text string |
|---|
<reponame>jhabriel/mixdim-estimates
import scipy.sparse as sps
import numpy as np
import porepy as pp
# Disclaimer: Script copied or partially modified from 10.5281/zenodo.3374624
def run_flow(gb, partition, folder):
grid_variable = "pressure"
flux_variable = "flux"
mortar_variable = "mortar_flux"
... |
# -*- coding: utf-8 -*-
"""
This file contains MLTools class and all developed methods.
"""
# Python2 support
from __future__ import unicode_literals
from __future__ import division
from __future__ import absolute_import
from __future__ import print_function
import numpy as np
import pickle
class MLTools(obje... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Copyright 2019 <NAME>, <NAME>, <NAME>
Released under the Apache License, Version 2.0 (the "License");
you may not use this software except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Main clas... |
import os
import csv
import sys
import scipy.optimize as opt
import scipy.stats as stat
from operator import itemgetter
import random
import numpy as np
import numpy.ma as ma
import numpy.linalg as la
import numpy.testing as npt
from brain_diffusion.msd import fillin2, MSD_iteration, vectorized_MMSD_calcs
def test_f... |
<filename>spearmint/kernels/kernel_utils.py
# -*- coding: utf-8 -*-
# Spearmint
#
# Academic and Non-Commercial Research Use Software License and Terms
# of Use
#
# Spearmint is a software package to perform Bayesian optimization
# according to specific algorithms (the “Software”). The Software is
# designed to automa... |
<reponame>s2t2/tweet-analyzer-py<filename>app/ks_test/topic_analyzer.py
import os
from functools import lru_cache
from pprint import pprint
from dotenv import load_dotenv
import numpy as np
from scipy.stats import ks_2samp
from pandas import DataFrame, read_csv, concat
from app import DATA_DIR
from app.decorators.da... |
<gh_stars>0
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Oct 22 16:35:33 2019
@author: nico
"""
import sys
sys.path.append('/home/nico/Documentos/facultad/6to_nivel/pds/git/pdstestbench')
import os
import matplotlib.pyplot as plt
import numpy as np
from scipy.fftpack import fft
from pdsmodulos.sig... |
<gh_stars>0
% % time
# # Configure Jupyter so figures appear in the notebook
# %matplotlib inline
# # Configure Jupyter to display the assigned value after an assignment
# %config InteractiveShell.ast_node_interactivity='last_expr_or_assign'
# import functions from the modsim.py module
from modsim import *
from scip... |
"""
roc_w_tf-servimg.py
Sending RGB image tiles to a running tf-serving POST for prediction and plotting ROC curve
author: @DevelopmentSeed
usage:
python3 roc_w_tf-servimg.py --test_path=test \
--keyword1=not_school \
--keyword2=school \
--server_endpoint='http://localhost:8501/v1/models/2nd-... |
#!/usr/bin/env python
import sys
import rospy
from hybrid_control_api.srv import *
import numpy as np
import math
from scipy import linalg
from keras.models import load_model
from keras.models import Model
from utils import *
# cartesian position of the end effector
endEffectorPosition = ( ['end_arm_position_x', 0... |
"""
waveforms.py
------------
This module provides classes and functions for importing and saving ECG files.
By: <NAME>, Ph.D., 2018
"""
# Compatibility imports
from __future__ import absolute_import, division, print_function
# 3rd party imports
import os
import pickle
import pandas as pd
import scipy.io as sio
# Lo... |
#Edited 12/3/17 <NAME>
#Functions used to set up the algorithm and perform checks on the given
#variables
import scipy
import numpy
import sys
import random
import QJMCAA
import QJMCMath
#TESTED
def dimensionTest(H,jumpOps,eOps,psi0):
#Compare all to the hamiltonian
dim = H.get_shape()
c = 0
for item in jumpOps:... |
<reponame>kipkurui/gimmemotifs
# Copyright (c) 2009-2010 <NAME> <<EMAIL>>
#
# This module is free software. You can redistribute it and/or modify it under
# the terms of the MIT License, see the file COPYING included with this
# distribution.
""" Odds and ends that for which I didn't (yet) find another place """
# ... |
"""
Collection of functions used for the stitching.
