text string |
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<reponame>danmohad/PMC-thermodynamics
# -*- coding: utf-8 -*-
"""
Porous Media Combustor (PMC) Class
Copyright 2020, <NAME>, All rights reserved.
Refer to <NAME>, <NAME>, <NAME>, "Thermodynamic cycle analysis of superadiabatic matrix-stabilized combustion for gas turbine engines," Energy (207) 2020.
"""
imp... |
<reponame>harshul1610/DeepNeuralnets--Alzheimer
from celery.decorators import task
import numpy as np
import nibabel as nib
from keras.models import Sequential
import keras
import time
from celery import current_task, shared_task, result
from scipy.fftpack import fft
import random
import os
@task(name="predict_nii_fil... |
# -*- coding: utf-8 -*-
# farmeconomy.py module
# authors: <NAME> & <NAME>
# An OOP implementation
import numpy as np
from matplotlib import pyplot as plt
from scipy.optimize import minimize
from collections import namedtuple
class Economy(object):
""" Economy with an Equilibrium Farm Size Distribution
At p... |
<reponame>s1990i/EMOSworks
import numpy as np
# import HyperProTool as hyper
import scipy.io as sio
#from LRSR_1 import LRSR
import jcamp as jc
import matplotlib.pyplot as plt
from matplotlib.collections import EventCollection
jdx_data = jc.JCAMP_reader("C7H5N3O6NORMALX.jdx")
# data pre-precessing
data = sio.loadmat... |
<gh_stars>10-100
import numpy as np
import time
import distributions
import scipy.stats
import scipy.special
import mxnet as mx
from mxnet import nd
mx.random.seed(13343)
np.random.seed(2324)
def test_bernoulli_sampling():
n_samples = 10000
K = 10 # num factors
C = 2 # num classes
# latent variable is of ... |
# -*- coding:utf-8 -*-
##---IMPORTS
import scipy as sp
from scipy import linalg as sp_la
from common import TimeSeriesCovE
from plot import P, mcdata
from nsim.scene.noise import ArNoiseGen
from database import MunkSession
##---CONSTANTS
DB = MunkSession()
##---FUNCTIONS
def load_cmx():
... |
import numpy as np
from scipy.spatial.distance import cdist
class Silhouette:
def __init__(self, metric: str = "euclidean"):
"""
inputs:
metric: str
the name of the distance metric to use
"""
def score(self, X: np.ndarray, y: np.ndarray) -> np.ndarray:
... |
"""This file contains functions to generate random propositional formulas.
"""
# Copyright (C) 2016
# <NAME> <<EMAIL>>
# All rights reserved.
# MIT license.
import random
import sympy
from sympy.logic.boolalg import *
functions = [
And,
Or,
Implies,
Equivalent,
Not,
]
def random_form... |
<gh_stars>1-10
from collections import OrderedDict
import colorcet
import numpy as np
from bokeh.layouts import gridplot
from bokeh.models import LinearColorMapper, ColumnDataSource, ColorBar, FixedTicker, PrintfTickFormatter, HoverTool, \
LabelSet
from bokeh.plotting import figure
from pandas import DataFrame
fro... |
# _FeatureBarcodeMatrixModule.py
__module_name__ = "_FeatureBarcodeMatrixModule.py"
__author__ = ", ".join(["<NAME>"])
__email__ = ", ".join(["<EMAIL>",])
# package imports #
# --------------- #
import anndata as a
import pandas as pd
import scipy.io
import os
def _strip_file_extension(filepath):
return ".".j... |
<filename>Code/ModelSelection/datafold-master/datafold/dynfold/tests/helper.py
#!/usr/bin/env python3
"""Helper functions for testing. """
import logging
from typing import Optional
import diffusion_maps as legacy_dmap
import numpy as np
import numpy.testing as nptest
from scipy.sparse import csr_matrix
from datafo... |
import numpy as np
import math
import matplotlib.pyplot as plt
import gegenbauer
