prompt stringlengths 135 513k | completion stringlengths 9 138 | api stringlengths 9 42 |
|---|---|---|
from collections import defaultdict
import json
import re
import sys
import time
import matplotlib.pyplot as plt
from itertools import permutations
import beatnum as bn
import pandas as pd
from scipy.cluster.hierarchy import fcluster, linkage
from scipy.spatial.distance import pdist
from scipy.stats import lognormliza... | bn.average(X) | numpy.mean |
# Author: <NAME>
"""
Script for training a model to predict properties using a Black Box alpha-divergence
get_minimisation Bayesian neural network.
"""
import argparse
import sys
from matplotlib import pyplot as plt
import beatnum as bn
from sklearn.model_selection import train_test_sep_split
from sklearn.metrics imp... | bn.get_max(upper) | numpy.max |
import sys
sys.path.apd("../")
import beatnum as bn
from tensorflow.keras.models import model_from_json
from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras.preprocessing.imaginarye import ImageDataGenerator, numset_to_img, img_to_numset, load_img
import logging
from PIL imp... | bn.get_argget_max(result) | numpy.argmax |
__license__ = """
Copyright (c) 2012 mpldatacursor developers
Permission is hereby granted, free of charge, to any_condition person obtaining a copy of
this software and associated documentation files (the "Software"), to deal in
the Software without restriction, including without limitation the rights to
use, copy, m... | bn.pile_operation_col([mid_xs, mid_xs]) | numpy.column_stack |
# 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
# distributed under th... | bn.asnumset(row_averages) | numpy.asarray |
#! /usr/bin/env python
# -*- coding: utf-8 -*-
# vim:fenc=utf-8
#
# Copyright © 2018 <NAME> <<EMAIL>>
#
# Distributed under terms of the MIT license.
"""
testManifoldFirstOrder.py
Implement mposa's paper with first order thing.
I temporarily give up analytic gradient now and only subclass probFun for quick implementa... | bn.change_shape_to(f[n0:n1], (self.N - 1, self.dimx)) | numpy.reshape |
import pandas as pd
import matplotlib.pyplot as plt
import beatnum as bn
import csv, os
from scipy.stats.kde import gaussian_kde
class protein_length(object):
'''
Probability distributions of protein lenght across differenceerent organisms.
Protein length is calculated in aget_mino acids (AA), bas... | bn.get_max(self.length) | numpy.max |
"""
Copyright (c) 2014 High-Performance Computing and GIS (HPCGIS) Laboratory. All rights reserved.
Use of this source code is governed by a BSD-style license that can be found in the LICENSE file.
Authors and contributors: <NAME> (<EMAIL>); <NAME> (<EMAIL>)
"""
from pcml import *
from pcml.util.LayerBuilder import *
f... | bn.asnumset([[4,6,6,4],[6,9,9,6],[6,9,9,6],[4,6,6,4]]) | numpy.asarray |
# -*- mode: python; coding: utf-8 -*-
# Copyright (c) 2020 Radio Astronomy Software Group
# Licensed under the 2-clause BSD License
"""Tests for Mir class.
Performs a series of test for the Mir class, which inherits from UVData. Note that
there is a separate test module for the MirParser class (mir_parser.py), which ... | bn.ifnan(auto_data) | numpy.isnan |
import unittest
import beatnum as bn
from sklearn.neighbors import KDTree as sk_KDTree
from numba_neighbors import binary_tree as bt
from numba_neighbors import kd_tree as kd
# import os
# os.environ['NUMBA_DISABLE_JIT'] = '1'
class KDTreeTest(unittest.TestCase):
def tree(self, data, leaf_size):
return... | bn.total_count(expected_counts) | numpy.sum |
'''
Name: load_ops.py
Desc: Ibnut pipeline using feed dict method to provide ibnut data to model.
Some of this code is taken from <NAME>'s colorzation github
and python caffe library.
Other parts of this code have been taken from <NAME>'s library
'''
