text string |
|---|
<reponame>MaruvkaLab/MSMuTect_3.2
# cython: language_level=3
import numpy as np
from collections import namedtuple
from scipy.stats import binom
from src.IndelCalling.FisherTest import Fisher
from src.IndelCalling.MutationCall import MutationCall
from src.IndelCalling.AlleleSet import AlleleSet
from src.IndelCalling.Hi... |
<reponame>Fang-Ke/Fast-eTofts<gh_stars>1-10
import numpy as np
from utils.config import get_config
from scipy.optimize import least_squares as ls
from scipy.optimize import lsq_linear
config = get_config()
r1 = config.protocol.r1
TR = config.protocol.TR
alpha = config.protocol.alpha/180*np.pi
deltt = config.protocol.de... |
<reponame>geg58/cs194DRLProj
import gym
import random
import numpy as np
import tflearn
import time
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.estimator import regression
from statistics import median, mean
from collections import Counter
LR = 1e-3
env = gym.make("Humanoid... |
import argparse
from timeit import default_timer as timer
import numpy as np
from scipy.special import comb # binom function
parser = argparse.ArgumentParser()
parser.add_argument("-d", "--degree", help="Degree of polynomial features", default=2, type=int)
parser.add_argument("-i", "--iterations", help="Number of it... |
<gh_stars>1-10
from __future__ import absolute_import, division, print_function
name = "Signal Processing toolkit | utils"
import sys
if sys.version_info[:2] < (3, 3):
old_print = print
def print(*args, **kwargs):
flush = kwargs.pop('flush', False)
old_print(*args, **kwargs)
if flush:
... |
<reponame>Ashelywang/Kaggle_toxic
import numpy as np
from scipy import sparse
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.utils.validation import check_X_y, check_is_fitted
from sklearn.linear_model import LogisticRegression
from ker... |
from statistics import mean
import numpy as np
xs = np.array([1, 2, 3, 4, 5], dtype=np.float64)
ys = np.array([5, 4, 6, 5, 6], dtype=np.float64)
def best_fit_slope(xs, ys):
m = ((mean(xs) * mean(ys)) - mean(xs * ys)) / (mean(xs)**2 - mean(xs**2))
return m
m = best_fit_slope(xs, ys)
print(m)
|
"""sympify -- convert objects SymPy internal format"""
from inspect import getmro
from core import all_classes as sympy_classes
from sympy.core.compatibility import iterable
class SympifyError(ValueError):
def __init__(self, expr, base_exc=None):
self.expr = expr
self.base_exc = base_exc
def ... |
<filename>sympy/stats/tests/test_continuous_rv.py
from sympy.stats import (P, E, where, density, variance, covariance, skewness,
given, pspace, cdf, ContinuousRV, sample)
from sympy.stats import (Arcsin, Benini, Beta, BetaPrime, Cauchy, Chi, Dagum,
Exponential, Gamma, L... |
<gh_stars>0
from __future__ import division, print_function
#import matplotlib.pyplot as plt
import numpy as np
from numpy import log10
from sys import stderr, stdout, exit
from dispersion import DR_Solve, init, DR_point, PlotDR2d
from scipy.interpolate import InterpolatedUnivariateSpline as US
def banner(... |
<gh_stars>10-100
import matplotlib.pyplot as plt
import sys
from scipy.optimize import curve_fit
import numpy as np
# alaz files
try:
alfile = sys.argv[1]
azfile = sys.argv[2]
except IndexError:
print 'Usage: python plotbeam.py alfile azfile'
print 'Will then use offsets and power values in both files ... |
#! /usr/bin/env python
"""
File: unstable_ODE
Copyright (c) 2016 <NAME>
License: MIT
Course: PHYS227
Assignment: C.4
Date: April 7th, 2016
Email: <EMAIL>
Name: <NAME>
Description: Demonstrates the instability of an ODE
"""
from __future__ import division
import matplotlib.pyplot as plt
import numpy as np
import sympy... |
"""Generative adversarial network for toy Gaussian data
(Goodfellow et al., 2014).
