text string |
|---|
import os
import pickle
from scipy.ndimage import distance_transform_edt as distance
from skimage import segmentation as skimage_seg
import numpy as np
from nnunet.configuration import default_num_threads
from nnunet.preprocessing.preprocessing import GenericPreprocessor
def compute_sdf(img_gt):
"""
compute ... |
#__docformat__ = "restructuredtext en"
# ******NOTICE***************
# optimize.py module by <NAME>
#
# You may copy and use this module as you see fit with no
# guarantee implied provided you keep this notice in all copies.
# *****END NOTICE************
# A collection of optimization algorithms. Version 0.5
# CHANGE... |
import os
import sys
import cv2
import numpy as np
from copy import deepcopy
from scipy.spatial.transform import Rotation as rot
import torch, torchvision
from time import time
import math
import h5py
import json
import random
import argparse
from const import KPTS_15, SMPL_KPTS_15
from data_utils import sample_projec... |
import numpy as np
import matplotlib.pyplot as plt
import statistics
class GA(object):
def __init__(self, population_size, chromosome_size, N_gerneration, crossover_rate=0.8, mutation_rate=0.003, X_bound=[0,5]):
self.population_size = population_size # DNA length
self.chromosome_size ... |
"""Demonstration of optimizing Spong controller parameters for an acrobot
using a Monte Carlo scenario.
"""
import argparse
from contextlib import closing
import os
import subprocess
import sys
import tempfile
import numpy as np
from scipy.optimize import fmin
from pydrake.common import FindResourceOrThrow
from dra... |
import numpy as np
import sympy as sp
from sympy import Matrix,shape,symbols, diff, sin, cos, tan, sinh, tanh, acos, atan, sqrt, limit, oo
#this file specifies the coordinate bases used in the programs.
#symbols for coordinate bases
t, x, y, z = sp.symbols('t x y z')
t, r, theta, phi = sp.symbols('t r theta... |
<filename>runoodp.py
import sys
import os
import os.path as osp
import argparse
import gym
from gym import wrappers
import random
import numpy as np
import tensorflow as tf
import tensorflow.contrib.layers as layers
from scipy.stats import mode
from agent import NaiveAgent
from oodpmodel import *
from uti... |
from memory import BasicBuffer
from DQN_Model import ieee2_net,ieee4_net
import torch.nn as nn
from torch.autograd import Variable
from setup import powerGrid_ieee2
import numpy as np
import torch
import os
import matplotlib.pyplot as plt
import copy
import statistics as stat
from torch.utils.tensorboard import Summary... |
<gh_stars>0
#import pickle
import pickle
import numpy as np
import pandas as pd
import argparse
from scipy.stats import norm
import matplotlib.pyplot as plt
import random
import itertools
from collections import Counter, defaultdict
from sklearn import preprocessing
import torch
from torch.nn.utils.rnn import pad_seque... |
from __future__ import print_function
from __future__ import division
from __future__ import absolute_import
import os.path as osp
import sys
sys.path.append(osp.dirname(osp.dirname(osp.abspath(__file__))))
import time
import numpy as np
import argparse
from scipy import stats
import gpflowSlim as gfs
from gpflowSlim... |
<gh_stars>0
import numpy
import scipy.interpolate
import collections
def getPolynomialFit(xList,yList,order=3,numPts=500):
# Get unique x,y value pairs and sort
valDict = {}
for x,y in zip(xList,yList):
try:
valDict[x].append(y)
except KeyError:
valDict[x] = [y]
... |
<gh_stars>0
import numpy as np
from scipy.integrate import odeint
def deg_to_rad(theta_deg):
""" Convert degrees to radians. """
return theta_deg * 0.0174532925
def rad_to_deg(theta_rad):
""" Convert radians to degrees. """
return theta_rad * 57.2957795
class DoublePendulum:
def __init__(self,... |
import numpy as np
import pandas as pd
from scipy.stats import norm, percentileofscore
from tqdm.notebook import tqdm
def rv_cc_estimator(sample,n=22):
"""
Realized volatility close to close calculation. Returns a time series of the realized volatility.
