text string |
|---|
<reponame>abisheckkathir/Exam-Authenticator
from flask import Flask, flash, redirect, render_template, Response, request, session, abort
from imutils import paths
import face_recognition
import pickle
import cv2
import os
import shutil
import pandas as pd
import csv
import numpy as np
import os
import matplo... |
<filename>budget-rnn/src/data_preparation/ford/audio_processing.py<gh_stars>1-10
import numpy as np
from scipy.signal import hamming
from typing import List
def amplify_signal(signal: np.ndarray, emphasis: float):
return np.append(signal[0], signal[1:] - emphasis * signal[:-1])
def frame_signal(signal: np.ndarr... |
import sys
def my_except_hook(exctype, value, traceback):
print('There has been an error in the system')
sys.excepthook = my_except_hook
import warnings
if not sys.warnoptions:
warnings.simplefilter("ignore")
import parselmouth
from parselmouth.praat import call, run_file
import glob
import errno
import csv... |
<filename>kgcnn/ops/polynom.py<gh_stars>0
import numpy as np
import scipy as sp
import scipy.special
import tensorflow as tf
from scipy.optimize import brentq
@tf.function
def tf_spherical_bessel_jn_explicit(x, n=0):
r"""Compute spherical bessel functions :math:`j_n(x)` for constant positive integer :math:`n` exp... |
from PyQt5.QtWidgets import QWidget, QVBoxLayout
import matplotlib.pyplot as plt
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.figure import Figure
import numpy as np
from scipy.interpolate import griddata
class MplPlotWidget(QWidget):
def __init__(self, parent... |
import numpy as np
import pandas as pd
import json
from scipy.stats import ttest_ind
# Loads files provided their path
# ===============================
def load_data(path):#,index):
# Loads the data
with open(path) as f:
g = json.load(f)
# Converts json dataset from dictionary to dataframe
#p... |
<filename>silx/opencl/test/test_medfilt.py<gh_stars>1-10
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Project: Median filter of images + OpenCL
# https://github.com/silx-kit/silx
#
# Permission is hereby granted, free of charge, to any person
# obtaining a copy of this software and associated docume... |
<reponame>amalroy/tipr-second-assignment
import numpy as np
from sklearn.utils import shuffle
from sklearn.metrics import accuracy_score,f1_score
from scipy.special import expit
def softmax(r):
shift=r-np.max(r)
exps=np.exp(shift)
return exps/np.sum(exps,axis=0)
def sigmoid(r):
return expit(r)
def swi... |
import os
import numpy as np
from toolkit.tvnet_pytorch.train_options import arguments
from toolkit.tvnet_pytorch.model.network import model
import scipy.io as sio
import cv2
import PIL.Image as Image
from toolkit.tvnet_pytorch.utils import *
from toolkit.datasets import DatasetFactory
from torchvision import transfor... |
import numpy as np
import torch
import random
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision as vision
import sys
from scipy.misc import imresize
from torchvision import transforms, utils
import models.modules as modules
import sc... |
import numpy as np
import scipy.sparse as sp
import torch
import torch.nn as nn
from tqdm import tqdm
import networkx as nx
import random
import math, os
from collections import defaultdict
import argparse
from models import DGI, LogReg
from utils import process
from attacker.attacker import Attacker
from estim... |
from GrStat import GroundStation, Reception
from sat import Satellite
from pathos.pools import ParallelPool
from scipy import interpolate
import pandas as pd
import numpy as np
import pickle
import tqdm
import datetime
import sys, os
# this file contains the functions used to estimate antenna sizes and di... |
# exercise 8.1.2
import matplotlib.pyplot as plt
import numpy as np
from scipy.io import loadmat
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from toolbox_02450 import rocplot, confmatplot
font_size = 15
plt.rcParams.update({'font.size': font_size})
# Load... |
<reponame>PengningChao/emdb-sphere
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Aug 5 21:46:50 2020
@author: pengning
"""
import numpy as np
import scipy.special as sp
import matplotlib.pyplot as plt
from .shell_domain import shell_rho_M, shell_rho_N
import mpmath
from mpmath import mp
from .dip... |
<gh_stars>1-10
from sympy.core import *
def test_rational():
a = Rational(1, 5)
assert a**Rational(1, 2) == a**Rational(1, 2)
assert 2 * a**Rational(1, 2) == 2 * a**Rational(1, 2)
assert a**Rational(3, 2) == a * a**Rational(1, 2)
assert 2 * a**Rational(3, 2) == 2*a * a**Rational(1, 2)
