKos/multiscaleRD
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 11 10:40:31 2019
@author: bzfkostr
"""
from __future__ import division
import numpy as np
import scipy.sparse
from numpy.linalg import inv
from math import exp
from Parameters_Prolieferation import *
'''
This code re... |
<reponame>juancalheiros/voronoi-diagrams
import numpy as np
import matplotlib.pyplot as plt
from scipy.spatial import Voronoi, voronoi_plot_2d, Delaunay
def display_voronoi(points_x, points_y, COLOR_POINT):
plt.plot(points_x, points_y,'o', color=COLOR_POINT)
def display_delaunay(points_x, points_y, points_indice... |
# -*- coding: utf-8 -*-
#
# This file is part of the pyFDA project hosted at https://github.com/chipmuenk/pyfda
#
# Copyright © pyFDA Project Contributors
# Licensed under the terms of the MIT License
# (see file LICENSE in root directory for details)
"""
Widget for plotting impulse and general transient responses
"""... |
<reponame>1eg1on/dimreducers-crusher
from .AbstractDataset import AbstractDataset
from typing import Union
import numpy as np
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
import scipy.sparse as sp... |
<gh_stars>1-10
from __future__ import print_function
import os
import argparse
import torch
import torch.backends.cudnn as cudnn
import numpy as np
import time
import model
from data import DatasetLoader
import cv2
import torchvision.models as models
import numpy as np
from scipy.optimize import leastsq
import random
i... |
from scipy import stats
import numpy as np
from matplotlib import use
use('Agg')
from matplotlib import pyplot as plt
from collections import Counter, defaultdict
import pandas as pd
import subsamplex
from glob import glob
suboral = pd.read_table('cold/suboral.tsv', squeeze=True, index_col=0)
SUBSAMPLE = True
counts... |
import dash
import dash_core_components as dcc
import dash_html_components as html
import plotly.graph_objs as go
import os
import numpy as np
import pandas as pd
pd.options.mode.chained_assignment = None # default='warn'
import scipy.spatial.distance as dist
import scipy.cluster.hierarchy as sch
# fn = os.path.... |
import numpy as np
import tie_decay_epidemics as tde
from numpy.testing import assert_array_equal
from scipy.sparse import csr_matrix
def test_tie_decay_graph():
nodes = np.array(["A", "B", "C"])
graph = tde.TieDecay_Graph(nodes)
graph.update_tie_strength("A", "B", 10)
graph.update_tie_strength("B", ... |
'''
Feature selection as in G-algorithm but with success rates.
To be run after session 2.
We use a one-sample t-test.
This time we comopute the t-test based on all blocks at once.
'''
from copy import deepcopy
import itertools
import numpy as np
import pandas as pd
import pickle
import random
from scipy import stats... |
<reponame>LafLaurine/imac2-projetTUT
import numpy as np
import torch
from torch.autograd import Variable
import torch.utils.data as data
from tqdm import tqdm
from sklearn import metrics
from scipy.optimize import brentq
from scipy.interpolate import interp1d
from sklearn.metrics import roc_curve
from torchvision.dat... |
from scipy import signal
import matplotlib.pyplot as plt
import numpy as np
t = np.linspace(-1, 1, 201)
x = (0.3*np.sin(2*np.pi*0.75*t*(1-t) + 2.1) +0.5*np.sin(2*np.pi*2*t + 1) +2*np.cos(2*np.pi*1*t))
xn = x + np.random.randn(len(t)) * 0.5
b, a = signal.butter(5, 0.17)
zi = signal.lfilter_zi(b, a)
z, _ = signal.lfi... |
<gh_stars>1-10
import dill
import yaml
import os
import json
import numpy as np
import matplotlib.patheffects as pe
from scipy.ndimage import rotate
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
import matplotlib.pyplot as plt
import torch
import gc
import sys
from torch import nn, optim, utils
from ysd... |
## parse annotation
import xml.etree.ElementTree as ET
import mne
import numpy as np
import sys
import os
import scipy.io
import cv2
path1='/ssd/hongyang/2018/physionet/data/shhs/'
size= 2**20
scale_pool=5
freq=125
# all shhs1 records
id_all=[]
f=open('id_shhs1.txt','r')
for line in f:
id_all.append(line.rstrip(... |
<reponame>vishalbelsare/S3M
#!/usr/bin/env python3
import collections
import csv
import json
import math
import mpmath
import tqdm
import sys
import numpy as np
import matplotlib.pyplot as plt
import scipy.spatial.distance as ss
from scipy.stats import chi2
# Decimal digits of precision for th... |
# lucky imaging library functions
import numpy as np
from skimage.registration import phase_cross_correlation
from skimage.measure import centroid
from skimage.transform import AffineTransform, warp
from scipy.ndimage import fourier_shift
from .image_processing import frame_center, shift_frame
def lucky_image(
... |
import math
import time
from random import randrange
from typing import Optional
import numpy
from scipy.ndimage import sobel, laplace
from aydin.util.fast_uniform_filter.uniform_filter import uniform_filter_auto
from aydin.util.log.log import lprint
def representative_crop(
image,
mode: str = 'sobelmin',
... |
<reponame>marcinjurek/pyMRA
import scipy.optimize as opt
import gc
import logging
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
import pdb
import time
import sys
import scipy.linalg as lng
sys.path.append('../..')
