text string |
|---|
# This file shows some example usage of Python functions to read an OCT file.
# To use exectute this test reader, scroll to the bottom and pass an OCT file to the function unzip_OCTFile.
# Find the comment #Example usage.
#
# Additional modules to be installed should be 'xmltodict', 'shutil', and 'gdown'.
# Tested in P... |
#!/usr/bin/env python
import os,sys,pdb,scipy,glob
from pylab import *
import urllib, urllib2
import xml.dom.minidom
import datetime
def ADS():
thisMirror = 'http://adsabs.harvard.edu/'
print 'Retrieving from ',thisMirror
baseUrl = thisMirror + 'cgi-bin/nph-abs_connect?'
return baseUrl
def get_text(... |
<filename>src/fftIfftTests.py
import subprocess as sp
import scikits.audiolab
import numpy as np
from scipy.fftpack import fft, ifft
from scipy.io import wavfile
#--CONVERT MP3 TO WAV------------------------------------------
song_path = '/home/gris/Music/vblandr/test_small/punk/07 Alkaline Trio - Only Love.mp3'
comm... |
<gh_stars>0
import numpy as np
import h5py
import keras
import keras.backend as K
from glob import glob
import json
import math, scipy
from scipy.optimize import linear_sum_assignment
import time
from collections import OrderedDict
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import kera... |
<reponame>ppik/loxodon<filename>prep_cars_data.py
#!/usr/bin/env python
import os
from os.path import basename, dirname, exists
from glob import glob
from random import seed, sample
from math import ceil
import shutil
from scipy.io import loadmat
DATA_PATH = 'data/'
VALID_RATIO = 0.2
seed(20171111)
info = loadmat(... |
<reponame>YuzhongHuangCS/journal-citation-cartels<filename>notebooks/construct-network/construct_network.py
import numpy as np
import pandas as pd
import py2neo
import pickle
import os,sys
from scipy import sparse
def edges2adj(edges, raw_edges, year, pcount):
# Uniqify edges
def uniqify_edges(edges, pcount):... |
<filename>chaospy/distributions/collection/gompertz.py
"""Gompertz distribution."""
import numpy
from scipy import special
from ..baseclass import SimpleDistribution, ShiftScaleDistribution
class gompertz(SimpleDistribution):
"""Gompertz distribution."""
def __init__(self, c):
super(gompertz, self).... |
<filename>TFQ/barren_plateaus/bp_tfq.py
import tensorflow as tf
import tensorflow_quantum as tfq
import cirq
import sympy
import numpy as np
import matplotlib.pyplot as plt
# https://www.tensorflow.org/quantum/tutorials/barren_plateaus#2_generating_random_circuits
def generate_circuit(qubits, depth, param):
circui... |
<filename>IRIS_data_download/IRIS_download_support/obspy/signal/tests/test_filter.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
The Filter test suite.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
from future.builtins import * # NOQA
import gzi... |
# -*- coding: utf-8 -*-
"""
price calculations
~~~~~~~~~
WARNING: This is specific to US income data.
If this needs to be changed,
you will need to update the BINS values and some of the regex expressions.
:copyright: (c) 2015 by <NAME>, Santa Fe Institute.
:license: MIT
"""
import numpy a... |
<reponame>cyhsu/leaflet-velocity
import os, sys, json
import numpy as np
import xarray as xr
from glob import glob
from datetime import datetime
from netCDF4 import Dataset, num2date, date2num
from scipy.interpolate import griddata
#- HYCOM GLBv0.08/latest (daily-mean) present + forecast
#- Detail info: https://www.h... |
"""Compute rank correlations between word vector cosine similarities and human ratings of semantic similarity."""
import numpy as np
import pandas as pd
import argparse
import os
import scipy.spatial.distance
import scipy.stats
from .vecs import Vectors
from .utensils import log_timer
import logging
logging.b... |
"""
Calculates distance of some (bpp, metric) point (for some metric) to some codec on some dataset.
