text string |
|---|
"""
=========================================
ANALYTICAL RESULTS FOR REALIZED PROCESSES
=========================================
"""
import numpy as np
import scipy
from numpy import sqrt, exp, log
###################################
# Statistics for realized processes
###################################
# when use... |
<filename>compute_similarities.py
import argparse
import os
import sys
from shutil import rmtree, copyfile
import numpy as np
import nibabel as nib
from scipy.spatial.distance import correlation, dice
from pickle import dump
from joblib import Parallel, delayed
parser = argparse.ArgumentParser('Computes the similarity... |
#!/usr/bin/python
# -*- coding: utf8 -*-
"""
Main function of Learning-guided Graph Dual Adversarial Domain Alignment (LG-DADA)framework
for predicting a target brain graph from a source brain graph.
The original paper can be found in: https://www.sciencedirect.com/science/article/pii/S13618415203... |
<filename>dftools/smooth.py
from scipy.ndimage import gaussian_filter1d
from scipy.interpolate import UnivariateSpline
def unispline_to_gausfilter(
x, y, w=None, spline_kw={"k": 2}, filter_kw={"sigma": 1},
):
if w is not None:
spline_kw["w"] = w
s = UnivariateSpline(x, y, **spline_kw)
return g... |
<gh_stars>0
# -*- coding: utf-8 -*-
"""
Created on Tue Jun 29 11:01:00 2021
@author: <NAME>
This is a function that will be used to automatically generate synthetic topo-
graphy from a specified elevation profile.
The 'site_file_name' variable must be a csv file containing a column for distance
labeled 'Di... |
<filename>pyapprox/sympy_utilities.py
#!/usr/bin/env python
import os, sympy as sp
import subprocess
def convert_sympy_equations_to_latex(equations,tex_filename,compile_pdf=True):
out = r"""
\documentclass[]{article}
\usepackage{amsmath,amssymb}
\begin{document}
"""
print(len(equations))
#li... |
import skimage.feature
import skimage.transform
import skimage.filters
import scipy.interpolate
import scipy.ndimage
import scipy.spatial
import scipy.optimize
import numpy as np
import pandas
import plot
class ParticleFinder:
def __init__(self, image):
"""
Class for finding circular particles
... |
<gh_stars>1-10
import sys
from . import globals
import numpy as np
import pandas as pd
import scipy as sp
import logging
import json
from pathlib import Path
import src.globals
pypsapath = "C:/dev/py/PyPSA/"
if sys.path[0] != pypsapath:
sys.path.insert(0, pypsapath)
import pypsa
from tqdm import tqdm
class Sci... |
import calendar
import numpy as np
from pandas import Timestamp
from scipy.sparse import csr_matrix, vstack, isspmatrix_csr
from tqdm import tqdm
def choose_last_day(year_in, month_in):
return str(calendar.monthrange(int(year_in), int(month_in))[1])
def year2pandas_latest_date(year_in, month_in):
if year_i... |
from __future__ import division
import numpy as np
from sympy import *
class ThePablos:
def __init__(self, l1=1, l2=1, l3=1, l4=1, r=1e-2, t=1):
#Configurar dimensiones
self.l = np.array([l1,l2,l3,l4])
self.r = r
#Posiciones locales de los centros de masa
... |
<reponame>wfreinhart/composite_geometry
import numpy as np
from scipy.spatial.transform import Rotation
class SphereReinforcement(object):
"""
Example call:
SphereReinforcement(radius=5 * 1e-3, lattice=BCCLattice(spacing=12 * 1e-3))
"""
# TODO: provide an option for offset
def __init__(self, r... |
import numpy as np
import scipy.linalg as LA
import scipy.sparse.linalg as spLA
from project.poisson1d import Poisson1D
from project.weighted_jacobi import WeightedJacobi
# from project.gauss_seidel import GaussSeidel
from project.linear_transfer import LinearTransfer
from project.mymultigrid import MyMultigrid
if _... |
<filename>nodes/viewport_definer.py
#!/usr/bin/env python3
# ROS imports
import roslib; roslib.load_manifest('freemovr_engine')
import rospy
import freemovr_engine.srv
import freemovr_engine.msg
import freemovr_engine.display_client as display_client
import json
import argparse
import tempfile, os, sys
# Major libra... |
from typing import Union
import scipy.stats as stats
from beartype import beartype
from UQpy.distributions.baseclass import DistributionContinuous1D
class GeneralizedExtreme(DistributionContinuous1D):
@beartype
def __init__(
self,
c: Union[None, float, int],
loc: Union[None, float, ... |
"""
Imports and extends the ``sympy`` library for symbolic mathematics.
