text string |
|---|
<filename>utils.py
# -*- encoding: utf-8 -*-
import torch
import math
import numpy as np
import scipy.sparse as sp
from scipy.special import iv
from scipy.optimize import linear_sum_assignment as linear_assignment
from sklearn.metrics import accuracy_score
from sklearn.metrics import f1_score
from sklearn.metrics.clus... |
__source__ = 'https://leetcode.com/problems/rotate-array/'
# https://github.com/kamyu104/LeetCode/blob/master/Python/rotate-array.py
# Time: O(n)
# Space: O(1)
# Array
#
# Description: Leetcode # 189. Rotate Array
#
# Rotate an array of n elements to the right by k steps.
#
# For example, with n = 7 and k = 3, the arr... |
# -*- coding: utf-8 -*-
"""
create test sound for YouTube Music
===================================
"""
# import standard libraries
import os
# import third-party libraries
import numpy as np
from scipy.io import wavfile
import wavio
import cv2
# import my libraries
# information
__author__ = '<NAME>'
__copyright__... |
import numpy as np
from glob import glob
import os
import json
from neuralparticles.tools.param_helpers import *
from neuralparticles.tools.data_helpers import particle_radius
from neuralparticles.tools.shell_script import *
from neuralparticles.tools.uniio import writeParticlesUni, writeNumpyRaw, readNumpyOBJ, writeNu... |
<reponame>antisymmetric/sumo
# coding: utf-8
# Copyright (c) Scanlon Materials Theory Group
# Distributed under the terms of the MIT License.
"""
Module containing functions to process dielectric and optical absorption data.
TODO:
* Remove magic values
"""
import os
import numpy as np
from scipy.ndimage.filters... |
<reponame>CheukHinHoJerry/3DCNN-SUPER-2021-pytorch
# -*- coding: utf-8 -*-
import numpy
import h5py
import scipy
from scipy import misc
import cv2
import math
from PIL import Image
def load_h5_data(filename, datasetname, AMOUNT):
f = h5py.File(filename, 'r')
data = f[datasetname][-AMOUNT:, :, :, :]
[AMOUN... |
<reponame>twmobius/kaggle-instacart
# mobius
import pandas as pd
import numpy as np
from time import time
import sys
# import gensim
from pathlib import Path
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.multiclass import OneVsOneClassifier
from sklearn.multiclass import OneVsRestClassifie... |
import numpy as np
import scipy.odr
import numba
import skimage.io
import skimage.measure
from matplotlib import path
class SimpleImageCollection(object):
"""
Load a collection of images.
Parameters
----------
load_pattern : string or list
If string, uses glob to generate list of files ... |
# -*- coding: utf-8 -*-
import numpy as np
from scipy import stats, interpolate
import matplotlib.pyplot as plt
from ReflectivitySolver import ReflectivitySolver
from sourcefunction import SourceFunctionGenerator
from utils import create_timevector, create_frequencyvector
def plot_PT_summary(samplers, burn_in=0):
... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import csv
import random
import glob
import os
import sys
import numpy
import scipy.io
import pylab
from svmutil import *
N_MONTH = 4
N_DAY_PER_MONTH = 31
BASE_MONTH = 4
TYPE_LENGTH = 4
class User(object):
def __init__(self, id, info):
self.id = id;
... |
import math as mt
def poly(r,N=20,b=1):
return (mt.exp(-3*r**2/(2*N*b**2))*4*mt.pi*r**2)*((3/(2*mt.pi*N*b**2))**(3/2))
ti=[]
t0=0 # Waktu Awal
t1=1 # Waktu Akhir
n=10
h=(t1-t0)/n
fa=poly(t0)
fn=poly(t1)
## pengisian matriks
while t0<t1+h:
ti.append(t0)
t0=t0+h
print('nilai t=',ti)
panjang=len(ti)
####
velo=... |
<gh_stars>0
#!/usr/bin/env python
"""
We now introduce a refinement to the SIR model (Program 2.2) which takes into account a latent period. The process of
transmission often occurs due to an initial inoculation with a very small number of pathogen units (e.g., a few
bacterial cells or virions). A period of time then e... |
#!/usr/bin/env python
import itertools as it
import numpy as np
import pytest
import scipy.ndimage as ndimage
import fsl.data.image as fslimage
import fsl.transform.affine as affine
import fsl.utils.image.resample as resample
from . import make_random_image
def random_affine()... |
<filename>mskpy/image/analysis.py
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
image.analysis --- Analyze (astronomical) images.