Inspired by a blog post by <NAME>.
Note there are several common failure modes, such as
(1) saturation of either discriminative or generative network;
(2) the generator running into a local optima that produces a Gaussian
somewhere aro... |
<filename>src/python/packages/metadapter/processors/mdoAzimuth_processor.py
# -*- coding: utf-8 -*-
"""
Created on Fri Feb 03 2017
@author: <NAME>
"""
import numpy
import math
from sys import version_info
if version_info.major <= 2:
import OSC
else:
# Use the self-made port for Python 3 (experimental)
f... |
# -*- coding: utf-8 -*-
"""
cosmology utils.
... use astropy.cosmology. that is a full furnished util.
Created on Sun Jun 28 18:31:23 2015
@author: hoseung
"""
from ..general import defaults
#dfl = defaults.Default()
#dir_repo = dfl.dir_repo
from scipy.integrate import cumtrapz
from numpy.core.records import fromarr... |
from __future__ import print_function, division
import sys
import os
from os.path import expanduser
home = expanduser("~")
path_to_cosmodc2 = os.path.join(home, 'cosmology/cosmodc2')
if 'mira-home' in home:
sys.path.insert(0, '/gpfs/mira-home/ekovacs/.local/lib/python2.7/site-packages')
sys.path.insert(0, path_to_... |
<gh_stars>1-10
import numpy as np
import pandas as pd
import os, errno
import datetime
import uuid
import itertools
import yaml
import subprocess
import scipy.sparse as sp
from scipy.spatial.distance import squareform
from sklearn.decomposition.nmf import non_negative_factorization
from sklearn.cluster import KMeans
... |
<filename>Simulation_Result_Analysis.py
# -*- coding: utf-8 -*-
"""
Created on Fri Aug 05 15:59:30 2016
@author: <NAME>
"""
import numpy
import matplotlib
from matplotlib import pyplot
from scipy import polyval, polyfit
#hyper parameters
matplotlib.rc('font', **{'sans-serif' : 'Arial','family' : 'sans-... |
##########################################################################
##########################################################################
##
## What are you doing looking at this file?
##
##########################################################################
##############################... |
<reponame>NMinhNguyen/wordsandbuttons<gh_stars>100-1000
from sympy import *
x1, y1, x2, y2, x3, y3, a, b, c = symbols('x1 y1 x2 y2 x3 y3 a b c')
print(solve([
a * x1 * x1 + b * x1 + c - y1,
a * x2 * x2 + b * x2 + c - y2,
a * x3 * x3 + b * x3 + c - y3,
], (a, b, c)))
|
'''
Calculate and plot Fisher information over subsets of r.
'''
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import cProfile, pstats
import sys
import os
import numpy as np
from scipy.special import spherical_jn
from sst import Fisher
from sst import cam... |
#a method for ranking sites in an alignment according to GC bias, for filtering purposes. Inspired by the Munoz-Gomez et al. (2018) zed score for amino acid data
from Bio import SeqIO, AlignIO
import sys, operator
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from scipy im... |
# -*- coding:utf-8 -*-
# ------------------------
# written by <NAME>
# 2019-02
# ------------------------
import h5py
import scipy.io as io
import glob
import warnings
import os
import numpy as np
import skimage.io
from gaussian_filter import gaussian_filter_density
warnings.filterwarnings("ignore")
path = "../../d... |
#!/usr/bin/env python
'''
This script creates test functions for verification of Redi tendancy terms.
