text string |
|---|
from logging import error, warning
from unittest.loader import VALID_MODULE_NAME
from matplotlib.patches import Polygon
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
#from shapely.geometry import Poligon
from scipy.interpolate import make_interp_spli... |
import sys
import threading
import queue
import numpy as np
import scipy as sp
import scipy.signal
import matplotlib.pyplot as plt
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
import pyaudio
import tkinter
import serial
import serial.tools.list_ports
f_sampling = 44100
duration = 10.... |
<reponame>kimjaed/simpeg
from SimPEG import Mesh, Maps, Utils, Tests
from SimPEG.EM import FDEM
import numpy as np
from scipy.constants import mu_0
import unittest
MuMax = 50.
TOL = 1e-10
EPS = 1e-20
np.random.seed(105)
def setupMeshModel():
cs = 10.
nc = 20.
npad = 15.
hx = [(cs, nc), (cs, npad, 1... |
<reponame>singhster96/Mini_Projs
from scipy import array, linspace
from scipy import integrate
from matplotlib.pyplot import *
def vector_field(X, t, r1, K1, c1, r2, K2, c2):
# Competing Species differential equations model
# from Section 9.4 of Boyce & DiPrima
# The differential equations are
... |
<gh_stars>1-10
import theano
import scipy
import theano.tensor as tt
from functools import partial
from .rv import RandomVariable, param_supp_shape_fn
# We need this so that `multipledispatch` initialization occurs
from .unify import *
# Continuous Numpy-generated variates
class UniformRVType(RandomVariable):
... |
"""
Module for building and manipulating astronomical catalogues.
@author: A.Ruiz
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from six.moves import zip, range
from io import open
import os
import warnings
import tempfile
import subprocess
from copy ... |
<filename>logit-stacker.py<gh_stars>0
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import pandas as pd
import numpy as np
from scip... |
<reponame>hhk998402/NaiveBayesClassifier<gh_stars>0
from collections import defaultdict
from feature.vectors.feature_vectors import FeatureVectors
from statistics import gaussian_pdf
from statistics import mean
from statistics import variance
class ContinuousFeatureVectors(FeatureVectors):
"""Collection of conti... |
import scipy as sci
import numpy as np
from DataCreator import *
from PODCreator import *
from dolfin import *
from tools import *
import csv
class rid(data_creator):
def __init__(self,meshInput,lzInput,numberModeInputForRID,ssListInput):
data_creator.__init__(self,meshInput,lzInput,ssListInput)
#create dir and... |
from flask import render_template, request, Blueprint
from scipy.misc import imread, imresize
import numpy as np
import os
import base64
import re
import sys
## BLUEPRINT INIT
algorithms_blueprint = Blueprint(
'algorithms', __name__,
template_folder="templates"
)
from .load import init_model
sys.path.appe... |
# -*- encoding: utf-8 -*-
from collections import namedtuple
from pathlib import Path
from typing import Union, List, Set
from astropy.time import Time
import csv
import h5py
import numpy as np
from datetime import datetime
from scipy.interpolate import interp1d
from .biases import BiasConfiguration
__all__ = [
... |
<reponame>wilfredkisku/generative-adversarial-networks
import matplotlib.pyplot as plt
import numpy as np
from sklearn import cluster, datasets, mixture
from scipy.stats import multivariate_normal
from sklearn.datasets import make_spd_matrix
plt.rcParams["axes.grid"] = False
n_samples = 100
# define the mean points... |
#!/usr/bin/env python
import matplotlib
matplotlib.use('Agg')
import os, sys
from importlib import import_module
import scipy as sp
import matplotlib.pyplot as pl
from mpl_toolkits.basemap.cm import sstanom
from matplotlib.cm import jet
from matplotlib import dates
import g5lib.plotters as ptrs
from g5lib import g5ds... |
<filename>Scripts/percolation.py
#!/usr/bin/env python3
import sys
import json
import random
from heapq import heappush, heappop
from statistics import mean