import scipy as sp
import scipy.special
import scipy.optimize
# useful function for ReLU
def f(phi, L):
if L==1:
return np.arccos(1/math.pi * np.sin(phi) + (1 - 1/math.pi * np.arccos(np.cos(phi)) ) * np.cos(phi))
eli... |
<filename>tick/linear_model/tests/model_poisreg_test.py
# License: BSD 3 clause
import unittest
import numpy as np
from scipy.sparse import csr_matrix
from tick.linear_model import SimuPoisReg, ModelPoisReg
from tick.base_model.tests.generalized_linear_model import TestGLM
class ModelPoisRegTest(object):
def t... |
import numpy as np
import h5py
from scipy.interpolate import interp1d
from pyspi.utils.livedets import get_live_dets_pointing
from pyspi.io.package_data import get_path_of_internal_data_dir
#from scipy.special import erfc
from math import erfc
from scipy.integrate import quad
from numba import njit, float64
import os
... |
'''
TACO: Multi-sample transcriptome assembly from RNA-Seq
'''
import numpy as np
from scipy.stats import distributions
from taco.lib.scipy.norm_sf import norm_sf
from scipy.stats import mannwhitneyu as scipy_mwu
from taco.lib.stats import mannwhitneyu as mwu
def test_mannwhitneyu():
x = [1, 2, 3, 4, 5]
y =... |
<filename>ml/pca.py<gh_stars>0
import numpy as np
import scipy
from .preprocessing import standardize
class PCA:
def __init__(self, n_component, random_state=None):
self.n_component = n_component
self.components = None
self.variance_ratio = None
if random_state is not None:
... |
<gh_stars>10-100
import numpy as np
import matplotlib as plt
import scipy.stats as st #for gaussian kernel
import scipy.misc
import os
from random import randint, gauss
from math import floor
from skimage import io, feature, transform
from IPython.display import clear_output
#for gif making
import imagei... |
import sys
from numpy import *
import numpy as np
from sklearn import metrics
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import scipy.ndimage as ndimage
def clamp(val, minimum=0, maximum=255):
if val < minimum:
return minimum
if val > maximum:
return maximum
ret... |
<filename>BioExp/clusters/clusters.py
import matplotlib
matplotlib.use('Agg')
import keras
import numpy as np
import tensorflow as tf
import os
from matplotlib import pyplot as plt
from scipy.cluster.hierarchy import dendrogram
from sklearn.cluster import AgglomerativeClustering
from sklearn.metrics import silhouette_s... |
<reponame>rsulli55/automates
import re
from string import ascii_uppercase
from collections import defaultdict
from typing import Union
from sympy.parsing.latex import parse_latex, LaTeXParsingError
from automates.equation_reading.latex_tokenizer import LatexTokenizer, Token
RESERVED_WORDS = {
"_max",
"_min"... |
import matplotlib.pyplot as plt
import copy
import graphClass as gc
import numpy as np
from fractions import Fraction
# INPUT HERE
# what level affine carpet would you like:
precarpet_level = 2
# how large would you like the center hole to be:
sideOfCenterHole = 1/2
# the above two are the only parameters, since sid... |
from tts import TextToMel, MelToWav
from transliterate import XlitEngine
from num_to_word_on_sent import normalize_nums
import re
import numpy as np
from scipy.io.wavfile import write
from mosestokenizer import *
from indicnlp.tokenize import sentence_tokenize
import argparse
_INDIC = ["as", "bn", "gu", "hi", "kn",... |
""" Provides a class to simulate an ultrashort optical pulse using its envelope
description.
The temporal envelope is denoted as `field` and the spectral envelope as
`spectrum` in the code and the function signatures.