sample: series or dataframe of closing prices indexed by date... |
import pandas as pd
import numpy as np
from scipy import stats, signal, fft
def spec_pgram(
x,
xfreq=1,
spans=None,
kernel=None,
taper=0.1,
pad=0,
fast=True,
demean=False,
detrend=True,
minimal=True,
option_summary=False,
**kwargs
):
"""Computes the spectral density... |
<filename>car_racing/planning/planner_helper.py
import numpy as np
from scipy.interpolate import interp1d
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import copy
from utils.constants import *
def get_agent_range(s_agent, ey_agent, epsi_agent, length, width):
ey_agent_max = ey_agent + 0.5 ... |
<filename>notebooks/libraries/io.py
import os
import numpy as np
import scipy.io
import pandas as pd
from pathlib import Path
class FileWizard():
def __init__(self):
pass
def load_data(path, db):
directory = Path(path)
# Access file path in filesystem of given fil... |
from scipy.stats import wasserstein_distance
from sklearn.preprocessing import scale
from math import cos, sin
import os
import cv2
import numpy as np
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
from tqdm import tqdm
import csv
from sklearn.metrics import mean_squared_error
from sklearn.metrics impor... |
<filename>cogdl/wrappers/tools/wrapper_utils.py<gh_stars>1000+
from typing import Dict
import random
import numpy as np
import scipy.sparse as sp
from collections import defaultdict
import torch
import torch.nn as nn
import torch.nn.functional as F
from sklearn.utils import shuffle as skshuffle
from sklearn.linear_m... |
<reponame>YiyiLiao/deep_marching_cubes<gh_stars>100-1000
import torch
from torch.autograd import Variable
import torch.nn.functional as F
import numpy as np
import scipy.ndimage
from utils.util import gaussian_kernel, offset_to_normal
from model.cffi.modules.point_triangle_distance import DistanceModule
from model.cff... |
<reponame>hemanshu116/MWDB--11K-Images<filename>Main/featureDescriptors/CM.py
import os
import cv2
from scipy.stats.mstats import skew
import numpy as np
# a method that partitions a input image into 100 * 100 windows
from Main import config
from Main.helper import progress
def get_100_by_100_windows(input_image):
... |
import abc
from typing import List
import numpy as np
from scipy.spatial.ckdtree import cKDTree
from gym_guppy.guppies._base_agents import Agent, TorqueThrustAgent, TurnBoostAgent, TurnSpeedAgent
class Guppy(Agent, abc.ABC):
# TODO Guppy decides when to call compute next action, this allows to run at different ... |
##############################################################################
#
# Author: <NAME>
# Date: 30 April 2019
# Name: record_orbcomm.py
# Description:
# This script will record samples from any overhead satellite (or it will wait
# until a satellite is overhead). It will create 100 2-second recordings. The
# ... |
'''
Author: <NAME>
Main_utils script to run training and evaluation
of a residual network model on chest x-ray images
'''
import os
from tqdm import tqdm, trange
import logging
from scipy.stats import logistic
import numpy as np
import sklearn
from sklearn.metrics import precision_recall_fscore_support
from sklearn.... |
#!/usr/bin/env python
# Version: 1.0
# Author: <NAME> (updated by <NAME>)
from __future__ import division, print_function
import sys, math, os
import numpy as np
import scipy.stats
import datetime
import matplotlib.pyplot as plt
import glob
from collections import deque
import astropy.units as u
from astropy import lo... |
# coding: utf-8
from brian2 import *
try:
from python_utils import *
except:
try:
from utils import *
except:
pass
import numpy as np
import scipy.io as sio
import os, time, warnings
this_seed = 4321
seed(this_seed)
np.random.seed(this_seed)
# Determine where the .mat data saved
savePath... |
"""Benchmarking utilities."""
import functools
import logging
from statistics import mean
from time import time
DELIM_LENGTH = 15
logging.basicConfig(filename='benchmark.log', level=logging.INFO)
def timeit(_func=None, *, fname=None, n=1, delim=False):
"""Time function duration."""
