text string |
|---|
<filename>pspnet/psp_tf/pspnet.py
#!/usr/bin/env python
"""
This module is a Keras/Tensorflow based implementation of Pyramid Scene Parsing Networks.
Original paper & code published by Hengshuang Zhao et al. (2017)
"""
from __future__ import print_function
from __future__ import division
from os.path import splitext, ... |
# =============================================================================================== #
# meanFieldIsing.py
# Author : <NAME>
#
# MIT License
#
# Copyright (c) 2019 <NAME>, <NAME>
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentat... |
import numpy as np
from pyprobml_utils import save_fig
import matplotlib.pyplot as plt
from scipy.special import betaln
theta = 0.7
N = 5
alpha = 1
alphaH = alpha
alphaT = alpha
# instantiate a number of datastructures
flips = np.zeros((2**N, N))
Nh = np.zeros(2**N)
Nt = np.zeros(2**N)
marginal_lik = np.zeros(2**N)
l... |
import math
import numpy as np
import numpy.linalg as la
import matplotlib.pyplot as plt
import sympy
from sympy.parsing.sympy_parser import parse_expr
class LTISystem(object):
def __init__(self):
pass
def reset(self, dt):
raise NotImplementedError()
def step(self, u, dt, t):
ra... |
<filename>chords/preprocessing/pitch_class_profiling.py
import matplotlib.pyplot as plt
import numpy as np
from scipy.io import wavfile
from scipy.fftpack import fft
from math import log2
class PitchClassProfiler():
def __init__(self, file_name):
self.file_name = file_name
self.read = False
de... |
# https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.random.html
from sys import argv
from scipy.sparse import random
from scipy import stats
import numpy as np
class CustomRandomState(object):
def randint(self, k):
i = np.random.randint(k)
return i - i % 2
if len(arg... |
#!/usr/bin/env python
import sys
import os
from matplotlib import transforms
from matplotlib.colors import get_named_colors_mapping
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure, get
import numpy as np
from matplotlib.patches import Circle
from pandas.core import algorithms
from scipy import clu... |
import numbers
import numpy as np
import scipy.sparse as ss
import warnings
from .base import _BaseSparray
from .compat import (
broadcast_to, broadcast_shapes, ufuncs_with_fixed_point_at_zero,
intersect1d_sorted, union1d_sorted, combine_ranges, len_range
)
# masks for kinds of multidimensional indexing
EMPTY... |
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import os, sys
import subprocess
from scipy import stats
from scipy.cluster.hierarchy import distance, linkage, fcluster
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
from util... |
<gh_stars>0
"""Usage:
outputDocVec.py -v <vectorsFile> -d <dataset> [options]
outputDocVec.py (-h | --help)
Arguments:
-v <vectorsFile> to specify VSM input file
-d <dataset> to specify dataset
Options:
-o <outputDir> set output directory
-f <w2vFormat> ... |
<filename>scripts/analysis.py
#!/usr/bin/env python
import sys
import os
import re
from collections import OrderedDict
# scipy is kinda necessary
import scipy
import scipy.stats
import numpy as np
import math
def mean_nonan(l):
filtered = [x for x in l if not math.isnan(x)]
return np.mean(filtered)
def gmea... |
<reponame>lanl/nubhlight<filename>test/fornax.py<gh_stars>10-100
# #
# COMPARISON TO FORNAX #
# #
###########... |
<reponame>qniksefat/macaque_brain_causality_test<filename>code/elephant/elephant/statistics.py
# -*- coding: utf-8 -*-
"""
Statistical measures of spike trains (e.g., Fano factor) and functions to estimate firing rates.
:copyright: Copyright 2014-2016 by the Elephant team, see AUTHORS.txt.
