text string |
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<gh_stars>10-100
# -*- coding: utf-8 -*-
"""
Created on Thu Jan 18 14:34:01 2018
@author: <NAME>
"""
import numpy as np
import itertools
import scipy.stats
from sklearn.utils.validation import check_random_state
class AbstractHyper(object):
""" abstract class representing an hyperparameter (or a set of hyperp... |
import numpy as np
from sklearn.neighbors import NearestNeighbors
from collections import Counter
from collections import deque
from scipy.stats import norm
import sdbscan_merge_chain
# from sklearn.preprocessing import PowerTransformer
from sklearn.base import BaseEstimator, ClusterMixin
import pandas as pd
from f... |
<reponame>cjayross/riccipy
"""
Name: Schwarzschild
Coordinates: Spherical
Symmetry:
- Spherical
- Static
Notes: Isotropic Coordinates
"""
from sympy import Rational, diag, sin, symbols
coords = symbols("t r theta phi", real=True)
variables = symbols("M", constant=True)
functions = ()
t, r, th, ph = coords
M = ... |
# vim: set fileencoding=<utf-8> :
# Copyright 2018-2020 <NAME> and <NAME>
'''Network functions'''
# universal
import os
import sys
import re
# additional
import glob
import operator
import shutil
import subprocess
import numpy as np
import pandas as pd
from scipy.stats import rankdata
from tempfile import mkstemp, mk... |
<gh_stars>0
import numpy as np
import scipy.io as sio
import tables
import os
from ecogdata.util import Bunch
# these segments are intended for snipping pre-processed data at load
# (or possibly clipping beginning/end segments?)
_load_prune_db = dict()
_load_prune_db['cat1.2010-05-19_test_41_filtered'] = (
(200, ... |
"""This module contains a base class for bivariate copulas."""
import json
import warnings
from enum import Enum
import numpy as np
from scipy import stats
from scipy.optimize import brentq
from copulas import EPSILON, NotFittedError, random_state
from copulas.bivariate.utils import split_matrix
class CopulaTypes(... |
<reponame>rjderosa/ImPlaneIA
#! /usr/bin/env python
# Mathematica nb from Alex & Laurent
# <EMAIL> major reorg as LG++ 2018 01
# python3 required (int( (len(coeffs) -1)/2 )) because of float int/int result change from python2
import numpy as np
import scipy.special
import numpy.linalg as linalg
import sys
from scip... |
__source__ = 'https://leetcode.com/problems/unique-paths/description/'
# https://github.com/kamyu104/LeetCode/blob/master/Python/unique-paths.py
# Time: O(m * n)
# Space: O(m + n)
# DP
#
# Description: Leetcode # 62. Unique Paths
#
# A robot is located at the top-left corner of a m x n grid (marked 'Start' in the diag... |
#Chapter 1 - Extracting and transforming data
#Positional and labeled indexing
# Assign the row position of election.loc['Bedford']: x
x = 4
# Assign the column position of election['winner']: y
y = 4
# Print the boolean equivalence
print(election.iloc[x, y] == election.loc['Bedford', 'winner'])
... |
# -*- coding: utf-8 -*-
"""
Created on Fri Sep 14 12:29:15 2018
@author: Pooja
"""
#Bounding Boxes and Segmented Images
import os
import numpy as np
import cv2
import pandas as pd
from matplotlib import pyplot as plt #cv2 images
from scipy.io import loadmat
from scipy.misc import imsave
from imageio im... |
<filename>evolugap.py
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from scipy import linalg as la
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
from cycler import cycler
#FUNCAO CONTINUA
def potv(xa,multi,lw):
x=abs... |
<reponame>hmshreyas7/SketchyGAN
import os
import cv2
import numpy as np
import tensorflow as tf
from data_processing.tfrecord import *
from scipy import ndimage
from config import Config
# TODO Change to Dataset API
sketchy_dir = './tfrecords/sketchy'
flickr_dir = './tfrecords/flickr_output'
paired_filenames_1 = [... |