from collections import defaultdict
class Network:
# TODO: Improve efficiency by converting
# string identifiers to ints
def __init__(self, ne... |
<reponame>ymohit/fkigp
import os
import time
import scipy
import argparse
import logging
import numpy as np
from pprint import pprint
from fkigp.configs import Structdict
from fkigp.configs import GridSizeFunc
from fkigp.configs import GsGPType
from fkigp.configs import Frameworks
from fkigp.configs import DatasetTyp... |
<filename>yacht/environments/reward_schemas.py
import warnings
from abc import ABC, abstractmethod
from typing import Union, List
import numpy as np
from scipy.stats import norm
from yacht import utils
from yacht.config import Config
from yacht.config.proto.environment_pb2 import EnvironmentConfig
from yacht.data.dat... |
<gh_stars>1-10
import numpy as np
import os
from scipy import interpolate
from scipy.ndimage import binary_fill_holes
from skimage.morphology import binary_dilation, disk, medial_axis
from skimage import transform
from skimage.measure import find_contours
import matplotlib.pyplot as plt
from matplotlib import rc
rc('fo... |
##########################################################################################################################################
### GETTING RID OF THIS ERROR: https://stackoverflow.com/questions/71106940/cannot-import-name-centered-from-scipy-signal-signaltools ###
########################################... |
<filename>adafdr/util.py
import numpy as np
import scipy as sp
from scipy import stats
import matplotlib.pyplot as plt
from scipy.stats import rankdata
import logging
import pickle
import os
"""
basic functions
"""
def get_grid_1d(n_grid):
"""
return an equally spaced covariate covering the 1d space..... |
'''
Builds the bus admittance matrix and branch admittance matrices.
Returns the full bus admittance matrix (i.e. for all buses) and the
matrices C{Yf} and C{Yt} which, when multiplied by a complex voltage
vector, yield the vector currents injected into each line from the
"from" and "to" buses respectively of each line... |
from yaferp.analysis import analyser
from yaferp.misc import tapering
from yaferp.misc import constants
from yaferp.general import fermions
import pickle
DATA_DIR = '/home/andrew/data/BKData/'
TAPERED_DIR = DATA_DIR + 'hamiltonian/tapered/'
'''
FLOW:
READ NUMBER OF ELECTRONS FROM FILE -> GET HF STATE
-> generate tape... |
<gh_stars>0
import argparse
import os
import time
from typing import Callable
import arviz
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as spst
import scipy.special as spsp
import tqdm
import hmc
# np.random.seed(0)
parser = argparse.ArgumentParser(description='Bias in Hamiltonian Monte Car... |
<filename>HackerRank-Python-main/Polar Coordinates.py<gh_stars>1-10
# Enter your code here. Read input from STDIN. Print output to STDOUT
from cmath import polar
print ('{}\n{}'.format(*polar(complex(input()))))
|
<reponame>enzo-bc/qteeg
from scipy.stats import zscore
from typing import Dict, Tuple
import numpy as np
import pandas as pd
from scipy.stats import median_absolute_deviation
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
MIN_TIME_BETWEEN_SPIK... |
import torch
from torch_geometric.data import Data
import math
from numpy.linalg import inv
from scipy.spatial.transform import Rotation as R
import h5py
from body_movement_vis import pyKinect
import numpy as np
import body_movement_vis.visualize_sign as visualize_sign
import threading
# from h5_control_single import k... |
#!/usr/bin/env python
import logging
from numpy import absolute, asfortranarray, diff, ones, inf, empty_like, isfinite
from scipy.optimize import minimize
from scipy.interpolate import interp1d
from numpy.linalg import norm
from time import time
from warnings import warn
#
from .transcararc import getColumnVER
from .p... |
"""
Problems
--------
This part of the package implement classes describing "problems".
Problems are required inputs for simulation and inference.