Contains tools for converting Sympy expressions to Python modules and functions.
"""
import re
from typing import Callable, Optional, Union, Literal
from functools import lru_cache
import numpy as np
import sympy as sp
from sympy.utilities.lambdi... |
<reponame>ryscet/pySeries
# -*- coding: utf-8 -*-
"""
Created on Wed Jun 8 11:21:35 2016
@author: user
"""
import sys
sys.path.insert(0, '/Users/user/Desktop/repo_for_pyseries/pyseries')
import pyseries.LoadingData as loading
import pyseries.Preprocessing as prep
import pyseries.Analysis as analysis
import matplotli... |
import numpy as np
import sys, csv, os
import torch
from torch.utils.data import DataLoader
from dgl.data.utils import split_dataset
from model import training, inference
from dataset import GraphDataset
from util import collate_reaction_graphs
from model import reactionMPNN
from sklearn.metrics import r2_score, mean... |
# coding: utf-8
# # this function will perform a hierarchical clustering on the raw behavioral scores
# Written by <NAME> & CBIG under MIT license:
# https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from scipy imp... |
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""Measure image properties.
"""
from __future__ import print_function, division
import numpy as np
from gammapy.image.utils import coordinates
__all__ = ['BoundingBox',
'bbox',
'find_max',
'lookup',
'lookup_ma... |
import numpy as np
from astropy.wcs import WCS
import re
from astropy.io import fits
from astropy import nddata
from scipy import ndimage
#https://docs.scipy.org/doc/scipy-1.3.0/reference/
# https://docs.astropy.org/en/stable/nddata/index.html
class FITSImage(WCS):
SIP = ('A','B','AP','BP')
def __i... |
<filename>viewers/cdxml2gnr.py<gh_stars>1-10
# pylint: disable=no-member
"""Widget to convert SMILES to nanoribbons."""
import numpy as np
from scipy.stats import mode
import re
from IPython.display import clear_output
import ipywidgets as ipw
import nglview
from traitlets import Instance
from ase import Atoms
from... |
import numpy as np
import scipy.misc
import scipy.io
import tensorflow as tf
VGG_MODEL = 'saved_models/VGG19/imagenet-vgg-verydeep-19.mat'
# The mean to subtract from the input to the VGG model. This is the mean that
# when the VGG was used to train. Minor changes to this will make a lot of
# difference to the perform... |
import importlib_resources
import numpy as np
import toml
import torch
import torch.nn as nn
import torch.nn.functional as F
from scipy.stats import betabinom
class Tacotron(nn.Module):
def __init__(self, encoder, decoder):
super().__init__()
self.input_size = 2 * decoder["input_size"]
sel... |
<reponame>redwankarimsony/UniFAD
"""Server class for visualizing images and datasets.
"""
# 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 Softwa... |
<reponame>palmercd/epic
import logging
from scipy.stats import poisson
from numpy import log
from epic.config.constants import BIN_SIZE, E_VALUE_THRESHOLD
from epic.statistics.generate_cumulative_distribution import generate_cumulative_dist
from epic.statistics.add_to_island_expectations import add_to_island_expectati... |
import scipy.io as sio
import numpy as np
data = sio.loadmat('../../data/train_data_10k.mat')
print('{}'.format(data.keys()))
print('{}'.format(data['positive_images'].shape))
#cameras = data['cameras']
#features = data['features']
#print(data.keys())
#print('{} {}'.format(cameras.shape, features.shape)) |
###################################################################################
# Copyright 2021 National Technology & Engineering Solutions of Sandia, #
# LLC (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the #
# U.S. Government retains certain rights in this software. ... |
<reponame>alm818/epipy
import unittest
from epipy.sparse import rigid_csr_matrix
from scipy.sparse import csr_matrix, dia_matrix
from test import generate_test, sum_duplicated_coo
import numpy as np
@unittest.skip("Finished tested, disable for faster unittest")
class TestTransform(unittest.TestCase):
def test_tran... |
################################################################################
#
# Copyright (c) 2009 The MadGraph5_aMC@NLO Development team and Contributors
#
# This file is a part of the MadGraph5_aMC@NLO project, an application which
# automatically generates Feynman diagrams and matrix elements for arbitrary
# hi... |
from collections import defaultdict
import os
from PIL import Image
from glob import glob
import tensorflow as tf
import numpy as np