=================================================
.. autosummary::
:toctree: generated/
anphot
apphot
apphot_by_wcs
azavg
azmed
bgfit
bgphot
... |
import os
from scanpy import read_10x_h5
from scipy import sparse
from anndata import AnnData, concat
import gc
import h5py
joint_url = "http://data.nemoarchive.org/biccn/lab/zeng/transcriptome/scell/10X/processed/analysis/RNASeq_integrated/"
# C = Chromium 10X, SS = SmartSeq
data_url = {
'scCv2':
"http://data... |
<gh_stars>0
## Partie des Imports
import numpy as np
from random import random
from random import randrange
from random import shuffle
from statistics import mean
import matplotlib.pyplot as plt
# antColonyAlg - Algorithme de colonies de fourmis
def antColonyAlg(dataPondArray, nombreCamions):
## Définition des va... |
from os.path import join, basename, splitext
import os, glob, random
import numpy
import scipy.io
import mne
import pandas
from autoreject import AutoReject
from eegprep.bids.naming import filename2tuple
from eegprep.guess import guess_montage
from eegprep.util import (
resample_events_on_resampled_epochs,
plot... |
from enum import Enum
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from sklearn.metrics.pairwise import paired_cosine_distances, paired_euclidean_distances, paired_manhattan_distances
from scipy.stats import pearsonr, spearmanr
import csv
import logging
import os
import numpy as np
from ty... |
import string
import scipy
from nose.tools import assert_equal
from datetime import datetime as dt
from .meta_graph import convert_to_meta_graph, \
convert_to_original_graph, \
convert_to_meta_graph_undirected
from .interactions import InteractionsUtil as IU, \
clean_decom_unzip, clean_unzip
from .test_ut... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 29 09:21:06 2021
@author: philippbst
"""
def main():
#%% -----------------------------------------------------------------------------------------------
import os
import time
import matplotlib.pyplot as plt
from matplotlib impor... |
"""Integration across experimental conditions or single cell modalities"""
import numpy as np
import anndata as ad
# from sklearn.metrics.pairwise import pairwise_distances
from sklearn.utils.extmath import randomized_svd
from scipy.sparse import csr_matrix, find
from ._utils import _knn
def infer_edges(adata_ref,
... |
<reponame>uhoefel/coordinates
import sympy as sym
from metric import Metric
from coordinate_system_implementation_generator import JavaCoordinateSystemCreator
r = sym.symbols('r', real=True, positive=True)
theta = sym.symbols('theta', real=True)
m = Metric.fromTransformation([r, theta], to_base_point=[r*sym.cos(thet... |
<gh_stars>1-10
import math
import scipy
params = {}
# Plate parameters
params['plate'] = {
'width' : 10.0,
'height' : 1.8,
'thickness' : 0.379,
'radius' : 5/64.0,
'numParts' : 5,
'partSpacing' : 2.0,
}
totalLength = (params['plate']['numPa... |
<gh_stars>1-10
import numpy as np
from scipy.special import expit
import matplotlib.pyplot as plt
from scipy.stats import norm
from scipy.stats import multivariate_normal
import pandas as pd
from scipy.stats import truncnorm
import matplotlib.pyplot as plt
# LOADING DATASET
df = pd.read_csv("data/data/bank-note/train.... |