assert... |
######################################
## save_and_check_Phase1.py ##
## <NAME> ##
## Version 2020.04.21 ##
######################################
# This is based on Linc's save_and_check_twopoint.
# It has been adapted to make .fits files for the Phase1 r... |
def load_ground_truth(gt_file: str):
ground_truth = []
with open(gt_file, 'r') as f:
for idx, line in enumerate(f):
ground_truth.append(int(line))
return ground_truth
def load_imagenet_meta(meta_file: str):
import scipy.io
mat = scipy.io.loadmat(meta_file)
return mat['... |
<reponame>fpcasale/limix
import sys
import h5py
import pdb
import scipy as SP
import scipy.stats as ST
import scipy.linalg as LA
import time as TIME
import copy
import warnings
import os
import csv
def splitGeno(
pos,
method='slidingWindow',
size=5e4,
step=None,
annotation_file... |
<filename>ground_truth_labeling_jobs/video_annotations_quality_assessment/quality_metrics_cli.py
import os
import json
import numpy as np
import argh
import boto3
from argh import arg
from tqdm import tqdm
from scipy.spatial import distance
from plotting_funcs import *
s3 = boto3.client('s3')
def compute_dist(img_emb... |
<gh_stars>0
from itertools import accumulate,chain,combinations,groupby,permutations,product
from collections import deque,Counter
from bisect import bisect_left,bisect_right
from math import gcd,sqrt,sin,cos,tan,degrees,radians
from fractions import Fraction
from decimal import Decimal
import sys
input = lambda: sys.s... |
import numpy as np
import pandas as pd
import scipy.special
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
plt.style.use(['../optdynim.mplstyle'])
import palettable
import sys
sys.path.append('../lib')
import optdynlib
import plotting
import misc
df = misc.loadnpz('data/data.npz')
df['tauc']... |
import imageio
import numpy as np
import scipy.ndimage
start_img = imageio.imread(
"http://static.cricinfo.com/db/PICTURES/CMS/263600/263697.20.jpg"
)
gray_inv_img = 255 - np.dot(start_img[..., :3], [0.299, 0.587, 0.114])
blur_img = scipy.ndimage.filters.gaussian_filter(gray_inv_img, sigma=5)
def dodge(front, b... |
#!/usr/bin/python
# import osqp
import sys, os
from klampt import *
from klampt import vis
from klampt.vis.glrobotprogram import GLSimulationPlugin
import numpy as np
import string
import scipy as sp
import scipy.sparse as sparse
from klampt.model.trajectory import Trajectory
import time
import math
sys.path.insert(0... |
import numpy as np
from scipy.sparse import csr_matrix
from feature_mining.em_base import ExpectationMaximization
class ExpectationMaximizationVector(ExpectationMaximization):
"""
Vectorized implementation of EM algorithm.
"""
def __init__(self, dump_path="../tests/data/em_01/"):
print(type(s... |
<gh_stars>10-100
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# File : hotrgTc.py
# Author : Xinliang(Bruce) Lyu <<EMAIL>>
# Date : 22.02.2021
# Last Modified Date: 22.02.2021
# Last Modified By : Xinliang(Bruce) Lyu <<EMAIL>>
# -*- coding: utf-8 -*-
"""
Created on Sun Aug 16 16:... |
import logging
import numpy as np
import scipy.stats
def mean_confidence_interval(data, confidence=0.95):
n = len(data)
m, se = np.mean(data), scipy.stats.sem(data)
h = se * scipy.stats.t.ppf((1 + confidence) / 2., n-1)
return m, m-h, m+h
def print_latency_stats(data, ident, log=False):
npdata =... |
<gh_stars>0
import math
import numpy as np
from multiprocessing import Pool
from scipy.spatial import cKDTree
from scipy.linalg import orth
from scipy.linalg.interpolative import svd as rsvd
from scipy.sparse import issparse
from numba import jit, float32, int32, int8
from . import settings
from .irlb import lanczos
... |
"""Randomized LU decomposition."""