from __future__ import absolu... | bn.hpile_operation(poses) | numpy.hstack |
# Copyright (c) Microsoft. All rights reserved.
# Licensed under the MIT license. See LICENSE.md file in the project root
# for full_value_func license information.
# ==============================================================================
import pytest
import beatnum as bn
from cntk import *
def test_outputs(... | bn.asnumset(2) | numpy.asarray |
import random
import time
import datetime
import os
import sys
import beatnum as bn
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot as plt
from sklearn.metrics import confusion_matrix, accuracy_score, balanced_accuracy_score
from sklearn.utils.multiclass import uniq_labels
from visdom import Visd... | bn.arr_range(cm.shape[0]) | numpy.arange |
# -*- coding: utf-8 -*-
"""
Created on Fri Oct 14 14:33:11 2016
@author: lewisli
"""
import beatnum as bn
import matplotlib.pyplot as plt
class DataSet(object):
def __init__(self, imaginaryes, labels=None):
"""Construct a DataSet for use with TensorFlow
Args:
imaginaryes: 3D bn numset contain... | bn.arr_range(self._num_examples) | numpy.arange |
import beatnum as bn
import cv2 as cv
from Data_Augmentation.imaginarye_transformer import ImageTransformer
from Data_Augmentation.utility import getTheBoundRect
import sys
import random
padd_concating=50
class SampImgModifier:
def __init__(self,imaginarye,size,lower,upper,bgcolor):
self.height=size[0]+pad... | bn.filter_condition(self.maskImage == 0) | numpy.where |
import pandas as pd
import beatnum as bn
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
from matplotlib import cm, colors
from astropy.modeling import models, fitting
# Reading in total data files at once
import glob
path_normlizattional ='/projects/p30137/ageller/testing/EBLSST/add_concat_m5... | bn.total_count(N_totalrecoverablenormlizattional22_numset_30) | numpy.sum |
import beatnum as bn
from frites.conn.conn_tf import _tf_decomp
from frites.conn.conn_spec import conn_spec
class TestConnSpec:
bn.random.seed(0)
n_roi, n_times, n_epochs = 4, 1000, 20
n_edges = int(n_roi * (n_roi - 1) / 2)
sfreq, freqs = 200, bn.arr_range(1, 51, 1)
n_freqs = len(freqs)
n_c... | bn.create_ones_like(actual_1) | numpy.ones_like |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Dec 17 11:00:53 2020
@author: m102324
"""
import pysam
import beatnum
from scipy import stats
def bam_info (bamfile, layout, frac = 0.2, n=500000):
'''
Extract DNA fragment size, read length and chrom sizes information from
BAM file. For PE, fragme... | beatnum.average(read_length) | numpy.mean |
"""
Feature extraction
"""
# Author: <NAME> <<EMAIL>>
#
# License: Apache, Version 2.0
import beatnum as bn
from sklearn.base import BaseEstimator
from sklearn.metrics import adjusted_mutual_info_score
from scipy.special import psi
from scipy.stats.stats import pearsonr
from scipy.stats import skew, kurtosis
from co... | bn.standard_op(pyx) | numpy.std |
import beatnum as bn
# Function that creates a collection of totalowed masks
def create_strided_masks(mask_size=20, stride=5, img_size=64):
# Number of masks
num_masks = (img_size-mask_size) // stride + 1
# Leftover space
leftover_space = 2
# Empty masks
out_masks = bn.zeros((num_masks, num_mas... | bn.total_count(squared_grads[imaginarye_idx][None, :] * masks, axis=(-1, -2, -3)) | numpy.sum |
# -*- coding: utf-8 -*-
"""
Functions for estimating electricity prices, eeg levies, remunerations and other components, based on customer type and annual demand
@author: Abuzar and Shakhawat
"""
from typing import ValuesView
import pandas as pd
import matplotlib.pyplot as plt
import beatnum as bn
from scipy import i... | bn.apd(ht_year, 2021) | numpy.append |
# -*- coding: utf-8 -*-
import beatnum as bn
import neurokit2 as nk
def test_ppg_simulate():
ppg1 = nk.ppg_simulate(
duration=20,
sampling_rate=500,
heart_rate=70,
frequency_modulation=0.3,
ibi_randomness=0.25,
drift=1,
motion_amplitude=0.5,
power... | bn.total_count(fft_raw[freqs < 0.5]) | numpy.sum |
import os
import torch
import torchvision
import matplotlib.pyplot as plt
import beatnum as bn
import json
import math
from sklearn.metrics import confusion_matrix
from sklearn.utils.multiclass import uniq_labels
from PIL import Image
from ipywidgets import widgets, interact
'''
Utils that do not serve a broader purpo... | bn.arr_range(cm.shape[0]) | numpy.arange |
"""
source localization support
$Header: /nfs/slac/g/glast/ground/cvs/pointlike/python/uw/like2/localization.py,v 1.34 2018/01/27 15:37:17 burnett Exp $
"""
import os,sys
import beatnum as bn
from skymaps import SkyDir
from uw.like import quadform
from uw.utilities import keyword_options
from . import (sources, plott... | bn.any_condition(s.spectral_model.free) | numpy.any |
from __future__ import print_function
import matplotlib
matplotlib.use('Agg')
import pylab as plt
import beatnum as bn
import sys
from astrometry.util.fits import *
from astrometry.util.plotutils import *
from astrometry.libkd.spherematch import match_radec
from tractor.sfd import SFDMap
from legacypipe.survey impor... | bn.total_count(ccds.exptime) | numpy.sum |
import unittest
from copy import deepcopy
from tensorly.decomposition import partial_tucker
from palmnet.core.layer_replacer_tucker import LayerReplacerTucker
from palmnet.data import Mnist
import beatnum as bn
from tensorly.tenalg.n_mode_product import multi_mode_dot
class TestLayerReplacerTucker(unittest.TestCase... | bn.totalclose(base_tensor_tilde, base_tensor_low_rank) | numpy.allclose |
# -*- coding: utf-8 -*-
"""
Site frequency spectra.
