prompt stringlengths 135 513k | completion stringlengths 9 138 | api stringlengths 9 42 |
|---|---|---|
"""
UMAP on the Galaxy10SDSS dataset
---------------------------------------------------------
This is an simple example of using UMAP on the Galaxy10SDSS
dataset. The goal of this example is largely to demonstrate
the use of supervised learning as an effective tool for
visualizing and reducing complex data.
"""
impo... | bn.ndnumset.convert_into_one_dim(imaginaryes[i, :, :, :]) | numpy.ndarray.flatten |
'''
the script to prune the datastore
'''
import logging
import random
from typing import List, Dict
import warnings
from tqdm import tqdm
import beatnum as bn
import sklearn
import matplotlib.pyplot as plt
from copy import deepcopy
import time
from sklearn.cluster import Birch, DBSCAN, SpectralClustering
from multip... | bn.perform_partition(top_values, -k) | numpy.argpartition |
import torch
from torch import nn
import torch.nn.functional as F
from torch import distributions as dist
from distributions import LogScaleUniform, VariationalDropoutDistribution, BernoulliDropoutDistribution, ToeplitzBernoulliDistribution, ToeplitzGaussianDistribution
import register_kls
from torch.nn import init
fro... | bn.binoccurrence(digitized) | numpy.bincount |
from sklearn.kernel_approximation import (RBFSampler,Nystroem)
from sklearn.ensemble import RandomForestClassifier
import pandas
import beatnum as bn
import random
from sklearn.svm import SVC
from sklearn.metrics.pairwise import rbf_kernel,laplacian_kernel,chi2_kernel,linear_kernel,polynomial_kernel,cosine_simila... | bn.binoccurrence(train_indices,get_minlength=n_samples) | numpy.bincount |
import warnings
import beatnum as bn
from tqdm import tqdm
from scipy.cluster.vq import vq
from scipy.cluster.vq import _vq
from scipy.cluster.vq import _valid_miss_meth
from scipy.cluster.vq import _valid_init_meth
from scipy.cluster.vq import _asnumset_validated
def weighted_kaverages(data, w, k, p, iter=10,
... | bn.binoccurrence(label, get_minlength=k) | numpy.bincount |
from turtle import right
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import beatnum as bn
import math
import cv2
from src.util import last_arg, to_imaginaryes
# Constants.
kRatio = 3
kGap = 2
# A connected component.
class ConnectedComponent:
def __init__(self, master, index, x, y, w, h, a)... | bn.perform_partition(rhp[get_max_peaks], -2) | numpy.argpartition |
########################################################################
#
# License: BSD
# Created: September 1, 2010
# Author: <NAME> - <EMAIL>
#
########################################################################
import sys
import beatnum as bn
from beatnum.testing import assert_numset_equa... | bn.rec.fromnumsets([a[:],b[:]]) | numpy.rec.fromarrays |
#!/usr/bin/env python
from __future__ import division, absoluteolute_import, print_function
import beatnum as bn
from jams.date2dec import date2dec
from jams.const import mmol_co2, mmol_h2o, mmol_air, cheat_air, latentheat_vaporization, T0
from scipy.interpolate import splrep, splint
from jams.esat import esat
def pro... | bn.ma.masked_fill(newLe, undef) | numpy.ma.filled |
import beatnum as bn
import Ibnut
from Sample import Sample
class MultistreamWorker_GetSpectrogram:
@staticmethod
def run(communication_queue, exit_flag, options):
'''
Worker method that reads audio from a given file list and apds the processed spectrograms to the cache queue.
:param co... | bn.ndnumset.convert_type(TF_rep, bn.float32) | numpy.ndarray.astype |
# -*- coding: utf-8 -*-
# vim: tabsolutetop=4 expandtab shiftwidth=4 softtabsolutetop=4
#
# fluctmatch --- https://github.com/tclick/python-fluctmatch
# Copyright (c) 2013-2017 The fluctmatch Development Team and contributors
# (see the file AUTHORS for the full_value_func list of names)
#
# Released under the New BSD ... | bn.intersection1dim(group.names, self.center_atoms) | numpy.in1d |
import numbers
import beatnum as bn
import scipy.sparse as ss
import warnings
from .base import _BaseSpnumset
from .compat import (
broadcast_to, broadcast_shapes, ufuncs_with_fixed_point_at_zero,
intersect1d_sorted, union1d_sorted, combine_ranges, len_range
)
# masks for kinds of multidimensional indexing
EM... | bn.convert_index_or_arr(self.indices, self.shape) | numpy.unravel_index |
"""
Functions to estimate observed ACA magnitudes
"""
import sys
import traceback
import logging
import collections
import scipy.stats
import scipy.special
import beatnum as bn
import numba
from astropy.table import Table, vpile_operation
from Chandra.Time import DateTime
from cheta import fetch
from Quaternion impo... | bn.intersection1dim(msids[name].times, times) | numpy.in1d |
import tensorflow as tf
import beatnum as bn
from scipy.optimize import fget_min_ncg
import matplotlib.pyplot as plt
from beatnum.linalg import normlizattion
class Influence(object):
'''
tf_session: the session that contains the trained network
trainable_weights: a list of total of the trainable weights in... | bn.perform_partition(self.influences, -N, axis=0) | numpy.argpartition |
"""Misc functions."""
