text string |
|---|
'''
Commands to aggregate the frames to MP4:
for high resolution:
ffmpeg -r 48 -i frames/primaryschool/%05d.png -vcodec libx264 -pix_fmt yuv420p -strict -2 -acodec aac clips/primaryschool.mp4
for twitter:
ffmpeg -i frames/primaryschool2/%05d.png -pix_fmt yuv420p -vcodec libx264 -vf 'scale=640:trunc(ow/a/2)*2' -ac... |
# make_png_tdata.py
#
# Reads original text data file and generates a PNG image.
#
import sys
import numpy as np
import scipy.misc
def run(data_fname):
img_ll = []
with open(data_fname) as fin:
for rlin in fin:
if len(rlin) < 30:
continue
lin = rlin.strip()
... |
<gh_stars>0
import warnings
def warn(*arg,**kwargs):pass
warnings.warn= warn
from sklearn.svm import SVC
from sklearn.model_selection import StratifiedKFold
cv = StratifiedKFold(n_splits=5, shuffle=True)
from statistics import mode
import numpy as np
from sklearn.metrics import accuracy_score, \
log_loss, \
... |
import scipy.fftpack
import numpy as np
import cv2
from ..hasher import ImageHasher
from .. import tools
class PHash(ImageHasher):
"""Also known as the DCT hash, a hash based on discrete cosine transforms of images.
See `complete paper <https://www.phash.org/docs/pubs/thesis_zauner.pdf>`_ for
details. Im... |
# Authors:
# <NAME> <<EMAIL>>
# <NAME> <<EMAIL>>
#
# License: BSD 3 clause
from .assembling import buildElasticityMatrix
from .bc import bcApplyWestMat, bcApplyWest_vec
from .cg import cg
from .projection import projection, GenEO_V0, minimal_V0, coarse_operators
from petsc4py import PETSc
from slepc4py import S... |
<reponame>gvvynplaine/jina<gh_stars>0
__copyright__ = "Copyright (c) 2020 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import gzip
from os import path
from typing import Optional, List, Union, Tuple
import numpy as np
from . import BaseVectorIndexer
from ...helper import cached_property
class ... |
import numpy as np
from scipy.optimize import minimize
class ExponentialFilter(object):
def __init__(self, min_time=1, max_time=10, n_subfilters=2, dt=1):
self.min_time = min_time
self.max_time = max_time
self.filter_values = np.zeros(n_subfilters)
self.tau = np.linspace(min_time, m... |
#!/usr/bin/env python
"""
TODO:
1) Get rid of unused functions
2) Possibly move the cmap lookup to tool_utils.
"""
import matplotlib as mpl
mpl.use('Agg')
from .sentinel_map import SentinelMap,SentinelNorm
import glob
#import matplotlib.numerix.ma as ma
from . import md_analysis_utils
import sys
import pylab,matplot... |
<reponame>mlodel/gym-exploration-2d<filename>gym_collision_avoidance/envs/Map.py<gh_stars>0
from copy import copy
import numpy as np
import imageio
import scipy.misc
import matplotlib.pyplot as plt
from PIL import Image
import time
class Map():
def __init__(self, x_width, y_width, grid_cell_size, map_filename=None... |
#!/usr/bin/env python
#coding:utf-8
# Third Party
from scipy.stats import multivariate_normal
# Self-made Modules
from __init__ import *
class Converter():
#ROSのmap 座標系をPython内の2-dimension array index 番号に対応付ける
def Map_coordinates_To_Array_index(self, X):
X = np.array(X)
Index = np.round(... |
<gh_stars>10-100
# -*- coding: utf-8 -*-
import json
import math
import queue
import os
import scipy.io
import numpy as np
IMAGE_LENGTH = IMAGE_WIDTH = IMAGE_HEIGHT = 768
NEAR_DISTANCE = 200
TYPES = ["unmovable", "tree", "movable"]
CATEGORIES_UNMOVABLE = ["house", "bus", "truck", "car", "bench", "chair"]
CATEGORIES... |
<filename>zisan/Seg/utils.py<gh_stars>10-100
from __future__ import division
import torch
from torch.autograd import Variable
from torch.utils import data
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
import torch.utils.model_zoo as model_zoo
from torchvision import models
# gener... |
<reponame>jaeilepp/eggie
# Author: <NAME> <<EMAIL>>
# <NAME> <<EMAIL>>
#
# License: BSD (3-clause)
import os.path as op
from copy import deepcopy
import numpy as np
from nose.tools import assert_raises, assert_true, assert_equal
from scipy.io import savemat
from mne.channels import (rename_channels, read_ch... |