IMPORTANT:
The identification of the organization of the fovs in the composite image
can be simplified if the (0,0) coords of the stage/camera will
be set to the same position for all machine used in the analysis.
In our case we started running experiments with the... |
def denoise(im, U_init, tolerance=0.1, tau=0.125, tv_weight=100):
""" 使用<NAME>（2005）在公式（11）中的计算步骤实现Rudin-Osher-Fatemi（ROF）去噪模型
输入：含有噪声的输入图像（灰度图像）、U 的初始值、TV 正则项权值、步长、停业条件
输出：去噪和去除纹理后的图像、纹理残留"""
m, n = im.shape # 噪声图像的大小
# 初始化
U = U_init
Px = im # 对偶域的x 分量
Py = im # 对偶域的y 分量
e... |
<filename>tests/benchmark_performance_tests/benchmark_performance_report.py
import statistics
from typing import List, Tuple, TypedDict
class BenchmarkPerformanceReportConfig:
model_id: int
model_name: str
model_xml_path: str
dataset_id: int
dataset_name: int
dataset_path: str
device_name:... |
<filename>Loan-Approval-Analysis/code.py
# --------------
# Import packages
import numpy as np
import pandas as pd
from scipy.stats import mode
bank=pd.read_csv(path)
print(bank)
# code starts here
categorical_var=bank.select_dtypes(include = 'object')
print(categorical_var)
numerical_var=bank.select_dtypes(in... |
import numpy as np
from scipy.ndimage.morphology import binary_dilation
def postprocess_ilastik_predictions(prediction,
dilation_iterations=1,
threshold=.5):
"""
Postprocess ilastik predictions to get a prediction mask.
Arguments:
... |
import os
import sys
import time
import pandas as pd
import numpy as np
from scipy.stats import kurtosis
from scipy.stats import skew
from statsmodels import robust
columns_intermediate = ['frame_no', 'ts', 'ts_delta', 'protocols', 'frame_len', 'eth_src',
'eth_dst', 'ip_src', 'ip_dst', 'tcp_src... |
<filename>darch/datasets.py
import numpy as np
import scipy as sp
import tensorflow as tf
try:
import cPickle
except ImportError:
import pickle as cPickle
import gc
import os, sys, tarfile, urllib
import scipy.io as sio
from scipy.misc import *
import argparse
import glob
from PIL import Image
import random
c... |
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Aug 26 16:34:40 2020
@author: skyjones
"""
import os
import re
import shutil
import pandas as pd
from glob import glob
import nibabel as nib
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
from sklearn.ensemble import Isola... |
from sympy import (
Basic,
Symbol,
sin,
cos,
atan,
exp,
sqrt,
Rational,
Float,
re,
pi,
sympify,
Add,
Mul,
Pow,
Mod,
I,
log,
S,
Max,
symbols,
oo,
zoo,
Integer,
sign,
im,
nan,
Dummy,
factorial,
comp,
... |
import csv
import random
from timeit import default_timer as timer
from datetime import timedelta
import statistics as st
Main_list=[] #a String list include alle the file's info
with open('european_cities.csv', 'r') as csv_file:
csv_file=csv.reader(csv_file)
for line in csv_file:
for e i... |
<filename>facerec_camera/infer_facerec.py
import tensorflow as tf
k = tf.keras
import sys
sys.path.insert(0, '.')