:license: Modified BSD, see ... |
# An attempt at a WaveToy using the NRPy+ infrastructure
# TODO: Parity on grid functions
import os, re
from datetime import date
from sympy import symbols, Function, diff
import grid
import NRPy_param_funcs as par
import finite_difference as fin
from outputC import indent_Ccode, add_to_Cfunction_dict, outCfunction, c... |
import os
import numpy as np
import pylab as plt
import h5py as hdf5
from mmap import mmap
from scipy.io import loadmat
def fig2png(filename, title, rat, begin, end):
"""
Args:
filename:
title:
rat:
begin:
end:
"""
raise NotImplemented
matfile = loadmat(filename, squeeze_me=True, struct_as_record=Fals... |
# coding=utf-8
# Utils used with tensorflow implemetation
import tensorflow.compat.v1 as tf
import numpy as np
import scipy.misc as misc
import os, sys
from six.moves import urllib
import tarfile
import zipfile
import scipy.io
from functools import reduce
tf.disable_eager_execution()
# 下载VGG模型的数据
def get_model_data(fi... |
import matplotlib.pyplot as plt
from scipy import integrate
import scipy.stats as stats
import numpy as np
alpha = 2.1
total_num = 50000
# M = np.sqrt(2.0*np.pi/np.e)
M = 1.3
lb = -5
hb = 5
def g(x):
result = (alpha/2)*np.exp(-alpha*np.abs(x))
return result
def f(x):
result = (1.0/np.sqr... |
<filename>HW4/ex1_ex2/ex2.1.9.py
from lib import Simulation
import numpy as np
from numpy import mean, min, max, median, quantile, sqrt
from matplotlib import pyplot as plt
from scipy.stats import expon, norm, erlang
from time import time
from math import factorial as fact
λ = 10
µ = 15
c = 2 # number of servers
max_... |
import matplotlib
matplotlib.use('Agg')
import pyart
from matplotlib import pyplot as plt
import numpy as np
import glob
import os
from copy import deepcopy
from ipyparallel import Client
from time import sleep
import time
import time_procedures
import sys
# File paths
berr_data_file_path = '/lcrc/group/earthscience/r... |
<gh_stars>10-100
import argparse
import copy
import itertools
import os
import sys
# import matplotlib.pyplot as plt
import numpy as np
import scipy
import scipy.io
import shapely.geometry as geom
from descartes.patch import PolygonPatch
from scipy.interpolate import RegularGridInterpolator
from shapely.geos import To... |
<reponame>chris-jh-cho/abides<gh_stars>1-10
import argparse
import sys
sys.path.append("..")
from util.formatting.convert_order_stream import dir_path
import glob
import re
import pandas as pd
import matplotlib.pyplot as plt
from realism_utils import get_plot_colors
import numpy as np
from scipy import stats
from matpl... |
from .utils import write_to_logger, mask_img, data_to_img
from .rsa_searchlight import SearchLight as RSASearchlight
from .cross_searchlight import SearchLight
import numpy as np
from datetime import datetime
from nilearn.input_data import NiftiMasker
from scipy.signal import savgol_filter
from nipy.modalities.fmri.de... |
# -*- coding: utf-8 -*-
"""
A minimalistic Echo State Networks demo with Mackey-Glass (delay 17) data
in "plain" scientific Python.
by <NAME>¡eviÄ?ius 2012
http://minds.jacobs-university.de/mantas
---
Modified by <NAME>: 2015-2016
http://www.xavierhinaut.com
"""
# from matplotlib.pyplot import *
import matplotlib.pyplo... |
import numpy as np
import matplotlib
import platform
if platform.system() == 'Darwin':
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
from matplotlib import rcParams
import keras
import tensorflow as tf
import datetime
import time
import pickle
import os
#from IPython.display import SVG
#from keras.utils... |
<gh_stars>0
r"""
Low-level module containing miscalleneous mathematical functions.
