text string |
|---|
<filename>legacy/scrap.py
from mc import Xzy, Slot
from scipy.spatial.distance import euclidean
from scipy.linalg import solve
import time
import sys
import random
import itertools
import numpy
import os
import cPickle
import math
import json
def mine(bot, types=[14, 15]):
TORCH = bot._block_ids['torch']
DIRT = bo... |
# -*- coding: utf-8 -*-
"""
@author:XuMing(<EMAIL>)
@description: 智能标注
"""
import os
from time import time
import cleanlab
import numpy as np
from cleanlab.pruning import get_noise_indices
from scipy.sparse import csr_matrix
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train... |
<reponame>haribharadwaj/codebasket
from anlffr.helper import biosemi2mne as bs
import mne
import numpy as np
import os
import fnmatch
from scipy.signal import savgol_filter as sg
from scipy.io import savemat
# Setup bayesian-weighted averaging
def bayesave(x, trialdim=0, timedim=1, method='mean', smoothtrials=19):
... |
"""
This module provides fittable models based on 2D images.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import warnings
import logging
import numpy as np
import copy
from astropy.modeling import Fittable2DModel
from astropy.modeling.parameters imp... |
<reponame>yjy941124/PPR-FCN
import caffe
import scipy.io as sio
import os
import cv2
import numpy as np
import yaml
from multiprocessing import Process, Queue
import random
import h5py
import fast_rcnn.bbox_transform
from fast_rcnn.nms_wrapper import nms
from utils.cython_bbox import bbox_overlaps
import numpy as np
i... |
import math
import datetime
import collections
import statistics
import itertools
def is_prime(num):
for i in range(2, int(math.sqrt(num)) + 1):
if num % i == 0:
return False
return True
def input_list():
ll = list(map(int, input().split(" ")))
return ll
tc = int(input())
for ... |
<filename>skijumpdesign/utils.py
import numpy as np
import sympy as sm
from sympy.utilities.autowrap import autowrap
EPS = np.finfo(float).eps
# NOTE : These parameters are more associated with an environment, but this
# doesn't warrant making a class for them. Maybe a namedtuple would be useful
# though.
GRAV_ACC = ... |
<reponame>mmckerns/diffpy.srxplanar
import numpy as np
import scipy as sp
import os
from functools import partial
from scipy.optimize import minimize, leastsq, fmin_bfgs, fmin_l_bfgs_b, fmin_tnc, minimize_scalar, fmin_powell, \
fmin_cg, fmin_slsqp, brent, golden
from matplotlib import rcPara... |
<filename>OptionPricing.py<gh_stars>0
import numpy as np
from scipy.stats import norm
from abc import ABCMeta, abstractmethod
def st(z, s0, r, sigma, T):
return s0 * np.exp((r - sigma ** 2 / 2) * T + sigma * np.sqrt(T) * z)
s0 = 80;
r = 0.1;
sigma = 0.2;
T = 5;
K = 100
def call(s0, r, sigma, T, K):
d1 = ... |
<filename>2-resources/_Past-Projects/LambdaSQL-master/LambdaSQL-master/module1/rpg_db.py
"""
Unit 3 Sprint 2 SQL Module 1
Part 1 Querying a Database
"""
import statistics
import sqlite3 as sql
from collections import defaultdict
# Connect to local database
connection = sql.connect("rpg_db.sqlite3").cursor()
# connect... |
<filename>newsolver.py<gh_stars>1-10
import numpy as np
import pandas as pd
from scipy.integrate import ode
import json
from scipy.integrate import odeint
from numba import njit
import time
@njit()
def odeSys(t, zeta, Lambda):
z = np.exp(zeta)
term1 = np.dot(Lambda,z)
term2 = np.dot(z.T,term1)
dzetadt ... |
import numpy as np
from scipy.special import logit, expit
from seaborn import kdeplot
from scipy import sparse
from scipy.stats import gaussian_kde
import pandas as pd
import six
import sys
sys.path.append("..")