from facerec_camera.make_database import RetinaFace, FaceRec
from scipy.special import softmax
import numpy as np
import cv2
from typing import List
import argparse
def main():
physical_devices = tf.con... |
# (c) 2019 by Authors
# This file is a part of centroFlye program.
# Released under the BSD license (see LICENSE file)
import argparse
import os
import subprocess
import math
import statistics
import edlib
from collections import defaultdict
from utils.os_utils import smart_makedirs
from ncrf_parser import NCRF_Report... |
import numpy as np
from scipy.optimize import curve_fit
import matplotlib.pyplot as plt
def func_log(x, a, b, c, d):
temp = b*x + c
return a * np.log(np.where(temp <= 0, 0.0001, temp)) + d
def func_gp(x, a, b, c, d):
return (a * (1 - np.power(b, x)) / (1 - b)) - c
epochs = np.array([
0.6175,
0.6461,
0.6828,... |
<reponame>fhessman/pyFU
#!/usr/bin/env python3
# pyfu/wavcal.py
import bisect
import logging
import numpy as np
import yaml
from astropy.io import fits
from astropy.table import Table
from scipy import signal, optimize
from matplotlib import pyplot as plt
from numpy.polynomial.polynomial import polyfi... |
<gh_stars>0
from random import randrange
import sys
from scipy.sparse.csgraph import dijkstra
from scipy.sparse import csr_matrix
def resolve(input):
N, Q = map(int, input().split())
ab = [list(map(int, input().split())) for _ in range(N - 1)]
cd = [list(map(int, input().split())) for _ in range(Q)]
... |
<reponame>duttm/Octahedra_Nanoparticle_Project
import numpy as np
import scipy.special as ss
from vecPBC import vecPBC
def sl_local(self, l):
# Define the box dimensions
if self.pbc==True:
Lx = self.boxBounds[0][1] - self.boxBounds[0][0]
Ly = self.boxBounds[1][1] - self.boxBounds[1][0]
if self.dim==... |
<reponame>akleeman/xray
from io import BytesIO
import numpy as np
import warnings
from .. import Variable
from ..conventions import cf_encoder
from ..core.pycompat import iteritems, basestring, unicode_type, OrderedDict
from ..core.utils import Frozen, FrozenOrderedDict
from ..core.variable import NumpyArrayAdapter
... |
# -*- coding: utf-8 -*-
import numpy as np
import scipy.io as scio
from scipy.spatial.distance import pdist
from scipy.spatial.distance import squareform
from scipy.sparse import coo_matrix
def bins(value):
intervals = [(0,2), (2,2.5), (2.5,3), (3,3.5), (3.5,4),
(4,4.5), (4.5,5), (5,5.5),... |
<reponame>Woffee/deformer
import collections
from abc import ABC
import numpy as np
from scipy.special import softmax
from sklearn.metrics import accuracy_score
from sklearn.metrics import f1_score
from common import tf
from models import BaseModel
class ClassifierModel(BaseModel, ABC):
@staticmethod
def g... |
<filename>geoscilabs/seismic/syntheticSeismogram.py
import numpy as np
import matplotlib.pyplot as plt
import scipy.io
from ipywidgets import interact, interactive, IntSlider, widget, FloatText, FloatSlider
def getPlotLog(d, log, dmax=200):
d = np.array(d, dtype=float)
log = np.array(log, dtype=float)
dp... |
#coding:utf-8
import os
import torch
import torch.utils.data as data
from PIL import Image
from scipy.io import loadmat
import numpy as np
import glob
from torchvision import transforms
import random
import matplotlib.pyplot as plt
def colormap(N=256, normalized=False):
def bitget(byteval, idx):
return ... |
import abc
import itertools
import inspect
import pandas as pd
import numpy as np
from skimage.measure import regionprops_table, perimeter
from scipy.ndimage.measurements import labeled_comprehension
from scipy.ndimage.morphology import distance_transform_edt
from scipy.ndimage import find_objects
# TODO test for 3D ... |
<filename>dechorate/dechorate_pre2_defined_geometry.py
import numpy as np
import scipy as sp
import pandas as pd
import matplotlib.pyplot as plt
import pyroomacoustics as pra
from dechorate.utils.file_utils import load_from_matlab
## LOAD POSITIONS
# load arrays' barycenters positions from file generated in excell
#... |
"""
Aplicação do modelo COPPE-COSENZA.
Este exemplo foi baseado na dissertação de mestrado de:
MACHADO, <NAME>.