Functions
---------
* :func:`normalize_orbs`: normalize KS orbitals within defined sphere
* :func:`int_sphere`: integral :math:`4\pi \int \mathrm{d}r r^2 f(r)`
* :func:`laplace`: compute the second-order derivative :math:`d^2 y(x) / dx^... |
import os
import numpy as np
import pandas as pd
from .ridge_regression import RidgeRegression
from scipy import optimize
class CustomRegressor(RidgeRegression):
def __init__(self, l2_penality=1, max_iter = 1000):
self.l2_penality = l2_penality
self.max_iter = max_iter
self.W = None
... |
# -*- coding: utf-8 -*-
# Imports
from sklearn.svm import SVC, SVR
import os,sys
import argparse as ap
import pandas as pd
from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler,PowerTransformer, QuantileTransformer
from sklearn.model_selection import train_test_split, cross_val_score, GridSearc... |
import numpy as np
import pandas as pd
from scipy.stats import norm
import unittest
import networkx as nx
from context import grama as gr
from context import models
class TestFORM(unittest.TestCase):
"""Test implementations of FORM
"""
def setUp(self):
## Linear limit state w/ MPP off initial gu... |
<gh_stars>0
import backend as F
import numpy as np
import scipy as sp
import dgl
from dgl.contrib.sampling.sampler import create_full_nodeflow, NeighborSampler
from dgl import utils
import dgl.function as fn
from functools import partial
import itertools
def generate_rand_graph(n, connect_more=False, complete=False):... |
<filename>tests/test_tools.py
import unittest
import numpy as np
from scipy.stats import multivariate_normal
from apollon import tools
class TestPca(unittest.TestCase):
def setUp(self) -> None:
mu = (0, 0)
cov = ((10, 0), (0, 12))
n = 1000
self.data = multivariate_normal(mu, cov)... |
import numpy as np
import pandas as pd
import pickle
from matplotlib import pyplot as plt
from sklearn.decomposition import TruncatedSVD
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
from sklearn.metrics.pairwise import euclidean_distances
from sklearn import metrics
from scipy.spa... |
import numpy as np
import csv, sys
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.font_manager as font_manager
from scipy.stats import wilcoxon, ttest_rel, ttest_ind
def set_box_color(bp, color):
plt.setp(bp['boxes'], color=color)
for patch in bp['boxes']:
patch.set(facec... |
import numpy as np
import pandas as pd
from scipy import misc
import torch
from tqdm import tqdm_notebook
from collections import Counter
import gc
from sklearn.model_selection import train_test_split
def eval_model_per_cell(model, loader, file_path, path_data, sub_df, device='cuda', sub_file='/artifacts/submission.c... |
<filename>pydl/median.py
# Licensed under a 3-clause BSD style license - see LICENSE.rst
# -*- coding: utf-8 -*-
def median(array, width=None, axis=None, even=False):
"""Replicate the IDL ``MEDIAN()`` function.
Parameters
----------
array : array-like
Compute the median of this array.
wid... |
# Author: <NAME>
# Created on: August 2020
# Last modified on: September 17 2020
import numpy as np
from scipy.integrate import odeint
import matplotlib.pyplot as plt
from ipywidgets import interact, interact_manual, widgets, Layout, VBox, HBox, Button
from IPython.display import display, Javascript, Markdown, HTML, c... |
<filename>autotrain.py
import subprocess
import tensorflow as tf
import glob
import scipy.io as sio
import numpy as np
base_path = 'Test/mhgd'
for i in range(5):
subprocess.call('python KD_methods_with_TF/train_w_distill.py '
+'--train_dir=%s%d '%(base_path,i)
+'--model_name=R... |
import sys
from itertools import zip_longest
from typing import Generator
import pandas as pd
from scipy.stats import fisher_exact
from statsmodels.stats.multitest import fdrcorrection
def promoter_size(s):
value = int(s)
if value > 2000:
raise argparse.ArgumentTypeError("Promoter size has to be less... |
"""
wcshdu.py
Defines a new class that is essentially a fits PrimaryHDU class but with
some of the WCS information in the header split out into separate
attributes of the class
"""
import os
import sys
from math import fabs
import numpy as np
from numpy.fft import fft2, ifft2, fftshift
from scipy import ndimage
fro... |
"""
:mod:`convolve` -- convolve two 2-d fields
==========================================
.. module:: convolve
:synopsis: Convolve two 2-d fields to apply a smoother (e.g. directional
filtering, gaussian smoother).