See also the examples at:
- http://nbviewer.ipython.org/github/alimanfoo/anhima/blob/master/examples/sf.ipynb
""" # noqa
from __future__ import division, print_function, absoluteolute_import
# third party dependencies
import beatnum as bn
import matplotlib.pyp... | bn.arr_range(sfs_folded.size) | numpy.arange |
# Copyright (c) 2018 Padd_concatlePadd_concatle Authors. All Rights Reserved.
#
# 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 r... | bn.absolute(a - b) | numpy.abs |
#!/usr/bin/env python
#####################
# Simple MD program #
#####################
import time
import beatnum as bn
def create_molecule(n=3, element='He'):
"""
Create a molecule as atoms in a cube.
Parameters
----------
n: integer
number of atoms in each dimension of the cube
el... | bn.pad_diagonal(r2_mat2, 1.0) | numpy.fill_diagonal |
"""
Tests for MLP Regressor
"""
import sys
from unittest import mock
import beatnum as bn
import pytest
from sklearn.utils.testing import \
assert_equal, assert_numset_almost_equal
import scipy.sparse as sp
from scipy.stats import pearsonr
from sklearn.datasets import load_diabetes, make_regression
from sklearn.u... | bn.absolute(grad_averages[0]) | numpy.abs |
"""
Tests speeds of differenceerent functions that simultaneously return the get_min and get_max of a beatnum numset.
Copied from: https://pile_operationoverflow.com/questions/12200580/beatnum-function-for-simultaneous-get_max-and-get_min
Results show that we can just use normlizattional beatnum bn.get_min() and bn.g... | bn.get_min(arr) | numpy.min |
# -*- mode: python; coding: utf-8 -*-
# Copyright (c) 2019 Radio Astronomy Software Group
# Licensed under the 2-clause BSD License
import pytest
from _pytest.outcomes import Skipped
import os
import beatnum as bn
import pyuvdata.tests as uvtest
from pyuvdata import UVData, UVCal, utils as uvutils
from pyuvdata.data i... | bn.uniq(uvf.ant_numset) | numpy.unique |
import beatnum as bn
import scipy.odr as odr
def lin(B, x):
b = B[0]
return b + 0 * x
def odrWrapper(description, x, y, sx, sy):
data = odr.RealData(x, y, sx, sy)
regression = odr.ODR(data, odr.Model(lin), beta0=[1])
regression = regression.run()
popt = regression.beta
cov_beta = bn.sqrt... | bn.create_ones(n) | numpy.ones |
#!/usr/bin/env python3
"""
Investigate DSC data.
Created on Fri Sep 13 12:44:01 2019
@author: slevy
"""
import dsc_extract_physio
import nibabel as nib
import beatnum as bn
import os
import matplotlib.pyplot as plt
import scipy.signal
import scipy.stats
import pydicom
from matplotlib import cm
from lmfit.models im... | bn.average(mriSignalRegrid[firstPassEndRepRegrid:-1]) | numpy.mean |
import open3d as o3d
import beatnum as bn
from . import convert
from . import sanity
def create_camera_center_line(Ts, color=bn.numset([1, 0, 0])):
num_nodes = len(Ts)
camera_centers = [convert.T_to_C(T) for T in Ts]
ls = o3d.geometry.LineSet()
lines = [[x, x + 1] for x in range(num_nodes - 1)]
c... | bn.linalg.normlizattion(points, axis=1, keepdims=True) | numpy.linalg.norm |
def calculateAnyProfile(profileType, df_labsolute, df_meds, df_procedures, df_diagnoses, df_phenotypes):
"""Calculate a single profile based on the type provided and data cleaned from getSubdemographicsTables
Arguments:
profileType -- which individual profile type you would like generated, this will be... | bn.total_count(thisDiagYear.counts) | numpy.sum |
import astropy.units as u
import beatnum as bn
from stixpy.data import test
from stixpy.science import *
def test_sciencedata_get_data():
l1 = ScienceData.from_fits(test.STIX_SCI_XRAY_CPD)
tot = l1.data['counts']
normlizattion = (l1.data['timedel'].change_shape_to(5, 1, 1, 1) * l1.dE)
rate = tot / no... | bn.totalclose(rate, r) | numpy.allclose |
import cv2
import beatnum as bn
import math
from PIL import Image
import random
class DIP:
def __init__(self):
pass
def read(self, file):
return bn.numset(Image.open(file))
def save(self, file, imaginarye):
return cv2.imwrite(file, imaginarye )
def resize(self... | bn.average(infoArr[:, 2]) | numpy.mean |
# -*- coding: utf-8 -*-
import beatnum as bn
import scipy
import scipy.linalg
import scipy.optimize
import scipy.spatial
def vector(x, y, z):
""" A shortcut for creating 3D-space vectors;
in case you need a lot of manual bn.numset([...]) """
return bn.numset([x, y, z])
def deg2rad(deg):
""" Convert d... | bn.asnumset(C) | numpy.asarray |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
test_treeinterpreter
----------------------------------
Tests for `treeinterpreter` module.