import utils
import pymc3 as pm
import tqdm
import itertools
import matplotlib.pyplot as plt
from mpl_t... |
<gh_stars>0
"""
Dveloper: vujadeyoon
E-mail: <EMAIL>
Github: https://github.com/vujadeyoon/vujade
Title: vujade_metric.py
Description: A module to measure performance for a developed DNN model
Acknowledgement:
1. This implementation is highly inspired from HolmesShuan.
2. Github: https://github.com/HolmesShua... |
import numpy as np
import skimage.draw as skd
import scipy.ndimage as simg
import torch
def get_random_smps(x_tr, y_tr, x_va, y_va, n_tr, n_va, n_tot_tr, n_tot_va, n_c):
tridxs = [np.random.choice(n_tot_tr, n_tr) for _ in range(n_c)]
vaidxs = [np.random.choice(n_tot_va, n_va) for _ in range(n_c)]
Xtr = tor... |
# ===============================================================================
# Copyright 2016 <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/LI... |
import pandas as pd
import scipy.stats
import random
def generate_bus_speed(n):
bus_speed_list = []
for i in range(0,n):
speed_temp = random.uniform(15,30)
bus_speed_list.append(speed_temp)
#print(randomlist)
return(bus_speed_list)
|
#!/usr/bin/env python3
# import keyboard
from dependency import np, pd, sp
# from S3Synth import S3Synth, Envelope
from S3Utils import freq_calc, find_Ns, get_note, find_maxsig, make_octaves
from S3DataUtils import train_S3, create_FunctionFrame
from matplotlib import pyplot as plt
from scipy.io.wavfile import write
... |
r"""$Z$ partial widths in the SM.
Based on arXiv:1401.2447"""
from math import log
from scipy import constants
# units: GeV=hbar=c=1
GeV = constants.giga * constants.eV
s = GeV / constants.hbar
m = s / constants.c
b = 1.e-28 * m**2
pb = constants.pico * b
# Table 5 of 1401.2447
cdict = {
'Gammae,mu': [83.966, -0... |
<filename>py_qt/nonparam_regression.py
"""
:Author: <NAME> <<EMAIL>>
Module implementing non-parametric regressions using kernel methods.
"""
import numpy as np
from scipy import linalg
import kde_bandwidth
import kernels
import npr_methods
class NonParamRegression(object):
r"""
Class performing kernel-bas... |
import numpy as np
import scipy.spatial.distance as dist
from permaviss.simplicial_complexes.differentials import complex_differentials
from permaviss.simplicial_complexes.vietoris_rips import vietoris_rips
from permaviss.persistence_algebra.PH_classic import persistent_homology
def test_persistent_homology():
... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
r"""
ARG model
=========
The code is an implementation of ARG model given in [1]_.
Its major features include:
* simulation of stochastic volatility and returns
* estimation using both MLE and GMM
* option pricing
References
----------
.. [1] <NAME> and <NAM... |
<gh_stars>1-10
# copyright <NAME> (2018)
# Released uder Lesser Gnu Public License (LGPL)
# See LICENSE file for details.
import ase
from ase import Atoms, Atom
import numpy as np
from numpy.linalg import norm
import itertools
import fractions
from math import pi, floor
from ase.build import cut, make_supercell
from a... |
<filename>GS2/GS2run.py
#!/usr/bin/env python3
################################################
################################################
stellDesigns=['WISTELL-A','NZ1988','HSX','KuQHS48','Drevlak','NCSX','ARIES-CS','QAS2','ESTELL','CFQS','Henneberg']
normalizedfluxvec = [0.01]
import os
from os import path, ... |
from typing import Any, Dict, Optional, Tuple
import numpy as np
from xaitk_saliency.interfaces.perturb_image import PerturbImage
from skimage.draw import ellipse
from scipy.ndimage.filters import gaussian_filter
class SlidingRadial (PerturbImage):
"""
Produce perturbation matrices generated by sliding a radi... |
<gh_stars>1-10
import numpy as np
import matplotlib.pyplot as plt
import logging
import sys
from scipy import linalg
import reltest.util as util
from reltest.mctest import MCTestPSI
from reltest.mmd import MMD_Linear, MMD_U
from reltest.ksd import KSD_U, KSD_Linear
from reltest import kernel
from kmod.mctest import ... |
<gh_stars>1-10
import numpy as np
from scipy import sparse
from sklearn.utils.extmath import randomized_svd
from datetime import datetime
import logging
from multiprocessing import Pool
def getvectors(X):
for col in range(X.shape[1]):
yield X[:, col]
def dotprod(v):