import numpy as np
import scipy.linalg as la
from typing import Tuple
PQLU = Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]
def randomized_lu(A: np.ndarray, k: int, l: int, seed: int = 0) -> PQLU:
"""Performs a randomized rank-k LU decomposition of A.
Adapted fr... |
__all__ = []
from .rbig import *
from .feature_map import *
from . import rbig
from . import feature_map
__all__ += rbig.__all__
__all__ += feature_map.__all__
del rbig
del feature_map
from scipy._lib._testutils import PytestTester
test = PytestTester(__name__)
del PytestTester |
<gh_stars>1-10
import numpy as np
from sparse_soft_impute import SoftImpute, SPLR
from scipy.sparse import coo_matrix, csr_matrix, csc_matrix, lil_matrix
from sklearn.utils.testing import assert_raises, assert_equal, assert_array_equal
import unittest
class TestPredict(unittest.TestCase):
''' Unit Tests for the S... |
import pickle
import numpy as np
import gym
# import pybobyqa
import tensorflow as tf
import matplotlib.pyplot as plt
import pandas as pd
from simulated_tango import SimTangoConnection
class FelLocalEnv(gym.Env):
def __init__(self, tango, **kwargs):
self.max_steps = 10
print('init env ' * 20)
... |
#! /usr/bin/env python3
import os
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from multiprocessing import Pool
from datetime import datetime
import arrow
data_dir = 'clean_data/'
out_dir = 'curves/'
out_dir = os.path.dirname(out_dir) + '/'
if out_d... |
import numpy as np
import scipy.sparse as sp
def subsetNpMatrix(matrix, row_bounds, column_bounds):
rows = np.array([x for x in range(row_bounds[0], row_bounds[1]) if 0 <= int(x) < matrix.shape[0]])
cols = np.array([y for y in range(column_bounds[0], column_bounds[1]) if 0 <= int(y) < matrix.shape[1]])
if... |
<gh_stars>10-100
"""
Finite-difference solver for wave equation:
u_yy = u_xx.
Initial and boundary conditions:
u(x, 0) = init(x), 0 <= x <= xf,
u_y(x, 0) = d_init(x), 0 <= x <= xf,
u(0, y) = bound_x0(y), 0 <= y <= yf,
u(xf, y) = bound_xf(y), 0 <= y <= yf.
"""
import numpy as np
from sci... |
<reponame>magnusmorton/trace-analysis
import re
import sys
import copy
import operator
import numpy as np
import instructions
import trace
from scipy.optimize import nnls
from scipy.linalg import solve
from sets import Set
from scipy.io import savemat
import pdb
high_cost = ['ARRAYLEN_GC_OP',
'STRLEN_OP',
'ST... |
#!/usr/local/bin env
import pprint as pprint
import statistics
import numpy as np
from datetime import datetime
from PIL.PngImagePlugin import PngImageFile, PngInfo
import random
import string
def running_mean(l, N):
"""From a list of values (N), calculate the running mean with a
window of (l) items. How larg... |
<gh_stars>0
"""Plot some basic BPT distributions
"""
import os, sys
from matplotlib import pyplot as plt
import numpy as np
from scipy.stats import expon, gamma, weibull_min, invgauss
mu = 100
alphas = [ 0.5, 1., 2., 5., 10.]