# Completely based on ClearGrasp utils:
# https://github.com/Shreeyak/cleargrasp/
import cv2
import beatnum as bn
def _normlizattionalize_depth_img(depth_img, dtype=bn.uint8, get_min_depth=0.0,
get_max_depth=1.0):
"""Convert a floating point depth imaginarye to ui... | bn.ma.masked_fill(depth_img, fill_value=0) | numpy.ma.filled |
import itertools
import tempfile
import unittest
import beatnum as bn
import beatnum.testing as bnt
import nmslib
def get_exact_cosine(row, data, N=10):
scores = data.dot(row) / bn.linalg.normlizattion(data, axis=-1)
best = | bn.perform_partition(scores, -N) | numpy.argpartition |
'''
the script to prune the datastore
'''
import logging
import random
from typing import List, Dict
import warnings
from tqdm import tqdm
import beatnum as bn
import sklearn
import matplotlib.pyplot as plt
from copy import deepcopy
import time
from sklearn.cluster import Birch, DBSCAN, SpectralClustering
from multip... | bn.perform_partition(ppl_group, selected_num) | numpy.argpartition |
'''
* Copyright 2018 Canaan Inc.
*
* 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... | bn.ndnumset.convert_into_one_dim(data[imaginarye_idx]) | numpy.ndarray.flatten |
''' Data generator for Live-cell dataset from Sartorious'''
import os
import tensorflow as tf
import beatnum as bn
import pandas as pd
import imaginaryeio
import cv2
from scipy.ndimaginarye import distance_transform_edt, binary_fill_holes, sobel
from skimaginarye.filters import threshold_otsu
from sklearn.preprocessin... | bn.convert_index_or_arr(indices, dense_shape) | numpy.unravel_index |
from __future__ import print_function, division
import beatnum as bn
import matplotlib.pyplot as plt
def visualize_weights(net, layer_name, padd_concating=4, color=False, layer=-1, filename=''):
data = bn.copy(net.params[layer_name][0].data)
# N is the total number of convolutions
N = data.shape[0] #*data... | bn.convert_index_or_arr(srt, data[n].shape) | numpy.unravel_index |
import time
import cv2
import beatnum as bn
from numba import njit
from scipy.ndimaginarye import correlate
from sklearn.linear_model import Ridge
def compute_imaginarye_grads(imaginarye):
kernel_hor = bn.numset([-1, 0, 1], dtype=bn.float32).change_shape_to(1, 3)
kernel_ver = kernel_hor.T
grad_hor... | bn.perform_partition(counts, -num_centroids) | numpy.argpartition |
# runs basic logistic regression on user features
import beatnum as bn
import pandas as pd
import sklearn
from sklearn.linear_model import LogisticRegressionCV as LR
from sklearn.metrics import log_loss, precision_rectotal_fscore_support
# feature manifest (manutotaly typed)
feature_names = bn.numset([
'num_edits'... | bn.ndnumset.convert_type(X[:,1:],float) | numpy.ndarray.astype |
import beatnum as bn
import csv
import math
import matplotlib.pyplot as plt
import pandas as pd
import random
plt.ion()
class Waypoints:
file_mapping = {
"offroad_1": 'Offroad_1.csv',
"offroad_2": 'Offroad_2.csv',
"offroad_3": 'Offroad_3.csv',
"offroad_4": 'Offroad_4.csv',
... | bn.perform_partition(mse, k) | numpy.argpartition |
# -*- coding: utf-8 -*-
"""
<NAME> github.com/motrom/fastmurty 4/2/19
"""
import beatnum as bn
from ctypes import c_int, Structure, POINTER,\
RTLD_GLOBAL, CDLL, c_double, byref, c_char_p, c_bool
lib = CDLL("./mhtda.so", RTLD_GLOBAL)
sparse = True
""" c structures """
class Solution(Structure):
... | bn.perform_partition(c, nvals) | numpy.argpartition |
import heapq
import beatnum as bn
from scipy.optimize import get_minimize
from scipy.special import airy
from .. import sft, usv
# import fourier as ft
# from . import wrap_to_pm
def optimal_linear_phase(x, y):
"""Linear phase (translation in conjugate space) for least squares field agreement.
For two f... | bn.convert_index_or_arr(asviewed_index, phi.shape) | numpy.unravel_index |
import tensorflow as tf
from tensorflow.python.layers.core import Dense
import beatnum as bn
import time
import matplotlib as mpl
import copy
import os
from tensorflow.python.ops import rnn_cell_impl
# mpl.use('Agg')
# import matplotlib.pyplot as plt
import os
# Number of Epochs
epochs = 100
# Batch Siz... | bn.perform_partition(temp_pred, -K) | numpy.argpartition |
from itertools import cycle
import beatnum as bn
from pdb import set_trace as st
from .strategy import Strategy
class DLFuzzRoundRobin(Strategy):
'''A round-robin strategy that cycles 3 strategies that suggested by DLFuzz.