<gh_stars>0
# -*- coding: utf-8 -*-
import numpy as np
import math
from scipy.stats.qmc import Sobol, Halton, LatinHypercube
from src.functions.particle import Particle
from src.functions.moment_matching import shift_samples
class Samples:
"""
Class for generating list of Particles given various initial condi... |
import os
import sys
import math
import numpy as np
from PIL import Image
import scipy.linalg
import chainer
import chainer.cuda
from chainer import Variable
from chainer import serializers
from chainer import cuda
import chainer.functions as F
sys.path.append(os.path.dirname(__file__))
sys.path.append('../')
from s... |
#%%
import numpy as np
import torch
import scipy.integrate as itg
import gym
from utils import ArmDynamicsFun, Jacobian, Jacobian_dot, Hand2Joint, Joint2Hand, dist_from_straight, rand_targ_circle, fibonacci_samples
from arm_params import *
#%%
# arm movement constraints :
# The Human Arm Kinematics and Dynamics
# D... |
<reponame>lucaskeiler/AlgoritmosTCC<filename>Algorithms/bipartiteK3/Correctness/testsVisualization.py
import numpy as np
import matplotlib.pyplot as plt
from scipy import interpolate
def loadTimeFile(fileName):
size = []
totalList = []
correctList = []
with open(fileName) as file:
line = file.r... |
import os
import errno
import random
import yaml
import json
import re
import numpy as np
import torch
import matplotlib.pyplot as plt
from utils import multiview
from scipy.optimize import least_squares
def config_to_str(config):
return yaml.dump(yaml.safe_load(json.dumps(config))) # fuck yeah
def update_after_... |
<reponame>tribhuvanesh/visual-privacy-advisor
#!/usr/bin/python
"""Common utilities
Replace this with a more detailed description of what this file contains.
"""
import json
import time
import pickle
import sys
import csv
import argparse
import os
import os.path as osp
import shutil
import numpy as np
import matplotl... |
<gh_stars>10-100
import numpy as np
# import matplotlib.pyplot as plt
from scipy.special import comb
import warnings
def wavefront_map(rho, theta, index):
"""Generate a map of the Zernike polynomial, normalised over the unit disk.
If index is a 1D array, the linear indexing is used.
If index is a 2D array... |
<reponame>C4IROcean/python_sdk_example_notebooks
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import cmocean
import cartopy
import cartopy.crs as ccrs
from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter
import cartopy.feature as cfeature
from cartopy.mpl.... |
import numpy as np
from numpy.linalg import norm
from ase import Atoms
from ase.data import covalent_radii
from ase.neighborlist import NeighborList
import ase.neighborlist
import scipy.stats
from scipy.constants import physical_constants
import itertools
from IPython.display import display, clear_output, HTML
import n... |
<gh_stars>10-100
import numpy as np
import matplotlib.pyplot as plt
from scipy.misc import toimage
from keras.datasets import cifar10
def draw(X):
"""Xは4次元テンソルの画像集合、最初の16枚の画像を描画する"""
assert X.shape[0] >= 16
plt.figure()
pos = 1
for i in range(16):
plt.subplot(4, 4, pos)
img = toim... |
# -*- coding: utf-8 -*-
"""
Functions/Class for regressions.