return v.transpose().dot(v).toden... |
<filename>RL_dispersion.py
# According to <NAME> "On waves in an elastic plate"
# He used xi for k (spatial frequency)
# sigma for omega (radial freq.)
# f for h/2 (half thickness)
# Making the relevant changes we get the following code for Si and Ai
from scipy import *
from pylab import *
from... |
import warnings
import numpy as np
from scipy.integrate import IntegrationWarning, quad, quad_vec
# Load the C library
import os.path
from pathlib import Path
import ctypes
# # Commands to manually generate
# gcc -Wall -fPIC -c voigt.c
# gcc -shared -o libvoigt.so voigt.o
dllabspath = Path(os.path.dirname(os.path.abs... |
"""
This file is part of Autognuplotpy, autogpy.
"""
from __future__ import print_function
import os
import numpy as np
import warnings
from collections import OrderedDict
import re
from . import autognuplot_terms
from . import plot_helpers
try:
import pandas as pd
import pandas
pandas_support_enabled ... |
<filename>analysis/xmodularity_informe.py<gh_stars>1-10
# -*- coding: utf-8 -*-
from src.env import DATA
from src.postproc.utils import load_elec_file, order_dict
from analysis.fig1_fig2_and_stats import plot_matrix, multipage
from analysis.bha import cross_modularity
import os
from os.path import join as opj
import n... |
import math
import dlib
import appdirs
import requests
import bz2
import cv2
import numpy as np
from scipy.spatial import distance as dist
from os import makedirs, path
from imutils import face_utils, resize
from imutils.video import VideoStream, FileVideoStream
# def dist(a, b):
# return math.sqrt((a * a) + (b *... |
<filename>Project 3/3.1.py
# Computes the volume of a 10-dimensional sphere using midpoint integration
import math as math
import numpy as np
from scipy.optimize import curve_fit
from time import process_time
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
from matplotlib.ticker import MaxNLocator
... |
<reponame>vinnamkim/GEM-Benchmark
from gem.evaluation import visualize_embedding as viz
from gem.utils import graph_util, plot_util
from .static_graph_embedding import StaticGraphEmbedding
import sys
from time import time
import scipy.sparse.linalg as lg
import scipy.sparse as sp
import scipy.io as sio
import n... |
<reponame>marrcio/relate-kanji
import pygame, sys, traceback
import pygame.freetype
from pygame.locals import *
import toolbox as tb
from statistics import Statistics
WINDOWWIDTH = 640
X_CENTER = 320
WINDOWHEIGHT = 480
NEXT_FLAG = 1
EXIT_FLAG = -1
SUCCESS_FLAG = 0
RIGHT_FLAG = 2
LEFT_FLAG = 3
REDRAW_FLAGS = {SUCCESS_F... |
from __future__ import division, print_function
__author__ = "adrn <<EMAIL>>"
# Third-party
import astropy.units as u
import numpy as np
from scipy.optimize import root
from gala.potential import HernquistPotential
from gala.units import galactic
# use the same mass function as Gnedin
from .gnedin import sample_mas... |
<reponame>wsgan001/AnomalyDetection
import math
import numpy as np
from sklearn.neighbors import DistanceMetric
from scipy.spatial.distance import mahalanobis;
from sklearn.metrics.pairwise import cosine_similarity
# https://spectrallyclustered.wordpress.com/2010/06/05/sprint-1-k-means-spectral-clustering/
def gaussia... |
import random
import numpy as np
import os
import argparse
import decimal
import warnings
import sys
sys.path.append('..')
from multiprocessing import Pool
from functools import partial
from operator import is_not
from scipy import optimize
import logging
import astropy.units as u
import SNReviewed as SN
def parse_comm... |
import numpy as np
from numpy.random import random_sample, randint
import pandas as pd
from scipy.stats import multivariate_normal
from scipy.special import logsumexp
from .system import System, multivariate_gaussian_logpdf, decompose
from numba import njit, objmode
import time
from tqdm import tqdm
@njit
def po... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri May 10 14:46:37 2019
Funções de forma para a viga de 4 nós de Euler-Bernouilli
Completo!