Currently, there are two types of problems:
* A :class:`ODEProblem` is a system of differential equations describing
the temporal behaviour of the system. They are typ... |
import numpy as np
from numpy import zeros,cos,tan,log,exp,sqrt,pi,clip,real,argwhere,append,linspace,squeeze,isscalar,save
from scipy.integrate import quad
import numba
from numba import jit,njit
from numba import cfunc,carray
from numba.types import intc, CPointer, float64
import matplotlib.pyplot as plt
@njit
def... |
<filename>examples/sudoku_9x9/baselines/cnn_lstm.py<gh_stars>0
import pandas as pd
import numpy as np
import torch.optim as optim
import json
import sys
import time
import torch
import math
from torch import nn
#from skorch import NeuralNetBinaryClassifier
from rf import get_tree_info, create_unstructured_example, pro... |
<reponame>theleokul/Real-ESRGAN<filename>realesrgan/losses/losses.py
import math
import torch
from torch import autograd as autograd
from torch import nn as nn
from torch.nn import functional as F
import torchvision as tv
import numpy as np
from scipy import linalg as sp_linalg
from basicsr.utils.registry import LOSS... |
import numpy as np
import matplotlib.pyplot as plt
import scipy.interpolate as si
plt.style.use('seaborn-whitegrid')
def f(x) :
s = 1/(1+25*np.power(x,2))
return s
def Lagrance(n) :
x_nodes=np.linspace(-1,1,n)
y_val=f(x_nodes)
polynomial = si.lagrange(x_no... |
<filename>gtrick/dgl/position_encoding.py
import dgl
import torch
import torch.nn.functional as F
import numpy as np
from scipy import sparse as sp
def position_encoding(g, max_freqs):
n = g.number_of_nodes()
A = g.adjacency_matrix_scipy(return_edge_ids=False).astype(float)
N = sp.diags(dgl.backend.asnum... |
"""
This is the web tool to visualize normal distributions and calcualted overlape.
Users need to input mean and stdev of normal distributions.
This web tool is developed using dash.
Before run this file, pelase import Dash packages.
"""
import dash
import dash_core_components as dcc
import dash_html_components as ht... |
import numpy as np
import cv2
import skimage
import math
from skimage import io
import matplotlib.pyplot as plt
from scipy.ndimage import gaussian_filter
from skimage.morphology import reconstruction
from scipy import stats
from statistics import mean
from collections import OrderedDict
import plotly.graph_objects as ... |
<gh_stars>0
import logging, os
import matplotlib.pyplot as plt
import numpy as np
from scipy.ndimage import map_coordinates
from muDIC.elements.b_splines import BSplineSurface
from muDIC.elements.q4 import Q4, Subsets
class Fields(object):
# TODO: Remove Q4 argument. This should be detected automaticaly
def ... |
<reponame>xuweigogogo/DSACA
import os
import re
import cv2
import glob
import h5py
import math
import time
import numpy as np
import scipy.spatial
import scipy.io as io
from shutil import copyfile
from scipy.ndimage.filters import gaussian_filter
root = '../dataset/RSOC'
test_label_pth = os.path.join(r... |
from scipy import ndimage
from scipy import misc
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten, Reshape
from keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D
from keras.utils import np_utils
import numpy as np
np.random.... |
"""Tests for the general many-body problem.
Some general tensor operations requiring range and dummies in the drudge are
also tested here.