<gh_stars>10-100
from pydec.testing import *
from scipy import arange, array
from pydec.dec.cube_array import *
class test_cube_array_search(TestCase):
def test_simple1(self):
k_face_array = array([[0,0,0],
[0,0,1],
[0,1,0],
... |
import collections
from scipy.special import comb
import numpy as np
def _iter_key_sorted_dct(dct):
for k in sorted(dct.keys()):
yield k, dct[k]
def make_sum(dct_values, base=None):
"""base is some previous result"""
sum_cnt = collections.defaultdict(int)
if base is not None:
sum_cnt... |
<reponame>Noodles-321/RegistrationEval<filename>evaluate.py
# -*- coding: utf-8 -*-
# evaluate registration error and write into csv
import pandas as pd
import skimage.io as skio
import skimage.transform as skt
import scipy.io as sio
from tqdm import tqdm
import os, cv2, argparse
import numpy as np
from glob i... |
<gh_stars>1-10
"""
"Data science" is just about as broad of a term as they come.
It may be easiest to describe what it is by listing its more concrete components:
Data Exploration & Analysis (EDA):
Included here: Pandas; NumPy; SciPy;
Data visualization:
A pretty self-explanatory name.
Taking dat... |
<gh_stars>0
import numpy as np
import accel.math.pyceres as ceres
""" linalg -> osqp -> pyceres
linalg:
min r^Tr
s.t. r = Ax - b
osqp:
min r^Tr
"""
# TODO
import numpy as np
from scipy.optimize import linprog
class LinearProgram(object):
MIN = 0
MAX = 1
EQ = 2
GEQ = 3
LEQ = 4
"""A flexin... |
"""
Polarized millimeter-wave atmospheric emission model
Copyright (c) 2019 <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
u... |
"""Simple class to deproject spectra and measure the rotation velocity."""
import celerite
import numpy as np
from scipy.stats import binned_statistic
from scipy.optimize import curve_fit
from scipy.optimize import minimize_scalar
from scipy.interpolate import interp1d
class ensemble(object):
def __init__(self,... |
<reponame>regina404/school<gh_stars>0
from django.db.models import Count
from fractions import Fraction
from typing import List
from pint import UnitRegistry
from .models import *
menu = [{'title': "О школе", 'url_name': 'about'},
{'title': "Добавить урок", 'url_name': 'add_page'},
{'title': ... |
# base.py
# Author: <NAME> <<EMAIL>>
"""
This file contains code that implements the core of the submodular selection
algorithms.
"""
import numpy
from tqdm import tqdm
from ..optimizers import BaseOptimizer
from ..optimizers import NaiveGreedy
from ..optimizers import LazyGreedy
from ..optimizers import Approximat... |
############################## Import Libraries ###############################
## Math Library
import numpy as np
## Library used to fit a 2d function
import scipy.optimize as opt
############################## Local Definitions ##############################
def background_stats(data):
# Reshapes and reorganiz... |
<reponame>Coricos/Challenger
# Author: <NAME>
# Date: 01/03/2019
# Project: optimizers
# General
import os
import six
import time
import json
import joblib
import logging
import warnings
import numpy as np
import pandas as pd
from functools import partial
# Testing
from sklearn.pipeline import Pipeline
from sklea... |
<reponame>ZeitgeberH/FISH-VIEWER
import os
import numpy as np
import zipfile
import io
import sys
import shutil
from PIL import Image
from gr_sys_utils import get_subdirs, listdir
from gr_io import request, check_file_exists
import pdb
import glob
import pandas as pd
from biothings_client import get_client
... |
<gh_stars>0
# -*- coding: utf-8 -*-
"""
@author: <NAME>
"""
from scipy.spatial.transform import Rotation as R
from pylab import (
rcParams,
savefig,
scatter,
pi,
cross,
array,
arccos,
arctan,
dot,
norm,
transpose,
zeros,
sqrt,
floor,
figure,
close,
pl... |
#This really does not work - don't try to use this
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plot
from matplotlib.figure import Figure
from io import BytesIO, StringIO
from sympy.parsing.sympy_parser import parse_expr, standard_transformations, implicit_multiplication_application, convert_xor