#Tools to study and correct for trends in spectroscopic succes rate (ssr)
#Initial LRG model fitting taken from <NAME> notebook
import sys, os, glob, time, warnings, gc
import numpy as np
import matplotlib.pyplot as plt
from astropy.table import Table, vstack, hstack, join
import fitsio
from scipy.optimize import curv... |
import numpy as np
import scipy.sparse as sp
import matplotlib.pyplot as plt
from scipy import signal
from scipy.ndimage.filters import gaussian_filter1d
from multiprocessing import Process, Manager
def blank_diagonal2(matr, strata = False):
"""
in: edgelist, strata (n entries off main diagonal to zero)
ou... |
import os
import collections
import torch
import torchvision
import numpy as np
import scipy.misc as m
import matplotlib.pyplot as plt
from torch.utils import data
from ptsemseg.augmentations import *
import cv2 as cv
from torchvision import transforms
class myLoader(data.Dataset):
def __init__(
self,
... |
import sys
if "" not in sys.path : sys.path.append("")
import numpy as np
import scipy
import scipy.stats
import matplotlib.pyplot as plt
import os
import warnings
import nibabel as nib
import pandas as pd
import seaborn as sns
from keras.models import load_model
from skimage.metrics import structural_similarity
def... |
from copy import deepcopy
import matplotlib.pyplot as plt
import numpy as np
import wandb
from src.utils.threshold import *
from scipy.stats import norm
from sklearn.calibration import calibration_curve
from sklearn.metrics import (auc, average_precision_score, det_curve,
matthews_corrcoef... |
<filename>utils/sunrgbd_utils.py
import numpy as np
import os
import pickle
from PIL import Image
import json
from scipy.io import loadmat
from libs.tools import get_world_R, normalize_point, yaw_pitch_roll_from_R, R_from_yaw_pitch_roll
from utils.sunrgbd_config import SUNRGBD_CONFIG, SUNRGBD_DATA
import pandas as pd
i... |
<reponame>BTETON/finance_ml<filename>finance_ml/distance.py
import numpy as np
import pandas as pd
import scipy.stats as ss
from sklearn.metrics import mutual_info_score
def _fix_corr(corr):
corr[corr > 1] = 1
corr[corr < -1] = -1
return corr.fillna(0)
def corr_metric(corr, use_abs=False):
corr = _fix... |
import numpy as np
from math import sqrt
from scipy.optimize import minimize, Bounds
from .functions import gp, link_gp
class kernel:
"""
Class that defines the GPs in the DGP hierarchy.
Args:
length (ndarray): a numpy 1d-array, whose length equals to:
1. one if the lengths... |
<gh_stars>0
import numpy as np
from scipy.signal import argrelextrema
def get_peaks(x):
maxpeaks = argrelextrema(x, np.greater, order=2)
minpeaks = argrelextrema(x, np.less, order=2)
return maxpeaks[0], minpeaks[0] |
# -*- coding: utf-8 -*-
"""
Created on Sat Jun 06 09:49:33 2015
@author: JMS
"""
import random
from abc import ABCMeta, abstractmethod
import numpy as np
import pandas as pd
from scipy.linalg import orth
from occupancy_map import Map,ZMap
from ptp import LocalArea,PointToPoint,matrixrank, anglebetween
from math impo... |
<reponame>lihenryhfl/SpectralVAEGAN<filename>vdae/vdae_util.py
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
import ot
import annoy
import scipy
# for each point in x, determine neighborhood in x, and then compute... |
<filename>DeepLearning/nnmath.py<gh_stars>1-10
__license__ = "MIT"
__author__ = "<NAME> (BGT) @ Johns Hopkins University"
__startdate__ = "2016.01.19"
__name__ = "nnmath"
__module__ = "Network"
__lastdate__ = "2016.01.19"
__version__ = "0.01"
__comments__ = "math utils for neural net work"
import numpy as np
from sci... |
# -*- coding: utf-8 -*-
"""
We are going to modify it so that we can get symbolic arrm matrix and also its jacobian for a 3 joint planar robot and then plot the
vectors in blender for understanding
Functions for calculating Basic Transformation Matrices in 3D space.