import random
import scipy.misc
from tqdm import tqdm
# TODO: should be able to use tf queue's for this somehow and not have to load entire dataset into memory. main problem is dynamical... |
<reponame>UKPLab/acl2020-interactive-entity-linking
from typing import List, Dict, Any
import numpy as np
import pandas as pd
from scipy import linalg
from sklearn import svm
from sklearn.preprocessing import normalize
from gleipnir.evaluation.metrics import EvaluationResult, compute_letor_scores
from gleipnir.mode... |
<filename>engine/model/transformers.py
import pickle as pkl
import pandas as pd
from tqdm import tqdm
import scipy
def sentence_embeddings(sentences, embedder):
print('TODO model training')
# Corpus with example sentences
corpus = sentences
corpus_embeddings = embedder.encode(corpus,show_progress_b... |
from __future__ import division, print_function, absolute_import
from collections import OrderedDict
from rep.metaml.gridsearch import SubgridParameterOptimizer, \
RegressionParameterOptimizer, AbstractParameterGenerator, \
AnnealingParameterOptimizer, RandomParameterOptimizer
import numpy
from tests import re... |
<reponame>conlain-k/srm_motor<gh_stars>0
import numpy as np
import numpy.random as nprand
import time
import scipy
import scipy.interpolate
from cProfile import Profile
from pstats import Stats
from matplotlib import pyplot as plt
from ../srm_system import *
import ../shapes
from ../constants import *
prof = Profile(... |
#!/usr/bin/env python3
# -*- encoding: utf-8 -*-
import healpy
import numpy as np
import matplotlib.pyplot as plt
from astropy.io import fits
import statistics as sts
from scipy.linalg import lstsq
SPEED_OF_LIGHT_M_S = 2.99792458e8
PLANCK_H_MKS = 6.62606896e-34
BOLTZMANN_K_MKS = 1.3806504e-23
SOLSYSSPEED_M_S = 370082... |
"""Docstring for Optimization module."""
import time
import inspect
import statistics
from copy import copy
from random import Random, randint
class Optimization(object):
"""Optimization class is where the problem put in. In here we define the
mathematical model together with other constraints, variables' ty... |
def flip_sign(string):
if string == '-':
return '+'
elif string == '+':
return '-'
else:
pass
def analyse_nodes(infile, outpref):
import numpy as np
from statistics import mean
stop_codons = ["TAA", "TGA", "TAG", "TTA", "TCA", "CTA"]
start_codons = ["AT... |
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
import numpy as np
from scipy.cluster.hierarchy import linkage, dendrogram
def _plot_rectangle(frameloc, color='k', linewidth... |
<gh_stars>0
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Apr 20 01:29:46 2020
@author: krishna
"""
import time
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
data=pd.read_csv('All.csv')
column_names=list(data.columns)
data['URL_Type_obf_Type'].value_counts()
#creating a ... |
<reponame>claresinger/StratoClim_H2O_Intercomparison<gh_stars>0
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import matplotlib.gridspec as gridspec
import seaborn
import datetime
import scipy.stats as stats
flno = [2,3,4,6,7,8]
colors = np.array(["k","#045275","#0C7BDC","#7CCB... |
"""
Some code taken from https://github.com/caglarcakan/stimulus_neural_populations
Copyright (c) 2019, <NAME> BSD 2-Clause License
"""
from scipy import ndimage, signal
import numpy as np
import matplotlib.pyplot as plt
from numba import jit
def plot_kuramoto_example(traces):
kur, phases, peakslist, traces = fa... |
"""Module dedicated to extraction of complexity metafeatures."""
import typing as t
import itertools
import numpy as np
import sklearn
import sklearn.pipeline
import scipy.spatial
from pymfe.general import MFEGeneral
from pymfe.clustering import MFEClustering
from pymfe import _utils
class MFEComplexity:
"""Ke... |
<filename>models/ijssel_system.py<gh_stars>1-10
# -*- coding: utf-8 -*-
__author__ = '<NAME>'
__copyright__ = 'Copyright 2018'
__license__ = 'GNU GPL'
import numpy as np
import pandas as pd
from scipy.stats import gumbel_r
import sys
sys.path.append('/Users/lraso/Dropbox/Monitoring_for_DAPP/Experiments/') #... |
<reponame>GBruening/succes_predictor<gh_stars>0
#%%
from os import stat_result
import numpy as np
import psycopg2
from sqlalchemy import create_engine
import pandas as pd
import requests
from contextlib import closing
from datetime import datetime
from scipy import stats
from matplotlib import pyplot as plt
server = '... |
<reponame>arthus701/algopy
"""
Univariate nth derivatives of several numpy and scipy functions.