def deco... |
import numpy as np
from ..Fourier.FFT import FFT
from scipy.signal import detrend
from .GetWindows import GetWindows
from ..LombScargle.LombScargle import LombScargle
from .DetectGaps import DetectGaps
from ..CrossPhase.CrossPhase import CrossPhase
from ..Tools.PolyDetrend import PolyDetrend
from ..Tools.RemoveStep imp... |
#!/usr/bin/env python
import datetime
import numpy as np
import math
import itertools
import argparse
import matplotlib
matplotlib.use('PDF')
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import warnings
warnings.filterwarnings("ignore") #Suppress matplotlib tight_layout() warning
from matplotlib... |
<reponame>vivekkalyan/fine-grained-image-classification<gh_stars>1-10
import os
import numpy as np
import pandas as pd
from scipy.io import loadmat
def to_pandas(mat):
dataset = []
for row in mat:
# row
# bbox_x1: array([[39]], dtype=uint8),
# bbox_y1: array([[116]], dtype=uint8),
... |
<filename>gardenbot/weather.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import requests
import statistics
from time import mktime
from functools import lru_cache
from datetime import date, timedelta
class WeatherInfo(object):
"""Represents the weather information for a given location and time.
Has att... |
<filename>CellAverage/diffraction_statistics.py
# tested influence of
# - Nr: no
# - Nphi: no
# - dp: Bragg-Peak intensity increases with dp
# seems to be due to some stray intensity
# from Bragg Peak that is cut-off (reduced
# if line-artefacts in FFT are reduced by
# edge sm... |
import glob, os, time, sys
import numpy as np
import scipy.linalg as sl, scipy.stats, scipy.special
class OutlierGibbs(object):
"""Gibbs-based pulsar-timing outlier analysis.
Based on:
Article by <NAME>:
"Robust and Accurate Inference via a Mixture
of Gaussian and Studen... |
from ctypes import c_char_p
import jieba
from jieba.analyse import extract_tags
import warnings
warnings.filterwarnings(action='ignore', category=UserWarning, module='gensim')
import gensim
from gensim.models import word2vec
import codecs
import time
import pandas as pd
import numpy as np
from wordcloud import WordClou... |
import matplotlib.pylab as plt
import numpy as np
from scipy.optimize import leastsq
x = np.linspace(start = 0,
stop = 4,
num = 100)
def gaussian(x, x0, k):
std = np.sqrt( x0**2 / (8*k))
a = 2 / (1 - np.exp(-k))
c = 2 - a
y = a*np.exp(-((x-(x0/2))**2)/(2*std**2)) +... |
<reponame>SirCarrius/epidemiology
import nltk, os, json, csv, string, cPickle
from scipy.stats import scoreatpercentile
from pprint import pprint
from progress.bar import Bar
READ = 'rb'
WRITE = 'wb'
stopwords = set(open('stopwords',READ).read().splitlines())
exclude = set(string.punctuation)