.. moduleauthor:: <NAME> <<EMAIL>>
"""
import numpy as np
from scipy import signal... |
# Copyright (C) 2021 Members of the Simons Observatory collaboration.
# Please refer to the LICENSE file in the root of this repository.
import os
import ref
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.transforms as mtransforms
import matplotlib.colors as colors
import matplotlib.cm as cm
from... |
<filename>pyFiDEL/ranks.py
'''
ranks.py - rank based metric calculation for structured learning
<NAME>
'''
__author__ = '<NAME>'
__version__ = '1.0.0'
import numpy as np
import pandas as pd
import scipy
def auc_rank(scores: list, y: list) -> float:
''' calculate AUC using rank formula '''
if len(scores) !... |
#!/usr/bin/env python3
"""
translation protein sequences in Clusters or lists of Proteins.
<NAME>
"""
import logging
import uuid
from collections import defaultdict, OrderedDict
from functools import partial
from itertools import combinations, product
from multiprocessing import Pool
import numpy as np
from scipy... |
<reponame>muhammedhassanm/NWPU-Crowd-Sample-Code
from matplotlib import pyplot as plt
import matplotlib
import os
import random
import torch
from torch.autograd import Variable
import torchvision.transforms as standard_transforms
import misc.transforms as own_transforms
import pandas as pd
from models.CC import Crowd... |
<filename>sympy/combinatorics/pc_groups.py
from sympy import isprime
from sympy.combinatorics.perm_groups import PermutationGroup
from sympy.printing.defaults import DefaultPrinting
from sympy.combinatorics.free_groups import free_group
class PolycyclicGroup(DefaultPrinting):
is_group = True
is_solvable = Tr... |
'''Load image/labels/boxes from an annotation file.
The list file is like:
img.jpg xmin ymin xmax ymax label xmin ymin xmax ymax label ...
'''
from __future__ import print_function
import os
import sys
import random
import torch
import torch.utils.data as data
import torchvision.transforms as transforms
import... |
"""
.. module:: gphoton_utils
:synopsis: Read, plot, time conversion, and other functionality useful when
dealing with gPhoton data.
"""
from __future__ import absolute_import, division, print_function
# Core and Third Party imports.
from astropy.time import Time
import scipy.stats
import matplotlib.pyplot a... |
<gh_stars>10-100
import numpy as np
from catboost import Pool, CatBoostClassifier
from catboost.utils import read_cd
from gbdt_uncertainty.data import process_classification_dataset
from gbdt_uncertainty.assessment import prr_class, ood_detect, nll_class
from gbdt_uncertainty.uncertainty import entropy_of_expected... |
<gh_stars>1-10
# -*- coding: utf-8 -*-
"""
Created on Mon Oct 22 19:21:14 2018
@author: arjun
"""
#HAVING!!!!
import pandas as pd
import numpy as np
import sqlite3 as sq
from math import sqrt
from scipy import stats, spatial
from scipy.sparse import csr_matrix
#TO-DO: SHOULD I ONLY DO THIS WITH USERS WITH OVER A CE... |
<filename>vibration_toolbox/vibesystem.py
import numpy as np
import scipy.linalg as la
import scipy.signal as signal
import matplotlib as mpl
import matplotlib.pyplot as plt
__all__ = ['VibeSystem']
plt.style.use('seaborn-white')
color_palette = ["#4C72B0", "#55A868", "#C44E52",
"#8172B2", "#CCB974"... |
from scipy.signal import argrelextrema
import pandas as pd
import numpy as np
from pyautofinance.common.learn.predicter import Predicter
class TaLibPredicter(Predicter):
def _copy(self, other):
self._model = other._model
self.ta_strategy = other.ta_strategy
self._dataframe = other._dataf... |
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as mptches
import seaborn as sns
import scipy.stats as sts
def draw_correlation_matrix(sigma, data):
# Get correlation matrix and draw it
corr_matrix = np.corrcoef(sigma)
features = data.columns.values.tolist()
sns.set(styl... |
<reponame>rzumer/VideoLowLevelVision
"""
Copyright: <NAME> 2017-2018
Author: <NAME>
Email: <EMAIL>
Created Date: May 17th 2018
Updated Date: May 17th 2018
Training environment callbacks preset
"""
from pathlib import Path
from functools import partial
import numpy as np