Modelagem COPPE-Cosenza de Hierarquia Fuzzy em indicadores de
sustentabilidade de distribuidoras de energia elétrica.
<NAME>, orientador.
Niterói, 2019.
78 f. : il.
Fonte: <https:... |
"""
test: True
"""
from six.moves import range
import numpy as np
import sympy as sp
import mpi4py.MPI as mpi
import pylbm
X, Y = sp.symbols('X, Y')
rho, qx, qy, T, LA = sp.symbols('rho, qx, qy, T, LA', real=True)
def init_T(x, y, Td, Tu, xmin, xmax, ymin, ymax):
xmid = (xmax+xmin)/2
print((Tu-Td)*(x<1.2*xm... |
from sklearn.ensemble import RandomForestClassifier
import numpy as np
import os
from scipy.misc import imsave,imread
from sklearn.grid_search import GridSearchCV
from datetime import datetime
import cPickle
def load_subset(subset):
images_labels = {}
path_to_images = '/storage/hpc_anna/Kaggle_DRD/imag... |
# ----------------------------------------FILTER FUNCTIONS MODULE----------------------------------- #
# Module to filter the preprocessed raster stacks to minimize the influence of soil moisture a.s.o.
# It is highly recommended to execute this module before analyzing the raster stack!
# Make use of small filter size... |
import numpy as np
import matplotlib.pyplot as plt
from scipy import ndimage
import json
import pytz
from datetime import datetime
by_mh = np.load('by_mh.npy')
data = np.load('data_by_cams_filtered.npy')
data[10, 6, 17:22] = 0
data_with_clouds = np.copy(data)
for i in range(18):
for frame in range(40):
ho... |
<filename>pandapower/pypower/dAbr_dV.py
# Copyright (c) 1996-2015 PSERC. All rights reserved.
# Use of this source code is governed by a BSD-style
# license that can be found in the LICENSE file.
"""Partial derivatives of squared flow magnitudes w.r.t voltage.
"""
from scipy.sparse import csr_matrix
def dAbr_dV(dSf... |
#!/usr/bin/python
# -*- coding: utf-8 -*-
#-------------------------------------------------------------------------------
# Name: metabolicmodel.py
# Purpose: MetabolicModel class in mfapy
#
# Author: Fumio_Matsuda
#
# Created: 12/06/2018
# Copyright: (c) Fumio_Matsuda 2018
# Licence: MIT li... |
<gh_stars>1-10
import numpy as np
from matplotlib import pyplot as plt
import stat_tools as st
from datetime import datetime,timedelta
import pysolar.solar as ps
from skimage.morphology import remove_small_objects
from scipy.ndimage import morphology,sobel
from scipy.ndimage.filters import maximum_filter
import mncc, g... |
<reponame>SFGLab/ChromoLooping<gh_stars>0
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
@author: zparteka
"""
from peak_stats.reader.peaks import Image, Group, Peak
from scipy.spatial import ConvexHull
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
# Histograms glob... |
<gh_stars>0
import sys, pickle
sys.path.extend(["..","../networks","../simulations"])