... |
# optimize.py
#
# <NAME>
# 5.8.2014
#
# Branching modified versions from
# SloppyCell.Optimization.
#
import scipy
import SloppyCell.lmopt as lmopt
def fmin_lm_log_params(m, params, *args, **kwargs):
"""
Minimize the cost of a model using Levenberg-Marquardt
in terms of log parameters.
"""
jac = ... |
# ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: percent
# format_version: '1.2'
# jupytext_version: 1.2.4
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# %% {"language": "html"}
# <style>
# div.input {
# ... |
<filename>math/spline.py
import numpy as np
import matplotlib.pyplot as plt
from scipy import interpolate
x = np.arange(0, 2*np.pi+np.pi/4, 2*np.pi/8)
y = np.sin(x)
tck = interpolate.splrep(x, y, s=0)
xnew = np.arange(0, 2*np.pi, np.pi/50)
ynew = interpolate.splev(xnew, tck, der=0)
plt.figure()
plt.plot(x, y, 'x', xn... |
<gh_stars>0
# -*- coding: utf-8 -*-
"""
***********************************************
BASIC USAGE
***********************************************
"""
# Import the main module
import polyline_hausdorff as ph
# Create two polylines
A = [(15, 1), (28, 11), (13, 26), (1, 18)]
B = [(26, 8), ... |
<reponame>WilliamJamieson/gwcs
# Licensed under a 3-clause BSD style license - see LICENSE.rst
import functools
import itertools
import warnings
import numpy as np
import numpy.linalg as npla
from scipy import optimize
from astropy import units as u
from astropy.modeling.core import Model
from astropy.modeling.models i... |
<filename>sureal/tools/stats.py
import numpy as np
import scipy
import scipy.signal
from .inverse import inversefunc
import warnings
# import multiprocessing
# pool = multiprocessing.Pool()
from .misc import parallel_map
__copyright__ = "Copyright 2016-2018, Netflix, Inc."
__license__ = "Apache, Version 2.0"
def v... |
<filename>yolonew.py
# -*- coding: utf-8 -*-
"""
Created on Mon Apr 27 14:15:06 2020
@author: <NAME>
"""
from keras.models import load_model
from glob import glob
import argparse
import os
import matplotlib.pyplot as plt
from matplotlib.pyplot import imshow
import scipy.io
import scipy.misc
import numpy ... |
import argparse
import gc
import numpy as np
import torch
from matplotlib import pyplot as plt
from scipy.special import logsumexp
from prob_mbrl import models, utils
def main():
# model parameters
parser = argparse.ArgumentParser("BNN regression example")
parser.add_argument('--seed', type=int, default=... |
from typing import Text, Sequence
import functools
import jax
import jax.numpy as jnp
import numpy as np
import scipy
import scipy.linalg
import tensornetwork as tn
#############################################################################
# Polar decomposition
#####################################################... |
<filename>mab/gd/potential.py
# -*- coding: utf-8 -*-
"""Contain some common profiles
"""
#from constants import *
from numpy import *
import mab.constants
import mab.astrounits
import numpy
import math
from math import pi
import mab.cosmology
from mab.gd import gdfast
import sys
from scipy.optimize import fsolve, ... |
<gh_stars>10-100
#! /usr/bin/env python
# -*- coding: utf-8 -*
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
"""python_speech_features based feature extraction.