from PIL.Image import Image
from ..Util.ImageP... |
# coding=utf-8
import PIL.Image
import matplotlib.image as mpimg
import scipy.ndimage
import cv2 # For Sobel etc
import glob
import numpy as np
import matplotlib.pyplot as plt
import os
import tensorflow as tf
np.set_printoptions(suppress=True, linewidth=200) # Better printing of arrays
featureA = tf.feature_column.n... |
"""
Compare various IIR filters
"""
import numpy as np
from scipy import signal
import matplotlib.pyplot as plt
def freq2rad(freq, fs):
return freq * np.pi / (fs/2)
def rad2freq(rad, fs):
return rad * (fs/2) / np.pi
# MAIN PARAMETER
pole_coef = 0.95
fs = 16000
# prepare figure
ALPHA = 0.8
f_max = 4000
p... |
<reponame>emarkou/scikit-learn
"""
Distribution functions used in GLM
"""
# Author: <NAME> <<EMAIL>>
# License: BSD 3 clause
from abc import ABCMeta, abstractmethod
from collections import namedtuple
import numbers
import numpy as np
from scipy.special import xlogy
DistributionBoundary = namedtuple("DistributionBo... |
from keras.utils import Sequence
import numpy as np
import math
import scipy
class FMData(Sequence):
def __init__(self, inputs, output, batch_size, implicit_samples=0,splits=None, feature_extraction=None, sample_probabilities={}, mask=None, shuffle=True, nce=None):
#validate inputs:
check_length ... |
<filename>logger.py
import os
import sys
import numpy as np
import statistics as stat
class Logger(object):
def __init__(self, log_path, on=True):
self.log_path = log_path
self.on = on
if self.on:
while os.path.isfile(self.log_path):
self.log_path += '+'
... |
"""
Proper Scoring Rules for assessing the quality of predictive
uncertainty quantification.
"""
import numpy as np
from scipy import stats
def nll_gaussian(y_pred, y_std, y_true, scaled=True):
"""
Return negative log likelihood for held out data (y_true) given predictive
uncertainty with mean (y_pred) a... |
# coding=utf-8
import numpy as np
import scipy.sparse as sp
from pymg.problem_base import ProblemBase
class Poisson1D(ProblemBase):
"""Implementation of the 1D Poission problem.
Here we define the 1D Poisson problem :math:`-\Delta u = 0` with
Dirichlet-Zero boundary conditions. This is the homogeneous p... |
<reponame>timtonthat/batch8_ceebios
# -*- coding: utf-8 -*-
"""
Created on Fri Sep 25 14:08:30 2020
Module de recherche sur la base gbif https://www.gbif.org/fr/
http://tecfa.unige.ch/perso/lombardf/calvin/teaching/mammiferes-fr-latin.html
@author: CHRISTIAN
"""
import os
import time
import pprint
import json
impo... |
# coding: utf-8
# ### Import
# In[5]:
import numpy as np
import pandas as pd
import xgboost
import xgboost as xgb
from xgboost.sklearn import XGBClassifier
from sklearn.metrics import *
from IPython.core.display import Image
from sklearn.datasets import make_classification
from sklearn.ensemble import ExtraTreesC... |
"""
N(z) check - for comparing across codes using different mass function implementations etc..
NOTE: We messed up when setting this task, so the mass limit is M500c > 5e13 MSun/h
"""
import os
import sys
import numpy as np
import pylab as plt
import astropy.table as atpy
from astLib import *
from scipy import stat... |
<reponame>IsaiahPressman/Kaggle_Hungry_Geese<gh_stars>0
import itertools
# import threading
from datetime import datetime
from time import time
import numpy as np
from numba import jit
from numba.experimental import jitclass
from numba.types import int32, float32, void, Tuple
from kaggle_environments.envs.hungry_gee... |
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets
from sklearn.metrics import roc_curve, auc
from sklearn.preprocessing import label_binarize
from sklearn.multiclass import OneVsRestClassifier
from scipy import interp
import getopt
from glob import glob
from natsort import natsorted
... |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import math
from scipy.optimize import curve_fit
from Funcoes_Bib import splitPlusMinus
df = pd.read_excel ('C:\Users\observer\Desktop\Ensaios_e_Caracterizacoes\Planilhas\Ganho_EM\HSS30MHz\EMPropaga... |
#!/usr/bin/env python
"""DETECTION.PY - Detection algorithms
"""