@author: markinho
"""
import sympy as sp
import numpy as np
import matplotlib.pyplot as plt
#para viga
L = sp.Symbol('L')
x1 = -L/2
x2 = -L/6
x3 = L/6
x4 = L/2... |
import numpy as np
import tifffile
import subprocess
import os
import csv
from tabulate import tabulate
from types import SimpleNamespace
from pathlib import Path
from itertools import zip_longest
from glob import glob
from scipy import ndimage
from scipy.ndimage import label, zoom
from scipy.... |
<reponame>SPOC-lab/gel-imaging-system<filename>long-yuv-array.py
# https://picamera.readthedocs.io/en/release-1.13/recipes1.html
from picamera import PiCamera
from time import sleep
from fractions import Fraction
# Force sensor mode 3 (the long exposure mode), set
# the framerate to 1/6fps, the shutter speed to 6s,
#... |
<filename>train.py
#!/usr/bin/env python
"""Train ANN"""
import sys
import glob
import datetime
import time
import pickle
from numpy import array, zeros, r_
from numpy.random import seed, randn
from cost_function import cost_function, gradients
from scipy.optimize import fmin_l_bfgs_b
from scipy.misc import imread, imr... |
<gh_stars>10-100
import numpy as np
from catboost import Pool, CatBoostRegressor
from gbdt_uncertainty.data import load_regression_dataset, make_train_val_test
from scipy.stats import ttest_rel
from gbdt_uncertainty.assessment import prr_regression, nll_regression, calc_rmse, ens_nll_regression, ood_detect, ens_rms... |
<gh_stars>0
#envi_perc.py
#multilayer perceptron to reconstruct SSTDR waveforms from environment data
import torch
import torch.nn as nn
#import matplotlib.pyplot as plt #done below; import procedure differs depending on SHOW_PLOTS
import random
import numpy as np
from tkinter.filedialog import askopenfilename... |
# Copyright 2013 <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-2.0
#
# Unless required by applicable law or agreed to in wri... |
from tensorforce.environments import Environment
from tensorforce.agents import Agent
import numpy as np
import torch.nn.functional as F
from statistics import mean
environment = Environment.create(
environment=HelpdeskEnv, max_episode_timesteps=100
)
agent = Agent.create(
agent='ppo', environment=environment... |
<gh_stars>1-10
"""
This component of the gamma_analysis code is in charge of identifying peaks
in a given energy spectra. The peaks are identified by the difference in counts
relative to its surrounding bins.
"""
from __future__ import print_function
import numpy as np
import math as mt
from matplotlib import pyplot as... |
import numpy as np
from scipy.fftpack import fft , fft2 , rfft
import matplotlib.pyplot as plt
import time
freq = 32
sampling_rate=55
t= np.linspace (0, 2, 2 *sampling_rate, endpoint=False)
x1= np.sin(freq* 2* np.pi * t)
x2= np.cos(5* 2* np.pi * t)
x3= np.sin(25* 2* np.pi * t)
x=x1+x2+x3
fig = plt.figure(1)
ax1 = f... |
<reponame>hitsh95/DespeckleNet
import numpy as np
import torch
import PIL.Image as Image
import time
import cv2
from scipy.io import loadmat
import math
import os
import shutil
from tensorboardX import SummaryWriter
def generate_random_phase():
p = np.random.rand(512, 512)
p = np.where(p<0.7, 0.... |
import typing
from pathlib import Path
import numpy as np
import scipy.fftpack
from scipy import signal
import einops
import functools
import torch
import paderbox as pb
import padertorch as pt
import padercontrib as pc
from padertorch.contrib.cb.transform import stft as pt_stft, istft as pt_istft
from padertorch.c... |
import numpy as np
import scipy.stats
import pytest
from skypy.utils.photometry import HAS_SPECLITE
@pytest.mark.flaky
def test_sampling_coefficients():
from skypy.galaxies.spectrum import dirichlet_coefficients
alpha0 = np.array([2.079, 3.524, 1.917, 1.992, 2.536])
alpha1 = np.array([2.265, 3.862, 1.92... |
<reponame>4DNucleome/big-fish
# -*- coding: utf-8 -*-
# Author: <NAME> <<EMAIL>>
# License: BSD 3 clause
"""Filtering functions."""