#alpha = 1
x_vals = np.arange(0, 4*mu)
# Plot for a range of alpha values
for alpha in alphas... |
<filename>scripts/real_data_novility.py
# coding: utf-8
# In[1]:
import drama as drm
import numpy as np
import matplotlib.pylab as plt
from matplotlib import gridspec
from sklearn.metrics import roc_auc_score
import os
import glob
import h5py
import scipy.io as sio
get_ipython().magic(u'matplotlib inline')
# In... |
import ipywidgets
import numpy as np
import pandas as pd
import pathlib
from scipy.stats import linregress
from bokeh.io import push_notebook, show, output_notebook
from bokeh.plotting import figure
from bokeh.models import ColumnDataSource, RangeTool, Circle, Slope, Label, Legend, LegendItem, LinearColorMapper
from bo... |
<filename>utils/pitch_tools.py
#########
# world
#########
import librosa
import parselmouth
import numpy as np
import torch
import torch.nn.functional as F
from pycwt import wavelet
from scipy.interpolate import interp1d
gamma = 0
mcepInput = 3 # 0 for dB, 3 for magnitude
alpha = 0.45
en_floor = 10 ** (-80 / 20)
FFT... |
from typing import Tuple, Union
import numpy as np
from scipy.special import erf
class RectifiedGaussianDistribution(object):
"""Implementation of the rectified Gaussian distribution.
To see what is the rectified Gaussian distribution, visit:
https://en.wikipedia.org/wiki/Rectified_Gaussian_distribu... |
<filename>distpy/workers/strainrate2summary.py
# (C) 2020, Schlumberger. Refer to LICENSE
import numpy
import datetime
import scipy.signal
import os
import distpy.io_help.io_helpers as io_helpers
import distpy.io_help.directory_services as directory_services
import distpy.calc.pub_command_set as pub_command_set
impor... |
'''
accuracy_utils
Module for checking accuracy of retrieval
'''
from scipy.optimize import fsolve
from scipy.spatial import distance
import numpy as np
from ._scaler import Scaler
class RetrievalMetricCalculator:
def __init__(self, parameter_limits):
'''
The RetrievalMetricCalculator generate... |
"""
.. _multi-taper-psd:
===============================
Multi-taper spectral estimation
===============================
The distribution of power in a signal, as a function of frequency, known as the
power spectrum (or PSD, for power spectral density) can be estimated using
variants of the discrete Fourier transfor... |
import logging
import itertools
import numpy as np
from scipy.optimize import OptimizeResult, minimize_scalar
import scipy.constants
from .util import find_vertex_x_of_positive_parabola
def scalar_discrete_gap_filling_minimizer(
fun, bracket, args=(), tol=1.0, maxfev=None, maxiter=100, callback=None, verbos... |
<reponame>talkowski-lab/gnomad-sv-v3-qc
#!/usr/bin/env python
from scipy import stats
import numpy as np
import os
import os.path
from sklearn import mixture
def Deltest(F,M,E,length,crit=0.01,thres1=0.0005): # calculate the Del statistic given a FME combo in het files
# if True:
thres1=min(50/length,thres1)
... |
import sys
from basic import *
import tcr_distances
import parse_tsv
import numpy as np
from scipy.cluster import hierarchy
from scipy.spatial import distance
import util
import html_colors
from all_genes import all_genes
with Parser(locals()) as p:
#p.str('args').unspecified_default().multiple().required()
p... |
#!/usr/bin/python3
import numpy as np
from numpy import matlib
from numpy import random
import sys
import copy
import scipy.signal
import scipy.stats.stats
from matplotlib import pyplot as plt
import unittest
def Norm(t):
while t > np.pi:
t -= 2 * np.pi
while t < -np.pi:
t += 2 * np.pi
return t
def Sign... |
<reponame>parejkoj/specutils
from abc import ABC, abstractmethod
import numpy as np
from scipy.interpolate import CubicSpline
from astropy.units import Quantity
from astropy.nddata import StdDevUncertainty, VarianceUncertainty, InverseVariance
from ..spectra import Spectrum1D
__all__ = ['ResamplerBase', 'FluxConserv... |
<gh_stars>1-10
"""
test_util_stats.py
Author: <NAME>
Affiliation: McGill
Created on: Tue 24 Mar 2020 22:11:31 EDT
Description:
"""
import numpy as np
from scipy.interpolate import interp1d
from ares.util.Math import interp1d_wrapper, forward_difference, \