"""
import os
import numpy as np
import scipy.interpolate
from utils import other_codecs
import constants
from utils import logdir_helpers
from collections import defaultdict
from fjcommon import functools_ext as ft
f... |
<reponame>awwong1/topic-traceability
#!/usr/bin/env python3
"""Calculate distances using the topic models on the course material/discussion
posts feature vectors.
"""
import os
import numpy as np
from datetime import datetime
from numpy import ravel
from pickle import load
from json import dump
from scipy.spatial.dista... |
<filename>InjectionTools.py
import numpy as np
import urllib
from lxml import etree
import batman
import ldtk
from scipy.interpolate import interp1d
# from joblib import Parallel, delayed
import random
import matplotlib.pyplot as plt
from scipy.spatial.qhull import QhullError
import multiprocessing
import logging
impor... |
import numpy as np
import scipy
import scipy.io
import pickle
from scipy.special import lpmv, spherical_jn, spherical_yn
class Directivity:
def __init__(self, data_path, rho0, c0, freq_vec, simulated_ir_duration, measurement_radius, sh_order, type, sample_rate=44100, **kwargs):
''... |
<gh_stars>100-1000
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import os
import sys
import torch
import numpy as np
import scipy.misc as m
import matplotlib.pyplot as plt
import matplotlib.image as imgs
from PIL import Image
import random
import scipy.io as io
from tqdm import tqdm
from s... |
from scipy.integrate import solve_ivp
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error
from hyperopt import hp, fmin, tpe
from typing import Tuple
def SIR(t, y, N, kappa, tau, nu):
"""
Expresses SIR model in initial value ODE format, including time,
... |
<gh_stars>1-10
import numpy as np
import collections
try:
from scipy.stats import scoreatpercentile
except:
# in case no scipy
scoreatpercentile = False
def _confidence_interval_1d(A, alpha=.05, metric=np.mean, numResamples=10000, interpolate=True):
"""Calculates bootstrap confidence interval along one... |
# <NAME>
# <EMAIL>
import numpy as np
from scipy.interpolate import interp2d
import copy
class GrismApCorr:
"""
GrismApCorr is a class containing tables for aperture correction (i.e., apcorr = f(wavelength, apsize)) in aXe reduction. These tables are from ISRs. Interpolaton model is also prepared.
- Avail... |
# -*- encoding: utf-8 -*-
'''
@File : lr_1d.py.py
@Modify Time @Author @Desciption
------------ ------- -----------
2021/7/5 22:51 Jonas None
'''
import numpy as np
import math
import matplotlib.pyplot as plt
from scipy.stats import norm
train_data = np.loadtxt("lin_reg_tr... |
<reponame>Wisc-HCI/CoFrame<filename>evd_ros_backend/evd_ros_core/src/evd_sim/pose_interpolator.py
from geometry_msgs.msg import Pose
from scipy.interpolate import interp1d
from pyquaternion import Quaternion
class PoseInterpolator:
def __init__(self, poseStart, poseEnd, velocity, minTime=1):
'''
... |
<gh_stars>1-10
import pysam
import pandas as pd
import mappy as mp
from itertools import chain
from statistics import median
from collections import Counter
from scipy.stats import entropy
MINLEN=50
MAXLEN=50000
def summary(splitter,caller):
try:
minsig = splitter.minSignal
minfrac = splitter._... |
<gh_stars>1-10
# LICENSE
# Copyright (c) 2013-2016, <NAME> (<EMAIL>)
# 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,... |
#! -*- coding: utf-8 -*-
# Keras implement of Glow
# Glow模型的Keras版
# https://blog.openai.com/glow/
from keras.layers import *
from keras.models import Model
from keras.datasets import cifar10
from keras.callbacks import Callback
from keras.optimizers import Adam
from flow_layers import *
import imageio
import numpy as... |
<reponame>tsmonteiro/fmri_proc
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 17 09:39:23 2020
@author: u0101486
"""
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Dec 5 12:26:49 2019
@author: u0101486
"""
# Aggregate QC measures
import os
import sys
import numpy as np
im... |
<gh_stars>0
import numpy as np
import networkx as nx
import sympy as sp
import matplotlib.pyplot as plt
import matplotlib as mpl
from sys import exit
from scipy.optimize import curve_fit
from scipy.integrate import odeint
class Model(nx.DiGraph):
"""
Base class for compartmental models.