https://github.com/jameslyons/python_speech_features
"""
import subprocess
import num... |
import numpy as np
from scipy.sparse import load_npz
from cov_constructor import CovConstructor, CovMatrix
def get_band(mat, band_size, tri=True):
i1 = np.tri(mat.shape[0], mat.shape[1], k=band_size)
i2 = np.tri(mat.shape[0], mat.shape[1], k=-band_size - 1)
mat[ np.logical_or(i1 == 0, i2 == 1) ] = 0
i... |
<gh_stars>0
# -*- coding: utf-8 -*-
import sys
# import io
from collections import OrderedDict
from tabulate import tabulate
import numpy as np
from scipy import stats
import pandas as pd
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt
##################################################... |
import itertools
from collections import defaultdict
from math import ceil
import numpy as np
from pycompss.api.api import compss_wait_on
from pycompss.api.parameter import Type, COLLECTION_IN, Depth, COLLECTION_INOUT
from pycompss.api.task import task
from scipy import sparse as sp
from scipy.sparse import issparse, ... |
"""
Contains an abstract base class that supports data transformations.
"""
import os
import logging
import time
import warnings
from typing import Any, List, Optional, Tuple, Union
import numpy as np
import scipy
logger = logging.getLogger(__name__)
class Transformer(object):
"""
Abstract base class for d... |
import gym
import tensorflow as tf
import random
import numpy as np
from statistics import mean, median
env = gym.make('CartPole-v0').env
env.reset()
goal_steps = 700
score_requirement = 60
initial_games = 10000
def model_data_preparation():
training_data = []
scores = []
accepted_scores = []
for game... |
<filename>scripts/post-processing.py
#!/usr/bin/env python3
# Copyright 2021 IBM Corporation
#
# 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.... |
<reponame>jgoodknight/spectroscopy
# -*- coding: utf-8 -*-
"""
Created on Fri Sep 27 15:40:19 2013
@author: joey
"""
import multiprocessing
import itertools
import sys
sys.path.append('..')
import numpy as np
import pickle
import matplotlib
import scipy.integrate
import scipy.interpolate
#matplotlib.use('Agg')
import... |
import subprocess
import sys
def install(package):
subprocess.check_call([sys.executable, "-m", "pip", "install", "-i",
"https://pypi.tuna.tsinghua.edu.cn/simple", "-U", package])
install("imgaug")
import os
import cv2
import time
import random
import pickle
import torch
import torch.... |
# ~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~
# MIT License
#
# Copyright (c) 2021 <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 res... |
#!/usr/bin/env python2.7
# -*- coding: utf-8 -*-
"""
@file py102-example2-zernike.py
@brief Fitting a surface in Python example for Python 102 lecture
@author <NAME> (<EMAIL>)
@url http://python101.vanwerkhoven.org
@date 20111012
Created by <NAME> (<EMAIL>) on 2011-10-12
Copyright (c) 2011 <NAME>. All rights reserved... |
<gh_stars>1-10
# -*- coding: utf-8 -*-
import numpy as np
import scipy.io as sio
def compute_pure_rotation(K, H):
R = np.dot(np.dot(np.linalg.pinv(K), H), K)
for i in range(0, R.shape[1]):
R[:, i] /= np.linalg.norm(R[:, i])
return R
def compute_pose(K, H):
Rt = np.dot(np.linalg.pinv(K), H)
... |
<filename>katsdpscripts/RTS/spectral_baseline.py
from __future__ import absolute_import
from __future__ import print_function
import os
import numpy as np
from numpy.ma import MaskedArray
import scipy.interpolate as interpolate
import scipy.ndimage as ndimage
import katdal
from katdal import averager
import matplotl... |
<filename>style_transfer/algorithms/gatys/neural_style_upload.py
œimport os
os.chdir('/home/ec2-user/neural-style')
import urllib2
import cStringIO
from scipy import ndimage
from skimage.color import rgb2gray
import scipy.misc
from flask import Flask, request
import boto3
from datetime import datetime as dt
import str... |
"""
Solvers for the identification modules.