central_difference, five_pt_stencil, LinearNDInterpolat... |
# -*- coding: utf-8 -*-
"""
Created on Tue Feb 11 13:07:06 2020
@author: <NAME>
Professor: Dr. <NAME>
"""
import numpy as np
print('\nExercise 1\n')
# Exercise 1
# =============================================
x = [1, 346432, 68, 1223, 5, 47, 678]
max_val = x[0]
for elem in x:
if elem > max_val:
... |
<reponame>jhkim6467/input_distill<filename>rein_train.py
from __future__ import division, unicode_literals
import argparse
import time
import math
import random
import torch.nn as nn, torch
import torch.nn.init as init
import torch.optim as optim
import os
import numpy as np
import pickle
from torch.autograd import V... |
<reponame>AIKICo/Steganalysis-By-Frame<filename>features_extractions.py
import numpy as np
import math as ma
import os
from pywt import wavedec
from pyeeg import hfd, pfd
from scipy.io import wavfile as wav
from python_speech_features.sigproc import framesig
from python_speech_features import mfcc, fbank, logfbank
from... |
"""Definitions for the `DiffusionCSM` class."""
from collections import OrderedDict
import numpy as np
from scipy.interpolate import interp1d
from mosfit.constants import C_CGS, DAY_CGS, M_SUN_CGS, AU_CGS
from mosfit.modules.transforms.transform import Transform
# Important: Only define one ``Module`` class per fil... |
<gh_stars>1-10
'''
MATPOWER
Copyright (c) 1996-2016 by Power System Engineering Research Center (PSERC) by <NAME>, PSERC Cornell
This code follows part of MATPOWER.
See http://www.pserc.cornell.edu/matpower/ for more info.
Modified by Oak Ridge National Laboratory (<NAME>) to be used in the parareal algorithm.
'''
imp... |
#!/usr/bin/env python3
# pipe input from benchmark binary into this script to plot throughput vs. compression ratio
import csv
import sys
from collections import defaultdict
from operator import itemgetter
from argparse import ArgumentParser
from math import floor, ceil, log10
import numpy as np
import scipy.stats a... |
import librosa
import numpy as np
from scipy.signal import lfilter, butter
import sigproc
import constants as c
def load_wav(filename, sample_rate):
audio, sr = librosa.load(filename, sr=sample_rate, mono=True)
audio = audio.flatten()
return audio
def normalize_frames(m,epsilon=1e-12):
return np.array([(v - np... |
import os
from PIL import Image
import cv2
import numpy as np
from scipy.ndimage import gaussian_filter
def join_path(*dirs):
if len(dirs) == 0:
return ''
path = dirs[0]
for d in dirs[1:]:
path = os.path.join(path, d)
return path
def make_filepath(fpath, dir_name=None, ext_name=None,... |
<reponame>JuliusvR/L5NeuronSimulation
"""
Contains the functions and class (SonataWriter) necessary for generating and saving the input spike rasters.
"""
import numpy as np
import scipy.signal as ss
import scipy
import scipy.stats as st
import matplotlib.pyplot as plt
import h5py
from bmtk.utils.reports.spike_trains ... |
<reponame>adolgert/cascade<filename>src/cascade/model/priors.py
from copy import copy
from functools import total_ordering
import numpy as np
import scipy.stats as stats
from cascade.core import getLoggers
CODELOG, MATHLOG = getLoggers(__name__)
# A description of how dismod interprets these distributions and their ... |
<reponame>slowy07/medical-BCDU
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
import models as M
import numpy as np
import scipy
import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve
from sklearn.metrics import roc_auc_score
from sklearn.metrics import confusion_matrix
from sklearn.metrics import pr... |
import numpy as np
import pylab as pl
from time import time
import logging
from motionstruct.functions import init_logging, asciiL, recursive_dict_update
from motionstruct.classes import PhiWorld
import scipy.io as sio
import os
# Help string and argument parsing
from argparse import ArgumentParser, RawTextHelpFormatt... |
<reponame>zfang-slim/PysitForPython3<gh_stars>0
import sys
import time
import copy
import numpy as np
import scipy.io as sio
__all__=['OptimizationBase']
__docformat__ = "restructuredtext en"
class OptimizationBase(object):
""" Base class for descent-like optimization routines.