__authors__ = '<NAME> <<EMAIL>?'
__version__ = '20210912' # yyyymmdd
import os
import sys
import numpy as np
import warnings
from astropy.io import fits
from astropy.table import Table
import astropy.units as u
from scipy.optimize import curve_fit, ... |
<filename>src/PCE_Codes/UQPCE.py
#!/usr/bin/env python
from builtins import setattr, getattr
from enum import auto, Enum
from fractions import Fraction
import math
from multiprocessing import Process
from multiprocessing import Process, Manager
import os
from warnings import showwarning, warn
from numpy.lina... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Sep 9 10:30:00 2021
@author: mac
"""
import pandas as pd
import numpy as np
from scipy.integrate import odeint
## Build training set:
## parameters of Lorenz system:
rho = 28.0
sigma = 10.0
beta = 8.0 / 3.0
def f(state, t):
x, y, z = state #... |
<gh_stars>1-10
# -*- coding: utf-8 -*-
"""
Interpolate sounding data onto a regular grid
"""
from scipy.interpolate import griddata
import numpy as np
from dembuilder.kriging import kriging
from shapely.ops import cascaded_union, polygonize, unary_union
from scipy.spatial import Delaunay
from scipy.interpolate ... |
<gh_stars>0
# -*- coding: utf-8 -*-
"""
Function:
Implementation approach of Noisy Input Gaussian Processing (NIGP); also the Paper implementation :
"Gaussian Process Training with Input Noise"
Direct calculate the posterior mean and covariance of GP, even the posterior distribution is... |
import datetime
import numpy as np
import scipy.optimize as opt
def logistic(t, a, b, c):
return c / (1 + np.exp(-(t - b) / a))
def logistic_deriv(t, a, b, c):
return np.exp(-(t - b) / a) * c / (a * (1 + np.exp(-(t - b) / a))**2)
# The date when the logistic function has reached a fraction `perc_flat` of
... |
<filename>tests/test_sparse.py
import numpy as np
import scipy.sparse
import tectosaur.util.sparse as sparse
import logging
logger = logging.getLogger(__name__)
def test_bsrmv():
A = np.zeros((4,4))
A[:2,:2] = np.random.rand(2,2)
A[2:,2:] = np.random.rand(2,2)