"""
import numpy as np
import statsmodels.api as sm
import pandas as pd
from itertools import combinations
from scipy import log, exp, mean, stats, special
from statsmodels.tools import eval_measures
from copy import copy as cp
from hydrolm.util import autoco... |
<gh_stars>1-10
'''
Created on 2015/05/24
@author: admin
'''
import numpy as np
import scipy
import scipy.linalg
class ES:
def __init__(self,ite,pop,c=0.85,sigma=1.0,torus=False,best=None,ftarget=-np.inf,threads=1):
self.ite = ite
self.pop = pop
self.c = c
self.firstSigma = sigma
... |
<reponame>BeyondLongLab/ecg_analysis
#!/usr/bin/env python
# coding: utf-8
import scipy.io
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from io import StringIO
from datetime import date, time, datetime, timedelta
from math import exp, log, sqrt, e
import ecg_analysis.EEMD as EMD
def Shanno... |
# %% [markdown]
# ## 0 | Import packages and load test data
# %%
import os
import tkinter
from tkinter.filedialog import askopenfilename, askopenfilenames, askdirectory
import h5py
from collections import defaultdict
from nptdms import TdmsFile
import numpy as np
import pandas as pd
import seaborn as sns
from scipy im... |
from scipy.signal import periodogram, spectrogram
from peakdetect import peakdet
def find_peak(Fs, signal):
""" Find the signal frequency and maximum value
"""
f,s = periodogram(signal, Fs, 'blackman', 1024*32, 'linear', False, scaling='spectrum')
threshold = max(s)*0.9 # only 0.4 ... 1.0 of max value... |
<reponame>xSakix/AI_playground
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from catboost import CatBoostRegressor
from scipy.stats import skew
from sklearn.dummy import DummyRegressor
from sklearn.gaussian_process import GaussianProcessRegressor
from skle... |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
# Power by <NAME> 2019-09-01 19:35:06
import sys
sys.path.append('./datasets')
import random
import scipy
import torch
import torch.nn.functional as F
import torch.nn as nn
import numpy as np
from math import pi
from .DnCNN import DnCNN
from .UNet import UNet
from utils imp... |
<reponame>mortazavilab/swan_vis<gh_stars>10-100
import networkx as nx
import numpy as np
import pandas as pd
import pickle
from statsmodels.stats.multitest import multipletests
import scipy.stats as st
import matplotlib.pyplot as plt
import os
import copy
from collections import defaultdict
from tqdm import tqdm
from s... |
from copy import deepcopy
import logging
import numpy as np
import torch
import torch.nn as nn
from scipy.stats import kendalltau
from functools import reduce
from nasws.cnn.policy.cnn_search_configs import build_default_args
from sklearn.linear_model import LinearRegression
from .lib import base_ops
from .lib impor... |
from __future__ import print_function, division
import os, sys, warnings, platform
from time import time
import numpy as np
if "PyPy" not in platform.python_implementation():
from scipy.io import loadmat, savemat
from Florence.Tensor import makezero, itemfreq, unique2d, in2d
from Florence.Utils import insensitive
f... |
<reponame>fkwai/geolearn
import scipy
from hydroDL import kPath, utils
from hydroDL.app import waterQuality
from hydroDL.master import basins
from hydroDL.master import slurm
from hydroDL.post import axplot, figplot
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import os
import json
import skl... |
# Copyright 2017 <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 writing, software
... |
import numpy as np
import logging
from sklearn import tree, linear_model, ensemble
from sklearn.metrics import accuracy_score
from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split
from scipy.sparse import csr_matrix, lil_matrix
import operator
import code
from functools import reduce
c... |
<reponame>tchamabe1979/exareme<gh_stars>0
import setpath
import functions
import math
import json
import re
from scipy import stats
registered=True
class ttest_independent(functions.vtable.vtbase.VT):
def VTiter(self, *parsedArgs,**envars):
largs, dictargs = self.full_parse(parsedArgs)
if 'query... |
"""
@brief test log(time=3s)
"""
import unittest
import pickle
from io import BytesIO
import numpy
import scipy.sparse
import pandas
from sklearn import __version__ as sklver
from sklearn.datasets import make_blobs
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris
from skl... |
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats
import tensorflow as tf
def gensign(h1, h2, t1, t2, noise=0.005, size=1000, return_y_values=True):
if [False for i in [h1, h2, t1, t2, noise, size] if i < 0].__contains__(False):
raise ValueError('argument cannot be less than ... |
<gh_stars>1-10
"""
The Code contains functions to calcualte the statistical vector.
Cross validation and repeated random sampling can be used to
increase the diversity of training set and calibration set
and improve the accuracy of probability vector and statistical vector.