"""
from numpy import append, array, amax, concatenate, dot, shape, empty, dot, zeros
from scipy.linalg import qr, solve, toeplitz
# Variables
__all__ = ['ls', 'qrsol', 'burg', 'levinson']
# functions
def ls(na, nb, nk, u, y):
''' Least Squares solution
:param ... |
<gh_stars>0
from __future__ import annotations
import timeit
from contextlib import nullcontext
from io import StringIO
import fire
import matplotlib.pyplot as plt
import numpy as np
from scipy.ndimage import convolve
KERNEL = np.array([[1, 2, 4], [8, 16, 32], [64, 128, 256]])
def main(input_file: str = "input.txt... |
<gh_stars>10-100
"""Project 04 - Advanced Lane Detection
Usage:
project04.py <input_video> <output_video> [-c <camera_file>]
project04.py (-h | --help)
Options:
-h --help Show this screen.
-c <camera_file> Specify camera calibration file [default: camera_data.npz]
"""
import os
import cv2
import glob... |
<reponame>apfeuti/high-altitude-balloon
import time
from datetime import datetime
import threading
import logging
import piexif
from fractions import Fraction
from picamera import PiCamera
class PictureCapturer:
""" Takes pictures with the pi-camera """
def __init__(self, capturing_frequency_sec, gps):
... |
<reponame>xi2pi/elastance-function
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 4 08:55:48 2018
@author: <NAME>
"""
# -*- coding: utf-8 -*-
"""
Created on Mon Sep 25 16:24:34 2017
@author: <NAME>
"""
import pandas as pd
import numpy as np
from cycler import cycler
import matplotlib.pyplot as plt
import glob, os
... |
# Copyright 2019 Toyota Research Institute. All rights reserved.
"""
Module and scripts for generating descriptors (quantities listed
in cell_analysis.m) from cycle-level summary statistics.
Usage:
featurize [INPUT_JSON]
Options:
-h --help Show this screen
--version Show version
The `featu... |
<gh_stars>1-10
"""Functions for plane manipulations."""
import numpy as np
import scipy.ndimage
def unit_vector(data, axis=None, out=None):
"""Return ndarray normalized by length, i.e. Euclidean norm, along axis.
"""
if out is None:
data = np.array(data, dtype=np.float64, copy=True)
if d... |
<gh_stars>0
import numpy as np
import pandas as pd
import scipy.stats as stats
import torch
import os
import time
import sys
sys.path.insert(1, os.path.dirname(__file__))
import genotypeio, eigenmt
from core import *
def logsumexp(x, dim=0):
mmax,_ = torch.max(x, dim=dim, keepdim=True)
return mmax + (x-mmax).... |
"""
http://en.wikipedia.org/wiki/Partial_correlation#Using_linear_regression
Taking X and Y two variables of interest and Z the matrix with all the variable minus {X, Y},
the algorithm can be summarized as
1) perform a normal linear least-squares regression with X as the target and Z as the predictor
2) calcula... |
#!/usr/bin/python
from __future__ import division
import math
import signal
import sys
import numpy as np
from scipy.spatial import distance
from munkres import munkres
from . import Matcher
from itertools import izip
from scipy.stats import kendalltau
from matteautils.base import printd
import matteautils.config as co... |
import numpy as np
from imutils import face_utils
import cv2
import dlib
from scipy.spatial import distance as dist
import time
from firebase import firebase
FBconn = firebase.FirebaseApplication('https://ed-workshop.firebaseio.com/', None)
def MAR(mouth):
return (dist.euclidean(mouth[0],mouth[1]) + dist.euclid... |
<gh_stars>1-10
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Wed Feb 6 21:10:46 2019
@author: rdamseh
"""
from VascGraph.Tools.CalcTools import *
import scipy as sp
import numpy as np
def Tmodel(noisy=False, smooth=False):
tr1=np.zeros((21,21))
ind=(np.array([ 0, 0, 0, 0, 0, 0, 0, ... |
<filename>srcAna/Learn_BG.py
import pylab as plt
import numpy as np
import matplotlib.patches as mpatches
from matplotlib.colors import LinearSegmentedColormap
from scipy import stats
import itertools
"""
Load 1 simualtion (main experiment)
Plot weights IT-StrD1-GPi, IT-STN-GPi, IT-StrD2-GPe for different time poin... |
<filename>main.py<gh_stars>10-100
import copy
import glob
import os
import time
import gym
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from baselines.common.vec_env.dummy_vec_env import DummyVecEnv
from baselines.common.vec_env.subproc_vec_env impo... |
"""
This module defines solvers that use multiple starting points in order to have a higher chance at finding the global minimum.