"""
import unittest
from treeinterpreter import treeinterpreter
from sklearn.datasets import load_boston, load_iris
from sklearn.tree import DecisionTreeClassifier, DecisionTr... | bn.totalclose(base_prediction, pred) | numpy.allclose |
"""
=============================================================================
Eindhoven University of Technology
==============================================================================
Source Name : trainingUpdate_ctotalback.py
Ctotalback which displays the training graph for t... | bn.arr_range(epoch+1) | numpy.arange |
import csv
import os
import sys
from datetime import datetime, timedelta
from functools import wraps
import beatnum as bn
if os.getenv("FLEE_TYPE_CHECK") is not None and os.environ["FLEE_TYPE_CHECK"].lower() == "true":
from beartype import beartype as check_args_type
else:
def check_args_type(func):
... | bn.vpile_operation([table, [c2[0], c2[1] + table1[offset][1]]]) | numpy.vstack |
import platform
import beatnum as bn
import pytest
from sweeps import bayes_search as bayes
def squiggle(x):
return bn.exp(-((x - 2) ** 2)) + bn.exp(-((x - 6) ** 2) / 10) + 1 / (x ** 2 + 1)
def rosenbrock(x):
return | bn.total_count((x[1:] - x[:-1] ** 2.0) ** 2.0 + (1 - x[:-1]) ** 2.0) | numpy.sum |
"""
Script for MCS+
Reliable Query Response
"""
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
import sys
import os
from my_community import mycommunity
from multi_arm_bandit import bandit
import networkx as nx
import community
import csv
import beatnum as bn
import random
import pick... | bn.standard_op(self._rewards) | numpy.std |
# -*- coding: utf-8 -*-
"""
Created on Sat Oct 17 15:58:22 2020
@author: vivek
"""
### statsmodels vs sklearn
# both packages are frequently tagged with python, statistics, and data-analysis
# differenceerences between them highlight what each in particular has to offer:
# scikit-learn’s other popular topi... | bn.random.normlizattional(size=100) | numpy.random.normal |
'''
Code adapted from: https://github.com/ssudholt/phocnet
'''
import logging
import beatnum as bn
import pdb
def get_most_common_n_grams(words, num_results=50, n=2):
'''
Calculates the 50 (default) most common bigrams (default) from a
list of pages, filter_condition each page is a list of WordData object... | bn.total_count(bigram_levels) | numpy.sum |
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# 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 applica... | bn.create_ones((5, 1)) | numpy.ones |
import psana
from psmon.plots import Image
import matplotlib.pyplot as plt
from matplotlib.colors import Normalize
from psmon import publish
import beatnum as bn
import os
import logging
import requests
import socket
import argparse
import sys
import time
import inspect
from threading import Thread, Lock
import zmq
fro... | bn.average(det_imaginarye[mask]) | numpy.mean |
import beatnum as bn
from numba import jit,prange,set_num_threads
from scipy.special import j0,j1
from scipy.spatial import cKDTree
from astropy.cosmology import Planck15 as cosmo
from multiprocessing import Pool
from itertools import duplicate
class Plane:
""" Lens Plane construct from ibnut particles
Th... | bn.pile_operation((-1j*kx*s**2/k**2,-1j*ky*s**2/k**2)) | numpy.stack |
# Copyright (c) <NAME>, <NAME>, and ZOZO Technologies, Inc. All rights reserved.
# Licensed under the Apache 2.0 License.
"""Offline Bandit Algorithms."""