"""
from math import cos, radians, sin
from numpy i... |
<filename>dppp/utils.py
import os
import functools
import itertools
import math
import re
from typing import Tuple
import h5py
import scipy.io
import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
import tensorflow_addons as tfa
import tensorflow_datasets_bw as tfdsbw
from dppp.types import... |
import sys
import string
import scipy
from scipy.stats import beta
import numpy
def parse_data(filename):
x, y = [], []
f = open(filename, "r")
for line in f.readlines():
tokens = string.split(line)
x.append(float(tokens[0]))
y.append(float(tokens[1]))
f.close()
return... |
<reponame>eltrompetero/maxent_fim<filename>pyutils/utils.py
# ====================================================================================== #
# Quick access to useful modules for pivotal components projects.
#
# Author : <NAME>, <EMAIL>
# =======================================================================... |
from .utility import array64, CacheError, Function
from collections import OrderedDict
import numpy as np
from scipy.stats import norm
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process import kernels as sk_kern
import warnings
class NelderMead:
def __init__(self,
... |
"""
model v1: baseline tree model
Light GBM
Doc: https://lightgbm.readthedocs.io/en/latest/Python-API.html
features: count-based (categorical) or tfidf (weights)
model: Light GBM - DART and GBDT with different seeds
"""
import gc
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.utils.validation im... |
<gh_stars>1-10
from matplotlib.offsetbox import AnchoredText
from scipy.optimize import curve_fit
from scipy import stats, signal
from smooth_spline import get_natural_cubic_spline_model
import statsmodels.formula.api as smf
import matplotlib.pyplot as plt
from matplotlib import gridspec
import numpy as np
import pan... |
from scipy.stats import norm
class UtilityFunction(object):
"""
An object to compute the acquisition functions.
"""
def __init__(self, k... |
<reponame>hughsyx/1D-DOST<gh_stars>0
#!/usr/bin/env python
import numpy as np
import scipy as sp
import os, time, glob
import matplotlib.pyplot as plt
import pdb
def dost(time_series):
rows_time_series = time_series.shape[0]
dost_coefficients = np.zeros(time_series.shape,dtype = complex)
# partition of the frequenc... |
import sys,time,datetime,copy,subprocess,itertools,pickle,warnings
import numpy as np
import scipy as sp
import pandas as pd
from matplotlib import pyplot as plt
import matplotlib as mpl
import scipy.sparse as spm
from .StatTool import Quasi_Newton,Bayesian_Smoothing
def Estimate_exp(Data,t,prior=[],opt=[]):
... |
import matplotlib.pyplot as plt
import numpy as np
import itertools as itt
from src.data.load import load
from src.metrics.reliability import signal_reliability
from src.metrics import trp_dispersion as ndisp
from src.data.cache import make_cache, get_cache
from src.data import rasters as tp
import pandas a... |
<filename>gcn_train.py<gh_stars>0
import torch
import numpy as np
from torchvision.datasets import mnist
from torch import nn
from torch.autograd import Variable
import matplotlib.pyplot as plt
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torch.utils.data import TensorDataset,DataLoader
... |
<gh_stars>100-1000
import numpy as np
from numpy import power
from scipy.sparse import diags
from scipy.sparse.linalg import norm as spnorm
import pandas as pd
from polara.tools.random import check_random_state
def split_holdout(matrix, sample_max_rated=True, random_state=None):
'''
Uses CSR format to efficie... |
from statistics import mean
from typing import Any
from dataclasses import dataclass, field
import random
import utils.blockworld as blockworld
from model.utils.Search_Tree import *
from model.Astar_Agent import Astar_Agent, Stochastic_Priority_Queue
import os
import sys
proj_dir = os.path.dirname(os.path.dirname(os.pa... |
<filename>work/initiate.py
# MODULES
# 2019.08.?? MADE BY <NAME>
#============================================================