import numpy as np
from .utils import check_array
from .utils import check_parameter
from .preprocess import cast_img_float32
from .preprocess import cast_img_float64
from .preprocess ... |
<reponame>NeonOcean/Environment
import operator
import random
import services
import sims4.resources
from sims4.localization import TunableLocalizedString
from sims4.tuning.instances import HashedTunedInstanceMetaclass
from sims4.tuning.tunable import HasTunableReference, OptionalTunable, Tunable, TunableEnumEntry, Tu... |
import os
import numpy as np
from PIL import Image
class FileLoader():
def cache(self, path):
return True
def save_cache(self, cache_path):
pass
def load_cache(self, cache_path):
pass
def __call__(self, path):
raise NotImplemented
class ImageLoader(FileLoader):
de... |
# Standard imports
import argparse
import asyncio
import json
import os
import socket
import statistics as stats
import sys
import traceback
from datetime import datetime
from difflib import get_close_matches
from random import choice, randint
from sys import stderr
from time import time
import pytz
import requests
i... |
<filename>attention_models/original_attention.py
import matplotlib
matplotlib.use('Agg')
from scipy import io
import tensorflow as tf
import pandas as pd
import numpy as np
import os, h5py, sys, argparse
import pdb
import time
import json
from collections import defaultdict
import time
import cv2
import argparse
im... |
"""
Implements grid search for naive fitting
"""
import numpy as np
from scipy.optimize import OptimizeResult
__all__ = ['grid_search']
def grid_search(func, x0, args=(), options={}, callback=None):
"""
Optimize with naive grid search in a way that outputs an OptimizeResult
Parameters
----------
... |
#!/usr/bin/python
from numpy import savetxt, loadtxt, array
from deap import base, creator, tools
from scipy import interpolate
from pickle import load, dump
from os import system, access, remove, path
from time import sleep
from glob import glob
from queue import Queue, Empty
from threading import Thread
fro... |
<reponame>lidiaxp/plannie
# -*- coding: utf-8 -*-
# import rospy
import math
import numpy as np
import matplotlib.pyplot as plt
from scipy import interpolate
from curves.bezier import Bezier
from curves import bSpline
import psutil
import os
from curves.spline3D import generate_curve
from mpl_toolkits.mplot3d.art3d imp... |
import typing as t
import numpy as np
import scipy.stats
def z_test(
samples: t.Sequence[float],
true_var: float,
hypothesis_mean: float,
tail: str = "both",
):
r"""Z-test for population mean with normal data of known variance.