#lemmatizer
lmtzr = nltk... |
# ENG Construct an FD matrix approximating the 2D Laplace operator with
# the infamous "five-point stencil." We assume that the difference h=1;
#
# FIN Kootaan differenssimatriisi Laplacen operaattorille käyttäen viiden
# pisteen klassista lähestymistapaa.
#
# <NAME> 2021
# Matlab -> Python Ville Tilvis May 2021
fr... |
<filename>assign.py
from scipy.optimize import linear_sum_assignment
import numpy as np
import argparse
import csv
parser = argparse.ArgumentParser()
parser.add_argument("input")
args = parser.parse_args()
if __name__ == "__main__":
names = []
cost = []
with open(args.input, "rt") as f:
for row i... |
<filename>8-PDEs.py
# coding: utf-8
# In[1]:
import numpy as np
get_ipython().magic('matplotlib inline')
from matplotlib import pyplot as plt
# Adapted from http://kitchingroup.cheme.cmu.edu/pycse/pycse.html#sec-10-4
#
# ## Plane Poiseuille flow - BVP solve by shooting method
#
# One approach to solving BVPs is ... |
import numpy as np
import logging as log
from openvino.inference_engine import IECore
import sqlalchemy
from sqlalchemy.orm import sessionmaker
import ngtpy
import scipy
from add_vector2ngt import prepare_net, prepare_images, infer
from model.image_feature import ImageDoubleFeature
class ModelHelper:
def __ini... |
<gh_stars>1-10
# imports
import numpy as np
import skfuzzy as fuzz
from scipy.stats import skew
import os
from alibi.utils.discretizer import Discretizer
from alibi.datasets import fetch_adult
import pandas as pd
import helper as h
import matplotlib.pyplot as plt
percentiles = np.arange(10, 110, 10)
def get_stats_... |
<gh_stars>1-10
import warnings
import math
import numpy as np
import pandas as pd
from scipy.stats import norm
import statsmodels.api as sm
import statsmodels.formula.api as smf
from statsmodels.genmod.families import links
from tabulate import tabulate
from zepid.calc.utils import (risk_ci, incidence_rate_ci, risk_ra... |
'''
@Author: <NAME>
@Version: 11.30.2017
'''
import numpy
import csv
from scipy.stats import multivariate_normal
class Naive_Bayes_Classifier_V2:
# USE ODD VALUES FOR K
def __init__(self):
self.__DATA_FILE_PATH = 'Iris_Dataset/bezdekIris.data'
self.__data = []
self.__debug = True
... |
# -*- coding: utf-8 -*-
# Author: <NAME> <<EMAIL>>
# pylint: disable=E1101
"""
This module deals with pixel-wise calibration for eis_prep - zero values, dark
current, hot, warm and dusty pixels.
"""
import datetime as dt
import numpy as np
from scipy.io import readsav
import locale
import heapq
from bs4 import Beautifu... |
<gh_stars>1-10
import numpy as np
import matplotlib.pyplot as plt
from KIDs import resonance_fitting
from KIDs import calibrate
from scipy import interpolate
import pickle
from scipy.stats import binned_statistic
def calibrate_single_tone(fine_f,fine_z,gain_f,gain_z,stream_f,stream_z,plot_period = 1,interp = "quadra... |
import math
import statistics
def get_average(values):
total = sum(values)
amount = len(values)
return total / amount
def calc_uncertainty(values, version = "simple"):
if version == "stddev":
return statistics.stdev(values)
else:
max_val = max(values)
min_val = min(values... |
import QuantLib as ql
from scipy.optimize import brentq
CALL_PUT = {'call': 1, 'put': -1}
class _Vanilla_option:
def __init__(self, yc, p0, dividend_yield, strike, callput, settlement_date, expiry_date, dt_valuation_date):
p0, dividend_yield, strike = tuple(map(float, (p0, dividend_yield, strike)))
... |
from scipy.sparse.csgraph import connected_components
from sklearn.model_selection import train_test_split
from tqdm import tqdm
import numpy as np
import os
import os.path as osp
import scipy.sparse as sp
import tensorflow.compat.v1 as tf
def xavier_init(size):
""" The initiation from the Xavier's paper.
... |
<filename>Calculator.py
import socketio
import numpy as np
import cv2
import json
from scipy.integrate import solve_ivp
# Y : [ x, x_dot, theta, theta_dot] 即y[0]为小车位置，y[1]为速度，y[2]为摆杆角度，y[3]为角速度
def func3( t, y ):
g = 9.8 # 万有引力常数
L = 1.5 # 摆杆长度
m = 1.0 #摆杆质量 (kg)
M = 5.0 #小车质量 (kg)
x_ddot = -... |
<gh_stars>0
import time
import logging
import numpy as np
import emcee
from typing import Union
import torch
import torch.nn as nn
import torch.optim as optim
from scipy import optimize
from scipy.stats import norm
from bnnbench.models.mlp import MLP
from bnnbench.models.bayesian_linear_regression import BayesianLine... |
import numpy as np
import numpy.linalg as npl
import numpy.random as npr
import scipy.linalg as spl
import scipy.optimize as spo
import scipy.sparse as sps
from time import time
from geom import Geom
from bc import BC
from darcy import DarcyExp
from dasa import DASAExpLM
from se_kernel import SEKernel
def compute_Lreg... |
#!/usr/bin/env python
"""This script scans through 'raw-input.csv' and tests if
these lists satisfy the five previously known relations.