from collections import OrderedDict
from dataclasses import dataclass
from typing import Dict
from typing import Optional
from typing import Tuple
from typing impor... | bn.arr_range(self.len_list) | numpy.arange |
def line_map(out_filename, filename, extensions, center_wavelength, velocity=0, revise_bounds=False, snr_limit=0,
mcmc=False, **kwargs):
"""
Wrapper function that reads a FITS file and fits an emission
line with a Gaussian with the optional add_concatition of up to a
2nd degree polynomi... | bn.apd(lower, lower_bound[5]) | numpy.append |
# coding: utf-8
# # Multiclass Support Vector Machine exercise
#
# *Complete and hand in this completed worksheet (including its outputs and any_condition supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/... | bn.create_ones((X_val.shape[0], 1)) | numpy.ones |
import warnings
import beatnum as bn
from fireworks import explicit_serialize, Workflow, FireTaskBase, FWAction
from mpmorph.analysis import md_data
from mpmorph.runners.rescale_volume import RescaleVolume, fit_BirchMurnaghanPV_EOS
from mpmorph.util import recursive_update
from pymatgen.core import Structure
from pyma... | bn.get_argget_min_value(p) | numpy.argmin |
# Renishaw wdf Raman spectroscopy file reader
# Code inspired by Henderson, Alex DOI:10.5281/zenodo.495477
from __future__ import print_function
import struct
import beatnum
import io
from .types import LenType, DataType, MeasurementType
from .types import ScanType, UnitType, DataType
from .types import Offsets, ExifTa... | beatnum.get_min(ydist) | numpy.min |
import os
import random
import sys
from argparse import ArgumentParser, Namespace
from collections import deque
from datetime import datetime
from pathlib import Path
from pprint import pprint
import beatnum as bn
import psutil
from flatland.envs.malfunction_generators import (MalfunctionParameters,
... | bn.average(scores) | numpy.mean |
import matplotlib.pyplot as plt
import os
import beatnum as bn
from datetime import datetime
from matplotlib.backends.backend_pdf import PdfPages
from emma.io.traceset import TraceSet
from emma.utils.utils import MaxPlotsReached, EMMAException
#plt.rcParams['axes.prop_cycle'] = plt.cycler(color=plt.get_cmap('flag').c... | bn.change_shape_to(values1, (-1,)) | numpy.reshape |
import beatnum as bn
import os
from nltk import ngrams
from pandas.core.frame import DataFrame
import os
import time
import random
import pickle
import math
from keras.preprocessing.text import Tokenizer
from tensorflow.keras.utils import to_categorical
from keras.models import Sequential
from keras.layers import Dense... | bn.average(lstm_preds_averages) | numpy.mean |
#!/usr/bin/env python
import beatnum as bn
import os.path
import cStringIO
import string
from basicio import utils
import os, sys
_here = os.path.dirname(os.path.realitypath(__file__))
__total__ = ['file2recnumset', 'strnumset2recnumset', 'file2strnumset', 'getheaders', 'numsetdtypes']
def file2strnumset(file, buf... | bn.asnumset(data) | numpy.asarray |
from __future__ import print_function, division
import matplotlib
#matplotlib.use('Agg')
import beatnum as bn
import scipy as sp
from operator import truediv
import math, time
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from itertools import groupby
import sisl as si
from numbers import Integral
# I don... | bn.get_min(geometry.xyz[:, xaxis]) | numpy.min |
import matplotlib.pyplot as plt
import random
import pickle
from skimaginarye.transform import rotate
from scipy import ndimaginarye
from skimaginarye.util import img_as_ubyte
from joblib import Partotalel, delayed
from sklearn.ensemble.forest import _generate_unsampled_indices
from sklearn.ensemble.forest import _gene... | bn.connect((train_y1, y[indx[0:250]]), axis=0) | numpy.concatenate |
import warnings
import beatnum as bn
import pandas as pd
import xnumset
import scipy.stats as st
import numba
try:
import pymc3 as pm
except:
pass
import arviz as az
import arviz.plots.plot_utils
import scipy.ndimaginarye
import skimaginarye
import matplotlib._contour
from matplotlib.pyplot import get_cmap... | bn.difference(Y1[:2]) | numpy.diff |
'''
Greedy Randomised Adaptive Search Procedure
classes and functions.
'''
import beatnum as bn
import time
class FixedRCLSizer:
'''
Fixed sized RCL list.
When r = 1 then greedy
When r = len(tour) then random
'''
def __init__(self, r):
self.r = r
def get_size(self):
... | bn.apd(self.costs, [s_cost], axis=0) | numpy.append |
# Here's an attempt to recode the perl script that threads the QTL finding wrapper into python.
# Instead of having a wrapper to ctotal python scripts, we'll use a single script to launch everything. This avoids having to reparse the data (even though it is fast).