"""
import pytest
from sympy import IndexedBase, conjugate, Symbol, symbols, I, exp, pi, sqrt
from drudge import GenMBDrudge, CR, AN, Range
@pytest.fixture(scope='module')
def ... |
<reponame>yogabonito/seir_hawkes
# from scipy.optimize import fmin_l_bfgs_b
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import fmin_l_bfgs_b
from sympy import derive_by_array, exp, lambdify, log, Piecewise, symbols
def exp_intensity_sigma_neq_gamma(history, sum_less_equal=True):
"""
... |
<filename>niscv_v2/basics/qtl.py<gh_stars>0
import numpy as np
from niscv_v2.basics.kde2 import KDE2
from niscv_v2.basics import utils
from wquantiles import quantile
import sklearn.linear_model as lm
import scipy.optimize as opt
import scipy.stats as st
import warnings
from datetime import datetime as dt
warnings.fil... |
<filename>testdata.py
# -*- coding: utf-8 -*-
"""
Created on Tue Sep 22 18:36:26 2020
@author: <NAME>
This script is regarding everything relating to the test data
"""
import numpy as np
import matplotlib.pyplot as plt
from scipy import interpolate
import importing
def cubic(x,val1,val2):
tck = inter... |
<gh_stars>0
#Linear classification
from sklearn import metrics
from sklearn.cross_validation import train_test_split
from sklearn import preprocessing
from sklearn import datasets
from sklearn.linear_model import SGDClassifier
import matplotlib.pyplot as plt
from matplotlib import style
import numpy as np
st... |
<reponame>Quan-y/regAnalyst
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: ericyuan
requirement:
(1) numpy='1.15.4'
(2) matplotlib='3.0.2'
(3) seaborn='0.9.0'
(4) pandas='0.24.0'
(5) scipy='1.1.0'
(6) statsmodels='0.9.0'
(7) sklearn='0.20.2'
"""
import numpy as np
import pandas ... |
<gh_stars>0
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jun 21 12:26:10 2021
G333 Smoothing and Dendrogram
@author: pmazumdar
"""
import numpy as np
from astropy import units as u
from spectral_cube import SpectralCube
from astropy.convolution import Gaussian1DKernel
import matplotlib.pyplot as... |
<filename>x3.Nuclear/ISL/main.py
import numpy as np
import matplotlib.pyplot as plt
import pint
from uncertainties import ufloat, umath
from uncertainties.unumpy import uarray
plt.rcParams['text.usetex'] = True
# To fit the modulation's sin
from scipy.optimize import curve_fit
# To calculate errors in fit parameters
fr... |
<filename>stimgen.py
import numpy as np
from scipy.io.wavfile import write
from IPython import embed
if __name__ == '__main__':
f0 = 1000. # Start frequency in Hz
fe = 200. * 1000. # End frequency in Hz
t0 = 0. # Start time in s
te = 1 # End time in s
samp_freq = 500. * 1000. # in Hz
time... |
<reponame>nkhn37/python-tech-sample-source
"""Collectionsモジュール
名前付きタプル namedtupleの使いどころ
関数の戻り値で使用する
[説明ページ]
https://tech.nkhn37.net/python-collections-namedtuple/#i
"""
import collections
import statistics
def calculate_stat(data):
min_v = min(data)
max_v = max(data)
mean_v = statistics.mean(data)
va... |
<reponame>perpetualVJ/greyatom-python-for-data-science<filename>Loan-Approval-Analysis/code.py
# --------------
# Importing header files
import numpy as np
import pandas as pd
from scipy.stats import mode
import warnings
warnings.filterwarnings('ignore')
#Reading file
bank_data = pd.read_csv(path)
#... |
<filename>arima/stat_test.py
import numpy as np
import scipy.stats as sst
from arima.utils import *
def wald_stat(X1, eps, beta):
# assert X1.shape[0] < X1.shape[1], "X dim: " + str(X1.shape) + " should be rotated"
if X1.shape[0] > X1.shape[1]:
X1 = X1.T
beta_covariance = np.var(eps) * np.linalg... |
import sys
import os
import time
from torchvision import transforms
import torch, torchaudio
import yarp
import numpy as np
from speechbrain.pretrained import EncoderClassifier
from project.voiceRecognition.speaker_embeddings import EmbeddingsHandler
from project.faceRecognition.utils import format_face_coord, face_al... |
<gh_stars>0
import matplotlib.pyplot as plt
import numpy as np
import random as rd
from scipy import interpolate
days_in_months = [0, 31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31];
def trans_time_to_hour (month, date, hour):
all_hour = (date - 1) * 24 + hour
for i in range(1, month):
all_hour += days_in_months[... |
<filename>rt1d/physics/Cosmology.py
"""
Cosmology.py
Author: <NAME>
Affiliation: University of Colorado at Boulder
Created on 2010-03-01.