"""
import re
from fractions import Fraction
# Functions to compute the Lefschetz, Baum-Bott and Camacho-Sad sums
def test_L1(list1, list2):
"""Test Lefschetz relation 1: sum... |
<filename>src/data_loader.py<gh_stars>0
import os
import numpy as np
import pandas as pd
from keras.utils import to_categorical
from tqdm import tqdm as tqdm
from tqdm import trange
import skimage as skim
from scipy.ndimage import rotate
from scipy import stats
from sklearn.preprocessing import MinMaxScaler
from edcuti... |
<reponame>hiaoxui/span-finder<gh_stars>1-10
from typing import *
import numpy as np
import torch
from scipy.optimize import linear_sum_assignment
from torch.nn.utils.rnn import pad_sequence
def num2mask(
nums: torch.Tensor,
max_length: Optional[int] = None
) -> torch.Tensor:
"""
E.g. input a ... |
"""
Replicate DCGAN on MNIST.
arXiv:1511.06434v2
Unsupervised Representation Learning with Deep Convolutional Generative
Adversarial Networks
"""
import numpy as np
import os
import scipy.misc
import input_data
from model import Dcgan
def make_dir(dir_path):
"""
Helper function to make a directory if it doe... |
import sys;
import abc;
import math;
import multiprocessing;
import psutil;
import numpy as np;
from scipy.stats import t, f;
import DataHelper;
class LinearRegression:
__DEFAULT_SIG_LEVEL = 0.05;
@staticmethod
def calcVIF(X):
if X is None:
raise ValueError("matrix X is None");
... |
import numpy as np
import matplotlib.pyplot as plt
plt.switch_backend('agg')
from matplotlib import animation, cm
from numpy.linalg import inv
from mpl_toolkits.mplot3d import Axes3D
import tables as tb
import subprocess
from scipy.optimize import brentq
import sys
def quick_plot(input_filename=None, filename=None, st... |
<reponame>lukenew2/ml-from-scratch
"""Module containing classes for supervised linear regression models."""
import numpy as np
from scipy.linalg import lstsq
from mlscratch.utils.metrics import mean_squared_error
from mlscratch.utils.metrics import r2_score
from mlscratch.utils.regularization import l1_regularization
... |
<filename>tests/test_gap.py
import numpy as np
from scipy.spatial import distance_matrix
import gaptrain as gt
from gaptrain.ase_calculators import expanded_atoms
import os
here = os.path.abspath(os.path.dirname(__file__))
h2o = gt.Molecule(os.path.join(here, 'data', 'h2o.xyz'))
methane = gt.Molecule(os.path.join(her... |
"""Ofrece una clase para manejo de la distribución geometrica."""
from sympy import Piecewise
from sympy import Expr
from estadistica.distribuciones.dist_disc import DistDisc
from estadistica.distribuciones.dist_conc import DistConc
class Geometrica(DistConc):
"""Ofrece funcionalidades para distribución geometric... |
<filename>gaussian_process.py
"""
Script for the Gaussian Process class.
See our paper for details on this implementation,
together with the papers
"Preference Learning with Gaussian Processes" by Chu & Gharamani (2005)
and
"A tutorial on Bayesian optimization of expensive cost functions,
with appl... |
"""Performance metrics for photo-z prediction."""