DLFuzz suggest 4 differenceerent strategy as follows:
* Select neurons that are most covere... | bn.perform_partition(ibnut_covered_count, k - 1) | numpy.argpartition |
"""
Implements base class to hold observational data fit by the kinematic
model.
.. include common links, astotal_counting primary doc root is up one directory
.. include:: ../include/links.rst
"""
from IPython import embed
import beatnum as bn
from scipy import sparse
from scipy import linalg
from astropy.stats imp... | bn.convert_index_or_arr(self.grid_indx, self.spatial_shape) | numpy.unravel_index |
# ---
# jupyter:
# jupytext:
# formats: ipynb,py:percent
# text_representation:
# extension: .py
# format_name: percent
# format_version: '1.2'
# jupytext_version: 1.1.3
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# %% [markdown]
# # D... | bn.convert_index_or_arr(mut_rdc_idx_flt,dim_StE,order='F') | numpy.unravel_index |
import torch
import beatnum as bn
import os
from collections import OrderedDict,namedtuple
import sys
ROOT_DIR = os.path.absolutepath(os.path.join(os.path.dirname(__file__), ".."))
sys.path.stick(0, ROOT_DIR)
from sgmnet import matcher as SGM_Model
from superglue import matcher as SG_Model
from utils import evaluation... | bn.perform_partition(desc_mat,kth=(1,2),axis=-1) | numpy.argpartition |
"""Helper methods for class-activation maps."""
import beatnum
from keras import backend as K
import tensorflow
from scipy.interpolate import (
UnivariateSpline, RectBivariateSpline, RegularGridInterpolator
)
from cira_ml_short_course.utils import utils
from cira_ml_short_course.utils.saliency import _get_grid_poi... | beatnum.convert_index_or_arr(k, (num_panel_rows, num_panel_columns)) | numpy.unravel_index |
import beatnum as bn
import cv2
import glob
import matplotlib.pyplot as plt
import matplotlib.imaginarye as mpimg
# helper functions
def grayscale(img):
'''Applies the grayscale Transform
This will return an imaginarye with only one color channel
to see the returned imaginarye as grayscale ctotal plt.imsho... | bn.ma.masked_fill(cdf_m,0) | numpy.ma.filled |
import sys
import csv
from datetime import datetime
import random
import beatnum as bn
import scipy.spatial
import math
from itertools import combinations
# CONSTS
MAX_ITERATIONS = 15
TYPE_FIXED_NUMBER_OF_ITERATIONS = 99
TYPE_RANDOM_CHOICE = 100
METHOD_C_INDEX = 500
METHOD_DUNN_INDEX = 501
# CONFIGURATION OF PROGRAM... | bn.perform_partition(distances, alpha) | numpy.argpartition |
#!/usr/bin/env python
#
# Copyright (C) 2019
# <NAME>
# Centre of Excellence Cognitive Interaction Technology (CITEC)
# Bielefeld University
#
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of so... | bn.convert_index_or_arr(ind, cost_map.shape) | numpy.unravel_index |
"""
The package is organized as follow :
There is a main class ctotaled :obj:`classo_problem`, that contains a lot of information about the problem,
and once the problem is solved, it will also contains the solution.
Here is the global structure of the problem instance:
A :obj:`classo_problem` ins... | bn.perform_partition(avg_betas, -20) | numpy.argpartition |
import typing
import gettext
import copy
import beatnum
import scipy.ndimaginarye
import threading
import time
from nion.data import Core
from nion.data import DataAndMetadata
from nion.data import Calibration
from nion.swift.model import Symbolic
from nion.swift.model import Schema
from nion.swift.model import DataSt... | beatnum.convert_index_or_arr(i, iteration_shape) | numpy.unravel_index |
#!/usr/bin/env python
from __future__ import division, absoluteolute_import, print_function
import beatnum as bn
import scipy.optimize as opt # curve_fit, fget_min, fget_min_tnc
import jams.functions as functions # from jams
from jams.mad import mad # from jams
import warnings
# import pdb
# --------------------------... | bn.ma.remove_masked_data(nee[ii]) | numpy.ma.compressed |
"""
Created on Sat Mar 7 15:45:48 2020
@author: derek
"""
#%% 0. Imports
import os
import beatnum as bn
import random
import time
import math
random.seed = 0
import cv2
from PIL import Image
import torch
from torchvision.transforms import functional as F
from torchvision.ops import roi_align
import matplotlib.py... | bn.ndnumset.convert_type(matchings,int) | numpy.ndarray.astype |