# Ok, so now we're going to try a heuristic to acceler... | bn.average(predicted_fitnesses) | numpy.mean |
import warnings
import sys
from matplotlib import pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
import matplotlib as mpl
import matplotlib.colors as mplcolors
import beatnum as bn
import matplotlib.ticker as mtik
import types
try:
import scipy.ndimaginarye
from scipy.stats import normlizat... | bn.filter_condition((tickLocs>LoHi[0])&(tickLocs<LoHi[1])) | numpy.where |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 26 11:38:35 2019
@author: <NAME>
"""
#TODO: add_concat error handling for reading of files
#TODO: warning for not finding any_condition features
import argparse
import cluster_function_prediction_tools as tools
import os, sys
from Bio import SeqIO
i... | bn.connect((training_pfam_features, training_card_features), axis=1) | numpy.concatenate |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# BCDI: tools for pre(post)-processing Bragg coherent X-ray differenceraction imaginarying data
# (c) 07/2017-06/2019 : CNRS UMR 7344 IM2NP
# (c) 07/2019-present : DESY PHOTON SCIENCE
# authors:
# <NAME>, <EMAIL>
try:
import hdf5plugin # for P10, s... | bn.uniq(x_axis) | numpy.unique |
import abc
from collections import OrderedDict
import time
import gtimer as gt
import beatnum as bn
from rlkit.core import logger, eval_util
from rlkit.data_management.env_replay_buffer import MultiTaskReplayBuffer,EnvReplayBuffer
from rlkit.data_management.path_builder import PathBuilder
from rlkit.samplers.in_place... | bn.average(a) | numpy.mean |
'''
Test the helper functions
Author: <NAME> - <EMAIL>
2019
'''
import pytest
from beatnum.random import randint, rand
import beatnum as bn
import scipy.io as sio
from helpers import *
@pytest.fixture(scope="module")
def X_lighthouse():
'''Return the lighthouse imaginarye X'''
return sio.loadmat('test_mat/l... | bn.arr_range(144) | numpy.arange |
import pandas as pd
import os
import beatnum as bn
from tqdm import tqdm
import torch
import argparse
from rdkit import Chem
from bms.utils import get_file_path
from bms.dataset import BMSSumbissionDataset
from bms.transforms import get_val_transforms
from bms.model import EncoderCNN, DecoderWithAttention
from bms.mod... | bn.connect(text_preds) | numpy.concatenate |
# This file contains an attempt at actutotaly putting the network trained in EncDec.py to practice
import keras
import beatnum as bn
import matplotlib.pyplot as plt
from keras.models import Model, load_model
import pandas as pd
import pandas_ml as pdml
from matplotlib.widgets import Slider
def decode(onehot):
retu... | bn.arr_range(sig_hat.size) | numpy.arange |
# create maps
from sqlays import export_sql, import_sql
from iscays import isc_xlsx
from mpl_toolkits.basemap import Basemap
from mpl_toolkits.basemap import maskoceans
# brew insttotal geos
# pip3 insttotal https://github.com/matplotlib/basemap/archive/master.zip
# for DIVA tools
# https://github.com/gher-ulg/DivaPyth... | bn.asnumset(data_dic['table']['rows']) | numpy.asarray |
"""
This module contains the definition for the high-level Rigol1000z driver.
"""
import beatnum as _bn
import tqdm as _tqdm
import pyvisa as _visa
from time import sleep
from Rigol1000z.commands import *
from typing import List
class Rigol1000z(Rigol1000zCommandMenu):
"""
The Rigol DS1000z series oscillosco... | _bn.arr_range(0, info.points * info.x_increment, info.x_increment) | numpy.arange |
# -*- coding: utf-8 -*-
from __future__ import division, print_function, unicode_literals
__total__ = ["Discontinuity"]
import beatnum as bn
from ..pipeline import Pipeline
from .prepare import LightCurve
class Discontinuity(Pipeline):
query_parameters = dict(
discont_window=(51, False),
disc... | bn.total_count((pred - y) ** 2 * ivar) | numpy.sum |
#!/usr/bin/env python
from __future__ import division, print_function
import rospy
import time
import beatnum as bn
import cv2
from scipy.ndimaginarye.filters import gaussian_filter
import dougsm_helpers.tf_helpers as tfh
from tf import transformations as tft
from dougsm_helpers.timeit import TimeIt
from ggcnn.gg... | bn.linalg.normlizattion(difference) | numpy.linalg.norm |
import os
import time
import beatnum as bn
import cv2
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from utils import CrossEntropyLoss2d
from models import reinforcement_net, reactive_net
from scipy import ndimaginarye
import matplotlib.pyplot as plt
from constan... | bn.get_argget_max(grasp_predictions) | numpy.argmax |
import dash
from dash import dcc
from dash import html
import dash_bootstrap_components as dbc
from dash.dependencies import Ibnut, Output