These functions are intended for two purposes.
They can be used for testing,
and they can also be used as components of
unsophisticated implementations of more complicated functions.
The functions in this module do not supp... |
<reponame>hardik-vala/2015-giller-prize-predictor
"""
Predicts the 2015 Giller prize winner.
@author: Hardik
"""
import logging
import numpy as np
import os
import sys
from scipy.spatial.distance import cosine
from sklearn import linear_model
from sklearn.decomposition import PCA
from sklearn.feature_extraction.text... |
import os.path as op
import numpy as np
import mne
from mne.datasets import sample
from mne.simulation import simulate_raw, add_noise
from neurolib.utils import atlases
def _simulate_raw_eeg(aal2_atlas, cortex, model_data):
data_path = sample.data_path()
subjects_dir = op.join(data_path, 'subjects')
sub... |
import os, sys, pdb, gc, pickle, pathlib, argparse
from collections import OrderedDict
import time, math, random
import numpy as np
import scipy as sp
import torch
import torch.nn.functional as F
import torch.nn as nn
import definitions
import data.data_loader as data
from pytorch.utils import *
from pytorch.layers ... |
"""Test DSS functions."""
import matplotlib.pyplot as plt
import numpy as np
import pytest
from numpy.testing import assert_allclose
from scipy import signal
from meegkit import dss
from meegkit.utils import fold, rms, tscov, unfold
def create_data(n_samples=100 * 3, n_chans=30, n_trials=100, noise_dim=20,
... |
<filename>main.py
import gym
import random
import numpy as np
import tflearn
from statistics import mean, median
from collections import Counter
from tqdm import tqdm
from createData import initial_population
from train_model import create_and_train_model
from model import create_model
from process_data import proces... |
<reponame>jpozin/Math-Projects
# Calculate the first four moments of a random variable, stored in an iterable data type or Pandas data frame
# Also calculate the mean, variance, standard deviation, skewness, and kurtosis of the data set
# Created by <NAME> on August 1, 2017
import scipy.stats as stats
from math... |
#!/usr/bin/env python
import copy
from collections import deque, defaultdict
from utils.utils import get_input, ints, tuple_add
import re
import networkx as nx
from fractions import Fraction
import math
from pprint import pprint
def part1(number_list, times=100):
base_pattern = [0, 1, 0, -1]
for _ in range(... |
import sys
import numpy as np
from scipy.sparse import csr_matrix
from scipy.sparse.csgraph import dijkstra
I = np.array(sys.stdin.read().split(), dtype=np.int64)
h, w = I[:2]
c = I[2:102].reshape(10, 10).T
a = I[102:].reshape(h, w)
def main():
cost = dijkstra(csr_matrix(c), directed=True, indices=... |
<reponame>brandonfranz13/aa274-sections
#!/usr/bin/env python
#This script will introduce us to Scipy, a library useful for scientific computation
#Adapted from https://docs.scipy.org/doc/scipy/reference/tutorial/integrate.html
#Integration
#Using known function
print("Integration:")
from scipy.integrate import quad... |
<gh_stars>0
from multiprocessing import Pool
from scipy.signal import max_len_seq
import numpy as np
def awgn_channel(signal, eb_n0_dB=0):
"""
Assume signal has a power of 1
"""
n_dB = -eb_n0_dB
n = 10 ** (n_dB / 10)
noise = np.random.normal(0, np.sqrt(n), len(signal))
return signal + n... |
<gh_stars>0
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 5 10:56:54 2018
cms - cross match simple
@author: csh4
"""
from scipy import spatial
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import gc
import os
import time
import sys
from matplotlib.colors import L... |
<filename>play_atari.py
import os
from argparse import ArgumentParser
from multiprocessing import Process, Queue, set_start_method
from queue import Empty
import multiprocessing
from random import randint
from functools import reduce
import statistics
parser = ArgumentParser("Play and evaluate Atari games with a train... |
import os
import pickle
import sys
import time
from collections import OrderedDict
import sklearn.metrics as skm
from taggers.lample_lstm_tagger.utils import create_input
from taggers.lample_lstm_tagger.utils import models_path
from taggers.lample_lstm_tagger.loader import word_mapping, char_mapping
from taggers.lamp... |
#! /usr/bin/env python
##################################################
# Author: <NAME>, 2019
# License: MIT
# Contact: <EMAIL>
# Those functions were implemented from pbdlib-python maintained by <NAME>
# (https://gitlab.idiap.ch/rli/pbdlib-python)
##################################################