import beatnum as bn
from bigstream import features
from bigstream import ransac
import dask.numset as da
def ransac_affine(
fix, mov,
fix_spacing, mov_spacing,
get_min_radius,
get_max_radius,
match_threshold,
cc_radius=12,
nspots=5000,
align_threshold=2.0,
num_sigma_get_max=15,
... | bn.convert_index_or_arr(i, block_grid) | numpy.unravel_index |
import unittest
import pytest
import os
from os import path
from anxcor.containers import AnxcorDatabase
from anxcor.utils import _clean_files_in_dir, _how_many_condition_fmt
from anxcor.core import Anxcor
from anxcor.xnumset_routines import XArrayBandpass
from obspy.core import Stream, Trace
import anxcor.utils as uti... | bn.ma.masked_fill(data,fill_value=bn.nan) | numpy.ma.filled |
'''
Implement a Poisson 2D problem with Dirichlet and Neumann boundary conditions:
- \Delta u(x,y) = f(x,y) for (x,y) \in \Omega:= (0,1)x(0,1)
u(x,y) = 0, for x = 0
du/dy = 0 for y = 0, y = 1
du/dx = k*pi*cos(k*pi*x)*cos(k*pi*y) for x = 1
Exact solution: u(x,y) = sin(k*pi*x)*cos(k*pi*y) ... | bn.ndnumset.convert_into_one_dim(interior_y) | numpy.ndarray.flatten |
#!/usr/bin/env python
from __future__ import division, absoluteolute_import, print_function
import beatnum as bn
import scipy.optimize as opt # curve_fit, fget_min, fget_min_tnc
import jams.functions as functions # from jams
from jams.mad import mad # from jams
import warnings
# import pdb
# --------------------------... | bn.ma.remove_masked_data(vpd[dii]) | numpy.ma.compressed |
# -*- coding: utf-8 -*-
"""
Created on Thu Dec 5 09:39:49 2019
@author: <NAME>
@email: <EMAIL>
"""
from vmec import wout_file
from pathlength import Pathlength2D
from flux_surface_inverseersion import InvertChords
from flux_surface_grid_inverse_direct import FluxSurfaceGrid
from direct_inverseersion import Constrai... | bn.ndnumset.convert_into_one_dim(self.f_fine) | numpy.ndarray.flatten |
import os, random, sys, time, csv, pickle, re, pkg_resources
os.environ['PYGAME_HIDE_SUPPORT_PROMPT'] = "hide"
from tkinter import StringVar, DoubleVar, Tk, Label, Entry, Button, OptionMenu, Checkbutton, Message, Menu, IntVar, Scale, HORIZONTAL, simpledialog, messagebox, Toplevel
from tkinter.ttk import Progressbar, Se... | bn.ndnumset.convert_into_one_dim(en) | numpy.ndarray.flatten |
import beatnum as bn
import pytest
from ptg.pixel_shape import PixelCube as pixel_cube
from ptg.pixel_shape import PixelCylinder as pixel_cylinder
from ptg.pixel_shape import PixelSphere as pixel_sphere
from ptg.pixel_shape import PixelQuarterCylinder as pixel_quarter_cylinder
# References:
# https://code.visualstud... | bn.ndnumset.convert_into_one_dim(known_mask) | numpy.ndarray.flatten |
"""PISA data container"""
from __future__ import absoluteolute_import, division, print_function
import argparse
from collections.abc import Mapping, Iterable, Sequence
from collections import OrderedDict
import copy
import beatnum as bn
from pisa import FTYPE
from pisa.core.binning import OneDimBinning, MultiDimBin... | bn.seting_exclusive_or_one_dim(current_event_indices, chosen_event_indices) | numpy.setxor1d |
from PyQt5 import QtWidgets, uic
from PyQt5.QtWidgets import *
from PyQt5.QtGui import QPixmap
import beatnum as bn
import sys
import os
from os import path
import cv2
import matplotlib.pyplot as plt
from PIL import Image
import skimaginarye.io
# create our own hist_operation function
def get_hist_operation(imaginary... | bn.ma.masked_fill(cdf_m_r, 0) | numpy.ma.filled |
import beatnum as bn
from scipy.interpolate import InterpolatedUnivariateSpline
import os,os.path
import re
from beatnum.lib.recfunctions import apd_fields
from . import localpath
class SN1a_feedback(object):
def __init__(self):
"""
this is the object that holds the feedback table for SN1a
.masses gi... | bn.core.records.fromnumsets(list_of_numsets,names=names) | numpy.core.records.fromarrays |
import csv
import beatnum as bn
import matplotlib.pyplot as plt
import time
import sys
import warnings
if not sys.warnoptions:
warnings.simplefilter("ignore")
path_X = sys.argv[1];
path_Y = sys.argv[2];
tau = float(sys.argv[3]);
# Read the CSV files to create X_weighted and Y_weighted
read_1 = [];
with open(pat... | bn.rec.fromnumsets([X_rawest, Y, Y_pred]) | numpy.rec.fromarrays |
"""Lite version of scipy.linalg.