Assumptions: data i.i.d. x_{1}, ..., x_{n} ~ N(mu, sigma^{2}), w... |
<reponame>93xiaoming/RL_state_preparation
import numpy as np
from scipy.linalg import expm
class Env( object):
def __init__(self,
dt=np.pi/10):
super(Env, self).__init__()
self.n_actions = 2
self.n_states = 4
self.state = np.array([1,0,0,0])
self.nstep=0 ##count num... |
import os
import argparse
import pandas as pd
import numpy as np
from vlpi.data.ClinicalDataset import ClinicalDataset,ClinicalDatasetSampler
from vlpi.vLPI import vLPI
from sklearn.metrics import average_precision_score
from scipy.stats import linregress
"""
This script performs is assess increase in case severity f... |
<reponame>ericgorday/SubjuGator
import pickle
import numpy as np
import matplotlib.pyplot as plt
from sub8_vision_tools import machine_learning as ml
from scipy.ndimage import convolve
from sklearn import linear_model, metrics
from sklearn.cross_validation import train_test_split
from sklearn.neural_network import Ber... |
<filename>correspondence/product_manifold_filters/degenerate_assignment_problem.py<gh_stars>1-10
## Standard Libraries ##
import sys
import os
from typing import List
## Numerical Libraries ##
import numpy as np
import math
## Local Imports ##
cur_dir = os.path.dirname(__file__)
sys.path.insert(1,os.path.join(cur_dir... |
#!/usr/bin/env python
from remimi.monodepth.bilateral_filtering import sparse_bilateral_filtering
from remimi.monodepth.dpt import DPTDepthEstimator
from remimi.utils.depth import colorize2
import torch
import torchvision
import base64
# import cupy
import cv2
import getopt
import glob
import h5py
import io
import ma... |
<reponame>bt402/pypercolate
# encoding: utf-8
"""
Low-level routines to implement the Newman-Ziff algorithm for HPC
See also
--------
percolate : The high-level module
"""
from __future__ import (absolute_import, division,
print_function, unicode_literals)
from future.builtins import dict, n... |
<reponame>kieranrcampbell/curver-python
"""
Main Curver file:
Curve reconstruction from noisy data
Based on "Curve reconstruction from unorganized points",
In-<NAME>, Computer Aided Geometric Design 17
<EMAIL>
"""
import numpy as np
import statsmodels.api as sm
from scipy.optimize import minimize
"""
NB in general... |
<reponame>brunorijsman/euler-problems-python
from fractions import Fraction
def fixed():
return 2
def nth(n):
if n % 3 == 1:
return (n // 3 + 1) * 2
else:
return 1
def tail(start_term, total_terms):
if start_term == total_terms:
return Fraction(1, nth(start_term))
else:
a = Fraction(nth(sta... |
<gh_stars>0
import numpy as np
import scipy as sp
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb
database = sb.load_dataset("flights")
print(database)
#Default kindnnya = "strip"
#sb.catplot(x="month",y="passengers",data=database,kind='violin')
sb.catplot(x="month",y="passengers",data=databa... |
<gh_stars>1-10
# 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 to in writing, softwa... |
import numpy as np
import torch
import os
import sys
from matplotlib import pyplot as plt
import torch.nn as nn
from xplain.attr import LayerIntegratedGradients, LayerGradientXActivation
import skimage.io
import torchvision
import pickle
import pandas as pd
import scipy.interpolate as interpolate
from torch.utils.data ... |
#!/usr/bin/env python3
import os
import statistics
dir_path = os.path.dirname(os.path.realpath(__file__))
file = open(dir_path + "/input.txt", "r")
ints = [int(n) for n in file.read().strip().split(',')]
# ints = [16,1,2,0,4,2,7,1,2,14]
median = statistics.median(sorted(ints))
fuel_costs = int(sum([abs(n-median) fo... |
import numpy as np
import MeshFEM, mesh
import registration
import os
import pickle, gzip
def load(path):
"""
load a pickled gzip object
"""
return pickle.load(gzip.open(path, 'rb'))
def save(obj, path):
"""
save an object to a pickled gzip
"""
pickle.dump(obj, gzip.open(path, 'wb'))
... |
<filename>utils/pascal_ctxt.py
import os
from os.path import join as pjoin
import collections
import json
import numpy as np
from skimage.io import imsave, imread
import scipy.io as io
import matplotlib.pyplot as plt
import glob
class pascalVOCContextLoader:
"""Data loader for the Pascal VOC semantic segmentation... |
# -*- coding: utf-8 -*-
"""
Created on Tue Dec 15 15:28:30 2020
@author:
"""
import numpy as np
from numpy import sqrt, arctan2, pi as π, cos, sin
from scipy.spatial.transform import Rotation
import sys
_vec_0 = np.array([0., 0., 0.])