"""
import numpy as np
from . import io
from . import lib
from .frequencies import convert
... |
<filename>figuras/Pycharm_Papoulis_Probability_Report/flip_coin_posterior_probability.py
import matplotlib.pyplot as plt
import numpy as np
import math
from scipy.stats import beta
from matplotlib import rc
__author__ = 'ernesto'
# if use latex or mathtext
rc('text', usetex=False)
rc('mathtext', fontset='cm')
#####... |
<filename>tests/unit/test_craps_game_point_on.py
from fractions import Fraction
import pytest
import casino.main
import tests.conftest
class TestCrapsGamePointOn:
@pytest.fixture(autouse=True)
def _setup(self, monkeypatch, mock_craps_game, mock_table, mock_throw):
monkeypatch.setattr(casino.main, "C... |
import os
import matplotlib.pyplot as plt
import numpy as np
import torch
from torch import nn
from sanitize_data import read_from_tar, TORCH_FILENAME
from weather_format import WeatherDataset, WeatherRow
from model import WeatherLSTM
from config import WINDOW_SIZE, DEVICE, DTYPE, TRAIN_END, VALIDATE_END, BATCH_SIZE,... |
<filename>tools/audio/wav_spectrogram.py
import argparse
import scipy.io.wavfile as wf
import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import stft
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='%(prog)s combine generate the spectrogram of a wav file')
parser.add_... |
#!/bin/python3
import scipy.stats
import numpy as np
from scipy.ndimage import gaussian_filter1d
import pandas as pd
from scipy import spatial, optimize, interpolate
import matplotlib.pyplot as plt
## Description of the collection of functions
def isUniformSpacing(vector):
"""Checks if the spacing is uniform... |
<reponame>marcociav/PyHarmonic
"""
A module for working with the Quantum Harmonic Oscillator.
@author Marco, <EMAIL>
"""
import numpy as np
from scipy import special
iterable = list or tuple or set or np.array # custom iterable type
class QuantumOscillator:
def __init__(self, n_states: iterable, co... |
<filename>geonetworkx/utils/geograph_utils.py
# -*- coding: utf-8 -*-
import math
import numpy as np
import networkx as nx
from shapely.geometry import Point, LineString, MultiPoint, MultiLineString
import geopy.distance
import pyproj
from geonetworkx.geometry_operations import coordinates_almost_equal, insert_point_in... |
<gh_stars>1-10
# The basic tests, that are needed in the series facility work, but the more
# complicated don't, they are commented out. Use the general limit algorithm
# for those, see limits.py. Ideally, we'll get rid of limits_series completely.
from sympy import *
from sympy.series.limits_series import mrv_compare... |
import pysam
import pandas as pd
import numpy as np
import re
import os
import sys
import collections
import scipy
from scipy import stats
import statsmodels
from statsmodels.stats.multitest import fdrcorrection
try:
from . import global_para
except ImportError:
import global_para
try:
from .consensus_seq ... |
import __folder_params
import sys
sys.path.insert(0, __folder_params.home)
import utils
from skimage.feature import hog
from scipy.stats import itemfreq
import cv2
def processHog(img_path, pixels_per_cell=(32, 32), cells_per_block=(1, 1)):
final_path = utils.adress_file(img_path, "Hog")
# http://scikit-imag... |
<gh_stars>10-100
from __future__ import division
import numpy as np
import pdb
import scipy.sparse
from scipy.sparse import coo_matrix
import cvxopt
import cvxopt.cholmod
from utils import deleterowcol
class Problem(object):
@staticmethod
def lk(E=1.):
"""element stiffness matrix"""
nu = ... |
# -*- coding: utf-8 -*-
import argparse
import sys
from statistics import Statistics
from interfaces.gui import DogaGUI
from thread_jobs import Job
from logs.generator import LogGenerator
from parsers.payload import PayloadParser
from parsers.packet import PacketParser
from interfaces.sockets import SocketInterface
f... |
<gh_stars>0
import numpy as np
import os
import fnmatch
import pylab as pl
from scipy.io import loadmat
from anlffr.dpss import dpss_windows
from statsmodels.robust.scale import stand_mad as mad
def rejecttrials(x, thresh=5.0, bipolar=True):
"""Simple function to reject trials from numpy array data
Parameter... |
<filename>2015/07/online_old.py
'''Functions for estimating an adjustment to the posterior prediction
over subtypes when making predictions online.