from pyMRA.MRATree import MRATree
from pyMRA import MRATools as mt
if ... |
<gh_stars>1-10
# -*- coding: utf-8 -*-
"""
Created on Fri Jul 17 02:02:36 2020
@author: admin
"""
##Functions for replication study
# Import modules.
import pandas as pd
import statsmodels.api as sm
import numpy as np
from scipy import stats
from linearmodels import IV2SLS
# Get significance asterix.
def signific... |
import keras
from keras import applications
from keras import backend as K
import tensorflow as tf
import numpy as np
import scipy
import scipy.misc
import matplotlib.pyplot as plt
import time
import imageio
#from scipy.misc import imsave
class cmv:
#out put index used in the paper
#99 goose
#968 cup
#... |
from tensorflow.keras.layers import Activation, UpSampling2D, Reshape, Input, Conv2D, \
LeakyReLU, BatchNormalization, Flatten, Dropout, Dense
from tensorflow.keras.models import Model
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam
import numpy as np
import pandas as pd
... |
<gh_stars>0
# Reference:
# 1. rankGauss by <NAME>
# https://github.com/michaeljahrer/rankGauss/blob/master/rankGaussMain.cpp
# 2. a beginning of scikit-learn compatible implementation of GaussRank
# https://github.com/zygmuntz/gaussrank/blob/master/gaussrank.py
# Extended by 오병화, 20180903
from sklearn.utils.validation ... |
<gh_stars>1000+
# Copyright Materialize, Inc. and contributors. All rights reserved.
#
# Use of this software is governed by the Business Source License
# included in the LICENSE file at the root of this repository.
#
# As of the Change Date specified in that file, in accordance with
# the Business Source License, use ... |
"""
Script calcules EOFs of sea ice thickness anomalies from LENS
Notes
-----
Source : http://www.esrl.noaa.gov/psd/data/gridded/data.
ncep.reanalysis.derived.html
Reference : Lindsay and Zhang [2005]
Author : <NAME>
Date : 1 May 2017
"""
### Import modules
import numpy as np
import ma... |
from collections import defaultdict
from typing import Dict
import numpy as np
import scipy.constants as constants
from bridge_sim.model import Config
from bridge_sim.sim.build import det_shells
from bridge_sim.sim.model import SimParams, DeckShells
def opensees_self_weight_loads(
config: Config, sim_params: Si... |
import cv2
import numpy as np
from PIL import Image
import urllib.request
import math
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from random import randint
from scipy import ndimage, misc
from scipy.spatial.distance import hamming
#from selenium import webdriver
from time i... |
from scipy import ndimage
def load_color(path, *args, **kwargs):
return ndimage.imread(path, mode='RGB', *args, **kwargs)
def load_gray(path, *args, **kwargs):
return ndimage.imread(path, flatten=True, *args, **kwargs) |
<gh_stars>1-10
import pandas as pd
import numpy as np
from numpy.random import normal
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
import plotly.plotly as py # tools to communicate with Plotly's server
import csv
import scipy.stats as stats
import pandas as pd
#from statsmodels.graphics import tsaplo... |
<filename>ufp_analysis_code.py
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats.stats import pearsonr, spearmanr, kendalltau
import seaborn as sbn
from scipy.stats import norm
from statsmodels.graphics.gofplots import qqplot
from sklearn.linear_model import LinearRegression
from s... |
"""
Purpose: MXNet Word Embedding
Date created: 2020-05-04
https://gluon-nlp.mxnet.io/examples/word_embedding/word_embedding_training.html
Contributor(s):
<NAME>.