_vec_x = np.array([1., 0., 0.])
_vec_y = np.array([0., 1., 0.])
_vec_z = np.array(... |
#!/usr/bin/env python
import glob
import os
import sys
import subprocess
import scipy.stats
import numpy
#DEBUG_MCNEMAR = True
DEBUG_MCNEMAR = False
ground_truth_dirname = os.path.expanduser("~/src/audio-research/")
#ground_truth_dirname = os.path.expanduser("~/src/audio-research/not-now")
results_subdir = "mfs/"
... |
<gh_stars>10-100
import pickle
from collections import Counter
import scipy.sparse as sp
import numpy as np
original_file = './movie_metadata_3class.csv'
movie_idx_map = {}
actor_idx_map = {}
director_idx_map = {}
keyword_idx_map = {}
with open('movie_idx_map.pickle', 'rb') as m:
movie_idx_map = pickle.load(m)
wi... |
import copy
import logging
import sys
from typing import Callable
import numpy as np
import torch
from pandas.api.types import is_numeric_dtype
from scipy.interpolate import interp1d
from torchinterp1d import Interp1d
# Some useful functions
# Set double precision by default
torch.set_default_tensor_type(torch.Doubl... |
<reponame>shubham526/SIGIR2021-Short-Final-Code-Release
from typing import Dict, List
import argparse
import sys
import json
import tqdm
from scipy import spatial
import numpy as np
import operator
from bert_serving.client import BertClient
bc = BertClient()
def load_run_file(file_path: str) -> Dict[str, List[str]]:... |
<gh_stars>0
import pandas as pd
import numpy as np
import ast
from scipy.spatial.distance import pdist, squareform
import pdb
from utils import *
def error_func(gt_labels, pred_labels):
assert gt_labels.shape == pred_labels.shape, "Groundtruth labels should have the same shape as the prediction labels"
if l... |
"""
run_experiment.py
Run proposed method and baseline on the testing set
"""
import warnings
warnings.simplefilter('always', UserWarning)
import numpy as np
import scipy.signal
import os
import soundfile as sf
import librosa.core
from utils.datasets import get_audio_files_DSD, get_audio_files_librispeech
from utils.... |
import numpy as np
from scipy import stats
from scipy import integrate
from scipy import special
class Low(object):
"""Class for fatigue life estimation using frequency domain
method by Low[1].
Notes
-----
Numerical implementation supports only integer values of
S-N curve parameter k (inver... |
__author__ = '<NAME>'
import numpy as np
import scipy
import numba
from ..abstract_scale_factor import ScaleFactorABC
########################################################################################
# Utility Functions
########################################################################################
... |
from datasets import Examples
from utils import euclidean_distance
from collections import Counter
from scipy.spatial import distance
from sklearn.metrics.pairwise import pairwise_distances
import numpy as np
class kNN(object):
"""
Implementation of kNN algorithm
"""
def __init__(self, dataset):
... |
from __future__ import division
from __future__ import print_function
import time
import tensorflow as tf
from utils import *
from models import DSSGCN_GC_BATCH
from tensorflow import set_random_seed
import matplotlib.pyplot as plt
import scipy.io as sio
from sklearn.model_selection import StratifiedKFold
import nump... |
#!/usr/bin/env python
from __future__ import print_function
import math
import numpy
import matplotlib
matplotlib.use("PDF")
fig_size = [8.3,11.7] # din A4
params = {'backend': 'pdf',
'axes.labelsize': 10,
'text.fontsize': 10,
'legend.fontsize': 10,
'xtick.labelsize': 8,
'yti... |
"""
isicarchive.imfunc
This module provides image helper functions and doesn't have to be
imported from outside the main package functionality (IsicApi).