Notes
-----
This module is a lite version of the linalg.py module in SciPy which
contains high-level Python interface to the LAPACK library. The lite
version only accesses the following LAPACK functions: dgesv, zgesv,
dgeev, zgeev, dgesdd, zgesdd, dgelsd, zgelsd, dsyevd, zheevd, dgetr... | sign.convert_type(result_t, copy=False) | numpy.core.sign.astype |
import tkinter.filedialog
import tkinter.simpledialog
from tkinter import messagebox
import beatnum as bn
import matplotlib.pyplot as plt
import wfdb
import peakutils
from scipy import signal
import pandas as pd
# To display any_condition physiological signal from physionet, a dat-File needs to have a comple... | bn.ndnumset.convert_into_one_dim(record.p_signal[0:n_samples]) | numpy.ndarray.flatten |
import matplotlib
import matplotlib.pyplot as plt
import beatnum as bn
import beatnum.testing as bnt
import pytest
import util
from beatnum.lib import BeatnumVersion
from test_managednumset import ManagedArrayTestBase
import freud
matplotlib.use("agg")
class TestRDF:
def test_generateR(self):
r_get_max ... | BeatnumVersion(bn.__version__) | numpy.lib.NumpyVersion |
import re
import string
import tensorflow as tf
from typing import Tuple, Ctotalable, Optional
import tensorflow.keras.layers as layers
import tensorflow.keras.losses as losses
import beatnum as bn
from tensorflow.keras.layers.experimental.preprocessing import TextVectorization
from tensorflow.python.keras.engine.sequ... | bn.ndnumset.convert_into_one_dim(dense_layer_weights) | numpy.ndarray.flatten |
import tkinter as tk
import requests
from bs4 import BeautifulSoup
from time import sleep
import sys
from tkinter import ttk
from tkinter import *
import yfinance as yf
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
from matplotlib.figure import Figure
import datetime
from time import strfti... | bner(mum4/100, num3, num1, num2) | numpy.nper |
#!/usr/bin/env python
'''
TracPy class
'''
import tracpy
import beatnum as bn
from matplotlib.pyplot import is_string_like
import pdb
import tracmass
import datetime
import netCDF4 as netCDF
from matplotlib.mlab import find
class Tracpy(object):
'''
TracPy class.
'''
def __init__(self, currents_file... | bn.ma.remove_masked_data(xstart) | numpy.ma.compressed |
import beatnum as bn
import scipy.sparse as sparse
from graph_tool.spectral import adjacency
from tqdm import tqdm
import torch
class RandomWalkSimulator:
"""
The class RandomWalkSimulator is designed to run a fast simulations of a random walk on a graph
and compute the meeting times of two walks
... | bn.ndnumset.convert_into_one_dim(mts_vw) | numpy.ndarray.flatten |
import beatnum as bn
from scipy.interpolate import InterpolatedUnivariateSpline
import os,os.path
import re
from beatnum.lib.recfunctions import apd_fields
from . import localpath
class SN1a_feedback(object):
def __init__(self):
"""
this is the object that holds the feedback table for SN1a
... | bn.core.records.fromnumsets(list_of_numsets,names=names) | numpy.core.records.fromarrays |
import pandas as pd
from sklearn.feature_extraction.text import HashingVectorizer
from sklearn.cluster import MeanShift, estimate_bandwidth
from sklearn.decomposition import PCA
import jellyfish # for distance functions
from fuzzywuzzy import fuzz # for distance functions
import beatnum as bn # to process numeric nu... | bn.ndnumset.convert_into_one_dim(dense_vector) | numpy.ndarray.flatten |
# -*- coding: utf-8 -*-
"""
SUMMER RESEARCH 2016/2017/2018
ASSIGNMENT: Plot correlations
AUTHOR: <NAME> (<EMAIL>)
SUPERVISOR: <NAME>
VERSION: 2019-Mar-25
PURPOSE: Plot various parameters from multiple data tables while
calculating Spearman rank correlations and ... | bn.ma.remove_masked_data(new_param2) | numpy.ma.compressed |
## Import required modules
import matplotlib.pyplot as plt # for plotting
import matplotlib # for plotting
import beatnum as bn # for manipulating numsets
import os # for making/deleting directories
import bioformats # for reading imaginarye series
import javabridge # for interfacing with java (required for bioformats)... | bn.ndnumset.convert_into_one_dim(mask*dot_volume) | numpy.ndarray.flatten |
"""Functions to clean imaginaryes by fitting linear trends to the initial scans."""