<NAME>, LANL, Nov 2019
followed example at:
https://pythonhosted.org/algopy/symbolic_differentiation.html
'''
import sympy as sp
import numpy as np
# define algabraic variables:
x, y, z = sp.symbols('x y z')
fkx, fky... |
<filename>sampling.py<gh_stars>100-1000
from lightning_model import NuWave
from omegaconf import OmegaConf as OC
import os
import argparse
import datetime
from glob import glob
import torch
import librosa as rosa
from scipy.io.wavfile import write as swrite
import matplotlib.pyplot as plt
from utils.stft import STFTMag... |
<reponame>samtx/pyapprox
from __future__ import (absolute_import, division,
print_function, unicode_literals)
import numpy as np
from scipy import special as sp
def charlier_recurrence(N, a):
r"""
Compute the recursion coefficients of the polynomials which are
orthonormal with resp... |
# 作者Hai
import tkinter
from scipy import special
import math
import openpyxl
import sys
# 主窗体
top = tkinter.Tk(className='Epsilon calculation', )
# 定义窗体大小及位置
width = 650
height = 240
screenwidth = top.winfo_screenwidth()
screenheight = top.winfo_screenheight()
alignstr = '%dx%d+%d+%d' % (width, height,... |
<filename>Code/constants.py<gh_stars>0
import numpy as np
import os
from glob import glob
import shutil
from datetime import datetime
from scipy.ndimage import imread
from time import gmtime, strftime
##
# Data
##
def get_date_str():
"""
@return: A string representing the current date/time that can be used as ... |
<reponame>alifeee/ElecSus
# Copyright 2014-2016 <NAME>, <NAME>, <NAME>, <NAME>,
# <NAME> and <NAME>.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-... |
<reponame>GEOS-ESM/GMAO_Shared
from g5lib import dset
import netCDF4 as nc
import scipy as sp
__all__=['ctl']
class Ctl(dset.NCDset):
def __init__(self):
name='HadISST'
flist=['/gpfsm/dnb42/projects/p16/ssd/ocean/kovach/odas-2/obs/HADISST/HadISST_sst.nc']
f=nc.Dataset(flist[0])
tt... |
import margin_leverage
import util
import Investor
import Market
import TaxRates
import BrokerageAccount
import plots
from datetime import datetime
import os
import shutil
import re
import math
from scipy.stats import norm
#from scipy.optimize import fsolve # don't need this anymore
import numpy
import time
import leve... |
from math import pi,sin,cos
from biogeme import *
from headers import *
from loglikelihood import *
from statistics import *
###define k for trigonometric function, n for #covariates and ps for power series of dur####
k=4
n=4
ps=3
begin=range(1,49)
end=range(1,49)
choiceset=range(1,1177)
arrmidpo... |
<reponame>amadavan/Stuka
import numpy as np
import scipy as sp
import scipy.sparse
import stukapy as st
c = np.array([2, 1])
A_ub = sp.sparse.bmat([[-1, 1],
[-1, -1],
[0, -1],
[1, -2]], 'csc')
b_ub = np.array([1, -2, 0, 4])
lp = st.LinearProgram(c=... |
<gh_stars>1-10
import astropy.io.fits as fits
import matplotlib.pyplot as plt
import numpy as np
import scipy.interpolate
import scipy.ndimage.filters as scipy_filter
import scipy.signal
import sys,json
import glob
def load_spc(file):
"""
Load a spectrum fits file.