import numpy as n... |
<filename>archive/MCQ/utils/lsqr.py<gh_stars>0
import scipy.sparse.linalg as lng
import numpy as np
import scipy.sparse as spe
import multiprocessing as mp
class CInitializer(object):
def __init__(self, X, M, K):
self._B = np.random.randint(K, size=[X.shape[0], M])
self.M = M
self.K = K
... |
<reponame>ccolas/funky_lenia
import numpy as np # pip3 install numpy
import reikna.fft, reikna.cluda # pip3 install pyopencl/pycuda, reikna
import PIL.Image, PIL.ImageTk # pip3 install pillow
import PIL.ImageDraw, PIL.ImageFont
from src.board import Board
from src.automaton import Automaton
from src.analyzer import ... |
"""
This file stores fisheries and runs + stores simulation results
The approach here is:
"""
from unit_gears.query import GearModel
from random import randrange
from math import prod
from statistics import mean, stdev
from collections import defaultdict
class FisheryResultSet(object):
"""
This stores a set ... |
<reponame>amirhosein-vedadi/GrippingForcePrediction
import pandas as pd
import glob
import numpy as np
import os
import matplotlib.pyplot as plt
import csv
from scipy.signal import butter, filtfilt
from scipy import signal
import scipy.signal as signal
from datetime import datetime
from sklearn import preproc... |
import os
import pickle
from collections import defaultdict
from os.path import join
import numpy as np
from scipy.special import softmax
from tqdm import tqdm
from pytorch_pretrained_vit.utils import *
# from utils.evaluate_utils import contrastive_evaluate
EPS=1e-8
class FeatureExtractor():
def __init__(self,... |
"""
Dynamic models.
These are the actual classes to send to IPOPT.
"""
import numpy as np
import opensim as osim
from scipy import interpolate
from static_optim.constraints import ConstraintAccelerationTarget
from static_optim.forces import ResidualForces, ExternalForces
from static_optim.kinematic import KinematicM... |
<reponame>Frizzles7/genre_classification<filename>check_data/test_data.py<gh_stars>1-10
import scipy.stats
import pandas as pd
def test_column_presence_and_type(data):
# Disregard the reference dataset
_, data = data
required_columns = {
"time_signature": pd.api.types.is_integer_dtype,
"... |
"""Create plots of signals generated by chirp() and sweep_poly()."""
import numpy as np
from scipy.signal.waveforms import chirp, sweep_poly
from numpy import poly1d
from pylab import figure, plot, show, xlabel, ylabel, subplot, grid, title, \
yscale, savefig, clf
FIG_SIZE = (7.5, 3.75)
def make... |
<reponame>uestcbingo/keras-retinanet<filename>keras_retinanet/bin/extra_callbacks.py
import keras.callbacks as cbks
import tensorflow as tf
import csv
import random
import sys
import cv2
import numpy as np
import matplotlib.pyplot as plt
import io
plt.rcParams['figure.figsize']=(20,15)
import os
from time import gmti... |
# coding: utf-8
# /*##########################################################################
#
# Copyright (c) 2017 European Synchrotron Radiation Facility
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
#... |
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style("dark")
plt.rcParams['figure.figsize'] = 16, 12
from glob import glob
import os
import pandas as pd
from PIL import Image
from tqdm import tqdm
from skimage import transform
import itertools as it
from ... |
<gh_stars>1-10
# --------------------------------------------------------
# Tensorflow ProtoNN for Multi-label learning
# Licensed under The MIT License [see LICENSE for details]
# Written by <NAME>
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import... |
import json
import math
from scipy.stats import poisson
import sys
import logging
import numpy as np
logging.basicConfig(filename=snakemake.log[0], level=logging.DEBUG,format="%(asctime)s:%(levelname)s:%(message)s")
#Read counts
expected_counts = json.load(open(snakemake.input['expectedCounts'],'r'))
actual_counts... |
from typing import Sequence
import numpy as np
import copy
from scipy import stats
from gmhazard_calc.constants import EventType
def mc_sampling(
nhypo: int,
planes: Sequence,
event_type: EventType,
total_length: float,
seed: int = None,
):
"""
Straight Monte Carlo using distributions al... |
<reponame>rockers7414/house_eval
#!/usr/bin/env python
from bs4 import BeautifulSoup
from functools import reduce
from statistics import mean, median, stdev
def load_html_doc(path):
with open(path) as f:
doc = f.read()
return doc
def normalize(doc):
soup = BeautifulSoup(doc, 'html.parser')
tot... |
"""Reduce size of embeddings by aligning their vocabularies."""