from networkConstants import *
from stimuliConstants import *
from simset_odor import *
from moose_utils import *
from neuro_utils import * # has the dual exponential functions
from data_utils import *
from pylab import * # part of... |
<filename>src/solvers/QAOA.py<gh_stars>10-100
# Copyright 2021 The QUARK Authors. All Rights Reserved.
#
# 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/licen... |
<reponame>Gibbsdavidl/GeneSignalProc
# performing graph segmentation on the scale space
import numpy as np
import igraph as ig
import copy
from scipy import stats
from sklearn.cluster import KMeans
from sklearn.cluster import MiniBatchKMeans
import sklearn.preprocessing
def loadSignal(filename, header=1, column=1):... |
import scipy.sparse as sps
class Relation(object):
def __init__(self, ot1, ot2, matrix, weight=1):
self.matrix = matrix
self.ot1 = ot1
self.ot2 = ot2
if matrix is not None:
self.matrix = sps.csr_matrix(matrix)
if not self.ot1.length or ot1.length <... |
from collections import Counter
from django.shortcuts import render
from django.apps import apps
from nltk import word_tokenize
from nltk.util import ngrams
from statistics import median
import datetime
import re
def search (request):
'''
This function renders the natural language data for the corporate
t... |
<filename>examples/full_gmm_procedure_simul_data.py
import numpy as np
import dill
import pandas as pd
from scipy import optimize as opt
import time
import sys
sys.path.append('../')
import src
#GMM parameters
maxiters = 50 #120. About 2 minutes per iteration
# Simulation parameters \
########################
σerror=... |
import os
import random
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils.rnn import PackedSequence
from torch.nn.utils.rnn import pad_packed_sequence
from torch.autograd import Variable
from torch.utils.data import Dataset, DataLoader
from sklearn.neighbors impor... |
import networkx as nx
import scipy
import matplotlib.pyplot as plt
from networkx.drawing.nx_agraph import write_dot
from networkx.drawing.nx_agraph import to_agraph
from IPython.display import Image
import pygraphviz as pgv
def graph(G, color="#cccccc", filename="/tmp/simple.png"):
for u, v in G.edges:
... |
<reponame>AIRI-Institute/DeepCT
import functools
import numpy as np
import scipy.stats
import sklearn.metrics as metrics
# Metric helpers which convert float values to binary (0/1).
# Regression metrics helper functions.
def _to_binary(x: np.ndarray, threshold=0.5) -> np.ndarray:
return np.where(x > threshold, ... |
import numpy as np
import scipy.interpolate as spinterp
import scipy.optimize as spopt
import vtk
import recon
#user supplied data
#save_path="./20200922_120x60CrossPlyCFRPTestPlate"
save_path = "./20201019_120x60CrossPlyCFRPTestPlate_FBH"
data_path = "%s" % save_path
file_root = "Output_at"
data_to_process = "V2"
t_... |
<reponame>duken72/CV2_WS20_RWTH
import argparse
import scipy.io
from glob import glob
import cv2
import numpy as np
import plot_utils as utils
import pretty_print
from tracker import Tracker
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--input_path", type=str, default=".... |
<gh_stars>0
import ipdb
import numpy as np
import pandas as pd
import os.path
import scipy.spatial.distance as sd
from skip_thoughts import configuration
from skip_thoughts import encoder_manager
from new_model import preprocess_news_df
from new_model import clean_sentence
def load_with_unidirectional_model():
v... |
import scipy.io
# mat = scipy.io.loadmat('105_label0.mat')
# print(mat['time_stamps'].flatten())
mat = scipy.io.loadmat('categories.mat')
print(mat['categories'].flatten())
|
"""
based on https://github.com/xunzheng/notears
"""
import igraph as ig
import matplotlib.pyplot as plt
import numpy as np
import scipy.linalg as slin
import scipy.optimize as sopt
from scipy.special import expit as sigmoid
def is_dag(W):
G = ig.Graph.Weighted_Adjacency(W.tolist())
return G.is_dag()
def s... |
import torch
import numpy as np
from scipy.optimize import linear_sum_assignment
from pcdet.utils import common_utils
from pcdet.ops.iou3d_nms import iou3d_nms_utils
from pcdet.models.model_utils.model_nms_utils import class_agnostic_nms
def consistency_ensemble(gt_infos_a, gt_infos_b, memory_ensemble_cfg):
"""
... |
"""Some functions to generate simulated data"""
try:
from os import link
except ImportError:
# Hack for windows
from shutil import copy2
def link(src, dst):
copy2(src, dst)
from numpy.random import randn, rand, permutation, randint, seed
from numpy import where, nonzero, sqrt, real
from numpy ... |
<gh_stars>0
#!/usr/bin/env python3
# To run script install libraries using command:
# pip install pyabf
import numpy as np
import matplotlib.pyplot as plt
import pyabf
import pyabf.tools.memtest
from statistics import mean
from math import sqrt
# Список імен abf файлів без росширення, в лапках, розділені комами
FI... |
"""Core tensor network tools.