return two nArray for wavelen... |
import scipy
from scipy.sparse import csc_matrix, save_npz
from scipy.sparse.linalg import eigsh
import numpy as np, enum
import sparse_matrices
class MassMatrixType(enum.Enum):
IDENTITY = 1
FULL = 2
LUMPED = 3
def compute_vibrational_modes(obj, fixedVars = [], mtype = MassMatrixType.FULL, n = 7, sigma=-0... |
<filename>rgbmcmr.py
from __future__ import division, print_function
from collections import namedtuple
import numpy as np
from scipy.special import erf, erfc
import emceemr
from astropy import units as u
MINF = -np.inf
class RGBModel(emceemr.Model):
"""
Note if biasfunc is used, the sense is mag_real = m... |
<filename>cloneOLD.py
import csv
import cv2
import numpy as np
from scipy import ndimage
from keras.models import Model
lines = []
with open('/home/workspace/CarND-Behavioral-Cloning-P3/run1/driving_log.csv') as csvfile:
reader = csv.reader(csvfile)
for line in reader:
lines.append(line)
imag... |
""" Copyright chriskeraly
Copyright (c) 2019 Lumerical Inc. """
import numpy as np
import scipy as sp
import scipy.constants
import lumapi
from lumopt.utilities.fields import Fields, FieldsNoInterp
def get_lambda_from_cad(fdtd, field_result_name):
fdtd.eval("wl = {0}.E.lambda;".format(field_result_n... |
import numpy as np
from scipy.signal import convolve
from time import perf_counter as timer
with open('input17.txt') as f:
data = (np.array([list(i.strip()) for i in f]) == '#').astype(np.uint8)
def cycle(init, dim, gen=6):
state = init.reshape([1] * (dim - init.ndim) + list(init.shape))
kernel = np.ones(... |
<gh_stars>0
# -*- coding: utf-8 -*-
# Name: em_cascades.py
# Authors: <NAME>
# Constructs an electromagnetic cascade, defined by the emitted photons
import logging
import numpy as np
import pickle
import pkgutil
from scipy.special import gamma as gamma_func
from .config import config
try:
import jax.numpy as jnp
... |
<filename>pysb/export/potterswheel.py
"""
Module containing a class for converting a PySB model to an equivalent set of
ordinary differential equations for integration or analysis in PottersWheel_.
.. _PottersWheel: http://www.potterswheel.de
For information on how to use the model exporters, see the documentation
fo... |
<reponame>alisiahkoohi/HINT
import os, glob, json
import numpy as np
import torch
import torch.utils.data
import pickle
from numpy.random import rand, randn
from scipy.io import loadmat
from scipy.spatial.distance import pdist, squareform
from matplotlib import pyplot as plt
from collections import defaultdict
from sha... |
# -*- coding: utf-8 -*-
from scipy import ndimage
import imageio
import matplotlib.pyplot as plt
import numpy
# Utilização do modulo imageio para ler as imagens
mars1 = imageio.imread('Z:\DRPI\questoes_aula\Mars_Reconnaissance_11.tif')
mars2 = imageio.imread('Z:\DRPI\questoes_aula\Mars_Reconnaissance_22.tif')
def ... |
<reponame>Enucatl/machine-learning-aging-brains
from __future__ import division, print_function
import os
import click
import numpy as np
import tqdm
import sklearn.neighbors as skn
import sklearn.gaussian_process as skg
import sklearn.base
import sklearn.metrics
import sklearn.model_selection
import sklearn.preprocess... |
<gh_stars>0
# USAGE:
# python scan.py (--images <IMG_DIR> | --image <IMG_PATH>) [-i]
# For example, to scan a single image with interactive mode:
# python scan.py --image sample_images/desk.JPG -i
# To scan all images in a directory automatically:
# python scan.py --images sample_images
# Scanned images will be output... |
<filename>venv/Lib/site-packages/pybrain/tools/xml/handling.py
__author__ = '<NAME>, <EMAIL>'
from xml.dom.minidom import parse, getDOMImplementation
from pybrain.utilities import fListToString
from scipy import zeros
import string
class XMLHandling:
""" general purpose methods for reading, writing and editing XM... |
<gh_stars>0
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Copyright 2020-2022 <NAME>. All Rights Reserved.