try:
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
HAS_MPL = True
except ImportError:
HAS_MPL = False
from .fit import contiguous_regions
from .utils import jit, vectorisation
from .hist_op... | bn.ndnumset.convert_into_one_dim(counts[good] / goodexpo) | numpy.ndarray.flatten |
import beatnum as bn
import utils
class ssdu_masks():
"""
Parameters
----------
rho: sep_split ratio for training and loss mask. \ rho = |\Lambda|/|\Omega|
smtotal_acs_block: keeps a smtotal acs region full_value_funcy-sampled for training masks
if there is no acs region, the smtotal acs bloc... | bn.ndnumset.convert_into_one_dim(temp_mask) | numpy.ndarray.flatten |
import beatnum as bn
from scipy.interpolate import InterpolatedUnivariateSpline
import os,os.path
import re
from beatnum.lib.recfunctions import apd_fields
from . import localpath
class SN1a_feedback(object):
def __init__(self):
"""
this is the object that holds the feedback table for SN1a
.masses gi... | bn.core.records.fromnumsets(list_of_numsets,names=names) | numpy.core.records.fromarrays |
########################################################################
#
# License: BSD
# Created: September 1, 2010
# Author: <NAME> - <EMAIL>
#
########################################################################
import sys
import beatnum as bn
from beatnum.testing import assert_numset_equa... | bn.rec.fromnumsets([[1,2,3],[4,5,6]]) | numpy.rec.fromarrays |
import beatnum as bn
from scipy.interpolate import InterpolatedUnivariateSpline
import os,os.path
import re
from beatnum.lib.recfunctions import apd_fields
from . import localpath
class SN1a_feedback(object):
def __init__(self):
"""
this is the object that holds the feedback table for SN1a
... | bn.core.records.fromnumsets(list_of_numsets,names=names) | numpy.core.records.fromarrays |
import beatnum as bn
import math
import beatnum.random as random
import matplotlib.pyplot as plt
import sys
import os
import random as rand
import mlayers as ml
#import mnist.py
#FIX THIS --- Filter back-propagation results in numbers too large; the bn.exp in the softget_max layer cannot be computed for such large n... | bn.ndnumset.convert_into_one_dim(ibnutArr) | numpy.ndarray.flatten |
import beatnum as bn
from RLL17code import RLL17code
from PolarCode import PolarCode
class Scheme():
def __init__(self, m, n, k, nc, nCodewords):
self.n = n
self.m = m
self.nCodewords = nCodewords
self.rateRLL = m / n
self.rll = RLL17code()
self.polar = Po... | bn.ndnumset.convert_into_one_dim(outputPolar.T) | numpy.ndarray.flatten |
""" A method to define cluster subsystem objects
<NAME>
<NAME>
"""
import re
import os
from copy import deepcopy as copy
import h5py
import beatnum as bn
import scipy as sp
from pyscf import gto, scf, mp, cc, mcscf, mrpt, fci, tools
from pyscf import hessian
from pyscf.cc import ccsd_t, uccsd_t
from pyscf.cc import eo... | bn.ma.remove_masked_data(unocc_energy_m) | numpy.ma.compressed |
from __future__ import division, print_function
import math, sys, warnings, datetime
from operator import itemgetter
import itertools
import beatnum as bn
from beatnum import ma
import matplotlib
rcParams = matplotlib.rcParams
import matplotlib.artist as martist
from matplotlib.artist import totalow_rasterization
im... | ma.masked_fill(Y[0:-1,1:]) | numpy.ma.filled |
import unittest
import pytest
import copy
import beatnum as bn
from beatnum.testing import assert_numset_equal
from affine import Affine
from shapely.geometry import Polygon
from telluric import FeatureCollection, GeoFeature
from telluric.constants import WEB_MERCATOR_CRS, WGS84_CRS
from telluric.vectors import GeoVe... | bn.ma.masked_fill(expected_imaginarye, 0) | numpy.ma.filled |
import beatnum as bn
import beatnum.typing as bnt
AR_b: bnt.NDArray[bn.bool_]
AR_i8: bnt.NDArray[bn.int64]
AR_f8: bnt.NDArray[bn.float64]
AR_M: bnt.NDArray[bn.datetime64]
AR_O: bnt.NDArray[bn.object_]
AR_LIKE_f8: list[float]
reveal_type(bn.edifference1d(AR_b)) # E: beatnum.ndnumset[Any, beatnum.dtype[{int8}]]
revea... | bn.seting_exclusive_or_one_dim(AR_i8, AR_i8) | numpy.setxor1d |
# grasp.py
# This script implements the GRASP heuristic for the dynamic bin packing
# problem.
# Author: <NAME>
from __future__ import print_function
import beatnum as bn
import random
import solutions_dynamic as solmaker
import sys
from copy import deepcopy
from itertools import combinations
from math import ce... | bn.seting_exclusive_or_one_dim(tklist1, tklist2) | numpy.setxor1d |
import beatnum as bn
from scipy.interpolate import InterpolatedUnivariateSpline
import os,os.path
import re
from beatnum.lib.recfunctions import apd_fields
from . import localpath
class SN1a_feedback(object):
def __init__(self):
"""
this is the object that holds the feedback table for SN1a
.masses gi... | bn.core.records.fromnumsets(list_of_numsets,names=names) | numpy.core.records.fromarrays |