"""
import os
os.chdir(r"C:\Users\Work1\Desktop\Info\GitHub\python-examples-main\notebook-samples")
CSV_PATH = rf"{os.getcwd()}\csv-files\kiva-data.c... |
import keras
from keras.preprocessing.image import load_img, img_to_arraytarget_image_path= 'target.jpg' #target image
style_image_path='style.jpg' #style image
width,height =load_img(target_image_path).size
image_height =400
image_width=int(width * image_height /height)
import numpy as np
from keras.applications impo... |
# import required libraries
import math
import pandas as pd
import scipy.stats as stats
# HELPER FUNCTIONS
def prop_error(n, p, alpha = 0.05):
"""
Return the confidence interval for a proportion at the given alpha level
"""
z_critical = stats.norm.ppf(1-alpha/2.0)
returnstr = str(round(p, 2))
... |
<gh_stars>0
import numpy as np
import pandas as pd
import scipy.sparse
from cirrocumulus.io_util import cirro_id
class SimpleData:
def __init__(self, X, obs, var):
self.obs = obs
self.var = var
self.uns = {}
if X is not None:
if len(X.shape) == 1:
X = ... |
<gh_stars>1-10
#!/usr/bin/env python3
import argparse
from collections import Counter
import statistics
import norman
def main(args):
with open(args.file) as fh:
gs = list(norman.robust_load(fh.read()))
re_map = norman.load_reifications(args.reify)
co_map = norman.load_dereifications(args.collap... |
<reponame>ericlee0920/Cluster-Viz
import itertools
import numpy as np
import pandas as pd
import networkx as nx
from scipy.stats import zscore
from scipy.spatial.distance import squareform, pdist
import matplotlib.pyplot as plt
"""
How to use:
expression_matrix, location_matrix, axis_matrix, size = read_data... |
import os
from collections import Counter
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
from matplotlib import image as mpimg
import statistics
# 2194
def getColors(img):
pixels = img.reshape((72*72, 4)) .tolist()
# filter pixels for all with A > .9 (almos... |
<gh_stars>0
# %%
import numpy as np
from scipy.integrate import odeint
import matplotlib.pyplot as plt
%matplotlib inline
import matplotlib.pyplot as plt
# %%
KON = 1
KD = 5
KOFF = KD/KON
def binding_one_to_one(concs, t, KD):
KON = 1
KOFF = KD/KON
dPdt = -KON*concs[0]*concs[1]+(KOFF)*... |
<reponame>pombreda/pyamg<filename>Examples/Visualization_ConnectionViewer/demo.py
##
# Import ConnectionViewer writers
import numpy, scipy
from pyamg import gallery, rootnode_solver
from pyamg.gallery import stencil_grid
from pyamg.gallery.diffusion import diffusion_stencil_2d
from cvoutput import *
from convergence_... |
import wfdb
from wfdb import processing
from ecg_model import MultiEcg, Ecg
import numpy as np
import scipy.signal as signal
import matplotlib.pyplot as plt
from array_tool import resample
import shutil
import os
from array_tool import med_filter
def detect_qrs(sig, fs):
# qrs_idx = processing.xqrs_detect(sig, fs... |
from scipy.io import loadmat
from numpy import transpose
import skimage.io as sio
from utils import visualize
import numpy as np
import os
import argparse
def main(args):
detection = loadmat('/home/tju/pytorch-pose/evaluation/data/detections.mat')
det_idxs = detection['RELEASE_img_index']
debug = 1
thr... |
<reponame>s-neilson/CORNISH-CNN-classifier
import copy
import os
from astropy.io import fits
from astropy.visualization import MinMaxInterval
from scipy.misc import imresize
import numpy
import keras_preprocessing
class ImagedObject:
name=""
label=()
imageSize=0
imageData=None #All of the data ... |
<reponame>rhwhite/rhwhitepackages3
# Module for performing various processing of data
# Written by rhwhite <EMAIL>
import netCDF4
import numpy as np
import xarray as xr
from scipy import stats,signal
import scipy.fftpack as fftpack
# Lanczos function weight calculation. confirmed it matches that from NCL when normaliz... |
import numpy as np
import scipy.ndimage.filters as spf
from scipy import ndimage
from skimage.restoration import denoise_nl_means
from skimage.transform import integral_image as sk_integral_image
from gputools.convolve import median_filter, uniform_filter, gaussian_filter
from gputools.transforms import scale, map_coor... |
"""
'power.py' module serves mainly for interacting with C++ library fastsim.py
- translate all power spectra, growth functions, correlations functions, etc.
into C++ functions for speed
- handles numpy arrays
- cosmo == C++ class Cosmo_Param, accessible through SimInfo.sim.cosmo
- FTYPE_t=[float, double, long... |
<filename>NASA SPACEAPPS CHALLENGE/Solution/Software part/Astronomical Data and Python Libraries/Astropy/astropy-1.1.2/astropy/utils/compat/fractions.py
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Handles backports of the standard library's `fractions.py`.