Author: <NAME>
'''
import numpy as np
from scipy.optimize import minimize
from sklearn.linear_model import LogisticRegressionCV
from sklearn.cross_validation import KFo... |
from typing import Callable
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import streamlit as st
from sympy import diff, lambdify, parse_expr
from src.common.consts import COLOR, TRANSFORMATIONS
from src.common.methods.numerical_differentiation.first_derivative_finder import FirstDerivative... |
from scipy.fft import fft, fftfreq
import numpy as np
import matplotlib.pyplot as plt
#Chebyshev Filter Coefficients
b = [ 0.00757702, -0.02666634, 0.06433529, -0.09739344, 0.11965053, -0.10339635,
0.07472005, -0.0214037, -0.0214037, 0.07472005, -0.10339635, 0.11965053,
-0.09739344, 0.06433529, -0.02666634, ... |
'''This module contains a collection of function to perform differential dynamic microscopy analysis of videos'''
from ipywidgets import interactive
import matplotlib.pyplot as plt
from scipy import fftpack
import pandas as pd
import numpy as np
import ipywidgets
def browse_images_FFT(video,interval=1,muperpix=1):
... |
import numpy as np
from nse_opinf_poddmd.load_data import get_matrices, load_snapshots
from nse_opinf_poddmd.plotting_tools import plotting_SVD_decay, plotting_obv_vel, plotting_abs_error
from nse_opinf_poddmd.optinf_tools import deriv_approx_data, optinf_quad_svd, pod_model, optinf_linear
import nse_opinf_poddmd.opti... |
<filename>triku_nb_code/comparing_feat_sel.py
import gc
import os
from itertools import product
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import ray
import scanpy as sc
import scipy.sparse as spr
import seaborn as sns
from matplotlib.lines import Line2D
from scikit_posthocs import (
po... |
<reponame>marcovaas/uclametrics<gh_stars>0
# -*- coding: utf-8 -*-
"""
Created on Sat Mar 26 10:50:56 2022
@author: marco
"""
import pandas as pd
import numpy as np
import os
from scipy.linalg import pinv as pinv
from scipy.linalg import inv as inv
from scipy.stats import f
import math
import random
impo... |
<reponame>aframires/freesound-loop-annotator
#!/usr/local/bin/python
# coding=utf-8
import os
import numpy as np
from algorithms.Edmkey.conversions import name_to_class
from matplotlib import pyplot as plt
import librosa.display
def plot_chroma(chromagram):
plt.figure(figsize=(10, 4))
librosa.display.specsho... |
# Copyright (c) 2022, NVIDIA CORPORATION.
# 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 i... |
<reponame>enricomeloni/covid-tools<filename>diffeq_test.py
from torch_euler import euler, Heun, RK4
from torchdiffeq import odeint, odeint_adjoint
from scipy.integrate import odeint as scipy_odeint
import torch
from matplotlib import pyplot as plt
gamma = torch.tensor([0.3])
beta = torch.tensor([0.8])
population = 1... |
# Author: <NAME>, <NAME>
# module for xgboost
import ctypes
import os
# optinally have scipy sparse, though not necessary
import numpy
import sys
import numpy.ctypeslib
import scipy.sparse as scp
# set this line correctly
XGBOOST_PATH = os.path.dirname(__file__)+'/libxgboostwrapper.so'
# load in xgboost library
xglib... |
# Author: <NAME>
# Date: 22 November, 2018
# Description: A file for implementing the Dataset interface of PyTorch
import scipy.sparse as sp
import numpy as np
import pandas as pd
import torch
from torch.utils.data import Dataset
np.random.seed(7)
#.train.rating ------------> trainMatrix -------> user_input, item_in... |
# -*- coding: utf-8 -*-
## Copyright 2015-2021 PyPSA Developers
## You can find the list of PyPSA Developers at
## https://pypsa.readthedocs.io/en/latest/developers.html
## PyPSA is released under the open source MIT License, see
## https://github.com/PyPSA/PyPSA/blob/master/LICENSE.txt
"""
Functionality for contin... |
#!/usr/bin/env python
import sys
import os
import math
import pysam
import argparse
import multiprocessing as mp
from statistics import mean
def printBinInfo(info):
if (info[3] + info[4] != 0): print(info[0] + '\t' + info[1] + '\t' + info[2] + '\t' + str(info[3]) + '\t' + str(info[4]))
def outputUnmappedLowQual... |
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
from math import exp, sqrt, log
'''
Question:
1) Implement Monte Carlo (MC) simulation to solve the below spread option,
=> E[(S2 - S1 - K)^+]
=> Given S1, S2 are dependent GBMs with correlation 'rho'
2) Find a closed form approximation ... |
from PIL import Image, ImageFilter
# FILTERING
from scipy import ndimage
import scipy.ndimage as nd
import numpy as np
import matplotlib.pyplot as plt
im = Image.open( 'ball_in_rough.jpg' )