See also:
-... |
import pickle
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as scp_stats
import pandas as pd
import f_rate_t_by_type_functions as frtbt
N_trials = 15
# Decide which systems we are doing analysis for.
sys_dict = {}
sys_dict['all_mice'] = { 'cells_file': '../build/ll1.csv', 'f_1': '/data/mat... |
<gh_stars>0
import logging
from itertools import cycle
from typing import Dict, List, Optional, Tuple, Union
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from matplotlib.axes import Axes
from matplotlib.figure import Figure
from matplotlib.ticker import ScalarFormat... |
"""
Implementation of the hierarchical poisson glm model, with a precinct-specific
term, an ethnicity specific term, and an offset term.
The data are tuples of (ethnicity, precinct, num_stops, total_arrests), where
the count variables num_stops and total_arrests refer to the number of stops
and total arrests of an eth... |
"""Contains the n dimensional inverted pendulum environment."""
import warnings
from typing import Optional
import matplotlib.pyplot as plt
import numpy as np
from numpy import ndarray
from polytope import polytope
from scipy.integrate import ode
from scipy.spatial.qhull import ConvexHull
from ..utils import assert_s... |
import contextlib
import unittest
from test import support
from itertools import permutations, product
from random import randrange, sample, choice
import warnings
import sys, array, io
from decimal import Decimal
from fractions import Fraction
try:
from _testbuffer import *
except ImportError:
ndarray = None
t... |
<reponame>andsteing/being<filename>tests/test_serialization.py
import unittest
import enum
from typing import NamedTuple
import numpy as np
from numpy.testing import assert_equal
from scipy.interpolate import PPoly, CubicSpline, BPoly
from being.serialization import (
ENUM_LOOKUP, EOT, NAMED_TUPLE_LOOKUP, FlyByDe... |
"""/**
* @author [<NAME>]
* @email [<EMAIL>]
* @create date 2020-05-21 11:55:58
* @modify date 2020-06-16 23:33:58
* @desc [
SC_Difficulty class with methods to set speed challenge difficulty:
- Ask for difficulties
- Acknowledge difficulty message.
]
*/
"""
##########
# Imports
##########
from sta... |
<filename>Python_Projects/Global_Model/Working/calculation.py
#!/usr/bin/env python
# coding: utf-8
import xs
import numpy as np
from scipy.integrate import odeint
from math import isclose
from constants import *
class Global_model:
def __init__(self, p, input_power, duty, period, time_resolution=1e-8):
... |
<reponame>knuu/competitive-programming
from heapq import heapify, heappush, heappop
from collections import Counter, defaultdict, deque, OrderedDict
from sys import setrecursionlimit, maxsize
from bisect import bisect_left, bisect, insort_left, insort
from math import ceil, log, factorial, hypot, pi
from fractions impo... |
<gh_stars>1-10
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Aug 20 21:29:32 2021
@author: qcao
Analysis code for example_topop_tb_v3.py
Parses and cleans load-driven phantoms. Computes Radiomic signatures. Compares with BvTv.