Conformal Prediction: 1. https://pypi.org/p... |
# =============================================================================
# Plots a SIRD model according input parameters
# =============================================================================
import numpy as np
from scipy.integrate import odeint
import matplotlib.pyplot as plt
import seaborn as sns
sn... |
# -*- coding: utf-8 -*-
import os
import pathlib
import sys
from PIL import Image
import numpy as np
import tensorflow as tf
from scipy import misc
from facenet.src import facenet
from facenet.src.align import detect_face
def align(image_paths, image_size=160, margin=32, gpu_memory_fraction=1.0):
minsize = 2... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Feb 11 23:18:16 2020
@author: leonidkotov
"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from dataset import create_dataset, load_from_file_with_index
from subplots import plot_signal, plot_examples, plot_signals, plot_signa... |
import operator
import sympy as sp
from means.approximation.mea.mea_helpers import make_k_chose_e
def _make_alpha(n_vec, k_vec, ymat):
return reduce(operator.mul, [y ** (n - m) for y, n, m in zip(ymat, n_vec, k_vec)])
def _make_min_one_pow_n_minus_k(n_vec, k_vec):
return reduce(operator.mul, [(-1) ** (n - k... |
import scipy.io
import scipy.misc
from glob import glob
import os
import numpy as np
from ops import *
import tensorflow as tf
from tensorflow import contrib
from menpo_functions import *
from logging_functions import *
from data_loading_functions import *
class DeepHeatmapsModel(object):
"""facial landmark loca... |
import random
import numpy as np
import scipy as sc
import util
import log
from scipy import special
from scipy import stats
def variableSelection(x0, y0, beta_tilde, c, clim):
'''
Performs variable (model) selection by computing the evidence of each model
@params:
x0 (np.array): array of input da... |
# Prefer newest SciPy interface
try:
from scipy.fft import rfft, irfft, rfftfreq # noqa
except ImportError:
from numpy.fft import rfft, irfft, rfftfreq # noqa
|
from itertools import chain
from sympy import Symbol, solve, Piecewise
from sympy.core import sympify
import cncframework.events.actions as actions
def tag_expr(tag, out_var):
"""Return out_var = tag as a SymPy expression."""
# since sympify will automatically equate to zero, we convert it to:
# tag_expr... |
<reponame>marisgg/pca_bats
import numpy as np
from tqdm import tqdm
from scipy.stats import norm
import pca
class Bat:
def __init__(self, d, pop, numOfGenerations, a, r, q_min, q_max, lower_bound, upper_bound, function, use_pca=True, levy=False, seed=0, alpha=1, gamma=1):
# Number of dimensions
sel... |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
import palettable
import itertools
from functools import partial
from scipy.spatial.distance import squareform
from matplotlib.gridspec import GridSpec
from matplotlib import cm
import scipy.cluster.hierarchy as sch
from c... |
import os, time, sys, platform
import numpy as np
import array, random
import glob
from scipy.io import wavfile
dataset_link = "https://storage.cloud.google.com/download.tensorflow.org/data/speech_commands_v0.01.tar.gz"
filedir = "D:\\speech_commands_v0.01/"
if platform.system().lower() != "windows":
filedir = "/us... |
<reponame>AniNair14/IE-7374-Machine-Learning<filename>Naive_Bayes.py
#Importing the required libraries:
import pandas as pd
import numpy as np
from sklearn.datasets import make_blobs
from sklearn.model_selection import train_test_split
from scipy.stats import norm
#Creating the Data:
X, y = make_blobs(n_samples = 1000... |
<reponame>marioharper182/OptionsPricing
__author__ = 'HarperMain'
import numpy as np
from numpy import exp, log, sqrt
from scipy.stats import norm
class Vanilla(object):
def __init__(self, flag, S, K, r, v, T, div):
self.Vanilla = self.BlackSholes(flag, float(S),
flo... |
# %% [markdown]
## Imports
# %%
# Data Processing
import pandas as pd
import matplotlib.pyplot as plt
plt.rcParams["font.family"] = "Times New Roman"
plt.rcParams["font.size"] = 12
plt.rcParams["axes.labelsize"] = 'x-large'
from matplotlib.collections import LineCollection
import scipy as scp
from scipy import interp... |
import networkx as nx
import numpy as np
import scipy
from numba import jit
from scipy.sparse import isspmatrix
from scipy.special import comb
from . import comdet_functions as cd
from . import cp_functions as cp
def compute_neighbours(adj):
lista_neigh = []
for ii in np.arange(adj.shape[0]):
lista_n... |
import os, sys, inspect
sys.path.insert(1, os.path.join(sys.path[0], '..'))