im2 = im.filter(ImageFilter.MinFilter)
im2.save('ball_proc.jpg')
im = nd.imread('ball_proc.jpg', True)
im = im.astype('in... |
from typing import Dict, Any
import GPy
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.axes import Axes
from numpy import newaxis
from .bayesquad.batch_selection import select_batch, LOCAL_PENALISATION, KRIGING_BELIEVER, KRIGING_OPTIMIST
from .bayesquad.gps import WsabiLGP, WsabiMGP, LogMGP
from .... |
<gh_stars>0
"""
This file loads a directory of images into a keras image dataset
based off of https://gist.github.com/fchollet/0830affa1f7f19fd47b06d4cf89ed44d
https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html
https://stackoverflow.com/questions/43239897/create-readable-im... |
<reponame>jmborr/LDRDSANS
"""
Thanks to Jorn's Blog
<https://joernhees.de/blog/2015/08/26/scipy-hierarchical-clustering-and-dendrogram-tutorial/>
"""
# needed imports
from matplotlib import pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage
import numpy as np
Z=np.loadtxt("linkage_matrix")
plt.titl... |
<gh_stars>0
import cv2
import numpy as np
from numpy import linalg as npla
import scipy as sp
def color_transfer_sot(src,trg, steps=10, batch_size=5, reg_sigmaXY=16.0, reg_sigmaV=5.0):
"""
Color Transform via Sliced Optimal Transfer
ported by @iperov from https://github.com/dcoeurjo/OTColorTransfer
sr... |
<gh_stars>10-100
# Data : http://www.gaussianprocess.org/gpml/data/
import json
import lightgbm as lgb
import numpy as np
import pandas as pd
import scipy.io
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
if __name__ == "__main__":
n = 5
train_np = scipy... |
<filename>pyts/quantization/tests/test_quantization.py
"""Tests for :mod:`pyts.quantization` module."""
from __future__ import division
from __future__ import unicode_literals
from __future__ import print_function
from __future__ import absolute_import
from future import standard_library
from itertools import product
... |
from PyQt5.QtCore import QThread, QObject, pyqtSignal, pyqtSlot
import serial
import statistics
import pandas as pd
import numpy as np
import scipy.signal
import vector
import matplotlib.pyplot as plt
from datetime import datetime as dt
class SerialReceiver(QObject):
finished = pyqtSignal()
minuteReport = pyqtS... |
import numpy as np
import pandas as pd
import copy
from sklearn.model_selection import StratifiedKFold
import warnings
with warnings.catch_warnings():
warnings.simplefilter("ignore")
from sklearn.metrics import roc_curve, precision_recall_curve, auc, f1_score, accuracy_score, roc_auc_score, make_scorer
from sklearn ... |
#!/usr/bin/env python
# coding: utf-8
# In[211]:
import numpy as np
# TRAINING
from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier, ExtraTreesClassifier
# METRICS
from sklearn.metrics import classification_report, roc_auc_score, confusion_matrix, accuracy_score, recall_score, precision_recall_fs... |
import numpy as np
import pytest
import pyspherical as pysh
@pytest.fixture
def mw_sampling():
# MW sampling of a sphere
Nt = 701 # Number of samples in theta (must be odd)
Nf = 401 # Samples in phi
theta, phi = pysh.utils.get_grid_sampling(Nt=Nt, Nf=Nf)
gtheta, gphi = np.meshgrid(theta, phi)
... |
import numpy as np
import pandas as pd
import datetime as datetime
from scipy.signal import find_peaks, peak_prominences
from scipy.interpolate import interp1d
from scipy import signal
from scipy.integrate import trapz
'''
Feature Engineering of Wearable Sensors:
Metrics computed:
Mean Heart Rate... |
<filename>evaluator.py
import keras
import sounddevice as sd
import numpy as np
import scipy.io.wavfile as wav
import librosa
import librosa.display
import matplotlib.pyplot as plt
from data_utils import my_calc_mfs
fs=16000
duration = 5 # seconds
CONFIG={
"n_filts": 64,
"n_ceps_raw": 64,
"n_ceps": ... |
from itertools import product
import json
import numpy as np
from scipy._lib.doccer import indentcount_lines
def extract_patches(image, patch_size=(100, 100)):
h, w, _ = image.shape
patches = list()
for k, l in product(range(h // patch_size[0]), range(w // patch_size[1])):
patches.append(image[k ... |
import time
import numpy as np
import scipy as sp
import theano
import theano.tensor as T
from tqdm import tqdm
import lasagne
from astropy.io import fits
from sklearn import cluster
from .patch import extract_patches, augment, nanomaggie_to_luptitude
from .params import save_params, load_params
from .models import bu... |
# -*- coding: utf-8 -*-
"""
Module to simulate a spectra observed by a telescope given the
instruments specifications.