# MODULE
#------------------------------------------------------------
import healpy as hp
import numpy as np
import time
import os, glob, sys
from astropy.table import Table, Column, Masked... |
from statistics import stdev
from scipy.signal import savgol_filter
from cloudpredictionframework.anomaly_detection.algorithms.base_algorithm import BaseAlgorithm
class SavgolAlgorithm(BaseAlgorithm):
def __init__(self, window_length=7, poly_order=3, tolerance_multiplier=1, min_tolerance=1):
super().__i... |
<gh_stars>1-10
import os
import sys
from io import BytesIO
import numpy as np
from numpy.testing import (assert_equal, assert_, assert_array_equal,
suppress_warnings)
import pytest
from pytest import raises, warns
from scipy.io import wavfile
def datafile(fn):
return os.path.join(os.p... |
<reponame>charelF/ComplexSystems
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
from numba import njit, prange
import scipy
from scipy import special, spatial, sparse
import ... |
<filename>draftplot_APrunoff.py
from scipy.integrate import odeint
from scipy.interpolate import UnivariateSpline
import os
import matplotlib as mpl
import numpy as np
import matplotlib.pyplot as plt
import sys
dd = 5.
hstep = .1
tstep = .01
hmin = 0.
hmax = dd
tmin = 0.
rr = np.array([.3,1.,3.])
LL1 = 1.08
LL3 = ... |
<reponame>StillEvan/alpha_shape_analysis
import numpy as np
import warnings
import pandas as pd
from scipy.spatial import Delaunay
from alpha_shape_analysis.simplex_property_determination import *
from alpha_shape_analysis.alpha_hull import *
from alpha_shape_analysis.alpha_heuristics import *
from alpha_shape_analysi... |
<gh_stars>10-100
import numpy as np
import random
import os
import sys
sys.path.append('../')
import pyedflib
from constants import INCLUDED_CHANNELS, FREQUENCY, ALL_LABEL_DICT
from scipy.fftpack import fft
from scipy.signal import resample, correlate
def computeFFT(signals, n):
"""
Args:
signals: EEG... |
import json
import logging
import matplotlib.pyplot as pl
import numpy as np
from scipy.cluster.hierarchy import dendrogram, linkage
log = logging.getLogger(__name__)
def analyze(data):
# Convert this to python data for us to be able to run ML algorithms
json_to_python = json.loads(data)
# Data pre-pro... |
# Copyright (c) IMToolkit Development Team
# This toolkit is released under the MIT License, see LICENSE.txt
import os
import sys
import glob
import re
import time
import shutil
from scipy import special
import numpy as np
import itertools
from imtoolkit import *
def getHammingDistanceTable(MCK, indsdec):
# This ... |
<reponame>SteffenPL/PartiallyKineticSystems<gh_stars>0
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jul 26 19:17:11 2019
@author: plunder
"""
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jul 25 09:44:55 2019
@author: plunder
"""
import matplotlib.pyplot as plt
import nump... |
# -*- coding: utf-8 -*-
"""
Test data augmentation of the small slices data set
Created on Thu Dec 21 14:59:42 2017
@author: mbarbier
"""
#from keras import model
from data_small import loadData
import numpy as np
from keras import backend as K
from module_model_unet import get_unet, preprocess
from module_callbacks ... |
<reponame>chung-ejy/comet_chaser_api
from cmath import nan
import pandas as pd
import pickle
from database.comet_historian import CometHistorian
import os
from dotenv import load_dotenv
load_dotenv()
mongouser = os.getenv("MONGOUSER")
mongokey = os.getenv("MONGOKEY")
class EntryStrategy(object):
@classmethod
d... |
import time
import numpy as np
import scipy.integrate
import scipy.linalg
import ross
from ross.units import Q_, check_units
from .abs_defect import Defect
from .integrate_solver import Integrator
__all__ = [
"Rubbing",
]
class Rubbing(Defect):
"""Contains a rubbing model for applications on finite elemen... |
<reponame>Prakshal2607/pythonml
import numpy as np
import argparse
import os
import string
import sys
from skimage.io import imread
from sklearn.model_selection import ShuffleSplit
from TFANN import ANNC
import tensorflow as tf
from scipy.stats import mode as Mode