Compare with ROIs
"""
# FEA and BoneBox Imports
import os
import sy... |
"""
PURPOSE:
Run feature selection mettestd available from sci-kit learn on a given dataframe
Must set path to Miniconda in HPC: export PATH=/mnt/testme/azodichr/miniconda3/bin:$PATH
INPUT:
-df Feature file for ML. If class/Y values are in a separate file use -df for features and -df2 for class/Y
-alg ... |
<gh_stars>1-10
import numpy as np
from scipy.fft import ifft
def generate_waveforms(data: np.ndarray) -> np.ndarray:
"""
Generate waveforms from frequency domains
:param data: frequency domains (where first n/2 examples consist of real values and the rest consists
of imaginary values)
:return: nu... |
#!/usr/bin/env python
#
# NSC_INSTCAL_SEXDAOPHOT.PY -- Run SExtractor and DAOPHOT on an exposure
#
from __future__ import print_function
__authors__ = '<NAME> <<EMAIL>>'
__version__ = '20180819' # yyyymmdd
import os
import sys
import numpy as np
import warnings
from astropy.io import fits
from astropy.wcs import WC... |
from datetime import datetime, timedelta
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy.optimize as opt
def area_chart(ds, dateFmt):
# create a subplot
fig, ax = plt.subplots()
# set figure size and dpi
fig.set_size_inches(10, 5)
... |
<filename>bin/NormalizeReadCounts.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Created on Mon Feb 13 09:23:51 2017
@author: philipp
"""
# Analyze count distribution
# =======================================================================
# Imports
from __future__ import division # floating point division by d... |
<reponame>twiecki/edward<filename>examples/convolutional_vae.py
#!/usr/bin/env python
"""
Convolutional variational auto-encoder for MNIST data. The model is
written in TensorFlow, with neural networks using Pretty Tensor.
Probability model
Prior: Normal
Likelihood: Bernoulli parameterized by convolutional NN
... |
<filename>sparkdq/models/dbscan/DBSCAN.py
from operator import add
import numpy as np
from pyspark.sql.types import StructField, StructType, IntegerType
from scipy.spatial.distance import euclidean
import sklearn.cluster as skc
from sparkdq.conf.Context import Context
from sparkdq.models.CommonUtils import DEFAULT_CL... |
<gh_stars>0
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""Functions to compute TS images."""
import functools
import logging
import warnings
import numpy as np
import scipy.optimize
from astropy.coordinates import Angle
from gammapy.datasets.map import MapEvaluator
from gammapy.maps import Map, Wcs... |
<reponame>eugeniu1994/Stereo-Camera-LiDAR-calibration<gh_stars>1-10
''' CONFIDENTIAL
Copyright (c) 2021 <NAME>,
Department of Remote Sensing and Photogrammetry,
Finnish Geospatial Research Institute (FGI), National Land Survey of Finland (NLS)
PERMISSION IS HEREBY LIMITED TO FGI'S INTERNAL USE ON... |
from deduplication import simhash, lsimhash
from pathlib import Path
from scipy.spatial.distance import hamming
import numpy as np
import textwrap
def test_main():
with open(Path(__file__).resolve().parent / 'data' / 'wiki_nlp.txt', 'r') as f:
content = f.read()
hval = lsimhash.lsimhash(content)
... |
<filename>ocr-server/ocr_server/lines.py
from typing import List
import numpy as np
import cv2
import scipy.signal
def find_line(image: np.ndarray, window_size: int = 30) -> np.ndarray:
"""Extracts a single line from the image"""
image_inverted = cv2.bitwise_not(image)
image_as_column = np.sum(image_inve... |
<filename>utils.py
# Description: A library of common utilities
# Author: <NAME>
import numpy as np
import cv2
from scipy.signal import convolve2d
import matplotlib.pyplot as plt
def imagify(fft):
'''
2D ffts usually have real and imaginary components, and they also usually
have way too much dynamic range... |
import numpy as np
from scipy.optimize import line_search
import locale
locale.setlocale(locale.LC_ALL, '')
class cd_res(object):
def __init__(self, x, fun):
self.x = x
self.fun = fun
print_stop_iteration = 1
def cdl_step(score,
guess,
jac,
val = Non... |
import numpy as np
from numpy import *
import pandas as pd
from pandas import DataFrame, Series
from numpy.random import randn
import tensorflow as tf
import matplotlib.pyplot as plt
from PIL import Image
import re
from skimage.io import imread, imshow
from termcolor import colored
import keras
import h5py
... |
import functools
import itertools
import operator
import re
import numpy as np
import pandas as pd
from pandas.api.types import is_numeric_dtype
import toolz
from genopandas import plotting as gplot
from genopandas.util.pandas_ import DfWrapper
from .frame import GenomicDataFrame, GenomicSlice
RANGED_REGEX = r'(?P<... |
<reponame>CarlGriffinsteed/UVM-ME144-Heat-Transfer<gh_stars>1-10
"""
Object name: HorizontalCylinder
Functions: Gr(g,beta,DT,D,nu) gives the Grashoff number based on:
gravity g, thermal expansion coefficient beta, Temperature difference DT,
length scale D, viscosity nu
Ra(g,beta,DT,... |
"""Modules for graph embedding methods."""