Description: Cosmology calculator based on Peebles 1993, with additions from
Britton Smith's cosmology calculator in yt.
Notes:
-Everything here uses cgs.
-I have a... |
# -*- coding: utf-8 -*-
"""Normal distibuted membership function
.. code-block:: python
TruncNorm(alpha0=[1, 3], alpha1=None, number_of_alpha_levels=15)
.. figure:: TruncNorm.png
:scale: 90 %
:alt: TruncNorm fuzzy number
TruncNorm fuzzy number
.. code-block:: python
TruncGenNorm(alpha0=[1, 4]... |
<reponame>HughPaynter/PyGRB
import numpy as np
from scipy.special import gammaln
from bilby import Likelihood as bilbyLikelihood
from PyGRB.backend.makekeys import MakeKeys
from PyGRB.backend.rate_functions import *
class PoissonRate(MakeKeys, bilbyLikelihood):
"""
Custom Poisson rate class inheriting from ... |
<gh_stars>0
from scipy.ndimage.morphology import binary_dilation as dilate
from numpy import array
from src.queue import Queue
class FoundPath(Exception):
'''Raised when dest is found
Exception is used to immediatelly leave recursion
'''
class Pathfinder:
'''Path-finder class. Implemets jump-point s... |
import BCI
import os, scipy.io
import numpy as np
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.metrics import cohen_kappa_score, accuracy_score
from keras.models import Sequential
from keras.layers import Dense,Flatten, Conv1D, Conv2D, Dropout, MaxPooling2D, MaxPooling3D, Activation... |
<filename>face_register/reps_checker.py
from sklearn.preprocessing import LabelEncoder
from scipy.spatial import distance
import scipy.stats as stats
import os
import pandas as pd
from operator import itemgetter
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
import numpy as np
def drow_distribution(X, ... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
MIT License
Copyright (c) 2019 maxvelasques
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 th... |
<filename>application/api/data_quality_index.py<gh_stars>0
"""File to handle DQI reports."""
import datetime
from collections import OrderedDict
from datetime import date
from datetime import datetime as dt
from statistics import mean
from flask_restful import Resource, reqparse
from sqlalchemy import Date
from appli... |
import numpy as np
from scipy.stats import mannwhitneyu
try:
import cPickle as pickle
except:
import pickle
def merge_dict(results, result):
# Merge nested dictionaries
for key in result:
if type(result[key]) == dict:
if key not in results:
results[key] = {}
... |
<reponame>luigi-borriello00/Metodi_SIUMerici
# -*- coding: utf-8 -*-
"""
Es 2
"""
import sympy as sym
import numpy as np
import matplotlib.pyplot as plt
import allM as fz
scelta = input("Scegli l'integrale da calcolare ")
x = sym.Symbol("x")
integrali = {
"1" : [sym.log(x), 1, 2],
"2" : [sym.sqr... |
import os
from scipy.io import loadmat
import pandas as pd
from ..utils import format_dataframe
"""Parsing nutrient mat files"""
# column renaming map
COL_MAP = {
'Event_Number': 'event_number',
'Event_Number_Niskin': 'event_number_niskin',
'Latitude': 'latitude',
'Longitude': 'longitude',
'Depth': 'depth... |
"""Utility functions for plots."""
import natsort
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib import gridspec
from scipy.signal import medfilt
from nilearn import plotting as ni_plt
from tqdm import tqdm
from pynwb import NWBHDF5IO
from dandi.dandiapi i... |
<filename>python/helpers/window_func.py
from typing import Union
import numpy as np
import scipy
def window_func(name: str, m: int, **kwargs: Union[float, int]) -> np.ndarray:
"""Design a window for a given window function.