NC = len(string.ascii_letters + string.digits + ' ') ... |
<reponame>aestrivex/PySurfer
import os
from os.path import join as pjoin
from warnings import warn
import numpy as np
from scipy import stats, ndimage, misc
from matplotlib.colors import colorConverter
import nibabel as nib
from mayavi import mlab
from mayavi.tools.mlab_scene_model import MlabSceneModel
from mayavi.... |
import os
from glob import glob
import numpy as np
import h5py
import argparse
from scipy import signal
import matplotlib.pyplot as plt
import matplotlib as mpl
import freqent.freqent as fe
plt.close('all')
mpl.rcParams['pdf.fonttype'] = 42
mpl.rcParams['font.size'] = 12
mpl.rcParams['axes.linewidth'] = 2
mpl.rcParams... |
<reponame>bmorris3/mosfire_wasp6
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 24 09:10:53 2015
@author: bmmorris
"""
import numpy as np
from matplotlib import pyplot as plt
def initialwalkers(pos, genmodel, Nbins, times, lightcurve,
lightcurve_errors, ch1, ch2, period, t0_roughfit,
... |
import json
import statistics
from django import template
from django.template.defaultfilters import stringfilter
from django.utils.safestring import mark_safe
register = template.Library()
@register.filter
@stringfilter
def split(value, arg):
return value.split(arg)
@register.filter(is_safe=True)
def js(obj)... |
# Modules for algebraic manipulation and pretty printing
from xmlrpc.client import Boolean
from sympy import *
from IPython.display import Math,display
def make_fn(expr:str):
''' Just a helper function to make a function given a rule expr
'''
x = symbols('x')
class f(Function):
@classmethod
... |
import os
import glob
import torch
from torch.utils.data import DataLoader
import numpy as np
from skimage import img_as_float, metrics, io
from scipy.signal import correlate2d
import networks, datasets, utils, kernels
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
n_out = 5 # number of HQS... |
<filename>model.py
import numpy as np
from scipy import ndimage
import csv
def load_images():
lines = []
with open('/opt/data/driving_log.csv') as csvfile:
reader = csv.reader(csvfile)
for line in reader:
lines.append(line)
images = []
measurements = []
for line in l... |
<reponame>jackypheno/flavio
import unittest
import numpy as np
from . import amplitude, observables
from math import sin, asin, cos, pi
from flavio.physics.eft import WilsonCoefficients
from flavio import Observable
from flavio.parameters import default_parameters
import copy
import flavio
import cmath
from wilson impo... |
<filename>FLife/tools.py<gh_stars>1-10
import numpy as np
from scipy import stats
import tkinter as tk
from tkinter.filedialog import asksaveasfilename
from matplotlib.figure import Figure
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2Tk
import time
def relative_error(value, value_... |
import torch
import numpy as np
import scipy.sparse as sp
from torch.autograd import Function
from utils import sparse_mx_to_torch_sparse_tensor
class ImplicitFunction(Function):
#ImplicitFunction.apply(input, A, U, self.X_0, self.W, self.Omega_1, self.Omega_2)
@staticmethod
def forward(ctx, W, X_0, A, B... |
<reponame>stupoole/LEEM-analysis
import numpy as np
import os
import numba
import time
import dask
import dask.array as da
import dask.array.image as daim
from dask.delayed import delayed
from dask.distributed import Client, LocalCluster
from scipy.optimize import least_squares
import scipy.ndimage as ndi... |
import relative_permeability as relperm
import numpy as np
import scipy.optimize as opt
from scipy.interpolate import interp1d
def frac_flow_wf(muw=1e-3, muo=2e-3, ut=1e-5, phi=0.2, \
k=1e-12, swc=0.1, sor=0.05, kro0=0.9, no=2.0, krw0=0.4, \
nw=2.0, sw0=0.0, sw_inj=1.0, L=1.0, pv_inj=5.0):
# sws(sw::Real)=(... |
import numpy as np
import scipy.misc
from gym.spaces.box import Box
from scipy.misc import imresize
from cached_property import cached_property
# TODO: move this to folder with different files
class BaseTransformer(object):
"""
Base transformer interface, inherited objects should conform to this
"""
... |
"""Testing copyfile functions."""