See LICENCE file for details
"""
import scipy.stats as stats
import random
import sys
import pandas as pd
import matplotlib.pyplot as plt
from scipy.signal import convolve
sys.path.append('../../')
from PD... |
<gh_stars>10-100
from abc import ABC, abstractmethod
import numpy as np
import pandas as pd
from scipy.sparse import csr_matrix, csc_matrix
from sklearn.base import BaseEstimator
class BaseTree(ABC, BaseEstimator):
"""
Abstract base class of all Bayesian decision tree models (classification and regression). ... |
import argparse
import glob
import os.path
import gzip
import pickle
import sys
import logging
import re
import numpy as np
import pandas as pd
import scipy.stats as sp_stats
from run_with_gridsearch import dict2fn
from ssvm.data_structures import CandSQLiteDB_Massbank
# ================
# Setup the Logger
LOGGER ... |
import numpy as np
import os
from scanorama import *
from scipy.sparse import vstack
from sklearn.cluster import KMeans
from sklearn.metrics import roc_auc_score
from sklearn.preprocessing import normalize, LabelEncoder
from experiments import *
from process import load_names
from utils import *
np.random.seed(0)
NA... |
'''
@author: <NAME>
'''
import sys, logging
import numpy as np
import ibcc
from scipy.linalg import block_diag
from scipy.special import gammaln
def state_to_alpha(logodds, var):
alpha1 = 1/var * (1+np.exp(logodds))
alpha2 = alpha1 * (1+np.exp(-logodds))
return alpha1.reshape(logodds.shape), alpha2.reshape... |
<filename>TotalActivation/filters/hrf.py
import numpy as np
from scipy import signal
from TotalActivation.filters.cons import cons
def bold_parameters():
eps = 0.54
ts = 1.54
tf = 2.46
t0 = 0.98
alpha = 0.33
E0 = 0.34
V0 = 1
k1 = 7 * E0
k2 = 2
k3 = 2 * E0 - 0.2
c = (1 + (1... |
import numpy as np
import copy
import scipy.stats as stats
import networkx as nx
import matplotlib.pyplot as plt
from model import get_data,get_status,stat,statA,data,a,d,T as ori_T,p
beta = a+d
T = ori_T
inc_prob = 1-stats.lognorm.cdf(np.arange(0,999)/4.17,s=0.66)
remove_prob = 1-stats.norm.cdf((np.arange(0,999)... |
import numpy as np
import time
import cv2
from scipy.misc import imresize
class Window:
def __init__(self, name):
self._window = name
self._fps = 15
self._batch_size = 100
def show(self, generator):
for img in generator:
cv2.imshow(self._window, imresize(img, (300, 300)))
if cv2.waitK... |
import os
import numpy as np
import matplotlib as mat
import matplotlib.pyplot as plt
import copy
import sys
import scipy.stats
from KMC_Run import *
from utils import *
import time
import scipy
class Replicates:
'''
Performs statistical data analysis for muliple kMC trajectories with the same input, but dif... |
<filename>inversetoon/core/intersect.py<gh_stars>1-10
# -*- coding: utf-8 -*-
## @package inversetoon.core.intersect
#
# Polyline intersection via internal subdivision.
# @author tody
# @date 2015/08/12
import numpy as np
from scipy.interpolate import UnivariateSpline
import matplotlib.pyplot as plt
fr... |
<filename>sknetwork/ranking/harmonic.py
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on November 19 2019
@author: <NAME> <<EMAIL>>
"""
from typing import Union, Optional
import numpy as np
from scipy import sparse
from sknetwork.path.shortest_path import distance
from sknetwork.ranking.base import BaseR... |
<reponame>elijahc/ML_V1<filename>pretrained_feature_extraction/stats.py
import scipy.io as sio
import numpy as np
def gen_y_fake(y, sem_y):
loc = np.zeros_like(y)
z = np.random.normal(loc,sem_y)
return (y + z)
def pairwise_pcc(y,y_pred):
# Expects data in shape [nsamples, ncells]
ncells = y.shape... |
"""
plasma functions
"""
from __future__ import annotations
import typing as T
import logging
import numpy as np
import xarray
from scipy.integrate import cumtrapz
from scipy.interpolate import interp1d, interp2d, interpn
from . import read
from . import LSP, SPECIES
from . import write
from .web import url_retrieve... |
from __future__ import division
import numpy as np
import scipy as sp
from scipy.sparse import diags
import multiprocessing as mp
import itertools
import time
import sys
from suftware.src import deft_core
from suftware.src import maxent
from suftware.src import utils
from suftware.src.utils import ControlledError
x_M... |
# Copyright (C) 2020 NumS Development Team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed ... |
# Copyright 2016 Sandia Corporation and the National Renewable Energy
# Laboratory
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#... |
import os
import tensorflow as tf
import numpy as np
import pandas as pd
from glob import glob
from ivis import Ivis
from ghost import BinaryGHOST
from raise_utils.learners import Autoencoder, RandomForest, LogisticRegressionClassifier