Parameters
----------
name: str
name of the window, can be any of the... |
<reponame>NTU-CompHydroMet-Lab/pyBL
import numpy as np
from scipy import special as sp
import math
import matplotlib.pyplot as plt
def OPTBINS(target, maxBins):
"""
:param target: array with size (1, N)
:param maxBins: int
:return optBins: int
"""
if len(target.shape) > 1:
print("The ... |
<reponame>ronny3050/MobileNet
"""Validate a face recognizer on the "Labeled Faces in the Wild" dataset (http://vis-www.cs.umass.edu/lfw/).
Embeddings are calculated using the pairs from http://vis-www.cs.umass.edu/lfw/pairs.txt and the ROC curve
is calculated and plotted. Both the model metagraph and the model paramete... |
<gh_stars>1-10
#! /usr/bin/env python
# -*- coding: utf-8 -*-
# vim:fenc=utf-8
#
# Copyright © 2018 <NAME> <<EMAIL>>
#
# Distributed under terms of the MIT license.
"""
collocDemo.py
Use the collocation version of problem.
"""
import sys, os, time
import numpy as np
import matplotlib.pyplot as plt
from trajoptlib.io ... |
"""
The MIT License (MIT)
Copyright (c) 2017 <NAME>
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import collections
from collections import OrderedDict
import json
import logging
import sys
import random
import types
import torch
import ... |
<reponame>fmi-basel/dl-utils<filename>dlutils/training/targets/separators.py
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from scipy.ndimage.morphology import grey_closing
from scipy.ndimage.morphology import grey_d... |
import sharpy.utils.solver_interface as solver_interface
import os
import numpy as np
import scipy.sparse as scsp
import sharpy.linear.src.libsparse as libsp
import sharpy.utils.cout_utils as cout
import sharpy.utils.algebra as algebra
import sharpy.utils.settings as settings
@solver_interface.solver
class StabilityD... |
<gh_stars>0
from pathlib import Path
import xml.etree.ElementTree as ET
import os
import cv2
import io
from PIL import Image
import pickle
from torchvision import transforms, models
from torch.autograd import Variable
import random
import numpy as np
import torch
from scipy.spatial import distance as dist
datapath = ... |
<gh_stars>100-1000
## Mostly from Physical Acoustics V.IIIB - W.P. Mason ed. 1965 [534 M412p] Ch. 1-2
## Also in Journal of Alloys and Compounds 353 (2003) 74–85
## Data here for Co3O4
# need to determine units for these values
mass = 240.79500 / 7 / 1000.0 / 6.0221415e23 # average atomic mass in ?kg/atom?
N = ... |
"""Helper functions for plotting."""
import matplotlib.pyplot as _plt
import numpy as _np
import sympy as _sp
def plot_slopes_1d(slopes, values, grid, scale=1, ax=None, **kwargs):
"""Plot incoming and outging slopes for 1D spline."""
if ax is None:
ax = _plt.gca()
slopes = _np.asarray(slopes)
... |
<gh_stars>0
datadir='/Users/michielk/M3_S1_GNU_NP/train'
datadir='/data/ndcn-fmrib-water-brain/ndcn0180/EM/Neuroproof/M3_S1_GNU_NP/train'
dset_name='m000_01000-01500_01000-01500_00030-00460'
datadir='/Users/michielk/M3_S1_GNU_NP/test'
datadir='/data/ndcn-fmrib-water-brain/ndcn0180/EM/Neuroproof/M3_S1_GNU_NP/test'
dset_... |
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from __future__ import division
from __future__ import absolute_import
from __future__ import print_function
import numpy as np
import pandas as pd
from scipy.spatial.distance import cdist
from . import AbstractCostFunction
from .gap_close import Abs... |
<filename>exercise-1/ex_1.py<gh_stars>0
# -*- coding: utf-8 -*-
"""
Enunciado:
Qual é o menor valor de entrada n (considere n > 0) tal que um algoritmo
cujo tempo de execução é 10n2 é mais rápido que um algoritmo cujo tempo de
execução é 2n na mesma máquina? Qual desses algoritmos você considera mais
ef... |
"""
Classes for acqoptimizers implemented with scipy.optimize.