# Authors: <NAME> <<EMAIL>>
# <NAME> <<EMAIL>>
# <NAME> <<EMAIL>>
#
# License: BSD (3-clause)
import os.path as op
import pytest
from scipy.io import savemat
import mne
from mne.datasets import testing
from mne.utils import _TempDir
from mne_bids.copyfiles import (_... |
<filename>hmc/linalg/solve_tridiagonal.py
import numpy as np
import scipy.linalg as spla
def solve_tridiagonal(tri: np.ndarray, rhs: np.ndarray) -> np.ndarray:
"""The special structure of a tridiagonal matrix permits it to be used in
solving a linear system in linear time instead of the usual cubic time.
... |
<filename>optbinning/binning/piecewise/binning_statistics.py
"""
Binning tables for optimal continuous binning.
"""
# <NAME> <<EMAIL>>
# Copyright (C) 2020
import numbers
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy import stats
from ...binning.binning_statistics import _check_... |
<gh_stars>1-10
import scipy.misc
import numpy as np
def color_grid_vis(X, nh, nw, save_path):
h, w = X[0].shape[:2]
img = np.zeros((h * nh, w * nw, 3))
for n, x in enumerate(X):
j = int(n / nw)
i = n % nw
img[j * h:j * h + h, i * w:i * w + w, :] = x
scipy.misc.imsave(save_path,... |
import warnings
import numpy as np
import pandas as pd
import scipy.sparse as sp
from scipy.interpolate import interp1d
from astropy import units as u
from tardis import constants as const
from tardis.montecarlo.montecarlo import formal_integral
from tardis.montecarlo.spectrum import TARDISSpectrum
class Integration... |
#Load in necessary packages
from scipy.stats import norm
import matplotlib.pyplot as plt
#construct normal distribution
a=norm.rvs(size=1000000,loc=-2, scale=1.5)
h=plt.hist(a,bins=100,normed=True)
#construct uniform distribution
from scipy.stats import uniform
b=uniform.rvs(size=1000000,loc=-1,scale=2)
i=plt.hist(b... |
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader, WeightedRandomSampler
from sklearn.model_selection import train_test_split
from scipy.spatial import distance
from datetime import date
import time
# expose version from _version file
fr... |
<reponame>babahui/Superpixels<filename>evaluate.py
import os
from os.path import basename, join, isfile
from imageio import imread, imwrite
from scipy.io import loadmat
from skimage.segmentation import find_boundaries
import numpy as np
# read
def evaluate(img_dir, gt_dir, soft_thres=1):
img_list = [join(img_dir,... |
import GPy
import numpy as np
from GPy.inference.latent_function_inference.posterior import PosteriorExact as Posterior
from GPy.util.linalg import pdinv, dpotrs, tdot, dtrtrs, dpotri, symmetrify
from GPy.util import diag
from scipy import stats
log_2_pi = np.log(2*np.pi)
def __setattr_patch__(self, name, val):
#... |
<reponame>Syler1984/seismo-ml-phase-picker<filename>utils/predict_tools.py
import numpy as np
from scipy.signal import find_peaks
from .h5_tools import write_batch
import matplotlib.pyplot as plt
def cut_spans_to_slices(cut_spans, start_time, end_time):
"""
Utility function for reversing list of time spans to... |
<reponame>wukevin/tcr-bert
from typing import *
import collections
import logging
import numpy as np
import pandas as pd
from anndata import AnnData
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import sklearn.metrics as metrics
from scipy import stats
from adjustText import adjust_text
import... |
<reponame>ricvolpi/domain-shift-robustness<filename>src/search_ops.py
import tensorflow as tf
import tensorflow.contrib.slim as slim
import numpy as np