Functions
---------
color_superpixel
Paint the pixels belong to a superpixel list in a specific color
column_period
Guess periodicity of data (image) column... |
<filename>examples/wip_plot_spin_test.py
# -*- coding: utf-8 -*-
"""
Spatial permutations for significance testing
=============================================
This example shows how to perform spatial permutations tests (a.k.a spin-tests;
`Alexander-Bloch et al., 2018, NeuroImage <https://www.ncbi.nlm.nih.gov/pmc/
a... |
import argparse
import os
import torch
from attrdict import AttrDict
from sgan.data.loader import data_loader
from sgan.models import TrajectoryGenerator
from sgan.losses import displacement_error, final_displacement_error
from sgan.utils import relative_to_abs, get_dset_path
import collections
import pickle
impor... |
<gh_stars>1-10
import numpy as np
from scipy.stats import linregress
from matplotlib import pyplot as pl
def circles_monte_bad(n = 20, m = 1e7):
m = int(m)
def counts(r):
rmax = x.max()
rx = rmax * (2 * np.random.random(m) - 1)
ry = rmax * (2 * np.random.random(m) - 1)
return np... |
<reponame>jcrist/pydy
#!/usr/bin/env python
import os
import shutil
import glob
import numpy as np
from numpy.testing import assert_allclose
from sympy import symbols
import sympy.physics.mechanics as me
from ...system import System
from ..shapes import Sphere
from ..visualization_frame import VisualizationFrame
fro... |
from scipy.optimize import linear_sum_assignment
import pandas as pd
from graph_definition import compute_compatibility_matrix
from host_response import HostResponse
from guest_response import GuestResponse
# download sample data
guest_responses_df = pd.read_csv('sample_data/sample_guest_responses.csv')
host_response... |
def function_f_x_k(funcs, args, x_0, mu=None):
'''
Parameters
----------
funcs : sympy.matrices.dense.MutableDenseMatrix
当前目标方程
args : sympy.matrices.dense.MutableDenseMatrix
参数列表
x_0 : list or tuple
初始迭代点列表（或元组）
mu : float
正则化参数
... |
<filename>src/single_pulse.py
""" Command line tool for single-pulse shapelet analysis """
import argparse
import logging
import os
import bilby
from scipy.stats import normaltest
from . import flux
from . import plot
from .priors import update_toa_prior
from .data import TimeDomainData
from .likelihood import Pulsar... |
<filename>source/demodulator.py
#!/usr/bin/env python
"""
File Name: demodulator.py
Author: <NAME>
Date: 13 Apr 2008
Purpose: Takes waveform arrays as input and returns their
estimated binary string.
Usage:
from demodulator import *
demodinstance = demodulator()
outputstring = demodinstance.run(inputarray)
peri... |
<reponame>maxxxxxdlp/code_share
import numpy
import json
import pandas
import pydotplus
import matplotlib.pyplot as plt
import matplotlib.image as pltimg
from scipy import stats
from sklearn import tree
from sklearn import linear_model
from sklearn.metrics import r2_score
from sklearn.preprocessing import StandardScale... |
<filename>notebooks/__code/registration/export_registration.py
import numpy as np
import copy
from qtpy import QtGui
from skimage import transform
from scipy.ndimage.interpolation import shift
from NeuNorm.normalization import Normalization
class ExportRegistration:
def __init__(self, parent=None, export_folder... |
#!/opt/conda/envs/pCRACKER_p27/bin/python
# All rights reserved.
from collections import Counter, defaultdict, OrderedDict
import cPickle as pickle
import errno
from itertools import combinations, permutations
import itertools
import os
import shutil
import subprocess
import matplotlib
matplotlib.use('Agg')
import mat... |
# coding: utf-8
# Density Based [k-means] Bootstrap
from __future__ import print_function
import logging as log
from itertools import groupby
from math import fabs, sqrt
from operator import itemgetter as iget
import numpy as np
from ellipse import ellipse_intersect, ellipse_polyline
from matplotlib.mlab import norm... |
<filename>src/tiling.py
import numpy as np
import os
import re
from scipy import misc
from keras.preprocessing.image import array_to_img, img_to_array, load_img
from PIL import Image
from PIL import ImageDraw
from PIL import ImageFont
import platform
def tiling_flat(input_directory='prediction_inter'):
root = ''
... |
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