"""
import numpy as np
from scipy.interpolate import interp1d
#from astropy.convolution import convolve, Gaussian1DKernel
from scipy.ndimage import gaussian_filter1d
from scipy.optimize import brentq
... |
from scipy import spatial
import simplejson
import json
import requests
# G is embedding of nodes.
f = open("graph.txt","r")
G = simplejson.load(f)
f.close()
#print(G)
# G2 is int to uid mapping
G2 = {}
f = open("node_id_mapping.txt","r")
G2 = simplejson.load(f)
f.close()
#G3 is the original graph.
G3 = {}
f = open... |
<filename>GenePanelAnalyzer.py
#! /usr/bin/env python
"""
# -----------------------------------------------------------------------
# encoding: utf-8
# DIAMOnD.py
# <NAME>, <NAME>
# Last Modified: 2020-22-09
# This code runs the DIAMOnD algorithm as described in
#
# A DIseAse MOdule Detection (DIAMOnD) Algorithm deriv... |
import json
import os
import numpy as np
from keras.optimizers import RMSprop, Optimizer
from keras.models import Model
from keras.layers import Input, Dense
from keras.initializers import RandomNormal
from keras.utils import plot_model
from keras.layers import Layer
import keras.backend as K
from PIL import Image
from... |
from numpy.linalg import pinv
from numpy.linalg import matrix_power,matrix_rank,eig,eigh
from scipy.linalg import expm,pinvh,solve
from sklearn.svm import SVC,SVR
from sklearn.kernel_ridge import KernelRidge
from support.ConvNTK import *
from support.tools import *
from copy import deepcopy
from time import time
from m... |
<reponame>botprof/agv-examples
"""
Example vanilla_SLAM.py
Author: <NAME> <<EMAIL>>
GitHub: https://github.com/botprof/agv-examples
"""
# %%
# SIMULATION SETUP
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import chi2
from matplotlib import patches
# Set the simulation time [s] and the sample p... |
'''Utility functions for activation_maximization.
This codes in this repository are based on CNN preferred image (cnnpref) https://github.com/KamitaniLab/cnnpref, which is written for 'Caffe'. These scripts are released under the MIT license.
Copyright (c) 2020 Kamitani Lab (<http://kamitani-lab.ist.i.kyoto-u.ac.jp/>)... |
from typing import List, Dict, Iterable, Callable
import logging
import pandas as pd
import numpy as np
import scipy.stats
from py_muvr.data_structures import FeatureRanks
def average_scores(scores: List[Dict]) -> Dict[int, float]:
avg_score = pd.DataFrame(scores).fillna(0).mean().to_dict()
return avg_score
... |
<reponame>keskarnitish/NQN
import numpy
import copy
import sys
def fmin_l_bfgs_b(funObj, x, gradObj, bounds=None, m=20, M=1, pgtol=1e-5,
iprint=-1, maxfun=15000, maxiter=15000, callback=None, factr=0.):
'''
Termination Flag:
0 => Converged
1 => Reached Maximum Iterations
2 => Rea... |
<filename>synctoolbox/feature/filterbank.py
import numpy as np
from scipy import signal
FILTERBANK_SETTINGS = [
{
'fs': 22050,
'midi_min': 96,
'midi_max': 120
},
{
'fs': 4410,
'midi_min': 60,
'midi_max': 95
},
{
'fs': 882,
'midi_min': ... |
from sympy import (
sin,
cos,
tan,
sec,
csc,
cot,
log,
exp,
atan,
asin,
acos,
Symbol,
Integral,
integrate,
pi,
Dummy,
Derivative,
diff,
I,
sqrt,
erf,
Piecewise,
Ne,
symbols,
Rational,
And,
Heaviside,
S,
a... |
#
# Module: Convert - Converts from several Term-Frequency Dictionaries/NArrays/PyTables/Matricies to a 2D Matrix/Array etc.