from raise_utils.hyperparams import DODGE
from raise_utils.transforms import Transfo... |
<filename>projects/amygActivation/amygActivation.py
# main script to run the processing of the experiment
import os
import glob
import numpy as np
import json
from datetime import datetime
from dateutil import parser
from subprocess import call
import time
import nilearn
from nilearn.masking import apply_mask
from sc... |
# -*- coding: utf-8 -*-
"""
Created on Thu Jun 17 11:33:40 2021
@author: lukepinkel
"""
import numpy as np
import scipy as sp
import scipy.stats
TWOPI = 2.0 * np.pi
LN2PI = np.log(TWOPI)
class SASH:
@staticmethod
def loglike(y, mu, sigma, nu, tau):
return _logpdf(y, mu, sigma, nu, tau)
... |
from scipy.io import loadmat
import torch
def load_and_reconfigure_mat_data(filename, im_size=(192, 192), device='cpu'):
data = loadmat(filename)
n1, n2 = im_size
x = data['data'][0][0][0]
y = data['data'][0][0][1]
x = torch.stack((torch.tensor(x.real), torch.tensor(x.imag)), dim=2).view(
... |
# ------------------------------------------------------------------------------
# Beam environment for damping compensation
# ------------------------------------------------------------------------------
import sys
sys.path.append('../')
from pathlib import Path
import time
import os
import pickle
from argparse impo... |
import unittest
import numpy as np
from scipy.spatial.transform import Rotation
from d3d.abstraction import ObjectTag, ObjectTarget3D, Target3DArray
from d3d.dataset.kitti import KittiObjectClass
from d3d.tracking.matcher import (DistanceTypes, HungarianMatcher,
NearestNeighborMatche... |
import numpy as np
from scipy.stats import rankdata
class CornerScore(object):
@staticmethod
def get_scores(cat_word_counts, not_cat_word_counts):
pos = CornerScore.get_scores_for_category(cat_word_counts, not_cat_word_counts)
neg = CornerScore.get_scores_for_category(not_cat_word_counts, cat_word_counts)
sco... |
# 14 July 2018 <NAME>
# Python bootcamp, lesson 40: Image processing practice with Python
# Import modules
import numpy as np
import matplotlib.pyplot as plt
import scipy.ndimage
import skimage.io
import skimage.segmentation
import skimage.morphology
# Import some pretty Seaborn settings
import seaborn as sns
rc={'... |
<gh_stars>10-100
import numpy as np
from scipy.spatial.ckdtree import cKDTree
class DecisionMaking:
def __init__(self, normalize=True, ideal_point=None, nadir_point=None) -> None:
super().__init__()
self.normalize = normalize
self.ideal_point, self.nadir_point = ideal_point, nadir_point
... |
#!/usr/bin/env python
"""Module for getting and plotting some basic statstics of segments"""
from __future__ import absolute_import
import glob
import os
import os.path
import random
import math
from collections import defaultdict
import pandas as pd
import scipy.stats
import numpy as np
import matplotlib.pyplot as p... |
<reponame>sharif1093/dextron
import numpy as np
from copy import deepcopy
from digideep.utility.toolbox import get_class
from digideep.utility.logging import logger
# from digideep.utility.profiling import KeepTime
from digideep.utility.monitoring import monitor
from digideep.agent.agent_base import AgentBase
from sc... |
<gh_stars>0
# 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\objects\components\statistic_component.py
# Compiled at: 2020-10-08 06:20:44
# Size of s... |
import argparse
import BLISS as bliss
import exoparams
import json
import numpy as np
from sklearn.externals import joblib
from scipy import spatial
from statsmodels.robust import scale
y,x = 0,1
ppm = 1e6
def setup_BLISS_inputs_from_file(dataDir, xBinSize=0.01, yBinSize=0.01,
xSigmaR... |
<gh_stars>0
from socialsent import util
import functools
import numpy as np
from socialsent import embedding_transformer
from scipy.sparse import csr_matrix
from multiprocessing import Pool
from sklearn.linear_model import LogisticRegression, Ridge
from socialsent.graph_construction import similarity_matrix, transitio... |
#$ header function f(double[:],double[:,:,:],int)
@sympy
def g(v,w,i):
from sympy import Lambda, Function ,symbols ,IndexedBase,Idx ,Max, Sum
x = Function('x')
i, n, j, dim, k =symbols('i, n, j, dim, k')
v=IndexedBase('v')
w=IndexedBase('w')
net = Lambda((i, n, dim, k), Max(0.0, Sum(x(k)*w[n, k,... |
#coding=utf-8
'''
Created on 2013.12.13
@author: dell
'''
import numpy as np
from scipy.stats.stats import pearsonr
import matplotlib.pyplot as plt
#from matplotlib.figure import Figure
from matplotlib.pyplot import figure as Figure
import os
import struct
from ..data_transform import Ion2Vector
from .ion_calc impo... |
# coding: utf-8
# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.