The fractions module in 2.6 does not h... |
import numpy as np
import scipy.linalg as LA
import torch
import torch.nn as nn
import torch.nn.functional as F
EPS = 1e-8
LOG_2 = 0.69314718056
EPS_TENSOR = torch.tensor(EPS)
# 3D Levi-Civita tensor
EIJK = torch.zeros(3, 3, 3, requires_grad=False)
EIJK[0, 1, 2] = EIJK[1, 2, 0] = EIJK[2, 0, 1] = 1
EIJK[0, 2, 1] = EIJ... |
import numpy as np
import cv2, serial, time, os, sys
from scipy import stats
def getSerialPort(intASCII): # intASCII is the integer which can be recognized as a correct signal (e.g. laser level) by our Arduino UNO
for portNum in range(1, 11):
try:
portName = 'COM' + str(portNum)
... |
# Copyright (c) 2015, <NAME> and <NAME> All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and ... |
<reponame>evgeniy97/taskgenerator
# coding: utf-8
# In[ ]:
"""
Numerical Methods, lab 3
"""
import sympy as sy
from sympy import Rational as syR
from sympy import exp, sin, cos, sqrt, log, ln
from sympy import pi, cot, sinh, cosh, atan, tan
# Это был Task_db
Tasks_db = {
'Task1':
[
# 3.1.1
{'a':... |
<reponame>yuwenhuisama/AudioSeparationExperiment<gh_stars>0
'''
File: \resource.py
Project: AudioSourceSeparation
Created Date: Tuesday April 24th 2018
Author: Huisama
-----
Last Modified: Sunday May 6th 2018 12:58:23 am
Modified By: Huisama
-----
Copyright (c) 2018 Hui
'''
import numpy as np
import musdb... |
# -*- coding: utf-8 -*-#
'''
# Name: GMM
# Description: 高斯混合模型实现
# Author: super
# Date: 2020/5/10
'''
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse
from scipy.stats import multivariate_normal
plt.style.use('seaborn')
def generate_X(true_Mu, true_Var)... |
import os, sys, pdb, time, gzip, pickle, math
from collections import OrderedDict as odict
from copy import copy
import torch, osqp
import cvxpy as cp, numpy as np, matplotlib.pyplot as plt, line_profiler as lp
import scipy.sparse as sp, scipy.sparse.linalg as spla
from matplotlib.patches import Rectangle
from tqdm im... |
<filename>toy_models/expected_max_value.py<gh_stars>1-10
import random as rand
import matplotlib.pyplot as plt
from scipy.stats import norm
from scipy.integrate import quad
import numpy as np
def normal_prob(i):
"""
Calculates the probability of taking a step to the right, left,
or staying at the ... |
<reponame>chcaru/gcnet<gh_stars>10-100
print 'Loading dependencies...'
import os, math, sys
import numpy as np
from scipy.misc import imsave
from scipy.optimize import fmin_l_bfgs_b
from keras.preprocessing.image import load_img, img_to_array
from keras.applications import vgg16 as vgg16
from keras.layers import Inp... |
'''
2017-02-28
place a colorbar.