import numpy as np
from scipy import stats
from matplotlib import pyplot as plt
import core.bounds as bounds
import seaborn as sns
import pdb
def map_bounds_R(bnds,Rs,delta,n,B,num_grid,sigmahat_factor,maxiters):
Rs = Rs/B
out = []
... |
import numpy as np
import numpy as np
import pandas as pd
from sklearn import preprocessing
import pprint
from os import chdir
from sklearn.ensemble import RandomForestClassifier
import sys
#sys.path.insert(0, '//Users/babakmac/Documents/HypDB/relational-causal-inference/source/HypDB')
#from core.cov_selection import... |
<gh_stars>1-10
import numpy as np
import pandas as pd
from astropy.table import Table, Column
from scipy.interpolate import interp1d
from astropy.cosmology import Planck18 as cosmo # noqa
from redback.utils import calc_kcorrected_properties, interpolated_barnes_and_kasen_thermalisation_efficiency, \
electron_frac... |
"""
Purpose: To simulate expected educational attainment gains from embryo selection between families.
Date: 10/09/2019
"""
import numpy as np
import pandas as pd
from scipy.stats import norm
from between_family_ea_simulation import (
get_random_index,
get_max_pgs_index,
select_embryos_by_index,
calc_... |
import sys
sys.path.insert(0, '../..')
from TheSoundOfAIOSR.stt.wavenet.inference import WaveNet
import argparse
import soundfile as sf
from scipy.signal import resample
import torch
from torchaudio.transforms import Resample
parser = argparse.ArgumentParser(description="ASR with recorded audio (offline)")
parser.add... |
<filename>interface.py
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import sys,os
from PyQt5.QtWidgets import QApplication, QWidget, QInputDialog, QLineEdit, QFileDialog, QCheckBox, QProgressBar
from PyQt5.QtGui import QIcon, QMatrix2x3, QPixmap
from PyQt5 import QtCore, QtWidgets
from PyQt5.QtWidgets import ... |
import numpy as np
from numpy.random import Generator
from scipy.signal import butter, filtfilt
#
# from src import float_service as fs, float_service_dev_utils as fsdu, globals as g
import src.float_service as fs
import src.float_service_utils as fsdu
# def test_low_pass_filter_input():
# # TODO: FIX t... |
# Copyright 2021 Lawrence Livermore National Security, LLC and other MuyGPyS
# Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: MIT
"""Convenience functions for optimizing :class:`MuyGPyS.gp.muygps.MuyGPS`
objects
Currently wraps :class:`scipy.optimize.opt` multiparamete... |
<filename>sybil.py
import networkx as nx
from scipy.sparse import csr_matrix, lil_matrix
import numpy as np
import random
from math import log, e
import math
def connect_sybils(G, num_sybils, stake_sybils, frac_naive, stake_naive, verbose=True):
if verbose:
print(" --- Graph ---")
print("Number G... |
import numpy as np
from scipy import sparse
import strawC # different
class HiCFile:
def __init__(self, filepath: str, resolutions: list, norm: str):
self.__filepath = filepath
self.__default_resolution = resolutions[0]
self.__all_resolutions = resolutions
self.__norm = norm
... |
import numpy as np
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import torch
import scipy.interpolate as spi
from scipy.io import loadmat
tensor_type = torch.DoubleTensor
def _swap_colums(ar, i, j):
aux = np.copy(ar[:, i])
ar[:, i] = np.copy(ar[:, j])
ar[:, j] = np.copy(aux)
return... |
<gh_stars>1-10
import os
import numpy as np
import struct
import PIL.Image
#http://www.nlpr.ia.ac.cn/databases/download/feature_data/HWDB1.1trn_gnt.zip
#http://www.nlpr.ia.ac.cn/databases/download/feature_data/HWDB1.1tst_gnt.zip
# 格式提取工具https://github.com/QiaXi/GntDecoder
train_data_dir = "../../data/HWDB1.1trn_gnt"
te... |
<filename>asct/src/SummaryNet.py
import torch.nn as nn
import torch.nn.functional as F
import torch
import numpy as np
from scipy import stats
#Information to calculate 1D CNN output size and maxpool output size.