"""
from argparse import Namespace
import numpy as np
from scipy.optimize import minimize
from .acqopt import AcqOptimizer
from ..util.misc_util import dict_to_namespace
class SpoAcqOptimizer(AcqOptimizer):
"""AcqOptimizer using algorithms from scip... |
<filename>model/rendnet.py
from __future__ import division
import os
import math
import time
import tensorflow as tf
import numpy as np
import scipy
import re
import pdb
import tensorflow.contrib.slim as slim
from .nnlib import *
from .parameters import arch_para, hparams, Parameters
from util import read_image, comp_c... |
# -*- coding: utf-8 -*-
import cv2
import matplotlib.pyplot as plt
import os
from mpl_toolkits.mplot3d import Axes3D
from scipy.ndimage.interpolation import rotate as R
import numpy as np
from matplotlib import animation
def get_volume_views(volume, save_dir, n_itr, idx, test=False, save_gif=False, color_map="bone", ... |
import numpy as np
import scipy.linalg as la
import pdb
from .submodular_funcs import *
class SubmodularOpt():
def __init__(self, V=None, v=None, **kwargs):
self.v = v
self.V = V
def initialize_function(self, lam, a1=1.0, a2=1.0, b1=1.0, b2= 1.0):
self.a1 = a1
self.a2 = a2
... |
from pines_analysis_toolkit.utils import pines_dir_check, short_name_creator
from astropy import units as u
from astropy.coordinates import SkyCoord
from astropy.coordinates import EarthLocation, AltAz
from astropy.time import Time
from astropy.utils.data import clear_download_cache
from astropy.constants import R_ear... |
import numpy as np
from scipy.sparse import csr_matrix, csc_matrix, lil_matrix, dok_matrix
n = 1000
np.random.seed(0)
d = np.random.randint(1, n, n*10)
i = np.random.randint(0, n, n*10)
j = np.random.randint(0, n, n*10)
csr = csr_matrix((d, (i, j)), (n, n))
csc = csr.tocsc()
lil = csr.tolil()
dok = csr.todok()
%%ti... |
# coding: utf-8
# ### Compute results for task 1 on the humour dataset.
#
# Please see the readme for instructions on how to produce the GPPL predictions that are required for running this script.
#
# Then, set the variable resfile to point to the ouput folder of the previous step.
#
import string
import pandas as p... |
<filename>python/load.py
import sys, os, h5py, corner
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from scipy import stats, signal, ndimage, interpolate
import astropy
from astropy.io import fits
###################################################################
### Get the Expected ... |
# --------------
import pandas as pd
import scipy.stats as stats
import math
import numpy as np
import warnings
warnings.filterwarnings('ignore')
#Sample_Size
sample_size=2000
#Z_Critical Score
z_critical = stats.norm.ppf(q = 0.95)
# path [File location variable]
data = pd.read_csv(path)
#C... |
<reponame>diehlpk/muDIC
import random
import numpy as np
from scipy.ndimage import gaussian_filter
try:
from noise import pnoise2
except ImportError as e:
print(e)
print("The package: noise, is not installed. Perlin speckle is not available")
def pnoise2(*args, **kwargs):
raise ImportError("... |
<reponame>TonioBall/braindecode
import numpy as np
import mne
from scipy.io import loadmat
class BCICompetition4Set2A(object):
def __init__(self, filename, load_sensor_names=None, labels_filename=None):
assert load_sensor_names is None
self.__dict__.update(locals())
del self.self
def ... |
<gh_stars>1-10
""" Defines the DataComparator class used to compare multiple DataSets."""