import numpy.random as npr
from ConfigParser import *
import os
import cPickle
import scipy.io
import sys
import glob
from numpy.linalg import norm
from scipy import mi... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 24 09:04:44 2021
@author: Jen
"""
### Standard loading of libraries
import pandas
import numpy
### setting my working directory here, because I'm always working out of random folders it seems##
from os import chdir, getcwd
wd=getcwd()
chdir(wd)
i... |
'''
@author <NAME>
@date 20/06/2014
@copyright CERN
'''
import numpy as np
from scipy.special import k0
from scipy.constants import c, e
from PyHEADTAIL.general.element import Element
class TransverseDamper(Element):
def __init__(self, dampingrate_x, dampingrate_y, phase=90,
local_beta_functi... |
"""Tools to easily make multi voxel models"""
import numpy as np
from numpy.lib.stride_tricks import as_strided
from tqdm import tqdm
from dipy.reconst.ivim import BOUNDS, f_D_star_error, IvimFit
from dipy.core.ndindex import ndindex
from dipy.reconst.quick_squash import quick_squash as _squash
from dipy.reconst.bas... |
<gh_stars>0
# The following line helps with future compatibility with Python 3
# print must now be used as a function, e.g print('Hello','World')
from __future__ import (absolute_import, division, print_function, unicode_literals)
import scipy
import numpy as np
import mslice.cli as m
import matplotlib
import matplot... |
# -*- coding: utf-8 -*-
"""
Created on Thu Mar 09 18:07:03 2017
@author: scram
"""
from gensim import corpora, models, similarities
import gensim
import json
import os
import pickle
from pymongo import MongoClient
import unicodedata as uniD
import sys
import nltk
import networkx as nx
import numpy as np
import iterto... |
#
# BSD 3-Clause License
#
# Copyright (c) 2020, <NAME>
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list... |
<gh_stars>1-10
import numpy as np
import uncertainties.unumpy as unp
from uncertainties import ufloat
from scipy.stats import sem
print('====================')
print('Kondensator ANFANG')
print('====================')
print('Wert 1 ANFANG')
cao2 = 994 * 10**(-9)
pot1 = 6.03
c2 = ufloat(cao2, cao2 * 0.002)
pot2 = 10 - ... |
<filename>wofs_ml_severe/common/classifier.py
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import GradientBoostingClassifier, HistGradientBoostingClassifier
from sklearn.calibration import CalibratedClassifierCV
from xgboost import XGBClas... |
# 平方根是一个数字，乘以它会产生指定的数量。
from sympy import sqrt, pprint, Mul
x = sqrt(2)
y = sqrt(2)
pprint(Mul(x, y, evaluate=False))
print('equals to ')
print(x * y)
|
import numpy as np
from .skeleton import Policy
from typing import Union
from scipy.special import softmax
class TabularSoftmax(Policy):
"""
A Tabular Softmax Policy (bs)
Parameters
----------
numStates (int): the number of states the tabular softmax policy has
numActions (int): the number o... |
<reponame>LBJ-Wade/HaloGraphNet
#-------------------------------------
# Apply the GNN already trained for the MW and M31 halos to infer their masses
# Author: <NAME>
# Last update: 5/11/21
#-------------------------------------
from main import *
from Hyperparameters.params_TNG import params as params_TNG
from Hyperp... |
import warnings
import pandas as pd
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
from xgboost import XGBClassifier
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.impute import KNNImputer, SimpleImpu... |
<filename>src/evaluate.py