#
# Author: <NAME>
#
# License: BSD Style
#
# Last update: Please refer to the GIT tracking
#
""" html2vect.base.convert.convert: submodule of `html2vect` module defines the... |
# -*- coding: utf-8 -*-
"""
License: MIT
@author: gaj
E-mail: <EMAIL>
"""
import numpy as np
import cv2
import os
from scipy import signal
from PIL import Image
import torch
from methods.Bicubic import Bicubic
from methods.Brovey import Brovey
from methods.PCA import PCA
from methods.IHS import IHS
fr... |
<reponame>CNES/decloud
# -*- coding: utf-8 -*-
"""
Copyright (c) 2020-2022 INRAE
Permission is hereby granted, free of charge, to any person obtaining a
copy of this software and associated documentation files (the "Software"),
to deal in the Software without restriction, including without limitation
the rights to use... |
<reponame>ekand/spotipy-hits
import statistics
from pathlib import Path
from src.data.pickle_util import load_pickle, save_pickle
# get the project directory
current_file_path = Path(__file__)
project_dir = Path(__file__).resolve().parents[2]
def get_avg_volume(list_of_track_feature_dicts):
t = []
for track... |
## ECCO SPATIAL CORRELATION
import numpy as np
import matplotlib.pyplot as plt
import time as tictoc
import scipy.stats as stats
## LOAD DATA
theta = np.load('python/ecco2_dT/dtheta_sfc_NA_ano_v2.npy')
eccomask_NA = np.load('python/ecco2/ecco_mask_NA.npy')
(time,latna,lonna) = np.load('python/ecco2/ecco_dim_NA.npy'... |
import numpy as np
from .Templates import Estimator_mimo_ML, Descriptor
from SCM3GPP.toeplitz_helpers import vec2mat, mat2vec
from training_CNN_mimo import pilot_matrix
from scipy.linalg import toeplitz
class ML(Estimator_mimo_ML, Descriptor):
_object_counter = 1
def __init__(self, snr, transform, name=None):... |
import itertools
import numpy as np
import torch
import hydra
from scipy.spatial.distance import pdist
from scipy.spatial.distance import cdist
from hydra.experimental import compose
from hydra import initialize_config_dir
from pathlib import Path
import smact
from smact.screening import pauling_test
from cdvae.comm... |
<gh_stars>1-10
import numpy as np
from scipy.interpolate import interp1d
def ramp_growth_rate(t, start, slope):
gr = np.maximum(0, slope*(t-start))
return(gr)
def ramp_biomass(t, od0, start, slope):
logod = np.maximum(0, ((t-start)**2)/2)
od = od0 * np.exp(logod)
return(od)
def step_growth_rate(t... |
import numpy as np
import astropy.units as u
from astropy.constants import c, m_p, m_e
from scipy import integrate
from ambient_CR_kinetic import ambient_CR_kinetic
from distr2spectr_kinetic import distr2spectr_kinetic
def reacc_kinetic(ene, co, br, s, particle):
# Check if input energy is MeV
if not ene.uni... |
<reponame>LSaldyt/curry
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from scipy import stats
from collections import defaultdict
from pprint import pprint
def histogram(results):
counts = defaultdict(lambda : 0)
for trial, trial_measurements in results.items():
... |
<reponame>veragluscevic/npoint-fgs<filename>scripts/bispectrum_fisher.py
import numpy as np
import pylab as pl
import matplotlib
if __name__=='__main__':
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from astropy.io import fits
from mpl_toolkits.axes_grid1 import make_axes_locatable
import matplotlib as mp... |
<gh_stars>10-100
import sklearn as sk # Solving static TLS
import torch
import numpy as np
from scipy import ndimage
from sklearn.metrics import f1_score
from utils import flowlib
def minimax(im, per_image=True):
if per_image:
batch_size = im.shape[0]
min_vals = im.min(1)[0].min(1)[0].min(1)[0].r... |
import numpy as np
import os
from PIL import Image
import scipy.io as sio
from nd2reader import ND2Reader
try:
from . import myutils
except Exception as e:
import myutils
### Helper Functions
def get_metadata_from_nd2(path, drop_z_coords=True):
"""
function to extract the meta data of a .nd2 file.