import logging
import gensim
import networkx as nx
import numpy as np
import pandas as pd
# For GCN
import stellargraph as sg
import tensorflow as tf
from graph_embeddings import samplers, utils
from scipy import sparse
from sklearn import model_selection
from stellargraph.... |
import numpy as np
import matplotlib.pyplot as plt
import math
from scipy.io.wavfile import read as read_wav
from scipy.io.wavfile import write as write_wav
from scipy.fftpack import fft, fftshift
from scipy.signal import lfilter, firwin
def filt(sig, Fc=0.5, NFIR=101):
fir_taps = firwin(NFIR, Fc, window=('blackma... |
"""
Core module.
Normally, do not add new construction methods here, do this in scene.py instead.
"""
from enum import Enum, auto, unique
import itertools
import re
import sympy as sp
from typing import List
from .figure import Figure
from .reason import Reason
from .util import LazyComment, Comment, divide
class Co... |
<reponame>arminnh/ma2-computer-vision
import numpy as np
import scipy.spatial.distance
from scipy import linalg
import procrustes_analysis
import util
from Landmark import Landmark
from models.CenterInitializationModel import CenterInitializationModel
class ToothModel:
def __init__(self, name, landmarks, pcaComp... |
from simba import transfer_function_to_graph, tf2rss
from sympy import symbols
# passive realisation (g = 0)
s = symbols('s')
gamma_f, omega_s = symbols('gamma_f omega_s', real=True, positive=True)
tf = (s**2 + s * gamma_f + omega_s**2) / (s**2 - s * gamma_f + omega_s**2)
h_int = tf2rss(tf).to_slh().split().interac... |
# Erstelle aus gegebnen Daten eine Ausgleichskurve
# Und Plotte diese Kurve + die Daten
# wechsle die Working Directory zum Versuchsordner, damit das Python-Script von überall ausgeführt werden kann
import os,pathlib
project_path = pathlib.Path(__file__).absolute().parent.parent
os.chdir(project_path)
# benutze die ma... |
<filename>waveform_analysis/tests/test_ITU_R_468_weighting.py
import pytest
from scipy import signal
from scipy.interpolate import interp1d
import numpy as np
from numpy import pi
# This package must first be installed with `pip install -e .` or similar
from waveform_analysis import (ITU_R_468_weighting_analog,
... |
<gh_stars>10-100
import scipy.interpolate
import scipy.signal
from builtins import staticmethod
import numpy as np
from scripts.utils.ArrayUtils import ArrayUtils
class MatlabUtils:
@staticmethod
def max(array: np.ndarray):
if len(array) > 1:
return np.amax(array, axis=0)
else:
... |
import h5py
import pandas as pd
import numpy as np
import json
import scipy as sp
import nibabel as nib
from glob import glob
import fnmatch
import os
run_name_dict = {
"REST1": "REST1_7T_PA",
"REST2": "REST2_7T_AP",
"REST3": "REST3_7T_PA",
"REST4": "REST4_7T_AP",
"MOVIE1": 'MOVIE1_CC1',
"MOV... |
<filename>jabble/dataset.py
import numpy as np
# import matplotlib.pyplot as plt
import astropy.table as at
import astropy.units as u
import astropy.coordinates as coord
import astropy.constants as const
import astropy.time as atime
import scipy.ndimage as ndimage
import numpy.polynomial as polynomial
# import jabble... |
<reponame>McCoyGroup/Coordinerds
"""
Redoes what was originally PyDVR but in the _right_ way using proper subclassing and abstract properties
"""