#https://towardsdatascience.com/pytorch-basics-how-to-train-your-neural-net-intro-to-cnn-26a14c2ea29
#M... |
import torch
import numpy as np
from scipy.signal import get_window
import librosa
def window_sumsquare(window, n_frames, hop_length, win_length,
n_fft, dtype=np.float32, norm=None):
"""
# from librosa 0.6
Compute the sum-square envelope of a window function at a given hop length.
... |
<filename>optvaedatasets/wikicorp/tokenizer.py
from sklearn.feature_extraction.text import CountVectorizer
import numpy as np
import re,time,os
from nltk.corpus import wordnet as wn
from nltk.corpus import stopwords
import inflect
import h5py,os
from utils.sparse_utils import saveSparseHDF5
from scipy.sparse import csc... |
<filename>PyParadise/ssplibrary.py
import astropy.io.fits as pyfits
import numpy
from .spectrum1d import Spectrum1D
from scipy import ndimage
from scipy import interpolate
from scipy import optimize
from collections import UserDict
class SSPlibrary(UserDict):
"""A library of template spectra that can be used to f... |
<filename>surf/rf.py
# -*- coding: utf-8 -*-
"""
/*------------------------------------------------------*
| Spatial Uncertainty Research Framework |
| |
| Author: <NAME>, UC Berkeley, <EMAIL> |
| ... |
<filename>src/utils/qgis/algorithms/attach.py
# -*- coding: utf-8 -*-
"""
***************************************************************************
* *
* This program is free software; you can redistribute it and/or modify *
* it under the ... |
<reponame>luyang93/ROSALIND
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# @File : lia.py
# @Date : 2019-02-16
# @Author : luyang(<EMAIL>)
from math import factorial
from scipy.stats import binom
# Binomial_distribution
# f(x) = n!/x!(n-x)!*p^x*(1-p)^(n-x)
def Binomial_distribution(k, n, p):
return f... |
import sys
import os
import numpy as np
from IPython.core.debugger import set_trace
import importlib
import pandas as pd
from scipy.spatial import cKDTree
from tqdm.auto import tqdm
from default_config.masif_opts import masif_opts
import pickle
from masif_modules.protein import Protein
from sklearn.neighbors import KDT... |
<reponame>xinglunju/tdviz
from traits.api import HasTraits, Button, Instance, List, Str, Enum, Float, File, Int
from traitsui.api import View, Item, VGroup, HSplit, HGroup, FileEditor
from tvtk.pyface.scene_editor import SceneEditor
from mayavi.tools.mlab_scene_model import MlabSceneModel
from mayavi.core.ui.mayavi_sce... |
<filename>src/trainer.py
"""
Codes for training recommenders used in the real-world experiments
in the paper "Unbiased Pairwise Learning from Biased Implicit Feedback".