These are stateful algori... |
<filename>Matsuoka/tripple_pend_ex.py
# -*- coding: utf-8 -*-
"""
Created on Thu Mar 11 11:25:26 2021
@author: jsalm
"""
import matplotlib.pyplot as plt
import numpy as np
from sympy import symbols
from sympy.physics import mechanics
from sympy import Dummy, lambdify
from scipy.integrate import odeint
#animation fu... |
<reponame>Ouranosinc/hailstorm<gh_stars>1-10
# noqa: D205,D400
"""
SDBA Diagnostic Testing Module
==============================
This module is meant to compare results with those expected from papers, or create figures illustrating the
behavior of sdba methods and utilities.
"""
from __future__ import annotations
im... |
<reponame>lindenmp/NormativeNeuroDev_CrossSec_DWI<filename>1_code/cluster/predict_symptoms_scv_grid.py
import argparse
# Essentials
import os, sys, glob
import pandas as pd
import numpy as np
import copy
import json
# Stats
import scipy as sp
from scipy import stats
# Sklearn
from sklearn.pipeline import Pipeline
fr... |
<filename>pyhrt/continuous.py
import os
import sys
import numpy as np
import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.optim as optim
from scipy.stats import norm
from scipy.stats.mstats import gmean
from pyhrt.utils import batches, create_folds, logsumexp
#############################... |
<gh_stars>0
import csv
import datetime
from scipy.stats import norm
from regional_poll_interpolator import RegionalPollInterpolator
import riding_poll_model
party_long_names = {
'cpc': 'Conservative/Conservateur',
'lpc': 'Liberal/Lib',
'ndp': 'NDP-New Democratic Party/NPD-Nouveau Parti d',
'gpc': 'Gr... |
<reponame>dungvtdev/upsbayescpm
################################################################################
# Copyright (C) 2013 <NAME>
#
# This file is licensed under the MIT License.
################################################################################
"""
Unit tests for bayespy.utils.misc module.
"... |
<filename>data_preparation/align_caricature_data.py
import numpy as np
import scipy.ndimage
import os
import PIL.Image
txtpath = './caricature.txt'
lmpath = './WebCaricature/FacialPoints/'
impath = './WebCaricature/OriginalImages/'
outpath = './Caricature/'
def image_align(src_file, dst_file, face_landmarks, output_s... |
<gh_stars>0
import empowermentexploration.utils.data_handle as data_handle
import empowermentexploration.utils.helpers as helpers
import matplotlib as mpl
import matplotlib.colors as c
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from scipy.stats import sem
mpl.use('Agg')
class Visualizati... |
import sys
import math
import json
from os import path
sys.path.append(path.dirname(path.dirname(path.abspath(__file__))) + '/utils/')
import numpy as np
import scipy.special as special
from algorithm_utils import get_parameters, set_algorithms_output_data
from pearsonc_lib import PearsonCorrelationLocalD... |
<filename>venv/lib/python3.8/site-packages/seaborn_qqplot/plots.py
# BSD 3-Clause License
#
# Copyright (c) 2019, <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 s... |
<reponame>lgbouma/earhart<gh_stars>0
"""
The stellar spin periods and planetary orbital periods originally collected by
Penev et al (2018) are shown in Figure~\ref{fig:Pspin_vs_Porb}. We only show
hot Jupiter systems with spin period S/N ratios of at least 5, and have colored
the hot Jupiters by whether their stellar r... |
from tcga_encoder.utils.helpers import *
from scipy import stats
def auc_standard_error( theta, nA, nN ):
# from: <NAME> McNeil (1982), The Meaning and Use of the Area under the ROC Curve
# theta: estimated AUC, can be 0.5 for a random test
# nA size of population A
# nN size of population N
Q1=theta/(2.0... |
from scipy import optimize,arange
from math import *
import sys
import csv
import numpy as np
import matplotlib.pyplot as plt
#matplotlib inline
#vectorised 2P-3T cournot now using basinhopping
#1. get in the data, etc. ... CHECK!