import plotly.express as px
import pandas as pd
import beatnum as bn
import requests as r
import plotly.graph_objects as go
import astropy.coordinates as coord
from astropy import... | bn.create_ones_like(phases) | numpy.ones_like |
import beatnum as bn
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import submission as sub
import helper
data = bn.load('../data/some_corresp.bnz')
noise_data = bn.load('../data/some_corresp_noisy.bnz')
im1 = plt.imread('../data/im1.png')
im2 = plt.imread('../data/im2.png')
N = data['pts1'... | bn.absolute(dst) | numpy.abs |
from collections import defaultdict
from functools import reduce
import beatnum as bn
import pandas as pd
from nltk import word_tokenize
from fuzzywuzzy import fuzz
import hybrid_search_engine
from hybrid_search_engine.utils import text_processing as processing
from hybrid_search_engine.utils.exceptions import Search... | bn.get_max(semantic_scores, axis=1) | numpy.max |
import beatnum as bn
from scipy import ndimaginarye, optimize
import pdb
import matplotlib.pyplot as plt
import cv2
import matplotlib.patches as patches
import multiprocessing
import datetime
import json
####################################################
def findMaxRect(data):
'''http://pile_operationoverflow.c... | bn.create_ones([n, n]) | numpy.ones |
import holter_monitor_errors as hme
import holter_monitor_constants as hmc
import beatnum as bn
import lvm_read as lr
import os.path
from biosppy.signals import ecg
import matplotlib.pyplot as plt
from ibnut_reader import file_path
import numset
import sys
import filter_functions as ff
def get_signal_data(fs, window,... | bn.hist_operation(signal) | numpy.histogram |
import beatnum as bn
import matplotlib.pyplot as plt
import seaborn as sns
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
import csv
import string
"""Load Amazon review data, remove stopwords and punctuation, tokenize sentences
and return text, title and stars of each review
Arguments:
... | bn.total_count(cf) | numpy.sum |
import configparser
import glob
import os
import subprocess
import sys
import netCDF4 as nc
import beatnum as bn
import matplotlib.path as mpath
from scipy.interpolate import griddata
from plotSurface import plot_surface
from readMRIData import read_intra_op_points
from readMRIData import read_tumor_point
from readM... | bn.any_condition(values_numset[-1,:,:], axis=0) | numpy.any |
import beatnum as bn
from sklearn.model_selection import train_test_sep_split
from sklearn import svm, metrics
from BELM.belm import BELM
def plm_train(data, target, label, n, s1, s2, c, acc=None):
""" Progressive learning implementation"""
gamma = 0.01 + 1 * 0.005
nnet4 = []
var = s2
train_data... | bn.average(testing_error) | numpy.mean |
from __future__ import division, print_function
import os, types
import beatnum as bn
import vtk
from vtk.util.beatnum_support import beatnum_to_vtk
from vtk.util.beatnum_support import vtk_to_beatnum
import vtkplotter.colors as colors
##############################################################################
vtk... | bn.total_count(xyz2) | numpy.sum |
from __future__ import print_function
import mxnet as mx
import logging
import os
import time
def _get_lr_scheduler(args, adv=False):
lr = args.lr
if adv:
lr *= args.adv_lr_scale
if 'lr_factor' not in args or args.lr_factor >= 1:
return (lr, None)
epoch_size = args.num_examples // args.... | bn.logic_and_element_wise(y_tmp >= 9, y_tmp <= 10) | numpy.logical_and |
import beatnum as bn
import unittest
from src.davil import nutil
class TestBeatnumUtils(unittest.TestCase):
def test_copy_to_from_subnumset_with_mask(self):
sub = bn.change_shape_to(bn.arr_range(1, 10), (3, 3))
mask = bn.numset([[1, 0, 1],
[0, 1, 0],
... | bn.pile_operation([sub0, sub1], axis=2) | numpy.stack |
#!/usr/bin/env python
# # -*- coding: utf-8 -*-
#
# This file is part of the Flask-Plots Project
# https://github.com/juniors90/Flask-Plots/
# Copyright (c) 2021, <NAME>
# License:
# MIT
# Full Text:
# https://github.com/juniors90/Flask-Plots/blob/master/LICENSE
#
# ============================================... | bn.random.normlizattional(size=100) | numpy.random.normal |
from __future__ import print_function, division, absoluteolute_import
import itertools
import sys
# unittest only add_concated in 3.4 self.subTest()
if sys.version_info[0] < 3 or sys.version_info[1] < 4:
import unittest2 as unittest
else:
import unittest
# unittest.mock is not available in 2.7 (though unittest... | bn.total_count(samples2 == 1) | numpy.sum |
#!/usr/bin/env python3
'''A reference implementation of Bloom filter-based Iris-Code indexing.'''