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.colors import ListedColormap
import mpl_scatter_density
import numpy as np
import scipy
from scipy.special import softmax
from scipy.stats import gaussian_kde
params = {
"legend.fontsize": ... |
from scipy.stats import norm
from nltk.util import ngrams
from ordered_set import OrderedSet
docs = [ "My, my, I was forgetting all about the children and the mysterious fern seed." ]
def smoothing_fn(x, mu, sigma):
print(norm.pdf(x, mu, sigma))
print(norm.cdf(x))
if 0 <= x <= 1:
return ... |
<filename>estimators/abstract_estimator.py
import networkx as nx
import scipy as sp
from math import log
import util
from random_walker import RandomWalker
class AbstractEstimator(object):
def __init__(self, G, edge_weight_cache=None, node_weight_cache=None):
self.G = G
# Optimisation, avoids call... |
<filename>multiviewdata/torchdatasets/cars3d.py
import glob
import os
import PIL
import matplotlib.pyplot as plt
import numpy as np
import scipy.io as sio
import torch
from torch.utils.data.dataset import Dataset
from torchvision.datasets.utils import download_and_extract_archive
class Cars(Dataset):
def __init_... |
<gh_stars>1-10
import numpy as np
import pickle
from scipy import misc
from tqdm import tqdm
import pandas as pd
import imageio
def unpickle(file):
with open(file, 'rb') as fo:
res = pickle.load(fo, encoding='bytes')
return res
meta = unpickle('cifar-100-python/meta')
fine_label_names = [t.decode('ut... |
<filename>nirps/sandbox/shift_patch/fix_shift.py
from astropy.io import fits
import glob
import numpy as np
import os
import sys
from scipy.signal import convolve2d
from astropy.table import Table
def mk_isolated_nans(image):
# input image is known to have its pixels in the right position
# without shifts
... |
# -*- coding: utf-8 -*-
"""
Created on Sat Jul 14 14:40:02 2018
@author: <NAME>
"""
import tv1d, metv1d, mctv1d
import parameter as pa
import numpy as np
import scipy.io as sio
import random as rd
import matplotlib.pyplot as plt
def rmse(X, Y):
return np.sqrt(np.mean((X - Y) ** 2))
def sub_plot(sig, noi_sig, n... |
<filename>mqtt_subscriber.py
import random
import paho.mqtt.client as mqtt
import time
from multiprocessing import Process
import numpy as np
import sys
from tqdm import tqdm
import statistics
import json
from datetime import datetime
def getRandomNumber(min, max):
return random.uniform(min, max)
class Subscribe... |
<reponame>NaveenSehgal/3d-pose-baseline<gh_stars>0
'''
Takes in path of synthetic data folder and converts labels to h5 file
structure your data as follows:
SYNTHETIC_FOLDER:
-> SYN_RR_amir_....
-> SYN_RR_naveen_...
-> images
-> joints_gt.mat
-> joints_gt3d.mat
...
..
... |
<reponame>certik/hermes1d-llnl
#! /usr/bin/env python
import os
from jinja2 import Environment, FileSystemLoader
from sympy import var, pprint, ccode
from orthogonalization import (gram_schmidt, l2_inner_product,
h1_inner_product, integrate)
N = 20
precision = 25
def check(basis):
print "orthonormality... |
import numpy as np
import pandas as pd
from scipy.stats import truncnorm
import os
import copy
X_LOW = -5
X_HIGH = 5
Y_HIGH = 2.5
Y_LOW = -2.5
PROJECT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
DATA_DIR = os.path.join(PROJECT_DIR, 'data')
MNIST_DIR = os.path.join(DATA_DIR, 'mnist')
PHYSIONET_... |
"""Class to perform over-sampling using ADASYN."""
# Authors: <NAME> <<EMAIL>>
# <NAME>
# License: MIT
import numpy as np
from scipy import sparse
from sklearn.utils import check_random_state
from sklearn.utils import _safe_indexing
from imblearn.over_sampling.base import BaseOverSampler
from imblearn.util... |
<filename>dataio.py
import csv
import glob
import math
import os
from unicodedata import normalize
import matplotlib.colors as colors
import numpy as np
import scipy.io as spio
import torch
from torch.utils.data import Dataset
from torchvision.transforms import Resize, Compose, ToTensor, Normalize
import utils
import... |
<gh_stars>0
""" This contains the list of all drawn plots on the log plotting page """
from html import escape
from bokeh.layouts import widgetbox
from bokeh.models import Range1d
from bokeh.models.widgets import Div, Button
from bokeh.io import curdoc
from scipy.interpolate import interp1d
from config import *
from... |
'''
AUTHOR: <NAME>
DATE: 02/11/2019
UPDATED: ---
DESCRIPTION:
This python file contains functions written and used for the Denver Crime
dataset analysis.