"""
import yaml
from pathlib import Path
from typing import Tuple
import pandas as pd
import numpy as np
import tensorflow as tf
from scipy import spa... |
# Correlation between CNN category similarity matrics in training and the one after training.
from os.path import join as pjoin
import numpy as np
import os
from scipy import stats
from scipy import io as sio
import matplotlib.pyplot as plt
from torchvision import models, transforms
import torch
from cnntools import cn... |
<reponame>Ombarus/freelancer-theme<gh_stars>0
import sys
import os
os.environ["path"] = os.path.dirname(sys.executable) + ";" + os.environ["path"]
import glob
import operator
import re
import datetime
import dateutil.relativedelta
import win32gui
import win32ui
import win32con
import win32api
import numpy
import json
i... |
<gh_stars>0
#Imports
from scipy.integrate import tplquad
#Constants
LowerLimit_x = 0.0
UpperLimit_x = 1.0
LowerLimit_y = 0.0
UpperLimit_y = 2.0
LowerLimit_z = 0.0
UpperLimit_z = 3.0
#Defining Function
def function(x,y,z):
return (x**2)+(y**2)+(z**2)
# Turning the constant limits into a function. This makes the co... |
<gh_stars>1-10
import logging
import numpy as np
import xarray as xr
from scipy.ndimage import uniform_filter
from wind_repower_usa.calculations import calc_simulated_energy
from wind_repower_usa.constants import KM_TO_METER
from wind_repower_usa.geographic_coordinates import geolocation_distances
from wind_repower_u... |
import numpy as np
from scipy.ndimage.measurements import center_of_mass
def argmax2d(data: np.ndarray) -> (int, int):
return np.unravel_index(np.argmax(data), data.shape)
def subpixel_argmax2d(heatmap: np.ndarray,
window_size=10):
w, h = argmax2d(heatmap)
window_size = min(window... |
import cv2
import numpy as np
import time
import pyautogui as gui
import pyautogui.tweens
gui.FAILSAFE = False
from collections import deque
from scipy import stats
# Function to find angle between two vectors
screenX, screenY = pyautogui.size()
frameX, frameY = 1000, 600
def angle(v1,v2):
dot = np.dot(v1,v2)
... |
<reponame>yilei0620/3D_Conditional_Gan
import theano
from theano import tensor as T
import scipy.io
import numpy as np
from lib.data_utils import OneHot, shuffle, iter_data
def load_shapenet_train(obj):
theano.config.floatX = 'float32'
mat = scipy.io.loadmat('models_stats.mat')
mat = mat['models']
num = np.arra... |
#!/usr/bin/python3
# -*- coding: utf-8 -*-
import os
import numpy as np
import matplotlib as mp
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
from PIL import Image
from fractions import Fraction
from pandas import DataFrame, read_table
from .model import ImageSet
class RegionSet(object... |
<reponame>corentinravoux/lelantos
import scipy as sp
import numpy as np
from scipy import random
PI = np.pi
def ComputeXYZdeg(ra,dec,R,ra0,dec0) :
return ComputeXYZ(np.radians(ra),np.radians(dec),R,np.radians(ra0),np.radians(dec0))
def ComputeXYZ(ra,dec,R,ra0,dec0) :
'''
XYZ of a point P (ra,dec,R) in a ... |
<reponame>jcchin/Hyperloop_v2
"""
Model for a Single Sided Linear Induction Motor(SLIM), using Circuit Model.
Evaluates thrust generated by a single, single sided linear induction motor using the simplified circuit model.
Inspired from the paper: DESIGN OF A SINGLE SIDED LINEAR INDUCTION MOTOR(SLIM) USING A USER INTER... |
<gh_stars>0
import statistics as stat
sum_array = []
for i in range(10):
nb_tax_array = []
time_array = []
first_line = True
with open("./experiment/" + str(i) + "/expeTax.csv", "r") as f:
for line in f:
if first_line:
first_line = False
else:
tmp, nb_t, t = line[:-1].split(";")
nb_tax_... |
from sympy import Symbol as _Symbol
from sympy import symbols as _symbols
from sympy import Array as _Array
class Wave:
def __init__(self, varidx='', k=None, w=None):
self.varidx = varidx
if k is None:
k_x, k_y, k_z= _symbols('k_x_{varidx}, k_y_{varidx}, k_z_{varidx}'.format(varidx... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.