"""
An interface to the excellent spglib library by <NAME>
(http://spglib.sourceforge.net/) for pymatgen.
v1.0 - Now works with both ordered and disordered structure.
v2.0 - Updated for spglib 1.6.
v3.0 - pyma... |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import pandas as pd;
import numpy as np;
import scipy as sp;
import sklearn;
import sys;
from nltk.corpus import stopwords;
import nltk;
from nltk.stem import WordNetLemmatizer, SnowballStemmer
from nltk.stem.porter import *
from gensim.models import ldamodel
import gen... |
<gh_stars>0
""" Prompt vs. Delayed model of the SN population """
import pandas
import numpy as np
from scipy import stats
from .tools import asym_gaussian
class PromptDelayModel(object):
def __init__(self):
""" """
# ====================== #
# Methods #
# ===================... |
# -*- coding: utf-8 -*-
"""
Copyright (c) 2016 <NAME>, <NAME>, and <NAME>
Permission is hereby granted, free of charge, to any person obtaining a copy of this software
and associated documentation files (the "Software"), to deal in the Software without restriction,
including without limitation the rights to use, copy... |
<gh_stars>1-10
"""
Tests for simulation of time series
Author: <NAME>
License: Simplified-BSD
"""
import numpy as np
from numpy.testing import assert_allclose
import pytest
from scipy.signal import lfilter
from statsmodels.tools.sm_exceptions import SpecificationWarning, \
EstimationWarning
from statsmodels.tsa.... |
<reponame>luiarthur/CytofDensityEstimation<gh_stars>0
from scipy.special import expit, logit, logsumexp
from scipy import stats
import matplotlib.pyplot as plt
import pystan
import numpy as np
import mcmc
from tqdm import trange
def update_beta(y, p, sd):
log_numer = np.log(p) - sum((y - 1) ** 2) / (2 * sd * sd)
... |
"""Validate a face recognizer on the "Labeled Faces in the Wild" dataset (http://vis-www.cs.umass.edu/lfw/).