"""
from . import utils, logging
from .solvers import Solver, default_solver
import numpy as np
import scipy as sp
import scipy.optimize
from qsrs import native_from_object
import time
from ... |
<reponame>aksarkar/anmf<filename>tests/test_modules.py
import anmf
import numpy as np
import scipy.stats as st
import torch
import torch.utils.data as td
from fixtures import *
def test_Encoder():
enc = anmf.modules.Encoder(input_dim=100, hidden_dim=50, output_dim=10)
x = torch.zeros([1, 100])
# if torch.cuda.i... |
<filename>Implementations/otsu.py
# Como executar:
# $ python otsu.py <img_entrada> <img_saida>
import sys
import numpy as np
import matplotlib.pyplot as plt
from scipy import misc
from skimage import img_as_float, filters
def loadImg(arg):
return misc.imread(arg)
# Lê a imagem a partir de um arquivo
img_1 = loa... |
<filename>src/Classes/MSDS400/Module 6/m6_discussion.py
from sympy import solve, Limit, lambdify, symbols, diff
import matplotlib.pyplot as plt
import numpy as np
domain_end = 20
g_xlim = [-2, 40]
g_ylim = [-2, 70]
# eq
x = symbols('x', positive = True ) # 0 <= x
R = 11000 - x ** 3 + 42 * x ** 2 + 800 * x # Revenue... |
# ############################################################################################### #
# #
# Implements the WEAT score calculation presented in [1]. #
# ... |
import sys
sys.path.append("/home/fs01/se237/ProdRank/build")
from z3 import *
import argparse
import time
from random import randint
#from xorLength import f_star
import random
import math
import scipy
import scipy.misc
def toSMT2Benchmark(f, status="unknown", name="benchmark", logic=""):
v = (Ast * 0)... |
<filename>scripts/build_goal_oriented_suggester.py
from suggestion import clustering
from scipy.special import entr
import tqdm
import logging
import pandas as pd
import pickle
import numpy as np
import cytoolz
logging.basicConfig(level=logging.INFO)
logging.info("Load all the reviews.")
data = pickle.load(open('yel... |
import numpy as np
import matplotlib
# don't use xwindow
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from scipy.stats.mstats import gmean
accelout = "FINAL_PROCESSED"
a = open(accelout, 'r')
acceldat = a.readlines()
a.close()
acceldat_arrays_ser = []
acceldat_arrays_des = []
boomdat_arrays_ser = []
boomda... |
import pathlib
import time
import scipy.misc
from mxnet import nd
import mxnet as mx
import h5py
import numpy as np
from mxnet import gluon
class DeepLatentGaussianModel(gluon.HybridBlock):
def __init__(self):
super().__init__()
with self.name_scope():
self.log_prior = GaussianLogProb()
# genera... |
<reponame>landdafku11/CogAlg
import os
import cv2
import argparse
import numpy as np
from scipy import misc
from time import time
from collections import deque
''' Temporal blob composition over a sequence of frames in a video:
pixels are compared to rng adjacent pixels over lateral x, vertical y, temporal t coor... |
# --------------
# Import packages
import numpy as np
import pandas as pd
from scipy.stats import mode
# code starts here
bank=pd.read_csv(path)
categorical_var=bank.select_dtypes(include='object')
print(categorical_var)
numerical_var=bank.select_dtypes(include='number')
print(numerical_var)
# code ends here
#... |
<filename>frites/utils/preproc.py<gh_stars>1-10
"""Pre- and post-processing functions."""