import os
import logging
import numpy as np
from scipy import sparse
from tqdm import tqdm
import embeddix.utils.files as futils
__all__ = ('reduce_sparse', 'reduce_dense')
logger = logging.getLogger(__name__)
# pylint: disable=C0103
def reduce_spar... |
<reponame>federico-terzi/koda
import cv2
import numpy as np
from matplotlib import pyplot as plt
from collections import defaultdict
import itertools
from scipy.signal import argrelextrema
from abc import ABC, abstractmethod
import time
import os
from koda.edge.network import UNetEdgeDetector, TARGET_IMAGE_SIZE
from .u... |
import json
import numpy as np
import pandas as pd
from scipy.optimize import linear_sum_assignment
from scipy.spatial import KDTree, distance_matrix
from tqdm import tqdm
import sys
sys.path.insert(1, '/home/xview3/src') # use an appropriate path if not in the docker volume
from xview3.processing.constants import P... |
import numpy as np
import matplotlib.pyplot as plt
from scipy.integrate import quad, simps, odeint
from scipy.interpolate import interp1d
from glob import glob
import pandas as pd
from plotting import plot_rho, plot_dwarfs, plot_mass, plot_mass_JR
from data import get_dwarf, create_inner_df, load_rho, load_M, load_M_J... |
<reponame>lintondf/MorrisonPolynomialFiltering
'''
Created on Feb 15, 2019
@author: NOOK
'''
import time
from typing import Tuple;
from netCDF4 import Dataset
from math import sin, cos, exp
import numpy as np
from numpy import array, array2string, diag, eye, ones, transpose, zeros, sqrt, mean, std, var,\
isscalar... |
import os
import re
import numpy as np
import scipy.io as sio
from scipy.fftpack import fft
import pandas as pd
from .movie import Movie, FullFieldFlashMovie
pd.set_option('display.width', 1000)
pd.set_option('display.max_columns', 100)
#################################################
def chunks(l, n):
"""Yiel... |
<filename>detect_blur.py<gh_stars>1-10
from facenet_code.detection import Detection
from facenet_code.encoder import Encoder
from scipy.linalg import svd
from imutils import paths
import numpy as np
import argparse
import cv2
import os
class DetectBlur(object):
def __init__(self, video, threshold=0.8):
sel... |
import time
import numpy as np
from scipy.integrate import solve_ivp
from scipy.interpolate import interp1d
from scipy.constants import c as c_luz #metros/segundos
c_luz_km = c_luz/1000
import sys
import os
from os.path import join as osjoin
from pc_path import definir_path
path_git, path_datos_global = definir_path()... |
<filename>GUI_Adquisicion/Ultracortex_16CH.py
import sys
sys.path.append('C:/Python37/Lib/site-packages')
from IPython.display import clear_output
from pyqtgraph.Qt import QtGui, QtCore
import pyqtgraph as pg
import random
from pyOpenBCI import OpenBCICyton
import threading
import time
import numpy as np
from scipy imp... |
# Copyright 2013 <NAME> and <NAME>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... |
<reponame>prashankkadam/IDMP-Data-Analysis-Tool
# -*- coding:/ utf-8 -*-
"""
This piece of software is bound by The MIT License (MIT)
Copyright (c) 2020 <NAME>
Code written by : <NAME>
Email ID : <EMAIL>
Created on - 03/14/2020
version : 1.0
"""
"""
This tool is a part of the Introduction to Data Management
and Proces... |
<filename>src/attrbench/suite/plot/cluster_plot.py
from typing import Dict, Tuple
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
import numpy as np
from scipy.cluster.hierarchy import linkage
class ClusterPlot:
def __init__(self, dfs: Dict[... |
"""
pyscf.py
Defines the modified effective potential veff_mod that allows the integration
of NeuralXC models in PySCF calculations. Provides utility functions for NeuralXC
PySCF interoperability.