... |
<filename>predict_class.py
# Copyright (C) 2021 <NAME>, ETH Zürich, Information Security Group
# Released under the MIT License
"""
Using a pretrained model, and given cookie data in JSON format, predict labels for each cookie.
Can choose between the three tree boosters.
Usage:
predict_class <model_path> <json_dat... |
<reponame>metodmove/finTDPSOM<gh_stars>10-100
"""
Array processing utilities
"""
import numpy as np
import scipy.stats as sp_stats
def nan_ratio(in_arr):
return np.sum(in_arr)/in_arr.size
def print_nan_stats(in_arr):
print("[0.0: {:.5f}, 1.0: {:.5f}, NAN: {:.5f}]".format(np.sum(in_arr==0.0)/in_arr.size,np.su... |
"""Infiltration module.
"""
import numpy as np
from scipy.optimize import fsolve
def _green_ampt_cum_eq(F_t1, F_t0, psi, dtheta, K, dt):
"""The Green-Ampt cumulative infiltration equation
"""
tmp = psi*dtheta
# np.log(x) computes ln(x)
return F_t1 - F_t0 - tmp*np.log((F_t1 + tmp)/(F_t0 + tmp)) -... |
from FinanceToolbox import imports
import pandas as pd
import numpy as np
import yfinance as yf
from sklearn.linear_model import LinearRegression
import statsmodels
import statsmodels.api as sm
import statsmodels.tsa.stattools as ts
import datetime
import scipy.stats
import math
import openpyxl as pyxl
from scipy im... |
<reponame>usmansaleem542/diabetic_retinopathy<filename>preprocess/PPImage.py
from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
import zipfile
import cv2
import io
from skimage import exposure
from scipy.ndimage.morphology import binary_fill_holes
import copy
class PPImage:
def __init__(self... |
# coding: utf-8
import re
import numpy as np
from scipy import stats
from abc import ABCMeta, abstractmethod
from ..utils import mk_class_get
class KerasyAbstKernel(metaclass=ABCMeta):
def __init__(self):
self.name = re.sub(r"([a-z])([A-Z])", r"\1_\2", self.__class__.__name__).lower()
@abstractmethod... |
import torch
import torch.nn as nn
import numpy as np
from cnnseq import utils
from cnnseq.utils_models import set_optimizer, flatten_audio
from skimage.io import imsave
import json
import os
# Recurrent neural network (many-to-one)
class Seq2Seq(nn.Module):
def __init__(self, params, device=torch.device('cpu')):... |
import scipy.stats, numpy as np
import matplotlib.pyplot as plt, matplotlib
np.random.seed(0)
matplotlib.style.use("ggplot")
plt.subplot(2,2,1)
plt.title('Continuous uniform')
plt.xlim(xmin=-1,xmax=1)
plt.ylim(ymin=-0.1,ymax=1.2)
plt.plot([-1,-0.5,-0.5,0.5,0.5,1],[0,0,1,1,0,0],'-')
plt.subplot(2,2,2)
plt.title('Nor... |
<filename>book_seller/crawler/views.py
#!/usr/bin/python3
# -*- coding: utf8 -*-
# -*- Mode: Python; py-indent-offset: 4 -*-
"""View that start crawler for justbook.fr"""
import math
import logging
import statistics
from scrapyd_api import ScrapydAPI
from datetime import timedelta
from django.views import View
from d... |
from sympy import primefactors
from fpack import primelist_till_x as primo
from time import time
def phi_n(x):
l = primefactors(x)
if len(l) == 2: # because we want to maximize phi(n)
phi_n = x
for ele in l:
phi_n *= 1 - 1 / ele
return int(phi_n)
else:
return 0... |
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