"""
import copy
import functools
from operator import add
import contextlib
import numpy as np
import scipy.sparse.linalg as spla
import opt_einsum as oe
from opt_einsum.contract import parse_backend, _tensordot, _transpose
from autoray import conj
from ...utils import (oset, valmap, chec... |
import numpy as np
import scipy
import os
from scipy.stats import t
from yass.template import align_get_shifts_with_ref, shift_chans
def connected_components(img, x, y, cc):
pixel = [x, y]
if (pixel in cc) or (x < 0) or (x >= img.shape[0]) or (y < 0) or (y >= img.shape[1]) or (img[x,y] == 0):
return c... |
<reponame>lfzarazuaa/LiderSeguidor
#!/usr/bin/env python2
# encoding: utf-8
import numpy as np
import os
import path_parser
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
from scipy.spatial import KDTree
... |
<filename>project.py
import nltk
import random
import os
from nltk.corpus.reader import CategorizedPlaintextCorpusReader
corpusdir = 'txt_sentoken/' # Directory of corpus.
mycorpus = nltk.corpus.reader.CategorizedPlaintextCorpusReader(r"C:\Users\amanb_2jhwatd\Desktop\Machine Learning\txt_sentoken",r'(?!\.).*\.... |
import numpy as np
from scipy.stats import linregress
import matplotlib.pyplot as plt
import bisect
def find_ge(a, x):
"""Find leftmost item greater than or equal to x"""
i = bisect.bisect_left(a, x)
if i != len(a):
return i
raise ValueError
def mean_clone_size_fit(times, rlam):
"""For th... |
<gh_stars>1-10
#!/usr/bin/env python
#-----------------------------------------------------------------------------
# Title : pysmurf tune module - SmurfTuneMixin class
#-----------------------------------------------------------------------------
# File : pysmurf/tune/smurf_tune.py
# Created : 2018-08-31... |
<reponame>SimoKorkolainen/TrafficDataScienceProject
#!/usr/bin/env python3
from PIL import Image
Image.MAX_IMAGE_PIXELS = 1000000000
from scipy import misc
import numpy as np
import math
north = 8388608.00
west = -548576.00
tile_size = 1024
#zoom_level = 7
#source_files = ['Taustakartta_320']
#zoom_level = 8
#so... |
<gh_stars>0
from numpy import array, linalg, dot
from scipy.linalg import lu
# System of Equations
A = array([[3, -.1, -.2],
[.1, 7, -.3],
[.3, -.2, 10]], float)
B = array([10.3, 33.6, 60.2], float)
# Linalg.Solve Function:
X = linalg.solve(A, B)
print("linalg.solve(A, B) = ", X, end='\n\n'... |
import streamlit as st
import pandas as pd
import pylab as plt
import seaborn as sns
import datetime as dt
import altair as alt
import numpy as np
from scipy.stats import pearsonr
#import geopandas as gpd
st.write('hello')
df = pd.read_csv('data/df_india_may9.csv')
df.ds = pd.to_datetime(df.ds)
df = df.set_index('ds'... |
<reponame>NumEconCopenhagen/projects-2019-bcg
#The OLG Model
#%%
# 1. Setup
#importing the necessary packages
import numpy as np
import sympy as sm
import matplotlib.pyplot as plt
import ipywidgets as widgets
# 2. Symbolic solution of the Household's (i) and Firm's (ii) problems
# (i) Household's problem
#%%
# Acti... |
<reponame>eduardagoulart/my_anime_playlist
from pre_processing import DataProcessing
import numpy as np
from scipy.spatial import distance
obj = DataProcessing()
type_animes = obj.anime_type()
qt_ep = obj.ep()
grades = obj.rating()
num_members = obj.members()
gender = obj.gender()
id_anime = obj.id_anime()
nodes = op... |
# low mass transit lens, no animation.
# import modules
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
import scipy
from scipy import integrate
import project.lensing_function as lensing
import matplotlib.widgets as widgets
from scipy.signal import find_peaks
import timeit
# %%
# start the ... |
"""
quakestats
Computes some basic statistics for earthquakes in a USGS data feed.