"""
# from sympy import N
from glob import glob
from pylibnxc.pyscf import RKS as RKSrad
from pyscf import dft, gto
from ... |
import os
from math import ceil, floor
import imageio
import random
from PIL import Image, ImageDraw
import numpy as np
from scipy import stats
import torch
import torch.nn.functional as F
from torch.autograd import Variable
from torchvision.utils import make_grid, save_image
TRAIN_FILE = "train_losses.log"
DECIMAL_... |
import pickle
import pprint
from math import *
import collections
import numpy as np
from scipy.stats import norm, beta
import os
import scipy.special as scispec
import time
import magis
## TODO: profile this against the itertools.tee version
def unzip(xys):
return [[x[i] for x in xys] for i in range(len(xys[0]))... |
<reponame>apls777/tacotron2
import logging
import re
import subprocess
import sys
from shutil import which, rmtree
import numpy as np
from hparams import create_hparams
from text.cleaners import english_cleaners
from train import load_model
from text import text_to_sequence
from scipy.io.wavfile import write
import tor... |
from scipy.integrate import odeint
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(font_scale=1.25)
from pyDOE import * #function name >>> lhs
#Tipping limits, see Schellnhuber, et al., 2016:
limits_gis = [0.8, 3.2]
limits_thc = [3.5, 6.0]
limits_wais = [0.8, 5.5]
limits_a... |
<gh_stars>1-10
import mne
import numpy as np
import scipy.interpolate as interpolate
import scipy.signal as signal
from tabulate import tabulate
def interpolate_raw_dataset(dataset, orig_raw_dataset):
"""
Interpolate the downsampled dataset to the original sampling rate
:param mne.io.RawArray dataset: o... |
import numpy as np
from scipy.optimize import approx_fprime
from numpy.testing import assert_array_almost_equal
from sklearn.metrics.pairwise import pairwise_kernels
from sklearn.gaussian_process.kernels import RBF
from sklearn.datasets import make_regression
from ofdft_ml.statslib.kernel import rbf_kernel, rbf_kernel... |
<gh_stars>0
## <NAME>
## 3 de febrero de 2020
import sounddevice as sd
import matplotlib.pylab as plt
import scipy.io.wavfile as wavfile
import numpy as np
import scipy as sp
import itertools
import os
import pyaudio
import wavio
import wave
from tkinter import *
from playsound import playsound
from scipy import sig... |
<reponame>wdr123/DARP-SBIR
import numpy as np
from bresenham import bresenham
import scipy.ndimage
import random
def mydrawPNG(vector_images, Sample = 25, Side = 256):
for vector_image in vector_images:
pixel_length = 0
# number_of_samples = random
sample_freq = list(np.round(np.linspace(0... |
# Standard library
import pickle
# Third-party
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import binned_statistic_2d
import yaml
# Joaquin
from joaquin import Joaquin
from joaquin.data import JoaquinData
from joaquin.config import Config
from joaquin.logger import logger
from joaquin.plot imp... |
#!/usr/bin/env python3
"""Run signal-to-reference alignments
"""
from __future__ import print_function
import numpy as np
import glob
import shutil
import subprocess
import os
import sys
from argparse import ArgumentParser
import scipy
import math
from signalalign.utils.sequenceTools import reverse_complement
# signa... |
#!/usr/bin/env python
# -*- coding=utf-8 -*-
###########################################################################
# Copyright (C) 2013-2016 by Caspar. All rights reserved.
# File Name: gsx_extrc.py
# Author: <NAME>
# E-mail: <EMAIL>
# Created Time: 2016-03-16 15:56:16
############################################... |
<gh_stars>0
from random_forests import RandomForest
from dataset import Dataset
from dparser import DParser
from entry import Entry
import numpy as np
import statistics
import random
import time
import sys
class KFoldValidation():
def __init__(self, dparser: DParser, k: int, treeCount: int):
"""
... |
#!/usr/bin/env python
# <NAME>
""" visualize similarities between networks
1. find random subset of network edges
2. subset all cell type specific networks for 1
3. cosine similarities between cell types
"""
import os
import sys
import csv
import argparse
def select_random_edges(celltype, filesize, offsets):
... |
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