#***************************************************************************************************
# Copyright 2015, 2019 National Technology & Engineering Solutions of Sandia, LLC (NTESS).
# Under the terms of Contract DE-NA000... |
import matplotlib.pyplot as plt
import scipy.io
import requests
def plot_ex5data1(X, y):
plt.figure(figsize=(8,5))
plt.xlabel('Mudança no nível da água (x)')
plt.ylabel('Água saindo da barragem (y)')
plt.plot(X,y,'rx')
|
import abc
from scipy.stats import norm
from numpy.random import normal
import streamlit as st
class OptionClass(metaclass=abc.ABCMeta):
'''Template Class for credit spread options trading'''
def __init__(self, principal, stockprice, sigma, numTrades=1000):
# Amount in the account
self.principa... |
"""This code can be used for risk prediction heatmap generation """
import numpy as np
import os
import cv2
import glob
from glob import glob
#import pandas as pd
from matplotlib import pyplot as plt
from PIL import Image
import pdb
from skimage import io
# skimage image processing packages
from skimage import measure... |
<filename>data.py
import os
import random
from copy import deepcopy
from functools import wraps
import numpy as np
import pandas as pd
import torch
from scipy.sparse.construct import rand
from sklearn.metrics.pairwise import cosine_similarity
from torch.utils.data import Dataset
from tqdm import tqdm
from const impor... |
# Licensed under a 3-clause BSD style license - see LICENSE.rst
from __future__ import absolute_import, division, print_function, unicode_literals
from collections import OrderedDict
import logging
import numpy as np
import astropy.units as u
from astropy.convolution import Tophat2DKernel, CustomKernel
from ..image imp... |
# -*- coding: utf-8 -*-
"""
Created on Fri May 18 16:03:11 2018
@author: Administrator
"""
import qcodes_measurements as qcm
from qcodes_measurements import pyplot
from qcodes_measurements.plot import plot_tools
from scipy import signal
import math
midas.sw_mode('distributed')
midas.filter_mode('5k')
midas.raster_ra... |
from math import sqrt, floor
from numpy import var
from scipy.stats import ttest_ind
from statsmodels.stats.power import tt_ind_solve_power
from oeda.log import warn, error
from oeda.analysis import Analysis
from numpy import mean
class TwoSampleTest(Analysis):
def run(self, data, knobs):
if len(data) <... |
<reponame>smartdatalake/pathlearn
import csv
import networkx as nx
import sys
import numpy as np
import time
import pandas as pd
import random as rnd
#from ampligraph.datasets import load_fb15k
import traceback
from datetime import datetime
#import mp_counts
from itertools import product
#import preprocessing as prc
#f... |
"""
Copyright 2019 <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 writing, software
distri... |
<reponame>bytedance/Hammer
# python3.7
"""Utility functions used for computing metrics."""
import numpy as np
import scipy.linalg
import torch
__all__ = [
'compute_fid', 'compute_fid_from_feature', 'kid_kernel',
'compute_kid_from_feature', 'compute_is', 'compute_pairwise_distance',
'compute_gan_precision... |
import numpy as np
from scipy.optimize import linear_sum_assignment
class MODA:
"""An addable metric class to track the components of MODA"""
def __init__(
self, false_negatives: int = 0, false_positives: int = 0, n_truth: int = 0
) -> None:
self.false_negatives = false_negatives
... |
# -*- coding: utf-8 -*-
# @Author: <NAME>
# @Email: <EMAIL>
# @Date: 2018-09-26 17:11:28
# @Last Modified by: <NAME>
# @Last Modified time: 2021-06-22 15:14:30
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import FormatStrFormatter
from scipy import signal
from PySONIC.core import Look... |
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