The desired feed is specified using severity level and period.
Severity level can be "significant", "4.5", "2.5", "1.0", "all".
Period can be "hour", "day", "week", "month".
Usage: quakestats [options] <level> <period>
Options:
-h ... |
import numpy as np
import joblib
from scipy.optimize import fmin_bfgs
from sklearn.datasets import load_digits
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
# Set random seed for reproducibility
np.random.seed(1000)
nb_unlabeled = 150
# Create a training Lo... |
# noinspection PyPackageRequirements
import datawrangler as dw
import numpy as np
import pandas as pd
import scipy.interpolate as interpolate
from .common import Manipulator
from ..core import get
def fitter(data, **kwargs):
def listify_dicts(dicts):
if len(dicts) == 0:
return {}
ld ... |
# -*- coding: utf-8 -*-
"""
Regularization path OT solvers
"""
# Author: <NAME> <<EMAIL>>
# License: MIT License
import numpy as np
import scipy.sparse as sp
def recast_ot_as_lasso(a, b, C):
r"""This function recasts the l2-penalized UOT problem as a Lasso problem.
Recall the l2-penalized UOT problem defin... |
# Explanations here about what this file does:
# https://github.com/guillaume-chevalier/python-signal-filtering-stft
# However our signal here is sampled and processed differently.
# See function filter_opportunity_datasets_accelerometers below.
__author__ = 'gchevalier'
import numpy as np
from scipy import signal
... |
from scipy.io import loadmat
import numpy as np
import torch
import matplotlib.pyplot as plt
from models.clstm import cLSTM, train_model_accumulated_ista
import argparse
import random, os
parser = argparse.ArgumentParser()
parser.add_argument("-seed", "--seed", help = "0, 1")
parser.add_argument("-lam", "--lam", help ... |
<filename>FinsterTab/W2020/DataForecast.py
# import libraries to be used in this code module
import pandas as pd
from statsmodels.tsa.arima_model import ARIMA
from sklearn.ensemble import RandomForestRegressor
from sklearn.svm import SVR
from math import sqrt
from statistics import stdev
import numpy as np
import xgboo... |
<filename>dannce/engine/generator.py
"""Generator module for dannce training.
"""
import os
import numpy as np
from tensorflow import keras
from dannce.engine import processing as processing
from dannce.engine import ops as ops
from dannce.engine.video import LoadVideoFrame
import imageio
import warnings
import time
im... |
<reponame>daniel-weisse/irreducible_polynom
"""
Generate a list of irreducible polynoms over GF(2^n) by testing all possibles
"""
from pyGF2 import gf2_mul, gf2_div, gf2_add
from numpy import array, append, binary_repr, flip, uint8, pad
from sympy import mobius, divisors
from datetime import datetime
from mult... |
<reponame>benbokor/secat
import pandas as pd
import numpy as np
import scipy as sp
import click
import sqlite3
import pickle
import os
import sys
try:
import matplotlib
matplotlib.use('Agg')
from matplotlib.backends.backend_pdf import PdfPages
import matplotlib.pyplot as plt
except ImportError:
plt... |
<gh_stars>0
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import math
import random
from time import time
import numpy as np
from scipy.stats import truncnorm
import torch
import torch.nn as nn
import torch.nn.functional as F
from open_... |
# Collection of small helper functions
import numpy as np
import pyaudio
from scipy.fftpack import fft
from .codec import audio_read
import logging
import decimal
import math
class _error(Exception):
pass
def linlin(x, smi, sma, dmi, dma):
"""Linear mapping
Parameters
----------
x : float
... |
<filename>src/openeo_processes/cubes.py<gh_stars>0
from datetime import datetime
from os.path import splitext
from typing import Any, Dict, List
import numpy as np
import odc.algo