import abc, numpy as np, scipy.sparse as sp, scipy.interpolate as interp
from McUtils.Data import UnitsData
__all__ = ["BaseDVR", "DVRResults", "DVRException"]
class Base... |
# Code from Chapter 18 of Machine Learning: An Algorithmic Perspective (2nd Edition)
# by <NAME> (http://stephenmonika.net)
# You are free to use, change, or redistribute the code in any way you wish for
# non-commercial purposes, but please maintain the name of the original author.
# This code comes with no warranty ... |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import xarray as xr
from scipy import signal
import timeit
from numba import jit
import datashader as ds
import xarray as xr
from datashader import transfer_functions as tf
#1. Define the boundary conditions
# Needed: surface temperature forcing (s... |
<reponame>parkerwray/tmm
"""
Import relevant modules
"""
from __future__ import division, print_function, absolute_import
#from tmm.tmm_core import (coh_tmm, unpolarized_RT, ellips,
# position_resolved, find_in_structure_with_inf)
from wptherml.wptherml.datalib import datalib
import tmm.tmm_cor... |
<reponame>AleFeli/momepy
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# dimension.py
# definitions of dimension characters
import math
import numpy as np
import pandas as pd
import scipy as sp
from shapely.geometry import LineString, Point, Polygon
from tqdm import tqdm
from .shape import _make_circle
__all__ = [... |
<reponame>saberzuko/MachineLearningAlgorithms<filename>KMeansClustering/clustering.py<gh_stars>0
import numpy as np
from scipy.spatial import distance
import random
def mu_generator(X, K):
# Function to initialize the cluster centers
# The input is the training data X and the number of cluster centers
mu =... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Apr 18 09:08:20 2022
A script to plot mean daily cores for intercomparison of features as a function of time through a season.
@author: michaeltown
"""
#libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
import... |
# imports
from .ball import Pallino
from .throw import Throw
from .cv.ballfinder import BallFinder
from scipy.spatial import distance as dist
# for now, these are "pixels" (not "inches" or "cm")
TOO_CLOSE_MARGIN = 5
class Frame:
def __init__(self, frameNumber, throwingEnd, pallinoThrowingTeam,
teamHome, ... |
"""I3D feature extration using a tensorflow model.
Copyright 2018 Mitsubishi Electric Research Labs
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import h5py
import numpy as np
import tensorflow as tf
import time
import os
import scipy.io as sio
im... |
# -*- coding: utf-8 -*-
from __future__ import division, print_function
__all__ = ["prepare_characterization"]
import kplr
import transit
import numpy as np
from scipy.stats import beta
import matplotlib.pyplot as pl
import george
from george import kernels
from ..prepare import Prepare
from ..download import Down... |
#!/bin/python
from deap import tools
from copy import deepcopy
import random
from deap import algorithms
import promoterz
import statistics
from .. import evolutionHooks
def checkPopulation(population, message):
if not (len(population)):
print(message)
def standard_loop(World, locale):
# --assertion... |
<gh_stars>0
import numpy as np
import scipy.sparse as ss
import logging
import time
import warnings
from .feature_selection import get_significant_genes
from .feature_selection import calculate_minmax
warnings.simplefilter("ignore")
logging.basicConfig(format='%(process)d - %(levelname)s : %(asctime)s - %(message)s'... |
#!/usr/bin/env python2
import numpy as np
import os
import scipy
VIS_DIR = "vis"
class Visualizer:
def __init__(self):
self.active = False
def begin(self, dest, max_entries):
self.lines = []
self.active = True
self.max_entries = max_entries
self.next_entry = 0
... |