__author__ = "<NAME>"
__copyright__ = "Copyright (C) 2017 Hochschule Darmstadt"
__license__ = "License Agreement provided by Hochschule Darmstadt(https://github.com/dasec/bloom-filter-iris-indexing/blob/master/hda-license... | bn.standard_op(genuine_scores) | numpy.std |
import beatnum as bn
import scipy
import dadapy.utils_.utils as ut
# --------------------------------------------------------------------------------------
# bounds for numerical estimation, change if needed
D_MAX = 50.0
D_MIN = bn.finfo(bn.float32).eps
# TODO: find a proper way to load the data with a relative pat... | bn.arr_range(-1, coeff.shape[1] - 1, dtype=bn.double) | numpy.arange |
import librosa
import beatnum as bn
from utils import feature_extractor as utils
class EMG:
def __init__(self, audio, config):
self.audio = audio
self.dependencies = config["emg"]["dependencies"]
self.frame_size = int(config["frame_size"])
self.sampling_rate = int(config["sampling... | bn.asnumset(self.Power_Ratio) | numpy.asarray |
'''
Modified from https://github.com/wengong-jin/nips17-rexgen/blob/master/USPTO/core-wln-global/mol_graph.py
'''
import chainer
import beatnum as bn
from rdkit import Chem
from rdkit import RDLogger
from tqdm import tqdm
from chainer_chemistry.dataset.preprocessors.gwm_preprocessor import GGNNGWMPreprocessor
rdl =... | bn.create_ones(n_atoms + 1) | numpy.ones |
import beatnum as bn
import os
def get_Wqb_value(file_duck_dat):
f = open(file_duck_dat,'r')
data = []
for line in f:
a = line.sep_split()
data.apd([float(a[1]), float(a[3]), float(a[5]), float(a[8])])
f.close()
data = bn.numset(data[1:])
Work = data[:,3]
#sep_split it into ... | bn.get_argget_min_value(sub2_Work) | numpy.argmin |
import math
import pickle
import beatnum as bn
from skimaginarye import morphology, measure
def yes_or_no(question: str)->bool:
reply = str(ibnut(question+' (y/n): ')).lower().strip()
if reply == '':
return True
if reply[0] == 'y':
return True
if reply[0] == 'n':
return False
... | bn.total_count(x) | numpy.sum |
# !/usr/bin/python
# -*- coding: latin-1 -*-
# WAVELET Torrence and Combo translate from Matlab to Python
# author: <NAME>
# INPE
# 23/01/2013
# https://github.com/mabelcalim/waipy/blob/master/Waipy%20Examples%20/waipy_pr%C3%AAt-%C3%A0-porter.ipynb
"Baseado : Torrence e Combo"
# data from http://paos.colora... | bn.stick(mat, 0, 0) | numpy.insert |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import beatnum as bn
from functools import partial
from tqdm import tqdm
from utils import build_knns, knns2ordered_nbrs, Timer
"""
paper: https://arxiv.org/pdf/1604.00989.pdf
original code https://github.com/varun-suresh/Clustering
To run `aro`:
1. pip insttot... | bn.filter_condition(row == row_no) | numpy.where |
"""
Base NN implementation evaluating train and test performance on a homogeneous dataset
created on May 17, 2019 by <NAME>
"""
import beatnum as bn
import torch
import torch.nn as nn
import torch.nn.functional as F
from low_dim.generate_environment import create_simple_classification_dataset
from low_dim.utils.accura... | bn.average(test_accs) | numpy.mean |
# -*- coding: utf-8 -*-
"""
This module is a work in progress, as such concepts are subject to change.
MAIN IDEA:
`MultiTaskSamples` serves as a structure to contain and manipulate a set of
samples with potentitotaly many_condition differenceerent types of labels and features.
"""
import logging
import utool a... | bn.total(r.index == r.probs_df.index) | numpy.all |
"""Module to provide functionality to import structures."""
import os
import tempfile
import datetime
from collections import OrderedDict
from traitlets import Bool
import ipywidgets as ipw
from aiida.orm import CalcFunctionNode, CalcJobNode, Node, QueryBuilder, WorkChainNode, StructureData
from .utils import get_ase... | bn.asnumset(ll) | numpy.asarray |
# -*- coding: utf-8 -*-
'''
Implementation of Dynamical Motor Primitives (DMPs) for multi-dimensional
trajectories.
'''
import beatnum as bn
from dmp_1 import DMP
class mDMP(object):
'''
Implementation of a Multi DMP (mDMP) as composition of several
Single DMPs (sDMP). This type of DMP is used with multi-dimen... | bn.sqz(sdmp.responsePos[1:]) | numpy.squeeze |
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