A_bsr = sparse.from_scipy_bsr(scipy.spars... |
from commonLib.nerscLib import *
import sys
from . import filemanager as fm
import numpy as np
from numpy.random import *
from commonLib import nerscPlot
import scipy.cluster.vq as vq
#from scipy.spatial import Voronoi, voronoi_plot_2d
from scipy.stats import scoreatpercentile
from commonLib import timeLib
from commo... |
<reponame>sforazz/nipype
# -*- coding: utf-8 -*-
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
import pickle
import os.path as op
import numpy as np
import nibabel as nb
import networkx as nx
from ... import logging
from ...utils.filemanip import spl... |
import pandas as pd
import numpy as np
import torch.nn.functional as F
from torch import optim
import seaborn as sns
from scipy import stats
import matplotlib as mpl
import scipy.sparse as sp
import networkx as nx
import time
import os
import glob
import csv
import torch
import torchvision
from models_al... |
<filename>tests/orbits/keplerian_test.py
# -*- coding: utf-8 -*-
import aesara_theano_fallback.tensor as tt
import astropy.units as u
import numpy as np
import pytest
from aesara_theano_fallback import aesara as theano
from astropy.constants import c
from scipy.optimize import minimize
from exoplanet.orbits.keplerian... |
<reponame>MACIEK1JAREMA/Gravitational-Lensing-python
# 2 body motion in pixels
# 1 dark and 1 bright, of comparable size, lensed and analysis.
# import modules
import numpy as np
import matplotlib.pyplot as plt
import scipy
from scipy import integrate
import project.lensing_function as lensing
import project.codes_phy... |
<filename>methods/sr-unit-test-03.py
# %%
import pandas as pd
import numpy as np
from datetime import datetime
import os
import pickle
import matplotlib.pyplot as plt
import scipy.special as sc
from scipy.stats import norm
from scipy.stats import lognorm
import copy
import matplotlib.pyplot as plt
exec(open('../env_... |
<gh_stars>10-100
#!/usr/bin/env python3
# coding: utf-8
"""
@author: <NAME> <EMAIL>
@last modified by: <NAME>
@file:neighbors.py
@time:2021/09/01
"""
from ..log_manager import logger
from scipy.sparse import issparse, coo_matrix, csr_matrix
from sklearn.neighbors import NearestNeighbors
from sklearn.utils import check... |
<reponame>joshandali52/textis
"""
@author: <NAME>, <NAME>, <NAME> (in alphabetic order)
@institution: University of Liechtenstein, Fuerst-Franz-Josef Strasse 21, 9490 Vaduz, Liechtenstein
@funding: European Commission, part of an Erasmus+ project (Project Reference: 2017-1-LI01-KA203-000083)
@copyright: Copyright (c) 2... |
# Copyright 2017 Google Inc.
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,... |
import numpy as np
import scipy.signal
def mean_relative_error(y, x):
return np.mean(np.absolute(y - x) / (1 + np.absolute(y)))
def mean_absolute_error(y, x):
return np.mean(np.absolute(y - x))
def mean_squared_error(y, x):
return np.mean(np.square(y - x))
def root_mean_squared_error(y, x):
return ... |
<gh_stars>1-10
import random
import os
import numpy as np
import scipy
from PIL import Image
from AnetLib.data.image_utils import RandomRotate, CenterCropNumpy, RandomCropNumpy, PoissonSubsampling, AddGaussianPoissonNoise, GaussianBlurring, AddGaussianNoise, ElasticTransform
from datasets import TUBULIN, NUCLEAR_PORE
... |
<gh_stars>1-10
"""Python implementation of MCMC on neurons."""
import numpy as np
from numpy import linalg as LA
import McNeuron.visualize
import McNeuron.swc_util
import McNeuron.dis_util
import matplotlib.pyplot as plt
from matplotlib import gridspec
from copy import deepcopy
from scipy.stats import chi2
from scipy.... |
<gh_stars>0
#
# Solution to Project Euler problem 267
# Copyright (c) Project Nayuki. All rights reserved.
#
# https://www.nayuki.io/page/project-euler-solutions
# https://github.com/nayuki/Project-Euler-solutions
#
import eulerlib, fractions, math
# When you win a coin toss, your capital is multiplied by (1 + 2f... |
<gh_stars>100-1000
# This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT.
# See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details.
# Author(s): <NAME>
#
# Copyright (C) 2019 Inria
#
# Modification(s):
# - YYYY/MM Author: Description of th... |
import os
import time
import logging
import numpy as np
from scipy.interpolate import interp1d
from scipy.optimize import fsolve
from scipy.integrate import solve_ivp, trapz, quad
from .utils import InvalidJumpError
from .utils import GRAV_ACC, EPS
from .utils import compute_dist_from_flat, vel2speed
if 'ONHEROKU' ... |
from collections import defaultdict
from typing import Union, Optional, List, Iterable, Mapping, Sequence
import warnings
import numpy as np
import pandas as pd
from scipy.sparse import issparse
import scanpy as sc
from anndata import AnnData
import matplotlib.pyplot as plt
from matplotlib.axes import Axes
import sea... |
from lib.device import Camera
from lib.processors_noopenmdao import findFaceGetPulse
from lib.interface import plotXY, imshow, waitKey, destroyWindow
import argparse
import numpy as np
import datetime
#TODO: work on serial port comms, if anyone asks for it
#from serial import Serial
import socket
import sys
from cv2 im... |
__author__ = 'eiscar'
import numpy as np
import json
import math
from scipy.integrate import simps, romb
import matplotlib.pyplot as plt
import logging
logger = logging.getLogger(__name__)
class Sensor:
def __init__(self):
self.name = None
self.resolution_x = 1000.