<reponame>mattpitkin/GraWIToNStatisticsLectures
#!/usr/bin/env python
"""
Make plots of the Student's t-distribution for different degrees of freedom
"""
import matplotlib.pyplot as pl
from scipy.stats import norm
from scipy.stats import t
import numpy as np
mu = 0. # the mean, mu
nus = [1., 2., 5, 10, 100] # standa... |
import logging
import time
from contextlib import contextmanager
import numpy as np
import pandas as pd
import scipy.stats
from openml import datasets, runs
from sklearn.model_selection import train_test_split
logger = logging.getLogger("dashboard")
logger.setLevel(logging.DEBUG)
def get_run_df(run_id: int):
ru... |
#%%
import numpy as np
from itertools import repeat
from itertools import starmap
from scipy.stats import norm
class ABCer:
def __init__(self, iterations, particles, observations):
self.iterations = iterations
self.particles = particles
self.observations = observations
def initialize_... |
<gh_stars>10-100
#from scikits.talkbox.features import mfcc
import scipy.io.wavfile
import numpy as np
import sys
import os
import glob
from utils1 import GENRE_DIR, GENRE_LIST
from python_speech_features import mfcc
#from librosa.feature import mfcc
# Given a wavfile, computes mfcc and saves mfcc data
def create_ce... |
from typing import (
Any,
Callable,
List,
NamedTuple,
Optional,
Tuple,
Type,
Union,
overload,
)
import numpy as np
from scipy import special
Array = Union[np.ndarray]
Numeric = Union[int, float]
# Lists = Union[Numeric, List['Lists']]
Tuplist = Union[Tuple[int, ...], List[int]]
Dim... |
<filename>FigureGeneration/makeFigure1.py
import matplotlib.pyplot as plt
from scipy.optimize import root
import matplotlib
import numpy as np
def makeFigure1():
def fun(x):
return [(x[0]**qq)/(1+x[0]**qq) - bb*x[0]]
b = [0.4,0.3,0.2,0.1]
q = [2.5,3,3.5,4]
x = np.arang... |
import torch
import torch.nn as nn
import torch.nn.functional as F
from config import _C as C
from models.layers.GNN_dmwater import GraphNet
from scipy import spatial
import numpy as np
import utils
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.node_dim_in = C.NET.NODE... |
"""Export data"""
from scipy.io import savemat
def mat(filename, mdict):
"""Export dictionary to .mat file for MATLAB"""
savemat(filename, mdict)
|
<filename>wouldyouci_database/recommendation/contents_based_filtering.py<gh_stars>1-10
import os
import time
import pymysql
import pandas as pd
from decouple import config
from datetime import datetime
from sklearn.linear_model import Lasso
from sklearn.linear_model import LinearRegression
from sklearn.model_selection ... |
#a2.t4 #This program is to create a function to check carbondioxide content in air
#taking advantage of python statistics library
import statistics
def check_air_quality(carbondioxide_data):
if statistics.median(carbondioxide_data) >= 400 and statistics.median(carbondioxide_data) < 700:
return "EXCELLENT"
... |
# speaker_2_sound.py
# 한 스피커로 녹음해서 정위상, 역위상 wav를 생성한 다음 정위상은 왼쪽, 역위상은 오른쪽 스피커에서 재생시키는 소스코드
# (정위상, 역위상 파일을 하나의 스테레오 wav로 만듦)
# 음성(소음) 녹음, 재생 하는 패키지(wav파일)
import pyaudio
import wave
# 위상 반전, 파장 결합(Merge), 소리 재생 하는 패키지
from pydub import AudioSegment
from pydub.playback import play
from scipy.io import wavfile
import... |
<reponame>isabellewei/deephealth<gh_stars>0
from time import time
from sklearn.preprocessing import StandardScaler
from sklearn import model_selection
from sklearn.model_selection import train_test_split, KFold, cross_val_score
from sklearn.metrics import classification_report,confusion_matrix,accuracy_score
from ... |
from dolfyn.tests import test_read_adp as tr
from dolfyn.tests import base
from dolfyn.rotate.api import rotate2
from numpy.testing import assert_allclose
import numpy as np
import scipy.io as sio
"""
Testing against velocity and bottom-track velocity data in Nortek mat files
exported from SignatureDeployment.