'''
import sys
import scipy
import pickle
import scipy.misc
import numpy as np
import SimpleITK as sitk
import PyQt5
import pyqtgraph.opengl as gl
import matplotlib.pyplot as plt
import pyqtgraph as pg
from scipy import ndimage
from PyQt5 import QtCore, QtGui
# from pyqtgr... |
<filename>archive/vc.py
#!/usr/bin/env python3
# https://librosa.org/doc/latest/auto_examples/plot_music_sync.html#sphx-glr-auto-examples-plot-music-sync-py
# pyrubberband may offer a higher quality stretch/compress function
import argparse
import librosa # pip install librosa
import librosa.display... |
__author__ = "<NAME>"
__email_ = "<EMAIL>"
import matplotlib.pyplot as plt
from scipy.stats import linregress
import scipy.constants as con
import numpy as np
import seaborn as sbn
import ipywidgets as widgets
from IPython.display import display
from ipywidgets import Layout, Button, Box, FloatText, Textarea, Dropdo... |
<reponame>WhoIsJack/chemokine_buffering_paper
# -*- coding: utf-8 -*-
"""
Created on Sun May 14 13:54:22 2017
@author: <NAME> @ Gilmour group @ EMBL Heidelberg
@descript: Preprocessing before landmark extraction for analysis of cxcr7
distribution. This includes a masking function to mask the entire
... |
'''
Code for downloading and processing Caltech Pedestrian Dataset - test part (P. Dollar et al. 2009, http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/).
Based on code related to PredNet - Lotter et al. 2016 (https://arxiv.org/abs/1605.08104 https://github.com/coxlab/prednet).
Method of resizing was spe... |
<filename>Stick_breaking.py<gh_stars>0
# -*- coding: utf-8 -*-
"""
@author: William
"""
from matplotlib import pyplot as plt
import numpy as np
import scipy as sp
plt.rcParams['figure.dpi'] = 100
plt.rcParams['font.size'] = 10
N = 20
K = 30
alpha = 2.
P0 = sp.stats.norm
beta = sp.stats.beta.rvs(1, al... |
import matplotlib
import matplotlib.gridspec as gs
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import tensorflow as tf
# Prevent text from rendering as paths
new_rc_params = {'text.usetex': False,
"svg.fonttype": 'none'
}
matplotlib.rcP... |
<reponame>daniel620/QunatPlayground<filename>src/ou_noise/ou.py
""" OU (Ornstein-Uhlenbeck) process
dX = -A(X-alpha)dt + v dB
"""
import math
import scipy.constants
import scipy.stats
import scipy.optimize
import numpy
from . import quadratic_variation
def mle(t, x, start=None):
"""Maximum-likelihood es... |
<reponame>eth-sri/ACE
import numpy as np
import torch
import torchvision
import matplotlib.pyplot as plt
import pickle
from scipy.stats import norm
import re
import json
import os
from layers import Linear, Conv2d
from networks import FFNN, ConvMedBig, MyResnet, myNet, EfficientNet
from itertools import combinations
... |
<reponame>KmolYuan/fourbar-simscape<filename>hw4.py
# -*- coding: utf-8 -*-
from math import degrees, radians
from numpy import array, arange, linspace, concatenate, zeros, pi, sin, cos, sqrt
from scipy.interpolate import interp1d
import matplotlib.pyplot as plt
l2 = 38e-3
l3 = 133e-3
r2 = 0.3 * l2
r3 = 0.36 * l3
the... |
import argparse, gc, gzip, multiprocessing, random, sys, time, traceback
import numpy as np
import scipy.misc
start = time.time()
def echo(msg):
global start
seconds = time.time() - start
m, s = divmod(seconds, 60)
h, m = divmod(m, 60)
hms = "%02d:%02d:%02d" % (h, m, s)
print('['+hms+'] ' + msg)
def parse_a... |
<gh_stars>0
"""
This version of the extraction code has started with Lindsay's example
but has been modified to work with the code in esi2d.py
"""
import sys
import numpy as np
from scipy import ndimage,interpolate
from matplotlib import pyplot as plt
try:
from astropy.io import fits as pf
except ImportError:
... |
# %matplotlib inline
import numpy as np
# for the sigmoid function we need expit() from scipy
import scipy.special
# library for plotting arrays
import matplotlib.pyplot as plt
# A particularly interesting backend, provided by IPython, is the inline backend.
# This is available only for the Jupyter Notebook and the Jup... |
# dd_models.py - Distance distribtion parametric models
# ---------------------------------------------------------------------------
# This file is a part of DeerLab. License is MIT (see LICENSE.md).
# Copyright(c) 2019-2021: <NAME>, <NAME> and other contributors.
import math as m
import numpy as np
import scipy.spe... |
<reponame>Pablo-Arias/STIM<gh_stars>1-10
#-*- coding: utf-8 -*-
# --------------------------------------------------------------------#
# --------------------------------------------------------------------#
# ---------- Made by <NAME> @ircam on 11/2015
# ---------- Copyright (c) 2018 CREAM Lab // CNRS / IRCAM / Sorbon... |
<gh_stars>10-100
# -*- coding: utf-8 -*-
"""
Created on Sun Sep 1 11:15:41 2019 at ISM, Tokyo
Author: <NAME> @ IGP-CEA
Bei ZHANGE @ IGP-CEA
Co-Author: <NAME> @ ISM-Tokyo
####################################################
MIT license
Copyright @ pyBACGS 2019
All right rese... |
<filename>tools/palantir/packages/CNV_infer/plotCnvArr.py
import sys
import math
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import scipy.cluster.hierarchy as sch
def plotcnv(cnvArr, chrArr, title, norm=None, norm_vals=None, norm_inds=None,
cmap=plt.cm.bwr,... |
<gh_stars>1-10
# Digital Signal Processing - hwk2 - Part 2.5
# <NAME> - 03117037
# Exercise 2.5: Digital Filter Design
import numpy as np
import matplotlib.pyplot as plt
import scipy as sp
from scipy import signal
plt.close('all')
counter =0
fs = 0.001
omega = np.arange(0,2*np.pi,fs)
Hcont = 5/(np.sqrt... |
import typing
import pandas as pd
import scipy.stats
from pyextremes.models.model_emcee import Emcee
from pyextremes.models.model_mle import MLE
def get_model(
model: str,
extremes: pd.Series,
distribution: typing.Union[str, scipy.stats.rv_continuous],
distribution_kwargs: typing.Optional[dict] = No... |
#!/usr/bin/env python
"""
show what outliers are using a poisson distirbution
"""
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as stats
plt.style.use('seaborn')
SMALL_SIZE = 18
MEDIUM_SIZE = 20
LARGE_SIZE = 22
plt.rc('font', size=SMALL_SIZE) # controls default text sizes
plt.rc(... |
<gh_stars>1-10
# Copyright 2020 <NAME>, <NAME>, <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 appl... |
import numpy as np
import json
import os
from scipy.io import loadmat
from pandas import DataFrame
import xgboost as xgb
from config_name_creator import create_fft_data_name
##xgb
def load_train_data_xgb(data_path, subject):
read_dir = data_path + '/' + subject
filenames = sorted(os.listdir(read_dir))
trai... |
<filename>pymirror/experimental/more_links.py
#!/usr/bin/env python3
# coding: utf-8
import argparse
import inspect
import itertools
import signal
import statistics
import time
from pathlib import Path
from typing import Optional
from dracula import DraculaPalette as Dp
from selenium.webdriver.common.by import By
fr... |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
from torch_geometric.utils import remove_self_loops, add_self_loops, softmax
from tqdm import tqdm, trange
from scipy import sparse
class ClassBoundaryLoss(_Loss):
__constants__ = ['reduc... |
"""This module contains the public api of the EasyAgents reinforcement learning library.
It consist mainly of the class hierarchy of the available agents (algorithms), registrations and
the management of the available backends. In their implementation the agents forward their calls
to the chosen backend.
"... |
from __future__ import unicode_literals, absolute_import, division
import os
import numpy as np
from scipy.stats import binned_statistic
from .base import BaseValidationTest, TestResult
from .plotting import plt
import healpy as hp
__all__ = ['DensityVersusSkyPosition']
def create_hp_map(ra, dec, nside):
"""
... |
"""
Created on 10:25 at 08/07/2021/
@author: bo
"""
import argparse
import os
import numpy as np
import pickle
import data.rruff as rruff
from sklearn.metrics import roc_curve, auc
from scipy.special import expit, softmax
import const
import test
import vis_utils as vis_utils
import data.prepare_data as pdd
import matp... |
<filename>test_sc_pix3d.py<gh_stars>10-100
from __future__ import print_function
from __future__ import division
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import matplotlib.pyplot as plt
import time
print("PyTorch Version: ",torch.__version__)
import pickle
import os
import scip... |
<gh_stars>1-10
from threading import Lock
import random
import sympy
from datetime import datetime
mutex = Lock()
def modexp(b, e, m):
bits = [(e >> bit) & 1 for bit in range(e.bit_length())]
s = b
v = 1
for bit in bits:
if bit == 1:
v *= s
v %= m
s *= s
... |
'''
Functions for loading and slicing data
'''
from datetime import datetime
from pkg_resources import resource_filename
import numpy as np
from scipy.io import loadmat
__all__ = ['get_data',
'get_samplerate_mstimer',
'get_samplerate_datetime',
'_sliding_window',
'rolling_... |
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