self.resolution_y = 1... |
# uncompyle6 version 3.7.4
# Python bytecode 3.7 (3394)
# Decompiled from: Python 3.7.9 (tags/v3.7.9:13c94747c7, Aug 17 2020, 18:58:18) [MSC v.1900 64 bit (AMD64)]
# Embedded file name: T:\InGame\Gameplay\Scripts\Server\interactions\base\cheat_interaction.py
# Compiled at: 2020-10-09 00:03:45
# Size of source mod 2**32... |
<reponame>EkremBayar/bayar
import os
import numpy as np
import tempfile
from pytest import raises as assert_raises
from numpy.testing import assert_equal, assert_
from scipy.sparse import (csc_matrix, csr_matrix, bsr_matrix, dia_matrix,
coo_matrix, save_npz, load_npz, dok_matrix)
DATA_DIR ... |
<gh_stars>1-10
"""Base Dice tests."""
from fractions import Fraction
from dice_stats import Dice
def test_always_true():
"""Generic test to ensure system is setup correctly."""
assert True
def test_reroll():
"""Test basic rerolls."""
d6 = Dice.from_dice(6)
assert d6.reroll([1]) == Dice.from_ex... |
<reponame>CiceroAraujo/SB
from .....data_class.data_manager import DataManager
import numpy as np
import scipy.sparse as sp
from scipy.sparse import linalg
import time
class AMSTpfa:
# name = 'AMSTpfa_'
id = 1
def __init__(self,
internals,
faces,
edges,
vertices,
gi... |
<reponame>gaudel/ranking_bandits<gh_stars>1-10
#### Bandits
## Packages
import numpy as np
import random as rd
from random import sample
from random import random
from numpy.random import beta
from random import uniform
from copy import deepcopy
from mpl_toolkits.mplot3d import Axes3D
import scipy.stats as s... |
<reponame>dburkhardt/diffxpy
import anndata
try:
from anndata.base import Raw
except ImportError:
from anndata import Raw
import batchglm.api as glm
import numpy as np
import pandas as pd
import patsy
import scipy.sparse
from typing import List, Tuple, Union
# Relay util functions for diffxpy api.
# design_ma... |
<filename>lib/python2.7/site-packages/sklearn/utils/stats.py
import numpy as np
from scipy.stats import rankdata as _sp_rankdata
from .fixes import bincount
# To remove when we support scipy 0.13
def _rankdata(a, method="average"):
"""Assign ranks to data, dealing with ties appropriately.
Ranks begin at 1. T... |
<gh_stars>0
import numpy
import glob
from PIL import Image
import os
import thr_counter
import numpy as np
from scipy import misc
def to_categorical(n_classes,y):
return numpy.eye(n_classes)[y]
def loadChars74k(path,num_classes,num_samples):
# list of directories
labels=[]
images=numpy.zeros... |
<reponame>giangtranml/framgia-training<filename>svm/svm.py
"""
Author: <NAME>.
"""
from cvxopt import matrix, solvers
import numpy as np
from scipy.spatial.distance import cdist
class SVM:
kernels = {"linear": "_linear_kernel", "poly": "_polynomial_kernel", "rbf": "_gaussian_kernel",
"sigmoid": "_... |
<reponame>tonino102008/openfast<filename>ExampleCases/OpFAST_FLORIS_WF3x1/plotyaw.py<gh_stars>0
import matplotlib.pyplot as plt
import numpy
import pandas as pd
import control.matlab as cnt
import cp
import scipy.optimize as optim
dfdata = pd.read_csv('t1.T1.out', sep='\t', header=None, skiprows=10)
datadata = dfdata.... |
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