#2. make arbitrary to nxm ... CHECK!
#3. make investment game ... v7
#4. div;expl
#vec... |
<reponame>uofuseismo/YPMLRecalibration
"""
Regularisation Tests
This file contains a set of functions that were used to test and optimise
the effects of regularisation.
This file can also be imported as a module and contains the following
functions:
* func()
Blurb about func.
"""
import os
import numpy ... |
<reponame>RileyWClarke/flarubin
from rubin_sim.photUtils import SignalToNoise
from rubin_sim.photUtils import PhotometricParameters
from rubin_sim.photUtils import Bandpass, Sed
from rubin_sim.data import get_data_dir
import numpy as np
from scipy.constants import *
from functools import wraps
import os
import h5py
im... |
<reponame>azane/chomp
import numpy as np
import theano as th
import sympy as sm
import theano.tensor as tt
from typing import *
def slow_fdiff_1(n: int) -> np.ndarray:
K = np.diag(np.ones(n) * -1, 0)
K += np.diag(np.ones(n - 1) * 1, 1)
K = np.vstack((np.zeros(n), K))
K[0, 0] = 1.
K[-1, -1] = -1.
... |
import numpy as np
import scipy.optimize as sciopt
def UniformBeamBendingModes(Type,EI,rho,A,L,w=None,x=None,Mtop=0,norm='tip_norm',nModes=4):
"""
returns Mode shapes and frequencies for a uniform beam in bending
References:
Inman : Engineering variation
Author: <NAME>"""
if x is None o... |
<filename>data/scripts/model.py
import csv
import importlib
import sys
sys.path.append('..')
import os
import json
import argparse
import copy
from enum import IntEnum
from datetime import datetime
import numpy as np
import scipy.integrate as solve
import scipy.optimize as opt
import matplotlib.pylab as plt
from scri... |
<filename>deep_dream.py
from keras.applications import inception_v3
from keras import backend as K
import scipy
import imageio
from keras.preprocessing import image
import numpy as np
K.set_learning_phase(0)
model = inception_v3.InceptionV3(weights='imagenet', include_top=False)
layer_contributions = {'mixed2': 0.2,... |
# -*- coding: utf-8 -*-
'''
Module contains all of the functions to create a radio telemetry project.'''
# import modules required for function dependencies
import numpy as np
import pandas as pd
import os
import sqlite3
import datetime
import matplotlib.pyplot as plt
import matplotlib
import matplotlib.dates as mdat... |
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import animation
from scipy.special import erf, erfinv
import cPickle as pickle
import glob
import os
import scipy
import scipy.ndimage.interpolation
#print glob.glob(os.path.expanduser("~/storage/metadata/kaggle-heart/predictions/j7_jeroen_ch.pkl"))