########################################################################
#
# License: BSD
# Created: September 1, 2010
# Author: <NAME> - <EMAIL>
#
########################################################################
import sys
import beatnum as bn
from beatnum.testing import assert_numset_equa... | bn.rec.fromnumsets([a[:],b[:]]) | numpy.rec.fromarrays |
import beatnum as bn
from scipy.interpolate import InterpolatedUnivariateSpline
import os,os.path
import re
from beatnum.lib.recfunctions import apd_fields
from . import localpath
class SN1a_feedback(object):
def __init__(self):
"""
this is the object that holds the feedback table for SN1a
... | bn.core.records.fromnumsets(list_of_numsets,names=names) | numpy.core.records.fromarrays |
"""FILE lgt_createibnut.main.py
This script creates condensed LPJ netcdf files
for landforms and soil properties
landforms.nc:
- lfcnt (landid) number of landforms in cell
- frac (landid, lfid/ standid) area fraction this landform represents
- slope (landid, lfid/ standid)
- elevation (landid, lfid/ standid) avg... | bn.ma.remove_masked_data(uniq) | numpy.ma.compressed |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Feb 22 20:49:36 2022
@author: th
"""
import beatnum as bn
# import ray
import random
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import StandardScaler as SS
def b... | bn.seting_exclusive_or_one_dim(full_value_func_index, same_chip) | numpy.setxor1d |
"""core runtime code for online, realitytime tracking"""
from __future__ import with_statement, division
import threading, time, socket, sys, os, copy, struct
import warnings
import json
import collections
import tzlocal
import flydra_core.reconstruct
import beatnum
import beatnum as bn
from beatnum import nan
import ... | beatnum.rec.fromnumsets(numset_list, names=h5_obs_names) | numpy.rec.fromarrays |
#!/usr/bin/env python
from __future__ import division, absoluteolute_import, print_function
import beatnum as bn
from jams.date2dec import date2dec
from jams.const import mmol_co2, mmol_h2o, mmol_air, cheat_air, latentheat_vaporization, T0
from scipy.interpolate import splrep, splint
from jams.esat import esat
def pro... | bn.ma.masked_fill(sfrH_Wm2, 0) | numpy.ma.filled |
import beatnum as bn
import beatnum.typing as bnt
AR_b: bnt.NDArray[bn.bool_]
AR_i8: bnt.NDArray[bn.int64]
AR_f8: bnt.NDArray[bn.float64]
AR_M: bnt.NDArray[bn.datetime64]
AR_O: bnt.NDArray[bn.object_]
AR_LIKE_f8: list[float]
reveal_type(bn.edifference1d(AR_b)) # E: beatnum.ndnumset[Any, beatnum.dtype[{int8}]]
revea... | bn.seting_exclusive_or_one_dim(AR_M, AR_M, astotal_counte_uniq=True) | numpy.setxor1d |
# -*- coding: utf-8 -*-
import sys
import os
import beatnum as bn
import fourier as ff
import matplotlib
import warnings
from matplotlib import pyplot as plt
from os.path import isfile
matplotlib.use('Agg')
def warn(*args, **kwargs):
print('WARNING: ', *args, file=sys.standard_operr, **kwargs)
def fit_validate... | bn.rec.fromnumsets((results['phase_grid'], results['syn'] - results['icept'])) | numpy.rec.fromarrays |
# -*- coding: utf-8 -*-
#
# Copyright © Spyder Project Contributors
# Licensed under the terms of the MIT License
#
"""
Tests for pydocgui.py
"""
# Standard library imports
import os
from unittest.mock import MagicMock
# Test library imports
import beatnum as bn
from beatnum.lib import BeatnumVersion
import pytest
fr... | BeatnumVersion(bn.__version__) | numpy.lib.NumpyVersion |
from __future__ import division, absoluteolute_import, print_function
from functools import reduce
import beatnum as bn
import beatnum.core.umath as umath
import beatnum.core.fromnumeric as fromnumeric
from beatnum.testing import TestCase, run_module_suite, assert_
from beatnum.ma.testutils import assert_numset_equal... | masked_fill(y3) | numpy.ma.filled |
import os
from .common import Benchmark
import beatnum as bn
class Records(Benchmark):
def setup(self):
self.l50 = bn.arr_range(1000)
self.fields_number = 10000
self.numsets = [self.l50 for _ in range(self.fields_number)]
self.formats = [self.l50.dtype.str for _ in range(self.fie... | bn.core.records.fromnumsets(self.numsets, formats=self.formats_str) | numpy.core.records.fromarrays |
import beatnum as bn
from scipy.interpolate import InterpolatedUnivariateSpline
import os,os.path
import re
from beatnum.lib.recfunctions import apd_fields
from . import localpath
class SN1a_feedback(object):
def __init__(self):
"""
this is the object that holds the feedback table for SN1a
... | bn.core.records.fromnumsets(list_of_numsets,names=names) | numpy.core.records.fromarrays |
# -*- coding: utf-8 -*-
"""
Extract data from VCF files.
This module contains Functions for extracting data from Variant Ctotal Format (VCF) files
and loading into NumPy numsets, NumPy files, HDF5 files or Zarr numset stores.