'''
import pandas as pd
import numpy as np
from scipy.stats import ttest_rel
def table_info(df1, df2, write='w'):
'''
Writes the tabl... |
r"""
This module contains :py:meth:`~sympy.solvers.ode.dsolve` and different helper
functions that it uses.
:py:meth:`~sympy.solvers.ode.dsolve` solves ordinary differential equations.
See the docstring on the various functions for their uses. Note that partial
differential equations support is in ``pde.py``. Note t... |
<reponame>neurodata/hyppo<filename>hyppo/kgof/fssd.py
from __future__ import division
from builtins import str, range, object
from past.utils import old_div
import autograd.numpy as np
from ._utils import outer_rows
from .base import GofTest
from abc import ABC, abstractmethod
import logging
import scipy
import scip... |
<reponame>lixianyi/audiomentations<gh_stars>1-10
import os
import random
from pathlib import Path
import numpy as np
import time
from scipy.io import wavfile
from audiomentations import (
AddGaussianNoise,
TimeStretch,
PitchShift,
Shift,
Normalize,
FrequencyMask,
TimeMask,
AddGaussianS... |
from __future__ import absolute_import
from __future__ import division
import os
import glob
import re
import sys
import urllib
import tarfile
import zipfile
import os.path as osp
from scipy.io import loadmat
import numpy as np
import h5py
from scipy.misc import imsave
import scipy.io as sio
from manage_data.get_dens... |
"""Testing for TransitionGraph"""
import numpy as np
import pytest
from scipy.sparse import csr_matrix
from sklearn.exceptions import NotFittedError
from giotto.graphs import TransitionGraph
X_tg = np.array([[[1, 0], [2, 3], [5, 4]],
[[0, 1], [3, 2], [4, 5]]])
X_tg_res = np.array([
csr_matrix((... |
<filename>app/server.py
import keras
import tensorflow as tf
from keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D, Lambda, MaxPool2D, BatchNormalization
from keras.utils import np_utils
from keras.utils import model_to_dot
from keras.utils.np_utils import to_categorical
from keras.prepro... |
# <NAME>
import numpy as np
from os import listdir
from skimage import io
from scipy.misc import imresize
from keras.preprocessing.image import array_to_img, img_to_array, load_img
def get_img(data_path):
# Getting image array from path:
img_size = 64
img = io.imread(data_path)
img = imresize(img, (im... |
import numpy as np
from numpy.random import seed as set_seed
from scipy.ndimage.filters import gaussian_filter
from brian2 import *
prefs.codegen.target = 'numpy'
def random_covariance(X, cov=0.1, K=2, seed=None, dt=None):
"""Add covariance between K randomly selected electrode pairs."""
set_seed(seed)
... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
""" advancedlogging.py
"""
__author__ = "<NAME>"
__copyright__ = "Copyright 2020, <NAME>"
__credits__ = ["<NAME>"]
__license__ = ""
__version__ = "0.1.0"
__maintainer__ = "<NAME>"
__email__ = ""
__status__ = "Beta"
# Default Libraries #
import abc
import copy
import datet... |
"""Core functions of the PPO algorithm."""
import gym
import numpy as np
import scipy.signal
import tensorflow as tf
EPS = 1e-8
LOG_STD_MAX = 2
LOG_STD_MIN = -20
def distribute_value(value, num_proc):
"""Adjusts training parameters for distributed training.
In case of distributed training frequencies expr... |
<gh_stars>0
"""
Outlier detection with FPCA
===========================
Example of using the inverse_transform method
in the FPCA class to detect outlier(s) from
the reconstruction (truncation) error.