#... |
import argparse
from itertools import product
import warnings
from joblib import Parallel, delayed
import librosa
import numpy as np
import pandas as pd
from scipy import signal, stats
from sklearn.linear_model import LinearRegression
from tqdm import tqdm
from tsfresh.feature_extraction import feature_calculators
fr... |
<gh_stars>100-1000
# fileio_serializers.py
#
# This file is part of scqubits: a Python package for superconducting qubits,
# arXiv:2107.08552 (2021). https://arxiv.org/abs/2107.08552
#
# Copyright (c) 2019 and later, <NAME> and <NAME>
# All rights reserved.
#
# This source code is licensed under the BSD-style ... |
# Copyright 2016 <NAME>
# Governed by the license described in LICENSE.txt
import libtcodpy as libtcod
import cProfile
import scipy.spatial.kdtree
import config
import algebra
import map
import log
from components import *
import miscellany
import bestiary
import ai
import actions
import spells
import quest
import co... |
<gh_stars>1-10
"""Scripts for second stage of labelling - ear and tail segmentations"""
from vis.utils import *
from dataset_production.mturk_processor import *
from scipy.ndimage import center_of_mass as COM
from matplotlib import colors
def extract_colours(rgb_array):
"""Given an array of (r,g,b) values, returns ... |
# -*- coding: utf-8 -*-
"""
Created on Mon Jun 6 16:59:19 2016
@author: tvzyl
"""
import data
from sklearn.datasets import make_spd_matrix
from pandas import DataFrame
from numpy import mean, diag, eye, rot90, dot, array, abs
from numpy import zeros, ones, arange
from numpy.random import uniform
from scipy.stats ... |
import numpy as np
import tensorflow as tf
import elbow.util as util
from elbow import ConditionalDistribution
import scipy.stats
from elbow.gaussian_messages import MVGaussianMeanCov, reverse_message, forward_message
from elbow.parameterization import unconstrained, psd_matrix, psd_diagonal
class LinearGaussian(C... |
<reponame>shijiale0609/Python_Data_Analysis
import numpy as np
import statsmodels.api as sm
import matplotlib.pyplot as plt
from scipy.fftpack import rfft
from scipy.fftpack import fftshift
data_loader = sm.datasets.sunspots.load_pandas()
sunspots = data_loader.data["SUNACTIVITY"].values
transformed = fftshift(rfft(s... |
<reponame>rebryk/SPbAU-Speech-Recognition<filename>task02/laughter_classification/sspnet_data_sampler.py<gh_stars>1-10
import os
from os.path import join
import numpy as np
import pandas as pd
import scipy.io.wavfile as wav
from laughter_classification.utils import chunks, in_any, interv_to_range, get_sname
from laug... |
"""
Binary vectors provide the basis for a representational approach
developed by <NAME> known as the Binary Spatter Code, with
the following key components:
(1) Randomly generated bit vectors with a .5 probability of a set bit in each component
(2) A *superposition* operator: this combines bit vectors elementwise via ... |
<filename>scripts/PMP_setup.py<gh_stars>0
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Sep 9 06:19:09 2020
@author: virati
Simple PMP buildup script
"""
import scipy.signal as sig
import matplotlib.pyplot as plt
import numpy as npo
import jax.numpy as np
from jax import grad, jit, vmap, jvp
from ... |
from abc import ABCMeta, abstractmethod
import sys
import numpy as np
from scipy import linalg
from scipy import stats
import pandas as pd
from vmaf.core.mixin import TypeVersionEnabled
from vmaf.tools.misc import import_python_file, indices
from vmaf.mos.dataset_reader import RawDatasetReader
__copyright__ = "Copyr... |
import warnings
import numpy as np
from scipy.linalg import pinvh
def get_generator(random_state=None):
"""Get an instance of a numpy random number generator object.
This instance uses `SFC64 <https://tinyurl.com/y2jtyly7>`_ bitgenerator,
which is the fastest numpy currently has to offer as of version 1... |
<reponame>certik/sympy-oldcore
import sys
sys.path.append("..")
from sympy import sqrt, symbols, eye
w, x, y, z = symbols("wxyz")
L = [x,y,z]
V = eye(len(L))
for i in range(len(L)):
for j in range(len(L)):
V[i,j] = L[i]**j
det = 1
for i in range(len(L)):
det *= L[i]-L[i-1]
print "matrix"
print V
print... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# ------------------------------------------------------------------------------
#
# Copyright 2022 <NAME>
# Copyright 2018-2021 Fetch.AI Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance wi... |
<filename>whizzlibrary/plotting.py
import numpy as np
import scipy.stats as st # for pearsonr, has to be imported explicitly
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.inset_locator import inset_axes # to use the inset in subplot
from mpl_toolkits.axes_grid1 import make_axes_locatable # to ... |
<gh_stars>0
import numpy as np
from scipy.special import logsumexp
from scipy.special.basic import psi
class MACEWorker():
# Worker model: MACE-like spammer model --------------------------------------------------------------------------------
# alpha[0,:] and alpha[1,:] are parameters for the spamming proba... |
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