inst2... |
<reponame>morales-gregorio/NetworkUnit
import numpy as np
from scipy.stats import entropy
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import seaborn as sns
import sciunit
class kl_divergence(sciunit.Score):
"""
Kullback-Leibner Divergence D_KL(P||Q)
Calculates the difference of two... |
# Cálculo da razão áurea (phi)
import sympy
d = 20
phi = sympy.symbols('phi', nonnegative=True)
eqn = sympy.Eq(1/phi, phi - 1)
sol = sympy.solve(eqn)
sympy.pprint(sol)
phiAprox = sympy.N(sol[0], d)
print('Para ', d, ' dígitos significativos, ϕ = ', phiAprox)
|
<gh_stars>0
"""multipy: Python library for multicomponent mass transfer"""
__author__ = "<NAME>, <NAME>"
__copyright__ = "Copyright (c) 2022, <NAME>, <NAME>"
__license__ = "MIT"
__version__ = "1.0.0"
__maintainer__ = ["<NAME>"]
__email__ = ["<EMAIL>"]
__status__ = "Production"
import numpy as np
import pandas as pd
i... |
<filename>create_dataset.py
import os
import lmdb # install lmdb by "pip install lmdb"
import cv2
import numpy as np
from tool.xml_parser import page_images
from glob import glob
import re
import sys
import io
import argparse
from scipy.spatial import distance
encoding = 'utf-8'
stdout = sys.stdout
reload(sys)
sys.s... |
<filename>dl/cifar_python_data_layer.py
# imports
import caffe
import numpy as np
from random import shuffle
import cPickle as cp
import scipy.io as sio
class PythonDataLayer(caffe.Layer):
"""
This is a simple syncronous datalayer for training a multilabel model on
CIFAR.
"""
def setup(self, b... |
# Licensed under a 3-clause BSD style license - see LICENSE.rst
from __future__ import absolute_import, division, print_function, unicode_literals
import numpy as np
from numpy.testing import assert_allclose
from astropy.io import fits
from astropy.units import Quantity
from astropy.coordinates.angles import Angle
from... |
import networkx as nx
import numpy as np
import scipy
import graph
import itertools
from collections import defaultdict
def calculate_persistence(crystal, other, minimum_value, G, function_vals):
minimums = []
min_vertices = []
other = set(other)
for vertex in crystal:
neighbors = set(G.neighbo... |
<gh_stars>0
# Example illustrating the application of MBAR to compute a 1D PMF from an umbrella sampling simulation.
#
# The data represents an umbrella sampling simulation for the magnetization of the Ising model
# Adapted from one of the pymbar example scripts for 1D PMFs
import numpy as np # numerical array library... |
<gh_stars>1-10
# encoding=utf-8
import os
import fire
import numpy as np
from scipy.sparse.csr import csr_matrix
from sklearn.base import BaseEstimator
from sklearn.model_selection import cross_validate
from sklearn.preprocessing import normalize
from sklearn.feature_extraction.text import CountVectorizer, TfidfTrans... |
<gh_stars>1-10
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
from collections import deque
from sklearn.neighbors import KernelDensity
from scipy.stats import entropy as scientropy
import random
class NoveltyMemory:
def __init__(self,... |
from .prepare import make_train_test
import os
import tempfile
import scipy.io as sio
from hashlib import sha256
try:
import urllib.request as urllib_request # for Python 3
except ImportError:
import urllib2 as urllib_request # for Python 2
urls = {
"chembl-IC50-346targets.mm" :
(
... |
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