import numpy as np
import xarray as xr
from scipy.signal import savgol_filter as savgol
from scipy.signal import fftconvolve
from frites.io import set_log_level, logger
def savgol_filter(x, h_freq, axis=None, sfreq=None, polyor... |
<reponame>AaronGlanville/Barry
import sys
sys.path.append("..")
from barry.samplers import DynestySampler
from barry.cosmology.camb_generator import getCambGenerator
from barry.postprocessing import BAOExtractor
from barry.config import setup
from barry.models import PowerSeo2016, PowerBeutler2017, PowerDing2018, Powe... |
<filename>_build/jupyter_execute/content/Module01/M01_N05_SampleStatistics.py
#!/usr/bin/env python
# coding: utf-8
# # Sample Statistics
# Reading: Emile-Geay Chapter 4.I and 4.II (p51-58)
#
# Other resources:
# https://en.wikipedia.org/wiki/Sampling_distribution
#
# https://en.wikipedia.org/wiki/Central_limit_th... |
<reponame>guohaoqiang/gcn<gh_stars>0
from __future__ import division
from __future__ import print_function
import time
import torch
import numpy as np
from numpy import argmax
import torch.nn.functional as F
from pygcn.gcnio.data import dataio
from pygcn.gcnio.util import utils
from pygcn.gcn1 import GCN
import scipy.... |
import skimage
from skimage.io import imread, imsave
from skimage import measure
import matplotlib.pyplot as plt
from skimage.segmentation import quickshift, felzenszwalb, slic
from skimage.segmentation import mark_boundaries
from skimage.filters import gaussian, median
from skimage.morphology import disk
import nump... |
#파이썬으로 상관분석 회기분석테스트
import numpy as np
import pandas as pd
#csv 파일 읽어오기
hdr = ['V1','V2','V3','V4','V5','V6','V7','V8','V9']
df = pd.read_csv('c:/java/phone-02.csv', header=None,names=hdr)
print(df)
#상관분석
dfc=df.corr()
print(dfc)
# df97=df['V9'].corr(df['V7'])
# or
df97=df.V9.corr(df.V7)
#회기분석
from scipy import s... |
<filename>Homework_9/sample.py<gh_stars>0
import numpy as np
from helpers import *
from scipy.optimize import least_squares
P = np.zeros((3, 4))
P[:, :3] = np.eye(3)
print np.linalg.pinv(P)
|
<filename>eval_schedules.py<gh_stars>1-10
from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
import os
from time import time, localtime, strftime, sleep
import pickle
import numpy as np
import tensorflow as tf
import scipy.io as sio
from dotmap import DotMap
#... |
<gh_stars>1-10
from scipy import sparse
from os import path
from src import config
from src.config import artifact_stores
from src.utils.preprocess import merge_edge_lists
from src.utils import io
from src.utils.converters import edge_list_to_adj_matrix
OUTFILE_NAME = "adjacency_matrix.npz"
def main():
node_labe... |
<reponame>michielkleinnijenhuis/EM<filename>snippets/3DEM/knossos/scratch_knossos.py
# knossos data prep
git clone https://github.com/knossos-project/knossos_python_tools.git
scriptdir="$HOME/workspace/EM"
DATA="$HOME/oxdata"
datadir="$DATA/P01/EM/M3/M3_S1_GNU"
dataset='m000'
z=30; Z=460;
x=1000; y=1000;
[ $x == 5000 ... |
<filename>detectors/algorithms/Polygonify.py<gh_stars>1-10
import numpy as np
from scipy.spatial import ConvexHull
from scipy.ndimage.interpolation import rotate
class Polygonify:
def __init__(self, points):
self.points = points
def find_polygon(self, rectangle=False):
if (rectangle):
... |
<reponame>APS-XSD-OPT-Group/wavepytools
'''
find the 1D line in the integrated phase in the csv file after wavepy processing
'''
import os
import sys
import glob
import numpy as np
import csv
from matplotlib import pylab as plt
import scipy.io as sio
def line_profile_process(Folder_path):
# Folder name
... |
<reponame>SaiRav95/Financial-Calculator
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import math