In this example, we illustrate the utility of the inverse_transform method
of the FPCA class to perform functional ou... |
#coding:utf-8
###########################################################
# SpCoTMHPi: Spatial Concept-based Path-Planning Program for SIGVerse
# Path-Planning Program by A star algorithm (ver. approximate inference)
# Path Selection: minimum cost (- log-likelihood) in a path trajectory
# <NAME> 2022/02/07
# Spacial T... |
<gh_stars>1-10
# Authors: <NAME> <<EMAIL>>
# <NAME> <<EMAIL>>
# <NAME> <<EMAIL>>
#
# License: BSD (3-clause)
from copy import deepcopy
from distutils.version import LooseVersion
import itertools as itt
from math import log
import os
import numpy as np
from scipy import linalg, sparse
from .defaults... |
<reponame>MasterXin2020/DL-based-Intelligent-Diagnosis-Benchmark<filename>AE_Datasets/O_A/datasets/MFPTSlice.py
import os
import numpy as np
import pandas as pd
from scipy.io import loadmat
from datasets.MatrixDatasets import dataset
from datasets.matrix_aug import *
from tqdm import tqdm
import pickle
import ... |
from collections import OrderedDict
import os.path as op
import os
import logging
import tempfile
import numpy as np
from scipy.stats import sem
import pandas as pd
import imageio as io
import IPython.display as display
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
from tqdm... |
<reponame>RandLive/Avito-Demand-Prediction-Challenge
import os
import numpy as np
import pandas as pd
import cv2
from tqdm import tqdm
import gzip
import gc
from keras.preprocessing import image
import keras.applications.resnet50 as resnet50
import keras.applications.xception as xception
import keras.applications.incep... |
"""
Changelog:
==========
0.0.2:
* Standardize the structure of the meta information
0.0.1:
* First implementation
"""
import logging
import time
from typing import Union, Tuple, Dict, List
import ConfigSpace as CS
import numpy as np
from scipy import sparse
from sklearn import pipeline
from sklearn import svm
fro... |
<reponame>samkberry/prysm
"""Coordinate conversions."""
from scipy import interpolate
from .conf import config
from prysm import mathops as m
def cart_to_polar(x, y):
'''Return the (rho,phi) coordinates of the (x,y) input points.
Parameters
----------
x : `numpy.ndarray` or number
x coordina... |
"""The Gamma distribution."""
from equadratures.distributions.template import Distribution
import numpy as np
from scipy.stats import gamma
RECURRENCE_PDF_SAMPLES = 8000
class Gamma(Distribution):
"""
The class defines a Gamma object. It is the child of Distribution.
:param double shape:
Shape parameter ... |
# MIT License
# ----------------------------- #
# Copyright 2020 <NAME> #
# ----------------------------- # -------------------------------------------------- #
# Redistribution and use in source and binary forms, with or without modification, #
# are permitted provided that the following conditions are met: ... |
<reponame>sophiarawlings/great_expectations<filename>contrib/experimental/great_expectations_experimental/expectations/expect_column_wasserstein_distance_to_be_less_than.py
import json
from typing import Any, Dict, Optional, Tuple
import numpy as np
import pandas as pd
from scipy import stats as stats
from great_expe... |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""
Utility functions for constrained optimization.
"""
from __future__ import annotations
from functools import par... |
<filename>bin/Python27/Lib/site-packages/scipy/signal/cont2discrete.py
"""
Continuous to discrete transformations for state-space and transfer function.
"""
from __future__ import division, print_function, absolute_import
# Author: <NAME> <<EMAIL>>
# March 29, 2011
import numpy as np
from scipy import linalg... |
import time
import os
import cv2
import argparse
import numpy as np
from scipy import signal
from math import ceil, floor
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
def align_images(input_img_1, input_img_2, pts_img_1, pts_img_2,
save_images=False):
# Load images
... |
<filename>tests/test_prior.py
import warnings
import numpy as np
from scipy.integrate import quad, dblquad, tplquad
import dpmm
from test_utils import timer
@timer
def test_GaussianMeanKnownVariance():
mu_0 = 0.15
sigsqr_0 = 1.2
sigsqr = 0.15
model = dpmm.GaussianMeanKnownVariance(mu_0, sigsqr_0, sig... |
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