import rioxarray # needed by save_result even if not directly called
import xarray as xr
from openeo_processes.extension.odc import write_... |
<reponame>msk-mind/luna
# General imports
import os, logging, sys
import click
from luna.common.custom_logger import init_logger
init_logger()
logger = logging.getLogger('extract_stain_texture')
from luna.common.utils import cli_runner
_params_ = [('input_slide_image', str), ('input_slide_mask', str), ('output_di... |
"""
This script contains the main slise functions, and classes
"""
from __future__ import annotations
from typing import Union, Tuple, Callable, List
from warnings import warn
from matplotlib.pyplot import Figure
import numpy as np
from scipy.special import expit as sigmoid
from slise.data import (
DataScaling... |
<gh_stars>0
import numpy as np
from sklearn.metrics.pairwise import pairwise_distances
from tqdm import tqdm
import scipy.ndimage as ndi
from .utils import decompose
from torchlib import post_processing_func
class PQ(object):
def __init__(self):
pass
def iou(self,obj1,obj2):
obj1=obj1.... |
from io import FileIO
import numpy as np
from scipy import ndimage
from matplotlib import pyplot as plt
import yaml
import csv
from PIL import Image
import ReferenceModification.LibFunctions as lib
class TrackMap:
def __init__(self, map_name) -> None:
self.map_name = map_name
# map info
... |
<reponame>WenyinWei/wagglepy<gh_stars>0
from sympy import sin, sinh, pi, symbols
a = symbols("a", positive=True)
b, t = symbols("b, t", real=True)
find_all_trig_period(sin((b**2)*t)+sin(b*t), t) |
from __future__ import absolute_import, division, print_function
import numpy as np
import tensorflow as tf
import tensorflow.contrib.keras.api.keras.backend as K
from scipy.fftpack import idct
from scipy.linalg import pinv
from tensorflow.contrib.keras.api.keras.models import Model
from tensorflow.contrib.keras.api.ke... |
<reponame>ansobolev/PseudoGenerator<filename>pseudogen/generate.py
#!/usr/bin/env python
"""
generate.py contains classes and functions needed to generate and test pseudopotential
"""
import os
import shutil
import subprocess
import numpy as np
from scipy.optimize import minimize
orbitals = [(1, 0),(2, 0),(2, 1),
... |
<reponame>nuthanmunaiah/xloc
import csv
import os
import sys
import statistics
from optparse import make_option, OptionValueError
from django.conf import settings
from django.core.management.base import BaseCommand
from app.lib import logger
from app.models import Function, File
class Command(BaseCommand):
opti... |
<gh_stars>0
#!/usr/bin/env python3
from typing import List
from fractions import Fraction
import pathlib
import json
import shutil
import glob
import os
import argparse
import subprocess
import re
import time
def get_files(f, args) -> List[str]:
"""
given a folder will return all the files that are not txt,
... |
<reponame>chenqim/Python3<filename>example/sqrt.py<gh_stars>1-10
# 计算实数和复数平方根
# 导入复数数学模块
import cmath
num = int(input("请输入一个数字: "))
num_sqrt = cmath.sqrt(num)
print('{0} 的平方根为 {1:0.3f}+{2:0.3f}j'.format(num, num_sqrt.real, num_sqrt.imag)) |
<reponame>lee-jingu/SSMOECHS<filename>SSMOECHS/main.py
import numpy as np
import config as cf
import networkx as nx
import matplotlib.pyplot as plt
from network import Energy
from network import Network
from network import Node
from Optimizer import LEACH, PSOECHS, SSMO_MH, SSMO, BSMO, LEACH_C, SSMO2,PSO_C
from... |
import dolfin as df
import numpy as np
import scipy.integrate.ode as scipy_ode
import matplotlib.pyplot as plt
import time
from finmag.drivers.llg_integrator import llg_integrator
from llb import LLB
from finmag.energies import Zeeman
from test_exchange import BaryakhtarExchange
def cross_times(a,b):
assert(... |
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