import scipy.stats as st
def ExpectedLoss(PD, EA, LR):
## PD is the probability of default or Expected Default frequency (EDF) ##
## EA is the Exposure Amount (Total loan amount) ##
## LR is the loss rate... |
<gh_stars>0
import numpy as np
from scipy.optimize import fsolve
class conversion_models:
d= 10.0/100.0 # diameter in meters
length =6.36 #meters
k = 0.25 # (min)^-1
sigma_sq = 6.15 #minutes^2
t_mean = 5.1 #minutes
def closed_vessel_dispersion(self):
d = self.d
... |
<filename>partition_data.py
import glob
import sys
import os
import random
import pdb
from PIL import Image, ImageOps
import cPickle as pickle
import numpy as np
from scipy import misc
from constants import *
if not os.path.isdir(BLOB_TRAIN_IMAGE_DIR):
os.makedirs(BLOB_TRAIN_IMAGE_DIR)
if not os.path.isdir(BLOB_T... |
<reponame>razvanc92/ST-WA<gh_stars>0
import os
import random
from scipy.sparse.linalg import eigs
import numpy as np
import torch
from componenets.normalization import NScaler, MinMax01Scaler, MinMax11Scaler, StandardScaler, ColumnMinMaxScaler
def normalize_dataset(data, normalizer, column_wise=False):
if normal... |
<reponame>luulinh90s/my-gans
import argparse
import os
import numpy as np
import math
import torchvision.transforms as transforms
from torchvision.utils import save_image
from torch.utils.data import DataLoader
from torchvision import datasets
from torch.autograd import Variable
import torch.nn as nn
import torch.nn... |
<filename>python/principalpath.py
#References:
#[1] 'Finding Prinicpal Paths in Data Space', M.J.Ferrarotti, W.Rocchia, S.Decherchi
#[2] 'Design and HPC Implementation of Unsupervised Kernel Methods in the Context of Molecular Dynamics', M.J.Ferrarotti, PhD Thesis.
#[3] https://github.com/mjf-89/PrincipalPath/blob/mast... |
"""
An example to illustrate the recovery of 5 Dirac deltas with two distinct horizontal and
vertical locations only:
(x2, y1)
(x1, y2) (x2, y2) (x3, y2)
(x2, y3)
"""
from __future__ import division
import os
import subprocess
import warnings
import numpy as np
from scipy import linalg
from alg_joint_... |
#%%
import pandas as pd
import numpy as np
import holoviews as hv
import hvplot.pandas
from scipy.sparse.linalg import svds
from scipy.stats import chisquare, chi2_contingency
from sklearn.decomposition import TruncatedSVD
from umoja.ca import CA
hv.extension('bokeh')
#%%
X = context.io.load('xente_train')
Y = contex... |
<filename>additional code/main.py<gh_stars>0
#!/usr/bin/python
import os
from generate_samples import dataset_for_sampling
from src.vipurpca.PCA import PCA
#from data_mnist import get_mnist_dataset
from Animation import Animation
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec
import plot... |
<filename>CameraQEresponse.py
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from scipy.optimize import curve_fit
from scipy.integrate import trapz
def QECurveFit(x,a,b,c,d,e,f,g,h,i,l,m,n):
return n+a*x+b*x**2+c*x**3+d*x**4+e*x**5+f*x**6+g*x**7+h*x**8+i*x**9+l*x**10+m*x**11
def TrapzInteg... |
import os
import numpy as np
import scipy.sparse
from sklearn import datasets
def load_dataset(args):
path = f"{args.data_folder}/{args.function_name}"
X, y = datasets.load_svmlight_file(f"{path}.svm")
w = np.load(f"{path}.npy")
return X, y, w
def store_dataset(X, y, w, args):
folder = f"{args.... |
<filename>attitude/geom/symbolic_math.py
"""
Functions for conic geometry implemented for
use with the `sympy` symbolic math module.
Generally, for use in testing and validation.
"""
from sympy import Matrix
def center(conic):
ec = conic[:-1,:-1].inv()
eo = -conic[:-1,-1]
return ec*eo
def dual(conic):
... |
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