Embeddings are calculated using the pairs from http://vis-www.cs.umass.edu/lfw/pairs.txt and the ROC curve
is calculated and plotted. Both the model metagraph and the model parameters need to exist
in the same d... |
from __future__ import division
import numpy as np
import sys
from sklearn.linear_model import OrthogonalMatchingPursuit
from sklearn.linear_model import OrthogonalMatchingPursuitCV
def CSSK(h,const=5.0,noise=0.0000001):
"""Compressed Sensing replacement of Fourier Transform on 1D array h
* REQUIRES CVXPY P... |
#===========================================#
# #
# #
#----------CROSSWALK RECOGNITION------------#
#-----------WRITTEN BY N.DALAL--------------#
#-----------------2017 (c)------------------#
# ... |
<filename>code/methodology/count.py
import re
import os
import matplotlib.pyplot as plt
import re
import numpy as np
from scipy import stats
from matplotlib.patches import Rectangle
fig = plt.figure()
ax = fig.add_subplot(111)
PROJECTS_LIST = "../../info/settings-project.txt"
RESULT_PATH="../../data/cleaned-complex... |
<reponame>blackpigg/RL_landmark_finder
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Apr 7 23:31:43 2017
@author: wd
"""
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
import gym
from gym import spaces
from gym.utils import s... |
<filename>search.py
import numpy as np
import scipy
from multiprocessing import Pool
from os.path import join
from sklearn.svm import SVC
from sklearn.model_selection import GridSearchCV
from util import load_data
import argparse
import pandas as pd
def search(dataset, data_dir):
gram = np.load(join(data_dir, 'gra... |
<reponame>raylu/PoE.py<filename>poe/price.py<gh_stars>10-100
import operator
import statistics as stats
import numpy as np
class PriceQuery:
def __init__(self, league, listings):
self.league = league
self.listings = listings
def lowest(self, results=3):
return self.listings[:results]... |
<reponame>goccert25/PowerSimData<filename>powersimdata/output/output_data.py<gh_stars>0
import os
import pickle
import numpy as np
import pandas as pd
from scipy.sparse import coo_matrix
from powersimdata.data_access.context import Context
from powersimdata.input.input_data import get_bus_demand
from powersimdata.uti... |
import numpy as np
from scipy import ndimage
from skimage import measure, morphology
def find_edges(mask, level=0.5):
edges = measure.find_contours(mask, level)[0]
print(type(edges))
ys = edges[:, 0]
xs = edges[:, 1]
return xs, ys
def plot_contours(arr, aux=None, level=0.5, ax=None, **kwargs):
... |
import argparse
import argparse
import os
import shutil
import numpy as np
import pandas as pd
import torch
from scipy.optimize import linear_sum_assignment
from scipy.special import softmax
from sklearn.metrics import confusion_matrix, accuracy_score, f1_score, \
classification_report
import src.utils.plotting_u... |
<reponame>pec27/lizard
from lizard.lizard_c import *
import sys
def test():
""" Test that the linear interpolation gives the same result as scipy """
n = 50 # grid size (n,n,n)
npts = 10 # number of points to interpolate
from numpy import arange
from numpy.random import random
from scipy.ndimag... |
"""
分段线性插值
"""
import sympy as sp
from sympy import Rational as r
def piecewise_linear_inter(X, Y):
"""
分段线性插值
:param X: 一系列x的一维向量
:param Y: 一系列y的一维向量
:return: 分段线性插值多项式
"""
x = sp.Symbol('x')
for i in range(len(X) - 1): # i代表当前第几段
print('在[{}, {}]上的线性插值为：'.format(X[i], X[i +... |
<reponame>albertopoljak/code-jam-5
import datetime
import os
import pickle
from pathlib import Path
import numpy as np
from scipy.interpolate import interp1d
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
from practical_porcupines.flask_api.models import LevelMo... |
<reponame>IronCretin/mandelbrot
import argparse
import sys
import stdio
import stddraw
import time
import color
from math import *
import cmath
from picture import Picture
from colorsys import hsv_to_rgb
# This bit just sets up the argument handling
parser = argparse.ArgumentParser(description='Generate Mandelbrot se... |
# -*- coding: utf-8 -*-
import numpy as np
import numpy.matlib
import scipy.misc
from PIL import Image
import scipy.io
import os
import scipy
import sys
caffe_root = '/home/lixiaoxing/code/PixelNet/tools/caffe'
sys.path.insert(0, caffe_root+'python/')
import caffe
# Use GPU?
use_gpu = 1;
gpu_id = 3;
net_struct = '/ho... |
import pylab as p
import glob
import numpy as n
import astropy.cosmology as co
aa=co.Planck13
import astropy.units as uu
import cPickle
import sys
from scipy.interpolate import interp1d
import glob
snL=glob.glob("/data2/DATA/eBOSS/Multidark-properties/MDPL/*0023*.cat.gz")
import numpy as n
import cPickle
massB=n.aran... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.