import os
import sys
import utils # local import
import skimage
import numpy as np
from numpy import log
from numpy import std
from numpy import exp
from math import floor
from numpy import mean
from numpy import cov
from numpy import trace
import tensorflow as tf
from numpy import asarray
fr... |
import numpy as np
from scipy.misc import imread
imgFolder = '/home/ljm/NiuChuang/AuroraObjectData/img/'
imgNames = '/home/ljm/NiuChuang/AuroraObjectData/images.txt'
img_type = '.jpg'
f = open(imgNames, 'r')
lines = f.readlines()
num_img = len(lines)
mean_sum = 0
for i in range(num_img):
name = lines[i][0:-1]
... |
<reponame>WilliamYi96/AnnotatedSAM<filename>vae_keras.py<gh_stars>1-10
#! -*- coding: utf-8 -*-
'''
VAE implemented by using keras (TensorFlow as backend)
'''
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm
from keras.layers import Input, Dense, Lambda
from keras.models import Model... |
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as stats
def main():
a = np.arange(16)
lambda_ = [1.5, 4.5]
colours = ["#348ABD", "#A60628"]
plt.bar(a, stats.poisson.pmf(a, lambda_[0]), color=colours[0],
label="$\lambda = %.1f$" % lambda_[0], alpha=0.60,
... |
<gh_stars>1-10
import time
def getTime():
return time.strftime("%H:%M:%S", time.localtime())
from scipy import misc
from tools.mask_tool import mask_tool as mt
from tools.mask_tool import makeMask as mk
from matplotlib import pyplot as plt
import numpy as np, cv2, json, requests
def Bottom(imgname, save_bottom = 1, ... |
<filename>SubspaceLearningAlgorithms/pca.py
"""Principal Component Analysis.
"""
# Copyright (c) 2022, <NAME>;
# Copyright (c) 2007-2022 The scikit-learn developers.
# License: BSD 3 clause
import numpy as np
from scipy import linalg
class PCA:
"""Principal Component Analysis (PCA).
Linear subspace learnin... |
import numpy as np
import pylab as plt
from skimage.util import montage
import matplotlib as mpl
from matplotlib import cm
from scipy.ndimage.filters import median_filter
from PIL import Image
import cv2
import imgaug.augmenters as iaa
def norm_01(a: np.ndarray):
return (a - a.min()) / (a.max() - a.min())
def b... |
import numpy as np
import torch as th
import torch.utils.data as data
from PIL import Image
import os
import pickle
from scipy import signal
from sconv.functional.sconv import spherical_conv
from tqdm import tqdm
import numbers
import cv2
from functools import lru_cache
from random import Random
class VRSaliency(data... |
<filename>pyigm/utils.py
""" Utilities for IGM calculations
"""
import numpy as np
import pdb
from astropy import constants as const
from astropy import units as u
from astropy.units.quantity import Quantity
from astropy import cosmology
from astropy.coordinates import SkyCoord
from pyigm.field.galaxy import Galaxy
... |
<reponame>dakota-hawkins/intensipy<filename>intensipy/models.py
"""
Normalize intensity in 3D image stacks.
Implements the Intensify3D algorithm as described by Yoyan et al.
References
----------
1.Yayon, N. et al. Intensify3D: Normalizing signal intensity in large
heterogenic image stacks. Scientific Reports 8, 431... |
from niio import loaded, write
import numpy as np
import scipy.io as sio
def mat2func(in_mat, out_func, hemisphere):
"""
Method to quickly convert between Matlab .mat and Gifti .func.gii files.
Generally, the Matlab file will be a 1-dimensional array. If it is not,
each column (dimension) will be sav... |
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