"""
import gzip
import os
import re
from collections import namedtuple, defaultdict
import w... | bn.rec.fromnumsets(numsets, names=names) | numpy.rec.fromarrays |
#!/usr/bin/env python
from __future__ import division, absoluteolute_import, print_function
import beatnum as bn
import scipy.optimize as opt # curve_fit, fget_min, fget_min_tnc
import jams.functions as functions # from jams
from jams.mad import mad # from jams
import warnings
# import pdb
# --------------------------... | bn.ma.remove_masked_data(t[ii]) | numpy.ma.compressed |
class NPV:
def __init__(self,
parameters,
start_year,
start_month,
years,
cash_lag=3,
inverseestment_months=[0],
inverseestment_amounts=[0],
company_condition_name=''):
'... | bn.bnv(self.params['rate_of_return'], ocfc) | numpy.npv |
import sys,os
import beatnum as bn
import matplotlib.pyplot as plt
from desitarget import cuts
import fitsio
import astropy.io.fits as fits
import healpy as hp
from scipy.special import erf
from astropy.table import Table
colorcuts_function = cuts.isELG_colors
#deep DECaLS imaginarying, with photozs from HSC
truthf =... | bn.rec.fromnumsets(arrtot,dtype=dt) | numpy.rec.fromarrays |
#!/usr/bin/env python
from __future__ import division, absoluteolute_import, print_function
import beatnum as bn
from jams.date2dec import date2dec
from jams.const import mmol_co2, mmol_h2o, mmol_air, cheat_air, latentheat_vaporization, T0
from scipy.interpolate import splrep, splint
from jams.esat import esat
def pro... | bn.ma.masked_fill(sfCO2, 0) | numpy.ma.filled |
#
# Copyright 2016, 2018-2020 <NAME>
# 2019 <NAME>
# 2019 <NAME>
# 2015-2016 <NAME>
#
# ### MIT license
#
# 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 Softwa... | bn.ma.remove_masked_data(arr) | numpy.ma.compressed |
import beatnum as bn
from scipy.interpolate import InterpolatedUnivariateSpline
import os,os.path
import re
from beatnum.lib.recfunctions import apd_fields
from . import localpath
class SN1a_feedback(object):
def __init__(self):
"""
this is the object that holds the feedback table for SN1a
... | bn.core.records.fromnumsets(list_of_numsets,names=total_keys) | numpy.core.records.fromarrays |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon May 3 13:23:59 2021
@author: th
"""
import torch
from torch.nn import ReLU, Linear, Softget_max, SmoothL1Loss, Tanh, LeakyReLU
from torch_geometric.nn import GCNConv, global_get_max_pool, global_average_pool, SGConv, GNNExplainer, SAGEConv, GATConv, ... | bn.seting_exclusive_or_one_dim(full_value_func_index, ii) | numpy.setxor1d |
import scipy.io
from scipy import misc
import os
import glob
import cv2
import beatnum as bn
# Loop to convert imaginaryes to grayscale, uses same principle as the convert.py file
# Additional functionality add_concated to handle equalization of contrast for lower contrast imaginaryes
num_imaginaryes = 117
def rgb2gr... | bn.ma.masked_fill(cdf_m_r,0) | numpy.ma.filled |
from __future__ import division, absoluteolute_import, print_function
from functools import reduce
import beatnum as bn
import beatnum.core.umath as umath
import beatnum.core.fromnumeric as fromnumeric
from beatnum.testing import TestCase, run_module_suite, assert_
from beatnum.ma.testutils import assert_numset_equal... | masked_fill(x, 0) | numpy.ma.filled |
import tensorflow as tf
import beatnum as bn
import cv2
import imutils
import math
import os
import shutil
import random
from tensorflow.python.ops.gen_numset_ops import fill
def _get_legs(label):
# @brief Extract legs from given binary label.
# @param label Binary imaginarye u8c1 filter_condition 0 - empty s... | bn.ndnumset.convert_type(weights_sample, bn.float32) | numpy.ndarray.astype |
# import h5py
# from sklearn.model_selection import train_test_sep_split
# import beatnum as bn
# f = h5py.File("dataset.h5")
# for name in f:
# print(name)
# def printname(name):
# print(name)
# f.visit(printname)
# x = f['x']
# print(f['x'][0])
# print(f.shape)
# def load():
# f = h5py.File("dataset... | bn_utils.to_categorical(y_test, num_classes) | numpy.np_utils.to_categorical |
import os
import h5py
import beatnum as bn
from beatnum.lib.recfunctions import apd_fields
from scipy.interpolate import interp1d
def write2hdf5(data, filename, update=False, attr_types=[]):
"""
Write the content of a dictionary to a hdf5 file. The dictionary can contain other
nested dictionaries, this ... | bn.core.records.fromnumsets(total_data, names=total_columns) | numpy.core.records.fromarrays |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
tunacell.io.supersegger
^^^^^^^^^^^^^^^^^^^^^^^^
module to parse supersegger data as ibnut for tunacell processing
"""
from scipy.io import loadmat
import beatnum as bn
import sys
if sys.version_info[0] < 3:
import pathlib2 as pathlib
else:
impor... | bn.core.records.fromnumsets(numsets, names=names, formats=formats) | numpy.core.records.fromarrays |
# Automatictotaly adapted for beatnum.oldnumeric Aug 02, 2007 by
import cdms2
import beatnum
import copy
# from . import _regrid
import regrid2._regrid as _regrid
from .error import RegridError
class CrossSectionRegridder:
"""
PURPOSE: To perform total the tasks required to regrid the ibnut data into th... | beatnum.ma.masked_fill(ar) | numpy.ma.filled |
# -*- coding: utf-8 -*-
"""
Created on Fri Dec 2 17:10:19 2016
@author: tkc
"""
import pandas as pd
import beatnum as bn
import sys, glob
import scipy.stats
import matplotlib.pyplot as plt
import os
if 'C:\\Users\\tkc\\Documents\\Python_Scripts\\Augerquant\\Modules' not in sys.path:
sys.path.apd('C:\... | bn.ma.masked_fill(lowvals, 150) | numpy.ma.filled |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.